wehub-resource-sync
381 lines
14 KiB
Python
381 lines
14 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 Bailing MoE v2.5 MTP model."""
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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.configuration_utils import PretrainedConfig
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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 import get_pp_group
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from vllm.model_executor.layers.fused_moe import (
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fused_moe_make_expert_params_mapping,
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)
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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.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 (
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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.bailing_moe_linear import (
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BailingMoeV25,
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BailingMoeV25MLAAttention,
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)
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from vllm.sequence import IntermediateTensors
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from .utils import PPMissingLayer, is_pp_missing_parameter, maybe_prefix
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def _get_draft_hf_config(vllm_config: VllmConfig) -> PretrainedConfig:
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speculative_config = vllm_config.speculative_config
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if speculative_config is not None:
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draft_model_config = speculative_config.draft_model_config
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if draft_model_config is not None:
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return draft_model_config.hf_config
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return vllm_config.model_config.hf_config
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class BailingMTPSharedHead(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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vllm_config: VllmConfig,
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) -> None:
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super().__init__()
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self.head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=vllm_config.quant_config,
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prefix=maybe_prefix(prefix, "head"),
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return hidden_states
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class BailingMoeV25MultiTokenPredictorLayer(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str,
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layer_id: int,
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) -> None:
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super().__init__()
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config = _get_draft_hf_config(vllm_config)
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self.config = config
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self.layer_id = layer_id
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self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.self_attn = BailingMoeV25MLAAttention(
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config,
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quant_config=vllm_config.quant_config,
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layer_id=layer_id,
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prefix=maybe_prefix(prefix, "self_attn"),
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cache_config=vllm_config.cache_config,
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)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.mlp = BailingMoeV25(
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config,
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quant_config=vllm_config.quant_config,
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layer_id=layer_id,
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prefix=maybe_prefix(prefix, "mlp"),
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)
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self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.shared_head = BailingMTPSharedHead(
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config,
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maybe_prefix(prefix, "shared_head"),
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vllm_config,
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)
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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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previous_hidden_states: torch.Tensor,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
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inputs_embeds = self.enorm(inputs_embeds)
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previous_hidden_states = self.hnorm(previous_hidden_states)
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hidden_states = self.eh_proj(
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torch.cat([inputs_embeds, previous_hidden_states], dim=-1)
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)
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(hidden_states, positions)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states.to(residual.device)
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return self.final_layernorm(hidden_states)
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class BailingMoeV25MultiTokenPredictor(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = _get_draft_hf_config(vllm_config)
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self.mtp_start_layer_idx = config.num_hidden_layers
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self.num_mtp_layers = config.num_nextn_predict_layers
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self.layers = nn.ModuleDict(
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{
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str(idx): BailingMoeV25MultiTokenPredictorLayer(
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vllm_config,
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f"{prefix}.layers.{idx}",
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idx,
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)
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for idx in range(
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self.mtp_start_layer_idx,
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self.mtp_start_layer_idx + self.num_mtp_layers,
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)
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}
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)
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if get_pp_group().is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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prefix=maybe_prefix(prefix, "embed_tokens"),
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.logits_processor = LogitsProcessor(config.vocab_size)
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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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previous_hidden_states: torch.Tensor,
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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 inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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current_step_idx = spec_step_idx % self.num_mtp_layers
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return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
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input_ids,
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positions,
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previous_hidden_states,
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inputs_embeds,
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current_step_idx,
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)
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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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lm_head: nn.Module | None = None,
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) -> torch.Tensor:
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current_step_idx = spec_step_idx % self.num_mtp_layers
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mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
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head = lm_head if lm_head is not None else mtp_layer.shared_head.head
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return self.logits_processor(
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head,
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mtp_layer.shared_head(hidden_states),
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)
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@support_torch_compile
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class BailingMoeV25MTPModel(nn.Module):
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packed_modules_mapping = {
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"gate_up_proj": ["gate_proj", "up_proj"],
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"fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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self.config = _get_draft_hf_config(vllm_config)
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self.lm_head: nn.Module | None = None
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self.model = BailingMoeV25MultiTokenPredictor(
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vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"),
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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 | None,
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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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return self.model(
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input_ids,
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positions,
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hidden_states,
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inputs_embeds,
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spec_step_idx,
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)
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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:
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return self.model.compute_logits(hidden_states, spec_step_idx, self.lm_head)
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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return fused_moe_make_expert_params_mapping(
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self,
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.num_experts,
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num_redundant_experts=0,
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)
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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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(".fused_qkv_a_proj", ".q_a_proj", 0),
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(".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1),
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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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expert_params_mapping = list(self.get_expert_mapping())
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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loaded_mtp_layers: set[int] = set()
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def load_param(
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name: str,
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loaded_weight: torch.Tensor,
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shard_id=None,
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) -> bool:
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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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return False
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if name not in params_dict or is_pp_missing_parameter(name, self):
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return False
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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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if shard_id is None:
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weight_loader(param, loaded_weight)
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elif isinstance(shard_id, int):
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weight_loader(param, loaded_weight, shard_id)
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else:
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weight_loader(
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param,
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loaded_weight,
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name,
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expert_id=shard_id[0],
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shard_id=shard_id[1],
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)
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loaded_params.add(name)
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return True
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def get_spec_layer_idx(name: str) -> int | None:
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if not name.startswith("model.layers."):
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return None
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try:
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layer_idx = int(name.split("model.layers.", 1)[1].split(".", 1)[0])
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except (IndexError, ValueError):
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return None
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mtp_idx = layer_idx - self.config.num_hidden_layers
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if 0 <= mtp_idx < self.config.num_nextn_predict_layers:
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return layer_idx
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return None
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def normalize_name(name: str) -> str:
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name = name.replace(".attention.dense", ".self_attn.o_proj")
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name = name.replace(".attention.", ".self_attn.")
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return name.replace(
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"mlp.gate.e_score_correction_bias",
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"mlp.gate.expert_bias",
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)
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def load_lm_head(loaded_weight: torch.Tensor) -> None:
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for layer_idx in range(
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self.model.mtp_start_layer_idx,
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self.model.mtp_start_layer_idx + self.model.num_mtp_layers,
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):
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name = f"model.layers.{layer_idx}.shared_head.head.weight"
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load_param(name, loaded_weight)
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if name == "model.word_embeddings.weight":
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load_param("model.embed_tokens.weight", loaded_weight)
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continue
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if name == "lm_head.weight":
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load_lm_head(loaded_weight)
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continue
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spec_layer = get_spec_layer_idx(name)
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if spec_layer is None:
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continue
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name = normalize_name(name)
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loaded = False
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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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if "mlp.experts." in name and name not in params_dict:
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continue
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mapped_name = name.replace(weight_name, param_name)
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if load_param(mapped_name, loaded_weight, shard_id):
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loaded = True
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break
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if loaded:
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loaded_mtp_layers.add(spec_layer)
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continue
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if "mlp.experts" in name:
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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mapped_name = name.replace(weight_name, param_name)
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if load_param(
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mapped_name,
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loaded_weight,
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(expert_id, shard_id),
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):
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loaded = True
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break
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if loaded:
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loaded_mtp_layers.add(spec_layer)
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continue
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if load_param(name, loaded_weight):
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loaded_mtp_layers.add(spec_layer)
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for layer_idx in range(
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self.model.mtp_start_layer_idx,
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self.model.mtp_start_layer_idx + self.model.num_mtp_layers,
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):
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if layer_idx not in loaded_mtp_layers:
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raise ValueError(
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f"Bailing MTP speculative decoding layer {layer_idx} "
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"weights are missing from checkpoint. Use a checkpoint "
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"that includes MTP layer weights, or disable speculative "
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"decoding."
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)
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return loaded_params
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