def do_predict(args):
place = "gpu"
place = paddle.set_device(place)
reader.adapt_vocab_size(args)
# Define model
transformer = FasterTransformer(
src_vocab_size=args.src_vocab_size,
trg_vocab_size=args.trg_vocab_size,
max_length=args.max_length + 1,
num_encoder_layers=args.n_layer,
num_decoder_layers=args.n_layer,
n_head=args.n_head,
d_model=args.d_model,
d_inner_hid=args.d_inner_hid,
dropout=args.dropout,
weight_sharing=args.weight_sharing,
bos_id=args.bos_idx,
eos_id=args.eos_idx,
decoding_strategy=args.decoding_strategy,
beam_size=args.beam_size,
max_out_len=args.max_out_len,
decoding_lib=args.decoding_lib,
use_fp16_decoding=args.use_fp16_decoding,
enable_faster_encoder=args.enable_faster_encoder,
use_fp16_encoder=args.use_fp16_encoder,
rel_len=args.use_rel_len,
alpha=args.alpha)
# Set evaluate mode
transformer.eval()
# Load checkpoint.
transformer.load(init_from_params=os.path.join(args.init_from_params,
"transformer.pdparams"))
# Convert dygraph model to static graph model
transformer = paddle.jit.to_static(
transformer,
input_spec=[
# src_word
paddle.static.InputSpec(shape=[None, None], dtype="int64"),
# trg_word
# Support exporting model which support force decoding
# NOTE: Data type MUST be int32 !
# paddle.static.InputSpec(
# shape=[None, None], dtype="int32")
])
# Save converted static graph model
paddle.jit.save(transformer,
os.path.join(args.inference_model_dir, "transformer"))
logger.info("Transformer has been saved to {}".format(
args.inference_model_dir))
def do_predict(args):
paddle.enable_static()
place = "gpu"
place = paddle.set_device(place)
reader.adapt_vocab_size(args)
test_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(test_program, startup_program):
src_word = paddle.static.data(name="src_word",
shape=[None, None],
dtype="int64")
# Define model
transformer = FasterTransformer(
src_vocab_size=args.src_vocab_size,
trg_vocab_size=args.trg_vocab_size,
max_length=args.max_length + 1,
num_encoder_layers=args.n_layer,
num_decoder_layers=args.n_layer,
n_head=args.n_head,
d_model=args.d_model,
d_inner_hid=args.d_inner_hid,
dropout=args.dropout,
weight_sharing=args.weight_sharing,
bos_id=args.bos_idx,
eos_id=args.eos_idx,
decoding_strategy=args.decoding_strategy,
beam_size=args.beam_size,
max_out_len=args.max_out_len,
decoding_lib=args.decoding_lib,
use_fp16_decoding=args.use_fp16_decoding,
rel_len=args.use_rel_len,
alpha=args.alpha)
finished_seq = transformer(src_word=src_word)
test_program = test_program.clone(for_test=True)
exe = paddle.static.Executor(place)
exe.run(startup_program)
# Load checkpoint.
transformer.export_params(init_from_params=os.path.join(
args.init_from_params, "transformer.pdparams"),
place=place)
paddle.static.save_inference_model(os.path.join(args.inference_model_dir,
"transformer"),
feed_vars=src_word,
fetch_vars=finished_seq,
executor=exe,
program=test_program)
def do_export(args):
# Adapt vocabulary size
reader.adapt_vocab_size(args)
# Define model
transformer = InferTransformerModel(
src_vocab_size=args.src_vocab_size,
trg_vocab_size=args.trg_vocab_size,
max_length=args.max_length + 1,
num_encoder_layers=args.n_layer,
num_decoder_layers=args.n_layer,
n_head=args.n_head,
d_model=args.d_model,
d_inner_hid=args.d_inner_hid,
dropout=args.dropout,
weight_sharing=args.weight_sharing,
bos_id=args.bos_idx,
eos_id=args.eos_idx,
beam_size=args.beam_size,
max_out_len=args.max_out_len)
# Load the trained model
assert args.init_from_params, (
"Please set init_from_params to load the infer model.")
model_dict = paddle.load(
os.path.join(args.init_from_params, "transformer.pdparams"))
# To avoid a longer length than training, reset the size of position
# encoding to max_length
model_dict["encoder.pos_encoder.weight"] = position_encoding_init(
args.max_length + 1, args.d_model)
model_dict["decoder.pos_encoder.weight"] = position_encoding_init(
args.max_length + 1, args.d_model)
transformer.load_dict(model_dict)
# Set evaluate mode
transformer.eval()
# Convert dygraph model to static graph model
transformer = paddle.jit.to_static(
transformer,
input_spec=[
# src_word
paddle.static.InputSpec(
shape=[None, None], dtype="int64")
])
# Save converted static graph model
paddle.jit.save(transformer,
os.path.join(args.inference_model_dir, "transformer"))
logger.info("Transformer has been saved to {}".format(
args.inference_model_dir))