DCE as early as possible so that committed is not dependent on DCE'…

…s vars

PiperOrigin-RevId: 521879918

jax/_src/interpreters/pxla.py

@@ -666,7 +666,6 @@ def make_sharded_device_array(
       aval.shape, sharding, device_buffers)  # type: ignore
 
 
-
 if TYPE_CHECKING:
   ShardedDeviceArray = Any
 else:
@@ -2365,6 +2364,22 @@ def lower_sharding_computation(
     global_out_avals = fun_or_jaxpr.out_avals
     consts = fun_or_jaxpr.consts
 
+  if (keep_unused or
+      any(hasattr(a, "shape") and not core.is_constant_shape(a.shape)
+          for a in global_in_avals)):
+    kept_var_idx = set(range(len(global_in_avals)))
+  else:
+    jaxpr, kept_const_idx, kept_var_idx = dispatch._prune_unused_inputs(jaxpr)
+    consts = [c for i, c in enumerate(consts) if i in kept_const_idx]
+    global_in_avals = tuple(a for i, a in enumerate(global_in_avals) if i in kept_var_idx)
+    in_shardings = tuple(s for i, s in enumerate(in_shardings) if i in kept_var_idx)
+    donated_invars = tuple(x for i, x in enumerate(donated_invars) if i in kept_var_idx)
+    del kept_const_idx
+
+  jaxpr = dispatch.apply_outfeed_rewriter(jaxpr)
+  closed_jaxpr = core.ClosedJaxpr(jaxpr, consts)
+  jaxpr = closed_jaxpr.jaxpr
+
   kept_outputs = [True] * len(global_out_avals)
 
   if _is_unspecified(out_shardings):
@@ -2383,9 +2398,6 @@ def lower_sharding_computation(
                 for js, source_info in jaxpr_sharding]),
       devices_from_context)
 
-  # TODO(yashkatariya): Make this logic work after DCE because there can be
-  # equations inside the jaxpr that don't affect the output so whether the
-  # output(s) are committed or not should not depend on it.
   committed = bool(
       devices_from_context or
       len(device_assignment) > 1 or
@@ -2402,17 +2414,6 @@ def lower_sharding_computation(
               "Argument mapping: %s.",
               fun_name, global_in_avals, in_shardings)
 
-  if keep_unused or any(hasattr(a, "shape") and not core.is_constant_shape(a.shape)
-                        for a in global_in_avals):
-    kept_var_idx = set(range(len(global_in_avals)))
-  else:
-    jaxpr, kept_const_idx, kept_var_idx = dispatch._prune_unused_inputs(jaxpr)
-    consts = [c for i, c in enumerate(consts) if i in kept_const_idx]
-    global_in_avals = tuple(a for i, a in enumerate(global_in_avals) if i in kept_var_idx)
-    in_shardings = tuple(s for i, s in enumerate(in_shardings) if i in kept_var_idx)
-    donated_invars = tuple(x for i, x in enumerate(donated_invars) if i in kept_var_idx)
-    del kept_const_idx
-
   local_device_assignment = [d for d in device_assignment
                              if d.process_index == d.client.process_index()]
   if len(device_assignment) != len(local_device_assignment):
@@ -2438,7 +2439,6 @@ def lower_sharding_computation(
           "`with jax.spmd_mode('allow_all'):` context manager.")
 
   has_outfeed = core.jaxpr_uses_outfeed(jaxpr)
-  jaxpr = dispatch.apply_outfeed_rewriter(jaxpr)
 
   # Computations that only produce constants and/or only rearrange their inputs,
   # which are often produced from partial evaluation, don't need compilation,
@@ -2498,7 +2498,6 @@ def lower_sharding_computation(
     axis_env = xla.AxisEnv(nreps, (), ())
     axis_ctx = mlir.ReplicaAxisContext(axis_env)
 
-  closed_jaxpr = core.ClosedJaxpr(jaxpr, consts)
   module_name = f"{api_name}_{fun_name}"
 
   if len(device_assignment) > 1: