Implementation of a Transformer, but completely in Triton

Related tags

Deep Learningdeep-learningtransformersartificial-intelligenceattention-mechanismgpu-programming
Overview

Transformer in Triton (wip)

Implementation of a Transformer, but completely in Triton. I'm completely new to lower-level neural net code, so this repository will mostly be a learning experience, with the end-goal being a vanilla transformer that is faster and more efficient to train.

Install

$ pip install triton-transformer

Usage

import torch
from triton_transformer import Transformer

model = Transformer(
    num_tokens = 256,
    max_seq_len = 1024,
    dim = 512,
    depth = 6,
    heads = 8,
    dim_head = 64
)

x = torch.randint(0, 256, (1, 1024))
mask = torch.ones(1, 1024).bool()

logits = model(x, mask = mask) # (1, 1024, 256)

Citations

@article{Tillet2019TritonAI,
    title   = {Triton: an intermediate language and compiler for tiled neural network computations},
    author  = {Philippe Tillet and H. Kung and D. Cox},
    journal = {Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages},
    year    = {2019}
}
@misc{vaswani2017attention,
    title   = {Attention Is All You Need}, 
    author  = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year    = {2017},
    eprint  = {1706.03762},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}
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Comments
  • Question concerning PyTorch build

    Question concerning PyTorch build

    Hello. I find your project very interesting and I have seen your comparison between PyTorch and Triton implementations.

    However, I am curious whether your PyTorch environment is a source build optimized for your machine or a pip/conda install.

    Source building has faster runtimes and if a conda install is being used for comparison, the difference in speed may simply be due to Triton optimizing CUDA for the run environment.

    Thank you again for your interesting project.

    opened by veritas9872 13
  • _layernorm implementation forward result not equal F.layer_norm

    _layernorm implementation forward result not equal F.layer_norm

    I have a try on your triton-transformer and test the layernorm module alone. It's very weird that the forward result is different while the backward result is equal.

    code: from triton_transformer.layernorm import layernorm import torch import torch.nn as nn

    torch.manual_seed(0) x = torch.randn(2,5).cuda() x.requires_grad_(True) dy = .1*torch.randn_like(x).cuda() dim = 5 norm = nn.LayerNorm(dim).cuda()

    y1 = layernorm(x, norm.weight, norm.bias, use_triton = True) y2 = layernorm(x, norm.weight, norm.bias, use_triton = False) print(y1, y2) print(torch.allclose(y1, y2))

    y1.backward(dy, retain_graph=True) dx_y1 = x.grad.clone()

    x.grad = None

    y2.backward(dy, retain_graph=True) dx_y2 = x.grad.clone() print(dx_y1, dx_y2) print(torch.allclose(dx_y1, dx_y2))

    result: `tensor([[ 0.9492, -0.0021, -0.9797, 0.4449, -0.4123], [-0.7624, 0.4399, 0.7299, -0.3091, -0.0983]], device='cuda:0', grad_fn=<_layernormBackward>) tensor([[ 1.4217, -0.0031, -1.4674, 0.6663, -0.6175], [-1.4342, 0.8276, 1.3732, -0.5815, -0.1850]], device='cuda:0', grad_fn=) False

    tensor([[-0.0706, 0.0288, -0.0813, 0.0446, 0.0785], [ 0.0218, -0.0152, 0.0141, -0.0522, 0.0315]], device='cuda:0') tensor([[-0.0706, 0.0288, -0.0813, 0.0446, 0.0785], [ 0.0218, -0.0152, 0.0141, -0.0522, 0.0315]], device='cuda:0') True`

    opened by Tengxu-Sun 1
  • Current state of benchmarking & contributing?

    Current state of benchmarking & contributing?

    Hey @lucidrains - hope you're doing well! I have some time to hack the next couple weeks, just wanted to get a sense of:

    • Current state of benchmarking (what Triton kernels provide how much lift, aggregate lift over a "vanilla Transformer implementation"
    • If there's anything I could help with, especially as I learn Triton!
    opened by siddk 0
  • Official layer norm added

    Official layer norm added

    Hi @lucidrains , in Triton layer norm was just added in examples, https://github.com/openai/triton/commit/d4baad426db72b83c5222e1c83c929c1860cae54 I tested it, it's twice as fast as Torch, often faster then Apex.

    I'm looking forward for your implementation of attention, so far the Torch implementation is the fastest with 12.3 / 14.5 (forw / back) vs the other Triton implementation in DeepSpeed which is 17.3/ 23.0 on my data.

    opened by olegklimov 2
Releases(0.1.1)
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
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