support Python built-in function 'enumerate'

mindspore/_extends/parse/resources.py

@@ -116,6 +116,7 @@ convert_object_map = {
     T.partial:      F.partial,
     T.zip:          C.zip_operation,
     T.print:        F.print_,
+    T.enumerate:    M.enumerate_,
 
     # custom define operation
     T.iter:         M.ms_iter,

mindspore/_extends/parse/standard_method.py

@@ -104,6 +104,15 @@ def bool_(x):
     return x.__bool__()
 
 
+def enumerate_(x, start=0):
+    """Enumerate list or tuple."""
+    x_type = F.typeof(x)
+    ret = ()
+    if check_is_tuple_or_list(x_type, "enumerate"):
+        ret = zip(range(start, start + len(x)), x)
+    return ret
+
+
 def while_cond(x):
     """For while condtion, if the condition is a tensor, the loop will not be unrolled"""
     if F.issubclass_(F.typeof(x), F.typeof(mstype.tensor)):
@@ -113,6 +122,13 @@ def while_cond(x):
     return x
 
 
+@constexpr
+def check_is_tuple_or_list(x, op_name):
+    """check whether x is list or tuple."""
+    if isinstance(x, (mstype.list_type, mstype.tuple_type)):
+        return True
+    raise TypeError(f"For '{op_name}', the input parameter should be tuple or list, but got {x}.")
+
 @constexpr
 def check_is_tensor_bool_cond(shp):
     """check if tensor is a bool condition"""

mindspore/_extends/parse/trope.py

@@ -27,7 +27,7 @@ from operator import (  # noqa
 
 # support system function call
 from builtins import (  # noqa
-    bool, getattr, setattr, len, iter, next, pow, range, map, zip, print
+    bool, getattr, setattr, len, iter, next, pow, range, map, zip, print, enumerate
 )
 
 # support functools
@@ -44,7 +44,7 @@ __all__ = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'mod', 'eq', 'ne', 'lt',
            'not_', 'and_', 'or_', 'xor', 'lshift', 'rshift', 'invert', 'is_', 'is_not', 'contains',
            'matmul', 'getitem', 'setitem',
            'bool', 'getattr', 'setattr', 'len', 'iter', 'next', 'pow', 'range', 'map', 'zip',
-           'partial', 'print',
+           'partial', 'print', 'enumerate',
            'exp', 'log', 'sin', 'cos', 'tan']
 
 

mindspore/ccsrc/operator/composite/map.cc

@@ -181,7 +181,7 @@ AnfNodePtr Map::FullMakeClass(const std::shared_ptr<Class> &type, const FuncGrap
 }
 
 AnfNodePtr Map::Make(const FuncGraphPtr &func_graph, const AnfNodePtr &fn_arg, const ArgsPairList &arg_pairs) {
-  if (arg_pairs.size() < 1) {
+  if (arg_pairs.empty()) {
     MS_EXCEPTION(TypeError) << "map() must have at least two arguments";
   }
   bool found = false;

mindspore/ccsrc/operator/composite/zip_operation.cc

@@ -18,44 +18,44 @@
 
 #include "operator/composite/zip_operation.h"
 #include <algorithm>
-#include <utility>
 
 #include "pipeline/static_analysis/abstract_value.h"
 #include "ir/anf.h"
 #include "pipeline/static_analysis/dshape.h"
-#include "pipeline/static_analysis/param_validator.h"
 #include "operator/cc_implementations.h"
 #include "optimizer/opt.h"
-#include "utils/symbolic.h"
-#include "./common.h"
 #include "pybind_api/api_register.h"
 
 namespace mindspore {
 // namespace to support composite operators definition
 namespace prim {
 using mindspore::abstract::AbstractBase;
+using mindspore::abstract::AbstractList;
+using mindspore::abstract::AbstractSequeue;
+using mindspore::abstract::AbstractSequeuePtr;
 using mindspore::abstract::AbstractTuple;
 
 FuncGraphPtr ZipOperation::GenerateFuncGraph(const AbstractBasePtrList &args_spec_list) {
   // zip operation:
   // input: tuple arguments
   // output: tuple of items of input iterated on every input
-  if (args_spec_list.size() == 0) {
-    MS_LOG(EXCEPTION) << "zip arguments input should not be empty";
+  if (args_spec_list.empty()) {
+    MS_LOG(EXCEPTION) << "For 'zip', there is at least one input.";
   }
 
-  auto is_all_tuple = std::all_of(args_spec_list.begin(), args_spec_list.end(), [](const AbstractBasePtr &abs) -> bool {
-    MS_EXCEPTION_IF_NULL(abs);
-    return abs->isa<AbstractTuple>();
-  });
-  if (!is_all_tuple) {
-    MS_LOG(EXCEPTION) << "zip input args should be tuple";
+  auto is_all_sequeue =
+    std::all_of(args_spec_list.begin(), args_spec_list.end(), [](const AbstractBasePtr &abs) -> bool {
+      MS_EXCEPTION_IF_NULL(abs);
+      return abs->isa<AbstractSequeue>();
+    });
+  if (!is_all_sequeue) {
+    MS_LOG(EXCEPTION) << "For 'zip', all inputs must be sequence.";
   }
 
-  auto min_abs = std::min_element(args_spec_list.begin(), args_spec_list.end(),
-                                  [](const AbstractBasePtr &x, const AbstractBasePtr &y) {
-                                    return (x->cast<AbstractTuplePtr>()->size() < y->cast<AbstractTuplePtr>()->size());
-                                  });
+  auto min_abs = std::min_element(
+    args_spec_list.begin(), args_spec_list.end(), [](const AbstractBasePtr &x, const AbstractBasePtr &y) {
+      return (x->cast<AbstractSequeuePtr>()->size() < y->cast<AbstractSequeuePtr>()->size());
+    });
   FuncGraphPtr ret_graph = std::make_shared<FuncGraph>();
   ret_graph->set_flag(FUNC_GRAPH_FLAG_CORE, true);
   for (size_t idx = 0; idx < args_spec_list.size(); idx++) {
@@ -65,12 +65,14 @@ FuncGraphPtr ZipOperation::GenerateFuncGraph(const AbstractBasePtrList &args_spe
   // generate tuple output of ziped arguments input
   std::vector<AnfNodePtr> make_tuple_nodes;
   make_tuple_nodes.push_back(NewValueNode(prim::kPrimMakeTuple));
-  for (size_t idx = 0; idx < (*min_abs)->cast<AbstractTuplePtr>()->size(); idx++) {
+  for (size_t idx = 0; idx < (*min_abs)->cast<AbstractSequeuePtr>()->size(); idx++) {
     std::vector<AnfNodePtr> make_tuple_zip_nodes;
     make_tuple_zip_nodes.push_back(NewValueNode(prim::kPrimMakeTuple));
+    std::string module_name = "mindspore.ops.composite.multitype_ops.getitem_impl";
+    ValuePtr op = prim::GetPythonOps("getitem", module_name);
     for (size_t arg_idx = 0; arg_idx < args_spec_list.size(); arg_idx++) {
-      std::vector<AnfNodePtr> tuple_get_item_nodes{NewValueNode(prim::kPrimTupleGetItem),
-                                                   ret_graph->parameters()[arg_idx], NewValueNode(SizeToInt(idx))};
+      std::vector<AnfNodePtr> tuple_get_item_nodes{NewValueNode(op), ret_graph->parameters()[arg_idx],
+                                                   NewValueNode(SizeToInt(idx))};
       auto tuple_get_item_op = ret_graph->NewCNode(tuple_get_item_nodes);
       make_tuple_zip_nodes.push_back(tuple_get_item_op);
     }

mindspore/ccsrc/operator/prim_statement.cc

@@ -229,6 +229,7 @@ AbstractBasePtr InferImplNotInDict(const AnalysisEnginePtr &, const PrimitivePtr
   // Inputs: x, t
   return std::make_shared<AbstractScalar>(!IsInDict(primitive, args_spec_list));
 }
+
 AbstractBasePtr InferImplIsConstant(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                     const AbstractBasePtrList &args_spec_list) {
   // statement: isconstant(x)

mindspore/ccsrc/pipeline/parse/parse.cc

@@ -1048,11 +1048,10 @@ FunctionBlockPtr Parser::ParseFor(const FunctionBlockPtr &block, const py::objec
   CNodePtr app = body_block->func_graph()->NewCNode({op_next, iter_param});
   CNodePtr target_app = body_block->func_graph()->NewCNode({op_getitem, app, NewValueNode(0)});
   py::object target_node = python_adapter::GetPyObjAttr(node, "target");
-  auto name_id = py::cast<std::string>(python_adapter::GetPyObjAttr(target_node, "id"));
-  target_app->debug_info()->set_name(name_id);
 
   CNodePtr iter2_app = body_block->func_graph()->NewCNode({op_getitem, app, NewValueNode(1)});
-  body_block->WriteVariable(name_id, target_app);
+  WriteAssignVars(body_block, target_node, target_app);
+
   // link the variable name with the target
   auto it_info = std::make_shared<TraceIterator>(target_app->debug_info());
   iter_param->debug_info()->set_trace_info(it_info);

mindspore/ccsrc/pipeline/static_analysis/param_validator.h

@@ -67,6 +67,7 @@ ABSTRACT_REPORT_NAME_TRAITS(Type)
 ABSTRACT_REPORT_NAME_TRAITS(KeywordArg)
 ABSTRACT_REPORT_NAME_TRAITS(Class)
 ABSTRACT_REPORT_NAME_TRAITS(IndexedSlices)
+ABSTRACT_REPORT_NAME_TRAITS(Sequeue)
 
 template <typename T>
 std::shared_ptr<T> CheckArg(const std::string &op, const AbstractBasePtrList &args_spec_list, size_t index) {

mindspore/ccsrc/pynative/pynative_execute.cc

@@ -226,11 +226,8 @@ void PynativeInfer(const PrimitivePyPtr &prim, const py::list &py_args, OpExecIn
   AbstractBasePtrList args_spec_list;
   for (size_t i = 0; i < size; i++) {
     ValuePtr input_value = PyAttrValue(py_args[i]);
-    if (!py::hasattr(prim->GetPyObj(), "const_value") && input_value->isa<tensor::Tensor>()) {
-      args_spec_list.emplace_back(abstract::FromValueInside(input_value, true));
-    } else {
-      args_spec_list.emplace_back(abstract::FromValueInside(input_value, false));
-    }
+    args_spec_list.emplace_back(abstract::FromValueInside(
+      input_value, !py::hasattr(prim->GetPyObj(), "const_value") && input_value->isa<tensor::Tensor>()));
   }
   AbstractBasePtr infer_res = EvalOnePrim(prim, args_spec_list)->abstract();
   op_exec_info->abstract = infer_res;
@@ -512,7 +509,7 @@ py::object RunOpInMs(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *stat
   return result;
 }
 
-py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecInfoPtr op_exec_info,
+py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecInfoPtr &op_exec_info,
                                   PynativeStatusCode *const status) {
   MS_EXCEPTION_IF_NULL(status);
   py::object result;
@@ -550,7 +547,7 @@ py::object RunOpWithBackendPolicy(MsBackendPolicy backend_policy, const OpExecIn
 }
 
 AnfNodePtr PynativeExecutor::MakeCNode(const OpExecInfoPtr &op_exec_info, const py::args &args, const py::tuple &out) {
-  if (!grad_flag_ || graph_info_map_.size() == 0) {
+  if (!grad_flag_ || graph_info_map_.empty()) {
     return nullptr;
   }
   std::vector<AnfNodePtr> inputs;
@@ -753,7 +750,7 @@ AnfNodePtr PynativeExecutor::GetInput(const py::object &obj, const py::object &o
     if (py::isinstance<py::none>(name_attr)) {
       MS_LOG(EXCEPTION) << "Parameter object should have name attribute";
     }
-    std::string param_name = py::cast<std::string>(name_attr);
+    auto param_name = py::cast<std::string>(name_attr);
     if (graph_info_map_[df_builder_].param_map.count(obj_id) == 0) {
       auto free_param = df_builder_->add_parameter();
       free_param->set_name(param_name);

mindspore/common/dtype.py

@@ -97,6 +97,8 @@ tensor_type = typing.TensorType
 anything_type = typing.TypeAnything
 slice_type = typing.Slice
 ellipsis_type = typing.TypeEllipsis
+list_type = typing.List
+tuple_type = typing.Tuple
 
 number_type = (int8,
                int16,

mindspore/ops/composite/multitype_ops/_compile_utils.py

@@ -65,9 +65,9 @@ def _generate_indices_from_tuple_of_mixed_tensors(data, tuple_index, op_name):
     tuple_len = len(tuple_index)
     for i in range(tuple_len):
         if i in int_positions:
-            tuple_index_new = tuple_index_new + (F.scalar_to_tensor(tuple_index[i], mstype.int32),)
+            tuple_index_new += (F.scalar_to_tensor(tuple_index[i], mstype.int32),)
         else:
-            tuple_index_new = tuple_index_new + (tuple_index[i],)
+            tuple_index_new += (tuple_index[i],)
     indexes_types = hyper_map(F.typeof, tuple_index_new)
     tensor_positions, slice_positions, ellipsis_position = \
         const_utils.separate_mixed_tensors_index(indexes_types, op_name)

mindspore/ops/operations/array_ops.py

@@ -1469,7 +1469,7 @@ class Concat(PrimitiveWithInfer):
 def _get_pack_shape(x_shape, x_type, axis, prim_name):
     """for pack output shape"""
     validator.check_value_type("shape", x_shape, [tuple, list], prim_name)
-    validator.check_integer("len of input_x", len(x_shape), 1, Rel.GT, prim_name)
+    validator.check_integer("len of input_x", len(x_shape), 1, Rel.GE, prim_name)
     validator.check_subclass("input_x[0]", x_type[0], mstype.tensor, prim_name)
     rank_base = len(x_shape[0])
     N = len(x_shape)

tests/ut/python/ops/test_ops.py

@@ -1747,6 +1747,10 @@ test_case_array_ops = [
         'desc_inputs': [[128, 128], [128, 128]],
         'desc_bprop': [[2, 128, 128]],
     }),
+    ('Pack_3', {
+        'block': NetForPackInput(P.Pack()),
+        'desc_inputs': [[2, 2]],
+        'desc_bprop': [[1, 2, 2]]}),
     ('Unpack_0', {
         'block': NetForUnpackInput(P.Unpack(axis=0)),
         'desc_inputs': [[2, 4]],
@@ -2206,10 +2210,6 @@ raise_set = [
                         Tensor(np.ones((2, 2), np.float32)),
                         Tensor(np.ones((2,), np.float32))),
         'desc_bprop': [[2, 3]]}),
-    ('Pack', {
-        'block': (NetForPackInput(P.Pack()), {'exception': ValueError}),
-        'desc_inputs': [[2, 2]],
-        'desc_bprop': [[1, 2, 2]]}),
     ('PReLU', {
         'block': (P.PReLU(), {'exception': ValueError}),
         'desc_inputs': [[2], [1]],

tests/ut/python/pipeline/parse/test_enumerate.py

+# 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 enumerate"""
+import numpy as np
+import pytest
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_enumerate_list_const():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = [11, 22, 33, 44]
+
+        def construct(self):
+            index_sum = 0
+            value_sum = 0
+            for i, j in enumerate(self.value):
+                index_sum += i
+                value_sum += j
+            return index_sum, value_sum
+
+    net = Net()
+    assert net() == (6, 110)
+
+
+def test_enumerate_tuple_const():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = (11, 22, 33, 44)
+
+        def construct(self):
+            index_sum = 0
+            value_sum = 0
+            for i, j in enumerate(self.value):
+                index_sum += i
+                value_sum += j
+            return index_sum, value_sum
+
+    net = Net()
+    assert net() == (6, 110)
+
+
+def test_enumerate_list_parameter():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x, y, z):
+            index_sum = 0
+            value = [x, y, z]
+            ret = ()
+            for i, j in enumerate(value):
+                index_sum += i
+                ret += (j,)
+            return index_sum, ret
+
+    x = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)))
+    net = Net()
+    net(x, x, x)
+
+
+def test_enumerate_tuple_parameter():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x, y, z):
+            index_sum = 0
+            value = (x, y, z)
+            ret = ()
+            for i, j in enumerate(value):
+                index_sum += i
+                ret += (j,)
+            return index_sum, ret
+    x = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)))
+    net = Net()
+    net(x, x, x)
+
+
+def test_enumerate_tuple_const_1():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = (11, 22, 33, 44)
+
+        def construct(self):
+            index_sum = 0
+            value_sum = 0
+            for i in enumerate(self.value):
+                index_sum += i[0]
+                value_sum += i[1]
+            return index_sum, value_sum
+
+    net = Net()
+    assert net() == (6, 110)
+
+
+def test_enumerate_tuple_parameter_1():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x, y, z):
+            index_sum = 0
+            value = (x, y, z)
+            ret = ()
+            for i in enumerate(value):
+                index_sum += i[0]
+                ret += (i[1],)
+            return index_sum, ret
+    x = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)))
+    net = Net()
+    net(x, x, x)
+
+def test_enumerate_tuple_const_2():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = (11, 22, 33, 44)
+
+        def construct(self):
+            index_sum = 0
+            value_sum = 0
+            for i in enumerate(self.value, 1):
+                index_sum += i[0]
+                value_sum += i[1]
+            return index_sum, value_sum
+
+    net = Net()
+    assert net() == (10, 110)
+
+
+def test_enumerate_tuple_parameter_2():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x, y, z):
+            index_sum = 0
+            value = (x, y, z)
+            ret = ()
+            for i in enumerate(value, 2):
+                index_sum += i[0]
+                ret += (i[1],)
+            return index_sum, ret
+    x = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)))
+    net = Net()
+    net(x, x, x)
+
+
+def test_enumerate_parameter_type_error():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            return enumerate(x)
+    x = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)))
+    net = Net()
+    with pytest.raises(TypeError) as ex:
+        net(x)
+    assert  "For 'enumerate', the input parameter should be tuple or list" in str(ex.value)