mirror of
https://github.com/hpcaitech/ColossalAI.git
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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>
This commit is contained in:
duanjunwen
GitHub
@@ -10,6 +10,7 @@ from colossalai.shardformer.layer import (
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LayerNorm,
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Linear1D_Col,
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Linear1D_Row,
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LinearWithGradAccum,
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PaddingEmbedding,
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PaddingLMHead,
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VocabParallelEmbedding1D,
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@@ -76,6 +77,8 @@ class OPTPolicy(Policy):
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self.shard_config.enable_sequence_parallelism = False
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warnings.warn("OPT doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")
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use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
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if self.shard_config.enable_tensor_parallelism:
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assert (
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self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
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@@ -85,10 +88,16 @@ class OPTPolicy(Policy):
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=Linear1D_Col,
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kwargs=dict(
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use_zbv=use_zbv,
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),
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=Linear1D_Row,
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kwargs=dict(
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use_zbv=use_zbv,
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),
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),
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]
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)
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@@ -104,6 +113,7 @@ class OPTPolicy(Policy):
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target_module=Linear1D_Col,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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SubModuleReplacementDescription(
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@@ -111,6 +121,7 @@ class OPTPolicy(Policy):
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target_module=Linear1D_Col,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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SubModuleReplacementDescription(
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@@ -118,6 +129,7 @@ class OPTPolicy(Policy):
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target_module=Linear1D_Col,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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SubModuleReplacementDescription(
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@@ -125,11 +137,67 @@ class OPTPolicy(Policy):
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target_module=Linear1D_Row,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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],
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)
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elif use_zbv:
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policy[OPTDecoderLayer] = ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=LinearWithGradAccum,
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kwargs=dict(
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use_zbv=use_zbv,
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),
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=LinearWithGradAccum,
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kwargs=dict(
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use_zbv=use_zbv,
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),
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),
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]
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)
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policy[attn_cls] = ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="q_proj",
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target_module=LinearWithGradAccum,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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SubModuleReplacementDescription(
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suffix="k_proj",
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target_module=LinearWithGradAccum,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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SubModuleReplacementDescription(
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suffix="v_proj",
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target_module=LinearWithGradAccum,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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SubModuleReplacementDescription(
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suffix="out_proj",
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target_module=LinearWithGradAccum,
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kwargs={
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"fp8_communication": self.shard_config.fp8_communication,
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"use_zbv": use_zbv,
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},
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),
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],
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)
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if embedding_cls is not None:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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@@ -221,15 +289,30 @@ class OPTPolicy(Policy):
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held_layers = []
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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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held_layers.append(module.embed_tokens)
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held_layers.append(module.embed_positions)
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held_layers.append(module.project_in)
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start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.final_layer_norm)
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held_layers.append(module.project_out)
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if stage_manager.is_interleave:
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assert stage_manager.num_model_chunks is not None
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if stage_manager.is_first_stage(ignore_chunk=True):
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held_layers.append(module.embed_tokens)
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held_layers.append(module.embed_positions)
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held_layers.append(module.project_in)
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stage_indices = stage_manager.get_stage_index(layers_per_stage)
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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.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.final_layer_norm)
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held_layers.append(module.project_out)
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else:
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if stage_manager.is_first_stage():
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held_layers.append(module.embed_tokens)
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held_layers.append(module.embed_positions)
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held_layers.append(module.project_in)
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start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.final_layer_norm)
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held_layers.append(module.project_out)
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return held_layers
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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@@ -323,8 +406,15 @@ class OPTForCausalLMPolicy(OPTPolicy):
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def get_held_layers(self) -> List[nn.Module]:
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held_layers = super().get_held_layers()
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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.lm_head)
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stage_manager = self.pipeline_stage_manager
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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 self.pipeline_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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@@ -395,8 +485,15 @@ class OPTForQuestionAnsweringPolicy(OPTPolicy):
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def get_held_layers(self) -> List[nn.Module]:
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held_layers = super().get_held_layers()
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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.qa_outputs)
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stage_manager = self.pipeline_stage_manager
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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.qa_outputs)
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else:
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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.qa_outputs)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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