wehub-resource-sync
690 lines
25 KiB
Python
690 lines
25 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest, get_device_place, is_custom_device
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import paddle
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from paddle.base import core
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def graph_send_recv_wrapper(
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x, src_index, dst_index, reduce_op="sum", out_size=None, name=None
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):
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return paddle.geometric.send_u_recv(
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x, src_index, dst_index, reduce_op.lower(), out_size, name
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)
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class TestGraphSendRecvMaxOp(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 20)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'MAX'}
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out, self.gradient = compute_graph_send_recv_for_min_max(
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self.inputs, self.attrs
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)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'], 'Out', user_defined_grads=[self.gradient], check_pir=True
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)
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class TestGraphSendRecvMinOp(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 20)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'MIN'}
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out, self.gradient = compute_graph_send_recv_for_min_max(
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self.inputs, self.attrs
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)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'], 'Out', user_defined_grads=[self.gradient], check_pir=True
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)
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class TestGraphSendRecvSumOp(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 20)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'SUM'}
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out, _ = compute_graph_send_recv_for_sum_mean(self.inputs, self.attrs)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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class TestGraphSendRecvMeanOp(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 20)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'MEAN'}
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out, dst_count = compute_graph_send_recv_for_sum_mean(
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self.inputs, self.attrs
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)
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self.outputs = {'Out': out, 'Dst_count': dst_count}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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class TestGraphSendRecvMaxOp_ZeroSize(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 0)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'MAX'}
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out, self.gradient = compute_graph_send_recv_for_min_max(
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self.inputs, self.attrs
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)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), check_pir=True)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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core.CPUPlace(),
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['X'],
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'Out',
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user_defined_grads=[self.gradient],
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check_pir=True,
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)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(
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get_device_place(),
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['X'],
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'Out',
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user_defined_grads=[self.gradient],
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check_pir=True,
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)
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class TestGraphSendRecvMinOp_ZeroSize(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 0)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'MIN'}
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out, self.gradient = compute_graph_send_recv_for_min_max(
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self.inputs, self.attrs
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)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), check_pir=True)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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core.CPUPlace(),
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['X'],
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'Out',
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user_defined_grads=[self.gradient],
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check_pir=True,
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)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(
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get_device_place(),
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['X'],
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'Out',
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user_defined_grads=[self.gradient],
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check_pir=True,
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)
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class TestGraphSendRecvSumOp_ZeroSize(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 0)).astype("float64")
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index = np.random.randint(0, 10, (15, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'SUM'}
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out, _ = compute_graph_send_recv_for_sum_mean(self.inputs, self.attrs)
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), check_pir=True)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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core.CPUPlace(), ['X'], 'Out', check_pir=True
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)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(
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get_device_place(), ['X'], 'Out', check_pir=True
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)
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class TestGraphSendRecvMeanOp_ZeroSize(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.python_api = graph_send_recv_wrapper
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self.python_out_sig = ["Out"]
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self.op_type = "graph_send_recv"
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x = np.random.random((10, 20)).astype("float64")
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index = np.random.randint(0, 10, (0, 2)).astype(np.int64)
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src_index = index[:, 0]
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dst_index = index[:, 1]
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self.inputs = {'X': x, 'Src_index': src_index, 'Dst_index': dst_index}
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self.attrs = {'reduce_op': 'MEAN'}
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out, dst_count = compute_graph_send_recv_for_sum_mean(
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self.inputs, self.attrs
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)
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self.outputs = {'Out': out, 'Dst_count': dst_count}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), check_pir=True)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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core.CPUPlace(), ['X'], 'Out', check_pir=True
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)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(
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get_device_place(), ['X'], 'Out', check_pir=True
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)
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def compute_graph_send_recv_for_sum_mean(inputs, attributes):
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x = inputs['X']
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src_index = inputs['Src_index']
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dst_index = inputs['Dst_index']
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reduce_op = attributes['reduce_op']
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gather_x = x[src_index]
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target_shape = list(x.shape)
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results = np.zeros(target_shape, dtype=x.dtype)
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if reduce_op == 'SUM':
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for index, s_id in enumerate(dst_index):
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results[s_id, :] += gather_x[index, :]
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elif reduce_op == 'MEAN':
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count = np.zeros(target_shape[0], dtype=np.int32)
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for index, s_id in enumerate(dst_index):
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results[s_id, :] += gather_x[index, :]
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count[s_id] += 1
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results = results / count.reshape([-1, 1])
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results[np.isnan(results)] = 0
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else:
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raise ValueError("Invalid reduce_op, only SUM, MEAN supported!")
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count = np.zeros(target_shape[0], dtype=np.int32)
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for index, s_id in enumerate(dst_index):
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count[s_id] += 1
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return results, count
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def compute_graph_send_recv_for_min_max(inputs, attributes):
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x = inputs['X']
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src_index = inputs['Src_index']
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dst_index = inputs['Dst_index']
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reduce_op = attributes['reduce_op']
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gather_x = x[src_index]
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target_shape = list(x.shape)
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results = np.zeros(target_shape, dtype=x.dtype)
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gradient = np.zeros_like(x)
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# Calculate forward output
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if reduce_op == "MAX":
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first_set = set()
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for index, s_id in enumerate(dst_index):
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if s_id not in first_set:
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results[s_id, :] += gather_x[index, :]
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first_set.add(s_id)
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else:
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results[s_id, :] = np.maximum(
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results[s_id, :], gather_x[index, :]
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)
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elif reduce_op == "MIN":
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first_set = set()
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for index, s_id in enumerate(dst_index):
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if s_id not in first_set:
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results[s_id, :] += gather_x[index, :]
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first_set.add(s_id)
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else:
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results[s_id, :] = np.minimum(
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results[s_id, :], gather_x[index, :]
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)
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else:
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raise ValueError("Invalid reduce_op, only MAX, MIN supported!")
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# Calculate backward gradient
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index_size = len(src_index)
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for i in range(index_size):
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forward_src_idx = src_index[i]
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forward_dst_idx = dst_index[i]
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gradient[forward_src_idx] += 1 * (
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x[forward_src_idx] == results[forward_dst_idx]
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)
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return results, gradient / results.size
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class API_GraphSendRecvOpTest(unittest.TestCase):
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def test_static(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
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src_index = paddle.static.data(name="src", shape=[4], dtype="int32")
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dst_index = paddle.static.data(name="dst", shape=[4], dtype="int32")
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res_sum = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "sum"
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)
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res_mean = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "mean"
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)
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res_max = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "max"
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)
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res_min = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "min"
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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data1 = np.array([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype='float32')
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data2 = np.array([0, 1, 2, 0], dtype="int32")
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data3 = np.array([1, 2, 1, 0], dtype="int32")
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np_sum = np.array(
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[[0, 2, 3], [2, 8, 10], [1, 4, 5]], dtype="float32"
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)
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np_mean = np.array(
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[[0, 2, 3], [1, 4, 5], [1, 4, 5]], dtype="float32"
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)
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np_max = np.array(
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[[0, 2, 3], [2, 6, 7], [1, 4, 5]], dtype="float32"
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)
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np_min = np.array(
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[[0, 2, 3], [0, 2, 3], [1, 4, 5]], dtype="float32"
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)
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ret = exe.run(
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feed={'x': data1, 'src': data2, 'dst': data3},
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fetch_list=[res_sum, res_mean, res_max, res_min],
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)
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for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
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np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
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def test_dygraph(self):
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paddle.disable_static()
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x = paddle.to_tensor(
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np.array([[0, 2, 3], [1, 4, 5], [2, 6, 7]]), dtype="float32"
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)
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src_index = paddle.to_tensor(np.array([0, 1, 2, 0]), dtype="int32")
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dst_index = paddle.to_tensor(np.array([1, 2, 1, 0]), dtype="int32")
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res_sum = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "sum"
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)
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res_mean = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "mean"
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)
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res_max = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "max"
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)
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res_min = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "min"
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)
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np_sum = np.array([[0, 2, 3], [2, 8, 10], [1, 4, 5]], dtype="float32")
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np_mean = np.array([[0, 2, 3], [1, 4, 5], [1, 4, 5]], dtype="float32")
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np_max = np.array([[0, 2, 3], [2, 6, 7], [1, 4, 5]], dtype="float32")
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np_min = np.array([[0, 2, 3], [0, 2, 3], [1, 4, 5]], dtype="float32")
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ret = [res_sum, res_mean, res_max, res_min]
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for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
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np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
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def test_int32_input(self):
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paddle.disable_static()
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x = paddle.to_tensor(
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np.array([[0, 2, 3], [1, 4, 5], [2, 6, 6]]), dtype="int32"
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)
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src_index = paddle.to_tensor(np.array([0, 1, 2, 0, 1]), dtype="int32")
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dst_index = paddle.to_tensor(np.array([1, 2, 1, 0, 1]), dtype="int32")
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res_sum = paddle.incubate.graph_send_recv(
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x, src_index, dst_index, "sum"
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)
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res_mean = paddle.incubate.graph_send_recv(
|
|
x, src_index, dst_index, "mean"
|
|
)
|
|
res_max = paddle.incubate.graph_send_recv(
|
|
x, src_index, dst_index, "max"
|
|
)
|
|
res_min = paddle.incubate.graph_send_recv(
|
|
x, src_index, dst_index, "min"
|
|
)
|
|
|
|
np_sum = np.array([[0, 2, 3], [3, 12, 14], [1, 4, 5]], dtype="int32")
|
|
np_mean = np.array([[0, 2, 3], [1, 4, 4], [1, 4, 5]], dtype="int32")
|
|
np_max = np.array([[0, 2, 3], [2, 6, 6], [1, 4, 5]], dtype="int32")
|
|
np_min = np.array([[0, 2, 3], [0, 2, 3], [1, 4, 5]], dtype="int32")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
|
|
|
|
def test_set_outsize_gpu(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(
|
|
np.array([[0, 2, 3], [1, 4, 5], [2, 6, 6]]), dtype="float32"
|
|
)
|
|
src_index = paddle.to_tensor(np.array([0, 0, 1]), dtype="int32")
|
|
dst_index = paddle.to_tensor(np.array([0, 1, 1]), dtype="int32")
|
|
res = paddle.incubate.graph_send_recv(x, src_index, dst_index, "sum")
|
|
out_size = paddle.max(dst_index) + 1
|
|
res_set_outsize = paddle.incubate.graph_send_recv(
|
|
x, src_index, dst_index, "sum", out_size
|
|
)
|
|
|
|
np_res = np.array([[0, 2, 3], [1, 6, 8], [0, 0, 0]], dtype="float32")
|
|
np_res_set_outsize = np.array([[0, 2, 3], [1, 6, 8]], dtype="float32")
|
|
|
|
np.testing.assert_allclose(np_res, res, rtol=1e-05, atol=1e-06)
|
|
np.testing.assert_allclose(
|
|
np_res_set_outsize, res_set_outsize, rtol=1e-05, atol=1e-06
|
|
)
|
|
|
|
def test_out_size_tensor_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
|
|
src_index = paddle.static.data(name="src", shape=[3], dtype="int32")
|
|
dst_index = paddle.static.data(name="dst", shape=[3], dtype="int32")
|
|
out_size = paddle.static.data(
|
|
name="out_size", shape=[1], dtype="int32"
|
|
)
|
|
|
|
res_sum = paddle.incubate.graph_send_recv(
|
|
x, src_index, dst_index, "sum", out_size
|
|
)
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
data1 = np.array([[0, 2, 3], [1, 4, 5], [2, 6, 6]], dtype='float32')
|
|
data2 = np.array([0, 0, 1], dtype="int32")
|
|
data3 = np.array([0, 1, 1], dtype="int32")
|
|
data4 = np.array([2], dtype="int32")
|
|
|
|
np_sum = np.array([[0, 2, 3], [1, 6, 8]], dtype="float32")
|
|
|
|
ret = exe.run(
|
|
feed={
|
|
'x': data1,
|
|
'src': data2,
|
|
'dst': data3,
|
|
'out_size': data4,
|
|
},
|
|
fetch_list=[res_sum],
|
|
)
|
|
np.testing.assert_allclose(np_sum, ret[0], rtol=1e-05, atol=1e-06)
|
|
|
|
|
|
class API_GeometricSendURecvTest(unittest.TestCase):
|
|
def test_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
|
|
src_index = paddle.static.data(name="src", shape=[4], dtype="int32")
|
|
dst_index = paddle.static.data(name="dst", shape=[4], dtype="int32")
|
|
|
|
res_sum = paddle.geometric.send_u_recv(
|
|
x, src_index, dst_index, "sum"
|
|
)
|
|
res_mean = paddle.geometric.send_u_recv(
|
|
x, src_index, dst_index, "mean"
|
|
)
|
|
res_max = paddle.geometric.send_u_recv(
|
|
x, src_index, dst_index, "max"
|
|
)
|
|
res_min = paddle.geometric.send_u_recv(
|
|
x, src_index, dst_index, "min"
|
|
)
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
data1 = np.array([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype='float32')
|
|
data2 = np.array([0, 1, 2, 0], dtype="int32")
|
|
data3 = np.array([1, 2, 1, 0], dtype="int32")
|
|
|
|
np_sum = np.array(
|
|
[[0, 2, 3], [2, 8, 10], [1, 4, 5]], dtype="float32"
|
|
)
|
|
np_mean = np.array(
|
|
[[0, 2, 3], [1, 4, 5], [1, 4, 5]], dtype="float32"
|
|
)
|
|
np_max = np.array(
|
|
[[0, 2, 3], [2, 6, 7], [1, 4, 5]], dtype="float32"
|
|
)
|
|
np_min = np.array(
|
|
[[0, 2, 3], [0, 2, 3], [1, 4, 5]], dtype="float32"
|
|
)
|
|
|
|
ret = exe.run(
|
|
feed={'x': data1, 'src': data2, 'dst': data3},
|
|
fetch_list=[res_sum, res_mean, res_max, res_min],
|
|
)
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
|
|
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(
|
|
np.array([[0, 2, 3], [1, 4, 5], [2, 6, 7]]), dtype="float32"
|
|
)
|
|
src_index = paddle.to_tensor(np.array([0, 1, 2, 0]), dtype="int32")
|
|
dst_index = paddle.to_tensor(np.array([1, 2, 1, 0]), dtype="int32")
|
|
res_sum = paddle.geometric.send_u_recv(x, src_index, dst_index, "sum")
|
|
res_mean = paddle.geometric.send_u_recv(x, src_index, dst_index, "mean")
|
|
res_max = paddle.geometric.send_u_recv(x, src_index, dst_index, "max")
|
|
res_min = paddle.geometric.send_u_recv(x, src_index, dst_index, "min")
|
|
|
|
np_sum = np.array([[0, 2, 3], [2, 8, 10], [1, 4, 5]], dtype="float32")
|
|
np_mean = np.array([[0, 2, 3], [1, 4, 5], [1, 4, 5]], dtype="float32")
|
|
np_max = np.array([[0, 2, 3], [2, 6, 7], [1, 4, 5]], dtype="float32")
|
|
np_min = np.array([[0, 2, 3], [0, 2, 3], [1, 4, 5]], dtype="float32")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
|
|
|
|
def test_int32_input(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(
|
|
np.array([[0, 2, 3], [1, 4, 5], [2, 6, 6]]), dtype="int32"
|
|
)
|
|
src_index = paddle.to_tensor(np.array([0, 1, 2, 0, 1]), dtype="int32")
|
|
dst_index = paddle.to_tensor(np.array([1, 2, 1, 0, 1]), dtype="int32")
|
|
res_sum = paddle.geometric.send_u_recv(x, src_index, dst_index, "sum")
|
|
res_mean = paddle.geometric.send_u_recv(x, src_index, dst_index, "mean")
|
|
res_max = paddle.geometric.send_u_recv(x, src_index, dst_index, "max")
|
|
res_min = paddle.geometric.send_u_recv(x, src_index, dst_index, "min")
|
|
|
|
np_sum = np.array([[0, 2, 3], [3, 12, 14], [1, 4, 5]], dtype="int32")
|
|
np_mean = np.array([[0, 2, 3], [1, 4, 4], [1, 4, 5]], dtype="int32")
|
|
np_max = np.array([[0, 2, 3], [2, 6, 6], [1, 4, 5]], dtype="int32")
|
|
np_min = np.array([[0, 2, 3], [0, 2, 3], [1, 4, 5]], dtype="int32")
|
|
|
|
ret = [res_sum, res_mean, res_max, res_min]
|
|
|
|
for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
|
|
np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)
|
|
|
|
def test_set_outsize_gpu(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(
|
|
np.array([[0, 2, 3], [1, 4, 5], [2, 6, 6]]), dtype="float32"
|
|
)
|
|
src_index = paddle.to_tensor(np.array([0, 0, 1]), dtype="int32")
|
|
dst_index = paddle.to_tensor(np.array([0, 1, 1]), dtype="int32")
|
|
res = paddle.geometric.send_u_recv(x, src_index, dst_index, "sum")
|
|
out_size = paddle.max(dst_index) + 1
|
|
res_set_outsize = paddle.geometric.send_u_recv(
|
|
x, src_index, dst_index, "sum", out_size
|
|
)
|
|
|
|
np_res = np.array([[0, 2, 3], [1, 6, 8], [0, 0, 0]], dtype="float32")
|
|
np_res_set_outsize = np.array([[0, 2, 3], [1, 6, 8]], dtype="float32")
|
|
|
|
np.testing.assert_allclose(np_res, res, rtol=1e-05, atol=1e-06)
|
|
np.testing.assert_allclose(
|
|
np_res_set_outsize, res_set_outsize, rtol=1e-05, atol=1e-06
|
|
)
|
|
|
|
def test_out_size_tensor_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
|
|
src_index = paddle.static.data(name="src", shape=[3], dtype="int32")
|
|
dst_index = paddle.static.data(name="dst", shape=[3], dtype="int32")
|
|
out_size = paddle.static.data(
|
|
name="out_size", shape=[1], dtype="int32"
|
|
)
|
|
|
|
res_sum = paddle.geometric.send_u_recv(
|
|
x, src_index, dst_index, "sum", out_size
|
|
)
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
data1 = np.array([[0, 2, 3], [1, 4, 5], [2, 6, 6]], dtype='float32')
|
|
data2 = np.array([0, 0, 1], dtype="int32")
|
|
data3 = np.array([0, 1, 1], dtype="int32")
|
|
data4 = np.array([2], dtype="int32")
|
|
|
|
np_sum = np.array([[0, 2, 3], [1, 6, 8]], dtype="float32")
|
|
|
|
ret = exe.run(
|
|
feed={
|
|
'x': data1,
|
|
'src': data2,
|
|
'dst': data3,
|
|
'out_size': data4,
|
|
},
|
|
fetch_list=[res_sum],
|
|
)
|
|
np.testing.assert_allclose(np_sum, ret[0], rtol=1e-05, atol=1e-06)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|