1759 lines
68 KiB
Python
1759 lines
68 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only DeepseekV2/DeepseekV3 model."""
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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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from typing import Any
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import torch
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from torch import nn
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from transformers import DeepseekV2Config, DeepseekV3Config
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.attention import Attention
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from vllm.attention.backends.abstract import AttentionBackend
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from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, ParallelConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
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get_ep_group,
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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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tensor_model_parallel_all_gather,
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)
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from vllm.forward_context import get_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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per_token_group_quant_fp8,
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)
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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,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.model_executor.models.utils import sequence_parallel_chunk
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits
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from vllm.utils.torch_utils import direct_register_custom_op
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backends.mla.indexer import (
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DeepseekV32IndexerBackend,
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DeepseekV32IndexerMetadata,
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)
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from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
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from .interfaces import MixtureOfExperts, SupportsEagle, SupportsLoRA, SupportsPP
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from .utils import (
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PPMissingLayer,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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if current_platform.is_cuda_alike():
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from vllm import _custom_ops as ops
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elif current_platform.is_xpu():
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from vllm._ipex_ops import ipex_ops as ops
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import ixformer.inference.functions as ixfops
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logger = init_logger(__name__)
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class DeepseekAttention(nn.Module):
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"""Normal MHA implementation used by Deepseek v1."""
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def __init__(
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self,
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vllm_config: VllmConfig,
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config: DeepseekV2Config | DeepseekV3Config,
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hidden_size: int,
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num_heads: int,
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rope_theta: float = 10000,
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rope_scaling: dict[str, Any] | None = None,
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max_position_embeddings: int = 8192,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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**kwargs,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.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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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.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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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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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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)
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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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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class DeepseekV2MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: QuantizationConfig | None = None,
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reduce_results: bool = True,
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is_sequence_parallel=False,
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prefix: str = "",
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) -> None:
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super().__init__()
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# If is_sequence_parallel, the input and output tensors are sharded
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# across the ranks within the tp_group. In this case the weights are
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# replicated and no collective ops are needed.
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# Otherwise we use standard TP with an allreduce at the end.
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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disable_tp=is_sequence_parallel,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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disable_tp=is_sequence_parallel,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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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 DeepseekV2MoE(nn.Module):
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def __init__(
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self,
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config: DeepseekV2Config | DeepseekV3Config,
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parallel_config: ParallelConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
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self.ep_group = get_ep_group().device_group
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self.ep_rank = get_ep_group().rank_in_group
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self.ep_size = self.ep_group.size()
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self.n_routed_experts: int = config.n_routed_experts
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self.n_shared_experts: int = config.n_shared_experts
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self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
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if config.hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now."
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.n_routed_experts,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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if getattr(config, "topk_method", None) == "noaux_tc":
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.empty(config.n_routed_experts)
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)
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else:
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self.gate.e_score_correction_bias = None
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# Load balancing settings.
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eplb_config = parallel_config.eplb_config
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self.enable_eplb = parallel_config.enable_eplb
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_logical_experts = self.n_routed_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
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if config.n_shared_experts is None or self.is_rocm_aiter_moe_enabled:
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self.shared_experts = None
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else:
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intermediate_size = config.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = DeepseekV2MLP(
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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is_sequence_parallel=self.is_sequence_parallel,
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reduce_results=False,
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prefix=f"{prefix}.shared_experts",
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)
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self.experts = SharedFusedMoE(
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shared_experts=self.shared_experts,
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gate=self.gate,
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num_experts=config.n_routed_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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use_grouped_topk=True,
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num_expert_group=getattr(config, "n_group", 1),
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topk_group=getattr(config, "topk_group", 1),
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prefix=f"{prefix}.experts",
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scoring_func=getattr(config, "scoring_func", "softmax"),
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# we do scaling outside, set factor to 1.0 to avoid double mul
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# aiter applies routed_scaling_factor internally
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routed_scaling_factor=1.0
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if not self.is_rocm_aiter_moe_enabled
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else self.routed_scaling_factor,
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e_score_correction_bias=self.gate.e_score_correction_bias,
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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is_sequence_parallel=self.is_sequence_parallel,
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n_shared_experts=config.n_shared_experts
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if rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
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else None,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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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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# Chunk the hidden states so they aren't replicated across TP ranks.
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# This avoids duplicate computation in self.experts.
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# TODO: We can replace the all_reduce at the end of attn with a
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# reduce_scatter instead of chunking here.
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if self.is_sequence_parallel:
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hidden_states = sequence_parallel_chunk(hidden_states)
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if self.experts.is_internal_router:
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# In this case, the gate/router runs inside the FusedMoE class
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fused_moe_out = self.experts(
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hidden_states=hidden_states, router_logits=hidden_states
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)
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else:
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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fused_moe_out = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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shared_output, final_hidden_states = fused_moe_out
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if self.shared_experts is None:
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assert shared_output is None
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# Fix FP16 overflow
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# See DeepseekV2DecoderLayer for more details.
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if hidden_states.dtype != torch.float16:
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if not self.is_rocm_aiter_moe_enabled:
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final_hidden_states *= self.routed_scaling_factor
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elif self.shared_experts is not None:
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assert shared_output is not None
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shared_output *= 1.0 / self.routed_scaling_factor
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if self.shared_experts is not None:
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assert shared_output is not None
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final_hidden_states += shared_output
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|
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if self.is_sequence_parallel:
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final_hidden_states = tensor_model_parallel_all_gather(
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final_hidden_states, 0
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)
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final_hidden_states = final_hidden_states[:num_tokens]
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elif self.tp_size > 1:
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final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
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final_hidden_states
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)
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return final_hidden_states.view(num_tokens, hidden_dim)
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def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
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import math
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if scale <= 1:
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return 1.0
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return 0.1 * mscale * math.log(scale) + 1.0
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|
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|
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class DeepseekV2Attention(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: 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: int,
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: dict[str, Any] | None = None,
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max_position_embeddings: int = 8192,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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topk_indices_buffer: torch.Tensor | None = None,
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prefix: str = "",
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) -> None:
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super().__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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assert topk_indices_buffer is None, (
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"topk_indices_buffer is not \
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supported for DeepseekV2Attention"
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)
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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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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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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q_lora_rank,
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self.num_heads * 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",
|
|
)
|
|
else:
|
|
self.q_proj = ColumnParallelLinear(
|
|
self.hidden_size,
|
|
self.num_heads * self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.q_proj",
|
|
)
|
|
|
|
self.kv_a_proj_with_mqa = ReplicatedLinear(
|
|
self.hidden_size,
|
|
self.kv_lora_rank + self.qk_rope_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.kv_a_proj_with_mqa",
|
|
)
|
|
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
|
|
self.kv_b_proj = ColumnParallelLinear(
|
|
self.kv_lora_rank,
|
|
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.kv_b_proj",
|
|
)
|
|
# O projection.
|
|
self.o_proj = RowParallelLinear(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
)
|
|
if rope_scaling:
|
|
rope_scaling["rope_type"] = "deepseek_yarn"
|
|
|
|
self.rotary_emb = get_rope(
|
|
qk_rope_head_dim,
|
|
rotary_dim=qk_rope_head_dim,
|
|
max_position=max_position_embeddings,
|
|
base=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
is_neox_style=False,
|
|
)
|
|
|
|
if rope_scaling:
|
|
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
|
|
scaling_factor = rope_scaling["factor"]
|
|
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
|
|
self.scaling = self.scaling * mscale * mscale
|
|
|
|
self.attn = Attention(
|
|
self.num_local_heads,
|
|
self.qk_head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_local_heads,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
)
|
|
|
|
def forward_opt(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
if self.q_lora_rank is not None:
|
|
q_latent_kpe = self.q_a_proj(hidden_states)[0]
|
|
q, kv_a, k_pe = q_latent_kpe.split([self.q_lora_rank, self.kv_lora_rank, self.qk_rope_head_dim], dim=1)
|
|
q = self.q_a_layernorm(q)
|
|
q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
|
|
else:
|
|
q_latent_kpe = self.q_proj(hidden_states)[0]
|
|
q, kv_a, k_pe = q_latent_kpe.split([self.num_heads * self.qk_head_dim, self.kv_lora_rank, self.qk_rope_head_dim], dim=1)
|
|
q = q.view(-1, self.num_local_heads, self.qk_head_dim)
|
|
|
|
_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
|
|
|
kv_a = self.kv_a_layernorm(kv_a)
|
|
kv = self.kv_b_proj(kv_a)[0]
|
|
kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
|
|
k_nope, v_nope = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
|
|
|
k = torch.empty_like(q)
|
|
v = torch.empty(q.shape[0], self.num_local_heads, self.v_head_dim, device=q.device, dtype=q.dtype)
|
|
ixfops.mla_rope(positions, q_pe, k_pe, k[...,self.qk_nope_head_dim:], self.rotary_emb.cos_sin_cache)
|
|
ixfops.mla_copy_kv(k_nope, v_nope, k, v)
|
|
|
|
attn_output = self.attn(q, k, v)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor
|
|
) -> torch.Tensor:
|
|
if self.q_lora_rank is not None:
|
|
q = self.q_a_proj(hidden_states)[0]
|
|
kv_a, k_pe = self.kv_a_proj_with_mqa(hidden_states)[0].split([self.kv_lora_rank, self.qk_rope_head_dim], dim=1)
|
|
q = self.q_a_layernorm(q)
|
|
q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
|
|
else:
|
|
q = self.q_proj(hidden_states)[0]
|
|
kv_a, k_pe = self.kv_a_proj_with_mqa(hidden_states)[0].split([self.kv_lora_rank, self.qk_rope_head_dim], dim=1)
|
|
q = q.view(-1, self.num_local_heads, self.qk_head_dim)
|
|
|
|
_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
|
|
|
kv_a = self.kv_a_layernorm(kv_a)
|
|
kv = self.kv_b_proj(kv_a)[0]
|
|
kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
|
|
k_nope, v_nope = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
|
|
|
q[..., self.qk_nope_head_dim :] = q_pe
|
|
k = torch.empty_like(q)
|
|
k[..., : self.qk_nope_head_dim] = k_nope
|
|
k[..., self.qk_nope_head_dim :] = k_pe
|
|
# padding value to qk_head_dim for alignment
|
|
v = torch.nn.functional.pad(
|
|
v, [0, self.qk_head_dim - self.v_head_dim], value=0
|
|
).view(-1, self.num_local_heads * self.qk_head_dim)
|
|
attn_output = self.attn(q, k, v)
|
|
attn_output = attn_output.view(-1, self.num_local_heads, self.qk_head_dim)[
|
|
..., : self.v_head_dim
|
|
].reshape(-1, self.num_local_heads * self.v_head_dim)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
|
|
class DeepseekV32IndexerCache(torch.nn.Module, AttentionLayerBase):
|
|
def __init__(
|
|
self, head_dim: int, dtype: torch.dtype, prefix: str, cache_config: CacheConfig
|
|
):
|
|
super().__init__()
|
|
self.kv_cache = [torch.tensor([])]
|
|
self.head_dim = head_dim
|
|
self.prefix = prefix
|
|
self.cache_config = cache_config
|
|
self.dtype = dtype
|
|
compilation_config = get_current_vllm_config().compilation_config
|
|
if prefix in compilation_config.static_forward_context:
|
|
raise ValueError(f"Duplicate layer name: {prefix}")
|
|
compilation_config.static_forward_context[prefix] = self
|
|
|
|
def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
|
|
return MLAAttentionSpec( # Only has one vector instead of K + V
|
|
block_size=self.cache_config.block_size,
|
|
num_kv_heads=1,
|
|
head_size=self.head_dim,
|
|
dtype=self.dtype,
|
|
)
|
|
|
|
def forward(self): ...
|
|
|
|
def get_attn_backend(self) -> AttentionBackend:
|
|
return DeepseekV32IndexerBackend
|
|
|
|
@torch.inference_mode()
|
|
def cp_gather_indexer_k_quant_cache(
|
|
kv_cache, # [num_blocks, block_size, head_dim]
|
|
dst_value, # [cu_seq_lens[-1], head_dim]
|
|
block_table, # [batch_size, num_blocks]
|
|
cu_seq_lens, # [batch_size + 1, ]
|
|
batch_size,
|
|
):
|
|
num_blocks, block_size, _ = kv_cache.shape
|
|
head_dim = dst_value.shape[-1]
|
|
kv_cache = kv_cache.view(num_blocks, -1)
|
|
|
|
expected_value = []
|
|
# expected_scale = []
|
|
for b in range(batch_size):
|
|
s = cu_seq_lens[b + 1] - cu_seq_lens[b]
|
|
if s == 0:
|
|
continue
|
|
tot = cdiv(s, block_size)
|
|
blocks = block_table[b, :tot]
|
|
|
|
value = []
|
|
scale = []
|
|
full_block = torch.arange(tot - 1,
|
|
device=kv_cache.device,
|
|
dtype=torch.int32)
|
|
non_remaining_value = kv_cache[blocks[full_block], :block_size *
|
|
head_dim].view(-1, head_dim)
|
|
# non_remaining_scale = kv_cache[blocks[full_block],
|
|
# block_size * head_dim:].view(-1, 4)
|
|
|
|
remaining = s - (tot - 1) * block_size
|
|
|
|
value = torch.cat([
|
|
non_remaining_value,
|
|
kv_cache[blocks[-1], :remaining * head_dim].view(-1, head_dim)
|
|
],
|
|
dim=0)
|
|
# scale = torch.cat([
|
|
# non_remaining_scale,
|
|
# kv_cache[blocks[-1], block_size * head_dim:block_size * head_dim +
|
|
# remaining * 4].view(-1, 4)
|
|
# ],
|
|
# dim=0)
|
|
|
|
expected_value.append(value)
|
|
# expected_scale.append(scale)
|
|
|
|
gather_value = torch.cat(expected_value, dim=0).view(-1, head_dim)
|
|
# gather_scale = torch.cat(expected_scale, dim=0).view(-1, 4)
|
|
gather_value = gather_value.view(torch.bfloat16)
|
|
# gather_scale = gather_scale.view(torch.float32)
|
|
dst_value.copy_(gather_value)
|
|
# dst_scale.copy_(gather_scale)
|
|
|
|
|
|
def sparse_attn_indexer(
|
|
hidden_states: torch.Tensor,
|
|
k_cache_prefix: str,
|
|
kv_cache: torch.Tensor,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
weights: torch.Tensor,
|
|
topk_tokens: int,
|
|
head_dim: int,
|
|
max_model_len: int,
|
|
total_seq_lens: int,
|
|
topk_indices_buffer: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
# careful! this will be None in dummy run
|
|
attn_metadata = get_forward_context().attn_metadata
|
|
# assert isinstance(attn_metadata, dict)
|
|
if not isinstance(attn_metadata, dict):
|
|
return sparse_attn_indexer_fake(
|
|
hidden_states,
|
|
k_cache_prefix,
|
|
kv_cache,
|
|
q,
|
|
k,
|
|
weights,
|
|
topk_tokens,
|
|
head_dim,
|
|
max_model_len,
|
|
total_seq_lens,
|
|
topk_indices_buffer,
|
|
)
|
|
attn_metadata = attn_metadata[k_cache_prefix]
|
|
assert isinstance(attn_metadata, DeepseekV32IndexerMetadata)
|
|
slot_mapping = attn_metadata.slot_mapping
|
|
has_decode = attn_metadata.num_decodes > 0
|
|
has_prefill = attn_metadata.num_prefills > 0
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
|
|
ops.indexer_k_cache(
|
|
k,
|
|
kv_cache,
|
|
slot_mapping
|
|
)
|
|
|
|
topk_indices_buffer[: hidden_states.shape[0]] = -1
|
|
if has_prefill:
|
|
prefill_metadata = attn_metadata.prefill
|
|
for chunk in prefill_metadata.chunks:
|
|
k = torch.empty(
|
|
[chunk.total_seq_lens, head_dim],
|
|
device=k.device,
|
|
dtype=torch.bfloat16,
|
|
)
|
|
# k_scale = torch.empty(
|
|
# [chunk.total_seq_lens, 4],
|
|
# device=k.device,
|
|
# dtype=torch.uint8,
|
|
# )
|
|
cp_gather_indexer_k_quant_cache(
|
|
kv_cache,
|
|
k,
|
|
chunk.block_table,
|
|
chunk.cu_seq_lens,
|
|
chunk.num_reqs,
|
|
)
|
|
logits = ops.ref_mqa_logits(
|
|
q[chunk.token_start:chunk.token_end],
|
|
k,
|
|
weights[chunk.token_start : chunk.token_end],
|
|
chunk.cu_seqlen_ks,
|
|
chunk.cu_seqlen_ke,
|
|
)
|
|
topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]),
|
|
dim=-1)[1]
|
|
topk_indices -= chunk.cu_seqlen_ks[:, None]
|
|
mask_lo = topk_indices >= 0
|
|
mask_hi = topk_indices - (chunk.cu_seqlen_ke -
|
|
chunk.cu_seqlen_ks)[:, None] < 0
|
|
mask = torch.full_like(topk_indices,
|
|
False,
|
|
dtype=torch.bool,
|
|
device=topk_indices.device)
|
|
mask = mask_lo & mask_hi
|
|
topk_indices = topk_indices.masked_fill(~mask, -1)
|
|
topk_indices_buffer[
|
|
chunk.token_start:chunk.token_end, :topk_indices.
|
|
shape[-1]] = topk_indices.to(dtype=torch.int32)
|
|
|
|
if has_decode:
|
|
decode_metadata = attn_metadata.decode
|
|
# kv_cache size requirement [num_block, block_size, n_head, head_dim],
|
|
# we only have [num_block, block_size, head_dim],
|
|
kv_cache = kv_cache.unsqueeze(-2)
|
|
decode_lens = decode_metadata.decode_lens
|
|
if decode_metadata.requires_padding:
|
|
# pad in edge case where we have short chunked prefill length <
|
|
# decode_threshold since we unstrictly split
|
|
# prefill and decode by decode_threshold
|
|
# (currently set to 1 + speculative tokens)
|
|
padded_q_decode_tokens = pack_seq_triton(
|
|
q[:num_decode_tokens], decode_lens)
|
|
else:
|
|
padded_q_decode_tokens = q[:num_decode_tokens].reshape(
|
|
decode_lens.shape[0], -1, *q.shape[1:])
|
|
# TODO: move and optimize below logic with triton kernels
|
|
batch_size = padded_q_decode_tokens.shape[0]
|
|
next_n = padded_q_decode_tokens.shape[1]
|
|
assert batch_size == decode_metadata.seq_lens.shape[0]
|
|
num_padded_tokens = batch_size * next_n
|
|
logits = ops.ref_paged_mqa_logits(
|
|
padded_q_decode_tokens,
|
|
kv_cache,
|
|
weights[:num_padded_tokens],
|
|
decode_metadata.seq_lens,
|
|
decode_metadata.block_table,
|
|
max_model_len=max_model_len,
|
|
)
|
|
# padded query len
|
|
current_device = padded_q_decode_tokens.device
|
|
padded_num_tokens = batch_size * next_n
|
|
positions = torch.arange(max_model_len,
|
|
device=current_device).unsqueeze(0).expand(
|
|
batch_size * next_n, -1)
|
|
row_indices = torch.arange(padded_num_tokens,
|
|
device=current_device) // next_n
|
|
next_n_offset = torch.arange(
|
|
padded_num_tokens,
|
|
device=padded_q_decode_tokens.device) % next_n
|
|
index_end_pos = (decode_metadata.seq_lens[row_indices] - next_n +
|
|
next_n_offset).unsqueeze(1)
|
|
# index_end_pos: [B * N, 1]
|
|
mask = positions <= index_end_pos
|
|
# mask: [B * N, L]
|
|
logits = logits.masked_fill(~mask, float('-inf'))
|
|
topk_indices = logits.topk(topk_tokens,
|
|
dim=-1)[1].to(torch.int32) # [B * N, K]
|
|
# ensure we don't set indices for the top k
|
|
# that is out of range(masked already)
|
|
# this will happen if context length is shorter than K
|
|
topk_indices[topk_indices > index_end_pos] = -1
|
|
if decode_metadata.requires_padding:
|
|
# if padded, we need to unpack
|
|
# the topk indices removing padded tokens
|
|
topk_indices = unpack_seq_triton(
|
|
topk_indices.reshape(batch_size, -1, topk_indices.shape[-1]),
|
|
decode_lens,
|
|
)
|
|
topk_indices_buffer[:num_decode_tokens, :topk_indices.
|
|
shape[-1]] = topk_indices.to(dtype=torch.int32)
|
|
|
|
return topk_indices_buffer
|
|
|
|
|
|
def sparse_attn_indexer_fake(
|
|
hidden_states: torch.Tensor,
|
|
k_cache_prefix: str,
|
|
kv_cache: torch.Tensor,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
weights: torch.Tensor,
|
|
topk_tokens: int,
|
|
head_dim: int,
|
|
max_model_len: int,
|
|
total_seq_lens: int,
|
|
topk_indices_buffer: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
# profile run
|
|
# NOTE(Chen): create the max possible flattened_kv. So that
|
|
# profile_run can get correct memory usage.
|
|
_flattened_kv = torch.empty([total_seq_lens, head_dim],
|
|
device=k.device,
|
|
dtype=torch.bfloat16)
|
|
_k = _flattened_kv[..., :head_dim].view(
|
|
torch.bfloat16).contiguous()
|
|
# _k_scale = _flattened_kv[..., head_dim:].view(torch.float32).contiguous()
|
|
return topk_indices_buffer
|
|
|
|
|
|
direct_register_custom_op(
|
|
op_name="sparse_attn_indexer",
|
|
op_func=sparse_attn_indexer,
|
|
mutates_args=["topk_indices_buffer"],
|
|
fake_impl=sparse_attn_indexer_fake,
|
|
dispatch_key=current_platform.dispatch_key,
|
|
)
|
|
|
|
|
|
class Indexer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
config: DeepseekV2Config | DeepseekV3Config,
|
|
hidden_size: int,
|
|
q_lora_rank: int,
|
|
quant_config: QuantizationConfig | None,
|
|
cache_config: CacheConfig | None,
|
|
topk_indices_buffer: torch.Tensor | None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.vllm_config = vllm_config
|
|
self.config = config
|
|
# self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"]
|
|
self.topk_tokens = config.index_topk
|
|
self.n_head = config.index_n_heads # 64
|
|
self.head_dim = config.index_head_dim # 128
|
|
self.rope_dim = config.qk_rope_head_dim # 64
|
|
self.q_lora_rank = q_lora_rank # 1536
|
|
# no tensor parallel, just replicated
|
|
self.wq_b = ReplicatedLinear(
|
|
self.q_lora_rank,
|
|
self.head_dim * self.n_head,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.wq_b",
|
|
)
|
|
self.wk = ReplicatedLinear(
|
|
hidden_size,
|
|
self.head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.wk",
|
|
)
|
|
self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
|
|
self.weights_proj = ReplicatedLinear(
|
|
hidden_size, self.n_head, quant_config=None, prefix=f"{prefix}.weights_proj"
|
|
)
|
|
self.softmax_scale = self.head_dim**-0.5
|
|
|
|
self.scale_fmt = "ue8m0"
|
|
self.quant_block_size = 128 # TODO: get from config
|
|
self.topk_indices_buffer = topk_indices_buffer
|
|
|
|
# NOTE: (zyongye) we use fp8 naive cache,
|
|
# where we store value in fp8 and scale in fp32
|
|
# per self.quant_block_size element
|
|
self.k_cache = DeepseekV32IndexerCache(
|
|
head_dim=self.head_dim,
|
|
dtype=torch.bfloat16,
|
|
prefix=f"{prefix}.k_cache",
|
|
cache_config=cache_config,
|
|
)
|
|
self.max_model_len = vllm_config.model_config.max_model_len
|
|
self.prefix = prefix
|
|
from vllm.v1.attention.backends.mla.indexer import get_max_prefill_buffer_size
|
|
|
|
self.max_total_seq_len = get_max_prefill_buffer_size(vllm_config)
|
|
|
|
def forward(
|
|
self, hidden_states: torch.Tensor, qr: torch.Tensor, positions, rotary_emb
|
|
) -> torch.Tensor:
|
|
q, _ = self.wq_b(qr)
|
|
q = q.view(-1, self.n_head, self.head_dim)
|
|
q_pe, q_nope = torch.split(
|
|
q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
|
|
)
|
|
|
|
k, _ = self.wk(hidden_states)
|
|
k = self.k_norm(k)
|
|
k_pe, k_nope = torch.split(
|
|
k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
|
|
)
|
|
|
|
q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1))
|
|
q = torch.cat([q_pe, q_nope], dim=-1)
|
|
k = torch.cat([k_pe.squeeze(1), k_nope], dim=-1)
|
|
|
|
# we only quant q here since k quant is fused with cache insertion
|
|
# q = q.view(-1, self.head_dim)
|
|
# q_fp8, q_scale = per_token_group_quant_fp8(
|
|
# q,
|
|
# self.quant_block_size,
|
|
# column_major_scales=False,
|
|
# use_ue8m0=self.scale_fmt is not None,
|
|
# )
|
|
# q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
|
|
# q_scale = q_scale.view(-1, self.n_head, 1)
|
|
|
|
weights, _ = self.weights_proj(hidden_states)
|
|
weights = (
|
|
weights.unsqueeze(-1) * self.softmax_scale * self.n_head**-0.5
|
|
)
|
|
weights = weights.squeeze(-1)
|
|
|
|
return torch.ops.vllm.sparse_attn_indexer(
|
|
hidden_states,
|
|
self.k_cache.prefix,
|
|
self.k_cache.kv_cache[0],
|
|
q,
|
|
k,
|
|
weights,
|
|
self.topk_tokens,
|
|
self.head_dim,
|
|
self.max_model_len,
|
|
self.max_total_seq_len,
|
|
self.topk_indices_buffer,
|
|
)
|
|
|
|
|
|
class DeepseekV2MLAAttention(nn.Module):
|
|
"""
|
|
Main reference: DeepseekV2 paper, and FlashInfer Implementation
|
|
(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
|
|
|
|
For more info see MLACommonImpl in:
|
|
vllm/v1/attention/backends/mla/utils.py
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
config: DeepseekV2Config | DeepseekV3Config,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
qk_nope_head_dim: int,
|
|
qk_rope_head_dim: int,
|
|
v_head_dim: int,
|
|
q_lora_rank: int | None,
|
|
kv_lora_rank: int,
|
|
rope_theta: float = 10000,
|
|
rope_scaling: dict[str, Any] | None = None,
|
|
max_position_embeddings: int = 8192,
|
|
cache_config: CacheConfig | None = None,
|
|
quant_config: QuantizationConfig | None = None,
|
|
prefix: str = "",
|
|
topk_indices_buffer: torch.Tensor | None = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = hidden_size
|
|
self.qk_nope_head_dim = qk_nope_head_dim
|
|
self.qk_rope_head_dim = qk_rope_head_dim
|
|
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
|
|
self.v_head_dim = v_head_dim
|
|
|
|
self.q_lora_rank = q_lora_rank
|
|
self.kv_lora_rank = kv_lora_rank
|
|
|
|
self.num_heads = num_heads
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
assert num_heads % tp_size == 0
|
|
self.num_local_heads = num_heads // tp_size
|
|
|
|
self.scaling = self.qk_head_dim**-0.5
|
|
self.rope_theta = rope_theta
|
|
self.max_position_embeddings = max_position_embeddings
|
|
|
|
if self.q_lora_rank is not None:
|
|
self.q_a_proj = ReplicatedLinear(self.hidden_size,
|
|
self.q_lora_rank,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.q_a_proj")
|
|
self.q_a_layernorm = RMSNorm(self.q_lora_rank,
|
|
eps=config.rms_norm_eps)
|
|
self.q_b_proj = ColumnParallelLinear(self.q_lora_rank,
|
|
self.num_heads *
|
|
self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.q_b_proj")
|
|
else:
|
|
self.q_proj = ColumnParallelLinear(self.hidden_size,
|
|
self.num_heads *
|
|
self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.q_proj")
|
|
|
|
self.kv_a_proj_with_mqa = ReplicatedLinear(
|
|
self.hidden_size,
|
|
self.kv_lora_rank + self.qk_rope_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.kv_a_proj_with_mqa")
|
|
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
|
|
eps=config.rms_norm_eps)
|
|
self.kv_b_proj = ColumnParallelLinear(
|
|
self.kv_lora_rank,
|
|
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.kv_b_proj",
|
|
)
|
|
self.o_proj = RowParallelLinear(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
)
|
|
|
|
if rope_scaling:
|
|
rope_scaling["rope_type"] = "deepseek_yarn"
|
|
self.rotary_emb = get_rope(
|
|
qk_rope_head_dim,
|
|
rotary_dim=qk_rope_head_dim,
|
|
max_position=max_position_embeddings,
|
|
base=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
is_neox_style=False,
|
|
)
|
|
if rope_scaling:
|
|
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
|
|
scaling_factor = rope_scaling["factor"]
|
|
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
|
|
self.scaling = self.scaling * mscale * mscale
|
|
|
|
self.is_v32 = hasattr(config, "index_topk")
|
|
|
|
if self.is_v32:
|
|
self.indexer = Indexer(
|
|
vllm_config,
|
|
config,
|
|
hidden_size,
|
|
q_lora_rank,
|
|
quant_config,
|
|
cache_config,
|
|
topk_indices_buffer,
|
|
f"{prefix}.indexer",
|
|
)
|
|
else:
|
|
self.indexer = None
|
|
|
|
mla_modules = MLAModules(
|
|
kv_a_layernorm=self.kv_a_layernorm,
|
|
kv_b_proj=self.kv_b_proj,
|
|
rotary_emb=self.rotary_emb,
|
|
o_proj=self.o_proj,
|
|
q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
|
|
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa
|
|
if self.q_lora_rank is None
|
|
else None,
|
|
q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None,
|
|
q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
|
|
q_proj=self.q_proj if self.q_lora_rank is None else None,
|
|
indexer=self.indexer,
|
|
is_sparse=self.is_v32,
|
|
topk_indices_buffer=topk_indices_buffer,
|
|
)
|
|
|
|
self.mla_attn = MultiHeadLatentAttentionWrapper(
|
|
self.hidden_size,
|
|
self.num_local_heads,
|
|
self.scaling,
|
|
self.qk_nope_head_dim,
|
|
self.qk_rope_head_dim,
|
|
self.v_head_dim,
|
|
self.q_lora_rank,
|
|
self.kv_lora_rank,
|
|
mla_modules,
|
|
cache_config,
|
|
quant_config,
|
|
prefix,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self.mla_attn(positions, hidden_states)
|
|
|
|
|
|
class DeepseekV2DecoderLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
prefix: str,
|
|
config: DeepseekV2Config | None = None,
|
|
topk_indices_buffer: torch.Tensor | None = None,
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
if config is None:
|
|
config = vllm_config.model_config.hf_config
|
|
model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
parallel_config = vllm_config.parallel_config
|
|
|
|
self.hidden_size = config.hidden_size
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
|
moe_layer_freq = getattr(config, "moe_layer_freq", 1)
|
|
# DecoderLayers are created with `make_layers` which passes the prefix
|
|
# with the layer's index.
|
|
layer_idx = int(prefix.split(sep=".")[-1])
|
|
self.layer_idx = layer_idx
|
|
|
|
# verify MLA attention specific fields
|
|
qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
|
|
qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
|
|
v_head_dim = getattr(config, "v_head_dim", 0)
|
|
kv_lora_rank = getattr(config, "kv_lora_rank", 0)
|
|
use_mha = config.model_type == "deepseek" or all(
|
|
dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
|
|
)
|
|
|
|
if use_mha:
|
|
attn_cls = DeepseekAttention
|
|
elif model_config.use_mla:
|
|
attn_cls = DeepseekV2MLAAttention
|
|
else:
|
|
attn_cls = DeepseekV2Attention
|
|
self.self_attn = attn_cls(
|
|
vllm_config=vllm_config,
|
|
config=config,
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
qk_nope_head_dim=qk_nope_head_dim,
|
|
qk_rope_head_dim=qk_rope_head_dim,
|
|
v_head_dim=v_head_dim,
|
|
q_lora_rank=config.q_lora_rank if hasattr(config, "q_lora_rank") else None,
|
|
kv_lora_rank=kv_lora_rank,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.self_attn",
|
|
topk_indices_buffer=topk_indices_buffer,
|
|
)
|
|
|
|
if (
|
|
config.n_routed_experts is not None
|
|
and layer_idx >= config.first_k_dense_replace
|
|
and layer_idx % moe_layer_freq == 0
|
|
):
|
|
self.mlp = DeepseekV2MoE(
|
|
config=config,
|
|
parallel_config=parallel_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
else:
|
|
self.mlp = DeepseekV2MLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
residual: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
# Self Attention
|
|
if residual is None:
|
|
residual = hidden_states.clone()
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
)
|
|
|
|
if (
|
|
not isinstance(self.self_attn, DeepseekAttention)
|
|
and hidden_states.dtype == torch.float16
|
|
):
|
|
# Fix FP16 overflow
|
|
# We scale both hidden_states and residual before
|
|
# rmsnorm, and rmsnorm result would not affect by scale.
|
|
hidden_states *= 1.0 / self.routed_scaling_factor
|
|
if self.layer_idx == 0:
|
|
# The residual is shared by all layers, we only scale it on
|
|
# first layer.
|
|
residual *= 1.0 / self.routed_scaling_factor
|
|
|
|
# Fully Connected
|
|
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
if isinstance(self.mlp, DeepseekV2MLP) and hidden_states.dtype == torch.float16:
|
|
# Fix FP16 overflow
|
|
# Scaling the DeepseekV2MLP output, it is the input of
|
|
# input_layernorm of next decoder layer.
|
|
# The scaling of DeepseekV2MOE output would be done in the forward
|
|
# of DeepseekV2MOE
|
|
hidden_states *= 1.0 / self.routed_scaling_factor
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
@support_torch_compile
|
|
class DeepseekV2Model(nn.Module):
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.device = current_platform.device_type
|
|
|
|
self.vocab_size = config.vocab_size
|
|
self.is_v32 = hasattr(config, "index_topk")
|
|
if self.is_v32:
|
|
topk_tokens = config.index_topk
|
|
topk_indices_buffer = torch.empty(
|
|
vllm_config.scheduler_config.max_num_batched_tokens,
|
|
topk_tokens,
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
else:
|
|
topk_indices_buffer = None
|
|
|
|
if get_pp_group().is_first_rank:
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.embed_tokens",
|
|
)
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: DeepseekV2DecoderLayer(
|
|
vllm_config, prefix, topk_indices_buffer=topk_indices_buffer
|
|
),
|
|
prefix=f"{prefix}.layers",
|
|
)
|
|
|
|
if get_pp_group().is_last_rank:
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size
|
|
)
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None,
|
|
inputs_embeds: 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)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
|
|
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 DeepseekV2MixtureOfExperts(MixtureOfExperts):
|
|
moe_mlp_layers: list[DeepseekV2MoE]
|
|
"""
|
|
List of MoE MLP layers in the model.
|
|
"""
|
|
|
|
def extract_moe_parameters(self, example_moe: DeepseekV2MoE | 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
|
|
logger.warning("DeepSeekV2: No DeepseekV2MoE layer found in model.layers.")
|
|
else:
|
|
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 DeepseekV2ForCausalLM(
|
|
nn.Module, SupportsPP, DeepseekV2MixtureOfExperts, SupportsLoRA, SupportsEagle
|
|
):
|
|
packed_modules_mapping = {
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
|
|
qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
|
|
qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
|
|
self.use_mha = config.model_type == "deepseek" or all(
|
|
dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
|
|
)
|
|
|
|
if self.use_mha:
|
|
self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]
|
|
|
|
# `packed_modules_mapping` needs to be modified before
|
|
# initializing DeepseekV2Model, as it is passed inplace to
|
|
# quantization config init and may be used to select the
|
|
# quant_method for relevant layers during initialization.
|
|
# self.fuse_qkv_a_proj = (
|
|
# hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
|
|
# )
|
|
# if self.fuse_qkv_a_proj:
|
|
# self.packed_modules_mapping["fused_qkv_a_proj"] = [
|
|
# "q_a_proj",
|
|
# "kv_a_proj_with_mqa",
|
|
# ]
|
|
|
|
self.model = DeepseekV2Model(
|
|
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,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
# Set MoE hyperparameters
|
|
self.num_moe_layers = (
|
|
self.config.num_hidden_layers - self.config.first_k_dense_replace
|
|
)
|
|
self.set_moe_parameters()
|
|
|
|
def set_moe_parameters(self):
|
|
self.expert_weights = []
|
|
|
|
self.num_expert_groups = getattr(self.config, "n_group", 1)
|
|
|
|
self.moe_layers = []
|
|
self.moe_mlp_layers = []
|
|
example_moe = None
|
|
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
assert isinstance(layer, DeepseekV2DecoderLayer)
|
|
if isinstance(layer.mlp, DeepseekV2MoE):
|
|
# Pick last one layer since the first ones may be dense layers.
|
|
example_moe = layer.mlp
|
|
if example_moe is None:
|
|
raise RuntimeError("No DeepseekV2MoE layer found in model.layers.")
|
|
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 set_eplb_state(
|
|
self,
|
|
expert_load_view: torch.Tensor,
|
|
logical_to_physical_map: torch.Tensor,
|
|
logical_replica_count: torch.Tensor,
|
|
) -> None:
|
|
for layer_idx, layer in enumerate(self.moe_layers):
|
|
# Register the expert weights.
|
|
self.expert_weights.append(layer.get_expert_weights())
|
|
layer.set_eplb_state(
|
|
moe_layer_idx=layer_idx,
|
|
expert_load_view=expert_load_view,
|
|
logical_to_physical_map=logical_to_physical_map,
|
|
logical_replica_count=logical_replica_count,
|
|
)
|
|
|
|
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 layer in self.model.layers:
|
|
if isinstance(layer.mlp, DeepseekV2MoE):
|
|
moe = layer.mlp
|
|
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()
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.embed_input_ids(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
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 SharedFusedMoE.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,
|
|
num_redundant_experts=0,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
rocm_aiter_moe_shared_expert_enabled = (
|
|
rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
|
|
)
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
# mla_params_mapping = [
|
|
# ("fused_qkv_a_proj", "q_a_proj", 0),
|
|
# ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
|
|
# ]
|
|
mha_params_mapping = [
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
if self.use_mha:
|
|
stacked_params_mapping.extend(mha_params_mapping)
|
|
# else:
|
|
# stacked_params_mapping.extend(mla_params_mapping)
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = SharedFusedMoE.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
|
|
+ (
|
|
self.config.n_shared_experts
|
|
if rocm_aiter_moe_shared_expert_enabled
|
|
else 0
|
|
),
|
|
num_redundant_experts=self.num_redundant_experts,
|
|
)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
try:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
|
if spec_layer is not None:
|
|
continue # skip spec decode layers for main model
|
|
|
|
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:
|
|
is_expert_weight = False
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
# Anyway, this is an expert weight and should not be
|
|
# attempted to load as other weights later
|
|
is_expert_weight = True
|
|
|
|
# Do not modify `name` since the loop may continue here
|
|
# Instead, create a new variable
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
|
|
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=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
|
|
# 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)
|
|
except:
|
|
pass
|
|
opt_support_quant_method = ["GGUFLinearMethod", "UnquantizedLinearMethod", "CompressedTensorsW8A8Int8", "AWQMarlinLinearMethod"]
|
|
# add your opt here..
|
|
def inject_layer(layer, quant_method, is_mla):
|
|
q_lora_rank = getattr(layer, "q_lora_rank", None)
|
|
if quant_method in ["UnquantizedLinearMethod", "CompressedTensorsW8A8Int8"]:
|
|
if q_lora_rank is not None:
|
|
layer.q_a_proj.weight.data = torch.cat([layer.q_a_proj.weight, layer.kv_a_proj_with_mqa.weight], dim=0)
|
|
if hasattr(layer.q_a_proj, "weight_scale"):
|
|
layer.q_a_proj.weight_scale.data = torch.cat([layer.q_a_proj.weight_scale, layer.kv_a_proj_with_mqa.weight_scale], dim=0)
|
|
del layer.kv_a_proj_with_mqa.weight_scale
|
|
elif not is_mla:
|
|
layer.q_proj.weight.data = torch.cat([layer.q_proj.weight, layer.kv_a_proj_with_mqa.weight], dim=0)
|
|
if hasattr(layer.q_proj, "weight_scale"):
|
|
layer.q_proj.weight_scale.data = torch.cat([layer.q_proj.weight_scale, layer.kv_a_proj_with_mqa.weight_scale], dim=0)
|
|
del layer.kv_a_proj_with_mqa.weight_scale
|
|
else:
|
|
return
|
|
del layer.kv_a_proj_with_mqa.weight
|
|
del layer.kv_a_proj_with_mqa
|
|
if is_mla:
|
|
layer.mla_attn.forward = layer.mla_attn.forward_opt
|
|
else:
|
|
layer.forward = layer.forward_opt
|
|
elif quant_method == "GGUFLinearMethod":
|
|
pass
|
|
elif quant_method == "AWQMarlinLinearMethod":
|
|
dtype = layer.kv_a_proj_with_mqa.qweight.dtype
|
|
assert dtype == torch.int32
|
|
if layer.q_lora_rank is not None:
|
|
layer.q_a_proj.qweight.data = torch.cat([layer.q_a_proj.qweight, layer.kv_a_proj_with_mqa.qweight], dim=1)
|
|
layer.q_a_proj.scales.data = torch.cat([layer.q_a_proj.scales, layer.kv_a_proj_with_mqa.scales], dim=1)
|
|
del layer.kv_a_proj_with_mqa.scales
|
|
layer.q_a_proj.qzeros.data = torch.cat([layer.q_a_proj.qzeros, layer.kv_a_proj_with_mqa.qzeros], dim=1)
|
|
del layer.kv_a_proj_with_mqa.qzeros
|
|
elif not is_mla:
|
|
layer.q_proj.weight.data = torch.cat([layer.q_proj.weight, layer.kv_a_proj_with_mqa.weight], dim=1)
|
|
layer.q_proj.scales.data = torch.cat([layer.q_proj.scales, layer.kv_a_proj_with_mqa.scales], dim=1)
|
|
del layer.kv_a_proj_with_mqa.scales
|
|
layer.q_proj.qzeros.data = torch.cat([layer.q_proj.qzeros, layer.kv_a_proj_with_mqa.qzeros], dim=1)
|
|
del layer.kv_a_proj_with_mqa.qzeros
|
|
else:
|
|
return
|
|
|
|
del layer.kv_a_proj_with_mqa.qweight
|
|
del layer.kv_a_proj_with_mqa
|
|
if is_mla:
|
|
layer.mla_attn.forward = layer.mla_attn.forward_opt
|
|
else:
|
|
layer.forward = layer.forward_opt
|
|
else:
|
|
pass
|
|
|
|
for _, layer in self.model.named_modules():
|
|
if layer.__class__.__name__ in ["DeepseekV2Attention","DeepseekV2MLAAttention"]:
|
|
if hasattr(layer.kv_a_proj_with_mqa, "scheme"):
|
|
quant_method = layer.kv_a_proj_with_mqa.scheme.__class__.__name__
|
|
else:
|
|
quant_method = layer.kv_a_proj_with_mqa.quant_method.__class__.__name__
|
|
if quant_method not in opt_support_quant_method:
|
|
break
|
|
|
|
inject_layer(layer, quant_method, is_mla = layer.__class__.__name__ == "DeepseekV2MLAAttention")
|
|
|
|
return loaded_params
|
|
|
|
|
|
class DeepseekForCausalLM(DeepseekV2ForCausalLM):
|
|
pass
|
|
|
|
|
|
class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
|
|
pass
|
|
|
|
|
|
# Compatibility with
|
|
# https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/blob/main/configuration_deepseek.py
|
|
def get_spec_layer_idx_from_weight_name(
|
|
config: DeepseekV2Config | DeepseekV3Config, weight_name: str
|
|
) -> int | None:
|
|
if (
|
|
hasattr(config, "num_nextn_predict_layers")
|
|
and config.num_nextn_predict_layers > 0
|
|
):
|
|
layer_idx = config.num_hidden_layers
|
|
for i in range(config.num_nextn_predict_layers):
|
|
if weight_name.startswith(f"model.layers.{layer_idx + i}."):
|
|
return layer_idx + i
|
|
return None
|