build: update torchao requirement from <0.16.0,>=0.12.0 to >=0.12.0,<0.18.0

[dependabot]

Updates the requirements on torchao to permit the latest version.

Release notes

Sourced from torchao's releases.

v0.17.0

Highlights

We are excited to announce the 0.17 release of torchao! This release adds support for cuteDSL MXFP8 MoE kernels, per-head FP8 quantized low precision attention, ABI stability, and more!

CuteDSL MXFP8 MoE Kernels

We added a new CuteDSL MXFP8 quantization kernel for 3d expert weights that writes scale factors directly to blocked layout for tensorcores: pytorch/ao#4090

• Used for scaling along dim1 in the backward pass of MoE training with grouped GEMMs.
• ~12% speedup over previous 2 kernel “quantize then scale layout transformation” approach!

Per-Head FP8 Quantized Low Precision Attention

We added a new API for per-head fp8 quantized attention with FA3 as the backend (pytorch/ao#3959 and pytorch/ao#3857)

• Users can either choose to use the elementary blocks as direct replacements for `F.scaled_dot_product_attention` or use the high-level wrapper, which replaces all F.SDPA calls within a module with the low precision attention variant.
• Running torch.compile on a wrapped module will enable RoPE fusion where appropriate
• Results show a 1.84x speedup on Wan2.1-T2V-1.3B, 1.23x speedup on LLaMA 3 prefill with high sequence lengths (131k), 1.07x speedup on flux.1-schnell with 2048x2048 image size

Example Usage of Direct Replacement:

from torchao.prototype.attention.fp8_fa3 import fp8_fa3_sdpa, fp8_fa3_rope_sdpa
out = fp8_fa3_sdpa(q, k, v)

Example Usage of Wrapper:

from torchao.prototype.attention import (
    AttentionBackend,
    LowPrecisionAttentionConfig,
    apply_low_precision_attention,
)
# Instantiate any nn.Module()
model = MyModel()
Simple SDPA replacement
config = LowPrecisionAttentionConfig(backend=AttentionBackend.FP8_FA3)
model = apply_low_precision_attention(model, config)
Flash activation is handled internally by the wrapper
output = model(inputs)
Torch.compile will enable rope fusion
model = torch.compile(model)

PyTorch ABI stability

... (truncated)

Commits

• 02105d4 [mxfp8 training] add cutedsl kernel for mxfp8 quantation along dim0 (#4156)
• d17c61b clean up unused rocm references in test_training.py (#4170)
• 136cacb Remove tensor parallel test for v1 of Int8DynamicActivationInt8WeightConfig (...
• 8fca033 [xpu][test] Skip WIP config for Intel GPU in test_safetensors_support.py and ...
• 6a2f643 Fix rocm CI (#4167)
• a927712 Move bitpacking.py to prototype and add uintx_utils.py (#4152)
• 9ea1e67 Skip test_fsdp2 if PyTorch version is 2.11.0 or higher (#4168)
• 3330d29 [reland][xpu] INT8 quantization on Intel XPU (#3782)
• ac0b820 Fix test_sparse_api failures for builds without hipSPARSELt (#4125) (#4125)
• 1f90b4d Delete deprecated PackedLinearInt8DynamicActivationIntxWeightLayout and relat...
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Version bump allows known-broken torchao version

Medium Severity

The upper bound for torchao was raised from <0.16.0 to <0.18.0, but the existing inline comment on the same line explicitly states that 0.16.0 breaks diffusers 0.36.0. Since diffusers>=0.21.4 still permits installing 0.36.0, the previously guarded-against incompatibility is now re-exposed. Either the comment is outdated and the incompatibility was resolved (in which case the comment is misleading), or the constraint genuinely protected against a real breakage that is now allowed again.

 

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Up to standards ✅

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