1108 lines
45 KiB
Python
1108 lines
45 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 regex as re
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import torch
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import torch.nn as nn
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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 (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.forward_context import get_forward_context, is_forward_context_available
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from vllm.logger import init_logger
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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.mhc import MHCPostOp, MHCPreDelayedOp
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from vllm.model_executor.layers.quantization import QuantizationConfig
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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 default_weight_loader
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from vllm.model_executor.models.interfaces import (
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EagleModelMixin,
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MixtureOfExperts,
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SupportsEagle3,
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SupportsLoRA,
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SupportsPP,
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)
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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WeightsMapper,
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extract_layer_index,
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is_pp_missing_parameter,
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make_layers,
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maybe_prefix,
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)
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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_v4.amd.model import (
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DeepseekV4MoE as DeepseekV4MoEBase,
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)
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from vllm.models.deepseek_v41.amd.rocm import DeepseekV41ROCMAiterMLAAttention
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from vllm.models.deepseek_v41.attention import DeepseekV4Attention
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from vllm.sequence import IntermediateTensors
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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from vllm.v1.worker.ubatching import dbo_current_ubatch_id
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from ..common.engram import EngramLayout, NgramHashState
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from ..common.mm_preprocess import IMAGE_SENTINEL_BASE_ID, image_sentinel_mask
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# Engram host offload and its prefetch stream are neither ROCm- nor
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# NVIDIA-specific, so they are imported rather than duplicated.
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from ..nvidia.engram import Engram
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if typing.TYPE_CHECKING:
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from vllm.v1.attention.backends.mla.sparse_swa import DeepseekSparseSWAMetadata
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logger = init_logger(__name__)
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class DeepseekV4MoE(DeepseekV4MoEBase):
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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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use_sequence_parallel: bool = False,
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):
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config = vllm_config.model_config.hf_config
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n_routed_experts = config.n_routed_experts
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n_activated_experts = config.num_experts_per_tok
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if extract_layer_index(prefix) >= config.num_hidden_layers:
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n_routed_experts = (
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getattr(config, "dspark_n_routed_experts", 0) or n_routed_experts
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)
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n_activated_experts = (
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getattr(config, "dspark_num_experts_per_tok", 0) or n_activated_experts
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)
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# The reused ROCm V4 MoE reads expert counts from the HF config. Draft
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# layers use V4.1's smaller DSpark expert set, so temporarily expose
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# the per-layer counts while constructing the module, then restore the
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# shared config for the remaining backbone layers.
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old_routed = config.n_routed_experts
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old_activated = config.num_experts_per_tok
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old_hash_layers = getattr(config, "num_hash_layers", None)
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config.n_routed_experts = n_routed_experts
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config.num_experts_per_tok = n_activated_experts
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config.num_hash_layers = 0
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try:
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super().__init__(vllm_config, prefix=prefix)
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finally:
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config.n_routed_experts = old_routed
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config.num_experts_per_tok = old_activated
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if old_hash_layers is None:
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delattr(config, "num_hash_layers")
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else:
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config.num_hash_layers = old_hash_layers
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# The V4.1 checkpoint contains a second per-expert routing bias for
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# image sentinel tokens. The reused ROCm V4 MoE predates this field,
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# so register it here and attach the same Parameter to the router,
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# matching the CUDA V4.1 implementation. Text tokens continue to use
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# e_score_correction_bias.
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self.gate.bias_vl = None
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self.image_sentinel_lo = 0
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if getattr(config, "vision_n_layers", 0) > 0:
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self.gate.bias_vl = nn.Parameter(
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torch.empty(self.n_routed_experts, dtype=torch.float32),
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requires_grad=False,
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)
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self.image_sentinel_lo = IMAGE_SENTINEL_BASE_ID
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self.experts.router.bias_vl = self.gate.bias_vl
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self.experts.router.image_sentinel_lo = self.image_sentinel_lo
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self.use_mega_moe = False
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# Expose the MixtureOfExperts protocol fields expected by the V4.1
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# outer model. The current ROCm runner has no redundant EPLB copies,
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# so logical and physical expert counts are identical.
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self.n_logical_experts = self.n_routed_experts
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self.n_physical_experts = self.n_routed_experts
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self.n_local_physical_experts = self.n_local_experts
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self.n_redundant_experts = 0
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def _select_dsv4_attn_cls(vllm_config: VllmConfig) -> type[DeepseekV4Attention]:
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"""Pick the ROCm sparse-MLA implementation for DeepSeek V4.1."""
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backend = vllm_config.attention_config.backend
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if backend is not None and (
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backend is not AttentionBackendEnum.ROCM_FLASHMLA_SPARSE_DSV4
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):
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raise ValueError(
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f"{backend.name} is not supported for DeepSeek V4.1 on ROCm; "
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"use ROCM_FLASHMLA_SPARSE_DSV4."
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)
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return DeepseekV41ROCMAiterMLAAttention
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def _use_sequence_parallel(vllm_config: VllmConfig) -> bool:
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# The ROCm fused-MoE path currently uses ordinary tensor/expert parallel.
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return False
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class DeepseekV4DecoderLayer(nn.Module):
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def __init__(
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self,
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vllm_config,
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prefix,
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topk_indices_buffer: torch.Tensor | None = None,
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aux_stream_list: list[torch.cuda.Stream] | None = None,
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candidate_block_buffer: torch.Tensor | None = None,
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engram_layout: EngramLayout | None = None,
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):
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.hidden_size = config.hidden_size
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self.use_sequence_parallel = _use_sequence_parallel(vllm_config)
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self.engram: Engram | None = None
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if engram_layout is not None:
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layer_id = extract_layer_index(prefix)
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if layer_id in engram_layout.layer_ids:
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self.engram = Engram(
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config,
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vllm_config.quant_config,
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engram_layout,
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engram_layout.layer_ids.index(layer_id),
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use_sequence_parallel=self.use_sequence_parallel,
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prefix=f"{prefix}.engram",
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)
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self.rms_norm_eps = config.rms_norm_eps
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self.attn = _select_dsv4_attn_cls(vllm_config)(
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vllm_config,
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prefix=f"{prefix}.attn",
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topk_indices_buffer=topk_indices_buffer,
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aux_stream_list=aux_stream_list,
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candidate_block_buffer=candidate_block_buffer,
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)
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if self.use_sequence_parallel:
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self.attn.wo_b.reduce_results = False
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self.ffn = DeepseekV4MoE(
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vllm_config,
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prefix=f"{prefix}.ffn",
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use_sequence_parallel=self.use_sequence_parallel,
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)
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self.attn_norm = RMSNorm(self.hidden_size, self.rms_norm_eps)
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self.ffn_norm = RMSNorm(self.hidden_size, self.rms_norm_eps)
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self.hc_mult = config.hc_mult
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self.hc_sinkhorn_iters = config.hc_sinkhorn_iters
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self.hc_eps = config.hc_eps
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self.hc_post_alpha = 2.0
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mix_hc = (2 + self.hc_mult) * self.hc_mult
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hc_dim = self.hc_mult * self.hidden_size
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self.hc_attn_fn = nn.Parameter(
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torch.empty(
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(mix_hc, hc_dim),
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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self.hc_attn_fn_broadcast: torch.Tensor | None = None
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self.hc_ffn_fn = nn.Parameter(
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torch.empty(
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(mix_hc, hc_dim),
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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self.hc_attn_base = nn.Parameter(
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torch.empty(
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mix_hc,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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self.hc_ffn_base = nn.Parameter(
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torch.empty(
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mix_hc,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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self.hc_attn_scale = nn.Parameter(
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torch.empty(
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3,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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self.hc_ffn_scale = nn.Parameter(
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torch.empty(
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3,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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self.mhc_pre_delayed = MHCPreDelayedOp()
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self.mhc_post = MHCPostOp()
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@staticmethod
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def _hc_collapse(x: torch.Tensor, pre_mix: torch.Tensor) -> torch.Tensor:
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"""Hyper-connection collapse used by DSpark on ROCm."""
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return torch.ops.vllm.hc_collapse_triton(x, pre_mix)
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def forward(
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self,
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x: torch.Tensor,
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positions: torch.Tensor,
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input_ids: torch.Tensor | None,
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pre_mix: torch.Tensor | None = None,
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post_mix: torch.Tensor | None = None,
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res_mix: torch.Tensor | None = None,
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residual: torch.Tensor | None = None,
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engram_hashes: torch.Tensor | None = None,
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engram_mask: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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# The reference collapses each sublayer's input with the *previous*
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# sublayer's pre-mix: attention uses the pre-mix carried in (identity
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# for the first layer), the FFN uses this layer's attention pre-mix.
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if residual is None:
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if x.dim() == 2:
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# First layer: the stream is the embedding broadcast to hc
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# copies and the identity pre-mix selects copy 0.
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assert self.hc_attn_fn_broadcast is not None
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residual = x.unsqueeze(1).expand(-1, self.hc_mult, -1).contiguous()
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residual, post_mix, res_mix, x, attn_pre = self.mhc_pre_delayed(
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residual,
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self.hc_attn_fn_broadcast,
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self.hc_attn_scale,
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self.hc_attn_base,
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self.rms_norm_eps,
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self.hc_eps,
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self.hc_eps,
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self.hc_post_alpha,
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self.hc_sinkhorn_iters,
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x=x,
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)
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else:
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residual = x
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residual, post_mix, res_mix, x, attn_pre = self.mhc_pre_delayed(
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residual,
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self.hc_attn_fn,
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self.hc_attn_scale,
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self.hc_attn_base,
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self.rms_norm_eps,
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self.hc_eps,
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self.hc_eps,
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self.hc_post_alpha,
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self.hc_sinkhorn_iters,
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pre_mix=pre_mix,
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)
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else:
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pre_args = (
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self.hc_attn_fn,
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self.hc_attn_scale,
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self.hc_attn_base,
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self.rms_norm_eps,
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self.hc_eps,
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self.hc_eps,
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self.hc_post_alpha,
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self.hc_sinkhorn_iters,
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)
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if self.engram is not None and engram_hashes is not None:
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# Engram injection happens between the previous sublayer's
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# post and this block's pre, on the full hc stream, so the
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# mix coefficients see the injected stream. That read of the
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# residual is what stops the post from fusing into the pre
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# here, unlike the attention seam below.
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residual = self.mhc_post(x, residual, post_mix, res_mix)
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residual = self.engram(
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residual,
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engram_hashes[:, self.engram.layer_hash_index],
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engram_mask,
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)
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residual, post_mix, res_mix, x, attn_pre = self.mhc_pre_delayed(
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residual, *pre_args, pre_mix=pre_mix
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)
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else:
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(
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residual,
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post_mix,
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res_mix,
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x,
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attn_pre,
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) = self.mhc_pre_delayed(
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residual,
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*pre_args,
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pre_mix=pre_mix,
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sublayer_out=x,
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post_layer_mix=post_mix,
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comb_res_mix=res_mix,
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)
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x = self.attn_norm(x)
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if self.use_sequence_parallel:
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x = sp_all_gather(x)[: positions.shape[0]]
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x = self.attn(positions, x, None)
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if self.use_sequence_parallel:
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x = sp_reduce_scatter(x)
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residual, post_mix, res_mix, x, ffn_pre = self.mhc_pre_delayed(
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residual,
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self.hc_ffn_fn,
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self.hc_ffn_scale,
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self.hc_ffn_base,
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self.rms_norm_eps,
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self.hc_eps,
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self.hc_eps,
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self.hc_post_alpha,
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self.hc_sinkhorn_iters,
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pre_mix=attn_pre,
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sublayer_out=x,
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post_layer_mix=post_mix,
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comb_res_mix=res_mix,
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)
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x = self.ffn_norm(x)
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x = self.ffn(x, input_ids)
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return x, residual, post_mix, res_mix, ffn_pre
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|
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class DeepseekV4Model(nn.Module, EagleModelMixin):
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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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self.quant_config = quant_config
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self.parallel_config = vllm_config.parallel_config
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|
self.use_mega_moe = False
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self.use_sequence_parallel = _use_sequence_parallel(vllm_config)
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if self.use_mega_moe and not vllm_config.parallel_config.enable_expert_parallel:
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raise NotImplementedError(
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"DeepSeek V4 MegaMoE currently requires expert parallel. "
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"Enable it with --enable-expert-parallel, or pick a different "
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"moe backend."
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)
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self.vocab_size = config.vocab_size
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self.hc_eps = config.hc_eps
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self.hc_mult = config.hc_mult
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self.hc_dim = self.hc_mult * config.hidden_size
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self.rms_norm_eps = config.rms_norm_eps
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|
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# Three aux streams: one per non-default input GEMM in
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# DeepseekV4Attention._run_parallel_input_projections
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# (compressor kv_score, indexer.weights_proj). fused_wqa_wkv stays on
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# the default stream.
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aux_stream_list = [torch.cuda.Stream() for _ in range(3)]
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# Reserved topk indices buffer for all Indexer layers to reuse.
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self.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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)
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# Two-level candidate filtering: the indexer at
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# candidate_source_layer_id publishes the top candidate blocks of
|
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# compressed positions here; later ratio-1 indexers (24/28/32/36)
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# mask their scores with it.
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candidate_source_layer = getattr(config, "candidate_source_layer_id", -1)
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candidate_topk_blocks = getattr(config, "candidate_topk_blocks", 0)
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if candidate_source_layer >= 0 and candidate_topk_blocks > 0:
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self.candidate_block_buffer = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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candidate_topk_blocks,
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dtype=torch.int32,
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)
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else:
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self.candidate_block_buffer = None
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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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quant_config=quant_config,
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prefix=f"{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.engram_layout = EngramLayout.from_config(config)
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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: DeepseekV4DecoderLayer(
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vllm_config,
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prefix=prefix,
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topk_indices_buffer=self.topk_indices_buffer,
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aux_stream_list=aux_stream_list,
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candidate_block_buffer=self.candidate_block_buffer,
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engram_layout=self.engram_layout,
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),
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prefix=f"{prefix}.layers",
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)
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# The n-gram hash needs a slot-keyed rolling store of compressed ids
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# (chunked prefill / decode lookback); key it off the first local
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# layer's sliding-window KV cache. Only PP ranks owning an engram
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# layer need it.
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self.engram_hash: NgramHashState | None = None
|
|
self.engram_swa_prefix: str | None = None
|
|
if self.engram_layout is not None:
|
|
local_engram = any(
|
|
isinstance(layer, DeepseekV4DecoderLayer) and layer.engram is not None
|
|
for layer in islice(self.layers, self.start_layer, self.end_layer)
|
|
)
|
|
if local_engram:
|
|
first_layer = next(
|
|
iter(islice(self.layers, self.start_layer, self.end_layer))
|
|
)
|
|
swa_cache_module = first_layer.attn.swa_cache_layer
|
|
self.engram_hash = NgramHashState(
|
|
vllm_config, self.engram_layout, swa_cache_module
|
|
)
|
|
self.engram_swa_prefix = swa_cache_module.prefix
|
|
|
|
if get_pp_group().is_last_rank:
|
|
self.norm = RMSNorm(config.hidden_size, self.rms_norm_eps)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
|
|
self.mhc_post = MHCPostOp()
|
|
|
|
spec_config = vllm_config.speculative_config
|
|
needs_mtp_hidden_states = spec_config is not None and (
|
|
spec_config.use_eagle() or spec_config.uses_draft_model()
|
|
)
|
|
if get_pp_group().is_last_rank and needs_mtp_hidden_states:
|
|
self._mtp_hidden_buffer = torch.empty(
|
|
vllm_config.scheduler_config.max_num_batched_tokens,
|
|
self.hc_dim,
|
|
dtype=vllm_config.model_config.dtype,
|
|
)
|
|
else:
|
|
self._mtp_hidden_buffer = None
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def make_empty_intermediate_tensors(
|
|
self,
|
|
batch_size: int,
|
|
dtype: torch.dtype,
|
|
device: torch.device,
|
|
) -> IntermediateTensors:
|
|
# PP intermediate tensors carry the multi-stream hidden_states
|
|
# of shape (num_tokens, hc_mult, hidden_size) — V4 expands the
|
|
# token embedding to hc_mult streams before the first decoder
|
|
# layer and keeps that shape until the final hc collapse — plus the
|
|
# (num_tokens, hc_mult) pre-mix the next rank's first layer needs
|
|
# for its attention collapse.
|
|
return IntermediateTensors(
|
|
{
|
|
"hidden_states": torch.zeros(
|
|
(batch_size, self.hc_mult, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device,
|
|
),
|
|
"pre_mix": torch.zeros(
|
|
(batch_size, self.hc_mult),
|
|
dtype=torch.float32,
|
|
device=device,
|
|
),
|
|
}
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
lookback_token_ids: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.embed_input_ids(input_ids)
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
|
|
if self.use_mega_moe:
|
|
input_ids = input_ids.to(torch.int64)
|
|
|
|
# Engram n-gram hashes for the whole (flattened) batch, computed once
|
|
# on the full token stream — before any sequence-parallel sharding —
|
|
# and consumed by the engram layers (1 and 14) below. Skipped on
|
|
# profile runs (KV cache unbound -> no slot space to key the rolling
|
|
# compressed-id cache into).
|
|
engram_hashes: torch.Tensor | None = None
|
|
engram_mask: torch.Tensor | None = None
|
|
if (
|
|
self.engram_hash is not None
|
|
and input_ids is not None
|
|
and is_forward_context_available()
|
|
):
|
|
attn_metadata = get_forward_context().attn_metadata
|
|
if isinstance(attn_metadata, list):
|
|
attn_metadata = attn_metadata[dbo_current_ubatch_id()]
|
|
if isinstance(attn_metadata, dict) and self.engram_hash.ensure_cache():
|
|
assert self.engram_swa_prefix is not None
|
|
swa_metadata = typing.cast(
|
|
"DeepseekSparseSWAMetadata", attn_metadata[self.engram_swa_prefix]
|
|
)
|
|
# Image-span tokens are dead: they break n-grams (hash op
|
|
# takes True=dead) and their gate is zeroed (Engram.forward
|
|
# takes True=keep).
|
|
image_mask = image_sentinel_mask(input_ids)
|
|
engram_mask = ~image_mask
|
|
if lookback_token_ids is None:
|
|
if not self.engram_hash.use_slot_cache:
|
|
raise NotImplementedError(
|
|
"engram needs `lookback_token_ids` from the model "
|
|
"runner (the DBO/ubatch wrapper drops model kwargs)"
|
|
)
|
|
num_reqs = swa_metadata.num_decodes + swa_metadata.num_prefills
|
|
lookback_token_ids = input_ids.new_full(
|
|
(num_reqs, self.engram_hash.lookback_depth), -1
|
|
)
|
|
engram_hashes = self.engram_hash(
|
|
input_ids,
|
|
positions,
|
|
swa_metadata.query_start_loc,
|
|
image_mask,
|
|
lookback_token_ids,
|
|
image_sentinel_mask(lookback_token_ids),
|
|
swa_metadata.slot_mapping,
|
|
swa_metadata.block_table,
|
|
)
|
|
# Gather all Engram rows before entering the decoder layers.
|
|
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
|
engram = getattr(layer, "engram", None)
|
|
if engram is not None:
|
|
engram.prepare_embeddings(
|
|
engram_hashes[:, engram.layer_hash_index]
|
|
)
|
|
|
|
full_num_tokens = positions.shape[0]
|
|
if self.use_sequence_parallel:
|
|
if envs.VLLM_MOE_SKIP_PADDING and is_forward_context_available():
|
|
forward_context = get_forward_context()
|
|
forward_context.is_padding = sp_padding_mask(
|
|
forward_context.is_padding, hidden_states
|
|
)
|
|
hidden_states = sp_shard(hidden_states)
|
|
input_ids = sp_shard(input_ids)
|
|
|
|
residual, post_mix, res_mix = None, None, None
|
|
pre_mix: torch.Tensor | None = None
|
|
if not get_pp_group().is_first_rank:
|
|
assert intermediate_tensors is not None
|
|
pre_mix = intermediate_tensors["pre_mix"]
|
|
aux_hidden_states: list[torch.Tensor] = []
|
|
final_aux_recon: torch.Tensor | None = None # avoid duplicate mhc_post call
|
|
for idx, layer in enumerate(
|
|
islice(self.layers, self.start_layer, self.end_layer),
|
|
start=self.start_layer,
|
|
):
|
|
hidden_states, residual, post_mix, res_mix, pre_mix = layer(
|
|
hidden_states,
|
|
positions,
|
|
input_ids,
|
|
pre_mix,
|
|
post_mix,
|
|
res_mix,
|
|
residual,
|
|
engram_hashes,
|
|
engram_mask,
|
|
)
|
|
if idx + 1 in self.aux_hidden_state_layers:
|
|
# Reconstruct the aux hidden state for draft models
|
|
aux_recon = self.mhc_post(hidden_states, residual, post_mix, res_mix)
|
|
aux_hidden_state = aux_recon.mean(dim=1)
|
|
if self.use_sequence_parallel:
|
|
aux_hidden_state = sp_all_gather(aux_hidden_state)[:full_num_tokens]
|
|
aux_hidden_states.append(aux_hidden_state)
|
|
final_aux_recon = aux_recon
|
|
if layer is not None:
|
|
# Reuse if the last layer was captured as an aux hidden state
|
|
if self.end_layer in self.aux_hidden_state_layers:
|
|
hidden_states = final_aux_recon
|
|
else:
|
|
hidden_states = self.mhc_post(
|
|
hidden_states, residual, post_mix, res_mix
|
|
)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors(
|
|
{"hidden_states": hidden_states, "pre_mix": pre_mix}
|
|
)
|
|
|
|
if self.use_sequence_parallel:
|
|
hidden_states = sp_all_gather(hidden_states)[:full_num_tokens]
|
|
pre_mix = sp_all_gather(pre_mix)[:full_num_tokens]
|
|
|
|
if self._mtp_hidden_buffer is not None:
|
|
num_tokens = hidden_states.shape[0]
|
|
self._mtp_hidden_buffer[:num_tokens].copy_(hidden_states.flatten(1))
|
|
|
|
# Collapse the hc copies with the pre-mix from the last layer's FFN
|
|
# mixes — the mix the reference applies via
|
|
# ``last_layer.hc_pre(h, pre_mix)`` (v4.1 has no learned hc_head).
|
|
assert pre_mix is not None
|
|
hidden_states = torch.ops.vllm.hc_collapse_triton(hidden_states, pre_mix)
|
|
hidden_states = self.norm(hidden_states)
|
|
if len(aux_hidden_states) > 0:
|
|
return hidden_states, aux_hidden_states
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("gate_up_proj", "w1", 0),
|
|
("gate_up_proj", "w3", 1),
|
|
("attn.fused_wqa_wkv", "attn.wq_a", 0),
|
|
("attn.fused_wqa_wkv", "attn.wkv", 1),
|
|
("compressor.fused_wkv_wgate", "compressor.wkv", 0),
|
|
("compressor.fused_wkv_wgate", "compressor.wgate", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
|
|
def _resolve_param_name(name: str) -> str:
|
|
"""Resolve scale naming across ROCm FP8 and MXFP8 methods."""
|
|
if name.endswith("_inv"):
|
|
direct_name = name.removesuffix("_inv")
|
|
if direct_name in params_dict:
|
|
return direct_name
|
|
inv_name = f"{name}_inv"
|
|
if name not in params_dict and inv_name in params_dict:
|
|
return inv_name
|
|
return name
|
|
|
|
# TP for attention
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
n_head = self.config.num_attention_heads
|
|
n_local_head = n_head // tp_size
|
|
head_rank_start = n_local_head * tp_rank
|
|
head_rank_end = n_local_head * (tp_rank + 1)
|
|
|
|
# Pre-compute expert mapping ONCE.
|
|
expert_mapping = self.get_expert_mapping()
|
|
|
|
# Block-FP8 shared experts: pad the intermediate up to the TP-uniform
|
|
# block count so the standard loaders below slice it evenly (trailing
|
|
# ranks land on the zero pad). SP / unquantized ones need no padding.
|
|
pad_shared_expert = (
|
|
getattr(self.quant_config, "weight_block_size", None) is not None
|
|
and not self.use_sequence_parallel
|
|
)
|
|
|
|
for name, loaded_weight in weights:
|
|
if name.startswith(("vision.", "aligner.", "image_")):
|
|
# Vision weights are loaded by the outer multimodal wrapper.
|
|
logger.warning_once("Skipping non-text weight: %s", name)
|
|
continue
|
|
if pad_shared_expert and ".shared_experts." in name:
|
|
loaded_weight = self._pad_shared_expert_weight(
|
|
self.quant_config, name, loaded_weight
|
|
)
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if ".experts." in name:
|
|
continue
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
break
|
|
if name not in params_dict:
|
|
head, _, leaf = name.rpartition(".")
|
|
suffixed = f"{head}.base_layer.{leaf}"
|
|
if suffixed in params_dict:
|
|
name = suffixed
|
|
name = _resolve_param_name(name)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
loaded_params.add(name)
|
|
break
|
|
else:
|
|
if ".experts." in name:
|
|
# E8M0 scales are stored as float8_e8m0fnu in
|
|
# checkpoints but the MoE param is uint8. copy_()
|
|
# would do a numeric conversion (e.g. 2^-7 → 0),
|
|
# destroying the raw exponent bytes.
|
|
if (
|
|
"weight_scale" in name
|
|
and loaded_weight.dtype == torch.float8_e8m0fnu
|
|
):
|
|
loaded_weight = loaded_weight.view(torch.uint8)
|
|
for mapping in expert_mapping:
|
|
param_name, weight_name, expert_id, expert_shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
name_mapped = _resolve_param_name(name_mapped)
|
|
param = params_dict[name_mapped]
|
|
# We should ask the weight loader to return success or not
|
|
# here since otherwise we may skip experts with other
|
|
# available replicas.
|
|
weight_loader = typing.cast(
|
|
Callable[..., bool], param.weight_loader
|
|
)
|
|
success = weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=expert_shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True,
|
|
)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
loaded_params.add(name_mapped)
|
|
continue
|
|
elif "attn_sink" in name:
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
narrow_weight = loaded_weight[head_rank_start:head_rank_end]
|
|
n = narrow_weight.shape[0]
|
|
params_dict[name][:n].copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
else:
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# Non-LoRA params on a LoRA-wrapped module live at
|
|
# ``<head>.base_layer.<leaf>``; the checkpoint is plain.
|
|
if name not in params_dict:
|
|
head, _, leaf = name.rpartition(".")
|
|
suffixed = f"{head}.base_layer.{leaf}"
|
|
if suffixed in params_dict:
|
|
name = suffixed
|
|
name = _resolve_param_name(name)
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
|
|
return loaded_params
|
|
|
|
@staticmethod
|
|
def _pad_shared_expert_weight(
|
|
quant_config: QuantizationConfig | None,
|
|
name: str,
|
|
loaded_weight: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
"""Zero-pad a block-FP8 shared-expert weight/scale on its intermediate
|
|
axis so the standard TP loaders split it into even, block-aligned shards
|
|
(trailing ranks get the zero pad). gate (w1)/up (w3) [I, H] pad dim 0;
|
|
down (w2 -> down_proj) [H, I] pads dim 1.
|
|
"""
|
|
block_size = getattr(quant_config, "weight_block_size", None)
|
|
assert block_size is not None
|
|
# Round the intermediate axis up to a whole number of TP shards. The axis
|
|
# is in elements for weights (step = block) and in blocks for scales.
|
|
step = (
|
|
1 if name.endswith(("weight_scale_inv", "weight_scale")) else block_size[0]
|
|
)
|
|
dim = 1 if ".down_proj." in name else 0
|
|
mult = get_tensor_model_parallel_world_size() * step
|
|
pad = cdiv(loaded_weight.shape[dim], mult) * mult - loaded_weight.shape[dim]
|
|
if pad == 0:
|
|
return loaded_weight
|
|
pad_shape = list(loaded_weight.shape)
|
|
pad_shape[dim] = pad
|
|
return torch.cat([loaded_weight, loaded_weight.new_zeros(pad_shape)], dim=dim)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return fused_moe_make_expert_params_mapping(
|
|
self,
|
|
ckpt_gate_proj_name="w1",
|
|
ckpt_down_proj_name="w2",
|
|
ckpt_up_proj_name="w3",
|
|
num_experts=self.config.n_routed_experts,
|
|
)
|
|
|
|
def finalize_mega_moe_weights(self) -> None:
|
|
return
|
|
|
|
def finalize_mhc_broadcast_weights(self) -> None:
|
|
if not get_pp_group().is_first_rank or self.start_layer >= self.end_layer:
|
|
return
|
|
layer = self.layers[self.start_layer]
|
|
if isinstance(layer, DeepseekV4DecoderLayer):
|
|
broadcast = (
|
|
layer.hc_attn_fn.detach()
|
|
.view(-1, layer.hc_mult, layer.hidden_size)
|
|
.sum(dim=1)
|
|
)
|
|
if layer.hc_attn_fn_broadcast is None:
|
|
layer.hc_attn_fn_broadcast = broadcast
|
|
else:
|
|
layer.hc_attn_fn_broadcast.copy_(broadcast)
|
|
|
|
|
|
def _linear_scale_param_name(vllm_config: VllmConfig, expert_dtype: str) -> str:
|
|
"""Parameter name the linear quant method registers for weight scales.
|
|
|
|
Native MXFP8 checkpoints ([32, 32] blocks with MXFP4 experts) route linear
|
|
layers through ModelOptLinearMethod, which registers ``weight_scale``;
|
|
block-FP8 linear layers register ``weight_scale_inv``.
|
|
"""
|
|
use_mxfp8 = (
|
|
getattr(vllm_config.quant_config, "weight_block_size", None) == [32, 32]
|
|
and expert_dtype == "fp4"
|
|
)
|
|
return "weight_scale" if use_mxfp8 else "weight_scale_inv"
|
|
|
|
|
|
def _make_deepseek_v4_weights_mapper(
|
|
expert_dtype: str, linear_scale_name: str = "weight_scale_inv"
|
|
) -> WeightsMapper:
|
|
if expert_dtype == "fp4":
|
|
# MXFP4 experts use Mxfp4MoEMethod, which registers scales as
|
|
# ``w{1,2,3}_weight_scale`` (no _inv suffix). Linear scales use the
|
|
# parameter name registered by their quantization method.
|
|
scale_regex = {
|
|
# ``.base_layer.``-namespace variant (LoRA-wrapped experts).
|
|
re.compile(
|
|
r"(\.experts\.\d+\.w[123]\.base_layer)\.scale$"
|
|
): r"\1.weight_scale",
|
|
re.compile(r"(\.experts\.\d+\.w[123])\.scale$"): r"\1.weight_scale",
|
|
# The ``embed.weight`` -> ``embed_tokens.weight`` suffix rule
|
|
# renames the engram fp8 table but not its scale; route the
|
|
# scale explicitly to the same module.
|
|
re.compile(r"(engram\.embed)\.scale$"): r"\1_tokens.weight_scale_inv",
|
|
re.compile(r"\.scale$"): f".{linear_scale_name}",
|
|
}
|
|
else:
|
|
# FP8 experts use Fp8MoEMethod (block_quant=True), which registers
|
|
# scales as ``w{13,2}_weight_scale_inv``. Map all ``.scale`` keys
|
|
# there.
|
|
scale_regex = {
|
|
# ``.base_layer.``-namespace variant of the above.
|
|
re.compile(
|
|
r"(\.experts\.\d+\.w[123]\.base_layer)\.scale$"
|
|
): r"\1.weight_scale_inv",
|
|
# Same engram reroute as the fp4 branch above.
|
|
re.compile(r"(engram\.embed)\.scale$"): r"\1_tokens.weight_scale_inv",
|
|
re.compile(r"\.scale$"): f".{linear_scale_name}",
|
|
}
|
|
return WeightsMapper(
|
|
orig_to_new_prefix={
|
|
"layers.": "model.layers.",
|
|
"embed.": "model.embed.",
|
|
"norm.": "model.norm.",
|
|
"mtp.": "model.mtp.",
|
|
},
|
|
orig_to_new_regex=scale_regex,
|
|
orig_to_new_suffix={
|
|
"head.weight": "lm_head.weight",
|
|
"embed.weight": "embed_tokens.weight",
|
|
".ffn.gate.bias": ".ffn.gate.e_score_correction_bias",
|
|
},
|
|
orig_to_new_substr={
|
|
".shared_experts.w2": ".shared_experts.down_proj",
|
|
"mtp.": None,
|
|
# The v4.1 checkpoint declares the VL arch even for text-only
|
|
# use; the text backbone drops the vision-tower weights.
|
|
"vision.": None,
|
|
"aligner.": None,
|
|
"image_": None,
|
|
},
|
|
)
|
|
|
|
|
|
class DeepseekV4MixtureOfExperts(MixtureOfExperts):
|
|
moe_mlp_layers: list["DeepseekV4MoE"]
|
|
|
|
def extract_moe_parameters(self, example_moe: "DeepseekV4MoE | None") -> None:
|
|
if example_moe is None:
|
|
self.num_moe_layers = 0
|
|
self.num_expert_groups = 0
|
|
self.num_logical_experts = 0
|
|
self.num_physical_experts = 0
|
|
self.num_local_physical_experts = 0
|
|
self.num_routed_experts = 0
|
|
self.num_shared_experts = 0
|
|
self.num_redundant_experts = 0
|
|
return
|
|
self.num_logical_experts = example_moe.n_logical_experts
|
|
self.num_physical_experts = example_moe.n_physical_experts
|
|
self.num_local_physical_experts = example_moe.n_local_physical_experts
|
|
self.num_routed_experts = example_moe.n_routed_experts
|
|
self.num_shared_experts = example_moe.n_shared_experts
|
|
self.num_redundant_experts = example_moe.n_redundant_experts
|
|
|
|
def update_physical_experts_metadata(
|
|
self,
|
|
num_physical_experts: int,
|
|
num_local_physical_experts: int,
|
|
) -> None:
|
|
assert self.num_local_physical_experts == num_local_physical_experts
|
|
self.num_physical_experts = num_physical_experts
|
|
self.num_local_physical_experts = num_local_physical_experts
|
|
self.num_redundant_experts = num_physical_experts - self.num_logical_experts
|
|
for moe in self.moe_mlp_layers:
|
|
moe.n_local_physical_experts = num_local_physical_experts
|
|
moe.n_physical_experts = num_physical_experts
|
|
moe.n_redundant_experts = self.num_redundant_experts
|
|
moe.experts.update_expert_map()
|
|
|
|
|
|
class DeepseekV41LLMForCausalLM(
|
|
nn.Module,
|
|
SupportsPP,
|
|
SupportsEagle3,
|
|
SupportsLoRA,
|
|
DeepseekV4MixtureOfExperts,
|
|
):
|
|
model_cls = DeepseekV4Model
|
|
|
|
# Default mapper assumes the original FP4-expert checkpoint layout.
|
|
# Overridden per-instance in __init__ when expert_dtype != "fp4".
|
|
hf_to_vllm_mapper = _make_deepseek_v4_weights_mapper("fp4")
|
|
|
|
packed_modules_mapping = {
|
|
"gate_up_proj": ["w1", "w3"],
|
|
"fused_wqa_wkv": ["wq_a", "wkv"],
|
|
"fused_wkv_wgate": ["wkv", "wgate"],
|
|
}
|
|
|
|
# The MTP draft head is not LoRA-adapted.
|
|
lora_skip_prefixes = ["mtp."]
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
self.config = config
|
|
expert_dtype = getattr(config, "expert_dtype", "fp4")
|
|
self.hf_to_vllm_mapper = _make_deepseek_v4_weights_mapper(
|
|
expert_dtype, _linear_scale_param_name(vllm_config, expert_dtype)
|
|
)
|
|
|
|
self.model = self.model_cls(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
if get_pp_group().is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = ( # type: ignore[method-assign]
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
self.set_moe_parameters()
|
|
|
|
def set_moe_parameters(self) -> None:
|
|
self.num_expert_groups = getattr(self.config, "n_group", 1)
|
|
self.num_moe_layers = self.config.num_hidden_layers
|
|
self.moe_layers: list[nn.Module] = []
|
|
self.moe_mlp_layers: list[DeepseekV4MoE] = []
|
|
example_moe: DeepseekV4MoE | None = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
if not isinstance(layer, DeepseekV4DecoderLayer):
|
|
continue
|
|
if isinstance(layer.ffn, DeepseekV4MoE):
|
|
example_moe = layer.ffn
|
|
self.moe_mlp_layers.append(layer.ffn)
|
|
self.moe_layers.append(layer.ffn.experts)
|
|
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
self.extract_moe_parameters(example_moe)
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.embed_input_ids(input_ids)
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def compute_logits_local(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self.logits_processor(self.lm_head, hidden_states, skip_gather=True)
|
|
|
|
@staticmethod
|
|
def get_model_state_cls():
|
|
from ..nvidia.model_state import DeepseekV41ModelState
|
|
|
|
return DeepseekV41ModelState
|
|
|
|
@property
|
|
def token_lookback_depth(self) -> int:
|
|
"""Tokens before a chunk start the engram hash needs; the model runner
|
|
passes them as `lookback_token_ids`."""
|
|
engram_hash = self.model.engram_hash
|
|
return engram_hash.lookback_depth if engram_hash is not None else 0
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
lookback_token_ids: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids,
|
|
positions,
|
|
intermediate_tensors,
|
|
inputs_embeds,
|
|
lookback_token_ids=lookback_token_ids,
|
|
)
|
|
return hidden_states
|
|
|
|
def get_mtp_target_hidden_states(self) -> torch.Tensor | None:
|
|
"""Pre-collapse residual stream buffer (max_num_batched_tokens,
|
|
hc_mult * hidden_size) for the MTP draft model. Populated by
|
|
forward(); valid after each target step."""
|
|
return getattr(self.model, "_mtp_hidden_buffer", None)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
loaded_params = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
|
self.process_weights_after_loading()
|
|
return loaded_params
|
|
|
|
def process_weights_after_loading(self) -> None:
|
|
self.model.finalize_mega_moe_weights()
|
|
self.model.finalize_mhc_broadcast_weights()
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|