Remvoe stray if

src/targets/gpu/gemm_impl.cpp

@@ -158,8 +158,6 @@ struct gemm_impl
         {
             beta = 0;
         }
-        if(arg_type == rocblas_datatype_f16_r)
-            compute_type = rocblas_datatype_f32_r;
 
         rocblas_gemm_flags flag = rocblas_gemm_flags_none;
 #if ROCBLAS_VERSION_MAJOR < 3
@@ -200,43 +198,43 @@ struct gemm_impl
         {
             output_type = rocblas_datatype_i32_r;
         }
-            compute_type = output_type;
-            if(compute_fp32)
-            {
+        compute_type = output_type;
+        if(compute_fp32)
+        {
             if(arg_type == rocblas_datatype_f16_r)
                 compute_type = rocblas_datatype_f32_r;
-            }
+        }
 
-            int8_flag = int8_x4_format ? rocblas_gemm_flags_pack_int8x4 : rocblas_gemm_flags_none;
+        int8_flag = int8_x4_format ? rocblas_gemm_flags_pack_int8x4 : rocblas_gemm_flags_none;
 
-            auto a_lens = input_shapes[0].lens();
-            auto b_lens = input_shapes[1].lens();
+        auto a_lens = input_shapes[0].lens();
+        auto b_lens = input_shapes[1].lens();
 
-            auto out_lens = output_shape.lens();
-            m             = out_lens[dim_0];
-            n             = out_lens[dim_1];
-            k             = input_shapes[0].lens()[dim_1];
-            if(input_shapes[0].type() == shape::int8_type and (k % 4) != 0 and int8_x4_format)
-            {
-            MIGRAPHX_THROW("ROCBLAS_GEMM: k size of int8 type input must be multiple of 4!");
-            }
+        auto out_lens = output_shape.lens();
+        m             = out_lens[dim_0];
+        n             = out_lens[dim_1];
+        k             = input_shapes[0].lens()[dim_1];
+        if(input_shapes[0].type() == shape::int8_type and (k % 4) != 0 and int8_x4_format)
+        {
+        MIGRAPHX_THROW("ROCBLAS_GEMM: k size of int8 type input must be multiple of 4!");
+        }
 
-            a_stride     = get_batch_stride(input_shapes[0]);
-            b_stride     = get_batch_stride(input_shapes[1]);
-            c_stride     = get_batch_stride(input_shapes[2]);
-            d_stride     = is_3inputs ? get_batch_stride(input_shapes[3]) : c_stride;
-            num_matrices = std::accumulate(out_lens.rbegin() + 2,
-                                           out_lens.rend(),
-                                           std::size_t{1},
-                                           std::multiplies<std::size_t>());
-            if(num_matrices == 1 or (num_matrices > 1 and b_stride == 0))
-            {
-            // If the batch dimension of B is broadcasted, then we can
-            // multiply m by the batch_size and use rocblas_gemm_ex
-            // instead of rocblas_gemm_strided_batched_ex.
-            m *= num_matrices;
-            strided_batched = false;
-            }
+        a_stride     = get_batch_stride(input_shapes[0]);
+        b_stride     = get_batch_stride(input_shapes[1]);
+        c_stride     = get_batch_stride(input_shapes[2]);
+        d_stride     = is_3inputs ? get_batch_stride(input_shapes[3]) : c_stride;
+        num_matrices = std::accumulate(out_lens.rbegin() + 2,
+                                        out_lens.rend(),
+                                        std::size_t{1},
+                                        std::multiplies<std::size_t>());
+        if(num_matrices == 1 or (num_matrices > 1 and b_stride == 0))
+        {
+        // If the batch dimension of B is broadcasted, then we can
+        // multiply m by the batch_size and use rocblas_gemm_ex
+        // instead of rocblas_gemm_strided_batched_ex.
+        m *= num_matrices;
+        strided_batched = false;
+        }
     }
 
     void run(context& ctx, const std::vector<argument>& input_args, int32_t solution_idx = 0) const