wehub-resource-sync
85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from torch.nn.parameter import Parameter
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from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
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MXFP8_BLOCK_SIZE,
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MXFP8_SCALE_DTYPE,
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dequant_mxfp8_to_bf16,
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)
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from .Mxfp8LinearKernel import Mxfp8LinearKernel, Mxfp8LinearLayerConfig
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class EmulationMxfp8LinearKernel(Mxfp8LinearKernel):
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"""Software emulation fallback for MXFP8 (dequant to BF16)."""
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@classmethod
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def is_supported(
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cls, compute_capability: int | None = None
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) -> tuple[bool, str | None]:
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return True, None
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@classmethod
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def can_implement(cls, c: Mxfp8LinearLayerConfig) -> tuple[bool, str | None]:
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return True, None
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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weight = layer.weight.data # [N, K]
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N, K = weight.shape
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scale_k = K // MXFP8_BLOCK_SIZE
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weight_scale = layer.weight_scale.data[:N, :scale_k].contiguous()
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# Dequantize MXFP8 -> BF16 ONCE here, at load time, so apply_weights runs
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# a plain BF16 linear with no per-step dequant -- i.e. run as if from a
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# BF16 checkpoint. The 1-byte MXFP8 weight is replaced by BF16 (2x its
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# size, but linear weights are small vs the MoE experts); the tiny E8M0
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# scale is kept for the dtype/ndim asserts but is otherwise unused.
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# Opt out (VLLM_MXFP8_EMULATION_DEQUANT_AT_LOAD=0) to keep the MXFP8
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# weight and dequant per-step in apply_weights instead.
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import vllm.envs as envs
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if envs.VLLM_MXFP8_EMULATION_DEQUANT_AT_LOAD:
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weight = dequant_mxfp8_to_bf16(weight.contiguous(), weight_scale)
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layer.weight = Parameter(weight.contiguous(), requires_grad=False)
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layer.weight_scale = Parameter(weight_scale, requires_grad=False)
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def apply_weights(
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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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weight = layer.weight
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# Load-time dequant path: weights are already BF16/FP16 (>= 2-byte), so
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# run a plain linear -- no per-step dequant. (MXFP8 weights are 1-byte.)
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if weight.element_size() >= 2:
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# F.linear requires x and weight share a dtype; .to() is a no-op when
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# they already match (e.g. both BF16).
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output = torch.nn.functional.linear(x, weight.to(x.dtype), bias)
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return output.to(x.dtype)
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# Fallback: weights still in MXFP8 -- dequant on the fly (other archs /
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# if a future caller skips the load-time conversion above).
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weight_scale = layer.weight_scale
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if weight_scale.dtype != MXFP8_SCALE_DTYPE:
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raise ValueError(
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f"Emulation backend requires {MXFP8_SCALE_DTYPE} "
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f"weight_scale dtype, got {weight_scale.dtype}."
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)
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if weight_scale.ndim != 2:
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raise ValueError(
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f"Emulation backend requires 2D weight_scale, "
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f"got {weight_scale.ndim}D. "
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f"Ensure process_weights_after_loading was called."
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)
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# Cast to x's dtype: dequant yields BF16, but F.linear needs both operands
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# to match (e.g. an FP16 model). No-op when x is already BF16.
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weight_bf16 = dequant_mxfp8_to_bf16(weight, weight_scale).to(x.dtype)
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output = torch.nn.functional.linear(x, weight_bf16, bias)
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return output.to(x.dtype)
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