!17297 dynamic_shape_pipeline

Merge pull request !17297 from wanyiming/dynamic_shape_pipeline

mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc

@@ -411,6 +411,11 @@ void AscendBackendUBFusionOptimization(const std::shared_ptr<session::KernelGrap
     MS_LOG(INFO) << "UBFusion is not enable, skip";
     return;
   }
+
+  if (kernel_graph->is_dynamic_shape()) {
+    MS_LOG(WARNING) << "Dynamic shape skip fusion";
+    return;
+  }
   bool save_graphs = context_ptr->get_param<bool>(MS_CTX_SAVE_GRAPHS_FLAG);
   if (save_graphs) {
     std::string file_name = "hwopt_d_ub_fusion_before_graph_" + std::to_string(kernel_graph->graph_id()) + ".ir";

mindspore/ccsrc/backend/optimizer/ascend/format_type/insert_cast.cc

@@ -54,7 +54,11 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo
     auto imm = std::make_shared<Int64Imm>(output_idx);
     idx->set_abstract(std::make_shared<abstract::AbstractScalar>(imm));
     auto getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), cnode, idx});
-    AnfAlgo::SetOutputTypeAndDetailShape({origin_type}, {origin_shape}, getitem.get());
+    auto abs = cnode->abstract()->cast<abstract::AbstractTuplePtr>();
+    MS_EXCEPTION_IF_NULL(abs);
+    auto abs_i = abs->elements()[output_idx];
+    MS_EXCEPTION_IF_NULL(abs_i);
+    getitem->set_abstract(abs_i);
     const auto dev_fmt = AnfAlgo::GetOutputFormat(cnode, output_idx);
     const auto device_type = AnfAlgo::GetOutputDeviceDataType(cnode, output_idx);
     if (origin_type != device_type) {

mindspore/ccsrc/backend/optimizer/common/helper.cc

@@ -492,9 +492,11 @@ CNodePtr CreatTupleGetItemNode(const FuncGraphPtr &func_graph, const AnfNodePtr
   CNodePtr tuple_getitem = func_graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx});
   MS_EXCEPTION_IF_NULL(tuple_getitem);
   tuple_getitem->set_scope(node->scope());
-  auto origin_shape = AnfAlgo::GetOutputDetailShape(node, output_idx);
-  TypeId origin_type = AnfAlgo::GetOutputInferDataType(node, output_idx);
-  AnfAlgo::SetOutputTypeAndDetailShape({origin_type}, {origin_shape}, tuple_getitem.get());
+  auto abs = node->abstract()->cast<abstract::AbstractTuplePtr>();
+  MS_EXCEPTION_IF_NULL(abs);
+  auto abs_i = abs->elements()[output_idx];
+  MS_EXCEPTION_IF_NULL(abs_i);
+  tuple_getitem->set_abstract(abs_i);
   return tuple_getitem;
 }
 

mindspore/ccsrc/runtime/device/ascend/executor/ai_cpu_dynamic_kernel.cc

@@ -165,10 +165,6 @@ bool AiCpuDynamicKernel::UpdateExtInfo() {
 }
 
 bool AiCpuDynamicKernel::UpdateOutputShapeFromExtInfo() {
-  if (input_num_ == 0) {
-    MS_LOG(WARNING) << "input num is 0";
-    return true;
-  }
   MS_LOG(INFO) << "UpdateOutputShapeFromExtInfo start";
   auto ret = rtMemcpy(ext_info_handler_->GetExtInfo(), ext_info_handler_->GetExtInfoLen(), ext_info_addr_dev_,
                       ext_info_size_, RT_MEMCPY_DEVICE_TO_HOST);

mindspore/ccsrc/runtime/device/executor/dynamic_kernel.cc

@@ -73,9 +73,6 @@ void DynamicKernel::RebuildDependTensor() {
 }
 
 void DynamicKernel::InferShape() {
-  if (!is_input_dynamic_shape_ && is_output_dynamic_shape_ && !have_depends()) {
-    return;
-  }
   auto cnode = cnode_ptr_.lock();
   MS_EXCEPTION_IF_NULL(cnode);
   MS_LOG(INFO) << "InferShape start, node:" << cnode->fullname_with_scope();

mindspore/ccsrc/utils/utils.h

@@ -602,7 +602,7 @@ const std::set<TypeId> kFloatDataTypeSet = {kNumberTypeFloat16, kNumberTypeFloat
 
 const std::set<std::string> kComputeDepend = {kUniqueOpName,       kComputeAccidentalHitsOpName, kSubAndFilterOpName,
                                               kPadAndShiftOpName,  kCTCGreedyDecoderOpName,      kDropoutGenMaskOpName,
-                                              kMaskedSelectOpName, kDynamicStitchOpName};
+                                              kMaskedSelectOpName, kDynamicStitchOpName,         kGetNextOpName};
 
 const std::set<std::string> k3DFormatSet = {kOpFormat_NCDHW, kOpFormat_NDC1HWC0, kOpFormat_FRACTAL_Z_3D,
                                             kOpFormat_NDHWC, kOpFormat_DHWCN,    kOpFormat_DHWNC};

mindspore/core/abstract/utils.cc

@@ -361,13 +361,11 @@ AbstractBasePtr MakeAbstractTensor(const ShapePtr &shape, const TypePtr &type) {
   ShapeVector min_shape_vec;
   ShapeVector max_shape_vec;
 
-  if (shape->IsDynamic()) {
-    if (!shape->min_shape().empty()) {
-      min_shape_vec = shape->min_shape();
-    }
-    if (!shape->max_shape().empty()) {
-      max_shape_vec = shape->max_shape();
-    }
+  if (!shape->min_shape().empty()) {
+    min_shape_vec = shape->min_shape();
+  }
+  if (!shape->max_shape().empty()) {
+    max_shape_vec = shape->max_shape();
   }
 
   auto ret_shape = std::make_shared<abstract::Shape>(ret_vec, min_shape_vec, max_shape_vec);

mindspore/core/base/core_ops.h

@@ -32,6 +32,7 @@ inline const std::unordered_map<std::string, ValuePtr> kSideEffectPropagate = {
   {mindspore::GRAPH_FLAG_SIDE_EFFECT_PROPAGATE, kValueOne},
 };
 
+constexpr auto kGetNext = "GetNext";
 constexpr auto kGather = "Gather";
 // Arithmetic
 constexpr auto kScalarAdd = "ScalarAdd";
@@ -92,6 +93,9 @@ constexpr auto kDropoutGrad = "DropoutGrad";
 constexpr auto kConv2DTranspose = "Conv2DTranspose";
 
 // Here list all primitives used in backend or some special primitives used by core.
+// GetNext
+inline const PrimitivePtr kPrimGetNext = std::make_shared<Primitive>(kGetNext);
+
 // Arithmetic
 inline const PrimitivePtr kPrimScalarAdd = std::make_shared<Primitive>(kScalarAdd);
 inline const PrimitivePtr kPrimScalarSub = std::make_shared<Primitive>(kScalarSub);

mindspore/core/ops/getnext.cc

@@ -0,0 +1,96 @@
+/**
+ * Copyright 2021 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 <set>
+#include <string>
+#include <vector>
+#include <memory>
+#include <algorithm>
+#include "ops/getnext.h"
+#include "ops/op_utils.h"
+#include "utils/check_convert_utils.h"
+#include "utils/tensor_construct_utils.h"
+#include "abstract/primitive_infer_map.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+
+void GetShapeVector(const ValuePtr &shape_attr, std::vector<std::vector<int64_t>> *shape_vec) {
+  if (shape_attr == nullptr) {
+    return;
+  }
+  std::vector<ValuePtr> shape = shape_attr->isa<ValueTuple>() ? shape_attr->cast<ValueTuplePtr>()->value()
+                                                              : shape_attr->cast<ValueListPtr>()->value();
+  for (ValuePtr shape_elements : shape) {
+    std::vector<ValuePtr> shape_elements_list = shape_elements->isa<ValueTuple>()
+                                                  ? shape_elements->cast<ValueTuplePtr>()->value()
+                                                  : shape_elements->cast<ValueListPtr>()->value();
+    std::vector<int64_t> shape_vec_item;
+    (void)std::transform(std::begin(shape_elements_list), std::end(shape_elements_list),
+                         std::back_inserter(shape_vec_item),
+                         [](const ValuePtr &e) -> int64_t { return GetValue<int64_t>(e); });
+    shape_vec->push_back(shape_vec_item);
+  }
+}
+
+bool IsDynamic(const std::vector<ShapeVector> &shape) {
+  for (auto shape_vec : shape) {
+    if (std::find(shape_vec.begin(), shape_vec.end(), -1) != shape_vec.end()) {
+      return true;
+    }
+  }
+  return false;
+}
+
+abstract::AbstractBasePtr GetnextInferShape(const PrimitivePtr &primitive,
+                                            const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto types = GetValue<std::vector<TypePtr>>(primitive->GetAttr("types"));
+  ValuePtr shape_attr = primitive->GetAttr("shapes");
+  ValuePtr min_shape_attr = primitive->GetAttr("min_shapes");
+  ValuePtr max_shape_attr = primitive->GetAttr("max_shapes");
+
+  std::vector<ShapeVector> shape;
+  std::vector<ShapeVector> min_shape;
+  std::vector<ShapeVector> max_shape;
+
+  GetShapeVector(shape_attr, &shape);
+  GetShapeVector(min_shape_attr, &min_shape);
+  GetShapeVector(max_shape_attr, &max_shape);
+
+  bool is_dynamic = IsDynamic(shape);
+
+  AbstractBasePtrList output;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    auto ret_shape = !min_shape.empty() && !max_shape.empty() && is_dynamic
+                       ? std::make_shared<abstract::Shape>(shape[i], min_shape[i], max_shape[i])
+                       : std::make_shared<abstract::Shape>(shape[i]);
+    auto element = std::make_shared<abstract::AbstractScalar>(kAnyValue, types[i]);
+    auto tensor = std::make_shared<abstract::AbstractTensor>(element, ret_shape);
+    output.push_back(tensor);
+  }
+  return std::make_shared<abstract::AbstractTuple>(output);
+}
+}  // namespace
+
+AbstractBasePtr GetNextInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                             const std::vector<AbstractBasePtr> &input_args) {
+  return GetnextInferShape(primitive, input_args);
+}
+REGISTER_PRIMITIVE_EVAL_IMPL(GetNext, prim::kPrimGetNext, GetNextInfer, nullptr, true);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/getnext.h

@@ -0,0 +1,43 @@
+
+/**
+ * Copyright 2020-2021 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_CORE_OPS_GETNEXT_H_
+#define MINDSPORE_CORE_OPS_GETNEXT_H_
+#include <map>
+#include <vector>
+#include <string>
+#include <memory>
+#include "ops/primitive_c.h"
+#include "abstract/abstract_value.h"
+#include "utils/check_convert_utils.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameGetNext = prim::kGetNext;
+class GetNext : public PrimitiveC {
+ public:
+  GetNext() : PrimitiveC(prim::kPrimGetNext->name()) {}
+  ~GetNext() = default;
+  MS_DECLARE_PARENT(GetNext, PrimitiveC);
+  void Init() {}
+};
+AbstractBasePtr GetNextInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                             const std::vector<AbstractBasePtr> &input_args);
+using PrimMulPtr = std::shared_ptr<GetNext>;
+}  // namespace ops
+}  // namespace mindspore
+#endif  // MINDSPORE_CORE_OPS_GETNEXT_H_

mindspore/ops/operations/nn_ops.py

@@ -3498,7 +3498,7 @@ class FastGeLU(PrimitiveWithInfer):
         return input_x
 
 
-class GetNext(PrimitiveWithInfer):
+class GetNext(Primitive):
     """
     Returns the next element in the dataset queue.
 
@@ -3545,12 +3545,6 @@ class GetNext(PrimitiveWithInfer):
         validator.check("types length", len(types), "shapes length", len(shapes), Rel.EQ, self.name)
         validator.check_value_type("output_num", output_num, [int], self.name)
 
-    def infer_shape(self):
-        return tuple(self.shapes)
-
-    def infer_dtype(self):
-        return tuple(self.types)
-
 
 class PReLU(PrimitiveWithInfer):
     r"""

mindspore/train/dataset_helper.py

@@ -60,13 +60,18 @@ class _DataWrapper(nn.Cell):
     dataset channel 'queue_name' and performs the forward computation.
     """
 
-    def __init__(self, network, dataset_types, dataset_shapes, queue_name):
+    def __init__(self, network, dataset_types, dataset_shapes, queue_name, min_shapes=None, max_shapes=None):
         super(_DataWrapper, self).__init__(auto_prefix=False, flags=network.get_flags())
         # Also copy the flag in `network` construct
         flags = getattr(network.__class__.construct, "_mindspore_flags", {})
         self.info = (dataset_types, dataset_shapes)
         self.add_flags(**flags)
         self.get_next = P.GetNext(dataset_types, dataset_shapes, len(dataset_types), queue_name)
+        if min_shapes is not None and max_shapes is not None:
+            Validator.check_value_type("min_shapes", min_shapes, [list, tuple])
+            Validator.check_value_type("max_shapes", max_shapes, [list, tuple])
+            self.get_next.add_prim_attr("min_shapes", min_shapes)
+            self.get_next.add_prim_attr("max_shapes", max_shapes)
         self.network = network
 
     def construct(self):
@@ -74,9 +79,24 @@ class _DataWrapper(nn.Cell):
         return self.network(*outputs)
 
 
-def _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types, queue_name):
+def _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types, queue_name,
+                                    min_shapes=None, max_shapes=None):
     if not isinstance(network, _DataWrapper):
-        network = _DataWrapper(network, dataset_types, dataset_shapes, queue_name)
+        network = _DataWrapper(network, dataset_types, dataset_shapes, queue_name, min_shapes, max_shapes)
+    return network
+
+def has_dynamic_shape(dataset_shapes):
+    for shape in dataset_shapes:
+        if -1 in shape:
+            return True
+    return False
+
+def _generate_network_with_dataset(network, dataset_helper, queue_name):
+    dataset_types, dataset_shapes = dataset_helper.types_shapes()
+    (min_shapes, max_shapes) = (None, None) if not has_dynamic_shape(dataset_shapes) \
+        else dataset_helper.dynamic_min_max_shapes()
+    network = _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types,
+                                              queue_name, min_shapes, max_shapes)
     return network
 
 
@@ -151,8 +171,7 @@ def connect_network_with_dataset(network, dataset_helper):
        not context.get_context("enable_ge") and \
        context.get_context("device_target") in ("Ascend", "GPU"):
         dataset.__me_inited__ = True
-        dataset_types, dataset_shapes = dataset_helper.types_shapes()
-        network = _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types, queue_name)
+        network = _generate_network_with_dataset(network, dataset_helper, queue_name)
 
     if hasattr(dataset_iter, "sink_size") and \
        dataset_iter.sink_size == 1 and \
@@ -286,7 +305,7 @@ class _DatasetIter:
         self.release = dataset.__transfer_dataset__.release
         self.continue_send = dataset.__transfer_dataset__.continue_send
         self.get_data_info = dataset.__transfer_dataset__.get_data_info
-        self.dynamic_min_max_shapes = dataset.__transfer_dataset__.dynamic_min_max_shapes
+        self.dynamic_min_max_shapes = dataset.dynamic_min_max_shapes
         self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
 
     def __iter__(self):

tests/st/dynamic_shape/test_getnext_dynamic_pipeline.py

@@ -0,0 +1,81 @@
+# Copyright 2021 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.
+# ==============================================================================
+import numpy as np
+import pytest
+from mindspore import nn, context
+from mindspore import ops as P
+from mindspore.train import DatasetHelper, connect_network_with_dataset
+import mindspore.dataset as ds
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+def _exec_preprocess(network, is_train, dataset, dataset_sink_mode, sink_size=-1, epoch_num=1, dataset_helper=None):
+    if dataset_sink_mode and not is_train:
+        dataset.__loop_size__ = 1
+
+    if dataset_helper is None:
+        dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)
+
+    if dataset_sink_mode:
+        network = connect_network_with_dataset(network, dataset_helper)
+
+    network.set_train(is_train)
+
+    return dataset_helper, network
+
+
+def _eval_dataset_sink_process(network, valid_dataset):
+    dataset_helper, eval_network = _exec_preprocess(network, is_train=False, dataset=valid_dataset,
+                                                    dataset_sink_mode=True)
+    for inputs1, inputs2 in zip(dataset_helper, valid_dataset.create_dict_iterator()):
+        outputs = eval_network(*inputs1)
+        for elem1, (_, elem2) in zip(outputs, inputs2.items()):
+            assert elem1.shape == elem2.shape
+
+def dataset_generator():
+    for i in range(1, 10):
+        yield (
+            np.ones((32, i), dtype=np.float32), np.zeros((32, i, i, 3), dtype=np.int32),
+            np.ones((32,), dtype=np.float32),
+            np.ones((32, i, 8), dtype=np.float32), np.ones((32, 8, 8), dtype=np.float32))
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.relu = P.ReLU()
+
+    def construct(self, x1, x2, x3, x4, x5):
+        x1 = self.relu(x1)
+        x1 = self.relu(x1)
+
+        x2 = self.relu(x2)
+
+        x3 = self.relu(x3)
+        x3 = self.relu(x3)
+
+        x4 = self.relu(x4)
+
+        x5 = self.relu(x5)
+        return x1, x2, x3, x4, x5
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_getnext_dynamic_pipeline():
+    network = Net()
+    dataset = ds.GeneratorDataset(dataset_generator, ["data1", "data2", "data3", "data4", "data5"])
+    dataset.set_dynamic_columns(columns={"data1": [32, None], "data2": [32, None, None, 3],
+                                         "data3": [32], "data4": [32, None, 8], "data5": [32, 8, 8]})
+    _eval_dataset_sink_process(network, dataset)