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vLLM/vllm/model_executor/layers/quantization/modelopt.py
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Juhi MittalClaude Opus 4.8mgoin
 7a977c0699 [ModelOpt] Redesign the LinearMethod classes using the generic QuantKey-driven method (#49381) 
Signed-off-by: Juhi Mittal <juhim@nvidia.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
2026-09-01 16:28:56 -04:00

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Callable
from dataclasses import dataclass
from enum import Enum
from fnmatch import fnmatch
from typing import TYPE_CHECKING, Any, cast
import torch
from torch.nn.parameter import Parameter
import vllm.envs as envs
from vllm.config import get_current_vllm_config_or_none
from vllm.config.quantization import QuantSpec
from vllm.logger import init_logger
from vllm.model_executor.kernels.linear import (
init_fp8_linear_kernel,
init_mxfp8_linear_kernel,
init_nvfp4_linear_kernel,
)
from vllm.model_executor.layers.attention import Attention, MLAAttention
from vllm.model_executor.layers.fused_moe import (
FusedMoEConfig,
FusedMoEMethodBase,
FusedMoEQuantConfig,
FusedMoeWeightScaleSupported,
RoutedExperts,
SharedExperts,
)
from vllm.model_executor.layers.fused_moe.moe_output import UnfinalizedMoEOutput
from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
Fp8MoeBackend,
convert_to_fp8_moe_kernel_format,
make_fp8_moe_kernel,
make_fp8_moe_quant_config,
select_fp8_moe_backend,
)
from vllm.model_executor.layers.fused_moe.oracle.mxfp8 import (
select_mxfp8_moe_backend,
)
from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
convert_to_nvfp4_moe_kernel_format,
is_global_sf_supported_for_nvfp4_backend,
make_nvfp4_moe_kernel,
make_nvfp4_moe_quant_config,
select_nvfp4_moe_backend,
)
from vllm.model_executor.layers.fusion.quant_activation import (
expose_input_quant_key,
)
from vllm.model_executor.layers.linear import (
LinearBase,
LinearMethodBase,
UnquantizedLinearMethod,
register_weight_loader_v2_supported_method,
)
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig,
QuantizeMethodBase,
)
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
FP8_SCALE_SENTINEL,
process_fp8_input_tensor_strategy_moe,
process_fp8_weight_channel_strategy,
process_fp8_weight_tensor_strategy_moe,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
get_marlin_input_dtype,
)
from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
MXFP8_BLOCK_SIZE,
MXFP8_SCALE_DTYPE,
MXFP8_VALUE_DTYPE,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
FP4_DTYPE,
QuantKey,
is_layer_skipped,
kFp8Dynamic128Sym,
kFp8DynamicTokenSym,
kFp8Static128BlockSym,
kFp8StaticTensorSym,
kFp8StaticTokenSym,
kMxfp8Dynamic,
kMxfp8Static,
kNvfp4Dynamic,
kNvfp4Static,
)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
requantize_with_max_scale,
)
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.parameter import (
BlockQuantScaleParameter,
ChannelQuantScaleParameter,
ModelWeightParameter,
PerTensorScaleParameter,
)
from vllm.model_executor.utils import replace_parameter, set_weight_attrs
from vllm.utils.math_utils import cdiv
if TYPE_CHECKING:
from vllm.model_executor.models.utils import WeightsMapper
logger = init_logger(__name__)
# Single source of truth for the ModelOpt linear algos.
#
# Each entry is (owning config's name, mixed-precision sub-config attribute).
# ``resolve`` turns the algo into a QuantSpec; this table only records which
# config object holds the checkpoint parameters resolve may read -- today just
# group_size, and only for the NVFP4 family.
#
# To add a format: one row here plus one row in resolve(). Every consumer below
# is derived, so nothing else needs editing.
LINEAR_ALGOS: dict[str, tuple[str, str]] = {
# FP8 per-tensor weight + optional static activation scale.
"FP8": ("modelopt", "fp8_config"),
# FP8 per-channel weight scale + per-token activation scale.
"FP8_PER_CHANNEL_PER_TOKEN": ("modelopt", "fp8_config"),
# FP8 128x128 per-block weight (ModelOpt may emit this lowercase).
"FP8_PB_WO": ("modelopt", "fp8_config"),
# NVFP4 W4A4: 4-bit float weights AND 4-bit float activations.
"NVFP4": ("modelopt_fp4", "nvfp4_config"),
# W4A16 NVFP4: 4-bit float weights, fp16/bf16 activations.
"W4A16_NVFP4": ("modelopt_fp4", "w4a16_nvfp4_config"),
# MXFP8: e4m3 weights with per-32-block e8m0 scales.
"MXFP8": ("modelopt_mxfp8", "mxfp8_config"),
}
# MIXED_PRECISION is not a linear algo; it selects a per-layer algo from the
# table above.
QUANT_ALGOS = [*LINEAR_ALGOS, "MIXED_PRECISION"]
def algos_owned_by(config_name: str) -> tuple[str, ...]:
"""The linear algos a given config accepts, for its quant_algo validation."""
return tuple(a for a, (owner, _) in LINEAR_ALGOS.items() if owner == config_name)
class ModelOptKVCacheMethod(BaseKVCacheMethod):
"""
Supports loading kv-cache scaling factors from FP8 or NVFP4 checkpoints.
"""
def __init__(self, quant_config: "ModelOptQuantConfigBase"):
super().__init__(quant_config)
class ModelOptQuantConfigBase(QuantizationConfig):
# ModelOpt quant-algo string, set by each subclass. Fed to resolve() to
# build the QuantSpec for the generic ModelOptLinearMethod. The mixed
# config resolves the algo per-prefix instead and does not set this.
quant_method: str
FusedMoEMethodCls: type = FusedMoEMethodBase
KVCacheMethodCls: type = BaseKVCacheMethod
def __init__(
self,
exclude_modules: list[str],
):
super().__init__()
self.exclude_modules: list[str] = exclude_modules
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 ModelOpt wildcard matching.
The ModelOpt exclude_modules list is a list of wildcards.
"""
if len(self.exclude_modules) == 0:
return False
# First check exact matching with fused layer support
if is_layer_skipped(prefix, self.exclude_modules, self.packed_modules_mapping):
return True
# TODO: This special hard coded logic is not needed for quantized checkpoints
# generated by ModelOpt >= 0.39.0 where they are handled natually by the
# exclude_modules config. But need to keep them for loading quantized
# checkpoints generated by older versions. Then check substring matching
# for patterns not caught by exact match
for exclude_module in self.exclude_modules:
# Skip exact matches already handled above
if exclude_module != prefix and (
exclude_module in prefix
or (
prefix.startswith("language_model.")
and exclude_module in prefix.removeprefix("language_model.")
)
):
return True
# modelopt exclude modules are not simple strings, they are wildcards
for wildcard_pattern in self.exclude_modules:
if fnmatch(prefix, wildcard_pattern):
return True
return False
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> "QuantizeMethodBase | None":
# handle kv-cache first so we can focus only on weight quantization thereafter
if isinstance(layer, (Attention, MLAAttention)):
return self.KVCacheMethodCls(self)
# handle exclusion
if self.is_layer_excluded(prefix):
if isinstance(layer, (LinearBase, ParallelLMHead)):
return UnquantizedLinearMethod()
return None
# TODO: This special hard coded logic is not needed for quantized checkpoints
# generated by ModelOpt >= 0.39.0 where they are handled natually by the
# exclude_modules config. But need to keep them for loading quantized
# checkpoints generated by older versions. Then check substring matching
# for patterns not caught by exact match
if (
"vision_tower" in prefix
or "vision_model" in prefix
or "vit_large_projector" in prefix
):
return UnquantizedLinearMethod()
# now, the layer is quantized, handle it here
if isinstance(layer, (LinearBase, ParallelLMHead)):
return build_linear_method(self, self.quant_method, prefix)
elif isinstance(layer, RoutedExperts):
quant_method = self.FusedMoEMethodCls(
quant_config=self, moe_config=layer.moe_config
)
if getattr(quant_method, "backend", "") == "marlin":
quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
return quant_method
return None
def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
if len(self.exclude_modules) > 0:
# This is a workaround for the weights remapping issue:
# https://github.com/vllm-project/vllm/issues/28072
# Right now, the Nvidia ModelOpt library use just one wildcard pattern:
# module_path*
# It gets applied if the whole tree of modules rooted at module_path
# is not quantized. Here we replace such pattern by 2 patterns that are
# collectively equivalent to the original pattern:
# module_path
# module_path.*
new_exclude_modules = []
for exclude in self.exclude_modules:
if len(exclude) >= 2 and exclude[-1] == "*" and exclude[-2] != ".":
new_exclude_modules.append(exclude[:-1])
new_exclude_modules.append(exclude[:-1] + ".*")
else:
new_exclude_modules.append(exclude)
self.exclude_modules = hf_to_vllm_mapper.apply_list(new_exclude_modules)
@staticmethod
def _extract_modelopt_quant_algo(
hf_quant_cfg: dict[str, Any] | None,
) -> str | None:
"""Extract upper-cased quant_algo from a modelopt config.
Returns the quant_algo string (upper-cased), or None if the config
is not a modelopt config.
"""
if hf_quant_cfg is None:
return None
if not hf_quant_cfg.get("quant_method", "").lower().startswith("modelopt"):
return None
if "quantization" in hf_quant_cfg:
quant_config = hf_quant_cfg["quantization"]
if isinstance(quant_config, dict):
return str(quant_config.get("quant_algo", "")).upper()
return None
return str(hf_quant_cfg.get("quant_algo", "")).upper()
@staticmethod
def get_config_filenames() -> list[str]:
return ["hf_quant_config.json"]
@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,
) -> "ModelOptQuantConfigBase":
raise NotImplementedError("Please implement this function in sub classes")
@classmethod
def from_config(cls, config: dict[str, Any]) -> "ModelOptQuantConfigBase":
# Handle both 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")
# Handle kv_cache_quant_algo with proper type validation
kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")
# Handle group_size with proper type validation
group_size_raw = quant_config.get("group_size")
# "exclude_modules" is the key in the legacy hf_quant_config.json
exclude_modules = quant_config.get("exclude_modules", [])
else:
# Compressed-tensors style format (config.json quantization_config):
# {"quant_algo": "...", "quant_method": "modelopt"}
quant_method = config.get("quant_algo")
# "kv_cache_scheme" (a dict) instead of "kv_cache_quant_algo" (a string).
kv_cache_scheme = config.get("kv_cache_scheme")
if isinstance(kv_cache_scheme, dict) and (
kv_cache_scheme.get("type") == "float"
and kv_cache_scheme.get("num_bits") == 8
):
kv_cache_quant_method = "FP8"
else:
kv_cache_quant_method = None
# "ignore" is the key in config.json
exclude_modules = config.get("ignore", [])
group_size_raw = config.get("group_size")
if not quant_method:
raise ValueError("Missing 'quant_algo' in quantization config")
# Normalize quant_algo for robust matching (ModelOpt may emit lowercase).
quant_method = str(quant_method).upper()
if kv_cache_quant_method is None:
# No KV cache quantization, keep this branch just to have this comment
pass
elif not isinstance(kv_cache_quant_method, str):
raise ValueError(
f"kv_cache_quant_algo must be a string, got "
f"{type(kv_cache_quant_method)}"
)
else:
kv_cache_quant_method = kv_cache_quant_method.upper()
if not isinstance(exclude_modules, list):
raise ValueError(
f"exclude_modules must be a list, got {type(exclude_modules)}"
)
if group_size_raw is None:
group_size = None
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
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."
)
return cls._from_config(
quant_method=quant_method,
kv_cache_quant_method=kv_cache_quant_method,
exclude_modules=exclude_modules,
group_size=group_size,
original_config=config,
)
class ModelOptFp8Config(ModelOptQuantConfigBase):
"""Config class for ModelOpt FP8."""
def __init__(
self,
quant_method: str,
is_checkpoint_fp8_serialized: bool,
kv_cache_quant_method: str | None,
exclude_modules: list[str],
) -> None:
super().__init__(exclude_modules)
self.quant_method = quant_method
self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
self.kv_cache_quant_method = kv_cache_quant_method
# Validate the quant_algo; resolve() maps it to a QuantSpec at dispatch.
supported = algos_owned_by("modelopt")
if self.quant_method not in supported:
raise ValueError(
"Unsupported ModelOpt FP8 quant_algo for vLLM: "
f"{self.quant_method}. Supported: {' / '.join(supported)}."
)
def has_blocked_weights(self) -> bool:
# Gates "+quant_fp8" in VllmConfig. FP8_PB_WO runs the block-scaled
# kernels, whose DeepGEMM path needs UE8M0-packed group scales; without
# the op enabled QuantFP8 falls back to forward_native, which emits
# unpacked fp32 scales and the GEMM fails to launch.
return self.quant_method == "FP8_PB_WO"
def get_name(self) -> QuantizationMethods:
return "modelopt"
def get_supported_act_dtypes(self) -> list[torch.dtype]:
return [torch.bfloat16, torch.half]
@classmethod
def get_min_capability(cls) -> int:
return 80
@classmethod
def override_quantization_method(
cls, hf_quant_cfg, user_quant, hf_config=None
) -> QuantizationMethods | None:
algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
if algo is not None and algo == "FP8":
return "modelopt"
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,
) -> "ModelOptFp8Config":
is_checkpoint_fp8_serialized = "FP8" in quant_method
return cls(
quant_method,
is_checkpoint_fp8_serialized,
kv_cache_quant_method,
exclude_modules,
)
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 create_weights(
self,
layer: RoutedExperts,
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: RoutedExperts,
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)
assert self.experts_cls is not None
self.moe_kernel = make_fp8_moe_kernel(
moe_quant_config=self.moe_quant_config,
moe_config=self.moe,
fp8_backend=self.fp8_backend,
experts_cls=self.experts_cls,
routing_tables=layer._expert_routing_tables(),
)
def process_weights_after_loading(self, layer: RoutedExperts) -> 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,
layer.moe_config.moe_parallel_config.enable_eplb,
)
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: RoutedExperts) -> FusedMoEQuantConfig:
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,
swiglu_limit=getattr(layer, "swiglu_limit", None),
layer=layer,
)
def apply_monolithic(
self,
layer: RoutedExperts,
x: torch.Tensor,
router_logits: torch.Tensor,
input_ids: torch.Tensor | None = None,
) -> torch.Tensor:
assert self.is_monolithic
assert self.moe_kernel is not None
return self.moe_kernel.apply_monolithic(
x,
layer.w13_weight,
layer.w2_weight,
router_logits,
activation=layer.activation,
global_num_experts=layer.global_num_experts,
expert_map=layer.expert_map,
apply_router_weight_on_input=layer.apply_router_weight_on_input,
num_expert_group=layer.num_expert_group,
topk_group=layer.topk_group,
e_score_correction_bias=layer.e_score_correction_bias,
routed_scaling_factor=layer.routed_scaling_factor,
)
def apply(
self,
layer: RoutedExperts,
x: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
shared_experts: SharedExperts | None,
shared_experts_input: torch.Tensor | None,
) -> torch.Tensor:
assert not self.is_monolithic
assert self.moe_kernel is not None
return self.moe_kernel.apply(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
activation=layer.activation,
global_num_experts=layer.global_num_experts,
expert_map=layer.expert_map,
apply_router_weight_on_input=layer.apply_router_weight_on_input,
shared_experts=shared_experts,
shared_experts_input=shared_experts_input,
)
ModelOptFp8Config.FusedMoEMethodCls = ModelOptFp8MoEMethod
ModelOptFp8Config.KVCacheMethodCls = ModelOptKVCacheMethod
class ModelOptNvFp4Config(ModelOptQuantConfigBase):
"""Config class for ModelOpt FP4."""
def __init__(
self,
quant_method: str = "NVFP4",
is_checkpoint_nvfp4_serialized: bool = False,
kv_cache_quant_algo: str | None = None,
exclude_modules: list[str] | None = None,
group_size: int = 16,
) -> None:
if exclude_modules is None:
exclude_modules = []
super().__init__(exclude_modules)
self.quant_method = quant_method
self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
if is_checkpoint_nvfp4_serialized:
self.group_size = group_size
self.kv_cache_quant_algo = kv_cache_quant_algo
# Validate the quant_algo; resolve() maps it to a QuantSpec at dispatch.
# NVFP4 -> W4A4: cutlass NVFP4 GEMM with input quantization
# W4A16_NVFP4 -> W4A16: FP4 Marlin GEMM with bf16/fp16 activations
supported = algos_owned_by("modelopt_fp4")
if quant_method not in supported:
raise ValueError(
f"Unsupported ModelOpt NVFP4 quant_algo: {quant_method}. "
f"Supported: {' / '.join(supported)}."
)
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, hf_config=None
) -> QuantizationMethods | None:
algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
if algo is not None and ("NVFP4" in algo or "FP4" in algo):
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
# A checkpoint can declare quant_algo "NVFP4" yet quantize weights
# only (input_activations is null in every config group). The W4A4
# MoE path then folds an uninitialized input_scale that was never
# loaded and can zero every expert, so route it through W4A16.
if quant_method == "NVFP4":
config_groups = original_config.get("config_groups")
if (
isinstance(config_groups, dict)
and config_groups
and all(
isinstance(group, dict) and group.get("input_activations") is None
for group in config_groups.values()
)
):
quant_method = "W4A16_NVFP4"
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(
quant_method,
is_checkpoint_nvfp4_serialized,
kv_cache_quant_method,
exclude_modules,
group_size,
)
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
# W4A16 mode fires for W4A16_NVFP4 on-disk checkpoints. With
# activation_key=None every W4A4 backend's _supports_quant_scheme
# rejects itself (they all require (kNvfp4Static, kNvfp4Dynamic)
# exactly); only Marlin survives. Marlin's MoE path drops
# activation scales in convert_to_nvfp4_moe_kernel_format, so no
# other change is needed.
self.use_a16 = quant_config.quant_method == "W4A16_NVFP4"
self.nvfp4_backend, self.experts_cls = select_nvfp4_moe_backend(
config=self.moe,
weight_key=kNvfp4Static,
activation_key=None if self.use_a16 else kNvfp4Dynamic,
)
self.use_global_sf = is_global_sf_supported_for_nvfp4_backend(
self.nvfp4_backend
)
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: RoutedExperts,
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: RoutedExperts) -> 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,
use_a16=self.use_a16,
)
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.
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
assert self.experts_cls is not None
self.moe_kernel = make_nvfp4_moe_kernel(
moe_quant_config=self.moe_quant_config,
moe_config=self.moe,
experts_cls=self.experts_cls,
backend=self.nvfp4_backend,
routing_tables=layer._expert_routing_tables(),
)
self.moe_kernel.fused_experts.process_weights_after_loading(layer)
def get_fused_moe_quant_config(self, layer: RoutedExperts) -> FusedMoEQuantConfig:
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,
swiglu_limit=getattr(layer, "swiglu_limit", None),
swiglu_alpha=getattr(layer, "swiglu_alpha", None),
swiglu_beta=getattr(layer, "swiglu_beta", None),
layer=layer,
use_a16=self.use_a16,
)
@property
def supports_eplb(self) -> bool:
return True
def apply_monolithic(
self,
layer: RoutedExperts,
x: torch.Tensor,
router_logits: torch.Tensor,
input_ids: torch.Tensor | None = None,
) -> torch.Tensor | UnfinalizedMoEOutput:
assert self.is_monolithic
assert self.moe_kernel is not None
return self.moe_kernel.apply_monolithic(
x,
layer.w13_weight,
layer.w2_weight,
router_logits,
activation=layer.activation,
global_num_experts=layer.global_num_experts,
expert_map=layer.expert_map,
apply_router_weight_on_input=layer.apply_router_weight_on_input,
num_expert_group=layer.num_expert_group,
topk_group=layer.topk_group,
e_score_correction_bias=layer.e_score_correction_bias,
routed_scaling_factor=layer.routed_scaling_factor,
)
def apply(
self,
layer: RoutedExperts,
x: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
shared_experts: SharedExperts | None,
shared_experts_input: torch.Tensor | None,
) -> torch.Tensor:
assert not self.is_monolithic
assert self.moe_kernel is not None
return self.moe_kernel.apply(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
activation=layer.activation,
global_num_experts=layer.global_num_experts,
expert_map=layer.expert_map,
apply_router_weight_on_input=layer.apply_router_weight_on_input,
shared_experts=shared_experts,
shared_experts_input=shared_experts_input,
)
ModelOptNvFp4Config.FusedMoEMethodCls = ModelOptNvFp4FusedMoE
ModelOptNvFp4Config.KVCacheMethodCls = ModelOptKVCacheMethod
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.quant_method = "MXFP8"
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."
)
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:
# Marlin kernel supports MXFP8 on SM80+
return 80
@classmethod
def override_quantization_method(
cls, hf_quant_cfg, user_quant, hf_config=None
) -> QuantizationMethods | None:
algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
if algo is not None and "MXFP8" in algo:
return "modelopt_mxfp8"
return None
@classmethod
def from_config(cls, config: dict[str, Any]) -> "ModelOptMxFp8Config":
# MiniMax-style checkpoints tag `quant_method: "mxfp8"` + `ignored_layers`
# (same on-disk format as ModelOpt MXFP8); normalize to the ModelOpt
# schema and reuse the shared parser.
if "quantization" not in config and not config.get("quant_algo"):
config = {
"quant_method": "modelopt",
"quantization": {
"quant_algo": "MXFP8",
"kv_cache_quant_algo": config.get("kv_cache_quant_algo"),
"exclude_modules": config.get("ignored_layers", []) or [],
},
}
return cast("ModelOptMxFp8Config", super().from_config(config))
@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 ModelOptMxFp8FusedMoE(FusedMoEMethodBase):
"""FlashInfer TRTLLM MXFP8 block-scale MoE for ModelOpt checkpoints."""
def __init__(
self,
quant_config: ModelOptMxFp8Config,
moe_config: FusedMoEConfig,
) -> None:
super().__init__(moe_config)
self.weight_block_size = [1, MXFP8_BLOCK_SIZE]
self.quant_config = quant_config
assert self.quant_config.is_checkpoint_mxfp8_serialized
self.mxfp8_backend, self.experts_cls = select_mxfp8_moe_backend(config=self.moe)
def create_weights(
self,
layer: RoutedExperts,
num_experts: int,
hidden_size: int,
intermediate_size_per_partition: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
assert layer.intermediate_size_per_partition == intermediate_size_per_partition
assert layer.hidden_size == hidden_size
layer.orig_dtype = params_dtype
if hidden_size % MXFP8_BLOCK_SIZE != 0:
raise ValueError(
f"MXFP8 MoE requires hidden_size divisible by {MXFP8_BLOCK_SIZE}, "
f"got {hidden_size}."
)
if intermediate_size_per_partition % MXFP8_BLOCK_SIZE != 0:
raise ValueError(
"MXFP8 MoE requires intermediate_size_per_partition divisible by "
f"{MXFP8_BLOCK_SIZE}, got {intermediate_size_per_partition}."
)
layer.num_experts = num_experts
weight_loader = extra_weight_attrs.get("weight_loader")
w13_num_shards = 2 if self.moe.is_act_and_mul else 1
# GEMM 1 weights: [E, (2I or I), H]
w13_weight = ModelWeightParameter(
data=torch.empty(
num_experts,
w13_num_shards * intermediate_size_per_partition,
hidden_size,
dtype=MXFP8_VALUE_DTYPE,
),
input_dim=2,
output_dim=1,
weight_loader=weight_loader,
)
layer.register_parameter("w13_weight", w13_weight)
# GEMM 2 weights: [E, H, I]
w2_weight = ModelWeightParameter(
data=torch.empty(
num_experts,
hidden_size,
intermediate_size_per_partition,
dtype=MXFP8_VALUE_DTYPE,
),
input_dim=2,
output_dim=1,
weight_loader=weight_loader,
)
layer.register_parameter("w2_weight", w2_weight)
# Per-block (K=32) E8M0 scales.
w13_weight_scale = ModelWeightParameter(
data=torch.empty(
num_experts,
w13_num_shards * intermediate_size_per_partition,
hidden_size // MXFP8_BLOCK_SIZE,
dtype=MXFP8_SCALE_DTYPE,
),
input_dim=2,
output_dim=1,
weight_loader=weight_loader,
)
layer.register_parameter("w13_weight_scale", w13_weight_scale)
w2_weight_scale = ModelWeightParameter(
data=torch.empty(
num_experts,
hidden_size,
intermediate_size_per_partition // MXFP8_BLOCK_SIZE,
dtype=MXFP8_SCALE_DTYPE,
),
input_dim=2,
output_dim=1,
weight_loader=weight_loader,
)
layer.register_parameter("w2_weight_scale", w2_weight_scale)
# Ensure the generic MoE weight-loader treats these as block scales.
set_weight_attrs(
layer.w13_weight_scale,
{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value},
)
set_weight_attrs(
layer.w2_weight_scale,
{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value},
)
@staticmethod
def _check_weight_dtypes(layer: torch.nn.Module) -> None:
"""Validate weight and scale dtypes before processing."""
expected = {
"w13_weight": MXFP8_VALUE_DTYPE,
"w2_weight": MXFP8_VALUE_DTYPE,
"w13_weight_scale": MXFP8_SCALE_DTYPE,
"w2_weight_scale": MXFP8_SCALE_DTYPE,
}
for name, expected_dtype in expected.items():
actual = getattr(layer, name).dtype
if actual != expected_dtype:
raise ValueError(
f"Expected {name} dtype {expected_dtype}, got {actual}."
)
def _dequant_mxfp8_weights_to_bf16(self, layer: RoutedExperts) -> None:
"""One-time MXFP8->BF16 weight dequant for the emulation path.
On devices without a native MXFP8 MoE kernel (e.g. gfx942 / MI300),
``Mxfp8EmulationTritonExperts`` otherwise dequantizes every expert
weight to BF16 on *every* forward step -- the dominant cost (conc1
~1.3 tok/s). Doing the dequant once here and replacing the MXFP8
parameters with BF16 makes the MoE run exactly like a plain BF16
checkpoint (full precision, no per-step dequant); SwiGLU-OAI is still
applied by the experts' ``activation()`` override. The MXFP8 weights
are freed by ``replace_parameter`` (BF16 is 2x their size; the small
E8M0 scale tensors are left in place, unused).
"""
from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
dequant_mxfp8_to_bf16,
)
target_dtype = getattr(layer, "orig_dtype", torch.bfloat16)
num_experts = layer.w13_weight.shape[0]
# dequant_mxfp8_to_bf16 handles arbitrary leading dims (*x.shape[:-1]),
# so dequant the whole [E, N, K] weight in one vectorized call.
w13_bf16 = dequant_mxfp8_to_bf16(layer.w13_weight, layer.w13_weight_scale).to(
target_dtype
)
w2_bf16 = dequant_mxfp8_to_bf16(layer.w2_weight, layer.w2_weight_scale).to(
target_dtype
)
replace_parameter(layer, "w13_weight", w13_bf16)
replace_parameter(layer, "w2_weight", w2_bf16)
logger.info_once(
"MXFP8->BF16 load-time dequant complete (%d experts/layer); MoE "
"now runs in BF16 with no per-step dequant.",
num_experts,
)
def process_weights_after_loading(self, layer: RoutedExperts) -> None:
# TODO(bnell): why is this required only for mxfp8?
if getattr(layer, "_already_called_process_weights_after_loading", False):
return
layer._already_called_process_weights_after_loading = True
self._check_weight_dtypes(layer)
layer.weight_block_size = self.weight_block_size
w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
fp8_backend=self.mxfp8_backend,
layer=layer,
w13=layer.w13_weight,
w2=layer.w2_weight,
w13_scale=layer.w13_weight_scale,
w2_scale=layer.w2_weight_scale,
w13_input_scale=None,
w2_input_scale=None,
)
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)
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
assert self.moe_quant_config is not None
assert self.experts_cls is not None
self.moe_kernel = make_fp8_moe_kernel(
moe_quant_config=self.moe_quant_config,
moe_config=self.moe,
fp8_backend=self.mxfp8_backend,
experts_cls=self.experts_cls,
routing_tables=layer._expert_routing_tables(),
)
# No native MXFP8 MoE kernel on this device (e.g. gfx942): the emulation
# experts would dequant MXFP8->BF16 every forward step. Convert the
# weights to BF16 once, here, so the MoE runs like a BF16 checkpoint.
# Opt out (VLLM_MXFP8_EMULATION_DEQUANT_AT_LOAD=0) to keep the 1-byte
# MXFP8 weights and dequant per-step (~half the memory, much slower).
if (
self.mxfp8_backend == Fp8MoeBackend.EMULATION
and envs.VLLM_MXFP8_EMULATION_DEQUANT_AT_LOAD
):
self._dequant_mxfp8_weights_to_bf16(layer)
def get_fused_moe_quant_config(
self, layer: RoutedExperts
) -> FusedMoEQuantConfig | None:
return make_fp8_moe_quant_config(
fp8_backend=self.mxfp8_backend,
w1_scale=layer.w13_weight_scale,
w2_scale=layer.w2_weight_scale,
a1_scale=None,
a2_scale=None,
block_shape=self.weight_block_size,
swiglu_limit=getattr(layer, "swiglu_limit", None),
gemm1_alpha=getattr(layer, "swiglu_alpha", None),
gemm1_beta=getattr(layer, "swiglu_beta", None),
layer=layer,
)
def apply_monolithic(
self,
layer: RoutedExperts,
x: torch.Tensor,
router_logits: torch.Tensor,
input_ids: torch.Tensor | None = None,
) -> torch.Tensor:
assert self.is_monolithic
assert self.moe_kernel is not None
return self.moe_kernel.apply_monolithic(
x,
layer.w13_weight,
layer.w2_weight,
router_logits,
activation=layer.activation,
global_num_experts=layer.global_num_experts,
expert_map=layer.expert_map,
apply_router_weight_on_input=layer.apply_router_weight_on_input,
num_expert_group=layer.num_expert_group,
topk_group=layer.topk_group,
e_score_correction_bias=layer.e_score_correction_bias,
routed_scaling_factor=layer.routed_scaling_factor,
)
def apply(
self,
layer: RoutedExperts,
x: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
shared_experts: SharedExperts | None,
shared_experts_input: torch.Tensor | None,
) -> torch.Tensor:
assert not self.is_monolithic
assert self.moe_kernel is not None
return self.moe_kernel.apply(
x,
layer.w13_weight,
layer.w2_weight,
topk_weights,
topk_ids,
activation=layer.activation,
global_num_experts=layer.global_num_experts,
expert_map=layer.expert_map,
apply_router_weight_on_input=layer.apply_router_weight_on_input,
shared_experts=shared_experts,
shared_experts_input=shared_experts_input,
)
# Register the method classes for ModelOptMxFp8Config
ModelOptMxFp8Config.FusedMoEMethodCls = ModelOptMxFp8FusedMoE
ModelOptMxFp8Config.KVCacheMethodCls = ModelOptKVCacheMethod
class ModelOptMixedPrecisionConfig(ModelOptQuantConfigBase):
"""Config class for ModelOpt MIXED_PRECISION.
Supports checkpoints where different layers use different quantization
algorithms (e.g., FP8 for dense layers and NVFP4 for MoE experts).
The per-layer algorithm is specified in the ``quantized_layers`` dict
inside ``config.json``'s ``quantization_config`` (preferred) or the
legacy ``hf_quant_config.json``.
"""
def __init__(
self,
kv_cache_quant_method: str | None,
exclude_modules: list[str],
quantized_layers: dict[str, dict[str, Any]],
fp8_config: ModelOptFp8Config,
nvfp4_config: ModelOptNvFp4Config,
w4a16_nvfp4_config: ModelOptNvFp4Config,
mxfp8_config: ModelOptMxFp8Config,
) -> None:
super().__init__(exclude_modules)
self.kv_cache_quant_method = kv_cache_quant_method
self.quantized_layers = quantized_layers
self.fp8_config = fp8_config
self.nvfp4_config = nvfp4_config
self.w4a16_nvfp4_config = w4a16_nvfp4_config
self.mxfp8_config = mxfp8_config
def has_blocked_weights(self) -> bool:
# Same gate as ModelOptFp8Config.has_blocked_weights, resolved per
# layer: "+quant_fp8" must be on as soon as any layer is block-scaled.
return any(
info.get("quant_algo", "").upper() == "FP8_PB_WO"
for info in self.quantized_layers.values()
)
def get_name(self) -> QuantizationMethods:
return "modelopt_mixed"
def get_supported_act_dtypes(self) -> list[torch.dtype]:
return [torch.bfloat16, torch.half]
@classmethod
def get_min_capability(cls) -> int:
# Turing and up (SM75+): NVFP4 routed experts run via Marlin W4A16
# (SM75+), FP8 weight-only dense via MarlinFP8 (cc>=7.5), and FP8 MoE,
# if present, via Marlin (TritonExperts gates its FP8 schemes behind
# supports_fp8(), cc>=89). None of these paths require native FP8 tensor
# cores, so SM75 is sufficient. Validated end-to-end on a Tesla T4
# (SM75) and A100 (SM80). Pairs with the FlashInfer attention SM80
# lower bound so SM75 auto-selects a supported attention backend.
return 75
@classmethod
def override_quantization_method(
cls, hf_quant_cfg, user_quant, hf_config=None
) -> QuantizationMethods | None:
algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
if algo is not None and algo == "MIXED_PRECISION":
return "modelopt_mixed"
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,
) -> "ModelOptMixedPrecisionConfig":
if "quantization" in original_config:
quantized_layers = original_config["quantization"].get(
"quantized_layers", {}
)
else:
quantized_layers = original_config.get("quantized_layers", {})
if not quantized_layers:
raise ValueError(
"MIXED_PRECISION quant_algo requires a non-empty "
"'quantized_layers' mapping in the quantization config."
)
# Determine group_size from the first NVFP4-family entry if not
# provided. Both NVFP4 (W4A4) and W4A16_NVFP4 share the same packing
# + group-size convention; either entry resolves the value.
if group_size is None:
for layer_info in quantized_layers.values():
if layer_info.get("quant_algo", "").upper() in (
"NVFP4",
"W4A16_NVFP4",
):
group_size = layer_info.get("group_size", 16)
break
if group_size is None:
group_size = 16
fp8_config = ModelOptFp8Config(
quant_method="FP8",
is_checkpoint_fp8_serialized=True,
kv_cache_quant_method=kv_cache_quant_method,
exclude_modules=[],
)
nvfp4_config = ModelOptNvFp4Config(
is_checkpoint_nvfp4_serialized=True,
kv_cache_quant_algo=kv_cache_quant_method,
exclude_modules=[],
group_size=group_size,
)
# Sibling config for layers that declare quant_algo: "W4A16_NVFP4".
# get_quant_method resolves this sub-config to the (kNvfp4Static, None)
# QuantSpec (W4A16) for the generic ModelOptLinearMethod. The MoE side
# reads quant_config.quant_method == "W4A16_NVFP4" to set use_a16 →
# Marlin backend in ModelOptNvFp4FusedMoE.__init__.
w4a16_nvfp4_config = ModelOptNvFp4Config(
quant_method="W4A16_NVFP4",
is_checkpoint_nvfp4_serialized=True,
kv_cache_quant_algo=kv_cache_quant_method,
exclude_modules=[],
group_size=group_size,
)
mxfp8_config = ModelOptMxFp8Config(
is_checkpoint_mxfp8_serialized=True,
kv_cache_quant_algo=kv_cache_quant_method,
exclude_modules=[],
)
return cls(
kv_cache_quant_method=kv_cache_quant_method,
exclude_modules=exclude_modules,
quantized_layers=quantized_layers,
fp8_config=fp8_config,
nvfp4_config=nvfp4_config,
w4a16_nvfp4_config=w4a16_nvfp4_config,
mxfp8_config=mxfp8_config,
)
def _resolve_quant_algo(self, prefix: str) -> str | None:
"""Look up the quant_algo for a vLLM-side layer prefix.
Tries three strategies in order:
1. Direct lookup in ``quantized_layers``.
2. Packed/fused-layer lookup (unfuse via ``packed_modules_mapping``).
3. Prefix-based lookup for RoutedExperts (any child key starts with
``prefix + "."``).
Returns the upper-cased quant_algo string, or *None* if the prefix
is not found.
"""
# 1. Direct lookup
for candidate in self._quantized_layer_prefix_candidates(prefix):
if candidate in self.quantized_layers:
return self.quantized_layers[candidate]["quant_algo"].upper()
# 2. Packed / fused layer lookup
proj_name = prefix.rsplit(".", 1)[-1]
if self.packed_modules_mapping and proj_name in self.packed_modules_mapping:
algos: set[str] = set()
base = prefix.rsplit(".", 1)[0]
for base_candidate in self._quantized_layer_prefix_candidates(base):
for shard_name in self.packed_modules_mapping[proj_name]:
shard_prefix = f"{base_candidate}.{shard_name}"
if shard_prefix in self.quantized_layers:
algos.add(
self.quantized_layers[shard_prefix]["quant_algo"].upper()
)
if len(algos) == 1:
return algos.pop()
if len(algos) > 1:
raise ValueError(
f"Mixed quant_algo within fused layer {prefix}: "
f"{algos}. All shards must use the same quantization."
)
# 3. Prefix-based lookup (for RoutedExperts / parent modules)
for candidate in self._quantized_layer_prefix_candidates(prefix):
prefix_dot = candidate + "."
for key, info in self.quantized_layers.items():
if key.startswith(prefix_dot):
return info["quant_algo"].upper()
# RoutedExperts expert prefix is e.g. "...moe.experts", while ModelOpt's
# quantized_layers entries use "...moe.gate_proj" / "...moe.up_proj".
if prefix.endswith(".experts"):
parent_dot = prefix.rsplit(".experts", 1)[0] + "."
for key, info in self.quantized_layers.items():
if key.startswith(parent_dot):
return info["quant_algo"].upper()
# 4. Parent-prefix fallback for fused projections whose config lists
# shard names instead of vLLM's packed module name.
fused_projection_shards = {
"qkv_proj": ("q_proj", "k_proj", "v_proj"),
"gate_up_proj": ("gate_proj", "up_proj"),
}
shard_names = fused_projection_shards.get(proj_name)
if shard_names is not None:
for candidate in self._quantized_layer_prefix_candidates(prefix):
parent_dot = candidate.rsplit(".", 1)[0] + "."
shard_algos: set[str] = set()
for shard_name in shard_names:
shard_prefix = f"{parent_dot}{shard_name}"
if shard_prefix in self.quantized_layers:
algo = self.quantized_layers[shard_prefix]["quant_algo"].upper()
shard_algos.add(algo)
if len(shard_algos) == 1:
return shard_algos.pop()
if len(shard_algos) > 1:
raise ValueError(
f"Mixed quant_algo within fused layer {prefix}: "
f"{shard_algos}. All shards must use the same quantization."
)
return None
@staticmethod
def _quantized_layer_prefix_candidates(prefix: str) -> tuple[str, ...]:
candidates = [prefix]
if prefix.endswith(".lm_head"):
candidates.append("lm_head")
if prefix.startswith("language_model.model."):
candidates.append(
"model.language_model." + prefix[len("language_model.model.") :]
)
elif prefix.startswith("model.language_model."):
candidates.append(
"language_model.model." + prefix[len("model.language_model.") :]
)
return tuple(dict.fromkeys(candidates))
def get_quant_method(
self, layer: torch.nn.Module, prefix: str
) -> "QuantizeMethodBase | None":
"""Return quantize-method based on layer."""
# KV-cache quantization
if isinstance(layer, Attention):
if self.kv_cache_quant_method:
return ModelOptKVCacheMethod(self)
return None
# Excluded layers
if self.is_layer_excluded(prefix):
if isinstance(layer, (LinearBase, ParallelLMHead)):
return UnquantizedLinearMethod()
return None
quant_algo = self._resolve_quant_algo(prefix)
if isinstance(layer, (LinearBase, ParallelLMHead)):
# Per-prefix algo -> its sub-config, then the generic linear method.
# resolve() reads group_size off whichever sub-config it is handed.
# Read-only — never write back, or a linear-only change leaks into
# the MoE method that shares the sub-config.
if quant_algo is None or quant_algo not in LINEAR_ALGOS:
# Layer not in quantized_layers — leave unquantized
return UnquantizedLinearMethod()
_, subcfg_attr = LINEAR_ALGOS[quant_algo]
return build_linear_method(getattr(self, subcfg_attr), quant_algo, prefix)
if isinstance(layer, RoutedExperts):
if quant_algo == "FP8":
return ModelOptFp8MoEMethod(
quant_config=self.fp8_config,
moe_config=layer.moe_config,
)
if quant_algo == "NVFP4":
return ModelOptNvFp4FusedMoE(
quant_config=self.nvfp4_config,
moe_config=layer.moe_config,
)
if quant_algo == "W4A16_NVFP4":
return ModelOptNvFp4FusedMoE(
quant_config=self.w4a16_nvfp4_config,
moe_config=layer.moe_config,
)
if quant_algo == "MXFP8":
return ModelOptMxFp8FusedMoE(
quant_config=self.mxfp8_config,
moe_config=layer.moe_config,
)
return None
return None
def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
super().apply_vllm_mapper(hf_to_vllm_mapper)
if self.quantized_layers:
self.quantized_layers = hf_to_vllm_mapper.apply_dict(self.quantized_layers)
# ===========================================================================
# Generic QuantKey-driven linear method
#
# ``ModelOptLinearMethod`` serves every ModelOpt linear format. It composes a
# per-QuantKey weight scheme + activation scheme (the ``QuantSpec`` pair that
# ``resolve`` produces from the checkpoint's quant_algo), runs a fixed
# create/process lifecycle, selects the kernel from the pair, and applies.
#
# Adding a format:
# * Composes as a (weight, activation) key pair -> add a QuantKeyScheme per
# new key to SCHEME_FOR, plus a resolve() row returning the QuantSpec. No
# new method class; this is how every current format is built.
# * Needs format-wide state but the same lifecycle -> also return a
# FormatScheme subclass from that resolve() row (extra_weights / pre_process
# / post_process hooks).
# * Needs a different lifecycle entirely -> write a LinearMethodBase and
# register it in LINEAR_METHOD_BUILDERS by algo.
# In all cases accept the algo in the owning config's quant_method validation,
# so get_quant_method can dispatch it.
# ===========================================================================
class Role(Enum):
WEIGHT = "weight"
ACT = "activation"
WEIGHT = Role.WEIGHT
ACT = Role.ACT
@dataclass(frozen=True)
class CkptCtx:
"""Per-checkpoint facts a QuantKey cannot carry."""
group_size: int | None = None
@dataclass(frozen=True)
class RuntimeDtypes:
"""Runtime/model dtypes kernels need — format-agnostic."""
input_dtype: torch.dtype
out_dtype: torch.dtype
marlin_input_dtype: torch.dtype | None = None
@dataclass(frozen=True)
class Shapes:
"""Layer geometry from create_weights args."""
output_partition_sizes: list[int]
input_size_per_partition: int
params_dtype: torch.dtype
@property
def output_size_per_partition(self) -> int:
return sum(self.output_partition_sizes)
@property
def num_partitions(self) -> int:
return len(self.output_partition_sizes)
class QuantKeyScheme:
"""One scheme per QuantKey. Selected by key *content*; the base supplies the
``role`` from the slot it fills the key into (wkey->WEIGHT, akey->ACT).
Every scheme branches on ``role`` explicitly and ``reject``s any role it has
not validated — never falling through to the wrong role's registration,
which would allocate the wrong parameters and produce garbage silently.
"""
key: QuantKey
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
raise NotImplementedError
def process(self, layer, role) -> None:
pass
@staticmethod
def reject(role) -> None:
raise NotImplementedError(
f"role {role!r} not validated for this QuantKey scheme"
)
@staticmethod
def register_params(layer, name, shape, dtype, cls, wl, *, init=None, **dims):
p = cls(data=torch.empty(shape, dtype=dtype), weight_loader=wl, **dims)
if init is not None:
p.data.fill_(init)
layer.register_parameter(name, p)
class KNvfp4Static(QuantKeyScheme):
"""NVFP4 weight scheme (W4A4 and W4A16 share it). Weight-role only today."""
key = kNvfp4Static
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is not WEIGHT:
self.reject(role)
if shapes.input_size_per_partition % 16 != 0:
raise ValueError(
"Unsupported model when in features size is not multiple of 16"
)
# Packed NVFP4 weight: 2 fp4 items per byte along the input dim.
self.register_params(
layer,
"weight",
(shapes.output_size_per_partition, shapes.input_size_per_partition // 2),
torch.uint8,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
# Per-tensor global weight scale.
self.register_params(
layer,
"weight_scale_2",
(shapes.num_partitions,),
torch.float32,
PerTensorScaleParameter,
wl,
)
# Per-block (group_size) weight scale.
self.register_params(
layer,
"weight_scale",
(
shapes.output_size_per_partition,
shapes.input_size_per_partition // ctx.group_size,
),
torch.float8_e4m3fn,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
def process(self, layer, role) -> None:
if role is not WEIGHT:
self.reject(role)
if torch.unique(layer.weight_scale_2).numel() != 1:
logger.warning_once(
"In NVFP4 linear, the global weight scale differs across "
"parallel layers (e.g. q_proj, k_proj, v_proj). This will "
"likely reduce accuracy. Consider a checkpoint with the same "
"global NVFP4 scale for fused weights."
)
# Raw max, no reciprocation — Marlin/cutlass want ModelOpt's amax/2688.
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
class KNvfp4Dynamic(QuantKeyScheme):
"""NVFP4 activation scheme (W4A4). Has a static global input scale on disk;
the per-group scale is computed at runtime inside the kernel."""
key = kNvfp4Dynamic
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is not ACT:
self.reject(role)
self.register_params(
layer,
"input_scale",
(shapes.num_partitions,),
torch.float32,
PerTensorScaleParameter,
wl,
)
def process(self, layer, role) -> None:
if role is not ACT:
self.reject(role)
if torch.unique(layer.input_scale).numel() != 1:
logger.warning_once(
"In NVFP4 linear, the global input scale differs across "
"parallel layers (e.g. q_proj, k_proj, v_proj). This will "
"likely reduce accuracy. Consider a checkpoint with the same "
"global NVFP4 scale for fused weights."
)
input_global_scale = layer.input_scale.max().to(torch.float32)
layer.input_global_scale = Parameter(input_global_scale, requires_grad=False)
layer.input_global_scale_inv = Parameter(
(1.0 / layer.input_global_scale).to(torch.float32),
requires_grad=False,
)
del layer.input_scale
class KFp8StaticTensor(QuantKeyScheme):
"""Plain per-tensor static FP8 — bivalent: serves BOTH the weight slot and
the activation slot (W8A8). One key in both QuantSpec slots."""
key = kFp8StaticTensorSym
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is WEIGHT:
self.register_params(
layer,
"weight",
(shapes.output_size_per_partition, shapes.input_size_per_partition),
torch.float8_e4m3fn,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
layer.orig_dtype = shapes.params_dtype
self.register_params(
layer,
"weight_scale",
(shapes.num_partitions,),
torch.float32,
PerTensorScaleParameter,
wl,
init=FP8_SCALE_SENTINEL,
)
elif role is ACT:
self.register_params(
layer,
"input_scale",
(shapes.num_partitions,),
torch.float32,
PerTensorScaleParameter,
wl,
init=FP8_SCALE_SENTINEL,
)
else:
self.reject(role)
def process(self, layer, role) -> None:
if role is WEIGHT:
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
)
# Transposed here rather than in the kernel: ModelOpt maps each
# fp8 key to exactly one kernel, so the layout is key-determined.
layer.weight = Parameter(weight.t(), requires_grad=False)
# The plain Parameter drops ModelWeightParameter's dim attributes;
# restore them for the transposed layout, which Humming reads.
layer.weight.input_dim = 0
layer.weight.output_dim = 1
layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
elif role is ACT:
if torch.unique(layer.input_scale).numel() != 1:
logger.warning_once(
"In FP8 linear, the static input scale differs across "
"parallel layers (e.g. q_proj, k_proj, v_proj). Collapsing "
"them to the max will likely reduce accuracy. Consider a "
"checkpoint with the same input scale for fused weights."
)
layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
else:
self.reject(role)
class KFp8StaticChannel(QuantKeyScheme):
"""Per-channel static FP8 weight (the 'PcPt' weight). Weight-role only —
there is no static per-channel *activation* today."""
key = kFp8StaticTokenSym
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is not WEIGHT:
self.reject(role)
self.register_params(
layer,
"weight",
(shapes.output_size_per_partition, shapes.input_size_per_partition),
torch.float8_e4m3fn,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
self.register_params(
layer,
"weight_scale",
(shapes.output_size_per_partition,),
torch.float32,
ChannelQuantScaleParameter,
wl,
output_dim=0,
init=FP8_SCALE_SENTINEL,
)
def process(self, layer, role) -> None:
if role is not WEIGHT:
self.reject(role)
weight, weight_scale, _ = process_fp8_weight_channel_strategy(
layer.weight, layer.weight_scale.data
)
layer.weight = Parameter(weight.t(), requires_grad=False)
layer.weight_scale = Parameter(weight_scale, requires_grad=False)
class KFp8Block128(QuantKeyScheme):
"""128x128 block-static FP8 weight ('PbWo'). Weight-role only. ModelOpt
exports the scale 4-D [out_blk,1,in_blk,1]; process squeezes to 2-D.
No transpose (block kernel keeps [out,in])."""
key = kFp8Static128BlockSym
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is not WEIGHT:
self.reject(role)
out = shapes.output_size_per_partition
in_ = shapes.input_size_per_partition
# Scale sized by cdiv to match the checkpoint's block grid (the last
# output block may be partial). Weight is registered at its logical
# size; a non-128 output width is padded to a block boundary by the
# _Fp8PbWoPartialBlock format scheme. Input must be 128-aligned (same as
# #53132, which only pads the output).
if in_ % 128 != 0:
raise ValueError(f"FP8_PB_WO requires in divisible by 128, got {in_}")
self.register_params(
layer,
"weight",
(out, in_),
torch.float8_e4m3fn,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
ob, ib = cdiv(out, 128), in_ // 128
self.register_params(
layer,
"weight_scale",
(ob, 1, ib, 1),
torch.float32,
BlockQuantScaleParameter,
wl,
input_dim=2,
output_dim=0,
init=FP8_SCALE_SENTINEL,
)
layer.weight_block_size = [128, 128]
def process(self, layer, role) -> None:
if role is not WEIGHT:
self.reject(role)
layer.weight = Parameter(layer.weight.data, requires_grad=False)
s = layer.weight_scale
if s.dim() == 4:
s = s.squeeze(1).squeeze(-1) # [ob,1,ib,1] -> [ob,ib]
elif s.dim() != 2:
raise ValueError(
f"Unexpected FP8_PB_WO weight_scale shape {tuple(s.shape)}"
)
layer.weight_scale = Parameter(s.contiguous(), requires_grad=False)
class KMxfp8Static(QuantKeyScheme):
"""MXFP8 weight: fp8-e4m3 values + per-32-block e8m0 (uint8) scale.
Weight-role only. process is validate-only plus an idempotency guard."""
key = kMxfp8Static
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is not WEIGHT:
self.reject(role)
if shapes.input_size_per_partition % MXFP8_BLOCK_SIZE != 0:
raise ValueError(
f"MXFP8 requires input size divisible by {MXFP8_BLOCK_SIZE}, "
f"got {shapes.input_size_per_partition}"
)
self.register_params(
layer,
"weight",
(shapes.output_size_per_partition, shapes.input_size_per_partition),
MXFP8_VALUE_DTYPE,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
self.register_params(
layer,
"weight_scale",
(
shapes.output_size_per_partition,
shapes.input_size_per_partition // MXFP8_BLOCK_SIZE,
),
MXFP8_SCALE_DTYPE,
ModelWeightParameter,
wl,
input_dim=1,
output_dim=0,
)
def process(self, layer, role) -> None:
if role is not WEIGHT:
self.reject(role)
# Idempotency: the emulation kernel's post-load may replace the 1-byte
# MXFP8 weight with BF16 (VLLM_MXFP8_EMULATION_DEQUANT_AT_LOAD, default
# on). On a weight reload process runs again after that swap, so skip
# the fp8 asserts when the weight is already >=2-byte.
if layer.weight.element_size() >= 2:
return
assert layer.weight.ndim == 2 and layer.weight.dtype == MXFP8_VALUE_DTYPE
assert layer.weight_scale.ndim == 2
assert layer.weight_scale.dtype == MXFP8_SCALE_DTYPE
class KDynamicNoParam(QuantKeyScheme):
"""Dynamic activation with no stored scale (W8A8): quantized at runtime in
the kernel. NOT the same as activation=None (weight-only) — init_fp8 needs a
non-None activation key. Activation-role only. Serves the fp8 per-token, fp8
per-block, and mxfp8 dynamic activation keys."""
def create_weights(self, layer, role, ctx, shapes, wl) -> None:
if role is not ACT:
self.reject(role)
# dynamic -> nothing stored
def process(self, layer, role) -> None:
if role is not ACT:
self.reject(role)
SCHEME_FOR: dict[QuantKey | None, QuantKeyScheme] = {
kNvfp4Static: KNvfp4Static(),
kNvfp4Dynamic: KNvfp4Dynamic(),
kFp8StaticTensorSym: KFp8StaticTensor(),
kFp8StaticTokenSym: KFp8StaticChannel(),
kFp8Static128BlockSym: KFp8Block128(),
kMxfp8Static: KMxfp8Static(),
kFp8DynamicTokenSym: KDynamicNoParam(),
kFp8Dynamic128Sym: KDynamicNoParam(),
kMxfp8Dynamic: KDynamicNoParam(),
}
def maybe_fuse_global_scales(layer) -> None:
"""alpha = input_global_scale * weight_global_scale, presence-gated.
W4A4 has both -> computed; W4A16 has no input_global_scale -> skipped.
"""
if hasattr(layer, "weight_global_scale") and hasattr(layer, "input_global_scale"):
layer.alpha = Parameter(
layer.input_global_scale * layer.weight_global_scale,
requires_grad=False,
)
def select_linear_kernel(spec: QuantSpec, layer, rt: RuntimeDtypes):
"""Thin family dispatcher on the weight key: nvfp4 / mxfp8 / fp8."""
w = spec.weight
assert isinstance(w, QuantKey), f"resolve() must supply a weight key, got {w!r}"
if w.dtype == FP4_DTYPE:
# W4A16 (activation is None) → use_a16=True defaults to Marlin *and*
# honors --linear-backend (matches upstream ModelOptNvFp4W4A16LinearMethod
# after #50273); W4A4 → use_a16=False.
return init_nvfp4_linear_kernel(use_a16=spec.activation is None)
if w.scale.dtype == MXFP8_SCALE_DTYPE:
return init_mxfp8_linear_kernel()
# fp8 family: init_fp8 routes block-vs-plain itself off the activation key,
# and needs a real key -- weight-only fp8 is not a ModelOpt format.
act = spec.activation
assert isinstance(act, QuantKey), f"fp8 needs an activation key, got {act!r}"
return init_fp8_linear_kernel(
activation_quant_key=act,
weight_quant_key=w,
input_dtype=rt.input_dtype,
out_dtype=rt.out_dtype,
weight_shape=layer.weight.shape,
module_name=type(layer).__name__,
)
class FormatScheme:
"""Optional per-format hooks that compose *around* the QuantKey schemes.
Extension seam for residue that belongs to a format as a whole rather than a
single QuantKey — an extra parameter the weight/activation schemes don't
cover, or a tweak before/after their ``process``. All hooks default to
no-ops; most formats need none (they are a pure ``(weight, activation)`` key
pair). To add one: subclass, override the hooks you need, and return an
instance from the format's ``resolve()`` branch — no change to
``ModelOptLinearMethod`` itself.
"""
def extra_weights(self, layer, shapes: "Shapes", ctx: CkptCtx, wl) -> None:
"""Register format-level params (after the key schemes' weights)."""
def pre_process(self, layer) -> None:
"""Run before the key schemes' ``process``."""
def post_process(self, layer) -> None:
"""Run after the key schemes' ``process``, before the kernel's."""
def apply(self, layer, x, bias, kernel_apply):
"""Wrap the kernel's forward. ``kernel_apply(layer, x, bias) -> Tensor``.
Default: delegate unchanged. A format whose residue lives at compute
time (e.g. run the GEMM on a padded weight then slice the output back
to its logical width, #53132-style) overrides this and calls
``kernel_apply`` in the middle.
"""
return kernel_apply(layer, x, bias)
class _Fp8PbWoPartialBlock(FormatScheme):
"""FP8_PB_WO output width that is not a multiple of 128 (a partial trailing
block; e.g. GLM's replicated ``fused_qkv_a_proj`` = 2048 + 576 = 2624).
The block kernel needs full 128-blocks, so pad the weight up to the block
boundary with zeros before the kernel's post-load, then slice the GEMM
output back to the logical width. This is #53132's approach, expressed as
the format's compute-time residue on the generic method. The cdiv-sized
scale from ``KFp8Block128`` already matches the padded block count, so only
the weight rows and the output need adjusting.
"""
def post_process(self, layer) -> None:
w = layer.weight
out = w.shape[0]
pad = (-out) % 128
if not pad:
return
layer._pbwo_logical_out = out
padded = w.data.new_zeros(out + pad, w.shape[1])
padded[:out].copy_(w.data)
layer.weight = Parameter(padded, requires_grad=False)
def apply(self, layer, x, bias, kernel_apply):
logical = getattr(layer, "_pbwo_logical_out", None)
if logical is None: # width was block-aligned: nothing to trim
return kernel_apply(layer, x, bias)
# Run the GEMM on the padded weight without bias, then trim to the
# logical width and add bias (the padded rows produce zeros we drop).
out = kernel_apply(layer, x, None)
out = out[..., :logical].contiguous()
return out if bias is None else out.add_(bias)
_PB_WO_PARTIAL_BLOCK = _Fp8PbWoPartialBlock()
class _DropInputScale(FormatScheme):
"""Interim: register then drop a W4A16 checkpoint's on-disk input_scale.
Kept for backward compat with previously-exported ModelOpt checkpoints.
"""
def extra_weights(self, layer, shapes, ctx, wl) -> None:
data = torch.full((shapes.num_partitions,), torch.nan)
layer.register_parameter(
"input_scale", PerTensorScaleParameter(data=data, weight_loader=wl)
)
def post_process(self, layer) -> None:
scale = getattr(layer, "input_scale", None)
if scale is None:
return
if not torch.isnan(scale).all():
logger.warning_once(
"Dropping input_scale from a weight-only W4A16 NVFP4 "
"checkpoint (deprecated)."
)
del layer.input_scale
_DROP_INPUT_SCALE = _DropInputScale()
@register_weight_loader_v2_supported_method
class ModelOptLinearMethod(LinearMethodBase):
"""Generic, format-agnostic ModelOpt linear method. Holds a weight scheme +
an activation scheme (from the QuantSpec pair) and an optional
``FormatScheme``, runs a fixed lifecycle, selects the kernel from the pair,
and applies.
Registered for weight_loader_v2 (like every ModelOpt linear method) so the
``BasevLLMParameter`` params route through the v2 fused loader, not the
legacy shape-assert path.
"""
def __init__(
self,
spec: QuantSpec,
ctx: CkptCtx,
format_scheme=None,
) -> None:
self.spec = spec
self.ctx = ctx
self.fmt = format_scheme or FormatScheme()
self.wkey = SCHEME_FOR[spec.weight]
self.akey = None if spec.activation is None else SCHEME_FOR[spec.activation]
# Only the fp8/mxfp8 kernels read input_dtype; nvfp4 ignores it. During
# real serving model_config is always set; fall back defensively when
# there is no config context (bare unit-test dispatch).
model_config = getattr(get_current_vllm_config_or_none(), "model_config", None)
dtype = (
model_config.dtype
if model_config is not None
else torch.get_default_dtype()
)
self.out_dtype = dtype
self.input_dtype = dtype
self.marlin_input_dtype = None
# Kernel/backend are chosen in create_weights (after get_quant_method),
# so the front-end marlin poke stays dormant here — same as the old
# NVFP4 methods.
self.kernel: Any = None
def create_weights(
self,
layer,
input_size_per_partition: int,
output_partition_sizes: list[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
) -> None:
del input_size, output_size
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 = sum(output_partition_sizes)
# Humming reads both off the layer in
# prepare_humming_linear_layer_config. LinearBase sets them itself;
# ParallelLMHead does not, so supply them here.
layer.output_partition_sizes = output_partition_sizes
if not hasattr(layer, "has_bias"):
layer.has_bias = getattr(layer, "bias", None) is not None
shapes = Shapes(output_partition_sizes, input_size_per_partition, params_dtype)
self.wkey.create_weights(layer, WEIGHT, self.ctx, shapes, weight_loader)
if self.akey:
self.akey.create_weights(layer, ACT, self.ctx, shapes, weight_loader)
self.fmt.extra_weights(layer, shapes, self.ctx, weight_loader)
rt = RuntimeDtypes(self.input_dtype, self.out_dtype, self.marlin_input_dtype)
self.kernel = select_linear_kernel(self.spec, layer, rt)
expose_input_quant_key(layer, self.kernel)
def process_weights_after_loading(self, layer) -> None:
self.fmt.pre_process(layer)
self.wkey.process(layer, WEIGHT)
if self.akey:
self.akey.process(layer, ACT)
maybe_fuse_global_scales(layer)
self.fmt.post_process(layer)
self.kernel.process_weights_after_loading(layer)
def apply(self, layer, x, bias=None):
return self.fmt.apply(
layer,
x,
bias,
lambda lyr, inp, b: self.kernel.apply_weights(layer=lyr, x=inp, bias=b),
)
def resolve(algo: str, subcfg, prefix: str):
"""Turn a sub-config into (QuantSpec, CkptCtx, format_scheme).
Strictly read-only over ``subcfg``: in mixed precision the same sub-config
is shared with the MoE method, so writing back would leak a linear-only
change into MoE.
"""
if algo == "FP8":
# Plain per-tensor static FP8 (W8A8): same key in both slots (bivalent).
ctx = CkptCtx()
return (
QuantSpec(weight=kFp8StaticTensorSym, activation=kFp8StaticTensorSym),
ctx,
None,
)
if algo == "FP8_PER_CHANNEL_PER_TOKEN":
# PcPt: per-channel static weight, dynamic per-token activation (W8A8).
ctx = CkptCtx()
return (
QuantSpec(weight=kFp8StaticTokenSym, activation=kFp8DynamicTokenSym),
ctx,
None,
)
if algo == "FP8_PB_WO":
# PbWo: 128x128 block-static weight, dynamic per-block activation (W8A8).
# The block kernel's post-load runs here; CompressedTensors block-FP8
# is the reference for this path. The FormatScheme pads a non-128 output
# width to a block boundary and trims the output back (#53132) -- a no-op
# for the common block-aligned case.
ctx = CkptCtx()
return (
QuantSpec(weight=kFp8Static128BlockSym, activation=kFp8Dynamic128Sym),
ctx,
_PB_WO_PARTIAL_BLOCK,
)
if algo == "MXFP8":
# MXFP8: block(32) e4m3 weight + e8m0 scale, dynamic activation.
ctx = CkptCtx()
return QuantSpec(weight=kMxfp8Static, activation=kMxfp8Dynamic), ctx, None
# NVFP4 family (W4A4 / W4A16).
ctx = CkptCtx(group_size=subcfg.group_size)
if algo == "NVFP4":
# W4A4: static fp4 weight + dynamic fp4 activation (has a static global
# input scale). alpha = weight_gs * input_gs.
return QuantSpec(weight=kNvfp4Static, activation=kNvfp4Dynamic), ctx, None
if algo == "W4A16_NVFP4":
# W4A16: fp4 weight, no activation quant (-> Marlin). _DROP_INPUT_SCALE
# is a no-op for genuine exports; keeps legacy input_scale ckpts loading.
return QuantSpec(weight=kNvfp4Static, activation=None), ctx, _DROP_INPUT_SCALE
raise NotImplementedError(f"resolve: unsupported ModelOpt linear algo {algo!r}")
# Bespoke-method escape hatch. Almost every ModelOpt linear format is a
# (weight, activation) QuantKey pair handled by the generic ModelOptLinearMethod
# (optionally + a FormatScheme), so this stays empty. A format that genuinely
# cannot be expressed that way — a fundamentally different create/process/apply
# lifecycle — registers its own LinearMethodBase here, keyed by algo:
#
# LINEAR_METHOD_BUILDERS["FOO"] = lambda cfg, prefix: ModelOptFooLinearMethod(cfg)
#
# Keep the ModelOpt* class-name prefix and decorate the class with
# @register_weight_loader_v2_supported_method if it uses BasevLLMParameter
# params. Also add the algo to the owning config's quant_method validation.
LINEAR_METHOD_BUILDERS: dict[str, Callable[..., LinearMethodBase]] = {}
def build_linear_method(config, algo: str, prefix: str) -> LinearMethodBase:
"""Construct the linear method for ``algo``.
Returns a bespoke method if one is registered in ``LINEAR_METHOD_BUILDERS``,
else the generic ``ModelOptLinearMethod`` built from ``resolve(algo, config,
prefix)``. Single indirection point shared by the homogeneous and
mixed-precision dispatch, so a new format plugs in without editing either
``get_quant_method``.
"""
builder = LINEAR_METHOD_BUILDERS.get(algo)
if builder is not None:
return builder(config, prefix)
spec, ctx, format_scheme = resolve(algo, config, prefix)
return ModelOptLinearMethod(spec, ctx, format_scheme)
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