[jax_triton] Always use "triton_kernel_call" as custom call target name.

Now there is a `name` field in the serialized proto, there is an alternative way to differentiate kernel calls.

PiperOrigin-RevId: 559124166

jax_triton/triton_lib.py

@@ -30,7 +30,6 @@
 import jax.dlpack
 from jax.interpreters import mlir
 from jax.interpreters import xla
-from jax.lib import xla_client as xc
 import jax.numpy as jnp
 import numpy as np
 
@@ -259,7 +258,6 @@ def triton_kernel_call_lowering(
     fn,
     scalar_args,
     name,
-    call_name,
     out_shapes,
     grid,
     num_warps,
@@ -404,7 +402,7 @@ def prune_configs(configs, named_args):
         for input_idx, output_idx in input_output_aliases
     )
     kernel_call = triton_kernel_call_lib.TritonAutotunedKernelCall(
-        f"{fn.fn.__name__} ({call_name=}) {named_scalar_args}",
+        f"{kernel_call_name} ({fn.fn.__name__}) {named_scalar_args}",
         [(call, str(config)) for call, config in zip(kernel_calls, configs)],
         input_output_aliases_with_sizes,
     )
@@ -420,7 +418,7 @@ def prune_configs(configs, named_args):
   else:
     call_proto = kernel_call.to_proto(serialized_metadata)
   return jaxlib.hlo_helpers.custom_call(
-      call_target_name=call_name,
+      call_target_name="triton_kernel_call",
       out_types=out_types,
       operands=array_args,
       backend_config=zlib.compress(call_proto),
@@ -450,7 +448,6 @@ def triton_call(
     out_shape: Union[ShapeDtype, Sequence[ShapeDtype]],
     grid: GridOrLambda,
     name: str = "",
-    call_name: str = "triton_kernel_call",  # TODO(cjfj): Remove this.
     num_warps: int = 4,
     num_stages: int = 2,
     input_output_aliases: Optional[Dict[int, int]] = None,
@@ -545,9 +542,6 @@ def add(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
     raise ValueError(
         "`triton_call` is only available when `triton` is installed."
     )
-  xc.register_custom_call_target(
-      call_name, triton_kernel_call_lib.get_custom_call(), platform="CUDA"
-  )
   out_shape = tree_util.tree_map(
       lambda a: jax.ShapeDtypeStruct(a.shape, a.dtype), out_shape)
   flat_args, _ = tree_util.tree_flatten(args)
@@ -572,7 +566,6 @@ def add(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
       fn=kernel,
       scalar_args=tuple(scalar_args),
       name=name,
-      call_name=call_name,
       out_shapes=tuple(flat_out_shapes),
       grid=grid,
       num_warps=num_warps,