# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from fnmatch import fnmatch from typing import TYPE_CHECKING, Any, Optional import torch from torch.nn import Module from torch.nn.parameter import Parameter import vllm.envs as envs import vllm.model_executor.layers.fused_moe.modular_kernel as mk from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant from vllm.attention.layer import Attention from vllm.logger import init_logger from vllm.model_executor.layers.fused_moe.config import ( FusedMoEConfig, FusedMoEQuantConfig, ) from vllm.model_executor.layers.fused_moe.layer import ( FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported, ) from vllm.model_executor.layers.fused_moe.oracle.fp8 import ( Fp8MoeBackend, convert_to_fp8_moe_kernel_format, make_fp8_moe_kernel, make_fp8_moe_kernel_for_mkm, make_fp8_moe_quant_config, select_fp8_moe_backend, ) from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import ( NvFp4MoeBackend, convert_to_nvfp4_moe_kernel_format, is_global_sf_supported_for_nvfp4_backend, make_nvfp4_moe_kernel, make_nvfp4_moe_kernel_for_mkm, make_nvfp4_moe_quant_config, select_nvfp4_moe_backend, ) from vllm.model_executor.layers.linear import ( LinearBase, LinearMethodBase, UnquantizedLinearMethod, ) 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.kernels.scaled_mm import ( init_fp8_linear_kernel, ) from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import ( build_flashinfer_fp4_cutlass_moe_prepare_finalize, flashinfer_trtllm_fp4_moe, flashinfer_trtllm_fp4_routed_moe, ) from vllm.model_executor.layers.quantization.utils.flashinfer_utils import ( apply_fi_trtllm_fp8_per_tensor_moe, build_flashinfer_fp8_cutlass_moe_prepare_finalize, ) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( W8A8BlockFp8LinearOp, process_fp8_input_tensor_strategy_moe, 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.marlin_utils_fp4 import ( apply_fp4_marlin_linear, is_fp4_marlin_supported, prepare_fp4_layer_for_marlin, ) from vllm.model_executor.layers.quantization.utils.quant_utils import ( GroupShape, cutlass_fp4_supported, is_layer_skipped, kFp8DynamicTokenSym, kFp8StaticTensorSym, kFp8StaticTokenSym, kNvfp4Dynamic, kNvfp4Static, swizzle_blockscale, ) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( cutlass_block_fp8_supported, requantize_with_max_scale, ) from vllm.model_executor.parameter import ( BlockQuantScaleParameter, ChannelQuantScaleParameter, ModelWeightParameter, PerTensorScaleParameter, ) from vllm.model_executor.utils import replace_parameter from vllm.utils.flashinfer import ( flashinfer_scaled_fp4_mm, has_flashinfer, ) if TYPE_CHECKING: from vllm.model_executor.models.utils import WeightsMapper logger = init_logger(__name__) QUANT_ALGOS = [ # FP8 (per-tensor weight + optional static activation scale). "FP8", # FP8 per-channel weight scale + per-token activation scale. "FP8_PER_CHANNEL_PER_TOKEN", # FP8 per-block weight-only (ModelOpt may emit this as lowercase). "FP8_PB_WO", # FP4 "NVFP4", ] KV_CACHE_QUANT_ALGOS = ["FP8"] class ModelOptFp8KVCacheMethod(BaseKVCacheMethod): """ Supports loading kv-cache scaling factors from FP8 checkpoints. """ def __init__(self, quant_config: "ModelOptQuantConfigBase"): super().__init__(quant_config) class ModelOptQuantConfigBase(QuantizationConfig): LinearMethodCls: type = LinearMethodBase 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 ) -> Optional["QuantizeMethodBase"]: # handle kv-cache first so we can focus only on weight quantization thereafter if isinstance(layer, Attention): return self.KVCacheMethodCls(self) # handle exclusion if self.is_layer_excluded(prefix): if isinstance(layer, LinearBase): 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: return UnquantizedLinearMethod() # now, the layer is quantized, handle it here if isinstance(layer, LinearBase): quant_method = self.LinearMethodCls(self) if getattr(quant_method, "backend", "") == "marlin": quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix) return quant_method elif isinstance(layer, FusedMoE): 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 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: # {"quant_algo": "...", "quant_method": "modelopt"} quant_method = config.get("quant_algo") kv_cache_quant_method = config.get("kv_cache_quant_algo") # "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 if is_checkpoint_fp8_serialized: logger.warning( "Detected ModelOpt fp8 checkpoint (quant_algo=%s). Please note " "that the format is experimental and could change.", quant_method, ) # Select LinearMethod implementation based on quant_algo. if self.quant_method == "FP8": self.LinearMethodCls = ModelOptFp8LinearMethod elif self.quant_method == "FP8_PER_CHANNEL_PER_TOKEN": self.LinearMethodCls = ModelOptFp8PcPtLinearMethod elif self.quant_method == "FP8_PB_WO": self.LinearMethodCls = ModelOptFp8PbWoLinearMethod else: raise ValueError( "Unsupported ModelOpt FP8 quant_algo for vLLM: " f"{self.quant_method}. Supported: FP8 / " "FP8_PER_CHANNEL_PER_TOKEN / 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 89 @classmethod def override_quantization_method( cls, hf_quant_cfg, user_quant ) -> QuantizationMethods | None: """Detect if this ModelOpt config should be used based on quantization config.""" if hf_quant_cfg is None: return None # Use the community standard 'quant_method' quant_method = hf_quant_cfg.get("quant_method", "").lower() # Only proceed if the method is explicitly "modelopt" if quant_method != "modelopt": return None # Look for ModelOpt-specific config structure if "quantization" in hf_quant_cfg: quant_config = hf_quant_cfg["quantization"] if isinstance(quant_config, dict): quant_algo = str(quant_config.get("quant_algo", "")) if "FP8" in quant_algo.upper(): return "modelopt" else: # Check for compressed-tensors style config with specific quant_algo quant_algo = str(hf_quant_cfg.get("quant_algo", "")) if "FP8" in quant_algo.upper(): 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 ModelOptFp8LinearMethod(LinearMethodBase): """Linear method for Model Optimizer static quantization. Supports loading FP8 checkpoints with static weight scale and activation scale. Future support might be added for dynamic scales. Limitations: 1. Only support per-tensor quantization due to torch._scaled_mm support. 2. Only support float8_e4m3fn datatype Args: quant_config: The ModelOpt quantization config. """ def __init__(self, quant_config: ModelOptFp8Config) -> None: self.quant_config = quant_config self.fp8_linear = init_fp8_linear_kernel( activation_quant_key=kFp8StaticTensorSym, weight_quant_key=kFp8StaticTensorSym, out_dtype=torch.get_default_dtype(), module_name=self.__class__.__name__, ) def create_weights( self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): del input_size, output_size output_size_per_partition = sum(output_partition_sizes) weight_loader = extra_weight_attrs.get("weight_loader") layer.logical_widths = output_partition_sizes layer.input_size_per_partition = input_size_per_partition layer.output_size_per_partition = output_size_per_partition weight_dtype = ( torch.float8_e4m3fn if self.quant_config.is_checkpoint_fp8_serialized else params_dtype ) weight = ModelWeightParameter( data=torch.empty( output_size_per_partition, input_size_per_partition, dtype=weight_dtype ), input_dim=1, output_dim=0, weight_loader=weight_loader, ) layer.register_parameter("weight", weight) if self.quant_config.is_checkpoint_fp8_serialized: # WEIGHT SCALE weight_scale = PerTensorScaleParameter( data=torch.empty(len(output_partition_sizes), dtype=torch.float32), weight_loader=weight_loader, ) weight_scale[:] = torch.finfo(torch.float32).min layer.register_parameter("weight_scale", weight_scale) # INPUT SCALE scale = PerTensorScaleParameter( data=torch.empty(len(output_partition_sizes), dtype=torch.float32), weight_loader=weight_loader, ) scale[:] = torch.finfo(torch.float32).min layer.register_parameter("input_scale", scale) def process_weights_after_loading(self, layer: Module) -> None: weight = layer.weight max_w_scale = layer.weight_scale.max() if not (layer.weight_scale == layer.weight_scale[0]).all(): max_w_scale, weight = requantize_with_max_scale( layer.weight, layer.weight_scale, layer.logical_widths ) layer.weight = Parameter(weight.t(), requires_grad=False) layer.weight_scale = Parameter(max_w_scale, requires_grad=False) layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = None, ) -> torch.Tensor: return self.fp8_linear.apply_weights(layer, x, bias) class ModelOptFp8PcPtLinearMethod(LinearMethodBase): """Linear method for ModelOpt FP8_PER_CHANNEL_PER_TOKEN checkpoints. Expected checkpoint structure (per Linear): - weight: fp8-e4m3fn, shape [out, in] - weight_scale: fp32, shape [out] (per-output-channel) - no input_scale (activations are dynamically quantized per-token) """ def __init__(self, quant_config: ModelOptFp8Config) -> None: self.quant_config = quant_config self.fp8_linear = init_fp8_linear_kernel( activation_quant_key=kFp8DynamicTokenSym, weight_quant_key=kFp8StaticTokenSym, out_dtype=torch.get_default_dtype(), module_name=self.__class__.__name__, ) def create_weights( self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): del input_size, output_size if not self.quant_config.is_checkpoint_fp8_serialized: raise ValueError( "FP8_PER_CHANNEL_PER_TOKEN currently only supports " "FP8-serialized checkpoints." ) output_size_per_partition = sum(output_partition_sizes) weight_loader = extra_weight_attrs.get("weight_loader") layer.logical_widths = output_partition_sizes layer.input_size_per_partition = input_size_per_partition layer.output_size_per_partition = output_size_per_partition weight = ModelWeightParameter( data=torch.empty( output_size_per_partition, input_size_per_partition, dtype=torch.float8_e4m3fn, ), input_dim=1, output_dim=0, weight_loader=weight_loader, ) layer.register_parameter("weight", weight) weight_scale = ChannelQuantScaleParameter( data=torch.empty(output_size_per_partition, dtype=torch.float32), output_dim=0, weight_loader=weight_loader, ) weight_scale[:] = torch.finfo(torch.float32).min layer.register_parameter("weight_scale", weight_scale) def process_weights_after_loading(self, layer: Module) -> None: layer.weight = Parameter(layer.weight.t(), requires_grad=False) layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = None, ) -> torch.Tensor: return self.fp8_linear.apply_weights(layer, x, bias) class ModelOptFp8PbWoLinearMethod(LinearMethodBase): """Linear method for ModelOpt FP8_PB_WO checkpoints. ModelOpt exports `weight_scale` as a 4D tensor: [out_blk, 1, in_blk, 1] where block size is typically 128 for both dims. vLLM executes it as FP8 GEMM with *dynamic per-token* activation quant. """ _WEIGHT_BLOCK_SIZE: tuple[int, int] = (128, 128) def __init__(self, quant_config: ModelOptFp8Config) -> None: self.quant_config = quant_config block_n, block_k = self._WEIGHT_BLOCK_SIZE self.weight_block_size = list(self._WEIGHT_BLOCK_SIZE) self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp( weight_group_shape=GroupShape(block_n, block_k), act_quant_group_shape=GroupShape(1, block_k), cutlass_block_fp8_supported=cutlass_block_fp8_supported(), use_aiter_and_is_supported=False, ) def create_weights( self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: list[int], input_size: int, output_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): del input_size, output_size if not self.quant_config.is_checkpoint_fp8_serialized: raise ValueError( "FP8_PB_WO currently only supports FP8-serialized checkpoints." ) output_size_per_partition = sum(output_partition_sizes) weight_loader = extra_weight_attrs.get("weight_loader") layer.logical_widths = output_partition_sizes layer.input_size_per_partition = input_size_per_partition layer.output_size_per_partition = output_size_per_partition # Expose block size so the v2 weight loaders can translate offsets from # element-space -> block-space for BlockQuantScaleParameter. layer.weight_block_size = self.weight_block_size weight = ModelWeightParameter( data=torch.empty( output_size_per_partition, input_size_per_partition, dtype=torch.float8_e4m3fn, ), input_dim=1, output_dim=0, weight_loader=weight_loader, ) layer.register_parameter("weight", weight) block_n, block_k = self._WEIGHT_BLOCK_SIZE if output_size_per_partition % block_n != 0: raise ValueError( "ModelOpt FP8_PB_WO requires out_features divisible by " f"{block_n}, got {output_size_per_partition}." ) if input_size_per_partition % block_k != 0: raise ValueError( "ModelOpt FP8_PB_WO requires in_features divisible by " f"{block_k}, got {input_size_per_partition}." ) out_blks = output_size_per_partition // block_n in_blks = input_size_per_partition // block_k # Match ModelOpt's exported shape so weight loading works without a # custom loader: [out_blk, 1, in_blk, 1] weight_scale = BlockQuantScaleParameter( data=torch.empty((out_blks, 1, in_blks, 1), dtype=torch.float32), input_dim=2, output_dim=0, weight_loader=weight_loader, ) weight_scale[:] = torch.finfo(torch.float32).min layer.register_parameter("weight_scale", weight_scale) def process_weights_after_loading(self, layer: Module) -> None: # Keep weight in [out, in] layout for W8A8BlockFp8LinearOp. layer.weight = Parameter(layer.weight.data, requires_grad=False) scale = layer.weight_scale if scale.dim() == 4: # [out_blk, 1, in_blk, 1] -> [out_blk, in_blk] scale = scale.squeeze(1).squeeze(-1) elif scale.dim() != 2: raise ValueError( "Unexpected ModelOpt FP8_PB_WO weight_scale shape: " f"{tuple(scale.shape)}." ) layer.weight_scale = Parameter(scale.contiguous(), requires_grad=False) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = None, ) -> torch.Tensor: return self.w8a8_block_fp8_linear.apply( input=x, weight=layer.weight, weight_scale=layer.weight_scale, input_scale=None, bias=bias, ) class ModelOptFp8MoEMethod(FusedMoEMethodBase): """MoE method for ModelOpt FP8. Supports loading FP8 checkpoints with static weight scale and activation scale. Args: quant_config: The ModelOpt quantization config. """ def __init__( self, quant_config: ModelOptFp8Config, moe_config: FusedMoEConfig, ) -> None: super().__init__(moe_config) self.quant_config = quant_config assert self.quant_config.is_checkpoint_fp8_serialized # Select Fp8 MoE backend self.fp8_backend, self.experts_cls = select_fp8_moe_backend( config=self.moe, weight_key=kFp8StaticTensorSym, activation_key=kFp8StaticTensorSym, ) # Delay creation of the kernel until after process-weights. self.kernel: mk.FusedMoEModularKernel | None = None @property def topk_indices_dtype(self) -> torch.dtype | None: if self.kernel is not None: return self.kernel.prepare_finalize.topk_indices_dtype() return None def maybe_make_prepare_finalize( self, routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None, ) -> mk.FusedMoEPrepareAndFinalize | None: # TRT LLM not supported with all2all yet. if self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM: return None elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS: # For no-EP case, don't use the MKM framework. if not self.moe.moe_parallel_config.use_all2all_kernels: return None prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize( self.moe, use_deepseek_fp8_block_scale=False, ) logger.debug_once("%s", prepare_finalize.__class__.__name__) return prepare_finalize return super().maybe_make_prepare_finalize(routing_tables) def select_gemm_impl( self, prepare_finalize: mk.FusedMoEPrepareAndFinalize, layer: torch.nn.Module, ) -> mk.FusedMoEPermuteExpertsUnpermute: assert self.moe_quant_config is not None assert self.experts_cls is not None return make_fp8_moe_kernel_for_mkm( moe_config=self.moe, quant_config=self.moe_quant_config, experts_cls=self.experts_cls, prepare_finalize=prepare_finalize, ) def create_weights( self, layer: torch.nn.Module, num_experts: int, hidden_size: int, intermediate_size_per_partition: int, params_dtype: torch.dtype, **extra_weight_attrs, ): layer.orig_dtype = params_dtype layer.num_experts = num_experts # Use FP8 dtype if checkpoint is serialized weight_dtype = ( torch.float8_e4m3fn if self.quant_config.is_checkpoint_fp8_serialized else params_dtype ) weight_loader = extra_weight_attrs.get("weight_loader") w13_num_shards = 2 if self.moe.is_act_and_mul else 1 w13_weight = ModelWeightParameter( data=torch.empty( num_experts, w13_num_shards * intermediate_size_per_partition, hidden_size, dtype=weight_dtype, ), input_dim=2, output_dim=1, weight_loader=weight_loader, ) layer.register_parameter("w13_weight", w13_weight) w2_weight = ModelWeightParameter( data=torch.empty( num_experts, hidden_size, intermediate_size_per_partition, dtype=weight_dtype, ), input_dim=2, output_dim=1, weight_loader=weight_loader, ) layer.register_parameter("w2_weight", w2_weight) # WEIGHT SCALES - Per-tensor scaling for ModelOpts # For gated MoE, allocate 2 scales for w1 and w3 respectively. # They will be combined to a single scale after weight loading. # For non-gated MoE, allocate 1 scale for w13. w13_weight_scale = PerTensorScaleParameter( data=torch.full( (num_experts, w13_num_shards), 1.0, dtype=torch.float32, ), weight_loader=weight_loader, ) w2_weight_scale = PerTensorScaleParameter( data=torch.full((num_experts,), 1.0, dtype=torch.float32), weight_loader=weight_loader, ) layer.register_parameter("w13_weight_scale", w13_weight_scale) layer.register_parameter("w2_weight_scale", w2_weight_scale) # INPUT SCALES - Per-tensor scaling for ModelOpt w13_input_scale = PerTensorScaleParameter( data=torch.full((num_experts,), 1.0, dtype=torch.float32), weight_loader=weight_loader, ) w2_input_scale = PerTensorScaleParameter( data=torch.full((num_experts,), 1.0, dtype=torch.float32), weight_loader=weight_loader, ) layer.register_parameter("w13_input_scale", w13_input_scale) layer.register_parameter("w2_input_scale", w2_input_scale) def _setup_kernel( self, layer: torch.nn.Module, w13: torch.Tensor, w2: torch.Tensor, w13_scale: torch.Tensor, w2_scale: torch.Tensor, w13_input_scale: torch.Tensor, w2_input_scale: torch.Tensor, ): w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format( fp8_backend=self.fp8_backend, layer=layer, w13=w13, w2=w2, w13_scale=w13_scale, w2_scale=w2_scale, w13_input_scale=w13_input_scale, w2_input_scale=w2_input_scale, ) # Replace parameters with updated versions. Note that this helper # function ensures the replacement is compatible with RL weight reloads. replace_parameter(layer, "w13_weight", w13) replace_parameter(layer, "w2_weight", w2) replace_parameter(layer, "w13_weight_scale", w13_scale) replace_parameter(layer, "w2_weight_scale", w2_scale) # Setup modular kernel. self.moe_quant_config = self.get_fused_moe_quant_config(layer) if self.moe_quant_config: assert self.experts_cls is not None self.kernel, self.use_inplace = 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, ) def process_weights_after_loading(self, layer: torch.nn.Module) -> None: w13 = layer.w13_weight w2 = layer.w2_weight w13_scale = layer.w13_weight_scale w2_scale = layer.w2_weight_scale w13_input_scale = layer.w13_input_scale w2_input_scale = layer.w2_input_scale # Per tensor kernels require single activation scale. Use the max. w13_input_scale, w2_input_scale = process_fp8_input_tensor_strategy_moe( w13_input_scale, w2_input_scale ) replace_parameter(layer, "w13_input_scale", w13_input_scale) replace_parameter(layer, "w2_input_scale", w2_input_scale) # Per tensor kernels require single weight scale for w13 per expert, but # on disk there is a scale for w1 and w3. Use the max to requantize. shard_size = layer.intermediate_size_per_partition w13, w13_scale = process_fp8_weight_tensor_strategy_moe( w13, w13_scale, shard_size, num_experts=layer.w13_weight.shape[0], is_act_and_mul=self.moe.is_act_and_mul, ) # Shuffle weights to runtime format and setup kernel. self._setup_kernel( layer, w13, w2, w13_scale, w2_scale, w13_input_scale, w2_input_scale ) def get_fused_moe_quant_config( self, layer: torch.nn.Module ) -> FusedMoEQuantConfig | None: w1_scale = layer.w13_weight_scale w2_scale = layer.w2_weight_scale a1_scale = layer.w13_input_scale a2_scale = layer.w2_input_scale return make_fp8_moe_quant_config( fp8_backend=self.fp8_backend, w1_scale=w1_scale, w2_scale=w2_scale, a1_scale=a1_scale, a2_scale=a2_scale, ) @property def is_monolithic(self) -> bool: return self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM def apply_monolithic( self, layer: FusedMoE, x: torch.Tensor, router_logits: torch.Tensor, ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: assert self.is_monolithic assert self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM if layer.enable_eplb: raise NotImplementedError( "EPLB not supported for FlashInfer TRTLLM FP8 MoE Backend." ) # TODO(rob): this validation should happen at kernel selection # time in the oracle rather than here. assert layer.activation == "silu", ( f"Expected 'silu' activation but got {layer.activation}" ) assert not layer.renormalize return apply_fi_trtllm_fp8_per_tensor_moe( layer=layer, hidden_states=x, router_logits=router_logits, routing_bias=layer.e_score_correction_bias, global_num_experts=layer.global_num_experts, top_k=layer.top_k, num_expert_group=layer.num_expert_group, topk_group=layer.topk_group, apply_router_weight_on_input=layer.apply_router_weight_on_input, ) def apply( self, layer: FusedMoE, x: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: assert not self.is_monolithic # TODO(rob): this validation should happen at kernel selection # time in the oracle rather than here. if self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS: assert layer.activation in ("silu", "relu2_no_mul"), ( "Expected activation to be in ('silu', 'relu2_no_mul')," f"but got {layer.activation}" ) assert self.kernel is not None return self.kernel( x, layer.w13_weight, layer.w2_weight, topk_weights, topk_ids, inplace=self.use_inplace, 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, ) ModelOptFp8Config.LinearMethodCls = ModelOptFp8LinearMethod ModelOptFp8Config.FusedMoEMethodCls = ModelOptFp8MoEMethod ModelOptFp8Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod class ModelOptNvFp4Config(ModelOptQuantConfigBase): """Config class for ModelOpt FP4.""" def __init__( self, is_checkpoint_nvfp4_serialized: bool, kv_cache_quant_algo: str | None, exclude_modules: list[str], group_size: int = 16, ) -> None: super().__init__(exclude_modules) self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized if is_checkpoint_nvfp4_serialized: logger.warning( "Detected ModelOpt NVFP4 checkpoint. Please note that" " the format is experimental and could change in future." ) self.group_size = group_size self.kv_cache_quant_algo = kv_cache_quant_algo def get_name(self) -> QuantizationMethods: return "modelopt_fp4" def get_supported_act_dtypes(self) -> list[torch.dtype]: return [torch.bfloat16, torch.half, torch.float8_e4m3fn] @classmethod def get_min_capability(cls) -> int: return 75 @classmethod def override_quantization_method( cls, hf_quant_cfg, user_quant ) -> QuantizationMethods | None: """Detect if this ModelOpt FP4 config should be used based on quantization config.""" if hf_quant_cfg is None: return None # Use the community standard 'quant_method' quant_method = hf_quant_cfg.get("quant_method", "").lower() # Only proceed if the method is explicitly "modelopt" if quant_method != "modelopt": return None # Look for ModelOpt-specific config structure if "quantization" in hf_quant_cfg: quant_config = hf_quant_cfg["quantization"] if isinstance(quant_config, dict): quant_algo = quant_config.get("quant_algo", "") if "NVFP4" in quant_algo: return "modelopt_fp4" else: # Check for compressed-tensors style config with specific # quant_algo field quant_algo = hf_quant_cfg.get("quant_algo", "") if isinstance(quant_algo, str) and "FP4" in quant_algo.upper(): return "modelopt_fp4" return None @classmethod def _from_config( cls, *, quant_method: str, kv_cache_quant_method: str | None, exclude_modules: list[str], original_config: dict[str, Any], group_size: int | None, **kwargs: Any, ) -> "ModelOptNvFp4Config": is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method if group_size is None: group_size = 16 # Default value # For FP4, these fields are required if is_checkpoint_nvfp4_serialized and "quantization" in original_config: # Check if required fields are present in the quantization config quant_config = original_config["quantization"] required_fields = ["group_size", "kv_cache_quant_algo", "exclude_modules"] missing_fields = [ field for field in required_fields if field not in quant_config ] if missing_fields: raise ValueError( f"NVFP4 quantization requires the following fields in " f"hf_quant_config.json: {missing_fields}" ) return cls( is_checkpoint_nvfp4_serialized, kv_cache_quant_method, exclude_modules, group_size, ) class ModelOptNvFp4LinearMethod(LinearMethodBase): """Linear method for Model Optimizer NVFP4. Supports loading NVFP4 checkpoints with the following structure: input_scale: torch.float32, scalar , weight: NVFP4(represented as byte) Shape: [1, X, y/2] weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale, weight_scale_2: torch.float32, scalar, Args: quant_config: The ModelOpt quantization config. """ def __init__(self, quant_config: ModelOptNvFp4Config) -> None: self.quant_config = quant_config self.marlin_input_dtype = None self.backend = "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}" elif envs.VLLM_NVFP4_GEMM_BACKEND == "cutlass": self.backend = "cutlass" assert cutlass_fp4_supported(), f"Cutlass is required for {self.backend}" elif envs.VLLM_NVFP4_GEMM_BACKEND == "marlin": self.backend = "marlin" assert is_fp4_marlin_supported(), f"Marlin 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, input_dtype=self.marlin_input_dtype, ) 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, is_sf_swizzled_layout=True, backend=self.backend ) # 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_config: FusedMoEConfig, ) -> None: super().__init__(moe_config) self.quant_config = quant_config # Select experts implementation. self.nvfp4_backend, self.experts_cls = select_nvfp4_moe_backend( config=self.moe, weight_key=kNvfp4Static, activation_key=kNvfp4Dynamic, ) # Delay creation of the kernel until after process-weights. self.kernel: mk.FusedMoEModularKernel | None = None self.use_global_sf = is_global_sf_supported_for_nvfp4_backend( self.nvfp4_backend ) @property def topk_indices_dtype(self) -> torch.dtype | None: if self.kernel is not None: return self.kernel.prepare_finalize.topk_indices_dtype() return None def maybe_make_prepare_finalize( self, routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None, ) -> mk.FusedMoEPrepareAndFinalize | None: if self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM: return None elif self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_CUTLASS: # For no-EP case, don't use the MKM framework. if not self.moe.moe_parallel_config.use_all2all_kernels: return None # 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(routing_tables) def select_gemm_impl( self, prepare_finalize: mk.FusedMoEPrepareAndFinalize, layer: torch.nn.Module, ) -> mk.FusedMoEPermuteExpertsUnpermute: assert self.moe_quant_config is not None assert self.experts_cls is not None return make_nvfp4_moe_kernel_for_mkm( moe_config=self.moe, quant_config=self.moe_quant_config, experts_cls=self.experts_cls, prepare_finalize=prepare_finalize, ) def uses_weight_scale_2_pattern(self) -> bool: """ FP4 variants use 'weight_scale_2' pattern for per-tensor weight scales. """ return True def create_weights( self, layer: torch.nn.Module, num_experts: int, hidden_size: int, intermediate_size_per_partition: int, params_dtype: torch.dtype, **extra_weight_attrs, ): assert self.quant_config.is_checkpoint_nvfp4_serialized layer.num_experts = num_experts layer.params_dtype = params_dtype layer.quant_config = self.quant_config weight_dtype = torch.uint8 weight_scale_dtype = torch.float8_e4m3fn weight_loader = extra_weight_attrs.get("weight_loader") global_num_experts = extra_weight_attrs.get("global_num_experts") w13_num_shards = 2 if self.moe.is_act_and_mul else 1 # GEMM 1 w13_weight = ModelWeightParameter( data=torch.empty( num_experts, w13_num_shards * intermediate_size_per_partition, # 2 fp4 items are packed in the input dimension hidden_size // 2, dtype=weight_dtype, ), input_dim=1, output_dim=2, weight_loader=weight_loader, ) layer.register_parameter("w13_weight", w13_weight) # GEMM 2 w2_weight = ModelWeightParameter( data=torch.empty( num_experts, hidden_size, # 2 fp4 items are packed in the input dimension intermediate_size_per_partition // 2, dtype=weight_dtype, ), input_dim=1, output_dim=2, weight_loader=weight_loader, ) layer.register_parameter("w2_weight", w2_weight) w13_weight_scale = ModelWeightParameter( data=torch.empty( num_experts, w13_num_shards * intermediate_size_per_partition, # 2 fp4 items are packed in the input dimension hidden_size // self.quant_config.group_size, dtype=weight_scale_dtype, ), input_dim=1, output_dim=2, weight_loader=weight_loader, ) layer.register_parameter("w13_weight_scale", w13_weight_scale) w2_weight_scale = ModelWeightParameter( data=torch.empty( num_experts, hidden_size, # 2 fp4 items are packed in the input dimension intermediate_size_per_partition // self.quant_config.group_size, dtype=weight_scale_dtype, ), input_dim=1, output_dim=2, weight_loader=weight_loader, ) layer.register_parameter("w2_weight_scale", w2_weight_scale) extra_weight_attrs.update( {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value} ) w13_weight_scale_2 = PerTensorScaleParameter( data=torch.empty(num_experts, w13_num_shards, dtype=torch.float32), weight_loader=weight_loader, ) layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2) w2_weight_scale_2 = PerTensorScaleParameter( data=torch.empty(num_experts, dtype=torch.float32), weight_loader=weight_loader, ) layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2) extra_weight_attrs.update( {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value} ) global_sf_num_experts = ( global_num_experts if self.use_global_sf else num_experts ) w13_input_scale = PerTensorScaleParameter( data=torch.empty( global_sf_num_experts, w13_num_shards, dtype=torch.float32, ), weight_loader=weight_loader, ) layer.register_parameter("w13_input_scale", w13_input_scale) w2_input_scale = PerTensorScaleParameter( data=torch.empty(global_sf_num_experts, dtype=torch.float32), weight_loader=weight_loader, ) layer.register_parameter("w2_input_scale", w2_input_scale) def process_weights_after_loading(self, layer: torch.nn.Module) -> None: """ Convert NVFP4 MoE weights into kernel format and setup the kernel. """ # Use a single gscale for w13. if self.moe.is_act_and_mul and not torch.allclose( layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1] ): logger.warning_once( "w1_weight_scale_2 must match w3_weight_scale_2. " "Accuracy may be affected." ) w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0].contiguous() ( w13, w13_scale, w13_scale_2, a13_scale, w2, w2_scale, w2_scale_2, a2_scale, ) = convert_to_nvfp4_moe_kernel_format( nvfp4_backend=self.nvfp4_backend, layer=layer, w13=layer.w13_weight, w13_scale=layer.w13_weight_scale, w13_scale_2=w13_weight_scale_2, a13_scale=layer.w13_input_scale, w2=layer.w2_weight, w2_scale=layer.w2_weight_scale, w2_scale_2=layer.w2_weight_scale_2, a2_scale=layer.w2_input_scale, is_act_and_mul=self.moe.is_act_and_mul, ) replace_parameter(layer, "w13_weight", w13) replace_parameter(layer, "w13_weight_scale", w13_scale) replace_parameter(layer, "w13_weight_scale_2", w13_scale_2) replace_parameter(layer, "w13_input_scale", a13_scale) replace_parameter(layer, "w2_weight", w2) replace_parameter(layer, "w2_weight_scale", w2_scale) replace_parameter(layer, "w2_weight_scale_2", w2_scale_2) replace_parameter(layer, "w2_input_scale", a2_scale) # Setup modular kernel for TP case and naive DP/EP case. # In non-naive DP/EP case, we will create a ModularKernelMethod. # TODO(rob): unify these so FP8MoEMethod owns the ModularKernel # in both cases. self.moe_quant_config = self.get_fused_moe_quant_config(layer) if self.moe_quant_config and ( (not self.moe.moe_parallel_config.use_all2all_kernels) or self.moe.moe_parallel_config.use_naive_all2all_kernels ): assert self.experts_cls is not None self.kernel = make_nvfp4_moe_kernel( moe_quant_config=self.moe_quant_config, moe_config=self.moe, experts_cls=self.experts_cls, ) @property def do_post_quant_allgather(self): return self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM def prepare_dp_allgather_tensor( self, layer: FusedMoE, hidden_states: torch.Tensor, router_logits: torch.Tensor, ) -> tuple[torch.Tensor, list[torch.Tensor]]: """Optionally prepare extra tensors to carry through DP allgather/EP.""" if self.nvfp4_backend != NvFp4MoeBackend.FLASHINFER_TRTLLM: raise RuntimeError( "prepare_dp_allgather_tensor is only supported for " "FlashInfer TRTLLM NVFP4 MoE backend." ) import flashinfer hidden_states_fp4, hidden_states_sf = flashinfer.fp4_quantize( hidden_states, layer.a1_gscale, is_sf_swizzled_layout=False, ) extra_tensors: list[torch.Tensor] = [hidden_states_sf] return hidden_states_fp4, extra_tensors def get_fused_moe_quant_config( self, layer: torch.nn.Module ) -> FusedMoEQuantConfig | None: return make_nvfp4_moe_quant_config( backend=self.nvfp4_backend, w13_scale=layer.w13_weight_scale, w2_scale=layer.w2_weight_scale, w13_scale_2=layer.w13_weight_scale_2, w2_scale_2=layer.w2_weight_scale_2, a13_scale=layer.w13_input_scale, a2_scale=layer.w2_input_scale, ) @property def supports_eplb(self) -> bool: return True @property def is_monolithic(self) -> bool: return ( self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM and not self.moe.moe_parallel_config.enable_eplb ) def apply_monolithic( self, layer: FusedMoE, x: torch.Tensor, router_logits: torch.Tensor, ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: assert self.is_monolithic assert ( self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM and not layer.enable_eplb ) return flashinfer_trtllm_fp4_moe( layer=layer, x=x, router_logits=router_logits, top_k=layer.top_k, activation=layer.activation, global_num_experts=layer.global_num_experts, num_expert_group=layer.num_expert_group, topk_group=layer.topk_group, custom_routing_function=layer.custom_routing_function, e_score_correction_bias=layer.e_score_correction_bias, ) def apply( self, layer: FusedMoE, x: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: assert not self.is_monolithic # EPLB path if self.nvfp4_backend == NvFp4MoeBackend.FLASHINFER_TRTLLM: assert layer.enable_eplb return flashinfer_trtllm_fp4_routed_moe( layer=layer, x=x, topk_ids=topk_ids, topk_weights=topk_weights, top_k=layer.top_k, activation=layer.activation, global_num_experts=layer.global_num_experts, ) else: assert self.kernel is not None return self.kernel( x, layer.w13_weight, layer.w2_weight, topk_weights, topk_ids, inplace=False, 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, ) ModelOptNvFp4Config.LinearMethodCls = ModelOptNvFp4LinearMethod ModelOptNvFp4Config.FusedMoEMethodCls = ModelOptNvFp4FusedMoE ModelOptNvFp4Config.KVCacheMethodCls = ModelOptFp8KVCacheMethod