Optimize with new batch kernel when batch_size=1 on LNL (#12419)

* Add use batch kernel condition for LNL

* Fix for other device judgement

* Fix based on comment

python/llm/src/ipex_llm/transformers/lookup.py

@@ -167,7 +167,7 @@ class PromptLookupCandidateGenerator():
         self.num_output_tokens = num_output_tokens
         self.max_matching_ngram_size = max_matching_ngram_size if max_matching_ngram_size else 2
 
-        if device == "mtl":
+        if device in ["mtl", "lnl"]:
             self.max_candidates = 3
             self.min_candidates = 0
         else:
@@ -418,7 +418,7 @@ def lookup_generate(self,
             accept_rate = self.n_matched/self.n_drafted if self.n_drafted > 0 else 1
             self.accept_rate.append(accept_rate)
             # Update the candidate generation strategy if needed
-            if device_name != 'mtl':
+            if device_name not in ["mtl", "lnl"]:
                 candidates_generator.update_candidate_strategy(candidate_length, n_matches,
                                                                accept_rate)
 

python/llm/src/ipex_llm/transformers/low_bit_linear.py

@@ -389,6 +389,7 @@ def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
             batch_size > 1
             or (device in ["arc", "flex"] and qtype in [SYM_INT8, FP4])
             or (device in ["arc", "flex", "mtl"] and qtype in [FP8E4])
+            or (device in ["lnl"] and qtype in [SYM_INT4] and x.shape[1] % 512 == 0)
         )
     return False
 

python/llm/src/ipex_llm/transformers/models/utils.py

@@ -92,7 +92,7 @@ def use_quantize_kv_cache(linear: torch.nn.Module, x: torch.Tensor, kv_group: in
 
 
 def kv_cache_device_check(x: torch.Tensor, kv_group: int) -> bool:
-    return (get_xpu_device_type(x) == "mtl" and kv_group <= 1) or \
+    return (get_xpu_device_type(x) in ["mtl", "lnl"] and kv_group <= 1) or \
         ((get_xpu_device_type(x) == "arc" or get_xpu_device_type(x) == "flex") and
             1 < x.size(0) and x.size(0) <= 8)
 
@@ -348,7 +348,7 @@ def mlp_fusion_check(x, qtype, training):
         return False
     if qtype == FP6:
         device = get_xpu_device_type(x)
-        if device == "mtl":
+        if device in ["mtl", "lnl"]:
             return False
     return True
 
@@ -395,7 +395,7 @@ def use_fused_layer_norm(x: torch.Tensor, training: bool):
     return (
         not training
         and not x.requires_grad
-        and device in ["arc", "flex", "pvc", "mtl"]  # fused layer norm cannot run on UHD
+        and device in ["arc", "flex", "pvc", "mtl", "lnl"]  # fused layer norm cannot run on UHD
         and x.numel() // x.size(-1) == 1  # fused layer norm is slower in first token
     )
 
@@ -474,7 +474,7 @@ def should_use_compresskv(x: torch.Tensor, prompt_len: int):
     else:
         if use_compress_kv is None:
             return (
-                get_xpu_device_type(x) == "mtl"
+                get_xpu_device_type(x) in ["mtl", "lnl"]
                 and prompt_len >= 1800
                 and prompt_len <= 4500
             )

python/llm/src/ipex_llm/transformers/utils.py

@@ -175,6 +175,9 @@ def get_xpu_device_type(x):
     if name.startswith("Intel(R) Arc(TM) A"):
         return "arc"
     elif name.startswith("Intel(R) Arc(TM)"):
+        if 'V' in name:
+            return "lnl"
+        else:
             return "mtl"
     elif name.startswith("Intel(R) Data Center GPU Flex"):
         return "flex"