c1b6f39a11
In vLLM v0.6.2, ParallelLMHead.forward() raises RuntimeError since its weights should be used through LogitsProcessor.linear_method.apply(). Pass lm_head as first arg to LogitsProcessor which handles the hidden_states -> logits projection internally.
143 lines
5.3 KiB
Python
143 lines
5.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Copyright 2024 The vLLM team.
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"""Transformers modeling backend mixin for causal language models.
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This module provides CausalMixin that adds causal language model specific
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functionality (lm_head, compute_logits, sample) to the Base class.
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Following latest vLLM architecture:
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- TransformersForCausalLM = CausalMixin + Base
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- lm_head is created at the wrapper level (not inside self.model)
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"""
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from typing import TYPE_CHECKING, Optional
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import torch
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from vllm.logger import init_logger
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.models.utils import PPMissingLayer, maybe_prefix
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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logger = init_logger(__name__)
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class CausalMixin:
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"""
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Mixin class that adds causal language model functionality.
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This mixin provides:
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- ParallelLMHead for language model head (created at wrapper level)
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- LogitsProcessor for logits computation
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- Sampler for token sampling
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- compute_logits method for VllmModelForTextGeneration protocol
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- sample method for VllmModelForTextGeneration protocol
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Following latest vLLM architecture:
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- lm_head is a direct attribute of TransformersForCausalLM (not inside self.model)
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- hf_to_vllm_mapper maps "model.lm_head." -> "lm_head." to handle this
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- For tied embeddings, lm_head weight loading is skipped and weights are tied
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Should be used with Base class:
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class TransformersForCausalLM(CausalMixin, Base): ...
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"""
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def __init__(self, *, vllm_config: "VllmConfig", prefix: str = "") -> None:
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# Call next class in MRO (should be Base)
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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# Handle tied word embeddings - skip loading lm_head weights
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tie_word_embeddings = getattr(self.text_config, "tie_word_embeddings", False)
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if tie_word_embeddings:
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self.skip_prefixes.append("lm_head.")
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logger.info("Model has tied word embeddings, will tie lm_head weights")
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# Create lm_head at wrapper level (following latest vLLM architecture)
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# This is outside self.model, so weights map "model.lm_head." -> "lm_head."
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if self.pp_group.is_last_rank:
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self.lm_head = ParallelLMHead(
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self.vocab_size,
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self.hidden_size,
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quant_config=self.quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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# Tie weights if needed
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if tie_word_embeddings:
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input_embeddings = self.model.get_input_embeddings()
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if input_embeddings is not None:
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self.lm_head = self.lm_head.tie_weights(input_embeddings)
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logger.info("Tied lm_head weights with input embeddings")
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# Setup logits processor
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logit_scale = getattr(self.text_config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(
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self.vocab_size,
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logits_as_input=False,
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scale=logit_scale,
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)
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logger.info("CausalMixin initialized (vocab_size=%d, hidden_size=%d, logit_scale=%s)",
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self.vocab_size, self.hidden_size, logit_scale)
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else:
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# For non-last PP ranks, use PPMissingLayer
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self.lm_head = PPMissingLayer()
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self.logits_processor = None
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logger.info("CausalMixin initialized (PP non-last rank, using PPMissingLayer)")
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# Setup sampler
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self.sampler = Sampler()
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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"""
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Compute logits from hidden states.
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This method conforms to the VllmModelForTextGeneration protocol.
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Args:
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hidden_states: Hidden states from the model [seq_len, hidden_size]
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sampling_metadata: Sampling metadata
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Returns:
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Logits tensor or None
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"""
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if self.logits_processor is None:
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# Non-last PP rank
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return None
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# In v0.6.2, LogitsProcessor handles the lm_head projection internally
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# via lm_head.linear_method.apply(). Pass lm_head as the first arg.
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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"""
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Sample tokens from logits.
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This method conforms to the VllmModelForTextGeneration protocol.
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Args:
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logits: Logits tensor
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sampling_metadata: Sampling metadata
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Returns:
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SamplerOutput with sampled tokens
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"""
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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