Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

TEST: Add llama logits tests #30835

Merged
merged 15 commits into from
May 17, 2024
Merged

TEST: Add llama logits tests #30835

Changes from all commits
Commits
Show all changes
15 commits
Select commit Hold shift + click to select a range
f832128
add llama logits test
younesbelkada May 15, 2024
e21f4ba
fix
younesbelkada May 15, 2024
802051e
fix tests
younesbelkada May 16, 2024
891e5e5
fix for a10
younesbelkada May 16, 2024
e2d9538
format
younesbelkada May 16, 2024
3241d26
format
younesbelkada May 16, 2024
353d2f8
fix
younesbelkada May 16, 2024
2050efd
[run-slow] remove fmt: skip
younesbelkada May 17, 2024
09f1194
Your commit message
younesbelkada May 17, 2024
b66e01e
test commit
younesbelkada May 17, 2024
9d90982
Revert "test commit"
younesbelkada May 17, 2024
2de1ef2
[run-slow]llama
younesbelkada May 17, 2024
3be7a11
Update tests/models/llama/test_modeling_llama.py
younesbelkada May 17, 2024
cef9eb3
[run-slow]llama
younesbelkada May 17, 2024
2903db9
empty commit
younesbelkada May 17, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
204 changes: 61 additions & 143 deletions tests/models/llama/test_modeling_llama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,6 @@
require_flash_attn,
require_read_token,
require_torch,
require_torch_accelerator,
require_torch_gpu,
require_torch_sdpa,
slow,
Expand All @@ -45,7 +44,6 @@
import torch

from transformers import (
CodeLlamaTokenizer,
LlamaForCausalLM,
LlamaForQuestionAnswering,
LlamaForSequenceClassification,
Expand Down Expand Up @@ -605,76 +603,79 @@ def setUpClass(cls):
# 8 is for A100 / A10 and 7 for T4
cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]

@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
@slow
def test_model_7b_logits(self):
@require_read_token
def test_model_7b_logits_bf16(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
out = model(torch.tensor([input_ids]))
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]) # fmt: skip
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)

@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
@slow
def test_model_13b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", device_map="auto")
out = model(torch.tensor(input_ids))
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]) # fmt: skip
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
model = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager"
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

we need to use eager otherwise bf16 + SDPA fails

)

@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
@slow
def test_model_13bf_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", device_map="auto")
out = model(torch.tensor(input_ids))
with torch.no_grad():
out = model(torch.tensor([input_ids]).to(torch_device))
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]) # fmt: skip
torch.testing.assert_close(out.mean(-1), EXPECTED_SLICE, atol=1e-2, rtol=1e-2)

@unittest.skip(
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"
)
@slow
def test_model_70b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf", device_map="auto")
out = model(torch.tensor(input_ids))

EXPECTED_MEAN = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]], dtype=torch.float32
# fmt: off
EXPECTED_MEAN = {
7: torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]),
8: torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]])
}

self.assertTrue(torch.allclose(EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device), out.logits.mean(-1), atol=1e-2, rtol=1e-2))

# slicing logits[0, 0, 0:15]
EXPECTED_SLICE = {
7: torch.tensor([[-12.5000, -7.0625, -0.6289, -7.8750, -6.9688, -7.8125, -6.4688, -7.4375, -7.6875, -6.9375, -6.0312, -7.0000, -1.8594, 1.8438, -8.5000]]),
8: torch.tensor([[-12.5625, -7.1250, -0.6289, -7.8750, -6.9688, -7.8125, -6.5000, -7.4375, -7.6562, -6.9688, -6.0312, -7.0312, -1.8203, 1.8750, -8.5000]])
}
# fmt: on

self.assertTrue(
torch.allclose(
EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
out.logits[0, 0, :15],
atol=1e-3,
rtol=1e-3,
)
)
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]) # fmt: skip
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)

@unittest.skip("Model is curently gated")
@slow
def test_model_13b_greedy_generation(self):
EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi"""
prompt = "Simply put, the theory of relativity states that "
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
input_ids = tokenizer.encode(prompt, return_tensors="pt")
@require_read_token
def test_model_7b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]

model = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-chat-hf", device_map="sequential", use_safetensors=False
"meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.float16
)

# greedy generation outputs
generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
with torch.no_grad():
out = model(torch.tensor([input_ids]).to(torch_device))

# fmt: off
# Expected mean on dim = -1
EXPECTED_MEAN = {
7: torch.tensor([[-6.6420, -4.1227, -4.9809, -3.2041, 0.8261, -3.0052, 1.2957, -3.3648]]),
8: torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]])
}

self.assertTrue(torch.allclose(EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device), out.logits.mean(-1), atol=1e-2, rtol=1e-2))

# slicing logits[0, 0, 0:15]
EXPECTED_SLICE = {
7: torch.tensor([-12.8125, -7.3359, -0.4846, -8.0234, -7.2383, -7.9922, -6.4805, -7.7344, -7.8125, -7.0078, -6.1797, -7.1094, -1.8633, 1.9736, -8.6016]),
8: torch.tensor([-12.8281, -7.4609, -0.4668, -8.0703, -7.2539, -8.0078, -6.4961, -7.7734, -7.8516, -7.0352, -6.2188, -7.1367, -1.8564, 1.9922, -8.6328])
}
# fmt: on

self.assertTrue(
torch.allclose(
EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
out.logits[0, 0, :15],
atol=1e-3,
rtol=1e-3,
)
)

@slow
@require_torch_gpu
Expand Down Expand Up @@ -736,89 +737,6 @@ def test_compile_static_cache(self):
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)


@require_torch
class CodeLlamaIntegrationTest(unittest.TestCase):
PROMPTS = [
'''def remove_non_ascii(s: str) -> str:
""" <FILL_ME>
return result
''',
"""# Installation instructions:
```bash
<FILL_ME>
```
This downloads the LLaMA inference code and installs the repository as a local pip package.
""",
"""class InterfaceManagerFactory(AbstractManagerFactory):
def __init__(<FILL_ME>
def main():
factory = InterfaceManagerFactory(start=datetime.now())
managers = []
for i in range(10):
managers.append(factory.build(id=i))
""",
"""/-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/
theorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :
π₁ P = 0 ↔ <FILL_ME> = 0 :=
begin
split,
{ intros h f,
rw pi_1_etalisation at h,
simp [h],
refl
},
{ intro h,
have := @quasi_adjoint C D P,
simp [←pi_1_etalisation, this, h],
refl
}
end
""",
]

@require_torch_accelerator
@slow
@unittest.skip("Model is too large")
def test_model_7b_logits(self):
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

As long as the tokenizer is tested with this, LGMT to remove

model = LlamaForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf").to(torch_device)
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
# Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
# meaning by default this supports passing splitted list of inputs
processed_text = tokenizer.batch_decode(tokenizer(self.PROMPTS)["input_ids"], add_special_tokens=False)
# fmt: off
EXPECTED_TEXT = [
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>',
'<s> <PRE> # Installation instructions:\n ```bash\n <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID>',
'<s> <PRE> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__( <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID>',
'<s> <PRE> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID>'
]
# fmt: on
self.assertEqual(processed_text, EXPECTED_TEXT)
processed_text_suffix_first = tokenizer.batch_decode(
tokenizer(self.PROMPTS, suffix_first=True, add_special_tokens=False)["input_ids"]
)

# fmt: off
EXPECTED_TEXT = [
'<PRE> <SUF>\n return result\n <MID> def remove_non_ascii(s: str) -> str:\n """ ',
'<PRE> <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID> # Installation instructions:\n ```bash\n',
'<PRE> <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__(',
'<PRE> <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ '
]
EXPECTED_IDS = torch.tensor([[1, 32007, 822, 3349, 29918, 5464, 29918, 294, 18869, 29898, 29879, 29901, 851, 29897, 1599, 851, 29901, 13, 1678, 9995, 29871, 32008, 13, 1678, 736, 1121, 13, 32009, 15941, 1661, 29899, 28599, 2687, 4890, 515, 263, 1347, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 269, 29901, 450, 1347, 304, 3349, 1661, 29899, 28599, 2687, 4890, 515, 29889, 13, 13, 1678, 16969, 29901, 13, 4706, 450, 1347, 411, 1661, 29899, 28599, 2687, 4890, 6206, 29889, 13, 1678, 9995, 13, 1678, 1121, 353, 5124, 13, 1678, 363, 274, 297, 269, 29901, 13, 4706, 565, 4356, 29898, 29883, 29897, 529, 29871, 29896, 29906, 29947, 29901, 13, 9651, 1121, 4619, 274, 32010, 2]])
# fmt: on
self.assertEqual(processed_text_suffix_first, EXPECTED_TEXT)
input_ids = tokenizer(self.PROMPTS[0], return_tensors="pt")["input_ids"]
generated_ids = model.generate(input_ids.to(torch_device), max_new_tokens=128)
torch.testing.assert_close(generated_ids, EXPECTED_IDS)

EXPECTED_INFILLING = [
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>Remove non-ASCII characters from a string.\n\n Args:\n s: The string to remove non-ASCII characters from.\n\n Returns:\n The string with non-ASCII characters removed.\n """\n result = ""\n for c in s:\n if ord(c) < 128:\n result += c <EOT></s>'
]
infilling = tokenizer.batch_decode(generated_ids)
self.assertEqual(infilling, EXPECTED_INFILLING)


@slow
@require_torch_gpu
class Mask4DTestHard(unittest.TestCase):
Expand Down
Loading