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# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
import gguf
import torch
from tqdm import tqdm
TORCH_COMPATIBLE_QTYPES = (None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16)
def is_torch_compatible(tensor):
return tensor is None or getattr(tensor, "tensor_type", None) in TORCH_COMPATIBLE_QTYPES
def is_quantized(tensor):
return not is_torch_compatible(tensor)
def dequantize_tensor(tensor, dtype=None, dequant_dtype=None):
qtype = getattr(tensor, "tensor_type", None)
oshape = getattr(tensor, "tensor_shape", tensor.shape)
if qtype in TORCH_COMPATIBLE_QTYPES:
return tensor.to(dtype)
elif qtype in dequantize_functions:
dequant_dtype = dtype if dequant_dtype == "target" else dequant_dtype
return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype)
else:
# this is incredibly slow
tqdm.write(f"Falling back to numpy dequant for qtype: {getattr(qtype, 'name', repr(qtype))}")
new = gguf.quants.dequantize(tensor.cpu().numpy(), qtype)
return torch.from_numpy(new).to(tensor.device, dtype=dtype)
def dequantize(data, qtype, oshape, dtype=None):
"""
Dequantize tensor back to usable shape/dtype
"""
block_size, type_size = gguf.GGML_QUANT_SIZES[qtype]
dequantize_blocks = dequantize_functions[qtype]
rows = data.reshape(
(-1, data.shape[-1])
).view(torch.uint8)
n_blocks = rows.numel() // type_size
blocks = rows.reshape((n_blocks, type_size))
blocks = dequantize_blocks(blocks, block_size, type_size, dtype)
return blocks.reshape(oshape)
def to_uint32(x):
# no uint32 :(
x = x.view(torch.uint8).to(torch.int32)
return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1)
def to_uint16(x):
x = x.view(torch.uint8).to(torch.int32)
return (x[:, 0] | x[:, 1] << 8).unsqueeze(1)
def split_block_dims(blocks, *args):
n_max = blocks.shape[1]
dims = list(args) + [n_max - sum(args)]
return torch.split(blocks, dims, dim=1)
# Full weights #
def dequantize_blocks_BF16(blocks, block_size, type_size, dtype=None):
return (blocks.view(torch.int16).to(torch.int32) << 16).view(torch.float32)
# Legacy Quants #
def dequantize_blocks_Q8_0(blocks, block_size, type_size, dtype=None):
d, x = split_block_dims(blocks, 2)
d = d.view(torch.float16).to(dtype)
x = x.view(torch.int8)
return (d * x)
def dequantize_blocks_Q5_1(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, m, qh, qs = split_block_dims(blocks, 2, 2, 4)
d = d.view(torch.float16).to(dtype)
m = m.view(torch.float16).to(dtype)
qh = to_uint32(qh)
qh = qh.reshape((n_blocks, 1)) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
ql = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1)
qh = (qh & 1).to(torch.uint8)
ql = (ql & 0x0F).reshape((n_blocks, -1))
qs = (ql | (qh << 4))
return (d * qs) + m
def dequantize_blocks_Q5_0(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, qh, qs = split_block_dims(blocks, 2, 4)
d = d.view(torch.float16).to(dtype)
qh = to_uint32(qh)
qh = qh.reshape(n_blocks, 1) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
ql = qs.reshape(n_blocks, -1, 1, block_size // 2) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1)
qh = (qh & 1).to(torch.uint8)
ql = (ql & 0x0F).reshape(n_blocks, -1)
qs = (ql | (qh << 4)).to(torch.int8) - 16
return (d * qs)
def dequantize_blocks_Q4_1(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, m, qs = split_block_dims(blocks, 2, 2)
d = d.view(torch.float16).to(dtype)
m = m.view(torch.float16).to(dtype)
qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1)
qs = (qs & 0x0F).reshape(n_blocks, -1)
return (d * qs) + m
def dequantize_blocks_Q4_0(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, qs = split_block_dims(blocks, 2)
d = d.view(torch.float16).to(dtype)
qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
qs = (qs & 0x0F).reshape((n_blocks, -1)).to(torch.int8) - 8
return (d * qs)
# K Quants #
QK_K = 256
K_SCALE_SIZE = 12
def get_scale_min(scales):
n_blocks = scales.shape[0]
scales = scales.view(torch.uint8)
scales = scales.reshape((n_blocks, 3, 4))
d, m, m_d = torch.split(scales, scales.shape[-2] // 3, dim=-2)
sc = torch.cat([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], dim=-1)
min = torch.cat([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], dim=-1)
return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))
def dequantize_blocks_Q6_K(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
ql, qh, scales, d, = split_block_dims(blocks, QK_K // 2, QK_K // 4, QK_K // 16)
scales = scales.view(torch.int8).to(dtype)
d = d.view(torch.float16).to(dtype)
d = (d * scales).reshape((n_blocks, QK_K // 16, 1))
ql = ql.reshape((n_blocks, -1, 1, 64)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
qh = (qh & 0x03).reshape((n_blocks, -1, 32))
q = (ql | (qh << 4)).to(torch.int8) - 32
q = q.reshape((n_blocks, QK_K // 16, -1))
return (d * q).reshape((n_blocks, QK_K))
def dequantize_blocks_Q5_K(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, dmin, scales, qh, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE, QK_K // 8)
d = d.view(torch.float16).to(dtype)
dmin = dmin.view(torch.float16).to(dtype)
sc, m = get_scale_min(scales)
d = (d * sc).reshape((n_blocks, -1, 1))
dm = (dmin * m).reshape((n_blocks, -1, 1))
ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([i for i in range(8)], device=d.device, dtype=torch.uint8).reshape((1, 1, 8, 1))
ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
qh = (qh & 0x01).reshape((n_blocks, -1, 32))
q = (ql | (qh << 4))
return (d * q - dm).reshape((n_blocks, QK_K))
def dequantize_blocks_Q4_K(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, dmin, scales, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE)
d = d.view(torch.float16).to(dtype)
dmin = dmin.view(torch.float16).to(dtype)
sc, m = get_scale_min(scales)
d = (d * sc).reshape((n_blocks, -1, 1))
dm = (dmin * m).reshape((n_blocks, -1, 1))
qs = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
qs = (qs & 0x0F).reshape((n_blocks, -1, 32))
return (d * qs - dm).reshape((n_blocks, QK_K))
def dequantize_blocks_Q3_K(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
hmask, qs, scales, d = split_block_dims(blocks, QK_K // 8, QK_K // 4, 12)
d = d.view(torch.float16).to(dtype)
lscales, hscales = scales[:, :8], scales[:, 8:]
lscales = lscales.reshape((n_blocks, 1, 8)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 2, 1))
lscales = lscales.reshape((n_blocks, 16))
hscales = hscales.reshape((n_blocks, 1, 4)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 4, 1))
hscales = hscales.reshape((n_blocks, 16))
scales = (lscales & 0x0F) | ((hscales & 0x03) << 4)
scales = (scales.to(torch.int8) - 32)
dl = (d * scales).reshape((n_blocks, 16, 1))
ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
qh = hmask.reshape(n_blocks, -1, 1, 32) >> torch.tensor([i for i in range(8)], device=d.device, dtype=torch.uint8).reshape((1, 1, 8, 1))
ql = ql.reshape((n_blocks, 16, QK_K // 16)) & 3
qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & 1) ^ 1
q = (ql.to(torch.int8) - (qh << 2).to(torch.int8))
return (dl * q).reshape((n_blocks, QK_K))
def dequantize_blocks_Q2_K(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
scales, qs, d, dmin = split_block_dims(blocks, QK_K // 16, QK_K // 4, 2)
d = d.view(torch.float16).to(dtype)
dmin = dmin.view(torch.float16).to(dtype)
# (n_blocks, 16, 1)
dl = (d * (scales & 0xF)).reshape((n_blocks, QK_K // 16, 1))
ml = (dmin * (scales >> 4)).reshape((n_blocks, QK_K // 16, 1))
shift = torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & 3
qs = qs.reshape((n_blocks, QK_K // 16, 16))
qs = dl * qs - ml
return qs.reshape((n_blocks, -1))
# IQ quants
KVALUES = torch.tensor([-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113], dtype=torch.int8)
def dequantize_blocks_IQ4_NL(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, qs = split_block_dims(blocks, 2)
d = d.view(torch.float16).to(dtype)
qs = qs.reshape((n_blocks, -1, 1, block_size//2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
qs = (qs & 0x0F).reshape((n_blocks, -1, 1)).to(torch.int64)
kvalues = KVALUES.to(qs.device).expand(*qs.shape[:-1], 16)
qs = torch.gather(kvalues, dim=-1, index=qs).reshape((n_blocks, -1))
del kvalues # should still be view, but just to be safe
return (d * qs)
def dequantize_blocks_IQ4_XS(blocks, block_size, type_size, dtype=None):
n_blocks = blocks.shape[0]
d, scales_h, scales_l, qs = split_block_dims(blocks, 2, 2, QK_K // 64)
d = d.view(torch.float16).to(dtype)
scales_h = to_uint16(scales_h)
shift_a = torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2))
shift_b = torch.tensor([2 * i for i in range(QK_K // 32)], device=d.device, dtype=torch.uint8).reshape((1, -1, 1))
scales_l = scales_l.reshape((n_blocks, -1, 1)) >> shift_a.reshape((1, 1, 2))
scales_h = scales_h.reshape((n_blocks, -1, 1)) >> shift_b.reshape((1, -1, 1))
scales_l = scales_l.reshape((n_blocks, -1)) & 0x0F
scales_h = scales_h.reshape((n_blocks, -1)).to(torch.uint8) & 0x03
scales = (scales_l | (scales_h << 4)).to(torch.int8) - 32
dl = (d * scales.to(dtype)).reshape((n_blocks, -1, 1))
qs = qs.reshape((n_blocks, -1, 1, 16)) >> shift_a.reshape((1, 1, 2, 1))
qs = qs.reshape((n_blocks, -1, 32, 1)) & 0x0F
kvalues = KVALUES.to(qs.device).expand(*qs.shape[:-1], 16)
qs = torch.gather(kvalues, dim=-1, index=qs.to(torch.int64)).reshape((n_blocks, -1, 32))
del kvalues # see IQ4_NL
del shift_a
del shift_b
return (dl * qs).reshape((n_blocks, -1))
dequantize_functions = {
gguf.GGMLQuantizationType.BF16: dequantize_blocks_BF16,
gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0,
gguf.GGMLQuantizationType.Q5_1: dequantize_blocks_Q5_1,
gguf.GGMLQuantizationType.Q5_0: dequantize_blocks_Q5_0,
gguf.GGMLQuantizationType.Q4_1: dequantize_blocks_Q4_1,
gguf.GGMLQuantizationType.Q4_0: dequantize_blocks_Q4_0,
gguf.GGMLQuantizationType.Q6_K: dequantize_blocks_Q6_K,
gguf.GGMLQuantizationType.Q5_K: dequantize_blocks_Q5_K,
gguf.GGMLQuantizationType.Q4_K: dequantize_blocks_Q4_K,
gguf.GGMLQuantizationType.Q3_K: dequantize_blocks_Q3_K,
gguf.GGMLQuantizationType.Q2_K: dequantize_blocks_Q2_K,
gguf.GGMLQuantizationType.IQ4_NL: dequantize_blocks_IQ4_NL,
gguf.GGMLQuantizationType.IQ4_XS: dequantize_blocks_IQ4_XS,
}
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