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[GenerationOutputs] Fix GenerationOutputs Tests (#9443)

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* fix generation models

* fix led

* fix docs

* add is_decoder

* fix last docstrings

* make style

* fix t5 cross attentions

* correct t5
This commit is contained in:
Patrick von Platen
2021-01-06 19:37:02 +01:00
committed by GitHub
parent 0c96262f7d
commit b8462b5b2a
9 changed files with 89 additions and 53 deletions
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26
src/transformers/generation_utils.py
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@@ -126,8 +126,7 @@ class SampleDecoderOnlyOutput(ModelOutput):
sequence_length)`.
hidden_states (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
:obj:`torch.FloatTensor` of shape :obj:`(num_return_sequences * batch_size, generated_length,
hidden_size)`.
:obj:`torch.FloatTensor` of shape :obj:`(num_return_sequences*batch_size, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
@@ -153,8 +152,8 @@ class SampleEncoderDecoderOutput(ModelOutput):
at each generation step. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of
shape :obj:`(batch_size*num_return_sequences, config.vocab_size)`).
encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer of the decoder) of shape :obj:`(batch_size *
num_return_sequences, num_heads, sequence_length, sequence_length)`.
Tuple of :obj:`torch.FloatTensor` (one for each layer of the decoder) of shape
:obj:`(batch_size*num_return_sequences, num_heads, sequence_length, sequence_length)`.
encoder_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*num_return_sequences, sequence_length, hidden_size)`.
@@ -164,8 +163,7 @@ class SampleEncoderDecoderOutput(ModelOutput):
sequence_length)`.
decoder_hidden_states (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
:obj:`torch.FloatTensor` of shape :obj:`(batch_size * num_return_sequences, generated_length,
hidden_size)`.
:obj:`torch.FloatTensor` of shape :obj:`(batch_size*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
@@ -190,8 +188,8 @@ class BeamSearchDecoderOnlyOutput(ModelOutput):
scores (:obj:`tuple(torch.FloatTensor)` `optional`, returned when ``output_scores=True`` is passed or when ``config.output_scores=True``):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape :obj:`(batch_size
* num_beams * num_return_sequences, config.vocab_size)`).
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape
:obj:`(batch_size*num_beams*num_return_sequences, config.vocab_size)`).
attentions (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
:obj:`torch.FloatTensor` of shape :obj:`(batch_size*num_beams, num_heads, generated_length,
@@ -225,8 +223,8 @@ class BeamSearchEncoderDecoderOutput(ModelOutput):
scores (:obj:`tuple(torch.FloatTensor)` `optional`, returned when ``output_scores=True`` is passed or when ``config.output_scores=True``):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape :obj:`(batch_size
* num_beams, config.vocab_size)`).
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape
:obj:`(batch_size*num_beams, config.vocab_size)`).
attentions (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer of the decoder) of shape :obj:`(batch_size,
@@ -267,8 +265,8 @@ class BeamSampleDecoderOnlyOutput(ModelOutput):
scores (:obj:`tuple(torch.FloatTensor)` `optional`, returned when ``output_scores=True`` is passed or when ``config.output_scores=True``):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape :obj:`(batch_size
* num_beams * num_return_sequences, config.vocab_size)`).
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape
:obj:`(batch_size*num_beams*num_return_sequences, config.vocab_size)`).
attentions (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
:obj:`torch.FloatTensor` of shape :obj:`(batch_size*num_beams, num_heads, generated_length,
@@ -301,8 +299,8 @@ class BeamSampleEncoderDecoderOutput(ModelOutput):
scores (:obj:`tuple(torch.FloatTensor)` `optional`, returned when ``output_scores=True`` is passed or when ``config.output_scores=True``):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape :obj:`(batch_size
* num_beams, config.vocab_size)`).
. :obj:`(max_length,)`-shaped tuple of :obj:`torch.FloatTensor` with each tensor of shape
:obj:`(batch_size*num_beams, config.vocab_size)`).
encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer of the decoder) of shape :obj:`(batch_size,
num_heads, sequence_length, sequence_length)`.

2
src/transformers/models/bert/modeling_bert.py
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@@ -1227,7 +1227,7 @@ class BertLMHeadModel(BertPreTrainedModel):
if past is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask}
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
def _reorder_cache(self, past, beam_idx):
reordered_past = ()

2
src/transformers/models/bert_generation/modeling_bert_generation.py
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@@ -570,7 +570,7 @@ class BertGenerationDecoder(BertGenerationPreTrainedModel):
if past is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask}
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
def _reorder_cache(self, past, beam_idx):
reordered_past = ()

2
src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
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@@ -455,7 +455,7 @@ class EncoderDecoderModel(PreTrainedModel):
"decoder_attention_mask": decoder_attention_mask,
"decoder_input_ids": decoder_inputs["input_ids"],
"encoder_outputs": encoder_outputs,
"past_key_values": past,
"past_key_values": decoder_inputs["past_key_values"],
"use_cache": use_cache,
}
return input_dict

2
src/transformers/models/roberta/modeling_roberta.py
View File

@@ -962,7 +962,7 @@ class RobertaForCausalLM(RobertaPreTrainedModel):
if past is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask}
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
def _reorder_cache(self, past, beam_idx):
reordered_past = ()

2
src/transformers/models/t5/modeling_t5.py
View File

@@ -1148,6 +1148,7 @@ class T5Model(T5PreTrainedModel):
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = T5Stack(encoder_config, self.shared)
@@ -1325,6 +1326,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = T5Stack(encoder_config, self.shared)

2
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py
View File

@@ -1132,7 +1132,7 @@ class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_m
if past is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask}
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
def _reorder_cache(self, past, beam_idx):
reordered_past = ()

32
tests/test_generation_utils.py
View File

@@ -522,6 +522,7 @@ class GenerationTesterMixin:
return
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
@@ -730,6 +731,7 @@ class GenerationTesterMixin:
beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_beam, output_generate = self._beam_search_generate(
model=model,
@@ -962,12 +964,7 @@ class GenerationTesterMixin:
# Attentions
if config.is_encoder_decoder:
# encoder
encoder_expected_shape = (batch_size, config.num_attention_heads, seq_length, seq_length)
self.assertIsInstance(output.encoder_attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in output.encoder_attentions],
[encoder_expected_shape] * len(output.encoder_attentions),
)
self._check_encoder_attention_for_generate(output.encoder_attentions, batch_size, config, seq_length)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
@@ -993,11 +990,8 @@ class GenerationTesterMixin:
# Hidden States
if config.is_encoder_decoder:
# encoder
encoder_expected_shape = (batch_size, seq_length, config.hidden_size)
self.assertIsInstance(output.encoder_hidden_states, tuple)
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in output.encoder_hidden_states],
[encoder_expected_shape] * len(output.encoder_hidden_states),
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, seq_length
)
# decoder
@@ -1052,6 +1046,14 @@ class GenerationTesterMixin:
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, config.num_attention_heads, seq_length, seq_length)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
@@ -1071,6 +1073,14 @@ class GenerationTesterMixin:
[expected_shape] * len(iter_hidden_states),
)
def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length):
encoder_expected_shape = (batch_size, seq_length, config.hidden_size)
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in hidden_states],
[encoder_expected_shape] * len(hidden_states),
)
@require_torch
class UtilsFunctionsTest(unittest.TestCase):

26
tests/test_modeling_led.py
View File

@@ -327,6 +327,32 @@ class LEDModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
# longformer cannot keep gradients in attentions or hidden states
return
def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
# make sure tgt_length is padded
tgt_length = (
seq_length // config.attention_window[0] + (seq_length % config.attention_window[0] != 0)
) * config.attention_window[0]
encoder_expected_shape = (batch_size, config.num_attention_heads, tgt_length, seq_length)
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[layer_attentions.shape for layer_attentions in attentions],
[encoder_expected_shape] * len(attentions),
)
def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length):
# make sure seq_length is padded
seq_length = (
seq_length // config.attention_window[0] + (seq_length % config.attention_window[0] != 0)
) * config.attention_window[0]
encoder_expected_shape = (batch_size, seq_length, config.hidden_size)
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in hidden_states],
[encoder_expected_shape] * len(hidden_states),
)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True