quant/fused_mlp.py

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

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': 256,
                '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': 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': 128,
                '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=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),  # 3090
            triton.Config({
                'BLOCK_SIZE_M': 128,
                'BLOCK_SIZE_N': 16,
                'BLOCK_SIZE_K': 32,
                'GROUP_SIZE_M': 8
            }, num_stages=4, num_warps=4),  # 3090
            triton.Config({
                'BLOCK_SIZE_M': 32,
                'BLOCK_SIZE_N': 32,
                'BLOCK_SIZE_K': 128,
                'GROUP_SIZE_M': 8
            }, num_stages=2, num_warps=4),  # 3090
            triton.Config({
                'BLOCK_SIZE_M': 64,
                'BLOCK_SIZE_N': 16,
                'BLOCK_SIZE_K': 64,
                'GROUP_SIZE_M': 8
            }, num_stages=4, num_warps=4),  # 3090
            triton.Config({
                'BLOCK_SIZE_M': 64,
                'BLOCK_SIZE_N': 32,
                'BLOCK_SIZE_K': 64,
                'GROUP_SIZE_M': 8
            }, num_stages=4, num_warps=4),  # 3090
        ],
        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 fusedmatmul_248_kernel(a_ptr, c_ptr, b1_ptr, scales1_ptr, zeros1_ptr, g1_ptr, b2_ptr, scales2_ptr, zeros2_ptr, g2_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):
        """
        Computes: C = silu(A * B1) * (A * B2)
        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 (1, N) float16
        zeros is of shape (1, N//8) 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
        b1_ptrs = b1_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)
        b2_ptrs = b2_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)
        g1_ptrs = g1_ptr + offs_k
        g2_ptrs = g2_ptr + offs_k
        # shifter is used to extract the N bits of each element in the 32-bit word from B
        scales1_ptrs = scales1_ptr + offs_bn[None, :]
        scales2_ptrs = scales2_ptr + offs_bn[None, :]
        zeros1_ptrs = zeros1_ptr + (offs_bn[None, :] // infearure_per_bits)
        zeros2_ptrs = zeros2_ptr + (offs_bn[None, :] // infearure_per_bits)

        shifter = (offs_k % infearure_per_bits) * bits
        zeros_shifter = (offs_bn % infearure_per_bits) * bits
        accumulator1 = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
        accumulator2 = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
        for k in range(0, num_pid_k):
            g1_idx = tl.load(g1_ptrs)
            g2_idx = tl.load(g2_ptrs)

            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
            scales1 = tl.load(scales1_ptrs + g1_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            scales2 = tl.load(scales2_ptrs + g2_idx[:, None] * stride_scales)

            zeros1 = tl.load(zeros1_ptrs + g1_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            zeros1 = (zeros1 >> zeros_shifter[None, :]) & maxq
            zeros1 = (zeros1 + 1)

            zeros2 = tl.load(zeros2_ptrs + g2_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
            zeros2 = (zeros2 >> zeros_shifter[None, :]) & maxq
            zeros2 = (zeros2 + 1)

            a = tl.load(a_ptrs, mask=a_mask, other=0.)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
            b1 = tl.load(b1_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
            b2 = tl.load(b2_ptrs)

            # Now we need to unpack b (which is N-bit values) into 32-bit values
            b1 = (b1 >> shifter[:, None]) & maxq  # Extract the N-bit values
            b1 = (b1 - zeros1) * scales1  # Scale and shift
            accumulator1 += tl.dot(a, b1)

            b2 = (b2 >> shifter[:, None]) & maxq
            b2 = (b2 - zeros2) * scales2
            accumulator2 += tl.dot(a, b2)

            a_ptrs += BLOCK_SIZE_K
            b1_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
            b2_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
            g1_ptrs += BLOCK_SIZE_K
            g2_ptrs += BLOCK_SIZE_K

        accumulator1 = silu(accumulator1)
        c = accumulator1 * accumulator2
        c = c.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, c, mask=c_mask)

    @triton.jit
    def silu(x):
        return x * tl.sigmoid(x)
except:
    print('triton not installed.')


class QuantLlamaMLP(nn.Module):

    def __init__(
        self,
        gate_proj,
        down_proj,
        up_proj,
    ):
        super().__init__()
        self.register_buffer('gate_proj_qweight', gate_proj.qweight)
        self.register_buffer('gate_proj_scales', gate_proj.scales)
        self.register_buffer('gate_proj_qzeros', gate_proj.qzeros)
        self.register_buffer('gate_proj_g_idx', gate_proj.g_idx)
        self.register_buffer('up_proj_qweight', up_proj.qweight)
        self.register_buffer('up_proj_scales', up_proj.scales)
        self.register_buffer('up_proj_qzeros', up_proj.qzeros)
        self.register_buffer('up_proj_g_idx', up_proj.g_idx)

        self.infeatures = gate_proj.infeatures
        self.intermediate_size = gate_proj.outfeatures
        self.outfeatures = down_proj.outfeatures
        self.bits = gate_proj.bits
        self.maxq = gate_proj.maxq

        self.down_proj = down_proj

    def forward(self, x):
        return self.down_proj(self.triton_llama_mlp(x))

    def triton_llama_mlp(self, x):
        with torch.cuda.device(x.device):
            out_shape = x.shape[:-1] + (self.intermediate_size, )
            x = x.reshape(-1, x.shape[-1])
            M, K = x.shape
            N = self.intermediate_size
            c = torch.empty((M, N), device=x.device, dtype=torch.float16)
            grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
            fusedmatmul_248_kernel[grid](x, c, self.gate_proj_qweight, self.gate_proj_scales, self.gate_proj_qzeros, self.gate_proj_g_idx, self.up_proj_qweight, self.up_proj_scales,
                                         self.up_proj_qzeros, self.up_proj_g_idx, M, N, K, self.bits, self.maxq, x.stride(0), x.stride(1), self.gate_proj_qweight.stride(0),
                                         self.gate_proj_qweight.stride(1), c.stride(0), c.stride(1), self.gate_proj_scales.stride(0), self.gate_proj_qzeros.stride(0))
            c = c.reshape(out_shape)
            return c

    def fused2cuda(self):
        self.gate_proj_qweight = self.gate_proj_qweight.cuda()
        self.gate_proj_scales = self.gate_proj_scales.cuda()
        self.gate_proj_qzeros = self.gate_proj_qzeros.cuda()
        self.gate_proj_g_idx = self.gate_proj_g_idx.cuda()
        self.up_proj_qweight = self.up_proj_qweight.cuda()
        self.up_proj_scales = self.up_proj_scales.cuda()
        self.up_proj_qzeros = self.up_proj_qzeros.cuda()
        self.up_proj_g_idx = self.up_proj_g_idx.cuda()

    def fused2cpu(self):
        self.gate_proj_qweight = self.gate_proj_qweight.cpu()
        self.gate_proj_scales = self.gate_proj_scales.cpu()
        self.gate_proj_qzeros = self.gate_proj_qzeros.cpu()
        self.gate_proj_g_idx = self.gate_proj_g_idx.cpu()
        self.up_proj_qweight = self.up_proj_qweight.cpu()
        self.up_proj_scales = self.up_proj_scales.cpu()
        self.up_proj_qzeros = self.up_proj_qzeros.cpu()
        self.up_proj_g_idx = self.up_proj_g_idx.cpu()


def make_fused_mlp(m, parent_name=''):
    """
    Replace all LlamaMLP modules with QuantLlamaMLP modules, which fuses many of the operations.
    """
    if isinstance(m, LlamaMLP):
        return QuantLlamaMLP(m.gate_proj, m.down_proj, m.up_proj)

    for name, child in m.named_children():
        child = make_fused_mlp(child, parent_name=f"{parent_name}.{name}")

        if isinstance(child, QuantLlamaMLP):
            setattr(m, name, child)
    return m


def autotune_warmup_fused(model):
    """
    Pre-tunes the quantized kernel
    """
    from tqdm import tqdm

    kn_values = {}

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

        k = m.infeatures
        n = m.intermediate_size

        m.fused2cuda()
        if (k, n) not in kn_values:
            kn_values[(k, n)] = m

    print(f'Found {len(kn_values)} unique fused mlp 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), (modules) in kn_values.items():
                a = torch.randn(m, k, dtype=torch.float16, device='cuda')
                modules.triton_llama_mlp(a)

        for (k, n), (modules) in kn_values.items():
            a = torch.randn(m, k, dtype=torch.float16, device='cuda')
            modules.fused2cpu()
    del kn_values