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

Jump to bottom

Jamba: update integration tests #32250

Merged
merged 11 commits into from
Aug 22, 2024
Merged

Jamba: update integration tests #32250

Changes from 9 commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
520bec2
try test updates
gante Jul 26, 2024
2237449
a few more changes
gante Jul 26, 2024
94e81a1
a few more changes
gante Jul 26, 2024
4ea6855
a few more changes
gante Jul 26, 2024
a305dda
[run slow] jamba
gante Jul 26, 2024
fbd83c2
skip logits checks on older gpus
gante Jul 26, 2024
619d88f
[run slow] jamba
gante Jul 26, 2024
1b81b63
oops
gante Jul 26, 2024
c372fd9
[run slow] jamba
gante Jul 26, 2024
27ce70a
Update tests/models/jamba/test_modeling_jamba.py
gante Aug 22, 2024
9912a22
Update tests/models/jamba/test_modeling_jamba.py
gante Aug 22, 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
128 changes: 79 additions & 49 deletions tests/models/jamba/test_modeling_jamba.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -650,82 +650,112 @@ def test_new_cache_format(self, num_beams, do_sample):
class JambaModelIntegrationTest(unittest.TestCase):
model = None
tokenizer = None
# 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
Comment on lines +653 to +655
Copy link
Member Author

Choose a reason for hiding this comment

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

This cuda_compute_capability_major_version pattern is copied from other models like e.g. gemma


@classmethod
def setUpClass(cls):
model_id = "ai21labs/Jamba-tiny-random"
cls.model = JambaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
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]

@slow
def test_simple_generate(self):
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
7: "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
8: "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew llam bb",
9: "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew llam bb",
}

self.model.to(torch_device)

input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
"input_ids"
].to(torch_device)
out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
output_sentence = self.tokenizer.decode(out[0, :])
self.assertEqual(
output_sentence,
"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
)
self.assertEqual(output_sentence, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])

with torch.no_grad():
logits = self.model(input_ids=input_ids).logits
# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
if self.cuda_compute_capability_major_version == 8:
with torch.no_grad():
logits = self.model(input_ids=input_ids).logits

EXPECTED_LOGITS_NO_GRAD = torch.tensor(
[
0.0140, -0.2246, 0.0408, -0.1016, 0.0471, 0.2715, -0.1465, 0.1631,
-0.2949, -0.0297, 0.0250, -0.5586, -0.2139, -0.1426, -0.1602, 0.1309,
0.0703, 0.2236, 0.1729, -0.2285, -0.1152, -0.1177, -0.1367, 0.0289,
0.1245, 0.2363, 0.0442, 0.1094, -0.1348, -0.2295, 0.1494, -0.3945,
0.1777, -0.4570, -0.0408, 0.2412, 0.1562, -0.1943, 0.2373, -0.0593
]
, dtype=torch.float32) # fmt: skip
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
[
0.0134, -0.2197, 0.0396, -0.1011, 0.0459, 0.2793, -0.1465, 0.1660,
-0.2930, -0.0278, 0.0269, -0.5586, -0.2109, -0.1426, -0.1553, 0.1279,
0.0713, 0.2246, 0.1660, -0.2314, -0.1187, -0.1162, -0.1377, 0.0292,
gante marked this conversation as resolved.
Show resolved Hide resolved
0.1245, 0.2275, 0.0374, 0.1089, -0.1348, -0.2305, 0.1484, -0.3906,
0.1709, -0.4590, -0.0447, 0.2422, 0.1592, -0.1855, 0.2441, -0.0562
]
, dtype=torch.float32) # fmt: skip

torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)

@slow
def test_simple_batched_generate_with_padding(self):
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
7: [
"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
],
8: [
"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
],
9: [
"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
],
}

self.model.to(torch_device)

inputs = self.tokenizer(
["Hey how are you doing on this lovely evening?", "Tell me a story"], padding=True, return_tensors="pt"
).to(torch_device)
out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
output_sentences = self.tokenizer.batch_decode(out)
self.assertEqual(
output_sentences[0],
"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
)
self.assertEqual(
output_sentences[1],
"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
)
self.assertEqual(output_sentences[0], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][0])
self.assertEqual(output_sentences[1], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][1])

with torch.no_grad():
logits = self.model(input_ids=inputs["input_ids"]).logits

EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
[
0.0140, -0.2246, 0.0408, -0.1016, 0.0471, 0.2715, -0.1465, 0.1631,
-0.2949, -0.0297, 0.0250, -0.5586, -0.2139, -0.1426, -0.1602, 0.1309,
0.0703, 0.2236, 0.1729, -0.2285, -0.1152, -0.1177, -0.1367, 0.0289,
0.1245, 0.2363, 0.0442, 0.1094, -0.1348, -0.2295, 0.1494, -0.3945,
0.1777, -0.4570, -0.0408, 0.2412, 0.1562, -0.1943, 0.2373, -0.0593
]
, dtype=torch.float32) # fmt: skip

EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
[
-0.1289, 0.2363, -0.4180, -0.0302, -0.0476, 0.0327, 0.2578, 0.0874,
0.1484, 0.2305, -0.1152, -0.1396, -0.1494, -0.1113, -0.0021, -0.2832,
0.2002, -0.2676, 0.0598, -0.1982, -0.2539, -0.1133, -0.1973, 0.2148,
0.0559, 0.1670, 0.1846, 0.1270, 0.1680, -0.1250, -0.2656, -0.2871,
0.2344, 0.2637, 0.0510, -0.1855, 0.2158, -0.1289, 0.1758, 0.0074
]
, dtype=torch.float32) # fmt: skip

torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)
# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
if self.cuda_compute_capability_major_version == 8:
Copy link
Collaborator

Choose a reason for hiding this comment

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

maybe better to use

self.skipTest(reason="Skipping for T4 runners because ...")

Copy link
Member Author

Choose a reason for hiding this comment

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

oops, merged before seeing this comment!

You have a good point, in fact we should split the test in two to test (/skip) the logits separately

with torch.no_grad():
logits = self.model(input_ids=inputs["input_ids"]).logits

EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
[
0.0166, -0.2227, 0.0396, -0.1035, 0.0459, 0.2754, -0.1445, 0.1641,
-0.2910, -0.0273, 0.0227, -0.5547, -0.2139, -0.1396, -0.1582, 0.1289,
0.0713, 0.2256, 0.1699, -0.2295, -0.1182, -0.1167, -0.1387, 0.0261,
0.1270, 0.2285, 0.0403, 0.1108, -0.1318, -0.2334, 0.1455, -0.3945,
0.1729, -0.4609, -0.0410, 0.2412, 0.1572, -0.1895, 0.2402, -0.0583
]
, dtype=torch.float32) # fmt: skip

EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
[
-0.1318, 0.2354, -0.4160, -0.0325, -0.0461, 0.0342, 0.2578, 0.0874,
0.1484, 0.2266, -0.1182, -0.1396, -0.1494, -0.1089, -0.0019, -0.2852,
0.1973, -0.2676, 0.0586, -0.1992, -0.2520, -0.1147, -0.1973, 0.2129,
0.0520, 0.1699, 0.1816, 0.1289, 0.1699, -0.1216, -0.2656, -0.2891,
gante marked this conversation as resolved.
Show resolved Hide resolved
0.2363, 0.2656, 0.0488, -0.1875, 0.2148, -0.1250, 0.1816, 0.0077
]
, dtype=torch.float32) # fmt: skip

torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)
Loading