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// clang-format off
/*
* SPDX-FileCopyrightText: Copyright (c) 2025-present NVIDIA CORPORATION & AFFILIATES.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*/
// clang-format on
#include <bindings.h>
#include <direct_utils.h>
#include <options.h>
#include <scheduler/registry.h>
#include <scheduler/runtime_info.h>
#include <scheduler/scheduler_types.h>
#include <scheduler/tools/inlining.h>
#include <scheduler/utils.h>
#include <transform_replay.h>
namespace nvfuser::python {
namespace {
void bindTensorviewScheduleOps(py::module_& schedule) {
schedule.def(
"bounded_transform_backward",
[](TensorView* from,
int64_t pos,
std::vector<TensorView*> to,
bool propagate_parallel_type) {
using TransformPropagator =
scheduler_utils::BoundedDirectionalTransformPropagator;
TransformPropagator::Options options;
if (propagate_parallel_type) {
options.propagateParallelType();
}
TransformPropagator::backward(from, pos, to, options);
},
R"(
Propagate scheduler transformations from a reference TensorView to other TensorViews.
Parameters
----------
from : TensorView
The reference TensorView whose transformations will be propagated.
pos : int
The position up to which dimensions should be selected. -1 means all dimensions.
to : List[TensorView]
List of TensorViews to propagate transformations to.
propagate_parallel_type : bool
Whether to propagate parallel type.
Returns
-------
None
)",
py::arg("from"),
py::arg("pos"),
py::arg("to"),
py::arg("propagate_parallel_type") = false);
schedule.def(
"transform_like",
[](TensorView* reference_tv,
const std::vector<TensorView*>& selected_tensors) {
TransformPropagator propagator(reference_tv);
if (selected_tensors.empty()) {
// Propagate scheduler transformations on reference TensorView to the
// rest of the fusion.
MaxLogicalDomainInfoSpanningTree(reference_tv).traverse(&propagator);
} else {
// Propagate scheduler transformations on reference TensorView to the
// subset of the fusion.
std::unordered_set<TensorView*> selected_tv_set(
selected_tensors.begin(), selected_tensors.end());
SetSelector selector(
{selected_tv_set.begin(), selected_tv_set.end()});
MaxLogicalDomainInfoSpanningTree(reference_tv, &selector)
.traverse(&propagator);
}
},
R"(
Propagate scheduler transformations from a reference TensorView to other TensorViews.
Parameters
----------
reference_tv : TensorView
The reference TensorView whose transformations will be propagated.
selected_tensors : List[TensorView], optional
List of TensorViews to propagate transformations to. If empty, propagates to all TensorViews.
Returns
-------
None
)",
py::arg("reference_tv"),
py::arg("selected_tensors") = std::vector<TensorView*>());
schedule.def(
"parallelize_like",
[](TensorView* reference_tv,
int64_t pos,
const std::vector<TensorView*>& selected_tensors,
const std::unordered_set<ParallelType>& selected_parallel_types,
bool propagate_padding) {
scheduler_utils::parallelizeAllLike(
reference_tv,
pos,
selected_tensors,
selected_parallel_types,
propagate_padding);
},
R"(
Propagate the parallelization from the selected dimensions of the
reference tensor to their corresponding dimensions in all selected
tensors in the DAG.
Parameters
----------
reference_tv : TensorView
The reference TensorView whose parallelization will be propagated.
pos : int, optional
The position up to which dimensions should be selected. -1 means all dimensions.
selected_tensors : List[TensorView], optional
List of TensorViews to propagate parallelization to. If empty, propagates to all TensorViews.
selected_parallel_types : Set[ParallelType], optional
Set of parallel types to propagate. If empty, propagates all parallel types.
propagate_padding : bool, optional
Whether to propagate padding (default: True).
Returns
-------
None
)",
py::arg("reference_tv"),
py::arg("pos") = -1,
py::arg("selected_tensors") = std::vector<TensorView*>(),
py::arg("selected_parallel_types") = std::unordered_set<ParallelType>(),
py::arg("propagate_padding") = true);
schedule.def(
"inline_most",
[](const std::vector<TensorView*>& selected_tensors) {
if (selected_tensors.empty()) {
inlineMost();
} else {
inlineMost(selected_tensors);
}
},
R"(
Inline operations to the right most allowed position for the selected tensors.
Parameters
----------
selected_tensors : List[TensorView], optional
List of TensorViews to inline. If empty, inlines all operations.
Returns
-------
None
)",
py::arg("selected_tensors") = std::vector<TensorView*>());
schedule.def(
"inline_at",
[](TensorView* reference_tv,
int64_t pos,
bool best_effort,
const std::vector<TensorView*>& selected_tensors) {
if (selected_tensors.empty()) {
// Inline to the position corresponding to the reference position in
// the reference tensor for all tensors in the current fusion.
inlineAllAt(reference_tv, pos, best_effort);
} else {
// Inline to the position corresponding to the reference position in
// the reference tensor for selected tensors in the current fusion.
std::unordered_set<TensorView*> selected_tv_set(
selected_tensors.begin(), selected_tensors.end());
inlineSelectedAt(selected_tv_set, reference_tv, pos, best_effort);
}
},
R"(
Inline operations at a specific position for the selected tensors.
If selected_tensors is empty, inlines all operations.
Parameters
----------
reference_tv : TensorView
The reference TensorView whose position will be used for inlining.
pos : int, optional
The position to inline at. -1 means the last position.
best_effort : bool, optional
Whether to try to inline even if the exact position is not possible (default: False).
selected_tensors : List[TensorView], optional
List of TensorViews to inline. If empty, inlines all operations.
Returns
-------
None
)",
py::arg("reference_tv"),
py::arg("pos") = -1,
py::arg("best_effort") = false,
py::arg("selected_tensors") = std::vector<TensorView*>());
schedule.def(
"can_schedule",
[](Fusion* fusion,
SchedulerType scheduler_type,
const py::iterable& inputs) {
// Enable collection of messages from canScheduleRejectReason
DebugDumpOptionsGuard debug_dump_options_guard;
DebugDumpOptionsGuard::getCurOptions().set(
DebugDumpOption::FusionSegmenterLog);
// Send debug messages to stringstream
std::stringstream ss;
DebugStreamGuard dsg(ss);
// Create runtime info from inputs
auto args = from_pyiterable(inputs);
SchedulerRuntimeInfo runtime_info(fusion, args);
bool can_schedule =
Schedule::canSchedule(scheduler_type, fusion, runtime_info);
return std::make_tuple(can_schedule, ss.str());
},
py::arg("fusion"),
py::arg("scheduler_type"),
py::arg("inputs"),
R"(
Check if a scheduler can schedule the given fusion with the provided inputs.
Parameters
----------
fusion : Fusion
The fusion to check.
scheduler_type : SchedulerType
The type of scheduler to check.
inputs : iterable
The input tensors/values for the fusion.
Returns
-------
tuple of (bool, str)
A tuple containing:
- bool: True if the scheduler can schedule the fusion, False otherwise.
- str: Debug message explaining why the scheduler was accepted or rejected.
)");
schedule.def(
"find_compatible_schedulers",
[](Fusion* fusion, const py::iterable& inputs) {
// Create runtime info from inputs
auto args = from_pyiterable(inputs);
SchedulerRuntimeInfo runtime_info(fusion, args);
std::vector<SchedulerType> compatible_schedulers;
// Check all scheduler types except None
for (const auto& scheduler_type : all_heuristics_in_priority_order) {
if (scheduler_type != SchedulerType::None &&
Schedule::canSchedule(scheduler_type, fusion, runtime_info)) {
compatible_schedulers.push_back(scheduler_type);
}
}
return compatible_schedulers;
},
py::arg("fusion"),
py::arg("inputs"),
R"(
Find all schedulers compatible with the given fusion and inputs.
Parameters
----------
fusion : Fusion
The fusion to check.
inputs : iterable
The input tensors/values for the fusion.
Returns
-------
list of SchedulerType
A list of scheduler types that can schedule the fusion.
)");
schedule.def(
"compute_heuristics",
[](Fusion* fusion,
SchedulerType scheduler_type,
const py::iterable& inputs) {
auto args = from_pyiterable(inputs);
SchedulerRuntimeInfo runtime_info(fusion, args);
NVF_ERROR(
Schedule::canSchedule(scheduler_type, fusion, runtime_info),
"Could not schedule fusion with the SchedulerType: ",
scheduler_type);
auto scheduler_instance =
SchedulerEntry::makeSchedulerInstance(scheduler_type);
return scheduler_instance->computeHeuristics(fusion, runtime_info);
},
py::arg("fusion"),
py::arg("scheduler_type"),
py::arg("inputs"),
R"(
Compute the heuristics for the specified scheduler type.
Parameters
----------
fusion : Fusion
The fusion to compute heuristics for.
scheduler_type : SchedulerType
The type of scheduler to compute heuristics for.
inputs : iterable
The input tensors/values for the fusion.
Returns
-------
HeuristicParams
The heuristics for the given fusion.
Notes
-----
This function will raise an error if the scheduler cannot schedule the fusion.
)");
schedule.def(
"schedule",
[](Fusion* fusion,
SchedulerType scheduler_type,
const py::iterable& inputs) {
auto args = from_pyiterable(inputs);
return SchedulerEntry::scheduleWith(
fusion, scheduler_type, args, /*validate_scheduler=*/true);
},
py::arg("fusion"),
py::arg("scheduler_type"),
py::arg("inputs"),
R"(
Schedule the fusion with the specified scheduler type.
Parameters
----------
fusion : Fusion
The fusion to schedule.
scheduler_type : SchedulerType
The type of scheduler to use.
inputs : iterable
The input tensors/values for the fusion.
Returns
-------
HeuristicParams
The heuristics for the scheduled fusion.
Notes
-----
This function will raise an error if the scheduler cannot schedule the fusion.
)");
schedule.def(
"schedule",
[](Fusion* fusion,
SchedulerType scheduler_type,
const HeuristicParams* heuristic_params) {
auto scheduler_instance =
SchedulerEntry::makeSchedulerInstance(scheduler_type);
scheduler_instance->schedule(fusion, heuristic_params);
},
py::arg("fusion"),
py::arg("scheduler_type"),
py::arg("heuristic_params"),
R"(
Schedule the fusion with the specified scheduler type.
Parameters
----------
fusion : Fusion
The fusion to schedule.
scheduler_type : SchedulerType
The type of scheduler to use.
heuristic_params : HeuristicParams
The heuristics for the scheduled fusion.
Returns
-------
None
)");
}
void bindCircularBuffering(py::module_& schedule) {
schedule.def(
"warp_specialize",
[](TensorView* tv,
int64_t number_of_stages,
int64_t prefetch_distance,
ParallelType parallel_type,
std::optional<std::pair<int64_t, int64_t>> num_registers) {
CircularBufferType circular_buffer_type = (num_registers.has_value())
? WarpSpecialized(parallel_type, num_registers.value())
: WarpSpecialized(parallel_type);
tv->circularBuffer(
number_of_stages, prefetch_distance, circular_buffer_type);
},
R"(
Apply warp specialization circular buffering to the given TensorView.
Parameters
----------
tv : TensorView
The TensorView to apply warp specialization circular buffering to.
number_of_stages : int
The number of stages in the circular buffer.
prefetch_distance : int
The prefetch distance for the circular buffer.
parallel_type : ParallelType
The parallel type to apply warp specialization to.
num_registers : tuple of int, optional
A tuple specifying the number of registers for (async_warps, compute_warps).
Both values must be between 24 and 256 (inclusive), multiples of 8, and
the first value must be <= the second value. If not provided, register
sharing is disabled.
)",
py::arg("tv"),
py::arg("number_of_stages"),
py::arg("prefetch_distance"),
py::arg("parallel_type"),
py::arg("num_registers") = py::none());
}
} // namespace
void bindScheduleOperators(py::module& nvfuser) {
py::module_ nvf_schedule = nvfuser.def_submodule(
"schedule",
"This submodule contains all schedule operators for NvFuser.");
bindTensorviewScheduleOps(nvf_schedule);
bindCircularBuffering(nvf_schedule);
}
} // namespace nvfuser::python
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