enable 2 llama UT cases on xpu (#37126)
* enable tests/models/llama/test_modeling_llama.py::LlamaIntegrationTest::test_model_7b_logits and tests/models/llama/test_modeling_llama.py::LlamaIntegrationTest::test_model_7b_logits_bf16 on xpu Signed-off-by: YAO Matrix <matrix.yao@intel.com> * switch to use Expectations Signed-off-by: YAO Matrix <matrix.yao@intel.com> * fix style Signed-off-by: YAO Matrix <matrix.yao@intel.com> * extract gen bits from architecture and use it Signed-off-by: YAO Matrix <matrix.yao@intel.com> * add cross refererence Signed-off-by: YAO Matrix <matrix.yao@intel.com> * fix style Signed-off-by: YAO Matrix <matrix.yao@intel.com> --------- Signed-off-by: YAO Matrix <matrix.yao@intel.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
This commit is contained in:
Yao Matrix
@@ -22,6 +22,7 @@ from parameterized import parameterized
|
||||
from transformers import AutoTokenizer, LlamaConfig, StaticCache, is_torch_available, set_seed
|
||||
from transformers.generation.configuration_utils import GenerationConfig
|
||||
from transformers.testing_utils import (
|
||||
Expectations,
|
||||
cleanup,
|
||||
require_read_token,
|
||||
require_torch,
|
||||
@@ -429,16 +430,6 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
|
||||
|
||||
@require_torch_accelerator
|
||||
class LlamaIntegrationTest(unittest.TestCase):
|
||||
# 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]
|
||||
|
||||
def tearDown(self):
|
||||
# TODO (joao): automatic compilation, i.e. compilation when `cache_implementation="static"` is used, leaves
|
||||
# some memory allocated in the cache, which means some object is not being released properly. This causes some
|
||||
@@ -490,14 +481,17 @@ class LlamaIntegrationTest(unittest.TestCase):
|
||||
# Expected mean on dim = -1
|
||||
|
||||
# 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]])
|
||||
}
|
||||
expected_means = Expectations(
|
||||
{
|
||||
("xpu", 3): torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]]),
|
||||
("cuda", 7): torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]),
|
||||
("cuda", 8): torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]])
|
||||
})
|
||||
|
||||
expected_mean = expected_means.get_expectation()
|
||||
self.assertTrue(
|
||||
torch.allclose(
|
||||
EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device),
|
||||
expected_mean.to(torch_device),
|
||||
out.logits.float().mean(-1),
|
||||
atol=1e-2,
|
||||
rtol=1e-2
|
||||
@@ -505,15 +499,17 @@ class LlamaIntegrationTest(unittest.TestCase):
|
||||
)
|
||||
|
||||
# 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]])
|
||||
}
|
||||
expected_slices = Expectations(
|
||||
{
|
||||
("xpu", 3): 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]]),
|
||||
("cuda", 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]]),
|
||||
("cuda", 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
|
||||
|
||||
expected_slice = expected_slices.get_expectation()
|
||||
self.assertTrue(
|
||||
torch.allclose(
|
||||
EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
|
||||
expected_slice.to(torch_device),
|
||||
out.logits[0, 0, :15].float(),
|
||||
atol=1e-2,
|
||||
rtol=1e-2,
|
||||
@@ -534,14 +530,17 @@ class LlamaIntegrationTest(unittest.TestCase):
|
||||
|
||||
# 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]])
|
||||
}
|
||||
expected_means = Expectations(
|
||||
{
|
||||
("xpu", 3): torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]]),
|
||||
("cuda", 7): torch.tensor([[-6.6420, -4.1227, -4.9809, -3.2041, 0.8261, -3.0052, 1.2957, -3.3648]]),
|
||||
("cuda", 8): torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]]),
|
||||
})
|
||||
|
||||
expected_mean = expected_means.get_expectation()
|
||||
self.assertTrue(
|
||||
torch.allclose(
|
||||
EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device),
|
||||
expected_mean.to(torch_device),
|
||||
out.logits.float().mean(-1),
|
||||
atol=1e-2,
|
||||
rtol=1e-2
|
||||
@@ -549,15 +548,18 @@ class LlamaIntegrationTest(unittest.TestCase):
|
||||
)
|
||||
|
||||
# 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])
|
||||
}
|
||||
expected_slices = Expectations(
|
||||
{
|
||||
("xpu", 3): 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]),
|
||||
("cuda", 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]),
|
||||
("cuda", 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
|
||||
|
||||
expected_slice = expected_slices.get_expectation()
|
||||
self.assertTrue(
|
||||
torch.allclose(
|
||||
EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
|
||||
expected_slice.to(torch_device),
|
||||
out.logits[0, 0, :15].float(),
|
||||
atol=1e-2,
|
||||
rtol=1e-2,
|
||||
|
||||
Reference in New Issue
Block a user