merge

operators/cuda/add_mul.h

@@ -29,7 +29,7 @@ struct AddOrMulSharedInput {
     auto length_c = tensor_c.NumberOfElement();
 
     T* output_data_ab = output_ab.Allocate(length_a <= length_b ? tensor_b.Shape() : tensor_a.Shape());
-    T* output_data_ac = output_ab.Allocate(length_a <= length_c ? tensor_c.Shape() : tensor_a.Shape());
+    T* output_data_ac = output_ac.Allocate(length_a <= length_c ? tensor_c.Shape() : tensor_a.Shape());
 
     if (0 == input_data_a || 0 == input_data_b || 0 == input_data_c) {
       return {};

operators/cuda/cuda_ops.cc

@@ -7,7 +7,11 @@
 #include "cuda/add_mul.h"
 #include "cuda/fast_gelu.h"
 #include "cuda/negxplus1.h"
+<<<<<<< HEAD
 #include "cuda/scatter_nd_of_shape.h"
+=======
+#include "cuda/transpose_cast.h"
+>>>>>>> 79f3b048d4d195b6684f2d1b6ca5bfe1ab9ea8d6
 #endif
 
 FxLoadCustomOpFactory LoadCustomOpClasses_Contrib = []() -> CustomOpArray& {
@@ -18,6 +22,8 @@ FxLoadCustomOpFactory LoadCustomOpClasses_Contrib = []() -> CustomOpArray& {
 #if ORT_API_VERSION >= 16
   using AddSharedInputFloat16Type = typename contrib::AddOrMulSharedInput<ortc::MFloat16, true>;
   using MulSharedInputFloat16Type = typename contrib::AddOrMulSharedInput<ortc::MFloat16, false>;
+  using Transpose2DCastFloat32ToFloat16Type = typename contrib::Transpose2DCast<float, ortc::MFloat16>;
+  using Transpose2DCastFloat16ToFloat32Type = typename contrib::Transpose2DCast<ortc::MFloat16, float>;
 #endif
 
 
@@ -39,7 +45,9 @@ FxLoadCustomOpFactory LoadCustomOpClasses_Contrib = []() -> CustomOpArray& {
       CustomCudaStructV2("MaskedScatterNDOfShape", contrib::MaskedScatterNDOfShape<ortc::MFloat16>),
       CustomCudaStructV2("MulSharedInput", MulSharedInputFloat16Type),
       CustomCudaStructV2("NegXPlus1", contrib::NegXPlus1<ortc::MFloat16>),
-      CustomCudaStructV2("ScatterNDOfShape", contrib::ScatterNDOfShape<ortc::MFloat16>)
+      CustomCudaStructV2("ScatterNDOfShape", contrib::ScatterNDOfShape<ortc::MFloat16>),
+      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

@@ -31,6 +31,20 @@ 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):
     @staticmethod
     def _create_negpos_test_model(domain="ai.onnx.contrib"):
@@ -167,8 +181,6 @@ def test_cuda_negxplus1(self):
         self._negxplus1_cuda(TensorProto.FLOAT16)
 
     def _addmul_shared_input_cuda(self, itype, op_type, shapea=(3, 2, 3), shapeb=(3, 2, 3), shapec=(3, 2, 3)):
-        from onnx_extended.ortops.optim.cuda import get_ort_ext_libs
-
         model1 = helper.make_model(
             helper.make_graph(
                 [
@@ -197,7 +209,7 @@ def _addmul_shared_input_cuda(self, itype, op_type, shapea=(3, 2, 3), shapeb=(3,
                         f"{op_type}SharedInput",
                         ["X", "Y", "Z"],
                         ["XY", "XZ"],
-                        domain="onnx_extended.ortops.optim.cuda",
+                        domain="ai.onnx.contrib",
                     )
                 ],
                 "nd",
@@ -213,7 +225,7 @@ def _addmul_shared_input_cuda(self, itype, op_type, shapea=(3, 2, 3), shapeb=(3,
             ),
             opset_imports=[
                 helper.make_opsetid("", 18),
-                helper.make_opsetid("onnx_extended.ortops.optim.cuda", 1),
+                helper.make_opsetid("ai.onnx.contrib", 1),
             ],
             ir_version=9,
         )
@@ -228,7 +240,7 @@ def _addmul_shared_input_cuda(self, itype, op_type, shapea=(3, 2, 3), shapeb=(3,
         expected = ref.run(None, feeds1)
 
         opts = _ort.SessionOptions()
-        opts.register_custom_ops_library(get_ort_ext_libs()[0])
+        opts.register_custom_ops_library(_get_library_path())
         sess = _ort.InferenceSession(model2.SerializeToString(), opts, providers=["CUDAExecutionProvider"])
         got = sess.run(None, feeds1)
         for i in range(2):
@@ -466,6 +478,62 @@ def test_masked_scatternd_of_shape_standalone_cuda_big(self):
         self._masked_scatternd_of_shape_cuda("add", 1, TensorProto.FLOAT, True)
         self._masked_scatternd_of_shape_cuda("add", 1, TensorProto.FLOAT16, True)
 
+    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)
+
+    @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__":
     unittest.main(verbosity=2)