mirror of https://github.com/hpcaitech/ColossalAI
427 lines
17 KiB
Python
427 lines
17 KiB
Python
from functools import partial
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from typing import Callable, Dict, List, Union
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import torch.nn as nn
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from torch import Tensor
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from torch.nn import Module
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from colossalai.shardformer.layer import (
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FusedRMSNorm,
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Linear1D_Col,
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Linear1D_Row,
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PaddingEmbedding,
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PaddingLMHead,
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RMSNorm,
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VocabParallelEmbedding1D,
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VocabParallelLMHead1D,
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)
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from ..modeling.llama import (
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LlamaPipelineForwards,
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get_llama_flash_attention_forward,
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get_llama_flash_attention_model_forward,
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get_lm_forward_with_dist_cross_entropy,
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)
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ["LlamaPolicy", "LlamaForCausalLMPolicy", "LlamaForSequenceClassificationPolicy"]
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class LlamaPolicy(Policy):
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def config_sanity_check(self):
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pass
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def preprocess(self):
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self.tie_weight = self.tie_weight_check()
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self.origin_attn_implement = self.model.config._attn_implementation
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return self.model
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def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
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from transformers.models.llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaFlashAttention2,
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LlamaModel,
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LlamaSdpaAttention,
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)
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ATTN_IMPLEMENTATION = {
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"eager": LlamaAttention,
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"flash_attention_2": LlamaFlashAttention2,
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"sdpa": LlamaSdpaAttention,
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}
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policy = {}
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attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement]
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embedding_cls = None
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if self.shard_config.enable_tensor_parallelism:
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embedding_cls = VocabParallelEmbedding1D
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else:
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if self.tie_weight:
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embedding_cls = PaddingEmbedding
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if self.shard_config.enable_fused_normalization:
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norm_cls = FusedRMSNorm
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else:
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norm_cls = RMSNorm
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sp_mode = self.shard_config.sequence_parallelism_mode or None
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sp_size = self.shard_config.sequence_parallel_size or None
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sp_group = self.shard_config.sequence_parallel_process_group or None
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sp_partial_derived = sp_mode in ["split_gather", "ring"]
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if sp_mode == "ring_attn" and not self.is_causal:
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raise ValueError("Ring attention is only meant for causal language modeling.")
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tp_size = self.shard_config.tensor_parallel_size
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# Modified by SP and TP
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num_q_heads = self.model.config.num_attention_heads
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num_kv_heads = getattr(self.model.config, "num_key_value_heads", None)
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if sp_mode == "all_to_all":
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num_q_heads //= sp_size
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decoder_attribute_replacement = {"num_heads": num_q_heads}
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if num_kv_heads:
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num_kv_heads //= sp_size
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decoder_attribute_replacement["num_key_value_heads"] = num_kv_heads
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policy[attn_cls] = ModulePolicyDescription(
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attribute_replacement=decoder_attribute_replacement,
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)
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if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism:
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self.append_or_create_method_replacement(
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description={
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"forward": get_llama_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group),
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},
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policy=policy,
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target_key=attn_cls,
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)
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if self.pipeline_stage_manager is None:
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self.append_or_create_method_replacement(
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description={
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"forward": get_llama_flash_attention_model_forward(
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self.shard_config,
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sp_mode=sp_mode,
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sp_size=sp_size,
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sp_group=sp_group,
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),
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},
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policy=policy,
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target_key=LlamaModel,
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)
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if self.shard_config.enable_tensor_parallelism:
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assert (
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num_q_heads % tp_size == 0
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), f"The number of attention heads must be divisible by tensor parallel size."
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if hasattr(self.model.config, "num_key_value_heads"):
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assert (
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num_kv_heads >= tp_size and num_kv_heads % tp_size == 0
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), f"The number of key_value heads must be divisible by, and must not be less than tensor parallel size."
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num_q_heads //= tp_size
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decoder_attribute_replacement = {
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"self_attn.hidden_size": self.model.config.hidden_size // tp_size,
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"self_attn.num_heads": num_q_heads,
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}
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if getattr(self.model.config, "num_key_value_heads", False):
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num_kv_heads //= tp_size
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decoder_attribute_replacement["self_attn.num_key_value_heads"] = num_kv_heads
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policy[LlamaDecoderLayer] = ModulePolicyDescription(
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attribute_replacement=decoder_attribute_replacement,
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="self_attn.q_proj",
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target_module=Linear1D_Col,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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),
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SubModuleReplacementDescription(
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suffix="self_attn.k_proj",
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target_module=Linear1D_Col,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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),
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SubModuleReplacementDescription(
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suffix="self_attn.v_proj",
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target_module=Linear1D_Col,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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),
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SubModuleReplacementDescription(
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suffix="self_attn.o_proj",
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target_module=Linear1D_Row,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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),
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SubModuleReplacementDescription(
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suffix="mlp.gate_proj",
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target_module=Linear1D_Col,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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),
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SubModuleReplacementDescription(
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suffix="mlp.up_proj",
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target_module=Linear1D_Col,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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),
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SubModuleReplacementDescription(
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suffix="mlp.down_proj",
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target_module=Linear1D_Row,
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kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication),
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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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suffix="embed_tokens",
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target_module=embedding_cls,
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kwargs=(
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{
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"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by,
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"fp8_communication": self.shard_config.fp8_communication,
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}
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if self.shard_config.enable_tensor_parallelism
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else {"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}
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),
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),
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policy=policy,
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target_key=LlamaModel,
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)
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# optimization configuration
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="input_layernorm",
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target_module=norm_cls,
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kwargs={"sp_partial_derived": sp_partial_derived},
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),
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SubModuleReplacementDescription(
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suffix="post_attention_layernorm",
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target_module=norm_cls,
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kwargs={"sp_partial_derived": sp_partial_derived},
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),
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],
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policy=policy,
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target_key=LlamaDecoderLayer,
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)
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="norm",
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target_module=norm_cls,
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kwargs={"sp_partial_derived": sp_partial_derived},
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),
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policy=policy,
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target_key=LlamaModel,
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)
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return policy
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def postprocess(self):
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return self.model
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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"""If under pipeline parallel setting, replacing the original forward method of huggingface
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to customized forward method, and add this changing to policy."""
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if self.pipeline_stage_manager is None:
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return
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "LlamaModel":
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module = self.model
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else:
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module = self.model.model
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if stage_manager.is_interleave:
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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stage_manager.stage_indices = stage_manager.get_stage_index(layers_per_stage)
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method_replacement = {
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"forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
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}
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else:
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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stage_index = stage_manager.get_stage_index(layers_per_stage)
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method_replacement = {
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"forward": partial(
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new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
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)
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}
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self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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assert self.pipeline_stage_manager is not None
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if self.model.__class__.__name__ == "LlamaModel":
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module = self.model
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else:
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module = self.model.model
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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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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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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stage_indices = stage_manager.get_stage_index(layers_per_stage)
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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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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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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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held_layers.append(module.embed_tokens)
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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.norm)
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return held_layers
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class LlamaModelPolicy(LlamaPolicy):
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def module_policy(self):
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policy = super().module_policy()
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from transformers.models.llama.modeling_llama import LlamaModel
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=LlamaModel, new_forward=LlamaPipelineForwards.llama_model_forward, policy=policy
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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held_layers = super().get_held_layers()
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in llama model"""
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return []
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class LlamaForCausalLMPolicy(LlamaPolicy):
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def module_policy(self):
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from transformers import LlamaForCausalLM
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self.is_causal = True
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for causal lm
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new_item = {
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LlamaForCausalLM: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=VocabParallelLMHead1D,
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kwargs={
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"gather_output": not self.shard_config.parallel_output,
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"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by,
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"fp8_communication": self.shard_config.fp8_communication,
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},
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)
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],
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)
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}
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else:
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new_item = {
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LlamaForCausalLM: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=PaddingLMHead,
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kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by},
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)
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],
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)
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}
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policy.update(new_item)
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=LlamaForCausalLM, new_forward=LlamaPipelineForwards.llama_for_causal_lm_forward, policy=policy
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)
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elif self.shard_config.enable_tensor_parallelism or self.shard_config.enable_sequence_parallelism:
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# Compute loss distributedly along the sequence dimension
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new_item[LlamaForCausalLM].method_replacement = {
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"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)
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}
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return policy
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def get_held_layers(self) -> List[Module]:
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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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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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llama_model = self.model.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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if (
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id(llama_model.embed_tokens.weight) == id(self.model.lm_head.weight)
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and self.pipeline_stage_manager.num_stages > 1
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):
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# tie weights
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return [
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{
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0: llama_model.embed_tokens.weight,
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self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
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}
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]
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return []
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class LlamaForSequenceClassificationPolicy(LlamaPolicy):
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def module_policy(self):
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from transformers import LlamaForSequenceClassification
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for sequence classification
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new_item = {
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LlamaForSequenceClassification: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="score",
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target_module=Linear1D_Col,
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kwargs=dict(
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gather_output=True,
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fp8_communication=self.shard_config.fp8_communication,
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),
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)
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]
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)
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}
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policy.update(new_item)
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# to be confirmed
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=LlamaForSequenceClassification,
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new_forward=LlamaPipelineForwards.llama_for_sequence_classification_forward,
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policy=policy,
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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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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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in llama for sequence classification model"""
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return []
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