Sync from v0.13
This commit is contained in:
446
vllm/model_executor/models/deepseek_mtp.py
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446
vllm/model_executor/models/deepseek_mtp.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import typing
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from collections.abc import Callable, Iterable
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from .deepseek_v2 import (
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DeepseekV2DecoderLayer,
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DeepseekV2MixtureOfExperts,
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DeepseekV2MoE,
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get_spec_layer_idx_from_weight_name,
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)
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from .interfaces import SupportsPP
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from .utils import maybe_prefix
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logger = init_logger(__name__)
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class SharedHead(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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quant_config: QuantizationConfig | None = None,
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) -> None:
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super().__init__()
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "head"),
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return self.norm(hidden_states)
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class DeepSeekMultiTokenPredictorLayer(nn.Module):
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def __init__(self, vllm_config: VllmConfig, prefix: str) -> None:
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super().__init__()
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config = vllm_config.speculative_config.draft_model_config.hf_config
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self.config = config
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quant_config = vllm_config.quant_config
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self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
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self.device = current_platform.device_type
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self.is_v32 = hasattr(config, "index_topk")
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if self.is_v32:
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topk_tokens = config.index_topk
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topk_indices_buffer = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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topk_tokens,
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dtype=torch.int32,
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device=self.device,
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)
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else:
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topk_indices_buffer = None
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self.shared_head = SharedHead(
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config=config, prefix=prefix, quant_config=quant_config
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)
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self.mtp_block = DeepseekV2DecoderLayer(
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vllm_config,
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prefix,
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config=self.config,
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topk_indices_buffer=topk_indices_buffer,
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)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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previous_hidden_states: torch.Tensor,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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# masking inputs at position 0, as not needed by MTP
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inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
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inputs_embeds = self.enorm(inputs_embeds)
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previous_hidden_states = self.hnorm(previous_hidden_states)
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hidden_states = self.eh_proj(
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torch.cat([inputs_embeds, previous_hidden_states], dim=-1)
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)
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hidden_states, residual = self.mtp_block(
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positions=positions, hidden_states=hidden_states, residual=None
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)
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hidden_states = residual + hidden_states
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return hidden_states
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class DeepSeekMultiTokenPredictor(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.mtp_start_layer_idx = config.num_hidden_layers
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self.num_mtp_layers = config.num_nextn_predict_layers
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# to map the exact layer index from weights
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self.layers = torch.nn.ModuleDict(
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{
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str(idx): DeepSeekMultiTokenPredictorLayer(
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vllm_config, f"{prefix}.layers.{idx}"
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)
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for idx in range(
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self.mtp_start_layer_idx,
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self.mtp_start_layer_idx + self.num_mtp_layers,
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)
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}
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)
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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previous_hidden_states: torch.Tensor,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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current_step_idx = spec_step_idx % self.num_mtp_layers
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return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
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input_ids,
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positions,
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previous_hidden_states,
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inputs_embeds,
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current_step_idx,
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)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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current_step_idx = spec_step_idx % self.num_mtp_layers
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mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
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logits = self.logits_processor(
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mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states)
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)
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return logits
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@support_torch_compile
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class DeepSeekMTP(nn.Module, SupportsPP, DeepseekV2MixtureOfExperts):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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self.config = vllm_config.model_config.hf_config
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self.model = DeepSeekMultiTokenPredictor(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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# Set MoE hyperparameters
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self.set_moe_parameters()
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def set_moe_parameters(self):
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self.expert_weights = []
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self.num_moe_layers = self.config.num_nextn_predict_layers
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self.num_expert_groups = self.config.n_group
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self.moe_layers = []
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self.moe_mlp_layers = []
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example_moe = None
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for layer in self.model.layers.values():
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assert isinstance(layer, DeepSeekMultiTokenPredictorLayer)
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layer = layer.mtp_block
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assert isinstance(layer, DeepseekV2DecoderLayer)
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if isinstance(layer.mlp, DeepseekV2MoE):
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example_moe = layer.mlp
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self.moe_mlp_layers.append(layer.mlp)
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self.moe_layers.append(layer.mlp.experts)
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self.extract_moe_parameters(example_moe)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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hidden_states = self.model(
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input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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spec_step_idx: int = 0,
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) -> torch.Tensor | None:
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return self.model.compute_logits(hidden_states, spec_step_idx)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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rocm_aiter_moe_shared_expert_enabled = (
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rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
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)
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stacked_params_mapping = [
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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("fused_qkv_a_proj", "q_a_proj", 0),
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("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
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]
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expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts
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+ (
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self.config.n_shared_experts
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if rocm_aiter_moe_shared_expert_enabled
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else 0
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),
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)
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
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if spec_layer is None:
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continue
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is_fusion_moe_shared_experts_layer = (
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rocm_aiter_moe_shared_expert_enabled and ("mlp.shared_experts" in name)
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)
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name = self._rewrite_spec_layer_name(spec_layer, name)
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for param_name, weight_name, shard_id in stacked_params_mapping:
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# Skip non-stacked layers and experts (experts handled below).
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if weight_name not in name:
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continue
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# We have mlp.experts[0].gate_proj in the checkpoint.
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# Since we handle the experts below in expert_params_mapping,
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# we need to skip here BEFORE we update the name, otherwise
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# name will be updated to mlp.experts[0].gate_up_proj, which
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# will then be updated below in expert_params_mapping
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# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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if ("mlp.experts." in name) and name not in params_dict:
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continue
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if is_fusion_moe_shared_experts_layer:
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continue
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name_mapped = name.replace(weight_name, param_name)
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# QKV fusion is optional, fall back to normal
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# weight loading if it's not enabled
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if (
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param_name == "fused_qkv_a_proj"
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) and name_mapped not in params_dict:
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continue
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else:
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name = name_mapped
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Special handling: when AITER fusion_shared_experts is enabled,
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# checkpoints may provide a single widened shared_experts tensor
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# without explicit expert indices
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# (e.g. ...mlp.shared_experts.gate_proj.weight).
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# For models with multiple shared experts, split that tensor
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# evenly into per-shared-expert slices and load them into
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# appended expert slots mlp.experts.{n_routed_experts + j}.*
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# accordingly.
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num_chunks = 1
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if is_fusion_moe_shared_experts_layer:
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num_chunks = getattr(self.config, "n_shared_experts", 1) or 1
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# Determine split axis based on op type
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# gate/up: ColumnParallel → split along dim 0
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# down: RowParallel → split along dim 1
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split_dim = 1 if "down_proj.weight" in name else 0
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total = loaded_weight.shape[split_dim]
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assert total % num_chunks == 0, (
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f"Shared expert weight dim {total} "
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f"not divisible by num_chunks {num_chunks}"
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)
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chunk_size = total // num_chunks
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for j in range(num_chunks):
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chunk_name = name
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weight_to_load = loaded_weight
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if is_fusion_moe_shared_experts_layer:
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if split_dim == 0:
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weight_to_load = loaded_weight[
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j * chunk_size : (j + 1) * chunk_size, :
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]
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else:
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weight_to_load = loaded_weight[
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:, j * chunk_size : (j + 1) * chunk_size
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]
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# Synthesize an expert-style name so expert mapping
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# can route it
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chunk_name = name.replace(
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"mlp.shared_experts",
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f"mlp.experts.{self.config.n_routed_experts + j}",
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)
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# Use expert_params_mapping to locate the destination
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# param and delegate to its expert-aware weight_loader
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# with expert_id.
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is_expert_weight = False
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in chunk_name:
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continue
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# Anyway, this is an expert weight and should not be
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# attempted to load as other weights later
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is_expert_weight = True
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# Do not modify `name` since the loop may continue here
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# Instead, create a new variable
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name_mapped = chunk_name.replace(weight_name, param_name)
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param = params_dict[name_mapped]
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# We should ask the weight loader to return success or
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# not here since otherwise we may skip experts with
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# other available replicas.
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weight_loader = typing.cast(
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Callable[..., bool], param.weight_loader
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)
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success = weight_loader(
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param,
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weight_to_load,
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name_mapped,
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shard_id=shard_id,
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expert_id=expert_id,
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return_success=True,
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)
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if success:
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if not is_fusion_moe_shared_experts_layer:
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name = name_mapped
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else:
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loaded_params.add(name_mapped)
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break
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else:
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if is_expert_weight:
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# We've checked that this is an expert weight
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# However it's not mapped locally to this rank
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# So we simply skip it
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continue
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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# According to DeepSeek-V3 Technical Report, MTP modules
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# shares embedding layer. We only load the first weights.
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if (
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spec_layer != self.model.mtp_start_layer_idx
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and ".layers" not in name
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):
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continue
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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if not is_fusion_moe_shared_experts_layer:
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loaded_params.add(name)
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return loaded_params
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def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
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"""
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Rewrite the weight name to match the format of the original model.
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Add .mtp_block for modules in transformer layer block for spec layer
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and rename shared layer weights to be top level.
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"""
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spec_layer_weight_names = [
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"embed_tokens",
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"enorm",
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"hnorm",
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"eh_proj",
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"shared_head",
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]
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shared_weight_names = ["embed_tokens"]
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spec_layer_weight = False
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shared_weight = False
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for weight_name in spec_layer_weight_names:
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if weight_name in name:
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spec_layer_weight = True
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if weight_name in shared_weight_names:
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shared_weight = True
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break
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if not spec_layer_weight:
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# treat rest weights as weights for transformer layer block
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name = name.replace(
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f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
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)
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elif shared_weight:
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# treat shared weights as top level weights
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name = name.replace(f"model.layers.{spec_layer}.", "model.")
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return name
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