1804 lines
64 KiB
Python
1804 lines
64 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 fnmatch import fnmatch
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from typing import TYPE_CHECKING, Any
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import torch
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from torch.nn.parameter import Parameter
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import vllm.model_executor.layers.fused_moe.modular_kernel as mk
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe.activation import MoEActivation
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from vllm.model_executor.layers.fused_moe.config import (
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FusedMoEConfig,
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FusedMoEQuantConfig,
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)
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE,
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FusedMoEMethodBase,
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FusedMoeWeightScaleSupported,
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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.nvfp4 import (
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NvFp4MoeBackend,
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convert_to_nvfp4_moe_kernel_format,
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is_global_sf_supported_for_nvfp4_backend,
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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.linear import (
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LinearBase,
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LinearMethodBase,
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UnquantizedLinearMethod,
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)
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
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init_fp8_linear_kernel,
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)
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from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
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from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
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flashinfer_trtllm_fp4_moe,
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flashinfer_trtllm_fp4_routed_moe,
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)
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from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
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apply_fi_trtllm_fp8_per_tensor_moe,
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)
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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W8A8BlockFp8LinearOp,
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process_fp8_input_tensor_strategy_moe,
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process_fp8_weight_tensor_strategy_moe,
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)
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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get_marlin_input_dtype,
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)
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from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
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MXFP8_BLOCK_SIZE,
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MXFP8_SCALE_DTYPE,
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MXFP8_VALUE_DTYPE,
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Mxfp8LinearBackend,
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Mxfp8LinearOp,
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)
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from vllm.model_executor.layers.quantization.utils.nvfp4_utils import (
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apply_nvfp4_linear,
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convert_to_nvfp4_linear_kernel_format,
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select_nvfp4_linear_backend,
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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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is_layer_skipped,
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kFp8DynamicTokenSym,
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kFp8StaticTensorSym,
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kFp8StaticTokenSym,
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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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cutlass_block_fp8_supported,
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requantize_with_max_scale,
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)
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from vllm.model_executor.parameter import (
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BlockQuantScaleParameter,
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ChannelQuantScaleParameter,
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ModelWeightParameter,
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PerTensorScaleParameter,
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)
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from vllm.model_executor.utils import replace_parameter
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if TYPE_CHECKING:
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from vllm.model_executor.models.utils import WeightsMapper
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logger = init_logger(__name__)
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QUANT_ALGOS = [
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# FP8 (per-tensor weight + optional static activation scale).
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"FP8",
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# FP8 per-channel weight scale + per-token activation scale.
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"FP8_PER_CHANNEL_PER_TOKEN",
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# FP8 per-block weight-only (ModelOpt may emit this as lowercase).
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"FP8_PB_WO",
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# FP4
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"NVFP4",
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# MXFP8
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"MXFP8",
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]
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KV_CACHE_QUANT_ALGOS = ["FP8"]
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class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
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"""
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Supports loading kv-cache scaling factors from FP8 checkpoints.
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"""
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def __init__(self, quant_config: "ModelOptQuantConfigBase"):
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super().__init__(quant_config)
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class ModelOptQuantConfigBase(QuantizationConfig):
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LinearMethodCls: type = LinearMethodBase
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FusedMoEMethodCls: type = FusedMoEMethodBase
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KVCacheMethodCls: type = BaseKVCacheMethod
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def __init__(
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self,
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exclude_modules: list[str],
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):
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super().__init__()
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self.exclude_modules: list[str] = exclude_modules
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def is_layer_excluded(self, prefix: str) -> bool:
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"""
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Check if a layer should be excluded from quantization.
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Handles both exact matching (for fused layers) and ModelOpt wildcard matching.
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The ModelOpt exclude_modules list is a list of wildcards.
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"""
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if len(self.exclude_modules) == 0:
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return False
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# First check exact matching with fused layer support
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if is_layer_skipped(prefix, self.exclude_modules, self.packed_modules_mapping):
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return True
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# TODO: This special hard coded logic is not needed for quantized checkpoints
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# generated by ModelOpt >= 0.39.0 where they are handled natually by the
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# exclude_modules config. But need to keep them for loading quantized
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# checkpoints generated by older versions. Then check substring matching
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# for patterns not caught by exact match
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for exclude_module in self.exclude_modules:
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# Skip exact matches already handled above
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if exclude_module != prefix and (
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exclude_module in prefix
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or (
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prefix.startswith("language_model.")
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and exclude_module in prefix.removeprefix("language_model.")
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)
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):
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return True
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# modelopt exclude modules are not simple strings, they are wildcards
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for wildcard_pattern in self.exclude_modules:
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if fnmatch(prefix, wildcard_pattern):
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return True
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return False
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> "QuantizeMethodBase | None":
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# handle kv-cache first so we can focus only on weight quantization thereafter
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if isinstance(layer, Attention):
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return self.KVCacheMethodCls(self)
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# handle exclusion
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if self.is_layer_excluded(prefix):
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if isinstance(layer, LinearBase):
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return UnquantizedLinearMethod()
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return None
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# TODO: This special hard coded logic is not needed for quantized checkpoints
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# generated by ModelOpt >= 0.39.0 where they are handled natually by the
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# exclude_modules config. But need to keep them for loading quantized
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# checkpoints generated by older versions. Then check substring matching
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# for patterns not caught by exact match
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if "vision_tower" in prefix or "vision_model" in prefix:
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return UnquantizedLinearMethod()
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# now, the layer is quantized, handle it here
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if isinstance(layer, LinearBase):
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quant_method = self.LinearMethodCls(self)
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if getattr(quant_method, "backend", "") == "marlin":
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quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
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return quant_method
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elif isinstance(layer, FusedMoE):
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quant_method = self.FusedMoEMethodCls(
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quant_config=self, moe_config=layer.moe_config
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)
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if getattr(quant_method, "backend", "") == "marlin":
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quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
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return quant_method
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return None
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def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
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if len(self.exclude_modules) > 0:
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# This is a workaround for the weights remapping issue:
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# https://github.com/vllm-project/vllm/issues/28072
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# Right now, the Nvidia ModelOpt library use just one wildcard pattern:
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# module_path*
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# It gets applied if the whole tree of modules rooted at module_path
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# is not quantized. Here we replace such pattern by 2 patterns that are
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# collectively equivalent to the original pattern:
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# module_path
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# module_path.*
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new_exclude_modules = []
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for exclude in self.exclude_modules:
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if len(exclude) >= 2 and exclude[-1] == "*" and exclude[-2] != ".":
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new_exclude_modules.append(exclude[:-1])
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new_exclude_modules.append(exclude[:-1] + ".*")
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else:
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new_exclude_modules.append(exclude)
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self.exclude_modules = hf_to_vllm_mapper.apply_list(new_exclude_modules)
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@staticmethod
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def get_config_filenames() -> list[str]:
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return ["hf_quant_config.json"]
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@classmethod
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def _from_config(
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cls,
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*,
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quant_method: str,
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kv_cache_quant_method: str | None,
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exclude_modules: list[str],
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original_config: dict[str, Any],
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group_size: int | None,
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) -> "ModelOptQuantConfigBase":
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raise NotImplementedError("Please implement this function in sub classes")
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "ModelOptQuantConfigBase":
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# Handle both ModelOpt format and compressed-tensors style format
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if "quantization" in config:
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# Traditional ModelOpt format:
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# {"quantization": {"quant_algo": "..."}}
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quant_config = cls.get_from_keys(config, ["quantization"])
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if not isinstance(quant_config, dict):
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raise ValueError("Expected 'quantization' to be a dictionary in config")
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quant_method = quant_config.get("quant_algo")
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# Handle kv_cache_quant_algo with proper type validation
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kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")
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# Handle group_size with proper type validation
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group_size_raw = quant_config.get("group_size")
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# "exclude_modules" is the key in the legacy hf_quant_config.json
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exclude_modules = quant_config.get("exclude_modules", [])
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else:
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# Compressed-tensors style format:
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# {"quant_algo": "...", "quant_method": "modelopt"}
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quant_method = config.get("quant_algo")
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kv_cache_quant_method = config.get("kv_cache_quant_algo")
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# "ignore" is the key in config.json
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exclude_modules = config.get("ignore", [])
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group_size_raw = config.get("group_size")
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if not quant_method:
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raise ValueError("Missing 'quant_algo' in quantization config")
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# Normalize quant_algo for robust matching (ModelOpt may emit lowercase).
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quant_method = str(quant_method).upper()
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if kv_cache_quant_method is None:
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# No KV cache quantization, keep this branch just to have this comment
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pass
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elif not isinstance(kv_cache_quant_method, str):
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raise ValueError(
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f"kv_cache_quant_algo must be a string, got "
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f"{type(kv_cache_quant_method)}"
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)
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else:
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kv_cache_quant_method = kv_cache_quant_method.upper()
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if not isinstance(exclude_modules, list):
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raise ValueError(
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f"exclude_modules must be a list, got {type(exclude_modules)}"
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)
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if group_size_raw is None:
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group_size = None
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elif isinstance(group_size_raw, int):
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group_size = group_size_raw
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else:
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try:
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group_size = int(group_size_raw)
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except (ValueError, TypeError):
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raise ValueError(
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f"group_size must be an integer, got {type(group_size_raw)}"
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) from None
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if quant_method not in QUANT_ALGOS:
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raise ValueError(
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f"ModelOpt currently only supports: {QUANT_ALGOS} "
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"quantizations in vLLM. Please check the "
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"`hf_quant_config.json` file for your model's "
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"quant configuration."
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)
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return cls._from_config(
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quant_method=quant_method,
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kv_cache_quant_method=kv_cache_quant_method,
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exclude_modules=exclude_modules,
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group_size=group_size,
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original_config=config,
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)
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class ModelOptFp8Config(ModelOptQuantConfigBase):
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"""Config class for ModelOpt FP8."""
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def __init__(
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self,
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quant_method: str,
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is_checkpoint_fp8_serialized: bool,
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kv_cache_quant_method: str | None,
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exclude_modules: list[str],
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) -> None:
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super().__init__(exclude_modules)
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self.quant_method = quant_method
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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self.kv_cache_quant_method = kv_cache_quant_method
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if is_checkpoint_fp8_serialized:
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logger.warning(
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"Detected ModelOpt fp8 checkpoint (quant_algo=%s). Please note "
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"that the format is experimental and could change.",
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quant_method,
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)
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# Select LinearMethod implementation based on quant_algo.
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if self.quant_method == "FP8":
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self.LinearMethodCls = ModelOptFp8LinearMethod
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elif self.quant_method == "FP8_PER_CHANNEL_PER_TOKEN":
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self.LinearMethodCls = ModelOptFp8PcPtLinearMethod
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elif self.quant_method == "FP8_PB_WO":
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self.LinearMethodCls = ModelOptFp8PbWoLinearMethod
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else:
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raise ValueError(
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"Unsupported ModelOpt FP8 quant_algo for vLLM: "
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f"{self.quant_method}. Supported: FP8 / "
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"FP8_PER_CHANNEL_PER_TOKEN / FP8_PB_WO."
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)
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def get_name(self) -> QuantizationMethods:
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return "modelopt"
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def get_supported_act_dtypes(self) -> list[torch.dtype]:
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return [torch.bfloat16, torch.half]
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@classmethod
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def get_min_capability(cls) -> int:
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return 89
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@classmethod
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def override_quantization_method(
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cls, hf_quant_cfg, user_quant
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) -> QuantizationMethods | None:
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"""Detect if this ModelOpt config should be used based on
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quantization config."""
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if hf_quant_cfg is None:
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return None
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# Use the community standard 'quant_method'
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quant_method = hf_quant_cfg.get("quant_method", "").lower()
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# Only proceed if the method is explicitly "modelopt"
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if quant_method != "modelopt":
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return None
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# Look for ModelOpt-specific config structure
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if "quantization" in hf_quant_cfg:
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quant_config = hf_quant_cfg["quantization"]
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if isinstance(quant_config, dict):
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quant_algo = str(quant_config.get("quant_algo", ""))
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if quant_algo.upper() == "FP8":
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return "modelopt"
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else:
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# Check for compressed-tensors style config with specific quant_algo
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quant_algo = str(hf_quant_cfg.get("quant_algo", ""))
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if quant_algo.upper() == "FP8":
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return "modelopt"
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return None
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@classmethod
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def _from_config(
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cls,
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*,
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quant_method: str,
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kv_cache_quant_method: str | None,
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exclude_modules: list[str],
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original_config: dict[str, Any],
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**kwargs: Any,
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) -> "ModelOptFp8Config":
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is_checkpoint_fp8_serialized = "FP8" in quant_method
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return cls(
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quant_method,
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is_checkpoint_fp8_serialized,
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kv_cache_quant_method,
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exclude_modules,
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)
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class ModelOptFp8LinearMethod(LinearMethodBase):
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"""Linear method for Model Optimizer static quantization.
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Supports loading FP8 checkpoints with static weight scale and
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activation scale. Future support might be added for dynamic
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scales.
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Limitations:
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1. Only support per-tensor quantization due to torch._scaled_mm support.
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2. Only support float8_e4m3fn datatype
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Args: quant_config: The ModelOpt quantization config.
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"""
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def __init__(self, quant_config: ModelOptFp8Config) -> None:
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self.quant_config = quant_config
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self.fp8_linear = init_fp8_linear_kernel(
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activation_quant_key=kFp8StaticTensorSym,
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weight_quant_key=kFp8StaticTensorSym,
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out_dtype=torch.get_default_dtype(),
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module_name=self.__class__.__name__,
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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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input_size_per_partition: int,
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output_partition_sizes: list[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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del input_size, output_size
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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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layer.logical_widths = output_partition_sizes
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layer.input_size_per_partition = input_size_per_partition
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layer.output_size_per_partition = output_size_per_partition
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weight_dtype = (
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torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_serialized
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else params_dtype
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)
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weight = ModelWeightParameter(
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data=torch.empty(
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output_size_per_partition, input_size_per_partition, dtype=weight_dtype
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight", weight)
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if self.quant_config.is_checkpoint_fp8_serialized:
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# WEIGHT SCALE
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weight_scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader,
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)
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weight_scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("weight_scale", weight_scale)
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# INPUT SCALE
|
|
scale = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
|
|
scale[:] = torch.finfo(torch.float32).min
|
|
layer.register_parameter("input_scale", scale)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
weight = layer.weight
|
|
max_w_scale = layer.weight_scale.max()
|
|
if not (layer.weight_scale == layer.weight_scale[0]).all():
|
|
max_w_scale, weight = requantize_with_max_scale(
|
|
layer.weight, layer.weight_scale, layer.logical_widths
|
|
)
|
|
layer.weight = Parameter(weight.t(), requires_grad=False)
|
|
layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
|
|
layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
return self.fp8_linear.apply_weights(layer, x, bias)
|
|
|
|
|
|
class ModelOptFp8PcPtLinearMethod(LinearMethodBase):
|
|
"""Linear method for ModelOpt FP8_PER_CHANNEL_PER_TOKEN checkpoints.
|
|
|
|
Expected checkpoint structure (per Linear):
|
|
- weight: fp8-e4m3fn, shape [out, in]
|
|
- weight_scale: fp32, shape [out] (per-output-channel)
|
|
- no input_scale (activations are dynamically quantized per-token)
|
|
"""
|
|
|
|
def __init__(self, quant_config: ModelOptFp8Config) -> None:
|
|
self.quant_config = quant_config
|
|
self.fp8_linear = init_fp8_linear_kernel(
|
|
activation_quant_key=kFp8DynamicTokenSym,
|
|
weight_quant_key=kFp8StaticTokenSym,
|
|
out_dtype=torch.get_default_dtype(),
|
|
module_name=self.__class__.__name__,
|
|
)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: list[int],
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
del input_size, output_size
|
|
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
raise ValueError(
|
|
"FP8_PER_CHANNEL_PER_TOKEN currently only supports "
|
|
"FP8-serialized checkpoints."
|
|
)
|
|
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
layer.logical_widths = output_partition_sizes
|
|
layer.input_size_per_partition = input_size_per_partition
|
|
layer.output_size_per_partition = output_size_per_partition
|
|
|
|
weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
output_size_per_partition,
|
|
input_size_per_partition,
|
|
dtype=torch.float8_e4m3fn,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight", weight)
|
|
|
|
weight_scale = ChannelQuantScaleParameter(
|
|
data=torch.empty(output_size_per_partition, dtype=torch.float32),
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
weight_scale[:] = torch.finfo(torch.float32).min
|
|
layer.register_parameter("weight_scale", weight_scale)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
layer.weight = Parameter(layer.weight.t(), requires_grad=False)
|
|
layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
return self.fp8_linear.apply_weights(layer, x, bias)
|
|
|
|
|
|
class ModelOptFp8PbWoLinearMethod(LinearMethodBase):
|
|
"""Linear method for ModelOpt FP8_PB_WO checkpoints.
|
|
|
|
ModelOpt exports `weight_scale` as a 4D tensor:
|
|
[out_blk, 1, in_blk, 1]
|
|
where block size is typically 128 for both dims.
|
|
|
|
vLLM executes it as FP8 GEMM with *dynamic per-token* activation quant.
|
|
"""
|
|
|
|
_WEIGHT_BLOCK_SIZE: tuple[int, int] = (128, 128)
|
|
|
|
def __init__(self, quant_config: ModelOptFp8Config) -> None:
|
|
self.quant_config = quant_config
|
|
block_n, block_k = self._WEIGHT_BLOCK_SIZE
|
|
self.weight_block_size = list(self._WEIGHT_BLOCK_SIZE)
|
|
self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
|
|
weight_group_shape=GroupShape(block_n, block_k),
|
|
act_quant_group_shape=GroupShape(1, block_k),
|
|
cutlass_block_fp8_supported=cutlass_block_fp8_supported(),
|
|
use_aiter_and_is_supported=False,
|
|
)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: list[int],
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
del input_size, output_size
|
|
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
raise ValueError(
|
|
"FP8_PB_WO currently only supports FP8-serialized checkpoints."
|
|
)
|
|
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
layer.logical_widths = output_partition_sizes
|
|
layer.input_size_per_partition = input_size_per_partition
|
|
layer.output_size_per_partition = output_size_per_partition
|
|
|
|
# Expose block size so the v2 weight loaders can translate offsets from
|
|
# element-space -> block-space for BlockQuantScaleParameter.
|
|
layer.weight_block_size = self.weight_block_size
|
|
|
|
weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
output_size_per_partition,
|
|
input_size_per_partition,
|
|
dtype=torch.float8_e4m3fn,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight", weight)
|
|
|
|
block_n, block_k = self._WEIGHT_BLOCK_SIZE
|
|
if output_size_per_partition % block_n != 0:
|
|
raise ValueError(
|
|
"ModelOpt FP8_PB_WO requires out_features divisible by "
|
|
f"{block_n}, got {output_size_per_partition}."
|
|
)
|
|
if input_size_per_partition % block_k != 0:
|
|
raise ValueError(
|
|
"ModelOpt FP8_PB_WO requires in_features divisible by "
|
|
f"{block_k}, got {input_size_per_partition}."
|
|
)
|
|
|
|
out_blks = output_size_per_partition // block_n
|
|
in_blks = input_size_per_partition // block_k
|
|
|
|
# Match ModelOpt's exported shape so weight loading works without a
|
|
# custom loader: [out_blk, 1, in_blk, 1]
|
|
weight_scale = BlockQuantScaleParameter(
|
|
data=torch.empty((out_blks, 1, in_blks, 1), dtype=torch.float32),
|
|
input_dim=2,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
weight_scale[:] = torch.finfo(torch.float32).min
|
|
layer.register_parameter("weight_scale", weight_scale)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# Keep weight in [out, in] layout for W8A8BlockFp8LinearOp.
|
|
layer.weight = Parameter(layer.weight.data, requires_grad=False)
|
|
|
|
scale = layer.weight_scale
|
|
if scale.dim() == 4:
|
|
# [out_blk, 1, in_blk, 1] -> [out_blk, in_blk]
|
|
scale = scale.squeeze(1).squeeze(-1)
|
|
elif scale.dim() != 2:
|
|
raise ValueError(
|
|
"Unexpected ModelOpt FP8_PB_WO weight_scale shape: "
|
|
f"{tuple(scale.shape)}."
|
|
)
|
|
|
|
layer.weight_scale = Parameter(scale.contiguous(), requires_grad=False)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
return self.w8a8_block_fp8_linear.apply(
|
|
input=x,
|
|
weight=layer.weight,
|
|
weight_scale=layer.weight_scale,
|
|
input_scale=None,
|
|
bias=bias,
|
|
)
|
|
|
|
|
|
class ModelOptFp8MoEMethod(FusedMoEMethodBase):
|
|
"""MoE method for ModelOpt FP8.
|
|
Supports loading FP8 checkpoints with static weight scale and
|
|
activation scale.
|
|
Args:
|
|
quant_config: The ModelOpt quantization config.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
quant_config: ModelOptFp8Config,
|
|
moe_config: FusedMoEConfig,
|
|
) -> None:
|
|
super().__init__(moe_config)
|
|
self.quant_config = quant_config
|
|
assert self.quant_config.is_checkpoint_fp8_serialized
|
|
|
|
# Select Fp8 MoE backend
|
|
self.fp8_backend, self.experts_cls = select_fp8_moe_backend(
|
|
config=self.moe,
|
|
weight_key=kFp8StaticTensorSym,
|
|
activation_key=kFp8StaticTensorSym,
|
|
)
|
|
|
|
def maybe_make_prepare_finalize(
|
|
self,
|
|
routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
|
|
) -> mk.FusedMoEPrepareAndFinalize | None:
|
|
raise ValueError(
|
|
f"{self.__class__.__name__} uses the new modular kernel initialization "
|
|
"logic. This function should not be called."
|
|
)
|
|
|
|
def select_gemm_impl(
|
|
self,
|
|
prepare_finalize: mk.FusedMoEPrepareAndFinalize,
|
|
layer: torch.nn.Module,
|
|
) -> mk.FusedMoEPermuteExpertsUnpermute:
|
|
raise ValueError(
|
|
f"{self.__class__.__name__} uses the new modular kernel initialization "
|
|
"logic. This function should not be called."
|
|
)
|
|
|
|
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.orig_dtype = params_dtype
|
|
layer.num_experts = num_experts
|
|
|
|
# Use FP8 dtype if checkpoint is serialized
|
|
weight_dtype = (
|
|
torch.float8_e4m3fn
|
|
if self.quant_config.is_checkpoint_fp8_serialized
|
|
else params_dtype
|
|
)
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
|
|
w13_num_shards = 2 if self.moe.is_act_and_mul else 1
|
|
|
|
w13_weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
num_experts,
|
|
w13_num_shards * intermediate_size_per_partition,
|
|
hidden_size,
|
|
dtype=weight_dtype,
|
|
),
|
|
input_dim=2,
|
|
output_dim=1,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
|
|
w2_weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition,
|
|
dtype=weight_dtype,
|
|
),
|
|
input_dim=2,
|
|
output_dim=1,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
|
|
# WEIGHT SCALES - Per-tensor scaling for ModelOpts
|
|
# For gated MoE, allocate 2 scales for w1 and w3 respectively.
|
|
# They will be combined to a single scale after weight loading.
|
|
# For non-gated MoE, allocate 1 scale for w13.
|
|
w13_weight_scale = PerTensorScaleParameter(
|
|
data=torch.full(
|
|
(num_experts, w13_num_shards),
|
|
1.0,
|
|
dtype=torch.float32,
|
|
),
|
|
weight_loader=weight_loader,
|
|
)
|
|
w2_weight_scale = PerTensorScaleParameter(
|
|
data=torch.full((num_experts,), 1.0, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
|
|
# INPUT SCALES - Per-tensor scaling for ModelOpt
|
|
w13_input_scale = PerTensorScaleParameter(
|
|
data=torch.full((num_experts,), 1.0, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
w2_input_scale = PerTensorScaleParameter(
|
|
data=torch.full((num_experts,), 1.0, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
|
|
def _setup_kernel(
|
|
self,
|
|
layer: FusedMoE,
|
|
w13: torch.Tensor,
|
|
w2: torch.Tensor,
|
|
w13_scale: torch.Tensor,
|
|
w2_scale: torch.Tensor,
|
|
w13_input_scale: torch.Tensor,
|
|
w2_input_scale: torch.Tensor,
|
|
):
|
|
w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
|
|
fp8_backend=self.fp8_backend,
|
|
layer=layer,
|
|
w13=w13,
|
|
w2=w2,
|
|
w13_scale=w13_scale,
|
|
w2_scale=w2_scale,
|
|
w13_input_scale=w13_input_scale,
|
|
w2_input_scale=w2_input_scale,
|
|
)
|
|
|
|
# Replace parameters with updated versions. Note that this helper
|
|
# function ensures the replacement is compatible with RL weight reloads.
|
|
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)
|
|
|
|
# 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_mk = 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._maybe_init_expert_routing_tables(),
|
|
shared_experts=layer.shared_experts,
|
|
)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
w13 = layer.w13_weight
|
|
w2 = layer.w2_weight
|
|
w13_scale = layer.w13_weight_scale
|
|
w2_scale = layer.w2_weight_scale
|
|
w13_input_scale = layer.w13_input_scale
|
|
w2_input_scale = layer.w2_input_scale
|
|
|
|
# Per tensor kernels require single activation scale. Use the max.
|
|
w13_input_scale, w2_input_scale = process_fp8_input_tensor_strategy_moe(
|
|
w13_input_scale, w2_input_scale
|
|
)
|
|
replace_parameter(layer, "w13_input_scale", w13_input_scale)
|
|
replace_parameter(layer, "w2_input_scale", w2_input_scale)
|
|
|
|
# Per tensor kernels require single weight scale for w13 per expert, but
|
|
# on disk there is a scale for w1 and w3. Use the max to requantize.
|
|
shard_size = layer.intermediate_size_per_partition
|
|
w13, w13_scale = process_fp8_weight_tensor_strategy_moe(
|
|
w13,
|
|
w13_scale,
|
|
shard_size,
|
|
num_experts=layer.w13_weight.shape[0],
|
|
is_act_and_mul=self.moe.is_act_and_mul,
|
|
)
|
|
|
|
# Shuffle weights to runtime format and setup kernel.
|
|
self._setup_kernel(
|
|
layer, w13, w2, w13_scale, w2_scale, w13_input_scale, w2_input_scale
|
|
)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
w1_scale = layer.w13_weight_scale
|
|
w2_scale = layer.w2_weight_scale
|
|
a1_scale = layer.w13_input_scale
|
|
a2_scale = layer.w2_input_scale
|
|
|
|
return make_fp8_moe_quant_config(
|
|
fp8_backend=self.fp8_backend,
|
|
w1_scale=w1_scale,
|
|
w2_scale=w2_scale,
|
|
a1_scale=a1_scale,
|
|
a2_scale=a2_scale,
|
|
)
|
|
|
|
@property
|
|
def is_monolithic(self) -> bool:
|
|
return self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM
|
|
|
|
def apply_monolithic(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
assert self.is_monolithic
|
|
assert self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM
|
|
if layer.enable_eplb:
|
|
raise NotImplementedError(
|
|
"EPLB not supported for FlashInfer TRTLLM FP8 MoE Backend."
|
|
)
|
|
# TODO(rob): this validation should happen at kernel selection
|
|
# time in the oracle rather than here.
|
|
SUPPORTED_ACTIVATIONS = [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
|
|
assert layer.activation in SUPPORTED_ACTIVATIONS, (
|
|
f"Only {SUPPORTED_ACTIVATIONS} activations are supported for FlashInfer "
|
|
f"TRTLLM FP4 MoE, {layer.activation} found instead."
|
|
)
|
|
return apply_fi_trtllm_fp8_per_tensor_moe(
|
|
layer=layer,
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
routing_bias=layer.e_score_correction_bias,
|
|
global_num_experts=layer.global_num_experts,
|
|
top_k=layer.top_k,
|
|
num_expert_group=layer.num_expert_group,
|
|
topk_group=layer.topk_group,
|
|
apply_router_weight_on_input=layer.apply_router_weight_on_input,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
assert not self.is_monolithic
|
|
|
|
# TODO(rob): this validation should happen at kernel selection
|
|
# time in the oracle rather than here.
|
|
if self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
|
|
assert layer.activation in (
|
|
MoEActivation.SILU,
|
|
MoEActivation.RELU2_NO_MUL,
|
|
), (
|
|
"Expected activation to be in ('silu', 'relu2_no_mul'),"
|
|
f"but got {layer.activation}"
|
|
)
|
|
|
|
assert self.moe_mk is not None
|
|
return self.moe_mk(
|
|
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_input=shared_experts_input,
|
|
)
|
|
|
|
|
|
ModelOptFp8Config.LinearMethodCls = ModelOptFp8LinearMethod
|
|
ModelOptFp8Config.FusedMoEMethodCls = ModelOptFp8MoEMethod
|
|
ModelOptFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod
|
|
|
|
|
|
class ModelOptNvFp4Config(ModelOptQuantConfigBase):
|
|
"""Config class for ModelOpt FP4."""
|
|
|
|
def __init__(
|
|
self,
|
|
is_checkpoint_nvfp4_serialized: bool,
|
|
kv_cache_quant_algo: str | None,
|
|
exclude_modules: list[str],
|
|
group_size: int = 16,
|
|
) -> None:
|
|
super().__init__(exclude_modules)
|
|
self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
|
|
if is_checkpoint_nvfp4_serialized:
|
|
logger.warning(
|
|
"Detected ModelOpt NVFP4 checkpoint. Please note that"
|
|
" the format is experimental and could change in future."
|
|
)
|
|
|
|
self.group_size = group_size
|
|
self.kv_cache_quant_algo = kv_cache_quant_algo
|
|
|
|
def get_name(self) -> QuantizationMethods:
|
|
return "modelopt_fp4"
|
|
|
|
def get_supported_act_dtypes(self) -> list[torch.dtype]:
|
|
return [torch.bfloat16, torch.half, torch.float8_e4m3fn]
|
|
|
|
@classmethod
|
|
def get_min_capability(cls) -> int:
|
|
return 75
|
|
|
|
@classmethod
|
|
def override_quantization_method(
|
|
cls, hf_quant_cfg, user_quant
|
|
) -> QuantizationMethods | None:
|
|
"""Detect if this ModelOpt FP4 config should be used based on
|
|
quantization config."""
|
|
if hf_quant_cfg is None:
|
|
return None
|
|
|
|
# Use the community standard 'quant_method'
|
|
quant_method = hf_quant_cfg.get("quant_method", "").lower()
|
|
|
|
# Only proceed if the method is explicitly "modelopt"
|
|
if quant_method != "modelopt":
|
|
return None
|
|
|
|
# Look for ModelOpt-specific config structure
|
|
if "quantization" in hf_quant_cfg:
|
|
quant_config = hf_quant_cfg["quantization"]
|
|
if isinstance(quant_config, dict):
|
|
quant_algo = quant_config.get("quant_algo", "")
|
|
if "NVFP4" in quant_algo:
|
|
return "modelopt_fp4"
|
|
else:
|
|
# Check for compressed-tensors style config with specific
|
|
# quant_algo field
|
|
quant_algo = hf_quant_cfg.get("quant_algo", "")
|
|
if isinstance(quant_algo, str) and "FP4" in quant_algo.upper():
|
|
return "modelopt_fp4"
|
|
|
|
return None
|
|
|
|
@classmethod
|
|
def _from_config(
|
|
cls,
|
|
*,
|
|
quant_method: str,
|
|
kv_cache_quant_method: str | None,
|
|
exclude_modules: list[str],
|
|
original_config: dict[str, Any],
|
|
group_size: int | None,
|
|
**kwargs: Any,
|
|
) -> "ModelOptNvFp4Config":
|
|
is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
|
|
|
|
if group_size is None:
|
|
group_size = 16 # Default value
|
|
|
|
# For FP4, these fields are required
|
|
if is_checkpoint_nvfp4_serialized and "quantization" in original_config:
|
|
# Check if required fields are present in the quantization config
|
|
quant_config = original_config["quantization"]
|
|
required_fields = ["group_size", "kv_cache_quant_algo", "exclude_modules"]
|
|
missing_fields = [
|
|
field for field in required_fields if field not in quant_config
|
|
]
|
|
if missing_fields:
|
|
raise ValueError(
|
|
f"NVFP4 quantization requires the following fields in "
|
|
f"hf_quant_config.json: {missing_fields}"
|
|
)
|
|
|
|
return cls(
|
|
is_checkpoint_nvfp4_serialized,
|
|
kv_cache_quant_method,
|
|
exclude_modules,
|
|
group_size,
|
|
)
|
|
|
|
|
|
class ModelOptNvFp4LinearMethod(LinearMethodBase):
|
|
"""Linear method for Model Optimizer NVFP4.
|
|
Supports loading NVFP4 checkpoints with the following structure:
|
|
|
|
input_scale: torch.float32, scalar ,
|
|
weight: NVFP4(represented as byte) Shape: [1, X, y/2]
|
|
weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
|
|
weight_scale_2: torch.float32, scalar,
|
|
Args: quant_config: The ModelOpt quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: ModelOptNvFp4Config) -> None:
|
|
self.quant_config = quant_config
|
|
self.marlin_input_dtype = None
|
|
self.backend = select_nvfp4_linear_backend()
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: list[int],
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
del input_size, output_size
|
|
if not self.quant_config.is_checkpoint_nvfp4_serialized:
|
|
raise ValueError(
|
|
"NVFP4 quantization was selected, "
|
|
" dynamic quantization is not supported."
|
|
)
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
layer.logical_widths = output_partition_sizes
|
|
layer.input_size_per_partition = input_size_per_partition
|
|
layer.output_size_per_partition = output_size_per_partition
|
|
|
|
if input_size_per_partition % 16 != 0:
|
|
raise ValueError(
|
|
"Unsupported model when in features size is not multiple of 16"
|
|
)
|
|
# The nvfp4 weight is still represented as
|
|
weight_dtype = (
|
|
torch.float8_e4m3fn
|
|
if self.quant_config.is_checkpoint_nvfp4_serialized
|
|
else params_dtype
|
|
)
|
|
# Weight
|
|
weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
# 2 fp4 items are packed in the input dimension
|
|
layer.output_size_per_partition,
|
|
layer.input_size_per_partition // 2,
|
|
dtype=torch.uint8,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight", weight)
|
|
|
|
# Input Global Scale
|
|
input_global_scale = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("input_scale", input_global_scale)
|
|
|
|
# Weight Global Scale
|
|
weight_global_scale = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight_scale_2", weight_global_scale)
|
|
|
|
# Per Block Weight Scale
|
|
weight_scale = ModelWeightParameter(
|
|
data=torch.empty(
|
|
output_size_per_partition,
|
|
input_size_per_partition // self.quant_config.group_size,
|
|
dtype=weight_dtype,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
|
|
layer.register_parameter("weight_scale", weight_scale)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# Rename ModelOpt checkpoint names to standardized names
|
|
input_global_scale = layer.input_scale.max().to(torch.float32)
|
|
layer.input_global_scale = Parameter(input_global_scale, requires_grad=False)
|
|
del layer.input_scale
|
|
weight_global_scale = layer.weight_scale_2.max().to(torch.float32)
|
|
layer.weight_global_scale = Parameter(weight_global_scale, requires_grad=False)
|
|
del layer.weight_scale_2
|
|
|
|
# Pre-compute alpha and inverse for runtime quantization
|
|
layer.alpha = Parameter(
|
|
layer.input_global_scale * layer.weight_global_scale, requires_grad=False
|
|
)
|
|
layer.input_global_scale_inv = Parameter(
|
|
(1.0 / layer.input_global_scale).to(torch.float32), requires_grad=False
|
|
)
|
|
|
|
# Convert layer to NVFP4 linear kernel format
|
|
convert_to_nvfp4_linear_kernel_format(self.backend, layer)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
return apply_nvfp4_linear(
|
|
backend=self.backend,
|
|
layer=layer,
|
|
x=x,
|
|
bias=bias,
|
|
)
|
|
|
|
|
|
class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
|
|
"""
|
|
MoE Method for FP4 Quantization.
|
|
Args:
|
|
quant_config: NVFP4 Quant Config
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
quant_config: ModelOptNvFp4Config,
|
|
moe_config: FusedMoEConfig,
|
|
) -> None:
|
|
super().__init__(moe_config)
|
|
self.quant_config = quant_config
|
|
# Select experts implementation.
|
|
self.nvfp4_backend, self.experts_cls = select_nvfp4_moe_backend(
|
|
config=self.moe,
|
|
weight_key=kNvfp4Static,
|
|
activation_key=kNvfp4Dynamic,
|
|
)
|
|
|
|
self.use_global_sf = is_global_sf_supported_for_nvfp4_backend(
|
|
self.nvfp4_backend
|
|
)
|
|
|
|
def maybe_make_prepare_finalize(
|
|
self,
|
|
routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
|
|
) -> mk.FusedMoEPrepareAndFinalize | None:
|
|
raise ValueError(
|
|
f"{self.__class__.__name__} uses the new modular kernel initialization "
|
|
"logic. This function should not be called."
|
|
)
|
|
|
|
def select_gemm_impl(
|
|
self,
|
|
prepare_finalize: mk.FusedMoEPrepareAndFinalize,
|
|
layer: torch.nn.Module,
|
|
) -> mk.FusedMoEPermuteExpertsUnpermute:
|
|
raise ValueError(
|
|
f"{self.__class__.__name__} uses the new modular kernel initialization "
|
|
"logic. This function should not be called."
|
|
)
|
|
|
|
def uses_weight_scale_2_pattern(self) -> bool:
|
|
"""
|
|
FP4 variants use 'weight_scale_2' pattern for per-tensor weight scales.
|
|
"""
|
|
return True
|
|
|
|
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,
|
|
):
|
|
assert self.quant_config.is_checkpoint_nvfp4_serialized
|
|
|
|
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
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
global_num_experts = extra_weight_attrs.get("global_num_experts")
|
|
w13_num_shards = 2 if self.moe.is_act_and_mul else 1
|
|
# GEMM 1
|
|
w13_weight = ModelWeightParameter(
|
|
data=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,
|
|
),
|
|
input_dim=1,
|
|
output_dim=2,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
|
|
# GEMM 2
|
|
w2_weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
# 2 fp4 items are packed in the input dimension
|
|
intermediate_size_per_partition // 2,
|
|
dtype=weight_dtype,
|
|
),
|
|
input_dim=1,
|
|
output_dim=2,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
|
|
w13_weight_scale = ModelWeightParameter(
|
|
data=torch.empty(
|
|
num_experts,
|
|
w13_num_shards * intermediate_size_per_partition,
|
|
# 2 fp4 items are packed in the input dimension
|
|
hidden_size // self.quant_config.group_size,
|
|
dtype=weight_scale_dtype,
|
|
),
|
|
input_dim=1,
|
|
output_dim=2,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
|
|
w2_weight_scale = ModelWeightParameter(
|
|
data=torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
# 2 fp4 items are packed in the input dimension
|
|
intermediate_size_per_partition // self.quant_config.group_size,
|
|
dtype=weight_scale_dtype,
|
|
),
|
|
input_dim=1,
|
|
output_dim=2,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
|
|
)
|
|
|
|
w13_weight_scale_2 = PerTensorScaleParameter(
|
|
data=torch.empty(num_experts, w13_num_shards, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
|
|
|
|
w2_weight_scale_2 = PerTensorScaleParameter(
|
|
data=torch.empty(num_experts, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
|
|
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
|
)
|
|
|
|
global_sf_num_experts = (
|
|
global_num_experts if self.use_global_sf else num_experts
|
|
)
|
|
w13_input_scale = PerTensorScaleParameter(
|
|
data=torch.empty(
|
|
global_sf_num_experts,
|
|
w13_num_shards,
|
|
dtype=torch.float32,
|
|
),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
|
|
|
w2_input_scale = PerTensorScaleParameter(
|
|
data=torch.empty(global_sf_num_experts, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
|
|
def process_weights_after_loading(self, layer: FusedMoE) -> None:
|
|
"""
|
|
Convert NVFP4 MoE weights into kernel format and setup the kernel.
|
|
"""
|
|
|
|
# Use a single gscale for w13.
|
|
if self.moe.is_act_and_mul and not torch.allclose(
|
|
layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]
|
|
):
|
|
logger.warning_once(
|
|
"w1_weight_scale_2 must match w3_weight_scale_2. "
|
|
"Accuracy may be affected."
|
|
)
|
|
w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0].contiguous()
|
|
|
|
(
|
|
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,
|
|
w2=layer.w2_weight,
|
|
w2_scale=layer.w2_weight_scale,
|
|
w2_scale_2=layer.w2_weight_scale_2,
|
|
a2_scale=layer.w2_input_scale,
|
|
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", 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", a2_scale)
|
|
|
|
# Setup modular kernel for TP case and naive DP/EP case.
|
|
# In non-naive DP/EP case, we will create a ModularKernelMethod.
|
|
# TODO(rob): unify these so FP8MoEMethod owns the ModularKernel
|
|
# in both cases.
|
|
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_mk = make_nvfp4_moe_kernel(
|
|
moe_quant_config=self.moe_quant_config,
|
|
moe_config=self.moe,
|
|
experts_cls=self.experts_cls,
|
|
shared_experts=layer.shared_experts,
|
|
routing_tables=layer._maybe_init_expert_routing_tables(),
|
|
)
|
|
|
|
@property
|
|
def do_post_quant_allgather(self):
|
|
return self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM
|
|
|
|
def prepare_dp_allgather_tensor(
|
|
self,
|
|
layer: FusedMoE,
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
) -> tuple[torch.Tensor, list[torch.Tensor]]:
|
|
"""Optionally prepare extra tensors to carry through DP allgather/EP."""
|
|
if self.nvfp4_backend != NvFp4MoeBackend.FLASHINFER_TRTLLM:
|
|
raise RuntimeError(
|
|
"prepare_dp_allgather_tensor is only supported for "
|
|
"FlashInfer TRTLLM NVFP4 MoE backend."
|
|
)
|
|
|
|
import flashinfer
|
|
|
|
hidden_states_fp4, hidden_states_sf = flashinfer.fp4_quantize(
|
|
hidden_states,
|
|
layer.a1_gscale,
|
|
is_sf_swizzled_layout=False,
|
|
)
|
|
extra_tensors: list[torch.Tensor] = [hidden_states_sf]
|
|
return hidden_states_fp4, extra_tensors
|
|
|
|
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,
|
|
a2_scale=layer.w2_input_scale,
|
|
)
|
|
|
|
@property
|
|
def supports_eplb(self) -> bool:
|
|
return True
|
|
|
|
@property
|
|
def is_monolithic(self) -> bool:
|
|
return (
|
|
self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM
|
|
and not self.moe.moe_parallel_config.enable_eplb
|
|
)
|
|
|
|
def apply_monolithic(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
assert self.is_monolithic
|
|
assert (
|
|
self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM
|
|
and not layer.enable_eplb
|
|
)
|
|
|
|
return flashinfer_trtllm_fp4_moe(
|
|
layer=layer,
|
|
x=x,
|
|
router_logits=router_logits,
|
|
top_k=layer.top_k,
|
|
activation=layer.activation,
|
|
global_num_experts=layer.global_num_experts,
|
|
num_expert_group=layer.num_expert_group,
|
|
topk_group=layer.topk_group,
|
|
custom_routing_function=layer.custom_routing_function,
|
|
e_score_correction_bias=layer.e_score_correction_bias,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: FusedMoE,
|
|
x: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
shared_experts_input: torch.Tensor | None,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
assert not self.is_monolithic
|
|
|
|
# EPLB path
|
|
if self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM:
|
|
assert layer.enable_eplb
|
|
return flashinfer_trtllm_fp4_routed_moe(
|
|
layer=layer,
|
|
x=x,
|
|
topk_ids=topk_ids,
|
|
topk_weights=topk_weights,
|
|
top_k=layer.top_k,
|
|
activation=layer.activation,
|
|
global_num_experts=layer.global_num_experts,
|
|
)
|
|
else:
|
|
assert self.moe_mk is not None
|
|
return self.moe_mk(
|
|
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_input=shared_experts_input,
|
|
)
|
|
|
|
|
|
ModelOptNvFp4Config.LinearMethodCls = ModelOptNvFp4LinearMethod
|
|
ModelOptNvFp4Config.FusedMoEMethodCls = ModelOptNvFp4FusedMoE
|
|
ModelOptNvFp4Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod
|
|
|
|
|
|
class ModelOptMxFp8Config(ModelOptQuantConfigBase):
|
|
"""Config class for ModelOpt MXFP8."""
|
|
|
|
def __init__(
|
|
self,
|
|
is_checkpoint_mxfp8_serialized: bool,
|
|
kv_cache_quant_algo: str | None,
|
|
exclude_modules: list[str],
|
|
) -> None:
|
|
super().__init__(exclude_modules)
|
|
self.is_checkpoint_mxfp8_serialized = is_checkpoint_mxfp8_serialized
|
|
|
|
if not is_checkpoint_mxfp8_serialized:
|
|
raise ValueError(
|
|
"MXFP8 quantization requires a serialized checkpoint. "
|
|
"Dynamic quantization is not supported."
|
|
)
|
|
|
|
logger.warning(
|
|
"Detected ModelOpt MXFP8 checkpoint. Please note that "
|
|
"the format is experimental and could change in future."
|
|
)
|
|
|
|
self.kv_cache_quant_algo = kv_cache_quant_algo
|
|
|
|
def get_name(self) -> QuantizationMethods:
|
|
return "modelopt_mxfp8"
|
|
|
|
def get_supported_act_dtypes(self) -> list[torch.dtype]:
|
|
return [torch.bfloat16]
|
|
|
|
@classmethod
|
|
def get_min_capability(cls) -> int:
|
|
# MXFP8 hardware acceleration requires Blackwell (SM100) or newer
|
|
return 100
|
|
|
|
def get_quant_method(
|
|
self, layer: torch.nn.Module, prefix: str
|
|
) -> "QuantizeMethodBase | None":
|
|
# MXFP8 does not yet support MoE models
|
|
if isinstance(layer, FusedMoE):
|
|
raise NotImplementedError(
|
|
"MXFP8 quantization does not yet support MoE models. "
|
|
"Please use FP8 or NVFP4 quantization for MoE models."
|
|
)
|
|
return super().get_quant_method(layer, prefix)
|
|
|
|
@classmethod
|
|
def override_quantization_method(
|
|
cls, hf_quant_cfg, user_quant
|
|
) -> QuantizationMethods | None:
|
|
"""Detect if this ModelOpt MXFP8 config should be used based on
|
|
quantization config."""
|
|
if hf_quant_cfg is None:
|
|
return None
|
|
|
|
# Use the community standard 'quant_method'
|
|
quant_method = hf_quant_cfg.get("quant_method", "").lower()
|
|
|
|
# Only proceed if the method is explicitly "modelopt"
|
|
if quant_method != "modelopt":
|
|
return None
|
|
|
|
# Look for ModelOpt-specific config structure
|
|
if "quantization" in hf_quant_cfg:
|
|
quant_config = hf_quant_cfg["quantization"]
|
|
if isinstance(quant_config, dict):
|
|
quant_algo = str(quant_config.get("quant_algo", "")).upper()
|
|
if "MXFP8" in quant_algo:
|
|
return "modelopt_mxfp8"
|
|
else:
|
|
# Check for compressed-tensors style config with specific quant_algo
|
|
quant_algo = str(hf_quant_cfg.get("quant_algo", "")).upper()
|
|
if "MXFP8" in quant_algo:
|
|
return "modelopt_mxfp8"
|
|
|
|
return None
|
|
|
|
@classmethod
|
|
def _from_config(
|
|
cls,
|
|
*,
|
|
quant_method: str,
|
|
kv_cache_quant_method: str | None,
|
|
exclude_modules: list[str],
|
|
original_config: dict[str, Any],
|
|
**kwargs: Any,
|
|
) -> "ModelOptMxFp8Config":
|
|
is_checkpoint_mxfp8_serialized = "MXFP8" in quant_method.upper()
|
|
|
|
# For MXFP8, validate required fields in the config
|
|
if is_checkpoint_mxfp8_serialized and "quantization" in original_config:
|
|
quant_config = original_config["quantization"]
|
|
required_fields = ["kv_cache_quant_algo", "exclude_modules"]
|
|
missing_fields = [
|
|
field for field in required_fields if field not in quant_config
|
|
]
|
|
if missing_fields:
|
|
raise ValueError(
|
|
f"MXFP8 quantization requires the following fields in "
|
|
f"hf_quant_config.json: {missing_fields}"
|
|
)
|
|
|
|
return cls(
|
|
is_checkpoint_mxfp8_serialized,
|
|
kv_cache_quant_method,
|
|
exclude_modules,
|
|
)
|
|
|
|
|
|
class ModelOptMxFp8LinearMethod(LinearMethodBase):
|
|
"""Linear method for ModelOpt MXFP8 quantization."""
|
|
|
|
def __init__(self, quant_config: ModelOptMxFp8Config) -> None:
|
|
self.quant_config = quant_config
|
|
|
|
if not self.quant_config.is_checkpoint_mxfp8_serialized:
|
|
raise ValueError(
|
|
"MXFP8 currently only supports serialized checkpoints. "
|
|
"Dynamic quantization is not supported."
|
|
)
|
|
|
|
backend: Mxfp8LinearBackend = Mxfp8LinearBackend.EMULATION
|
|
self.mxfp8_linear_op = Mxfp8LinearOp(backend=backend)
|
|
logger.info_once("Using %s backend for MXFP8 GEMM", backend.value)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: list[int],
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
del input_size, output_size
|
|
|
|
if not self.quant_config.is_checkpoint_mxfp8_serialized:
|
|
raise ValueError(
|
|
"MXFP8 quantization was selected, but checkpoint is not "
|
|
"MXFP8 serialized. Dynamic quantization is not supported."
|
|
)
|
|
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
layer.logical_widths = output_partition_sizes
|
|
layer.input_size_per_partition = input_size_per_partition
|
|
layer.output_size_per_partition = output_size_per_partition
|
|
|
|
if input_size_per_partition % MXFP8_BLOCK_SIZE != 0:
|
|
raise ValueError(
|
|
f"MXFP8 requires input dimension to be divisible by "
|
|
f"{MXFP8_BLOCK_SIZE}, got {input_size_per_partition}"
|
|
)
|
|
|
|
# Weight tensor: FP8 E4M3 format
|
|
weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
output_size_per_partition,
|
|
input_size_per_partition,
|
|
dtype=MXFP8_VALUE_DTYPE,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight", weight)
|
|
|
|
# Weight scale tensor (E8M0 encoded as uint8), one scale per block of 32 along K
|
|
weight_scale = ModelWeightParameter(
|
|
data=torch.empty(
|
|
output_size_per_partition,
|
|
input_size_per_partition // MXFP8_BLOCK_SIZE,
|
|
dtype=MXFP8_SCALE_DTYPE,
|
|
),
|
|
input_dim=1,
|
|
output_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight_scale", weight_scale)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
if layer.weight.ndim != 2:
|
|
raise ValueError(
|
|
f"MXFP8 weight must be 2D tensor [N, K], got {layer.weight.ndim}D "
|
|
f"with shape {tuple(layer.weight.shape)}"
|
|
)
|
|
|
|
if layer.weight.dtype != MXFP8_VALUE_DTYPE:
|
|
raise ValueError(
|
|
f"MXFP8 weight must be {MXFP8_VALUE_DTYPE} (FP8 E4M3), "
|
|
f"got {layer.weight.dtype}. The checkpoint may not be properly "
|
|
f"quantized with MXFP8."
|
|
)
|
|
|
|
weight = layer.weight.data # [N, K]
|
|
N, K = weight.shape
|
|
scale_k = K // MXFP8_BLOCK_SIZE
|
|
|
|
# Slice weight_scale to match weight dimensions (handles padding)
|
|
weight_scale = layer.weight_scale.data[:N, :scale_k].contiguous()
|
|
|
|
layer.weight = Parameter(weight.contiguous(), requires_grad=False)
|
|
layer.weight_scale = Parameter(weight_scale, requires_grad=False)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
if layer.weight.dtype != MXFP8_VALUE_DTYPE:
|
|
raise ValueError(
|
|
f"Weight dtype {layer.weight.dtype} != expected {MXFP8_VALUE_DTYPE}"
|
|
)
|
|
if layer.weight_scale.dtype != MXFP8_SCALE_DTYPE:
|
|
raise ValueError(
|
|
f"Weight scale dtype {layer.weight_scale.dtype} != "
|
|
f"expected {MXFP8_SCALE_DTYPE}"
|
|
)
|
|
|
|
return self.mxfp8_linear_op.apply(
|
|
input=x,
|
|
weight=layer.weight,
|
|
weight_scale=layer.weight_scale,
|
|
out_dtype=x.dtype,
|
|
bias=bias,
|
|
)
|
|
|
|
|
|
# Register the method classes for ModelOptMxFp8Config
|
|
ModelOptMxFp8Config.LinearMethodCls = ModelOptMxFp8LinearMethod
|
|
ModelOptMxFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod
|