Update thinc.ai to v8.1.1 #762
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* Ops: replace FloatsType by constrained typevar
Ops used the `FloatsType`, which had `FloatsXd` as its bound. MyPy could
not infer that code such as the following is correct,
```
def dish(self, X: FloatsType, inplace: bool = False) -> FloatsType:
tmp = X * X
# ...
```
because the inferred type is the union (or a subtype). If we instead
constrain the type variable as follows:
```
FloatsType = TypeVar("FloatsType",
Floats1d, Floats2d, Floats3d, Floats4d)
```
the type paramater will be instantiated with a single concrete type,
solving such issues.
* Remove a bunch of casts and ignores that are not necessary anymore
* Unroll `argmax` in `maxout` for small sizes of `P` `maxout` uses the `argmax` function to determine the index of the maximum value of each `P` inputs. `argmax` uses a generic array loop, which impedes speculative execution and `could` also prevent unrolling of the outer `maxout` loop. This change unrolls `argmax` with small values of `P` using a variadic template. This leads to a small performance improvement. * Unmodernize struct initialization
This is purely a cosmetic change, but less confusing than thinc-io :).
* Add with_signpost_interval layer This layer wraps a layer, adding macOS interval signposts for the forward and backward pass. These intervals can then be visualized in the macOS Instruments.app timeline. * Fix reference in api-layers.md Co-authored-by: Madeesh Kannan <[email protected]> * End message is optional since signpost 0.0.3 * with_signpost_interval: also wrap init callback * docs: we wrap init as well * Add documentation fixes Suggested by @svlandeg. Co-authored-by: Madeesh Kannan <[email protected]>
* Add Ops.(backprop_)dish and CUDA kernel Dish is a Swish/GELU-like activation function. Since it does not rely on elementary operations like `exp` or `erf`, it can generally be computed faster than Swish and GELU: https://twitter.com/danieldekok/status/1484898130441166853 * Make mypy happy Apparently, X * X does not typecheck (?!?). * test_compare_activations_to_torch: test with different dY Also fix the backprop_dish CUDA kernel, which would fail now (thanks @shadeMe). * test_compare_activations_to_torch: be slightly more (absolute) tolerant Or the Dish test would fail (possibly different accuracies for sqrt?). * doc fix * Update dish types to use `FloatsXdT` * docs: add version tag to `(backprop_)dish` * Add Dish Thinc layer * Add Dish layer docs Also update description as suggested by @kadarakos. * Fix dish description Co-authored-by: Madeesh Kannan <[email protected]> Co-authored-by: Madeesh Kannan <[email protected]>
Co-authored-by: explosion-bot <[email protected]>
* Remove redundant tests. Add confection to requirement.txt and setup.cfg. Adjust cnfig.py. * Add reference to confection in website/docs/usage-config.md. * Update confection reference in docs. * Extend imports fro confection for backwards compatibility.
* `PyTorchGradScaler`: Cache `_found_inf` on the CPU This prevents unnecessary overhead from launching kernels on the GPU in hot backward passes. * Only pin `_found_inf` to the CPU * Always store `_found_inf` as a `bool`
* work with cupy arrays and 2d arrays * force mypy pass * addressing comments * return correct shape empty array * test remap_ids with Ints2d * Update thinc/layers/remap_ids.py Co-authored-by: Daniël de Kok <[email protected]> * use numpy array * remove cupy import * mini fix * more strict typing * adjust test * Update thinc/layers/remap_ids.py Co-authored-by: Adriane Boyd <[email protected]> * remove check * Update thinc/layers/remap_ids.py Co-authored-by: Adriane Boyd <[email protected]> * address reviews * Update thinc/layers/remap_ids.py Co-authored-by: Adriane Boyd <[email protected]> * simplify casting * Update thinc/layers/remap_ids.py Co-authored-by: Adriane Boyd <[email protected]> * Update thinc/layers/remap_ids.py Co-authored-by: Adriane Boyd <[email protected]> * remap_ids legacy * legacy * test version 1 and 2 * rename legacy to v1 * adding old test back * remap_ids docs update * Update website/docs/api-layers.md Co-authored-by: Adriane Boyd <[email protected]> * Update website/docs/api-layers.md Co-authored-by: Adriane Boyd <[email protected]> * make init/forward attribute setting more clear * Update website/docs/api-layers.md Co-authored-by: Adriane Boyd <[email protected]> * Update website/docs/api-layers.md Co-authored-by: Adriane Boyd <[email protected]> * Update website/docs/api-layers.md Co-authored-by: Adriane Boyd <[email protected]> * prettier * update model type * prettier * Use new _v2 instead of renamed _v1 Co-authored-by: Daniël de Kok <[email protected]> Co-authored-by: Adriane Boyd <[email protected]>
Co-authored-by: explosion-bot <[email protected]>
…w` (explosion#757) `tf.experimental.dlpack.from_dlpack` expects a `PyCapsule` object.
* Remove references to FastAPI being an Explosion product. * Remove period at end of subheader.
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