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| -rw-r--r-- | docs/user-guide/a1-02-slangpy.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/user-guide/a1-02-slangpy.md b/docs/user-guide/a1-02-slangpy.md index 8def87cea..b704e215a 100644 --- a/docs/user-guide/a1-02-slangpy.md +++ b/docs/user-guide/a1-02-slangpy.md @@ -153,7 +153,7 @@ print(output_grad) `slangpy` also binds the forward-mode version of your kernel (propagate derivatives of inputs to the output) which can be invoked the same way using `module.square.fwd()` -You can refer to [this documentation](07-autodiff.md) for a detailed reference of Slang's automatic differentiation feature. +You can refer to [this documentation](08-autodiff.md) for a detailed reference of Slang's automatic differentiation feature. ### Wrapping your kernels as pytorch functions @@ -452,7 +452,7 @@ in the `inputGradToPropagateTo` tensor. Therefore, after running `boxFilter_bwd` back propagated derivative values. Again, to understand all the details of the automatic differentiation system, please refer to the -[Automatic Differentiation](07-autodiff.md) chapter for a detailed explanation. +[Automatic Differentiation](08-autodiff.md) chapter for a detailed explanation. ## Manually binding kernels `[AutoPyBindCUDA]` works for most use cases, but in certain situations, it may be necessary to write the *host* function by hand. The host function can also be written in Slang, and `slangpy` handles its compilation to C++. @@ -805,4 +805,4 @@ Calling `myFunc` from python will result in a python tuple in the form of [[tensor, tensor, tensor], float] ``` -The same transform rules apply to parameter types.
\ No newline at end of file +The same transform rules apply to parameter types. |