# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py from __future__ import annotations import logging from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union import torch import torch.nn.functional as F from torch.nn import Module from torch.nn.parameter import Parameter try: from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import ( apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin, ) MARLIN_FP8_AVAILABLE = True except ImportError: MARLIN_FP8_AVAILABLE = False def dummy_func(*args, **kwargs): raise ImportError( "marlin FP8 requires some operators from vllm. Please install vllm." ) apply_fp8_marlin_linear = prepare_fp8_layer_for_marlin = dummy_func from sglang.srt.distributed import get_tensor_model_parallel_world_size from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading from sglang.srt.layers.parameter import ( BlockQuantScaleParameter, ModelWeightParameter, PerTensorScaleParameter, ) from sglang.srt.layers.quantization.base_config import ( FusedMoEMethodBase, LinearMethodBase, QuantizationConfig, QuantizeMethodBase, ) from sglang.srt.layers.quantization.fp8_kernel import ( fp8_dtype, is_fp8_fnuz, per_token_group_quant_fp8, scaled_fp8_quant, ) from sglang.srt.layers.quantization.fp8_utils import ( apply_fp8_linear, cutlass_fp8_supported, dispatch_w8a8_block_fp8_linear, input_to_float8, normalize_e4m3fn_to_e4m3fnuz, ) from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod from sglang.srt.layers.quantization.utils import ( all_close_1d, convert_to_channelwise, is_layer_skipped, per_tensor_dequantize, requantize_with_max_scale, ) from sglang.srt.layers.utils import is_sm100_supported from sglang.srt.utils import ( cpu_has_amx_support, get_bool_env_var, is_cpu, is_cuda, is_hip, is_npu, log_info_on_rank0, print_warning_once, set_weight_attrs, use_intel_amx_backend, ) if TYPE_CHECKING: from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config _is_hip = is_hip() _is_cuda = is_cuda() _is_npu = is_npu() _is_cpu_amx_available = cpu_has_amx_support() _is_cpu = is_cpu() _is_fp8_fnuz = is_fp8_fnuz() _use_hip_int4 = get_bool_env_var("SGLANG_INT4_WEIGHT") _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip if _is_hip and (_use_aiter or _use_hip_int4): from aiter import ActivationType, QuantType from aiter.fused_moe import fused_moe from aiter.fused_moe_bf16_asm import asm_moe, ck_moe_2stages from aiter.ops.shuffle import shuffle_weight if not (_is_cuda or _is_npu or (_is_cpu and _is_cpu_amx_available)): from vllm._custom_ops import scaled_fp8_quant ACTIVATION_SCHEMES = ["static", "dynamic"] logger = logging.getLogger(__name__) 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: List[int] = None, ) -> None: self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized if is_checkpoint_fp8_serialized: log_info_on_rank0(logger, "Detected fp8 checkpoint.") 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( f"The block-wise quantization only supports fp8-serialized checkpoint for now." ) if len(weight_block_size) != 2: raise ValueError( f"The quantization block size of weight must have 2 dimensions, but got {len(weight_block_size)} dimensions." ) if activation_scheme != "dynamic": raise ValueError( f"The block-wise quantization only supports dynamic activation scheme for now, but got {activation_scheme} activation scheme." ) self.weight_block_size = weight_block_size @classmethod def get_name(cls) -> str: 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 [] @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) 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 sglang.srt.layers.linear import LinearBase from sglang.srt.layers.moe.fused_moe_triton import FusedMoE if isinstance(layer, LinearBase): if is_layer_skipped(prefix, self.ignored_layers): return UnquantizedLinearMethod() return Fp8LinearMethod(self) elif isinstance(layer, FusedMoE): return Fp8MoEMethod(self) return None def get_scaled_act_names(self) -> List[str]: return [] 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: Union[Fp8Config, W4AFp8Config]): self.quant_config = quant_config self.cutlass_fp8_supported = cutlass_fp8_supported() # For GPUs that lack FP8 hardware support, we can leverage the Marlin # kernel for fast weight-only FP8 quantization self.use_marlin = ( get_bool_env_var("SGLANG_FORCE_FP8_MARLIN") and MARLIN_FP8_AVAILABLE ) # Disable marlin for ROCm if _is_hip: self.use_marlin = False self.block_quant = self.quant_config.weight_block_size is not None if self.block_quant: # Marlin doesn't support block-wise fp8 self.use_marlin = False self.w8a8_block_fp8_linear = dispatch_w8a8_block_fp8_linear() 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, ): output_size_per_partition = sum(output_partition_sizes) weight_loader = extra_weight_attrs.get("weight_loader") tp_size = get_tensor_model_parallel_world_size() if self.block_quant: 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: if 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 if ( tp_size > 1 and output_size // output_size_per_partition == tp_size ) or len(output_partition_sizes) > 1: for output_partition_size in output_partition_sizes: 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}." ) 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 # 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 self.block_quant: if hasattr(self.quant_config, "activation_scheme"): assert self.quant_config.activation_scheme == "dynamic" elif hasattr(self.quant_config, "linear_activation_scheme"): assert self.quant_config.linear_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 layer.register_parameter("weight_scale_inv", scale) else: 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("weight_scale", scale) # INPUT ACTIVATION SCALE if ( hasattr(self.quant_config, "activation_scheme") and self.quant_config.activation_scheme == "static" ) or ( hasattr(self.quant_config, "linear_activation_scheme") and self.quant_config.linear_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 layer.register_parameter("input_scale", scale) else: layer.register_parameter("input_scale", None) def process_weights_after_loading(self, layer: Module) -> None: # Block quant doesn't need to process weights after loading if self.block_quant: # If ROCm, normalize the weights and scales to e4m3fnuz if _is_fp8_fnuz: # activation_scheme: dynamic weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=layer.weight, weight_scale=layer.weight_scale_inv, input_scale=None, ) layer.input_scale = None elif _is_cpu: assert ( _is_cpu_amx_available ), "Fp8LinearMethod on CPU requires that CPU has AMX support" _amx_process_weight_after_loading(layer, ["weight"]) return else: weight, weight_scale = layer.weight.data, layer.weight_scale_inv.data layer.weight = torch.nn.Parameter(weight, requires_grad=False) layer.weight_scale_inv = torch.nn.Parameter( weight_scale, requires_grad=False ) return layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False) # If checkpoint not serialized fp8, quantize the weights. if not self.quant_config.is_checkpoint_fp8_serialized: if self.cutlass_fp8_supported or self.use_marlin: # apply per-channel quantization default, as cutlass sgl-kernel and marlin only support per-channel scale qweight, weight_scale = per_token_group_quant_fp8( layer.weight, layer.weight.shape[-1] ) weight_scale = weight_scale.t().contiguous() else: # per-tensor quantization qweight, weight_scale = input_to_float8(layer.weight) # 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 ( hasattr(self.quant_config, "activation_scheme") and self.quant_config.activation_scheme == "static" ) or ( hasattr(self.quant_config, "linear_activation_scheme") and self.quant_config.linear_activation_scheme == "static" ): layer.input_scale = torch.nn.Parameter( layer.input_scale.data, requires_grad=False ) # cutlass sgl-kernel and marlin only support per-channel scale if self.cutlass_fp8_supported or self.use_marlin: weight = layer.weight weight_scale = convert_to_channelwise( layer.weight_scale, layer.logical_widths ) else: # Dequant -> Quant with max scale so we can run per tensor. weight = layer.weight weight_scale = layer.weight_scale # If ROCm, normalize the weights and scales to e4m3fnuz if _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, ) # Update layer with new values. layer.weight = Parameter(weight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) if ( hasattr(self.quant_config, "activation_scheme") and self.quant_config.activation_scheme == "static" ) or ( hasattr(self.quant_config, "linear_activation_scheme") and self.quant_config.linear_activation_scheme == "static" ): layer.input_scale = Parameter( layer.input_scale.max(), requires_grad=False ) if self.use_marlin: prepare_fp8_layer_for_marlin(layer) # Activations not quantized for marlin. del layer.input_scale 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: if use_intel_amx_backend(layer): return torch.ops.sgl_kernel.fp8_scaled_mm_cpu( x, layer.weight, layer.weight_scale_inv, self.quant_config.weight_block_size, bias, x.dtype, True, # is_vnni ) return self.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=None, bias=bias, ) return apply_fp8_linear( input=x, weight=layer.weight, weight_scale=layer.weight_scale, input_scale=layer.input_scale, bias=bias, cutlass_fp8_supported=self.cutlass_fp8_supported, use_per_token_if_dynamic=False, ) 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): self.quant_config = quant_config self.block_quant = self.quant_config.weight_block_size is not None self.cutlass_fp8_supported = cutlass_fp8_supported() def create_weights( self, layer: Module, num_experts: int, hidden_size: int, intermediate_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported if self.quant_config.is_checkpoint_fp8_serialized: params_dtype = torch.uint32 if _use_hip_int4 else torch.float8_e4m3fn tp_size = get_tensor_model_parallel_world_size() if self.block_quant: block_n, block_k = ( self.quant_config.weight_block_size[0], self.quant_config.weight_block_size[1], ) # NOTE(HandH1998): 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 % block_n != 0: raise ValueError( f"The output_size of gate's and up's weight = " f"{intermediate_size} is not divisible by " f"weight quantization block_n = {block_n}." ) if tp_size > 1: # Required by row parallel if intermediate_size % block_k != 0: raise ValueError( f"The input_size of down's weight = " f"{intermediate_size} is not divisible by " f"weight quantization block_k = {block_k}." ) # WEIGHTS if _is_hip and _use_hip_int4: # INT4 MoE weight - INT32 packed w13_weight = torch.nn.Parameter( torch.empty( num_experts, 2 * intermediate_size, hidden_size // 8, dtype=params_dtype, ), requires_grad=False, ) w2_weight = torch.nn.Parameter( torch.empty( num_experts, hidden_size, intermediate_size // 8, dtype=params_dtype ), requires_grad=False, ) else: w13_weight = torch.nn.Parameter( torch.empty( num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype ), requires_grad=False, ) w2_weight = torch.nn.Parameter( torch.empty( num_experts, hidden_size, intermediate_size, dtype=params_dtype ), requires_grad=False, ) layer.register_parameter("w13_weight", w13_weight) set_weight_attrs(w13_weight, extra_weight_attrs) layer.register_parameter("w2_weight", w2_weight) set_weight_attrs(w2_weight, extra_weight_attrs) # WEIGHT_SCALES if self.block_quant: w13_weight_scale = torch.nn.Parameter( torch.ones( num_experts, 2 * ((intermediate_size + 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 + 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" if ( get_bool_env_var("SGLANG_CUTLASS_MOE") and self.cutlass_fp8_supported and is_sm100_supported() ): self.ab_strides1 = torch.full( (num_experts,), hidden_size, device=w13_weight.device, dtype=torch.int64, ) self.c_strides1 = torch.full( (num_experts,), 2 * intermediate_size, device=w13_weight.device, dtype=torch.int64, ) self.ab_strides2 = torch.full( (num_experts,), intermediate_size, device=w2_weight.device, dtype=torch.int64, ) self.c_strides2 = torch.full( (num_experts,), hidden_size, device=w2_weight.device, dtype=torch.int64, ) self.workspace = torch.empty( 90000, device=w13_weight.device, dtype=torch.uint8 ) self.a_ptr = torch.empty( num_experts, device=w13_weight.device, dtype=torch.int64 ) self.b_ptr = torch.empty( num_experts, device=w13_weight.device, dtype=torch.int64 ) self.out_ptr = torch.empty( num_experts, device=w13_weight.device, dtype=torch.int64 ) self.a_scales_ptr = torch.empty( num_experts, device=w13_weight.device, dtype=torch.int64 ) self.b_scales_ptr = torch.empty( num_experts, device=w13_weight.device, dtype=torch.int64 ) self.expert_offsets = torch.empty( num_experts + 1, device=w13_weight.device, dtype=torch.int32 ) self.problem_sizes1 = torch.empty( num_experts, 3, device=w13_weight.device, dtype=torch.int32 ) self.problem_sizes2 = torch.empty( num_experts, 3, device=w13_weight.device, dtype=torch.int32 ) else: # 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) if _is_hip: # _use_aiter: TODO: add check back after triton kernel # ROCm - using column scaling, duplicate scaling numbers in case per tensor scaling w13_weight_scale1 = torch.nn.Parameter( torch.ones(num_experts, 2 * intermediate_size, dtype=torch.float32), requires_grad=False, ) w2_weight_scale1 = torch.nn.Parameter( torch.ones(num_experts, hidden_size, dtype=torch.float32), requires_grad=False, ) layer.register_parameter("w13_weight_scale1", w13_weight_scale1) layer.register_parameter("w2_weight_scale1", w2_weight_scale1) # 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) if _is_hip and _use_hip_int4: extra_weight_attrs.update( {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value} ) set_weight_attrs(w13_weight_scale1, extra_weight_attrs) set_weight_attrs(w2_weight_scale1, 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: if _is_hip and _use_hip_int4: self.process_weights_hip_int4(layer) return # Block quant doesn't need to process weights after loading if self.block_quant: # If ROCm, normalize the weights and scales to e4m3fnuz if _is_fp8_fnuz: # activation_scheme: dynamic w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=layer.w13_weight, weight_scale=layer.w13_weight_scale_inv, input_scale=None, ) w2_weight, w2_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=layer.w2_weight, weight_scale=layer.w2_weight_scale_inv, input_scale=None, ) # Reset the parameter layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False) layer.w13_weight_scale_inv = torch.nn.Parameter( w13_weight_scale, requires_grad=False ) layer.w13_input_scale = None layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False) layer.w2_weight_scale_inv = torch.nn.Parameter( w2_weight_scale, requires_grad=False ) layer.w2_input_scale = None if _use_aiter: # Pre-shuffle weights layer.w13_weight.data = shuffle_weight( layer.w13_weight.contiguous(), (16, 16) ) layer.w2_weight.data = shuffle_weight( layer.w2_weight.contiguous(), (16, 16) ) if _is_cpu: assert ( _is_cpu_amx_available ), "Fp8MoEMethod on CPU requires that CPU has AMX support" _amx_process_weight_after_loading(layer, ["w13_weight", "w2_weight"]) return # If checkpoint is fp16 or bfloat16, quantize in place. if not self.quant_config.is_checkpoint_fp8_serialized: # If ROCm, fp8_dtype will be float8_e4m3fnuz (MI300x HW) 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.num_experts, dtype=torch.float32, device=w13_weight.device ), requires_grad=False, ) for expert in range(layer.num_experts): w13_weight[expert, :, :], layer.w13_weight_scale[expert] = ( scaled_fp8_quant(layer.w13_weight.data[expert, :, :]) ) w2_weight[expert, :, :], layer.w2_weight_scale[expert] = ( 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 _is_hip: self.process_weights_hip_scale_padding(layer) return # 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 ): print_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 ROCm, normalize the weights and scales to e4m3fnuz if _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.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, :], _, ) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id]) start += shard_size layer.w13_weight_scale = torch.nn.Parameter( max_w13_scales, requires_grad=False ) if _is_hip: self.process_weights_hip_scale_padding(layer) return def process_weights_hip_int4(self, layer: Module): # TODO: _use_aiter: add after triton kernel added # INT4-FP8 (INT4 MoE Weight, FP8 Compute) # Weight Permutation layer.w13_weight = torch.nn.Parameter( shuffle_weight(layer.w13_weight.data, (16, 16)), requires_grad=False, ) torch.cuda.empty_cache() layer.w2_weight = torch.nn.Parameter( shuffle_weight(layer.w2_weight.data, (16, 16)), requires_grad=False, ) torch.cuda.empty_cache() # INT4-FP8 : offset INT4 w13_weight_scale1 to single w13_weight_scale # Fp8 moe kernel needs single fp8 w13_weight_scale for w13 per expert. # We won't do requant each expert's fp8 weight (not direct available), # instead we adjust half of INT4 w13_weight_scale1 numbers 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.num_experts): start = 0 max_w13_scale_fp8 = max_w13_scales[expert_id] for shard_id in range(2): if layer.w13_weight_scale[expert_id][shard_id] != max_w13_scale_fp8: int4_rescale = ( layer.w13_weight_scale[expert_id][shard_id] / max_w13_scale_fp8 ) layer.w13_weight_scale1[expert_id][ start : start + shard_size ] *= int4_rescale start += shard_size layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False) # special hack to asm_moe, which takes (weight_scale1 * weight_scale) as post GEMM scaling # optimal design - shall apply per-column weight_scale1 before GEMM, and weight_scale post for expert_id in range(layer.num_experts): layer.w13_weight_scale1[expert_id] *= max_w13_scales[expert_id] layer.w2_weight_scale1[expert_id] *= layer.w2_weight_scale[expert_id] def process_weights_hip_scale_padding(self, layer: Module): from sglang.srt.layers.moe.fused_moe_triton.fused_moe import ( padding_size, # Avoid circular import ) if _use_aiter: layer.w13_weight = torch.nn.Parameter( shuffle_weight(layer.w13_weight.data, (16, 16)), requires_grad=False, ) torch.cuda.empty_cache() layer.w2_weight = torch.nn.Parameter( shuffle_weight(layer.w2_weight.data, (16, 16)), requires_grad=False, ) torch.cuda.empty_cache() # ROCm (_use_aiter): using column-wise scaling layer.w13_weight_scale1 *= layer.w13_weight_scale.unsqueeze(-1) layer.w2_weight_scale1 *= layer.w2_weight_scale.unsqueeze(-1) elif get_bool_env_var("SGLANG_MOE_PADDING"): # If ROCm, apply weight padding (min. Mem channel contention) only if set layer.w13_weight = torch.nn.Parameter( F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0), requires_grad=False, ) torch.cuda.empty_cache() layer.w2_weight = torch.nn.Parameter( F.pad(layer.w2_weight.data, (0, padding_size), "constant", 0), requires_grad=False, ) torch.cuda.empty_cache() def apply( self, layer: torch.nn.Module, x: torch.Tensor, router_logits: torch.Tensor, top_k: int, renormalize: bool, use_grouped_topk: bool, topk_group: Optional[int] = None, num_expert_group: Optional[int] = None, num_fused_shared_experts: int = 0, custom_routing_function: Optional[Callable] = None, correction_bias: Optional[torch.Tensor] = None, activation: str = "silu", apply_router_weight_on_input: bool = False, inplace: bool = True, no_combine: bool = False, routed_scaling_factor: Optional[float] = None, ) -> torch.Tensor: from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts from sglang.srt.layers.moe.topk import select_experts # Expert selection topk_weights, topk_ids = 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, num_fused_shared_experts=num_fused_shared_experts, custom_routing_function=custom_routing_function, correction_bias=correction_bias, routed_scaling_factor=routed_scaling_factor, ) if use_intel_amx_backend(layer): from sglang.srt.layers.moe.topk import apply_topk_weights_cpu x, topk_weights = apply_topk_weights_cpu( apply_router_weight_on_input, topk_weights, x ) return torch.ops.sgl_kernel.fused_experts_cpu( x, layer.w13_weight, layer.w2_weight, topk_weights, topk_ids, False, # inplace See [Note] inplace should be False in fused_experts. False, # use_int8_w8a8 True, # use_fp8_w8a16 layer.w13_weight_scale_inv, # w1_scale layer.w2_weight_scale_inv, # w2_scale self.quant_config.weight_block_size, # block_size None, # a1_scale None, # a2_scale True, # is_vnni ) if _is_hip: ret = self.maybe_apply_hip_fused_experts( layer, x, topk_weights, topk_ids, activation, no_combine, ) if ret is not None: return ret if ( get_bool_env_var("SGLANG_CUTLASS_MOE") and self.cutlass_fp8_supported and self.block_quant and is_sm100_supported() ): from sglang.srt.layers.moe.cutlass_moe import cutlass_fused_experts_fp8 return cutlass_fused_experts_fp8( x, layer.w13_weight.transpose(1, 2), layer.w2_weight.transpose(1, 2), layer.w13_weight_scale_inv.transpose(1, 2), layer.w2_weight_scale_inv.transpose(1, 2), topk_weights, topk_ids, self.ab_strides1, self.c_strides1, self.ab_strides2, self.c_strides2, self.workspace, self.a_ptr, self.b_ptr, self.out_ptr, self.a_scales_ptr, self.b_scales_ptr, self.expert_offsets, self.problem_sizes1, self.problem_sizes2, use_fp8_blockscale=True, ) # Expert fusion with FP8 quantization return fused_experts( x, layer.w13_weight, layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, inplace=inplace and not no_combine, activation=activation, apply_router_weight_on_input=apply_router_weight_on_input, use_fp8_w8a8=True, 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, no_combine=no_combine, routed_scaling_factor=routed_scaling_factor, ) def maybe_apply_hip_fused_experts( self, layer: torch.nn.Module, x: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, activation: str = "silu", no_combine: bool = False, ) -> Optional[torch.Tensor]: if _use_hip_int4: # TODO: add triton kernel and add check _use_aiter assert not no_combine, f"{no_combine=} is not supported." return fused_moe( x, layer.w13_weight, layer.w2_weight, topk_weights, topk_ids, quant_type=QuantType.per_Token, w1_scale=layer.w13_weight_scale1, w2_scale=layer.w2_weight_scale1, activation=( ActivationType.Silu if activation == "silu" else ActivationType.Gelu ), ) if _use_aiter: assert not no_combine, f"{no_combine=} is not supported." if self.block_quant: return fused_moe( x, layer.w13_weight, layer.w2_weight, topk_weights, topk_ids, w1_scale=layer.w13_weight_scale_inv, w2_scale=layer.w2_weight_scale_inv, quant_type=QuantType.per_128x128, activation=( ActivationType.Silu if activation == "silu" else ActivationType.Gelu ), expert_mask=None, ) else: return fused_moe( x, layer.w13_weight, layer.w2_weight, topk_weights, topk_ids, quant_type=QuantType.per_Token, w1_scale=layer.w13_weight_scale1, w2_scale=layer.w2_weight_scale1, activation=( ActivationType.Silu if activation == "silu" else ActivationType.Gelu ), ) return None class Fp8EPMoEMethod(Fp8MoEMethod): """MoE method for FP8. Supports loading FP8 checkpoints with static weight scale and dynamic/static activation scale. Args: quant_config: The quantization config. """ def __init__(self, quant_config: Fp8Config): self.quant_config = quant_config self.block_quant = self.quant_config.weight_block_size is not None def create_weights( self, layer: Module, num_experts_per_partition: int, hidden_size: int, intermediate_size: int, params_dtype: torch.dtype, **extra_weight_attrs, ): from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported if self.quant_config.is_checkpoint_fp8_serialized: params_dtype = torch.float8_e4m3fn tp_size = get_tensor_model_parallel_world_size() if self.block_quant: block_n, block_k = ( self.quant_config.weight_block_size[0], self.quant_config.weight_block_size[1], ) # NOTE(HandH1998): 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 % block_n != 0: raise ValueError( f"The output_size of gate's and up's weight = " f"{intermediate_size} is not divisible by " f"weight quantization block_n = {block_n}." ) if tp_size > 1: # Required by row parallel if intermediate_size % block_k != 0: raise ValueError( f"The input_size of down's weight = " f"{intermediate_size} is not divisible by " f"weight quantization block_k = {block_k}." ) # WEIGHTS w13_weight = torch.nn.Parameter( torch.empty( num_experts_per_partition, 2 * intermediate_size, 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_per_partition, hidden_size, intermediate_size, dtype=params_dtype, ), requires_grad=False, ) layer.register_parameter("w2_weight", w2_weight) set_weight_attrs(w2_weight, extra_weight_attrs) # WEIGHT_SCALES if self.block_quant: w13_weight_scale = torch.nn.Parameter( torch.ones( num_experts_per_partition, 2 * ((intermediate_size + 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_per_partition, (hidden_size + block_n - 1) // block_n, (intermediate_size + 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" else: # WEIGHT_SCALES # Allocate 2 scales for w1 and w3 respectively. w13_weight_scale = torch.nn.Parameter( torch.ones(num_experts_per_partition, 2, dtype=torch.float32), requires_grad=False, ) layer.register_parameter("w13_weight_scale", w13_weight_scale) w2_weight_scale = torch.nn.Parameter( torch.ones(num_experts_per_partition, dtype=torch.float32), requires_grad=False, ) layer.register_parameter("w2_weight_scale", w2_weight_scale) # 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_per_partition, 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_per_partition, 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: # If checkpoint is fp16, quantize in place. if not self.quant_config.is_checkpoint_fp8_serialized: # If rocm, use float8_e4m3fnuz as dtype fp8_dtype = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype) w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype) layer.w13_weight_scale = torch.nn.Parameter( torch.ones( layer.num_experts_per_partition, dtype=torch.float32, device=w13_weight.device, ), requires_grad=False, ) for expert in range(layer.num_experts_per_partition): w13_weight[expert, :, :], layer.w13_weight_scale[expert] = ( scaled_fp8_quant(layer.w13_weight.data[expert, :, :]) ) w2_weight[expert, :, :], layer.w2_weight_scale[expert] = ( 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) return # 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: 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." ) layer.w13_weight_scale = torch.nn.Parameter( torch.max(layer.w13_weight_scale, dim=1).values, requires_grad=False, ) if self.block_quant: # If ROCm, normalize the weights and scales to e4m3fnuz if _is_fp8_fnuz: # activation_scheme: dynamic w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=layer.w13_weight, weight_scale=layer.w13_weight_scale_inv, input_scale=None, ) w2_weight, w2_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=layer.w2_weight, weight_scale=layer.w2_weight_scale_inv, input_scale=None, ) # Reset the parameter layer.w13_weight = torch.nn.Parameter( w13_weight, requires_grad=False ) layer.w13_weight_scale_inv = torch.nn.Parameter( w13_weight_scale, requires_grad=False ) layer.w13_input_scale = None layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False) layer.w2_weight_scale_inv = torch.nn.Parameter( w2_weight_scale, requires_grad=False ) layer.w2_input_scale = None if _use_aiter: layer.w13_weight = torch.nn.Parameter( shuffle_weight(layer.w13_weight.data, (16, 16)), requires_grad=False, ) layer.w2_weight = torch.nn.Parameter( shuffle_weight(layer.w2_weight.data, (16, 16)), requires_grad=False, ) return def apply( self, layer: torch.nn.Module, x: torch.Tensor, router_logits: torch.Tensor, top_k: int, renormalize: bool, use_grouped_topk: bool, topk_group: Optional[int] = None, num_expert_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None, ) -> torch.Tensor: raise NotImplementedError class Fp8KVCacheMethod(BaseKVCacheMethod): """ Supports loading kv-cache scaling factors from FP8 checkpoints. """ def __init__(self, quant_config: Fp8Config): super().__init__(quant_config)