restrict transformers version for now...

huggingface · Nov 3, 2023 · 0bcb2b5 · 0bcb2b5
1 parent f73f469
commit 0bcb2b5
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Showing 3 changed files with 9 additions and 24 deletions.
diff --git a/optimum/intel/openvino/trainer.py b/optimum/intel/openvino/trainer.py
@@ -65,25 +65,11 @@
 from transformers.trainer_utils import (
     EvalPrediction,
     HPSearchBackend,
+    ShardedDDPOption,
     TrainOutput,
     has_length,
     speed_metrics,
 )
-
-
-try:
-    from transformers.trainer_utils import ShardedDDPOption
-except ImportError:
-    from transformers.utils import ExplicitEnum
-
-    class ShardedDDPOption(ExplicitEnum):
-        SIMPLE = "simple"
-        ZERO_DP_2 = "zero_dp_2"
-        ZERO_DP_3 = "zero_dp_3"
-        OFFLOAD = "offload"
-        AUTO_WRAP = "auto_wrap"
-
-
 from transformers.utils import (
     WEIGHTS_NAME,
     is_apex_available,
@@ -310,10 +296,9 @@ def _inner_training_loop(
             else:
                 debug_overflow = DebugUnderflowOverflow(self.model)  # noqa
 
-        sharded_ddp = getattr(self, "sharded_ddp", None)
         delay_optimizer_creation = (
-            sharded_ddp is not None
-            and sharded_ddp != ShardedDDPOption.SIMPLE
+            self.sharded_ddp is not None
+            and self.sharded_ddp != ShardedDDPOption.SIMPLE
             or is_sagemaker_mp_enabled()
             or self.fsdp is not None
         )
@@ -526,7 +511,7 @@ def _inner_training_loop(
                     if args.max_grad_norm is not None and args.max_grad_norm > 0 and not self.deepspeed:
                         # deepspeed does its own clipping
 
-                        if getattr(self, "do_grad_scaling", False):
+                        if self.do_grad_scaling:
                             # AMP: gradients need unscaling
                             self.scaler.unscale_(self.optimizer)
 
@@ -549,7 +534,7 @@ def _inner_training_loop(
                     optimizer_was_run = True
                     if self.deepspeed:
                         pass  # called outside the loop
-                    elif getattr(self, "do_grad_scaling", False):
+                    elif self.do_grad_scaling:
                         scale_before = self.scaler.get_scale()
                         self.scaler.step(self.optimizer)
                         self.scaler.update()

diff --git a/setup.py b/setup.py
@@ -42,7 +42,7 @@
         "onnx",
         "onnxruntime<1.15.0",
     ],
-    "openvino": ["openvino>=2023.1.0", "onnx", "onnxruntime"],
+    "openvino": ["openvino>=2023.1.0", "onnx", "onnxruntime", "transformers<4.35.0"],
     "nncf": ["nncf>=2.6.0"],
     "ipex": ["transformers<4.32.0", "intel-extension-for-pytorch", "onnx"],
     "diffusers": ["diffusers"],

diff --git a/tests/openvino/test_modeling.py b/tests/openvino/test_modeling.py
@@ -493,7 +493,7 @@ def test_compare_to_transformers(self, model_arch):
         set_seed(SEED)
         ov_model = OVModelForCausalLM.from_pretrained(model_id, export=True)
         self.assertIsInstance(ov_model.config, PretrainedConfig)
-        transformers_model = AutoModelForCausalLM.from_pretrained(model_id)
+        transformers_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
         tokenizer = AutoTokenizer.from_pretrained(model_id)
         tokens = tokenizer(
             "This is a sample", return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None
@@ -504,7 +504,8 @@ def test_compare_to_transformers(self, model_arch):
         with torch.no_grad():
             transformers_outputs = transformers_model(**tokens)
         # Compare tensor outputs
-        self.assertTrue(torch.allclose(ov_outputs.logits, transformers_outputs.logits, atol=1e-2))
+        self.assertTrue(torch.allclose(ov_outputs.logits, transformers_outputs.logits, atol=1e-4),
+                         f"Max diff {torch.abs(ov_outputs.logits - transformers_outputs.logits).max()}")
         del transformers_model
         del ov_model
         gc.collect()
@@ -1238,7 +1239,6 @@ def test_compare_to_transformers(self, model_arch):
 
             ov_outputs = ov_model(**features, **decoder_inputs)
             self.assertIn("logits", ov_outputs)
-            self.assertIsInstance(ov_outputs.logits, TENSOR_ALIAS_TO_TYPE[input_type])
             # Compare tensor outputs
             self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), transformers_outputs.logits, atol=1e-3))