Merge branch '0.6.2-w8a8' into 'v0.6.2-dev'

0.6.2 w8a8

See merge request dcutoolkit/deeplearing/vllm!43

README.md

@@ -9,7 +9,7 @@ vLLM是一个快速且易于使用的LLM推理和服务库,使用PageAttention
 
 ## 支持模型结构列表
 | 结构 | 模型 | FP16/BF16 | AWQ | GPTQ |
-| :------: | :------: | :------: | :------: |
+| :------: | :------: | :------: | :------: |:------: |
 | LlamaForCausalLM      | Llama 3.1,Llama 3,Llama 2,Llama,Yi,Codellama,deepseek       | Yes | Yes | Yes |  
 | QWenLMHeadModel       | QWen,Qwen-VL                                                | Yes | Yes | Yes |
 | Qwen2ForCausalLM      | QWen2,QWen1.5,CodeQwen1.5                                   | Yes | Yes | Yes |

vllm/_custom_ops.py

@@ -12,7 +12,7 @@ from vllm.platforms import current_platform
 try:
     from lmslim import quant_ops 
 except Exception:
-    print("INFO: Please install lmslim if you want to infer gptq or awq model.\n") 
+    print("INFO: Please install lmslim if you want to infer gptq or awq  or w8a8 model.\n") 
 
 logger = init_logger(__name__)
 
@@ -706,9 +706,9 @@ def cutlass_scaled_mm(a: torch.Tensor,
     # torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
 
     # return out
+    #return quant_ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)
 
-
 def rocblas_scaled_mm(a: torch.Tensor,
                       b: torch.Tensor,
                       scale_a: torch.Tensor,

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -4,12 +4,12 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils import is_hip
+from vllm.utils import is_hip,W8a8GetCacheJSON
 
 # Input scaling factors are no longer optional in _scaled_mm starting
 # from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
 TORCH_DEVICE_IDENTITY = torch.ones(1).cuda() if is_hip() else None
-
+W8A8_TRITONJSON=W8a8GetCacheJSON()
 
 def cutlass_fp8_supported() -> bool:
     # cutlass is not supported on Rocm
@@ -200,12 +200,37 @@ def apply_int8_linear(
     x_q, x_scale, _ = ops.scaled_int8_quant(input, input_scale)
 
     if w8a8_strategy==1:
+        m=x_q.shape[0]
+        k=x_q.shape[1]
+        n=weight.shape[1]
+        if f"{m}_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict[0]:
+            best_config=W8A8_TRITONJSON.triton_json_dict[0][f"{m}_{n}_{k}"]
+            #print("json files:",best_config)
+        elif f"1_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict[0]:
+            if m<64:
+                m_= 32
+            elif m<128:
+                m_=64
+            elif m<256:
+                m_=128
+            elif m<512:
+                m_=256
+            elif m<1024:
+                m_=512
+            else:
+                m_=1024
+            best_config=W8A8_TRITONJSON.triton_json_dict[0][f"{m_}_{n}_{k}"]
+            
+        else: 
+            best_config=None
+            print("config not found!")
+            
         return ops.triton_scaled_mm(x_q,
                                 weight,
                                 scale_a=x_scale,
                                 scale_b=weight_scale,
                                 out_dtype=input.dtype,
-                                bias=bias)
+                                bias=bias,best_config=best_config)
     elif w8a8_strategy==2:
         return ops.cutlass_scaled_mm(x_q,
                                     weight,

vllm/model_executor/models/llama.py

@@ -51,7 +51,7 @@ from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
-from vllm.utils import is_hip
+from vllm.utils import is_hip,W8a8GetCacheJSON
 
 from .interfaces import SupportsLoRA
 from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
@@ -424,6 +424,8 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
                                 quant_config,
                                 lora_config=lora_config,
                                 prefix="model")
+        self.tritonsingleton= W8a8GetCacheJSON()
+        
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
             if lora_config:
@@ -459,6 +461,7 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
         self.use_fa_pad = os.environ.get('FA_PAD') == '1'
         self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
+        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '0'))
 
     def forward(
         self,
@@ -648,6 +651,7 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
                         qweight_pad= torch.zeros(dim_n,int(group_size//4),dtype=torch.int32).cuda()
                         qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()
             
+        #当为triton支持推理的时候不能进行处理
         if self.quant_method == "compressed_tensors":
             lay_key_words = [
                 "self_attn.qkv_proj.weight",
@@ -656,15 +660,39 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA):
                 "mlp.down_proj.weight",
             ]
             combined_words = "|".join(lay_key_words)
+            weight_shapes=[]
+            all_json={}
             
             for layername, weight in params_dict.items():  
                 matches = re.findall(combined_words, layername)
-                if matches:
+                if matches and "scale" not in layername:
                     weight_data =params_dict[layername]
-                    k=weight_data.shape[0]
-                    _weight=weight_data.T.contiguous().reshape(k,-1)
+                    n=weight_data.shape[0]
+                    
+                    #rocblas和cutlass目前都需要weight做处理，但是triton不用
+                    if self.w8a8_strategy!=1:
+                        _weight=weight_data.T.contiguous().reshape(n,-1)
                         weight_data.data.copy_(_weight)  
                     
+                    #下面是针对模型记录模型出现k和n值 
+                    elif len(weight_shapes)<4: 
+                        k=weight_data.shape[1]
+                        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:
+                            all_json.update(configs_dict)
+                                              
+            if self.w8a8_strategy==1:
+                self.tritonsingleton.triton_json_dict.append(all_json)
+                #找到的所有config都进行一次warmup
+                for key, value in all_json.items():
+                    m=int(key.split('_')[0])
+                    n=int(key.split('_')[1])
+                    k=int(key.split('_')[2])
+                    ops._int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,best_config=value)
+             
     # If this function is called, it should always initialize KV cache scale
     # factors (or else raise an exception). Thus, handled exceptions should
     # make sure to leave KV cache scale factors in a known good (dummy) state

vllm/model_executor/models/qwen.py

@@ -48,7 +48,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SequenceData
-from vllm.utils import is_list_of
+from vllm.utils import is_list_of,W8a8GetCacheJSON
 
 from .utils import flatten_bn, is_pp_missing_parameter, make_layers
 from vllm import _custom_ops as ops
@@ -904,6 +904,7 @@ class QWenLMHeadModel(nn.Module, SupportsMultiModal):
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
         self.use_fa_pad = os.environ.get('FA_PAD') == '1'
         self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
+        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '0'))
 
     def _get_image_input_type(
             self,
@@ -1100,11 +1101,35 @@ class QWenLMHeadModel(nn.Module, SupportsMultiModal):
                 "mlp.c_proj.weight",
             ]
             combined_words = "|".join(lay_key_words)
+            weight_shapes=[]
+            all_json={}
             
             for layername, weight in params_dict.items():  
                 matches = re.findall(combined_words, layername)
-                if matches:
+                if matches and "scale" not in layername:
                     weight_data =params_dict[layername]
-                    k=weight_data.shape[0]
-                    _weight=weight_data.T.contiguous().reshape(k,-1)
+                    n=weight_data.shape[0]
+                    
+                    #rocblas和cutlass目前都需要weight做处理，但是triton不用
+                    if self.w8a8_strategy!=1:
+                        _weight=weight_data.T.contiguous().reshape(n,-1)
                         weight_data.data.copy_(_weight)  
+                    
+                    #下面是针对模型记录模型出现k和n值 
+                    elif len(weight_shapes)<4: 
+                        k=weight_data.shape[1]
+                        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:
+                            all_json.update(configs_dict)
+                                              
+            if self.w8a8_strategy==1:
+                self.tritonsingleton.triton_json_dict.append(all_json)
+                #找到的所有config都进行一次warmup
+                for key, value in all_json.items():
+                    m=int(key.split('_')[0])
+                    n=int(key.split('_')[1])
+                    k=int(key.split('_')[2])
+                    ops._int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,best_config=value)

vllm/utils.py

@@ -16,6 +16,7 @@ import threading
 import uuid
 import warnings
 import weakref
+import json
 from asyncio import FIRST_COMPLETED, ensure_future
 from functools import lru_cache, partial, wraps
 from platform import uname
@@ -1334,3 +1335,88 @@ class AtomicCounter:
     @property
     def value(self):
         return self._value
+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):
+        self.triton_json_dir=(os.getenv('TRITON_JSON_DIR', './cache'))
+        self.triton_json_dict=[]
+        
+    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_tune(self,file_path,n,k):
+        #tuning的时候使用，当文件不存在时候，则创建文件夹
+        
+        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:
+            folder_path = os.path.dirname(file_path)
+            os.makedirs(folder_path, exist_ok=True)
+            cachedata = {}
+            # 写入空数据到新的JSON文件
+            with open(file_path, 'w') as file:
+                json.dump(cachedata, file)   
+                    
+        #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_value={
+                    'SPLIT_K': int(sub_value["SPLIT_K"]),
+                    'BLOCK_SIZE_M': int(sub_value["BLOCK_SIZE_M"]),
+                    'BLOCK_SIZE_N': int(sub_value["BLOCK_SIZE_N"]),
+                    'BLOCK_SIZE_K': int(sub_value["BLOCK_SIZE_K"]),
+                    'GROUP_SIZE_M': int(sub_value["GROUP_SIZE_M"]),
+                    'num_stages':int(sub_value['num_stages']),
+                    'num_warps':int(sub_value['num_warps'])
+                }
+                configs_dict[configs_key]=configs_value
+        return configs_dict
+    
+    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_value={
+                    'SPLIT_K': int(sub_value["SPLIT_K"]),
+                    'BLOCK_SIZE_M': int(sub_value["BLOCK_SIZE_M"]),
+                    'BLOCK_SIZE_N': int(sub_value["BLOCK_SIZE_N"]),
+                    'BLOCK_SIZE_K': int(sub_value["BLOCK_SIZE_K"]),
+                    'GROUP_SIZE_M': int(sub_value["GROUP_SIZE_M"]),
+                    'num_stages':int(sub_value['num_stages']),
+                    'num_warps':int(sub_value['num_warps'])
+                }
+                configs_dict[configs_key]=configs_value
+        return configs_dict
+  
+    def get_w8a8json_name(self,n,k):
+        device_name = current_platform.get_device_name().replace(" ", "_")
+        return self.triton_json_dir+f"/W8A8_{n}_{k}_DCU{device_name}.json"
+        
\ No newline at end of file