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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>
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Yao Matrix
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@ -202,9 +202,11 @@ if is_torch_available():
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IS_ROCM_SYSTEM = torch.version.hip is not None
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IS_CUDA_SYSTEM = torch.version.cuda is not None
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IS_XPU_SYSTEM = torch.version.xpu is not None
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else:
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IS_ROCM_SYSTEM = False
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IS_CUDA_SYSTEM = False
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IS_XPU_SYSTEM = False
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logger = transformers_logging.get_logger(__name__)
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@ -3097,6 +3099,14 @@ def get_device_properties() -> DeviceProperties:
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return ("rocm", major)
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else:
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return ("cuda", major)
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elif IS_XPU_SYSTEM:
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import torch
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# To get more info of the architecture meaning and bit allocation, refer to https://github.com/intel/llvm/blob/sycl/sycl/include/sycl/ext/oneapi/experimental/device_architecture.def
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arch = torch.xpu.get_device_capability()["architecture"]
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gen_mask = 0x000000FF00000000
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gen = (arch & gen_mask) >> 32
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return ("xpu", gen)
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else:
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return (torch_device, None)
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@ -22,6 +22,7 @@ from parameterized import parameterized
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from transformers import AutoTokenizer, LlamaConfig, StaticCache, is_torch_available, set_seed
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from transformers.generation.configuration_utils import GenerationConfig
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from transformers.testing_utils import (
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Expectations,
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cleanup,
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require_read_token,
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require_torch,
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@ -429,16 +430,6 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
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@require_torch_accelerator
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class LlamaIntegrationTest(unittest.TestCase):
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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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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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def tearDown(self):
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# TODO (joao): automatic compilation, i.e. compilation when `cache_implementation="static"` is used, leaves
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# some memory allocated in the cache, which means some object is not being released properly. This causes some
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@ -490,14 +481,17 @@ class LlamaIntegrationTest(unittest.TestCase):
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# Expected mean on dim = -1
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# fmt: off
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EXPECTED_MEAN = {
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7: torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]),
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8: torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]])
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}
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expected_means = Expectations(
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{
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("xpu", 3): torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]]),
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("cuda", 7): torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]),
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("cuda", 8): torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536, 0.8127, -2.9811, 1.2918, -3.3848]])
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})
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expected_mean = expected_means.get_expectation()
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self.assertTrue(
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torch.allclose(
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EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device),
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expected_mean.to(torch_device),
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out.logits.float().mean(-1),
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atol=1e-2,
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rtol=1e-2
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@ -505,15 +499,17 @@ class LlamaIntegrationTest(unittest.TestCase):
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)
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# slicing logits[0, 0, 0:15]
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EXPECTED_SLICE = {
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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]]),
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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]])
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}
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expected_slices = Expectations(
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{
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("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]]),
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("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]]),
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("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]])
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})
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# fmt: on
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expected_slice = expected_slices.get_expectation()
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self.assertTrue(
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torch.allclose(
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EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
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expected_slice.to(torch_device),
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out.logits[0, 0, :15].float(),
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atol=1e-2,
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rtol=1e-2,
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@ -534,14 +530,17 @@ class LlamaIntegrationTest(unittest.TestCase):
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# fmt: off
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# Expected mean on dim = -1
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EXPECTED_MEAN = {
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7: torch.tensor([[-6.6420, -4.1227, -4.9809, -3.2041, 0.8261, -3.0052, 1.2957, -3.3648]]),
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8: torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]])
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}
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expected_means = Expectations(
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{
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("xpu", 3): torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]]),
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("cuda", 7): torch.tensor([[-6.6420, -4.1227, -4.9809, -3.2041, 0.8261, -3.0052, 1.2957, -3.3648]]),
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("cuda", 8): torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456, 0.8261, -3.0124, 1.2971, -3.3641]]),
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})
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expected_mean = expected_means.get_expectation()
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self.assertTrue(
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torch.allclose(
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EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device),
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expected_mean.to(torch_device),
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out.logits.float().mean(-1),
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atol=1e-2,
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rtol=1e-2
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@ -549,15 +548,18 @@ class LlamaIntegrationTest(unittest.TestCase):
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)
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# slicing logits[0, 0, 0:15]
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EXPECTED_SLICE = {
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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]),
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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])
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}
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expected_slices = Expectations(
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{
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("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]),
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("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]),
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("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])
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})
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# fmt: on
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expected_slice = expected_slices.get_expectation()
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self.assertTrue(
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torch.allclose(
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EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
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expected_slice.to(torch_device),
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out.logits[0, 0, :15].float(),
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atol=1e-2,
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rtol=1e-2,
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@ -13,14 +13,16 @@ class ExpectationsTest(unittest.TestCase):
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("rocm", 8): 4,
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("rocm", None): 5,
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("cpu", None): 6,
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("xpu", 3): 7,
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}
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)
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def check(value, key):
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assert expectations.find_expectation(key) == value
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# xpu has no matches so should find default expectation
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check(1, ("xpu", None))
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# npu has no matches so should find default expectation
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check(1, ("npu", None))
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check(7, ("xpu", 3))
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check(2, ("cuda", 8))
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check(3, ("cuda", 7))
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check(4, ("rocm", 9))
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