mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-17 15:11:20 +00:00
[shardformer] update transformers (#5583)
* flash_attention forward upgrade * llama_model_forward * remove useless comment * update the requirements.txt * add the transformers version requirements * remove the LATEST VERSION try * [shardformer] update bloom model (#5518) * update bloom model * remove the version restriction * [shardformer] update_falcon (#5520) * [shardformer] update mistral model (#5511) * [shardformer] update gpt2 (#5502) * [shardformer] update gptj model (#5503) * [shardformer] update opt (#5522) * [shardformer] update t5 model (#5524) * [shardformer] update whisper model (#5529) * [shardformer] update vit model (#5530) * update vit model * remove the output_hidden_states * [shardformer] fix llama modeling * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [zero] support multiple (partial) backward passes (#5596) * [zero] support multiple (partial) backward passes * [misc] update requirements * [zero] support multiple (partial) backward passes (#5596) * [zero] support multiple (partial) backward passes * [misc] update requirements * fix conflicts * [doc] fix ColossalMoE readme (#5599) * fix readme * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * merge with main * merge with main * llama_model_forward * remove useless comment * remove the LATEST VERSION try * [shardformer] update bloom model (#5518) * update bloom model * remove the version restriction * [shardformer] update mistral model (#5511) * [shardformer] update opt (#5522) * [shardformer] update whisper model (#5529) * [shardformer] fix llama modeling * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [hotfix] Fix examples no pad token & auto parallel codegen bug; (#5606) * fix no pad token bug * fixed some auto parallel codegen bug, but might not run on torch 2.1 --------- Co-authored-by: Edenzzzz <wtan45@wisc.edu> * [shardformer] fix pipeline grad ckpt (#5620) * [shardformer] fix pipeline grad ckpt * [shardformer] fix whisper (#5628) * [test] fix llama model test * fix the opt upgrade (#5634) * [shardformer] fix attn replacement (#5636) * [shardformer] update flashattention replacement (#5637) * update transformers update transformers fix fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [test] fix llama test (#5638) * [gemini] fix buffer cast (#5639) * Fix shardformer upgrade (#5640) * fix llama model * fix the mistral * fix the shardformer model * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [shardformer]support pipeline parallelism for mistral. (#5642) * [shardformer] fix attn replacement (#5636) * [shardformer] update flashattention replacement (#5637) * update transformers update transformers fix fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [Feature] Support LLaMA-3 CPT and ST (#5619) * support LLaMA-3 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Run pre-commit --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [exampe] update llama example (#5626) * [plugin] support dp inside for hybriad parallel * [example] update llama benchmark * [example] update llama benchmark * [example] update llama readme * [example] update llama readme * [example] llama3 (#5631) * release llama3 * [release] llama3 * [release] llama3 * [release] llama3 * [release] llama3 * [test] fix llama test (#5638) * [gemini] fix buffer cast (#5639) * support pp for mistral * fix * fix fix fix * fix --------- Co-authored-by: Hongxin Liu <lhx0217@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Tong Li <tong.li352711588@gmail.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> --------- Co-authored-by: Hongxin Liu <lhx0217@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Co-authored-by: Edenzzzz <wenxuan.tan@wisc.edu> Co-authored-by: Edenzzzz <wtan45@wisc.edu> Co-authored-by: flybird11111 <1829166702@qq.com> Co-authored-by: Tong Li <tong.li352711588@gmail.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com>
This commit is contained in:
Wang Binluo
GitHub
@@ -1,9 +1,16 @@
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import math
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.distributed as dist
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from torch.distributed import ProcessGroup
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_attn_mask_utils import (
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AttentionMaskConverter,
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_prepare_4d_causal_attention_mask,
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_prepare_4d_causal_attention_mask_for_sdpa,
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)
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from transformers.modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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@@ -99,11 +106,17 @@ def get_tp_falcon_decoder_layer_forward():
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hidden_states: torch.Tensor,
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alibi: Optional[torch.Tensor],
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attention_mask: torch.Tensor,
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position_ids: Optional[torch.LongTensor] = None,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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head_mask: Optional[torch.Tensor] = None,
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use_cache: bool = False,
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output_attentions: bool = False,
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**kwargs,
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):
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
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)
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residual = hidden_states
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if self.config.new_decoder_architecture:
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@@ -117,10 +130,12 @@ def get_tp_falcon_decoder_layer_forward():
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attention_layernorm_out,
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layer_past=layer_past,
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attention_mask=attention_mask,
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position_ids=position_ids,
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alibi=alibi,
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head_mask=head_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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**kwargs,
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)
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attention_output = attn_outputs[0]
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@@ -154,87 +169,6 @@ def get_tp_falcon_decoder_layer_forward():
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return forward
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def get_falcon_flash_attention_forward():
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try:
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from xformers.ops import memory_efficient_attention as me_attention
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except:
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raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.")
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from transformers.models.falcon.modeling_falcon import FalconAttention
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def forward(
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self: FalconAttention,
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hidden_states: torch.Tensor,
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alibi: Optional[torch.Tensor],
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attention_mask: torch.Tensor,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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head_mask: Optional[torch.Tensor] = None,
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use_cache: bool = False,
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output_attentions: bool = False,
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):
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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, query_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, query_length, self.head_dim)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size * num_kv_heads,
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query_length,
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self.head_dim,
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)
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value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim)
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past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
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query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
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if layer_past is not None:
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past_key, past_value = layer_past
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# concatenate along seq_length dimension:
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# - key: [batch_size * self.num_heads, kv_length, head_dim]
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# - value: [batch_size * self.num_heads, kv_length, head_dim]
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key_layer = torch.cat((past_key, key_layer), dim=1)
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value_layer = torch.cat((past_value, value_layer), dim=1)
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_, kv_length, _ = key_layer.shape
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if use_cache:
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present = (key_layer, value_layer)
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else:
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present = None
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attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
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query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim).transpose(1, 2).contiguous()
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key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim).transpose(1, 2).contiguous()
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value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim).transpose(1, 2).contiguous()
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if alibi is not None:
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attention_mask_float = (
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attention_mask_float + alibi.view(batch_size, self.num_heads, 1, kv_length) * self.beta
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)
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batch_size, src_len = query_layer_.size()[0], query_layer_.size()[1]
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tgt_len = key_layer_.size()[1]
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attention_mask_float = attention_mask_float.expand(batch_size, self.num_heads, src_len, tgt_len).contiguous()
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context_layer = me_attention(
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query_layer_,
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key_layer_,
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value_layer_,
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attn_bias=attention_mask_float,
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scale=self.inv_norm_factor,
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p=self.attention_dropout.p,
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)
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batch_size, seq_length, _, _ = context_layer.shape
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context_layer = context_layer.reshape(batch_size, seq_length, -1)
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output_tensor = self.dense(context_layer)
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return output_tensor, present
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return forward
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class FalconPipelineForwards:
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"""
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This class serves as a micro library for falcon pipeline forwards.
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@@ -246,6 +180,7 @@ class FalconPipelineForwards:
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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@@ -274,17 +209,6 @@ class FalconPipelineForwards:
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if past_key_values is None:
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past_key_values = tuple([None] * len(self.h))
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else:
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past_key_values = self._convert_to_rw_cache(past_key_values)
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape batch_size x num_heads x N x N
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# head_mask has shape n_layer x batch x num_heads x N x N
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head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
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# case: First stage of training
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if stage_manager.is_first_stage():
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if input_ids is not None and inputs_embeds is not None:
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@@ -295,16 +219,22 @@ class FalconPipelineForwards:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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hidden_states = inputs_embeds
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else:
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input_shape = hidden_states.shape[:-1]
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batch_size, seq_length = input_shape
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if past_key_values is None:
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past_key_values = tuple([None] * len(self.h))
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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presents = () if use_cache else None
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all_self_attentions = () if output_attentions else None
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all_hidden_states = () if output_hidden_states else None
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@@ -312,22 +242,80 @@ class FalconPipelineForwards:
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# Compute alibi tensor: check build_alibi_tensor documentation
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past_key_values_length = 0
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if past_key_values[0] is not None:
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past_key_values_length = past_key_values[0][0].shape[1] # 1 because RW-cache, not standard format
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=hidden_states.device)
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else:
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attention_mask = attention_mask.to(hidden_states.device)
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past_key_values_length = past_key_values[0][0].shape[-2]
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if self.use_alibi:
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alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
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mask = (
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torch.ones(
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(batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long
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)
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if attention_mask is None
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else attention_mask
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)
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alibi = build_alibi_tensor(mask, self.num_heads, dtype=hidden_states.dtype)
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else:
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alibi = None
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0)
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causal_mask = self._prepare_attn_mask(
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attention_mask,
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input_shape=(batch_size, seq_length),
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past_key_values_length=past_key_values_length,
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)
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if self._use_flash_attention_2:
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# 2d mask is passed through the layers
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attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
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elif self._use_sdpa and not output_attentions:
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# output_attentions=True can not be supported when using SDPA, and we fall back on
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# the manual implementation that requires a 4D causal mask in all cases.
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if alibi is None:
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attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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)
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elif head_mask is None:
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alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
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attention_mask_2d = attention_mask
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# We don't call _prepare_4d_causal_attention_mask_for_sdpa as we need to mask alibi using the 4D attention_mask untouched.
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
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)
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# We take care to integrate alibi bias in the attention_mask here.
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if attention_mask_2d is None:
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attention_mask = alibi / math.sqrt(self.config.hidden_size // self.num_heads)
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else:
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attention_mask = torch.masked_fill(
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alibi / math.sqrt(self.config.hidden_size // self.num_heads),
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attention_mask < -1,
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torch.finfo(alibi.dtype).min,
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)
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# From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
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# produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
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if seq_length > 1:
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attention_mask = AttentionMaskConverter._unmask_unattended(
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attention_mask, attention_mask_2d, unmasked_value=0.0
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)
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else:
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# PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case.
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
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)
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else:
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# 4d mask is passed through the layers
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
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)
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape batch_size x num_heads x N x N
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# head_mask has shape n_layer x batch x num_heads x N x N
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head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
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start_idx, end_idx = stage_index[0], stage_index[1]
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for i, (block, layer_past) in enumerate(
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@@ -337,31 +325,23 @@ class FalconPipelineForwards:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
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return custom_forward
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outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(block),
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outputs = self._gradient_checkpointing_func(
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block.__call__,
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hidden_states,
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alibi,
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causal_mask,
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attention_mask,
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position_ids,
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head_mask[i],
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layer_past,
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use_cache,
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output_attentions,
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)
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else:
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outputs = block(
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hidden_states,
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layer_past=layer_past,
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attention_mask=causal_mask,
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attention_mask=attention_mask,
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position_ids=position_ids,
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head_mask=head_mask[i],
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use_cache=use_cache,
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output_attentions=output_attentions,
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@@ -382,9 +362,6 @@ class FalconPipelineForwards:
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if presents is not None:
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presents = self._convert_cache_to_standard_format(presents, batch_size)
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if stage_manager.is_last_stage():
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if not return_dict:
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return tuple(
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