1003 lines
39 KiB
Python
1003 lines
39 KiB
Python
"""
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ConvRot quantization backends (convrot4 / convrot8 qtypes).
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convrot4 is the paper's W4A4 NVFP4 method described below. convrot8 pairs the same
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rotation with per-token / per-output-channel symmetric int8 (W8A8) and
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torch._int_mm: near-lossless (~1% weight error), and the fast path runs on any int8
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tensor-core gpu (Ampere+), not just Blackwell. The rotation is what makes the coarse
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per-row scales safe — it spreads outliers so a whole row shares one scale without
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clipping damage (the classic SmoothQuant failure mode).
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Implements "ConvRot: Rotation-Based Plug-and-Play 4-bit Quantization for Diffusion
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Transformers" (arXiv:2512.03673) as an OstrisQuantizer backend, self-contained on
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top of torch (no torchao version requirements).
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Method: weights and activations are rotated with a block *regular* Hadamard
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transform (R4 = [[1,1,1,-1],[1,1,-1,1],[1,-1,1,1],[-1,1,1,1]]/2 Kronecker-powered
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to rot_size, a power of 4, default 256). Unlike the standard Hadamard whose all-ones
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row concentrates the block mean into one coordinate, the regular Hadamard has
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constant row sums, smoothing row-wise and column-wise outliers symmetrically. The
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rotation is folded into the weight offline and applied to the activation at runtime,
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so it cancels in the matmul. Both sides are then quantized to NVFP4 (fp4 e2m1 values,
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fp8 e4m3 scale per 16 elements, plus one fp32 per-tensor scale) and multiplied with
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the Blackwell fp4 tensor cores via torch._scaled_mm — a real ~5-6x gemm speedup, ~2x
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at the layer level after rotation + activation-quant overhead.
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Paths:
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- inference (no grad): rotate -> fused triton nvfp4 activation quant ->
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hardware fp4 gemm. Requires sm_100+ (Blackwell); otherwise falls back to the
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dequantized matmul below.
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- training (grad enabled): rotate -> straight-through fake-quant of the
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activation (so adapters train against the same W4A4 numerics that deployment
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uses) -> bf16 matmul with the dequantized rotated weight. Fully differentiable
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w.r.t. the input.
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Everything is deterministic: the rotation is a fixed matrix (no randomness at all)
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and quantization is pure rounding.
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Quantized state attached to each module:
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cr_qdata packed e2m1 codes (uint8, out x in/2; low nibble = even element)
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cr_scales e4m3 block scales (out x in/16)
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cr_scales_blocked the same scales pre-swizzled for torch._scaled_mm
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cr_pts fp32 per-tensor scale (scalar)
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cr_rot / module.cr_rot_size rotation block size
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"""
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from toolkit.print import print_acc
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from toolkit.util.ostris_quant import OstrisQuantizer
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CONVROT_QTYPES = ("convrot4", "convrot8")
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def get_convrot_quantizer(qtype: str):
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if qtype == "convrot4":
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return ConvRotQuantizer(rot_size=256)
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if qtype == "convrot8":
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return ConvRotInt8Quantizer(rot_size=256)
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return None
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F4_MAX = 6.0
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F8_E4M3_MAX = 448.0
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BLOCK = 16 # nvfp4 scale block
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_E2M1_EDGES = [0.25, 0.75, 1.25, 1.75, 2.5, 3.5, 5.0]
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_hadamard_cache = {}
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_edges_cache = {}
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_skip_warned = set()
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def _cached(cache, key, build):
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if key not in cache:
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cache[key] = build()
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return cache[key]
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def regular_hadamard(rot_size: int, device, dtype=torch.bfloat16) -> torch.Tensor:
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"""The ConvRot rotation: Kronecker powers of the 4x4 regular Hadamard matrix,
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orthonormal. Symmetric and orthogonal, so it is its own inverse."""
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key = (rot_size, str(device), dtype)
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def build():
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r4 = torch.tensor(
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[[1.0, 1, 1, -1], [1, 1, -1, 1], [1, -1, 1, 1], [-1, 1, 1, 1]],
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dtype=torch.float64,
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)
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h = r4.clone()
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while h.shape[0] < rot_size:
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h = torch.kron(h, r4)
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if h.shape[0] != rot_size:
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raise ValueError(f"rot_size {rot_size} is not a power of 4")
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return (h / rot_size**0.5).to(device=device, dtype=dtype)
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return _cached(_hadamard_cache, key, build)
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def largest_pow4_divisor(d: int) -> int:
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h = 1
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while d % (h * 4) == 0:
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h *= 4
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return h
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def rotate(x: torch.Tensor, rot_size: int) -> torch.Tensor:
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"""Apply the block regular-Hadamard rotation along the last dim (self-inverse)."""
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if rot_size == 1:
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return x
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h = regular_hadamard(rot_size, x.device, x.dtype)
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shape = x.shape
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xb = x.reshape(-1, shape[-1] // rot_size, rot_size)
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return torch.matmul(xb, h).reshape(shape)
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def to_blocked(m: torch.Tensor) -> torch.Tensor:
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"""Rearrange an (R, C) scale matrix into the swizzled layout torch._scaled_mm
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expects for block-scaled fp4 (cublas 128x4-tile layout)."""
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rows, cols = m.shape
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rb, cb = -(-rows // 128), -(-cols // 4)
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if (rows, cols) != (rb * 128, cb * 4):
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padded = torch.zeros(rb * 128, cb * 4, device=m.device, dtype=m.dtype)
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padded[:rows, :cols] = m
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m = padded
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blocks = m.view(rb, 128, cb, 4).permute(0, 2, 1, 3)
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return blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16).flatten()
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def quantize_nvfp4(x: torch.Tensor, pts: Optional[torch.Tensor] = None):
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"""Quantize (rows, K) to nvfp4. Returns (packed uint8 (rows, K/2),
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e4m3 scales (rows, K/16), fp32 per-tensor scale)."""
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rows, K = x.shape
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xf = x.float()
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if pts is None:
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pts = xf.abs().amax() / (F4_MAX * F8_E4M3_MAX)
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pts = torch.where(pts > 0, pts, torch.ones_like(pts))
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xb = xf.view(rows, K // BLOCK, BLOCK)
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scales = (xb.abs().amax(dim=-1) / (F4_MAX * pts)).to(torch.float8_e4m3fn)
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denom = (scales.float() * pts).unsqueeze(-1)
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z = (xb / torch.where(denom > 0, denom, torch.ones_like(denom))).clamp(
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-F4_MAX, F4_MAX
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)
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edges = _cached(
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_edges_cache, str(x.device), lambda: torch.tensor(_E2M1_EDGES, device=x.device)
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)
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mag = torch.bucketize(z.abs(), edges).to(torch.uint8)
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codes = (mag | ((z < 0).to(torch.uint8) << 3)).view(rows, K)
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packed = ((codes[:, 1::2] << 4) | codes[:, ::2]).contiguous()
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return packed, scales, pts
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def dequantize_nvfp4(
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packed: torch.Tensor,
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scales: torch.Tensor,
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pts: torch.Tensor,
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rows: int,
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K: int,
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dtype: torch.dtype,
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) -> torch.Tensor:
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# single-pass triton path when available: the torch chain below is ~7 full-size
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# elementwise passes with fp32 intermediates, which made every convrot4 training
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# backward pay a dequant cost comparable to the gradient matmul itself
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if _triton_available() and packed.is_cuda and dtype in (torch.bfloat16, torch.float16, torch.float32):
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return _fp4_dequant_op(
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packed, scales.view(torch.uint8), pts.reshape(1).view(torch.uint8),
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str(dtype).split(".")[-1],
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)
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codes = torch.stack([packed & 15, packed >> 4], dim=-1).view(rows, K)
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# the lookup table is built inline (NOT module-cached): this function runs inside
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# custom-op backwards, which torch.compile traces with fake tensors where a
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# pre-existing real tensor is illegal; an in-trace constructed constant is fine
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vals = torch.tensor(
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[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], device=packed.device
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)
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mag = torch.index_select(vals, 0, (codes & 7).flatten().to(torch.int32)).view(rows, K)
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v = mag * torch.where((codes & 8) > 0, -1.0, 1.0)
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v = v.view(rows, K // BLOCK, BLOCK) * (scales.float() * pts).unsqueeze(-1)
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return v.view(rows, K).to(dtype)
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# ---------------- fused triton activation quant ----------------
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_triton_ok = None
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def _triton_available() -> bool:
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global _triton_ok
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if _triton_ok is None:
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try:
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import triton # noqa: F401
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import triton.language as tl # noqa: F401
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_triton_ok = True
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except Exception:
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_triton_ok = False
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print_acc(
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"ConvRot: triton is not available. The fused activation-quant kernel is "
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"disabled and activations will be quantized with plain torch ops instead "
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"— inference gets slower (most of the fp4 speedup is lost), but quality "
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"and training are unaffected."
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)
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return _triton_ok
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_kernel = None
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def _get_kernel():
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global _kernel
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if _kernel is not None:
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return _kernel
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import triton
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import triton.language as tl
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@triton.jit
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def nvfp4_act_quant_kernel(
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x_ptr,
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out_ptr,
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scale_ptr,
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pts_ptr,
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K,
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n_col_tiles,
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BLOCK_K: tl.constexpr,
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BLOCKED_SCALES: tl.constexpr,
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):
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pid_m = tl.program_id(0)
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pid_k = tl.program_id(1)
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pts = tl.load(pts_ptr)
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offs = pid_k * BLOCK_K + tl.arange(0, BLOCK_K)
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mask = offs < K
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x = tl.load(x_ptr + pid_m * K + offs, mask=mask, other=0.0).to(tl.float32)
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xb = tl.reshape(x, (BLOCK_K // 16, 16))
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amax = tl.max(tl.abs(xb), axis=1)
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# clamp to the e4m3 max so an oversized block can't overflow the fp8
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# conversion (matters now that activations use a fixed pts=1)
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scale8 = tl.minimum(amax / (6.0 * pts), 448.0).to(tl.float8e4nv)
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denom = scale8.to(tl.float32) * pts
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denom = tl.where(denom > 0, denom, 1.0)
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# note: triton fp32 division on this backend is ~1ulp off ieee (even with
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# tl.fdiv ieee_rounding=True), so values landing exactly on a code boundary
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# can round to the adjacent code vs the torch path. ties are equidistant, so
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# this changes nothing quantitatively; activation codes are transient (never
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# stored), and the kernel itself is deterministic.
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z = xb / denom[:, None]
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z = tl.minimum(tl.maximum(z, -6.0), 6.0)
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az = tl.abs(z)
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# strict > so exact midpoints go to the lower code, matching torch.bucketize
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code = (
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(az > 0.25).to(tl.uint8)
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+ (az > 0.75).to(tl.uint8)
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+ (az > 1.25).to(tl.uint8)
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+ (az > 1.75).to(tl.uint8)
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+ (az > 2.5).to(tl.uint8)
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+ (az > 3.5).to(tl.uint8)
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+ (az > 5.0).to(tl.uint8)
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)
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code = code | ((z < 0).to(tl.uint8) << 3)
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lo, hi = tl.split(tl.reshape(code, (BLOCK_K // 2, 2)))
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byte = lo | (hi << 4)
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offs_b = pid_k * (BLOCK_K // 2) + tl.arange(0, BLOCK_K // 2)
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tl.store(out_ptr + pid_m * (K // 2) + offs_b, byte, mask=offs_b < K // 2)
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s_idx = pid_k * (BLOCK_K // 16) + tl.arange(0, BLOCK_K // 16)
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if BLOCKED_SCALES:
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# store straight into the cublas 128x4-tile swizzle (see to_blocked)
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r_t = pid_m // 128
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r_in = pid_m % 128
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c_t = s_idx // 4
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c = s_idx % 4
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offs_s = (
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((r_t * n_col_tiles + c_t) * 32 + (r_in % 32)) * 16
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+ (r_in // 32) * 4
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+ c
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)
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else:
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offs_s = pid_m * (K // 16) + s_idx
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tl.store(scale_ptr + offs_s, scale8, mask=s_idx < K // 16)
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_kernel = nvfp4_act_quant_kernel
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return _kernel
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def _launch_nvfp4_kernel(x, packed, scales, pts, blocked_scales: bool):
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rows, K = x.shape
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n_col_tiles = -(-(K // BLOCK) // 4)
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BLOCK_K = 2048 if K >= 2048 else K
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grid = (rows, -(-K // BLOCK_K))
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_get_kernel()[grid](
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x, packed, scales, pts,
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K, n_col_tiles,
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BLOCK_K=BLOCK_K, BLOCKED_SCALES=blocked_scales, num_warps=4,
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)
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# registered as a custom op so torch.compile treats the triton launch as an opaque
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# node with known output shapes; tracing raw JITFunction calls breaks inductor's
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# autotune arg-cloning (seen on wan 2.2 under compile). the op also pads its output
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# rows to a multiple of 16 for torch._scaled_mm (callers slice the mm result) so
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# the compiled graph never contains a symbolic constant_pad_nd, which inductor
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# mis-handles when fused with the op input.
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@torch.library.custom_op("ostris::convrot_nvfp4_act_quant", mutates_args=())
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def _nvfp4_act_quant_op(x: torch.Tensor) -> list[torch.Tensor]:
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rows, K = x.shape
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rows_pad = -(-rows // 16) * 16
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x = x.contiguous()
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# activations use a FIXED per-tensor scale of 1: their block scales fit the
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# e4m3 range natively (unlike tiny weight magnitudes, which keep dynamic pts),
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# measured quality-neutral on realistic distributions — and it removes a full
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# activation read (global amax) plus a device sync from every forward
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pts = torch.ones((), device=x.device)
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packed = torch.empty(rows_pad, K // 2, device=x.device, dtype=torch.uint8)
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if rows_pad != rows:
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packed[rows:].zero_()
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n_col_tiles = -(-(K // BLOCK) // 4)
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# zero-init: rows are padded to 128-tiles and the pad region must be zero
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scales = torch.zeros(
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(-(-rows_pad // 128)) * 128 * n_col_tiles * 4,
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device=x.device, dtype=torch.float8_e4m3fn,
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)
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_launch_nvfp4_kernel(x, packed, scales, pts, blocked_scales=True)
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return [packed, scales.view(torch.uint8), pts]
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@_nvfp4_act_quant_op.register_fake
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def _nvfp4_act_quant_fake(x):
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rows, K = x.shape
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rows_pad = -(-rows // 16) * 16
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n_col_tiles = -(-(K // BLOCK) // 4)
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return [
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torch.empty(rows_pad, K // 2, device=x.device, dtype=torch.uint8),
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torch.empty((-(-rows_pad // 128)) * 128 * n_col_tiles * 4, device=x.device, dtype=torch.uint8),
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torch.empty((), device=x.device, dtype=torch.float32),
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]
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def quantize_nvfp4_fused(x: torch.Tensor, blocked_scales: bool = False):
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"""Triton path of quantize_nvfp4 for the inference hot loop: one read of x,
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writes packed codes + e4m3 scales (row-major, or directly in the swizzled
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layout torch._scaled_mm wants when blocked_scales=True). Falls back to the
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torch ops (row-major only)."""
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rows, K = x.shape
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if not (_triton_available() and x.is_cuda and K % 16 == 0):
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if blocked_scales:
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# match the custom op: rows padded to a multiple of 16 for _scaled_mm,
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# fixed pts=1 for activations (see _nvfp4_act_quant_op)
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rows_pad = -(-rows // 16) * 16
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if rows_pad != rows:
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x = F.pad(x, (0, 0, 0, rows_pad - rows))
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packed, scales, pts = quantize_nvfp4(x, pts=torch.ones((), device=x.device))
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return packed, to_blocked(scales), pts
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return quantize_nvfp4(x)
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if blocked_scales:
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packed, scales_u8, pts = _nvfp4_act_quant_op(x)
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return packed, scales_u8.view(torch.float8_e4m3fn), pts
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# row-major variant (used by tests/tools, not the compiled hot path)
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pts = x.float().abs().amax() / (F4_MAX * F8_E4M3_MAX)
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pts = torch.where(pts > 0, pts, torch.ones_like(pts))
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x = x.contiguous()
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packed = torch.empty(rows, K // 2, device=x.device, dtype=torch.uint8)
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scales = torch.empty(rows, K // BLOCK, device=x.device, dtype=torch.float8_e4m3fn)
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_launch_nvfp4_kernel(x, packed, scales, pts, blocked_scales=False)
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return packed, scales, pts
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# ---------------- fp4 dequant kernel (backward hot path) ----------------
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_dequant_kernel = None
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def _get_dequant_kernel():
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global _dequant_kernel
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if _dequant_kernel is not None:
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return _dequant_kernel
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import triton
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import triton.language as tl
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@triton.jit
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def nvfp4_dequant_kernel(
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q_ptr, s_ptr, pts_ptr, out_ptr, K,
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BLOCK_B: tl.constexpr,
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):
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row = tl.program_id(0)
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pid_k = tl.program_id(1)
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pts = tl.load(pts_ptr)
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offs_b = pid_k * BLOCK_B + tl.arange(0, BLOCK_B)
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bmask = offs_b < K // 2
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byte = tl.load(q_ptr + row * (K // 2) + offs_b, mask=bmask, other=0)
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codes = tl.interleave(byte & 15, byte >> 4) # (2*BLOCK_B,), column order
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m = (codes & 7).to(tl.float32)
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# arithmetic e2m1 decode ([0, .5, 1, 1.5, 2, 3, 4, 6]), exact
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mag = tl.where(m < 2, m * 0.5, tl.exp2(tl.floor(m / 2) - 1) * (1 + (m % 2) * 0.5))
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v = tl.where((codes & 8) > 0, -mag, mag)
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n_s: tl.constexpr = (2 * BLOCK_B) // 16
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offs_s = pid_k * n_s + tl.arange(0, n_s)
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s = tl.load(s_ptr + row * (K // 16) + offs_s, mask=offs_s < K // 16, other=0.0)
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vb = tl.reshape(v, (n_s, 16)) * (s.to(tl.float32) * pts)[:, None]
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out = tl.reshape(vb, (2 * BLOCK_B,))
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offs_v = pid_k * (2 * BLOCK_B) + tl.arange(0, 2 * BLOCK_B)
|
|
tl.store(out_ptr + row * K + offs_v, out.to(out_ptr.dtype.element_ty), mask=offs_v < K)
|
|
|
|
_dequant_kernel = nvfp4_dequant_kernel
|
|
return _dequant_kernel
|
|
|
|
|
|
# custom op so the kernel stays opaque where it matters most: inside the fp4
|
|
# training op's registered backward, which torch.compile traces with fake tensors
|
|
@torch.library.custom_op("ostris::convrot_fp4_dequant", mutates_args=())
|
|
def _fp4_dequant_op(
|
|
packed: torch.Tensor,
|
|
scales_u8: torch.Tensor,
|
|
pts_u8: torch.Tensor,
|
|
out_dtype: str,
|
|
) -> torch.Tensor:
|
|
rows, half = packed.shape
|
|
out = torch.empty(rows, half * 2, device=packed.device, dtype=getattr(torch, out_dtype))
|
|
kernel = _get_dequant_kernel()
|
|
block_b = 1024
|
|
grid = (rows, -(-half // block_b))
|
|
kernel[grid](
|
|
packed.contiguous(),
|
|
scales_u8.view(torch.float8_e4m3fn),
|
|
pts_u8.view(torch.float32),
|
|
out, half * 2,
|
|
BLOCK_B=block_b, num_warps=4,
|
|
)
|
|
return out
|
|
|
|
|
|
@_fp4_dequant_op.register_fake
|
|
def _fp4_dequant_fake(packed, scales_u8, pts_u8, out_dtype):
|
|
rows, half = packed.shape
|
|
return torch.empty(rows, half * 2, device=packed.device, dtype=getattr(torch, out_dtype))
|
|
|
|
|
|
# ---------------- backend ----------------
|
|
|
|
|
|
_warned_no_fp4 = False
|
|
|
|
|
|
def _fp4_gemm_supported(device) -> bool:
|
|
global _warned_no_fp4
|
|
device = torch.device(device)
|
|
supported = (
|
|
device.type == "cuda"
|
|
and torch.cuda.is_available()
|
|
and torch.cuda.get_device_capability(device)[0] >= 10 # Blackwell
|
|
)
|
|
if not supported and not _warned_no_fp4:
|
|
_warned_no_fp4 = True
|
|
print_acc(
|
|
f"ConvRot: no fp4 tensor-core support on this device ({device}; needs an "
|
|
"NVIDIA Blackwell GPU, sm_100+). Inference falls back to dequantized bf16 "
|
|
"matmuls: correct output but NO speedup, and inference activations stay "
|
|
"unquantized (W4A16 numerics instead of W4A4). The training path is "
|
|
"unaffected (it always simulates W4A4 via fake-quant)."
|
|
)
|
|
return supported
|
|
|
|
|
|
# the fp4 training-path linear: forward VALUE is the real fp4 tensor-core gemm
|
|
# (bit-identical to the inference path), gradient is the straight-through estimate
|
|
# d y / d x_rot ~= dequant(W'). the backward re-dequantizes the weight from the fp4
|
|
# codes instead of saving a bf16 copy per layer (F.linear in the old fake-quant path
|
|
# retained a dequantized weight for every layer of the graph — a full bf16 model of
|
|
# extra train-step vram) and x is not saved at all.
|
|
@torch.library.custom_op("ostris::convrot_fp4_linear_ste", mutates_args=())
|
|
def _fp4_linear_ste_op(
|
|
x2d: torch.Tensor,
|
|
qdata: torch.Tensor,
|
|
scales_u8: torch.Tensor,
|
|
scales_blocked_u8: torch.Tensor,
|
|
pts_u8: torch.Tensor,
|
|
bias: Optional[torch.Tensor],
|
|
out_dtype: str,
|
|
) -> torch.Tensor:
|
|
m = x2d.shape[0]
|
|
aq, a_scales_blocked, a_pts = quantize_nvfp4_fused(x2d, blocked_scales=True)
|
|
out = torch._scaled_mm(
|
|
aq.view(torch.float4_e2m1fn_x2),
|
|
qdata.view(torch.float4_e2m1fn_x2).t(),
|
|
a_scales_blocked.view(torch.float8_e4m3fn),
|
|
scales_blocked_u8.view(torch.float8_e4m3fn),
|
|
out_dtype=getattr(torch, out_dtype),
|
|
)
|
|
if out.shape[0] != m:
|
|
out = out[:m]
|
|
s = (a_pts * pts_u8.view(torch.float32).reshape(())).to(out.dtype)
|
|
if bias is not None:
|
|
return torch.addcmul(bias, out, s)
|
|
return out * s
|
|
|
|
|
|
@_fp4_linear_ste_op.register_fake
|
|
def _fp4_linear_ste_fake(x2d, qdata, scales_u8, scales_blocked_u8, pts_u8, bias, out_dtype):
|
|
return torch.empty(
|
|
x2d.shape[0], qdata.shape[0], device=x2d.device, dtype=getattr(torch, out_dtype)
|
|
)
|
|
|
|
|
|
def _fp4_linear_ste_setup(ctx, inputs, output):
|
|
x2d, qdata, scales_u8, scales_blocked_u8, pts_u8, bias, out_dtype = inputs
|
|
ctx.save_for_backward(qdata, scales_u8, pts_u8)
|
|
|
|
|
|
def _fp4_linear_ste_backward(ctx, grad):
|
|
qdata, scales_u8, pts_u8 = ctx.saved_tensors
|
|
out_f, in_half = qdata.shape
|
|
w = dequantize_nvfp4(
|
|
qdata, scales_u8.view(torch.float8_e4m3fn),
|
|
pts_u8.view(torch.float32).reshape(()),
|
|
out_f, in_half * 2, grad.dtype,
|
|
)
|
|
return grad @ w, None, None, None, None, None, None
|
|
|
|
|
|
_fp4_linear_ste_op.register_autograd(
|
|
_fp4_linear_ste_backward, setup_context=_fp4_linear_ste_setup
|
|
)
|
|
|
|
|
|
class ConvRotQuantizer(OstrisQuantizer):
|
|
"""ConvRot W4A4 backend. One instance per qtype, shareable across modules."""
|
|
|
|
def __init__(self, rot_size: int = 256):
|
|
self.rot_size = rot_size
|
|
|
|
def _rot_for(self, d: int) -> int:
|
|
return min(self.rot_size, largest_pow4_divisor(d))
|
|
|
|
def can_quantize(self, module: torch.nn.Linear) -> bool:
|
|
d = module.in_features
|
|
rot = self._rot_for(d)
|
|
if d % BLOCK != 0 or module.out_features % BLOCK != 0 or rot < 16:
|
|
if d not in _skip_warned:
|
|
_skip_warned.add(d)
|
|
print_acc(
|
|
f"ConvRot: skipping linears with in_features={d} "
|
|
f"(needs in/out divisible by 16 and a power-of-4 block >= 16)"
|
|
)
|
|
return False
|
|
return True
|
|
|
|
def quantize_(self, module: torch.nn.Linear, weight_fp32: torch.Tensor) -> None:
|
|
rot = self._rot_for(module.in_features)
|
|
w_rot = rotate(weight_fp32, rot)
|
|
packed, scales, pts = quantize_nvfp4(w_rot)
|
|
# scales/pts are stored as uint8 byte views: nn.Module._apply dtype-casts
|
|
# every floating buffer (module.to(dtype=...) would silently convert the
|
|
# e4m3 scales to bf16 and fp32 pts to bf16, corrupting them). integer
|
|
# buffers are only ever moved, never cast.
|
|
module.register_buffer("cr_qdata", packed, persistent=False)
|
|
module.register_buffer("cr_scales", scales.view(torch.uint8), persistent=False)
|
|
module.register_buffer(
|
|
"cr_scales_blocked", to_blocked(scales).view(torch.uint8), persistent=False
|
|
)
|
|
module.register_buffer(
|
|
"cr_pts",
|
|
pts.detach().clone().reshape(1).view(torch.uint8),
|
|
persistent=False,
|
|
)
|
|
module.cr_rot_size = rot
|
|
|
|
@staticmethod
|
|
def _pts(module) -> torch.Tensor:
|
|
return module.cr_pts.view(torch.float32).reshape(())
|
|
|
|
def _dequantize_rotated(self, module, dtype: torch.dtype) -> torch.Tensor:
|
|
return dequantize_nvfp4(
|
|
module.cr_qdata,
|
|
module.cr_scales.view(torch.float8_e4m3fn),
|
|
self._pts(module),
|
|
module.out_features,
|
|
module.in_features,
|
|
dtype,
|
|
)
|
|
|
|
def dequantize(self, module) -> torch.Tensor:
|
|
w = self._dequantize_rotated(module, torch.float32)
|
|
return rotate(w, module.cr_rot_size) # self-inverse
|
|
|
|
def requantize_(self, module, fp_weight: torch.Tensor) -> None:
|
|
w = fp_weight.to(device=module.cr_qdata.device, dtype=torch.float32)
|
|
w_rot = rotate(w, module.cr_rot_size)
|
|
packed, scales, pts = quantize_nvfp4(w_rot)
|
|
module.cr_qdata = packed
|
|
module.cr_scales = scales.view(torch.uint8)
|
|
module.cr_scales_blocked = to_blocked(scales).view(torch.uint8)
|
|
module.cr_pts = pts.detach().clone().reshape(1).view(torch.uint8)
|
|
|
|
def forward(self, module, x: torch.Tensor) -> torch.Tensor:
|
|
rot = module.cr_rot_size
|
|
in_f, out_f = module.in_features, module.out_features
|
|
m = x.numel() // in_f
|
|
|
|
if x.requires_grad:
|
|
# training path, gated on requires_grad alone (not is_grad_enabled) so
|
|
# both passes of gradient checkpointing take the same branch
|
|
if _fp4_gemm_supported(x.device):
|
|
# fp4 tensor-core forward (bit-identical to the inference path)
|
|
# with a straight-through analytic backward
|
|
x2d = rotate(x, rot).reshape(-1, in_f)
|
|
out = _fp4_linear_ste_op(
|
|
x2d, module.cr_qdata, module.cr_scales,
|
|
module.cr_scales_blocked, module.cr_pts, module.bias,
|
|
str(x.dtype).split(".")[-1],
|
|
)
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
# no fp4 hardware: straight-through fake-quant of the activation and a
|
|
# differentiable bf16 matmul against the dequantized rotated weight
|
|
x2d = rotate(x, rot).reshape(-1, in_f)
|
|
with torch.no_grad():
|
|
aq, a_scales, a_pts = quantize_nvfp4(x2d.detach())
|
|
x_dq = dequantize_nvfp4(aq, a_scales, a_pts, m, in_f, x.dtype)
|
|
w = self._dequantize_rotated(module, x.dtype)
|
|
x_ste = x2d + (x_dq - x2d).detach()
|
|
out = F.linear(x_ste, w, module.bias)
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
|
|
if _fp4_gemm_supported(x.device):
|
|
# row padding for _scaled_mm happens inside the act-quant op (compile
|
|
# safety); slice the mm output back to m rows (a contiguous prefix).
|
|
# NOTE: no fused rotate+quant here (unlike convrot8): the e2m1 packing
|
|
# needs tl.reshape/tl.split of dot-derived tensors, which this triton
|
|
# backend miscompiles when the dot sits in/after a loop (element order
|
|
# scrambles). the int8 kernel avoids those primitives and fuses safely.
|
|
aq, a_scales_blocked, a_pts = quantize_nvfp4_fused(
|
|
rotate(x, rot).reshape(-1, in_f), blocked_scales=True
|
|
)
|
|
out = torch._scaled_mm(
|
|
aq.view(torch.float4_e2m1fn_x2),
|
|
module.cr_qdata.view(torch.float4_e2m1fn_x2).t(),
|
|
a_scales_blocked.view(torch.float8_e4m3fn),
|
|
module.cr_scales_blocked.view(torch.float8_e4m3fn),
|
|
out_dtype=x.dtype,
|
|
)
|
|
if out.shape[0] != m:
|
|
out = out[:m]
|
|
s = (a_pts * self._pts(module)).to(x.dtype)
|
|
if module.bias is not None:
|
|
out = torch.addcmul(module.bias, out, s)
|
|
else:
|
|
out = out * s
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
|
|
# no fp4 hardware: dequantized matmul (correct, no speedup)
|
|
w = self._dequantize_rotated(module, x.dtype)
|
|
out = F.linear(rotate(x, rot).reshape(-1, in_f), w, module.bias)
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
|
|
|
|
# ---------------- convrot8: W8A8 int8 backend ----------------
|
|
|
|
|
|
def quantize_int8_rows(x: torch.Tensor):
|
|
"""Symmetric per-row int8 quantization. Returns (int8 (rows, K), fp32 scales (rows,))."""
|
|
xf = x.float()
|
|
scales = xf.abs().amax(dim=1) / 127.0
|
|
scales = torch.where(scales > 0, scales, torch.ones_like(scales))
|
|
q = torch.round(xf / scales.unsqueeze(1)).clamp_(-127, 127).to(torch.int8)
|
|
return q, scales
|
|
|
|
|
|
_int8_kernels = None
|
|
|
|
|
|
def _get_int8_kernels():
|
|
global _int8_kernels
|
|
if _int8_kernels is not None:
|
|
return _int8_kernels
|
|
import triton
|
|
import triton.language as tl
|
|
from triton.language.extra import libdevice
|
|
|
|
@triton.jit
|
|
def int8_act_quant_kernel(x_ptr, q_ptr, s_ptr, K, BLOCK_K: tl.constexpr):
|
|
row = tl.program_id(0)
|
|
base = row * K
|
|
acc = tl.zeros((BLOCK_K,), tl.float32)
|
|
for k0 in range(0, K, BLOCK_K):
|
|
offs = k0 + tl.arange(0, BLOCK_K)
|
|
v = tl.load(x_ptr + base + offs, mask=offs < K, other=0.0).to(tl.float32)
|
|
acc = tl.maximum(acc, tl.abs(v))
|
|
amax = tl.max(acc, axis=0)
|
|
scale = tl.where(amax > 0, amax / 127.0, 1.0)
|
|
for k0 in range(0, K, BLOCK_K):
|
|
offs = k0 + tl.arange(0, BLOCK_K)
|
|
mask = offs < K
|
|
v = tl.load(x_ptr + base + offs, mask=mask, other=0.0).to(tl.float32)
|
|
# rint = round-half-to-even, matching torch.round in the reference path
|
|
q = libdevice.rint(v / scale)
|
|
q = tl.minimum(tl.maximum(q, -127.0), 127.0)
|
|
tl.store(q_ptr + base + offs, q.to(tl.int8), mask=mask)
|
|
tl.store(s_ptr + row, scale)
|
|
|
|
@triton.jit
|
|
def int8_epilogue_kernel(
|
|
i_ptr,
|
|
as_ptr,
|
|
ws_ptr,
|
|
b_ptr,
|
|
o_ptr,
|
|
N,
|
|
HAS_BIAS: tl.constexpr,
|
|
BLOCK_N: tl.constexpr,
|
|
):
|
|
row = tl.program_id(0)
|
|
cb = tl.program_id(1)
|
|
offs = cb * BLOCK_N + tl.arange(0, BLOCK_N)
|
|
mask = offs < N
|
|
acc = tl.load(i_ptr + row * N + offs, mask=mask, other=0).to(tl.float32)
|
|
a_s = tl.load(as_ptr + row)
|
|
w_s = tl.load(ws_ptr + offs, mask=mask, other=0.0)
|
|
out = acc * (a_s * w_s)
|
|
if HAS_BIAS:
|
|
out += tl.load(b_ptr + offs, mask=mask, other=0.0).to(tl.float32)
|
|
tl.store(o_ptr + row * N + offs, out.to(o_ptr.dtype.element_ty), mask=mask)
|
|
|
|
_int8_kernels = (int8_act_quant_kernel, int8_epilogue_kernel)
|
|
return _int8_kernels
|
|
|
|
|
|
# registered as custom ops so torch.compile treats the triton launches as opaque
|
|
# nodes with known output shapes (see _nvfp4_act_quant_op). rows are padded to a
|
|
# multiple of 32 inside the op for torch._int_mm; callers slice the mm output.
|
|
@torch.library.custom_op("ostris::convrot_int8_act_quant", mutates_args=())
|
|
def _int8_act_quant_op(x: torch.Tensor) -> list[torch.Tensor]:
|
|
rows, K = x.shape
|
|
rows_pad = -(-rows // 32) * 32
|
|
x = x.contiguous()
|
|
q = torch.empty(rows_pad, K, device=x.device, dtype=torch.int8)
|
|
scales = torch.empty(rows_pad, device=x.device, dtype=torch.float32)
|
|
if rows_pad != rows:
|
|
q[rows:].zero_()
|
|
scales[rows:].fill_(1.0)
|
|
kernel, _ = _get_int8_kernels()
|
|
kernel[(rows,)](x, q, scales, K, BLOCK_K=2048 if K >= 2048 else K, num_warps=8)
|
|
return [q, scales]
|
|
|
|
|
|
@_int8_act_quant_op.register_fake
|
|
def _int8_act_quant_fake(x):
|
|
rows, K = x.shape
|
|
rows_pad = -(-rows // 32) * 32
|
|
return [
|
|
torch.empty(rows_pad, K, device=x.device, dtype=torch.int8),
|
|
torch.empty(rows_pad, device=x.device, dtype=torch.float32),
|
|
]
|
|
|
|
|
|
@torch.library.custom_op("ostris::convrot_int8_epilogue", mutates_args=())
|
|
def _int8_epilogue_op(
|
|
i32: torch.Tensor,
|
|
a_scales: torch.Tensor,
|
|
w_scales: torch.Tensor,
|
|
bias: Optional[torch.Tensor],
|
|
out_dtype: str,
|
|
) -> torch.Tensor:
|
|
m, n = i32.shape
|
|
out = torch.empty(m, n, device=i32.device, dtype=getattr(torch, out_dtype))
|
|
_, kernel = _get_int8_kernels()
|
|
grid = (m, -(-n // 1024))
|
|
kernel[grid](
|
|
i32,
|
|
a_scales,
|
|
w_scales,
|
|
bias if bias is not None else a_scales,
|
|
out,
|
|
n,
|
|
HAS_BIAS=bias is not None,
|
|
BLOCK_N=1024,
|
|
num_warps=4,
|
|
)
|
|
return out
|
|
|
|
|
|
@_int8_epilogue_op.register_fake
|
|
def _int8_epilogue_fake(i32, a_scales, w_scales, bias, out_dtype):
|
|
m, n = i32.shape
|
|
return torch.empty(m, n, device=i32.device, dtype=getattr(torch, out_dtype))
|
|
|
|
|
|
def quantize_int8_rows_fused(x: torch.Tensor):
|
|
"""Triton path of quantize_int8_rows: one extra read of x instead of the
|
|
multi-kernel torch chain. Falls back to the torch ops."""
|
|
if not (_triton_available() and x.is_cuda):
|
|
return quantize_int8_rows(x)
|
|
q, scales = _int8_act_quant_op(x)
|
|
rows = x.shape[0]
|
|
return q[:rows], scales[:rows]
|
|
|
|
|
|
def _int8_act_quant_padded(x: torch.Tensor):
|
|
"""Act quant with rows padded to a multiple of 32 for torch._int_mm."""
|
|
if _triton_available() and x.is_cuda:
|
|
q, scales = _int8_act_quant_op(x)
|
|
return q, scales
|
|
q, scales = quantize_int8_rows(x)
|
|
rows = q.shape[0]
|
|
rows_pad = -(-rows // 32) * 32
|
|
if rows_pad != rows:
|
|
q = F.pad(q, (0, 0, 0, rows_pad - rows))
|
|
scales = F.pad(scales, (0, rows_pad - rows), value=1.0)
|
|
return q, scales
|
|
|
|
|
|
# the training-path linear: forward VALUE is the real int8 tensor-core gemm (bit
|
|
# identical to the inference path), gradient is the straight-through estimate
|
|
# d y / d x_rot ~= dequant(W'), registered as a custom-op autograd so it works
|
|
# under torch.compile. the backward re-dequantizes the weight from int8 instead of
|
|
# saving a bf16 copy, and x is not saved at all — less memory than F.linear.
|
|
@torch.library.custom_op("ostris::convrot_int8_linear_ste", mutates_args=())
|
|
def _int8_linear_ste_op(
|
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x2d: torch.Tensor,
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qdata: torch.Tensor,
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w_scales_u8: torch.Tensor,
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bias: Optional[torch.Tensor],
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out_dtype: str,
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) -> torch.Tensor:
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m = x2d.shape[0]
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aq, a_s = _int8_act_quant_padded(x2d)
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i32 = torch._int_mm(aq, qdata.t())
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return _int8_epilogue(
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i32[:m], a_s[:m], w_scales_u8.view(torch.float32), bias,
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getattr(torch, out_dtype),
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)
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@_int8_linear_ste_op.register_fake
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def _int8_linear_ste_fake(x2d, qdata, w_scales_u8, bias, out_dtype):
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return torch.empty(
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x2d.shape[0], qdata.shape[0], device=x2d.device, dtype=getattr(torch, out_dtype)
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)
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def _int8_linear_ste_setup(ctx, inputs, output):
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x2d, qdata, w_scales_u8, bias, out_dtype = inputs
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ctx.save_for_backward(qdata, w_scales_u8)
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def _int8_linear_ste_backward(ctx, grad):
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qdata, w_scales_u8 = ctx.saved_tensors
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w_scales = w_scales_u8.view(torch.float32).to(grad.dtype)
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w = qdata.to(grad.dtype) * w_scales.unsqueeze(1)
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return grad @ w, None, None, None, None
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_int8_linear_ste_op.register_autograd(
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_int8_linear_ste_backward, setup_context=_int8_linear_ste_setup
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)
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def _int8_epilogue(
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i32: torch.Tensor,
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a_scales: torch.Tensor,
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w_scales: torch.Tensor,
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bias,
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out_dtype: torch.dtype,
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) -> torch.Tensor:
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"""out = i32 * a_scales[:, None] * w_scales[None, :] (+ bias), in out_dtype."""
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if _triton_available() and i32.is_cuda:
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return _int8_epilogue_op(
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i32, a_scales, w_scales, bias, str(out_dtype).split(".")[-1]
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)
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out = i32.float() * w_scales
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out = out * a_scales.unsqueeze(1)
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if bias is not None:
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out = out + bias.float()
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return out.to(out_dtype)
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_int8_mm_ok = None
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def _int8_gemm_supported(device) -> bool:
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global _int8_mm_ok
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device = torch.device(device)
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if device.type != "cuda" or not torch.cuda.is_available():
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supported = False
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else:
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if _int8_mm_ok is None:
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try:
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a = torch.zeros(32, 64, dtype=torch.int8, device=device)
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b = torch.zeros(64, 32, dtype=torch.int8, device=device)
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torch._int_mm(a, b)
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_int8_mm_ok = True
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except Exception:
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_int8_mm_ok = False
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supported = _int8_mm_ok
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global _warned_no_int8
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if not supported and not _warned_no_int8:
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_warned_no_int8 = True
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print_acc(
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f"ConvRot: int8 matmul (torch._int_mm) is not usable on this device "
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f"({device}). Inference falls back to dequantized bf16 matmuls: correct "
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"output but NO speedup, and inference activations stay unquantized "
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"(W8A16 numerics instead of W8A8). The training path is unaffected "
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"(it always simulates W8A8 via fake-quant)."
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)
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return supported
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_warned_no_int8 = False
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|
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class ConvRotInt8Quantizer(OstrisQuantizer):
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"""ConvRot W8A8 backend: shared regular-Hadamard rotation + per-token /
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per-output-channel symmetric int8 with torch._int_mm. One instance per qtype,
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shareable across modules."""
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def __init__(self, rot_size: int = 256):
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self.rot_size = rot_size
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def _rot_for(self, d: int) -> int:
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return min(self.rot_size, largest_pow4_divisor(d))
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def can_quantize(self, module: torch.nn.Linear) -> bool:
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d = module.in_features
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if d % BLOCK != 0 or module.out_features % 8 != 0 or self._rot_for(d) < 16:
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if d not in _skip_warned:
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_skip_warned.add(d)
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print_acc(
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f"ConvRot: skipping linears with in_features={d} "
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|
f"(needs in divisible by 16, out by 8, and a power-of-4 block >= 16)"
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)
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return False
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return True
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|
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def quantize_(self, module: torch.nn.Linear, weight_fp32: torch.Tensor) -> None:
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rot = self._rot_for(module.in_features)
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q, scales = quantize_int8_rows(rotate(weight_fp32, rot))
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module.register_buffer("cr8_qdata", q, persistent=False)
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# fp32 scales stored as a uint8 byte view (see convrot4: module.to(dtype=...)
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# would otherwise cast them)
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module.register_buffer("cr8_scales", scales.view(torch.uint8), persistent=False)
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module.cr8_rot_size = rot
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|
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|
@staticmethod
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def _scales(module) -> torch.Tensor:
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return module.cr8_scales.view(torch.float32)
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|
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def _dequantize_rotated(self, module, dtype: torch.dtype) -> torch.Tensor:
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|
w = module.cr8_qdata.float() * self._scales(module).unsqueeze(1)
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|
return w.to(dtype)
|
|
|
|
def dequantize(self, module) -> torch.Tensor:
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|
return rotate(
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|
self._dequantize_rotated(module, torch.float32), module.cr8_rot_size
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|
)
|
|
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|
def requantize_(self, module, fp_weight: torch.Tensor) -> None:
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|
w = fp_weight.to(device=module.cr8_qdata.device, dtype=torch.float32)
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|
q, scales = quantize_int8_rows(rotate(w, module.cr8_rot_size))
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module.cr8_qdata = q
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|
module.cr8_scales = scales.view(torch.uint8)
|
|
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|
def forward(self, module, x: torch.Tensor) -> torch.Tensor:
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|
rot = module.cr8_rot_size
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|
in_f, out_f = module.in_features, module.out_features
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|
m = x.numel() // in_f
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|
|
|
if x.requires_grad:
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|
# training: gated on requires_grad alone so both gradient-checkpoint
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|
# passes take the same branch
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|
if _int8_gemm_supported(x.device):
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|
# int8 tensor-core forward (bit-identical to the inference path)
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|
# with a straight-through analytic backward. the rotation stays a
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|
# cublas matmul: an in-kernel tl.dot rotation was tried and measured
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|
# SLOWER on every shape (small-tile dots at low tensor-core
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|
# utilization, computed twice for the amax and quant passes, cost
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|
# more than the activation round-trips they saved)
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|
out = _int8_linear_ste_op(
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|
rotate(x, rot).reshape(-1, in_f),
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|
module.cr8_qdata, module.cr8_scales, module.bias,
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|
str(x.dtype).split(".")[-1],
|
|
)
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|
return out.reshape(*x.shape[:-1], out_f)
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|
# no int8 hardware: straight-through fake-quant + bf16 matmul
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|
x2d = rotate(x, rot).reshape(-1, in_f)
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|
with torch.no_grad():
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|
aq, a_s = quantize_int8_rows(x2d.detach())
|
|
x_dq = (aq.float() * a_s.unsqueeze(1)).to(x.dtype)
|
|
w = self._dequantize_rotated(module, x.dtype)
|
|
x_ste = x2d + (x_dq - x2d).detach()
|
|
out = F.linear(x_ste, w, module.bias)
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
|
|
if _int8_gemm_supported(x.device):
|
|
# row padding for _int_mm happens inside the act-quant op (compile
|
|
# safety); slice the mm output back to m rows (a contiguous prefix)
|
|
aq, a_s = _int8_act_quant_padded(rotate(x, rot).reshape(-1, in_f))
|
|
i32 = torch._int_mm(aq, module.cr8_qdata.t())
|
|
out = _int8_epilogue(i32[:m], a_s[:m], self._scales(module), module.bias, x.dtype)
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
|
|
w = self._dequantize_rotated(module, x.dtype)
|
|
out = F.linear(rotate(x, rot).reshape(-1, in_f), w, module.bias)
|
|
return out.reshape(*x.shape[:-1], out_f)
|
|
|