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[Sharderformer] Support zbv in Sharderformer Policy (#6150)
* [feat] Sharderformer support zbv * [feat] support chatglm2, command, deepseek for zbv * [feat] support zbv in shardformer policy: falcon,gptj,mistral,opt,qwen2,t5, vit, whisper * [feat] support GPT2FusedLinearConv1D * [feat] support GPT2FusedLinear (without tp) * [fix] debug FusedConvLinear * [shardfromer] support gpt2 policy for zbv, support GPT2FusedLinearConv Col and Row. * [Shardformer] support FusedLinear1D base for zbv * [shardformer] support zbv in FusedLinear1D base, Col, Row * [shardformer] support zbv in blip2 and sam policy * [shardformer] fix bug incorrect number of gradients; add fusedLinear base testcase; * [fix] fix incorrect number of gradients ; * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [Shardformer] add en doc for zbv; * [fix] fix typo in Model compatibility table * [fix] fix API Reference typo * [Shardformer] add zh-Han doc for zbv * [fix] fix Linear name; update en & zh doc * [fix] fix shardformer doc import err * [fix] fix shardconfig import in doc * [fix] fix shardformer doc * [fix] fix shardconfig doc * [fix] fix config * [fix] remove shardconfig * [fix] fix doc * [feat] add zbv doc string * [fix] rm doc * [fix] fix doc * [fix] empty zbv doc * [fix] ifx torch version * [fix] fix torch version * [fix] fix torch versions * [fix] fix torch versions * [fix] fix pyramid versions * [fix] fix pyramid, zope version * [fix] try fix workflow * [fix] try import ShardConfig in yml * [fix] fix workflow * [fix] fix workflow * [fix] fix workflow * [fix] fix workflow * [fix] fix ci * [fix] fix zbv doc * [fix] fix param for qkv linear, gpt2fused linear; fix requirments; * [fix] fix policy use fused_linear * [fix] fix weight grad none, err caused by weight ptr change * [fix] fix comm in WeightGradStore * [fix] fix WeightGradStore pop param * [fix] remove useless param in doc; fix gpt2 qkv test; * [shardformer] simplify execute_w_pass_grad_accum; * [fix] rm useless comments * [shardformer] simplify execute_w_pass_grad_accum & execute_w_pass * [shardformer] Run meaningful doc test * [shadformer] fix doc test cmd; --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@@ -324,9 +324,10 @@ class MistralPolicy(Policy):
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held_layers.append(module.embed_tokens)
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for start_idx, end_idx in stage_indices:
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage(ignore_chunk=True):
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if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
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not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
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):
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held_layers.append(module.norm)
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else:
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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if stage_manager.is_first_stage():
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@@ -419,8 +420,14 @@ class MistralForCausalLMPolicy(MistralPolicy):
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage(ignore_chunk=True):
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held_layers.append(self.model.lm_head)
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if stage_manager.is_interleave:
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if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
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not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
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):
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held_layers.append(self.model.lm_head)
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else:
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if stage_manager.is_last_stage():
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held_layers.append(self.model.lm_head)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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@@ -475,8 +482,14 @@ class MistralForSequenceClassificationPolicy(MistralPolicy):
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage(ignore_chunk=True):
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held_layers.append(self.model.score)
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if stage_manager.is_interleave:
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if (stage_manager.use_zbv and stage_manager.is_first_stage(ignore_chunk=True)) or (
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not stage_manager.use_zbv and stage_manager.is_last_stage(ignore_chunk=True)
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):
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held_layers.append(self.model.score)
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else:
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if stage_manager.is_last_stage():
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held_layers.append(self.model.score)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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