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model_executor/models/deepseek_eagle.py
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260
model_executor/models/deepseek_eagle.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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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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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.distributed.parallel_state import get_pp_group
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from vllm.model_executor.layers.fused_moe import FusedMoE
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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.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.model_executor.models.deepseek_v2 import (
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DeepseekV2DecoderLayer,
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DeepseekV3ForCausalLM,
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)
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from vllm.utils import init_logger
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from .utils import AutoWeightsLoader, maybe_prefix, process_eagle_weight
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logger = init_logger(__name__)
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@support_torch_compile
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class DeepseekV2Model(nn.Module):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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start_layer_id: int = 0,
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) -> None:
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super().__init__()
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self.config = vllm_config.speculative_config.draft_model_config.hf_config
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quant_config = vllm_config.quant_config
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self.vocab_size = self.config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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self.config.vocab_size,
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self.config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "embed_tokens"),
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)
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self.layers = nn.ModuleList(
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[
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DeepseekV2DecoderLayer(
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vllm_config,
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prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
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config=self.config,
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)
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for i in range(self.config.num_hidden_layers)
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]
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)
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self.fc = nn.Linear(
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self.config.model.hidden_size * 2,
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self.config.model.hidden_size,
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bias=False,
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)
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self.enorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
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self.hnorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
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self.norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
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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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hidden_states: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_embeds = self.embed_tokens(input_ids)
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inputs = torch.cat(
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[self.enorm(input_embeds), self.hnorm(hidden_states)], dim=-1
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)
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hidden_states = self.fc(inputs)
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states, hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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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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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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expert_params_mapping = FusedMoE.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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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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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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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 go with fusion option, then update name
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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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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 name:
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continue
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(
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param,
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loaded_weight,
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name,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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break
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else:
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# if PP disabled then draft will share embed with target
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if get_pp_group().world_size == 1 and "embed_tokens." in name:
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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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# Remapping the name of FP8 kv-scale.
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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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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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loaded_params.add(name)
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return loaded_params
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class EagleDeepseekV3ForCausalLM(DeepseekV3ForCausalLM):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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self.config = vllm_config.speculative_config.draft_model_config.hf_config
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quant_config = vllm_config.quant_config
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target_layer_num = vllm_config.model_config.get_num_layers(
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vllm_config.parallel_config
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)
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self.model = DeepseekV2Model(
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vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num
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)
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self.lm_head = ParallelLMHead(
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self.config.vocab_size,
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self.config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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logit_scale = getattr(self.config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(
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self.config.vocab_size, scale=logit_scale
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)
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# Set MoE hyperparameters
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self.num_moe_layers = self.config.num_hidden_layers
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self.set_moe_parameters()
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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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inputs_embeds: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if inputs_embeds is not None:
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raise NotImplementedError(
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f"{type(self).__name__} does not support multimodal inputs yet."
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)
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return self.model(input_ids, positions, 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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) -> torch.Tensor | None:
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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def transform(inputs):
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name, loaded_weight = inputs
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if "lm_head" not in name:
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name = "model." + name
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process_eagle_weight(self, name)
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return name, loaded_weight
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=None,
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)
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loader.load_weights(map(transform, weights))
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