def test_dygraph(self):
paddle.disable_static()
for place in self.places:
with fluid.dygraph.guard(place):
np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
x = fluid.layers.assign(np_x)
x = fluid.layers.cast(x, 'bool')
out1 = paddle.all(x)
np_out1 = out1.numpy()
expect_res1 = np.all(np_x)
self.assertTrue((np_out1 == expect_res1).all())
out2 = paddle.all(x, axis=0)
np_out2 = out2.numpy()
expect_res2 = np.all(np_x, axis=0)
self.assertTrue((np_out2 == expect_res2).all())
out3 = paddle.all(x, axis=-1)
np_out3 = out3.numpy()
expect_res3 = np.all(np_x, axis=-1)
self.assertTrue((np_out3 == expect_res3).all())
out4 = paddle.all(x, axis=1, keepdim=True)
np_out4 = out4.numpy()
expect_res4 = np.all(np_x, axis=1, keepdims=True)
self.assertTrue((np_out4 == expect_res4).all())
paddle.enable_static()
def check_initial_inverse_hessian_estimate(H0):
r"""Check whether the specified initial_inverse_hessian_estimate is symmetric and positive definite.
Raise errors when precondition not met.
Note:
In static graph can not raise error directly, so use py_func make raise_func as a op,
and use paddle.static.nn.cond to decide if put the op in net.
cholesky is the fast way to check positive definition, but in static graph can not catch
exception to raise value error, so use eigvals rather than cholesky in static graph.
"""
is_symmetric = paddle.all(paddle.equal(H0, H0.t()))
def raise_func():
raise ValueError(
"The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not."
)
if paddle.in_dynamic_mode():
if not is_symmetric:
raise_func()
try:
paddle.linalg.cholesky(H0)
except RuntimeError as error:
raise_func()
else:
def create_tmp_var(program, name, dtype, shape):
return program.current_block().create_var(
name=name, dtype=dtype, shape=shape)
out_var = create_tmp_var(
paddle.static.default_main_program(),
name='output',
dtype='float32',
shape=[-1])
def false_fn():
paddle.static.nn.py_func(
func=raise_func, x=is_symmetric, out=out_var)
paddle.static.nn.cond(is_symmetric, None, false_fn)
# eigvals only support cpu
paddle.set_device("cpu")
eigvals = paddle.paddle.linalg.eigvals(H0)
is_positive = paddle.all(eigvals.real() > 0.) and paddle.all(
eigvals.imag() == 0.)
paddle.static.nn.cond(is_positive, None, false_fn)
def check_static_result(self, place):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input = fluid.data(name="input", shape=[4, 4], dtype="bool")
result = paddle.all(x=input)
input_np = np.random.randint(0, 2, [4, 4]).astype("bool")
exe = fluid.Executor(place)
fetches = exe.run(fluid.default_main_program(),
feed={"input": input_np},
fetch_list=[result])
self.assertTrue(np.allclose(fetches[0], np.all(input_np)))
def _unscale(self, optimizer):
if not self._enable:
return
param_grads_dict = defaultdict(list)
dist_param_grads_dict = defaultdict(list)
if getattr(optimizer, '_param_groups', None) and isinstance(
optimizer._param_groups[0], dict):
for group in optimizer._param_groups:
for param in group['params']:
if not param.is_distributed:
if param._grad_ivar() is not None:
param_grads_dict[param._grad_ivar().dtype].append(
param._grad_ivar())
else:
if param._grad_ivar() is not None:
dist_param_grads_dict[
param._grad_ivar().dtype].append(
param._grad_ivar())
else:
for param in optimizer._parameter_list:
if not param.is_distributed:
if param._grad_ivar() is not None:
param_grads_dict[param._grad_ivar().dtype].append(
param._grad_ivar())
else:
if param._grad_ivar() is not None:
dist_param_grads_dict[param._grad_ivar().dtype].append(
param._grad_ivar())
for dtype in dist_param_grads_dict:
for grad in dist_param_grads_dict[dtype]:
self._found_inf = paddle.logical_not(
paddle.all(paddle.isfinite(grad)))
if self._found_inf:
print('Found inf or nan in classifier, dtype is', dtype)
return
for dtype in param_grads_dict:
param_grads = param_grads_dict[dtype]
_C_ops.check_finite_and_unscale(param_grads, self._scale,
param_grads, self._found_inf)
if self._found_inf:
print('Found inf or nan in backbone, dtype is', dtype)
break
def step(self, optimizer):
if int(self.sample_ratio) < 1:
warnings.warn(
"Explicitly call the function paddle._C_ops.sparse_momentum is a temporary manner. "
"We will merge it to optimizer in the future, please don't follow.")
found_inf = paddle.logical_not(
paddle.all(paddle.isfinite(self._parameter_list[0].grad)))
if found_inf:
print('Found inf or nan in classifier')
else:
if self.weight.name not in optimizer._accumulators[
optimizer._velocity_acc_str]:
optimizer._add_accumulator(optimizer._velocity_acc_str,
self.weight)
velocity = optimizer._accumulators[
optimizer._velocity_acc_str][self.weight.name]
_, _ = paddle._C_ops.sparse_momentum(
self.weight,
self._parameter_list[0].grad,
velocity,
self.index,
paddle.to_tensor(
optimizer.get_lr(), dtype='float32'),
self.weight,
velocity,
'mu',
optimizer._momentum,
'use_nesterov',
optimizer._use_nesterov,
'regularization_method',
optimizer._regularization_method,
'regularization_coeff',
optimizer._regularization_coeff,
'axis',
1)
def forward(self, inputs):
"""
forward
"""
x = paddle.all(inputs, axis=self.axis, keepdim=self.keepdim)
return x
def sync_gradient_and_unscale(self, optimizer):
if self.world_size <= 1 and self.grad_norm_clip is None and not self._enable:
return
# data parallel
param_grads_dict = defaultdict(list)
# model parallel
dist_param_grads_dict = defaultdict(list)
if getattr(optimizer, '_param_groups', None) and isinstance(
optimizer._param_groups[0], dict):
for group in optimizer._param_groups:
for param in group['params']:
if not param.is_distributed:
if param._grad_ivar() is not None:
param_grads_dict[param._grad_ivar().dtype].append(
param._grad_ivar())
else:
if param._grad_ivar() is not None:
dist_param_grads_dict[param._grad_ivar(
).dtype].append(param._grad_ivar())
elif getattr(param, 'sparse_grad', None) is not None:
grad = getattr(param, 'sparse_grad')
dist_param_grads_dict[grad.dtype].append(grad)
else:
for param in optimizer._parameter_list:
if not param.is_distributed:
if param._grad_ivar() is not None:
param_grads_dict[param._grad_ivar().dtype].append(
param._grad_ivar())
else:
if param._grad_ivar() is not None:
dist_param_grads_dict[param._grad_ivar().dtype].append(
param._grad_ivar())
elif getattr(param, 'sparse_grad', None) is not None:
grad = getattr(param, 'sparse_grad')
dist_param_grads_dict[grad.dtype].append(grad)
if self._enable:
for dtype in dist_param_grads_dict:
for grad in dist_param_grads_dict[dtype]:
self._found_inf = paddle.logical_not(
paddle.all(paddle.isfinite(grad)))
if self._found_inf:
print(
'Found inf or nan of distributed parameter, dtype is',
dtype)
return
grads_fp32 = []
grads_fp16 = []
if len(param_grads_dict[paddle.float32]) > 0:
coalesced_grads_and_vars_fp32 = \
paddle.fluid.dygraph.parallel.build_groups(param_grads_dict[paddle.float32], 128 * 1024 * 1024)
for coalesced_grad, _, _ in coalesced_grads_and_vars_fp32:
if self.world_size > 1:
paddle.distributed.all_reduce(coalesced_grad)
grads_fp32.append(coalesced_grad)
if self._enable:
_C_ops.check_finite_and_unscale(grads_fp32, self._scale,
grads_fp32, self._found_inf)
if self._found_inf:
print(
'Found inf or nan of non distributed parameter, dtype is',
paddle.float32)
return
if len(param_grads_dict[paddle.float16]) > 0:
coalesced_grads_and_vars_fp16 = \
paddle.fluid.dygraph.parallel.build_groups(param_grads_dict[paddle.float16], 128 * 1024 * 1024)
for coalesced_grad, _, _ in coalesced_grads_and_vars_fp16:
if self.world_size > 1:
paddle.distributed.all_reduce(coalesced_grad)
grads_fp16.append(coalesced_grad)
if self._enable:
_C_ops.check_finite_and_unscale(grads_fp16, self._scale,
grads_fp16, self._found_inf)
if self._found_inf:
print(
'Found inf or nan non distributed parameter, dtype is',
paddle.float16)
return
if self.grad_norm_clip is not None:
clip_grad_norm_(grads_fp32, grads_fp16, self.grad_norm_clip,
self.grad_norm_clip_max)
if len(param_grads_dict[paddle.float16]) > 0:
paddle.fluid.dygraph.parallel._split_tensors(
coalesced_grads_and_vars_fp16)
if len(param_grads_dict[paddle.float32]) > 0:
paddle.fluid.dygraph.parallel._split_tensors(
coalesced_grads_and_vars_fp32)
def greedy_search(self,
src_word,
max_len=256,
waitk=-1,
caches=None,
bos_id=None):
"""
greedy_search uses streaming reader. It doesn't need calling
encoder many times, an a sub-sentence just needs calling encoder once.
So, it needs previous state(caches) and last one of generated
tokens id last time.
"""
src_max_len = paddle.shape(src_word)[-1]
base_attn_bias = paddle.cast(
src_word == self.bos_id,
dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e9
src_slf_attn_bias = base_attn_bias
src_slf_attn_bias.stop_gradient = True
trg_src_attn_bias = paddle.tile(base_attn_bias, [1, 1, 1, 1])
src_pos = paddle.cast(
src_word != self.bos_id, dtype="int64") * paddle.arange(
start=0, end=src_max_len)
src_emb = self.src_word_embedding(src_word)
src_pos_emb = self.src_pos_embedding(src_pos)
src_emb = src_emb + src_pos_emb
enc_input = F.dropout(
src_emb, p=self.dropout,
training=self.training) if self.dropout else src_emb
enc_outputs = [self.encoder(enc_input, src_mask=src_slf_attn_bias)]
# constant number
batch_size = enc_outputs[-1].shape[0]
max_len = (
enc_outputs[-1].shape[1] + 20) if max_len is None else max_len
end_token_tensor = paddle.full(
shape=[batch_size, 1], fill_value=self.eos_id, dtype="int64")
predict_ids = []
log_probs = paddle.full(
shape=[batch_size, 1], fill_value=0, dtype="float32")
if not bos_id:
trg_word = paddle.full(
shape=[batch_size, 1], fill_value=self.bos_id, dtype="int64")
else:
trg_word = paddle.full(
shape=[batch_size, 1], fill_value=bos_id, dtype="int64")
# init states (caches) for transformer
if not caches:
caches = self.decoder.gen_cache(enc_outputs[-1], do_zip=False)
for i in range(max_len):
trg_pos = paddle.full(
shape=trg_word.shape, fill_value=i, dtype="int64")
trg_emb = self.trg_word_embedding(trg_word)
trg_pos_emb = self.trg_pos_embedding(trg_pos)
trg_emb = trg_emb + trg_pos_emb
dec_input = F.dropout(
trg_emb, p=self.dropout,
training=self.training) if self.dropout else trg_emb
if waitk < 0 or i >= len(enc_outputs):
# if the decoder step is full sent or longer than all source
# step, then read the whole src
_e = enc_outputs[-1]
dec_output, caches = self.decoder(
dec_input, [_e], None,
trg_src_attn_bias[:, :, :, :_e.shape[1]], caches)
else:
_e = enc_outputs[i]
dec_output, caches = self.decoder(
dec_input, [_e], None,
trg_src_attn_bias[:, :, :, :_e.shape[1]], caches)
dec_output = paddle.reshape(
dec_output, shape=[-1, dec_output.shape[-1]])
logits = self.linear(dec_output)
step_log_probs = paddle.log(F.softmax(logits, axis=-1))
log_probs = paddle.add(x=step_log_probs, y=log_probs)
scores = log_probs
topk_scores, topk_indices = paddle.topk(x=scores, k=1)
finished = paddle.equal(topk_indices, end_token_tensor)
trg_word = topk_indices
log_probs = topk_scores
predict_ids.append(topk_indices)
if paddle.all(finished).numpy():
break
predict_ids = paddle.stack(predict_ids, axis=0)
finished_seq = paddle.transpose(predict_ids, [1, 2, 0])
finished_scores = topk_scores
return finished_seq, finished_scores, caches
def greedy_search(self, src_word, max_len=256, waitk=-1):
src_max_len = paddle.shape(src_word)[-1]
base_attn_bias = paddle.cast(
src_word == self.bos_id,
dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e9
src_slf_attn_bias = base_attn_bias
src_slf_attn_bias.stop_gradient = True
trg_src_attn_bias = paddle.tile(base_attn_bias, [1, 1, 1, 1])
src_pos = paddle.cast(src_word != self.bos_id,
dtype="int64") * paddle.arange(start=0,
end=src_max_len)
src_emb = self.src_word_embedding(src_word)
src_pos_emb = self.src_pos_embedding(src_pos)
src_emb = src_emb + src_pos_emb
enc_input = F.dropout(
src_emb, p=self.dropout,
training=self.training) if self.dropout else src_emb
if waitk < 0 or waitk > src_max_len:
enc_outputs = [self.encoder(enc_input, src_mask=src_slf_attn_bias)]
else:
enc_outputs = []
for i in range(waitk, src_max_len + 1):
enc_output = self.encoder(
enc_input[:, :i, :],
src_mask=src_slf_attn_bias[:, :, :, :i])
enc_outputs.append(enc_output)
# constant number
batch_size = enc_outputs[-1].shape[0]
max_len = (enc_outputs[-1].shape[1] +
20) if max_len is None else max_len
end_token_tensor = paddle.full(shape=[batch_size, 1],
fill_value=self.eos_id,
dtype="int64")
predict_ids = []
log_probs = paddle.full(shape=[batch_size, 1],
fill_value=0,
dtype="float32")
trg_word = paddle.full(shape=[batch_size, 1],
fill_value=self.bos_id,
dtype="int64")
# init states (caches) for transformer
caches = self.decoder.gen_cache(enc_outputs[-1], do_zip=False)
for i in range(max_len):
trg_pos = paddle.full(shape=trg_word.shape,
fill_value=i,
dtype="int64")
trg_emb = self.trg_word_embedding(trg_word)
trg_pos_emb = self.trg_pos_embedding(trg_pos)
trg_emb = trg_emb + trg_pos_emb
dec_input = F.dropout(
trg_emb, p=self.dropout,
training=self.training) if self.dropout else trg_emb
if waitk < 0 or i >= len(enc_outputs):
# Avoid getting the whole source in advance, a diff from:
# https://github.com/autosimtrans/SimulTransBaseline/blob/master/model.py#L1207
# if the decoder step is full sent or longer than all source
# step, then read the whole src
_e = enc_outputs[-1]
dec_output, caches = self.decoder(
dec_input, [_e], None,
trg_src_attn_bias[:, :, :, :_e.shape[1]], caches)
else:
_e = enc_outputs[i]
dec_output, caches = self.decoder(
dec_input, [_e], None,
trg_src_attn_bias[:, :, :, :_e.shape[1]], caches)
dec_output = paddle.reshape(dec_output,
shape=[-1, dec_output.shape[-1]])
logits = self.linear(dec_output)
step_log_probs = paddle.log(F.softmax(logits, axis=-1))
log_probs = paddle.add(x=step_log_probs, y=log_probs)
scores = log_probs
topk_scores, topk_indices = paddle.topk(x=scores, k=1)
finished = paddle.equal(topk_indices, end_token_tensor)
trg_word = topk_indices
log_probs = topk_scores
predict_ids.append(topk_indices)
if paddle.all(finished).numpy():
break
predict_ids = paddle.stack(predict_ids, axis=0)
finished_seq = paddle.transpose(predict_ids, [1, 2, 0])
finished_scores = topk_scores
return finished_seq, finished_scores
def forward(self, src_word):
src_max_len = paddle.shape(src_word)[-1]
mem_seq_lens = paddle.sum(paddle.cast(src_word != self.bos_id,
dtype="int32"),
axis=-1,
keepdim=True)
src_slf_attn_bias = paddle.cast(
src_word == self.bos_id,
dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e9
src_slf_attn_bias.stop_gradient = True
src_pos = paddle.cast(src_word != self.bos_id,
dtype="int64") * paddle.arange(start=0,
end=src_max_len)
src_emb = self.src_word_embedding(src_word)
src_pos_emb = self.src_pos_embedding(src_pos)
src_emb = src_emb + src_pos_emb
enc_input = F.dropout(
src_emb, p=self.dropout,
training=self.training) if self.dropout else src_emb
enc_output = self.transformer.encoder(enc_input,
src_mask=src_slf_attn_bias)
batch_size = enc_output.shape[0]
end_token_tensor = paddle.full(shape=[batch_size, 1],
fill_value=self.eos_id,
dtype="int64")
predict_ids = []
log_probs = paddle.full(shape=[batch_size, 1],
fill_value=0,
dtype="float32")
trg_word = paddle.full(shape=[batch_size, 1],
fill_value=self.bos_id,
dtype="int64")
if self.use_fp16_decoder:
enc_output = paddle.cast(enc_output, "float16")
# Init cache
self_cache = paddle.zeros(
shape=[self.num_decoder_layers, 2, 0, batch_size, self.d_model],
dtype=enc_output.dtype)
mem_cache = paddle.zeros(shape=[
self.num_decoder_layers, 2, batch_size, src_max_len, self.d_model
],
dtype=enc_output.dtype)
for i in range(self.max_out_len):
trg_pos = paddle.full(shape=trg_word.shape,
fill_value=i,
dtype="int64")
trg_emb = self.trg_word_embedding(trg_word)
trg_pos_emb = self.trg_pos_embedding(trg_pos)
trg_emb = trg_emb + trg_pos_emb
dec_input = F.dropout(
trg_emb, p=self.dropout,
training=self.training) if self.dropout else trg_emb
# TODO(gongenlei): do cast in op
if self.use_fp16_decoder:
dec_input = paddle.cast(dec_input, "float16")
dec_output, self_cache, mem_cache = self.decoder(
from_tensor=dec_input,
memory_tensor=enc_output,
mem_seq_len=mem_seq_lens,
self_cache=self_cache,
mem_cache=mem_cache)
if self.use_fp16_decoder:
dec_output = paddle.cast(dec_output, "float32")
dec_output = paddle.reshape(dec_output,
shape=[-1, dec_output.shape[-1]])
logits = self.linear(dec_output)
step_log_probs = paddle.log(F.softmax(logits, axis=-1))
log_probs = paddle.add(x=step_log_probs, y=log_probs)
scores = log_probs
topk_scores, topk_indices = paddle.topk(x=scores, k=1)
finished = paddle.equal(topk_indices, end_token_tensor)
trg_word = topk_indices
log_probs = topk_scores
predict_ids.append(topk_indices)
# TODO(gongenlei): support static graph
if paddle.all(finished).numpy():
break
predict_ids = paddle.stack(predict_ids, axis=0)
finished_seq = paddle.transpose(predict_ids, [1, 2, 0])
finished_scores = topk_scores
return finished_seq, finished_scores
def __call__(self, value):
return paddle.all(value >= 0, axis=-1) and (
(value.sum(-1) - 1).abs() < 1e-6)
def merge_semantic_and_instance(semantic, instance, label_divisor, thing_list,
stuff_area, ignore_index):
"""
Post-processing for panoptic segmentation, by merging semantic segmentation label and class agnostic
instance segmentation label.
Args:
semantic (Tensor): A Tensor of shape [1, H, W], predicted semantic label.
instance (Tensor): A Tensor of shape [1, H, W], predicted instance label.
label_divisor (int): An Integer, used to convert panoptic id = semantic id * label_divisor + instance_id.
thing_list (list): A List of thing class id.
stuff_area (int): An Integer, remove stuff whose area is less tan stuff_area.
ignore_index (int): Specifies a value that is ignored.
Returns:
Tensor: A Tensor of shape [1, H, W] . The pixels whose value equaling ignore_index is ignored.
The stuff class is represented as format like class_id, while
thing class as class_id * label_divisor + ins_id and ins_id begin from 1.
"""
# In case thing mask does not align with semantic prediction
pan_seg = paddle.zeros_like(semantic) + ignore_index
thing_seg = instance > 0
semantic_thing_seg = paddle.zeros_like(semantic)
for thing_class in thing_list:
semantic_thing_seg += semantic == thing_class
# keep track of instance id for each class
class_id_tracker = {}
# paste thing by majority voting
ins_ids = paddle.unique(instance)
for ins_id in ins_ids:
if ins_id == 0:
continue
# Make sure only do majority voting within semantic_thing_seg
thing_mask = paddle.logical_and(instance == ins_id,
semantic_thing_seg == 1)
if paddle.all(paddle.logical_not(thing_mask)):
continue
# get class id for instance of ins_id
sem_ins_id = paddle.gather(semantic.reshape(
(-1, )), paddle.nonzero(thing_mask.reshape(
(-1, )))) # equal to semantic[thing_mask]
v, c = paddle.unique(sem_ins_id, return_counts=True)
class_id = paddle.gather(v, c.argmax())
class_id = class_id.numpy()[0]
if class_id in class_id_tracker:
new_ins_id = class_id_tracker[class_id]
else:
class_id_tracker[class_id] = 1
new_ins_id = 1
class_id_tracker[class_id] += 1
# pan_seg[thing_mask] = class_id * label_divisor + new_ins_id
pan_seg = pan_seg * (paddle.logical_not(thing_mask)) + (
class_id * label_divisor + new_ins_id) * thing_mask.astype('int64')
# paste stuff to unoccupied area
class_ids = paddle.unique(semantic)
for class_id in class_ids:
if class_id.numpy() in thing_list:
# thing class
continue
# calculate stuff area
stuff_mask = paddle.logical_and(semantic == class_id,
paddle.logical_not(thing_seg))
area = paddle.sum(stuff_mask.astype('int64'))
if area >= stuff_area:
# pan_seg[stuff_mask] = class_id
pan_seg = pan_seg * (paddle.logical_not(stuff_mask)
) + stuff_mask.astype('int64') * class_id
return pan_seg
