[BUGFIX] qwen2.5-vl failed after PR24444, provide a temp solution

vllm_gaudi/ops/hpu_rotary_embedding.py

@@ -655,44 +655,45 @@ def forward_oot(
         key: torch.Tensor,
         offsets: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        from habana_frameworks.torch.hpex.kernels import (RotaryPosEmbeddingMode, apply_rotary_pos_emb)
-
-        # Prepare cos-sin caches for long-context + LoRA with offsets for every
-        # forward, since the offset information wasn't available previously
-        if not hasattr(self, "sin") or self.recompute_cos_sin:
-            self.prepare_cos_sin(positions, offsets, recompute_cos_sin=True)
-        if hasattr(self, "scaling_factors") or hasattr(self, "scaling_factor") or self.sin is None:
-            self.prepare_cos_sin(positions, offsets)
-        num_tokens = positions.shape[0] * positions.shape[1]
-        # HPU RoPE kernel requires hidden dimension for cos and sin to be equal
-        # to query hidden dimension, so the original tensors need to be
-        # expanded
-        # GPT-NeoX kernel requires position_ids = None, offset, mode = BLOCKWISE
-        # and expansion of cos/sin tensors via concatenation
-        # GPT-J kernel requires position_ids = None, offset = 0, mode = PAIRWISE
-        # and expansion of cos/sin tensors via repeat_interleave
-        rope_mode: RotaryPosEmbeddingMode
-        rope_mode = RotaryPosEmbeddingMode.BLOCKWISE if self.is_neox_style else RotaryPosEmbeddingMode.PAIRWISE
-        sin = self.sin
-        cos = self.cos
-        query_shape = query.shape
-        key_shape = key.shape
-        query = query.view(num_tokens, -1, self.head_size)
-        key = key.view(num_tokens, -1, self.head_size)
-
-        if self.head_size == self.rotary_dim:
-            # Avoid unnecessary slicing and concatenation
-            query = apply_rotary_pos_emb(query, cos, sin, None, 0, rope_mode)
-            key = apply_rotary_pos_emb(key, cos, sin, None, 0, rope_mode)
-            return query.reshape(query_shape), key.reshape(key_shape)
-
-        query_rot = query[..., :self.rotary_dim]
-        query_pass = query[..., self.rotary_dim:]
-        query_rot = apply_rotary_pos_emb(query_rot, cos, sin, None, 0, rope_mode)
-        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
-
-        key_rot = key[..., :self.rotary_dim]
-        key_pass = key[..., self.rotary_dim:]
-        key_rot = apply_rotary_pos_emb(key_rot, cos, sin, None, 0, rope_mode)
-        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
-        return query, key
+        return self.forward_native(positions, query, key, offsets)
+        # from habana_frameworks.torch.hpex.kernels import (RotaryPosEmbeddingMode, apply_rotary_pos_emb)
+
+        # # Prepare cos-sin caches for long-context + LoRA with offsets for every
+        # # forward, since the offset information wasn't available previously
+        # if not hasattr(self, "sin") or self.recompute_cos_sin:
+        #     self.prepare_cos_sin(positions, offsets, recompute_cos_sin=True)
+        # if hasattr(self, "scaling_factors") or hasattr(self, "scaling_factor") or self.sin is None:
+        #     self.prepare_cos_sin(positions, offsets)
+        # num_tokens = positions.shape[0] * positions.shape[1]
+        # # HPU RoPE kernel requires hidden dimension for cos and sin to be equal
+        # # to query hidden dimension, so the original tensors need to be
+        # # expanded
+        # # GPT-NeoX kernel requires position_ids = None, offset, mode = BLOCKWISE
+        # # and expansion of cos/sin tensors via concatenation
+        # # GPT-J kernel requires position_ids = None, offset = 0, mode = PAIRWISE
+        # # and expansion of cos/sin tensors via repeat_interleave
+        # rope_mode: RotaryPosEmbeddingMode
+        # rope_mode = RotaryPosEmbeddingMode.BLOCKWISE if self.is_neox_style else RotaryPosEmbeddingMode.PAIRWISE
+        # sin = self.sin
+        # cos = self.cos
+        # query_shape = query.shape
+        # key_shape = key.shape
+        # query = query.view(num_tokens, -1, self.head_size)
+        # key = key.view(num_tokens, -1, self.head_size)
+
+        # if self.head_size == self.rotary_dim:
+        #     # Avoid unnecessary slicing and concatenation
+        #     query = apply_rotary_pos_emb(query, cos, sin, None, 0, rope_mode)
+        #     key = apply_rotary_pos_emb(key, cos, sin, None, 0, rope_mode)
+        #     return query.reshape(query_shape), key.reshape(key_shape)
+
+        # query_rot = query[..., :self.rotary_dim]
+        # query_pass = query[..., self.rotary_dim:]
+        # query_rot = apply_rotary_pos_emb(query_rot, cos, sin, None, 0, rope_mode)
+        # query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
+
+        # key_rot = key[..., :self.rotary_dim]
+        # key_pass = key[..., self.rotary_dim:]
+        # key_rot = apply_rotary_pos_emb(key_rot, cos, sin, None, 0, rope_mode)
+        # key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        # return query, key