initial commit

fix ci

fix ci

remove old sequence mask api

fix ci

fix ci

remove old seuqence mask tests

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/sequence_mask_gpu_kernel.cc

@@ -0,0 +1,35 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cstdint>
+
+#include "backend/kernel_compiler/gpu/arrays/sequence_mask_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+
+// keep this as TWO but output is always bool, just in case framework can
+// support passing optional dtype and then we can be identical to tf
+MS_REG_GPU_KERNEL_TWO(
+  SequenceMask,
+  KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
+  SequenceMaskGpuKernel, int32_t, bool)
+
+MS_REG_GPU_KERNEL_TWO(
+  SequenceMask,
+  KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
+  SequenceMaskGpuKernel, int64_t, bool)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/sequence_mask_gpu_kernel.h

@@ -0,0 +1,101 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SEQUENCE_MASK_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SEQUENCE_MASK_GPU_KERNEL_H_
+
+#include "backend/kernel_compiler/gpu/cuda_impl/sequence_mask_impl.cuh"
+
+#include <cuda_runtime.h>
+
+#include <vector>
+
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T, typename S>
+class SequenceMaskGpuKernel : public GpuKernel {
+ public:
+  SequenceMaskGpuKernel() { ResetResource(); }
+  ~SequenceMaskGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    T *lengths_device_address = GetDeviceAddress<T>(inputs, 0);
+    T *maxlen_device_address = GetDeviceAddress<T>(inputs, 1);
+    S *output_device_address = GetDeviceAddress<S>(outputs, 0);
+
+    CalSequenceMask(lengths_device_address, maxlen_device_address, output_device_address, output_size_,
+                    reinterpret_cast<cudaStream_t>(stream_ptr));
+
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_count = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_count != 2) {
+      MS_LOG(EXCEPTION) << input_count << " inputs were provided, but SequenceMaskGpuKernel expects 2.";
+    }
+
+    input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    for (const int &e : input_shape_) {
+      lengths_size_ *= e;
+    }
+
+    std::vector<size_t> inferred_output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+    for (const size_t &e : inferred_output_shape) {
+      output_size_ *= e;
+    }
+
+    InitSizeLists();
+
+    return true;
+  }
+
+  void ResetResource() noexcept override {
+    output_size_ = 1;
+    lengths_size_ = 1;
+    input_size_list_.clear();
+    output_size_list_.clear();
+    workspace_size_list_.clear();
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(lengths_size_ * sizeof(T));
+    input_size_list_.push_back(sizeof(T));
+    output_size_list_.push_back(output_size_);
+  }
+
+ private:
+  std::vector<size_t> input_shape_;
+  size_t lengths_size_;
+  size_t output_size_;
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SEQUENCE_MASK_GPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/sequence_mask_impl.cu

@@ -0,0 +1,50 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuda_runtime.h>
+
+#include "sequence_mask_impl.cuh"
+#include "runtime/device/gpu/cuda_common.h"
+
+__global__ void ValidateArgs(int *maxlen, const int lengths_size, const int max_output_size) {
+  int maxlen_value = *maxlen;
+  if (maxlen_value < 0 || lengths_size * maxlen_value > max_output_size) {
+    asm("trap;");
+  }
+}
+
+template <typename T, typename S>
+__global__ void SequenceMask(
+    const T *input, T *maxlen, S *output, const size_t output_size) {
+  T maxlen_value = *maxlen;
+
+  for (size_t gt_id = blockIdx.x * blockDim.x + threadIdx.x; gt_id < output_size; gt_id += gridDim.x * blockDim.x) {
+    T mask_comparison_value = gt_id % maxlen_value;
+    T input_comparison_index = (gt_id - mask_comparison_value) / maxlen_value;
+    S result = mask_comparison_value < input[input_comparison_index];
+    output[gt_id] = result;
+  }
+}
+
+template <typename T, typename S>
+void CalSequenceMask(const T *lengths, T *maxlen, S *output, const size_t output_size, cudaStream_t cuda_stream) {
+  SequenceMask<<<GET_BLOCKS(output_size), GET_THREADS, 0, cuda_stream>>>(lengths, maxlen, output, output_size);
+}
+
+template void CalSequenceMask<int, bool>(const int *lengths, int *maxlen, bool *output, const size_t output_size,
+                                         cudaStream_t cuda_stream);
+
+template void CalSequenceMask<int64_t, bool>(const int64_t *lengths, int64_t *maxlen, bool *output,
+                                             const size_t output_size, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/sequence_mask_impl.cuh

@@ -0,0 +1,25 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SEQUENCE_MASK_CUH_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SEQUENCE_MASK_CUH_
+
+#include <cuda_runtime.h>
+
+template <typename T, typename S>
+void CalSequenceMask(const T *lengths, T *maxlen, S *output, const size_t output_size, cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SEQUENCE_MASK_CUH_

mindspore/core/abstract/infer_functions.h

@@ -263,6 +263,9 @@ AbstractBasePtr InferImplPad(const AnalysisEnginePtr &, const PrimitivePtr &prim
                              const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplSplit(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplSequenceMask(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                      const AbstractBasePtrList &args_spec_list);
+
 template <typename T>
 AbstractBasePtr InferTupleOrListOrDictLen(const std::string &op_name, const AbstractBasePtrList &args_spec_list) {
   // Inputs: a tuple or list or dict.

mindspore/core/abstract/prim_arrays.cc

@@ -771,5 +771,56 @@ AbstractBasePtr InferImplSplit(const AnalysisEnginePtr &, const PrimitivePtr &pr
   }
   return std::make_shared<AbstractTuple>(output_list);
 }
+
+AbstractBasePtr InferImplSequenceMask(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                      const AbstractBasePtrList &args_spec_list) {
+  const std::string &op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 2);
+
+  AbstractTensorPtr lengths = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
+  (void)CheckTensorDType(lengths, {kInt32, kInt64}, "Input 1 (lengths) for SequenceMask should be one of: %s");
+
+  int64_t maxlen_value = 0;
+
+  if (args_spec_list[1]->isa<AbstractScalar>()) {
+    AbstractScalarPtr maxlen = CheckArg<AbstractScalar>(op_name, args_spec_list, 1);
+    (void)CheckScalarType(maxlen, {kInt32, kInt64}, "Input 0 (maxlen) for SequenceMask should be one of: %s");
+
+    TypePtr maxlen_type = nullptr;
+    maxlen_type = maxlen->GetTypeTrack();
+    MS_EXCEPTION_IF_NULL(maxlen_type);
+
+    if (maxlen_type->type_id() == TypeId::kNumberTypeInt32) {
+      maxlen_value = static_cast<int64_t>(GetValue<int32_t>(maxlen->BuildValue()));
+    } else if (maxlen_type->type_id() == TypeId::kNumberTypeInt64) {
+      maxlen_value = GetValue<int64_t>(maxlen->BuildValue());
+    }
+  } else if (args_spec_list[1]->isa<AbstractTensor>()) {
+    auto maxlen_tensor_ptr = args_spec_list[1]->cast<AbstractTensorPtr>();
+    MS_EXCEPTION_IF_NULL(maxlen_tensor_ptr);
+    auto maxlen_value_ptr = maxlen_tensor_ptr->BuildValue();
+    MS_EXCEPTION_IF_NULL(maxlen_value_ptr);
+    auto maxlen_tensor = maxlen_value_ptr->cast<tensor::TensorPtr>();
+    MS_EXCEPTION_IF_NULL(maxlen_tensor);
+    maxlen_value = *static_cast<int64_t *>(maxlen_tensor->data_c());
+  }
+
+  ShapeVector lengths_shape = lengths->shape()->shape();
+  ShapeVector lengths_shape_min = lengths->shape()->min_shape();
+  if (lengths_shape_min.empty()) {
+    lengths_shape_min = lengths_shape;
+  }
+  ShapeVector lengths_shape_max = lengths->shape()->max_shape();
+  if (lengths_shape_max.empty()) {
+    lengths_shape_max = lengths_shape;
+  }
+
+  lengths_shape.push_back(maxlen_value);
+  lengths_shape_min.push_back(maxlen_value);
+  lengths_shape_max.push_back(maxlen_value);
+
+  ShapePtr output_shape = std::make_shared<Shape>(lengths_shape, lengths_shape_min, lengths_shape_max);
+  return std::make_shared<AbstractTensor>(kBool, output_shape);
+}
 }  // namespace abstract
 }  // namespace mindspore

mindspore/core/abstract/primitive_infer_map.cc

@@ -71,6 +71,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimTranspose, {InferImplTranspose, true}},
     {prim::kPrimReshape, {InferImplReshape, true}},
     {prim::kPrimSplit, {InferImplSplit, true}},
+    {prim::kPrimSequenceMask, {InferImplSequenceMask, true}},
     // Structure
     {prim::kPrimMakeTuple, {InferImplMakeTuple, true}},
     {prim::kPrimMakeList, {InferImplMakeList, true}},

mindspore/core/base/core_ops.h

@@ -119,6 +119,7 @@ inline const PrimitivePtr kPrimDynamicGRUV2Grad = std::make_shared<Primitive>("D
 inline const PrimitivePtr kPrimScatterAdd = std::make_shared<Primitive>("ScatterAdd");
 inline const PrimitivePtr kPrimScatterUpdate = std::make_shared<Primitive>("ScatterUpdate");
 inline const PrimitivePtr kPrimSplit = std::make_shared<Primitive>("Split");
+inline const PrimitivePtr kPrimSequenceMask = std::make_shared<Primitive>("SequenceMask");
 
 // NN
 inline const PrimitivePtr kPrimFlatten = std::make_shared<Primitive>("Flatten");

mindspore/ops/operations/__init__.py

@@ -22,7 +22,7 @@ A collection of operators to build neural networks or to compute functions.
 from .image_ops import (CropAndResize)
 from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Diag, DiagPart, DType, ExpandDims, Eye,
-                        Fill, Ones, Zeros, SequenceMask, GatherNd, GatherV2, SparseGatherV2, InvertPermutation,
+                        Fill, Ones, Zeros, GatherNd, GatherV2, SparseGatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue, Meshgrid,
                         SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin,
@@ -33,7 +33,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin, UnsortedSegmentMax,
                         UnsortedSegmentProd, UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
                         SpaceToBatchND, BatchToSpaceND, BroadcastTo, InplaceUpdate, ReverseSequence, EmbeddingLookup,
-                        Unique, GatherD, Identity)
+                        Unique, GatherD, Identity, SequenceMask)
 from .comm_ops import (AllGather, AllReduce, _AlltoAll, AllSwap, ReduceScatter, Broadcast,
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice, Send, Receive,
@@ -394,6 +394,7 @@ __all__ = [
     "Pull",
     "ReLUV2",
     "SparseToDense",
+    "SequenceMask",
 ]
 
 __all__.sort()

mindspore/ops/operations/array_ops.py

@@ -1216,68 +1216,6 @@ class Zeros(PrimitiveWithInfer):
         return out
 
 
-class SequenceMask(PrimitiveWithInfer):
-    r"""
-    Generates sequence mask according to input lengths.
-
-    Creates a mask tensor which retains the first N elements in tensor by setting the values
-    to be True or one. The rest values in mask are set to False or zero.
-
-    Args:
-        max_length (int): Nonnegative integer, size of the last dimension in mask. Default: None.
-
-    Inputs:
-        - **lengths** (Union[tuple[int], list[int]]) - Defines the first N elements that are retained.
-          Only constant value is allowed.
-        - **dtype** (mindspore.dtype) - The specified type of output tensor. Only constant value is allowed.
-
-    Outputs:
-        Tensor.
-        If max_length is set, the shape of the output is (lengths.shape, max_length).
-        If max_length is not set and the biggest value in lengths is x. Then, the shape of
-        the output is (lengths.shape, x).
-
-    Supported Platforms:
-    ``Ascend`` ``GPU`` ``CPU``
-
-    Examples:
-        >>> from mindspore.ops import operations as P
-        >>> sequence_mask = P.SequenceMask()
-        >>> mask = sequence_mask([2, 2, 4], mindspore.int32)
-        >>> print(mask)
-        [[1, 1, 0, 0],
-         [1, 1, 0, 0],
-         [1, 1, 1, 1]]
-
-    """
-
-    @prim_attr_register
-    def __init__(self):
-        """Initialize SequenceMask"""
-
-    def __infer__(self, lengths, dtype, max_length=None):
-        validator.check_value_type("shape", lengths['value'], [tuple, list], self.name)
-        valid_types = [mstype.bool_, mstype.int8, mstype.int16, mstype.int32, mstype.int64,
-                       mstype.uint8, mstype.uint16, mstype.uint32, mstype.uint64,
-                       mstype.float16, mstype.float32, mstype.float64]
-        validator.check_subclass("dtype", dtype['value'], valid_types, self.name)
-        nptype = mstype.dtype_to_nptype(dtype['value'])
-        if max_length is None:
-            max_length = np.max(lengths['value'])
-        else:
-            validator.check_non_negative_int(max_length['value'])
-            max_length = max_length['value']
-        row_vector = np.arange(0, max_length)
-        col_matrix = np.expand_dims(lengths['value'], -1)
-        result = (row_vector < col_matrix).astype(nptype)
-        out = {
-            'value': Tensor(result),
-            'shape': result.shape,
-            'dtype': dtype['value']
-        }
-        return out
-
-
 class OnesLike(PrimitiveWithInfer):
     """
     Creates a new tensor. The values of all elements are 1.
@@ -4655,3 +4593,47 @@ class Identity(PrimitiveWithInfer):
                'dtype': x['dtype'],
                'value': None}
         return out
+
+
+class SequenceMask(PrimitiveWithCheck):
+    """
+    Returns a mask tensor representing the first N positions of each cell.
+
+    If lengths has shape [d_1, d_2, ..., d_n], then the resulting tensor mask has type dtype and shape
+    [d_1, d_2, ..., d_n, maxlen], with mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
+
+    Inputs:
+        - **lengths** (Tensor) - Tensor to calculate the mask for. All values in this tensor must be
+          less than `maxlen`. Must be type int32 or int64.
+
+        - **maxlen** (int) - size of the last dimension of returned tensor. Must be positive and same
+          tyupe as elements in `lengths`.
+
+    Outputs:
+        One mask tensor of shape lengths.shape + (maxlen,).
+
+    Supported Platforms:
+        ``GPU``
+
+    Examples:
+        >>> x = Tensor(np.array([[1, 3], [2, 0]])
+        >>> sequence_mask = P.SequenceMask()
+        >>> output = sequence_mask(x, 3)
+        >>> print(output)
+        [[[True, False, False],
+          [True, True, True]],
+         [[True, True, False],
+          [False, False, False]]]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=["lengths", "maxlen"], outputs=["mask"])
+
+    def check_shape(self, lengths_shape, maxlen_shape):
+        validator.check("lengths_shape", len(lengths_shape), "", 0, Rel.GT, self.name)
+        validator.check("maxlen_shape", len(maxlen_shape), "", 0, Rel.EQ, self.name)
+
+    def check_dtype(self, lengths_dtype, maxlen_dtype):
+        validator.check_subclass("lengths_dtype", lengths_dtype, mstype.tensor, self.name)
+        validator.check_subclass("maxlen", maxlen_dtype, mstype.number, self.name)

tests/st/ops/gpu/test_sequence_mask_op.py

@@ -0,0 +1,117 @@
+import numpy as np
+import pytest
+
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops.operations import _inner_ops as inner
+import mindspore.nn as nn
+import mindspore.context as context
+
+def sequence_mask(x, maxlen):
+    sequence_mask_op = P.SequenceMask()
+    return sequence_mask_op(Tensor(x.astype(np.int32)), maxlen)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sequence_mask_1d():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    a = np.array([2, 3, 1])
+
+    maxlen = 4
+    ms_out = sequence_mask(a, maxlen)
+    expected_out = Tensor(np.array([[True, True, False, False],
+                                    [True, True, True, False],
+                                    [True, False, False, False]]))
+    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sequence_mask_2d():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    a = np.array([[0, 1, 3, 2], [1, 4, 4, 2]])
+
+    maxlen = 6
+    ms_out = sequence_mask(a, maxlen)
+    expected_out = Tensor(np.array([[[False, False, False, False, False, False],
+                                     [True, False, False, False, False, False],
+                                     [True, True, True, False, False, False],
+                                     [True, True, False, False, False, False]],
+                                    [[True, False, False, False, False, False],
+                                     [True, True, True, True, False, False],
+                                     [True, True, True, True, False, False],
+                                     [True, True, False, False, False, False]]]))
+    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sequence_mask_3d():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    a = np.array([[[2, 2], [1, 1]],
+                  [[2, 0], [2, 1]],
+                  [[0, 0], [0, 0]]])
+
+    maxlen = 2
+    ms_out = sequence_mask(a, maxlen)
+    expected_out = Tensor(np.array([[[[True, True], [True, True]], [[True, False], [True, False]]],
+                                    [[[True, True], [False, False]], [[True, True], [True, False]]],
+                                    [[[False, False], [False, False]], [[False, False], [False, False]]]]))
+
+    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sequence_mask_maxlen_1():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    a = np.array([[[0, 1], [1, 1]],
+                  [[1, 0], [1, 1]],
+                  [[0, 1], [0, 1]]])
+
+    maxlen = 1
+    ms_out = sequence_mask(a, maxlen)
+    expected_out = Tensor(np.array([[[[False], [True]], [[True], [True,]]],
+                                    [[[True], [False]], [[True], [True]]],
+                                    [[[False], [True]], [[False], [True]]]]))
+
+    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sequence_mask_dynamic():
+    class SequenceMaskDynamicNet(nn.Cell):
+        def __init__(self, maxlen):
+            super(SequenceMaskDynamicNet, self).__init__()
+            self.maxlen = maxlen
+            self.convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
+            self.sequence_mask = P.SequenceMask()
+
+        def construct(self, x):
+            converted_to_dynamic_shape = self.convert_to_dynamic_shape(x)
+            return self.sequence_mask(converted_to_dynamic_shape, self.maxlen)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    sequence_mask_net = SequenceMaskDynamicNet(4)
+
+    a = Tensor(np.array([0, 1, 0, 2, 0, 5]))
+    ms_out = sequence_mask_net(a)
+    expected_out = Tensor(np.array([[False, False, False, False],
+                                    [True, False, False, False],
+                                    [False, False, False, False],
+                                    [True, True, False, False],
+                                    [False, False, False, False],
+                                    [True, True, True, True]]))
+    np.testing.assert_array_equal(expected_out.asnumpy(), ms_out.asnumpy())
+
+    a = Tensor(np.array([[4, 3, 0], [0, 1, 3]]))
+    ms_out = sequence_mask_net(a)
+    expected_out = Tensor(np.array([[[True, True, True, True],
+                                     [True, True, True, False],
+                                     [False, False, False, False]],
+                                    [[False, False, False, False],
+                                     [True, False, False, False],
+                                     [True, True, True, False]]]))

tests/ut/python/ops/test_array_ops.py

@@ -42,28 +42,6 @@ def test_expand_dims():
     assert output.asnumpy().shape == (1, 2, 2)
 
 
-def test_sequence_mask():
-    list_ = [2, 2, 4]
-    sequence_mask = P.SequenceMask()
-    mask1 = sequence_mask(list_, mstype.int32)
-    mask2 = sequence_mask(list_, mstype.int32, 5)
-    assert mask1.shape == (3, 4)
-    assert mask1.dtype == mstype.int32
-    assert mask2.shape == (3, 5)
-    assert mask2.dtype == mstype.int32
-
-
-def test_sequence_mask_1():
-    list_ = [[2, 2, 4], [3, 4, 4]]
-    sequence_mask = P.SequenceMask()
-    mask1 = sequence_mask(list_, mstype.bool_)
-    mask2 = sequence_mask(list_, mstype.bool_, 5)
-    assert mask1.shape == (2, 3, 4)
-    assert mask1.dtype == mstype.bool_
-    assert mask2.shape == (2, 3, 5)
-    assert mask2.dtype == mstype.bool_
-
-
 def test_cast():
     input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_x = Tensor(input_np)