[generate_vmap_rule] Delete unused output_shapes #92024
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We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/92024
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This was referenced Jan 11, 2023
We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
soulitzer
approved these changes
Jan 13, 2023
| def reductify(grad_input, grad_input_bdim, input_bdim, input_shape_without_bdim, batch_size, | ||
| allow_expanded_grad=True): | ||
| def reductify(grad_input, grad_input_bdim, input_bdim, batch_size, | ||
| reduce_to_input_shape_without_bdim=None): |
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nit: "reduce_to_input_shape_without_bdim" sounds like a bool because it begins with a verb (when it is supposed to be a shape). Just to shuffle that around, maybe "{target,input}_shape_without_bdim{_to_reduce_to,}" is better?
We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
We don't actually need `output_shapes` to implement `generate_vmap_rule=True` support for autograd.Function. - We need this in the vjp (backward) case because autograd automatically reduces grad_inputs to inputs and we need to replicate that behavior. In order to replicate that behavior, we recorded the original input shapes so we know how to reduce the grad_input. - There is no such behavior for forward-mode AD, so we don't need to pass an `output_shapes` to reductify. This PR simplifies the API of `reductify` and `reductify_leaf`. Instead of accepting `input_shape_without_bdim` and `allow_expanded_grad`, we now combine these into a single argument, `reduce_to_input_shape_without_bdim`. - if it is None, then we don't do anything - if it is not-None and a shape, then we will reduce the grad to the provided shape. Test Plan: - updated original unittests - wait for test suite [ghstack-poisoned]
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Stack from ghstack:
We don't actually need
output_shapesto implementgenerate_vmap_rule=Truesupport for autograd.Function.reduces grad_inputs to inputs and we need to replicate that behavior.
In order to replicate that behavior, we recorded the original input
shapes so we know how to reduce the grad_input.
pass an
output_shapesto reductify.This PR simplifies the API of
reductifyandreductify_leaf. Insteadof accepting
input_shape_without_bdimandallow_expanded_grad, wenow combine these into a single argument,
reduce_to_input_shape_without_bdim.provided shape.
Test Plan: