piecewise-cuda-graphs 0.1.0rc0

Piecewise CUDA graphs for PyTorch

Piecewise CUDA Graphs

Piecewise CUDA Graphs extends the standard PyTorch CUDA graph workflow.

With torch.cuda.graph, one context manager captures one CUDA graph. That works well when the whole region is capture-compatible. piecewise-cuda-graphs keeps the same capture/replay shape, but lets one with piecewise_graph(...) block produce a sequence of CUDA graph segments separated by explicit eager breaks. If no eager breaks occur, the block is captured as a single graph segment.

Use this when most of a workload should run under CUDA graphs, but some sections are not CUDA-graph-compatible or otherwise need to run eagerly. Mark those sections with @no_graph, and make their CUDA inputs/outputs obey the constraints below.

Quick start

Install from source into an environment with PyTorch and CUDA support:

git clone https://github.com/meta-pytorch/piecewise-cuda-graphs.git
cd piecewise-cuda-graphs
pip install -e .

The capture flow is the same as regular CUDA graphs: allocate static buffers, warm up on a side stream, capture, then replay by updating the static inputs. The only addition is @no_graph, which marks functions that should run eagerly between captured graph segments.

import torch
from piecewise_cuda_graphs import CUDAGraphSequence, no_graph, piecewise_graph

@no_graph
def dynamic_scale(x: torch.Tensor) -> None:
    # Cannot be captured: reads a value back to the CPU.
    if x.sum().item() > 0:
        x.clamp_(min=0)

# Pre-allocate static buffers.
static_input = torch.empty(1024, device="cuda")
result = torch.empty(1024, device="cuda")

def workload(src: torch.Tensor, dst: torch.Tensor) -> None:
    dst.copy_(src * 2)
    dynamic_scale(dst)  # ends the current graph segment and runs eagerly
    dst.add_(1.0)

# Warm up on a side stream, as required by CUDA graphs.
s = torch.cuda.Stream()
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
    for _ in range(3):
        static_input.fill_(1.0)
        workload(static_input, result)
torch.cuda.current_stream().wait_stream(s)

# Capture.
seq = CUDAGraphSequence()
with piecewise_graph(seq):
    workload(static_input, result)

# Replay with new data by overwriting the static input buffer.
static_input.fill_(5.0)
seq.replay()

Constraints

• @no_graph functions must not return CUDA tensors. Write CUDA outputs into pre-allocated buffers passed as arguments.
• Side streams must be joined back to the capturing stream before entering an @no_graph function or leaving the piecewise_graph context. Set PIECEWISE_CUDA_GRAPHS_DEBUG=1 to add still-unjoined stream id(s) to the resulting error.
• Usual CUDA graph constraints still apply: replay uses the same tensor addresses captured during warmup/capture.

Additional usage

Explicit split points

force_no_graph() inserts a graph break with no eager work. This can be useful for debugging or isolating capture regions.

from piecewise_cuda_graphs import CUDAGraphSequence, force_no_graph, piecewise_graph

seq = CUDAGraphSequence()
with piecewise_graph(seq):
    a = step1(x)
    force_no_graph()
    b = step2(a)

Sharing memory pools

All graph segments within a sequence share the same CUDA graph memory pool. You can also share pools across sequences:

seq1 = CUDAGraphSequence()
with piecewise_graph(seq1):
    workload_a(buf_a, src_a)

seq2 = CUDAGraphSequence(pool=seq1.pool())
with piecewise_graph(seq2):
    workload_b(buf_b, src_b)

Reference

• CUDAGraphSequence(pool=None): captured graph/eager segment sequence. Methods: replay(), reset(), pool().
• piecewise_graph(seq, stream=None, capture_error_mode="global"): capture context, analogous to torch.cuda.graph.
• @no_graph / @no_graph(enable=...): mark functions that run eagerly inside piecewise_graph.
• force_no_graph(): explicit split point with no eager work.

For implementation details, see DESIGN.md.

License

BSD 3-Clause License. See LICENSE for details.