code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def set_mem_length(self, mem_length: int):
"""
Parameters
----------
mem_length
The memory length of the model
"""
self._cfg.defrost()
self._cfg.MODEL.mem_length = mem_length
self._cfg.freeze() |
Parameters
----------
mem_length
The memory length of the model
| set_mem_length | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer_xl.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer_xl.py | Apache-2.0 |
def forward(self, data, target, mem_l, rel_positions=None, data_mem_mask=None,
causal_only=False, detach_memory=True):
"""
Parameters
----------
data
The input data
- layout = 'NT'
Shape (B, T)
- layout = 'TN'
... |
Parameters
----------
data
The input data
- layout = 'NT'
Shape (B, T)
- layout = 'TN'
Shape (T, B)
target
The ground truth
- layout = 'NT'
Shape (B, T)
- layout =... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer_xl.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer_xl.py | Apache-2.0 |
def step_forward(self, step_data, mem_l):
"""Forward for just one step
Parameters
----------
step_data
Shape (B,)
mem_l
A list of memory objects
- layout = 'NT'
Shape (B, T_mem, units)
- layout = 'TN'
... | Forward for just one step
Parameters
----------
step_data
Shape (B,)
mem_l
A list of memory objects
- layout = 'NT'
Shape (B, T_mem, units)
- layout = 'TN'
Shape (T_mem, B, units)
Returns
-... | step_forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer_xl.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer_xl.py | Apache-2.0 |
def get_pretrained_xlmr(model_name: str = 'fairseq_xlmr_base',
root: str = get_model_zoo_home_dir(),
load_backbone: bool = True,
load_mlm: bool = False) \
-> Tuple[CN, SentencepieceTokenizer, str, str]:
"""Get the pretrained XLM-R weigh... | Get the pretrained XLM-R weights
Parameters
----------
model_name
The name of the xlmr model.
root
The downloading root
load_backbone
Whether to load the weights of the backbone network
load_mlm
Whether to load the weights of MLM
Returns
-------
cfg
... | get_pretrained_xlmr | python | dmlc/gluon-nlp | src/gluonnlp/models/xlmr.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/xlmr.py | Apache-2.0 |
def gen_self_attn_mask(data,
valid_length=None,
attn_type: str = 'full',
layout: str = 'NT'):
"""Generate the mask used for the encoder, i.e, self-attention.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data wi... | Generate the mask used for the encoder, i.e, self-attention.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data with two samples:
data =
[['I', 'can', 'now', 'use', 'numpy', 'in', 'Gluon@@', 'NLP' ],
['May', 'the', 'force', 'be', 'with', 'you', '<PAD>', ... | gen_self_attn_mask | python | dmlc/gluon-nlp | src/gluonnlp/torch/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/attention_cell.py | Apache-2.0 |
def gen_mem_attn_mask(mem, mem_valid_length, data, data_valid_length=None,
layout: str = 'NT'):
"""Generate the mask used for the decoder. All query slots are attended to the memory slots.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data + mem with a batch... | Generate the mask used for the decoder. All query slots are attended to the memory slots.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data + mem with a batch of two samples:
mem = [['I', 'can', 'now', 'use'],
['May', 'the', 'force', '<PAD>']]
mem_valid_lengt... | gen_mem_attn_mask | python | dmlc/gluon-nlp | src/gluonnlp/torch/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/attention_cell.py | Apache-2.0 |
def masked_softmax(att_score, mask, axis: int = -1):
"""Ignore the masked elements when calculating the softmax.
The mask can be broadcastable.
Parameters
----------
att_score : Symborl or NDArray
Shape (..., length, ...)
mask : Symbol or NDArray or None
Shape (..., length, ...... | Ignore the masked elements when calculating the softmax.
The mask can be broadcastable.
Parameters
----------
att_score : Symborl or NDArray
Shape (..., length, ...)
mask : Symbol or NDArray or None
Shape (..., length, ...)
1 --> The element is not masked
0 --> The ... | masked_softmax | python | dmlc/gluon-nlp | src/gluonnlp/torch/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/attention_cell.py | Apache-2.0 |
def multi_head_dot_attn(query, key, value,
mask=None,
edge_scores=None,
dropout: float = 0.0,
scaled: bool = True, normalized: bool = False,
eps: float = 1E-6,
layout: str = 'N... | Multihead dot product attention between the query, key, value.
scaled is False, normalized is False:
D(h_q, h_k) = <h_q, h_k>
scaled is True, normalized is False:
D(h_q, h_k) = <h_q, h_k> / sqrt(dim_q)
scaled is False, normalized is True:
D(h_q, h_k) = <h_q / ||h_q||, h_k / ||h_k||>... | multi_head_dot_attn | python | dmlc/gluon-nlp | src/gluonnlp/torch/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/attention_cell.py | Apache-2.0 |
def relative_position_bucket(relative_position, bidirectional: bool = True, num_buckets: int = 32,
max_distance: int = 128):
"""Map the relative position to buckets.
The major difference between our implementation and
that in [mesh_tensorflow](https://github.com/tensorflow/mesh... | Map the relative position to buckets.
The major difference between our implementation and
that in [mesh_tensorflow](https://github.com/tensorflow/mesh/blob/c59988047e49b4d2af05603e3170724cdbadc467/mesh_tensorflow/transformer/transformer_layers.py#L595-L637)
is that we use 'query_i - mem_j' as the (i, j)-th... | relative_position_bucket | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def get_activation(act, inplace=False):
"""
Parameters
----------
act
Name of the activation
inplace
Whether to perform inplace activation
Returns
-------
activation_layer
The activation
"""
if act is None:
return lambda x: x
if isinstance(ac... |
Parameters
----------
act
Name of the activation
inplace
Whether to perform inplace activation
Returns
-------
activation_layer
The activation
| get_activation | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def get_norm_layer(normalization: str = 'layer_norm', axis: int = -1, epsilon: float = 1e-5,
in_channels: int = 0, **kwargs):
"""Get the normalization layer based on the provided type
Parameters
----------
normalization
The type of the layer normalization from ['layer_norm']
... | Get the normalization layer based on the provided type
Parameters
----------
normalization
The type of the layer normalization from ['layer_norm']
axis
The axis to normalize the
epsilon
The epsilon of the normalization layer
in_channels
Input channel
Returns... | get_norm_layer | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def __init__(self, units: int = 512, hidden_size: int = 2048, activation_dropout: float = 0.0,
dropout: float = 0.1, gated_proj: bool = False, activation='relu',
normalization: str = 'layer_norm', layer_norm_eps: float = 1E-5,
pre_norm: bool = False):
"""
... |
Parameters
----------
units
hidden_size
activation_dropout
dropout
activation
normalization
layer_norm or no_norm
layer_norm_eps
pre_norm
Pre-layer normalization as proposed in the paper:
"[ACL2018] The ... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def forward(self, data):
"""
Parameters
----------
data :
Shape (B, seq_length, C_in)
Returns
-------
out :
Shape (B, seq_length, C_out)
"""
residual = data
if self._pre_norm:
data = self.layer_norm(dat... |
Parameters
----------
data :
Shape (B, seq_length, C_in)
Returns
-------
out :
Shape (B, seq_length, C_out)
| forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def __init__(self, units: int, learnable=False):
"""Use a geometric sequence of timescales.
It is calculated as
[sin(wi x), cos(wi x), sin(wi x), cos(wi x), ...]
By default, we initialize wi to be (1 / 10000) ^ (1 / (units//2 - 1))
Parameters
----------
units
... | Use a geometric sequence of timescales.
It is calculated as
[sin(wi x), cos(wi x), sin(wi x), cos(wi x), ...]
By default, we initialize wi to be (1 / 10000) ^ (1 / (units//2 - 1))
Parameters
----------
units
The number of units for positional embedding
... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def forward(self, positions):
"""
Parameters
----------
positions : th.Tensor
Shape (..., )
Returns
-------
ret :
Shape (..., units)
"""
emb = positions.unsqueeze(-1) * self.freq
sin_emb = th.sin(emb)
cos_e... |
Parameters
----------
positions : th.Tensor
Shape (..., )
Returns
-------
ret :
Shape (..., units)
| forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/layers.py | Apache-2.0 |
def to_torch_dtype(dtype):
"""Convert the dtype to pytorch data type
Parameters
----------
dtype
The input dtype
Returns
-------
ret
Converted dtype
"""
if isinstance(dtype, th.dtype) or dtype is None:
return dtype
dtype = np.dtype(dtype)
if dtype in... | Convert the dtype to pytorch data type
Parameters
----------
dtype
The input dtype
Returns
-------
ret
Converted dtype
| to_torch_dtype | python | dmlc/gluon-nlp | src/gluonnlp/torch/utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/utils.py | Apache-2.0 |
def to_numpy_dtype(dtype):
"""Convert the dtype to numpy dtype
Parameters
----------
dtype
Input dtype
Returns
-------
ret
The converted dtype
"""
if dtype is None:
return None
if dtype in torch_dtype_to_numpy_dict:
return torch_dtype_to_numpy_di... | Convert the dtype to numpy dtype
Parameters
----------
dtype
Input dtype
Returns
-------
ret
The converted dtype
| to_numpy_dtype | python | dmlc/gluon-nlp | src/gluonnlp/torch/utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/utils.py | Apache-2.0 |
def share_parameters(source, target):
"""Share parameters recursively from source model to target model.
For example, if you want ``dense1`` to share ``dense0``'s weights, you can do::
dense0 = nn.Linear(20)
dense1 = nn.Linear(20)
share_parameters(dense0, dense)
which equals to
... | Share parameters recursively from source model to target model.
For example, if you want ``dense1`` to share ``dense0``'s weights, you can do::
dense0 = nn.Linear(20)
dense1 = nn.Linear(20)
share_parameters(dense0, dense)
which equals to
dense1.weight = dense0.weight
d... | share_parameters | python | dmlc/gluon-nlp | src/gluonnlp/torch/utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/utils.py | Apache-2.0 |
def _named_members(module, get_members_fn, prefix='', recurse=True):
r"""Helper method for yielding various names + members of modules.
Unlike upstream torch implementation, this implementation returns
members that are known under multiple names, such as shared
parameters.
"""
... | Helper method for yielding various names + members of modules.
Unlike upstream torch implementation, this implementation returns
members that are known under multiple names, such as shared
parameters.
| _named_members | python | dmlc/gluon-nlp | src/gluonnlp/torch/utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/utils.py | Apache-2.0 |
def move_to(obj, device=None):
"""
Parameters
----------
obj
Nested torch object
device
The target device
Returns
-------
new_obj
The objects that have been moved to device.
"""
if th.is_tensor(obj):
return obj.to(device)
elif isinstance(obj,... |
Parameters
----------
obj
Nested torch object
device
The target device
Returns
-------
new_obj
The objects that have been moved to device.
| move_to | python | dmlc/gluon-nlp | src/gluonnlp/torch/utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/utils.py | Apache-2.0 |
def _pad_arrs_to_max_length(arrs, pad_val, dtype, batch_dim=0, round_to=None):
"""Inner Implementation of the Pad batchify
Parameters
----------
arrs
List of arrays
pad_val
The padding value
dtype
The type of the tensor
batch_dim
The dimension to insert the b... | Inner Implementation of the Pad batchify
Parameters
----------
arrs
List of arrays
pad_val
The padding value
dtype
The type of the tensor
batch_dim
The dimension to insert the batch dimension.
This controls how we should construct the mini-batch.
roun... | _pad_arrs_to_max_length | python | dmlc/gluon-nlp | src/gluonnlp/torch/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/data/batchify.py | Apache-2.0 |
def __call__(self, data):
"""Batchify the input data.
The input can be list of numpy.ndarray, list of numbers or list of
th.Tensor. The arrays will be padded to the largest dimension at `axis` and then
stacked to form the final output.
Parameters
----------
data... | Batchify the input data.
The input can be list of numpy.ndarray, list of numbers or list of
th.Tensor. The arrays will be padded to the largest dimension at `axis` and then
stacked to form the final output.
Parameters
----------
data : List[np.ndarray] or List[List[dtyp... | __call__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/data/batchify.py | Apache-2.0 |
def _stack_arrs(arrs, batch_dim, dtype):
"""
Parameters
----------
arrs
batch_dim
The batch dimension
dtype
torch dtype
Returns
-------
stacked_arr
The resulting stacked array
"""
if isinstance(arrs[0], np.ndarray):
stacked_arr = np.stack(ar... |
Parameters
----------
arrs
batch_dim
The batch dimension
dtype
torch dtype
Returns
-------
stacked_arr
The resulting stacked array
| _stack_arrs | python | dmlc/gluon-nlp | src/gluonnlp/torch/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/data/batchify.py | Apache-2.0 |
def __call__(self, data: t_List[t_Dict]) -> t_Dict:
"""
Parameters
----------
data
The samples to batchify. Each sample should be a dictionary
Returns
-------
ret
The resulting dictionary that stores the merged samples.
"""
... |
Parameters
----------
data
The samples to batchify. Each sample should be a dictionary
Returns
-------
ret
The resulting dictionary that stores the merged samples.
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/data/batchify.py | Apache-2.0 |
def __call__(self, data: t_List[t_NamedTuple]) -> t_NamedTuple:
"""Batchify the input data.
Parameters
----------
data
The samples to batchfy. Each sample should be a namedtuple.
Returns
-------
ret
A namedtuple of length N. Contains the ba... | Batchify the input data.
Parameters
----------
data
The samples to batchfy. Each sample should be a namedtuple.
Returns
-------
ret
A namedtuple of length N. Contains the batchified result of each attribute in the input.
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/data/batchify.py | Apache-2.0 |
def forward(self, data, valid_length):
"""
Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
F
data
- layout = 'NT'
Shape (batch_size, seq_length, C)
... |
Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
F
data
- layout = 'NT'
Shape (batch_size, seq_length, C)
- layout = 'TN'
Shape (seq_length,... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def forward(self, inputs, token_types, valid_length):
# pylint: disable=arguments-differ
"""Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape... | Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def get_initial_embedding(self, inputs, token_types=None):
"""Get the initial token embeddings that considers the token type and positional embeddings
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
... | Get the initial token embeddings that considers the token type and positional embeddings
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
token_types
... | get_initial_embedding | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def apply_pooling(self, sequence):
"""Generate the representation given the inputs.
This is used for pre-training or fine-tuning a bert model.
Get the first token of the whole sequence which is [CLS]
sequence
- layout = 'NT'
Shape (batch_size, sequence_lengt... | Generate the representation given the inputs.
This is used for pre-training or fine-tuning a bert model.
Get the first token of the whole sequence which is [CLS]
sequence
- layout = 'NT'
Shape (batch_size, sequence_length, units)
- layout = 'TN'
... | apply_pooling | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def from_cfg(cls, cfg, use_pooler=True) -> 'BertModel':
"""
Parameters
----------
cfg
Configuration
use_pooler
Whether to output the pooled feature
Returns
-------
ret
The constructed BertModel
"""
cfg ... |
Parameters
----------
cfg
Configuration
use_pooler
Whether to output the pooled feature
Returns
-------
ret
The constructed BertModel
| from_cfg | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def __init__(self, backbone_cfg):
"""
Parameters
----------
backbone_cfg
The cfg of the backbone model
"""
super().__init__()
self.backbone_model = BertModel.from_cfg(backbone_cfg)
# Construct nsp_classifier for next sentence prediction
... |
Parameters
----------
backbone_cfg
The cfg of the backbone model
| __init__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def forward(self, inputs, token_types, valid_length, masked_positions):
"""Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)... | Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def forward(self, inputs, token_types, valid_length, masked_positions):
"""Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)... | Generate the representation given the inputs.
This is used in training or fine-tuning a bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/bert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/bert.py | Apache-2.0 |
def __init__(self, units: int = 512, hidden_size: int = 2048, num_heads: int = 8,
attention_dropout_prob: float = 0.1, hidden_dropout_prob: float = 0.1,
activation_dropout_prob: float = 0.0, layer_norm_eps: float = 1e-12,
pre_norm: bool = False, use_qkv_bias: bool = Tr... |
Parameters
----------
units
hidden_size
num_heads
attention_dropout_prob
hidden_dropout_prob
activation_dropout_prob
layer_norm_eps
pre_norm
Whether to attach the normalization layer before attention layer
If pre_no... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def forward(self, data, attn_mask):
"""
Parameters
----------
data :
If layout == 'NT'
Shape (batch_size, seq_length, C_in)
Else
Shape (seq_length, batch_size, C_in)
attn_mask :
Shape (batch_size, seq_length, seq... |
Parameters
----------
data :
If layout == 'NT'
Shape (batch_size, seq_length, C_in)
Else
Shape (seq_length, batch_size, C_in)
attn_mask :
Shape (batch_size, seq_length, seq_length)
Returns
-------
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def __init__(self, units: int = 512, mem_units: Optional[int] = None, hidden_size: int = 2048,
num_heads: int = 8, activation_dropout: float = 0.0, dropout: float = 0.1,
attention_dropout: float = 0.1, layer_norm_eps: float = 1E-5,
activation: str = 'relu', gated_proj:... |
Parameters
----------
units
mem_units
The number of units in the memory. By default, it is initialized to be the
same as the units.
hidden_size
num_heads
activation_dropout
dropout
attention_dropout
layer_norm_eps
... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def forward(self, data, mem, self_causal_mask, mem_attn_mask):
"""
Parameters
----------
data :
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
mem :
- la... |
Parameters
----------
data :
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
mem :
- layout = 'NT'
Shape (batch_size, mem_length, C_mem)
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def init_states(self, batch_size, device=None, dtype='float32'):
"""Initialize the states required for incremental decoding
Parameters
----------
batch_size
device
dtype
Returns
-------
init_key
- layout = 'NT'
Shape (... | Initialize the states required for incremental decoding
Parameters
----------
batch_size
device
dtype
Returns
-------
init_key
- layout = 'NT'
Shape (batch_size, 0, N, C_key)
- layout = 'TN'
Shape (... | init_states | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def incremental_decode(self, data, states, mem, mem_valid_length, mem_attn_mask=None):
"""Incrementally generate the output given the decoder input.
Parameters
----------
data
Shape (batch_size, C_in)
states
The previous states, contains
1. la... | Incrementally generate the output given the decoder input.
Parameters
----------
data
Shape (batch_size, C_in)
states
The previous states, contains
1. layout = 'NT':
- prev_multi_key
Shape (batch_size, prev_seq_leng... | incremental_decode | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def forward(self, data, valid_length, mem_data, mem_valid_length):
"""Run forward
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
valid_le... | Run forward
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
valid_length
Shape (batch_size,)
mem_data
- layout = '... | forward | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def init_states(self, batch_size, device=None, dtype='float32'):
"""Initialize the states required for incremental decoding
Parameters
----------
batch_size
The batch size
device
The device
dtype
The data type of the states
Re... | Initialize the states required for incremental decoding
Parameters
----------
batch_size
The batch size
device
The device
dtype
The data type of the states
Returns
-------
states
A list of states, each incl... | init_states | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def incremental_decode(self, data, states, mem, mem_valid_length):
"""Incrementally generate the output given the decoder input.
Parameters
----------
data
Shape (batch_size, C_in)
states
The previous states, contain a list of
1. layout = 'NT'... | Incrementally generate the output given the decoder input.
Parameters
----------
data
Shape (batch_size, C_in)
states
The previous states, contain a list of
1. layout = 'NT'
- prev_multi_key
Shape (batch_size, prev_... | incremental_decode | python | dmlc/gluon-nlp | src/gluonnlp/torch/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/models/transformer.py | Apache-2.0 |
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for... | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | dmlc/gluon-nlp | src/gluonnlp/torch/optimizers/fused_lans.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/optimizers/fused_lans.py | Apache-2.0 |
def get_warmup_linear_const_decay_poly_schedule(optimizer, total_steps, warmup_ratio=0.002,
const_ratio=0., degree=1.0, last_epoch=-1):
"""Create a schedule with a learning rate that decreases linearly from the
initial lr set in the optimizer to 0, after a warmup ... | Create a schedule with a learning rate that decreases linearly from the
initial lr set in the optimizer to 0, after a warmup period during which it
increases linearly from 0 to the initial lr set in the optimizer and a
constant period.
Args:
optimizer (:class:`~torch.optim.Optimizer`):
... | get_warmup_linear_const_decay_poly_schedule | python | dmlc/gluon-nlp | src/gluonnlp/torch/optimizers/schedules.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/torch/optimizers/schedules.py | Apache-2.0 |
def clone_merge(self, cfg_filename_or_other_cfg):
"""Create a new cfg by cloning and merging with the given cfg
Parameters
----------
cfg_filename_or_other_cfg
Returns
-------
"""
ret = self.clone()
if isinstance(cfg_filename_or_other_cfg, str):... | Create a new cfg by cloning and merging with the given cfg
Parameters
----------
cfg_filename_or_other_cfg
Returns
-------
| clone_merge | python | dmlc/gluon-nlp | src/gluonnlp/utils/config.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/config.py | Apache-2.0 |
def glob(url, separator=','):
"""Return a list of paths matching a pathname pattern.
The pattern may contain simple shell-style wildcards.
Input may also include multiple patterns, separated by separator.
Parameters
----------
url : str
The name of the files
separator : str, defaul... | Return a list of paths matching a pathname pattern.
The pattern may contain simple shell-style wildcards.
Input may also include multiple patterns, separated by separator.
Parameters
----------
url : str
The name of the files
separator : str, default is ','
The separator in url... | glob | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def file_line_number(path: str) -> int:
"""
Parameters
----------
path
The path to calculate the number of lines in a file.
Returns
-------
ret
The number of lines
"""
ret = 0
with open(path, 'rb') as f:
for _ in f:
ret += 1
return re... |
Parameters
----------
path
The path to calculate the number of lines in a file.
Returns
-------
ret
The number of lines
| file_line_number | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def md5sum(filename):
"""Calculate the md5sum of a file
Parameters
----------
filename
Name of the file
Returns
-------
ret
The md5sum
"""
with open(filename, mode='rb') as f:
d = hashlib.md5()
for buf in iter(functools.partial(f.read, 1024*100), b''... | Calculate the md5sum of a file
Parameters
----------
filename
Name of the file
Returns
-------
ret
The md5sum
| md5sum | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def sha1sum(filename):
"""Calculate the sha1sum of a file
Parameters
----------
filename
Name of the file
Returns
-------
ret
The sha1sum
"""
with open(filename, mode='rb') as f:
d = hashlib.sha1()
for buf in iter(functools.partial(f.read, 1024*100),... | Calculate the sha1sum of a file
Parameters
----------
filename
Name of the file
Returns
-------
ret
The sha1sum
| sha1sum | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def logging_config(folder: Optional[str] = None,
name: Optional[str] = None,
logger: logging.Logger = logging.root,
level: int = logging.INFO,
console_level: int = logging.INFO,
console: bool = True,
overwr... | Config the logging module. It will set the logger to save to the specified file path.
Parameters
----------
folder
The folder to save the log
name
Name of the saved
logger
The logger
level
Logging level
console_level
Logging level of the console log
... | logging_config | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def logerror(logger: logging.Logger = logging.root):
"""A decorator that wraps the passed in function and logs exceptions.
Parameters
----------
logger: logging.Logger
The logger to which to log the error.
"""
def log_wrapper(function):
@functools.wraps(function)
def wra... | A decorator that wraps the passed in function and logs exceptions.
Parameters
----------
logger: logging.Logger
The logger to which to log the error.
| logerror | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def grouper(iterable, n, fillvalue=None):
"""Collect data into fixed-length chunks or blocks"""
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx
args = [iter(iterable)] * n
return itertools.zip_longest(*args, fillvalue=fillvalue) | Collect data into fixed-length chunks or blocks | grouper | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def repeat(iterable, count=None):
"""Repeat a basic iterator for multiple rounds
Parameters
----------
iterable
The basic iterable
count
Repeat the basic iterable for "count" times. If it is None, it will be an infinite iterator.
Returns
-------
new_iterable
A n... | Repeat a basic iterator for multiple rounds
Parameters
----------
iterable
The basic iterable
count
Repeat the basic iterable for "count" times. If it is None, it will be an infinite iterator.
Returns
-------
new_iterable
A new iterable in which the basic iterator h... | repeat | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def load_checksum_stats(path: str) -> dict:
"""
Parameters
----------
path
Path to the stored checksum
Returns
-------
file_stats
"""
file_stats = dict()
with open(path, 'r', encoding='utf-8') as f:
for line in f:
name, hex_hash, file_size = line.str... |
Parameters
----------
path
Path to the stored checksum
Returns
-------
file_stats
| load_checksum_stats | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def download_file_from_google_drive(file_id, dest_path, overwrite=False, showsize=False):
"""Downloads a shared file from google drive into a given folder.
Optionally unzips it.
Parameters
----------
file_id: str
the file identifier.
You can obtain it fro... | Downloads a shared file from google drive into a given folder.
Optionally unzips it.
Parameters
----------
file_id: str
the file identifier.
You can obtain it from the sharable link.
dest_path: str
the destination where to save the downloaded ... | download_file_from_google_drive | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def download(url: str,
path: Optional[str] = None,
overwrite: Optional[bool] = False,
sha1_hash: Optional[str] = None,
retries: Optional[int] = 5,
verify_ssl: Optional[bool] = True,
anonymous_credential: Optional[bool] = True) -> str:
"""... | Download a given URL
Parameters
----------
url
URL to download
path
Destination path to store downloaded file. By default stores to the
current directory with same name as in url.
overwrite
Whether to overwrite destination file if already exists.
sha1_hash
... | download | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def check_version(min_version: str,
warning_only: bool = False,
library: Optional[ModuleType] = None):
"""Check the version of gluonnlp satisfies the provided minimum version.
An exception is thrown if the check does not pass.
Parameters
----------
min_version
... | Check the version of gluonnlp satisfies the provided minimum version.
An exception is thrown if the check does not pass.
Parameters
----------
min_version
Minimum version
warning_only
Printing a warning instead of throwing an exception.
library
The target library for ver... | check_version | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def init_comm(backend, gpus):
"""Init communication backend
Parameters
----------
backend
The communication backend
gpus
Returns
-------
store
The kvstore
num_workers
The total number of workers
rank
local_rank
is_master_node
ctx_l
"""
... | Init communication backend
Parameters
----------
backend
The communication backend
gpus
Returns
-------
store
The kvstore
num_workers
The total number of workers
rank
local_rank
is_master_node
ctx_l
| init_comm | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def get_mxnet_visible_ctx():
"""Get the visible contexts in MXNet.
- If GPU is available
it will return all the visible GPUs, which can be controlled via "CUDA_VISIBLE_DEVICES".
- If no GPU is available
it will return the cpu device.
Returns
-------
ctx_l
The recommende... | Get the visible contexts in MXNet.
- If GPU is available
it will return all the visible GPUs, which can be controlled via "CUDA_VISIBLE_DEVICES".
- If no GPU is available
it will return the cpu device.
Returns
-------
ctx_l
The recommended contexts to use for MXNet
| get_mxnet_visible_ctx | python | dmlc/gluon-nlp | src/gluonnlp/utils/misc.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py | Apache-2.0 |
def __init__(self, params=None):
"""Maintain a set of shadow variables "v" that is calculated by
v[:] = (1 - 1/t) v + 1/t \theta
The t is the number of training steps.
It is also known as "Polyak-Rupert averaging" applied to SGD and was rediscovered in
"Towards Optimal One... | Maintain a set of shadow variables "v" that is calculated by
v[:] = (1 - 1/t) v + 1/t heta
The t is the number of training steps.
It is also known as "Polyak-Rupert averaging" applied to SGD and was rediscovered in
"Towards Optimal One Pass Large Scale Learning withAveraged Stoch... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def apply(self, params):
""" Tell the moving average tracker which parameters we are going to track.
Parameters
----------
params : ParameterDict
The parameters that we are going to track and calculate the moving average.
"""
assert self._track_params is None... | Tell the moving average tracker which parameters we are going to track.
Parameters
----------
params : ParameterDict
The parameters that we are going to track and calculate the moving average.
| apply | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def copy_back(self, params=None):
""" Copy the average parameters back to the given parameters
Parameters
----------
params : ParameterDict
The parameters that we will copy tha average params to.
If it is not given, the tracked parameters will be updated
... | Copy the average parameters back to the given parameters
Parameters
----------
params : ParameterDict
The parameters that we will copy tha average params to.
If it is not given, the tracked parameters will be updated
| copy_back | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def grad_global_norm(parameters: Iterable[Parameter]) -> float:
"""Calculate the 2-norm of gradients of parameters, and how much they should be scaled down
such that their 2-norm does not exceed `max_norm`, if `max_norm` if provided.
If gradients exist for more than one context for a parameter, user needs t... | Calculate the 2-norm of gradients of parameters, and how much they should be scaled down
such that their 2-norm does not exceed `max_norm`, if `max_norm` if provided.
If gradients exist for more than one context for a parameter, user needs to explicitly call
``trainer.allreduce_grads`` so that the gradients... | grad_global_norm | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def clip_grad_global_norm(parameters: Iterable[Parameter],
max_norm: float,
check_isfinite: bool = True) -> Tuple[float, float, bool]:
"""Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`.
If gradients exist for more t... | Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`.
If gradients exist for more than one context for a parameter, user needs to explicitly call
``trainer.allreduce_grads`` so that the gradients are summed first before calculating
the 2-norm.
.. note::
T... | clip_grad_global_norm | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def move_to_ctx(arr, ctx):
"""Move a nested structure of array to the given context
Parameters
----------
arr
The input array
ctx
The MXNet context
Returns
-------
new_arr
The array that has been moved to context
"""
if isinstance(arr, tuple):
re... | Move a nested structure of array to the given context
Parameters
----------
arr
The input array
ctx
The MXNet context
Returns
-------
new_arr
The array that has been moved to context
| move_to_ctx | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def deduplicate_param_dict(param_dict):
"""Get a parameter dict that has been deduplicated
Parameters
----------
param_dict
The parameter dict returned by `model.collect_params()`
Returns
-------
dedup_param_dict
"""
dedup_param_dict = dict()
param_uuid_set = set()
... | Get a parameter dict that has been deduplicated
Parameters
----------
param_dict
The parameter dict returned by `model.collect_params()`
Returns
-------
dedup_param_dict
| deduplicate_param_dict | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def count_parameters(params) -> Tuple[int, int]:
"""
Parameters
----------
params
The input parameter dict
Returns
-------
num_params
The number of parameters that requires gradient
num_fixed_params
The number of parameters that does not require gradient
"""... |
Parameters
----------
params
The input parameter dict
Returns
-------
num_params
The number of parameters that requires gradient
num_fixed_params
The number of parameters that does not require gradient
| count_parameters | python | dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py | Apache-2.0 |
def get_trimmed_lengths(lengths: List[int],
max_length: int,
do_merge: bool = False) -> np.ndarray:
"""Get the trimmed lengths of multiple text data. It will make sure that
the trimmed length is smaller than or equal to the max_length
- do_merge is True
... | Get the trimmed lengths of multiple text data. It will make sure that
the trimmed length is smaller than or equal to the max_length
- do_merge is True
Make sure that sum(trimmed_lengths) <= max_length.
The strategy is to always try to trim the longer lengths.
- do_merge is False
Mak... | get_trimmed_lengths | python | dmlc/gluon-nlp | src/gluonnlp/utils/preprocessing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/preprocessing.py | Apache-2.0 |
def match_tokens_with_char_spans(token_offsets: np.ndarray,
spans: np.ndarray) -> np.ndarray:
"""Match the span offsets with the character-level offsets.
For each span, we perform the following:
1: Cutoff the boundary
span[0] = max(span[0], token_offsets[0, 0])
... | Match the span offsets with the character-level offsets.
For each span, we perform the following:
1: Cutoff the boundary
span[0] = max(span[0], token_offsets[0, 0])
span[1] = min(span[1], token_offsets[-1, 1])
2: Find start + end
We try to select the smallest number of tokens that c... | match_tokens_with_char_spans | python | dmlc/gluon-nlp | src/gluonnlp/utils/preprocessing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/preprocessing.py | Apache-2.0 |
def register(self, *args):
"""
Register the given object under either the nickname or `obj.__name__`. It can be used as
either a decorator or not. See docstring of this class for usage.
"""
if len(args) == 2:
# Register an object with nick name by function call
... |
Register the given object under either the nickname or `obj.__name__`. It can be used as
either a decorator or not. See docstring of this class for usage.
| register | python | dmlc/gluon-nlp | src/gluonnlp/utils/registry.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/registry.py | Apache-2.0 |
def create(self, name: str, *args, **kwargs) -> object:
"""Create the class object with the given args and kwargs
Parameters
----------
name
The name in the registry
args
kwargs
Returns
-------
ret
The created object
... | Create the class object with the given args and kwargs
Parameters
----------
name
The name in the registry
args
kwargs
Returns
-------
ret
The created object
| create | python | dmlc/gluon-nlp | src/gluonnlp/utils/registry.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/registry.py | Apache-2.0 |
def serialize(path, tbl):
"""Serialize tbl with out-of-band data to path for zero-copy shared memory usage.
If the object to be serialized itself, or the objects it uses for data
storage (such as numpy arrays) implement the the pickle protocol version 5
pickle.PickleBuffer type in __reduce_ex__, then t... | Serialize tbl with out-of-band data to path for zero-copy shared memory usage.
If the object to be serialized itself, or the objects it uses for data
storage (such as numpy arrays) implement the the pickle protocol version 5
pickle.PickleBuffer type in __reduce_ex__, then this function can store
these ... | serialize | python | dmlc/gluon-nlp | src/gluonnlp/utils/shm.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/shm.py | Apache-2.0 |
def load(path):
"""Load serialized object with out-of-band data from path based on zero-copy shared memory.
Parameters
----------
path : pathlib.Path
Folder used to save serialized data with serialize(). Usually a folder /dev/shm
"""
num_buffers = len(list(path.iterdir())) - 1 # exclu... | Load serialized object with out-of-band data from path based on zero-copy shared memory.
Parameters
----------
path : pathlib.Path
Folder used to save serialized data with serialize(). Usually a folder /dev/shm
| load | python | dmlc/gluon-nlp | src/gluonnlp/utils/shm.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/shm.py | Apache-2.0 |
def is_match_states_batch_size(states, states_batch_axis, batch_size) -> bool:
"""Test whether the generated states have the specified batch size
Parameters
----------
states
The states structure
states_batch_axis
The states batch axis structure
batch_size
The batch size... | Test whether the generated states have the specified batch size
Parameters
----------
states
The states structure
states_batch_axis
The states batch axis structure
batch_size
The batch size
Returns
-------
ret
| is_match_states_batch_size | python | dmlc/gluon-nlp | src/gluonnlp/utils/testing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py | Apache-2.0 |
def verify_nmt_model(model, batch_size: int = 4,
src_seq_length: int = 5,
tgt_seq_length: int = 10,
atol: float = 1E-4,
rtol: float = 1E-3):
"""Verify the correctness of an NMT model. Raise error message if it detects problems.
... | Verify the correctness of an NMT model. Raise error message if it detects problems.
Parameters
----------
model
The machine translation model
batch_size
The batch size to test the nmt model
src_seq_length
Length of the source sequence
tgt_seq_length
Length of the... | verify_nmt_model | python | dmlc/gluon-nlp | src/gluonnlp/utils/testing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py | Apache-2.0 |
def verify_nmt_inference(train_model, inference_model,
batch_size=4, src_seq_length=5,
tgt_seq_length=10, atol=1E-4, rtol=1E-3):
"""Verify the correctness of an NMT inference model. Raise error message if it detects
any problems.
Parameters
----------
... | Verify the correctness of an NMT inference model. Raise error message if it detects
any problems.
Parameters
----------
train_model
The training model
inference_model
The inference model
batch_size
Batch size
src_seq_length
Length of the source sequence
t... | verify_nmt_inference | python | dmlc/gluon-nlp | src/gluonnlp/utils/testing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py | Apache-2.0 |
def _cast_nested_to_fp16(nested_dat):
"""Cast the nested input to fp16
Parameters
----------
dat
The input nested data structure
Returns
-------
output
The casted output data
"""
if isinstance(nested_dat, (mx.np.ndarray, np.ndarray)):
if nested_dat.dtype == ... | Cast the nested input to fp16
Parameters
----------
dat
The input nested data structure
Returns
-------
output
The casted output data
| _cast_nested_to_fp16 | python | dmlc/gluon-nlp | src/gluonnlp/utils/testing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py | Apache-2.0 |
def verify_backbone_fp16(model_cls, cfg, ctx, inputs,
atol=1E-2, rtol=1E-2, check_amp=True):
"""Test whether the backbone model has the comparable parameter gradient +
Parameters
----------
model_cls
The modeling class
cfg
The configuration
ctx
T... | Test whether the backbone model has the comparable parameter gradient +
Parameters
----------
model_cls
The modeling class
cfg
The configuration
ctx
The context
inputs
The input tensors of the model. We will
atol
The absolute tolerance
rtol
... | verify_backbone_fp16 | python | dmlc/gluon-nlp | src/gluonnlp/utils/testing.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py | Apache-2.0 |
def get_ec2_tvm_flags() -> Dict[str, Dict]:
r"""Return the recommended flags for TVM compilation in AWS EC2 instances.
Including C4, C5, G4, P3.
For more details about AWS EC2 instances, refer to https://aws.amazon.com/ec2/instance-types/.
Returns
-------
info_dict
A dictionary that c... | Return the recommended flags for TVM compilation in AWS EC2 instances.
Including C4, C5, G4, P3.
For more details about AWS EC2 instances, refer to https://aws.amazon.com/ec2/instance-types/.
Returns
-------
info_dict
A dictionary that contains the mapping between instance type and the
... | get_ec2_tvm_flags | python | dmlc/gluon-nlp | src/gluonnlp/utils/tvm_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/tvm_utils.py | Apache-2.0 |
def update_tvm_convert_map() -> None:
"""A Monkey Patch to update convert map in tvm/relay/frontend/mxnet.py"""
op = (('masked_softmax', _mx_masked_softmax),)
_convert_map.update({key: value for key, value in op}) | A Monkey Patch to update convert map in tvm/relay/frontend/mxnet.py | update_tvm_convert_map | python | dmlc/gluon-nlp | src/gluonnlp/utils/tvm_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/tvm_utils.py | Apache-2.0 |
def test_test():
"""Test that fixing a random seed works."""
py_rnd = random.randint(0, 100)
np_rnd = np.random.randint(0, 100)
mx_rnd = mx.nd.random_uniform(shape=(1, )).asscalar()
random.seed(1)
mx.random.seed(1)
np.random.seed(1)
assert py_rnd == random.randint(0, 100)
assert np... | Test that fixing a random seed works. | test_test | python | dmlc/gluon-nlp | tests/test_pytest.py | https://github.com/dmlc/gluon-nlp/blob/master/tests/test_pytest.py | Apache-2.0 |
def is_image_file(filename):
"""Checks if a file is an image.
Args:
filename (string): path to a file
Returns:
bool: True if the filename ends with a known image extension
"""
filename_lower = filename.lower()
return any(filename_lower.endswith(ext) for ext in IMG_EXTENSIONS) | Checks if a file is an image.
Args:
filename (string): path to a file
Returns:
bool: True if the filename ends with a known image extension
| is_image_file | python | ajbrock/BigGAN-PyTorch | datasets.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py | MIT |
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
if self.load_in_mem:
img = self.data[index]
target = self.labels[index]
else:
path, target = self.imgs[index]
... |
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
| __getitem__ | python | ajbrock/BigGAN-PyTorch | datasets.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py | MIT |
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
... |
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
| __getitem__ | python | ajbrock/BigGAN-PyTorch | datasets.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py | MIT |
def torch_cov(m, rowvar=False):
'''Estimate a covariance matrix given data.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`,
then the covariance matrix element `C_{ij}` is the covariance of
`x_i` and `x_j`. The el... | Estimate a covariance matrix given data.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`,
then the covariance matrix element `C_{ij}` is the covariance of
`x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`... | torch_cov | python | ajbrock/BigGAN-PyTorch | inception_utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py | MIT |
def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
"""Numpy implementation of the Frechet Distance.
Taken from https://github.com/bioinf-jku/TTUR
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1... | Numpy implementation of the Frechet Distance.
Taken from https://github.com/bioinf-jku/TTUR
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Stable version by Dougal J. Sutherland.
Params:
... | numpy_calculate_frechet_distance | python | ajbrock/BigGAN-PyTorch | inception_utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py | MIT |
def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
"""Pytorch implementation of the Frechet Distance.
Taken from https://github.com/bioinf-jku/TTUR
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C... | Pytorch implementation of the Frechet Distance.
Taken from https://github.com/bioinf-jku/TTUR
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Stable version by Dougal J. Sutherland.
Params... | torch_calculate_frechet_distance | python | ajbrock/BigGAN-PyTorch | inception_utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py | MIT |
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
size = (min(img.size), min(img.size))
# Only step forward along this edge if it's the long edge
i = (0 if size[0] == img.size[0]
else np.random.randint(l... |
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
| __call__ | python | ajbrock/BigGAN-PyTorch | utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py | MIT |
def log(self, record=None, **kwargs):
"""
Assumption: no newlines in the input.
"""
if record is None:
record = {}
record.update(kwargs)
record['_stamp'] = time.time()
with open(self.fname, 'a') as f:
f.write(json.dumps(record, ensure_ascii=True) + '\n') |
Assumption: no newlines in the input.
| log | python | ajbrock/BigGAN-PyTorch | utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py | MIT |
def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'):
"""
Returns a generator over `items`, printing the number and percentage of
items processed and the estimated remaining processing time before yielding
the next item. `total` gives the total number of items (required if `items`
has no... |
Returns a generator over `items`, printing the number and percentage of
items processed and the estimated remaining processing time before yielding
the next item. `total` gives the total number of items (required if `items`
has no length), and `min_delay` gives the minimum time in seconds between
subsequent ... | progress | python | ajbrock/BigGAN-PyTorch | utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py | MIT |
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in g... | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
| step | python | ajbrock/BigGAN-PyTorch | utils.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py | MIT |
def _data_parallel_master(self, intermediates):
"""Reduce the sum and square-sum, compute the statistics, and broadcast it."""
# Always using same "device order" makes the ReduceAdd operation faster.
# Thanks to:: Tete Xiao (http://tetexiao.com/)
intermediates = sorted(intermediates, ke... | Reduce the sum and square-sum, compute the statistics, and broadcast it. | _data_parallel_master | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/batchnorm.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/batchnorm.py | MIT |
def _compute_mean_std(self, sum_, ssum, size):
"""Compute the mean and standard-deviation with sum and square-sum. This method
also maintains the moving average on the master device."""
assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
mean = sum... | Compute the mean and standard-deviation with sum and square-sum. This method
also maintains the moving average on the master device. | _compute_mean_std | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/batchnorm.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/batchnorm.py | MIT |
def __init__(self, master_callback):
"""
Args:
master_callback: a callback to be invoked after having collected messages from slave devices.
"""
self._master_callback = master_callback
self._queue = queue.Queue()
self._registry = collections.OrderedDict()
... |
Args:
master_callback: a callback to be invoked after having collected messages from slave devices.
| __init__ | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/comm.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py | MIT |
def register_slave(self, identifier):
"""
Register an slave device.
Args:
identifier: an identifier, usually is the device id.
Returns: a `SlavePipe` object which can be used to communicate with the master device.
"""
if self._activated:
assert ... |
Register an slave device.
Args:
identifier: an identifier, usually is the device id.
Returns: a `SlavePipe` object which can be used to communicate with the master device.
| register_slave | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/comm.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py | MIT |
def run_master(self, master_msg):
"""
Main entry for the master device in each forward pass.
The messages were first collected from each devices (including the master device), and then
an callback will be invoked to compute the message to be sent back to each devices
(including t... |
Main entry for the master device in each forward pass.
The messages were first collected from each devices (including the master device), and then
an callback will be invoked to compute the message to be sent back to each devices
(including the master device).
Args:
... | run_master | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/comm.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py | MIT |
def execute_replication_callbacks(modules):
"""
Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
Note that, as all modules are isomorphism, we assign eac... |
Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
Note that, as all modules are isomorphism, we assign each sub-module with a context
(shared among multi... | execute_replication_callbacks | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/replicate.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/replicate.py | MIT |
def patch_replication_callback(data_parallel):
"""
Monkey-patch an existing `DataParallel` object. Add the replication callback.
Useful when you have customized `DataParallel` implementation.
Examples:
> sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
> sync_bn = DataParal... |
Monkey-patch an existing `DataParallel` object. Add the replication callback.
Useful when you have customized `DataParallel` implementation.
Examples:
> sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
> sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
> patch_replic... | patch_replication_callback | python | ajbrock/BigGAN-PyTorch | sync_batchnorm/replicate.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/replicate.py | MIT |
def dump_tfhub_to_hdf5(module_path, hdf5_path, redownload=False):
"""Loads TFHub weights and saves them to intermediate HDF5 file.
Args:
module_path ([Path-like]): Path to TFHub module.
hdf5_path ([Path-like]): Path to output HDF5 file.
Returns:
[h5py.File]: Loaded hdf5 file containing module weight... | Loads TFHub weights and saves them to intermediate HDF5 file.
Args:
module_path ([Path-like]): Path to TFHub module.
hdf5_path ([Path-like]): Path to output HDF5 file.
Returns:
[h5py.File]: Loaded hdf5 file containing module weights.
| dump_tfhub_to_hdf5 | python | ajbrock/BigGAN-PyTorch | TFHub/converter.py | https://github.com/ajbrock/BigGAN-PyTorch/blob/master/TFHub/converter.py | MIT |
def read_img(t_imgfname, input_size, img_mean): # optional pre-processing arguments
"""Read one image and its corresponding mask with optional pre-processing.
Args:
input_queue: tf queue with paths to the image and its mask.
input_size: a tuple with (height, width) values.
If not given, return images of... | Read one image and its corresponding mask with optional pre-processing.
Args:
input_queue: tf queue with paths to the image and its mask.
input_size: a tuple with (height, width) values.
If not given, return images of original size.
random_scale: whether to randomly scale the images prior
to rand... | read_img | python | iyah4888/SIGGRAPH18SSS | main_hyper.py | https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/main_hyper.py | MIT |
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