) grouped_matmul_kernel[grid]( a_ptrs, b_ptrs, c_ptrs, sizes, lds, group_size, )def torch_perf_fn(group_A, group_B): for a, b in zip(group_A, group_B): torch.matmul(a, b)@triton.testing.perf_report( triton...
def grouped_matmul_kernel( # device tensor of matrices pointers # 设备张量矩阵指针 group_a_ptrs, group_b_ptrs, group_c_ptrs, # device tensor of gemm sizes. its shape is [group_size, 3] # 设备张量的 GEMM(General Matrix Multiply)大小。其形状为 [group_size, 3] # dim 0 is group_si...
so one threadblock may need 2 SMs to accomplish the gemm accumulate. grouped gemm above only include one gemm. as the name implies, grouped gemm includes several(2,3) gemm in a kernel. assume the first gemm is the above naive gemm with 9 blocks, the second is a gemm with 4 bl...
上文的向量加就取于Triton的python/tutorials/01-vector-add.py,他还提供了包括fused-attention、fused-softmax、grouped-gemm在内的示例。 部分示例使用了@triton.autotune,即自动优化参数针对不同的硬件选择最优的配置。其内的benchmark也和torch内置的以及cuBLAS等做了比较,基本达到了各有胜负的水平。此外triton还在...
# Simulate a grouped gemm # 模拟一个分组的GEMM (General Matrix Multiply) 操作。 if ni == tiles_per_update: tl.extra.cuda.experimental_device_tensormap_create2d(desc_ptr=a_desc_ptr, global_address=a_ptr, load_size=[BLOCK_SIZE_M, ...
都是9。然后这里每次要计算的小块大小为BLOCK_SIZE_M x BLOCK_SIZE_M,对于Row-major odreding来说,BLOCK_SIZE_M为1,BLOCK_SIZE_N为9,而对于Grouped ordering来说,BLOCK_SIZE_M=BLOCK_SIZE_N=3。所以: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # 程序ID pid = tl.program_id(axis=0) # ...
0x10 GEMM代码详细解读 首先是对于搜索空间的定义,这里 代码语言:javascript 代码运行次数:0 运行 AI代码解释 @triton.autotune( configs=[ triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), triton.Config({'BL...
0x01 GEMM代码示例 全部编译好后,就可以通过执行下面的代码得到对应的 GEMM 在 AMD 显卡上针对 Triton和 rocBLAS 的 benchmark 了。 importtorch importtriton importtriton.languageastl importsys importargparse importpytest #`triton.jit`'edfunctionscanbeauto-tunedbyusingthe`triton.autotune`decorator,whichconsum...
ampere_fp16_s16816gemm_fp16_128x128_ldg8_f2f_stages_32x5_tn AI检测代码解析 import argparse import torch import triton import triton.language as tl import triton.tools.experimental_descriptor import triton.profiler as proton from contextlib import contextmanageriftorch.cuda.is_available():from tri...
from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache import torch from typing import Optional from kernels.basic_gemm import matmul # change to your path from torch import nn device = "cuda" class Triton_myMLP(nn.Module): def __init__(self, llama_mlp_layer): super()....