support implicit type conversion for pynative mode

mindspore/ccsrc/operator/composite/do_signature.cc

@@ -31,12 +31,10 @@
 namespace mindspore {
 // namespace to support composite operators definition
 namespace prim {
-namespace {
-using PatternListType = std::initializer_list<BaseRef>;
 const std::map<TypeId, size_t> type_map = {{kNumberTypeBool, 1},    {kNumberTypeInt8, 2},    {kNumberTypeUInt8, 3},
                                            {kNumberTypeInt16, 4},   {kNumberTypeInt32, 5},   {kNumberTypeInt64, 6},
                                            {kNumberTypeFloat16, 7}, {kNumberTypeFloat32, 8}, {kNumberTypeFloat64, 9}};
-
+namespace {
 const std::vector<Signature> &GetSignature(const ValuePtr &function) {
   static const auto empty = std::vector<Signature>();
   if (function->isa<Primitive>() && function->cast<PrimitivePtr>()->has_signature()) {

mindspore/ccsrc/operator/composite/do_signature.h

@@ -56,6 +56,8 @@ class DoSignatureMetaFuncGraph : public MetaFuncGraph {
 };
 using RWSignaturePtr = std::shared_ptr<DoSignatureMetaFuncGraph>;
 
+extern const std::map<TypeId, size_t> type_map;
+
 AnfNodePtr GenerateCNode(const FuncGraphPtr &func_graph, const std::string &func_name, const ValuePtr &function,
                          const AbstractBasePtrList &args_spec_list, const AnfNodePtrList &old_node_inputs);
 }  // namespace prim

mindspore/ccsrc/pynative/pynative_execute.cc

@@ -160,36 +160,102 @@ std::map<SignatureEnumDType, std::vector<size_t>> GetTypeIndex(const std::vector
   return type_indexes;
 }
 
-std::map<SignatureEnumDType, size_t> GetDstType(const py::tuple &py_args,
+std::map<SignatureEnumDType, TypeId> GetDstType(const py::tuple &py_args,
                                                 const std::map<SignatureEnumDType, std::vector<size_t>> &type_indexes) {
-  std::map<SignatureEnumDType, size_t> dst_type;
+  std::map<SignatureEnumDType, TypeId> dst_type;
   for (auto it = type_indexes.begin(); it != type_indexes.end(); (void)++it) {
     auto type = it->first;
     auto indexes = it->second;
-    if (indexes.size() < 2) {
+    if (type == SignatureEnumDType::kDTypeEmptyDefaultValue || indexes.size() < 2) {
       continue;
     }
-    size_t m_index = indexes[0];
-    for (size_t i = 1; i < indexes.size(); ++i) {
-      if (py::isinstance<tensor::Tensor>(py_args[indexes[i]])) {
-        m_index = indexes[i];
+    size_t priority = 0;
+    TypeId max_type = TypeId::kTypeUnknown;
+    bool has_float = false;
+    bool has_int = false;
+    for (size_t index : indexes) {
+      if (!has_float && py::isinstance<py::float_>(py_args[index])) {
+        has_float = true;
+      }
+      if (!has_int && !py::isinstance<py::bool_>(py_args[index]) && py::isinstance<py::int_>(py_args[index])) {
+        has_int = true;
+      }
+      if (py::isinstance<tensor::Tensor>(py_args[index])) {
+        auto arg = py::cast<tensor::TensorPtr>(py_args[index]);
+        TypeId arg_type_id = arg->data_type();
+        auto type_priority = prim::type_map.find(arg_type_id);
+        if (type_priority->second > priority) {
+          max_type = type_priority->first;
+          priority = type_priority->second;
+        }
+      }
+    }
+    if (max_type == TypeId::kNumberTypeBool) {
+      if (has_int) {
+        max_type = TypeId::kNumberTypeInt32;
+      }
+      if (has_float) {
+        max_type = TypeId::kNumberTypeFloat32;
       }
     }
-    (void)dst_type.insert(std::make_pair(type, m_index));
+    (void)dst_type.insert(std::make_pair(type, max_type));
   }
   return dst_type;
 }
 
+std::string TypeIdToMsTypeStr(const TypeId &type_id) {
+  switch (type_id) {
+    case kNumberTypeFloat16:
+      return "float16";
+    case kNumberTypeFloat32:
+      return "float32";
+    case kNumberTypeFloat64:
+      return "float64";
+    case kNumberTypeInt8:
+      return "int8";
+    case kNumberTypeInt16:
+      return "int16";
+    case kNumberTypeInt32:
+      return "int32";
+    case kNumberTypeInt64:
+      return "int64";
+    case kNumberTypeUInt8:
+      return "uint8";
+    case kNumberTypeUInt16:
+      return "uint16";
+    case kNumberTypeUInt32:
+      return "uint32";
+    case kNumberTypeUInt64:
+      return "uint64";
+    case kNumberTypeBool:
+      return "bool_";
+    default:
+      MS_LOG(EXCEPTION) << "For implicit type conversion, not support the type: " << TypeIdToType(type_id);
+  }
+}
+py::object DoAutoCast(const py::object arg, const TypeId &type_id) {
+  py::tuple args(3);
+  std::string module_name = "mindspore.ops.functional";
+  std::string op_name = "cast";
+  args[0] = parse::python_adapter::GetPyFn(module_name, op_name);
+  args[1] = "Cast";
+
+  std::string dst_type_str = TypeIdToMsTypeStr(type_id);
+  module_name = "mindspore.common.dtype";
+  py::object dst_type = parse::python_adapter::GetPyFn(module_name, dst_type_str);
+  py::tuple inputs(2);
+  inputs[0] = arg;
+  inputs[1] = dst_type;
+  args[2] = inputs;
+
+  return RunOp(args)[0];
+}
 py::tuple ConvertInputs(const PrimitivePyPtr &prim, const py::list &args, py::tuple *const out_args,
                         py::list *const out_args_list) {
   auto &py_args = *out_args;
   py::tuple input_mask(args.size());
   for (size_t i = 0; i < args.size(); ++i) {
-    if (py::hasattr(args[i], "__parameter__")) {
-      input_mask[i] = true;
-    } else {
-      input_mask[i] = false;
-    }
+    input_mask[i] = py::hasattr(args[i], "__parameter__");
     py_args[i] = GetTupleObj(args[i]);
   }
   auto signature = prim->signatures();
@@ -197,26 +263,36 @@ py::tuple ConvertInputs(const PrimitivePyPtr &prim, const py::list &args, py::tu
   (void)std::transform(signature.begin(), signature.end(), std::back_inserter(dtypes),
                        [](const Signature &sig) { return sig.dtype; });
   int empty_dtype_count = std::count(dtypes.begin(), dtypes.end(), SignatureEnumDType::kDTypeEmptyDefaultValue);
-  if (dtypes.size() == 0 || static_cast<int>(dtypes.size()) == empty_dtype_count) {
+  if (dtypes.empty() || static_cast<int>(dtypes.size()) == empty_dtype_count) {
     return input_mask;
   }
   auto type_indexes = GetTypeIndex(dtypes);
   auto dst_type = GetDstType(py_args, type_indexes);
-  for (size_t i = 0; i < py_args.size(); ++i) {
+
+  for (size_t i = 0; i < dtypes.size(); ++i) {
+    if (dtypes[i] == SignatureEnumDType::kDTypeEmptyDefaultValue) {
+      continue;
+    }
     auto it = dst_type.find(dtypes[i]);
-    if (it != dst_type.end() && it->second != i &&
-        (py::isinstance<py::int_>(py_args[i]) || py::isinstance<py::float_>(py_args[i]))) {
-      auto tensor_ptr = py::cast<tensor::TensorPtr>(py_args[it->second]);
-      if (py::isinstance<py::int_>(py_args[i])) {
-        py_args[i] = std::make_shared<tensor::Tensor>(py::cast<py::int_>(py_args[i]), tensor_ptr->Dtype());
-        (*out_args_list)[i] = py_args[i];
-      } else {
-        double arg_value = py::cast<py::float_>(py_args[i]);
-        py_args[i] = std::make_shared<tensor::Tensor>(arg_value, tensor_ptr->Dtype());
-        (*out_args_list)[i] = py_args[i];
-      }
+    if (it == dst_type.end() || it->second == kTypeUnknown) {
       continue;
     }
+    if (py::isinstance<tensor::Tensor>(py_args[i])) {
+      auto arg = py::cast<tensor::TensorPtr>(py_args[i]);
+      if (arg->data_type() == it->second) {
+        continue;
+      }
+      if (signature[i].rw == SignatureEnumRW::kRWWrite) {
+        MS_LOG(EXCEPTION) << "In op '" << prim->name() << "', \n"
+                          << "the type of writable argument is '" << TypeIdToMsTypeStr(arg->data_type()) << "', "
+                          << "but the largest type in the same SignatureEumDtype is '" << TypeIdToMsTypeStr(it->second)
+                          << "'. The writable arg type is not equal to the largest type, "
+                          << "so can not cast automatically.";
+      }
+    }
+    py::object cast_output = DoAutoCast(py_args[i], it->second);
+    (*out_args)[i] = cast_output;
+    (*out_args_list)[i] = cast_output;
   }
   return input_mask;
 }

tests/st/pynative/test_implicit_conversion.py

@@ -0,0 +1,81 @@
+# 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.
+# ============================================================================
+""" test implicit conversion """
+import numpy as np
+
+from mindspore import Tensor
+
+
+def test_float_tensor_and_int_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = 2
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_bool_tensor_and_float_add():
+    x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
+    y = 3.3
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_bool_tensor_and_int_add():
+    x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
+    y = 3
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_bool_and_int_tensor_add():
+    x = True
+    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+def test_float_tensor_and_int_tensor_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_float_tensor_and_float_tensor_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float64))
+    y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float64))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_int_tensor_and_int_tensor_add():
+    x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16))
+    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_float_tensor_and_bool_tensors_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[1.1, 1.2, 1.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

tests/ut/python/pynative_mode/test_implicit_conversion.py

@@ -0,0 +1,81 @@
+# 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.
+# ============================================================================
+""" test implicit conversion """
+import numpy as np
+
+from mindspore import Tensor
+
+
+def test_float_tensor_and_int_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = 2
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_bool_tensor_and_float_add():
+    x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
+    y = 3.3
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_bool_tensor_and_int_add():
+    x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
+    y = 3
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_bool_and_int_tensor_add():
+    x = True
+    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+def test_float_tensor_and_int_tensor_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_float_tensor_and_float_tensor_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float16))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_int_tensor_and_int_tensor_add():
+    x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
+    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
+
+
+def test_float_tensor_and_bool_tensors_add():
+    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
+    ret_actual = x + y
+    ret_expect = Tensor(np.array([[1.1, 1.2, 1.3], [0.4, 0.5, 0.6]], dtype=np.float32))
+    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

tests/vm_impl/vm_me.py

@@ -403,7 +403,7 @@ def max_pool_grad(x, dout, pool_h, pool_w, stride):
     """Grad of max pooling."""
     dout = dout.transpose(0, 2, 3, 1)
     pool_size = pool_h * pool_w
-    dmax = np.zeros((dout.size, pool_size))
+    dmax = np.zeros((dout.size, pool_size), dout.dtype)
     col = im2col(x, pool_h, pool_w, stride)
     col = col.reshape(-1, pool_h * pool_w)
     arg_max = np.argmax(col, axis=1)
@@ -418,7 +418,7 @@ def max_pool_grad_with_argmax(x, dout, arg_max, pool_h, pool_w, stride):
     """Grad of max pooling with argmax."""
     dout = dout.transpose(0, 2, 3, 1)
     pool_size = pool_h * pool_w
-    dmax = np.zeros((dout.size, pool_size))
+    dmax = np.zeros((dout.size, pool_size), dout.dtype)
     dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
     dmax = dmax.reshape(dout.shape + (pool_size,))
     dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)