- 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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```
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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# 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
# What does this PR do?
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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
## Before submitting
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---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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.
[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`
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>
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>
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 .
```
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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## Before submitting
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Fixes # (issue)
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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>
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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
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
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Here are the
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- [ ] Did you write any new necessary tests?
## Who can review?
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passed. Feel free to tag
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@Narsil what do you think?
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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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# What does this PR do?
Fix#1095
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