adaptation part w4A8 quantization
(cherry picked from commit 68277eac30)
This commit is contained in:
lizhigong
@@ -614,6 +614,7 @@ class ModelConfig:
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"petit_nvfp4",
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"quark",
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"mxfp4",
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"slimquant_w4a8_marlin",
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]
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optimized_quantization_methods = [
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"fp8",
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@@ -633,6 +634,7 @@ class ModelConfig:
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"qoq",
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"w4afp8",
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"petit_nvfp4",
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"slimquant_w4a8_marlin",
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]
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compatible_quantization_methods = {
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"modelopt_fp4": ["modelopt"],
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@@ -57,6 +57,7 @@ from sglang.srt.layers.quantization.qoq import QoQConfig
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from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
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from sglang.srt.layers.quantization.w8a8_fp8 import W8A8Fp8Config
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from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
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from sglang.srt.layers.quantization.slimquant_w4a8_marlin import SlimQuantW4A8Int8MarlinConfig
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from sglang.srt.utils import is_cuda, is_hip, mxfp_supported
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_is_mxfp_supported = mxfp_supported()
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@@ -83,6 +84,7 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
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"w4afp8": W4AFp8Config,
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"petit_nvfp4": PetitNvFp4Config,
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"fbgemm_fp8": FBGEMMFp8Config,
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"slimquant_w4a8_marlin":SlimQuantW4A8Int8MarlinConfig,
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}
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408
python/sglang/srt/layers/quantization/slimquant_w4a8.py
Normal file
408
python/sglang/srt/layers/quantization/slimquant_w4a8.py
Normal file
@@ -0,0 +1,408 @@
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from typing import Any, Callable, Dict, List, Optional
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import torch
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from sglang.srt.layers.linear import set_weight_attrs
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from torch.nn.parameter import Parameter
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.quantization.base_config import LinearMethodBase, QuantizationConfig, QuantizeMethodBase, FusedMoEMethodBase
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from sglang.srt.layers.parameter import (
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ChannelQuantScaleParameter,
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_ColumnvLLMParameter,
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RowvLLMParameter,
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)
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from lmslim.layers.gemm.int8_utils import (
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per_token_group_quant_int8,
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per_token_quant_int8)
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from sglang.srt import _custom_ops as ops
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from vllm.utils import W8a8GetCacheJSON
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import os
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class ModelWeightParameter(_ColumnvLLMParameter, RowvLLMParameter):
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"""
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Parameter class for linear layer weights. Uses both column and
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row parallelism.
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"""
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pass
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W8A8_TRITONJSON=W8a8GetCacheJSON()
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def baseline_scaled_mm(a: torch.Tensor,
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b: torch.Tensor,
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scale_a: torch.Tensor,
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scale_b: torch.Tensor,
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out_dtype: torch.dtype,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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scales= scale_a* scale_b.T
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gemmout= torch.mm(
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a.to(dtype=torch.float32), b.to(dtype=torch.float32))
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output = (scales *gemmout).to(out_dtype)
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if bias is not None:
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output = output + bias
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return output.to(out_dtype)
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class SlimQuantW4A8Int8Config(QuantizationConfig):
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"""Config class for W8A8 Int8 Quantization.
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- Weight: static, per-channel, symmetric
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- Activation: dynamic, per-token, symmetric
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"""
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def __init__(self):
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pass
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.float16, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 75
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@classmethod
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def get_name(self) -> str:
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return "slimquant_w4a8"
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "SlimQuantW4A8Int8Config":
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return cls()
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def get_quant_method(
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self,
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layer: torch.nn.Module,
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prefix: str,
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) -> Optional["QuantizeMethodBase"]:
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from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
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if isinstance(layer, LinearBase):
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return SlimQuantW4A8Int8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return SlimQuantW4A8Int8MoEMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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return []
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class SlimQuantW4A8Int8LinearMethod(LinearMethodBase):
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def __init__(self, quantization_config: SlimQuantW4A8Int8Config):
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self.quantization_config = quantization_config
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self.tritonsingleton= W8a8GetCacheJSON()
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self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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n=layer.weight.shape[0]
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k=layer.weight.shape[1]
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if self.w8a8_strategy==1:
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if {n,k} not in self.tritonsingleton.weight_shapes:
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self.tritonsingleton.weight_shapes.append({n,k})
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json_file=self.tritonsingleton.get_w8a8json_name(n,k)
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configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
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if configs_dict:
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self.tritonsingleton.triton_json_dict.update(configs_dict)
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for key, value in configs_dict.items():
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m=int(key.split('_')[0])
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ops.triton_int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,device=layer.weight.device,best_config=value)
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else:
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weight_data=layer.weight.data
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_weight=weight_data.T.contiguous().reshape(n,-1)
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layer.weight.data=_weight
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layer.weight = Parameter(layer.weight.t(), requires_grad=False)
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layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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weight_loader = extra_weight_attrs.get("weight_loader")
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self.logical_widths = output_partition_sizes
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weight = ModelWeightParameter(
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data=torch.empty(
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sum(output_partition_sizes), input_size_per_partition, dtype=torch.int8
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight", weight)
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weight_scale = ChannelQuantScaleParameter(
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data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight_scale", weight_scale)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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input_quant_args: Optional[list[torch.Tensor]] = None,
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silu_quant_args: Optional[list[torch.Tensor]] = None
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):
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# if envs.USE_FUSED_RMS_QUANT and input_quant_args is not None:
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# assert len(input_quant_args) == 2
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# x_q, x_scale = input_quant_args
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# elif envs.USE_FUSED_SILU_MUL_QUANT and silu_quant_args is not None:
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# x_q, x_scale = silu_quant_args
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# else:
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x_q, x_scale = per_token_quant_int8(x)
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if self.w8a8_strategy==1:
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m=x_q.shape[0]
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k=x_q.shape[1]
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n=layer.weight.shape[1]
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if len(W8A8_TRITONJSON.triton_json_dict)==0:
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best_config=None
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elif f"1_{n}_{k}" in W8A8_TRITONJSON.triton_json_dict:
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if m<=16:
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m_=m
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elif m<=64:
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m_= (m + 3) & -4 #取值到最近的4的倍数
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elif m<=160:
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m_=(m + 7) & -8
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elif m<200: #256
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m_=160
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elif m<480: #512
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m_=256
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elif m<960: #1024
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m_=512
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elif m<2048:
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m_=1024
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elif m<4096:
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m_=2048
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elif m<6000:
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m_=4096
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else:
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m_=8192
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best_config=W8A8_TRITONJSON.triton_json_dict[f"{m_}_{n}_{k}"]
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else:
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best_config=None
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#if best_config==None:
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# print("m:{},n:{},k:{}".format(m,n,k))
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# print("config not found!")
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return ops.triton_scaled_mm(x_q,
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layer.weight,
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scale_a=x_scale,
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scale_b=layer.weight_scale,
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out_dtype=x.dtype,
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bias=bias,best_config=best_config)
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elif self.w8a8_strategy==2:
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return ops.cutlass_scaled_mm(x_q,
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layer.weight,
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scale_a=x_scale,
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scale_b=layer.weight_scale,
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out_dtype=x.dtype,
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bias=bias)
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else:
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return ops.rocblas_scaled_mm(x_q,
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layer.weight,
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scale_a=x_scale,
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scale_b=layer.weight_scale,
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out_dtype=x.dtype,
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bias=bias)
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class SlimQuantW4A8Int8MoEMethod:
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"""MoE method for W4A8INT8.
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Supports loading INT8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Also supports loading quantized FP16/BF16 model checkpoints with dynamic
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activation scaling. The weight scaling factor will be initialized after
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the model weights are loaded.
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Args:
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quant_config: The quantization config.
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"""
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def __new__(cls, *args, **kwargs):
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from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
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if not hasattr(cls, "_initialized"):
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original_init = cls.__init__
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new_cls = type(
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cls.__name__,
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(FusedMoEMethodBase,),
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{
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"__init__": original_init,
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**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
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},
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)
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obj = super(new_cls, new_cls).__new__(new_cls)
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obj.__init__(*args, **kwargs)
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return obj
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return super().__new__(cls)
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def __init__(self, quant_config):
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self.quant_config = quant_config
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self.tritonsingleton= W8a8GetCacheJSON()
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def create_weights(
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self,
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layer: torch.nn.Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
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tp_size = get_tensor_model_parallel_world_size()
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, 2 * intermediate_size, hidden_size//2, dtype=torch.int8
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(num_experts, hidden_size, intermediate_size//2, dtype=torch.int8),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
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)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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w13_input_scale = None
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layer.register_parameter("w13_input_scale", w13_input_scale)
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w2_input_scale = None
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layer.register_parameter("w2_input_scale", w2_input_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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E=layer.w13_weight.shape[0]
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N1=layer.w13_weight.shape[1]
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N2=layer.w2_weight.shape[1]
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K=N1//2
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if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
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self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
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TOPK= self.tritonsingleton.topk
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json_file=self.tritonsingleton.get_moeint8json_name(E,N1,N2,K,TOPK,use_int4_w4a8=True)
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configs_dict=self.tritonsingleton.get_moeint8_triton_cache(json_file,E,N1,N2,K,TOPK)
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#warmup
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if configs_dict:
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self.tritonsingleton.triton_moejson_dict.update(configs_dict)
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layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
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layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
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layer.w13_weight_scale = Parameter(
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layer.w13_weight_scale.data, requires_grad=False
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)
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layer.w2_weight_scale = Parameter(
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layer.w2_weight_scale.data, requires_grad=False
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)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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enable_eplb: bool = False,
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use_nn_moe: Optional[bool] = False,
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routed_scaling_factor: Optional[float] = None,
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use_fused_gate: Optional[bool] = False,
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**_
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) -> torch.Tensor:
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from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
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from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
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if enable_eplb:
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raise NotImplementedError(
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"EPLB not supported for `SlimQuantW4A8Int8MoEMethod` yet.")
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# Expert selection
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topk_weights, topk_ids = FusedMoE.select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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routed_scaling_factor=routed_scaling_factor,
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use_fused_gate=use_fused_gate
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)
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return fused_experts(
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x,
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layer.w13_weight,
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layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=True,
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use_int4_w4a8=True,
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per_channel_quant=True,
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activation=activation,
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expert_map=expert_map,
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apply_router_weight_on_input=apply_router_weight_on_input,
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||||
global_num_experts=global_num_experts,
|
||||
w1_scale=(layer.w13_weight_scale),
|
||||
w2_scale=(layer.w2_weight_scale),
|
||||
a1_scale=layer.w13_input_scale,
|
||||
a2_scale=layer.w2_input_scale,
|
||||
use_nn_moe=use_nn_moe,
|
||||
)
|
||||
272
python/sglang/srt/layers/quantization/slimquant_w4a8_marlin.py
Normal file
272
python/sglang/srt/layers/quantization/slimquant_w4a8_marlin.py
Normal file
@@ -0,0 +1,272 @@
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
import torch
|
||||
from sglang.srt import _custom_ops as ops
|
||||
from sglang.srt.utils import set_weight_attrs
|
||||
from sglang.srt.distributed import get_tensor_model_parallel_world_size
|
||||
from torch.nn.parameter import Parameter
|
||||
from sglang.srt.layers.linear import LinearBase
|
||||
from sglang.srt.layers.quantization import QuantizationConfig
|
||||
from sglang.srt.layers.quantization.w4a8_utils import w4a8_weight_repack_impl
|
||||
from sglang.srt.layers.quantization.base_config import (FusedMoEMethodBase, QuantizeMethodBase)
|
||||
from sglang.srt.layers.quantization.slimquant_w4a8 import SlimQuantW4A8Int8LinearMethod
|
||||
|
||||
try:
|
||||
from lmslim.layers.fused_moe.fuse_moe_w4a8_marlin import fused_experts_impl_w4a8_marlin
|
||||
except Exception:
|
||||
print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
|
||||
|
||||
|
||||
class MarlinMoeWorkspace:
|
||||
"""
|
||||
Singleton manager for device-specific workspace buffers used by w4a8 Marlin-MoE.
|
||||
global_reduce_buffer will take 1.5MB * cus (about 120MB for BW200) memoery in each device
|
||||
"""
|
||||
_instances = {}
|
||||
def __new__(cls, device):
|
||||
if device not in cls._instances:
|
||||
instance = super().__new__(cls)
|
||||
instance._initialized = False
|
||||
cls._instances[device] = instance
|
||||
return cls._instances[device]
|
||||
|
||||
def __init__(self, device):
|
||||
if self._initialized:
|
||||
return
|
||||
sms = torch.cuda.get_device_properties(device).multi_processor_count
|
||||
self.workspace = torch.zeros(
|
||||
500, dtype=torch.int, device=device, requires_grad=False
|
||||
)
|
||||
self.global_reduce_buffer = torch.zeros(
|
||||
sms * 6 * 128 * 512, dtype=torch.int, device=device, requires_grad=False
|
||||
)
|
||||
self._initialized = True
|
||||
|
||||
def get_buffers(self):
|
||||
return self.workspace, self.global_reduce_buffer
|
||||
|
||||
def baseline_scaled_mm(a: torch.Tensor,
|
||||
b: torch.Tensor,
|
||||
scale_a: torch.Tensor,
|
||||
scale_b: torch.Tensor,
|
||||
out_dtype: torch.dtype,
|
||||
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
||||
|
||||
scales= scale_a* scale_b.T
|
||||
gemmout= torch.mm(
|
||||
a.to(dtype=torch.float32), b.to(dtype=torch.float32))
|
||||
output = (scales *gemmout).to(out_dtype)
|
||||
if bias is not None:
|
||||
output = output + bias
|
||||
return output.to(out_dtype)
|
||||
|
||||
|
||||
class SlimQuantW4A8Int8MarlinConfig(QuantizationConfig):
|
||||
"""Config class for W4A8 Int8 Quantization.
|
||||
- Weight: static, per-channel, symmetric
|
||||
- Activation: dynamic, per-token, symmetric
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
|
||||
return [torch.float16, torch.bfloat16]
|
||||
|
||||
@classmethod
|
||||
def get_min_capability(cls) -> int:
|
||||
return 75
|
||||
|
||||
@classmethod
|
||||
def get_name(self) -> str:
|
||||
return "slimquant_w4a8_marlin"
|
||||
|
||||
@classmethod
|
||||
def get_config_filenames(cls) -> List[str]:
|
||||
return []
|
||||
|
||||
@classmethod
|
||||
def from_config(cls, config: Dict[str, Any]) -> "SlimQuantW4A8Int8MarlinConfig":
|
||||
return cls()
|
||||
@classmethod
|
||||
def override_quantization_method(
|
||||
cls, hf_quant_cfg, user_quant) -> Optional[str]:
|
||||
if hf_quant_cfg.get("quant_method") == "slimquant_w4a8" \
|
||||
and user_quant == "slimquant_w4a8_marlin":
|
||||
return cls.get_name()
|
||||
return None
|
||||
def get_quant_method(
|
||||
self,
|
||||
layer: torch.nn.Module,
|
||||
prefix: str,
|
||||
) -> Optional["QuantizeMethodBase"]:
|
||||
from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
|
||||
|
||||
if isinstance(layer, LinearBase):
|
||||
return SlimQuantW4A8Int8LinearMethod(self)
|
||||
elif isinstance(layer, FusedMoE):
|
||||
return SlimQuantW4A8Int8MarlinMoEMethod(self)
|
||||
return None
|
||||
|
||||
def get_scaled_act_names(self) -> List[str]:
|
||||
return []
|
||||
|
||||
|
||||
class SlimQuantW4A8Int8MarlinMoEMethod:
|
||||
"""MoE method for W4A8INT8 Marlin.
|
||||
Supports loading INT8 checkpoints with static weight scale and
|
||||
dynamic/static activation scale.
|
||||
Args:
|
||||
quant_config: The quantization config.
|
||||
"""
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
|
||||
|
||||
if not hasattr(cls, "_initialized"):
|
||||
original_init = cls.__init__
|
||||
new_cls = type(
|
||||
cls.__name__,
|
||||
(FusedMoEMethodBase,),
|
||||
{
|
||||
"__init__": original_init,
|
||||
**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
|
||||
},
|
||||
)
|
||||
obj = super(new_cls, new_cls).__new__(new_cls)
|
||||
obj.__init__(*args, **kwargs)
|
||||
return obj
|
||||
return super().__new__(cls)
|
||||
|
||||
def __init__(self, quant_config):
|
||||
self.quant_config = quant_config
|
||||
|
||||
def create_weights(
|
||||
self,
|
||||
layer: torch.nn.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 (FusedMoE, FusedMoeWeightScaleSupported)
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
|
||||
# WEIGHTS
|
||||
w13_weight = torch.nn.Parameter(
|
||||
torch.empty(
|
||||
num_experts, 2 * intermediate_size, hidden_size//2, dtype=torch.int8
|
||||
),
|
||||
requires_grad=False,
|
||||
)
|
||||
layer.register_parameter("w13_weight", w13_weight)
|
||||
set_weight_attrs(w13_weight, extra_weight_attrs)
|
||||
|
||||
w2_weight = torch.nn.Parameter(
|
||||
torch.empty(num_experts, hidden_size, intermediate_size//2, dtype=torch.int8),
|
||||
requires_grad=False,
|
||||
)
|
||||
layer.register_parameter("w2_weight", w2_weight)
|
||||
set_weight_attrs(w2_weight, extra_weight_attrs)
|
||||
|
||||
w13_weight_scale = torch.nn.Parameter(
|
||||
torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
|
||||
requires_grad=False,
|
||||
)
|
||||
w2_weight_scale = torch.nn.Parameter(
|
||||
torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
|
||||
requires_grad=False,
|
||||
)
|
||||
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
||||
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
||||
|
||||
extra_weight_attrs.update(
|
||||
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
|
||||
)
|
||||
|
||||
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
||||
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
||||
|
||||
w13_input_scale = None
|
||||
layer.register_parameter("w13_input_scale", w13_input_scale)
|
||||
|
||||
w2_input_scale = None
|
||||
layer.register_parameter("w2_input_scale", w2_input_scale)
|
||||
|
||||
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
||||
layer.w13_weight_scale = Parameter(
|
||||
layer.w13_weight_scale.data, requires_grad=False
|
||||
)
|
||||
layer.w2_weight_scale = Parameter(
|
||||
layer.w2_weight_scale.data, requires_grad=False
|
||||
)
|
||||
|
||||
layer.w13_weight = Parameter(w4a8_weight_repack_impl(layer.w13_weight), requires_grad=False)
|
||||
layer.w2_weight = Parameter(w4a8_weight_repack_impl(layer.w2_weight), requires_grad=False)
|
||||
|
||||
def apply(
|
||||
self,
|
||||
layer: torch.nn.Module,
|
||||
x: torch.Tensor,
|
||||
router_logits: torch.Tensor,
|
||||
top_k: int,
|
||||
renormalize: bool,
|
||||
use_grouped_topk: bool = False,
|
||||
topk_group: Optional[int] = None,
|
||||
num_expert_group: Optional[int] = None,
|
||||
global_num_experts: int = -1,
|
||||
expert_map: Optional[torch.Tensor] = None,
|
||||
custom_routing_function: Optional[Callable] = None,
|
||||
scoring_func: str = "softmax",
|
||||
e_score_correction_bias: Optional[torch.Tensor] = None,
|
||||
apply_router_weight_on_input: bool = False,
|
||||
activation: str = "silu",
|
||||
enable_eplb: bool = False,
|
||||
use_nn_moe: Optional[bool] = False,
|
||||
routed_scaling_factor: Optional[float] = None,
|
||||
use_fused_gate: Optional[bool] = False,
|
||||
**_
|
||||
) -> torch.Tensor:
|
||||
from sglang.srt.layers.moe.fused_moe_triton import (FusedMoE, FusedMoeWeightScaleSupported)
|
||||
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
|
||||
if enable_eplb:
|
||||
raise NotImplementedError(
|
||||
"EPLB not supported for `SlimQuantW4A8Int8MarlinMoEMethod` yet.")
|
||||
# Expert selection
|
||||
topk_weights, topk_ids = FusedMoE.select_experts(
|
||||
hidden_states=x,
|
||||
router_logits=router_logits,
|
||||
use_grouped_topk=use_grouped_topk,
|
||||
top_k=top_k,
|
||||
renormalize=renormalize,
|
||||
topk_group=topk_group,
|
||||
num_expert_group=num_expert_group,
|
||||
custom_routing_function=custom_routing_function,
|
||||
scoring_func=scoring_func,
|
||||
e_score_correction_bias=e_score_correction_bias,
|
||||
routed_scaling_factor=routed_scaling_factor,
|
||||
use_fused_gate=use_fused_gate
|
||||
)
|
||||
workspace, global_reduce_buffer = MarlinMoeWorkspace(x.device).get_buffers()
|
||||
return fused_experts_impl_w4a8_marlin(
|
||||
x,
|
||||
layer.w13_weight,
|
||||
layer.w2_weight,
|
||||
topk_weights=topk_weights,
|
||||
topk_ids=topk_ids,
|
||||
workspace=workspace,
|
||||
global_reduce_buffer=global_reduce_buffer,
|
||||
inplace=True,
|
||||
use_int4_w4a8=True,
|
||||
per_channel_quant=True,
|
||||
activation=activation,
|
||||
expert_map=expert_map,
|
||||
apply_router_weight_on_input=apply_router_weight_on_input,
|
||||
global_num_experts=global_num_experts,
|
||||
w1_scale=(layer.w13_weight_scale),
|
||||
w2_scale=(layer.w2_weight_scale),
|
||||
a1_scale=layer.w13_input_scale,
|
||||
a2_scale=layer.w2_input_scale,
|
||||
use_nn_moe=use_nn_moe,
|
||||
)
|
||||
92
python/sglang/srt/layers/quantization/w4a8_utils.py
Normal file
92
python/sglang/srt/layers/quantization/w4a8_utils.py
Normal file
@@ -0,0 +1,92 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
try:
|
||||
from lightop import awq_marlin_repack_w4a8
|
||||
use_lightop = False
|
||||
except Exception:
|
||||
use_lightop = False
|
||||
|
||||
def unpack_int8_to_int4(tensor_int8: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
将[N, K//2]大小的torch.int8 Tensor，转换为[N, K]大小的torch.int32 Tensor。
|
||||
每个int8包含两个int4，分别提取到int32的低4位，其余位为0。
|
||||
|
||||
Args:
|
||||
tensor_int8 (torch.Tensor): 输入张量，形状为[N, K//2]，类型为torch.int8。
|
||||
|
||||
Returns:
|
||||
torch.Tensor: 输出张量，形状为[N, K]，类型为torch.int32。
|
||||
"""
|
||||
if tensor_int8.dtype != torch.int8:
|
||||
raise ValueError("Input tensor must be of type torch.int8")
|
||||
|
||||
N, K_half = tensor_int8.shape
|
||||
tensor_uint8 = tensor_int8.to(torch.uint8)
|
||||
high4 = tensor_uint8 & 0x0F
|
||||
low4 = (tensor_uint8 >> 4) & 0x0F
|
||||
unpacked = torch.empty((N, K_half * 2), dtype=torch.int32, device=tensor_int8.device)
|
||||
unpacked[:, 0::2] = low4.to(torch.int32)
|
||||
unpacked[:, 1::2] = high4.to(torch.int32)
|
||||
|
||||
return unpacked
|
||||
|
||||
def get_weight_perms(interleave: bool=True):
|
||||
perm = []
|
||||
for i in range(64):
|
||||
|
||||
for col in range(4):
|
||||
cur_col = (i % 16) * 4 + col
|
||||
for row in range(8):
|
||||
cur_row = (i // 16) * 8 + row
|
||||
cur_idx = cur_row * 64 + cur_col
|
||||
perm.append(cur_idx)
|
||||
|
||||
perm = np.array(perm)
|
||||
if interleave:
|
||||
interleave = np.array([4, 0, 5, 1, 6, 2, 7, 3])
|
||||
perm = perm.reshape((-1, 8))[:, interleave].ravel()
|
||||
|
||||
perm = torch.from_numpy(perm)
|
||||
|
||||
return perm
|
||||
|
||||
def marlin_weights(q_w,weight_perm,k_tile=32,n_tile=64,pack_factor=8):
|
||||
size_k, size_n = q_w.shape
|
||||
q_w = q_w.reshape((size_k // k_tile, k_tile, size_n // n_tile, n_tile))
|
||||
q_w = q_w.permute((0, 2, 1, 3))
|
||||
q_w = q_w.reshape((size_k // k_tile, size_n * k_tile))
|
||||
q_w = q_w.reshape((-1, weight_perm.numel()))[:, weight_perm].reshape(q_w.shape)
|
||||
|
||||
orig_device = q_w.device
|
||||
q_w = q_w.contiguous().to(torch.int32)
|
||||
M, N = q_w.shape
|
||||
assert N % pack_factor == 0, f"size_n ({N}) must be divisible by pack_factor ({pack_factor})"
|
||||
q_packed = torch.zeros((M, N // pack_factor), dtype=torch.int32, device=orig_device)
|
||||
for i in range(pack_factor):
|
||||
q_packed += q_w[:, i::pack_factor] << (4 * i)
|
||||
|
||||
return q_packed
|
||||
|
||||
def w4a8_2_marlin_weight(w4a8_w):
|
||||
full_w4a8_w = unpack_int8_to_int4(w4a8_w)
|
||||
full_w4a8_w = full_w4a8_w.T
|
||||
weight_perm = get_weight_perms()
|
||||
marlin_q_w = marlin_weights(full_w4a8_w, weight_perm, k_tile=32, n_tile=64, pack_factor=8)
|
||||
return marlin_q_w
|
||||
|
||||
def w4a8_weight_repack_impl(input):
|
||||
if use_lightop:
|
||||
size_batch = input.shape[0]
|
||||
size_n = input.shape[1]
|
||||
size_k = input.shape[2] * 2
|
||||
output = torch.zeros((size_batch, size_k // 32, size_n * 4), device=input.device, dtype=torch.int32)
|
||||
awq_marlin_repack_w4a8(input, output, size_batch, size_k, size_n)
|
||||
else:
|
||||
w_marlin_list = []
|
||||
for e in range(input.shape[0]):
|
||||
w_marlin_in = w4a8_2_marlin_weight(input[e])
|
||||
w_marlin_list.append(w_marlin_in)
|
||||
output = torch.stack(w_marlin_list, dim=0)
|
||||
|
||||
return output
|
||||
@@ -93,6 +93,7 @@ QUANTIZATION_CHOICES = [
|
||||
"w4afp8",
|
||||
"mxfp4",
|
||||
"compressed-tensors", # for Ktransformers
|
||||
"slimquant_w4a8_marlin",
|
||||
]
|
||||
|
||||
ATTENTION_BACKEND_CHOICES = [
|
||||
|
||||
Reference in New Issue
Block a user