Fix ESM checkpoints for tests (#20436)

* Re-enable TF ESM tests, make sure we use facebook checkpoints

* make fixup

tests/models/esm/test_modeling_esm.py

@@ -274,7 +274,7 @@ class EsmModelIntegrationTest(TestCasePlus):
     @slow
     def test_inference_masked_lm(self):
         with torch.no_grad():
-            model = EsmForMaskedLM.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D")
+            model = EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
             model.eval()
             input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
             output = model(input_ids)[0]
@@ -292,7 +292,7 @@ class EsmModelIntegrationTest(TestCasePlus):
     @slow
     def test_inference_no_head(self):
         with torch.no_grad():
-            model = EsmModel.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D")
+            model = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
             model.eval()
 
             input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])

tests/models/esm/test_modeling_esmfold.py

@@ -247,7 +247,7 @@ class EsmFoldModelTest(ModelTesterMixin, unittest.TestCase):
 class EsmModelIntegrationTest(TestCasePlus):
     @slow
     def test_inference_protein_folding(self):
-        model = EsmForProteinFolding.from_pretrained("Rocketknight1/esmfold_v1").float()
+        model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1").float()
         model.eval()
         input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
         position_outputs = model(input_ids)["positions"]

tests/models/esm/test_modeling_tf_esm.py

@@ -254,9 +254,9 @@ class TFEsmModelTest(TFModelTesterMixin, unittest.TestCase):
 
 @require_tf
 class TFEsmModelIntegrationTest(unittest.TestCase):
-    @unittest.skip("Temporarily disabled as we update ESM model checkpoints")
+    @slow
     def test_inference_masked_lm(self):
-        model = TFEsmForMaskedLM.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D")
+        model = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
 
         input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
         output = model(input_ids)[0]
@@ -264,13 +264,19 @@ class TFEsmModelIntegrationTest(unittest.TestCase):
         self.assertEqual(list(output.numpy().shape), expected_shape)
         # compare the actual values for a slice.
         expected_slice = tf.constant(
-            [[[15.0963, -6.6414, -1.1346], [-0.2209, -9.9633, 4.2082], [-1.6045, -10.0011, 1.5882]]]
+            [
+                [
+                    [8.920963, -10.591399, -6.467397],
+                    [-6.3980846, -13.913257, -1.1291938],
+                    [-7.7815733, -13.951929, -3.7438734],
+                ]
+            ]
         )
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
 
-    @unittest.skip("Temporarily disabled as we update ESM model checkpoints")
+    @slow
     def test_inference_no_head(self):
-        model = TFEsmModel.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D")
+        model = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
 
         input_ids = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
         output = model(input_ids)[0]
@@ -278,9 +284,9 @@ class TFEsmModelIntegrationTest(unittest.TestCase):
         expected_slice = tf.constant(
             [
                 [
-                    [0.144337, 0.541198, 0.32479298],
-                    [0.30328932, 0.00519154, 0.31089523],
-                    [0.32273883, -0.24992886, 0.34143737],
+                    [0.14422388, 0.5411936, 0.3249576],
+                    [0.30342406, 0.00549317, 0.31096306],
+                    [0.32278833, -0.24974644, 0.34135976],
                 ]
             ]
         )