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Add model-last saving mechanism to pretraining (#12459)
* Adjust pretrain command * chane naming and add finally block * Add unit test * Add unit test assertions * Update spacy/training/pretrain.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * change finally block * Add to docs * Update website/docs/usage/embeddings-transformers.mdx * Add flag to skip saving model-last --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
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Edward
GitHub
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@ -23,6 +23,7 @@ def pretrain_cli(
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resume_path: Optional[Path] = Opt(None, "--resume-path", "-r", help="Path to pretrained weights from which to resume pretraining"),
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epoch_resume: Optional[int] = Opt(None, "--epoch-resume", "-er", help="The epoch to resume counting from when using --resume-path. Prevents unintended overwriting of existing weight files."),
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
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skip_last: bool = Opt(False, "--skip-last", "-L", help="Skip saving model-last.bin"),
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# fmt: on
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):
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"""
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@ -74,6 +75,7 @@ def pretrain_cli(
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epoch_resume=epoch_resume,
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use_gpu=use_gpu,
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silent=False,
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skip_last=skip_last,
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)
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msg.good("Successfully finished pretrain")
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@ -165,7 +165,8 @@ def test_pretraining_default():
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@pytest.mark.parametrize("objective", CHAR_OBJECTIVES)
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def test_pretraining_tok2vec_characters(objective):
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@pytest.mark.parametrize("skip_last", (True, False))
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def test_pretraining_tok2vec_characters(objective, skip_last):
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"""Test that pretraining works with the character objective"""
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config = Config().from_str(pretrain_string_listener)
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config["pretraining"]["objective"] = objective
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@ -178,10 +179,14 @@ def test_pretraining_tok2vec_characters(objective):
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filled["paths"]["raw_text"] = file_path
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filled = filled.interpolate()
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assert filled["pretraining"]["component"] == "tok2vec"
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pretrain(filled, tmp_dir)
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pretrain(filled, tmp_dir, skip_last=skip_last)
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assert Path(tmp_dir / "model0.bin").exists()
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assert Path(tmp_dir / "model4.bin").exists()
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assert not Path(tmp_dir / "model5.bin").exists()
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if skip_last:
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assert not Path(tmp_dir / "model-last.bin").exists()
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else:
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assert Path(tmp_dir / "model-last.bin").exists()
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@pytest.mark.parametrize("objective", VECTOR_OBJECTIVES)
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@ -237,6 +242,7 @@ def test_pretraining_tagger_tok2vec(config):
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pretrain(filled, tmp_dir)
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assert Path(tmp_dir / "model0.bin").exists()
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assert Path(tmp_dir / "model4.bin").exists()
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assert Path(tmp_dir / "model-last.bin").exists()
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assert not Path(tmp_dir / "model5.bin").exists()
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@ -24,6 +24,7 @@ def pretrain(
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epoch_resume: Optional[int] = None,
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use_gpu: int = -1,
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silent: bool = True,
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skip_last: bool = False,
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):
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msg = Printer(no_print=silent)
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if config["training"]["seed"] is not None:
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@ -60,10 +61,14 @@ def pretrain(
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row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
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msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
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def _save_model(epoch, is_temp=False):
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def _save_model(epoch, is_temp=False, is_last=False):
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is_temp_str = ".temp" if is_temp else ""
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with model.use_params(optimizer.averages):
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with (output_dir / f"model{epoch}{is_temp_str}.bin").open("wb") as file_:
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if is_last:
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save_path = output_dir / f"model-last.bin"
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else:
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save_path = output_dir / f"model{epoch}{is_temp_str}.bin"
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with (save_path).open("wb") as file_:
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file_.write(model.get_ref("tok2vec").to_bytes())
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log = {
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"nr_word": tracker.nr_word,
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@ -76,22 +81,26 @@ def pretrain(
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# TODO: I think we probably want this to look more like the
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# 'create_train_batches' function?
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for epoch in range(epoch_resume, P["max_epochs"]):
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for batch_id, batch in enumerate(batcher(corpus(nlp))):
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docs = ensure_docs(batch)
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loss = make_update(model, docs, optimizer, objective)
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progress = tracker.update(epoch, loss, docs)
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if progress:
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msg.row(progress, **row_settings)
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if P["n_save_every"] and (batch_id % P["n_save_every"] == 0):
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_save_model(epoch, is_temp=True)
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try:
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for epoch in range(epoch_resume, P["max_epochs"]):
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for batch_id, batch in enumerate(batcher(corpus(nlp))):
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docs = ensure_docs(batch)
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loss = make_update(model, docs, optimizer, objective)
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progress = tracker.update(epoch, loss, docs)
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if progress:
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msg.row(progress, **row_settings)
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if P["n_save_every"] and (batch_id % P["n_save_every"] == 0):
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_save_model(epoch, is_temp=True)
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if P["n_save_epoch"]:
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if epoch % P["n_save_epoch"] == 0 or epoch == P["max_epochs"] - 1:
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if P["n_save_epoch"]:
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if epoch % P["n_save_epoch"] == 0 or epoch == P["max_epochs"] - 1:
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_save_model(epoch)
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else:
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_save_model(epoch)
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else:
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_save_model(epoch)
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tracker.epoch_loss = 0.0
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tracker.epoch_loss = 0.0
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finally:
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if not skip_last:
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_save_model(P["max_epochs"], is_last=True)
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def ensure_docs(examples_or_docs: Iterable[Union[Doc, Example]]) -> List[Doc]:
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@ -1122,17 +1122,18 @@ auto-generated by setting `--pretraining` on
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$ python -m spacy pretrain [config_path] [output_dir] [--code] [--resume-path] [--epoch-resume] [--gpu-id] [overrides]
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```
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| Name | Description |
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| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
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| `output_dir` | Directory to save binary weights to on each epoch. ~~Path (positional)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--resume-path`, `-r` | Path to pretrained weights from which to resume pretraining. ~~Optional[Path] \(option)~~ |
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| `--epoch-resume`, `-er` | The epoch to resume counting from when using `--resume-path`. Prevents unintended overwriting of existing weight files. ~~Optional[int] \(option)~~ |
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| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--training.dropout 0.2`. ~~Any (option/flag)~~ |
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| **CREATES** | The pretrained weights that can be used to initialize `spacy train`. |
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| Name | Description |
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| -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. If `-`, the data will be [read from stdin](/usage/training#config-stdin). ~~Union[Path, str] \(positional)~~ |
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| `output_dir` | Directory to save binary weights to on each epoch. ~~Path (positional)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--resume-path`, `-r` | Path to pretrained weights from which to resume pretraining. ~~Optional[Path] \(option)~~ |
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| `--epoch-resume`, `-er` | The epoch to resume counting from when using `--resume-path`. Prevents unintended overwriting of existing weight files. ~~Optional[int] \(option)~~ |
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| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
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| `--skip-last`, `-L` <Tag variant="new">3.5.2</Tag> | Skip saving `model-last.bin`. Defaults to `False`. ~~bool (flag)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--training.dropout 0.2`. ~~Any (option/flag)~~ |
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| **CREATES** | The pretrained weights that can be used to initialize `spacy train`. |
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## evaluate {id="evaluate",version="2",tag="command"}
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@ -746,13 +746,16 @@ this by setting `initialize.init_tok2vec` to the filename of the `.bin` file
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that you want to use from pretraining.
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A pretraining step that runs for 5 epochs with an output path of `pretrain/`, as
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an example, produces `pretrain/model0.bin` through `pretrain/model4.bin`. To
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make use of the final output, you could fill in this value in your config file:
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an example, produces `pretrain/model0.bin` through `pretrain/model4.bin` plus a
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copy of the last iteration as `pretrain/model-last.bin`. Additionally, you can
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configure `n_save_epoch` to tell pretraining in which epoch interval it should
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save the current training progress. To use the final output to initialize your
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`tok2vec` layer, you could fill in this value in your config file:
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```ini {title="config.cfg"}
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[paths]
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init_tok2vec = "pretrain/model4.bin"
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init_tok2vec = "pretrain/model-last.bin"
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[initialize]
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init_tok2vec = ${paths.init_tok2vec}
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