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  1. 17 May, 2024 1 commit
    • amyeroberts's avatar
      Remove deprecated logic and warnings (#30743) · 57c965a8
      amyeroberts authored 
      * Remove deprecated logic and warnings

* Add back some code that seems to be important...

* Let's just add all he nllb stuff back; removing it is a bit more involved

* Remove kwargs

* Remove more kwargs
      57c965a8
  3. 16 May, 2024 2 commits
  5. 14 May, 2024 1 commit
  7. 09 May, 2024 1 commit
    • Lysandre Debut's avatar
      Removal of deprecated maps (#30576) · 297b732b
      Lysandre Debut authored 
      * [test_all] Remove all imports

Remove remaining ARCHIVE MAPS

Remove remaining PRETRAINED maps

* review comments

* [test_all] empty commit to trigger tests
      297b732b
  9. 30 Apr, 2024 1 commit
  11. 24 Apr, 2024 1 commit
    • Gustavo de Rosa's avatar
      Phi-3 (#30423) · c9693db2
      Gustavo de Rosa authored 
      * chore(root): Initial commit of Phi-3 files.

* fix(root): Fixes Phi-3 missing on readme.

* fix(root): Ensures files are consistent.

* fix(phi3): Fixes unit tests.

* fix(tests): Fixes style of phi-3 test file.

* chore(tests): Adds integration tests for Phi-3.

* fix(phi3): Removes additional flash-attention usage, .e.g, swiglu and rmsnorm.

* fix(phi3): Fixes incorrect docstrings.

* fix(phi3): Fixes docstring typos.

* fix(phi3): Adds support for Su and Yarn embeddings.

* fix(phi3): Improves according first batch of reviews.

* fix(phi3): Uses up_states instead of y in Phi3MLP.

* fix(phi3): Uses gemma rotary embedding to support torch.compile.

* fix(phi3): Improves how rotary embedding classes are defined.

* fix(phi3): Fixes inv_freq not being re-computed for extended RoPE.

* fix(phi3): Adds last suggestions to modeling file.

* fix(phi3): Splits inv_freq calculation in two lines.
      c9693db2
  13. 17 Apr, 2024 1 commit
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  41. 08 Dec, 2023 3 commits
    • fxmarty's avatar
      F.scaled_dot_product_attention support (#26572) · 80377eb0
      fxmarty authored 
      

* add sdpa

* wip

* cleaning

* add ref

* yet more cleaning

* and more :)

* wip llama

* working llama

* add output_attentions=True support

* bigcode sdpa support

* fixes

* gpt-bigcode support, require torch>=2.1.1

* add falcon support

* fix conflicts falcon

* style

* fix attention_mask definition

* remove output_attentions from attnmaskconverter

* support whisper without removing any Copied from statement

* fix mbart default to eager renaming

* fix typo in falcon

* fix is_causal in SDPA

* check is_flash_attn_2_available in the models init as well in case the model is not initialized through from_pretrained

* add warnings when falling back on the manual implementation

* precise doc

* wip replace _flash_attn_enabled by config.attn_implementation

* fix typo

* add tests

* style

* add a copy.deepcopy on the config in from_pretrained, as we do not want to modify it inplace

* obey to config.attn_implementation if a config is passed in from_pretrained

* fix is_torch_sdpa_available when torch is not installed

* remove dead code

* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/bart/modeling_bart.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* remove duplicate pretraining_tp code

* add dropout in llama

* precise comment on attn_mask

* add fmt: off for _unmask_unattended docstring

* precise num_masks comment

* nuke pretraining_tp in LlamaSDPAAttention following Arthur's suggestion

* cleanup modeling_utils

* backward compatibility

* fix style as requested

* style

* improve documentation

* test pass

* style

* add _unmask_unattended tests

* skip meaningless tests for idefics

* hard_check SDPA requirements when specifically requested

* standardize the use if XXX_ATTENTION_CLASSES

* fix SDPA bug with mem-efficient backend on CUDA when using fp32

* fix test

* rely on SDPA is_causal parameter to handle the causal mask in some cases

* fix FALCON_ATTENTION_CLASSES

* remove _flash_attn_2_enabled occurences

* fix test

* add OPT to the list of supported flash models

* improve test

* properly test on different SDPA backends, on different dtypes & properly handle separately the pad tokens in the test

* remove remaining _flash_attn_2_enabled occurence

* Update src/transformers/modeling_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update docs/source/en/perf_infer_gpu_one.md
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* remove use_attn_implementation

* fix docstring & slight bug

* make attn_implementation internal (_attn_implementation)

* typos

* fix tests

* deprecate use_flash_attention_2=True

* fix test

* add back llama that was removed by mistake

* fix tests

* remove _flash_attn_2_enabled occurences bis

* add check & test that passed attn_implementation is valid

* fix falcon torchscript export

* fix device of mask in tests

* add tip about torch.jit.trace and move bt doc below sdpa

* fix parameterized.expand order

* move tests from test_modeling_attn_mask_utils to test_modeling_utils as a relevant test class is already there

* update sdpaattention class with the new cache

* Update src/transformers/configuration_utils.py
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/bark/modeling_bark.py

* address review comments

* WIP torch.jit.trace fix. left: test both eager & sdpa

* add test for torch.jit.trace for both eager/sdpa

* fix falcon with torch==2.0 that needs to use sdpa

* fix doc

* hopefully last fix

* fix key_value_length that has no default now in mask converter

* is it flacky?

* fix speculative decoding bug

* tests do pass

* fix following #27907

---------
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>
      80377eb0
    • Joao Gante's avatar
      Generate: SinkCache can handle iterative prompts (#27907) · ce0bbd51
      Joao Gante authored
      ce0bbd51
    • Tom Aarsen's avatar
      Generate: New `Cache` abstraction and Attention Sinks support (#26681) · 633215ba
      Tom Aarsen authored 
      * Draft version of new KV Caching

This should allow Attention Sinks (https://github.com/tomaarsen/attention_sinks)
/ StreamingLLM (https://arxiv.org/abs/2309.17453) to be easily implemented
in a third-party or in transformers directly

* Address numerous PR suggestions

1. Move layer_idx from cache to ...Attention. Removes confusing set_layer_idx magic.
2. Always convert past_key_values to Cache instance at the start of ...Attention, removes all other isinstance calls.
3. Remove __bool__ and __getitem__ magic as they're confusing.
4. past_key_values.update(key, value, idx) now returns key, value.
5. Add use_legacy_cache flag, defaults to None, i.e. Falsey. This breaks generate for now, until 1) the cache is used is generate() or 2) use_legacy_cache is defaulted to True in generate() until we change it in another PR.
6. Separate key_cache and value_cache.

Some work is still needed to see if the SinkCache can conveniently be implemented with just one update method.

* Implement the SinkCache through backward+forward rotations

* Integrate (Sink)Cache with Llama FA2

* Set use_legacy_cache=True as default, allows for test passes

* Move from/to_legacy_cache to ...Model class

* Undo unnecessary newline change

* Remove copy utility from deprecated OpenLlama

* Match import style

* manual rebase with main

* Cache class working with generate (#1)

* Draft version of new KV Caching

This should allow Attention Sinks (https://github.com/tomaarsen/attention_sinks)
/ StreamingLLM (https://arxiv.org/abs/2309.17453

) to be easily implemented
in a third-party or in transformers directly

* Address numerous PR suggestions

1. Move layer_idx from cache to ...Attention. Removes confusing set_layer_idx magic.
2. Always convert past_key_values to Cache instance at the start of ...Attention, removes all other isinstance calls.
3. Remove __bool__ and __getitem__ magic as they're confusing.
4. past_key_values.update(key, value, idx) now returns key, value.
5. Add use_legacy_cache flag, defaults to None, i.e. Falsey. This breaks generate for now, until 1) the cache is used is generate() or 2) use_legacy_cache is defaulted to True in generate() until we change it in another PR.
6. Separate key_cache and value_cache.

Some work is still needed to see if the SinkCache can conveniently be implemented with just one update method.

* Integrate (Sink)Cache with Llama FA2

* Move from/to_legacy_cache to ...Model class

* Undo unnecessary newline change

* Match import style

* working generate

* Add tests; Simplify code; Apply changes to Mistral and Persimmon

* fix rebase mess

* a few more manual fixes

* last manual fix

* propagate changes to phi

* upgrade test

* add use_legacy_cache docstring; beef up tests

* reintroduce unwanted deletes

---------
Co-authored-by: default avatarTom Aarsen <Cubiegamedev@gmail.com>

* move import

* add default to model_kwargs.get('use_legacy_cache')

* correct failing test

* Apply suggestions from code review
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>

* apply PR suggestions

* fix failing test

* Apply suggestions from code review
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: default avatarTom Aarsen <37621491+tomaarsen@users.noreply.github.com>

* PR comments

* tmp commit

* add docstrings

* more tests, more docstrings, add to docs

* derp

* tmp commit

* tmp dbg

* more dbg

* fix beam search bug

* cache can be a list of tuples in some models

* fix group beam search

* all but sinkcache integration tests

* fix sink cache and add hard integration test

* now also compatible with input_embeds input

* PR comments

* add Cache support to Phi+FA2

* make fixup

---------
Co-authored-by: default avatarJoao Gante <joao@huggingface.co>
Co-authored-by: default avatarJoao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      633215ba
  43. 04 Dec, 2023 1 commit
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  61. 27 Oct, 2023 2 commits
  63. 26 Oct, 2023 1 commit
  65. 25 Oct, 2023 1 commit
    • Younes Belkada's avatar
      [`core`] Refactor of `gradient_checkpointing` (#27020) · 06e782da
      Younes Belkada authored 
      * v1

* fix

* remove `create_custom_forward`

* fixup

* fixup

* add test and fix all failing GC tests

* remove all remaining `create_custom_forward` methods

* fix idefics bug

* fixup

* replace with `__call__`

* add comment

* quality
      06e782da