refine mxfp4 shuffling log (#9194)
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@@ -1,5 +1,18 @@
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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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 https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/mxfp4.py
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from __future__ import annotations
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@@ -209,6 +222,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
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super().__init__()
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self.prefix = prefix
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self.topk_indices_dtype = None
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self.use_triton_kernels = global_server_args_dict["enable_triton_kernel_moe"]
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self.with_bias = False
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@@ -332,7 +346,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
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if self.use_flashinfer:
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log_info_on_rank0(
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logger,
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"Shuffling MoE weights for FlashInfer MXFP4 moe kernel, it might take a while...",
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f"Shuffling MoE weights for FlashInfer MXFP4 moe kernel (layer: {self.prefix}), it might take a while...",
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)
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layer.gemm1_alpha = Parameter(
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torch.tensor([1.702] * self.num_experts, dtype=torch.float32).cuda(),
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