repository stringclasses 166
values | file_path stringlengths 6 125 | url stringlengths 89 210 | code stringlengths 413 290k | chunk stringlengths 56 175k |
|---|---|---|---|---|
lucidrains/lion-pytorch | lion_pytorch/triton.py | https://github.com/lucidrains/lion-pytorch/blob/6a74fdc0ba572ab5683dc0270c66c20ecbc02d09/lion_pytorch/triton.py | import torch
try:
import triton
import triton.language as tl
except ImportError as e:
print('triton is not installed, please install by running `pip install triton>=2.2.0`')
exit()
# triton cuda kernel
@triton.autotune(configs = [
triton.Config({'BLOCK_SIZE': 128}, num_warps = 4),
triton.Conf... | @triton.jit
def update_fn_kernel(
p_ptr,
grad_ptr,
exp_avg_ptr,
lr,
wd,
beta1,
beta2,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis = 0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
... |
jax-ml/jax-triton | examples/add.py | https://github.com/jax-ml/jax-triton/blob/9aff06677a24d07e510f3632532a88b6804324dc/examples/add.py | # Copyright 2024 The jax_triton Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... | @triton.jit
def add_kernel(
x_ptr,
y_ptr,
output_ptr,
block_size: tl.constexpr,
):
"""Adds two vectors."""
pid = tl.program_id(axis=0)
block_start = pid * block_size
offsets = block_start + tl.arange(0, block_size)
mask = offsets < 8
x = tl.load(x_ptr + offsets, mask=mask)
y = tl.load(y_p... |
josStorer/RWKV-Runner | finetune/lora/v6/fla/ops/hgrn/chunk.py | https://github.com/josStorer/RWKV-Runner/blob/ad6170816a776bfc312837aafc9a3ff889a3cdd3/finetune/lora/v6/fla/ops/hgrn/chunk.py | # -*- coding: utf-8 -*-
# Copyright (c) 2024, Yu Zhang, Songlin Yang
# this function implements the chunkwise form of HGRN, inspired by
# [Volodymyr Kyrylov in his blog post](https://proger.github.io/posts/scan/chunk.html)
# also refer to the `accelerated-scan` lib: https://github.com/proger/accelerated-scan
# from ... | @triton.jit
def chunk_hgrn_fwd_kernel_h(
x,
g,
gc,
o,
h0,
T: tl.constexpr,
D: tl.constexpr,
BT: tl.constexpr,
BD: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr
):
i_d, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
o_d = i_d * BD + tl.arange(0, BD)
... |
josStorer/RWKV-Runner | finetune/lora/v6/fla/ops/hgrn/chunk.py | https://github.com/josStorer/RWKV-Runner/blob/ad6170816a776bfc312837aafc9a3ff889a3cdd3/finetune/lora/v6/fla/ops/hgrn/chunk.py | # -*- coding: utf-8 -*-
# Copyright (c) 2024, Yu Zhang, Songlin Yang
# this function implements the chunkwise form of HGRN, inspired by
# [Volodymyr Kyrylov in his blog post](https://proger.github.io/posts/scan/chunk.html)
# also refer to the `accelerated-scan` lib: https://github.com/proger/accelerated-scan
# from ... | @triton.jit
def chunk_hgrn_fwd_kernel_o(
gc,
o,
s_h,
s_t,
s_d,
T: tl.constexpr,
D: tl.constexpr,
BT: tl.constexpr,
BD: tl.constexpr
):
i_d, i_bh = tl.program_id(0), tl.program_id(1)
o_d = i_d * BD + tl.arange(0, BD)
mask = o_d < D
for i_t in range(1, tl.cdiv(T, BT))... |
josStorer/RWKV-Runner | finetune/lora/v6/fla/ops/hgrn/chunk.py | https://github.com/josStorer/RWKV-Runner/blob/ad6170816a776bfc312837aafc9a3ff889a3cdd3/finetune/lora/v6/fla/ops/hgrn/chunk.py | # -*- coding: utf-8 -*-
# Copyright (c) 2024, Yu Zhang, Songlin Yang
# this function implements the chunkwise form of HGRN, inspired by
# [Volodymyr Kyrylov in his blog post](https://proger.github.io/posts/scan/chunk.html)
# also refer to the `accelerated-scan` lib: https://github.com/proger/accelerated-scan
# from ... | @triton.jit
def chunk_hgrn_bwd_kernel_h(
g,
gc,
dx,
do,
T: tl.constexpr,
D: tl.constexpr,
BT: tl.constexpr,
BD: tl.constexpr
):
i_d, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
o_d = i_d * BD + tl.arange(0, BD)
mask = o_d < D
BC = min(BT, T - i_t * B... |
josStorer/RWKV-Runner | finetune/lora/v6/fla/ops/hgrn/chunk.py | https://github.com/josStorer/RWKV-Runner/blob/ad6170816a776bfc312837aafc9a3ff889a3cdd3/finetune/lora/v6/fla/ops/hgrn/chunk.py | # -*- coding: utf-8 -*-
# Copyright (c) 2024, Yu Zhang, Songlin Yang
# this function implements the chunkwise form of HGRN, inspired by
# [Volodymyr Kyrylov in his blog post](https://proger.github.io/posts/scan/chunk.html)
# also refer to the `accelerated-scan` lib: https://github.com/proger/accelerated-scan
# from ... | @triton.jit
def chunk_hgrn_bwd_kernel_o(
g,
gc,
o,
dx,
dg,
s_h,
s_t,
s_d,
T: tl.constexpr,
D: tl.constexpr,
BT: tl.constexpr,
BD: tl.constexpr
):
i_d, i_bh = tl.program_id(0), tl.program_id(1)
o_d = i_d * BD + tl.arange(0, BD)
mask = o_d < D
for i_t in r... |
INT-FlashAttention2024/INT-FlashAttention | flash_atten_full_int8.py | https://github.com/INT-FlashAttention2024/INT-FlashAttention/blob/7f7bfb00bcd26b2cef49e7783f51ef610e05abf7/flash_atten_full_int8.py | import pytest
import torch
import triton
import triton.language as tl
from configs import *
@triton.jit
def _attn_fwd_inner_full_int8(acc, l_i, m_i, q, #
K_block_ptr, V_block_ptr, #
q_scale, K_block_scale_ptr, v_scale,#
start_m, qk_scale, #
... | @triton.jit
def _attn_fwd_inner_full_int8(acc, l_i, m_i, q, #
K_block_ptr, V_block_ptr, #
q_scale, K_block_scale_ptr, v_scale,#
start_m, qk_scale, #
BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr, #
... |
INT-FlashAttention2024/INT-FlashAttention | flash_atten_full_int8.py | https://github.com/INT-FlashAttention2024/INT-FlashAttention/blob/7f7bfb00bcd26b2cef49e7783f51ef610e05abf7/flash_atten_full_int8.py | import pytest
import torch
import triton
import triton.language as tl
from configs import *
@triton.jit
def _attn_fwd_inner_full_int8(acc, l_i, m_i, q, #
K_block_ptr, V_block_ptr, #
q_scale, K_block_scale_ptr, v_scale,#
start_m, qk_scale, #
... | @triton.jit
def _attn_fwd_full_int8(Q, K, V, Q_scale, K_scale, V_scale, sm_scale, Out, #
stride_qz, stride_qh, stride_qm, stride_qk, #
stride_kz, stride_kh, stride_kn, stride_kk, #
stride_vz, stride_vh, stride_vk, stride_vn, #
stride_oz, stride_oh, stride_om,... |
TD87/triton-kernels | gemm_matmul.py | https://github.com/TD87/triton-kernels/blob/17a97ede7b6d0ca7356db68b56d0e5b6a9080ad4/gemm_matmul.py | import math
import torch # type: ignore
import triton # type: ignore
import triton.language as tl # type: ignore
@triton.jit()
def matmul_kernel(x_ptr, y_ptr, out_ptr, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr,
BLOCK_K: tl.constexpr):
pid_r = tl.program_id(0)
pid_c = tl.program_i... | @triton.jit
()
def matmul_kernel(x_ptr, y_ptr, out_ptr, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr,
BLOCK_K: tl.constexpr):
pid_r = tl.program_id(0)
pid_c = tl.program_id(1)
row_start = pid_r * BLOCK_M
row_offsets = row_start + tl.arange(0, BLOCK_M)
col_start = pid_c ... |
xiaonans/triton-gemm-benchmark | kernels/basic_matmul.py | https://github.com/xiaonans/triton-gemm-benchmark/blob/436ee5a77e01ede7e4a1fe015f533dfdc53b31d3/kernels/basic_matmul.py | import triton
import triton.language as tl
import torch
from .autotune_config import get_autotune_config
# `triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` decorator, which consumes:
# - A list of `triton.Config` objects that define different configurations of
# meta-parameters (e.g.,... | @triton.jit
def matmul_kernel(
# Pointers to matrices
a_ptr, b_ptr, c_ptr,
# Matrix dimensions
M, N, K,
# The stride variables represent how much to increase the ptr by when moving by 1
# element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`... |
xiaonans/triton-gemm-benchmark | kernels/basic_matmul.py | https://github.com/xiaonans/triton-gemm-benchmark/blob/436ee5a77e01ede7e4a1fe015f533dfdc53b31d3/kernels/basic_matmul.py | import triton
import triton.language as tl
import torch
from .autotune_config import get_autotune_config
# `triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` decorator, which consumes:
# - A list of `triton.Config` objects that define different configurations of
# meta-parameters (e.g.,... | @triton.jit
def leaky_relu(x):
return tl.where(x >= 0, x, 0.01 * x)
def matmul(a, b, activation=""):
# Check constraints.
assert a.shape[1] == b.shape[0], "Incompatible dimensions"
assert a.is_contiguous(), "Matrix A must be contiguous"
M, K = a.shape
K, N = b.shape
# Allocates output.
... |
xiaohuguo2023/scripts | others/tune_gemm1.py | https://github.com/xiaohuguo2023/scripts/blob/b6de80a590c78e78a4f8d64346c34ef445e2aa17/others/tune_gemm1.py | import argparse
import sys
import yaml
import os
import glob
import subprocess
import torch
import triton
import triton.language as tl
from matmul_kernel import matmul_kernel
from datetime import datetime
import pandas as pd
import torch.distributed as dist
from torch.multiprocessing import spawn
def get_full_tuning_s... | @triton.jit
def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
input = tl.load(input_ptr + offsets, mask=mask)
output = input
tl.store(output_ptr + ... |
phlippe/liger_kernels | liger_kernels/utils.py | https://github.com/phlippe/liger_kernels/blob/0abb152b752e66e1c3e0c78a7eb56daea9a07f42/liger_kernels/utils.py | import jax
import numpy as np
import triton
import triton.language as tl
@triton.jit
def element_mul_kernel(
_, # alias for X_ptr
grad_output_ptr,
X_ptr,
X_stride,
n_cols,
BLOCK_SIZE: tl.constexpr,
):
"""
This function multiplies each element of the tensor pointed by X_ptr with the va... | @triton.jit
def element_mul_kernel(
_, # alias for X_ptr
grad_output_ptr,
X_ptr,
X_stride,
n_cols,
BLOCK_SIZE: tl.constexpr,
):
"""
This function multiplies each element of the tensor pointed by X_ptr with the value pointed by grad_output_ptr.
The multiplication is performed in-pla... |
yifuwang/symm-mem-recipes | triton_utils.py | https://github.com/yifuwang/symm-mem-recipes/blob/8ee5d5b8f53efb9c051e6cdf0ca62270c2b43c34/triton_utils.py | import triton
import triton.language as tl
@triton.jit
def get_tid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %tid.x;
mov.u32 $1, %tid.y;
mov.u32 $2, %tid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True... | @triton.jit
def get_tid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %tid.x;
mov.u32 $1, %tid.y;
mov.u32 $2, %tid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True,
pack=1,
)
|
yifuwang/symm-mem-recipes | triton_utils.py | https://github.com/yifuwang/symm-mem-recipes/blob/8ee5d5b8f53efb9c051e6cdf0ca62270c2b43c34/triton_utils.py | import triton
import triton.language as tl
@triton.jit
def get_tid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %tid.x;
mov.u32 $1, %tid.y;
mov.u32 $2, %tid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True... | @triton.jit
def get_ntid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %ntid.x;
mov.u32 $1, %ntid.y;
mov.u32 $2, %ntid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True,
pack=1,
)
|
yifuwang/symm-mem-recipes | triton_utils.py | https://github.com/yifuwang/symm-mem-recipes/blob/8ee5d5b8f53efb9c051e6cdf0ca62270c2b43c34/triton_utils.py | import triton
import triton.language as tl
@triton.jit
def get_tid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %tid.x;
mov.u32 $1, %tid.y;
mov.u32 $2, %tid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True... | @triton.jit
def get_flat_tid():
tid_x, tid_y, tid_z = get_tid()
ntid_x, ntid_y, _ = get_ntid()
return tid_z * ntid_y * ntid_x + tid_y * ntid_x + tid_x
|
yifuwang/symm-mem-recipes | triton_utils.py | https://github.com/yifuwang/symm-mem-recipes/blob/8ee5d5b8f53efb9c051e6cdf0ca62270c2b43c34/triton_utils.py | import triton
import triton.language as tl
@triton.jit
def get_tid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %tid.x;
mov.u32 $1, %tid.y;
mov.u32 $2, %tid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True... | @triton.jit
def get_flat_bid():
return (
tl.program_id(2) * tl.num_programs(1) * tl.num_programs(0)
+ tl.program_id(1) * tl.num_programs(0)
+ tl.program_id(0)
)
|
yifuwang/symm-mem-recipes | triton_utils.py | https://github.com/yifuwang/symm-mem-recipes/blob/8ee5d5b8f53efb9c051e6cdf0ca62270c2b43c34/triton_utils.py | import triton
import triton.language as tl
@triton.jit
def get_tid():
return tl.inline_asm_elementwise(
"""
mov.u32 $0, %tid.x;
mov.u32 $1, %tid.y;
mov.u32 $2, %tid.z;
""",
"=r,=r,=r",
[],
dtype=(tl.uint32, tl.uint32, tl.uint32),
is_pure=True... | @triton.jit
def sync_threads():
tl.inline_asm_elementwise(
"bar.sync 0;", "=r", [], dtype=tl.int32, is_pure=False, pack=1
)
|
Terapines/AI-Benchmark | src/triton/resize.py | https://github.com/Terapines/AI-Benchmark/blob/0ae8cd849a833d4c35a4b25b722ce98c5af2fe34/src/triton/resize.py | import torch
import triton
import triton.language as tl
import os
USE_GPU = False
triton.runtime.driver.set_active_to_cpu()
def get_resize_kernel_autotune_config():
configs = [
triton.Config({'BLOCK_SIZE_W': 1}),
triton.Config({'BLOCK_SIZE_W': 2}),
triton.Config({'BLOCK_SIZE_W': 4}),
... | @triton.jit
def resize_kernel(
src_ptr,
out_ptr,
channel,
height,
width,
BLOCK_SIZE_W: tl.constexpr,
):
pid_h = tl.program_id(axis=0)
pid_c = tl.program_id(axis=1)
dst_height = 2 * height # 2x upsample
dst_width = 2 * width
hw_fl = 7
h_idx = pid_h
input_y = h_idx... |
khulnasoft/divest | divest/kernels/swiglu.py | https://github.com/khulnasoft/divest/blob/53b878ed6cf9f8e172a496bf26a2b22ff3a30a51/divest/kernels/swiglu.py | import triton
import triton.language as tl
import torch
from .utils import calculate_settings
@triton.jit
def _fg_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
block_idx = tl.program_id(0)
offsets = block_idx*BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
e_row = tl.load... | @triton.jit
def _fg_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
block_idx = tl.program_id(0)
offsets = block_idx*BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
e_row = tl.load(e + offsets, mask = mask, other = 0).to(tl.float32)
g_row = tl.load(g + offsets, mask = ma... |
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