support feeding scalar when runing program , test=develop (#23214) (#23397)

* support feed_python_builtin, test=develop

* add test, test=develop

* support CompiledProgram, test=develop

* support fluid.data, test=develop

* fix ci problems, test=develop

* follow comments, test=develop

python/paddle/fluid/executor.py

@@ -372,7 +372,7 @@ def _get_program_cache_key(feed, fetch_list):
     return str(feed_var_names + fetch_var_names)
 
 
-def _as_lodtensor(data, place):
+def _as_lodtensor(data, place, dtype=None):
     """
         Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
         For higher dimensional sequence data, please use LoDTensor directly.
@@ -387,6 +387,8 @@ def _as_lodtensor(data, place):
 
         Args:
             data(numpy.ndarray): a instance of array
+            data(core.Place): the place of created tensor
+            dtype(core.VarDesc.VarType): the expected data type of created tensor
 
         Returns:
             LoDTensor
@@ -397,6 +399,15 @@ def _as_lodtensor(data, place):
                 ndarray to LoDTensor. Please convert data to LoDTensor \
                 directly before feeding the data.\
                 ")
+
+    #NOTE(zhiqiu): convert python builtin ,like float and int, to numpy array
+    if not isinstance(data, np.ndarray):
+        if np.isscalar(data):
+            assert dtype is not None, 'dtype should be given when casting python scalar to tensor'
+            dtype = convert_dtype(dtype) if isinstance(
+                dtype, core.VarDesc.VarType) else dtype
+            data = np.array([data]).astype(dtype)
+
     # single tensor case
     tensor = core.LoDTensor()
     tensor.set(data, place)
@@ -574,9 +585,9 @@ class Executor(object):
             if op.desc.type() == 'feed':
                 feed_target_name = op.desc.output('Out')[0]
                 cur_feed = feed[feed_target_name]
-                if not isinstance(cur_feed, core.LoDTensor):
-                    cur_feed = _as_lodtensor(cur_feed, self.place)
                 var = global_block.var(feed_target_name)
+                if not isinstance(cur_feed, core.LoDTensor):
+                    cur_feed = _as_lodtensor(cur_feed, self.place, var.dtype)
                 check_feed_shape_type(var, cur_feed)
                 idx = op.desc.attr('col')
                 core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
@@ -631,16 +642,14 @@ class Executor(object):
             feed_tensor_dict = dict()
             for feed_name in feed:
                 feed_tensor = feed[feed_name]
+                var = global_block.var(feed_name) if need_check_feed else None
                 if not isinstance(feed_tensor, core.LoDTensor):
-                    feed_tensor = core.LoDTensor()
                     # always set to CPU place, since the tensor need to be split
                     # it is fast in CPU
-                    assert isinstance( feed[feed_name], np.ndarray ), \
-                        "The input({}) should be numpy.array, but not {}.".format(
-                        feed_name, type(feed[feed_name]))
-                    feed_tensor.set(feed[feed_name], core.CPUPlace())
+                    feed_tensor = _as_lodtensor(feed[feed_name],
+                                                core.CPUPlace(), var.dtype
+                                                if var else None)
                 if need_check_feed:
-                    var = global_block.var(feed_name)
                     check_feed_shape_type(var, feed_tensor, exe.device_count())
                 feed_tensor_dict[feed_name] = feed_tensor
 
@@ -659,15 +668,13 @@ class Executor(object):
                 res_dict = dict()
                 for feed_name in each:
                     tensor = each[feed_name]
+                    var = global_block.var(
+                        feed_name) if need_check_feed else None
                     if not isinstance(tensor, core.LoDTensor):
-                        tmp = core.LoDTensor()
-                        assert isinstance(each[feed_name], np.ndarray), \
-                            "The input({}) should be numpy.array, but not {}.".format(
-                            feed_name, type(each[feed_name]))
-                        tmp.set(tensor, program._places[i])
-                        tensor = tmp
+                        tensor = _as_lodtensor(each[feed_name],
+                                               program._places[i], var.dtype
+                                               if var else None)
                     if need_check_feed:
-                        var = global_block.var(feed_name)
                         check_feed_shape_type(var, tensor)
                     res_dict[feed_name] = tensor
                 res.append(res_dict)

python/paddle/fluid/tests/unittests/test_executor_feed_scalar.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+
+import numpy
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+
+
+class TestExecutor(unittest.TestCase):
+    def net(self):
+        lr = fluid.data(name="lr", shape=[1], dtype='float32')
+        x = fluid.data(name="x", shape=[None, 1], dtype='float32')
+        y = fluid.data(name="y", shape=[None, 1], dtype='float32')
+        y_predict = fluid.layers.fc(input=x, size=1, act=None)
+
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+
+        opt = fluid.optimizer.Adam(learning_rate=lr)
+        opt.minimize(avg_cost)
+
+        return lr, avg_cost
+
+    def test_program_feed_float(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(main_program, startup_program):
+            with fluid.scope_guard(scope):
+                cpu = fluid.CPUPlace()
+                exe = fluid.Executor(cpu)
+                lr, cost = self.net()
+                exe.run(startup_program)
+                train_data = numpy.array(
+                    [[1.0], [2.0], [3.0], [4.0]]).astype('float32')
+                y_true = numpy.array(
+                    [[2.0], [4.0], [6.0], [8.0]]).astype('float32')
+                a = 0.01
+                _lr, _ = exe.run(feed={'x': train_data,
+                                       'y': y_true,
+                                       'lr': a},
+                                 fetch_list=[lr, cost],
+                                 return_numpy=False)
+            self.assertEqual(_lr._dtype(), lr.dtype)
+            self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
+            self.assertEqual(type(a), float)
+
+    def test_program_feed_int(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(main_program, startup_program):
+            with fluid.scope_guard(scope):
+                cpu = fluid.CPUPlace()
+                exe = fluid.Executor(cpu)
+                lr, cost = self.net()
+                exe.run(startup_program)
+                train_data = numpy.array(
+                    [[1.0], [2.0], [3.0], [4.0]]).astype('float32')
+                y_true = numpy.array(
+                    [[2.0], [4.0], [6.0], [8.0]]).astype('float32')
+                a = 0
+                _lr, _ = exe.run(feed={'x': train_data,
+                                       'y': y_true,
+                                       'lr': a},
+                                 fetch_list=[lr, cost],
+                                 return_numpy=False)
+            self.assertEqual(_lr._dtype(), lr.dtype)
+            self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
+            self.assertEqual(type(a), int)
+
+    def test_compiled_program_feed_scalar(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(main_program, startup_program):
+            with fluid.scope_guard(scope):
+                lr, cost = self.net()
+                cpu = fluid.CPUPlace()
+                exe = fluid.Executor(cpu)
+                exe.run(startup_program)
+                compiled_prog = fluid.CompiledProgram(
+                    main_program).with_data_parallel(loss_name=cost.name)
+                train_data = numpy.array(
+                    [[1.0], [2.0], [3.0], [4.0]]).astype('float32')
+                y_true = numpy.array(
+                    [[2.0], [4.0], [6.0], [8.0]]).astype('float32')
+                a = 0.01
+                _lr, _ = exe.run(compiled_prog,
+                                 feed={'x': train_data,
+                                       'y': y_true,
+                                       'lr': a},
+                                 fetch_list=[lr, cost],
+                                 return_numpy=False)
+                self.assertEqual(_lr._dtype(), lr.dtype)
+                self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
+                self.assertEqual(type(a), float)
+
+
+class TestAsLodTensor(unittest.TestCase):
+    def test_as_lodtensor_int32(self):
+        cpu = fluid.CPUPlace()
+        tensor = fluid.executor._as_lodtensor(1.0, cpu,
+                                              fluid.core.VarDesc.VarType.INT32)
+        self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.INT32)
+
+    def test_as_lodtensor_fp64(self):
+        cpu = fluid.CPUPlace()
+        tensor = fluid.executor._as_lodtensor(1, cpu,
+                                              fluid.core.VarDesc.VarType.FP64)
+        self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.FP64)
+
+    def test_as_lodtensor_error(self):
+        cpu = fluid.CPUPlace()
+        self.assertRaises(AssertionError, fluid.executor._as_lodtensor, 1, cpu)
+
+
+if __name__ == '__main__':
+    unittest.main()