ee0f50e68f
* [Feature] support deepseek v3/r1/v3.2 * fix gpt_oss * update readme * update readme --------- Co-authored-by: hanhaowen <hanhaowen@baidu.com>
1445 lines
59 KiB
Python
1445 lines
59 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, Optional, Union
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import torch
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from torch.library import custom_op
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from torch import nn
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from transformers import DeepseekV2Config, DeepseekV3Config
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from vllm_kunlun.ops.attention.layer 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,
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get_current_vllm_config)
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from vllm.distributed import (get_ep_group, 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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from vllm.forward_context import get_forward_context
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from vllm.logger import init_logger
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from vllm_kunlun.ops.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.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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RowParallelLinear)
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from vllm_kunlun.ops.linear import ReplicatedLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm_kunlun.ops.attention.mla import MLAModules, MultiHeadLatentAttention
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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.shared_fused_moe import SharedFusedMoE
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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 (
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default_weight_loader, maybe_remap_kv_scale_name)
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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 import cdiv, direct_register_custom_op
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from vllm_kunlun.ops.deep_gemm import int8_mqa_logits, int8_paged_mqa_logits
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from vllm.v1.attention.backends.mla.indexer import DeepseekV32IndexerBackend
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from vllm_kunlun.v1.attention.backends.mla.indexer import DeepseekV32IndexerMetadata
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from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
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from vllm.model_executor.models.interfaces import MixtureOfExperts, SupportsLoRA, 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 DeepseekV32IndexerCache
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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 xspeedgate_ops
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_is_kunlun = True
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logger = init_logger(__name__)
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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: Optional[QuantizationConfig] = 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, [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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self.down_proj = RowParallelLinear(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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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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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: Union[DeepseekV2Config, DeepseekV3Config],
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parallel_config: ParallelConfig,
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quant_config: Optional[QuantizationConfig] = 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 = config.routed_scaling_factor
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self.ep_group = get_ep_group().device_group
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self.ep_rank = self.ep_group.rank()
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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(f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now.")
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self.gate = ReplicatedLinear(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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if config.topk_method == "noaux_tc":
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self.gate.e_score_correction_bias = nn.Parameter(
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torch.empty(config.n_routed_experts, dtype=torch.float32))
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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 +
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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 *
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self.n_local_physical_experts)
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self.physical_expert_end = (self.physical_expert_start +
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self.n_local_physical_experts)
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if config.n_shared_experts is None:
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self.experts = FusedMoE(
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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=config.n_group,
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topk_group=config.topk_group,
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prefix=f"{prefix}.experts",
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scoring_func=config.scoring_func,
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# we do scaling outside, set factor to 1.0 to avoid double mul
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routed_scaling_factor=1.0,
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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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)
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self.shared_experts = None
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else:
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intermediate_size = (config.moe_intermediate_size *
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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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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=config.n_group,
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topk_group=config.topk_group,
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prefix=f"{prefix}.experts",
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scoring_func=config.scoring_func,
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# we do scaling outside, set factor to 1.0 to avoid double mul
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routed_scaling_factor=1.0,
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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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)
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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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# 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(hidden_states=hidden_states,
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router_logits=router_logits)
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if self.shared_experts is not None:
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shared_output, final_hidden_states = fused_moe_out
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else:
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shared_output = None
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final_hidden_states = fused_moe_out
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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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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. / 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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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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final_hidden_states = final_hidden_states[:num_tokens]
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elif self.tp_size > 1:
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final_hidden_states = (
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self.experts.maybe_all_reduce_tensor_model_parallel(
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final_hidden_states))
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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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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: 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: 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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topk_indices_buffer: Optional[torch.Tensor] = 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, "topk_indices_buffer is not \
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supported for DeepseekV2Attention"
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if self.q_lora_rank is not None:
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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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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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# O projection.
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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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if rope_scaling:
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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.attn = Attention(self.num_local_heads,
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self.qk_head_dim,
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self.scaling,
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num_kv_heads=self.num_local_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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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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if self.q_lora_rank is not None:
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q = self.q_a_proj(hidden_states)[0]
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q = self.q_a_layernorm(q)
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q = self.q_b_proj(q)[0].view(-1, self.num_local_heads,
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self.qk_head_dim)
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else:
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q = self.q_proj(hidden_states)[0].view(-1, self.num_local_heads,
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self.qk_head_dim)
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q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
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dim=-1)
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latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
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kv_a, _ = latent_cache.split(
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[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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latent_cache = latent_cache.unsqueeze(1)
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kv_a = self.kv_a_layernorm(kv_a)
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kv = self.kv_b_proj(kv_a)[0]
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||
kv = kv.view(-1, self.num_local_heads,
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self.qk_nope_head_dim + self.v_head_dim)
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k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
||
k_pe = latent_cache[:, :, self.kv_lora_rank:]
|
||
|
||
q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
|
||
|
||
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
|
||
|
||
@torch.inference_mode()
|
||
def cp_gather_indexer_k_quant_cache(
|
||
kv_cache, # [num_blocks, block_size, head_dim + 1]
|
||
block_table, # [batch_size, num_blocks]
|
||
cu_seq_lens, # [batch_size + 1, ]
|
||
batch_size,
|
||
head_dim,
|
||
):
|
||
num_blocks, block_size, _ = kv_cache.shape
|
||
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.int8)
|
||
gather_scale = gather_scale.view(torch.float32)
|
||
return gather_value, gather_scale
|
||
|
||
@torch.inference_mode()
|
||
def kunlun_indexer_k_quant_cache(
|
||
k, #[num_tokens, head_dim]
|
||
kv_cache, # [num_blocks, cache_block_size, head_dim + 1]
|
||
slot_mapping, # [num_tokens]
|
||
quant_block_size,
|
||
):
|
||
num_blocks, cache_block_size, cache_stride = kv_cache.shape
|
||
# num_tokens, head_dim = k.shape
|
||
head_dim = k.shape[1]
|
||
num_tokens = slot_mapping.shape[0]
|
||
assert head_dim % quant_block_size == 0
|
||
kv_cache = kv_cache.view(num_blocks, -1)
|
||
|
||
k_fp8 = torch.empty(
|
||
k.shape,
|
||
device=k.device,
|
||
dtype=torch.int8,
|
||
)
|
||
k_scale = torch.empty(
|
||
[k.shape[0], 1],
|
||
device=k.device,
|
||
dtype=torch.float32,
|
||
)
|
||
|
||
torch.ops._C.quant2d(k, k_fp8, k_scale, force_sdnn=True)
|
||
k_scale /= 127
|
||
for token_idx in range(num_tokens):
|
||
slot_idx = slot_mapping[token_idx]
|
||
if slot_idx < 0:
|
||
continue
|
||
block_idx = slot_idx // cache_block_size
|
||
block_offset = slot_idx % cache_block_size
|
||
v_offset = block_offset * head_dim
|
||
kv_cache[block_idx, v_offset:v_offset + head_dim] = k_fp8[token_idx, :].view(torch.uint8).contiguous()
|
||
s_offset = cache_block_size * head_dim + block_offset * 4
|
||
kv_cache[block_idx, s_offset:s_offset + 4] = k_scale[token_idx, :].view(torch.uint8).contiguous()
|
||
kv_cache = kv_cache.view(num_blocks, cache_block_size, cache_stride)
|
||
|
||
@custom_op("vllm::sparse_attn_indexer_vllm_kunlun", mutates_args=())
|
||
def sparse_attn_indexer_vllm_kunlun(
|
||
hidden_states: torch.Tensor,
|
||
k_cache_prefix: str,
|
||
kv_cache: torch.Tensor,
|
||
q_fp8: torch.Tensor,
|
||
k: torch.Tensor,
|
||
weights: torch.Tensor,
|
||
quant_block_size: int,
|
||
scale_fmt: Optional[str],
|
||
topk_tokens: int,
|
||
head_dim: int,
|
||
max_model_len: int,
|
||
total_seq_lens: int,
|
||
topk_indices_buffer: Optional[torch.Tensor],
|
||
) -> None:
|
||
|
||
# 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):
|
||
sparse_attn_indexer_vllm_kunlun_fake(
|
||
hidden_states,
|
||
k_cache_prefix,
|
||
kv_cache,
|
||
q_fp8,
|
||
k,
|
||
weights,
|
||
quant_block_size,
|
||
scale_fmt,
|
||
topk_tokens,
|
||
head_dim,
|
||
max_model_len,
|
||
total_seq_lens,
|
||
topk_indices_buffer,
|
||
)
|
||
return
|
||
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
|
||
|
||
# kunlun_indexer_k_quant_cache(
|
||
# k,
|
||
# kv_cache,
|
||
# slot_mapping,
|
||
# quant_block_size,
|
||
# )
|
||
|
||
torch.ops.xspeedgate_ops.indexer_k_quant_and_cache(
|
||
k,
|
||
kv_cache,
|
||
slot_mapping,
|
||
quant_block_size,
|
||
scale_fmt,
|
||
)
|
||
topk_indices_buffer[:hidden_states.shape[0]] = -1
|
||
if has_prefill:
|
||
prefill_metadata = attn_metadata.prefill
|
||
for chunk in prefill_metadata.chunks:
|
||
k_fp8, k_scale = cp_gather_indexer_k_quant_cache(
|
||
kv_cache,
|
||
chunk.block_table,
|
||
chunk.cu_seq_lens,
|
||
chunk.num_reqs,
|
||
head_dim,
|
||
)
|
||
|
||
logits = int8_mqa_logits(
|
||
q_fp8[chunk.token_start:chunk.token_end],
|
||
(k_fp8, k_scale),
|
||
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_fp8_decode_tokens = pack_seq_triton(
|
||
q_fp8[:num_decode_tokens], decode_lens)
|
||
else:
|
||
padded_q_fp8_decode_tokens = q_fp8[:num_decode_tokens].reshape(
|
||
decode_lens.shape[0], -1, *q_fp8.shape[1:])
|
||
# TODO: move and optimize below logic with triton kernels
|
||
batch_size = padded_q_fp8_decode_tokens.shape[0]
|
||
next_n = padded_q_fp8_decode_tokens.shape[1]
|
||
assert batch_size == decode_metadata.seq_lens.shape[0]
|
||
num_padded_tokens = batch_size * next_n
|
||
logits = int8_paged_mqa_logits(
|
||
padded_q_fp8_decode_tokens,
|
||
kv_cache,
|
||
weights[:num_padded_tokens],
|
||
decode_metadata.seq_lens,
|
||
decode_metadata.seq_lens_cpu,
|
||
decode_metadata.block_table,
|
||
decode_metadata.schedule_metadata,
|
||
max_model_len=max_model_len,
|
||
)
|
||
# padded query len
|
||
current_device = padded_q_fp8_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_fp8_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_vllm_kunlun_fake(
|
||
hidden_states: torch.Tensor,
|
||
k_cache_prefix: str,
|
||
kv_cache: torch.Tensor,
|
||
q_fp8: torch.Tensor,
|
||
k: torch.Tensor,
|
||
weights: torch.Tensor,
|
||
quant_block_size: int,
|
||
scale_fmt: Optional[str],
|
||
topk_tokens: int,
|
||
head_dim: int,
|
||
max_model_len: int,
|
||
total_seq_lens: int,
|
||
topk_indices_buffer: Optional[torch.Tensor],
|
||
) -> None:
|
||
return
|
||
|
||
sparse_attn_indexer_vllm_kunlun.register_fake(sparse_attn_indexer_vllm_kunlun_fake)
|
||
|
||
class Indexer(nn.Module):
|
||
|
||
def __init__(self,
|
||
vllm_config: VllmConfig,
|
||
config: Union[DeepseekV2Config, DeepseekV3Config],
|
||
hidden_size: int,
|
||
q_lora_rank: int,
|
||
quant_config: Optional[QuantizationConfig],
|
||
cache_config: Optional[CacheConfig],
|
||
topk_indices_buffer: Optional[torch.Tensor],
|
||
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,
|
||
bias=False,
|
||
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 +
|
||
self.head_dim // self.quant_block_size * 4,
|
||
dtype=torch.uint8,
|
||
prefix=f"{prefix}.k_cache",
|
||
cache_config=cache_config)
|
||
self.max_model_len = vllm_config.model_config.max_model_len
|
||
if self.max_model_len % cache_config.block_size != 0: #由于I8_paged_mqa_logits输入参数的限制，最大长度必须为block_zise的整数倍
|
||
self.max_model_len = self.max_model_len + cache_config.block_size - (self.max_model_len % cache_config.block_size)
|
||
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 = torch.empty(
|
||
q.shape,
|
||
device=q.device,
|
||
dtype=torch.int8,
|
||
)
|
||
q_scale = torch.empty(
|
||
[q.shape[0], 1],
|
||
device=q.device,
|
||
dtype=torch.float32,
|
||
)
|
||
torch.ops._C.quant2d(q, q_fp8, q_scale, force_sdnn=True)
|
||
q_scale /= 127
|
||
q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
|
||
q_scale = q_scale.view(-1, self.n_head)
|
||
weights, _ = self.weights_proj(hidden_states)
|
||
weights = weights * self.n_head**-0.5
|
||
weights = weights * q_scale * self.softmax_scale
|
||
|
||
torch.ops.vllm.sparse_attn_indexer_vllm_kunlun(
|
||
hidden_states,
|
||
self.k_cache.prefix,
|
||
self.k_cache.kv_cache[0],
|
||
q_fp8,
|
||
k,
|
||
weights,
|
||
self.quant_block_size,
|
||
self.scale_fmt,
|
||
self.topk_tokens,
|
||
self.head_dim,
|
||
self.max_model_len,
|
||
self.max_total_seq_len,
|
||
self.topk_indices_buffer,
|
||
)
|
||
return 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: Union[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: Optional[int],
|
||
kv_lora_rank: int,
|
||
rope_theta: float = 10000,
|
||
rope_scaling: Optional[dict[str, Any]] = None,
|
||
max_position_embeddings: int = 8192,
|
||
cache_config: Optional[CacheConfig] = None,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
prefix: str = "",
|
||
topk_indices_buffer: Optional[torch.Tensor] = 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.fused_qkv_a_proj = MergedColumnParallelLinear(
|
||
self.hidden_size,
|
||
[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
|
||
bias=False,
|
||
quant_config=quant_config,
|
||
prefix=f"{prefix}.fused_qkv_a_proj",
|
||
disable_tp=True)
|
||
else:
|
||
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")
|
||
|
||
if self.q_lora_rank is not None:
|
||
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_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,
|
||
fused_qkv_a_proj=self.fused_qkv_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 = MultiHeadLatentAttention(
|
||
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,
|
||
topk_indices_buffer: Optional[torch.Tensor] = None) -> None:
|
||
super().__init__()
|
||
|
||
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)
|
||
# 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
|
||
if 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=config.qk_nope_head_dim,
|
||
qk_rope_head_dim=config.qk_rope_head_dim,
|
||
v_head_dim=config.v_head_dim,
|
||
q_lora_rank=config.q_lora_rank
|
||
if hasattr(config, "q_lora_rank") else None,
|
||
kv_lora_rank=config.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 % config.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 = config.routed_scaling_factor
|
||
|
||
def forward(
|
||
self,
|
||
positions: torch.Tensor,
|
||
hidden_states: torch.Tensor,
|
||
residual: Optional[torch.Tensor],
|
||
) -> 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 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. / 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. / 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. / 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.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="cuda")
|
||
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),
|
||
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 get_input_embeddings(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: Optional[IntermediateTensors],
|
||
inputs_embeds: Optional[torch.Tensor] = None,
|
||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||
if get_pp_group().is_first_rank:
|
||
if inputs_embeds is not None:
|
||
hidden_states = inputs_embeds
|
||
else:
|
||
hidden_states = self.get_input_embeddings(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 DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts,
|
||
SupportsLoRA):
|
||
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
|
||
|
||
# `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)
|
||
self.expert_weights = []
|
||
|
||
# Set MoE hyperparameters
|
||
self.num_moe_layers = (config.num_hidden_layers -
|
||
config.first_k_dense_replace)
|
||
self.num_expert_groups = config.n_group
|
||
|
||
self.moe_layers: list[FusedMoE] = []
|
||
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
|
||
self.moe_layers.append(layer.mlp.experts)
|
||
|
||
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 get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||
return self.model.get_input_embeddings(input_ids)
|
||
|
||
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]:
|
||
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
||
inputs_embeds)
|
||
return hidden_states
|
||
|
||
def compute_logits(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
) -> Optional[torch.Tensor]:
|
||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||
return logits
|
||
|
||
def load_weights(self, weights: Iterable[tuple[str,
|
||
torch.Tensor]]) -> set[str]:
|
||
stacked_params_mapping = [
|
||
# (param_name, shard_name, shard_id)
|
||
("gate_up_proj", "gate_proj", 0),
|
||
("gate_up_proj", "up_proj", 1),
|
||
("fused_qkv_a_proj", "q_a_proj", 0),
|
||
("fused_qkv_a_proj", "kv_a_proj_with_mqa", 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,
|
||
num_redundant_experts=self.num_redundant_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
|
||
|
||
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_mapped = name.replace(weight_name, param_name)
|
||
|
||
# QKV fusion is optional, fall back to normal
|
||
# weight loading if it's not enabled
|
||
# if go with fusion option, then update name
|
||
if ((param_name == "fused_qkv_a_proj")
|
||
and name_mapped not in params_dict):
|
||
continue
|
||
else:
|
||
name = name_mapped
|
||
# 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
|
||
if name not in params_dict:
|
||
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
|
||
if name_mapped not in params_dict:
|
||
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
|
||
if name not in params_dict:
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = getattr(param, "weight_loader",
|
||
default_weight_loader)
|
||
weight_loader(param, loaded_weight)
|
||
loaded_params.add(name)
|
||
|
||
return loaded_params
|
||
|
||
|
||
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: Union[DeepseekV2Config,
|
||
DeepseekV3Config],
|
||
weight_name: str) -> Optional[int]:
|
||
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 |