From ba981604fdf6e50041453d47369d113e2d5a65e0 Mon Sep 17 00:00:00 2001 From: JiabinYang Date: Fri, 25 Jan 2019 13:05:49 +0000 Subject: [PATCH] fix split --- paddle/fluid/framework/operator.cc | 21 +- python/paddle/fluid/imperative/nn.py | 12 +- .../fluid/tests/unittests/test_imperative.py | 1 - .../unittests/test_imperative_ptb_rnn.py | 265 ++++++++++++++++++ .../tests/unittests/test_imperative_split.py | 45 +++ 5 files changed, 322 insertions(+), 22 deletions(-) create mode 100644 python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py create mode 100644 python/paddle/fluid/tests/unittests/test_imperative_split.py diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index ec5cd1c4c..a8cc66b12 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -555,18 +555,17 @@ Tensor* ExecutionContext::LegacyOutput(const std::string& name) const { template <> std::vector ExecutionContext::MultiOutput( const std::string& name) const { - auto names = op().Outputs(name); + auto it = ctx_.outputs.find(name); + if (it == ctx_.outputs.end()) { + return {}; + } + const std::vector& vars = it->second; std::vector res; - res.reserve(names.size()); - std::transform(names.begin(), names.end(), std::back_inserter(res), - [&](const std::string& sub_name) -> Tensor* { - auto var = scope_.FindVar(sub_name); - if (var == nullptr) return nullptr; - PADDLE_ENFORCE( - var->IsType(), - "%s should be LoDTensor, but the received type is %s", - sub_name, ToTypeName(var->Type())); - return var->GetMutable(); + res.reserve(vars.size()); + std::transform(vars.begin(), vars.end(), std::back_inserter(res), + [&](Variable* var) -> Tensor* { + return var == nullptr ? nullptr + : var->GetMutable(); }); return res; } diff --git a/python/paddle/fluid/imperative/nn.py b/python/paddle/fluid/imperative/nn.py index 68fffdfa3..b5c049e92 100644 --- a/python/paddle/fluid/imperative/nn.py +++ b/python/paddle/fluid/imperative/nn.py @@ -22,13 +22,7 @@ from . import layers from ..framework import Variable, OpProtoHolder from ..param_attr import ParamAttr from ..initializer import Normal, Constant -__all__ = [ - 'Conv2D', - 'Pool2D', - 'FC', - 'BatchNorm', - 'EMBEDDING' -] +__all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'EMBEDDING'] class Conv2D(layers.Layer): @@ -419,8 +413,6 @@ class BatchNorm(layers.Layer): # Currently, we don't support inplace in imperative mode return self._helper.append_activation(batch_norm_out) - outputs={'Out': [bias_out]}, - class EMBEDDING(layers.Layer): @@ -438,7 +430,7 @@ class EMBEDDING(layers.Layer): self._is_distributed = is_distributed self._padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else ( - size[0] + padding_idx) + size[0] + padding_idx) self._param_attr = param_attr self._dtype = dtype diff --git a/python/paddle/fluid/tests/unittests/test_imperative.py b/python/paddle/fluid/tests/unittests/test_imperative.py index fab60ae75..6cfac57f5 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative.py +++ b/python/paddle/fluid/tests/unittests/test_imperative.py @@ -338,7 +338,6 @@ class TestImperative(unittest.TestCase): dy_grad_i2h = simple_rnn._cell._i2h_w._gradient() with new_program_scope(): - print("im here") inp = fluid.layers.data( name="inp", shape=[1, 4, 3], append_batch_size=False) simple_rnn = SimpleRNN() diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py new file mode 100644 index 000000000..c64d5964e --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py @@ -0,0 +1,265 @@ +# 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 paddle.fluid as fluid +from paddle.fluid.imperative.nn import EMBEDDING +import paddle.fluid.framework as framework +from paddle.fluid.optimizer import SGDOptimizer +from paddle.fluid.imperative.base import to_variable +import numpy as np +from paddle.fluid.backward import append_backward + + +class SimpleLSTMRNN(fluid.imperative.Layer): + def __init__(self, + hidden_size, + num_steps, + num_layers=2, + init_scale=0.1, + dropout=None): + super(SimpleLSTMRNN, self).__init__() + self._hidden_size = hidden_size + self._num_layers = num_layers + self._init_scale = init_scale + self._dropout = dropout + self.input = None + self.num_steps = num_steps + + def _build_once(self, input_embedding, init_hidden=None, init_cell=None): + self.weight_1_arr = [] + self.weight_2_arr = [] + self.bias_arr = [] + self.hidden_array = [] + self.cell_array = [] + self.mask_array = [] + + for i in range(self._num_layers): + weight_1 = fluid.layers.create_parameter( + shape=[self._hidden_size * 2, self._hidden_size * 4], + dtype="float32", + name="fc_weight1_" + str(i), + default_initializer=fluid.initializer.UniformInitializer( + low=-self._init_scale, high=self._init_scale)) + self.weight_1_arr.append(weight_1) + bias_1 = fluid.layers.create_parameter( + [self._hidden_size * 4], + dtype="float32", + name="fc_bias1_" + str(i), + default_initializer=fluid.initializer.Constant(0.0)) + self.bias_arr.append(bias_1) + + pre_hidden = fluid.layers.slice( + init_hidden, axes=[0], starts=[i], ends=[i + 1]) + pre_cell = fluid.layers.slice( + init_cell, axes=[0], starts=[i], ends=[i + 1]) + pre_hidden = fluid.layers.reshape( + pre_hidden, shape=[-1, self._hidden_size]) + pre_cell = fluid.layers.reshape( + pre_cell, shape=[-1, self._hidden_size]) + self.hidden_array.append(pre_hidden) + self.cell_array.append(pre_cell) + + def forward(self, input_embedding, init_hidden=None, init_cell=None): + res = [] + for index in range(self.num_steps): + self.input = fluid.layers.slice( + input_embedding, axes=[1], starts=[index], ends=[index + 1]) + self.input = fluid.layers.reshape( + self.input, shape=[-1, self._hidden_size]) + for k in range(self._num_layers): + pre_hidden = self.hidden_array[k] + print("pre_hidden shape is:{}".format(pre_hidden.shape)) + print("input shape is:{}".format(self.input.shape)) + pre_cell = self.cell_array[k] + weight_1 = self.weight_1_arr[k] + bias = self.bias_arr[k] + + nn = fluid.layers.concat([self.input, pre_hidden], 1) + gate_input = fluid.layers.matmul(x=nn, y=weight_1) + + gate_input = fluid.layers.elementwise_add(gate_input, bias) + print("gate_input shape is: {}".format(gate_input.shape)) + print("gate_input value is :{}".format(gate_input._numpy())) + print("gate_input desc is :{}".format(gate_input)) + # i, j, f, o = fluid.layers.split(gate_input, num_or_sections=4, dim=-1) + # # + # # c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid( + # # i) * fluid.layers.tanh(j) + # # m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o) + # # + # # self.hidden_array[k] = m + # # self.cell_array[k] = c + # # self.input = m + # # + # # if self.dropout is not None and self.dropout > 0.0: + # # self.input = fluid.layers.dropout( + # # self.input, + # # dropout_prob=self.dropout, + # # dropout_implementation='upscale_in_train') + # # + # # res.append( + # # fluid.layers.reshape( + # # input, shape=[1, -1, self._hidden_size])) + # # real_res = fluid.layers.concat(res, 0) + # # real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2]) + # # last_hidden = fluid.layers.concat(self.hidden_array, 1) + # # last_hidden = fluid.layers.reshape( + # # last_hidden, shape=[-1, self._num_layers, self._hidden_size]) + # # last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2]) + # # last_cell = fluid.layers.concat(self.cell_array, 1) + # # last_cell = fluid.layers.reshape( + # # last_cell, shape=[-1, self._num_layers, self._hidden_size]) + # # last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2]) + # # + # return real_res, last_hidden, last_cell + return [1], [2], [3] + + +class PtbModel(fluid.imperative.Layer): + def __init__(self, + hidden_size, + vocab_size, + num_layers=2, + num_steps=20, + init_scale=0.1, + dropout=None): + super(PtbModel, self).__init__() + self.hidden_size = hidden_size + self.vocab_size = vocab_size + self.init_scale = init_scale + self.num_layers = num_layers + self.num_steps = num_steps + self.dropout = dropout + self.simple_lstm_rnn = SimpleLSTMRNN( + hidden_size, + num_steps, + num_layers=num_layers, + init_scale=init_scale, + dropout=dropout) + self.embedding = EMBEDDING( + size=[vocab_size, hidden_size], + dtype='float32', + is_sparse=False, + param_attr=fluid.ParamAttr( + name='embedding_para', + initializer=fluid.initializer.UniformInitializer( + low=-init_scale, high=init_scale))) + + def _build_once(self, input, label, init_hidden, init_cell): + self.softmax_weight = fluid.layers.create_parameter( + [self.hidden_size, self.vocab_size], + dtype="float32", + name="softmax_weight", + default_initializer=fluid.initializer.UniformInitializer( + low=-self.init_scale, high=self.init_scale)) + self.softmax_bias = fluid.layers.create_parameter( + [self.vocab_size], + dtype="float32", + name='softmax_bias', + default_initializer=fluid.initializer.UniformInitializer( + low=-self.init_scale, high=self.init_scale)) + + def forward(self, input, label, init_hidden, init_cell): + + init_h = fluid.layers.reshape( + init_hidden, shape=[self.num_layers, -1, self.hidden_size]) + + init_c = fluid.layers.reshape( + init_cell, shape=[self.num_layers, -1, self.hidden_size]) + + x_emb = self.embedding(input) + x_emb = fluid.layers.reshape( + x_emb, shape=[-1, self.num_steps, self.hidden_size]) + if self.dropout is not None and self.dropout > 0.0: + x_emb = fluid.layers.dropout( + x_emb, + dropout_prob=self.drop_out, + dropout_implementation='upscale_in_train') + print("init_c is {}".format(init_c)) + rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h, + init_c) + rnn_out = fluid.layers.reshape( + rnn_out, shape=[-1, self.num_steps, self.hidden_size]) + projection = fluid.layers.reshape(rnn_out, self.softmax_weight) + projection = fluid.layers.elementwise_add(projection, self.softmax_bias) + projection = fluid.layers.reshape( + projection, shape=[-1, self.vocab_size]) + projection = fluid.layers.reshape( + projection, shape=[-1, self.vocab_size]) + loss = fluid.layers.softmax_with_cross_entropy( + logits=projection, label=label, soft_label=False) + loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps]) + loss = fluid.layers.reduce_mean(loss, dim=[0]) + loss = fluid.layers.reduce_sum(loss) + loss.permissions = True + + return loss, last_hidden, last_cell + + +class TestImperativePtbRnn(unittest.TestCase): + def test_mnist_cpu_float32(self): + seed = 90 + hidden_size = 10 + vocab_size = 1000 + num_layers = 1 + num_steps = 3 + init_scale = 0.1 + batch_size = 4 + + with fluid.imperative.guard(): + fluid.default_startup_program().random_seed = seed + fluid.default_main_program().random_seed = seed + # TODO: marsyang1993 Change seed to + ptb_model = PtbModel( + hidden_size=hidden_size, + vocab_size=vocab_size, + num_layers=num_layers, + num_steps=num_steps, + init_scale=init_scale) + + sgd = SGDOptimizer(learning_rate=1e-3) + print("q") + for i in range(2): + x_data = np.arange(12).reshape(4, 3).astype('int64') + y_data = np.arange(1, 13).reshape(4, 3).astype('int64') + x_data = x_data.reshape((-1, num_steps, 1)) + y_data = y_data.reshape((-1, 1)) + init_hidden_data = np.zeros( + (num_layers, batch_size, hidden_size), dtype='float32') + init_cell_data = np.zeros( + (num_layers, batch_size, hidden_size), dtype='float32') + x = to_variable(x_data) + y = to_variable(y_data) + init_hidden = to_variable(init_hidden_data) + init_cell = to_variable(init_cell_data) + dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden, + init_cell) + dy_param_init = dict() + if i == 0: + for param in fluid.default_main_program().global_block( + ).all_parameters(): + dy_param_init[param.name] = param._numpy() + dy_loss._backward() + sgd.minimize(dy_loss) + dy_param_updated = dict() + for param in fluid.default_main_program().global_block( + ).all_parameters(): + dy_param_updated[param.name] = param._numpy() + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_imperative_split.py b/python/paddle/fluid/tests/unittests/test_imperative_split.py new file mode 100644 index 000000000..5dee51f39 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_imperative_split.py @@ -0,0 +1,45 @@ +# Copyright (c) 2019 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 paddle.fluid as fluid +from paddle.fluid.imperative.base import to_variable +import numpy as np + + +class Split_test(fluid.imperative.Layer): + def __init__(self): + super(Split_test, self).__init__() + + def _build_once(self, input): + pass + + def forward(self, input): + out = fluid.layers.split(input, num_or_sections=4, dim=-1) + return out + + +class TestImperativePtbRnn(unittest.TestCase): + def test_spilt(self): + with fluid.imperative.guard(): + inp = to_variable(np.arange(160).reshape(4, 40).astype('float32')) + st = Split_test() + out = st(inp) + print(out) + + +if __name__ == '__main__': + unittest.main() -- GitLab