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Commit 1484d67d authored by thomwolf's avatar thomwolf
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[LARGE] updating all tests and API

parent 4f8b5f68
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Showing with 1479 additions and 1347 deletions
pytorch_pretrained_bert/model_utils.py
View file @ 1484d67d
......@@ -41,6 +41,12 @@ class PretrainedConfig(object):
"""
pretrained_config_archive_map = {}
def __init__(self, **kwargs):
self.finetuning_task = kwargs.pop('finetuning_task', None)
self.num_labels = kwargs.pop('num_labels', 2)
self.output_attentions = kwargs.pop('output_attentions', False)
self.output_hidden_states = kwargs.pop('output_hidden_states', False)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
"""
......@@ -114,6 +120,9 @@ class PretrainedConfig(object):
text = reader.read()
return cls.from_dict(json.loads(text))
def __eq__(self, other):
return self.__dict__ == other.__dict__
def __repr__(self):
return str(self.to_json_string())
......@@ -133,12 +142,11 @@ class PretrainedConfig(object):
class PreTrainedModel(nn.Module):
""" An abstract class to handle weights initialization and
""" An abstract class to handle storing model config and
a simple interface for dowloading and loading pretrained models.
"""
config_class = PretrainedConfig
pretrained_model_archive_map = {}
pretrained_config_archive_map = {}
load_tf_weights = lambda model, config, path: None
base_model_prefix = ""
......@@ -151,8 +159,16 @@ class PreTrainedModel(nn.Module):
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
))
# Save config in model
self.config = config
def prune_heads(self, heads_to_prune):
""" Prunes heads of the base model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
model_to_prune = getattr(self, self.base_model_prefix, self) # get the base model if needed
model_to_prune._prune_heads(heads_to_prune)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
"""
......@@ -175,24 +191,22 @@ class PreTrainedModel(nn.Module):
*inputs, **kwargs: additional input for the specific XLNet class
(ex: num_labels for XLNetForSequenceClassification)
"""
state_dict = kwargs.get('state_dict', None)
kwargs.pop('state_dict', None)
cache_dir = kwargs.get('cache_dir', None)
kwargs.pop('cache_dir', None)
from_tf = kwargs.get('from_tf', False)
kwargs.pop('from_tf', None)
state_dict = kwargs.pop('state_dict', None)
cache_dir = kwargs.pop('cache_dir', None)
from_tf = kwargs.pop('from_tf', None)
# Load config
config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
# Load model
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
else:
if from_tf:
# Directly load from a TensorFlow checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
else:
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
......@@ -210,47 +224,15 @@ class PreTrainedModel(nn.Module):
', '.join(cls.pretrained_model_archive_map.keys()),
archive_file))
return None
try:
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
except EnvironmentError:
if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
logger.error(
"Couldn't reach server at '{}' to download pretrained model configuration file.".format(
config_file))
else:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
', '.join(cls.pretrained_config_archive_map.keys()),
config_file))
return None
if resolved_archive_file == archive_file and resolved_config_file == config_file:
if resolved_archive_file == archive_file:
logger.info("loading weights file {}".format(archive_file))
logger.info("loading configuration file {}".format(config_file))
else:
logger.info("loading weights file {} from cache at {}".format(
archive_file, resolved_archive_file))
logger.info("loading configuration file {} from cache at {}".format(
config_file, resolved_config_file))
# Load config
config = cls.config_class.from_json_file(resolved_config_file)
# Update config with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
setattr(config, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info("Model config {}".format(config))
# Instantiate model.
model = cls(config, *inputs, **kwargs)
model = cls(config)
if state_dict is None and not from_tf:
state_dict = torch.load(resolved_archive_file, map_location='cpu')
if from_tf:
......@@ -275,7 +257,7 @@ class PreTrainedModel(nn.Module):
if child is not None:
load(child, prefix + name + '.')
# Be able to load base models as well as derived models (with heads)
# Make sure we are able to load base models as well as derived models (with heads)
start_prefix = ''
model_to_load = model
if not hasattr(model, cls.base_model_prefix) and any(s.startswith(cls.base_model_prefix) for s in state_dict.keys()):
......
pytorch_pretrained_bert/modeling.py
View file @ 1484d67d
......@@ -155,7 +155,7 @@ class BertConfig(PretrainedConfig):
pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file,
vocab_size_or_config_json_file=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
......@@ -167,7 +167,7 @@ class BertConfig(PretrainedConfig):
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
finetuning_task=None):
**kwargs):
"""Constructs BertConfig.
Args:
......@@ -192,8 +192,8 @@ class BertConfig(PretrainedConfig):
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
layer_norm_eps: The epsilon used by LayerNorm.
finetuning_task: name of the glue task on which the model was fine-tuned if any
"""
super(BertConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
......@@ -213,7 +213,6 @@ class BertConfig(PretrainedConfig):
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.finetuning_task = finetuning_task
else:
raise ValueError("First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)")
......@@ -270,13 +269,13 @@ class BertEmbeddings(nn.Module):
class BertSelfAttention(nn.Module):
def __init__(self, config, output_attentions=False):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads))
self.output_attentions = output_attentions
self.output_attentions = config.output_attentions
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
......@@ -344,10 +343,9 @@ class BertSelfOutput(nn.Module):
class BertAttention(nn.Module):
def __init__(self, config, output_attentions=False):
def __init__(self, config):
super(BertAttention, self).__init__()
self.output_attentions = output_attentions
self.self = BertSelfAttention(config, output_attentions=output_attentions)
self.self = BertSelfAttention(config)
self.output = BertSelfOutput(config)
def prune_heads(self, heads):
......@@ -404,10 +402,9 @@ class BertOutput(nn.Module):
class BertLayer(nn.Module):
def __init__(self, config, output_attentions=False):
def __init__(self, config):
super(BertLayer, self).__init__()
self.output_attentions = output_attentions
self.attention = BertAttention(config, output_attentions=output_attentions)
self.attention = BertAttention(config)
self.intermediate = BertIntermediate(config)
self.output = BertOutput(config)
......@@ -421,11 +418,11 @@ class BertLayer(nn.Module):
class BertEncoder(nn.Module):
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertEncoder, self).__init__()
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
layer = BertLayer(config, output_attentions=output_attentions)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
layer = BertLayer(config)
self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
def forward(self, hidden_states, attention_mask, head_mask=None):
......@@ -546,9 +543,6 @@ class BertPreTrainedModel(PreTrainedModel):
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
def __init__(self, *inputs, **kwargs):
super(BertPreTrainedModel, self).__init__(*inputs, **kwargs)
def init_weights(self, module):
""" Initialize the weights.
"""
......@@ -612,19 +606,19 @@ class BertModel(BertPreTrainedModel):
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertModel, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config, output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
self.encoder = BertEncoder(config)
self.pooler = BertPooler(config)
self.apply(self.init_weights)
def prune_heads(self, heads_to_prune):
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
......@@ -730,14 +724,12 @@ class BertForPreTraining(BertPreTrainedModel):
masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForPreTraining, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.bert = BertModel(config, output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
self.bert = BertModel(config)
self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None,
......@@ -809,13 +801,12 @@ class BertForMaskedLM(BertPreTrainedModel):
masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForMaskedLM, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.bert = BertModel(config, output_attentions=output_attentions )
self.bert = BertModel(config)
self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
......@@ -880,12 +871,10 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForNextSentencePrediction, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.bert = BertModel(config, output_attentions=output_attentions)
self.bert = BertModel(config)
self.cls = BertOnlyNSPHead(config)
self.apply(self.init_weights)
......@@ -954,15 +943,13 @@ class BertForSequenceClassification(BertPreTrainedModel):
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_labels=2, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForSequenceClassification, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.num_labels = num_labels
self.num_labels = config.num_labels
self.bert = BertModel(config, output_attentions=output_attentions)
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, num_labels)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
self.apply(self.init_weights)
......@@ -997,7 +984,6 @@ class BertForMultipleChoice(BertPreTrainedModel):
`config`: a BertConfig class instance with the configuration to build a new model
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
`output_hidden_states`: If True, also output hidden states computed by the model at each layer. Default: False
`num_choices`: the number of classes for the classifier. Default = 2.
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
......@@ -1030,25 +1016,23 @@ class BertForMultipleChoice(BertPreTrainedModel):
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
num_choices = 2
model = BertForMultipleChoice(config, num_choices)
model = BertForMultipleChoice(config)
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_choices=2, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForMultipleChoice, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.num_choices = num_choices
self.bert = BertModel(config, output_attentions=output_attentions)
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
""" Input shapes should be [bsz, num choices, seq length] """
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
......@@ -1057,7 +1041,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, self.num_choices)
reshaped_logits = logits.view(-1, num_choices)
outputs = [reshaped_logits] + outputs[2:] # add hidden states and attention if they are here
......@@ -1118,15 +1102,13 @@ class BertForTokenClassification(BertPreTrainedModel):
logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, num_labels=2, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForTokenClassification, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.num_labels = num_labels
self.num_labels = config.num_labels
self.bert = BertModel(config, output_attentions=output_attentions)
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, num_labels)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.apply(self.init_weights)
......@@ -1204,12 +1186,12 @@ class BertForQuestionAnswering(BertPreTrainedModel):
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(BertForQuestionAnswering, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.bert = BertModel(config, output_attentions=output_attentions)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.num_labels = config.num_labels
self.bert = BertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.apply(self.init_weights)
......
pytorch_pretrained_bert/modeling_gpt2.py
View file @ 1484d67d
This diff is collapsed.
pytorch_pretrained_bert/modeling_openai.py
View file @ 1484d67d
......@@ -147,7 +147,8 @@ class OpenAIGPTConfig(PretrainedConfig):
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
predict_special_tokens=True
predict_special_tokens=True,
**kwargs
):
"""Constructs OpenAIGPTConfig.
......@@ -172,6 +173,8 @@ class OpenAIGPTConfig(PretrainedConfig):
initializing all weight matrices.
predict_special_tokens: should we predict special tokens (when the model has a LM head)
"""
super(OpenAIGPTConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
......@@ -205,7 +208,7 @@ class OpenAIGPTConfig(PretrainedConfig):
class Attention(nn.Module):
def __init__(self, nx, n_ctx, config, scale=False, output_attentions=False, keep_multihead_output=False):
def __init__(self, nx, n_ctx, config, scale=False):
super(Attention, self).__init__()
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implem]
......@@ -215,9 +218,7 @@ class Attention(nn.Module):
self.split_size = n_state
self.scale = scale
self.output_attentions = output_attentions
self.keep_multihead_output = keep_multihead_output
self.multihead_output = None
self.output_attentions = config.output_attentions
self.c_attn = Conv1D(n_state * 3, nx)
self.c_proj = Conv1D(n_state, nx)
......@@ -256,9 +257,10 @@ class Attention(nn.Module):
if head_mask is not None:
w = w * head_mask
outputs = [torch.matmul(w, v)]
if self.output_attentions:
return w, torch.matmul(w, v)
return torch.matmul(w, v)
outputs.append(w)
return outputs
def merge_heads(self, x):
x = x.permute(0, 2, 1, 3).contiguous()
......@@ -280,19 +282,15 @@ class Attention(nn.Module):
key = self.split_heads(key, k=True)
value = self.split_heads(value)
a = self._attn(query, key, value, head_mask)
if self.keep_multihead_output:
self.multihead_output = a
self.multihead_output.retain_grad()
attn_outputs = self._attn(query, key, value, head_mask)
a = attn_outputs[0]
if self.output_attentions:
attentions, a = a
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
if self.output_attentions:
return attentions, a
return a
outputs = [a] + attn_outputs[1:]
return outputs # a, (attentions)
class MLP(nn.Module):
......@@ -311,25 +309,24 @@ class MLP(nn.Module):
class Block(nn.Module):
def __init__(self, n_ctx, config, scale=False, output_attentions=False, keep_multihead_output=False):
def __init__(self, n_ctx, config, scale=False):
super(Block, self).__init__()
nx = config.n_embd
self.output_attentions = output_attentions
self.attn = Attention(nx, n_ctx, config, scale, output_attentions, keep_multihead_output)
self.attn = Attention(nx, n_ctx, config, scale)
self.ln_1 = LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
self.ln_2 = LayerNorm(nx, eps=config.layer_norm_epsilon)
def forward(self, x, head_mask=None):
a = self.attn(x, head_mask=head_mask)
if self.output_attentions:
attentions, a = a
attn_outputs = self.attn(x, head_mask=head_mask)
a = attn_outputs[0]
n = self.ln_1(x + a)
m = self.mlp(n)
h = self.ln_2(n + m)
if self.output_attentions:
return attentions, h
return h
outputs = [h] + attn_outputs[1:]
return outputs
class OpenAIGPTLMHead(nn.Module):
......@@ -368,10 +365,15 @@ class OpenAIGPTMultipleChoiceHead(nn.Module):
nn.init.normal_(self.linear.weight, std=0.02)
nn.init.normal_(self.linear.bias, 0)
def forward(self, hidden_states, mc_token_ids):
# Classification logits
# hidden_state (bsz, num_choices, seq_length, hidden_size)
# mc_token_ids (bsz, num_choices)
def forward(self, hidden_states, mc_token_ids=None):
""" Extract classification token hidden state and project it using self.linear
hidden_state: hidden state of shape (bsz, num_choices, seq_length, hidden_size)
mc_token_ids: [optional] index of the classification token, shape (bsz, num_choices)
if mc_token_ids=None we take the last token of the sequence as classification token
"""
if mc_token_ids is None:
mc_token_ids = torch.full_like(hidden_states[:, :, :1, :], hidden_states.shape[2] - 1, dtype=torch.long)
else:
mc_token_ids = mc_token_ids.unsqueeze(-1).unsqueeze(-1).expand(-1, -1, -1, hidden_states.size(-1))
# (bsz, num_choices, 1, hidden_size)
multiple_choice_h = hidden_states.gather(2, mc_token_ids).squeeze(2)
......@@ -388,13 +390,9 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel):
"""
config_class = OpenAIGPTConfig
pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_openai_gpt
base_model_prefix = "transformer"
def __init__(self, *inputs, **kwargs):
super(OpenAIGPTPreTrainedModel, self).__init__(*inputs, **kwargs)
def init_weights(self, module):
""" Initialize the weights.
"""
......@@ -495,14 +493,15 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
```
"""
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
def __init__(self, config):
super(OpenAIGPTModel, self).__init__(config)
self.output_attentions = output_attentions
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.tokens_embed = nn.Embedding(config.total_tokens_embeddings, config.n_embd)
self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
block = Block(config.n_ctx, config, scale=True, output_attentions=output_attentions,
keep_multihead_output=keep_multihead_output)
block = Block(config.n_ctx, config, scale=True)
self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(config.n_layer)])
self.apply(self.init_weights)
......@@ -521,19 +520,13 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
# Copy word embeddings from the previous weights
self.tokens_embed.weight.data[:self.config.vocab_size, :] = old_embed.weight.data[:self.config.vocab_size, :]
def prune_heads(self, heads_to_prune):
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
def get_multihead_outputs(self):
""" Gather all multi-head outputs.
Return: list (layers) of multihead module outputs with gradients
"""
return [h.attn.multihead_output for h in self.h]
def forward(self, input_ids, position_ids=None, token_type_ids=None, head_mask=None):
if position_ids is None:
# This was used when we had a single embedding matrice from position and token embeddings
......@@ -574,19 +567,26 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
output_shape = input_shape + (hidden_states.size(-1),)
all_attentions = []
all_hidden_states = [hidden_states.view(*output_shape)]
all_hidden_states = []
for i, block in enumerate(self.h):
if self.output_hidden_states:
all_hidden_states.append(hidden_states.view(*output_shape))
outputs = block(hidden_states, head_mask[i])
hidden_states = outputs[0]
if self.output_attentions:
attentions, hidden_states = outputs
all_attentions.append(attentions)
else:
hidden_states = outputs
all_attentions.append(outputs[1])
# Add last layer
if self.output_hidden_states:
all_hidden_states.append(hidden_states.view(*output_shape))
outputs = [hidden_states.view(*output_shape)]
if self.output_hidden_states:
outputs.append(all_hidden_states)
if self.output_attentions:
return all_attentions, all_hidden_states
return all_hidden_states
outputs.append(all_attentions)
return outputs # last hidden state, (all hidden states), (all attentions)
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
......@@ -650,10 +650,9 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
```
"""
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
def __init__(self, config):
super(OpenAIGPTLMHeadModel, self).__init__(config)
self.transformer = OpenAIGPTModel(config, output_attentions=output_attentions,
keep_multihead_output=keep_multihead_output)
self.transformer = OpenAIGPTModel(config)
self.lm_head = OpenAIGPTLMHead(self.transformer.tokens_embed.weight, config)
self.apply(self.init_weights)
......@@ -666,12 +665,11 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
self.lm_head.set_embeddings_weights(self.transformer.tokens_embed.weight, predict_special_tokens=predict_special_tokens)
def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, head_mask=None):
hidden_states = self.transformer(input_ids, position_ids, token_type_ids, head_mask)
if self.transformer.output_attentions:
all_attentions, hidden_states = hidden_states
hidden_states = hidden_states[-1]
transformer_outputs = self.transformer(input_ids, position_ids, token_type_ids, head_mask)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
outputs = [lm_logits] + transformer_outputs[1:]
if lm_labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
......@@ -680,10 +678,9 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
return loss
if self.transformer.output_attentions:
return all_attentions, lm_logits
return lm_logits
outputs = [loss] + outputs
return outputs # (loss), lm_logits, (all hidden states), (all attentions)
class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
......@@ -752,10 +749,9 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
```
"""
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
def __init__(self, config):
super(OpenAIGPTDoubleHeadsModel, self).__init__(config)
self.transformer = OpenAIGPTModel(config, output_attentions=output_attentions,
keep_multihead_output=keep_multihead_output)
self.transformer = OpenAIGPTModel(config)
self.lm_head = OpenAIGPTLMHead(self.transformer.tokens_embed.weight, config)
self.multiple_choice_head = OpenAIGPTMultipleChoiceHead(config)
self.apply(self.init_weights)
......@@ -768,26 +764,26 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.tokens_embed.weight, predict_special_tokens=predict_special_tokens)
def forward(self, input_ids, mc_token_ids, lm_labels=None, mc_labels=None, token_type_ids=None,
def forward(self, input_ids, mc_token_ids=None, lm_labels=None, mc_labels=None, token_type_ids=None,
position_ids=None, head_mask=None):
hidden_states = self.transformer(input_ids, position_ids, token_type_ids, head_mask)
if self.transformer.output_attentions:
all_attentions, hidden_states = hidden_states
hidden_states = hidden_states[-1]
transformer_outputs = self.transformer(input_ids, position_ids, token_type_ids, head_mask)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
losses = []
outputs = [lm_logits, mc_logits] + transformer_outputs[1:]
if mc_labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)),
mc_labels.view(-1))
outputs = [loss] + outputs
if lm_labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss(ignore_index=-1)
losses.append(loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)))
if mc_labels is not None:
loss_fct = CrossEntropyLoss()
losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)))
if losses:
return losses
if self.transformer.output_attentions:
return all_attentions, lm_logits, mc_logits
return lm_logits, mc_logits
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = [loss] + outputs
return outputs # (lm loss), (mc loss), lm logits, mc logits, (all hidden_states), (attentions)
pytorch_pretrained_bert/modeling_xlm.py
View file @ 1484d67d
This diff is collapsed.
pytorch_pretrained_bert/modeling_xlnet.py
View file @ 1484d67d
......@@ -198,7 +198,7 @@ class XLNetConfig(PretrainedConfig):
pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file,
vocab_size_or_config_json_file=32000,
d_model=1024,
n_layer=24,
n_head=16,
......@@ -221,7 +221,12 @@ class XLNetConfig(PretrainedConfig):
bi_data=False,
clamp_len=-1,
same_length=False,
finetuning_task=None):
finetuning_task=None,
num_labels=2,
summary_type="last",
use_proj=True,
**kwargs):
"""Constructs XLNetConfig.
Args:
......@@ -265,6 +270,8 @@ class XLNetConfig(PretrainedConfig):
same_length: bool, whether to use the same attention length for each token.
finetuning_task: name of the glue task on which the model was fine-tuned if any
"""
super(XLNetConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
......@@ -297,7 +304,11 @@ class XLNetConfig(PretrainedConfig):
self.bi_data = bi_data
self.clamp_len = clamp_len
self.same_length = same_length
self.finetuning_task = finetuning_task
self.num_labels = num_labels
self.summary_type = summary_type
self.use_proj = use_proj
else:
raise ValueError("First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)")
......@@ -323,9 +334,10 @@ except ImportError:
return self.weight * x + self.bias
class XLNetRelativeAttention(nn.Module):
def __init__(self, config, output_attentions=False):
def __init__(self, config):
super(XLNetRelativeAttention, self).__init__()
self.output_attentions = output_attentions
self.output_attentions = config.output_attentions
if config.d_model % config.n_head != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
......@@ -533,10 +545,9 @@ class XLNetFeedForward(nn.Module):
return output
class XLNetLayer(nn.Module):
def __init__(self, config, output_attentions=False, ):
def __init__(self, config):
super(XLNetLayer, self).__init__()
self.output_attentions = output_attentions
self.rel_attn = XLNetRelativeAttention(config, output_attentions=output_attentions)
self.rel_attn = XLNetRelativeAttention(config)
self.ff = XLNetFeedForward(config)
self.dropout = nn.Dropout(config.dropout)
......@@ -562,7 +573,6 @@ class XLNetPreTrainedModel(PreTrainedModel):
"""
config_class = XLNetConfig
pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_xlnet
base_model_prefix = "transformer"
......@@ -589,10 +599,10 @@ class XLNetPreTrainedModel(PreTrainedModel):
class XLNetModel(XLNetPreTrainedModel):
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(XLNetModel, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.mem_len = config.mem_len
self.reuse_len = config.reuse_len
......@@ -601,25 +611,17 @@ class XLNetModel(XLNetPreTrainedModel):
self.attn_type = config.attn_type
self.bi_data = config.bi_data
self.clamp_len = config.clamp_len
self.n_layer = config.n_layer
self.word_embedding = nn.Embedding(config.n_token, config.d_model)
self.mask_emb = nn.Parameter(torch.Tensor(1, 1, config.d_model))
layer = XLNetLayer(config, output_attentions=output_attentions)
layer = XLNetLayer(config)
self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.n_layer)])
self.dropout = nn.Dropout(config.dropout)
def prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.layer[layer].attention.prune_heads(heads)
def get_multihead_outputs(self):
""" Gather all multi-head outputs.
Return: list (layers) of multihead module outputs with gradients
"""
return [layer.attention.self.multihead_output for layer in self.layer]
def _prune_heads(self, heads_to_prune):
logger.info("Head pruning is not implemented for XLNet")
pass
def create_mask(self, qlen, mlen):
""" create causal attention mask.
......@@ -708,11 +710,11 @@ class XLNetModel(XLNetPreTrainedModel):
pos_emb = pos_emb.to(next(self.parameters()))
return pos_emb
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None, head_mask=None):
"""
Args:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
input_ids: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
......@@ -751,7 +753,7 @@ class XLNetModel(XLNetPreTrainedModel):
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension
# so we move here the first dimension (batch) to the end
inp_k = inp_k.transpose(0, 1).contiguous()
input_ids = input_ids.transpose(0, 1).contiguous()
token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
......@@ -759,7 +761,7 @@ class XLNetModel(XLNetPreTrainedModel):
target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
inp_q = inp_q.transpose(0, 1).contiguous() if inp_q is not None else None
qlen, bsz = inp_k.shape[0], inp_k.shape[1]
qlen, bsz = input_ids.shape[0], input_ids.shape[1]
mlen = mems[0].shape[0] if mems is not None else 0
klen = mlen + qlen
......@@ -810,7 +812,7 @@ class XLNetModel(XLNetPreTrainedModel):
non_tgt_mask = None
##### Word embeddings and prepare h & g hidden states
word_emb_k = self.word_embedding(inp_k)
word_emb_k = self.word_embedding(input_ids)
output_h = self.dropout(word_emb_k)
if inp_q is not None:
if target_mapping is not None:
......@@ -838,20 +840,20 @@ class XLNetModel(XLNetPreTrainedModel):
pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
pos_emb = self.dropout(pos_emb)
##### Head mask if needed (for bertology/pruning)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [n_layer x num_heads]
# and head_mask is converted to shape [n_layer x batch x num_heads x seq_length x seq_length]
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
# and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.config.n_layer
head_mask = [None] * self.n_layer
new_mems = []
if mems is None:
......@@ -870,7 +872,7 @@ class XLNetModel(XLNetPreTrainedModel):
head_mask=head_mask[i])
output_h, output_g = outputs[:2]
if self.output_attentions:
attentions.append(outputs[2:])
attentions.append(outputs[2])
# Add last hidden state
if self.output_hidden_states:
......@@ -887,6 +889,7 @@ class XLNetModel(XLNetPreTrainedModel):
hidden_states = [hs.permute(1, 0, 2).contiguous() for hs in hidden_states]
outputs.append(hidden_states)
if self.output_attentions:
attentions = list(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
outputs.append(attentions)
return outputs # outputs, new_mems, (hidden_states), (attentions)
......@@ -902,7 +905,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
This can be used to compute head importance metrics. Default: False
Inputs:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
input_ids: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
......@@ -953,16 +956,12 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(XLNetLMHeadModel, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.attn_type = config.attn_type
self.same_length = config.same_length
self.transformer = XLNetModel(config, output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
self.transformer = XLNetModel(config)
self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
# Tie weights
......@@ -975,12 +974,12 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
"""
self.lm_loss.weight = self.transformer.word_embedding.weight
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
labels=None, head_mask=None):
"""
Args:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
input_ids: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
input_mask: float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
......@@ -1008,7 +1007,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
summary_type: str, "last", "first", "mean", or "attn". The method
to pool the input to get a vector representation.
"""
transformer_outputs = self.transformer(inp_k, token_type_ids, input_mask, attention_mask,
transformer_outputs = self.transformer(input_ids, token_type_ids, input_mask, attention_mask,
mems, perm_mask, target_mapping, inp_q, head_mask)
logits = self.lm_loss(transformer_outputs[0])
......@@ -1025,14 +1024,14 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
return outputs # return (loss), logits, (mems), (hidden states), (attentions)
class XLNetSequenceSummary(nn.Module):
def __init__(self, config, summary_type="last", use_proj=True):
def __init__(self, config):
super(XLNetSequenceSummary, self).__init__()
self.summary_type = summary_type
if use_proj:
self.summary_type = config.summary_type
if config.use_proj:
self.summary = nn.Linear(config.d_model, config.d_model)
else:
self.summary = None
if summary_type == 'attn':
if config.summary_type == 'attn':
# We should use a standard multi-head attention module with absolute positional embedding for that.
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
......@@ -1069,7 +1068,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
to pool the input to get a vector representation. Default: last
Inputs:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
input_ids: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
input_mask: float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
......@@ -1121,30 +1120,21 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, summary_type="last", use_proj=True, num_labels=2,
output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(XLNetForSequenceClassification, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.attn_type = config.attn_type
self.same_length = config.same_length
self.summary_type = summary_type
self.num_labels = num_labels
self.transformer = XLNetModel(config, output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
self.transformer = XLNetModel(config)
self.sequence_summary = XLNetSequenceSummary(config)
self.logits_proj = nn.Linear(config.d_model, config.num_labels)
self.sequence_summary = XLNetSequenceSummary(config, summary_type=summary_type, use_proj=use_proj)
self.logits_proj = nn.Linear(config.d_model, num_labels)
self.apply(self.init_weights)
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
labels=None, head_mask=None):
"""
Args:
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
input_ids: int32 Tensor in shape [bsz, len], the input token IDs.
token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
input_mask: float32 Tensor in shape [bsz, len], the input mask.
0 for real tokens and 1 for padding.
......@@ -1169,7 +1159,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
Only used during pretraining for two-stream attention.
Set to None during finetuning.
"""
transformer_outputs = self.transformer(inp_k, token_type_ids, input_mask, attention_mask,
transformer_outputs = self.transformer(input_ids, token_type_ids, input_mask, attention_mask,
mems, perm_mask, target_mapping, inp_q, head_mask)
output = transformer_outputs[0]
......@@ -1247,20 +1237,18 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, output_attentions=False, output_hidden_states=False):
def __init__(self, config):
super(XLNetForQuestionAnswering, self).__init__(config)
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.transformer = XLNetModel(config, output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.transformer = XLNetModel(config)
self.qa_outputs = nn.Linear(config.d_model, config.num_labels)
self.apply(self.init_weights)
def forward(self, inp_k, token_type_ids=None, input_mask=None, attention_mask=None,
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
start_positions=None, end_positions=None, head_mask=None):
transformer_outputs = self.transformer(inp_k, token_type_ids, input_mask, attention_mask,
transformer_outputs = self.transformer(input_ids, token_type_ids, input_mask, attention_mask,
mems, perm_mask, target_mapping, inp_q, head_mask)
logits = self.qa_outputs(transformer_outputs[0])
......
pytorch_pretrained_bert/tests/model_tests_commons.py 0 → 100644
View file @ 1484d67d
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import json
import random
import torch
def create_and_check_for_headmasking(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
config.output_hidden_states = True
model = model_class(config=config)
model.eval()
head_mask = torch.zeros(tester.num_hidden_layers, tester.num_attention_heads)
# Set that after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask.requires_grad_(requires_grad=True)
outputs = model(**inputs_dict, head_mask=head_mask)
# Compute some gradients
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
tester.parent.assertEqual(len(multihead_outputs), tester.num_hidden_layers)
# self.parent.assertListEqual(
# list(multihead_outputs[0].size()),
# [self.batch_size, self.num_attention_heads,
# self.seq_length, self.hidden_size // self.num_attention_heads])
# self.parent.assertEqual(
# len(multihead_outputs[0][:, 1:(self.num_attention_heads-1), :, :].nonzero()),
# 0)
# self.parent.assertEqual(
# len(multihead_outputs[0][:, 0, :, :].nonzero()),
# self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
# self.parent.assertEqual(
# len(multihead_outputs[0][:, self.num_attention_heads-1, :, :].nonzero()),
# self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
# self.parent.assertListEqual(
# list(multihead_outputs[1].size()),
# [self.batch_size, self.num_attention_heads,
# self.seq_length, self.hidden_size // self.num_attention_heads])
# self.parent.assertEqual(
# len(multihead_outputs[1].nonzero()),
# multihead_outputs[1].numel())
# self.parent.assertListEqual(
# list(multihead_outputs[-1].size()),
# [self.batch_size, self.num_attention_heads,
# self.seq_length, self.hidden_size // self.num_attention_heads])
# self.parent.assertEqual(
# len(multihead_outputs[-1][:, 1:, :, :].nonzero()),
# 0)
# self.parent.assertEqual(
# len(multihead_outputs[-1][:, 0, :, :].nonzero()),
# self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
def create_and_check_for_head_pruning(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
model = model_class(config=config)
model.eval()
heads_to_prune = {0: list(range(1, tester.num_attention_heads)),
-1: [0]}
model.prune_heads(heads_to_prune)
outputs = model(**inputs_dict)
# output = sum(t.sum() for t in outputs[0])
# output = output.sum()
# output.backward()
# multihead_outputs = bert_model.get_multihead_outputs()
# self.parent.assertEqual(len(multihead_outputs), self.num_hidden_layers)
# self.parent.assertListEqual(
# list(multihead_outputs[0].size()),
# [self.batch_size, 1,
# self.seq_length, self.hidden_size // self.num_attention_heads])
# self.parent.assertListEqual(
# list(multihead_outputs[1].size()),
# [self.batch_size, self.num_attention_heads,
# self.seq_length, self.hidden_size // self.num_attention_heads])
# self.parent.assertListEqual(
# list(multihead_outputs[-1].size()),
# [self.batch_size, self.num_attention_heads-1,
# self.seq_length, self.hidden_size // self.num_attention_heads])
def create_and_check_for_attentions(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
tester.parent.assertEqual(model.config.output_attentions, True)
tester.parent.assertEqual(model.config.output_hidden_states, False)
tester.parent.assertEqual(len(attentions), tester.num_hidden_layers)
tester.parent.assertListEqual(
list(attentions[0].shape[-3:]),
[tester.num_attention_heads,
tester.seq_length,
tester.key_len if hasattr(tester, 'key_len') else tester.seq_length])
out_len = len(outputs)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
tester.parent.assertEqual(out_len+1, len(outputs))
tester.parent.assertEqual(model.config.output_attentions, True)
tester.parent.assertEqual(model.config.output_hidden_states, True)
attentions = outputs[-1]
tester.parent.assertEqual(len(attentions), tester.num_hidden_layers)
tester.parent.assertListEqual(
list(attentions[0].shape[-3:]),
[tester.num_attention_heads,
tester.seq_length,
tester.key_len if hasattr(tester, 'key_len') else tester.seq_length])
def create_and_check_for_hidden_states(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
tester.parent.assertEqual(model.config.output_attentions, False)
tester.parent.assertEqual(model.config.output_hidden_states, True)
tester.parent.assertEqual(len(hidden_states), tester.num_hidden_layers + 1)
tester.parent.assertListEqual(
list(hidden_states[0].shape[-2:]),
[tester.seq_length, tester.hidden_size])
def create_and_check_commons(tester, config, inputs_dict):
create_and_check_for_attentions(tester, tester.all_model_classes, config, inputs_dict)
create_and_check_for_headmasking(tester, tester.all_model_classes, config, inputs_dict)
create_and_check_for_head_pruning(tester, tester.all_model_classes, config, inputs_dict)
create_and_check_for_hidden_states(tester, tester.all_model_classes, config, inputs_dict)
def ids_tensor(shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
class ConfigTester(object):
def __init__(self, parent, config_class=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.inputs_dict = kwargs
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
json_file_path = "/tmp/config.json"
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
os.remove(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def run_common_tests(self):
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
class GPTModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_special=1,
n_positions=33,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
config_class=None,
base_model_class=None,
lm_head_model_class=None,
double_head_model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_special = n_special
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.config_class = config_class
self.base_model_class = base_model_class
self.lm_head_model_class = lm_head_model_class
self.double_head_model_class = double_head_model_class
self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
def prepare_config_and_inputs(self):
total_num_tokens = self.vocab_size + self.n_special
input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
position_ids = None
if self.use_position_ids:
position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size
token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = self.config_class(
vocab_size_or_config_json_file=self.vocab_size,
n_special=self.n_special,
n_positions=self.n_positions,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
initializer_range=self.initializer_range)
return (config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids)
def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.base_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids)
hidden_state = outputs[0]
self.parent.assertListEqual(
list(hidden_state.size()),
[self.batch_size, self.n_choices, self.seq_length, self.hidden_size])
def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.lm_head_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
total_voc = self.n_special + self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(loss.size()),
[])
def create_and_check_presents(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
outputs = model(input_ids)
presents = outputs[-1]
self.parent.assertEqual(self.num_hidden_layers, len(presents))
self.parent.assertListEqual(
list(presents[0].size()),
[2, self.batch_size * self.n_choices, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.double_head_model_class(config)
model.eval()
outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
token_type_ids=token_type_ids, position_ids=position_ids)
lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
loss = [lm_loss, mc_loss]
total_voc = self.n_special + self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(mc_logits.size()),
[self.batch_size, self.n_choices])
self.parent.assertListEqual(
[list(l.size()) for l in loss],
[[], []])
def create_and_check_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_pretrained_bert_test/"
for model_name in list(self.base_model_class.PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.parent.assertIsNotNone(model)
def create_and_check_commons(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
inputs_dict = {'input_ids': input_ids}
create_and_check_commons(self, config, inputs_dict)
def run_common_tests(self, test_presents=False):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_base_model(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_lm_head(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_double_heads(*config_and_inputs)
if test_presents:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_presents(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_commons(*config_and_inputs)
def run_slow_tests(self):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_model_from_pretrained(*config_and_inputs)
pytorch_pretrained_bert/tests/model_utils_test.py 0 → 100644
View file @ 1484d67d
# coding=utf-8
# Copyright 2018 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
import json
import random
import shutil
import pytest
import torch
from pytorch_pretrained_bert import PretrainedConfig, PreTrainedModel
from pytorch_pretrained_bert.modeling import BertModel, BertConfig, PRETRAINED_MODEL_ARCHIVE_MAP, PRETRAINED_CONFIG_ARCHIVE_MAP
class ModelUtilsTest(unittest.TestCase):
def test_model_from_pretrained(self):
for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
if __name__ == "__main__":
unittest.main()
pytorch_pretrained_bert/tests/modeling_gpt2_test.py 0 → 100644
View file @ 1484d67d
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
import json
import random
import shutil
import pytest
import torch
from pytorch_pretrained_bert import (GPT2Config, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel)
from .model_tests_commons import (create_and_check_for_attentions, create_and_check_for_head_pruning,
create_and_check_for_headmasking, create_and_check_for_hidden_states,
ConfigTester, GPTModelTester)
class GPT2ModelTest(unittest.TestCase):
def test_config(self):
config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
config_tester.run_common_tests()
def test_model(self):
model_tester = GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
lm_head_model_class=GPT2LMHeadModel,
double_head_model_class=GPT2DoubleHeadsModel)
model_tester.run_common_tests(test_presents=True)
@pytest.mark.slow
def test_pretrained(self):
model_tester = GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
lm_head_model_class=GPT2LMHeadModel,
double_head_model_class=GPT2DoubleHeadsModel)
model_tester.run_slow_tests()
if __name__ == "__main__":
unittest.main()
pytorch_pretrained_bert/tests/modeling_openai_test.py 0 → 100644
View file @ 1484d67d
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
import json
import random
import shutil
import pytest
import torch
from pytorch_pretrained_bert import (OpenAIGPTConfig, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel)
from .model_tests_commons import (create_and_check_for_attentions, create_and_check_for_head_pruning,
create_and_check_for_headmasking, create_and_check_for_hidden_states,
ConfigTester, GPTModelTester)
class OpenAIModelTest(unittest.TestCase):
def test_config(self):
config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
config_tester.run_common_tests()
def test_model(self):
model_tester = GPTModelTester(self, config_class=OpenAIGPTConfig, base_model_class=OpenAIGPTModel,
lm_head_model_class=OpenAIGPTLMHeadModel,
double_head_model_class=OpenAIGPTDoubleHeadsModel)
model_tester.run_common_tests(test_presents=False)
@pytest.mark.slow
def test_pretrained(self):
model_tester = GPTModelTester(self, config_class=OpenAIGPTConfig, base_model_class=OpenAIGPTModel,
lm_head_model_class=OpenAIGPTLMHeadModel,
double_head_model_class=OpenAIGPTDoubleHeadsModel)
model_tester.run_slow_tests()
if __name__ == "__main__":
unittest.main()
tests/modeling_transfo_xl_test.py → pytorch_pretrained_bert/tests/modeling_transfo_xl_test.py
View file @ 1484d67d
......@@ -28,6 +28,8 @@ import torch
from pytorch_pretrained_bert import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel)
from pytorch_pretrained_bert.modeling_transfo_xl import PRETRAINED_MODEL_ARCHIVE_MAP
from .model_tests_commons import ConfigTester, create_and_check_commons, ids_tensor
class TransfoXLModelTest(unittest.TestCase):
class TransfoXLModelTester(object):
......@@ -41,54 +43,58 @@ class TransfoXLModelTest(unittest.TestCase):
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
d_model=32,
hidden_size=32,
d_embed=32,
n_head=4,
num_attention_heads=4,
d_head=8,
d_inner=128,
div_val=2,
n_layer=5,
num_hidden_layers=5,
scope=None,
seed=1):
seed=1,
all_model_classes=(TransfoXLModel, TransfoXLLMHeadModel),
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
self.key_len = seq_length + mem_len
self.clamp_len = clamp_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.d_model = d_model
self.hidden_size = hidden_size
self.d_embed = d_embed
self.n_head = n_head
self.num_attention_heads = num_attention_heads
self.d_head = d_head
self.d_inner = d_inner
self.div_val = div_val
self.n_layer = n_layer
self.num_hidden_layers = num_hidden_layers
self.scope = scope
self.seed = seed
self.all_model_classes = all_model_classes
def prepare_config_and_inputs(self):
input_ids_1 = TransfoXLModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = TransfoXLModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
lm_labels = None
if self.use_labels:
lm_labels = TransfoXLModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = TransfoXLConfig(
vocab_size_or_config_json_file=self.vocab_size,
mem_len=self.mem_len,
clamp_len=self.clamp_len,
cutoffs=self.cutoffs,
d_model=self.d_model,
d_model=self.hidden_size,
d_embed=self.d_embed,
n_head=self.n_head,
n_head=self.num_attention_heads,
d_head=self.d_head,
d_inner=self.d_inner,
div_val=self.div_val,
n_layer=self.n_layer)
n_layer=self.num_hidden_layers)
return (config, input_ids_1, input_ids_2, lm_labels)
......@@ -113,37 +119,34 @@ class TransfoXLModelTest(unittest.TestCase):
def check_transfo_xl_model_output(self, result):
self.parent.assertListEqual(
list(result["hidden_states_1"].size()),
[self.batch_size, self.seq_length, self.d_model])
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(result["hidden_states_2"].size()),
[self.batch_size, self.seq_length, self.d_model])
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
model = TransfoXLLMHeadModel(config)
model.eval()
loss_1, mems_1a = model(input_ids_1, labels=lm_labels)
lm_logits_1, mems_1b = model(input_ids_1)
loss_2, mems_2a = model(input_ids_2, labels=lm_labels, mems=mems_1a)
lm_logits_2, mems_2b = model(input_ids_2, mems=mems_1b)
lm_logits_1, mems_1 = model(input_ids_1)
loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels)
lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1)
loss_2, _, mems_2 = model(input_ids_2, labels=lm_labels, mems=mems_1)
outputs = {
"loss_1": loss_1,
"mems_1a": mems_1a,
"mems_1": mems_1,
"lm_logits_1": lm_logits_1,
"mems_1b": mems_1b,
"loss_2": loss_2,
"mems_2a": mems_2a,
"mems_2": mems_2,
"lm_logits_2": lm_logits_2,
"mems_2b": mems_2b,
}
return outputs
......@@ -155,14 +158,8 @@ class TransfoXLModelTest(unittest.TestCase):
list(result["lm_logits_1"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1a"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1b"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
self.parent.assertListEqual(
list(mem[~torch.isnan(mem)].sum() for mem in result["mems_1a"]),
list(mem[~torch.isnan(mem)].sum() for mem in result["mems_1b"]))
list(list(mem.size()) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(result["loss_2"].size()),
......@@ -171,31 +168,19 @@ class TransfoXLModelTest(unittest.TestCase):
list(result["lm_logits_2"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2a"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2b"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
self.parent.assertListEqual(
list(mem[~torch.isnan(mem)].sum() for mem in result["mems_2a"]),
list(mem[~torch.isnan(mem)].sum() for mem in result["mems_2b"]))
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_transfo_xl_commons(self, config, input_ids_1, input_ids_2, lm_labels):
inputs_dict = {'input_ids': input_ids_1}
create_and_check_commons(self, config, inputs_dict)
def test_default(self):
self.run_tester(TransfoXLModelTest.TransfoXLModelTester(self))
def test_config_to_json_string(self):
config = TransfoXLConfig(vocab_size_or_config_json_file=96, d_embed=37)
obj = json.loads(config.to_json_string())
self.assertEqual(obj["n_token"], 96)
self.assertEqual(obj["d_embed"], 37)
def test_config_to_json_file(self):
config_first = TransfoXLConfig(vocab_size_or_config_json_file=96, d_embed=37)
json_file_path = "/tmp/config.json"
config_first.to_json_file(json_file_path)
config_second = TransfoXLConfig.from_json_file(json_file_path)
os.remove(json_file_path)
self.assertEqual(config_second.to_dict(), config_first.to_dict())
def test_config(self):
config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
config_tester.run_common_tests()
@pytest.mark.slow
def test_model_from_pretrained(self):
......@@ -209,28 +194,18 @@ class TransfoXLModelTest(unittest.TestCase):
config_and_inputs = tester.prepare_config_and_inputs()
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
output_result = tester.create_transfo_xl_model(*config_and_inputs)
tester.check_transfo_xl_model_output(output_result)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
output_result = tester.create_transfo_xl_lm_head(*config_and_inputs)
tester.check_transfo_xl_lm_head_output(output_result)
@classmethod
def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_transfo_xl_commons(*config_and_inputs)
if __name__ == "__main__":
......
tests/modeling_xlnet_test.py → pytorch_pretrained_bert/tests/modeling_xlnet_test.py
View file @ 1484d67d
......@@ -25,9 +25,11 @@ import pytest
import torch
from pytorch_pretrained_bert import (XLNetConfig, XLNetModel, XLNetLMHeadModel)
from pytorch_pretrained_bert import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering)
from pytorch_pretrained_bert.modeling_xlnet import PRETRAINED_MODEL_ARCHIVE_MAP
from .model_tests_commons import ConfigTester, create_and_check_commons, ids_tensor
class XLNetModelTest(unittest.TestCase):
class XLNetModelTester(object):
......@@ -42,43 +44,48 @@ class XLNetModelTest(unittest.TestCase):
use_labels=True,
vocab_size=99,
cutoffs=[10, 50, 80],
d_model=32,
n_head=4,
hidden_size=32,
num_attention_heads=4,
d_inner=128,
n_layer=5,
num_hidden_layers=5,
max_position_embeddings=10,
untie_r=True,
bi_data=False,
same_length=False,
seed=1,
type_vocab_size=2):
type_vocab_size=2,
all_model_classes=(XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForQuestionAnswering),
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.mem_len = mem_len
# self.key_len = seq_length + mem_len
self.clamp_len = clamp_len
self.reuse_len = reuse_len
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.cutoffs = cutoffs
self.d_model = d_model
self.n_head = n_head
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.d_inner = d_inner
self.n_layer = n_layer
self.num_hidden_layers = num_hidden_layers
self.max_position_embeddings = max_position_embeddings
self.bi_data = bi_data
self.untie_r = untie_r
self.same_length = same_length
self.seed = seed
self.type_vocab_size = type_vocab_size
self.all_model_classes = all_model_classes
def prepare_config_and_inputs(self):
input_ids_1 = XLNetModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = XLNetModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
segment_ids = XLNetModelTest.ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
input_ids_q = XLNetModelTest.ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float)
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float)
......@@ -89,8 +96,8 @@ class XLNetModelTest(unittest.TestCase):
# token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
# input_mask: float32 Tensor in shape [bsz, len], the input mask.
# 0 for real tokens and 1 for padding.
# mems: a list of float32 Tensors in shape [bsz, mem_len, d_model], memory
# from previous batches. The length of the list equals n_layer.
# mems: a list of float32 Tensors in shape [bsz, mem_len, hidden_size], memory
# from previous batches. The length of the list equals num_hidden_layers.
# If None, no memory is used.
# perm_mask: float32 Tensor in shape [bsz, len, len].
# If perm_mask[k, i, j] = 0, i attend to j in batch k;
......@@ -108,14 +115,14 @@ class XLNetModelTest(unittest.TestCase):
lm_labels = None
if self.use_labels:
lm_labels = XLNetModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = XLNetConfig(
vocab_size_or_config_json_file=self.vocab_size,
d_model=self.d_model,
n_head=self.n_head,
d_model=self.hidden_size,
n_head=self.num_attention_heads,
d_inner=self.d_inner,
n_layer=self.n_layer,
n_layer=self.num_hidden_layers,
untie_r=self.untie_r,
max_position_embeddings=self.max_position_embeddings,
mem_len=self.mem_len,
......@@ -159,7 +166,7 @@ class XLNetModelTest(unittest.TestCase):
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.d_model]] * self.n_layer)
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
self.parent.assertListEqual(
list(result["loss_2"].size()),
......@@ -169,24 +176,18 @@ class XLNetModelTest(unittest.TestCase):
[self.batch_size, self.seq_length, self.vocab_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_commons(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, target_mapping, inp_q, segment_ids, lm_labels):
inputs_dict = {'input_ids': input_ids_1}
create_and_check_commons(self, config, inputs_dict)
def test_default(self):
self.run_tester(XLNetModelTest.XLNetModelTester(self))
def test_config_to_json_string(self):
config = XLNetConfig(vocab_size_or_config_json_file=96, d_model=16*4)
obj = json.loads(config.to_json_string())
self.assertEqual(obj["n_token"], 96)
self.assertEqual(obj["d_model"], 16*4)
def test_config_to_json_file(self):
config_first = XLNetConfig(vocab_size_or_config_json_file=96, d_model=16*4)
json_file_path = "/tmp/config.json"
config_first.to_json_file(json_file_path)
config_second = XLNetConfig.from_json_file(json_file_path)
os.remove(json_file_path)
self.assertEqual(config_second.to_dict(), config_first.to_dict())
def test_config(self):
config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
config_tester.run_common_tests()
@pytest.mark.slow
def test_model_from_pretrained(self):
......@@ -197,27 +198,14 @@ class XLNetModelTest(unittest.TestCase):
self.assertIsNotNone(model)
def run_tester(self, tester):
config_and_inputs = tester.prepare_config_and_inputs()
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
output_result = tester.create_transfo_xl_lm_head(*config_and_inputs)
tester.check_transfo_xl_lm_head_output(output_result)
@classmethod
def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlnet_commons(*config_and_inputs)
@classmethod
def mask_tensor(cls, shape, vocab_size, rng=None, name=None):
......
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