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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 transformers import LlamaConfig
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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.logger import init_logger
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from vllm.model_executor.layers.linear import ReplicatedLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
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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.llama import LlamaDecoderLayer, LlamaForCausalLM
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from .utils import (
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AutoWeightsLoader,
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get_draft_quant_config,
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maybe_prefix,
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process_eagle_weight,
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)
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logger = init_logger(__name__)
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class LlamaDecoderLayer(LlamaDecoderLayer):
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def __init__(
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self,
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vllm_config: VllmConfig,
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disable_input_layernorm: bool,
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prefix: str = "",
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config: LlamaConfig | None = None,
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) -> None:
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super().__init__(vllm_config, prefix=prefix, config=config)
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# Skip the input_layernorm
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# https://github.com/SafeAILab/EAGLE/blob/35c78f6cdc19a73e05cf5c330b4c358dad970c6a/eagle/model/cnets.py#L427
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if disable_input_layernorm:
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del self.input_layernorm
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self.input_layernorm = nn.Identity()
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def get_quant_config(self, vllm_config: VllmConfig) -> QuantizationConfig | None:
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"""Use drafter's quantization config instead of verifier's."""
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return get_draft_quant_config(vllm_config)
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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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) -> 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.vocab_size = self.config.vocab_size
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# Get drafter's quantization config
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self.quant_config = get_draft_quant_config(vllm_config)
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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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self.layers = nn.ModuleList(
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[
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LlamaDecoderLayer(
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vllm_config,
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i == 0,
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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 = ReplicatedLinear(
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input_size=self.config.hidden_size * 2,
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output_size=self.config.hidden_size,
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bias=False,
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params_dtype=vllm_config.model_config.dtype,
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quant_config=self.quant_config,
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prefix=maybe_prefix(prefix, "fc"),
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return_bias=False,
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)
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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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hidden_states = self.fc(torch.cat((input_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 = 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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# Handle kv cache quantization scales
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if self.quant_config is not None and (
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scale_name := self.quant_config.get_cache_scale(name)
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):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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loaded_weight = (
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loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
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)
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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# Remapping the name FP8 kv-scale
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if "scale" in name:
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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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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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# 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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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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return loaded_params
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class EagleLlamaForCausalLM(LlamaForCausalLM):
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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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# Ensure draft_vocab_size is set
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# default to the base vocab size when absent
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if getattr(self.config, "draft_vocab_size", None) is None:
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base_vocab_size = getattr(self.config, "vocab_size", None)
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self.config.draft_vocab_size = base_vocab_size
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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 = LlamaModel(
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vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num
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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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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 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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