Check that ET and Eager are numericaly equivalent

Differential Revision: D61494957

Pull Request resolved: #4779

Author: JacobSzwejbka

exir/tests/TARGETS

@@ -109,6 +109,7 @@ python_unittest(
     deps = [
         "//caffe2:torch",
         "//executorch/exir:lib",
+        "//executorch/extension/pybindings:portable_lib",
     ],
 )
 
@@ -209,6 +210,7 @@ python_unittest(
         "//executorch/exir/passes:debug_handle_generator_pass",
         "//executorch/exir/passes:insert_write_back_for_buffers_pass",
         "//executorch/exir/passes:lib",
+        "//executorch/exir/passes:memory_format_ops_pass",
         "//executorch/exir/passes:normalize_view_copy_base_pass",
         "//executorch/exir/passes:remove_graph_asserts_pass",
         "//executorch/exir/passes:remove_mixed_type_operators",

exir/tests/test_joint_graph.py

@@ -11,6 +11,10 @@
 import torch._dynamo
 
 from executorch.exir import to_edge
+
+from executorch.extension.pybindings.portable_lib import (
+    _load_for_executorch_from_buffer,
+)
 from torch.export._trace import _export
 from torch.export.experimental import _export_forward_backward
 from torch.export.exported_program import OutputKind
@@ -89,3 +93,18 @@ def forward(self, x, y):
             .val.allocation_info.memory_offset_low,
             48,
         )
+
+        loss = m(*example_inputs)
+        loss.backward()
+        et_mod = _load_for_executorch_from_buffer(et.buffer)
+        et_outputs = et_mod.forward(
+            example_inputs
+        )  # ET outputs are [loss, grads, weights]
+
+        self.assertTrue(torch.allclose(loss, et_outputs[0]))
+        self.assertTrue(
+            torch.allclose(m.linear.weight.grad, et_outputs[1])  # pyre-ignore[6]
+        )
+        self.assertTrue(torch.allclose(m.linear.bias.grad, et_outputs[2]))
+        self.assertTrue(torch.allclose(m.linear.weight, et_outputs[3]))
+        self.assertTrue(torch.allclose(m.linear.bias, et_outputs[4]))