File size: 11,349 Bytes
dc9bb20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 | from collections.abc import Sequence
import ctypes as ct
import logging
from math import prod
import torch
from bitsandbytes.functional import get_ptr, has_avx512bf16
from ..._ops import register_kernel
from ...cextension import ErrorHandlerMockBNBNativeLibrary, lib
logger = logging.getLogger(__name__)
_has_avx512 = torch.backends.cpu.get_cpu_capability() == "AVX512"
# torch._int_mm for s8@s8->s32 is supported on CPU from torch 2.4+.
# However, we can overflow if we use this without AVX512_VNNI support.
# This is fixed in torch 2.6+, so we set this as the minimum to be safe.
# For more information: https://github.com/pytorch/pytorch/pull/136942
# TODO(matthewdouglas): aarch64?
if torch.__version__ >= (2, 6):
@register_kernel("bitsandbytes::int8_linear_matmul", "cpu")
def _(A: torch.Tensor, B: torch.Tensor):
return torch._int_mm(
A.reshape(-1, A.shape[-1]),
B.t(),
).reshape(*A.shape[:-1], B.shape[0])
if not isinstance(lib, ErrorHandlerMockBNBNativeLibrary):
@register_kernel("bitsandbytes::quantize_blockwise", "cpu")
def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]:
torch._check_is_size(blocksize)
n = A.numel()
# Only FP32 has c++ kernrl
if A.dtype == torch.float32:
blocks = -(n // -blocksize)
absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32)
out = torch.empty_like(A, dtype=torch.uint8)
lib.cquantize_blockwise_cpu_fp32(
get_ptr(code),
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(n),
)
else:
rem = n % blocksize
has_rem = rem > 0
blocks = n // blocksize + has_rem
absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32)
A_reshaped = A.reshape(n)
A_com = A_reshaped[: n - rem]
A_com_reshaped = A_com.reshape(n // blocksize, blocksize)
absmax[: blocks - has_rem] = torch.abs(A_com_reshaped).max(dim=-1)[0]
scaled_A = torch.clamp(A_com_reshaped * (1 / absmax[: blocks - has_rem].view(-1, 1)), -1, 1)
scaled_A = scaled_A.reshape(-1)
if has_rem:
absmax[-1] = torch.abs(A_reshaped[n - rem :]).max()
scaled_A_rem = torch.clamp(A_reshaped[n - rem :] * (1 / absmax[-1]), -1, 1)
scaled_A = torch.cat([scaled_A, scaled_A_rem], dim=0)
diff = torch.abs(scaled_A.unsqueeze(-1) - code.to(scaled_A.device))
out = torch.argmin(diff, dim=-1).to(torch.uint8).to(scaled_A.device).reshape(A.shape)
return out, absmax
@register_kernel("bitsandbytes::dequantize_blockwise", "cpu")
def _(
A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype
) -> torch.Tensor:
torch._check_is_size(blocksize)
torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
out = torch.empty_like(A, dtype=dtype)
if dtype == torch.float32:
lib.cdequantize_blockwise_cpu_fp32(
get_ptr(code),
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(A.numel()),
)
elif dtype == torch.bfloat16:
lib.cdequantize_blockwise_cpu_bf16(
get_ptr(code),
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(A.numel()),
)
elif dtype == torch.float16:
lib.cdequantize_blockwise_cpu_fp16(
get_ptr(code),
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(A.numel()),
)
else:
out = code[A.reshape(-1).int()]
blocks = out.shape[-1] // blocksize
res = out.shape[-1] % blocksize
if res != 0:
out = torch.nn.functional.pad(out, (0, blocksize - res), mode="constant", value=0)
out = (out.view(-1, blocksize) * absmax.view(-1, 1)).to(dtype).reshape(-1)
out = out[: blocks * blocksize + res]
out = out.reshape(A.shape)
return out
@register_kernel("bitsandbytes::dequantize_4bit", "cpu")
def _(
A: torch.Tensor,
absmax: torch.Tensor,
blocksize: int,
quant_type: str,
shape: Sequence[int],
dtype: torch.dtype,
) -> torch.Tensor:
torch._check_is_size(blocksize)
torch._check(quant_type in ("nf4", "fp4"), lambda: f"quant_type must be nf4 or fp4, got {quant_type}")
torch._check(
dtype in [torch.bfloat16, torch.float16, torch.float32],
lambda: f"Blockwise 4bit dequantization only supports 16/32-bit floats, but got {dtype}",
)
# Fallback as AVX512 implementation has accuracy issues with fp16/fp32 and blocksize >= 2048
# Note: this is not a common use case.
avx512_fallback = _has_avx512 and blocksize >= 2048 and dtype != torch.bfloat16
# Odd shape is not supported by this kernel; fallback to generic implementation
shape_fallback = shape[-1] % 2 != 0
if avx512_fallback or shape_fallback:
from ..default.ops import _dequantize_4bit_impl
return _dequantize_4bit_impl(A, absmax, blocksize, quant_type, shape, dtype)
# Enable non uint8 dtype
if A.dtype != torch.uint8:
A = A.view(torch.uint8)
# TODO: support half precision absmax
if absmax.dtype != torch.float32:
absmax = absmax.float()
if len(shape) == 1:
shape = (1, shape[0])
m = prod(shape[:-1])
n = shape[-1]
A = A.reshape(m, n // 2)
out = torch.empty(shape, dtype=dtype, device=A.device)
if quant_type == "fp4":
if dtype == torch.float32:
lib.cdequantize_blockwise_cpu_fp4_fp32(
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(m),
ct.c_longlong(n),
)
elif dtype == torch.bfloat16:
lib.cdequantize_blockwise_cpu_fp4_bf16(
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(m),
ct.c_longlong(n),
)
elif dtype == torch.float16:
lib.cdequantize_blockwise_cpu_fp4_fp16(
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(m),
ct.c_longlong(n),
)
elif quant_type == "nf4":
if dtype == torch.float32:
lib.cdequantize_blockwise_cpu_nf4_fp32(
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(m),
ct.c_longlong(n),
)
elif dtype == torch.bfloat16:
lib.cdequantize_blockwise_cpu_nf4_bf16(
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(m),
ct.c_longlong(n),
)
elif dtype == torch.float16:
lib.cdequantize_blockwise_cpu_nf4_fp16(
get_ptr(A),
get_ptr(absmax),
get_ptr(out),
ct.c_longlong(blocksize),
ct.c_longlong(m),
ct.c_longlong(n),
)
else:
raise ValueError
return out
if has_avx512bf16():
gemm_4bit_forward_kernel = None
try:
from kernels import get_kernel
gemm_4bit_forward_kernel = get_kernel("kernels-community/quantization_bitsandbytes").gemm_4bit_forward
except Exception as exc: # pragma: no cover - best effort fallback
gemm_4bit_forward_kernel = None
logger.warning(
"Failed to load CPU gemm_4bit_forward from kernels-community: %s. Please make sure you already `pip install kernels` and the kernels >= 0.11.1",
exc,
)
@register_kernel("bitsandbytes::gemv_4bit", "cpu")
def _(
A: torch.Tensor,
B: torch.Tensor,
shapeB: Sequence[int],
absmax: torch.Tensor,
code: torch.Tensor,
blocksize: int,
) -> torch.Tensor:
assert B.dtype == torch.uint8, "Only support uint8 qweight"
dtype = A.dtype
quant_type = "fp4" if code[1] > 0 else "nf4"
# cpu fused op only support bf16 for now.
if dtype != torch.bfloat16:
A = A.to(torch.bfloat16)
final_out_shape = (*A.shape[:-1], shapeB[0])
A = A.reshape(-1, A.shape[-1])
out_shape = (*A.shape[:-1], shapeB[0])
if gemm_4bit_forward_kernel is not None:
quant_type_num = 1 if quant_type == "fp4" else 0
out = gemm_4bit_forward_kernel(A, B, absmax, blocksize, quant_type_num)
else:
out = torch.empty(out_shape, dtype=A.dtype, device=A.device)
M = A.shape[0]
N = shapeB[0]
K = A.shape[1]
x_strideM = A.stride(0)
out_strideM = out.stride(0)
if quant_type == "fp4":
lib.gemv_4bit_inference_cpu_fp4_bf16(
ct.c_int64(M),
ct.c_int64(N),
ct.c_int64(K),
get_ptr(A),
get_ptr(B),
get_ptr(absmax),
get_ptr(out),
ct.c_int64(blocksize),
ct.c_int64(x_strideM),
ct.c_int64(out_strideM),
)
elif quant_type == "nf4":
lib.gemv_4bit_inference_cpu_nf4_bf16(
ct.c_int64(M),
ct.c_int64(N),
ct.c_int64(K),
get_ptr(A),
get_ptr(B),
get_ptr(absmax),
get_ptr(out),
ct.c_int64(blocksize),
ct.c_int64(x_strideM),
ct.c_int64(out_strideM),
)
if dtype != torch.bfloat16:
out = out.to(dtype)
return out.reshape(final_out_shape)
|