Merge branch 'v0.7.2-dev' of http://10.6.10.68/dcutoolkit/deeplearing/vllm into v0.7.2-dev

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -16,6 +16,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.vocab_parallel_embedding import UnquantizedEmbeddingMethod
 from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
@@ -86,11 +87,11 @@ class CompressedTensorsConfig(QuantizationConfig):
         if should_ignore_layer(prefix,
                                ignore=self.ignore,
                                fused_mapping=self.packed_modules_mapping):
-            return UnquantizedLinearMethod()
+            return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
         if isinstance(layer, LinearBase):
             scheme = self.get_scheme(layer=layer, layer_name=prefix)
             if scheme is None:
-                return UnquantizedLinearMethod()
+                return UnquantizedEmbeddingMethod()#UnquantizedLinearMethod()
             layer.scheme = scheme
             return CompressedTensorsLinearMethod(self)
         if isinstance(layer, Attention):

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

@@ -255,8 +255,10 @@ def apply_int8_linear(
         k=x_q.shape[1]
         n=weight.shape[1]
         #print("m:{},n:{},k:{}".format(m,n,k))
+        if len(W8A8_TRITONJSON.triton_json_dict)==0:
+            best_config=None
             
-        if f"1_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict[0]:
+        elif f"1_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict[0]:
             if m<=16:
                 m_=m
                 #best_config=W8A8_TRITONJSON.triton_json_dict[0][f"{m}_{n}_{k}"]

vllm/model_executor/models/gpt_neox.py

@@ -18,7 +18,8 @@
 # limitations under the License.
 """Inference-only GPT-NeoX model compatible with HuggingFace weights."""
 from typing import Iterable, List, Optional, Set, Tuple, Union
-
+import os
+import re
 import torch
 from torch import nn
 from transformers import GPTNeoXConfig
@@ -45,7 +46,8 @@ from .interfaces import SupportsPP
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
-
+from vllm.utils import is_hip,W8a8GetCacheJSON
+from vllm import _custom_ops as ops
 
 class GPTNeoXAttention(nn.Module):
 
@@ -278,6 +280,13 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
         self.make_empty_intermediate_tensors = (
             self.gpt_neox.make_empty_intermediate_tensors)
         
+        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        self.quant_method = None
+        if quant_config is not None:
+            self.quant_method=quant_config.get_name()
+            self.quant_config=quant_config
+        self.tritonsingleton= W8a8GetCacheJSON()
+        
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.gpt_neox.get_input_embeddings(input_ids)
 
@@ -349,4 +358,52 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
             loaded_params.add(name)
+        #当为triton支持推理的时候不能进行处理
+        if self.quant_method == "compressed_tensors":
+            os.environ['LM_NN'] = '0'
+            lay_key_words = [
+                "attention.query_key_value.weight",
+                "attention.dense.weight",
+                "mlp.dense_h_to_4h.weight",
+                "mlp.dense_4h_to_h.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 and "scale" not in layername:
+                    weight_data =params_dict[layername]
+                    n=weight_data.shape[0]
+                    k=weight_data.shape[1]
+       
+                    #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]
+                        #print("n:{},k:{}".format(n,k))
+                        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)
+            #("weight_shapes:",weight_shapes)
+                                            
+            if self.w8a8_strategy==1:
+                self.tritonsingleton.triton_json_dict.append(all_json)
+                #print("self.tritonsingleton.triton_json_dict:",self.tritonsingleton.triton_json_dict)
+                #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.triton_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)
+                   
+
         return loaded_params

vllm/model_executor/models/llama.py

@@ -355,6 +355,7 @@ class LlamaModel(nn.Module):
             self.quant_method=quant_config.get_name()
             self.quant_config=quant_config
             
+        self.tritonsingleton= W8a8GetCacheJSON()      
         self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
         # self.use_lm_nn = os.environ.get('LM_NN') == '1'
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
@@ -549,6 +550,7 @@ class LlamaModel(nn.Module):
             
         #当为triton支持推理的时候不能进行处理
         if self.quant_method == "compressed_tensors":
+            os.environ['LM_NN'] = '0'
             lay_key_words = [
                 "self_attn.qkv_proj.weight",
                 "self_attn.o_proj.weight",
@@ -644,7 +646,7 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.model = self._init_model(vllm_config=vllm_config,
                                       prefix=maybe_prefix(prefix, "model"))
         
-        self.tritonsingleton= W8a8GetCacheJSON()
+        
 
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size

vllm/model_executor/models/qwen.py

@@ -1174,6 +1174,7 @@ class QWenBaseModel(nn.Module, SupportsPP, SupportsLoRA):
                         qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()
                        
         if self.quant_method == "compressed_tensors":
+            os.environ['LM_NN'] = '0'
             lay_key_words = [
                 "attn.c_attn.weight",
                 "attn.c_proj.weight",

vllm/model_executor/models/qwen2.py

@@ -527,6 +527,7 @@ class Qwen2Model(nn.Module):
                         qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()
                    
         if self.quant_method == "compressed_tensors":
+            os.environ['LM_NN'] = '0'
             lay_key_words = [
                 "self_attn.qkv_proj.weight",
                 "self_attn.o_proj.weight",

vllm/utils.py

@@ -58,6 +58,7 @@ if TYPE_CHECKING:
     from vllm.config import VllmConfig
 
 logger = init_logger(__name__)
+import json
 
 # Exception strings for non-implemented encoder/decoder scenarios
 
@@ -1608,6 +1609,7 @@ class W8a8GetCacheJSON:
         return configs_dict
   
     def get_w8a8json_name(self,n,k):
+        from vllm.platforms import current_platform
         device_name = current_platform.get_device_name().replace(" ", "_")
         return self.triton_json_dir+f"/W8A8_{n}_{k}_{device_name}.json"