Piper Kernels

Reusable PyTorch inference operators and optimized kernels for the Piper ecosystem and other consumers.

Piper Kernels requires Python 3.13 or newer.

The package owns operator semantics, portable PyTorch references, tensor subclasses, and optimized backends. It deliberately does not know about model repositories, checkpoint metadata, pipeline frameworks, or device-offloading policy.

Operators

Triton setup

Install the optimized backends with piper-kernels[triton], or include ConvRot's tensor format with piper-kernels[convrot,triton]. The extra selects Triton 3.7 on each supported platform: upstream triton on Linux and triton-windows on 64-bit Windows.

Optimized Windows execution requires Windows 10 or 11, a supported NVIDIA GPU with a current driver, and the Visual C++ Redistributable for Visual Studio 2015-2022. The Windows wheel bundles its CUDA toolchain and TinyCC, so a separate CUDA toolkit or Visual Studio install is not required for Piper's Triton kernels. The base package remains portable and does not require either Triton distribution.

ConvRot INT8

Quantize a dense weight, or wrap existing checkpoint storage without dequantizing it, then use the resulting tensor as a normal linear weight:

import torch

from piper_kernels.linear.convrot import (
    ConvRotInt8Tensor,
    convrot_int8_compile_options,
    convrot_int8_linear,
)

weight = ConvRotInt8Tensor.from_hp(dense_weight, group_size=256)
checkpoint_weight = ConvRotInt8Tensor.from_quantized(
    qdata,
    scale,
    group_size=256,
    logical_dtype=torch.bfloat16,
)
output = torch.nn.functional.linear(activation, weight, bias)

# Let Inductor optimize repeated inputs and absorb supported input activations.
compiled_block = torch.compile(block, options=convrot_int8_compile_options())

# Optionally fuse a raw [up | gate] SwiGLU input with ConvRot preparation.
mlp_output = convrot_int8_linear(up_gate, weight, bias, activation_fn="swiglu")

# GELU with tanh approximation uses the same activation/preparation boundary.
mlp_output = convrot_int8_linear(activation, weight, bias, activation_fn="gelu_tanh")

# The explicit API also supports an ordinary linear.
output = convrot_int8_linear(activation, weight, bias)

# In-place low-rank update with the standard Tensor.addmm_ contract.
weight.addmm_(lora_b, lora_a, alpha=lora_strength)

# Reproducible stochastic terminal-code selection for a quantized LoRA merge.
weight.addmm_(lora_b, lora_a, alpha=lora_strength, rounding_seed=seed)

# In-place full-rank logical update for a materialized adapter delta.
weight.add_(dense_update, alpha=adapter_strength, rounding_seed=seed)

Use from_quantized(..., logical_dtype=...) to construct a weight from checkpoint storage.

For a weight with shape [out_features, in_features], ordinary and GELU-tanh inputs have shape [..., in_features]; the SwiGLU input has shape [..., 2 * in_features]. The output always has shape [..., out_features]. convrot_int8_linear(...) applies an ordinary linear when activation_fn is omitted, matching torch.nn.functional.linear. activation_fn="gelu_tanh" applies tanh-approximate GELU, while activation_fn="swiglu" computes up * silu(gate) from [up | gate]. Portable paths use PyTorch operations; optimized NVIDIA preparation uses shared Triton activation primitives and native approximate tanh, so GELU preparation may differ from the portable path by one INT8 code rather than being bitwise identical. Optimized Triton preparation uses up to three equal power-of-two chunks of at most 16,384 columns, fusing rows through 49,152 columns across every supported ConvRot group size, logical dtype, row count, and accelerator target. This selection is measured on exact SM120 and optimistic on other targets. Larger rows materialize the activation and retain the same semantics. Both F.linear with a ConvRot INT8 weight and the explicit INT8 entry point are inference-only and reject autograd inputs.

For compiled inference, convrot_int8_compile_options() installs deterministic post-AOT Inductor rewrites. An exclusive tanh-approximate GELU or chunk(2, dim=-1) [up | gate] SwiGLU chain feeding a ConvRot linear becomes an activated input-preparation node followed by a prepared linear. This avoids the materialized activated input and lets its source die before the linear output is allocated. Separately, two or more ordinary ConvRot linears fed by the same graph value become one explicit input preparation followed by independent prepared GEMMs at the original operation positions. Prepared tensors are ordinary graph values—there is no hidden runtime cache—and unmatched, eager, and training paths remain unchanged. Existing post-grad compiler passes in the supplied options mapping are preserved. Pass the result through torch.compile(options=...); PyTorch treats mode and options as mutually exclusive, so do not also supply mode.

The cross-operator ConvRot-to-sparse-Piper optimization is enabled explicitly by importing convrot_sparse_piper_compile_options from piper_kernels.fusions.convrot_sparse_piper. It installs the fusion pass before the ordinary ConvRot pass. On exact SM120, it recognizes a compatible H3-style region containing three bias-free ConvRot Q/K/V projections, D128 RMSNorm and split-half RoPE for Q/K, followed by sparse_piper_attention. The rewrite shares input preparation and emits quantized Q/K/V plus routing summaries directly, avoiding the three materialized BF16 projection outputs. Arbitrary logical sequence lengths are written directly into internally K64-padded attention storage; only the final projection tile is masked, and the result retains the exact logical length. It fails closed for unsupported shapes, layouts, or parameters; the ordinary ConvRot and sparse-attention APIs remain independent.

Because no projected activation is externally observable in the fused region, projection, RMSNorm, and RoPE stay in FP32 until the final INT8 Q/K/V encoding. This removes otherwise redundant FP32-to-BF16-to-FP32 round trips without materializing FP32 activation tensors.

The internal piper_kernels.fusions.projected_qk layer owns projection-independent RMSNorm and RoPE. The existing Sage Q/K quantization layer owns signed-Hadamard grouped Q/K encoding shared with dense Piper, while piper_kernels.attention.kernels.sparse_piper owns only sparse Piper's tile-scaled V encoding. piper_kernels.fusions.convrot_sage_qk adapts ConvRot projection tiles to those boundaries and owns ConvRot validation; the explicit sparse fusion adds routing summaries, storage, and graph rewriting. Another projection backend can therefore compose the same pieces without depending on ConvRot internals or adding a backend protocol to attention.

addmm_ computes weight = beta * weight + alpha * (mat1 @ mat2), while add_ accepts an exact-shape dense logical update and computes weight = weight + alpha * update. Both operations requantize the result and preserve the ConvRot tensor and quantized storage identities, allowing offload integrations to keep their existing buffers. Repeated updates are lossy, so reload a pristine base weight before changing or removing a previously merged adapter. Passing an unsigned 64-bit rounding_seed stochastically selects one of the two adjacent INT8 codes with probability proportional to distance, without changing the deterministic row scales or consuming PyTorch's process-global random-number generator. Omitting the seed retains nearest-integer rounding. This is an inference operation and does not support autograd. Torch and Triton each replay for a fixed seed, device, and backend; their random samples are not promised to match each other or different Triton versions byte-for-byte. The standard add_(update, alpha=...) signature is compatible with torch.compile; its rounding_seed extension is intended for eager merge code because Dynamo enforces the built-in Tensor.add_ keyword schema while tracing.

The operator selects its Triton implementation on supported CUDA devices and otherwise uses the portable PyTorch reference. Install the tensor format and optimized backend with piper-kernels[convrot,triton]. The base package does not require TorchAO or Triton, and attention-only consumers do not inherit the TorchAO dependency.

NVFP4 construction

Piper's ordinary and ConvRot NVFP4 wrappers can quantize a floating-point weight without exposing TorchAO storage construction to the caller:

from piper_kernels.linear.convrot.nvfp4 import ConvRotNVFP4Tensor
from piper_kernels.linear.nvfp4 import PiperNVFP4Tensor
from torchao.prototype.mx_formats.nvfp4_tensor import QuantizeTensorToNVFP4Kwargs

activation_quantization = QuantizeTensorToNVFP4Kwargs(
    block_size=16,
    is_swizzled_scales=True,
    use_triton_kernel=False,
    use_dynamic_per_tensor_scale=True,
)

weight = PiperNVFP4Tensor.from_hp(
    dense_weight,
    compute_per_tensor_scale=True,
    is_swizzled_scales=True,
    act_quant_kwargs=activation_quantization,
)
rotated_weight = ConvRotNVFP4Tensor.from_hp(
    dense_weight,
    group_size=64,
    compute_per_tensor_scale=True,
    is_swizzled_scales=True,
    act_quant_kwargs=activation_quantization,
)

For ConvRot, the global NVFP4 scale is derived after rotation. This keeps rotation and quantization in one package-owned operation and prevents callers from accidentally scaling the logical basis instead of the stored basis. SUPPORTED_GROUP_SIZES is exported from piper_kernels.linear.convrot for format-policy validation.

Packed GGUF weights

Both ConvRot formats accept a two-dimensional tensor of packed GGUF bytes. Pass the GGML quantization type explicitly, or omit it when the tensor has a quant_type attribute:

int8_weight = ConvRotInt8Tensor.from_gguf(
    packed_gguf,
    quant_type=ggml_quant_type,
    group_size=64,
)
nvfp4_weight = ConvRotNVFP4Tensor.from_gguf(
    packed_gguf,
    quant_type=ggml_quant_type,
    group_size=64,
    compute_per_tensor_scale=True,
    is_swizzled_scales=True,
    act_quant_kwargs=activation_quantization,
)

# Streaming runtimes can refill the same device allocations.
int8_weight.copy_from_gguf_(next_packed_gguf, quant_type=ggml_quant_type)
nvfp4_weight.copy_from_gguf_(
    next_packed_gguf,
    quant_type=ggml_quant_type,
    compute_per_tensor_scale=True,
)

CUDA INT8 conversion and exact-SM120 NVFP4 conversion decode GGUF values in registers and feed the existing ConvRot quantization epilogues, so no dense weight is allocated. Computing an NVFP4 global scale requires one row-amax pass and one packing pass. Unsupported devices fail closed instead of allocating a dense fallback: INT8 requires CUDA, while NVFP4 requires exact SM120. Piper Kernels does not parse GGUF files and does not require a GGUF parser at runtime; the caller owns file loading, tensor-name mapping, and the packed bytes plus quant-type metadata.

Piper Attention

Piper Attention is the package's key-scaled integer-PV attention algorithm:

from piper_kernels import piper_attention

output = piper_attention(query, key, value, is_causal=False)

It follows FlashAttention's fused online-softmax structure and SageAttention's K smoothing plus INT8 QK quantization. Before quantization, it applies the same fixed signed, normalized Hadamard transform across each Q head and centered K head. This orthogonal change of basis preserves their exact dot products while smoothing outliers for the subsequent integer quantizers. Its distinct PV path quantizes each V key row with one signed-INT8 scale, folds those scales into nonnegative probabilities, and uses UINT8 x INT8 -> INT32 tensor-core products. The probability multiplier remains FP32 so every finite FP16 input scale is representable without a conversion in the hot loop. The online-softmax state, denominator, and PV numerator also remain FP32. The numerator stays in UINT8 probability-code units during the recurrence, and the common factor of 255 is removed once in the output epilogue.

For centered V, Piper Attention uses the exact identity

softmax(QK) @ V = softmax(QK) @ (V - mean_sequence(V)) + mean_sequence(V)

For non-causal attention, it stores only the compact FP32 [batch, head, feature] mean, subtracts it while quantizing V, and restores it in the attention epilogue. This improves signed-INT8 precision when V has a large feature bias and preserves constant V exactly. Causal attention leaves V uncentered so per-row INT8 rounding cannot make an earlier output depend on future V rows. Both paths preserve the original K/V sequence order.

Native mixed-sign MMA is selected on the supported NVIDIA backend through the packaged stock-Triton extension. The integer-PV benchmark retains the exact affine identity u @ v = (u - 128) @ v + 128 * sum(v) as a signed-INT8 correctness control; unsupported production targets use the portable quantized reference instead. The public optimized dispatch supports NVIDIA SM8x and consumer Blackwell SM12x, whose Triton lowering uses the MMAv2 instruction rewritten by the packaged extension. Exact SM120 uses packed four-code probability conversion for D64 and non-causal D128, while causal D128 retains the faster stock conversion. SM89 and exact SM120 have measured schedules; other supported targets use the generic schedule. Production plan selection depends on target, head dimension, and causal mode, not sequence length. Hopper lowers the operation through unsupported WGMMA and therefore uses the slow portable quantized reference. Native ROCm mixed-sign lowering remains future work.

Piper Attention is an independently developed Sage-derived design. The per-key quantizer, centering identity, and online-softmax lineage are not claimed as novel in isolation; the name identifies this package's selected combination and fused recurrence.

Sparse Piper Attention

Sparse Piper is a separate non-causal SM120 operator for pre-tiled H3-style self-attention:

from piper_kernels import SparsePiperAttention

attention = SparsePiperAttention(
    (0.2, 0.4, 0.6),
    routing="mean",
)
output = attention(
    query,
    key,
    value,
    sparse_key_blocks=1036,
    sparse_query_blocks=1024,  # optional leading routed-query K64 blocks
    block_lengths=block_lengths,  # optional valid-front padded K64 storage
)

Inputs use pre-tiled [batch, sequence, heads, 128] BF16 layout. Without block_lengths, every row participates in attention and the sequence length may be arbitrary; the operator pads only its internal quantized storage to K64. Supplying one contiguous device INT32 length in [1, 64] per physical K64 block instead selects valid-front padded storage. The output retains that physical layout so the caller can apply its existing gather; padded query rows are unspecified. sparse_key_blocks is a runtime count of complete routeable physical K64 prefix tiles, so any compact partial final tile belongs to the dense suffix. Routing defaults to FP32 min/max pooling; passing routing="mean" instead scores FP32 Q64/K64 mean summaries. Both policies select the same per-head block budget over the sparse prefix, after which every query attends to every remaining K/V row in the same softmax. By default every query block uses that policy. Supplying sparse_query_blocks makes only that many leading K64 query blocks routed; later query blocks attend every K/V block densely. This supports packed video-first layouts followed by dense non-video queries using one runtime scalar rather than a per-block mask. Engine owns only the semantic per-layer ratio profile. Each opaque attention call derives its temporary physical keep counts, packed offsets, and exact route storage from that immutable model configuration and the current prefix length. Dynamic compiled graphs accept changed prefix lengths and their resulting route capacities without compiling another graph or SM120 attention kernel. Routes remain call-local because both policies depend on the current Q/K values. Compatible ConvRot INT8, NVFP4, and ConvRot NVFP4 compiler rewrites preserve the selected policy while producing its summaries directly from fused projections.

Sparse Piper also exposes a routing-selectable Q/K/V-derived coarse-attention residual:

from piper_kernels import sparse_piper_coarse_residual

coarse_output = sparse_piper_coarse_residual(
    query,
    key,
    value,
    coarse_gate,
    routing="mean",  # or "minmax"
    coarse_key_blocks=total_key_blocks,
    coarse_scale=coarse_scale,
    block_lengths=block_lengths,
)
output = fine_output + coarse_output

The selected policy derives mean- or extrema-based Q/K block scores, mean-pools V blocks, applies dense coarse attention, expands each result over its physical K64 query block, multiplies the caller-provided gate directly without an implicit activation, and returns the independent residual for the caller to compose. The optional coarse_key_blocks prefix may include a partial compact tail and defaults to every available block. block_lengths is optional for compact storage and selects valid-front internally padded storage when supplied. coarse_attention_residual remains available for learned or already-materialized block scores. These composable implementations are the correctness and training contract; compatible compiled ConvRot INT8, NVFP4, and ConvRot NVFP4 graphs fuse the shared route scores, wider coarse attention, and gated residual, including valid-front padded storage. When a compatible static ConvRot INT8, NVFP4, or ConvRot NVFP4 projection immediately consumes the quantized attention result, the bounded output rewrite also supports block_lengths and the coarse residual together with sparse_query_blocks. It passes the coarse result and coarse gate into each ranged attention launch and projects that chunk directly, so the full BF16 attention output is not materialized.

The SM120 path writes packed UINT16 routes, pairs two logical K64 tiles in one physical K128 recurrence, and uses one centered-V INT8 scale per logical tile. Its online numerator and pre-rounding denominator remain FP32. Unsupported devices use a slow portable implementation of the same quantized Sparse Piper arithmetic. A separate exact-BF16 sparse reference serves as its quality oracle; it is not the public fallback.

SageAttention2++

The package provides an independently written, pure-Triton backend for the canonical SageAttention2++ 8+8 algorithm:

from piper_kernels import sage_attention_2pp

output = sage_attention_2pp(query, key, value, is_causal=False)

Inputs use [batch, heads, sequence, head_dim] layout and may be FP16 or BF16. The optimized backend requires NVIDIA FP8 tensor cores with FP16 accumulation (SM89 or newer); measured schedules currently cover consumer SM89 and SM120 GPUs, while other SM12x targets retain grouped Q/K quantization with generic scheduling. It supports head dimensions 64 and 128, equal query/KV head counts, arbitrary positive sequence lengths, rectangular non-causal attention, strided sequence dimensions, and torch.compile. It is inference-only and does not support autograd. Its production execution plans are also sequence-length invariant.

This is SageAttention2++, not a Piper Attention-specific algorithm: K is smoothed, the same fixed signed, normalized Hadamard transform is applied to Q and centered K, Q/K are quantized to INT8 with the canonical architecture-specific granularity, V and the online-softmax probabilities are quantized to E4M3, each 64-key P x V tile accumulates in FP16, and tile results are buffered in FP32. All optimized device code is Triton; the package contains no CUDA extension. Unsupported devices use the slow portable quantized reference.

Install either optimized attention backend with piper-kernels[triton]. The official CUDA SageAttention package is a revision-pinned, optional benchmark dependency only; it is not imported by production code. See benchmarks/README.md for the reproducible provider comparison.

Dependency direction

Applications such as Piper consume this package. Integrations such as torch-offload may optionally recognize its tensor types, but piper-kernels does not depend on either project.

Development

uv sync --dev
uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv build

GPU tests use the gpu pytest marker. The pre-commit test hook hides CUDA so commits run the portable suite; run uv run pytest directly to exercise installed GPU backends.

Releases

Releases follow the compatibility and release policy in VERSIONING.md. Distribution artifacts are built from version tags and published to PyPI by GitHub Actions using Trusted Publishing; maintainers do not upload releases from local environments.