text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class CodeLlamaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class CpmTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class DebertaV2Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class ErnieMTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class XLMProphetNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class FNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,559 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class GemmaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class GPTSw3Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class LayoutXLMTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class LlamaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class M2M100Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class MarianTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class MBartTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class MBart50Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class MLukeTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class MT5Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class NllbTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class PegasusTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class PLBartTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class ReformerTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class RemBertTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class SeamlessM4TTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class SiglipTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class Speech2TextTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class SpeechT5Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class T5Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class UdopTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class XGLMTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class XLMRobertaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class XLNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"]) | 2,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_sentencepiece_objects.py |
class TFBertTokenizer(metaclass=DummyObject):
_backends = ["tensorflow_text"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tensorflow_text"]) | 2,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tensorflow_text_objects.py |
class Pop2PianoFeatureExtractor(metaclass=DummyObject):
_backends = ["music"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["music"]) | 2,585 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_music_objects.py |
class Pop2PianoTokenizer(metaclass=DummyObject):
_backends = ["music"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["music"]) | 2,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_music_objects.py |
class HFProxy(Proxy):
"""
Proxy that uses metadata to handle data-dependent control-flow.
"""
def install_metadata(self, metadata):
self._metadata = metadata
@property
def shape(self):
return self.tracer.create_proxy("call_method", "size", (self,), {})
@property
def de... | 2,587 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def __getattr__(self, k):
if k == "_metadata":
return self.__getattribute__(k)
# note: not added to the graph yet, if this is a method call
# we peephole optimize to the method invocation
return HFAttribute(self, k)
def __setitem__(self, indices, values):
return ... | 2,587 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
class HFAttribute(HFProxy):
def __init__(self, root, attr: str):
self.root = root
self.attr = attr
self.tracer = root.tracer
self._node = None
if hasattr(self.root, "_metadata"):
self.install_metadata(getattr(self.root._metadata, attr))
@property
def nod... | 2,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
class MetaDeviceAttribute(HFAttribute):
pass | 2,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
class HFCacheProxy(HFProxy):
"""
Proxy that represents an instance of `transformers.cache_utils.Cache`.
"""
def install_orig_cache_cls(self, orig_cache_cls: Type[Cache]):
self._orig_cache_cls = orig_cache_cls
@property
def __class__(self):
if not hasattr(self, "_orig_cache_cls"... | 2,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
class HFProxyableClassMeta(type):
"""
Metaclass that creates a class with its main methods wrapped to be proxyable.
""" | 2,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def __new__(
cls,
name: str,
bases: Tuple[Type, ...],
attrs: Dict[str, Any],
proxy_factory_fn: Optional[Callable[[Node], Proxy]] = None,
):
cls = super().__new__(cls, name, bases, attrs)
for attr_name in dir(cls):
attr = getattr(cls, attr_name, Non... | 2,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
class HFTracer(Tracer):
"""
Tracer that is able to symbolically trace models from the library. To do that, it uses the HFProxy instead of the
regular PyTorch torch.fx.Proxy.
"""
# Feature flag for proxying accesses to buffer values
proxy_buffer_attributes: bool = True
allow_insert_stateless... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def __init__(self, autowrap_modules=(math,), autowrap_functions=()):
super().__init__(autowrap_modules=autowrap_modules, autowrap_functions=autowrap_functions)
if not is_torch_fx_available():
raise ImportError(
f"Found an incompatible version of torch. Found version {get_tor... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
# when tracing a model with KV cache, we simply need to unsure that the KV cache length is larger than one to
# rightfully pass certain controlflows (Example: https://github.com/huggingface/transformers/blob/5c8d941d66734811d2ef6f57f15b44f7fb7a98c4/src/transformers/modeling_attn_mask_utils.py#L162).
# A... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if input_name in ["labels", "start_positions", "end_positions"]:
batch_size = shape[0]
if model_class_name in [
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
*get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES),
*get_values(MODEL_F... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
inputs_dict["end_positions"] = torch.zeros(batch_size, dtype=torch.long, device=device)
elif model_class_name in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES):
if not hasattr(model.config, "problem_type") or model.config.problem_type is None:
raise ValueErro... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if model.config.problem_type == "regression":
labels_shape = (batch_size, model.config.num_labels)
labels_dtype = torch.float32
elif model.config.problem_type == "single_label_classification":
labels_shape = (batch_size,)
la... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
elif model_class_name in [
*get_values(MODEL_FOR_PRETRAINING_MAPPING_NAMES),
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES),
*get_val... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
f"Generating the dummy input named {input_name} for {model_class_name} is not supported yet."
)
elif "pixel_values" in input_name:
batch_size = shape[0]
image_size = getattr(model.config, "image_size", None)
if image_size is None:
if hasattr(mo... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
# If no num_channels is in the config, use some arbitrary value.
num_channels = getattr(model.config, "num_channels", 3)
if not isinstance(image_size, collections.abc.Iterable):
image_size = (image_size, image_size)
height, width = image_size
inputs_dict[i... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if (
getattr(model.config, "embedding_size", None) is not None
and model.config.model_type != "megatron-bert"
):
embedding_size = model.config.embedding_size
else:
embedding_size = model.config.hidden_size
if len(shape)... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
inputs_dict[input_name] = torch.zeros(embedding_shape, dtype=torch.float, device=device)
elif "visual_feats" in input_name:
inputs_dict[input_name] = torch.zeros(
shape
+ [
model.config.visual_feat_dim,
],
dtype=torc... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
inputs_dict[input_name] = torch.zeros(batch_size, seq_length, dtype=torch.float, device=device)
elif "mask" in input_name:
if "past_key_values" in input_names:
mask_shape = [shape[0], shape[1] + kv_cache_length]
else:
mask_shape = shape | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
inputs_dict[input_name] = torch.zeros(mask_shape, dtype=torch.long, device=device)
elif "ids" in input_name:
inputs_dict[input_name] = torch.zeros(shape, dtype=torch.long, device=device)
elif "past_key_values" in input_name:
if model.config.model_type not in _FX_SUPPORTED_MODELS_... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
cache_shape = (shape[0], num_heads, kv_cache_length, head_dim)
pkv = tuple(
(
torch.rand(cache_shape, dtype=torch.float, device=device),
torch.rand(cache_shape, dtype=torch.float, device=device),
)
for i in range(model.c... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if target in self.orig_fns:
# NOTE: tensor constructors in PyTorch define the `device` argument as
# *kwargs-only*. That is why this works. If you add methods to
# _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
# this will break and you will likely see... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if kind == "call_function":
meta_target = _MANUAL_META_OVERRIDES.get(target, target)
meta_out = meta_target(*args_metas, **kwargs_metas)
if isinstance(meta_out, torch.Tensor):
meta_out = meta_out.to(device="meta")
elif kind == "call_method"... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
meta_out = self.orig_forward(*args_metas, **kwargs_metas)
elif kind == "get_attr":
attr_itr = self.root
atoms = target.split(".")
for atom in atoms:
attr_itr = getattr(attr_itr, atom)
if isinstance(attr_itr, torch.Tensor):
... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if should_install_metadata:
if not isinstance(rv, Proxy):
raise ValueError("Don't support composite output yet")
rv.install_metadata(meta_out)
except Exception as e:
if _IS_IN_DEBUG_MODE:
warnings.warn(f"Could not compute metadata ... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def maybe_get_proxy_for_attr(attr_val, collection_to_search, parameter_proxy_cache):
for n, p in collection_to_search:
if attr_val is p:
if n not in parameter_proxy_cache:
kwargs = {}
if "proxy_factory_fn... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
if isinstance(attr_val, torch.nn.Parameter):
maybe_parameter_proxy = maybe_get_proxy_for_attr(
attr_val, self.root.named_parameters(), parameter_proxy_cache
)
if maybe_parameter_proxy is not None:
return maybe_parameter_proxy
... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def call_module(self, m, forward, args, kwargs):
if getattr(self, "_disable_call_module", False):
return forward(*args, **kwargs)
self.orig_forward = forward
return super().call_module(m, forward, args, kwargs)
def proxy(self, node):
return HFProxy(node, self)
@cont... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
# Patching classes
patched = []
module_of_model = inspect.getmodule(root)
for name, mod in sys.modules.items():
if module_of_model is not None and mod is not module_of_model:
continue
if not name.startswith("transformers"):
continue
... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def trace(
self,
root: Union[torch.nn.Module, Callable[..., Any]],
concrete_args: Optional[Dict[str, Any]] = None,
dummy_inputs: Optional[Dict[str, Any]] = None,
complete_concrete_args_with_inputs_not_in_dummy_inputs: bool = True,
) -> Graph:
"""
Traces `root`... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
Args:
root (`torch.nn.Module` or `Callable`):
Either a `torch.nn.Module`` or a function to be traced through. If root is not a
[`~transformers.PreTrainedModel`], then `dummy_inputs` must be passed, otherwise tracing will fail.
concrete_args (`Dict[str, Any], *opt... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
`dummy_inputs` and not in `concrete_args` will be added to `concrete_args`, otherwise does nothing. | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
Returns:
`torch.fx.Graph`:
A FX `torch.fx.Graph` representing the semantics of the passed-in `root`.
"""
sig = inspect.signature(root.forward if isinstance(root, torch.nn.Module) else root)
if concrete_args is None:
concrete_args = {}
if dummy_i... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
input_names = sig.parameters.keys() - concrete_args.keys()
# Creating a random input shape to generate dummy inputs.
batch_size = _generate_random_int()
sequence_length = _generate_random_int()
shape = [batch_size, sequence_length]
if root.__class__.__name__ in get_values(MODEL... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
inputs = dict(dummy_inputs) if dummy_inputs is not None else {}
for input_name in input_names:
if input_name in inputs:
continue
# We enforce that root must either be a PreTrainedModel or deserialized from a serialized traced model to
# be able to use HFTracer... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
concrete_metas = pytree.tree_map(to_meta, inputs)
for param in sig.parameters.values():
if param.kind == inspect.Parameter.VAR_KEYWORD and param.name not in input_names:
concrete_metas[f"**{param.name}"] = {}
self.meta_args = concrete_metas
global _CURRENT_TRACER
... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
# This is necessary because concrete args are added as input to the traced module since
# https://github.com/pytorch/pytorch/pull/55888.
for node in self.graph.nodes:
if node.op == "placeholder":
# Removing default values for inputs as the forward pass will fail with them.
... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
to_visit += list(n.users.keys()) | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
for user in reversed(to_delete.keys()):
self.graph.erase_node(user)
# TODO: solves GraphModule creation.
# Without this, return type annotation "Tuple" is causing code execution failure.
if node.op == "output":
node.type = None
return... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def _insert_module_as_submodule(self, mod: nn.Module) -> str:
"""
Helper method which tries to insert a module that was not declared as submodule.
"""
# If one of the module attributes is a Proxy, it means that its instantiation is input-dependent.
# It is not possible to insert ... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
def path_of_module(self, mod: nn.Module) -> str:
"""
Helper method to find the qualified name of `mod` in the Module hierarchy of `root`. For example, if `root` has
a submodule named `foo`, which has a submodule named `bar`, passing `bar` into this function will return the
string "foo.ba... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
@compatibility(is_backward_compatible=True)
def keys(self, obj: "Proxy") -> Any:
"""Called when a proxy object is has the keys() method called.
This is what happens when ** is called on a proxy. This should return an iterator if ** is supposed to work in
your custom tracer.
"""
... | 2,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/fx.py |
class NotebookProgressBar:
"""
A progress par for display in a notebook.
Class attributes (overridden by derived classes)
- **warmup** (`int`) -- The number of iterations to do at the beginning while ignoring `update_every`.
- **update_every** (`float`) -- Since calling the time takes some... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
Args:
total (`int`):
The total number of iterations to reach.
prefix (`str`, *optional*):
A prefix to add before the progress bar.
leave (`bool`, *optional*, defaults to `True`):
Whether or not to leave the progress bar once it's completed. You can always call... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
warmup = 5
update_every = 0.2
def __init__(
self,
total: int,
prefix: Optional[str] = None,
leave: bool = True,
parent: Optional["NotebookTrainingTracker"] = None,
width: int = 300,
):
self.total = total
self.prefix = "" if prefix is None else... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
Args:
value (`int`):
The value to use. Must be between 0 and `total`.
force_update (`bool`, *optional*, defaults to `False`):
Whether or not to force and update of the internal state and display (by default, the bar will wait for
`value` to reach t... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total):
if self.first_calls > 0:
self.first_calls -= 1
current_time = time.time()
self.elapse... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
self.update_bar(value)
self.last_value = value
self.last_time = current_time
if (self.average_time_per_item is None) or (self.average_time_per_item == 0):
self.wait_for = 1
else:
self.wait_for = max(int(self.update_every / self.average_time... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def update_bar(self, value, comment=None):
spaced_value = " " * (len(str(self.total)) - len(str(value))) + str(value)
if self.elapsed_time is None:
self.label = f"[{spaced_value}/{self.total} : < :"
elif self.predicted_remaining is None:
self.label = f"[{spaced_value}/{se... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def display(self):
self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width)
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is No... | 2,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
class NotebookTrainingTracker(NotebookProgressBar):
"""
An object tracking the updates of an ongoing training with progress bars and a nice table reporting metrics.
Args:
num_steps (`int`): The number of steps during training. column_names (`List[str]`, *optional*):
The list of column n... | 2,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def display(self):
self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width)
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table)
if self.child_bar is not None:
self.html_code += self.child_bar.html_... | 2,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
Args:
values (`Dict[str, float]`): The values to display.
"""
if self.inner_table is None:
self.inner_table = [list(values.keys()), list(values.values())]
else:
columns = self.inner_table[0]
for key in values.keys():
if key not in c... | 2,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
new_values[c] = last_values[columns.index(c)]
self.inner_table[-1] = [new_values[c] for c in columns]
else:
self.inner_table.append([values[c] for c in columns]) | 2,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def add_child(self, total, prefix=None, width=300):
"""
Add a child progress bar displayed under the table of metrics. The child progress bar is returned (so it can be
easily updated).
Args:
total (`int`): The number of iterations for the child progress bar.
pref... | 2,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
class NotebookProgressCallback(TrainerCallback):
"""
A [`TrainerCallback`] that displays the progress of training or evaluation, optimized for Jupyter Notebooks or
Google colab.
"""
def __init__(self):
self.training_tracker = None
self.prediction_bar = None
self._force_next_... | 2,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def on_step_end(self, args, state, control, **kwargs):
epoch = int(state.epoch) if int(state.epoch) == state.epoch else f"{state.epoch:.2f}"
self.training_tracker.update(
state.global_step + 1,
comment=f"Epoch {epoch}/{state.num_train_epochs}",
force_update=self._forc... | 2,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def on_predict(self, args, state, control, **kwargs):
if self.prediction_bar is not None:
self.prediction_bar.close()
self.prediction_bar = None
def on_log(self, args, state, control, logs=None, **kwargs):
# Only for when there is no evaluation
if args.eval_strategy == I... | 2,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
if self.first_column == "Epoch":
values["Epoch"] = int(state.epoch)
else:
values["Step"] = state.global_step
metric_key_prefix = "eval"
for k in metrics:
if k.endswith("_loss"):
metric_key_prefix = re.sub(r"\_loss$",... | 2,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
values[name] = v
self.training_tracker.write_line(values)
self.training_tracker.remove_child()
self.prediction_bar = None
# Evaluation takes a long time so we should force the next update.
self._force_next_update = True | 2,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
def on_train_end(self, args, state, control, **kwargs):
self.training_tracker.update(
state.global_step,
comment=f"Epoch {int(state.epoch)}/{state.num_train_epochs}",
force_update=True,
)
self.training_tracker = None | 2,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/notebook.py |
class DummyObject(type):
"""
Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
`requires_backend` each time a user tries to access any method of that class.
"""
def __getattribute__(cls, key):
if key.startswith("_") and key != "_from_conf... | 2,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
class _LazyModule(ModuleType):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
# Very heavily inspired by optuna.integration._IntegrationModule
# https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
def _... | 2,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
for backends, module in import_structure.items():
missing_backends = []
for backend in backends:
if backend not in BACKENDS_MAPPING:
raise ValueError(
f"Error: the following backend: '{backend}' was specified around ... | 2,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
for value in values:
self._class_to_module[value] = key
if len(missing_backends):
self._object_missing_backend[value] = missing_backends
_import_structure.setdefault(key, []).extend(values)
# Needed for auto... | 2,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
# This can be removed once every exportable object has a `export()` export.
else:
self._modules = set(import_structure.keys())
self._class_to_module = {}
for key, values in import_structure.items():
for value in values:
self._class_to_modul... | 2,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
# Needed for autocompletion in an IDE
def __dir__(self):
result = super().__dir__()
# The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether
# they have been accessed or not. So we only add the elements of self.__all__ that are not already... | 2,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/import_utils.py |
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