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Fix GPTQ for models which do not have float16 at the default dtype (simpler) (#1953) 

# What does this PR do?

Fix GPTQ for models which do not have float16 at the default dtype

Before this change GPTQ models would not work if the model's default
data type is not `float16`. For example, Gemma GPTQ models would fail
because the default dtype of Gemma is `bfloat16`. There are two issues:

If the default `dtype` is not `float16`, the quantizer's `float16`
parameters get converted to that dtype. The kernels cannot deal
with non-`float16` types. The same applies to inputs of quantized ops.

This is resolved by setting the dtype of gptq/awq-quantized models to
`float16`.

Simpler version of #1951.

**Draft:** just testing...

## 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),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

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passed. Feel free to tag
members/contributors who may be interested in your PR.

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This commit is contained in:
Daniël de Kok 2024-05-27 14:41:28 +02:00 committed by GitHub
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5 changed files with 605 additions and 3 deletions
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89
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq.json Normal file
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"id": 2,
"logprob": null,
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},
{
"id": 2015,
"logprob": -9.640625,
"text": "Test"
},
{
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"text": " request"
}
],
"seed": null,
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{
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{
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"text": "</strong>"
}
],
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},
"generated_text": " for the following:\n\n* <strong>Name</strong>"
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89
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json Normal file
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@ -0,0 +1,89 @@
{
"details": {
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"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.65625,
"text": "Test"
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{
"id": 3853,
"logprob": -10.3671875,
"text": " request"
}
],
"seed": 0,
"tokens": [
{
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{
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"special": false,
"text": "2"
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{
"id": 235265,
"logprob": -1.9443359,
"special": false,
"text": "."
},
{
"id": 235284,
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"special": false,
"text": "2"
},
{
"id": 235308,
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"special": false,
"text": "5"
},
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"id": 235290,
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358
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_load.json Normal file
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[
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"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.6484375,
"text": "Test"
},
{
"id": 3853,
"logprob": -10.359375,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 604,
"logprob": -2.4277344,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob": -2.4394531,
"special": false,
"text": " the"
},
{
"id": 2412,
"logprob": -2.8613281,
"special": false,
"text": " following"
},
{
"id": 235292,
"logprob": -2.1523438,
"special": false,
"text": ":"
},
{
"id": 109,
"logprob": -0.76220703,
"special": false,
"text": "\n\n"
},
{
"id": 235287,
"logprob": -1.3642578,
"special": false,
"text": "*"
},
{
"id": 235248,
"logprob": -2.0175781,
"special": false,
"text": " "
},
{
"id": 199,
"logprob": -1.4238281,
"special": false,
"text": "<strong>"
},
{
"id": 1232,
"logprob": -4.328125,
"special": false,
"text": "Name"
},
{
"id": 208,
"logprob": -0.8881836,
"special": false,
"text": "</strong>"
}
],
"top_tokens": null
},
"generated_text": " for the following:\n\n* <strong>Name</strong>"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.6484375,
"text": "Test"
},
{
"id": 3853,
"logprob": -10.34375,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 604,
"logprob": -2.4238281,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob": -2.4453125,
"special": false,
"text": " the"
},
{
"id": 2412,
"logprob": -2.859375,
"special": false,
"text": " following"
},
{
"id": 235292,
"logprob": -2.1445312,
"special": false,
"text": ":"
},
{
"id": 109,
"logprob": -0.7631836,
"special": false,
"text": "\n\n"
},
{
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"special": false,
"text": "*"
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"id": 235248,
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"special": false,
"text": " "
},
{
"id": 199,
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"special": false,
"text": "<strong>"
},
{
"id": 1232,
"logprob": -4.3359375,
"special": false,
"text": "Name"
},
{
"id": 208,
"logprob": -0.8847656,
"special": false,
"text": "</strong>"
}
],
"top_tokens": null
},
"generated_text": " for the following:\n\n* <strong>Name</strong>"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.640625,
"text": "Test"
},
{
"id": 3853,
"logprob": -10.3671875,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 604,
"logprob": -2.4257812,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob": -2.4453125,
"special": false,
"text": " the"
},
{
"id": 2412,
"logprob": -2.8789062,
"special": false,
"text": " following"
},
{
"id": 235292,
"logprob": -2.1367188,
"special": false,
"text": ":"
},
{
"id": 109,
"logprob": -0.76171875,
"special": false,
"text": "\n\n"
},
{
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"special": false,
"text": "*"
},
{
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"logprob": -1.9873047,
"special": false,
"text": " "
},
{
"id": 199,
"logprob": -1.4169922,
"special": false,
"text": "<strong>"
},
{
"id": 1232,
"logprob": -4.3320312,
"special": false,
"text": "Name"
},
{
"id": 208,
"logprob": -0.8930664,
"special": false,
"text": "</strong>"
}
],
"top_tokens": null
},
"generated_text": " for the following:\n\n* <strong>Name</strong>"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.6484375,
"text": "Test"
},
{
"id": 3853,
"logprob": -10.359375,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 604,
"logprob": -2.4179688,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob": -2.4492188,
"special": false,
"text": " the"
},
{
"id": 2412,
"logprob": -2.8574219,
"special": false,
"text": " following"
},
{
"id": 235292,
"logprob": -2.1445312,
"special": false,
"text": ":"
},
{
"id": 109,
"logprob": -0.7519531,
"special": false,
"text": "\n\n"
},
{
"id": 235287,
"logprob": -1.3623047,
"special": false,
"text": "*"
},
{
"id": 235248,
"logprob": -1.9707031,
"special": false,
"text": " "
},
{
"id": 199,
"logprob": -1.4267578,
"special": false,
"text": "<strong>"
},
{
"id": 1232,
"logprob": -4.3359375,
"special": false,
"text": "Name"
},
{
"id": 208,
"logprob": -0.88427734,
"special": false,
"text": "</strong>"
}
],
"top_tokens": null
},
"generated_text": " for the following:\n\n* <strong>Name</strong>"
}
]

62
integration-tests/models/test_flash_gemma_gptq.py Normal file
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@ -0,0 +1,62 @@
import pytest
@pytest.fixture(scope="module")
def flash_gemma_gptq_handle(launcher):
with launcher("TechxGenus/gemma-2b-GPTQ", num_shard=1, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_gemma_gptq(flash_gemma_gptq_handle):
await flash_gemma_gptq_handle.health(300)
return flash_gemma_gptq_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_gemma_gptq(flash_gemma_gptq, response_snapshot):
response = await flash_gemma_gptq.generate(
"Test request", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_gemma_gptq_all_params(flash_gemma_gptq, response_snapshot):
response = await flash_gemma_gptq.generate(
"Test request",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_gemma_gptq_load(
flash_gemma_gptq, generate_load, response_snapshot
):
responses = await generate_load(
flash_gemma_gptq, "Test request", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot

4
server/text_generation_server/models/__init__.py
View File

@ -263,6 +263,10 @@ def get_model(
trust_remote_code: bool, trust_remote_code: bool,
) -> Model: ) -> Model:
if dtype is None: if dtype is None:
if quantize in ["awq", "gptq"]:
# These quantizers only work with float16 params.
dtype = torch.float16
else:
# Keep it as default for now and let # Keep it as default for now and let
# every model resolve their own default dtype. # every model resolve their own default dtype.
dtype = None dtype = None