skip fp8

csrc/ops.h

@@ -166,15 +166,15 @@ void gptq_shuffle(
   torch::Tensor q_perm,
   int bit);
 
-void static_scaled_fp8_quant(
-  torch::Tensor& out,
-  torch::Tensor& input,
-  torch::Tensor& scale);
-
-void dynamic_scaled_fp8_quant(
-  torch::Tensor& out,
-  torch::Tensor& input,
-  torch::Tensor& scale);
+// void static_scaled_fp8_quant(
+//   torch::Tensor& out,
+//   torch::Tensor& input,
+//   torch::Tensor& scale);
+
+// void dynamic_scaled_fp8_quant(
+//   torch::Tensor& out,
+//   torch::Tensor& input,
+//   torch::Tensor& scale);
 
 void moe_align_block_size(
   torch::Tensor topk_ids,

csrc/pybind.cpp

@@ -75,8 +75,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   ops.def("gptq_gemm", &gptq_gemm, "Quantized GEMM for GPTQ");
   ops.def("gptq_shuffle", &gptq_shuffle, "Post processing for GPTQ");
   ops.def("squeezellm_gemm", &squeezellm_gemm, "Quantized GEMM for SqueezeLLM");
-  ops.def("static_scaled_fp8_quant", &static_scaled_fp8_quant, "Compute FP8 quantized tensor for given scaling factor");
-  ops.def("dynamic_scaled_fp8_quant", &dynamic_scaled_fp8_quant, "Compute FP8 quantized tensor and scaling factor");
+  // ops.def("static_scaled_fp8_quant", &static_scaled_fp8_quant, "Compute FP8 quantized tensor for given scaling factor");
+  // ops.def("dynamic_scaled_fp8_quant", &dynamic_scaled_fp8_quant, "Compute FP8 quantized tensor and scaling factor");
   ops.def(
     "moe_align_block_size",
     &moe_align_block_size,

vllm/_custom_ops.py

@@ -186,17 +186,17 @@ def gptq_marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
 
 
 # fp8
-def scaled_fp8_quant(
-    input: torch.Tensor,
-    scale: Optional[torch.Tensor] = None,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    output = torch.empty_like(input, dtype=torch.float8_e4m3fn)
-    if scale is None:
-        scale = torch.zeros(1, device=input.device, dtype=torch.float32)
-        vllm_ops.dynamic_scaled_fp8_quant(output, input, scale)
-    else:
-        vllm_ops.static_scaled_fp8_quant(output, input, scale)
-    return output, scale
+# def scaled_fp8_quant(
+#     input: torch.Tensor,
+#     scale: Optional[torch.Tensor] = None,
+# ) -> Tuple[torch.Tensor, torch.Tensor]:
+#     output = torch.empty_like(input, dtype=torch.float8_e4m3fn)
+#     if scale is None:
+#         scale = torch.zeros(1, device=input.device, dtype=torch.float32)
+#         vllm_ops.dynamic_scaled_fp8_quant(output, input, scale)
+#     else:
+#         vllm_ops.static_scaled_fp8_quant(output, input, scale)
+#     return output, scale
 
 
 # moe

vllm/model_executor/model_loader/loader.py

@@ -54,10 +54,9 @@ def _get_quantization_config(
                 f"{model_config.dtype} is not supported for quantization "
                 f"method {model_config.quantization}. Supported dtypes: "
                 f"{supported_dtypes}")
-        return quant_config
-    
         if quant_config != None:
             os.environ['LLAMA_NN'] = '0'
+        return quant_config
         
     return None