mirror of
https://github.com/handsomezhuzhu/vllm-npu-plugin.git
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1302 lines
56 KiB
Python
1302 lines
56 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# # Adapted from
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# # vllm-project/vllm/blob/main/vllm/model_executor/models/deepseek_v2.py
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# # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# # vllm-project/vllm/vllm/model_executor/models/deepseek_v2.py
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# """Inference-only DeepseekV2/DeepseekV3 model."""
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from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
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import torch
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import torch_npu
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, ModelConfig, VllmConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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get_tp_group, split_tensor_along_last_dim,
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tensor_model_parallel_all_gather,
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tensor_model_parallel_all_reduce,
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tensor_model_parallel_reduce_scatter)
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from vllm.distributed.parallel_state import get_dp_group, get_ep_group
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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UnquantizedLinearMethod)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.models.deepseek_v2 import \
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DeepseekV2ForCausalLM # noqa: E501
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from vllm.model_executor.models.deepseek_v2 import \
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yarn_get_mscale # noqa: E501
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from vllm.model_executor.models.deepseek_v2 import (
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DeepseekV2Attention, DeepseekV2DecoderLayer, DeepseekV2MLAAttention,
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get_spec_layer_idx_from_weight_name)
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from vllm.model_executor.models.utils import (
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PPMissingLayer, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
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from vllm.sequence import IntermediateTensors
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from vllm_npu import envs
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from vllm_npu.ascend_config import get_ascend_config
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from vllm_npu.models.layers.sfa import Indexer
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from vllm_npu.ops.weight_prefetch import maybe_npu_prefetch
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from vllm_npu.quantization.quant_config import AscendLinearMethod
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from vllm_npu.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE
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from vllm_npu.torchair.quantization.torchair_w8a8_dynamic import \
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TorchairAscendW8A8DynamicLinearMethod
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from vllm_npu.utils import dispose_tensor, oproj_tp_enable
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class TorchairDeepseekV2SiluAndMul(SiluAndMul):
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def __init__(self,
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*,
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weight_scale: Optional[Callable[[], torch.Tensor]] = None):
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super().__init__()
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self.weight_scale = weight_scale
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def forward_oot(self, x: Union[torch.Tensor, Tuple[torch.Tensor,
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torch.Tensor]]):
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if isinstance(x, tuple):
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assert self.weight_scale is not None
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# For AscendW8A8DynamicLinearMethod:
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# a dynamic scale is passed along with the quantized value.
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quantized_x, dynamic_scale = x
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return torch_npu.npu_dequant_swiglu_quant(
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x=quantized_x,
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weight_scale=self.weight_scale(),
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activation_scale=dynamic_scale,
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activate_left=True,
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quant_mode=1)
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else:
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return super().forward_oot(x)
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class TorchairDeepseekV2MergedReplicatedLinear(ReplicatedLinear):
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def __init__(
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self,
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input_size: int,
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output_sizes: list[int],
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bias: bool = True,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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self.output_sizes = output_sizes
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super().__init__(input_size,
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sum(output_sizes),
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bias=bias,
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quant_config=quant_config,
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prefix=prefix)
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def weight_loader(self, param: torch.nn.Parameter,
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loaded_weight: torch.Tensor, loaded_shard_id: int):
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# With no support for GGUF format yet.
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assert not getattr(param, "is_gguf_weight", False)
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assert not getattr(param, "is_gguf_weight_type", False)
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assert loaded_shard_id < len(self.output_sizes)
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shard_offset = sum(self.output_sizes[:loaded_shard_id])
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shard_size = self.output_sizes[loaded_shard_id]
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shard = param.data.narrow(param.output_dim, shard_offset, shard_size)
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assert shard.size() == loaded_weight.size(), (
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f"Tried to load weights of size {loaded_weight.size()}"
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f"to a parameter shard of id {loaded_shard_id} size {shard.size()}"
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)
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shard.copy_(loaded_weight)
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class TorchairDeepseekV2RowParallelLinearReplaceAllreduce(RowParallelLinear):
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def forward(
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self,
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input_,
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is_prefill=True,
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is_force_scatter=False
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[nn.Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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tp_rank = get_tensor_model_parallel_rank()
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[tp_rank].contiguous()
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# Matrix multiply.
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assert self.quant_method is not None
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# Only fuse bias add into GEMM for rank 0 (this ensures that
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# bias will not get added more than once in TP>1 case)
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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output_parallel = self.quant_method.apply(self,
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input_parallel,
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bias=bias_)
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forward_context = get_forward_context()
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if self.reduce_results and self.tp_size > 1:
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num_tokens = output_parallel.shape[0]
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if is_force_scatter and num_tokens % self.tp_size:
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output_parallel = nn.functional.pad(
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output_parallel, (0, 0, 0, -num_tokens % self.tp_size))
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if is_force_scatter or (not forward_context.with_prefill
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and output_parallel.shape[0] % self.tp_size
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== 0):
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output = tensor_model_parallel_reduce_scatter(output_parallel,
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dim=0)
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else:
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output = tensor_model_parallel_all_reduce(output_parallel)
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else:
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output = output_parallel
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output_bias = self.bias if self.skip_bias_add else None
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if not self.return_bias:
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return output
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return output, output_bias
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class TorchairDeepseekV2RowParallelLinear(RowParallelLinear):
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def forward(
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self,
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input_,
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is_prefill=True,
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is_force_scatter=False
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) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[nn.Parameter]]]:
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if self.input_is_parallel:
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input_parallel = input_
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else:
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tp_rank = get_tensor_model_parallel_rank()
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splitted_input = split_tensor_along_last_dim(
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input_, num_partitions=self.tp_size)
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input_parallel = splitted_input[tp_rank].contiguous()
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# Matrix multiply.
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assert self.quant_method is not None
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# Only fuse bias add into GEMM for rank 0 (this ensures that
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# bias will not get added more than once in TP>1 case)
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bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
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output_parallel = self.quant_method.apply(self,
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input_parallel,
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bias=bias_)
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if self.reduce_results and self.tp_size > 1:
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output = tensor_model_parallel_all_reduce(output_parallel)
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else:
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output = output_parallel
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output_bias = self.bias if self.skip_bias_add else None
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if not self.return_bias:
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return output
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return output, output_bias
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class TorchairDeepseekV2MLP(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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force_replicate: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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if not force_replicate:
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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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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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prefix=f"{prefix}.down_proj")
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else:
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self.gate_up_proj = TorchairDeepseekV2MergedReplicatedLinear(
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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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prefix=f"{prefix}.gate_up_proj")
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self.down_proj = ReplicatedLinear(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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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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quant_method = self.gate_up_proj.quant_method
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if isinstance(quant_method, UnquantizedLinearMethod):
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self.act_fn = TorchairDeepseekV2SiluAndMul()
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elif (isinstance(quant_method, AscendLinearMethod)
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and isinstance(quant_method.quant_method,
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TorchairAscendW8A8DynamicLinearMethod)):
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# TODO(sdmyzlp): Currently preserved as before:
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# 1. The only quantization supported for silu is W8A8Dynamic
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# 2. Output dtype of gate_up/down is fixed to be int32/bfloat16
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#
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# Maybe one can implement a better and more general configuration
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# scheme, e.g. by somehow passing around the tweaked `quant_config`
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self.act_fn = TorchairDeepseekV2SiluAndMul(
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# Use lazy binding, for `weight_scale_fp32` is accessible
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# only after `process_weights_after_loading`.
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weight_scale=lambda: self.gate_up_proj.weight_scale_fp32)
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# To be consumed by AscendW8A8DynamicLinearMethod.apply()
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self.gate_up_proj._ascend_quant_config = {
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"output_dtype": torch.int32,
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"pertoken_scale": False,
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"return_scale": True,
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}
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self.down_proj._ascend_quant_config = {
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"output_dtype": torch.bfloat16,
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"pertoken_scale": True,
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"return_scale": False,
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}
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else:
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raise NotImplementedError(
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f"Quantization with [{type(quant_method)}] is NOT supported")
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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 TorchairDeepseekV2MoE(nn.Module):
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top_k: int
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def __init__(
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self,
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config: PretrainedConfig,
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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.routed_scaling_factor = config.routed_scaling_factor
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self.n_shared_experts = config.n_shared_experts
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if self.tp_size > config.n_routed_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.n_routed_experts}.")
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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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ascend_config = get_ascend_config()
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self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
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self.multistream_overlap_shared_expert = \
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ascend_config.multistream_overlap_shared_expert and \
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self.torchair_graph_enabled
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self.enable_super_kernel = ascend_config.torchair_graph_config.enable_super_kernel
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self.params_dtype = torch.float32 if self.enable_super_kernel else \
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torch.get_default_dtype()
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# Converting gate weight to fp32 is to adapt to the super kernel feature.
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# Super kernel feature currently cannot fuse operators such as cast, stridedslice, and add.
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# In the moe stage, Cast will interrupt the fusion of the super kernel. To avoid this problem,
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# modifications will be made in the initialization stage.
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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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params_dtype=self.params_dtype,
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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=self.params_dtype))
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else:
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self.gate.e_score_correction_bias = None
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self.experts = TorchairAscendFusedMoE(
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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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e_score_correction_bias=self.gate.e_score_correction_bias)
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if config.n_shared_experts is not None:
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self.all_reduce_merge = self.experts.all_reduce_merge
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reduce_results = not self.all_reduce_merge
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intermediate_size = (config.moe_intermediate_size *
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config.n_shared_experts)
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enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
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self.shared_experts = TorchairDeepseekV2MLP(
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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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reduce_results=reduce_results,
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force_replicate=self.multistream_overlap_shared_expert
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or enable_shared_expert_dp,
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prefix=f"{prefix}.shared_experts",
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)
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else:
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self.shared_experts = None # type: ignore
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TorchairDeepseekV2MoE.top_k = config.num_experts_per_tok
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self.dp_size = get_dp_group().world_size
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self.tp_group = get_tp_group().device_group
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self.tp_rank = get_tp_group().rank_in_group
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self.ep_group = get_ep_group()
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self.params_dtype = torch.get_default_dtype()
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self.rm_router_logits = self.experts.rm_router_logits
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def forward(self,
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hidden_states: torch.Tensor,
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attn_metadata: Optional[AttentionMetadata] = None,
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replace_allreduce: bool = False) -> torch.Tensor:
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forward_context = get_forward_context()
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# when profile runs, force experts to load balanced tokens
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# to avoid high memory consumption on a single rank.
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enable_force_load_balance = forward_context.in_profile_run
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is_prefill = forward_context.with_prefill
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# router_logits: (num_tokens, n_experts)
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router_logits = None
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if not self.rm_router_logits and not self.multistream_overlap_shared_expert:
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router_logits, _ = self.gate(hidden_states)
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experts_hidden_states = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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is_prefill=is_prefill,
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top_k=TorchairDeepseekV2MoE.top_k,
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enable_force_load_balance=enable_force_load_balance,
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shared_experts=self.shared_experts,
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gate=self.gate,
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replace_allreduce=replace_allreduce)
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hidden_states = (
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experts_hidden_states[0] * self.routed_scaling_factor +
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experts_hidden_states[1])
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if self.all_reduce_merge:
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# When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
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hidden_states = tensor_model_parallel_all_reduce(hidden_states)
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return hidden_states
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class TorchairDeepseekV2MLAAttention(DeepseekV2MLAAttention):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: Optional[int],
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kv_lora_rank: int,
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rope_theta: float = 10000,
|
|
rope_scaling: Optional[Dict[str, Any]] = None,
|
|
max_position_embeddings: int = 8192,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
decoder_layer=None,
|
|
) -> None:
|
|
nn.Module.__init__(self)
|
|
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
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
assert num_heads % self.tp_size == 0
|
|
self.num_local_heads = num_heads // self.tp_size
|
|
self.layers = config.num_hidden_layers
|
|
self.first_k_dense_replace = config.first_k_dense_replace
|
|
|
|
self.scaling = self.qk_head_dim**-0.5
|
|
self.rope_theta = rope_theta
|
|
self.max_position_embeddings = max_position_embeddings
|
|
|
|
self.prefix = prefix
|
|
self.debug_layer_idx = int(self.prefix.split(".")[-2])
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
|
|
self.enable_multistream_mla = \
|
|
ascend_config.torchair_graph_config.enable_multistream_mla
|
|
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
|
|
|
|
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(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")
|
|
|
|
if oproj_tp_enable():
|
|
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")
|
|
elif (config.n_routed_experts is not None
|
|
and self.debug_layer_idx >= config.first_k_dense_replace
|
|
and self.debug_layer_idx % config.moe_layer_freq == 0
|
|
and (ascend_config.multistream_overlap_shared_expert
|
|
or self.enable_shared_expert_dp)):
|
|
self.o_proj = TorchairDeepseekV2RowParallelLinearReplaceAllreduce(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj")
|
|
else:
|
|
self.o_proj = TorchairDeepseekV2RowParallelLinear(
|
|
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
|
|
|
|
# In the MLA backend, kv_cache includes both k_c and
|
|
# pe (i.e. decoupled position embeddings). In particular,
|
|
# the concat_and_cache_mla op requires
|
|
# k_c.size(1) + k_pe.size(1) == kv_cache.size(2)
|
|
# i.e.
|
|
# kv_lora_rank + qk_rope_head_dim == head_size
|
|
self.mla_attn = Attention(
|
|
num_heads=self.num_local_heads,
|
|
head_size=self.kv_lora_rank + self.qk_rope_head_dim,
|
|
scale=self.scaling,
|
|
num_kv_heads=1,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
use_mla=True,
|
|
# MLA Args
|
|
q_lora_rank=self.q_lora_rank,
|
|
kv_lora_rank=self.kv_lora_rank,
|
|
qk_nope_head_dim=self.qk_nope_head_dim,
|
|
qk_rope_head_dim=self.qk_rope_head_dim,
|
|
qk_head_dim=self.qk_head_dim,
|
|
v_head_dim=self.v_head_dim,
|
|
rotary_emb=self.rotary_emb,
|
|
q_proj=self.q_proj if self.q_lora_rank is None else None,
|
|
q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
|
|
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
|
|
kv_a_layernorm=self.kv_a_layernorm,
|
|
kv_b_proj=self.kv_b_proj,
|
|
o_proj=self.o_proj,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: Optional[torch.Tensor] = None,
|
|
attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
|
|
forward_context = get_forward_context()
|
|
enable_multistream_mla = (self.enable_multistream_mla
|
|
and attn_metadata is not None
|
|
and not forward_context.with_prefill
|
|
and attn_metadata.num_decodes > 0)
|
|
forward_kwargs = {"enable_multistream_mla": enable_multistream_mla}
|
|
if self.q_lora_rank is not None:
|
|
maybe_npu_prefetch(self.q_a_proj.weight,
|
|
hidden_states,
|
|
enabled=enable_multistream_mla)
|
|
ckq = self.q_a_proj(hidden_states)[0]
|
|
hidden_states_or_q_c = self.q_a_layernorm(ckq)
|
|
forward_kwargs['ckq'] = ckq
|
|
else:
|
|
hidden_states_or_q_c = hidden_states
|
|
if self.torchair_graph_enabled:
|
|
output_shape = hidden_states.shape
|
|
output = torch.empty(output_shape,
|
|
dtype=hidden_states_or_q_c.dtype,
|
|
device=hidden_states_or_q_c.device)
|
|
forward_kwargs['output'] = output
|
|
output = self.mla_attn.impl.forward(self.mla_attn,
|
|
hidden_states_or_q_c,
|
|
hidden_states, None, kv_cache,
|
|
attn_metadata,
|
|
**forward_kwargs)
|
|
output = output.view(-1, output_shape[-1])
|
|
return output
|
|
else:
|
|
kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
if self.enable_shared_expert_dp and self.debug_layer_idx > self.first_k_dense_replace and self.debug_layer_idx < self.layers:
|
|
hidden_states_or_q_c = get_tp_group().all_gather(
|
|
hidden_states_or_q_c, 0)
|
|
kv_no_split = get_tp_group().all_gather(kv_no_split, 0)
|
|
|
|
kv_c, k_pe = kv_no_split.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
|
|
if not self.enable_shared_expert_dp or self.debug_layer_idx < self.first_k_dense_replace:
|
|
output_shape = hidden_states.shape
|
|
else:
|
|
num_tokens = hidden_states_or_q_c.shape[0]
|
|
rows = num_tokens // self.tp_size
|
|
if num_tokens % self.tp_size:
|
|
rows += 1
|
|
output_shape = (rows, hidden_states.shape[1])
|
|
return self.mla_attn(hidden_states_or_q_c,
|
|
kv_c_normed,
|
|
k_pe,
|
|
output_shape=output_shape)
|
|
|
|
|
|
class TorchairDeepseekV2SFAAttention(DeepseekV2MLAAttention):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
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 = "",
|
|
decoder_layer=None,
|
|
) -> None:
|
|
nn.Module.__init__(self)
|
|
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
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
assert num_heads % self.tp_size == 0
|
|
self.num_local_heads = num_heads // self.tp_size
|
|
self.layers = config.num_hidden_layers
|
|
self.first_k_dense_replace = config.first_k_dense_replace
|
|
|
|
self.scaling = self.qk_head_dim**-0.5
|
|
self.rope_theta = rope_theta
|
|
self.max_position_embeddings = max_position_embeddings
|
|
|
|
self.prefix = prefix
|
|
self.debug_layer_idx = int(self.prefix.split(".")[-2])
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
|
|
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
|
|
|
|
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",
|
|
return_bias=False,
|
|
)
|
|
self.q_a_layernorm = RMSNorm(self.q_lora_rank,
|
|
eps=config.rms_norm_eps)
|
|
self.q_b_proj = ColumnParallelLinear(
|
|
q_lora_rank,
|
|
self.num_heads * self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.q_b_proj",
|
|
return_bias=False,
|
|
)
|
|
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",
|
|
return_bias=False,
|
|
)
|
|
|
|
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",
|
|
return_bias=False,
|
|
)
|
|
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",
|
|
return_bias=False,
|
|
)
|
|
if (config.n_routed_experts is not None
|
|
and self.debug_layer_idx >= config.first_k_dense_replace
|
|
and self.debug_layer_idx % config.moe_layer_freq == 0
|
|
and (ascend_config.multistream_overlap_shared_expert
|
|
or self.enable_shared_expert_dp)):
|
|
self.o_proj = TorchairDeepseekV2RowParallelLinearReplaceAllreduce(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
return_bias=False,
|
|
)
|
|
else:
|
|
self.o_proj = TorchairDeepseekV2RowParallelLinear(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
return_bias=False,
|
|
)
|
|
|
|
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.dim: int = config.hidden_size # 7168
|
|
# TODO(zzzzwwjj): wait transformers add these params
|
|
self.n_heads: int = 64 # 64
|
|
self.head_dim: int = 128 # 128
|
|
self.index_topk: int = 2048 # 2048
|
|
self.indexer = Indexer(
|
|
config,
|
|
quant_config=quant_config,
|
|
dim=self.dim,
|
|
n_heads=self.n_heads,
|
|
head_dim=self.head_dim,
|
|
index_topk=self.index_topk,
|
|
prefix=f"{prefix}.indexer",
|
|
)
|
|
|
|
self.sfa_attn = Attention(
|
|
num_heads=self.num_local_heads,
|
|
head_size=self.kv_lora_rank + self.qk_rope_head_dim,
|
|
scale=self.scaling,
|
|
num_kv_heads=1,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
use_mla=True,
|
|
use_sparse=True,
|
|
# SFA Args
|
|
q_lora_rank=self.q_lora_rank,
|
|
kv_lora_rank=self.kv_lora_rank,
|
|
qk_nope_head_dim=self.qk_nope_head_dim,
|
|
qk_rope_head_dim=self.qk_rope_head_dim,
|
|
qk_head_dim=self.qk_head_dim,
|
|
v_head_dim=self.v_head_dim,
|
|
rotary_emb=self.rotary_emb,
|
|
q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
|
|
q_a_layernorm=self.q_a_layernorm
|
|
if self.q_lora_rank is not None else None,
|
|
q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
|
|
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
|
|
kv_a_layernorm=self.kv_a_layernorm,
|
|
kv_b_proj=self.kv_b_proj,
|
|
o_proj=self.o_proj,
|
|
indexer=self.indexer,
|
|
decoder_layer=decoder_layer,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: Optional[torch.Tensor] = None,
|
|
attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
|
|
forward_context = get_forward_context()
|
|
if not self.torchair_graph_enabled:
|
|
if forward_context.attn_metadata is not None and isinstance(
|
|
forward_context.attn_metadata, dict):
|
|
attn_metadata = next(
|
|
iter(forward_context.attn_metadata.values()), None)
|
|
else:
|
|
attn_metadata = forward_context.attn_metadata
|
|
if kv_cache is None:
|
|
kv_cache = self.sfa_attn.kv_cache[
|
|
forward_context.virtual_engine]
|
|
|
|
num_tokens = hidden_states.shape[0]
|
|
need_gather_q_kv = False
|
|
# if self.enable_shared_expert_dp and self.debug_layer_idx > self.first_k_dense_replace and self.debug_layer_idx < self.layers:
|
|
# # Simulate all gather to calculate output shape
|
|
# num_tokens = num_tokens * self.tp_size
|
|
# need_gather_q_kv = True
|
|
if not self.enable_shared_expert_dp or self.debug_layer_idx != self.first_k_dense_replace:
|
|
output_shape = hidden_states.shape
|
|
if self.enable_shared_expert_dp and (
|
|
self.debug_layer_idx == self.first_k_dense_replace
|
|
or self.debug_layer_idx == self.layers):
|
|
rows = num_tokens // self.tp_size
|
|
if num_tokens % self.tp_size:
|
|
rows += 1
|
|
output_shape = (rows, hidden_states.shape[1])
|
|
output = torch.empty(output_shape,
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device)
|
|
self.sfa_attn.impl.forward(hidden_states, kv_cache, attn_metadata,
|
|
need_gather_q_kv, output)
|
|
output = output.view(-1, output_shape[-1])
|
|
return output
|
|
|
|
|
|
class TorchairDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
prefix: str,
|
|
model_config: ModelConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
nn.Module.__init__(self)
|
|
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
|
|
self.layers = config.num_hidden_layers
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.tp_rank = get_tp_group().rank_in_group
|
|
ascend_config = get_ascend_config()
|
|
self.use_mla = False
|
|
self.use_sparse = False
|
|
# TODO: enable mla in vllm-ascend
|
|
if model_config.use_mla:
|
|
if hasattr(model_config.hf_config, "index_topk"):
|
|
attn_cls = TorchairDeepseekV2SFAAttention
|
|
self.use_sparse = True
|
|
else:
|
|
attn_cls = TorchairDeepseekV2MLAAttention # type: ignore[assignment]
|
|
self.use_mla = True
|
|
else:
|
|
attn_cls = DeepseekV2Attention
|
|
self.self_attn = attn_cls(
|
|
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",
|
|
decoder_layer=self,
|
|
)
|
|
|
|
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 = TorchairDeepseekV2MoE(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
self.mla_moe_communication = ascend_config.multistream_overlap_shared_expert \
|
|
and model_config.use_mla and self.tp_size > 1
|
|
else:
|
|
self.mlp = TorchairDeepseekV2MLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
self.mla_moe_communication = False
|
|
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
|
|
self.first_k_dense_replace = config.first_k_dense_replace
|
|
self.tp_group = get_tp_group().device_group
|
|
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
residual: Optional[torch.Tensor],
|
|
kv_cache: Optional[torch.Tensor] = None,
|
|
attn_metadata: Optional[AttentionMetadata] = None,
|
|
replace_allreduce: bool = False,
|
|
) -> torch.Tensor:
|
|
# Self Attention
|
|
forward_context = get_forward_context()
|
|
if attn_metadata is not None:
|
|
decoding_condition_met = (
|
|
not attn_metadata.is_prefill if self.use_sparse else
|
|
not forward_context.with_prefill if self.use_mla else False)
|
|
mla_moe_communication = decoding_condition_met and self.mla_moe_communication and replace_allreduce
|
|
else:
|
|
mla_moe_communication = False
|
|
|
|
if (envs.vllm_npu_ENABLE_MLAPO
|
|
and isinstance(self.self_attn, TorchairDeepseekV2SFAAttention)
|
|
and attn_metadata is not None
|
|
and not forward_context.with_prefill):
|
|
if residual is not None:
|
|
hidden_states = hidden_states + residual
|
|
residual = hidden_states
|
|
else:
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
previous_hidden_states, previous_residual = hidden_states, residual
|
|
hidden_states, residual = self.input_layernorm(
|
|
hidden_states, residual)
|
|
# Dispose hidden_states and residual from the previous layer
|
|
# to save npu memory because they're no longer used.
|
|
dispose_tensor(previous_hidden_states)
|
|
dispose_tensor(previous_residual)
|
|
if mla_moe_communication and self.layer_idx > self.first_k_dense_replace and self.layer_idx < self.layers:
|
|
hidden_states = tensor_model_parallel_all_gather(hidden_states,
|
|
dim=0)
|
|
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
|
|
if mla_moe_communication and residual.shape[0] != hidden_states.shape[
|
|
0]:
|
|
chunk_hidden_states = torch.tensor_split(residual,
|
|
self.tp_size,
|
|
dim=0)
|
|
residual = chunk_hidden_states[self.tp_rank]
|
|
|
|
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
|
|
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
if self.enable_shared_expert_dp and (
|
|
self.layer_idx == self.first_k_dense_replace
|
|
or self.layer_idx == self.layers) and tp_size > 1:
|
|
num_tokens, _ = residual.shape
|
|
if num_tokens % tp_size:
|
|
residual = nn.functional.pad(residual,
|
|
(0, 0, 0, -num_tokens % tp_size))
|
|
chunk_residual = torch.tensor_split(residual, tp_size, dim=0)
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
residual = chunk_residual[tp_rank]
|
|
|
|
# Fully Connected
|
|
hidden_states, residual = self.post_attention_layernorm(
|
|
hidden_states, residual)
|
|
|
|
if isinstance(self.mlp, TorchairDeepseekV2MoE):
|
|
hidden_states = self.mlp(hidden_states,
|
|
attn_metadata,
|
|
replace_allreduce=mla_moe_communication)
|
|
else:
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
if isinstance(self.mlp, TorchairDeepseekV2MLP
|
|
) 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
|
|
if mla_moe_communication and self.layer_idx >= self.layers - 1:
|
|
hidden_states = tensor_model_parallel_all_gather(hidden_states,
|
|
dim=0)
|
|
residual = tensor_model_parallel_all_gather(residual, dim=0)
|
|
|
|
# for last layer of main model and mtp layer.
|
|
if self.enable_shared_expert_dp and self.layer_idx >= (
|
|
self.layers - 1) and tp_size > 1:
|
|
hidden_states = get_tp_group().all_gather(hidden_states, 0)
|
|
residual = get_tp_group().all_gather(residual, 0)
|
|
|
|
attn_metadata = get_forward_context().attn_metadata
|
|
if attn_metadata is not None and isinstance(attn_metadata, dict):
|
|
attn_metadata = next(iter(attn_metadata.values()), None)
|
|
if attn_metadata is not None:
|
|
num_tokens = attn_metadata.num_actual_tokens
|
|
else:
|
|
num_tokens = hidden_states.shape[0]
|
|
|
|
if num_tokens < hidden_states.shape[0]:
|
|
hidden_states = hidden_states[:num_tokens]
|
|
residual = residual[:num_tokens]
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
class TorchairDeepseekV2Model(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
|
|
model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
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: TorchairDeepseekV2DecoderLayer(
|
|
config,
|
|
prefix,
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
),
|
|
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,
|
|
kv_caches: Optional[List[torch.Tensor]] = None,
|
|
attn_metadata: Optional[AttentionMetadata] = None,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
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"]
|
|
|
|
replace_allreduce = hidden_states.shape[0] % self.tp_size == 0
|
|
|
|
for i in range(self.start_layer, self.end_layer):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
residual,
|
|
kv_caches[i -
|
|
self.start_layer] if kv_caches is not None else None,
|
|
attn_metadata,
|
|
replace_allreduce=replace_allreduce)
|
|
|
|
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 TorchairDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
|
|
# add `packed_modules_mapping` in `DeepseekV2ForCausalLM` to support weight merging
|
|
packed_modules_mapping = {
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
"experts":
|
|
["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
nn.Module.__init__(self)
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.num_dense_layers = self.config.first_k_dense_replace
|
|
self.num_moe_layers = self.config.num_hidden_layers - self.num_dense_layers
|
|
self.quant_config = quant_config
|
|
self.model = TorchairDeepseekV2Model(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)
|
|
|
|
# NOTE: This `load_weights` is mainly copied from
|
|
# https://github.com/vllm-project/vllm/commit/07b8fae219b1fff51ef115c38c44b51395be5bb5
|
|
# to fix CI, and it is different from the implementation in main
|
|
# TODO: support eplb style load_weights
|
|
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),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = TorchairAscendFusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "module" 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:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=False)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: Optional[List[torch.Tensor]] = None,
|
|
attn_metadata: Optional[AttentionMetadata] = None,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, intermediate_tensors,
|
|
inputs_embeds)
|
|
return hidden_states
|