# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # # 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. from typing import OrderedDict import paddle import paddle.distributed.fleet as fleet import paddle.nn as nn from paddle.distributed.fleet.meta_parallel import ( LayerDesc, PipelineLayer, SharedLayerDesc, ) from paddle.distributed.fleet.recompute.recompute import recompute from paddlenlp.transformers.refined_recompute import get_skip_recompute_ops from paddlenlp.transformers.refined_recompute import recompute as rr_recompute from ...utils.tools import get_env_device from ..dpo_criterion import DPOCriterion from ..model_utils import PipelinePretrainedModel from .modeling import ( Qwen2Config, Qwen2DecoderLayer, Qwen2LMHead, Qwen2Model, Qwen2PretrainedModel, Qwen2PretrainingCriterion, Qwen2RMSNorm, ) __all__ = [ "Qwen2ForCausalLMPipe", ] def parse_args(args): if isinstance(args, tuple): if len(args) == 4: hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids = args elif len(args) == 3: hidden_states, attention_mask, attn_mask_startend_row_indices = args position_ids = None elif len(args) == 2: hidden_states, attention_mask = args attn_mask_startend_row_indices, position_ids = None, None else: hidden_states = args attention_mask, attn_mask_startend_row_indices, position_ids = None, None, None if position_ids is not None: position_ids.stop_gradient = True if attention_mask is not None: attention_mask.stop_gradient = True if attn_mask_startend_row_indices is not None: attn_mask_startend_row_indices.stop_gradient = True return hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids def return_args(hidden_states, attention_mask=None, attn_mask_startend_row_indices=None, position_ids=None): ret = (hidden_states,) if attention_mask is not None: ret += (attention_mask.clone(),) if attn_mask_startend_row_indices is not None: ret += (attn_mask_startend_row_indices.clone(),) if position_ids is not None: ret += (position_ids.clone(),) if len(ret) == 1: ret = ret[0] return ret def get_attr(layer, name): if getattr(layer, name, None) is not None: return getattr(layer, name, None) else: return get_attr(layer._layer, name) class Qwen2EmbeddingPipe(nn.Layer): """Extends QWenEmbeddings to forward attention_mask through the pipeline.""" def __init__(self, config: Qwen2Config): super(Qwen2EmbeddingPipe, self).__init__() self.config = config self.sequence_parallel = config.sequence_parallel self.hidden_size = config.hidden_size if config.tensor_parallel_degree > 1 and config.vocab_size % config.tensor_parallel_degree == 0: self.embed_tokens = fleet.meta_parallel.VocabParallelEmbedding( config.vocab_size, config.hidden_size, weight_attr=paddle.ParamAttr(initializer=nn.initializer.XavierNormal()), ) else: self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) @property def embedding_weight(self): return get_attr(self.embed_tokens, "weight") def forward(self, args): """_summary_ Args: input (_type_): _description_ Returns: _type_: _description_ """ input_ids, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args) input_embeds = self.embed_tokens(input_ids) if self.config.sequence_parallel: from paddlenlp.transformers import ScatterOp # [bs, seq_len, num_head * head_dim] -> [bs * seq_len, num_head * head_dim] bs, seq_len, hidden_size = input_embeds.shape input_embeds = paddle.reshape_(input_embeds, [bs * seq_len, hidden_size]) # [seq_len * bs / n, num_head * head_dim] (n is mp parallelism) input_embeds = ScatterOp.apply(input_embeds) batch_size, seq_length = input_ids.shape if attention_mask is not None: assert ( attn_mask_startend_row_indices is None ), "attention_mask and attn_mask_startend_row_indices can not be set at same time" attention_mask = Qwen2Model._prepare_decoder_attention_mask( attention_mask, (batch_size, seq_length), 0, input_embeds.dtype ) attention_mask.stop_gradient = True if get_env_device() == "npu": attention_mask = attention_mask.astype("bool") elif get_env_device() == "npu": attention_mask = paddle.tril(paddle.ones((seq_length, seq_length), dtype="bool")) attention_mask.stop_gradient = True return return_args(input_embeds, attention_mask, attn_mask_startend_row_indices, position_ids) class Qwen2DecoderLayerPipe(Qwen2DecoderLayer): def forward(self, args): hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args) has_gradient = not hidden_states.stop_gradient if attention_mask is not None and attention_mask.dtype == paddle.int32: attention_mask, attn_mask_startend_row_indices, position_ids = ( None, attention_mask, attn_mask_startend_row_indices, ) elif attention_mask is not None and attention_mask.dtype == paddle.int64: attention_mask, attn_mask_startend_row_indices, position_ids = None, None, attention_mask elif attn_mask_startend_row_indices is not None and attn_mask_startend_row_indices.dtype == paddle.int64: attn_mask_startend_row_indices, position_ids = None, attn_mask_startend_row_indices if self.enable_recompute and self.config.recompute_granularity == "full" and has_gradient: recompute_fn = rr_recompute if any(self.skip_recompute_ops.values()) else recompute if attention_mask is not None or attn_mask_startend_row_indices is not None: hidden_states = recompute_fn( super().forward, hidden_states, position_ids=position_ids, attention_mask=attention_mask, attn_mask_startend_row_indices=attn_mask_startend_row_indices, use_reentrant=False, ) else: # for pretrain hidden_states = recompute_fn( super().forward, hidden_states, position_ids=position_ids, attn_mask_startend_row_indices=attn_mask_startend_row_indices, use_reentrant=self.config.recompute_use_reentrant, ) else: hidden_states = super().forward( hidden_states, position_ids=position_ids, attention_mask=attention_mask, attn_mask_startend_row_indices=attn_mask_startend_row_indices, ) return return_args(hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids) class Qwen2RMSNormPipe(nn.Layer): def __init__(self, config): super().__init__() self.norm = Qwen2RMSNorm(config) def forward(self, args): hidden_states, attention_mask, attn_mask_startend_row_indices, position_ids = parse_args(args) return self.norm(hidden_states) class Qwen2LMHeadPipe(Qwen2LMHead): def __init__(self, config, transpose_y=False): super(Qwen2LMHeadPipe, self).__init__(config, transpose_y=transpose_y) @property def embedding_weight(self): return get_attr(self, "weight") class Qwen2ForCausalLMPipe(PipelinePretrainedModel, PipelineLayer): """QWenForPretraining adapted for pipeline parallelism. The largest change is flattening the QWenModel class so we can express it as a sequence of layers including embedding, transformer layers, and output. """ config_class = Qwen2Config _get_tensor_parallel_mappings = Qwen2PretrainedModel._get_tensor_parallel_mappings _init_weights = Qwen2PretrainedModel._init_weights _keys_to_ignore_on_load_unexpected = Qwen2PretrainedModel._keys_to_ignore_on_load_unexpected _get_model_flops = Qwen2PretrainedModel._get_model_flops _get_hardware_flops = Qwen2PretrainedModel._get_hardware_flops _tied_weights_keys = ["lm_head.weight"] # DONOT Add base_model_prefix !!!! @classmethod def _prepare_pipeline_inputs_func(cls, inputs): first_stage_keys = ["input_ids", "attention_mask", "attn_mask_startend_row_indices", "position_ids"] last_stage_keys = ["labels"] def get_expected_keys(inputs, keys): ret = tuple([inputs.pop(k) if k in inputs else None for k in keys]) if len(ret) == 1: ret = ret[0] return ret if type(inputs) is dict or type(inputs) is OrderedDict: return [ get_expected_keys(inputs, first_stage_keys), get_expected_keys(inputs, last_stage_keys), ] keys = list(inputs[0].keys()) inputs_batch = {key: [data.pop(key) for data in inputs] for key in keys} return [ get_expected_keys(inputs_batch, first_stage_keys), get_expected_keys(inputs_batch, last_stage_keys), ] def __init__(self, config: Qwen2Config): self.config = config # Note that we will actually perform a recompute only if both enable_recompute and layerwise_recompute are set to True # Enable_recompute defaults to False and is controlled by Trainer self.enable_recompute = False self.recompute_granularity = self.config.recompute_granularity self.pp_recompute_interval = self.config.pp_recompute_interval self.no_recompute_layers = config.no_recompute_layers if config.no_recompute_layers is not None else [] if self.recompute_granularity == "full": assert len(self.no_recompute_layers) == 0, "for pp with full recompute, no_recompute_layers is not support" virtual_pp_degree = getattr(self.config, "virtual_pp_degree", 1) def get_hcg(): return fleet.get_hybrid_communicate_group() hcg = get_hcg() tensor_parallel_degree = max(hcg.get_model_parallel_world_size(), 1) tensor_parallel_rank = max(hcg.get_model_parallel_rank(), 0) # TODO: fix tensor_parallel_degree rewrite in here config.tensor_parallel_degree = tensor_parallel_degree config.tensor_parallel_rank = tensor_parallel_rank if config.tie_word_embeddings: self.add_sequential_layer( SharedLayerDesc( "qwen2_shared_weight", Qwen2EmbeddingPipe, shared_weight_attr="embedding_weight", config=config ), "qwen2", ) else: self.add_sequential_layer(LayerDesc(Qwen2EmbeddingPipe, config=config), "qwen2") for i in range(config.num_hidden_layers): self.add_sequential_layer( LayerDesc( Qwen2DecoderLayerPipe, config=config, layerwise_recompute=i not in self.no_recompute_layers, skip_recompute_ops=get_skip_recompute_ops(config, i), ), f"qwen2.layers.{i}", ) self.add_sequential_layer(LayerDesc(Qwen2RMSNormPipe, config=config), "qwen2") if config.tie_word_embeddings: self.add_sequential_layer( SharedLayerDesc( "qwen2_shared_weight", Qwen2LMHeadPipe, shared_weight_attr="embedding_weight", config=config, **{"transpose_y": True}, ), "lm_head", ) else: self.add_sequential_layer(LayerDesc(Qwen2LMHeadPipe, config=config), "lm_head") recompute_interval = 0 if self.enable_recompute and self.recompute_granularity == "full": assert self.config.pp_recompute_interval <= config.num_hidden_layers // ( virtual_pp_degree * get_hcg().topology().get_dim_size("pipe") ), "pp recompute interval should smaller than num layers of each pp chunk" recompute_interval = self.config.pp_recompute_interval seg_method = "layer:Qwen2DecoderLayer" if config.num_hidden_layers % get_hcg().topology().get_dim_size("pipe") != 0: seg_method = "uniform" PipelineLayer.__init__( self, layers=self.get_sequential_layers(), loss_fn=self.get_loss_fn(config), topology=get_hcg().topology(), seg_method=seg_method, recompute_interval=recompute_interval, recompute_ctx={ "mp_group": get_hcg().get_model_parallel_group(), "offload": False, "partition": False, }, num_virtual_pipeline_stages=virtual_pp_degree, ) # You should call init here, since there is a diamond inheritance problem self.apply(self._init_weights) # DON'T init PipelinePretrainedModel # PipelinePretrainedModel.__init__(self.super(), config=config) def get_loss_fn(self, config): if config.dpo_config is not None: return DPOCriterion(config, use_infohub=True) else: return Qwen2PretrainingCriterion(config)