Revert "Added BF16 & FP16 models to PTQ tests" (#3124)

Reverts #2922 ### Related tickets 158536
openvinotoolkit · Dec 2, 2024 · 0799634 · 0799634
1 parent 03c0540
commit 0799634
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Showing 3 changed files with 8 additions and 51 deletions.
diff --git a/tests/post_training/data/ptq_reference_data.yaml b/tests/post_training/data/ptq_reference_data.yaml
@@ -26,14 +26,6 @@ hf/hf-internal-testing/tiny-random-gpt2_backend_OV:
   metric_value: null
 hf/hf-internal-testing/tiny-random-gpt2_backend_TORCH:
   metric_value: null
-hf/bert-base-uncased_fp16_backend_FP32:
-  metric_value: null
-hf/bert-base-uncased_fp16_backend_OV:
-  metric_value: null
-hf/bert-base-uncased_bf16_backend_FP32:
-  metric_value: null
-hf/bert-base-uncased_bf16_backend_OV:
-  metric_value: null
 torchvision/resnet18_backend_FP32:
   metric_value: 0.6978
 torchvision/resnet18_backend_OV:

diff --git a/tests/post_training/model_scope.py b/tests/post_training/model_scope.py
@@ -12,8 +12,6 @@
 import copy
 from typing import Dict, List
 
-import torch
-
 import nncf
 from nncf import ModelType
 from nncf import QuantizationPreset
@@ -83,30 +81,6 @@
         },
         "backends": [BackendType.TORCH, BackendType.OV, BackendType.OPTIMUM],
     },
-    {
-        "reported_name": "hf/bert-base-uncased_fp16",
-        "model_id": "bert-base-uncased",
-        "pipeline_cls": MaskedLanguageModelingHF,
-        "compression_params": {
-            "preset": QuantizationPreset.MIXED,
-            "model_type": ModelType.TRANSFORMER,
-            "subset_size": 2,
-        },
-        "backends": [BackendType.OV],
-        "params": {"base_precision": torch.float16},
-    },
-    {
-        "reported_name": "hf/bert-base-uncased_bf16",
-        "model_id": "bert-base-uncased",
-        "pipeline_cls": MaskedLanguageModelingHF,
-        "compression_params": {
-            "preset": QuantizationPreset.MIXED,
-            "model_type": ModelType.TRANSFORMER,
-            "subset_size": 2,
-        },
-        "backends": [BackendType.OV],
-        "params": {"base_precision": torch.bfloat16},
-    },
     # Torchvision models
     {
         "reported_name": "torchvision/resnet18",

diff --git a/tests/post_training/pipelines/masked_language_modeling.py b/tests/post_training/pipelines/masked_language_modeling.py
@@ -29,26 +29,14 @@ class MaskedLanguageModelingHF(PTQTestPipeline):
     """Pipeline for masked language models from Hugging Face repository"""
 
     def prepare_model(self) -> None:
-        torch_dtype = self.params.get("base_precision", torch.float32)
         if self.backend in PT_BACKENDS:
-            self.model_hf = transformers.AutoModelForSequenceClassification.from_pretrained(
-                self.model_id, torch_dtype=torch_dtype
-            )
+            self.model_hf = transformers.AutoModelForSequenceClassification.from_pretrained(self.model_id)
             self.model = self.model_hf
             self.model.config.torchscript = True  # Set to export by convert_model via torch.jit.trace
             self.dummy_tensor = self.model_hf.dummy_inputs["input_ids"]
         if self.backend in OV_BACKENDS + [BackendType.FP32]:
-            if torch_dtype != torch.float32:
-                # Since optimum-intel does not produce custom-type models, this workaround handles it.
-                self.model_hf = transformers.AutoModelForSequenceClassification.from_pretrained(
-                    self.model_id, torch_dtype=torch_dtype
-                )
-                self.model = ov.convert_model(self.model_hf, example_input=self.model_hf.dummy_inputs)
-            else:
-                self.model_hf = OVModelForSequenceClassification.from_pretrained(
-                    self.model_id, export=True, compile=False
-                )
-                self.model = self.model_hf.model
+            self.model_hf = OVModelForSequenceClassification.from_pretrained(self.model_id, export=True, compile=False)
+            self.model = self.model_hf.model
 
         if self.backend == BackendType.ONNX:
             self.model_hf = ORTModelForSequenceClassification.from_pretrained(self.model_id, export=True)
@@ -87,10 +75,13 @@ def transform_func(data):
                 return torch.tensor([data["input_ids"]]).type(dtype=torch.LongTensor).to(device)
 
         else:
-            input_names = [p.get_friendly_name() for p in self.model.get_parameters()]
 
             def transform_func(data):
-                return {n: np.expand_dims(data[n], axis=0) for n in input_names}
+                return {
+                    "input_ids": np.expand_dims(data["input_ids"], axis=0),
+                    "token_type_ids": np.expand_dims(data["token_type_ids"], axis=0),
+                    "attention_mask": np.expand_dims(data["attention_mask"], axis=0),
+                }
 
         return transform_func