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Merge pull request #13299 from danieldk/copy/master
Sync main with latests changes from master (v3)
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Daniël de Kok
GitHub
commit
1052cba9f3
2
.github/workflows/gputests.yml
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.github/workflows/gputests.yml
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@ -9,7 +9,7 @@ jobs:
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strategy:
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fail-fast: false
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matrix:
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branch: [master, v4]
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branch: [master, main]
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if: github.repository_owner == 'explosion'
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runs-on: ubuntu-latest
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steps:
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2
.github/workflows/slowtests.yml
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.github/workflows/slowtests.yml
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@ -9,7 +9,7 @@ jobs:
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strategy:
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fail-fast: false
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matrix:
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branch: [master, v4]
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branch: [master, main]
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if: github.repository_owner == 'explosion'
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runs-on: ubuntu-latest
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steps:
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@ -452,10 +452,9 @@ and plugins in spaCy v3.0, and we can't wait to see what you build with it!
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spaCy website. If you're sharing your project on Twitter, feel free to tag
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[@spacy_io](https://twitter.com/spacy_io) so we can check it out.
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- Once your extension is published, you can open an issue on the
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[issue tracker](https://github.com/explosion/spacy/issues) to suggest it for the
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[resources directory](https://spacy.io/usage/resources#extensions) on the
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website.
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- Once your extension is published, you can open a
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[PR](https://github.com/explosion/spaCy/pulls) to suggest it for the
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[Universe](https://spacy.io/universe) page.
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📖 **For more tips and best practices, see the [checklist for developing spaCy extensions](https://spacy.io/usage/processing-pipelines#extensions).**
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@ -13,7 +13,7 @@ from .. import util
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from ..language import Language
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from ..tokens import Doc
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from ..training import Corpus
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from ._util import Arg, Opt, benchmark_cli, setup_gpu
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from ._util import Arg, Opt, benchmark_cli, import_code, setup_gpu
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@benchmark_cli.command(
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@ -30,12 +30,14 @@ def benchmark_speed_cli(
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
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n_batches: int = Opt(50, "--batches", help="Minimum number of batches to benchmark", min=30,),
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warmup_epochs: int = Opt(3, "--warmup", "-w", min=0, help="Number of iterations over the data for warmup"),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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# fmt: on
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):
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"""
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Benchmark a pipeline. Expects a loadable spaCy pipeline and benchmark
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data in the binary .spacy format.
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"""
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import_code(code_path)
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setup_gpu(use_gpu=use_gpu, silent=False)
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nlp = util.load_model(model)
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@ -171,5 +173,5 @@ def print_outliers(sample: numpy.ndarray):
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def warmup(
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nlp: Language, docs: List[Doc], warmup_epochs: int, batch_size: Optional[int]
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) -> numpy.ndarray:
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docs = warmup_epochs * docs
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docs = [doc.copy() for doc in docs * warmup_epochs]
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return annotate(nlp, docs, batch_size)
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@ -1830,6 +1830,7 @@ class Language:
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# is done, so that they can exit gracefully.
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for q in texts_q:
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q.put(_WORK_DONE_SENTINEL)
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q.close()
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# Otherwise, we are stopping because the error handler raised an
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# exception. The sentinel will be last to go out of the queue.
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@ -2464,7 +2465,8 @@ def _apply_pipes(
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# Stop working if we encounter the end-of-work sentinel.
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if isinstance(texts_with_ctx, _WorkDoneSentinel):
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return
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sender.close()
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receiver.close()
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docs = (
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ensure_doc(doc_like, context) for doc_like, context in texts_with_ctx
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@ -2488,7 +2490,8 @@ def _apply_pipes(
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# Parent has closed the pipe prematurely. This happens when a
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# worker encounters an error and the error handler is set to
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# stop processing.
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return
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sender.close()
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receiver.close()
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class _Sender:
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@ -15,7 +15,12 @@ def doc_w_attrs(en_tokenizer):
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Token.set_extension("_test_token", default="t0")
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doc[1]._._test_token = "t1"
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return doc
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yield doc
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Doc.remove_extension("_test_attr")
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Doc.remove_extension("_test_prop")
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Doc.remove_extension("_test_method")
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Token.remove_extension("_test_token")
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def test_serialize_ext_attrs_from_bytes(doc_w_attrs):
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@ -1267,13 +1267,14 @@ the [binary `.spacy` format](/api/data-formats#binary-training). The pipeline is
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warmed up before any measurements are taken.
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```cli
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$ python -m spacy benchmark speed [model] [data_path] [--batch_size] [--no-shuffle] [--gpu-id] [--batches] [--warmup]
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$ python -m spacy benchmark speed [model] [data_path] [--code] [--batch_size] [--no-shuffle] [--gpu-id] [--batches] [--warmup]
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```
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| Name | Description |
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| -------------------- | -------------------------------------------------------------------------------------------------------- |
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| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `model` | Pipeline to benchmark the speed of. Can be a package or a path to a data directory. ~~str (positional)~~ |
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| `data_path` | Location of benchmark data in spaCy's [binary format](/api/data-formats#training). ~~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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| `--batch-size`, `-b` | Set the batch size. If not set, the pipeline's batch size is used. ~~Optional[int] \(option)~~ |
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| `--no-shuffle` | Do not shuffle documents in the benchmark data. ~~bool (flag)~~ |
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| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
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@ -1295,6 +1296,9 @@ input formats are:
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When a directory is provided it is traversed recursively to collect all files.
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When loading a .spacy file, any potential annotations stored on the `Doc` that are not overwritten by the pipeline will be preserved.
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If you want to evaluate the pipeline on raw text only, make sure that the .spacy file does not contain any annotations.
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```bash
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$ python -m spacy apply [model] [data-path] [output-file] [--code] [--text-key] [--force-overwrite] [--gpu-id] [--batch-size] [--n-process]
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```
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@ -1507,7 +1507,7 @@ These models all take the same parameters:
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> ```ini
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> [components.llm.model]
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> @llm_models = "spacy.Llama2.v1"
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> name = "llama2-7b-hf"
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> name = "Llama-2-7b-hf"
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> ```
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Currently, these models are provided as part of the core library:
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@ -13,7 +13,7 @@ between `Doc` objects.
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<Infobox variant="warning">
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Note that a `Vocab` instance is not static. It increases in size as texts with
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new tokens are processed.
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new tokens are processed. Some models may have an empty vocab at initialization.
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</Infobox>
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@ -93,6 +93,7 @@ given string, you need to look it up in
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> #### Example
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>
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> ```python
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> nlp("I'm eating an apple")
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> apple = nlp.vocab.strings["apple"]
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> oov = nlp.vocab.strings["dskfodkfos"]
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> assert apple in nlp.vocab
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