Automated tutorials push

Author: pytorchbot

_downloads/0ccffddcfee1f815c02241b985844376/torch_compile_user_defined_triton_kernel_tutorial.py

@@ -257,9 +257,9 @@ def sin_triton(x):
 # Prefer this to using ``torch.autograd.Function`` (which has various composability footguns
 # with ``torch.compile``).
 
-def backward(ctx, grad_output):
+def backward(ctx, grad):
     x, = ctx.saved_tensors
-    return grad_input * x.cos()
+    return grad * x.cos()
 
 def setup_context(ctx, inputs, output):
     x, = inputs
@@ -293,9 +293,9 @@ def mycos(x: torch.Tensor) -> torch.Tensor:
     wrap_triton(cos_kernel)[(n_elements,)](x, out, n_elements, BLOCK_SIZE=4)
     return out
 
-def backward(ctx, grad_output):
+def backward(ctx, grad):
     x, = ctx.saved_tensors
-    return grad_input * mycos(x)
+    return grad * mycos(x)
 
 def setup_context(ctx, inputs, output):
     x, = inputs

_downloads/f827f181506a79226f4ffbcf7c9a5a50/torch_compile_user_defined_triton_kernel_tutorial.ipynb

@@ -354,9 +354,9 @@
    },
    "outputs": [],
    "source": [
-    "def backward(ctx, grad_output):\n",
+    "def backward(ctx, grad):\n",
     "    x, = ctx.saved_tensors\n",
-    "    return grad_input * x.cos()\n",
+    "    return grad * x.cos()\n",
     "\n",
     "def setup_context(ctx, inputs, output):\n",
     "    x, = inputs\n",
@@ -404,9 +404,9 @@
     "    wrap_triton(cos_kernel)[(n_elements,)](x, out, n_elements, BLOCK_SIZE=4)\n",
     "    return out\n",
     "\n",
-    "def backward(ctx, grad_output):\n",
+    "def backward(ctx, grad):\n",
     "    x, = ctx.saved_tensors\n",
-    "    return grad_input * mycos(x)\n",
+    "    return grad * mycos(x)\n",
     "\n",
     "def setup_context(ctx, inputs, output):\n",
     "    x, = inputs\n",