[FRONTEND] Added support for element-wise function defined in external LLVM bitcode (e.g., libdevice) (#562)

python/tutorials/07-libdevice-function.py

@@ -0,0 +1,74 @@
+"""
+Libdevice function
+===============
+Triton can invoke a custom function from an external library.
+In this example, we will use the `libdevice` library to apply `asin` on a tensor.
+Please refer to https://docs.nvidia.com/cuda/libdevice-users-guide/index.html regarding the semantics of all available libdevice functions.
+
+In `trition/language/libdevice.py`, we try to aggregate functions with the same computation but different data types together.
+For example, both `__nv_asin` and `__nvasinf` calculate the principal value of the arc sine of the input, but `__nv_asin` operates on `double` and `__nv_asinf` operates on `float`.
+Using triton, you can simply call `tl.libdevice.asinf`.
+triton automatically selects the correct underlying device function to invoke based on input and output types.
+"""
+
+# %%
+#  asin Kernel
+# --------------------------
+
+import torch
+
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def asin_kernel(
+    x_ptr,
+    y_ptr,
+    n_elements,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    offsets = block_start + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+    x = tl.load(x_ptr + offsets, mask=mask)
+    x = tl.libdevice.asin(x)
+    tl.store(y_ptr + offsets, x, mask=mask)
+
+# %%
+#  Using the default libdevice library path
+# --------------------------
+# We can use the default libdevice library path encoded in `triton/language/libdevice.py`
+
+
+torch.manual_seed(0)
+size = 98432
+x = torch.rand(size, device='cuda')
+output_triton = torch.zeros(size, device='cuda')
+output_torch = torch.asin(x)
+assert x.is_cuda and output_triton.is_cuda
+n_elements = output_torch.numel()
+grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
+asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024)
+print(output_torch)
+print(output_triton)
+print(
+    f'The maximum difference between torch and triton is '
+    f'{torch.max(torch.abs(output_torch - output_triton))}'
+)
+
+# %%
+#  Customize the libdevice library path
+# --------------------------
+# We can also customize the libdevice library path by passing the path to the `libdevice` library to the `asin` kernel.
+
+output_triton = torch.empty_like(x)
+asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024,
+                  extern_libs={'libdevice': '/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'})
+print(output_torch)
+print(output_triton)
+print(
+    f'The maximum difference between torch and triton is '
+    f'{torch.max(torch.abs(output_torch - output_triton))}'
+)