103f0de565
Signed-off-by: Bowen Bao <bowenbao@amd.com> Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
1334 lines
52 KiB
Python
1334 lines
52 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 import envs
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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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FusedMoE,
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FusedMoEConfig,
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FusedMoEMethodBase,
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FusedMoeWeightScaleSupported,
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MoEActivation,
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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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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.fused_marlin_moe import fused_marlin_moe
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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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convert_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.quantization.utils.marlin_utils_fp8 import (
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prepare_fp8_moe_layer_for_marlin,
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)
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from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
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_swizzle_mxfp4,
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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 GroupShape
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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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from vllm.scalar_type import scalar_types
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logger = init_logger(__name__)
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__all__ = [
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"QuarkMoEMethod",
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"QuarkOCP_MX_MoEMethod",
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"QuarkOCP_MX_MoEMethod_OSS",
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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: 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_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_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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emulate = not current_platform.supports_mx() or not (
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rocm_aiter_ops.is_fused_moe_enabled()
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)
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if (
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input_config is not None
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and input_config.get("dtype") == "fp8_e4m3"
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and not input_config.get("is_dynamic")
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and not emulate
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):
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return QuarkOCP_MX_MoEMethod_OSS(
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weight_config, input_config, module.moe_config
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)
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else:
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return QuarkOCP_MX_MoEMethod(
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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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# For GPUs that lack FP8 hardware support, we can leverage the Marlin
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# kernel for fast weight-only FP8 quantization
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self.use_marlin = (
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not current_platform.has_device_capability(89)
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or envs.VLLM_TEST_FORCE_FP8_MARLIN
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)
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# Disable marlin for rocm
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if current_platform.is_rocm():
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self.use_marlin = False
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self.rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
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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: 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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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 FusedMoE.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 FusedMoE.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: 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.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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# Property to determine if AITER is used
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if self.rocm_aiter_moe_enabled:
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# reshaping weights is required for aiter moe kernel.
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shuffled_w13, shuffled_w2 = rocm_aiter_ops.shuffle_weights(
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layer.w13_weight.data, layer.w2_weight.data
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)
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layer.w13_weight = torch.nn.Parameter(shuffled_w13, requires_grad=False)
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layer.w2_weight = torch.nn.Parameter(shuffled_w2, requires_grad=False)
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elif self.use_marlin:
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w13_weight, w2_weight, w13_weight_scale, w2_weight_scale = (
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prepare_fp8_moe_layer_for_marlin(
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layer,
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layer.w13_weight,
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layer.w2_weight,
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layer.w13_weight_scale,
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layer.w2_weight_scale,
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)
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)
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# TODO(rob): once we apply refactor to Quark, switch to using
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# replace_parameter for compatibility with reloading in RL.
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layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
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layer.w2_weight = torch.nn.Parameter(w2_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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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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def get_fused_moe_quant_config(
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self, layer: torch.nn.Module
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) -> FusedMoEQuantConfig | None:
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return fp8_w8a8_moe_quant_config(
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w1_scale=layer.w13_weight_scale,
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w2_scale=layer.w2_weight_scale,
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a1_scale=layer.w13_input_scale,
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a2_scale=layer.w2_input_scale,
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w1_bias=layer.w13_bias,
|
|
w2_bias=layer.w2_bias,
|
|
per_act_token_quant=self.input_qscheme == "per_channel",
|
|
per_out_ch_quant=self.weight_qscheme == "per_channel",
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
if self.rocm_aiter_moe_enabled:
|
|
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_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,
|
|
)
|
|
elif self.use_marlin:
|
|
assert layer.activation == MoEActivation.SILU, (
|
|
f"{layer.activation} not supported for Marlin MoE."
|
|
)
|
|
return fused_marlin_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
None,
|
|
None,
|
|
layer.w13_weight_scale,
|
|
layer.w2_weight_scale,
|
|
topk_weights,
|
|
topk_ids,
|
|
quant_type_id=scalar_types.float8_e4m3fn.id,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
global_num_experts=layer.global_num_experts,
|
|
expert_map=layer.expert_map,
|
|
inplace=not self.moe.disable_inplace,
|
|
)
|
|
else:
|
|
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,
|
|
inplace=not self.moe.disable_inplace,
|
|
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: torch.nn.Module,
|
|
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: torch.nn.Module) -> 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,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_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."
|
|
)
|
|
|
|
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
|
|
self.w13_precision_config = None
|
|
self.w2_precision_config = None
|
|
|
|
if self.ocp_mx_scheme == "w_mxfp4":
|
|
self.mxfp4_backend, self.experts_cls = select_mxfp4_moe_backend(moe)
|
|
elif self.ocp_mx_scheme.startswith("w_mxfp4"):
|
|
# TODO(bowenbao): refactor and introduce backends for other OCP MX schemes.
|
|
self.mxfp4_backend = Mxfp4MoeBackend.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
|
|
|
|
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.use_rocm_aiter_moe = rocm_aiter_ops.is_fused_moe_enabled()
|
|
|
|
self.model_type = getattr(
|
|
get_current_vllm_config().model_config.hf_config, "model_type", None
|
|
)
|
|
|
|
self.emulate = (
|
|
not current_platform.supports_mx()
|
|
or not self.ocp_mx_scheme.startswith("w_mxfp4")
|
|
) and (
|
|
self.mxfp4_backend is Mxfp4MoeBackend.NONE or not self.use_rocm_aiter_moe
|
|
)
|
|
|
|
if self.emulate:
|
|
logger.warning_once(
|
|
f"The current mode (supports_mx={current_platform.supports_mx()}, "
|
|
f"use_rocm_aiter_moe={self.use_rocm_aiter_moe}, "
|
|
f"ocp_mx_scheme={self.ocp_mx_scheme}) "
|
|
"does not support native MXFP4/MXFP6 "
|
|
"computation. Simulated weight dequantization and activation "
|
|
"QDQ (quantize and dequantize) will be used, with the linear "
|
|
"layers computed in high precision."
|
|
)
|
|
else:
|
|
logger.warning_once(
|
|
"The current mode supports native MoE MXFP4 computation"
|
|
)
|
|
|
|
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,
|
|
)
|
|
if self.mxfp4_backend is not None:
|
|
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: torch.nn.Module,
|
|
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):
|
|
if self.static_input_scales and self.input_dtype == "fp8":
|
|
# firstly, process activations if fp8 static input
|
|
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 "
|
|
"fp8 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
|
|
)
|
|
|
|
if current_platform.is_fp8_fnuz():
|
|
# Normalize the weights and scales
|
|
_, _, w13_input_scale = normalize_e4m3fn_to_e4m3fnuz(
|
|
torch.empty_like(layer.w13_weight, dtype=torch.float8_e4m3fn),
|
|
torch.empty_like(
|
|
layer.w13_weight_scale, dtype=layer.w13_weight_scale.dtype
|
|
),
|
|
layer.w13_input_scale,
|
|
)
|
|
_, _, w2_input_scale = normalize_e4m3fn_to_e4m3fnuz(
|
|
torch.empty_like(layer.w2_weight, dtype=torch.float8_e4m3fn),
|
|
torch.empty_like(
|
|
layer.w2_weight_scale, dtype=layer.w13_weight_scale.dtype
|
|
),
|
|
layer.w2_input_scale,
|
|
)
|
|
# Reset the parameter
|
|
if w13_input_scale is not None:
|
|
layer.w13_input_scale = torch.nn.Parameter(
|
|
w13_input_scale, requires_grad=False
|
|
)
|
|
if w2_input_scale is not None:
|
|
layer.w2_input_scale = torch.nn.Parameter(
|
|
w2_input_scale, requires_grad=False
|
|
)
|
|
|
|
# For w_mxfp4, use oracle functions
|
|
if (
|
|
self.ocp_mx_scheme == "w_mxfp4"
|
|
and self.mxfp4_backend != Mxfp4MoeBackend.NONE
|
|
):
|
|
self._setup_kernel_via_oracle(layer)
|
|
return
|
|
|
|
# TODO(bowenbao): gradually migrate to oracles.
|
|
# secondly, process mxfp weights for other schemes
|
|
if self.emulate:
|
|
# Build quant config for emulation path
|
|
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
|
|
torch.accelerator.empty_cache()
|
|
return
|
|
|
|
# Existing AITER path for w_mxfp4_a_mxfp4 and other schemes
|
|
from aiter.utility.fp4_utils import e8m0_shuffle
|
|
|
|
# Pre-shuffle weight scales
|
|
s0, s1, _ = layer.w13_weight_scale.shape
|
|
w13_weight_scale = layer.w13_weight_scale.view(s0 * s1, -1)
|
|
w13_weight_scale = e8m0_shuffle(w13_weight_scale)
|
|
layer.w13_weight_scale.data = w13_weight_scale.view(s0, s1, -1)
|
|
|
|
s0, s1, _ = layer.w2_weight_scale.shape
|
|
w2_weight_scale = layer.w2_weight_scale.view(s0 * s1, -1)
|
|
w2_weight_scale = e8m0_shuffle(w2_weight_scale)
|
|
layer.w2_weight_scale.data = w2_weight_scale.view(s0, s1, -1)
|
|
|
|
if self.fp4_dtype is not None:
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
layer.w13_weight.view(self.fp4_dtype),
|
|
requires_grad=layer.w13_weight.requires_grad,
|
|
)
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
layer.w2_weight.view(self.fp4_dtype),
|
|
requires_grad=layer.w2_weight.requires_grad,
|
|
)
|
|
# Pre-shuffle weight
|
|
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)
|
|
layer.w13_weight.is_shuffled = True
|
|
layer.w2_weight.is_shuffled = True
|
|
|
|
# Build quant config for AITER path
|
|
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
|
|
torch.accelerator.empty_cache()
|
|
|
|
def _setup_kernel_via_oracle(self, layer: FusedMoE):
|
|
"""Setup kernel using oracle functions for w_mxfp4 scheme."""
|
|
w13 = layer.w13_weight
|
|
w2 = layer.w2_weight
|
|
w13_scale = layer.w13_weight_scale
|
|
w2_scale = layer.w2_weight_scale
|
|
w13_bias = getattr(layer, "w13_bias", None)
|
|
w2_bias = getattr(layer, "w2_bias", None)
|
|
|
|
# Convert weights to kernel format
|
|
w13, w2, w13_scale, w2_scale, w13_bias, w2_bias = (
|
|
convert_to_mxfp4_moe_kernel_format(
|
|
mxfp4_backend=self.mxfp4_backend,
|
|
layer=layer,
|
|
w13_weight=w13,
|
|
w2_weight=w2,
|
|
w13_weight_scale=w13_scale,
|
|
w2_weight_scale=w2_scale,
|
|
w13_bias=w13_bias,
|
|
w2_bias=w2_bias,
|
|
)
|
|
)
|
|
|
|
# For TRITON backends, weights are wrapped tensors from triton_kernels
|
|
# that don't support .detach(). Manually assign parameters.
|
|
if self.mxfp4_backend not in TRITON_BACKENDS:
|
|
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)
|
|
else:
|
|
layer.w13_weight = w13
|
|
layer.w2_weight = w2
|
|
self.w13_precision_config = w13_scale
|
|
self.w2_precision_config = 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)
|
|
|
|
# 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._maybe_init_expert_routing_tables(),
|
|
shared_experts=layer.shared_experts,
|
|
)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
# For w_mxfp4 with oracle backend, use oracle function
|
|
if (
|
|
self.ocp_mx_scheme == "w_mxfp4"
|
|
and self.mxfp4_backend != Mxfp4MoeBackend.NONE
|
|
):
|
|
w1_scale = layer.w13_weight_scale
|
|
w2_scale = layer.w2_weight_scale
|
|
if self.mxfp4_backend in TRITON_BACKENDS:
|
|
w1_scale = self.w13_precision_config
|
|
w2_scale = self.w2_precision_config
|
|
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),
|
|
)
|
|
|
|
# Existing code for other schemes
|
|
# TODO(bowenbao): kept for emulation fallback, to be refactored into
|
|
# dedicated emulation backend.
|
|
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,
|
|
)
|
|
|
|
@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: FusedMoE,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor:
|
|
# For w_mxfp4 with oracle kernel
|
|
if 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,
|
|
)
|
|
|
|
# Existing code for emulation/AITER paths
|
|
if not self.emulate:
|
|
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
|
|
rocm_aiter_fused_experts,
|
|
)
|
|
|
|
return rocm_aiter_fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
activation=layer.activation,
|
|
quant_config=self.moe_quant_config,
|
|
moe_config=layer.moe_config,
|
|
expert_map=layer.expert_map,
|
|
)
|
|
else:
|
|
from vllm.model_executor.layers.fused_moe import fused_experts
|
|
|
|
return fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=not self.moe.disable_inplace,
|
|
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,
|
|
quant_config=self.moe_quant_config,
|
|
)
|
|
|
|
def apply_monolithic(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
) -> 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 QuarkOCP_MX_MoEMethod_OSS(QuarkOCP_MX_MoEMethod):
|
|
def __init__(
|
|
self,
|
|
weight_config: dict[str, Any],
|
|
input_config: dict[str, Any],
|
|
moe: FusedMoEConfig,
|
|
):
|
|
super().__init__(weight_config, input_config, moe)
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
|
|
|
|
w13_bias = layer.w13_bias.to(torch.float32)
|
|
w2_bias = layer.w2_bias.to(torch.float32)
|
|
|
|
layer.w13_bias = torch.nn.Parameter(w13_bias, requires_grad=False)
|
|
layer.w2_bias = torch.nn.Parameter(w2_bias, requires_grad=False)
|
|
|
|
# FIXME warp need to be adjusted based on batch size
|
|
# only apply to batched mode
|
|
if self.moe.use_ep:
|
|
num_warps = 4 if envs.VLLM_MOE_DP_CHUNK_SIZE <= 512 else 8
|
|
else:
|
|
num_warps = 8
|
|
|
|
w13_weight, w13_flex, w13_scale = _swizzle_mxfp4(
|
|
layer.w13_weight, layer.w13_weight_scale, num_warps
|
|
)
|
|
w2_weight, w2_flex, w2_scale = _swizzle_mxfp4(
|
|
layer.w2_weight, layer.w2_weight_scale, num_warps
|
|
)
|
|
|
|
self.w13_weight_triton_tensor = w13_weight
|
|
self.w2_weight_triton_tensor = w2_weight
|
|
|
|
# need to delete the original weights to save memory on single GPU
|
|
del layer.w13_weight
|
|
del layer.w2_weight
|
|
layer.w13_weight = None
|
|
layer.w2_weight = None
|
|
torch.accelerator.empty_cache()
|
|
|
|
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 "
|
|
"fp8 MoE layer. Using the maximum across experts "
|
|
"for each layer."
|
|
)
|
|
|
|
layer.w13_input_scale = torch.nn.Parameter(
|
|
layer.w13_input_scale.max().to(torch.float32), requires_grad=False
|
|
)
|
|
layer.w2_input_scale = torch.nn.Parameter(
|
|
layer.w2_input_scale.max().to(torch.float32), requires_grad=False
|
|
)
|
|
|
|
from triton_kernels.numerics import InFlexData
|
|
|
|
lhs_data13 = InFlexData(scale=layer.w13_input_scale)
|
|
lhs_data2 = InFlexData(scale=layer.w2_input_scale)
|
|
|
|
self.w13_precision_config = PrecisionConfig(
|
|
weight_scale=w13_scale,
|
|
flex_ctx=FlexCtx(rhs_data=w13_flex, lhs_data=lhs_data13),
|
|
)
|
|
|
|
self.w2_precision_config = PrecisionConfig(
|
|
weight_scale=w2_scale,
|
|
flex_ctx=FlexCtx(rhs_data=w2_flex, lhs_data=lhs_data2),
|
|
)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
return mxfp4_w4a8_moe_quant_config(
|
|
w1_scale=self.w13_precision_config,
|
|
w2_scale=self.w2_precision_config,
|
|
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,
|
|
)
|
|
|
|
@property
|
|
def is_monolithic(self) -> bool:
|
|
return True
|
|
|
|
def apply_monolithic(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
expert_map: torch.Tensor | None = None,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
if layer.enable_eplb:
|
|
raise NotImplementedError(
|
|
"EPLB not supported for `QuarkW4MXFp4MoEMethod_OSS` yet."
|
|
)
|
|
|
|
from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import ( # noqa: E501
|
|
triton_kernel_moe_forward,
|
|
)
|
|
|
|
assert self.moe.hidden_dim_unpadded is not None
|
|
assert self.moe.intermediate_size_per_partition_unpadded is not None
|
|
return triton_kernel_moe_forward(
|
|
hidden_states=x,
|
|
w1=self.w13_weight_triton_tensor,
|
|
w2=self.w2_weight_triton_tensor,
|
|
gating_output=router_logits,
|
|
topk=layer.top_k,
|
|
renormalize=layer.renormalize,
|
|
global_num_experts=layer.global_num_experts,
|
|
expert_map=expert_map,
|
|
quant_config=self.moe_quant_config,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
unpadded_N_w1=self.moe.intermediate_size_per_partition_unpadded * 2,
|
|
unpadded_K_w1=self.moe.hidden_dim_unpadded,
|
|
unpadded_N_w2=self.moe.hidden_dim_unpadded,
|
|
unpadded_K_w2=self.moe.intermediate_size_per_partition_unpadded,
|
|
)
|