diff --git a/paddle/operators/shrink_rnn_memory_op.cc b/paddle/operators/shrink_rnn_memory_op.cc index e5ef0740b6f385de7f17a3a419000cb8c897d986..b37269b471b4d71b42c41641fd14c7a64d2719d6 100644 --- a/paddle/operators/shrink_rnn_memory_op.cc +++ b/paddle/operators/shrink_rnn_memory_op.cc @@ -116,9 +116,9 @@ class ShrinkRNNMemoryGradOp : public ArrayOp { auto height = dout_tensor.dims()[0]; auto slice = dx_tensor.Slice(0, static_cast(height)); framework::CopyFrom(dout_tensor, dout_tensor.place(), dev_ctx, &slice); - if (dx_tensor.dims()[0] < height) { + if (dx_tensor.dims()[0] > height) { auto rest_tensor = dx_tensor.Slice( - static_cast(height), static_cast(dout_tensor.dims()[0])); + static_cast(height), static_cast(dx_tensor.dims()[0])); math::set_constant(dev_ctx, &rest_tensor, 0.0f); } } diff --git a/paddle/operators/sum_op.h b/paddle/operators/sum_op.h index eaa36aa1aea53e0b37ef6c578d8bb1cda230ded0..552b48f608b7e0248f03dbea940a83f112a67712 100644 --- a/paddle/operators/sum_op.h +++ b/paddle/operators/sum_op.h @@ -37,11 +37,11 @@ class SumKernel : public framework::OpKernel { bool in_place = out_var == in_vars[0]; if (out_var->IsType()) { - auto *out = context.Output("Out"); - out->mutable_data(context.GetPlace()); - + auto *out = context.Output("Out"); + if (!in_place) { + out->mutable_data(context.GetPlace()); + } auto result = EigenVector::Flatten(*out); - if (!in_place) { math::SetConstant constant_functor; constant_functor(context.template device_context(), out, diff --git a/paddle/operators/tensor_array_read_write_op.cc b/paddle/operators/tensor_array_read_write_op.cc index 53e38ec70336ca7f2d7c142e5fb1bbe427ab2957..d5ff3e3fce29b1a888b2cd4d307c2655669e3e4c 100644 --- a/paddle/operators/tensor_array_read_write_op.cc +++ b/paddle/operators/tensor_array_read_write_op.cc @@ -130,9 +130,9 @@ class ReadFromArrayOp : public ArrayOp { auto &x_array = x->Get(); auto *out = scope.FindVar(Output("Out")); PADDLE_ENFORCE(out != nullptr, "Out must be set"); - auto *out_tensor = out->GetMutable(); size_t offset = GetOffset(scope, place); if (offset < x_array.size()) { + auto *out_tensor = out->GetMutable(); platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); auto &dev_ctx = *pool.Get(place); diff --git a/paddle/pybind/tensor_py.h b/paddle/pybind/tensor_py.h index 4d5e73e2c266b301de4f19e09be7ab4009c936d3..6b4290972bade585d1a0c2ae919a2e712bdf308c 100644 --- a/paddle/pybind/tensor_py.h +++ b/paddle/pybind/tensor_py.h @@ -77,10 +77,10 @@ struct CastToPyBufferImpl { } else if (paddle::platform::is_cpu_place(tensor.place())) { dst_tensor = tensor; } - return py::buffer_info( - dst_tensor.mutable_data(dst_tensor.place()), - sizeof(CUR_TYPE), py::format_descriptor::format(), - (size_t)framework::arity(dst_tensor.dims()), dims_outside, strides); + return py::buffer_info(dst_tensor.data(), sizeof(CUR_TYPE), + py::format_descriptor::format(), + (size_t)framework::arity(dst_tensor.dims()), + dims_outside, strides); } else { constexpr bool less = I + 1 < std::tuple_size>::value; return CastToPyBufferImpl()(tensor); diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py index 2c91afb363bf72f2791e60c6df0d9130ccd698c5..1d6c594b41a2c295e3818fb119362d1daba1de33 100644 --- a/python/paddle/v2/fluid/executor.py +++ b/python/paddle/v2/fluid/executor.py @@ -1,12 +1,31 @@ import numpy as np +import contextlib +from framework import Program, default_main_program from . import core -from framework import Program, default_main_program, Parameter, Variable -__all__ = ['Executor', 'g_scope'] +__all__ = ['Executor', 'global_scope', 'scope_guard', 'switch_scope'] g_scope = core.Scope() +def global_scope(): + return g_scope + + +def switch_scope(scope): + global g_scope + ex = g_scope + g_scope = scope + return ex + + +@contextlib.contextmanager +def scope_guard(scope): + ex = switch_scope(scope) + yield + switch_scope(ex) + + def as_numpy(tensor): if isinstance(tensor, list): return [as_numpy(t) for t in tensor] @@ -117,7 +136,7 @@ class Executor(object): raise TypeError() if scope is None: - scope = g_scope + scope = global_scope() program = program.clone() global_block = program.global_block() diff --git a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py index c3591a613acafb268a5bd70618cd4555450bac29..8acd470c5ed5fa8eeda396f1e9182db4ecdd7016 100644 --- a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py +++ b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py @@ -170,7 +170,7 @@ def main(): exe.run(fluid.default_startup_program()) - embedding_param = fluid.g_scope.find_var(embedding_name).get_tensor() + embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor() embedding_param.set( load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place) diff --git a/python/paddle/v2/fluid/tests/decorators.py b/python/paddle/v2/fluid/tests/decorators.py new file mode 100644 index 0000000000000000000000000000000000000000..154619b0e93455922700a12d734967b4d20c4f13 --- /dev/null +++ b/python/paddle/v2/fluid/tests/decorators.py @@ -0,0 +1,29 @@ +import paddle.v2.fluid as fluid + +__all__ = ['many_times', 'prog_scope'] + + +def many_times(times): + def __impl__(fn): + def __fn__(*args, **kwargs): + for _ in range(times): + fn(*args, **kwargs) + + return __fn__ + + return __impl__ + + +def prog_scope(): + def __impl__(fn): + def __fn__(*args, **kwargs): + prog = fluid.Program() + startup_prog = fluid.Program() + scope = fluid.core.Scope() + with fluid.scope_guard(scope): + with fluid.program_guard(prog, startup_prog): + fn(*args, **kwargs) + + return __fn__ + + return __impl__ diff --git a/python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py b/python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py new file mode 100644 index 0000000000000000000000000000000000000000..c02c59284e1ca2e28ba2f6c5ec13b241c15fc288 --- /dev/null +++ b/python/paddle/v2/fluid/tests/test_dynrnn_gradient_check.py @@ -0,0 +1,347 @@ +import numpy +import random +import collections +import paddle.v2.fluid as fluid +import unittest +from decorators import * + + +class Memory(object): + def __init__(self, shape, dtype='float32'): + self.ex = numpy.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 ex(self): + return self.ex + + def next(self): + self.ex = self.cur + self.cur = None + + def __next__(self): + self.next() + + def reset(self): + self.ex = numpy.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 xrange(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 xrange(seq_len): + lst.append(numpy.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] = numpy.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 xrange(self.num_seq): + for mname in self.mems: + self.mems[mname].reset() + for out in self.outputs: + self.outputs[out].next_sequence() + + iname0 = self.inputs.keys()[0] + seq_len = len(self.inputs[iname0][seq_id]) + + for step_id in xrange(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] = numpy.array(retv[out]) + return retv + + def to_feed(self, place): + feed_dict = dict() + + for iname in self.inputs: + lod = [0] + np_flatten = [] + for seq_id in xrange(len(self.inputs[iname])): + seq_len = len(self.inputs[iname][seq_id]) + lod.append(lod[-1] + seq_len) + np_flatten.extend(self.inputs[iname][seq_id]) + + t = fluid.Tensor() + t.set(numpy.array(np_flatten), place) + t.set_lod([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 = numpy.zeros(shape=p.shape, dtype=p.dtype) + + for i in xrange(p.shape[0]): + for j in xrange(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 = numpy.zeros(shape=item.shape, dtype=item.dtype) + if len(item.shape) != 1: + raise ValueError("Not support") + + for i in xrange(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 xrange(len(grad)): + grad[i] = numpy.concatenate(grad[i]) + grad = numpy.concatenate(grad) + return grad + + def _exe_mean_out_(self): + outs = self.exe() + return numpy.array([o.mean() for o in outs.itervalues()]).mean() + + +class TestSimpleMul(unittest.TestCase): + 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(numpy.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 = fluid.layers.mean(x=out) + fluid.backward.append_backward(loss) + + cpu = fluid.CPUPlace() + exe = fluid.Executor(cpu) + out, w_g, i_g = map(numpy.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] + self.assertTrue(numpy.allclose(out, out_by_python)) + w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME) + self.assertTrue(numpy.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) + self.assertTrue(numpy.allclose(i_g_num, i_g, rtol=0.05)) + + +class TestSimpleMulWithMemory(unittest.TestCase): + 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 = numpy.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 = fluid.layers.mean(x=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 = map(numpy.array, + exe.run(feed=feed, + fetch_list=[ + last, self.PARAM_NAME + "@GRAD", + self.DATA_NAME + "@GRAD" + ], + return_numpy=False)) + last_by_py, = py_rnn.exe().values() + w_g_num = py_rnn.get_numeric_gradient_of_param(self.PARAM_NAME) + self.assertTrue(numpy.allclose(last_np, last_by_py)) + + self.assertTrue(numpy.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 + self.assertTrue(numpy.allclose(i_g_num, i_g, rtol=0.1)) + + +if __name__ == '__main__': + unittest.main()