zeroslike dynamic shape no opt

fix ci

fix ci

fix ci

fix ci

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

@@ -0,0 +1,41 @@
+/**
+ * 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/zeroslike_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
+                      ZerosLikeGpuKernel, bool)
+
+MS_REG_GPU_KERNEL_ONE(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
+                      ZerosLikeGpuKernel, int8_t)
+
+MS_REG_GPU_KERNEL_ONE(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
+                      ZerosLikeGpuKernel, uint8_t)
+
+MS_REG_GPU_KERNEL_ONE(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                      ZerosLikeGpuKernel, int32_t)
+
+MS_REG_GPU_KERNEL_ONE(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                      ZerosLikeGpuKernel, half)
+
+MS_REG_GPU_KERNEL_ONE(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      ZerosLikeGpuKernel, float)
+
+}  // namespace kernel
+}  // namespace mindspore

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

@@ -0,0 +1,88 @@
+/**
+ * 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_ARRAYS_ZEROSLIKE_GPU_KERNEL_H
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_ZEROSLIKE_GPU_KERNEL_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>
+class ZerosLikeGpuKernel : public GpuKernel {
+ public:
+  ZerosLikeGpuKernel() { ResetResource(); }
+  ~ZerosLikeGpuKernel() override = 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 *output_device_address = GetDeviceAddress<T>(outputs, 0);
+
+    CHECK_CUDA_RET_WITH_EXCEPT(
+      kernel_node_,
+      // have to use a float literal instead of an int literal beacuse of ambigious half() overload.
+      cudaMemsetAsync(output_device_address, static_cast<T>(0.0), input_size_ * sizeof(T),
+                      reinterpret_cast<cudaStream_t>(stream_ptr)),
+      "cudaMemset failed");
+
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    kernel_node_ = kernel_node;
+
+    std::vector<size_t> input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
+    for (size_t i = 0; i < input_shape.size(); i++) {
+      input_size_ *= input_shape[i];
+    }
+
+    InitSizeLists();
+
+    return true;
+  }
+
+  void ResetResource() noexcept override {
+    kernel_node_ = nullptr;
+    input_size_ = 1;
+    input_size_list_.clear();
+    output_size_list_.clear();
+    workspace_size_list_.clear();
+  }
+
+ protected:
+  void InitSizeLists() override {
+    // allocate space for input even though we don't need to do anything with the input
+    input_size_list_.push_back(input_size_ * sizeof(T));
+    output_size_list_.push_back(input_size_ * sizeof(T));
+  }
+
+ private:
+  CNodePtr kernel_node_;
+  size_t input_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_ARRAYS_ZEROSLIKE_GPU_KERNEL_H

mindspore/ccsrc/frontend/optimizer/irpass/special_op_eliminate.h

@@ -195,6 +195,13 @@ class ZeroLikeFillZero : public AnfVisitor {
     TypePtr tensor_type_ptr = tensor_abstract->element()->BuildType();
     std::vector<int64_t> tensor_shape = tensor_abstract->shape()->shape();
 
+    // if shape is unknown, don't optimize this operator away
+    for (const int64_t &dimension : tensor_shape) {
+      if (dimension < 0) {
+        return node;
+      }
+    }
+
     tensor::TensorPtr new_tensor_ptr = std::make_shared<tensor::Tensor>(tensor_type_ptr->type_id(), tensor_shape);
     size_t mem_size = GetTypeByte(tensor_type_ptr) * LongToSize(new_tensor_ptr->ElementsNum());
     char *data = reinterpret_cast<char *>(new_tensor_ptr->data_c());

mindspore/core/abstract/infer_functions.h

@@ -275,9 +275,9 @@ AbstractBasePtr InferImplSplit(const AnalysisEnginePtr &, const PrimitivePtr &pr
                                const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplSequenceMask(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                       const AbstractBasePtrList &args_spec_list);
-
 AbstractBasePtr InferImplAddN(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

@@ -767,9 +767,21 @@ AbstractBasePtr InferImplDynamicShape(const AnalysisEnginePtr &, const Primitive
 
 AbstractBasePtr InferImplZerosLike(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                    const AbstractBasePtrList &args_spec_list) {
-  // Inputs: a tensor.
-  CheckArgsSize(primitive->name(), args_spec_list, 1);
-  return args_spec_list[0]->Broaden();
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 1);
+  AbstractTensorPtr input_x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
+  ShapeVector x_shape = input_x->shape()->shape();
+  ShapeVector x_shape_min = input_x->shape()->min_shape();
+  if (x_shape_min.empty()) {
+    x_shape_min = x_shape;
+  }
+  ShapeVector x_shape_max = input_x->shape()->max_shape();
+  if (x_shape_max.empty()) {
+    x_shape_max = x_shape;
+  }
+
+  ShapePtr output_shape = std::make_shared<Shape>(x_shape, x_shape_min, x_shape_max);
+  return std::make_shared<AbstractTensor>(input_x->element(), output_shape);
 }
 
 AbstractBasePtr InferImplTranspose(const AnalysisEnginePtr &, const PrimitivePtr &primitive,

tests/st/ops/gpu/test_zeroslike_op.py

@@ -20,6 +20,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
+from mindspore.ops.operations import _inner_ops as inner
 
 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
 
@@ -74,3 +75,96 @@ def test_ZerosLike():
     error1 = np.ones(shape=expect1.shape) * 1.0e-5
     assert np.all(diff1 < error1)
     assert output1.shape == expect1.shape
+
+
+class ZerosLikeDynamicNet(nn.Cell):
+    def __init__(self):
+        super(ZerosLikeDynamicNet, self).__init__()
+        self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
+        self.zeros_like = P.ZerosLike()
+
+    def construct(self, x):
+        converted_to_dynamic = self.gpu_convert_to_dynamic_shape(x)
+        return self.zeros_like(converted_to_dynamic)
+
+
+def zeros_like_dynamic(x):
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    net = ZerosLikeDynamicNet()
+    return net(x)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_bool():
+    x = Tensor(np.arange(120).reshape(3, 4, 1, 2, 5).astype(np.bool))
+    output = zeros_like_dynamic(x)
+    expected = np.zeros([3, 4, 1, 2, 5])
+    np.testing.assert_array_equal(output.asnumpy(), expected)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_int8():
+    x = Tensor(np.arange(24).reshape(1, 4, 1, 6).astype(np.int8))
+    output = zeros_like_dynamic(x)
+    expected = np.zeros([1, 4, 1, 6])
+    print(output)
+    np.testing.assert_array_equal(output.asnumpy(), expected)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_uint8():
+    x = Tensor(np.arange(30).reshape(3, 2, 5).astype(np.uint8))
+    output = zeros_like_dynamic(x)
+    expected = np.zeros([3, 2, 5])
+    np.testing.assert_array_equal(output.asnumpy(), expected)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_int32():
+    x = Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(np.int32))
+    output = zeros_like_dynamic(x)
+    expected = np.zeros([2, 2, 2, 2])
+    np.testing.assert_array_equal(output.asnumpy(), expected)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_float16():
+    x = Tensor(np.arange(120).reshape(3, 4, 1, 2, 5).astype(np.float16))
+    output = zeros_like_dynamic(x)
+    expected = np.zeros([3, 4, 1, 2, 5])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_float32():
+    x = Tensor(np.arange(63).reshape(3, 7, 3).astype(np.float32))
+    output = zeros_like_dynamic(x)
+    expected = np.zeros([3, 7, 3])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_zeros_like_dynamic_multiple_inputs():
+    net = ZerosLikeDynamicNet()
+
+    x = Tensor(np.arange(4).reshape(4).astype(np.float32))
+    output = net(x)
+    expected = np.zeros([4])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
+    x = Tensor(np.arange(8).reshape(2, 1, 2, 2).astype(np.uint8))
+    output = net(x)
+    expected = np.zeros([2, 1, 2, 2])
+    np.testing.assert_array_equal(output.asnumpy(), expected)
+
+    x = Tensor(np.arange(1).reshape(1).astype(np.float16))
+    output = net(x)
+    expected = np.zeros([1])
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)