Fixed dict grad in pynative and jit scenario.

mindspore/ccsrc/pipeline/jit/ps/action.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019-2023 Huawei Technologies Co., Ltd
+ * Copyright 2019-2024 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.
@@ -62,6 +62,7 @@
 #include "include/backend/debug/data_dump/dump_json_parser.h"
 #include "backend/common/graph_kernel/graph_kernel_flags.h"
 #include "include/backend/debug/profiler/profiling.h"
+#include "frontend/optimizer/fallback_rewriter.h"
 #if defined(__linux__) && defined(WITH_BACKEND)
 #include "include/backend/distributed/cluster/cluster_context.h"
 #include "include/backend/distributed/ps/ps_context.h"
@@ -957,6 +958,18 @@ bool GetJitBpropGraph(const ResourcePtr &resource) {
   return pynative::PyNativeExecutor::GetInstance()->grad_executor()->jit()->GetJitGradGraph(resource);
 }
 
+bool RewriterAfterOptAPassAfterJitBprop(const ResourcePtr &resource) {
+  MS_EXCEPTION_IF_NULL(resource);
+  FuncGraphPtr func_graph = resource->func_graph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+  auto context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context);
+  context->set_not_convert_jit(false);
+  (void)mindspore::opt::RewriterAfterOptA(func_graph, resource);
+  UpdateArgsSpec(func_graph, resource);
+  return true;
+}
+
 bool EliminateSpecialOpNode(const ResourcePtr &resource) {
   MS_EXCEPTION_IF_NULL(resource);
   if (resource->manager() == nullptr) {
@@ -1648,6 +1661,9 @@ std::vector<ActionItem> VmPipeline(const ResourcePtr &resource) {
     // Eliminate forward cnode for grad graph
     (void)actions.emplace_back(std::make_pair(kGetJitBpropGraph, GetJitBpropGraph));
 
+    // Rewriter(dict convert pyexecute) after jit bprop.
+    (void)actions.emplace_back(std::make_pair(kRewriterAfterJitBprop, RewriterAfterOptAPassAfterJitBprop));
+
     // Eliminate the virtual mirror node
     (void)actions.emplace_back(std::make_pair(kEliminateSpecialOpNode, EliminateSpecialOpNode));
     (void)actions.emplace_back(std::make_pair(kValidate, ValidateAction));

mindspore/ccsrc/pipeline/jit/ps/pass.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019-2023 Huawei Technologies Co., Ltd
+ * Copyright 2019-2024 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.
@@ -94,7 +94,6 @@ using CompileGraphs = compile::CompileGraphs;
 using abstract::AnalysisResult;
 using mindspore::abstract::AnalysisContextPtr;
 using mindspore::validator::Validate;
-namespace {
 void UpdateArgsSpec(const FuncGraphPtr &func_graph, const ResourcePtr &resource) {
   MS_EXCEPTION_IF_NULL(func_graph);
   MS_EXCEPTION_IF_NULL(resource);
@@ -105,7 +104,6 @@ void UpdateArgsSpec(const FuncGraphPtr &func_graph, const ResourcePtr &resource)
                        [](const AnfNodePtr &p) { return p->abstract(); });
   resource->set_args_abs(args_abs);
 }
-}  // namespace
 
 bool PyInterpretToExecutePass(const ResourcePtr &resource) {
   const auto allow_fallback_runtime = (fallback::GetJitSyntaxLevel() == kLax);
@@ -149,6 +147,11 @@ bool TransformTopGraphPass(const ResourcePtr &resource) {
 }
 
 bool RewriterAfterOptAPass(const ResourcePtr &resource) {
+  auto context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context);
+  if (context->not_convert_jit()) {
+    return true;
+  }
   MS_EXCEPTION_IF_NULL(resource);
   FuncGraphPtr func_graph = resource->func_graph();
   MS_EXCEPTION_IF_NULL(func_graph);

mindspore/ccsrc/pipeline/jit/ps/pass.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2024 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.
@@ -58,6 +58,7 @@ FuncGraphPtr PrimBpOptPassStep2(const opt::irpass::OptimizeIRPassLib &irpass, co
                                 const std::vector<bool> &need_grad_flags);
 FuncGraphPtr JitBpropGraphPass(const ResourcePtr &resource, bool need_renormalize);
 FuncGraphPtr FinalBpropGraphPass(const ResourcePtr &resource, bool has_control_flow);
+void UpdateArgsSpec(const FuncGraphPtr &func_graph, const ResourcePtr &resource);
 }  // namespace pipeline
 }  // namespace mindspore
 

mindspore/ccsrc/pipeline/jit/ps/resource.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019-2023 Huawei Technologies Co., Ltd
+ * Copyright 2019-2024 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.
@@ -70,6 +70,7 @@ const char kPipelineSplit[] = "pipeline_split";
 const char kOptimize[] = "optimize";
 const char kAutoMonadReorder[] = "auto_monad_reorder";
 const char kGetJitBpropGraph[] = "get_jit_bprop_graph";
+const char kRewriterAfterJitBprop[] = "rewriter_after_jit_bprop_graph";
 const char kEliminateSpecialOpNode[] = "eliminate_special_op_node";
 const char kValidate[] = "validate";
 const char kLoadMindir[] = "load_mindir";

mindspore/ccsrc/pipeline/pynative/grad/auto_grad.cc

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * Copyright 2022-2024 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.
@@ -1785,7 +1785,7 @@ AnfNodePtr AutoGradCellImpl::TraceShape(const FunctionNodePtr &fn, const ValuePt
     }
     return new_din;
   } else if (out_value->isa<ValueDictionary>()) {
-    TraceShapeForDict(fn, out_value, out_abs, input_tensor, din);
+    return TraceShapeForDict(fn, out_value, out_abs, input_tensor, din);
   }
   MS_LOG(DEBUG) << "Get non tensor input " << out_value->ToString();
   return BuildSpecialNode(ad_param()->tape_, out_value, out_abs, SpecialType::kZerosLikeType);

mindspore/ccsrc/pybind_api/utils/ms_context_py.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2023 Huawei Technologies Co., Ltd
+ * Copyright 2020-2024 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.
@@ -149,6 +149,7 @@ void RegMsContext(const py::module *m) {
          "Return whether this MindSpore package supports specified device.")
     .def("_enable_cell_recompute", &mindspore::MsContext::EnableCellRecompute, "Set a cell to be recomputed.")
     .def("load_plugin_error", &mindspore::MsContext::GetLoadPluginErrorStr,
-         "Return error message when loading plugins for this MindSpore package.");
+         "Return error message when loading plugins for this MindSpore package.")
+    .def("_set_not_convert_jit", &mindspore::MsContext::set_not_convert_jit, "Set not convert jit.");
 }
 }  // namespace mindspore

mindspore/core/utils/ms_context.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019-2023 Huawei Technologies Co., Ltd
+ * Copyright 2019-2024 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.
@@ -265,6 +265,9 @@ class MS_CORE_API MsContext {
 
   std::string GetLoadPluginErrorStr() const { return load_plugin_error_(); }
 
+  void set_not_convert_jit(bool not_convert_jit) { not_convert_jit_ = not_convert_jit; }
+  bool not_convert_jit() { return not_convert_jit_; }
+
  private:
   void RefreshExecutionMode();
   void RefreshMemoryOffload();
@@ -296,6 +299,7 @@ class MS_CORE_API MsContext {
   static std::map<std::string, std::string> &PluginPathMap();
   enum CellReuseLevel cell_reuse_level_ = CellReuseLevel::kNoCellReuse;
   bool cell_recompute_{false};
+  bool not_convert_jit_{false};
 };
 
 // set method implementation for type bool/int/uint32_t/float/std::string

mindspore/python/mindspore/ops/composite/base.py

@@ -1,6 +1,6 @@
 # This is the Python adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
 #
-# Copyright 2020-2023 Huawei Technologies Co., Ltd
+# Copyright 2020-2024 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.
@@ -31,7 +31,7 @@ from mindspore._c_expression import GradOperation_, HyperMap_, Map_, MultitypeFu
     ListClear_, ListReverse_, ListExtend_, DictClear_, DictHasKey_, DictUpdate_, DictFromKeys_, \
     ZerosLike_, TensorIndexGetitem_, TensorIndexSetitem_, ListAdd_, DictSetItem_, \
     HandleBoolTensor_, HandleEmptySlice_, PreSetitemByTuple_, HandleScalarTensorIndex_, StarredGetItem_,\
-    StarredUnpack_, StarredUnpackMerge_, IterConverter_, HasNext_, Next_
+    StarredUnpack_, StarredUnpackMerge_, IterConverter_, HasNext_, Next_, MSContext
 from mindspore.common import dtype as mstype
 from mindspore.common.api import jit, _pynative_executor, _wrap_func
 from mindspore.common.api import _add_flags, _core
@@ -380,6 +380,7 @@ class GradOperation(GradOperation_):
                 out = _grads_divided_by_device_num_if_recomputation(out)
                 return out
         else:
+            MSContext.get_instance()._set_not_convert_jit(True)
             grad_.pynative_ = True
             if not _pynative_executor.enable_grad():
                 raise RuntimeError("In no_grad context, you can not calculate gradient")
@@ -610,6 +611,7 @@ class _Grad(GradOperation_):
                     return out, res[1:]
                 return out
         else:
+            MSContext.get_instance()._set_not_convert_jit(True)
             if not _pynative_executor.enable_grad():
                 raise RuntimeError("In no_grad context, you can not calculate gradient")
             grad_.pynative_ = True

tests/st/fallback/test_graph_fallback_runtime_dict.py

@@ -1160,3 +1160,50 @@ def test_dict_inner_method_overrrided_3():
         return obj.to_tuple()
     ms_out = foo()
     assert ms_out == (1, 2)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_jit_dict_grad():
+    """
+    Feature: Return dict in forward graph.
+    Description: Support grad for dict return in pynative mode.
+    Expectation: No exception.
+    """
+
+    @ms.jit
+    def dict_net(a):
+        x = {'a': a, 'b': 2}
+        return x
+
+    ms.set_context(mode=ms.PYNATIVE_MODE)
+    out = ops.grad(dict_net)(ms.Tensor([1]))
+    assert out == 1
+    ms.set_context(mode=ms.GRAPH_MODE)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_jit_dict_grad_2():
+    """
+    Feature: Return dict in forward graph.
+    Description: Support grad for dict return in pynative mode.
+    Expectation: No exception.
+    """
+
+    @ms.jit
+    def dict_net(a):
+        x = {'a': a, 'b': 2}
+        return x
+
+    ms.set_context(mode=ms.PYNATIVE_MODE)
+    grad = ops.GradOperation()
+    out = grad(dict_net)(ms.Tensor([1]))
+    assert out == 1
+    ms.set_context(mode=ms.GRAPH_MODE)

tests/st/pynative/grad/test_common_grad.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2024 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.
@@ -122,3 +122,37 @@ def test_network_with_dict_output():
     ms_grad = GradOfFirstInput(ms_net, True)
     grad_out = ms_grad(Tensor(x), out)
     assert np.allclose(x, grad_out.asnumpy())
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_jit_network_with_dict_output():
+    """
+    Feature: Test sens dict in jit
+    Description: Net out is dict in jit
+    Expectation: Success
+    """
+    class DicNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = P.ReLU()
+
+        @jit
+        def construct(self, x):
+            y = self.relu(x)
+            out = {'a': y}
+            return out
+
+    x = np.array([[0.8, 0.6, 0.2], [1.8, 1.3, 1.1]])
+    ms_net = DicNet()
+    # No sens
+    ms_grad = GradOfFirstInput(ms_net, False)
+    grad_out = ms_grad(Tensor(x))
+    assert np.allclose(np.ones_like(x), grad_out.asnumpy())
+
+    # Have sens
+    out = ms_net(Tensor(x))
+    ms_grad = GradOfFirstInput(ms_net, True)
+    grad_out = ms_grad(Tensor(x), out)
+    assert np.allclose(x, grad_out.asnumpy())