* Quantized phi in a separate file.
* Add the quantized phi example + rework the model code.
* Improve the phi model.
* Get some generation out.
* Use the appropriate rope shape.
* Tweak the default prompt.
---------
Co-authored-by: Jane Doe <jane.doe@example.org>
* moondream implementation
* add moondream example
* change config default activation
* Add assets and integrate phi mixformer with example
* Make use of kv cache and fix seq_len bug; Clean up example code
* Add README link to example
* Remove pos_embed scaling; Remove assets; Add to README; Expand VisionConfig
* Delete image
* Use apply instead of forward
* Use latest release special token; Fix token/s accuracy; Use GeluPytorchTanh in VisionConfig v2
* Derive debug and clone traits for Moondream model.
* add basic unary bench for sqrt
* process unary commands in tiles of 4
* re-enable all benchmarks
* rename helper to unary
* modify approach to split up tiled and non-tiled operations
* undo bench ignore for other tests
* update tile size to 2
* only perform the optimization on the contiguous even numbered element case
* add support for l3b, new tokenizer
* add todo
* Add todo and use k_s model
* Use the official tokenizers.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Add the mmv kernels for smaller sizes.
* Support more mmv kernels.
* Use the new kernels.
* Fix the call.
* Silly fix.
* Improve the testing.
* Fix for dmmv.
* Add another dedicated test for the batching mmv.
* Utilize batches in Stable Diffusion that were already there, but unutilized.
Also refactor out the `save_image` function.
* Clippy + cosmetic fixes.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Fix for the batch dim in the quantized matmul example.
* Enable more tests on cuda.
* Add a test for qmm with a batch.
* Fix the zeros-dim test on metal.
* Hook the quantized matmul cuda kernels.
* Add a (currently broken) test.
* Kernel fixes.
* Fix by transposing the rhs matrix.
* Add the q4-1 kernels.
* Proper block sizes.
* More details in the tests.
* This change avoids crashes when running T5 models with F16 tensors on CPU.
* This enables running ProstT5's (https://huggingface.co/Rostlab/ProstT5) encoder-only mode in Candle. This ProstT5 mode stores it's embed_tokens weights within the encoder, as its decoding stage was replaced with a CNN. You could write more, like: This alone is not sufficient to run ProstT5 within Candle examples. We will develop a ProstT5 runner outside candle for now, but would be willing to upstream it to candle-examples at a later point.
* Revert "This enables running ProstT5's (https://huggingface.co/Rostlab/ProstT5) encoder-only mode in Candle. This ProstT5 mode stores it's embed_tokens weights within the encoder, as its decoding stage was replaced with a CNN. You could write more, like: This alone is not sufficient to run ProstT5 within Candle examples. We will develop a ProstT5 runner outside candle for now, but would be willing to upstream it to candle-examples at a later point."
This reverts commit d886d3ce5e.
* This change avoids crashes when running T5 models with F16 tensors on CPU.
* This enables running ProstT5's (https://huggingface.co/Rostlab/ProstT5) encoder-only mode in Candle. This ProstT5 mode stores it's embed_tokens weights within the encoder, as its decoding stage was replaced with a CNN. This alone is not sufficient to run ProstT5 within Candle examples. We will develop a ProstT5 runner outside candle for now, but would be willing to upstream it to candle-examples at a later point.
* Start adding the recurrent-gemma model.
* More griffin.
* Add the example + get the weights to load from the HF version.
* More inference code.
* Rope + kv-cache on the attention side.
* Add to the inference code.
* Add more to the recurrent gemma inference.
* Get some first inference to run.
* Add the softcap on logits.
* Fixes.
* Use partial rotary embeddings.
* Get inference to work.
* Add a comment.
* And add a readme.
* Move the metal kernels utils in a separate module.
* Use the BufferOffset for unary ops.
* Fix clippy lints.
* Use the new BufferOffset.
* Adapt the binary ops.
* Affine.
* More ops (powf, elu, cast).
