text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if not hasattr(self, "added_tokens"):
self.added_tokens = []
if not hasattr(self, "unk_token_id"):
self.unk_token_id = None
# Llama2 uses the field `unknown_token_id`
if hasattr(self, "unknown_token_id") and self.unk_token_id is None:
self.unk_token_id = sel... | 10,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class GGUFLlamaConverter(LlamaConverter):
def __init__(self, tokenizer_dict):
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.original_tokenizer = self.proto
self.additional_kwargs = {}
self.is_llama_3_tokenizer = getattr(self.proto, "tokenizer_type", "llama") != "llama"
... | 10,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
tokenizer = Tokenizer(
BPE(
bpe_vocab,
merges,
unk_token=unk_token,
fuse_unk=True,
byte_fallback=True,
)
)
special_tokens = []
if not hasattr(self.proto, "token_type"):
if unk_to... | 10,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
if len(special_tokens) != 0:
tokenizer.add_special_tokens(special_tokens)
if len(self.proto.added_tokens) != 0:
tokenizer.add_tokens(
[AddedToken(added_token, normalized=False, special=False) for added_token in self.proto.added_tokens]
)
self.additio... | 10,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
if self.is_llama_3_tokenizer:
sequence += [decoders.ByteLevel(add_prefix_space=False, trim_offsets=False, use_regex=True)]
if add_prefix_space:
sequence += [decoders.Strip(content=" ", left=1)]
return decoders.Sequence(sequence)
def converted(self):
# Copied partly ... | 10,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
tokenizer.decoder = self.decoder(replacement, add_prefix_space)
post_processor = self.post_processor()
if post_processor:
tokenizer.post_processor = post_processor
# HACK: patch the llama-3 tokenizer to use the correspinding pre-tokenizer
# and normalizer
if self.is_... | 10,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class GGUFQwen2Converter(Qwen2Converter):
def __init__(self, tokenizer_dict):
self.original_tokenizer = GGUFTokenizerSkeleton(tokenizer_dict)
self.additional_kwargs = {}
def converted(self) -> Tokenizer:
vocab = {word: i for i, word in enumerate(self.original_tokenizer.tokens)}
... | 10,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class GGUFPhi3Converter(LlamaConverter):
def __init__(self, tokenizer_dict):
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.original_tokenizer = self.proto
self.additional_kwargs = {}
def vocab(self, proto):
return list(zip(proto.tokens, proto.scores))
def merges(s... | 10,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
tokenizer = Tokenizer(BPE(bpe_vocab, merges))
# add the special tokens from phi3 tokenizer config
tokenizer.add_special_tokens(
[
AddedToken("</s>", rstrip=True, lstrip=False, normalized=False, special=True),
AddedToken("<|endoftext|>", normalized=False, speci... | 10,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
AddedToken("<|placeholder5|>", rstrip=True, normalized=False, special=True),
AddedToken("<|placeholder6|>", rstrip=True, normalized=False, special=True),
AddedToken("<|user|>", rstrip=True, normalized=False, special=True),
]
) | 10,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
self.additional_kwargs["unk_token"] = (
proto.tokens[proto.unk_token_id] if proto.unk_token_id is not None else None
)
self.additional_kwargs["eos_token"] = (
proto.tokens[proto.eos_token_id] if proto.eos_token_id is not None else None
)
self.additional_kwargs["bo... | 10,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
def converted(self) -> Tokenizer:
tokenizer = self.tokenizer(self.proto)
replacement = "▁"
add_prefix_space = True
if hasattr(self.original_tokenizer, "add_prefix_space"):
add_prefix_space = self.original_tokenizer.add_prefix_space
tokenizer.decoder = self.decoder(r... | 10,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class GGUFGPTConverter(GPT2Converter):
def __init__(self, tokenizer_dict):
self.original_tokenizer = GGUFTokenizerSkeleton(tokenizer_dict)
self.additional_kwargs = {}
def converted(self) -> Tokenizer:
vocab = {word: i for i, word in enumerate(self.original_tokenizer.tokens)}
mer... | 10,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class GGUFT5Converter(T5Converter):
def __init__(self, tokenizer_dict):
# set dummy data to avoid unnecessary merges calculation
tokenizer_dict["merges"] = ["dummy text"]
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.token2id = {k: v for v, k in enumerate(self.proto.tokens... | 10,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
def post_processor(self):
return processors.TemplateProcessing(
single=["$A", "</s>"],
pair=["$A", "</s>", "$B", "</s>"],
special_tokens=[
("</s>", self.token2id["</s>"]),
],
)
def converted(self) -> Tokenizer:
vocab_scores = s... | 10,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space)
if pre_tokenizer is not None:
tokenizer.pre_tokenizer = pre_tokenizer
tokenizer.decoder = self.decoder(replacement, add_prefix_space)
post_processor = self.post_processor()
if post_processor:
token... | 10,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class GGUFGemmaConverter(GemmaConverter):
def __init__(self, tokenizer_dict):
# set dummy data to avoid unnecessary merges calculation
tokenizer_dict["merges"] = ["dummy text"]
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.original_tokenizer = self.proto
self.addit... | 10,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
def decoder(self, replacement, add_prefix_space):
sequence = [
decoders.Replace("▁", " "),
decoders.ByteFallback(),
decoders.Fuse(),
]
if add_prefix_space:
sequence += [decoders.Strip(content=" ", left=1)]
return decoders.Sequence(sequence... | 10,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
tokenizer.decoder = self.decoder(replacement, add_prefix_space)
pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space)
if pre_tokenizer is not None:
tokenizer.pre_tokenizer = pre_tokenizer
return tokenizer | 10,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
class TorchExportableModuleWithStaticCache(torch.nn.Module):
"""
A wrapper module designed to make a `PreTrainedModel` exportable with `torch.export`,
specifically for use with static caching. This module ensures that the exported model
is compatible with further lowering and execution in `ExecuTorch`.
... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
Raises:
AssertionError: If the pretrained model does not have caching enabled or if it does
not use a 'static' caching implementation in `model.generation_config`.
"""
super().__init__()
# Sanity checks
if model.generation_config is None:
raise Assert... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
if model.generation_config.cache_implementation != "static":
raise AssertionError(
"The model must use a 'static' caching implementation to be exported with static caching. "
"Please set `generation_config.cache_implementation='static'`."
) | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
self.model = model
self.static_cache = StaticCache(
config=self.model.config,
batch_size=self.model.generation_config.cache_config.batch_size,
max_cache_len=self.model.generation_config.cache_config.max_cache_len,
dtype=self.model.dtype,
)
self.is_... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
Args:
input_ids (`torch.Tensor`): Tensor representing current input token id to the module.
cache_position (`torch.Tensor`): Tensor representing current input position in the cache.
Returns:
torch.Tensor: Logits output from the model.
This forward adapter serves two... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
2. **Ensuring Compatibility with `ExecuTorch` runtime**:
The adapter matches the model's forward signature with that in `executorch/extension/llm/runner`,
ensuring that the exported model can be executed in `ExecuTorch` out-of-the-box.
"""
_, seqlen = input_ids.shape
attn... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
This util function is designed to test exported models by simulating the generation process.
It processes the input prompt tokens sequentially (no parallel prefill).
This generate function is not intended to replace the original `generate` method, and the support
for leveraging the original `gen... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
Returns:
torch.Tensor: A tensor containing the generated sequence of token IDs, including the original prompt tokens.
"""
prompt_token_len = prompt_token_ids.shape[-1]
max_generation_length = prompt_token_len + max_new_tokens
for buffer_name, buffer in exported_program.named_... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
current_token = torch.argmax(result[:, -1, :], dim=-1).item()
response_tokens.append(current_token)
while len(response_tokens) < max_generation_length:
result = exported_program.module().forward(
input_ids=torch.tensor([[current_token]], dtype=torch.long),
ca... | 10,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py |
class TensorBoardCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard).
Args:
tb_writer (`SummaryWriter`, *optional*):
The writer to use. Will instantiate one if not set.
"""
def __init__(self, tb_write... | 10,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self._SummaryWriter = SummaryWriter
except ImportError:
self._SummaryWriter = None
else:
self._SummaryWriter = None
self.tb_writer = tb_writer
def _init_summary_writer(self, args, log_dir=None):
log_dir = log_dir or args.logging_dir
if... | 10,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self.tb_writer is not None:
self.tb_writer.add_text("args", args.to_json_string())
if "model" in kwargs:
model = kwargs["model"]
if hasattr(model, "config") and model.config is not None:
model_config_json = model.config.to_json_string()
... | 10,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self.tb_writer is not None:
logs = rewrite_logs(logs)
for k, v in logs.items():
if isinstance(v, (int, float)):
self.tb_writer.add_scalar(k, v, state.global_step)
elif isinstance(v, str):
self.tb_writer.add_text(k, v, sta... | 10,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class WandbLogModel(str, Enum):
"""Enum of possible log model values in W&B."""
CHECKPOINT = "checkpoint"
END = "end"
FALSE = "false"
@property
def is_enabled(self) -> bool:
"""Check if the value corresponds to a state where the `WANDB_LOG_MODEL` setting is enabled."""
return s... | 10,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
@classmethod
def _missing_(cls, value: Any) -> "WandbLogModel":
if not isinstance(value, str):
raise ValueError(f"Expecting to have a string `WANDB_LOG_MODEL` setting, but got {type(value)}")
if value.upper() in ENV_VARS_TRUE_VALUES:
raise DeprecationWarning(
... | 10,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class WandbCallback(TrainerCallback):
"""
A [`TrainerCallback`] that logs metrics, media, model checkpoints to [Weight and Biases](https://www.wandb.com/).
"""
def __init__(self):
has_wandb = is_wandb_available()
if not has_wandb:
raise RuntimeError("WandbCallback requires w... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
Environment:
- **WANDB_LOG_MODEL** (`str`, *optional*, defaults to `"false"`):
Whether to log model and checkpoints during training. Can be `"end"`, `"checkpoint"` or `"false"`. If set
to `"end"`, the model will be uploaded at the end of training. If set to `"checkpoint"`, the checkpoint... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
</Deprecated>
- **WANDB_WATCH** (`str`, *optional* defaults to `"false"`):
Can be `"gradients"`, `"all"`, `"parameters"`, or `"false"`. Set to `"all"` to log gradients and
parameters.
- **WANDB_PROJECT** (`str`, *optional*, defaults to `"huggingface"`):
Set this to a ... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if state.is_world_process_zero:
logger.info(
'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"'
)
combined_dict = {**args.to_dict()} | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if hasattr(model, "config") and model.config is not None:
model_config = model.config if isinstance(model.config, dict) else model.config.to_dict()
combined_dict = {**model_config, **combined_dict}
if hasattr(model, "peft_config") and model.peft_config is not None:
... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
"not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.",
repeat=False,
) | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self._wandb.run is None:
self._wandb.init(
project=os.getenv("WANDB_PROJECT", "huggingface"),
**init_args,
)
# add config parameters (run may have been created manually)
self._wandb.config.update(combined_dict, allow_val_... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
# keep track of model topology and gradients, unsupported on TPU
_watch_model = os.getenv("WANDB_WATCH", "false")
if not is_torch_xla_available() and _watch_model in ("all", "parameters", "gradients"):
self._wandb.watch(model, log=_watch_model, log_freq=max(100, state.logging_ste... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
# log the initial model architecture to an artifact
if self._log_model.is_enabled:
with tempfile.TemporaryDirectory() as temp_dir:
model_name = (
f"model-{self._wandb.run.id}"
if (args.run_name is None or args.run_name == ar... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
save_model_architecture_to_file(model, temp_dir) | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
for f in Path(temp_dir).glob("*"):
if f.is_file():
with model_artifact.new_file(f.name, mode="wb") as fa:
fa.write(f.read_bytes())
self._wandb.run.log_artifact(model_artifact, aliases=["base_model"])
... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_train_begin(self, args, state, control, model=None, **kwargs):
if self._wandb is None:
return
hp_search = state.is_hyper_param_search
if hp_search:
self._wandb.finish()
self._initialized = False
args.run_name = None
if not self._init... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
fake_trainer = Trainer(args=args, model=model, processing_class=tokenizer, eval_dataset=["fake"])
with tempfile.TemporaryDirectory() as temp_dir:
fake_trainer.save_model(temp_dir)
metadata = (
{
k: v
for k, v... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
f"model-{self._wandb.run.id}"
if (args.run_name is None or args.run_name == args.output_dir)
else f"model-{self._wandb.run.name}"
)
# add the model architecture to a separate text file
save_model_architecture_to_file(model, temp_dir... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
artifact = self._wandb.Artifact(name=model_name, type="model", metadata=metadata)
for f in Path(temp_dir).glob("*"):
if f.is_file():
with artifact.new_file(f.name, mode="wb") as fa:
fa.write(f.read_bytes())
self._wan... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self._wandb is None:
return
if not self._initialized:
self.setup(args, state, model)
if state.is_world_process_zero:
for k, v in logs.items():
if k in single_value_scalars:
self._wandb.run.summary[k] = v
non_scalar_lo... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_save(self, args, state, control, **kwargs):
if self._log_model == WandbLogModel.CHECKPOINT and self._initialized and state.is_world_process_zero:
checkpoint_metadata = {
k: v
for k, v in dict(self._wandb.summary).items()
if isinstance(v, numbers... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
ckpt_dir = f"checkpoint-{state.global_step}"
artifact_path = os.path.join(args.output_dir, ckpt_dir)
logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. ...")
checkpoint_name = (
f"model-{self._wandb.run.id}"
if (args.run_name is None or args.run... | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_predict(self, args, state, control, metrics, **kwargs):
if self._wandb is None:
return
if not self._initialized:
self.setup(args, state, **kwargs)
if state.is_world_process_zero:
metrics = rewrite_logs(metrics)
self._wandb.log(metrics) | 10,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class CometCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.com/site/).
"""
def __init__(self):
if _is_comet_installed is False or _is_comet_recent_enough is False:
raise RuntimeError(
f"CometCallback requires c... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
Environment:
- **COMET_MODE** (`str`, *optional*, default to `get_or_create`):
Control whether to create and log to a new Comet experiment or append to an existing experiment.
It accepts the following values:
* `get_or_create`: Decides automatically depending if
... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
Use the `--report_to` flag to control the integrations used
for logging result instead.
- **COMET_PROJECT_NAME** (`str`, *optional*):
Comet project name for experiments.
- **COMET_LOG_ASSETS** (`str`, *optional*, defaults to `TRUE`):
Whether or not to log traini... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
For a number of configurable items in the environment, see
[here](https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#explore-comet-configuration-options).
"""
self._initialized = True
log_assets = os.getenv("COMET_LOG_ASSETS", "FALSE").upper()
if log_assets... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if comet_old_mode == "online":
online = True
elif comet_old_mode == "offline":
online = False
elif comet_old_mode in ("get", "get_or_create", "create"):
mode = comet_old_mode
elif comet_old_mode:
... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
# Do not use the default run_name as the experiment name
if args.run_name is not None and args.run_name != args.output_dir:
experiment_config = comet_ml.ExperimentConfig(name=args.run_name)
else:
experiment_config = comet_ml.ExperimentConfig()
self._e... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self._experiment.__internal_api__log_parameters__(
params, framework="transformers", source="manual", flatten_nested=True
)
if state.is_hyper_param_search:
optimization_id = getattr(state, "trial_name", None)
optimization_params = getattr(state, "... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_log(self, args, state, control, model=None, logs=None, **kwargs):
if not self._initialized:
self.setup(args, state, model)
if state.is_world_process_zero:
if self._experiment is not None:
rewritten_logs = rewrite_logs(logs)
self._experiment.... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
# We create one experiment per trial in HPO mode
if state.is_hyper_param_search:
self._experiment.clean()
self._initialized = False
def on_predict(self, args, state, control, metrics, **kwargs):
if not self._initialized:
self.setup(args, state, model=... | 10,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class AzureMLCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/).
"""
def __init__(self, azureml_run=None):
if not is_azureml_available():
raise RuntimeError("AzureMLCallback requires azureml to be installed. R... | 10,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class MLflowCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting
environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`.
"""
def __init__(self):
if not is_mlflow_available():
raise RuntimeErro... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
Environment:
- **HF_MLFLOW_LOG_ARTIFACTS** (`str`, *optional*):
Whether to use MLflow `.log_artifact()` facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy each saved checkpoint on each save in
[`T... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
activated. If an experiment with this name does not exist, a new experiment with this name is created.
- **MLFLOW_TAGS** (`str`, *optional*):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
`os.environ['MLFLOW_TAGS']='{"release.candidate": ... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
Whether to flatten the parameters dictionary before logging.
- **MLFLOW_MAX_LOG_PARAMS** (`int`, *optional*):
Set the maximum number of parameters to log in the run.
"""
self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
self._... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
# "synchronous" flag is only available with mlflow version >= 2.8.0
# https://github.com/mlflow/mlflow/pull/9705
# https://github.com/mlflow/mlflow/releases/tag/v2.8.0
self._async_log = packaging.version.parse(self._ml_flow.__version__) >= packaging.version.parse("2.8.0") | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
logger.debug(
f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run},"
f" tracking_uri={self._tracking_uri}"
)
if state.is_world_process_zero:
if not self._ml_flow.is_tracking_uri_set():
if self._tracking_... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self._ml_flow.active_run() is None or self._nested_run or self._run_id:
if self._experiment_name:
# Use of set_experiment() ensure that Experiment is created if not exists
self._ml_flow.set_experiment(self._experiment_name)
self._ml_flow.start_r... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
# internally, all values are converted to str in MLflow
if len(str(value)) > self._MAX_PARAM_VAL_LENGTH:
logger.warning(
f'Trainer is attempting to log a value of "{value}" for key "{name}" as a parameter. MLflow\'s'
" log_param() only ... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
f"Reducing the number of parameters to log from {len(combined_dict_items)} to {max_log_params}."
)
combined_dict_items = combined_dict_items[:max_log_params]
for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH):
if self._async_l... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_train_begin(self, args, state, control, model=None, **kwargs):
if not self._initialized:
self.setup(args, state, model)
def on_log(self, args, state, control, logs, model=None, **kwargs):
if not self._initialized:
self.setup(args, state, model)
if state.is_wor... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self._async_log:
self._ml_flow.log_metrics(metrics=metrics, step=state.global_step, synchronous=False)
else:
self._ml_flow.log_metrics(metrics=metrics, step=state.global_step)
def on_train_end(self, args, state, control, **kwargs):
if self._initialized and sta... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_save(self, args, state, control, **kwargs):
if self._initialized and state.is_world_process_zero and self._log_artifacts:
ckpt_dir = f"checkpoint-{state.global_step}"
artifact_path = os.path.join(args.output_dir, ckpt_dir)
logger.info(f"Logging checkpoint artifacts in ... | 10,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class DagsHubCallback(MLflowCallback):
"""
A [`TrainerCallback`] that logs to [DagsHub](https://dagshub.com/). Extends [`MLflowCallback`]
"""
def __init__(self):
super().__init__()
if not is_dagshub_available():
raise ImportError("DagsHubCallback requires dagshub to be insta... | 10,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self.log_artifacts = os.getenv("HF_DAGSHUB_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
self.name = os.getenv("HF_DAGSHUB_MODEL_NAME") or "main"
self.remote = os.getenv("MLFLOW_TRACKING_URI")
self.repo = self.Repo(
owner=self.remote.split(os.sep)[-2],
name=sel... | 10,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self.repo.directory(str(self.path)).add_dir(args.output_dir) | 10,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class NeptuneMissingConfiguration(Exception):
def __init__(self):
super().__init__(
"""
------ Unsupported ---- We were not able to create new runs. You provided a custom Neptune run to
`NeptuneCallback` with the `run` argument. For the integration to work fully, provide your `ap... | 10,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class NeptuneCallback(TrainerCallback):
"""TrainerCallback that sends the logs to [Neptune](https://app.neptune.ai). | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
Args:
api_token (`str`, *optional*): Neptune API token obtained upon registration.
You can leave this argument out if you have saved your token to the `NEPTUNE_API_TOKEN` environment
variable (strongly recommended). See full setup instructions in the
[docs](https://docs.neptu... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
If True, logs all Trainer arguments and model parameters provided by the Trainer.
log_checkpoints (`str`, *optional*): If "same", uploads checkpoints whenever they are saved by the Trainer.
If "last", uploads only the most recently saved checkpoint. If "best", uploads the best checkpoint (among
... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
For instructions and examples, see the [Transformers integration
guide](https://docs.neptune.ai/integrations/transformers) in the Neptune documentation.
"""
integration_version_key = "source_code/integrations/transformers"
model_parameters_key = "model_parameters"
trial_name_key = "trial"
trial... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
try:
from neptune import Run
from neptune.internal.utils import verify_type
except ImportError:
from neptune.new.internal.utils import verify_type
from neptune.new.metadata_containers.run import Run
verify_type("api_token", api_token, (str, type(None)))
... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self._run = None
self._is_monitoring_run = False
self._run_id = None
self._force_reset_monitoring_run = False
self._init_run_kwargs = {"api_token": api_token, "project": project, "name": name, **neptune_run_kwargs}
self._volatile_checkpoints_dir = None
self._should_uploa... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def _initialize_run(self, **additional_neptune_kwargs):
try:
from neptune import init_run
from neptune.exceptions import NeptuneMissingApiTokenException, NeptuneMissingProjectNameException
except ImportError:
from neptune.new import init_run
from neptune.n... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def _ensure_run_with_monitoring(self):
if self._initial_run is not None:
self._use_initial_run()
else:
if not self._force_reset_monitoring_run and self._is_monitoring_run:
return
if self._run and not self._is_monitoring_run and not self._force_reset_m... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def _ensure_at_least_run_without_monitoring(self):
if self._initial_run is not None:
self._use_initial_run()
else:
if not self._run:
self._initialize_run(
with_id=self._run_id,
capture_stdout=False,
captu... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def _log_model_parameters(self, model):
from neptune.utils import stringify_unsupported
if model and hasattr(model, "config") and model.config is not None:
self._metadata_namespace[NeptuneCallback.model_parameters_key] = stringify_unsupported(
model.config.to_dict()
... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self._volatile_checkpoints_dir is not None:
consistent_checkpoint_path = os.path.join(self._volatile_checkpoints_dir, checkpoint)
try:
# Remove leading ../ from a relative path.
cpkt_path = relative_path.replace("..", "").lstrip(os.path.sep)
cop... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if self._should_clean_recently_uploaded_checkpoint and self._recent_checkpoint_path is not None:
self._metadata_namespace[self._target_checkpoints_namespace].delete_files(self._recent_checkpoint_path)
self._recent_checkpoint_path = relative_path
def on_init_end(self, args, state, control, **kw... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self._log_integration_version()
if self._log_parameters:
self._log_trainer_parameters(args)
self._log_model_parameters(model)
if state.is_hyper_param_search:
self._log_hyper_param_search_parameters(state)
def on_train_end(self, args, state, control, **kwargs):
... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_evaluate(self, args, state, control, metrics=None, **kwargs):
if self._log_checkpoints == "best":
best_metric_name = args.metric_for_best_model
if not best_metric_name.startswith("eval_"):
best_metric_name = f"eval_{best_metric_name}"
metric_value = me... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if logs is not None:
for name, value in rewrite_logs(logs).items():
if isinstance(value, (int, float)):
if name in NeptuneCallback.flat_metrics:
self._metadata_namespace[name] = value
else:
self._metadata... | 10,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class CodeCarbonCallback(TrainerCallback):
"""
A [`TrainerCallback`] that tracks the CO2 emission of training.
"""
def __init__(self):
if not is_codecarbon_available():
raise RuntimeError(
"CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install co... | 10,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_init_end(self, args, state, control, **kwargs):
if self.tracker is None and state.is_local_process_zero:
# CodeCarbon will automatically handle environment variables for configuration
self.tracker = self._codecarbon.EmissionsTracker(output_dir=args.output_dir)
def on_train_be... | 10,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class ClearMLCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/).
Environment:
- **CLEARML_PROJECT** (`str`, *optional*, defaults to `HuggingFace Transformers`):
ClearML project name.
- **CLEARML_TASK** (`str`, *optional*, defaults to `Tr... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
_hparams_section = "Transformers"
_model_config_section = "Model Configuration"
_ignore_hparams_overrides = "_ignore_hparams_ui_overrides_"
_ignoge_model_config_overrides = "_ignore_model_config_ui_overrides_"
_model_config_description = "The configuration of model number {}."
_model_config_descript... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self._initialized = False
self._clearml_task = None
self._log_model = False
self._checkpoints_saved = [] | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def setup(self, args, state, model, tokenizer, **kwargs):
if self._clearml is None:
return
if self._initialized:
return
ClearMLCallback._train_run_counter += 1
ClearMLCallback._model_connect_counter += 1
ClearMLCallback.log_suffix = (
"" if Cle... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
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