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apaszke
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Dec 29, 2016
apaszke
commented
Contributor
- Fixed all warnings from nn docs
- Fixed all warnings from optim docs
- Added autograd docs
soumith
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Dec 29, 2016
| @@ -1,10 +1,41 @@ | |||
| """ | |||
| torch.autograd provides classes and functions implementing automatic | |||
| differentiation of arbitrary scalar valued functions. It requires only minimal | |||
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| """ | ||
| torch.autograd provides classes and functions implementing automatic | ||
| differentiation of arbitrary scalar valued functions. It requires only minimal | ||
| changes to the existing code - you only need to wrap all tensors in |
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| class Variable(_C._VariableBase): | ||
| """Wraps a tensor and records operations applied to it. |
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| class Variable(_C._VariableBase): | ||
| """Wraps a tensor and records operations applied to it. | ||
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| Variable is only a thin wrapper around a Tensor object, that also holds |
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| """Wraps a tensor and records operations applied to it. | ||
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| Variable is only a thin wrapper around a Tensor object, that also holds | ||
| the gradient w.r.t. to it, and a reference to a function that output it. |
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| Variable is only a thin wrapper around a Tensor object, that also holds | ||
| the gradient w.r.t. to it, and a reference to a function that output it. | ||
| This reference allows of retracing the whole chain of operations that |
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| the gradient w.r.t. to it, and a reference to a function that output it. | ||
| This reference allows of retracing the whole chain of operations that | ||
| created the data. If the Variable has been created by the user, its creator | ||
| will be ``None`` and we call such objects leaf Variables. |
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| Parameters: | ||
| reward(Tensor): Tensor with per-element rewards. It has to match | ||
| the location and shape of Variable's data. |
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soumith
approved these changes
Dec 29, 2016
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2 of 12 tasks
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i fixed the typos and merged. |
mrshenli
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Apr 11, 2020
* [UPDATED] AWS distributed training tutorial * Minor fix
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Sep 23, 2020
* Get a crazy test example working. * Change problem size and tile size, still an issue with N > 32. * Add sync threads in loops that read from smem, to make sure we finish reading before writing. * Predicate off threads bound to a broadcast dim of an output when its in shared memory. * Predicate smem tiling writing based on broadcasted dims in consumer. * Cleanup example a bit. * Revert "Add sync threads in loops that read from smem, to make sure we finish reading before writing." This reverts commit dffaa76. Revert this in favor of pytorch#383 * Add _syncthreads for Write-After-Read Race (pytorch#383) * Basic Write-After-Read (WAR) check to add __syncthreads to end of for-loop * Enable Tiled GEMM example * Check that IterDomain iterates from zero to some positive integer Co-authored-by: Ryan Spring <[email protected]> * Refactor thread predication for writes to smem Co-authored-by: Naoya Maruyama <[email protected]> Co-authored-by: Ryan Spring <[email protected]> Co-authored-by: Ryan Spring <[email protected]>
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Sep 24, 2020
* Get a crazy test example working. * Change problem size and tile size, still an issue with N > 32. * Add sync threads in loops that read from smem, to make sure we finish reading before writing. * Predicate off threads bound to a broadcast dim of an output when its in shared memory. * Predicate smem tiling writing based on broadcasted dims in consumer. * Cleanup example a bit. * Revert "Add sync threads in loops that read from smem, to make sure we finish reading before writing." This reverts commit dffaa76. Revert this in favor of pytorch#383 * Add _syncthreads for Write-After-Read Race (pytorch#383) * Basic Write-After-Read (WAR) check to add __syncthreads to end of for-loop * Enable Tiled GEMM example * Check that IterDomain iterates from zero to some positive integer Co-authored-by: Ryan Spring <[email protected]> * Refactor thread predication for writes to smem Co-authored-by: Naoya Maruyama <[email protected]> Co-authored-by: Ryan Spring <[email protected]> Co-authored-by: Ryan Spring <[email protected]>
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Dec 14, 2020
* Fixes pytorch#320 Signed-off-by: Jason Plurad <[email protected]> Co-authored-by: Vinitra Swamy <[email protected]>
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Summary: X-link: meta-pytorch/data#368 This is PR aims to expose the right data-relate API. There are two more changes made in this PR to convert public api to private api `check_lambda_fn` -> `_check_lambda_fn` `deprecation_warning` -> `_deprecation_warning` Pull Request resolved: #76143 Reviewed By: albanD, NivekT Differential Revision: D35798311 Pulled By: ejguan fbshipit-source-id: b13fded5c88a533c706702fb2070c918c839dca4
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Summary: X-link: meta-pytorch/data#368 This is PR aims to expose the right data-relate API. There are two more changes made in this PR to convert public api to private api `check_lambda_fn` -> `_check_lambda_fn` `deprecation_warning` -> `_deprecation_warning` Pull Request resolved: #76143 Reviewed By: albanD, NivekT Differential Revision: D35798311 Pulled By: ejguan fbshipit-source-id: b13fded5c88a533c706702fb2070c918c839dca4 (cherry picked from commit 0b534b8)
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Summary: X-link: meta-pytorch/data#368 This is PR aims to expose the right data-relate API. There are two more changes made in this PR to convert public api to private api `check_lambda_fn` -> `_check_lambda_fn` `deprecation_warning` -> `_deprecation_warning` Pull Request resolved: pytorch#76143 Reviewed By: albanD, NivekT Differential Revision: D35798311 Pulled By: ejguan fbshipit-source-id: b13fded5c88a533c706702fb2070c918c839dca4 (cherry picked from commit 0b534b8)
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