feat: enable pytorch xpu support for non-attention models (#2561)

XPU backend is available natively (without IPEX) in pytorch starting
from pytorch 2.4. This commit extends TGI to cover the case when user
has XPU support thru pytorch 2.4, but does not have IPEX installed.
Models which don't require attention can work. For attention required
models more work is needed to provide attention implementation.

Tested with the following models:
* teknium/OpenHermes-2.5-Mistral-7B
* bigscience/bloom-560m
* google/gemma-7b
* google/flan-t5-xxl

Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

server/text_generation_server/models/causal_lm.py

@@ -517,14 +517,13 @@ class CausalLM(Model):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = default_dtype if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device(f"xpu:{rank}")
+            dtype = default_dtype if dtype is None else dtype
         elif SYSTEM == "ipex":
-            if hasattr(torch, "xpu") and torch.xpu.is_available():
-                device = torch.device(f"xpu:{rank}")
-                dtype = default_dtype if dtype is None else dtype
-            else:
-                device = torch.device("cpu")
-                # Float16 doesn't exist on target.
-                dtype = torch.bfloat16 if dtype is None else dtype
+            device = torch.device("cpu")
+            # Float16 doesn't exist on target.
+            dtype = torch.bfloat16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32 if dtype is None else dtype
@@ -593,8 +592,14 @@ class CausalLM(Model):
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
+        device_count = 0
         if torch.cuda.is_available():
             device = torch.device("cuda")
+            device_count = torch.cuda.device_count()
+            dtype = torch.float16 if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device("xpu")
+            device_count = torch.xpu.device_count()
             dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
@@ -616,18 +621,17 @@ class CausalLM(Model):
             torch_dtype=dtype,
             device_map=(
                 "auto"
-                if torch.cuda.is_available() and torch.cuda.device_count() > 1
+                if device_count > 1
                 else None
             ),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
         if (
-            torch.cuda.is_available()
-            and torch.cuda.device_count() == 1
+            device_count == 1
             and quantize != "bitsandbytes"
         ):
-            model = model.cuda()
+            model = model.to(device)
 
         if tokenizer.pad_token_id is None:
             if model.config.pad_token_id is not None:

server/text_generation_server/models/seq2seq_lm.py

@@ -558,14 +558,13 @@ class Seq2SeqLM(Model):
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
             dtype = default_dtype if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device(f"xpu:{rank}")
+            dtype = default_dtype if dtype is None else dtype
         elif SYSTEM == "ipex":
-            if hasattr(torch, "xpu") and torch.xpu.is_available():
-                device = torch.device(f"xpu:{rank}")
-                dtype = default_dtype if dtype is None else dtype
-            else:
-                device = torch.device("cpu")
-                # Float16 doesn't exist on target.
-                dtype = torch.bfloat16 if dtype is None else dtype
+            device = torch.device("cpu")
+            # Float16 doesn't exist on target.
+            dtype = torch.bfloat16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32 if dtype is None else dtype
@@ -630,8 +629,14 @@ class Seq2SeqLM(Model):
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
+        device_count = 0
         if torch.cuda.is_available():
             device = torch.device("cuda")
+            device_count = torch.cuda.device_count()
+            dtype = torch.float16 if dtype is None else dtype
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            device = torch.device("xpu")
+            device_count = torch.xpu.device_count()
             dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
@@ -646,14 +651,14 @@ class Seq2SeqLM(Model):
             torch_dtype=dtype,
             device_map=(
                 "auto"
-                if torch.cuda.is_available() and torch.cuda.device_count() > 1
+                if device_count > 1
                 else None
             ),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
-        if torch.cuda.is_available() and torch.cuda.device_count() == 1:
-            model = model.cuda()
+        if device_count == 1:
+            model = model.to(device)
 
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,

server/text_generation_server/utils/import_utils.py

@@ -66,6 +66,11 @@ elif is_ipex_available():
         empty_cache = noop
         synchronize = noop
         get_free_memory = get_cpu_free_memory
+elif hasattr(torch, "xpu") and torch.xpu.is_available():
+    SYSTEM = "xpu"
+    empty_cache = torch.xpu.empty_cache
+    synchronize = torch.xpu.synchronize
+    get_free_memory = get_xpu_free_memory
 else:
     SYSTEM = "cpu"