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This change adds `FlashGPT2ForCausalLM` and wires it up. The model
itself is pretty straightforward, the main difference from other models
is that it uses trained position embeddings and that all weight matrices
are transposed compared to other models (due to the use of Conv1D in the
upstream model).
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---------
Co-authored-by: Joshua Rosenkranz <joshua.rosenkranz@gmail.com>
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Add's support for the Falcon2 11B model architecture.
## Before submitting
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other checks if that's the case).
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---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: oOraph <13552058+oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Co-authored-by: abhishek thakur <1183441+abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
Co-authored-by: Christof Weickhardt <christof@weickhardt.ch>
Co-authored-by: Ikko Eltociear Ashimine <eltociear@gmail.com>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
Co-authored-by: Lucain <lucain@huggingface.co>
Co-authored-by: fxmarty <9808326+fxmarty@users.noreply.github.com>
Co-authored-by: Moritz Laurer <41862082+MoritzLaurer@users.noreply.github.com>
Co-authored-by: dr3s <dr3s@users.noreply.github.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Co-authored-by: Brandon Royal <2762697+brandonroyal@users.noreply.github.com>
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
Co-authored-by: Martin Iglesias Goyanes <martinigoyanes@hotmail.com>
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>
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Thank you so much for the work you are doing, this is my little
contribution to this great thing you have built. I hope it is useful and
helpful, please don't hesitate to discuss any matters that are not
clear!
I am basing my implementation of frequency penalty on OpenAI's
implementation:
https://platform.openai.com/docs/guides/text-generation/parameter-details
The problem I see with TGI's current implementation is that is not
taking into account the frequency of tokens which have already been
sampled in the current generation stream. Also, the scaling is of the
adjusted token logits is done differently for positive and negative
logits. While in OpenAI's implementation token frequency is taking into
account and the scaling is always done with a subtraction (if penalty is
positive) or add operation (if penalty is negative).
This leads to corrupt generations as I mentioned in issue #1810 .
Moreover, after my tests, other issues are also gone like the one about
some request's with ``penalty_frequency = 1.0`` overruling other
requests (with ``frequency_penalty = 0.0``) in the same batch and
therefore corrupting all generations in the batch. Basically, padding
does not affect this implementation so I believe this ``score *=
input_ids.ne(0)`` is not needed anymore.
Frequency penalty | -1.0 | 0.0 | 1.0
-- | -- | -- | --
Before my change | https://paste.mozilla.org/JxqGJkWY |
https://paste.mozilla.org/hrztJ56h | https://paste.mozilla.org/pBSEH2zw
After my change | https://paste.mozilla.org/7gXCi7zo |
https://paste.mozilla.org/ZR9rJ92g | https://paste.mozilla.org/gHaD2YnC
---------
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>
# What does this PR do?
Just unifying some branches and making intentions clearer (no cuda graph
when 0 all the way in the launcher)
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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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This PR resolves an issue with the penalty processors during batched
generation where extra padding tokens incorrectly impact the penalty
scores.
generation is impacted in the case where at least one item in the batch
includes a `frequency_penalty`
reproduction script below
```python
import requests
from concurrent import futures
import time
headers = {
"Content-Type": "application/json",
}
json_data = {
"inputs": "[INST] Whats the capitol of France? [/INST]",
"parameters": {
"max_new_tokens": 100,
"seed": 20,
"do_sample": False,
},
}
json_data2 = {
"inputs": "<s>[INST]Write a mind bending story: I saw a puppy a cat a rat and a raccoon during my bike ride in the park[/INST]",
"parameters": {
"max_new_tokens": 100,
"seed": 2,
"do_sample": False,
# OFFENDING LINE
"frequency_penalty": 1.05,
},
}
base_url = "http://localhost:3000/generate"
def req():
response = requests.post(base_url, headers=headers, json=json_data)
print("[req ]", response.json())
def req2():
response = requests.post(base_url, headers=headers, json=json_data2)
print("[req2]", response.json())
n = 1
for i in range(0, 3):
print(f"- {n} threads -")
with futures.ThreadPoolExecutor(max_workers=n) as executor:
executor.submit(req)
for i in range(3):
executor.submit(req2)
n += 1
# - 1 threads -
# [req ] {'generated_text': ' The capital of France is Paris.'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# - 2 threads -
# [req ] {'generated_text': ' The capital city'}
# [req2] {'generated_text': ' As""%\n================'}
# [req2] {'generated_text': ' As""%%$\n================'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# output with this PR's changes:
# - 1 threads -
# [req ] {'generated_text': ' The capital of France is Paris.'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# - 2 threads -
# [req ] {'generated_text': ' The capital city'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
```
**divergence from expected generation is easier to reproduce with
batched grammar requests as they are more sensitive to unexpected
outputs.
this PR resolves the issue by setting the penalty score to 0 where input
ids are padding tokens (0).
---------
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
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---------
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
# What does this PR do?
- Changed all models to extract `embed_tokens` in order to enable llava
to separately call the embeddings and the core model layers.
- Added VlmCausalLM to inherit from FlashMistral in order to be
maximally supported. The only added logics sits on top and parses images
into pixel values, preallocates input_ids space for the image
embeddings, and passes them for the model.
- Added Clip for the vision tower.
- Didn't add flash for the vision tower since there's no padding anyway.
- Added heuristic (potentially incomplete) to calculate number of
features *before* calculating the clip patches (allows for easier logic
reuse of the LLM under the hood).
Still needs to be done:
- [x] Implement the image parsing in the controller side, to avoid
downloading n times per TP shard and also refusing requests too large
early and avoid issues where the truncation actually truncates the
image.
- [ ] Make sure it works with quantization properly.
- [x] Make sure it works with TP>1
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# What does this PR do?
```
text-generation-launcher --model-id XXX # Uses cuda graphs by default
text-generation-launcher --model-id XXX --cuda-graphs "1,2" #Restrict the number of cuda graphs which saves VRAM
text-generation-launcher --model-id XXX --cuda-graphs "0" # Disabling it entirely
```
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This PR correctly handles batches with a mixture of constrained and non
constrained generations.
Currently if batch contains mixed generations the generation will throw
an error because it will incorrectly attempt to constrain a request with
an empty grammar.
We now handled `None` grammars and only apply the mask if needed
Fixes:
https://github.com/huggingface/text-generation-inference/issues/1643
# What does this PR do?
A few cases where you're using a mistral structure or mixtral structure
but not a llama tokenizer, why not make it to call the AutoTokenizer in
exception handling.
Similar PR #619
@Narsil
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This PR resolves a couple
- [X] adjusts the tool response to align with openai's tools response
type
- [X] bumps pydantic to `2.6.4` in all apps (resolves dependency issue
when running tests)
- [X] bump `outlines` version and fix import for new name
This PR adds `force_downcast_after` to `FastRMSNorm.forward` which is
used in the Gemma model. References
https://github.com/huggingface/transformers/pull/29402 and
https://github.com/huggingface/transformers/pull/29729
Setting `force_downcast_after=True` will perform the `hidden_states *
weight` multiplication in f32 and then downcast to half. This differs
slightly from the current implementation which first casts the
`hidden_states` to a half and then multiples.
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This PR fixes parallel grammar requests, currently grammar states are
not concatenated correctly when a new request is added to the batch and
this results in incorrect generation. This PR updates the `concatenate`
function to correctly include the previous states.
fixes: #1601
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Using a single `os.getenv` statement instead of multiple.
Should make truthful values easier to catch
In the end didn't move towards full CLI because modifying globals in
Python is error prone (depends on code import order).
Added an error when mamba is launched with TP.
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- Move float16 to bfloat16, which has less imprecisions (load test are
failing with the update kernels + f16, all working under bf16).
Another note, is that we are not respecting the layer norm in f32
defined in the configuration (this is OK in my book, but that could
impact the f16 precision)
- Moved to update kernels. Triton overhead is super high, removed by
switching to cuda graphs works great (update cuda graph is available
in TRT-LLM if needed, seems *exactly* like the regular ssm kernel.
- Moved inference_params struct in order to make only 2 tensors, to
reduce the overhead of copying back and forth to the cuda graphs.
- Left over overhead seems entirely in the tokenization bit. (Still 4
copies are paid before launching the graph)
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This PR adds the possibility to run AWQ models with Exllama/GPTQ
kernels, specifically for ROCm devices that support Exllama kernels but
not AWQ's GEMM.
This is done by :
- un-packing, reordering and re-packing AWQ weights when `--quantize
gptq` but the model's `quant_method=awq`.
- avoiding overflows when adding 1 to zeros in exllama and triton.
Ref: https://github.com/casper-hansen/AutoAWQ/pull/313
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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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This draft PR is a work in progress implementation of the mamba model.
This PR currently loads weights, and produces correct logits after a
single pass.
This PR still needs to correctly integrate this model so it produces
tokens as expected, and apply optimization to avoid all copies during
runtime/unnecessary operations.
#### Helpful resources
[Mamba: Linear-Time Sequence Modeling with Selective State Spaces
(Albert Gu and Tri Dao)](https://arxiv.org/abs/2312.00752)
https://github.com/johnma2006/mamba-minimalhttps://github.com/huggingface/candle/blob/main/candle-examples/examples/mamba-minimal/model.rshttps://github.com/huggingface/transformers/pull/28094
Notes: this dev work is currently targeting `state-spaces/mamba-130m`,
so if you want to test please use that model. Additionally when starting
the router the prefill needs to be limited: `cargo run --
--max-batch-prefill-tokens 768 --max-input-length 768`
## Update / Current State
Integration tests have been added and basic functionality such as model
loading is supported.
```bash
cd integration-tests
pytest -vv models/test_fused_kernel_mamba.py
```
- [x] add tests
- [x] load model
- [x] make simple request
- [ ] resolve warmup issue
- [ ] resolve output issues
fetching models tested during dev
```bash
text-generation-server download-weights state-spaces/mamba-130m
text-generation-server download-weights state-spaces/mamba-1.4b
text-generation-server download-weights state-spaces/mamba-2.8b
```
The server can be run
```bash
cd server
MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 python text_generation_server/cli.py serve state-spaces/mamba-2.8b
```
router
```bash
cargo run
```
make a request
```bash
curl -s localhost:3000/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json' | jq
```
response
```json
{
"generated_text": "\n\nDeep learning is a machine learning technique that uses a deep neural network to learn from data."
}
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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Add TensorRT-LLM weight-only GEMV kernel support. We extract GEMV kernel
from
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv)
to accelerate the decode speed of EETQ when batch_size is smaller or
equal to 4.
- Features
1. There is almost no loss of quantization accuracy.
2. The speed of decoding is 13% - 27% faster than original EETQ which
utilizes GEMM kernel.
- Test
Below is our test on 3090. Environment: torch=2.0.1, cuda=11.8, nvidia
driver: 525.78.01
prompt=1024, max_new_tokens=50
## Before submitting
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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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---------
Co-authored-by: Choon Meng Tan <choonmeng@aisingapore.org>
Co-authored-by: David Ong Tat-Wee <13075447+ongtw@users.noreply.github.com>
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This PR adds basic modeling for phi-2
run
```bash
text-generation-server \
serve \
microsoft/phi-2 \
--revision 834565c23f9b28b96ccbeabe614dd906b6db551a
```
test
```bash
curl -s localhost:3000/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json' | jq .
# {
# "generated_text": "\nDeep learning is a subset of machine learning that uses artificial neural networks to learn from data. These"
# }
```
notes
- recently (~1 day ago) the Phi weights and model were updated to
accommodate adding [GQA/MQA attention to the
model.](https://github.com/huggingface/transformers/pull/28163) This
impl expects the original model format so a fixed revision is required
at the moment.
- this PR only includes a basic implementation of the model and can
later be extended for support Flash and Sharded versions as well as make
use of better optimization
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Close#1418Close#1415
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local directory overloaded still needs the directory to locate the
weights files correctly.
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Works by removing adapter_model.safetensors from being detected as the
core model file (which skips the real peft detection).
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This PR adds support for AMD Instinct MI210 & MI250 GPUs, with paged
attention and FAv2 support.
Remaining items to discuss, on top of possible others:
* Should we have a
`ghcr.io/huggingface/text-generation-inference:1.1.0+rocm` hosted image,
or is it too early?
* Should we set up a CI on MI210/MI250? I don't have access to the
runners of TGI though.
* Are we comfortable with those changes being directly in TGI, or do we
need a fork?
---------
Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Your Name <you@example.com>
# What does this PR do?
See #1165
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---------
Co-authored-by: Florian Zimmermeister <flozi00.fz@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-24-153.ec2.internal>
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This forces the use of `bfloat16` for IDEFICS. The issue is that with
`float16` the 80b model gives garbage output. Let me know if this
solution is not appropriate and I'll adjust accordingly. For the details
see below.
The current behaviour:
```sh
$ curl 127.0.0.1:8080/generate -X POST -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' -H 'Content-Type: application/json'
{"generated_text":""}
```
On closer inspection with:
```python
import requests
headers = { "Content-Type": "application/json"}
query = "What is Deep Learning?"
data = {
"inputs": query,
"parameters": {
"max_new_tokens": 10,
"return_full_text": True,
"decoder_input_details": True,
"do_sample": False,
},
}
api_url = "http://127.0.0.1:8080"
response = requests.post(api_url + "/generate", headers=headers, json=data).json()
for i in ['prefill', 'tokens']:
print(f'### {i}')
print(repr(''.join([t['text'] for t in response['details'][i]])))
```
Prints:
```
### prefill
'<s>WhatisDeepLearning?'
### tokens
'<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>'
########
```
With the change in this PR it prints:
```
### prefill
'<s>WhatisDeepLearning?'
### tokens
'\n\nDeep Learning is a subset of machine'
```
Note, using the Transformers implementation (with
`IdeficsForVisionText2Text.from_pretrained`) produces the latter
(correct) output as well.
This only happens with the 80b model, the 9b model is not as sensitive
to the dtype (as also mentioned in the code).
The reason for "forcing" this in the IDEFICS init method, is because if
quantization is used, then the dtype cannot be set explicitly. And since
it's left as `None`, it's set to `float16` by default
[here](96a982ad8f/server/text_generation_server/models/__init__.py (L90)).
I.e. there's no other way to manually change the dtype if someone is
using quantization:
```sh
$ docker run .... ghcr.io/huggingface/text-generation-inference:latest --model-id HuggingFaceM4/idefics-80b-instruct --dtype bfloat16 --quantize bitsandbytes-nf4
.....
2023-10-31T12:42:26.710401Z INFO shard-manager: text_generation_launcher: Starting shard rank=0
2023-10-31T12:42:30.315734Z ERROR shard-manager: text_generation_launcher: Shard complete standard error output:
Traceback (most recent call last):
File "/opt/conda/bin/text-generation-server", line 8, in <module>
sys.exit(app())
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py", line 80, in serve
raise RuntimeError(
RuntimeError: Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model.
rank=0
Error: ShardCannotStart
2023-10-31T12:42:30.414010Z ERROR text_generation_launcher: Shard 0 failed to start
2023-10-31T12:42:30.414044Z INFO text_generation_launcher: Shutting down shards
```
## Before submitting
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other checks if that's the case).
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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
Enables PEFT weights to be loaded from a local directory, as opposed to
a hf hub repository. It is a continuation of the work in PR
https://github.com/huggingface/text-generation-inference/pull/762
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Fixes#1259
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this repo, and it doesn't look like this code is covered anyway**
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[forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case. **Yes, @Narsil asked for a PR in [this
comment](https://github.com/huggingface/text-generation-inference/pull/762#issuecomment-1728089505)**
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**I didn't see any documentation added to the [original
PR](https://github.com/huggingface/text-generation-inference/pull/762),
and am not sure where this belongs. Let me know and I can add some**
- [x] Did you write any new necessary tests? **I didn't see any existing
test coverage for this python module**
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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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>
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Fixes#1017
Not sure if there's a mistake here but
- NousResearch/Yarn-Llama-2-7b-128k seems to be working fine
- TheBloke/Yarn-Llama-2-13B-128K-GPTQ outputs garbage
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Fixes#1098
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# What does this PR do?
Fixing a list type hint definition (I believe this was a typo).
Allows backward compatibility with Python 3.8 (relevant for
JetPack-enabled systems).
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# What does this PR do?
This PR is meant to handle cases in which the images provided are
invalid.
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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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`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
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---------
Co-authored-by: bangoz <ch_xie@pku.edu.cn>
# What does this PR do?
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---------
Co-authored-by: zhaosida <zhaosida@corp.netease.com>
# What does this PR do?
Upgrade all relevant versions and dependencies.
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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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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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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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N/A
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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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Fixes # (issue)
## Before submitting
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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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## Before submitting
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if there's no cuda. disable custom kernels
# What does this PR do?
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## Before submitting
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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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# 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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# 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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@Narsil, you wrote both commits I referenced in this PR. I think you'll
understand this change :)
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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
# What does this PR do?
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Co-Authored-By: Victor Sanh <victorsanh@gmail.com>
# What does this PR do?
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Co-authored-by: Victor Sanh <victorsanh@gmail.com>
# What does this PR do?
- New "falcon" layout on this repo
- No alibi
- `transformers` already modifying cache layout in our stead (same
modifications).
- Output is garbage. Not sure why.
Does not fix#826 but it's a step.
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# What does this PR do?
Fixes#843
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# What does this PR do?
A few tokenizer_config in huggingface use LlamaTokenizer, so I think I
would have selected `LlamaTokenizer` before.
For a few cases where you're using a llama structure but not a llama
tokenizer, why not make it to call the AutoTokenizer in exception
handling.
In the case of `decapoda-research/llama-7b-hf`, LLamaTokenizer is still
being used in config.json, so it should be called through`
LlamaTokenizer`.
Also, if an exception is thrown by LlamaTokenizer, it will cause
`LlamaTokenzierFast` to be called from AutoTokenizer.
Fixes # 560
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Fixes#816
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# What does this PR do?
Fixes#531
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# What does this PR do?
Reflecting
https://github.com/huggingface/transformers/pull/24998
Current status wants to make sure integration tests *are* broken with
this.
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# What does this PR do?
Fixes#787
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Fixes#784
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Typo
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# What does this PR do?
See #626
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---------
Co-authored-by: krzim <zimmerk4@live.com>
# What does this PR do?
Fixes#732
And remove duplicate AutoTokenizer import
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- Will detect `peft` model by finding `adapter_config.json`.
- This triggers a totally dedicated `download-weights` path
- This path, loads the adapter config, finds the base model_id
- It loads the base_model
- Then peft_model
- Then `merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.
- The chosen location is a **local folder with the name of the user
chosen model id**
PROs:
- Easier than to expect user to merge manually
- Barely any change outside of `download-weights` command.
- This means everything will work in a single load.
- Should enable out of the box SM + HFE
CONs:
- Creates a local merged model in unusual location, potentially
not saved across docker reloads, or ovewriting some files if the PEFT
itself was local and containing other files in addition to the lora
Alternatives considered:
- Add `local_files_only=True` every where (discard because of massive
code change for not a good enough reason)
- Return something to `launcher` about the new model-id (a cleaner
location for this new model), but it would
introduce new communication somewhere where we didn't need it before.
- Using the HF cache folder and *stopping* the flow after
`download-weights` and asking user to restart with the actual local
model location
Fix#482
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# What does this PR do?
Fixes#749
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Co-authored-by: p_spozzhang <p_spozzhang@tencent.com>
# What does this PR do?
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# What does this PR do?
Fixed typo.
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implemetation -> implementation
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# What does this PR do?
- Adds Rope NTK scaling.
Done because
https://github.com/huggingface/text-generation-inference/pull/529 was
closed
Took some code from
https://github.com/huggingface/transformers/pull/24653
- `--rope-scaling` and `--rope-factor` are added separately. I
considered having a single one and parsing something line ("linear:4.0"
, or "dynamic") but decided against
it because it would push more parsing+validation a bit everywhere (both
in the launcher and the server).
Fixes#512
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# What does this PR do?
Redoes #719
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- Current PR is not great because we're side stepping the
`Weights.__init__` but Weights shouldn't requires anything related
to the config or the model_id as it aims to be a simple Wrapper
over multi file loading.
- Ideal solution would be to use something like Rust enum
```
enum Quantize{
Bitandbytes(Bitsandbytes),
GPTQ(bits: usize, groupsize: usize)
```
And passing that around during load. Unfortunately we don't
have access to this, so for now, side-stepping seems easier.
- Re-enabling groupsize<0 with exllama (confirmed it works.)
Helps #601
In next steps we should make sure our quantization script uses that
format and make it standard.
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This PR directly load GPTBigCode to specified device, avoiding moving
model between devices.
# What does this PR do?
This PR directly load GPTBigCode to specified device, avoiding moving
model between devices.
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Just trying to get the integration tests to pass.
# What does this PR do?
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Fixes # (issue)
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# What does this PR do?
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Fixes a bug appeared with MR #587 fixing issue #552.
See the discussion in #552.
With MR #587 the trust_remote_code variable is not passed to
AutoModelForCausalLM, but is found in the function signature. This
prevents models like falcon to be quantized, because trust_remote_code
is required. This MR fixes the issue.
## Before submitting
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other checks if that's the case).
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Pull Request section?
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but should work on more configurations (no need for 2 GPUs, less RAM
usage).
# What does this PR do?
Reworking the quantization script so it's still universal (not llama
specific)
but should work on more configurations (no need for 2 GPUs, less RAM
usage).
Still need to investigate the potential differences in quantization
results.
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# What does this PR do?
When passing in environment variables like gptq_bits, we still get
errors thrown from TGI because the try/catch block is catching the wrong
type of error. This PR aims to fix that.
@Narsil - let me know if this is how you want this formatted. My Python
is a little shaky, so I hope this syntax is correct.
- The code is relatively easy (just disable the checks on Embedding and
Head)
This cannot be done in the same easy fashion for hidden_dim/head_dim.
It's relatively easy on some models (classic MHA) but it would make the
other
models (MQA) much more complex, and GPTQ quantization another quite
hairy piece
of code.
# What does this PR do?
This fixes a typo and extends the GPTP_BITS environment variables
through to the second method which requires the same logic. Please let
me know if there's anything I've misunderstood in this change.
Thanks @Narsil for the original fix.
# What does this PR do?
Some models are already converted, and do not have those values in the
file, this enables users to use them with less friction.
Went for pure env based because adding flags would end up (imo) very
tedious to maintain. There's a lot of sanitation to do: those flags
would be errors if not used in conjuction with `--quantize gptq`.
Then the flags need to exist in the launcher and the server passing them
all throughout all function calls.
This PR is intended as an easy escape hatch, not the defacto method to
use gptq in TGI.
Fixes#500
This PR allows the MPT model to be loaded from local files. Without this
change, an exception will be thrown by `hf_hub_download` function if
`model_id` is a local path.
# What does this PR do?
For consistency and ease of use (you can just run `make` to install vllm
without any extra steps).
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Let's start discussing implementation.
- Need to expose the quantization scripts (either included here or add
doc on how to use https://github.com/qwopqwop200/GPTQ-for-LLaMa)
- Make sure GPTQ works for multiple models (priority to Falcon).
Currently it means that every place we use `get_{tensor|sharded}` to
check for quantization.
My idea is to reintegrate as much as possible into `utils/layer.py` by
expanding `load_multi` to be a bit more generic.
This might require some thinking, but ultimately the
`qweight,qzeros,scales,g_idx` should be in a single place, and
independant of bias presence.
# What does this PR do?
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Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Should be more robust to shared tensors (ok when using
`from_pretrained). But forcing us to add new checks in our loading
code (since the chosen key to keep might be different from
`transformers`).
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
# What does this PR do?
Reworked the loading logic. Idea is to use cleaner loading code:
- Remove need for `no_init_weights`
- Remove all weird `bnb_linear` and `load_weights` and
`post_load_weights`.
New code layout:
- New class `Weights` in charge of handling loading the weights from
multiple files into appropiate tensors (potentially sharded)
- TP layers now are "shells", they contain the code to know what kind of
sharding we need + eventual `all_reduce`. They do not inherit from
linear, but they contain some kind of Linear instead
- the contained linear can be either FastLinear, BnbLinear or GPTq
Linear next.
- All modeling code is explictly made for sharding, process group is
just no-ops for non sharded code (removes a lot of test cases)
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.taildb5d.ts.net>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Daniël de Kok