wehub-resource-sync
210 lines
7.4 KiB
Python
210 lines
7.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only ExaoneMoe MTP model."""
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from collections.abc import Iterable
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import torch
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from torch import 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.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.linear import ColumnParallelLinear
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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.models.exaone_moe import ExaoneMoeDecoderLayer
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from vllm.sequence import IntermediateTensors
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from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
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logger = init_logger(__name__)
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KVCache = tuple[torch.Tensor, torch.Tensor]
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@support_torch_compile
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class ExaoneMoeMultiTokenPredictor(nn.Module):
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_stacked={
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".q_proj": (".qkv_proj", "q"),
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".k_proj": (".qkv_proj", "k"),
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".v_proj": (".qkv_proj", "v"),
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# Scope to dense mlp; experts are handled separately.
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".mlp.gate_proj": (".mlp.gate_up_proj", 0),
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".mlp.up_proj": (".mlp.gate_up_proj", 1),
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}
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)
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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model_config = vllm_config.model_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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config = model_config.hf_config
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self.config = config
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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self.mtp_start_layer_idx = config.num_hidden_layers
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self.num_mtp_layers = getattr(config, "num_nextn_predict_layers", 1)
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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self.fc = ColumnParallelLinear(
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self.config.hidden_size * 2,
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self.config.hidden_size,
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gather_output=True,
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bias=False,
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return_bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.fc",
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)
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self.layers = nn.ModuleList(
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ExaoneMoeDecoderLayer(
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vllm_config.model_config.hf_config,
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quant_config=quant_config,
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prefix=f"{prefix}.layers.{idx}",
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mtp_layer=True,
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)
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for idx in range(self.num_mtp_layers)
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.pre_fc_norm_hidden = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.pre_fc_norm_embedding = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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def get_input_embeddings(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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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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if get_pp_group().is_first_rank:
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if inputs_embeds is None:
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inputs_embeds = self.get_input_embeddings(input_ids)
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assert hidden_states.shape[-1] == inputs_embeds.shape[-1]
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inputs_embeds = self.pre_fc_norm_embedding(inputs_embeds)
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hidden_states = self.pre_fc_norm_hidden(hidden_states)
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hidden_states = torch.cat([inputs_embeds, hidden_states], dim=-1)
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hidden_states = self.fc(hidden_states)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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current_step_idx = spec_step_idx % self.num_mtp_layers
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hidden_states, residual = self.layers[current_step_idx](
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positions=positions,
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hidden_states=hidden_states,
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residual=residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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@support_torch_compile
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class ExaoneMoeMTP(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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config = vllm_config.model_config.hf_config
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self.vllm_config = vllm_config
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self.quant_config = vllm_config.quant_config
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super().__init__()
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self.config = config
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self.model = ExaoneMoeMultiTokenPredictor(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "mtp")
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)
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self.unpadded_vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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# padding_size=DEFAULT_VOCAB_PADDING_SIZE,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(
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self.unpadded_vocab_size, config.vocab_size
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(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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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_idx: int = 0,
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**kwargs: object,
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):
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hidden_states = self.model(
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input_ids,
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positions,
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hidden_states,
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intermediate_tensors,
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inputs_embeds,
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spec_step_idx,
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)
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return 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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spec_step_idx: int = 0,
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) -> torch.Tensor | None:
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return self.logits_processor(self.lm_head, hidden_states)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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shared_weight_names = ["embed_tokens", "lm_head"]
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def remap_weight_names(weights):
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for name, weight in weights:
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if name.startswith("mtp."):
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name = name.replace("mtp.", "model.")
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elif not any(key in name for key in shared_weight_names):
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continue
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yield name, weight
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loader = AutoWeightsLoader(self)
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return loader.load_weights(remap_weight_names(weights))
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