enable mllama in intel platform (#2610)

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

server/text_generation_server/models/custom_modeling/mllama.py

@@ -19,7 +19,12 @@ from typing import Optional, Tuple, List
 import torch
 import torch.utils.checkpoint
 from torch import nn
-import flash_attn_2_cuda
+from text_generation_server.utils.import_utils import SYSTEM
+
+if SYSTEM == "ipex":
+    import intel_extension_for_pytorch as ipex
+else:
+    import flash_attn_2_cuda
 
 from transformers.activations import ACT2FN
 import torch.nn.functional as F
@@ -698,29 +703,60 @@ class MllamaTextCrossAttention(nn.Module):
         # logger.info(
         #     f"Q: {query_states.shape} -K {key_states.shape} - V{value_states.shape}"
         # )
-        attn_output = flash_attn_2_cuda.varlen_fwd(
-            query_states,
-            key_states,
-            value_states,
-            None,
-            cu_seqlen_q,
-            cu_seqlen_k,
-            None,
-            None,
-            None,  # block_tables
-            None,
-            max_q,
-            max_k,
-            0.0,
-            self.softmax_scale,
-            False,
-            causal,  # Causal
-            -1,  # window_size_left,
-            -1,
-            0.0,  # softcap
-            False,
-            None,
-        )[0]
+        if SYSTEM == "ipex":
+            attn_output = torch.empty_like(query_states)
+            ipex.llm.functional.varlen_attention(
+                (
+                    query_states.contiguous()
+                    if query_states.device.type == "xpu"
+                    else query_states
+                ),
+                (
+                    key_states.contiguous()
+                    if key_states.device.type == "xpu"
+                    else key_states
+                ),
+                (
+                    value_states.contiguous()
+                    if value_states.device.type == "xpu"
+                    else value_states
+                ),
+                attn_output,
+                cu_seqlen_q,
+                cu_seqlen_k,
+                max_q,
+                max_k,
+                0.0,
+                self.softmax_scale,
+                False,
+                causal,
+                False,
+                None,
+            )
+        else:
+            attn_output = flash_attn_2_cuda.varlen_fwd(
+                query_states,
+                key_states,
+                value_states,
+                None,
+                cu_seqlen_q,
+                cu_seqlen_k,
+                None,
+                None,
+                None,  # block_tables
+                None,
+                max_q,
+                max_k,
+                0.0,
+                self.softmax_scale,
+                False,
+                causal,  # Causal
+                -1,  # window_size_left,
+                -1,
+                0.0,  # softcap
+                False,
+                None,
+            )[0]
         attn_output = self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
 
         return attn_output