443 lines
18 KiB
Python
443 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import typing
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from collections.abc import Callable, Iterable
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from itertools import islice
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import torch
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import vllm.envs as envs
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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.forward_context import get_forward_context, is_forward_context_available
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from vllm.model_executor.layers.fused_embed_norm import (
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fused_embed_norm,
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has_full_vocab_on_rank,
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make_input_embedding,
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)
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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.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.deepseek_v2 import (
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DeepseekV2ForCausalLM,
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DeepseekV2MLP,
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DeepseekV2MoE,
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_try_load_fp8_indexer_wk,
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get_spec_layer_idx_from_weight_name,
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)
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from vllm.model_executor.models.utils import (
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PPMissingLayer,
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get_pp_missing_layer_names,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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)
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from vllm.models.common.ops.fused_allreduce_rms_norm import fused_allreduce_rms_norm
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from vllm.models.common.ops.sequence_parallel import (
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sp_all_gather,
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sp_padding_mask,
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sp_reduce_scatter,
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sp_shard,
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)
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from vllm.models.deepseek_v32.attention import DeepseekV32Attention
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from vllm.sequence import IntermediateTensors
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from .glm52_low_latency_gemm import enable_glm52_low_latency_gemm
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class DeepseekV32DecoderLayer(torch.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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config=None,
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topk_indices_buffer: torch.Tensor | None = None,
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) -> None:
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super().__init__()
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if config is None:
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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parallel_config = vllm_config.parallel_config
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self.hidden_size = config.hidden_size
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moe_layer_freq = getattr(config, "moe_layer_freq", 1)
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layer_idx = int(prefix.split(sep=".")[-1])
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self.layer_idx = layer_idx
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self.use_mha = False
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self.use_sequence_parallel = (
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parallel_config.use_sequence_parallel_moe
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and parallel_config.pipeline_parallel_size == 1
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)
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self.self_attn = DeepseekV32Attention(
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vllm_config=vllm_config,
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config=config,
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prefix=f"{prefix}.self_attn",
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topk_indices_buffer=topk_indices_buffer,
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)
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if (
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config.n_routed_experts is not None
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and layer_idx >= config.first_k_dense_replace
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and layer_idx % moe_layer_freq == 0
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):
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# Keep the MoE output un-reduced. Non-SP fuses its all-reduce into
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# the next layer norm; SP keeps the token shard local.
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self.mlp = DeepseekV2MoE(
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config=config,
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parallel_config=parallel_config,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.mlp",
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apply_routed_scale_to_output=False,
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)
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else:
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self.mlp = DeepseekV2MLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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reduce_results=False,
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is_sequence_parallel=self.use_sequence_parallel,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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attn_in: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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full_num_tokens = positions.shape[0]
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if residual is None:
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# First layer: hidden_states is the embedding (the residual).
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residual = hidden_states
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# ``attn_in`` is input_layernorm(embedding) already computed fused
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# with the embedding gather; otherwise apply it here.
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hidden_states = (
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attn_in if attn_in is not None else self.input_layernorm(hidden_states)
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)
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elif self.use_sequence_parallel:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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else:
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# The previous layer's MLP/MoE output is left un-reduced; fuse its
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# all-reduce into this input_layernorm.
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hidden_states, residual = fused_allreduce_rms_norm(
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hidden_states, residual, self.input_layernorm
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)
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if self.use_sequence_parallel:
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hidden_states = sp_all_gather(hidden_states)[:full_num_tokens]
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# self_attn's o_proj runs reduce_results=False; reduce before RMSNorm.
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hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
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if self.use_sequence_parallel:
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hidden_states = sp_reduce_scatter(hidden_states)
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual
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)
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else:
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hidden_states, residual = fused_allreduce_rms_norm(
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hidden_states, residual, self.post_attention_layernorm
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)
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if self.use_sequence_parallel and isinstance(self.mlp, DeepseekV2MoE):
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hidden_states = self.mlp(hidden_states, already_sequence_parallel=True)
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else:
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class DeepseekV32Model(torch.nn.Module):
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fall_back_to_pt_during_load = False
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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from vllm.platforms import current_platform
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self.device = current_platform.device_type
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self.vocab_size = config.vocab_size
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parallel_config = vllm_config.parallel_config
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self.use_sequence_parallel = (
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parallel_config.use_sequence_parallel_moe
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and parallel_config.pipeline_parallel_size == 1
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)
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# DSA is always sparse (has index_topk); allocate the shared top-k
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# buffer the indexer writes and the sparse MLA backend reads.
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self.is_v32 = True
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topk_indices_buffer = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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config.index_topk,
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dtype=torch.int32,
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device=self.device,
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)
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if get_pp_group().is_first_rank:
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self.embed_tokens = make_input_embedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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tie_word_embeddings=getattr(config, "tie_word_embeddings", False),
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)
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else:
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self.embed_tokens = PPMissingLayer()
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# The fused embed+norm gather needs the full table on-rank.
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self.replicated_embed = has_full_vocab_on_rank(self.embed_tokens)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: DeepseekV32DecoderLayer(
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vllm_config=vllm_config,
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prefix=prefix,
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topk_indices_buffer=topk_indices_buffer,
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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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self.aux_hidden_state_layers = tuple[int, ...]()
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self.num_redundant_experts = parallel_config.eplb_config.num_redundant_experts
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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 | None,
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positions: 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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) -> torch.Tensor | IntermediateTensors:
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attn_in = None
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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elif self.replicated_embed:
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assert input_ids is not None
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# Full table on-rank: gather the embedding and the first layer's
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# input_layernorm in one launch. ``attn_in`` is the pre-normed
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# attention input; ``hidden_states`` is the (residual) embedding.
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hidden_states, attn_in = fused_embed_norm(
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input_ids,
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self.embed_tokens.weight,
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chain_weight=self.layers[self.start_layer].input_layernorm.weight,
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eps=self.config.rms_norm_eps,
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)
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else:
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assert input_ids is not None
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hidden_states = self.embed_input_ids(input_ids)
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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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full_num_tokens = positions.shape[0]
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if self.use_sequence_parallel:
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if envs.VLLM_MOE_SKIP_PADDING and is_forward_context_available():
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forward_context = get_forward_context()
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forward_context.is_padding = sp_padding_mask(
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forward_context.is_padding, hidden_states
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)
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hidden_states = sp_shard(hidden_states)
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if attn_in is not None:
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attn_in = sp_shard(attn_in)
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assert residual is None, "Currently, SP is not supported with PP"
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aux_hidden_states = []
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for idx, layer in enumerate(
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islice(self.layers, self.start_layer, self.end_layer),
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start=self.start_layer,
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):
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if idx in self.aux_hidden_state_layers:
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aux_hidden_states.append(
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hidden_states if residual is None else hidden_states + residual
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)
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hidden_states, residual = layer(positions, hidden_states, residual, attn_in)
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attn_in = None
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if not get_pp_group().is_last_rank:
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assert not self.use_sequence_parallel, (
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"Currently, SP is not supported with PP"
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)
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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if self.use_sequence_parallel:
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hidden_states, _ = self.norm(hidden_states, residual)
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if aux_hidden_states:
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hidden_size = hidden_states.shape[-1]
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packed_hidden_states = torch.cat(
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[hidden_states, *aux_hidden_states], dim=-1
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)
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packed_hidden_states = sp_all_gather(packed_hidden_states)
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packed_hidden_states = packed_hidden_states[:full_num_tokens]
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hidden_states, *aux_hidden_states = packed_hidden_states.split(
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hidden_size, dim=-1
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)
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else:
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hidden_states = sp_all_gather(hidden_states)[:full_num_tokens]
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else:
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hidden_states, _ = fused_allreduce_rms_norm(
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hidden_states, residual, self.norm
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)
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if len(aux_hidden_states) > 0:
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return hidden_states, aux_hidden_states
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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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# DSA-only: MLA (fused_qkv_a_proj) + the fused indexer wk/weights_proj +
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# routed experts. No MHA (qkv_proj) or ROCm shared-expert-fusion paths.
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stacked_params_mapping = [
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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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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("wk_weights_proj", "wk", 0),
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("wk_weights_proj", "weights_proj", 1),
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]
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expert_params_mapping = 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.n_routed_experts,
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num_redundant_experts=self.num_redundant_experts,
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)
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pp_missing_layer_names = get_pp_missing_layer_names(self)
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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_pending_wk_fp8: dict = {}
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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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# MTP / nextn layers are loaded by the MTP model, not here.
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if get_spec_layer_idx_from_weight_name(self.config, name) is not None:
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continue
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if _try_load_fp8_indexer_wk(
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name,
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loaded_weight,
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_pending_wk_fp8,
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params_dict,
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loaded_params,
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pp_missing_layer_names,
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):
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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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# Experts are handled below; skip here before the name rewrite.
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if ("mlp.experts." in name) and name not in params_dict:
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continue
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name_mapped = name.replace(weight_name, param_name)
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if (
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param_name == "fused_qkv_a_proj"
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) and name_mapped not in params_dict:
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continue
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name = name_mapped
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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param.weight_loader(param, loaded_weight, shard_id)
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break
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else:
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is_expert_weight = False
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping # type: ignore[assignment]
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if weight_name not in name:
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continue
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is_expert_weight = True
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name_mapped = name.replace(weight_name, param_name)
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if is_pp_missing_parameter(name_mapped, self):
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continue
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param = params_dict[name_mapped]
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weight_loader = typing.cast(
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Callable[..., bool], param.weight_loader
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)
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success = weight_loader(
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param,
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loaded_weight,
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name_mapped,
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shard_id=shard_id,
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expert_id=expert_id,
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return_success=True,
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)
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if success:
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name = name_mapped
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break
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else:
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if is_expert_weight:
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continue
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if name.endswith(".bias") and name not in params_dict:
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continue
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name = maybe_remap_kv_scale_name(name, params_dict) # type: ignore[assignment]
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if name is None:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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loader = getattr(param, "weight_loader", default_weight_loader)
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loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class DeepseekV32ForCausalLM(DeepseekV2ForCausalLM):
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"""DSA causal LM — DeepSeek V2/V3 orchestration with the DSA backbone.
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Serves DeepSeek V3.2 and any architecture reusing DSA (e.g. GLM-5.2).
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"""
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model_cls = DeepseekV32Model
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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if self.config.model_type == "glm_moe_dsa":
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enable_glm52_low_latency_gemm(self, vllm_config.model_config.dtype)
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def set_moe_parameters(self):
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# Same as the base, but keyed on the MoE block type rather than the
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# decoder-layer type (DeepseekV32DecoderLayer is a plain nn.Module).
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self.num_expert_groups = getattr(self.config, "n_group", 1)
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self.moe_layers = []
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self.moe_mlp_layers = []
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example_moe = None
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for layer in self.model.layers:
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if isinstance(layer, PPMissingLayer):
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continue
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if isinstance(layer.mlp, DeepseekV2MoE):
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example_moe = layer.mlp
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self.moe_mlp_layers.append(layer.mlp)
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self.moe_layers.append(layer.mlp.experts)
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self.extract_moe_parameters(example_moe)
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