41d3071918
Signed-off-by: jiahanc <173873397+jiahanc@users.noreply.github.com> Co-authored-by: Michael Goin <mgoin64@gmail.com>
1770 lines
67 KiB
Python
1770 lines
67 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 collections.abc import Callable
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from typing import TYPE_CHECKING, Any, Optional
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import torch
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from torch.nn import Module
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from torch.nn.parameter import Parameter
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import vllm.envs as envs
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import vllm.model_executor.layers.fused_moe.modular_kernel as mk
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from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
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from vllm.logger import init_logger
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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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fp8_w8a8_moe_quant_config,
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nvfp4_moe_quant_config,
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)
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from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
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is_valid_flashinfer_cutlass_fused_moe,
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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.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.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.kv_cache import BaseKVCacheMethod
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from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
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build_flashinfer_fp4_cutlass_moe_prepare_finalize,
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reorder_w1w3_to_w3w1,
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select_nvfp4_gemm_impl,
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)
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from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
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FlashinferMoeBackend,
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apply_flashinfer_per_tensor_scale_fp8,
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build_flashinfer_fp8_cutlass_moe_prepare_finalize,
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flashinfer_cutlass_moe_fp8,
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get_flashinfer_moe_backend,
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register_moe_scaling_factors,
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rotate_flashinfer_fp8_moe_weights,
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select_cutlass_fp8_gemm_impl,
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swap_w13_to_w31,
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)
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
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apply_fp4_marlin_linear,
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is_fp4_marlin_supported,
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prepare_fp4_layer_for_marlin,
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prepare_moe_fp4_layer_for_marlin,
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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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cutlass_fp4_supported,
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is_layer_skipped,
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swizzle_blockscale,
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)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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Fp8LinearOp,
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requantize_with_max_scale,
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)
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from vllm.model_executor.parameter import ModelWeightParameter, PerTensorScaleParameter
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from vllm.scalar_type import scalar_types
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from vllm.utils.flashinfer import (
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flashinfer_scaled_fp4_mm,
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has_flashinfer,
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has_flashinfer_moe,
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)
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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 = ["FP8", "NVFP4"]
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KV_CACHE_QUANT_ALGOS = ["FP8"]
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class ModelOptFp8Config(QuantizationConfig):
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"""Config class for ModelOpt FP8."""
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def __init__(
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self,
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is_checkpoint_fp8_serialized: bool = False,
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kv_cache_quant_method: str | None = None,
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exclude_modules: list[str] | None = None,
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) -> None:
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super().__init__()
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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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self.exclude_modules = exclude_modules or []
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if is_checkpoint_fp8_serialized:
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logger.warning(
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"Detected ModelOpt fp8 checkpoint. Please note that"
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" the format is experimental and could change."
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)
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "modelopt"
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@classmethod
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def get_supported_act_dtypes(cls) -> 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 get_config_filenames(cls) -> list[str]:
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return ["hf_quant_config.json"]
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def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
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if self.exclude_modules is not None:
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self.exclude_modules = hf_to_vllm_mapper.apply_list(self.exclude_modules)
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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 = quant_config.get("quant_algo", "")
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if "FP8" in quant_algo:
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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 = hf_quant_cfg.get("quant_algo", "")
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if isinstance(quant_algo, str) and "FP8" in quant_algo:
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return "modelopt"
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return None
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config":
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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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# ModelOpt format: {"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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if not quant_method:
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raise ValueError("Missing 'quant_algo' in quantization config")
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kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")
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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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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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is_checkpoint_fp8_serialized = "FP8" in quant_method
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return cls(is_checkpoint_fp8_serialized, kv_cache_quant_method, 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 substring matching.
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This method handles both regular models and multimodal models that use
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the language_model prefix. For multimodal models, it checks if the
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module name (without the language_model prefix) is in the exclude list.
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"""
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if self.exclude_modules is None:
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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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# Then check substring matching for patterns not caught by exact match
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for module in self.exclude_modules:
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# Skip exact matches already handled above
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if module != prefix and (
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module in prefix
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or (
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prefix.startswith("language_model.")
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and module in prefix.removeprefix("language_model.")
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)
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):
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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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) -> Optional["QuantizeMethodBase"]:
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from vllm.attention.layer import Attention # Avoid circular import
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if isinstance(layer, LinearBase):
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if self.is_layer_excluded(prefix):
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return UnquantizedLinearMethod()
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# Check if this is a vision model layer that should not be quantized
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if "vision_tower" in prefix or "vision_model" in prefix:
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return UnquantizedLinearMethod()
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return ModelOptFp8LinearMethod(self)
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elif isinstance(layer, Attention):
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return ModelOptFp8KVCacheMethod(self)
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elif isinstance(layer, FusedMoE):
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return ModelOptFp8MoEMethod(self, layer)
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return None
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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 = Fp8LinearOp(
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act_quant_static=True, act_quant_group_shape=GroupShape.PER_TENSOR
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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
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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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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("input_scale", scale)
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def process_weights_after_loading(self, layer: Module) -> None:
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weight = layer.weight
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max_w_scale = layer.weight_scale.max()
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if not (layer.weight_scale == layer.weight_scale[0]).all():
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max_w_scale, weight = requantize_with_max_scale(
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layer.weight, layer.weight_scale, layer.logical_widths
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)
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layer.weight = Parameter(weight.t(), requires_grad=False)
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layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
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layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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return self.fp8_linear.apply(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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input_scale=layer.input_scale,
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bias=bias,
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)
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class ModelOptFp8MoEMethod(FusedMoEMethodBase):
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"""MoE method for ModelOpt FP8.
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Supports loading FP8 checkpoints with static weight scale and
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activation scale.
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Args:
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quant_config: The ModelOpt quantization config.
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"""
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def __init__(
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self,
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quant_config: ModelOptFp8Config,
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layer: torch.nn.Module,
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) -> None:
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super().__init__(layer.moe_config)
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self.layer = layer
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self.quant_config = quant_config
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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cutlass_fp8_supported,
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)
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self.cutlass_fp8_supported = cutlass_fp8_supported()
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self.flashinfer_moe_backend: FlashinferMoeBackend | None = None
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if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe():
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self.flashinfer_moe_backend = get_flashinfer_moe_backend()
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logger.info_once(
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f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
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)
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def maybe_make_prepare_finalize(
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self,
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) -> mk.FusedMoEPrepareAndFinalize | None:
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# TRT LLM not supported with all2all yet.
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if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
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return None
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elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
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prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
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self.moe
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)
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logger.debug_once("%s", prepare_finalize.__class__.__name__)
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return prepare_finalize
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else:
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return super().maybe_make_prepare_finalize()
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def select_gemm_impl(
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self,
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prepare_finalize: mk.FusedMoEPrepareAndFinalize,
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layer: torch.nn.Module,
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) -> mk.FusedMoEPermuteExpertsUnpermute:
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assert self.moe_quant_config is not None
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experts = select_cutlass_fp8_gemm_impl(
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self.moe,
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self.moe_quant_config,
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)
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logger.debug_once("Using %s", experts.__class__.__name__)
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return experts
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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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# Use FP8 dtype if checkpoint is serialized
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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_loader = extra_weight_attrs.get("weight_loader")
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w13_weight = ModelWeightParameter(
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data=torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size,
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dtype=weight_dtype,
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),
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input_dim=2,
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output_dim=1,
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weight_loader=weight_loader,
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)
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layer.register_parameter("w13_weight", w13_weight)
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w2_weight = ModelWeightParameter(
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data=torch.empty(
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num_experts,
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hidden_size,
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intermediate_size_per_partition,
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dtype=weight_dtype,
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),
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input_dim=2,
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output_dim=1,
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weight_loader=weight_loader,
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)
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layer.register_parameter("w2_weight", w2_weight)
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if self.quant_config.is_checkpoint_fp8_serialized:
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# WEIGHT SCALES - Per-tensor scaling for ModelOpts
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# Allocate 2 scales for w1 and w3 respectively.
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# They will be combined to a single scale after weight loading.
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w13_weight_scale = PerTensorScaleParameter(
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data=torch.full(
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(num_experts, 2),
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1.0,
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dtype=torch.float32,
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),
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weight_loader=weight_loader,
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)
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w2_weight_scale = PerTensorScaleParameter(
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data=torch.full((num_experts,), 1.0, dtype=torch.float32),
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weight_loader=weight_loader,
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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# Set weight loader attributes for scales
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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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# INPUT SCALES - Per-tensor scaling for ModelOpt
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w13_input_scale = PerTensorScaleParameter(
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data=torch.full((num_experts,), 1.0, dtype=torch.float32),
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weight_loader=weight_loader,
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)
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w2_input_scale = PerTensorScaleParameter(
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data=torch.full((num_experts,), 1.0, dtype=torch.float32),
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weight_loader=weight_loader,
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)
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layer.register_parameter("w13_input_scale", w13_input_scale)
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layer.register_parameter("w2_input_scale", w2_input_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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"""Process FP8 MoE weights after loading from serialized checkpoint.
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Only supports pre-quantized checkpoints with FP8 weights and scales.
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"""
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layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
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layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
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|
|
from vllm._custom_ops import scaled_fp8_quant
|
|
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
|
|
per_tensor_dequantize,
|
|
)
|
|
|
|
# Handle scale parameters
|
|
if hasattr(layer, "w13_weight_scale") and layer.w13_weight_scale is not None:
|
|
# Fp8 moe kernel needs single weight scale for w13 per expert.
|
|
# We take the max of the w1 and w3 scales
|
|
# then dequant and requant each expert.
|
|
if layer.w13_weight_scale.dim() == 2:
|
|
# Get the maximum scale across w1 and w3 for each expert
|
|
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
|
|
|
|
# Requantize each expert's weights using the combined scale
|
|
# w13_weight (num_experts, 2 * intermediate_size, hidden_size)
|
|
# where the first intermediate_size rows are w1, the next are w3
|
|
intermediate_size = layer.w13_weight.shape[1] // 2
|
|
for expert_id in range(layer.w13_weight.shape[0]):
|
|
start = 0
|
|
for shard_id in range(2): # w1 and w3
|
|
# Dequantize using the original scale for this shard
|
|
dq_weight = per_tensor_dequantize(
|
|
layer.w13_weight[expert_id][
|
|
start : start + intermediate_size, :
|
|
],
|
|
layer.w13_weight_scale[expert_id][shard_id],
|
|
)
|
|
# Requantize using the combined max scale
|
|
|
|
(
|
|
layer.w13_weight[expert_id][
|
|
start : start + intermediate_size, :
|
|
],
|
|
_,
|
|
) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
|
|
|
|
start += intermediate_size
|
|
|
|
# Update the scale parameter to be per-expert
|
|
layer.w13_weight_scale = Parameter(max_w13_scales, requires_grad=False)
|
|
else:
|
|
layer.w13_weight_scale = Parameter(
|
|
layer.w13_weight_scale.data, requires_grad=False
|
|
)
|
|
|
|
if hasattr(layer, "w2_weight_scale") and layer.w2_weight_scale is not None:
|
|
layer.w2_weight_scale = Parameter(
|
|
layer.w2_weight_scale.data, requires_grad=False
|
|
)
|
|
# Input scales must be equal for each expert in fp8 MoE layers.
|
|
if hasattr(layer, "w13_input_scale") and layer.w13_input_scale is not None:
|
|
layer.w13_input_scale = Parameter(
|
|
layer.w13_input_scale.max(), requires_grad=False
|
|
)
|
|
if hasattr(layer, "w2_input_scale") and layer.w2_input_scale is not None:
|
|
layer.w2_input_scale = Parameter(
|
|
layer.w2_input_scale.max(), requires_grad=False
|
|
)
|
|
|
|
if self.flashinfer_moe_backend is not None:
|
|
layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
|
|
register_moe_scaling_factors(layer)
|
|
if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
|
|
rotate_flashinfer_fp8_moe_weights(layer.w13_weight, layer.w2_weight)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
|
|
return None
|
|
|
|
return fp8_w8a8_moe_quant_config(
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
per_act_token_quant=False,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: int | None = None,
|
|
num_expert_group: int | None = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: torch.Tensor | None = None,
|
|
custom_routing_function: Callable | None = None,
|
|
scoring_func: str = "softmax",
|
|
routed_scaling_factor: float = 1.0,
|
|
e_score_correction_bias: torch.Tensor | None = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
enable_eplb: bool = False,
|
|
expert_load_view: torch.Tensor | None = None,
|
|
logical_to_physical_map: torch.Tensor | None = None,
|
|
logical_replica_count: torch.Tensor | None = None,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
if enable_eplb:
|
|
raise NotImplementedError(
|
|
"EPLB not supported for `ModelOptFp8MoEMethod` yet."
|
|
)
|
|
|
|
if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM:
|
|
assert self.fused_experts is None
|
|
assert activation == "silu", (
|
|
f"Expected 'silu' activation but got {activation}"
|
|
)
|
|
assert not renormalize
|
|
return apply_flashinfer_per_tensor_scale_fp8(
|
|
layer=layer,
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
routing_bias=e_score_correction_bias,
|
|
global_num_experts=global_num_experts,
|
|
top_k=top_k,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
|
|
# Expert selection
|
|
topk_weights, topk_ids, _ = FusedMoE.select_experts(
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
use_grouped_topk=use_grouped_topk,
|
|
top_k=top_k,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
routed_scaling_factor=routed_scaling_factor,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
indices_type=self.topk_indices_dtype,
|
|
)
|
|
|
|
#
|
|
# Note: the order here is important. self.fused_experts can override
|
|
# cutlass or fused_experts.
|
|
#
|
|
if self.fused_experts is not None:
|
|
return self.fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
inplace=False,
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS:
|
|
assert not renormalize
|
|
assert activation == "silu", (
|
|
f"Expected 'silu' activation but got {activation}"
|
|
)
|
|
return flashinfer_cutlass_moe_fp8(
|
|
x,
|
|
layer,
|
|
topk_weights,
|
|
topk_ids,
|
|
inplace=False,
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
else:
|
|
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
|
|
|
|
assert self.moe_quant_config is not None
|
|
|
|
return fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=True,
|
|
activation=activation,
|
|
quant_config=self.moe_quant_config,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
|
|
|
|
class ModelOptNvFp4Config(QuantizationConfig):
|
|
"""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__()
|
|
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
|
|
self.exclude_modules = exclude_modules
|
|
|
|
@classmethod
|
|
def get_name(cls) -> QuantizationMethods:
|
|
return "modelopt_fp4"
|
|
|
|
@classmethod
|
|
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
|
|
return [torch.bfloat16, torch.half, torch.float8_e4m3fn]
|
|
|
|
@classmethod
|
|
def get_min_capability(cls) -> int:
|
|
return 80
|
|
|
|
@classmethod
|
|
def get_config_filenames(cls) -> list[str]:
|
|
return ["hf_quant_config.json"]
|
|
|
|
def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
|
|
if self.exclude_modules is not None:
|
|
self.exclude_modules = hf_to_vllm_mapper.apply_list(self.exclude_modules)
|
|
|
|
@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, config: dict[str, Any]) -> "ModelOptNvFp4Config":
|
|
# Handle both traditional ModelOpt format and compressed-tensors
|
|
# style format
|
|
if "quantization" in config:
|
|
# Traditional ModelOpt format:
|
|
# {"quantization": {"quant_algo": "..."}}
|
|
quant_config = cls.get_from_keys(config, ["quantization"])
|
|
if not isinstance(quant_config, dict):
|
|
raise ValueError("Expected 'quantization' to be a dictionary in config")
|
|
|
|
quant_method = quant_config.get("quant_algo", "")
|
|
if not quant_method:
|
|
raise ValueError("Missing 'quant_algo' in quantization config")
|
|
|
|
# Handle kv_cache_quant_algo with proper type validation
|
|
kv_cache_quant_algo_raw = quant_config.get("kv_cache_quant_algo")
|
|
if kv_cache_quant_algo_raw is None:
|
|
# No KV cache quantization by default
|
|
kv_cache_quant_algo = None
|
|
elif isinstance(kv_cache_quant_algo_raw, str):
|
|
kv_cache_quant_algo = kv_cache_quant_algo_raw
|
|
else:
|
|
raise ValueError(
|
|
f"kv_cache_quant_algo must be a string, got "
|
|
f"{type(kv_cache_quant_algo_raw)}"
|
|
)
|
|
|
|
# Handle group_size with proper type validation
|
|
group_size_raw = quant_config.get("group_size")
|
|
if group_size_raw is None:
|
|
group_size = 16 # Default value
|
|
elif isinstance(group_size_raw, int):
|
|
group_size = group_size_raw
|
|
else:
|
|
try:
|
|
group_size = int(group_size_raw)
|
|
except (ValueError, TypeError):
|
|
raise ValueError(
|
|
f"group_size must be an integer, got {type(group_size_raw)}"
|
|
) from None
|
|
|
|
# "exclude_modules" is the key in the legacy hf_quant_config.json
|
|
exclude_modules = quant_config.get("exclude_modules", [])
|
|
if not isinstance(exclude_modules, list):
|
|
raise ValueError(
|
|
f"exclude_modules must be a list, got {type(exclude_modules)}"
|
|
)
|
|
else:
|
|
# Compressed-tensors style format:
|
|
# {"quant_algo": "...", "quant_method": "modelopt"}
|
|
quant_method = config.get("quant_algo", "")
|
|
|
|
# Handle kv_cache_quant_algo with proper type validation
|
|
kv_cache_quant_algo_raw = config.get("kv_cache_quant_algo")
|
|
if kv_cache_quant_algo_raw is None:
|
|
# No KV cache quantization by default
|
|
kv_cache_quant_algo = None
|
|
elif isinstance(kv_cache_quant_algo_raw, str):
|
|
kv_cache_quant_algo = kv_cache_quant_algo_raw
|
|
else:
|
|
raise ValueError(
|
|
f"kv_cache_quant_algo must be a string, got "
|
|
f"{type(kv_cache_quant_algo_raw)}"
|
|
)
|
|
|
|
# Handle group_size with proper type validation
|
|
group_size_raw = config.get("group_size")
|
|
if group_size_raw is None:
|
|
group_size = 16 # Default value
|
|
elif isinstance(group_size_raw, int):
|
|
group_size = group_size_raw
|
|
else:
|
|
try:
|
|
group_size = int(group_size_raw)
|
|
except (ValueError, TypeError):
|
|
raise ValueError(
|
|
f"group_size must be an integer, got {type(group_size_raw)}"
|
|
) from None
|
|
|
|
# "ignore" is the key in config.json
|
|
exclude_modules = config.get("ignore", [])
|
|
if not isinstance(exclude_modules, list):
|
|
raise ValueError(
|
|
f"exclude_modules must be a list, got {type(exclude_modules)}"
|
|
)
|
|
|
|
if quant_method not in QUANT_ALGOS:
|
|
raise ValueError(
|
|
f"ModelOpt currently only supports: {QUANT_ALGOS} "
|
|
"quantizations in vLLM. Please check the "
|
|
"`hf_quant_config.json` file for your model's "
|
|
"quant configuration."
|
|
)
|
|
is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
|
|
|
|
# For FP4, these fields are required
|
|
if is_checkpoint_nvfp4_serialized and "quantization" in config:
|
|
# Check if required fields are present in the quantization config
|
|
quant_config = 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_algo,
|
|
exclude_modules,
|
|
group_size,
|
|
)
|
|
|
|
def is_layer_excluded(self, prefix: str) -> bool:
|
|
"""
|
|
Check if a layer should be excluded from quantization.
|
|
Handles both exact matching (for fused layers) and pattern matching.
|
|
"""
|
|
# First check exact matching with fused layer support
|
|
if is_layer_skipped(prefix, self.exclude_modules, self.packed_modules_mapping):
|
|
return True
|
|
|
|
# Check regex pattern matching for patterns not caught by exact match
|
|
import regex as re
|
|
|
|
for pattern in self.exclude_modules:
|
|
# Skip patterns that would be caught by exact matching
|
|
if "*" in pattern or "." in pattern:
|
|
regex_str = pattern.replace(".", r"\.").replace("*", r".*")
|
|
if re.fullmatch(regex_str, prefix):
|
|
return True
|
|
return False
|
|
|
|
def get_quant_method(
|
|
self, layer: torch.nn.Module, prefix: str
|
|
) -> Optional["QuantizeMethodBase"]:
|
|
from vllm.attention.layer import Attention # Avoid circular import
|
|
|
|
skip_layer = self.is_layer_excluded(prefix)
|
|
if isinstance(layer, LinearBase):
|
|
if skip_layer:
|
|
return UnquantizedLinearMethod()
|
|
# Check if this is a vision model layer that should not be quantized
|
|
if "vision_tower" in prefix or "vision_model" in prefix:
|
|
return UnquantizedLinearMethod()
|
|
return ModelOptNvFp4LinearMethod(self)
|
|
elif isinstance(layer, Attention):
|
|
return ModelOptFp8KVCacheMethod(self)
|
|
elif isinstance(layer, FusedMoE):
|
|
if skip_layer:
|
|
return None
|
|
return ModelOptNvFp4FusedMoE(self, layer.moe_config, layer)
|
|
return None
|
|
|
|
|
|
class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
|
|
"""
|
|
Supports loading kv-cache scaling factors from FP8 checkpoints.
|
|
"""
|
|
|
|
def __init__(self, quant_config: ModelOptFp8Config | ModelOptNvFp4Config):
|
|
super().__init__(quant_config)
|
|
|
|
|
|
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.backend = "none"
|
|
if envs.VLLM_NVFP4_GEMM_BACKEND is None:
|
|
if has_flashinfer():
|
|
self.backend = "flashinfer-cutlass"
|
|
elif cutlass_fp4_supported():
|
|
self.backend = "cutlass"
|
|
elif is_fp4_marlin_supported():
|
|
self.backend = "marlin"
|
|
elif envs.VLLM_NVFP4_GEMM_BACKEND.startswith("flashinfer-"):
|
|
self.backend = envs.VLLM_NVFP4_GEMM_BACKEND
|
|
assert has_flashinfer(), f"FlashInfer is required for {self.backend}"
|
|
|
|
if self.backend == "none":
|
|
raise ValueError(
|
|
"No valid NVFP4 GEMM backend found. "
|
|
"Please check your platform capability."
|
|
)
|
|
|
|
logger.info_once(f"Using {self.backend} for NVFP4 GEMM")
|
|
|
|
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 Weight Scale
|
|
input_scale = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("input_scale", input_scale)
|
|
|
|
# Global Weight Scale
|
|
weight_scale_2 = PerTensorScaleParameter(
|
|
data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("weight_scale_2", weight_scale_2)
|
|
|
|
# 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: Module) -> None:
|
|
# global scales:
|
|
input_scale_2 = layer.input_scale.max().to(torch.float32)
|
|
layer.input_scale = Parameter(input_scale_2, requires_grad=False)
|
|
|
|
weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
|
|
layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
|
|
|
|
layer.alpha = Parameter(
|
|
layer.input_scale * layer.weight_scale_2, requires_grad=False
|
|
)
|
|
|
|
# Calculate `1 / input_scale` so that we don't need to do so at runtime
|
|
layer.input_scale_inv = Parameter(
|
|
(1 / layer.input_scale).to(torch.float32), requires_grad=False
|
|
)
|
|
|
|
# Swizzle the weight blockscale.
|
|
# contracting dimension is input dimension
|
|
# block_size = 16;
|
|
assert layer.weight_scale.dtype == torch.float8_e4m3fn, (
|
|
"Weight Block scale must be represented as FP8-E4M3"
|
|
)
|
|
|
|
if self.backend == "marlin":
|
|
prepare_fp4_layer_for_marlin(layer)
|
|
del layer.alpha
|
|
del layer.input_scale
|
|
elif self.backend == "flashinfer-trtllm":
|
|
# FlashInfer TRTLLM FP4 GEMM requires a different weight layout.
|
|
# FlashInfer provides nvfp4_quantize to quantize + shuffle the
|
|
# layout but we use our own quantization so we have to call
|
|
# shuffles ourselves.
|
|
from flashinfer import shuffle_matrix_a, shuffle_matrix_sf_a
|
|
|
|
weight = layer.weight.data
|
|
weight_scale = layer.weight_scale.data
|
|
|
|
epilogue_tile_m = 128
|
|
weight = shuffle_matrix_a(weight.view(torch.uint8), epilogue_tile_m)
|
|
weight_scale = (
|
|
shuffle_matrix_sf_a(weight_scale.view(torch.uint8), epilogue_tile_m)
|
|
.reshape(weight_scale.shape)
|
|
.view(torch.float8_e4m3fn)
|
|
)
|
|
|
|
layer.weight_scale = Parameter(weight_scale, requires_grad=False)
|
|
layer.weight = Parameter(weight, requires_grad=False)
|
|
else:
|
|
swizzled_weight_scale = swizzle_blockscale(layer.weight_scale)
|
|
layer.weight_scale = Parameter(swizzled_weight_scale, requires_grad=False)
|
|
layer.weight = Parameter(layer.weight.data, requires_grad=False)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
if self.backend == "marlin":
|
|
return apply_fp4_marlin_linear(
|
|
input=x,
|
|
weight=layer.weight,
|
|
weight_scale=layer.weight_scale,
|
|
weight_scale_2=layer.weight_scale_2,
|
|
workspace=layer.workspace,
|
|
size_n=layer.output_size_per_partition,
|
|
size_k=layer.input_size_per_partition,
|
|
bias=bias,
|
|
)
|
|
|
|
output_dtype = x.dtype
|
|
output_shape = [x.shape[0], layer.weight.shape[0]]
|
|
|
|
# quantize BF16 or FP16 to (FP4 and interleaved block scale)
|
|
x_fp4, x_blockscale = scaled_fp4_quant(x, layer.input_scale_inv)
|
|
|
|
# validate dtypes of quantized input, input block scale,
|
|
# weight and weight_blockscale
|
|
assert x_fp4.dtype == torch.uint8
|
|
assert layer.weight.dtype == torch.uint8
|
|
assert x_blockscale.dtype == torch.float8_e4m3fn
|
|
assert layer.weight_scale.dtype == torch.float8_e4m3fn
|
|
assert layer.alpha.dtype == torch.float32
|
|
|
|
mm_args = (
|
|
x_fp4,
|
|
layer.weight,
|
|
x_blockscale,
|
|
layer.weight_scale,
|
|
layer.alpha,
|
|
output_dtype,
|
|
)
|
|
if self.backend.startswith("flashinfer-"):
|
|
backend_name = self.backend[len("flashinfer-") :]
|
|
out = flashinfer_scaled_fp4_mm(*mm_args, backend=backend_name)
|
|
else:
|
|
assert self.backend == "cutlass"
|
|
out = cutlass_scaled_fp4_mm(*mm_args)
|
|
|
|
if bias is not None:
|
|
out = out + bias
|
|
return out.view(*output_shape)
|
|
|
|
|
|
class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
|
|
"""
|
|
MoE Method for FP4 Quantization.
|
|
Args:
|
|
quant_config: NVFP4 Quant Config
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
quant_config: ModelOptNvFp4Config,
|
|
moe: FusedMoEConfig,
|
|
layer: torch.nn.Module,
|
|
) -> None:
|
|
from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import ( # noqa: E501
|
|
detect_nvfp4_moe_support,
|
|
)
|
|
|
|
super().__init__(moe)
|
|
self.quant_config = quant_config
|
|
self.layer = layer
|
|
_nvfp4 = detect_nvfp4_moe_support(self.__class__.__name__)
|
|
self.cutlass_nvfp4_supported = _nvfp4.cutlass_supported
|
|
self.allow_flashinfer = _nvfp4.allow_flashinfer
|
|
self.use_marlin = _nvfp4.use_marlin
|
|
self.flashinfer_moe_backend = None
|
|
self._cache_permute_indices: dict[torch.Size, torch.Tensor] = {}
|
|
if self.allow_flashinfer:
|
|
self.flashinfer_moe_backend = get_flashinfer_moe_backend()
|
|
logger.info_once(
|
|
f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
|
|
" for ModelOptNvFp4FusedMoE."
|
|
)
|
|
|
|
def maybe_make_prepare_finalize(self) -> mk.FusedMoEPrepareAndFinalize | None:
|
|
if self.use_marlin or (
|
|
self.allow_flashinfer
|
|
and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
|
|
):
|
|
return None
|
|
elif (
|
|
self.allow_flashinfer
|
|
and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
|
|
):
|
|
# For now, fp4 moe only works with the flashinfer dispatcher.
|
|
prepare_finalize = build_flashinfer_fp4_cutlass_moe_prepare_finalize(
|
|
self.moe
|
|
)
|
|
logger.debug_once("%s", prepare_finalize.__class__.__name__)
|
|
return prepare_finalize
|
|
else:
|
|
return super().maybe_make_prepare_finalize()
|
|
|
|
def select_gemm_impl(
|
|
self,
|
|
prepare_finalize: mk.FusedMoEPrepareAndFinalize,
|
|
layer: torch.nn.Module,
|
|
) -> mk.FusedMoEPermuteExpertsUnpermute:
|
|
assert self.moe_quant_config is not None
|
|
experts = select_nvfp4_gemm_impl(
|
|
self.moe,
|
|
self.moe_quant_config,
|
|
allow_flashinfer=self.allow_flashinfer,
|
|
)
|
|
logger.debug_once("Using %s", experts.__class__.__name__)
|
|
return experts
|
|
|
|
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,
|
|
):
|
|
if not self.quant_config.is_checkpoint_nvfp4_serialized:
|
|
raise ValueError(
|
|
"NVFP4 quantization was selected, "
|
|
" dynamic quantization is not supported."
|
|
)
|
|
|
|
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")
|
|
# GEMM 1
|
|
w13_weight = ModelWeightParameter(
|
|
data=torch.empty(
|
|
num_experts,
|
|
2 * 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,
|
|
2 * 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, 2, 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}
|
|
)
|
|
|
|
w13_input_scale = PerTensorScaleParameter(
|
|
data=torch.empty(num_experts, 2, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
|
|
|
w2_input_scale = PerTensorScaleParameter(
|
|
data=torch.empty(num_experts, dtype=torch.float32),
|
|
weight_loader=weight_loader,
|
|
)
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
|
|
|
def prepare_static_weights_for_trtllm_fp4_moe(
|
|
self,
|
|
# args_dequant,
|
|
# args,
|
|
gemm1_weights,
|
|
gemm2_weights,
|
|
gemm1_scales_linear_fp4_bytes,
|
|
gemm2_scales_linear_fp4_bytes,
|
|
hidden_size,
|
|
intermediate_size,
|
|
num_experts,
|
|
):
|
|
from flashinfer import nvfp4_block_scale_interleave
|
|
from flashinfer.fused_moe.core import (
|
|
_maybe_get_cached_w3_w1_permute_indices,
|
|
get_w2_permute_indices_with_cache,
|
|
)
|
|
|
|
"""Prepare quantized weights for kernel (done offline with weights)."""
|
|
epilogue_tile_m = 128 # FIXME: this depends on the kernel internals
|
|
|
|
# Convert quantized weights to proper formats
|
|
gemm1_weights_fp4 = gemm1_weights.view(torch.float8_e4m3fn).reshape(
|
|
num_experts, 2 * intermediate_size, hidden_size // 2
|
|
) # packed fp4
|
|
gemm1_scales_linear_fp4 = gemm1_scales_linear_fp4_bytes.view(
|
|
torch.float8_e4m3fn
|
|
).reshape(
|
|
num_experts, 2 * intermediate_size, hidden_size // 16
|
|
) # fp8 scaling factors
|
|
|
|
gemm2_weights_fp4 = gemm2_weights.view(torch.float8_e4m3fn).reshape(
|
|
num_experts, hidden_size, intermediate_size // 2
|
|
) # packed fp4
|
|
gemm2_scales_linear_fp4 = gemm2_scales_linear_fp4_bytes.view(
|
|
torch.float8_e4m3fn
|
|
).reshape(
|
|
num_experts, hidden_size, intermediate_size // 16
|
|
) # fp8 scaling factors
|
|
|
|
gemm1_weights_fp4_shuffled = []
|
|
gemm1_scales_fp4_shuffled = []
|
|
gemm2_weights_fp4_shuffled = []
|
|
gemm2_scales_fp4_shuffled = []
|
|
for i in range(num_experts):
|
|
# Calculate the permute indices for the following:
|
|
# 1. Reorder rows of W1 and scales for fused gated activation
|
|
# 2. Shuffle weights and scaling factors for transposed mma output
|
|
# for both w3_w1 and w2 weights and scale factors
|
|
permute_indices = _maybe_get_cached_w3_w1_permute_indices(
|
|
self._cache_permute_indices,
|
|
gemm1_weights_fp4[i].view(torch.uint8),
|
|
epilogue_tile_m,
|
|
)
|
|
gemm1_weights_fp4_shuffled.append(
|
|
gemm1_weights_fp4[i]
|
|
.view(torch.uint8)[permute_indices.to(gemm1_weights_fp4.device)]
|
|
.contiguous()
|
|
)
|
|
|
|
permute_sf_indices = _maybe_get_cached_w3_w1_permute_indices(
|
|
self._cache_permute_indices,
|
|
gemm1_scales_linear_fp4[i].view(torch.uint8),
|
|
epilogue_tile_m,
|
|
num_elts_per_sf=16,
|
|
)
|
|
gemm1_scales_fp4_shuffled.append(
|
|
nvfp4_block_scale_interleave(
|
|
gemm1_scales_linear_fp4[i]
|
|
.view(torch.uint8)[
|
|
permute_sf_indices.to(gemm1_scales_linear_fp4.device)
|
|
]
|
|
.contiguous()
|
|
)
|
|
)
|
|
|
|
permute_indices = get_w2_permute_indices_with_cache(
|
|
self._cache_permute_indices,
|
|
gemm2_weights_fp4[i].view(torch.uint8),
|
|
epilogue_tile_m,
|
|
)
|
|
gemm2_weights_fp4_shuffled.append(
|
|
gemm2_weights_fp4[i]
|
|
.view(torch.uint8)[permute_indices.to(gemm2_weights_fp4.device)]
|
|
.contiguous()
|
|
)
|
|
|
|
permute_sf_indices = get_w2_permute_indices_with_cache(
|
|
self._cache_permute_indices,
|
|
gemm2_scales_linear_fp4[i].view(torch.uint8),
|
|
epilogue_tile_m,
|
|
num_elts_per_sf=16,
|
|
)
|
|
gemm2_scales_fp4_shuffled.append(
|
|
nvfp4_block_scale_interleave(
|
|
gemm2_scales_linear_fp4[i]
|
|
.view(torch.uint8)[
|
|
permute_sf_indices.to(gemm2_scales_linear_fp4.device)
|
|
]
|
|
.contiguous()
|
|
)
|
|
)
|
|
|
|
# Stack weights for all experts
|
|
gemm1_weights_fp4_shuffled = torch.stack(gemm1_weights_fp4_shuffled)
|
|
gemm1_scales_fp4_shuffled = (
|
|
torch.stack(gemm1_scales_fp4_shuffled)
|
|
.view(torch.float8_e4m3fn)
|
|
.reshape(num_experts, 2 * intermediate_size, hidden_size // 16)
|
|
)
|
|
|
|
gemm2_weights_fp4_shuffled = torch.stack(gemm2_weights_fp4_shuffled)
|
|
gemm2_scales_fp4_shuffled = (
|
|
torch.stack(gemm2_scales_fp4_shuffled)
|
|
.view(torch.float8_e4m3fn)
|
|
.reshape(num_experts, hidden_size, intermediate_size // 16)
|
|
)
|
|
return (
|
|
gemm1_weights_fp4_shuffled,
|
|
gemm1_scales_fp4_shuffled,
|
|
gemm2_weights_fp4_shuffled,
|
|
gemm2_scales_fp4_shuffled,
|
|
)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# GEMM 1 processing
|
|
gemm1_weight = layer.w13_weight.data
|
|
gemm1_weight_scale = layer.w13_weight_scale.data
|
|
|
|
if self.allow_flashinfer:
|
|
gemm1_weight, gemm1_weight_scale = reorder_w1w3_to_w3w1(
|
|
gemm1_weight, gemm1_weight_scale, dim=-2
|
|
)
|
|
|
|
layer.w13_weight = Parameter(gemm1_weight, requires_grad=False)
|
|
layer.w13_weight_scale = Parameter(gemm1_weight_scale, requires_grad=False)
|
|
|
|
# Common processing for w13_weight_scale_2
|
|
if 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]
|
|
layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2, requires_grad=False)
|
|
|
|
# Common processing for input scales and alphas
|
|
w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(torch.float32)
|
|
layer.g1_alphas = Parameter(
|
|
(w13_input_scale * w13_weight_scale_2).to(torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
|
|
# This is for quantization, so we need to invert it.
|
|
layer.w13_input_scale_quant = Parameter(
|
|
(1 / w13_input_scale).to(torch.float32), requires_grad=False
|
|
)
|
|
|
|
# GEMM 2 processing
|
|
layer.g2_alphas = Parameter(
|
|
(layer.w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
|
|
# This is for quantization, so we need to invert it.
|
|
layer.w2_input_scale_quant = Parameter(
|
|
(1 / layer.w2_input_scale).to(torch.float32), requires_grad=False
|
|
)
|
|
|
|
# TensorRT-LLM specific processing
|
|
if (
|
|
self.allow_flashinfer
|
|
and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
|
|
):
|
|
# Prepare static weights for TRT-LLM kernel
|
|
# alternate: prepare_static_weight_layouts_for_trtllm_moe
|
|
(
|
|
gemm1_weights_fp4_shuffled,
|
|
gemm1_scales_fp4_shuffled,
|
|
gemm2_weights_fp4_shuffled,
|
|
gemm2_scales_fp4_shuffled,
|
|
) = self.prepare_static_weights_for_trtllm_fp4_moe(
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
layer.w13_weight_scale,
|
|
layer.w2_weight_scale,
|
|
layer.w2_weight.size(-2), # hidden_size
|
|
layer.w13_weight.size(-2) // 2, # intermediate_size
|
|
layer.w13_weight.size(0), # num_experts
|
|
)
|
|
logger.debug_once("Finished shuffling weights for TRT-LLM MOE")
|
|
|
|
layer.gemm1_weights_fp4_shuffled = Parameter(
|
|
gemm1_weights_fp4_shuffled, requires_grad=False
|
|
)
|
|
layer.gemm2_weights_fp4_shuffled = Parameter(
|
|
gemm2_weights_fp4_shuffled, requires_grad=False
|
|
)
|
|
layer.gemm1_scales_fp4_shuffled = Parameter(
|
|
gemm1_scales_fp4_shuffled, requires_grad=False
|
|
)
|
|
layer.gemm2_scales_fp4_shuffled = Parameter(
|
|
gemm2_scales_fp4_shuffled, requires_grad=False
|
|
)
|
|
|
|
# Additional parameter needed for TRT-LLM
|
|
layer.g1_scale_c = Parameter(
|
|
(layer.w2_input_scale_quant * layer.g1_alphas).to(torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
|
|
# Clean up weights that won't be used by TRT-LLM
|
|
del layer.w2_weight
|
|
del layer.w2_weight_scale
|
|
del layer.w13_weight
|
|
del layer.w13_weight_scale
|
|
elif self.use_marlin:
|
|
# Marlin processing
|
|
prepare_moe_fp4_layer_for_marlin(layer)
|
|
del layer.g1_alphas
|
|
del layer.g2_alphas
|
|
del layer.w13_input_scale_quant
|
|
del layer.w2_input_scale_quant
|
|
else:
|
|
# Non-TRT-LLM processing (Cutlass or non-flashinfer)
|
|
w13_blockscale_swizzled = swizzle_blockscale(layer.w13_weight_scale)
|
|
layer.w13_weight_scale = Parameter(
|
|
w13_blockscale_swizzled, requires_grad=False
|
|
)
|
|
|
|
w2_blockscale_swizzled = swizzle_blockscale(layer.w2_weight_scale)
|
|
layer.w2_weight_scale = Parameter(
|
|
w2_blockscale_swizzled, requires_grad=False
|
|
)
|
|
layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
|
|
|
|
def get_fused_moe_quant_config(
|
|
self, layer: torch.nn.Module
|
|
) -> FusedMoEQuantConfig | None:
|
|
if (
|
|
self.use_marlin
|
|
or self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
|
|
):
|
|
return None
|
|
|
|
return nvfp4_moe_quant_config(
|
|
w1_scale=layer.w13_weight_scale,
|
|
w2_scale=layer.w2_weight_scale,
|
|
g1_alphas=layer.g1_alphas,
|
|
g2_alphas=layer.g2_alphas,
|
|
a1_gscale=layer.w13_input_scale_quant,
|
|
a2_gscale=layer.w2_input_scale_quant,
|
|
)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: int | None = None,
|
|
num_expert_group: int | None = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: torch.Tensor | None = None,
|
|
custom_routing_function: Callable | None = None,
|
|
scoring_func: str = "softmax",
|
|
routed_scaling_factor: float = 1.0,
|
|
e_score_correction_bias: torch.Tensor | None = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
enable_eplb: bool = False,
|
|
expert_load_view: torch.Tensor | None = None,
|
|
logical_to_physical_map: torch.Tensor | None = None,
|
|
logical_replica_count: torch.Tensor | None = None,
|
|
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
|
|
if enable_eplb:
|
|
raise NotImplementedError(
|
|
"EPLB not supported for `ModelOptNvFp4FusedMoE` yet."
|
|
)
|
|
assert activation == "silu", "Only SiLU activation is supported."
|
|
|
|
if (
|
|
self.allow_flashinfer
|
|
and self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM
|
|
):
|
|
import flashinfer
|
|
|
|
from vllm.model_executor.models.llama4 import Llama4MoE
|
|
|
|
assert self.fused_experts is None
|
|
|
|
a1_gscale = layer.w13_input_scale_quant
|
|
(hidden_states_fp4, hidden_states_scale_linear_fp4) = (
|
|
flashinfer.fp4_quantize(
|
|
x,
|
|
a1_gscale,
|
|
is_sf_swizzled_layout=False,
|
|
)
|
|
)
|
|
use_llama4_routing = (
|
|
custom_routing_function is Llama4MoE.custom_routing_function
|
|
)
|
|
routing_method_type = flashinfer.RoutingMethodType.DeepSeekV3
|
|
if use_llama4_routing:
|
|
routing_method_type = flashinfer.RoutingMethodType.Llama4
|
|
routing_bias = e_score_correction_bias
|
|
if routing_bias is not None:
|
|
routing_bias = routing_bias.to(torch.bfloat16)
|
|
out = flashinfer.fused_moe.trtllm_fp4_block_scale_moe(
|
|
routing_logits=router_logits
|
|
if use_llama4_routing
|
|
else router_logits.to(torch.float32),
|
|
routing_bias=routing_bias,
|
|
hidden_states=hidden_states_fp4,
|
|
hidden_states_scale=hidden_states_scale_linear_fp4.view(
|
|
torch.float8_e4m3fn
|
|
).flatten(),
|
|
gemm1_weights=layer.gemm1_weights_fp4_shuffled.data,
|
|
gemm1_weights_scale=layer.gemm1_scales_fp4_shuffled.data.view(
|
|
torch.float8_e4m3fn
|
|
),
|
|
gemm1_bias=None,
|
|
gemm1_alpha=None,
|
|
gemm1_beta=None,
|
|
gemm1_clamp_limit=None,
|
|
gemm2_weights=layer.gemm2_weights_fp4_shuffled.data,
|
|
gemm2_weights_scale=layer.gemm2_scales_fp4_shuffled.data.view(
|
|
torch.float8_e4m3fn
|
|
),
|
|
gemm2_bias=None,
|
|
output1_scale_scalar=layer.g1_scale_c.data,
|
|
output1_scale_gate_scalar=layer.g1_alphas.data,
|
|
output2_scale_scalar=layer.g2_alphas.data,
|
|
num_experts=global_num_experts,
|
|
top_k=top_k,
|
|
n_group=num_expert_group if num_expert_group is not None else 0,
|
|
topk_group=topk_group if topk_group is not None else 0,
|
|
intermediate_size=layer.intermediate_size_per_partition,
|
|
local_expert_offset=layer.ep_rank * layer.local_num_experts,
|
|
local_num_experts=layer.local_num_experts,
|
|
routed_scaling_factor=None,
|
|
tile_tokens_dim=None,
|
|
routing_method_type=routing_method_type,
|
|
do_finalize=True,
|
|
)[0]
|
|
return out
|
|
|
|
topk_weights, topk_ids, _ = FusedMoE.select_experts(
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
use_grouped_topk=use_grouped_topk,
|
|
top_k=top_k,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
routed_scaling_factor=routed_scaling_factor,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
indices_type=self.topk_indices_dtype,
|
|
)
|
|
|
|
#
|
|
# Note: the order here is important. self.fused_experts can override
|
|
# flashinfer cutlass, cutlass fp4 or fused_experts but not marlin or
|
|
# trtllm.
|
|
#
|
|
if self.use_marlin:
|
|
assert self.fused_experts is None
|
|
return fused_marlin_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
None,
|
|
None,
|
|
layer.w13_weight_scale,
|
|
layer.w2_weight_scale,
|
|
router_logits,
|
|
topk_weights,
|
|
topk_ids,
|
|
global_scale1=layer.w13_weight_scale_2,
|
|
global_scale2=layer.w2_weight_scale_2,
|
|
quant_type_id=scalar_types.float4_e2m1f.id,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
workspace=layer.workspace,
|
|
)
|
|
|
|
elif self.fused_experts is not None:
|
|
assert (
|
|
self.allow_flashinfer
|
|
and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
|
|
)
|
|
|
|
assert is_valid_flashinfer_cutlass_fused_moe(
|
|
x, layer.w13_weight, layer.w2_weight
|
|
), "Flashinfer CUTLASS Fused MoE not applicable!"
|
|
|
|
return self.fused_experts(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=False, # TODO(shuw): fix later, now output is high prec
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
elif (
|
|
self.allow_flashinfer
|
|
and self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS
|
|
):
|
|
from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import ( # noqa: E501
|
|
flashinfer_cutlass_moe_fp4,
|
|
)
|
|
|
|
assert self.moe_quant_config is not None
|
|
|
|
return flashinfer_cutlass_moe_fp4(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
quant_config=self.moe_quant_config,
|
|
inplace=False,
|
|
activation=activation,
|
|
global_num_experts=global_num_experts,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
)
|
|
else:
|
|
# If no modular kernel is provided, use cutlass_moe_fp4 for TP case
|
|
# only (no EP).
|
|
from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
|
|
|
|
assert self.moe_quant_config is not None
|
|
return cutlass_moe_fp4(
|
|
a=x,
|
|
w1_fp4=layer.w13_weight,
|
|
w2_fp4=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
quant_config=self.moe_quant_config,
|
|
expert_map=expert_map,
|
|
apply_router_weight_on_input=apply_router_weight_on_input,
|
|
# TODO: derive from arguments
|
|
m=x.shape[0],
|
|
n=layer.w2_weight.shape[2] * 2,
|
|
k=x.shape[1],
|
|
e=layer.w13_weight.shape[0],
|
|
)
|