wehub-resource-sync
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
# Copyright (c) 2021 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 numpy as np
|
|
from op_test_ipu import IPUOpTest
|
|
|
|
import paddle
|
|
import paddle.static
|
|
|
|
|
|
class TestTopKOp(IPUOpTest):
|
|
def setUp(self):
|
|
self.set_atol()
|
|
self.set_training()
|
|
self.set_data_feed()
|
|
self.set_feed_attr()
|
|
self.set_test_op()
|
|
self.set_op_attrs()
|
|
|
|
def set_test_op(self):
|
|
self.op = paddle.topk
|
|
|
|
def set_data_feed(self):
|
|
data = np.random.uniform(size=[3, 5])
|
|
self.feed_fp32 = {"in_0": data.astype(np.float32)}
|
|
self.feed_fp16 = {"in_0": data.astype(np.float16)}
|
|
|
|
def set_feed_attr(self):
|
|
self.feed_shape = [x.shape for x in self.feed_fp32.values()]
|
|
self.feed_list = list(self.feed_fp32.keys())
|
|
|
|
def set_op_attrs(self):
|
|
self.use_k_as_const_variable = False
|
|
self.attrs = {}
|
|
if not self.use_k_as_const_variable:
|
|
self.attrs["k"] = 3
|
|
|
|
@IPUOpTest.static_graph
|
|
def build_model(self):
|
|
x = paddle.static.data(
|
|
name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
|
|
)
|
|
if not self.use_k_as_const_variable:
|
|
topk_values, topk_indices = self.op(x, **self.attrs)
|
|
else:
|
|
# !important, popart cannot accept non const tensor
|
|
K_t = paddle.tensor.fill_constant(
|
|
shape=[1], dtype='int32', value=self.k, name="in_2"
|
|
)
|
|
topk_values, topk_indices = self.op(x, K_t, **self.attrs)
|
|
self.fetch_list = [topk_values.name, topk_indices.name]
|
|
|
|
def run_model(self, exec_mode):
|
|
self.run_op_test(exec_mode)
|
|
|
|
def test(self):
|
|
for m in IPUOpTest.ExecutionMode:
|
|
if not self.skip_mode(m):
|
|
self.build_model()
|
|
self.run_model(m)
|
|
|
|
value_dict = {}
|
|
index_dict = {}
|
|
for k, v in self.output_dict.items():
|
|
value_dict[k] = v[0]
|
|
index_dict[k] = v[1]
|
|
self.check(output_dict=value_dict)
|
|
self.check(output_dict=index_dict)
|
|
|
|
|
|
class TestCase2(TestTopKOp):
|
|
def set_test_op(self):
|
|
self.op = paddle.topk
|
|
|
|
|
|
@unittest.skip("Trying to get data as int64 but it is of type int32")
|
|
class TestCase3(TestTopKOp):
|
|
def set_op_attrs(self):
|
|
self.use_k_as_const_variable = True
|
|
self.attrs = {}
|
|
self.k = 2
|
|
|
|
|
|
@unittest.skip("Trying to get data as int64 but it is of type int32")
|
|
class TestCase4(TestCase3):
|
|
def set_test_op(self):
|
|
self.op = paddle.topk
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|