add triton support

auto_gptq/modeling/_base.py

@@ -338,6 +338,12 @@ class BaseGPTQForCausalLM(nn.Module):
         use_triton: bool = False
     ):
         """load quantized model from local disk"""
+        if use_triton:
+            from ..nn_modules.qlinear_triton import autotune_warmup_linear
+
+            logger.warning("use_triton will force moving the hole model to GPU, make sure you have enough VRAM.")
+            device = "cuda:0"
+
         config = AutoConfig.from_pretrained(save_dir, trust_remote_code=True)
         if config.model_type not in SUPPORTED_MODELS:
             raise TypeError(f"{config.model_type} isn't supported yet.")
@@ -386,6 +392,9 @@ class BaseGPTQForCausalLM(nn.Module):
         model.eval()
         model.to(device)
 
+        if use_triton:
+            autotune_warmup_linear(model, seqlen=model.seqlen)
+
         return cls(model, True, quantize_config)
 
 

auto_gptq/modeling/_utils.py

@@ -3,7 +3,7 @@ from logging import getLogger
 import torch.nn as nn
 from transformers import AutoConfig
 
-from ._const import SUPPORTED_MODELS
+from ._const import SUPPORTED_MODELS, CUDA
 
 
 logger = getLogger(__name__)
@@ -26,7 +26,7 @@ def get_module_by_name(model, module_name: str):
 
 def make_quant(module, names, bits, groupsize, name='', use_triton=False):
     if use_triton:
-        raise NotImplementedError("triton not supported yet")
+        from ..nn_modules.qlinear_triton import QuantLinear
     else:
         from ..nn_modules.qlinear import QuantLinear
 
@@ -42,9 +42,9 @@ def make_quant(module, names, bits, groupsize, name='', use_triton=False):
         make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
 
 
-def pack_model(model, quantizers, bits, group_size, use_triton=False):
+def pack_model(model, quantizers, bits, group_size, use_triton=False, autotune_warmup: bool = False):
     if use_triton:
-        raise NotImplementedError("triton not supported yet.")
+        from ..nn_modules.qlinear_triton import QuantLinear, autotune_warmup_linear
     else:
         from ..nn_modules.qlinear import QuantLinear
 
@@ -60,6 +60,12 @@ def pack_model(model, quantizers, bits, group_size, use_triton=False):
         qlayers[name].pack(layers[name], scale, zero, g_idx)
     logger.info('Model packed.')
 
+    if use_triton and autotune_warmup:
+        logger.warning(
+            "using autotune_warmup will move model to GPU, make sure you have enough VRAM to load the hole model."
+        )
+        autotune_warmup_linear(model.to(CUDA), seqlen=model.seqlen)
+
 
 def check_and_get_model_type(model_dir):
     config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)

auto_gptq/nn_modules/qlinear_triton.py

@@ -0,0 +1,455 @@
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.cuda.amp import custom_bwd, custom_fwd
+from logging import getLogger
+
+logger = getLogger(__name__)
+
+try:
+    import triton
+    import triton.language as tl
+    from .triton_utils 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.)  # (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 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_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 ImportError:
+    logger.warning('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='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 autotune_warmup_linear(model, transpose=False, seqlen=2048):
+    """
+    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)
+
+    logger.info(f'Found {len(kn_values)} unique KN Linear values.')
+    logger.info('Warming up autotune cache ...')
+    with torch.no_grad():
+        for m in tqdm(range(0, math.ceil(math.log2(seqlen)) + 1)):
+            m = 2 ** m
+            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
+
+
+__all__ = [
+    "QuantLinear",
+    "autotune_warmup_linear"
+]

auto_gptq/nn_modules/triton_utils/custom_autotune.py

@@ -0,0 +1,174 @@
+import builtins
+import math
+import time
+from typing import Dict
+
+import triton
+
+
+#  code based https://github.com/fpgaminer/GPTQ-triton
+"""
+Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
+"""
+
+
+class CustomizedTritonAutoTuner(triton.KernelInterface):
+    def __init__(
+        self,
+        fn,
+        arg_names,
+        configs,
+        key,
+        reset_to_zero,
+        prune_configs_by: Dict = None,
+        nearest_power_of_two: bool = False
+    ):
+        if not configs:
+            self.configs = [triton.Config({}, num_warps=4, num_stages=2)]
+        else:
+            self.configs = configs
+        self.key_idx = [arg_names.index(k) for k in key]
+        self.nearest_power_of_two = nearest_power_of_two
+        self.cache = {}
+        # hook to reset all required tensor to zeros before relaunching a kernel
+        self.hook = lambda args: 0
+        if reset_to_zero is not None:
+            self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
+
+            def _hook(args):
+                for i in self.reset_idx:
+                    args[i].zero_()
+
+            self.hook = _hook
+        self.arg_names = arg_names
+        # prune configs
+        if prune_configs_by:
+            perf_model, top_k = prune_configs_by['perf_model'], prune_configs_by['top_k']
+            if 'early_config_prune' in prune_configs_by:
+                early_config_prune = prune_configs_by['early_config_prune']
+        else:
+            perf_model, top_k, early_config_prune = None, None, None
+        self.perf_model, self.configs_top_k = perf_model, top_k
+        self.early_config_prune = early_config_prune
+        self.fn = fn
+
+    def _bench(self, *args, config, **meta):
+        # check for conflicts, i.e. meta-parameters both provided
+        # as kwargs and by the autotuner
+        conflicts = meta.keys() & config.kwargs.keys()
+        if conflicts:
+            raise ValueError(f"Conflicting meta-parameters: {', '.join(conflicts)}."
+                             " Make sure that you don't re-define auto-tuned symbols.")
+        # augment meta-parameters with tunable ones
+        current = dict(meta, **config.kwargs)
+
+        def kernel_call():
+            if config.pre_hook:
+                config.pre_hook(self.nargs)
+            self.hook(args)
+            self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **current)
+
+        try:
+            # In testings using only 40 reps seems to be close enough and it appears to be what PyTorch uses
+            # PyTorch also sets fast_flush to True, but I didn't see any speedup so I'll leave the default
+            return triton.testing.do_bench(kernel_call, percentiles=(0.5, 0.2, 0.8), rep=40)
+        except triton.compiler.OutOfResources:
+            return (float('inf'), float('inf'), float('inf'))
+
+    def run(self, *args, **kwargs):
+        self.nargs = dict(zip(self.arg_names, args))
+        if len(self.configs) > 1:
+            key = tuple(args[i] for i in self.key_idx)
+
+            # This reduces the amount of autotuning by rounding the keys to the nearest power of two
+            # In my testing this gives decent results, and greatly reduces the amount of tuning required
+            if self.nearest_power_of_two:
+                key = tuple([2 ** int(math.log2(x) + 0.5) for x in key])
+
+            if key not in self.cache:
+                # prune configs
+                pruned_configs = self.prune_configs(kwargs)
+                bench_start = time.time()
+                timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
+                bench_end = time.time()
+                self.bench_time = bench_end - bench_start
+                self.cache[key] = builtins.min(timings, key=timings.get)
+                self.hook(args)
+                self.configs_timings = timings
+            config = self.cache[key]
+        else:
+            config = self.configs[0]
+        self.best_config = config
+        if config.pre_hook is not None:
+            config.pre_hook(self.nargs)
+        return self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **kwargs, **config.kwargs)
+
+    def prune_configs(self, kwargs):
+        pruned_configs = self.configs
+        if self.early_config_prune:
+            pruned_configs = self.early_config_prune(self.configs, self.nargs)
+        if self.perf_model:
+            top_k = self.configs_top_k
+            if isinstance(top_k, float) and top_k <= 1.0:
+                top_k = int(len(self.configs) * top_k)
+            if len(pruned_configs) > top_k:
+                est_timing = {
+                    config: self.perf_model(**self.nargs, **kwargs, **config.kwargs, num_stages=config.num_stages,
+                                            num_warps=config.num_warps) for config in pruned_configs}
+                pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k]
+        return pruned_configs
+
+    def warmup(self, *args, **kwargs):
+        self.nargs = dict(zip(self.arg_names, args))
+        for config in self.prune_configs(kwargs):
+            self.fn.warmup(
+                *args,
+                num_warps=config.num_warps,
+                num_stages=config.num_stages,
+                **kwargs,
+                **config.kwargs,
+            )
+        self.nargs = None
+
+
+def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False):
+    def decorator(fn):
+        return CustomizedTritonAutoTuner(
+            fn, fn.arg_names, configs, key, reset_to_zero, prune_configs_by, nearest_power_of_two
+        )
+
+    return decorator
+
+
+def matmul248_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
+        )
+
+
+__all__ = ["autotune"]

auto_gptq/quantization/quantizer.py

@@ -1,7 +1,5 @@
-import math
 from logging import getLogger
 
-import numpy as np
 import torch
 import torch.nn as nn