You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
331 lines
11 KiB
331 lines
11 KiB
# 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.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest, skip_check_grad_ci, check_out_dtype
|
|
import paddle.fluid.core as core
|
|
from paddle.fluid.op import Operator
|
|
import paddle.compat as cpt
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import Program, program_guard
|
|
import paddle.nn.functional as F
|
|
|
|
|
|
class TestLookupTableOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "lookup_table"
|
|
table = np.random.random((17, 31)).astype("float64")
|
|
ids = np.random.randint(0, 17, 4).astype("int64")
|
|
ids_expand = np.expand_dims(ids, axis=1)
|
|
self.inputs = {'W': table, 'Ids': ids_expand}
|
|
self.outputs = {'Out': table[ids]}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
|
|
|
|
|
|
class TestLookupTableOpWithTensorIds(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "lookup_table"
|
|
table = np.random.random((17, 31)).astype("float64")
|
|
ids = np.random.randint(
|
|
low=0, high=17, size=(2, 4, 5, 1)).astype("int64")
|
|
self.inputs = {'W': table, 'Ids': ids}
|
|
self.outputs = {'Out': table[ids.flatten()].reshape((2, 4, 5, 31))}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="Since paddings are not trainable and fixed in forward,"
|
|
"the gradient of paddings makes no sense and we don't "
|
|
"test the gradient here.")
|
|
class TestLookupTableOpWithPadding(TestLookupTableOp):
|
|
def test_check_output(self):
|
|
ids = np.squeeze(self.inputs['Ids'])
|
|
padding_idx = np.random.choice(ids, 1)[0]
|
|
self.outputs['Out'][ids == padding_idx] = np.zeros(31)
|
|
self.attrs = {'padding_idx': int(padding_idx)}
|
|
self.check_output()
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="Since paddings are not trainable and fixed in forward,"
|
|
"the gradient of paddings makes no sense and we don't "
|
|
"test the gradient here.")
|
|
class TestLookupTableOpWithTensorIdsAndPadding(TestLookupTableOpWithTensorIds):
|
|
def test_check_output(self):
|
|
ids = self.inputs['Ids']
|
|
flatten_idx = ids.flatten()
|
|
padding_idx = np.random.choice(flatten_idx, 1)[0]
|
|
self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31)
|
|
self.attrs = {'padding_idx': cpt.long_type(padding_idx)}
|
|
self.check_output()
|
|
|
|
|
|
class TestLookupTableWIsSelectedRows(unittest.TestCase):
|
|
def prepare_ids(self, scope, place):
|
|
ids_tensor = scope.var('Ids').get_tensor()
|
|
ids_array = np.array([[0], [4], [3], [5]]).astype("int64")
|
|
ids_tensor.set(ids_array, place)
|
|
return ids_array
|
|
|
|
def prepare_w(self, scope, place):
|
|
rows = [0, 1, 2, 3, 4, 5, 6]
|
|
row_numel = 12
|
|
|
|
w_selected_rows = scope.var('W').get_selected_rows()
|
|
w_selected_rows.set_height(len(rows))
|
|
w_selected_rows.set_rows(rows)
|
|
w_array = np.ones((len(rows), row_numel)).astype("float32")
|
|
for i in range(len(rows)):
|
|
w_array[i] *= i
|
|
w_tensor = w_selected_rows.get_tensor()
|
|
w_tensor.set(w_array, place)
|
|
|
|
def create_out_tensor(self, scope, place):
|
|
return scope.var('Out').get_tensor()
|
|
|
|
def check_result(self, ids_array, result_array):
|
|
# all(): return True if all elements of the iterable are true (or if the iterable is empty)
|
|
for idx, row in enumerate(ids_array):
|
|
assert (row[0] == result_array[idx]).all()
|
|
|
|
def check_with_place(self, place):
|
|
scope = core.Scope()
|
|
|
|
ids_array = self.prepare_ids(scope, place)
|
|
|
|
self.prepare_w(scope, place)
|
|
|
|
out_tensor = self.create_out_tensor(scope, place)
|
|
|
|
# create and run lookup_table operator
|
|
lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
|
|
lookup_table.run(scope, place)
|
|
|
|
# get result from Out
|
|
result_array = np.array(out_tensor)
|
|
|
|
self.check_result(ids_array, result_array)
|
|
|
|
def test_w_is_selected_rows(self):
|
|
places = [core.CPUPlace()]
|
|
# currently only support CPU
|
|
for place in places:
|
|
self.check_with_place(place)
|
|
|
|
|
|
class TestLookupTableWithTensorIdsWIsSelectedRows(
|
|
TestLookupTableWIsSelectedRows):
|
|
def prepare_ids(self, scope, place):
|
|
ids_tensor = scope.var('Ids').get_tensor()
|
|
ids_array = np.random.randint(
|
|
low=0, high=6, size=(2, 4, 3, 1)).astype("int64")
|
|
ids_tensor.set(ids_array, place)
|
|
return ids_array
|
|
|
|
def check_result(self, ids_array, result_array):
|
|
for idx, row in np.ndenumerate(ids_array):
|
|
assert (row == result_array[idx]).all()
|
|
|
|
|
|
class TestEmbedOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
input_data = np.random.randint(0, 10, (4, 1)).astype("int64")
|
|
|
|
def test_Variable():
|
|
# the input type must be Variable
|
|
fluid.layers.embedding(input=input_data, size=(10, 64))
|
|
|
|
self.assertRaises(TypeError, test_Variable)
|
|
|
|
def test_input_dtype():
|
|
# the input dtype must be int64
|
|
input = fluid.data(name='x', shape=[4, 1], dtype='float32')
|
|
fluid.layers.embedding(input=input, size=(10, 64))
|
|
|
|
self.assertRaises(TypeError, test_input_dtype)
|
|
|
|
def test_param_dtype():
|
|
# dtype must be float32 or float64
|
|
input2 = fluid.data(name='x2', shape=[4, 1], dtype='int64')
|
|
fluid.layers.embedding(
|
|
input=input2, size=(10, 64), dtype='int64')
|
|
|
|
self.assertRaises(TypeError, test_param_dtype)
|
|
|
|
input3 = fluid.data(name='x3', shape=[4, 1], dtype='int64')
|
|
fluid.layers.embedding(input=input3, size=(10, 64), dtype='float16')
|
|
|
|
|
|
class TestLookupTableOpInt8(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "lookup_table"
|
|
table = np.random.randint(
|
|
low=-128, high=127, size=(17, 31)).astype("int8")
|
|
ids = np.random.randint(0, 17, 4).astype("int64")
|
|
ids_expand = np.expand_dims(ids, axis=1)
|
|
self.inputs = {'W': table, 'Ids': ids_expand}
|
|
self.outputs = {'Out': table[ids]}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
# since int8 type only be used in test and inference, there is
|
|
# no gradient implement, so we don't need to test it
|
|
pass
|
|
|
|
|
|
class TestLookupTableOpWithTensorIdsInt8(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "lookup_table"
|
|
table = np.random.randint(
|
|
low=-128, high=127, size=(17, 31)).astype("int8")
|
|
ids = np.random.randint(
|
|
low=0, high=17, size=(2, 4, 5, 1)).astype("int64")
|
|
self.inputs = {'W': table, 'Ids': ids}
|
|
self.outputs = {'Out': table[ids.flatten()].reshape((2, 4, 5, 31))}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
# since int8 type only be used in test and inference, there is
|
|
# no gradient implement, so we don't need to test it
|
|
pass
|
|
|
|
|
|
class TestLookupTableOpWithPaddingInt8(TestLookupTableOpInt8):
|
|
def test_check_output(self):
|
|
ids = np.squeeze(self.inputs['Ids'])
|
|
padding_idx = np.random.choice(ids, 1)[0]
|
|
self.outputs['Out'][ids == padding_idx] = np.zeros(31)
|
|
self.attrs = {'padding_idx': int(padding_idx)}
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
# Since paddings are not trainable and fixed in forward, the gradient of
|
|
# paddings makes no sense and we don't test the gradient here.
|
|
pass
|
|
|
|
|
|
class TestLookupTableOpWithTensorIdsAndPaddingInt8(
|
|
TestLookupTableOpWithTensorIdsInt8):
|
|
def test_check_output(self):
|
|
ids = self.inputs['Ids']
|
|
flatten_idx = ids.flatten()
|
|
padding_idx = np.random.choice(flatten_idx, 1)[0]
|
|
self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31)
|
|
self.attrs = {'padding_idx': cpt.long_type(padding_idx)}
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
# Since paddings are not trainable and fixed in forward, the gradient of
|
|
# paddings makes no sense and we don't test the gradient here.
|
|
pass
|
|
|
|
|
|
class TestLookupTableWIsSelectedRowsInt8(unittest.TestCase):
|
|
def prepare_ids(self, scope, place):
|
|
ids_tensor = scope.var('Ids').get_tensor()
|
|
ids_array = np.array([[0], [4], [3], [5]]).astype("int64")
|
|
ids_tensor.set(ids_array, place)
|
|
return ids_array
|
|
|
|
def prepare_w(self, scope, place):
|
|
rows = [0, 1, 2, 3, 4, 5, 6]
|
|
row_numel = 12
|
|
|
|
w_selected_rows = scope.var('W').get_selected_rows()
|
|
w_selected_rows.set_height(len(rows))
|
|
w_selected_rows.set_rows(rows)
|
|
w_array = np.ones((len(rows), row_numel)).astype("int8")
|
|
for i in range(len(rows)):
|
|
w_array[i] *= i
|
|
w_tensor = w_selected_rows.get_tensor()
|
|
w_tensor.set(w_array, place)
|
|
|
|
def create_out_tensor(self, scope, place):
|
|
return scope.var('Out').get_tensor()
|
|
|
|
def check_result(self, ids_array, result_array):
|
|
# all(): return True if all elements of the iterable are true (or if the iterable is empty)
|
|
for idx, row in enumerate(ids_array):
|
|
assert (row[0] == result_array[idx]).all()
|
|
|
|
def check_with_place(self, place):
|
|
scope = core.Scope()
|
|
|
|
ids_array = self.prepare_ids(scope, place)
|
|
|
|
self.prepare_w(scope, place)
|
|
|
|
out_tensor = self.create_out_tensor(scope, place)
|
|
|
|
# create and run lookup_table operator
|
|
lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
|
|
lookup_table.run(scope, place)
|
|
|
|
# get result from Out
|
|
result_array = np.array(out_tensor)
|
|
|
|
self.check_result(ids_array, result_array)
|
|
|
|
def test_w_is_selected_rows(self):
|
|
places = [core.CPUPlace()]
|
|
# currently only support CPU
|
|
for place in places:
|
|
self.check_with_place(place)
|
|
|
|
|
|
class TestLookupTableWithTensorIdsWIsSelectedRowsInt8(
|
|
TestLookupTableWIsSelectedRowsInt8):
|
|
def prepare_ids(self, scope, place):
|
|
ids_tensor = scope.var('Ids').get_tensor()
|
|
ids_array = np.random.randint(
|
|
low=0, high=6, size=(2, 4, 3, 1)).astype("int64")
|
|
ids_tensor.set(ids_array, place)
|
|
return ids_array
|
|
|
|
def check_result(self, ids_array, result_array):
|
|
for idx, row in np.ndenumerate(ids_array):
|
|
assert (row == result_array[idx]).all()
|
|
|
|
|
|
class TestOutDtype(unittest.TestCase):
|
|
def test_dtype(self):
|
|
api_fn = F.embedding
|
|
check_out_dtype(
|
|
api_fn,
|
|
in_specs=[([10, 16], 'int64'), ([100, 64], )],
|
|
expect_dtypes=['float32', 'float64'],
|
|
target_index=1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|