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Fix slow tests for important models to be compatible with A10 runners (#29905)

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* fix mistral and mixtral

* add pdb

* fix mixtral tesst

* fix

* fix mistral ?

* add fix gemma

* fix mistral

* fix

* test

* anoter test

* fix

* fix

* fix mistral tests

* fix them again

* final fixes for mistral

* fix padding right

* fix whipser fa2

* fix

* fix

* fix gemma

* test

* fix llama

* fix

* fix

* fix llama gemma

* add class attribute

* fix CI

* clarify whisper

* compute_capability

* rename names in some comments

* Add   # fmt: skip

* make style

* Update tests/models/mistral/test_modeling_mistral.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* update

* update

---------

Co-authored-by: Younes Belkada <younesbelkada@gmail.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2024-04-09 13:28:54 +02:00
committed by GitHub
parent e9c23fa056
commit 08a194fcd6
6 changed files with 246 additions and 110 deletions
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141
tests/models/gemma/test_modeling_gemma.py
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@@ -21,6 +21,7 @@ from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, GemmaConfig, is_torch_available
from transformers.testing_utils import (
is_flaky,
require_bitsandbytes,
require_flash_attn,
require_read_token,
@@ -379,40 +380,6 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
def test_past_key_values_format(self):
pass
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_generate_padding_right(self):
import torch
for model_class in self.all_generative_model_classes:
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(
torch_device
)
dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)
model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)
model = model_class.from_pretrained(
tmpdirname,
torch_dtype=torch.float16,
attn_implementation="flash_attention_2",
low_cpu_mem_usage=True,
).to(torch_device)
with self.assertRaises(ValueError):
_ = model.generate(
dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False
)
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@@ -500,6 +467,7 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
@require_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@is_flaky
@slow
def test_flash_attn_2_equivalence(self):
for model_class in self.all_model_classes:
@@ -531,12 +499,21 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
assert torch.allclose(logits_fa, logits, atol=3e-3)
@require_torch_gpu
@slow
@require_read_token
@require_torch_gpu
class GemmaIntegrationTest(unittest.TestCase):
input_text = ["Hello I am doing", "Hi today"]
# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
# Depending on the hardware we get different logits / generations
cuda_compute_capability_major_version = None
@classmethod
def setUpClass(cls):
if is_torch_available() and torch.cuda.is_available():
# 8 is for A100 / A10 and 7 for T4
cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
@require_read_token
def test_model_2b_fp32(self):
model_id = "google/gemma-2b"
EXPECTED_TEXTS = [
@@ -554,6 +531,7 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_model_2b_fp16(self):
model_id = "google/gemma-2b"
EXPECTED_TEXTS = [
@@ -573,6 +551,7 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_model_2b_fp16_static_cache(self):
model_id = "google/gemma-2b"
EXPECTED_TEXTS = [
@@ -594,12 +573,19 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_model_2b_bf16(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",
]
EXPECTED_TEXTS = {
7: [
"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",
],
8: [
"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>Kaju Kat",
],
}
model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
torch_device
@@ -611,14 +597,21 @@ class GemmaIntegrationTest(unittest.TestCase):
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)
self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
@require_read_token
def test_model_2b_eager(self):
model_id = "google/gemma-2b"
EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I am looking for some information on the ",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
]
EXPECTED_TEXTS = {
7: [
"Hello I am doing a project on the 1990s and I am looking for some information on the ",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
8: [
"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>Kaju Kat",
],
}
model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
@@ -631,15 +624,22 @@ class GemmaIntegrationTest(unittest.TestCase):
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)
self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
@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",
]
EXPECTED_TEXTS = {
7: [
"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",
],
8: [
"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>Kaju Kat",
],
}
model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa"
@@ -652,10 +652,11 @@ class GemmaIntegrationTest(unittest.TestCase):
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)
self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
@pytest.mark.flash_attn_test
@require_flash_attn
@require_read_token
def test_model_2b_flash_attn(self):
model_id = "google/gemma-2b"
EXPECTED_TEXTS = [
@@ -677,6 +678,7 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_bitsandbytes
@require_read_token
def test_model_2b_4bit(self):
model_id = "google/gemma-2b"
EXPECTED_TEXTS = [
@@ -695,6 +697,7 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@unittest.skip("The test will not fit our CI runners")
@require_read_token
def test_model_7b_fp32(self):
model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
@@ -712,6 +715,7 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_model_7b_fp16(self):
model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
@@ -731,12 +735,19 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_model_7b_bf16(self):
model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
"""Hello I am doing a project on a 1991 240sx and I am trying to find""",
"Hi today I am going to show you how to make a very simple and easy to make a very simple and",
]
EXPECTED_TEXTS = {
7: [
"""Hello I am doing a project on a 1991 240sx and I am trying to find""",
"Hi today I am going to show you how to make a very simple and easy to make a very simple and",
],
8: [
"Hello I am doing a project for my school and I am trying to make a program that will read a .txt file",
"Hi today I am going to show you how to make a very simple and easy to make a very simple and",
],
}
model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
torch_device
@@ -748,8 +759,9 @@ class GemmaIntegrationTest(unittest.TestCase):
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)
self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
@require_read_token
def test_model_7b_fp16_static_cache(self):
model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
@@ -772,12 +784,19 @@ class GemmaIntegrationTest(unittest.TestCase):
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_bitsandbytes
@require_read_token
def test_model_7b_4bit(self):
model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
"Hello I am doing a project for my school and I am trying to make a program that will take a number and then",
"""Hi today I am going to talk about the new update for the game called "The new update" and I""",
]
EXPECTED_TEXTS = {
7: [
"Hello I am doing a project for my school and I am trying to make a program that will take a number and then",
"""Hi today I am going to talk about the new update for the game called "The new update" and I""",
],
8: [
"Hello I am doing a project for my school and I am trying to make a program that will take a number and then",
"Hi today I am going to talk about the best way to get rid of acne. miniaturing is a very",
],
}
model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True)
@@ -787,4 +806,4 @@ class GemmaIntegrationTest(unittest.TestCase):
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)
self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])