huggingface/text-generation-inference
5
0
Fork 0
mirror of https://github.com/huggingface/text-generation-inference.git synced 2025-09-11 20:34:54 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source 
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`.
This commit is contained in:
Daniël de Kok 2024-05-25 08:48:01 +00:00
parent 9231098f3a
commit 6f30a13afa
5 changed files with 605 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

89
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq.json Normal file
View File

@ -0,0 +1,89 @@
{
"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": -10.34375,
"text": " request"
}
],
"seed": null,
"tokens": [
{
"id": 604,
"logprob": -2.4296875,
"special": false,
"text": " for"
},
{
"id": 573,
"logprob": -2.4453125,
"special": false,
"text": " the"
},
{
"id": 2412,
"logprob": -2.8632812,
"special": false,
"text": " following"
},
{
"id": 235292,
"logprob": -2.1328125,
"special": false,
"text": ":"
},
{
"id": 109,
"logprob": -0.76660156,
"special": false,
"text": "\n\n"
},
{
"id": 235287,
"logprob": -1.3837891,
"special": false,
"text": "*"
},
{
"id": 235248,
"logprob": -1.9746094,
"special": false,
"text": " "
},
{
"id": 199,
"logprob": -1.4189453,
"special": false,
"text": "<strong>"
},
{
"id": 1232,
"logprob": -4.34375,
"special": false,
"text": "Name"
},
{
"id": 208,
"logprob": -0.8852539,
"special": false,
"text": "</strong>"
}
],
"top_tokens": null
},
"generated_text": " for the following:\n\n* <strong>Name</strong>"
}

89
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_all_params.json Normal file
View File

@ -0,0 +1,89 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 2015,
"logprob": -9.65625,
"text": "Test"
},
{
"id": 3853,
"logprob": -10.3671875,
"text": " request"
}
],
"seed": 0,
"tokens": [
{
"id": 604,
"logprob": -0.36938477,
"special": false,
"text": " for"
},
{
"id": 235248,
"logprob": -1.8046875,
"special": false,
"text": " "
},
{
"id": 235274,
"logprob": -0.46240234,
"special": false,
"text": "1"
},
{
"id": 235284,
"logprob": -1.7460938,
"special": false,
"text": "2"
},
{
"id": 235265,
"logprob": -1.9443359,
"special": false,
"text": "."
},
{
"id": 235284,
"logprob": -1.4550781,
"special": false,
"text": "2"
},
{
"id": 235308,
"logprob": -1.0205078,
"special": false,
"text": "5"
},
{
"id": 235290,
"logprob": -1.0283203,
"special": false,
"text": "-"
},
{
"id": 235274,
"logprob": -1.2783203,
"special": false,
"text": "1"
},
{
"id": 235284,
"logprob": 0.0,
"special": false,
"text": "2"
}
],
"top_tokens": null
},
"generated_text": "Test request for 12.25-12"
}

358
integration-tests/models/__snapshots__/test_flash_gemma_gptq/test_flash_gemma_gptq_load.json Normal file
View File

@ -0,0 +1,358 @@
[
{
"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": -2.4277344,
"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"
},
{
"id": 235287,
"logprob": -1.3642578,
"special": false,
"text": "*"
},
{
"id": 235248,
"logprob": -1.9960938,
"special": false,
"text": " "
},
{
"id": 199,
"logprob": -1.4179688,
"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"
},
{
"id": 235287,
"logprob": -1.3515625,
"special": false,
"text": "*"
},
{
"id": 235248,
"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
View File

@ -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

10
server/text_generation_server/models/__init__.py
View File

@ -263,9 +263,13 @@ def get_model(
trust_remote_code: bool, trust_remote_code: bool,
) -> Model: ) -> Model:
if dtype is None: if dtype is None:
# Keep it as default for now and let if quantize in ["awq", "gptq"]:
# every model resolve their own default dtype. # These quantizers only work with float16 params.
dtype = None dtype = torch.float16
else:
# Keep it as default for now and let
# every model resolve their own default dtype.
dtype = None
elif dtype == "float16": elif dtype == "float16":
dtype = torch.float16 dtype = torch.float16
elif dtype == "bfloat16": elif dtype == "bfloat16":