disable optimization

Author: mattjj

jax/_src/pjit.py

@@ -76,7 +76,7 @@
 from jax._src.util import (
     HashableFunction, safe_map, safe_zip, wraps,
     distributed_debug_log, split_list, weakref_lru_cache,
-    merge_lists, flatten, unflatten, subs_list)
+    merge_lists, flatten, unflatten)
 
 map, unsafe_map = safe_map, map
 zip, unsafe_zip = safe_zip, zip
@@ -1808,34 +1808,37 @@ def _pjit_partial_eval(trace, *in_tracers,
   def keep_where(l, should_keep):
     return tuple(x for x, keep in zip(l, should_keep) if keep)
 
-  # Input-to-output forwarding: compute which outputs are just forwarded inputs.
-  num_out_primals = len(known_jaxpr.out_avals) - num_residuals
-  in_fwd: list[int | None] = pe._jaxpr_forwarding(known_jaxpr.jaxpr)
-  # Only forward primal outputs when corresponding out_sharding is UNSPECIFIED.
-  in_fwd_primal, in_fwd_res = split_list(in_fwd, [num_out_primals])
-  in_fwd = [fwd if is_unspecified(os) else None for os, fwd in
-            zip(keep_where(out_shardings, known_outs), in_fwd_primal)
-            ] + in_fwd_res
-  del in_fwd_primal, in_fwd_res
-  # Prune jaxpr outputs and out_shardings by removing the input-forwards.
-  keep = [f is None for f in in_fwd]
-  known_jaxpr = pe.prune_closed_jaxpr_outputs(known_jaxpr, keep)
   known_out_shardings = keep_where(out_shardings, known_outs) + res_shardings
-  known_out_shardings = keep_where(known_out_shardings, keep)
-  # Update num_out_primals to reflect pruning.
-  kept_primals, kept_res = split_list(keep, [num_out_primals])
-  num_out_primals = sum(f is None for f in kept_primals)
-  del keep, kept_primals, kept_res
-
-  # Output-to-output forwarding: compute which residuals are just primal outputs
-  out_vars, res_vars = split_list(known_jaxpr.jaxpr.outvars, [num_out_primals])
-  idx_map = {id(v): i for i, v in enumerate(out_vars)}
-  out_fwd = [None] * num_out_primals + [idx_map.get(id(v)) for v in res_vars]
-  # Prune jaxpr outputs and out_shardings by removing forwarded residuals.
-  keep = [f is None for f in out_fwd]
-  known_jaxpr = pe.prune_closed_jaxpr_outputs(known_jaxpr, keep)
-  known_out_shardings = keep_where(known_out_shardings, keep)
-  del keep
+
+  # TODO(mattjj): un-disable this optimization after we have more tests
+  # # Input-to-output forwarding: compute which outputs are just forwarded inputs.
+  # num_out_primals = len(known_jaxpr.out_avals) - num_residuals
+  # in_fwd: list[int | None] = pe._jaxpr_forwarding(known_jaxpr.jaxpr)
+  # # Only forward primal outputs when corresponding out_sharding is UNSPECIFIED.
+  # in_fwd_primal, in_fwd_res = split_list(in_fwd, [num_out_primals])
+  # in_fwd = [fwd if is_unspecified(os) else None for os, fwd in
+  #           zip(keep_where(out_shardings, known_outs), in_fwd_primal)
+  #           ] + in_fwd_res
+  # del in_fwd_primal, in_fwd_res
+  # # Prune jaxpr outputs and out_shardings by removing the input-forwards.
+  # keep = [f is None for f in in_fwd]
+  # known_jaxpr = pe.prune_closed_jaxpr_outputs(known_jaxpr, keep)
+  # known_out_shardings = keep_where(known_out_shardings, keep)
+  # # Update num_out_primals to reflect pruning.
+  # kept_primals, kept_res = split_list(keep, [num_out_primals])
+  # num_out_primals = sum(f is None for f in kept_primals)
+  # del keep, kept_primals, kept_res
+
+  # TODO(mattjj): un-disable this optimization after we have more tests
+  # # Output-to-output forwarding: compute which residuals are just primal outputs
+  # out_vars, res_vars = split_list(known_jaxpr.jaxpr.outvars, [num_out_primals])
+  # idx_map = {id(v): i for i, v in enumerate(out_vars)}
+  # out_fwd = [None] * num_out_primals + [idx_map.get(id(v)) for v in res_vars]
+  # # Prune jaxpr outputs and out_shardings by removing forwarded residuals.
+  # keep = [f is None for f in out_fwd]
+  # known_jaxpr = pe.prune_closed_jaxpr_outputs(known_jaxpr, keep)
+  # known_out_shardings = keep_where(known_out_shardings, keep)
+  # del keep
 
   known_params = dict(
       jaxpr=known_jaxpr, in_shardings=keep_where(in_shardings, known_ins),
@@ -1847,10 +1850,11 @@ def keep_where(l, should_keep):
   # Bind known things to pjit_p.
   known_inputs = [pv.get_known() for pv in in_pvals if pv.is_known()]
   all_known_outs = pjit_p.bind(*known_inputs, **known_params)
-  # Add back in the output fwds.
-  all_known_outs = subs_list(out_fwd, all_known_outs, all_known_outs)
-  # Add back in the input fwds.
-  all_known_outs = subs_list(in_fwd, known_inputs, all_known_outs)
+  # TODO(mattjj): un-disable this optimization after we have more tests
+  # # Add back in the output fwds.
+  # all_known_outs = subs_list(out_fwd, all_known_outs, all_known_outs)
+  # # Add back in the input fwds.
+  # all_known_outs = subs_list(in_fwd, known_inputs, all_known_outs)
 
   known_out_vals, residual_vals = \
       split_list(all_known_outs, [len(all_known_outs) - num_residuals])

tests/api_test.py

@@ -4565,6 +4565,15 @@ def foo(self):
     gc.collect()
     assert a() is None
 
+  def test_forwarding_bug(self):
+    # Test for issue #20267.
+    def f(x):
+        @jax.jit
+        def inner(a, x):
+            return a, jnp.exp(x)
+        return inner(0., x)[0]
+    jax.grad(f)(1.)  # don't crash
+
 
 class RematTest(jtu.JaxTestCase):
 

tests/core_test.py

@@ -368,6 +368,8 @@ def body(c, _):
     dropvar, b = jaxpr.eqns[0].outvars
     self.assertEqual(dropvar.aval, aval)
 
+  # TODO(mattjj): un-skip
+  @unittest.skip('temporarily skipping until we can add more tests')
   def test_input_residual_forwarding(self):
     # https://github.com/google/jax/pull/11151
     x = jnp.arange(3 * 4.).reshape(3, 4)