wehub-resource-sync
257 lines
8.5 KiB
Python
257 lines
8.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Online MXFP8 (microscaling FP8, block-32) quantization methods."""
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from typing import TYPE_CHECKING
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import torch
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from torch.nn import Module
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if TYPE_CHECKING:
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import vllm.model_executor.layers.fused_moe.modular_kernel as mk
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from vllm.model_executor.layers.fused_moe import (
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FusedMoEQuantConfig,
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RoutedExperts,
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)
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from vllm.model_executor.layers.fused_moe.oracle.fp8 import Fp8MoeBackend
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from vllm.model_executor.kernels.linear import init_mxfp8_linear_kernel
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from vllm.model_executor.layers.fused_moe.oracle.mxfp8 import (
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select_mxfp8_moe_backend,
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)
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from vllm.model_executor.layers.quantization.online.fp8 import (
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_Fp8OnlineLinearBase,
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)
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from vllm.model_executor.layers.quantization.online.moe_base import (
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OnlineMoEMethodBase,
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)
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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_e4m3_quantize,
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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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class Mxfp8OnlineLinearMethod(_Fp8OnlineLinearBase):
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"""Online MXFP8 linear method.
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Loads bf16/fp16 checkpoints and quantizes weights to MXFP8 (microscaling
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FP8 with block-32 scales) during weight loading.
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"""
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def __init__(self):
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super().__init__()
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self.kernel = init_mxfp8_linear_kernel()
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: list[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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if input_size_per_partition % MXFP8_BLOCK_SIZE != 0:
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raise ValueError(
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f"MXFP8 requires input_size_per_partition "
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f"({input_size_per_partition}) to be divisible by "
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f"{MXFP8_BLOCK_SIZE}."
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)
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super().create_weights(
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layer,
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input_size_per_partition,
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output_partition_sizes,
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input_size,
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output_size,
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params_dtype,
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**extra_weight_attrs,
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)
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def process_weights_after_loading(self, layer: Module) -> None:
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if getattr(layer, "_already_called_process_weights_after_loading", False):
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return
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weight_fp8, weight_scale = mxfp8_e4m3_quantize(layer.weight.contiguous())
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layer.input_scale = None
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replace_parameter(layer, "weight", weight_fp8.data)
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replace_parameter(layer, "weight_scale", weight_scale.data)
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self.kernel.process_weights_after_loading(layer)
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layer._already_called_process_weights_after_loading = True
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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 self.kernel.apply_weights(layer, x, bias)
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class Mxfp8OnlineMoEMethod(OnlineMoEMethodBase):
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"""MoE method for online MXFP8 (block) quantization."""
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fp8_backend: "Fp8MoeBackend"
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experts_cls: "type[mk.FusedMoEExperts] | None"
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def __init__(self, *, layer: torch.nn.Module):
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super().__init__(layer.moe_config)
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self.weight_block_size: list[int] = [1, MXFP8_BLOCK_SIZE]
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self.weight_scale_name = "weight_scale"
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self.fp8_backend, self.experts_cls = select_mxfp8_moe_backend(config=self.moe)
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def create_weights(
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self,
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layer: Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size_per_partition: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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if (
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hidden_size % MXFP8_BLOCK_SIZE != 0
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or intermediate_size_per_partition % MXFP8_BLOCK_SIZE != 0
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):
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raise ValueError(
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"Online MXFP8 MoE requires hidden/intermediate sizes divisible "
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f"by {MXFP8_BLOCK_SIZE}."
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)
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super().create_weights(
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layer=layer,
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num_experts=num_experts,
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hidden_size=hidden_size,
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intermediate_size_per_partition=intermediate_size_per_partition,
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params_dtype=params_dtype,
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**extra_weight_attrs,
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)
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layer.weight_block_size = [1, MXFP8_BLOCK_SIZE]
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def _quantize_mxfp8_moe_weight(
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self, weight: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""Batch quantization: bf16/fp16 weights -> MXFP8 (fp8 + uint8 scales)."""
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E = weight.size(0)
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first_q, first_s = mxfp8_e4m3_quantize(weight[0], is_sf_swizzled_layout=False)
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# Pre-allocate the output tensors rather than stacking.
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# This is important for consistent memory layout.
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w_quant = torch.empty(
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(E, *first_q.shape), dtype=first_q.dtype, device=weight.device
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)
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w_scales = torch.empty(
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(E, *first_s.shape), dtype=first_s.dtype, device=weight.device
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)
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w_quant[0] = first_q
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w_scales[0] = first_s
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for i in range(1, E):
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w_quant[i], w_scales[i] = mxfp8_e4m3_quantize(
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weight[i], is_sf_swizzled_layout=False
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)
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return w_quant, w_scales
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def _setup_kernel(
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self,
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layer: "RoutedExperts",
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w13: torch.Tensor,
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w2: torch.Tensor,
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w13_scale: torch.Tensor,
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w2_scale: torch.Tensor,
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w13_input_scale: torch.Tensor | None,
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w2_input_scale: torch.Tensor | None,
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) -> None:
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from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
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convert_to_fp8_moe_kernel_format,
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make_fp8_moe_kernel,
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)
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# Shuffle weights to runtime format.
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w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
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fp8_backend=self.fp8_backend,
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layer=layer,
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w13=w13,
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w2=w2,
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w13_scale=w13_scale,
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w2_scale=w2_scale,
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w13_input_scale=w13_input_scale,
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w2_input_scale=w2_input_scale,
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)
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replace_parameter(layer, "w13_weight", w13)
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replace_parameter(layer, "w2_weight", w2)
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replace_parameter(layer, f"w13_{self.weight_scale_name}", w13_scale)
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replace_parameter(layer, f"w2_{self.weight_scale_name}", w2_scale)
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self.moe_quant_config = self.get_fused_moe_quant_config(layer)
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if self.moe_quant_config:
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assert self.experts_cls is not None
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self.moe_kernel = make_fp8_moe_kernel(
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moe_quant_config=self.moe_quant_config,
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moe_config=self.moe,
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fp8_backend=self.fp8_backend,
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experts_cls=self.experts_cls,
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routing_tables=layer._expert_routing_tables(),
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layer=layer,
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)
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def get_fused_moe_quant_config(
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self, layer: torch.nn.Module
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) -> "FusedMoEQuantConfig":
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from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
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make_fp8_moe_quant_config,
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)
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w1_scale = getattr(layer, f"w13_{self.weight_scale_name}")
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w2_scale = getattr(layer, f"w2_{self.weight_scale_name}")
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a1_scale = layer.w13_input_scale
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a2_scale = layer.w2_input_scale
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return make_fp8_moe_quant_config(
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fp8_backend=self.fp8_backend,
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w1_scale=w1_scale,
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w2_scale=w2_scale,
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a1_scale=a1_scale,
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a2_scale=a2_scale,
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w1_bias=getattr(layer, "w13_bias", None),
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w2_bias=getattr(layer, "w2_bias", None),
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block_shape=self.weight_block_size,
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swiglu_limit=getattr(layer, "swiglu_limit", None),
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gemm1_alpha=getattr(layer, "swiglu_alpha", None),
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gemm1_beta=getattr(layer, "swiglu_beta", None),
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layer=layer,
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)
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def process_weights_after_loading(self, layer: Module) -> None:
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if getattr(layer, "_already_called_process_weights_after_loading", False):
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return
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fp8_dtype = current_platform.fp8_dtype()
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w13 = torch.empty_like(layer.w13_weight, dtype=fp8_dtype)
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w2 = torch.empty_like(layer.w2_weight, dtype=fp8_dtype)
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layer.w13_input_scale = None
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layer.w2_input_scale = None
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w13, w13_scale = self._quantize_mxfp8_moe_weight(layer.w13_weight)
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w2, w2_scale = self._quantize_mxfp8_moe_weight(layer.w2_weight)
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self._setup_kernel(
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layer,
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w13,
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w2,
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w13_scale,
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w2_scale,
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layer.w13_input_scale,
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layer.w2_input_scale,
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)
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layer._already_called_process_weights_after_loading = True
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