pax_global_header00006660000000000000000000000064145342300660014515gustar00rootroot0000000000000052 comment=1d877f697f777dfd58f071bc43a155f6186dbd08 pengchengmind/000077500000000000000000000000001453423006600136675ustar00rootroot00000000000000pengchengmind/README.md000066400000000000000000000176201453423006600151540ustar00rootroot00000000000000PengCheng.Mind 又称鹏城·脑海，是鹏城实验室基于[鹏程·盘古α](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha)开发、开源、开放的基于Transformer架构的自回归式语言模型，目前包含7B、200B两个版本。模型全流程基于中国算力网的全自主安全可控国产软硬件平台进行开发和训练，采用MindSpore框架实现在大规模集群上长期稳定的多维分布式并行训练。鹏城·脑海模型主要聚焦中文核心能力，兼顾英文和部分多语言能力。当前模型已完成训练1.5T Tokens数据量，正式对外发布进阶版，可以在申请表中申请试用，模型仍在持续训练迭代中。 随着中国算力网的不断发展壮大，我们希望通过模型训练全流程开源开放的方式，将鹏城·脑海200B 模型培育为算力网上一个种子应用，吸引产学研界更多人参与到通用大语言模型基座的研究和应用中，一起助力国产智能软硬件生态的发展。
## 要点 - **稠密型两千亿参数量语言模型「鹏城·脑海」** - **200B/7B 最新迭代模型逐步全开源** - **MindSpore多维度分布式并行训练** - **模型基于国产全栈式软硬件协同生态（算力网+MindSpore+CANN+昇腾910+ModelArts）** ### 模型结构 | 结构参数 | N(params) | N(layers)| D(model) | N(heads) | D(head) |seq_length | vocab_size| |:--------------------|:-------------|:---------|:----------|:---------|:--------|:--------|:--------| | PengCheng.Mind 200B | 201.1 B | 104 | 12672 | 96 | 132 | 4096 | 49984 | - N: number 数目 - D: dimension 维度 ### 环境要求 支持 python >= 3.7.5, mindspore >= 2.0.0 版本. 推荐使用mindspore的官方 docker 镜像。 | 硬件平台 | 操作系统 |框架 | 7B推理 | 200B推理 | 200B 微调| | :------------|:--------------- |:---------- |:------------|:-----------|:-----------| | Ascend 910 | EulerOS-aarch64 |MindSpore | 1卡 | 16卡 | 144卡 | ### 模型文件 [鹏城·脑海模型申请](https://openi.pcl.ac.cn/extension/mind) ### 推理 #### 200B 鹏城·脑海模型推理 ```bash python predict_200B.py \ --run_type predict \ --mode 200B \ --distribute true \ --use_pynative_op 1 \ --op_level_model_parallel_num 16 \ --device_num 16 \ --use_rope True \ --stage_num 1 \ --restore_checkpoint_bucket_dir /path/to/checkpoint/ \ --restore_ckpt_name_prefix PengCheng_Mind-200B.ckpt \ --top_p 1.0 \ --top_k_num 3 \ --max_generate_length 100 ``` ### 微调 #### 1、准备训练数据 参考[数据](#数据)部分 #### 2、预训练模型加载，增量训练 运行脚本:(这里以144卡增量训练为例) ```bash python train.py \ --data_url YOUR_OUTPUT_PATH \ --mode 200B \ --embedding_size 12672 \ --num_layers 104 \ --num_heads 96 \ --seq_length 4096 \ --device_num 144 \ --stage_num 18 \ --op_level_model_parallel 8 \ --micro_size 32 \ --optimizer_shard 0 \ --start_lr 5e-6 \ --end_lr 1e-6 \ --decay_steps 10000 \ --warmup_step 200 \ --per_batch_size 1 \ --epoch_size 1 \ --sink_size 4 \ --param_init_type fp16 \ --save_checkpoint True \ --save_checkpoint_steps 100 \ --recompute True \ --use_rope True \ --use_flash_attention True \ --sequence_parallel True \ --softmax_compute_fp32 FP32 \ --save_checkpoint_bucket_dir YOUR_SFT_OBS_PATH \ --pre_trained True \ --restore_checkpoint_bucket_dir PRETRAINED_MODEL_OBS_DIR \ --restore_ckpt_name_prefix PengCheng-Mind-200B_500bTokens.ckpt \ --pipeline_config_filename YOUR_PIPLINE_CONFIG_PATH ``` - restore_checkpoint_bucket_dir: 待加载的预训练模型的obs路径 - restore_ckpt_name_prefix：待加载的预训练模型名称，例如：PC_Mind_200B_exp000_rst40_\*-325_4.ckpt，‘\*’位置用于替换rank_id信息 - pipeline_config_filename：流水线并行和重算的相关配置信息，配置文件保存在’./search/BaSys_FineGrain/‘，gpt_104layer_1dp_8mp_18pp_29mem_32ms.json为144卡训练使用配置文件 - 该配置训练总batch size=micro_size*dp(这里dp=1)*per_batch_size=32 #### 3、模型分片转换 训练模型包含优化器参数，采用分片保存，鹏城·脑海 200B模型训练保存为3456个模型分片，finetune可在Ascend 910 144卡上进行，如果要进行微调，由于使用算力资源的差异，需要对模型切片进行合并。 推理模型我们提供的是转换好的16个分片的模型，无需进行模型分片的转换，如有需要可参考微调转换流程。 参考脚本 ```python import mindspore # 1、融合训练保存的分片策略文件 train_strategy_files = "/path/to/train_strategy/" train_strategy_file_merged = "/path/to/train_strategy_merged.ckpt" mindspore.merge_pipeline_strategys(src_strategy_dirs=train_strategy_files, dst_strategy_file=train_strategy_file_merged) # 2、融合微调保存的分片策略文件（需要根据并行策略配置先跑通生成策略文件） finetune_strategy_files = "/path/to/train_strategy/" finetune_strategy_file_merged = "/path/to/train_strategy_merged.ckpt" mindspore.merge_pipeline_strategys(src_strategy_dirs=train_strategy_files, dst_strategy_file=train_strategy_file_merged) # 3、根据训练和微调策略文件调用接口进行分片模型文件的转换，考虑到机器内存限制采用分片逐个转换 for transform_rank in range(144): # 获取转换transform_rank分片所需的源模型分片列表 rank_list = mindspore.rank_list_for_transform(rank_id = transform_rank, src_strategy_file=train_strategy_file_merged, dst_strategy_file=finetune_strategy_file_merged) rank_list.sort(reverse=True) checkpoint_files_map = {} for k in rank_list: # 对应rank分片模型的位置 checkpoint_files_map[k] = f"/path/to/rank_{k}/checkpoint_rank_{k}.ckpt" # 转换得到对应分片模型 save_checkpoint_file_name = f"/save/to/checkpoint_rank_{transform_rank}.ckpt" mindspore.transform_checkpoint_by_rank(rank_id = transform_rank, checkpoint_files_map = checkpoint_files_map, save_checkpoint_file_name = save_checkpoint_file_name, src_strategy_file=train_strategy_file_merged, dst_strategy_file=train_strategy_file_merged) ``` ### 数据 ##### 生成训练数据 参考脚本：`/tools/pre_process_data.py` 在 YOUR_DATASET_PATH 目录下存放多个 `xxx.json` 文件，如果训练数据较多，最好每个 `json` 文件大小统一，且分开多个 `json` 存放， 大小可以 1M 一个文件。如果有繁体文字，需要转成简体，可以使用`zhconv`。 每个 `json` 文本格式为（需要换行符号分割不同样本）： ```txt {"text": "sample1"} {"text": "sample2"} {"text": "sample2"} ``` ```bash python pre_process_data.py --input_globs "YOUR_DATASET_PATH/*.json" --output_file "YOUR_OUTPUT_PATH/mindrecord" --SEQ_LEN 4097 ``` 将会在YOUR_OUTPUT_PATH目录下生成mindrecord* 文件。 ### NPU 模型转 GPU 参考[鹏城·脑海7B模型分支-NPU模型转GPU](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PengCheng.Mind/src/branch/7B#user-content-npu-%E6%A8%A1%E5%9E%8B%E8%BD%AC-gpu) 部分 ### 声明 我们尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用鹏城·脑海开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
pengchengmind/flash_attention/000077500000000000000000000000001453423006600170515ustar00rootroot00000000000000pengchengmind/flash_attention/__init__.py000066400000000000000000000001101453423006600211520ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. pengchengmind/flash_attention/attention.py000066400000000000000000000357661453423006600214510ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. from abc import ABCMeta from abc import abstractmethod from functools import partial from collections import defaultdict import te.platform as tbe_platform from tbe import tik from tbe.common.platform import get_soc_spec from constants import FP16 from constants import FP32 from constants import GM from constants import MASK_FILL_VALUE from constants import UB from tiling_strategy.strategy import TilingPara from tiling_strategy.strategy import TilingStrategy from tiling_strategy.xunfei_tiling import XunfeiTiling from tik_ops_utils import TikOpsUtils class FlashAttention(metaclass=ABCMeta): """The base class of FlashAttention""" def __init__(self, q, k, v, attn_mask, dropout_mask, alibi_mask, kernel_name, tiling_stgy_cls, prev_block_num=65536, next_block_num=65536, high_precision=False, disable_debug=True): """ Init parameter shape :param q: with shape: (B, h, N, d) :param k: with shape: (B, h, N, d) :param v: with shape: (B, h, N, d) :param attn_mask: with shape: (1, N, N) or (B, N, N) :param dropout_mask: with shape: (B, h, N, N) :param alibi_mask: with shape: (B, h, 1, N) :param kernel_name: :param tiling_stgy_cls: :param prev_block_num: :param next_block_num: :param disable_debug: """ self.tik_instance = tik.Tik(disable_debug=disable_debug) self.core_num = get_soc_spec(tbe_platform.CORE_NUM) self.M = tbe_platform.get_soc_spec(tbe_platform.L1_SIZE) self.kernel_name = kernel_name self.cont_data_mv_1_bust = partial(self.tik_instance.data_move, sid=0, nburst=1, src_stride=0, dst_stride=0) self.tik_ops_utils = TikOpsUtils(self.tik_instance) self.parse_input_shape(alibi_mask, attn_mask, dropout_mask, k, q, v) # NZ _, _, N1, M1, M0, N0 = self.q_shape self.M1 = M1 self.N1 = N1 self.M0 = M0 self.N0 = N0 self.d = N1 * N0 # ND batch_size, h, Nq, actual_d = self.q_ori_shape self.head_num = h self.B, self.Nq = batch_size * h, Nq self.N = self.k_ori_shape[2] self.actual_d = actual_d self.l_shape = [batch_size, h, self.Nq] self.m_shape = [batch_size, h, self.Nq] self.O_shape = self.q_shape self.prev_block_num = prev_block_num self.next_block_num = next_block_num self.high_precision = high_precision if self.high_precision: self.precision_type = FP32 else: self.precision_type = FP16 if tiling_stgy_cls is None: self.tiling_stgy = XunfeiTiling(self.Nq, self.N, self.d) else: self.tiling_stgy: TilingStrategy = tiling_stgy_cls(self.Nq, self.N, self.d) # NZ get offset def get_gm_offset(self, batch_start, batch_idx, h, w, block_h, block_idx): gm_offset = (batch_start + batch_idx) * h * w + block_idx * block_h * self.N0 return gm_offset # ND get offset @staticmethod def get_l_m_gm_offset(batch_start, batch_idx, h, block_h, block_idx): gm_offset = (batch_start + batch_idx) * h + block_idx * block_h return gm_offset @staticmethod def get_alibi_gm_offset(batch_start, batch_idx, w, block_w, block_idx): gm_offset = (batch_start + batch_idx) * w + block_idx * block_w return gm_offset @staticmethod def get_drop_mask_gm_offset(batch_start, batch_idx, h, w, block_h, block_h_idx, block_w, block_w_idx): gm_offset = (batch_start + batch_idx) * h * w + block_h_idx * (w * block_h) + block_w_idx * block_w return gm_offset @abstractmethod def define_custom_inputs(self): raise NotImplemented @abstractmethod def define_outputs(self): raise NotImplemented @abstractmethod def collect_inputs(self): raise NotImplemented @abstractmethod def collect_outputs(self): raise NotImplemented def get_cur_tr_block_num(self, tr_idx): cur_prev_block_num = min(tr_idx, self.prev_block_num) cur_next_block_num = min(self.next_block_num, self.Tc - tr_idx - 1) block_num = cur_prev_block_num + 1 + cur_next_block_num return block_num def get_total_block_num(self): block_num = 0 for b_idx in range(self.B): for tr_idx in range(self.Tr): block_num += self.get_cur_tr_block_num(tr_idx) return block_num def update_core_task_map(self, core_b_map, core_b_tr_map, core_idx, b_start, b_end, tr_start, tr_end): core_b_map[core_idx][0] = min(core_b_map[core_idx][0], b_start) if tr_end == 0: # 跨head，但跨过的head不会被当前的core处理 core_b_map[core_idx][1] = max(core_b_map[core_idx][1], b_end - 1) else: core_b_map[core_idx][1] = max(core_b_map[core_idx][1], b_end) for b_idx in range(b_start, b_end + 1): if b_idx == b_end and tr_end == 0: # 跨head，但跨过的head不会被当前的core处理 break elif b_idx == b_start and b_idx == b_end: # 没跨head core_b_tr_map[core_idx][b_idx] = (tr_start, tr_end) elif b_idx == b_start: # 跨head，第一个head core_b_tr_map[core_idx][b_idx] = (tr_start, self.Tr) elif b_idx == b_end: # 跨head，最后一个head core_b_tr_map[core_idx][b_idx] = (0, tr_end) else: # 跨head，中间的head core_b_tr_map[core_idx][b_idx] = (0, self.Tr) def convert_py_dict_to_tik_tensor(self, core_b_map, core_b_tr_map): # python dict -> tik tensor # [batch_start, batch_idx_end] -> [batch_start, batch_num] # [tr_start, tr_idx_end] -> [tr_start, tr_idx_end) core_idx_to_batch_info = self.tik_instance.Tensor( "int32", (self.core_num, 2), name="core_idx_to_batch_info", scope=UB ) core_idx_to_tr_info = self.tik_instance.Tensor( "int32", (self.core_num, self.B, 2), name="core_idx_to_tr_info", scope=UB ) for core_idx in core_b_map.keys(): batch_start, batch_end = core_b_map[core_idx] core_idx_to_batch_info[core_idx, 0] = batch_start core_idx_to_batch_info[core_idx, 1] = batch_end - batch_start + 1 for batch_idx in core_b_tr_map[core_idx].keys(): tr_start, tr_end = core_b_tr_map[core_idx][batch_idx] core_idx_to_tr_info[core_idx, batch_idx, 0] = tr_start core_idx_to_tr_info[core_idx, batch_idx, 1] = tr_end return core_idx_to_batch_info, core_idx_to_tr_info def get_core_bath_info(self): """ Get batch start and batch number of each NPU core. :return: Tensor([[core_1_batch_start, core_1_batch_num],...,[core_m_batch_start, core_m_batch_num]]), Tensor([[[core_1_batch_1_Tr_start, core_1_batch_1_Tr_end],...[core_1_batch_n_Tr_start, core_1_batch_n_Tr_end]],...,[[core_m_batch_1_Tr_start, core_m_batch_1_Tr_end],...[core_m_batch_n_Tr_start, core_m_batch_n_Tr_end]] """ if self.core_num > self.B * self.Tr: self.core_num = self.B * self.Tr total_blk_num = self.get_total_block_num() b_start = 0 tr_start = 0 remain_blk_num = total_blk_num core_b_map = defaultdict(lambda: [100000, -1]) core_b_tr_map = defaultdict(lambda: defaultdict(list)) for core_idx in range(self.core_num): cur_core_blk_num = 0 cur_each_core_blk_num = remain_blk_num // (self.core_num - core_idx) cur_core_finished = False b_end = b_start tr_end = tr_start while b_end < self.B: while tr_end < self.Tr: cur_tr_blk_num = self.get_cur_tr_block_num(tr_end) if abs(cur_core_blk_num - cur_each_core_blk_num) <= \ (cur_core_blk_num + cur_tr_blk_num - cur_each_core_blk_num): self.update_core_task_map(core_b_map, core_b_tr_map, core_idx, b_start, b_end, tr_start, tr_end) remain_blk_num -= cur_core_blk_num cur_core_finished = True break else: cur_core_blk_num += cur_tr_blk_num tr_end += 1 if tr_end == self.Tr: tr_end = 0 b_end += 1 if cur_core_finished: b_start = b_end tr_start = tr_end break core_idx_to_batch_info, core_idx_to_tr_info = self.convert_py_dict_to_tik_tensor(core_b_map, core_b_tr_map) return core_idx_to_batch_info, core_idx_to_tr_info def get_attn_mask_gm_offset(self, batch_start, batch_idx, h, w, block_h, block_h_idx, block_w, block_w_idx): if self.att_mask_shape[0] == 1: gm_offset = block_w_idx * (h * block_w) + block_h_idx * block_h * self.N0 else: gm_offset = ((batch_start + batch_idx) // self.head_num) * h * w \ + block_w_idx * (h * block_w) + block_h_idx * block_h * self.N0 return gm_offset def parse_input_shape(self, alibi_mask, attn_mask, dropout_mask, k, q, v): """parser input shape""" self.has_attn_mask = False self.has_drop_mask = False self.has_alibi_mask = False # NZ self.q_shape = q["shape"] self.k_shape = k["shape"] self.v_shape = v["shape"] # ND self.q_ori_shape = q["ori_shape"] self.k_ori_shape = k["ori_shape"] if attn_mask is not None: self.has_attn_mask = True self.att_mask_shape = attn_mask["shape"] if dropout_mask is not None: self.has_drop_mask = True self.drop_mask_shape = dropout_mask["shape"] if alibi_mask is not None: self.has_alibi_mask = True self.alibi_mask_shape = alibi_mask["shape"] def define_inputs_outputs(self): self.define_common_inputs() self.define_custom_inputs() self.define_outputs() def init(self): tiling_para: TilingPara = self.tiling_stgy.tiling() self.Br = tiling_para.Br self.last_Br = tiling_para.last_Br self.Bc = tiling_para.Bc self.last_Bc = tiling_para.last_Bc self.Tr = tiling_para.Tr self.Tc = tiling_para.Tc self.define_inputs_outputs() def define_common_inputs(self): """define common input gm tensors""" self.Q_gm = self.tik_instance.Tensor(FP16, self.q_shape, name="Q_gm", scope=GM) self.K_gm = self.tik_instance.Tensor(FP16, self.k_shape, name="K_gm", scope=GM) self.V_gm = self.tik_instance.Tensor(FP16, self.v_shape, name="V_gm", scope=GM) if self.has_attn_mask: self.att_mask_gm = self.tik_instance.Tensor(FP16, self.att_mask_shape, name="att_mask_gm", scope=GM) if self.has_drop_mask: self.drop_mask_gm = self.tik_instance.Tensor(FP16, self.drop_mask_shape, name="drop_mask_gm", scope=GM) if self.has_alibi_mask: self.alibi_mask_gm = self.tik_instance.Tensor(FP16, self.alibi_mask_shape, name="alibi_mask_gm", scope=GM) def do_alibi_mask(self, Sij_ub, alibi_mask_gm_offset, m_aligned, n_aligned): """load alibi mask from gm to ub, then add Sij""" with self.tik_instance.new_stmt_scope(disable_sync=False): alibi_mask_ub = self.tik_instance.Tensor(FP16, (1, n_aligned), scope=UB, name="alibi_mask_ub") self.tik_instance.data_move(alibi_mask_ub, self.alibi_mask_gm[alibi_mask_gm_offset], 0, 1, n_aligned // 16, 0, 0) alibi_mask_ub_broadcast = self.tik_ops_utils.broadcast_row(alibi_mask_ub, (m_aligned, n_aligned)) self.tik_instance.h_add(Sij_ub, Sij_ub, alibi_mask_ub_broadcast) def do_att_mask(self, Sij_ub_N1MN0, attn_mask_gm_offset, q_blk_height, kv_blk_height, q_blk_h_aligned, kv_blk_h_aligned): """load attn mask from gm to ub, then mul it by MASK_FILL_VALUE and add Sij""" with self.tik_instance.new_stmt_scope(disable_sync=False): att_mask_ub = self.tik_instance.Tensor(FP16, (kv_blk_h_aligned // self.N0, q_blk_h_aligned, self.N0), scope=UB, name="att_mask_ub") self.tik_instance.data_move(att_mask_ub, self.att_mask_gm[attn_mask_gm_offset], 0, kv_blk_height // self.N0, q_blk_height * self.N0 // 16, (self.Nq - q_blk_height) * self.N0 // 16, 0) self.tik_instance.h_mul(att_mask_ub, att_mask_ub, MASK_FILL_VALUE) self.tik_instance.h_add(Sij_ub_N1MN0, Sij_ub_N1MN0, att_mask_ub) def do_dropout_mask(self, Pij_ub, dropout_mask_gm_offset, kv_blk_h_aligned, kv_blk_height, q_blk_h_aligned, q_blk_height, precision_type=FP16, workspace=None): """load drop mask from gm to ub, then mul it by Pij""" with self.tik_instance.new_stmt_scope(disable_sync=False): dropout_mask_ub = self.tik_instance.Tensor(FP16, (q_blk_h_aligned, kv_blk_h_aligned), scope=UB, name="drop_mask_ub") self.tik_instance.data_move(dropout_mask_ub, self.drop_mask_gm[dropout_mask_gm_offset], 0, q_blk_height, kv_blk_height // 16, (self.N - kv_blk_height) // 16, 0) dropout_mask_ub = dropout_mask_ub.reshape((kv_blk_height // self.N0, q_blk_height, self.N0)) if precision_type == FP32: dropout_mask_ub_fp32 = self.tik_instance.Tensor(FP32, (kv_blk_h_aligned // self.N0, q_blk_h_aligned, self.N0), scope=UB, name="dropout_mask_ub_fp32") self.tik_instance.h_cast(dropout_mask_ub_fp32, dropout_mask_ub, "none") if workspace is None: self.tik_instance.h_mul(Pij_ub, Pij_ub, dropout_mask_ub_fp32) else: self.tik_instance.h_mul(workspace, Pij_ub, dropout_mask_ub_fp32) else: if workspace is None: self.tik_instance.h_mul(Pij_ub, Pij_ub, dropout_mask_ub) else: self.tik_instance.h_mul(workspace, Pij_ub, dropout_mask_ub) pengchengmind/flash_attention/constants.py000066400000000000000000000010051453423006600214330ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. from tbe import tik BLOCK_NUM = 16 FP16 = "float16" INT8 = "int8" INT32 = "int32" FP32 = "float32" REPEAT_SZ = 128 BLK_STRIDE = 1 REPEAT_STRIDE = 8 TRANS_CUBE_TGT = 8 FP16_MIN_VAL = -65504.0 MASK_FILL_VALUE = -10000.0 GM = tik.scope_gm L1 = tik.scope_cbuf L1OUT = tik.scope_cbuf_out UB = tik.scope_ubuf L0A = tik.scope_ca L0B = tik.scope_cb L0C = tik.scope_cc DTYPE_SIZE = { "int8": 1, "float16": 2, "int16": 2, "float32": 4, } pengchengmind/flash_attention/flash_attention_bwd.py000066400000000000000000000720511453423006600234460ustar00rootroot00000000000000import os import sys from tbe import tik sys.path.append(os.path.dirname(__file__)) from tiling_strategy.strategy import TilingStrategy from attention import FlashAttention from constants import FP16 from constants import FP32 from constants import INT32 from constants import GM from constants import L1 from constants import UB class FlashAttentionBwd(FlashAttention): """The implementation of FlashAttention backward This function contains the flash attention backward implementation used in flash attention (see paper) `FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness ` """ def __init__(self, q, k, v, O, dO, l, m, attn_mask, dropout_mask, alibi_mask, prev_block_num, next_block_num, high_precision, kernel_name, tiling_stgy: TilingStrategy, disable_debug): super().__init__(q, k, v, attn_mask, dropout_mask, alibi_mask, kernel_name, tiling_stgy, prev_block_num, next_block_num, high_precision, disable_debug) if isinstance(q, dict): self.dO_shape = dO["shape"] # [B, Nq, d] else: self.dO_shape = dO.shape self.dV_shape = self.v_shape self.dQ_shape = self.q_shape self.dK_shape = self.k_shape def define_outputs(self): """define output gm tensors""" self.dQ_gm = self.tik_instance.Tensor(FP32, self.dQ_shape, name="dQ_gm", scope=GM, is_atomic_add=True) self.dK_gm = self.tik_instance.Tensor(FP32, self.dK_shape, name="dK_gm", scope=GM, is_atomic_add=True) self.dV_gm = self.tik_instance.Tensor(FP32, self.dV_shape, name="dV_gm", scope=GM, is_atomic_add=True) def define_custom_inputs(self): """define input gm tensors""" self.O_gm = self.tik_instance.Tensor(FP16, self.O_shape, name="O_gm", scope=GM) self.dO_gm = self.tik_instance.Tensor(FP16, self.dO_shape, name="dO_gm", scope=GM) self.l_gm = self.tik_instance.Tensor(self.precision_type, self.l_shape, name="l_gm", scope=GM) self.m_gm = self.tik_instance.Tensor(FP16, self.m_shape, name="m_gm", scope=GM) def collect_inputs(self): """collect all input gm tensors into input_gm_list, the input list should keep order with the para order in Primitive and init """ input_gm_list = [ self.Q_gm, self.K_gm, self.V_gm, self.O_gm, self.dO_gm, self.l_gm, self.m_gm ] if self.has_attn_mask: input_gm_list.append(self.att_mask_gm) if self.has_drop_mask: input_gm_list.append(self.drop_mask_gm) if self.has_alibi_mask: input_gm_list.append(self.alibi_mask_gm) return input_gm_list def compute_Pij(self, Qi_l1_K1MK0_ed, KjT_l1_K1NK0_ed, m, k, n, lm_gm_offset, attn_mask_gm_offset, dropout_mask_gm_offset, alibi_mask_gm_offset): """Refer to Algorithm 4 line11-14 in FlashAttention implement Pij computation""" m_aligned = self.tik_ops_utils.up_align_to_K0(m) n_aligned = self.tik_ops_utils.up_align_to_K0(n) # Sij <- Qi * KjT Sij_ub = self.tik_ops_utils.matmul_compute(Qi_l1_K1MK0_ed, KjT_l1_K1NK0_ed, m, k, n, N1MN0_to_MN=False) if self.has_drop_mask: Pij_drop_ed_ub = self.tik_instance.Tensor(FP16, (n_aligned // self.N0, m_aligned, self.N0), name="Pij_drop_ed_ub", scope=UB) with self.tik_instance.new_stmt_scope(disable_sync=False): # Sij <- Sij / sqrt(d) TODO NZ 适配 alibi if self.has_alibi_mask: self.do_alibi_mask(Sij_ub, alibi_mask_gm_offset, m_aligned, n_aligned) # att_mask if self.has_attn_mask: self.do_att_mask(Sij_ub, attn_mask_gm_offset, m, n, m_aligned, n_aligned) # move li (ith block of l_gm) and mi (ith block of m_gm) from gm to ub li_ub = self.tik_instance.Tensor(self.precision_type, (m_aligned,), name="li_ub", scope=UB) mi_ub = self.tik_instance.Tensor(FP16, (m_aligned,), name="mi_ub", scope=UB) self.tik_ops_utils.move_vector_from_gm_to_ub(li_ub, self.l_gm, lm_gm_offset, m) self.tik_ops_utils.move_vector_from_gm_to_ub(mi_ub, self.m_gm, lm_gm_offset, m) # Sij <- Sij - mi (Br, Bc) (Br) n1 = n_aligned // self.N0 with self.tik_instance.new_stmt_scope(disable_sync=False): broadcast_mi_ub = self.tik_ops_utils.broadcast(mi_ub, (m, self.N0)) broadcast_mi_ub = broadcast_mi_ub.reshape((1, m, self.N0)) for idx in range(n1): self.tik_instance.h_sub(Sij_ub[idx, :, :], Sij_ub[idx, :, :], broadcast_mi_ub) # Sij <- diag(li)^-1 * Sij li_rec_ub = self.tik_ops_utils.calc_vec_rec(li_ub, m) with self.tik_instance.new_stmt_scope(disable_sync=False): if self.high_precision: # fp16 -> fp32 Sij_ub_fp32 = self.tik_instance.Tensor(FP32, (n_aligned // self.N0, m_aligned, self.N0), name="Sij_ub_fp32", scope=UB) self.tik_instance.h_cast(Sij_ub_fp32, Sij_ub, "none") # Sij <- exp(Sij) self.tik_instance.h_exp(Sij_ub_fp32, Sij_ub_fp32) # Sij <- diag(li)^-1 * Sij # TODO: fp32广播优化 cur_row_sum_rec = self.tik_instance.Tensor(FP32, (m_aligned, self.N0), name="cur_row_sum_rec", scope=UB) for i in range(m_aligned): src_scalar = self.tik_instance.Scalar(init_value=li_rec_ub[i], dtype=FP32) self.tik_instance.h_duplicate(cur_row_sum_rec[i, :], src_scalar) cur_row_sum_rec = cur_row_sum_rec.reshape((1, m_aligned, self.N0)) with self.tik_instance.for_range(0, n_aligned // self.N0) as idx: self.tik_instance.h_mul(Sij_ub_fp32[idx, :, :], Sij_ub_fp32[idx, :, :], cur_row_sum_rec) # fp32 -> fp16 self.tik_instance.h_cast(Sij_ub, Sij_ub_fp32, "none") else: # Sij <- exp(Sij) self.tik_instance.h_exp(Sij_ub, Sij_ub) broadcast_li_rec_ub = self.tik_ops_utils.broadcast(li_rec_ub, (m_aligned, self.N0)) broadcast_li_rec_ub = broadcast_li_rec_ub.reshape((1, m_aligned, self.N0)) for idx in range(n1): self.tik_instance.h_mul(Sij_ub[idx, :, :], Sij_ub[idx, :, :], broadcast_li_rec_ub) # dropout_mask if self.has_drop_mask: self.do_dropout_mask(Sij_ub, dropout_mask_gm_offset, n_aligned, n, m_aligned, m, workspace=Pij_drop_ed_ub) else: Pij_drop_ed_ub = Sij_ub return Sij_ub, Pij_drop_ed_ub def compute_Di(self, Di_ub, dOi_ub, qo_gm_offset, q_blk_height): """Refer to Algorithm 4 line19 in FlashAttention implement Di computation""" q_blk_height_aligned = self.tik_ops_utils.up_align_to_K0(q_blk_height) with self.tik_instance.new_stmt_scope(disable_sync=False): Oi_ub = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_height_aligned, self.N0), scope=UB, name="Oi_ub") self.tik_instance.data_move(dst=Oi_ub, src=self.O_gm[qo_gm_offset], sid=0, nburst=self.N1, burst=q_blk_height * self.N0 // 16, src_stride=(self.Nq - q_blk_height) * self.N0 // 16, dst_stride=0) # dOi*Oi (elementwise) self.tik_instance.h_mul(Oi_ub, dOi_ub, Oi_ub) # rowsum (dOi*Oi) dOi_Oi_l1 = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_height_aligned, self.N0), name="dOi_Oi_l1", scope=L1) self.cont_data_mv_1_bust(dst=dOi_Oi_l1, src=Oi_ub, burst=q_blk_height_aligned * self.d // 16) self.tik_ops_utils.row_sum_cube_impl(dOi_Oi_l1, Di_ub, q_blk_height, self.actual_d, precision_type=FP16) def compute_dSij(self, Pij_ub, dOi_l1_K1MK0_ed, VjT_K1NK0_ed, Di_ub, kv_blk_height, q_blk_height, dropout_mask_gm_offset): """Refer to Algorithm 4 line20 in FlashAttention implement dSij computation""" with self.tik_instance.new_stmt_scope(disable_sync=False): # 为了释放dPij_ub dPij_ub = self.tik_ops_utils.matmul_compute(dOi_l1_K1MK0_ed, VjT_K1NK0_ed, q_blk_height, self.actual_d, kv_blk_height, N1MN0_to_MN=False) q_blk_height_aligned = self.tik_ops_utils.up_align_to_K0(q_blk_height) kv_blk_height_aligned = self.tik_ops_utils.up_align_to_K0(kv_blk_height) # dropout_mask if self.has_drop_mask: self.do_dropout_mask(dPij_ub, dropout_mask_gm_offset, kv_blk_height_aligned, kv_blk_height, q_blk_height_aligned, q_blk_height) # dPij - Di with self.tik_instance.new_stmt_scope(disable_sync=False): broadcast_Di_ub = self.tik_ops_utils.broadcast(Di_ub, (q_blk_height_aligned, self.N0)) broadcast_Di_ub = broadcast_Di_ub.reshape((1, q_blk_height_aligned, self.N0)) n1 = kv_blk_height_aligned // self.N0 for idx in range(n1): self.tik_instance.h_sub(dPij_ub[idx, :, :], dPij_ub[idx, :, :], broadcast_Di_ub) # Pij * (dPij - Di) (elementwise) self.tik_instance.h_mul(Pij_ub, Pij_ub, dPij_ub) # 复用Pij_ub内存 return Pij_ub def update_dVj(self, PijT_l1_K1MK0_ed, dOi_l1_K1NK0_ed, kv_gm_offset, kv_blk_height, q_blk_height): """Refer to Algorithm 4 line16 in FlashAttention implement dVj update""" with self.tik_instance.new_stmt_scope(disable_sync=False): PijT_Oi_ub = self.tik_ops_utils.matmul_compute(PijT_l1_K1MK0_ed, dOi_l1_K1NK0_ed, kv_blk_height, q_blk_height, self.actual_d, N1MN0_to_MN=False, precision_type=FP32) self.tik_instance.set_atomic_add(1) # 实测 2 (fp16) 不支持 self.tik_instance.data_move(dst=self.dV_gm[kv_gm_offset], src=PijT_Oi_ub, sid=0, nburst=self.N1, burst=kv_blk_height * self.N0 // 8, src_stride=0, dst_stride=(self.Nq - kv_blk_height) * self.N0 // 8) self.tik_instance.set_atomic_add(0) def update_dQi(self, dSij_l1_K1MK0_ed, Kj_l1_K1NK0_ed, qo_gm_offset, q_blk_height, kv_blk_height): """Refer to Algorithm 4 line21 in FlashAttention implement dQi update""" with self.tik_instance.new_stmt_scope(disable_sync=False): dSij_Kj_ub = self.tik_ops_utils.matmul_compute(dSij_l1_K1MK0_ed, Kj_l1_K1NK0_ed, q_blk_height, kv_blk_height, self.actual_d, N1MN0_to_MN=False, precision_type=FP32) self.tik_instance.set_atomic_add(1) self.tik_instance.data_move(dst=self.dQ_gm[qo_gm_offset], src=dSij_Kj_ub, sid=0, nburst=self.d // self.N0, burst=q_blk_height * self.N0 // 8, src_stride=0, dst_stride=(self.Nq - q_blk_height) * self.N0 // 8) self.tik_instance.set_atomic_add(0) def update_dKj(self, dSijT_l1_K1MK0_ed, Qi_l1_K1NK0_ed, kv_gm_offset, kv_blk_height, q_blk_height): """Refer to Algorithm 4 line22 in FlashAttention implement dKi update""" with self.tik_instance.new_stmt_scope(disable_sync=False): dSijT_Qi_ub = self.tik_ops_utils.matmul_compute(dSijT_l1_K1MK0_ed, Qi_l1_K1NK0_ed, kv_blk_height, q_blk_height, self.actual_d, N1MN0_to_MN=False, precision_type=FP32) self.tik_instance.set_atomic_add(1) self.tik_instance.data_move(dst=self.dK_gm[kv_gm_offset], src=dSijT_Qi_ub, sid=0, nburst=self.d // self.N0, burst=kv_blk_height * self.N0 // 8, src_stride=0, dst_stride=(self.Nq - kv_blk_height) * self.N0 // 8) self.tik_instance.set_atomic_add(0) def compute_in_each_kv_block(self, batch_start, batch_idx, kv_blk_idx, kv_blk_height, core_idx_to_tr_info, core_idx): """The backward computation in each outer loop""" kv_blk_height_aligned = self.tik_ops_utils.up_align_to_K0(kv_blk_height) kv_gm_offset = self.get_gm_offset(batch_start, batch_idx, self.N, self.d, self.Bc, kv_blk_idx) # load KjT Kj_l1_1_K1MK0 = self.tik_instance.Tensor(FP16, (self.d // self.N0, kv_blk_height_aligned, self.N0), name="Kj_l1_1_K1MK0", scope=L1) # for Qi*KjT (算法11行) self.tik_instance.data_move(dst=Kj_l1_1_K1MK0, src=self.K_gm[kv_gm_offset], sid=0, nburst=self.N1, burst=kv_blk_height_aligned * self.N0 // 16, src_stride=(self.N - kv_blk_height_aligned) * self.N0 // 16, dst_stride=0) # load Kj Kj_l1_2 = self.tik_instance.Tensor(FP16, (kv_blk_height_aligned, self.d), name="Kj_l1_2", scope=L1) # for dSij*Kj （算法21行） with self.tik_instance.new_stmt_scope(disable_sync=False): Kj_ub = self.tik_instance.Tensor(FP16, (self.d // self.N0, kv_blk_height_aligned, self.N0), name="Kj_ub", scope=UB) self.tik_instance.data_move(dst=Kj_ub, src=self.K_gm[kv_gm_offset], sid=0, nburst=self.N1, burst=kv_blk_height_aligned * self.N0 // 16, src_stride=(self.N - kv_blk_height_aligned) * self.N0 // 16, dst_stride=0) # (N1, K, N0) -> (K, N) Kj_ub = self.tik_ops_utils.N1MN0_TO_MN(Kj_ub) # (K, N) -> (K1, N, K0) Kj_l1_2_K1NK0_ed = self.tik_ops_utils.KN_TO_K1NK0(Kj_ub, workspace_tensor=Kj_l1_2) # load VjT Vj_l1 = self.tik_instance.Tensor(FP16, (self.d // self.N0, kv_blk_height_aligned, self.N0), name="Vj_l1", scope=L1) self.tik_instance.data_move(dst=Vj_l1, src=self.V_gm[kv_gm_offset], sid=0, nburst=self.N1, burst=kv_blk_height_aligned * self.N0 // 16, src_stride=(self.N - kv_blk_height_aligned) * self.N0 // 16, dst_stride=0) tr_start_s = self.tik_instance.Scalar("int32", name="tr_start_s") tr_end_s = self.tik_instance.Scalar("int32", name="tr_end_s") tr_start_s.set_as(core_idx_to_tr_info[core_idx, batch_start + batch_idx, 0]) tr_end_s.set_as(core_idx_to_tr_info[core_idx, batch_start + batch_idx, 1]) with self.tik_instance.for_range(tr_start_s, tr_end_s, name="q_blk_idx") as q_blk_idx: # 根据 atten_mask倒三角特性，过滤无效计算 with self.tik_instance.if_scope(tik.all(kv_blk_idx - self.next_block_num <= q_blk_idx, q_blk_idx <= kv_blk_idx + self.prev_block_num)): with self.tik_instance.if_scope(q_blk_idx != self.Tr - 1): self.compute_in_each_q_block(Kj_l1_1_K1MK0, Kj_l1_2_K1NK0_ed, Vj_l1, batch_idx, batch_start, kv_gm_offset, kv_blk_height, self.Br, kv_blk_idx, q_blk_idx) with self.tik_instance.else_scope(): self.compute_in_each_q_block(Kj_l1_1_K1MK0, Kj_l1_2_K1NK0_ed, Vj_l1, batch_idx, batch_start, kv_gm_offset, kv_blk_height, self.last_Br, kv_blk_idx, q_blk_idx) def compute_in_each_q_block(self, KjT_l1_K1NK0_ed, Kj_l1_K1NK0_ed, VjT_l1_K1NK0_ed, batch_idx, batch_start, kv_gm_offset, kv_blk_height, q_blk_height, kv_blk_idx, q_blk_idx): """The backward computation in each inner loop""" kv_blk_height_alig = self.tik_ops_utils.up_align_to_K0(kv_blk_height) q_blk_height_alig = self.tik_ops_utils.up_align_to_K0(q_blk_height) # load Qi并做分形 (L1: 256K) qo_gm_offset = self.get_gm_offset(batch_start, batch_idx, self.Nq, self.d, self.Br, q_blk_idx) # load dOi并做分形，提前计算Di，否则dOi_ub无法释放，或者后面计算Di时需要再次load dOi (L1: 320K) dOi_l1_right = self.tik_instance.Tensor(FP16, (q_blk_height_alig, self.d), name="dOi_l1_right", scope=L1) # for PijT*dOi (算法16行) Di_ub = self.tik_instance.Tensor(FP16, (q_blk_height_alig,), name="Di_ub", scope=UB) # 为了释放dOi_ub，提前计算Di with self.tik_instance.new_stmt_scope(disable_sync=False): dOi_ub = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_height_alig, self.N0), name="dOi_ub", scope=UB) self.tik_instance.data_move(dst=dOi_ub, src=self.dO_gm[qo_gm_offset], sid=0, nburst=self.N1, burst=q_blk_height_alig * self.N0 // 16, src_stride=(self.Nq - q_blk_height_alig) * self.N0 // 16, dst_stride=0) self.compute_Di(Di_ub, dOi_ub, qo_gm_offset, q_blk_height) # (N1, K, N0) -> (K, N) dOi_ub = self.tik_ops_utils.N1MN0_TO_MN(dOi_ub) # (K, N) -> (K1, N, K0) dOi_l1_K1NK0_ed = self.tik_ops_utils.KN_TO_K1NK0(dOi_ub, workspace_tensor=dOi_l1_right) Qi_l1_K1MK0 = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_height_alig, self.N0), name="Qi_l1_K1MK0", scope=L1) # for Qi*KjT (算法11行) self.tik_instance.data_move(dst=Qi_l1_K1MK0, src=self.Q_gm[qo_gm_offset], sid=0, nburst=self.N1, burst=q_blk_height_alig * self.N0 // 16, src_stride=(self.Nq - q_blk_height_alig) * self.N0 // 16, dst_stride=0) # load Qi_right Qi_l1_right = self.tik_instance.Tensor(FP16, (q_blk_height_alig, self.d), name="Qi_l1_right", scope=L1) # for dSijT*Qi (算法22行) with self.tik_instance.new_stmt_scope(disable_sync=False): Qi_ub = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_height_alig, self.N0), name="Qi_ub", scope=UB) # ub是不是可以重复利用，上面的datamove就直接用这个move给l1 self.tik_instance.data_move(dst=Qi_ub, src=self.Q_gm[qo_gm_offset], sid=0, nburst=self.N1, burst=q_blk_height_alig * self.N0 // 16, src_stride=(self.N - q_blk_height_alig) * self.N0 // 16, dst_stride=0) # (N1, K, N0) -> (K, N) Qi_ub = self.tik_ops_utils.N1MN0_TO_MN(Qi_ub) # (K, N) -> (K1, N, K0) Qi_l1_K1NK0_ed = self.tik_ops_utils.KN_TO_K1NK0(Qi_ub, workspace_tensor=Qi_l1_right) lm_gm_offset = self.get_l_m_gm_offset(batch_start, batch_idx, self.Nq, self.Br, q_blk_idx) attn_mask_gm_offset, dropout_mask_gm_offset, alibi_mask_gm_offset = None, None, None if self.has_attn_mask: attn_mask_gm_offset = self.get_attn_mask_gm_offset(batch_start, batch_idx, self.Nq, self.N, self.Br, q_blk_idx, self.Bc, kv_blk_idx) if self.has_drop_mask: dropout_mask_gm_offset = self.get_drop_mask_gm_offset(batch_start, batch_idx, self.Nq, self.N, self.Br, q_blk_idx, self.Bc, kv_blk_idx) if self.has_alibi_mask: alibi_mask_gm_offset = self.get_alibi_gm_offset(batch_start, batch_idx, self.N, self.Bc, kv_blk_idx) # 算法11~15行 Pij_ub, Pij_drop_ed_ub = self.compute_Pij(Qi_l1_K1MK0, KjT_l1_K1NK0_ed, q_blk_height, self.actual_d, kv_blk_height, lm_gm_offset, attn_mask_gm_offset, dropout_mask_gm_offset, alibi_mask_gm_offset) dOi_l1_K1MK0 = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_height_alig, self.N0), name="dOi_l1_K1MK0", scope=L1) # for dOi * VjT (算法17行) okk self.tik_instance.data_move(dst=dOi_l1_K1MK0, src=self.dO_gm[qo_gm_offset], sid=0, nburst=self.N1, burst=q_blk_height_alig * self.N0 // 16, src_stride=(self.Nq - q_blk_height_alig) * self.N0 // 16, dst_stride=0) # 分形，为PijT*dOi(算法16行)做准备 # 左矩阵转置后做MK->K1MK0分形等价于不做转置的KN->K1NK0分形 (L1: 384K) Pij_l1 = self.tik_instance.Tensor(FP16, (q_blk_height_alig, kv_blk_height_alig), name="Pij_l1", scope=L1) Pij_drop_ed_ub = self.tik_ops_utils.N1MN0_TO_MN(Pij_drop_ed_ub) PijT_l1_K1MK0_ed = self.tik_ops_utils.KN_TO_K1NK0(Pij_drop_ed_ub, workspace_tensor=Pij_l1) # 算法24行：write dKj dVj (放入内循环=>增大tiling的blocksize) self.update_dVj(PijT_l1_K1MK0_ed, dOi_l1_K1NK0_ed, kv_gm_offset, kv_blk_height, q_blk_height) # (L1: 512K) dSij_l1_K1MK0_ed = self.tik_instance.Tensor(FP16, (kv_blk_height_alig // self.N0, q_blk_height_alig, self.N0), name="dSij_l1_1", scope=L1) # for dSij*Kj (算法21行) dSij_l1_2 = self.tik_instance.Tensor(FP16, (q_blk_height_alig, kv_blk_height_alig), name="dSij_l1_2", scope=L1) # for dSijT*Qi (算法22行) with self.tik_instance.new_stmt_scope(disable_sync=False): # 为了释放dSij_ub dSij_ub = self.compute_dSij(Pij_ub, dOi_l1_K1MK0, VjT_l1_K1NK0_ed, Di_ub, kv_blk_height, q_blk_height, dropout_mask_gm_offset) # for dSij*Kj (算法21行) self.cont_data_mv_1_bust(dst=dSij_l1_K1MK0_ed, src=dSij_ub, burst=kv_blk_height_alig * q_blk_height_alig // 16) dSij_ub = self.tik_ops_utils.N1MN0_TO_MN(dSij_ub) # for dSijT*Qi (算法22行) dSijT_l1_K1MK0_ed = self.tik_ops_utils.KN_TO_K1NK0(dSij_ub, workspace_tensor=dSij_l1_2) # 算法21行 self.update_dQi(dSij_l1_K1MK0_ed, Kj_l1_K1NK0_ed, qo_gm_offset, q_blk_height, kv_blk_height) # 算法22行 self.update_dKj(dSijT_l1_K1MK0_ed, Qi_l1_K1NK0_ed, kv_gm_offset, kv_blk_height, q_blk_height) def compute_one_core(self, batch_start_sc, batch_num_sc, core_idx_to_tr_info, core_idx): """The computation of FlashAttention backward on each core""" with self.tik_instance.for_range(0, batch_num_sc, name="batch_index") as batch_idx: with self.tik_instance.for_range(0, self.Tc, name="kv_blk_idx") as kv_blk_idx: with self.tik_instance.if_scope(kv_blk_idx != self.Tc - 1): self.compute_in_each_kv_block(batch_start_sc, batch_idx, kv_blk_idx, self.Bc, core_idx_to_tr_info, core_idx) with self.tik_instance.else_scope(): self.compute_in_each_kv_block(batch_start_sc, batch_idx, kv_blk_idx, self.last_Bc, core_idx_to_tr_info, core_idx) def compute_process(self): """The compute process of FlashAttention backward""" self.init() core_idx_to_batch_info, core_idx_to_tr_info = self.get_core_bath_info() with self.tik_instance.for_range(begint=0, endt=self.core_num, name="core_index", block_num=self.core_num) as core_idx: batch_start_s = self.tik_instance.Scalar("int32", name="batch_start_s") batch_num_s = self.tik_instance.Scalar("int32", name="batch_num_s") batch_start_s.set_as(core_idx_to_batch_info[core_idx, 0]) batch_num_s.set_as(core_idx_to_batch_info[core_idx, 1]) self.compute_one_core(batch_start_s, batch_num_s, core_idx_to_tr_info, core_idx) self.tik_instance.BuildCCE( kernel_name=self.kernel_name, inputs=self.collect_inputs(), outputs=self.collect_outputs(), config={"dump_cce_code": False, "save_temp_cce_file": True, "enable_const_fold": True}, enable_l2=True ) def collect_outputs(self): """collect all output gm tensors into output_gm_list, the output list should keep order with the para order in Primitive and init """ output_gm_list = [self.dQ_gm, self.dK_gm, self.dV_gm] return output_gm_list def flash_attention_grad(Q, K, V, O, dO, l, m, attn_mask, dropout_mask, alibi_mask, dq, dk, dv, prev_block_num=65536, next_block_num=65536, high_precision=False, tiling_stgy_name='xunfei', kernel_name="flash_attention_grad", disable_debug=True): """ algorithm: flash_attention_backward Parameters ---------- Q : dict. shape and dtype of input, only support float16 K : dict. shape and dtype of input, only support float16 V: dict. shape and dtype of input, only support float16 O: dict. shape and dtype of input, only support float16 dO: dict. shape and dtype of input, only support float16 l: dict. shape and dtype of input, only support float16 m: dict. shape and dtype of input, only support float16 dropout_mask: dict. shape and dtype of input, only support float16 dropout_mask: dict. shape and dtype of input, only support float16 alibi_mask: dict. shape and dtype of input, only support float16 dq: dict. shape and dtype of output, only support float16 dk: dict. shape and dtype of output, only support float16 dv: dict. shape and dtype of output, only support float16 prev_block_num: int. an attribute used to define sparse attention next_block_num: int. an attribute used to define sparse attention tiling_stgy_name: str. an attribute used to choose the tiling strategy kernel_name: str. cce kernel name, default value is real_div disable_debug: bool. whether disable debug Returns ------- tik_instance """ fa_grad = FlashAttentionBwd(Q, K, V, O, dO, l, m, attn_mask, dropout_mask, alibi_mask, prev_block_num=prev_block_num, next_block_num=next_block_num, high_precision=high_precision, kernel_name=kernel_name, tiling_stgy=TilingStrategy.from_strategy_name(tiling_stgy_name), disable_debug=disable_debug) fa_grad.compute_process() return fa_grad.tik_instance pengchengmind/flash_attention/flash_attention_fwd.py000066400000000000000000001074461453423006600234610ustar00rootroot00000000000000import os import sys from tbe import tik sys.path.append(os.path.dirname(__file__)) from tiling_strategy.strategy import TilingStrategy from constants import INT32 from constants import FP16 from constants import FP32 from constants import GM from constants import L1 from constants import UB from constants import DTYPE_SIZE from attention import FlashAttention class FlashAttentionFwd(FlashAttention): """The implementation of flash attention forward This function contains the flash attention forward implementation used in flash attention (see paper) `FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness ` """ def __init__(self, q, k, v, attn_mask, dropout_mask, alibi_mask, kernel_name, tiling_stgy: TilingStrategy, prev_block_num=65536, next_block_num=65536, high_precision=False, disable_debug=True): super().__init__(q, k, v, attn_mask, dropout_mask, alibi_mask, kernel_name, tiling_stgy, prev_block_num, next_block_num, high_precision, disable_debug) def define_custom_inputs(self): pass def define_outputs(self): """define output gm tensors""" self.O_gm = self.tik_instance.Tensor(FP16, self.O_shape, name="O_gm", scope=GM, is_atomic_add=True) if self.high_precision: # 累加更新O FP32临时空间 self.O_gm_workspace = self.tik_instance.Tensor(FP32, self.O_shape, name="O_gm_workspace", scope=GM, is_workspace=True, is_atomic_add=True) self.l_gm = self.tik_instance.Tensor(self.precision_type, self.l_shape, name="l_gm", scope=GM, is_atomic_add=True) self.m_gm = self.tik_instance.Tensor(FP16, self.m_shape, name="m_gm", scope=GM, is_atomic_add=True) def softmax_compute(self, Sij_ub, mij_ub, lij_ub, m, n): """Refer to Algorithm 2 line12 Calculate softmax. :param Sij_ub: with shape M x N or N1 x M x N0, 使用Sij空间返回Pij 提高UB利用率 :param mij_ub: :param lij_ub: :param m: :param n: :return: """ m_aligned = self.tik_ops_utils.up_align_to_K0(m) n_aligned = self.tik_ops_utils.up_align_to_K0(n) n0 = 16 n1 = n // 16 # 当前只支持 n % 16 == 0 # TODO：当 n % 16 ！=0时，无效的尾块不能参与计算rowmax # calc rowmax of Sij with self.tik_instance.new_stmt_scope(disable_sync=False): mn0_block_max = self.tik_instance.Tensor(FP16, (1, m, n0), name="mn0_block_max", scope=UB) self.cont_data_mv_1_bust(dst=mn0_block_max, src=Sij_ub, burst=m) # 用第一个(m, n0)始化，少一次duplicate和max with self.tik_instance.for_range(1, n1) as idx: self.tik_instance.h_max(mn0_block_max, mn0_block_max, Sij_ub[idx, :, :]) mn0_block_max = mn0_block_max.reshape((m, n0)) self.tik_instance.h_reduce_max(mij_ub, mn0_block_max, 1) # Sij - mij with self.tik_instance.new_stmt_scope(disable_sync=False): broadcast_mij_ub = self.tik_ops_utils.broadcast(mij_ub, (m, n0)) broadcast_mij_ub = broadcast_mij_ub.reshape((1, m, n0)) for idx in range(n1): self.tik_instance.h_sub(Sij_ub[idx, :, :], Sij_ub[idx, :, :], broadcast_mij_ub) # exp if self.high_precision: # fp16 -> fp32 Sij_ub_fp32 = self.tik_instance.Tensor( FP32, (n_aligned // 16, m_aligned, 16), name="Sij_ub_fp32", scope=UB ) with self.tik_instance.new_stmt_scope(disable_sync=False): self.tik_instance.h_cast(Sij_ub_fp32, Sij_ub, "none") self.tik_instance.h_exp(Sij_ub_fp32, Sij_ub_fp32) self.tik_instance.h_cast(Sij_ub, Sij_ub_fp32, "none") else: self.tik_instance.h_exp(Sij_ub, Sij_ub) # cube impl rowsum Sij_l1_K1MK0_ed = self.tik_instance.Tensor(FP16, (n_aligned // 16, m_aligned, 16), name="Sij_l1_K1MK0_ed", scope=L1) self.cont_data_mv_1_bust(dst=Sij_l1_K1MK0_ed, src=Sij_ub, burst=m * n // 16) Sij_row_sum_ub = self.tik_ops_utils.row_sum_cube_impl(Sij_l1_K1MK0_ed, lij_ub, m, n, self.precision_type) if self.high_precision: return Sij_ub_fp32, mij_ub, Sij_row_sum_ub if self.has_drop_mask: return Sij_ub, mij_ub, Sij_row_sum_ub return Sij_l1_K1MK0_ed, mij_ub, Sij_row_sum_ub def update_m_l(self, mi_old_ub, mij_ub, li_old_ub, lij_ub, vec_len): """Refer to Algorithm 2 line13 mi_new = max(mi, mij), li_new = exp(mi-mi_new)*li + exp(mij - mi_new) * lij """ dtype = li_old_ub.dtype vec_len_aligned = self.tik_ops_utils.up_align_to_K0(vec_len) mi_new_ub = self.tik_instance.Tensor(FP16, (vec_len_aligned,), name="mi_new_ub", scope=UB) li_new_ub = self.tik_instance.Tensor(dtype, (vec_len_aligned,), name="li_new_ub", scope=UB) # 1 calculate mi_new = max(mi, mij) self.tik_instance.h_max(mi_new_ub, mi_old_ub, mij_ub) # 2 calculate li_new = exp(mi-mi_new)*li + exp(mij-mi_new)*lij # 2.1 相减，求指数 self.tik_instance.h_sub(mi_old_ub, mi_old_ub, mi_new_ub) # mi-mi_new self.tik_instance.h_exp(mi_old_ub, mi_old_ub) # exp(mi-mi_new) # 2.2 相减，求指数 self.tik_instance.h_sub(mij_ub, mij_ub, mi_new_ub) # mij-mi_new self.tik_instance.h_exp(mij_ub, mij_ub) # exp(mij-mi_new) # 2.3 相乘，相加 exp(m_ij_ub-mi_new)*li + exp(m_ij_ub-mi_new) * lij with self.tik_instance.new_stmt_scope(disable_sync=False): mul_li_ub = self.tik_instance.Tensor(dtype, (vec_len_aligned,), scope=UB, name="mul_li_ub") mul_lij_ub = self.tik_instance.Tensor(dtype, (vec_len_aligned,), scope=UB, name="mul_lij_ub") if self.high_precision: self.tik_instance.h_cast(mul_li_ub, mi_old_ub, "none") self.tik_instance.h_cast(mul_lij_ub, mij_ub, "none") self.tik_instance.h_mul(mul_li_ub, mul_li_ub, li_old_ub) self.tik_instance.h_mul(mul_lij_ub, mul_lij_ub, lij_ub) else: self.tik_instance.h_mul(mul_li_ub, mi_old_ub, li_old_ub) self.tik_instance.h_mul(mul_lij_ub, mij_ub, lij_ub) self.tik_instance.h_add(li_new_ub, mul_li_ub, mul_lij_ub) return mi_new_ub, li_new_ub # TODO fp32未NZ适配 def update_o_m_l_fp32(self, Pij_ub_fp32, Vj_l1_K1NK0_ed, Pij_ub, mij_ub, lij_ub, batch_start, batch_idx, kv_blk_idx, kv_blk_height, q_blk_idx, block_h): """ load o m l from gm and update them in ub, then write them back to gm :param Pij_Vj_ub: input tensor with shape of (q_blk_h_aligned, self.d) :param mij_ub: input tensor with shape of (Br) :param lij_ub: input tensor with shape of (Br) :param batch_start: :param batch_idx: :param kv_blk_idx: :param q_blk_idx: :param block_h: :return: None """ vec_gm_offset = self.get_l_m_gm_offset(batch_start, batch_idx, self.Nq, self.Br, q_blk_idx) o_gm_offset = self.get_gm_offset(batch_start, batch_idx, self.Nq, self.d, self.Br, q_blk_idx) block_h_aligned = self.tik_ops_utils.up_align_to_K0(block_h) block_k_aligned_aligned = self.tik_ops_utils.up_align_to_K0(kv_blk_height) n1 = block_k_aligned_aligned // self.N0 # 第一次外循环时，不用走更新逻辑， 减少的无用计算 # mi_new = mij, li_new=lij. Oi <- Pij*Vj/Li_new with self.tik_instance.if_scope(tik.any(kv_blk_idx == 0, kv_blk_idx + self.prev_block_num == q_blk_idx)): self.tik_ops_utils.move_vector_from_ub_to_gm(self.l_gm, lij_ub, vec_gm_offset, block_h) self.tik_ops_utils.move_vector_from_ub_to_gm(self.m_gm, mij_ub, vec_gm_offset, block_h) li_new_rec_ub = self.tik_ops_utils.calc_vec_rec(lij_ub, block_h) # TODO：FP32广播接口，去掉循环 => 提升性能 vec_ub = self.tik_instance.Tensor(FP32, (block_h, self.N0), name="vec_ub", scope=UB) for i in range(block_h): src_scalar = self.tik_instance.Scalar(init_value=li_new_rec_ub[i], dtype=FP32) self.tik_instance.h_duplicate(vec_ub[i, :], src_scalar) vec_ub = vec_ub.reshape((1, block_h, self.N0)) with self.tik_instance.for_range(0, n1) as idx: self.tik_instance.h_mul(Pij_ub_fp32[idx, :, :], Pij_ub_fp32[idx, :, :], vec_ub) self.tik_instance.h_cast(Pij_ub, Pij_ub_fp32, "none") # ub -> l1 Pij_l1_K1MK0_ed = self.tik_instance.Tensor( FP16, (block_k_aligned_aligned // 16, block_h_aligned, 16), name="Pij_l1_K1MK0_ed", scope=L1 ) # Pij_l1_K1MK0_ed = self.tik_ops_utils.MK_TO_K1MK0(Pij_ub, workspace_tensor=Pij_l1_K1MK0_ed) self.cont_data_mv_1_bust(dst=Pij_l1_K1MK0_ed, src=Pij_ub, burst=block_k_aligned_aligned * block_h_aligned // 16) # UB 使用: Pij_ub + Pij_ub_f32 * 2 Pij_Vj_matmul_res_ub = self.tik_ops_utils.matmul_compute(Pij_l1_K1MK0_ed, Vj_l1_K1NK0_ed, block_h, kv_blk_height, self.actual_d, N1MN0_to_MN=False, precision_type=self.precision_type) # Pij*Vj self.tik_instance.data_move(dst=self.O_gm_workspace[o_gm_offset], src=Pij_Vj_matmul_res_ub, sid=0, nburst=self.N1, burst=block_h * self.N0 // 8, src_stride=0, dst_stride=(self.Nq - block_h_aligned) * self.N0 // 8) with self.tik_instance.else_scope(): mi_ub = self.tik_instance.Tensor(FP16, (block_h_aligned,), name="mi_old_ub", scope=UB) li_ub = self.tik_instance.Tensor(FP32, (block_h_aligned,), name="li_ub", scope=UB) self.tik_ops_utils.move_vector_from_gm_to_ub(mi_ub, self.m_gm, vec_gm_offset, block_h) self.tik_ops_utils.move_vector_from_gm_to_ub(li_ub, self.l_gm, vec_gm_offset, block_h) # 更新 l, m mi_new_ub, li_new_ub = self.update_m_l(mi_ub, mij_ub, li_ub, lij_ub, block_h) self.tik_ops_utils.move_vector_from_ub_to_gm(self.l_gm, li_new_ub, vec_gm_offset, block_h) self.tik_ops_utils.move_vector_from_ub_to_gm(self.m_gm, mi_new_ub, vec_gm_offset, block_h) exp_m_old_fp32 = self.tik_instance.Tensor(FP32, (block_h_aligned,), scope=UB, name="exp_m_old_fp32") exp_m_cur_fp32 = self.tik_instance.Tensor(FP32, (block_h_aligned,), scope=UB, name="exp_m_cur_fp32") self.tik_instance.h_cast(exp_m_old_fp32, mi_ub, "none") self.tik_instance.h_cast(exp_m_cur_fp32, mij_ub, "none") li_new_rec_ub = self.tik_ops_utils.calc_vec_rec(li_new_ub, block_h) self.tik_instance.h_mul(exp_m_cur_fp32, exp_m_cur_fp32, li_new_rec_ub) exp_m_cur_fp32_vec_ub = self.tik_instance.Tensor(FP32, (block_h, self.N0), name="exp_m_cur_fp32_vec_ub", scope=UB) for i in range(block_h): src_scalar = self.tik_instance.Scalar(init_value=exp_m_cur_fp32[i], dtype=FP32) self.tik_instance.h_duplicate(exp_m_cur_fp32_vec_ub[i, :], src_scalar) exp_m_cur_fp32_vec_ub = exp_m_cur_fp32_vec_ub.reshape((1, block_h, self.N0)) with self.tik_instance.for_range(0, n1) as idx: self.tik_instance.h_mul(Pij_ub_fp32[idx, :, :], Pij_ub_fp32[idx, :, :], exp_m_cur_fp32_vec_ub) self.tik_instance.h_cast(Pij_ub, Pij_ub_fp32, "none") # ub -> l1 Pij_l1_K1MK0_ed = self.tik_instance.Tensor( FP16, (block_k_aligned_aligned // 16, block_h_aligned, 16), name="Pij_l1_K1MK0_ed", scope=L1 ) self.cont_data_mv_1_bust(dst=Pij_l1_K1MK0_ed, src=Pij_ub, burst=block_k_aligned_aligned * block_h_aligned // 16) Pij_Vj_matmul_res_ub = self.tik_ops_utils.matmul_compute(Pij_l1_K1MK0_ed, Vj_l1_K1NK0_ed, block_h, kv_blk_height, self.actual_d, N1MN0_to_MN=False, precision_type=self.precision_type) # Pij*Vj n1, m, n0 = Pij_Vj_matmul_res_ub.shape self.tik_instance.h_mul(li_new_rec_ub, li_new_rec_ub, li_ub) self.tik_instance.h_mul(li_new_rec_ub, li_new_rec_ub, exp_m_old_fp32) li_new_rec_vec_ub = self.tik_instance.Tensor(FP32, (block_h, self.N0), name="li_new_rec_vec_ub", scope=UB) for i in range(block_h): src_scalar = self.tik_instance.Scalar(init_value=li_new_rec_ub[i], dtype=FP32) self.tik_instance.h_duplicate(li_new_rec_vec_ub[i, :], src_scalar) li_new_rec_vec_ub = li_new_rec_vec_ub.reshape((1, block_h, self.N0)) # 载入Oi 到 UB n1_front = n1 // 2 n1_tail = n1 - n1_front with self.tik_instance.new_stmt_scope(disable_sync=False): Oi_ub = self.tik_instance.Tensor(FP32, (n1_front, m, n0), name="Oi_ub", scope=UB) self.tik_instance.data_move(dst=Oi_ub, src=self.O_gm_workspace[o_gm_offset], sid=0, nburst=n1_front, burst=m * self.N0 // 8, src_stride=(self.Nq - m) * self.N0 // 8, dst_stride=0) with self.tik_instance.for_range(0, n1_front) as idx: self.tik_instance.h_mul(Oi_ub[idx, :, :], Oi_ub[idx, :, :], li_new_rec_vec_ub) self.tik_instance.h_add(Oi_ub, Oi_ub, Pij_Vj_matmul_res_ub[:n1_front, :, :]) self.tik_instance.data_move(dst=self.O_gm_workspace[o_gm_offset], src=Oi_ub, sid=0, nburst=n1_front, burst=block_h * self.N0 // 8, src_stride=0, dst_stride=(self.Nq - block_h_aligned) * self.N0 // 8) with self.tik_instance.new_stmt_scope(disable_sync=False): Oi_ub = self.tik_instance.Tensor(FP32, (n1_tail, m, n0), name="Oi_ub", scope=UB) o_cur_offset = o_gm_offset + n1_front * self.Nq * n0 self.tik_instance.data_move(dst=Oi_ub, src=self.O_gm_workspace[o_cur_offset], sid=0, nburst=n1_tail, burst=m * self.N0 // 8, src_stride=(self.Nq - m) * self.N0 // 8, dst_stride=0) with self.tik_instance.for_range(0, n1_tail) as idx: self.tik_instance.h_mul(Oi_ub[idx, :, :], Oi_ub[idx, :, :], li_new_rec_vec_ub) self.tik_instance.h_add(Oi_ub, Oi_ub, Pij_Vj_matmul_res_ub[n1_front:, :, :]) self.tik_instance.data_move(dst=self.O_gm_workspace[o_cur_offset], src=Oi_ub, sid=0, nburst=n1_tail, burst=block_h * self.N0 // 8, src_stride=0, dst_stride=(self.Nq - block_h_aligned) * self.N0 // 8) def exp_Pij_Vj(self, exp_mij_sub_mi_new, Pij_Vj_ub, block_h_aligned): """Refer to Algorithm 2 line15 exp(mij - mi_new) * Pij * Vj """ with self.tik_instance.new_stmt_scope(disable_sync=False): broadcast_exp_mij_sub_mi_new = self.tik_ops_utils.broadcast(exp_mij_sub_mi_new, (block_h_aligned, self.d)) self.tik_instance.h_mul(Pij_Vj_ub, Pij_Vj_ub, broadcast_exp_mij_sub_mi_new) return Pij_Vj_ub def update_o_m_l(self, Pij_l1_K1MK0_ed, Vj_l1_K1NK0_ed, mij_ub, lij_ub, batch_start, batch_idx, kv_blk_idx, kv_blk_height, q_blk_idx, block_h): """Refer to Algorithm 2 line13 and line15 in FlashAttention load o m l from gm and update them in ub, then write them back to gm :param Pij_Vj_ub: input tensor with shape of (q_blk_h_aligned, self.d) :param mij_ub: input tensor with shape of (Br) :param lij_ub: input tensor with shape of (Br) :param batch_start: :param batch_idx: :param kv_blk_idx: :param q_blk_idx: :param block_h: :return: None """ vec_gm_offset = self.get_l_m_gm_offset(batch_start, batch_idx, self.Nq, self.Br, q_blk_idx) o_gm_offset = self.get_gm_offset( batch_start, batch_idx, self.Nq, self.d, self.Br, q_blk_idx ) block_h_aligned = self.tik_ops_utils.up_align_to_K0(block_h) Pij_Vj_matmul_res_ub = self.tik_ops_utils.matmul_compute(Pij_l1_K1MK0_ed, Vj_l1_K1NK0_ed, block_h, kv_blk_height, self.actual_d, N1MN0_to_MN=False) # Pij*Vj n1, m, n0 = Pij_Vj_matmul_res_ub.shape # 第一次外循环时，不用走更新逻辑， 减少的无用计算 # calculate mi_new = mij, li_new=lij. Oi <- Pij*Vj/Li_new with self.tik_instance.if_scope(tik.any(kv_blk_idx == 0, kv_blk_idx + self.prev_block_num == q_blk_idx)): self.tik_ops_utils.move_vector_from_ub_to_gm(self.l_gm, lij_ub, vec_gm_offset, block_h) self.tik_ops_utils.move_vector_from_ub_to_gm(self.m_gm, mij_ub, vec_gm_offset, block_h) li_new_rec_ub = self.tik_ops_utils.calc_vec_rec(lij_ub, block_h) # TODO：NZ版double buffer broadcast_li_new_rec_ub = self.tik_ops_utils.broadcast(li_new_rec_ub, (m, n0)) broadcast_li_new_rec_ub = broadcast_li_new_rec_ub.reshape((1, m, n0)) with self.tik_instance.for_range(0, n1) as idx: self.tik_instance.h_mul(Pij_Vj_matmul_res_ub[idx, :, :], Pij_Vj_matmul_res_ub[idx, :, :], broadcast_li_new_rec_ub) self.tik_instance.data_move(dst=self.O_gm[o_gm_offset], src=Pij_Vj_matmul_res_ub, sid=0, nburst=self.N1, burst=block_h * self.N0 // 16, src_stride=0, dst_stride=(self.Nq - block_h_aligned) * self.N0 // 16) with self.tik_instance.else_scope(): mi_ub = self.tik_instance.Tensor(FP16, (block_h_aligned,), name="mi_old_ub", scope=UB) li_ub = self.tik_instance.Tensor(FP16, (block_h_aligned,), name="li_ub", scope=UB) self.tik_ops_utils.move_vector_from_gm_to_ub(mi_ub, self.m_gm, vec_gm_offset, block_h) self.tik_ops_utils.move_vector_from_gm_to_ub(li_ub, self.l_gm, vec_gm_offset, block_h) # 更新 l, m mi_new_ub, li_new_ub = self.update_m_l(mi_ub, mij_ub, li_ub, lij_ub, block_h) self.tik_ops_utils.move_vector_from_ub_to_gm(self.l_gm, li_new_ub, vec_gm_offset, block_h) self.tik_ops_utils.move_vector_from_ub_to_gm(self.m_gm, mi_new_ub, vec_gm_offset, block_h) exp_mi_sub_mi_new = mi_ub exp_mij_sub_mi_new = mij_ub li_new_rec_ub = self.tik_ops_utils.calc_vec_rec(li_new_ub, block_h) # TODO：NZ版double buffer # scale1 <- li * exp(mi - mi_new) / li_new self.tik_instance.h_mul(li_ub, li_ub, exp_mi_sub_mi_new) self.tik_instance.h_mul(li_ub, li_ub, li_new_rec_ub) scale1 = li_ub # scale2 <- exp(mij - mi_new) / li_new self.tik_instance.h_mul(exp_mij_sub_mi_new, exp_mij_sub_mi_new, li_new_rec_ub) scale2 = exp_mij_sub_mi_new Oi_ub = self.tik_instance.Tensor(FP16, (n1, m, n0), name="Oi_ub", scope=UB) self.tik_instance.data_move(dst=Oi_ub, src=self.O_gm[o_gm_offset], sid=0, nburst=self.N1, burst=m * self.N0 // 16, src_stride=(self.Nq - m) * self.N0 // 16, dst_stride=0) broadcast_scale1 = self.tik_ops_utils.broadcast(scale1, (m, n0)) broadcast_scale1 = broadcast_scale1.reshape((1, m, n0)) with self.tik_instance.for_range(0, n1) as idx: self.tik_instance.h_mul(Oi_ub[idx, :, :], Oi_ub[idx, :, :], broadcast_scale1) # vec: scale2 * Pij_Vj broadcast_scale2 = self.tik_ops_utils.broadcast(scale2, (m, n0)) broadcast_scale2 = broadcast_scale2.reshape((1, m, n0)) with self.tik_instance.for_range(0, n1) as idx: self.tik_instance.h_mul(Pij_Vj_matmul_res_ub[idx, :, :], Pij_Vj_matmul_res_ub[idx, :, :], broadcast_scale2) self.tik_instance.h_add(Oi_ub, Oi_ub, Pij_Vj_matmul_res_ub) self.tik_instance.data_move(dst=self.O_gm[o_gm_offset], src=Oi_ub, sid=0, nburst=self.N1, burst=block_h * self.N0 // 16, src_stride=0, dst_stride=(self.Nq - block_h_aligned) * self.N0 // 16) def compute_in_each_kv_block(self, batch_start, batch_idx, kv_blk_idx, kv_blk_height, core_idx_to_tr_info, core_idx): """The forward computation in each outer loop""" kv_blk_height_aligned = self.tik_ops_utils.up_align_to_K0(kv_blk_height) kv_gm_offset = self.get_gm_offset(batch_start, batch_idx, self.N, self.d, self.Bc, kv_blk_idx) # load Kj (kv_blk_idx_th block of K_gm) KjT_l1_K1MK0_ed = self.tik_instance.Tensor(FP16, (self.d // self.N0, kv_blk_height_aligned, self.N0), name="KjT_l1_K1MK0_ed", scope=L1) self.tik_instance.data_move(dst=KjT_l1_K1MK0_ed, src=self.K_gm[kv_gm_offset], sid=0, nburst=self.N1, burst=kv_blk_height_aligned * self.N0 // 16, src_stride=(self.N - kv_blk_height_aligned) * self.N0 // 16, dst_stride=0) # load Vj (kv_blk_idx_th block of V_gm), then reorder for Pij*Vj Vj_l1 = self.tik_instance.Tensor(FP16, (kv_blk_height_aligned, self.d), name="Vj_l1", scope=L1) with self.tik_instance.new_stmt_scope(disable_sync=False): # TODO: 外部转置或V的快速转换方案探索 Vj_ub = self.tik_instance.Tensor(FP16, (self.d // self.N0, kv_blk_height_aligned, self.N0), name="Vj_ub", scope=UB) self.tik_instance.data_move(dst=Vj_ub, src=self.V_gm[kv_gm_offset], sid=0, nburst=self.N1, burst=kv_blk_height_aligned * self.N0 // 16, src_stride=(self.N - kv_blk_height_aligned) * self.N0 // 16, dst_stride=0) # (N1, K, N0) -> (K, N) Vj_ub = self.tik_ops_utils.N1MN0_TO_MN(Vj_ub) # (K, N) -> (K1, N, K0) Vj_l1_K1NK0_ed = self.tik_ops_utils.KN_TO_K1NK0(Vj_ub, workspace_tensor=Vj_l1) tr_start_s = self.tik_instance.Scalar("int32", name="tr_start_s") tr_end_s = self.tik_instance.Scalar("int32", name="tr_end_s") tr_start_s.set_as(core_idx_to_tr_info[core_idx, batch_start + batch_idx, 0]) tr_end_s.set_as(core_idx_to_tr_info[core_idx, batch_start + batch_idx, 1]) with self.tik_instance.for_range(tr_start_s, tr_end_s, name="q_blk_idx") as q_blk_idx: # 根据atten_mask倒三角特性，过滤无效计算 with self.tik_instance.if_scope(tik.all(kv_blk_idx - self.next_block_num <= q_blk_idx, q_blk_idx <= kv_blk_idx + self.prev_block_num)): with self.tik_instance.if_scope(q_blk_idx != self.Tr - 1): self.compute_in_each_q_block(KjT_l1_K1MK0_ed, Vj_l1_K1NK0_ed, batch_idx, batch_start, kv_blk_height, self.Br, q_blk_idx, kv_blk_idx) with self.tik_instance.else_scope(): self.compute_in_each_q_block(KjT_l1_K1MK0_ed, Vj_l1_K1NK0_ed, batch_idx, batch_start, kv_blk_height, self.last_Br, q_blk_idx, kv_blk_idx) def compute_in_each_q_block(self, KjT_l1_K1MK0_ed, Vj_l1_K1NK0_ed, batch_idx, batch_start, kv_blk_height, q_blk_height, q_blk_idx, kv_blk_idx): """The forward computation in each inner loop""" kv_blk_h_aligned = self.tik_ops_utils.up_align_to_K0(kv_blk_height) q_blk_h_aligned = self.tik_ops_utils.up_align_to_K0(q_blk_height) # load Qi (q_blk_idx_th block of Q_gm), then reorder it fo Qi*KjT q_gm_offset = self.get_gm_offset(batch_start, batch_idx, self.Nq, self.d, self.Br, q_blk_idx) Qi_l1_K1MK0_ed = self.tik_instance.Tensor(FP16, (self.d // self.N0, q_blk_h_aligned, self.N0), scope=L1, name="Qi_l1_K1MK0_ed") self.tik_instance.data_move(dst=Qi_l1_K1MK0_ed, src=self.Q_gm[q_gm_offset], sid=0, nburst=self.N1, burst=q_blk_h_aligned * self.N0 // 16, src_stride=(self.Nq - q_blk_h_aligned) * self.N0 // 16, dst_stride=0) lij_ub = self.tik_instance.Tensor(self.precision_type, (q_blk_h_aligned,), scope=UB, name="lij_ub") mij_ub = self.tik_instance.Tensor(FP16, (q_blk_h_aligned,), scope=UB, name="mij_ub") # QK^T Q shape: (q_blk_h_aligned, self.d), K^T shape: (self.d, kv_blk_h_aligned) # M, N (q_blk_h_aligned, kv_blk_h_aligned) Sij_ub_N1MN0 = self.tik_ops_utils.matmul_compute(Qi_l1_K1MK0_ed, KjT_l1_K1MK0_ed, m=q_blk_height, k=self.actual_d, n=kv_blk_height, N1MN0_to_MN=False) # Qi*KjT if self.has_alibi_mask: # TODO NZ需要维护 alibi_mask_gm_offset = self.get_alibi_gm_offset(batch_start, batch_idx, self.N, self.Bc, kv_blk_idx) self.do_alibi_mask(Sij_ub_N1MN0, alibi_mask_gm_offset, q_blk_h_aligned, kv_blk_h_aligned) # att_mask if self.has_attn_mask: attn_mask_gm_offset = self.get_attn_mask_gm_offset(batch_start, batch_idx, self.Nq, self.N, self.Br, q_blk_idx, self.Bc, kv_blk_idx) self.do_att_mask(Sij_ub_N1MN0, attn_mask_gm_offset, q_blk_height, kv_blk_height, q_blk_h_aligned, kv_blk_h_aligned) Pij_N1MN0, mij_ub, lij_ub = self.softmax_compute( Sij_ub_N1MN0, mij_ub, lij_ub, q_blk_height, kv_blk_height ) # self.high_precision=True, Pij_ub return type fp32 # dropout_mask if self.has_drop_mask: dropout_mask_gm_offset = self.get_drop_mask_gm_offset(batch_start, batch_idx, self.Nq, self.N, self.Br, q_blk_idx, self.Bc, kv_blk_idx) self.do_dropout_mask(Pij_N1MN0, dropout_mask_gm_offset, kv_blk_h_aligned, kv_blk_height, q_blk_h_aligned, q_blk_height, precision_type=self.precision_type) if not self.high_precision: Pij_l1_K1MK0_ed = self.tik_instance.Tensor(FP16, (kv_blk_h_aligned // self.N0, q_blk_h_aligned, self.N0), name="Pij_l1_K1MK0_ed", scope=L1) self.cont_data_mv_1_bust(dst=Pij_l1_K1MK0_ed, src=Pij_N1MN0, burst=kv_blk_h_aligned * q_blk_h_aligned // 16) Pij_N1MN0 = Pij_l1_K1MK0_ed if self.high_precision: self.update_o_m_l_fp32( Pij_N1MN0, Vj_l1_K1NK0_ed, Sij_ub_N1MN0, # fp16, 与Pij_ub内存共用, fp32, 内存保留做类型转换使用，避免重新申请; mij_ub, lij_ub, batch_start, batch_idx, kv_blk_idx, kv_blk_height, q_blk_idx, q_blk_height ) else: self.update_o_m_l( Pij_N1MN0, Vj_l1_K1NK0_ed, mij_ub, lij_ub, batch_start, batch_idx, kv_blk_idx, kv_blk_height, q_blk_idx, q_blk_height ) def compute_one_core(self, batch_start_sc, batch_num_sc, core_idx_to_tr_info, core_idx): """The computation of FlashAttention forward on each core""" with self.tik_instance.for_range(0, batch_num_sc, name="batch_index") as batch_idx: with self.tik_instance.for_range(0, self.Tc, name="kv_blk_idx") as kv_blk_idx: with self.tik_instance.if_scope(kv_blk_idx != self.Tc - 1): self.compute_in_each_kv_block(batch_start_sc, batch_idx, kv_blk_idx, self.Bc, core_idx_to_tr_info, core_idx) with self.tik_instance.else_scope(): self.compute_in_each_kv_block(batch_start_sc, batch_idx, kv_blk_idx, self.last_Bc, core_idx_to_tr_info, core_idx) if self.high_precision: # O_gm_workspace -> O_gm block_h = 128 gm_offset = (batch_start_sc + batch_idx) * (self.Nq * self.d) temp_ub = self.tik_instance.Tensor(FP32, (block_h, self.d), name="temp_ub", scope=UB) temp_ub_fp16 = self.tik_instance.Tensor(FP16, (block_h, self.d), name="temp_ub_fp16", scope=UB) if self.Nq // block_h // 2 > 0: with self.tik_instance.for_range(0, self.Nq // block_h // 2) as i: with self.tik_instance.for_range(0, 2, thread_num=2) as t_idx: index = i * 2 + t_idx gm_offset += index * (block_h * self.d) self.cont_data_mv_1_bust(dst=temp_ub, src=self.O_gm_workspace[gm_offset], burst=block_h * self.d * DTYPE_SIZE[FP32] // 32) self.tik_instance.h_cast(temp_ub_fp16, temp_ub, "none") self.cont_data_mv_1_bust(dst=self.O_gm[gm_offset], src=temp_ub_fp16, burst=block_h * self.d * DTYPE_SIZE[FP16] // 32) if self.Nq % (block_h * 2) > 0: # 尾块处理 gm_offset = (batch_start_sc + batch_idx) * (self.Nq * self.d) + \ (self.Nq // (block_h * 2) * 2) * (block_h * self.d) last_block_h = self.Nq % (block_h * 2) self.cont_data_mv_1_bust(dst=temp_ub, src=self.O_gm_workspace[gm_offset], burst=last_block_h * self.d * DTYPE_SIZE[FP32] // 32) self.tik_instance.h_cast(temp_ub_fp16, temp_ub, "none") self.cont_data_mv_1_bust(dst=self.O_gm[gm_offset], src=temp_ub_fp16, burst=last_block_h * self.d * DTYPE_SIZE[FP16] // 32) def collect_inputs(self): """collect all input gm tensors into input_gm_list, the input list should keep order with the para order in Primitive and init """ input_gm_list = [self.Q_gm, self.K_gm, self.V_gm] if self.has_attn_mask: input_gm_list.append(self.att_mask_gm) if self.has_drop_mask: input_gm_list.append(self.drop_mask_gm) if self.has_alibi_mask: input_gm_list.append(self.alibi_mask_gm) return input_gm_list def collect_outputs(self): """collect all output gm tensors into output_gm_list, the output list should keep order with the para order in Primitive and init """ output_gm_list = [self.O_gm, self.l_gm, self.m_gm] return output_gm_list def compute_process(self): """The compute process of FlashAttention forward""" self.init() core_idx_to_batch_info, core_idx_to_tr_info = self.get_core_bath_info() with self.tik_instance.for_range(begint=0, endt=self.core_num, name="core_index", block_num=self.core_num) as core_idx: batch_start_s = self.tik_instance.Scalar("int32", name="batch_start_s") batch_num_s = self.tik_instance.Scalar("int32", name="batch_num_s") batch_start_s.set_as(core_idx_to_batch_info[core_idx, 0]) batch_num_s.set_as(core_idx_to_batch_info[core_idx, 1]) self.compute_one_core(batch_start_s, batch_num_s, core_idx_to_tr_info, core_idx) self.tik_instance.BuildCCE( kernel_name=self.kernel_name, inputs=self.collect_inputs(), outputs=self.collect_outputs(), config={"dump_cce_code": False, "save_temp_cce_file": True, "enable_const_fold": True}, enable_l2=True ) def flash_attention(q, k, v, attn_mask, dropout_mask, alibi_mask, y, l, m, prev_block_num=65536, next_block_num=65536, high_precision=False, tiling_stgy_name='xunfei', kernel_name="flash_attention", disable_debug=True): """ algorithm: flash_attention_backward Parameters ---------- q : dict. shape and dtype of input, only support float16 k : dict. shape and dtype of input, only support float16 v: dict. shape and dtype of input, only support float16 attn_mask: dict. shape and dtype of input, only support float16 dropout_mask: dict. shape and dtype of input, only support float16 dropout_mask: dict. shape and dtype of input, only support float16 alibi_mask: dict. shape and dtype of input, only support float16 y: dict. shape and dtype of output, only support float16 l: dict. shape and dtype of output, only support float16 m: dict. shape and dtype of output, only support float16 prev_block_num: int. an attribute used to define sparse attention next_block_num: int. an attribute used to define sparse attention tiling_stgy_name: str. an attribute used to choose the tiling strategy kernel_name: str. cce kernel name, default value is real_div disable_debug: bool. whether disable debug Returns ------- tik_instance """ fa = FlashAttentionFwd(q=q, k=k, v=v, attn_mask=attn_mask, dropout_mask=dropout_mask, alibi_mask=alibi_mask, kernel_name=kernel_name, tiling_stgy=TilingStrategy.from_strategy_name(tiling_stgy_name), prev_block_num=prev_block_num, next_block_num=next_block_num, high_precision=high_precision, disable_debug=disable_debug) fa.compute_process() return fa.tik_instance pengchengmind/flash_attention/flash_attention_impl.py000066400000000000000000000126311453423006600236310ustar00rootroot00000000000000from __future__ import absolute_import import os import sys from mindspore.ops import DataType from mindspore.ops import TBERegOp from mindspore.ops import op_info_register sys.path.append(os.path.dirname(__file__)) from flash_attention_fwd import flash_attention from flash_attention_bwd import flash_attention_grad kernel_name = "flash_attention" cus_flash_atten_op_info = TBERegOp("FlashAttentionPrimitive") \ .fusion_type("OPAQUE") \ .partial_flag(True) \ .async_flag(False) \ .binfile_name("flash_attention.so") \ .compute_cost(10) \ .kernel_name(kernel_name) \ .attr("prev_block_num", "required", "int", "all", "65536") \ .attr("next_block_num", "required", "int", "all", "65536") \ .attr("high_precision", "required", "bool", "all", "false") \ .attr("tiling_stgy_name", "required", "str", "all", "xunfei") \ .input(0, "q", False, "required", "all") \ .input(1, "k", False, "required", "all") \ .input(2, "v", False, "required", "all") \ .input(3, "attn_mask", False, "optional", "all") \ .input(4, "dropout_mask", False, "optional", "all") \ .input(5, "alibi_mask", False, "optional", "all") \ .output(0, "y", False, "required", "all") \ .output(1, "l", False, "required", "all") \ .output(2, "m", False, "required", "all") \ .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default) \ .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F32_Default, DataType.F16_Default) \ .get_op_info() # Binding kernel info with the kernel implementation. @op_info_register(cus_flash_atten_op_info) def flash_attention_impl(query, key, value, attn_mask, dropout_mask, alibi_mask, y, l, m, prev_block_num, next_block_num, high_precision, tiling_stgy_name): flash_attention(query, key, value, attn_mask, dropout_mask, alibi_mask, y, l, m, prev_block_num, next_block_num, high_precision=high_precision, kernel_name=kernel_name, tiling_stgy_name=tiling_stgy_name) kernel_name = "flash_attention_grad" cus_flash_atten_grad_op_info = TBERegOp("FlashAttentionGradPrimitive") \ .fusion_type("OPAQUE") \ .partial_flag(True) \ .async_flag(False) \ .binfile_name("flash_attention_grad.so") \ .compute_cost(10) \ .kernel_name(kernel_name) \ .attr("prev_block_num", "required", "int", "all", "65536") \ .attr("next_block_num", "required", "int", "all", "65536") \ .attr("high_precision", "required", "bool", "all", "false") \ .attr("tiling_stgy_name", "required", "str", "all", "xunfei") \ .input(0, "q", False, "required", "all") \ .input(1, "k", False, "required", "all") \ .input(2, "v", False, "required", "all") \ .input(3, "o", False, "required", "all") \ .input(4, "do", False, "required", "all") \ .input(5, "l", False, "required", "all") \ .input(6, "m", False, "required", "all") \ .input(7, "attn_mask", False, "optional", "all") \ .input(8, "dropout_mask", False, "optional", "all") \ .input(9, "alibi_mask", False, "optional", "all") \ .output(0, "dq", False, "required", "all") \ .output(1, "dk", False, "required", "all") \ .output(2, "dv", False, "required", "all") \ .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ,) \ .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F32_Default, DataType.F16_Default, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \ .get_op_info() # Binding kernel info with the kernel implementation. @op_info_register(cus_flash_atten_grad_op_info) def flash_attention_grad_impl(q, k, v, o, dout, l, m, attn_mask, dropout_mask, alibi_mask, dq, dk, dv, prev_block_num, next_block_num, high_precision, tiling_stgy_name="xunfei"): flash_attention_grad(q, k, v, o, dout, l, m, attn_mask, dropout_mask, alibi_mask, dq, dk, dv, prev_block_num, next_block_num, high_precision=high_precision, kernel_name=kernel_name, tiling_stgy_name=tiling_stgy_name) pengchengmind/flash_attention/flash_attention_primitive.py000066400000000000000000000064071453423006600247040ustar00rootroot00000000000000# Copyright 2022 Huawei Technologies Co., Ltd import mindspore.ops as ops from mindspore import dtype as mstype from mindspore.ops import PrimitiveWithInfer from mindspore.ops import prim_attr_register from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like class FlashAttentionGradPrimitive(PrimitiveWithInfer): @prim_attr_register def __init__(self, name="flash_attention_grad"): super().__init__(name) from flash_attention.flash_attention_impl import flash_attention_grad_impl self.init_prim_io_names( inputs=["q", "k", "v", "o", "do", "l", "m", "attn_mask", "dropout_mask", "alibi_mask"], outputs=["dq", "dk", "dv"] ) def infer_shape(self, q_shape, k_shape, v_shape, o_shape, do_shape, l_shape, m_shape, att_mask_shape, dropout_mask_shape, alibi_mask_shape): return q_shape, k_shape, v_shape def infer_dtype(self, q_dtype, k_dtype, v_dtype, o_dytpe, do_dtype, l_dtype, m_dtype, attn_mask_dtype, dropout_mask_dtype, alibi_mask_type): return mstype.float32, mstype.float32, mstype.float32 class FlashAttentionPrimitive(PrimitiveWithInfer): @prim_attr_register def __init__(self, prev_block_num=65536, next_block_num=65536, high_precision=False, name="flash_attention", tiling_stgy_name='xunfei'): super().__init__(name) from flash_attention.flash_attention_impl import flash_attention_impl self.init_prim_io_names( inputs=["q", "k", "v", "attn_mask", "dropout_mask", "alibi_mask"], outputs=["y", "l", "m"] ) self.prev_block_num = prev_block_num self.next_block_num = next_block_num self.high_precision = high_precision self.tiling_stgy_name = tiling_stgy_name def infer_shape(self, q_shape, k_shape, v_shape, attn_mask_shape=None, dropout_mask_shape=None, alibi_mask_shape=None): b, h, N, d = q_shape l_shape = (b, h, N) m_shape = (b, h, N) return q_shape, l_shape, m_shape def infer_dtype(self, q_dtype, k_dtype, v_dtype, attn_mask_dtype=None, dropout_mask_dtype=None, alibi_mask_type=None): l_dtype = mstype.float16 if self.high_precision: l_dtype = mstype.float32 return q_dtype, l_dtype, q_dtype def get_bprop(self): flash_attention_grad = FlashAttentionGradPrimitive() flash_attention_grad.add_prim_attr("prev_block_num", self.prev_block_num) flash_attention_grad.add_prim_attr("next_block_num", self.next_block_num) flash_attention_grad.add_prim_attr("high_precision", self.high_precision) flash_attention_grad.add_prim_attr("tiling_stgy_name", self.tiling_stgy_name) def bprop(q, k, v, attn_mask, dropout_mask, alibi_mask, out, douts): o, l, m = out dout, dl, dm = douts dq, dk, dv = flash_attention_grad(q, k, v, o, dout, l, m, attn_mask, dropout_mask, alibi_mask) dq = ops.cast(dq, mstype.float16) dk = ops.cast(dk, mstype.float16) dv = ops.cast(dv, mstype.float16) return dq, dk, dv, zeros_like(attn_mask), \ zeros_like(dropout_mask), zeros_like(alibi_mask) return bprop pengchengmind/flash_attention/tik_ops_utils.py000066400000000000000000000444511453423006600223230ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. from functools import partial from constants import DTYPE_SIZE from constants import FP16 from constants import FP32 from constants import L0C from constants import L1 from constants import UB class TikOpsUtils: def __init__(self, tik_instance): self.tik_instance = tik_instance self.dtype = "float16" self.cont_data_mv_1_bust = partial(self.tik_instance.data_move, sid=0, nburst=1, src_stride=0, dst_stride=0) def MK_TO_K1MK0(self, mk_input_tensor, workspace_tensor=None): """change data shape from (M, K) to (K1, M, K0), K1 = K // K0, the effect is equant to: new_tensor = np.stack(np.hsplit(mk_input_tensor, K1), axis=0) :param mk_input_tensor: input tensor in GM with shape: (M, K) :param workspace_tensor: workspace tensor with shape: (K1, M, K0) tensor will be changed, otherwise the new data will be copied to the workspace tensor, and input tensor will stay unchanged. :return: Tensor with shape (K1,M, K0) """ dtype = mk_input_tensor.dtype m, k = mk_input_tensor.shape K0 = 16 K1 = k // K0 M = self.up_align_to_K0(m) if workspace_tensor is not None: with self.tik_instance.for_range(0, K1) as i: self.tik_instance.data_move( workspace_tensor[i * M * K0:], mk_input_tensor[i * K0:], 0, M, K0 * DTYPE_SIZE[dtype] // 32, (K1 - 1) * K0 * DTYPE_SIZE[dtype] // 32, 0, ) return workspace_tensor.reshape((K1, M, K0)) else: with self.tik_instance.new_stmt_scope(disable_sync=False): tmp_ub = self.tik_instance.Tensor(dtype, (K1, M, K0), name="tmp_ub", scope=UB) # data_move(m,k) --> (k1,m,K0) with self.tik_instance.for_range(0, K1) as i: self.tik_instance.data_move( tmp_ub[i * M * K0:], mk_input_tensor[i * K0:], 0, M, K0 * DTYPE_SIZE[dtype] // 32, (K1 - 1) * K0 * DTYPE_SIZE[dtype] // 32, 0, ) self.cont_data_mv_1_bust( dst=mk_input_tensor, src=tmp_ub, burst=K1 * M * K0 * DTYPE_SIZE[dtype] // 32) return mk_input_tensor.reshape((K1, M, K0)) def transpose_matrix(self, src_ub, dst_ub, N, nk0=False): """ transpose matrix, default support shape: (16, n) -> (n, 16) if nk0 is true, support shape: (n, 16) -> (16, n) """ K0 = 16 rep_times = N // K0 if nk0: src_list = [src_ub[16 * i] for i in range(16)] dst_list = [dst_ub[N * i] for i in range(16)] else: src_list = [src_ub[N * i] for i in range(16)] dst_list = [dst_ub[16 * i] for i in range(16)] dst_rep_stride = K0 src_rep_stride = 1 if rep_times == 1: dst_rep_stride = 0 src_rep_stride = 0 if nk0: src_rep_stride, dst_rep_stride = dst_rep_stride, src_rep_stride self.tik_instance.vec_trans_scatter( False, False, dst_list, src_list, rep_times, dst_rep_stride, src_rep_stride ) return dst_ub def KN_TO_K1NK0(self, kn_input_tensor, workspace_tensor=None): """change data shape from (K,N) to (K1, N, K0), K1 = K // K0, the effect is equvilent to: new_tensor = np.reshape(kn_input_tensor, newshape=(K1, K0, N)).swapaxes(1, 2) :param kn_input_tensor: input tensor with shape: (K, N) :param workspace_tensor: workspace tensor with shape: (K1, N, K0) tensor will be changed, otherwise the new data will be copied to the workspace tensor, and input tensor will stay unchanged. :return: Tensor with shape: (K1, N, K0) """ dtype = kn_input_tensor.dtype k, n = kn_input_tensor.shape K0 = 16 K1 = k // K0 N = n with self.tik_instance.for_range(0, K1) as index: k1nk0_ub = self.tik_instance.Tensor(dtype, (N, K0), UB, "k1nk0_ub") src_ub = self.tik_instance.Tensor(dtype, (K0, N), UB, "src_ub") burst_len = K0 * N * DTYPE_SIZE[dtype] // 32 self.cont_data_mv_1_bust(dst=src_ub, src=kn_input_tensor[index * K0 * N], burst=burst_len) k1nk0_ub = self.transpose_matrix(src_ub, k1nk0_ub, N) if workspace_tensor is None: self.cont_data_mv_1_bust(dst=kn_input_tensor[index * K0 * N], src=k1nk0_ub, burst=burst_len) else: self.cont_data_mv_1_bust(dst=workspace_tensor[index * K0 * N], src=k1nk0_ub, burst=burst_len) if workspace_tensor is None: return kn_input_tensor.reshape((K1, N, K0)) else: return workspace_tensor.reshape((K1, N, K0)) def N1MN0_TO_MN(self, N1MN0_input): """change data shape from (N1, M, N0) to (M, N), N0=16, N = N1 * K0, the effect is equant to: N1MN0_input = np.concatenate(list(map(np.squeeze, np.split(N1MN0_input, N1))), axis=1) :param N1MN0_input: input tensor with shape (N, M, N0) in GM or L1. :return: """ dtype = N1MN0_input.dtype N1, M, N0 = N1MN0_input.shape with self.tik_instance.new_stmt_scope(disable_sync=False): tmp_ub = self.tik_instance.Tensor(dtype, (M, N1 * N0), name="tmp_ub", scope=UB) # data_move (n1,m,n0) --> (m,n) with self.tik_instance.for_range(0, N1) as i: self.tik_instance.data_move( tmp_ub[i * N0:], N1MN0_input[i * M * N0:], 0, M, N0 * DTYPE_SIZE[dtype] // 32, 0, (N1 - 1) * N0 * DTYPE_SIZE[dtype] // 32, ) # data_move out self.cont_data_mv_1_bust(dst=N1MN0_input, src=tmp_ub, burst=M * N1 * N0 * DTYPE_SIZE[ dtype] // 32) return N1MN0_input.reshape((M, N1 * N0)) def broadcast(self, vec_ub, shape): """ broadcast a vector to a matrix :param vec_ub: a tensor in UB with shape of (M,), and dtype is float16 :param shape: the target shape, a tuple with value (M, N)，M and N are integer multiples of 16 :return: a tensor in UB with shape of (M, N) """ M, N = shape dst_ub = self.tik_instance.Tensor(FP16, shape, name="dst_ub", scope=UB) with self.tik_instance.new_stmt_scope(disable_sync=False): # (M,) -> (2, M) -> (4, M) -> (8, M) -> (16, M) tmp_ub1 = self.tik_instance.Tensor(FP16, (16, M), name="tmp_ub1", scope=UB) self.tik_instance.data_move(tmp_ub1, vec_ub, 0, 1, M // 16, 0, 0) times = self.tik_instance.Scalar("int32", name="times", init_value=1) with self.tik_instance.for_range(begint=0, endt=16) as idx: with self.tik_instance.if_scope(times <= 8): offset = times * M burst = times * M // 16 self.cont_data_mv_1_bust(dst=tmp_ub1[offset], src=tmp_ub1, burst=burst) with self.tik_instance.else_scope(): self.tik_instance.tik_break() times.set_as(times * 2) # (16, M) -> (M, 16) tmp_ub2 = self.tik_instance.Tensor(FP16, (M, 16), name="tmp_ub2", scope=UB) tmp_ub2_transposed = self.transpose_matrix(tmp_ub1, tmp_ub2, M) # (M, 16) -> (M, 32) -> (M, 64) -> ... -> (M, N) self.tik_instance.data_move(dst_ub, tmp_ub2_transposed, 0, M, 1, 0, N // 16 - 1) times.set_as(1) with self.tik_instance.for_range(begint=0, endt=N) as idx: offset = times * 16 with self.tik_instance.if_scope(offset * 2 <= N): burst = offset // 16 src_stride = N // 16 - burst dst_stride = N // 16 - burst self.tik_instance.data_move(dst_ub[offset], dst_ub, 0, M, burst, src_stride, dst_stride) with self.tik_instance.else_scope(): burst = (N - offset) // 16 src_stride = N // 16 - burst dst_stride = N // 16 - burst with self.tik_instance.if_scope(burst > 0): self.tik_instance.data_move(dst_ub[offset], dst_ub, 0, M, burst, src_stride, dst_stride) self.tik_instance.tik_break() times.set_as(times * 2) return dst_ub def broadcast_row(self, vec_ub, shape): M, N = shape dst_ub = self.tik_instance.Tensor(FP16, shape, name="dst_ub", scope=UB) self.tik_instance.data_move(dst_ub, vec_ub, 0, 1, N // 16, 0, 0) times = self.tik_instance.Scalar("int32", name="times", init_value=1) # (1, N) -> (2, M) -> (4, N) -> ... -> (M, N) with self.tik_instance.for_range(begint=0, endt=M) as row_idx: with self.tik_instance.if_scope(times * 2 <= M): burst = times * N // 16 offset = times * N self.tik_instance.data_move(dst_ub[offset], dst_ub, 0, 1, burst, 0, 0) with self.tik_instance.else_scope(): burst = (M - times) * N // 16 offset = times * N with self.tik_instance.if_scope(burst > 0): self.tik_instance.data_move(dst_ub[offset], dst_ub, 0, 1, burst, 0, 0) self.tik_instance.tik_break() times.set_as(times * 2) return dst_ub def get_K0(self, dtype=None): if dtype is None: dtype = self.dtype return 32 // DTYPE_SIZE[dtype] def up_align_to_K0(self, n, dtype=None): if dtype is None: dtype = self.dtype K0 = 32 // DTYPE_SIZE[dtype] return (n + K0 - 1) // K0 * K0 def calc_vec_rec(self, vec_ub, vec_len): dtype = vec_ub.dtype vec_len_aligned = self.up_align_to_K0(vec_len) vec_rec_ub = self.tik_instance.Tensor(dtype, (vec_len_aligned,), scope=UB, name="li_new_rec_ub") mask_len = 256 // DTYPE_SIZE[dtype] block_len = 32 // DTYPE_SIZE[dtype] work_size = 8 // DTYPE_SIZE[dtype] with self.tik_instance.new_stmt_scope(disable_sync=False): repeat_times = vec_len // mask_len if repeat_times > 0: dst_rep_stride = 8 src_rep_stride = 8 src_extent_size = (repeat_times - 1) * src_rep_stride * block_len + mask_len wk_size_unit = ((src_extent_size + block_len - 1) // block_len) * block_len wk_size = work_size * wk_size_unit # 定义work_tensor work_tensor_ub = self.tik_instance.Tensor( "float32", (wk_size,), name="work_tensor_ub", scope=UB ) # 如果work_tensor有索引，需要写成 work_tensor[index:] self.tik_instance.vec_rec_high_preci( mask_len, vec_rec_ub[0:], vec_ub[0:], work_tensor_ub[0:], repeat_times, dst_rep_stride, src_rep_stride, ) mask_len = vec_len - repeat_times * mask_len if mask_len > 0: wk_size = work_size * ((mask_len + block_len - 1) // block_len) * block_len work_tensor_ub2 = self.tik_instance.Tensor( "float32", (wk_size,), name="work_tensor_ub2", scope=UB ) self.tik_instance.vec_rec_high_preci( mask_len, vec_rec_ub[repeat_times * 128:], vec_ub[repeat_times * 128:], work_tensor_ub2[0:], 1, 0, 0, ) return vec_rec_ub def row_sum_cube_impl(self, matrix_l1_K1MK0_ed, rowsum_ub, m, k, precision_type): """用cube实现矩阵行和：右乘一个shape=(n,1)全一矩阵 :param matrix_l1_K1MK0_ed: input tensor with shape (K1, M, K0) :param rowsum_ub: output tensor stores the row sum of input tensor. :param m: actual tensor height :param k: actual tensor width :return: row sum of the output tensor """ K1, M, K0 = matrix_l1_K1MK0_ed.shape K = K1 * K0 # 构造全一右矩阵，由于cube无法处理shape=(n, 1)，所以shape=(n, 16)，全一矩阵不需分形 right_all_one_matrix_ub = self.tik_instance.Tensor( FP16, (K, 16), name="right_all_one_matrix_ub", scope=UB ) self.tik_instance.h_duplicate(right_all_one_matrix_ub, 1.0) right_all_one_matrix_l1 = self.tik_instance.Tensor( FP16, (K1 * K0, 16), name="right_all_one_matrix_l1", scope=L1 ) self.cont_data_mv_1_bust(dst=right_all_one_matrix_l1, src=right_all_one_matrix_ub, burst=K) # 调用matmul实现rowsum，结果shape=(m, 16)，取每行的第一个数 with self.tik_instance.new_stmt_scope(disable_sync=False): row_sum_ub_N1MN0 = self.matmul_compute(matrix_l1_K1MK0_ed, right_all_one_matrix_l1, m, k, 16, N1MN0_to_MN=False, precision_type=precision_type) row_sum_ub_MN_ed = row_sum_ub_N1MN0.reshape((M, 16)) if precision_type == FP32: # TODO: fp32转置接口，代替循环 => 提升性能 for idx in range(0, m): cur_row_sum = self.tik_instance.Scalar(FP32, init_value=row_sum_ub_MN_ed[idx, 0]) rowsum_ub[idx].set_as(cur_row_sum) else: # row_sum_ub_MN_ed 先转置，然后取一行, 替换原来按行操作: lij_ub[i].set_as(row_sum_ub_MN_ed[i, 0]) row_sum_ub_trans = self.tik_instance.Tensor(FP16, (16, M), name="row_sum_ub_trans", scope=UB) row_sum_ub_trans = self.transpose_matrix(row_sum_ub_MN_ed, row_sum_ub_trans, M, True) self.cont_data_mv_1_bust(dst=rowsum_ub, src=row_sum_ub_trans, burst=M // 16) return rowsum_ub def matmul_compute(self, A_l1, B_l1, m, k, n, N1MN0_to_MN=True, precision_type=FP16): """calculate matrix multiplication A_l1 * B_l1, and move the result to C_ub, then rearrange C_ub :param A_l1: input tensor in L1 with shape of (K1, M, K0) :param B_l1: input tensor in L1 with shape of (K1, N, K0) :param m: the actual number of rows of A_l1 :param k: the actual number of cols of A_l1 :param n: the actual number of cols of B_l1 :param N1MN0_to_MN: Whether reorder the result tensor. :return: C_ub with tensor with shape of (M, N) if N1MN0_to_MN else (N1, M, N0) """ M = self.up_align_to_K0(m) N = self.up_align_to_K0(n) # 根据精度类型，决定matmul返回的ub精度类型，如果fp16, tensor_move做随路精度转换 C_ub = self.tik_instance.Tensor(precision_type, (N // 16, M, 16), name="C_ub", scope=UB) with self.tik_instance.new_stmt_scope(disable_sync=False): # matmul C_l0c = self.tik_instance.Tensor( FP32, (N // 16, M, 16), scope=L0C, name="C_l0c" ) # n1mn0 (n0=16) self.tik_instance.matmul(C_l0c, A_l1, B_l1, m, k, n) # L0C -> ub, fp32 -> fp16 (tensor_mov可做随路转换) self.tik_instance.tensor_mov(C_ub, C_l0c, "m", 1, M * N * DTYPE_SIZE[FP32] // 1024, 0, 0) if N1MN0_to_MN: return self.N1MN0_TO_MN(C_ub) else: return C_ub def move_vector_from_gm_to_ub(self, dst_tensor, src_tensor, gm_offset, vec_len): """load the vector from gm to ub :param dst_tensor: :param src_tensor: :param gm_offset: :return: """ a_burst_num = 32 // DTYPE_SIZE[src_tensor.dtype] full_tik_blk_num, tail_num = divmod(vec_len, a_burst_num) with self.tik_instance.if_scope(full_tik_blk_num > 0): self.cont_data_mv_1_bust(dst=dst_tensor, src=src_tensor[gm_offset], burst=full_tik_blk_num) # 地址回退处理尾部数据 with self.tik_instance.if_scope(tail_num > 0): offset = vec_len - a_burst_num last_blk_ub = self.tik_instance.Tensor(FP16, (a_burst_num,), name="last_blk_ub", scope=UB) self.cont_data_mv_1_bust(dst=last_blk_ub, src=src_tensor[gm_offset + offset], burst=1) with self.tik_instance.for_range(0, a_burst_num) as idx: # offset非32bytes对齐，无法用datamove dst_tensor[offset + idx].set_as(last_blk_ub[idx]) def move_vector_from_ub_to_gm(self, dst_tensor, src_tensor, gm_offset, block_h): """write the vector back to gm :param dst_tensor: :param src_tensor: :param gm_offset: :param block_h: :return: """ a_burst_num = 32 // DTYPE_SIZE[src_tensor.dtype] full_tik_blk_num = block_h // a_burst_num with self.tik_instance.if_scope(full_tik_blk_num > 0): self.cont_data_mv_1_bust(dst=dst_tensor[gm_offset], src=src_tensor, burst=full_tik_blk_num) tail_num = block_h % a_burst_num with self.tik_instance.if_scope(tail_num > 0): offset = block_h - a_burst_num tmp_ub = self.tik_instance.Tensor(FP16, (a_burst_num,), name="tmp_ub", scope=UB) with self.tik_instance.for_range(0, a_burst_num) as idx: tmp_ub[idx].set_as(src_tensor[offset + idx]) self.cont_data_mv_1_bust(dst=dst_tensor[gm_offset + offset], src=tmp_ub, burst=1) def scale_compute_vector(self, Sij_ub, dim): scale_value = dim ** -0.5 scale = self.tik_instance.Scalar(dtype=FP16) scale.set_as(scale_value) self.tik_instance.h_mul(Sij_ub, Sij_ub, scale) return Sij_ub pengchengmind/flash_attention/tiling_strategy/000077500000000000000000000000001453423006600222615ustar00rootroot00000000000000pengchengmind/flash_attention/tiling_strategy/__init__.py000066400000000000000000000001101453423006600243620ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. pengchengmind/flash_attention/tiling_strategy/strategy.py000066400000000000000000000025551453423006600245040ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. from abc import ABCMeta from abc import abstractmethod from collections import namedtuple TilingPara = namedtuple("TilingPara", "Br last_Br Bc last_Bc Tr Tc") class TilingStrategy(metaclass=ABCMeta): """Tiling strategy interface. All implementations should be defined in this module, otherwise, the UT will fail. """ _strategies = {} def __init__(self, Nq, N, head_dim) -> None: super().__init__() self.Nq = Nq self.N = N self.Br = None self.last_Br = None self.Bc = None self.last_Bc = None self.Tr = None self.Tc = None self.d = head_dim def __init_subclass__(cls, **kwargs): TilingStrategy._strategies[cls.strategy_name()] = cls @classmethod @abstractmethod def strategy_name(cls): raise NotImplemented @classmethod def from_strategy_name(cls, stgy_name: str): stgy_clz = TilingStrategy._strategies.get(stgy_name) if stgy_clz is None: raise Exception(f"Strategy:{stgy_name} not supported") return stgy_clz @abstractmethod def tiling(self) -> TilingPara: raise NotImplemented def gen_tiling_para(self) -> TilingPara: return TilingPara(self.Br, self.last_Br, self.Bc, self.last_Bc, self.Tr, self.Tc) pengchengmind/flash_attention/tiling_strategy/wukong_tiling.py000066400000000000000000000034251453423006600255170ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. from tiling_strategy.strategy import TilingPara from tiling_strategy.strategy import TilingStrategy class WukongTiling(TilingStrategy): """A tiling strategy implementation for wukonghuahua model shape""" @classmethod def strategy_name(cls): return "wukong" def tiling(self) -> TilingPara: """ 反向的空间分布待详细分析 N = (4096, 1024, 256, 64) 或 77 Nq = (4096, 1024, 256, 64) d = dv = (40, 80, 160， 160) """ if self.N <= 77: # [77, 64] # cross-attention or self-attention of (64, 64, 160) self.Bc = self.N self.Tc = self.N // self.Bc if self.d <= 80: # [40, 80] # 内存瓶颈为在ub中对P*V结果[Br, dv]进行cast # ub: 512 * 80 * 6 // 1024 = 240KB self.Br = min(self.Nq, 64) self.Tr = self.Nq // self.Br else: # ub: dv = 160， 256 * 160 * 6 // 1024 = 240KB self.Br = min(self.Nq, 64) self.Tr = self.Nq // self.Br else: # self-attention if self.N == 256: self.Bc = 64 self.Tc = 1 # 内存瓶颈为在ub中对Q*K的结果[Br, Bc]进行cast # ub: 128 * 256 * 6 // 1024 = 192KB self.Br = 64 self.Tr = self.Nq // self.Br else: self.Bc = 64 self.Tc = self.N // self.Bc # ub: 64 * 512 * 6 // 1024 = 192KB self.Br = 64 self.Tr = self.Nq // self.Br self.last_Br = self.Br self.last_Bc = self.Bc return self.gen_tiling_para()pengchengmind/flash_attention/tiling_strategy/xunfei_tiling.py000066400000000000000000000016011453423006600254750ustar00rootroot00000000000000# Copyright (C) 2021.Huawei Technologies Co., Ltd. All rights reserved. from tiling_strategy.strategy import TilingPara from tiling_strategy.strategy import TilingStrategy class XunfeiTiling(TilingStrategy): """A tiling strategy implementation for xunfei ipt model shape""" @classmethod def strategy_name(cls): return "xunfei" def tiling(self) -> TilingPara: self.Br = min(128, self.Nq) self.Bc = min(128, self.N) self.Tr = self.Nq // self.Br self.Tc = self.N // self.Bc if self.Nq % self.Br != 0: self.last_Br = self.Nq - self.Tr * self.Br self.Tr += 1 else: self.last_Br = self.Br if self.N % self.Bc != 0: self.last_Bc = self.N - self.Tc * self.Bc self.Tc += 1 else: self.last_Bc = self.Bc return self.gen_tiling_para()pengchengmind/ma-pre-start.sh000066400000000000000000000043311453423006600165400ustar00rootroot00000000000000#!/bin/bash # 查看包的版本 cat /home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/mindspore/.commit_id pip install regex pip install zhconv pip install sentencepiece==0.1.94 pip install transformers==v4.29.0 pwd echo "********************" sudo mkdir /cache sudo chown -R 1101:1101 /cache sudo chmod -R 777:777 /cache cd /cache sudo mkdir /cache/Ckpt sudo mkdir /cache/Graph sudo mkdir /cache/compiler_cache sudo chown -R 1101:1101 /cache/Ckpt sudo chmod 777 /cache/Ckpt -R sudo mkdir /home/ma-user/modelarts/user-job-dir/Data sudo chown -R 1101:1101 /home/ma-user/modelarts/user-job-dir/Data sudo mkdir /cache/Data sudo chown -R 1101:1101 /cache/Data sudo mkdir /cache_gpt #sudo mkdir /cache_gpt/exp01-2 #sudo chown -R 1101:1101 /cache_gpt/exp01-2 sudo chmod 777 /cache_gpt -R sudo chmod -R 777:777 /cache_gpt sudo mkdir /cache/EvalData_dev sudo chown -R 1101:1101 /cache/EvalData_dev sudo chmod 777 /cache/EvalData_dev -R sudo mkdir /cache/EvalData_test sudo chown -R 1101:1101 /cache/EvalData_test sudo chmod 777 /cache/EvalData_test -R ls -l /cache ls -l / echo "********************" ls -ld Ckpt sudo chmod -R 777:777 /cache/Ckpt sudo chmod 777:777 /cache/Ckpt -R sudo chmod 777 /cache/Ckpt -R ls -ld Ckpt sudo chmod 777:777 /cache/Graph -R sudo chmod 777 /cache/Graph -R sudo chmod 777:777 /cache/compiler_cache -R sudo chmod 777 /cache/compiler_cache -R echo "********************" df -hl # 配合新包，打印建链时间 export GLOG_v=2 #export GLOG_v=3 #export MS_SUBMODULE_LOG_v="{DEVICE:1}" # 查看分配的task数目有没有问题 export ASCEND_GLOBAL_LOG_LEVEL=3 export ASCEND_SLOG_PRINT_TO_STDOUT=0 export HCCL_CONNECT_TIMEOUT=3600 export HCCL_EXEC_TIMEOUT=800 #export HCCL_EXEC_TIMEOUT=1800 export PIPELINE_SLICE_SKIP_REDISTRIBUTION=1 #export MS_BUILD_PROCESS_NUM=12 #export COMBINE_LINK_GRAPHS=1 # task 任务优化 #export MS_ENABLE_TASK_OPT=1 ##export MS_REDUNDANCY_TASK_NUM=10 #export MS_REDUNDANCY_TASK_NUM=4 export MS_DEV_REDUNDANCY_TASK_NUM=4 # compiletion 编译优化 export MS_DEV_CELL_REUSE=2 # link 闪断，256ms*7,修改到4s export HCCL_RDMA_TIMEOUT=19 # save graph # export MS_COMPILER_CACHE_ENABLE=1 # export MS_COMPILER_CACHE_PATH=/cache/compiler_cache/ free -g pengchengmind/parallel_speed_up.json000066400000000000000000000002611453423006600202410ustar00rootroot00000000000000{ "recompute_comm_overlap": false, "matmul_grad_comm_overlap": false, "enable_task_opt": true, "interleaved_matmul_comm": false, "interleaved_layernorm_comm": false } pengchengmind/predict_2.6B.py000066400000000000000000000400441453423006600163640ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PengChengMind predict run """ import json import os import requests import datetime import glob import numpy as np from tqdm import tqdm import mindspore.common.dtype as mstype import mindspore.communication.management as D import mindspore as ms from mindspore import context, Tensor from mindspore import export from mindspore.context import ParallelMode from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.train.model import Model from mindspore.train.serialization import load_distributed_checkpoint, load_checkpoint from mindspore.nn.transformer.transformer import TransformerOpParallelConfig from src.generate import get_scores from src.pengcheng_mind import EvalNet, PengChengMindModel, EvalNet_200B from src.pengcheng_mind_config import set_parse, PengChengMindConfig from src.utils import get_args from mindspore.common import Parameter from mindspore.common.tensor import Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net from src.utils import download_ckpt_from_obs def restore_checkpoint(args_param, network, cache_url='/cache/Ckpt/'): r""" Load checkpoint process. """ restore_ranks = D.get_rank() print("======start single checkpoint", flush=True) ckpt_name = os.path.join(cache_url, f"rank_{restore_ranks}.ckpt") if not ckpt_name: print(f"There is no ckpt file in {ckpt_name}, " f"current ckpt_files found is {ckpt_name} " f"with pattern {ckpt_name}, so skip the loading.") time_stamp = datetime.datetime.now() print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')} pre trained ckpt model {ckpt_name} loading", flush=True) # Load checkpoint files latest file print(f'Start to load from {ckpt_name}') param_dict = load_checkpoint(ckpt_name) for k, v in param_dict.items(): print("rank: ", restore_ranks, k) if "embedding" not in k and ".layernorm" not in k: param_dict[k] = Parameter(Tensor(param_dict[k].data.asnumpy(), dtype=mstype.float16), name=k) load_param_into_net(network, param_dict, strict_load=True) def set_auto_parallel_context(args_opt): """Set the auto parallel context""" rank = 0 device_num = 1 context.reset_auto_parallel_context() # context.set_auto_parallel_context( # strategy_ckpt_load_file=args_opt.strategy_load_ckpt_path) if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() print("rank_id is {}, device_num is {}".format(rank, device_num)) context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=True, loss_repeated_mean=True, enable_parallel_optimizer=False, strategy_ckpt_save_file=f'/cache/strategy_{rank}.ckpt', pipeline_stages=args_opt.stage_num) set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() return rank, device_num def load_model(args_opt): r""" The main function for load model """ context.set_context(mode=context.GRAPH_MODE) # Set parallel context rank, device_num = set_auto_parallel_context(args_opt) context.set_context(variable_memory_max_size="30GB") context.set_context(save_graphs=False, save_graphs_path="/cache/graphs_of_device_id_" + str(rank), device_target=args_opt.device_target) strategy_local_file = f"/cache/inference_strategy_100b_d8_mp8_dp1-{rank}.ckpt" ms.set_auto_parallel_context(strategy_ckpt_save_file=strategy_local_file) if args_opt.eval_task: use_past = False else: use_past = True if args_opt.export else (args_opt.use_past == "true") print('local_rank:{}, start to run...'.format(rank), flush=True) # Set model property, rewrite the model parallel if device_num < args_opt.op_level_model_parallel_num: print(f"The op_level_model_parallel_num {args_opt.op_level_model_parallel_num} is smaller than the device num，" f"so change it to the {device_num}", flush=True) args_opt.op_level_model_parallel_num = device_num model_parallel_num = args_opt.op_level_model_parallel_num data_parallel_num = int(device_num / (model_parallel_num*args_opt.stage_num)) parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, vocab_emb_dp=False, recompute=False) # add sequence_parallel parallel_config.sequence_parallel = args_opt.sequence_parallel # add select_recompute parallel_config.select_recompute = args_opt.select_recompute per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num # Now only support single batch_size for predict if args_opt.run_type == "predict": batch_size = 1 # download ckpt to local D.init() device_num = D.get_group_size() rank_id = D.get_rank() # if args_opt.softmax_compute_fp32 == "FP32": # softmax_compute_type = mstype.float32 # elif args_opt.softmax_compute_fp32 == "FP16": # softmax_compute_type = mstype.float16 # else: # raise ValueError(f"Unknown softmax_compute_fp32 {args_opt.softmax_compute_fp32}") # # if args_opt.top_query_softmax_fp32 == "FP32": # top_query_softmax = mstype.float32 # elif args_opt.top_query_softmax_fp32 == "FP16": # top_query_softmax = mstype.float16 # else: # raise ValueError(f"Unknown top_query_softmax_fp32 {args_opt.top_query_softmax_fp32}") softmax_compute_type = mstype.float16 top_query_softmax = mstype.float16 config = PengChengMindConfig( batch_size=batch_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, hidden_size=args_opt.embedding_size, num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, post_layernorm_residual=False, dropout_rate=0.0, ffn_hidden_size=args_opt.embedding_size * 4, use_past=use_past, eod_reset=False, parallel_config=parallel_config, load_ckpt_path=None, run_type=args_opt.run_type, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, use_rope=args_opt.use_rope, use_flash_attention=args_opt.use_flash_attention, pipeline_config_filename=args_opt.pipeline_config_filename ) config.softmax_compute_fp32 = softmax_compute_type config.top_query_softmax_fp32 = top_query_softmax print("===config is: ", config, flush=True) print("=====args_opt is: ", args_opt, flush=True) # Define network pengcheng_mind = PengChengMindModel(config) from src.pengcheng_mind import PengChengMindLossWithPrompt from mindspore.nn.transformer import CrossEntropyLoss # loss = CrossEntropyLoss() # eval_net = PengChengMindLossWithPrompt(config, pengcheng_mind, loss) eval_net = EvalNet_200B(pengcheng_mind, pad_token=args_opt.padding_id, seq_length=args_opt.seq_length) eval_net.set_train(False) # # 完整模型加载，要在构图之前 # import time # time.sleep((rank % 8)*20) # load_checkpoint(local_ckpt_path, net=eval_net) model_predict = Model(eval_net) # Compile network and obtain tensor layout for loading ckpt inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) if args_opt.distribute == "false": predict_layout = None else: # Compiling only needs the shape current_index = Tensor(np.array([0]), mstype.int32) predict_layout = model_predict.infer_predict_layout(inputs_np, current_index) # if args_opt.save_strategy_bucket_dir and args_opt.save_strategy_name: # import moxing as mox # mox.file.copy(src_url=strategy_local_file, # dst_url=args_opt.save_strategy_bucket_dir+args_opt.save_strategy_name.replace('*', str(rank_id))) # import moxing as mox # obs_graphs_path = f"obs://research-my/taoht-100b/graphs_tmp/" # mox.file.copy_parallel(src_url="/cache/", dst_url=obs_graphs_path) cache_url = '/cache/Ckpt/' download_ckpt_from_obs(args_opt, cache_url, rank=rank_id) restore_checkpoint(args_opt, eval_net, cache_url=cache_url) print("================load param ok=================", flush=True) return model_predict, config def run_predict(model_predict, config, args_opt): """run predict""" from src.generate import generate, generate_increment, generate_100b, generate_100b_task import time D.init() rank_id = D.get_rank() device_num = D.get_group_size() # generate_func = generate_increment if config.use_past else generate generate_func = generate_100b_task # # Define tokenizer # from transformers import LlamaTokenizer # vocab_file = '/home/ma-user/modelarts/user-job-dir/PengCheng.Mind/tokenizer/llama_vocab/llama_zh_hf/tokenizer_2.model' # tokenizer = LlamaTokenizer.from_pretrained(vocab_file) # Define tokenizer work_dir = '/home/ma-user/modelarts/user-job-dir/PengCheng.Mind/tokenizer/spm_13w' from tokenizer.spm_13w.tokenizer import SpmTokenizer, langs_ID, translate_ID vocab_file = work_dir + '/spm.128k.model.1' tokenizer = SpmTokenizer(vocab_file) # data = ["你今天中午吃的什么？", "你好", "这是一个错误的印象吗？", # "我不指望有什么好东西", "他真的失去了他不给的东西吗？", # "我也没有在那里吃饭。","这些测试有多准确？"] # 我不能上天, 因为地下有人。 # 至于负相关, 则没有这种限制。 # 我给你二十分钟出现在桥下。 # 他以同样的方式回敬了他的侮辱。 # 嘿, 我应该留住他的, 布鲁斯·李! # 爸爸到第一人民医院了。 # 将面团放在一个温暖的地方, 直到它的大小翻倍(发面)。 data = ["لا أستطيع الذهاب إلى الجنة لأن هناك شخص ما تحت الأرض .", "أما بالنسبة للإرتباطات السلبية فليس هناك مثل هذا التقييد .", "سأعطيك عشرين دقيقة لتظهر تحت الجسر .", "هو رد إهاناته بنفس الطريقة .", "يجب أن أبقيه يا بروس لي .", "وصل أبي إلى مستشفى الشعب الأول .", "ضعي العجينة في مكان دافئ حتى يتضاعف حجمها .",] for input_txt in data: # Tokenize input sentence to ids # input_txt = print("===="*20) print(f"Input: {input_txt}") input_ids = tokenizer.tokenize(input_txt) tag_langs = 'zh' src_langs = 'ar' # Call inference src_input_ids = [langs_ID[src_langs], langs_ID[src_langs], langs_ID[src_langs]] + \ input_ids + \ [translate_ID, translate_ID, translate_ID] + \ [langs_ID[tag_langs], langs_ID[tag_langs], langs_ID[tag_langs]] # tag_output_max_length = min(len(input_ids) * 2 + 100, 512) # output_ids = generate_func(model_predict, np.array([src_input_ids]), args_opt, dynamic_generate_length=tag_output_max_length) output_ids = generate_func(model_predict, np.array([src_input_ids]), args_opt, top_p=args_opt.top_p, top_k_num=args_opt.top_k_num, max_generate_length=args_opt.max_generate_length, duRepeate=args_opt.duRepeate) # Decode output ids to sentence output_txt = tokenizer.detokenize(output_ids[len(src_input_ids):].tolist()) if tag_langs == 'zh': output_txt = output_txt.replace(" ", '') print('Output is:', output_txt, flush=True) # # EOT = tokenizer.eos_token_id # PAD = tokenizer.unk_token_id # # test_sample = ["中国的四大发明有哪些？", # "请把‘We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90% quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. ‘翻译为中文：", # "北极熊和灰熊的科学名称分别是什么？", # "我要做蛋炒饭，请按步骤写一个详细的制作教程：", # "中国有哪些好玩的城市？请选取其中一个城市详细介绍当地景点和游玩攻略。", # ] # # time_all = [] # time_word = [] # for idx, sample in enumerate(test_sample): # # # Tokenize input sentence to ids # start_sentence = tokenizer.encode(sample, add_special_tokens=False) # # start_sentence = tokenizer.encode(sample) # input_ids = np.array(start_sentence).reshape(1, -1) # time_start = time.time() # output_ids = generate_func(model_predict, # input_ids, # args_opt, # top_p=args_opt.top_p, # top_k_num=args_opt.top_k_num, # max_generate_length=args_opt.max_generate_length, # duRepeate=args_opt.duRepeate)#args_opt.duRepeate # time_use = time.time() - time_start # # Call inference # if rank_id %8==0: # # # output_ids = generate_func(model_predict, input_ids, args_opt) # # generate_100b_task(model, origin_inputs, config, top_p=1.0, top_k_num = 3, max_generate_length=100, duRepeate=True): # # # Decode output ids to sentence # output_samples = tokenizer.decode(output_ids[input_ids.shape[-1]:].tolist(), skip_special_tokens=True) # time_all.append(time_use/max(1, len(output_ids[len(input_ids):].tolist()))) # time_word.append(time_use/max(1, len(output_samples))) # print(f"----------------------{idx}------------------------") # print(f"Input is: {sample}\n") # # print(f"ids : {output_ids[input_ids.shape[-1]:].tolist()}\n") # print(f'Output is: {output_samples}\n') # # print(f"{time_use/len(output_ids[len(input_ids):].tolist())} s/tokens") # # print(f"{time_use/len(output_samples)} s/字") # print(f"Average time: {np.average(time_all)} s/tokens") # # print(f"Average time: {np.average(time_word)} s/字\n") def main(): """Main process for predict or export model""" opt = get_args(True) set_parse(opt) model_predict, config = load_model(opt) run_predict(model_predict, config, opt) if __name__ == "__main__": main() pengchengmind/predict_200B.py000066400000000000000000000304531453423006600163630ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PengChengMind predict run """ import json import os import requests import datetime import glob import numpy as np from tqdm import tqdm import mindspore.common.dtype as mstype import mindspore.communication.management as D import mindspore as ms from mindspore import context, Tensor from mindspore import export from mindspore.context import ParallelMode from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.train.model import Model from mindspore.train.serialization import load_distributed_checkpoint, load_checkpoint from mindspore.nn.transformer.transformer import TransformerOpParallelConfig from src.generate import get_scores from src.pengcheng_mind import EvalNet, PengChengMindModel, EvalNet_200B from src.pengcheng_mind_config import set_parse, PengChengMindConfig from src.utils import get_args from mindspore.common import Parameter from mindspore.common.tensor import Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net from src.utils import download_ckpt_from_obs def restore_checkpoint(args_param, network, cache_url='/cache/Ckpt/'): r""" Load checkpoint process. """ restore_ranks = D.get_rank() print("======start single checkpoint", flush=True) ckpt_name = os.path.join(cache_url, f"rank_{restore_ranks}.ckpt") if not ckpt_name: print(f"There is no ckpt file in {ckpt_name}, " f"current ckpt_files found is {ckpt_name} " f"with pattern {ckpt_name}, so skip the loading.") time_stamp = datetime.datetime.now() print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')} pre trained ckpt model {ckpt_name} loading", flush=True) # Load checkpoint files latest file print(f'Start to load from {ckpt_name}') param_dict = load_checkpoint(ckpt_name) # for k, v in param_dict.items(): # print("rank: ", restore_ranks, k) load_param_into_net(network, param_dict, strict_load=False) def set_auto_parallel_context(args_opt): """Set the auto parallel context""" rank = 0 device_num = 1 context.reset_auto_parallel_context() # context.set_auto_parallel_context( # strategy_ckpt_load_file=args_opt.strategy_load_ckpt_path) if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() print("rank_id is {}, device_num is {}".format(rank, device_num)) context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=True, loss_repeated_mean=True, enable_parallel_optimizer=False, strategy_ckpt_save_file=f'/cache/strategy_{rank}.ckpt', pipeline_stages=args_opt.stage_num) set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() return rank, device_num def load_model(args_opt): r""" The main function for load model """ context.set_context(mode=context.GRAPH_MODE) # Set parallel context rank, device_num = set_auto_parallel_context(args_opt) context.set_context(variable_memory_max_size="30GB") context.set_context(save_graphs=False, save_graphs_path="/cache/graphs_of_device_id_" + str(rank), device_target=args_opt.device_target) strategy_local_file = f"/cache/inference_strategy_100b_d8_mp8_dp1-{rank}.ckpt" ms.set_auto_parallel_context(strategy_ckpt_save_file=strategy_local_file) if args_opt.eval_task: use_past = False else: use_past = True if args_opt.export else (args_opt.use_past == "true") print('local_rank:{}, start to run...'.format(rank), flush=True) # Set model property, rewrite the model parallel if device_num < args_opt.op_level_model_parallel_num: print(f"The op_level_model_parallel_num {args_opt.op_level_model_parallel_num} is smaller than the device num，" f"so change it to the {device_num}", flush=True) args_opt.op_level_model_parallel_num = device_num model_parallel_num = args_opt.op_level_model_parallel_num data_parallel_num = int(device_num / (model_parallel_num*args_opt.stage_num)) parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, vocab_emb_dp=False, recompute=False) # add sequence_parallel parallel_config.sequence_parallel = args_opt.sequence_parallel # add select_recompute parallel_config.select_recompute = args_opt.select_recompute per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num # Now only support single batch_size for predict if args_opt.run_type == "predict": batch_size = 1 # download ckpt to local D.init() device_num = D.get_group_size() rank_id = D.get_rank() # if args_opt.softmax_compute_fp32 == "FP32": # softmax_compute_type = mstype.float32 # elif args_opt.softmax_compute_fp32 == "FP16": # softmax_compute_type = mstype.float16 # else: # raise ValueError(f"Unknown softmax_compute_fp32 {args_opt.softmax_compute_fp32}") # # if args_opt.top_query_softmax_fp32 == "FP32": # top_query_softmax = mstype.float32 # elif args_opt.top_query_softmax_fp32 == "FP16": # top_query_softmax = mstype.float16 # else: # raise ValueError(f"Unknown top_query_softmax_fp32 {args_opt.top_query_softmax_fp32}") softmax_compute_type = mstype.float16 top_query_softmax = mstype.float16 config = PengChengMindConfig( batch_size=batch_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, hidden_size=args_opt.embedding_size, num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, post_layernorm_residual=False, dropout_rate=0.0, ffn_hidden_size=args_opt.embedding_size * 4, use_past=use_past, eod_reset=False, parallel_config=parallel_config, load_ckpt_path=None, run_type=args_opt.run_type, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, use_rope=args_opt.use_rope, use_flash_attention=args_opt.use_flash_attention, pipeline_config_filename=args_opt.pipeline_config_filename ) config.softmax_compute_fp32 = softmax_compute_type config.top_query_softmax_fp32 = top_query_softmax print("===config is: ", config, flush=True) print("=====args_opt is: ", args_opt, flush=True) # Define network pengcheng_mind = PengChengMindModel(config) from src.pengcheng_mind import PengChengMindLossWithPrompt from mindspore.nn.transformer import CrossEntropyLoss # loss = CrossEntropyLoss() # eval_net = PengChengMindLossWithPrompt(config, pengcheng_mind, loss) eval_net = EvalNet_200B(pengcheng_mind, pad_token=args_opt.padding_id, seq_length=args_opt.seq_length) eval_net.set_train(False) # # 完整模型加载，要在构图之前 # import time # time.sleep((rank % 8)*20) # load_checkpoint(local_ckpt_path, net=eval_net) model_predict = Model(eval_net) # Compile network and obtain tensor layout for loading ckpt inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) if args_opt.distribute == "false": predict_layout = None else: # Compiling only needs the shape current_index = Tensor(np.array([0]), mstype.int32) predict_layout = model_predict.infer_predict_layout(inputs_np, current_index) # if args_opt.save_strategy_bucket_dir and args_opt.save_strategy_name: # import moxing as mox # mox.file.copy(src_url=strategy_local_file, # dst_url=args_opt.save_strategy_bucket_dir+args_opt.save_strategy_name.replace('*', str(rank_id))) # import moxing as mox # obs_graphs_path = f"obs://research-my/taoht-100b/graphs_tmp/" # mox.file.copy_parallel(src_url="/cache/", dst_url=obs_graphs_path) cache_url = '/cache/Ckpt/' download_ckpt_from_obs(args_opt, cache_url, rank=rank_id) restore_checkpoint(args_opt, eval_net, cache_url=cache_url) print("================load param ok=================", flush=True) return model_predict, config def run_predict(model_predict, config, args_opt): """run predict""" from src.generate import generate, generate_increment, generate_100b, generate_100b_task import time D.init() rank_id = D.get_rank() device_num = D.get_group_size() # generate_func = generate_increment if config.use_past else generate generate_func = generate_100b_task # Define tokenizer from transformers import LlamaTokenizer vocab_file = '/home/ma-user/modelarts/user-job-dir/PengCheng.Mind/tokenizer/llama_vocab/llama_zh_hf/tokenizer_2.model' tokenizer = LlamaTokenizer.from_pretrained(vocab_file) EOT = tokenizer.eos_token_id PAD = tokenizer.unk_token_id test_sample = ["中国的四大发明有哪些？", "请把‘We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90% quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. ‘翻译为中文：", "北极熊和灰熊的科学名称分别是什么？", "我要做蛋炒饭，请按步骤写一个详细的制作教程：", "中国有哪些好玩的城市？请选取其中一个城市详细介绍当地景点和游玩攻略。",] time_all = [] time_word = [] for idx, sample in enumerate(test_sample): # Tokenize input sentence to ids start_sentence = tokenizer.encode(sample, add_special_tokens=False) # start_sentence = tokenizer.encode(sample) input_ids = np.array(start_sentence).reshape(1, -1) time_start = time.time() output_ids = generate_func(model_predict, input_ids, args_opt, top_p=args_opt.top_p, top_k_num=args_opt.top_k_num, max_generate_length=args_opt.max_generate_length, duRepeate=args_opt.duRepeate)#args_opt.duRepeate time_use = time.time() - time_start # Call inference if rank_id == 7 or rank_id == 15: # Decode output ids to sentence output_samples = tokenizer.decode(output_ids[input_ids.shape[-1]:].tolist(), skip_special_tokens=True) time_all.append(time_use/max(1, len(output_ids[len(input_ids):].tolist()))) time_word.append(time_use/max(1, len(output_samples))) print(f"----------------------{idx}------------------------") print(f"Input is: {sample}\n") print(f'Output is: {output_samples}\n') def main(): """Main process for predict or export model""" opt = get_args(True) set_parse(opt) model_predict, config = load_model(opt) run_predict(model_predict, config, opt) if __name__ == "__main__": main() pengchengmind/predict_7B.py000066400000000000000000000310141453423006600162220ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PengChengMind predict run """ import json import os import requests import datetime import glob import numpy as np from tqdm import tqdm import mindspore.common.dtype as mstype import mindspore.communication.management as D import mindspore as ms from mindspore import context, Tensor from mindspore import export from mindspore.context import ParallelMode from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.train.model import Model from mindspore.train.serialization import load_distributed_checkpoint, load_checkpoint from mindspore.nn.transformer.transformer import TransformerOpParallelConfig from src.generate import get_scores from src.pengcheng_mind import EvalNet, PengChengMindModel, EvalNet_200B from src.pengcheng_mind_config import set_parse, PengChengMindConfig from src.utils import get_args from mindspore.common import Parameter from mindspore.common.tensor import Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net from src.utils import download_ckpt_from_obs def download_OneCKPT_from_obs(obs_ckpt_url, local_ckpt_url, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ print("Modify the time out from 300 to 30000") print(f"begin download ckpt from {obs_ckpt_url}") if rank % 8 == 0: time.sleep(rank % 4) mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) os.system(f"chmod 777 {local_ckpt_url}") print(f"Rank {rank} download ckpt {obs_ckpt_url} succeed!", flush=True) f = open(f"/tmp/restore.txt", 'w') f.close() # stop while not os.path.exists(f"/tmp/restore.txt"): time.sleep(1) def restore_checkpoint(args_param, network, cache_url='/cache/Ckpt/'): r""" Load checkpoint process. """ restore_ranks = D.get_rank() print("======start single checkpoint", flush=True) ckpt_name = os.path.join(cache_url, f"rank_{restore_ranks}.ckpt") if not ckpt_name: print(f"There is no ckpt file in {ckpt_name}, " f"current ckpt_files found is {ckpt_name} " f"with pattern {ckpt_name}, so skip the loading.") time_stamp = datetime.datetime.now() print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')} pre trained ckpt model {ckpt_name} loading", flush=True) # Load checkpoint files latest file print(f'Start to load from {ckpt_name}') param_dict = load_checkpoint(ckpt_name) # for k, v in param_dict.items(): # print("rank: ", restore_ranks, k) load_param_into_net(network, param_dict, strict_load=False) def set_auto_parallel_context(args_opt): """Set the auto parallel context""" rank = 0 device_num = 1 context.reset_auto_parallel_context() # context.set_auto_parallel_context( # strategy_ckpt_load_file=args_opt.strategy_load_ckpt_path) if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() print("rank_id is {}, device_num is {}".format(rank, device_num)) context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=True, loss_repeated_mean=True, enable_parallel_optimizer=False, strategy_ckpt_save_file=f'/cache/strategy_{rank}.ckpt', pipeline_stages=args_opt.stage_num) set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() return rank, device_num def load_model(args_opt): r""" The main function for load model """ context.set_context(mode=context.GRAPH_MODE) # Set parallel context rank, device_num = set_auto_parallel_context(args_opt) context.set_context(variable_memory_max_size="30GB") context.set_context(save_graphs=False, save_graphs_path="/cache/graphs_of_device_id_" + str(rank), device_target=args_opt.device_target) strategy_local_file = f"/cache/inference_strategy_100b_d8_mp8_dp1-{rank}.ckpt" ms.set_auto_parallel_context(strategy_ckpt_save_file=strategy_local_file) if args_opt.eval_task: use_past = False else: use_past = True if args_opt.export else (args_opt.use_past == "true") print('local_rank:{}, start to run...'.format(rank), flush=True) # Set model property, rewrite the model parallel if device_num < args_opt.op_level_model_parallel_num: print(f"The op_level_model_parallel_num {args_opt.op_level_model_parallel_num} is smaller than the device num，" f"so change it to the {device_num}", flush=True) args_opt.op_level_model_parallel_num = device_num model_parallel_num = args_opt.op_level_model_parallel_num data_parallel_num = int(device_num / (model_parallel_num*args_opt.stage_num)) parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, vocab_emb_dp=False, recompute=False) # add sequence_parallel parallel_config.sequence_parallel = args_opt.sequence_parallel # add select_recompute parallel_config.select_recompute = args_opt.select_recompute per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num # Now only support single batch_size for predict if args_opt.run_type == "predict": batch_size = 1 # download ckpt to local D.init() device_num = D.get_group_size() rank_id = D.get_rank() softmax_compute_type = mstype.float16 top_query_softmax = mstype.float16 config = PengChengMindConfig( batch_size=batch_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, hidden_size=args_opt.embedding_size, num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, post_layernorm_residual=False, dropout_rate=0.0, ffn_hidden_size=args_opt.embedding_size * 4, use_past=use_past, eod_reset=False, parallel_config=parallel_config, load_ckpt_path=None, run_type=args_opt.run_type, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, use_rope=args_opt.use_rope,) config.softmax_compute_fp32 = softmax_compute_type config.top_query_softmax_fp32 = top_query_softmax print("===config is: ", config, flush=True) print("=====args_opt is: ", args_opt, flush=True) # Define network pengcheng_mind = PengChengMindModel(config) from src.pengcheng_mind import PengChengMindLossWithPrompt from mindspore.nn.transformer import CrossEntropyLoss # loss = CrossEntropyLoss() # eval_net = PengChengMindLossWithPrompt(config, pengcheng_mind, loss) eval_net = EvalNet_200B(pengcheng_mind, pad_token=args_opt.padding_id) eval_net.set_train(False) # # 完整模型加载，要在构图之前 # import time # time.sleep((rank % 8)*20) # load_checkpoint(local_ckpt_path, net=eval_net) model_predict = Model(eval_net) # Compile network and obtain tensor layout for loading ckpt inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) if args_opt.distribute == "false": predict_layout = None else: # Compiling only needs the shape current_index = Tensor(np.array([0]), mstype.int32) predict_layout = model_predict.infer_predict_layout(inputs_np, current_index) local_ckpt_path = '/cache/pretrained_7b_fp16.ckpt' if not os.path.exists(local_ckpt_path): try: STEP = 500 download_OneCKPT_from_obs( obs_ckpt_url=f'obs://YOUR_PATH/PengCheng-Mind-7b_{STEP}bTokens.ckpt', local_ckpt_url=local_ckpt_path, rank=rank) param_dict = load_checkpoint(local_ckpt_path) load_param_into_net(eval_net, param_dict) print("================load param ok=================", flush=True) except: pass return model_predict, config def run_predict(model_predict, config, args_opt): """run predict""" from src.generate import generate, generate_increment import time D.init() rank_id = D.get_rank() device_num = D.get_group_size() generate_func = generate_increment if config.use_past else generate # Define tokenizer from transformers import LlamaTokenizer vocab_file = './tokenizer/llama_vocab/llama_zh_hf/tokenizer_2.model' tokenizer = LlamaTokenizer.from_pretrained(vocab_file) EOT = tokenizer.eos_token_id PAD = tokenizer.unk_token_id test_sample = [ "四大发明来源于哪个国家？请用专业的知识给大家介绍。", "请把‘We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90% quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. ‘翻译为中文：", "假设你是一个唐代的诗人，以’风、雪‘为关键词，根据你自己此时的心境，写一首七言绝句：", "你读了’《咏雪》唐·白居易\n已讶衾枕冷，\n复见窗户明。\n夜深知雪重，\n时闻折竹声。‘，即兴而发，以’风、雪‘为主题，写了一首七言绝句：", "我要做蛋炒饭，请按步骤写一个详细的制作教程：", ] time_all = [] time_word = [] for idx, sample in enumerate(test_sample): # Tokenize input sentence to ids start_sentence = tokenizer.encode(sample, add_special_tokens=False) # start_sentence = tokenizer.encode(sample) input_ids = np.array(start_sentence).reshape(1, -1) time_start = time.time() output_ids = generate_func(model_predict, input_ids, args_opt, top_p=args_opt.top_p, top_k_num=args_opt.top_k_num, max_generate_length=args_opt.max_generate_length, duRepeate=args_opt.duRepeate)#args_opt.duRepeate time_use = time.time() - time_start output_samples = tokenizer.decode(output_ids[input_ids.shape[-1]:].tolist(), skip_special_tokens=True) time_all.append(time_use/max(1, len(output_ids[len(input_ids):].tolist()))) time_word.append(time_use/max(1, len(output_samples))) print(f"----------------------{idx}------------------------") print(f"Input is: {sample}\n") # print(f"ids : {output_ids[input_ids.shape[-1]:].tolist()}\n") print(f'Output is: {output_samples}\n') # print(f"{time_use/len(output_ids[len(input_ids):].tolist())} s/tokens") # print(f"{time_use/len(output_samples)} s/字") print(f"Average time: {np.average(time_all)} s/tokens") # print(f"Average time: {np.average(time_word)} s/字\n") def main(): """Main process for predict or export model""" opt = get_args(True) set_parse(opt) model_predict, config = load_model(opt) run_predict(model_predict, config, opt) if __name__ == "__main__": main() 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"ATTENTION_ATTEN": 0, "ATTENTION_QKV": 9, "stage_idx": 9, "layer_range": [ 105, 116 ] }, { "LINEAR_GELU": 0, "LINEAR_MAPPING": 0, "LINEAR_INPUT": 6, "ATTENTION_BMM": 0, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 12, "stage_idx": 10, "layer_range": [ 117, 128 ] }, { "LINEAR_GELU": 0, "LINEAR_MAPPING": 0, "LINEAR_INPUT": 6, "ATTENTION_BMM": 0, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 16, "stage_idx": 11, "layer_range": [ 129, 140 ] }, { "LINEAR_GELU": 0, "LINEAR_MAPPING": 0, "LINEAR_INPUT": 6, "ATTENTION_BMM": 2, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 18, "stage_idx": 12, "layer_range": [ 141, 152 ] }, { "LINEAR_GELU": 1, "LINEAR_MAPPING": 0, "LINEAR_INPUT": 6, "ATTENTION_BMM": 5, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 18, "stage_idx": 13, "layer_range": [ 153, 164 ] }, { "LINEAR_GELU": 1, "LINEAR_MAPPING": 1, "LINEAR_INPUT": 6, "ATTENTION_BMM": 6, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 18, "stage_idx": 14, "layer_range": [ 165, 176 ] }, { "LINEAR_GELU": 1, "LINEAR_MAPPING": 3, "LINEAR_INPUT": 6, "ATTENTION_BMM": 6, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 18, "stage_idx": 15, "layer_range": [ 177, 188 ] }, { "LINEAR_GELU": 3, "LINEAR_MAPPING": 6, "LINEAR_INPUT": 6, "ATTENTION_BMM": 6, "ATTENTION_SOFTMAX": 0, "ATTENTION_ATTEN": 0, "ATTENTION_QKV": 18, "stage_idx": 16, "layer_range": [ 189, 200 ] }, { "LINEAR_GELU": 4, "LINEAR_MAPPING": 4, "LINEAR_INPUT": 4, "ATTENTION_BMM": 4, "ATTENTION_SOFTMAX": 1, "ATTENTION_ATTEN": 4, "ATTENTION_QKV": 12, "stage_idx": 17, "layer_range": [ 201, 209 ] } ]pengchengmind/search/README.md000066400000000000000000000005211453423006600164110ustar00rootroot00000000000000# PengCheng.Mind-200B ## generate pipeline configuration 1. Set the dp(optimizer shard), tp, pp, layer_num, etc. in `search/searcher.py`. 2. Generate the configuration file under `BaSys_FineGrain`. ``` python3 searcher.py ``` 3. run the training with `pipeline_config_filename={WORK_DIR}/search/BaSys_FineGrain/{strategy_name}.json` pengchengmind/search/searcher.py000066400000000000000000000554401453423006600173120ustar00rootroot00000000000000from dataclasses import dataclass from enum import Enum import time import numpy as np import json class Config: def __init__(self, micro_batch_size, seq_len, hidden_size, head_num, num_layers, micro_size, vocab_size, mp, dp, pp, device_memory=30): self.micro_batch_size = micro_batch_size self.seq_len = seq_len self.hidden_size = hidden_size self.head_num = head_num self.num_layers = num_layers self.micro_size = micro_size self.vocab_size = vocab_size self.mp = mp self.dp = dp self.pp = pp self.dtype_size = 2 # FP16 self.expand_ratio = 4 # sub attention mask self.use_device_memory = device_memory self.device_memory = device_memory * 1024 * 1024 * 1024 - self.dtype_size * self.micro_size * self.seq_len * self.seq_len def get_next_embedding_memory(self): # top query weight_params = self.seq_len * self.hidden_size weight_params += self.vocab_size * self.hidden_size / self.mp weight_memory = weight_params * 4 * 2 optimizer_memory = weight_params * 4 * 2 whole_weight_memory = (weight_memory + optimizer_memory) + self.micro_batch_size * self.seq_len * self.hidden_size * self.dtype_size / self.mp return whole_weight_memory def get_prev_embedding_memory(self): weight_params = self.vocab_size * self.hidden_size / self.mp weight_params += self.seq_len * self.hidden_size weight_memory = weight_params * 4 * 2 optimizer_memory = weight_params * 4 * 2 whole_weight_memory = (weight_memory + optimizer_memory) return whole_weight_memory def get_prev_embedding_compute_time(self): return 12, 16 def get_next_embedding_compute_time(self): return 22.5, 21.87 def get_attention_memory(self): weight_params = self.hidden_size * self.hidden_size * 3 // self.mp weight_params += self.hidden_size * self.hidden_size // self.mp weight_memory = weight_params * self.dtype_size weight_memory += weight_params * self.dtype_size // self.dp optimizer_memory = weight_params * 4 * 2 // self.dp weight_memory += weight_params * self.dtype_size whole_weight_memory = (optimizer_memory + weight_memory) return whole_weight_memory def get_attention_forward_memory(self): forward_memory = 0.0 # layer norm forward_memory += self.micro_batch_size * self.seq_len * self.hidden_size * self.dtype_size * 2 / self.mp # compute qkv forward_memory += self.micro_batch_size * self.seq_len * self.hidden_size * self.dtype_size # query, key, value forward_memory += self.micro_batch_size * self.seq_len * self.hidden_size * self.dtype_size * 3 / self.mp # softmax forward_memory += self.micro_batch_size * self.head_num * self.seq_len * self.seq_len * 4 * 3 / self.mp # bmm forward_memory += self.micro_batch_size * self.seq_len * self.hidden_size * self.dtype_size / self.mp return forward_memory def get_linear_memory(self): weight_params = self.hidden_size * self.hidden_size * self.expand_ratio // self.mp # (weight)linear weight_params += self.hidden_size * self.hidden_size * self.expand_ratio // self.mp weight_memory = weight_params * self.dtype_size weight_memory += weight_params * self.dtype_size // self.dp optimizer_memory = weight_params * 4 * 2 // self.dp weight_memory += weight_params * self.dtype_size whole_weight_memory = (weight_memory + optimizer_memory) return whole_weight_memory def get_attention_compute_time(self): # forward # recompute: allgather is overlaped return (35, 33 + 53) def get_linear_compute_time(self): return (19.19, 9 + 33) def get_linear_operators(self): return { OperatorType.LINEAR_INPUT: 1, #OperatorType.LINEAR_X: 1, OperatorType.LINEAR_MAPPING: 1, OperatorType.LINEAR_GELU: 1 } def get_attention_operators(self): return { #OperatorType.ATTENTION_X: 1, OperatorType.ATTENTION_QKV: 3, OperatorType.ATTENTION_ATTEN: 1, OperatorType.ATTENTION_SOFTMAX: 1, OperatorType.ATTENTION_BMM: 1 } class OperatorType(str, Enum): #ATTENTION_X = "ATTENTION_X" ATTENTION_QKV = "ATTENTION_QKV" ATTENTION_ATTEN = "ATTENTION_ATTEN" ATTENTION_SOFTMAX = "ATTENTION_SOFTMAX" ATTENTION_BMM = "ATTENTION_BMM" LINEAR_INPUT = "LINEAR_INPUT" #LINEAR_X = "LINEAR_X" LINEAR_MAPPING = "LINEAR_MAPPING" LINEAR_GELU = "LINEAR_GELU" class IntraStageDPQuery: def __init__(self, config:Config, cost_map, divisor, ref_compute_time): self.config = config self.cost_map = cost_map self.divisor = divisor self.ref_compute_time = ref_compute_time self.cached_result = {} self.cached_solution = {} def get_intra_stage(self, stage_idx, layer_start, layer_end): begin_flag = layer_start == 0 end_flag = layer_end == self.config.num_layers * 2 + 1 # generate layer graph from [layer_start, layer_end] weight_memory = 0.0 ftime, btime = 0.0, 0.0 # process first embedding layer if layer_start == 0: weight_memory += self.config.get_prev_embedding_memory() f, b = self.config.get_prev_embedding_compute_time() ftime += f btime += b layer_start += 1 if layer_end == 2 * self.config.num_layers + 1: weight_memory += self.config.get_next_embedding_memory() f, b = self.config.get_next_embedding_compute_time() ftime += f btime += b layer_end -= 1 # process other layers layer_num = layer_end - layer_start + 1 atten_num = 0 if layer_start % 2 == 1: # begin with linear layer atten_num = (layer_num + 1) // 2 else: atten_num = layer_num // 2 key = f"{stage_idx}_{layer_num}" if layer_start % 2 == 0: # linear key += "_linear" else: key += "_atten" if begin_flag: key += "_begin" if end_flag: key += "_end" if key in self.cached_result: return self.cached_result[key], self.cached_solution[key] weight_memory += atten_num * self.config.get_attention_memory() f, b = self.config.get_attention_compute_time() ftime += atten_num * f btime += atten_num * b weight_memory += (layer_num - atten_num) * self.config.get_linear_memory() f, b = self.config.get_linear_compute_time() ftime += (layer_num - atten_num) * f btime += (layer_num - atten_num) * b # left memory left_memory = (self.config.device_memory - weight_memory) left_memory -= self.config.get_attention_forward_memory() #print(f"left memory: {left_memory / 1024 / 1024 / 1024} GB") left_memory = left_memory / self.divisor / (self.config.pp - stage_idx) # reduce default recomputation left_memory -= atten_num * self.cost_map[OperatorType.LINEAR_INPUT][0] if layer_start % 2 == 0: # begin with linear layer left_memory -= self.cost_map[OperatorType.LINEAR_INPUT][0] # add default recompute if layer_end % 2 == 1: # end with attention layer btime -= self.cost_map[OperatorType.LINEAR_INPUT][1] # beibao wenti operator_count = { #OperatorType.ATTENTION_X: 0, OperatorType.ATTENTION_QKV: 0, OperatorType.ATTENTION_ATTEN: 0, OperatorType.ATTENTION_SOFTMAX: 0, OperatorType.ATTENTION_BMM: 0, OperatorType.LINEAR_INPUT: 0, #OperatorType.LINEAR_X: 0, OperatorType.LINEAR_MAPPING: 0, OperatorType.LINEAR_GELU: 0} linear_operators = self.config.get_linear_operators() attention_operators = self.config.get_attention_operators() for op, num in linear_operators.items(): operator_count[op] += num * (layer_num - atten_num) for op, num in attention_operators.items(): operator_count[op] += num * atten_num if layer_start % 2 == 0: operator_count[OperatorType.LINEAR_INPUT] -= 1 # pre-test: test_compute_time = ftime + btime for op, num in operator_count.items(): mem, cost = self.cost_map[op] test_compute_time -= cost * num if test_compute_time > self.ref_compute_time: return None, None # beibao wenti left_memory = int(left_memory) if left_memory < 0: return None, None op_num = len(operator_count) l = np.zeros((op_num + 1, left_memory + 1), np.float32) trace = np.zeros((op_num + 1, left_memory + 1), np.int32) operator_keys = list(operator_count.keys()) op_idx = 1 for op in operator_keys: num = operator_count[op] mem, cost = self.cost_map[op] for i in range(0, left_memory + 1): l[op_idx][i] = l[op_idx - 1][i] for j in range(1, num + 1): if i - mem * j >= 0: if l[op_idx][i] < l[op_idx - 1][i - mem * j] + cost * j: l[op_idx][i] = l[op_idx - 1][i - mem * j] + cost * j trace[op_idx][i] = j op_idx += 1 final_time = ftime + btime - l[op_num][left_memory] recompute_op_dict = None record_time = (ftime, btime - l[op_num][left_memory]) if final_time > self.ref_compute_time: final_time = None record_time = None else: # trace back recompute_op_dict = {} for i in range(op_num, 0, -1): recompute_op_dict[operator_keys[i - 1]] = trace[i][left_memory].tolist() left_memory -= trace[i][left_memory] * self.cost_map[operator_keys[i - 1]][0] self.cached_result[key] = record_time self.cached_solution[key] = recompute_op_dict return record_time, recompute_op_dict @dataclass class UniInterStageDPItem: """Items used in intra stage DP algorithm. Attributes: 1. main cost: the computation time and comm time of activations. 2. weight cost: the comm time to all gather and reduce scatter gradients 3. cur_state: (device_mesh, l_s, l_e), point to intra stage 4. next_state: (prev_dn,) point to prev item """ main_cost: float = 0.0 next_warmup_time: float = 0.0 next_steady_time: float = 0.0 iter_time: float = 0.0 def stage_dp_algorithm(config:Config, intra_stage_dp_query: IntraStageDPQuery, output_filename): layer_num = config.num_layers * 2 + 2 stage_max = [[None] * layer_num for _ in range(config.pp)] prev_pointers = [[-1] * layer_num for _ in range(config.pp)] stage_idx = config.pp - 1 for i in range(layer_num - 1, -1, -1): result, _ = intra_stage_dp_query.get_intra_stage(stage_idx, i, layer_num - 1) if result is None: break iter_time = result[0] + result[1] new_item = UniInterStageDPItem( main_cost=iter_time * config.micro_size, iter_time=iter_time) new_item.next_steady_time = (config.micro_size - 2) * iter_time new_item.next_warmup_time = iter_time new_item.next_cool_time = iter_time if config.micro_size == 1: new_item.next_steady_time = iter_time new_item.next_warmup_time = 0 new_item.next_cool_time = 0 stage_max[stage_idx][i] = new_item #print(f"init: {new_item}") for s_n in range(config.pp - 2, -1, -1): begin = time.time() for l_s in range(layer_num - (config.pp - s_n), -1, -1): for l_m in range(l_s, layer_num - (config.pp - s_n) + 1): #print(f"process stage_idx: {s_n}, l_s: {l_s}, l_m: {l_m}") cur_item = stage_max[s_n][l_s] #print(f"cur_item: {cur_item}") next_item = stage_max[s_n + 1][l_m + 1] #print(f"next_item: {next_item}") if next_item is None: continue mid_item, _ = intra_stage_dp_query.get_intra_stage(s_n, l_s, l_m) if mid_item is None: break cur_iter_time = mid_item[0] + mid_item[1] next_iter_time = next_item.iter_time max_iter_time = max(cur_iter_time, next_iter_time) iter_num = config.micro_size - (config.pp - s_n) if iter_num >= 0: steady_time = max_iter_time * iter_num else: steady_time = next_item.next_steady_time # warmup stage time warmup_time = mid_item[0] if iter_num >= 0: warmup_time += max( mid_item[0] * (config.pp - s_n - 1), next_item.next_warmup_time) else: warmup_time += max( mid_item[0] * (config.micro_size - 1), next_item.next_warmup_time) # cool stage time cool_time = mid_item[1] if iter_num >= 0: cool_time += max( mid_item[1] * (config.pp - s_n - 1), next_item.next_cool_time) else: cool_time += max( mid_item[1] * (config.micro_size - 1), next_item.next_cool_time) # new main_cost new_main_cost = steady_time + warmup_time + cool_time if cur_item is None or cur_item.main_cost > new_main_cost: new_item = UniInterStageDPItem( main_cost=new_main_cost, iter_time = max_iter_time) if iter_num >= 1: new_item.next_steady_time = (steady_time - max_iter_time) if cur_iter_time >= max_iter_time: new_item.next_warmup_time = ( warmup_time + mid_item[1]) new_item.next_cool_time = ( cool_time + mid_item[0]) else: new_item.next_warmup_time = ( warmup_time + max_iter_time - mid_item[1]) new_item.next_cool_time = ( cool_time + max_iter_time - mid_item[0]) else: new_item.next_steady_time = ( steady_time + mid_item[0] + mid_item[1]) new_item.next_warmup_time = warmup_time new_item.next_cool_time = cool_time stage_max[s_n][l_s] = new_item prev_pointers[s_n][l_s] = l_m end = time.time() print(f"stage idx: {s_n}, elapsed time: {end - begin} s") # find the best item best_solution = stage_max[0][0] print(f"best solution: {best_solution}") # print result solution = [] stage_idx = 0 layer_idx = 0 while stage_idx < config.pp: next_layer_idx = prev_pointers[stage_idx][layer_idx] if stage_idx == config.pp - 1: next_layer_idx = layer_num - 1 stage_time, stage_solution = intra_stage_dp_query.get_intra_stage(stage_idx, layer_idx, next_layer_idx) print(f"stage idx: {stage_idx}, layer: [{layer_idx}, {next_layer_idx}] stage time: {stage_time}") stage_solution["stage_idx"] = stage_idx stage_solution["layer_range"] = [layer_idx, next_layer_idx] solution.append(stage_solution) layer_idx = next_layer_idx + 1 stage_idx += 1 with open(output_filename, "w") as f: json.dump(solution, f, indent=4) def print_elapsed_time(config:Config, intra_stage_dp_query: IntraStageDPQuery, solution): last_solution = solution[config.pp - 1] layer_range = last_solution["layer_range"] stage_time, _ = intra_stage_dp_query.get_intra_stage(config.pp - 1, layer_range[0], layer_range[1]) iter_time = stage_time[0] + stage_time[1] cur_item = UniInterStageDPItem( main_cost=iter_time * config.micro_size, iter_time=iter_time) cur_item.next_steady_time = (config.micro_size - 2) * iter_time cur_item.next_warmup_time = iter_time cur_item.next_cool_time = iter_time if config.micro_size == 1: cur_item.next_steady_time = iter_time cur_item.next_warmup_time = 0 cur_item.next_cool_time = 0 for stage_idx in range(config.pp - 2, -1, -1): stage_config = solution[stage_idx] layer_range = stage_config["layer_range"] mid_item, _ = intra_stage_dp_query.get_intra_stage(stage_idx, layer_range[0], layer_range[1]) cur_iter_time = mid_item[0] + mid_item[1] next_iter_time = cur_item.iter_time max_iter_time = max(cur_iter_time, next_iter_time) iter_num = config.micro_size - (config.pp - stage_idx) if iter_num >= 0: steady_time = max_iter_time * iter_num else: steady_time = cur_item.next_steady_time # warmup stage time warmup_time = mid_item[0] if iter_num >= 0: warmup_time += max( mid_item[0] * (config.pp - stage_idx - 1), cur_item.next_warmup_time) else: warmup_time += max( mid_item[0] * (config.micro_size - 1), cur_item.next_warmup_time) # cool stage time cool_time = mid_item[1] if iter_num >= 0: cool_time += max( mid_item[1] * (config.pp - stage_idx - 1), cur_item.next_cool_time) else: cool_time += max( mid_item[1] * (config.micro_size - 1), cur_item.next_cool_time) # new main_cost new_main_cost = steady_time + warmup_time + cool_time new_item = UniInterStageDPItem( main_cost=new_main_cost, iter_time = max_iter_time) if iter_num >= 1: new_item.next_steady_time = (steady_time - max_iter_time) if cur_iter_time >= max_iter_time: new_item.next_warmup_time = ( warmup_time + mid_item[1]) new_item.next_cool_time = ( cool_time + mid_item[0]) else: new_item.next_warmup_time = ( warmup_time + max_iter_time - mid_item[1]) new_item.next_cool_time = ( cool_time + max_iter_time - mid_item[0]) else: new_item.next_steady_time = ( steady_time + mid_item[0] + mid_item[1]) new_item.next_warmup_time = warmup_time new_item.next_cool_time = cool_time cur_item = new_item print(cur_item) def finegrain_dp_algorithm(config:Config, intra_stage_dp_query: IntraStageDPQuery, output_filename): layer_num = config.num_layers * 2 + 2 # add prev embedding and later embedding # print result solution = [] stage_idx = config.pp - 1 layer_idx = layer_num - 1 while stage_idx >= 0: layer_num_per_stage = (config.num_layers // config.pp + 1) * 2 if stage_idx == config.pp - 1 or stage_idx == 0: layer_num_per_stage -= 3 prev_layer_idx = layer_idx - layer_num_per_stage stage_time, stage_solution = intra_stage_dp_query.get_intra_stage(stage_idx, prev_layer_idx + 1, layer_idx) print(f"stage idx: {stage_idx}, layer: [{prev_layer_idx + 1}, {layer_idx}] stage time: {stage_time}") stage_solution["stage_idx"] = stage_idx stage_solution["layer_range"] = [prev_layer_idx + 1, layer_idx] solution.append(stage_solution) layer_idx = prev_layer_idx stage_idx -= 1 solution.reverse() print_elapsed_time(gpt_config, intra_stage_dp_query, solution) with open(output_filename, "w") as f: json.dump(solution, f, indent=4) def gcd(a, b): while b != 0: temp = a % b a = b b = temp return a gpt_config = Config( micro_batch_size=1, seq_len=4096, hidden_size=12672, head_num=96, num_layers=20, micro_size=64, vocab_size=49984, mp=4, dp=4, pp=4, device_memory=29) atten_x = gpt_config.micro_batch_size * gpt_config.seq_len * gpt_config.hidden_size * gpt_config.dtype_size // gpt_config.mp # Float32 atten_atten = gpt_config.micro_batch_size * gpt_config.head_num * gpt_config.seq_len * gpt_config.seq_len * 2 // gpt_config.mp # cost of GPT3 model, mp = 4 cost_map_mp4 = { #OperatorType.ATTENTION_X: (atten_x * gpt_config.mp, 0.276), OperatorType.ATTENTION_QKV: (atten_x, 1.7), OperatorType.ATTENTION_ATTEN: (atten_atten, 10.544), OperatorType.ATTENTION_SOFTMAX: (atten_atten * 3, 7.5), # mul 3 OperatorType.ATTENTION_BMM: (atten_x, 1.3), OperatorType.LINEAR_INPUT: (atten_x, 9.1), #OperatorType.LINEAR_X: (atten_x * gpt_config.mp, 0.276), OperatorType.LINEAR_MAPPING: (atten_x * gpt_config.expand_ratio * 2, 5.84), # mul 2 OperatorType.LINEAR_GELU: (atten_x * gpt_config.expand_ratio, 2.07), } max_divisor = gcd(atten_x, atten_atten) for op, (mem, cost) in cost_map_mp4.items(): cost_map_mp4[op] = (mem // max_divisor, cost) # get reference running time ref_layer_num = gpt_config.num_layers // gpt_config.pp + 1 ref_compute_time = (sum(gpt_config.get_attention_compute_time()) + sum(gpt_config.get_linear_compute_time())) * ref_layer_num first_stage = ref_compute_time + sum(gpt_config.get_prev_embedding_compute_time()) last_stage = ref_compute_time + sum(gpt_config.get_next_embedding_compute_time()) ref_compute_time = max(first_stage, last_stage) print(f"ref iteration time: {ref_compute_time}") intra_stage_dp_query = IntraStageDPQuery(gpt_config, cost_map_mp4, max_divisor, ref_compute_time) #output_filename = f"BaSys/gpt_{gpt_config.num_layers}layer_{gpt_config.dp}dp_{gpt_config.mp}mp_{gpt_config.pp}pp_{gpt_config.micro_size}micro.json" #stage_dp_algorithm(gpt_config, intra_stage_dp_query, output_filename) output_filename = f"BaSys_FineGrain/gpt_{gpt_config.num_layers}layer_{gpt_config.dp}dp_{gpt_config.mp}mp_{gpt_config.pp}pp_{gpt_config.use_device_memory}mem_{gpt_config.micro_size}ms.json" finegrain_dp_algorithm(gpt_config, intra_stage_dp_query, output_filename) pengchengmind/src/000077500000000000000000000000001453423006600144565ustar00rootroot00000000000000pengchengmind/src/__init__.py000066400000000000000000000001031453423006600165610ustar00rootroot00000000000000from .loss import CrossEntropyLoss __all__ = ["CrossEntropyLoss"] pengchengmind/src/adam.py000066400000000000000000000266131453423006600157420ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """AdamWeightDecay, a customized Adam for pengcheng mind. Input: gradient.""" import numpy as np from mindspore.common import dtype as mstype from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.common.initializer import initializer from mindspore.common.tensor import Tensor from mindspore.common.parameter import Parameter, ParameterTuple from mindspore._checkparam import Validator as validator from mindspore._checkparam import Rel from mindspore.nn.optim.optimizer import Optimizer _adam_opt = C.MultitypeFuncGraph("adam_opt") _scaler_one = Tensor(1, mstype.int32) _scaler_ten = Tensor(10, mstype.float32) _cpu_div = P.RealDiv().add_prim_attr("primitive_target", "CPU") @_adam_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool") def _update_run_kernel(opt, clip_value, beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, decay_flags, optim_filter): """ Update parameters by AdamWeightDecay op. """ success = True if optim_filter: if decay_flags: next_param = opt(param, m, v, lr, beta1, beta2, eps, weight_decay, P.Cast()(gradient, mstype.float16), clip_value) else: next_param = opt(param, m, v, lr, beta1, beta2, eps, 0.0, P.Cast()(gradient, mstype.float16), clip_value) return F.depend(success, next_param) return success def _check_param_value(beta1, beta2, eps, prim_name): """Check the type of inputs.""" validator.check_value_type("beta1", beta1, [float], prim_name) validator.check_value_type("beta2", beta2, [float], prim_name) validator.check_value_type("eps", eps, [float], prim_name) validator.check_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name) validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name) validator.check_positive_float(eps, "eps", prim_name) class AdamWeightDecayOp(Optimizer): """ Implements the Adam algorithm to fix the weight decay. It is a complete operator, not a combination of other ops. Note: When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive. To improve parameter groups performance, the customized order of parameters can be supported. Args: params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated, the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed. - params: Required. The value must be a list of `Parameter`. - lr: Optional. If "lr" is in the keys, the value of the corresponding learning rate will be used. If not, the `learning_rate` in the API will be used. - weight_decay: Optional. If "weight_decay" is in the keys, the value of the corresponding weight decay will be used. If not, the `weight_decay` in the API will be used. - order_params: Optional. If "order_params" is in the keys, the value must be the order of parameters and the order will be followed in the optimizer. There are no other keys in the `dict` and the parameters which in the 'order_params' must be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use the dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. Default: 1e-3. beta1 (float): The exponential decay rate for the 1st moment estimations. Default: 0.9. Should be in range (0.0, 1.0). beta2 (float): The exponential decay rate for the 2nd moment estimations. Default: 0.999. Should be in range (0.0, 1.0). eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6. Should be greater than 0. weight_decay (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: tuple[bool], all elements are True. Supported Platforms: ``CPU`` Examples: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = AdamWeightDecayOp(params=net.trainable_params()) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = AdamWeightDecayOp(group_params, learning_rate=0.1, weight_decay=0.0) >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = Model(net, loss_fn=loss, optimizer=optim) """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0, clip_norm=1.0, param_init_type=mstype.float32): super(AdamWeightDecayOp, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) self.beta1 = Tensor(np.array([beta1]).astype(np.float32)) self.beta2 = Tensor(np.array([beta2]).astype(np.float32)) self.eps = Tensor(np.array([eps]).astype(np.float32)) self.clip_norm = Tensor([clip_norm], mstype.float32) self.enable_init_fp16 = (param_init_type == mstype.float16) if self.enable_init_fp16: self.moments1 = self.clone_param32(prefix="adam_m", init='zeros') self.moments2 = self.clone_param32(prefix="adam_v", init='zeros') self.opt = P.FusedCastAdamWeightDecay() else: self.moments1 = self.parameters.clone(prefix="adam_m", init='zeros') self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros') self.opt = P.AdamWeightDecay() self.hyper_map = C.HyperMap() self.opt.add_prim_attr("primitive_target", "CPU") def construct(self, gradients, clip_value): """AdamWeightDecayOp""" lr = self.get_lr() cond = P.GreaterEqual()(clip_value, self.clip_norm) global_norm = F.select(cond, clip_value, self.clip_norm) global_norm = P.Cast()(global_norm, mstype.float16) if self.is_group: if self.is_group_lr: optim_result = self.map_reverse(F.partial(_adam_opt, self.opt, global_norm, self.beta1, self.beta2, self.eps), lr, self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.map_reverse(F.partial(_adam_opt, self.opt, global_norm, self.beta1, self.beta2, self.eps, lr), self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.map_reverse(F.partial(_adam_opt, self.opt, global_norm, self.beta1, self.beta2, self.eps, lr, self.weight_decay), self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: self.broadcast_params(optim_result) return optim_result def clone_param32(self, prefix, init=None): """ Clone the parameters in ParameterTuple element-wisely to generate a new ParameterTuple with float32 data type. Inputs: prefix (str): The prefix name of the parameters. init (Union[Tensor, str, numbers.Number]): Initialize the shape and dtype of the parameters. The definition of `init` is the same as in `Parameter` API. If `init` is 'same', the parameters in the new parameter tuple are the same as those in the original parameter tuple. Default: 'same'. Returns: Tuple, the new Parameter tuple. """ new = [] for old_param in self.parameters: param_init = init if init is None: param_init = old_param.init new_state = Parameter(initializer(param_init, shape=old_param.shape, dtype=mstype.float32)) new_state.param_info = old_param.param_info.clone() new_state.is_init = False new_state.is_param_ps = old_param.is_param_ps new_state.init_in_server = old_param.init_in_server new_state.cache_enable = old_param.cache_enable new_state.requires_aggr = old_param.requires_aggr if old_param.cache_shape: new_state.cache_shape = old_param.cache_shape new_state.name = prefix + '.' + new_state.name new.append(new_state) return ParameterTuple(new) pengchengmind/src/callbacks.py000066400000000000000000000204401453423006600167470ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ Callbacks """ import time import math import numpy as np import moxing as mox from mindspore.train.callback import Callback from mindspore import context from mindspore.context import ParallelMode from mindspore.communication.management import get_rank from mindspore.train.summary import SummaryRecord from multiprocessing import Process from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype class LossCallBack(Callback): """ Monitor the loss in training. If the loss in NAN or INF terminating training. """ def __init__(self, dataset_size=-1, local_rank=0, has_trained_epoch=0, has_trained_step=0, micro_size=1, is_last_stage=True): super(LossCallBack, self).__init__() self._dataset_size = dataset_size self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.micro_size = micro_size self.is_last_stage = is_last_stage # self.log_filename = local_log_file # self.bucket = log_upload_bucket_path print("Load the trained epoch :{} and step: {}".format(has_trained_epoch, has_trained_step), flush=True) def step_end(self, run_context): """ Print loss after each step """ cb_params = run_context.original_args() if self._dataset_size > 0 and self.local_rank % 8 == 0: percent, epoch_num = math.modf(cb_params.cur_step_num / self._dataset_size) if percent == 0: epoch_num -= 1 date = time.asctime(time.localtime(time.time())) loss_value = 'no loss for this stage' if self.is_last_stage: loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size loss_value = np.mean(loss_value) print("time: {} local_rank: {}, epoch: {}, step: {}, loss is {}, overflow is {}, loss scale is {}, global norm value is {}". format(date, int(self.local_rank), int(epoch_num) + int(self.has_trained_epoch), cb_params.cur_step_num + int(self.has_trained_step), loss_value, cb_params.net_outputs[1].asnumpy(), cb_params.net_outputs[2].asnumpy(), cb_params.net_outputs[3].asnumpy())) # if int(cb_params.cur_step_num)%4==0: # self.syn_files() # print("time: {} local_rank: {}, epoch: {}, step: {}, loss is {}, overflow is {}, loss scale is {}, lr is {}". # format(date, int(self.local_rank), int(epoch_num) + int(self.has_trained_epoch), # cb_params.cur_step_num + int(self.has_trained_step), # loss_value, # cb_params.net_outputs[1].asnumpy(), # cb_params.net_outputs[2].asnumpy(), # cb_params.net_outputs[3].asnumpy())) # def syn_files(self): # process = Process(target=mox.file.copy, args=(self.log_filename, self.bucket), name="file_sync") # process.start() class LossSummaryCallback(Callback): def __init__(self, summary_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, micro_size=1, is_last_stage=True, bucket='obs://mindspore-file/loss_file/summary/', syn_times=20): self._summary_dir = summary_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.bucket = bucket self.syn_times = syn_times self.micro_size = micro_size self.is_last_stage = is_last_stage if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) # print("entering") self.summary_record = SummaryRecord(self._summary_dir) def step_end(self, run_context): cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + int(self.has_trained_step) if self.is_last_stage: loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size loss_value = Tensor(np.mean(loss_value), dtype=mstype.float32) # create a confusion matric image, and record it to summary file self.summary_record.add_value('scalar', 'loss', loss_value) self.summary_record.add_value('scalar', 'loss scale', cb_params.net_outputs[2]) if len(cb_params.net_outputs) > 3: self.summary_record.add_value('scalar', 'global_norm', cb_params.net_outputs[3]) # if len(cb_params.net_outputs) > 4: # self.summary_record.add_value('scalar', 'global_norm', cb_params.net_outputs[4]) self.summary_record.record(cur_step) if cur_step % self.syn_times == 0: print("Copying summary to the bueckets start", flush=True) self.summary_record.flush() self.syn_files() print("Copying summary to the bueckets ends", flush=True) def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self._summary_dir, self.bucket), name="file_sync") process.start() class EvalCallBack(Callback): """ Monitor the ppl loss in evaluating. Note: If per_print_times is 0, do NOT print loss. Args: print_per_step (int): Print loss every times. Default: 1. """ def __init__(self, model, eval_dataset, ppl_metric, print_per_step=20, has_trained_step=0): super(EvalCallBack, self).__init__() if not isinstance(print_per_step, int) or print_per_step < 0: raise ValueError("print_per_step must be int and >= 0.") self.print_per_step = print_per_step self.model = model self.eval_dataset = eval_dataset self.pplMetric = ppl_metric self.has_trained_step = has_trained_step self.pplMetric.clear() self.parallel_mode = context.get_auto_parallel_context("parallel_mode") self.strategy_ckpt_save_file = context.get_auto_parallel_context("strategy_ckpt_save_file") self.strategy_ckpt_load_file = context.get_auto_parallel_context("strategy_ckpt_load_file") def step_end(self, run_context): """ step end """ cb_params = run_context.original_args() current_step = cb_params.cur_step_num + self.has_trained_step if current_step % self.print_per_step != 0: return self.pplMetric.clear() if self.parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL): context.set_auto_parallel_context(strategy_ckpt_save_file="", strategy_ckpt_load_file=self.strategy_ckpt_save_file) rank_id = 0 if self.parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL, ParallelMode.DATA_PARALLEL): rank_id = get_rank() start_time = time.time() out = self.model.eval(self.eval_dataset, dataset_sink_mode=True) end_time = time.time() eval_time = int(end_time - start_time) time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime()) out_str = "{} == Rank: {} == EvalCallBack model.eval(): {}; eval_time: {}s". \ format(time_str, rank_id, out.values(), eval_time) print(out_str) context.set_auto_parallel_context(strategy_ckpt_save_file=self.strategy_ckpt_save_file, strategy_ckpt_load_file=self.strategy_ckpt_load_file) pengchengmind/src/dataset.py000066400000000000000000000230201453423006600164520ustar00rootroot00000000000000# Copyright 2021-2022 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ Create dataset for training and evaluating """ import os import numpy as np import mindspore.dataset as ds # import mindspore.dataset.transforms as C import mindspore.dataset.transforms.c_transforms as C import mindspore.common.dtype as mstype from mindspore import context def get_input_data_batch_slice_map(input_ids, eod_id, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for rank: the current rank dis: the slice value for each rank eod_reset: whether to open eod reset or not returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) input_ids = input_ids[rank * dis: (rank + 1) * dis] if not eod_reset: return input_ids seq_length = input_ids.shape[1] - 1 # Initialize position_ids and attention_mask batch_input_ids = input_ids batch_position_ids = np.ones((dis, seq_length), dtype=np.int32) batch_attention_mask = np.ones((dis, seq_length, seq_length), dtype=np.int8) # Loop through batches for bs_i, _ in enumerate(range(len(input_ids))): # Get normal position_ids and attention_mask local_ids = input_ids[bs_i] batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length), dtype=np.int8)) batch_position_ids[bs_i] = np.arange(seq_length) # Find eod_of_document eod_index = batch_position_ids[bs_i, local_ids[:-1] == eod_id].astype(np.int32) prev_index = 0 for i in range(eod_index.size): # Reset position_ids and attention_mask considering EOD index = eod_index[i] batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 batch_position_ids[bs_i, (index + 1):] -= (index + 1 - prev_index) prev_index = index + 1 return batch_input_ids, batch_position_ids, batch_attention_mask def get_input_data_batch_slice_map2(input_ids, position_id, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) position_id = position_id[rank * dis: (rank + 1) * dis] input_ids = input_ids[rank * dis: (rank + 1) * dis] if not eod_reset: return position_id seq_length = position_id.shape[1] tril_dev = np.tril(np.ones(shape=(seq_length, seq_length), dtype=np.int8)) # Initialize position_ids and attention_mask batch_attention_mask = np.ones((len(position_id), seq_length, seq_length), dtype=np.int8) batch_position_ids = np.ones((dis, seq_length), dtype=np.int32) # Loop through batches for bs_i in range(len(position_id)): # Get normal position_ids and attention_mask batch_attention_mask[bs_i] = tril_dev # Find eod_of_document local_ids = position_id[bs_i] batch_position_ids[bs_i] = np.arange(seq_length) eod_index = batch_position_ids[bs_i, local_ids == 0].astype(np.int32)[1:] - 1 for i in range(eod_index.size): # Reset position_ids and attention_mask considering EOD index = eod_index[i] batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 return input_ids, position_id, batch_attention_mask def get_input_data_batch_slice_map3(input_ids, position_id, eod_index, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) position_id = position_id[rank * dis: (rank + 1) * dis] input_ids = input_ids[rank * dis: (rank + 1) * dis] eod_id = eod_index[rank * dis: (rank + 1) * dis] if not eod_reset: return position_id seq_length = position_id.shape[1] # Initialize position_ids and attention_mask batch_attention_mask = np.ones((len(position_id), seq_length, seq_length)) # Loop through batches for bs_i in range(len(position_id)): # Get normal position_ids and attention_mask batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length))) # Find eod_of_document for index in eod_id[bs_i]: # Reset position_ids and attention_mask considering EOD batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 return input_ids, position_id, batch_attention_mask def create_dataset(batch_size, data_path, device_num=1, rank=0, drop=True, full_batch=False, data_start_index=0, eod_reset=False, eod_id=9, column_name='input_ids', epoch=1, num_samples=None): """ Create dataset Inputs: batch_size: batch size data_path: path of your MindRecord files device_num: total device number rank: current rank id drop: whether drop remainder eod_reset: whether enable position reset and attention mask reset eod_id: the id for column_name: the column name of the mindrecord file. Default is input_ids epoch: The repeat times of the dataset Returns: dataset_restore: the dataset for training or evaluating """ ds.config.set_seed(1) # Control the size of data queue in the consideration of the memory ds.config.set_prefetch_size(4) is_data_parallel = context.get_auto_parallel_context( "parallel_mode") == context.ParallelMode.DATA_PARALLEL # Get path for source data files home_path = os.path.join(os.getcwd(), data_path) files = os.listdir(data_path) data = [ os.path.join(home_path, name) for name in files if not name.endswith(".db") ] # Ensure the order of mindrecords is same in all machines, otherwise it will meet loss converge problem. data.sort() print("************\n"*10) print("load_data_list: ", data) # Load data files and preprocess # dataset = ds.MindDataset(data[data_start_index:], columns_list=[column_name], # shuffle=False, num_samples=num_samples) dataset = ds.MindDataset(data[data_start_index:], columns_list=[column_name, "position_id"], shuffle=False, num_samples=num_samples) type_cast_op = C.TypeCast(mstype.int32) type_cast_op_float = C.TypeCast(mstype.float16) if full_batch or is_data_parallel: # no need to slice from the inputs rank = 0 dis = batch_size else: # Each card slice a small batch from the full batch dis = int(batch_size / device_num) if batch_size % device_num != 0: raise ValueError( f"batch size {batch_size} should be a multiple of device number {device_num}." " You should change the args: per_batch_size.") # map_func = (lambda input_ids: get_input_data_batch_slice_map(input_ids, eod_id, rank, dis, eod_reset)) map_func = (lambda input_ids, position_id: get_input_data_batch_slice_map2(input_ids, position_id, rank, dis, eod_reset)) # If eod_reset enabled, another two inputs will be generated through input_ids num_parallel_workers = 8 dataset = dataset.map(input_columns=column_name, operations=type_cast_op, num_parallel_workers=num_parallel_workers) if eod_reset: dataset = dataset.batch(batch_size, drop_remainder=drop) # dataset = dataset.map(operations=map_func, input_columns=[column_name], # output_columns=[column_name, "position_id", "attention_mask"], num_parallel_workers=num_parallel_workers) dataset = dataset.map(operations=map_func, input_columns=["input_ids", "position_id"], output_columns=["input_ids", "position_id", "attention_mask"], num_parallel_workers=num_parallel_workers) dataset = dataset.map(input_columns="position_id", operations=type_cast_op, num_parallel_workers=num_parallel_workers) # dataset = dataset.map(input_columns="attention_mask", operations=type_cast_op_float, num_parallel_workers=8) else: dataset = dataset.map(input_columns=[column_name], operations=type_cast_op) dataset = dataset.batch(batch_size, drop_remainder=drop) # dataset = dataset.map(operations=map_func, input_columns=[column_name], # output_columns=[column_name]) # dataset = dataset.repeat(epoch) return dataset pengchengmind/src/generate.py000066400000000000000000000461021453423006600166250ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ TopK for text generation """ import numpy as np from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.ops import operations as P import sys sys.path.append("..") def compute_CrossEntropyLoss(logits, labels, mask): from .loss import CrossEntropyLoss logits = Tensor(logits, mstype.float32) input_mask = Tensor(mask) labels = Tensor(labels) loss = CrossEntropyLoss(logits, labels, input_mask) print(f"loss.shape: {loss.shape}, {loss}") return loss def convert_text_to_ids(text, tokenizer, seq_length, pad, plus=0): input_ids = tokenizer.encode(text) # input_ids = tokenizer.convert_tokens_to_ids(input_ids) input_ids = np.array(input_ids).reshape(1, -1) if input_ids.shape[-1] > seq_length: input_ids = input_ids[:, :seq_length + plus] pad_length = seq_length + plus - input_ids.shape[-1] input_ids = np.pad(input_ids, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, pad)) return input_ids def gather(data, index): result = [] for i in range(data.shape[0]): result.append(data[i, index[i]]) return np.array(result) def compute_loss(logits, labels, mask): labels = labels.astype(np.int32) select = gather(logits, labels[0]) loss = -select * mask loss = np.mean(loss) return loss def get_scores(model_predict, item, tokenizer, pad_length=1024): input_sentence = item['input_str'] prompt = item['prompt'] tokens = convert_text_to_ids(input_sentence, tokenizer, pad_length, pad=tokenizer.pad_id, plus=0) prompt_ids = convert_text_to_ids(prompt, tokenizer, pad_length, pad=tokenizer.pad_id, plus=0) labels = np.concatenate((tokens[:, 1:], np.ones((tokens.shape[0], 1)) * tokenizer.pad_id), axis=-1) logits, mask = model_predict.predict(Tensor(np.array(tokens, np.int32)), Tensor(np.array(prompt_ids, np.int32))) loss = compute_loss(logits.asnumpy(), labels, mask.asnumpy()) return loss def get_scores_100b(model_predict, item, tokenizer, pad_length=4096): input_sentence = item tokens = convert_text_to_ids(input_sentence, tokenizer, pad_length, pad=tokenizer.pad_id, plus=0) labels = np.concatenate((tokens[:, 1:], np.ones((tokens.shape[0], 1)) * tokenizer.pad_id), axis=-1) logits, mask = model_predict.predict(Tensor(np.array(tokens, np.int32))) loss = compute_CrossEntropyLoss(logits.asnumpy(), labels, mask.asnumpy()) return loss def topk_fun(logits, topk=5): """Get topk""" target_column = logits[0].tolist() sorted_array = [(k, v) for k, v in enumerate(target_column)] sorted_array.sort(key=lambda x: x[1], reverse=True) topk_array = sorted_array[:topk] index, value = zip(*topk_array) index = np.array([index]) value = np.array([value]) return value, index def sampler(log_probs_revised, top_p, top_k_num, use_pynative=False): """Convert the log_probs to probability""" if use_pynative: logits = P.Pow()(np.e, Tensor(log_probs_revised, mstype.float32)) else: logits = np.power(np.e, np.array(log_probs_revised, np.float32)) # If top_p is less than 1.0, use top_p sampling if top_p < 1.0: # Only consider the 5000 largest logits to reduce computation if use_pynative: sorted_logits, index = P.TopK(sorted=True)(logits, 5000) cumsum_logits = P.CumSum()(sorted_logits, 1) cumsum_logits = cumsum_logits.asnumpy() index = index.asnumpy() sorted_logits = sorted_logits.asnumpy() else: sorted_logits, index = topk_fun(logits, 5000) cumsum_logits = np.cumsum(sorted_logits, 1) cumsum_logits = cumsum_logits[0] index = index[0] sorted_logits = sorted_logits[0] top_p_num = sum(cumsum_logits < top_p) + 1 # In case the probability is smooth, the sum of 5000 largest probabilities are not large enough if top_p_num == 0: top_p_num = 5000 # Get the corresponding probs and indices probs = sorted_logits[:top_p_num] p_args = index[:top_p_num] p = probs / sum(probs) # if top_p is set to 1.0, use top_k sampling else: # Get the corresponding probs and indices if use_pynative: probs, p_args = P.TopK(sorted=True)(logits, top_k_num) probs = probs.asnumpy() p_args = p_args.asnumpy() else: probs, p_args = topk_fun(logits, top_k_num) probs = probs[0] p_args = p_args[0] # Avoid rounding error if sum(probs) == 0: probs = np.array([1 / top_k_num for _ in range(top_k_num)]) p = probs / sum(probs) return p, p_args def generate(model, origin_inputs, config): """ Text generation Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations return_scores: return the score on the given sentence rather than generating the words. Default False. Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k_num = config.top_k_num max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # print("input_ids shape :", input_ids.shape) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: inputs = Tensor(input_ids, mstype.int32) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 current_index = Tensor([current_index], mstype.int32) # print(f"current_index: {current_index}") # Call a single inference log_probs = model.predict(inputs, current_index) # print(f"log_probs: {log_probs}\nlog_probs[:]: {log_probs[0].asnumpy().shape}\n{log_probs[1].asnumpy().shape}") log_probs = log_probs[0] # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs = log_probs.asnumpy().reshape(1, config.vocab_size) log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: outputs = input_ids break # update frequency list target = p_args[target_index] # frequency_list[0][target] = frequency_list[0][target] + 1 # Modify input_ids with newly generated token input_ids[0][valid_length] = p_args[target_index] valid_length += 1 # Return valid outputs out of padded outputs length = np.sum(outputs != 0) outputs = outputs[0][:length] return outputs def generate_100b(model, origin_inputs, config): """ Text generation Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations return_scores: return the score on the given sentence rather than generating the words. Default False. Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k_num = config.top_k_num max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # print("input_ids shape :", input_ids.shape) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: inputs = Tensor(input_ids, mstype.int32) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 current_index = Tensor([current_index], mstype.int32) # print(f"current_index: {current_index}") # Call a single inference log_probs = model.predict(inputs, current_index) # print(f"log_probs: {log_probs}\nlog_probs[:]: {log_probs[0].asnumpy().shape}\n{log_probs[1].asnumpy().shape}") log_probs = log_probs[0] # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs = log_probs.asnumpy().reshape(1, config.vocab_size) log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1 or p_args[target_index] == 0: outputs = input_ids break # update frequency list target = p_args[target_index] frequency_list[0][target] = frequency_list[0][target] + 1 # Modify input_ids with newly generated token input_ids[0][valid_length] = p_args[target_index] valid_length += 1 # Return valid outputs out of padded outputs length = np.sum(outputs != 0) outputs = outputs[0][:length] # outputs = outputs[0] return outputs def generate_100b_task(model, origin_inputs, config, top_p=1.0, top_k_num = 3, max_generate_length=100, duRepeate=True): """ Text generation Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations return_scores: return the score on the given sentence rather than generating the words. Default False. Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = top_p top_k_num = top_k_num max_generate_length = max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # print("input_ids shape :", input_ids.shape) outputs = [] # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: inputs = Tensor(input_ids, mstype.int32) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 current_index = Tensor([current_index], mstype.int32) # print(f"current_index: {current_index}") # Call a single inference log_probs = model.predict(inputs, current_index) # print(f"log_probs: {log_probs}\nlog_probs[:]: {log_probs[0].asnumpy().shape}\n{log_probs[1].asnumpy().shape}") log_probs = log_probs[0] # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs = log_probs.asnumpy().reshape(1, config.vocab_size) log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: outputs = input_ids break # update frequency list target = p_args[target_index] if duRepeate: frequency_list[0][target] = frequency_list[0][target] + 1 # Modify input_ids with newly generated token input_ids[0][valid_length] = p_args[target_index] valid_length += 1 # Return valid outputs out of padded outputs length = np.sum(outputs != 0) outputs = outputs[0][:length+1] # outputs = outputs[0] return outputs def generate_increment(model, origin_inputs, config, top_p=1.0, top_k_num = 3, max_generate_length=100, duRepeate=True): """ Text generation for incremental inference Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = top_p top_k_num = top_k_num max_generate_length = max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # Init outputs with original inputs outputs = [origin_inputs[0][i] for i in range(valid_length)] # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 batch_valid_length = Tensor(np.array([current_index]), mstype.int32) current_index = Tensor(np.array([current_index]), mstype.int32) # For first graph, not_init should be false init_true = Tensor([True], mstype.bool_) init_false = Tensor([False], mstype.bool_) init = init_false # Claim the first graph model.predict_network.add_flags_recursive(is_first_iteration=True) # Call a single inference with input size of (bs, seq_length) logits = model.predict(Tensor(input_ids, mstype.int32), current_index, init, batch_valid_length) # Claim the second graph and set not_init to true init = init_true model.predict_network.add_flags_recursive(is_first_iteration=False) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: # Reshape the output logits logits = logits.asnumpy() log_probs = logits.reshape(1, config.vocab_size) # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: break # Update frequency list target = p_args[target_index] if duRepeate: frequency_list[0][target] = frequency_list[0][target] + 1 valid_length += 1 batch_valid_length = Tensor(np.array([valid_length - 1]), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) input_id = Tensor([[target]], mstype.int32) # Update outputs with current generated token outputs.append(int(target)) # Call a single inference with input size of (bs, 1) logits = model.predict(input_id, current_index, init, batch_valid_length) # Return valid outputs out of padded outputs return np.array(outputs) if __name__ == "__main__": import numpy as np from mindspore import dtype as mstype from loss import CrossEntropyLoss from mindspore import Tensor loss = CrossEntropyLoss() logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), mstype.float32) labels_np = np.array([1]).astype(np.int32) input_mask = Tensor(np.ones(1).astype(np.float32)) labels = Tensor(labels_np) output = loss(logits, labels, input_mask) print(output.shape, output[0]) pengchengmind/src/loss.py000066400000000000000000000304531453423006600160150ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ Parallel Loss for the Parallel Training This is an experimental interface that is subject to change or deletion. """ from __future__ import absolute_import from mindspore.parallel import set_algo_parameters from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn import Cell from mindspore.nn.loss.loss import _check_is_tensor from mindspore.parallel._utils import _get_parallel_mode, _is_sharding_propagation from mindspore.context import ParallelMode from mindspore.parallel._utils import _get_device_num, _get_pipeline_stages # from mindspore.nn.transformer.layers import _check_input_dtype, _check_input_shape from mindspore.nn.transformer.op_parallel_config import default_dpmp_config, OpParallelConfig __all__ = ["CrossEntropyLoss"] class _Softmax(Cell): """ Calculate the softmax results with given logits. Note: The bprop of the cell is rewritten, just returns the accepted dout as returns. This cell should be used together with _NLLoss, to optimize the bprop of the cross entroy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **logits** (Tensor) - Tensor of shape (N, C). Data type must be float16 or float32. The output logits of the backbone. Outputs: Tensor. The corresponding softmax results. """ def __init__(self): super(_Softmax, self).__init__() # on/off value for onehot, for smooth labeling, modify the off_value self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) self.sum = P.ReduceSum() self.max = P.ArgMaxWithValue(axis=-1, keep_dims=True) self.sub = P.Sub() self.exp = P.Exp() self.div = P.RealDiv() self.onehot = P.OneHot() def construct(self, logits, label): # LogSoftmax for logits over last dimension logits = F.cast(logits, mstype.float32) _, logit_max = self.max(logits) logit_sub = self.sub(logits, logit_max) logit_exp = self.exp(logit_sub) exp_sum = self.sum(logit_exp, -1) exp_sum = P.Reshape()(exp_sum, (F.shape(exp_sum)[0], 1)) softmax_result = self.div(logit_exp, exp_sum) one_hot_label = self.onehot(label, F.shape(logits)[-1], self.on_value, self.off_value) return softmax_result, one_hot_label def bprop(self, logits, label, out, dout): """just return the loss of the dout. Note this should be used together with _NLLLoss""" d_logits = F.cast(dout[0], F.dtype(logits)) return d_logits, F.zeros_like(label) class _NLLLoss(Cell): """ Calculate the NLLLoss results with given softmax results and the label. Note: The bprop of the cell is rewritten. This cell should be used together with _Softmax, to optimize the bprop of the cross entroy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **loss** (Tensor) - Tensor of shape (N, C). Data type is float32. Outputs: Tensor. The corresponding loss results. """ def __init__(self): super(_NLLLoss, self).__init__() self.repeat_loss = 1 self.eps_const = Tensor(1e-24, mstype.float32) # In auto parallel, there will be a virtual div in the back propagation begins. As we use custom bprop function # we need to eliminate this virtual div by adding a factor "mp". if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation(): self.sum = P.ReduceSum() self.mul = P.Mul() self.neg = P.Neg() self.log = P.Log() self.add = P.Add() else: self.sum = P.ReduceSum() self.mul = P.Mul() self.neg = P.Neg() self.log = P.Log() self.add = P.Add() def construct(self, softmax_result, one_hot_label): log_softmax_result = self.log(self.add(softmax_result, self.eps_const)) loss = self.mul(log_softmax_result, one_hot_label) loss_unsum = self.neg(loss) loss_reduce = self.sum(loss_unsum, -1) return loss_reduce def bprop(self, softmax_result, one_hot_label, out, dout): """A simplified function. Note this should be used together with _Softmax""" logits = softmax_result - one_hot_label logits = logits * P.ExpandDims()(dout, -1) * self.repeat_loss return logits, F.zeros_like(one_hot_label) class CrossEntropyLoss(Cell): """ Calculate the cross entropy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **logits** (Tensor) - Tensor of shape (N, C). Data type must be float16 or float32. The output logits of the backbone. - **labels** (Tensor) - Tensor of shape (N, ). The ground truth label of the sample. - **input_mask** (Tensor) - Tensor of shape (N, ). input_mask indicates whether there are padded inputs and for padded inputs it will not be counted into loss. Outputs: Tensor. The corresponding cross entropy loss. Examples: >>> import numpy as np >>> from mindspore import dtype as mstype >>> from mindspore.nn.transformer import CrossEntropyLoss >>> from mindspore import Tensor >>> loss = CrossEntropyLoss() >>> logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), mstype.float32) >>> labels_np = np.array([1]).astype(np.int32) >>> input_mask = Tensor(np.ones(1).astype(np.float32)) >>> labels = Tensor(labels_np) >>> output = loss(logits, labels, input_mask) >>> print(output.shape) (1,) """ def __init__(self): super(CrossEntropyLoss, self).__init__() self.enable_force_redistribute = False if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL, ParallelMode.SEMI_AUTO_PARALLEL): self.enable_force_redistribute = True self.add = P.Add().add_prim_attr("keep_alive", True) self.add_label = P.Add().add_prim_attr("keep_alive", True) self.sum2 = P.ReduceSum() self.mul2 = P.Mul() self.add2 = P.Add() self.div2 = P.RealDiv() self.relu = P.ReLU() self._softmax = _Softmax() self._nllloss = _NLLLoss() @staticmethod def _check_and_modify_sharding_context(dp): device_num = _get_device_num() stages = _get_pipeline_stages() if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and dp * stages != device_num: set_algo_parameters(fully_use_devices=False) def construct(self, logits, label, input_mask): # self._check_input(logits, label, input_mask) # The add is used for forcing the redistribution before stepping in sub graphs, when semi/auto parallel enabled. if self.enable_force_redistribute: logits = self.add(logits, 0) label = self.add_label(label, 0) softmax, one_hot_label = self._softmax(logits, label) loss_reduce = self._nllloss(softmax, one_hot_label) # Using input_mask to mask the loss input_mask = P.Reshape()(input_mask, (-1,)) numerator = self.sum2(self.mul2(loss_reduce, input_mask)) denominator = self.add2( self.sum2(input_mask), P.Cast()(F.tuple_to_array((1e-5,)), mstype.float32)) loss = self.div2(numerator, denominator) return loss # def _check_input(self, logits, label, input_mask): # r"""Check the input tensor shape and type""" # _check_is_tensor('logits', logits, self.cls_name) # _check_is_tensor('label', label, self.cls_name) # _check_is_tensor('input_mask', input_mask, self.cls_name) # _check_input_dtype(F.dtype(logits), "logits", [mstype.float32, mstype.float16], self.cls_name) # _check_input_dtype(F.dtype(label), "label", [mstype.int32], self.cls_name) # _check_input_dtype(F.dtype(input_mask), "input_mask", [mstype.float32], self.cls_name) # _check_input_shape(F.shape(logits), "logits", self.cls_name, 2) # _check_input_shape(F.shape(label), "label", self.cls_name, 1) # _check_input_shape(F.shape(input_mask), "input_mask", self.cls_name, 1) # return True class CrossEntropyLoss_eval(Cell): """ Calculate the cross entropy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **logits** (Tensor) - Tensor of shape (N, C). Data type must be float16 or float32. The output logits of the backbone. - **labels** (Tensor) - Tensor of shape (N, ). The ground truth label of the sample. - **input_mask** (Tensor) - Tensor of shape (N, ). input_mask indicates whether there are padded inputs and for padded inputs it will not be counted into loss. Outputs: Tensor. The corresponding cross entropy loss. Examples: >>> import numpy as np >>> from mindspore import dtype as mstype >>> from mindspore.nn.transformer import CrossEntropyLoss >>> from mindspore import Tensor >>> loss = CrossEntropyLoss() >>> logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), mstype.float32) >>> labels_np = np.array([1]).astype(np.int32) >>> input_mask = Tensor(np.ones(1).astype(np.float32)) >>> labels = Tensor(labels_np) >>> output = loss(logits, labels, input_mask) >>> print(output.shape) (1,) """ def __init__(self): super(CrossEntropyLoss_eval, self).__init__() self.enable_force_redistribute = False if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL, ParallelMode.SEMI_AUTO_PARALLEL): self.enable_force_redistribute = True self.add = P.Add().add_prim_attr("keep_alive", True) self.add_label = P.Add().add_prim_attr("keep_alive", True) self.sum2 = P.ReduceSum() self.mul2 = P.Mul() self.add2 = P.Add() self.div2 = P.RealDiv() self.relu = P.ReLU() self._softmax = _Softmax() self._nllloss = _NLLLoss() @staticmethod def _check_and_modify_sharding_context(dp): device_num = _get_device_num() stages = _get_pipeline_stages() if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and dp * stages != device_num: set_algo_parameters(fully_use_devices=False) def construct(self, logits, label, input_mask): # self._check_input(logits, label, input_mask) # The add is used for forcing the redistribution before stepping in sub graphs, when semi/auto parallel enabled. if self.enable_force_redistribute: logits = self.add(logits, 0) label = self.add_label(label, 0) softmax, one_hot_label = self._softmax(logits, label) loss_reduce = self._nllloss(softmax, one_hot_label) # Using input_mask to mask the loss input_mask = P.Reshape()(input_mask, (-1,)) numerator = self.sum2(self.mul2(loss_reduce, input_mask)) denominator = self.add2( self.sum2(input_mask), P.Cast()(F.tuple_to_array((1e-5,)), mstype.float32)) loss = self.div2(numerator, denominator) return losspengchengmind/src/pengcheng_mind.py000066400000000000000000001331331453423006600200010ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """PengChengMind model""" import os import copy import numpy as np import mindspore.nn as nn import mindspore.numpy as np2 import mindspore.common.dtype as mstype from mindspore.common.initializer import initializer from mindspore import Tensor, Parameter from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn import Cell from mindspore.nn.transformer.transformer import VocabEmbedding, TransformerEncoder, TransformerEncoderLayer, \ AttentionMask from mindspore.nn.transformer import MoEConfig from mindspore.nn.transformer.layers import _LayerNorm, _Dropout from mindspore._extends import cell_attr_register from src.pengcheng_mind_config import OperatorType from src.pengcheng_mind_pipeline_layer import PipeTransformerEncoder, PipeTransformerEncoderLayer class EmbeddingLayer(nn.Cell): r"""Embedding layer of the PengChengMind Model""" def __init__(self, config): super(EmbeddingLayer, self).__init__() # Only for the pipeline mode, the embedding needs to be row sliced. ###################taoht############################### # if config.run_type == 'predict': # dp = 1 # mp = 1 # else: dp = config.parallel_config.embedding_dp_mp_config.data_parallel mp = config.parallel_config.embedding_dp_mp_config.model_parallel self.word_embedding = VocabEmbedding(vocab_size=config.vocab_size, embedding_size=config.hidden_size, param_init=initializer("normal", [config.vocab_size, config.hidden_size], dtype=mstype.float32), parallel_config=config.parallel_config.embedding_dp_mp_config) self.word_embedding.gather.shard(((mp, 1), (dp, 1))) self.word_embedding.embedding_table.parallel_optimizer = True copied_parallel_config = copy.deepcopy(config.parallel_config) copied_parallel_config.vocab_emb_dp = True # if config.run_type == 'predict': # copied_parallel_config.vocab_emb_dp = False self.position_embedding = None self.use_rope = config.use_rope if not self.use_rope: self.position_embedding = VocabEmbedding(vocab_size=config.seq_length, embedding_size=config.hidden_size, param_init=initializer("normal", [config.seq_length, config.hidden_size], dtype=mstype.float32), parallel_config=copied_parallel_config.embedding_dp_mp_config) # self.split = P.Split(1, 2).shard( # ((config.parallel_config.data_parallel, 1, 1),) # ) self.add = P.Add().shard( ((config.parallel_config.data_parallel, 1, 1), (config.parallel_config.data_parallel, 1, 1))) self.dropout = _Dropout(1 - config.dropout_rate) self.dropout.shard( ((config.parallel_config.data_parallel, 1, 1),) ) self.is_first_iteration = True self.use_past = config.use_past self.batch_size = config.batch_size def construct(self, input_ids, input_position, init_reset, batch_valid_length): embed, word_table = self.word_embedding(input_ids) if not self.use_rope: if self.use_past and not self.is_first_iteration: _, seq_length = F.shape(input_ids) input_position = batch_valid_length.view(self.batch_size, seq_length) position_embedding, _ = self.position_embedding(input_position) embed = self.add(embed, position_embedding) embed = self.dropout(embed) return embed, word_table def get_word_embedding_weight(self): return self.word_embedding.embedding_table class QueryLayer(PipeTransformerEncoderLayer): r"""Query Layer at the final layer.""" def __init__(self, batch_size, hidden_size, ffn_hidden_size, num_heads, seq_length, attention_dropout_rate=0.1, hidden_dropout_rate=0.1, post_layernorm_residual=False, param_init_type=mstype.float32, hidden_act='fast_gelu', use_past=False, parallel_config=None, softmax_compute_type=mstype.float32, use_rope=False): super(QueryLayer, self).__init__(batch_size=batch_size, hidden_size=hidden_size, ffn_hidden_size=ffn_hidden_size, num_heads=num_heads, seq_length=seq_length, attention_dropout_rate=attention_dropout_rate, hidden_dropout_rate=hidden_dropout_rate, post_layernorm_residual=post_layernorm_residual, param_init_type=param_init_type, hidden_act=hidden_act, use_past=use_past, parallel_config=parallel_config.dp_mp_config, softmax_compute_type=softmax_compute_type, use_rope=use_rope) def construct(self, x, query_vector, input_mask, init_reset=True, batch_valid_length=None): r""" The forward process of the block. """ # [bs * seq_length, embedding_size] input_x = self.layernorm1(x) input_x = F.cast(input_x, self.dtype) # indicate whether reset saved states key_reset = None value_reset = None if self.use_past: # reset states, init_reset True for reuse and False for reset key_reset = self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype))) value_reset = self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype))) # add dependency for desired execution order input_x = F.depend(input_x, key_reset) input_x = F.depend(input_x, value_reset) attention, layer_present = self.attention(query_vector, input_x, input_x, input_mask, self.key_past, self.value_past, batch_valid_length) # For post-layernorm the inputs for residual path are output of self-attention and output of layernorm if self.post_layernorm_residual: x = self.add(input_x, attention) # For pre-layernorm the inputs for residual path are output of self-attention and input of this layer else: x = self.add(x, attention) output_x = self.layernorm2(x) output_x = F.cast(output_x, self.dtype) mlp_logit = self.output(output_x) value_update = None key_update = None if self.use_past: # current key and value key_present, value_present = layer_present # update key and value calculated this step key_update = self.assign(self.key_past, key_present) value_update = self.assign(self.value_past, value_present) # add dependency for desired execution order key_update = F.depend(key_update, key_reset) value_update = F.depend(value_update, value_reset) # add dependency for desired execution order mlp_logit = F.depend(mlp_logit, value_update) mlp_logit = F.depend(mlp_logit, key_update) if self.post_layernorm_residual: output = self.add(output_x, mlp_logit) else: output = self.add(x, mlp_logit) return output, layer_present class PengChengMindHead(Cell): """ Head to get the logits of each token in the vocab Args: config(): the config of network Inputs: state: the output of the backbone embedding_table: the embedding table of the vocabulary Returns: logits: Tensor, the logits of the corresponding inputs """ def __init__(self, hidden_size, compute_type=mstype.float16, parallel_config=None): super(PengChengMindHead, self).__init__() if parallel_config.vocab_emb_dp: self.matmul = P.MatMul(transpose_b=True).shard(((parallel_config.data_parallel, 1), (1, 1))) else: self.matmul = P.MatMul(transpose_b=True).shard(((parallel_config.data_parallel, 1), ( parallel_config.model_parallel, 1))) self.hidden_size = hidden_size self.dtype = compute_type self.cast = P.Cast() def construct(self, state, embed): state = P.Reshape()(state, (-1, self.hidden_size)) # output logits over vocabulary [bs*seq_length, vocab_size] logits = self.matmul(self.cast(state, self.dtype), self.cast(embed, self.dtype)) return logits # def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers): # r""" # Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. # # # Args: # network(Cell) - Represents the transformer block # layer_id(int) - Means the layer index for the current module, counts from zero. # offset(int) - Means the layer_index needs a offset, if there are other modules in the net. # layers(int) - The total layers used for the model. # """ # # Used for the pipeline's stages setting # # As the final layer is not included here, so we need to manually add here. # # original: if set two stages, layers on two stages will be [15, 16+1] # # with 1 added, the layers on two stages will be [16, 15 +1] # pp_dis = max(int((layers + 1)/ parallel_config.pipeline_stage), 1) # # the pipeline stage must be in [0, parallel_config.pipeline_stage - 1] # pp_id = min((layer_id + offset) // pp_dis, parallel_config.pipeline_stage - 1) # network.pipeline_stage = pp_id # print(f"pipeline stage id is {pp_id}", flush=True) # # # Used for optimizer's fusion tag # dis = max(int((layers + 1) / parallel_config.gradient_aggregation_group), 1) # if parallel_config.pipeline_stage > 1: # # we give the fusion in pipeline mode a fixed value, otherwise the performance may become worse. # network.set_comm_fusion(2) # else: # network.set_comm_fusion(int((layer_id + offset) / dis) + 1) # # Used for enabling recomputation of the block # if isinstance(parallel_config.recompute, bool): # if parallel_config.recompute: # network.recompute() # else: # if parallel_config.recompute.recompute: # network.recompute(recompute_slice_activation=parallel_config.recompute.recompute_slice_activation) def set_recompute_block(block, layer_config, use_flash_attention): if OperatorType.LINEAR_INPUT in layer_config: if layer_config[OperatorType.LINEAR_INPUT] == 1: block.attention.projection.matmul.recompute(mode=False) block.attention.projection.matmul.add_prim_attr("recompute_comm_op", False) block.attention.projection.bias_add.recompute(mode=False) #block.attention.dropout.dropout.recompute(mode=False) if OperatorType.ATTENTION_QKV in layer_config: atten_recomp = layer_config[OperatorType.ATTENTION_QKV] if atten_recomp > 0: block.attention.dense1.matmul.recompute(mode=False) block.attention.dense1.bias_add.recompute(mode=False) block.attention.transpose_q.recompute(mode=False) #block.attention.real_div_q.recompute(mode=False) atten_recomp -= 1 if atten_recomp > 0: block.attention.dense2.matmul.recompute(mode=False) block.attention.dense2.bias_add.recompute(mode=False) block.attention.transpose_k.recompute(mode=False) #block.attention.transpose_back.recompute(mode=False) #block.attention.real_div_k.recompute(mode=False) atten_recomp -= 1 if atten_recomp > 0: block.attention.dense3.matmul.recompute(mode=False) block.attention.dense3.bias_add.recompute(mode=False) block.attention.transpose_v.recompute(mode=False) atten_recomp -= 1 if OperatorType.LINEAR_MAPPING in layer_config: if layer_config[OperatorType.LINEAR_MAPPING] == 1: block.output.mapping.matmul.recompute(mode=False) block.output.mapping.bias_add.recompute(mode=False) if not use_flash_attention: if OperatorType.ATTENTION_SOFTMAX in layer_config: if layer_config[OperatorType.ATTENTION_SOFTMAX] == 1: block.attention.softmax_3d.softmax.recompute(mode=False) block.attention.softmax_cast.recompute(mode=False) block.attention.softmax_reshape.recompute(mode=False) block.attention.add_mask.recompute(mode=False) if OperatorType.ATTENTION_ATTEN in layer_config: if layer_config[OperatorType.ATTENTION_ATTEN] == 1: block.attention.batch_matmul1.recompute(mode=False) if OperatorType.ATTENTION_BMM in layer_config: if layer_config[OperatorType.ATTENTION_BMM] == 1: block.attention.batch_matmul.recompute(mode=False) #if OperatorType.ATTENTION_X in layer_config: # if layer_config[OperatorType.ATTENTION_X] == 1: # block.layernorm1.layer_norm.add_prim_attr("recompute_comm_op", False) #if OperatorType.LINEAR_X in layer_config: # if layer_config[OperatorType.LINEAR_X] == 1: # block.layernorm2.layer_norm.add_prim_attr("recompute_comm_op", False) if OperatorType.LINEAR_GELU in layer_config: if layer_config[OperatorType.LINEAR_GELU] == 1: block.output.mapping.activation.fast_gelu.recompute(mode=False) def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers, pipeline_config, use_flash_attention=False): r""" Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. Args: network(Cell) - Represents the transformer block layer_id(int) - Means the layer index for the current module, counts from zero. offset(int) - Means the layer_index needs a offset, if there are other modules in the net. layers(int) - The total layers used for the model. """ # Used for the pipeline's stages setting # As the final layer is not included here, so we need to manually add here. # original: if set two stages, layers on two stages will be [15, 16+1] # with 1 added, the layers on two stages will be [16, 15 +1] layer_config = pipeline_config[layer_id] network.pipeline_stage = layer_config["stage_idx"] print(f"Layer id is {layer_id}, pipeline stage id is {layer_config['stage_idx']}", flush=True) # Used for enabling recomputation of the block network.layernorm1.set_comm_fusion(layer_id * 2 + 1) network.attention.set_comm_fusion(layer_id * 2 + 1) network.layernorm2.set_comm_fusion(layer_id * 2 + 2) network.output.set_comm_fusion(layer_id * 2 + 2) if parallel_config.recompute: network.recompute(recompute_slice_activation=True) set_recompute_block(network, layer_config, use_flash_attention) class PengChengMind_Model(Cell): r"""The base backbone of the PengChengMind model""" def __init__(self, config): super(PengChengMind_Model, self).__init__() self.is_pipeline = config.parallel_config.pipeline_stage > 1 self.embedding = EmbeddingLayer(config) self.config = config self.layernorm = _LayerNorm((config.hidden_size,)).to_float(mstype.float32) self.layernorm.set_comm_fusion(2 * config.num_layers + 4) self.layernorm.shard(((config.parallel_config.data_parallel, 1),)) self.layernorm.pipeline_stage = config.parallel_config.pipeline_stage - 1 # Configure the shard configure of the Embedding layer self.embedding.pipeline_stage = 0 self.num_layers = config.num_layers if config.use_moe: moe_config = MoEConfig(expert_num=config.expert_num, num_experts_chosen=config.per_token_num_experts_chosen) else: moe_config = MoEConfig(expert_num=1) # The shard setting of Transformer is set within the class StackedTransformer self.blocks = PipeTransformerEncoder( num_layers=config.num_layers - 1, batch_size=config.batch_size, hidden_size=config.hidden_size, ffn_hidden_size=config.ffn_hidden_size, num_heads=config.num_heads, seq_length=config.seq_length, attention_dropout_rate=config.dropout_rate, hidden_dropout_rate=config.dropout_rate, lambda_func=set_parallel_configure_for_layer, hidden_act=config.hidden_act,## taoht param_init_type=config.param_init_type, use_past=config.use_past, parallel_config=config.parallel_config, moe_config=moe_config, softmax_compute_type=config.softmax_compute_fp32, use_rope=config.use_rope, use_flash_attention=config.use_flash_attention, pipeline_config=config.pipeline_config).blocks for block in self.blocks: block.attention.dense1.bias.parallel_optimizer = False block.attention.dense2.bias.parallel_optimizer = False block.attention.dense3.bias.parallel_optimizer = False block.output.mapping.bias.parallel_optimizer = False mp = config.parallel_config.model_parallel dp = config.parallel_config.data_parallel if config.parallel_config.sequence_parallel: print("===using sequence parallel", flush=True) for block in self.blocks: block.attention.projection.bias_add.shard(((dp * mp, 1), (1,))) block.attention.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) block.attention.dropout.dropout.shard(((dp * mp, 1),)) block.add.shard(((dp * mp, 1), (dp * mp, 1),)) block.layernorm1.shard(((dp * mp, 1),)) block.layernorm2.shard(((dp * mp, 1),)) block.output.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) block.output.projection.bias_add.shard(((dp * mp, 1), (1,))) block.output.dropout.dropout.shard(((dp * mp, 1),)) if config.parallel_config.select_recompute: print("===using select recompute", flush=True) for block in self.blocks: block.layernorm1.layer_norm.add_prim_attr("recompute_comm_op", True) block.layernorm2.layer_norm.add_prim_attr("recompute_comm_op", True) block.attention.expand_dims.recompute() block.attention.batch_matmul.recompute() block.attention.softmax.softmax.recompute() block.attention.softmax_3d.softmax.recompute() block.attention.add.recompute() block.attention.prob_dropout.dropout.recompute() block.attention.sub.recompute() block.attention.mul.recompute() copied_parallel_config = copy.deepcopy(config.parallel_config) copied_parallel_config.vocab_emb_dp = True self.top_query_embedding = VocabEmbedding(vocab_size=config.seq_length, embedding_size=config.hidden_size, param_init=initializer("normal", [config.seq_length, config.hidden_size], dtype=mstype.float32), parallel_config=copied_parallel_config.embedding_dp_mp_config) self.top_query_embedding.pipeline_stage = config.parallel_config.pipeline_stage - 1 self.top_query_embedding.set_comm_fusion(2 * config.num_layers + 1) self.top_query_layer = QueryLayer(batch_size=config.batch_size, hidden_size=config.hidden_size, ffn_hidden_size=config.ffn_hidden_size, num_heads=config.num_heads, seq_length=config.seq_length, attention_dropout_rate=config.dropout_rate, hidden_dropout_rate=config.dropout_rate, hidden_act=config.hidden_act, param_init_type=config.param_init_type, use_past=config.use_past, softmax_compute_type=config.top_query_softmax_fp32, parallel_config=config.parallel_config, use_rope=config.use_rope) self.top_query_layer.attention.dense1.bias.parallel_optimizer = False self.top_query_layer.attention.dense2.bias.parallel_optimizer = False self.top_query_layer.attention.dense3.bias.parallel_optimizer = False self.top_query_layer.output.mapping.bias.parallel_optimizer = False if config.parallel_config.sequence_parallel: self.layernorm.shard(((dp * mp, 1),)) self.top_query_layer.attention.projection.bias_add.shard(((dp * mp, 1), (1,))) self.top_query_layer.attention.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) self.top_query_layer.attention.dropout.dropout.shard(((dp * mp, 1),)) self.top_query_layer.add.shard(((dp * mp, 1), (dp * mp, 1),)) self.top_query_layer.layernorm1.shard(((dp * mp, 1),)) self.top_query_layer.layernorm2.shard(((dp * mp, 1),)) self.top_query_layer.output.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) self.top_query_layer.output.projection.bias_add.shard(((dp * mp, 1), (1,))) if config.parallel_config.select_recompute: self.top_query_layer.layernorm1.layer_norm.add_prim_attr("recompute_comm_op", True) self.top_query_layer.layernorm2.layer_norm.add_prim_attr("recompute_comm_op", True) self.layernorm.layer_norm.add_prim_attr("recompute_comm_op", True) self.top_query_layer.attention.expand_dims.recompute() self.top_query_layer.attention.batch_matmul.recompute() self.top_query_layer.attention.softmax.softmax.recompute() self.top_query_layer.attention.softmax_3d.softmax.recompute() self.top_query_layer.attention.add.recompute() self.top_query_layer.attention.prob_dropout.dropout.recompute() self.top_query_layer.attention.sub.recompute() self.top_query_layer.attention.mul.recompute() if config.parallel_config.recompute: self.top_query_layer.recompute(recompute_slice_activation=True, parallel_optimizer_comm_recompute=False) layer_config = config.pipeline_config[-1] #set_recompute_block(self.top_query_layer, layer_config) self.top_query_layer.layernorm1.set_comm_fusion(config.num_layers * 2 + 2) self.top_query_layer.attention.set_comm_fusion(config.num_layers * 2 + 2) self.top_query_layer.layernorm2.set_comm_fusion(config.num_layers * 2 + 3) self.top_query_layer.output.set_comm_fusion(config.num_layers * 2 + 3) self.top_query_layer.pipeline_stage = config.parallel_config.pipeline_stage - 1 self.dtype = mstype.float16 if config.load_ckpt_path: self.load_embedding_from_ckpt(config.load_ckpt_path) self.run_type = config.run_type def construct(self, input_ids, input_position, encoder_masks, init_reset=True, batch_valid_length=None): r"""forward pass of the model""" embed, word_table = self.embedding(input_ids, input_position, init_reset, batch_valid_length) hidden_state = P.Cast()(embed, self.dtype) # the input of the incremental prediction is 3d #########################taoht############################ # if self.run_type != 'predict': # hidden_state = self.reshape_to_2d(hidden_state) hidden_state = self.reshape_to_2d(hidden_state) # ignored predict to speed up if self.blocks is not None: for i in range(self.num_layers - 1): hidden_state, _ = self.blocks[i](hidden_state, encoder_masks, init_reset, batch_valid_length) if self.is_pipeline: top_query_hidden_states, _ = self.top_query_embedding(input_position) top_query_hidden_states = self.reshape_to_2d(top_query_hidden_states) encoder_output, _ = self.top_query_layer(hidden_state, top_query_hidden_states, encoder_masks, init_reset, batch_valid_length) encoder_output = self.layernorm(encoder_output) else: # hidden_state = self.reshape_to_2d(hidden_state) # hidden_state = P.Cast()(hidden_state, self.dtype) top_query_hidden_states, _ = self.top_query_embedding(input_position) top_query_hidden_states = self.reshape_to_2d(top_query_hidden_states) encoder_output, _ = self.top_query_layer(hidden_state, top_query_hidden_states, encoder_masks, init_reset, batch_valid_length) encoder_output = self.layernorm(encoder_output) # hidden_state = self.reshape_to_2d(hidden_state) # encoder_output = self.layernorm(hidden_state) # encoder_output = P.Cast()(encoder_output, self.dtype) # top_query_hidden_states, _ = self.top_query_embedding(input_position) # top_query_hidden_states = self.reshape_to_2d(top_query_hidden_states) # encoder_output, _ = self.top_query_layer(encoder_output, top_query_hidden_states, # encoder_masks, init_reset, batch_valid_length) return encoder_output, word_table def reshape_to_2d(self, x): r"""reshape nd tensor to 2d, if n <= 2, keep original shape.""" shape = F.shape(x) if len(shape) <= 2: return x x = F.reshape(x, (-1, shape[-1])) return x def load_embedding_from_ckpt(self, load_ckpt_path): r"""load the weights from the checkpoint""" def load_param(path): if os.path.exists(path): p_table = np.load(path) table_param = Tensor(p_table, mstype.float32) else: raise ValueError(f"{path} file not exits, " f"please check whether embedding file exit.") return table_param # three embedding needed to be loaded # Loading the embedding table from the ckpt path: position_embedding_path = os.path.join(load_ckpt_path, 'position_embedding.npy') word_embedding_path = os.path.join(load_ckpt_path, 'word_embedding.npy') top_query_embedding_path = os.path.join(load_ckpt_path, 'top_query_embedding.npy') self.embedding.word_embedding.embedding_table = Parameter(initializer(load_param(word_embedding_path), [self.config.vocab_size, self.config.hidden_size]), name='word_embedding_table', parallel_optimizer=False) self.embedding.position_embedding.embedding_table = Parameter(initializer(load_param(position_embedding_path), [self.config.seq_length, self.config.hidden_size]), name='position_embedding_table', parallel_optimizer=False) self.top_query_embedding.embedding_table = Parameter(initializer(load_param(top_query_embedding_path), [self.config.seq_length, self.config.hidden_size]), name='query_embedding_table', parallel_optimizer=False) class PengChengMindModel(nn.Cell): """ The PengChengMind network consisting of two parts the backbone and the head Args: config(PengChengMindConfig): the config of network Inputs: input_ids: the tokenized inputs input_mask: the mask indicating whether each position is a valid input past: the previous feature map Returns: logits: Tensor: the logits of the corresponding inputs with shape (batch_size, seq_length, vocab_size) """ def __init__(self, config): super(PengChengMindModel, self).__init__() # Network head to get logits over vocabulary copied_parallel_config = copy.deepcopy(config.parallel_config) if copied_parallel_config.pipeline_stage > 1: copied_parallel_config.vocab_emb_dp = False self.head = PengChengMindHead(hidden_size=config.hidden_size, parallel_config=copied_parallel_config) self.head.pipeline_stage = config.parallel_config.pipeline_stage - 1 self.backbone = PengChengMind_Model(config) self.backbone.embedding.word_embedding.embedding_table.add_pipeline_stage(self.head.pipeline_stage) # self.cast = P.Cast() def construct(self, input_ids, input_position, attention_mask, init_reset=True, batch_valid_length=None): #######################taoht#############################NO # attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## output_states, word_table = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) logits = self.head(output_states, word_table) return logits class PengChengMindWithLoss(Cell): """ PengChengMind training loss for generation. Args: config(PengChengMindConfig) Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ @cell_attr_register def __init__(self, config, network, loss): super(PengChengMindWithLoss, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = config.parallel_config.data_parallel self.network = network self.pad_token = config.pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.cast = P.Cast() def construct(self, input_ids, input_position=None, attention_mask=None): r"""Forward process of the pengcheng mind model""" # tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) # input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) # print("===================="*10) # P.Print()("enter input_ids", input_ids) # P.Print()("enter input_position", input_position) # print("====================" * 10) tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) #######################taoht############################## attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## decoder_attention_masks = self.slice2(attention_mask, (0, 0, 0), (self.batch_size, self.len, self.len), (1, 1, 1)) input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) logits = self.network(tokens, input_position, decoder_attention_masks) # Get label corresponding to input tokens labels = self.slice(input_ids, (0, 1), (self.batch_size, self.len + 1), (1, 1)) labels = P.Reshape()(labels, (-1,)) input_mask = P.Reshape()(input_mask, (-1,)) output = self.loss(logits, labels, input_mask) return output class PengChengMindLossWithPrompt(Cell): """ PengChengMind training loss for generation. Args: config(PengChengMindConfig) Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss): super(PengChengMindLossWithPrompt, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = config.parallel_config.data_parallel self.network = network self.pad_token = config.pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(config.seq_length) self.equal = P.Equal() self.expand = P.ExpandDims() def construct(self, input_ids, prompt_ids): r"""Forward process of the pengcheng mind model""" tokens = input_ids input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) input_position = F.tuple_to_array(F.make_range(self.len)) input_position = P.Tile()(self.expand(input_position, 0), (self.batch_size, 1)) input_mask_a = F.cast(self.equal(prompt_ids, self.pad_token), mstype.float32) attention_mask = self.get_attention_mask(input_mask) logits = self.network(tokens, input_position, attention_mask) log_probs = self.log_softmax(logits) input_mask_b = input_mask * input_mask_a return log_probs, input_mask_b class PengChengMindLossWith_notPrompt(Cell): """ PengChengMind training loss for generation. Args: config(PengChengMindConfig) Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss, pad_token=0, seq_length=4096): super(PengChengMindLossWith_notPrompt, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = 1 self.network = network self.pad_token = pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.equal = P.Equal() self.expand = P.ExpandDims() def construct(self, input_ids, mask_ids_input=None): r"""Forward process of the pengcheng mind model""" # input_ids = np2.array(input_ids) # tokens = Tensor(input_ids, mstype.int32) tokens = input_ids input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) input_position = F.tuple_to_array(F.make_range(self.len)) input_position = P.Tile()(self.expand(input_position, 0), (self.batch_size, 1)) attention_mask = self.get_attention_mask(input_mask) logits = self.network(tokens, input_position, attention_mask) # log_probs = self.log_softmax(logits) # input_mask_b = input_mask # Get label corresponding to input tokens labels = F.cast(np2.concatenate((input_ids[:, 1:], np2.ones((input_ids.shape[0], 1)) * self.pad_token), axis=-1), mstype.int32) # labels = self.slice(input_labels, (0, 1), (self.batch_size, self.len + 1), # (1, 1)) labels = P.Reshape()(labels, (-1,)) # input_mask = P.Reshape()(input_mask, (-1,)) # if mask_ids_input == None: # # model.infer_predict_layout() # output = self.loss(logits, labels, input_mask) # else: # # inference # # mask_ids_input = F.cast(mask_ids_input, mstype.float32) # # mask_ids_input = P.Reshape()(mask_ids_input, (-1,)) output = self.loss(logits, labels, mask_ids_input) return output #log_probs, input_mask_b # log_probs = self.log_softmax(logits) # return log_probs class EvalNet(nn.Cell): """ PengChengMind evaluation net Args: backbone: backbone network of PengChengMind generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=128297, seq_length=4096): super(EvalNet, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.expand = P.ExpandDims().shard(((1, 1, 1),)) # used for incremental prediction self.all_ones_attention_mask = Tensor(np.ones((1, 1, seq_length)), mstype.float32) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) # taoht, not use use_past # if self.is_first_iteration is False: # attention_mask = P.Tile()(self.all_ones_attention_mask, (bs, 1, 1)) # else: # attention_mask = self.get_attention_mask(input_mask) attention_mask = self.get_attention_mask(input_mask) # For speed, ignore not first_iteration cond. logits = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) # log_probs = self.log_softmax(logits) # index = current_index.view(1,) # logits = self.gather(log_probs, index, 0) # logits = logits.view(bs, 1, -1) # return logits log_probs = self.log_softmax(logits) index = current_index.view(1,) logits = self.gather(log_probs, index, 0) logits = logits.view(bs, 1, -1) return logits # # print(type(logits)) # # print(logits) # # print("logits: ", logits.data.asnumpy().shape) # # print(logits) # index = current_index.view(1,) # logits = self.gather(logits, index, 0) # logits = logits.view(bs, 1, -1) # log_probs = self.log_softmax(logits) # return log_probs class EvalNet_use_past(nn.Cell): """ PengChengMind evaluation net Args: backbone: backbone network of PengChengMind generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=0, seq_length=4096): super(EvalNet_use_past, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.expand = P.ExpandDims().shard(((1, 1, 1),)) # used for incremental prediction self.all_ones_attention_mask = Tensor(np.ones((1, 1, seq_length)), mstype.float32) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) # taoht, not use use_past if self.is_first_iteration is False: attention_mask = P.Tile()(self.all_ones_attention_mask, (bs, 1, 1)) else: attention_mask = self.get_attention_mask(input_mask) # attention_mask = self.get_attention_mask(input_mask) # For speed, ignore not first_iteration cond. logits = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) log_probs = self.log_softmax(logits) index = current_index.view(1,) logits = self.gather(log_probs, index, 0) logits = logits.view(bs, 1, -1) return logits class EvalNet_200B(nn.Cell): """ PengChengMind evaluation net Args: backbone: backbone network of PengChengMind generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=0, seq_length=4096): super(EvalNet_200B, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.expand = P.ExpandDims().shard(((1, 1, 1),)) # used for incremental prediction self.all_ones_attention_mask = Tensor(np.ones((1, 1, seq_length)), mstype.float32) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) attention_mask = self.get_attention_mask(input_mask) # For speed, ignore not first_iteration cond. logits = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) log_probs = self.log_softmax(logits) index = current_index.view(1, ) logits = self.gather(log_probs, index, 0) logits = logits.view(bs, 1, -1) return logits pengchengmind/src/pengcheng_mind_7B.py000066400000000000000000001064111453423006600203300ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """PengChengMind model""" import os import copy import numpy as np import mindspore.nn as nn import mindspore.numpy as np2 import mindspore.common.dtype as mstype from mindspore.common.initializer import initializer from mindspore import Tensor, Parameter from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn import Cell from mindspore.nn.transformer.transformer import VocabEmbedding, TransformerEncoder, TransformerEncoderLayer, \ AttentionMask from mindspore.nn.transformer import MoEConfig from mindspore.nn.transformer.layers import _LayerNorm, _Dropout from mindspore._extends import cell_attr_register from src.pengcheng_mind_config import OperatorType from src.pengcheng_mind_pipeline_layer import PipeTransformerEncoder, PipeTransformerEncoderLayer class EmbeddingLayer(nn.Cell): r"""Embedding layer of the PengChengMind Model""" def __init__(self, config): super(EmbeddingLayer, self).__init__() # Only for the pipeline mode, the embedding needs to be row sliced. ###################taoht############################### # if config.run_type == 'predict': # dp = 1 # mp = 1 # else: dp = config.parallel_config.embedding_dp_mp_config.data_parallel mp = config.parallel_config.embedding_dp_mp_config.model_parallel self.word_embedding = VocabEmbedding(vocab_size=config.vocab_size, embedding_size=config.hidden_size, param_init=initializer("normal", [config.vocab_size, config.hidden_size], dtype=mstype.float32), parallel_config=config.parallel_config.embedding_dp_mp_config) self.word_embedding.gather.shard(((mp, 1), (dp, 1))) self.word_embedding.embedding_table.parallel_optimizer = True copied_parallel_config = copy.deepcopy(config.parallel_config) copied_parallel_config.vocab_emb_dp = True # if config.run_type == 'predict': # copied_parallel_config.vocab_emb_dp = False self.position_embedding = None self.use_rope = config.use_rope if not self.use_rope: self.position_embedding = VocabEmbedding(vocab_size=config.seq_length, embedding_size=config.hidden_size, param_init=initializer("normal", [config.seq_length, config.hidden_size], dtype=mstype.float32), parallel_config=copied_parallel_config.embedding_dp_mp_config) # self.split = P.Split(1, 2).shard( # ((config.parallel_config.data_parallel, 1, 1),) # ) self.add = P.Add().shard( ((config.parallel_config.data_parallel, 1, 1), (config.parallel_config.data_parallel, 1, 1))) self.dropout = _Dropout(1 - config.dropout_rate) self.dropout.shard( ((config.parallel_config.data_parallel, 1, 1),) ) self.is_first_iteration = True self.use_past = config.use_past self.batch_size = config.batch_size def construct(self, input_ids, input_position, init_reset, batch_valid_length): embed, word_table = self.word_embedding(input_ids) if not self.use_rope: if self.use_past and not self.is_first_iteration: _, seq_length = F.shape(input_ids) input_position = batch_valid_length.view(self.batch_size, seq_length) position_embedding, _ = self.position_embedding(input_position) embed = self.add(embed, position_embedding) embed = self.dropout(embed) return embed, word_table def get_word_embedding_weight(self): return self.word_embedding.embedding_table class PengChengMindHead(Cell): """ Head to get the logits of each token in the vocab Args: config(): the config of network Inputs: state: the output of the backbone embedding_table: the embedding table of the vocabulary Returns: logits: Tensor, the logits of the corresponding inputs """ def __init__(self, hidden_size, compute_type=mstype.float16, parallel_config=None): super(PengChengMindHead, self).__init__() if parallel_config.vocab_emb_dp: self.matmul = P.MatMul(transpose_b=True).shard(((parallel_config.data_parallel, 1), (1, 1))) else: self.matmul = P.MatMul(transpose_b=True).shard(((parallel_config.data_parallel, 1), ( parallel_config.model_parallel, 1))) self.hidden_size = hidden_size self.dtype = compute_type self.cast = P.Cast() def construct(self, state, embed): state = P.Reshape()(state, (-1, self.hidden_size)) # output logits over vocabulary [bs*seq_length, vocab_size] logits = self.matmul(self.cast(state, self.dtype), self.cast(embed, self.dtype)) return logits # def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers): # r""" # Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. # # # Args: # network(Cell) - Represents the transformer block # layer_id(int) - Means the layer index for the current module, counts from zero. # offset(int) - Means the layer_index needs a offset, if there are other modules in the net. # layers(int) - The total layers used for the model. # """ # # Used for the pipeline's stages setting # # As the final layer is not included here, so we need to manually add here. # # original: if set two stages, layers on two stages will be [15, 16+1] # # with 1 added, the layers on two stages will be [16, 15 +1] # pp_dis = max(int((layers + 1)/ parallel_config.pipeline_stage), 1) # # the pipeline stage must be in [0, parallel_config.pipeline_stage - 1] # pp_id = min((layer_id + offset) // pp_dis, parallel_config.pipeline_stage - 1) # network.pipeline_stage = pp_id # print(f"pipeline stage id is {pp_id}", flush=True) # # # Used for optimizer's fusion tag # dis = max(int((layers + 1) / parallel_config.gradient_aggregation_group), 1) # if parallel_config.pipeline_stage > 1: # # we give the fusion in pipeline mode a fixed value, otherwise the performance may become worse. # network.set_comm_fusion(2) # else: # network.set_comm_fusion(int((layer_id + offset) / dis) + 1) # # Used for enabling recomputation of the block # if isinstance(parallel_config.recompute, bool): # if parallel_config.recompute: # network.recompute() # else: # if parallel_config.recompute.recompute: # network.recompute(recompute_slice_activation=parallel_config.recompute.recompute_slice_activation) def set_recompute_block(block, layer_config, use_flash_attention): if OperatorType.LINEAR_INPUT in layer_config: if layer_config[OperatorType.LINEAR_INPUT] == 1: block.attention.projection.matmul.recompute(mode=False) block.attention.projection.matmul.add_prim_attr("recompute_comm_op", False) block.attention.projection.bias_add.recompute(mode=False) #block.attention.dropout.dropout.recompute(mode=False) if OperatorType.ATTENTION_QKV in layer_config: atten_recomp = layer_config[OperatorType.ATTENTION_QKV] if atten_recomp > 0: block.attention.dense1.matmul.recompute(mode=False) block.attention.dense1.bias_add.recompute(mode=False) block.attention.transpose_q.recompute(mode=False) #block.attention.real_div_q.recompute(mode=False) atten_recomp -= 1 if atten_recomp > 0: block.attention.dense2.matmul.recompute(mode=False) block.attention.dense2.bias_add.recompute(mode=False) block.attention.transpose_k.recompute(mode=False) #block.attention.transpose_back.recompute(mode=False) #block.attention.real_div_k.recompute(mode=False) atten_recomp -= 1 if atten_recomp > 0: block.attention.dense3.matmul.recompute(mode=False) block.attention.dense3.bias_add.recompute(mode=False) block.attention.transpose_v.recompute(mode=False) atten_recomp -= 1 if OperatorType.LINEAR_MAPPING in layer_config: if layer_config[OperatorType.LINEAR_MAPPING] == 1: block.output.mapping.matmul.recompute(mode=False) block.output.mapping.bias_add.recompute(mode=False) if not use_flash_attention: if OperatorType.ATTENTION_SOFTMAX in layer_config: if layer_config[OperatorType.ATTENTION_SOFTMAX] == 1: block.attention.softmax_3d.softmax.recompute(mode=False) block.attention.softmax_cast.recompute(mode=False) block.attention.softmax_reshape.recompute(mode=False) block.attention.add_mask.recompute(mode=False) if OperatorType.ATTENTION_ATTEN in layer_config: if layer_config[OperatorType.ATTENTION_ATTEN] == 1: block.attention.batch_matmul1.recompute(mode=False) if OperatorType.ATTENTION_BMM in layer_config: if layer_config[OperatorType.ATTENTION_BMM] == 1: block.attention.batch_matmul.recompute(mode=False) #if OperatorType.ATTENTION_X in layer_config: # if layer_config[OperatorType.ATTENTION_X] == 1: # block.layernorm1.layer_norm.add_prim_attr("recompute_comm_op", False) #if OperatorType.LINEAR_X in layer_config: # if layer_config[OperatorType.LINEAR_X] == 1: # block.layernorm2.layer_norm.add_prim_attr("recompute_comm_op", False) if OperatorType.LINEAR_GELU in layer_config: if layer_config[OperatorType.LINEAR_GELU] == 1: block.output.mapping.activation.fast_gelu.recompute(mode=False) def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers, pipeline_config, use_flash_attention=False): r""" Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. Args: network(Cell) - Represents the transformer block layer_id(int) - Means the layer index for the current module, counts from zero. offset(int) - Means the layer_index needs a offset, if there are other modules in the net. layers(int) - The total layers used for the model. """ # Used for the pipeline's stages setting # As the final layer is not included here, so we need to manually add here. # original: if set two stages, layers on two stages will be [15, 16+1] # with 1 added, the layers on two stages will be [16, 15 +1] layer_config = pipeline_config[layer_id] network.pipeline_stage = layer_config["stage_idx"] print(f"Layer id is {layer_id}, pipeline stage id is {layer_config['stage_idx']}", flush=True) # Used for enabling recomputation of the block network.layernorm1.set_comm_fusion(layer_id * 2 + 1) network.attention.set_comm_fusion(layer_id * 2 + 1) network.layernorm2.set_comm_fusion(layer_id * 2 + 2) network.output.set_comm_fusion(layer_id * 2 + 2) if parallel_config.recompute: network.recompute(recompute_slice_activation=True) set_recompute_block(network, layer_config, use_flash_attention) class PengChengMind_Model(Cell): r"""The base backbone of the PengChengMind model""" def __init__(self, config): super(PengChengMind_Model, self).__init__() self.is_pipeline = config.parallel_config.pipeline_stage > 1 self.embedding = EmbeddingLayer(config) self.config = config self.layernorm = _LayerNorm((config.hidden_size,)).to_float(mstype.float32) self.layernorm.set_comm_fusion(2 * config.num_layers + 4) self.layernorm.shard(((config.parallel_config.data_parallel, 1),)) self.layernorm.pipeline_stage = config.parallel_config.pipeline_stage - 1 # Configure the shard configure of the Embedding layer self.embedding.pipeline_stage = 0 self.num_layers = config.num_layers if config.use_moe: moe_config = MoEConfig(expert_num=config.expert_num, num_experts_chosen=config.per_token_num_experts_chosen) else: moe_config = MoEConfig(expert_num=1) # The shard setting of Transformer is set within the class StackedTransformer self.blocks = PipeTransformerEncoder( num_layers=config.num_layers, batch_size=config.batch_size, hidden_size=config.hidden_size, ffn_hidden_size=config.ffn_hidden_size, num_heads=config.num_heads, seq_length=config.seq_length, attention_dropout_rate=config.dropout_rate, hidden_dropout_rate=config.dropout_rate, lambda_func=set_parallel_configure_for_layer, hidden_act=config.hidden_act,## taoht param_init_type=config.param_init_type, use_past=config.use_past, parallel_config=config.parallel_config, moe_config=moe_config, softmax_compute_type=config.softmax_compute_fp32, use_rope=config.use_rope, use_flash_attention=config.use_flash_attention, pipeline_config=config.pipeline_config).blocks for block in self.blocks: block.attention.dense1.bias.parallel_optimizer = False block.attention.dense2.bias.parallel_optimizer = False block.attention.dense3.bias.parallel_optimizer = False block.output.mapping.bias.parallel_optimizer = False mp = config.parallel_config.model_parallel dp = config.parallel_config.data_parallel if config.parallel_config.sequence_parallel: print("===using sequence parallel", flush=True) for block in self.blocks: block.attention.projection.bias_add.shard(((dp * mp, 1), (1,))) block.attention.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) block.attention.dropout.dropout.shard(((dp * mp, 1),)) block.add.shard(((dp * mp, 1), (dp * mp, 1),)) block.layernorm1.shard(((dp * mp, 1),)) block.layernorm2.shard(((dp * mp, 1),)) block.output.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) block.output.projection.bias_add.shard(((dp * mp, 1), (1,))) block.output.dropout.dropout.shard(((dp * mp, 1),)) if config.parallel_config.select_recompute: print("===using select recompute", flush=True) for block in self.blocks: block.layernorm1.layer_norm.add_prim_attr("recompute_comm_op", True) block.layernorm2.layer_norm.add_prim_attr("recompute_comm_op", True) block.attention.expand_dims.recompute() block.attention.batch_matmul.recompute() block.attention.softmax.softmax.recompute() block.attention.softmax_3d.softmax.recompute() block.attention.add.recompute() block.attention.prob_dropout.dropout.recompute() block.attention.sub.recompute() block.attention.mul.recompute() copied_parallel_config = copy.deepcopy(config.parallel_config) copied_parallel_config.vocab_emb_dp = True self.dtype = mstype.float16 if config.load_ckpt_path: self.load_embedding_from_ckpt(config.load_ckpt_path) self.run_type = config.run_type def construct(self, input_ids, input_position, encoder_masks, init_reset=True, batch_valid_length=None): r"""forward pass of the model""" embed, word_table = self.embedding(input_ids, input_position, init_reset, batch_valid_length) hidden_state = P.Cast()(embed, self.dtype) # the input of the incremental prediction is 3d #########################taoht############################ # if self.run_type != 'predict': # hidden_state = self.reshape_to_2d(hidden_state) hidden_state = self.reshape_to_2d(hidden_state) # ignored predict to speed up if self.blocks is not None: for i in range(self.num_layers): hidden_state, _ = self.blocks[i](hidden_state, encoder_masks, init_reset, batch_valid_length) encoder_output = self.layernorm(hidden_state) return encoder_output, word_table def reshape_to_2d(self, x): r"""reshape nd tensor to 2d, if n <= 2, keep original shape.""" shape = F.shape(x) if len(shape) <= 2: return x x = F.reshape(x, (-1, shape[-1])) return x def load_embedding_from_ckpt(self, load_ckpt_path): r"""load the weights from the checkpoint""" def load_param(path): if os.path.exists(path): p_table = np.load(path) table_param = Tensor(p_table, mstype.float32) else: raise ValueError(f"{path} file not exits, " f"please check whether embedding file exit.") return table_param # three embedding needed to be loaded # Loading the embedding table from the ckpt path: position_embedding_path = os.path.join(load_ckpt_path, 'position_embedding.npy') word_embedding_path = os.path.join(load_ckpt_path, 'word_embedding.npy') self.embedding.word_embedding.embedding_table = Parameter(initializer(load_param(word_embedding_path), [self.config.vocab_size, self.config.hidden_size]), name='word_embedding_table', parallel_optimizer=False) self.embedding.position_embedding.embedding_table = Parameter(initializer(load_param(position_embedding_path), [self.config.seq_length, self.config.hidden_size]), name='position_embedding_table', parallel_optimizer=False) class PengChengMindModel(nn.Cell): """ The PengChengMind network consisting of two parts the backbone and the head Args: config(PengChengMindConfig): the config of network Inputs: input_ids: the tokenized inputs input_mask: the mask indicating whether each position is a valid input past: the previous feature map Returns: logits: Tensor: the logits of the corresponding inputs with shape (batch_size, seq_length, vocab_size) """ def __init__(self, config): super(PengChengMindModel, self).__init__() # Network head to get logits over vocabulary copied_parallel_config = copy.deepcopy(config.parallel_config) if copied_parallel_config.pipeline_stage > 1: copied_parallel_config.vocab_emb_dp = False self.head = PengChengMindHead(hidden_size=config.hidden_size, parallel_config=copied_parallel_config) self.head.pipeline_stage = config.parallel_config.pipeline_stage - 1 self.backbone = PengChengMind_Model(config) self.backbone.embedding.word_embedding.embedding_table.add_pipeline_stage(self.head.pipeline_stage) # self.cast = P.Cast() def construct(self, input_ids, input_position, attention_mask, init_reset=True, batch_valid_length=None): #######################taoht#############################NO # attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## output_states, word_table = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) logits = self.head(output_states, word_table) return logits class PengChengMindWithLoss(Cell): """ PengChengMind training loss for generation. Args: config(PengChengMindConfig) Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ @cell_attr_register def __init__(self, config, network, loss): super(PengChengMindWithLoss, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = config.parallel_config.data_parallel self.network = network self.pad_token = config.pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.cast = P.Cast() def construct(self, input_ids, input_position=None, attention_mask=None): r"""Forward process of the pengcheng mind model""" # tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) # input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) # print("===================="*10) # P.Print()("enter input_ids", input_ids) # P.Print()("enter input_position", input_position) # print("====================" * 10) tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) #######################taoht############################## attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## decoder_attention_masks = self.slice2(attention_mask, (0, 0, 0), (self.batch_size, self.len, self.len), (1, 1, 1)) input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) logits = self.network(tokens, input_position, decoder_attention_masks) # Get label corresponding to input tokens labels = self.slice(input_ids, (0, 1), (self.batch_size, self.len + 1), (1, 1)) labels = P.Reshape()(labels, (-1,)) input_mask = P.Reshape()(input_mask, (-1,)) output = self.loss(logits, labels, input_mask) return output class PengChengMindLossWithPrompt(Cell): """ PengChengMind training loss for generation. Args: config(PengChengMindConfig) Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss): super(PengChengMindLossWithPrompt, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = config.parallel_config.data_parallel self.network = network self.pad_token = config.pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(config.seq_length) self.equal = P.Equal() self.expand = P.ExpandDims() def construct(self, input_ids, prompt_ids): r"""Forward process of the pengcheng mind model""" tokens = input_ids input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) input_position = F.tuple_to_array(F.make_range(self.len)) input_position = P.Tile()(self.expand(input_position, 0), (self.batch_size, 1)) input_mask_a = F.cast(self.equal(prompt_ids, self.pad_token), mstype.float32) attention_mask = self.get_attention_mask(input_mask) logits = self.network(tokens, input_position, attention_mask) log_probs = self.log_softmax(logits) input_mask_b = input_mask * input_mask_a return log_probs, input_mask_b class PengChengMindLossWith_notPrompt(Cell): """ PengChengMind training loss for generation. Args: config(PengChengMindConfig) Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss, pad_token=0, seq_length=4096): super(PengChengMindLossWith_notPrompt, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = 1 self.network = network self.pad_token = pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.equal = P.Equal() self.expand = P.ExpandDims() def construct(self, input_ids, mask_ids_input=None): r"""Forward process of the pengcheng mind model""" # input_ids = np2.array(input_ids) # tokens = Tensor(input_ids, mstype.int32) tokens = input_ids input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) input_position = F.tuple_to_array(F.make_range(self.len)) input_position = P.Tile()(self.expand(input_position, 0), (self.batch_size, 1)) attention_mask = self.get_attention_mask(input_mask) logits = self.network(tokens, input_position, attention_mask) # log_probs = self.log_softmax(logits) # input_mask_b = input_mask # Get label corresponding to input tokens labels = F.cast(np2.concatenate((input_ids[:, 1:], np2.ones((input_ids.shape[0], 1)) * self.pad_token), axis=-1), mstype.int32) # labels = self.slice(input_labels, (0, 1), (self.batch_size, self.len + 1), # (1, 1)) labels = P.Reshape()(labels, (-1,)) # input_mask = P.Reshape()(input_mask, (-1,)) # if mask_ids_input == None: # # model.infer_predict_layout() # output = self.loss(logits, labels, input_mask) # else: # # inference # # mask_ids_input = F.cast(mask_ids_input, mstype.float32) # # mask_ids_input = P.Reshape()(mask_ids_input, (-1,)) output = self.loss(logits, labels, mask_ids_input) return output #log_probs, input_mask_b # log_probs = self.log_softmax(logits) # return log_probs class EvalNet(nn.Cell): """ PengChengMind evaluation net Args: backbone: backbone network of PengChengMind generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=128297, seq_length=4096): super(EvalNet, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.expand = P.ExpandDims().shard(((1, 1, 1),)) # used for incremental prediction self.all_ones_attention_mask = Tensor(np.ones((1, 1, seq_length)), mstype.float32) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) # taoht, not use use_past # if self.is_first_iteration is False: # attention_mask = P.Tile()(self.all_ones_attention_mask, (bs, 1, 1)) # else: # attention_mask = self.get_attention_mask(input_mask) attention_mask = self.get_attention_mask(input_mask) # For speed, ignore not first_iteration cond. logits = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) # log_probs = self.log_softmax(logits) # index = current_index.view(1,) # logits = self.gather(log_probs, index, 0) # logits = logits.view(bs, 1, -1) # return logits log_probs = self.log_softmax(logits) index = current_index.view(1,) logits = self.gather(log_probs, index, 0) logits = logits.view(bs, 1, -1) return logits # # print(type(logits)) # # print(logits) # # print("logits: ", logits.data.asnumpy().shape) # # print(logits) # index = current_index.view(1,) # logits = self.gather(logits, index, 0) # logits = logits.view(bs, 1, -1) # log_probs = self.log_softmax(logits) # return log_probs class EvalNet_use_past(nn.Cell): """ PengChengMind evaluation net Args: backbone: backbone network of PengChengMind generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=0, seq_length=4096): super(EvalNet_use_past, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.expand = P.ExpandDims().shard(((1, 1, 1),)) # used for incremental prediction self.all_ones_attention_mask = Tensor(np.ones((1, 1, seq_length)), mstype.float32) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) # taoht, not use use_past if self.is_first_iteration is False: attention_mask = P.Tile()(self.all_ones_attention_mask, (bs, 1, 1)) else: attention_mask = self.get_attention_mask(input_mask) # attention_mask = self.get_attention_mask(input_mask) # For speed, ignore not first_iteration cond. logits = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) log_probs = self.log_softmax(logits) index = current_index.view(1,) logits = self.gather(log_probs, index, 0) logits = logits.view(bs, 1, -1) return logits class EvalNet_200B(nn.Cell): """ PengChengMind evaluation net Args: backbone: backbone network of PengChengMind generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=0, seq_length=4096): super(EvalNet_200B, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1),)) self.get_attention_mask = AttentionMask(seq_length) self.expand = P.ExpandDims().shard(((1, 1, 1),)) # used for incremental prediction self.all_ones_attention_mask = Tensor(np.ones((1, 1, seq_length)), mstype.float32) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) attention_mask = self.get_attention_mask(input_mask) # For speed, ignore not first_iteration cond. logits = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) log_probs = self.log_softmax(logits) index = current_index.view(1, ) logits = self.gather(log_probs, index, 0) logits = logits.view(bs, 1, -1) return logits pengchengmind/src/pengcheng_mind_config.py000066400000000000000000000271711453423006600213320ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ network config setting """ from enum import Enum import mindspore.common.dtype as mstype import json class OperatorType(str, Enum): ATTENTION_X = "ATTENTION_X" ATTENTION_QKV = "ATTENTION_QKV" ATTENTION_ATTEN = "ATTENTION_ATTEN" ATTENTION_SOFTMAX = "ATTENTION_SOFTMAX" ATTENTION_BMM = "ATTENTION_BMM" LINEAR_INPUT = "LINEAR_INPUT" LINEAR_X = "LINEAR_X" LINEAR_MAPPING = "LINEAR_MAPPING" LINEAR_GELU = "LINEAR_GELU" class PengChengMindConfig: """ PengChengMindConfig config class which defines the model size """ def __init__(self, batch_size=32, seq_length=4096,# vocab_size=49984,# , 49984 hidden_size=768, ffn_hidden_size=768, num_layers=12, num_heads=12, load_ckpt_path=None, param_init_type=mstype.float16, post_layernorm_residual=False, dropout_rate=0.1, eod_token=2,# 2 128298 pad_token=0,# 0 128297 use_past=False, hidden_act='fast_gelu', eod_reset=True, enable_offload=False, use_moe=False, expert_num=1, per_token_num_experts_chosen=1, parallel_config=None, run_type='train', use_rope=False, use_flash_attention=False, pipeline_config_filename=None): # softmax_compute_fp32=mstype.float16, self.batch_size = batch_size self.seq_length = seq_length self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_layers = num_layers self.num_heads = num_heads self.eod_token = eod_token # Use post-layernorm or pre-layernrom, default:pre-layernorm self.post_layernorm_residual = post_layernorm_residual self.load_ckpt_path = load_ckpt_path self.param_init_type = param_init_type self.dropout_rate = dropout_rate self.compute_dtype = mstype.float16 self.parallel_config = parallel_config self.ffn_hidden_size = ffn_hidden_size self.hidden_act = hidden_act self.use_past = use_past self.eod_reset = eod_reset self.enable_offload = enable_offload self.use_moe = bool(use_moe) self.expert_num = expert_num self.per_token_num_experts_chosen = per_token_num_experts_chosen self.run_type = run_type self.pad_token = pad_token self.use_rope = use_rope self.use_flash_attention = use_flash_attention # self.softmax_compute_fp32 = softmax_compute_fp32 # if softmax_compute_fp32: # self.softmax_compute_type = mstype.float32 # else: # self.softmax_compute_type = mstype.float16 # if self.run_type == "predict": # self.softmax_compute_type = mstype.float32 pipeline_strategy = None with open(pipeline_config_filename, 'r') as f: content = f.read() pipeline_strategy = json.loads(content) self.pipeline_config = [] for stage_idx, stage_config in enumerate(pipeline_strategy): layer_range = stage_config["layer_range"] if stage_idx == 0: layer_range[0] = 1 elif stage_idx == self.parallel_config.pipeline_stage - 1: layer_range[1] = self.num_layers * 2 + 1 layer_num = (layer_range[1] - layer_range[0] + 1) // 2 for _ in range(layer_num): layer_config = { OperatorType.LINEAR_INPUT: 1, OperatorType.LINEAR_X: 1, OperatorType.ATTENTION_X: 1, OperatorType.ATTENTION_BMM: 1, OperatorType.ATTENTION_QKV: 3, OperatorType.ATTENTION_ATTEN: 1, OperatorType.ATTENTION_SOFTMAX: 1, OperatorType.LINEAR_MAPPING: 1, OperatorType.LINEAR_GELU: 1, } key_list = list(layer_config.keys()) for op in key_list: if op in stage_config: left_num = stage_config[op] m = min(left_num, layer_config[op]) layer_config[op] = m stage_config[op] -= m else: layer_config[op] = 0 layer_config["stage_idx"] = stage_idx self.pipeline_config.append(layer_config) def __str__(self): info = '===' * 10 + "[PengChengMindConfig]" + '===' * 10 + '\n' for k, v in self.__dict__.items(): var_info = "--{}:{}\n".format(k, v) info += var_info info += '=' * 10 return info def set_parse_200B(args_opt): r""" Set config for 13B mode """ args_opt.embedding_size = 12672 args_opt.num_layers = 104 args_opt.num_heads = 96 if args_opt.run_type == "train": # args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload args_opt.micro_batch_interleaved = 1 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": # args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse_100B(args_opt): r""" Set config for 13B mode """ args_opt.embedding_size = 10240 # args_opt.num_layers = 80 # args_opt.num_heads = 80 # args_opt.word_emb_dp = 4# 1 # args_opt.op_level_model_parallel_num = 4 # 8 if args_opt.run_type == "train": # args_opt.start_lr = 4e-5# 5e-5 # args_opt.end_lr = 1e-6 # args_opt.stage_num = 16 # args_opt.micro_size = 32 # args_opt.op_level_model_parallel_num = 16 # if args_opt.optimizer_shard == 1: # args_opt.op_level_model_parallel_num = 8 # args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload args_opt.micro_batch_interleaved = 1 # if args_opt.per_batch_size == 0: # args_opt.per_batch_size = 2 # 8 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": # args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse_13B(args_opt): r""" Set config for 13B mode """ args_opt.embedding_size = 5120 args_opt.num_layers = 40 args_opt.num_heads = 40 args_opt.word_emb_dp = 1 args_opt.op_level_model_parallel_num = 8 if args_opt.run_type == "train": args_opt.start_lr = 5e-5 args_opt.end_lr = 1e-6 args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload args_opt.micro_batch_interleaved = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 8 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse_7B(args_opt): r""" Set config for 7B mode """ args_opt.embedding_size = 4096 args_opt.num_layers = 32 args_opt.num_heads = 32 # args_opt.word_emb_dp = 4# 1 # args_opt.op_level_model_parallel_num = 4 # 8 if args_opt.run_type == "train": # args_opt.start_lr = 4e-5# 5e-5 # args_opt.end_lr = 1e-6 # args_opt.stage_num = 16 # args_opt.micro_size = 32 # args_opt.op_level_model_parallel_num = 16 # if args_opt.optimizer_shard == 1: # args_opt.op_level_model_parallel_num = 8 # # args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload args_opt.micro_batch_interleaved = 1 # if args_opt.per_batch_size == 0: # args_opt.per_batch_size = 2 # 8 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": # args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse_2_6B(args_opt): r""" Set config for 2.6B mode """ args_opt.embedding_size = 2560 args_opt.num_layers = 32 args_opt.num_heads = 32 args_opt.op_level_model_parallel_num = 8 if args_opt.run_type == "train": args_opt.start_lr = 1e-4 args_opt.end_lr = 1e-6 args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload if args_opt.per_batch_size == 0: args_opt.per_batch_size = 16 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse_1_3B(args_opt): r""" Set config for 1.3B mode """ args_opt.embedding_size = 1024 args_opt.num_layers = 16 args_opt.num_heads = 32 args_opt.op_level_model_parallel_num = 8 if args_opt.run_type == "train": args_opt.start_lr = 1e-4 args_opt.end_lr = 1e-6 args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload if args_opt.per_batch_size == 0: args_opt.per_batch_size = 16 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse_350M(args_opt): r""" Set config for 13B mode """ args_opt.embedding_size = 1024 args_opt.num_layers = 24 args_opt.num_heads = 16 if args_opt.run_type == "train": # args_opt.optimizer_shard = 1 args_opt.full_batch = args_opt.opt_offload args_opt.micro_batch_interleaved = 1 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 elif args_opt.run_type == "predict": # args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 def set_parse(args_opt): r""" Set config according to the mode """ parse_fn_dict = {"200B": set_parse_200B, "100B": set_parse_100B, "13B": set_parse_13B, "7B": set_parse_7B, "2.6B": set_parse_2_6B, "1.3B": set_parse_1_3B, "350M": set_parse_350M} if args_opt.mode not in parse_fn_dict.keys(): raise ValueError("Invalid mode: {}. Optional mode: 200B, 13B, 2.6B and 1.3B".format(args_opt.mode)) parse_fn_dict[args_opt.mode](args_opt) pengchengmind/src/pengcheng_mind_pipeline_layer.py000066400000000000000000001667701453423006600230770ustar00rootroot00000000000000from mindspore.nn import Cell import mindspore from mindspore import ops from mindspore.common.tensor import Tensor from mindspore.common.parameter import Parameter from mindspore.common.initializer import initializer from mindspore import nn from mindspore import context import mindspore.common.dtype as mstype from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn.cell import Cell from mindspore._checkparam import Validator from mindspore import log as logger from mindspore.parallel._utils import _get_parallel_mode, _is_sharding_propagation from mindspore.context import ParallelMode from mindspore.log import _LogActionOnce from mindspore.nn.transformer.layers import _LayerNorm, _Linear, \ _args_type_validator_check, _valid_type_checks, _valid_value_checks, \ _check_past_none_input_none, _check_input_dtype from mindspore.nn.transformer import FeedForward, TransformerOpParallelConfig from mindspore.nn.transformer.op_parallel_config import default_dpmp_config, OpParallelConfig, \ _Config, _check_config, MoEParallelConfig from mindspore.nn.transformer.moe import default_moe_config, MoE, _check_moe_config import numpy as np import math from flash_attention.flash_attention_primitive import FlashAttentionPrimitive def _get_lambda_func(total_layer=None): r""" A wrapper function of specifying pipeline stage and gradient aggregation fusion. If the total layer is not None, for example, set in the transformer model, the pipeline stage setting function will be `(layer_id + 0) // (total_layers / parallel_config.pipeline_stage)` for the encoder and, `(layer_id + offset) // (total_layers / parallel_config.pipeline_stage)` for the decoder, where `offset` is the layers in the encoder. """ def _set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers): r""" Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. Args: network(Cell) - Represents the transformer block layer_id(int) - Means the layer index for the current module, counts from zero. offset(int) - Means the layer_index needs an offset, if there are other modules in the net. layers(int) - The total layers used for the model. """ # override the layers if total_layer: layers = total_layer # Used for the pipeline's stages setting if layers < parallel_config.pipeline_stage: raise ValueError(f"layers {layers} must be larger than pipeline stage {parallel_config.pipeline_stage}") pp_dis = max(layers // parallel_config.pipeline_stage, 1) # the pipeline stage must be in [0, parallel_config.pipeline_stage - 1] pp_id = min((layer_id + offset) // pp_dis, parallel_config.pipeline_stage - 1) network.pipeline_stage = pp_id # Used for optimizer's fusion tag dis = max(layers // parallel_config.gradient_aggregation_group, 1) network.set_comm_fusion((layer_id + offset) // dis + 1) # Used for enabling recomputation of the block if isinstance(parallel_config.recompute, bool): if parallel_config.recompute: network.recompute() else: if parallel_config.recompute.recompute: paralel_op_comm_compute = parallel_config.recompute.parallel_optimizer_comm_recompute network.recompute(parallel_optimizer_comm_recompute=paralel_op_comm_compute, mp_comm_recompute=parallel_config.recompute.mp_comm_recompute, recompute_slice_activation=parallel_config.recompute.recompute_slice_activation) return _set_parallel_configure_for_layer class RoPE(nn.Cell): """ 定义RoPE位置Embedding """ sin_matrix = None cos_matrix = None def __init__(self, num_heads, hidden_size, seq_length, parallel_config, compute_type): super().__init__(auto_prefix=False) self.dtype = np.float64 self.max_seq_len = seq_length self.output_dim = hidden_size // num_heads self.shape = P.Shape() self.reshape = P.Reshape() self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) dp = parallel_config.data_parallel mp = parallel_config.model_parallel self.mul = P.Mul().shard(((dp, mp, 1, 1), (1, 1, 1, 1))) self.add = P.Add().shard(((dp, mp, 1, 1), (dp, mp, 1, 1))) self.split = P.Split(axis=-1, output_num=2).shard(((dp, mp, 1, 1, 1),)) self.concat = P.Concat(axis=-1).shard(((dp, mp, 1, 1, 1), (dp, mp, 1, 1, 1))) self.neg = P.Neg().shard(((dp, mp, 1, 1, 1),)) if self.cos_matrix is None: position_ids = np.arange(0, self.max_seq_len, dtype=self.dtype)[None] indices = np.arange(0, self.output_dim // 2., dtype=self.dtype) indices = 1. / np.power(10000.0, 2 * indices / self.output_dim, dtype=self.dtype) embeddings = np.einsum('bn,d->bnd', position_ids, indices, dtype=self.dtype) embeddings = np.repeat(embeddings, 2, axis=-1).reshape(1, 1, self.max_seq_len, self.output_dim) RoPE.sin_matrix = Tensor(np.sin(embeddings, dtype=self.dtype), dtype=compute_type) RoPE.cos_matrix = Tensor(np.cos(embeddings, dtype=self.dtype), dtype=compute_type) def rotate_half(self, x, input_shape): shape_5d = (input_shape[0], input_shape[1], input_shape[2], -1, 2) x = self.reshape(x, shape_5d) x0, x1 = self.split(x) rotate = self.concat([self.neg(x1), x0]) rotate = self.reshape(rotate, input_shape) return rotate def construct(self, inputs): # P.Print()("enter RoPE", inputs) input_shape = self.shape(inputs) seq_len = input_shape[2] sin_matrix = self.slice(self.sin_matrix, (0, 0, 0, 0), (1, 1, seq_len, input_shape[3]), (1, 1, 1, 1)) cos_matrix = self.slice(self.cos_matrix, (0, 0, 0, 0), (1, 1, seq_len, input_shape[3]), (1, 1, 1, 1)) output1 = self.mul(inputs, cos_matrix) output2 = self.mul(self.rotate_half(inputs, input_shape), sin_matrix) output = self.add(output1, output2) return output class FlashAttention(nn.Cell): def __init__(self, head_dim, dropout_rate=0.0, prev_block_num=65536, next_block_num=65536, tiling_stgy_name="xunfei", dp=1, mp=1, ): super(FlashAttention, self).__init__() self.flash_attention = FlashAttentionPrimitive( prev_block_num=prev_block_num, next_block_num=next_block_num, high_precision=True ) self.flash_attention.add_prim_attr("primitive_target", "Ascend") self.scale_factor = Tensor([1. / math.sqrt(math.sqrt(head_dim))], dtype=mstype.float16) self.dim_mask = Tensor([1 for _ in range(head_dim)], dtype=mstype.int8) self.scale_mul = ops.Mul().shard(((dp, mp, 1, 1), (1,))) self.keep_prob = Tensor(1 - dropout_rate, dtype=mstype.float16) self.fill_v2 = ops.FillV2().shard(((dp, mp, 1, 1), ())) self.tensor_one = Tensor(1.0, mstype.float16) self.drop_gen_mask = ops.DropoutGenMask() self.do_dropout = ops.DropoutDoMask().shard(((dp, mp, 1, 1),)) self.depend = ops.Depend() def shard(self, in_strategy=None, out_strategy=None): """Distributed configuration of FlashAttention :param in_strategy: Describe the split strategy of operator input. Default: None. :param out_strategy: Describe the split strategy of operator output, it is only for certain operators, such as MatMul. Default: None. :return: """ if in_strategy is not None: shard_stgy = list(in_strategy) # shard_stgy.insert(3, (1,)) # dim_mask shard_stgy = tuple(shard_stgy) else: # default: dp=1, mp=1, construct inputs only contain q, k, v shard_stgy = ( (1, 1, 1, 1), (1, 1, 1, 1), (1, 1, 1, 1), # (1,), # dim_mask ) self.flash_attention.shard(shard_stgy) def construct(self, q, k, v, attn_mask=None, alibi_mask=None): """FlashAttention forward :param q: [bsz, head_num, seq_len, head_dim] :param k: [bsz, head_num, seq_len, head_dim] :param v: [bsz, head_num, seq_len, head_dim] :param attn_mask: [1 or bsz, seq_len, seq_len], if not None :param alibi_mask: [bsz, head_num, 1, seq_len], if not None :return: o [bsz, head_num, seq_len, head_dim] """ q = self.scale_mul(q, self.scale_factor) k = self.scale_mul(k, self.scale_factor) bsz, head_num, seq_len, _ = q.shape drop_mask_bits = self.drop_gen_mask((bsz, head_num, seq_len, seq_len), self.keep_prob) ones = self.fill_v2((bsz, head_num, seq_len, seq_len), self.tensor_one) ones = self.depend(ones, q) # ones = self.depend(ones, k) drop_mask = self.do_dropout(ones, drop_mask_bits, self.keep_prob) o, _, _ = self.flash_attention(q, k, v, attn_mask, drop_mask, None) return o class PipeMultiHeadAttention(Cell): @_LogActionOnce(logger=logger, key='MultiHeadAttention', no_warning=_get_parallel_mode() in (ParallelMode.STAND_ALONE,)) @_args_type_validator_check(hidden_size=Validator.check_positive_int, num_heads=Validator.check_positive_int, src_seq_length=Validator.check_positive_int, tgt_seq_length=Validator.check_positive_int, attention_dropout_rate=Validator.check_non_negative_float, hidden_dropout_rate=Validator.check_non_negative_float, compute_dtype=_valid_value_checks([mstype.float32, mstype.float16], "MultiHeadAttention"), softmax_compute_type=_valid_value_checks([mstype.float32, mstype.float16], "MultiHeadAttention"), param_init_type=_valid_value_checks([mstype.float32, mstype.float16], "MultiHeadAttention"), parallel_config=_valid_type_checks([OpParallelConfig], "MultiHeadAttention"), use_past=Validator.check_bool) def __init__(self, batch_size, src_seq_length, tgt_seq_length, hidden_size, num_heads, hidden_dropout_rate=0.1, attention_dropout_rate=0.1, compute_dtype=mstype.float16, softmax_compute_type=mstype.float32, param_init_type=mstype.float32, use_past=False, parallel_config=default_dpmp_config, use_rope=False, use_flash_attention=False): super(PipeMultiHeadAttention, self).__init__() self._is_ascend = context.get_context('device_target') in ["Ascend"] self.dp = parallel_config.data_parallel self.is_parallel_mode = _get_parallel_mode() in ( ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if batch_size: Validator.check_positive_int(batch_size) _check_config(parallel_config) self.src_seq_length = src_seq_length self.tgt_seq_length = tgt_seq_length self.hidden_size = hidden_size self.batch_size = batch_size # for simplicity, assert not (_get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation()) if hidden_dropout_rate < 0 or hidden_dropout_rate >= 1: raise ValueError("For 'MultiHeadAttention', the class variable 'hidden_dropout_rate' must be " "in range [0, 1.0), but got the value : {}.".format(hidden_dropout_rate)) if attention_dropout_rate < 0 or attention_dropout_rate >= 1: raise ValueError("For 'MultiHeadAttention', the class variable 'attention_dropout_rate' must be " "in range [0, 1.0), but got the value : {}.".format(attention_dropout_rate)) if hidden_size % num_heads != 0: raise ValueError("For 'MultiHeadAttention', the class variable 'hidden_size' must be a multiple " "of 'num_heads', but got the hidden_size is {} and the num_heads is {}." .format(hidden_size, num_heads)) if num_heads % parallel_config.model_parallel != 0: raise ValueError("For 'MultiHeadAttention', the class variable 'num_heads' must be a multiple of " "'parallel_config.model_parallel', but got the num_heads is {} " "and the parallel_config.model_parallel is {}." .format(num_heads, parallel_config.model_parallel)) self.is_first_iteration = True # Output layer self.projection = _Linear(in_channels=hidden_size, out_channels=hidden_size, transpose_b=False, compute_dtype=compute_dtype, param_init_type=param_init_type) self.projection.shard(strategy_bias=((parallel_config.data_parallel, 1), (1,)), strategy_matmul=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel, 1))) self.projection.bias.parallel_optimizer = False self.transpose = P.Transpose().shard( ((parallel_config.data_parallel, 1, parallel_config.model_parallel, 1),)) self.transpose_q = P.Transpose().shard( ((parallel_config.data_parallel, 1, parallel_config.model_parallel, 1),)) self.transpose_k = P.Transpose().shard( ((parallel_config.data_parallel, 1, parallel_config.model_parallel, 1),)) self.transpose_v = P.Transpose().shard( ((parallel_config.data_parallel, 1, parallel_config.model_parallel, 1),)) self.transpose_back = P.Transpose().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.merger_head_transpose = P.Transpose().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.reshape = P.Reshape() self.n_head = num_heads # embedding size per head self.size_per_head = hidden_size // self.n_head self.concat_k = P.Concat(axis=3) self.concat_v = P.Concat(axis=2) self.multiply_data = Tensor([ -10000.0, ], dtype=softmax_compute_type) self.batch_matmul = P.BatchMatMul().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), (parallel_config.data_parallel, parallel_config.model_parallel, 1, 1))) self.batch_matmul1 = P.BatchMatMul().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), (parallel_config.data_parallel, parallel_config.model_parallel, 1, 1))) self.real_div = P.RealDiv().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), ())) self.real_div_q = P.RealDiv().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), ())) self.real_div_k = P.RealDiv().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), ())) self.sub = P.Sub().shard( ((1,), (parallel_config.data_parallel, 1, 1, 1))) self.mul = P.Mul().shard( ((parallel_config.data_parallel, 1, 1, 1), (1,))) self.add_mask = P.Add().shard( ((parallel_config.data_parallel, 1, 1, 1), (parallel_config.data_parallel, parallel_config.model_parallel, 1, 1))) # Normalize factor for attention, sqrt(dk) as widely used self.scale_factor = Tensor(math.sqrt(math.sqrt(self.size_per_head))) self.use_past = use_past self.dropout = nn.Dropout(1 - hidden_dropout_rate) self.dropout.dropout.shard(((parallel_config.data_parallel, 1),)) self.prob_dropout = nn.Dropout(1 - attention_dropout_rate) self.prob_dropout.dropout.shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.softmax = nn.Softmax().to_float(softmax_compute_type) self.softmax.softmax.shard(((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.softmax_3d = nn.Softmax().to_float(softmax_compute_type) self.softmax_3d.softmax.shard(((parallel_config.data_parallel, parallel_config.model_parallel, 1),)) self.expand_dims = P.ExpandDims().shard(((parallel_config.data_parallel, 1, 1),)) # Query self.dense1 = _Linear(hidden_size, hidden_size, compute_dtype=compute_dtype, param_init_type=param_init_type) self.dense1.shard(strategy_matmul=((parallel_config.data_parallel, 1), (parallel_config.model_parallel, 1)), strategy_bias=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel,))) # Key self.dense2 = _Linear(hidden_size, hidden_size, compute_dtype=compute_dtype, param_init_type=param_init_type) self.dense2.shard(strategy_matmul=((parallel_config.data_parallel, 1), (parallel_config.model_parallel, 1)), strategy_bias=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel,))) # Value self.dense3 = _Linear(hidden_size, hidden_size, compute_dtype=compute_dtype, param_init_type=param_init_type) self.dense3.shard(strategy_matmul=((parallel_config.data_parallel, 1), (parallel_config.model_parallel, 1)), strategy_bias=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel,))) self.dtype = compute_dtype self.softmax_dtype = softmax_compute_type if self.use_past: # operators used for state reuse seq_range = np.arange(src_seq_length).reshape(1, 1, -1) self.range = Tensor(np.tile(seq_range, (batch_size, 1, 1)), mstype.int32) self.seq_length = src_seq_length self.attention_mask = Tensor(np.tril(np.ones(shape=(self.seq_length, self.seq_length))), mstype.int32) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.expand_dims = P.ExpandDims().shard(((1, 1, 1),)) self.tensor_le = P.LessEqual().shard(((1, 1, 1), (1, 1, 1))) self.add = P.Add().shard(((1, 1, 1, 1), (1, 1, 1, 1))) self.equal = P.Equal().shard(((1, 1, 1), (1, 1, 1))) self.sub1 = P.Sub().shard(((1,), ())) self.tile = P.Tile().shard(((1, 1, 1, 1),)) self.less = P.Less().shard(((1, 1, 1), (1, 1, 1))) self.mul1 = P.Mul().shard(((1, 1, 1, 1), (1, 1, 1, 1))) self.use_flash_attention = use_flash_attention if self.use_flash_attention: self.head_dim = hidden_size // num_heads dp = parallel_config.data_parallel mp = parallel_config.model_parallel self.flash_attention = FlashAttention(head_dim=self.head_dim, dropout_rate=attention_dropout_rate, prev_block_num=65536, next_block_num=0, dp=parallel_config.data_parallel, mp=parallel_config.model_parallel) q_shard_stgy = (dp, mp, 1, 1) k_shard_stgy = (dp, mp, 1, 1) v_shard_stgy = (dp, mp, 1, 1) attn_mask_shard_stgy = (dp, 1, 1) dropout_shard_stgy = (dp, mp, 1, 1) in_stgy = (q_shard_stgy, k_shard_stgy, v_shard_stgy, attn_mask_shard_stgy, dropout_shard_stgy) self.flash_attention.shard(in_stgy) self.pad_tensor = Tensor(np.zeros((self.batch_size, self.n_head, self.src_seq_length, 16 - self.head_dim % 16)), dtype=compute_dtype) self.zeroslike = P.ZerosLike().shard(((dp, mp, 1, 1),)) self.concat = P.Concat(axis=-1).shard(((dp, mp, 1, 1), (dp, mp, 1, 1))) self.slice = P.Slice().shard(((dp, mp, 1, 1),)) self.output_shape = (self.batch_size, self.n_head, self.src_seq_length, self.head_dim) self.softmax_reshape = P.Reshape() self.softmax_cast = P.Cast() self.use_rope = use_rope self.rope = None if self.use_rope: self.rope = RoPE(num_heads, hidden_size, tgt_seq_length, parallel_config, compute_dtype) def construct(self, query_tensor, key_tensor, value_tensor, attention_mask, key_past=None, value_past=None, batch_valid_length=None): self._check_inputs(query_tensor, key_tensor, value_tensor, attention_mask, key_past, value_past, batch_valid_length) ori_shape = F.shape(query_tensor) batch_size = self._get_batch_size_from_query(query_tensor) query_tensor, key_tensor, value_tensor = self._convert_to_2d_tensor(query_tensor, key_tensor, value_tensor, attention_mask) ori_dtype = F.dtype(query_tensor) query_tensor = F.cast(query_tensor, self.dtype) key_tensor = F.cast(key_tensor, self.dtype) value_tensor = F.cast(value_tensor, self.dtype) # multi head attention: query, key, value are derived from the same inputs query = self.dense1(query_tensor) key = self.dense2(key_tensor) value = self.dense3(value_tensor) # the returned shape is [bs, num_heads, seq_length, size_per_head] query = self.transpose_q( F.reshape( query, (batch_size, self._get_seq_length_under_incremental(self.src_seq_length), self.n_head, self.size_per_head)), (0, 2, 1, 3)) # the returned shape is [bs, size_per_head, seq_length, num_heads] key = self.transpose_k( F.reshape( key, (batch_size, self._get_seq_length_under_incremental(self.tgt_seq_length), self.n_head, self.size_per_head)), (0, 2, 1, 3)) if self.use_rope: query = self.rope(query) key = self.rope(key) # the returned shape is [bs, num_heads, seq_length, size_per_head] value = self.transpose_v( F.reshape( value, (batch_size, self._get_seq_length_under_incremental(self.tgt_seq_length), self.n_head, self.size_per_head)), (0, 2, 1, 3)) # support input shape is [bs, seq, seq] or [bs, heads, seq, seq] if attention_mask is not None and len(F.shape(attention_mask)) == 3: # expand attention mask from [bs, seq, seq] -> [bs, 1, seq, seq] attention_mask = self.expand_dims(attention_mask, 1) # key and value for current token(s) key_present = key value_present = value if self.use_past: # The first graph with the input size of (bs, seq_length) if self.is_first_iteration: # Get the valid input length without padding valid_length_vector = F.cast(self.less(self.range, batch_valid_length.view(-1, 1, 1)), self.dtype) # Cover the key and value numbers corresponding to the padding position key_present = self.mul1(key, self.expand_dims(valid_length_vector, 3)) value_present = self.mul1(value, self.expand_dims(valid_length_vector, 3)) # The second graph with the inpus size of (bs, 1) # the shape of query is (bs, num_heads, 1, size_per_head) # the shape of key is (bs, num_heads, size_per_head, 1) # the shape of value is (bs, num_heads, 1, size_per_head) else: # Get the current token position index valid_length = self.reducesum(F.cast(self.not_equal(self.slice(key_past, (0, 0, 0, 0), (F.shape(key_tensor)[0], 1, 1, self.src_seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) valid_length = F.reshape(valid_length, (-1, 1, 1)) valid_length_vector = F.cast(self.equal(valid_length, self.range), self.dtype) # Pad the key and value to seq_length with only the position index not zero current_key = self.mul1(self.tile(key, (1, 1, self.seq_length, 1)), self.expand_dims(valid_length_vector, 3)) current_value = self.mul1(self.tile(value, (1, 1, self.seq_length, 1)), self.expand_dims(valid_length_vector, 3)) # Concat the previous saved state and current state key = self.add(key_past, current_key) value = self.add(value_past, current_value) # Update key_present and value_present for state update key_present = key value_present = value attention_mask = F.reshape(self.attention_mask, (self.seq_length, self.seq_length, 1, 1)) layer_present = (key_present, value_present) if self.use_flash_attention: attention_mask = 1.0 - attention_mask.squeeze(1) attention = self.flash_attention(self.concat((query, self.zeroslike(self.pad_tensor))), self.concat((key, self.zeroslike(self.pad_tensor))), self.concat((value, self.zeroslike(self.pad_tensor))), attention_mask) # attention = self.flash_attention(query, key, value, attention_mask) attention = self.slice(attention, (0, 0, 0, 0), self.output_shape) attention = self._merge_heads(attention) else: key = self.transpose_back(key, (0, 1, 3, 2)) attention = self._attn(query, key, value, attention_mask) # multi head attention considering attention mask # the return shape is [bs * seq_length, hidden_size] #attention = self._attn(query, key, value, attention_mask) # Output output = self.projection(attention) output = self.dropout(output) output = F.reshape(output, ori_shape) output = F.cast(output, ori_dtype) return output, layer_present def _get_batch_size_from_query(self, query): r"""Get the batch size from query tensor""" # For the incremental prediction, the seq length for the input is 1. if len(F.shape(query)) == 2 and ((self.use_past and self.is_first_iteration) or (not self.use_past)): return F.shape(query)[0] // self.src_seq_length return F.shape(query)[0] def _get_seq_length_under_incremental(self, length): r"""Return the length of the tensor. For the incremental prediction, the seq length for the input is 1. """ if self.use_past and not self.is_first_iteration: return 1 return length def _check_inputs(self, query_tensor, key_tensor, value_tensor, attention_mask, key_past=None, value_past=None, batch_valid_length=None): r"""Check inputs""" _check_input_dtype(F.dtype(query_tensor), "query_tensor", [mstype.float32, mstype.float16], self.cls_name) _check_input_dtype(F.dtype(key_tensor), "key_tensor", [mstype.float32, mstype.float16], self.cls_name) _check_input_dtype(F.dtype(value_tensor), "value_tensor", [mstype.float32, mstype.float16], self.cls_name) if attention_mask is not None: _check_input_dtype(F.dtype(attention_mask), "attention_mask", [mstype.float32, mstype.float16], self.cls_name) key_is_tensor = isinstance(key_past, Tensor) value_is_tensor = isinstance(value_past, Tensor) batch_valid_length_is_tensor = isinstance(batch_valid_length, Tensor) key_is_default = key_past is None value_is_default = value_past is None batch_is_default = batch_valid_length is None _check_past_none_input_none(self.use_past, "key_past", self.cls_name, None, key_is_tensor, key_is_default) _check_past_none_input_none(self.use_past, "value_past", self.cls_name, None, value_is_tensor, value_is_default) _check_past_none_input_none(self.use_past, "batch_valid_length", self.cls_name, None, batch_valid_length_is_tensor, batch_is_default) if self.use_past: _check_input_dtype(F.dtype(key_past), "key_past", [mstype.float16], self.cls_name) _check_input_dtype(F.dtype(value_past), "value_past", [mstype.float16], self.cls_name) _check_input_dtype(F.dtype(batch_valid_length), "batch_valid_length", [mstype.int32], self.cls_name) return True def _convert_to_2d_tensor(self, query_tensor, key_tensor, value_tensor, attention_mask): """convert a nd tensor to a 2d tensor""" query_shape = F.shape(query_tensor) query_tensor = F.reshape(query_tensor, (-1, query_shape[-1])) key_shape = F.shape(key_tensor) key_tensor = F.reshape(key_tensor, (-1, key_shape[-1])) value_shape = F.shape(value_tensor) value_tensor = F.reshape(value_tensor, (-1, value_shape[-1])) return query_tensor, key_tensor, value_tensor def _merge_heads(self, x): """ convert a 4d input to a 2d output Inputs: x: input tensor Output: x_merge: the 2d output """ x = self.merger_head_transpose( x, (0, 2, 1, 3)) # bs, seq_length, head, size_per_head x_shape = P.Shape()(x) new_shape = (-1, x_shape[-2] * x_shape[-1]) x_merge = self.reshape(x, new_shape) return x_merge def _softmax(self, attention_scores): """ For the consideration of the performance, do softmax according to different situations :param attention_scores: a 3d tensor before softmax :return: the attention scores. """ if self._is_ascend and self.softmax_dtype == mstype.float16 or not self._is_ascend: attention_probs = self.softmax(attention_scores) else: shape = F.shape(attention_scores) # attention probs attention_probs = self.softmax_3d( self.softmax_reshape(attention_scores, (shape[0], -1, shape[-1]))) attention_probs = self.softmax_reshape(attention_probs, shape) return attention_probs def _attn(self, query, key, value, attention_mask): """ Get the weighted score along the seq_length Inputs: query: the query matrix key: the key matrix value: the value matrix attention_mask: the attention mask matrix with shape (batch_size, 1, seq_length, seq_length) Outputs: weighted_values: Tensor, the weighted sum scores """ # Normalize query and key before MatMul, default off # Attention score [bs, num_heads, seq_length, seq_length] factor = P.Cast()(self.scale_factor, P.DType()(query)) query = self.real_div_q(query, factor) key = self.real_div_k(key, factor) score = self.batch_matmul1(query, key) ori_dtype = P.DType()(score) attention_scores = self.softmax_cast(score, self.softmax_dtype) # for input size of (bs, 1) namely the second graph, # the shape of attention_mask matrix should be (bs, 1, 1, seq_length) if attention_mask is not None: if self.use_past and not self.is_first_iteration: # Calculate the current total token current_index = self.reducesum(F.cast(self.not_equal(self.slice(key, (0, 0, 0, 0), (F.shape(query)[0], 1, 1, self.seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) # Get the precise position index index = self.sub1(F.cast(current_index, mstype.int32), 1) index = F.reshape(index, (-1, 1, 1)) # Calculate the attention_mask matrix via the position index attention_mask = F.cast(self.tensor_le(self.range, index), mstype.int32) attention_mask = self.expand_dims(attention_mask, 2) # Minus 10000 for the position where masked to exclude them from softmax multiplu_out = self.sub( P.Cast()(F.tuple_to_array((1.0,)), P.DType()(attention_scores)), P.Cast()(attention_mask, P.DType()(attention_scores))) adder = self.mul(multiplu_out, self.multiply_data) attention_scores = self.add_mask(adder, attention_scores) # attention probs attention_probs = self._softmax(attention_scores) attention_probs = self.softmax_cast(attention_probs, ori_dtype) attention_probs = self.prob_dropout(attention_probs) # Weighted sum output [bs, num_heads, seq_length, size_per_head] weighted_values = self.batch_matmul(attention_probs, value) attention_merge = self._merge_heads(weighted_values) return attention_merge class PipeTransformerEncoderLayer(Cell): def __init__(self, batch_size, hidden_size, ffn_hidden_size, num_heads, seq_length, attention_dropout_rate=0.1, hidden_dropout_rate=0.1, post_layernorm_residual=False, layernorm_compute_type=mstype.float32, softmax_compute_type=mstype.float32, param_init_type=mstype.float32, hidden_act='gelu', use_past=False, moe_config=default_moe_config, parallel_config=default_dpmp_config, use_rope=False, use_flash_attention=False): super(PipeTransformerEncoderLayer, self).__init__() if batch_size or use_past: Validator.check_positive_int(batch_size) self.batch_size = batch_size if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation(): _check_config(parallel_config) if num_heads % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'num_heads' must be divisibled by the " "'parallel_config.model_parallel', but got the num_heads is {} and " "parallel_config.model_parallel is {}.".format(num_heads, parallel_config.model_parallel)) if hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'hidden_size' must be divisibled by " "the 'parallel_config.model_parallel', but got the hidden_size is {} and parallel_config." " model_parallel is {}.".format(hidden_size, parallel_config.model_parallel)) if ffn_hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'ffn_hidden_size' must be divisibled " "by the 'parallel_config.model_parallel', but got the ffn_hidden_size is {} " "and parallel_config. model_parallel is {}." .format(ffn_hidden_size, parallel_config.model_parallel)) _check_moe_config(moe_config, parallel_config) self.use_moe = (moe_config.expert_num > 1) self.use_past = use_past self.seq_length = seq_length self.hidden_size = hidden_size self.layernorm1 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type) self.layernorm2 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type) attention_parallel_config = parallel_config.dpmp if self.use_moe else parallel_config self.attention = PipeMultiHeadAttention(batch_size=batch_size, src_seq_length=seq_length, tgt_seq_length=seq_length, hidden_size=hidden_size, num_heads=num_heads, hidden_dropout_rate=hidden_dropout_rate, attention_dropout_rate=attention_dropout_rate, softmax_compute_type=softmax_compute_type, param_init_type=param_init_type, use_past=use_past, parallel_config=attention_parallel_config, use_rope=use_rope, use_flash_attention=use_flash_attention) if self.use_moe: self.output = MoE(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, hidden_act=hidden_act, moe_config=moe_config, parallel_config=parallel_config) else: # Feed Forward Network, FFN self.output = FeedForward(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, hidden_act=hidden_act, parallel_config=parallel_config) self.post_layernorm_residual = post_layernorm_residual self.add = P.Add().shard(((parallel_config.data_parallel, 1), (parallel_config.data_parallel, 1))) self.add_3d = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1))) self.dtype = mstype.float16 self.key_past = None self.value_past = None if self.use_past: # operator used for state reuse self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) size_per_head = hidden_size // num_heads self.key_shape = (batch_size, num_heads, size_per_head, seq_length) self.value_shape = (batch_size, num_heads, seq_length, size_per_head) # parameters saving key and value states self.key_past = Parameter(Tensor(np.zeros(shape=self.key_shape), self.dtype), name="key_past") self.value_past = Parameter(Tensor(np.zeros(shape=self.value_shape), self.dtype), name="value_past") self.tile = P.Tile().shard(((1, 1),)) self.mul = P.Mul().shard(((1, 1, 1, 1), (1,))) self.assign = P.Assign().shard(((1, 1, 1, 1), (1, 1, 1, 1))) elif _get_parallel_mode() not in (ParallelMode.AUTO_PARALLEL,): _check_config(parallel_config) if num_heads % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'num_heads' must be divisibled by the " "'parallel_config.model_parallel', but got the num_heads is {} and " "parallel_config.model_parallel is {}.".format(num_heads, parallel_config.model_parallel)) if hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'hidden_size' must be divisibled by " "the 'parallel_config.model_parallel', but got the hidden_size is {} and parallel_config." " model_parallel is {}.".format(hidden_size, parallel_config.model_parallel)) if ffn_hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'ffn_hidden_size' must be divisibled " "by the 'parallel_config.model_parallel', but got the ffn_hidden_size is {} " "and parallel_config. model_parallel is {}." .format(ffn_hidden_size, parallel_config.model_parallel)) _check_moe_config(moe_config, parallel_config) self.use_moe = (moe_config.expert_num > 1) self.use_past = use_past self.seq_length = seq_length self.hidden_size = hidden_size self.layernorm1 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type) self.layernorm1.shard(((parallel_config.data_parallel, 1),)) self.layernorm2 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type) self.layernorm2.shard(((parallel_config.data_parallel, 1),)) attention_parallel_config = parallel_config.dpmp if self.use_moe else parallel_config self.attention = PipeMultiHeadAttention(batch_size=batch_size, src_seq_length=seq_length, tgt_seq_length=seq_length, hidden_size=hidden_size, num_heads=num_heads, hidden_dropout_rate=hidden_dropout_rate, attention_dropout_rate=attention_dropout_rate, softmax_compute_type=softmax_compute_type, param_init_type=param_init_type, use_past=use_past, parallel_config=attention_parallel_config, use_rope=use_rope, use_flash_attention=use_flash_attention) if self.use_moe: self.output = MoE(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, hidden_act=hidden_act, moe_config=moe_config, parallel_config=parallel_config) else: # Feed Forward Network, FFN self.output = FeedForward(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, hidden_act=hidden_act, parallel_config=parallel_config) self.post_layernorm_residual = post_layernorm_residual self.add = P.Add().shard(((parallel_config.data_parallel, 1), (parallel_config.data_parallel, 1))) self.add_3d = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1))) self.dtype = mstype.float16 self.key_past = None self.value_past = None if self.use_past: # operator used for state reuse self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) size_per_head = hidden_size // num_heads self.key_shape = (batch_size, num_heads, size_per_head, seq_length) self.value_shape = (batch_size, num_heads, seq_length, size_per_head) # parameters saving key and value states self.key_past = Parameter(Tensor(np.zeros(shape=self.key_shape), self.dtype), name="key_past") self.value_past = Parameter(Tensor(np.zeros(shape=self.value_shape), self.dtype), name="value_past") self.tile = P.Tile().shard(((1, 1),)) self.mul = P.Mul().shard(((1, 1, 1, 1), (1,))) self.assign = P.Assign().shard(((1, 1, 1, 1), (1, 1, 1, 1))) else: raise RuntimeError(f"The {self.cls_name} only support sharding propagation or " f"semi-auto parallel mode now.") def construct(self, x, input_mask=None, init_reset=True, batch_valid_length=None): self._check_input(x, input_mask, init_reset, batch_valid_length) x_shape = F.shape(x) x = F.reshape(x, (-1, x_shape[-1])) if self.post_layernorm_residual: input_x = x else: input_x = self.layernorm1(x) input_x = F.cast(input_x, self.dtype) # indicate whether reset saved states key_reset = None value_reset = None if self.use_past: # reset states, init_reset True for reuse and False for reset self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype))) key_reset = self.key_past self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype))) value_reset = self.value_past # add dependency for desired execution order input_x = F.depend(input_x, key_reset) input_x = F.depend(input_x, value_reset) attention, layer_present = self.attention(input_x, input_x, input_x, input_mask, self.key_past, self.value_past, batch_valid_length) # For post-layernorm the inputs for residual path are output of self-attention and output of layernorm if self.post_layernorm_residual: x = self.add(input_x, attention) # For pre-layernorm the inputs for residual path are output of self-attention and input of this layer else: x = self.add(x, attention) output_x = self.layernorm2(x) output_x = F.cast(output_x, self.dtype) aux_loss = None if self.use_moe: mlp_logit, aux_loss = self.output(output_x) else: mlp_logit = self.output(output_x) value_update = None key_update = None if self.use_past: # current key and value key_present, value_present = layer_present # update key and value calculated this step self.assign(self.key_past, key_present) key_update = self.key_past self.assign(self.value_past, value_present) value_update = self.value_past # add dependency for desired execution order key_update = F.depend(key_update, key_reset) value_update = F.depend(value_update, value_reset) # add dependency for desired execution order mlp_logit = F.depend(mlp_logit, value_update) mlp_logit = F.depend(mlp_logit, key_update) # if shape is 3d, we reshape the inputs of the add if len(x_shape) == 3: output_x = P.Reshape()(output_x, x_shape) mlp_logit = P.Reshape()(mlp_logit, x_shape) x = P.Reshape()(x, x_shape) if self.post_layernorm_residual: output = self.add_3d(output_x, mlp_logit) output = F.reshape(output, (-1, x_shape[-1])) output = self.layernorm1(output) output = F.reshape(output, x_shape) else: output = self.add_3d(x, mlp_logit) else: if self.post_layernorm_residual: output = self.add(output_x, mlp_logit) output = self.layernorm1(output) else: output = self.add(x, mlp_logit) output = F.reshape(output, x_shape) if self.use_moe: return output, layer_present, aux_loss return output, layer_present def _check_input(self, x, input_mask, init_reset, batch_valid_length): r"""Check inputs""" _check_input_dtype(F.dtype(x), "x", [mstype.float32, mstype.float16], self.cls_name) if input_mask is not None: _check_input_dtype(F.dtype(input_mask), "input_mask", [mstype.float32, mstype.float16], self.cls_name) init_reset_is_tensor = isinstance(init_reset, Tensor) init_reset_is_default = init_reset is True batch_valid_length_is_tensor = isinstance(batch_valid_length, Tensor) batch_is_default = batch_valid_length is None _check_past_none_input_none(self.use_past, "init_reset", self.cls_name, True, init_reset_is_tensor, init_reset_is_default) _check_past_none_input_none(self.use_past, "batch_valid_length", self.cls_name, None, batch_valid_length_is_tensor, batch_is_default) if self.use_past: _check_input_dtype(F.dtype(init_reset), "init_reset", [mstype.bool_], self.cls_name) _check_input_dtype(F.dtype(batch_valid_length), "batch_valid_length", [mstype.int32], self.cls_name) return True default_transformer_config = TransformerOpParallelConfig() class PipeTransformerEncoder(Cell): @_LogActionOnce(logger=logger, key='TransformerEncoder', no_warning=_get_parallel_mode() in (ParallelMode.STAND_ALONE,)) @_args_type_validator_check(batch_size=Validator.check_positive_int, hidden_size=Validator.check_positive_int, num_heads=Validator.check_positive_int, ffn_hidden_size=Validator.check_positive_int, seq_length=Validator.check_positive_int, num_layers=Validator.check_positive_int, offset=Validator.check_non_negative_int, attention_dropout_rate=Validator.check_non_negative_float, hidden_dropout_rate=Validator.check_non_negative_float, post_layernorm_residual=Validator.check_bool, layernorm_compute_type=_valid_value_checks([mstype.float32, mstype.float16], "TransformerEncoder"), softmax_compute_type=_valid_value_checks([mstype.float32, mstype.float16], "TransformerEncoder"), param_init_type=_valid_value_checks([mstype.float32, mstype.float16], "TransformerEncoder"), parallel_config=_valid_type_checks([TransformerOpParallelConfig], "TransformerEncoder"), use_past=Validator.check_bool, use_rope=Validator.check_bool) def __init__(self, batch_size, num_layers, hidden_size, ffn_hidden_size, seq_length, num_heads, attention_dropout_rate=0.1, hidden_dropout_rate=0.1, hidden_act='gelu', post_layernorm_residual=False, layernorm_compute_type=mstype.float32, softmax_compute_type=mstype.float32, param_init_type=mstype.float32, lambda_func=None, offset=0, use_past=False, moe_config=default_moe_config, parallel_config=default_transformer_config, use_rope=False, use_flash_attention=False, pipeline_config=None): super(PipeTransformerEncoder, self).__init__() _check_config(parallel_config) _check_moe_config(moe_config, parallel_config) self.use_moe = (moe_config.expert_num > 1) config_to_layer = parallel_config.moe_parallel_config if self.use_moe else parallel_config.dp_mp_config if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation(): self.add = P.Add() self.aux_loss = Tensor(0.0, mstype.float32) self.num_layers = num_layers self.blocks = nn.CellList() for i in range(num_layers): block = PipeTransformerEncoderLayer( hidden_size=hidden_size, batch_size=batch_size, ffn_hidden_size=ffn_hidden_size, seq_length=seq_length, attention_dropout_rate=attention_dropout_rate, hidden_dropout_rate=hidden_dropout_rate, layernorm_compute_type=layernorm_compute_type, softmax_compute_type=softmax_compute_type, num_heads=num_heads, hidden_act=hidden_act, post_layernorm_residual=post_layernorm_residual, param_init_type=param_init_type, use_past=use_past, moe_config=moe_config, parallel_config=config_to_layer, use_rope=use_rope, use_flash_attention=use_flash_attention) # If the user doesn't pass the fusion function, use the default one if not lambda_func: lambda_func = _get_lambda_func() lambda_func(block, layer_id=i, layers=num_layers, offset=offset, parallel_config=parallel_config, pipeline_config=pipeline_config, use_flash_attention=use_flash_attention) self.blocks.append(block) elif _get_parallel_mode() not in (ParallelMode.AUTO_PARALLEL,): self.add = P.Add().shard(((), ())) self.aux_loss = Tensor(0.0, mstype.float32) logger.warning("For parallel mode, sharding propagation is recommended, you can use it by setting " "'set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, " "search_mode=\"sharding_propagation\")' and " "'set_algo_parameters(elementwise_op_strategy_follow=False, fully_use_devices=False)'") self.num_layers = num_layers self.blocks = nn.CellList() for i in range(num_layers): block = PipeTransformerEncoderLayer( hidden_size=hidden_size, batch_size=batch_size, ffn_hidden_size=ffn_hidden_size, seq_length=seq_length, attention_dropout_rate=attention_dropout_rate, hidden_dropout_rate=hidden_dropout_rate, layernorm_compute_type=layernorm_compute_type, softmax_compute_type=softmax_compute_type, num_heads=num_heads, hidden_act=hidden_act, post_layernorm_residual=post_layernorm_residual, param_init_type=param_init_type, use_past=use_past, moe_config=moe_config, parallel_config=config_to_layer, use_rope=use_rope, use_flash_attention=use_flash_attention) # If the user doesn't pass the fusion function, use the default one if not lambda_func: lambda_func = _get_lambda_func() lambda_func(block, layer_id=i, layers=num_layers, offset=offset, parallel_config=parallel_config, pipeline_config=pipeline_config, use_flash_attention=use_flash_attention) self.blocks.append(block) else: raise RuntimeError(f"The {self.cls_name} only support sharding propagation or " f"semi-auto parallel mode now.") def construct(self, hidden_states, attention_mask, init_reset=True, batch_valid_length=None): present_layer = () if self.use_moe: accum_loss = self.aux_loss for i in range(self.num_layers): hidden_states, present, aux_loss = self.blocks[i](hidden_states, attention_mask, init_reset, batch_valid_length) present_layer = present_layer + (present,) accum_loss = self.add(accum_loss, aux_loss) return hidden_states, present_layer, accum_loss for i in range(self.num_layers): hidden_states, present = self.blocks[i](hidden_states, attention_mask, init_reset, batch_valid_length) present_layer = present_layer + (present,) return hidden_states, present_layer pengchengmind/src/pengcheng_mind_wrapcell.py000066400000000000000000000307051453423006600216730ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """GPT training wrapper""" import mindspore.nn as nn from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.nn.wrap.loss_scale import TrainOneStepWithLossScaleCell from mindspore import context, Parameter from mindspore.context import ParallelMode from mindspore.nn.wrap.grad_reducer import DistributedGradReducer from mindspore.communication.management import get_group_size from mindspore.parallel._utils import _get_enable_parallel_optimizer from src.utils import GlobalNorm, ClipByGlobalNorm GRADIENT_CLIP_TYPE = 1 GRADIENT_CLIP_VALUE = 1.0 clip_grad = C.MultitypeFuncGraph("clip_grad") @clip_grad.register("Number", "Number", "Tensor") def _clip_grad(clip_type, clip_value, grad): """ Clip gradients. Inputs: clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'. clip_value (float): Specifies how much to clip. grad (tuple[Tensor]): Gradients. Outputs: tuple[Tensor], clipped gradients. """ if clip_type not in [0, 1]: return grad dt = F.dtype(grad) # 0 for clip_by_value and 1 for clip_by_norm if clip_type == 0: new_grad = C.clip_by_value( grad, F.cast(F.tuple_to_array((-clip_value,)), dt), F.cast(F.tuple_to_array((clip_value,)), dt)) else: new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt)) return new_grad grad_scale = C.MultitypeFuncGraph("grad_scale") shard_grad_scale = C.MultitypeFuncGraph("shard_grad_scale") reciprocal = P.Reciprocal() @grad_scale.register("Tensor", "Tensor") def tensor_grad_scale(scale, grad): return grad * P.Cast()(reciprocal(scale), F.dtype(grad)) @grad_scale.register("Tensor", "Tensor", "Tensor") def tensor_grad_scale_pipeline(scale, grad, accu_grad): accu_grad = F.depend(accu_grad, grad) new_grad = accu_grad * reciprocal(scale) accu_grad = F.depend(accu_grad, new_grad) zeros = F.tensor_mul(accu_grad, 0.0) new_grad = F.depend(new_grad, F.assign(accu_grad, zeros)) return new_grad @shard_grad_scale.register("Tensor", "Tensor", "Tensor") def tensor_shard_grad_scale_pipeline(scale, grad, accu_grad): new_grad = grad * reciprocal(scale) accu_grad = F.depend(accu_grad, new_grad) new_grad = F.depend(new_grad, F.assign(accu_grad, F.zeros_like(accu_grad))) return new_grad class PengChengMindTrainOneStepWithLossScaleCell(TrainOneStepWithLossScaleCell): """ Encapsulation class of PengChengMind network training. Append an optimizer to the training network after that the construct function can be called to create the backward graph. Args: network (Cell): The training network. Note that loss function should have been added. optimizer (Optimizer): Optimizer for updating the weights. scale_update_cell (Cell): Cell to do the loss scale. Default: None. """ def __init__(self, network, optimizer, scale_update_cell=None, enable_global_norm=False, config=None): super(PengChengMindTrainOneStepWithLossScaleCell, self).__init__(network, optimizer, scale_update_cell) self.network = network self.config = config self.weights = optimizer.parameters self.optimizer = optimizer self.default_lr = Tensor([0.0], dtype=mstype.float32) self.enable_global_norm = enable_global_norm self.enable_offload = config.enable_offload self.clip_value = Tensor([1.0], dtype=mstype.float32) if config.enable_offload: self.clip = GlobalNorm(self.weights, config) else: self.clip = ClipByGlobalNorm(self.weights, config) self.cast = P.Cast() def construct(self, input_ids, input_position, attention_mask, layer_past=None, sens=None): """Defines the computation performed.""" weights = self.weights # Forward process loss = self.network(input_ids, input_position, attention_mask) scaling_sens = self.scale_sense # alloc status and clear should be right before gradoperation status, scaling_sens = self.start_overflow_check(loss, scaling_sens) scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss)) # Backward process using loss scale grads = self.grad(self.network, weights)(input_ids, input_position, attention_mask, scaling_sens_filled) # apply grad reducer on grads grads = self.grad_reducer(grads) grads = self.hyper_map( F.partial(grad_scale, scaling_sens), grads) clip_value = self.clip_value if self.enable_global_norm: grads, clip_value = self.clip(grads) else: grads = self.hyper_map( F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads) # Check whether overflow cond = self.get_overflow_status(status, grads) overflow = self.process_loss_scale(cond) # If overflow, surpass weights update # if not, update weights if not overflow: if self.enable_offload: self.optimizer(grads, clip_value) else: self.optimizer(grads) return loss, cond, scaling_sens.value() class PengChengMindTrainPipelineWithLossScaleCell(nn.Cell): """ Encapsulation class of PengChengMind network training. Append an optimizer to the training network after that the construct function can be called to create the backward graph. Args: network (Cell): The training network. Note that loss function should have been added. optimizer (Optimizer): Optimizer for updating the weights. scale_update_cell (Cell): Cell to do the loss scale. Default: None. """ def __init__(self, network, optimizer, config, scale_update_cell=None, enable_global_norm=True): super(PengChengMindTrainPipelineWithLossScaleCell, self).__init__(auto_prefix=False) self.config = config self.network = network self.network.add_flags(defer_inline=True) self.weights = optimizer.parameters self.accu_grads = self.weights.clone(prefix="accu_grads", init="zeros") self.optimizer = optimizer self.enable_global_norm = enable_global_norm self.grad = C.GradOperation(get_by_list=True, sens_param=True) self.reducer_flag = False self.allreduce = P.AllReduce() self.parallel_mode = context.get_auto_parallel_context("parallel_mode") if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]: self.reducer_flag = True self.grad_reducer = F.identity self.degree = 1 if self.reducer_flag: self.degree = get_group_size() self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree) self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE) self.cast = P.Cast() self.alloc_status = P.NPUAllocFloatStatus() self.get_status = P.NPUGetFloatStatus() self.clear_before_grad = P.NPUClearFloatStatus() self.reduce_sum = P.ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = P.LessEqual() self.hyper_map = C.HyperMap() self.loss_scale = None self.reshape = P.Reshape() self.loss_scaling_manager = scale_update_cell if scale_update_cell: self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32), name="loss_scale") self.clip = ClipByGlobalNorm(self.weights, self.config) self.micro_size = config.parallel_config.micro_batch_num self.opt_shard = _get_enable_parallel_optimizer() self.enable_offload = config.enable_offload self.clip_value = Tensor([1.0], dtype=mstype.float32) if config.enable_offload: self.clip = GlobalNorm(self.weights, config) else: self.clip = ClipByGlobalNorm(self.weights, config) @C.add_flags(has_effect=True) def construct(self, input_ids, input_position, attention_mask, past=None, sens=None): """Defines the computation performed.""" weights = self.weights loss = self.network(input_ids, input_position, attention_mask) if sens is None: scaling_sens = self.loss_scale scaling_sens = self.reshape(scaling_sens, (1,)) else: scaling_sens = sens # alloc status and clear should be right before gradoperation init = self.alloc_status() status_clear = self.clear_before_grad(init) #######################taoht############################# # attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## grads = self.grad(self.network, weights)(input_ids, input_position, attention_mask, self.cast(scaling_sens / self.micro_size, mstype.float32)) init = F.depend(init, grads) get_status = self.get_status(init) init = F.depend(init, get_status) flag_sum = self.reduce_sum(init, (0,)) loss = F.depend(loss, status_clear) ## taoht if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) cond = self.less_equal(self.base, flag_reduce) else: cond = self.less_equal(self.base, flag_sum) grads = F.depend(grads, cond) # if erase it will make performance worse # apply grad reducer on grads if self.opt_shard: grads = self.grad_reducer(grads) grads = self.hyper_map(F.partial(shard_grad_scale, scaling_sens * self.degree), grads, self.accu_grads) else: accu_grads = self.grad_reducer(self.accu_grads) grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads, accu_grads) clip_value = self.clip_value if self.enable_global_norm: grads, global_norm_value = self.clip(grads) else: global_norm_value = None grads = self.hyper_map( F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads) # if self.is_distributed: # # sum overflow flag over devices # flag_reduce = self.allreduce(flag_sum) # cond = self.less_equal(self.base, flag_reduce) # else: # cond = self.less_equal(self.base, flag_sum) # grads = F.depend(grads, cond) # if erase it will make performance worse overflow = cond if sens is None: overflow = self.loss_scaling_manager(self.loss_scale, cond) if not overflow: if self.enable_offload: # _, lr = self.optimizer(grads, clip_value) self.optimizer(grads, clip_value) else: # _, lr = self.optimizer(grads) self.optimizer(grads) # self.optimizer(grads) # ignored enable_offload to speed up # else: # lr = self.default_lr return (loss, overflow, scaling_sens.value(), global_norm_value)#, lr) pengchengmind/src/preprocess.py000066400000000000000000000211351453423006600172170ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ transform wikitext-2, wikitext-103, lambada, openwebtext dataset to mindrecord. """ import argparse import glob import json import os import re from functools import partial from multiprocessing import Pool, current_process import numpy as np from mindspore.mindrecord import FileWriter from src.tokenization_jieba import JIEBATokenizer def chunks(lst, n): """ yield n sized chunks from list""" for i in range(0, len(lst), n): yield lst[i:i+n] def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def clean_wikitext(string): """ cleaning wikitext dataset""" # contractions string = string.replace("s '", "s'") string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) # number separators string = string.replace(" @-@ ", "-") string = string.replace(" @,@ ", ",") string = string.replace(" @.@ ", ".") # punctuation string = string.replace(" : ", ": ") string = string.replace(" ; ", "; ") string = string.replace(" . ", ". ") string = string.replace(" ! ", "! ") string = string.replace(" ? ", "? ") string = string.replace(" , ", ", ") # double brackets string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) # miscellaneous string = string.replace("= = = =", "====") string = string.replace("= = =", "===") string = string.replace("= =", "==") string = string.replace(" "+chr(176)+" ", chr(176)) string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" N ", " 1 ") string = string.replace(" 's", "'s") return string def tokenize_openwebtext(tokenizer, iterator, seq_length, eot): """ tokenize openwebtext dataset""" for file_path in iterator: if os.path.getsize(file_path) == 0: continue content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_wiki(tokenizer, file_path, seq_length, eot): """tokenize wikitext-2/wikitext-103 dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in clean_wikitext(f.read()).split("\n\n"): if para and para.strip().startswith('=') is False: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_lambada(tokenizer, file_path, seq_length, eot): """tokenize lambada dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for line in f.readlines(): para = json.loads(line)['text'].replace( "“", '"').replace("”", '"').strip().strip(".") tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def task_unit(iterator, tokenizer, seq_length, eot, parallel_writer=True): """task for each process""" p = current_process() index = p.pid if p.pid else 0 item_iter = tokenize_openwebtext(tokenizer, iterator, seq_length, eot) batch_size = 1024 # size of write batch count = 0 while True: data_batch = [] try: for _ in range(batch_size): data_batch.append(next(item_iter)) count += 1 writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) except StopIteration: if data_batch: writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) break if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--dataset_type', type=str, default='openwebtext') parser.add_argument('--input_glob', type=str, default='*.txt') parser.add_argument('--output_file', type=str, default='./output/transfered_mindrecord') parser.add_argument('--tokenizer', type=str, default='gpt', choices=['gpt', 'jieba']) parser.add_argument('--model_file', type=str, default=None) parser.add_argument('--file_partition', type=int, default=1) parser.add_argument('--file_batch_size', type=int, default=1024) parser.add_argument('--num_process', type=int, default=64) parser.add_argument('--seq_length', type=int, default=1025) parser.add_argument('--eot', type=int, default=3, help="Eod of text depends on the vocab file.") parser.add_argument('--data_column_name', type=str, default='input_ids') args = parser.parse_args() out_dir, out_file = os.path.split(os.path.abspath(args.output_file)) if not os.path.exists(out_dir): os.mkdir(out_dir) schema = {args.data_column_name: {"type": "int32", "shape": [-1]},} writer = FileWriter(file_name=args.output_file, shard_num=args.file_partition) writer.add_schema(schema, args.dataset_type) writer.open_and_set_header() # Start to load tokenizer if args.tokenizer == 'gpt': try: from transformers import GPT2Tokenizer except ModuleNotFoundError: print("module 'transformers' not installed.") word_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") else: if not os.path.exists(args.model_file): raise FileNotFoundError(f"file {args.model_file} do not exists.") word_tokenizer = JIEBATokenizer(model_file=args.model_file) transforms_count = 0 if args.dataset_type == 'wiki': for x in tokenize_wiki(word_tokenizer, args.input_glob, args.seq_length, args.eot): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'lambada': for x in tokenize_lambada(word_tokenizer, args.input_glob, args.seq_length, args.eot): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'openwebtext': file_iter = glob.iglob(args.input_glob) with Pool(processes=args.num_process) as pool: map_func = partial(task_unit, tokenizer=word_tokenizer, seq_length=args.seq_length, eot=args.eot) pool.map(map_func, package_file(file_iter, args.file_batch_size)) else: raise ValueError( "Not support dataset type: {}".format(args.dataset_type)) writer.commit() out_file = args.output_file if args.file_partition > 1: out_file += '0' print("Transform finished, output files refer: {}".format(out_file)) pengchengmind/src/utils.py000066400000000000000000001706671453423006600162110ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ network config setting, gradient clip function and dynamic learning rate function """ import argparse import ast import os, shutil import time import hashlib import numpy as np import mindspore.nn as nn from mindspore import context from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F import mindspore.communication.management as D import mindspore.common.dtype as mstype from mindspore.common.tensor import Tensor from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR, CosineDecayLR from mindspore.parallel._auto_parallel_context import auto_parallel_context from mindspore.context import ParallelMode from mindspore.communication.management import get_rank, get_group_size, create_group from mindspore.nn import AdamWeightDecay from mindspore.common import Parameter, ParameterTuple from mindspore.common.initializer import initializer from mindspore.train.callback import Callback import moxing as mox from multiprocessing import Process from mindspore.nn.optim.optimizer import Optimizer from mindspore._checkparam import Validator as validator from mindspore._checkparam import Rel from mindspore.common.api import jit ''' _adam_opt = C.MultitypeFuncGraph("adam_opt") _fused_adam_weight_decay = C.MultitypeFuncGraph("fused_adam_weight_decay") def _check_param_value(beta1, beta2, eps, prim_name): """Check the type of inputs.""" validator.check_value_type("beta1", beta1, [float], prim_name) validator.check_value_type("beta2", beta2, [float], prim_name) validator.check_value_type("eps", eps, [float], prim_name) validator.check_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name) validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name) validator.check_positive_float(eps, "eps", prim_name) @_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool") class AdamWeightDecay(Optimizer): r""" Implements the Adam algorithm with weight decay. .. math:: \begin{array}{l} &\newline &\hline \\ &\textbf{Parameters}: \: 1^{\text {st }}\text {moment vector} \: m , \: 2^{\text {nd}} \: \text{moment vector} \: v , \\ &\: gradients \: g, \: \text{learning rate} \: \gamma, \text {exponential decay rates for the moment estimates} \: \beta_{1} \: \beta_{2} , \\ &\:\text {parameter vector} \: w_{0}, \:\text{timestep} \: t, \: \text{weight decay} \: \lambda \\ &\textbf{Init}: m_{0} \leftarrow 0, \: v_{0} \leftarrow 0, \: t \leftarrow 0, \: \text{init parameter vector} \: w_{0} \\[-1.ex] &\newline &\hline \\ &\textbf{repeat} \\ &\hspace{5mm} t \leftarrow t+1 \\ &\hspace{5mm}\boldsymbol{g}_{t} \leftarrow \nabla f_{t}\left(\boldsymbol{w}_{t-1}\right) \\ &\hspace{5mm}\boldsymbol{m}_{t} \leftarrow \beta_{1} \boldsymbol{m}_{t-1}+\left(1-\beta_{1}\right) \boldsymbol{g}_{t} \\ &\hspace{5mm}\boldsymbol{v}_{t} \leftarrow \beta_{2} \boldsymbol{v}_{t-1}+\left(1-\beta_{2}\right) \boldsymbol{g}_{t}^{2} \\ &\hspace{5mm}\boldsymbol{w}_{t} \leftarrow \boldsymbol{w}_{t-1}-\left(\gamma \hat{\boldsymbol{m}}_{t} /\left(\sqrt{\hat{\boldsymbol{v}}_{t}}+\epsilon\right)+\lambda \boldsymbol{w}_{t-1}\right) \\ &\textbf{until}\text { stopping criterion is met } \\[-1.ex] &\newline &\hline \\[-1.ex] &\textbf{return} \: \boldsymbol{w}_{t} \\[-1.ex] &\newline &\hline \\[-1.ex] \end{array} :math:`m` represents the 1st moment vector `moment1`, :math:`v` represents the 2nd moment vector `moment2`, :math:`g` represents `gradients`, :math:`\gamma` represents `learning_rate`, :math:`\beta_1, \beta_2` represent `beta1` and `beta2`, :math:`t` represents the current step, :math:`w` represents `params`, :math:`\gamma` represents `weight_decay`. Note: There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'. As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer document `LossScale `_ to process `loss_scale` correctly. If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without 'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When parameters are grouped, each group can set `weight_decay`. If not, the `weight_decay` in optimizer will be applied. Args: params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the `params` is a list of `dict`, the string "params", "lr", "weight_decay", and "order_params" are the keys can be parsed. - params: Required. Parameters in current group. The value must be a list of `Parameter`. - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used. If not, the `learning_rate` in optimizer will be used. Fixed and dynamic learning rate are supported. - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay will be used. If not, the `weight_decay` in the optimizer will be used. It should be noted that weight decay can be a constant value or a Cell. It is a Cell only when dynamic weight decay is applied. Dynamic weight decay is similar to dynamic learning rate, users need to customize a weight decay schedule only with global step as input, and during training, the optimizer calls the instance of WeightDecaySchedule to get the weight decay value of current step. - order_params: Optional. When parameters is grouped, this usually is used to maintain the order of parameters that appeared in the network to improve performance. The value should be parameters whose order will be followed in optimizer. If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in one group of `params`. learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]): Default: 1e-3. - float: The fixed learning rate value. Must be equal to or greater than 0. - int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float. - Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied. For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate. - Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate. - LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of LearningRateSchedule with step as the input to get the learning rate of current step. beta1 (float): The exponential decay rate for the 1st moment estimations. Default: 0.9. Should be in range (0.0, 1.0). beta2 (float): The exponential decay rate for the 2nd moment estimations. Default: 0.999. Should be in range (0.0, 1.0). eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6. Should be greater than 0. weight_decay (Union[float, int, Cell]): Weight decay (L2 penalty). Default: 0.0. - float: The fixed weight decay value. Must be equal to or greater than 0. - int: The fixed weight decay value. Must be equal to or greater than 0. It will be converted to float. - Cell: Weight decay is dynamic. During training, the optimizer calls the instance of the Cell with step as the input to get the weight decay value of current step. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: tuple[bool], all elements are True. Raises: TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule. TypeError: If element of `parameters` is neither Parameter nor dict. TypeError: If `beta1`, `beta2` or `eps` is not a float. TypeError: If `weight_decay` is neither float nor int. ValueError: If `eps` is less than or equal to 0. ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0). ValueError: If `weight_decay` is less than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore as ms >>> from mindspore import nn >>> >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.AdamWeightDecay(params=net.trainable_params()) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = nn.AdamWeightDecay(group_params, learning_rate=0.1, weight_decay=0.0) >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = ms.Model(net, loss_fn=loss, optimizer=optim) """ _support_parallel_optimizer = True def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(AdamWeightDecay, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) self.beta1 = Tensor(np.array([beta1]).astype(np.float32)) self.beta2 = Tensor(np.array([beta2]).astype(np.float32)) self.eps = Tensor(np.array([eps]).astype(np.float32)) self.moments1 = self._parameters.clone(prefix="adam_m", init='zeros') self.moments2 = self._parameters.clone(prefix="adam_v", init='zeros') self.fused_opt = P.AdamWeightDecay() if context.get_context("device_target") == "CPU": self.use_fused_opt = True else: self.use_fused_opt = False @jit def construct(self, gradients): gradients = self.flatten_gradients(gradients) weight_decay = self.get_weight_decay() lr = self.get_lr() if self.use_fused_opt: if self.is_group: if self.is_group_lr: optim_result = self.hyper_map( F.partial(_fused_adam_weight_decay, self.fused_opt, self.beta1, self.beta2, self.eps), lr, weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map( F.partial(_fused_adam_weight_decay, self.fused_opt, self.beta1, self.beta2, self.eps, lr), weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map( F.partial(_fused_adam_weight_decay, self.fused_opt, self.beta1, self.beta2, self.eps, lr, weight_decay), self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: if self.is_group: if self.is_group_lr: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps), lr, weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr), weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr, weight_decay), self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: self.broadcast_params(optim_result) return optim_result, lr @Optimizer.target.setter def target(self, value): """ If the input value is set to "CPU", the parameters will be updated on the host using the Fused optimizer operation. """ self._set_base_target(value) if value == 'CPU': self.fused_opt.set_device("CPU") self.use_fused_opt = True else: self.use_fused_opt = False ''' class FP32StateAdamWeightDecay(AdamWeightDecay): r""" This class is almost same with the mindspore's AdamWeightDecay implements, the only difference is the optimizer's state will be always initialized with float32, where the original AdamWeightDecay will initialize the optimizer's state with float16, if the parameters are initialized with fp16. This setting will avoid overflow in training PengChengMind-Alpha model using fp16. """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(FP32StateAdamWeightDecay, self).__init__(params, learning_rate=learning_rate, beta1=beta1, beta2=beta2, eps=eps, weight_decay=weight_decay) self.moments1 = self.clone_state(self.parameters, prefix='adam_m', init='zeros') self.moments2 = self.clone_state(self.parameters, prefix='adam_v', init='zeros') def clone_state(self, parameter_tuple, prefix, init): r""" parameter_tuple: ParameterTuple. The parameters of the network prefix: str. The prefix name of the parameters init: str. The initialization method """ new = [] for old_param in parameter_tuple: new_state = Parameter(initializer(init, shape=old_param.shape, dtype=mstype.float32)) new_state.param_info = old_param.param_info.clone() if hasattr(old_param.param_info, "cloned_obj"): old_param.param_info.cloned_obj.append(new_state) else: old_param.param_info.cloned_obj = [new_state] new_state.param_info.obj = new_state new_state.is_init = False new_state.set_data(initializer(init, shape=old_param.shape, dtype=mstype.float32)) new_state.name = prefix + '.' + new_state.name new.append(new_state) return ParameterTuple(new) get_square_sum = C.MultitypeFuncGraph("get_square_sum") @get_square_sum.register("Tensor", "Number") def _get_square_sum(grad, value): #P.Print()("grad:", grad) norm = P.ReduceSum(False)(F.square(grad), ()) / value #P.Print()("grad norm:", norm) norm = F.expand_dims(F.cast(norm, mstype.float32), 0) return norm apply_global_norm = C.MultitypeFuncGraph("apply_global_norm") @apply_global_norm.register("Bool", "Tensor", "Tensor", "Tensor") def _apply_global_norm(enable_grad_fp16, clip_norm, global_norm, grad): if enable_grad_fp16: grad = P.Cast()(grad * clip_norm / global_norm, mstype.float16) else: grad = grad * clip_norm / global_norm return grad def _get_model_parallel_group(mp): """ Calculate the communication group of model parallel dim in one pipeline stage """ rank = get_rank() stage_nums = auto_parallel_context().get_pipeline_stages() device_nums = get_group_size() per_stage_device_nums = device_nums // stage_nums stage_id = rank // per_stage_device_nums local_stage_rank_id = rank % per_stage_device_nums index = local_stage_rank_id // mp group = range(0, mp) rank_str_list = [str(x + index * mp + stage_id * per_stage_device_nums) for x in group] rank_list_str = "-".join(rank_str_list) rank_list = [x + index * mp + stage_id * per_stage_device_nums for x in group] return rank_list, rank_list_str def _get_pipeline_group(): """ Calculate the communication group between all pipeline stages """ rank = get_rank() stage_nums = auto_parallel_context().get_pipeline_stages() device_nums = get_group_size() per_stage_device_nums = device_nums // stage_nums local_stage_rank_id = rank % per_stage_device_nums group = range(0, stage_nums) rank_list = [local_stage_rank_id + x * per_stage_device_nums for x in group] rank_str_list = [str(local_stage_rank_id + x * per_stage_device_nums) for x in group] rank_list_str = "-".join(rank_str_list) return rank_list, rank_list_str class GlobalNorm(nn.Cell): """ Calculate the global norm value of given tensors """ def __init__(self, params, config): super(GlobalNorm, self).__init__() self.norm = nn.Norm() self.hyper_map = C.HyperMap() self.is_pipeline = context.get_auto_parallel_context("pipeline_stages") > 1 # self.is_data_parallel = context.get_auto_parallel_context("parallel_mode") == ParallelMode.DATA_PARALLEL # self.config = config # self.group_size = 1 # if self.is_data_parallel: # self.merge_op = P.identity() # else: # self.merge_op = P.AllReduce() if self.is_pipeline: if context.get_auto_parallel_context("enable_parallel_optimizer"): group_size = get_group_size() // config.parallel_config.pipeline_stage else: group_size = config.parallel_config.model_parallel group_list, group_name = _get_model_parallel_group(group_size) # In avoid of the group name too long hashed = hashlib.md5(group_name.encode()).hexdigest()[:48] print(f"Creating hash value for the group_name hash({group_name})={hashed}") group_name = str(hashed) create_group(group_name, group_list) self.allreduce = P.AllReduce(group=group_name) pipeline_group_list, pipeline_group_name = _get_pipeline_group() hashed = hashlib.md5(pipeline_group_name.encode()).hexdigest()[:48] print(f"Creating hash value for the group_name hash({pipeline_group_name})={hashed}") pipeline_group_name = str(hashed) create_group(pipeline_group_name, pipeline_group_list) self.allreduce2 = P.AllReduce(group=pipeline_group_name) else: group_size = get_group_size() zero_repeated_size = 1.0 if context.get_auto_parallel_context("enable_parallel_optimizer") and \ context.get_auto_parallel_context("optimizer_weight_shard_size") > 1: zero_shard_size = context.get_auto_parallel_context("optimizer_weight_shard_size") zero_repeated_size = config.parallel_config.data_parallel // zero_shard_size self.allreduce_group_size = () # if self.is_data_parallel: # self.allreduce_group_size = (1,) * len(params) # else: # self.allreduce_group_size = self._get_scale_for_gradient_norm(params) for x in params: if "projection.bias" not in x.name and "layernorm" not in x.name and "embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (zero_repeated_size,) elif "embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (group_size * 1.0,) else: if not config.parallel_config.vocab_emb_dp and "position_embedding.embedding_table" not in x.name and \ "top_query_embedding.embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (config.parallel_config.data_parallel * 1.0,) else: self.allreduce_group_size = self.allreduce_group_size + (group_size * 1.0,) def construct(self, grads): """Calculate global norm construct""" square_sum = self.hyper_map(get_square_sum, grads, self.allreduce_group_size) square_reduce_sum = F.addn(square_sum) #P.Print()("square sum: ", square_sum) if self.is_pipeline: stage_square_reduce_sum = self.allreduce(square_reduce_sum) #P.Print()("stage square sum: ", stage_square_reduce_sum) global_square_reduce_sum = self.allreduce2(stage_square_reduce_sum) #P.Print()("global square sum: ", global_square_reduce_sum) global_norms = F.sqrt(global_square_reduce_sum) else: global_norms = F.sqrt(P.AllReduce()(square_reduce_sum)) return grads, global_norms # def _get_scale_for_gradient_norm(self, params): # allreduce_group_size = () # for x in params: # if "projection.bias" not in x.name and "layernorm" not in x.name and "embedding_table" not in x.name: # allreduce_group_size = allreduce_group_size + (1.0,) # elif "embedding_table" not in x.name: # allreduce_group_size = allreduce_group_size + (self.group_size * 1.0,) # else: # if not self.config.parallel_config.vocab_emb_dp and "position_embedding.embedding_table" not in x.name \ # and "top_query_embedding_table" not in x.name: # allreduce_group_size = allreduce_group_size + \ # (self.config.parallel_config.data_parallel * 1.0,) # else: # allreduce_group_size = allreduce_group_size + (self.group_size * 1.0,) # return allreduce_group_size class ClipByGlobalNorm(nn.Cell): """ Clip grads by global norm """ def __init__(self, params, config, clip_norm=1.0): super(ClipByGlobalNorm, self).__init__() self.global_norm = GlobalNorm(params, config) self.clip_norm = Tensor([clip_norm], mstype.float32) self.hyper_map = C.HyperMap() if config.param_init_type == mstype.float16 and config.enable_offload: self.enable_grad_fp16 = True else: self.enable_grad_fp16 = False def construct(self, grads): """Clip grads by global norm construct""" grads, global_norm_value = self.global_norm(grads) cond = P.GreaterEqual()(global_norm_value, self.clip_norm) global_norm = F.select(cond, global_norm_value, self.clip_norm) grads = self.hyper_map(F.partial(apply_global_norm, self.enable_grad_fp16, self.clip_norm, global_norm), grads) return grads, global_norm_value class LearningRate(LearningRateSchedule): """ Warmup-decay learning rate for PengChengMind network. """ def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power=1.0, use_cosine=True): super(LearningRate, self).__init__() self.warmup_flag = False if warmup_steps > 0: self.warmup_flag = True self.warmup_lr = WarmUpLR(learning_rate, warmup_steps) self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power) self.cosine_decay_lr = CosineDecayLR(end_learning_rate, learning_rate, decay_steps) self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32)) self.greater = P.Greater() self.one = Tensor(np.array([1.0]).astype(np.float32)) self.cast = P.Cast() self.use_cosine = use_cosine def construct(self, global_step): """dynamic learning rate""" if not self.use_cosine: decay_lr = self.decay_lr(global_step) else: decay_lr = self.cosine_decay_lr(global_step) if self.warmup_flag: is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32) warmup_lr = self.warmup_lr(global_step) lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr else: lr = decay_lr return lr def add_inference_params(opt): """Add inference params""" opt.add_argument("--frequency_penalty", type=float, default=1.5, help="coefficient for frequency_penalty") opt.add_argument("--presence_penalty", type=float, default=0.3, help="coefficient for presence_penalty") opt.add_argument("--max_generate_length", type=int, default=500, help="the maximum number of generated token") opt.add_argument("--top_k_num", type=int, default=3, help="the number for top_k sampling") opt.add_argument("--top_p", type=float, default=1.0, help="top_p sampling threshold, enabled if less than 1.0") opt.add_argument("--end_token", type=int, default=2, help="the token id for ") opt.add_argument("--use_pynative_op", type=int, default=1, help="Whether use pynative op for postproecess") opt.add_argument("--use_past", type=bool, default=False, help="Whether enable state reuse") def add_training_params(opt): """Add training params""" opt.add_argument("--seq_length", type=int, default=4096, help="sequence length, default is 1024.") opt.add_argument("--vocab_size", type=int, default=49984, help="vocabulary size, default is 40000.") opt.add_argument("--embedding_size", type=int, default=10240, help="embedding table size, default is 16384.") opt.add_argument("--num_layers", type=int, default=80, help="total layers, default is 64.") opt.add_argument("--num_heads", type=int, default=80, help="head size, default is 128.") opt.add_argument("--stage_num", type=int, default=1, help="Pipeline stage num, default is 1.") opt.add_argument("--micro_size", type=int, default=32, help="Pipeline micro_size, default is 1.") opt.add_argument("--eod_reset", type=int, default=1, help="Enable eod mask, default is 1.") opt.add_argument("--warmup_step", type=int, default=1000, help="Warmup step, default is 2000.") opt.add_argument("--decay_steps", type=int, default=150000, help="Decay step, default is 200000.") opt.add_argument("--optimizer", type=str, default="adam", choices=["adam", "lamb"], help="select which optimizer to be used, default adam") opt.add_argument("--opt_offload", type=int, default=0, help="Enable optimizer status offload to host CPU, default is 0") opt.add_argument("--use_moe", type=int, default=0, help="Use moe, default is 0") opt.add_argument("--expert_num", type=int, default=1, help="Expert number, only effective when applying moe, Default is 1") opt.add_argument("--per_token_num_experts_chosen", type=int, default=1, help="Expert nums chosen by each token, only effective when applying moe, default is 1") opt.add_argument("--eod_id", type=int, default=2, help="The id of end of document") opt.add_argument("--padding_id", type=int, default=0, help="The padding id of dataset") opt.add_argument("--epoch_size", type=int, default=1, help="The training epoch") opt.add_argument("--sink_size", type=int, default=4, help="The sink size of the training. default is 2") opt.add_argument("--full_batch", default=0, type=int, help="Import the full size of a batch for each card, default is 1") opt.add_argument("--optimizer_shard", type=int, default=8, help="Enable optimizer parallel, default is 1") opt.add_argument("--per_batch_size", type=int, default=1, help="The batch size for each data parallel way. default 0") opt.add_argument("--start_lr", type=float, default=6e-5, help="The start learning rate. default 5e-5") opt.add_argument("--end_lr", type=float, default=6e-7, help="The end learning rate. default 1e-6") opt.add_argument("--op_level_model_parallel_num", type=int, default=2, help="The model parallel way. default 8") opt.add_argument("--expert_parallel_num", type=int, default=1, help="The expert parallel way, only effective when applying moe. Default 1") opt.add_argument("--word_emb_dp", type=int, default=1, choices=[0, 1], help="Whether do data parallel in word embedding. default 1") opt.add_argument("--gradient_aggregation_group", type=int, default=4, help="The gradient communication fusion group. default 4") opt.add_argument("--data_column_name", type=str, default="input_ids", help="Column name of datasets") opt.add_argument("--micro_batch_interleaved", type=int, default=1, help="Parallel split num of batch size. default 2") # opt.add_argument("--recompute_slice_activation", # type=int, default=0, # help="Enable slice the recompute activation state. default 0") opt.add_argument("--recompute", type=bool, default=False, help="Enable recompute. default False") opt.add_argument("--softmax_compute_fp32", type=str, default='FP16', choices=['FP32', 'FP16'], help="enable softmax_compute_type fp32") opt.add_argument("--top_query_softmax_fp32", type=str, default='FP32', choices=['FP32', 'FP16'], help="enable top_query softmax_compute_type fp32") opt.add_argument("--load_compiler_cache", help="Enable download and load compiler cache in obs", type=bool, default=False) opt.add_argument("--compiler_name", type=str, default="obs://research-my/taoht-100b/compiler_cache/2520_pp21_mp2dp60ms96", help="compiler_name") def add_context_args_mode(opt): """Add context args params""" opt.add_argument("--parallel_mode", type=str, default="semi_auto_parallel", choices=['data_parallel', "semi_auto_parallel", "auto_parallel"], help="The parallel context mode") def add_downstream_params(opt): opt.add_argument("--eval_task", help="Enable evaluating the tasks. Currently supports WPLC, C3.", default=None, choices=['wplc', 'ocnli', 'c3', 'chid'], type=str) opt.add_argument("--enable_client", help="Enable evaluation as an client.", action='store_true') opt.add_argument("--server_ip", help="The server ip of the model. The evaluation will send the data to the server, so the option" "should be enabled with enable_client together", type=str, default="127.0.0.1") opt.add_argument("--port", help="The server port of the model. The evaluation will send the data to the server, so the option" "should be enabled with enable_client together", type=str, default="1500") def add_retrain_params(opt): """ Add parameters about retrain. """ opt.add_argument("--pre_trained", type=str, default=None, help="Pretrained checkpoint path.") opt.add_argument("--restore_steps", type=int, default=600, help="restore checkpoint steps.") opt.add_argument("--restore_ckpt_name_prefix", type=str, default='PengChengMind_delta', help="restore checkpoint name.") opt.add_argument("--restore_checkpoint_bucket_dir", type=str, default="s3://research-my/taoht-100b/ckpt_100b/PengChengMind_delta_exp00/", help="restore checkpoint obs path.") opt.add_argument("--save_checkpoint_path", type=str, default="/cache/ckpt_new", help="Save checkpoint path.") opt.add_argument("--save_checkpoint_bucket_dir", type=str, default="s3://research-my/taoht-100b/ckpt_100b/test", help="Save checkpoint path.") opt.add_argument("--save_strategy_bucket_dir", type=str, default="obs://research-my/taoht_200b/strategy/predict_strategy/test/", help="Save checkpoint path.") opt.add_argument("--save_summary_bucket_dir", type=str, default="obs://research-my/taoht_200b/summary/test/", help="Save checkpoint path.") opt.add_argument("--keep_checkpoint_max", type=int, default=1, help="Max checkpoint save number.") opt.add_argument("--save_checkpoint_steps", type=int, default=300, help="Save checkpoint step number.") opt.add_argument("--save_checkpoint", type=ast.literal_eval, default=True, help="Whether save checkpoint in local disk.") opt.add_argument("--ckpt_name_prefix", type=str, default="PengChengMind_delta_2400rst-1", help="Saving checkpoint name prefix.") opt.add_argument("--has_trained_epoches", type=int, default=0, help="Epoches has been trained before.") opt.add_argument("--has_trained_steps", type=int, default=0, help="Steps has been trained before.") opt.add_argument("--data_start_index", type=int, default=0, help="datasets has been trained before. 0") def get_args(inference=False): """train function for PengChengMind""" parser = argparse.ArgumentParser(description="PengChengMind training") parser.add_argument('--device_id', type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=256, help="Use device nums, default is 128.") parser.add_argument("--distribute", type=str, default="true", choices=["true", "false"], help="Run distribute, default is true.") parser.add_argument("--load_ckpt_name", type=str, default='PengChengMind3.ckpt', help="checkpint file name.") parser.add_argument("--load_ckpt_path", type=str, default=None, help="predict file path.") parser.add_argument('--data_url', default=None, help='Location of data.') parser.add_argument('--eval_data_url', default=None, help='Location of eval data.') parser.add_argument('--train_url', default="/cache/Graph/", help='Location of training outputs.') parser.add_argument("--run_type", type=str, default="train", choices=["train", "predict"], help="The run type") parser.add_argument("--mode", type=str, default="100B", choices=["200B", "13B", "7B", "2.6B", "1.3B", "100B", "350M"], help="The scale of the model parameters") parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"], help="The running device") parser.add_argument("--strategy_load_ckpt_path", type=str, default="", help="The training prallel strategy for the model.") parser.add_argument("--tokenizer_path", type=str, default="./tokenizer_path", help="The path where stores vocab and vocab model file") parser.add_argument("--param_init_type", type=str, default="fp16", help="The initialization type for parameters. Default fp16.") parser.add_argument("--offline", type=int, default=1, help="Running on cloud of not. Default 1.") parser.add_argument("--export", type=int, default=0, help="Whether export mindir for serving.") parser.add_argument("--incremental_training", type=int, default=0, help="Enable incremental training. Default 0.") parser.add_argument("--train_and_eval_mode", type=int, default=0, help="Enable evaling while training. Default 0.") parser.add_argument("--save_strategy_name", type=str, default="strategy_inference_d16_mp16_dp1_rank-*.ckpt", help="Save checkpoint path.") parser.add_argument("--eval_steps", type=int, default=10, help="The eval step in train and eval mode. Default 10.") parser.add_argument("--enable_alltoall", type=int, default=0, help="Enable alltoall communication, only effective when applying moe. Default 0") parser.add_argument("--hccl_connect_time", type=int, default=7200, help="Set the hccl build time out, only effective on Ascend. Default 6000") # add RoPE parser.add_argument('--use_rope', type=ast.literal_eval, default=True, help="Use rotary position embedding") # use flash attention parser.add_argument('--use_flash_attention', type=ast.literal_eval, default=False, help="Use rotary position embedding") # add sequence parallel parser.add_argument('--sequence_parallel', type=ast.literal_eval, default=False, help="Use sequence parallel") # add select recompute parser.add_argument('--select_recompute', type=ast.literal_eval, default=False, help="Use select recompute") parser.add_argument('--duRepeate', type=bool, default=True, help="Use select recompute") # enable profiler parser.add_argument('--enable_profiler', type=ast.literal_eval, default=False, help="Profile timeline") # save ir parser.add_argument('--save_graph', type=ast.literal_eval, default=False, help="save ir graph") # add pipeline config parser.add_argument('--pipeline_config_filename', type=str, default="/home/ma-user/modelarts/user-job-dir/PengCheng.Mind/search/BaSys_FineGrain/gpt_104layer_16dp_4mp_18pp_29mem_64ms.json", help="configuration for pipeline") add_context_args_mode(parser) add_training_params(parser) add_retrain_params(parser) if inference: add_inference_params(parser) add_downstream_params(parser) args_opt = parser.parse_args() return args_opt def ckpt_copy_tar_new(obs_path, target_path="/cache/ckpt"): """ requires the obs_path to be a complete name Copy tar file from the obs to the /cache/ """ sub_name_list = ['part_0.tar', 'part_1.tar', 'part_2.tar', 'part_3.tar'] for item in sub_name_list: sub_name = obs_path + item tmp_name = 'model.tar' mox.file.copy(sub_name, os.path.join(target_path, tmp_name)) os.system('cd {}; tar -xvf {}'.format(target_path, tmp_name)) def get_ckpt_file_list(ckpt_path, device_num): # path = os.listdir(ckpt_path) # print('Get path list:', path, flush=True) returned_list = [] for i in range(0, device_num): returned_list.append('filerted_{}.ckpt'.format(i)) # filtered = [item for item in path if 'embedding' not in item]save_checkpoint_bucket_dir # filtered.sort(key = lambda x: int(x.split('.')[0].split('_')[1])) returned_list = [os.path.join(ckpt_path, item) for item in returned_list if 'embedding' not in item] print("Sorted list", returned_list) for item in returned_list: fsize = os.path.getsize(item) f_gb = fsize / float(1024) / 1024 print(item, " :{:.2f} MB".format(f_gb)) return returned_list class CheckpointSaveCallback(Callback): def __init__(self, local_ckpt_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, bucket='obs://ckeckpoint_file/path/', syn_obs_steps=100): self.local_ckpt_dir = local_ckpt_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.bucket = bucket self.syn_obs_steps = syn_obs_steps self.obs_save_rank_dir = os.path.join(bucket, "rank_" + str(local_rank)) if not mox.file.exists(self.obs_save_rank_dir): print("Creating ckeckpoint bueckt dir {}".format(self.obs_save_rank_dir)) mox.file.make_dirs(self.obs_save_rank_dir) def step_end(self, run_context): # try: # os.system('ls /cache/ckpt_new/rank_0/') # except: # pass cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + self.has_trained_step if cur_step % self.syn_obs_steps == 0: try: print("Copying ckpt to the buckets start", flush=True) self.syn_files() # time.sleep(self.local_rank % 16) # mox.file.copy_parallel(self.local_ckpt_dir, self.obs_save_rank_dir) print("Copying ckpt to the buckets ends", flush=True) except Exception as error: print(error) print('\n###########Upload ckpt Error#############\n') print("Copying ckpt to the buckets again", flush=True) self.syn_files() # time.sleep((self.local_rank+15) % 16) # mox.file.copy_parallel(self.local_ckpt_dir, self.obs_save_rank_dir) print("Copying ckpt to the buckets ends", flush=True) def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.local_ckpt_dir, self.obs_save_rank_dir), name="file_sync") process.start() class CheckpointSaveCallback_Exception(): def __init__(self, local_ckpt_dir, local_rank=0, bucket='obs://ckeckpoint_file/path/'): self.local_ckpt_dir = local_ckpt_dir self.local_rank = local_rank self.bucket = bucket self.obs_save_rank_dir = os.path.join(bucket, "rank_" + str(local_rank)) if not mox.file.exists(self.obs_save_rank_dir): print("Creating ckeckpoint bueckt dir {}".format(self.obs_save_rank_dir)) mox.file.make_dirs(self.obs_save_rank_dir) self.obs_save_rank_dir = os.path.join(self.obs_save_rank_dir, local_ckpt_dir.split("/")[-1]) def step_end(self): try: print("Copying ckpt to the buckets start", flush=True) self.syn_files() print("Copying ckpt to the buckets ends", flush=True) except Exception as error: print(error) print('\n###########Upload ckpt Error#############\n') print("Copying ckpt to the buckets again", flush=True) self.syn_files() print("Copying ckpt to the buckets ends", flush=True) def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.local_ckpt_dir, self.obs_save_rank_dir), name="file_sync") process.start() class CheckpointSaveCallback_7B(Callback): def __init__(self, args, local_ckpt_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, bucket='obs://ckeckpoint_file/path/', syn_obs_steps=100): self.local_ckpt_dir = local_ckpt_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.bucket = bucket self.syn_obs_steps = syn_obs_steps self.stage_num = args.stage_num D.init() self.device_num = D.get_group_size() rank = D.get_rank() if self.local_rank % (self.device_num // self.stage_num) == 0: self.obs_save_rank_dir = os.path.join(bucket, "rank_" + str(local_rank)) if not mox.file.exists(self.obs_save_rank_dir): print("Creating ckeckpoint bueckt dir {}".format(self.obs_save_rank_dir)) mox.file.make_dirs(self.obs_save_rank_dir) def step_end(self, run_context): cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + self.has_trained_step if cur_step % self.syn_obs_steps == 0: if self.local_rank % (self.device_num // self.stage_num) == 0: try: print("Copying ckpt to the buckets start", flush=True) self.syn_files() # time.sleep(self.local_rank % 16) print("Copying ckpt to the buckets ends", flush=True) except Exception as error: print(error) print('\n###########Upload ckpt Error#############\n') print("Copying ckpt to the buckets again", flush=True) self.syn_files() # time.sleep((self.local_rank+15) % 16) print("Copying ckpt to the buckets ends", flush=True) def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.local_ckpt_dir, self.obs_save_rank_dir), name="file_sync") process.start() class Strategy_and_compilerSaveCallback(Callback): def __init__(self, strategy_path, local_rank=0, has_trained_step=0, bucket='obs://mindspore-file/strategy_ckpt/', compiler_name='2520_pp21_mp2dp60ms96', sym_step=100): self.strategy_path = strategy_path self.local_rank = local_rank self.sym_step = sym_step self.bucket = bucket self.has_trained_step = has_trained_step self.has_send = False self.file_name = strategy_path.split('/')[-1] self.compiler_cache_local_path = f"/cache/compiler_cache/rank_{local_rank}/" self.compiler_cache_obs_path = f"{compiler_name}/rank_{local_rank}/" if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) if not mox.file.exists(self.compiler_cache_obs_path): mox.file.make_dirs(self.compiler_cache_obs_path) def step_end(self, run_context): cb_params = run_context.original_args() if self.has_send is False: print("Send strategy file to the obs") self.syn_files() print("Send compiler cache file to the obs") time.sleep((self.local_rank % 8)*30) self.syn_files2() self.has_send = True def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.strategy_path, self.bucket + self.file_name), name="file_sync") process.start() def syn_files2(self): process = Process(target=mox.file.copy_parallel, args=(self.compiler_cache_local_path, self.compiler_cache_obs_path), name="file_sync") process.start() class StrategySaveCallback(Callback): def __init__(self, strategy_path, local_rank=0, has_trained_step=0, bucket='obs://mindspore-file/strategy_ckpt/', sym_step=100): self.strategy_path = strategy_path self.local_rank = local_rank self.sym_step = sym_step self.bucket = bucket self.has_trained_step = has_trained_step self.has_send = False self.file_name = strategy_path.split('/')[-1] if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) def step_end(self, run_context): cb_params = run_context.original_args() if self.has_send is False: print("Send strategy file to the obs") self.syn_files() self.has_send = True def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.strategy_path, self.bucket + self.file_name), name="file_sync") process.start() class GraphsSaveCallback(Callback): def __init__(self, Graph_path, local_rank=0, has_trained_step=0, bucket='obs://research-my/taoht-100b/graphs_100b/', sym_step=100): self.Graph_path = Graph_path self.local_rank = local_rank self.sym_step = sym_step self.bucket_obs = bucket + f"rank_{local_rank}" self.has_trained_step = has_trained_step self.has_send = False self.file_name = Graph_path.split('/')[-1] if not mox.file.exists(self.bucket_obs): print("Creating graph bueckt dir {}".format(self.bucket_obs)) mox.file.make_dirs(self.bucket_obs) def step_end(self, run_context): if self.has_send is False: print("Send Graph file to the obs") self.syn_files() self.has_send = True def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.Graph_path, self.bucket_obs), name="file_sync") process.start() def download_data(src_data_url, tgt_data_path, rank, flag="0"): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ cache_url = tgt_data_path EXEC_PATH = '/tmp' if rank % 8 == 0: import moxing as mox print("Modify the time out from 300 to 30000") print(os.listdir(cache_url)) for files in os.listdir(cache_url): files_path = os.path.join(cache_url, files) try: shutil.rmtree(files_path) except OSError: os.remove(files_path) print(f"Delete dataset in path {cache_url}...") print("begin download dataset", flush=True) if not os.path.exists(cache_url): os.makedirs(cache_url, exist_ok=True) mox.file.copy_parallel(src_url=src_data_url, dst_url=cache_url) print("Dataset download succeed!", flush=True) f = open(f"/tmp/install_{flag}.txt", 'w') f.close() # stop while not os.path.exists(f"/tmp/install_{flag}.txt"): time.sleep(1) def download_compiler_cache_from_obs(args_opt, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ compiler_cache_obs_path = f"{args_opt.compiler_name}/rank_{rank}/" compiler_cache_local_path = f"/cache/compiler_cache/rank_{rank}/" time.sleep(rank%16) mox.file.copy_parallel(src_url=compiler_cache_obs_path, dst_url=compiler_cache_local_path) print(f"Rank {rank} download compiler cache {compiler_cache_obs_path} succeed!", flush=True) f = open(f"/tmp/restore_compiler_{rank}.txt", 'w') f.close() while not os.path.exists(f"/tmp/restore_compiler_{rank}.txt"): time.sleep(1) def download_ckpt_from_obs(args_opt, cache_url, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ local_ckpt_url = os.path.join(cache_url, f"rank_{rank}.ckpt") # # if args_opt.restore_steps == 0: # obs_ckpt_url = os.path.join(args_opt.restore_checkpoint_bucket_dir, # f"rank_{rank}", # args_opt.restore_ckpt_name_prefix.replace('*', str(rank))) # else: obs_ckpt_url = os.path.join(args_opt.restore_checkpoint_bucket_dir, f"rank_{rank}", f"{args_opt.restore_ckpt_name_prefix.replace('*', str(rank))}") import moxing as mox print("Modify the time out from 300 to 30000") # print(f"begin download ckpt from {obs_ckpt_url}") # if not os.path.exists(os.path.join(cache_url, f"rank_{rank}")): # os.makedirs(os.path.join(cache_url, f"rank_{rank}"), exist_ok=True) # if not os.path.exists(cache_url): # os.makedirs(cache_url, exist_ok=True) time.sleep(rank % 4) try: mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) except Exception as error: print(f"Error: {error}, download again...") time.sleep(10) mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) print(f"Rank {rank} download ckpt succeed!", flush=True) # print(f"Rank {rank} download ckpt {obs_ckpt_url} succeed!", flush=True) f = open(f"/tmp/restore_{rank}.txt", 'w') f.close() # stop while not os.path.exists(f"/tmp/restore_{rank}.txt"): time.sleep(1) print(os.listdir(cache_url)) def download_ckpt_from_obs_7B(args_opt, cache_url, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ # print(f"begin download ckpt from {obs_ckpt_url}") # if not os.path.exists(os.path.join(cache_url, f"rank_{rank}")): # os.makedirs(os.path.join(cache_url, f"rank_{rank}"), exist_ok=True) # if not os.path.exists(cache_url): # os.makedirs(cache_url, exist_ok=True) # time.sleep(rank % 4) if rank%8 ==0: local_ckpt_url = os.path.join(cache_url, f"rank.ckpt") stage_num = args_opt.stage_num D.init() device_num = D.get_group_size() stage_device_num = device_num // stage_num rank_download = (rank // stage_device_num) * stage_device_num obs_ckpt_url = os.path.join(args_opt.restore_checkpoint_bucket_dir, f"rank_{rank_download}", f"{args_opt.restore_ckpt_name_prefix}_{rank_download}-{args_opt.restore_steps}_4.ckpt") import moxing as mox print("Modify the time out from 300 to 30000") try: mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) except Exception as error: print(f"Error: {error}, download again...") time.sleep(3) mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) print(f"Rank {rank} download ckpt succeed!", flush=True) # print(f"Rank {rank} download ckpt {obs_ckpt_url} succeed!", flush=True) f = open(f"/tmp/restore.txt", 'w') f.close() # stop while not os.path.exists(f"/tmp/restore.txt"): time.sleep(1) print(os.listdir(cache_url)) def download_merged_ckpt_from_obs(args_opt, cache_url, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ local_ckpt_url = os.path.join(cache_url, f"merged.ckpt") obs_ckpt_url = "obs://research-my/taoht-100b/ckpt_100b/merged_100b/PengChengMind_delta_rst11_36900-3980-5548_layers_fp16.ckpt" import moxing as mox print("Modify the time out from 300 to 30000") print("begin download ckpt", flush=True) if rank%8 == 0: mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) f = open(f"/tmp/restore.txt", 'w') f.close() while not os.path.exists(f"/tmp/restore.txt"): time.sleep(1) print(f"Rank {rank} download ckpt succeed!", flush=True) pengchengmind/strategy/000077500000000000000000000000001453423006600155315ustar00rootroot00000000000000pengchengmind/strategy/d144_pp18_mp8_strategy.ckpt000066400000000000000000022552531453423006600224640ustar00rootroot00000000000000@ ,LayerNorm_backbone.blocks.7.layernorm2.gamma9 )Add_backbone.blocks.7.output.mapping.bias ; +Add_backbone.blocks.7.attention.dense1.bias < ,Add_backbone.blocks.5.output.projection.bias C 1MatMul_backbone.blocks.9.output.projection.weight@ .MatMul_backbone.blocks.4.output.mapping.weight; 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1MatMul_backbone.blocks.66.attention.dense2.weight C 1MatMul_backbone.blocks.67.attention.dense2.weight C 1MatMul_backbone.blocks.69.attention.dense1.weight D 2MatMul_backbone.blocks.64.output.projection.weight < ,Add_backbone.blocks.69.attention.dense1.bias  < ,Add_backbone.blocks.69.attention.dense3.bias  C 1MatMul_backbone.blocks.66.attention.dense3.weight < ,Add_backbone.blocks.64.attention.dense1.bias  = -Add_backbone.blocks.65.output.projection.bias  A -LayerNorm_backbone.blocks.64.layernorm2.gamma C 1MatMul_backbone.blocks.68.attention.dense2.weight A -LayerNorm_backbone.blocks.66.layernorm1.gamma A /MatMul_backbone.blocks.64.output.mapping.weight C 1MatMul_backbone.blocks.65.attention.dense1.weight A /MatMul_backbone.blocks.67.output.mapping.weight C 1MatMul_backbone.blocks.64.attention.dense2.weight A -LayerNorm_backbone.blocks.65.layernorm1.gamma C 1MatMul_backbone.blocks.64.attention.dense1.weight A -LayerNorm_backbone.blocks.68.layernorm1.gamma A -LayerNorm_backbone.blocks.66.layernorm2.gamma C 1MatMul_backbone.blocks.67.attention.dense1.weight C 1MatMul_backbone.blocks.69.attention.dense2.weight @ 0Add_backbone.blocks.68.attention.projection.bias  @ 0Add_backbone.blocks.65.attention.projection.bias  A /MatMul_backbone.blocks.69.output.mapping.weight A -LayerNorm_backbone.blocks.69.layernorm1.gamma : *Add_backbone.blocks.68.output.mapping.bias  < ,Add_backbone.blocks.66.attention.dense3.bias  : *Add_backbone.blocks.66.output.mapping.bias  D 2MatMul_backbone.blocks.65.output.projection.weight = +Receive_backbone.blocks.64.layernorm1.gamma < ,Add_backbone.blocks.69.attention.dense2.bias  < ,Add_backbone.blocks.67.attention.dense2.bias  < ,Add_backbone.blocks.68.attention.dense1.bias  = -Add_backbone.blocks.69.output.projection.bias  < ,Add_backbone.blocks.65.attention.dense3.bias  C 1MatMul_backbone.blocks.66.attention.dense1.weight < ,Add_backbone.blocks.64.attention.dense3.bias  : *Add_backbone.blocks.64.output.mapping.bias  @ 0Add_backbone.blocks.67.attention.projection.bias  G 5MatMul_backbone.blocks.65.attention.projection.weight D 2MatMul_backbone.blocks.66.output.projection.weight < ,Add_backbone.blocks.68.attention.dense3.bias  = -Add_backbone.blocks.64.output.projection.bias  A -LayerNorm_backbone.blocks.67.layernorm2.gamma C 1MatMul_backbone.blocks.67.attention.dense3.weight < ,Add_backbone.blocks.64.attention.dense2.bias  G 5MatMul_backbone.blocks.66.attention.projection.weight G 5MatMul_backbone.blocks.68.attention.projection.weight : *Add_backbone.blocks.69.output.mapping.bias  D 2MatMul_backbone.blocks.67.output.projection.weight A -LayerNorm_backbone.blocks.64.layernorm1.gamma < ,Add_backbone.blocks.66.attention.dense2.bias  C 1MatMul_backbone.blocks.69.attention.dense3.weight = -Add_backbone.blocks.66.output.projection.bias  G 5MatMul_backbone.blocks.69.attention.projection.weight = -Add_backbone.blocks.68.output.projection.bias  : *Add_backbone.blocks.67.output.mapping.bias  < ,Add_backbone.blocks.65.attention.dense2.bias  D 2MatMul_backbone.blocks.68.output.projection.weight C 1MatMul_backbone.blocks.65.attention.dense3.weight A /MatMul_backbone.blocks.66.output.mapping.weight A -LayerNorm_backbone.blocks.68.layernorm2.gamma < ,Add_backbone.blocks.68.attention.dense2.bias  < ,Add_backbone.blocks.65.attention.dense1.bias  C 1MatMul_backbone.blocks.68.attention.dense3.weight G 5MatMul_backbone.blocks.64.attention.projection.weight @ 0Add_backbone.blocks.69.attention.projection.bias  A -LayerNorm_backbone.blocks.65.layernorm2.gamma : *Add_backbone.blocks.65.output.mapping.bias  G 5MatMul_backbone.blocks.67.attention.projection.weight A -LayerNorm_backbone.blocks.67.layernorm1.gamma A -LayerNorm_backbone.blocks.69.layernorm2.gamma D 2MatMul_backbone.blocks.69.output.projection.weight < ,Add_backbone.blocks.67.attention.dense1.bias  < ,Add_backbone.blocks.66.attention.dense1.bias  C 1MatMul_backbone.blocks.64.attention.dense3.weight A /MatMul_backbone.blocks.68.output.mapping.weight C 1MatMul_backbone.blocks.68.attention.dense1.weight @ 0Add_backbone.blocks.66.attention.projection.bias  A /MatMul_backbone.blocks.65.output.mapping.weight = -Add_backbone.blocks.67.output.projection.bias  @ 0Add_backbone.blocks.64.attention.projection.bias  < ,Add_backbone.blocks.67.attention.dense3.bias  C 1MatMul_backbone.blocks.65.attention.dense2.weight G 5MatMul_backbone.blocks.87.attention.projection.weightC 1MatMul_backbone.blocks.84.attention.dense3.weight< ,Add_backbone.blocks.86.attention.dense2.bias C 1MatMul_backbone.blocks.86.attention.dense1.weightA -LayerNorm_backbone.blocks.86.layernorm1.gamma: *Add_backbone.blocks.87.output.mapping.bias < ,Add_backbone.blocks.83.attention.dense1.bias : *Add_backbone.blocks.86.output.mapping.bias C 1MatMul_backbone.blocks.83.attention.dense2.weightA /MatMul_backbone.blocks.87.output.mapping.weight< ,Add_backbone.blocks.87.attention.dense1.bias A 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1MatMul_backbone.blocks.83.attention.dense3.weightA -LayerNorm_backbone.blocks.84.layernorm1.gammaC 1MatMul_backbone.blocks.85.attention.dense2.weightC 1MatMul_backbone.blocks.82.attention.dense1.weightD 2MatMul_backbone.blocks.86.output.projection.weight< ,Add_backbone.blocks.83.attention.dense2.bias = -Add_backbone.blocks.86.output.projection.bias : *Add_backbone.blocks.85.output.mapping.bias C 1MatMul_backbone.blocks.85.attention.dense3.weight: *Add_backbone.blocks.84.output.mapping.bias D 2MatMul_backbone.blocks.87.output.projection.weightA -LayerNorm_backbone.blocks.84.layernorm2.gammaA -LayerNorm_backbone.blocks.87.layernorm1.gammaC 1MatMul_backbone.blocks.84.attention.dense1.weightA /MatMul_backbone.blocks.85.output.mapping.weightG 5MatMul_backbone.blocks.82.attention.projection.weightG 5MatMul_backbone.blocks.83.attention.projection.weight= -Add_backbone.blocks.84.output.projection.bias D 2MatMul_backbone.blocks.82.output.projection.weight@ 0Add_backbone.blocks.82.attention.projection.bias C 1MatMul_backbone.blocks.86.attention.dense2.weightA /MatMul_backbone.blocks.83.output.mapping.weight< ,Add_backbone.blocks.84.attention.dense3.bias < ,Add_backbone.blocks.86.attention.dense1.bias A -LayerNorm_backbone.blocks.83.layernorm2.gammaD 2MatMul_backbone.blocks.84.output.projection.weight: *Add_backbone.blocks.83.output.mapping.bias A -LayerNorm_backbone.blocks.85.layernorm1.gammaC 1MatMul_backbone.blocks.82.attention.dense3.weightA -LayerNorm_backbone.blocks.87.layernorm2.gammaA /MatMul_backbone.blocks.82.output.mapping.weightC 1MatMul_backbone.blocks.87.attention.dense1.weightC 1MatMul_backbone.blocks.86.attention.dense3.weight= +Receive_backbone.blocks.82.layernorm1.gamma< ,Add_backbone.blocks.86.attention.dense3.bias C 1MatMul_backbone.blocks.83.attention.dense1.weight: *Add_backbone.blocks.82.output.mapping.bias < ,Add_backbone.blocks.85.attention.dense2.bias C 1MatMul_backbone.blocks.85.attention.dense1.weightC 1MatMul_backbone.blocks.84.attention.dense2.weightC 1MatMul_backbone.blocks.82.attention.dense2.weight@ 0Add_backbone.blocks.84.attention.projection.bias @ 0Add_backbone.blocks.86.attention.projection.bias < ,Add_backbone.blocks.82.attention.dense2.bias = -Add_backbone.blocks.83.output.projection.bias = -Add_backbone.blocks.87.output.projection.bias A /MatMul_backbone.blocks.86.output.mapping.weightA -LayerNorm_backbone.blocks.82.layernorm1.gamma< ,Add_backbone.blocks.83.attention.dense3.bias < ,Add_backbone.blocks.84.attention.dense2.bias C 1MatMul_backbone.blocks.87.attention.dense3.weight= -Add_backbone.blocks.85.output.projection.bias < ,Add_backbone.blocks.85.attention.dense3.bias C 1MatMul_backbone.blocks.87.attention.dense2.weightD 2MatMul_backbone.blocks.83.output.projection.weightG 5MatMul_backbone.top_query_layer.output.mapping.weightB 0MatMul_backbone.blocks.101.output.mapping.weight; +Add_backbone.blocks.101.output.mapping.bias > 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-LayerNorm_backbone.blocks.43.layernorm1.gammaA -LayerNorm_backbone.blocks.44.layernorm1.gamma= -Add_backbone.blocks.45.output.projection.bias G 5MatMul_backbone.blocks.41.attention.projection.weightA -LayerNorm_backbone.blocks.43.layernorm2.gammaC 1MatMul_backbone.blocks.44.attention.dense1.weightC 1MatMul_backbone.blocks.99.attention.dense2.weightA -LayerNorm_backbone.blocks.97.layernorm2.gamma< ,Add_backbone.blocks.97.attention.dense1.bias A -LayerNorm_backbone.blocks.99.layernorm1.gamma< ,Add_backbone.blocks.97.attention.dense2.bias G 5MatMul_backbone.blocks.96.attention.projection.weight< ,Add_backbone.blocks.96.attention.dense2.bias < ,Add_backbone.blocks.98.attention.dense2.bias D 2MatMul_backbone.blocks.97.output.projection.weightG 5MatMul_backbone.blocks.94.attention.projection.weightA -LayerNorm_backbone.blocks.98.layernorm2.gammaA /MatMul_backbone.blocks.96.output.mapping.weightG 5MatMul_backbone.blocks.98.attention.projection.weightC 1MatMul_backbone.blocks.97.attention.dense1.weightC 1MatMul_backbone.blocks.96.attention.dense2.weight< ,Add_backbone.blocks.99.attention.dense2.bias A -LayerNorm_backbone.blocks.98.layernorm1.gammaA /MatMul_backbone.blocks.98.output.mapping.weight< ,Add_backbone.blocks.98.attention.dense3.bias @ 0Add_backbone.blocks.94.attention.projection.bias D 2MatMul_backbone.blocks.94.output.projection.weightC 1MatMul_backbone.blocks.96.attention.dense1.weightC 1MatMul_backbone.blocks.95.attention.dense2.weightC 1MatMul_backbone.blocks.96.attention.dense3.weight< ,Add_backbone.blocks.95.attention.dense3.bias D 2MatMul_backbone.blocks.98.output.projection.weightC 1MatMul_backbone.blocks.94.attention.dense1.weight= -Add_backbone.blocks.96.output.projection.bias < ,Add_backbone.blocks.97.attention.dense3.bias @ 0Add_backbone.blocks.95.attention.projection.bias = -Add_backbone.blocks.97.output.projection.bias A -LayerNorm_backbone.blocks.94.layernorm2.gamma= -Add_backbone.blocks.94.output.projection.bias D 2MatMul_backbone.blocks.96.output.projection.weightA -LayerNorm_backbone.blocks.95.layernorm2.gamma= +Receive_backbone.blocks.94.layernorm1.gammaG 5MatMul_backbone.blocks.95.attention.projection.weight: *Add_backbone.blocks.97.output.mapping.bias < ,Add_backbone.blocks.94.attention.dense1.bias C 1MatMul_backbone.blocks.94.attention.dense3.weightG 5MatMul_backbone.blocks.99.attention.projection.weight< ,Add_backbone.blocks.95.attention.dense2.bias < ,Add_backbone.blocks.98.attention.dense1.bias G 5MatMul_backbone.blocks.97.attention.projection.weight: *Add_backbone.blocks.99.output.mapping.bias A /MatMul_backbone.blocks.94.output.mapping.weightC 1MatMul_backbone.blocks.97.attention.dense2.weight@ 0Add_backbone.blocks.98.attention.projection.bias : *Add_backbone.blocks.94.output.mapping.bias < ,Add_backbone.blocks.95.attention.dense1.bias : *Add_backbone.blocks.95.output.mapping.bias : *Add_backbone.blocks.98.output.mapping.bias D 2MatMul_backbone.blocks.99.output.projection.weight= -Add_backbone.blocks.99.output.projection.bias A -LayerNorm_backbone.blocks.95.layernorm1.gammaA -LayerNorm_backbone.blocks.94.layernorm1.gammaD 2MatMul_backbone.blocks.95.output.projection.weight< ,Add_backbone.blocks.94.attention.dense3.bias < ,Add_backbone.blocks.96.attention.dense1.bias C 1MatMul_backbone.blocks.98.attention.dense2.weight< ,Add_backbone.blocks.94.attention.dense2.bias C 1MatMul_backbone.blocks.97.attention.dense3.weightC 1MatMul_backbone.blocks.99.attention.dense1.weight< ,Add_backbone.blocks.99.attention.dense3.bias = -Add_backbone.blocks.98.output.projection.bias @ 0Add_backbone.blocks.96.attention.projection.bias : *Add_backbone.blocks.96.output.mapping.bias @ 0Add_backbone.blocks.99.attention.projection.bias A -LayerNorm_backbone.blocks.96.layernorm1.gamma< ,Add_backbone.blocks.99.attention.dense1.bias C 1MatMul_backbone.blocks.95.attention.dense1.weightC 1MatMul_backbone.blocks.99.attention.dense3.weightA /MatMul_backbone.blocks.99.output.mapping.weight< ,Add_backbone.blocks.96.attention.dense3.bias @ 0Add_backbone.blocks.97.attention.projection.bias C 1MatMul_backbone.blocks.94.attention.dense2.weightA -LayerNorm_backbone.blocks.96.layernorm2.gammaA /MatMul_backbone.blocks.95.output.mapping.weight= -Add_backbone.blocks.95.output.projection.bias C 1MatMul_backbone.blocks.98.attention.dense3.weightA /MatMul_backbone.blocks.97.output.mapping.weightC 1MatMul_backbone.blocks.95.attention.dense3.weightA -LayerNorm_backbone.blocks.97.layernorm1.gammaC 1MatMul_backbone.blocks.98.attention.dense1.weightA -LayerNorm_backbone.blocks.99.layernorm2.gammaA -LayerNorm_backbone.blocks.56.layernorm1.gamma : *Add_backbone.blocks.54.output.mapping.bias  A /MatMul_backbone.blocks.53.output.mapping.weight = -Add_backbone.blocks.56.output.projection.bias  < ,Add_backbone.blocks.56.attention.dense1.bias  : *Add_backbone.blocks.57.output.mapping.bias  A /MatMul_backbone.blocks.56.output.mapping.weight A /MatMul_backbone.blocks.57.output.mapping.weight D 2MatMul_backbone.blocks.55.output.projection.weight A -LayerNorm_backbone.blocks.52.layernorm2.gamma G 5MatMul_backbone.blocks.55.attention.projection.weight = -Add_backbone.blocks.52.output.projection.bias  < ,Add_backbone.blocks.55.attention.dense3.bias  < ,Add_backbone.blocks.52.attention.dense2.bias  C 1MatMul_backbone.blocks.53.attention.dense3.weight C 1MatMul_backbone.blocks.55.attention.dense3.weight < ,Add_backbone.blocks.56.attention.dense2.bias  < ,Add_backbone.blocks.56.attention.dense3.bias  < ,Add_backbone.blocks.52.attention.dense3.bias  = -Add_backbone.blocks.55.output.projection.bias  C 1MatMul_backbone.blocks.56.attention.dense2.weight C 1MatMul_backbone.blocks.54.attention.dense3.weight G 5MatMul_backbone.blocks.56.attention.projection.weight C 1MatMul_backbone.blocks.53.attention.dense1.weight < ,Add_backbone.blocks.53.attention.dense3.bias  A -LayerNorm_backbone.blocks.57.layernorm1.gamma : *Add_backbone.blocks.55.output.mapping.bias  A -LayerNorm_backbone.blocks.54.layernorm1.gamma C 1MatMul_backbone.blocks.55.attention.dense1.weight C 1MatMul_backbone.blocks.52.attention.dense2.weight A -LayerNorm_backbone.blocks.54.layernorm2.gamma C 1MatMul_backbone.blocks.57.attention.dense3.weight < ,Add_backbone.blocks.53.attention.dense1.bias  C 1MatMul_backbone.blocks.55.attention.dense2.weight < ,Add_backbone.blocks.52.attention.dense1.bias  = -Add_backbone.blocks.53.output.projection.bias  A -LayerNorm_backbone.blocks.55.layernorm1.gamma A -LayerNorm_backbone.blocks.56.layernorm2.gamma A -LayerNorm_backbone.blocks.55.layernorm2.gamma A -LayerNorm_backbone.blocks.53.layernorm2.gamma @ 0Add_backbone.blocks.57.attention.projection.bias  < ,Add_backbone.blocks.57.attention.dense3.bias  < ,Add_backbone.blocks.55.attention.dense2.bias  < ,Add_backbone.blocks.55.attention.dense1.bias  D 2MatMul_backbone.blocks.54.output.projection.weight < ,Add_backbone.blocks.57.attention.dense2.bias  : *Add_backbone.blocks.52.output.mapping.bias  D 2MatMul_backbone.blocks.57.output.projection.weight A -LayerNorm_backbone.blocks.57.layernorm2.gamma C 1MatMul_backbone.blocks.53.attention.dense2.weight < ,Add_backbone.blocks.57.attention.dense1.bias  < ,Add_backbone.blocks.53.attention.dense2.bias  G 5MatMul_backbone.blocks.53.attention.projection.weight @ 0Add_backbone.blocks.55.attention.projection.bias  A -LayerNorm_backbone.blocks.52.layernorm1.gamma C 1MatMul_backbone.blocks.57.attention.dense1.weight C 1MatMul_backbone.blocks.57.attention.dense2.weight = +Receive_backbone.blocks.52.layernorm1.gamma G 5MatMul_backbone.blocks.57.attention.projection.weight C 1MatMul_backbone.blocks.56.attention.dense3.weight < ,Add_backbone.blocks.54.attention.dense1.bias  @ 0Add_backbone.blocks.53.attention.projection.bias  = -Add_backbone.blocks.57.output.projection.bias  A /MatMul_backbone.blocks.52.output.mapping.weight : *Add_backbone.blocks.56.output.mapping.bias  @ 0Add_backbone.blocks.52.attention.projection.bias  = -Add_backbone.blocks.54.output.projection.bias  @ 0Add_backbone.blocks.54.attention.projection.bias  < ,Add_backbone.blocks.54.attention.dense2.bias  G 5MatMul_backbone.blocks.52.attention.projection.weight C 1MatMul_backbone.blocks.56.attention.dense1.weight C 1MatMul_backbone.blocks.52.attention.dense1.weight D 2MatMul_backbone.blocks.52.output.projection.weight C 1MatMul_backbone.blocks.54.attention.dense2.weight D 2MatMul_backbone.blocks.56.output.projection.weight : *Add_backbone.blocks.53.output.mapping.bias  C 1MatMul_backbone.blocks.52.attention.dense3.weight C 1MatMul_backbone.blocks.54.attention.dense1.weight A -LayerNorm_backbone.blocks.53.layernorm1.gamma A /MatMul_backbone.blocks.54.output.mapping.weight D 2MatMul_backbone.blocks.53.output.projection.weight G 5MatMul_backbone.blocks.54.attention.projection.weight < ,Add_backbone.blocks.54.attention.dense3.bias  A /MatMul_backbone.blocks.55.output.mapping.weight @ 0Add_backbone.blocks.56.attention.projection.bias  < ,Add_backbone.blocks.61.attention.dense2.bias  A /MatMul_backbone.blocks.63.output.mapping.weight G 5MatMul_backbone.blocks.62.attention.projection.weight : *Add_backbone.blocks.61.output.mapping.bias  C 1MatMul_backbone.blocks.58.attention.dense3.weight : *Add_backbone.blocks.59.output.mapping.bias  A -LayerNorm_backbone.blocks.60.layernorm1.gamma : *Add_backbone.blocks.63.output.mapping.bias  C 1MatMul_backbone.blocks.63.attention.dense1.weight C 1MatMul_backbone.blocks.61.attention.dense2.weight < ,Add_backbone.blocks.60.attention.dense2.bias  < ,Add_backbone.blocks.63.attention.dense3.bias  D 2MatMul_backbone.blocks.59.output.projection.weight G 5MatMul_backbone.blocks.61.attention.projection.weight A -LayerNorm_backbone.blocks.62.layernorm1.gamma < ,Add_backbone.blocks.59.attention.dense3.bias  C 1MatMul_backbone.blocks.62.attention.dense1.weight @ 0Add_backbone.blocks.62.attention.projection.bias  C 1MatMul_backbone.blocks.60.attention.dense1.weight < ,Add_backbone.blocks.61.attention.dense3.bias  A -LayerNorm_backbone.blocks.59.layernorm1.gamma < ,Add_backbone.blocks.62.attention.dense2.bias  A /MatMul_backbone.blocks.60.output.mapping.weight < ,Add_backbone.blocks.58.attention.dense2.bias  : *Add_backbone.blocks.58.output.mapping.bias  D 2MatMul_backbone.blocks.62.output.projection.weight A -LayerNorm_backbone.blocks.58.layernorm1.gamma C 1MatMul_backbone.blocks.59.attention.dense2.weight D 2MatMul_backbone.blocks.58.output.projection.weight C 1MatMul_backbone.blocks.63.attention.dense2.weight A /MatMul_backbone.blocks.62.output.mapping.weight @ 0Add_backbone.blocks.61.attention.projection.bias  G 5MatMul_backbone.blocks.59.attention.projection.weight < ,Add_backbone.blocks.59.attention.dense2.bias  : *Add_backbone.blocks.60.output.mapping.bias  A -LayerNorm_backbone.blocks.62.layernorm2.gamma A -LayerNorm_backbone.blocks.59.layernorm2.gamma A -LayerNorm_backbone.blocks.61.layernorm2.gamma D 2MatMul_backbone.blocks.63.output.projection.weight C 1MatMul_backbone.blocks.59.attention.dense3.weight C 1MatMul_backbone.blocks.62.attention.dense2.weight C 1MatMul_backbone.blocks.59.attention.dense1.weight < ,Add_backbone.blocks.62.attention.dense1.bias  < ,Add_backbone.blocks.58.attention.dense1.bias  < ,Add_backbone.blocks.61.attention.dense1.bias  D 2MatMul_backbone.blocks.61.output.projection.weight < ,Add_backbone.blocks.63.attention.dense2.bias  = -Add_backbone.blocks.60.output.projection.bias  @ 0Add_backbone.blocks.60.attention.projection.bias  A /MatMul_backbone.blocks.61.output.mapping.weight < ,Add_backbone.blocks.62.attention.dense3.bias  A -LayerNorm_backbone.blocks.63.layernorm1.gamma C 1MatMul_backbone.blocks.63.attention.dense3.weight C 1MatMul_backbone.blocks.58.attention.dense2.weight < ,Add_backbone.blocks.60.attention.dense1.bias  A -LayerNorm_backbone.blocks.61.layernorm1.gamma C 1MatMul_backbone.blocks.58.attention.dense1.weight = -Add_backbone.blocks.58.output.projection.bias  = +Receive_backbone.blocks.58.layernorm1.gamma C 1MatMul_backbone.blocks.60.attention.dense3.weight < ,Add_backbone.blocks.63.attention.dense1.bias  = -Add_backbone.blocks.59.output.projection.bias  A /MatMul_backbone.blocks.59.output.mapping.weight < ,Add_backbone.blocks.59.attention.dense1.bias  @ 0Add_backbone.blocks.59.attention.projection.bias  C 1MatMul_backbone.blocks.62.attention.dense3.weight A -LayerNorm_backbone.blocks.58.layernorm2.gamma C 1MatMul_backbone.blocks.61.attention.dense3.weight = -Add_backbone.blocks.63.output.projection.bias  = -Add_backbone.blocks.62.output.projection.bias  < ,Add_backbone.blocks.58.attention.dense3.bias  < ,Add_backbone.blocks.60.attention.dense3.bias  C 1MatMul_backbone.blocks.61.attention.dense1.weight C 1MatMul_backbone.blocks.60.attention.dense2.weight = -Add_backbone.blocks.61.output.projection.bias  D 2MatMul_backbone.blocks.60.output.projection.weight G 5MatMul_backbone.blocks.63.attention.projection.weight A -LayerNorm_backbone.blocks.60.layernorm2.gamma A /MatMul_backbone.blocks.58.output.mapping.weight @ 0Add_backbone.blocks.58.attention.projection.bias  G 5MatMul_backbone.blocks.58.attention.projection.weight A -LayerNorm_backbone.blocks.63.layernorm2.gamma @ 0Add_backbone.blocks.63.attention.projection.bias  G 5MatMul_backbone.blocks.60.attention.projection.weight : *Add_backbone.blocks.62.output.mapping.bias  A -LayerNorm_backbone.blocks.28.layernorm2.gammaC 1MatMul_backbone.blocks.31.attention.dense1.weightC 1MatMul_backbone.blocks.30.attention.dense3.weightD 2MatMul_backbone.blocks.31.output.projection.weightD 2MatMul_backbone.blocks.28.output.projection.weightA -LayerNorm_backbone.blocks.28.layernorm1.gamma< ,Add_backbone.blocks.28.attention.dense1.bias < ,Add_backbone.blocks.29.attention.dense2.bias A -LayerNorm_backbone.blocks.30.layernorm2.gammaG 5MatMul_backbone.blocks.29.attention.projection.weightG 5MatMul_backbone.blocks.30.attention.projection.weightG 5MatMul_backbone.blocks.33.attention.projection.weight@ 0Add_backbone.blocks.29.attention.projection.bias < ,Add_backbone.blocks.32.attention.dense2.bias A -LayerNorm_backbone.blocks.31.layernorm1.gamma= -Add_backbone.blocks.31.output.projection.bias @ 0Add_backbone.blocks.30.attention.projection.bias < ,Add_backbone.blocks.31.attention.dense1.bias D 2MatMul_backbone.blocks.32.output.projection.weightA -LayerNorm_backbone.blocks.31.layernorm2.gamma@ 0Add_backbone.blocks.33.attention.projection.bias = -Add_backbone.blocks.33.output.projection.bias : *Add_backbone.blocks.30.output.mapping.bias C 1MatMul_backbone.blocks.31.attention.dense2.weightG 5MatMul_backbone.blocks.31.attention.projection.weightA -LayerNorm_backbone.blocks.32.layernorm1.gamma: *Add_backbone.blocks.29.output.mapping.bias < ,Add_backbone.blocks.30.attention.dense3.bias < ,Add_backbone.blocks.32.attention.dense1.bias < ,Add_backbone.blocks.30.attention.dense2.bias : *Add_backbone.blocks.28.output.mapping.bias D 2MatMul_backbone.blocks.30.output.projection.weight= -Add_backbone.blocks.30.output.projection.bias C 1MatMul_backbone.blocks.32.attention.dense3.weightA -LayerNorm_backbone.blocks.30.layernorm1.gamma: *Add_backbone.blocks.32.output.mapping.bias A /MatMul_backbone.blocks.28.output.mapping.weightA -LayerNorm_backbone.blocks.33.layernorm2.gamma< ,Add_backbone.blocks.33.attention.dense1.bias A /MatMul_backbone.blocks.33.output.mapping.weightC 1MatMul_backbone.blocks.33.attention.dense2.weightD 2MatMul_backbone.blocks.33.output.projection.weightA -LayerNorm_backbone.blocks.29.layernorm2.gammaA /MatMul_backbone.blocks.32.output.mapping.weight< ,Add_backbone.blocks.29.attention.dense1.bias C 1MatMul_backbone.blocks.29.attention.dense1.weightC 1MatMul_backbone.blocks.32.attention.dense1.weight< ,Add_backbone.blocks.31.attention.dense2.bias A /MatMul_backbone.blocks.31.output.mapping.weight< ,Add_backbone.blocks.33.attention.dense3.bias = -Add_backbone.blocks.29.output.projection.bias A /MatMul_backbone.blocks.29.output.mapping.weightC 1MatMul_backbone.blocks.29.attention.dense2.weight= -Add_backbone.blocks.28.output.projection.bias < ,Add_backbone.blocks.28.attention.dense2.bias C 1MatMul_backbone.blocks.32.attention.dense2.weightC 1MatMul_backbone.blocks.30.attention.dense2.weight: *Add_backbone.blocks.31.output.mapping.bias C 1MatMul_backbone.blocks.33.attention.dense1.weight< ,Add_backbone.blocks.32.attention.dense3.bias C 1MatMul_backbone.blocks.29.attention.dense3.weight@ 0Add_backbone.blocks.28.attention.projection.bias C 1MatMul_backbone.blocks.30.attention.dense1.weightA -LayerNorm_backbone.blocks.32.layernorm2.gamma@ 0Add_backbone.blocks.31.attention.projection.bias A -LayerNorm_backbone.blocks.29.layernorm1.gamma< ,Add_backbone.blocks.31.attention.dense3.bias A -LayerNorm_backbone.blocks.33.layernorm1.gammaA /MatMul_backbone.blocks.30.output.mapping.weightC 1MatMul_backbone.blocks.33.attention.dense3.weight< ,Add_backbone.blocks.29.attention.dense3.bias C 1MatMul_backbone.blocks.28.attention.dense2.weightG 5MatMul_backbone.blocks.32.attention.projection.weightC 1MatMul_backbone.blocks.28.attention.dense3.weightC 1MatMul_backbone.blocks.31.attention.dense3.weight: *Add_backbone.blocks.33.output.mapping.bias < ,Add_backbone.blocks.30.attention.dense1.bias = -Add_backbone.blocks.32.output.projection.bias G 5MatMul_backbone.blocks.28.attention.projection.weight< ,Add_backbone.blocks.28.attention.dense3.bias < ,Add_backbone.blocks.33.attention.dense2.bias D 2MatMul_backbone.blocks.29.output.projection.weight@ 0Add_backbone.blocks.32.attention.projection.bias = +Receive_backbone.blocks.28.layernorm1.gammaC 1MatMul_backbone.blocks.28.attention.dense1.weightA -LayerNorm_backbone.blocks.46.layernorm1.gammaA -LayerNorm_backbone.blocks.47.layernorm2.gammaA /MatMul_backbone.blocks.48.output.mapping.weight< ,Add_backbone.blocks.46.attention.dense3.bias D 2MatMul_backbone.blocks.46.output.projection.weightA -LayerNorm_backbone.blocks.47.layernorm1.gammaC 1MatMul_backbone.blocks.46.attention.dense2.weight: *Add_backbone.blocks.48.output.mapping.bias A -LayerNorm_backbone.blocks.49.layernorm1.gamma< ,Add_backbone.blocks.50.attention.dense3.bias C 1MatMul_backbone.blocks.50.attention.dense3.weight= -Add_backbone.blocks.49.output.projection.bias D 2MatMul_backbone.blocks.48.output.projection.weightC 1MatMul_backbone.blocks.46.attention.dense1.weight< ,Add_backbone.blocks.48.attention.dense3.bias C 1MatMul_backbone.blocks.51.attention.dense1.weight@ 0Add_backbone.blocks.51.attention.projection.bias = -Add_backbone.blocks.48.output.projection.bias = -Add_backbone.blocks.51.output.projection.bias < ,Add_backbone.blocks.46.attention.dense2.bias < ,Add_backbone.blocks.49.attention.dense2.bias A /MatMul_backbone.blocks.46.output.mapping.weight= -Add_backbone.blocks.50.output.projection.bias < ,Add_backbone.blocks.46.attention.dense1.bias = -Add_backbone.blocks.47.output.projection.bias D 2MatMul_backbone.blocks.50.output.projection.weightC 1MatMul_backbone.blocks.49.attention.dense2.weight@ 0Add_backbone.blocks.50.attention.projection.bias = -Add_backbone.blocks.46.output.projection.bias < ,Add_backbone.blocks.47.attention.dense2.bias = +Receive_backbone.blocks.46.layernorm1.gamma: *Add_backbone.blocks.49.output.mapping.bias @ 0Add_backbone.blocks.48.attention.projection.bias G 5MatMul_backbone.blocks.46.attention.projection.weight< ,Add_backbone.blocks.47.attention.dense3.bias D 2MatMul_backbone.blocks.51.output.projection.weightG 5MatMul_backbone.blocks.49.attention.projection.weightA -LayerNorm_backbone.blocks.51.layernorm2.gamma@ 0Add_backbone.blocks.47.attention.projection.bias < ,Add_backbone.blocks.49.attention.dense1.bias A /MatMul_backbone.blocks.51.output.mapping.weight< ,Add_backbone.blocks.48.attention.dense2.bias A /MatMul_backbone.blocks.50.output.mapping.weightD 2MatMul_backbone.blocks.49.output.projection.weight< ,Add_backbone.blocks.47.attention.dense1.bias A /MatMul_backbone.blocks.49.output.mapping.weightC 1MatMul_backbone.blocks.47.attention.dense2.weightC 1MatMul_backbone.blocks.49.attention.dense3.weightC 1MatMul_backbone.blocks.48.attention.dense3.weight: *Add_backbone.blocks.46.output.mapping.bias A /MatMul_backbone.blocks.47.output.mapping.weightC 1MatMul_backbone.blocks.46.attention.dense3.weightC 1MatMul_backbone.blocks.47.attention.dense3.weightG 5MatMul_backbone.blocks.50.attention.projection.weightG 5MatMul_backbone.blocks.51.attention.projection.weightA -LayerNorm_backbone.blocks.51.layernorm1.gamma< ,Add_backbone.blocks.51.attention.dense3.bias : *Add_backbone.blocks.50.output.mapping.bias C 1MatMul_backbone.blocks.49.attention.dense1.weightC 1MatMul_backbone.blocks.47.attention.dense1.weightA -LayerNorm_backbone.blocks.48.layernorm1.gamma< ,Add_backbone.blocks.50.attention.dense2.bias < ,Add_backbone.blocks.51.attention.dense1.bias C 1MatMul_backbone.blocks.51.attention.dense3.weightC 1MatMul_backbone.blocks.48.attention.dense1.weightG 5MatMul_backbone.blocks.47.attention.projection.weight< ,Add_backbone.blocks.50.attention.dense1.bias @ 0Add_backbone.blocks.46.attention.projection.bias A -LayerNorm_backbone.blocks.48.layernorm2.gammaC 1MatMul_backbone.blocks.50.attention.dense2.weightA -LayerNorm_backbone.blocks.46.layernorm2.gamma: *Add_backbone.blocks.47.output.mapping.bias < ,Add_backbone.blocks.49.attention.dense3.bias : *Add_backbone.blocks.51.output.mapping.bias < ,Add_backbone.blocks.51.attention.dense2.bias G 5MatMul_backbone.blocks.48.attention.projection.weight@ 0Add_backbone.blocks.49.attention.projection.bias C 1MatMul_backbone.blocks.50.attention.dense1.weightA -LayerNorm_backbone.blocks.50.layernorm1.gammaD 2MatMul_backbone.blocks.47.output.projection.weightC 1MatMul_backbone.blocks.51.attention.dense2.weight< ,Add_backbone.blocks.48.attention.dense1.bias C 1MatMul_backbone.blocks.48.attention.dense2.weightA -LayerNorm_backbone.blocks.50.layernorm2.gammaA -LayerNorm_backbone.blocks.49.layernorm2.gammaD 2MatMul_backbone.blocks.24.output.projection.weight< ,Add_backbone.blocks.26.attention.dense2.bias : *Add_backbone.blocks.27.output.mapping.bias A -LayerNorm_backbone.blocks.22.layernorm1.gamma@ 0Add_backbone.blocks.25.attention.projection.bias A /MatMul_backbone.blocks.27.output.mapping.weightC 1MatMul_backbone.blocks.22.attention.dense3.weightC 1MatMul_backbone.blocks.24.attention.dense3.weight@ 0Add_backbone.blocks.22.attention.projection.bias < ,Add_backbone.blocks.25.attention.dense2.bias = -Add_backbone.blocks.22.output.projection.bias = +Receive_backbone.blocks.22.layernorm1.gammaA -LayerNorm_backbone.blocks.24.layernorm2.gammaA /MatMul_backbone.blocks.24.output.mapping.weightG 5MatMul_backbone.blocks.26.attention.projection.weightA -LayerNorm_backbone.blocks.25.layernorm2.gamma= -Add_backbone.blocks.26.output.projection.bias : *Add_backbone.blocks.22.output.mapping.bias : *Add_backbone.blocks.23.output.mapping.bias < ,Add_backbone.blocks.24.attention.dense2.bias : *Add_backbone.blocks.24.output.mapping.bias < ,Add_backbone.blocks.25.attention.dense3.bias = -Add_backbone.blocks.25.output.projection.bias = -Add_backbone.blocks.27.output.projection.bias C 1MatMul_backbone.blocks.25.attention.dense1.weightA /MatMul_backbone.blocks.25.output.mapping.weightC 1MatMul_backbone.blocks.25.attention.dense3.weight< ,Add_backbone.blocks.23.attention.dense2.bias < ,Add_backbone.blocks.22.attention.dense3.bias D 2MatMul_backbone.blocks.22.output.projection.weightA -LayerNorm_backbone.blocks.25.layernorm1.gamma< ,Add_backbone.blocks.24.attention.dense1.bias C 1MatMul_backbone.blocks.27.attention.dense1.weight< ,Add_backbone.blocks.24.attention.dense3.bias < ,Add_backbone.blocks.23.attention.dense3.bias G 5MatMul_backbone.blocks.23.attention.projection.weight< ,Add_backbone.blocks.22.attention.dense2.bias C 1MatMul_backbone.blocks.25.attention.dense2.weightC 1MatMul_backbone.blocks.24.attention.dense1.weight< ,Add_backbone.blocks.26.attention.dense1.bias = -Add_backbone.blocks.24.output.projection.bias A /MatMul_backbone.blocks.22.output.mapping.weightA -LayerNorm_backbone.blocks.22.layernorm2.gammaG 5MatMul_backbone.blocks.24.attention.projection.weightA -LayerNorm_backbone.blocks.24.layernorm1.gammaC 1MatMul_backbone.blocks.26.attention.dense2.weight< ,Add_backbone.blocks.27.attention.dense2.bias C 1MatMul_backbone.blocks.23.attention.dense3.weightC 1MatMul_backbone.blocks.22.attention.dense2.weightC 1MatMul_backbone.blocks.22.attention.dense1.weightC 1MatMul_backbone.blocks.23.attention.dense1.weightA -LayerNorm_backbone.blocks.26.layernorm2.gammaA /MatMul_backbone.blocks.26.output.mapping.weightG 5MatMul_backbone.blocks.22.attention.projection.weightC 1MatMul_backbone.blocks.26.attention.dense1.weight< ,Add_backbone.blocks.23.attention.dense1.bias : *Add_backbone.blocks.25.output.mapping.bias C 1MatMul_backbone.blocks.26.attention.dense3.weightD 2MatMul_backbone.blocks.26.output.projection.weight@ 0Add_backbone.blocks.24.attention.projection.bias < ,Add_backbone.blocks.27.attention.dense3.bias D 2MatMul_backbone.blocks.23.output.projection.weightA -LayerNorm_backbone.blocks.26.layernorm1.gammaA -LayerNorm_backbone.blocks.23.layernorm2.gamma@ 0Add_backbone.blocks.26.attention.projection.bias A -LayerNorm_backbone.blocks.23.layernorm1.gamma< ,Add_backbone.blocks.25.attention.dense1.bias < ,Add_backbone.blocks.26.attention.dense3.bias A /MatMul_backbone.blocks.23.output.mapping.weightA -LayerNorm_backbone.blocks.27.layernorm1.gammaG 5MatMul_backbone.blocks.27.attention.projection.weightD 2MatMul_backbone.blocks.25.output.projection.weight: *Add_backbone.blocks.26.output.mapping.bias C 1MatMul_backbone.blocks.27.attention.dense2.weightC 1MatMul_backbone.blocks.23.attention.dense2.weightC 1MatMul_backbone.blocks.24.attention.dense2.weightG 5MatMul_backbone.blocks.25.attention.projection.weight@ 0Add_backbone.blocks.23.attention.projection.bias C 1MatMul_backbone.blocks.27.attention.dense3.weightA -LayerNorm_backbone.blocks.27.layernorm2.gammaD 2MatMul_backbone.blocks.27.output.projection.weight@ 0Add_backbone.blocks.27.attention.projection.bias = -Add_backbone.blocks.23.output.projection.bias < ,Add_backbone.blocks.22.attention.dense1.bias < ,Add_backbone.blocks.27.attention.dense1.bias < ,Add_backbone.blocks.76.attention.dense1.bias  D 2MatMul_backbone.blocks.80.output.projection.weight < ,Add_backbone.blocks.78.attention.dense3.bias  A -LayerNorm_backbone.blocks.81.layernorm2.gamma < ,Add_backbone.blocks.77.attention.dense3.bias  @ 0Add_backbone.blocks.77.attention.projection.bias  C 1MatMul_backbone.blocks.77.attention.dense1.weight D 2MatMul_backbone.blocks.77.output.projection.weight D 2MatMul_backbone.blocks.81.output.projection.weight A -LayerNorm_backbone.blocks.79.layernorm2.gamma : *Add_backbone.blocks.76.output.mapping.bias  C 1MatMul_backbone.blocks.78.attention.dense2.weight A /MatMul_backbone.blocks.79.output.mapping.weight A -LayerNorm_backbone.blocks.77.layernorm1.gamma A -LayerNorm_backbone.blocks.76.layernorm1.gamma A -LayerNorm_backbone.blocks.78.layernorm2.gamma C 1MatMul_backbone.blocks.81.attention.dense3.weight < ,Add_backbone.blocks.79.attention.dense2.bias  @ 0Add_backbone.blocks.81.attention.projection.bias  A /MatMul_backbone.blocks.78.output.mapping.weight < ,Add_backbone.blocks.78.attention.dense2.bias  C 1MatMul_backbone.blocks.76.attention.dense2.weight C 1MatMul_backbone.blocks.81.attention.dense1.weight C 1MatMul_backbone.blocks.79.attention.dense3.weight : *Add_backbone.blocks.81.output.mapping.bias  D 2MatMul_backbone.blocks.79.output.projection.weight < ,Add_backbone.blocks.79.attention.dense1.bias  G 5MatMul_backbone.blocks.80.attention.projection.weight : *Add_backbone.blocks.79.output.mapping.bias  A /MatMul_backbone.blocks.76.output.mapping.weight C 1MatMul_backbone.blocks.80.attention.dense3.weight A -LayerNorm_backbone.blocks.77.layernorm2.gamma C 1MatMul_backbone.blocks.80.attention.dense1.weight C 1MatMul_backbone.blocks.80.attention.dense2.weight C 1MatMul_backbone.blocks.78.attention.dense3.weight < ,Add_backbone.blocks.81.attention.dense2.bias  < ,Add_backbone.blocks.80.attention.dense2.bias  C 1MatMul_backbone.blocks.76.attention.dense3.weight G 5MatMul_backbone.blocks.76.attention.projection.weight A -LayerNorm_backbone.blocks.78.layernorm1.gamma A -LayerNorm_backbone.blocks.80.layernorm1.gamma C 1MatMul_backbone.blocks.81.attention.dense2.weight A -LayerNorm_backbone.blocks.79.layernorm1.gamma = -Add_backbone.blocks.80.output.projection.bias  @ 0Add_backbone.blocks.80.attention.projection.bias  < ,Add_backbone.blocks.80.attention.dense3.bias  A /MatMul_backbone.blocks.81.output.mapping.weight = -Add_backbone.blocks.79.output.projection.bias  < ,Add_backbone.blocks.76.attention.dense3.bias  < ,Add_backbone.blocks.77.attention.dense1.bias  < ,Add_backbone.blocks.81.attention.dense1.bias  C 1MatMul_backbone.blocks.76.attention.dense1.weight G 5MatMul_backbone.blocks.77.attention.projection.weight : *Add_backbone.blocks.77.output.mapping.bias  : *Add_backbone.blocks.80.output.mapping.bias  C 1MatMul_backbone.blocks.77.attention.dense2.weight @ 0Add_backbone.blocks.78.attention.projection.bias  G 5MatMul_backbone.blocks.78.attention.projection.weight < ,Add_backbone.blocks.80.attention.dense1.bias  A -LayerNorm_backbone.blocks.76.layernorm2.gamma = -Add_backbone.blocks.78.output.projection.bias  C 1MatMul_backbone.blocks.79.attention.dense1.weight C 1MatMul_backbone.blocks.79.attention.dense2.weight = +Receive_backbone.blocks.76.layernorm1.gamma < ,Add_backbone.blocks.79.attention.dense3.bias  < ,Add_backbone.blocks.76.attention.dense2.bias  A /MatMul_backbone.blocks.80.output.mapping.weight G 5MatMul_backbone.blocks.79.attention.projection.weight A /MatMul_backbone.blocks.77.output.mapping.weight C 1MatMul_backbone.blocks.78.attention.dense1.weight < ,Add_backbone.blocks.77.attention.dense2.bias  D 2MatMul_backbone.blocks.76.output.projection.weight @ 0Add_backbone.blocks.79.attention.projection.bias  = -Add_backbone.blocks.81.output.projection.bias  A -LayerNorm_backbone.blocks.80.layernorm2.gamma < ,Add_backbone.blocks.78.attention.dense1.bias  C 1MatMul_backbone.blocks.77.attention.dense3.weight = -Add_backbone.blocks.77.output.projection.bias  D 2MatMul_backbone.blocks.78.output.projection.weight @ 0Add_backbone.blocks.76.attention.projection.bias  G 5MatMul_backbone.blocks.81.attention.projection.weight : *Add_backbone.blocks.78.output.mapping.bias  < ,Add_backbone.blocks.81.attention.dense3.bias  A -LayerNorm_backbone.blocks.81.layernorm1.gamma = -Add_backbone.blocks.76.output.projection.bias  @ 0Add_backbone.blocks.72.attention.projection.bias  A -LayerNorm_backbone.blocks.71.layernorm2.gamma = -Add_backbone.blocks.70.output.projection.bias  G 5MatMul_backbone.blocks.71.attention.projection.weight = -Add_backbone.blocks.75.output.projection.bias  < ,Add_backbone.blocks.74.attention.dense3.bias  C 1MatMul_backbone.blocks.71.attention.dense2.weight < ,Add_backbone.blocks.72.attention.dense2.bias  A -LayerNorm_backbone.blocks.70.layernorm2.gamma C 1MatMul_backbone.blocks.70.attention.dense3.weight < ,Add_backbone.blocks.73.attention.dense1.bias  A -LayerNorm_backbone.blocks.74.layernorm1.gamma C 1MatMul_backbone.blocks.75.attention.dense1.weight : *Add_backbone.blocks.72.output.mapping.bias  A /MatMul_backbone.blocks.73.output.mapping.weight A /MatMul_backbone.blocks.71.output.mapping.weight D 2MatMul_backbone.blocks.72.output.projection.weight : *Add_backbone.blocks.70.output.mapping.bias  @ 0Add_backbone.blocks.70.attention.projection.bias  < ,Add_backbone.blocks.70.attention.dense2.bias  : *Add_backbone.blocks.75.output.mapping.bias  C 1MatMul_backbone.blocks.73.attention.dense1.weight C 1MatMul_backbone.blocks.71.attention.dense3.weight A -LayerNorm_backbone.blocks.74.layernorm2.gamma < ,Add_backbone.blocks.75.attention.dense1.bias  C 1MatMul_backbone.blocks.73.attention.dense3.weight A /MatMul_backbone.blocks.72.output.mapping.weight G 5MatMul_backbone.blocks.75.attention.projection.weight < ,Add_backbone.blocks.71.attention.dense2.bias  = -Add_backbone.blocks.72.output.projection.bias  : *Add_backbone.blocks.73.output.mapping.bias  A /MatMul_backbone.blocks.74.output.mapping.weight D 2MatMul_backbone.blocks.70.output.projection.weight < ,Add_backbone.blocks.72.attention.dense3.bias  A -LayerNorm_backbone.blocks.72.layernorm2.gamma : *Add_backbone.blocks.71.output.mapping.bias  A -LayerNorm_backbone.blocks.73.layernorm2.gamma A /MatMul_backbone.blocks.75.output.mapping.weight G 5MatMul_backbone.blocks.73.attention.projection.weight < ,Add_backbone.blocks.73.attention.dense3.bias  < ,Add_backbone.blocks.70.attention.dense1.bias  C 1MatMul_backbone.blocks.70.attention.dense1.weight C 1MatMul_backbone.blocks.74.attention.dense1.weight C 1MatMul_backbone.blocks.70.attention.dense2.weight < ,Add_backbone.blocks.75.attention.dense2.bias  C 1MatMul_backbone.blocks.75.attention.dense3.weight @ 0Add_backbone.blocks.75.attention.projection.bias  C 1MatMul_backbone.blocks.73.attention.dense2.weight A /MatMul_backbone.blocks.70.output.mapping.weight D 2MatMul_backbone.blocks.71.output.projection.weight A -LayerNorm_backbone.blocks.71.layernorm1.gamma C 1MatMul_backbone.blocks.75.attention.dense2.weight < ,Add_backbone.blocks.74.attention.dense1.bias  C 1MatMul_backbone.blocks.74.attention.dense2.weight A -LayerNorm_backbone.blocks.70.layernorm1.gamma C 1MatMul_backbone.blocks.72.attention.dense3.weight A -LayerNorm_backbone.blocks.72.layernorm1.gamma = +Receive_backbone.blocks.70.layernorm1.gamma < ,Add_backbone.blocks.75.attention.dense3.bias  C 1MatMul_backbone.blocks.72.attention.dense1.weight A -LayerNorm_backbone.blocks.75.layernorm2.gamma D 2MatMul_backbone.blocks.73.output.projection.weight G 5MatMul_backbone.blocks.74.attention.projection.weight A -LayerNorm_backbone.blocks.73.layernorm1.gamma G 5MatMul_backbone.blocks.72.attention.projection.weight A -LayerNorm_backbone.blocks.75.layernorm1.gamma C 1MatMul_backbone.blocks.71.attention.dense1.weight : *Add_backbone.blocks.74.output.mapping.bias  C 1MatMul_backbone.blocks.72.attention.dense2.weight < ,Add_backbone.blocks.73.attention.dense2.bias  G 5MatMul_backbone.blocks.70.attention.projection.weight < ,Add_backbone.blocks.72.attention.dense1.bias  C 1MatMul_backbone.blocks.74.attention.dense3.weight = -Add_backbone.blocks.71.output.projection.bias  D 2MatMul_backbone.blocks.75.output.projection.weight @ 0Add_backbone.blocks.73.attention.projection.bias  < ,Add_backbone.blocks.74.attention.dense2.bias  @ 0Add_backbone.blocks.71.attention.projection.bias  < ,Add_backbone.blocks.71.attention.dense3.bias  < ,Add_backbone.blocks.70.attention.dense3.bias  @ 0Add_backbone.blocks.74.attention.projection.bias  < ,Add_backbone.blocks.71.attention.dense1.bias  = -Add_backbone.blocks.73.output.projection.bias  = -Add_backbone.blocks.74.output.projection.bias  D 2MatMul_backbone.blocks.74.output.projection.weight D 2MatMul_backbone.blocks.16.output.projection.weightA -LayerNorm_backbone.blocks.16.layernorm1.gammaC 1MatMul_backbone.blocks.18.attention.dense3.weightC 1MatMul_backbone.blocks.19.attention.dense1.weight< ,Add_backbone.blocks.16.attention.dense2.bias < ,Add_backbone.blocks.17.attention.dense3.bias D 2MatMul_backbone.blocks.17.output.projection.weight: *Add_backbone.blocks.16.output.mapping.bias D 2MatMul_backbone.blocks.20.output.projection.weight= -Add_backbone.blocks.17.output.projection.bias = -Add_backbone.blocks.18.output.projection.bias G 5MatMul_backbone.blocks.18.attention.projection.weightC 1MatMul_backbone.blocks.16.attention.dense2.weightD 2MatMul_backbone.blocks.19.output.projection.weightA /MatMul_backbone.blocks.16.output.mapping.weightA -LayerNorm_backbone.blocks.21.layernorm1.gamma< ,Add_backbone.blocks.16.attention.dense1.bias @ 0Add_backbone.blocks.20.attention.projection.bias : *Add_backbone.blocks.21.output.mapping.bias C 1MatMul_backbone.blocks.17.attention.dense1.weightA -LayerNorm_backbone.blocks.20.layernorm2.gamma@ 0Add_backbone.blocks.16.attention.projection.bias C 1MatMul_backbone.blocks.18.attention.dense2.weightA -LayerNorm_backbone.blocks.16.layernorm2.gamma: *Add_backbone.blocks.17.output.mapping.bias = -Add_backbone.blocks.20.output.projection.bias C 1MatMul_backbone.blocks.20.attention.dense3.weightC 1MatMul_backbone.blocks.21.attention.dense1.weight< ,Add_backbone.blocks.19.attention.dense3.bias < ,Add_backbone.blocks.17.attention.dense1.bias = -Add_backbone.blocks.21.output.projection.bias < ,Add_backbone.blocks.19.attention.dense1.bias D 2MatMul_backbone.blocks.21.output.projection.weight= -Add_backbone.blocks.19.output.projection.bias A /MatMul_backbone.blocks.17.output.mapping.weightA -LayerNorm_backbone.blocks.17.layernorm2.gammaC 1MatMul_backbone.blocks.17.attention.dense3.weight@ 0Add_backbone.blocks.19.attention.projection.bias A -LayerNorm_backbone.blocks.20.layernorm1.gammaG 5MatMul_backbone.blocks.16.attention.projection.weight= -Add_backbone.blocks.16.output.projection.bias C 1MatMul_backbone.blocks.20.attention.dense2.weightC 1MatMul_backbone.blocks.18.attention.dense1.weightA -LayerNorm_backbone.blocks.18.layernorm1.gamma: *Add_backbone.blocks.19.output.mapping.bias @ 0Add_backbone.blocks.21.attention.projection.bias D 2MatMul_backbone.blocks.18.output.projection.weight@ 0Add_backbone.blocks.17.attention.projection.bias A /MatMul_backbone.blocks.19.output.mapping.weight< ,Add_backbone.blocks.18.attention.dense3.bias = +Receive_backbone.blocks.16.layernorm1.gamma< ,Add_backbone.blocks.20.attention.dense2.bias < ,Add_backbone.blocks.19.attention.dense2.bias C 1MatMul_backbone.blocks.17.attention.dense2.weightA -LayerNorm_backbone.blocks.18.layernorm2.gammaC 1MatMul_backbone.blocks.19.attention.dense2.weightC 1MatMul_backbone.blocks.20.attention.dense1.weightA -LayerNorm_backbone.blocks.19.layernorm1.gamma< ,Add_backbone.blocks.20.attention.dense1.bias C 1MatMul_backbone.blocks.21.attention.dense3.weightA -LayerNorm_backbone.blocks.19.layernorm2.gammaC 1MatMul_backbone.blocks.19.attention.dense3.weight: *Add_backbone.blocks.20.output.mapping.bias < ,Add_backbone.blocks.16.attention.dense3.bias < ,Add_backbone.blocks.21.attention.dense2.bias < ,Add_backbone.blocks.17.attention.dense2.bias < ,Add_backbone.blocks.21.attention.dense1.bias A -LayerNorm_backbone.blocks.17.layernorm1.gammaC 1MatMul_backbone.blocks.16.attention.dense3.weight< ,Add_backbone.blocks.18.attention.dense2.bias G 5MatMul_backbone.blocks.19.attention.projection.weightA /MatMul_backbone.blocks.18.output.mapping.weightG 5MatMul_backbone.blocks.21.attention.projection.weightG 5MatMul_backbone.blocks.20.attention.projection.weight< ,Add_backbone.blocks.21.attention.dense3.bias @ 0Add_backbone.blocks.18.attention.projection.bias A -LayerNorm_backbone.blocks.21.layernorm2.gamma< ,Add_backbone.blocks.20.attention.dense3.bias C 1MatMul_backbone.blocks.21.attention.dense2.weightA /MatMul_backbone.blocks.20.output.mapping.weightC 1MatMul_backbone.blocks.16.attention.dense1.weight< ,Add_backbone.blocks.18.attention.dense1.bias A /MatMul_backbone.blocks.21.output.mapping.weight: *Add_backbone.blocks.18.output.mapping.bias G 5MatMul_backbone.blocks.17.attention.projection.weightA /MatMul_backbone.blocks.39.output.mapping.weightA -LayerNorm_backbone.blocks.37.layernorm2.gammaG 5MatMul_backbone.blocks.39.attention.projection.weight< ,Add_backbone.blocks.38.attention.dense3.bias < ,Add_backbone.blocks.39.attention.dense3.bias A -LayerNorm_backbone.blocks.37.layernorm1.gammaC 1MatMul_backbone.blocks.37.attention.dense1.weightC 1MatMul_backbone.blocks.34.attention.dense2.weightA -LayerNorm_backbone.blocks.36.layernorm2.gammaA /MatMul_backbone.blocks.36.output.mapping.weight: *Add_backbone.blocks.34.output.mapping.bias D 2MatMul_backbone.blocks.37.output.projection.weightC 1MatMul_backbone.blocks.36.attention.dense3.weightC 1MatMul_backbone.blocks.35.attention.dense1.weight= -Add_backbone.blocks.35.output.projection.bias A -LayerNorm_backbone.blocks.39.layernorm2.gammaA /MatMul_backbone.blocks.37.output.mapping.weight< ,Add_backbone.blocks.36.attention.dense1.bias < ,Add_backbone.blocks.37.attention.dense2.bias G 5MatMul_backbone.blocks.34.attention.projection.weight< ,Add_backbone.blocks.35.attention.dense3.bias C 1MatMul_backbone.blocks.35.attention.dense3.weight@ 0Add_backbone.blocks.36.attention.projection.bias A /MatMul_backbone.blocks.38.output.mapping.weightG 5MatMul_backbone.blocks.38.attention.projection.weight< ,Add_backbone.blocks.38.attention.dense1.bias C 1MatMul_backbone.blocks.37.attention.dense3.weight< ,Add_backbone.blocks.35.attention.dense2.bias C 1MatMul_backbone.blocks.34.attention.dense3.weightC 1MatMul_backbone.blocks.37.attention.dense2.weightC 1MatMul_backbone.blocks.38.attention.dense1.weightG 5MatMul_backbone.blocks.36.attention.projection.weight< ,Add_backbone.blocks.34.attention.dense2.bias A -LayerNorm_backbone.blocks.39.layernorm1.gammaD 2MatMul_backbone.blocks.35.output.projection.weightA /MatMul_backbone.blocks.34.output.mapping.weight@ 0Add_backbone.blocks.34.attention.projection.bias C 1MatMul_backbone.blocks.38.attention.dense3.weightD 2MatMul_backbone.blocks.36.output.projection.weight@ 0Add_backbone.blocks.37.attention.projection.bias C 1MatMul_backbone.blocks.36.attention.dense1.weight< ,Add_backbone.blocks.37.attention.dense3.bias @ 0Add_backbone.blocks.35.attention.projection.bias < ,Add_backbone.blocks.34.attention.dense1.bias : *Add_backbone.blocks.35.output.mapping.bias A -LayerNorm_backbone.blocks.34.layernorm1.gammaC 1MatMul_backbone.blocks.34.attention.dense1.weight= -Add_backbone.blocks.34.output.projection.bias < ,Add_backbone.blocks.36.attention.dense2.bias A -LayerNorm_backbone.blocks.38.layernorm1.gammaC 1MatMul_backbone.blocks.39.attention.dense1.weight: *Add_backbone.blocks.38.output.mapping.bias A -LayerNorm_backbone.blocks.35.layernorm1.gamma< ,Add_backbone.blocks.39.attention.dense1.bias < ,Add_backbone.blocks.34.attention.dense3.bias < ,Add_backbone.blocks.38.attention.dense2.bias = -Add_backbone.blocks.37.output.projection.bias < ,Add_backbone.blocks.35.attention.dense1.bias C 1MatMul_backbone.blocks.39.attention.dense3.weightA -LayerNorm_backbone.blocks.36.layernorm1.gammaC 1MatMul_backbone.blocks.39.attention.dense2.weightC 1MatMul_backbone.blocks.38.attention.dense2.weight@ 0Add_backbone.blocks.38.attention.projection.bias D 2MatMul_backbone.blocks.34.output.projection.weightD 2MatMul_backbone.blocks.39.output.projection.weightA -LayerNorm_backbone.blocks.34.layernorm2.gamma@ 0Add_backbone.blocks.39.attention.projection.bias : *Add_backbone.blocks.39.output.mapping.bias : *Add_backbone.blocks.36.output.mapping.bias C 1MatMul_backbone.blocks.35.attention.dense2.weight< ,Add_backbone.blocks.36.attention.dense3.bias < ,Add_backbone.blocks.37.attention.dense1.bias A -LayerNorm_backbone.blocks.35.layernorm2.gamma= -Add_backbone.blocks.38.output.projection.bias = -Add_backbone.blocks.39.output.projection.bias = -Add_backbone.blocks.36.output.projection.bias C 1MatMul_backbone.blocks.36.attention.dense2.weightG 5MatMul_backbone.blocks.35.attention.projection.weightA -LayerNorm_backbone.blocks.38.layernorm2.gamma= +Receive_backbone.blocks.34.layernorm1.gammaD 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1MatMul_backbone.blocks.11.attention.dense2.weightD 2MatMul_backbone.blocks.15.output.projection.weight< ,Add_backbone.blocks.10.attention.dense1.bias @ 0Add_backbone.blocks.10.attention.projection.bias A -LayerNorm_backbone.blocks.11.layernorm1.gammaA /MatMul_backbone.blocks.11.output.mapping.weightC 1MatMul_backbone.blocks.14.attention.dense1.weight< ,Add_backbone.blocks.12.attention.dense2.bias @ 0Add_backbone.blocks.12.attention.projection.bias C 1MatMul_backbone.blocks.15.attention.dense2.weight= -Add_backbone.blocks.10.output.projection.bias < ,Add_backbone.blocks.11.attention.dense1.bias : *Add_backbone.blocks.13.output.mapping.bias C 1MatMul_backbone.blocks.14.attention.dense2.weight@ 0Add_backbone.blocks.11.attention.projection.bias D 2MatMul_backbone.blocks.10.output.projection.weightD 2MatMul_backbone.blocks.11.output.projection.weightC 1MatMul_backbone.blocks.11.attention.dense1.weightC 1MatMul_backbone.blocks.11.attention.dense3.weight: 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1MatMul_backbone.blocks.88.attention.dense1.weightC 1MatMul_backbone.blocks.93.attention.dense3.weightA -LayerNorm_backbone.blocks.89.layernorm1.gamma< ,Add_backbone.blocks.89.attention.dense3.bias < ,Add_backbone.blocks.93.attention.dense1.bias C 1MatMul_backbone.blocks.89.attention.dense3.weight< ,Add_backbone.blocks.90.attention.dense2.bias C 1MatMul_backbone.blocks.89.attention.dense2.weight= -Add_backbone.blocks.90.output.projection.bias A /MatMul_backbone.blocks.88.output.mapping.weightA -LayerNorm_backbone.blocks.88.layernorm2.gamma< ,Add_backbone.blocks.92.attention.dense2.bias @ 0Add_backbone.blocks.88.attention.projection.bias A /MatMul_backbone.blocks.89.output.mapping.weight< ,Add_backbone.blocks.88.attention.dense3.bias < ,Add_backbone.blocks.91.attention.dense2.bias A -LayerNorm_backbone.blocks.88.layernorm1.gamma= -Add_backbone.blocks.88.output.projection.bias C 1MatMul_backbone.blocks.90.attention.dense3.weight< ,Add_backbone.blocks.93.attention.dense2.bias C 1MatMul_backbone.blocks.93.attention.dense2.weightG 5MatMul_backbone.blocks.89.attention.projection.weight< ,Add_backbone.blocks.93.attention.dense3.bias C 1MatMul_backbone.blocks.91.attention.dense2.weight< ,Add_backbone.blocks.90.attention.dense3.bias G 5MatMul_backbone.blocks.93.attention.projection.weightA /MatMul_backbone.blocks.92.output.mapping.weightD 2MatMul_backbone.blocks.89.output.projection.weightC 1MatMul_backbone.blocks.90.attention.dense1.weightG 5MatMul_backbone.blocks.88.attention.projection.weightA -LayerNorm_backbone.blocks.90.layernorm2.gammaC 1MatMul_backbone.blocks.92.attention.dense2.weight= -Add_backbone.blocks.93.output.projection.bias = -Add_backbone.blocks.91.output.projection.bias : *Add_backbone.blocks.91.output.mapping.bias < ,Add_backbone.blocks.92.attention.dense3.bias D 2MatMul_backbone.blocks.90.output.projection.weight: *Add_backbone.blocks.90.output.mapping.bias C 1MatMul_backbone.blocks.91.attention.dense3.weight@ 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/MatMul_backbone.blocks.77.output.mapping.weightA -LayerNorm_backbone.blocks.35.layernorm2.gamma@ 0Add_backbone.blocks.96.attention.projection.bias C 1MatMul_backbone.blocks.27.attention.dense2.weightD 2MatMul_backbone.blocks.102.attention.dense2.weightA /MatMul_backbone.blocks.42.output.mapping.weight@ 0Add_backbone.blocks.69.attention.projection.bias C 1MatMul_backbone.blocks.16.attention.dense3.weightA -LayerNorm_backbone.blocks.65.layernorm2.gamma= -Add_backbone.blocks.81.output.projection.bias = -Add_backbone.blocks.39.output.projection.bias C 1MatMul_backbone.blocks.41.attention.dense3.weight= -Add_backbone.blocks.100.attention.dense1.bias : *Add_backbone.blocks.47.output.mapping.bias < ,Add_backbone.blocks.51.attention.dense2.bias C 1MatMul_backbone.blocks.21.attention.dense2.weight@ 0Add_backbone.blocks.71.attention.projection.bias G 3LayerNorm_backbone.top_query_layer.layernorm2.gammaC 1MatMul_backbone.blocks.54.attention.dense1.weightD 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0Add_backbone.blocks.30.attention.projection.bias = -Add_backbone.blocks.26.output.projection.bias < ,Add_backbone.blocks.84.attention.dense1.bias < ,Add_backbone.blocks.31.attention.dense1.bias A -LayerNorm_backbone.blocks.13.layernorm2.gammaG 5MatMul_backbone.blocks.61.attention.projection.weight= -Add_backbone.blocks.82.output.projection.bias < ,Add_backbone.blocks.59.attention.dense3.bias @ 0Add_backbone.blocks.88.attention.projection.bias @ 0Add_backbone.blocks.51.attention.projection.bias C 1MatMul_backbone.blocks.60.attention.dense1.weightC 1MatMul_backbone.blocks.64.attention.dense1.weight: *Add_backbone.blocks.70.output.mapping.bias G 5MatMul_backbone.blocks.38.attention.projection.weightG 5MatMul_backbone.blocks.36.attention.projection.weightA -LayerNorm_backbone.blocks.15.layernorm1.gammaC 1MatMul_backbone.blocks.71.attention.dense3.weightD 2MatMul_backbone.blocks.40.output.projection.weightC 1MatMul_backbone.blocks.73.attention.dense3.weight< 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1MatMul_backbone.blocks.13.attention.dense3.weightG 5MatMul_backbone.blocks.86.attention.projection.weightA /MatMul_backbone.blocks.98.output.mapping.weight< ,Add_backbone.blocks.13.attention.dense1.bias @ .MatMul_backbone.blocks.0.output.mapping.weight@ 0Add_backbone.blocks.70.attention.projection.bias < ,Add_backbone.blocks.30.attention.dense3.bias C 1MatMul_backbone.blocks.93.attention.dense2.weight: *Add_backbone.blocks.28.output.mapping.bias < ,Add_backbone.blocks.58.attention.dense2.bias < ,Add_backbone.blocks.17.attention.dense1.bias B 0MatMul_backbone.blocks.7.attention.dense2.weightA -LayerNorm_backbone.blocks.42.layernorm1.gamma< ,Add_backbone.blocks.75.attention.dense1.bias A /MatMul_backbone.blocks.69.output.mapping.weightD 2MatMul_backbone.blocks.36.output.projection.weightA -LayerNorm_backbone.blocks.69.layernorm1.gammaD 2MatMul_backbone.blocks.100.attention.dense1.weightG 5MatMul_backbone.blocks.88.attention.projection.weightA -LayerNorm_backbone.blocks.44.layernorm2.gammaA -LayerNorm_backbone.blocks.24.layernorm1.gamma< ,Add_backbone.blocks.37.attention.dense3.bias : *Add_backbone.blocks.35.output.mapping.bias = -Add_backbone.blocks.53.output.projection.bias C 1MatMul_backbone.blocks.26.attention.dense2.weight= -Add_backbone.blocks.79.output.projection.bias < ,Add_backbone.blocks.86.attention.dense1.bias C 1MatMul_backbone.blocks.66.attention.dense1.weightC 1MatMul_backbone.blocks.20.attention.dense2.weight: *Add_backbone.blocks.25.output.mapping.bias C 1MatMul_backbone.blocks.75.attention.dense3.weightG 5MatMul_backbone.blocks.68.attention.projection.weight< ,Add_backbone.blocks.20.attention.dense2.bias < ,Add_backbone.blocks.80.attention.dense1.bias C 1MatMul_backbone.blocks.49.attention.dense3.weightA -LayerNorm_backbone.blocks.76.layernorm2.gamma< ,Add_backbone.blocks.85.attention.dense2.bias < ,Add_backbone.blocks.42.attention.dense2.bias < ,Add_backbone.blocks.60.attention.dense1.bias C 1MatMul_backbone.blocks.21.attention.dense3.weight@ 0Add_backbone.blocks.28.attention.projection.bias F 4MatMul_backbone.blocks.9.attention.projection.weight< ,Add_backbone.blocks.54.attention.dense1.bias D 2MatMul_backbone.blocks.68.output.projection.weightA /MatMul_backbone.blocks.52.output.mapping.weight= -Add_backbone.blocks.44.output.projection.bias @ ,LayerNorm_backbone.blocks.2.layernorm2.gamma; +Add_backbone.blocks.0.attention.dense1.bias C 1MatMul_backbone.blocks.28.attention.dense2.weightA -LayerNorm_backbone.blocks.75.layernorm1.gamma@ 0Add_backbone.blocks.79.attention.projection.bias A -LayerNorm_backbone.blocks.48.layernorm2.gamma9 )Add_backbone.blocks.1.output.mapping.bias ; +Add_backbone.blocks.3.attention.dense1.bias C 1MatMul_backbone.blocks.45.attention.dense1.weight< ,Add_backbone.blocks.83.attention.dense3.bias A -LayerNorm_backbone.blocks.69.layernorm2.gammaG 5MatMul_backbone.blocks.70.attention.projection.weight; +Add_backbone.blocks.1.attention.dense2.bias C 1MatMul_backbone.blocks.68.attention.dense1.weight< ,Add_backbone.blocks.11.attention.dense3.bias C 1MatMul_backbone.blocks.88.attention.dense2.weightA /MatMul_backbone.blocks.97.output.mapping.weightG 5MatMul_backbone.blocks.28.attention.projection.weight: *Add_backbone.blocks.78.output.mapping.bias D 2MatMul_backbone.blocks.38.output.projection.weight@ 0Add_backbone.blocks.32.attention.projection.bias < ,Add_backbone.blocks.81.attention.dense3.bias : *Add_backbone.blocks.37.output.mapping.bias A /MatMul_backbone.blocks.21.output.mapping.weightA -LayerNorm_backbone.blocks.50.layernorm2.gammaG 5MatMul_backbone.blocks.13.attention.projection.weightD 2MatMul_backbone.blocks.92.output.projection.weight@ ,LayerNorm_backbone.blocks.1.layernorm1.gammaA -LayerNorm_backbone.blocks.45.layernorm1.gamma@ 0Add_backbone.blocks.41.attention.projection.bias < ,Add_backbone.blocks.56.attention.dense1.bias : *Add_backbone.blocks.40.output.mapping.bias C 1MatMul_backbone.blocks.1.output.projection.weight: *Add_backbone.blocks.16.output.mapping.bias @ 0Add_backbone.blocks.40.attention.projection.bias = -Add_backbone.blocks.17.output.projection.bias G 5MatMul_backbone.blocks.55.attention.projection.weightC 1MatMul_backbone.blocks.46.attention.dense2.weight: *Add_backbone.blocks.10.output.mapping.bias < ,Add_backbone.blocks.93.attention.dense1.bias C 1MatMul_backbone.blocks.50.attention.dense3.weightA -LayerNorm_backbone.blocks.78.layernorm2.gammaA /MatMul_backbone.blocks.16.output.mapping.weight= -Add_backbone.blocks.35.output.projection.bias < ,Add_backbone.blocks.24.attention.dense2.bias : *Add_backbone.blocks.44.output.mapping.bias A -LayerNorm_backbone.blocks.62.layernorm1.gammaA -LayerNorm_backbone.blocks.65.layernorm1.gamma< ,Add_backbone.blocks.78.attention.dense2.bias < ,Add_backbone.blocks.23.attention.dense2.bias < ,Add_backbone.blocks.22.attention.dense3.bias = -Add_backbone.blocks.48.output.projection.bias < ,Add_backbone.blocks.62.attention.dense2.bias < ,Add_backbone.blocks.82.attention.dense3.bias C 1MatMul_backbone.blocks.38.attention.dense1.weight< ,Add_backbone.blocks.49.attention.dense2.bias C 1MatMul_backbone.blocks.95.attention.dense2.weightC 1MatMul_backbone.blocks.32.attention.dense3.weight= -Add_backbone.blocks.100.attention.dense3.bias = -Add_backbone.blocks.47.output.projection.bias @ 0Add_backbone.blocks.68.attention.projection.bias A -LayerNorm_backbone.blocks.54.layernorm2.gamma= -Add_backbone.blocks.12.output.projection.bias C 1MatMul_backbone.blocks.12.attention.dense1.weightG 5MatMul_backbone.blocks.75.attention.projection.weight@ 0Add_backbone.blocks.37.attention.projection.bias C 1MatMul_backbone.blocks.24.attention.dense1.weightC 1MatMul_backbone.blocks.92.attention.dense2.weight= -Add_backbone.blocks.19.output.projection.bias : *Add_backbone.blocks.66.output.mapping.bias D 2MatMul_backbone.blocks.51.output.projection.weight< ,Add_backbone.blocks.34.attention.dense1.bias D 2MatMul_backbone.blocks.45.output.projection.weight< ,Add_backbone.blocks.94.attention.dense1.bias C 1MatMul_backbone.blocks.14.attention.dense1.weight< ,Add_backbone.blocks.84.attention.dense3.bias A /MatMul_backbone.blocks.81.output.mapping.weight< ,Add_backbone.blocks.41.attention.dense3.bias < ,Add_backbone.blocks.55.attention.dense2.bias @ 0Add_backbone.blocks.11.attention.projection.bias < ,Add_backbone.blocks.47.attention.dense1.bias < ,Add_backbone.blocks.34.attention.dense3.bias C 1MatMul_backbone.blocks.26.attention.dense1.weightC 1MatMul_backbone.blocks.87.attention.dense1.weight< ,Add_backbone.blocks.95.attention.dense1.bias D 2MatMul_backbone.blocks.23.output.projection.weightG 5MatMul_backbone.blocks.53.attention.projection.weightC 1MatMul_backbone.blocks.57.attention.dense1.weight< ,Add_backbone.blocks.74.attention.dense1.bias C 1MatMul_backbone.blocks.38.attention.dense2.weightC 1MatMul_backbone.blocks.82.attention.dense2.weight< ,Add_backbone.blocks.4.output.projection.bias C 1MatMul_backbone.blocks.49.attention.dense1.weight< ,Add_backbone.blocks.65.attention.dense2.bias @ 0Add_backbone.blocks.53.attention.projection.bias G 5MatMul_backbone.blocks.11.attention.projection.weightA /MatMul_backbone.blocks.59.output.mapping.weightA /MatMul_backbone.blocks.23.output.mapping.weight< ,Add_backbone.blocks.40.attention.dense3.bias : *Add_backbone.blocks.45.output.mapping.bias D 2MatMul_backbone.blocks.73.output.projection.weight< ,Add_backbone.blocks.21.attention.dense2.bias G 5MatMul_backbone.blocks.72.attention.projection.weightA -LayerNorm_backbone.blocks.13.layernorm1.gamma< ,Add_backbone.blocks.54.attention.dense2.bias @ 0Add_backbone.blocks.46.attention.projection.bias G 5MatMul_backbone.blocks.63.attention.projection.weightC 1MatMul_backbone.blocks.56.attention.dense1.weight< ,Add_backbone.blocks.89.attention.dense1.bias C 1MatMul_backbone.blocks.74.attention.dense3.weight@ 0Add_backbone.blocks.73.attention.projection.bias @ ,LayerNorm_backbone.blocks.5.layernorm1.gammaC 1MatMul_backbone.blocks.52.attention.dense3.weight@ ,LayerNorm_backbone.blocks.6.layernorm2.gammaB .LayerNorm_backbone.blocks.101.layernorm1.gammaD 2MatMul_backbone.blocks.44.output.projection.weightD 2MatMul_backbone.blocks.83.output.projection.weight< ,Add_backbone.blocks.71.attention.dense1.bias = -Add_backbone.blocks.73.output.projection.bias < ,Add_backbone.blocks.39.attention.dense2.bias A -LayerNorm_backbone.blocks.99.layernorm2.gammaD 2MatMul_backbone.blocks.24.output.projection.weightA -LayerNorm_backbone.blocks.89.layernorm2.gamma< ,Add_backbone.blocks.90.attention.dense1.bias A -LayerNorm_backbone.blocks.47.layernorm2.gamma: *Add_backbone.blocks.57.output.mapping.bias < ,Add_backbone.blocks.39.attention.dense3.bias C 1MatMul_backbone.blocks.71.attention.dense2.weightM ;MatMul_backbone.top_query_layer.attention.projection.weightC 1MatMul_backbone.blocks.13.attention.dense2.weightA /MatMul_backbone.blocks.87.output.mapping.weight: *Add_backbone.blocks.72.output.mapping.bias C 1MatMul_backbone.blocks.61.attention.dense2.weightC 1MatMul_backbone.blocks.36.attention.dense3.weight: *Add_backbone.blocks.22.output.mapping.bias : *Add_backbone.blocks.24.output.mapping.bias < ,Add_backbone.blocks.55.attention.dense3.bias : *Add_backbone.blocks.21.output.mapping.bias A /MatMul_backbone.blocks.67.output.mapping.weight@ 0Add_backbone.blocks.81.attention.projection.bias C 1MatMul_backbone.blocks.51.attention.dense1.weight@ 0Add_backbone.blocks.36.attention.projection.bias A -LayerNorm_backbone.blocks.25.layernorm1.gammaB 0MatMul_backbone.blocks.5.attention.dense1.weightC 1MatMul_backbone.blocks.40.attention.dense3.weightA -LayerNorm_backbone.blocks.84.layernorm1.gammaB 2Add_backbone.top_query_layer.attention.dense2.bias C 1MatMul_backbone.blocks.43.attention.dense2.weightD 2MatMul_backbone.blocks.102.attention.dense3.weightA 1Add_backbone.blocks.100.attention.projection.bias = -Add_backbone.blocks.72.output.projection.bias C 1MatMul_backbone.blocks.90.attention.dense1.weightC 1MatMul_backbone.blocks.84.attention.dense1.weight< ,Add_backbone.blocks.53.attention.dense1.bias A /MatMul_backbone.blocks.32.output.mapping.weightD 2MatMul_backbone.blocks.70.output.projection.weightD 2MatMul_backbone.blocks.82.output.projection.weightA /MatMul_backbone.blocks.11.output.mapping.weightA -LayerNorm_backbone.blocks.95.layernorm2.gammaA /MatMul_backbone.blocks.83.output.mapping.weightC 1MatMul_backbone.blocks.34.attention.dense1.weightC 1MatMul_backbone.blocks.15.attention.dense2.weight< ,Add_backbone.blocks.11.attention.dense1.bias A -LayerNorm_backbone.blocks.85.layernorm1.gammaA -LayerNorm_backbone.blocks.87.layernorm2.gammaC 1MatMul_backbone.blocks.32.attention.dense2.weightD 2MatMul_backbone.blocks.67.output.projection.weight: *Add_backbone.blocks.82.output.mapping.bias : *Add_backbone.blocks.11.output.mapping.bias < ,Add_backbone.blocks.12.attention.dense1.bias G 5MatMul_backbone.blocks.45.attention.projection.weight@ ,LayerNorm_backbone.blocks.4.layernorm2.gamma: *Add_backbone.blocks.92.output.mapping.bias = -Add_backbone.blocks.68.output.projection.bias @ 0Add_backbone.blocks.38.attention.projection.bias F 6Add_backbone.top_query_layer.attention.projection.bias = -Add_backbone.blocks.14.output.projection.bias C 1MatMul_backbone.blocks.13.attention.dense1.weightD 2MatMul_backbone.blocks.43.output.projection.weightA -LayerNorm_backbone.blocks.68.layernorm2.gammaC 1MatMul_backbone.blocks.62.attention.dense3.weight< ,Add_backbone.blocks.58.attention.dense3.bias I 7MatMul_backbone.top_query_layer.attention.dense2.weightC 1MatMul_backbone.blocks.2.output.projection.weightA -LayerNorm_backbone.blocks.33.layernorm1.gamma< ,Add_backbone.blocks.17.attention.dense2.bias C 1MatMul_backbone.blocks.28.attention.dense3.weightA /MatMul_backbone.blocks.86.output.mapping.weightA -LayerNorm_backbone.blocks.96.layernorm1.gammaC 1MatMul_backbone.blocks.10.attention.dense2.weight@ ,LayerNorm_backbone.blocks.0.layernorm2.gamma: *Add_backbone.blocks.74.output.mapping.bias A /MatMul_backbone.blocks.99.output.mapping.weightG 5MatMul_backbone.blocks.21.attention.projection.weightG 5MatMul_backbone.blocks.67.attention.projection.weight< ,Add_backbone.blocks.72.attention.dense1.bias @ 0Add_backbone.blocks.23.attention.projection.bias ; +Add_backbone.blocks.1.attention.dense3.bias D 2MatMul_backbone.blocks.56.output.projection.weight; +Add_backbone.blocks.5.attention.dense1.bias < ,Add_backbone.blocks.28.attention.dense3.bias : *Add_backbone.blocks.15.output.mapping.bias B 0MatMul_backbone.blocks.1.attention.dense1.weightD 2MatMul_backbone.blocks.80.output.projection.weightA -LayerNorm_backbone.blocks.22.layernorm1.gammaC 1MatMul_backbone.blocks.19.attention.dense1.weight< ,Add_backbone.blocks.97.attention.dense2.bias D 2MatMul_backbone.blocks.102.attention.dense1.weight9 )Add_backbone.blocks.7.output.mapping.bias A /MatMul_backbone.blocks.40.output.mapping.weightD 2MatMul_backbone.blocks.46.output.projection.weight< ,Add_backbone.blocks.25.attention.dense2.bias ; +Add_backbone.blocks.3.attention.dense3.bias ; +Add_backbone.blocks.9.attention.dense2.bias < ,Add_backbone.blocks.87.attention.dense1.bias A -LayerNorm_backbone.blocks.49.layernorm1.gamma< ,Add_backbone.blocks.32.attention.dense2.bias @ 0Add_backbone.blocks.87.attention.projection.bias : *Add_backbone.blocks.43.output.mapping.bias C 1MatMul_backbone.blocks.65.attention.dense1.weightD 2MatMul_backbone.blocks.37.output.projection.weight< ,Add_backbone.blocks.63.attention.dense3.bias : *Add_backbone.blocks.23.output.mapping.bias < ,Add_backbone.blocks.16.attention.dense1.bias C 1MatMul_backbone.blocks.46.attention.dense1.weight@ 0Add_backbone.blocks.83.attention.projection.bias C 1MatMul_backbone.blocks.41.attention.dense2.weight: *Add_backbone.blocks.17.output.mapping.bias = -Add_backbone.blocks.33.output.projection.bias A -LayerNorm_backbone.blocks.82.layernorm2.gamma@ 0Add_backbone.top_query_layer.output.mapping.bias = -Add_backbone.blocks.55.output.projection.bias C 1MatMul_backbone.blocks.56.attention.dense2.weightI 7MatMul_backbone.top_query_layer.attention.dense1.weight= -Add_backbone.blocks.88.output.projection.bias B 0MatMul_backbone.blocks.6.attention.dense2.weight< ,Add_backbone.blocks.93.attention.dense2.bias C 1MatMul_backbone.blocks.67.attention.dense1.weightA -LayerNorm_backbone.blocks.54.layernorm1.gammaC 1MatMul_backbone.blocks.80.attention.dense3.weightG 5MatMul_backbone.blocks.93.attention.projection.weight< ,Add_backbone.blocks.71.attention.dense2.bias B .LayerNorm_backbone.blocks.102.layernorm1.gamma< ,Add_backbone.blocks.80.attention.dense2.bias C 1MatMul_backbone.blocks.76.attention.dense3.weightA -LayerNorm_backbone.blocks.78.layernorm1.gammaA -LayerNorm_backbone.blocks.94.layernorm2.gammaA -LayerNorm_backbone.blocks.73.layernorm2.gammaA -LayerNorm_backbone.blocks.59.layernorm2.gamma< ,Add_backbone.blocks.65.attention.dense3.bias A -LayerNorm_backbone.blocks.61.layernorm2.gammaB 0MatMul_backbone.blocks.0.attention.dense2.weightC 1MatMul_backbone.blocks.23.attention.dense1.weight< ,Add_backbone.blocks.62.attention.dense1.bias < ,Add_backbone.blocks.0.output.projection.bias : *Add_backbone.blocks.83.output.mapping.bias G 5MatMul_backbone.blocks.99.attention.projection.weight< ,Add_backbone.blocks.95.attention.dense2.bias F 4MatMul_backbone.blocks.6.attention.projection.weightC 1MatMul_backbone.blocks.26.attention.dense3.weightC 1MatMul_backbone.blocks.47.attention.dense2.weight@ 0Add_backbone.blocks.17.attention.projection.bias G 5MatMul_backbone.blocks.90.attention.projection.weight@ 0Add_backbone.blocks.24.attention.projection.bias ; +Add_backbone.blocks.8.attention.dense2.bias A -LayerNorm_backbone.blocks.71.layernorm1.gamma< ,Add_backbone.blocks.53.attention.dense2.bias < ,Add_backbone.blocks.62.attention.dense3.bias A -LayerNorm_backbone.blocks.18.layernorm2.gamma: *Add_backbone.blocks.98.output.mapping.bias C 1MatMul_backbone.blocks.75.attention.dense2.weightA -LayerNorm_backbone.blocks.70.layernorm1.gammaA -LayerNorm_backbone.blocks.40.layernorm2.gammaC 1MatMul_backbone.blocks.30.attention.dense1.weightA -LayerNorm_backbone.blocks.34.layernorm2.gammaA -LayerNorm_backbone.blocks.32.layernorm2.gamma< ,Add_backbone.blocks.96.attention.dense1.bias : *Add_backbone.blocks.39.output.mapping.bias < ,Add_backbone.blocks.59.attention.dense1.bias < ,Add_backbone.blocks.82.attention.dense2.bias < ,Add_backbone.blocks.40.attention.dense1.bias B 0MatMul_backbone.blocks.7.attention.dense3.weightA -LayerNorm_backbone.blocks.75.layernorm2.gammaC 1MatMul_backbone.blocks.40.attention.dense2.weight@ 0Add_backbone.blocks.54.attention.projection.bias D 2MatMul_backbone.blocks.14.output.projection.weight< ,Add_backbone.blocks.44.attention.dense2.bias G 5MatMul_backbone.blocks.74.attention.projection.weight< ,Add_backbone.blocks.77.attention.dense2.bias = -Add_backbone.blocks.61.output.projection.bias @ 0Add_backbone.blocks.45.attention.projection.bias C 1MatMul_backbone.blocks.99.attention.dense3.weightC 1MatMul_backbone.blocks.71.attention.dense1.weight< ,Add_backbone.blocks.50.attention.dense1.bias = -Add_backbone.blocks.36.output.projection.bias G 5MatMul_backbone.blocks.25.attention.projection.weightA -LayerNorm_backbone.blocks.67.layernorm1.gamma< ,Add_backbone.blocks.30.attention.dense1.bias C 1MatMul_backbone.blocks.41.attention.dense1.weight@ 0Add_backbone.blocks.76.attention.projection.bias D 2MatMul_backbone.blocks.101.attention.dense2.weightB 0MatMul_backbone.blocks.100.output.mapping.weight@ 0Add_backbone.blocks.49.attention.projection.bias A -LayerNorm_backbone.blocks.63.layernorm2.gammaG 5MatMul_backbone.blocks.35.attention.projection.weightB 0MatMul_backbone.blocks.2.attention.dense3.weight< ,Add_backbone.blocks.76.attention.dense1.bias C 1MatMul_backbone.blocks.31.attention.dense1.weight< ,Add_backbone.blocks.78.attention.dense3.bias A -LayerNorm_backbone.blocks.81.layernorm2.gammaG 5MatMul_backbone.blocks.71.attention.projection.weightC 1MatMul_backbone.blocks.30.attention.dense3.weightD 2MatMul_backbone.blocks.28.output.projection.weightD 2MatMul_backbone.blocks.20.output.projection.weight< ,Add_backbone.blocks.28.attention.dense1.bias A -LayerNorm_backbone.blocks.37.layernorm1.gammaC 1MatMul_backbone.blocks.42.attention.dense2.weightG 5MatMul_backbone.blocks.30.attention.projection.weightA -LayerNorm_backbone.blocks.14.layernorm2.gamma< ,Add_backbone.blocks.73.attention.dense1.bias A -LayerNorm_backbone.blocks.77.layernorm1.gammaA -LayerNorm_backbone.blocks.66.layernorm1.gammaB 2Add_backbone.top_query_layer.attention.dense3.bias A -LayerNorm_backbone.blocks.60.layernorm1.gammaA -LayerNorm_backbone.blocks.85.layernorm2.gamma; +Add_backbone.blocks.6.attention.dense1.bias A /MatMul_backbone.blocks.96.output.mapping.weight= -Add_backbone.blocks.52.output.projection.bias @ 0Add_backbone.blocks.42.attention.projection.bias @ .MatMul_backbone.blocks.6.output.mapping.weightA /MatMul_backbone.blocks.88.output.mapping.weightD 2MatMul_backbone.blocks.59.output.projection.weightC 1MatMul_backbone.blocks.53.attention.dense3.weight; +Add_backbone.blocks.5.attention.dense2.bias A /MatMul_backbone.blocks.78.output.mapping.weight< ,Add_backbone.blocks.35.attention.dense3.bias = -Add_backbone.blocks.20.output.projection.bias G 5MatMul_backbone.blocks.85.attention.projection.weightC 1MatMul_backbone.blocks.55.attention.dense3.weight< ,Add_backbone.blocks.52.attention.dense3.bias < ,Add_backbone.blocks.8.output.projection.bias : *Add_backbone.blocks.29.output.mapping.bias C 1MatMul_backbone.blocks.37.attention.dense3.weight= -Add_backbone.blocks.43.output.projection.bias C 1MatMul_backbone.blocks.82.attention.dense1.weight= -Add_backbone.blocks.50.output.projection.bias < ,Add_backbone.blocks.12.attention.dense3.bias C 1MatMul_backbone.blocks.49.attention.dense2.weight= -Add_backbone.blocks.24.output.projection.bias < ,Add_backbone.blocks.66.attention.dense3.bias A /MatMul_backbone.blocks.74.output.mapping.weightD 2MatMul_backbone.blocks.58.output.projection.weightD 2MatMul_backbone.blocks.65.output.projection.weight< ,Add_backbone.blocks.72.attention.dense3.bias A 1Add_backbone.blocks.101.attention.projection.bias A -LayerNorm_backbone.blocks.34.layernorm1.gamma@ 0Add_backbone.blocks.82.attention.projection.bias G 5MatMul_backbone.blocks.95.attention.projection.weightG 5MatMul_backbone.blocks.59.attention.projection.weightA -LayerNorm_backbone.blocks.62.layernorm2.gamma< ,Add_backbone.blocks.33.attention.dense3.bias C 1MatMul_backbone.blocks.22.attention.dense1.weight< ,Add_backbone.blocks.2.output.projection.bias B 0MatMul_backbone.blocks.5.attention.dense3.weightC 1MatMul_backbone.blocks.88.attention.dense3.weight= -Add_backbone.blocks.28.output.projection.bias C 1MatMul_backbone.blocks.76.attention.dense1.weight@ ,LayerNorm_backbone.blocks.7.layernorm1.gammaC 1MatMul_backbone.blocks.67.attention.dense3.weightA -LayerNorm_backbone.blocks.35.layernorm1.gamma< ,Add_backbone.blocks.39.attention.dense1.bias = -Add_backbone.blocks.60.output.projection.bias B 0MatMul_backbone.blocks.102.output.mapping.weight= -Add_backbone.blocks.78.output.projection.bias C 1MatMul_backbone.blocks.79.attention.dense2.weight: *Add_backbone.blocks.88.output.mapping.bias A /MatMul_backbone.blocks.47.output.mapping.weightD 2MatMul_backbone.blocks.99.output.projection.weightA -LayerNorm_backbone.blocks.26.layernorm1.gammaC 1MatMul_backbone.blocks.11.attention.dense3.weight= -Add_backbone.blocks.99.output.projection.bias < ,Add_backbone.blocks.15.attention.dense1.bias C 1MatMul_backbone.blocks.84.attention.dense2.weightA -LayerNorm_backbone.blocks.23.layernorm1.gamma@ 0Add_backbone.blocks.31.attention.projection.bias < ,Add_backbone.blocks.51.attention.dense1.bias C 1MatMul_backbone.blocks.51.attention.dense3.weight@ 0Add_backbone.blocks.90.attention.projection.bias C 1MatMul_backbone.blocks.35.attention.dense2.weight@ ,LayerNorm_backbone.blocks.4.layernorm1.gamma@ ,LayerNorm_backbone.blocks.9.layernorm2.gamma< ,Add_backbone.blocks.36.attention.dense3.bias A -LayerNorm_backbone.blocks.27.layernorm1.gamma< ,Add_backbone.blocks.94.attention.dense2.bias = -Add_backbone.blocks.62.output.projection.bias G 5MatMul_backbone.blocks.79.attention.projection.weight< ,Add_backbone.blocks.37.attention.dense1.bias < ,Add_backbone.blocks.29.attention.dense3.bias A -LayerNorm_backbone.blocks.17.layernorm1.gamma@ 0Add_backbone.blocks.99.attention.projection.bias < ,Add_backbone.blocks.10.attention.dense3.bias D 2MatMul_backbone.blocks.78.output.projection.weight: *Add_backbone.blocks.33.output.mapping.bias A -LayerNorm_backbone.blocks.96.layernorm2.gammaD 2MatMul_backbone.blocks.69.output.projection.weightA /MatMul_backbone.blocks.95.output.mapping.weightC 1MatMul_backbone.blocks.87.attention.dense3.weightD 2MatMul_backbone.blocks.75.output.projection.weight@ 0Add_backbone.blocks.18.attention.projection.bias G 5MatMul_backbone.blocks.58.attention.projection.weight= -Add_backbone.blocks.23.output.projection.bias @ 0Add_backbone.blocks.63.attention.projection.bias G 5MatMul_backbone.blocks.60.attention.projection.weight: *Add_backbone.blocks.62.output.mapping.bias G 5MatMul_backbone.blocks.17.attention.projection.weight< ,Add_backbone.blocks.27.attention.dense1.bias = -Add_backbone.blocks.70.output.projection.bias = -Add_backbone.blocks.11.output.projection.bias C 1MatMul_backbone.blocks.18.attention.dense3.weight: *Add_backbone.blocks.61.output.mapping.bias C 1MatMul_backbone.blocks.88.attention.dense1.weightB 0MatMul_backbone.blocks.0.attention.dense3.weightG 5MatMul_backbone.blocks.18.attention.projection.weightA -LayerNorm_backbone.blocks.79.layernorm2.gamma@ .MatMul_backbone.blocks.4.output.mapping.weightC 1MatMul_backbone.blocks.70.attention.dense3.weightD 2MatMul_backbone.blocks.100.attention.dense2.weightC 1MatMul_backbone.blocks.75.attention.dense1.weightA -LayerNorm_backbone.blocks.83.layernorm1.gammaC 1MatMul_backbone.blocks.63.attention.dense1.weight< ,Add_backbone.blocks.60.attention.dense2.bias = -Add_backbone.blocks.25.output.projection.bias A -LayerNorm_backbone.blocks.39.layernorm2.gammaB 0MatMul_backbone.blocks.9.attention.dense2.weight< ,Add_backbone.blocks.36.attention.dense1.bias 9 )Add_backbone.blocks.2.output.mapping.bias @ 0Add_backbone.blocks.33.attention.projection.bias C 1MatMul_backbone.blocks.79.attention.dense3.weight< ,Add_backbone.blocks.85.attention.dense1.bias C 1MatMul_backbone.blocks.96.attention.dense3.weightA /MatMul_backbone.blocks.34.output.mapping.weightF 4MatMul_backbone.blocks.0.attention.projection.weightA /MatMul_backbone.blocks.72.output.mapping.weightA -LayerNorm_backbone.blocks.33.layernorm2.gammaB 0MatMul_backbone.blocks.4.attention.dense1.weight< ,Add_backbone.blocks.22.attention.dense2.bias C 1MatMul_backbone.blocks.94.attention.dense1.weight< ,Add_backbone.blocks.26.attention.dense1.bias C 1MatMul_backbone.blocks.11.attention.dense2.weightA /MatMul_backbone.blocks.22.output.mapping.weightF 4MatMul_backbone.blocks.2.attention.projection.weightA -LayerNorm_backbone.blocks.90.layernorm2.gamma< ,Add_backbone.blocks.47.attention.dense2.bias C 1MatMul_backbone.blocks.44.attention.dense3.weight< ,Add_backbone.blocks.3.output.projection.bias @ 0Add_backbone.blocks.19.attention.projection.bias C 1MatMul_backbone.blocks.93.attention.dense1.weightC 1MatMul_backbone.blocks.70.attention.dense1.weightC 1MatMul_backbone.blocks.59.attention.dense1.weight< ,Add_backbone.blocks.77.attention.dense1.bias C 1MatMul_backbone.blocks.39.attention.dense1.weightA -LayerNorm_backbone.blocks.67.layernorm2.gammaA /MatMul_backbone.blocks.82.output.mapping.weightD 2MatMul_backbone.blocks.18.output.projection.weightC 1MatMul_backbone.blocks.92.attention.dense3.weight= -Add_backbone.blocks.37.output.projection.bias C 1MatMul_backbone.blocks.53.attention.dense2.weightA -LayerNorm_backbone.blocks.52.layernorm1.gamma@ .MatMul_backbone.blocks.3.output.mapping.weightC 1MatMul_backbone.blocks.44.attention.dense2.weightC 1MatMul_backbone.blocks.19.attention.dense2.weightC 1MatMul_backbone.blocks.85.attention.dense1.weightA -LayerNorm_backbone.blocks.23.layernorm2.gammaA -LayerNorm_backbone.blocks.61.layernorm1.gammaC 1MatMul_backbone.blocks.58.attention.dense1.weight= -Add_backbone.blocks.101.attention.dense2.bias A -LayerNorm_backbone.blocks.72.layernorm1.gamma< ,Add_backbone.blocks.25.attention.dense1.bias A -LayerNorm_backbone.blocks.19.layernorm2.gamma< ,Add_backbone.blocks.76.attention.dense2.bias @ 0Add_backbone.blocks.52.attention.projection.bias = -Add_backbone.blocks.54.output.projection.bias A -LayerNorm_backbone.blocks.11.layernorm2.gammaI 7MatMul_backbone.top_query_layer.attention.dense3.weightD 2MatMul_backbone.blocks.88.output.projection.weight= -Add_backbone.blocks.38.output.projection.bias G 5MatMul_backbone.blocks.52.attention.projection.weightC 1MatMul_backbone.blocks.77.attention.dense3.weight< ,Add_backbone.blocks.49.attention.dense3.bias = -Add_backbone.blocks.71.output.projection.bias A /MatMul_backbone.blocks.68.output.mapping.weight= -Add_backbone.blocks.13.output.projection.bias < ,Add_backbone.blocks.89.attention.dense2.bias A /MatMul_backbone.blocks.54.output.mapping.weightA -LayerNorm_backbone.blocks.38.layernorm2.gammaF 4MatMul_backbone.blocks.5.attention.projection.weightC 1MatMul_backbone.blocks.99.attention.dense2.weightG 5MatMul_backbone.blocks.87.attention.projection.weightA -LayerNorm_backbone.blocks.97.layernorm2.gammaA -LayerNorm_backbone.blocks.40.layernorm1.gammaG 5MatMul_backbone.blocks.62.attention.projection.weight< ,Add_backbone.blocks.74.attention.dense3.bias < ,Add_backbone.blocks.69.attention.dense3.bias D 2MatMul_backbone.blocks.17.output.projection.weightC 1MatMul_backbone.blocks.12.attention.dense2.weightA /MatMul_backbone.blocks.57.output.mapping.weightC 1MatMul_backbone.blocks.83.attention.dense2.weightC 1MatMul_backbone.blocks.68.attention.dense2.weightA -LayerNorm_backbone.blocks.70.layernorm2.gamma; +Add_backbone.blocks.2.attention.dense2.bias C 1MatMul_backbone.blocks.37.attention.dense1.weightC 1MatMul_backbone.blocks.17.attention.dense1.weightD 2MatMul_backbone.blocks.72.output.projection.weight@ 0Add_backbone.blocks.85.attention.projection.bias < ,Add_backbone.blocks.99.attention.dense2.bias B 0MatMul_backbone.blocks.8.attention.dense2.weight@ .MatMul_backbone.blocks.2.output.mapping.weightB 0MatMul_backbone.blocks.1.attention.dense2.weight@ 0Add_backbone.blocks.94.attention.projection.bias A -LayerNorm_backbone.blocks.68.layernorm1.gamma< ,Add_backbone.blocks.30.attention.dense2.bias A /MatMul_backbone.blocks.46.output.mapping.weight= -Add_backbone.blocks.21.output.projection.bias D 2MatMul_backbone.blocks.62.output.projection.weight@ 0Add_backbone.blocks.34.attention.projection.bias = -Add_backbone.blocks.96.output.projection.bias E 3MatMul_backbone.blocks.100.output.projection.weightD 2MatMul_backbone.blocks.15.output.projection.weight: *Add_backbone.blocks.49.output.mapping.bias A -LayerNorm_backbone.blocks.87.layernorm1.gamma: *Add_backbone.blocks.41.output.mapping.bias D 2MatMul_backbone.blocks.42.output.projection.weightA /MatMul_backbone.blocks.43.output.mapping.weight< ,Add_backbone.blocks.45.attention.dense3.bias G 5MatMul_backbone.blocks.83.attention.projection.weight= -Add_backbone.blocks.94.output.projection.bias A -LayerNorm_backbone.blocks.72.layernorm2.gammaC 1MatMul_backbone.blocks.32.attention.dense1.weight< ,Add_backbone.blocks.31.attention.dense2.bias A -LayerNorm_backbone.blocks.56.layernorm2.gammaD 2MatMul_backbone.blocks.90.output.projection.weightC 1MatMul_backbone.blocks.22.attention.dense2.weightA -LayerNorm_backbone.blocks.20.layernorm1.gamma= -Add_backbone.blocks.100.attention.dense2.bias = -Add_backbone.blocks.16.output.projection.bias C 1MatMul_backbone.blocks.59.attention.dense3.weight= -Add_backbone.blocks.29.output.projection.bias < ,Add_backbone.blocks.76.attention.dense3.bias D 2MatMul_backbone.blocks.66.output.projection.weight@ .MatMul_backbone.blocks.7.output.mapping.weightD 2MatMul_backbone.blocks.61.output.projection.weight: *Add_backbone.blocks.19.output.mapping.bias C 1MatMul_backbone.blocks.30.attention.dense2.weightA /MatMul_backbone.blocks.19.output.mapping.weight= -Add_backbone.blocks.89.output.projection.bias F 4MatMul_backbone.blocks.4.attention.projection.weight9 )Add_backbone.blocks.4.output.mapping.bias @ 0Add_backbone.blocks.60.attention.projection.bias A /MatMul_backbone.blocks.61.output.mapping.weightA -LayerNorm_backbone.blocks.63.layernorm1.gammaG 5MatMul_backbone.blocks.69.attention.projection.weightC 1MatMul_backbone.blocks.56.attention.dense3.weight@ 0Add_backbone.blocks.39.attention.projection.bias B 0MatMul_backbone.blocks.8.attention.dense1.weight@ .MatMul_backbone.blocks.1.output.mapping.weight= -Add_backbone.blocks.59.output.projection.bias = -Add_backbone.blocks.83.output.projection.bias < ,Add_backbone.blocks.43.attention.dense3.bias C 1MatMul_backbone.blocks.68.attention.dense3.weight< ,Add_backbone.blocks.21.attention.dense1.bias ? /Add_backbone.blocks.9.attention.projection.bias = -Add_backbone.blocks.87.output.projection.bias C 1MatMul_backbone.blocks.24.attention.dense2.weightA -LayerNorm_backbone.blocks.46.layernorm2.gamma= -Add_backbone.blocks.101.attention.dense1.bias ; +Add_backbone.blocks.8.attention.dense1.bias = -Add_backbone.blocks.95.output.projection.bias @ ,LayerNorm_backbone.blocks.6.layernorm1.gamma< ,Add_backbone.blocks.1.output.projection.bias G 5MatMul_backbone.blocks.81.attention.projection.weightA /MatMul_backbone.blocks.65.output.mapping.weightA -LayerNorm_backbone.blocks.92.layernorm1.gamma< ,Add_backbone.blocks.22.attention.dense1.bias < ,Add_backbone.blocks.54.attention.dense3.bias A -LayerNorm_backbone.blocks.44.layernorm1.gamma; +Add_backbone.blocks.3.attention.dense2.bias A -LayerNorm_backbone.blocks.43.layernorm2.gammaC 1MatMul_backbone.blocks.51.attention.dense2.weightC 1MatMul_backbone.blocks.48.attention.dense2.weight= -Add_backbone.blocks.76.output.projection.bias C 1MatMul_backbone.blocks.44.attention.dense1.weight@ ,LayerNorm_backbone.blocks.7.layernorm2.gammaC 1MatMul_backbone.blocks.67.attention.dense2.weightC 1MatMul_backbone.blocks.69.attention.dense1.weight@ 0Add_backbone.blocks.72.attention.projection.bias < ,Add_backbone.blocks.86.attention.dense2.bias A /MatMul_backbone.blocks.48.output.mapping.weightC 1MatMul_backbone.blocks.66.attention.dense3.weightC 1MatMul_backbone.blocks.22.attention.dense3.weightA -LayerNorm_backbone.blocks.86.layernorm1.gamma; +Add_backbone.blocks.7.attention.dense1.bias D 2MatMul_backbone.blocks.77.output.projection.weight= -Add_backbone.blocks.18.output.projection.bias G 5MatMul_backbone.blocks.94.attention.projection.weight< ,Add_backbone.blocks.40.attention.dense2.bias @ 0Add_backbone.blocks.29.attention.projection.bias : *Add_backbone.blocks.34.output.mapping.bias F 4MatMul_backbone.blocks.1.attention.projection.weightG 5MatMul_backbone.blocks.26.attention.projection.weightC 1MatMul_backbone.blocks.89.attention.dense3.weightA /MatMul_backbone.blocks.71.output.mapping.weight: *Add_backbone.blocks.14.output.mapping.bias = -Add_backbone.blocks.27.output.projection.bias ? /Add_backbone.blocks.8.attention.projection.bias < ,Add_backbone.blocks.37.attention.dense2.bias C 1MatMul_backbone.blocks.25.attention.dense3.weight@ 0Add_backbone.blocks.16.attention.projection.bias A /MatMul_backbone.blocks.89.output.mapping.weightA -LayerNorm_backbone.blocks.59.layernorm1.gamma= -Add_backbone.blocks.51.output.projection.bias < ,Add_backbone.blocks.70.attention.dense2.bias @ ,LayerNorm_backbone.blocks.2.layernorm1.gamma= -Add_backbone.blocks.102.attention.dense2.bias G 5MatMul_backbone.blocks.56.attention.projection.weightC 1MatMul_backbone.blocks.21.attention.dense1.weightC 1MatMul_backbone.blocks.85.attention.dense2.weightA -LayerNorm_backbone.blocks.12.layernorm1.gamma= -Add_backbone.blocks.86.output.projection.bias D 2MatMul_backbone.blocks.87.output.projection.weightC 1MatMul_backbone.blocks.80.attention.dense2.weightC 1MatMul_backbone.blocks.78.attention.dense3.weight= -Add_backbone.blocks.46.output.projection.bias = -Add_backbone.blocks.97.output.projection.bias C 1MatMul_backbone.blocks.36.attention.dense1.weightG 5MatMul_backbone.blocks.46.attention.projection.weightC 1MatMul_backbone.blocks.63.attention.dense2.weight@ 0Add_backbone.blocks.35.attention.projection.bias C 1MatMul_backbone.blocks.86.attention.dense2.weight@ 0Add_backbone.blocks.47.attention.projection.bias C 1MatMul_backbone.blocks.29.attention.dense2.weightC 1MatMul_backbone.blocks.18.attention.dense1.weight< ,Add_backbone.blocks.81.attention.dense1.bias = -Add_backbone.blocks.42.output.projection.bias A /MatMul_backbone.blocks.94.output.mapping.weightD 2MatMul_backbone.blocks.10.output.projection.weight< ,Add_backbone.blocks.57.attention.dense2.bias A /MatMul_backbone.blocks.70.output.mapping.weight: *Add_backbone.blocks.52.output.mapping.bias @ ,LayerNorm_backbone.blocks.8.layernorm1.gammaA -LayerNorm_backbone.blocks.57.layernorm2.gamma9 )Add_backbone.blocks.6.output.mapping.bias C 1MatMul_backbone.blocks.33.attention.dense1.weight: *Add_backbone.blocks.95.output.mapping.bias @ ,LayerNorm_backbone.blocks.0.layernorm1.gamma< ,Add_backbone.blocks.32.attention.dense3.bias C 1MatMul_backbone.blocks.20.attention.dense1.weightC 1MatMul_backbone.blocks.47.attention.dense3.weightG 5MatMul_backbone.blocks.50.attention.projection.weightD 2MatMul_backbone.blocks.34.output.projection.weight< ,Add_backbone.blocks.50.attention.dense2.bias < ,Add_backbone.blocks.63.attention.dense1.bias E 3MatMul_backbone.blocks.101.output.projection.weight: *Add_backbone.blocks.20.output.mapping.bias C 1MatMul_backbone.blocks.14.attention.dense3.weight< ,Add_backbone.blocks.68.attention.dense2.bias A /MatMul_backbone.blocks.12.output.mapping.weightH 6MatMul_backbone.blocks.102.attention.projection.weightD 2MatMul_backbone.blocks.76.output.projection.weight< ,Add_backbone.blocks.18.attention.dense2.bias G 5MatMul_backbone.blocks.19.attention.projection.weight< ,Add_backbone.blocks.78.attention.dense1.bias C 1MatMul_backbone.blocks.89.attention.dense1.weight@ ,LayerNorm_backbone.blocks.3.layernorm2.gamma< ,Add_backbone.blocks.73.attention.dense2.bias < ,Add_backbone.blocks.13.attention.dense2.bias C 1MatMul_backbone.blocks.95.attention.dense3.weight@ 0Add_backbone.blocks.74.attention.projection.bias E 3Gather_backbone.top_query_embedding.embedding_tableD 2MatMul_backbone.blocks.74.output.projection.weightD 2MatMul_backbone.blocks.101.attention.dense3.weightA -LayerNorm_backbone.blocks.49.layernorm2.gammaC 1MatMul_backbone.blocks.65.attention.dense2.weightA /MatMul_backbone.blocks.39.output.mapping.weight@ 0Add_backbone.blocks.25.attention.projection.bias A /MatMul_backbone.blocks.53.output.mapping.weight= -Add_backbone.blocks.65.output.projection.bias < ,Add_backbone.blocks.96.attention.dense2.bias @ ,LayerNorm_backbone.blocks.8.layernorm2.gamma< ,Add_backbone.blocks.87.attention.dense3.bias A /MatMul_backbone.blocks.73.output.mapping.weightC 1MatMul_backbone.blocks.43.attention.dense1.weight9 )Add_backbone.blocks.9.output.mapping.bias A -LayerNorm_backbone.blocks.98.layernorm1.gamma< ,Add_backbone.blocks.56.attention.dense2.bias C 1MatMul_backbone.blocks.20.attention.dense3.weightC 1MatMul_backbone.blocks.10.attention.dense3.weight< ,Add_backbone.blocks.35.attention.dense2.bias A -LayerNorm_backbone.blocks.10.layernorm1.gammaC 1MatMul_backbone.blocks.27.attention.dense1.weightC 1MatMul_backbone.blocks.69.attention.dense2.weight< ,Add_backbone.blocks.90.attention.dense3.bias D 2MatMul_backbone.blocks.41.output.projection.weight< ,Add_backbone.blocks.9.output.projection.bias D 2MatMul_backbone.blocks.89.output.projection.weightA -LayerNorm_backbone.blocks.84.layernorm2.gammaH 6MatMul_backbone.blocks.100.attention.projection.weight@ .MatMul_backbone.blocks.8.output.mapping.weightC 1MatMul_backbone.blocks.4.output.projection.weight< ,Add_backbone.blocks.44.attention.dense1.bias D 2MatMul_backbone.blocks.33.output.projection.weight6 "LayerNorm_backbone.layernorm.gamma9 )Add_backbone.blocks.5.output.mapping.bias D 2MatMul_backbone.blocks.96.output.projection.weightC 1MatMul_backbone.blocks.91.attention.dense3.weight< ,Add_backbone.blocks.68.attention.dense1.bias A -LayerNorm_backbone.blocks.55.layernorm2.gamma< ,Add_backbone.blocks.27.attention.dense2.bias < ,Add_backbone.blocks.80.attention.dense3.bias = -Add_backbone.blocks.10.output.projection.bias = -Add_backbone.blocks.92.output.projection.bias @ 0Add_backbone.blocks.89.attention.projection.bias C 1MatMul_backbone.blocks.14.attention.dense2.weight: *Add_backbone.blocks.38.output.mapping.bias A /MatMul_backbone.blocks.26.output.mapping.weight@ 0Add_backbone.blocks.21.attention.projection.bias C 1MatMul_backbone.blocks.97.attention.dense2.weight< ,Add_backbone.blocks.63.attention.dense2.bias G 5MatMul_backbone.blocks.22.attention.projection.weightA /MatMul_backbone.blocks.49.output.mapping.weightD 2MatMul_backbone.blocks.71.output.projection.weightD 2MatMul_backbone.blocks.26.output.projection.weight9 )Add_backbone.blocks.3.output.mapping.bias < ,Add_backbone.blocks.18.attention.dense3.bias C 1MatMul_backbone.blocks.48.attention.dense3.weightC 1MatMul_backbone.blocks.39.attention.dense3.weight< ,Add_backbone.blocks.19.attention.dense2.bias A -LayerNorm_backbone.blocks.36.layernorm1.gamma= -Add_backbone.blocks.66.output.projection.bias ? /Add_backbone.blocks.5.attention.projection.bias C 1MatMul_backbone.blocks.57.attention.dense2.weight< ,Add_backbone.blocks.14.attention.dense2.bias < ,Add_backbone.blocks.88.attention.dense2.bias : *Add_backbone.blocks.67.output.mapping.bias @ 0Add_backbone.blocks.84.attention.projection.bias : *Add_backbone.blocks.36.output.mapping.bias < ,Add_backbone.blocks.75.attention.dense3.bias C 1MatMul_backbone.blocks.98.attention.dense2.weightA -LayerNorm_backbone.blocks.58.layernorm2.gamma? /Add_backbone.blocks.1.attention.projection.bias A /MatMul_backbone.blocks.41.output.mapping.weightA /MatMul_backbone.blocks.93.output.mapping.weight< ,Add_backbone.blocks.60.attention.dense3.bias C 1MatMul_backbone.blocks.23.attention.dense2.weightC 1MatMul_backbone.blocks.60.attention.dense2.weight< ,Add_backbone.blocks.99.attention.dense1.bias C 1MatMul_backbone.blocks.31.attention.dense3.weightC 1MatMul_backbone.blocks.50.attention.dense2.weightA /MatMul_backbone.blocks.18.output.mapping.weightA /MatMul_backbone.blocks.58.output.mapping.weight< ,Add_backbone.blocks.21.attention.dense3.bias = -Add_backbone.blocks.85.output.projection.bias G 5MatMul_backbone.blocks.15.attention.projection.weightC 1MatMul_backbone.blocks.50.attention.dense1.weight= -Add_backbone.blocks.45.output.projection.bias C 1MatMul_backbone.blocks.16.attention.dense1.weightD 2MatMul_backbone.blocks.13.output.projection.weight= -Add_backbone.blocks.74.output.projection.bias < ,Add_backbone.blocks.67.attention.dense3.bias A -LayerNorm_backbone.blocks.28.layernorm2.gammaA -LayerNorm_backbone.blocks.56.layernorm1.gamma: *Add_backbone.blocks.54.output.mapping.bias = -Add_backbone.blocks.75.output.projection.bias C 1MatMul_backbone.blocks.86.attention.dense1.weightG 5MatMul_backbone.blocks.39.attention.projection.weight: *Add_backbone.blocks.12.output.mapping.bias < ,Add_backbone.blocks.5.output.projection.bias C 1MatMul_backbone.blocks.9.output.projection.weightA -LayerNorm_backbone.blocks.36.layernorm2.gamma= -Add_backbone.blocks.22.output.projection.bias A /MatMul_backbone.blocks.36.output.mapping.weight; +Add_backbone.blocks.0.attention.dense2.bias G 5MatMul_backbone.blocks.40.attention.projection.weight9 )Add_backbone.blocks.0.output.mapping.bias = -Add_backbone.blocks.31.output.projection.bias A -LayerNorm_backbone.blocks.25.layernorm2.gammaA -LayerNorm_backbone.blocks.21.layernorm1.gamma< ,Add_backbone.blocks.25.attention.dense3.bias < ,Add_backbone.blocks.15.attention.dense2.bias = -Add_backbone.blocks.90.output.projection.bias A -LayerNorm_backbone.blocks.88.layernorm2.gammaA -LayerNorm_backbone.blocks.31.layernorm2.gammaC 1MatMul_backbone.blocks.18.attention.dense2.weightA /MatMul_backbone.blocks.84.output.mapping.weightD 2MatMul_backbone.blocks.85.output.projection.weightA -LayerNorm_backbone.blocks.88.layernorm1.gamma< ,Add_backbone.blocks.24.attention.dense1.bias C 1MatMul_backbone.blocks.37.attention.dense2.weight@ 0Add_backbone.blocks.13.attention.projection.bias < ,Add_backbone.blocks.32.attention.dense1.bias C 1MatMul_backbone.blocks.83.attention.dense3.weight< ,Add_backbone.blocks.79.attention.dense1.bias A -LayerNorm_backbone.blocks.57.layernorm1.gamma: *Add_backbone.blocks.58.output.mapping.bias : *Add_backbone.blocks.55.output.mapping.bias A /MatMul_backbone.blocks.76.output.mapping.weightA -LayerNorm_backbone.blocks.74.layernorm2.gammaC 1MatMul_backbone.blocks.52.attention.dense2.weight? /Add_backbone.blocks.0.attention.projection.bias C 1MatMul_backbone.blocks.38.attention.dense3.weightD 2MatMul_backbone.blocks.50.output.projection.weightA /MatMul_backbone.blocks.92.output.mapping.weightC 1MatMul_backbone.blocks.85.attention.dense3.weightG 5MatMul_backbone.blocks.42.attention.projection.weight: *Add_backbone.blocks.73.output.mapping.bias A -LayerNorm_backbone.blocks.58.layernorm1.gamma@ .MatMul_backbone.blocks.9.output.mapping.weightA -LayerNorm_backbone.blocks.22.layernorm2.gammaG 5MatMul_backbone.blocks.76.attention.projection.weightC 1MatMul_backbone.blocks.81.attention.dense2.weightA /MatMul_backbone.blocks.62.output.mapping.weight: *Add_backbone.blocks.91.output.mapping.bias C 1MatMul_backbone.blocks.29.attention.dense1.weightA /MatMul_backbone.blocks.75.output.mapping.weight: *Add_backbone.blocks.97.output.mapping.bias = -Add_backbone.blocks.69.output.projection.bias D 2MatMul_backbone.blocks.84.output.projection.weight< ,Add_backbone.blocks.49.attention.dense1.bias A /MatMul_backbone.blocks.51.output.mapping.weight< ,Add_backbone.blocks.58.attention.dense1.bias C 1MatMul_backbone.blocks.0.output.projection.weightC 1MatMul_backbone.blocks.74.attention.dense1.weight: *Add_backbone.blocks.99.output.mapping.bias < ,Add_backbone.blocks.23.attention.dense1.bias C 1MatMul_backbone.blocks.73.attention.dense2.weightC 1MatMul_backbone.blocks.77.attention.dense2.weight; +Add_backbone.blocks.0.attention.dense3.bias D 2MatMul_backbone.blocks.57.output.projection.weightD 2MatMul_backbone.blocks.11.output.projection.weight< ,Add_backbone.blocks.35.attention.dense1.bias C 1MatMul_backbone.blocks.42.attention.dense1.weight= -Add_backbone.blocks.102.attention.dense1.bias < ,Add_backbone.blocks.86.attention.dense3.bias C 1MatMul_backbone.blocks.79.attention.dense1.weightC 1MatMul_backbone.blocks.17.attention.dense2.weight< ,Add_backbone.blocks.51.attention.dense3.bias D 2MatMul_backbone.blocks.39.output.projection.weightD 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0MatMul_backbone.blocks.3.attention.dense3.weightD 2MatMul_backbone.blocks.27.output.projection.weightA /MatMul_backbone.blocks.20.output.mapping.weight< ,Add_backbone.blocks.33.attention.dense2.bias < ,Add_backbone.blocks.48.attention.dense1.bias < ,Add_backbone.blocks.26.attention.dense2.bias C 1MatMul_backbone.blocks.84.attention.dense3.weightA -LayerNorm_backbone.blocks.90.layernorm1.gamma< ,Add_backbone.blocks.14.attention.dense1.bias < ,Add_backbone.blocks.69.attention.dense1.bias A -LayerNorm_backbone.blocks.37.layernorm2.gamma< ,Add_backbone.blocks.16.attention.dense2.bias A -LayerNorm_backbone.blocks.45.layernorm2.gamma; +Add_backbone.blocks.7.attention.dense2.bias A -LayerNorm_backbone.blocks.24.layernorm2.gammaA /MatMul_backbone.blocks.64.output.mapping.weightG 5MatMul_backbone.blocks.98.attention.projection.weightG 5MatMul_backbone.blocks.84.attention.projection.weightC 1MatMul_backbone.blocks.81.attention.dense3.weightA -LayerNorm_backbone.blocks.20.layernorm2.gamma< 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/MatMul_backbone.blocks.85.output.mapping.weight= -Add_backbone.blocks.93.output.projection.bias G 5MatMul_backbone.blocks.43.attention.projection.weight= -Add_backbone.blocks.84.output.projection.bias = -Add_backbone.blocks.91.output.projection.bias < ,Add_backbone.blocks.92.attention.dense3.bias A -LayerNorm_backbone.blocks.17.layernorm2.gammaA -LayerNorm_backbone.blocks.55.layernorm1.gammaG 5MatMul_backbone.blocks.73.attention.projection.weightA -LayerNorm_backbone.blocks.53.layernorm2.gamma@ 0Add_backbone.blocks.93.attention.projection.bias < ,Add_backbone.blocks.70.attention.dense1.bias @ 0Add_backbone.blocks.12.attention.projection.bias D 2MatMul_backbone.blocks.63.output.projection.weightA -LayerNorm_backbone.blocks.38.layernorm1.gammaA -LayerNorm_backbone.blocks.26.layernorm2.gammaA /MatMul_backbone.blocks.50.output.mapping.weightG 5MatMul_backbone.blocks.97.attention.projection.weightA /MatMul_backbone.blocks.44.output.mapping.weight: 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1MatMul_backbone.blocks.90.attention.dense2.weight@ 0Add_backbone.blocks.27.attention.projection.bias A -LayerNorm_backbone.blocks.53.layernorm1.gamma@ 0Add_backbone.blocks.66.attention.projection.bias G 5MatMul_backbone.blocks.91.attention.projection.weightG 5MatMul_backbone.blocks.14.attention.projection.weightC 1MatMul_backbone.blocks.28.attention.dense1.weight; +Add_backbone.blocks.102.output.mapping.bias G 5MatMul_backbone.blocks.10.attention.projection.weightA -LayerNorm_backbone.blocks.99.layernorm1.gammaA -LayerNorm_backbone.blocks.91.layernorm2.gammaA -LayerNorm_backbone.blocks.93.layernorm1.gammaA -LayerNorm_backbone.blocks.41.layernorm2.gammaD 2MatMul_backbone.blocks.55.output.projection.weight< ,Add_backbone.blocks.98.attention.dense2.bias @ 0Add_backbone.blocks.22.attention.projection.bias D 2MatMul_backbone.blocks.97.output.projection.weightG 5MatMul_backbone.blocks.29.attention.projection.weightC 1MatMul_backbone.blocks.78.attention.dense2.weightG 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'ཁ་བརྡ་གློད་པ་ 妈妈 整体 姆 过程 硬 不到 玩家 以为  ཁ་བཤལ་བ་ 皮肤 上涨 句 演员 女人 财 爾 男人 戴  ཁ་མོ་ 改革 月份 宣布 消费 不少 优势 机会 杰  ▁为什么 侧 警 暗 文件 刻 结合 想要 怀 范围 一天 教学 耳 风格 心理 睡 责任 众 來 取得 首先 这里 淡 商业 爆 事情 独立 移动 找到 晚上 虚 患者 聚  不同的 软 举行 庆  的事情 承 零 几乎 也会 小说 羊 男子 联合 新的 让我 形式 恢复 郑 加强 球员  消费者 铁路 医 一条 最大 担任 适合 答 刷 晓 看看 征 这次 证券 丰 同学 我是 当地 孔 里面 行动 炮 执行 点击  是一种 下面 挂 证明 文学 思想 露 角色 搞 跌  一定要 双方 杜 第四  也可以 抢 人生 过去 评论 陆 横 涨 一家 广州 晋 略 厚 设置 因素 事业 首次 降低 观众 借 自动 宣传  的东西 位置 免费 分享 烧 洪 附近 化学  过程中 认识 距离 猫 我就  问答社区  大部分 陵 婚  有限公司 郭 真正 运行 钟 怕 鸟 数量 遇到 艾 来源 青年  的时间 技能 暴 一名 更新 三个 图片 生命 扬 欲  ཁ་ཚ། 蒂 免 是在 为主 腿 净 毕业 满足 哦 紧 的小 资产 物质 即可 苹果 ▁而 教授 预 保险 给我 体系 父 食品  的一个 逐渐 乘  有没有 至少 带来 经历 足球 然而 评价 哪些 混 人士 宣 避免 网友 折 宜 资 南京 引起 兼 促进 猪 虫 指出 推出 一切 本人 紫 秒 快速 出了 让你 从而 程序 纪 年度 考生 皆 优秀  ཁ་ཡ་ 结束 每年 创作 喷 统一 ▁但 成员 伟 劳 居民 1 *ཁ་ཡང་སྤྱོད་ཡང་ ▁从 稳定 用于  是因为 土地 制造 革命  ཁ་ལག་ 协会  的发展 爱情 股票 缘 上市 迷 称为 印度 方便  了一个 症 本身 处于 依  就可以 优 讨论 方案  越来越 呼 每个 值得 补充 沉 同样 泡 农村 网上 探 墙 冬  并没有 胸 我想 攻击 延 儿童 附 惠 护 预计 钢 一场 数学 ( !ཁ་ལག་བཟོ་བ། 肯定 床 透 麦 谢谢 高中 家里  有一个 黄金 联赛 主义 咨询 基地 预期 到底 照片 表演 泽 团队 缺 凯 文章 终于 大型 三年 冠 继 我也 范 时尚  ཁ་ཤས། 不足 作者 计算 社区 两人 简  ཁ་སང་ 不得 她的 赛季 商品  尤其是 高考 费用 针对 荣 幸福 确实 赞 深圳 接触 还要 矿 英雄 滑 每次  电视剧  平方公里 看了 汇 突破 性能 念  ཁ་སྐོང་། ▁这 抓 培养 战略 此次 力量 及时  俄罗斯 明确 各位 怎样 培训 出生 它们 相比 实力 公开 隔 父亲 不用 固 出版 指导 抽 杂 教师 疾病 成长 特色 豆 很好 动力 腰 蛋 惊 呈 记录 曰 官方 不了  ཁ་སྦུབ་ 厅 儿子 相当 很难  人民币 卫生 潮 工具 公共 赫 人才 术 臣 將 坏 措施 营养 掌 艺 里的 份  ཁག་ 昨日 铜 禁 更是 而言 觉 创业 依然 安排 性的 闻 箱 险 安装  ▁中国  ཁག་ཐེག་ 拜 似乎 镜 女子  的影响 废 组成 好像 现象 数字 贷款 娱乐 耐 迅速 凉 著名 犯 飞机 中学 摩 内部 今日 离开  特别是  计算机  ཁང་ 暖 明星 恶 售  ཁང་པ། 围 內  ཁང། 背景 备 投入 绝对 累 主题 协议 平时 熟 燕 仍然 习惯 扎 采取 报名 课程 甘 阵 感到 男生  他们的 申 巨 凡 终 下跌 现实 身上 饰 帕  的情况 毕竟 启动 魏 娜 慢 即使 使得  进行了 配置 影片 顿 严 就能 重大 膜  ཁཎྜ་ 直到 大概  主要是 如今 個 文明  亿美元 意义 主席 鲜  ཁད་ 乳 销 准 回到 墓 有所 不想 动作 当年  ཁན་ 它的 诸 尖  ཁབ་ལེན། 死亡 上面 多次 会有 鼓 金属 推动 集中 柱 打开 阅读  ཁབ། 有个 限制 争 保障 锅 主动  如果你 鞋 酒店 驻 敌 墨 % ཁམ་ཕུ་ཁྲེ། 享受 打造 令人 感情 浮 薄 粗 珠 是指  意大利 女儿 统计 舍 童 行情 装备 回来 往往 感谢 联盟  ▁如何评价 最新 机械 组合 肥 母亲  ཁམ་བུ། 合同 丰富 手术 年龄 上升 休 仓 推进 插 家长 朗  ཁམས་ 测试 批 竞争 佩 支付  的原因 从事 其它  很多人 山东 军事 形象 实验 不再  ཁམས། 哭 灰 菲 刺激 的事 员工 刺  并不是 逃 亚洲  有很多 桑 困难 升级 籍 ▁据 回家 符合 埃 小学 检 采 认真 湾 主任 运营  的故事 改善 股份 概念 天下 发表 车辆  更多的 尊 稳 排名 心里 懂 旗 哪个 温度 版本 原来  ཁའེ་ 尿 伤害 对象 演出 歌曲 兄弟 弗 两年 玛 益 公布 熊 积  的生活 群众 菌 追求 害 殿 弄 胶 创造  的情况下 放在 欢迎 狂 太多 坑  的感觉 情绪 特殊 万人 广东 有着 工资 磨  ཁལ་ 例如 屏 智能 搭配 勇 核心 东北 不知 泥 敬 随后 吸 发挥 参考 呀 下载 另一 發  ▁由于 播 食物 做好 分为 岁的 描述 根本 表达 曹 都不 公园 给你  的朋友 戰 先后 隐 楚 成了 分子 愿 学术 + $ཁས་མི་ལེན་པ་ 敏 状况 壁 近期 卢 數 不够 辛 太阳 年轻 加上 霍  ཁས་ལེན་ 天津 变成  ཁས་ལེན། 中文 斑  的方式 至今 来到 物理  。。。  我们的 医学  怎么办 此外 流行 很大 就业 餐 高级  之间的 都在 就像 学会 岸 办法 永远 鼻 甜 宏 那样 配合 注册 资格 类型 济 勤 超级 难以 柳 广泛 瘦 是由 焦 浅 一张 残  在中国 迁  也没有 距 比例 他在  ཁཱ་ 十二 昨天 意见 高校 瓶  的作用 文字 寿  西班牙 扣 庙 普遍 贤 分布 反对 赶 经典 那种 生态 详细 红色 振 沃 遗 邀请 骂 及其 徒 须 板块 拒绝 赢 掌握 眼睛 维持 莉  的重要 观察 奉 机关 白色 姓 部队  可能是 对手  俱乐部 专辑 唯一 贸易 特点 凤 岭  ཁིལ་ 医疗 众多 芳 特征 女孩 绿色 透露 表面 帅 侯  ▁如何 摆 营销 枝 高速 贡献 某些 豪 总统 一段 他说 上了  ▁但是 旁 人数 错误 出口 尽管 沟 举办 地址 老人 汗 歌手 老板 似 ▁李 办理 轴 也许  的工作 武器 挑战 大陆 ”( 仪 吨 奶 判断 忙 担心 洁 乃 时候 意识 有了 资本 症状 开放 胃 嘴 粉丝 无论 之外 论坛 逼 构成 腹 患 回复  的方法 四川 议 本科  应该是 地位  ཁུ་བ། 不错 多个 重庆 體 润 币 灭 快乐 来了 东方 下去 警方 刚刚 充分 航空 家人  ཁུ་ཚུར་ 裂 自我 在于 颜色 % ཁུ་སིམ་པོ།  ཁུག་མ། 清楚 利益 一份 身边 不太 博士 陶 指标 遇  的主要 机场 完善 颜 慢慢 浙江 市民 如下 俊  实际上 虽 兽 工艺 煮 做了 偷 支撑 榜 》《 自身 邦 扩大 骑 姜 乙 失败 寄 地球 皇帝 纹 苗 前往 针 吹 学科  ཁུག་རྟ་ 类似 衣服  ཁུང་ 名称 另 她们 琴 加工 翼 签 真是 接近 应当  出来的 突出 法院 最低 赴 精彩 强调 瓜 还在 即将 元素 朝鲜  ཁུངས་ 不久 男性 稿 专门 告诉 " ཁུངས་མེད་ 娘 湿 吸引 抱 羽 动画 审 请问 公路 杆 黑色 % ཁུངས་འདེད་  ཁུན་ 连接  ཁུའེ་ 成都 召开 兩 幼 战斗 共有 稍 深入 规范 的大 有时 一旦 访 提前 炒 武汉 规则 不敢 细节 寻 街道 就要 蔡 严格 萌 在此 壮 业绩 还能 购 展开 划 迎 肝 趋势 机制 巧 道路 传播 过来  在这里 感受 辞 乔 遭  研究生 劲 玄 姐 铺 手段 药物 档 做到 上述 扫 跨  ཁུར་ 此前 忍 疏 神经 愿意 之中 反弹 股市 贾 推广 所谓  ཁུར། 闹 生长 很少 公告 杭州 指挥 关键 英文 號 说话 中间 大小 做出 增强 性格 主演 进攻  怎么样 ▁当  ཁུལ། 亩 不管 修改 出去 走势  ཁེ་ 坡  工作人员 信号 拼 此时 堡 ( !ཁེ་བཟང་ཟ་བ། 收益 利润  ཁེ་ལས་ 放弃 具备 則 境内 搭 成熟 胜利 妈 通知 斯特 讯 下午 河南 新浪 百度 丑 一致  发动机 厘米 撞  ཁེངས་པ།  重要的  ▁因为 石油 浪 吸收 制定 将会 都要 庭 设立 相同 肠 策略 对此 人家  也就是  ▁如何看待 好好 危险 监 实践 第五 平衡 胎  都可以 毁 天气 都能 刑  都没有  ཁེབས་ 縣 戈 期待 谱 品种 柔 思考 祥 固定 会计 亞 响  ཁེམ་བུ། 宾 译 狼 :< 荒 估计  ཁོ་ན་ ▁其 韦 寻找 反映 什 兹 适应 带着 批准 名字 大幅 拔 缺乏 才是 搜索 少年 酷 国外 ▁该 诚 耶 青春 本次 西部 這 同意 损失 意思 警察 记忆 正是 啥 目的  ཁོག་  ▁此外 席  男朋友 留下 原油 能量 當 就在 立即  而不是  可能会 分手 防止 遍 一個 实行 珍 多种 震 谋 家族 脉 给予 吃饭 塘 轻松  ཁོང་ 挑 事实 充  ཁོང་ཁྲོ་ 幅 献 内心 效率 大盘 感染 肺 牧 年底 广场 胆 甚 年前 承担 突 + $ཁོང་ཁྲོ་ཟ་བ་ 不如 造型  ཁོངས། 当前 提醒 主持 破坏 十年 蛇 玻璃 乎 的新 读书 俄 各个  女朋友 观点 监管 装修 奔 哪里 剧情 变得 海外 合理  ཁོད་རྒྱ། 一只  ཁོམ་པ། 大众 岳 阻  的回答 表明 正确 近日 高度 民警 約 记载 是我  ▁另外 污染 设施 覆盖 广大 二十 纪念 杂志 车型 维护 煤 疑 ”“ 述 就有 沈 戒 财政  有可能 桃 复杂 邓 纵 环保 桂 伏  还可以 收购 承认 逆 劳动 矛盾 为何 怒 宗教 角度 或许 犯罪 孟 女士 较大 猛 研发  不可能 爽 尽量 教练 一句 原则 西方 亡 爸爸 老公  所有的 晶 看着 婚姻  ཁྭ་ཏ། 决 亭 龄 完美 事故 军队 开心 招生 梦想  ཁྱད་པར། 通信 会员  ▁现在 稀  ཁྱད། 当天 身份 腾 辉 聊 壳 在线 技巧 引发 空气 饮食 闪  大多数  ཁྱམས་ར།  国务院 提示 穆 也很 出演  ཁྱི། 还会 烂 也就  ཁྱིམ་ 涂 窗 真实 就算 提到 锦 摄影 肾 登记 想到 期货 居住 输入 持有 闲 键 地铁 尘 病毒  出现了 确认 展示  ▁所以 俗 董 民间 小编 吕 农民 % ཁྱིམ་མཚེས་ 冯 妹 理念 烈  第一个 定位 成就 從 优惠 癌  的一种 施工 沟通 看来 本来  大学生 租 或是 . 'ཁྱིའུ་དབྱིབས་ 浩 这位 拖 收到 一生 截至 一款 饰演  方面的 色彩  ཁྱུ། 做的 涉及 融 驾驶 莲 姑娘 则是 意外 江苏 去了 理想 敢 录取 碎 运输 镜头 恐怖 旅 域 荷  ཁྱུང་ 人体 也没 监督  ཁྱུང་མོ་ 案件 就不 企 显得 大约 棒 妻子 " ཁྱེད་རང་། 中华 这款 各类 早期 包含 ▁王 货币 剪 改造 四个 撒 选手 频 幕 粒 顾 产量 民主  办公室 古代 妙 一座 缓  基本上 紧张 危机 重视 多年 少女 财务 嘛 袭 运用 科研  历史上 思维  ▁其中 直播 遂  有时候 主力 舒  最好的 唯 劝 而已  ▁因此 成果 面临 舰 盆 锁 后面  是为了 干部 摸 听到 领先  ཁྱེར་བ། 适 葬 大师 恨 迹 让他 添加  公司的 尝试 鹿 两种 来的 等级 柏 祭 演唱 知名 打算 一边 欣 作出 学者 检测 沿  维生素 相互 闭 刊 旺 播出 鸣 进口  ཁྱོ་ག  房地产 搜 耗 三十 陪 气候 該 互 准确 习 恒 误 岗 建成 赏 成分 材 纽约 欢  负责人 西北 美國 周围 西南 渠道 日前 我在 丘 很快 挡 多了  ཁྱོགས་ 弟 夹 始终  真正的 毫米 一部 作家 司机 己 韵 执 电池 道德 赚 并非 顺利 浓  ▁对于 将在 融资 巴西 看见  ཁྲ་ 腔  ཁྲ་ཐིག་ 拿到 论文  ཁྲ་མདའ།  中国人 西安 倾 脂肪 也要  ཁྲག 房子 脂 旅行 分类 署 总结 南方 周期 拟 素质  ཁྲག་རྭ་ 充满 淘宝 盈利 两次  ཁྲང་ 總  ཁྲབ་ 也能 翻译 QQ 能源 汁 彭  现在的 反而  ཁྲལ། 地点 确保  的表现 袁  ཁྲི་ 校园 侠 阳光 琳 链  ཁྲིད། 预测 袋 俩 发出 整理  之类的 逐步 丢 减肥 森林 诉 氧  世界杯 忘 启 用来 买了 便宜 兴趣 情感  了很多 景区  一段时间 彻底 画面 家具 漫画  还没有 震荡 基于 潘  平方米 委员 好了 潜 察  ཁྲིམས་ 磁  差不多 激 哲学 实在 滴 缩 囊 涨幅 球迷 魂 醒  在北京  的同时 屏幕 湖南 肩 說 垃圾 头发 否则 棋 混合  的机会 炸 出席 痛苦 有限 坛 惨 氧化 竟然 万吨 萧 明白 至于 盟 瞬间 夺 性质 砂 湘 滚 摇 晒 逻辑 旋 坚  不喜欢 频道 肌 休息 面前 品质 给他 日常 授 信心  会不会  大家都 和平 逢 想象 ▁张 肯 游客 触  看起来 已有 打击 纷纷 凭 律 还没  ▁同年 捕 摄 过了 利率 享 五年 健身 记得  ▁有哪些 " ཁྲིམས་ཆད། 粮 凌 识别  ཁྲིམས་ར་ 停止 锋 移民  一个月 队伍 先进  ཁྲུང་ 策 迈 将军 種 每日 大会  ཁྲུང་། 原理  这就是 ( !ཁྲུང་ཁྲུང་། 驾  ཁྲུན་ 悲 茨 够 崇  ཁྲུའུ་ 差异 避 对比 食用 朴 泪  加拿大 等人 辅 一块 规 裤 名单 小孩 保留 代理 坦 冒 很有 南部 旗下 恋爱 巨大 海洋 奏 鹏 加快  ཁྲུས་ 栏 ▁本 喊 声音 辖 外观 植 小米 青岛 也在 粘 填  另一个 含量 漫 鼓励 关心 霸 地下 难道 院校 吊 四年 失去 试验 妻 我说  的关系 识 碑 凭借 ▁如 公主 主场  良好的 短期 怀孕 区别 不上 最多 理由 原本 午  提供了 一片 城镇 协 晨 ▁三 拆  ཁྲེ་ 更好 地面 骗 水果  不得不 新疆  ▁不过 定义 爆发 看过  ཁྲེང་ 态度 汪 不行  在美国 孤 各地 苏联 肌肉 服装  博物馆 考研 咖啡 后期 半年 进步 邮 一项 受伤 规律 宇宙 说的  你可以 道理 取消 其次 账 天然 雾  的行为 等待 飞行 涉 当中 斤 讨 声明 较为 星期  同比增长 一套  ཁྲེན་ 下的 柯 一度 危  是中国 麻烦  没什么 智慧 ( !ཁྲེལ་སྐྱེད།  什么样的 作战 聊天 賽 生存 分配 稱 装饰 男女 卵 等地 忌 竞  的历史 愈 芬 丈夫 吐 伸 灾 同事 老婆 册 高达 過 集体 三星 不懂 文艺 队员 传说 加速 十八  ▁根据 依据 男友 预防 偶 相似 多数 北部 常常 ▁它 锻炼 初中 优质 但在 决赛 碗 了一 海军 训 损 既然 醉 iPhone 中國 外国 不起 将于 良好 互相 云南  新加坡 代码 佛教 用的 弃 日起 示范 形态 妹子 留学 中医 听说  ཁྲོ་ 适当 离婚 说了 不良  主持人  一部分 蘭 好多 夏天 布局  一系列 总体  ཁྲོ་བ། 居然 属性 碰 不出 慧 访问 协调 那里 漏 妮 抵 擦  ཁྲོན་ 标志 流量 浦  不需要 尊重 點 莎 而不 都很  著名的 轨 繁 蔬菜 在这 火箭 学历  ▁虽然 早上 钙 收费 暂时 算是 役  市场的 肿 柜  无论是 官员 映 饼 前面 加盟 之下  那么多 群体 砖 股权  ཁྲོན་པ། 线路 长江  ▁这个 鼠 女友 个股 胖 地震  研究所 辽 最初 第十 城区 地产  ཁྲོབ་ 并在 经理 小区 一步 仪式 消失 几年 毅 上班 鼎 筹  ▁然而 我要 到达 北方 面试 舌 三大 僧 辰 尤 能否 雕 上下 加大  ཁྲོམ་  ▁首先 周边 ( !ཁྲོམ་བཞུགས། 保存 曝光  的产品 黎 据悉 霜  世界上 穷 销量  如果是 踏 都市 天天  ▁这是 消化 鹰 高端 基因  ག་གེ་མོ། 收藏 成交 他人 融合 股价 临床 他是  ག་ཚོད།  ▁目前 债 查看 防守 提问 應 解放 绕 渣 马上 反复 有一 辣 链接 这类 爬  的位置 嫁 伍 伪 炼 练习 后的 客观 遭遇 秘  得到了 妖 舟 棉  中华人民共和国 将其 差距  ག་རེ། 公益 现有 大赛 埋 飘  གང་ 更为 金额 竟  我现在  意味着 上映 睡觉  ▁其实  获得了 呼吸 房屋 葛 高手 实用 ▁第 背后 通道 染 阁 的确 地理 纤维 牵 ▁此 大多 割 回忆 一半 傻 见到 全新 身高 爆炸 招聘 时刻 影视 几天 艳 去世  ▁请问 伴 部位 出场 主流 主体  电视台 汽 逐 内存  的名字  གང་བུ་ 责 舞台 第六 坊 病人 体现 再度  年轻人  场比赛 美丽 自主 异常 年间  什么的 篮球 碧 孕 依旧 律师  运动员  不一样 话题 全市 堆 因而 热情 互动 涛 调节  གང་བྱུང་ 冲击 巡  澳大利亚 一周 锡 织 罗马 不大 十六  也不会 希腊 盗 實 維 赔 河北 + $གང་སར་ཁྱབ་པ་ 蛋白 工人 怀疑  长时间 仲 一级 桶 邀  的要求 十一 贷 物品  每一个  的能力 谓 肖 禁止 阿尔 類 萬 公元 体重 呈现 遭到  གངས་ 鸿 禅 资讯 逸  很好的 幽 心中 气质 难度 祝 临时  》:“ 罚 崔 密码  གཅིག 途 % གཅིག་གྱུར་ 大大 困 幻 脏 钻 威胁 都没 中共 茂 赚钱 机器 腾讯 尤其  一定的 续 驱 把握 合并 台灣 发射  གཅིག་ནས་ 强化 妇 含有 犬 垂 场景  发展的 感动 哲  事实上 弯  中国的 福建 可见 前期 物流 商务  接下来 姚  没想到 对我 屈 呆 显然 贺  གཅིག་པུ། 走向  很容易  带来的 人群 漂亮 室内 撤 窝 翔 累计 上网 沪 必然 成為 " གཅིག་འདྲ་ 運  万美元 创建 这时 层次 援 舒适 魅力 订 各自 内地 美食 前后 饮  的孩子 清晰 美女  也不是 现金 购物 替 给了 继承  的学生 工厂 站在 ( !གཅིག་ལྕོགས་ 题目 敌人 进去 NBA 进球 日期 苍  比较好 宅 亏 吓 满意 變 志愿 财富 夫妻 枚 關 心情 依法 优化 一件  གཅིན་ 般 允许 硕士 职工 国王 周末 登陆 妆 喂  这也是  གཅུང་པོ་ 猜  的内容 探索  的研究 有多 绵 抛 才会 获取  这么多 公安 美术 篮 勾 强烈 上午 释放 咸 蒙古 神秘 模型 卫星 一批 低于 烤  གཅུང་མོ་ 出发 了吗 ▁陈 合成 開始 相机 循环 本地 深度 电力 挖 对外 达成 十五 裁 ▁刘  告诉我 节奏 級  一直在  གཅུར།  གཅུས་ " གཅུས་གདན།  ▁同时 环节 旦 下方 驱动 凝 又是 渐 有机 辆 种植 开启 司法 好看 帝国 路线 公斤 体内  据了解 " གཅུས་སྒོ་ 原料 身材  地区的 黄色 廷 流程 纪录 敏感  被称为 东南  ▁我们 赛事 力度 畅  一句话 生日 男孩 邪 霞 径  生活中  北京市 缝 效应 污  股份有限公司 扑 岗位 锐 统治 斜 地图 屬 总理 房间  出版社  的想法 引导 障碍 传奇 完整 邻  གཅེན་པོ་ 盈 卓 山西 芽 舞蹈  གཅེན་པོ། 陷 原创 形势 冲突 号码 捷 你要  གཅེན་མོ། 随便 评估 说我 眉 吻  的价格  在这个 害怕 电信 沈阳  གཅེར་བུ།  大量的 辐射  以上的 财产 别的 新鲜 四大 住宅  让我们 淮 一路 采访 最为 妇女 强度 一大 卧 股东 炉 元年 灌 起了 新增  一方面 证书 况 咬 各项 傳 从此 伦敦 昂 凶 扮演 ( !གཅེས་སྐྱོང་ 赖 个性 日电 休闲 理财 梨 悟 路上 仿  གཅེས།  图书馆  ▁谢邀 腐 十三 坝 轉 公众 延续 帶 猎 说法 位於  ▁为了 膏  丰富的  出现在  等方面 权力  所谓的 放入 還 上方  不错的 蒋 恐 克斯 改为  范围内 耀 广西  更好的 蒸 帮忙 第七  专业的 文物 垫  我自己 高于  成为了 二年 省级 据说 齿 虾 之处 ▁二 硬件  གཅོད་པ་ 激烈 社交 大神  གཉན་ཚད། 酶 疼 燃 种类  巨大的 冻 输出 东部 氢 认可  གཉིད་ཁུག  的作品 团体 荣誉 鸭 一线 更有 转移 泛 得知 弦 喜剧 著作 干净  国家队 发育  的答案 动态 江西 女神  我知道 积分 明天 兆 无数 照顾 活跃 宿舍 作业 暂  服务器 究竟 传媒 寸  形成了 印象 福利 廉  གཉིད་ཉལ། 播放 巴黎  个小时 商家 扶 营业 供应 年初 一人  ▁最近 大力 州市 安徽 原子 不多  可以在 认证 臭 付出  的意思 好友  简单的 各国 注重 和谐 和我 遗址 督 空调 厂商 落实 我不 应对 欠  གཉིད། 中有 姐姐 仔 衡  我认为 回归 蓝色 回应  གཉིས་ཀ་ 房价 长沙 过于 过的 村民 视觉  གཉིས་པ།  另一方面  གཉིས།  该公司  的设计 徽 蛮 嫩 也可 自信 妃 踢 合适 周年 截 债券 迟 所在  的基本 丛 国产 的好 睡眠 预算 聪明 拨 离子 尺寸 充足 搬 喜爱 二人 予 衰 昆 投票 向上 日至  有利于 劇 设定 首都 南北  ▁美国 趣 權 而成 由此 清洁 走了  演唱会 实习 赐 列车 ( !གཉེན་སྒྲིག་ 湖北  蛋白质 整合  打电话 不但 溶 证据 报考 促 接到  的态度 际 选举 几次  设计师 太大 日报 本周  གཉེར་མ་ 要是 违法 卿 包装  གཏན།  高速公路  朋友圈 烦  上市公司 陕西 思路 显著 最早 未能 非洲 批评 这部 东京 那就 而在 荡 创意 哥哥 书记  ▁第二 辈 事物 删除  གཏམ་དཔེ། 戏剧 一本 题材 储 漆 娇 无限 纲 同志 培 宠  发生了 裙 情节 人工 事务  ▁我是 诊 ▁小  的比赛  གཏམ་བཤད། 盒  གཏམ། 厦门 大气 跃 裸  的变化  最重要的 贞 我会 漂 月底 原始 格式 这两 说是 尚未 工商 当初 有望 授权 悦 一是 长大 它是 吾  小时候 魔法 名为 下了  གཏའ་འཇོག 短信 强势 退出 姑 螺 鸡蛋 越南 想法 修复 一支  什么时候 并未 苏州 務 寨 广播 又有 观念 博客 命令 释 但我 土壤  市场上 积累  的角度 桌 联邦  情况下 卫视 化的 地质 坪 我很 是以 住房 无线 全省 骑士 公交 宴 慈 爷 荷兰 疯狂 重量 重复 蕾 不住  的第一 潭 线上 季节 击败 酱  到现在 摊  产生的 危害  因为我 微软 奖励 毫无  能不能 冬季  日下午  公务员 人气 一辆 足够 特性 交换 两天 简称 信仰 看完 :《 透明 " གཏའ་འཛིན། 反正 明年 " གཏིང་ཐུང་ 怨 山区  总面积 % གཏུབ་གཏུབ་ 对你 格局 概 穴  ▁关于  གཏུབ། 理性  要注意  奥运会 梯  的样子 消防 彼此 转换 被人 遮  简介编辑 引进 考察 超越 領 展现 叔 海南 欣赏  相当于 治理 夏季  是怎么  最后一 围绕  གཏུམ་པོ།  有一些 海拔 仔细 你会  གཏེར་ཁ་ 少数 返回 校长 ( !གཏོགས་ཁུངས། 郑州 主角  和其他 初期 摘 平安  的过程 一百 砍 抑制  གཏོང་། 氣 陷入 强大 读者 劫 每一 用了 种子 可爱  གཏོང་བ་ 龟  གཏོར་བ་ 碳 承诺 铝 收获 叫做  的效果  གད་མོ།  采用了 接口 疾 箭 执法  真的是  分析师 查询 十七 武装 对他 学家  同时也 建造 你是 " གད་སྙིགས་ 账户  གདན་ 大连 得分 弟弟  近年来 4 -གདན་འདྲེན་ཞུ་བ་ 員 号线 下滑 浪漫 起到 日在  གདན། 场所  全世界 " གདམ་གསེས།  一样的 财经 塑 通讯 王朝  ▁后来 % གདུག་རྩུབ།  第二次 澳 两位 HK 瑟  董事长 月初 见面 职位 和他  的需求 條 二是 組  没有任何 渔 偶尔 斩 转变  会出现  孩子的 定期 信任 正面 收集 熟悉 景观 冈 办公 答主 不算 大道 验 风景 蜜 帽  之所以  ▁作为 沒有 好处 简直  数据显示 为此  的结果 栋  教育部 一把 強  影响力 温暖 敲  གདུགས་ 大學  但是我 交往 说说 看好 浆 各大 叉 小姐  གདུང་མ་ 消耗 但他  身份证 小组 较高 辅助  的目的 毕 " གདུང་སྒམ། 处罚 以外 筑 发动 赔偿  国家级 西藏 某个  གདུབ་  经济学 英寸 写的 院长 又称 阔 妹妹  实验室 死了 廣 全身 关闭  ▁今天 侵  第二天 县城 相反 生理 权利 較 装置 内的 葱 叠 !< 亲自 我家 消除 战士 上周  社会主义 专利  两个人 发明 颗 迎来  གདོང་ 入选  使用的 艘 處 呵呵 屯 报价 指定 " གདོང་རིས་  在哪里 油价 披露 跟我 硕 儒  在网上 ▁她 随时 函数 气象 命名 前景  གན་རྒྱ།  གནང་བ་  可以用 得以 兔 笼 」, 江南 依靠 出售 热点 同一 签订 血管 不对 小型 是最 尴尬  的一些 较多 觀 泰国 济南 非法 晴 坐在 债务 存储 公民  把自己 门口 阵容 编剧 仅仅 柴 三国 后者 挤  人民政府 阅  一开始 转载 旁边 我都 真心 绘 诊断 那是 千万 你就 会在  处理器 特定 索尼 不见 玲  ▁然后 束 级别 " གནད་འཁེལ། 开了 姿  强大的 车站 落后 丙  的基础上 初步 了吧 兄 信用  གནད། 夫人  有人说 虚拟 赠 不然 民众 出身 贡 零售  了一下  ▁这种  作为一个 对待 案例 出的 跌幅  གནམ་ " གནམ་གཤིས་ 熙  日上午 队长 她说 给出 流动 厉害 市区 功效 争议 五十 观看 更换 姬 上有 心态 地上 做法 蜀 生成 汉语 勃 薛  不是很 也将 职务 存款 干燥 悬 策划 铭 芝 米兰 艺人 耕 公平 利亚 密切 出台  分别为 过多 却是 + $གནམ་དྭངས་པོ་ 贼 空中  科学家 罢 测量 同比  正确的 缓解  青少年 书法 认定 筒 带有 侍 ▁新 薪 )< 婚礼 尼亚  增加了 乘客  这一点 发达 醋 生涯  大规模 党员 仇 参赛 赌 " གནམ་ལྷའི་ 地區 决策 百万 收录 个体 将来 质疑 一代 谦 以往 聘 步骤 想起 美好 顾问 牛奶  毕业生  系统的 可用 战胜 通用 给人 配套 客场  的照片 一颗 太子  找不到 流通 游泳 授予 皇后 带动 國家 浪费 家居  变成了 」。  看到了 官网  没有什么 占据  发现了 涌 十九 煎 貌 靖 砸 魚 婆 " གནའ་བོའི་ 中午 忘记 没人  产品的  独特的 跟着 队的 接着 子女 性和 穿着 想想 频率 波动 的那 写作  很重要 我妈 支出 陸  工作的 差别 饱 暴力 他們 途径 " གནའ་ཤུགས། 保养 趋 密度 秘密 常见 臂  ▁这些  传统的 看出 售价 硬盘 全年 版权 缺点 粮食 私人 百姓 模拟 取代 的心 对话 象征 硅 因為 必要 华为  གནས་  生活的  广东省 他还 鹤 仰 央行 疼痛  ▁例如  的消息 开通 議  上半年 可惜 打破 高峰 颇 唇  了解到 一首 当代 维修 % གནས་དབྱུང་  工程师 阶 其余 塑料  优秀的 体制 激素  གནས་ཚུལ་ 逾 兑 阿里 医药 故障 趁 无人 碱 结论 脊 对其 要么  女孩子 餐厅 嘉宾 年后 扯 队友  想知道 外交 表情  གནས་ཚུལ། 進行  的世界 逊 会上 面向 味道  相关的 扇  参加了 相处 奋 二级 忧 滨 弓 一带 新型 虹 丧 复习 伊朗 请求 年至  的目标 一群  不容易 你说 血液 十大 獎 房产 蜂 签约 过度  这两个 家中 夸  གནས་ཡུལ་  心理学 不变  造成的 慎 书籍 高清 尺 发起  这种情况 改编 回去 懒 谨慎 . 'གནས་ལ་འགྲོ་བ་ 共享  取得了 讨厌 蓄 还不 走出 图书 分离 台北 裕 本文 演技 今后 库存  在国内 是谁 铃 势力 裝 问我  太平洋 納 一对 平面 " གནས་ལུགས་ 制品 卒  的看法 偶像  真的很  提出了 无奈 大臣 争取  明显的  很大的 适量 贫困  ▁当然 局部 试图  不愿意 司马 " གནས་སྐབས་ 冬天 斌 基层 乐队 月经 电动 做过  ▁本人 握 自行 形容  总经理  的声音 跑步 防御 中部  这件事 提名 合法  གནས་སྤོ།  གནས།  告诉记者 大致 資 傅 滞 保健 翠 战役 证实 轨道 質 抚 惟 单独  联合国 绳 解答 截止 来讲  也就是说 退休 从小  了一些 贫 奧 提交 诞生 谭 黨 等于 高温 主张 歌词 曼联 清朝  觉得自己 大战  副主任 监测 罐 婴儿 曾在 礼物  的特点  微信号 辽宁 泄 会被 气温  机器人 亏损 后卫 是有 " གནོད་འབུ། 夕 知友 平原 收盘 淘汰 报警 钢铁 掉了 国民  ▁比如 依赖  ”——  在上海 圖  的信息 谷歌 灵活 分泌 超市  ▁谢谢 是不 士兵 玉米  完成了  电子商务 宪 肌肤 火车 傲 很高 设有 袭击 行驶  可以说 人文 改进 边缘  的角色 ◆ 专用 首发  གནོད། 监控  比赛中  每个人 外界 机身 覆 化工 醇 日晚 滋  相关问题 相应 視 央视 三种 长的 者的 电商 自杀 优点 而来 雌  英格兰  གནོན། 长安 網 部署 坠  有效的  的食物  在一个 带领 巾 召 专项 兴奋 战术 清理 捐 一层 哈哈 动漫 在家 鉴定 宠物 中山  གབ་པ། 矣 晕 第八 图像 有过  ▁还有 單 教材 ▁黄  གབ་ཚིག་  笔记本 新人  的状态 还将 黑暗 导弹 抄 涵 生于  企业的 旅客 不满 紧急 一道  幼儿园 您的 巷  公众号 义务 二次 罩 化妆 不必  相应的  的观点  གཙང་མ། 赋 王子  གཙང་སེལ་  的一部分 給 匹 是很 文献 天使 天地 进展 涉嫌 乐观  经济发展 亮相 遥 传递  གཙོད། 春秋 现状 那时 见过  让自己 斗争 乡村 抹 大脑 称号 愁 陆续 委托  严重的  གཞང་དཀར་ ▁阿 遗憾  གཞན་ཡང་ 主人 联想 贪  就开始 押  ▁一般 在我  有一天  发布会 审核 神奇 这也 该剧  གཞས་ 花园 故意 白银 上线 抓住 试点 大于 棚 当你  美联储 筋 人均 了个 人心 式的 氛围 奶奶 合约 桐 高兴  告诉你  原因是  什么是 夷 ( !གཞི་འཛིན་ས།  一定会 同步 延伸 早已 出门 蓬 ▁请 以此 手续 帖  是否有  གཞི། 乾隆 闷 效力 可谓 该片 勿 错过 普及 参数 眼镜 对抗 担 之後 实体 娃 优雅  国民党 得很 四十 + $གཞིར་འཛིན་པ་ 古典 新兴 部长 养殖 长度 不去 尹 千米 容量 昆明 球场 幅度 巫 单身 腺 電影 转型 手中 精力 缴 赢得 携带 吐槽 采购 吃了  很喜欢 是对  formula  可以通过 顾客 上课 尝  གཞུ་ 盾 坎 地说 涅 肿瘤 某种  的游戏  行业的  不一定  གཞུག 生意 随意 認為  གཞུང་  的可能性  或者是 约翰 加油 交警  གཞུང་། 姓名 总部 糊 扔 权威 审查  阿拉伯 防治 溶液 这场  有一种 闯 中期 疗 煤炭 笔者 剩  ▁随着  结束后  给大家 后悔 ▁金 聯 瑞士 懂得  所以我 高效  的支持  希望能 狱 孤独 有钱 予以 袖 楼主  གཞུང་ཡིག 正规 帆 简介  ▁北京  派出所 一篇  的说法 凤凰 喀 抬 专科 前提 六年 三次 均匀 控股 复制 陀 谈判 长春  达到了 踩  的力量 禄 浴  གཞོག་པ། 这条  གཞོགས་ 遣 浸 充电 获奖 奴 琪 + $གཞོགས་འདེགས། 便利 尽快 廊 滩 竞技  གཞོང་པ། 欧美 一遍 淫 拍照 事项 贵州 两者  有些人 让她  的过程中 當時 槽 正好  自行车 细菌 独特 放松 时光 辩 人在 展览  再加上 搭载 区间 重组 出国  总人口 三角 优先 一番  当时的 不及 辛苦  ”,“ 感冒  的基础  གཟབ་གཟབ་ 待遇 损害 他也  公元前 诗人  给自己  གཟབ་ནན་ ▁自  是如何 脸上 几十 灵魂  哈哈哈 说过 " གཟའ་འཁོར། 蟹 主办  的形式 递 检验  那时候 电源 发放 郁 尉 皇马  江苏省 都说  其实是 巢 摔  གཟར་པོ། 拒 每月 热门 协助 现货 豪华 违 长得 朵 催 合格 芯片  有机会 是从 伞 进程  考虑到 回落 出血 回报 面積 去做  最多的 水泥 稻  གཟས་པ།  གཟི། 斯坦 网页 也被  的日子  产生了  ▁除了 年来 出于  的精神  做什么 十字 誉 吞 哀  གཟིག་ 分数 可怕 当日 给她 慢性 上去  的经历  如果有 皇家 一看 水库 划分 一同 敦 精品 牺牲  国内外 % གཟིགས་དང་། 的老 比分 媒 真相 两大  的数据 此事 昏  天然气 错了 刮 估值 还原 赶紧 留在 苑 民国 获悉 诸多 每周 一时 新建 免疫 景点 堵 奥运 溢  研究院 董事 南海  董事会 美容 更高 團 承受 立法 ”; 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標  གསོད། 桓 书中 麟 鹅 彩票 理科 仍在 像素 烦恼 欧元 按摩 足协 两市  中国人民 下行  出来了 一波 无所 彈 作為 电梯 彬 渴望 周三 警告  ▁—— 大一 辖区 修正 朝阳  河北省 屑 勇气  གསོན་པོ། 战国 公寓 北美 环球 偶然  གསོལ་ཇ། 援助 % གསོལ་ཚིགས། 没事  必要的 舆论 比重  实在是  就知道 出手  更大的 颗粒 忽视 1 *གསོལ་རས་གནང་བ་ 鄂  都不会 周四  根本不 异性  摄像头 党委 卻 遇见  的风险 最快  切尔西 弹性  绝大多数 一根 楼上 爱你 底部  没办法 中场 讲究 刃  你觉得  我已经 清除 航班 想去 暗示 电压 伟大  为什么会  的方向 你在 开出  的人生 % གསོལ་སྟོན་ 轻轻 疲劳 购房  说什么 大厦 转化 尼斯 春季  གུ་ 吏 眼里 蜡 分散 风光 〉 衬 海域 闸  还是要  还需要  的形象 捉 日语 彻  我没有 而非 某一  ▁上海 众人 纠纷 惊人 ▁白 绿化  分辨率 名词 过渡 向下  设计的 产能 甘肃 九年  可以说是 极限 汽油 般的  意义上 % གུ་དོག་པོ།  了许多  གུག་ 未经 严肃 庞大 轻易  可以看到  解决方案 另有 :「  有许多 提起 接待 答题 岁月  讲述了 点赞 小幅 情人  日本人 褐色  就不会 樓 招商 枫 回头 我爸 愉快  ▁我觉得  万像素 看似 頓 网易 癌症  的空间  གུང་  怎么做 街头  会导致 度过 只想 恶意 阳县 規  不应该 歪 阮 风云 天才  གུང་།  གུད་པ། 商场 这点 我看 收取 詞  少数民族  གུས་པ་ 肢 单一 出品 面上 逝世 线条 外表 氨  的原则 我去 军区 新高 纯粹 提取  གུས་པ། 一千  较高的  已成为 仪器  为自己  我们在 海关  化合物 订阅 安静 興 奇迹  才知道 眠 庞 贩  请大家 环比 一声 雇  ▁今年 委会 时报 跟你 意志 唐代 狄 剧院 各有 条约 截图 运气 最有 瞎 + $གུས་ཕྱག་འཚལ། 婴  强烈的 黛 卑 永久 出現 抵达 熱 回升 屠 耳机 ▁西 」( 酒精  真实的 体质 饶  科学技术 进而 建国 杭 为其 防护 祖父 魔术 助攻  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右侧 骄傲 字体 射手 偷偷 渗 阿拉  好朋友 纪律 发掘  在家里 桌面 节约 打败 芭 西汉  新西兰  和发展 月亮  一次性 特里 溶解 哎 声称  中国科学院  》,《 就此  三季度 蔚  解决问题 屎 至此  就行了 珠宝 走路 华东 低价 创下 牛逼 不肯 起床  充满了 寶 容器  的习惯  医学院  二等奖  不舒服 德拉 雷达  不好意思 测定 識  企业家 棱  建议你 无锡 古装 ( !གྲོས་བསྡུར་ 麓 道教  任何人 卡片  中共中央 担当 局长 绝望  很高的 不顾 万多  然后就 当成 数值 拌  གླ་བ།  驾驶员 领土 赞同 血压 精美 类别 储存 帐号 首选 理事  石家庄 要比 斯托  中央电视台  以下是  许多人  གླང་ཆེན་ 确立  成熟的  电影节 为准  现在已经 竭 遵循 富含 不当 外资 当晚 头痛 戊 战队 社保 阶层 圍 歷史 绪 评审 派遣 物资 单反  ---------------< ▁按 七月 水域 尼克  接受了  供应商 清明 三四  需要注意 護 民营  最喜欢的 将近 晋级 棉花 专注 健全 卡拉 手上  有很大  改变了 破产  才发现  安全性  比上年增长 批发 三百 严谨 听力 相传  做出了 炎症 前三  གླས་པ། 楼盘 一副 亦有 米的  第一名  吸引力 斷 书院  གླིང་ " གླིང་ཕྲན།  就是这样 终止 出租 因其  留下了 ~~~ 社团  甚至是 当局 毫  年增长  信息化 带给 》; 歌唱  要知道 书写 举措 方形 舉行  ▁新浪娱乐讯 參與  的风格 河道 終  当然是 哪种 每人 辩论 该机 几千 贴吧  被认为是  不明白 早盘  གླིང་བུ་  小小的 时常 洪水 朔 扩散  一直都 牡丹 巅峰  却没有  知识产权  极大的 進入 用作 注定 已知 衡量 芝麻  ▁随后 恤 词语 内饰 笋 iPad  的话题 县级  注意事项  是日本 歐 柠檬 侨  奥地利  百分之  གླིང་བུ། 进军 情商 布里  在未来 受理  是否会 霖 眼光 动手  时间内  为主的  只有一个  搜狐汽车 敞 问他 蝴蝶 媛 三月  胡萝卜  大多数人 哟 压制 優 之心 淀粉 甄 搅拌  གླེང་། 侦 徵 參加 官兵 古人  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锥 春晚 套路 相声 多多 最长  ང་ཚོ་ 右手 教训 增值 万物  认识的  主人公  ང་རྒྱལ་ 这名 栓  ངང་  女演员  第一批 熏 不做 USB  熟悉的 相亲  他表示 痕迹 幼儿 再来 祁 写出  的高度 繼 师生  理事会 魄  ངང་དཀར། 多头 扎实 都将 雕塑 廠  巧克力  ངང་པ། 衣物 不通  中饰演  巴基斯坦 有序 指甲 投放  Facebook  中国古代 四月  发现自己  这首歌  希拉里 佬 大腿 老大 马云 欺 ★★ 丢失 哨 御史 不来 爸  周围的  个人认为 广阔 筹备  我当时 开门 法定 庶 前端  ངན་པ་  全方位 靈 保卫  阿富汗 括 安卓 抄袭 合计 】: 并购 路径 裴 几句  可是我  中小企业 毯 镀 赞助 无穷 还算 俄国 谐 之人 其后 打进 更改 遊 方针  显示器 捐赠  州刺史 粪 忆 嘱 戏曲 兴起  东南亚 征收 奥迪 彼得 烷 浸泡 掘  已经开始 身亡 分歧 传球  ངན་པ། 回事  劳动力 沉积 娅 橘  ▁中新网 酱油 倡导 例外 並且  代表大会 相遇  肯定会 理学 拆除 囚 酒吧 建于 支流 头脑  ངན་རེངས་ 生病 物价  什么呢 斐 好感 特意 不過  个篮板 脖子 巴尔 合资 舊 英镑 城县 礁  总觉得 兼任 手游 女星 热血 法制 现已 坟 蠢  的责任 录像 交互 甜蜜 ▁該  抑郁症  时间为 入围 全景 禾 穿上 皱 鱼类 徐州 汶  >---------------  银行卡  子公司 位居  理工大学 国道 戚 移植 路面 橄榄  制作人 主播 行程 千克 门将  ངན་སྐུལ་ 麥 曼城 贮 逆转 布鲁 自家 原型 宋代  奥斯卡  成立于  影响到 三观 酥 問 靡 " ངར་ལངས་པ་  比利时 建築 习俗 哺乳 缅甸 下令 首播 里亚 杖 換  ངར་ལྕགས་ 大海 越高 马丁  开玩笑  可以直接  很不错 全文 穿过 细致 4 -ངར་སྐད་སྒྲོག་པ་ 即位  准备好  在全球 解说 达人 其间 我能 南宋 简约 观光 底线 分割 卖家  分公司 之家 签名  就是要 专栏 宾馆 大国  合作伙伴 颠  分钟后 新款  对应的  存在着 主教 蹄 檀  给他们 八月 斯蒂 輪 旬  反映了 老实 新娘 火灾 準 莲花 大人  短期内 西瓜 异地 当我 阿姨 同类  上赛季  ངལ་གསོ་ 分別  的第一个  小孩子 卤 毛病 茜  ངལ་དུབ་ 哑  ངལ་དུབ། 口感 动员 一轮  知名度  ངུ་བ། 极高 饲养 肋 絕 弱势 编曲  嫌疑人  对自己的  新闻发布会 兜 夜晚  活动中 大选  这本书 门户  就不能 两队 好转 酬 漏洞 跨越 城堡 便秘 提倡  ངུ་ཤུམ་ 公分 实质 tic 兔子  给孩子 齐全 桂林 菜单 入境 拉开 意图 光泽  知道自己 过得  世界上最 行使 老子 没钱 贵阳 鲁迅  如果我  ངུར་བ་  本场比赛 娃娃 配件 挪威 躺在 热烈  以下简称  科技大学 荫 秉 很低 庫 车道 奴隶  特朗普 鞍 域名  才可以 捣  因为他们 好久 主義 插入  ངེས་པར་  干什么 网址 写真  这么大 本月 伽  在问答社区 湿地 首页 大叔 填报 镁 聂 电网 用力 決定  ངོ་  江西省 宁夏 佩戴 此处 米尔  让大家  代表作 办事 血糖  ངོ་མ། 举例 进士  ངོ་མཚར་ 天王 罢了  ངོ་ཚ་  )、《 做饭 剧场 中考  资本主义 重返 产权 万户 為了 开花 总共 排队 籽 国足 大涨 心疼  我希望 踪  中国人民解放军 军官 柔软 切成 净化 出资 马路 IPO 书面 油脂 水量  奥林匹克 隐私 希尔  ངོ་ཚ་བ།  省的一个市镇  ངོ་ཚ།  三角形 % ངོ་འཕྲད་པ་  总产值  元左右 抑  代表性 瓣  不稳定  受欢迎 带上 乐视 楷 高档 演艺 友情 潜艇  第二季 陣 东汉 编写 樊  一般都  广州市 诠释 欧阳 平方  ངོ་ལོག་ 九月 驳 还真 最少 深处 灵感 侧面  市公安局 全区  社会科学 浊 帅气 选购  国际机场 香味 格尔 叙 矿物 沉淀 抛弃 一顿  的定义 波特  环境中 海口  广播电视 操控  ངོགས་ 货运 再生 泼 雅思 美观  并不能 就连  在国外  ངོམ་ 巴斯  信息编辑 省委 死刑 哈利  支球队 餘 助手 交界 该股 离职 概括 检察 这事  ངོས་ 歌舞 增添 汇报 审理 因子  ▁由於 百合 屁股  标准化 ཅ་  个人觉得 公子 讳 峰会 特效  同时还  主义者 隶  ཅང་། 农场  是多少 人身  能看到  的局面 呐  ▁作为一个 咱 补助 倒入  资本市场 动机 患有 绑 !!!! 扳  职员表 二者 传出 贬值 万桶  经典的 联手 鸽 暮 深夜 態 有种 勉强 眼球 支队 复出 法人 恋人 骑兵 号称  这一切 修订  但如果 强奸 真理 增幅  得不到 证件 一幅  成为一个  也非常 仓库  英特尔 专区 换了 精度  风景区 衔 男方 杀害  的一面 船舶 扒  人力资源 % ཅང་ཅེ་ཧྲི། 叶片 民事 載 最終 名额 终结  无疑是 避孕 兩個  ཅན་ 就行 跑到 质感  的大小 恶劣 企图  博士学位  传统文化  过哪些 配方 日线 和尚 硬化 乳房 高管 内阁 感慨  都需要 去过 长约 诞  以外的  因为它 三世  是法国 俯 之作 变革 前任 俏 左手 三千 炸弹 受损 心思  国际化 默认 货车 悲伤 射门  ཅཱ་ 廳 ▁̧? 不准 搓 趟 口语 初恋 理工 进度 场均 秋冬 钧 毁灭 老鼠 隶属 备案 梓  商业银行 魅族 出处 木材 膳 执教 侦查  请各位  相关部门  我该怎么办 低迷  副作用 争论  到最后  很多时候  显示屏  上世纪  市中心  ▁全年 留意 加剧 嚴 阎  基金经理 香蕉 播种 淡水 央 恶魔 苔 病变  绝对是  范冰冰  体育场 入住 同情  放弃了  ཅི་ 上限 精细  也比较  二十年 自拍 富豪  副总裁 外援  基本面  呈现出  立方米  怎么说  我不想 祖先  但是他 迅  演员表  很明显 攒 丝毫  ཅི་འདྲ་ 约会  命名为 碎片  小学生  发达国家  综合性 底层 瓦尔 五百 即是 湿度 峻 鲁能 做多 这块 外围  日报道  产业发展 气势 擁有  ཅིན་ 更快 轰炸 恒星 惩 灾难  普通话 认购 腥  也许是  ཅིའུ་ 學院 两只 咖 重心 贯彻 量化 排在 稳健  服务中心 触摸  有限责任公司  跟我说 捐款 音频 东海 投篮 失踪 改名 庵  或其他  我们会 猿  如果您 嫁给 韶 名誉 定居 小吃 艰苦 打入 協 门外 泻 岚 常态  ཅུང་  ▁你好 零食  辽宁省  ▁镜像问题  ཅུན་ 圆满  竞争对手  完全是  外交部 瀑布 励志 吼 咪  不断地  说的是 流失  理想的 尊敬 铁矿 污水 线性 被告 不难  胆固醇 分红 忍受 归来 改成  改革开放 德语 螺旋 准则 乞  ཅུས་  ཅོད་པན་ 不含 俭 漫长 恶化 临近 招收 行车 切换 发热 历经  是什么意思  如何看待 终极 吸烟  网络游戏  ▁新浪体育讯 紫色  张专辑  这几天 薯 实战 却被  想问问  的前提下 爱人 序列 世间 泰山 丘陵 买家 榨  还有很多 两周  较好的 氟  几十年 哥们  世界各地  介绍编辑  会议上  有名的  是一种怎样的体验 学业 管制 歸 趾 手臂 做空 色素  不允许 梦幻 吸毒 空白 获胜 描绘  集团有限公司 哭了 原先 惊讶 砌 敬请 加之 路过 嵩 一双 陋  ཅོན་ 均线 分校 纠正  安全感  文物保护单位 剧集  地理位置 真人 李某 伤口 用法  不少人  中小学 彤 承办 件事 减弱 过高  马拉松 非要 状元  不了解 呕吐  只能说 痘痘 海底 底下  蒋介石 村落 仄  高科技 ཆ་ 睁 半天 丫 相爱 观望 信念  微生物  ཆ་ག་པ་ 不服 感恩 谥 欺负 父子 火焰 参照 半个 肥胖 找了 即时 悼 内衣 抑郁  有两种 一角  亚马逊  ཆ་གཉིས། 法案 覺  日正式 週 着手 势头 彪  值得一提的是  的秘密 达尔  用什么 感应 收回 对付 釉 若是 换成  ཆ་རྐྱེན། 炳 命题 总督 万里 糟  涉及到 凿 法國 两点 外贸 向往 恳 湖泊 高出 找出  年平均 吃完  有效地 华南 安县 持久 王某 尼亞 品味 明清 現在  ▁通过 茄 笑容 阻碍 婚后 满了 化解 脆弱  本科生  只是一个 一向  一直以来 順 那你  ▁而在 〕 结晶 降温  重庆市 衬衫  所得税 冰川  第二届 特別 可供 得起 扮 密封 位列 谨  ཆ་སྒམ་ 涂料 侄  委員會 来临 差点 亚太  以至于  大学毕业 安娜 儿女 科尔 厦  小组赛 干预  中華民國 淋巴 聚合 太高 弯曲 天子 陇  离开了  增长率 溃疡 球隊 重启  ཆ་སྙོམས། 孜 才华  这段时间 〔  ཆག་པ་ 减持  直升机 蜘蛛 从前 两边 查处 剩下 沽 香菇 昔 格林  历史文化  硕士学位 尔顿 馨 征集  这个词 棕  伴随着 越野  特点是 京剧  主管部门 疑惑 约合 寝室 颤 毫克  氨基酸 跌破 壤 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鮭 铩 剎 瘰 驛 ぃ 聒 黻 韪 鳐 珽 枰 脍 霑 缦 僰 弇 讪 鱇 ぷ 缁 柵 ㄐ 樨 碜 墘 檻 속 鷲 肟 貊 嚯 锛 蓁 翮 箜 赜 訥 朊 疔 诔 鲚 蜿 頔 悫 渲 抉 葩 跻 娩 窒 郸 璀 菠 诅 蟑 痊 骷 徊 萄 憧 咙 娛 鸯 瘙 跆 榈 蝙 雳 憬 酝 傀 肪 髅 婪 惫 尴 淆 疙 楂 圾 稣 娠 逅 赁 貔 侥 徘 馒 骼 瘩 蝴 飓 豌 痪 蜘 邂 貅 饪 螃 礎 鹉 儡 ;/large_experiments/theorem/datasets/MERGED/all.test1.merged-spm_model_32k_200M_charcov099995_allowWSO__v2 0:textU?X_p=}@?P  ⁇  identity 2pengchengmind/tokenizer/llama_vocab/llama_zh_hf/tokenizer_config.json000066400000000000000000000013301453423006600266340ustar00rootroot00000000000000{ "add_bos_token": false, "add_eos_token": false, "bos_token": { "__type": "AddedToken", "content": "", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false }, "clean_up_tokenization_spaces": false, "eos_token": { "__type": "AddedToken", "content": "", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false }, "model_max_length": 1000000000000000019884624838656, "pad_token": null, "sp_model_kwargs": {}, "tokenizer_class": "LlamaTokenizer", "unk_token": { "__type": "AddedToken", "content": "", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false } } pengchengmind/tools/000077500000000000000000000000001453423006600150275ustar00rootroot00000000000000pengchengmind/tools/PengCheng-Mind-logo.png000066400000000000000000011534151453423006600212300ustar00rootroot00000000000000PNG  IHDRDJ@ pHYs  ֿIDATxw]u~Nv s3)fʲ-4xf~~Dr-Y)1S")1$"nt9X8o7 ${Y}sT޵K\W4?f_Ћ>k"Z+v%@pm㵱1 oDtObw#^FUǻMvPkH!I  &]pt۰K.԰Oozgࡧ<~iާ8Q?ADt?wՙAF頵nj<~f$RMQ |dP q槷]=Ol{rVYwF-WhCJ2vxїx|s\޻~?oN^b8ݍ) ^hEW'ѷY#T-]6Xuk7SwBX.R1^O°lk,S9ݸk12͉M)e'ǿv9pozT=mÃep^:R}R*ܰy#t4 XyzBoj? f|[_c{ bq{/߿W{~'|af*xt:0$P.^Y\6\u g@]d '1.̿15 C<3X 5滂?ƜTcu( S+%'?[g}tlz+I) ®`ge\"v2B>M V#AYOqg_%'fӜ*#oL:t$-4F7AtJC41d'B؀ZF(HZ+0~,Q2/=O"egQ%aݚ fՄ!6HOГ;\Ce 6ʼz0}BRh8n=KBX@;0jFTkô׹{%DDzljD%: !h9Чbq "_(6 8 UMf%FPZXr=BQŰ&D{PF40$к@"EOC~xln ad*Ƹ.%t|Z=;ANUXf B@~ƟY[@ H+*/F5hNf wi!X•* ƪ8YۍcGe2N/ )mNTm2`|MiǥpO9Hb7oF6F@GL gVpd. ^f7skځ'v$;r 2ƋiX8[KNZC-pw"?/0CaB0ݬ*n%UNr{Nsl`hg"Sn."Ox;gF^beSE`H ZcH 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R֠Igcx+DOݟ'}7>/lTlae$Ub)\.AWog'x;܏tzx7@.E||xdԇEKDSo V,9)oqߟO߶zj Qyi'jXmߟ[X{MBåSQ#GO HFZhU6XꪣӲ{6T;4K0D*:\a=9lj$fWLI0κzkc cFqA?ݚ>hk v? !㫩amFA41dp!\fTsګ;mOʝg4gtaSi/agA<V%9wp~.7ۙDHwi=F*;I@p`W<UE+k56?FZKPbZoWQ練-(9])1zmtzYٚtQ/5 5U c} ?BEpIENDB`pengchengmind/tools/pre_process_data.py000066400000000000000000000601051453423006600207200ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ transform wikitext-2, wikitext-103, lambada, openwebtext dataset to mindrecord. """ import argparse import glob import json import os import re import random from multiprocessing import Pool, current_process import numpy as np from tokenization import FullTokenizer from tokenization_jieba import JIEBATokenizer import tokenizationBBPE as BBPETokenizer from tokenization_transformer import TranTokenizer import jieba from zhconv import convert import sys sys.setrecursionlimit(10000) try: from transformers import GPT2Tokenizer except ModuleNotFoundError: print("module 'transformers' not installed.") from mindspore.mindrecord import FileWriter #python src/pre_process_chinese.py --input_glob= --output_file= --tokenizer= --SEQ_LEN= --file_batch_size= parser = argparse.ArgumentParser() parser.add_argument('--dataset_type', type=str, default='openwebtext') ########################### # parser.add_argument('--input_glob1', type=str, default='/userhome/dataset/chinese_txt/**/*.txt') parser.add_argument('--input_glob', type=str, default='/userhome/dataset/chinese_txt/webtext2019zh/*.txt') ############################# parser.add_argument('--output_file', type=str, default='/userhome/dataset/chinese_txt/webtext2019zh_mindrecord/webtext2019zh_EOT30000_mindrecord') parser.add_argument('--file_partition', type=int, default=3) parser.add_argument('--file_batch_size', type=int, default=300) #不影响结果, 影响打乱程度 parser.add_argument('--num_process', type=int, default=100)#96 parser.add_argument('--tokenizer', type=str, default='llama') parser.add_argument('--SEQ_LEN', type=int, default=4097) parser.add_argument('--rankOfCluster', type=str, default='0of1') args = parser.parse_args() SEQ_LEN = args.SEQ_LEN # the length of sample # EOT = 50256 # id of endoftext # tokenizer = GPT2Tokenizer.from_pretrained("gpt2") working_dir = os.getcwd() ############################## if args.tokenizer == '4w-bpe': vocab_path = working_dir + '/bpe_4w_pcl/vocab.vocab' model_file = working_dir + '/bpe_4w_pcl/vocab.model' tokenizer = JIEBATokenizer(vocab_path, model_file) ############################ ############################## if args.tokenizer == '3w-bpe': vocab_path = working_dir + '/bpe_3w_pcl/vocab.vocab' model_file = working_dir + '/bpe_3w_pcl/vocab.model' tokenizer = JIEBATokenizer(vocab_path, model_file) ############################ ############################## if args.tokenizer == 'spm-bpe': vocab_path = working_dir + '/bpe_3w_spm/vocab.vocab' model_file = working_dir + '/bpe_3w_spm/vocab.model' tokenizer = JIEBATokenizer(vocab_path, model_file) ############################ ############################## if args.tokenizer == 'bbpe-5Lang': import tokenizationBBPE_notwithjiabacut as BBPETokenizer vocab_file = working_dir + '/bbpe_4w_5Lang/vocab_bbpe.txt' tokenizer = BBPETokenizer.FullTokenizer( vocab_file=vocab_file, do_lower_case=False) ############################ ############################## if args.tokenizer == 'bbpe-7Lang-5w': import tokenizationBBPE_notwithjiabacut as BBPETokenizer vocab_file = working_dir + '/bbpe_5w_7Lang/vocab_bbpe.txt' tokenizer = BBPETokenizer.FullTokenizer( vocab_file=vocab_file, do_lower_case=False) ############################ ############################## if args.tokenizer == 'bbpe-4w': vocab_file = working_dir + '/bbpe_4w_zh/120g_vocab_bbpe_40513.txt' tokenizer = BBPETokenizer.FullTokenizer( vocab_file=vocab_file, do_lower_case=False) ############################ ############################## if args.tokenizer == 'bpe-50w-tran': vocab_file = working_dir + '/bpe_50w_transformer' tokenizer = TranTokenizer(vocab_file) ############################ ############################## if args.tokenizer == 'langs_23': from spm_13w.tokenizer import SpmTokenizer from spm_13w.zh_process_jieba import zh_process vocab_file = working_dir + '/spm_13w/spm.133952.PanGu.model' tokenizer = SpmTokenizer(vocab_file) if args.tokenizer == 'llama': from transformers import LlamaTokenizer vocab_file = working_dir + '/tokenizer/llama_vocab/llama_zh_hf/tokenizer_2.model' tokenizer = LlamaTokenizer.from_pretrained(vocab_file) EOT = tokenizer.eos_token_id PAD = tokenizer.unk_token_id ############################ # PAD = tokenizer.pad_id # EOT = tokenizer.eot_id print('pad id :', PAD)# 128297 print('eot id :', EOT)# 128298 # print('vocab size :', tokenizer.vocab_size) ##-----------------------corpus change------------------- ### pad id : 128297 ### eot id : 128298 ### vocab size : 128320 langs_ID = {'zh': 128301, 'ko': 128302, 'vi': 128303, 'de': 128317, 'en': 128318, 'nl': 128132, 'ms': 128109, 'id': 128110, 'tl': 128111, 'mn': 128103, 'my': 128104, 'th': 128105, 'lo': 128106, 'km':128107, 'lt': 128112, 'et': 128113, 'lv': 128133, 'hu': 128115, 'pl': 128116, 'cs': 128117, 'sk': 128118, 'sl': 128119, 'hr': 128120, 'bs': 128121, 'sr': 128306, 'bg': 128304, 'mk': 128122, 'ru': 128305, 'uk': 128307, 'be': 128123, 'sq': 128124, 'el': 128125, 'ka': 128126, 'hy': 128127, 'ro': 128108, 'fr': 128100, 'es': 128102, 'pt': 128101, 'fa': 128310, 'he': 128311, 'ar': 128308, 'ps': 128309, 'tr': 128128, 'kk': 128129, 'uz': 128130, 'az': 128131, 'hi': 128315, 'ta': 128316, 'ur': 128313, 'bn': 128312, 'si': 128314, 'ne': 128114} translate_ID = 128300 ##-------------------------------------------------------- def chunks(lst, n): """ yield n sized chunks from list""" for i in range(0, len(lst), n): yield lst[i:i+n] # 将一个list尽量均分成n份，限制len(list)==n，份数大于原list内元素个数则分配空list[] def divideIntoNstrand(listTemp, n): twoList = [[] for i in range(n)] for i,e in enumerate(listTemp): twoList[i%n].append(e) return twoList def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def clean_wikitext(string): """ cleaning wikitext dataset""" # contractions string = string.replace("s '", "s'") string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) # number separators string = string.replace(" @-@ ", "-") string = string.replace(" @,@ ", ",") string = string.replace(" @.@ ", ".") # punctuation string = string.replace(" : ", ": ") string = string.replace(" ; ", "; ") string = string.replace(" . ", ". ") string = string.replace(" ! ", "! ") string = string.replace(" ? ", "? ") string = string.replace(" , ", ", ") # double brackets string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) # miscellaneous string = string.replace("= = = =", "====") string = string.replace("= = =", "===") string = string.replace("= =", "==") string = string.replace(" "+chr(176)+" ", chr(176)) string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" N ", " 1 ") string = string.replace(" 's", "'s") return string def padding_eot(chunk): pad = [PAD] * (SEQ_LEN - len(chunk)) chunk.extend(pad) return chunk def neighbour_mix(content, content_new=[]): content_new = content_new ceil = [] del_idx = [] count = 0 for idx, i in enumerate(content[:10]): # find if len(ceil) + len(i) <= (SEQ_LEN-1): ceil.extend(i) del_idx.append(i) else: count += 1 content_new.append(ceil) for i in del_idx: content.remove(i) if len(content) > 1: return neighbour_mix(content, content_new) elif len(content) == 1: content_new.append(content[0]) len_data = [len(i) for i in content_new] print("***"*3, len(content_new), np.average(len_data)) return content_new else: len_data = [len(i) for i in content_new] print("***"*3, len(content_new), np.average(len_data)) return content_new def neighbour_mix2(content): content_new = [] ceil = [] # len_c = len(content) # flag = True for idx, i in enumerate(content): if len(ceil) + len(i) <= (SEQ_LEN-1): ceil.extend(i) else: content_new.append(ceil) ceil = [] len_data = [len(i) for i in content_new] print("***"*3, len(content_new), np.average(len_data)) return content_new def neighbour_mix3(content): content_new = [] ceil = [] del_ceil = [] len_c = len(content) # flag = True for idx, i in enumerate(content): # if i not in del_ceil: if len(ceil) + len(i) <= (SEQ_LEN-1): ceil.extend(i) # elif (idx+1) returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ seq_length = input_ids.shape[1] - 1 # Initialize position_ids and attention_mask batch_input_ids = input_ids batch_position_ids = np.ones((len(input_ids), seq_length)) # batch_attention_mask = np.ones((len(input_ids), seq_length, seq_length)) batch_eod_index = [] # Loop through batches for bs_i, _ in enumerate(range(len(input_ids))): # Get normal position_ids and attention_mask local_ids = input_ids[bs_i] # batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length))) batch_position_ids[bs_i] = np.arange(seq_length) # Find eod_of_document eod_index = batch_position_ids[bs_i, local_ids[:-1] == eod_id].astype(np.int32) prev_index = 0 for i in range(eod_index.size): index = eod_index[i] # Reset position_ids and attention_mask considering EOD # batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 batch_position_ids[bs_i, (index + 1):] -= (index + 1 - prev_index) prev_index = index + 1 return batch_position_ids def get_input_data_batch_slice_map3(input_ids, eod_id): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ seq_length = input_ids.shape[1] - 1 # Initialize position_ids and attention_mask batch_input_ids = input_ids batch_position_ids = np.ones((len(input_ids), seq_length)) batch_attention_mask = np.ones((len(input_ids), seq_length, seq_length)) batch_eod_index = [] # Loop through batches for bs_i, _ in enumerate(range(len(input_ids))): # Get normal position_ids and attention_mask local_ids = input_ids[bs_i] batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length))) batch_position_ids[bs_i] = np.arange(seq_length) # Find eod_of_document eod_index = batch_position_ids[bs_i, local_ids[:-1] == eod_id].tolist() eod_index.extend([int(0) for i in range(len(eod_index), seq_length//2)]) batch_eod_index.append(eod_index) prev_index = 0 for index in eod_index: if index != 0: index = int(index) # Reset position_ids and attention_mask considering EOD batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 batch_position_ids[bs_i, (index + 1):] -= (index + 1 - prev_index) prev_index = index + 1 else: break batch_eod_index = np.array(batch_eod_index, dtype=np.int32) return batch_input_ids, batch_position_ids, batch_attention_mask, batch_eod_index def tokenize_openwebtext(iterator): """ tokenize openwebtext dataset""" content = [] for file_path in iterator: if os.path.getsize(file_path) == 0: continue if '.json' not in file_path: print(file_path, "jump...") continue else: with open(file_path, 'r', encoding='utf-8') as fl: for idx, info_one in enumerate(fl): try: para = eval(info_one)['text'] # para = info_one if len(para)>=6:# and para != '\n': #if 'res_' in file_path: # para = zh_process(para) #tokenized_id = tokenizer.encode(''+para) tokenized_id = tokenizer.encode(''+para, add_special_tokens=False) # langs_id = tokenizer.convert_tokens_to_ids(tokenized_text_langs) if len(tokenized_id) < (SEQ_LEN-1): content.append(tokenized_id + [EOT]) else: if 'code_' not in file_path: content.append(tokenized_id + [EOT]) ''' ratio = int(len(tokenized_id)/(SEQ_LEN-2)) len_r = SEQ_LEN-2 for mn in range(ratio): content.append(tokenized_id[mn*len_r:(mn+1)*len_r] + [EOT]) if len(tokenized_id[ratio*len_r:]) >= 80: content.append(tokenized_id[ratio*len_r:] + [EOT]) ''' else: content.append(tokenized_id + [EOT]) #if len(tokenized_id) <= (SEQ_LEN + 2048): # content.append(tokenized_id[:SEQ_LEN-2] + [EOT]) except Exception as error: print("eval info Error, jump...", error) #print(content[:40]) #print(content[0]) #print(len(content), content[0]) # read files batch default 300 random.shuffle(content) #content_new = [] #content_new = neighbour_mix3(content) #random.shuffle(content_new) content_new = [] for i in content: content_new += i #print(len(content_new), type(content_new[0])) #exit() for chunk in chunks(content_new, SEQ_LEN): #for chunk in content_new:# chunks(content_new, SEQ_LEN): # print(type(chunk), type(chunk[0]), len(chunk)) # print(chunk[0]) sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) batch_position_ids = get_input_data_batch_slice_map( np.array([sample['input_ids']], dtype=np.int32), eod_id=EOT) sample['position_id'] = np.array(batch_position_ids[0], dtype=np.int32) # sample['eod_index'] = np.array(batch_eod_index[0], dtype=np.int32) yield sample else: sample['input_ids'] = np.array(padding_eot(chunk), dtype=np.int32) batch_position_ids = get_input_data_batch_slice_map( np.array([sample['input_ids']], dtype=np.int32), eod_id=EOT) sample['position_id'] = np.array(batch_position_ids[0], dtype=np.int32) # sample['eod_index'] = np.array(batch_eod_index[0], dtype=np.int32) # print(sample) yield sample def tokenize_openwebtext_padEachPara(iterator): """ tokenize openwebtext dataset""" content = [] for file_path in iterator: if os.path.getsize(file_path) == 0: continue with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: content = [] ############################################### tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [EOT] ########################################### for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample else: sample['input_ids'] = np.array(padding_eot(chunk), dtype=np.int32) yield sample def tokenize_wiki(file_path): """tokenize wikitext-2/wikitext-103 dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in clean_wikitext(f.read()).split("\n\n"): if para and para.strip().startswith('=') is False: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ EOT] for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_lambada(file_path): """tokenize lambada dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for line in f.readlines(): para = json.loads(line)['text'].replace( "“", '""').replace("”", '"').strip().strip(".") tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [EOT] for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def task_unit(iterator, parallel_writer=True): """task for each process""" p = current_process() index = p.pid if p.pid else 0 item_iter = tokenize_openwebtext(iterator) # item_iter = tokenize_openwebtext_padEachPara(iterator) batch_size = 1024 # size of write batch count = 0 while True: data_batch = [] try: for _ in range(batch_size): data_batch.append(next(item_iter)) count += 1 random.shuffle(data_batch) # print(data_batch[0].keys()) # print(data_batch[0]['attention_mask']) writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) except StopIteration: try: if data_batch: random.shuffle(data_batch) # print(data_batch[0].keys()) # print(data_batch[0]['attention_mask'][:100]) writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process again {} transformed {} records.".format( index, count)) except Exception as error: print(error) break def conver_words_to_ids_ch(words, word2id, numchword): ids = [] for word in words: if word in word2id: ids.append(word2id[word]) # else: # tokenized_text = tokenizer.tokenize(word) # tmp = tokenizer.convert_tokens_to_ids(tokenized_text) # ids.extend(list(np.array(tmp) + numchword)) return ids #python src/pre_process_chinese.py --input_glob "/raid-50/commmon-crawl/common-crawl-WET-ReCleaned-filter-v3-length200-by08-v2-reClean2-sample30G/train/*" # --output_file="/raid-50/commmon-crawl/common-crawl-WET-ReCleaned-filter-v3-length200-by08-v2-reClean2-sample30G-mindrecord/train/ # common-crawl-WET-ReCleaned-filter-v3-length200-by08-v2-reClean2-sample30G-mindrecord" if __name__ == '__main__': ### out_dir, out_file = os.path.split(os.path.abspath(args.output_file)) if not os.path.exists(out_dir): os.mkdir(out_dir) schema = {"input_ids": {"type": "int32", "shape": [-1]}, "position_id": {"type": "int32", "shape": [-1]},} # "eod_index": {"type": "int32", "shape": [-1]}} writer = FileWriter(file_name=args.output_file, shard_num=args.file_partition) writer.add_schema(schema, args.dataset_type) writer.open_and_set_header() ### transforms_count = 0 if args.dataset_type == 'wiki': for x in tokenize_wiki(args.input_glob): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'lambada': for x in tokenize_lambada(args.input_glob): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'openwebtext': # file_iter = glob.iglob(args.input_glob) input_files = list(glob.iglob(args.input_glob)) # input_files = input_files*2 input_files.sort() random.seed(10) random.shuffle(input_files) # input_files = input_files[:le] num_worker = int(args.rankOfCluster.split('of')[1]) rank = int(args.rankOfCluster.split('of')[0]) print(rank, ' of ', num_worker) print('num files of cluster : ',len(input_files)) input_files = divideIntoNstrand(input_files, num_worker)[rank] print('num files of this machine : ', len(input_files)) # input_files = input_files[:int(len(input_files)*50/2000)] # print('num input_files : ',len(input_files)) file_iter = (x for x in input_files) with Pool(processes=args.num_process) as pool: pool.map(task_unit, package_file(file_iter, args.file_batch_size)) ################################ # file_iter = glob.iglob(args.input_glob2) # with Pool(processes=args.num_process) as pool: # pool.map(task_unit, package_file(file_iter, args.file_batch_size)) ################################ else: raise ValueError( "Not support dataset type: {}".format(args.dataset_type)) writer.commit() out_file = args.output_file if args.file_partition > 1: out_file += '0' print("Transform finished, output files refer: {}".format(out_file)) pengchengmind/tools/qrcode.jpg000066400000000000000000004152571453423006600170240ustar00rootroot00000000000000JFIFC   #,%!*!&4'*./121%6:60:,010C  0  000000000000000000000000000000000000000000000000004" }!1AQa"q2#BR$3br %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz w!1AQaq"2B #3Rbr $4%&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz ?( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( dK8L]hsi{iH:#V,w{?#ހ6?yGͿ:gYuSӰtH~tFz~uIWu?Q񴖖O!T/~t _4^_q[Ij y@IW}Qo>gI_絛O(hK ӡh%aGsjygH=ԯe#MB#դuuʐExi]mDY|I3xk`[.@rNxHKfO#GQH2:ZЀ(((((((((((((((((((((((((((((((((((((((((((((((((((?V?QMsçҺ `sđ} +yV9:.tBX`wS6~GSNŝ zUǡkWSʾ{ַ9ar)\ǭs)msS]3=ejE"\xE3S]zLbzһҙ i(56 UsHL$Rsڷ-}fR7Nn`?HZՍD-a='ދ"W̰qLu 8AhMke"["\t-,|ʇs`|?GLֺS2kUEofQ/W(-6xZv{:3/]ƿk>BMºh`z[9ʃ) j0LMU_iOׇƞ_O,Op_ykڣ9gD vhaU׏ڪiڽ3(|s'PZͬ%-Ӳ'@$z' ?T 7C MrG^+_?H?w>?ր=~ⶁ?n|Dދ+kjƻv1ϭd?/H6a#XYxoi[ea"F4x ";{dW>0p15e6IY'F` &0A>ȻQKr>ޫu&YuWq |@՞կ io~i, Iy$?nƾ)5 1k[*Hx|&OY@ PGO R;3;$$A珥PG˯Yjh֒"A0x~q[[5Ԭ' @؊|DԮ>I4=Pp['<ڼo;cd3p#ъ_O MuHY``Wn@-sP_ɉb($c3@SyM;z%Գ\ L TG#I((((ŝiZ4ZB=:5 j_]y%_ީFt#P7I`e &s}h<CH6Z_A,Pn '9aڻ_}׉,h- cqUFA T߸€=;)yq,iFнv~&𮛬DkASW#.W/UT8J Xiֶ 9=TM}I_&?M}M;i6o1@%Fh_$d#(rJ;s^W6m*K2InVu>FMBl/q`cgf-6_j&D2[C23vy+±/v9@:<oa Ps# AeX\GS籩~Go3k$QpɁ;X}+{_W-s{' Yă' ?S޺A'NI@BGcO?#?1J| 'o"O?u*uSVjxw>#lmHًhV@Tś捔``w"ƀ (2gc+nn_vAQX|\o7L׏~SOo^>(zMVёY[jH'@C׎| Gxnh]U~+>]^;g2~\dּ.iLz>izN d>5OuKyw4O8=oYh"xה|8VM3+6lHq+?[fشAw\r8?>kC2h~> XmΪzsްEjgT!'nݿ9dWxo3ͪ[-w ~f 8ǩxZ:"dy۱_hĚ)[jHz[5OZŶ\l[חyXHL+އxC/d-cD H>w~G/_zj_Ux?77;Ccfm5|2G֚ơwGq;HIL+'5/?<Fi6'_c_2x_Bqi\2[t`fꠑH#O}ۖe6%OEdOr(ݼ-ovV>Vo+v U,(E5.yלʺ ̿xO|W1pŚ~< WnG O{?3ދA?€//9G/ Ϟ~bxc{qt/Dʣn͘뺶$|aXkBFRS3@/ Ϟ~b㔱|ȨZXfP|?5|'Wk\z/ndi#08 62q@eEV l E| __'2 @~1@E֓&a OG N3^٧^è[ڶ.#Yc>Ğ$|O%:\\*IO5MnAڧk 9qʓր>U a?|G]7WԄG+2#LUA@EPEPEPEPEPEPEPEPEPEPEPAտE5x;=Fd%o]k~1hiΓv. k?:\)=+կa]Y :U-cr fismoztOcpvb㓚mgΪW]myΥ$^$jdbނzҰ?P?5Ҹ(i}ߥ#kk* =KDP56*V eƽmhz>ly+գ|w2 &ͼJK>CLgM~oLҬ<>+xƸ ? |%{W7 ׯMcE DU EG0P Sa=PW[hӘ{r^ 4}[mqB(n_ūgdtҊnOM7F7QaO, >-7DK9RTA,{7^U,o:\bGw|bF/n0By8K+t/'k4lQgXN[F}洀I,Ra[> { \&jZp : vc@F>'vEYF}#VWqQ@-P/C+^_/~5ؐM֙?:W~? 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# you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PengChengMind train script """ import datetime import json import glob import os import math import time from mindspore import context from mindspore.train.model import Model import mindspore.communication.management as D from mindspore.context import ParallelMode import mindspore.nn as nn from mindspore.train.callback import TimeMonitor from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell import mindspore.common.dtype as mstype from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell, MicroBatchInterleaved from mindspore.nn.transformer import TransformerOpParallelConfig, CrossEntropyLoss, TransformerRecomputeConfig from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.serialization import load_distributed_checkpoint, load_checkpoint, load_param_into_net from src.adam import AdamWeightDecayOp from src.dataset import create_dataset from src.pengcheng_mind import PengChengMindWithLoss, PengChengMindModel from src.pengcheng_mind_wrapcell import PengChengMindTrainOneStepWithLossScaleCell, PengChengMindTrainPipelineWithLossScaleCell from src.pengcheng_mind_config import set_parse, PengChengMindConfig from src.utils import LearningRate, get_args, FP32StateAdamWeightDecay from src.callbacks import EvalCallBack, LossCallBack, LossSummaryCallback from src.utils import download_data, ckpt_copy_tar_new, get_ckpt_file_list, download_ckpt_from_obs from src.utils import StrategySaveCallback, CheckpointSaveCallback, GraphsSaveCallback from mindspore.common import Parameter from mindspore.common.tensor import Tensor import numpy as np from mindspore import Profiler from mindspore.train import Callback import mindspore project_root = os.path.abspath( os.path.dirname(os.path.realpath(__file__)) + os.path.sep + "..") print('project_root:', project_root) class ProfileStep(Callback): def __init__(self, start_step, stop_step, output_path): super(ProfileStep, self).__init__() self.start_step = start_step self.stop_step = stop_step self.profiler = Profiler(start_profile=False, output_path=output_path) def step_begin(self, run_context): cb_params = run_context.original_args() step_num = cb_params.cur_step_num if step_num == self.start_step: self.profiler.start() def step_end(self, run_context): cb_params = run_context.original_args() step_num = cb_params.cur_step_num if step_num == self.stop_step: self.profiler.stop() self.profiler.analyse() def set_weight_decay(params): """ Set weight decay coefficient, zero for bias and layernorm, 1e-1 for rest """ decay_filter = lambda x: 'layernorm' not in x.name.lower() and "bias" not in x.name.lower() decay_params = list(filter(decay_filter, params)) other_params = list(filter(lambda x: not decay_filter(x), params)) group_params = [{ 'params': decay_params, 'weight_decay': 1e-1 }, { 'params': other_params, 'weight_decay': 0.0 }, { 'order_params': params }] return group_params def add_checkpoint_callback_policy(args_param, callback, rank_id): r""" Add checkpoint policy to callback. """ if args_param.save_checkpoint: if not os.path.exists(args_param.save_checkpoint_path): os.makedirs(args_param.save_checkpoint_path, exist_ok=True) # checkpoint store epoch_num and step_num info ckpt_append_info = [{"epoch_num": args_param.has_trained_epoches, "step_num": args_param.has_trained_steps}] ckpt_config = CheckpointConfig(save_checkpoint_steps=args_param.save_checkpoint_steps, keep_checkpoint_max=1, integrated_save=False, append_info=ckpt_append_info, exception_save=True)# 临终遗言 save_dir_rank = os.path.join(args_param.save_checkpoint_path, f"rank_{rank_id}") save_ckptfile_name = args_param.ckpt_name_prefix + '_' + str(rank_id) if not os.path.exists(save_dir_rank): os.makedirs(save_dir_rank, exist_ok=True) ckpoint_cb = ModelCheckpoint(prefix=save_ckptfile_name, directory=save_dir_rank, config=ckpt_config) ckpt_save_obs_cb = CheckpointSaveCallback(local_ckpt_dir=save_dir_rank, local_rank=rank_id, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, bucket=args_param.save_checkpoint_bucket_dir, syn_obs_steps=args_param.save_checkpoint_steps) callback.append(ckpoint_cb) callback.append(ckpt_save_obs_cb) if rank_id == (args_param.device_num-1): callback.append(LossSummaryCallback(summary_dir="summary", local_rank=rank_id, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, micro_size=args_param.micro_size, bucket=args_param.save_summary_bucket_dir, syn_times=10)) callback.append(StrategySaveCallback(strategy_path=f'/cache/strategy_{rank_id}.ckpt', local_rank=rank_id, has_trained_step=args_param.has_trained_steps, bucket=args_param.save_strategy_bucket_dir)) def restore_checkpoint(args_param, sink_size, dataset, model, network, epoch, cache_url='/cache/Ckpt/'): r""" Load checkpoint process. """ print("======start single checkpoint", flush=True) ckpt_name = args_param.restore_ckpt_name_prefix restore_ranks = D.get_rank() ckpt_files = os.path.join(cache_url, f"rank_{restore_ranks}.ckpt") # f"{ckpt_name}_{restore_ranks}-{args_param.restore_steps}_4.ckpt") # f"rank_{restore_ranks}", # ckpt_files = glob.glob(ckpt_pattern) if not ckpt_files: print(f"There is no ckpt file in {cache_url}, in rank {restore_ranks}" f"current ckpt_files found is {os.listdir(cache_url)} " f"with pattern {ckpt_files}, so skip the loading.") time_stamp = datetime.datetime.now() print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')} pre trained ckpt model {ckpt_files} loading", flush=True) # Load checkpoint files latest file print(f'Start to load from {ckpt_files}') param_dict = load_checkpoint(ckpt_files) # print(f"Load rank {restore_ranks} param_dict: \n", param_dict.keys()) # print(f"===global_step: {param_dict['global_step'].data.asnumpy()}") # print(f"===current_iterator_step: {param_dict['current_iterator_step'].data.asnumpy()}") # print(f"===step_num: {param_dict['step_num'].data.asnumpy()}") # print(f"===loss_scale: {param_dict['loss_scale'].data.asnumpy()}") # param_dict["loss_scale"] = Parameter(Tensor(268435456, dtype=mstype.float32), name="loss_scale") param_dict["loss_scale"] = Parameter(Tensor(param_dict['loss_scale'].data.asnumpy(), dtype=mstype.float32), name="loss_scale") param_dict["global_step"] = Parameter(Tensor([int(param_dict['global_step'].data.asnumpy())], dtype=mstype.int32), name="global_step") param_dict["current_iterator_step"] = Parameter(Tensor(int(param_dict['current_iterator_step'].data.asnumpy()), dtype=mstype.int32), name="current_iterator_step") param_dict["step_num"] = Parameter(Tensor(int(param_dict['step_num'].data.asnumpy()), dtype=mstype.int32), name="step_num") param_dict["epoch_num"] = Parameter(Tensor(int(param_dict['epoch_num'].data.asnumpy()), dtype=mstype.int32), name="epoch_num") param_dict["last_overflow_iterator_step"] = Parameter(Tensor(int(param_dict['last_overflow_iterator_step'].data.asnumpy()), dtype=mstype.int32), name="last_overflow_iterator_step") if param_dict.get("epoch_num") and param_dict.get("step_num"): # args_param.has_trained_epoches = int(param_dict["epoch_num"].data.asnumpy()) args_param.has_trained_step = int(param_dict["step_num"].data.asnumpy()) model.build(train_dataset=dataset, sink_size=sink_size, epoch=epoch) load_param_into_net(network, param_dict, strict_load=False) def set_pipeline_parallel_context(args_opt): r"""Set pipeline parallel context.""" D.init() device_num = D.get_group_size() rank_id = D.get_rank() print("rank_id is {}, device_num is {}".format(rank_id, device_num)) context.reset_auto_parallel_context() context.set_auto_parallel_context( parallel_mode=args_opt.parallel_mode, gradients_mean=False, full_batch=bool(args_opt.full_batch), loss_repeated_mean=True, device_num=device_num, enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_size=args_opt.optimizer_shard, parallel_optimizer_config={"gradient_accumulation_shard": False}, pipeline_stages=args_opt.stage_num, strategy_ckpt_save_file=f'/cache/strategy_{rank_id}.ckpt', enable_alltoall=bool(args_opt.enable_alltoall), speedup_config_path="/home/ma-user/modelarts/user-job-dir/pengcheng_mind-r2.0beta_rotary/parallel_speed_up.json") set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() return rank_id, device_num def run_train_pipeline(args_opt): r"""The main training process in pipeline.""" if args_opt.save_graph: graphs_url = '/home/ma-user/modelarts/outputs/train_url_0' print(f"graphs_url: {graphs_url}\n") context.set_context(save_graphs=2, save_graphs_path=graphs_url, mode=context.GRAPH_MODE, device_target=args_opt.device_target, op_timeout=0) else: context.set_context(save_graphs=False, mode=context.GRAPH_MODE, device_target=args_opt.device_target, op_timeout=0)# task优化 context.set_context(max_device_memory="31GB") mindspore.set_seed(1234) rank_id = 0 device_num = 1 if args_opt.distribute == "true": rank_id, device_num = set_pipeline_parallel_context(args_opt) # copy data from the cloud to the /cache/Data cache_url = '/cache/Data/' if args_opt.offline: cache_url = args_opt.data_url print(os.listdir(cache_url)) else: download_data(src_data_url=args_opt.data_url, tgt_data_path=cache_url, rank=rank_id) # download_data(src_data_url=args_opt.eval_data_url, tgt_data_path=eval_cache_url, rank=rank_id) model_parallel_num = args_opt.op_level_model_parallel_num stage_device_num = int(device_num / args_opt.stage_num) data_parallel_num = int(stage_device_num / model_parallel_num) is_last_stage = (rank_id // stage_device_num) == args_opt.stage_num - 1 per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num * args_opt.micro_size micro_batch_interleaved = args_opt.micro_batch_interleaved # recompute_config = TransformerRecomputeConfig(recompute=True, # recompute_slice_activation=bool(args_opt.recompute_slice_activation)) recompute_config = args_opt.recompute parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, optimizer_shard=bool(args_opt.optimizer_shard), vocab_emb_dp=bool(args_opt.word_emb_dp), recompute=recompute_config) # add sequence_parallel parallel_config.sequence_parallel = args_opt.sequence_parallel # add select_recompute parallel_config.select_recompute = args_opt.select_recompute if args_opt.softmax_compute_fp32 == "FP32": softmax_compute_type = mstype.float32 elif args_opt.softmax_compute_fp32 == "FP16": softmax_compute_type = mstype.float16 else: raise ValueError(f"Unknown softmax_compute_fp32 {args_opt.softmax_compute_fp32}") if args_opt.top_query_softmax_fp32 == "FP32": top_query_softmax = mstype.float32 elif args_opt.top_query_softmax_fp32 == "FP16": top_query_softmax = mstype.float16 else: raise ValueError(f"Unknown top_query_softmax_fp32 {args_opt.top_query_softmax_fp32}") print(f"softmax_compute_type: {softmax_compute_type}") print(f"top_query_softmax: {top_query_softmax}") config = PengChengMindConfig(batch_size=batch_size // parallel_config.micro_batch_num // micro_batch_interleaved, num_heads=args_opt.num_heads, hidden_size=args_opt.embedding_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, use_moe=bool(args_opt.use_moe), eod_token=args_opt.eod_id, num_layers=args_opt.num_layers, ffn_hidden_size=args_opt.embedding_size * 4, eod_reset=bool(args_opt.eod_reset), load_ckpt_path=args_opt.load_ckpt_path, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, enable_offload=bool(args_opt.opt_offload), parallel_config=parallel_config, use_rope=args_opt.use_rope, use_flash_attention=args_opt.use_flash_attention, pipeline_config_filename=args_opt.pipeline_config_filename) # softmax_compute_fp32=softmax_compute_type) config.softmax_compute_fp32 = softmax_compute_type config.top_query_softmax_fp32 = top_query_softmax print("[Configure] is: ", config, flush=True) pengcheng_mind = PengChengMindModel(config=config) loss = CrossEntropyLoss(config.parallel_config.dp_mp_config) pengcheng_mind_with_loss_net = PipelineCell(MicroBatchInterleaved(PengChengMindWithLoss(config, pengcheng_mind, loss), micro_batch_interleaved), config.parallel_config.micro_batch_num) pengcheng_mind_with_loss = _VirtualDatasetCell(pengcheng_mind_with_loss_net) print("[args_opt] is: ", args_opt, flush=True) lr = LearningRate(learning_rate=args_opt.start_lr, end_learning_rate=args_opt.end_lr, warmup_steps=args_opt.warmup_step, decay_steps=args_opt.decay_steps) params = pengcheng_mind_with_loss.trainable_params() group_params = set_weight_decay(params) if args_opt.optimizer == "lamb": optimizer = nn.Lamb(group_params, learning_rate=lr) elif args_opt.opt_offload: optimizer = AdamWeightDecayOp(group_params, learning_rate=lr, eps=1e-8, beta1=0.9, beta2=0.96, param_init_type=config.param_init_type) else: optimizer = FP32StateAdamWeightDecay(group_params, learning_rate=lr, beta1=0.9, beta2=0.96, eps=1e-8) ds = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, data_path=cache_url, device_num=stage_device_num, rank=rank_id % stage_device_num, eod_reset=True, eod_id=args_opt.eod_id, data_start_index=args_opt.data_start_index, full_batch=context.get_auto_parallel_context("full_batch"), column_name=args_opt.data_column_name) # print(f"train full batch: {context.get_auto_parallel_context('full_batch')}") epoch_num = args_opt.epoch_size step_per_epoch = ds.get_dataset_size() callback_size = args_opt.sink_size actual_epoch_num = int(epoch_num * step_per_epoch / callback_size) loss_callback = LossCallBack(step_per_epoch, rank_id, is_last_stage=is_last_stage, micro_size=parallel_config.micro_batch_num * micro_batch_interleaved) # loss_callback = LossCallBack(local_log_file="/tmp/log/train.log", # log_upload_bucket_path="obs://research-my/taoht_200B_log/data_test/" + str(rank_id) + ".txt", # dataset_size=step_per_epoch, # local_rank=rank_id, # is_last_stage=is_last_stage, # micro_size=parallel_config.micro_batch_num * micro_batch_interleaved) callback = [TimeMonitor(callback_size), loss_callback] if args_opt.enable_profiler: callback.append(ProfileStep(12, 20, f"/home/ma-user/modelarts/outputs/train_url_0/profile_{rank_id}")) loss_scale_value = math.pow(2, 27) update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=200) pengcheng_mind_with_grads = PengChengMindTrainPipelineWithLossScaleCell( pengcheng_mind_with_loss, optimizer=optimizer, config=config, scale_update_cell=update_cell) if args_opt.train_and_eval_mode: # dev_cache_url = '/cache/EvalData_dev/' # download_data(src_data_url="obs://research-my/taoht_200b/datasets/200B_llma_4096_dev/", # tgt_data_path=dev_cache_url, rank=rank_id, flag="dev") # dev_cache_url = args_opt.eval_data_url test_cache_url = '/cache/EvalData_test/' download_data(src_data_url="obs://research-my/taoht_200b/datasets/lama_4096_6G_test", tgt_data_path=test_cache_url, rank=rank_id, flag="test") # test_cache_url = args_opt.eval_data_url # print(f"eval full batch: {context.get_auto_parallel_context('full_batch')}") # ds_eval_dev = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, # data_path=dev_cache_url, # device_num=stage_device_num, # rank=rank_id % stage_device_num, # eod_reset=True, # eod_id=args_opt.eod_id, # data_start_index=0, # full_batch=context.get_auto_parallel_context("full_batch"),#bool(args_opt.full_batch), # column_name=args_opt.data_column_name,) # # num_samples=args_opt.eval_steps * config.batch_size) ds_eval_test = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, data_path=test_cache_url, device_num=stage_device_num, rank=rank_id % stage_device_num, eod_reset=True, eod_id=args_opt.eod_id, data_start_index=0, full_batch=context.get_auto_parallel_context("full_batch"),#bool(args_opt.full_batch), column_name=args_opt.data_column_name) # num_samples=args_opt.eval_steps * config.batch_size) print(f"Eval dataset size: {ds_eval_test.get_dataset_size()}") ppl_metric = PPLMetric(config.seq_length) pengcheng_mind_with_loss_eval_net = _VirtualDatasetCell(PengChengMindWithLoss(config, pengcheng_mind, loss)) model = Model(pengcheng_mind_with_grads, eval_network=pengcheng_mind_with_loss_eval_net, metrics={"ppl": ppl_metric}) model.build(ds, ds_eval_test, sink_size=callback_size) # eval_callback_dev = EvalCallBack(model, ds_eval_dev, ppl_metric) eval_callback_test = EvalCallBack(model, ds_eval_test, ppl_metric) # callback.append(eval_callback_dev) callback.append(eval_callback_test) else: model = Model(pengcheng_mind_with_grads) if args_opt.pre_trained: # # copy ckpt from the cloud to the /cache/Ckpt cache_url = '/cache/Ckpt/' download_ckpt_from_obs(args_opt, cache_url, rank=rank_id) restore_checkpoint(args_opt, callback_size, ds, model, pengcheng_mind_with_grads, epoch=actual_epoch_num, cache_url=cache_url) loss_callback.has_trained_step = args_opt.has_trained_steps add_checkpoint_callback_policy(args_opt, callback, rank_id) if rank_id%8 == 0: count_parameters = 0 params_all = pengcheng_mind_with_loss.get_parameters() for param in params_all: # print(param) param_size = param.shape if len(param_size) == 1: count_parameters += param_size[0] else: sum_ceil = 1 for i in param_size: sum_ceil *= i count_parameters += sum_ceil print("====="*10) print("PC Mind parameters :", count_parameters) print("====="*10) model.train(actual_epoch_num, ds, callbacks=callback, sink_size=callback_size, dataset_sink_mode=True) # try: # model.train(actual_epoch_num, ds, callbacks=callback, # sink_size=callback_size, dataset_sink_mode=True) # except Exception as error: # # 增加临终遗言 # print(error) # from src.utils import CheckpointSaveCallback_Exception # # save_dir_rank = os.path.join(args_opt.save_checkpoint_path, f"rank_{rank_id}") # files = os.listdir(save_dir_rank) # home_path = os.path.join(os.getcwd(), save_dir_rank) # ckpts = [os.path.join(home_path, name) for name in files if name.endswith(".ckpt")] # ckpts = sorted(ckpts, reverse=True) # print(f"last word local ckpts: {files}") # print(f"last word ckpts: {ckpts}") # # test = CheckpointSaveCallback_Exception(local_ckpt_dir=ckpts[0], # local_rank=rank_id, # bucket=args_opt.save_checkpoint_bucket_dir) # test.step_end() if __name__ == "__main__": opt = get_args() set_parse(opt) if opt.per_batch_size == 0: raise ValueError("The per_batch_size has not been configured.") if bool(opt.enable_alltoall) is True and bool(opt.use_moe) is False: raise ValueError("The alltoall communication is only effective when applying moe") os.environ['HCCL_CONNECT_TIMEOUT'] = str(opt.hccl_connect_time) if opt.stage_num > 1: run_train_pipeline(opt) else: print(f"Set pipline stage number for {opt.stage_num}") run_train_pipeline(opt) pengchengmind/train_7B.py000066400000000000000000000610431453423006600157120ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PengChengMind train script """ import datetime import json import glob import os import math import time from mindspore import context from mindspore.train.model import Model import mindspore.communication.management as D from mindspore.context import ParallelMode import mindspore.nn as nn from mindspore.train.callback import TimeMonitor from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell import mindspore.common.dtype as mstype from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell, MicroBatchInterleaved from mindspore.nn.transformer import TransformerOpParallelConfig, CrossEntropyLoss, TransformerRecomputeConfig from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.serialization import load_distributed_checkpoint, load_checkpoint, load_param_into_net from src.adam import AdamWeightDecayOp from src.dataset import create_dataset from src.pengcheng_mind_7B import PengChengMindWithLoss, PengChengMindModel from src.pengcheng_mind_wrapcell import PengChengMindTrainOneStepWithLossScaleCell, PengChengMindTrainPipelineWithLossScaleCell from src.pengcheng_mind_config import set_parse, PengChengMindConfig from src.utils import LearningRate, get_args, FP32StateAdamWeightDecay from src.callbacks import EvalCallBack, LossCallBack, LossSummaryCallback from src.metrics import PPLMetric from src.utils import download_data, ckpt_copy_tar_new, get_ckpt_file_list, download_ckpt_from_obs from src.utils import StrategySaveCallback, CheckpointSaveCallback, GraphsSaveCallback from mindspore.common import Parameter from mindspore.common.tensor import Tensor import numpy as np from mindspore import Profiler from mindspore.train import Callback import mindspore project_root = os.path.abspath( os.path.dirname(os.path.realpath(__file__)) + os.path.sep + "..") print('project_root:', project_root) class ProfileStep(Callback): def __init__(self, start_step, stop_step, output_path): super(ProfileStep, self).__init__() self.start_step = start_step self.stop_step = stop_step self.profiler = Profiler(start_profile=False, output_path=output_path) def step_begin(self, run_context): cb_params = run_context.original_args() step_num = cb_params.cur_step_num if step_num == self.start_step: self.profiler.start() def step_end(self, run_context): cb_params = run_context.original_args() step_num = cb_params.cur_step_num if step_num == self.stop_step: self.profiler.stop() self.profiler.analyse() def set_weight_decay(params): """ Set weight decay coefficient, zero for bias and layernorm, 1e-1 for rest """ decay_filter = lambda x: 'layernorm' not in x.name.lower() and "bias" not in x.name.lower() decay_params = list(filter(decay_filter, params)) other_params = list(filter(lambda x: not decay_filter(x), params)) group_params = [{ 'params': decay_params, 'weight_decay': 1e-1 }, { 'params': other_params, 'weight_decay': 0.0 }, { 'order_params': params }] return group_params def add_checkpoint_callback_policy(args_param, callback, rank_id): r""" Add checkpoint policy to callback. """ if args_param.save_checkpoint: if not os.path.exists(args_param.save_checkpoint_path): os.makedirs(args_param.save_checkpoint_path, exist_ok=True) # checkpoint store epoch_num and step_num info ckpt_append_info = [{"epoch_num": args_param.has_trained_epoches, "step_num": args_param.has_trained_steps}] ckpt_config = CheckpointConfig(save_checkpoint_steps=args_param.save_checkpoint_steps, keep_checkpoint_max=1, integrated_save=False, append_info=ckpt_append_info, exception_save=True)# 临终遗言 save_dir_rank = os.path.join(args_param.save_checkpoint_path, f"rank_{rank_id}") save_ckptfile_name = args_param.ckpt_name_prefix + '_' + str(rank_id) if not os.path.exists(save_dir_rank): os.makedirs(save_dir_rank, exist_ok=True) ckpoint_cb = ModelCheckpoint(prefix=save_ckptfile_name, directory=save_dir_rank, config=ckpt_config) ckpt_save_obs_cb = CheckpointSaveCallback(local_ckpt_dir=save_dir_rank, local_rank=rank_id, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, bucket=args_param.save_checkpoint_bucket_dir, syn_obs_steps=args_param.save_checkpoint_steps) callback.append(ckpoint_cb) callback.append(ckpt_save_obs_cb) if rank_id == (args_param.device_num-1): callback.append(LossSummaryCallback(summary_dir="summary", local_rank=rank_id, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, micro_size=args_param.micro_size, bucket=args_param.save_summary_bucket_dir, syn_times=10)) callback.append(StrategySaveCallback(strategy_path=f'/cache/strategy_{rank_id}.ckpt', local_rank=rank_id, has_trained_step=args_param.has_trained_steps, bucket=args_param.save_strategy_bucket_dir)) def restore_checkpoint(args_param, sink_size, dataset, model, network, epoch, cache_url='/cache/Ckpt/'): r""" Load checkpoint process. """ print("======start single checkpoint", flush=True) ckpt_name = args_param.restore_ckpt_name_prefix restore_ranks = D.get_rank() ckpt_files = os.path.join(cache_url, f"rank_{restore_ranks}.ckpt") # f"{ckpt_name}_{restore_ranks}-{args_param.restore_steps}_4.ckpt") # f"rank_{restore_ranks}", # ckpt_files = glob.glob(ckpt_pattern) if not ckpt_files: print(f"There is no ckpt file in {cache_url}, in rank {restore_ranks}" f"current ckpt_files found is {os.listdir(cache_url)} " f"with pattern {ckpt_files}, so skip the loading.") time_stamp = datetime.datetime.now() print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')} pre trained ckpt model {ckpt_files} loading", flush=True) # Load checkpoint files latest file print(f'Start to load from {ckpt_files}') param_dict = load_checkpoint(ckpt_files) # print(f"Load rank {restore_ranks} param_dict: \n", param_dict.keys()) # print(f"===global_step: {param_dict['global_step'].data.asnumpy()}") # print(f"===current_iterator_step: {param_dict['current_iterator_step'].data.asnumpy()}") # print(f"===step_num: {param_dict['step_num'].data.asnumpy()}") # print(f"===loss_scale: {param_dict['loss_scale'].data.asnumpy()}") # param_dict["loss_scale"] = Parameter(Tensor(268435456, dtype=mstype.float32), name="loss_scale") param_dict["loss_scale"] = Parameter(Tensor(param_dict['loss_scale'].data.asnumpy(), dtype=mstype.float32), name="loss_scale") param_dict["global_step"] = Parameter(Tensor([int(param_dict['global_step'].data.asnumpy())], dtype=mstype.int32), name="global_step") param_dict["current_iterator_step"] = Parameter(Tensor(int(param_dict['current_iterator_step'].data.asnumpy()), dtype=mstype.int32), name="current_iterator_step") param_dict["step_num"] = Parameter(Tensor(int(param_dict['step_num'].data.asnumpy()), dtype=mstype.int32), name="step_num") param_dict["epoch_num"] = Parameter(Tensor(int(param_dict['epoch_num'].data.asnumpy()), dtype=mstype.int32), name="epoch_num") param_dict["last_overflow_iterator_step"] = Parameter(Tensor(int(param_dict['last_overflow_iterator_step'].data.asnumpy()), dtype=mstype.int32), name="last_overflow_iterator_step") if param_dict.get("epoch_num") and param_dict.get("step_num"): # args_param.has_trained_epoches = int(param_dict["epoch_num"].data.asnumpy()) args_param.has_trained_step = int(param_dict["step_num"].data.asnumpy()) model.build(train_dataset=dataset, sink_size=sink_size, epoch=epoch) load_param_into_net(network, param_dict, strict_load=False) def set_pipeline_parallel_context(args_opt): r"""Set pipeline parallel context.""" D.init() device_num = D.get_group_size() rank_id = D.get_rank() print("rank_id is {}, device_num is {}".format(rank_id, device_num)) context.reset_auto_parallel_context() context.set_auto_parallel_context( parallel_mode=args_opt.parallel_mode, gradients_mean=False, full_batch=bool(args_opt.full_batch), loss_repeated_mean=True, device_num=device_num, enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_size=args_opt.optimizer_shard, parallel_optimizer_config={"gradient_accumulation_shard": False}, pipeline_stages=args_opt.stage_num, strategy_ckpt_save_file=f'/cache/strategy_{rank_id}.ckpt', enable_alltoall=bool(args_opt.enable_alltoall), speedup_config_path="/home/ma-user/modelarts/user-job-dir/pengcheng_mind-r2.0beta_rotary/parallel_speed_up.json") set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() return rank_id, device_num def run_train_pipeline(args_opt): r"""The main training process in pipeline.""" if args_opt.save_graph: graphs_url = '/home/ma-user/modelarts/outputs/train_url_0' print(f"graphs_url: {graphs_url}\n") context.set_context(save_graphs=2, save_graphs_path=graphs_url, mode=context.GRAPH_MODE, device_target=args_opt.device_target, op_timeout=0) else: context.set_context(save_graphs=False, mode=context.GRAPH_MODE, device_target=args_opt.device_target, op_timeout=0)# task优化 context.set_context(max_device_memory="31GB") mindspore.set_seed(1234) rank_id = 0 device_num = 1 if args_opt.distribute == "true": rank_id, device_num = set_pipeline_parallel_context(args_opt) # copy data from the cloud to the /cache/Data cache_url = '/cache/Data/' if args_opt.offline: cache_url = args_opt.data_url print(os.listdir(cache_url)) else: download_data(src_data_url=args_opt.data_url, tgt_data_path=cache_url, rank=rank_id) # download_data(src_data_url=args_opt.eval_data_url, tgt_data_path=eval_cache_url, rank=rank_id) model_parallel_num = args_opt.op_level_model_parallel_num stage_device_num = int(device_num / args_opt.stage_num) data_parallel_num = int(stage_device_num / model_parallel_num) is_last_stage = (rank_id // stage_device_num) == args_opt.stage_num - 1 per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num * args_opt.micro_size micro_batch_interleaved = args_opt.micro_batch_interleaved # recompute_config = TransformerRecomputeConfig(recompute=True, # recompute_slice_activation=bool(args_opt.recompute_slice_activation)) recompute_config = args_opt.recompute parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, optimizer_shard=bool(args_opt.optimizer_shard), vocab_emb_dp=bool(args_opt.word_emb_dp), recompute=recompute_config) # add sequence_parallel parallel_config.sequence_parallel = args_opt.sequence_parallel # add select_recompute parallel_config.select_recompute = args_opt.select_recompute if args_opt.softmax_compute_fp32 == "FP32": softmax_compute_type = mstype.float32 elif args_opt.softmax_compute_fp32 == "FP16": softmax_compute_type = mstype.float16 else: raise ValueError(f"Unknown softmax_compute_fp32 {args_opt.softmax_compute_fp32}") if args_opt.top_query_softmax_fp32 == "FP32": top_query_softmax = mstype.float32 elif args_opt.top_query_softmax_fp32 == "FP16": top_query_softmax = mstype.float16 else: raise ValueError(f"Unknown top_query_softmax_fp32 {args_opt.top_query_softmax_fp32}") print(f"softmax_compute_type: {softmax_compute_type}") print(f"top_query_softmax: {top_query_softmax}") config = PengChengMindConfig(batch_size=batch_size // parallel_config.micro_batch_num // micro_batch_interleaved, num_heads=args_opt.num_heads, hidden_size=args_opt.embedding_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, use_moe=bool(args_opt.use_moe), eod_token=args_opt.eod_id, num_layers=args_opt.num_layers, ffn_hidden_size=args_opt.embedding_size * 4, eod_reset=bool(args_opt.eod_reset), load_ckpt_path=args_opt.load_ckpt_path, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, enable_offload=bool(args_opt.opt_offload), parallel_config=parallel_config, use_rope=args_opt.use_rope, use_flash_attention=args_opt.use_flash_attention, pipeline_config_filename=args_opt.pipeline_config_filename) # softmax_compute_fp32=softmax_compute_type) config.softmax_compute_fp32 = softmax_compute_type config.top_query_softmax_fp32 = top_query_softmax print("[Configure] is: ", config, flush=True) pengcheng_mind = PengChengMindModel(config=config) loss = CrossEntropyLoss(config.parallel_config.dp_mp_config) pengcheng_mind_with_loss_net = PipelineCell(MicroBatchInterleaved(PengChengMindWithLoss(config, pengcheng_mind, loss), micro_batch_interleaved), config.parallel_config.micro_batch_num) pengcheng_mind_with_loss = _VirtualDatasetCell(pengcheng_mind_with_loss_net) print("[args_opt] is: ", args_opt, flush=True) lr = LearningRate(learning_rate=args_opt.start_lr, end_learning_rate=args_opt.end_lr, warmup_steps=args_opt.warmup_step, decay_steps=args_opt.decay_steps) params = pengcheng_mind_with_loss.trainable_params() group_params = set_weight_decay(params) if args_opt.optimizer == "lamb": optimizer = nn.Lamb(group_params, learning_rate=lr) elif args_opt.opt_offload: optimizer = AdamWeightDecayOp(group_params, learning_rate=lr, eps=1e-8, beta1=0.9, beta2=0.96, param_init_type=config.param_init_type) else: optimizer = FP32StateAdamWeightDecay(group_params, learning_rate=lr, beta1=0.9, beta2=0.96, eps=1e-8) ds = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, data_path=cache_url, device_num=stage_device_num, rank=rank_id % stage_device_num, eod_reset=True, eod_id=args_opt.eod_id, data_start_index=args_opt.data_start_index, full_batch=context.get_auto_parallel_context("full_batch"), column_name=args_opt.data_column_name) # print(f"train full batch: {context.get_auto_parallel_context('full_batch')}") epoch_num = args_opt.epoch_size step_per_epoch = ds.get_dataset_size() callback_size = args_opt.sink_size actual_epoch_num = int(epoch_num * step_per_epoch / callback_size) loss_callback = LossCallBack(step_per_epoch, rank_id, is_last_stage=is_last_stage, micro_size=parallel_config.micro_batch_num * micro_batch_interleaved) # loss_callback = LossCallBack(local_log_file="/tmp/log/train.log", # log_upload_bucket_path="obs://research-my/taoht_200B_log/data_test/" + str(rank_id) + ".txt", # dataset_size=step_per_epoch, # local_rank=rank_id, # is_last_stage=is_last_stage, # micro_size=parallel_config.micro_batch_num * micro_batch_interleaved) callback = [TimeMonitor(callback_size), loss_callback] if args_opt.enable_profiler: callback.append(ProfileStep(12, 20, f"/home/ma-user/modelarts/outputs/train_url_0/profile_{rank_id}")) loss_scale_value = math.pow(2, 27) update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=200) pengcheng_mind_with_grads = PengChengMindTrainPipelineWithLossScaleCell( pengcheng_mind_with_loss, optimizer=optimizer, config=config, scale_update_cell=update_cell) if args_opt.train_and_eval_mode: # dev_cache_url = '/cache/EvalData_dev/' # download_data(src_data_url="obs://research-my/taoht_200b/datasets/200B_llma_4096_dev/", # tgt_data_path=dev_cache_url, rank=rank_id, flag="dev") # dev_cache_url = args_opt.eval_data_url test_cache_url = '/cache/EvalData_test/' download_data(src_data_url="obs://research-my/taoht_200b/datasets/lama_4096_6G_test", tgt_data_path=test_cache_url, rank=rank_id, flag="test") # test_cache_url = args_opt.eval_data_url # print(f"eval full batch: {context.get_auto_parallel_context('full_batch')}") # ds_eval_dev = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, # data_path=dev_cache_url, # device_num=stage_device_num, # rank=rank_id % stage_device_num, # eod_reset=True, # eod_id=args_opt.eod_id, # data_start_index=0, # full_batch=context.get_auto_parallel_context("full_batch"),#bool(args_opt.full_batch), # column_name=args_opt.data_column_name,) # # num_samples=args_opt.eval_steps * config.batch_size) ds_eval_test = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, data_path=test_cache_url, device_num=stage_device_num, rank=rank_id % stage_device_num, eod_reset=True, eod_id=args_opt.eod_id, data_start_index=0, full_batch=context.get_auto_parallel_context("full_batch"),#bool(args_opt.full_batch), column_name=args_opt.data_column_name) # num_samples=args_opt.eval_steps * config.batch_size) print(f"Eval dataset size: {ds_eval_test.get_dataset_size()}") ppl_metric = PPLMetric(config.seq_length) pengcheng_mind_with_loss_eval_net = _VirtualDatasetCell(PengChengMindWithLoss(config, pengcheng_mind, loss)) model = Model(pengcheng_mind_with_grads, eval_network=pengcheng_mind_with_loss_eval_net, metrics={"ppl": ppl_metric}) model.build(ds, ds_eval_test, sink_size=callback_size) # eval_callback_dev = EvalCallBack(model, ds_eval_dev, ppl_metric) eval_callback_test = EvalCallBack(model, ds_eval_test, ppl_metric) # callback.append(eval_callback_dev) callback.append(eval_callback_test) else: model = Model(pengcheng_mind_with_grads) if args_opt.pre_trained: # # copy ckpt from the cloud to the /cache/Ckpt cache_url = '/cache/Ckpt/' download_ckpt_from_obs(args_opt, cache_url, rank=rank_id) restore_checkpoint(args_opt, callback_size, ds, model, pengcheng_mind_with_grads, epoch=actual_epoch_num, cache_url=cache_url) loss_callback.has_trained_step = args_opt.has_trained_steps add_checkpoint_callback_policy(args_opt, callback, rank_id) if rank_id%8 == 0: count_parameters = 0 params_all = pengcheng_mind_with_loss.get_parameters() for param in params_all: # print(param) param_size = param.shape if len(param_size) == 1: count_parameters += param_size[0] else: sum_ceil = 1 for i in param_size: sum_ceil *= i count_parameters += sum_ceil print("====="*10) print("PC Mind parameters :", count_parameters) print("====="*10) model.train(actual_epoch_num, ds, callbacks=callback, sink_size=callback_size, dataset_sink_mode=True) # try: # model.train(actual_epoch_num, ds, callbacks=callback, # sink_size=callback_size, dataset_sink_mode=True) # except Exception as error: # # 增加临终遗言 # print(error) # from src.utils import CheckpointSaveCallback_Exception # # save_dir_rank = os.path.join(args_opt.save_checkpoint_path, f"rank_{rank_id}") # files = os.listdir(save_dir_rank) # home_path = os.path.join(os.getcwd(), save_dir_rank) # ckpts = [os.path.join(home_path, name) for name in files if name.endswith(".ckpt")] # ckpts = sorted(ckpts, reverse=True) # print(f"last word local ckpts: {files}") # print(f"last word ckpts: {ckpts}") # # test = CheckpointSaveCallback_Exception(local_ckpt_dir=ckpts[0], # local_rank=rank_id, # bucket=args_opt.save_checkpoint_bucket_dir) # test.step_end() if __name__ == "__main__": opt = get_args() set_parse(opt) if opt.per_batch_size == 0: raise ValueError("The per_batch_size has not been configured.") if bool(opt.enable_alltoall) is True and bool(opt.use_moe) is False: raise ValueError("The alltoall communication is only effective when applying moe") os.environ['HCCL_CONNECT_TIMEOUT'] = str(opt.hccl_connect_time) if opt.stage_num > 1: run_train_pipeline(opt) else: print(f"Set pipline stage number for {opt.stage_num}") run_train_pipeline(opt)