Fix transformers v4.42.0 compatibility (#793)

* fix transformers v4.42.0 compatibility

* fix inc modeling

* update setup

* add missing argument

* fix patching

* format

* fix num quant op

* remove incompatible transformers generation

* udpate setup

.github/workflows/test_openvino.yml

@@ -21,7 +21,7 @@ jobs:
       fail-fast: false
       matrix:
         python-version: ["3.8", "3.12"]
-        transformers-version: ["4.36.0", "4.41.*"]
+        transformers-version: ["4.36.0", "4.42.*"]
         os: [ubuntu-latest]
 
     runs-on: ${{ matrix.os }}

optimum/exporters/openvino/model_patcher.py

@@ -1161,7 +1161,7 @@ def __exit__(self, exc_type, exc_value, traceback):
                 block.attention.forward = block.attention._orig_forward
 
 
-# Adapted from https://github.com/huggingface/transformers/blob/ccdabc5642bf84849af93f591e207dc625c8e1e1/src/transformers/models/phi3/modeling_phi3.py#L426
+# Adapted from https://github.com/huggingface/transformers/blob/ccdabc5642bf84849af93f591e207dc625c8e1e1/src/transformers/models/phi3/modeling_phi3.py#L729
 def _phi3_self_attn_sdpa_forward(
     self,
     hidden_states: torch.Tensor,
@@ -1170,6 +1170,7 @@ def _phi3_self_attn_sdpa_forward(
     past_key_value: Optional[Tuple[torch.Tensor]] = None,
     output_attentions: bool = False,
     use_cache: bool = False,
+    cache_position: Optional[torch.LongTensor] = None,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     if output_attentions:
         return self._orig_forward(
@@ -1181,10 +1182,9 @@ def _phi3_self_attn_sdpa_forward(
             use_cache=use_cache,
         )
 
-    # TO DO: remove llama imports when transformers with phi3 support will be released
-    try:
+    if is_transformers_version(">=", "4.41.0"):
         from transformers.models.phi3.modeling_phi3 import apply_rotary_pos_emb, repeat_kv
-    except ImportError:
+    else:
         from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
 
     bsz, q_len, _ = hidden_states.size()
@@ -1206,17 +1206,15 @@ def _phi3_self_attn_sdpa_forward(
     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
 
     if past_key_value is not None:
-        cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
         key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
     key_states = repeat_kv(key_states, self.num_key_value_groups)
     value_states = repeat_kv(value_states, self.num_key_value_groups)
 
+    causal_mask = attention_mask
     if attention_mask is not None:
-        if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-            )
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
 
     # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
     # Reference: https://github.com/pytorch/pytorch/issues/112577.
@@ -1229,7 +1227,7 @@ def _phi3_self_attn_sdpa_forward(
         query_states,
         key_states,
         value_states,
-        attn_mask=attention_mask,
+        attn_mask=causal_mask,
         dropout_p=self.attention_dropout if self.training else 0.0,
         # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
         is_causal=self.is_causal and attention_mask is None and q_len > 1,
@@ -1561,7 +1559,7 @@ def __exit__(self, exc_type, exc_value, traceback):
                 layer.attn._attn = layer.attn._orig_attn
 
 
-# adapted from https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/dbrx/modeling_dbrx.py#L763
+# Adapted from https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/dbrx/modeling_dbrx.py#L763
 def _dbrx_experts_forward(
     self, x: torch.Tensor, weights: torch.Tensor, top_weights: torch.Tensor, top_experts: torch.LongTensor
 ):
@@ -1606,7 +1604,7 @@ def _dbrx_experts_forward(
     return out
 
 
-# adapted from https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/dbrx/modeling_dbrx.py#L1228
+# Adapted from https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/dbrx/modeling_dbrx.py#L1228
 def _dbrx_update_causal_mask_legacy(
     self, attention_mask: Optional[torch.Tensor], input_tensor: torch.Tensor, cache_position: torch.Tensor
 ) -> Optional[torch.Tensor]:
@@ -1803,6 +1801,7 @@ def __exit__(self, exc_type, exc_value, traceback):
             block.ffn.experts.forward = block.ffn.experts._orig_forward
 
 
+# Adapted from https://github.com/huggingface/transformers/blob/v4.41.0/src/transformers/models/persimmon/modeling_persimmon.py#L264
 def _persimmon_self_attn_sdpa_forward(
     self,
     hidden_states: torch.Tensor,
@@ -1811,6 +1810,7 @@ def _persimmon_self_attn_sdpa_forward(
     past_key_value: Optional["Cache"] = None,
     output_attentions: bool = False,
     use_cache: bool = False,
+    cache_position: Optional[torch.LongTensor] = None,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     from transformers.models.persimmon.modeling_persimmon import apply_rotary_pos_emb
 
@@ -1865,14 +1865,23 @@ def _persimmon_self_attn_sdpa_forward(
 
     if past_key_value is not None:
         # Specific to RoPE models with partial rotation
-        cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
+        cache_kwargs = {
+            "sin": sin,
+            "cos": cos,
+            "partial_rotation_size": self.rotary_emb.dim,
+            "cache_position": cache_position,
+        }
         key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
+    causal_mask = attention_mask
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+
     attn_output = F.scaled_dot_product_attention(
         query_states,
         key_states,
         value_states,
-        attention_mask,
+        causal_mask,
         scale=1 / math.sqrt(self.head_dim),
         dropout_p=self.attention_dropout.p,
     )

optimum/intel/generation/modeling.py

@@ -90,6 +90,7 @@ class BaseModelForCausalLM(OptimizedModel, GenerationMixin):
     export_feature = "text-generation"
     main_input_name = "input_ids"
     base_model_prefix = "torch_script_model"
+    _supports_cache_class = False
 
     def __init__(
         self,

optimum/intel/ipex/modeling_base.py

@@ -128,6 +128,7 @@ class IPEXModel(OptimizedModel):
     base_model_prefix = "ipex_model"
     main_input_name = "input_ids"
     output_name = "last_hidden_state"
+    _supports_cache_class = False
 
     def __init__(
         self,

optimum/intel/neural_compressor/modeling_base.py

@@ -71,6 +71,7 @@ class INCModel(OptimizedModel):
     auto_model_class = AutoModel
     export_feature = "feature-extraction"
     base_model_prefix = "inc_model"
+    _supports_cache_class = False
 
     def __init__(
         self,

optimum/intel/neural_compressor/trainer.py

@@ -682,6 +682,7 @@ def _inner_training_loop(
     def save_model(
         self,
         output_dir: Optional[str] = None,
+        _internal_call: bool = False,
         save_onnx_model: bool = False,
     ):
         """
@@ -696,6 +697,7 @@ def save_model(
                 output_dir=output_dir,
                 save_onnx_model=save_onnx_model,
             )
+        # TODO: push to hub if self.args.push_to_hub and not _internal_call
 
     def _save(
         self,

optimum/intel/openvino/modeling_base.py

@@ -50,6 +50,7 @@
 class OVBaseModel(OptimizedModel):
     auto_model_class = None
     export_feature = None
+    _supports_cache_class = False
 
     def __init__(
         self,

setup.py

@@ -28,8 +28,9 @@
 
 INSTALL_REQUIRE = [
     "torch>=1.11",
-    "transformers>=4.36.0,<4.42.0",
-    "optimum~=1.20",
+    "transformers>=4.36.0,<4.43.0",
+    "optimum~=1.21",
+    # "optimum>=1.21.2,<1.22.0",
     "datasets>=1.4.0",
     "sentencepiece",
     "setuptools",

tests/openvino/test_modeling.py

@@ -697,7 +697,6 @@ def test_compare_to_transformers(self, model_arch):
         ov_model = OVModelForCausalLM.from_pretrained(model_id, export=True, ov_config=F32_CONFIG, **model_kwargs)
         self.assertIsInstance(ov_model.config, PretrainedConfig)
         self.assertTrue(ov_model.use_cache)
-        self.assertEqual(ov_model.stateful, ov_model.config.model_type not in not_stateful)
         tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=model_arch in self.REMOTE_CODE_MODELS)
         tokens = tokenizer("This is a sample output", return_tensors="pt")
         tokens.pop("token_type_ids", None)

tests/openvino/test_quantization.py

@@ -75,8 +75,8 @@
 
 class OVQuantizerTest(unittest.TestCase):
     SUPPORTED_ARCHITECTURES_TORCH_MODEL = (
-        (OVModelForSequenceClassification, "bert", 22, 35),
-        (OVModelForCausalLM, "gpt2", 41, 3),
+        (OVModelForSequenceClassification, "bert", 32 if is_transformers_version("<", "4.41.0") else 22, 35),
+        (OVModelForCausalLM, "gpt2", 41 if is_transformers_version("<", "4.42.0") else 21, 3),
     )
     SUPPORTED_ARCHITECTURES_OV_MODEL = (
         (OVModelForSequenceClassification, "bert", 32, 35),
@@ -90,9 +90,6 @@ def test_automodel_static_quantization(self, model_cls, model_name, expected_fak
         dataset_name, dataset_config_name, column_name = _TASK_TO_DATASET[task]
         file_name = "openvino_quantized_model.xml"
 
-        if model_name == "bert" and is_transformers_version("<", "4.41.0"):
-            expected_fake_quantize = 32
-
         def preprocess_function(examples, tokenizer):
             return tokenizer(examples[column_name], padding="max_length", max_length=128, truncation=True)