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# mypy: allow-untyped-defs
"""
Provides functionality for compiling PyTorch's autograd (automatic differentiation) system.
This module implements compiled autograd, which traces and optimizes backward pass
computations at runtime. The key components are:
- AutogradCompilerInstance: Traces and compiles autograd graphs using FX
- Context managers (_enable/_disable): Control when compiled autograd is active
- Utility functions: Support graph manipulation, tensor operations, and hooks
Compiled autograd can significantly improve backward pass performance by removing
Python overhead and enabling additional optimizations. It works by capturing
backward computations into an FX graph that can be compiled and optimized,
while maintaining the same semantics as eager mode autograd.
"""
import contextlib
import functools
import itertools
import operator
import time
from collections import Counter, defaultdict
from typing import Optional, TYPE_CHECKING, Union
import torch
import torch.utils._pytree as pytree
from torch._dynamo.external_utils import (
call_accumulate_grad,
call_backward,
call_hook,
FakeCompiledAutogradEngine,
unwrap_maybe_dynamic_int,
)
from torch._dynamo.source import GetItemSource, LocalSource
from torch._dynamo.utils import (
counters,
get_chromium_event_logger,
lazy_format_graph_code,
set_locals_to_steal,
)
from torch._functorch._aot_autograd.runtime_wrappers import (
AutogradLazyBackwardCompileInfo,
CachedAutogradLazyBackwardCompileInfo,
)
from torch._guards import compile_context, CompileContext, CompileId
from torch._logging import getArtifactLogger, trace_structured
from torch._prims_common import clone_preserve_strides
from torch._subclasses import FakeTensorMode
from torch.fx import GraphModule
from torch.fx.experimental._backward_state import BackwardState
from torch.fx.experimental.proxy_tensor import (
decompose,
disable_autocast_cache,
disable_proxy_modes_tracing,
fetch_object_proxy,
ProxyTorchDispatchMode,
PythonKeyTracer,
track_tensor_tree,
)
from torch.fx.experimental.symbolic_shapes import DimDynamic, ShapeEnv
from torch.fx.traceback import preserve_node_meta, set_stack_trace
from torch.utils._ordered_set import OrderedSet
from torch.utils._traceback import CapturedTraceback
if TYPE_CHECKING:
from torch.fx.proxy import Proxy
TURN_OFF_MSG = """You can turn off compiled autograd by either:
1. Moving the unsupported autograd call outside of the torch.compile'd region.
2. Wrapping the unsupported autograd call in the torch._dynamo.compiled_autograd._disable() context manager.
3. Setting torch._dynamo.config.compiled_autograd=False for the torch.compile call containing the unsupported autograd call.
4. Setting torch._dynamo.config.compiled_autograd=False at the start of the program."""
compiled_autograd_log = getArtifactLogger(__name__, "compiled_autograd")
verbose_log = getArtifactLogger(__name__, "compiled_autograd_verbose")
def snapshot_verbose_logging_enabled():
return torch._logging._internal.log_state.is_artifact_enabled(
"compiled_autograd_verbose"
)
def snapshot_cudagraph_enabled():
return torch._inductor.config.triton.cudagraphs
def maybe_clone(x):
if x is not None:
return clone_preserve_strides(x)
return x
def extract_bw_module(CompiledFunction):
if isinstance(
CompiledFunction._lazy_backward_info, AutogradLazyBackwardCompileInfo
):
return CompiledFunction._lazy_backward_info.bw_module
elif isinstance(
CompiledFunction._lazy_backward_info, CachedAutogradLazyBackwardCompileInfo
):
with torch._subclasses.fake_tensor.unset_fake_temporarily():
return CompiledFunction._lazy_backward_info.bw_module_fn()
else:
raise AssertionError(
"Unexpected Lazy Backward Compilation Info Type. Please file an issue."
)
# Note: [Anomaly Mode Semantics in Compiled Autograd]
# In the eager autograd engine, anomaly mode is able to detect NaNs
# after each node. This is useful, because the executed code with
# and without anomaly mode are the same. So assuming determinism,
# a NaN in regular mode should also happen in anomaly mode.
#
# With torch.compile, following eager semantics would require inserting
# runtime asserts to check for NaNs, which could prevent some fusions.
# This results in different code being run with and without anomaly mode.
# So different semantics are needed, this implementation below will check
# for NaNs at the end of the autograd call, instead of after each node
class NaNChecker:
def __init__(self, accumulate_grad: bool):
self.accumulate_grad = accumulate_grad
self.params_indices: list[int] = []
self.params_to_check: dict[str, torch.Tensor] = {}
self.output_names: list[str] = []
def prep_with_graph(self, graph: torch.fx.Graph):
inputs_node = next(iter(graph.nodes))
acc_grad_nodes = graph.find_nodes(
op="call_function", target=call_accumulate_grad
)
output_nodes = graph.find_nodes(op="output")[0].args[0]
assert self.accumulate_grad == bool(
acc_grad_nodes
) and self.accumulate_grad == (not output_nodes)
for node in acc_grad_nodes:
param_node = node.args[0]
# AccumulateGrad always saves a reference to the param
# so Compiled Autograd will always lift the param and
# this should always be true
assert (
param_node.target == operator.getitem
and param_node.args[0] is inputs_node # type: ignore[possibly-undefined]
and isinstance(param_node.args[1], int)
)
self.params_indices.append(param_node.args[1])
self.output_names = [node.name for node in output_nodes]
def prep_with_inputs(self, inputs: tuple[torch.Tensor]):
if not self.accumulate_grad:
# Using .grad, nothing to prep
return
# Using .backward, we must check existing grads on params if any
for idx in self.params_indices:
grad = inputs[idx].grad
if grad is not None:
assert not torch.isnan(grad).any(), (
f"Compiled autograd running under anomaly mode with inputs[{idx}] already "
"having NaN gradient. This is not supported. {TURN_OFF_MSG}"
)
self.params_to_check[f"inputs[{idx}]"] = inputs[idx]
def check(self, out: tuple[torch.Tensor]):
if self.accumulate_grad:
# Using .backward, graph outputs are empty
assert not out
nan_params: list[str] = []
for inputs_str, param in self.params_to_check.items():
assert param.grad is not None # not true for autograd.grad
if torch.isnan(param.grad).any():
nan_params.append(inputs_str)
if nan_params:
raise RuntimeError(
f"Compiled Autograd returned NaN gradients for parameters: {','.join(nan_params)}."
)
else:
# Using .grad, graph outputs are grads
nan_grads: list[str] = []
for i, grad in enumerate(out):
if torch.isnan(grad).any():
nan_grads.append(self.output_names[i])
if nan_grads:
raise RuntimeError(
f"Compiled Autograd returned NaN gradients for output nodes: {','.join(nan_grads)}."
)
# We lazily bind "functional backward" variants for PyTorch built-in autograd
# nodes to this class. Example: torch._dynamo.compiled_autograd.ops.MulBackward0
# Each "functional backward" is bound the first time the node's apply_with_saved
# function is called. It's possible to avoid lazy binding and instead bind
# all of this upfront (perhaps at import time) via codegen changes.
class OpNamespace:
def __init__(self):
self.custom_function_name_counter: Counter[str] = Counter()
def add(self, name, fn, is_custom_function, is_traceable):
if is_custom_function:
name = "CppNode" + name
count = self.custom_function_name_counter[name]
self.custom_function_name_counter[name] += 1
name = f"{name}{count}"
assert not hasattr(self, name)
result = Op(name, fn, is_custom_function)
if is_traceable:
setattr(self, name, torch._dynamo.allow_in_graph(result))
else:
# C++ autograd function was not marked as traceable
# Dynamo can't dry run it at compile time, so must fallback to eager
@torch._dynamo.disable
def run_non_traceable_cpp_in_eager(*args, **kwargs):
return result(*args, **kwargs)
setattr(self, name, run_non_traceable_cpp_in_eager)
return name
def get(self, name):
return getattr(self, name)
class Op:
def __init__(self, name, fn, is_custom_function):
self.fn = fn
self.is_custom_function = is_custom_function
self.__name__ = name
self.__module__ = "torch._dynamo.compiled_autograd.ops"
def __call__(self, *args, **kwargs):
return self.fn(*args, **kwargs)
def __repr__(self):
return self.__module__ + "." + self.__name__
ops = OpNamespace()
_graph_placeholders = ["inputs", "sizes", "scalars", "hooks", "packed_data"]
_impure_targets = OrderedSet(
[
call_hook,
call_backward,
FakeCompiledAutogradEngine._exec_final_callbacks_stub,
call_accumulate_grad,
]
)
COMPILE_COUNTER = itertools.count()
def make_compile_context(compiled_autograd_id):
return compile_context(
CompileContext(
CompileId(
compiled_autograd_id=compiled_autograd_id,
frame_id=None,
frame_compile_id=None,
)
)
)
class AutogradCompilerInstance:
def __init__(self, compiler_fn) -> None:
self.compiler_fn = compiler_fn
self.stack = contextlib.ExitStack()
self.close = self.stack.close
self.shape_env = ShapeEnv()
self.fake_tensor_mode = FakeTensorMode(
allow_fallback_kernels=True,
allow_non_fake_inputs=True,
shape_env=self.shape_env,
)
self.fx_tracer = PythonKeyTracer()
self.proxy_mode = ProxyTorchDispatchMode(self.fx_tracer, "symbolic")
self.hooks_proxy: Optional[Proxy] = None
def wrap_fake(self, x, source):
assert isinstance(x, torch.Tensor)
return self.fake_tensor_mode.from_tensor(x, source=source)
@staticmethod
def source(name, idx) -> GetItemSource:
return GetItemSource(LocalSource(name), idx)
def begin_capture(
self,
inputs: list[torch.Tensor],
sizes: list[int],
scalars: list[Union[int, float]],
origins: list[list[tuple[int, str]]],
accumulate_grad: bool,
check_nans: bool,
):
counters["compiled_autograd"]["captures"] += 1
self.id = next(COMPILE_COUNTER)
self.aot_id_counter: dict[int, int] = defaultdict(int)
self.compile_context = make_compile_context(self.id)
self.compile_context.__enter__()
self.nan_checker = NaNChecker(accumulate_grad) if check_nans else None
self.start_time_ns = time.time_ns()
get_chromium_event_logger().log_event_start(
"compiled_autograd",
self.start_time_ns,
{"graph_id": self.id},
log_pt2_compile_event=True,
)
self.fx_tracer.root = torch.nn.Module()
self.fx_tracer.graph = torch.fx.Graph(tracer_cls=PythonKeyTracer)
self.fx_tracer.tensor_attrs = {}
self.symnode_proxy_lookup = {}
(
args_proxy,
self.sizes_proxy,
self.scalars_proxy,
self.hooks_proxy,
self.packed_data_proxy,
) = (
self.fx_tracer.create_proxy("placeholder", name, (), {})
for name in _graph_placeholders
)
self.stack.enter_context(preserve_node_meta())
inputs_origins, sizes_origins, scalars_origins = origins
# tensor inputs to fake tensors
x = inputs[0] # mypy will complain about unbound x
try:
for idx, x in enumerate(inputs):
inputs[idx] = self.wrap_fake(x, self.source("inputs", idx))
except Exception as e:
raise NotImplementedError(
f"Found tensor of type {type(x)}, which is not supported by FakeTensorMode. {TURN_OFF_MSG}"
) from e
self.bind_objects_to_proxies(inputs, args_proxy, inputs_origins)
# size inputs to symints
sizes = [
self.shape_env.create_unspecified_symint_and_symbol(
val,
self.source("sizes", idx),
DimDynamic.DYNAMIC,
)
for idx, val in enumerate(sizes)
]
# We want to mark every size as dynamic, but since there's no way to
# mark a primitive `int` as dynamic, we need to wrap it in a tensor.
# In the graph, we unwrap it with `unwrap_maybe_dynamic_int` back into a primitive.
proxies = [self.sizes_proxy[i] for i in range(len(sizes))] # type: ignore[index]
for i, symint in enumerate(sizes):
proxies[i] = self.fx_tracer.create_proxy(
"call_function",
unwrap_maybe_dynamic_int,
(proxies[i],),
{},
)
self.symnode_proxy_lookup[symint.node] = proxies[i]
proxies = self.bind_objects_to_proxies(sizes, proxies, sizes_origins)
for idx, val in enumerate(scalars):
source = self.source("scalars", idx)
if isinstance(val, int):
scalars[idx] = self.shape_env.create_unspecified_symint_and_symbol(
val,
source,
DimDynamic.DYNAMIC,
)
elif isinstance(val, float):
scalars[idx] = self.shape_env.create_symfloatnode(
self.shape_env.create_unspecified_symbol(
val,
source=source,
dynamic_dim=DimDynamic.DYNAMIC,
),
hint=val,
source=source,
)
else:
raise AssertionError("Unexpected scalar type: ", type(val))
self.bind_objects_to_proxies(scalars, self.scalars_proxy, scalars_origins)
for i, symval in enumerate(scalars):
self.symnode_proxy_lookup[symval.node] = self.scalars_proxy[i] # type: ignore[union-attr]
# TODO(jansel): are all these modes needed?
self.stack.enter_context(decompose({}))
self.stack.enter_context(self.fake_tensor_mode)
self.stack.enter_context(self.proxy_mode)
self.stack.enter_context(disable_autocast_cache())
# Needed to make sure we don't accidentally specialize any symbols
assert self.fake_tensor_mode.shape_env is not None
env = self.fake_tensor_mode.shape_env
self.stack.enter_context(
torch.fx.experimental.symbolic_shapes._suppress_guards(env)
)
return (
str(CompileContext.current_compile_id()),
inputs,
sizes,
scalars,
)
def log_compile_reasons(
self,
compile_reasons: list[str],
):
assert compile_reasons
trace_structured(
"artifact",
metadata_fn=lambda: {
"name": "compiled_autograd_compile_reasons",
"encoding": "json",
},
payload_fn=lambda: compile_reasons,
)
def proxy_call_aot_backward(
self,
pinputs,
psaved_tensors,
saved_tensors,
pctx,
ctx,
maybe_backward_state_idx,
):
# The AOTBackward call consists of three things: the prologue, the
# backward graph, and the epilogue.
# Our strategy is:
# - allow_in_graph the prologue (in the CA graph and Dynamo graph),
# - copy-paste the backward graph into the CA graph so that CA passes and Dynamo can see it
# - trace directly through the epilogue. Anything that gets baked in is
# constant metadata (for example, metadata about the number of outputs, or removing
# RNG arguments or effect tokens).
# If Dynamo graph capture were better, then we could add a node for the prologue
# into the CA graph and have Dynamo trace into it.
psymints = [self.to_proxy(e) for e in ctx._get_compiled_autograd_symints()]
# NOTE: we should only close over constants
CompiledFunction = ctx._forward_cls
bw_module = extract_bw_module(CompiledFunction)
metadata = CompiledFunction.metadata
maybe_subclass_metadata = CompiledFunction.maybe_subclass_metadata
aot_id = CompiledFunction._aot_id
del CompiledFunction
if torch.is_grad_enabled():
for output_alias_info in metadata.output_info:
if output_alias_info.requires_grad:
raise RuntimeError(
"torch.compile does not currently support higher order gradients."
)
@torch._dynamo.allow_in_graph # type: ignore[misc]
def call_aot_bwd_prologue(ctx_saved_tensors, ctx_symints, *flat_args):
out = torch._functorch._aot_autograd.runtime_wrappers._backward_prologue_functional(
ctx_saved_tensors,
ctx_symints,
metadata,
maybe_subclass_metadata,
*flat_args,
)
return out
pgrads = self.fx_tracer.create_proxy(
kind="call_function",
target=call_aot_bwd_prologue,
args=(
psaved_tensors,
psymints,
*pinputs,
),
kwargs={},
)
pbackward_state = None
if maybe_backward_state_idx is not None:
pbackward_state = self.hooks_proxy[maybe_backward_state_idx] # type: ignore[index]
# Copy-paste the AOT backward graph into the compiled autograd graph
def copy_paste_aot_backward_graph():
def num_inputs(graph):
num_args = 0
for node in graph.nodes:
if node.op == "placeholder":
num_args += 1
continue
else:
break
return num_args
# set up the proxy inputs to bw_module
# the calling convention is: [*symints, *args (primals and tangents), backward_state]
num_args = num_inputs(bw_module.graph)
pall_args = [
pgrads[i] for i in range(num_args - int(pbackward_state is not None))
]
# replace the symints with our symints
symints = ctx._get_compiled_autograd_symints()
assert len(symints) == len(ctx.symints)
psymints = [self.to_proxy(e) for e in symints]
pall_args[: len(symints)] = psymints
# Add backward_state
if pbackward_state is not None:
pall_args.append(pbackward_state)
# run over all nodes of the aot_backward graph.
# copy and paste them all into the compiled autograd graph.
args_idx = 0
value_remap = {}
poutputs: Optional[list[torch.fx.Proxy]] = None
# names of nodes must appear only once in the fx.Graph
# dedup AOT backwards that appear multiple times
deduped_aot_id = str(aot_id)
if self.aot_id_counter[aot_id]:
deduped_aot_id += f"_{self.aot_id_counter[aot_id]}"
self.aot_id_counter[aot_id] += 1
def make_unique(node_name):
# make it both informative and unique
return f"aot{deduped_aot_id}_{node_name}"
for node in bw_module.graph.nodes:
if node.op == "placeholder":
ph = pall_args[args_idx].node
ph.name = make_unique(node.name)
value_remap[node] = ph
args_idx += 1
elif node.op == "output":
assert len(node.args) == 1
poutputs = [
torch.fx.Proxy(value_remap[n], self.fx_tracer)
if isinstance(n, torch.fx.Node)
else n
for n in node.args[0]
]
elif node.op == "get_attr":
name = node.target
qualname = self.fx_tracer.get_fresh_qualname(name)
setattr(self.fx_tracer.root, qualname, getattr(bw_module, name))
result = self.fx_tracer.create_node("get_attr", qualname, (), {})
result.name = make_unique(node.name)
value_remap[node] = result
elif node.op == "call_function":
if node.target == torch.ops.aten.view.default:
# this aot bwd graph is being lazily compiled
# we must manually apply the view_to_reshape post grad pass
# since it was already applied to the aot fwd, and baked into the gradients
node.target = torch.ops.aten.reshape.default
result = self.fx_tracer.graph.node_copy(
node, lambda n: value_remap[n]
)
result.name = make_unique(node.name)
value_remap[node] = result
elif node.op == "call_module":
name = node.target
qualname = self.fx_tracer.get_fresh_qualname(name)
setattr(self.fx_tracer.root, qualname, getattr(bw_module, name))
result = self.fx_tracer.graph.node_copy(
node, lambda n: value_remap[n]
)
result.target = qualname
value_remap[node] = result
else:
raise AssertionError("shouldn't get here")
assert poutputs is not None
# In general we don't know what the shapes of the outputs are, so allocate
# some dummy sizes for them.
def dummy():
with disable_proxy_modes_tracing():
return torch.zeros(0, 0, 0, 0, 123)
outputs = [
dummy() if isinstance(o, torch.fx.Proxy) else o for o in poutputs
]
self.bind_objects_to_proxies(outputs, poutputs)
return outputs
outputs = copy_paste_aot_backward_graph()
def proxy_subclass_constructor(subclass_meta, is_runtime, unwrapped_args):
@torch._dynamo.allow_in_graph
def make_subclass(*unwrapped_args):
return subclass_meta.creation_fn(unwrapped_args, is_runtime=is_runtime)
punwrapped_args = pytree.tree_map(self.to_proxy, unwrapped_args)
poutput = self.fx_tracer.create_proxy(
kind="call_function",
target=make_subclass,
args=tuple(punwrapped_args),
kwargs={},
)
output = self.allocate_dummy()
self.bind_objects_to_proxies([output], [poutput])
return output
results = torch._functorch._aot_autograd.runtime_wrappers._backward_epilogue_functional(
metadata,
maybe_subclass_metadata,
outputs,
make_subclass_override=proxy_subclass_constructor,
)
presults = pytree.tree_map(self.to_proxy, results)
return presults
def proxy_call_backward(
self,
inputs,
output_metadatas,
saved_tensors,
backward_idx: int,
ctx: torch.autograd.function.BackwardCFunction,
maybe_backward_state_idx: Optional[int],
):
assert self.hooks_proxy is not None
pctx = self.hooks_proxy[backward_idx] # type: ignore[index]
pinputs = self.to_proxy(inputs)
psaved_tensors = self.to_proxy(saved_tensors)
if hasattr(ctx._forward_cls, "_aot_id"): # type: ignore[attr-defined]
# AOT backward
proxies = self.proxy_call_aot_backward(
pinputs,
psaved_tensors,
saved_tensors,
pctx,
ctx,
maybe_backward_state_idx,
)
else:
proxies = self.fx_tracer.create_proxy(
kind="call_function",
target=call_backward,
args=(
pctx,
psaved_tensors,
*pinputs,
),
kwargs={},
)
assert proxies is not None
with disable_proxy_modes_tracing():
# create fake Tensors
grad_ins: list[Optional[torch.Tensor]] = []
for idx, output_metadata in enumerate(output_metadatas):
if output_metadata is None or proxies[idx] is None:
grad_ins.append(None)
continue
layout, device, dtype, size = output_metadata
grad_ins.append(
torch.empty(size=size, dtype=dtype, layout=layout, device=device)
)
self.bind_objects_to_proxies(grad_ins, proxies)
return tuple(grad_ins)
def call_copy_slices_prologue(
self,
inputs,
base_sizes,
base_strides,
base_storage_offset,
view_sizes,
view_strides,
view_storage_offset,
):
args = (
inputs,
self.to_proxy(base_sizes),
self.to_proxy(base_strides),
self.to_proxy(base_storage_offset),
self.to_proxy(view_sizes),
self.to_proxy(view_strides),
self.to_proxy(view_storage_offset),
)
return self.proxy_call(copy_slices_prologue, args, [None] * 3)
def call_copy_slices_epilogue(self, needs_input_grad, result, res, grad_slice):
return self.proxy_call(
copy_slices_epilogue,
(needs_input_grad, result, res, grad_slice),
[None] * len(needs_input_grad),
)
def allocate_dummy(self):
with disable_proxy_modes_tracing():
# Weird quantity so it's easy to grep
return torch.zeros([0, 123456789])
def bind_function(self, fn_name, fn, is_custom_function, is_traceable):
"""Binds ops.fn_name = fn"""
return ops.add(fn_name, fn, is_custom_function, is_traceable)
def apply_functional(self, fn_name, grads, args, output_metadata):
"""Proxies a call to ops.fn_name(grads, *args) into the graph"""
op = ops.get(fn_name)
return self.proxy_call(op, (grads, *args), output_metadata)
def proxy_call(self, fn, args, output_metadata):
"""Proxies a call to fn(*args) into the graph"""
flat_args, _ = pytree.tree_flatten(args)
proxy_args = pytree.tree_map(lambda e: self.to_proxy(e), args)
proxy_out = self.fx_tracer.create_proxy(
"call_function", fn, args=proxy_args, kwargs={}
)
result = [self.allocate_dummy() for _ in output_metadata]
self.bind_objects_to_proxies(result, [proxy_out[i] for i in range(len(result))])
return result
def validate_outputs(self, _, outputs, args, output_metadata):
"""Proxies a call to ops.validate_outputs(outputs, *args) into the graph"""
op = ops.get("validate_outputs")
proxy_args = pytree.tree_map(self.to_proxy, (outputs, *args))
new_proxy_outputs = self.fx_tracer.create_proxy(
"call_function", op, args=proxy_args, kwargs={}
)
assert len(output_metadata) == len(outputs)
self.bind_objects_to_proxies(outputs, new_proxy_outputs)
return outputs
def accumulate(self, old_var, new_var):
old_var_proxy = self.to_proxy(old_var)
new_var_proxy = self.to_proxy(new_var)
proxy_out = self.fx_tracer.create_proxy(
"call_function", torch.add, args=(old_var_proxy, new_var_proxy), kwargs={}
)
result = self.allocate_dummy()
self.bind_objects_to_proxies([result], [proxy_out])
return result
def accumulate_grad(self, variable, grad, has_post_hooks):
self.fx_tracer.create_proxy(
"call_function",
call_accumulate_grad,
args=(
self.to_proxy(variable),
self.to_proxy(grad),
has_post_hooks,
),
kwargs={},
)
def proxy_call_hook(self, hook, *args, **kwargs):
return self.fx_tracer.create_proxy(
"call_function",
call_hook,
(
hook,
*[self.to_proxy(x) for x in args],
),
kwargs,
)
def unpack_hook(self, hook_id, data_id):
assert self.hooks_proxy is not None
hook = self.hooks_proxy[hook_id] # type: ignore[index]
data = self.packed_data_proxy[data_id] # type: ignore[index]
proxy = self.proxy_call_hook(
hook,
data,
hook_type="unpack_hook",
)
out = self.allocate_dummy()
self.bind_objects_to_proxies([out], [proxy])
return out
def tensor_pre_hook(self, inputs, hook_id, i: int):
assert self.hooks_proxy is not None
hook = self.hooks_proxy[hook_id] # type: ignore[index]
proxy = self.proxy_call_hook(
hook,
inputs[i],
hook_type="tensor_pre_hook",
)
with disable_proxy_modes_tracing():
inputs[i] = maybe_clone(inputs[i])
self.bind_objects_to_proxies([inputs[i]], [proxy])
return inputs
def cpp_tensor_pre_hook(self, inputs: list[torch.Tensor], hook_id: int, i: int):
proxy = self.fx_tracer.create_proxy(
"call_function",
torch._C._dynamo.compiled_autograd.call_cpp_tensor_pre_hooks,
(hook_id, self.to_proxy(inputs[i])),
{},
)
with disable_proxy_modes_tracing():
inputs[i] = maybe_clone(inputs[i])
self.bind_objects_to_proxies([inputs[i]], [proxy])
return inputs
def pre_hook(self, inputs, hook_id):
assert self.hooks_proxy is not None
hook = self.hooks_proxy[hook_id] # type: ignore[index]
proxies = self.proxy_call_hook(
hook,
inputs,
hook_type="pre_hook",
)
with disable_proxy_modes_tracing():
inputs = [maybe_clone(x) for x in inputs]
self.bind_objects_to_proxies(inputs, proxies)
return inputs
def post_hook(self, outputs, inputs, hook_id):
assert self.hooks_proxy is not None
hook = self.hooks_proxy[hook_id] # type: ignore[index]
proxies = self.proxy_call_hook(
hook,
outputs,
inputs,
hook_type="post_hook",
)
with disable_proxy_modes_tracing():
outputs = [maybe_clone(x) for x in outputs]
self.bind_objects_to_proxies(outputs, proxies)
return outputs
def post_acc_grad_hook(self, input, hook_id):
assert isinstance(input, torch.Tensor)
assert self.hooks_proxy is not None
hook = self.hooks_proxy[hook_id] # type: ignore[index]
proxy = self.proxy_call_hook(
hook,
input,
hook_type="post_acc_grad_hook",
)
with disable_proxy_modes_tracing():
input = [maybe_clone(input)]
self.bind_objects_to_proxies(input, [proxy])
return input
# Note: [Compiled autograd and cudagraphs]
# Eager autograd backward implements scalars as 0-dim tensors, see DivBackward0::other_.
# When compiled autograd traces those nodes, it lifts the scalar tensors, resulting in a graph
# with some cpu 0-dim tensor inputs. To prevent the entire graph from skipping cudagraph, we move the
# scalars tensors to cuda. This works because ATen/prims ops will accept cuda 0-dim tensors too.
def move_graph_nodes_to_cuda(self, graph) -> list[int]:
to_move: dict[int, torch.fx.Node] = {}
has_cuda_inputs = False
nodes = list(graph.nodes)
assert nodes[0].target == "inputs"
inputs = nodes[0]
inputs_users = list(inputs.users.keys())
# input access nodes should immediately follow placeholder nodes
first_getitem_idx = len(_graph_placeholders)
assert nodes[first_getitem_idx] == inputs_users[0]
last_getitem_idx = first_getitem_idx + len(inputs_users) - 1
assert nodes[last_getitem_idx] == inputs_users[-1]
# getitem nodes on inputs
for i, node in enumerate(inputs_users):
if not has_cuda_inputs and node.meta["val"].device.type == "cuda":
has_cuda_inputs = True
continue
is_cpu = node.meta["val"].device.type == "cpu"
is_scalar = len(node.meta["val"].size()) == 0
if is_cpu and is_scalar:
node_users = list(node.users.keys())
# We can only move the cpu scalar if it is not exposed to user code.
if all(
(
isinstance(user.target, torch._ops.OpOverload)
and user.target.namespace in ("prims", "aten")
)
or (
isinstance(user.target, Op)
and not user.target.is_custom_function
)
for user in node_users
):
# all users are prims/aten, can move safely
to_move[i] = node
# only move cpu scalars to cuda if there were cuda activations in this graph,
# this is to handle the case where cudagraphs is enabled on a cpu-only graph
if has_cuda_inputs:
for node in to_move.values():
verbose_log.debug("Moving node %s from cpu to cuda", node)
node.meta["val"] = node.meta["val"].cuda()
# return runtime indices we need to move to cuda
return list(to_move.keys())
return []
def is_sym_node(self, node):
return (
isinstance(node, torch.fx.Node)
and node.op == "call_function"
and node.target
in [torch.ops.aten.sym_size.int, torch.ops.aten.sym_numel.default]
)
def dce(self):
# Most of these removed nodes would have been removed during Dynamo and AOTDispatch
# Remove some of these nodes earlier to improve compilation speed
# Dynamo guards will error instead of creating aliasing guards unless we unpack them in the graph
unpack_nodes: OrderedSet[torch.fx.Node] = OrderedSet()
for i, node in enumerate(self.fx_tracer.graph.find_nodes(op="placeholder")):
unpack_nodes.update(node.users.keys())
assert i == len(_graph_placeholders) - 1
def is_impure(node):
if node in unpack_nodes or (
node.op == "call_function" and node.target in _impure_targets
):
return True
return node.is_impure()
before = len(self.fx_tracer.graph.nodes)
self.fx_tracer.graph.eliminate_dead_code(is_impure)
after = len(self.fx_tracer.graph.nodes)
verbose_log.debug("DCE removed %d nodes", before - after)
def remove_unused_sizes(self):
used_sizes = []
unused_sizes = []
# seek placeholder, should be at nodes[1]
it = iter(self.fx_tracer.graph.nodes)
next(it)
sizes_node = next(it)
assert sizes_node.name == "sizes"
for getitem_node in sizes_node.users.keys():
assert getitem_node.target == operator.getitem
if getitem_node.users:
used_sizes.append(getitem_node)
else:
# remove from the graph
unused_sizes.append(getitem_node)
used_sizes_idx: set[int] = set()
for used in used_sizes:
assert isinstance(used.args, tuple)
assert used.args[0] == sizes_node
assert isinstance(used.args[1], int)
next_size_idx = len(used_sizes_idx)
# used later reindex the runtime sizes arg
used_sizes_idx.add(used.args[1])
# reindex the graph
used.args = (used.args[0], next_size_idx)
for unused in unused_sizes:
self.fx_tracer.graph.erase_node(unused)
return used_sizes_idx
def create_graph_module(self, id):
return GraphModule(self.fx_tracer.root, self.fx_tracer.graph, id)
def end_capture(self, outputs):
self.fx_tracer.create_proxy(
"call_function",
FakeCompiledAutogradEngine._exec_final_callbacks_stub,
(),
{},
)
self.stack.close()
self.fx_tracer.create_node(
"output",
"output",
(self.fx_tracer.create_arg(self.to_proxy(outputs)),),
{},
)
runtime_inputs_to_move: list[int] = []
if snapshot_cudagraph_enabled():
runtime_inputs_to_move = self.move_graph_nodes_to_cuda(self.fx_tracer.graph)
# We traced using dummy tensors. Delete all the metadata of the dummy tensors.
# It's probably better to refactor this class to use a different tracer
# than the make_fx tracer, but that is a larger change.
for node in self.fx_tracer.graph.nodes:
for field in ["tensor_meta", "example_value", "val"]:
if field in node.meta:
del node.meta[field]
trace_structured(
"artifact",
metadata_fn=lambda: {
"name": "compiled_autograd_graph_pre_reordering",
"encoding": "string",
},
payload_fn=lambda: GraphModule(
self.fx_tracer.root,
self.fx_tracer.graph,
f"CompiledAutograd{self.id}PreReordering",
).print_readable(print_output=False),
)
self.delay_unpack_hook_nodes()
self.reorder_tensor_pre_hook_nodes()
self.reorder_pre_hook_nodes_to_schedule_asap()
self.reorder_accumulate_grad_nodes()
self.reorder_pre_hook_nodes_to_mimic_eager()
self.reorder_post_acc_grad_hook_nodes()
self.reorder_post_hook_nodes()
# TODO(yf225): work around: remove dead codes like `sym_size` and `sym_numel` which are not used downstream. e.g.
# ```
# sym_numel_default = torch.ops.aten.sym_numel.default(sum_109); sum_109 = None
# eq_115 = 16 == sym_numel_default; sym_numel_default = eq_115 = None
# sym_size_int_39 = torch.ops.aten.sym_size.int(getitem_112, 1); getitem_112 = None
# eq_116 = 16 == sym_size_int_39; eq_116 = None
# eq_117 = 16 == sym_size_int_39; sym_size_int_39 = eq_117 = None
# ```
# Proper fix is Richard's Python compiled autograd effort which will avoid calling make_fx and
# should prevent these ops from going into the CA graph.
self.dce()
if self.nan_checker:
self.nan_checker.prep_with_graph(self.fx_tracer.graph)
# keep only sizes that are actually used in the graph
used_sizes_idx = self.remove_unused_sizes()
graph = self.create_graph_module(f"CompiledAutograd{self.id}")
set_locals_to_steal(graph, ["inputs"])
lazy_graph_code = lazy_format_graph_code(
"Compiled autograd graph",
graph,
include_device=True,
include_stride=True,
colored=True,
)
compiled_autograd_log.info("%s", lazy_graph_code)
verbose_log.debug("%s", lazy_graph_code)
trace_structured(
"compiled_autograd_graph",
payload_fn=lambda: graph.print_readable(print_output=False),
)
def runtime_wrapper(compiled_fn, inputs, sizes, scalars, hooks, packed_inputs):
global in_compiled_autograd_region
try:
in_compiled_autograd_region = True
if self.nan_checker:
self.nan_checker.prep_with_inputs(inputs)
filtered_sizes = []
for idx, integer in enumerate(sizes):
if idx in used_sizes_idx:
# can't create negative size
if integer > 0:
filtered_sizes.append(torch.empty(0, integer))
torch._dynamo.maybe_mark_dynamic(filtered_sizes[-1], 1)
else:
filtered_sizes.append(integer)
for i in runtime_inputs_to_move:
inputs[i] = inputs[i].pin_memory().cuda(non_blocking=True)
with _disable(), make_compile_context(self.id):
out = compiled_fn(
inputs, filtered_sizes, scalars, hooks, packed_inputs
)
if self.nan_checker:
self.nan_checker.check(out)
return out
finally:
in_compiled_autograd_region = False
get_chromium_event_logger().log_event_end(
"compiled_autograd",
time.time_ns(),
{"graph_id": self.id},
self.start_time_ns,
log_pt2_compile_event=True,
)
self.compile_context.__exit__(None, None, None)
return runtime_wrapper, self.compiler_fn(graph)
@staticmethod
def get_all_nodes(args):
# filter out non-Node args, like None
nodes = [n for n in args if type(n) is torch.fx.Node]
return nodes
@staticmethod
def is_placeholder(node):
if node.op == "placeholder" or (
node.op == "call_function"
and node.target == operator.getitem
and node.args[0].op == "placeholder"
):
return True
return False
def reorder_accumulate_grad_nodes(self):
"""
Usage of AOTAutograd causes all the accumulate_grad_ nodes to get pushed to the end of
the graph. This differs from eager mode, which schedules them as soon as possible. This
pass attempts to reorder the graph to mimic eager behavior.
"""
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_accumulate_grad
):
param_node, grad_node = node.args[0], node.args[1]
getitem_node = None
if grad_node.target == operator.getitem:
getitem_node = grad_node
grad_node = getitem_node.args[0]
arg = max([param_node, grad_node]) # last arg
if arg is not node.prev and not self.is_placeholder(arg):
arg.append(node)
if getitem_node is not None:
arg.append(getitem_node)
def delay_unpack_hook_nodes(self):
"""
We can delay unpack hooks until they are needed, even later than in the eager autograd engine.
"""
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_hook
):
if node.kwargs.get("hook_type", None) != "unpack_hook":
continue
first_user = min(node.users)
first_user.prepend(node)
def reorder_tensor_pre_hook_nodes(self):
"""
Usage of AOTAutograd causes all the tensor_pre_hook nodes to get pushed
to the end of the graph. This differs from eager mode, which schedules
them as soon as possible. This pass attempts to reorder the graph to
mimic eager behavior.
"""
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_hook
):
if node.kwargs.get("hook_type", None) != "tensor_pre_hook":
continue
getitem_node = node.args[0]
input_node = node.args[1] # tensor_pre_hook handle only one grad tensor
if input_node is not node.prev and not self.is_placeholder(input_node):
input_node.append(getitem_node)
getitem_node.append(node)
def reorder_pre_hook_nodes_to_schedule_asap(self):
"""
In this function, we schedule the pre hooks as soon as possible. This
does not match eager behavior (schedule pre hook right before its
registered node), but it can make acc grad be scheduled properly when
the pre hooks are registered to them. After reordering acc grad node, we
will reorder the pre hooks again to mimic eager behavior.
"""
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_hook
):
if node.kwargs.get("hook_type", None) != "pre_hook":
continue
getitem_node = node.args[0]
# pre_hook handle a tuple of grad tensors
input_nodes = self.get_all_nodes(node.args[1])
to_remove = []
to_append = []
hook_block = [node] # contain the hook and hook args getitem
for n in input_nodes:
if n.op == "call_function" and n.target == operator.getitem:
to_append.append(n.args[0])
to_remove.append(n)
hook_block.append(n)
for a, b in zip(to_remove, to_append):
input_nodes.remove(a)
input_nodes.append(b)
arg = max(input_nodes) # last input
if arg is not node.prev and not self.is_placeholder(arg):
arg.append(getitem_node)
for n in hook_block:
getitem_node.append(n)
def reorder_pre_hook_nodes_to_mimic_eager(self):
"""
Usage of AOTAutograd causes all the pre_hook nodes to get pushed to the
end of the graph. This differs from eager mode, which schedules them
right before their registered node execution. This pass attempts to
reorder the graph to mimic eager behavior.
"""
pre_hooks = []
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_hook
):
if node.kwargs.get("hook_type", None) != "pre_hook":
continue
pre_hooks.append(node)
for node in reversed(pre_hooks):
hook_getitem_node = node.args[0]
users = list(node.users.keys())
if len(users) == 0:
continue
# users are all getitem ops and they are used by same registered node
assert all(
user.op == "call_function" and user.target == operator.getitem
for user in users
)
registered_node = next(iter(users[0].users.keys()))
if registered_node is not node.next:
registered_node.prepend(hook_getitem_node)
registered_node.prepend(node)
for getitem in users:
registered_node.prepend(getitem)
def reorder_post_acc_grad_hook_nodes(self):
"""
Usage of AOTAutograd causes all the post_acc_grad_hook nodes to get
pushed to the end of the graph. This differs from eager mode, which
schedules them as soon as possible. This pass attempts to reorder the
graph to mimic eager behavior.
"""
post_acc_grad_hooks = []
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_hook
):
if node.kwargs.get("hook_type", None) != "post_acc_grad_hook":
continue
post_acc_grad_hooks.append(node)
# nodes in post_acc_grad_hooks are in topo order. For hooks registered
# to same node, we should keep their relative order
for node in reversed(post_acc_grad_hooks):
getitem_node = node.args[0]
param_node = node.args[1] # post_acc_grad_hook handle one param
# find the corresponding acc_grad node
acc_grad_node = None
for n in list(param_node.users.keys()):
if n.op == "call_function" and n.target == call_accumulate_grad:
acc_grad_node = n
break
assert acc_grad_node is not None, (
"post_acc_grad_hook must have corresponding acc grad node"
)
# append post_acc_grad_hook after acc_grad node
acc_grad_node.append(getitem_node)
getitem_node.append(node)
def reorder_post_hook_nodes(self):
"""
Usage of AOTAutograd causes all the post_hook nodes to get pushed to the
end of the graph. This differs from eager mode, which schedules them as
soon as possible. This pass attempts to reorder the graph to mimic eager
behavior.
"""
post_hooks = []
for node in self.fx_tracer.graph.find_nodes(
op="call_function", target=call_hook
):
if node.kwargs.get("hook_type", None) != "post_hook":
continue
post_hooks.append(node)
for node in reversed(post_hooks):
getitem_node = node.args[0]
output_nodes = node.args[1]
input_nodes = node.args[2]
if len(output_nodes) > 0:
continue
input_nodes_and_users = []
input_nodes_and_users.extend(list(input_nodes))
for input_node in input_nodes:
input_nodes_and_users.extend(
user
for user in list(input_node.users.keys())
if not (
user.op == "call_function"
and user.target == call_hook
and node.kwargs.get("hook_type", None) == "post_hook"
)
)
arg = max(input_nodes_and_users) # last input users
if arg.op == "call_function" and arg.target == call_accumulate_grad:
param_node = arg.args[0]
post_acc_grad_hook_node = None
for n in list(param_node.users.keys()):
if (
n.op == "call_function"
and n.target == call_hook
and n.kwargs.get("hook_type", None) == "post_acc_grad_hook"
):
post_acc_grad_hook_node = n
if post_acc_grad_hook_node is not None:
post_acc_grad_hook_node.append(getitem_node)
getitem_node.append(node)
continue
if arg is not node.prev and not self.is_placeholder(arg):
arg.append(getitem_node)
getitem_node.append(node)
def to_proxy(self, t):
if t is None:
return None
if isinstance(t, list):
return [self.to_proxy(x) for x in t]
if isinstance(t, tuple):
return tuple(self.to_proxy(x) for x in t)
if isinstance(t, (torch.SymInt, torch.SymFloat)):
return self.symnode_proxy_lookup[t.node]
if not isinstance(t, torch.Tensor):
# constant types like device, dtype, str
return t
proxy_tensor = fetch_object_proxy(self.fx_tracer, t)
assert isinstance(proxy_tensor, torch.fx.experimental.proxy_tensor._ProxyTensor)
return proxy_tensor.proxy
def bind_objects_to_proxies(
self, objects, proxies, origins: Optional[list[tuple[int, str]]] = None
):
if isinstance(proxies, torch.fx.Proxy):
if origins:
assert len(origins) == len(objects)
bound_proxies = []
for i in range(len(objects)):
nodecall_index, node_name = origins[i]
self.set_node_origin(node_name, nodecall_index, None)
bound_proxies.append(proxies[i]) # type: ignore[index]
proxies = bound_proxies
else:
proxies = [proxies[i] for i in range(len(objects))] # type: ignore[index]
assert len(objects) == len(proxies)
track_tensor_tree(objects, proxies, constant=None, tracer=self.fx_tracer)
return proxies
def bind_backward_state(self, index: int):
assert self.hooks_proxy is not None
proxy = self.hooks_proxy[index] # type: ignore[index]
bw_state = BackwardState()
track_tensor_tree(bw_state, proxy, constant=None, tracer=self.fx_tracer)
return bw_state
def set_node_origin(
self,
node_name: str,
nodecall_index: int,
pyobj: Optional[torch.autograd.Function],
):
maybe_aot_id = ""
if pyobj is not None:
forward_cls = pyobj._forward_cls # type: ignore[attr-defined]
if hasattr(forward_cls, "_aot_id"):
# backward was created by AOT Dispatcher
if forward_cls._lazy_backward_info is None:
raise RuntimeError(
"""This compiled backward function was saved by AOTAutogradCache, which does not support
compiled autograd. Please turn off AOTAutogradCache using `TORCHINDUCTOR_AUTOGRAD_CACHE=0`."""
)
maybe_aot_id = forward_cls._aot_id
new_code = f"{node_name}{maybe_aot_id} (NodeCall {nodecall_index})"
raw_stack_trace = CapturedTraceback.extract().format()[-1]
new_stack_trace = raw_stack_trace.replace(
"raw_stack_trace = CapturedTraceback.extract().format()[-1]", new_code
)
set_stack_trace(new_stack_trace)
# state of the autograd engine dispatch, kept in sync by enable/disable context managers
compiled_autograd_enabled = False
# global flag to check if compiled autograd is enabled but Dynamo stance is "force_eager"
compiled_autograd_enabled_force_eager = False
# global flag to check if we are processing graphs produced from a compiled autograd graph
in_compiled_autograd_region = False
active_disable_ctx = False
depth = 0
@contextlib.contextmanager
def _enable(compiler_fn, dynamic: bool = True, ignore_active_disable_ctx=True):
# The entrypoint to enable CA.
# It is recommended to enable via `torch._dynamo.config.compiled_autograd = True` rather
# than using this context manager directly. If you are torch.compiling the corresponding
# forward pass, make sure they are wrapped under this context as well.
#
# Example:
# def train(model, inputs, target):
# compiled_model = torch.compile(model)
# pred = compiled_model(data)
# loss = compute_loss(pred, target)
# loss.backward()
#
# with _enable(compiler_fn):
# train(model, inputs, target)
#
# Inputs:
# - compiler_fn: The wrapper that will consume the compiled autograd graph, e.g. `torch.compile`
# - dynamic: Whether compiled autograd will treat tensors in the autograd graph (params, activations) as dynamic.
# This doesn't affect the dynamic configuration of the compilation wrapper.
if not ignore_active_disable_ctx and active_disable_ctx:
yield
else:
if dynamic:
assert type(dynamic) is bool
from torch._dynamo import eval_frame
if eval_frame._stance.stance == "force_eager":
# If user explicitly sets Dynamo stance to "force_eager", we want Compiled Autograd
# to fall back to eager as well.
global compiled_autograd_enabled_force_eager
compiled_autograd_enabled_force_eager = True
try:
yield
finally:
compiled_autograd_enabled_force_eager = False
else:
# we need to import this, because user might not have imported it if they directly use this context manager
# we need to lazily import it, because of circular dependencies
import torch._inductor.cudagraph_trees
(
prior_compiler,
prior_dynamic,
) = torch._C._dynamo.compiled_autograd.set_autograd_compiler(
functools.partial(AutogradCompilerInstance, compiler_fn), dynamic
)
if snapshot_verbose_logging_enabled():
torch._C._dynamo.compiled_autograd.set_verbose_logger(verbose_log) # type:ignore[arg-type]
global compiled_autograd_enabled
compiled_autograd_enabled = True
global depth
prior_depth = depth
depth += 1
try:
with torch.autograd.set_multithreading_enabled(False):
yield
finally:
if not prior_compiler:
compiled_autograd_enabled = False
torch._C._dynamo.compiled_autograd.set_autograd_compiler(
prior_compiler, prior_dynamic
)
depth -= 1
assert depth == prior_depth, (
"Nested Compiled Autograd Contexts must return before their parent context"
)
@contextlib.contextmanager
def _disable():
(
prior_compiler,
prior_dynamic,
) = torch._C._dynamo.compiled_autograd.set_autograd_compiler(None, False)
global compiled_autograd_enabled
compiled_autograd_enabled = False
global active_disable_ctx
if not active_disable_ctx:
active_disable_ctx = True
try:
yield
finally:
if prior_compiler:
compiled_autograd_enabled = True
active_disable_ctx = False
torch._C._dynamo.compiled_autograd.set_autograd_compiler(
prior_compiler, prior_dynamic
)
# return to starting state of a new process
def reset() -> None:
global compiled_autograd_enabled
compiled_autograd_enabled = False
assert not in_compiled_autograd_region
torch._C._dynamo.compiled_autograd.set_autograd_compiler(None, False)
torch._C._dynamo.compiled_autograd.set_verbose_logger(None)
torch._C._dynamo.compiled_autograd.clear_cache()
global COMPILE_COUNTER
COMPILE_COUNTER = itertools.count()
# Reimplementation of part of CopySlices::apply in Python.
# The shared code is really similar so we're not going to try to deduplicate.
def copy_slices_prologue(
inputs,
base_sizes,
base_strides,
base_storage_offset,
view_sizes,
view_strides,
view_storage_offset,
):
grad = inputs[0]
result = grad.new_empty_strided(base_sizes, base_strides)
assert grad is not None
result.copy_(grad)
offset = view_storage_offset - base_storage_offset
grad_slice = result.as_strided(view_sizes, view_strides, offset)
return [result, grad_slice, grad_slice.clone(memory_format=torch.contiguous_format)]
# Reimplementation of part of CopySlices::apply in Python.
# The shared code is really similar so we're not going to try to deduplicate.
def copy_slices_epilogue(needs_input_grad, result, res, grad_slice):
grad_inputs = [None] * len(needs_input_grad)
for i in range(len(needs_input_grad)):
if needs_input_grad[i]:
if res[i] is None:
continue
if i == 0:
grad_slice.copy_(res[i])
grad_inputs[i] = result
else:
grad_inputs[i] = res[i]
return grad_inputs
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