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vllm/model_executor/models/llama4_eagle.py
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vllm/model_executor/models/llama4_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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# Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team.
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# All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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.logger import init_logger
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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.quantization.torchao import TorchAOConfig
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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 default_weight_loader
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from vllm.model_executor.models.llama4 import Llama4DecoderLayer, Llama4ForCausalLM
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from vllm.model_executor.models.utils import extract_layer_index
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from .interfaces import SupportsMultiModal
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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 LlamaModel(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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quant_config: QuantizationConfig | None = None,
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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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self.validate_and_update_config(start_layer_id, 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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prefix=maybe_prefix(prefix, "embed_tokens"),
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)
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# Temporarily modify vllm_config.quant_config for draft model layers
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original_quant_config = vllm_config.quant_config
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vllm_config.quant_config = quant_config
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try:
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self.layers = nn.ModuleList(
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[
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Llama4DecoderLayer(
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vllm_config=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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finally:
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# Restore original quant_config
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vllm_config.quant_config = original_quant_config
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self.fc = torch.nn.Linear(
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self.config.hidden_size * 2, self.config.hidden_size, bias=False
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)
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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 | None,
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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 None:
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inputs_embeds = self.embed_input_ids(input_ids)
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hidden_states = self.fc(torch.cat((inputs_embeds, hidden_states), dim=-1))
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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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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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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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name = name.removeprefix("model.")
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for param_name, weight_name, shard_id in stacked_params_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(param, loaded_weight, shard_id)
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break
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else:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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for name in params_dict:
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assert name in loaded_params, f"{name} is not loaded!"
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return loaded_params
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def validate_and_update_config(
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self, start_layer_id: int, quant_config: QuantizationConfig | None = None
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) -> None:
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# yoco and moe is not supported by draft model yet
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assert self.config.yoco_global_kv_layer is None
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assert self.config.yoco_local_kv_layer is None
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assert len(self.config.moe_layers) == 0
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# draft model layer index is increased by start_layer_id,
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# so we need to pad relevant configs accordingly
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self.config.no_rope_layers = [0] * start_layer_id + self.config.no_rope_layers
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# currently only TorchAO quantization is supported
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if isinstance(quant_config, TorchAOConfig):
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def pad_layer_name(layer: str) -> str:
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layer_index = extract_layer_index(layer)
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return layer.replace(
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str(layer_index), str(layer_index + start_layer_id)
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)
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torchao_config = quant_config.torchao_config
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torchao_config.module_fqn_to_config = {
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pad_layer_name(layer): quantization
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for layer, quantization in torchao_config.module_fqn_to_config.items()
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}
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class EagleLlama4ForCausalLM(Llama4ForCausalLM):
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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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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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# draft model quantization config may differ from target model
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quant_config = VllmConfig.get_quantization_config(
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vllm_config.speculative_config.draft_model_config, vllm_config.load_config
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)
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self.model = LlamaModel(
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vllm_config=vllm_config,
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prefix="model",
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start_layer_id=target_layer_num,
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quant_config=quant_config,
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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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self.lm_head = ParallelLMHead(
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self.config.draft_vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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# Set MoE hyperparameters
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self.set_moe_parameters()
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def get_language_model(self) -> torch.nn.Module:
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return self.model
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embed_input_ids = SupportsMultiModal.embed_input_ids # type: ignore
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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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return self.model(input_ids, positions, hidden_states, inputs_embeds)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> None:
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def transform(inputs):
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name, loaded_weight = inputs
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name, weight = self.permute_qk_weight_for_rotary(name, loaded_weight)
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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, weight
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loader = AutoWeightsLoader(
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self,
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# lm_head is tied with target model (Llama4ForCausalLM)
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skip_prefixes=([]),
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)
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loader.load_weights(map(transform, weights))
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