AutoGPTQ/auto_gptq/nn_modules/qlinear/qlinear_exllamav2.py

# Adapted from turboderp exllama: https://github.com/turboderp/exllamav2

from logging import getLogger

import torch
import torch.nn as nn
import math

logger = getLogger(__name__)

try:
    from exllamav2_kernels import make_q_matrix, gemm_half_q_half
except ImportError:
    logger.error('exllamav2_kernels not installed.')
    raise

# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
none_tensor = torch.empty((1, 1), device="meta")

def _torch_device(idx):
    if idx == -1: return "cpu"
    return f"cuda:{idx}"

def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
    """Matrix multiplication, returns x @ q4"""
    output_shape = x.shape[:-1] + (q4_width,)
    x = x.view(-1, x.shape[-1])
    output = torch.empty((x.shape[0], q4_width), dtype = torch.half, device = x.device)
    gemm_half_q_half(x, q_handle, output, force_cuda)
    return output.view(output_shape)

def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
    """
    Create Q matrix 
    """
    # EXL2
    # won't work as the moment because the tensors are not the same. 
    if "q_weight" in w:
        w["q_scale_max"] /= 256
        w["q_perm"] = w["q_perm"].short()
        w["q_invperm"] = w["q_invperm"].short()
        return make_q_matrix(w["q_weight"],
                                w["q_perm"],
                                w["q_invperm"],
                                w["q_scale"],
                                w["q_scale_max"],
                                w["q_groups"],
                                none_tensor,
                                none_tensor,
                                none_tensor,
                                temp_dq)
    # GPTQ
    elif "qweight" in w:
        if w["scales"].dtype == torch.float:
            w["scales"] = w["scales"].half()

        # GPTQ with g_idx (act_order)
        if "g_idx" in w and not (w["g_idx"] == 0).all().item():
            w["q_perm"] = torch.empty((w["qweight"].shape[0] * 8,), dtype = torch.short, device = w["qweight"].device)
            w["q_invperm"] = torch.empty_like(w["q_perm"])
            # make_q4 segfaults if g_idx is not on cpu in the act-order case. In the non act-order case, None needs to be passed for g_idx.
            return make_q_matrix(w["qweight"],
                                 w["q_perm"],
                                 w["q_invperm"],
                                 none_tensor,
                                 none_tensor,
                                 none_tensor,
                                 w["qzeros"],
                                 w["scales"],
                                 w["g_idx"].cpu(),
                                 temp_dq)
        # GPTQ without g_idx
        else:
            return make_q_matrix(w["qweight"],
                                none_tensor,
                                none_tensor,
                                none_tensor,
                                none_tensor,
                                none_tensor,
                                w["qzeros"],
                                w["scales"],
                                none_tensor,
                                temp_dq)

class QuantLinear(nn.Module):
    QUANT_TYPE = "exllamav2"

    """Linear layer implementation with per-group 4-bit quantization of the weights"""

    def __init__(self, bits, group_size, infeatures, outfeatures, bias, trainable=False, **kwargs):
        super().__init__()
        if bits != 4:
            raise ValueError(
                f"Exllamav2 kernel supports only bits=4, requested bits={bits}. Something is wrong in the model initialization.")
        if trainable:
            raise NotImplementedError("Exllamav2 kernel does not support training.")

        self.q_handle = None
        self.q_tensors = None
        self.padding = - outfeatures % 32
        
        self.infeatures = infeatures
        self.outfeatures = outfeatures + self.padding
        self.bits = bits
        self.group_size = group_size if group_size != -1 else infeatures
        self.trainable = trainable
        self.maxq = 2 ** self.bits - 1

        assert infeatures % 32 == 0
        assert infeatures % self.group_size == 0
        assert outfeatures % 32 == 0
        
        # I need to register the tensors, otherwise, we won't be able to load them easily using transformers ... 
        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=torch.float16)
        )
        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=torch.float16))
        else:
            self.bias = None

    def post_init(self, temp_dq):
        assert self.qweight.device.type == "cuda"
        assert self.qweight.device.index is not None
        self.q_tensors = {
            "qweight":self.qweight,
            "qzeros":self.qzeros,
            "scales":self.scales,
            "g_idx":self.g_idx
        }
        temp_dq = temp_dq.get_scratch_slice(self.temp_dq_size())
        self.q_handle = ext_make_q_matrix(
            self.q_tensors, temp_dq
        )
    
    def forward(self, x, force_cuda = False):
        output = ext_gemm_half_q_half(x, self.q_handle, self.outfeatures, force_cuda)

        if self.bias is not None:
            output.add_(self.bias)
        return output
    
    def temp_dq_size(self):
        return self.infeatures * self.outfeatures * 2 + 128
    
    def temp_fwd_size(self, max_input_len, max_batch_size):
        return self.outfeatures * max_input_len * max_batch_size * 4 + 128
    
    def scratch_space_fixed(self, max_input_len=2048, max_batch_size=8):
        return self.temp_dq_size() + self.temp_fwd_size(max_input_len, max_batch_size)
               
    
class ExLlamaV2DeviceTensors:

    device_idx: int
    scratch_bytes: int
    scratch_idx: int
    scratch: torch.tensor = None

    def __init__(self, device_idx, scratch_bytes):
        self.device_idx = device_idx
        self.scratch_bytes = scratch_bytes
    
    def prepare(self):
        self.scratch = torch.empty((self.scratch_bytes // 2,), dtype = torch.half, device = _torch_device(self.device_idx))

    def get_scratch_slice(self, size_bytes):

        if self.scratch is None: self.prepare()

        size_bytes = ((size_bytes + 127) // 128) * 128
        size_half = size_bytes // 2
        scratch_slice = self.scratch.narrow(0, 0, size_half)
        return scratch_slice