!20986 revert the modification of ExpandJPrim

Merge pull request !20986 from huangbingjian/revert_jprim

mindspore/ccsrc/frontend/optimizer/irpass/gradient_eliminate.cc

@@ -65,28 +65,37 @@ AnfNodePtr ExpandJ(const ValueNodePtr &vnode, const pipeline::ResourceBasePtr &r
 }
 }  // namespace internal
 
-bool ExpandJPrim::operator()(const FuncGraphPtr &root, const OptimizerPtr &optimizer) {
-  AnfNodePtr ret = root->get_return();
-  MS_EXCEPTION_IF_NULL(ret);
-  std::vector<AnfNodePtr> all_nodes = DeepScopedGraphSearch(ret);
-
+bool ExpandJPrim::operator()(const FuncGraphPtr &func_graph, const OptimizerPtr &optimizer) {
+  // Search all j nodes.
+  GetJPrim(func_graph);
+  // Get j nodes that don't have embed j nodes.
+  std::vector<CNodePtr> todo;
+  // If graph also contains J(FuncGraph) or J(Primitive), then ignore this graph.
+  // ExpandJ innermost graph or primitive first.
+  std::copy_if(j_nodes_.begin(), j_nodes_.end(), std::back_inserter(todo),
+               [](const CNodePtr &j_node) { return !internal::CheckIfEmbedJ(j_node); });
+  // Expand j nodes that don't have embed j nodes.
   bool change = false;
   auto manager = optimizer->manager();
-  for (auto &node : all_nodes) {
-    if (IsPrimitiveCNode(node, prim::kPrimJ)) {
-      auto j_node = node->cast<CNodePtr>();
-      // If graph also contains J(FuncGraph) or J(Primitive), then ignore this graph.
-      // ExpandJ innermost graph or primitive first.
-      if (internal::CheckIfEmbedJ(j_node)) {
-        continue;
-      }
-      auto expanded_j = internal::ExpandJ(j_node->input(1)->cast<ValueNodePtr>(), optimizer->resource());
-      manager->Replace(j_node, expanded_j);
-      change = true;
-    }
+  for (auto &j_node : todo) {
+    auto expanded_j = internal::ExpandJ(j_node->input(1)->cast<ValueNodePtr>(), optimizer->resource());
+    manager->Replace(j_node, expanded_j);
+    change = true;
   }
   return change;
 }
+
+void ExpandJPrim::GetJPrim(const FuncGraphPtr &func_graph) {
+  j_nodes_.clear();
+  AnfNodePtr ret = func_graph->get_return();
+  MS_EXCEPTION_IF_NULL(ret);
+  std::vector<AnfNodePtr> all_nodes = DeepScopedGraphSearch(ret);
+  for (auto &node : all_nodes) {
+    if (IsPrimitiveCNode(node, prim::kPrimJ)) {
+      j_nodes_.push_back(node->cast<CNodePtr>());
+    }
+  }
+}
 }  // namespace irpass
 }  // namespace opt
 }  // namespace mindspore

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

@@ -36,7 +36,11 @@ class ExpandJPrim {
  public:
   ExpandJPrim() = default;
   virtual ~ExpandJPrim() = default;
-  bool operator()(const FuncGraphPtr &root, const OptimizerPtr &optimizer);
+  bool operator()(const FuncGraphPtr &func_graph, const OptimizerPtr &optimizer);
+  void GetJPrim(const FuncGraphPtr &func_graph);
+
+ private:
+  std::vector<CNodePtr> j_nodes_;
 };
 }  // namespace irpass
 }  // namespace opt

tests/st/high_grad/test_highgrad_train.py

@@ -0,0 +1,115 @@
+# 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 pytest
+import numpy as np
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import TrainOneStepCell
+from mindspore.nn.optim import Momentum
+from mindspore.ops.composite import GradOperation
+from mindspore.common import ParameterTuple
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class _Grad(nn.Cell):
+    def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
+        super().__init__()
+        self.network = network
+        self.grad = grad
+        self.sens_param = self.grad.sens_param
+        self.wrt_params = wrt_params
+        self.real_inputs_count = real_inputs_count
+        if self.wrt_params:
+            self.params = ParameterTuple(self.network.trainable_params())
+
+    def construct(self, *inputs):
+        if self.real_inputs_count is None or self.sens_param is False:
+            if self.wrt_params:
+                return self.grad(self.network, self.params)(*inputs)
+            return self.grad(self.network)(*inputs)
+
+        real_inputs = inputs[:self.real_inputs_count]
+        sense_param_inputs = inputs[self.real_inputs_count:]
+        if self.wrt_params:
+            return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
+        return self.grad(self.network)(*real_inputs, sense_param_inputs)
+
+
+class GradOfFirstInput(_Grad):
+    """
+    get grad of first input
+    """
+
+    def __init__(self, network, sens_param=True, real_inputs_count=None):
+        super().__init__(grad=GradOperation(sens_param=sens_param),
+                         network=network, real_inputs_count=real_inputs_count)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.mul = ops.Mul()
+        self.add = ops.TensorAdd()
+        weight_np = np.array([2]).astype(np.float32)
+        bias_np = np.array([1]).astype(np.float32)
+        self.weight = Parameter(Tensor(weight_np),
+                                name='weight', requires_grad=True)
+        self.bias = Parameter(Tensor(bias_np),
+                              name="bias", requires_grad=True)
+
+    def construct(self, x):
+        xw = self.mul(x, self.weight)
+        output = self.add(xw, self.bias)
+        return output
+
+
+class WithLossCellLocal(nn.Cell):
+    def __init__(self, grad, loss):
+        super(WithLossCellLocal, self).__init__(auto_prefix=False)
+        self.grad = grad
+        self.loss = loss
+
+    def construct(self, data, label):
+        out = self.grad(data)
+        return self.loss(out, label)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_high_grad_train():
+    x_pure = np.random.randint(-10, 100, 32)
+    x_train = x_pure.astype(np.float32)
+    y_noise = 3 * x_pure + 2 + np.random.randn(32) / 10
+    y_train = y_noise.astype(np.float32)
+    net = Net()
+    grad_net = GradOfFirstInput(net, sens_param=False)
+    epoch = 2
+    momentum = 0.0
+    learning_rate = 0.001
+    optimizer = Momentum(filter(lambda x: x.requires_grad,
+                                grad_net.get_parameters()), learning_rate, momentum)
+    criterion = nn.loss.MSELoss()
+    net_with_criterion = WithLossCellLocal(grad_net, criterion)
+    train_network = TrainOneStepCell(net_with_criterion, optimizer)
+    train_network.set_train()
+    for i in range(epoch):
+        train_network(Tensor([x_train[i]]), Tensor([y_train[i]]))