402 lines
14 KiB
Python
402 lines
14 KiB
Python
import torch
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from torch.cuda.amp import custom_bwd, custom_fwd
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from logging import getLogger
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import triton
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import triton.language as tl
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from . import custom_autotune
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logger = getLogger(__name__)
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# code based https://github.com/fpgaminer/GPTQ-triton
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@custom_autotune.autotune(
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configs=[
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 256,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 128,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 128,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 64,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 128,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=2,
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num_warps=8
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)
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],
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key=['M', 'N', 'K'],
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nearest_power_of_two=True,
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prune_configs_by={
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'early_config_prune': custom_autotune.matmul248_kernel_config_pruner,
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'perf_model': None,
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'top_k': None,
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},
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)
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@triton.jit
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def quant_matmul_248_kernel(
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a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K,
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bits, maxq,
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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stride_scales, stride_zeros,
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr
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):
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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, K) float16
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B is of shape (K//8, N) int32
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C is of shape (M, N) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + (
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(offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn
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) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_k
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# shifter is used to extract the N bits of each element in the 32-bit word from B
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scales_ptrs = scales_ptr + offs_bn[None, :]
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zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
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shifter = (offs_k % infearure_per_bits) * bits
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zeros_shifter = (offs_bn % infearure_per_bits) * bits
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for k in range(0, num_pid_k):
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g_idx = tl.load(g_ptrs)
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# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
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scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1)
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a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
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# Now we need to unpack b (which is N-bit values) into 32-bit values
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b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
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b = (b - zeros) * scales # Scale and shift
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accumulator += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_K
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b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
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g_ptrs += BLOCK_SIZE_K
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c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
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c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
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tl.store(c_ptrs, accumulator, mask=c_mask)
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@custom_autotune.autotune(
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configs=[
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 256,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 128,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 128,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 32,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 64,
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'GROUP_SIZE_M': 8
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},
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num_stages=4,
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num_warps=4
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),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 128,
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'GROUP_SIZE_M': 8
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},
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num_stages=2,
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num_warps=8
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)
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],
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key=['M', 'N', 'K'],
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nearest_power_of_two=True
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)
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@triton.jit
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def transpose_quant_matmul_248_kernel(
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a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K,
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bits, maxq,
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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stride_scales, stride_zeros,
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr
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):
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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, N) float16
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B is of shape (K//8, N) int32
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C is of shape (M, K) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_in_group = GROUP_SIZE_M * num_pid_k
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_k = (pid % num_pid_in_group) // group_size_m
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
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offs_n = tl.arange(0, BLOCK_SIZE_N)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + (
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(offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn
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) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_bk
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g_idx = tl.load(g_ptrs)
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# shifter is used to extract the N bits of each element in the 32-bit word from B
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scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
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zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
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shifter = (offs_bk % infearure_per_bits) * bits
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zeros_shifter = (offs_n % infearure_per_bits) * bits
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
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for k in range(0, num_pid_n):
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# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
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scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1)
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a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
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b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
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# Now we need to unpack b (which is N-bit values) into 32-bit values
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b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
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b = (b - zeros) * scales # Scale and shift
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b = tl.trans(b)
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accumulator += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_N
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b_ptrs += BLOCK_SIZE_N
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scales_ptrs += BLOCK_SIZE_N
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zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
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c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
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c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
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tl.store(c_ptrs, accumulator, mask=c_mask)
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@triton.jit
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def silu(x):
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return x * tl.sigmoid(x)
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def quant_matmul_248(input, qweight, scales, qzeros, g_idx, bits, maxq):
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with torch.cuda.device(input.device):
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output = torch.empty((input.shape[0], qweight.shape[1]), device=input.device, dtype=input.dtype)
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grid = lambda META: (
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triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),
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)
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quant_matmul_248_kernel[grid](
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input, qweight, output,
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scales.to(input.dtype), qzeros, g_idx,
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input.shape[0], qweight.shape[1], input.shape[1],
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bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0)
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)
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return output
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def transpose_quant_matmul_248(input, qweight, scales, qzeros, g_idx, bits, maxq):
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with torch.cuda.device(input.device):
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output_dim = (qweight.shape[0] * 32) // bits
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output = torch.empty((input.shape[0], output_dim), device=input.device, dtype=input.dtype)
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grid = lambda META: (
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triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']),)
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transpose_quant_matmul_248_kernel[grid](
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input, qweight, output,
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scales.to(input.dtype), qzeros, g_idx,
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input.shape[0], qweight.shape[1], output_dim,
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bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0)
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)
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return output
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class QuantLinearFunction(torch.autograd.Function):
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@staticmethod
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@custom_fwd
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def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = quant_matmul_248(input, qweight, scales, qzeros, g_idx, bits, maxq)
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ctx.save_for_backward(qweight, scales, qzeros, g_idx)
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ctx.bits, ctx.maxq = bits, maxq
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return output
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_output):
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qweight, scales, qzeros, g_idx = ctx.saved_tensors
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bits, maxq = ctx.bits, ctx.maxq
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grad_input = None
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if ctx.needs_input_grad[0]:
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grad_input = transpose_quant_matmul_248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
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return grad_input, None, None, None, None, None, None
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def quant_matmul_inference_only_248(input, qweight, scales, qzeros, g_idx, bits, maxq):
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with torch.cuda.device(input.device):
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output = torch.empty((input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16)
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grid = lambda META: (
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triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),
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)
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quant_matmul_248_kernel[grid](
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input, qweight, output,
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scales, qzeros, g_idx,
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input.shape[0], qweight.shape[1], input.shape[1],
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bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0)
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)
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return output
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class QuantLinearInferenceOnlyFunction(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float16)
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def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = quant_matmul_248(input, qweight, scales, qzeros, g_idx, bits, maxq)
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return output
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