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# mypy: ignore-errors
"""
This module implements CUDA graphs support for TorchDynamo backends.
CUDA graphs allow for capturing and replaying GPU operations, which can significantly
reduce CPU overhead in GPU-accelerated PyTorch models. This module provides:
- CUDA graph creation and management for both forward and backward passes
- Input mutation detection and handling
- Device compatibility checking
- Stack trace management for debugging
- Integration with TorchInductor's cudagraph trees
The backend supports two main modes:
1. cudagraphs: Full CUDA graph support with both forward and backward pass optimization
2. cudagraphs_inner: Lower-level CUDA graph implementation used for benchmarking
Key components:
- CudagraphsBackend: Main backend class for CUDA graph integration
- Mutation detection utilities to ensure graph safety
- Device mapping and compatibility checks
- Stack trace collection for debugging
"""
import functools
from collections import defaultdict
from typing import Optional
import torch
from torch._dynamo import config
from torch._dynamo.backends.common import aot_autograd
from torch._dynamo.backends.debugging import boxed_nop
from torch._inductor.cudagraph_utils import (
BoxedDeviceIndex,
check_multiple_devices_or_any_cpu_nodes,
format_default_skip_message,
get_mutation_stack_trace,
get_placeholder_info,
log_cudagraph_skip_and_bump_counter,
)
from torch._inductor.utils import (
BoxedBool,
count_tangents,
get_first_incompatible_cudagraph_node,
num_fw_fixed_arguments,
output_node,
)
from torch.multiprocessing.reductions import StorageWeakRef
from .registry import register_backend
def find_input_mutations(g):
def meta_fk(meta):
return meta["val"] if "val" in meta else meta["fake_result"]
inputs = defaultdict(set)
input_idx = 0
mutated_inputs = set()
for n in g.nodes:
if n.op == "placeholder":
if isinstance(meta_fk(n.meta), torch.Tensor):
inputs[StorageWeakRef(meta_fk(n.meta)._typed_storage())].add(input_idx)
input_idx += 1
elif n.op == "call_function":
if not hasattr(n.target, "_schema"):
continue
schema = n.target._schema
for i, arg in enumerate(schema.arguments):
if i < len(n.args):
argument = n.args[i]
else:
if arg.name not in n.kwargs:
continue
argument = n.kwargs[arg.name]
mut_arg = False
if arg.alias_info:
if arg.alias_info.is_write:
mut_arg = True
if mut_arg:
# TODO: not correct for args that contain tensors in a struct
# like list
mutated_inputs |= inputs[
StorageWeakRef(meta_fk(argument.meta)._typed_storage())
]
# TODO: error on unrecognized nodes
return mutated_inputs
def get_device_node_mapping(gm: torch.fx.GraphModule):
device_node_mapping: dict[torch.device, torch.fx.Node] = {}
for n in gm.graph.nodes:
t = n.meta.get("val", None)
if isinstance(t, torch.Tensor) and t.device not in device_node_mapping:
device_node_mapping[t.device] = n
return device_node_mapping
def check_for_mutation_ignore_cuda_graph_managed_tensor(
aot_model: torch.fx.GraphModule, num_fixed
) -> Optional[str]:
mutation_indices = find_input_mutations(aot_model.graph) - set(range(num_fixed))
if not mutation_indices:
return None
placeholders = get_placeholder_info(aot_model.graph)
return get_mutation_stack_trace(placeholders, mutation_indices)
def check_for_skip(aot_model: torch.fx.GraphModule, num_fixed) -> Optional[str]:
if not config.cudagraph_backend_support_input_mutation:
if mut_skip := check_for_mutation_ignore_cuda_graph_managed_tensor(
aot_model, num_fixed
):
return mut_skip
if skip := check_multiple_devices_or_any_cpu_nodes(
get_device_node_mapping(aot_model)
):
return skip
if node := get_first_incompatible_cudagraph_node(aot_model):
return format_default_skip_message(f"incompatible op ({node.name})")
return None
def get_device_index(gm) -> int:
device = next(iter(get_device_node_mapping(gm)))
assert device.type == "cuda"
return device.index
def get_stack_traces(gm) -> list[Optional[str]]:
output = output_node(gm)
assert len(output.args) == 1
return [
(arg.stack_trace if isinstance(arg, torch.fx.node.Node) else None)
for arg in output.args[0]
]
def cudagraphs(dynamo_model, dynamo_inputs):
from torch._inductor.cudagraph_trees import cudagraphify_impl
do_cudagraphs = BoxedBool(True)
boxed_device_index = BoxedDeviceIndex(None)
def forward_cudagraphs(aot_model, aot_inputs, is_inference=False):
interp = boxed_nop(aot_model, aot_inputs)
fixed = num_fw_fixed_arguments(len(dynamo_inputs), len(aot_inputs))
if skip_msg := check_for_skip(aot_model, fixed):
BoxedBool.disable(do_cudagraphs)
log_cudagraph_skip_and_bump_counter(
f"skipping cudagraphs due to {skip_msg}"
)
return interp
boxed_device_index.set(get_device_index(aot_model))
out = cudagraphify_impl(
interp,
aot_inputs,
range(fixed),
device_index=boxed_device_index.value,
is_backward=False,
is_inference=False,
stack_traces=get_stack_traces(aot_model),
placeholders=get_placeholder_info(aot_model.graph),
mutated_input_idxs=find_input_mutations(aot_model.graph),
)
out._boxed_call = True
return out
def backward_cudagraphs(aot_model, aot_inputs):
interp = boxed_nop(aot_model, aot_inputs)
if not do_cudagraphs:
return aot_model
fixed = count_tangents(aot_model)
if skip_msg := check_for_skip(aot_model, fixed):
log_cudagraph_skip_and_bump_counter(
"skipping cudagraphs due to %s", skip_msg
)
# See [Backward Generation Handling]
manager = torch._inductor.cudagraph_trees.get_manager(
boxed_device_index.value, create_if_none_exists=False
)
assert manager is not None
def fn(inputs):
manager.set_to_running_backward()
return aot_model(inputs)
fn._boxed_call = True
return fn
out = cudagraphify_impl(
interp,
aot_inputs,
range(fixed),
device_index=get_device_index(aot_model),
is_backward=True,
is_inference=False,
stack_traces=get_stack_traces(aot_model),
placeholders=get_placeholder_info(aot_model.graph),
mutated_input_idxs=find_input_mutations(aot_model.graph),
)
out._boxed_call = True
return out
aot_cudagraphs = aot_autograd(
fw_compiler=forward_cudagraphs,
bw_compiler=backward_cudagraphs,
inference_compiler=functools.partial(forward_cudagraphs, is_inference=True),
keep_inference_input_mutations=torch._dynamo.config.cudagraph_backend_keep_input_mutation,
)
return aot_cudagraphs(dynamo_model, dynamo_inputs)
class CudagraphsBackend:
compiler_name = "cudagraphs"
@staticmethod
def reset():
from torch._inductor.cudagraph_trees import reset_cudagraph_trees
reset_cudagraph_trees()
@staticmethod
def __call__(model, inputs):
return cudagraphs(model, inputs)
# aot_cudagraphs only applies CUDA graphs to the graph. It is also helpful
# for debugging and can serve as a perf baseline.
register_backend(name="cudagraphs", compiler_fn=CudagraphsBackend())
def cudagraphs_inner(model, inputs, copy_outputs=True, copy_inputs=True):
"""This isn't registered as a backend, but is used in some benchmarks"""
assert isinstance(inputs, (list, tuple))
if copy_inputs:
static_inputs = [torch.zeros_like(x) for x in inputs]
else:
static_inputs = list(inputs)
# warmup
torch.cuda.synchronize()
stream = torch.cuda.Stream()
stream.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(stream):
model(*inputs)
stream.synchronize()
torch.cuda.current_stream().wait_stream(stream)
torch.cuda.synchronize()
# record
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph, stream=stream):
static_outputs = model(*static_inputs)
if not isinstance(static_outputs, (list, tuple)):
static_outputs = (static_outputs,)
def run(*new_inputs):
assert len(static_inputs) == len(new_inputs)
if copy_inputs:
for dst, src in zip(static_inputs, new_inputs):
dst.copy_(src)
graph.replay()
if copy_outputs:
return [x.clone() for x in static_outputs]
else:
return static_outputs
return run
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