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Lazy modules #1039
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Lazy modules #1039
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Summary: Pull Request resolved: facebookresearch#1039 Add lazy modules to PyText. These are modules which are able to infer some of their dimensions on their first forward pass. The main tool introduced is Lazy, which is a Module wrapping any other Module, and to which arguments can be passed that will be used to construct that wrapped Module after the first forward pass. If any of these arguments are Infer objects, those arguments will be replaced by calling the callback of the Infer object on the forward pass input. For instance, `Lazy(nn.Linear, Infer(lambda input: input.size(-1)), 4)` would take its in_features dimension from the last dimension of the input to its forward pass. This can be simplified to `Lazy(nn.Linear, Infer.dimension(-1)`, 4), or a partial can be created, for instance `LazyLinear = Lazy.partial(nn.Linear, Infer.dimension(-1)); LazyLinear(4)`. Finally, these Lazy objects explicitly forbid treating themselves normally; they must instead be replaced by calling `init_lazy_modules` on your model before training. For instance, ``` ll = lazy.Linear(4) seq = nn.Sequential(ll) final = init_lazy_modules(seq, torch.rand(1, 2) ``` `final` will be a full nn.Module graph with no lazy components; all of them will be "resolved" and replaced with their true module types. Differential Revision: D17837321 fbshipit-source-id: 0ef6062f04ead4af9692d0d17e8cbfb20a29a778
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This pull request was exported from Phabricator. Differential Revision: D17837321 |
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Summary: Pull Request resolved: facebookresearch#1039 Add lazy modules to PyText. These are modules which are able to infer some of their dimensions on their first forward pass. The main tool introduced is Lazy, which is a Module wrapping any other Module, and to which arguments can be passed that will be used to construct that wrapped Module after the first forward pass. If any of these arguments are Infer objects, those arguments will be replaced by calling the callback of the Infer object on the forward pass input. For instance, `Lazy(nn.Linear, Infer(lambda input: input.size(-1)), 4)` would take its in_features dimension from the last dimension of the input to its forward pass. This can be simplified to `Lazy(nn.Linear, Infer.dimension(-1)`, 4), or a partial can be created, for instance `LazyLinear = Lazy.partial(nn.Linear, Infer.dimension(-1)); LazyLinear(4)`. Finally, these Lazy objects explicitly forbid treating themselves normally; they must instead be replaced by calling `init_lazy_modules` on your model before training. For instance, ``` ll = lazy.Linear(4) seq = nn.Sequential(ll) final = init_lazy_modules(seq, torch.rand(1, 2) ``` `final` will be a full nn.Module graph with no lazy components; all of them will be "resolved" and replaced with their true module types. Differential Revision: D17837321 fbshipit-source-id: ff915ab7e73747652cfdca5fe524d13db9840e49
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Summary: Pull Request resolved: facebookresearch#1039 Add lazy modules to PyText. These are modules which are able to infer some of their dimensions on their first forward pass. The main tool introduced is Lazy, which is a Module wrapping any other Module, and to which arguments can be passed that will be used to construct that wrapped Module after the first forward pass. If any of these arguments are Infer objects, those arguments will be replaced by calling the callback of the Infer object on the forward pass input. For instance, `Lazy(nn.Linear, Infer(lambda input: input.size(-1)), 4)` would take its in_features dimension from the last dimension of the input to its forward pass. This can be simplified to `Lazy(nn.Linear, Infer.dimension(-1)`, 4), or a partial can be created, for instance `LazyLinear = Lazy.partial(nn.Linear, Infer.dimension(-1)); LazyLinear(4)`. Finally, these Lazy objects explicitly forbid treating themselves normally; they must instead be replaced by calling `init_lazy_modules` on your model before training. For instance, ``` ll = lazy.Linear(4) seq = nn.Sequential(ll) final = init_lazy_modules(seq, torch.rand(1, 2) ``` `final` will be a full nn.Module graph with no lazy components; all of them will be "resolved" and replaced with their true module types. Differential Revision: D17837321 fbshipit-source-id: 4f071a453ca2af6c234d57e83837bc15d50c68c2
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Summary:
Add lazy modules to PyText. These are modules which are able to infer some of their dimensions on their first forward pass.
The main tool introduced is Lazy, which is a Module wrapping any other Module, and to which arguments can be passed that will be used to construct that wrapped Module after the first forward pass. If any of these arguments are Infer objects, those arguments will be replaced by calling the callback of the Infer object on the forward pass input.
For instance,
Lazy(nn.Linear, Infer(lambda input: input.size(-1)), 4)would take its in_features dimension from the last dimension of the input to its forward pass. This can be simplified toLazy(nn.Linear, Infer.dimension(-1), 4), or a partial can be created, for instanceLazyLinear = Lazy.partial(nn.Linear, Infer.dimension(-1)); LazyLinear(4).Finally, these Lazy objects explicitly forbid treating themselves normally; they must instead be replaced by calling
init_lazy_moduleson your model before training. For instance,finalwill be a full nn.Module graph with no lazy components; all of them will be "resolved" and replaced with their true module types.Differential Revision: D17837321