diff --git a/modelopt/torch/quantization/ggml/codebooks.py b/modelopt/torch/quantization/ggml/codebooks.py index 5e015cd3765..22b14decb17 100644 --- a/modelopt/torch/quantization/ggml/codebooks.py +++ b/modelopt/torch/quantization/ggml/codebooks.py @@ -178,6 +178,33 @@ "Xf5hzoD21KEk6CpbkT/vBJbU" ) +# Compact byte representation of the canonical [1024, 8] IQ2_S grid. +_IQ2_S_GRID_ZLIB_B64 = ( + "eNqFmWF25DAIg//6CjoD979fG5sPI2bS7r48tttJYoOQhGet8ycySv4fkVFL9oHIqPAbIqPkD9DKB+SDIuPvx+zBkVHyF2mFvTAy" + "KnwBkfH3LluQlr4u7PfHc1/kz3mx4Mj43NU3EBmfZfYNacXXjUXG5+N9o1qyDUdGhSdAykRE/rwyISMxkVHKuOJrorieh/XEaelr" + "Ap/0RKalJzQyPtvtCX620RP9PO48xxMf6yRePflsjpe1gkTGnZ1WoL3aVqjIuN/SChcZd9laISOjlqywkXGnvxU6Mu40t6UBgMio" + "cEDstJx151Yi7wsDDLXhAkCRUUsGqMi4l9MAtl+nk3i11AG8yLg/1oAYGSUHplZ8BWhklBKwkT8vfQUwpaPWXAoHtuQAlxzoWnLA" + "JzPsVP3RAGCe3HIBktU3Ffny1igb3XH6IqGn3kCRcVe5NVRk3NmMzF7kbiN32Rpv/zayrSJ5qkF9NmRk3LBtDbrh2Bp0wyUSJpFw" + "sMYN9VbaC2sNrSVr7FhhDR6j0Z+7eysuIy/VHpaxqAqsgEXGQsrFXYKgFJIThgz9oBh0XiKJ1VEFii51LKvWJRzJiUeZpUlAQERy" + "QiKn/IWgIuOhmUtYkfHQySUw7iQFkfHQwCW2yHja+xIdb5yEB6RPO14CZGUQISsk9WCSmh7Y3x1MgjywvTuDMNnhG2HSYnAiPUxG" + "VicnSKQRK00BwZLB1Tddi7vEGxlPGi8Rk/lJyFAAt5403ApNwqZiq3Ehvd2JnNxyQexUehI8FMTSIXwQMYkfhLwJAFuHM0HSFASQ" + "9SYMD7915NGaoWY2mnCwVy6QunqTxi3JTlRD8hWYMGQXFTRKVispmkAHTGGiI6ZAPSp0nhd/CtWT3C5YSAJQQovouNVFt1zgFTbI" + "PtJ10aFT8AAloJjCd9TnCmCkANLhUwjp9cLickFE4qollr4KJJbqTShLg/LvWWikb42UmC6k+Yna0vkkTAS1h5q5bYJLT2FaEPu6" + "7hsv+TaBhkymUJ/kd8F2Zw1FUAKYcgo6zDmFPXbbXoHHil6hlwm+Nlyv8Bc1rWZGGzNPY3CTfH6AuUsadzridEf3DmAV43AfsaN8" + "LGUMZegog0FF5WMkvVXKsdzklBEBCfLxDvEpxVlOipBaGReQVGTg41YZGopFMUhmJcvHopIGWk8+xqDic0xBdVVY8bGiqE4+NhTy" + "5eNAKes0VHQIbAu7yu17GS25Da+t0WHSMGAyO1zSSBfJ7W0ZNLmNLYMGGuR2s6Aqt5OlZXKbWJ0vt4OV4+W2rlp5uV3DnpURlNuu" + "MoQwy7BX006VUZTbo5Iwuf0pppLbmuLsaSypgdx+1COm4dxq35zTmwGFIeUyX0uaxlQuz60FNtC2unZnRibl8liMjEfCQ+DkpsEF" + "e0PuprzdE4IXIwzzy+WlUl7FyUXK5eDjpCFpfNL3q6FGceQ0WaVeTnOT1kpr5TRWXLKcliYN1Zbk9FKmbRp4FBHzgLWQ00CR9fK2" + "LZJY3o4FWkAmb6tKBi+Rt0ddc0CQw/djUJDDrFppDg5yWFTuHoHrg4S8fGVJ5WUp6MjTXZ5sDh6wnDw9tec5kMi38zGYgBr5a4s6" + "5sAi//XH4MI/5gATW5E/BxmcETdiQXnAHHR44Bx4zjBzTTkv+m8A2q72vD/6QMQCl51+3wGJVmfhzDRsYA5ObAguYmPvA5WfzGFS" + "SACiTCLmwEVi3gYv7S19HcCaa82EtMGMxDISkOA5qJHwt4GNQrwNblhUigSlVjVOx1UhS2nS2nDvTl0b9LaXjeNJesGXf/3C1y01" + "CAIEKIOlMKsVkpd9XVGDItwAYMAqNeGaAEIhARJKiblafpxeQKO0AK7Ey4+1C4iQHICk6bkKXFnkI+d3UKVD83i2gAylAuhS8NQQ" + "AL76SN4Av7pktgYAsjTC8mO9agw0nwYpLswlsrc7KPsJMmpKQ6F2NFZJRG6VRnsbrCObiuLPQVun2z4G7kLNsmODatzaba6SSZCG" + "LseTZmGTYBsXD4ceDvQj5TACwDExvi0fz4og3gZ5JlGIg5VBIHPQB3NvA/8kGqQZwpmDf9rfIqK3AwCYYvUvLa/9qkxCYPOgAEJ7" + "OzA4hwGX8P47QIAIIZs4m9+02IlxHjDESWXNuDrVrOE7V1EnAkmLdQABwfKtLXRW31LmlxAQ7zyoKCgdZihzelIZ9ztJjhxWPyWK" + "PCXi5nm6cf8Jwb8deBykxb2yrCUlmYaoL1/rjOq8XmeEyR6tI6z6mK+2LS0/ln9+AIHIjsQ=" +) + @cache def iq1_s_grid_bytes() -> bytes: @@ -195,3 +222,9 @@ def iq2_xs_grid_bytes() -> bytes: def iq2_xxs_grid_bytes() -> bytes: """Decoded bytes of the [256, 8] IQ2_XXS magnitude table.""" return zlib.decompress(base64.b64decode(_IQ2_XXS_GRID_ZLIB_B64)) + + +@cache +def iq2_s_grid_bytes() -> bytes: + """Decoded bytes of the [1024, 8] IQ2_S magnitude table.""" + return zlib.decompress(base64.b64decode(_IQ2_S_GRID_ZLIB_B64)) diff --git a/modelopt/torch/quantization/ggml/iq2_s.py b/modelopt/torch/quantization/ggml/iq2_s.py new file mode 100644 index 00000000000..024965af048 --- /dev/null +++ b/modelopt/torch/quantization/ggml/iq2_s.py @@ -0,0 +1,217 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""IQ2_S fake quantization and GGML-compatible block packing. + +The encoder performs a single-pass squared-error grid search at a fixed, +empirically anchored super-block scale, mirroring :mod:`.iq2_xs`. Every 256 +logical values become one 82-byte block_iq2_s payload: + +* bytes 0..1: little-endian FP16 super-block scale d +* bytes 2..33: 32 low bytes of the grid index, four per sub-block +* bytes 34..65: 32 sign masks, four per sub-block +* bytes 66..73: eight bytes holding the grid index high 2 bits, four per byte +* bytes 74..81: 16 four-bit local scales, two per byte + +IQ2_S stores a full eight-bit sign mask per group rather than the seven-bit +parity-coded index used by IQ2_XS and IQ2_XXS, so the encoder can take the +input signs directly instead of flipping the weakest element to fix parity. + +The canonical 1024 x 8 magnitude grid lives in :mod:`.codebooks`, carried from +llama.cpp ggml-common.h revision 9b05354ec6fb58b4e665e9a39ebc40285c015638. +The matching dequantization formula is in ggml-quants.c at the same revision: +https://github.com/ggml-org/llama.cpp/blob/9b05354ec6fb58b4e665e9a39ebc40285c015638/ggml/src/ggml-quants.c#L2540-L2571 +""" + +import torch + +from .codebooks import iq2_s_grid_bytes +from .common import ( + GGML_BLOCK_SIZE, + narrow_to_float32, + validate_block_chunk_size, + validate_packed_weights, + validate_weight, +) + +__all__ = [ + "IQ2_S_BLOCK_BYTES", + "IQ2_S_BLOCK_SIZE", + "IQ2_S_EFFECTIVE_BITS", + "dequantize_iq2_s", + "iq2_s_grid", + "quantize_iq2_s", +] + +IQ2_S_BLOCK_SIZE = GGML_BLOCK_SIZE +IQ2_S_BLOCK_BYTES = 82 +IQ2_S_EFFECTIVE_BITS = IQ2_S_BLOCK_BYTES * 8 / IQ2_S_BLOCK_SIZE +_IQ2_S_GRID_ENTRIES = 1024 +_IQ2_S_LOCAL_SCALES = 16 +_IQ2_S_GROUPS = 32 +_IQ2_S_SUBBLOCKS = 8 +_IQ2_S_NATIVE_MAX = 43 * 31 / 8 +_IQ2_S_SCALE_ANCHOR_MIN = 0.65 +_IQ2_S_SCALE_ANCHOR_MAX = 0.92 +_IQ2_S_PEAK_TO_RMS_TAPER = 0.035 +# The grid is twice IQ2_XS's, so the same search tile costs twice the memory. +_DEFAULT_BLOCK_CHUNK_SIZE = 128 +_DEFAULT_DECODE_CHUNK_SIZE = 4096 + +_GRID_CACHE: dict[torch.device, torch.Tensor] = {} + + +def iq2_s_grid(device: torch.device | str | None = None) -> torch.Tensor: + """Return the canonical IQ2_S magnitude grid as float32.""" + resolved_device = torch.device(device or "cpu") + if resolved_device.type == "cuda" and resolved_device.index is None: + resolved_device = torch.device("cuda", torch.cuda.current_device()) + if resolved_device not in _GRID_CACHE: + values = torch.tensor(list(iq2_s_grid_bytes()), dtype=torch.float32) + _GRID_CACHE[resolved_device] = values.reshape(_IQ2_S_GRID_ENTRIES, 8).to( + device=resolved_device + ) + return _GRID_CACHE[resolved_device] + + +def _predict_iq2_s_scales(blocks: torch.Tensor) -> torch.Tensor: + """Predict one FP16 super-block scale for each flattened block.""" + x = narrow_to_float32(blocks) + amax = x.abs().amax(dim=1) + rms = x.square().mean(dim=1).sqrt() + peak_to_rms = torch.where(rms > 0, amax / rms, torch.zeros_like(rms)) + anchor_ratio = (1.0 - _IQ2_S_PEAK_TO_RMS_TAPER * peak_to_rms).clamp( + _IQ2_S_SCALE_ANCHOR_MIN, _IQ2_S_SCALE_ANCHOR_MAX + ) + return ((amax / _IQ2_S_NATIVE_MAX) * anchor_ratio).clamp(max=65504.0).to(torch.float16) + + +def _encode_blocks(blocks: torch.Tensor, grid: torch.Tensor) -> torch.Tensor: + """Encode a moderate-size batch of flattened 256-value blocks.""" + x = narrow_to_float32(blocks) + block_count = x.shape[0] + vectors = x.reshape(block_count, _IQ2_S_GROUPS, 8) + magnitudes = vectors.abs() + negative = vectors < 0 + + d = _predict_iq2_s_scales(x) + d_float = d.float() + + xnorm = vectors.square().sum(dim=-1) + qnorm = grid.square().sum(dim=-1) + shape = (block_count, _IQ2_S_GROUPS, _IQ2_S_LOCAL_SCALES) + best_error = torch.full(shape, torch.inf, dtype=torch.float32, device=x.device) + best_entry = torch.zeros(shape, dtype=torch.int64, device=x.device) + # All eight signs are storable, so the search compares magnitudes directly. + for entry_start in range(0, _IQ2_S_GRID_ENTRIES, 64): + grid_tile = grid[entry_start : entry_start + 64] + dot = (magnitudes.unsqueeze(2) * grid_tile.reshape(1, 1, -1, 8)).sum(dim=-1) + tile_qnorm = qnorm[entry_start : entry_start + 64].reshape(1, 1, -1) + + for local in range(_IQ2_S_LOCAL_SCALES): + scale = d_float.reshape(-1, 1, 1) * ((2 * local + 1) / 8.0) + error = ( + xnorm.unsqueeze(-1) - 2.0 * scale * dot + scale.square() * tile_qnorm + ).clamp_min_(0) + tile_error, tile_index = error.min(dim=-1) + replace = tile_error < best_error[:, :, local] + best_error[:, :, local] = torch.where(replace, tile_error, best_error[:, :, local]) + best_entry[:, :, local] = torch.where( + replace, tile_index + entry_start, best_entry[:, :, local] + ) + + # One local scale covers two groups (16 values), as in IQ2_XS. + pair_error = best_error.reshape(block_count, 16, 2, _IQ2_S_LOCAL_SCALES).sum(dim=2) + selected_local = pair_error.argmin(dim=-1) + group_local = selected_local.repeat_interleave(2, dim=1) + selected_entry = best_entry.gather(2, group_local.unsqueeze(-1)).squeeze(-1) + + sign_bits = torch.arange(8, dtype=torch.int64, device=x.device) + sign_mask = (negative.to(torch.int64) << sign_bits).sum(dim=-1) + + packed = torch.empty((block_count, IQ2_S_BLOCK_BYTES), dtype=torch.uint8, device=x.device) + packed[:, :2] = d.contiguous().view(torch.uint8).reshape(block_count, 2) + packed[:, 2:34] = (selected_entry & 0xFF).to(torch.uint8) + packed[:, 34:66] = sign_mask.to(torch.uint8) + high = (selected_entry >> 8).reshape(block_count, _IQ2_S_SUBBLOCKS, 4) + packed[:, 66:74] = ( + high[:, :, 0] | (high[:, :, 1] << 2) | (high[:, :, 2] << 4) | (high[:, :, 3] << 6) + ).to(torch.uint8) + packed[:, 74:] = (selected_local[:, 0::2] | (selected_local[:, 1::2] << 4)).to(torch.uint8) + return torch.where((d_float == 0).unsqueeze(1), 0, packed) + + +@torch.no_grad() +def quantize_iq2_s( + weight: torch.Tensor, *, block_chunk_size: int = _DEFAULT_BLOCK_CHUNK_SIZE +) -> tuple[torch.Tensor, torch.Tensor]: + """Pack a floating-point weight into GGML-compatible IQ2_S blocks. + + Returned shapes are ``[*weight.shape[:-1], weight.shape[-1] // 256, 82]`` + and ``[weight.ndim]``. + """ + validate_weight(weight, "IQ2_S") + validate_block_chunk_size(block_chunk_size) + + logical_shape = torch.tensor(weight.shape, dtype=torch.int64) + blocks = weight.contiguous().reshape(-1, IQ2_S_BLOCK_SIZE) + grid = iq2_s_grid(weight.device) + packed_shape = (*weight.shape[:-1], weight.shape[-1] // IQ2_S_BLOCK_SIZE, IQ2_S_BLOCK_BYTES) + chunks = [ + _encode_blocks(blocks[start : start + block_chunk_size], grid) + for start in range(0, blocks.shape[0], block_chunk_size) + ] + return torch.cat(chunks).reshape(packed_shape), logical_shape + + +@torch.no_grad() +def dequantize_iq2_s( + packed_weights: torch.Tensor, + weight_shape: torch.Tensor, + *, + dtype: torch.dtype = torch.bfloat16, + block_chunk_size: int = _DEFAULT_DECODE_CHUNK_SIZE, +) -> torch.Tensor: + """Decode GGML-compatible IQ2_S payload bytes.""" + shape = validate_packed_weights( + packed_weights, weight_shape, block_bytes=IQ2_S_BLOCK_BYTES, format_name="IQ2_S" + ) + validate_block_chunk_size(block_chunk_size) + + blocks = packed_weights.contiguous().reshape(-1, IQ2_S_BLOCK_BYTES) + bit_positions = torch.arange(8, dtype=torch.int64, device=blocks.device) + high_shifts = torch.tensor([0, 2, 4, 6], dtype=torch.int64, device=blocks.device) + grid = iq2_s_grid(blocks.device) + decoded = torch.empty((blocks.shape[0], IQ2_S_BLOCK_SIZE), dtype=dtype, device=blocks.device) + for start in range(0, blocks.shape[0], block_chunk_size): + stop = min(start + block_chunk_size, blocks.shape[0]) + block_chunk = blocks[start:stop] + count = block_chunk.shape[0] + d = block_chunk[:, :2].contiguous().view(torch.float16).reshape(-1).float() + low = block_chunk[:, 2:34].to(torch.int64).reshape(count, _IQ2_S_SUBBLOCKS, 4) + sign_mask = block_chunk[:, 34:66].to(torch.int64).reshape(count, _IQ2_S_SUBBLOCKS, 4) + qh = block_chunk[:, 66:74].to(torch.int64).reshape(count, _IQ2_S_SUBBLOCKS) + scale_bytes = block_chunk[:, 74:].to(torch.int64) + + entries = low | (((qh.unsqueeze(-1) >> high_shifts) & 0x3) << 8) + local = torch.empty((count, 16), dtype=torch.int64, device=blocks.device) + local[:, 0::2] = scale_bytes & 0x0F + local[:, 1::2] = scale_bytes >> 4 + scales = d.unsqueeze(-1) * (0.5 + local.float()) * 0.25 + signs = 1.0 - 2.0 * ((sign_mask.unsqueeze(-1) >> bit_positions) & 1).float() + values = grid[entries] * signs + chunk_decoded = values.reshape(count, 16, 2, 8) * scales.unsqueeze(-1).unsqueeze(-1) + decoded[start:stop] = chunk_decoded.reshape(-1, IQ2_S_BLOCK_SIZE) + return decoded.reshape(shape) diff --git a/tests/_test_utils/torch/quantization/iq_llama_cpp_vectors.py b/tests/_test_utils/torch/quantization/iq_llama_cpp_vectors.py index 6fe3a9ccd06..693a80179b4 100644 --- a/tests/_test_utils/torch/quantization/iq_llama_cpp_vectors.py +++ b/tests/_test_utils/torch/quantization/iq_llama_cpp_vectors.py @@ -141,6 +141,49 @@ "/qrzW/DbKMq7yFtqTG/oGeZ9U/1/TYe49w==" ), }, + "iq2_s": { + "source": "blk.35.attn_output.weight", + "block_bytes": 82, + "blocks": ( + "eNoB7AET/q4M+OCIlAAzLJjmBQAJuLQHGSwziwAmnlIDogF04URRrgOutsGuCoscGISHWf5Z7PnTZCXDydppD389LinF" + "F+K8HgxMCGQcABGwhFvXRv1Y11zFCvZOQw2g3hUdMb0vDvZmARjtM7DOQZ5kTxxzz/H+AAeVcDR0leZIoJDxcfhrr7Uv" + "kpvnsBWJYWuM7FOcANKeHe00igDNHJJDAYl2po2NiMp/XwiRcq1apgHtYw6m6g/Aao/ThGi7zgieTN49ZMg3FACEA/WD" + "VoudZA4dZAYThwSiFspPvOiNjuUxu11ujtJevf5VIolkIiygEQJrv56qq32nzw0KowLwPnr0CswZwQKXCL5JdIKYjwKX" + "nkrYcesAH00mlyNWkgcISZ9synXVqLyJ7IuNpgFD4XvoXfHtkou3P9FjihCFiMAeQwlxiWa7act/5XglDa3SmBz14UIO" + "vwsbhiMZ3oIAM4AAgmkAAXRzAAlEAHWHs6+UevSuXXqwriyP2frZUU5JYkM/84aPBbjS5XTjweCoMK6QHAYBoCQ0M2bP" + "hbc+UA3t0EhwrUstChnOAAkLEQRd7in2AFXGGaCDeNvvEgC4IkeqzF+vBg0/wJbAXSu4r42iDMm+Oqdr4+mcwHeTesEG" + "wEgAQgMAVoImQ/hldEZUNmfm3b4=" + ), + "expected": ( + "eNp1V22IXtURPjbWajDtVhJZxKQvRUStP9ZoNGZm263YmlIDi0K0fq62frIFNVASpGQtrYa0DYuCFcMmK5WgIrqiqNmZ" + "VxfxYzERV1EjiLBGhFhFFiWQH8F05tyZc+fevPnx8DzznDnnnjNzzsvuzMQinJlYxEM33cxp41kkGiMLwDh7mqta8jHP" + 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iq2_xxs_module from modelopt.torch.quantization.config import QuantizerAttributeConfig @@ -41,8 +42,12 @@ "iq1_s": (iq1_s_module, 50, 2048, 1.5625), "iq2_xxs": (iq2_xxs_module, 66, 256, 2.0625), "iq2_xs": (iq2_xs_module, 74, 512, 2.3125), + "iq2_s": (iq2_s_module, 82, 1024, 2.5625), } NAMES = sorted(FORMATS) +# The formats backend dispatch can reach. A codec can land before it is registered, so the +# tests that go through TensorQuantizer iterate these rather than every codec above. +DISPATCHED = sorted(IQ_FORMAT_REGISTRY) # IQ1 grids are ternary; IQ2 grids hold the magnitudes 8, 25 and 43. TERNARY = {"iq1_s"} @@ -211,7 +216,7 @@ def test_rejects_scalar_packed_payload(name): dequantize(torch.tensor(0, dtype=torch.uint8), torch.tensor([1, 256])) -@pytest.mark.parametrize("name", NAMES) +@pytest.mark.parametrize("name", DISPATCHED) def test_fake_quant_has_pass_through_gradient(name): quantizer = TensorQuantizer( QuantizerAttributeConfig(num_bits=name, block_sizes={-1: 256}, backend="ggml") @@ -265,16 +270,17 @@ def test_error_decreases_with_bit_width(): """More bits must buy less error, or a format's scale handling is wrong.""" generator = torch.Generator().manual_seed(7) weight = torch.randn((4, 1024), generator=generator) + ordered = sorted(DISPATCHED, key=lambda n: FORMATS[n][3]) errors = [] - for name in sorted(NAMES, key=lambda n: FORMATS[n][3]): + for name in ordered: quantizer = TensorQuantizer( QuantizerAttributeConfig(num_bits=name, block_sizes={-1: 256}, backend="ggml") ) errors.append(float((quantizer(weight) - weight).square().mean())) - assert errors == sorted(errors, reverse=True), dict(zip(sorted(NAMES), errors)) + assert errors == sorted(errors, reverse=True), dict(zip(ordered, errors)) def test_every_registered_format_is_covered(): """A format registered for dispatch must also be listed here, or it escapes this contract.""" - assert sorted(IQ_FORMAT_REGISTRY) == sorted(FORMATS) + assert set(IQ_FORMAT_REGISTRY) <= set(FORMATS)