Unittests concurrency (#8666)

Python Unit Tests for CSP

* Simple Channel Send and Receive test
* Daisy Chain test with 100 channels/Go ops

Author: cs2be

python/paddle/fluid/concurrency.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# TODO: Variables: make_channel
-# TODO: Operators: send, close_channel, recv, go, select
 from layers.control_flow import BlockGuard
-from layer_helper import LayerHelper
+from layer_helper import LayerHelper, unique_name
+from layers import fill_constant
 import core
+
 __all__ = [
     'Go',
     'make_channel',
@@ -46,27 +46,35 @@ def construct_go_op(self):
         parent_block = main_program.block(main_program.current_block()
                                           .parent_idx)
 
+        inner_outputs = set()
         x_name_list = set()
-        out_vars = []
         for op in go_block.ops:
             # Iterate over all operators, get all the inputs
             # and add as input to the Go operator.
             for iname in op.input_names:
                 for in_var_name in op.input(iname):
-                    x_name_list.add(in_var_name)
+                    if in_var_name not in inner_outputs:
+                        x_name_list.add(in_var_name)
 
-            # Iterate over all operators , get all the outputs
-            # add to the output list of Go operator only if
-            # they exist in the parent block.
             for oname in op.output_names:
                 for out_var_name in op.output(oname):
-                    if out_var_name in parent_block.vars:
-                        out_vars.add(parent_block.var(out_var_name))
+                    inner_outputs.add(out_var_name)
+
+        # Iterate over all operators , get all the outputs
+        # add to the output list of Go operator only if
+        # they exist in the parent block.
+        out_vars = []
+        for inner_out_name in inner_outputs:
+            if inner_out_name in parent_block.vars:
+                out_vars.append(parent_block.var(inner_out_name))
 
         parent_block.append_op(
             type='go',
-            inputs={'X': [parent_block.var(x_name) for x_name in x_name_list]},
-            outputs={'Out': out_vars},
+            inputs={
+                'X':
+                [parent_block.var_recursive(x_name) for x_name in x_name_list]
+            },
+            outputs={},
             attrs={'sub_block': go_block})
 
 
@@ -88,8 +96,8 @@ def make_channel(dtype, capacity=0):
     `channel_close`, and `Go` to design a concurrent Paddle program.
 
     Args:
-        dtype (ParamAttr|int): Data type of the data sent in the channel.
-        This data type should be one of the Paddle supported data types.
+        dtype (ParamAttr|string): Data type of the data sent in the channel.
+        This data type should be the string name of a numpy data type.
         capacity (ParamAttr|int): Size of the channel. Defaults to 0 for
         to create an unbuffered channel.
 
@@ -106,22 +114,24 @@ def make_channel(dtype, capacity=0):
           fluid.channel_send(ch, 100)
           fluid.channel_close(ch)
     """
-    helper = LayerHelper('make_channel', **locals())
+    helper = LayerHelper('channel_create', **locals())
     main_program = helper.main_program
     make_channel_block = main_program.current_block()
 
     # Make a channel variable (using the channel data type) and make sure it
     # persists into the global scope.
     channel = helper.create_variable(
-        dtype=core.VarDesc.VarType.CHANNEL, persistable=True)
+        name=unique_name.generate('channel'),
+        type=core.VarDesc.VarType.CHANNEL,
+        persistable=True)
 
     create_channel_op = make_channel_block.append_op(
         type="channel_create",
         outputs={"Out": channel},
         attrs={"data_type": dtype,
                "capacity": capacity})
 
-    return create_channel_op
+    return channel
 
 
 def channel_send(channel, value):
@@ -133,7 +143,7 @@ def channel_send(channel, value):
     Args:
         channel (Variable|Channel): Channel variable created using
         `make_channel`.
-
+        value (Variable): Value to send to channel
     Returns:
         Variable: The boolean status on whether or not the channel
                   successfully sent the passed value.
@@ -149,7 +159,11 @@ def channel_send(channel, value):
     helper = LayerHelper('channel_send', **locals())
     main_program = helper.main_program
     channel_send_block = main_program.current_block()
-    status = helper.create_variable(dtype=core.VarDesc.VarType.TENSOR)
+
+    status = helper.create_variable(
+        name=unique_name.generate('status'),
+        type=core.VarDesc.VarType.LOD_TENSOR,
+        dtype=core.VarDesc.VarType.BOOL)
 
     channel_send_op = channel_send_block.append_op(
         type="channel_send",
@@ -159,10 +173,10 @@ def channel_send(channel, value):
         },
         outputs={"Status": status})
 
-    return channel_send_op
+    return status
 
 
-def channel_recv(channel, dtype):
+def channel_recv(channel, return_value):
     """
     Receives a value through a channel variable. Used by an unbuffered or
     buffered channel within a concurrent Go block to get data from originally
@@ -172,11 +186,10 @@ def channel_recv(channel, dtype):
     Args:
         channel (Variable|Channel): Channel variable created using
         `make_channel`.
-        dtype (Variable|int): Data type of the data expected to be read in the
-        channel. This data type should be one of the Paddle supported data
-        types.
+        return_value (Variable): Variable to set as a result of running channel_recv_op
 
     Returns:
+        Variable: The received value from the channel.
         Variable: The boolean status on whether or not the channel
                   successfully received the passed value.
 
@@ -185,24 +198,26 @@ def channel_recv(channel, dtype):
 
           ch = fluid.make_channel(dtype='int32', capacity=10)
           with fluid.Go():
-            fluid.channel_recv(ch, 'int32')
+            returned_value = fluid.channel_recv(ch, 'int32')
 
           # Code to send data through the channel.
     """
     helper = LayerHelper('channel_recv', **locals())
     main_program = helper.main_program
     channel_recv_block = main_program.current_block()
 
-    return_value = helper.create_variable(dtype=dtype)
-    status = helper.create_variable(dtype=core.VarDesc.VarType.TENSOR)
+    status = helper.create_variable(
+        name=unique_name.generate('status'),
+        type=core.VarDesc.VarType.LOD_TENSOR,
+        dtype=core.VarDesc.VarType.BOOL)
 
     channel_recv_op = channel_recv_block.append_op(
         type="channel_recv",
         inputs={"Channel": channel},
         outputs={"Out": return_value,
                  "Status": status})
 
-    return channel_recv_op
+    return return_value, status
 
 
 def channel_close(channel):
@@ -228,5 +243,3 @@ def channel_close(channel):
 
     channel_close_op = channel_close_block.append_op(
         type="channel_close", inputs={"Channel": channel})
-
-    return channel_close_op

python/paddle/fluid/tests/notest_csp.py

@@ -0,0 +1,100 @@
+#   Copyright (c) 2018 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.
+
+import unittest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid import framework, unique_name
+from paddle.fluid.executor import Executor
+from paddle.fluid.layers import fill_constant
+
+
+class TestRoutineOp(unittest.TestCase):
+    def test_simple_routine(self):
+        ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+
+        # Create LOD_TENSOR<INT64> and put it into the scope.  This placeholder
+        # variable will be filled in and returned by fluid.channel_recv
+        result = self._create_tensor('return_value',
+                                     core.VarDesc.VarType.LOD_TENSOR,
+                                     core.VarDesc.VarType.INT64)
+
+        with fluid.Go():
+            input_value = fill_constant(
+                shape=[1], dtype=core.VarDesc.VarType.FP64, value=1234)
+            fluid.channel_send(ch, input_value)
+
+        result, status = fluid.channel_recv(ch, result)
+        fluid.channel_close(ch)
+
+        cpu = core.CPUPlace()
+        exe = Executor(cpu)
+
+        outs = exe.run(fetch_list=[result])
+        self.assertEqual(outs[0], 1234)
+
+    def test_daisy_chain(self):
+        '''
+        Mimics classic Daisy-chain test:  https://talks.golang.org/2012/concurrency.slide#39
+        '''
+        n = 100
+
+        leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+        left = leftmost
+
+        # TODO(thuan): Use fluid.While() after scope capture is implemented.
+        # https://github.com/PaddlePaddle/Paddle/issues/8502
+        for i in range(n):
+            right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+            with fluid.Go():
+                one_tensor = self._create_one_dim_tensor(1)
+                result = self._create_tensor('return_value',
+                                             core.VarDesc.VarType.LOD_TENSOR,
+                                             core.VarDesc.VarType.INT64)
+
+                result, status = fluid.channel_recv(right, result)
+                one_added = fluid.layers.elementwise_add(x=one_tensor, y=result)
+                fluid.channel_send(left, one_added)
+            left = right
+
+        # Trigger the channel propagation by sending a "1" to rightmost channel
+        with fluid.Go():
+            one_tensor = self._create_one_dim_tensor(1)
+            fluid.channel_send(right, one_tensor)
+
+        leftmost_result = self._create_tensor('return_value',
+                                              core.VarDesc.VarType.LOD_TENSOR,
+                                              core.VarDesc.VarType.INT64)
+        leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)
+
+        cpu = core.CPUPlace()
+        exe = Executor(cpu)
+        leftmost_data = exe.run(fetch_list=[leftmost_result])
+
+        # The leftmost_data should be equal to the number of channels + 1
+        self.assertEqual(leftmost_data[0][0], n + 1)
+
+    def _create_one_dim_tensor(self, value):
+        one_dim_tensor = fill_constant(
+            shape=[1], dtype=core.VarDesc.VarType.INT64, value=value)
+        one_dim_tensor.stop_gradient = True
+        return one_dim_tensor
+
+    def _create_tensor(self, name, type, dtype):
+        return framework.default_main_program().current_block().create_var(
+            name=unique_name.generate(name), type=type, dtype=dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()