* Fixing exl2 and other quanize tests again.
* Mark exl2 as non release (so CI tests them, needs to be removed latet).
* Fixing exl2 (by disabling cuda graphs)
* Fix quantization defaults without cuda graphs on exl2 (linked to new
issues with it).
* Removing serde override.
* Go back to released exl2 and remove log.
* Adding warnings for deprecated bitsandbytes + upgrade info to warn.
* (backend) use parking_lot crate for RwLock fairness
* (docker) let's put rust in the TRTLLM folder when building
* (docker) build ompi with SLURM support
* (launcher) default new server::run parameters to false for now
* (chore) fmt ... why?
* fix: improve completions to send a final chunk with usage details
* fix: include finish reason string
* fix: remove dev debug trait and unneeded mut
* fix: update openapi schema
This change adds support for prefix caching to the v3 router. This
is broken up from the backend support to ease reviewing.
For now prefix caching is only enabled with `USE_PREFIX_CACHING=1`
in this case, the router will switch to `RadixAllocator`. This
allocator uses a radix trie to keep track of prefills that were
seen prior. If a new prefill is a prefix of a previously-seen
prefil, the router will send a request with `prefix_len>0`, which
can be used by the backend to decide to reuse KV blocks from the
cache, rather than recomputing them.
Even though backend support is not added in this PR, the backend
will still work with prefix caching enabled. The prefix lengths
are just ignored and not used.
This change adds support for FlashInfer. FlashInfer can be enabled using
`FLASH_INFER=1` and is currently only implemented in `FlashCausalLM`.
Since this functionality is currently only for testing, FlashInfer is
not installed anywhere yet.
The FlashInfer API is quite different from FlashAttention/vLLM in that
it requires more global bookkeeping:
* A wrapper class needs to be contstructed (which we just call *state*).
Since this is fairly expensive (due to pinned host memory allocation),
we only do this once in a FlashCausalLM instance or for each CUDA
Graph size.
* Each model forward call needs to be wrapped in `begin_forward` and
`end_forward`. This sets up data structures that can be reused for all
calls to attention for that forward call.
When calling attention, we need access to the state object. To avoid
passing an argument down the call chain (which would require changes to
all models), we use a context variable.
Each model forward call is wrapped using a context manager that does all
the bookkeeping for such a call:
* Set the context variable to the forward call's state.
* Call `begin_forward` on the state.
* Yield.
* Call `end_forward` on the state.
* Reset the context variable.
We cannot use a single shared global variable for this, since e.g. CUDA
Graphs of different sizes each have their own state.
* Fix unsigned integer underflow
Passing --max-batch-size to the launcher actually had no effect
because after a few requests the max_size passed to State::next_batch
would underflow becoming a largo positive number.
In the scheduler, as soon as the cached batch size reached the
max_batch_size the max_size passed to next_batch becomes 0.
Since the only check in that funcion is
```
if Some(batch_requests.len()) == max_size {
break;
}
```
and it's called after the `batch_requests.len()` has
become 1, it doesn't do anything to prevent more than 0
requests from being batched.
Now we have cached batch in the server that is large than
max_batch_size and `max_size - batch_size as usize`
underflows.
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
* fix: update v3 scheduler and ensure max_batch_size > 0
---------
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Max de Bayser <mbayser@br.ibm.com>
* Update Quantization docs and minor doc fix.
* update readme with latest quants info
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* up
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* hotfix: fix xpu crash brought by code refine. torch.xpu rely on import ipex
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* reable gemma2 in xpu
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix in regression in ipex flashattention
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>
* Update __init__.py
Fix issue with NoneType comparison for max_input_tokens and sliding_window
- Add default values for max_input_tokens and sliding_window to handle None cases.
- Ensure the comparison between max_input_tokens and sliding_window is handled correctly to prevent TypeError.
- This change addresses the error: TypeError: '<=' not supported between instances of 'int' and 'NoneType'.
* Update __init__.py
Handle NoneType in sliding_window comparison to fix TypeError in __init__.py by ensuring the comparison logic accounts for NoneType values, preventing errors and improving code robustness.
* fix: syntax/style tweak
---------
Co-authored-by: Praz <prazanth2006@gmail.com>
* add gptj modeling
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix: update docs for model addition
* fix: adjust syntax typo
* fix: adjust syntax typo again
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>
* feat: implement a templated endpoint for visibility into chat requests
* feat: improve to tokenize too
* fix: adjust return type
* feat: simplify prepare_chat_input logic and adjust start stop chars
* fix: attempt forward on flash attn2 to check hardware support
* fix: warn window_size_left when using flash attn 1
* fix: prefer version check over test op and avoid window_size_left if not flash attn2
* fix: improve condtional and error message
* fix: update sliding window conditional
* fix: simplify changes and revert model changes
* fix: avoid changing conditional
* fix: typo tweak
- Always return the hidden states.
- Create the output tensor inside the `attention` and `paged_attention`
functions.
This removes the difference between how the output is handled between
attention (output parameter) and paged attention (return value). This
also removes the assumption that the attention implementation can
write to an output tensor (in preparation of FlashInfer).
Nicolas Patry