Merge remote-tracking branch 'origin/v0.6.2-dev' into v0.6.2-dev

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",
@@ -656,14 +660,38 @@ 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)
-                    weight_data.data.copy_(_weight)   
+                    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

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)
-                    weight_data.data.copy_(_weight)                  
+                    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/platforms/cpu.py

+import psutil
 import torch
 
 from .interface import Platform, PlatformEnum
@@ -9,6 +10,10 @@ class CpuPlatform(Platform):
     @classmethod
     def get_device_name(cls, device_id: int = 0) -> str:
         return "cpu"
+    
+    @classmethod
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        return psutil.virtual_memory().total
 
     @classmethod
     def inference_mode(cls):

vllm/platforms/interface.py

@@ -83,6 +83,11 @@ class Platform:
     def get_device_name(cls, device_id: int = 0) -> str:
         raise NotImplementedError
 
+    @classmethod
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        """Get the total memory of a device in bytes."""
+        raise NotImplementedError
+
     @classmethod
     def inference_mode(cls):
         """A device-specific wrapper of `torch.inference_mode`.

vllm/platforms/rocm.py

@@ -29,3 +29,8 @@ class RocmPlatform(Platform):
     @lru_cache(maxsize=8)
     def get_device_name(cls, device_id: int = 0) -> str:
         return torch.cuda.get_device_name(device_id)
+    
+    @classmethod
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        device_props = torch.cuda.get_device_properties(device_id)
+        return device_props.total_memory

vllm/platforms/tpu.py

@@ -9,6 +9,10 @@ class TpuPlatform(Platform):
     @classmethod
     def get_device_name(cls, device_id: int = 0) -> str:
         raise NotImplementedError
+    
+    @classmethod
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        raise NotImplementedError
 
     @classmethod
     def inference_mode(cls):

vllm/triton_utils/custom_cache_manager.py

@@ -45,7 +45,7 @@ class CustomCacheManager(FileCacheManager):
             self.cache_dir = os.getenv("TRITON_CACHE_DIR",
                                        "").strip() or default_cache_dir()
             if self.cache_dir:
-                self.cache_dir = f"{self.cache_dir}_{os.getpid()}"
+                # self.cache_dir = f"{self.cache_dir}_{os.getpid()}"
                 self.cache_dir = os.path.join(self.cache_dir, self.key)
                 self.lock_path = os.path.join(self.cache_dir, "lock")
                 os.makedirs(self.cache_dir, exist_ok=True)

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
@@ -119,6 +120,9 @@ STR_XFORMERS_ATTN_VAL: str = "XFORMERS"
 STR_FLASH_ATTN_VAL: str = "FLASH_ATTN"
 STR_INVALID_VAL: str = "INVALID"
 
+GB_bytes = 1_000_000_000
+"""The number of bytes in one gigabyte (GB)."""
+
 GiB_bytes = 1 << 30
 """The number of bytes in one gibibyte (GiB)."""
 
@@ -1331,3 +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