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https://github.com/hpcaitech/ColossalAI.git
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[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
@@ -5,6 +5,10 @@ from typing import List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.modeling_attn_mask_utils import (
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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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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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@@ -35,6 +39,8 @@ def _get_attention_mask(
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hidden_states: torch.Tensor,
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past_key_values_length: int,
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attention_mask: Optional[torch.FloatTensor],
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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):
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batch_size, seq_length = hidden_states.shape[:2]
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mask_seq_length = past_key_values_length + seq_length
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@@ -47,12 +53,20 @@ def _get_attention_mask(
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is_causal=True,
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)
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else:
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attention_mask = self._prepare_decoder_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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hidden_states,
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past_key_values_length,
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)
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input_shape = (batch_size, seq_length)
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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 head_mask is None and not output_attentions:
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# output_attentions=True & head_mask can not be supported when using SDPA.
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attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask, input_shape, hidden_states, 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, input_shape, hidden_states, past_key_values_length
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)
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return attention_mask
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@@ -539,18 +553,12 @@ class WhisperPipelineForwards:
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layer_outputs = (None, None)
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else:
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if self.gradient_checkpointing and self.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs, output_attentions)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(encoder_layer),
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layer_outputs = self._gradient_checkpointing_func(
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encoder_layer.__call__,
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hidden_states,
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None,
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(head_mask[idx] if head_mask is not None else None),
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output_attentions,
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)
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else:
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layer_outputs = encoder_layer(
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@@ -702,20 +710,16 @@ class WhisperPipelineForwards:
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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attention_mask = _get_attention_mask(
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self, shard_config, inputs_embeds, past_key_values_length, attention_mask
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)
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# embed positions
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if input_ids is not None:
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positions = self.embed_positions(input_ids, past_key_values_length=past_key_values_length)
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else:
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positions = self.embed_positions(inputs_embeds, past_key_values_length=past_key_values_length)
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attention_mask = _get_attention_mask(
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self,
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shard_config,
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inputs_embeds,
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past_key_values_length,
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attention_mask,
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)
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hidden_states = inputs_embeds + positions
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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@@ -732,7 +736,6 @@ class WhisperPipelineForwards:
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"hidden_states shouldn't be None for stages other than the first stage of encoder/decoder."
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)
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input_shape = hidden_states.size()[:-1]
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attention_mask = _get_attention_mask(
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self,
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shard_config,
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@@ -756,16 +759,8 @@ class WhisperPipelineForwards:
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past_key_value = past_key_values[idx] if past_key_values is not None else None
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if self.gradient_checkpointing and self.training:
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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, output_attentions, use_cache)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(decoder_layer),
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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attention_mask,
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encoder_hidden_states,
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@@ -773,6 +768,8 @@ class WhisperPipelineForwards:
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head_mask[idx] if head_mask is not None else None,
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(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
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None, # past_key_value
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output_attentions,
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use_cache,
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)
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else:
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layer_outputs = decoder_layer(
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