* fixed division by zero for fast operations
* made et closer to 0
* replace POW llop with SQRT
* updated mlops to swap SQRT and POW llops
* updated hlops to swap POW and SQRT
* added sqrt llop to cpu runtime
* added sqrt llop to cstyle codegen
* added POW llop to llvm ir codegen
* added SQRT llop to torch runtime
* moved pow from mlops to hlops
* found a better way to do reverse pow
* fixed indentation
* added SQRT llop to triton
* update docs to match new llops
* removed POW operator from assembly codegen
* added sqrt and rsqrt to pow hlop
* rewrote pow function in tensor.py
* Adjust tolerance
* Adjust for adamw
* Reduce for Adam too
* removed accidental leftover code
* removed all of accidental code
* added rsqrt test
* removed pow from mlops again
it was added back when resolving merge conflicts
---------
Co-authored-by: Jacky Lee <jla524@sfu.ca>
* Dedup params in optimizer
* Passing the same tensor multiple times in the set of learnable params passed to optimizers can result in models completely failing to learn, but no errors are produced. This dedups tensors to avoid the problem.
* Fix types
* Use new variable to satisfy linter
* Use `helpers.dedup` instead of `set()` to dedup params
* Add test for duped params in optimizers
* Split tests
Split tests into "Test CPU" and "Test GPU".
Add test flag "TEST_DEVICES" which is a comma separated list of devices:
CPU,GPU,ANE
* Run tests based on provided TEST_DEVICES flag
By default will run all "CPU,GPU,ANE"
* fix bad quote
* Revert changes and use GPU=1
This is done through setting the default Tensor Device to Device.CPU of
GPU=1 is set.
Run GPU tests: GPU=1 pytest -s -v
* Update all devices to be tested
ANE, CPU and OCL all now support all tests.
However tests are not currently passing on GPU and I cannot test on CPU.
Failing GPU test are not an issue caused by this update. Tests have not
been passing due to a missing "six" required installation.
OpenCL Tests have not been run since commit: 1a1c63a08b
devices have 3 types and are handle by a new DeviceTypes enum. (The goal
is to revert to Tensor.<type>, but this current setup allows for keyword
argument defaults: `device=DeviceType.CPU`)
All references to Tensor.GPU/CPU/ANE as been converted to the
corresponding `DeviceTypes` enum.
Refactor of the conversion code to allow for any device to any device
conversion.
* Add six dependency in requirements.txt
* Resolve failure to run tests
Move six into gpu required installs. Remove six from standard
installation.
* Remove repeated data conversion
* Refactor method names
Also reduce code with .to and .to_
* Dynamic device handlers
* Refactor DeviceTypes -> Device
* Add mem copy profiling back
* test_backward_pass_diamond_model passing
* Resolve Sum issue on GPU
* Revert batchnorm2d tests
* Update README with upadated API
* ANE testing with
* Last minute line gains
* Consistent GPU classes
Convert the existing GPU classes into one standard format.
Remove duplicated functions in `test_mnist` and create a TestMNISTGPU
class. This reduces line count and ensures consistency.
Use `@unittest.skipUnless(GPU, "Requires GPU")` instead of `if GPU:` to
skip GPU testing. This will ensure that skipped tests are displayed
accordingly in the pytest output.
* Optim Testing now supports GPU
* Tensor testing now supports GPU
jacobian and gradcheck auto skipped until GPU float64 support added.
* GPU support for custom constructor methods
* Remove GPU flag from Model constructors
It was requested that the `gpu` kwarg be removed from the model
constructor. GPU conversion is now handled in the train function.
This also required the conversion of Optimizer parameters as they are
constructed prior to execution of the `train` function and are dependant
on the model GPU state.
* Fix typo: float32->float64
* Clean `get_parameters` utility
Just a quick refactor w/ the new support for optimizers.
* Remove GPU kwarg from TinyNet
Remove `gpu` kwarg from tiny net to match test_mnist `train` function.
