from __future__ import annotations import logging from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import torch from torch.nn import Module from torch.nn.parameter import Parameter from sglang.multimodal_gen.runtime.distributed.parallel_state import ( get_tensor_model_parallel_world_size, ) from sglang.multimodal_gen.runtime.layers.linear import ( LinearMethodBase, UnquantizedLinearMethod, ) from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import ( QuantizationConfig, QuantizeMethodBase, ) from sglang.multimodal_gen.runtime.models.parameter import ( BlockQuantScaleParameter, ModelWeightParameter, PerTensorScaleParameter, ) from sglang.multimodal_gen.runtime.platforms import current_platform from sglang.multimodal_gen.runtime.platforms.aiter import USE_AITER from sglang.multimodal_gen.runtime.utils.common import ( cpu_has_amx_support, get_bool_env_var, use_intel_amx_backend, ) from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading from sglang.srt.layers.quantization.fp8_kernel import ( is_fp8_fnuz, per_token_group_quant_fp8, ) 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, normalize_e4m3fn_to_e4m3fnuz, requant_weight_ue8m0_inplace, ) from sglang.srt.layers.quantization.marlin_utils_fp8 import ( apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin, ) from sglang.srt.layers.quantization.utils import ( convert_to_channelwise, is_layer_skipped, requantize_with_max_scale, ) if TYPE_CHECKING: from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config _is_hip = current_platform.is_hip() _is_cuda = current_platform.is_cuda() _is_npu = current_platform.is_npu() _is_cpu_amx_available = cpu_has_amx_support() _is_cpu = current_platform.is_cpu() _is_fp8_fnuz = is_fp8_fnuz() _use_hip_int4 = get_bool_env_var("SGLANG_INT4_WEIGHT") and _is_hip if USE_AITER or _use_hip_int4: pass ACTIVATION_SCHEMES = ["static", "dynamic"] logger = logging.getLogger(__name__) class Fp8Config(QuantizationConfig): """Config class for FP8. No-arg ``Fp8Config()`` selects online (post-load) weight quantization: ``is_checkpoint_fp8_serialized=False`` with ``activation_scheme="dynamic"``. """ 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, packed_modules_mapping: Optional[Dict[str, List[str]]] = None, ) -> None: self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized if is_checkpoint_fp8_serialized: logger.info("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.packed_modules_mapping = packed_modules_mapping 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( 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", "modules_to_not_convert"], None ) if ignored_layers: # hacking ministral ignored_layers = [layer.replace("model.", "") for layer in ignored_layers] 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.multimodal_gen.runtime.layers.linear import LinearBase if isinstance(layer, LinearBase): if is_layer_skipped( prefix, self.ignored_layers, fused_mapping=self.packed_modules_mapping, ): return UnquantizedLinearMethod() return Fp8LinearMethod(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 = 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.block_quant = self.quant_config.weight_block_size is not None 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.format_ue8m0 = False 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: 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"]) layer.weight_scale_inv = torch.nn.Parameter( layer.weight_scale_inv.data, requires_grad=False ) 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 else: layer.weight = 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 = 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 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: 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.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=False, )