restore SDPA in gpt neo after 4.45

optimum/exporters/openvino/model_configs.py

@@ -30,6 +30,7 @@
     FalconOnnxConfig,
     GemmaOnnxConfig,
     GPTJOnnxConfig,
+    GPTNeoOnnxConfig,
     GPTNeoXOnnxConfig,
     IBertOnnxConfig,
     LlamaOnnxConfig,
@@ -68,6 +69,7 @@
     FluxTransfromerModelPatcher,
     Gemma2ModelPatcher,
     GptJModelPatcher,
+    GptNeoModelPatcher,
     GptNeoxJapaneseModelPatcher,
     GptNeoxModelPatcher,
     IBertModelPatcher,
@@ -790,6 +792,24 @@ def patch_model_for_export(
         return GptNeoxJapaneseModelPatcher(self, model, model_kwargs=model_kwargs)
 
 
+@register_in_tasks_manager(
+    "gpt-neo",
+    *[
+        "feature-extraction",
+        "feature-extraction-with-past",
+        "text-generation",
+        "text-generation-with-past",
+        "text-classification",
+    ],
+    library_name="transformers",
+)
+class GPTNeoOpenVINOConfig(GPTNeoOnnxConfig):
+    def patch_model_for_export(
+        self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+    ) -> "ModelPatcher":
+        return GptNeoModelPatcher(self, model, model_kwargs=model_kwargs)
+
+
 @register_in_tasks_manager(
     "gptj",
     *[

optimum/exporters/openvino/model_patcher.py

@@ -2681,6 +2681,96 @@ def __exit__(self, exc_type, exc_value, traceback):
         unpatch_update_causal_mask(self._model, "gpt_neox_japanese")
 
 
+def _gpt_neo_attn_forward(
+    self,
+    hidden_states,
+    attention_mask=None,
+    layer_past=None,
+    head_mask=None,
+    use_cache=False,
+    output_attentions=False,
+    cache_position=None,
+):
+    if output_attentions:
+        self._attn = self._orig_attn
+
+    return self._orig_forward(
+        hidden_states,
+        attention_mask=attention_mask,
+        layer_past=layer_past,
+        head_mask=head_mask,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        cache_position=cache_position,
+    )
+
+
+# Adopted from https://github.com/huggingface/optimum/blob/main/optimum/bettertransformer/models/attention.py#L185
+def _gpt_neo_attn_sdpa(
+    self,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    head_mask: Optional[torch.Tensor] = None,
+):
+    batch_size = query.shape[0]
+
+    mask_value = torch.finfo(torch.float16).min
+    mask_value = torch.full([], mask_value, dtype=value.dtype)
+
+    dropout_p = float(self.config.attention_dropout) if self.training else 0.0
+    if (batch_size == 1 or self.training) and self.attention_type == "global":
+        if query.shape[2] > 1:
+            sdpa_result = torch.nn.functional.scaled_dot_product_attention(
+                query, key, value, attn_mask=None, dropout_p=dropout_p, is_causal=True
+            )
+        else:
+            sdpa_result = torch.nn.functional.scaled_dot_product_attention(
+                query, key, value, attn_mask=None, dropout_p=dropout_p, is_causal=False, scale=1.0
+            )
+    else:
+        query_length, key_length = query.size(-2), key.size(-2)
+
+        causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
+
+        causal_mask = torch.where(causal_mask, 0, mask_value)
+        if batch_size > 1:
+            # torch.Tensor.expand does no memory copy
+            causal_mask = causal_mask.expand(batch_size, -1, -1, -1)
+
+        if attention_mask is not None:
+            attention_mask = causal_mask + attention_mask
+
+        sdpa_result = torch.nn.functional.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=dropout_p, is_causal=False, scale=1.0
+        )
+
+    return sdpa_result, None
+
+
+class GptNeoModelPatcher(DecoderModelPatcher):
+    def __enter__(self):
+        super().__enter__()
+        if is_transformers_version(">=", "4.45.0") and is_torch_version(">=", "2.1.0"):
+            self._model.config._orig_attn_implementation = self._model.config._attn_implementation
+            self._model.config._attn_implementation = "sdpa"
+            for layer in self._model.transformer.h:
+                self_attn = layer.attn.attention
+                self_attn._orig_attn = self_attn._attn
+                self_attn._attn = types.MethodType(_gpt_neo_attn_sdpa, self_attn)
+                self_attn._orig_forward = types.MethodType(_gpt_neo_attn_forward, self_attn)
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        super().__exit__(exc_type, exc_value, traceback)
+        if hasattr(self._model.config, "_orig_attn_implementation"):
+            self._model.config._attn_implementation = self._model.config._orig_attn_implementation
+            for layer in self._model.transformer.h:
+                for layer in self._model.transformer.h:
+                    layer.attn.attention.forward = layer.attn.attention._orig_forward
+                    layer.attn.attention._attn = layer.attn.attention._orig_attn
+
+
 class Gemma2ModelPatcher(LlamaModelPatcher):
     def __init__(
         self,

tests/openvino/test_modeling.py

@@ -1296,7 +1296,15 @@ def test_beam_search(self, model_arch):
             transformers_model._supports_cache_class = True
             from transformers.cache_utils import DynamicCache
         tokenizer.pad_token_id = tokenizer.eos_token_id
-        tokens = tokenizer(["Today is a nice day and I am longer", "This is me"], return_tensors="pt", padding=True)
+        tokenization_args = {}
+        if is_transformers_version(">=", "4.45") and model_arch == "gpt_neo":
+            tokenization_args["padding_side"] = "left"
+        tokens = tokenizer(
+            ["Today is a nice day and I am longer", "This is me"],
+            return_tensors="pt",
+            padding=True,
+            **tokenization_args,
+        )
         ov_model_stateful.generation_config.eos_token_id = None
         ov_model_stateless.generation_config.eos_token_id = None
         transformers_model.generation_config.eos_token_id = None