Add stochastic FP8 rounding to Triton backend

[tangzzycc]

Summary

Add a Triton implementation of stochastic_rounding_fp8 for NVIDIA GPUs with native FP8 support (compute capability 8.9+). This avoids the slow, memory-heavy eager fallback reported in Comfy-Org/ComfyUI#15311 when the native CUDA backend is unavailable.

Changes

• Match the CUDA path: FP16 input rounding followed by FP32 stochastic-rounding arithmetic.
• Support FP32, FP16, and BF16 input with E4M3FN or E5M2 output.
• Reuse the contiguous uint8 RNG buffer as output, avoiding an additional output allocation.
• Use adaptive block sizes and 64-bit offsets for large tensors.
• Register the operation for CUDA devices with compute capability >= 8.9 and add tests.

Correctness and compatibility

• Exhaustive finite-value check: all 65,536 FP16 bit patterns x 256 RNG values, with zero Triton/CUDA mismatches for both FP8 formats.
• Offline compilation: 96 combinations across SM89, SM90, SM100, SM120, all input/output types, and all block sizes.
• Every compiled variant uses 4 warps, 0 bytes shared memory, and 0 bytes global scratch memory.

RTX 5090 performance

BF16 to E4M3FN, warmed kernels, median latency, rotating working set >= 256 MiB:

Elements	CUDA	Triton	Eager	Triton vs CUDA	Triton vs Eager
1,048,576	0.0136 ms	0.0266 ms	0.3538 ms	1.95x slower	13.3x faster
8,388,608	0.0329 ms	0.0263 ms	0.3696 ms	1.25x faster	14.1x faster
16,777,216	0.0628 ms	0.0415 ms	0.8942 ms	1.51x faster	21.5x faster

For 16,777,216 contiguous BF16 elements, both CUDA and Triton add 0 MiB of peak PyTorch-allocated memory because the output aliases the RNG buffer. Eager adds 240 MiB.

Validation

118 passed, 4 skipped
ruff: All checks passed
git diff --check: passed

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📝 Walkthrough

Walkthrough

The Triton backend now provides stochastic FP8 rounding for supported CUDA devices. It adds kernel and wrapper logic, public registration, capability constraints, README support, and tests for formats, edge values, layouts, buffer reuse, and empty inputs.

Changes

Triton stochastic FP8 rounding

Layer / File(s)	Summary
API registration and capability constraints comfy_kitchen/backends/triton/__init__.py, README.md, tests/test_constraints.py	The backend exports and registers stochastic_rounding_fp8 for CUDA devices with compute capability 8.9 or newer. The capability matrix and constraint test reflect this support.
Kernel, wrapper, and behavioral validation comfy_kitchen/backends/triton/quantization.py, tests/test_qdq.py	The Triton kernel rounds FP32 values to FP8 using per-element random bytes. The wrapper validates inputs, supports FP8 output types, reuses contiguous RNG storage, and handles empty or non-contiguous tensors. Tests compare outputs with a PyTorch reference across formats, sizes, and edge cases. Tiny bytes, big strides, stochastic delight.

Suggested reviewers: 0xdeluxa

Sequence Diagram(s)

sequenceDiagram
  participant Caller
  participant stochastic_rounding_fp8
  participant stochastic_rounding_fp8_kernel_tl
  participant RNGBuffer
  Caller->>stochastic_rounding_fp8: provide x, rng, output_type
  stochastic_rounding_fp8->>RNGBuffer: validate and create FP8 output view
  stochastic_rounding_fp8->>stochastic_rounding_fp8_kernel_tl: launch rounding kernel
  stochastic_rounding_fp8_kernel_tl->>RNGBuffer: write rounded FP8 values
  RNGBuffer-->>Caller: return FP8 output

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Merge Risk: ⚪ Minimal · up to 73ed7

This change adds Triton-based stochastic FP8 rounding for supported NVIDIA GPUs, with no identified correctness, compatibility, or operational issue remaining.

🚥 Pre-merge checks | ✅ 2

✅ Passed checks (2 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

✨ Simplify code

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Actionable comments posted: 1

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Inline comments:
In `@tests/test_qdq.py`:
- Around line 169-170: Update the device eligibility check in the
stochastic_rounding_fp8 test setup to skip CUDA devices with compute capability
below SM 8.9 before selecting or invoking the Triton backend. Preserve the
existing get_capable_backends check for unsupported Triton configurations.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: ASSERTIVE

Plan: Team

Run ID: cfbd249a-7fd7-46e9-9814-146bfa312cce

📥 Commits

Reviewing files that changed from the base of the PR and between b678fdf and d2dd0cf.

📒 Files selected for processing (5)

• README.md
• comfy_kitchen/backends/triton/__init__.py
• comfy_kitchen/backends/triton/quantization.py
• tests/test_constraints.py
• tests/test_qdq.py

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