def fast_preprocess_layer(img, input_size, normalize, subtract_means, to_float, mean=MEANS, std=STD):
''' 对图片预处理。用paddle而不使用numpy来得到更快的速度。预测时使用。 '''
# NCHW
img = P.transpose(img, perm=[0, 3, 1, 2])
img = P.image_resize(img, out_shape=[input_size, input_size], resample="BILINEAR")
if normalize:
m = P.create_tensor(dtype='float32')
P.assign(np.array(mean).astype(np.float32), m)
m = P.reshape(m, (1, 3, 1, 1))
m = P.expand_as(m, target_tensor=img)
v = P.create_tensor(dtype='float32')
P.assign(np.array(std).astype(np.float32), v)
v = P.reshape(v, (1, 3, 1, 1))
v = P.expand_as(v, target_tensor=img)
img = (img - m) / v
elif subtract_means:
m = P.create_tensor(dtype='float32')
P.assign(np.array(mean).astype(np.float32), m)
m = P.reshape(m, (1, 3, 1, 1))
m = P.expand_as(m, target_tensor=img)
img = (img - m)
elif to_float: # 只是归一化
img = img / 255
# 换成RGB格式
img_rgb = P.concat([img[:, 2:3, :, :], img[:, 1:2, :, :], img[:, 0:1, :, :]], axis=1)
# Return value is in channel order [n, c, h, w] and RGB
return img_rgb
def not_test_raw_api(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = layers.less_than(x=label, y=limit)
true_image, false_image = split_lod_tensor(input=image, mask=cond)
true_out = layers.create_tensor(dtype='float32')
true_cond = ConditionalBlock([cond])
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=true_out)
false_out = layers.create_tensor(dtype='float32')
false_cond = ConditionalBlock([cond])
with false_cond.block():
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=false_out)
prob = merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image)
loss = layers.cross_entropy(input=prob, label=label)
avg_loss = layers.mean(loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=10)
place = core.CPUPlace()
exe = Executor(place)
exe.run(startup_prog)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print(outs[0])
if outs[0] < 1.0:
return
self.assertFalse(True)
def test_square_error_cost(self):
input_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
label_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
sub = input_val - label_val
np_result = sub * sub
input_var = layers.create_tensor(dtype="float32", name="input")
label_var = layers.create_tensor(dtype="float32", name="label")
layers.assign(input=input_val, output=input_var)
layers.assign(input=label_val, output=label_var)
output = layers.square_error_cost(input=input_var, label=label_var)
for use_cuda in ([False, True]
if core.is_compiled_with_cuda() else [False]):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
result = exe.run(fluid.default_main_program(),
feed={
"input": input_var,
"label": label_var
},
fetch_list=[output])
self.assertTrue(np.isclose(np_result, result).all())
def __call__(self, batch_C_prime, I_r_size):
C = self.build_C()
P = self.build_P(I_r_size)
inv_delta_C = self.build_inv_delta_C(C).astype('float32')
P_hat = self.build_P_hat(C, P).astype('float32')
inv_delta_C_tensor = layers.create_tensor(dtype='float32')
layers.assign(inv_delta_C, inv_delta_C_tensor)
inv_delta_C_tensor.stop_gradient = True
P_hat_tensor = layers.create_tensor(dtype='float32')
layers.assign(P_hat, P_hat_tensor)
P_hat_tensor.stop_gradient = True
batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
# batch_C_ex_part_tensor = create_tmp_var(
# fluid.default_main_program(),
# name='batch_C_ex_part_tensor',
# dtype='float32', shape=[-1, 3, 2])
# layers.py_func(func=get_batch_C_expand,
# x=[batch_C_prime], out=[batch_C_ex_part_tensor])
batch_C_ex_part_tensor.stop_gradient = True
batch_C_prime_with_zeros = layers.concat(
[batch_C_prime, batch_C_ex_part_tensor], axis=1)
batch_T = layers.matmul(inv_delta_C_tensor, batch_C_prime_with_zeros)
batch_P_prime = layers.matmul(P_hat_tensor, batch_T)
return batch_P_prime
def test_forward(self):
data = layers.data(name='X', shape=[1], dtype='float32')
data.stop_gradient = False
cond = layers.ConditionalBlock(inputs=[data])
out = layers.create_tensor(dtype='float32')
with cond.block():
hidden = layers.fc(input=data, size=10)
layers.assign(hidden, out)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe.run(default_startup_program())
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
print outs
loss = layers.mean(out)
append_backward(loss=loss)
outs = exe.run(
feed={'X': x},
fetch_list=[
default_main_program().block(0).var(data.name + "@GRAD")
])[0]
print outs
def test_forward(self):
data = layers.data(name='X', shape=[1], dtype='float32')
data.stop_gradient = False
cond = ConditionalBlock(inputs=[data])
out = layers.create_tensor(dtype='float32')
with cond.block():
hidden = layers.fc(input=data, size=10)
layers.assign(hidden, out)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe.run(default_startup_program())
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
print(outs)
loss = layers.mean(out)
append_backward(loss=loss)
outs = exe.run(feed={'X': x},
fetch_list=[
default_main_program().block(0).var(data.name +
"@GRAD")
])[0]
print(outs)
def test_uniform_name(self):
name = 'test_uniform'
uniform1 = Uniform(0.0, 1.0, name=name)
self.assertEqual(uniform1.name, name)
uniform2 = Uniform(0.0, 1.0)
self.assertEqual(uniform2.name, 'Uniform')
paddle.enable_static()
sample = uniform1.sample([2])
self.assertEqual(self.get_prefix(sample.name), name + '_sample')
entropy = uniform1.entropy()
self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)
lp = uniform1.log_prob(value_tensor)
self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
p = uniform1.probs(value_tensor)
self.assertEqual(self.get_prefix(p.name), name + '_probs')
def test_normal_name(self):
name = 'test_normal'
normal1 = Normal(0.0, 1.0, name=name)
self.assertEqual(normal1.name, name)
normal2 = Normal(0.0, 1.0)
self.assertEqual(normal2.name, 'Normal')
paddle.enable_static()
sample = normal1.sample([2])
self.assertEqual(self.get_prefix(sample.name), name + '_sample')
entropy = normal1.entropy()
self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)
lp = normal1.log_prob(value_tensor)
self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
p = normal1.probs(value_tensor)
self.assertEqual(self.get_prefix(p.name), name + '_probs')
kl = normal1.kl_divergence(normal2)
self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
def test_categorical_name(self):
name = 'test_categorical'
categorical1 = Categorical([0.4, 0.6], name=name)
self.assertEqual(categorical1.name, name)
categorical2 = Categorical([0.5, 0.5])
self.assertEqual(categorical2.name, 'Categorical')
paddle.enable_static()
sample = categorical1.sample([2])
self.assertEqual(self.get_prefix(sample.name), name + '_sample')
entropy = categorical1.entropy()
self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
kl = categorical1.kl_divergence(categorical2)
self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
value_npdata = np.array([0], dtype="int64")
value_tensor = layers.create_tensor(dtype="int64")
layers.assign(value_npdata, value_tensor)
p = categorical1.probs(value_tensor)
self.assertEqual(self.get_prefix(p.name), name + '_probs')
lp = categorical1.log_prob(value_tensor)
self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
def test_fetch_var(self):
self.set_input()
x = layers.create_tensor(dtype="int32", persistable=True, name="x")
layers.assign(input=self.val, output=x)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_main_program(), feed={}, fetch_list=[])
fetched_x = fluid.executor._fetch_var("x")
self.assertTrue(numpy.array_equal(fetched_x, self.val),
"fetch_x=%s val=%s" % (fetched_x, self.val))
self.assertEqual(fetched_x.dtype, self.val.dtype)
def test_assign(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
x = layers.create_tensor(dtype=self.dtype)
layers.assign(input=self.value, output=x)
exe = fluid.Executor(self.place)
[fetched_x] = exe.run(main_program, feed={}, fetch_list=[x])
self.assertTrue(numpy.array_equal(fetched_x, self.value),
"fetch_x=%s val=%s" % (fetched_x, self.value))
self.assertEqual(fetched_x.dtype, self.value.dtype)
def test_load(self):
main_prog = fluid.Program()
start_prog = fluid.Program()
with fluid.program_guard(main_prog, start_prog):
var = layers.create_tensor(dtype='float32')
layers.load(var, file_path='./model/w')
exe = fluid.Executor(fluid.CPUPlace())
exe.run(start_prog)
ret = exe.run(main_prog, fetch_list=[var.name])
self.assertTrue(np.array_equal(self.ones, ret[0]))
def test_fetch_var(self):
val = numpy.array([1, 3, 5]).astype(numpy.int32)
x = layers.create_tensor(dtype="int32", persistable=True, name="x")
layers.assign(input=val, output=x)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_main_program(), feed={}, fetch_list=[])
fetched_x = fluid.fetch_var("x")
self.assertTrue(
numpy.array_equal(fetched_x, val),
"fetch_x=%s val=%s" % (fetched_x, val))
self.assertEqual(fetched_x.dtype, val.dtype)
def test_assign(self):
val = (-100 + 200 * numpy.random.random(size=(2, 5))).astype(
numpy.int32)
x = layers.create_tensor(dtype="float32")
layers.assign(input=val, output=x)
exe = fluid.Executor(fluid.CPUPlace())
fetched_x = exe.run(fluid.default_main_program(),
feed={},
fetch_list=[x])[0]
self.assertTrue(numpy.array_equal(fetched_x, val),
"fetch_x=%s val=%s" % (fetched_x, val))
self.assertEqual(fetched_x.dtype, val.dtype)
def test_assign(self):
val = (
-100 + 200 * numpy.random.random(size=(2, 5))).astype(numpy.int32)
x = layers.create_tensor(dtype="float32")
layers.assign(input=val, output=x)
exe = fluid.Executor(fluid.CPUPlace())
fetched_x = exe.run(fluid.default_main_program(),
feed={},
fetch_list=[x])[0]
self.assertTrue(
numpy.array_equal(fetched_x, val),
"fetch_x=%s val=%s" % (fetched_x, val))
self.assertEqual(fetched_x.dtype, val.dtype)
def __call__(self, batch_C_prime, I_r_size):
"""
Generate the grid for the grid_sampler.
Args:
batch_C_prime: the matrix of the geometric transformation
I_r_size: the shape of the input image
Return:
batch_P_prime: the grid for the grid_sampler
"""
C = self.build_C()
P = self.build_P(I_r_size)
inv_delta_C = self.build_inv_delta_C(C).astype('float32')
P_hat = self.build_P_hat(C, P).astype('float32')
inv_delta_C_tensor = layers.create_tensor(dtype='float32')
layers.assign(inv_delta_C, inv_delta_C_tensor)
inv_delta_C_tensor.stop_gradient = True
P_hat_tensor = layers.create_tensor(dtype='float32')
layers.assign(P_hat, P_hat_tensor)
P_hat_tensor.stop_gradient = True
batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
# batch_C_ex_part_tensor = create_tmp_var(
# fluid.default_main_program(),
# name='batch_C_ex_part_tensor',
# dtype='float32', shape=[-1, 3, 2])
# layers.py_func(func=get_batch_C_expand,
# x=[batch_C_prime], out=[batch_C_ex_part_tensor])
batch_C_ex_part_tensor.stop_gradient = True
batch_C_prime_with_zeros = layers.concat(
[batch_C_prime, batch_C_ex_part_tensor], axis=1)
batch_T = layers.matmul(inv_delta_C_tensor, batch_C_prime_with_zeros)
batch_P_prime = layers.matmul(P_hat_tensor, batch_T)
return batch_P_prime
def test_masked_select(self):
mask_shape = [4, 1]
shape = [4, 4]
data = np.random.random(mask_shape).astype("float32")
input_data = np.random.random(shape).astype("float32")
mask_data = data > 0.5
mask_data_b = np.broadcast_to(mask_data, shape)
npresult = input_data[np.where(mask_data_b)]
input_var = layers.create_tensor(dtype="float32", name="input")
mask_var = layers.create_tensor(dtype="bool", name="mask")
output = layers.masked_select(input=input_var, mask=mask_var)
for use_cuda in ([False, True]
if core.is_compiled_with_cuda() else [False]):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
result = exe.run(fluid.default_main_program(),
feed={"input": input_data,
"mask": mask_data},
fetch_list=[output])
self.assertTrue(np.isclose(npresult, result).all())
def test_distribution_error(self):
distribution = Distribution()
self.assertRaises(NotImplementedError, distribution.sample)
self.assertRaises(NotImplementedError, distribution.entropy)
normal = Normal(0.0, 1.0)
self.assertRaises(NotImplementedError, distribution.kl_divergence,
normal)
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
self.assertRaises(NotImplementedError, distribution.log_prob,
value_tensor)
self.assertRaises(NotImplementedError, distribution.probs,
value_tensor)
def beam_search():
max_len = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=max_out_len,
force_cpu=True)
step_idx = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=0,
force_cpu=True)
cond = layers.less_than(x=step_idx,
y=max_len) # default force_cpu=True
while_op = layers.While(cond)
# array states will be stored for each step.
ids = layers.array_write(layers.reshape(start_tokens, (-1, 1)),
step_idx)
scores = layers.array_write(init_scores, step_idx)
# cell states will be overwrited at each step.
# caches contains states of history steps in decoder self-attention
# and static encoder output projections in encoder-decoder attention
# to reduce redundant computation.
caches = [
{
"k": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_key],
dtype=enc_output.dtype,
value=0),
"v": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_value],
dtype=enc_output.dtype,
value=0),
"static_k": # for encoder-decoder attention
layers.create_tensor(dtype=enc_output.dtype),
"static_v": # for encoder-decoder attention
layers.create_tensor(dtype=enc_output.dtype)
} for i in range(n_layer)
]
with while_op.block():
pre_ids = layers.array_read(array=ids, i=step_idx)
# Since beam_search_op dosen't enforce pre_ids' shape, we can do
# inplace reshape here which actually change the shape of pre_ids.
pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True)
pre_scores = layers.array_read(array=scores, i=step_idx)
# gather cell states corresponding to selected parent
pre_src_attn_bias = layers.gather(trg_src_attn_bias,
index=parent_idx)
pre_pos = layers.elementwise_mul(
x=layers.fill_constant_batch_size_like(
input=pre_src_attn_bias, # cann't use lod tensor here
value=1,
shape=[-1, 1, 1],
dtype=pre_ids.dtype),
y=step_idx,
axis=0)
logits = wrap_decoder(trg_vocab_size,
max_in_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
dec_inputs=(pre_ids, pre_pos, None,
pre_src_attn_bias),
enc_output=enc_output,
caches=caches,
gather_idx=parent_idx,
bos_idx=bos_idx)
# intra-beam topK
topk_scores, topk_indices = layers.topk(
input=layers.softmax(logits), k=beam_size)
accu_scores = layers.elementwise_add(x=layers.log(topk_scores),
y=pre_scores,
axis=0)
# beam_search op uses lod to differentiate branches.
accu_scores = layers.lod_reset(accu_scores, pre_ids)
# topK reduction across beams, also contain special handle of
# end beams and end sentences(batch reduction)
selected_ids, selected_scores, gather_idx = layers.beam_search(
pre_ids=pre_ids,
pre_scores=pre_scores,
ids=topk_indices,
scores=accu_scores,
beam_size=beam_size,
end_id=eos_idx,
return_parent_idx=True)
layers.increment(x=step_idx, value=1.0, in_place=True)
# cell states(caches) have been updated in wrap_decoder,
# only need to update beam search states here.
layers.array_write(selected_ids, i=step_idx, array=ids)
layers.array_write(selected_scores, i=step_idx, array=scores)
layers.assign(gather_idx, parent_idx)
layers.assign(pre_src_attn_bias, trg_src_attn_bias)
length_cond = layers.less_than(x=step_idx, y=max_len)
finish_cond = layers.logical_not(layers.is_empty(x=selected_ids))
layers.logical_and(x=length_cond, y=finish_cond, out=cond)
finished_ids, finished_scores = layers.beam_search_decode(
ids, scores, beam_size=beam_size, end_id=eos_idx)
return finished_ids, finished_scores
def test_raw_api(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0)
cond = layers.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_out = layers.create_tensor(dtype='float32')
true_cond = layers.ConditionalBlock([true_image])
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=true_out)
false_out = layers.create_tensor(dtype='float32')
false_cond = layers.ConditionalBlock([false_image])
with false_cond.block():
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=false_out)
prob = layers.merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image)
loss = layers.cross_entropy(input=prob, label=label)
avg_loss = layers.mean(loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=200)
place = core.CPUPlace()
exe = Executor(place)
exe.run(startup_prog)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
if outs[0] < 1.0:
return
self.assertFalse(True)
def beam_search():
"""Beam search function"""
max_len = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=self.max_out_len,
force_cpu=True)
min_len = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=self.min_out_len)
neg_inf = layers.fill_constant(shape=[1],
dtype='float32',
value=-INF)
step_idx = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=0,
force_cpu=True)
step_next_idx = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=1,
force_cpu=True)
cond = layers.less_than(x=step_idx,
y=max_len) # default force_cpu=True
while_op = layers.While(cond)
# array states will be stored for each step.
ids = layers.array_write(layers.reshape(start_tokens, (-1, 1)),
step_idx)
scores = layers.array_write(init_scores, step_idx)
# cell states will be overwrited at each step.
# caches contains states of history steps in decoder self-attention
# and static encoder output projections in encoder-decoder attention
# to reduce redundant computation.
caches = [
{
"k": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, self._n_head, 0, self._emb_size // self._n_head],
dtype=enc_words_output.dtype,
value=0),
"v": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, self._n_head, 0, self._emb_size // self._n_head],
dtype=enc_words_output.dtype,
value=0),
"static_k_word": # for encoder-decoder attention
layers.create_tensor(dtype=enc_words_output.dtype),
"static_v_word": # for encoder-decoder attention
layers.create_tensor(dtype=enc_words_output.dtype),
"static_k_sent": # for encoder-decoder attention
layers.create_tensor(dtype=enc_sents_output.dtype),
"static_v_sent": # for encoder-decoder attention
layers.create_tensor(dtype=enc_sents_output.dtype)
} for i in range(self._dec_n_layer)
]
trigram_blocking = TrigramBlocking(start_tokens,
self.tokenizer,
use_fp16=self._use_fp16,
beam_size=self.beam_size)
with while_op.block():
pre_ids = layers.array_read(array=ids, i=step_idx)
pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True)
# Since beam_search_op dosen't enforce pre_ids' shape, we can do
# inplace reshape here which actually change the shape of pre_ids.
# pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True)
pre_scores = layers.array_read(array=scores, i=step_idx)
# gather cell states corresponding to selected parent
pre_src_words_attn_bias = layers.gather(
tgt_src_words_attn_bias, index=parent_idx)
pre_src_sents_attn_bias = layers.gather(
tgt_src_sents_attn_bias, index=parent_idx)
pre_graph_attn_bias = layers.gather(graph_attn_bias,
index=parent_idx)
pre_pos = layers.elementwise_mul(
x=layers.fill_constant_batch_size_like(
input=
pre_src_sents_attn_bias, # cann't use lod tensor here
value=1,
shape=[-1, 1, 1],
dtype=pre_ids.dtype),
y=step_idx,
axis=0)
logits = self.decode(
dec_input=(pre_ids, pre_pos, None, pre_src_words_attn_bias,
pre_src_sents_attn_bias, pre_graph_attn_bias),
enc_words_output=enc_words_output,
enc_sents_output=enc_sents_output,
caches=caches,
gather_idx=parent_idx)
# prevent generating end token if length less than min_out_len
eos_index = layers.fill_constant(
shape=[layers.shape(logits)[0]],
dtype='int64',
value=self.eos_idx)
eos_index = fluid.one_hot(eos_index, depth=self.voc_size)
less_cond = layers.cast(layers.less_than(x=step_idx,
y=min_len),
dtype='float32')
less_val = layers.elementwise_mul(less_cond, neg_inf)
eos_val = layers.elementwise_mul(eos_index, less_val, axis=0)
revised_logits = layers.elementwise_add(logits,
eos_val,
axis=0)
# topK reduction across beams, also contain special handle of
# end beams and end sentences(batch reduction)
topk_scores, topk_indices = layers.topk(
input=layers.softmax(revised_logits), k=self.beam_size)
# Roll-Back previous-scores for length-penalty
# previous-scores has been length-penaltied, before this timestep length-penalty, need roll-back
# because of doing this, we need store the length-penaltied score in `scores`
# while calculating use the un-penaltied score
# -> safe for step_idx == 0 (initialization state), because previous-score == 0
pre_timestep_length_penalty = fluid.layers.pow(
((5.0 + fluid.layers.cast(step_idx, pre_scores.dtype)) /
6.0), self.len_penalty)
pre_scores_wo_len_penalty = fluid.layers.elementwise_mul(
pre_scores, pre_timestep_length_penalty)
# calc trigram-blocking delta scores for current alive sequence
if self.block_trigram:
trigram_blocking.update_seq(pre_ids, parent_idx)
trigram_blocking.expand_cand_seq(topk_indices)
fluid.layers.py_func(
func=trigram_blocking.blocking_forward,
x=[
trigram_blocking.cand_seq,
trigram_blocking.id2is_full_token
],
out=trigram_blocking.delta_score_out,
backward_func=None)
layers.Print(trigram_blocking.delta_score_out,
summarize=100,
message="trigram_blocking.delta_score_out")
pre_scores_wo_len_penalty = fluid.layers.elementwise_add(
x=trigram_blocking.delta_score_out,
y=pre_scores_wo_len_penalty,
axis=0)
# => [N, topk]
accu_scores = layers.elementwise_add(
x=layers.log(topk_scores),
y=pre_scores_wo_len_penalty,
axis=0)
cur_timestep_length_penalty = layers.pow(
((5.0 + layers.cast(step_next_idx, accu_scores.dtype)) /
6.0), self.len_penalty)
curr_scores = layers.elementwise_div(
accu_scores, cur_timestep_length_penalty)
# beam_search op uses lod to differentiate branches.
curr_scores = layers.lod_reset(curr_scores, pre_ids)
topk_indices = layers.lod_reset(topk_indices, pre_ids)
selected_ids, selected_scores, gather_idx = layers.beam_search(
pre_ids=pre_ids,
pre_scores=pre_scores,
ids=topk_indices,
scores=curr_scores,
beam_size=self.beam_size,
end_id=self.eos_idx,
return_parent_idx=True)
layers.increment(x=step_idx, value=1.0, in_place=True)
layers.increment(x=step_next_idx, value=1.0, in_place=True)
# cell states(caches) have been updated in wrap_decoder,
# only need to update beam search states here.
layers.array_write(selected_ids, i=step_idx, array=ids)
layers.array_write(selected_scores, i=step_idx, array=scores)
layers.assign(gather_idx, parent_idx)
layers.assign(pre_src_words_attn_bias, tgt_src_words_attn_bias)
layers.assign(pre_src_sents_attn_bias, tgt_src_sents_attn_bias)
layers.assign(pre_graph_attn_bias, graph_attn_bias)
length_cond = layers.less_than(x=step_idx, y=max_len)
finish_cond = layers.logical_not(
layers.is_empty(x=selected_ids))
layers.logical_and(x=length_cond, y=finish_cond, out=cond)
finished_ids, finished_scores = layers.beam_search_decode(
ids, scores, beam_size=self.beam_size, end_id=self.eos_idx)
return finished_ids, finished_scores
def fast_decode(src_vocab_size,
trg_vocab_size,
max_in_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
beam_size,
max_out_len,
bos_idx,
eos_idx,
model_input=None):
"""
Use beam search to decode. Caches will be used to store states of history
steps which can make the decoding faster.
"""
enc_inputs = (model_input.src_word, model_input.src_pos,
model_input.src_slf_attn_bias)
dec_inputs = (model_input.trg_word, model_input.init_score,
model_input.init_idx, model_input.trg_src_attn_bias)
enc_output = wrap_encoder(src_vocab_size,
max_in_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
enc_inputs,
bos_idx=bos_idx)
start_tokens, init_scores, parent_idx, trg_src_attn_bias = dec_inputs
max_len = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=max_out_len,
force_cpu=True)
step_idx = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=0,
force_cpu=True)
# array states will be stored for each step.
ids = layers.array_write(layers.reshape(start_tokens, (-1, 1)), step_idx)
scores = layers.array_write(init_scores, step_idx)
# cell states will be overwrited at each step.
# caches contains states of history steps in decoder self-attention
# and static encoder output projections in encoder-decoder attention
# to reduce redundant computation.
caches = []
for i in range(n_layer):
caches.append({
"k": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_key],
dtype=enc_output.dtype,
value=0),
"v": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_value],
dtype=enc_output.dtype,
value=0),
"static_k": # for encoder-decoder attention
layers.create_tensor(dtype=enc_output.dtype),
"static_v": # for encoder-decoder attention
layers.create_tensor(dtype=enc_output.dtype)
})
_do_beam_search(trg_vocab_size, max_in_len, n_layer, n_head, d_key,
d_value, d_model, d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd, weight_sharing, beam_size, max_len,
bos_idx, eos_idx, ids, scores, parent_idx,
trg_src_attn_bias, caches, enc_output, step_idx)
finished_ids, finished_scores = layers.beam_search_decode(
ids, scores, beam_size=beam_size, end_id=eos_idx)
return finished_ids, finished_scores
exe.run(start_prog)
# simulate saving model
fluid.io.save_persistables(exe, dirname="old", main_program=main_prog)
#############################################################################
# The following section illustrates what user should do to adjust parameter #
#############################################################################
# The target 'weight' is the concatenation of original 'weight' and a
# supplement weight filled 0 of shape (4, 8)
zeros = np.zeros((4, 8)).astype('float32')
main_prog = fluid.Program()
start_prog = fluid.Program()
with fluid.program_guard(main_prog, start_prog):
w_part1 = layers.create_tensor(dtype='float32')
layers.load(w_part1, file_path='old/weight')
w_part2 = layers.assign(zeros)
new_w = layers.concat([w_part1, w_part2], axis=0)
main_prog.current_block().append_op(type='save',
inputs={'X': [new_w]},
outputs={},
attrs={'file_path': 'new/weight'})
exe = fluid.Executor(fluid.CPUPlace())
exe.run(start_prog)
ret = exe.run(main_prog, fetch_list=[new_w.name])
target = np.concatenate((ones, zeros), axis=0)
assert np.array_equal(ret[0], target)
