From a6aa782ee5b0bfdb88c3b00fccbb72a6ae904195 Mon Sep 17 00:00:00 2001 From: phlrain Date: Fri, 30 Nov 2018 21:49:14 +0800 Subject: [PATCH] add unitest --- .../tests/unittests/test_lstm_cudnn_op.py | 154 ++++++++++++++++++ 1 file changed, 154 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py diff --git a/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py new file mode 100644 index 000000000..2741bf167 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py @@ -0,0 +1,154 @@ +# 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 + +import paddle.fluid.core as core +from op_test import OpTest + + +def lstm_naive( + input, + w, ): + seq_len, batch_size, hidden_size = input.shape + + offset = 0 + wi = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + wf = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + wc = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + wo = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + ri = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + rf = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + rc = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + ro = w[offset:offset + hidden_size * hidden_size].reshape( + (hidden_size, hidden_size)).transpose() + offset += hidden_size * hidden_size + + bi_1 = w[offset:offset + hidden_size] + offset += hidden_size + bf_1 = w[offset:offset + hidden_size] + offset += hidden_size + bc_1 = w[offset:offset + hidden_size] + offset += hidden_size + bo_1 = w[offset:offset + hidden_size] + offset += hidden_size + + bi_2 = w[offset:offset + hidden_size] + offset += hidden_size + bf_2 = w[offset:offset + hidden_size] + offset += hidden_size + bc_2 = w[offset:offset + hidden_size] + offset += hidden_size + bo_2 = w[offset:offset + hidden_size] + + def sigmoid(x): + return 1.0 / (1.0 + np.exp(-x)) + + def tanh(x): + return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x)) + + output = [] + pre_h = np.zeros((batch_size, hidden_size), dtype=input.dtype) + pre_c = np.zeros((batch_size, hidden_size), dtype=input.dtype) + + for i in range(seq_len): + emb_1 = input[i] + + input_gate = sigmoid( + np.matmul(emb_1, wi) + np.matmul(pre_h, ri) + bi_1 + bi_2) + forget_gate = sigmoid( + np.matmul(emb_1, wf) + np.matmul(pre_h, rf) + bf_1 + bf_2) + output_gate = sigmoid( + np.matmul(emb_1, wo) + np.matmul(pre_h, ro) + bo_1 + bo_2) + c_t_temp = tanh( + np.matmul(emb_1, wc) + np.matmul(pre_h, rc) + bc_1 + bc_2) + new_c = input_gate * c_t_temp + forget_gate * pre_c + new_h = output_gate * tanh(new_c) + + pre_h = new_h + pre_c = new_c + + output.append(new_h) + + output = np.concatenate(output, -1) + output = output.reshape((batch_size, -1, hidden_size)) + + output = output.transpose((1, 0, 2)) + + return output + + +class TestCUDNNLstmOp(OpTest): + def setUp(self): + self.op_type = "cudnn_lstm" + self.dtype = np.float32 + + num_steps = 50 + batch_size = 20 + hidden_size = 200 + + input_weight_size = (hidden_size * hidden_size) * 4 + hidden_weight_size = (hidden_size * hidden_size) * 4 + weight_size = input_weight_size + hidden_weight_size + weight_size += hidden_size * 8 + + input = np.random.random( + (num_steps, batch_size, hidden_size)).astype(self.dtype) + flat_w = np.random.random((weight_size)).astype(self.dtype) + + output = lstm_naive(input, flat_w) + + init_h = np.zeros((batch_size, hidden_size), dtype=np.float32) + init_c = np.zeros((batch_size, hidden_size), dtype=np.float32) + self.inputs = { + 'Input': OpTest.np_dtype_to_fluid_dtype(input), + 'W': OpTest.np_dtype_to_fluid_dtype(flat_w), + 'InitH': OpTest.np_dtype_to_fluid_dtype(init_h), + 'InitC': OpTest.np_dtype_to_fluid_dtype(init_c), + } + self.attrs = { + 'max_len': num_steps, + 'dropout_prob': 0.0, + 'is_bidirec': False, + 'input_size': hidden_size, + 'hidden_size': hidden_size, + 'num_layers': 1, + } + self.outputs = {'Out': output} + + def test_grad_with_place(self): + place = core.CUDAPlace(0) + self.check_grad_with_place(place, atol=1e-5) + + def test_output_with_place(self): + place = core.CUDAPlace(0) + self.check_output_with_place( + place, atol=1e-5, no_check_set=['last_h', 'last_c']) -- GitLab