# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import TYPE_CHECKING, Any, Callable, Optional import torch import torch.nn.functional as F from torch.nn import Module from torch.nn.parameter import Parameter import vllm.envs as envs from vllm import _custom_ops as ops from vllm.distributed import get_tensor_model_parallel_world_size from vllm.logger import init_logger from vllm.model_executor.layers.fused_moe import ( FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase, FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize, FusedMoeWeightScaleSupported) 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 ( apply_flashinfer_per_tensor_scale_fp8, register_moe_scaling_factors, rotate_flashinfer_fp8_moe_weights, swap_w13_to_w31) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( get_col_major_tma_aligned_tensor, requant_weight_ue8m0_inplace) 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, requantize_with_max_scale) 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 import has_deep_gemm from vllm.utils.deep_gemm import (is_blackwell_deep_gemm_e8m0_used, is_deep_gemm_supported) from vllm.utils.flashinfer import has_flashinfer_moe if TYPE_CHECKING: from vllm.model_executor.models.utils import WeightsMapper ACTIVATION_SCHEMES = ["static", "dynamic"] logger = init_logger(__name__) def _is_col_major(x: torch.Tensor) -> bool: assert x.dim() == 3 b, m, n = x.shape return x.stride(0) == m * n and x.stride(1) == 1 and x.stride(2) == m class Fp8Config(QuantizationConfig): """Config class for FP8.""" def __init__( self, is_checkpoint_fp8_serialized: bool = False, activation_scheme: str = "dynamic", ignored_layers: Optional[list[str]] = None, weight_block_size: Optional[list[int]] = 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_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["QuantizeMethodBase"]: from vllm.attention.layer import Attention # Avoid circular import 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): return Fp8MoEMethod(self, layer.moe_config) elif isinstance(layer, Attention): return Fp8KVCacheMethod(self) return None def get_cache_scale(self, name: str) -> Optional[str]: """ 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 # AITER is only supported on ROCm and only for FP8_FNUZ # and at the moment are MI300 series self.use_aiter_and_is_supported = (current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_LINEAR and current_platform.is_fp8_fnuz()) self.block_quant = self.quant_config.weight_block_size is not None self.act_q_static = self.quant_config.activation_scheme == "static" # 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 self.fp8_linear = Fp8LinearOp( act_quant_static=self.act_q_static, act_quant_group_shape=self.act_q_group_shape, cutlass_fp8_supported=cutlass_fp8_supported()) 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: tp_size = get_tensor_model_parallel_world_size() assert self.quant_config.weight_block_size is not None layer.weight_block_size = self.quant_config.weight_block_size block_n, block_k = ( self.quant_config.weight_block_size[0], self.quant_config.weight_block_size[1], ) # Required by row parallel if (tp_size > 1 and input_size // input_size_per_partition == tp_size and input_size_per_partition % block_k != 0): raise ValueError( f"Weight input_size_per_partition = " f"{input_size_per_partition} is not divisible by " f"weight quantization block_k = {block_k}.") # Required by column parallel or enabling merged weights is_tp_split = (tp_size > 1 and output_size // output_size_per_partition == tp_size) is_merged_gemm = len(output_partition_sizes) > 1 if is_tp_split or is_merged_gemm: sizes_to_check = output_partition_sizes if not is_tp_split and is_merged_gemm: # In case of merged matrices, we allow the last # matrix to not be a multiple of block size sizes_to_check = output_partition_sizes[:-1] for output_partition_size in sizes_to_check: if output_partition_size % block_n != 0: raise ValueError( f"Weight output_partition_size = " f"{output_partition_size} is not divisible by " f"weight quantization block_n = {block_n}.") # WEIGHT 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 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 = PerTensorScaleParameter( data=torch.empty(len(output_partition_sizes), dtype=torch.float32), weight_loader=weight_loader, ) scale[:] = torch.finfo(torch.float32).min set_weight_attrs(scale, {"scale_type": "weight_scale"}) layer.register_parameter("weight_scale", scale) else: assert self.quant_config.activation_scheme == "dynamic" scale = BlockQuantScaleParameter( data=torch.empty( (output_size_per_partition + block_n - 1) // block_n, (input_size_per_partition + block_k - 1) // block_k, dtype=torch.float32, ), input_dim=1, output_dim=0, weight_loader=weight_loader, ) scale[:] = torch.finfo(torch.float32).min 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.quant_config.activation_scheme == "static": scale = PerTensorScaleParameter(data=torch.empty( len(output_partition_sizes), dtype=torch.float32), weight_loader=weight_loader) scale[:] = torch.finfo(torch.float32).min set_weight_attrs(scale, {"scale_type": "input_scale"}) layer.register_parameter("input_scale", scale) else: layer.register_parameter("input_scale", None) def _maybe_pad_weight(self, weight: torch.Tensor) -> torch.Tensor: # Pad the weight tensor. This is an optimization on ROCm platform, which # can benefit from tensors located far enough from one another in memory if (envs.VLLM_ROCM_FP8_PADDING and current_platform.is_rocm() and weight.stride(-1) == 1 and (weight.stride(-2) * weight.element_size()) % 512 == 0): num_pad = 256 // weight.element_size() weight = F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad] torch.cuda.empty_cache() return weight def process_weights_after_loading(self, layer: Module) -> None: size_k_first = True # TODO(rob): refactor block quant into separate class. if self.block_quant: assert self.quant_config.activation_scheme == "dynamic" size_k_first = False if current_platform.is_fp8_fnuz(): weight, weight_scale_inv, _ = \ normalize_e4m3fn_to_e4m3fnuz( weight=layer.weight, weight_scale=layer.weight_scale_inv) else: weight = layer.weight.data weight_scale_inv = layer.weight_scale_inv.data weight = self._maybe_pad_weight(weight) # Torch.compile cannot use Parameter subclasses. layer.weight = Parameter(weight, requires_grad=False) layer.weight_scale_inv = Parameter(weight_scale_inv, requires_grad=False) # 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) # Update the layer with the new values. layer.weight = Parameter(qweight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) layer.input_scale = None # If checkpoint is fp8, handle that there are N scales for N # shards in a fused module else: layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data, requires_grad=False) if self.quant_config.activation_scheme == "static": layer.input_scale = torch.nn.Parameter(layer.input_scale.data, requires_grad=False) 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: # Dequant -> Quant with max scale so we can run per tensor. if current_platform.is_fp8_fnuz(): weight, weight_scale, input_scale = \ normalize_e4m3fn_to_e4m3fnuz( weight=weight, weight_scale=weight_scale, input_scale=layer.input_scale) if input_scale is not None: layer.input_scale = Parameter(input_scale, requires_grad=False) weight_scale, weight = requantize_with_max_scale( weight=weight, weight_scale=weight_scale, logical_widths=layer.logical_widths, ) weight = self._maybe_pad_weight(weight) # Update layer with new values. layer.weight = Parameter(weight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) if self.quant_config.activation_scheme == "static": layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False) if self.use_marlin: prepare_fp8_layer_for_marlin(layer, size_k_first) # Activations not quantized for marlin. del layer.input_scale # On B200, if E8M0 for DeepGemm is used, we need to # requantize the weight and input to the specific scale # at the same time. if is_blackwell_deep_gemm_e8m0_used(): assert layer.weight_block_size is not None block_sz = tuple(layer.weight_block_size) requant_weight_ue8m0_inplace( layer.weight.data, layer.weight_scale_inv.data if hasattr( layer, "weight_scale_inv") else layer.weight_scale.data, block_sz, ) def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: 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.quant_config.weight_block_size is not None return torch.ops.vllm.apply_w8a8_block_fp8_linear( input=x, weight=layer.weight, block_size=self.quant_config.weight_block_size, weight_scale=layer.weight_scale_inv, input_scale=layer.input_scale, bias=bias, cutlass_block_fp8_supported=self.cutlass_block_fp8_supported, use_aiter_and_is_supported=self.use_aiter_and_is_supported, ) 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, moe: FusedMoEConfig): super().__init__(moe) self.quant_config = quant_config self.block_quant = self.quant_config.weight_block_size is not None self.flashinfer_moe_enabled = False if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe(): logger.info_once( "Using FlashInfer MoE FP8 kernels for Fp8MoEMethod.") self.flashinfer_moe_enabled = True # 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 # Check for DeepGemm support. self.allow_deep_gemm = False if envs.VLLM_USE_DEEP_GEMM: if not has_deep_gemm(): logger.warning_once("Failed to import DeepGemm kernels.") elif not self.block_quant: logger.warning_once("Model is not block quantized. Not using " "DeepGemm kernels") elif (is_deep_gemm_supported()): logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.") self.allow_deep_gemm = True else: logger.warning_once( "DeepGemm not supported on the current platform.") # Check for CutlassBlockScaledGroupedGemm support. self.allow_cutlass_block_scaled_grouped_gemm = False if not self.block_quant: logger.debug_once("Model is not block quantized. Not using " "CutlassBlockScaledGroupedGemm kernels") elif (current_platform.is_cuda() and current_platform.is_device_capability(100)): logger.info_once( "Using CutlassBlockScaledGroupedGemm kernels for Fp8MoEMethod." ) self.allow_cutlass_block_scaled_grouped_gemm = True else: logger.warning_once( "CutlassBlockScaledGroupedGemm not supported on the current " "platform.") 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.quant_config.weight_block_size is not None layer.weight_block_size = self.quant_config.weight_block_size tp_size = get_tensor_model_parallel_world_size() block_n, block_k = ( self.quant_config.weight_block_size[0], self.quant_config.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 def process_weights_after_loading(self, layer: Module) -> None: # Lazy import to avoid importing triton too early. from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( is_rocm_aiter_moe_enabled, shuffle_weights) self.rocm_aiter_moe_enabled = is_rocm_aiter_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_enabled: # 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 if not self.block_quant: register_moe_scaling_factors(layer) rotate_flashinfer_fp8_moe_weights(w13_weight, w2_weight) 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 = 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 and not is_blackwell_deep_gemm_e8m0_used(): # Lazy import to avoid CUDA initialization problems. if _is_col_major(layer.w13_weight_scale_inv): layer.w13_weight_scale_inv = \ get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous() if _is_col_major(layer.w2_weight_scale_inv): layer.w2_weight_scale_inv = \ get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous() # 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 = 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 = 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.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 if is_blackwell_deep_gemm_e8m0_used(): assert layer.weight_block_size is not None # Re-quantise the expert weights so their scales are UE8M0. block_sz = tuple(layer.weight_block_size) requant_weight_ue8m0_inplace( layer.w13_weight.data, layer.w13_weight_scale_inv.data, block_sz, ) requant_weight_ue8m0_inplace( layer.w2_weight.data, layer.w2_weight_scale_inv.data, block_sz, ) # Ensure column-major TMA alignment expected by DeepGEMM. if _is_col_major(layer.w13_weight_scale_inv): layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor( layer.w13_weight_scale_inv).contiguous() if _is_col_major(layer.w2_weight_scale_inv): layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor( layer.w2_weight_scale_inv).contiguous() def select_gemm_impl( self, prepare_finalize: FusedMoEPrepareAndFinalize, moe: FusedMoEConfig, ) -> FusedMoEPermuteExpertsUnpermute: from vllm.model_executor.layers.fused_moe import ( BatchedTritonOrDeepGemmExperts, TritonOrDeepGemmExperts) assert not self.use_marlin and not self.rocm_aiter_moe_enabled, ( "Marlin and ROCm AITER are not supported with all2all yet.") 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 logger.debug( "BatchedTritonOrDeepGemmExperts(%s): " "max_tokens_per_rank=%s, block_size=%s, per_act_token=%s", self.__class__.__name__, max_num_tokens_per_rank, self.quant_config.weight_block_size, False) return BatchedTritonOrDeepGemmExperts( max_num_tokens=max_num_tokens_per_rank, num_dispatchers=prepare_finalize.num_dispatchers(), use_fp8_w8a8=True, block_shape=self.quant_config.weight_block_size, per_act_token_quant=False, allow_deep_gemm=self.allow_deep_gemm, ) else: logger.debug( "TritonOrDeepGemmExperts(%s): block_size=%s, per_act_token=%s", self.__class__.__name__, self.quant_config.weight_block_size, False) return TritonOrDeepGemmExperts( use_fp8_w8a8=True, block_shape=self.quant_config.weight_block_size, allow_deep_gemm=self.allow_deep_gemm, ) 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: Optional[int] = None, num_expert_group: Optional[int] = None, global_num_experts: int = -1, expert_map: Optional[torch.Tensor] = None, custom_routing_function: Optional[Callable] = None, scoring_func: str = "softmax", e_score_correction_bias: Optional[torch.Tensor] = None, apply_router_weight_on_input: bool = False, activation: str = "silu", enable_eplb: bool = False, expert_load_view: Optional[torch.Tensor] = None, logical_to_physical_map: Optional[torch.Tensor] = None, logical_replica_count: Optional[torch.Tensor] = None, ) -> 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 not self.flashinfer_moe_enabled: topk_weights, topk_ids = FusedMoE.select_experts( hidden_states=x, router_logits=router_logits, use_grouped_topk=use_grouped_topk, top_k=top_k, renormalize=renormalize, topk_group=topk_group, num_expert_group=num_expert_group, custom_routing_function=custom_routing_function, scoring_func=scoring_func, 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, ) if self.rocm_aiter_moe_enabled: from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( # noqa: E501 rocm_aiter_fused_experts) return rocm_aiter_fused_experts( x, layer.w13_weight, layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, activation=activation, use_fp8_w8a8=True, apply_router_weight_on_input=apply_router_weight_on_input, 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.quant_config.weight_block_size, expert_map=expert_map) elif self.use_marlin: assert activation == "silu", ( f"{activation} not supported for Marlin MoE.") return torch.ops.vllm.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) elif self.flashinfer_moe_enabled: assert activation == 'silu' assert scoring_func == 'sigmoid' if self.block_quant: assert (renormalize and use_grouped_topk and custom_routing_function is None) return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8( routing_logits=router_logits.to(torch.float32), 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.quant_config.weight_block_size, routed_scaling=1.0, ) else: assert (not renormalize and custom_routing_function is not None) return apply_flashinfer_per_tensor_scale_fp8( layer=layer, hidden_states=x, router_logits=router_logits, routing_bias=e_score_correction_bias, global_num_experts=global_num_experts, top_k=top_k, num_expert_group=num_expert_group, topk_group=topk_group, apply_router_weight_on_input=apply_router_weight_on_input) elif self.fused_experts is not None: return self.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, 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, ) else: from vllm.model_executor.layers.fused_moe import fused_experts return 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, 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, use_fp8_w8a8=True, block_shape=self.quant_config.weight_block_size, allow_deep_gemm=self.allow_deep_gemm, allow_cutlass_block_scaled_grouped_gemm=( self.allow_cutlass_block_scaled_grouped_gemm)) class Fp8KVCacheMethod(BaseKVCacheMethod): """ Supports loading kv-cache scaling factors from FP8 checkpoints. """ def __init__(self, quant_config: Fp8Config): super().__init__(quant_config)