quant.py

import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
import math
from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb

def quantize(x, scale, zero, maxq):
    if maxq < 0:
        return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
    q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
    return scale * (q - zero)

class Quantizer(nn.Module):

    def __init__(self, shape=1):
        super(Quantizer, self).__init__()
        self.register_buffer('maxq', torch.tensor(0))
        self.register_buffer('scale', torch.zeros(shape))
        self.register_buffer('zero', torch.zeros(shape))

    def configure(
        self,
        bits, perchannel=False, sym=True, 
        mse=False, norm=2.4, grid=100, maxshrink=.8,
        trits=False
        ):
        
        self.maxq = torch.tensor(2 ** bits - 1)
        self.perchannel = perchannel
        self.sym = sym
        self.mse = mse
        self.norm = norm
        self.grid = grid
        self.maxshrink = maxshrink 
        if trits:
            self.maxq = torch.tensor(-1) 

    def find_params(self, x, weight=False):
        dev = x.device
        self.maxq = self.maxq.to(dev)

        shape = x.shape
        if self.perchannel:
            if weight:
                x = x.flatten(1)
            else:
                if len(shape) == 4:
                    x = x.permute([1, 0, 2, 3])
                    x = x.flatten(1)
                if len(shape) == 3:
                    x = x.reshape((-1, shape[-1])).t()
                if len(shape) == 2:
                    x = x.t()
        else:
            x = x.flatten().unsqueeze(0)

        tmp = torch.zeros(x.shape[0], device=dev)
        xmin = torch.minimum(x.min(1)[0], tmp)
        xmax = torch.maximum(x.max(1)[0], tmp)

        if self.sym:
            xmax = torch.maximum(torch.abs(xmin), xmax)
            tmp = xmin < 0
            if torch.any(tmp):
                xmin[tmp] = -xmax[tmp]
        tmp = (xmin == 0) & (xmax == 0)
        xmin[tmp] = -1
        xmax[tmp] = +1

        if self.maxq < 0:
            self.scale = xmax
            self.zero = xmin
        else:
            self.scale = (xmax - xmin) / self.maxq
            if self.sym:
                self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
            else:
                self.zero = torch.round(-xmin / self.scale)

        if self.mse:
            best = torch.full([x.shape[0]], float('inf'), device=dev)
            for i in range(int(self.maxshrink * self.grid)):
                p = 1 - i / self.grid 
                xmin1 = p * xmin
                xmax1 = p * xmax
                scale1 = (xmax1 - xmin1) / self.maxq
                zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
                q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
                q -= x
                q.abs_()
                q.pow_(self.norm)
                err = torch.sum(q, 1)
                tmp = err < best
                if torch.any(tmp):
                    best[tmp] = err[tmp]
                    self.scale[tmp] = scale1[tmp]
                    self.zero[tmp] = zero1[tmp]
        if not self.perchannel:
            if weight:
                tmp = shape[0]
            else:
                tmp = shape[1] if len(shape) != 3 else shape[2]
            self.scale = self.scale.repeat(tmp)
            self.zero = self.zero.repeat(tmp)

        if weight:
            shape = [-1] + [1] * (len(shape) - 1)
            self.scale = self.scale.reshape(shape)
            self.zero = self.zero.reshape(shape)
            return
        if len(shape) == 4:
            self.scale = self.scale.reshape((1, -1, 1, 1))
            self.zero = self.zero.reshape((1, -1, 1, 1))
        if len(shape) == 3:
            self.scale = self.scale.reshape((1, 1, -1))
            self.zero = self.zero.reshape((1, 1, -1)) 
        if len(shape) == 2:
            self.scale = self.scale.unsqueeze(0)
            self.zero = self.zero.unsqueeze(0)

    def quantize(self, x):
        if self.ready():
            return quantize(x, self.scale, self.zero, self.maxq)
        return x

    def enabled(self):
        return self.maxq > 0

    def ready(self):
        return torch.all(self.scale != 0)
    
try:
    import triton
    import triton.language as tl
    import custom_autotune

    def matmul4_kernel_config_pruner(configs, nargs):
        """
        The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
        """
        m = max(2 ** int(math.ceil(math.log2(nargs['M']))), 16)
        n = max(2 ** int(math.ceil(math.log2(nargs['N']))), 16)
        k = max(2 ** int(math.ceil(math.log2(nargs['K']))), 16)

        used = set()
        for config in configs:
            block_size_m = min(m, config.kwargs['BLOCK_SIZE_M'])
            block_size_n = min(n, config.kwargs['BLOCK_SIZE_N'])
            block_size_k = min(k, config.kwargs['BLOCK_SIZE_K'])
            group_size_m = config.kwargs['GROUP_SIZE_M']

            if (block_size_m, block_size_n, block_size_k, group_size_m, config.num_stages, config.num_warps) in used:
                continue

            used.add((block_size_m, block_size_n, block_size_k, group_size_m, config.num_stages, config.num_warps))
            yield triton.Config({'BLOCK_SIZE_M': block_size_m, 'BLOCK_SIZE_N': block_size_n, 'BLOCK_SIZE_K': block_size_k, 'GROUP_SIZE_M': group_size_m}, num_stages=config.num_stages, num_warps=config.num_warps)

    # code based https://github.com/fpgaminer/GPTQ-triton
    @custom_autotune.autotune(
        configs=[
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
            triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
        ],
        key=['M', 'N', 'K'],
        nearest_power_of_two=True,
        prune_configs_by={
            'early_config_prune': matmul4_kernel_config_pruner,
            'perf_model': None,
            'top_k': None,
        },
    )
    
    @triton.jit
    def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
                          scales_ptr, zeros_ptr, g_ptr,
                          M, N, K, bits, maxq,
                          stride_am, stride_ak,
                          stride_bk, stride_bn,
                          stride_cm, stride_cn,
                          stride_scales, stride_zeros,
                          BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
                          GROUP_SIZE_M: tl.constexpr):
        """
        Compute the matrix multiplication C = A x B.
        A is of shape (M, K) float16
        B is of shape (K//8, N) int32
        C is of shape (M, N) float16
        scales is of shape (G, N) float16
        zeros is of shape (G, N) float16
        g_ptr is of shape (K) int32 
        """
        infearure_per_bits = 32 // bits

        pid = tl.program_id(axis=0)
        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
        num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
        num_pid_in_group = GROUP_SIZE_M * num_pid_n
        group_id = pid // num_pid_in_group
        first_pid_m = group_id * GROUP_SIZE_M
        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
        pid_m = first_pid_m + (pid % group_size_m)
        pid_n = (pid % num_pid_in_group) // group_size_m

        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
        offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
        offs_k = tl.arange(0, BLOCK_SIZE_K)
        a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
        a_mask = (offs_am[:, None] < M)
        # b_ptrs is set up such that it repeats elements along the K axis 8 times
        b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
        g_ptrs = g_ptr + offs_k
        # shifter is used to extract the N bits of each element in the 32-bit word from B
        scales_ptrs = scales_ptr + offs_bn[None, :]
        zeros_ptrs = zeros_ptr + (offs_bn[None, :]// infearure_per_bits) 
        
        shifter = (offs_k % infearure_per_bits) * bits
        zeros_shifter = (offs_bn % infearure_per_bits) * bits
        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
                
        for k in range(0, num_pid_k):
            g_idx = tl.load(g_ptrs)

            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
            scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            
            zeros = (zeros >> zeros_shifter[None, :]) & maxq
            zeros = (zeros + 1)
            
            a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
            b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

            # Now we need to unpack b (which is N-bit values) into 32-bit values
            b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
            b = (b - zeros) * scales  # Scale and shift

            accumulator += tl.dot(a, b)
            a_ptrs += BLOCK_SIZE_K
            b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
            g_ptrs += BLOCK_SIZE_K

        c = accumulator.to(tl.float16)
        c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
        c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
        tl.store(c_ptrs, accumulator, mask=c_mask)
        
    # code based https://github.com/fpgaminer/GPTQ-triton
    def transpose_matmul4_kernel_config_pruner(configs, nargs):
        """
        The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
        """
        m = max(2 ** int(math.ceil(math.log2(nargs['M']))), 16)
        n = max(2 ** int(math.ceil(math.log2(nargs['N']))), 16)
        k = max(2 ** int(math.ceil(math.log2(nargs['K']))), 16)

        used = set()
        for config in configs:
            block_size_m = min(m, config.kwargs['BLOCK_SIZE_M'])
            block_size_n = min(n, config.kwargs['BLOCK_SIZE_N'])
            block_size_k = min(k, config.kwargs['BLOCK_SIZE_K'])
            group_size_m = config.kwargs['GROUP_SIZE_M']

            if (block_size_m, block_size_n, block_size_k, group_size_m, config.num_stages, config.num_warps) in used:
                continue

            used.add((block_size_m, block_size_n, block_size_k, group_size_m, config.num_stages, config.num_warps))
            yield triton.Config({'BLOCK_SIZE_M': block_size_m, 'BLOCK_SIZE_N': block_size_n, 'BLOCK_SIZE_K': block_size_k, 'GROUP_SIZE_M': group_size_m}, num_stages=config.num_stages, num_warps=config.num_warps)

    @custom_autotune.autotune(
        configs=[
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),
            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
            triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),
        ],
        key=['M', 'N', 'K'],
        nearest_power_of_two=True,
        prune_configs_by={
            'early_config_prune': transpose_matmul4_kernel_config_pruner,
            'perf_model': None,
            'top_k': None,
        },
    )
    
    @triton.jit
    def transpose_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
                                    scales_ptr, zeros_ptr, g_ptr,
                                    M, N, K, bits, maxq,
                                    stride_am, stride_ak,
                                    stride_bk, stride_bn,
                                    stride_cm, stride_cn,
                                    stride_scales, stride_zeros,
                                    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
                                    GROUP_SIZE_M: tl.constexpr):
        """
        Compute the matrix multiplication C = A x B.
        A is of shape (M, N) float16
        B is of shape (K//8, N) int32
        C is of shape (M, K) float16
        scales is of shape (G, N) float16
        zeros is of shape (G, N) float16
        g_ptr is of shape (K) int32 
        """
        infearure_per_bits = 32 // bits

        pid = tl.program_id(axis=0)
        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
        num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
        num_pid_in_group = GROUP_SIZE_M * num_pid_k
        group_id = pid // num_pid_in_group
        first_pid_m = group_id * GROUP_SIZE_M
        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
        pid_m = first_pid_m + (pid % group_size_m)
        pid_k = (pid % num_pid_in_group) // group_size_m

        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
        offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
        offs_n = tl.arange(0, BLOCK_SIZE_N)
        a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
        a_mask = (offs_am[:, None] < M)
        # b_ptrs is set up such that it repeats elements along the K axis 8 times
        b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
        g_ptrs = g_ptr + offs_bk
        g_idx = tl.load(g_ptrs)
        
        # shifter is used to extract the N bits of each element in the 32-bit word from B
        scales_ptrs = scales_ptr + offs_n[None, :]  + g_idx[:, None] * stride_scales
        zeros_ptrs = zeros_ptr + (offs_n[None, :]// infearure_per_bits) + g_idx[:, None] * stride_zeros
        
        shifter = (offs_bk % infearure_per_bits) * bits
        zeros_shifter = (offs_n % infearure_per_bits) * bits
        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
        
        for k in range(0, num_pid_n):
            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
            scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            
            zeros = (zeros >> zeros_shifter[None, :]) & maxq
            zeros = (zeros + 1)
            
            a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
            b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

            # Now we need to unpack b (which is N-bit values) into 32-bit values
            b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
            b = (b - zeros) * scales  # Scale and shift
            b = tl.trans(b)

            accumulator += tl.dot(a, b)
            a_ptrs += BLOCK_SIZE_N
            b_ptrs += BLOCK_SIZE_N
            scales_ptrs += BLOCK_SIZE_N
            zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
            
        c = accumulator.to(tl.float16)
        c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
        c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
        tl.store(c_ptrs, accumulator, mask=c_mask)
except:
    print('trioton not installed.')

def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
    with torch.cuda.device(input.device):
        output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
        grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
        matmul_248_kernel[grid](input, qweight, output,
                                scales, qzeros, g_idx,
                                input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
                                input.stride(0), input.stride(1),
                                qweight.stride(0), qweight.stride(1),
                                output.stride(0), output.stride(1),
                                scales.stride(0), qzeros.stride(0))
        return output

def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
    with torch.cuda.device(input.device):
        output_dim = (qweight.shape[0] * 32) // bits
        output = torch.empty((input.shape[0], output_dim), device='cuda', dtype=torch.float16)
        grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']),)
        transpose_matmul_248_kernel[grid](input, qweight, output,
                                        scales, qzeros, g_idx,
                                        input.shape[0], qweight.shape[1], output_dim, bits, maxq,
                                        input.stride(0), input.stride(1),
                                        qweight.stride(0), qweight.stride(1),
                                        output.stride(0), output.stride(1),
                                        scales.stride(0), qzeros.stride(0))
        return output

class QuantLinearFunction(torch.autograd.Function):
    @staticmethod
    @custom_fwd(cast_inputs=torch.float16)
    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
        ctx.save_for_backward(qweight, scales, qzeros, g_idx)
        ctx.bits,ctx.maxq = bits, maxq
        return output
    
    @staticmethod
    @custom_bwd
    def backward(ctx, grad_output):
        qweight, scales, qzeros, g_idx = ctx.saved_tensors
        bits, maxq = ctx.bits, ctx.maxq
        grad_input = None

        if ctx.needs_input_grad[0]:
            grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
        return grad_input, None, None, None, None, None, None
    
class QuantLinear(nn.Module): 
    def __init__(self, bits, groupsize, infeatures, outfeatures, bias):
        super().__init__()
        if bits not in [2,4,8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")
        self.infeatures = infeatures
        self.outfeatures = outfeatures
        self.bits = bits
        self.maxq = 2 ** self.bits - 1
        self.groupsize = groupsize if groupsize != -1 else infeatures
        
        self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
        self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
        self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
        self.register_buffer('g_idx', torch.tensor([i // self.groupsize  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 pack(self, linear, scales, zeros, g_idx = None):
        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
        
        scales = scales.t().contiguous()
        zeros = zeros.t().contiguous()
        scale_zeros = zeros * scales
        self.scales = scales.clone().half()
        if linear.bias is not None:
            self.bias = linear.bias.clone().half()
            
        intweight = []
        for idx in range(self.infeatures):
            intweight.append(torch.round((linear.weight.data[:,idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(torch.int)[:,None])
        intweight = torch.cat(intweight,dim=1)
        intweight = intweight.t().contiguous()
        intweight = intweight.numpy().astype(np.uint32)
        qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
        i = 0
        row = 0
        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
            else:
                raise NotImplementedError("Only 2,4,8 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
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")
                
        qzeros = qzeros.astype(np.int32)
        self.qzeros = torch.from_numpy(qzeros) 
        
    def forward(self, x):
        out_shape = x.shape[:-1] + (self.outfeatures, )
        out = QuantLinearFunction.apply(x.reshape(-1,x.shape[-1]), self.qweight, self.scales, 
                                        self.qzeros, self.g_idx, self.bits, self.maxq)
        out = out + self.bias if self.bias is not None else out  
        return out.reshape(out_shape)
        
def make_quant_attn(model):
    """
    Replace all LlamaAttention modules with QuantLlamaAttention modules, fusing the q, k, v projections.
    """
    for name, m in model.named_modules():
        if not isinstance(m, LlamaAttention):
            continue

        q_proj = m.q_proj
        k_proj = m.k_proj
        v_proj = m.v_proj

        qweights = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=1)
        qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=1)
        scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=1)
        g_idx = torch.cat([q_proj.g_idx, k_proj.g_idx, v_proj.g_idx], dim=0)
        bias = torch.cat([q_proj.bias, k_proj.bias, v_proj.bias], dim=0) if q_proj.bias is not None else None

        qkv_layer = QuantLinear(q_proj.bits, q_proj.groupsize, q_proj.infeatures, q_proj.outfeatures + k_proj.outfeatures + v_proj.outfeatures, True if q_proj.bias is not None else False)
        qkv_layer.qweight = qweights
        qkv_layer.qzeros = qzeros
        qkv_layer.scales = scales
        qkv_layer.g_idx = g_idx
        qkv_layer.bias = bias

        attn = QuantLlamaAttention(m.hidden_size, m.num_heads, qkv_layer, m.o_proj, m.rotary_emb)

        if '.' in name:
            parent_name = name.rsplit('.', 1)[0]
            child_name = name[len(parent_name) + 1:]
            parent = model.get_submodule(parent_name)
        else:
            parent_name = ''
            parent = model
            child_name = name

        #print(f"Replacing {name} with quant_attn; parent: {parent_name}, child's name: {child_name}")

        setattr(parent, child_name, attn)


class QuantLlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self,hidden_size,num_heads,qkv_proj,o_proj,rotary_emb,):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads

        if (self.head_dim * num_heads) != self.hidden_size:
            raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"f" and `num_heads`: {num_heads}).")
        self.qkv_proj = qkv_proj
        self.o_proj = o_proj
        self.rotary_emb = rotary_emb

    def _shape(self, tensor, seq_len, bsz):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(self,hidden_states,past_key_value = None,attention_mask = None,position_ids = None, output_attentions = False,use_cache= False):
        """Input shape: Batch x Time x Channel"""

        bsz, q_len, _ = hidden_states.size()

        qkv_states = self.qkv_proj(hidden_states)
        query_states, key_states, value_states = torch.split(qkv_states, self.hidden_size, dim=2)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # [bsz, nh, t, hd]

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value
        
def autotune_warmup(model, transpose = False):
    """
    Pre-tunes the quantized kernel
    """
    from tqdm import tqdm

    kn_values  = {}

    for _, m in model.named_modules():
        if not isinstance(m, QuantLinear):
            continue

        k = m.infeatures
        n = m.outfeatures
        
        if (k, n) not in kn_values:
            kn_values[(k, n)] = (m.qweight.cuda(), m.scales.cuda(), m.qzeros.cuda(), m.g_idx.cuda(), m.bits, m.maxq)

    print(f'Found {len(kn_values)} unique KN values.')

    print('Warming up autotune cache ...')
    with torch.no_grad():
        for m in tqdm(range(0, 12)):
            m = 2 ** m   # [1, 2048]
            for (k, n), (qweight, scales, qzeros, g_idx, bits, maxq) in kn_values.items():
                a = torch.randn(m, k, dtype=torch.float16, device='cuda')
                matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq)
                if transpose:
                    a = torch.randn(m, n, dtype=torch.float16, device='cuda')
                    transpose_matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq)
    del kn_values
        
def make_quant(module, names, bits, groupsize, name=''):
    if isinstance(module, QuantLinear):
        return
    for attr in dir(module):
        tmp = getattr(module, attr)
        name1 = name + '.' + attr if name != '' else attr
        if name1 in names:
            delattr(module, attr)
            setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
    for name1, child in module.named_children():
        make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)