wehub-resource-sync
302 lines
9.6 KiB
Python
302 lines
9.6 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 (
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OpTest,
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convert_float_to_uint16,
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convert_uint16_to_float,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.framework import core
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SEED = 2021
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np.random.seed(SEED)
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def get_c_embedding(start, end, table, ids):
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index = ids.flatten()
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input_mask = (index < start) | (index >= end)
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masked_input = index - start
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masked_input[input_mask] = 0
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output = table[masked_input]
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output[input_mask] = 0.0
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return output
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def c_embedding_wrapper(table, index, start_index=0, vocab_size=-1):
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return paddle._C_ops.c_embedding(table, index, start_index, vocab_size)
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class TestCEmbeddingCPU(OpTest):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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if core.is_compiled_with_xpu():
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self.__class__.use_xpu = True
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elif core.is_compiled_with_cuda():
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self.__class__.exist_fp64_check_grad = True
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def initcase(self):
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self.op_type = "c_embedding"
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self.python_api = c_embedding_wrapper
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table = np.random.random((17, 64)).astype(self.dtype)
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ids = np.random.randint(low=0, high=17 * 2, size=(2, 4)).astype(
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self.ids_dtype
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)
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self.start_index = 10
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self.end_index = self.start_index + 17
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self.vocab_size = 34
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self.inputs = {'W': table, 'Ids': ids}
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np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
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self.outputs = {'Out': np_out.reshape((2, 4, 64))}
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self.attrs = {
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'start_index': self.start_index,
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'vocab_size': self.vocab_size,
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}
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if core.is_compiled_with_xpu():
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self.__class__.use_xpu = True
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace())
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def test_check_grad(self):
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self.check_grad_with_place(core.CPUPlace(), ['W'], 'Out')
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def init_dtype(self):
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self.dtype = "float32"
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self.ids_dtype = "int64"
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class TestCEmbeddingOpBase(TestCEmbeddingCPU):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place())
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elif core.is_compiled_with_xpu():
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self.check_output_with_place(core.XPUPlace(0))
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else:
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current_place = paddle.framework._current_expected_place()
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if isinstance(current_place, paddle.CustomPlace):
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self.check_output_with_place(current_place)
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def test_check_grad(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(get_device_place(), ['W'], 'Out')
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elif core.is_compiled_with_xpu():
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self.check_grad_with_place(core.XPUPlace(0), ['W'], 'Out')
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else:
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current_place = paddle.framework._current_expected_place()
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if isinstance(current_place, paddle.CustomPlace):
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self.check_grad_with_place(current_place, ['W'], 'Out')
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def init_dtype(self):
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if core.is_compiled_with_cuda():
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self.dtype = "float64"
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self.ids_dtype = "int64"
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elif core.is_compiled_with_xpu():
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self.dtype = "float32"
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self.ids_dtype = "int64"
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else:
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current_place = paddle.framework._current_expected_place()
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if isinstance(current_place, paddle.CustomPlace):
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self.dtype = "float32"
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self.ids_dtype = "int64"
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class TestCEmbeddingOpFP32(TestCEmbeddingOpBase):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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def initcase(self):
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self.op_type = "c_embedding"
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self.python_api = c_embedding_wrapper
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table = np.random.random((17, 64)).astype(self.dtype)
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ids = np.random.randint(low=0, high=17 * 2, size=(2, 4)).astype(
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self.ids_dtype
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)
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self.start_index = 10
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ids[0][1] = 12
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ids[0][2] = 12
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ids[1][2] = 12
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ids[1][3] = 12
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self.end_index = self.start_index + 17
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self.inputs = {'W': table, 'Ids': ids}
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np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
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self.outputs = {'Out': np_out.reshape((2, 4, 64))}
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self.attrs = {'start_index': self.start_index}
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if core.is_compiled_with_xpu():
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self.__class__.use_xpu = True
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elif core.is_compiled_with_cuda():
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self.__class__.exist_fp64_check_grad = True
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def init_dtype(self):
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self.dtype = "float32"
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self.ids_dtype = "int32"
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class TestCEmbeddingOpFP16(TestCEmbeddingOpBase):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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def initcase(self):
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self.op_type = "c_embedding"
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self.python_api = c_embedding_wrapper
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table = np.random.random((17, 64)).astype(self.dtype)
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ids = np.random.randint(low=0, high=17 * 2, size=(2, 4)).astype(
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self.ids_dtype
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)
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self.start_index = 10
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ids[0][1] = 12
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ids[0][2] = 12
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ids[1][2] = 12
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ids[1][3] = 12
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self.end_index = self.start_index + 17
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self.inputs = {'W': table, 'Ids': ids}
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np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
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self.outputs = {'Out': np_out.reshape((2, 4, 64))}
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self.attrs = {'start_index': self.start_index}
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if core.is_compiled_with_xpu():
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self.__class__.use_xpu = True
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elif core.is_compiled_with_cuda():
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self.__class__.exist_fp64_check_grad = True
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def init_dtype(self):
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self.dtype = "float16"
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self.ids_dtype = "int32"
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class TestCEmbeddingOpBF16(TestCEmbeddingOpBase):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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def initcase(self):
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self.op_type = "c_embedding"
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self.python_api = c_embedding_wrapper
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table = np.random.random((17, 64)).astype('float32')
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table_bf16 = convert_float_to_uint16(table)
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table = convert_uint16_to_float(table_bf16)
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ids = np.random.randint(low=0, high=17 * 2, size=(2, 4)).astype(
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self.ids_dtype
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)
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self.start_index = 10
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ids[0][1] = 12
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ids[0][2] = 12
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ids[1][2] = 12
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ids[1][3] = 12
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self.end_index = self.start_index + 17
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self.inputs = {'W': table_bf16, 'Ids': ids}
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np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
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np_out = convert_float_to_uint16(np_out)
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self.outputs = {'Out': np_out.reshape((2, 4, 64))}
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self.attrs = {'start_index': self.start_index}
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if core.is_compiled_with_xpu():
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self.__class__.use_xpu = True
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elif core.is_compiled_with_cuda():
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self.__class__.exist_fp64_check_grad = True
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def init_dtype(self):
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self.dtype = np.uint16
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self.ids_dtype = "int32"
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class TestCEmbeddingOpComplex64(TestCEmbeddingOpBase):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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def initcase(self):
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self.op_type = "c_embedding"
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self.python_api = c_embedding_wrapper
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table = (
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np.random.random((17, 64)) + 1j * np.random.random((17, 64))
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).astype(self.dtype)
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ids = np.random.randint(low=0, high=17 * 2, size=(2, 4)).astype(
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self.ids_dtype
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)
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self.start_index = 10
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ids[0][1] = 12
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ids[0][2] = 12
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ids[1][2] = 12
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ids[1][3] = 12
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self.end_index = self.start_index + 17
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self.inputs = {'W': table, 'Ids': ids}
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np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
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self.outputs = {'Out': np_out.reshape((2, 4, 64))}
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self.attrs = {'start_index': self.start_index}
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if core.is_compiled_with_cuda():
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self.__class__.exist_fp64_check_grad = True
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def init_dtype(self):
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self.dtype = "complex64"
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self.ids_dtype = "int32"
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class TestCEmbeddingOpComplex128(TestCEmbeddingOpBase):
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def setUp(self):
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self.init_dtype()
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self.initcase()
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def initcase(self):
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self.op_type = "c_embedding"
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self.python_api = c_embedding_wrapper
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table = (
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np.random.random((17, 64)) + 1j * np.random.random((17, 64))
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).astype(self.dtype)
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ids = np.random.randint(low=0, high=17 * 2, size=(2, 4)).astype(
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self.ids_dtype
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)
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self.start_index = 10
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ids[0][1] = 12
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ids[0][2] = 12
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ids[1][2] = 12
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ids[1][3] = 12
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self.end_index = self.start_index + 17
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self.inputs = {'W': table, 'Ids': ids}
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np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
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self.outputs = {'Out': np_out.reshape((2, 4, 64))}
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self.attrs = {'start_index': self.start_index}
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if core.is_compiled_with_cuda():
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self.__class__.exist_fp64_check_grad = True
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def init_dtype(self):
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self.dtype = "complex128"
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self.ids_dtype = "int32"
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if __name__ == "__main__":
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unittest.main()
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