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enable test_dispatch_model_tied_weights_memory_with_nested_offload_cpu on xpu (#3569)
* enable test_dispatch_model_tied_weights_memory_with_nested_offload_cpu case on XPU Signed-off-by: Matrix Yao <matrix.yao@intel.com> * replace hard-coded torch.cuda w/ device-dependent callings Signed-off-by: Matrix Yao <matrix.yao@intel.com> * fix style Signed-off-by: Matrix Yao <matrix.yao@intel.com> * use device agnostic clear_device_cache Signed-off-by: Matrix Yao <matrix.yao@intel.com> * fix style Signed-off-by: Matrix Yao <matrix.yao@intel.com> --------- Signed-off-by: Matrix Yao <matrix.yao@intel.com>
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Yao Matrix
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@ -36,7 +36,6 @@ from accelerate.big_modeling import (
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from accelerate.hooks import remove_hook_from_submodules
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from accelerate.test_utils import (
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require_bnb,
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require_cuda,
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require_cuda_or_xpu,
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require_multi_device,
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require_multi_gpu_or_xpu,
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@ -47,6 +46,7 @@ from accelerate.test_utils import (
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torch_device,
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)
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from accelerate.utils import is_hpu_available, offload_state_dict
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from accelerate.utils.memory import clear_device_cache
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from accelerate.utils.versions import is_torch_version
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@ -379,7 +379,7 @@ class BigModelingTester(unittest.TestCase):
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torch_accelerator_module = getattr(torch, torch_device_type)
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torch_accelerator_module.empty_cache() # Needed in case we run several tests in a row.
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clear_device_cache() # Needed in case we run several tests in a row.
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model = nn.Sequential(
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OrderedDict(
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@ -443,7 +443,7 @@ class BigModelingTester(unittest.TestCase):
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# Test that we do not duplicate tied weights at any point during dispatch_model call.
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torch_accelerator_module = getattr(torch, torch_device_type)
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torch_accelerator_module.empty_cache() # Needed in case we run several tests in a row.
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clear_device_cache() # Needed in case we run several tests in a row.
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class SubModule(torch.nn.Module):
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def __init__(self, ref_to_parameter):
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@ -521,7 +521,7 @@ class BigModelingTester(unittest.TestCase):
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torch.testing.assert_close(expected, output.cpu(), atol=ATOL, rtol=RTOL)
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torch_accelerator_module.empty_cache()
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clear_device_cache()
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free_memory_bytes_after_infer = torch_accelerator_module.mem_get_info(torch_device)[0]
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@ -536,14 +536,16 @@ class BigModelingTester(unittest.TestCase):
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# This test fails because sometimes data_ptr() of compute2.weight is the same as compute1.weight.
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# I checked that the values are not the same but it gives the same address. This does not happen on my local machine.
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@require_cuda
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@require_cuda_or_xpu
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@unittest.skip(
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"Flaky test, we should have enough coverage with test_dispatch_model_tied_weights_memory_with_nested_offload_cpu test"
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)
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def test_dispatch_model_tied_weights_memory_with_nested_offload_disk(self):
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# Test that we do not duplicate tied weights at any point during dispatch_model call.
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torch.cuda.empty_cache() # Needed in case we run several tests in a row.
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torch_accelerator_module = getattr(torch, torch_device_type)
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clear_device_cache() # Needed in case we run several tests in a row.
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class SubModule(torch.nn.Module):
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def __init__(self, ref_to_parameter):
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@ -589,27 +591,33 @@ class BigModelingTester(unittest.TestCase):
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expected = model(x)
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# Just to initialize CUDA context.
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a = torch.rand(5).to("cuda:0") # noqa: F841
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device_0 = f"{torch_device_type}:0"
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a = torch.rand(5).to(device_0) # noqa: F841
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free_memory_bytes = torch.cuda.mem_get_info("cuda:0")[0]
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free_memory_bytes = torch_accelerator_module.mem_get_info(device_0)[0]
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required_memory_bytes = 2 * 5000 * 5000 * (32 // 8) # 200 MB
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# Leaving 150 MB of free memory for possible buffers, etc.
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n_vals = (free_memory_bytes - required_memory_bytes - int(200e6)) // (32 // 8)
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foo = torch.rand(n_vals, device="cuda:0") # noqa: F841
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foo = torch.rand(n_vals, device=device_0) # noqa: F841
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free_memory_bytes_before_dispatch = torch.cuda.mem_get_info("cuda:0")[0]
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free_memory_bytes_before_dispatch = torch_accelerator_module.mem_get_info(device_0)[0]
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with TemporaryDirectory() as tmp_dir:
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dispatch_model(model, device_map, offload_dir=tmp_dir)
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free_memory_bytes_after_dispatch = torch.cuda.mem_get_info("cuda:0")[0]
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free_memory_bytes_after_dispatch = torch_accelerator_module.mem_get_info(device_0)[0]
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assert (free_memory_bytes_after_dispatch - free_memory_bytes_before_dispatch) * 1e-6 < 130
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oom_error = (
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torch.OutOfMemoryError
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if hasattr(torch, "OutOfMemoryError")
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else torch_accelerator_module.OutOfMemoryError
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)
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with torch.no_grad():
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try:
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output = model(x)
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except torch.cuda.OutOfMemoryError as e:
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raise torch.cuda.OutOfMemoryError(
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except oom_error as e:
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raise oom_error(
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f"OOM error in dispatch_model. This is a bug and should not happen, see test_dispatch_model_tied_weights_memory_with_nested_offload_disk. {e}"
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)
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except Exception as e:
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@ -617,9 +625,9 @@ class BigModelingTester(unittest.TestCase):
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torch.testing.assert_close(expected, output.cpu(), atol=ATOL, rtol=RTOL)
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torch.cuda.empty_cache()
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clear_device_cache()
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free_memory_bytes_after_infer = torch.cuda.mem_get_info("cuda:0")[0]
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free_memory_bytes_after_infer = torch_accelerator_module.mem_get_info(device_0)[0]
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# Check that we have no more references on GPU for the offloaded tied weight.
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n_non_empty = 0
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@ -12,7 +12,6 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import tempfile
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import unittest
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@ -543,8 +542,7 @@ class MixedInt8LoaddedModelTest(unittest.TestCase):
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del self.model_fp16
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del self.model_8bit
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gc.collect()
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torch.cuda.empty_cache()
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clear_device_cache(garbage_collection=True)
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def test_memory_footprint(self):
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r"""
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@ -663,8 +661,7 @@ class Bnb4BitEmptyModelTest(unittest.TestCase):
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del self.model_fp16
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del self.model_4bit
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gc.collect()
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torch.cuda.empty_cache()
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clear_device_cache(garbage_collection=True)
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def test_memory_footprint(self):
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r"""
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