mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
Revert "add/remove pytest marks to TF tests"
This reverts commit b522f933a9.
This commit is contained in:
Ean Garvey
@@ -1,3 +0,0 @@
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def pytest_addoption(parser):
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# Attaches SHARK command-line arguments to the pytest machinery.
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parser.addoption("--save_temps", action="store_true", default="False", help="Saves IREE reproduction artifacts for filing upstream issues.")
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@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
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from shark.iree_utils import check_device_drivers
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from shark.shark_inference import SharkInference
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import iree.compiler as ireec
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import unittest
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import pytest
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import numpy as np
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import tempfile
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class AlbertBaseModuleTester:
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def __init__(
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self,
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save_temps=False
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):
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self.save_temps = save_temps
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def create_and_check_module(self, dynamic, device):
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model, input, act_out = get_causal_lm_model("albert-base-v2")
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save_temps = self.save_temps
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if save_temps == True:
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if dynamic == True:
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repro_dir = f"albert_base_v2_dynamic_{device}"
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else:
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repro_dir = f"albert_base_v2_static_{device}"
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temp_dir = tempfile.mkdtemp(prefix=repro_dir)
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np.set_printoptions(threshold=np.inf)
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np.save(f"{temp_dir}/input1.npy", input[0])
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np.save(f"{temp_dir}/input2.npy", input[1])
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exp_out = act_out.numpy()
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with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
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out_file.write(np.array2string(exp_out))
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with ireec.tools.TempFileSaver(temp_dir):
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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else:
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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class AlbertBaseModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.module_tester = AlbertBaseModuleTester(self)
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self.module_tester.save_temps = pytestconfig.getoption("save_temps")
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def setUp(self):
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self.module_tester = AlbertBaseModuleTester()
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@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
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@pytest.mark.xfail
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def test_module_static_cpu(self):
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dynamic = False
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device = "cpu"
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@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
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from shark.iree_utils import check_device_drivers
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from shark.shark_inference import SharkInference
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import iree.compiler as ireec
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import unittest
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import pytest
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import numpy as np
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import tempfile
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class BertBaseUncasedModuleTester:
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def __init__(
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self,
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save_temps=False
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):
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self.save_temps = save_temps
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def create_and_check_module(self, dynamic, device):
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model, input, act_out = get_causal_lm_model("bert-base-uncased")
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save_temps = self.save_temps
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if save_temps == True:
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if dynamic == True:
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repro_dir = f"bert_base_uncased_dynamic_{device}"
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else:
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repro_dir = f"bert_base_uncased_static_{device}"
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temp_dir = tempfile.mkdtemp(prefix=repro_dir)
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np.set_printoptions(threshold=np.inf)
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np.save(f"{temp_dir}/input1.npy", input[0])
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np.save(f"{temp_dir}/input2.npy", input[1])
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exp_out = act_out.numpy()
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with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
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out_file.write(np.array2string(exp_out))
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with ireec.tools.TempFileSaver(temp_dir):
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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else:
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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class BertBaseUncasedModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.module_tester = BertBaseUncasedModuleTester(self)
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self.module_tester.save_temps = pytestconfig.getoption("save_temps")
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def setUp(self):
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self.module_tester = BertBaseUncasedModuleTester()
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@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
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@pytest.mark.xfail
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def test_module_static_cpu(self):
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dynamic = False
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device = "cpu"
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@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
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from shark.iree_utils import check_device_drivers
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from shark.shark_inference import SharkInference
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import iree.compiler as ireec
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import unittest
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import pytest
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import numpy as np
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import tempfile
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class CamemBertModuleTester:
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def __init__(
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self,
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save_temps=False
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):
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self.save_temps = save_temps
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def create_and_check_module(self, dynamic, device):
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model, input, act_out = get_causal_lm_model("camembert-base")
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save_temps = self.save_temps
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if save_temps == True:
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if dynamic == True:
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repro_dir = f"camembert-base_dynamic_{device}"
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else:
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repro_dir = f"camembert-base_static_{device}"
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temp_dir = tempfile.mkdtemp(prefix=repro_dir)
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np.set_printoptions(threshold=np.inf)
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np.save(f"{temp_dir}/input1.npy", input[0])
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np.save(f"{temp_dir}/input2.npy", input[1])
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exp_out = act_out.numpy()
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with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
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out_file.write(np.array2string(exp_out))
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with ireec.tools.TempFileSaver(temp_dir):
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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else:
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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class CamemBertModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.module_tester=CamemBertModuleTester(self)
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self.module_tester.save_temps = pytestconfig.getoption("save_temps")
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def setUp(self):
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self.module_tester = CamemBertModuleTester()
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@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
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@pytest.mark.xfail
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def test_module_static_cpu(self):
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dynamic = False
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device = "cpu"
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@@ -3,66 +3,31 @@ from tank.model_utils_tf import compare_tensors_tf
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from shark.iree_utils import check_device_drivers
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from shark.shark_inference import SharkInference
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import iree.compiler as ireec
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import unittest
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import pytest
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import numpy as np
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import tempfile
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class ConvBertModuleTester:
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def __init__(
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self,
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save_temps=False
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):
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self.save_temps = save_temps
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def create_and_check_module(self, dynamic, device):
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model, input, act_out = get_causal_lm_model(
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"dbmdz/convbert-base-turkish-cased")
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save_temps = self.save_temps
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if save_temps == True:
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if dynamic == True:
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repro_dir = f"convbert_base_dynamic_{device}"
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else:
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repro_dir = f"convbert_base_static_{device}"
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temp_dir = tempfile.mkdtemp(prefix=repro_dir)
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np.set_printoptions(threshold=np.inf)
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np.save(f"{temp_dir}/input1.npy", input[0])
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np.save(f"{temp_dir}/input2.npy", input[1])
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exp_out = act_out.numpy()
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with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
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out_file.write(np.array2string(exp_out))
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with ireec.tools.TempFileSaver(temp_dir):
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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else:
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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shark_module = SharkInference(model, (input,),
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device=device,
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dynamic=dynamic,
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jit_trace=True)
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shark_module.set_frontend("tensorflow")
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shark_module.compile()
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results = shark_module.forward((input))
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assert True == compare_tensors_tf(act_out, results)
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|
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|
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class ConvBertModuleTest(unittest.TestCase):
|
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.module_tester = ConvBertModuleTester(self)
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self.module_tester.save_temps = pytestconfig.getoption("save_temps")
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def setUp(self):
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self.module_tester = ConvBertModuleTester()
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|
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@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
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@pytest.mark.xfail
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def test_module_static_cpu(self):
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dynamic = False
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device = "cpu"
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|
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@@ -3,63 +3,28 @@ from tank.model_utils_tf import compare_tensors_tf
|
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from shark.iree_utils import check_device_drivers
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from shark.shark_inference import SharkInference
|
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|
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import iree.compiler as ireec
|
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import unittest
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import pytest
|
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import numpy as np
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import tempfile
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|
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|
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class DebertaModuleTester:
|
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|
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def __init__(
|
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self,
|
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save_temps=False
|
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):
|
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self.save_temps = save_temps
|
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|
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def create_and_check_module(self, dynamic, device):
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model, input, act_out = get_causal_lm_model("microsoft/deberta-base")
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save_temps = self.save_temps
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if save_temps == True:
|
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if dynamic == True:
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repro_dir = f"deberta-base_dynamic_{device}"
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else:
|
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repro_dir = f"deberta-base_static_{device}"
|
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temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
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np.set_printoptions(threshold=np.inf)
|
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np.save(f"{temp_dir}/input1.npy", input[0])
|
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np.save(f"{temp_dir}/input2.npy", input[1])
|
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exp_out = act_out.numpy()
|
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with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
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out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
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device=device,
|
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dynamic=dynamic,
|
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jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
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shark_module.compile()
|
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results = shark_module.forward((input))
|
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assert True == compare_tensors_tf(act_out, results)
|
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|
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else:
|
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shark_module = SharkInference(model, (input,),
|
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device=device,
|
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dynamic=dynamic,
|
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jit_trace=True)
|
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shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
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results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
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device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
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shark_module.compile()
|
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results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class DebertaModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = DebertaModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
|
||||
def setUp(self):
|
||||
self.module_tester = DebertaModuleTester()
|
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|
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@pytest.mark.xfail
|
||||
@pytest.mark.skip(reason="deberta currently failing in the lowering passes."
|
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|
||||
@@ -3,64 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class DistilBertModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("distilbert-base-uncased")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"distilbert_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"distilbert__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class DistilBertModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = DistilBertModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = DistilBertModuleTester()
|
||||
|
||||
@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,65 +3,31 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class ElectraModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("google/electra-small-discriminator")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"electra_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"electra__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
model, input, act_out = get_causal_lm_model(
|
||||
"google/electra-small-discriminator")
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class ElectraModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self):
|
||||
self.module_tester = ElectraModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = ElectraModuleTester()
|
||||
|
||||
@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,63 +3,28 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class FunnelModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("funnel-transformer/small")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"funnel_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"funnel__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class FunnelModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = FunnelModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = FunnelModuleTester()
|
||||
|
||||
@pytest.mark.skip(reason="funnel currently failing in the lowering passes.")
|
||||
def test_module_static_cpu(self):
|
||||
|
||||
@@ -3,66 +3,31 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class LayoutLmModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model(
|
||||
"microsoft/layoutlm-base-uncased")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"layoutlm_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"layoutlm__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class LayoutLmModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = LayoutLmModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = LayoutLmModuleTester()
|
||||
|
||||
@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,63 +3,29 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class LongFormerModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("allenai/longformer-base-4096")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"longformer_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"longformer__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
model, input, act_out = get_causal_lm_model(
|
||||
"allenai/longformer-base-4096")
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class LongFormerModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = LongFormerModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = LongFormerModuleTester()
|
||||
|
||||
@pytest.mark.skip(
|
||||
reason="longformer currently failing in the lowering passes.")
|
||||
|
||||
@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class MobileBertModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("google/mobilebert-uncased")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"mobilebert_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"mobilebert__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class MobileBertModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = MobileBertModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = MobileBertModuleTester()
|
||||
|
||||
@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class MpNetModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("microsoft/mpnet-base")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"mpnet_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"mpnet__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class MpNetModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = MpNetModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
|
||||
@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
|
||||
def setUp(self):
|
||||
self.module_tester = MpNetModuleTester()
|
||||
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,63 +3,28 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class RemBertModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("google/rembert")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"rembert_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"rembert__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class RemBertModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = RemBertModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = RemBertModuleTester()
|
||||
|
||||
@pytest.mark.skip(reason="rembert currently failing in the lowering passes."
|
||||
)
|
||||
|
||||
@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class RobertaModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("roberta-base")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"roberta_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"roberta__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class RobertaModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = RobertaModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = RobertaModuleTester()
|
||||
|
||||
@pytest.mark.xfail(reason="Upstream IREE issue, see https://github.com/google/iree/issues/9536")
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,63 +3,28 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class TapasBaseModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("google/tapas-base")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"tapas-base_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"tapas-base__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class TapasBaseModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = TapasBaseModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = TapasBaseModuleTester()
|
||||
|
||||
@pytest.mark.skip(reason="tapas currently failing in the lowering passes.")
|
||||
def test_module_static_cpu(self):
|
||||
|
||||
@@ -3,64 +3,31 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class FlauBertModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("hf-internal-testing/tiny-random-flaubert")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"flaubert_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"flaubert__static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
model, input, act_out = get_causal_lm_model(
|
||||
"hf-internal-testing/tiny-random-flaubert")
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class FlauBertModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = FlauBertModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = FlauBertModuleTester()
|
||||
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
@@ -3,65 +3,30 @@ from tank.model_utils_tf import compare_tensors_tf
|
||||
from shark.iree_utils import check_device_drivers
|
||||
from shark.shark_inference import SharkInference
|
||||
|
||||
import iree.compiler as ireec
|
||||
import unittest
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tempfile
|
||||
|
||||
|
||||
class XLMRobertaModuleTester:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
save_temps=False
|
||||
):
|
||||
self.save_temps = save_temps
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_causal_lm_model("xlm-roberta-base")
|
||||
save_temps = self.save_temps
|
||||
if save_temps == True:
|
||||
if dynamic == True:
|
||||
repro_dir = f"xlm_roberta_dynamic_{device}"
|
||||
else:
|
||||
repro_dir = f"xlm_roberta_static_{device}"
|
||||
temp_dir = tempfile.mkdtemp(prefix=repro_dir)
|
||||
np.set_printoptions(threshold=np.inf)
|
||||
np.save(f"{temp_dir}/input1.npy", input[0])
|
||||
np.save(f"{temp_dir}/input2.npy", input[1])
|
||||
exp_out = act_out.numpy()
|
||||
with open(f"{temp_dir}/expected_out.txt", "w") as out_file:
|
||||
out_file.write(np.array2string(exp_out))
|
||||
with ireec.tools.TempFileSaver(temp_dir):
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
else:
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
shark_module = SharkInference(model, (input,),
|
||||
device=device,
|
||||
dynamic=dynamic,
|
||||
jit_trace=True)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class XLMRobertaModuleTest(unittest.TestCase):
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = XLMRobertaModuleTester(self)
|
||||
self.module_tester.save_temps = pytestconfig.getoption("save_temps")
|
||||
def setUp(self):
|
||||
self.module_tester = XLMRobertaModuleTester()
|
||||
|
||||
@pytest.mark.skip(reason="Test currently hangs.")
|
||||
@pytest.mark.xfail
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
|
||||
Reference in New Issue
Block a user