Add Ascend NPU Support

install/install_requirements.sh

@@ -71,6 +71,9 @@ then
 elif [[ -x "$(command -v xpu-smi)" ]];
 then
   TORCH_NIGHTLY_URL="https://download.pytorch.org/whl/nightly/xpu"
+elif [[ -x "$(command -v npu-smi)" ]]
+then
+  TORCH_NIGHTLY_URL="https://download.pytorch.org/whl/test/cpu"
 else
   TORCH_NIGHTLY_URL="https://download.pytorch.org/whl/nightly/cpu"
 fi
@@ -83,6 +86,13 @@ then
     torchvision=="0.22.0.${VISION_NIGHTLY_VERSION}"
     #torchtune=="0.7.0" # no 0.6.0 on xpu nightly
   )
+elif [[ -x "$(command -v npu-smi)" ]];
+then
+  REQUIREMENTS_TO_INSTALL=(
+    torch=="2.7.0"
+    torchvision=="0.22.0"
+    torchtune=="0.6.0"
+  )
 else
   REQUIREMENTS_TO_INSTALL=(
     torch=="2.8.0.${PYTORCH_NIGHTLY_VERSION}"

torchchat/cli/builder.py

@@ -29,7 +29,7 @@
 from torchchat.utils.build_utils import (
     device_sync,
     is_cpu_device,
-    is_cuda_or_cpu_or_xpu_device,
+    is_supported_device,
     name_to_dtype,
 )
 from torchchat.utils.measure_time import measure_time
@@ -78,6 +78,8 @@ def __post_init__(self):
                 self.device = "cuda"
             elif torch.xpu.is_available():
                 self.device = "xpu"
+            elif hasattr(torch, "npu") and torch.npu.is_available():
+                self.device = "npu"
             else:
                 self.device = "cpu"
 
@@ -539,7 +541,7 @@ def _initialize_model(
         _set_gguf_kwargs(builder_args, is_et=is_pte, context="generate")
 
     if builder_args.dso_path:
-        if not is_cuda_or_cpu_or_xpu_device(builder_args.device):
+        if not is_supported_device(builder_args.device):
             print(
                 f"Cannot load specified DSO to {builder_args.device}. Attempting to load model to CPU instead"
             )
@@ -573,7 +575,7 @@ def do_nothing(max_batch_size, max_seq_length):
             raise RuntimeError(f"Failed to load AOTI compiled {builder_args.dso_path}")
 
     elif builder_args.aoti_package_path:
-        if not is_cuda_or_cpu_or_xpu_device(builder_args.device):
+        if not is_supported_device(builder_args.device):
             print(
                 f"Cannot load specified PT2 to {builder_args.device}. Attempting to load model to CPU instead"
             )

torchchat/cli/cli.py

@@ -176,8 +176,8 @@ def _add_model_config_args(parser, verb: str) -> None:
         "--device",
         type=str,
         default=None,
-        choices=["fast", "cpu", "cuda", "mps", "xpu"],
-        help="Hardware device to use. Options: fast, cpu, cuda, mps, xpu",
+        choices=["fast", "cpu", "cuda", "mps", "xpu", "npu"],
+        help="Hardware device to use. Options: fast, cpu, cuda, mps, xpu, npu",
     )
     model_config_parser.add_argument(
         "--attention-backend",

torchchat/generate.py

@@ -1213,8 +1213,10 @@ def callback(x, *, done_generating=False):
                     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
                 elif self.builder_args.device == "cuda":
                     print(prof.key_averages().table(sort_by="self_cuda_time_total"))
-                else:
+                elif self.builder_args.device == "xpu":
                     print(prof.key_averages().table(sort_by="self_xpu_time_total"))
+                elif self.builder_args.device == "npu":
+                    print(prof.key_averages().table(sort_by="self_npu_time_total"))
                 prof.export_chrome_trace(f"{self.profile}.json")
 
             if start_pos >= max_seq_length:
@@ -1299,8 +1301,10 @@ def callback(x, *, done_generating=False):
             )
         if torch.cuda.is_available():
             print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB")
-        if torch.xpu.is_available():
+        elif torch.xpu.is_available():
             print(f"Memory used: {torch.xpu.max_memory_reserved() / 1e9:.02f} GB")
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            print(f"Memory used: {torch.npu.max_memory_reserved() / 1e9:.02f} GB")
 
 
 
@@ -1595,7 +1599,6 @@ def sample(
 
         return idx_next, probs
 
-
 def run_generator(
     args,
     rank: Optional[int] =None
@@ -1628,8 +1631,10 @@ def run_generator(
         )
         if torch.cuda.is_available():
             torch.cuda.reset_peak_memory_stats()
-        if torch.xpu.is_available():
+        elif torch.xpu.is_available():
             torch.xpu.reset_peak_memory_stats()
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            torch.npu.reset_peak_memory_stats()
 
         for _ in gen.chat(generator_args):
             pass

torchchat/utils/build_utils.py

@@ -233,6 +233,8 @@ def device_sync(device="cpu"):
         torch.cuda.synchronize(device)
     elif "xpu" in device:
         torch.xpu.synchronize(device)
+    elif "npu" in device:
+        torch.npu.synchronize(device)
     elif ("cpu" in device) or ("mps" in device):
         pass
     else:
@@ -275,33 +277,36 @@ def is_mps_available() -> bool:
     # MPS, is that you?
     return True
 
+def select_device(device) -> str:
+    if torch.cuda.is_available():
+        return "cuda"
+    elif is_mps_available():
+        return "mps"
+    elif hasattr(torch, "npu") and torch.npu.is_available():
+        return "npu"
+    elif torch.xpu.is_available():
+        return "xpu"
+    else:
+        return "cpu"
+
 
 def get_device_str(device) -> str:
     if isinstance(device, str) and device == "fast":
-        device = (
-            "cuda"
-            if torch.cuda.is_available()
-            else "mps" if is_mps_available()
-            else "xpu" if torch.xpu.is_available()  else "cpu"
-        )
+        device = select_device(device)
         return device
     else:
         return str(device)
 
 
 def get_device(device) -> str:
     if isinstance(device, str) and device == "fast":
-        device = (
-            "cuda"
-            if torch.cuda.is_available()
-            else "mps" if is_mps_available()
-            else "xpu" if torch.xpu.is_available()  else "cpu"
-        )
+        device = select_device(device)
     return torch.device(device)
 
 
 def is_cpu_device(device) -> bool:
     return device == "" or str(device) == "cpu"
 
-def is_cuda_or_cpu_or_xpu_device(device) -> bool:
-    return is_cpu_device(device) or ("cuda" in str(device)) or ("xpu" in str(device))
+def is_supported_device(device) -> bool:
+    device_str = str(device)
+    return is_cpu_device(device) or any(dev in device_str for dev in ('cuda', 'xpu', 'npu'))

torchchat/utils/device_info.py

@@ -9,12 +9,11 @@
 
 import torch
 
-
 def get_device_info(device: str) -> str:
     """Returns a human-readable description of the hardware based on a torch.device.type
 
     Args:
-        device: A torch.device.type string: one of {"cpu", "cuda", "xpu"}.
+        device: A torch.device.type string: one of {"cpu", "cuda", "xpu", "npu"}.
     Returns:
         str: A human-readable description of the hardware or an empty string if the device type is unhandled.
 
@@ -46,4 +45,6 @@ def get_device_info(device: str) -> str:
             .split("\n")[0]
             .split("Device Name:")[1]
             )
+    if device == "npu":
+        return torch.npu.get_device_name(0)
     return ""

torchchat/utils/quantize.py

@@ -123,7 +123,7 @@ def quantize_model(
             raise RuntimeError(f"unknown quantizer {quantizer} specified")
         else:
             # Use tensor subclass API for int4 weight only.
-            if (device == "cuda" or device == "xpu") and quantizer == "linear:int4":
+            if (device in ["cuda", "xpu", "npu"]) and quantizer == "linear:int4":
                 quantize_(model, int4_weight_only(q_kwargs["groupsize"]))
                 if not support_tensor_subclass:
                     unwrap_tensor_subclass(model)