Quantized weights were loaded in the `Weights` class, but this was
getting quite unwieldy, where every higher level method to load weights
was a long conditional to cover all the different quantizers.
This change moves loading of quantized weights out of the `Weights`
class. This is done by defining a simple `WeightsLoader` interface
that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`,
and `MarlinWeightsLoader`. These implementations are in the quantizers'
respective modules. The `Weights` class provides the low-level load
operations (such as loading tensors or sharded tensors), but delegates
loads that need quantizer-specific weight processing to a loader. The
loaders still use the low-level functionality provided by `Weights`.
I initially tried making a hierarchy where a class like `GPTQWeights`
would inherit from `Weights`. But it is not very flexible (e.g. does
not work well with the new weight storage mock used in tests) and
the implicit indirections made the code harder to follow.
* Refactor dead code.
* First working step.
* Remove a lot of duplicated code.
* More dead code.
* More cleanup.
* Fix Santacoder test.
* Fixing the simple tests.
* Fixing sharding.
* Fixes for VLM.
* Fixing santacoder (num_kv_heads hardcoded).
* Removing more dead code.
* Fixing `config.n_head`.
* Stopping earlier because of `<end_of_utterance>` in idefics2.
* Addresses comments.
* Removing the dead code.
* Fuse back mistral into FlashCausalLM.
* Finish removal.
* Fixing docs + causal_lm `batch_class`.
* Fixing docs + causal.lm.
* Add default to Gemma Causality.
* Default value for gemma/gemma2.
* Wrong default.
* feat: first draft load multiple lora
* feat: load weights within layer and refactor lora pass
* fix: refactor and reduce lora math
* feat: baseline impl single request multi lora support
* feat: prefer lorax implementation and port loading logic
* fix: prefer adapter_data and refactors
* feat: perfer loraxs custom punica kernels and add mlp loras
* fix: adjust batch for bgmv
* fix: adjust adapter_segments logic when in batch
* fix: refactor and move changes to v3 proto
* fix: pass model_id for all flash causal lms
* fix: pass model_id for all causal and seq2seq lms
* fix: add model_id to model test
* feat: add lora support to mistral and refactors
* feat: prefer model id in request
* fix: include rust code for adapter id
* feat: bump launcher and add new lora docs
* feat: support base model generation and refactors
* fix: rename doc to retry ci build
* feat: support if vlm models
* fix: add adapter_data param and avoid missing layers
* fix: add adapter_data param to phi and neox
* fix: update all models forwards to include adapter_data
* fix: add model_id to IdeficsCausalLM
* Update lora.md
Fixed a typo
* Update lora.md
Fixing spam image
* fix: add lora kernel to dockerfile, support running without kernels and refactors
* fix: avoid dockerfile conflict
* fix: refactors and adjust flash llama lora logic
* fix: skip llama test due to CI issue (temp)
* fix: skip llama test CI (temp) 2
* fix: revert skips and prefer updated ci token for tests
* fix: refactors and helpful comments
* fix: add noop in TensorParallelAdapterRowLinear too
* fix: refactor and move shard_lora_weights logic
* fix: exit early if no adapter_data
---------
Co-authored-by: Derek <datavistics@gmail.com>
The router will now send the input as chunks besides as a single
string. This change modifies the server to process chunked input
rather than strings. This also allows us to remove the image
extraction code from the server.
# What does this PR do?
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Co-authored-by: Joshua Rosenkranz <joshua.rosenkranz@gmail.com>
# What does this PR do?
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# What does this PR do?
Superseeds #1459
The fix works as follows.
We updated next_token_chooser to return all logprbs, then
batch_top_n_tokens, now also gets accepted_ids + speculated_length (so
it knows how to interpret the flat logprobs).
We then update the code to return lists ot `Tokens` that it expects.
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This PR addresses an issue where calling `model = model.cuda()` would
throw a ValueError when `quantize` is set to "bitsandbytes".
```
> File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 147, in serve_inner
model = get_model(
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/__init__.py", line 295, in get_model
return CausalLM(
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/causal_lm.py", line 515, in __init__
model = model.cuda()
File "/opt/conda/lib/python3.9/site-packages/transformers/modeling_utils.py", line 1998, in cuda
raise ValueError(
ValueError: Calling `cuda()` is not supported for `4-bit` or `8-bit` quantized models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.
```
Co-authored-by: mmnga <mmnga1mmnga@gmail.com>
Superseed #1024
# What does this PR do?
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---------
Co-authored-by: bangoz <ch_xie@pku.edu.cn>
# What does this PR do?
I made an embarrassing mistake where I accidentally passed normal
softmax probabilities into `batch_top_tokens` for `CausalLM` and
`Seq2SeqLM`.
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@Narsil
if there's no cuda. disable custom kernels
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
# What does this PR do?
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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
@njhill,
temporary workaround to be able to run our CI as secrets are not
available to runners run by external contributors. I will ask around to
see if there is a better way.
Co-authored-by: Nick Hill <nickhill@us.ibm.com>
Changes only the type from `bool` to `Option<Enum>` pretty much
everywhere.
- Use `Optional[str]` in Python (easier to manage than importing type
everywhere). Except for the cli to get proper validation
- Updated all models to handle gracefully new values. (Error out if
unknown value, or gptq since not implemented).
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drbh