Merge pull request #26078 from gnecula:debug_info_jaxpr

PiperOrigin-RevId: 723151082

jax/_src/ad_checkpoint.py

@@ -420,9 +420,9 @@ def _trace_to_jaxpr(fun: Callable,
                     in_avals: Sequence[core.AbstractValue],
                     debug: core.DebugInfo
                     ) -> tuple[core.Jaxpr, Sequence[Any], PyTreeDef]:
-  flat_fun, out_tree = api_util.flatten_fun(lu.wrap_init(fun), in_tree)
+  flat_fun, out_tree = api_util.flatten_fun(lu.wrap_init(fun, debug_info=debug), in_tree)
   try:
-    jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals, debug)
+    jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals)
   except core.ConcretizationTypeError as e:
     msg, = e.args
     if 'for checkpoint' in msg:
@@ -699,7 +699,8 @@ def transposed(*args_flat):
     assert next(ins_iter, None) is None
     with source_info_util.extend_name_stack('rematted_computation'):
       lin_jaxpr, _, consts = pe.trace_to_jaxpr_nounits(
-          lu.wrap_init(core.jaxpr_as_fun(jaxpr)), in_pvals, False)
+          lu.wrap_init(core.jaxpr_as_fun(jaxpr), debug_info=jaxpr.jaxpr.debug_info),
+          in_pvals, False)
 
     # Transpose the linear jaxpr (which only has linear inputs).
     out_cts_iter = iter(out_cts_flat)

jax/_src/api.py

@@ -61,7 +61,7 @@
   flatten_axes, donation_vector,
   rebase_donate_argnums, _ensure_index, _ensure_index_tuple,
   apply_flat_fun_nokwargs, check_callable, debug_info,
-  result_paths, flat_out_axes)
+  flat_out_axes)
 from jax._src.lax import lax as lax_internal
 from jax._src.lib import jax_jit
 from jax._src.lib import xla_client as xc
@@ -1430,7 +1430,8 @@ def _prepare_pmap(fun: Callable, in_axes, out_axes, static_broadcasted_tuple,
       "pmap", fun, args, kwargs,
       static_argnums=static_broadcasted_tuple)
 
-  f = lu.wrap_init(fun)
+  f = lu.wrap_init(fun, debug_info=dbg)
+  del dbg
   if static_broadcasted_tuple:
     if max(static_broadcasted_tuple) >= len(args):
       raise ValueError(
@@ -1477,9 +1478,6 @@ def _prepare_pmap(fun: Callable, in_axes, out_axes, static_broadcasted_tuple,
     raise ValueError(msg) from None
   local_axis_size = _mapped_axis_size(fun, in_tree, args, in_axes_flat, "pmap")
 
-  f, res_paths = result_paths(f)
-  dbg = dbg.add_result_paths(res_paths)
-  f = lu.add_debug_info(f, dbg)
   f, out_axes_thunk = flat_out_axes(f, out_axes)
   flat_fun, out_tree = flatten_fun(f, in_tree)
 

jax/_src/api_util.py

@@ -590,7 +590,7 @@ def debug_info(
     static_argnums: tuple[int, ...] = (),
     static_argnames: tuple[str, ...] = (),
     result_paths_thunk: Callable[[], tuple[str, ...]] | None = None,
-    # TODO(necula): check if we really need this, e.g., to speed up tracing.
+    # TODO(necula): check if we really need this, e.g., to speed up tracing?
     sourceinfo: str | None = None,
     signature: inspect.Signature | None = None,
 ) -> core.DebugInfo:
@@ -674,29 +674,6 @@ def _non_static_arg_names(fn_signature: inspect.Signature | None,
   arg_names = args_arg_names + kwargs_arg_names
   return arg_names
 
-@lu.transformation_with_aux2
-def result_paths(_fun, _store, *args, **kwargs):
-  "linear_util transform to get output pytree paths of pre-flattened function."
-  ans = _fun(*args, **kwargs)
-  _store.store([keystr(path) for path, _ in generate_key_paths(ans)])
-  return ans
-
-# TODO(necula): simplify this function, all it needs is to add the trace_debug to the Jaxpr
-def add_jaxpr_debug_info(jaxpr: core.Jaxpr,
-                         debug: core.DebugInfo | None,
-                         result_paths: tuple[str, ...] | None = None,
-                         ) -> core.Jaxpr:
-  """Add debug info to jaxpr, given trace-time debug info and result paths."""
-  if debug is None:
-    return jaxpr
-  # TODO(necula): re-enable this safety check
-  # assert (result_paths is not None) ^ (trace_debug.result_paths_thunk is not None)
-  if result_paths is not None:
-    debug = debug._replace(result_paths=tuple(result_paths))
-  else:
-    debug = debug.resolve_result_paths()
-  return jaxpr.replace(debug_info=debug)
-
 def hoist_obj_attrs(f, flat_args):
   idxs, objs, flat_args_ = [], [], []
   for i, x in enumerate(flat_args):
@@ -721,7 +698,7 @@ def register_class_with_attrs(t: type) -> None:
 _class_with_attrs: set[type] = set()
 
 # TODO(mattjj): make this function faster
-def _check_no_aliased_ref_args(dbg, avals, args):
+def _check_no_aliased_ref_args(dbg: core.DebugInfo | None, avals, args):
   assert config.mutable_array_checks.value
   refs: dict[int, int] = {}
   for i, (a, x) in enumerate(zip(avals, args)):
@@ -735,7 +712,7 @@ def _check_no_aliased_ref_args(dbg, avals, args):
         if dbg else
         f"at both flat index {dup_idx} and flat index {i}") from None
 
-def _check_no_aliased_closed_over_refs(dbg, consts, args) -> None:
+def _check_no_aliased_closed_over_refs(dbg: core.DebugInfo | None, consts, args) -> None:
   assert config.mutable_array_checks.value
   refs: set[int] = {id(core.get_referent(c)) for c in consts
                     if isinstance(core.get_aval(c), AbstractRef)}
@@ -746,4 +723,4 @@ def _check_no_aliased_closed_over_refs(dbg, consts, args) -> None:
           f"when tracing {dbg.func_src_info} for {dbg.traced_for}, a mutable "
           f"array reference of type {a.str_short()} was both closed over and "
           f"passed as the argument "
-          f"{dbg.arg_names[i]}" if dbg else "at flat index {i}")
+          f"{dbg.safe_arg_names(len(args))[i]}" if dbg else "at flat index {i}")

jax/_src/checkify.py

@@ -1208,7 +1208,7 @@ def checked_fun(*args, **kwargs):
     fun_, out_tree = api_util.flatten_fun(lu.wrap_init(closed_f,
                                                        debug_info=debug),
                                           in_tree)
-    jaxpr_, _, consts, () = pe.trace_to_jaxpr_dynamic(fun_, (), debug)
+    jaxpr_, _, consts, () = pe.trace_to_jaxpr_dynamic(fun_, ())
     jaxpr = pe.close_jaxpr(pe.convert_constvars_jaxpr(jaxpr_))
     # checkify:
     error, out_flat = checkify_jaxpr(jaxpr, errors, init_error, *consts)

jax/_src/core.py

@@ -2398,7 +2398,8 @@ def bind_with_trace(self, trace, fun_and_args, params):
   def get_bind_params(self, params):
     new_params = dict(params)
     jaxpr = new_params.pop('call_jaxpr')
-    subfun = lu.hashable_partial(lu.wrap_init(eval_jaxpr), jaxpr, ())
+    subfun = lu.hashable_partial(lu.wrap_init(eval_jaxpr, debug_info=jaxpr.debug_info),
+                                 jaxpr, ())
     if config.dynamic_shapes.value:
       subfun = lu.annotate(subfun, _jaxpr_type_to_callable_annotation(jaxpr))
     return [subfun], new_params
@@ -2434,7 +2435,7 @@ def bind(self, *args, **params):
     return self._true_bind(*args, **params)
 
   def bind_with_trace(self, trace, fun_and_args, params):
-    fun = fun_and_args[0]
+    fun: lu.WrappedFun = fun_and_args[0]
     args = fun_and_args[1:]
     assert len(params['in_axes']) == len(args)
     return trace.process_map(self, fun, args, params)
@@ -2444,8 +2445,9 @@ def process(self, trace, fun, tracers, params):
 
   def get_bind_params(self, params):
     new_params = dict(params)
-    jaxpr = new_params.pop('call_jaxpr')
-    subfun = lu.hashable_partial(lu.wrap_init(eval_jaxpr), jaxpr, ())
+    jaxpr: Jaxpr = new_params.pop('call_jaxpr')
+    subfun = lu.hashable_partial(lu.wrap_init(eval_jaxpr,
+                                              debug_info=jaxpr.debug_info), jaxpr, ())
     axes = new_params.pop('out_axes')
     new_params['out_axes_thunk'] = HashableFunction(lambda: axes, closure=axes)
     return [subfun], new_params

jax/_src/custom_batching.py

@@ -153,7 +153,7 @@ def __call__(self, *args, **kwargs):
         lu.wrap_init(self.fun, debug_info=debug),
         in_tree)
     in_avals = [core.get_aval(x) for x in args_flat]
-    jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals, debug)
+    jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals)
     closed_call = core.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr), ())
     in_tree = treedef_tuple((tree_structure(consts), in_tree))
     assert self.vmap_rule is not None

jax/_src/custom_dce.py

@@ -147,11 +147,11 @@ def __call__(self, *args, **kwargs):
       )
       static_args = [args[i] for i in self.static_argnums]
       dce_rule = api_util.prepend_static_args(
-          lu.wrap_init(self.dce_rule), static_args
+          lu.wrap_init(self.dce_rule, debug_info=debug_rule), static_args
       )
     else:
       fun = lu.wrap_init(self.fun, debug_info=debug)
-      dce_rule = lu.wrap_init(self.dce_rule)
+      dce_rule = lu.wrap_init(self.dce_rule, debug_info=debug_rule)
       dyn_args = args
 
     args_flat, in_tree = tree_util.tree_flatten(dyn_args)
@@ -176,7 +176,7 @@ def dce_jaxpr_thunk(
       )
       assert self.dce_rule is not None
       dce_jaxpr, _, dce_consts, () = pe.trace_to_jaxpr_dynamic(
-          flat_rule, in_avals, debug_rule
+          flat_rule, in_avals
       )
 
       # This second round of DCE is used to work out which inputs are actually
@@ -191,7 +191,7 @@ def dce_jaxpr_thunk(
 
       return core.ClosedJaxpr(dce_jaxpr, dce_consts), used_ins
 
-    jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals, debug)
+    jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals)
     closed_call = pe.close_jaxpr(pe.convert_constvars_jaxpr(jaxpr))
     out_avals = closed_call.out_avals
     out_flat = custom_dce_p.bind(
@@ -366,7 +366,8 @@ def custom_dce_jvp(primals, tangents, *, fun_jaxpr: core.ClosedJaxpr, **_):
   # that most users of this API would compose this with a custom_jvp or
   # custom_vjp, which makes this less urgent.
   out = core.call_p.bind(
-      lu.wrap_init(core.jaxpr_as_fun(jvp_jaxpr)), *primals, *tangents
+      lu.wrap_init(core.jaxpr_as_fun(jvp_jaxpr),
+                   debug_info=jvp_jaxpr.jaxpr.debug_info), *primals, *tangents
   )
 
   out_primals, out_tangents = util.split_list(out, [len(out_nz)])

jax/_src/custom_partitioning.py

@@ -485,13 +485,13 @@ def __call__(self, *args, **kwargs):
       _check_for_tracers(static_args)
     else:
       static_args = []
-      f_, dyn_args = lu.wrap_init(self.fun), args
+      f_, dyn_args = lu.wrap_init(self.fun, debug_info=debug), args
     args_flat, in_tree = tree_util.tree_flatten(dyn_args)
     flat_fun, out_tree = api_util.flatten_fun_nokwargs(f_, in_tree)
     in_avals = [core.get_aval(x) for x in args_flat]
     mesh = mesh_lib.thread_resources.env.physical_mesh
     with core.extend_axis_env_nd(mesh.shape.items()):
-      jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals, debug)
+      jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals)
     assert not len(consts)
     closed_call = core.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr), ())
 

jax/_src/interpreters/ad.py

@@ -98,7 +98,7 @@ def linearize_subtrace(_f: Callable, _store, _tag, nzs_in, *primals, **params):
   nzs_out = tuple(type(t) is not Zero for t in out_tangents)
   out_tangents = tuple(t for t, nz in zip(out_tangents, nzs_out) if nz)
   out_tangents = map(tangent_trace.to_jaxpr_tracer, out_tangents)  # type: ignore[assignment]
-  jaxpr, consts, attrs_tracked = tangent_trace.to_jaxpr(out_tangents)
+  jaxpr, consts, attrs_tracked = tangent_trace.to_jaxpr(out_tangents, None)
   residual_avals = map(get_aval, consts)
   if attrs_tracked:
     raise NotImplementedError("TODO: attrs")
@@ -167,16 +167,17 @@ def new_arg(trace, primal_aval, nz):
     out_primals, out_tangents = unzip2(map(lin_trace.to_primal_tangent_pair, ans))
     del lin_trace, ans, tracers, new_arg
 
+  debug_info = jaxpr.jaxpr.debug_info
   nzs_out = [type(t) is not Zero for t in out_tangents]
   out_tangents = tuple(tangent_trace.to_jaxpr_tracer(t)
                        for (nz, t) in zip(nzs_out, out_tangents) if nz)
-  tangent_jaxpr, tangent_consts, attrs_tracked = tangent_trace.to_jaxpr(out_tangents)
+  tangent_jaxpr, tangent_consts, attrs_tracked = tangent_trace.to_jaxpr(out_tangents, debug_info)
   tangent_trace.invalidate()
   if attrs_tracked:
     raise NotImplementedError("TODO: attrs")
   residuals_and_primals = (*tangent_consts, *out_primals)
   residuals_and_primals = map(primal_trace.to_jaxpr_tracer, residuals_and_primals)  # type: ignore[assignment]
-  primal_jaxpr, primal_consts, attrs_tracked = primal_trace.to_jaxpr(residuals_and_primals)
+  primal_jaxpr, primal_consts, attrs_tracked = primal_trace.to_jaxpr(residuals_and_primals, debug_info)
   primal_trace.invalidate()
   num_residuals = len(tangent_consts)
   tangent_jaxpr = pe.close_jaxpr(convert_constvars_jaxpr_constvars_at_end(tangent_jaxpr))
@@ -209,7 +210,7 @@ def direct_linearize(traceable: lu.WrappedFun,
   out_nzs = [type(t) is not Zero for t in out_tangents]
   out_nz_tangents = [t for t, nz in zip(out_tangents, out_nzs) if nz]
   out_nz_tangents = map(tangent_trace.to_jaxpr_tracer, out_nz_tangents)  # type: ignore
-  jaxpr, consts, attrs_tracked = tangent_trace.to_jaxpr(out_nz_tangents)
+  jaxpr, consts, attrs_tracked = tangent_trace.to_jaxpr(out_nz_tangents, traceable.debug_info)
   tangent_trace.invalidate()
   out_tangents_pvals = [pe.PartialVal.unknown(core.get_aval(t)) if nz else
                         pe.PartialVal.known(zeros_like_aval(t.aval))
@@ -1026,12 +1027,14 @@ def jvp_jaxpr(jaxpr: core.ClosedJaxpr, nonzeros: Sequence[bool],
 def _jvp_jaxpr(jaxpr: core.ClosedJaxpr,
                nonzeros: Sequence[bool], instantiate: Sequence[bool]):
   assert len(jaxpr.in_avals) == len(nonzeros)
-  f = lu.wrap_init(core.jaxpr_as_fun(jaxpr))
+  debug_info = jaxpr.jaxpr.debug_info
+  f = lu.wrap_init(core.jaxpr_as_fun(jaxpr), debug_info=debug_info)
   f_jvp, out_nonzeros = f_jvp_traceable(jvp(f, instantiate=instantiate, transform_stack=False),
                                         nonzeros)
   tangent_avals = [aval.to_tangent_aval() for aval, nz in zip(jaxpr.in_avals, nonzeros) if nz]
   avals_in = list(it.chain(jaxpr.in_avals, tangent_avals))
-  jaxpr_out, avals_out, literals_out, () = pe.trace_to_jaxpr_dynamic(f_jvp, avals_in)
+  jaxpr_out, avals_out, literals_out, () = pe.trace_to_jaxpr_dynamic(
+      f_jvp, avals_in)
   return core.ClosedJaxpr(jaxpr_out, literals_out), out_nonzeros()
 
 @lu.transformation_with_aux2

jax/_src/interpreters/batching.py

@@ -758,7 +758,8 @@ def _batch_jaxpr2(
     axis_data,
     in_axes: tuple[int | NotMapped | RaggedAxis, ...],
   ) -> tuple[core.ClosedJaxpr, tuple[int | NotMapped, ...]]:
-  f = lu.wrap_init(core.jaxpr_as_fun(closed_jaxpr))
+  f = lu.wrap_init(core.jaxpr_as_fun(closed_jaxpr),
+                   debug_info=closed_jaxpr.jaxpr.debug_info)
   f, out_axes = _batch_jaxpr_inner(f, axis_data)
   f = _batch_jaxpr_outer(f, axis_data, in_axes)
   in_axes2, avals_in = unzip2([

jax/_src/interpreters/mlir.py

@@ -1406,7 +1406,6 @@ def lower_jaxpr_to_fun(
     MLIR func op
   """
   util.test_event("lower_jaxpr_to_fun", name)
-
   # The first dimension variable may be the platform index
   num_dim_vars = len(ctx.shape_poly_state.dim_vars)
   dim_var_avals = [core.ShapedArray((), dtypes.canonicalize_dtype(np.int64))] * num_dim_vars