fix merge conflicts

operators/cuda/add_mul.h

@@ -103,6 +103,7 @@ struct AddAndMul {
   OrtxStatus OnModelAttach(const TDict& dict) {
     int64_t default_value = 0;
     switchMiddelAxis_ = dict.TryToGetAttributeWithDefault("switchMiddleAxis", default_value) == 1;
+    return {};
   }
   OrtxStatus Compute(Ort::Custom::CUDAKernelContext* ctx,
                      const ortc::Tensor<T>& tensor_a,
@@ -157,9 +158,9 @@ template <typename T, bool subtract_first>
 struct SubAndMul {
   template <typename TDict>
   OrtxStatus OnModelAttach(const TDict& dict) {
-    //int64_t default_value = 0;
-    //negative_ = dict.TryToGetAttributeWithDefault("negative", default_value) == 1;
-    negative_ = false;
+    int64_t default_value = 0;
+    negative_ = dict.TryToGetAttributeWithDefault("negative", default_value) == 1;
+    return {};
   }
   OrtxStatus Compute(Ort::Custom::CUDAKernelContext* ctx,
                      const ortc::Tensor<T>& tensor_a,

operators/cuda/cuda_ops.cc

@@ -7,6 +7,7 @@
 #include "cuda/add_mul.h"
 #include "cuda/fast_gelu.h"
 #include "cuda/negxplus1.h"
+#include "cuda/transpose_cast.h"
 #endif
 
 FxLoadCustomOpFactory LoadCustomOpClasses_Contrib = []() -> CustomOpArray& {
@@ -34,6 +35,9 @@ FxLoadCustomOpFactory LoadCustomOpClasses_Contrib = []() -> CustomOpArray& {
 
   using SubAndMulFloat16Type = typename contrib::SubAndMul<ortc::MFloat16, true>;
   using MulAndSubFloat16Type = typename contrib::SubAndMul<ortc::MFloat16, false>;
+
+  using Transpose2DCastFloat32ToFloat16Type = typename contrib::Transpose2DCast<float, ortc::MFloat16>;
+  using Transpose2DCastFloat16ToFloat32Type = typename contrib::Transpose2DCast<ortc::MFloat16, float>;
 #endif
 
   static OrtOpLoader op_loader(
@@ -62,7 +66,9 @@ FxLoadCustomOpFactory LoadCustomOpClasses_Contrib = []() -> CustomOpArray& {
       CustomCudaStructV2("MulSharedInput", MulSharedInputFloat16Type),
       CustomCudaStructV2("MulSub", MulAndSubFloat16Type),
       CustomCudaStructV2("NegXPlus1", contrib::NegXPlus1<ortc::MFloat16>),
-      CustomCudaStructV2("SubMul", SubAndMulFloat16Type)
+      CustomCudaStructV2("SubMul", SubAndMulFloat16Type),
+      CustomCudaStructV2("Transpose2DCastFP16", Transpose2DCastFloat32ToFloat16Type),
+      CustomCudaStructV2("Transpose2DCastFP32", Transpose2DCastFloat16ToFloat32Type)
 #endif
 #endif
   );

operators/cuda/transpose_cast.h

@@ -0,0 +1,39 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+#include "ocos.h"
+#include "transpose_cast_impl.cuh"
+#include "ortx_common.h"
+
+namespace contrib {
+
+template <typename TIN, typename TOUT>
+struct Transpose2DCast {
+  template <typename TDict>
+  OrtxStatus OnModelAttach(const TDict& /*dict*/) {
+    return {};
+  }
+  OrtxStatus Compute(Ort::Custom::CUDAKernelContext* ctx,
+                       const ortc::Tensor<TIN>& input,
+                       ortc::Tensor<TOUT>& output) const {
+    const TIN* input_data = input.Data();
+    auto shape = input.Shape();
+    if (shape.size() != 2) {
+      ORTX_CXX_API_THROW("Input must be a 2D tensor", ORT_RUNTIME_EXCEPTION);
+    }
+    int n_rows = static_cast<int>(shape[0]);
+    int n_cols = static_cast<int>(shape[1]);
+
+    std::vector<int64_t> new_shape{static_cast<int64_t>(n_cols), static_cast<int64_t>(n_rows)};
+    TOUT* output_data = output.Allocate(new_shape);
+    if (0 == n_rows || 0 == n_cols) {
+      return {};
+    }
+    LaunchTranspose2DCastKernel<TIN, TOUT>(reinterpret_cast<cudaStream_t>(ctx->GetCudaStream()),
+                                           n_rows, n_cols, input_data, output_data);
+    return {};
+  }
+};
+
+}  // namespace contrib

operators/cuda/transpose_cast_impl.cu

@@ -0,0 +1,56 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "device_prop.cuh"
+#include "utils.cuh"
+#include "transpose_cast_impl.cuh"
+#include "cuda_type.h"
+
+using namespace Ort::Custom;
+
+#define TILE_DIM 32
+#define BLOCK_ROWS 8
+
+template <typename TOUT, typename TIN>
+__global__ void Transpose2DCastKernel(TOUT *output_data, const TIN *input_data, int n_rows, int n_cols) {
+  __shared__ TIN tile[TILE_DIM][TILE_DIM + 1];
+
+  int x = blockIdx.x * TILE_DIM + threadIdx.x;
+  int y = blockIdx.y * TILE_DIM + threadIdx.y;
+  // int width = gridDim.x * TILE_DIM;
+
+  for (int j = 0; j < TILE_DIM; j += BLOCK_ROWS)
+    tile[threadIdx.y + j][threadIdx.x] = input_data[(y + j) * n_cols + x];
+
+  __syncthreads();
+
+  x = blockIdx.y * TILE_DIM + threadIdx.x; // transpose block offset
+  y = blockIdx.x * TILE_DIM + threadIdx.y;
+
+  for (int j = 0; j < TILE_DIM; j += BLOCK_ROWS)
+    output_data[(y + j) * n_rows + x] = (TOUT)(tile[threadIdx.x][threadIdx.y + j]);
+}
+
+template <typename TIN, typename TOUT>
+cudaError_t _LaunchTranspose2DCastKernel(cudaStream_t stream, int n_rows, int n_cols,
+                                         const TIN* input, TOUT* output) {
+  dim3 dimGrid((n_cols + TILE_DIM - 1) / TILE_DIM, (n_rows + TILE_DIM - 1) / TILE_DIM, 1);
+  dim3 dimBlock(TILE_DIM, BLOCK_ROWS, 1);
+  using TTIN = typename contrib::CudaT<TIN>::MappedType;
+  using TTOUT = typename contrib::CudaT<TOUT>::MappedType;
+  Transpose2DCastKernel<TTOUT, TTIN><<<dimGrid, dimBlock, TILE_DIM * TILE_DIM + TILE_DIM, stream>>>(
+    reinterpret_cast<TTOUT*>(output), reinterpret_cast<const TTIN*>(input), n_rows, n_cols);
+  return cudaGetLastError();
+}
+
+template <>
+cudaError_t LaunchTranspose2DCastKernel<float, ortc::MFloat16>(cudaStream_t stream, int n_rows, int n_cols,
+                                                               const float* input, ortc::MFloat16* output) {
+  return _LaunchTranspose2DCastKernel(stream, n_rows, n_cols, input, output);
+}
+
+template <>
+cudaError_t LaunchTranspose2DCastKernel<ortc::MFloat16, float>(cudaStream_t stream, int n_rows, int n_cols,
+                                                               const ortc::MFloat16* input, float* output) {
+  return _LaunchTranspose2DCastKernel(stream, n_rows, n_cols, input, output);
+}

operators/cuda/transpose_cast_impl.cuh

@@ -0,0 +1,9 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+template <typename TIN, typename TOUT>
+cudaError_t LaunchTranspose2DCastKernel(cudaStream_t stream, int n_rows, int n_cols, const TIN* input, TOUT* output);

test/cuda/test_cudaops.py

@@ -21,6 +21,20 @@ def _run(self, X):
         return (1 - X,)
 
 
+class Transpose2DCastFP16(OpRun):
+    op_domain = "ai.onnx.contrib"
+
+    def _run(self, X):
+        return (X.T.to(np.float16),)
+
+
+class Transpose2DCastFP32(OpRun):
+    op_domain = "ai.onnx.contrib"
+
+    def _run(self, X):
+        return (X.T.to(np.float32),)
+
+
 class TestCudaOps(unittest.TestCase):
     def _addaddmulmul_cuda(self, itype, op_type, broad=False):
         model1 = helper.make_model(
@@ -337,6 +351,57 @@ def test_add_shared_input_cuda_broadcast2(self):
             shapec=(3, 2, 3),
         )
 
+    def _transpose_cast_cuda(self, itype):
+        dtype = np.float32 if itype == TensorProto.FLOAT else np.float16
+        itype2 = TensorProto.FLOAT if itype == TensorProto.FLOAT16 else TensorProto.FLOAT16
+        model1 = helper.make_model(
+            helper.make_graph(
+                [
+                    helper.make_node("Transpose", ["X"], ["t"], perm=[1, 0]),
+                    helper.make_node("Cast", ["t"], ["Y"], to=itype2),
+                ],
+                "nd",
+                [helper.make_tensor_value_info("X", itype, [None, None])],
+                [helper.make_tensor_value_info("Y", itype2, [None, None])],
+            ),
+            opset_imports=[helper.make_opsetid("", 18)],
+            ir_version=9,
+        )
+
+        model2 = helper.make_model(
+            helper.make_graph(
+                [
+                    helper.make_node(
+                        ("Transpose2DCastFP16" if itype2 == TensorProto.FLOAT16 else "Transpose2DCastFP32"),
+                        ["X"],
+                        ["Y"],
+                        domain="ai.onnx.contrib",
+                    )
+                ],
+                "nd",
+                [helper.make_tensor_value_info("X", itype, [None, None])],
+                [helper.make_tensor_value_info("Y", itype2, [None, None])],
+            ),
+            opset_imports=[
+                helper.make_opsetid("", 18),
+                helper.make_opsetid("ai.onnx.contrib", 1),
+            ],
+            ir_version=9,
+        )
+
+        dtype = np.float32 if itype == TensorProto.FLOAT else np.float16
+        x = (np.arange(32 * 32 * 3) + 1).reshape((32, 32 * 3)).astype(dtype)
+
+        feeds1 = dict(X=x)
+        ref = ReferenceEvaluator(model1, new_ops=[Transpose2DCastFP16, Transpose2DCastFP32])
+        expected = ref.run(None, feeds1)[0]
+
+        opts = _ort.SessionOptions()
+        opts.register_custom_ops_library(_get_library_path())
+        sess = _ort.InferenceSession(model2.SerializeToString(), opts, providers=["CUDAExecutionProvider"])
+        got = sess.run(None, feeds1)[0]
+        assert_almost_equal(expected, got, decimal=5)
+
     def _addmul_cuda(self, itype, op_type1, op_type2, broad=False, negative=False):
         model1 = helper.make_model(
             helper.make_graph(
@@ -440,6 +505,11 @@ def test_mulsub_cuda(self):
     def test_mulsub_cuda_negative(self):
         self._addmul_cuda(TensorProto.FLOAT, "Mul", "Sub", negative=True)
         self._addmul_cuda(TensorProto.FLOAT16, "Mul", "Sub", negative=True)
+
+    @unittest.skipIf(not has_cuda(), reason="cuda not available")
+    def test_transpose_cast_cuda(self):
+        self._transpose_cast_cuda(TensorProto.FLOAT)
+        self._transpose_cast_cuda(TensorProto.FLOAT16)
 
 
 if __name__ == "__main__":