659 lines
35 KiB
Python
659 lines
35 KiB
Python
from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, ModelConfig, VllmConfig
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from vllm.distributed import (get_pp_group, get_tp_group,
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get_tensor_model_parallel_world_size,get_tensor_model_parallel_rank,
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tensor_model_parallel_all_reduce)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from vllm.model_executor.models.interfaces import SupportsPP
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from vllm.model_executor.models.utils import (PPMissingLayer, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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from vllm.model_executor.models.deepseek_v2 import yarn_get_mscale, DeepseekV2MLAAttention, Indexer
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from vllm.logger import init_logger
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logger = init_logger(__name__)
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from .vars import *
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from ..ops.deepseek_fused_mlp_moe import (vacc_fused_decode_moe_fp8,
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vacc_fused_prefill_moe_fp8,
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vacc_fused_mlp_fp8)
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from .fused_forward import *
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import os
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test_layer_en = os.getenv("test_layer_en", "0")
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# class DeepseekV2MLAAttention(nn.Module):
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# def __init__(
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# self,
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# vllm_config: VllmConfig,
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# config: Union[DeepseekV2Config, DeepseekV3Config],
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# hidden_size: int,
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# num_heads: int,
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# qk_nope_head_dim: int,
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# qk_rope_head_dim: int,
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# v_head_dim: int,
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# q_lora_rank: Optional[int],
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# kv_lora_rank: int,
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# rope_theta: float = 10000,
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# rope_scaling: Optional[dict[str, Any]] = None,
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# max_position_embeddings: int = 8192,
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# cache_config: Optional[CacheConfig] = None,
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# quant_config: Optional[QuantizationConfig] = None,
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# prefix: str = "",
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# topk_indices_buffer: Optional[torch.Tensor] = None,
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# ) -> None:
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# super(DeepseekV2MLAAttention,self).__init__()
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# self.hidden_size = hidden_size
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# self.qk_nope_head_dim = qk_nope_head_dim
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# self.qk_rope_head_dim = qk_rope_head_dim
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# self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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# self.v_head_dim = v_head_dim
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# self.q_lora_rank = q_lora_rank
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# self.kv_lora_rank = kv_lora_rank
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# self.num_heads = num_heads
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# tp_size = get_tensor_model_parallel_world_size()
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# assert num_heads % tp_size == 0
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# self.num_local_heads = num_heads // tp_size
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# self.scaling = self.qk_head_dim**-0.5
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# self.rope_theta = rope_theta
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# self.max_position_embeddings = max_position_embeddings
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# if self.q_lora_rank is not None:
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# if USE_PARALLEL_Q_KV_GEN:
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# self.q_a_proj = RowParallelLinear(self.hidden_size,
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# self.q_lora_rank,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.q_a_proj")
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# else:
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# self.q_a_proj = ReplicatedLinear(self.hidden_size,
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# self.q_lora_rank,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.q_a_proj")
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# self.q_a_layernorm = RMSNorm(self.q_lora_rank,
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# eps=config.rms_norm_eps)
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# self.q_b_proj = ColumnParallelLinear(q_lora_rank,
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# self.num_heads *
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# self.qk_head_dim,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.q_b_proj")
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# else:
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# self.q_proj = ColumnParallelLinear(self.hidden_size,
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# self.num_heads *
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# self.qk_head_dim,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.q_proj")
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# if USE_PARALLEL_Q_KV_GEN:
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# self.kv_a_proj_with_mqa = RowParallelLinear(
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# self.hidden_size,
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# self.kv_lora_rank + self.qk_rope_head_dim,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.kv_a_proj_with_mqa")
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# else:
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# self.kv_a_proj_with_mqa = ReplicatedLinear(
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# self.hidden_size,
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# self.kv_lora_rank + self.qk_rope_head_dim,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.kv_a_proj_with_mqa")
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# self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
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# eps=config.rms_norm_eps)
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# self.kv_b_proj = ColumnParallelLinear(
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# self.kv_lora_rank,
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# self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.kv_b_proj")
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# self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
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# self.hidden_size,
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# bias=False,
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# quant_config=quant_config,
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# prefix=f"{prefix}.o_proj")
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# rope_scaling["rope_type"] = 'deepseek_yarn'
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# self.rotary_emb = get_rope(qk_rope_head_dim,
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# rotary_dim=qk_rope_head_dim,
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# max_position=max_position_embeddings,
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# base=rope_theta,
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# rope_scaling=rope_scaling,
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# is_neox_style=False)
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# if rope_scaling:
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# mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
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# scaling_factor = rope_scaling["factor"]
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# mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
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# self.scaling = self.scaling * mscale * mscale
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# self.is_v32 = hasattr(config, "index_topk")
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# if self.is_v32:
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# self.indexer = Indexer(vllm_config, config, hidden_size,
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# q_lora_rank, quant_config, cache_config,
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# topk_indices_buffer, f"{prefix}.indexer")
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# else:
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# self.indexer = None
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# self.mla_attn = Attention(
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# num_heads=self.num_local_heads,
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# head_size=self.kv_lora_rank,
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# scale=self.scaling,
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# num_kv_heads=1,
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# cache_config=cache_config,
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# quant_config=quant_config,
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# prefix=f"{prefix}.attn",
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# use_mla=True,
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# # MLA Args
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# q_lora_rank=self.q_lora_rank,
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# kv_lora_rank=self.kv_lora_rank,
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# qk_nope_head_dim=self.qk_nope_head_dim,
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# qk_rope_head_dim=self.qk_rope_head_dim,
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# qk_head_dim=self.qk_head_dim,
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# v_head_dim=self.v_head_dim,
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# rotary_emb=self.rotary_emb,
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# q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
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# kv_b_proj=self.kv_b_proj,
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# o_proj=self.o_proj,
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# )
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# self.prefix = prefix
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# self.debug_layer_idx = int(self.prefix.split(".")[-2])
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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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# kv_cache: torch.Tensor,
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# attn_metadata: AttentionMetadata,
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# ) -> torch.Tensor:
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# tp_size = get_tensor_model_parallel_world_size()
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# rank_id = get_tensor_model_parallel_rank()
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# last_dim = hidden_states.shape[-1]
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# if USE_PARALLEL_Q_KV_GEN: #tp qa and kva
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# hidden_states_split = hidden_states
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# if tp_size > 1:
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# hiddens_tp = last_dim//tp_size
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# hidden_states_split = hidden_states[...,rank_id*hiddens_tp : (rank_id+1)*hiddens_tp].contiguous()
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# if self.q_lora_rank is not None:
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# ckq = self.q_a_proj(hidden_states_split)[0]
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# hidden_states_or_q_c = self.q_a_layernorm(ckq)
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# else:
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# hidden_states_or_q_c = hidden_states
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# kv_c, k_pe = self.kv_a_proj_with_mqa(hidden_states_split)[0].split(
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# [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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# kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
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# return self.mla_attn(hidden_states_or_q_c, kv_c_normed, k_pe, kv_cache,
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# attn_metadata)
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# if self.q_lora_rank is not None:
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# ckq = self.q_a_proj(hidden_states)[0]
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# hidden_states_or_q_c = self.q_a_layernorm(ckq)
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# else:
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# hidden_states_or_q_c = hidden_states
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# kv_c, k_pe = self.kv_a_proj_with_mqa(hidden_states)[0].split(
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# [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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# kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
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# return self.mla_attn(hidden_states_or_q_c, kv_c_normed, k_pe, kv_cache,
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# attn_metadata)
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class DeepseekV2MoE(nn.Module):
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def forward(self, hidden_states: torch.Tensor, residual = None, rms_norm = None):
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# moe layer support prefill&decode vacc ops
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if residual is not None:
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try:
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reduce_result = self.tp_size > 1
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# decode moe, first seq
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if self.is_decode:
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hidden_states, residual = vacc_fused_decode_moe_fp8(self, self.shared_experts,
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hidden_states, residual,
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rms_norm, self.gate, self.experts,
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self.routed_scaling_factor,
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reduce_result)
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return hidden_states, residual
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# prefill moe, first expert
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else:
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hidden_states, residual = vacc_fused_prefill_moe_fp8(self, self.shared_experts,
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hidden_states, residual,
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rms_norm, self.gate, self.experts,
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self.routed_scaling_factor,
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reduce_result)
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return hidden_states, residual
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except Exception as e:
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logger.warning("vacc fused moe run fail, now use unfused ops %s", e)
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hidden_states, residual = rms_norm(hidden_states, residual)
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self.experts.is_decode = self.is_decode
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# 1. fuse_prefill_pre_moe
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.n_shared_experts is not None:
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try:
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shared_output = vacc_fused_mlp_fp8(self.shared_experts, hidden_states, moe_share=True)
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except Exception as e:
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logger.warning("fused mlp is Error, now use Default:%s", e)
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shared_output = self.shared_experts(hidden_states)
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router_logits, _ = self.gate(hidden_states)
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# 2. fused_moe
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits)
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# 3. add_reduce
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# now fuse share_mlp add to experts
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# if shared_output is not None:
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# # out = input + other * alpha
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# final_hidden_states = shared_output.add_(final_hidden_states, alpha=self.routed_scaling_factor)
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# else:
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# final_hidden_states = final_hidden_states * self.routed_scaling_factor
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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if residual is not None:
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return final_hidden_states.view(num_tokens, hidden_dim), residual
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return final_hidden_states.view(num_tokens, hidden_dim)
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class DeepseekV2MLP(nn.Module):
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def forward(self, x, residual = None, rms_norm = None):
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# use all fused ops
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if residual is not None:
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reduce_result = self.down_proj.reduce_results and self.down_proj.tp_size > 1
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hidden_states, residual = vacc_fused_mlp_fp8(self,
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x, residual,
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rms_norm,
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reduce_result)
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return hidden_states, residual
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# use default fuse ops
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try:
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output_parallel = vacc_fused_mlp_fp8(self, x, residual, rms_norm)
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if self.down_proj.reduce_results and self.down_proj.tp_size > 1:
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x = tensor_model_parallel_all_reduce(output_parallel)
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else:
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x = output_parallel
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except Exception as e:
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logger.warning("fuse_mlp run fail, now use default: %s", e)
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class DeepseekV2Model(nn.Module):
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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forward_context: ForwardContext = get_forward_context()
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attn_metadata = forward_context.attn_metadata
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if isinstance(attn_metadata, dict):
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attn_metadata = attn_metadata.items().__iter__().__next__()[1]
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first_k_dense_replace = self.config.first_k_dense_replace if hasattr(self.config, "first_k_dense_replace") else 3
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if not hasattr(self, "weight_capture"):
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from vllm_vacc.vllm.model_executor.models.weight_capture.deepseek_weight_capture import DeepseekWeightCapture
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self.weight_capture = DeepseekWeightCapture(self.layers, self.start_layer, self.end_layer)
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self.cached_weights_state = True
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self.cached_batch = 1
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self.layer_nums = self.end_layer - self.start_layer
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self.is_pipeline_first = get_pp_group().is_first_rank
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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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else:
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hidden_states = self.get_input_embeddings(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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if(attn_metadata.prefill_metadata is not None or not USE_DECODER_LAYER_FUSE_MODE):
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(positions, hidden_states, residual)
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else:
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# update global seq lens, use for serve infos
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# update_seqence_length(attn_metadata.decode_metadata.seq_lens)
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if FUSE_ALL_DECODER_LAYERS:
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self.weight_capture.update_attn_args(attn_metadata.decode_metadata.seq_lens,
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attn_metadata.slot_mapping,
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[self.layers[i].self_attn.mla_attn.kv_cache[forward_context.virtual_engine] for i in range(self.start_layer, first_k_dense_replace)],
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[self.layers[i].self_attn.mla_attn.kv_cache[forward_context.virtual_engine] for i in range(first_k_dense_replace, self.end_layer)],
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attn_metadata.decode_metadata.block_tables)
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hidden_states, residual = forward_mla_mlp_single_layer(hidden_states, residual, self.weight_capture, 0)
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hidden_states, residual = forward_mla_mlp_single_layer(hidden_states, residual, self.weight_capture, 1)
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hidden_states, residual = forward_mla_mlp_single_layer(hidden_states, residual, self.weight_capture, 2)
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if hidden_states.shape[0] != self.cached_batch:
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# batch切换，重新执行缓存
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self.cached_weights_state = True
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self.cached_batch = hidden_states.shape[0]
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if self.cached_weights_state:
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self.cached_weights_state = False
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hidden_states, residual = forward_mla_moe_layers_with_weights(hidden_states, residual, self.weight_capture)
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else:
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hidden_states, residual = forward_mla_moe_layers_without_weights(hidden_states, residual, self.weight_capture)
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else:
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from torch_vacc.vacc.custom_ops import fuse_mla_mlp_v2_allreduce_decode,fuse_mla_moe_v2_allreduce_decode
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for i in range(0, self.layer_nums):
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layer_id = i + self.start_layer
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layer = self.layers[layer_id]
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kv_cache = layer.self_attn.mla_attn.kv_cache[forward_context.virtual_engine]
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positions = [p - 1 for p in attn_metadata.decode_metadata.seq_lens]
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cos_cache = [layer.self_attn.mla_attn.impl.rotary_emb.cos_cache[p] for p in positions]
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sin_cache = [layer.self_attn.mla_attn.impl.rotary_emb.sin_cache[p] for p in positions]
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if layer_id < first_k_dense_replace:
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hidden_states, residual = fuse_mla_mlp_v2_allreduce_decode(
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hidden_states = hidden_states,
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residual = residual,
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hidden_states_norm_weight = self.weight_capture.layer_mlp.attn_args._a_hidden_states_norm_weight[i],
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q_a_proj_weight = self.weight_capture.layer_mlp.attn_args._0_merge_q_kv_weights[i],
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q_a_proj_weight_scale_inv = self.weight_capture.layer_mlp.attn_args._1_merge_q_kv_scale_inv[i],
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q_a_layernorm_weight = self.weight_capture.layer_mlp.attn_args._2_q_a_layernorm_weight[i],
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w_q = self.weight_capture.layer_mlp.attn_args._3_W_Q[i],
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w_q_scale = self.weight_capture.layer_mlp.attn_args._4_W_Q_scales[i],
|
||
w_uk = self.weight_capture.layer_mlp.attn_args._5_W_UK[i],
|
||
w_uk_scale = self.weight_capture.layer_mlp.attn_args._6_W_UK_scales[i],
|
||
w_qr = self.weight_capture.layer_mlp.attn_args._7_W_QR[i],
|
||
w_qr_scale = self.weight_capture.layer_mlp.attn_args._8_W_QR_scales[i],
|
||
kv_a_layernorm_weight = self.weight_capture.layer_mlp.attn_args._9_kv_a_layernorm_weight[i],
|
||
sin_cache = sin_cache,# self.weight_capture.layer_mlp.attn_args._10_sin_cache,
|
||
cos_cache = cos_cache,# self.weight_capture.layer_mlp.attn_args._11_cos_cache,
|
||
slot_mapping = attn_metadata.slot_mapping,#self.weight_capture.layer_mlp.attn_args._12_slot_mapping[i],
|
||
kv_cache = kv_cache,#self.weight_capture.layer_mlp.attn_args._13_kv_cache[i],
|
||
block_tables = attn_metadata.decode_metadata.block_tables,#self.weight_capture.layer_mlp.attn_args._14_block_tables[i],
|
||
block_group_size = self.weight_capture.layer_mlp.attn_args._15_env_blk_grp_size,
|
||
w_uv = self.weight_capture.layer_mlp.attn_args._16_W_UV[i],
|
||
w_uv_scale = self.weight_capture.layer_mlp.attn_args._17_W_UV_scales[i],
|
||
o_proj_weight = self.weight_capture.layer_mlp.attn_args._18_o_proj_weight[i],
|
||
o_proj_weight_scale_inv = self.weight_capture.layer_mlp.attn_args._19_o_proj_weight_scale_inv[i],
|
||
# mla params
|
||
seq_lens = attn_metadata.decode_metadata.seq_lens,
|
||
sm_scale = self.weight_capture.layer_mlp.attn_args._21_sm_scale,
|
||
head_num = self.weight_capture.layer_mlp.attn_args._22_head_num,
|
||
# flash attention
|
||
flash_attention = (USE_FLASH_ATTENTION==1),
|
||
# mlp weight
|
||
rms_weight = self.weight_capture.layer_mlp.mlp_args._0_mlp_rms_weight[i],
|
||
mlp_weight_13 = self.weight_capture.layer_mlp.mlp_args._1_mlp_w13[i],
|
||
mlp_weight_2 = self.weight_capture.layer_mlp.mlp_args._2_mlp_w2[i],
|
||
mlp_weight_scale_13 = self.weight_capture.layer_mlp.mlp_args._3_mlp_w13_scale[i],
|
||
mlp_weight_scale_2 = self.weight_capture.layer_mlp.mlp_args._4_mlp_w2_scale[i],
|
||
# mlp params
|
||
mlp_block_size_w13 = self.weight_capture.layer_mlp.mlp_args._5_mlp_w13_block_size,
|
||
mlp_block_size_w2 = self.weight_capture.layer_mlp.mlp_args._6_mlp_w2_block_size,
|
||
# vccl info
|
||
world_size = self.weight_capture.layer_mlp.dist_args._0_world_size,
|
||
rank = self.weight_capture.layer_mlp.dist_args._1_rank,
|
||
group_id = self.weight_capture.layer_mlp.dist_args._2_group_id,
|
||
dev_info = self.weight_capture.layer_mlp.dist_args._3_dev_info)
|
||
else:
|
||
wid = i - first_k_dense_replace if self.is_pipeline_first else i
|
||
hidden_states, residual = fuse_mla_moe_v2_allreduce_decode(
|
||
hidden_states = hidden_states,
|
||
residual = residual,
|
||
hidden_states_norm_weight = self.weight_capture.layer_moe.attn_args._a_hidden_states_norm_weight[wid],
|
||
q_a_proj_weight = self.weight_capture.layer_moe.attn_args._0_merge_q_kv_weights[wid],
|
||
q_a_proj_weight_scale_inv = self.weight_capture.layer_moe.attn_args._1_merge_q_kv_scale_inv[wid],
|
||
q_a_layernorm_weight = self.weight_capture.layer_moe.attn_args._2_q_a_layernorm_weight[wid],
|
||
w_q = self.weight_capture.layer_moe.attn_args._3_W_Q[wid],
|
||
w_q_scale = self.weight_capture.layer_moe.attn_args._4_W_Q_scales[wid],
|
||
w_uk = self.weight_capture.layer_moe.attn_args._5_W_UK[wid],
|
||
w_uk_scale = self.weight_capture.layer_moe.attn_args._6_W_UK_scales[wid],
|
||
w_qr = self.weight_capture.layer_moe.attn_args._7_W_QR[wid],
|
||
w_qr_scale = self.weight_capture.layer_moe.attn_args._8_W_QR_scales[wid],
|
||
kv_a_layernorm_weight = self.weight_capture.layer_moe.attn_args._9_kv_a_layernorm_weight[wid],
|
||
sin_cache = sin_cache,# self.weight_capture.layer_mlp.attn_args._10_sin_cache,
|
||
cos_cache = cos_cache,# self.weight_capture.layer_mlp.attn_args._11_cos_cache,
|
||
slot_mapping = attn_metadata.slot_mapping,#self.weight_capture.layer_mlp.attn_args._12_slot_mapping[i],
|
||
kv_cache = kv_cache,#self.weight_capture.layer_mlp.attn_args._13_kv_cache[i],
|
||
block_tables = attn_metadata.decode_metadata.block_tables,
|
||
block_group_size = self.weight_capture.layer_moe.attn_args._15_env_blk_grp_size,
|
||
w_uv = self.weight_capture.layer_moe.attn_args._16_W_UV[wid],
|
||
w_uv_scale = self.weight_capture.layer_moe.attn_args._17_W_UV_scales[wid],
|
||
o_proj_weight = self.weight_capture.layer_moe.attn_args._18_o_proj_weight[wid],
|
||
o_proj_weight_scale_inv = self.weight_capture.layer_moe.attn_args._19_o_proj_weight_scale_inv[wid],
|
||
# mla params
|
||
seq_lens = attn_metadata.decode_metadata.seq_lens,
|
||
sm_scale = self.weight_capture.layer_moe.attn_args._21_sm_scale,
|
||
head_num = self.weight_capture.layer_moe.attn_args._22_head_num,
|
||
# flash attention
|
||
flash_attention = (USE_FLASH_ATTENTION==1),
|
||
# moe weight
|
||
rms_weight = self.weight_capture.layer_moe.moe_args._0_moe_rms_weight[wid],
|
||
mlp_weight_13 = self.weight_capture.layer_moe.moe_args._1_moe_share_mlp_w13[wid],
|
||
mlp_weight_2 = self.weight_capture.layer_moe.moe_args._2_moe_share_mlp_w2[wid],
|
||
mlp_weight_scale_13 = self.weight_capture.layer_moe.moe_args._3_moe_share_mlp_w13_scale[wid],
|
||
mlp_weight_scale_2 = self.weight_capture.layer_moe.moe_args._4_moe_share_mlp_w2_scale[wid],
|
||
moe_weight_13 = self.weight_capture.layer_moe.moe_args._5_moe_w13[wid],
|
||
moe_weight_2 = self.weight_capture.layer_moe.moe_args._6_moe_w2[wid],
|
||
moe_weight_scale_13 = self.weight_capture.layer_moe.moe_args._7_moe_w13_scale[wid],
|
||
moe_weight_scale_2 = self.weight_capture.layer_moe.moe_args._8_moe_w2_scale[wid],
|
||
mm_weight = self.weight_capture.layer_moe.moe_args._9_gate_weight[wid],
|
||
moe_bias = self.weight_capture.layer_moe.moe_args._10_moe_bias[wid],
|
||
# moe params
|
||
mlp_block_size_w13 = self.weight_capture.layer_moe.moe_args._11_moe_mlp_w13_block_size,
|
||
mlp_block_size_w2 = self.weight_capture.layer_moe.moe_args._12_moe_mlp_w2_block_size,
|
||
moe_block_size_w13 = self.weight_capture.layer_moe.moe_args._13_moe_w13_block_size,
|
||
moe_block_size_w2 = self.weight_capture.layer_moe.moe_args._14_moe_w2_block_size,
|
||
# vccl info
|
||
world_size = self.weight_capture.layer_moe.dist_args._0_world_size,
|
||
rank = self.weight_capture.layer_moe.dist_args._1_rank,
|
||
group_id = self.weight_capture.layer_moe.dist_args._2_group_id,
|
||
dev_info = self.weight_capture.layer_moe.dist_args._3_dev_info)
|
||
|
||
if not get_pp_group().is_last_rank:
|
||
return IntermediateTensors({
|
||
"hidden_states": hidden_states,
|
||
"residual": residual
|
||
})
|
||
|
||
hidden_states, _ = self.norm(hidden_states, residual)
|
||
return hidden_states
|
||
|
||
class DeepseekV2ForCausalLM(nn.Module, SupportsPP):
|
||
|
||
def load_weights(self, weights: Iterable[Tuple[str,
|
||
torch.Tensor]]) -> Set[str]:
|
||
from .memory.memory_recycling import init_huge_memory_allocator
|
||
from .vars import LLM_MAX_PREFILL_SEQ_LEN
|
||
from vllm_vacc.vllm.config_manager import vllm_vacc_config_manager
|
||
|
||
# default is deepseek, config can set to ['deepseek_mtp',]
|
||
model_name = "deepseek"
|
||
config_infos = vllm_vacc_config_manager().get_model_infos()
|
||
if config_infos != "default":
|
||
if config_infos in ['mtp']:
|
||
model_name = "deepseek_mtp"
|
||
else:
|
||
model_name = config_infos
|
||
|
||
if not init_huge_memory_allocator(LLM_MAX_PREFILL_SEQ_LEN, self.config.hidden_size, vllm_model=model_name):
|
||
logger.warning("init huge memory allocator fail. prefill memory recycling will disable")
|
||
|
||
from vllm.model_executor.model_loader.weight_utils import maybe_remap_kv_scale_name
|
||
stacked_params_mapping = [
|
||
# (param_name, shard_name, shard_id)
|
||
("gate_up_proj", "gate_proj", 0),
|
||
("gate_up_proj", "up_proj", 1),
|
||
]
|
||
|
||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||
# (param_name, weight_name, expert_id, shard_id)
|
||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||
ckpt_gate_proj_name="gate_proj",
|
||
ckpt_down_proj_name="down_proj",
|
||
ckpt_up_proj_name="up_proj",
|
||
num_experts=self.config.n_routed_experts)
|
||
|
||
params_dict = dict(self.named_parameters())
|
||
loaded_params: Set[str] = set()
|
||
for name, loaded_weight in weights:
|
||
if "rotary_emb.inv_freq" in name:
|
||
continue
|
||
|
||
if test_layer_en == "1":
|
||
test_layer = 5
|
||
if name not in ['model.embed_tokens.weight', 'model.norm.weight', 'lm_head.weight']:
|
||
if int(name.split(".")[2]) > test_layer:
|
||
continue
|
||
# TODO(simon): support nextn predict layers
|
||
if hasattr(self.config, "num_nextn_predict_layers"
|
||
) and self.config.num_nextn_predict_layers > 0:
|
||
assert self.config.num_nextn_predict_layers == 1
|
||
layer_idx = self.config.num_hidden_layers
|
||
if name.startswith(f"model.layers.{layer_idx}"):
|
||
continue
|
||
|
||
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
||
# Skip non-stacked layers and experts (experts handled below).
|
||
if weight_name not in name:
|
||
continue
|
||
# We have mlp.experts[0].gate_proj in the checkpoint.
|
||
# Since we handle the experts below in expert_params_mapping,
|
||
# we need to skip here BEFORE we update the name, otherwise
|
||
# name will be updated to mlp.experts[0].gate_up_proj, which
|
||
# will then be updated below in expert_params_mapping
|
||
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
||
if (("mlp.experts." in name) and name not in params_dict):
|
||
continue
|
||
name = name.replace(weight_name, param_name)
|
||
# Skip loading extra bias for GPTQ models.
|
||
if name.endswith(".bias") and name not in params_dict:
|
||
continue
|
||
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
|
||
param = params_dict[name]
|
||
weight_loader = param.weight_loader
|
||
weight_loader(param, loaded_weight, shard_id)
|
||
break
|
||
else:
|
||
for mapping in expert_params_mapping:
|
||
param_name, weight_name, expert_id, shard_id = mapping
|
||
if weight_name not in name:
|
||
continue
|
||
name = name.replace(weight_name, param_name)
|
||
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
|
||
param = params_dict[name]
|
||
weight_loader = param.weight_loader
|
||
weight_loader(param,
|
||
loaded_weight,
|
||
name,
|
||
shard_id=shard_id,
|
||
expert_id=expert_id)
|
||
break
|
||
else:
|
||
# Skip loading extra bias for GPTQ models.
|
||
if name.endswith(".bias") and name not in params_dict:
|
||
continue
|
||
|
||
# Remapping the name of FP8 kv-scale.
|
||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||
if name is None:
|
||
continue
|
||
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
|
||
param = params_dict[name]
|
||
weight_loader = getattr(param, "weight_loader",
|
||
default_weight_loader)
|
||
weight_loader(param, loaded_weight)
|
||
loaded_params.add(name)
|
||
|
||
if USE_MERGE_Q_KV_GEN_AND_Q_QR:
|
||
for layer in self.model.layers:
|
||
if isinstance(layer, PPMissingLayer):
|
||
continue
|
||
layer.self_attn.merge_qkv_weights()
|
||
|
||
return loaded_params
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: torch.Tensor,
|
||
positions: torch.Tensor,
|
||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||
inputs_embeds: Optional[torch.Tensor] = None,
|
||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||
|
||
attn_metadata = get_forward_context().attn_metadata
|
||
if isinstance(attn_metadata, dict):
|
||
attn_metadata = attn_metadata.items().__iter__().__next__()[1]
|
||
if attn_metadata.prefill_metadata is not None:
|
||
from .memory.memory_recycling import alloc_memory_recycler
|
||
from vllm_vacc.vllm.config_manager import vllm_vacc_config_manager
|
||
if hasattr(attn_metadata, 'num_prefill_tokens'):
|
||
tokens = attn_metadata.num_prefill_tokens
|
||
else:
|
||
tokens = attn_metadata.prefill_metadata.num_prefill_tokens
|
||
|
||
vllm_model_mode = "deepseek"
|
||
config_infos = vllm_vacc_config_manager().get_model_infos()
|
||
if config_infos != "default":
|
||
if config_infos in ['mtp']:
|
||
vllm_model_mode = "deepseek_mtp"
|
||
else:
|
||
vllm_model_mode = config_infos
|
||
|
||
if get_tp_group().rank_in_group == 0:
|
||
memory_infos = f'[MemoryRecycler] enable: {vllm_model_mode}'
|
||
logger.info(memory_infos)
|
||
|
||
if not alloc_memory_recycler(tokens, vllm_model=vllm_model_mode, world_size=get_tp_group().world_size):
|
||
logger.warning("deepseek memory recycler allock fail. current request may inefficient %s", tokens)
|
||
|
||
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
||
inputs_embeds)
|
||
return hidden_states
|