* fix edge cases in memsize_to_str()
Inputs <= 1 now return "0.00 B" for 0 and "1.00 B" for 1, avoiding an
IndexError. Also, memsize_to_str(1000) now returns "1.00 KB" instead of
"1000.00 B".
Replaced the list comprehension with a next(...) generator for conciseness
and efficiency.
* simplify code using idiomatic python
- Remove the unused `memsize_to_str()` function in helpers.
- Use a tuple for checking multiple string prefixes/suffixes.
- Avoid unnecessary list construction by using iterables directly.
- Check None in @diskcache to ensure proper caching of falsy values.
* revert generators back to list comprehension
Sometimes building list first could be faster. Keep it as is.
* different way to write torch backend
* both backends
* more work
* simpler code
* more work
* test both
* imply unwrap/wrap
* FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add works
* ready to start making test_ops work in torch backend
* backward pass, TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add works
* FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_simple_conv2d works
* matmul backward is broken with as_strided
python time 45ms -> 9ms, it was spending time to schedule the shard
also init bert data on CLANG since it's from numpy, so we don't create the tensor on default device then shard into GPUS
`DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 EVAL_BS=6 GPUS=6 MODEL=bert python3 examples/mlperf/model_train.py` no longer oom. I think the buffer of random init weights caused the oom.
* WebGPU f16 support
* Don't enable f16 yet
* dtype tests passing after bitcast fix
* Maybe all WebGPU green?
* Require shader-f16 in examples
* Minor wgsl touchup
* 1 line shorter
* Simpler
* Add transcendetal support
* log2 nan location mismatch on Vulkan
* Nan skips
* start
* log severity
* only change this
* change abstraction so it's more usable for huggingface
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* MultiLazyBuffer is UOp [pr]
* this is new mlb
* this is the idea
* progress
* multitensor works
* more movement ops
* this
* MultiLazyBuffer is UOp
* cleanups
* multi axis
* fix more tests
* work
* not that
* add multi grad and move shard to ops
* mops not views
* no double contig
* sweet, all mt tests passing
* port old logic
* remove lbs
* fix realized
* whitespace
* assign tweak
* test_assign_kv_cache_multi passes
* fix is_realized
* fix JIT for multi
* just a few more lines i'll pay them back soon i swear please bro just a few more
* no split reduceop for multi
* this
* clean up
* more clean ups and improve debug msg
* more correct training toggler
* remove manual training toggling
* change some variable names
* actually just add the training toggle for LIMIT envvar too
* more refinement
* __call__ and OnnxRunner
* fix half pylint, other half is importing from onnx while this file is onnx.py, figure out later
* ahhhh found another mistake
* remove limit from __call__
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* is 67% considered fixed?
* move test up
* share function
* add qgemm too
* make sure qgemm comes out as int
* actually that note is not right
* remove qgemm (I did it wrong) and add it later lol.
