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[Seq2Seq] Fix a couple of bugs and clean examples (#7474)

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* clean T5

* fix t5 tests

* fix index typo

* fix tf common test

* fix examples

* change positional ordering for Bart and FSTM

* add signature test

* clean docs and add tests

* add docs to encoder decoder

* clean docs

* correct two doc strings

* remove sig test for TF Elektra & Funnel

* fix tf t5 slow tests

* fix input_ids to inputs in tf

* Update src/transformers/modeling_bart.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_bart.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* implement lysandre results

* make style

* fix encoder decoder typo

* fix tf slow tests

* fix slow tests

* renaming

* remove unused input

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Patrick von Platen
2020-10-01 17:38:50 +02:00
committed by GitHub
parent a42f62d34f
commit 62f5ae68ec
27 changed files with 686 additions and 414 deletions
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22
src/transformers/modeling_bart.py
View File

@@ -101,25 +101,25 @@ BART_INPUTS_DOCSTRING = r"""
Mask to avoid performing attention on padding token indices in input_ids.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`) is a sequence of hidden-states at the output of the last layer of the encoder.
Used in the cross-attention of the decoder.
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids right, following the paper.
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
If you want to change padding behavior, you should read :func:`~transformers.modeling_bart._prepare_decoder_inputs` and modify.
See diagram 1 in the paper for more info on the default strategy
encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`: :obj:`attentions`)
:obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`) is a sequence of hidden-states at the output of the last layer of the encoder.
Used in the cross-attention of the decoder.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains pre-computed key and value hidden-states of the attention blocks.
Can be used to speed up decoding.
If ``past_key_values`` are used, the user can optionally input only the last
If :obj:`past_key_values` are used, the user can optionally input only the last
``decoder_input_ids`` (those that don't have their past key value states given to this model) of shape
:obj:`(batch_size, 1)` instead of all ``decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If `use_cache` is True, ``past_key_values`` are returned and can be used to speed up decoding (see
``past_key_values``).
If :obj:`use_cache` is True, :obj:`past_key_values` are returned and can be used to speed up decoding (see
:obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
@@ -874,8 +874,8 @@ class BartModel(PretrainedBartModel):
input_ids,
attention_mask=None,
decoder_input_ids=None,
encoder_outputs: Optional[Tuple] = None,
decoder_attention_mask=None,
encoder_outputs: Optional[Tuple] = None,
past_key_values=None,
use_cache=None,
output_attentions=None,
@@ -1004,9 +1004,9 @@ class BartForConditionalGeneration(PretrainedBartModel):
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
labels=None,
use_cache=None,
@@ -1171,9 +1171,9 @@ class BartForSequenceClassification(PretrainedBartModel):
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
labels=None,
use_cache=None,
output_attentions=None,
@@ -1257,9 +1257,9 @@ class BartForQuestionAnswering(PretrainedBartModel):
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
start_positions=None,
end_positions=None,
use_cache=None,

10
src/transformers/modeling_ctrl.py
View File

@@ -251,11 +251,11 @@ CTRL_START_DOCSTRING = r"""
CTRL_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
:obj:`input_ids_length` = ``sequence_length`` if ``past_key_values`` is ``None`` else
:obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states).
Indices of input sequence tokens in the vocabulary.
If ``past_key_values`` is used, only input IDs that do not have their past calculated should be passed as
If :obj:`past_key_values` is used, only input IDs that do not have their past calculated should be passed as
``input_ids``.
Indices can be obtained using :class:`~transformers.CTRLTokenizer`.
@@ -265,7 +265,7 @@ CTRL_INPUTS_DOCSTRING = r"""
`What are input IDs? <../glossary.html#input-ids>`__
past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see ``past_key_values`` output below). Can be used to speed up sequential decoding.
(see :obj:`past_key_values` output below). Can be used to speed up sequential decoding.
The ``input_ids`` which have their past given to this model should not be passed as input ids as they have
already been computed.
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -301,8 +301,8 @@ CTRL_INPUTS_DOCSTRING = r"""
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.

53
src/transformers/modeling_encoder_decoder.py
View File

@@ -69,10 +69,6 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
:meth:`transformers.PreTrainedTokenizer.__call__` for details.
`What are input IDs? <../glossary.html#input-ids>`__
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
@@ -81,11 +77,6 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
- 0 for tokens that are **maked**.
`What are attention masks? <../glossary.html#attention-mask>`__
encoder_outputs (:obj:`tuple(torch.FloatTensor)`, `optional`):
This tuple must consist of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`: :obj:`attentions`)
:obj:`last_hidden_state` (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`)
is a tensor of hidden-states at the output of the last layer of the encoder.
Used in the cross-attention of the decoder.
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for sequence to sequence training to the decoder.
Indices can be obtained using :class:`~transformers.PretrainedTokenizer`.
@@ -94,6 +85,21 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
encoder_outputs (:obj:`tuple(torch.FloatTensor)`, `optional`):
This tuple must consist of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`: :obj:`attentions`)
:obj:`last_hidden_state` (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`)
is a tensor of hidden-states at the output of the last layer of the encoder.
Used in the cross-attention of the decoder.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
representation. This is useful if you want more control over how to convert :obj:`decoder_input_ids`
@@ -103,6 +109,15 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with
labels in ``[0, ..., config.vocab_size]``
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
If set to ``True``, the model will return a :class:`~transformers.file_utils.Seq2SeqLMOutput` instead of a
plain tuple.
@@ -328,13 +343,17 @@ class EncoderDecoderModel(PreTrainedModel):
def forward(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None, # TODO: (PVP) implement :obj:`use_cache`
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None, # TODO: (PVP) implement :obj:`use_cache`
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
@@ -378,20 +397,24 @@ class EncoderDecoderModel(PreTrainedModel):
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_encoder,
)
hidden_states = encoder_outputs[0]
encoder_hidden_states = encoder_outputs[0]
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
inputs_embeds=decoder_inputs_embeds,
attention_mask=decoder_attention_mask,
encoder_hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=attention_mask,
inputs_embeds=decoder_inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs_decoder,
)
@@ -423,7 +446,7 @@ class EncoderDecoderModel(PreTrainedModel):
"encoder_outputs": encoder_outputs,
}
# Ideally all models should have a `use_cache`
# Ideally all models should have a :obj:`use_cache`
# leave following to ifs until all have it implemented
if "use_cache" in decoder_inputs:
input_dict["decoder_use_cache"] = decoder_inputs["use_cache"]

16
src/transformers/modeling_fsmt.py
View File

@@ -227,10 +227,6 @@ FSMT_INPUTS_DOCSTRING = r"""
- 0 for tokens that are **maked**.
`What are attention masks? <../glossary.html#attention-mask>`__
encoder_outputs (:obj:`Tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`: :obj:`attentions`)
:obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the input_ids right, following the paper.
@@ -240,6 +236,10 @@ FSMT_INPUTS_DOCSTRING = r"""
If you want to change padding behavior, you should read
:func:`modeling_fstm._prepare_fstm_decoder_inputs` and modify.
See diagram 1 in the paper for more info on the default strategy
encoder_outputs (:obj:`Tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`: :obj:`attentions`)
:obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (:obj:`Tuple(torch.FloatTensor)` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks.
Can be used to speed up decoding.
@@ -248,8 +248,8 @@ FSMT_INPUTS_DOCSTRING = r"""
:obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape
:obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
@@ -910,8 +910,8 @@ class FSMTModel(PretrainedFSMTModel):
input_ids,
attention_mask=None,
decoder_input_ids=None,
encoder_outputs: Optional[Tuple] = None,
decoder_attention_mask=None,
encoder_outputs: Optional[Tuple] = None,
past_key_values=None,
use_cache=None,
output_attentions=None,
@@ -1045,9 +1045,9 @@ class FSMTForConditionalGeneration(PretrainedFSMTModel):
self,
input_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
labels=None,
use_cache=None,

10
src/transformers/modeling_funnel.py
View File

@@ -187,16 +187,16 @@ class FunnelAttentionStructure(nn.Module):
# dividide.
self.pooling_mult = None
def init_attention_inputs(self, input_embeds, attention_mask=None, token_type_ids=None):
def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None):
""" Returns the attention inputs associated to the inputs of the model. """
# input_embeds has shape batch_size x seq_len x d_model
# inputs_embeds has shape batch_size x seq_len x d_model
# attention_mask and token_type_ids have shape batch_size x seq_len
self.pooling_mult = 1
self.seq_len = seq_len = input_embeds.size(1)
position_embeds = self.get_position_embeds(seq_len, input_embeds.dtype, input_embeds.device)
self.seq_len = seq_len = inputs_embeds.size(1)
position_embeds = self.get_position_embeds(seq_len, inputs_embeds.dtype, inputs_embeds.device)
token_type_mat = self.token_type_ids_to_mat(token_type_ids) if token_type_ids is not None else None
cls_mask = (
F.pad(input_embeds.new_ones([seq_len - 1, seq_len - 1]), (1, 0, 1, 0))
F.pad(inputs_embeds.new_ones([seq_len - 1, seq_len - 1]), (1, 0, 1, 0))
if self.config.separate_cls
else None
)

16
src/transformers/modeling_gpt2.py
View File

@@ -365,7 +365,7 @@ class GPT2DoubleHeadsModelOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
:obj:`past_key_values` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -407,11 +407,11 @@ GPT2_START_DOCSTRING = r"""
GPT2_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
:obj:`input_ids_length` = ``sequence_length`` if ``past_key_values`` is ``None`` else
:obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states).
Indices of input sequence tokens in the vocabulary.
If ``past_key_values`` is used, only ``input_ids`` that do not have their past calculated should be passed
If :obj:`past_key_values` is used, only ``input_ids`` that do not have their past calculated should be passed
as ``input_ids``.
Indices can be obtained using :class:`~transformers.GPT2Tokenizer`.
@@ -421,7 +421,7 @@ GPT2_INPUTS_DOCSTRING = r"""
`What are input IDs? <../glossary.html#input-ids>`__
past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model
(see ``past_key_values`` output below). Can be used to speed up sequential decoding.
(see :obj:`past_key_values` output below). Can be used to speed up sequential decoding.
The ``input_ids`` which have their past given to this model should not be passed as ``input_ids`` as they
have already been computed.
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -457,11 +457,11 @@ GPT2_INPUTS_DOCSTRING = r"""
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
If ``past_key_values`` is used, optionally only the last :obj:`inputs_embeds` have to be input (see
``past_key_values``).
If :obj:`past_key_values` is used, optionally only the last :obj:`inputs_embeds` have to be input (see
:obj:`past_key_values`).
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.

14
src/transformers/modeling_outputs.py
View File

@@ -80,7 +80,7 @@ class BaseModelOutputWithPast(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
:obj:`past_key_values` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -110,13 +110,13 @@ class Seq2SeqModelOutput(ModelOutput):
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If ``past_key_values`` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
If :obj:`past_key_values` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -196,7 +196,7 @@ class CausalLMOutputWithPast(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
:obj:`past_key_values` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -261,7 +261,7 @@ class Seq2SeqLMOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -371,7 +371,7 @@ class Seq2SeqSequenceClassifierOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -517,7 +517,7 @@ class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.

8
src/transformers/modeling_rag.py
View File

@@ -52,7 +52,7 @@ class RetrievAugLMMarginOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see ``past_key_values`` input) to speed up sequential decoding.
(see :obj:`past_key_values` input) to speed up sequential decoding.
retrieved_doc_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.n_docs, hidden_size)`, `optional`, returned when `output_retrieved=True`):
Embedded documents retrieved by the retriever.
Is used with ``question_encoder_last_hidden_state`` to compute the ``doc_scores``.
@@ -137,7 +137,7 @@ class RetrievAugLMOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used
(see ``past_key_values`` input) to speed up sequential decoding.
(see :obj:`past_key_values` input) to speed up sequential decoding.
retrieved_doc_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.n_docs, hidden_size)`, `optional`, returned when `output_retrieved=True`):
Embedded documents retrieved by the retriever.
Is used with ``question_encoder_last_hidden_state`` to compute the ``doc_scores``.
@@ -447,8 +447,8 @@ RAG_FORWARD_INPUTS_DOCSTRING = r"""
to the forward pass. :obj:`context_attention_mask` are returned by
:meth:`~transformers.RagRetriever.__call__`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.

4
src/transformers/modeling_reformer.py
View File

@@ -1959,8 +1959,8 @@ REFORMER_INPUTS_DOCSTRING = r"""
Contains precomputed hidden-states and buckets (only relevant for LSH Self-Attention). Can be used to speed
up sequential decoding.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.

193
src/transformers/modeling_t5.py
View File

@@ -202,8 +202,9 @@ class T5LayerFF(nn.Module):
class T5Attention(nn.Module):
def __init__(self, config: T5Config, has_relative_attention_bias=False):
def __init__(self, config: T5Config, has_relative_attention_bias=False, is_bidirectional=False):
super().__init__()
self.is_bidirectional = is_bidirectional
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_bias
@@ -293,7 +294,7 @@ class T5Attention(nn.Module):
relative_position = memory_position - context_position # shape (qlen, klen)
rp_bucket = self._relative_position_bucket(
relative_position, # shape (qlen, klen)
bidirectional=not self.is_decoder,
bidirectional=self.is_bidirectional,
num_buckets=self.relative_attention_num_buckets,
)
rp_bucket = rp_bucket.to(self.relative_attention_bias.weight.device)
@@ -307,7 +308,7 @@ class T5Attention(nn.Module):
mask=None,
kv=None,
position_bias=None,
past_key_value_state=None,
past_key_value=None,
head_mask=None,
query_length=None,
use_cache=False,
@@ -318,17 +319,17 @@ class T5Attention(nn.Module):
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
# past_key_value_state[0] is (bs, n_heads, q_len - 1, dim_per_head)
# past_key_value[0] is (bs, n_heads, q_len - 1, dim_per_head)
bs, qlen, dim = input.size()
if past_key_value_state is not None:
if past_key_value is not None:
assert self.is_decoder is True, "Encoder cannot cache past key value states"
assert (
len(past_key_value_state) == 2
), "past_key_value_state should have 2 past states: keys and values. Got {} past states".format(
len(past_key_value_state)
len(past_key_value) == 2
), "past_key_value should have 2 past states: keys and values. Got {} past states".format(
len(past_key_value)
)
real_qlen = qlen + past_key_value_state[0].shape[2] if query_length is None else query_length
real_qlen = qlen + past_key_value[0].shape[2] if query_length is None else query_length
else:
real_qlen = qlen
@@ -350,18 +351,18 @@ class T5Attention(nn.Module):
if kv is None:
k = shape(self.k(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v(input)) # (bs, n_heads, qlen, dim_per_head)
elif past_key_value_state is None:
elif past_key_value is None:
k = v = kv
k = shape(self.k(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v(v)) # (bs, n_heads, qlen, dim_per_head)
if past_key_value_state is not None:
if past_key_value is not None:
if kv is None:
k_, v_ = past_key_value_state
k_, v_ = past_key_value
k = torch.cat([k_, k], dim=2) # (bs, n_heads, klen, dim_per_head)
v = torch.cat([v_, v], dim=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = past_key_value_state
k, v = past_key_value
if self.is_decoder and use_cache is True:
present_key_value_state = ((k, v),)
@@ -380,8 +381,8 @@ class T5Attention(nn.Module):
# if key and values are already calculated
# we want only the last query position bias
if past_key_value_state is not None:
position_bias = position_bias[:, :, -1:, :]
if past_key_value is not None:
position_bias = position_bias[:, :, -qlen:, :]
if mask is not None:
position_bias = position_bias + mask # (bs, n_heads, qlen, klen)
@@ -411,7 +412,9 @@ class T5Attention(nn.Module):
class T5LayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False):
super().__init__()
self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
self.SelfAttention = T5Attention(
config, has_relative_attention_bias=has_relative_attention_bias, is_bidirectional=not config.is_decoder
)
self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
@@ -421,7 +424,7 @@ class T5LayerSelfAttention(nn.Module):
attention_mask=None,
position_bias=None,
head_mask=None,
past_key_value_state=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
):
@@ -431,7 +434,7 @@ class T5LayerSelfAttention(nn.Module):
mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
past_key_value_state=past_key_value_state,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
@@ -444,7 +447,9 @@ class T5LayerSelfAttention(nn.Module):
class T5LayerCrossAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False):
super().__init__()
self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
self.EncDecAttention = T5Attention(
config, has_relative_attention_bias=has_relative_attention_bias, is_bidirectional=True
)
self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
@@ -455,7 +460,7 @@ class T5LayerCrossAttention(nn.Module):
attention_mask=None,
position_bias=None,
head_mask=None,
past_key_value_state=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
@@ -467,7 +472,7 @@ class T5LayerCrossAttention(nn.Module):
kv=kv,
position_bias=position_bias,
head_mask=head_mask,
past_key_value_state=past_key_value_state,
past_key_value=past_key_value,
use_cache=use_cache,
query_length=query_length,
output_attentions=output_attentions,
@@ -498,33 +503,33 @@ class T5Block(nn.Module):
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
head_mask=None,
past_key_value_state=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
):
if past_key_value_state is not None:
assert self.is_decoder, "Only decoder can use `past_key_value_states`"
expected_num_past_key_value_states = 2 if encoder_hidden_states is None else 4
if past_key_value is not None:
assert self.is_decoder, "Only decoder can use `past_key_values`"
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
expected_num_past_key_value_states,
"2 (past / key) for cross attention" if expected_num_past_key_value_states == 4 else "",
len(past_key_value_state),
expected_num_past_key_values,
"2 (past / key) for cross attention" if expected_num_past_key_values == 4 else "",
len(past_key_value),
)
assert len(past_key_value_state) == expected_num_past_key_value_states, error_message
assert len(past_key_value) == expected_num_past_key_values, error_message
self_attn_past_key_value_state = past_key_value_state[:2]
cross_attn_past_key_value_state = past_key_value_state[2:]
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value_state, cross_attn_past_key_value_state = None, None
self_attn_past_key_value, cross_attn_past_key_value = None, None
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
past_key_value_state=self_attn_past_key_value_state,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
@@ -545,7 +550,7 @@ class T5Block(nn.Module):
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
head_mask=head_mask,
past_key_value_state=cross_attn_past_key_value_state,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
@@ -673,7 +678,7 @@ class T5Stack(T5PreTrainedModel):
encoder_attention_mask=None,
inputs_embeds=None,
head_mask=None,
past_key_value_states=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
@@ -688,17 +693,18 @@ class T5Stack(T5PreTrainedModel):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
if self.is_decoder:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
if inputs_embeds is None:
assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
@@ -706,18 +712,13 @@ class T5Stack(T5PreTrainedModel):
batch_size, seq_length = input_shape
if past_key_value_states is not None:
assert seq_length == 1, "Input shape is {}, but should be {} when using past_key_value_sates".format(
input_shape, (batch_size, 1)
)
# required mask seq length can be calculated via length of past
# key value states and seq_length = 1 for the last token
mask_seq_length = past_key_value_states[0][0].shape[2] + seq_length
else:
mask_seq_length = seq_length
# required mask seq length can be calculated via length of past
mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
if use_cache is True:
assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self)
assert self.is_decoder, ":obj:`use_cache` can only be set to `True` if {} is used as a decoder".format(
self
)
if attention_mask is None:
attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
@@ -727,9 +728,9 @@ class T5Stack(T5PreTrainedModel):
batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
)
# initialize past_key_value_states with `None` if past does not exist
if past_key_value_states is None:
past_key_value_states = [None] * len(self.block)
# initialize past_key_values with `None` if past does not exist
if past_key_values is None:
past_key_values = [None] * len(self.block)
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)
@@ -749,7 +750,7 @@ class T5Stack(T5PreTrainedModel):
hidden_states = self.dropout(inputs_embeds)
for i, (layer_module, past_key_value_state) in enumerate(zip(self.block, past_key_value_states)):
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
@@ -761,7 +762,7 @@ class T5Stack(T5PreTrainedModel):
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
head_mask=head_mask[i],
past_key_value_state=past_key_value_state,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
@@ -845,10 +846,6 @@ T5_INPUTS_DOCSTRING = r"""
- 0 for tokens that are **maked**.
`What are attention masks? <../glossary.html#attention-mask>`__
encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`: `attentions`)
:obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for sequence to sequence training. T5 uses the :obj:`pad_token_id` as the starting token for
:obj:`decoder_input_ids` generation.
@@ -861,15 +858,23 @@ T5_INPUTS_DOCSTRING = r"""
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`: `attentions`)
:obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
@@ -883,13 +888,11 @@ T5_INPUTS_DOCSTRING = r"""
associated vectors than the model's internal embedding lookup matrix.
If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both
unset, :obj:`decoder_input_embeds` takes the value of :obj:`input_embeds`.
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
unset, :obj:`decoder_inputs_embeds` takes the value of :obj:`inputs_embeds`.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
@@ -952,14 +955,14 @@ class T5Model(T5PreTrainedModel):
self,
input_ids=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
use_cache=None,
head_mask=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
head_mask=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
@@ -975,10 +978,11 @@ class T5Model(T5PreTrainedModel):
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = T5Model.from_pretrained('t5-small')
>>> input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="pt") # Batch size 1
>>> outputs = model(input_ids=input_ids)
>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)
>>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
>>> last_hidden_states = outputs.last_hidden_state
"""
if "decoder_past_key_value_states" in kwargs:
warnings.warn(
@@ -1017,26 +1021,12 @@ class T5Model(T5PreTrainedModel):
hidden_states = encoder_outputs[0]
# If the model is only provided with either input_ids or inputs_embeds,
# use them as the inputs of the decoder. self.encoder checks for input_ids XOR inputs_embeds
if (decoder_input_ids is None) and (decoder_inputs_embeds is None):
decoder_input_ids = input_ids
decoder_inputs_embeds = inputs_embeds
# If decoding with past key value states, only the last tokens
# should be given as an input
if past_key_values is not None:
if decoder_input_ids is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_inputs_embeds is not None:
decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_value_states=past_key_values,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=head_mask,
@@ -1108,15 +1098,15 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
self,
input_ids=None,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
use_cache=None,
labels=None,
head_mask=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
head_mask=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
@@ -1139,14 +1129,14 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = T5ForConditionalGeneration.from_pretrained('t5-small', return_dict=True)
>>> input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="pt") # Batch size 1
>>> outputs = model(input_ids=input_ids, labels=input_ids)
>>> input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt').input_ids
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = T5ForConditionalGeneration.from_pretrained('t5-small', return_dict=True)
>>> input_ids = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="pt") # Batch size 1
>>> input_ids = tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="pt").input_ids # Batch size 1
>>> outputs = model.generate(input_ids)
"""
@@ -1212,7 +1202,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_value_states=past_key_values,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=head_mask,
@@ -1250,6 +1240,11 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
)
def prepare_inputs_for_generation(self, input_ids, past, attention_mask, use_cache, encoder_outputs, **kwargs):
# cut decoder_input_ids if past is used
if past is not None:
input_ids = input_ids[:, -1:]
return {
"decoder_input_ids": input_ids,
"past_key_values": past,

59
src/transformers/modeling_tf_electra.py
View File

@@ -1,3 +1,4 @@
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -743,7 +744,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
@replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
@@ -753,6 +754,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
Returns:
@@ -769,8 +771,15 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
>>> scores = outputs[0]
"""
return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
if inputs is None and "input_ids" in kwargs and isinstance(kwargs["input_ids"], (dict, BatchEncoding)):
warnings.warn(
"Using `input_ids` as a dictionary keyword argument is deprecated. Please use `inputs` instead."
)
inputs = kwargs["input_ids"]
discriminator_hidden_states = self.electra(
input_ids,
inputs,
attention_mask,
token_type_ids,
position_ids,
@@ -847,7 +856,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
)
def call(
self,
input_ids,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
@@ -858,6 +867,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
return_dict=None,
labels=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -868,16 +878,22 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
"""
return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
if isinstance(input_ids, (tuple, list)):
labels = input_ids[9] if len(input_ids) > 9 else labels
if inputs is None and "input_ids" in kwargs and isinstance(kwargs["input_ids"], (dict, BatchEncoding)):
warnings.warn(
"Using `input_ids` as a dictionary keyword argument is deprecated. Please use `inputs` instead."
)
inputs = kwargs["input_ids"]
if len(input_ids) > 9:
input_ids = input_ids[:9]
elif isinstance(input_ids, (dict, BatchEncoding)):
labels = input_ids.pop("labels", labels)
if isinstance(inputs, (tuple, list)):
labels = inputs[9] if len(inputs) > 9 else labels
if len(inputs) > 9:
inputs = inputs[:9]
elif isinstance(inputs, (dict, BatchEncoding)):
labels = inputs.pop("labels", labels)
generator_hidden_states = self.electra(
input_ids,
inputs,
attention_mask,
token_type_ids,
position_ids,
@@ -952,7 +968,7 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
)
def call(
self,
input_ids,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
@@ -963,6 +979,7 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
return_dict=None,
labels=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -973,16 +990,22 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
"""
return_dict = return_dict if return_dict is not None else self.electra.config.return_dict
if isinstance(input_ids, (tuple, list)):
labels = input_ids[9] if len(input_ids) > 9 else labels
if inputs is None and "input_ids" in kwargs and isinstance(kwargs["input_ids"], (dict, BatchEncoding)):
warnings.warn(
"Using `input_ids` as a dictionary keyword argument is deprecated. Please use `inputs` instead."
)
inputs = kwargs["input_ids"]
if len(input_ids) > 9:
input_ids = input_ids[:9]
elif isinstance(input_ids, (dict, BatchEncoding)):
labels = input_ids.pop("labels", labels)
if isinstance(inputs, (tuple, list)):
labels = inputs[9] if len(inputs) > 9 else labels
if len(inputs) > 9:
inputs = inputs[:9]
elif isinstance(inputs, (dict, BatchEncoding)):
labels = inputs.pop("labels", labels)
outputs = self.electra(
input_ids,
inputs,
attention_mask,
token_type_ids,
position_ids,

22
src/transformers/modeling_tf_funnel.py
View File

@@ -14,6 +14,7 @@
# limitations under the License.
""" TF 2.0 Funnel model. """
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -173,16 +174,16 @@ class TFFunnelAttentionStructure:
# dividide.
self.pooling_mult = None
def init_attention_inputs(self, input_embeds, attention_mask=None, token_type_ids=None, training=False):
def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None, training=False):
""" Returns the attention inputs associated to the inputs of the model. """
# input_embeds has shape batch_size x seq_len x d_model
# inputs_embeds has shape batch_size x seq_len x d_model
# attention_mask and token_type_ids have shape batch_size x seq_len
self.pooling_mult = 1
self.seq_len = seq_len = input_embeds.shape[1]
position_embeds = self.get_position_embeds(seq_len, dtype=input_embeds.dtype, training=training)
self.seq_len = seq_len = inputs_embeds.shape[1]
position_embeds = self.get_position_embeds(seq_len, dtype=inputs_embeds.dtype, training=training)
token_type_mat = self.token_type_ids_to_mat(token_type_ids) if token_type_ids is not None else None
cls_mask = (
tf.pad(tf.ones([seq_len - 1, seq_len - 1], dtype=input_embeds.dtype), [[1, 0], [1, 0]])
tf.pad(tf.ones([seq_len - 1, seq_len - 1], dtype=inputs_embeds.dtype), [[1, 0], [1, 0]])
if self.separate_cls
else None
)
@@ -1184,7 +1185,7 @@ class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
@replace_return_docstrings(output_type=TFFunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids,
inputs,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
@@ -1192,6 +1193,7 @@ class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs
):
r"""
Returns:
@@ -1209,8 +1211,14 @@ class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
"""
return_dict = return_dict if return_dict is not None else self.funnel.return_dict
if inputs is None and "input_ids" in kwargs and isinstance(kwargs["input_ids"], (dict, BatchEncoding)):
warnings.warn(
"Using `input_ids` as a dictionary keyword argument is deprecated. Please use `inputs` instead."
)
inputs = kwargs["input_ids"]
discriminator_hidden_states = self.funnel(
input_ids,
inputs,
attention_mask,
token_type_ids,
inputs_embeds,

2
src/transformers/modeling_tf_gpt2.py
View File

@@ -427,7 +427,7 @@ class TFGPT2DoubleHeadsModelOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
:obj:`past_key_values` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.

14
src/transformers/modeling_tf_outputs.py
View File

@@ -84,7 +84,7 @@ class TFBaseModelOutputWithPast(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
:obj:`past_key_values` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -114,13 +114,13 @@ class TFSeq2SeqModelOutput(ModelOutput):
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If ``past_key_values`` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
If :obj:`past_key_values` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -200,7 +200,7 @@ class TFCausalLMOutputWithPast(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
:obj:`past_key_values` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -265,7 +265,7 @@ class TFSeq2SeqLMOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -372,7 +372,7 @@ class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -518,7 +518,7 @@ class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
:obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see ``past_key_values`` input) to speed up sequential decoding.
used (see :obj:`past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.

316
src/transformers/modeling_tf_t5.py
View File

@@ -117,8 +117,9 @@ class TFT5LayerFF(tf.keras.layers.Layer):
class TFT5Attention(tf.keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
def __init__(self, config, has_relative_attention_bias=False, is_bidirectional=False, **kwargs):
super().__init__(**kwargs)
self.is_bidirectional = is_bidirectional
self.layer_id = next(TFT5Attention.NEW_ID)
self.is_decoder = config.is_decoder
self.use_cache = config.use_cache
@@ -202,7 +203,7 @@ class TFT5Attention(tf.keras.layers.Layer):
relative_position = memory_position - context_position # shape (qlen, klen)
rp_bucket = self._relative_position_bucket(
relative_position,
bidirectional=not self.is_decoder,
bidirectional=self.is_bidirectional,
num_buckets=self.relative_attention_num_buckets,
)
values = self.relative_attention_bias(rp_bucket) # shape (qlen, klen, num_heads)
@@ -215,8 +216,7 @@ class TFT5Attention(tf.keras.layers.Layer):
mask=None,
kv=None,
position_bias=None,
cache=None,
past_key_value_state=None,
past_key_value=None,
head_mask=None,
query_length=None,
use_cache=False,
@@ -228,17 +228,17 @@ class TFT5Attention(tf.keras.layers.Layer):
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
# past_key_value_state[0] is (bs, n_heads, q_len - 1, dim_per_head)
# past_key_value[0] is (bs, n_heads, q_len - 1, dim_per_head)
bs, qlen, dim = shape_list(input)
if past_key_value_state is not None:
if past_key_value is not None:
assert self.is_decoder is True, "Encoder cannot cache past key value states"
assert (
len(past_key_value_state) == 2
), "past_key_value_state should have 2 past states: keys and values. Got {} past states".format(
len(past_key_value_state)
len(past_key_value) == 2
), "past_key_value should have 2 past states: keys and values. Got {} past states".format(
len(past_key_value)
)
real_qlen = qlen + shape_list(past_key_value_state[0])[2] if query_length is None else query_length
real_qlen = qlen + shape_list(past_key_value[0])[2] if query_length is None else query_length
else:
real_qlen = qlen
@@ -260,18 +260,18 @@ class TFT5Attention(tf.keras.layers.Layer):
if kv is None:
k = shape(self.k(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v(input)) # (bs, n_heads, qlen, dim_per_head)
elif past_key_value_state is None:
elif past_key_value is None:
k = v = kv
k = shape(self.k(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v(v)) # (bs, n_heads, qlen, dim_per_head)
if past_key_value_state is not None:
if past_key_value is not None:
if kv is None:
k_, v_ = past_key_value_state
k_, v_ = past_key_value
k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head)
v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = past_key_value_state
k, v = past_key_value
# to cope with keras serialization
if self.is_decoder and cast_bool_to_primitive(use_cache, self.use_cache) is True:
@@ -288,8 +288,8 @@ class TFT5Attention(tf.keras.layers.Layer):
# if key and values are already calculated
# we want only the last query position bias
if past_key_value_state is not None:
position_bias = position_bias[:, :, -1:, :]
if past_key_value is not None:
position_bias = position_bias[:, :, -qlen:, :]
if mask is not None:
position_bias = position_bias + mask # (bs, n_heads, qlen, klen)
@@ -322,6 +322,7 @@ class TFT5LayerSelfAttention(tf.keras.layers.Layer):
self.SelfAttention = TFT5Attention(
config,
has_relative_attention_bias=has_relative_attention_bias,
is_bidirectional=not config.is_decoder,
name="SelfAttention",
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
@@ -333,7 +334,7 @@ class TFT5LayerSelfAttention(tf.keras.layers.Layer):
attention_mask=None,
position_bias=None,
head_mask=None,
past_key_value_state=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
@@ -344,7 +345,7 @@ class TFT5LayerSelfAttention(tf.keras.layers.Layer):
mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
past_key_value_state=past_key_value_state,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
@@ -361,6 +362,7 @@ class TFT5LayerCrossAttention(tf.keras.layers.Layer):
self.EncDecAttention = TFT5Attention(
config,
has_relative_attention_bias=has_relative_attention_bias,
is_bidirectional=True,
name="EncDecAttention",
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
@@ -373,7 +375,7 @@ class TFT5LayerCrossAttention(tf.keras.layers.Layer):
attention_mask=None,
position_bias=None,
head_mask=None,
past_key_value_state=None,
past_key_value=None,
query_length=None,
use_cache=False,
output_attentions=False,
@@ -386,7 +388,7 @@ class TFT5LayerCrossAttention(tf.keras.layers.Layer):
kv=kv,
position_bias=position_bias,
head_mask=head_mask,
past_key_value_state=past_key_value_state,
past_key_value=past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
@@ -430,34 +432,34 @@ class TFT5Block(tf.keras.layers.Layer):
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
head_mask=None,
past_key_value_state=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
):
if past_key_value_state is not None:
if past_key_value is not None:
assert self.is_decoder, "Only decoder can use `past_key_values`"
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
expected_num_past_key_values,
"2 (past / key) for cross attention" if expected_num_past_key_values == 4 else "",
len(past_key_value_state),
len(past_key_value),
)
assert len(past_key_value_state) == expected_num_past_key_values, error_message
assert len(past_key_value) == expected_num_past_key_values, error_message
self_attn_past_key_value_state = past_key_value_state[:2]
cross_attn_past_key_value_state = past_key_value_state[2:]
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value_state, cross_attn_past_key_value_state = None, None
self_attn_past_key_value, cross_attn_past_key_value = None, None
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
past_key_value_state=self_attn_past_key_value_state,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
@@ -479,7 +481,7 @@ class TFT5Block(tf.keras.layers.Layer):
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
head_mask=head_mask,
past_key_value_state=cross_attn_past_key_value_state,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
@@ -618,34 +620,38 @@ class TFT5MainLayer(tf.keras.layers.Layer):
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 10, "Too many inputs."
if "past_key_value_states" in inputs:
if "past_key_values" in inputs:
warnings.warn(
"The `past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
"The `past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = inputs.pop("past_key_value_states")
past_key_values = inputs.pop("past_key_values")
else:
input_ids = inputs
if "past_key_value_states" in kwargs:
if "past_key_values" in kwargs:
warnings.warn(
"The `past_key_value_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
"The `past_key_values` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
FutureWarning,
)
past_key_values = kwargs.pop("past_key_value_states")
past_key_values = kwargs.pop("past_key_values")
output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
use_cache = use_cache if use_cache is not None else self.use_cache
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both inputs and inputs_embeds at the same time")
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, (-1, input_shape[-1]))
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either inputs or inputs_embeds")
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
if inputs_embeds is None:
assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
@@ -653,15 +659,10 @@ class TFT5MainLayer(tf.keras.layers.Layer):
batch_size, seq_length = input_shape
if past_key_values is not None:
assert seq_length == 1, "Input shape is {}, but should be {} when using past_key_value_sates".format(
input_shape, (batch_size, 1)
)
# required mask seq length can be calculated via length of past
# key value states and seq_length = 1 for the last token
mask_seq_length = shape_list(past_key_values[0][0])[2] + seq_length
else:
mask_seq_length = seq_length
# required mask seq length can be calculated via length of past
mask_seq_length = (
shape_list(past_key_values[0][0])[2] + seq_length if past_key_values is not None else seq_length
)
if attention_mask is None:
attention_mask = tf.fill((batch_size, mask_seq_length), 1)
@@ -692,7 +693,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
causal_mask = tf.cast(causal_mask, dtype=tf.float32)
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
if past_key_values[0] is not None:
extended_attention_mask = extended_attention_mask[:, :, -1:, :]
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = attention_mask[:, None, None, :]
@@ -740,7 +741,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
hidden_states = self.dropout(inputs_embeds, training=training)
for i, (layer_module, past_key_value_state) in enumerate(zip(self.block, past_key_values)):
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
@@ -752,7 +753,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
head_mask=head_mask[i],
past_key_value_state=past_key_value_state,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
@@ -915,22 +916,19 @@ T5_INPUTS_DOCSTRING = r"""
- 0 for tokens that are **maked**.
`What are attention masks? <../glossary.html#attention-mask>`__
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
encoder_outputs (:obj:`tuple(tuple(tf.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`: `attentions`)
:obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a sequence of
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
past_key_values (:obj:`tuple(tuple(tf.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
ontains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, ``past_key_values`` key value states are returned and can be used to speed up
decoding (see ``past_key_values``).
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
@@ -944,7 +942,7 @@ T5_INPUTS_DOCSTRING = r"""
associated vectors than the model's internal embedding lookup matrix.
If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both
unset, :obj:`decoder_input_embeds` takes the value of :obj:`input_embeds`.
unset, :obj:`decoder_inputs_embeds` takes the value of :obj:`inputs_embeds`.
head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
@@ -952,6 +950,9 @@ T5_INPUTS_DOCSTRING = r"""
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
@@ -1017,12 +1018,12 @@ class TFT5Model(TFT5PreTrainedModel):
self,
inputs,
attention_mask=None,
encoder_outputs=None,
inputs_embeds=None,
head_mask=None,
past_key_values=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
head_mask=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
@@ -1040,20 +1041,22 @@ class TFT5Model(TFT5PreTrainedModel):
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5Model.from_pretrained('t5-small')
>>> inputs = tokenizer.encode("Hello, my dog is cute", return_tensors="tf") # Batch size 1
>>> outputs = model(inputs, decoder_input_ids=inputs)
>>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids # Batch size 1
>>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)
"""
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
encoder_outputs = inputs[2] if len(inputs) > 2 else encoder_outputs
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
head_mask = inputs[4] if len(inputs) > 4 else head_mask
past_key_values = inputs[5] if len(inputs) > 5 else past_key_values
decoder_input_ids = inputs[6] if len(inputs) > 6 else decoder_input_ids
decoder_attention_mask = inputs[7] if len(inputs) > 7 else decoder_attention_mask
decoder_input_ids = inputs[2] if len(inputs) > 2 else decoder_input_ids
decoder_attention_mask = inputs[3] if len(inputs) > 3 else decoder_attention_mask
encoder_outputs = inputs[4] if len(inputs) > 4 else encoder_outputs
past_key_values = inputs[5] if len(inputs) > 5 else head_mask
head_mask = inputs[6] if len(inputs) > 6 else head_mask
inputs_embeds = inputs[7] if len(inputs) > 7 else inputs_embeds
decoder_inputs_embeds = inputs[8] if len(inputs) > 8 else decoder_inputs_embeds
use_cache = inputs[9] if len(inputs) > 9 else use_cache
output_attentions = inputs[10] if len(inputs) > 10 else output_attentions
@@ -1066,17 +1069,16 @@ class TFT5Model(TFT5PreTrainedModel):
input_ids = inputs.get("inputs")
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
encoder_outputs = inputs.get("encoder_outputs", encoder_outputs)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
head_mask = inputs.get("head_mask", head_mask)
past_key_values = inputs.get("past_key_values", past_key_values)
decoder_input_ids = inputs.get("decoder_input_ids", decoder_input_ids)
decoder_attention_mask = inputs.get("decoder_attention_mask", decoder_attention_mask)
encoder_outputs = inputs.get("encoder_outputs", encoder_outputs)
past_key_values = inputs.get("past_key_values", past_key_values)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
decoder_inputs_embeds = inputs.get("decoder_inputs_embeds", decoder_inputs_embeds)
use_cache = inputs.get("use_cache", use_cache)
output_attentions = inputs.get("output_attentions", output_attentions)
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
return_dict = inputs.get("return_dict", return_dict)
assert len(inputs) <= 13, "Too many inputs."
if "past_key_value_states" in inputs:
@@ -1096,52 +1098,43 @@ class TFT5Model(TFT5PreTrainedModel):
past_key_values = kwargs.pop("past_key_value_states")
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.return_dict
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
[
input_ids,
attention_mask,
None,
None,
inputs_embeds,
head_mask,
None,
False,
output_attentions,
output_hidden_states,
],
input_ids,
attention_mask=attention_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
past_key_values=None,
use_cache=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training,
)
hidden_states = encoder_outputs[0]
# If decoding with past key value states, only the last tokens
# should be given as an input
if past_key_values is not None:
if decoder_input_ids is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
if decoder_inputs_embeds is not None:
decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]
# Decode
decoder_outputs = self.decoder(
[
decoder_input_ids,
decoder_attention_mask,
hidden_states,
attention_mask,
decoder_inputs_embeds,
head_mask,
past_key_values,
use_cache,
output_attentions,
output_hidden_states,
],
decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
inputs_embeds=decoder_inputs_embeds,
head_mask=head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training,
)
past = (
(encoder_outputs, decoder_outputs[1]) if cast_bool_to_primitive(use_cache, self.config.use_cache) else None
)
@@ -1150,12 +1143,6 @@ class TFT5Model(TFT5PreTrainedModel):
decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
return decoder_outputs + encoder_outputs
# If put before, this breaks the tf compilation.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# This is long and annoying but if we introduce return_dict at the TFT5MainLayer level (like in PyTorch)
# TF refuses to compile anymore.
if not cast_bool_to_primitive(use_cache, self.config.use_cache):
@@ -1227,18 +1214,18 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
self,
inputs,
attention_mask=None,
encoder_outputs=None,
inputs_embeds=None,
head_mask=None,
past_key_values=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
head_mask=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
**kwargs,
):
@@ -1253,33 +1240,35 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
>>> from transformers import T5Tokenizer, TFT5ForConditionalGeneration
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small', return_dict=True)
>>> model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
>>> inputs = tokenizer.encode("Hello, my dog is cute", return_tensors="tf") # Batch size 1
>>> outputs = model(inputs, decoder_input_ids=inputs)
>>> prediction_scores = outputs[0]
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
>>> inputs = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="tf") # Batch size 1
>>> inputs = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='tf').input_ids
labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='tf').input_ids
>>> outputs = model(inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
>>> inputs = tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="tf").input_ids # Batch size 1
>>> result = model.generate(inputs)
"""
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
encoder_outputs = inputs[2] if len(inputs) > 2 else encoder_outputs
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
head_mask = inputs[4] if len(inputs) > 4 else head_mask
past_key_values = inputs[5] if len(inputs) > 5 else past_key_values
decoder_input_ids = inputs[6] if len(inputs) > 6 else decoder_input_ids
decoder_attention_mask = inputs[7] if len(inputs) > 7 else decoder_attention_mask
decoder_input_ids = inputs[2] if len(inputs) > 2 else decoder_input_ids
decoder_attention_mask = inputs[3] if len(inputs) > 3 else decoder_attention_mask
encoder_outputs = inputs[4] if len(inputs) > 4 else encoder_outputs
past_key_values = inputs[5] if len(inputs) > 5 else head_mask
head_mask = inputs[6] if len(inputs) > 6 else head_mask
inputs_embeds = inputs[7] if len(inputs) > 7 else inputs_embeds
decoder_inputs_embeds = inputs[8] if len(inputs) > 8 else decoder_inputs_embeds
use_cache = inputs[9] if len(inputs) > 9 else use_cache
output_attentions = inputs[10] if len(inputs) > 10 else output_attentions
output_hidden_states = inputs[11] if len(inputs) > 11 else output_hidden_states
return_dict = inputs[12] if len(inputs) > 12 else return_dict
labels = inputs[13] if len(inputs) > 13 else labels
labels = inputs[9] if len(inputs) > 9 else labels
use_cache = inputs[10] if len(inputs) > 10 else use_cache
output_attentions = inputs[11] if len(inputs) > 11 else output_attentions
output_hidden_states = inputs[12] if len(inputs) > 12 else output_hidden_states
return_dict = inputs[13] if len(inputs) > 13 else return_dict
assert len(inputs) <= 14, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
if "inputs" in inputs:
@@ -1287,18 +1276,18 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
input_ids = inputs.get("inputs")
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
encoder_outputs = inputs.get("encoder_outputs", encoder_outputs)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
head_mask = inputs.get("head_mask", head_mask)
past_key_values = inputs.get("past_key_values", past_key_values)
decoder_input_ids = inputs.get("decoder_input_ids", decoder_input_ids)
decoder_attention_mask = inputs.get("decoder_attention_mask", decoder_attention_mask)
encoder_outputs = inputs.get("encoder_outputs", encoder_outputs)
past_key_values = inputs.get("past_key_values", past_key_values)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
decoder_inputs_embeds = inputs.get("decoder_inputs_embeds", decoder_inputs_embeds)
labels = inputs.get("labels", labels)
use_cache = inputs.get("use_cache", use_cache)
output_attentions = inputs.get("output_attentions", output_attentions)
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
return_dict = inputs.get("return_dict", return_dict)
labels = inputs.get("labels", labels)
assert len(inputs) <= 14, "Too many inputs."
if "past_key_value_states" in inputs:
@@ -1318,24 +1307,19 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
past_key_values = kwargs.pop("past_key_value_states")
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.return_dict
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
# Convert encoder inputs in embeddings if needed
encoder_outputs = self.encoder(
[
input_ids,
attention_mask,
None,
None,
inputs_embeds,
head_mask,
None,
False,
output_attentions,
output_hidden_states,
],
input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training,
)
@@ -1355,18 +1339,16 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
# Decode
decoder_outputs = self.decoder(
[
decoder_input_ids,
decoder_attention_mask,
hidden_states,
attention_mask,
decoder_inputs_embeds,
head_mask,
past_key_values,
use_cache,
output_attentions,
output_hidden_states,
],
decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
inputs_embeds=decoder_inputs_embeds,
head_mask=head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
training=training,
)
@@ -1422,6 +1404,10 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
else:
encoder_outputs, past_key_values = past[0], past[1]
# cut decoder_input_ids if past is used
if past_key_values is not None:
inputs = inputs[:, -1:]
return {
"inputs": None, # inputs don't have to be defined, but still need to be passed to make Keras.layer.__call__ happy
"decoder_input_ids": inputs, # inputs are the decoder_input_ids

2
src/transformers/modeling_tf_xlnet.py
View File

@@ -1065,7 +1065,7 @@ XLNET_INPUTS_DOCSTRING = r"""
decoding. The token ids which have their past given to this model should not be passed as
:obj:`input_ids` as they have already been computed.
:obj:`use_cache` has to be set to :obj:`True` to make use of :obj:`mems`.
:obj::obj:`use_cache` has to be set to :obj:`True` to make use of :obj:`mems`.
perm_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, sequence_length)`, `optional`):
Mask to indicate the attention pattern for each input token with values selected in ``[0, 1]``:

7
src/transformers/modeling_utils.py
View File

@@ -237,8 +237,15 @@ class ModuleUtilsMixin:
batch_size, seq_length = input_shape
seq_ids = torch.arange(seq_length, device=device)
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
if causal_mask.shape[1] < attention_mask.shape[1]:
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
causal_mask = torch.cat(
[torch.ones((batch_size, seq_length, prefix_seq_len), device=device), causal_mask], axis=-1
)
# causal and attention masks must have same type with pytorch version < 1.3
causal_mask = causal_mask.to(attention_mask.dtype)
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
else:
extended_attention_mask = attention_mask[:, None, None, :]

8
src/transformers/modeling_xlnet.py
View File

@@ -874,7 +874,7 @@ XLNET_INPUTS_DOCSTRING = r"""
decoding. The token ids which have their past given to this model should not be passed as
:obj:`input_ids` as they have already been computed.
:obj:`use_cache` has to be set to :obj:`True` to make use of :obj:`mems`.
:obj::obj:`use_cache` has to be set to :obj:`True` to make use of :obj:`mems`.
perm_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, sequence_length)`, `optional`):
Mask to indicate the attention pattern for each input token with values selected in ``[0, 1]``:
@@ -997,15 +997,15 @@ class XLNetModel(XLNetPreTrainedModel):
curr_out = curr_out[: self.reuse_len]
if self.mem_len is None or self.mem_len == 0:
# If `use_cache` is active but no `mem_len` is defined, the model behaves like GPT-2 at inference time
# If :obj:`use_cache` is active but no `mem_len` is defined, the model behaves like GPT-2 at inference time
# and returns all of the past and current hidden states.
cutoff = 0
else:
# If `use_cache` is active and `mem_len` is defined, the model returns the last `mem_len` hidden
# If :obj:`use_cache` is active and `mem_len` is defined, the model returns the last `mem_len` hidden
# states. This is the preferred setting for training and long-form generation.
cutoff = -self.mem_len
if prev_mem is None:
# if `use_cache` is active and `mem_len` is defined, the model
# if :obj:`use_cache` is active and `mem_len` is defined, the model
new_mem = curr_out[cutoff:]
else:
new_mem = torch.cat([prev_mem, curr_out], dim=0)[cutoff:]

15
tests/test_modeling_bart.py
View File

@@ -76,7 +76,7 @@ class ModelTester:
self.bos_token_id = 0
torch.manual_seed(0)
def prepare_config_and_inputs_for_common(self):
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
@@ -101,6 +101,13 @@ class ModelTester:
inputs_dict = prepare_bart_inputs_dict(config, input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"]
inputs_dict["use_cache"] = False
return config, inputs_dict
def prepare_bart_inputs_dict(
config,
@@ -139,7 +146,7 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
self.config_tester.run_common_tests()
def test_initialization_more(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
model = BartModel(config)
model.to(torch_device)
model.eval()
@@ -156,7 +163,7 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
_check_var(model.encoder.embed_positions)
def test_advanced_inputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
config.use_cache = False
inputs_dict["input_ids"][:, -2:] = config.pad_token_id
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_bart_decoder_inputs(
@@ -185,7 +192,7 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
_assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)

43
tests/test_modeling_common.py
View File

@@ -14,6 +14,7 @@
# limitations under the License.
import copy
import inspect
import os.path
import random
import tempfile
@@ -158,6 +159,28 @@ class ModelTesterMixin:
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = [
"input_ids",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
"encoder_outputs",
]
self.assertListEqual(arg_names[:5], expected_arg_names)
else:
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
@@ -187,7 +210,7 @@ class ModelTesterMixin:
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
outputs = model(**self._prepare_for_class(inputs_dict, model_class), return_dict=True)
attentions = outputs[-1]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
@@ -272,10 +295,22 @@ class ModelTesterMixin:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)["input_ids"] # Let's keep only input_ids
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
traced_gpt2 = torch.jit.trace(model, inputs)
if model.config.is_encoder_decoder:
model.config.use_cache = False # TODO: this should be deleted after bug #7474 is solved
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
traced_model = torch.jit.trace(
model, (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
)
else:
input_ids = inputs["input_ids"]
traced_model = torch.jit.trace(model, input_ids)
except RuntimeError:
self.fail("Couldn't trace module.")
@@ -283,7 +318,7 @@ class ModelTesterMixin:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_gpt2, pt_file_name)
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")

19
tests/test_modeling_fsmt.py
View File

@@ -71,7 +71,7 @@ class ModelTester:
# hack needed for modeling_common tests - despite not really having this attribute in this model
self.vocab_size = self.src_vocab_size
def prepare_config_and_inputs_for_common(self):
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.src_vocab_size).clamp(
3,
)
@@ -99,6 +99,13 @@ class ModelTester:
inputs_dict = prepare_fsmt_inputs_dict(config, input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"]
inputs_dict["use_cache"] = False
return config, inputs_dict
def prepare_fsmt_inputs_dict(
config,
@@ -142,7 +149,7 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
# XXX: override test_model_common_attributes / different Embedding type
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
@@ -152,7 +159,7 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
self.assertTrue(x is None or isinstance(x, torch.nn.modules.sparse.Embedding))
def test_initialization_more(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
model = FSMTModel(config)
model.to(torch_device)
model.eval()
@@ -170,7 +177,7 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
# self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2)
def test_advanced_inputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
config.use_cache = False
inputs_dict["input_ids"][:, -2:] = config.pad_token_id
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
@@ -200,7 +207,7 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
_assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
@@ -210,7 +217,7 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
self.assertEqual(info["missing_keys"], [])
def test_save_load_no_save_keys(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)

36
tests/test_modeling_gpt2.py
View File

@@ -261,6 +261,38 @@ class GPT2ModelTester:
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gpt2_model_past_large_inputs(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPT2Model(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2LMHeadModel(config)
model.to(torch_device)
@@ -357,6 +389,10 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
def test_gpt2_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs)
def test_gpt2_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)

66
tests/test_modeling_t5.py
View File

@@ -235,7 +235,7 @@ class T5ModelTester:
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past_key_value_states = outputs.to_tuple()
output, past_key_values = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -244,7 +244,7 @@ class T5ModelTester:
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_value_states=past_key_value_states)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -274,7 +274,7 @@ class T5ModelTester:
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past_key_value_states = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple()
output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -293,7 +293,7 @@ class T5ModelTester:
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_value_states=past_key_value_states, attention_mask=attn_mask)[
output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
"last_hidden_state"
]
@@ -305,7 +305,41 @@ class T5ModelTester:
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_generate_with_past_key_value_states(
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = T5Model(config=config).get_decoder().to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_generate_with_past_key_values(
self,
config,
input_ids,
@@ -439,7 +473,7 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_torchscript = True
test_resize_embeddings = False
is_encoder_decoder = True
@@ -470,9 +504,13 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_generate_with_past_key_value_states(self):
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_generate_with_past_key_value_states(*config_and_inputs)
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_generate_with_past_key_values(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs)
def test_encoder_decoder_shared_weights(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -495,10 +533,11 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
model,
config_and_inputs[1],
(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
f"{tmpdirname}/t5_test.onnx",
export_params=True,
opset_version=9,
input_names=["input_ids", "decoder_input_ids"],
)
@@ -527,7 +566,7 @@ class T5ModelIntegrationTests(unittest.TestCase):
ARTICLE_SUBWAY = 'New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the 2010 marriage license application, according to court documents. Prosecutors said the marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages. Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted. The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.'
expected_summaries = [
'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a cell phone video of the final seconds . "one can hear cries of \'My God\' in several languages," the magazine says .',
'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a cell phone video at the crash site . "one can hear cries of \'My God\' in several languages," one magazine says .',
"the Palestinians become the 123rd member of the international criminal court . the accession was marked by a ceremony at the Hague, where the court is based . as members of the court, Palestinians may be subject to counter-charges as well .",
"the u.s. and its negotiating partners reached a very strong framework agreement with Iran . aaron miller: the debate that has already begun since the announcement of the new framework will likely result in more heat than light . the deal would reduce Iran's low-enriched uranium stockpile, cut centrifuges and implement a rigorous inspection regime .",
'prosecutors say the marriages were part of an immigration scam . if convicted, barrientos faces two criminal counts of "offering a false instrument for filing in the first degree" she has been married 10 times, with nine of her marriages occurring between 1999 and 2002 .',
@@ -604,13 +643,6 @@ class T5ModelIntegrationTests(unittest.TestCase):
"sous forme "
"de points bleus."
)
# expected_translation = (
# "Cette section d'images provenant de l'enregistrement infrarouge effectué par le "
# "télescope Spitzer montre un « portrait familial » de générations innombrables de "
# "étoiles : les plus anciennes sont observées sous forme de pointes bleues, "
# "alors que les « nouveau-nés » de couleur rose dans la salle des accouchements doivent "
# "être plus difficiles "
# )
self.assertEqual(translation, new_truncated_translation)

37
tests/test_modeling_tf_common.py
View File

@@ -136,6 +136,29 @@ class TFModelTesterMixin:
outputs = run_in_graph_mode()
self.assertIsNotNone(outputs)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = [
"inputs",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
"encoder_outputs",
]
self.assertListEqual(arg_names[:5], expected_arg_names)
else:
expected_arg_names = ["inputs"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@slow
def test_saved_model_with_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -152,7 +175,12 @@ class TFModelTesterMixin:
tf.saved_model.save(model, tmpdirname)
model = tf.keras.models.load_model(tmpdirname)
outputs = model(inputs_dict)
output = outputs[list(outputs.keys())[-1]] if isinstance(outputs, dict) else outputs[-1]
if self.is_encoder_decoder:
output = outputs["encoder_hidden_states"] if isinstance(outputs, dict) else outputs[-1]
else:
output = outputs["hidden_states"] if isinstance(outputs, dict) else outputs[-1]
hidden_states = [t.numpy() for t in output]
self.assertEqual(len(outputs), num_out)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
@@ -185,7 +213,12 @@ class TFModelTesterMixin:
tf.saved_model.save(model, tmpdirname)
model = tf.keras.models.load_model(tmpdirname)
outputs = model(inputs_dict)
output = outputs[list(outputs.keys())[-1]] if isinstance(outputs, dict) else outputs[-1]
if self.is_encoder_decoder:
output = outputs["encoder_attentions"] if isinstance(outputs, dict) else outputs[-1]
else:
output = outputs["attentions"] if isinstance(outputs, dict) else outputs[-1]
attentions = [t.numpy() for t in output]
self.assertEqual(len(outputs), num_out)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

34
tests/test_modeling_tf_gpt2.py
View File

@@ -211,6 +211,36 @@ class TFGPT2ModelTester:
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-12)
def create_and_check_gpt2_model_past_large_inputs(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = TFGPT2Model(config=config)
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFGPT2LMHeadModel(config=config)
inputs = {
@@ -290,6 +320,10 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
def test_gpt2_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs)
def test_gpt2_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs)

72
tests/test_modeling_tf_t5.py
View File

File diff suppressed because one or more lines are too long

2
tests/test_modeling_xlnet.py
View File

@@ -511,7 +511,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_base_model_use_cache(self):
# checking that in auto-regressive mode, `use_cache` gives the same results
# checking that in auto-regressive mode, :obj:`use_cache` gives the same results
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_model_use_cache(*config_and_inputs)