【bug fix】修复w4a8编译慢 (#3510)

* 修复w4a8编译

* code style

* 修复tma copy

custom_ops/gpu_ops/w4afp8_gemm/w4afp8_gemm.cu

@@ -77,16 +77,7 @@ void DisPatchW4AFp8Gemm(
             max_tokens,
             stream)
     } else {
-        GEMM_SWITCH_FP16(
-            M, K, batch_size, token_padding_size, kBlockN, TailN,
-            weight,
-            input,
-            out,
-            weight_scale,
-            input_row_sum,
-            tokens,
-            max_tokens,
-            stream)
+        PD_THROW("Only supported dtype in ['BFLOAT16'].");
     }
 }
 
@@ -128,22 +119,7 @@ std::vector<paddle::Tensor> W4AFp8Gemm(
                 input.stream());
             return {out};
         } else {
-            paddle::Tensor out = paddle::empty({all_tokens, M}, paddle::DataType::FLOAT16, input.place());
-            phi::dtype::float16 *out_data = out.data<phi::dtype::float16>();
-            DisPatchW4AFp8Gemm(
-                reinterpret_cast<const cutlass::float_e4m3_t*>(input.data<phi::dtype::float8_e4m3fn>()),
-                reinterpret_cast<const cutlass::float_e4m3_t*>(weight.data<uint8_t>()),
-                tokens.data<int>(),
-                input_row_sum.data<float>(),
-                weight_scale.data<float>(),
-                reinterpret_cast<cutlass::half_t*>(out_data),
-                token_padding_size,
-                max_tokens,
-                batch_size,
-                M,
-                K,
-                input.stream());
-            return {out};
+            PD_THROW("Only supported dtype in ['BFLOAT16'].");
         }
     } else {
         if (is_bflot16) {
@@ -164,23 +140,7 @@ std::vector<paddle::Tensor> W4AFp8Gemm(
                 input.stream());
             return {out};
         } else {
-            paddle::Tensor out = paddle::empty({batch_size, token_padding_size, M}, paddle::DataType::FLOAT16, input.place());
-            phi::dtype::float16 * out_data = out.data<phi::dtype::float16>();
-
-            DisPatchW4AFp8Gemm(
-                reinterpret_cast<const cutlass::float_e4m3_t*>(input.data<phi::dtype::float8_e4m3fn>()),
-                reinterpret_cast<const cutlass::float_e4m3_t*>(weight.data<uint8_t>()),
-                tokens.data<int>(),
-                input_row_sum.data<float>(),
-                weight_scale.data<float>(),
-                reinterpret_cast<cutlass::half_t*>(out_data),
-                token_padding_size,
-                max_tokens,
-                batch_size,
-                M,
-                K,
-                input.stream());
-            return {out};
+            PD_THROW("Only supported dtype in ['BFLOAT16'].");
         }
     }
 }

custom_ops/utils/auto_gen_w4afp8_gemm_kernel.py

@@ -83,14 +83,9 @@ void w4afp8_gemm_M{M}_N{N}_TAILN{TAILN}_K{K}_B{BATCH}_P{PADDING}_{TYPE}(
 }}
 """
 
-gemm_case = [
-    [8192, 3584, 8, 0],  # eb45T ffn1
-    [8192, 3584, 8, 2048],  # eb45T ffn1
-    [7168, 8192, 8, 0],  # eb45T ffn2
-    [7168, 8192, 8, 2048],  # eb45T ffn2
-]
+gemm_case = [[256, 256, 1, 0]]
 
-dtype = ["BF16", "FP16"]
+dtype = ["BF16"]
 
 
 def get_cutlass_type(type):

tests/operators/test_w4afp8_gemm.py

@@ -44,10 +44,10 @@ def peruate_scale(weight_scale):
 
 
 paddle.seed(0)
-tokens_per_group = 32
-N = 8192
-K = 3584
-BATCH = 8
+tokens_per_group = 256
+N = 256
+K = 256
+BATCH = 1
 TokenPadding = 0
 
 tokens = [tokens_per_group] * BATCH