添加w8a8 rocblas非融合支持

README.md

@@ -9,25 +9,16 @@ 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 | 模型并行 | FP16 |
-| :----------: | :----------: | :------: | :--: |
-|    LlamaForCausalLM       |    LLaMA          |   Yes    | Yes  |
-|    LlamaForCausalLM       |    LLaMA-2        |   Yes    | Yes  |
-|    LlamaForCausalLM       |    LLaMA-3        |   Yes    | Yes  |
-|    LlamaForCausalLM       |    Codellama      |   Yes    | Yes  |
+| :------: | :------: | :------: | :------: |
+| LlamaForCausalLM      | LLaMA、LLaMA-2、LLaMA-3、Codellama、deepseek、Yi | Yes | Yes |
 | QWenLMHeadModel       | QWen                                            | Yes | Yes |
-|    Qwen2ForCausalLM       |    QWen1.5        |   Yes    | Yes  |
-|    Qwen2ForCausalLM       |    CodeQwen1.5    |   Yes    | Yes  |
-|    Qwen2ForCausalLM       |    QWen2          |   Yes    | Yes  |
-|    ChatGLMModel           |    chatglm2       |   Yes    | Yes  |
-|    ChatGLMModel           |    chatglm3       |   Yes    | Yes  |
-|    BaiChuanForCausalLM    |    Baichuan       |   Yes    | Yes  |
-|    BaiChuanForCausalLM    |    Baichuan2      |   Yes    | Yes  |
-|    BloomForCausalLM       |    BLOOM          |   Yes    | Yes  |
+| Qwen2ForCausalLM      | QWen1.5、CodeQwen1.5、QWen2                     | Yes | Yes |
+| ChatGLMModel          | chatglm2、chatglm3                              | Yes | Yes |
+| BaiChuanForCausalLM   | Baichuan、Baichuan2                             | Yes | Yes |
+| BloomForCausalLM      | BLOOM        | Yes | Yes |
 | InternLMForCausalLM   | InternLM     | Yes | Yes |
 | InternLM2ForCausalLM  | InternLM2    | Yes | Yes |
-|    LlamaForCausalLM       |    deepseek       |   Yes    | Yes  |
 | DeepseekV2ForCausalLM | DeepSeek-V2  | Yes | Yes |
-|    LlamaForCausalLM       |    Yi             |   Yes    | Yes  |
 | MixtralForCausalLM    | Mixtral-8x7B | Yes | Yes |
 
 

examples/offline_inference.py

 from vllm import LLM, SamplingParams
 
-if __name__ == '__main__':
-    # Sample prompts.
-    prompts = [
+# Sample prompts.
+prompts = [
     "Hello, my name is",
     "The president of the United States is",
     "The capital of France is",
     "The future of AI is",
-    ]
-    # Create a sampling params object.
-    sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=16)
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=16)
 
-    # Create an LLM.
-    llm = LLM(model="facebook/opt-125m",tensor_parallel_size=1, distributed_executor_backend="ray", dtype="float16",trust_remote_code=True, enforce_eager=True)
-    # Generate texts from the prompts. The output is a list of RequestOutput objects
-    # that contain the prompt, generated text, and other information.
-    outputs = llm.generate(prompts, sampling_params)
-    # Print the outputs.
-    for output in outputs:
+# Create an LLM.
+llm = LLM(model="facebook/opt-125m",tensor_parallel_size=1, distributed_executor_backend="ray", dtype="float16",trust_remote_code=True, enforce_eager=True)
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs[0].text
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

requirements-rocm.txt

@@ -15,4 +15,4 @@ torch == 2.3.0
 triton == 2.1.0
 flash_attn == 2.6.1
 xformers == 0.0.25
-lmslim == 0.1.1
+lmslim == 0.1.2
\ No newline at end of file

vllm/_custom_ops.py

@@ -615,18 +615,19 @@ def cutlass_scaled_mm(a: torch.Tensor,
                       scale_b: torch.Tensor,
                       out_dtype: torch.dtype,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-    assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
-    assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
-    assert bias is None or bias.shape[0] == b.shape[
-        1] and bias.dtype == out_dtype
+    # assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
+    # assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
+    # assert bias is None or bias.shape[0] == b.shape[
+    #     1] and bias.dtype == out_dtype
 
-    m = a.shape[0]
-    n = b.shape[1]
-    out = torch.empty((m, n), dtype=out_dtype, device=a.device)
+    # m = a.shape[0]
+    # n = b.shape[1]
+    # out = torch.empty((m, n), dtype=out_dtype, device=a.device)
 
-    torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
+    # torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
 
-    return out
+    # return out
+    return quant_ops.rocblas_scaled_mm_nn(a, b, scale_a, scale_b, out_dtype, bias)
 
 
 def cutlass_scaled_mm_azp(a: torch.Tensor,

vllm/model_executor/model_loader/loader.py

@@ -407,7 +407,7 @@ class DefaultModelLoader(BaseModelLoader):
 
             for _, module in model.named_modules():
                 quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None and quant_method!="awq" and quant_method!="gptq":
+                if quant_method is not None and quant_method != "awq" and quant_method != "gptq" and quant_method != "compressed_tensors":
                     # When quant methods need to process weights after loading
                     # (for repacking, quantizing, etc), they expect parameters
                     # to be on the global target device. This scope is for the

vllm/model_executor/model_loader/utils.py

@@ -28,8 +28,8 @@ def get_model_architecture(
             os.environ['LLAMA_NN'] = '1'
         if os.getenv('GEMM_PAD') != '1': 
             os.environ['GEMM_PAD'] = '0'
-        if os.getenv('FA_PAD') != '0': 
-            os.environ['FA_PAD'] = '1'
+        if os.getenv('FA_PAD') != '1': 
+            os.environ['FA_PAD'] = '0'
     else:
         os.environ['LLAMA_NN'] = '0'
         os.environ['GEMM_PAD'] = '0'

vllm/model_executor/models/llama.py

@@ -639,6 +639,23 @@ 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()
             
+        if self.quant_method == "compressed_tensors":
+            lay_key_words = [
+                "self_attn.qkv_proj.weight",
+                "self_attn.o_proj.weight",
+                "mlp.gate_up_proj.weight",
+                "mlp.down_proj.weight",
+            ]
+            combined_words = "|".join(lay_key_words)
+            
+            for layername, weight in params_dict.items():  
+                matches = re.findall(combined_words, layername)
+                if matches:
+                    weight_data =params_dict[layername]
+                    k=weight_data.shape[0]
+                    _weight=weight_data.T.contiguous().reshape(k,-1)
+                    weight_data.data.copy_(_weight)   
+             
     # 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

@@ -1091,3 +1091,19 @@ class QWenLMHeadModel(nn.Module, SupportsMultiModal):
                         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()
                        
+        if self.quant_method == "compressed_tensors":
+            lay_key_words = [
+                "attn.c_attn.weight",
+                "attn.c_proj.weight",
+                "mlp.gate_up_proj.weight",
+                "mlp.c_proj.weight",
+            ]
+            combined_words = "|".join(lay_key_words)
+            
+            for layername, weight in params_dict.items():  
+                matches = re.findall(combined_words, layername)
+                if matches:
+                    weight_data =params_dict[layername]
+                    k=weight_data.shape[0]
+                    _weight=weight_data.T.contiguous().reshape(k,-1)
+                    weight_data.data.copy_(_weight)                  

vllm/model_executor/models/qwen2.py

@@ -543,3 +543,19 @@ class Qwen2ForCausalLM(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()
                    
+        if self.quant_method == "compressed_tensors":
+            lay_key_words = [
+                "self_attn.qkv_proj.weight",
+                "self_attn.o_proj.weight",
+                "mlp.gate_up_proj.weight",
+                "mlp.down_proj.weight",
+            ]
+            combined_words = "|".join(lay_key_words)
+            
+            for layername, weight in params_dict.items():  
+                matches = re.findall(combined_words, layername)
+                if matches:
+                    weight_data =params_dict[layername]
+                    k=weight_data.shape[0]
+                    _weight=weight_data.T.contiguous().reshape(k,-1)
+                    weight_data.data.copy_(_weight)    
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