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 |
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def forward(self, inputs, token_types, valid_length,
masked_positions):
"""Getting the scores of the masked positions.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
... | Getting the scores of the masked positions.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
token_types
The type of the token. For example, if ... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/mobilebert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/mobilebert.py | Apache-2.0 |
def __init__(self, backbone_cfg,
weight_initializer=None,
bias_initializer=None):
"""
Parameters
----------
backbone_cfg
The cfg of the backbone model
weight_initializer
bias_initializer
"""
super().__init__()... |
Parameters
----------
backbone_cfg
The cfg of the backbone model
weight_initializer
bias_initializer
| __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/mobilebert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/mobilebert.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 mobile mobile bert model.
Parameters
----------
inputs
- layout = 'NT'
... | Generate the representation given the inputs.
This is used in training or fine-tuning a mobile mobile bert model.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/mobilebert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/mobilebert.py | Apache-2.0 |
def get_pretrained_mobilebert(model_name: str = 'google_uncased_mobilebert',
root: str = get_model_zoo_home_dir(),
load_backbone: str = True,
load_mlm: str = False)\
-> Tuple[CN, HuggingFaceWordPieceTokenizer, str, str]:
... | Get the pretrained mobile bert weights
Parameters
----------
model_name
The name of the mobile bert 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
---... | get_pretrained_mobilebert | python | dmlc/gluon-nlp | src/gluonnlp/models/mobilebert.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/mobilebert.py | Apache-2.0 |
def __init__(self,
vocab_size=50265,
units=768,
hidden_size=3072,
num_layers=12,
num_heads=12,
max_length=512,
hidden_dropout_prob=0.1,
attention_dropout_prob=0.1,
pos... |
Parameters
----------
vocab_size
units
hidden_size
num_layers
num_heads
max_length
hidden_dropout_prob
attention_dropout_prob
pos_embed_type
activation
pooler_activation
layer_norm_eps
embed_initial... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/roberta.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/roberta.py | Apache-2.0 |
def get_initial_embedding(self, inputs):
"""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 ... | 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)
Returns
--... | get_initial_embedding | python | dmlc/gluon-nlp | src/gluonnlp/models/roberta.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/roberta.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 mobile bert model.
Get the first token of the whole sequence which is [CLS]
Parameters
----------
sequence
- layout = 'NT'
... | Generate the representation given the inputs.
This is used for pre-training or fine-tuning a mobile bert model.
Get the first token of the whole sequence which is [CLS]
Parameters
----------
sequence
- layout = 'NT'
Shape (batch_size, sequence_length... | apply_pooling | python | dmlc/gluon-nlp | src/gluonnlp/models/roberta.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/roberta.py | Apache-2.0 |
def forward(self, inputs, valid_length, masked_positions):
"""Getting the scores of the masked positions.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
... | Getting the scores of the masked positions.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
valid_length
The valid length of each sequence
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/roberta.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/roberta.py | Apache-2.0 |
def get_pretrained_roberta(model_name: str = 'fairseq_roberta_base',
root: str = get_model_zoo_home_dir(),
load_backbone: bool = True,
load_mlm: bool = False) \
-> Tuple[CN, HuggingFaceByteBPETokenizer, str, str]:
"""Get the pr... | Get the pretrained RoBERTa weights
Parameters
----------
model_name
The name of the RoBERTa 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
-------
... | get_pretrained_roberta | python | dmlc/gluon-nlp | src/gluonnlp/models/roberta.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/roberta.py | Apache-2.0 |
def __init__(
self,
d_model,
d_kv,
d_ff,
is_decoder,
num_heads=12,
dropout_prob=0.1,
layer_norm_eps=1E-6,
activation='relu',
init_factor=1.0,
layout='NT',
dtype='float32'
):
"""
Parameters
... |
Parameters
----------
d_model
Equivalent to transformer's `units`.
d_kv
d_kv * num_heads (see below) = inner_dim.
d_ff
Equivalent to transformer's `hidden_size`.
is_decoder
If is_decoder, apply cross-attention.
... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def incremental_decode(
self,
step_hidden_states,
step_position_embeddings,
past_key_value,
mem_states,
step_mem_attn_mask
):
"""Incrementally generate the output given the decoder input.
Parameters
----------
step_hidden_states... | Incrementally generate the output given the decoder input.
Parameters
----------
step_hidden_states
Stepwise hidden states where L_seq = 1 as in `forward` case.
- layout = 'NT'
Shape (B, 1, d_model)
- layout = 'TN'
Shape (1,... | incremental_decode | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def forward(
self,
hidden_states,
self_attn_mask,
position_embeddings,
mem_states=None,
mem_attn_mask=None
):
"""
Parameters
----------
hidden_states
- layout = 'NT'
Shape (B, L_seq, d_model)
... |
Parameters
----------
hidden_states
- layout = 'NT'
Shape (B, L_seq, d_model)
- layout = 'TN'
Shape (L_seq, B, d_model)
self_attn_mask
if is_decoder, it should be a "causal" attention mask.
Shape (B, L_se... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def __init__(
self,
d_model,
d_kv,
d_ff,
num_layers=12,
num_heads=12,
dropout_prob=0.1,
layer_norm_eps=1E-6,
activation='relu',
init_factor=1.0,
layout='NT',
dtype='float32'
):
"""
Parameters... |
Parameters
----------
d_model
Equivalent to transformer's `units`.
d_kv
d_kv * num_heads (see below) = inner_dim.
d_ff
Equivalent to transformer's `hidden_size`.
num_layers
num_heads
dropout_prob
We use ... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def forward(self, hidden_states, valid_length):
"""
Parameters
----------
hidden_states
- layout = 'NT'
Shape (B, L_seq, d_model)
- layout = 'TN'
Shape (L_seq, B, d_model)
valid_length
Valid sequence length fo... |
Parameters
----------
hidden_states
- layout = 'NT'
Shape (B, L_seq, d_model)
- layout = 'TN'
Shape (L_seq, B, d_model)
valid_length
Valid sequence length for each sample feeded into the encoder.
Shape (B... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def __init__(
self,
d_model,
d_kv,
d_ff,
num_layers=12,
num_heads=12,
dropout_prob=0.1,
layer_norm_eps=1E-6,
activation='relu',
init_factor=1.0,
layout='NT',
dtype='float32'
):
"""
Parameters... |
Parameters
----------
d_model
Equivalent to transformer's `units`.
d_kv
d_kv * num_heads (see below) = inner_dim.
d_ff
Equivalent to transformer's `hidden_size`.
num_layers
num_heads
dropout_prob
We use ... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def incremental_decode(
self,
step_hidden_states,
position,
past_key_values,
mem_states,
mem_valid_length
):
"""Incrementally generate the output given the decoder input.
Parameters
----------
step_hidden_states
Step... | Incrementally generate the output given the decoder input.
Parameters
----------
step_hidden_states
Stepwise hidden states where L_seq = 1 as in `forward` case.
- layout = 'NT'
Shape (B, 1, d_model)
- layout = 'TN'
Shape (1,... | incremental_decode | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def forward(self, hidden_states, valid_length, mem_states, mem_valid_length):
"""
Parameters
----------
hidden_states
- layout = 'NT'
Shape (B, L_seq, d_model)
- layout = 'TN'
Shape (L_seq, B, d_model)
valid_length
... |
Parameters
----------
hidden_states
- layout = 'NT'
Shape (B, L_seq, d_model)
- layout = 'TN'
Shape (L_seq, B, d_model)
valid_length
Valid sequence length for each sample feeded into the decoder.
Shape (B... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def __init__(
self,
vocab_size=32128,
d_model=768,
d_kv=64,
d_ff=3072,
num_layers=12,
num_heads=12,
dropout_prob=0.1,
layer_norm_eps=1E-6,
activation='relu',
init_factor=1.0,
layout='NT',
dtype='float32'
... |
Parameters
----------
vocab_size
vocab_size should be no smaller than len(tokenizer._sp_model).
d_model
Equivalent to transformer's `units`.
d_kv
d_kv * num_heads (see below) = inner_dim.
d_ff
Equivalent to transformer'... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def encode(self, src_data, src_valid_length):
"""Encode the source data to memory states.
Parameters
----------
src_data
Token ids feeded into the encoder.
- layout = 'NT'
Shape (B, L_src_seq)
- layout = 'TN'
Shape ... | Encode the source data to memory states.
Parameters
----------
src_data
Token ids feeded into the encoder.
- layout = 'NT'
Shape (B, L_src_seq)
- layout = 'TN'
Shape (L_src_seq, B)
src_valid_length
Valid... | encode | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def decode(self, tgt_data, tgt_valid_length, mem_states, mem_valid_length):
"""Decode based on target data and memory states.
Parameters
----------
tgt_data
Token ids feeded into the decoder.
- layout = 'NT'
Shape (B, L_seq)
- layo... | Decode based on target data and memory states.
Parameters
----------
tgt_data
Token ids feeded into the decoder.
- layout = 'NT'
Shape (B, L_seq)
- layout = 'TN'
Shape (L_seq, B)
tgt_valid_length
Valid s... | decode | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def forward(self, src_data, src_valid_length, tgt_data, tgt_valid_length):
"""
Parameters
----------
src_data
Token ids feeded into the encoder.
- layout = 'NT'
Shape (B, L_src_seq)
- layout = 'TN'
Shape (L_src_seq, ... |
Parameters
----------
src_data
Token ids feeded into the encoder.
- layout = 'NT'
Shape (B, L_src_seq)
- layout = 'TN'
Shape (L_src_seq, B)
src_valid_length
Valid sequence length for each sample feeded i... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def state_batch_axis(self):
"""The returned 4-tuple corresponds to the batch axes of `init_states()` results.
Returns
-------
enc_out_batch_axis
src_valid_length_batch_axis
position_batch_axis
dec_layer_batch_axes
"""
if self.model.layout == 'NT... | The returned 4-tuple corresponds to the batch axes of `init_states()` results.
Returns
-------
enc_out_batch_axis
src_valid_length_batch_axis
position_batch_axis
dec_layer_batch_axes
| state_batch_axis | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def init_states(self, src_data, src_valid_length):
"""Initialize the states required for incremental decoding.
Parameters
----------
src_data
Token ids feeded into the encoder.
- layout = 'NT'
Shape (B, L_src_seq)
- layout = 'TN'
... | Initialize the states required for incremental decoding.
Parameters
----------
src_data
Token ids feeded into the encoder.
- layout = 'NT'
Shape (B, L_src_seq)
- layout = 'TN'
Shape (L_src_seq, B)
src_valid_length
... | init_states | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def forward(self, step_data, past_states):
"""
Parameters
----------
step_data
Stepwise batched token ids for incremental decoding.
Shape (B,)
past_states
A 4-tuple containing states of last incremental decoding step.
... |
Parameters
----------
step_data
Stepwise batched token ids for incremental decoding.
Shape (B,)
past_states
A 4-tuple containing states of last incremental decoding step.
1. mem_states
- layout = 'NT'
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/t5.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/t5.py | Apache-2.0 |
def transformer_base():
"""Configuration of Transformer WMT EN-DE Base"""
cfg = CN()
cfg.MODEL = CN()
cfg.MODEL.src_vocab_size = -1
cfg.MODEL.tgt_vocab_size = -1
cfg.MODEL.max_src_length = -1
cfg.MODEL.max_tgt_length = -1
cfg.MODEL.scale_embed = True
cfg.MODEL.pos_embed_type = "sinus... | Configuration of Transformer WMT EN-DE Base | transformer_base | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.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... |
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_n... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def forward(self, data, attn_mask):
"""
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
attn_mask
Shape (batch_size, seq_leng... |
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
attn_mask
Shape (batch_size, seq_length, seq_length)
Returns
------... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def __init__(self, num_layers=6, recurrent=False,
units=512, hidden_size=2048, num_heads=8,
activation_dropout=0.0, dropout=0.1, use_qkv_bias=True,
attention_dropout=0.1, layer_norm_eps=1E-5, data_norm=False,
pre_norm=False, weight_initializer=None, bi... |
Parameters
----------
num_layers :
The number of layers
recurrent : bool
Whether the layers share weights or not
units
hidden_size
num_heads
dropout
layer_norm_eps
data_norm
Whether to apply LayerNorm t... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def forward(self, data, valid_length):
"""
Parameters
----------
data :
- layout = 'NT'
Shape (batch_size, seq_length, C)
- layout = 'TN'
Shape (seq_length, batch_size, C)
valid_length :
Shape (batch_size,)
... |
Parameters
----------
data :
- layout = 'NT'
Shape (batch_size, seq_length, C)
- layout = 'TN'
Shape (seq_length, batch_size, C)
valid_length :
Shape (batch_size,)
Returns
-------
out
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/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,
... |
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/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/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
- layo... |
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/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def init_states(self, batch_size, ctx, dtype='float32'):
"""Initialize the states required for incremental decoding
Returns
-------
init_key
- layout = 'NT'
Shape (batch_size, 0, N, C_key)
- layout = 'TN'
Shape (0, batch_size, N, C... | Initialize the states required for incremental decoding
Returns
-------
init_key
- layout = 'NT'
Shape (batch_size, 0, N, C_key)
- layout = 'TN'
Shape (0, batch_size, N, C_key)
init_value
- layout = 'NT'
... | init_states | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/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. l... | 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_len... | incremental_decode | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def forward(self, data, valid_length, mem_data, mem_valid_length):
"""
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, seq_length, C_in)
- layout = 'TN'
Shape (seq_length, batch_size, C_in)
valid_length
... |
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 = 'NT'
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def init_states(self, batch_size, ctx, dtype='float32'):
"""Initialize the states required for incremental decoding
Returns
-------
states
A list of states, each includes:
- init_key
- layout = 'NT'
Shape (batch_si... | Initialize the states required for incremental decoding
Returns
-------
states
A list of states, each includes:
- init_key
- layout = 'NT'
Shape (batch_size, 0, N, C_key)
- layout = 'TN'
... | init_states | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/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/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def __init__(self, src_vocab_size: int,
tgt_vocab_size: int,
max_src_length: Optional[int] = None,
max_tgt_length: Optional[int] = None,
scale_embed: bool = True,
pos_embed_type="sinusoidal",
shared_embed: bool = True,... |
Parameters
----------
src_vocab_size
The vocabulary size of the source language
tgt_vocab_size
The vocabulary size of the target language
max_src_length
The maximal length of the source sequence.
If it's negative, we will use trea... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def encode(self, src_data, src_valid_length):
"""Encode the source data to memory
Parameters
----------
src_data
- layout = 'NT'
Shape (batch_size, src_length)
- layout = 'TN'
Shape (src_length, batch_size)
src_valid_lengt... | Encode the source data to memory
Parameters
----------
src_data
- layout = 'NT'
Shape (batch_size, src_length)
- layout = 'TN'
Shape (src_length, batch_size)
src_valid_length
Shape (batch_size,)
Returns
... | encode | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def decode_seq(self, tgt_data, tgt_valid_length, mem_data, mem_valid_length):
"""Decode a sequence of inputs
Parameters
----------
tgt_data
- layout = 'NT'
Shape (batch_size, tgt_length)
- layout = 'TN'
Shape (tgt_length, batch_siz... | Decode a sequence of inputs
Parameters
----------
tgt_data
- layout = 'NT'
Shape (batch_size, tgt_length)
- layout = 'TN'
Shape (tgt_length, batch_size)
tgt_valid_length
Shape (batch_size,)
mem_data
... | decode_seq | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def forward(self, src_data, src_valid_length, tgt_data, tgt_valid_length):
"""
Parameters
----------
src_data
- layout = 'NT'
Shape (batch_size, src_length)
- layout = 'TN'
Shape (src_length, batch_size)
src_valid_length
... |
Parameters
----------
src_data
- layout = 'NT'
Shape (batch_size, src_length)
- layout = 'TN'
Shape (src_length, batch_size)
src_valid_length
Shape (batch_size,)
tgt_data
- layout = 'NT'
... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def state_batch_axis(self) -> Tuple[int, int, int, List]:
"""Return a data structure that stores the batch axis of the internal states
of the inference model.
Returns
-------
enc_out_batch_axis
src_valid_length_batch_axis
position_batch_axis
dec_layer_ba... | Return a data structure that stores the batch axis of the internal states
of the inference model.
Returns
-------
enc_out_batch_axis
src_valid_length_batch_axis
position_batch_axis
dec_layer_batch_axis
| state_batch_axis | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def init_states(self, src_data, src_valid_length): # TODO(sxjscience) Revisit here, support auxiliary states?
"""Initialize the states required for incremental decoding
Parameters
----------
src_data
- layout = 'NT'
Shape (batch_size, src_length)
... | Initialize the states required for incremental decoding
Parameters
----------
src_data
- layout = 'NT'
Shape (batch_size, src_length)
- layout = 'TN'
Shape (src_length, batch_size)
src_valid_length
Shape (batch_size,)
... | init_states | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def forward(self, step_data, states):
"""
Parameters
----------
step_data
Shape (batch_size,)
states
It includes :
- layout = 'NT'
- mem_data : (batch_size, src_length, C_mem)
- mem_valid_length : (b... |
Parameters
----------
step_data
Shape (batch_size,)
states
It includes :
- layout = 'NT'
- mem_data : (batch_size, src_length, C_mem)
- mem_valid_length : (batch_size,)
- position : (bat... | forward | python | dmlc/gluon-nlp | src/gluonnlp/models/transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/models/transformer.py | Apache-2.0 |
def forward(self, data, mem, rel_positions, mask, query_r_bias, query_k_bias):
"""
Parameters
----------
data
The input data.
- layout = 'NT'
Shape (batch_size, query_length, units)
- layout = 'TN'
Shape (query_length,... |
Parameters
----------
data
The input data.
- layout = 'NT'
Shape (batch_size, query_length, units)
- layout = 'TN'
Shape (query_length, batch_size, units)
mem
The memory.
- layout = 'NT'
... | 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 forward(self, data, mem_l, rel_positions, mask):
"""
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, query_length)
- layout = 'TN'
Shape (query_length, batch_size)
mem_l
Contains a list of mem... |
Parameters
----------
data
- layout = 'NT'
Shape (batch_size, query_length)
- layout = 'TN'
Shape (query_length, batch_size)
mem_l
Contains a list of memory objects, each one will contain:
- layout = 'NT'... | 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 init_states(self, batch_size, ctx):
"""Initialize the states
Parameters
----------
batch_size
ctx
ctx of the initialized
Returns
-------
mems
A list of memory states
- layout = 'NT'
Shape (B, T, C)... | Initialize the states
Parameters
----------
batch_size
ctx
ctx of the initialized
Returns
-------
mems
A list of memory states
- layout = 'NT'
Shape (B, T, C)
- layout = 'TN'
Shape ... | init_states | 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 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 masked_logsoftmax(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, ...)
mask = 1 --> not masked
mask = 0 --> masked
... | masked_logsoftmax | 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):
"""Batchify the input data.
Parameters
----------
data : list
The samples to batchfy. Each sample should contain N attributes.
Returns
-------
ret : tuple
A tuple of length N. Contains the batchified result of ea... | Batchify the input data.
Parameters
----------
data : list
The samples to batchfy. Each sample should contain N attributes.
Returns
-------
ret : tuple
A tuple 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 __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 forward(self, inputs, token_types, valid_length, masked_positions):
"""Getting the scores of the masked positions.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, b... | Getting the scores of the masked positions.
Parameters
----------
inputs
- layout = 'NT'
Shape (batch_size, seq_length)
- layout = 'TN'
Shape (seq_length, batch_size)
token_types
If the inputs contain two sequences, we... | 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, 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 __init__(self, backbone_cfg):
"""
Parameters
----------
backbone_cfg
The cfg of the backbone model
"""
super().__init__()
self.backbone_model = BertModel.from_cfg(backbone_cfg)
self.quickthought = th.nn.Sequential(
th.nn.Linea... |
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 __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 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.