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Decouple the program desc with batch_size in Transformer. #783

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merged 9 commits into from
Apr 8, 2018
Merged

Decouple the program desc with batch_size in Transformer. #783

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f3c247d
Decouple the program desc with batch_size in Transformer.
guoshengCS Mar 27, 2018
ec70ff4
Fix the inference batch_size in Transformer.
guoshengCS Mar 28, 2018
b6ab2f4
Merge branch 'develop' of https://github.com/PaddlePaddle/models into…
guoshengCS Apr 2, 2018
2e7494d
Merge branch 'develop' of https://github.com/PaddlePaddle/models into…
guoshengCS Apr 4, 2018
8ca8903
Fix the merged conflicts in Transformer inference
guoshengCS Apr 4, 2018
86fe83f
Merge branch 'develop' of https://github.com/PaddlePaddle/models into…
guoshengCS Apr 4, 2018
f82f47c
Increase the batch size in inference config of Transformer
guoshengCS Apr 8, 2018
afe55c9
Append the <pad> token to the dict when converting ids to words
guoshengCS Apr 8, 2018
baa01f6
Refine the validation in Transformer.
guoshengCS Apr 8, 2018
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5 changes: 3 additions & 2 deletions fluid/neural_machine_translation/transformer/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,7 @@ class TrainTaskConfig(object):
class InferTaskConfig(object):
use_gpu = False
# the number of examples in one run for sequence generation.
# currently the batch size can only be set to 1.
batch_size = 1
batch_size = 10

# the parameters for beam search.
beam_size = 5
Expand Down Expand Up @@ -103,6 +102,7 @@ class ModelHyperParams(object):
"src_word",
"src_pos",
"src_slf_attn_bias",
"src_data_shape",
"src_slf_attn_pre_softmax_shape",
"src_slf_attn_post_softmax_shape", )

Expand All @@ -112,6 +112,7 @@ class ModelHyperParams(object):
"trg_pos",
"trg_slf_attn_bias",
"trg_src_attn_bias",
"trg_data_shape",
"trg_slf_attn_pre_softmax_shape",
"trg_slf_attn_post_softmax_shape",
"trg_src_attn_pre_softmax_shape",
Expand Down
90 changes: 61 additions & 29 deletions fluid/neural_machine_translation/transformer/infer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ def translate_batch(exe,
n_best,
batch_size,
n_head,
d_model,
src_pad_idx,
trg_pad_idx,
bos_idx,
Expand All @@ -43,6 +44,11 @@ def translate_batch(exe,
return_pos=True,
return_attn_bias=True,
return_max_len=False)
# Append the data shape input to reshape the output of embedding layer.
enc_in_data = enc_in_data + [
np.array(
[-1, enc_in_data[2].shape[-1], d_model], dtype="int32")
]
# Append the shape inputs to reshape before and after softmax in encoder
# self attention.
enc_in_data = enc_in_data + [
Expand All @@ -59,9 +65,14 @@ def translate_batch(exe,
scores = np.zeros((batch_size, beam_size), dtype="float32")
prev_branchs = [[] for i in range(batch_size)]
next_ids = [[] for i in range(batch_size)]
# Use beam_map to map the instance idx in batch to beam idx, since the
# Use beam_inst_map to map beam idx to the instance idx in batch, since the
# size of feeded batch is changing.
beam_map = range(batch_size)
beam_inst_map = {
beam_idx: inst_idx
for inst_idx, beam_idx in enumerate(range(batch_size))
}
# Use active_beams to recode the alive.
active_beams = range(batch_size)

def beam_backtrace(prev_branchs, next_ids, n_best=beam_size):
"""
Expand Down Expand Up @@ -98,8 +109,14 @@ def init_dec_in_data(batch_size, beam_size, enc_in_data, enc_output):
[-1e9]).astype("float32")
# This is used to remove attention on the paddings of source sequences.
trg_src_attn_bias = np.tile(
src_slf_attn_bias[:, :, ::src_max_length, :],
[beam_size, 1, trg_max_len, 1])
src_slf_attn_bias[:, :, ::src_max_length, :][:, np.newaxis],
[1, beam_size, 1, trg_max_len, 1]).reshape([
-1, src_slf_attn_bias.shape[1], trg_max_len,
src_slf_attn_bias.shape[-1]
])
# Append the shape input to reshape the output of embedding layer.
trg_data_shape = np.array(
[batch_size * beam_size, trg_max_len, d_model], dtype="int32")
# Append the shape inputs to reshape before and after softmax in
# decoder self attention.
trg_slf_attn_pre_softmax_shape = np.array(
Expand All @@ -112,22 +129,24 @@ def init_dec_in_data(batch_size, beam_size, enc_in_data, enc_output):
[-1, trg_src_attn_bias.shape[-1]], dtype="int32")
trg_src_attn_post_softmax_shape = np.array(
trg_src_attn_bias.shape, dtype="int32")
enc_output = np.tile(enc_output, [beam_size, 1, 1])
enc_output = np.tile(
enc_output[:, np.newaxis], [1, beam_size, 1, 1]).reshape(
[-1, enc_output.shape[-2], enc_output.shape[-1]])
return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, \
trg_slf_attn_pre_softmax_shape, trg_slf_attn_post_softmax_shape, \
trg_src_attn_pre_softmax_shape, trg_src_attn_post_softmax_shape, \
enc_output
trg_data_shape, trg_slf_attn_pre_softmax_shape, \
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape, \
trg_src_attn_post_softmax_shape, enc_output

def update_dec_in_data(dec_in_data, next_ids, active_beams):
def update_dec_in_data(dec_in_data, next_ids, active_beams, beam_inst_map):
"""
Update the input data of decoder mainly by slicing from the previous
input data and dropping the finished instance beams.
"""
trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, \
trg_slf_attn_pre_softmax_shape, trg_slf_attn_post_softmax_shape, \
trg_src_attn_pre_softmax_shape, trg_src_attn_post_softmax_shape, \
enc_output = dec_in_data
trg_cur_len = len(next_ids[0]) + 1 # include the <bos>
trg_data_shape, trg_slf_attn_pre_softmax_shape, \
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape, \
trg_src_attn_post_softmax_shape, enc_output = dec_in_data
trg_cur_len = trg_slf_attn_bias.shape[-1] + 1
trg_words = np.array(
[
beam_backtrace(prev_branchs[beam_idx], next_ids[beam_idx])
Expand All @@ -138,6 +157,7 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams):
trg_pos = np.array(
[range(1, trg_cur_len + 1)] * len(active_beams) * beam_size,
dtype="int64").reshape([-1, 1])
active_beams = [beam_inst_map[beam_idx] for beam_idx in active_beams]
active_beams_indice = (
(np.array(active_beams) * beam_size)[:, np.newaxis] +
np.array(range(beam_size))[np.newaxis, :]).flatten()
Expand All @@ -152,6 +172,10 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams):
trg_src_attn_bias = np.tile(trg_src_attn_bias[
active_beams_indice, :, ::trg_src_attn_bias.shape[2], :],
[1, 1, trg_cur_len, 1])
# Append the shape input to reshape the output of embedding layer.
trg_data_shape = np.array(
[len(active_beams) * beam_size, trg_cur_len, d_model],
dtype="int32")
# Append the shape inputs to reshape before and after softmax in
# decoder self attention.
trg_slf_attn_pre_softmax_shape = np.array(
Expand All @@ -166,9 +190,9 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams):
trg_src_attn_bias.shape, dtype="int32")
enc_output = enc_output[active_beams_indice, :, :]
return trg_words, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, \
trg_slf_attn_pre_softmax_shape, trg_slf_attn_post_softmax_shape, \
trg_src_attn_pre_softmax_shape, trg_src_attn_post_softmax_shape, \
enc_output
trg_data_shape, trg_slf_attn_pre_softmax_shape, \
trg_slf_attn_post_softmax_shape, trg_src_attn_pre_softmax_shape, \
trg_src_attn_post_softmax_shape, enc_output

dec_in_data = init_dec_in_data(batch_size, beam_size, enc_in_data,
enc_output)
Expand All @@ -177,15 +201,18 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams):
feed=dict(zip(dec_in_names, dec_in_data)),
fetch_list=dec_out_names)[0]
predict_all = np.log(
predict_all.reshape([len(beam_map) * beam_size, i + 1, -1])[:,
-1, :])
predict_all = (predict_all + scores[beam_map].reshape(
[len(beam_map) * beam_size, -1])).reshape(
[len(beam_map), beam_size, -1])
predict_all.reshape([len(beam_inst_map) * beam_size, i + 1, -1])
[:, -1, :])
predict_all = (predict_all + scores[active_beams].reshape(
[len(beam_inst_map) * beam_size, -1])).reshape(
[len(beam_inst_map), beam_size, -1])
if not output_unk: # To exclude the <unk> token.
predict_all[:, :, unk_idx] = -1e9
active_beams = []
for inst_idx, beam_idx in enumerate(beam_map):
for beam_idx in range(batch_size):
if not beam_inst_map.has_key(beam_idx):
continue
inst_idx = beam_inst_map[beam_idx]
predict = (predict_all[inst_idx, :, :]
if i != 0 else predict_all[inst_idx, 0, :]).flatten()
top_k_indice = np.argpartition(predict, -beam_size)[-beam_size:]
Expand All @@ -198,10 +225,14 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams):
next_ids[beam_idx].append(top_scores_ids % predict_all.shape[-1])
if next_ids[beam_idx][-1][0] != eos_idx:
active_beams.append(beam_idx)
beam_map = active_beams
if len(beam_map) == 0:
if len(active_beams) == 0:
break
dec_in_data = update_dec_in_data(dec_in_data, next_ids, active_beams)
dec_in_data = update_dec_in_data(dec_in_data, next_ids, active_beams,
beam_inst_map)
beam_inst_map = {
beam_idx: inst_idx
for inst_idx, beam_idx in enumerate(active_beams)
}

# Decode beams and select n_best sequences for each instance by backtrace.
seqs = [
Expand All @@ -215,10 +246,8 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams):
def main():
place = fluid.CUDAPlace(0) if InferTaskConfig.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# The current program desc is coupled with batch_size and the only
# supported batch size is 1 currently.

encoder_program = fluid.Program()
model.batch_size = InferTaskConfig.batch_size
with fluid.program_guard(main_program=encoder_program):
enc_output = encoder(
ModelHyperParams.src_vocab_size + 1,
Expand All @@ -228,7 +257,6 @@ def main():
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
ModelHyperParams.src_pad_idx, ModelHyperParams.pos_pad_idx)

model.batch_size = InferTaskConfig.batch_size * InferTaskConfig.beam_size
decoder_program = fluid.Program()
with fluid.program_guard(main_program=decoder_program):
predict = decoder(
Expand Down Expand Up @@ -273,6 +301,9 @@ def main():

trg_idx2word = paddle.dataset.wmt16.get_dict(
"de", dict_size=ModelHyperParams.trg_vocab_size, reverse=True)
# Append the <pad> token since the dict provided by dataset.wmt16 does
# not include it.
trg_idx2word[ModelHyperParams.trg_pad_idx] = "<pad>"
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Please fix this in next PR.

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Get it.


def post_process_seq(seq,
bos_idx=ModelHyperParams.bos_idx,
Expand Down Expand Up @@ -306,6 +337,7 @@ def post_process_seq(seq,
InferTaskConfig.n_best,
len(data),
ModelHyperParams.n_head,
ModelHyperParams.d_model,
ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx,
ModelHyperParams.bos_idx,
Expand Down
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