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Tests: upgrade test_eager_matches_sdpa_generate (#34386)

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This commit is contained in:
Joao Gante
2024-10-25 11:55:07 +01:00
committed by GitHub
parent 8814043c8c
commit 186b8dc190
22 changed files with 85 additions and 946 deletions
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62
tests/models/llama/test_modeling_llama.py
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@@ -32,7 +32,6 @@ from transformers.testing_utils import (
require_torch,
require_torch_accelerator,
require_torch_gpu,
require_torch_sdpa,
slow,
torch_device,
)
@@ -651,67 +650,6 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
if not has_flash:
raise ValueError("The flash model should have flash attention layers")
@require_torch_sdpa
@slow
def test_eager_matches_sdpa_generate(self):
"""
Overwritting the common test as the test is flaky on tiny models
"""
max_new_tokens = 30
tokenizer = LlamaTokenizer.from_pretrained("saibo/llama-1B")
model_sdpa = LlamaForCausalLM.from_pretrained(
"saibo/llama-1B",
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(torch_device)
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
model_eager = LlamaForCausalLM.from_pretrained(
"saibo/llama-1B",
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
attn_implementation="eager",
).to(torch_device)
self.assertTrue(model_eager.config._attn_implementation == "eager")
for name, submodule in model_eager.named_modules():
if "SdpaAttention" in submodule.__class__.__name__:
raise ValueError("The eager model should not have SDPA attention layers")
has_sdpa = False
for name, submodule in model_sdpa.named_modules():
if "SdpaAttention" in submodule.__class__.__name__:
has_sdpa = True
break
if not has_sdpa:
raise ValueError("The SDPA model should have SDPA attention layers")
texts = [
"hi here's a longer context, getting longer and",
"Hello this is a very long sentence my friend, very long for real",
"Today I am in Paris and",
]
for padding_side in ["left", "right"]:
tokenizer.padding_side = padding_side
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device)
res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
with self.subTest(f"{padding_side}"):
torch.testing.assert_close(
res_eager,
res_sdpa,
msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}",
)
@unittest.skip("Broken by the loss update will fix soon @ArthurZucker")
def test_torch_fx_output_loss(self, *args, **kwargs):
pass