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Jamba: update integration tests (#32250)
* try test updates * a few more changes * a few more changes * a few more changes * [run slow] jamba * skip logits checks on older gpus * [run slow] jamba * oops * [run slow] jamba * Update tests/models/jamba/test_modeling_jamba.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/jamba/test_modeling_jamba.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Joao Gante
GitHub
parent
3bb7b05229
commit
f6e2586a36
@ -650,15 +650,31 @@ class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
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class JambaModelIntegrationTest(unittest.TestCase):
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model = None
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tokenizer = None
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# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
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# Depending on the hardware we get different logits / generations
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cuda_compute_capability_major_version = None
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@classmethod
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def setUpClass(cls):
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model_id = "ai21labs/Jamba-tiny-random"
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cls.model = JambaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
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cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
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if is_torch_available() and torch.cuda.is_available():
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# 8 is for A100 / A10 and 7 for T4
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cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
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@slow
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def test_simple_generate(self):
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# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
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#
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# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
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# considering differences in hardware processing and potential deviations in generated text.
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EXPECTED_TEXTS = {
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7: "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
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8: "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew llam bb",
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9: "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew llam bb",
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}
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self.model.to(torch_device)
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input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
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@ -666,28 +682,46 @@ class JambaModelIntegrationTest(unittest.TestCase):
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].to(torch_device)
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out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
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output_sentence = self.tokenizer.decode(out[0, :])
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self.assertEqual(
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output_sentence,
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"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
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)
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self.assertEqual(output_sentence, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
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with torch.no_grad():
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logits = self.model(input_ids=input_ids).logits
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# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
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if self.cuda_compute_capability_major_version == 8:
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with torch.no_grad():
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logits = self.model(input_ids=input_ids).logits
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EXPECTED_LOGITS_NO_GRAD = torch.tensor(
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[
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0.0140, -0.2246, 0.0408, -0.1016, 0.0471, 0.2715, -0.1465, 0.1631,
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-0.2949, -0.0297, 0.0250, -0.5586, -0.2139, -0.1426, -0.1602, 0.1309,
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0.0703, 0.2236, 0.1729, -0.2285, -0.1152, -0.1177, -0.1367, 0.0289,
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0.1245, 0.2363, 0.0442, 0.1094, -0.1348, -0.2295, 0.1494, -0.3945,
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0.1777, -0.4570, -0.0408, 0.2412, 0.1562, -0.1943, 0.2373, -0.0593
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]
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, dtype=torch.float32) # fmt: skip
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EXPECTED_LOGITS_NO_GRAD = torch.tensor(
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[
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0.0134, -0.2197, 0.0396, -0.1011, 0.0459, 0.2793, -0.1465, 0.1660,
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-0.2930, -0.0278, 0.0269, -0.5586, -0.2109, -0.1426, -0.1553, 0.1279,
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0.0713, 0.2246, 0.1660, -0.2314, -0.1187, -0.1162, -0.1377, 0.0292,
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0.1245, 0.2275, 0.0374, 0.1089, -0.1348, -0.2305, 0.1484, -0.3906,
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0.1709, -0.4590, -0.0447, 0.2422, 0.1592, -0.1855, 0.2441, -0.0562
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]
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, dtype=torch.float32) # fmt: skip
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torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
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torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
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@slow
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def test_simple_batched_generate_with_padding(self):
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# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
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#
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# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
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# considering differences in hardware processing and potential deviations in generated text.
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EXPECTED_TEXTS = {
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7: [
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"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
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"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
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],
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8: [
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"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
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"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
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],
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9: [
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"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
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"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
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],
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}
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self.model.to(torch_device)
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inputs = self.tokenizer(
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@ -695,37 +729,33 @@ class JambaModelIntegrationTest(unittest.TestCase):
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).to(torch_device)
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out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
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output_sentences = self.tokenizer.batch_decode(out)
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self.assertEqual(
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output_sentences[0],
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"<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
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)
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self.assertEqual(
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output_sentences[1],
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"<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
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)
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self.assertEqual(output_sentences[0], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][0])
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self.assertEqual(output_sentences[1], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][1])
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with torch.no_grad():
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logits = self.model(input_ids=inputs["input_ids"]).logits
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# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
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if self.cuda_compute_capability_major_version == 8:
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with torch.no_grad():
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logits = self.model(input_ids=inputs["input_ids"]).logits
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EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
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[
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0.0140, -0.2246, 0.0408, -0.1016, 0.0471, 0.2715, -0.1465, 0.1631,
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-0.2949, -0.0297, 0.0250, -0.5586, -0.2139, -0.1426, -0.1602, 0.1309,
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0.0703, 0.2236, 0.1729, -0.2285, -0.1152, -0.1177, -0.1367, 0.0289,
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0.1245, 0.2363, 0.0442, 0.1094, -0.1348, -0.2295, 0.1494, -0.3945,
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0.1777, -0.4570, -0.0408, 0.2412, 0.1562, -0.1943, 0.2373, -0.0593
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]
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, dtype=torch.float32) # fmt: skip
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EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
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[
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0.0166, -0.2227, 0.0396, -0.1035, 0.0459, 0.2754, -0.1445, 0.1641,
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-0.2910, -0.0273, 0.0227, -0.5547, -0.2139, -0.1396, -0.1582, 0.1289,
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0.0713, 0.2256, 0.1699, -0.2295, -0.1182, -0.1167, -0.1387, 0.0261,
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0.1270, 0.2285, 0.0403, 0.1108, -0.1318, -0.2334, 0.1455, -0.3945,
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0.1729, -0.4609, -0.0410, 0.2412, 0.1572, -0.1895, 0.2402, -0.0583
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]
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, dtype=torch.float32) # fmt: skip
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EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
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[
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-0.1289, 0.2363, -0.4180, -0.0302, -0.0476, 0.0327, 0.2578, 0.0874,
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0.1484, 0.2305, -0.1152, -0.1396, -0.1494, -0.1113, -0.0021, -0.2832,
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0.2002, -0.2676, 0.0598, -0.1982, -0.2539, -0.1133, -0.1973, 0.2148,
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0.0559, 0.1670, 0.1846, 0.1270, 0.1680, -0.1250, -0.2656, -0.2871,
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0.2344, 0.2637, 0.0510, -0.1855, 0.2158, -0.1289, 0.1758, 0.0074
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]
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, dtype=torch.float32) # fmt: skip
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EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
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[
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-0.1318, 0.2354, -0.4160, -0.0325, -0.0461, 0.0342, 0.2578, 0.0874,
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0.1484, 0.2266, -0.1182, -0.1396, -0.1494, -0.1089, -0.0019, -0.2852,
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0.1973, -0.2676, 0.0586, -0.1992, -0.2520, -0.1147, -0.1973, 0.2129,
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0.0520, 0.1699, 0.1816, 0.1289, 0.1699, -0.1216, -0.2656, -0.2891,
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0.2363, 0.2656, 0.0488, -0.1875, 0.2148, -0.1250, 0.1816, 0.0077
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]
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, dtype=torch.float32) # fmt: skip
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torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
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torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)
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torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
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torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)
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