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? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
This commit is contained in:
Daniël de Kok
GitHub
parent
9231098f3a
commit
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import pytest
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@pytest.fixture(scope="module")
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def flash_gemma_gptq_handle(launcher):
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with launcher("TechxGenus/gemma-2b-GPTQ", num_shard=1, quantize="gptq") as handle:
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yield handle
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@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
|
||||
|
|
@ -263,9 +263,13 @@ def get_model(
|
|||
trust_remote_code: bool,
|
||||
) -> Model:
|
||||
if dtype is None:
|
||||
# Keep it as default for now and let
|
||||
# every model resolve their own default dtype.
|
||||
dtype = 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
|
||||
# every model resolve their own default dtype.
|
||||
dtype = None
|
||||
elif dtype == "float16":
|
||||
dtype = torch.float16
|
||||
elif dtype == "bfloat16":
|
||||
|
|
|
|||
Loading…
Reference in New Issue