360 lines
12 KiB
Python
360 lines
12 KiB
Python
import math
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.cuda.amp import custom_fwd
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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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# 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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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": 64,
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"GROUP_SIZE_M": 8,
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},
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num_stages=3,
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num_warps=8,
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),
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triton.Config(
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{
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"BLOCK_SIZE_M": 32,
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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=4,
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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 matmul_248_kernel(
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a_ptr,
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b_ptr,
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c_ptr,
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scales_ptr,
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zeros_ptr,
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g_ptr,
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M,
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N,
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K,
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bits,
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maxq,
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stride_am,
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stride_ak,
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stride_bk,
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stride_bn,
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stride_cm,
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stride_cn,
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stride_scales,
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stride_zeros,
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BLOCK_SIZE_M: tl.constexpr,
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BLOCK_SIZE_N: tl.constexpr,
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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 + (
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offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
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) # (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
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+ 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(
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scales_ptrs + g_idx[:, None] * stride_scales
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) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(
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zeros_ptrs + g_idx[:, None] * stride_zeros
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) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1) & maxq # eventually avoid overflow
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a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (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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def matmul248(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(
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(input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
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)
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def grid(META):
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return (
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triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
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* triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
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)
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matmul_248_kernel[grid](
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input,
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qweight,
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output,
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scales,
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qzeros,
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g_idx,
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input.shape[0],
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qweight.shape[1],
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input.shape[1],
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bits,
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maxq,
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input.stride(0),
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input.stride(1),
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qweight.stride(0),
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qweight.stride(1),
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output.stride(0),
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output.stride(1),
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scales.stride(0),
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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(cast_inputs=torch.float16)
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def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
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return output
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class QuantLinear(nn.Module):
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def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
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super().__init__()
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self.register_buffer("qweight", qweight)
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self.register_buffer("qzeros", qzeros)
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self.register_buffer("scales", scales)
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self.register_buffer("g_idx", g_idx)
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if bias is not None:
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self.register_buffer("bias", bias)
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else:
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self.bias = None
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if bits not in [2, 4, 8]:
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raise NotImplementedError("Only 2,4,8 bits are supported.")
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self.bits = bits
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self.maxq = 2**self.bits - 1
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self.groupsize = groupsize
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self.outfeatures = qweight.shape[1]
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self.infeatures = qweight.shape[0] * 32 // bits
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@classmethod
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def new(cls, bits, groupsize, infeatures, outfeatures, bias):
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if bits not in [2, 4, 8]:
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raise NotImplementedError("Only 2,4,8 bits are supported.")
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qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
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qzeros = torch.zeros(
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(math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
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dtype=torch.int32,
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)
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scales = torch.zeros(
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(math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
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)
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g_idx = torch.tensor(
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[i // groupsize for i in range(infeatures)], dtype=torch.int32
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)
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if bias:
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bias = torch.zeros((outfeatures), dtype=torch.float16)
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else:
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bias = None
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return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
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def pack(self, linear, scales, zeros, g_idx=None):
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self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
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scales = scales.t().contiguous()
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zeros = zeros.t().contiguous()
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scale_zeros = zeros * scales
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self.scales = scales.clone().half()
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if linear.bias is not None:
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self.bias = linear.bias.clone().half()
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intweight = []
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for idx in range(self.infeatures):
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intweight.append(
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torch.round(
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(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
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/ self.scales[self.g_idx[idx]]
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).to(torch.int)[:, None]
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)
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intweight = torch.cat(intweight, dim=1)
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intweight = intweight.t().contiguous()
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intweight = intweight.numpy().astype(np.uint32)
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qweight = np.zeros(
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(intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
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)
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i = 0
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row = 0
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while row < qweight.shape[0]:
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if self.bits in [2, 4, 8]:
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for j in range(i, i + (32 // self.bits)):
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qweight[row] |= intweight[j] << (self.bits * (j - i))
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i += 32 // self.bits
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row += 1
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else:
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raise NotImplementedError("Only 2,4,8 bits are supported.")
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qweight = qweight.astype(np.int32)
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self.qweight = torch.from_numpy(qweight)
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zeros -= 1
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zeros = zeros.numpy().astype(np.uint32)
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qzeros = np.zeros(
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(zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
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)
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i = 0
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col = 0
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while col < qzeros.shape[1]:
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if self.bits in [2, 4, 8]:
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for j in range(i, i + (32 // self.bits)):
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qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
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i += 32 // self.bits
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col += 1
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else:
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raise NotImplementedError("Only 2,4,8 bits are supported.")
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qzeros = qzeros.astype(np.int32)
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self.qzeros = torch.from_numpy(qzeros)
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.outfeatures,)
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out = QuantLinearFunction.apply(
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x.reshape(-1, x.shape[-1]),
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self.qweight,
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self.scales,
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self.qzeros,
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self.g_idx,
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self.bits,
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self.maxq,
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)
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out = out + self.bias if self.bias is not None else out
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return out.reshape(out_shape)
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