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update gemma tests #38384

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Merged
ydshieh merged 6 commits into from
May 26, 2025
Merged

update gemma tests #38384

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7c3f894
update
ydshieh May 26, 2025
64b4fc7
Merge branch 'main' into update_gemma_tests
ydshieh May 26, 2025
15b0145
update
ydshieh May 26, 2025
10d83f4
Merge branch 'update_gemma_tests' of https://github.com/huggingface/t…
ydshieh May 26, 2025
18b878e
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ydshieh May 26, 2025
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ydshieh May 26, 2025
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31 changes: 4 additions & 27 deletions tests/models/gemma/test_modeling_gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,6 @@
require_torch,
require_torch_accelerator,
require_torch_gpu,
require_torch_sdpa,
slow,
torch_device,
)
Expand Down Expand Up @@ -147,7 +146,7 @@ def test_model_2b_bf16(self):

EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
]

model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
Expand All @@ -168,34 +167,12 @@ def test_model_2b_eager(self):

EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
]

model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
)
model.to(torch_device)

tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

self.assertEqual(output_text, EXPECTED_TEXTS)

@require_torch_sdpa
@require_read_token
def test_model_2b_sdpa(self):
model_id = "google/gemma-2b"

EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
]

# bfloat16 gives strange values, likely due to it has lower precision + very short prompts
model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa"
model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16, attn_implementation="eager"
)
model.to(torch_device)

Expand Down