* bitcast & tests
* use to_dtype
* put disk tensor tests back
* tests
* bitmask
* no bitmask
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* handle float16 overflow in PYTHON
use `truncate` when constructing tensor from list to make sure all values are packable (might be slow, but should be correct). add truncate_fp16 to cast overflowed values to inf/-inf.
* all valid fmt supports truncate
replaced all dtype.np with _to_np_dtype defined in tensor.py.
after this, the only numpy usages are (1) Tensor(np.ndarray), (2) construct .numpy() output, (3) numpy random buffer
* compile raise CompileError and skip only RuntimeError in multiprocess beam
renderer error with multiprocess should not be skipped by beam
* use `==` for dtype to dtype comparison
* that needs to be is
* typo
* mockgpu nv
* works
* comment that out
* fix merge
* setup gpuocelot
* install packages
* not run all of them
* passes
* fix ci
* almost
* should pass
* linter
* linter 2
* try this?
* ugn, not supported
* ci
* remove ticket from description
* better descs
* fix mean underflow for half tensor
divide only the reduce factor. added unit test and non-nan assertion in resnet training. also added a failed test cast for symbolic shape var
* skip for python backend
* Shape changing bitcast
* only support it on disk
* basic test
* more tests
* RuntimeError instead of assert
* create unique temp files
* move tests that use disk to test_disk_tensor
* linter
* remove assert on error messages
* that's RuntimeError now
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* env var to change default float to fp16 or bf16
looking for standard names for these. we have FLOAT16 that does something to IMAGE and HALF to convert weights.
working on default bf16 too.
```
RuntimeError: compile failed: <null>(6): error: identifier "__bf16" is undefined
__bf16 cast0 = (nv_bfloat16)(val0);
```
remove that in cifar
* DEFAULT_FLOAT
* default of default
* unit test
* don't check default
* tests work on linux
* initialize Tensor grad same type as self
* also test different default float
* check dtype + try/finally
* don't test_gradient_dtype if f16 is not supported
* fix bad merge
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* diverse test value in test_dtype DATA based on dtype
* eh fix typo
* that too?
* PTX does not support i8 and s8
* skip that
* unused line
* pus the hack back
* remove that
* use int32 instead of default_int in simplify_phi_loops
indices are in int32 now and is separated from buffer dtype. fix#3823
* return early if not supported
* it's not that
* why is it failing for RHIP
