wehub-resource-sync
285 lines
7.8 KiB
Python
285 lines
7.8 KiB
Python
# Copyright (c) 2018 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 random
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import sys
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import unittest
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from pathlib import Path
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import numpy as np
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from op_test import OpTest, get_device_place
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import paddle
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from paddle.base import core
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# Add test/rnn to sys.path
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legacy_test_dir = Path(__file__).resolve().parents[1]
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sys.path.append(str(legacy_test_dir / "rnn"))
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sys.path.append("../../rnn")
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from convert import get_params_for_net
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from rnn_numpy import LSTM
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random.seed(2)
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np.set_printoptions(threshold=np.inf)
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paddle.enable_static()
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def rnn_wrapper(
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Input,
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PreState,
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WeightList=None,
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SequenceLength=None,
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dropout_prob=0.0,
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is_bidirec=False,
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input_size=10,
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hidden_size=100,
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num_layers=1,
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mode="LSTM",
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seed=0,
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is_test=False,
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):
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dropout_state_in = paddle.tensor.fill_constant([], "float32", 0.0)
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return paddle._C_ops.rnn(
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Input,
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PreState,
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WeightList,
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SequenceLength,
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dropout_state_in,
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dropout_prob,
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is_bidirec,
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input_size,
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hidden_size,
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num_layers,
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mode,
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seed,
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is_test,
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)
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class TestRNNOp(OpTest):
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def get_weight_names(self):
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weight_names = []
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for i in range(self.num_layers):
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for j in range(0, 2 * self.direction_num):
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weight_names.append(f"{i}.weight_{j}")
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for i in range(self.num_layers):
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for j in range(0, 2 * self.direction_num):
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weight_names.append(f"{i}.bias_{j}")
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return weight_names
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def setUp(self):
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self.op_type = "rnn"
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self.python_api = rnn_wrapper
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self.python_out_sig = ["Out", "DropoutState", "State"]
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self.python_out_sig_sub_name = {"State": ["last_hidden", "last_cell"]}
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self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
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self.sequence_length = (
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None
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if core.is_compiled_with_rocm()
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else np.array([12, 11, 10, 9, 8], dtype=np.int32)
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)
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self.num_layers = 1
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self.is_bidirec = False
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self.mode = "LSTM"
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self.is_test = False
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self.dropout = 0.0
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self.set_attrs()
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self.direction_num = 2 if self.is_bidirec else 1
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direction = "bidirectional" if self.is_bidirec else "forward"
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seq_length = 12
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batch_size = 5
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input_size = 3
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hidden_size = 2
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input = np.random.uniform(
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low=-0.1, high=0.1, size=(seq_length, batch_size, input_size)
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).astype(self.dtype)
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if self.sequence_length is not None:
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input[11][1:][:] = 0
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input[10][2:][:] = 0
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input[9][3:][:] = 0
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input[8][4:][:] = 0
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rnn1 = LSTM(
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input_size,
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hidden_size,
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num_layers=self.num_layers,
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time_major=True,
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direction=direction,
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dropout=self.dropout,
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dtype=self.dtype,
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)
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flat_w = get_params_for_net(rnn1)
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output, (last_hidden, last_cell) = rnn1(
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input, sequence_length=self.sequence_length
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)
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if core.is_compiled_with_rocm():
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def rocm_rnn_get_place():
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places = [get_device_place()]
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return places
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self._get_places = rocm_rnn_get_place
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init_h = np.zeros(
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(self.num_layers * self.direction_num, batch_size, hidden_size)
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).astype(self.dtype)
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init_c = np.zeros(
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(self.num_layers * self.direction_num, batch_size, hidden_size)
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).astype(self.dtype)
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state_out = np.ndarray(300).astype("uint8")
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self.inputs = {
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'Input': input,
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'WeightList': flat_w,
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'PreState': [('init_h', init_h), ('init_c', init_c)],
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'SequenceLength': self.sequence_length,
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}
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if self.sequence_length is None:
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self.inputs = {
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'Input': input,
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'WeightList': flat_w,
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'PreState': [('init_h', init_h), ('init_c', init_c)],
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}
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self.attrs = {
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'dropout_prob': self.dropout,
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'is_bidirec': self.is_bidirec,
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'input_size': input_size,
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'hidden_size': hidden_size,
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'num_layers': self.num_layers,
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'mode': self.mode,
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'is_test': self.is_test,
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}
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self.outputs = {
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'Out': output,
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"State": [('last_hidden', last_hidden), ('last_cell', last_cell)],
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'Reserve': np.ndarray(400).astype("uint8"),
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'DropoutState': state_out,
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}
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def test_output(self):
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self.check_output(
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no_check_set=['Reserve', 'DropoutState'],
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check_pir=True,
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check_symbol_infer=False,
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)
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def set_attrs(self):
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pass
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def test_grad(self):
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if not self.is_test:
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var_name_list = self.get_weight_names()
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grad_check_list = ['Input', 'init_h', 'init_c']
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grad_check_list.extend(var_name_list)
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self.check_grad(
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set(grad_check_list),
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['Out', 'last_hidden', 'last_cell'],
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check_pir=True,
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)
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def test_grad_only_input(self):
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if not self.is_test:
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var_name_list = self.get_weight_names()
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grad_check_list = ['Input']
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grad_check_list.extend(var_name_list)
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self.check_grad(
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set(grad_check_list),
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['Out', 'last_hidden', 'last_cell'],
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check_pir=True,
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)
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def test_grad_only_h(self):
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if not self.is_test:
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var_name_list = self.get_weight_names()
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grad_check_list = ['init_h']
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grad_check_list.extend(var_name_list)
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self.check_grad(
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set(grad_check_list),
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['Out', 'last_hidden', 'last_cell'],
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check_pir=True,
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)
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def test_grad_only_c(self):
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if not self.is_test:
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var_name_list = self.get_weight_names()
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grad_check_list = ['init_c']
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grad_check_list.extend(var_name_list)
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self.check_grad(
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set(grad_check_list),
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['Out', 'last_hidden', 'last_cell'],
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check_pir=True,
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)
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class TestRNNOp1(TestRNNOp):
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def set_attrs(self):
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self.sequence_length = None
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class TestRNNOp2(TestRNNOp):
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def set_attrs(self):
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self.sequence_length = None
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self.is_bidirec = True
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class TestRNNOp3(TestRNNOp):
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def set_attrs(self):
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self.is_test = True
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self.sequence_length = None
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class TestRNNOp4(TestRNNOp):
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def set_attrs(self):
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self.is_test = True
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self.sequence_length = None
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self.is_bidirec = True
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class TestRNNOp5(TestRNNOp):
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def set_attrs(self):
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self.num_layers = 2
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class TestRNNOp6(TestRNNOp):
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def set_attrs(self):
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self.num_layers = 2
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self.is_bidirec = True
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class TestRNNOp7(TestRNNOp):
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def set_attrs(self):
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self.num_layers = 2
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self.is_bidirec = True
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self.is_test = True
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class TestRNNOp8(TestRNNOp):
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def set_attrs(self):
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self.num_layers = 2
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self.is_bidirec = True
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self.sequence_length = None
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class TestRNNOp9(TestRNNOp):
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def set_attrs(self):
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self.num_layers = 3
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if __name__ == '__main__':
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unittest.main()
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