wehub-resource-sync
1607 lines
62 KiB
Python
1607 lines
62 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 typing import Any
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import torch
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import vllm.model_executor.layers.fused_moe.modular_kernel as mk
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from vllm import _custom_ops as ops
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.config import get_current_vllm_config
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe import (
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FusedMoEConfig,
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FusedMoEMethodBase,
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FusedMoeWeightScaleSupported,
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RoutedExperts,
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SharedExperts,
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)
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from vllm.model_executor.layers.fused_moe.config import (
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FusedMoEParallelConfig,
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FusedMoEQuantConfig,
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fp8_w8a8_moe_quant_config,
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int8_w8a8_moe_quant_config,
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mxfp4_w4a8_moe_quant_config,
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mxfp4_w4a16_moe_quant_config,
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ocp_mx_moe_quant_config,
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)
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from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
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Fp8MoeBackend,
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convert_to_fp8_moe_kernel_format,
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make_fp8_moe_kernel,
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make_fp8_moe_quant_config,
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select_fp8_moe_backend,
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)
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from vllm.model_executor.layers.fused_moe.oracle.mxfp4 import (
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TRITON_BACKENDS,
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Mxfp4MoeBackend,
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backend_to_kernel_cls,
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convert_gpt_oss_weight_to_mxfp4_moe_kernel_format,
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make_mxfp4_moe_kernel,
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make_mxfp4_moe_quant_config,
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mxfp4_round_up_hidden_size_and_intermediate_size,
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select_mxfp4_moe_backend,
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)
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from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
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convert_to_nvfp4_moe_kernel_format,
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make_nvfp4_moe_kernel,
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make_nvfp4_moe_quant_config,
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select_nvfp4_moe_backend,
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)
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from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import (
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OCP_MX_BLOCK_SIZE,
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OCP_MX_Scheme,
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)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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GroupShape,
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kFp8DynamicTensorSym,
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kFp8DynamicTokenSym,
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kFp8StaticChannelSym,
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kFp8StaticTensorSym,
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kMxfp4Dynamic,
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kNvfp4Dynamic,
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kNvfp4Static,
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)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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all_close_1d,
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normalize_e4m3fn_to_e4m3fnuz,
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per_tensor_dequantize,
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)
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from vllm.model_executor.utils import replace_parameter, set_weight_attrs
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from vllm.platforms import current_platform
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logger = init_logger(__name__)
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__all__ = [
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"QuarkMoEMethod",
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"QuarkW8A8Fp8MoEMethod",
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"QuarkOCP_MX_MoEMethod",
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"QuarkNvfp4MoEMethod",
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]
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class QuarkMoEMethod(FusedMoEMethodBase):
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def __init__(self, moe: FusedMoEConfig):
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super().__init__(moe)
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self.has_bias = self.moe.has_bias
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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: RoutedExperts,
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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_fp8_w4a8(weight_config, input_config):
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return QuarkW4A8Fp8MoEMethod(weight_config, input_config, module.moe_config)
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elif quant_config._is_nvfp4(weight_config, input_config):
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return QuarkNvfp4MoEMethod(
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weight_config, input_config, module.moe_config, quant_config
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)
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elif quant_config._is_fp8_w8a8(weight_config, input_config):
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return QuarkW8A8Fp8MoEMethod(weight_config, input_config, module.moe_config)
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elif quant_config._is_w_ocp_mx_a_x(weight_config, input_config):
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# All OCP MX schemes (W4A16, W4A8, etc.) handled by QuarkOCP_MX_MoEMethod
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# Backend selection happens inside via oracle
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return QuarkOCP_MX_MoEMethod(weight_config, input_config, module.moe_config)
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elif quant_config._is_static_tensor_w8a8(
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weight_config, input_config
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) or quant_config._is_dynamic_per_token_w8a8(weight_config, input_config):
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return QuarkW8A8Int8MoEMethod(
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weight_config, input_config, module.moe_config
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)
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else:
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raise RuntimeError("Unsupported FusedMoe scheme")
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class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
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def __init__(
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self,
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weight_config: dict[str, Any],
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input_config: dict[str, Any],
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moe: FusedMoEConfig,
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):
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super().__init__(moe)
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self.weight_quant = weight_config
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self.input_quant = input_config
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self.weight_qscheme = self.weight_quant.get("qscheme")
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self.input_qscheme = self.input_quant.get("qscheme")
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self.weight_dtype = self.weight_quant.get("dtype", "").replace(
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"fp8_e4m3", "fp8"
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)
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self.input_dtype = self.input_quant.get("dtype", "").replace("fp8_e4m3", "fp8")
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per_tensor = (
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self.weight_qscheme == "per_tensor" and self.input_qscheme == "per_tensor"
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)
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per_channel = (
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self.weight_qscheme == "per_channel" and self.input_qscheme == "per_channel"
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)
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self.act_quant_group_shape = (
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GroupShape.PER_TOKEN if per_channel else GroupShape.PER_TENSOR
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)
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if not (per_tensor or per_channel):
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raise ValueError(
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"For FP8 Fused MoE layers, only per-tensor and per-channel "
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"scales for weights and activations are supported. Found "
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f"{self.weight_qscheme}, {self.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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if self.static_input_scales and per_channel:
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raise ValueError(
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"For FP8 Fused MoE layer, we require either per tensor or "
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"channelwise, dynamic per token quantization."
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)
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# Determine quant keys for oracle backend selection
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if per_channel:
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weight_key = kFp8StaticChannelSym
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activation_key = kFp8DynamicTokenSym
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elif self.static_input_scales:
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weight_key = kFp8StaticTensorSym
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activation_key = kFp8StaticTensorSym
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else:
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weight_key = kFp8StaticTensorSym
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activation_key = kFp8DynamicTensorSym
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self.fp8_backend, self.experts_cls = select_fp8_moe_backend(
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config=moe,
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weight_key=weight_key,
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activation_key=activation_key,
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)
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self.model_type = getattr(
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get_current_vllm_config().model_config.hf_config, "model_type", None
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)
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def create_weights(
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self,
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layer: RoutedExperts,
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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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layer.num_experts = num_experts
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layer.orig_dtype = params_dtype
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layer.weight_block_size = None
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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.zeros(
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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.zeros(
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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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if self.weight_qscheme == "per_tensor":
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# Allocate 2 scales for w1 and w3 respectively.
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# They are combined to a single scale after weight loading.
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if self.model_type != "gpt_oss":
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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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else:
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# For gpt_oss, the w1(gate) & w3(up) are fused as one.
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# Therefore, only one weight scale for each expert.
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, 1, dtype=torch.float32), requires_grad=False
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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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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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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# Add PER-TENSOR quantization for RoutedExperts.weight_loader.
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
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)
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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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elif self.weight_qscheme == "per_channel":
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# quark's scale is 1 dim.
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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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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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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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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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# Add PER-CHANNEL quantization for RoutedExperts.weight_loader.
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
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)
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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.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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if self.has_bias:
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w13_bias = torch.nn.Parameter(
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torch.zeros(
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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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layer.register_parameter("w13_bias", w13_bias)
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set_weight_attrs(w13_bias, extra_weight_attrs)
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w2_bias = torch.nn.Parameter(
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torch.zeros(num_experts, hidden_size, dtype=torch.float32),
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requires_grad=False,
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)
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layer.register_parameter("w2_bias", w2_bias)
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set_weight_attrs(w2_bias, extra_weight_attrs)
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else:
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layer.w13_bias, layer.w2_bias = None, None
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def process_weights_after_loading(self, layer: RoutedExperts) -> 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.static_input_scales:
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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_once(
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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 current_platform.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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# For per-tensor case, Fp8 moe kernel needs single weight scale
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# for w13 per expert. Use max then dequant and requant each expert.
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if self.weight_qscheme == "per_tensor":
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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 gpt_oss, w1 and w3 are fused into a single combined
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# gate_up_proj tensor with size 2*intermediate_size_per_partition
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# and only one scale per expert.
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# Process the entire weight tensor as one shard.
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if self.model_type == "gpt_oss":
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for expert_id in range(layer.local_num_experts):
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# Process all 2*intermediate_size_per_partition rows at once
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dq_weight = per_tensor_dequantize(
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layer.w13_weight[expert_id],
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layer.w13_weight_scale[expert_id][0],
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)
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layer.w13_weight[expert_id], _ = ops.scaled_fp8_quant(
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dq_weight, max_w13_scales[expert_id]
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)
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else:
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# For non-gpt_oss, process w1 and w3 shards separately
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for expert_id in range(layer.local_num_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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) = ops.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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# quark's scale is 1 dim.
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elif self.weight_qscheme == "per_channel":
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if self.act_quant_group_shape == GroupShape.PER_TOKEN:
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w13_weight_scale = layer.w13_weight_scale.unsqueeze(-1)
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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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w2_weight_scale = layer.w2_weight_scale.unsqueeze(-1)
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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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self._setup_kernel(layer)
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def _setup_kernel(self, layer: RoutedExperts) -> None:
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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=layer.w13_weight,
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w2=layer.w2_weight,
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w13_scale=layer.w13_weight_scale,
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w2_scale=layer.w2_weight_scale,
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w13_input_scale=layer.w13_input_scale,
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w2_input_scale=layer.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, "w13_weight_scale", w13_scale)
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replace_parameter(layer, "w2_weight_scale", w2_scale)
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if self.fp8_backend == Fp8MoeBackend.AITER:
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layer.w13_weight.is_shuffled = True
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layer.w2_weight.is_shuffled = True
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self.moe_quant_config = self.get_fused_moe_quant_config(layer)
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assert self.moe_quant_config is not None
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assert self.experts_cls is not None
|
|
self.moe_kernel = make_fp8_moe_kernel(
|
|
moe_quant_config=self.moe_quant_config,
|
|
moe_config=self.moe,
|
|
fp8_backend=self.fp8_backend,
|
|
experts_cls=self.experts_cls,
|
|
routing_tables=layer._expert_routing_tables(),
|
|
)
|
|
|
|
def get_fused_moe_quant_config(self, layer: RoutedExperts) -> FusedMoEQuantConfig:
|
|
return make_fp8_moe_quant_config(
|
|
fp8_backend=self.fp8_backend,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
w1_bias=getattr(layer, "w13_bias", None),
|
|
w2_bias=getattr(layer, "w2_bias", None),
|
|
per_act_token_quant=self.input_qscheme == "per_channel",
|
|
per_out_ch_quant=self.weight_qscheme == "per_channel",
|
|
swiglu_limit=getattr(layer, "swiglu_limit", None),
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: RoutedExperts,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts: SharedExperts | None,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
assert self.moe_kernel is not None
|
|
return self.moe_kernel.apply(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=layer.activation,
|
|
global_num_experts=layer.global_num_experts,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
expert_map=layer.expert_map,
|
|
shared_experts=shared_experts,
|
|
shared_experts_input=shared_experts_input,
|
|
)
|
|
|
|
|
|
class QuarkW8A8Int8MoEMethod(QuarkMoEMethod):
|
|
"""Quark W8A8 INT8 MoE method."""
|
|
|
|
def __init__(
|
|
self,
|
|
weight_config: dict[str, Any],
|
|
input_config: dict[str, Any],
|
|
moe: FusedMoEConfig,
|
|
):
|
|
super().__init__(moe)
|
|
self.weight_quant = weight_config
|
|
self.input_quant = input_config
|
|
self.weight_qscheme = self.weight_quant.get("qscheme", "per_tensor")
|
|
self.static_input_scales = not self.input_quant.get("is_dynamic", False)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
layer.num_experts = num_experts
|
|
layer.orig_dtype = params_dtype
|
|
layer.weight_block_size = None
|
|
params_dtype = torch.int8
|
|
|
|
# WEIGHTS
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
hidden_size,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
if self.weight_qscheme == "per_channel":
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, hidden_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
else:
|
|
# per-tensor: one scalar per expert
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, 2, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
|
|
# INPUT_SCALES
|
|
if self.static_input_scales:
|
|
w13_input_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
|
set_weight_attrs(w13_input_scale, extra_weight_attrs)
|
|
|
|
w2_input_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
set_weight_attrs(w2_input_scale, extra_weight_attrs)
|
|
else:
|
|
layer.w13_input_scale = None
|
|
layer.w2_input_scale = None
|
|
|
|
# ZERO POINTS (loaded but discarded after loading; kernel uses symmetric)
|
|
w13_input_zero_point = torch.nn.Parameter(
|
|
torch.zeros(num_experts, 2, dtype=torch.int8),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_input_zero_point", w13_input_zero_point)
|
|
set_weight_attrs(w13_input_zero_point, extra_weight_attrs)
|
|
|
|
w2_input_zero_point = torch.nn.Parameter(
|
|
torch.zeros(num_experts, dtype=torch.int8),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_input_zero_point", w2_input_zero_point)
|
|
set_weight_attrs(w2_input_zero_point, extra_weight_attrs)
|
|
|
|
if self.weight_qscheme == "per_channel":
|
|
w13_weight_zero_point = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.int8,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_zero_point = torch.nn.Parameter(
|
|
torch.zeros(num_experts, hidden_size, dtype=torch.int8),
|
|
requires_grad=False,
|
|
)
|
|
else:
|
|
w13_weight_zero_point = torch.nn.Parameter(
|
|
torch.zeros(num_experts, 2, dtype=torch.int8),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_zero_point = torch.nn.Parameter(
|
|
torch.zeros(num_experts, dtype=torch.int8),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_zero_point", w13_weight_zero_point)
|
|
set_weight_attrs(w13_weight_zero_point, extra_weight_attrs)
|
|
layer.register_parameter("w2_weight_zero_point", w2_weight_zero_point)
|
|
set_weight_attrs(w2_weight_zero_point, extra_weight_attrs)
|
|
|
|
# BIAS
|
|
if self.has_bias:
|
|
w13_bias = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_bias", w13_bias)
|
|
set_weight_attrs(w13_bias, extra_weight_attrs)
|
|
w2_bias = torch.nn.Parameter(
|
|
torch.zeros(num_experts, hidden_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_bias", w2_bias)
|
|
set_weight_attrs(w2_bias, extra_weight_attrs)
|
|
else:
|
|
layer.w13_bias, layer.w2_bias = None, None
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# Discard zero points (INT8 fused MoE kernel uses symmetric quant)
|
|
for attr in (
|
|
"w13_input_zero_point",
|
|
"w2_input_zero_point",
|
|
"w13_weight_zero_point",
|
|
"w2_weight_zero_point",
|
|
):
|
|
if hasattr(layer, attr):
|
|
delattr(layer, attr)
|
|
|
|
# For static input scales, collapse per-expert scales to single max
|
|
if self.static_input_scales:
|
|
if layer.w13_input_scale is None or layer.w2_input_scale is None:
|
|
raise ValueError(
|
|
"QuantConfig has static quantization, but found "
|
|
"activation scales are None."
|
|
)
|
|
if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
|
|
layer.w2_input_scale
|
|
):
|
|
logger.warning_once(
|
|
"Found input_scales that are not equal for "
|
|
"INT8 MoE layer. Using the maximum across experts "
|
|
"for each layer."
|
|
)
|
|
layer.w13_input_scale = torch.nn.Parameter(
|
|
layer.w13_input_scale.max(), requires_grad=False
|
|
)
|
|
layer.w2_input_scale = torch.nn.Parameter(
|
|
layer.w2_input_scale.max(), requires_grad=False
|
|
)
|
|
|
|
# Per-channel scales: 2D [E, N] -> 3D [E, N, 1] for the int8 MoE kernel.
|
|
if self.weight_qscheme == "per_channel":
|
|
for attr in ("w13_weight_scale", "w2_weight_scale"):
|
|
param = getattr(layer, attr, None)
|
|
if param is not None and param.dim() == 2:
|
|
replace_parameter(
|
|
layer,
|
|
attr,
|
|
torch.nn.Parameter(
|
|
param.data.unsqueeze(-1).contiguous(),
|
|
requires_grad=False,
|
|
),
|
|
)
|
|
|
|
# For per-tensor weights, merge w1/w3 scales into single per-expert
|
|
if self.weight_qscheme == "per_tensor":
|
|
assert layer.w13_weight_scale is not None
|
|
shard_size = layer.intermediate_size_per_partition
|
|
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
|
|
|
|
for expert_id in range(layer.local_num_experts):
|
|
start = 0
|
|
for shard_id in range(2):
|
|
dq_weight = per_tensor_dequantize(
|
|
layer.w13_weight[expert_id][start : start + shard_size, :],
|
|
layer.w13_weight_scale[expert_id][shard_id],
|
|
)
|
|
layer.w13_weight[expert_id][start : start + shard_size, :], _, _ = (
|
|
ops.scaled_int8_quant(
|
|
dq_weight,
|
|
scale=max_w13_scales[expert_id],
|
|
)
|
|
)
|
|
start += shard_size
|
|
|
|
layer.w13_weight_scale = torch.nn.Parameter(
|
|
max_w13_scales, requires_grad=False
|
|
)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
if self.weight_qscheme == "per_channel" and not self.static_input_scales:
|
|
return int8_w8a8_moe_quant_config(
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
w1_bias=getattr(layer, "w13_bias", None),
|
|
w2_bias=getattr(layer, "w2_bias", None),
|
|
per_act_token_quant=True,
|
|
)
|
|
is_dynamic = not self.static_input_scales
|
|
is_per_channel = self.weight_qscheme == "per_channel"
|
|
return FusedMoEQuantConfig.make(
|
|
torch.int8,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
w1_bias=getattr(layer, "w13_bias", None),
|
|
w2_bias=getattr(layer, "w2_bias", None),
|
|
per_act_token_quant=is_dynamic,
|
|
per_out_ch_quant=is_per_channel,
|
|
block_shape=None,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: RoutedExperts,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts: SharedExperts | None,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
from vllm.model_executor.layers.fused_moe import fused_experts
|
|
|
|
return fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=layer.activation,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
global_num_experts=layer.global_num_experts,
|
|
expert_map=layer.expert_map,
|
|
quant_config=self.moe_quant_config,
|
|
)
|
|
|
|
|
|
class QuarkW4A8Fp8MoEMethod(QuarkMoEMethod):
|
|
def __init__(
|
|
self,
|
|
weight_config: dict[str, Any],
|
|
input_config: dict[str, Any],
|
|
moe: FusedMoEConfig,
|
|
):
|
|
super().__init__(moe)
|
|
self.weight_quant = weight_config
|
|
self.input_quant = input_config
|
|
|
|
assert rocm_aiter_ops.is_fused_moe_enabled(), (
|
|
"W4A8 FP8 MoE requires ROCm AITER fused MoE support."
|
|
)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: RoutedExperts,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
params_dtype = torch.uint32
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
hidden_size // 8, # INT32 packing for W4
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition // 8, # INT32 packing for W4
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# Per-tensor fp8 weight scales
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32), requires_grad=False
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
|
|
# Per-channel int4 weight scales
|
|
w13_weight_scale_2 = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale_2 = torch.nn.Parameter(
|
|
torch.ones(num_experts, hidden_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
|
|
layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale_2, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale_2, extra_weight_attrs)
|
|
|
|
def process_weights_after_loading(self, layer: RoutedExperts) -> None:
|
|
shuffled_w13, shuffled_w2 = rocm_aiter_ops.shuffle_weights(
|
|
layer.w13_weight.data, layer.w2_weight.data
|
|
)
|
|
layer.w13_weight = torch.nn.Parameter(shuffled_w13, requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(shuffled_w2, requires_grad=False)
|
|
|
|
# INT4-FP8 : offset INT4 w13_weight_scale1 to single w13_weight_scale
|
|
# Fp8 moe kernel needs single fp8 w13_weight_scale for w13 per expert.
|
|
# We won't do requant each expert's fp8 weight (not direct available),
|
|
# instead we adjust half of INT4 w13_weight_scale1 numbers
|
|
shard_size = layer.intermediate_size_per_partition
|
|
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
|
|
assert torch.all(max_w13_scales != 0), "fp8 weight scale cannot be zero."
|
|
for expert_id in range(layer.local_num_experts):
|
|
start = 0
|
|
max_w13_scale_fp8 = max_w13_scales[expert_id]
|
|
for shard_id in range(2):
|
|
if layer.w13_weight_scale[expert_id][shard_id] != max_w13_scale_fp8:
|
|
int4_rescale = (
|
|
layer.w13_weight_scale[expert_id][shard_id] / max_w13_scale_fp8
|
|
)
|
|
layer.w13_weight_scale_2[expert_id][start : start + shard_size] *= (
|
|
int4_rescale
|
|
)
|
|
start += shard_size
|
|
|
|
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False)
|
|
|
|
# special hack to asm_moe, which takes (weight_scale1 * weight_scale) as post
|
|
# GEMM scaling optimal design - shall apply per-column weight_scale1 before
|
|
# GEMM, and weight_scale post
|
|
for expert_id in range(layer.local_num_experts):
|
|
layer.w13_weight_scale_2[expert_id] *= max_w13_scales[expert_id]
|
|
layer.w2_weight_scale_2[expert_id] *= layer.w2_weight_scale[expert_id]
|
|
|
|
def get_fused_moe_quant_config(self, layer):
|
|
return fp8_w8a8_moe_quant_config(
|
|
w1_scale=layer.w13_weight_scale_2,
|
|
w2_scale=layer.w2_weight_scale_2,
|
|
per_out_ch_quant=True,
|
|
gemm1_clamp_limit=getattr(layer, "swiglu_limit", None),
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: RoutedExperts,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts: SharedExperts | None,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
|
|
rocm_aiter_fused_experts,
|
|
)
|
|
|
|
return rocm_aiter_fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=layer.activation,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
quant_config=self.moe_quant_config,
|
|
moe_config=layer.moe_config,
|
|
expert_map=layer.expert_map,
|
|
)
|
|
|
|
|
|
class QuarkOCP_MX_MoEMethod(QuarkMoEMethod):
|
|
def __init__(
|
|
self,
|
|
weight_config: dict[str, Any],
|
|
input_config: dict[str, Any] | None,
|
|
moe: FusedMoEConfig,
|
|
):
|
|
super().__init__(moe)
|
|
self.weight_quant = weight_config
|
|
self.input_quant = input_config
|
|
|
|
weight_qscheme = self.weight_quant.get("qscheme")
|
|
if not weight_qscheme == "per_group":
|
|
raise ValueError(
|
|
"For MX(FP4) Fused MoE layers, only per-group scales "
|
|
f"for weights are supported. Found {weight_qscheme}."
|
|
) # noqa E501
|
|
|
|
self.weight_dtype = self.weight_quant["dtype"].replace("fp", "mxfp")
|
|
if self.input_quant is not None:
|
|
input_quant = self.input_quant["dtype"]
|
|
if input_quant in ["fp4", "fp6_e3m2", "fp6_e2m3"]:
|
|
self.input_dtype = input_quant.replace("fp", "mxfp")
|
|
elif input_quant == "fp8_e4m3":
|
|
self.input_dtype = input_quant.replace("fp8_e4m3", "fp8")
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Current input dtype {input_quant} is not compatible \
|
|
with OCP MX (weight) MoE quantization. Please open an issue"
|
|
)
|
|
else:
|
|
self.input_dtype = None
|
|
|
|
self.fp4_dtype = getattr(torch, "float4_e2m1fn_x2", None)
|
|
|
|
self.ocp_mx_scheme = OCP_MX_Scheme.from_quant_dtype(
|
|
self.input_dtype, self.weight_dtype
|
|
)
|
|
|
|
if self.ocp_mx_scheme is None:
|
|
raise ValueError(
|
|
f"Unsupported OCP MX dtype combination for MoE: "
|
|
f"input_dtype={self.input_dtype}, weight_dtype={self.weight_dtype}. "
|
|
f"Please check that the combination is supported in OCP_MX_Scheme."
|
|
)
|
|
|
|
# TODO(bowenbao): refactor and introduce backends for other OCP MX schemes,
|
|
# use kernel abstraction for all OCP MX MOE implementations.
|
|
self.mxfp4_backend: Mxfp4MoeBackend = Mxfp4MoeBackend.NONE
|
|
self.experts_cls: type[mk.FusedMoEExperts] | None = None
|
|
self.moe_kernel: mk.FusedMoEKernel | None = None
|
|
|
|
# Used for triton kernel precision configs (W4A8, TRITON backends)
|
|
self.w13_precision_config = None
|
|
self.w2_precision_config = None
|
|
|
|
if self.input_quant is not None:
|
|
self.static_input_scales = not self.input_quant.get("is_dynamic")
|
|
else:
|
|
self.static_input_scales = False
|
|
|
|
# Select backend based on OCP MX scheme
|
|
if self.ocp_mx_scheme == "w_mxfp4":
|
|
# W4A16: weight-only MXFP4
|
|
self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
|
|
elif self.ocp_mx_scheme == "w_mxfp4_a_fp8" and self.static_input_scales:
|
|
# W4A8: MXFP4 weights + static FP8 activations
|
|
self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(
|
|
moe, activation_key=kFp8StaticTensorSym
|
|
)
|
|
elif self.ocp_mx_scheme == "w_mxfp4_a_mxfp4":
|
|
# W4A4: MXFP4 weights + MXFP4 activations
|
|
self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(
|
|
moe, activation_key=kMxfp4Dynamic
|
|
)
|
|
|
|
# Validation for unsupported schemes
|
|
if any(
|
|
self.ocp_mx_scheme.endswith(a_scheme)
|
|
for a_scheme in ["a_mxfp4", "a_mxfp6_e3m2", "a_mxfp6_e2m3"]
|
|
):
|
|
if self.static_input_scales:
|
|
raise NotImplementedError(
|
|
"QuarkOCP_MX_MoEMethod with static input scales is currently "
|
|
f"not implemented for OCP MX scheme {self.ocp_mx_scheme}. "
|
|
"Please open an issue."
|
|
)
|
|
elif self.ocp_mx_scheme.endswith("a_fp8") and not self.static_input_scales:
|
|
raise NotImplementedError(
|
|
"QuarkOCP_MX_MoEMethod with dynamic input scales is currently "
|
|
f"not implemented for OCP MX scheme {self.ocp_mx_scheme}. "
|
|
"Please open an issue."
|
|
)
|
|
|
|
self.model_type = getattr(
|
|
get_current_vllm_config().model_config.hf_config, "model_type", None
|
|
)
|
|
|
|
# If no native backend available, use emulation.
|
|
if self.mxfp4_backend is Mxfp4MoeBackend.NONE:
|
|
self.mxfp4_backend = Mxfp4MoeBackend.EMULATION
|
|
|
|
self.experts_cls = backend_to_kernel_cls(self.mxfp4_backend)[0]
|
|
|
|
logger.info_once(
|
|
f"Using {self.mxfp4_backend.value} backend for {self.ocp_mx_scheme}"
|
|
)
|
|
|
|
def maybe_roundup_sizes(
|
|
self,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
act_dtype: torch.dtype,
|
|
moe_parallel_config: FusedMoEParallelConfig,
|
|
) -> tuple[int, int]:
|
|
hidden_size, intermediate_size_per_partition = super().maybe_roundup_sizes(
|
|
hidden_size=hidden_size,
|
|
intermediate_size_per_partition=intermediate_size_per_partition,
|
|
act_dtype=act_dtype,
|
|
moe_parallel_config=moe_parallel_config,
|
|
)
|
|
# In case quantization emulation backend is used, there is no need to apply
|
|
# MXFP4-specific padding logic as the compute happens in higher precision.
|
|
if (
|
|
self.mxfp4_backend is not None
|
|
and self.mxfp4_backend != Mxfp4MoeBackend.EMULATION
|
|
):
|
|
hidden_size, intermediate_size_per_partition = (
|
|
mxfp4_round_up_hidden_size_and_intermediate_size(
|
|
self.mxfp4_backend, hidden_size, intermediate_size_per_partition
|
|
)
|
|
)
|
|
return hidden_size, intermediate_size_per_partition
|
|
|
|
def get_packed_dim(self, dim: int, quant_dtype: str):
|
|
if quant_dtype == "mxfp4":
|
|
assert dim % 2 == 0
|
|
return dim // 2
|
|
else:
|
|
# FP6 packs 4 * 6 = 24 bits on 3 bytes.
|
|
assert (dim * 3) % 4 == 0
|
|
return (dim * 3) // 4
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: RoutedExperts,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
# Add the quantization method used (per tensor/grouped/channel)
|
|
# to ensure the weight scales are loaded in properly
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
|
|
)
|
|
|
|
params_dtype = torch.uint8
|
|
|
|
# WEIGHTS
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
self.get_packed_dim(hidden_size, self.weight_dtype),
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
hidden_size,
|
|
self.get_packed_dim(intermediate_size_per_partition, self.weight_dtype),
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
hidden_size // OCP_MX_BLOCK_SIZE,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition // OCP_MX_BLOCK_SIZE,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
|
|
if self.has_bias:
|
|
w13_bias = torch.nn.Parameter(
|
|
torch.zeros(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_bias", w13_bias)
|
|
set_weight_attrs(w13_bias, extra_weight_attrs)
|
|
|
|
w2_bias = torch.nn.Parameter(
|
|
torch.zeros(num_experts, hidden_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_bias", w2_bias)
|
|
set_weight_attrs(w2_bias, extra_weight_attrs)
|
|
else:
|
|
layer.w13_bias, layer.w2_bias = None, None
|
|
|
|
# INPUT_SCALES
|
|
if self.static_input_scales:
|
|
w13_input_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32), requires_grad=False
|
|
)
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
|
set_weight_attrs(w13_input_scale, extra_weight_attrs)
|
|
|
|
w2_input_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32), requires_grad=False
|
|
)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
set_weight_attrs(w2_input_scale, extra_weight_attrs)
|
|
else:
|
|
layer.w13_input_scale = None
|
|
layer.w2_input_scale = None
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
self._setup_kernel(layer)
|
|
|
|
def _setup_kernel(self, layer: RoutedExperts):
|
|
"""Setup kernel using oracle functions for MXFP4 schemes (W4A16, W4A8)."""
|
|
w13_bias = getattr(layer, "w13_bias", None)
|
|
w2_bias = getattr(layer, "w2_bias", None)
|
|
|
|
# Convert weights to kernel format (handles all backend-specific logic)
|
|
w13, w2, w13_scale, w2_scale, w13_bias, w2_bias = (
|
|
convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
|
|
mxfp4_backend=self.mxfp4_backend,
|
|
layer=layer,
|
|
w13_weight=layer.w13_weight,
|
|
w2_weight=layer.w2_weight,
|
|
w13_weight_scale=layer.w13_weight_scale,
|
|
w2_weight_scale=layer.w2_weight_scale,
|
|
w13_bias=w13_bias,
|
|
w2_bias=w2_bias,
|
|
w13_input_scale=layer.w13_input_scale,
|
|
w2_input_scale=layer.w2_input_scale,
|
|
)
|
|
)
|
|
|
|
# Handle weight/scale assignment based on backend type
|
|
if self.mxfp4_backend in TRITON_BACKENDS or self.mxfp4_backend in (
|
|
Mxfp4MoeBackend.AITER_MXFP4_FP8,
|
|
):
|
|
# Triton-based backends: w13/w2 are triton_kernels.tensor.Tensor
|
|
# Store on layer for apply(), scales are PrecisionConfig
|
|
layer.w13_weight = w13
|
|
layer.w2_weight = w2
|
|
self.w13_precision_config = w13_scale
|
|
self.w2_precision_config = w2_scale
|
|
else:
|
|
# Standard backends: replace parameters
|
|
replace_parameter(layer, "w13_weight", w13)
|
|
replace_parameter(layer, "w2_weight", w2)
|
|
replace_parameter(layer, "w13_weight_scale", w13_scale)
|
|
replace_parameter(layer, "w2_weight_scale", w2_scale)
|
|
|
|
if w13_bias is not None and w2_bias is not None:
|
|
replace_parameter(layer, "w13_bias", w13_bias)
|
|
replace_parameter(layer, "w2_bias", w2_bias)
|
|
|
|
if self.mxfp4_backend == Mxfp4MoeBackend.AITER_MXFP4_MXFP4:
|
|
layer.w13_weight.is_shuffled = True
|
|
layer.w2_weight.is_shuffled = True
|
|
|
|
torch.accelerator.empty_cache()
|
|
|
|
# Build quant config and kernel
|
|
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
|
|
if self.moe_quant_config is not None and self.experts_cls is not None:
|
|
self.moe_kernel = make_mxfp4_moe_kernel(
|
|
moe_quant_config=self.moe_quant_config,
|
|
moe_config=self.moe,
|
|
mxfp4_backend=self.mxfp4_backend,
|
|
experts_cls=self.experts_cls,
|
|
routing_tables=layer._expert_routing_tables(),
|
|
)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: RoutedExperts
|
|
) -> FusedMoEQuantConfig | None:
|
|
# For oracle-based backends (W4A16, W4A8), use make_mxfp4_moe_quant_config
|
|
if self.mxfp4_backend not in (Mxfp4MoeBackend.NONE, Mxfp4MoeBackend.EMULATION):
|
|
# Determine scale source based on backend type
|
|
if self.mxfp4_backend in TRITON_BACKENDS or self.mxfp4_backend in (
|
|
Mxfp4MoeBackend.AITER_MXFP4_FP8,
|
|
):
|
|
w1_scale = self.w13_precision_config
|
|
w2_scale = self.w2_precision_config
|
|
else:
|
|
w1_scale = layer.w13_weight_scale
|
|
w2_scale = layer.w2_weight_scale
|
|
|
|
return make_mxfp4_moe_quant_config(
|
|
mxfp4_backend=self.mxfp4_backend,
|
|
w1_scale=w1_scale,
|
|
w2_scale=w2_scale,
|
|
w1_bias=getattr(layer, "w13_bias", None),
|
|
w2_bias=getattr(layer, "w2_bias", None),
|
|
a1_scale=getattr(layer, "w13_input_scale", None),
|
|
a2_scale=getattr(layer, "w2_input_scale", None),
|
|
gemm1_alpha=getattr(layer, "swiglu_alpha", None),
|
|
gemm1_beta=getattr(layer, "swiglu_beta", None),
|
|
swiglu_limit=getattr(layer, "swiglu_limit", None),
|
|
layer=layer,
|
|
)
|
|
|
|
# Emulation and other schemes
|
|
if self.ocp_mx_scheme == "w_mxfp4":
|
|
return mxfp4_w4a16_moe_quant_config(
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
w1_bias=layer.w13_bias,
|
|
w2_bias=layer.w2_bias,
|
|
)
|
|
elif self.ocp_mx_scheme == "w_mxfp4_a_fp8":
|
|
return mxfp4_w4a8_moe_quant_config(
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
w1_bias=layer.w13_bias,
|
|
w2_bias=layer.w2_bias,
|
|
block_shape=None,
|
|
)
|
|
elif self.ocp_mx_scheme in ["w_mxfp6_e3m2_a_fp8", "w_mxfp6_e2m3_a_fp8"]:
|
|
raise NotImplementedError(
|
|
"Currently there is no corresponding fused moe quant config configured "
|
|
f"in vLLM for OCP MX scheme {self.ocp_mx_scheme}. Please open an issue."
|
|
)
|
|
else:
|
|
return ocp_mx_moe_quant_config(
|
|
quant_dtype=self.input_dtype,
|
|
weight_dtype=self.weight_dtype,
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
w1_bias=layer.w13_bias,
|
|
w2_bias=layer.w2_bias,
|
|
a1_scale=None,
|
|
a2_scale=None,
|
|
block_shape=None,
|
|
gemm1_alpha=getattr(layer, "swiglu_alpha", None),
|
|
gemm1_beta=getattr(layer, "swiglu_beta", None),
|
|
gemm1_clamp_limit=getattr(layer, "swiglu_limit", None),
|
|
)
|
|
|
|
@property
|
|
def supports_eplb(self) -> bool:
|
|
# AITER shuffle keeps expert dim outermost, so EPLB row moves are layout-safe.
|
|
return True
|
|
|
|
@property
|
|
def is_monolithic(self) -> bool:
|
|
if self.moe_kernel is not None:
|
|
return self.moe_kernel.is_monolithic
|
|
return False
|
|
|
|
def apply(
|
|
self,
|
|
layer: RoutedExperts,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts: SharedExperts | None,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
assert self.moe_kernel is not None
|
|
return self.moe_kernel.apply(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=layer.activation,
|
|
global_num_experts=layer.global_num_experts,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
expert_map=layer.expert_map,
|
|
shared_experts_input=shared_experts_input,
|
|
)
|
|
|
|
def apply_monolithic(
|
|
self,
|
|
layer: RoutedExperts,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
input_ids: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
assert self.is_monolithic
|
|
assert self.moe_kernel is not None
|
|
return self.moe_kernel.apply_monolithic(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
router_logits=router_logits,
|
|
activation=layer.activation,
|
|
global_num_experts=layer.global_num_experts,
|
|
expert_map=layer.expert_map,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
)
|
|
|
|
|
|
class QuarkNvfp4MoEMethod(QuarkMoEMethod):
|
|
def __init__(
|
|
self,
|
|
weight_config: dict[str, Any],
|
|
input_config: dict[str, Any],
|
|
moe: FusedMoEConfig,
|
|
quant_config: "QuarkConfig", # type: ignore # noqa E501 # noqa F821
|
|
):
|
|
super().__init__(moe)
|
|
self.weight_quant = weight_config
|
|
self.input_quant = input_config
|
|
self.quant_config = quant_config
|
|
self.group_size = 16
|
|
|
|
# Select experts implementation.
|
|
self.nvfp4_backend, self.experts_cls = select_nvfp4_moe_backend(
|
|
config=self.moe,
|
|
weight_key=kNvfp4Static,
|
|
activation_key=kNvfp4Dynamic,
|
|
)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
layer.num_experts = num_experts
|
|
layer.params_dtype = params_dtype
|
|
layer.quant_config = self.quant_config
|
|
weight_dtype = torch.uint8
|
|
weight_scale_dtype = torch.float8_e4m3fn
|
|
w13_num_shards = 2 if self.moe.is_act_and_mul else 1
|
|
|
|
# GEMM 1 - w13 weight
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
w13_num_shards * intermediate_size_per_partition,
|
|
# 2 fp4 items are packed in the input dimension
|
|
hidden_size // 2,
|
|
dtype=weight_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
# GEMM 2 - w2 weight
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
# 2 fp4 items are packed in the input dimension
|
|
intermediate_size_per_partition // 2,
|
|
dtype=weight_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# Weight scales (per-group FP8 scales)
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
w13_num_shards * intermediate_size_per_partition,
|
|
hidden_size // self.group_size,
|
|
dtype=weight_scale_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.GROUP.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition // self.group_size,
|
|
dtype=weight_scale_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
|
|
# Global weight scales (per-tensor FP32 scales)
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
|
)
|
|
|
|
w13_weight_scale_2 = torch.nn.Parameter(
|
|
torch.empty(num_experts, w13_num_shards, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
|
|
set_weight_attrs(w13_weight_scale_2, extra_weight_attrs)
|
|
|
|
w2_weight_scale_2 = torch.nn.Parameter(
|
|
torch.empty(num_experts, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
|
|
set_weight_attrs(w2_weight_scale_2, extra_weight_attrs)
|
|
|
|
# Input global scales (per-tensor FP32 scales)
|
|
w13_input_scale_2 = torch.nn.Parameter(
|
|
torch.empty(num_experts, w13_num_shards, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_input_scale_2", w13_input_scale_2)
|
|
set_weight_attrs(w13_input_scale_2, extra_weight_attrs)
|
|
|
|
w2_input_scale_2 = torch.nn.Parameter(
|
|
torch.empty(num_experts, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_input_scale_2", w2_input_scale_2)
|
|
set_weight_attrs(w2_input_scale_2, extra_weight_attrs)
|
|
|
|
def process_weights_after_loading(self, layer: RoutedExperts) -> None:
|
|
"""
|
|
Convert NVFP4 MoE weights into kernel format and setup the kernel.
|
|
"""
|
|
|
|
if not torch.allclose(
|
|
layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]
|
|
):
|
|
raise ValueError("Different global scales for w1 and w3 is not supported.")
|
|
|
|
# Use a single gscale for w13
|
|
w13_weight_scale_2 = torch.maximum(
|
|
layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]
|
|
).contiguous()
|
|
|
|
w2_weight_scale_2 = layer.w2_weight_scale_2
|
|
|
|
(
|
|
w13,
|
|
w13_scale,
|
|
w13_scale_2,
|
|
a13_scale,
|
|
w2,
|
|
w2_scale,
|
|
w2_scale_2,
|
|
a2_scale,
|
|
) = convert_to_nvfp4_moe_kernel_format(
|
|
nvfp4_backend=self.nvfp4_backend,
|
|
layer=layer,
|
|
w13=layer.w13_weight,
|
|
w13_scale=layer.w13_weight_scale,
|
|
w13_scale_2=w13_weight_scale_2,
|
|
a13_scale=layer.w13_input_scale_2,
|
|
w2=layer.w2_weight,
|
|
w2_scale=layer.w2_weight_scale,
|
|
w2_scale_2=w2_weight_scale_2,
|
|
a2_scale=layer.w2_input_scale_2,
|
|
is_act_and_mul=self.moe.is_act_and_mul,
|
|
)
|
|
|
|
replace_parameter(layer, "w13_weight", w13)
|
|
replace_parameter(layer, "w13_weight_scale", w13_scale)
|
|
replace_parameter(layer, "w13_weight_scale_2", w13_scale_2)
|
|
replace_parameter(layer, "w13_input_scale_2", a13_scale)
|
|
|
|
replace_parameter(layer, "w2_weight", w2)
|
|
replace_parameter(layer, "w2_weight_scale", w2_scale)
|
|
replace_parameter(layer, "w2_weight_scale_2", w2_scale_2)
|
|
replace_parameter(layer, "w2_input_scale_2", a2_scale)
|
|
|
|
# Setup modular kernel.
|
|
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
|
|
if self.moe_quant_config:
|
|
assert self.experts_cls is not None
|
|
self.moe_kernel = make_nvfp4_moe_kernel(
|
|
moe_quant_config=self.moe_quant_config,
|
|
moe_config=self.moe,
|
|
experts_cls=self.experts_cls,
|
|
backend=self.nvfp4_backend,
|
|
routing_tables=layer._expert_routing_tables(),
|
|
layer=layer,
|
|
)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
return make_nvfp4_moe_quant_config(
|
|
backend=self.nvfp4_backend,
|
|
w13_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
w13_scale_2=layer.w13_weight_scale_2,
|
|
w2_scale_2=layer.w2_weight_scale_2,
|
|
a13_scale=layer.w13_input_scale_2,
|
|
a2_scale=layer.w2_input_scale_2,
|
|
layer=layer,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: RoutedExperts,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts: SharedExperts | None,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
assert self.moe_kernel is not None
|
|
return self.moe_kernel.apply(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
activation=layer.activation,
|
|
global_num_experts=layer.global_num_experts,
|
|
expert_map=layer.expert_map,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
shared_experts=shared_experts,
|
|
shared_experts_input=shared_experts_input,
|
|
)
|