# What does this PR do?
Fix#1095
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Fixes # (issue)
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Just removed `--` from the arguments.
With `--` bitsandbytes and bitsandbytes-nf4 are considered an option
which they are not
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Fixes # (issue)
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# What does this PR do?
This PR is meant to handle cases in which the images provided are
invalid.
## Who can review?
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passed. Feel free to tag
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@Narsil
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
This fixes flash attention v1 which was always
NotImplementedError("window_size_left is only available with flash attn
v2").
Currently flash_llama_modeling.py doesn't override the default value of
window_size_left when calling attention(..) (line 282). This means that
window_size_left will always be the default of -1, but flash attention
v1 throws an exception if `window_size_left != 0`.
To fix this, we should be checking `window_size_left != -1` before
throwing the NotImplementedError.
Fixes#1084
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# What does this PR do?
Fixes#1079
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Fixes # (issue)
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Adding a new line to escape between heading and codeblock. However, it
is a hotfix and I will work on a permanent solution on
https://github.com/huggingface/doc-builder
I had to open this PR since I initially worked from my fork, and it
requires a handful of work to trigger a new github action on my fork's
specific branch (couldn't find a way, at least, despite trying all of
them).
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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`FastLinear.load` requires 4 parameters, but in the following only 3 are
given. This PR fix this.
```python
# server/text_generation_server/models/custom_modeling/opt_modeling.py
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = FastLinear.load(
config, prefix="model.decoder.project_out", bias=False
)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = FastLinear.load(
config, prefix="model.decoder.project_in", bias=False
```
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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?
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Fixes # (issue)
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---------
Co-authored-by: zhaosida <zhaosida@corp.netease.com>
Added note on serving supported models from a different folder without
re-downloading them.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
Upgrade all relevant versions and dependencies.
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Fixes # (issue)
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# 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
# What does this PR do?
Install curl within base image, negligible regarding the image volume
and will allow to easily perform a better health check. Not sure about
the failing github actions though. Should I fix something ?
Signed-off-by: Raphael <oOraph@users.noreply.github.com>
Co-authored-by: Raphael <oOraph@users.noreply.github.com>
# What does this PR do?
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<!-- Remove if not applicable -->
Model Class attributes `_tied_weights_keys`, `
_keys_to_ignore_on_load_missing` can only be `None` or a List.
`getattr(class_, "_keys_to_ignore_on_load_missing", [])` will return
`None` if `_keys_to_ignore_on_load_missing` is None, and
`discard_names.extend(None)` will trigger an exception, even though
`_tied_weights_keys` exists.
## Who can review?
@OlivierDehaene @Narsil
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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<!-- Remove if not applicable -->
Support local config file to avoid unexpected `discard_names`, which
causes #1057.
In the case of launching local mode without `model.safetensors` file,
the original code will result `discard_names = []` when
`hf_hub_download` throws an connection error.
```python
# server/text_generation_server/cli.py
try:
import transformers
import json
config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
with open(config_filename, "r") as f:
config = json.load(f)
architecture = config["architectures"][0]
class_ = getattr(transformers, architecture)
# Name for this varible depends on transformers version.
discard_names = getattr(class_, "_tied_weights_keys", [])
discard_names.extend(getattr(class_, "_keys_to_ignore_on_load_missing", []))
except Exception as e:
discard_names = []
```
The expected `_tied_weights_keys` of OPT-1.3b is `["lm_head.weight"]`,
and its tied weight `"model.decoder.embed_tokens.weight"` will be kept
in the safetensors conversion. But the above empty `discard_names` will
lead to `"lm_head.weight"` being kept and
`"model.decoder.embed_tokens.weight"` being discard in the subsequent
method `_remove_duplicate_names`, which causes error #1057.
So add a local mode branch to get the expected `discard_names` like
follows. This modification also applies to other models
```python
# server/text_generation_server/cli.py
if is_local_model:
config_filename = os.path.join(model_id, "config.json")
else:
config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
```
In addition, when `_tied_weights_keys` or
`_keys_to_ignore_on_load_missing` is `None`, the above code will also
throw an error unexpectedly. This is fixed in PR #1052
## Before submitting
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# What does this PR do?
Fixes#1056
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# Add AWQ quantization inference support
Fixes
https://github.com/huggingface/text-generation-inference/issues/781
This PR (partially) adds support for AWQ quantization for inference.
More information on AWQ [here](https://arxiv.org/abs/2306.00978). In
general, AWQ is faster and more accurate than GPTQ, which is currently
supported by TGI.
This PR installs 4-bit GEMM custom CUDA kernels released by AWQ authors
(in `requirements.txt`, just one line change).
Quick way to test this PR would be bring up TGI as follows:
```
text-generation-server download-weights abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq
text-generation-launcher \
--huggingface-hub-cache ~/.cache/huggingface/hub/ \
--model-id abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq \
--trust-remote-code --port 8080 \
--max-input-length 2048 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
--quantize awq
```
Please note:
* This PR was tested with FlashAttention v2 and vLLM.
* This PR adds support for AWQ inference, not quantizing the models.
That needs to be done outside of TGI, instructions
[here](f084f40bd9).
* This PR only adds support for `FlashLlama` models for now.
* Multi-GPU setup has not been tested.
* No integration tests have been added so far, will add later if
maintainers are interested in this change.
* This PR can be tested on any of the models released
[here](https://huggingface.co/abhinavkulkarni?sort_models=downloads#models).
Please refer to the linked issue for benchmarks for
[abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
vs
[TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ).
Please note, AWQ has released faster (and in case of Llama, fused)
kernels for 4-bit GEMM, currently at the top of the `main` branch at
https://github.com/mit-han-lab/llm-awq, but this PR uses an older commit
that has been tested to work. We can switch to latest commit later on.
## Who can review?
@OlivierDehaene OR @Narsil
---------
# What does this PR do?
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---------
Co-authored-by: Abhinav M Kulkarni <abhinavkulkarni@gmail.com>
Co-authored-by: Abhinav Kulkarni <abhinav@concentric.ai>
# What does this PR do?
Fixes#1038
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# What does this PR do?
Fixes#1004
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if there's no cuda. disable custom kernels
# What does this PR do?
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
As more and more people begin to use Baichuan's open-source models, the
influence of Baichuan models is growing, especially in China. Many
community members are interested in adding support for Baichuan models
to TGI. Meanwhile, Baichuan is a very open company, and in the future,
it plans to open-source more and more models, taking all this into
consideration, we would like to add support for the Baichuan model to
TGI. To do this, we need to make some changes, which we hope can be
merged into the main branch of TGI. In the future, we would be happy to
help maintain support for Baichuan models in TGI. We sincerely hope that
our pull request can be accepted. Thank you.
By the way, the changes of this time mainly for supporting Baichuan-7B.
---------
Co-authored-by: xiaoyuze <xiaoyuze@baichuan.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
Fix __call__ vs forward.
To reproduce error just launch:
TheBloke/WizardLM-Uncensored-Falcon-7B-GPTQ with gptq (it fails because
falcon code uses `__call__` instead for `forward` calls)
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IDK what else to add in this guide, I looked for relevant code in TGI
codebase and saw that it's used in quantization as well (maybe I could
add that?)
PR for conceptual guide on flash attention. I will add more info unless
I'm told otherwise.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
# What does this PR do?
Disabling exllama on old compute.
Exllama + T4 don't play nice together, this will disable it right away
to avoid issues at runtime.
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This should allow users to use either Pydantic 2 or Pydantic 1.
I couldn't run all tests locally because I reran them too often and got
rate limited, but I believe this is sufficient.
# What does this PR do?
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# What does this PR do?
During the safetensor conversion, duplicate weights are removed.
However, which of the duplicates gets removed, differs per checkpoint.
In some, like `h2oai/h2ogpt-oig-oasst1-falcon-40b`, the weight
`transformer.word_embeddings.weightSafetensor` gets removed. In others,
`lm_head.weight` gets removed. Long story long, we need to support both.
Originally, f018143 mapped `lm_head` to `word_embeddings`. Then ac736fd
switched this around. This commit merges them and allows for both.
## Before submitting
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other checks if that's the case).
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## Who can review?
@Narsil, you wrote both commits I referenced in this PR. I think you'll
understand this change :)
Nicolas Patry