[Backport] Update Pallas user guide (#6965)

Author: alanwaketan

docs/pallas.md

@@ -0,0 +1,57 @@
+# Custom Kernels via Pallas
+
+With the rise of OpenAI [triton](https://openai.com/research/triton), custom kernels become more and more popular in the GPU community, for instance, the introduction of [FlashAttention](https://github.com/Dao-AILab/flash-attention) and [PagedAttention](https://blog.vllm.ai/2023/06/20/vllm.html). In order to provide the feature parity in the TPU world, Google has introduced [Pallas](http://go/jax-pallas) and [Mosaic](http://go/mosaic-tpu). For PyTorch/XLA to continue pushing the performance in TPU, we have to support custom kernels, and the best way is through Pallas and Mosaic. The design doc is [TBA]().
+
+Let's assume you have a Pallas kernel defined as follow:
+```python3
+import jax
+from jax.experimental import pallas as pl
+import jax.numpy as jnp
+
+def add_vectors_kernel(x_ref, y_ref, o_ref):
+  x, y = x_ref[...], y_ref[...]
+  o_ref[...] = x + y
+
+@jax.jit
+def add_vectors(x: jax.Array, y: jax.Array) -> jax.Array:
+  return pl.pallas_call(add_vectors_kernel,
+                        out_shape=jax.ShapeDtypeStruct(x.shape, x.dtype)
+                        )(x, y)
+```
+
+## Adopt the above kernel to be compatible with PyTorch/XLA
+
+Example usage:
+```python3
+q = torch.randn(3, 2, 128, 4).to("xla")
+k = torch.randn(3, 2, 128, 4).to("xla")
+v = torch.randn(3, 2, 128, 4).to("xla")
+
+# Adopts any Pallas kernel
+from torch_xla.experimental.custom_kernel import make_kernel_from_pallas
+pt_kernel = make_kernel_from_pallas(add_vectors, lambda x, y: [(x.shape, x.dtype)])
+output = pt_kernel(q, k)
+```
+For simple kernels, the adoption is just as simple as one liner. For more complicated kernels, you can refer to our Flash Attention implementation for details.
+
+## Use built-in kernels
+
+Besides manually wrapping external Pallas kernels, there are built-in kernels where the adoptions are done by PyTorch/XLA already.
+
+Example usage:
+```python3
+# Use built-in kernels
+from torch_xla.experimental.custom_kernel import flash_attention
+output = flash_attention(q, k, v)
+```
+
+You can just use it like any other torch.ops.
+
+## HuggingFace Llama 3 Example
+We have a fork of HF Llama 3 to demonstrate a potential integration [here](https://github.com/pytorch-tpu/transformers/tree/alanwaketan/flash_attention).
+
+## Dependencies
+The Pallas integration depends on JAX to function. However, not every JAX version is compatible with your installed PyTorch/XLA. To install the proper JAX:
+```bash
+pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
+```