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prompt_ids=list(input_ids),
max_new_tokens=max_new_tokens,
eos_token_id=self.eos_token_id,
status="failed",
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@ArthurZucker ArthurZucker marked this pull request as ready for review May 12, 2025 14:55
@ArthurZucker ArthurZucker merged commit 5c859ad into feat/stream_inputs_to_continuous_batch May 12, 2025
4 of 8 checks passed
@ArthurZucker ArthurZucker deleted the add-cuda-graphs branch May 12, 2025 14:58
ArthurZucker added a commit that referenced this pull request May 22, 2025
* stash for now

* initial commit

* small updated

* up

* up

* works!

* nits and fixes

* don't loop too much

* finish working example

* update

* fix the small freeblocks issue

* feat: stream inputs to continuous batch

* fix: update attn from `eager` to `sdpa`

* refactor: fmt

* refactor: cleanup unnecessary code

* feat: add `update` fn to `PagedAttentionCache`

* feat: broken optimal block size computation

* fix: debugging invalid cache logic

* fix: attention mask

* refactor: use custom prompts for example

* feat: add streaming output

* fix: prefill split

refactor: add doc strings and unsound/redundant logic
fix: compute optimal blocks logic

* fix: send decoded tokens when `prefilling_split` -> `decoding`

* refactor: move logic to appropriate parent class

* fix: remove truncation as we split prefilling anyways

refactor: early return when we have enough selected requests

* feat: add paged attention forward

* push Ggraoh>

* add paged sdpa

* update

* btter mps defaults

* feat: add progress bar for `generate_batch`

* feat: add opentelemetry metrics (ttft + batch fill %age)

* feat: add tracing

* Add cuda graphs (#38059)

* draft cudagraphs addition

* nits

* styling

* update

* fix

* kinda draft of what it should look like

* fixes

* lol

* not sure why inf everywhere

* can generate but output is shit

* some fixes

* we should have a single device synch

* broken outputs but it does run

* refactor

* updates

* updates with some fixes

* fix mask causality

* another commit that casts after

* add error

* simplify example

* update

* updates

* revert llama changes

* fix merge conflicts

* fix: tracing and metrics

* my updates

* update script default values

* fix block allocation issue

* fix prefill split attnetion mask

* no bugs

* add paged eager

* fix

* update

* style

* feat: add pytorch traces

* fix

* fix

* refactor: remove pytorch profiler data

* style

* nits

* cleanup

* draft test file

* fix

* fix

* fix paged and graphs

* small renamings

* cleanups and push

* refactor: move tracing and metrics logic to utils

* refactor: trace more blocks of code

* nits

* nits

* update

* to profile or not to profile

* refactor: create new output object

* causal by default

* cleanup but generations are still off for IDK what reason

* simplifications but not running still

* this does work.

* small quality of life updates

* nits

* updaet

* fix the scheduler

* fix warning

* ol

* fully fixed

* nits

* different generation parameters

* nice

* just style

* feat: add cache memory usage

* feat: add kv cache free memory

* feat: add active/waiting count & req latency

* do the sampling

* fix: synchronize CUDA only if available and improve error handling in ContinuousBatchingManager

* fix on mps

* feat: add dashboard & histogram buckets

* perf: improve waiting reqs data structures

* attempt to compile, but we should only do it on mps AFAIK

* feat: decouple scheduling logic

* just a draft

* c;eanup and fixup

* optional

* style

* update

* update

* remove the draft documentation

* fix import as well

* update

* fix the test

* style doomed

---------

Co-authored-by: Luc Georges <[email protected]>
redmoe-moutain pushed a commit to redmoe-moutain/transformers that referenced this pull request Jun 10, 2025
* stash for now

* initial commit

* small updated

* up

* up

* works!

* nits and fixes

* don't loop too much

* finish working example

* update

* fix the small freeblocks issue

* feat: stream inputs to continuous batch

* fix: update attn from `eager` to `sdpa`

* refactor: fmt

* refactor: cleanup unnecessary code

* feat: add `update` fn to `PagedAttentionCache`

* feat: broken optimal block size computation

* fix: debugging invalid cache logic

* fix: attention mask

* refactor: use custom prompts for example

* feat: add streaming output

* fix: prefill split

refactor: add doc strings and unsound/redundant logic
fix: compute optimal blocks logic

* fix: send decoded tokens when `prefilling_split` -> `decoding`

* refactor: move logic to appropriate parent class

* fix: remove truncation as we split prefilling anyways

refactor: early return when we have enough selected requests

* feat: add paged attention forward

* push Ggraoh>

* add paged sdpa

* update

* btter mps defaults

* feat: add progress bar for `generate_batch`

* feat: add opentelemetry metrics (ttft + batch fill %age)

* feat: add tracing

* Add cuda graphs (huggingface#38059)

* draft cudagraphs addition

* nits

* styling

* update

* fix

* kinda draft of what it should look like

* fixes

* lol

* not sure why inf everywhere

* can generate but output is shit

* some fixes

* we should have a single device synch

* broken outputs but it does run

* refactor

* updates

* updates with some fixes

* fix mask causality

* another commit that casts after

* add error

* simplify example

* update

* updates

* revert llama changes

* fix merge conflicts

* fix: tracing and metrics

* my updates

* update script default values

* fix block allocation issue

* fix prefill split attnetion mask

* no bugs

* add paged eager

* fix

* update

* style

* feat: add pytorch traces

* fix

* fix

* refactor: remove pytorch profiler data

* style

* nits

* cleanup

* draft test file

* fix

* fix

* fix paged and graphs

* small renamings

* cleanups and push

* refactor: move tracing and metrics logic to utils

* refactor: trace more blocks of code

* nits

* nits

* update

* to profile or not to profile

* refactor: create new output object

* causal by default

* cleanup but generations are still off for IDK what reason

* simplifications but not running still

* this does work.

* small quality of life updates

* nits

* updaet

* fix the scheduler

* fix warning

* ol

* fully fixed

* nits

* different generation parameters

* nice

* just style

* feat: add cache memory usage

* feat: add kv cache free memory

* feat: add active/waiting count & req latency

* do the sampling

* fix: synchronize CUDA only if available and improve error handling in ContinuousBatchingManager

* fix on mps

* feat: add dashboard & histogram buckets

* perf: improve waiting reqs data structures

* attempt to compile, but we should only do it on mps AFAIK

* feat: decouple scheduling logic

* just a draft

* c;eanup and fixup

* optional

* style

* update

* update

* remove the draft documentation

* fix import as well

* update

* fix the test

* style doomed

---------

Co-authored-by: Luc Georges <[email protected]>
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