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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
@@ -3,6 +3,7 @@ from typing import List, Optional, Tuple, Union
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import torch
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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@@ -42,7 +43,7 @@ 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.decoder._prepare_decoder_attention_mask(
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attention_mask = _prepare_4d_causal_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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@@ -57,6 +58,20 @@ class OPTPipelineForwards:
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under pipeline setting.
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"""
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@staticmethod
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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@staticmethod
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def opt_model_forward(
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self: OPTModel,
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@@ -112,7 +127,7 @@ class OPTPipelineForwards:
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inputs_embeds = decoder.project_in(inputs_embeds)
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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inputs_embeds.dtype
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hidden_states = inputs_embeds
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else:
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if hidden_states is None:
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raise ValueError("hidden_states shouldn't be None for intermediate stages.")
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@@ -125,12 +140,25 @@ class OPTPipelineForwards:
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# required mask seq length can be calculated via length of past
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mask_seq_length = past_key_values_length + seq_length
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(batch_size, mask_seq_length, device=device)
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elif attention_mask.shape[1] != mask_seq_length:
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raise ValueError(
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f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
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f"{mask_seq_length} (sum of the lengths of current and past inputs)"
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if self.decoder._use_flash_attention_2:
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# 2d mask is passed through the layers
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causal_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
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attention_mask = (
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torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
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if attention_mask is None
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else attention_mask
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)
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else:
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# 4d mask is passed through the layers
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if attention_mask is None:
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attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
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elif attention_mask.shape[1] != mask_seq_length:
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raise ValueError(
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f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
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f"{mask_seq_length} (sum of the lengths of current and past inputs)"
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)
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causal_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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if stage_manager.is_first_stage():
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@@ -205,20 +233,14 @@ class OPTPipelineForwards:
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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 decoder.gradient_checkpointing and decoder.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, None)
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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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causal_attention_mask,
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head_mask[idx] if head_mask is not None else None,
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None,
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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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