text-generation-inference/server/text_generation_server/utils/gptq/quant_linear.py

import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd

try:
    import triton
    import triton.language as tl
    from . import custom_autotune

    # 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": custom_autotune.matmul248_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=0.0)  # (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_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)

    @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,
    )
    @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 n 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=0.0)  # (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_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("triton 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=input.device, 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=input.device, 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, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
        super().__init__()
        self.qweight = qweight
        self.qzeros = qzeros
        self.scales = scales
        self.g_idx = g_idx
        self.bias = bias
        if bits not in [2, 4, 8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")
        self.bits = bits
        self.maxq = 2**self.bits - 1
        self.groupsize = groupsize

        self.outfeatures = qweight.shape[1]
        self.infeatures = qweight.shape[0] * 32 // 4

    @classmethod
    def new(cls, bits, groupsize, infeatures, outfeatures, bias):
        super().__init__()
        if bits not in [2, 4, 8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")

        qweight = torch.zeros(
            (infeatures // 32 * self.bits, outfeatures), dtype=torch.int32
        )
        qzeros = torch.zeros(
            (math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits),
            dtype=torch.int32,
        )
        scales = torch.zeros(
            (math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16
        )
        g_idx = torch.tensor(
            [i // self.groupsize for i in range(infeatures)], dtype=torch.int32
        )
        if bias:
            bias = torch.zeros((outfeatures), dtype=torch.float16)
        else:
            bias = None
        return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)

    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)