[Pallas] Introduce make_kernel_from_pallas (#6713)

Summary:
This pull request introduces make_kernel_from_pallas API which is the top level API to interact with the Pallas integration. It takes a pallas_call wrapper and than make it a custom pytorch op.

Test Plan:
python test/test_pallas.py

test/test_pallas.py

@@ -135,6 +135,48 @@ def add_vectors(x: jax.Array, y: jax.Array) -> jax.Array:
     # the most important fields are present.
     self.assertIn("custom_call_config", payload)
 
+  @unittest.skipIf(xr.device_type() != 'TPU', "This test only works on TPU.")
+  # TODO: This test cannot be ran individually, let's fix it.
+  def test_tpu_custom_call_pallas_wrap_add_payload(self):
+    import jax
+    import jax.numpy as jnp
+    import jax._src.pallas.mosaic.pallas_call_registration
+
+    from jax.experimental import pallas as pl
+
+    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)
+
+    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))
+
+    dtypes = [torch.float32, torch.float
+             ]  # TODO: torch.float64, torch.bfloat16, torch.float16 don't work.
+    for i in range(len(dtypes)):
+      x = torch.randn((i + 1, i + 1), dtype=dtypes[i]).to("xla")
+      y = torch.randn((i + 1, i + 1), dtype=dtypes[i]).to("xla")
+      expected_output = x + y
+      output = pt_kernel(x, y)
+      self.assertTrue(torch.allclose(output.cpu(), expected_output.cpu()))
+
+    dtypes = [
+        torch.int32, torch.int
+    ]  # TODO: torch.int64, torch.int16, torch.int8, torch.uint8 don't work.
+    for i in range(len(dtypes)):
+      x = torch.arange(i + 1, dtype=dtypes[i]).to("xla")
+      y = torch.arange(i + 1, dtype=dtypes[i]).to("xla")
+      expected_output = x + y
+      output = pt_kernel(x, y)
+      self.assertTrue(torch.allclose(output.cpu(), expected_output.cpu()))
+
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)

torch_xla/experimental/custom_kernel.py

@@ -1,7 +1,13 @@
+import functools
+import jax
+import jax.numpy as jnp
+import jax._src.pallas.mosaic.pallas_call_registration
 import torch
 import torch_xla
+import torch_xla.core.xla_model as xm
 
-from typing import List
+from jax.experimental import pallas as pl
+from typing import List, Callable
 from torch.library import impl
 from torch_xla.core.xla_model import XLA_LIB
 
@@ -56,3 +62,50 @@ def _extract_backend_config(
       if op.name == "stablehlo.custom_call":
         return op.backend_config.value
   return None
+
+
+def convert_torch_dtype_to_jax(dtype: torch.dtype) -> jnp.dtype:
+  if dtype == torch.float32:
+    return jnp.float32
+  elif dtype == torch.float64:
+    return jnp.float64
+  elif dtype == torch.float16:
+    return jnp.float16
+  elif dtype == torch.bfloat16:
+    return jnp.bfloat16
+  elif dtype == torch.int32:
+    return jnp.int32
+  elif dtype == torch.int64:
+    return jnp.int64
+  elif dtype == torch.int16:
+    return jnp.int16
+  elif dtype == torch.int8:
+    return jnp.int8
+  elif dtype == torch.uint8:
+    return jnp.uint8
+  else:
+    raise ValueError(f"Unsupported dtype: {dtype}")
+
+
+def make_kernel_from_pallas(kernel: Callable, output_shape_dtype_fn: Callable):
+  # TODO: Maybe we can cache the payload for the same input.
+  def wrapped_kernel(kernel: Callable, output_shape_dtype_fn: Callable, *args):
+    jax_args = []
+    for i, arg in enumerate(args):
+      if torch.is_tensor(arg):
+        # ShapedArray doesn't have any storage and thus is very suitable for generating the payload.
+        jax_meta_tensor = jax.core.ShapedArray(
+            arg.shape, convert_torch_dtype_to_jax(arg.dtype))
+        jax_args.append(jax_meta_tensor)
+      else:
+        # TODO: We can support more types here.
+        assert False, f"Unsupported argument type: {type(arg)}"
+
+    ir = jax.jit(kernel).lower(*jax_args).compiler_ir()
+    payload = _extract_backend_config(ir)
+    output_shape, output_dtype = output_shape_dtype_fn(*args)
+    output = torch.empty(output_shape, dtype=output_dtype).to(xm.xla_device())
+    torch_xla._XLAC._xla_tpu_custom_call_(output, args, payload)
+    return output
+
+  return functools.partial(wrapped_kernel, kernel, output_shape_dtype_fn)