Support Cambricon MLU (#6964)

Co-authored-by: huzhan <huzhan@cambricon.com>

comfy/model_management.py

@@ -95,6 +95,13 @@ try:
 except:
     npu_available = False
 
+try:
+    import torch_mlu  # noqa: F401
+    _ = torch.mlu.device_count()
+    mlu_available = torch.mlu.is_available()
+except:
+    mlu_available = False
+
 if args.cpu:
     cpu_state = CPUState.CPU
 
@@ -112,6 +119,12 @@ def is_ascend_npu():
         return True
     return False
 
+def is_mlu():
+    global mlu_available
+    if mlu_available:
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
@@ -127,6 +140,8 @@ def get_torch_device():
             return torch.device("xpu", torch.xpu.current_device())
         elif is_ascend_npu():
             return torch.device("npu", torch.npu.current_device())
+        elif is_mlu():
+            return torch.device("mlu", torch.mlu.current_device())
         else:
             return torch.device(torch.cuda.current_device())
 
@@ -153,6 +168,12 @@ def get_total_memory(dev=None, torch_total_too=False):
             _, mem_total_npu = torch.npu.mem_get_info(dev)
             mem_total_torch = mem_reserved
             mem_total = mem_total_npu
+        elif is_mlu():
+            stats = torch.mlu.memory_stats(dev)
+            mem_reserved = stats['reserved_bytes.all.current']
+            _, mem_total_mlu = torch.mlu.mem_get_info(dev)
+            mem_total_torch = mem_reserved
+            mem_total = mem_total_mlu
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -232,7 +253,7 @@ try:
         if torch_version_numeric[0] >= 2:
             if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
-    if is_intel_xpu() or is_ascend_npu():
+    if is_intel_xpu() or is_ascend_npu() or is_mlu():
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             ENABLE_PYTORCH_ATTENTION = True
 except:
@@ -316,6 +337,8 @@ def get_torch_device_name(device):
         return "{} {}".format(device, torch.xpu.get_device_name(device))
     elif is_ascend_npu():
         return "{} {}".format(device, torch.npu.get_device_name(device))
+    elif is_mlu():
+        return "{} {}".format(device, torch.mlu.get_device_name(device))
     else:
         return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
 
@@ -905,6 +928,8 @@ def xformers_enabled():
         return False
     if is_ascend_npu():
         return False
+    if is_mlu():
+        return False
     if directml_enabled:
         return False
     return XFORMERS_IS_AVAILABLE
@@ -936,6 +961,8 @@ def pytorch_attention_flash_attention():
             return True
         if is_ascend_npu():
             return True
+        if is_mlu():
+            return True
         if is_amd():
             return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
     return False
@@ -984,6 +1011,13 @@ def get_free_memory(dev=None, torch_free_too=False):
             mem_free_npu, _ = torch.npu.mem_get_info(dev)
             mem_free_torch = mem_reserved - mem_active
             mem_free_total = mem_free_npu + mem_free_torch
+        elif is_mlu():
+            stats = torch.mlu.memory_stats(dev)
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_mlu, _ = torch.mlu.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_mlu + mem_free_torch
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
@@ -1053,6 +1087,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if is_ascend_npu():
         return True
 
+    if is_mlu():
+        return True
+
     if torch.version.hip:
         return True
 
@@ -1121,6 +1158,11 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
             return False
 
     props = torch.cuda.get_device_properties(device)
+
+    if is_mlu():
+        if props.major > 3:
+            return True
+
     if props.major >= 8:
         return True