528 lines
20 KiB
Python
528 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Any
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import torch
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from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
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from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
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from sglang.srt.layers.quantization.base_config import FusedMoEMethodBase
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from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz, scaled_fp8_quant
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from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
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from sglang.srt.layers.quantization.utils import all_close_1d, per_tensor_dequantize
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from sglang.srt.utils import (
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get_bool_env_var,
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is_gfx95_supported,
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is_hip,
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set_weight_attrs,
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)
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if TYPE_CHECKING:
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from sglang.srt.layers.moe.token_dispatcher import (
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CombineInput,
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StandardDispatchOutput,
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)
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from sglang.srt.layers.quantization.quark.quark import QuarkConfig
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logger = logging.getLogger(__name__)
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_is_shuffle_moe_mxfp4 = is_gfx95_supported()
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__all__ = ["QuarkMoEMethod", "QuarkW4A4MXFp4MoEMethod"]
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_is_fp8_fnuz = is_fp8_fnuz()
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_is_hip = is_hip()
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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if _use_aiter:
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from aiter import ActivationType, QuantType
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from aiter.fused_moe import fused_moe
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from aiter.ops.shuffle import shuffle_weight
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from aiter.utility.fp4_utils import e8m0_shuffle
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from sglang.srt.layers.moe.rocm_moe_utils import rocm_fused_experts_tkw1
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OCP_MX_BLOCK_SIZE = 32
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if TYPE_CHECKING:
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from sglang.srt.layers.quantization import QuarkConfig
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class QuarkMoEMethod(FusedMoEMethodBase):
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def __init__(self, quant_config: QuarkConfig):
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self.quant_config = quant_config
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@staticmethod
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def get_moe_method(
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quant_config: QuarkConfig, # type: ignore # noqa E501 # noqa F821
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module: torch.nn.Module,
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layer_name: str,
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) -> "QuarkMoEMethod":
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layer_quant_config = quant_config._find_matched_config(layer_name, module)
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if layer_quant_config.get("output_tensors") or layer_quant_config.get("bias"):
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raise NotImplementedError(
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"Currently, Quark models with "
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"output_tensors and bias "
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"quantized are not supported"
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)
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weight_config = layer_quant_config.get("weight")
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input_config = layer_quant_config.get("input_tensors")
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if quant_config._is_mx_fp4(weight_config, input_config):
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return QuarkW4A4MXFp4MoEMethod(weight_config, input_config)
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elif quant_config._is_fp8_w8a8(weight_config, input_config):
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return QuarkW8A8FP8MoEMethod(weight_config, input_config)
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else:
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raise RuntimeError("Unsupported FusedMoe scheme")
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class QuarkW4A4MXFp4MoEMethod(QuarkMoEMethod):
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def __init__(self, weight_config: dict[str, Any], input_config: dict[str, Any]):
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self.weight_quant = weight_config
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self.input_quant = input_config
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weight_qscheme = self.weight_quant.get("qscheme")
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input_qscheme = self.input_quant.get("qscheme")
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if not (weight_qscheme == "per_group" and input_qscheme == "per_group"):
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raise ValueError(
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"For MX(FP4) Fused MoE layers, only per-group scales "
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"for weights and activations are supported. Found "
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f"{weight_qscheme}, {input_qscheme}"
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) # noqa E501
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self.static_input_scales = not self.input_quant.get("is_dynamic")
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self.with_bias = False
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def create_weights(
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self,
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layer: torch.nn.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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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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# Add the quantization method used (per tensor/grouped/channel)
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# to ensure the weight scales are loaded in properly
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
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)
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params_dtype = torch.uint8
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size // 2,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size,
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intermediate_size_per_partition // 2,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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# WEIGHT_SCALES
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size // OCP_MX_BLOCK_SIZE,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(
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num_experts,
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hidden_size,
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intermediate_size_per_partition // OCP_MX_BLOCK_SIZE,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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float_dtype = torch.get_default_dtype()
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# Pre-shuffle weight scales
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s0, s1, _ = layer.w13_weight_scale.shape
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w13_weight_scale = layer.w13_weight_scale.view(s0 * s1, -1)
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w13_weight_scale = e8m0_shuffle(w13_weight_scale)
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# layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale, requires_grad=False)
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layer.w13_weight_scale.data = w13_weight_scale.view(s0, s1, -1)
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s0, s1, _ = layer.w2_weight_scale.shape
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w2_weight_scale = layer.w2_weight_scale.view(s0 * s1, -1)
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w2_weight_scale = e8m0_shuffle(w2_weight_scale)
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# layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale, requires_grad=False)
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layer.w2_weight_scale.data = w2_weight_scale.view(s0, s1, -1)
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# Pre-shuffle weight
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if _is_shuffle_moe_mxfp4:
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layer.w13_weight.data = shuffle_weight(
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layer.w13_weight.contiguous(), (16, 16)
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)
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layer.w2_weight.data = shuffle_weight(
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layer.w2_weight.contiguous(), (16, 16)
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)
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layer.w13_weight.is_shuffled = True
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layer.w2_weight.is_shuffled = True
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def create_moe_runner(
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self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
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):
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self.moe_runner_config = moe_runner_config
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def apply(
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self,
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layer: torch.nn.Module,
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dispatch_output: StandardDispatchOutput,
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) -> CombineInput:
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from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
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x = dispatch_output.hidden_states
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topk_output = dispatch_output.topk_output
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moe_runner_config = self.moe_runner_config
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topk_weights, topk_ids, _ = topk_output
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if _is_hip:
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topk_weights = topk_weights.to(
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torch.float32
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) # aiter's moe_sorting requires topk_weights to be FP32
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if hasattr(torch, "float4_e2m1fn_x2"):
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w13_weight = layer.w13_weight.view(torch.float4_e2m1fn_x2)
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w2_weight = layer.w2_weight.view(torch.float4_e2m1fn_x2)
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else:
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w13_weight = layer.w13_weight
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w2_weight = layer.w2_weight
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if hasattr(layer.w13_weight, "is_shuffled"):
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w13_weight.is_shuffled = True
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w2_weight.is_shuffled = True
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output = fused_moe(
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x,
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w13_weight,
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w2_weight,
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topk_weights,
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topk_ids,
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quant_type=QuantType.per_1x32,
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w1_scale=layer.w13_weight_scale,
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w2_scale=layer.w2_weight_scale,
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activation=(
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ActivationType.Silu
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if moe_runner_config.activation == "silu"
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else ActivationType.Gelu
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),
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doweight_stage1=False,
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)
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return StandardCombineInput(hidden_states=output)
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class QuarkW8A8FP8MoEMethod(QuarkMoEMethod):
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def __init__(self, weight_config: dict[str, Any], input_config: dict[str, Any]):
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self.is_static_input_scheme: bool = False
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self.input_qscheme = None
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if input_config is not None:
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self.is_static_input_scheme = not input_config.get("is_dynamic")
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self.input_qscheme = input_config.get("qscheme")
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self.input_per_token = (
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not self.is_static_input_scheme and self.input_qscheme == "per_channel"
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)
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self.weight_qscheme = weight_config.get("qscheme")
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self.is_weight_per_channel = self.weight_qscheme == "per_channel"
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self.out_dtype = torch.get_default_dtype()
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@classmethod
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def get_min_capability(cls) -> int:
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# lovelace and up
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return 89
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def create_weights(
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self,
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layer: torch.nn.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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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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params_dtype = torch.float8_e4m3fn
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size,
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intermediate_size_per_partition,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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# WEIGHT_SCALES
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# per-tensor quantization
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if self.weight_qscheme == "per_tensor":
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# Allocate 2 scales for w1 and w3 respectively.
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# They will be combined to a single scale after weight loading.
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, dtype=torch.float32), requires_grad=False
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)
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weight_quant_method = FusedMoeWeightScaleSupported.TENSOR.value
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elif self.weight_qscheme == "per_channel":
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(
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num_experts,
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2 * intermediate_size_per_partition,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, hidden_size, dtype=torch.float32),
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requires_grad=False,
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)
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weight_quant_method = FusedMoeWeightScaleSupported.CHANNEL.value
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else:
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raise ValueError(
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f"Unsupported weight quantization strategy: {self.weight_qscheme}."
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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# Add the quantization method used (per tensor/grouped/channel)
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# to ensure the weight scales are loaded in properly
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extra_weight_attrs.update({"quant_method": weight_quant_method})
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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# INPUT_SCALES
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if self.is_static_input_scheme:
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assert (
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self.input_qscheme == "per_tensor"
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), "Only per-tensor quantization is supported for static input scales"
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w13_input_scale = torch.nn.Parameter(
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torch.ones(num_experts, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("w13_input_scale", w13_input_scale)
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set_weight_attrs(w13_input_scale, extra_weight_attrs)
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w2_input_scale = torch.nn.Parameter(
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torch.ones(num_experts, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("w2_input_scale", w2_input_scale)
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set_weight_attrs(w2_input_scale, extra_weight_attrs)
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else:
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layer.w13_input_scale = None
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layer.w2_input_scale = None
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# Fp8 moe kernels require a single activation scale.
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# We take the max of all the scales in case they differ.
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if self.is_static_input_scheme:
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if layer.w13_input_scale is None or layer.w2_input_scale is None:
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raise ValueError(
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"QuantConfig has static quantization, but found "
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"activation scales are None."
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)
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if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
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layer.w2_input_scale
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):
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logger.warning(
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"Found input_scales that are not equal for "
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"fp8 MoE layer. Using the maximum across experts "
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"for each layer."
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)
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layer.w13_input_scale = torch.nn.Parameter(
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layer.w13_input_scale.max(), requires_grad=False
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)
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layer.w2_input_scale = torch.nn.Parameter(
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layer.w2_input_scale.max(), requires_grad=False
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)
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if _is_fp8_fnuz:
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# Normalize the weights and scales
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w13_weight, w13_weight_scale, w13_input_scale = (
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normalize_e4m3fn_to_e4m3fnuz(
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layer.w13_weight, layer.w13_weight_scale, layer.w13_input_scale
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)
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)
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w2_weight, w2_weight_scale, w2_input_scale = normalize_e4m3fn_to_e4m3fnuz(
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layer.w2_weight, layer.w2_weight_scale, layer.w2_input_scale
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)
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# Reset the parameter
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layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
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layer.w13_weight_scale = torch.nn.Parameter(
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w13_weight_scale, requires_grad=False
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)
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if w13_input_scale is not None:
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layer.w13_input_scale = torch.nn.Parameter(
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w13_input_scale, requires_grad=False
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)
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layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
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layer.w2_weight_scale = torch.nn.Parameter(
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w2_weight_scale, requires_grad=False
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)
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if w2_input_scale is not None:
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layer.w2_input_scale = torch.nn.Parameter(
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w2_input_scale, requires_grad=False
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)
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if self.weight_qscheme == "per_tensor":
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# Fp8 moe kernel needs single weight scale for w13 per expert.
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# We take the max then dequant and requant each expert.
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assert layer.w13_weight_scale is not None
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shard_size = layer.intermediate_size_per_partition
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max_w13_scales = layer.w13_weight_scale.max(dim=1).values
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for expert_id in range(layer.num_local_experts):
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start = 0
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for shard_id in range(2):
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dq_weight = per_tensor_dequantize(
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layer.w13_weight[expert_id][start : start + shard_size, :],
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layer.w13_weight_scale[expert_id][shard_id],
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)
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(
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layer.w13_weight[expert_id][start : start + shard_size, :],
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_,
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) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
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start += shard_size
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layer.w13_weight_scale = torch.nn.Parameter(
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max_w13_scales, requires_grad=False
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)
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elif self.weight_qscheme == "per_channel":
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layer.w13_weight_scale = torch.nn.Parameter(
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layer.w13_weight_scale.unsqueeze(-1), requires_grad=False
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)
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layer.w2_weight_scale = torch.nn.Parameter(
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layer.w2_weight_scale.unsqueeze(-1), requires_grad=False
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)
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else:
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raise ValueError(
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f"Unsupported weight quantization strategy: {self.weight_qscheme}."
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)
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if (
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_use_aiter
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and self.is_weight_per_channel
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and self.moe_runner_config.apply_router_weight_on_input
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):
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with torch.no_grad():
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# Pre-shuffle weights
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layer.w13_weight = torch.nn.Parameter(
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shuffle_weight(layer.w13_weight.data, (16, 16)),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
shuffle_weight(layer.w2_weight.data, (16, 16)),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
def create_moe_runner(
|
|
self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
|
|
):
|
|
self.moe_runner_config = moe_runner_config
|
|
self.runner = MoeRunner(MoeRunnerBackend.TRITON, moe_runner_config)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
dispatch_output: StandardDispatchOutput,
|
|
) -> CombineInput:
|
|
|
|
from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
|
|
|
|
x = dispatch_output.hidden_states
|
|
topk_output = dispatch_output.topk_output
|
|
|
|
moe_runner_config = self.moe_runner_config
|
|
|
|
if (
|
|
_use_aiter
|
|
and self.is_weight_per_channel
|
|
and moe_runner_config.apply_router_weight_on_input
|
|
):
|
|
topk_weights, topk_ids, _ = topk_output
|
|
output = rocm_fused_experts_tkw1(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=moe_runner_config.activation,
|
|
apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
|
|
use_fp8_w8a8=True,
|
|
per_channel_quant=self.is_weight_per_channel,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
)
|
|
return StandardCombineInput(hidden_states=output)
|
|
else:
|
|
quant_info = TritonMoeQuantInfo(
|
|
w13_weight=layer.w13_weight,
|
|
w2_weight=layer.w2_weight,
|
|
use_fp8_w8a8=True,
|
|
per_channel_quant=self.is_weight_per_channel,
|
|
w13_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a13_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
)
|
|
return self.runner.run(dispatch_output, quant_info)
|