add sdpa for phi3 openvino model

optimum/exporters/openvino/model_configs.py

@@ -485,6 +485,12 @@ def patch_model_for_export(
     library_name="transformers",
 )
 class Phi3OpenVINOConfig(PhiOnnxConfig):
+    DUMMY_INPUT_GENERATOR_CLASSES = (
+        MistralDummyPastKeyValuesGenerator,
+    ) + TextDecoderOnnxConfig.DUMMY_INPUT_GENERATOR_CLASSES
+    DUMMY_PKV_GENERATOR_CLASS = MistralDummyPastKeyValuesGenerator
+    NORMALIZED_CONFIG_CLASS = NormalizedTextConfig.with_args(num_key_value_heads="num_key_value_heads", allow_new=True)
+
     def patch_model_for_export(
         self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
     ) -> "ModelPatcher":

optimum/exporters/openvino/model_patcher.py

@@ -951,15 +951,107 @@ 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
+def _phi3_self_attn_sdpa_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    if output_attentions:
+        return self._orig_forward(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+    # TO DO: remove llama imports when transformers with phi3 support will be released
+    try:
+        from transformers.models.phi3.modelling_phi3 import apply_rotary_pos_emb, repeat_kv
+    except ImportError:
+        from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
+
+    bsz, q_len, _ = hidden_states.size()
+
+    qkv = self.qkv_proj(hidden_states)
+    query_pos = self.num_heads * self.head_dim
+    query_states = qkv[..., :query_pos]
+    key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
+    value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+
+    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+    cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+    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
+        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)
+
+    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()}"
+            )
+
+    # 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.
+    if query_states.device.type == "cuda" and attention_mask is not None:
+        query_states = query_states.contiguous()
+        key_states = key_states.contiguous()
+        value_states = value_states.contiguous()
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query_states,
+        key_states,
+        value_states,
+        attn_mask=attention_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,
+    )
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value
+
+
 class Phi3ModelPatcher(DecoderModelPatcher):
     def __enter__(self):
         super().__enter__()
-
         # https://github.com/huggingface/transformers/blob/30ee508c6c92a1c0aa0281d193c7c0fb815b8d2f/src/transformers/models/phi3/modeling_phi3.py#L113
         # init inv_freq for torchscript tracing
         for layer in self._model.model.layers:
+            if is_torch_version(">=", "2.1.0"):
+                orig_self_attn_fwd = layer.self_attn.forward
+                layer.self_attn.forward = types.MethodType(_phi3_self_attn_sdpa_forward, layer.self_attn)
+                layer.self_attn._orig_forward = orig_self_attn_fwd
+
             if layer.self_attn.rotary_emb.inv_freq is None:
                 rotary_emb = layer.self_attn.rotary_emb
                 layer.self_attn.rotary_emb.inv_freq = 1.0 / (
                     rotary_emb.base ** (torch.arange(0, rotary_emb.dim, 2, dtype=torch.int64).float() / rotary_emb.dim)
                 )
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        super().__exit__(exc_type, exc_value, traceback)
+        for layer in self._model.model.layers:
+            if hasattr(layer.self_attn, "_orig_forward"):
+                layer.self_attn.forward = layer.self_attn._orig_forward