# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from collections.abc import Callable from enum import Enum from functools import partial 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 import _custom_ops as ops from vllm._aiter_ops import rocm_aiter_ops from vllm.distributed import get_tensor_model_parallel_world_size from vllm.logger import init_logger from vllm.model_executor.layers.batch_invariant import ( vllm_is_batch_invariant, ) from vllm.model_executor.layers.fused_moe import ( FusedMoE, FusedMoEActivationFormat, FusedMoEMethodBase, FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize, FusedMoeWeightScaleSupported, ) from vllm.model_executor.layers.fused_moe.config import ( FusedMoEQuantConfig, RoutingMethodType, fp8_w8a8_moe_quant_config, ) from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod 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.kv_cache import BaseKVCacheMethod from vllm.model_executor.layers.quantization.utils.flashinfer_utils import ( FlashinferMoeBackend, apply_flashinfer_per_tensor_scale_fp8, build_flashinfer_fp8_cutlass_moe_prepare_finalize, flashinfer_cutlass_moe_fp8, get_flashinfer_moe_backend, register_moe_scaling_factors, rotate_flashinfer_fp8_moe_weights, select_cutlass_fp8_gemm_impl, swap_w13_to_w31, ) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( W8A8BlockFp8LinearOp, create_fp8_input_scale, create_fp8_scale_parameter, create_fp8_weight_parameter, deepgemm_post_process_fp8_weight_block, maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy, process_fp8_weight_tensor_strategy, validate_fp8_block_shape, ) from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import ( apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin, prepare_moe_fp8_layer_for_marlin, ) from vllm.model_executor.layers.quantization.utils.quant_utils import ( GroupShape, is_layer_skipped, ) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( Fp8LinearOp, all_close_1d, cutlass_block_fp8_supported, cutlass_fp8_supported, maybe_create_device_identity, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize, ) from vllm.model_executor.parameter import ( BlockQuantScaleParameter, ModelWeightParameter, PerTensorScaleParameter, ) from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform from vllm.scalar_type import scalar_types from vllm.utils.deep_gemm import ( is_deep_gemm_e8m0_used, is_deep_gemm_supported, ) from vllm.utils.flashinfer import has_flashinfer_moe from vllm.utils.import_utils import has_deep_gemm if TYPE_CHECKING: from vllm.model_executor.models.utils import WeightsMapper ACTIVATION_SCHEMES = ["static", "dynamic"] logger = init_logger(__name__) class Fp8MoeBackend(Enum): NONE = 0 FLASHINFER_TRTLLM = 1 FLASHINFER_CUTLASS = 2 DEEPGEMM = 3 CUTLASS_BLOCK_SCALED_GROUPED_GEMM = 4 MARLIN = 5 TRITON = 6 def get_fp8_moe_backend(block_quant: bool) -> Fp8MoeBackend: """ Select the primary FP8 MoE backend Note: Shape-specific fallbacks may still occur at runtime. """ # Prefer FlashInfer backends on supported GPUs; allow SM90 and SM100. if ( current_platform.is_cuda() and ( current_platform.is_device_capability(100) or current_platform.is_device_capability(90) ) and envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe() ): backend = get_flashinfer_moe_backend() if backend == FlashinferMoeBackend.TENSORRT_LLM: logger.info_once("Using FlashInfer FP8 MoE TRTLLM backend for SM100") return Fp8MoeBackend.FLASHINFER_TRTLLM else: if block_quant and current_platform.is_device_capability(100): raise ValueError( "FlashInfer FP8 MoE throughput backend does not " "support block quantization. Please use " "VLLM_FLASHINFER_MOE_BACKEND=latency " "instead." ) logger.info_once("Using FlashInfer FP8 MoE CUTLASS backend for SM90/SM100") return Fp8MoeBackend.FLASHINFER_CUTLASS # weight-only path for older GPUs without native FP8 use_marlin = ( not current_platform.has_device_capability(89) or envs.VLLM_TEST_FORCE_FP8_MARLIN ) if current_platform.is_rocm(): use_marlin = False if use_marlin: logger.info_once("Using Marlin backend for FP8 MoE") return Fp8MoeBackend.MARLIN # deepGEMM on supported platforms with block-quantized weights if envs.VLLM_USE_DEEP_GEMM and envs.VLLM_MOE_USE_DEEP_GEMM and block_quant: if not has_deep_gemm(): logger.warning_once("DeepGEMM backend requested but not available.") elif is_deep_gemm_supported(): logger.info_once("Using DeepGEMM backend for FP8 MoE") return Fp8MoeBackend.DEEPGEMM # CUTLASS BlockScaled GroupedGemm on SM100 with block-quantized weights if ( current_platform.is_cuda() and current_platform.is_device_capability(100) and block_quant ): logger.info_once("Using Cutlass BlockScaled GroupedGemm backend for FP8 MoE") return Fp8MoeBackend.CUTLASS_BLOCK_SCALED_GROUPED_GEMM # default to Triton logger.info_once("Using Triton backend for FP8 MoE") return Fp8MoeBackend.TRITON class Fp8Config(QuantizationConfig): """Config class for FP8.""" def __init__( self, is_checkpoint_fp8_serialized: bool = False, activation_scheme: str = "dynamic", ignored_layers: list[str] | None = None, weight_block_size: list[int] | None = None, ) -> None: super().__init__() self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized if activation_scheme not in ACTIVATION_SCHEMES: raise ValueError(f"Unsupported activation scheme {activation_scheme}") self.activation_scheme = activation_scheme self.ignored_layers = ignored_layers or [] if weight_block_size is not None: if not is_checkpoint_fp8_serialized: raise ValueError( "The block-wise quantization only supports fp8-serialized " "checkpoint for now." ) if len(weight_block_size) != 2: raise ValueError( "The quantization block size of weight must have 2 " f"dimensions, but got {len(weight_block_size)} dimensions" ) if activation_scheme != "dynamic": raise ValueError( "The block-wise quantization only supports " "dynamic activation scheme for now, but got " f"{activation_scheme} activation scheme." ) self.weight_block_size = weight_block_size @classmethod def get_name(cls) -> QuantizationMethods: return "fp8" @classmethod def get_supported_act_dtypes(cls) -> list[torch.dtype]: return [torch.bfloat16, torch.half] @classmethod def get_min_capability(cls) -> int: return 80 @classmethod def get_config_filenames(cls) -> list[str]: return [] def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"): if self.ignored_layers is not None: self.ignored_layers = hf_to_vllm_mapper.apply_list(self.ignored_layers) @classmethod def from_config(cls, config: dict[str, Any]) -> "Fp8Config": quant_method = cls.get_from_keys(config, ["quant_method"]) is_checkpoint_fp8_serialized = "fp8" in quant_method activation_scheme = cls.get_from_keys(config, ["activation_scheme"]) ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None) weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None) if not ignored_layers: ignored_layers = cls.get_from_keys_or( config, ["modules_to_not_convert"], None ) return cls( is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized, activation_scheme=activation_scheme, ignored_layers=ignored_layers, weight_block_size=weight_block_size, ) def get_xpu_quant_method( self, layer: torch.nn.Module, prefix: str ) -> Optional["QuantizeMethodBase"]: from vllm.attention.layer import Attention from vllm.model_executor.layers.quantization.ipex_quant import ( XPUFp8LinearMethod, XPUFp8MoEMethod, ) fp8_config = Fp8Config( is_checkpoint_fp8_serialized=self.is_checkpoint_fp8_serialized, activation_scheme=self.activation_scheme, ignored_layers=self.ignored_layers, weight_block_size=self.weight_block_size, ) if isinstance(layer, LinearBase): if is_layer_skipped( prefix=prefix, ignored_layers=self.ignored_layers, fused_mapping=self.packed_modules_mapping, ): return UnquantizedLinearMethod() return XPUFp8LinearMethod(fp8_config) elif isinstance(layer, FusedMoE): return XPUFp8MoEMethod(fp8_config, layer) elif isinstance(layer, Attention): return Fp8KVCacheMethod(self) return None def get_quant_method( self, layer: torch.nn.Module, prefix: str ) -> Optional["QuantizeMethodBase"]: from vllm.attention.layer import Attention # Avoid circular import if current_platform.is_xpu(): return self.get_xpu_quant_method(layer, prefix) if isinstance(layer, LinearBase): if is_layer_skipped( prefix=prefix, ignored_layers=self.ignored_layers, fused_mapping=self.packed_modules_mapping, ): return UnquantizedLinearMethod() return Fp8LinearMethod(self) elif isinstance(layer, FusedMoE): if is_layer_skipped( prefix=prefix, ignored_layers=self.ignored_layers, fused_mapping=self.packed_modules_mapping, ): return UnquantizedFusedMoEMethod(layer.moe_config) return Fp8MoEMethod(self, layer) elif isinstance(layer, Attention): return Fp8KVCacheMethod(self) return None def get_cache_scale(self, name: str) -> str | None: """ Check whether the param name matches the format for k/v cache scales in compressed-tensors. If this is the case, return its equivalent param name expected by vLLM :param name: param name :return: matching param name for KV cache scale in vLLM """ if name.endswith(".output_scale") and ".k_proj" in name: return name.replace(".k_proj.output_scale", ".attn.k_scale") if name.endswith(".output_scale") and ".v_proj" in name: return name.replace(".v_proj.output_scale", ".attn.v_scale") if name.endswith(".output_scale") and ".q_proj" in name: return name.replace(".q_proj.output_scale", ".attn.q_scale") if name.endswith("self_attn.prob_output_scale"): return name.replace(".prob_output_scale", ".attn.prob_scale") # If no matches, return None return None class Fp8LinearMethod(LinearMethodBase): """Linear method for FP8. Supports loading FP8 checkpoints with static weight scale and dynamic/static activation scale. Also supports loading quantized FP16/BF16 model checkpoints with dynamic activation scaling. The weight scaling factor will be initialized after the model weights are loaded. Limitations: 1. Only support per-tensor quantization due to torch._scaled_mm support. 2. Only support float8_e4m3fn data type due to the limitation of torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856) Args: quant_config: The quantization config. """ def __init__(self, quant_config: Fp8Config): self.quant_config = quant_config self.cutlass_block_fp8_supported = cutlass_block_fp8_supported() self.out_dtype = torch.get_default_dtype() # For GPUs that lack FP8 hardware support, we can leverage the Marlin # kernel for fast weight-only FP8 quantization self.use_marlin = ( not current_platform.has_device_capability(89) or envs.VLLM_TEST_FORCE_FP8_MARLIN ) # Disable marlin for rocm if current_platform.is_rocm(): self.use_marlin = False if vllm_is_batch_invariant(): self.use_marlin = False self.use_aiter_and_is_supported = rocm_aiter_ops.is_linear_fp8_enaled() self.use_deep_gemm = is_deep_gemm_supported() self.weight_block_size = self.quant_config.weight_block_size self.block_quant = self.weight_block_size is not None self.act_q_static = self.quant_config.activation_scheme == "static" if self.weight_block_size: self.act_q_group_shape = GroupShape(1, self.weight_block_size[0]) else: # Use per-token quantization for better perf if dynamic and cutlass if not self.act_q_static and cutlass_fp8_supported(): self.act_q_group_shape = GroupShape.PER_TOKEN else: self.act_q_group_shape = GroupShape.PER_TENSOR if self.block_quant: assert not self.act_q_static assert self.weight_block_size is not None self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp( weight_group_shape=GroupShape(*self.weight_block_size), act_quant_group_shape=self.act_q_group_shape, cutlass_block_fp8_supported=self.cutlass_block_fp8_supported, use_aiter_and_is_supported=self.use_aiter_and_is_supported, ) else: self.fp8_linear = Fp8LinearOp( act_quant_static=self.act_q_static, act_quant_group_shape=self.act_q_group_shape, ) 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, ): maybe_create_device_identity() 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 layer.orig_dtype = params_dtype layer.weight_block_size = None if self.block_quant: assert self.weight_block_size is not None layer.weight_block_size = self.weight_block_size validate_fp8_block_shape( layer, input_size, output_size, input_size_per_partition, output_partition_sizes, self.weight_block_size, ) # WEIGHT if self.quant_config.is_checkpoint_fp8_serialized: weight = create_fp8_weight_parameter( output_size_per_partition, input_size_per_partition, weight_loader ) else: # For non-serialized checkpoints, use original dtype weight = ModelWeightParameter( data=torch.empty( output_size_per_partition, input_size_per_partition, dtype=params_dtype, ), input_dim=1, output_dim=0, weight_loader=weight_loader, ) layer.register_parameter("weight", weight) # If checkpoint is serialized fp8, load them. # Otherwise, wait until process_weights_after_loading. if self.quant_config.is_checkpoint_fp8_serialized: # WEIGHT SCALE if not self.block_quant: scale = create_fp8_scale_parameter( PerTensorScaleParameter, output_partition_sizes, input_size_per_partition, None, weight_loader, ) set_weight_attrs(scale, {"scale_type": "weight_scale"}) layer.register_parameter("weight_scale", scale) else: assert not self.act_q_static assert self.weight_block_size is not None scale = create_fp8_scale_parameter( BlockQuantScaleParameter, output_partition_sizes, input_size_per_partition, self.weight_block_size, weight_loader, ) set_weight_attrs(scale, {"scale_type": "weight_scale"}) # The weight_scale_inv name is intentional for deepseekv3 layer.register_parameter("weight_scale_inv", scale) # INPUT ACTIVATION SCALE if self.act_q_static: scale = create_fp8_input_scale(output_partition_sizes, weight_loader) set_weight_attrs(scale, {"scale_type": "input_scale"}) layer.register_parameter("input_scale", scale) else: layer.register_parameter("input_scale", None) def process_weights_after_loading(self, layer: Module) -> None: size_k_first = True input_scale = None # TODO(rob): refactor block quant into separate class. if self.block_quant: assert not self.act_q_static size_k_first = False weight, weight_scale = process_fp8_weight_block_strategy( layer.weight, layer.weight_scale_inv ) # Delete the weight_scale_inv parameter to avoid confusion # with the weight_scale parameter del layer.weight_scale_inv # If checkpoint not serialized fp8, quantize the weights. elif not self.quant_config.is_checkpoint_fp8_serialized: qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None) weight = qweight.t() # If checkpoint is fp8 per-tensor, handle that there are N scales for N # shards in a fused module else: weight = layer.weight weight_scale = layer.weight_scale # If using w8a8, torch._scaled_mm needs per tensor, so # requantize the logical shards as a single weight. if not self.use_marlin: weight, weight_scale, input_scale = process_fp8_weight_tensor_strategy( weight, weight_scale, layer.logical_widths, getattr(layer, "input_scale", None), ) if self.act_q_static: assert input_scale is not None input_scale = input_scale.max() weight = weight.t() # Update layer with new values. layer.weight = Parameter(weight.data, requires_grad=False) layer.weight_scale = Parameter(weight_scale.data, requires_grad=False) layer.input_scale = ( Parameter(input_scale, requires_grad=False) if input_scale is not None else None ) if self.use_marlin: prepare_fp8_layer_for_marlin(layer, size_k_first) # Activations not quantized for marlin. del layer.input_scale return if self.block_quant: maybe_post_process_fp8_weight_block(layer) def apply( self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None = None, ) -> torch.Tensor: # if batch invariant mode is enabled, prefer DeepGEMM FP8 path # we will use BF16 dequant when DeepGEMM is not supported. if vllm_is_batch_invariant(): if self.block_quant: assert self.weight_block_size is not None return self.w8a8_block_fp8_linear.apply( input=x, weight=layer.weight, weight_scale=layer.weight_scale, input_scale=layer.input_scale, bias=bias, ) else: # per-tensor/channel: dequant to BF16 and run GEMM weight_fp8 = layer.weight.to(torch.bfloat16) weight_scale = layer.weight_scale.to(torch.bfloat16) if weight_scale.numel() == 1: # Per-tensor: simple scalar multiplication weight_bf16 = weight_fp8 * weight_scale else: # Multiple scales (fused modules like QKV) # Try to infer correct broadcasting # weight is [K, N], scale could be [num_logical_weights] # Need to figure out how to broadcast - for now just try # direct multiplication if ( weight_scale.dim() == 1 and weight_scale.shape[0] == weight_fp8.shape[0] ): # Per-row scaling weight_bf16 = weight_fp8 * weight_scale.unsqueeze(1) else: # Fallback weight_bf16 = weight_fp8 * weight_scale return torch.nn.functional.linear(x, weight_bf16.t(), bias) if self.use_marlin: return apply_fp8_marlin_linear( input=x, weight=layer.weight, weight_scale=layer.weight_scale, workspace=layer.workspace, size_n=layer.output_size_per_partition, size_k=layer.input_size_per_partition, bias=bias, ) if self.block_quant: assert self.weight_block_size is not None return self.w8a8_block_fp8_linear.apply( input=x, weight=layer.weight, weight_scale=layer.weight_scale, input_scale=layer.input_scale, bias=bias, ) return self.fp8_linear.apply( input=x, weight=layer.weight, weight_scale=layer.weight_scale, out_dtype=self.out_dtype, input_scale=layer.input_scale, bias=bias, ) class Fp8MoEMethod(FusedMoEMethodBase): """MoE method for FP8. Supports loading FP8 checkpoints with static weight scale and dynamic/static activation scale. Also supports loading quantized FP16/BF16 model checkpoints with dynamic activation scaling. The weight scaling factor will be initialized after the model weights are loaded. Args: quant_config: The quantization config. """ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): super().__init__(layer.moe_config) self.layer = layer self.quant_config = quant_config self.weight_block_size = self.quant_config.weight_block_size self.block_quant: bool = self.weight_block_size is not None self.fp8_backend = get_fp8_moe_backend(self.block_quant) self.use_marlin = self.fp8_backend == Fp8MoeBackend.MARLIN self.flashinfer_moe_backend: FlashinferMoeBackend | None = None if self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM: self.flashinfer_moe_backend = FlashinferMoeBackend.TENSORRT_LLM elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS: self.flashinfer_moe_backend = FlashinferMoeBackend.CUTLASS if self.block_quant: assert self.weight_block_size == [128, 128], ( f"Only support weight_block_size == [128, 128], " f"got {self.weight_block_size}" ) self.flashinfer_moe_fn = partial( flashinfer_cutlass_moe_fp8, moe=self.moe, use_deepseek_fp8_block_scale=self.block_quant, ) self.allow_deep_gemm = self.fp8_backend == Fp8MoeBackend.DEEPGEMM self.allow_cutlass_block_scaled_grouped_gemm = ( self.fp8_backend == Fp8MoeBackend.CUTLASS_BLOCK_SCALED_GROUPED_GEMM ) def create_weights( self, layer: Module, num_experts: int, hidden_size: int, intermediate_size_per_partition: int, params_dtype: torch.dtype, **extra_weight_attrs, ): layer.intermediate_size_per_partition = intermediate_size_per_partition layer.hidden_size = hidden_size layer.num_experts = num_experts layer.orig_dtype = params_dtype layer.weight_block_size = None if self.quant_config.is_checkpoint_fp8_serialized: params_dtype = torch.float8_e4m3fn if self.block_quant: assert self.weight_block_size is not None layer.weight_block_size = self.weight_block_size tp_size = get_tensor_model_parallel_world_size() block_n, block_k = ( self.weight_block_size[0], self.weight_block_size[1], ) # NOTE: To ensure proper alignment of the block-wise quantization # scales, the output_size of the weights for both the gate and up # layers must be divisible by block_n. # Required by column parallel or enabling merged weights if intermediate_size_per_partition % block_n != 0: raise ValueError( f"The output_size of gate's and up's weight = " f"{intermediate_size_per_partition} is not divisible by " f"weight quantization block_n = {block_n}." ) if tp_size > 1 and intermediate_size_per_partition % block_k != 0: # Required by row parallel raise ValueError( f"The input_size of down's weight = " f"{intermediate_size_per_partition} is not divisible by " f"weight quantization block_k = {block_k}." ) # WEIGHTS w13_weight = torch.nn.Parameter( torch.empty( num_experts, 2 * intermediate_size_per_partition, hidden_size, dtype=params_dtype, ), requires_grad=False, ) layer.register_parameter("w13_weight", w13_weight) set_weight_attrs(w13_weight, extra_weight_attrs) w2_weight = torch.nn.Parameter( torch.empty( num_experts, hidden_size, intermediate_size_per_partition, dtype=params_dtype, ), requires_grad=False, ) layer.register_parameter("w2_weight", w2_weight) set_weight_attrs(w2_weight, extra_weight_attrs) # WEIGHT_SCALES if not self.block_quant: # Allocate 2 scales for w1 and w3 respectively. # They will be combined to a single scale after weight loading. w13_weight_scale = torch.nn.Parameter( torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False ) w2_weight_scale = torch.nn.Parameter( torch.ones(num_experts, dtype=torch.float32), requires_grad=False ) layer.register_parameter("w13_weight_scale", w13_weight_scale) layer.register_parameter("w2_weight_scale", w2_weight_scale) else: w13_weight_scale = torch.nn.Parameter( torch.ones( num_experts, 2 * ((intermediate_size_per_partition + block_n - 1) // block_n), (hidden_size + block_k - 1) // block_k, dtype=torch.float32, ), requires_grad=False, ) w2_weight_scale = torch.nn.Parameter( torch.ones( num_experts, (hidden_size + block_n - 1) // block_n, (intermediate_size_per_partition + block_k - 1) // block_k, dtype=torch.float32, ), requires_grad=False, ) layer.register_parameter("w13_weight_scale_inv", w13_weight_scale) layer.register_parameter("w2_weight_scale_inv", w2_weight_scale) assert self.quant_config.activation_scheme == "dynamic" # Add the quantization method used (per tensor/grouped/channel) # to ensure the weight scales are loaded in properly extra_weight_attrs.update( {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value} if self.block_quant else {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value} ) # If loading fp8 checkpoint, pass the weight loaders. # If loading an fp16 checkpoint, do not (we will quantize in # process_weights_after_loading() if self.quant_config.is_checkpoint_fp8_serialized: set_weight_attrs(w13_weight_scale, extra_weight_attrs) set_weight_attrs(w2_weight_scale, extra_weight_attrs) # INPUT_SCALES if self.quant_config.activation_scheme == "static": if not self.quant_config.is_checkpoint_fp8_serialized: raise ValueError( "Found static activation scheme for checkpoint that " "was not serialized fp8." ) w13_input_scale = torch.nn.Parameter( torch.ones(num_experts, dtype=torch.float32), requires_grad=False ) layer.register_parameter("w13_input_scale", w13_input_scale) set_weight_attrs(w13_input_scale, extra_weight_attrs) w2_input_scale = torch.nn.Parameter( torch.ones(num_experts, dtype=torch.float32), requires_grad=False ) layer.register_parameter("w2_input_scale", w2_input_scale) set_weight_attrs(w2_input_scale, extra_weight_attrs) else: layer.w13_input_scale = None layer.w2_input_scale = None self.rocm_aiter_moe_enabled = False def process_weights_after_loading(self, layer: Module) -> None: # Lazy import to avoid importing triton too early. self.rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled() # TODO (rob): refactor block quant into separate class. if self.block_quant: assert self.quant_config.activation_scheme == "dynamic" if current_platform.is_fp8_fnuz(): w13_weight, w13_weight_scale_inv, w13_input_scale = ( normalize_e4m3fn_to_e4m3fnuz( layer.w13_weight, layer.w13_weight_scale_inv, layer.w13_input_scale, ) ) w2_weight, w2_weight_scale_inv, w2_input_scale = ( normalize_e4m3fn_to_e4m3fnuz( layer.w2_weight, layer.w2_weight_scale_inv, layer.w2_input_scale ) ) elif self.flashinfer_moe_backend is not None: # NOTE: weights have to be swapped since the activation is # applied on different half for flashinfer vs vllm w13_weight = swap_w13_to_w31(layer.w13_weight.data) w13_weight_scale_inv = swap_w13_to_w31(layer.w13_weight_scale_inv.data) w2_weight = layer.w2_weight.data w2_weight_scale_inv = layer.w2_weight_scale_inv.data else: w13_weight = layer.w13_weight.data w13_weight_scale_inv = layer.w13_weight_scale_inv.data w2_weight = layer.w2_weight w2_weight_scale_inv = layer.w2_weight_scale_inv # torch.compile() cannot use Parameter subclasses. layer.w13_weight = Parameter(w13_weight, requires_grad=False) layer.w13_weight_scale_inv = Parameter( w13_weight_scale_inv, requires_grad=False ) layer.w2_weight = Parameter(w2_weight, requires_grad=False) layer.w2_weight_scale_inv = Parameter( w2_weight_scale_inv, requires_grad=False ) if self.rocm_aiter_moe_enabled: # reshaping weights is required for aiter moe kernel. shuffled_w13, shuffled_w2 = rocm_aiter_ops.shuffle_weights( layer.w13_weight.data, layer.w2_weight.data ) layer.w13_weight = torch.nn.Parameter(shuffled_w13, requires_grad=False) layer.w2_weight = torch.nn.Parameter(shuffled_w2, requires_grad=False) # DeepGemm scales need to be transposed and aligned. We try to do # it ahead of time for performance reasons. if self.allow_deep_gemm: dg_w13_weight, dg_w13_weight_scale_inv = ( deepgemm_post_process_fp8_weight_block( wq=layer.w13_weight.data, ws=layer.w13_weight_scale_inv.data, quant_block_shape=tuple(layer.weight_block_size), use_e8m0=is_deep_gemm_e8m0_used(), ) ) dg_w2_weight, dg_w2_weight_scale_inv = ( deepgemm_post_process_fp8_weight_block( wq=layer.w2_weight.data, ws=layer.w2_weight_scale_inv.data, quant_block_shape=tuple(layer.weight_block_size), use_e8m0=is_deep_gemm_e8m0_used(), ) ) layer.w13_weight = Parameter(dg_w13_weight, requires_grad=False) layer.w13_weight_scale_inv = Parameter( dg_w13_weight_scale_inv, requires_grad=False ) layer.w2_weight = Parameter(dg_w2_weight, requires_grad=False) layer.w2_weight_scale_inv = Parameter( dg_w2_weight_scale_inv, requires_grad=False ) # If checkpoint is fp16, quantize in place. elif not self.quant_config.is_checkpoint_fp8_serialized: fp8_dtype = current_platform.fp8_dtype() w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype) w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype) # Re-initialize w13_scale because we directly quantize # merged w13 weights and generate a single scaling factor. layer.w13_weight_scale = torch.nn.Parameter( torch.ones( layer.local_num_experts, dtype=torch.float32, device=w13_weight.device, ), requires_grad=False, ) for expert in range(layer.local_num_experts): w13_weight[expert, :, :], layer.w13_weight_scale[expert] = ( ops.scaled_fp8_quant(layer.w13_weight.data[expert, :, :]) ) w2_weight[expert, :, :], layer.w2_weight_scale[expert] = ( ops.scaled_fp8_quant(layer.w2_weight.data[expert, :, :]) ) layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False) layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False) if self.rocm_aiter_moe_enabled: # reshaping weights is required for aiter moe kernel. shuffled_w13, shuffled_w2 = rocm_aiter_ops.shuffle_weights( layer.w13_weight, layer.w2_weight ) layer.w13_weight = torch.nn.Parameter(shuffled_w13, requires_grad=False) layer.w2_weight = torch.nn.Parameter(shuffled_w2, requires_grad=False) # If checkpoint is fp8, we need to handle that the # MoE kernels require single activation scale and single weight # scale for w13 per expert. else: # Fp8 moe kernels require a single activation scale. # We take the max of all the scales in case they differ. if self.quant_config.activation_scheme == "static": if layer.w13_input_scale is None or layer.w2_input_scale is None: raise ValueError( "QuantConfig has static quantization, but found " "activation scales are None." ) if not all_close_1d(layer.w13_input_scale) or not all_close_1d( layer.w2_input_scale ): logger.warning_once( "Found input_scales that are not equal for " "fp8 MoE layer. Using the maximum across experts " "for each layer." ) layer.w13_input_scale = torch.nn.Parameter( layer.w13_input_scale.max(), requires_grad=False ) layer.w2_input_scale = torch.nn.Parameter( layer.w2_input_scale.max(), requires_grad=False ) if current_platform.is_fp8_fnuz(): # Normalize the weights and scales w13_weight, w13_weight_scale, w13_input_scale = ( normalize_e4m3fn_to_e4m3fnuz( layer.w13_weight, layer.w13_weight_scale, layer.w13_input_scale ) ) w2_weight, w2_weight_scale, w2_input_scale = ( normalize_e4m3fn_to_e4m3fnuz( layer.w2_weight, layer.w2_weight_scale, layer.w2_input_scale ) ) # Reset the parameter layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False) layer.w13_weight_scale = torch.nn.Parameter( w13_weight_scale, requires_grad=False ) if w13_input_scale is not None: layer.w13_input_scale = torch.nn.Parameter( w13_input_scale, requires_grad=False ) layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False) layer.w2_weight_scale = torch.nn.Parameter( w2_weight_scale, requires_grad=False ) if w2_input_scale is not None: layer.w2_input_scale = torch.nn.Parameter( w2_input_scale, requires_grad=False ) # Fp8 moe kernel needs single weight scale for w13 per expert. # We take the max then dequant and requant each expert. assert layer.w13_weight_scale is not None shard_size = layer.intermediate_size_per_partition max_w13_scales = layer.w13_weight_scale.max(dim=1).values for expert_id in range(layer.local_num_experts): start = 0 for shard_id in range(2): dq_weight = per_tensor_dequantize( layer.w13_weight[expert_id][start : start + shard_size, :], layer.w13_weight_scale[expert_id][shard_id], ) layer.w13_weight[expert_id][start : start + shard_size, :], _ = ( ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id]) ) start += shard_size if self.rocm_aiter_moe_enabled: shuffled_w13, shuffled_w2 = rocm_aiter_ops.shuffle_weights( layer.w13_weight, layer.w2_weight ) layer.w13_weight = torch.nn.Parameter(shuffled_w13, requires_grad=False) layer.w2_weight = torch.nn.Parameter(shuffled_w2, requires_grad=False) layer.w13_weight_scale = torch.nn.Parameter( max_w13_scales, requires_grad=False ) if self.flashinfer_moe_backend is not None: # NOTE: weights have to be swapped since the activation is # applied on different half for flashinfer vs vllm assert not self.block_quant register_moe_scaling_factors(layer) w13_weight = swap_w13_to_w31(layer.w13_weight.data) if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM: rotate_flashinfer_fp8_moe_weights(w13_weight, w2_weight) layer.w13_weight.data = w13_weight.data if self.use_marlin: prepare_moe_fp8_layer_for_marlin(layer, False) # Activations not quantized for marlin. del layer.w13_input_scale del layer.w2_input_scale def maybe_make_prepare_finalize(self) -> mk.FusedMoEPrepareAndFinalize | None: if ( self.rocm_aiter_moe_enabled or self.use_marlin or self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM ): return None elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS: if self.block_quant: assert self.weight_block_size == [128, 128], ( f"Only support weight_block_size == [128, 128], " f"got {self.weight_block_size}" ) # Wire block-scale flag through prepare/finalize when using CUTLASS prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize( self.moe, use_deepseek_fp8_block_scale=self.block_quant, ) logger.debug_once("%s", prepare_finalize.__class__.__name__) return prepare_finalize else: return super().maybe_make_prepare_finalize() def select_gemm_impl( self, prepare_finalize: FusedMoEPrepareAndFinalize, layer: torch.nn.Module, ) -> FusedMoEPermuteExpertsUnpermute: from vllm.model_executor.layers.fused_moe import ( BatchedDeepGemmExperts, BatchedTritonExperts, TritonOrDeepGemmExperts, ) assert not self.use_marlin and not self.rocm_aiter_moe_enabled, ( "Marlin and ROCm AITER are not supported with all2all yet." ) assert self.moe_quant_config is not None if ( prepare_finalize.activation_format == FusedMoEActivationFormat.BatchedExperts ): max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank() assert max_num_tokens_per_rank is not None experts_impl = ( BatchedDeepGemmExperts if self.allow_deep_gemm else BatchedTritonExperts ) logger.debug( "%s(%s): max_tokens_per_rank=%s, block_size=%s, per_act_token=%s", experts_impl.__name__, self.__class__.__name__, max_num_tokens_per_rank, self.weight_block_size, False, ) return experts_impl( max_num_tokens=max_num_tokens_per_rank, num_dispatchers=prepare_finalize.num_dispatchers(), quant_config=self.moe_quant_config, ) elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS: # Select GEMM experts with block-scale when weights are block-quantized experts = select_cutlass_fp8_gemm_impl( self.moe, self.moe_quant_config, use_deepseek_fp8_block_scale=self.block_quant, ) logger.debug_once("Using %s", experts.__class__.__name__) return experts else: logger.debug( "TritonOrDeepGemmExperts(%s): block_size=%s, per_act_token=%s", self.__class__.__name__, self.weight_block_size, False, ) return TritonOrDeepGemmExperts( quant_config=self.moe_quant_config, allow_deep_gemm=self.allow_deep_gemm, ) def get_fused_moe_quant_config( self, layer: torch.nn.Module ) -> FusedMoEQuantConfig | None: if self.use_marlin: return None return fp8_w8a8_moe_quant_config( w1_scale=( layer.w13_weight_scale_inv if self.block_quant else layer.w13_weight_scale ), w2_scale=( layer.w2_weight_scale_inv if self.block_quant else layer.w2_weight_scale ), a1_scale=layer.w13_input_scale, a2_scale=layer.w2_input_scale, block_shape=self.weight_block_size, ) @property def supports_eplb(self) -> bool: return True @property def allow_inplace(self) -> bool: return True def apply( self, layer: torch.nn.Module, x: torch.Tensor, router_logits: torch.Tensor, top_k: int, renormalize: bool, use_grouped_topk: bool = False, topk_group: int | None = None, num_expert_group: int | None = None, global_num_experts: int = -1, expert_map: torch.Tensor | None = None, custom_routing_function: Callable | None = None, scoring_func: str = "softmax", routed_scaling_factor: float = 1.0, e_score_correction_bias: torch.Tensor | None = None, apply_router_weight_on_input: bool = False, activation: str = "silu", enable_eplb: bool = False, expert_load_view: torch.Tensor | None = None, logical_to_physical_map: torch.Tensor | None = None, logical_replica_count: torch.Tensor | None = None, ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: if enable_eplb: assert expert_load_view is not None assert logical_to_physical_map is not None assert logical_replica_count is not None assert isinstance(layer, FusedMoE) if self.flashinfer_moe_backend == FlashinferMoeBackend.TENSORRT_LLM: assert activation == "silu", ( f"Expected 'silu' activation but got {activation}" ) if self.block_quant: import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe # noqa: E501, F401 e_score_correction_bias = ( e_score_correction_bias.to(x.dtype) if e_score_correction_bias is not None else None ) routing_method_type = layer.routing_method_type return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8( routing_logits=router_logits.to(torch.float32) if routing_method_type == RoutingMethodType.DeepSeekV3 else router_logits, routing_bias=e_score_correction_bias, x=x, w13_weight=layer.w13_weight, w13_weight_scale_inv=layer.w13_weight_scale_inv, w2_weight=layer.w2_weight, w2_weight_scale_inv=layer.w2_weight_scale_inv, global_num_experts=global_num_experts, top_k=top_k, num_expert_group=num_expert_group, topk_group=topk_group, intermediate_size=layer.intermediate_size_per_partition, expert_offset=layer.ep_rank * layer.local_num_experts, local_num_experts=layer.local_num_experts, block_shape=self.weight_block_size, routing_method_type=routing_method_type, routed_scaling=routed_scaling_factor, ) else: assert not renormalize and custom_routing_function is not None result = apply_flashinfer_per_tensor_scale_fp8( layer=layer, hidden_states=x, router_logits=router_logits, routing_bias=e_score_correction_bias, global_num_experts=global_num_experts, top_k=top_k, num_expert_group=num_expert_group, topk_group=topk_group, apply_router_weight_on_input=apply_router_weight_on_input, ) zero_expert_num = getattr(layer, "zero_expert_num", 0) zero_expert_type = getattr(layer, "zero_expert_type", None) select_result = FusedMoE.select_experts( hidden_states=x, router_logits=router_logits, use_grouped_topk=use_grouped_topk, top_k=top_k, renormalize=renormalize, topk_group=topk_group, num_expert_group=num_expert_group, custom_routing_function=custom_routing_function, scoring_func=scoring_func, routed_scaling_factor=routed_scaling_factor, e_score_correction_bias=e_score_correction_bias, indices_type=self.topk_indices_dtype, enable_eplb=enable_eplb, expert_map=expert_map, expert_load_view=expert_load_view, logical_to_physical_map=logical_to_physical_map, logical_replica_count=logical_replica_count, global_num_experts=global_num_experts, zero_expert_num=zero_expert_num, zero_expert_type=zero_expert_type, num_fused_shared_experts=layer.num_fused_shared_experts, ) topk_weights, topk_ids, zero_expert_result = select_result if self.rocm_aiter_moe_enabled: from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( # noqa: E501 rocm_aiter_fused_experts, ) result = rocm_aiter_fused_experts( x, layer.w13_weight, layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, activation=activation, apply_router_weight_on_input=apply_router_weight_on_input, expert_map=expert_map, quant_config=self.moe_quant_config, ) elif self.use_marlin: assert activation == "silu", f"{activation} not supported for Marlin MoE." result = fused_marlin_moe( x, layer.w13_weight, layer.w2_weight, None, None, layer.w13_weight_scale, layer.w2_weight_scale, router_logits, topk_weights, topk_ids, quant_type_id=scalar_types.float8_e4m3fn.id, apply_router_weight_on_input=apply_router_weight_on_input, global_num_experts=global_num_experts, expert_map=expert_map, workspace=layer.workspace, ) elif self.flashinfer_moe_backend == FlashinferMoeBackend.CUTLASS: assert activation == "silu", ( f"Expected 'silu' activation but got {activation}" ) if not self.block_quant: assert not renormalize and custom_routing_function is not None assert scoring_func == "sigmoid", ( f"Expected 'sigmoid' scoring func but got {scoring_func}" ) # Delegate to CUTLASS FlashInfer path; function already bound with # use_deepseek_fp8_block_scale for block-quant when applicable result = self.flashinfer_moe_fn( x, layer, topk_weights, topk_ids, inplace=False, activation=activation, global_num_experts=global_num_experts, expert_map=expert_map, apply_router_weight_on_input=apply_router_weight_on_input, ) else: from vllm.model_executor.layers.fused_moe import fused_experts result = fused_experts( hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, inplace=True, activation=activation, global_num_experts=global_num_experts, apply_router_weight_on_input=apply_router_weight_on_input, expert_map=expert_map, quant_config=self.moe_quant_config, allow_deep_gemm=self.allow_deep_gemm, allow_cutlass_block_scaled_grouped_gemm=( self.allow_cutlass_block_scaled_grouped_gemm ), ) if zero_expert_num != 0 and zero_expert_type is not None: assert not isinstance(result, tuple), ( "Shared + zero experts are mutually exclusive not yet supported" ) return result, zero_expert_result else: return result class Fp8KVCacheMethod(BaseKVCacheMethod): """ Supports loading kv-cache scaling factors from FP8 checkpoints. """ def __init__(self, quant_config: Fp8Config): super().__init__(quant_config)