# 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, Dict, List, Optional, Union import torch import torch.nn.functional as F from torch.nn import Module from torch.nn.parameter import Parameter from sglang.srt.distributed import get_tensor_model_parallel_world_size, get_tp_group from sglang.srt.distributed.device_communicators.pynccl_allocator import ( use_symmetric_memory, ) from sglang.srt.environ import envs from sglang.srt.layers.amx_utils import ( CPUQuantMethod, _amx_process_weight_after_loading, ) from sglang.srt.layers.dp_attention import is_allocation_symmetric from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig from sglang.srt.layers.moe.moe_runner.deep_gemm import DeepGemmMoeQuantInfo from sglang.srt.layers.moe.moe_runner.flashinfer_trtllm import ( FlashInferTrtllmFp8MoeQuantInfo, ) from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo from sglang.srt.layers.moe.utils import RoutingMethodType, get_moe_runner_backend 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, can_auto_enable_marlin_fp8, cutlass_fp8_supported, dispatch_w8a8_block_fp8_linear, input_to_float8, mxfp8_group_quantize, normalize_e4m3fn_to_e4m3fnuz, requant_weight_ue8m0_inplace, triton_mxfp8_blockscaled_linear, ) from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod from sglang.srt.layers.quantization.marlin_utils_fp8 import ( apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin, ) 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.utils import ( cpu_has_amx_support, get_bool_env_var, is_cpu, is_cuda, is_hip, is_npu, is_sm90_supported, is_sm100_supported, log_info_on_rank0, print_warning_once, set_weight_attrs, use_intel_amx_backend, ) if TYPE_CHECKING: from sglang.srt.layers.moe.token_dispatcher import CombineInput, DispatchOutput from sglang.srt.layers.moe.topk import TopKOutput 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") and _is_hip _use_aiter = envs.SGLANG_USE_AITER.get() and _is_hip if _use_aiter or _use_hip_int4: from aiter import ActivationType, QuantType from aiter.fused_moe import fused_moe from aiter.ops.shuffle import shuffle_weight 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, use_mxfp8: bool = False, ) -> 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 [] self.use_mxfp8 = use_mxfp8 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." ) if self.use_mxfp8: if weight_block_size is None: weight_block_size = [1, 32] elif weight_block_size != [1, 32]: raise ValueError("MXFP8 requires weight_block_size=[1, 32].") self.weight_block_size = weight_block_size def get_name(self) -> str: return "mxfp8" if self.use_mxfp8 else "fp8" @classmethod def get_supported_act_dtypes(cls) -> List[torch.dtype]: return [torch.bfloat16, torch.half] def get_min_capability(self) -> int: return 100 if self.use_mxfp8 else 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"]) use_mxfp8 = "mxfp8" in quant_method is_checkpoint_fp8_serialized = ("fp8" in quant_method) or use_mxfp8 activation_scheme = cls.get_from_keys(config, ["activation_scheme"]) ignored_layers = cls.get_from_keys_or( config, ["ignored_layers", "modules_to_not_convert"], None ) if ignored_layers: if "mistral3" in config.get("model_type", ""): # hack for ministral ignored_layers = [ layer.replace("model.", "") for layer in ignored_layers ] weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None) if use_mxfp8 and weight_block_size is not None: logger.warning( "MXFP8 ignoring incoming weight_block_size in config.json; it is fixed to [1, 32]." ) weight_block_size = [1, 32] return cls( is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized, activation_scheme=activation_scheme, ignored_layers=ignored_layers, weight_block_size=weight_block_size, use_mxfp8=use_mxfp8, ) 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 from sglang.srt.layers.radix_attention import RadixAttention if isinstance(layer, LinearBase): if is_layer_skipped(prefix, self.ignored_layers): return UnquantizedLinearMethod() return Fp8LinearMethod(self) elif isinstance(layer, FusedMoE): return Fp8MoEMethod(self) elif isinstance(layer, RadixAttention): return Fp8KVCacheMethod(self) return None def get_scaled_act_names(self) -> List[str]: return [] class Fp8LinearMethod(LinearMethodBase): """Linear method for FP8. It supports the following quantization schemes: - Per-channel weight quantization + per-token activation quantization - Per-tensor weight quantization + per-tensor activation quantization - Blockwise weight quantization + blockwise activation quantization It supports the following checkpoint formats: - FP8 checkpoint - FP16/BF16 checkpoint. In this case, the weights will be quantized to FP8 during the weight loading. Notes: - The activation quantization scheme can be static or dynamic. The dynamic activation quantization is more commonly used. - On NV platforms, the per-channel weight quantization is used by default, if block quantization is not enabled. 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 = False if _is_cuda: force_marlin = get_bool_env_var("SGLANG_FORCE_FP8_MARLIN") auto_enable = can_auto_enable_marlin_fp8() self.use_marlin = force_marlin or auto_enable self.use_mxfp8 = getattr(self.quant_config, "use_mxfp8", False) self.block_quant = ( self.use_mxfp8 or self.quant_config.weight_block_size is not None ) self.w8a8_block_fp8_linear = dispatch_w8a8_block_fp8_linear() self.is_checkpoint_fp8_serialized = ( self.quant_config.is_checkpoint_fp8_serialized ) self.use_aiter_fp8_per_token = envs.SGLANG_USE_AITER_FP8_PER_TOKEN.get() self.use_per_token_if_dynamic = False def validate_block_quant_shapes( self, input_size: int, input_size_per_partition: int, output_size: int, output_size_per_partition: int, output_partition_sizes: List[int], skip_block_quant_check: bool = False, ): 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], ) if skip_block_quant_check: print_warning_once( "Skipping block quantization checks for weight partition." ) else: # 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}." ) 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, skip_block_quant_check: bool = False, **extra_weight_attrs, ): # Copy the layer attributes output_size_per_partition = sum(output_partition_sizes) 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_loader = extra_weight_attrs.get("weight_loader") if self.block_quant: block_n, block_k = self.quant_config.weight_block_size self.validate_block_quant_shapes( input_size, input_size_per_partition, output_size, output_size_per_partition, output_partition_sizes, skip_block_quant_check, ) # Create the weight weight_dtype = ( torch.float8_e4m3fn if self.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.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" if self.use_mxfp8 and not self.is_checkpoint_fp8_serialized: raise ValueError( "MXFP8 requires fp8-serialized checkpoint for linear layers." ) scale_dtype = torch.uint8 if self.use_mxfp8 else torch.float32 scale_init = torch.zeros if scale_dtype == torch.uint8 else torch.empty scale = BlockQuantScaleParameter( data=scale_init( (output_size_per_partition + block_n - 1) // block_n, (input_size_per_partition + block_k - 1) // block_k, dtype=scale_dtype, ), input_dim=1, output_dim=0, weight_loader=weight_loader, ) scale.format_ue8m0 = self.use_mxfp8 if scale_dtype != torch.uint8: 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_block_quant(self, layer: Module) -> None: # 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"]) layer.weight_scale_inv = torch.nn.Parameter( layer.weight_scale_inv.data, requires_grad=False ) return elif self.use_mxfp8: if not self.is_checkpoint_fp8_serialized: self._quantize_mxfp8_weights(layer) return # MXFP8 scales are stored as UE8M0 uint8; no requantization here. # Keep parameter object to preserve weight_loader attrs for hot reload. layer.weight_scale_inv.requires_grad_(False) layer.weight_scale_inv.format_ue8m0 = True return else: # For fp8 linear weights run with deepgemm, the weights and scales need be requantized to ue8m0 from sglang.srt.layers.quantization.fp8_utils import ( deepgemm_w8a8_block_fp8_linear_with_fallback, ) from sglang.srt.model_loader.utils import ( should_deepgemm_weight_requant_ue8m0, ) if ( should_deepgemm_weight_requant_ue8m0( weight_block_size=getattr( self.quant_config, "weight_block_size", None ), ) and ( self.w8a8_block_fp8_linear is deepgemm_w8a8_block_fp8_linear_with_fallback ) and (not layer.weight_scale_inv.format_ue8m0) ): requant_weight_ue8m0_inplace( layer.weight, layer.weight_scale_inv, self.quant_config.weight_block_size, ) layer.weight_scale_inv.format_ue8m0 = True weight, weight_scale = layer.weight.data, layer.weight_scale_inv.data layer.weight.data = weight.data layer.weight_scale_inv.data = weight_scale.data def _quantize_mxfp8_weights(self, layer: Module) -> None: weight = layer.weight.data qweight, weight_scale = mxfp8_group_quantize(weight) # Keep parameter objects to preserve weight_loader attrs for hot reload. layer.weight.data = qweight layer.weight.requires_grad_(False) if hasattr(layer, "weight_scale_inv") and layer.weight_scale_inv is not None: layer.weight_scale_inv.data = weight_scale layer.weight_scale_inv.requires_grad_(False) else: # First-time online MXFP8 quantization (no serialized scales). layer.register_parameter( "weight_scale_inv", Parameter(weight_scale, requires_grad=False) ) layer.weight_scale_inv.format_ue8m0 = True layer.input_scale = None def process_weights_after_loading(self, layer: Module) -> None: if self.block_quant: self.process_weights_after_loading_block_quant(layer) else: layer.weight = Parameter(layer.weight.data, requires_grad=False) # If checkpoint not serialized fp8, quantize the weights. if not self.is_checkpoint_fp8_serialized: if ( self.cutlass_fp8_supported or self.use_marlin or (_use_aiter and self.use_aiter_fp8_per_token) ): # 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() if _use_aiter and self.use_aiter_fp8_per_token: self.use_per_token_if_dynamic = True qweight = shuffle_weight(qweight.contiguous(), (16, 16)) 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 = 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 = Parameter( layer.input_scale.data, requires_grad=False ) # cutlass sgl-kernel and marlin only support per-channel scale; aiter supports per-channel scale if ( self.cutlass_fp8_supported or self.use_marlin or (_use_aiter and self.use_aiter_fp8_per_token) ): weight = layer.weight weight_scale = convert_to_channelwise( layer.weight_scale, layer.logical_widths ) if _use_aiter and self.use_aiter_fp8_per_token: # Otherwise, by default, aiter only uses per-tensor quantization self.use_per_token_if_dynamic = True if _is_fp8_fnuz: weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=weight, weight_scale=weight_scale, ) weight = shuffle_weight(weight.contiguous(), (16, 16)) 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: if self.block_quant: layer.weight_block_size = self.quant_config.weight_block_size prepare_fp8_layer_for_marlin(layer, not self.block_quant) # 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.use_mxfp8: if isinstance(x, tuple): return triton_mxfp8_blockscaled_linear( input=x[0], weight=layer.weight, weight_scale=layer.weight_scale_inv, input_scale=x[1], bias=bias, ) return triton_mxfp8_blockscaled_linear( input=x, weight=layer.weight, weight_scale=layer.weight_scale_inv, input_scale=None, 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 ) if isinstance(x, tuple): return self.w8a8_block_fp8_linear( input=x[0], weight=layer.weight, block_size=self.quant_config.weight_block_size, weight_scale=layer.weight_scale_inv, input_scale=x[1], bias=bias, ) 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=self.use_per_token_if_dynamic, ) 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.use_mxfp8 = getattr(self.quant_config, "use_mxfp8", False) self.block_quant = ( self.use_mxfp8 or self.quant_config.weight_block_size is not None ) self.with_bias = False if get_moe_runner_backend().is_cutlass(): assert ( cutlass_fp8_supported() ), "cutlass_fp8 MoE requires CUDA 12.0+ with SM90 or CUDA 12.4+ with SM89" assert self.block_quant, "cutlass_fp8 MoE requires block quantization" assert is_sm100_supported() or is_sm90_supported() @staticmethod def is_deepgemm_moe_runner_backend_enabled() -> bool: """Check if MoE will actually use DeepGEMM runner for FP8.""" from sglang.srt.layers import deep_gemm_wrapper from sglang.srt.layers.moe.utils import get_moe_a2a_backend moe_runner_backend = get_moe_runner_backend() if moe_runner_backend.is_deep_gemm(): return True if moe_runner_backend.is_auto(): return deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and ( get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake() ) return False def create_weights( self, layer: Module, num_experts: int, hidden_size: int, intermediate_size_per_partition: int, params_dtype: torch.dtype, with_bias: bool = False, **extra_weight_attrs, ): self.with_bias = with_bias 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_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: # Required by row parallel if intermediate_size_per_partition % block_k != 0: 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 if _is_hip and _use_hip_int4: # INT4 MoE weight - INT32 packed w13_weight = torch.nn.Parameter( torch.empty( num_experts, 2 * intermediate_size_per_partition, hidden_size // 8, dtype=params_dtype, ), requires_grad=False, ) w2_weight = torch.nn.Parameter( torch.empty( num_experts, hidden_size, intermediate_size_per_partition // 8, dtype=params_dtype, ), requires_grad=False, ) else: w13_weight = torch.nn.Parameter( torch.empty( num_experts, 2 * intermediate_size_per_partition, hidden_size, dtype=params_dtype, ), requires_grad=False, ) w2_weight = torch.nn.Parameter( torch.empty( num_experts, hidden_size, intermediate_size_per_partition, 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) # BIAS (optional, e.g. GPT-OSS) if self.with_bias: w13_up_dim = ( 2 * intermediate_size_per_partition if layer.moe_runner_config.is_gated else intermediate_size_per_partition ) w13_weight_bias = torch.nn.Parameter( torch.empty(num_experts, w13_up_dim, dtype=torch.float32), requires_grad=False, ) layer.register_parameter("w13_weight_bias", w13_weight_bias) set_weight_attrs(w13_weight_bias, extra_weight_attrs) w2_weight_bias = torch.nn.Parameter( torch.empty(num_experts, hidden_size, dtype=torch.float32), requires_grad=False, ) layer.register_parameter("w2_weight_bias", w2_weight_bias) set_weight_attrs(w2_weight_bias, extra_weight_attrs) # WEIGHT_SCALES if self.block_quant: scale_dtype = torch.uint8 if self.use_mxfp8 else torch.float32 scale_init = torch.zeros if scale_dtype == torch.uint8 else torch.ones w13_weight_scale = torch.nn.Parameter( scale_init( num_experts, 2 * ((intermediate_size_per_partition + block_n - 1) // block_n), (hidden_size + block_k - 1) // block_k, dtype=scale_dtype, ), requires_grad=False, ) w2_weight_scale = torch.nn.Parameter( scale_init( num_experts, (hidden_size + block_n - 1) // block_n, (intermediate_size_per_partition + block_k - 1) // block_k, dtype=scale_dtype, ), requires_grad=False, ) # w13_weight and w2_weight are always requanted together w13_weight_scale.format_ue8m0 = self.use_mxfp8 w2_weight_scale.format_ue8m0 = self.use_mxfp8 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_moe_runner_backend().is_cutlass(): self._ensure_cutlass_buffers_initialized(layer) 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_per_partition, 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_block_quant(self, layer: Module) -> None: # 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: # add this section for MI300 # 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) ) elif _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) ) elif _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"]) elif self.use_mxfp8: self._process_mxfp8_moe_weights( layer, quantize=not self.quant_config.is_checkpoint_fp8_serialized ) else: # For fp8 moe run with deepgemm, the expert weights and scales need be requantized to ue8m0 from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE from sglang.srt.model_loader.utils import ( should_deepgemm_weight_requant_ue8m0, ) # Check if MoE will actually use DeepGEMM runner will_use_deepgemm = self.is_deepgemm_moe_runner_backend_enabled() if ( should_deepgemm_weight_requant_ue8m0( weight_block_size=getattr( self.quant_config, "weight_block_size", None ), ) and will_use_deepgemm and not layer.w13_weight_scale_inv.format_ue8m0 ): assert isinstance( layer, DeepEPMoE ), "DeepGemm MoE is only supported with DeepEPMoE" weight_block_size = self.quant_config.weight_block_size requant_weight_ue8m0_inplace( layer.w13_weight, layer.w13_weight_scale_inv, weight_block_size ) requant_weight_ue8m0_inplace( layer.w2_weight, layer.w2_weight_scale_inv, weight_block_size ) layer.w13_weight_scale_inv.format_ue8m0 = True layer.w2_weight_scale_inv.format_ue8m0 = True def _process_mxfp8_moe_weights(self, layer: Module, quantize: bool = True) -> None: if not (_is_cuda and is_sm100_supported()): raise RuntimeError("MXFP8 MoE quantization requires SM100.") def _quantize_and_swizzle_with_cutlass_es_kernel(weight: torch.Tensor): from sgl_kernel import es_sm100_mxfp8_blockscaled_grouped_quant weight = weight.contiguous() num_experts, m, k = weight.shape assert k % 32 == 0, f"{k=} must be divisible by 32 for MXFP8" weight_flat = weight.view(-1, k).contiguous() problem_sizes = torch.empty( (num_experts, 3), dtype=torch.int32, device=weight.device ) problem_sizes[:, 0] = m problem_sizes[:, 1] = 0 problem_sizes[:, 2] = k expert_offsets = torch.arange( 0, num_experts * m, m, dtype=torch.int32, device=weight.device ) aligned_m = ((m + 127) // 128) * 128 blockscale_offsets = torch.arange( 0, num_experts * aligned_m, aligned_m, dtype=torch.int32, device=weight.device, ) qweight = torch.empty_like(weight_flat, dtype=torch.float8_e4m3fn) scale = torch.empty( (num_experts * aligned_m, k // 32), dtype=torch.uint8, device=weight.device, ) es_sm100_mxfp8_blockscaled_grouped_quant( weight_flat, problem_sizes, expert_offsets, blockscale_offsets, qweight, scale, ) qweight = qweight.view_as(weight) scale = scale.view(num_experts, aligned_m, k // 32) if aligned_m != m: scale = scale[:, :m, :] return qweight, scale def _swizzle_mxfp8_sf(scale, num_warps): from triton_kernels.tensor import convert_layout, wrap_torch_tensor from triton_kernels.tensor_details import layout scale_layout, scale_layout_opts = ( layout.make_default_matmul_mxfp4_w_scale_layout( mx_axis=1, num_warps=num_warps ) ) scale = scale.transpose(-2, -1) scale = convert_layout( wrap_torch_tensor(scale), scale_layout, **scale_layout_opts ) return scale def _swizzle_with_triton_kernel( weight_shape: tuple[int, int, int], scale: torch.Tensor ): num_experts, m, k = weight_shape aligned_m = ((m + 127) // 128) * 128 scale = scale.view(num_experts, aligned_m, k // 32) num_warps = 8 scale = _swizzle_mxfp8_sf(scale, num_warps) scale = scale.data.view(num_experts, aligned_m, k // 32) return scale def _quantize_and_swizzle_with_triton_kernel(weight: torch.Tensor): weight = weight.contiguous() _, _, k = weight.shape assert k % 32 == 0, f"{k=} must be divisible by 32 for MXFP8" weight_flat = weight.view(-1, k).contiguous() qweight, scale = mxfp8_group_quantize(weight_flat) qweight = qweight.view_as(weight) scale = _swizzle_with_triton_kernel(weight.shape, scale) return qweight, scale if quantize: if get_moe_runner_backend().is_cutlass(): w13_q, w13_s = _quantize_and_swizzle_with_cutlass_es_kernel( layer.w13_weight.data ) w2_q, w2_s = _quantize_and_swizzle_with_cutlass_es_kernel( layer.w2_weight.data ) else: w13_q, w13_s = _quantize_and_swizzle_with_triton_kernel( layer.w13_weight.data ) w2_q, w2_s = _quantize_and_swizzle_with_triton_kernel( layer.w2_weight.data ) else: w13_q = layer.w13_weight.data w2_q = layer.w2_weight.data w13_s = _swizzle_with_triton_kernel( layer.w13_weight.data.shape, layer.w13_weight_scale_inv.data ) w2_s = _swizzle_with_triton_kernel( layer.w2_weight.data.shape, layer.w2_weight_scale_inv.data ) # Keep parameter objects to preserve weight_loader attrs for hot reload. # Prefer in-place copy; rebind only when shape/dtype changes (online quantize). def _copy_or_rebind(param: Parameter, new_value: torch.Tensor) -> None: if ( param.data.shape == new_value.shape and param.data.dtype == new_value.dtype ): param.data.copy_(new_value) else: param.data = new_value _copy_or_rebind(layer.w13_weight, w13_q) _copy_or_rebind(layer.w2_weight, w2_q) _copy_or_rebind(layer.w13_weight_scale_inv, w13_s) _copy_or_rebind(layer.w2_weight_scale_inv, w2_s) layer.w13_weight.requires_grad_(False) layer.w2_weight.requires_grad_(False) layer.w13_weight_scale_inv.requires_grad_(False) layer.w2_weight_scale_inv.requires_grad_(False) layer.w13_weight_scale_inv.format_ue8m0 = True layer.w2_weight_scale_inv.format_ue8m0 = True 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: self.process_weights_after_loading_block_quant(layer) 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_local_experts, dtype=torch.float32, device=w13_weight.device, ), requires_grad=False, ) for expert in range(layer.num_local_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_local_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) # Align FP8 weights to FlashInfer per-tensor kernel layout if enabled if get_moe_runner_backend().is_flashinfer_trtllm(): from sglang.srt.layers.moe.moe_runner.flashinfer_trtllm import ( align_fp8_moe_weights_for_flashinfer_trtllm, ) align_fp8_moe_weights_for_flashinfer_trtllm(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_local_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_local_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 create_moe_runner( self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig ): self.moe_runner_config = moe_runner_config moe_runner_backend = get_moe_runner_backend() if moe_runner_backend.is_auto(): if self.is_deepgemm_moe_runner_backend_enabled(): moe_runner_backend = MoeRunnerBackend.DEEP_GEMM else: moe_runner_backend = MoeRunnerBackend.TRITON if ( moe_runner_backend.is_deep_gemm() or moe_runner_backend.is_triton() or moe_runner_backend.is_flashinfer_trtllm() ): self.runner = MoeRunner(moe_runner_backend, moe_runner_config) else: # TODO(cwan): refactor other backends pass def apply( self, layer: torch.nn.Module, dispatch_output: DispatchOutput, ) -> CombineInput: from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput x = dispatch_output.hidden_states moe_runner_config = self.moe_runner_config if use_intel_amx_backend(layer): from sglang.srt.layers.moe.topk import apply_topk_weights_cpu topk_weights, topk_ids, _ = dispatch_output.topk_output x, topk_weights = apply_topk_weights_cpu( moe_runner_config.apply_router_weight_on_input, topk_weights, x ) output = 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. CPUQuantMethod.FP8_W8A16, layer.w13_weight_scale_inv, # w1_scale layer.w2_weight_scale_inv, # w2_scale None, # w1_zp None, # w2_zp self.quant_config.weight_block_size, # block_size True, # is_vnni ) return StandardCombineInput(hidden_states=output) if _is_hip: ret = self.maybe_apply_hip_fused_experts( layer, x, dispatch_output.topk_output, moe_runner_config.activation, moe_runner_config.no_combine, ) if ret is not None: return StandardCombineInput(hidden_states=ret) if get_moe_runner_backend().is_cutlass(): from sglang.srt.layers.moe.cutlass_moe import cutlass_fused_experts_fp8 with use_symmetric_memory( get_tp_group(), disabled=not is_allocation_symmetric() ): symm_output = torch.empty_like(x) topk_weights, topk_ids, _ = dispatch_output.topk_output use_mxfp8 = getattr(self.quant_config, "use_mxfp8", False) output = 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, use_mxfp8=use_mxfp8, output=symm_output, enable_es=(use_mxfp8, use_mxfp8), ) return StandardCombineInput(hidden_states=output) if self.runner.runner_backend.is_deep_gemm(): w13_weight = layer.w13_weight w2_weight = layer.w2_weight if self.block_quant: block_shape = self.quant_config.weight_block_size w13_scale = layer.w13_weight_scale_inv w2_scale = layer.w2_weight_scale_inv else: # Convert per-tensor quant to per-block quant by repeating scales for forward_deepgemm scale_block_size = 128 block_shape = [scale_block_size, scale_block_size] w13_scale_n = (w13_weight.shape[1] - 1) // scale_block_size + 1 w13_scale_k = (w13_weight.shape[2] - 1) // scale_block_size + 1 w13_scale = ( layer.w13_weight_scale.unsqueeze(1) .repeat_interleave(w13_scale_n, dim=1) .unsqueeze(2) .repeat_interleave(w13_scale_k, dim=2) ) w2_scale_n = (w2_weight.shape[1] - 1) // scale_block_size + 1 w2_scale_k = (w2_weight.shape[2] - 1) // scale_block_size + 1 w2_scale = ( layer.w2_weight_scale.unsqueeze(1) .repeat_interleave(w2_scale_n, dim=1) .unsqueeze(2) .repeat_interleave(w2_scale_k, dim=2) ) quant_info = DeepGemmMoeQuantInfo( w13_weight=w13_weight, w2_weight=w2_weight, use_fp8=True, w13_scale=w13_scale, w2_scale=w2_scale, block_shape=block_shape, ) elif self.runner.runner_backend.is_flashinfer_trtllm(): # FlashInfer TRT-LLM backend only supports fused execution and consumes # router logits directly (no separate apply_with_router_logits needed). global_num_experts = int(getattr(layer, "num_experts")) num_local_experts = int(getattr(layer, "num_local_experts")) moe_ep_rank = int(getattr(layer, "moe_ep_rank")) quant_info = FlashInferTrtllmFp8MoeQuantInfo( w13_weight=layer.w13_weight, w2_weight=layer.w2_weight, global_num_experts=global_num_experts, local_expert_offset=moe_ep_rank * num_local_experts, local_num_experts=num_local_experts, intermediate_size=layer.w2_weight.shape[2], routing_method_type=int( getattr(layer, "routing_method_type", RoutingMethodType.DeepSeekV3) ), block_quant=self.block_quant, weight_block_k=( None if self.quant_config.weight_block_size is None else self.quant_config.weight_block_size[1] ), w13_weight_scale_inv=( layer.w13_weight_scale_inv if self.block_quant else None ), w2_weight_scale_inv=( layer.w2_weight_scale_inv if self.block_quant else None ), w13_input_scale=layer.w13_input_scale if not self.block_quant else None, output1_scales_scalar=( getattr(layer, "output1_scales_scalar", None) if not self.block_quant else None ), output1_scales_gate_scalar=( getattr(layer, "output1_scales_gate_scalar", None) if not self.block_quant else None ), output2_scales_scalar=( getattr(layer, "output2_scales_scalar", None) if not self.block_quant else None ), ) elif self.runner.runner_backend.is_triton(): quant_info = TritonMoeQuantInfo( w13_weight=layer.w13_weight, w2_weight=layer.w2_weight, b13=getattr(layer, "w13_weight_bias", None), b2=getattr(layer, "w2_weight_bias", None), use_fp8_w8a8=True, w13_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 ), a13_scale=layer.w13_input_scale, a2_scale=layer.w2_input_scale, block_shape=self.quant_config.weight_block_size, ) else: raise NotImplementedError( "Unsupported runner backend: %s" % self.runner.runner_backend ) return self.runner.run(dispatch_output, quant_info) def _ensure_cutlass_buffers_initialized(self, layer: Module) -> None: if getattr(self, "_cutlass_buffers_ready", False): return device = layer.w13_weight.device num_experts = layer.w13_weight.shape[0] hidden_size = layer.w2_weight.shape[1] intermediate_size_per_partition = layer.intermediate_size_per_partition self.ab_strides1 = torch.full( (num_experts,), hidden_size, device=device, dtype=torch.int64 ) self.c_strides1 = torch.full( (num_experts,), 2 * intermediate_size_per_partition, device=device, dtype=torch.int64, ) self.ab_strides2 = torch.full( (num_experts,), intermediate_size_per_partition, device=device, dtype=torch.int64, ) self.c_strides2 = torch.full( (num_experts,), hidden_size, device=device, dtype=torch.int64 ) self.workspace = torch.empty(90000, device=device, dtype=torch.uint8) self.a_ptr = torch.empty(num_experts, device=device, dtype=torch.int64) self.b_ptr = torch.empty(num_experts, device=device, dtype=torch.int64) self.out_ptr = torch.empty(num_experts, device=device, dtype=torch.int64) self.a_scales_ptr = torch.empty(num_experts, device=device, dtype=torch.int64) self.b_scales_ptr = torch.empty(num_experts, device=device, dtype=torch.int64) self.expert_offsets = torch.empty( num_experts + 1, device=device, dtype=torch.int32 ) self.problem_sizes1 = torch.empty( num_experts, 3, device=device, dtype=torch.int32 ) self.problem_sizes2 = torch.empty( num_experts, 3, device=device, dtype=torch.int32 ) self._cutlass_buffers_ready = True def maybe_apply_hip_fused_experts( self, layer: torch.nn.Module, x: torch.Tensor, topk_output: TopKOutput, activation: str = "silu", no_combine: bool = False, ) -> Optional[torch.Tensor]: topk_weights, topk_ids, _ = topk_output 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=layer.expert_mask_gpu, ) 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 ), expert_mask=layer.expert_mask_gpu, ) return None class Fp8KVCacheMethod(BaseKVCacheMethod): """ Supports loading kv-cache scaling factors from FP8 checkpoints. """ def __init__(self, quant_config: Fp8Config): super().__init__(quant_config)