def reader():
self.inputs = []
cnt = 0
while True:
tensors = fluid.LoDTensorArray()
in_data = np.random.uniform(
low=0, high=1, size=(1, self.in_size)).astype('float32')
tensors.append(as_tensor(in_data))
label = np.random.random_integers(low=0,
high=self.class_num - 1,
size=(1, 1)).astype('int64')
tensors.append(as_tensor(label))
if cnt < self.iterations * self.batch_size * self.batch_size_times:
if cnt % (self.batch_size * self.batch_size_times) == 0:
self.inputs.append([in_data, label])
else:
self.inputs[-1][0] = np.concatenate(
(self.inputs[-1][0], in_data), axis=0)
self.inputs[-1][1] = np.concatenate(
(self.inputs[-1][1], label), axis=0)
elif not self.use_double_buffer:
break
yield tensors
cnt += 1
yield None
def test_pin_memory_pyreader(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
executor = fluid.Executor(place)
data_file = fluid.layers.py_reader(
capacity=self.capacity,
dtypes=self.dtypes,
lod_levels=self.lod_levels,
shapes=self.shapes)
# feed_queue = data_file.queue
read_out_data = fluid.layers.read_file(data_file)
self.inputs = []
for _ in range(10):
sample = np.random.uniform(
low=0, high=1, size=[3, 2, 1]).astype("float32")
label = np.random.uniform(
low=0, high=10, size=[1]).astype("int64")
self.inputs.append((sample, label))
self.input_tensors = []
for d, l in batch_feeder(
paddle.batch(
user_reader(self.inputs), batch_size=2),
pin_memory=True
if fluid.core.is_compiled_with_cuda() else False)():
ta = fluid.LoDTensorArray()
ta.append(d)
ta.append(l)
self.input_tensors.append(ta)
self.batched_inputs = []
for batch in paddle.batch(user_reader(self.inputs), batch_size=2)():
feed_d = []
feed_l = []
for d, l in batch:
feed_d.append(d)
feed_l.append([l])
self.batched_inputs.append([feed_d, feed_l])
data_file.decorate_tensor_provider(
batch_feeder(
paddle.batch(
user_reader(self.inputs), batch_size=2),
pin_memory=True
if fluid.core.is_compiled_with_cuda() else False))
executor.run(fluid.default_startup_program())
self.outputs = []
data_file.start()
for _ in self.input_tensors:
self.outputs.append(
executor.run(fetch_list=list(read_out_data)))
data_file.reset()
self.validate()
def main(self, use_thread=False):
with fluid.program_guard(fluid.Program(), fluid.Program()):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
executor = fluid.Executor(place)
data_file = fluid.layers.py_reader(
capacity=self.capacity,
dtypes=self.dtypes,
lod_levels=self.lod_levels,
shapes=self.shapes)
feed_queue = data_file.queue
read_out_data = fluid.layers.read_file(data_file)
self.inputs = []
for i in range(self.iterations):
in_data = fluid.LoDTensorArray()
batch_size = np.random.random_integers(self.batch_size_min,
self.batch_size_max)
for shape, dtype in zip(self.shapes, self.dtypes):
next_data = np.random.uniform(
low=0, high=1000,
size=(batch_size, ) + shape[1:]).astype(dtype)
in_data.append(
fluid.executor._as_lodtensor(next_data, place))
self.inputs.append(in_data)
executor.run(fluid.default_startup_program())
self.outputs = []
if use_thread:
thread = Thread(
target=feed_data, args=(feed_queue, self.inputs))
thread.start()
for in_data in self.inputs:
self.outputs.append(
executor.run(fetch_list=list(read_out_data)))
else:
for in_data in self.inputs:
feed_queue.push(in_data)
self.outputs.append(
executor.run(fetch_list=list(read_out_data)))
feed_queue.close()
self.validate()
def sample_list_to_tensor_array(sample_list):
slot_num = None
slots = None
for sample in sample_list:
if slot_num is None:
slot_num = len(sample)
slots = [None] * len(sample)
else:
assert slot_num == len(sample)
for slot_id, slot_item in enumerate(sample):
if slots[slot_id] is None:
slots[slot_id] = []
slots[slot_id].append(slot_item)
tensor_array = fluid.LoDTensorArray()
for slot in slots:
t = fluid.LoDTensor()
t.set(np.array(slot), fluid.CPUPlace())
tensor_array.append(t)
return tensor_array
def _slot_gate(encoder_outs, encoder_last_h, slots_embedding, sent_mask, words_emb, story):
slots_embedding1 = fluid.layers.transpose(x= slots_embedding, perm=[1,0,2])
slots_embedding1 = fluid.layers.reshape(x=slots_embedding1, shape=[args['all_slot_num'] * args['batch_size'], -1, args['slot_emb_dim']])
dec_input = fluid.layers.dropout(slots_embedding1, dropout_prob=args['dropout'])
hidden = fluid.layers.expand(encoder_last_h, expand_times=[args['all_slot_num'], 1])
# hidden2gate = fluid.layers.create_parameter(shape=[args['slot_emb_dim'], args['gate_kind']], dtype='float32')
# hidden2pgen = fluid.layers.create_parameter(shape=[args['slot_emb_dim'] * 3, 1],dtype='float32')
# all_point_outputs_list = fluid.Tensor()
# all_point_outputs_list = LodTensor_to_Tensor(all_point_outputs_list)
# all_point_outputs_list.set(np.zeros(shape=(args['all_slot_num'], args['batch_size'], 10 ,data_processor.get_vocab_size('utterances')),
# dtype='float32'),
# fluid.CPUPlace())
all_point_outputs_list = fluid.LoDTensorArray()
out_gate_probs = []
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
n = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
cond = fluid.layers.less_than(x=i, y=n)
while_op = fluid.layers.While(cond = cond)
with while_op.block():
# fluid.layers.arr
# i, n, hidden, dec_input, encoder_outs, words_emb, story, all_point_outputs_list
# i, n, hidden, dec_input, encoder_outs, words_emb, story, all_point_outputs_list = \
# fluid.layers.while_loop(cond=while_cond,
# body=while_body,
# loop_vars=[i, n, hidden, dec_input, encoder_outs, words_emb, story, all_point_outputs_list])
# for i in range(10):
# cell = fluid.layers.GRUCell(hidden_size=args['slot_emb_dim'])
# dec_outs, hidden = fluid.layers.rnn(cell = cell,
# inputs = dec_input,
# initial_states= hidden)
init_c = fluid.layers.fill_constant(shape=[1, args['batch_size'] * args['all_slot_num'], args['slot_emb_dim']], dtype='float32', value=0.0 )
dec_outs, hidden, _ = fluid.layers.lstm(input = dec_input,
init_h= fluid.layers.unsqueeze(hidden, axes=[0]),
init_c=init_c,
max_len=400,
hidden_size=args['slot_emb_dim'],
num_layers=1)
hidden = fluid.layers.squeeze(hidden, axes=[0])
enc_out = fluid.layers.expand(encoder_outs, expand_times=[args['all_slot_num'], 1, 1])
context, prob = attend(enc_out, hidden, sent_mask)
gate_probs = fluid.layers.fc(context, size = args['gate_kind'], act='softmax')
# gate_probs = fluid.layers.softmax(fluid.layers.matmul(context, hidden2gate))
if i == 0:
out_gate_probs = gate_probs
p_vocab = attend_vocab(words_emb, hidden)
p_gen_vec= fluid.layers.concat([fluid.layers.squeeze(dec_outs, axes=[1]), context, fluid.layers.squeeze(dec_input, axes=[1])], axis=-1)
vocab_pointer_switches = fluid.layers.fc(p_gen_vec, size=1,act='sigmoid')
# vocab_pointer_switches = fluid.layers.sigmoid(fluid.layers.matmul(p_gen_vec, hidden2pgen))
p_context_ptr = fluid.layers.fill_constant(shape=[args['batch_size'] * args['all_slot_num'], data_processor.get_vocab_size('utterances')],dtype='float32',value=0.0)
story_index = fluid.layers.unsqueeze(fluid.layers.expand(story, expand_times=[args['all_slot_num'], 1]),axes=[2])
updates = fluid.layers.expand(fluid.layers.unsqueeze(prob,axes=[2]), expand_times=[1,1,data_processor.get_vocab_size('utterances')])
p_context_ptr = fluid.layers.scatter_nd_add(ref = p_context_ptr, index = story_index, updates=updates)
# print('vocab_pointer_switches size: %s'%(str(vocab_pointer_switches.shape)))
# print('p_context_ptr size: %s'%(str(p_context_ptr.shape)))
# print('p_vocab size: %s'%(str(p_vocab.shape)))
final_p_vocab = fluid.layers.expand((1 - vocab_pointer_switches), expand_times=[1, data_processor.get_vocab_size('utterances')]) * p_context_ptr + \
fluid.layers.expand(vocab_pointer_switches, expand_times=[1, data_processor.get_vocab_size('utterances')]) * p_vocab
# p_final_word = fluid.layers.argmax(final_p_vocab, axis=1)
npfw = fluid.layers.reshape(final_p_vocab,shape=[args['all_slot_num'], args['batch_size'], -1 ,data_processor.get_vocab_size('utterances')])
# if i == 0:
# c = npfw
# else:
# c = fluid.layers.concat([c, npfw], axis=2)
all_point_outputs_list.append(npfw)
i=fluid.layers.increment(x=i, value=1, in_place=True)
fluid.layers.less_than(x=i, y=n, cond=cond)
# all_point_outputs_list.append(npfw)
# all_point_outputs_list[:, :, i, :] = npfw
# dec_input =
# gate_probs = all_point_outputs_list[0]
# all_point_outputs = fluid.layers.concat(all_point_outputs_list, axis=2)
c = fluid.layers.reshape(x=c, shape=[args['all_slot_num'], args['batch_size'], -1 ,data_processor.get_vocab_size('utterances')])
# all_point_outputs_list
return out_gate_probs, c
