# 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. import numpy as np import random import collections import paddle import paddle.fluid as fluid import unittest from decorator_helper import * class Memory(object): def __init__(self, shape, dtype='float32'): self.ex = np.zeros(shape=shape, dtype=dtype) self.cur = None def update(self, val): assert val.shape == self.ex.shape assert val.dtype == self.ex.dtype self.cur = val def next(self): self.ex = self.cur self.cur = None def __next__(self): self.next() def reset(self): self.ex = np.zeros(shape=self.ex.shape, dtype=self.ex.dtype) self.cur = None class Output(object): def __init__(self): self.outs = [] def next_sequence(self): self.outs.append([]) def out(self, val): self.outs[-1].append(val) def last(self): return self.outs[-1][-1] class BaseRNN(object): def __init__(self, ins, mems, params, outs, num_seq=5, max_seq_len=15): self.num_seq = num_seq self.inputs = collections.defaultdict(list) for _ in range(num_seq): seq_len = random.randint(1, max_seq_len - 1) for iname in ins: ishape = ins[iname].get('shape', None) idtype = ins[iname].get('dtype', 'float32') lst = [] for _ in range(seq_len): lst.append(np.random.random(size=ishape).astype(idtype)) self.inputs[iname].append(lst) self.mems = dict() for mname in mems: mshape = mems[mname].get('shape', None) mdtype = mems[mname].get('dtype', 'float32') self.mems[mname] = Memory(shape=mshape, dtype=mdtype) self.params = dict() for pname in params: pshape = params[pname].get('shape', None) pdtype = params[pname].get('dtype', 'float32') self.params[pname] = np.random.random(size=pshape).astype(pdtype) self.outputs = dict() for oname in outs: self.outputs[oname] = Output() def step(self, **kwargs): raise NotImplementedError() def exe(self): retv = dict() for out in self.outputs: retv[out] = [] for seq_id in range(self.num_seq): for mname in self.mems: self.mems[mname].reset() for out in self.outputs: self.outputs[out].next_sequence() iname0 = list(self.inputs.keys())[0] seq_len = len(self.inputs[iname0][seq_id]) for step_id in range(seq_len): xargs = dict() for iname in self.inputs: xargs[iname] = self.inputs[iname][seq_id][step_id] for mname in self.mems: xargs[mname] = self.mems[mname] for pname in self.params: xargs[pname] = self.params[pname] for out in self.outputs: xargs[out] = self.outputs[out] self.step(**xargs) for mname in self.mems: next(self.mems[mname]) for out in self.outputs: retv[out].append(self.outputs[out].last()) for out in retv: retv[out] = np.array(retv[out]) return retv def to_feed(self, place): feed_dict = dict() for iname in self.inputs: lod = [] np_flatten = [] for seq_id in range(len(self.inputs[iname])): seq_len = len(self.inputs[iname][seq_id]) lod.append(seq_len) np_flatten.extend(self.inputs[iname][seq_id]) t = fluid.Tensor() t.set(np.array(np_flatten), place) t.set_recursive_sequence_lengths([lod]) feed_dict[iname] = t for pname in self.params: feed_dict[pname] = self.params[pname] return feed_dict def get_numeric_gradient_of_param(self, param_name, delta=0.001): p = self.params[param_name] if len(p.shape) != 2: raise ValueError("Not support get numeric gradient of an parameter," " which is not matrix") g = np.zeros(shape=p.shape, dtype=p.dtype) for i in range(p.shape[0]): for j in range(p.shape[1]): o = p[i][j] p[i][j] += delta pos = self._exe_mean_out_() p[i][j] -= 2 * delta neg = self._exe_mean_out_() p[i][j] = o g[i][j] = (pos - neg) / (delta * 2) return g def get_numeric_gradient_of_input(self, input_name, delta=0.001, return_one_tensor=True): ipt = self.inputs[input_name] grad = [] for seq in ipt: seq_grad = [] for item in seq: item_grad = np.zeros(shape=item.shape, dtype=item.dtype) if len(item.shape) != 1: raise ValueError("Not support") for i in range(len(item)): o = item[i] item[i] += delta pos = self._exe_mean_out_() item[i] -= 2 * delta neg = self._exe_mean_out_() item[i] = o item_grad[i] = (pos - neg) / (delta * 2) seq_grad.append(item_grad) grad.append(seq_grad) if not return_one_tensor: return grad for i in range(len(grad)): grad[i] = np.concatenate(grad[i]) grad = np.concatenate(grad) return grad def _exe_mean_out_(self): outs = self.exe() return np.array([o.mean() for o in outs.values()]).mean() class SeedFixedTestCase(unittest.TestCase): @classmethod def setUpClass(cls): """Fix random seeds to remove randomness from tests""" cls._np_rand_state = np.random.get_state() cls._py_rand_state = random.getstate() np.random.seed(123) random.seed(124) @classmethod def tearDownClass(cls): """Restore random seeds""" np.random.set_state(cls._np_rand_state) random.setstate(cls._py_rand_state) class TestSimpleMul(SeedFixedTestCase): DATA_NAME = 'X' DATA_WIDTH = 32 PARAM_NAME = 'W' HIDDEN_WIDTH = 10 OUT_NAME = 'Out' class SimpleMul(BaseRNN): def __init__(self): base = TestSimpleMul super(base.SimpleMul, self).__init__({base.DATA_NAME: { 'shape': [base.DATA_WIDTH] }}, {}, { base.PARAM_NAME: { 'shape': [base.DATA_WIDTH, base.HIDDEN_WIDTH] } }, [base.OUT_NAME]) def step(self, X, W, Out): Out.out(np.matmul(X, W)) # Test many times in local to ensure the random seed cannot breaks CI # @many_times(10) @prog_scope() def test_forward_backward(self): py_rnn = TestSimpleMul.SimpleMul() dat = fluid.layers.data(name=self.DATA_NAME, shape=[self.DATA_WIDTH], lod_level=1) dat.stop_gradient = False rnn = fluid.layers.DynamicRNN() with rnn.block(): d = rnn.step_input(dat) o = fluid.layers.fc(input=d, param_attr=self.PARAM_NAME, bias_attr=False, size=self.HIDDEN_WIDTH, act=None) rnn.output(o) out = rnn() out = fluid.layers.sequence_pool(out, pool_type='last') loss = paddle.mean(out) fluid.backward.append_backward(loss) cpu = fluid.CPUPlace() exe = fluid.Executor(cpu) out, w_g, i_g = list( map( np.array, exe.run(feed=py_rnn.to_feed(cpu), fetch_list=[ out, self.PARAM_NAME + "@GRAD", self.DATA_NAME + "@GRAD" ], return_numpy=False))) out_by_python = py_rnn.exe()[self.OUT_NAME] np.testing.assert_allclose(out, out_by_python, rtol=1e-05) w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME) np.testing.assert_allclose(w_g_num, w_g, rtol=0.05) i_g_num = py_rnn.get_numeric_gradient_of_input( input_name=self.DATA_NAME) i_g_num = i_g_num.reshape(i_g.shape) np.testing.assert_allclose(i_g_num, i_g, rtol=0.05) class TestSimpleMulWithMemory(SeedFixedTestCase): DATA_WIDTH = 32 HIDDEN_WIDTH = 20 DATA_NAME = 'X' PARAM_NAME = 'W' class SimpleMulWithMemory(BaseRNN): def __init__(self): super(TestSimpleMulWithMemory.SimpleMulWithMemory, self).__init__( { TestSimpleMulWithMemory.DATA_NAME: { 'shape': [TestSimpleMulWithMemory.DATA_WIDTH] } }, {'Mem': { 'shape': [TestSimpleMulWithMemory.HIDDEN_WIDTH] }}, { TestSimpleMulWithMemory.PARAM_NAME: { 'shape': [ TestSimpleMulWithMemory.DATA_WIDTH, TestSimpleMulWithMemory.HIDDEN_WIDTH ] } }, ['Out']) def step(self, X, Mem, W, Out): o = np.matmul(X, W) assert isinstance(Mem, Memory) o += Mem.ex Mem.update(o) assert isinstance(Out, Output) Out.out(o) # many_times used locally for debug. Make sure the calculation is stable. # @many_times(10) @prog_scope() def test_forward_backward(self): py_rnn = TestSimpleMulWithMemory.SimpleMulWithMemory() data = fluid.layers.data(name=self.DATA_NAME, shape=[self.DATA_WIDTH], lod_level=1) data.stop_gradient = False rnn = fluid.layers.DynamicRNN() with rnn.block(): d = rnn.step_input(data) mem = rnn.memory(value=0.0, shape=[self.HIDDEN_WIDTH]) hidden = fluid.layers.fc(input=d, size=self.HIDDEN_WIDTH, param_attr=self.PARAM_NAME, bias_attr=False, act=None) o = fluid.layers.elementwise_add(x=hidden, y=mem) rnn.update_memory(mem, o) rnn.output(o) out = rnn() last = fluid.layers.sequence_pool(input=out, pool_type='last') loss = paddle.mean(last) fluid.backward.append_backward(loss) cpu = fluid.CPUPlace() exe = fluid.Executor(cpu) feed = py_rnn.to_feed(cpu) last_np, w_g, i_g = list( map( np.array, exe.run(feed=feed, fetch_list=[ last, self.PARAM_NAME + "@GRAD", self.DATA_NAME + "@GRAD" ], return_numpy=False))) last_by_py, = list(py_rnn.exe().values()) w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME) np.testing.assert_allclose(last_np, last_by_py, rtol=1e-05) np.testing.assert_allclose(w_g_num, w_g, rtol=0.1) i_g_num = py_rnn.get_numeric_gradient_of_input(self.DATA_NAME) i_g_num = i_g_num.reshape(i_g.shape) # Since this RNN has many float add. The number could be not stable. # rtol = 0.1 np.testing.assert_allclose(i_g_num, i_g, rtol=0.1) if __name__ == '__main__': unittest.main()