text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | 300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tens... | 300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states ... | 300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size,... | 300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFNextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `next_sentence_label` is provided):
N... | 301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`... | 302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
... | 303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | 303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tens... | 303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size,... | 303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSemanticSegmenterOutput(ModelOutput):
"""
Base class for outputs of semantic segmentation models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of... | 304 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each layer) of shape `(batch_si... | 304 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
loss: tf.Tensor | None = None
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 304 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSemanticSegmenterOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of semantic segmentation models that do not output attention scores.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.... | 305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each layer) of shape `(batch_si... | 305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFImageClassifierOutput(ModelOutput):
"""
Base class for outputs of image classification models. | 306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (b... | 306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. | 306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: tf.Tensor | None = None
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFMultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`tf.Tensor` of shape `(batch_size, num_choices)`... | 307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFTokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of unmasked labels, returned when `labels` is provided) :
Classification loss.
logits (`tf.Tensor` of... | 308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `start_positions` and `end_positions` are provided):
Total span extraction loss is the sum of a Cross-... | 309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for ... | 310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | 310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last... | 310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size,... | 310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
l... | 311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):... | 311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: tf.Tensor | None = None
logits: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple... | 311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`t... | 312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
loss: tf.Tensor | None = None
logits: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor, ...]] = None | 312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFMaskedImageModelingOutput(ModelOutput):
"""
Base class for outputs of masked image completion / in-painting models. | 313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Reconstruction loss.
reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Reconstructed / completed images.
hidden_states (`tuple(tf.Tensor)`, *... | 313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
""" | 313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
loss: tf.Tensor | None = None
reconstruction: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
@property
def logits(self):
warnings.warn(
"logits attribute is deprecated and will be removed in version 5 of Transformers."
... | 313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TensorFlowBenchmark(Benchmark):
args: TensorFlowBenchmarkArguments
configs: PretrainedConfig
framework: str = "TensorFlow"
@property
def framework_version(self):
return tf.__version__
def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
strategy = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow.")
_train = self._prepare_train_func(model_name, batch_size, s... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
def _train_memory(
self, model_name: str, batch_size: int, sequence_length: int
) -> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], True)
strategy = self.args.strategy
if strategy ... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
has_model_class_in_config = (
hasattr(config, "architectures")
and isinstance(config.architectures, list)
and len(config.architectures) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
model_class = "TF" + conf... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
# encoder-decoder has vocab size saved differently
vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size
input_ids = random_input_ids(batch_size, sequence_length, vocab_size)
@run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
def... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.")
if self.args.fp16:
raise NotImplementedError("Mixed precision is currently not supported.") | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
has_model_class_in_config = (
hasattr(config, "architectures")
and isinstance(config.architectures, list)
and len(config.architectures) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
model_class = "TF" + conf... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
# encoder-decoder has vocab size saved differently
vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size
input_ids = random_input_ids(batch_size, sequence_length, vocab_size)
@run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
def... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
def _measure_speed(self, func) -> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabi... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
def _measure_memory(self, func: Callable[[], None]) -> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`"
)
elif self.args.is... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
nvml.nvmlInit()
func()
handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
meminfo = nvml.nvmlDeviceGetMemoryInfo(handle)
max_bytes_in_use = meminfo.used
memory = Memory(max_bytes_in_use)
... | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
if self.args.trace_memory_line_by_line:
summary = stop_memory_tracing(trace)
if memory is None:
memory = summary.total
else:
summary = None | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"Doesn't fit on GPU. {e}")
return "N/A", None | 314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py |
class PyTorchBenchmark(Benchmark):
args: PyTorchBenchmarkArguments
configs: PretrainedConfig
framework: str = "PyTorch"
@property
def framework_version(self):
return torch.__version__
def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
_... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
def _train_memory(
self, model_name: str, batch_size: int, sequence_length: int
) -> [Memory, Optional[MemorySummary]]:
_train = self._prepare_train_func(model_name, batch_size, sequence_length)
return self._measure_memory(_train)
def _prepare_inference_func(self, model_name: str, batch... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
has_model_class_in_config = (
hasattr(config, "architectures")
and isinstance(config.architectures, list)
and len(config.architectures) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
model_class = config.arch... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
# encoder-decoder has vocab size saved differently
vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size
input_ids = torch.randint(vocab_size, (batch_size, sequence_length), dtype=torch.long, device=self.args.device)
if self.args.fp16:
logger... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
def encoder_decoder_forward():
with torch.no_grad():
outputs = inference_model(input_ids, decoder_input_ids=input_ids)
return outputs
def encoder_forward():
with torch.no_grad():
outputs = inference_model(input_ids)
return outputs
... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
has_model_class_in_config = (
hasattr(config, "architectures")
and isinstance(config.architectures, list)
and len(config.architectures) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
model_class = config.arch... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
if self.args.torchscript:
raise NotImplementedError("Training for torchscript is currently not implemented")
else:
train_model = model
model.train()
model.to(self.args.device)
# encoder-decoder has vocab size saved differently
vocab_size = config.vocab_s... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
def compute_loss_and_backprob_encoder():
loss = train_model(input_ids, labels=input_ids)[0]
loss.backward()
return loss
def compute_loss_and_backprob_encoder_decoder():
loss = train_model(input_ids, decoder_input_ids=input_ids, labels=input_ids)[0]
lo... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
runtimes = timeit.repeat(
func,
repeat=self.args.repeat,
number=10,
)
if self.args.is_tpu and self.args.torc... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"
" `--no-memory` or `args.memory=False`"
)
elif self.args.is_gpu:
... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
meminfo = nvml.nvmlDeviceGetMemoryInfo(handle)
max_bytes_in_use = meminfo.used
memory = Memory(max_bytes_in_use)
# shutdown nvml
nvml.nvmlShutdown()
... | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
if self.args.trace_memory_line_by_line:
summary = stop_memory_tracing(trace)
else:
summary = None
return memory, summary
except RuntimeError as e:
self.print_fn(f"Doesn't fit on GPU. {e}")
return "N/A", None | 315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py |
class PyTorchBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__(self, **kwargs):
"""
This __init__ is there for l... | 316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args.py |
self.torchscript = kwargs.pop("torchscript", self.torchscript)
self.torch_xla_tpu_print_metrics = kwargs.pop("torch_xla_tpu_print_metrics", self.torch_xla_tpu_print_metrics)
self.fp16_opt_level = kwargs.pop("fp16_opt_level", self.fp16_opt_level)
super().__init__(**kwargs)
torchscript: bool ... | 316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args.py |
@cached_property
def _setup_devices(self) -> Tuple["torch.device", int]:
requires_backends(self, ["torch"])
logger.info("PyTorch: setting up devices")
if not self.cuda:
device = torch.device("cpu")
n_gpu = 0
elif is_torch_xla_available():
device = ... | 316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args.py |
@property
def device(self) -> "torch.device":
requires_backends(self, ["torch"])
return self._setup_devices[0]
@property
def n_gpu(self):
requires_backends(self, ["torch"])
return self._setup_devices[1]
@property
def is_gpu(self):
return self.n_gpu > 0 | 316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args.py |
class BenchmarkArguments:
"""
BenchMarkArguments are arguments we use in our benchmark scripts **which relate to the training loop itself**.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
line.
"""
models: List[str] = list_fiel... | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
inference: bool = field(
default=True,
metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},
)
cuda: bool = field(
default=True,
metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.... | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
},
)
trace_memory_line_by_line: bool = field(default=False, metadata={"help": "Trace memory line by line"})
save_to_csv: bool = field(default=False, metadata={"help": "Save result to a CSV file"})
... | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
default=f"inference_time_{round(time())}.csv",
metadata={"help": "CSV filename used if saving time results to csv."},
)
inference_memory_csv_file: str = field(
default=f"inference_memory_{round(time())}.csv",
metadata={"help": "CSV filename used if saving memory results to csv."},
)
... | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
metadata={"help": "Log filename used if print statements are saved in log."},
)
repeat: int = field(default=3, metadata={"help": "Times an experiment will be run."})
only_pretrain_model: bool = field(
default=False,
metadata={
"help": (
"Instead of loading the mod... | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
def __post_init__(self):
warnings.warn(
f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
" are deprecated in general and it is advised to use external Benchmarking libraries "
" to benchmark Transformer models.",
FutureWarning,
... | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
@property
def do_multi_processing(self):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("Multiprocessing is currently not possible on TPU.")
return False
else:
return True | 317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py |
class Frame(NamedTuple):
"""
`Frame` is a NamedTuple used to gather the current frame state. `Frame` has the following fields:
- 'filename' (string): Name of the file currently executed
- 'module' (string): Name of the module currently executed
- 'line_number' (int): Number of the line ... | 318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class UsedMemoryState(NamedTuple):
"""
`UsedMemoryState` are named tuples with the following fields:
- 'frame': a `Frame` namedtuple (see below) storing information on the current tracing frame (current file,
location in current file)
- 'cpu_memory': CPU RSS memory state *before* exec... | 319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class Memory(NamedTuple):
"""
`Memory` NamedTuple have a single field `bytes` and you can get a human readable str of the number of mega bytes by
calling `__repr__`
- `byte` (integer): number of bytes,
"""
bytes: int
def __repr__(self) -> str:
return str(bytes_to_mega_bytes(se... | 320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class MemoryState(NamedTuple):
"""
`MemoryState` are namedtuples listing frame + CPU/GPU memory with the following fields:
- `frame` (`Frame`): the current frame (see above)
- `cpu`: CPU memory consumed at during the current frame as a `Memory` named tuple
- `gpu`: GPU memory consumed a... | 321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class MemorySummary(NamedTuple):
"""
`MemorySummary` namedtuple otherwise with the fields:
- `sequential`: a list of `MemoryState` namedtuple (see below) computed from the provided `memory_trace` by
subtracting the memory after executing each line from the memory before executing said line.
... | 322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
sequential: List[MemoryState]
cumulative: List[MemoryState]
current: List[MemoryState]
total: Memory | 322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class MemoryMeasureProcess(Process):
"""
`MemoryMeasureProcess` inherits from `Process` and overwrites its `run()` method. Used to measure the
memory usage of a process
"""
def __init__(self, process_id: int, child_connection: Connection, interval: float):
... | 323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
# send results to parent pipe
self.connection.send(self.mem_usage)
self.connection.send(self.num_measurements) | 323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class Benchmark(ABC):
"""
Benchmarks is a simple but feature-complete benchmarking script to compare memory and time performance of models in
Transformers.
"""
args: BenchmarkArguments
configs: PretrainedConfig
framework: str
def __init__(self, args: BenchmarkArguments = None, configs:... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if self.args.memory and os.getenv("TRANSFORMERS_USE_MULTIPROCESSING") == 0:
logger.warning(
"Memory consumption will not be measured accurately if `args.multi_process` is set to `False.` The"
" flag 'TRANSFORMERS_USE_MULTIPROCESSING' should only be disabled for debugging / te... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
@abstractmethod
def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
pass
@abstractmethod
def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
pass
@abstractmethod
def _inference_memory(
self, model... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
def inference_memory(self, *args, **kwargs) -> [Memory, Optional[MemorySummary]]:
return separate_process_wrapper_fn(self._inference_memory, self.args.do_multi_processing)(*args, **kwargs)
def train_memory(self, *args, **kwargs) -> [Memory, Optional[MemorySummary]]:
return separate_process_wrapper_... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
model_dict = {
"bs": self.args.batch_sizes,
"ss": self.args.sequence_lengths,
"result": {i: {} for i in self.args.batch_sizes},
}
inference_result_time[model_name] = copy.deepcopy(model_dict)
inference_result_memory[model_name] = copy.d... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
for batch_size in self.args.batch_sizes:
for sequence_length in self.args.sequence_lengths:
if self.args.inference:
if self.args.memory:
memory, inference_summary = self.inference_memory(model_name, batch_size, sequence_length)
... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if self.args.training:
if self.args.memory:
memory, train_summary = self.train_memory(model_name, batch_size, sequence_length)
train_result_memory[model_name]["result"][batch_size][sequence_length] = memory
if self.a... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if self.args.inference:
if self.args.speed:
self.print_fn("\n" + 20 * "=" + ("INFERENCE - SPEED - RESULT").center(40) + 20 * "=")
self.print_results(inference_result_time, type_label="Time in s")
self.save_to_csv(inference_result_time, self.args.inference_time... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if self.args.trace_memory_line_by_line:
self.print_fn("\n" + 20 * "=" + ("INFERENCE - MEMOMRY - LINE BY LINE - SUMMARY").center(40) + 20 * "=")
self.print_memory_trace_statistics(inference_summary)
if self.args.training:
if self.args.speed:
self.print... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if self.args.memory:
self.print_fn("\n" + 20 * "=" + ("TRAIN - MEMORY - RESULTS").center(40) + 20 * "=")
self.print_results(train_result_memory, type_label="Memory in MB")
self.save_to_csv(train_result_memory, self.args.train_memory_csv_file)
if self.args.tra... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if self.args.save_to_csv:
with open(self.args.env_info_csv_file, mode="w", newline="") as csv_file:
writer = csv.writer(csv_file)
for key, value in self.environment_info.items():
writer.writerow([key, value])
return BenchmarkOutput(
in... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
@property
def environment_info(self):
if self._environment_info is None:
info = {}
info["transformers_version"] = version
info["framework"] = self.framework
if self.framework == "PyTorch":
info["use_torchscript"] = self.args.torchscript
... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
info["only_pretrain_model"] = self.args.only_pretrain_model | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
if is_psutil_available():
info["cpu_ram_mb"] = bytes_to_mega_bytes(psutil.virtual_memory().total)
else:
logger.warning(
"Psutil not installed, we won't log available CPU memory. "
"Install psutil (pip install psutil) to log available CP... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
info["use_gpu"] = self.args.is_gpu
if self.args.is_gpu:
info["num_gpus"] = 1 # TODO(PVP) Currently only single GPU is supported
if is_py3nvml_available():
nvml.nvmlInit()
handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
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