fix lightop gemm smooth and add some trivial changes

python/sglang/srt/_custom_ops.py

@@ -332,6 +332,18 @@ def rocblas_scaled_mm(a: torch.Tensor,
 
     return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)
 
+def blaslt_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:
+    m = a.shape[0]
+    n = b.shape[0]
+    k = a.shape[1]
+    _, out = quant_ops.hipblaslt_w8a8_gemm(a, b, scale_a, scale_b, m, n, k, 'NT', out_dtype)
+    return out
+
 def triton_int8_gemm_helper(m: int,
                              n: int,
                              k: int,

python/sglang/srt/layers/moe/token_dispatcher/deepep.py

@@ -725,6 +725,7 @@ class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
         self.packed_recv_count = self.handle = None
         return combined_hidden_states, event, hook
 
+    @torch._dynamo.disable()
     def _get_buffer(self):
         DeepEPBuffer.set_dispatch_mode_as_low_latency()
         return DeepEPBuffer.get_deepep_buffer(

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py

 # Adapted from https://github.com/vllm-project/vllm/tree/v0.8.2/vllm/model_executor/layers/quantization/compressed_tensors
 # SPDX-License-Identifier: Apache-2.0
 from __future__ import annotations
-
+import os
 import logging
 from contextlib import suppress
 from typing import Any, Dict, List, Literal, NamedTuple, Optional, Tuple, cast
@@ -46,6 +46,9 @@ from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
 from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
 
+from sglang.srt import _custom_ops as ops
+from sglang.srt.utils import W8a8GetCacheJSON  # TODO: remove vllm dependency
+
 logger = logging.getLogger(__name__)
 
 __all__ = ["CompressedTensorsLinearMethod"]
@@ -590,8 +593,33 @@ class CompressedTensorsLinearMethod(LinearMethodBase):
 
     def __init__(self, quantization_config: CompressedTensorsConfig):
         self.quantization_config = quantization_config
+        self.tritonsingleton= W8a8GetCacheJSON()
+        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        n=layer.weight.shape[0]
+        k=layer.weight.shape[1]
+        
+        if self.w8a8_strategy==1:
+            if [n,k] not in self.tritonsingleton.weight_shapes:
+                self.tritonsingleton.weight_shapes.append([n,k])
+                json_file=self.tritonsingleton.get_w8a8json_name(n,k)
+                configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
+                
+                if configs_dict:
+                    self.tritonsingleton.triton_json_dict.update(configs_dict)
+                    
+                    for key, value in configs_dict.items():
+                        m=int(key.split('_')[0])
+                        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)
+        elif self.w8a8_strategy==3:
+            layer.weight.data = layer.weight.data.T
+        else: 
+            weight_data=layer.weight.data
+            _weight=weight_data.T.contiguous().reshape(n,-1)
+            layer.weight.data=_weight
+            
+        self.tritonsingleton.gen_model_json() 
         layer.scheme.process_weights_after_loading(layer)
 
     def create_weights(

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

 # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
 # SPDX-License-Identifier: Apache-2.0
 
+import os
 from typing import Callable, Optional
 
 import torch
@@ -19,11 +20,14 @@ from sglang.srt.layers.quantization.compressed_tensors.schemes import (
 from lmslim.layers.gemm.int8_utils import per_token_quant_int8
 from sglang.srt.layers.quantization.utils import requantize_with_max_scale
 from sglang.srt.utils import is_cuda
-from lmslim import quant_ops
+
+from sglang.srt import _custom_ops as ops
 _is_cuda = is_cuda()
 if _is_cuda:
     from sgl_kernel import int8_scaled_mm
-
+# TODO: remove vllm deps
+from sglang.srt.utils import W8a8GetCacheJSON
+W8A8_TRITONJSON=W8a8GetCacheJSON()
 
 class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
 
@@ -33,6 +37,7 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
         self.strategy = strategy
         self.is_static_input_scheme = is_static_input_scheme
         self.input_symmetric = input_symmetric
+        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))  # TODO
 
     @classmethod
     def get_min_capability(cls) -> int:
@@ -163,14 +168,70 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                 )
                 layer.register_parameter("input_zero_point", input_zero_point)
 
-    @torch._dynamo.disable()
     def apply_weights(
         self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor]
     ) -> torch.Tensor:
         # TODO: add cutlass_scaled_mm_azp support
         x_q, x_scale = per_token_quant_int8(x)
-
-        # TODO: fix with lmslim/lightop
-        return quant_ops.triton_scaled_mm(
-            x_q, layer.weight, x_scale, layer.weight_scale, out_dtype=x.dtype, bias=bias
-        )
+        # return quant_ops.custom_scaled_mm(x_q, layer.weight, x_scale, layer.weight_scale, out_dtype=x.dtype, bias=bias)
+        
+        if self.w8a8_strategy==1:
+            m=x_q.shape[0]
+            k=x_q.shape[1]
+            n=layer.weight.shape[1]
+            
+            if len(W8A8_TRITONJSON.triton_json_dict)==0:
+                best_config=None
+                
+            elif f"1_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict:
+                if m<=16:
+                    m_=m
+                elif m<=64:
+                    m_= (m + 3) & -4 #取值到最近的4的倍数
+                elif m<=160:
+                    m_=(m + 7) & -8
+                    
+                elif m<200: #256
+                    m_=160
+                elif m<480: #512
+                    m_=256
+                elif m<960: #1024
+                    m_=512
+                elif m<2048:
+                    m_=1024
+                elif m<4096:
+                    m_=2048
+                elif m<6000:
+                    m_=4096
+                else:
+                    m_=8192  
+
+                best_config=W8A8_TRITONJSON.triton_json_dict[f"{m_}_{n}_{k}"]
+                
+            else: 
+                best_config=None
+
+            return ops.triton_scaled_mm(
+                x_q, layer.weight, x_scale, layer.weight_scale, out_dtype=x.dtype, bias=bias, best_config=best_config
+            )
+        elif self.w8a8_strategy==2:
+            return ops.cutlass_scaled_mm(x_q,
+                                    layer.weight,
+                                    scale_a=x_scale,
+                                    scale_b=layer.weight_scale,
+                                    out_dtype=x.dtype,
+                                    bias=bias)
+        elif self.w8a8_strategy==3:
+            return ops.blaslt_scaled_mm(x_q,
+                                    layer.weight,
+                                    scale_a=x_scale,
+                                    scale_b=layer.weight_scale,
+                                    out_dtype=x.dtype,
+                                    bias=None)
+        else:
+            return ops.rocblas_scaled_mm(x_q,
+                                        layer.weight,
+                                        scale_a=x_scale,
+                                        scale_b=layer.weight_scale,
+                                        out_dtype=x.dtype,
+                                        bias=bias)

python/sglang/srt/layers/quantization/slimquant_w4a8.py

@@ -15,7 +15,7 @@ from lmslim.layers.gemm.int8_utils import (
     per_token_group_quant_int8,
     per_token_quant_int8)
 from sglang.srt import _custom_ops as ops
-from vllm.utils import W8a8GetCacheJSON
+from sglang.srt.utils import W8a8GetCacheJSON
 from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
 
 import os
@@ -157,7 +157,6 @@ class SlimQuantW4A8Int8LinearMethod(LinearMethodBase):
         )
         layer.register_parameter("weight_scale", weight_scale)
 
-    @torch._dynamo.disable()  # TODO: 性能优化需要lmslim/lightop配合
     def apply(
         self,
         layer: torch.nn.Module,
@@ -227,6 +226,13 @@ class SlimQuantW4A8Int8LinearMethod(LinearMethodBase):
                                         scale_b=layer.weight_scale,
                                         out_dtype=x.dtype,
                                         bias=bias)
+        elif self.w8a8_strategy==3:
+            return ops.blaslt_scaled_mm(x_q,
+                                    layer.weight,
+                                    scale_a=x_scale,
+                                    scale_b=layer.weight_scale,
+                                    out_dtype=x.dtype,
+                                    bias=None)
         else:
             return ops.rocblas_scaled_mm(x_q,
                                         layer.weight,

python/sglang/srt/model_executor/forward_batch_info.py

@@ -653,7 +653,7 @@ class ForwardBatch:
                     bs = self.batch_size,
                 )
             else:
-                logger.info("SGLANG_CREATE_CHUNKED_PREFIX_CACHE_KV_INDICES=0")
+                # logger.info("SGLANG_CREATE_CHUNKED_PREFIX_CACHE_KV_INDICES=0")
                 create_chunked_prefix_cache_kv_indices[(self.batch_size,)](
                     self.req_to_token_pool.req_to_token,
                     self.req_pool_indices,

python/sglang/srt/utils/common.py

@@ -3528,3 +3528,158 @@ def cached_triton_kernel(key_fn=None):
         return CachedKernel(fn, key_fn)
 
     return decorator
+
+# from vllm
+class W8a8GetCacheJSON:
+    _instance = None
+    
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            cls._instance = super(W8a8GetCacheJSON, cls).__new__(cls, *args, **kwargs)
+            cls._instance._initialize()
+        return cls._instance
+
+    def _initialize(self):
+        current_folder_path = os.path.dirname(os.path.abspath(__file__))
+        json_folder_path=current_folder_path+'/../../lmslim/configs/w8a8'
+    
+        self.triton_json_dir=(os.getenv('TRITON_JSON_DIR', json_folder_path))
+        self.triton_json_dict={}
+        self.triton_moejson_dict={}
+        self.triton_json_list=[]
+        self.weight_shapes=[]
+        self.moe_weight_shapes=[]
+        arch_name = torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0]
+        arch_cu = torch.cuda.get_device_properties(torch.cuda.current_device()).multi_processor_count
+        
+        device_name =arch_name+'_'+str(arch_cu)+'cu'
+        self.device_name=device_name
+        self.topk=1
+        self.quant_method=None
+
+    #析构函数，最后会生成model.json的配置文件
+    def gen_model_json(self,E:Optional[int]=0,block_size:Optional[list]=None):
+        json_dir = os.getenv('LMSLIM_TUNING_JSON', "None")
+        if json_dir != "None" and os.path.exists(json_dir):
+            #生成模型配置文件
+            # logger.info("model_tuning.json is at LMSLIM_TUNING_JSON:%s", json_dir)
+            config = {
+                "layers": {
+                    "linear": {
+                        "shapes": [],
+                        "m_range":"None",
+                    },
+                    "moe": {
+                        "shapes": [],
+                        "m_range": "None",
+                        "topk": self.topk
+                    }
+                },
+                "quantization_config": {
+                    "quant_method": self.quant_method,
+                    "weight_block_size": "None"
+                }
+            }
+            
+            # 处理 MoE shapes
+            for shape in self.moe_weight_shapes:
+                if len(shape) == 4:  # 假设 MoE shape 是 [N1, N2,K] 格式
+                    moe_config = {
+                        "E": shape[0],
+                        "N1": shape[1],
+                        "N2": shape[2],
+                        "K": shape[3],      # 默认值
+                    }
+                    config["layers"]["moe"]["shapes"].append(moe_config)
+                    
+            for shape in self.weight_shapes:
+                config["layers"]["linear"]["shapes"].append(shape)
+            
+            if block_size is not None:
+                config["quantization_config"]["weight_block_size"]=block_size
+                                    
+            with open(json_dir+"/model.json", 'w') as f:
+                json.dump(config, f, indent=4)
+        # else:
+        #     logger.info("LMSLIM_TUNING_JSON is not set")
+                   
+    def getspec_config(self,configs_dict,M,N,K):
+        if f"{M}_{N}_{K}" in configs_dict:
+            return configs_dict[f"{M}_{N}_{K}"]
+        else:
+            return None  
+        
+    def get_triton_cache(self,file_path,n,k):
+        #在非tuning的时候使用，当文件不存在则直接返回none
+        cache_json_file=file_path
+        
+        if os.path.exists(file_path):
+        #try:
+            with open(cache_json_file, 'r') as file:
+                cachedata = json.load(file)
+        else:
+            return None 
+                    
+        #把所有的cache解析成key:config的形式：[M_N_K]:[config]
+        configs_dict={}
+        for key, value in cachedata.items():
+            for sub_key, sub_value in value.items():
+                configs_key= f"{sub_key}_{key}"
+                configs_dict[configs_key]=sub_value
+        return configs_dict
+  
+    def get_w8a8json_name(self,n,k):
+        return self.triton_json_dir+f"/W8A8_{n}_{k}_{self.device_name}.json"
+    
+    def get_blockint8_triton_cache(self,file_path,n,k,block_n,block_k):
+        cache_json_file=file_path
+        
+        if os.path.exists(file_path):
+        #try:
+            with open(cache_json_file, 'r') as file:
+                cachedata = json.load(file)
+        else:
+            return None  
+                    
+        #把所有的cache解析成key:config的形式：[M_N_K]:[config]
+        configs_dict={}
+        for key, value in cachedata.items():
+            for sub_key, sub_value in value.items():
+                configs_key= f"{sub_key}_{key}"
+                configs_dict[configs_key]=sub_value
+        return configs_dict
+
+    def get_blockint8json_name(self,n,k,block_n,block_k):
+        return self.triton_json_dir+f"/linear_{n}_{k}_block[{block_n},{block_k}]_{self.device_name}.json"
+
+    def get_moeint8json_name(self,E,N1,N2,K,TOPK,
+                             block_size:Optional[list]=None,use_int4_w4a8:Optional[bool]=False):
+        if use_int4_w4a8:
+            if block_size is not None:
+                return self.triton_json_dir+f"/MOE_W4A8INT8[{block_size[0]},{block_size[1]}]_E={E}_N1={N1}_N2={N2}_K={K}_TOPK{TOPK}_{self.device_name}.json"
+            else:
+                return self.triton_json_dir+f"/MOE_W4A8INT8_E={E}_N1={N1}_N2={N2}_K={K}_TOPK{TOPK}_{self.device_name}.json"      
+        else:
+            if block_size is not None:
+                return self.triton_json_dir+f"/MOE_BLOCKINT8[{block_size[0]},{block_size[1]}]_E={E}_N1={N1}_N2={N2}_K={K}_TOPK{TOPK}_{self.device_name}.json"
+            else:
+                return self.triton_json_dir+f"/MOE_W8A8INT8_E={E}_N1={N1}_N2={N2}_K={K}_TOPK{TOPK}_{self.device_name}.json"
+
+    def get_moeint8_triton_cache(self,file_path,E,N1,N2,K,TOPK):
+        cache_json_file=file_path
+        
+        if os.path.exists(file_path):
+        #try:
+            with open(cache_json_file, 'r') as file:
+                cachedata = json.load(file)
+        else:
+            return None  
+                    
+        #把所有的cache解析成key:config的形式：[M_N_K]:[config1,config2]
+        configs_dict={}
+        for key, value in cachedata.items():
+            for sub_key, sub_value in value.items():
+                configs_key= f"{sub_key}_{key}"   
+                configs_dict[configs_key]=sub_value
+    
+        return configs_dict