fix stable diffusion on npu

README.md

@@ -100,7 +100,7 @@
     import mindnlp
     from diffusers import DiffusionPipeline
 
-    pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", ms_dtype=mindspore.float16)
+    pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", ms_dtype=mindspore.float16, device_map='cuda')
     pipeline("An image of a squirrel in Picasso style").images[0]
     ```
 

mindtorch/_apis/cpu.py

@@ -1240,3 +1240,9 @@ def scatter_nd_update(input, indices, updates):
 
 def triu_indices(row, col, offset, dtype):
     return legacy.triu_indices(row, col, offset, dtype)
+
+def cumprod(input, dim, dtype):
+    out = legacy.cum_prod(input, dim, False, False)
+    if dtype is not None:
+        out = cast(out, dtype)
+    return out

mindtorch/_apis/gpu.py

@@ -1247,3 +1247,9 @@ def fft(input, n=None, dim=-1, norm="backward"):
 
 def triu_indices(row, col, offset, dtype):
     return legacy.triu_indices(row, col, offset, dtype)
+
+def cumprod(input, dim, dtype):
+    out = legacy.cum_prod(input, dim, False, False)
+    if dtype is not None:
+        out = cast(out, dtype)
+    return out

mindtorch/_apis/npu.py

@@ -1656,3 +1656,9 @@ def repeat_interleave_tensor(input, repeats, dim, output_size):
 
 def triu_indices(row, col, offset, dtype):
     return legacy.triu_indices(row, col, offset, dtype)
+
+def cumprod(input, dim, dtype):
+    out = legacy.cum_prod(input, dim, False, False)
+    if dtype is not None:
+        out = cast(out, dtype)
+    return out

mindtorch/ops/other.py

@@ -100,6 +100,8 @@ def clone(input, *, memory_format=mindtorch.preserve_format):
 # cummin
 
 # cumprod
+def cumprod(input, dim, *, dtype=None, out=None):
+    return execute('cumprod', input, dim, dtype)
 
 # cumsum
 def cumsum(input, dim=None, dtype=None, **kwargs):
@@ -1131,6 +1133,7 @@ def cosine_similarity(x1, x2, dim=1, eps=1e-8):
     "clone",
     "contains",
     "cross",
+    "cumprod",
     "cumsum",
     "diag",
     "diagonal",

mindtorch/ops/pointwise.py

@@ -200,8 +200,15 @@ def div(input, other, *, rounding_mode=None):
             rounding_mode
         )
     else:
-        if not isinstance(other, numbers.Number) and not isinstance(input, numbers.Number) and other.device != input.device:
-            other = other.to(input.device)
+        if not isinstance(other, numbers.Number) and not isinstance(input, numbers.Number):
+            if other.device != input.device:
+                device = max([input.device, other.device])
+                other = other.to(device)
+                input = input.to(device)
+            if other.dtype != input.dtype:
+                dtype = min([input.dtype, other.dtype])
+                other = other.to(dtype)
+                input = input.to(dtype)
         output = execute("div", input, other)
     return output
 
@@ -380,7 +387,13 @@ def logical_xor(input, other):
 # mul
 def mul(input, other):
     if not isinstance(other, numbers.Number) and other.device != input.device:
-        other = other.to(input.device)
+        device = max([input.device, other.device])
+        other = other.to(device)
+        input = input.to(device)
+    if not isinstance(other, numbers.Number) and other.dtype != input.dtype:
+        dtype = min([input.dtype, other.dtype])
+        other = other.to(dtype)
+        input = input.to(dtype)
     #  and isinstance(input, torch.Tensor):
     #     return execute("muls", input, other)
     return execute("mul", input, other)