2064afe380
### What this PR does / why we need it?
- This PR fixes an issue with weight format conversion for unquantized
models running on Ascend 310P devices.
- The changes refactor the logic for converting weights to the
FRACTAL_NZ format. Previously, this was handled in a 310P-specific
linear layer implementation (`AscendUnquantizedLinearMethod310`). This
implementation has been removed, and the logic is now centralized in the
`maybe_trans_nz` utility function. This function now checks if the
device is a 310P and applies the NZ format cast accordingly for
`float16`/`bfloat16` weights.
- This refactoring simplifies the code by removing platform-specific
duplication and ensures correct weight handling for unquantized models
on 310P.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut and local test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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from __future__ import annotations
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import torch
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import torch.nn.functional as F
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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UnquantizedEmbeddingMethod,
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)
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from vllm_ascend.ops.vocab_parallel_embedding import AscendParallelLMHead, AscendVocabParallelEmbedding
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from vllm_ascend.utils import maybe_trans_nz
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class AscendUnquantizedEmbeddingMethod310(UnquantizedEmbeddingMethod):
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.weight_nz = maybe_trans_nz(layer.weight)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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return F.linear(x, layer.weight_nz, bias)
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class AscendVocabParallelEmbedding310(AscendVocabParallelEmbedding):
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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params_dtype: torch.dtype | None = None,
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org_num_embeddings: int | None = None,
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padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__(
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num_embeddings, embedding_dim, params_dtype, org_num_embeddings, padding_size, quant_config, prefix
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)
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if quant_config is None:
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self.quant_method = AscendUnquantizedEmbeddingMethod310()
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class AscendParallelLMHead310(AscendParallelLMHead):
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"""
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Register ParallelLMHead as a custom op for Atlas 310p.
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"""
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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bias: bool = False,
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params_dtype: torch.dtype | None = None,
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org_num_embeddings: int | None = None,
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padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__(
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num_embeddings, embedding_dim, bias, params_dtype, org_num_embeddings, padding_size, quant_config, prefix
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)
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if quant_config is None:
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self.quant_method = AscendUnquantizedEmbeddingMethod310()
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