wehub-resource-sync
119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Sequence
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import torch
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import vllm.envs as envs
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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process_fp8_weight_block_strategy,
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)
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
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apply_fp8_marlin_linear,
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is_fp8_marlin_supported,
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prepare_fp8_layer_for_marlin,
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)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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kFp8Static128BlockSym,
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)
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from vllm.model_executor.utils import replace_parameter
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from vllm.platforms import current_platform
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from .ScaledMMLinearKernel import (
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FP8ScaledMMLinearKernel,
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FP8ScaledMMLinearLayerConfig,
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)
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class MarlinFP8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
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"""
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FP8 Marlin kernel for GPUs that lack FP8 hardware support.
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Leverages the Marlin kernel for fast weight-only FP8 quantization.
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"""
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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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if not current_platform.is_cuda():
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return False, "requires CUDA."
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# Check if platform supports FP8 Marlin
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if not is_fp8_marlin_supported():
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return False, "FP8 Marlin requires compute capability 7.5 or higher"
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if envs.VLLM_BATCH_INVARIANT:
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return False, "FP8 Marlin not supported for batch invariant execution."
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if (
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compute_capability is not None
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and compute_capability >= 89
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and not envs.VLLM_TEST_FORCE_FP8_MARLIN
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):
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return (
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False,
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"To apply FP8 Marlin on high-capability GPUs, please set "
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"VLLM_TEST_FORCE_FP8_MARLIN=1",
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)
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return True, None
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@classmethod
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def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
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return True, None
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def __init__(
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self, c: FP8ScaledMMLinearLayerConfig, layer_param_names: Sequence[str]
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) -> None:
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super().__init__(c, layer_param_names)
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self.marlin_input_dtype = None
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self.block_quant = self.config.weight_quant_key in {kFp8Static128BlockSym}
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self.size_k_first = not self.block_quant
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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if self.block_quant:
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weight, weight_scale_inv = process_fp8_weight_block_strategy(
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layer.weight, layer.weight_scale_inv
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)
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# Update layer with new values
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replace_parameter(layer, "weight", weight.data)
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replace_parameter(layer, "weight_scale_inv", weight_scale_inv.data)
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# Non-block: callers must pass weight in (K, N) layout.
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layer.input_scale = None
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prepare_fp8_layer_for_marlin(
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layer, self.size_k_first, input_dtype=self.marlin_input_dtype
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)
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del layer.input_scale
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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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if self.block_quant:
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weight_scale = layer.weight_scale_inv
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else:
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weight_scale = layer.weight_scale
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return apply_fp8_marlin_linear(
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input=x,
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weight=layer.weight,
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weight_scale=weight_scale,
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workspace=layer.workspace,
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size_n=layer.output_size_per_partition,
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size_k=layer.input_size_per_partition,
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input_dtype=self.marlin_input_dtype,
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bias=bias,
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)
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def apply_scaled_mm(
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self,
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*,
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A: torch.Tensor,
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B: torch.Tensor,
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out_dtype: torch.dtype,
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As: torch.Tensor,
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Bs: torch.Tensor,
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bias: torch.Tensor | None,
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output_shape: list,
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) -> torch.Tensor:
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pass
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