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Add fairseq-hydra-train and update docs (#1449)
Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1449 Test Plan: Imported from OSS Reviewed By: alexeib Differential Revision: D25094525 Pulled By: myleott fbshipit-source-id: 430387d11196d3292933bb168cf09ea16ebc0d3b
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@ -1,57 +1,70 @@
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## Hydra
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[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python framework that simplifies the development of
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research and other complex applications. The key feature is the ability to dynamically create a hierarchical
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configuration by composition and override it through config files and the command line. The name Hydra comes from its
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ability to run multiple similar jobs - much like a Hydra with multiple heads.
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[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python
|
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framework that simplifies the development of research and other complex
|
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applications. The key feature is the ability to dynamically create a
|
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hierarchical configuration by composition and override it through config files
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and the command line. The name Hydra comes from its ability to run multiple
|
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similar jobs - much like a Hydra with multiple heads.
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## Motivation
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Until recently, all components in fairseq were configured through a shared "args" namespace that was created at
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application startup. Components declared their own "add_args" method to update the argparse parser, hoping that
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the names would not clash with arguments from other components. While this model works for smaller applications,
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as fairseq grew and became integrated into other applications, this became problematic.
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In order to determine how to configure each component, one needed to a) examine what args were added by this component, and
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b) read the code to figure out what shared arguments it is using that were added in other places. Reproducing
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models involved sharing commands that often contained dozens of command line switches.
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Until recently, all components in fairseq were configured through a shared
|
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`args` namespace that was created at application startup. Components declared
|
||||
their own `add_args` method to update the argparse parser, hoping that the names
|
||||
would not clash with arguments from other components. While this model works for
|
||||
smaller applications, as fairseq grew and became integrated into other
|
||||
applications, this became problematic. In order to determine how to configure
|
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each component, one needed to a) examine what args were added by this component,
|
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and b) read the code to figure out what shared arguments it is using that were
|
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added in other places. Reproducing models involved sharing commands that often
|
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contained dozens of command line switches.
|
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|
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The model described above is still supported by fairseq for backward compatibility, but will be deprecated some time
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in the future.
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The model described above is still supported by fairseq for backward
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compatibility, but will be deprecated some time in the future.
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New components in fairseq should now create a dataclass that encapsulates all parameters required to configure this
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component. The dataclass is registered along with the component, and fairseq takes care of constructing and
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providing this configuration object to the component's constructor. Note that sharing parameters can optionally
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still work, but one has to explicitly point to the "source of truth" (see inheritance example below).
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These changes make components in fairseq
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more independent and re-usable by other applications: all that is needed to create a component is to initialize its
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dataclass and overwrite some of the defaults.
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New components in fairseq should now create a dataclass that encapsulates all
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parameters required to configure this component. The dataclass is registered
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along with the component, and fairseq takes care of constructing and providing
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this configuration object to the component's constructor. Note that sharing
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parameters can optionally still work, but one has to explicitly point to the
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"source of truth" (see inheritance example below). These changes make components
|
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in fairseq more independent and re-usable by other applications: all that is
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needed to create a component is to initialize its dataclass and overwrite some
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of the defaults.
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|
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While configuring fairseq through command line (using either the legacy argparse based or the new Hydra based entry points) is still
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fully supported, you can now take advantage of configuring fairseq completely or piece-by-piece through
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hierarchical YAML configuration files. These files can also be shipped as examples that others can use to run
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an identically configured job.
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While configuring fairseq through command line (using either the legacy argparse
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based or the new Hydra based entry points) is still fully supported, you can now
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take advantage of configuring fairseq completely or piece-by-piece through
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hierarchical YAML configuration files. These files can also be shipped as
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examples that others can use to run an identically configured job.
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|
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Additionally, Hydra has a rich and growing
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[library of plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that provide functionality such as
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hyperparameter sweeping (including using bayesian optimization through the [Ax](https://github.com/facebook/Ax) library),
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job launching across various platforms, and more.
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Additionally, Hydra has a rich and growing [library of
|
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plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that
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provide functionality such as hyperparameter sweeping (including using bayesian
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optimization through the [Ax](https://github.com/facebook/Ax) library), job
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launching across various platforms, and more.
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## Creating or migrating components
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In general, each new (or updated) component should provide a companion [dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are typically located in the same
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file as the component and are passed as arguments to the register_*() functions. Top-level configs that should be
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present in every fairseq application are placed in the [global](fairseq/dataclass/configs.py) config file and added
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to the FairseqConfig object.
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In general, each new (or updated) component should provide a companion
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[dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are
|
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typically located in the same file as the component and are passed as arguments
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to the `register_*()` functions. Top-level configs that should be present in
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every fairseq application are placed in the
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[global](fairseq/dataclass/configs.py) config file and added to the
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`FairseqConfig` object.
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Each dataclass is a plain-old-data object, similar to a NamedTuple. These classes are decorated with a @dataclass
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decorator, and typically inherit from `FairseqDataclass` (which adds some functionality for backward compatibility).
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Each field must have a type, and generally has metadata (such as a help string) and a default value. Only primitive types or other config objects are allowed as
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Each dataclass is a plain-old-data object, similar to a `NamedTuple`. These
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classes are decorated with a `@dataclass` decorator, and typically inherit from
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`FairseqDataclass` (which adds some functionality for backward compatibility).
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Each field must have a type, and generally has metadata (such as a help string)
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and a default value. Only primitive types or other config objects are allowed as
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data types for each field.
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Example:
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#### Example:
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``` python
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```python
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from dataclasses import dataclass, field
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from fairseq.dataclass import FairseqDataclass
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@ -71,11 +84,12 @@ class InteractiveConfig(FairseqDataclass):
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### Inherting values
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Some components require sharing a value. For example, a learning rate scheduler and an optimizer may both need to
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know the initial learning rate value. One can declare a field that, by default, will
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inherit its value from another config node in the same hierarchy:
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Some components require sharing a value. For example, a learning rate scheduler
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and an optimizer may both need to know the initial learning rate value. One can
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declare a field that, by default, will inherit its value from another config
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node in the same hierarchy:
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``` python
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```python
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@dataclass
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FairseqAdamConfig(FairseqDataclass):
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...
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@ -83,18 +97,21 @@ FairseqAdamConfig(FairseqDataclass):
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...
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```
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`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"` , which is the value one can use in a YAML config file or through
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command line to achieve the same effect. Note that this assumes that there is an "optimization" config object
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in the root config and it has a field called "lr".
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`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"`, which is
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the value one can use in a YAML config file or through command line to achieve
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the same effect. Note that this assumes that there is an "optimization" config
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object in the root config and it has a field called "lr".
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### Tasks and Models
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Creating Tasks and Models works same as before, except that legacy implementations now inherit from Legacy* base classes,
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while new components inherit from FairseqTask and FairseqModel and provide a dataclass to the register_*() functions.
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Creating Tasks and Models works same as before, except that legacy
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implementations now inherit from `LegacyFairseq*` base classes, while new
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components inherit from `FairseqTask` and `FairseqModel` and provide a dataclass
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to the `register_*()` functions.
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Task example:
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#### Task example:
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``` python
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```python
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@dataclass
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class LanguageModelingConfig(FairseqDataclass):
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data: Optional[str] = field(
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@ -110,9 +127,9 @@ class LanguageModelingTask(LegacyFairseqTask):
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...
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```
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Model example:
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#### Model example:
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``` python
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```python
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@dataclass
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class TransformerLanguageModelConfig(FairseqDataclass):
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activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
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@ -131,9 +148,10 @@ class TransformerLanguageModel(FairseqLanguageModel):
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### Other components
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Other components work as before, but they now take their configuration dataclass as the only constructor argument:
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Other components work as before, but they now take their configuration dataclass
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as the only constructor argument:
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``` python
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```python
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@dataclass
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class MosesTokenizerConfig(FairseqDataclass):
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source_lang: str = field(default="en", metadata={"help": "source language"})
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@ -145,50 +163,61 @@ class MosesTokenizer(object):
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...
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```
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Note that if you are adding a new registry for a new set of components, you need to add it to the FairseqConfig object in
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fairseq/dataclass/configs.py:
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Note that if you are adding a new registry for a new set of components, you need
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to add it to the `FairseqConfig` object in `fairseq/dataclass/configs.py`:
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``` python
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```python
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@dataclass
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class FairseqConfig(object):
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...
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my_new_registry: Any = None
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```
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## Training with hydra_train.py
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## Training with `fairseq-hydra-train`
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To fully take advantage of configuration flexibility offered by Hydra, you may want to train new models using the
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hydra_train.py entry point located in the fairseq_cli directory. Legacy CLI tools such as train.py,
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will remain supported for the foreseeable future but will be deprecated eventually.
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To fully take advantage of configuration flexibility offered by Hydra, you may
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want to train new models using the `fairseq-hydra-train` entry point. Legacy CLI
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tools such as `fairseq-train` will remain supported for the foreseeable future
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but will be deprecated eventually.
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On startup, Hydra will create a configuration object that contains a hierarchy of all the necessary dataclasses
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populated with their default values in the code. The default values are overwritten by values found in YAML files in
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fairseq/config directory (which currently just set default task, optimizer, etc) and then further overwritten by values
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provided through command line arguments.
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On startup, Hydra will create a configuration object that contains a hierarchy
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of all the necessary dataclasses populated with their default values in the
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code. The default values are overwritten by values found in YAML files in
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`fairseq/config` directory (which currently sets minimal defaults) and then
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further overwritten by values provided through command line arguments.
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Some of the most common use cases are shown below:
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### 1. Overwrite default values through command line:
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### 1. Override default values through command line:
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```shell script
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python fairseq_cli/hydra_train.py distributed_training.distributed_world_size=1 dataset.batch_size=2 task.data=data-bin \
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model=transformer_lm/transformer_lm_gpt task=language_modeling optimization.max_update=5000
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$ fairseq-hydra-train \
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distributed_training.distributed_world_size=1 \
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dataset.batch_size=2 \
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task.data=data-bin \
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model=transformer_lm/transformer_lm_gpt \
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task=language_modeling \
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optimization.max_update=5000
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```
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Note that along with explicitly providing values for parameters such as dataset.batch_size, this also tells Hydra to overlay configuration found in `fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml`
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over the default values in the dataclass. If you want to train a model without specifying a particular architecture
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you can simply specify model=transformer_lm. This only works for migrated tasks and models.
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Note that along with explicitly providing values for parameters such as
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`dataset.batch_size`, this also tells Hydra to overlay configuration found in
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`fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml` over the default
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values in the dataclass. If you want to train a model without specifying a
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particular architecture you can simply specify `model=transformer_lm`. This only
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works for migrated tasks and models.
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### 2. Replace bundled configs with an external config:
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```shell script
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python fairseq_cli/hydra_train.py --config-path /path/to/external/configs --config-name wiki103
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$ fairseq-hydra-train \
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--config-path /path/to/external/configs \
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--config-name wiki103
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```
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where /path/to/external/configs/wiki103.yaml contains:
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where `/path/to/external/configs/wiki103.yaml` contains:
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``` yaml
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```yaml
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# @package _group_
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model:
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@ -211,24 +240,38 @@ lr_scheduler:
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_name: cosine
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```
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Note that here bundled configs from `fairseq/config` directory are not used, however the defaults from each dataclass will still be used (unless overwritten by your external config).
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Note that here bundled configs from `fairseq/config` directory are not used,
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however the defaults from each dataclass will still be used (unless overwritten
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by your external config).
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Additionally you can choose to break up your configs by creating a directory structure in the same location as your main config file, with the names of the top-level fields
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(such as "model", "dataset", etc), and placing config files with meaningful names that would populate that specific section of your
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top-level config file (for example, you might have model/small_transformer_lm.yaml, model/big_transformer_lm.yaml, etc). You can then specify the correct configuration via command line, defaults in the main config, or even launch all of them as a sweep (see Hydra documentation on how to do this).
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Additionally you can choose to break up your configs by creating a directory
|
||||
structure in the same location as your main config file, with the names of the
|
||||
top-level fields (such as "model", "dataset", etc), and placing config files
|
||||
with meaningful names that would populate that specific section of your
|
||||
top-level config file (for example, you might have
|
||||
`model/small_transformer_lm.yaml`, `model/big_transformer_lm.yaml`, etc). You
|
||||
can then specify the correct configuration via command line, defaults in the
|
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main config, or even launch all of them as a sweep (see Hydra documentation on
|
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how to do this).
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### 3. Add an external config directory to Hydra search path:
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This allows combining default configuration (including using any bundled config files), while specifying your own config files for some parts of the configuration.
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This allows combining default configuration (including using any bundled config
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files), while specifying your own config files for some parts of the
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configuration.
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```shell script
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python fairseq_cli/hydra_train.py distributed_training.distributed_world_size=1 dataset.batch_size=2 \
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task.data=/path/to/data/ model=transformer_lm/2_layers task=language_modeling optimization.max_update=5000 \
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--config-dir /path/to/external/configs
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$ fairseq-hydra-train \
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distributed_training.distributed_world_size=1 \
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dataset.batch_size=2 \
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task.data=/path/to/data/ \
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model=transformer_lm/2_layers \
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task=language_modeling \
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optimization.max_update=5000 \
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--config-dir /path/to/external/configs
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```
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where /path/to/external/configs has the following structure:
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where `/path/to/external/configs` has the following structure:
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```
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.
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+-- model
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@ -236,5 +279,6 @@ where /path/to/external/configs has the following structure:
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| | +-- 2_layers.yaml
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```
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and 2_layers.yaml contains a copy of transformer_lm_gpt.yaml but with decoder_layers set to 2. You can add
|
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other configs to configure other components as well.
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and `2_layers.yaml` contains a copy of `transformer_lm_gpt.yaml` but with
|
||||
`decoder_layers` set to 2. You can add other configs to configure other
|
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components as well.
|
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|
@ -56,8 +56,10 @@ This configuration was used for the base model trained on the Librispeech datase
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Note that the input is expected to be single channel, sampled at 16 kHz
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|
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```shell script
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$ python fairseq_cli/hydra_train.py task.data=/path/to/data \
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--config-path /path/to/fairseq-py/examples/wav2vec/config/pretraining --config-name wav2vec2_base_librispeech
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$ fairseq-hydra-train \
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task.data=/path/to/data \
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--config-path /path/to/fairseq-py/examples/wav2vec/config/pretraining \
|
||||
--config-name wav2vec2_base_librispeech
|
||||
```
|
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|
||||
Note: you can simulate 64 GPUs by using k GPUs and adding command line parameters (before --config-path)
|
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@ -68,8 +70,10 @@ Note: you can simulate 64 GPUs by using k GPUs and adding command line parameter
|
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This configuration was used for the large model trained on the Libri-light dataset in the wav2vec 2.0 paper
|
||||
|
||||
```shell script
|
||||
$ python fairseq_cli/hydra_train.py task.data=/path/to/data \
|
||||
--config-path /path/to/fairseq-py/examples/wav2vec/config/pretraining --config-name wav2vec2_large_librivox
|
||||
$ fairseq-hydra-train \
|
||||
task.data=/path/to/data \
|
||||
--config-path /path/to/fairseq-py/examples/wav2vec/config/pretraining \
|
||||
--config-name wav2vec2_large_librivox
|
||||
```
|
||||
|
||||
Note: you can simulate 128 GPUs by using k GPUs and adding command line parameters (before --config-path)
|
||||
@ -88,9 +92,12 @@ $ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $sp
|
||||
|
||||
Fine-tuning on 100h of Librispeech with letter targets:
|
||||
```shell script
|
||||
python fairseq_cli/hydra_train.py distributed_training.distributed_port=$PORT task.data=/path/to/data \
|
||||
model.w2v_path=/path/to/model.pt --config-path /path/to/fairseq-py/examples/wav2vec/config/finetuning \
|
||||
--config-name base_100h
|
||||
$ fairseq-hydra-train \
|
||||
distributed_training.distributed_port=$PORT \
|
||||
task.data=/path/to/data \
|
||||
model.w2v_path=/path/to/model.pt \
|
||||
--config-path /path/to/fairseq-py/examples/wav2vec/config/finetuning \
|
||||
--config-name base_100h
|
||||
```
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||||
|
||||
There are other config files in the config/finetuning directory that can be used to fine-tune on other splits.
|
||||
|
@ -1,10 +1,10 @@
|
||||
# @package _group_
|
||||
defaults:
|
||||
- task: language_modeling
|
||||
- task: null
|
||||
- model: null
|
||||
- criterion: cross_entropy
|
||||
- optimizer: adam
|
||||
- lr_scheduler: cosine
|
||||
- optimizer: null
|
||||
- lr_scheduler: fixed
|
||||
- bpe: null
|
||||
- tokenizer: null
|
||||
- scoring: null
|
||||
|
@ -173,6 +173,12 @@ class CommonConfig(FairseqDataclass):
|
||||
profile: bool = field(
|
||||
default=False, metadata={"help": "enable autograd profiler emit_nvtx"}
|
||||
)
|
||||
reset_logging: bool = field(
|
||||
default=True,
|
||||
metadata={
|
||||
"help": "when using Hydra, reset the logging at the beginning of training"
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
@ -26,12 +26,14 @@ try:
|
||||
import xentropy_cuda
|
||||
from apex.contrib import xentropy
|
||||
|
||||
logger.info("using fused cross entropy")
|
||||
|
||||
def cross_entropy(logits, target, ignore_index=-100, reduction="mean"):
|
||||
if logits.device == torch.device("cpu"):
|
||||
return _cross_entropy_pytorch(logits, target, ignore_index, reduction)
|
||||
else:
|
||||
if not getattr(cross_entropy, "_has_logged_once", False):
|
||||
logger.info("using fused cross entropy")
|
||||
cross_entropy._has_logged_once = True
|
||||
|
||||
half_to_float = logits.dtype == torch.half
|
||||
losses = xentropy.SoftmaxCrossEntropyLoss.apply(
|
||||
logits,
|
||||
|
@ -4,29 +4,32 @@
|
||||
# This source code is licensed under the MIT license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import hydra
|
||||
from omegaconf import OmegaConf
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
|
||||
from fairseq.dataclass.initialize import hydra_init
|
||||
from fairseq_cli.train import main as pre_main
|
||||
from fairseq import distributed_utils
|
||||
from fairseq.dataclass.configs import FairseqConfig
|
||||
|
||||
import logging
|
||||
import hydra
|
||||
import torch
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger = logging.getLogger("fairseq_cli.hydra_train")
|
||||
|
||||
|
||||
@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config")
|
||||
def hydra_main(cfg: FairseqConfig) -> None:
|
||||
|
||||
cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True, enum_to_str=True))
|
||||
|
||||
OmegaConf.set_struct(cfg, True)
|
||||
|
||||
if cfg.common.reset_logging:
|
||||
reset_logging() # Hydra hijacks logging, fix that
|
||||
|
||||
if cfg.common.profile:
|
||||
with torch.cuda.profiler.profile():
|
||||
with torch.autograd.profiler.emit_nvtx():
|
||||
@ -35,7 +38,22 @@ def hydra_main(cfg: FairseqConfig) -> None:
|
||||
distributed_utils.call_main(cfg, pre_main)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
def reset_logging():
|
||||
root = logging.getLogger()
|
||||
for handler in root.handlers:
|
||||
root.removeHandler(handler)
|
||||
root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
|
||||
handler = logging.StreamHandler(sys.stdout)
|
||||
handler.setFormatter(
|
||||
logging.Formatter(
|
||||
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
)
|
||||
root.addHandler(handler)
|
||||
|
||||
|
||||
def cli_main():
|
||||
try:
|
||||
from hydra._internal.utils import get_args
|
||||
|
||||
@ -46,3 +64,7 @@ if __name__ == "__main__":
|
||||
|
||||
hydra_init(cfg_name)
|
||||
hydra_main()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cli_main()
|
||||
|
17
setup.py
17
setup.py
@ -22,14 +22,18 @@ def write_version_py():
|
||||
|
||||
# append latest commit hash to version string
|
||||
try:
|
||||
sha = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("ascii").strip()
|
||||
sha = (
|
||||
subprocess.check_output(["git", "rev-parse", "HEAD"])
|
||||
.decode("ascii")
|
||||
.strip()
|
||||
)
|
||||
version += "+" + sha[:7]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# write version info to fairseq/version.py
|
||||
with open(os.path.join("fairseq", "version.py"), "w") as f:
|
||||
f.write("__version__ = \"{}\"\n".format(version))
|
||||
f.write('__version__ = "{}"\n'.format(version))
|
||||
return version
|
||||
|
||||
|
||||
@ -194,7 +198,8 @@ def do_setup(package_data):
|
||||
"tests",
|
||||
"tests.*",
|
||||
]
|
||||
) + extra_packages,
|
||||
)
|
||||
+ extra_packages,
|
||||
package_data=package_data,
|
||||
ext_modules=extensions,
|
||||
test_suite="tests",
|
||||
@ -202,6 +207,7 @@ def do_setup(package_data):
|
||||
"console_scripts": [
|
||||
"fairseq-eval-lm = fairseq_cli.eval_lm:cli_main",
|
||||
"fairseq-generate = fairseq_cli.generate:cli_main",
|
||||
"fairseq-hydra-train = fairseq_cli.hydra_train:cli_main",
|
||||
"fairseq-interactive = fairseq_cli.interactive:cli_main",
|
||||
"fairseq-preprocess = fairseq_cli.preprocess:cli_main",
|
||||
"fairseq-score = fairseq_cli.score:cli_main",
|
||||
@ -230,8 +236,11 @@ try:
|
||||
fairseq_examples = os.path.join("fairseq", "examples")
|
||||
if "build_ext" not in sys.argv[1:] and not os.path.exists(fairseq_examples):
|
||||
os.symlink(os.path.join("..", "examples"), fairseq_examples)
|
||||
|
||||
package_data = {
|
||||
"fairseq": get_files("fairseq/examples"),
|
||||
"fairseq": (
|
||||
get_files(fairseq_examples) + get_files(os.path.join("fairseq", "config"))
|
||||
)
|
||||
}
|
||||
do_setup(package_data)
|
||||
finally:
|
||||
|
Loading…
Reference in New Issue
Block a user