Remove DORT related tests since it was removed from PyTorch (#2465)

Signed-off-by: Justin Chu <[email protected]>

Author: justinchuby

onnxscript/tools/training_helper.py

@@ -9,7 +9,6 @@
 import onnxruntime
 import torch
 
-import onnxscript.tools.training_helper
 import onnxscript.tools.transformers_models
 import onnxscript.tools.transformers_models.llama
 from onnxscript._internal.version_utils import (
@@ -34,13 +33,7 @@ def test_llama_export_cpu(self):
         )
         input_tensors = input_tensors_many[0]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))
@@ -63,15 +56,9 @@ def test_llama_export_cpu_export_api(self):
         )
         input_tensors = input_tensors_many[0]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(
-                model, *input_tensors, export_api=True
-            )
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(
+            model, *input_tensors, export_api=True
+        )
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))
@@ -94,13 +81,7 @@ def test_llama_export_cuda(self):
         model = model.to("cuda")
         input_tensors = [i.to("cuda") for i in input_tensors_cpu]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.detach().cpu().numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))

onnxscript/tools/transformers_models/llama_test.py

@@ -9,9 +9,6 @@
 import onnxruntime
 import torch
 
-import onnxscript.optimizer
-import onnxscript.rewriter
-import onnxscript.tools.training_helper
 import onnxscript.tools.transformers_models
 import onnxscript.tools.transformers_models.mistral
 from onnxscript._internal.version_utils import (
@@ -36,13 +33,7 @@ def test_mistral_export_cpu(self):
         )
         input_tensors = input_tensors_many[0]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))
@@ -65,15 +56,9 @@ def test_mistral_export_cpu_export_api(self):
         )
         input_tensors = input_tensors_many[0]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(
-                model, *input_tensors, export_api=True
-            )
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(
+            model, *input_tensors, export_api=True
+        )
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))
@@ -95,13 +80,7 @@ def test_phi_export_cuda(self):
         model = model.to("cuda")
         input_tensors = [i.to("cuda") for i in input_tensors_cpu]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.detach().cpu().numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))

onnxscript/tools/transformers_models/mistral_test.py

@@ -9,9 +9,6 @@
 import onnxruntime
 import torch
 
-import onnxscript.optimizer
-import onnxscript.rewriter
-import onnxscript.tools.training_helper
 import onnxscript.tools.transformers_models
 import onnxscript.tools.transformers_models.phi3
 from onnxscript._internal.version_utils import (
@@ -35,13 +32,7 @@ def test_phi3_export_cpu(self):
         )
         input_tensors = input_tensors_many[0]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))
@@ -62,15 +53,9 @@ def test_phi3_export_cpu_export_api(self):
         )
         input_tensors = input_tensors_many[0]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(
-                model, *input_tensors, export_api=True
-            )
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(
+            model, *input_tensors, export_api=True
+        )
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))
@@ -93,13 +78,7 @@ def test_phi3_export_cuda(self):
         model = model.to("cuda")
         input_tensors = [i.to("cuda") for i in input_tensors_cpu]
         expected = model(*input_tensors)
-        try:
-            proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
-        except torch._export.verifier.SpecViolationError as e:  # pylint: disable=protected-access
-            # see https://github.com/pytorch/pytorch/issues/128394
-            if "Node.meta _enter_autocast is missing val field." in str(e):
-                raise unittest.SkipTest(str(e))
-            raise
+        proto = onnxscript.tools.transformers_models.export_to_onnx(model, *input_tensors)
         names = [i.name for i in proto.graph.input]
         np_input_tensors = [x.detach().cpu().numpy() for x in input_tensors]
         feeds = dict(zip(names, np_input_tensors))

onnxscript/tools/transformers_models/phi3_test.py

@@ -2,15 +2,13 @@
 # Licensed under the MIT License.
 # pylint: disable=not-callable
 
-import copy
 import sys
 import unittest
 
 import numpy as np
 import onnxruntime
 import torch
 
-import onnxscript.tools.training_helper
 import onnxscript.tools.transformers_models
 import onnxscript.tools.transformers_models.phi
 from onnxscript._internal.version_utils import (
@@ -79,33 +77,6 @@ def test_phi_export_cuda(self):
         results = sess.run(None, feeds)
         np.testing.assert_allclose(expected[0].detach().cpu().numpy(), results[0], atol=1e-5)
 
-    @unittest.skipIf(sys.platform == "win32", reason="not supported yet on Windows")
-    @unittest.skipIf(not has_transformers(), reason="transformers is missing")
-    @unittest.skipIf(
-        not hasattr(onnxruntime, "training"), reason="ORT training removed since 1.22"
-    )
-    @ignore_warnings(UserWarning)
-    def test_phi_dort_static(self):
-        model, input_tensors_many, _ = onnxscript.tools.transformers_models.phi.get_phi_model()
-        input_tensors = input_tensors_many[0]
-        expected = model(*input_tensors)
-
-        local_aot_ort = onnxscript.tools.training_helper.make_aot_ort()
-
-        compiled_model = torch.compile(
-            copy.deepcopy(model),
-            backend=local_aot_ort,
-            dynamic=False,
-            fullgraph=True,
-        )
-
-        results = compiled_model(*input_tensors)
-        torch.testing.assert_close(expected[0], results[0], atol=1e-5, rtol=1e-5)
-
-        expected_gradients = onnxscript.tools.training_helper.train_loop(model, *input_tensors)
-        gradients = onnxscript.tools.training_helper.train_loop(compiled_model, *input_tensors)
-        torch.testing.assert_close(expected_gradients[0], gradients[0], atol=1e-5, rtol=1e-5)
-
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)