Use Infinity as a separate backend and handle the model within the
common module. This separates out the embeddings model from the endpoint
which allows for model loading/unloading in core.
Signed-off-by: kingbri <bdashore3@proton.me>
Place OAI specific routes in the appropriate folder. This is in
preperation for adding new API servers that can be optionally enabled.
Signed-off-by: kingbri <bdashore3@proton.me>
This reverts commit 21516bd7b5.
This skips EOS and implementing it the proper way seems more
costly than necessary.
Signed-off-by: kingbri <bdashore3@proton.me>
This helps make the generation loop more efficient by skipping past
chunks that aren't providing any tokens anyways. The offset isn't
affected.
Signed-off-by: kingbri <bdashore3@proton.me>
Identify which request is being processed to help users disambiguate
which logs correspond to which request.
Signed-off-by: kingbri <bdashore3@proton.me>
If a job causes the generator to error, tabby stops working until
a relaunch. It's better to try establishing a system of redundancy
and remake the generator in the event that it fails.
May replace this with an exit signal for a fatal error instead, but
not sure.
Signed-off-by: kingbri <bdashore3@proton.me>
API keys are not allowed to view all the admin's models, templates,
draft models, loras, etc. Basically anything that can be viewed
on the filesystem outside of anything that's currently loaded is
not allowed to be returned unless an admin key is present.
This change helps preserve user privacy while not erroring out on
list endpoints that the OAI spec requires.
Signed-off-by: kingbri <bdashore3@proton.me>
If flash attention is already turned off by exllamaV2 itself, don't
try creating a paged generator. Also condense all the redundant
logic into one if statement.
Also check arch_compat_overrides to see if flash attention should
be disabled for a model arch (ex. Gemma 2)
Signed-off-by: kingbri <bdashore3@proton.me>
Move the large import errors into the check functions themselves.
This helps reduce the difficulty in interpreting where errors are
coming from.
Signed-off-by: kingbri <bdashore3@proton.me>
* Model: Clean up paged attention checks
* Model: Move cache_size checks after paged attn checks
Cache size is only relevant in paged mode
* Model: Fix no_flash_attention
* Model: Remove no_flash_attention
Ability to use flash attention is auto-detected, so this flag is unneeded. Uninstall flash attention to disable it on supported hardware.
* Model: More extensive checks for paged attention
Previously, TabbyAPI only checked for whether the user's hardware supports flash attention before deciding whether to enabled paged mode.
This adds checks for whether no_flash_attention is set, whether flash-attn is installed, and whether the installed version supports paged attention.
* Tree: Format
* Tree: Lint
* Model: Check GPU architecture first
Check GPU arch prior to checking whether flash attention 2 is installed
Cache size must be a multiple of 256 to work properly in ExllamaV2.
Take the config value and set the cache size to one multiple above
the remainder of the cache size divided by 256.
This is because cache size can never be lower than max_seq_len.
If max_seq_len isn't a multiple of 256, this method will never
yield a number that's lower than max_seq_len since it's no longer
a source of truth.
Signed-off-by: kingbri <bdashore3@proton.me>
Depending on the day of the week, Starlette can work with a CancelledError
or using await request.is_disconnected(). Run the same behavior for both
cases and allow cancellation.
Streaming requests now set an event to cancel the batched job and break
out of the generation loop.
Signed-off-by: kingbri <bdashore3@proton.me>
If a user is using GPU split, check compute capability on only those
GPUs. Autosplit assumes that all GPUs will be used.
Signed-off-by: kingbri <bdashore3@proton.me>
List comprehensions are the more "pythonic" way to approach mapping
values to a list. They're also more flexible across different collection
types rather than the inbuilt map method. It's best to keep one convention
rather than splitting down two.
Signed-off-by: kingbri <bdashore3@proton.me>
FA2 v2.5.7 and up is not supported below ampere and on AMD GPUs.
Clarify the error message and explain what happens as a result.
Signed-off-by: kingbri <bdashore3@proton.me>
Add a sequential lock and wait until jobs are completed before executing
any loading requests that directly alter the model. However, we also
need to block any new requests that come in until the load is finished,
so add a condition that triggers once the lock is free.
Signed-off-by: kingbri <bdashore3@proton.me>
The dynamic generator requires Flash attention 2.5.7 or higher to
be installed. This is only supported on Nvidia's 30 series and higher.
If a card is AMD or lower than the 30 series, switch to compatability
mode which functions the same way as the older generator, except
without parallel batching and any features that depend on it, such as
CFG.
Signed-off-by: kingbri <bdashore3@proton.me>
Dynamic generator needed multiple prompts to be tokenized and sent
for them to be sampled in serial, but generated in parallel.
Signed-off-by: kingbri <bdashore3@proton.me>
The new async dynamic job allows for native async support without the
need of threading. Also add logprobs and metrics back to responses.
Signed-off-by: kingbri <bdashore3@proton.me>
Dynamic gen takes in filters differently. Adjust to set the filter list
per class rather than in the generation function.
Signed-off-by: kingbri <bdashore3@proton.me>
Adds basic support for ExllamaV2's dynamic generator. Can generate
a streaming and non-streaming completion.
Signed-off-by: kingbri <bdashore3@proton.me>
