from typing import Iterable, List, Optional, Tuple import torch import torch.nn as nn from vllm.attention.backends.abstract import AttentionMetadata from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.sampler import SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models import ModelRegistry from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.eagle import EAGLEConfig class EAGLE(nn.Module): """This class implements the EAGLE draft model from the paper: https://arxiv.org/pdf/2401.15077 Reference implementation: https://github.com/SafeAILab/EAGLE Differences from reference implementation: 1. In reference, LlamaDecoderLayer implementation doesn't have input_layernorm for 1st decoder layer (https://github.com/SafeAILab/EAGLE/blob/7d065d084443fbfd386f88839efd7193c12be869/eagle/model/cnets.py#L427) but we do as HF implementation also does. 2. We allow any decoder layer to be used in EAGLE whereas in reference decoder layer is fixed to be LlamaDecoderLayer. 3. We have an optional token_map which reduces draft vocab to most frequently used tokens to give some additional speed-up by reducing sampling overhead. This is disabled unless the checkpoint file has explicit token_map tensor and config has an optional attribute truncated_vocab_size < vocab_size. To use this technique, one has to find the top-k most frequent tokens in target dataset and add that as a tensor in the draft checkpoint (using key token_map). Also, the draft config needs to have truncated_vocab_size (=k) as an attribute.""" def __init__(self, config: EAGLEConfig, *args, **kwargs) -> None: super().__init__() self.config = config architectures = getattr(self.config.model, "architectures", []) model_cls, _ = ModelRegistry.resolve_model_cls(architectures) self.model = model_cls(self.config.model, *args, **kwargs) self.fc = nn.Linear(config.model.hidden_size * 2, config.model.hidden_size, bias=False) self.orig_vocab_size = config.vocab_size self.truncated_vocab_size = config.truncated_vocab_size self.unpadded_vocab_size = self.truncated_vocab_size self.lm_head = ParallelLMHead( self.unpadded_vocab_size, config.hidden_size, org_num_embeddings=self.truncated_vocab_size, padding_size=DEFAULT_VOCAB_PADDING_SIZE, ) logit_scale = getattr(config, "logit_scale", 1.0) self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, self.truncated_vocab_size, logit_scale) # Token map is a idx to token mapping to reduce the vocab size for # the draft model. Using smaller vocab size for draft, containing # only most frequent tokens reduces the speculation overhead. This # doesn't affect the acceptance rate much and thus gives more speed # -up. By default, this is disabled and is only used if the EAGLE # checkpoint file has token_map tensor. self.token_map = None @property def sampler(self): return self.model.sampler def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, previous_hidden_states: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, ) -> torch.Tensor: tok_embeds = self.model.model.embed_tokens(input_ids) inputs_embeds = self.fc( torch.cat([tok_embeds, previous_hidden_states], dim=-1)) inputs_embeds[positions == 0] = 0 # masking inputs at position=0 hidden_states = self.model.model( input_ids=None, inputs_embeds=inputs_embeds, positions=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, intermediate_tensors=intermediate_tensors) return hidden_states def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata) -> torch.Tensor: logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) if self.token_map is not None: _logits = logits logits = -torch.inf * torch.ones( size=(*_logits.shape[:-1], self.orig_vocab_size), device=_logits.device, dtype=_logits.dtype) logits[..., self.token_map] = _logits return logits def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: next_tokens = self.sampler(logits, sampling_metadata) return next_tokens def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): # This implementation is incompitable with https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B # due to missing lm_head weights and its config being that of a # Llama model. Here's a compatible version with the same weights: # https://huggingface.co/abhigoyal/EAGLE-LLaMA3-Instruct-8B-vllm # Also, here's an example script for converting trained EAGLE # checkpoint to vLLM compatible version: https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d model_weights = {} for name, loaded_weight in weights: if name == "token_map": if self.config.truncated_vocab_size < self.config.vocab_size: self.token_map = nn.Parameter(loaded_weight, requires_grad=False) elif name.startswith("fc."): weight_loader = getattr(self.fc.weight, "weight_loader", default_weight_loader) weight_loader(self.fc.weight, loaded_weight) elif name.startswith("model.lm_head.") or name.startswith( "model.model."): model_weights[name.split("model.", 1)[-1]] = loaded_weight elif name.startswith("lm_head.") or name.startswith("model."): model_weights[name] = loaded_weight else: model_weights[f"model.{name}"] = loaded_weight lm_head_weight = model_weights.pop("lm_head.weight") if self.token_map is not None and\ lm_head_weight.shape[0] > self.token_map.shape[0]: lm_head_weight = lm_head_weight[self.token_map] weight_loader = getattr(self.lm_head.weight, "weight_loader", default_weight_loader) weight_loader(self.lm_head.weight, lm_head_weight) self.model.load_weights(model_weights.items())