internal/donttouch_unpacking_autogptq/qlinear_cuda_old.py.ori.py

import math
from logging import getLogger

import numpy as np
import torch
import torch.nn as nn
import transformers


logger = getLogger(__name__)
try:
    import autogptq_cuda_64
    import autogptq_cuda_256

    _autogptq_cuda_available = True
except ImportError:
    logger.warning("CUDA extension not installed.")
    autogptq_cuda_256 = None
    autogptq_cuda_64 = None
    _autogptq_cuda_available = False


class QuantLinear(nn.Module):
    QUANT_TYPE = "cuda-old"

    def __init__(
        self,
        bits,
        group_size,
        infeatures,
        outfeatures,
        bias,
        use_cuda_fp16=True,
        kernel_switch_threshold=128,
        trainable=False,
        weight_dtype=torch.float16,
    ):
        super().__init__()
        global _autogptq_cuda_available
        if bits not in [2, 3, 4, 8]:
            raise NotImplementedError("Only 2,3,4,8 bits are supported.")
        if trainable:
            _autogptq_cuda_available = False
        self.infeatures = infeatures
        self.outfeatures = outfeatures
        self.bits = bits
        self.group_size = group_size if group_size != -1 else infeatures
        self.maxq = 2**self.bits - 1

        self.register_buffer(
            "qweight",
            torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32),
        )
        self.register_buffer(
            "qzeros",
            torch.zeros(
                (
                    math.ceil(infeatures / self.group_size),
                    outfeatures // 32 * self.bits,
                ),
                dtype=torch.int32,
            ),
        )
        self.register_buffer(
            "scales",
            torch.zeros(
                (math.ceil(infeatures / self.group_size), outfeatures),
                dtype=weight_dtype,
            ),
        )
        self.register_buffer(
            "g_idx",
            torch.tensor([i // self.group_size for i in range(infeatures)], dtype=torch.int32),
        )

        if bias:
            self.register_buffer("bias", torch.zeros((outfeatures), dtype=weight_dtype))
        else:
            self.bias = None
        self.half_indim = self.infeatures // 2

        self.use_cuda_fp16 = use_cuda_fp16 if bits != 8 else False

        # is performed by unpacking the weights and using torch.matmul
        if self.bits in [2, 4, 8]:
            self.wf = torch.tensor(list(range(0, 32, self.bits)), dtype=torch.int32).unsqueeze(0)
        elif self.bits == 3:
            self.wf = torch.tensor(
                [
                    [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0],
                    [0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31],
                    [0, 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0],
                ],
                dtype=torch.int32,
            ).reshape(1, 3, 12)

        self.kernel_switch_threshold = kernel_switch_threshold
        self.autogptq_cuda_available = _autogptq_cuda_available
        self.autogptq_cuda = autogptq_cuda_256
        if infeatures % 256 != 0 or outfeatures % 256 != 0:
            self.autogptq_cuda = autogptq_cuda_64
        if infeatures % 64 != 0 or outfeatures % 64 != 0:
            self.autogptq_cuda_available = False

        self.trainable = trainable

    def post_init(self):
        pass

    def pack(self, linear, scales, zeros, g_idx):
        W = linear.weight.data.clone()
        if isinstance(linear, nn.Conv2d):
            W = W.flatten(1)
        if isinstance(linear, transformers.pytorch_utils.Conv1D):
            W = W.t()

        scales = scales.t().contiguous()
        zeros = zeros.t().contiguous()
        scale_zeros = zeros * scales
        self.scales = scales.clone().to(dtype=linear.weight.dtype)
        if linear.bias is not None:
            self.bias = linear.bias.clone().to(dtype=linear.weight.dtype)

        intweight = []
        for idx in range(self.infeatures):
            g_idx = idx // self.group_size
            intweight.append(torch.round((W[:, idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:, None])
        intweight = torch.cat(intweight, dim=1)
        intweight = intweight.t().contiguous()
        intweight = intweight.numpy().astype(np.uint32)
        
        i = 0
        row = 0
        qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
        while row < qweight.shape[0]:
            if self.bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.bits)):
                    qweight[row] |= intweight[j] << (self.bits * (j - i))
                i += 32 // self.bits
                row += 1
            elif self.bits == 3:
                for j in range(i, i + 10):
                    qweight[row] |= intweight[j] << (3 * (j - i))
                i += 10
                qweight[row] |= intweight[i] << 30
                row += 1
                qweight[row] |= (intweight[i] >> 2) & 1
                i += 1
                for j in range(i, i + 10):
                    qweight[row] |= intweight[j] << (3 * (j - i) + 1)
                i += 10
                qweight[row] |= intweight[i] << 31
                row += 1
                qweight[row] |= (intweight[i] >> 1) & 0x3
                i += 1
                for j in range(i, i + 10):
                    qweight[row] |= intweight[j] << (3 * (j - i) + 2)
                i += 10
                row += 1
            else:
                raise NotImplementedError("Only 2,3,4,8 bits are supported.")

        qweight = qweight.astype(np.int32)
        self.qweight = torch.from_numpy(qweight)

        zeros -= 1
        zeros = zeros.numpy().astype(np.uint32)
        qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
        i = 0
        col = 0
        while col < qzeros.shape[1]:
            if self.bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.bits)):
                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
                i += 32 // self.bits
                col += 1
            elif self.bits == 3:
                for j in range(i, i + 10):
                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
                i += 10
                qzeros[:, col] |= zeros[:, i] << 30
                col += 1
                qzeros[:, col] |= (zeros[:, i] >> 2) & 1
                i += 1
                for j in range(i, i + 10):
                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
                i += 10
                qzeros[:, col] |= zeros[:, i] << 31
                col += 1
                qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
                i += 1
                for j in range(i, i + 10):
                    qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
                i += 10
                col += 1
            else:
                raise NotImplementedError("Only 2,3,4,8 bits are supported.")

        qzeros = qzeros.astype(np.int32)
        self.qzeros = torch.from_numpy(qzeros)

    def forward(self, x):
        x_dtype = x.dtype
        out_shape = x.shape[:-1] + (self.outfeatures,)
        x = x.reshape(-1, x.shape[-1])
        if (
            x.device.type == "cuda"
            and self.autogptq_cuda_available is True
            and (self.kernel_switch_threshold is False or x.shape[0] < self.kernel_switch_threshold)
        ):
            out = torch.zeros(x.shape[0], out_shape[-1], dtype=torch.float, device=x.device)
            if self.use_cuda_fp16:
                if x_dtype != torch.float16:
                    logger.warning_once(
                        f"The cuda-old kernel for GPTQ with use_cuda_fp16=True requires a float16 input activation, while {x_dtype} was passed. Casting to float16.\nMake sure you loaded your model with torch_dtype=torch.float16, that the model definition does not inadvertently cast to float32, or disable AMP Autocast that may produce float32 intermediate activations in the model."
                    )

                if self.bits == 2:
                    self.autogptq_cuda.vecquant2matmul_faster_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                        self.half_indim,
                    )
                elif self.bits == 3:
                    self.autogptq_cuda.vecquant3matmul_faster_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                        self.half_indim,
                    )
                elif self.bits == 4:
                    self.autogptq_cuda.vecquant4matmul_faster_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                        self.half_indim,
                    )

                else:
                    raise NotImplementedError("Only 2,3,4 bits are supported.")
            else:
                x = x.to(torch.float32)  # This is required for autocast compatibility.
                if self.bits == 2:
                    self.autogptq_cuda.vecquant2matmul_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                    )
                elif self.bits == 3:
                    self.autogptq_cuda.vecquant3matmul_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                    )
                elif self.bits == 4:
                    self.autogptq_cuda.vecquant4matmul_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                    )
                elif self.bits == 8:
                    self.autogptq_cuda.vecquant8matmul_old(
                        x,
                        self.qweight,
                        out,
                        self.scales.float(),
                        self.qzeros,
                        self.group_size,
                    )
                else:
                    raise NotImplementedError("Only 2,3,4,8 bits are supported.")
        else:
            if self.wf.device != self.qzeros.device:
                self.wf = self.wf.to(self.qzeros.device)

            if self.bits in [2, 4, 8]:
                zeros = torch.bitwise_right_shift(
                    torch.unsqueeze(self.qzeros, 2).expand(-1, -1, 32 // self.bits),
                    self.wf.unsqueeze(0),
                ).to(torch.int16 if self.bits == 8 else torch.int8)

                zeros = zeros + 1
                zeros = torch.bitwise_and(
                    zeros, (2**self.bits) - 1
                )  # NOTE: It appears that casting here after the `zeros = zeros + 1` is important.

                zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])

                scales = self.scales
                scales = scales.reshape(-1, 1, scales.shape[-1])

                weight = torch.bitwise_right_shift(
                    torch.unsqueeze(self.qweight, 1).expand(-1, 32 // self.bits, -1),
                    self.wf.unsqueeze(-1),
                ).to(torch.int16 if self.bits == 8 else torch.int8)
                weight = torch.bitwise_and(weight, (2**self.bits) - 1)
                weight = weight.reshape(-1, self.group_size, weight.shape[2])
            elif self.bits == 3:
                zeros = self.qzeros.reshape(self.qzeros.shape[0], self.qzeros.shape[1] // 3, 3, 1).expand(
                    -1, -1, -1, 12
                )
                zeros = zeros >> self.wf.unsqueeze(0)
                zeros[:, :, 0, 10] = (zeros[:, :, 0, 10] & 0x3) | ((zeros[:, :, 1, 0] << 2) & 0x4)
                zeros[:, :, 1, 11] = (zeros[:, :, 1, 11] & 0x1) | ((zeros[:, :, 2, 0] << 1) & 0x6)
                zeros = zeros & 0x7
                zeros = torch.cat(
                    [zeros[:, :, 0, :11], zeros[:, :, 1, 1:12], zeros[:, :, 2, 1:11]],
                    dim=2,
                )

                zeros = zeros + 1
                zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])

                scales = self.scales
                scales = scales.reshape(-1, 1, scales.shape[-1])

                weight = self.qweight.reshape(self.qweight.shape[0] // 3, 3, 1, self.qweight.shape[1]).expand(
                    -1, -1, 12, -1
                )
                weight = (weight >> self.wf.unsqueeze(-1)) & 0x7
                weight[:, 0, 10] = (weight[:, 0, 10] & 0x3) | ((weight[:, 1, 0] << 2) & 0x4)
                weight[:, 1, 11] = (weight[:, 1, 11] & 0x1) | ((weight[:, 2, 0] << 1) & 0x6)
                weight = weight & 0x7
                weight = torch.cat([weight[:, 0, :11], weight[:, 1, 1:12], weight[:, 2, 1:11]], dim=1)
                weight = weight.reshape(-1, self.group_size, weight.shape[2])
            else:
                raise NotImplementedError("Only 2,3,4,8 bits are supported.")

            weight = scales * (weight - zeros)
            weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
            out = torch.matmul(x, weight)
        out = out.to(dtype=x_dtype).reshape(
            out_shape
        )  # A cast is needed here as for some reason the vecquant2matmul_faster_old still allocate a float32 output.
        out = out + self.bias if self.bias is not None else out
        return out


__all__ = ["QuantLinear"]