Merge f0caa15a17 into 3cd7b32f1b

comfy/ldm/flux/math.py

@@ -21,8 +21,15 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     else:
         device = pos.device
 
-    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+    if device.type == "musa":
-    omega = 1.0 / (theta**scale)
+        # XXX (MUSA): Unsupported tensor dtype in Neg: Double
+        scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float32, device=device)
+        if not isinstance(theta, torch.Tensor):
+            theta = torch.tensor(theta, dtype=torch.float32, device=device)
+        omega = torch.exp(-scale * torch.log(theta + 1e-6))
+    else:
+        scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+        omega = 1.0 / (theta**scale)
     out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)

comfy/model_management.py

@@ -139,6 +139,12 @@ try:
 except:
     ixuca_available = False
 
+try:
+    import torchada  # noqa: F401
+    musa_available = hasattr(torch, "musa") and torch.musa.is_available()
+except:
+    musa_available = False
+
 if args.cpu:
     cpu_state = CPUState.CPU
 
@@ -146,27 +152,24 @@ def is_intel_xpu():
     global cpu_state
     global xpu_available
     if cpu_state == CPUState.GPU:
-        if xpu_available:
+        return xpu_available
-            return True
     return False
 
 def is_ascend_npu():
     global npu_available
-    if npu_available:
+    return npu_available
-        return True
-    return False
 
 def is_mlu():
     global mlu_available
-    if mlu_available:
+    return mlu_available
-        return True
-    return False
 
 def is_ixuca():
     global ixuca_available
-    if ixuca_available:
+    return ixuca_available
-        return True
+
-    return False
+def is_musa():
+    global musa_available
+    return musa_available
 
 def get_torch_device():
     global directml_enabled
@@ -311,7 +314,7 @@ def amd_min_version(device=None, min_rdna_version=0):
     return False
 
 MIN_WEIGHT_MEMORY_RATIO = 0.4
-if is_nvidia():
+if is_nvidia() or is_musa():
     MIN_WEIGHT_MEMORY_RATIO = 0.0
 
 ENABLE_PYTORCH_ATTENTION = False
@@ -320,7 +323,7 @@ if args.use_pytorch_cross_attention:
     XFORMERS_IS_AVAILABLE = False
 
 try:
-    if is_nvidia():
+    if is_nvidia() or is_musa():
         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
@@ -375,7 +378,7 @@ if ENABLE_PYTORCH_ATTENTION:
 
 PRIORITIZE_FP16 = False  # TODO: remove and replace with something that shows exactly which dtype is faster than the other
 try:
-    if (is_nvidia() or is_amd()) and PerformanceFeature.Fp16Accumulation in args.fast:
+    if (is_nvidia() or is_amd() or is_musa()) and PerformanceFeature.Fp16Accumulation in args.fast:
         torch.backends.cuda.matmul.allow_fp16_accumulation = True
         PRIORITIZE_FP16 = True  # TODO: limit to cards where it actually boosts performance
         logging.info("Enabled fp16 accumulation.")
@@ -1020,7 +1023,7 @@ if args.async_offload is not None:
     NUM_STREAMS = args.async_offload
 else:
     #  Enable by default on Nvidia and AMD
-    if is_nvidia() or is_amd():
+    if is_nvidia() or is_amd() or is_musa():
         NUM_STREAMS = 2
 
 if args.disable_async_offload:
@@ -1117,7 +1120,7 @@ PINNED_MEMORY = {}
 TOTAL_PINNED_MEMORY = 0
 MAX_PINNED_MEMORY = -1
 if not args.disable_pinned_memory:
-    if is_nvidia() or is_amd():
+    if is_nvidia() or is_amd() or is_musa():
         if WINDOWS:
             MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
         else:
@@ -1261,6 +1264,8 @@ def pytorch_attention_flash_attention():
             return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
         if is_ixuca():
             return True
+        if is_musa():
+            return True
     return False
 
 def force_upcast_attention_dtype():
@@ -1392,6 +1397,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if torch.version.hip:
         return True
 
+    if is_musa():
+        return True
+
     props = torch.cuda.get_device_properties(device)
     if props.major >= 8:
         return True
@@ -1462,6 +1470,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
                 return True
             return False
 
+    if is_musa():
+        return True
+
     props = torch.cuda.get_device_properties(device)
 
     if is_mlu():
@@ -1484,25 +1495,27 @@ def supports_fp8_compute(device=None):
     if SUPPORT_FP8_OPS:
         return True
 
-    if not is_nvidia():
-        return False
-
     props = torch.cuda.get_device_properties(device)
-    if props.major >= 9:
+    if is_nvidia():
-        return True
+        if props.major >= 9:
-    if props.major < 8:
+            return True
-        return False
+        if props.major < 8:
-    if props.minor < 9:
+            return False
-        return False
+        if props.minor < 9:
-
-    if torch_version_numeric < (2, 3):
-        return False
-
-    if WINDOWS:
-        if torch_version_numeric < (2, 4):
             return False
 
-    return True
+        if torch_version_numeric < (2, 3):
+            return False
+
+        if WINDOWS:
+            if torch_version_numeric < (2, 4):
+                return False
+
+    elif is_musa():
+        if props.major >= 3:
+            return True
+
+    return False
 
 def supports_nvfp4_compute(device=None):
     if not is_nvidia():
@@ -1553,7 +1566,7 @@ def unload_all_models():
     free_memory(1e30, get_torch_device())
 
 def debug_memory_summary():
-    if is_amd() or is_nvidia():
+    if is_amd() or is_nvidia() or is_musa():
         return torch.cuda.memory.memory_summary()
     return ""
 

requirements.txt

@@ -28,3 +28,4 @@ kornia>=0.7.1
 spandrel
 pydantic~=2.0
 pydantic-settings~=2.0
+torchada>=0.1.11