def done(self):
arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape)
t = core.LoDTensor()
t.set(arr, self.place)
if self.lod_level > 0:
t.set_lod(self.lod)
return t
def done(self):
arr = numpy.array(self.data, dtype=self.dtype)
if self.shape:
arr = arr.reshape(self.shape)
t = core.LoDTensor()
t.set(arr, self.place)
if self.lod_level > 0:
t.set_recursive_sequence_lengths(self.lod)
return t
def create_lod_tensor(data, lod, place):
"""Create a lod tensor from a numpy array, a list, or an existing lod tensor.
Create a lod tensor by doing the following:
1. Check that the length-based input lod is valid.
2. Convert the length-based lod to a offset-based LoD.
3. Copy the data from a numpy array, a list or a existing lod tensor to
CPU or GPU device (based on input place).
4. Set the level of detail (LoD) using the offset-based LoD.
Use example:
Suppose we want LoDTensor to hold data for sequences of word, where each word is
represented by an integer. If we want to create a LoDTensor to represent two
sentences, one of 2 words, and one of 3 words.
Then 'data' can be a numpy array of integers with shape (5, 1).
'lod' will be [[2, 3]], indicating the length(# of words) in each sentence.
This length-based input lod [[2, 3]] will be converted to offset-based lod [[0, 2, 5]]
inside the function call.
Please refer to
github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/lod_tensor.md
for more details regarding LoD.
Args:
data: a numpy array or a LoDTensor or a list holding the data to be copied.
lod: a list of lists indicating the length-based LoD info specified by the user.
place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
Returns:
A fluid LoDTensor object with tensor data and lod info.
"""
if isinstance(data, core.LoDTensor):
return create_lod_tensor(np.array(data), lod, place)
elif isinstance(data, list):
# When input data is a list, it only deal with the case where the base element
# is an index of shape [1] and dtype int64 (e.g., word id). Hence, the generated
# LoDTensor will be of shape [n, 1] and dtype int64, where `n` is the total number
# of words or other indexes in the sequence.
new_lod = []
for seq in data:
new_lod.append(len(seq))
assert [new_lod] == lod, "data and lod do not match"
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
return create_lod_tensor(flattened_data, lod, place)
elif isinstance(data, np.ndarray):
assert _validate_lod(lod,
data.shape[0]), "the provided lod info is invalid"
tensor = core.LoDTensor()
tensor.set(data, place)
tensor.set_lod(_convert_lod(lod))
return tensor
else:
raise TypeError(
"data should be either a LoDTensor, a Numpy array or a list")
def run(self, fetch_list, feed=None, feed_dict=None):
"""
Run a parallel executor with fetch_list.
The feed parameter can be a dict or a list. If feed is a dict, the
feed data will be split into multiple devices. If feed is a list, we
assume the data has been splitted into multiple devices, the each
element in the list will be copied to each device directly.
For example, if the feed is a dict:
>>> exe = ParallelExecutor()
>>> # the image will be splitted into devices. If there is two devices
>>> # each device will process an image with shape (24, 1, 28, 28)
>>> exe.run(feed={'image': numpy.random.random(size=(48, 1, 28, 28))})
For example, if the feed is a list:
>>> exe = ParallelExecutor()
>>> # each device will process each element in the list.
>>> # the 1st device will process an image with shape (48, 1, 28, 28)
>>> # the 2nd device will process an image with shape (32, 1, 28, 28)
>>> #
>>> # you can use exe.device_count to get the device number.
>>> exe.run(feed=[{"image": numpy.random.random(size=(48, 1, 28, 28))},
>>> {"image": numpy.random.random(size=(32, 1, 28, 28))},
>>> ])
Args:
fetch_list(list): The fetched variable names
feed(list|dict|None): The feed variables. If the feed is a dict,
tensors in that dict will be splitted into each devices. If
the feed is a list, each element of the list will be copied
to each device.
feed_dict: Alias for feed parameter, for backward compatibility.
This parameter is deprecated.
Returns: fetched result list.
"""
if feed is None and feed_dict is not None:
feed = feed_dict
print >> sys.stderr, "`feed_dict` is deprecated. Please use `feed=`"
if isinstance(feed, dict):
feed_tensor_dict = dict()
for feed_name in feed:
feed_tensor = feed[feed_name]
if not isinstance(feed_tensor, core.LoDTensor):
feed_tensor = core.LoDTensor()
# always set to CPU place, since the tensor need to be splitted
# it is fast in CPU
feed_tensor.set(feed[feed_name], core.CPUPlace())
feed_tensor_dict[feed_name] = feed_tensor
self.executor.feed_and_split_tensor_into_local_scopes(
feed_tensor_dict)
elif isinstance(feed, list) or isinstance(feed, tuple):
if len(feed) != len(self._act_places):
raise ValueError(
"Feed a list of tensor, the list should be the same size as places"
)
res = list()
for i, each in enumerate(feed):
if not isinstance(each, dict):
raise TypeError(
"Each element of feed list should be a dict")
res_dict = dict()
for feed_name in each:
tensor = each[feed_name]
if not isinstance(tensor, core.LoDTensor):
tmp = core.LoDTensor()
tmp.set(tensor, self._act_places[i])
tensor = tmp
res_dict[feed_name] = tensor
res.append(res_dict)
self.executor.feed_tensors_into_local_scopes(res)
fetch_var_name = '@FETCHED_VAR_NAME@'
self.executor.run(fetch_list, fetch_var_name)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
return [arr[i] for i in range(len(arr))]
def create_lod_tensor(data, recursive_seq_lens, place):
"""
Create a lod tensor from a numpy array, a list, or an existing lod tensor.
Create a lod tensor by doing the following:
1. Check that the length-based level of detail (LoD) also known as
recursive_sequence_lengths of the input is valid.
2. Convert recursive_sequence_lengths to a offset-based LoD.
3. Copy the data from a numpy array, a list or a existing lod tensor to
CPU or GPU device (based on input place).
4. Set the level of detail (LoD) using the offset-based LoD.
Examples:
Suppose we want LoDTensor to hold data for sequences of word, where each
word is represented by an integer. If we want to create a LoDTensor to
represent two sentences, one of 2 words, and one of 3 words.
Then :code:`data` can be a numpy array of integers with shape (5, 1).
:code:`recursive_seq_lens` will be [[2, 3]], indicating the length(# of words) in each
sentence. This length-based :code:`recursive_seq_lens` [[2, 3]] will be converted to
offset-based LoD [[0, 2, 5]] inside the function call.
Please reference :ref:`api_guide_low_level_lod_tensor` for more details
regarding LoD.
Args:
data(numpy.ndarray|list|LoDTensor): a numpy array or a LoDTensor or a
list holding the data to be copied.
recursive_seq_lens(list): a list of lists indicating the length-based level of detail
info specified by the user.
place(Place): CPU or GPU place indicating where the data in the new
LoDTensor will be stored.
Returns:
A fluid LoDTensor object with tensor data and recursive_seq_lens info.
"""
if isinstance(data, core.LoDTensor):
return create_lod_tensor(np.array(data), recursive_seq_lens, place)
elif isinstance(data, list):
# When input data is a list, it only deal with the case where the base element
# is an index of shape [1] and dtype int64 (e.g., word id). Hence, the generated
# LoDTensor will be of shape [n, 1] and dtype int64, where `n` is the total number
# of words or other indexes in the sequence.
new_recursive_seq_lens = []
for seq in data:
new_recursive_seq_lens.append(len(seq))
assert [
new_recursive_seq_lens
] == recursive_seq_lens, "data and recursive_seq_lens do not match"
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
return create_lod_tensor(flattened_data, recursive_seq_lens, place)
elif isinstance(data, np.ndarray):
tensor = core.LoDTensor()
tensor.set(data, place)
tensor.set_recursive_sequence_lengths(recursive_seq_lens)
assert tensor.has_valid_recursive_sequence_lengths(
), "the provided lod info is invalid"
return tensor
else:
raise TypeError(
"data should be either a LoDTensor, a Numpy array or a list")