!66230 summary适配动态shape

Merge pull request !66230 from DavidFFFan/feature-2.3-summary-iter4

mindspore/ccsrc/debug/summary/summary.cc

@@ -81,8 +81,9 @@ void Summary::SummaryTensor(KernelGraph *graph) {
     auto node = output_item.second.first;
     size_t index = IntToSize(output_item.second.second);
     auto address = AnfAlgo::GetOutputAddr(node, index, false);
-    auto shape = common::AnfAlgo::GetOutputInferShape(node, index);
+    auto kt = AnfAlgo::GetOutputKernelTensor(node, index);
-    TypeId type_id = common::AnfAlgo::GetOutputInferDataType(node, index);
+    auto shape = kt->GetShapeVector();
+    TypeId type_id = kt->dtype_id();
     tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_id, shape);
     MS_EXCEPTION_IF_NULL(address);
     if (!address->GetPtr()) {

mindspore/core/ops/histogram_summary.cc

@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2024. All rights reserved.
+ *
+ * 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 "ops/histogram_summary.h"
+
+#include <memory>
+#include <vector>
+
+#include "abstract/abstract_value.h"
+#include "abstract/dshape.h"
+#include "abstract/ops/op_infer.h"
+#include "abstract/ops/primitive_infer_map.h"
+#include "base/base.h"
+#include "ir/anf.h"
+#include "ir/dtype/number.h"
+#include "ir/primitive.h"
+#include "mindapi/base/shape_vector.h"
+#include "mindapi/base/shared_ptr.h"
+#include "mindapi/ir/value.h"
+#include "mindapi/src/helper.h"
+#include "mindspore/core/ops/structure_ops.h"
+#include "ops/op_name.h"
+#include "ops/primitive_c.h"
+#include "utils/check_convert_utils.h"
+#include "utils/log_adapter.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+constexpr int BASE_SIZE = 1;
+abstract::ShapePtr HistogramSummaryInferShape(const PrimitivePtr &primitive,
+                                              const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  // check
+  MS_EXCEPTION_IF_NULL(input_args[1]);
+  auto v_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->GetShape())[kShape];
+  (void)CheckAndConvertUtils::CheckInteger("v rank", int64_t(v_shape.size()), kGreaterEqual, BASE_SIZE, prim_name);
+  return std::make_shared<abstract::Shape>(ShapeVector(1));
+}
+}  // namespace
+
+MIND_API_OPERATOR_IMPL(HistogramSummary, BaseOperator);
+void HistogramSummary::set_side_effect_io() { (void)this->AddAttr(kSideEffectIO, api::MakeValue(true)); }
+
+bool HistogramSummary::get_side_effect_io() const {
+  auto value_ptr = GetAttr(kSideEffectIO);
+  return GetValue<bool>(value_ptr);
+}
+
+void HistogramSummary::Init() { this->set_side_effect_io(); }
+
+class MIND_API HistogramSummaryInfer : public abstract::OpInferBase {
+ public:
+  BaseShapePtr InferShape(const PrimitivePtr &primitive,
+                          const std::vector<AbstractBasePtr> &input_args) const override {
+    primitive->AddAttr("dyn_input_sizes", MakeValue(std::vector<int64_t>{-1, 1}));
+    return HistogramSummaryInferShape(primitive, input_args);
+  }
+
+  TypePtr InferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
+    MS_EXCEPTION_IF_NULL(primitive);
+    // check
+    CheckAndConvertUtils::CheckSummaryParam(input_args[0], input_args[1], primitive->name());
+    return kInt32;
+  }
+};
+
+REGISTER_PRIMITIVE_OP_INFER_IMPL(HistogramSummary, prim::kPrimHistogramSummary, HistogramSummaryInfer, false);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/histogram_summary.h

@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2024. All rights reserved.
+ *
+ * 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_CORE_OPS_HISTOGRAM_SUMMARY_H_
+#define MINDSPORE_CORE_OPS_HISTOGRAM_SUMMARY_H_
+#include <map>
+#include <memory>
+#include <string>
+
+#include "mindapi/base/types.h"
+#include "ops/base_operator.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameHistogramSummary = "HistogramSummary";
+
+/// \brief Outputs a tensor to a protocol buffer through a tensor summary operator.
+/// Refer to Python API @ref mindspore.ops.HistogramSummary for more details.
+class MIND_API HistogramSummary : public BaseOperator {
+ public:
+  MIND_API_BASE_MEMBER(HistogramSummary);
+  /// \brief Constructor.
+  HistogramSummary() : BaseOperator(kNameHistogramSummary) {}
+  /// \brief Init.
+  void Init();
+  /// \brief Set side_effect_io.
+  void set_side_effect_io();
+  /// \brief Get side_effect_io.
+  ///
+  /// \return side_effect_io.
+  bool get_side_effect_io() const;
+};
+}  // namespace ops
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CORE_OPS_HISTOGRAM_SUMMARY_H_

mindspore/core/ops/image_summary.cc

@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2024. All rights reserved.
+ *
+ * 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 "ops/image_summary.h"
+
+#include <memory>
+#include <vector>
+
+#include "abstract/abstract_value.h"
+#include "abstract/dshape.h"
+#include "abstract/ops/op_infer.h"
+#include "abstract/ops/primitive_infer_map.h"
+#include "base/base.h"
+#include "ir/anf.h"
+#include "ir/dtype/number.h"
+#include "ir/primitive.h"
+#include "mindapi/base/shape_vector.h"
+#include "mindapi/base/shared_ptr.h"
+#include "mindapi/ir/value.h"
+#include "mindapi/src/helper.h"
+#include "mindspore/core/ops/structure_ops.h"
+#include "ops/op_name.h"
+#include "ops/primitive_c.h"
+#include "utils/check_convert_utils.h"
+#include "utils/log_adapter.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+constexpr int IMAGE_RANK = 4;
+abstract::ShapePtr ImageSummaryInferShape(const PrimitivePtr &primitive,
+                                          const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  // check
+  MS_EXCEPTION_IF_NULL(input_args[1]);
+  auto v_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->GetShape())[kShape];
+  (void)CheckAndConvertUtils::CheckInteger("v rank", int64_t(v_shape.size()), kEqual, IMAGE_RANK, prim_name);
+  return std::make_shared<abstract::Shape>(ShapeVector(1));
+}
+}  // namespace
+
+MIND_API_OPERATOR_IMPL(ImageSummary, BaseOperator);
+void ImageSummary::set_side_effect_io() { (void)this->AddAttr(kSideEffectIO, api::MakeValue(true)); }
+
+bool ImageSummary::get_side_effect_io() const {
+  auto value_ptr = GetAttr(kSideEffectIO);
+  return GetValue<bool>(value_ptr);
+}
+
+void ImageSummary::Init() { this->set_side_effect_io(); }
+
+class MIND_API ImageSummaryInfer : public abstract::OpInferBase {
+ public:
+  BaseShapePtr InferShape(const PrimitivePtr &primitive,
+                          const std::vector<AbstractBasePtr> &input_args) const override {
+    primitive->AddAttr("dyn_input_sizes", MakeValue(std::vector<int64_t>{-1, 1}));
+    return ImageSummaryInferShape(primitive, input_args);
+  }
+
+  TypePtr InferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
+    MS_EXCEPTION_IF_NULL(primitive);
+    // check
+    CheckAndConvertUtils::CheckSummaryParam(input_args[0], input_args[1], primitive->name());
+    return kInt32;
+  }
+};
+
+REGISTER_PRIMITIVE_OP_INFER_IMPL(ImageSummary, prim::kPrimImageSummary, ImageSummaryInfer, false);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/image_summary.h

@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2024. All rights reserved.
+ *
+ * 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_CORE_OPS_IMAGE_SUMMARY_H_
+#define MINDSPORE_CORE_OPS_IMAGE_SUMMARY_H_
+#include <map>
+#include <memory>
+#include <string>
+
+#include "mindapi/base/types.h"
+#include "ops/base_operator.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameImageSummary = "ImageSummary";
+
+/// \brief Outputs a tensor to a protocol buffer through a tensor summary operator.
+/// Refer to Python API @ref mindspore.ops.ImageSummary for more details.
+class MIND_API ImageSummary : public BaseOperator {
+ public:
+  MIND_API_BASE_MEMBER(ImageSummary);
+  /// \brief Constructor.
+  ImageSummary() : BaseOperator(kNameImageSummary) {}
+  /// \brief Init.
+  void Init();
+  /// \brief Set side_effect_io.
+  void set_side_effect_io();
+  /// \brief Get side_effect_io.
+  ///
+  /// \return side_effect_io.
+  bool get_side_effect_io() const;
+};
+}  // namespace ops
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CORE_OPS_IMAGE_SUMMARY_H_

mindspore/python/mindspore/ops/operations/debug_ops.py

@@ -116,7 +116,7 @@ class ScalarSummary(Primitive):
         _cache_summary_data(self.name, args[0], args[1])
 
 
-class ImageSummary(PrimitiveWithInfer):
+class ImageSummary(Primitive):
     """
     This operator will put an image tensor to a summary file with protocol buffer format. It must be used with
     SummaryRecord or SummaryCollector, which specify the directory of the summary file. The summary file can
@@ -163,18 +163,6 @@ class ImageSummary(PrimitiveWithInfer):
         self.add_prim_attr("channel_name", "ms_image_summary")
         self.add_prim_attr("dyn_input_sizes", [-1, 1])
 
-    def __infer__(self, name, value):
-        _check_summary_param(name, value, self.__class__.__name__)
-
-        # The shape dim of image should be 4.
-        v_shape = value['shape']
-        image_dim = 4
-        if len(v_shape) != image_dim:
-            raise ValueError(f"For '{self.name}', the dimension of 'value' must be {image_dim},"
-                             f" but got {len(v_shape)}.")
-
-        return SUMMARY_RETURN_VALUE
-
     def __call__(self, *args):
         _cache_summary_data(self.name, args[0], args[1])
 
@@ -320,7 +308,7 @@ class TensorDump(Primitive):
         TENSORDUMP_ID += 1
 
 
-class HistogramSummary(PrimitiveWithInfer):
+class HistogramSummary(Primitive):
     """
     This operator will calculate the histogram of a tensor and put it to a summary file with protocol buffer format.
     It must be used with SummaryRecord or SummaryCollector, which specify the directory of the summary file.
@@ -373,17 +361,6 @@ class HistogramSummary(PrimitiveWithInfer):
         self.add_prim_attr("channel_name", "ms_histogram_summary")
         self.add_prim_attr("dyn_input_sizes", [-1, 1])
 
-    def __infer__(self, name, value):
-        _check_summary_param(name, value, self.__class__.__name__)
-
-        v_shape = value['shape']
-        # In the summary, the histogram value should be a tensor whose shape is not [].
-        if not v_shape:
-            raise ValueError(f"For '{self.name}', the type of 'value' must be tensor, "
-                             f"its shape should not be [], but got {v_shape}.")
-
-        return SUMMARY_RETURN_VALUE
-
     def __call__(self, *args):
         _cache_summary_data(self.name, args[0], args[1])
 

tests/st/summary/test_summary_ops.py

@@ -21,6 +21,7 @@ import time
 import numpy as np
 import pytest
 
+import mindspore as ms
 from mindspore import nn, Tensor, context
 from mindspore.common.initializer import Normal
 from mindspore.train import Loss
@@ -50,6 +51,7 @@ class LeNet5(nn.Cell):
 
         self.scalar_summary = P.ScalarSummary()
         self.image_summary = P.ImageSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.tensor_summary = P.TensorSummary()
         self.channel = Tensor(num_channel)
 
@@ -57,6 +59,7 @@ class LeNet5(nn.Cell):
         """construct"""
         self.image_summary('x', x)
         self.tensor_summary('x', x)
+        self.histogram_summary('x', x)
         x = self.conv1(x)
         x = self.relu(x)
         x = self.max_pool2d(x)
@@ -112,10 +115,12 @@ class TestSummaryOps:
         summary_data = _get_summary_tensor_data()
         image_data = summary_data.get('x[:Image]').asnumpy()
         tensor_data = summary_data.get('x[:Tensor]').asnumpy()
+        histogram_data = summary_data.get('x[:Histogram]').asnumpy()
         x_fc3 = summary_data.get('x_fc3[:Scalar]').asnumpy()
 
         assert np.allclose(expected_data, image_data)
         assert np.allclose(expected_data, tensor_data)
+        assert np.allclose(expected_data, histogram_data)
         assert not np.allclose(0, x_fc3)
 
     @pytest.mark.level0
@@ -140,10 +145,12 @@ class TestSummaryOps:
         summary_data = _get_summary_tensor_data()
         image_data = summary_data.get('x[:Image]').asnumpy()
         tensor_data = summary_data.get('x[:Tensor]').asnumpy()
+        histogram_data = summary_data.get('x[:Histogram]').asnumpy()
         x_fc3 = summary_data.get('x_fc3[:Scalar]').asnumpy()
 
         assert np.allclose(expected_data, image_data)
         assert np.allclose(expected_data, tensor_data)
+        assert np.allclose(expected_data, histogram_data)
         assert not np.allclose(0, x_fc3)
 
     @pytest.mark.level0
@@ -169,10 +176,41 @@ class TestSummaryOps:
         summary_data = _get_summary_tensor_data()
         image_data = summary_data.get('x[:Image]').asnumpy()
         tensor_data = summary_data.get('x[:Tensor]').asnumpy()
+        histogram_data = summary_data.get('x[:Histogram]').asnumpy()
         x_fc3 = summary_data.get('x_fc3[:Scalar]').asnumpy()
 
         assert np.allclose(expected_data, image_data)
         assert np.allclose(expected_data, tensor_data)
+        assert np.allclose(expected_data, histogram_data)
         assert not np.allclose(0, x_fc3)
 
         del os.environ['GRAPH_OP_RUN']
+
+    @pytest.mark.level0
+    @pytest.mark.platform_x86_ascend_training
+    @pytest.mark.platform_arm_ascend_training
+    @pytest.mark.platform_x86_gpu_training
+    @pytest.mark.env_onecard
+    @security_off_wrap
+    def test_dynamic_shape_summary_ops(self):
+        context.set_context(mode=context.GRAPH_MODE, device_id=self.device_id)
+        ds_train = create_mnist_dataset('train', num_samples=1, batch_size=1)
+        ds_train_iter = ds_train.create_dict_iterator()
+        expected_data = next(ds_train_iter)['image'].asnumpy()
+
+        net = LeNet5()
+        dynamic_shape = Tensor(shape=[None, None, None, None], dtype=ms.float32)
+        net.set_inputs(dynamic_shape)
+        net(Tensor(expected_data))
+
+        time.sleep(0.5)
+        summary_data = _get_summary_tensor_data()
+        image_data = summary_data.get('x[:Image]').asnumpy()
+        tensor_data = summary_data.get('x[:Tensor]').asnumpy()
+        histogram_data = summary_data.get('x[:Histogram]').asnumpy()
+        x_fc3 = summary_data.get('x_fc3[:Scalar]').asnumpy()
+
+        assert np.allclose(expected_data, image_data)
+        assert np.allclose(expected_data, tensor_data)
+        assert np.allclose(expected_data, histogram_data)
+        assert not np.allclose(0, x_fc3)