This PR clarifies which features are supported on P100 via its tests,
though Pascal is not officially and fully supported by Triton.
## What this PR does
- Skip unsupported tests on P100.
- Atomic RMW
- `tl.dot()` (perhaps not all patterns, but basically most `tl.dot()`
tests do not work on P100).
- Add an explicit error if shared memory size >= 64K on P100.
- Otherwise it causes `Invalid CUDA argument` error at
`cuLaunchKernel()`, but this error is not very straightforward to
understand. Instead of this generic CUDA argument error, this PR makes
Triton show an error during codegen when `sm < 70`. This check happens
in C/C++ so won't add an overhead in Triton's Python runtime.
- 3 tests (see below) are currently failing, but these are not marked as
skipped because any codegen update in the future can change the kernel
size of the other tests.
- This change won't affect Triton-MLIR. Hopefully Triton-MLIR's generic
`tl.dot()` implementation would support P100.
Importantly, Triton passed all the other tests on P100. Though this
support is not official, it is great for, for example, PyTorch's
TorchDynamo/Inductor, which can use Triton (without `tl.dot()`) for its
backend (https://github.com/pytorch/torchdynamo/issues/1591).
### Results on P100 (Google Cloud)
```sh
$ pytest test/unit
...
================================================================================== short test summary info ==================================================================================
FAILED test/unit/language/test_core.py::test_reduce2d[argmin-float32-shape99-1] - RuntimeError: Device does not support shared memory of 65536bytes
FAILED test/unit/language/test_core.py::test_reduce2d[argmax-float32-shape113-1] - RuntimeError: Device does not support shared memory of 65536bytes
FAILED test/unit/language/test_core.py::test_permute[float32-shape5-perm5] - RuntimeError: Device does not support shared memory of 67584bytes
================================================================== 3 failed, 3824 passed, 952 skipped in 470.90s (0:07:50) ==================================================================
```
<details><summary> <b>Environment Details (collapsed)</b></summary>
<p>
### VM details (Google Cloud)
https://cloud.google.com/
```
# You need a paid account (free trial does not cover GPUs)
Google Cloud -> New Project -> Compute-Engine -> VM Instance
Machine:
GPU: NVIDIA Tesla P100 x 1
CPU: 2 vCPUs, 7.5GB memory
Boot disk:
OS: Ubuntu 18.04 LTS
Disk: 40GB (cannot build Triton on the default 10GB disk)
- When I tried, about $1.2 per hour.
- US instances were full when I tried. I used Asia or Australia.
- Needed a paid account (GPU is not covered by free trial)
- Needed quota request for any GPU instance (by default, no GPU instance is allowed). Needed to wait an hour for approval
```
### Reproducer
```sh
## 1. Install CUDA and a driver
# Update the apt key (https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/)
sudo apt-key del 7fa2af80
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb
# Download CUDA as instructed
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo apt-get update
sudo apt-get -y install cuda
# Are you using P100?
nvidia-smi | grep "Tesla P100"
## 2. Setup the build environment
sudo apt update
sudo apt install -y build-essential wget git libz-dev
wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
bash Anaconda3-2022.05-Linux-x86_64.sh -b -p $(pwd)/anaconda3
eval "$($(pwd)/anaconda3/bin/conda shell.bash hook)"
conda create -y --name triton_base
conda activate triton_base
conda install -y cmake setuptools
## 3. Build Triton
git clone https://github.com/openai/triton.git
cd triton/python
pip3 install -e '.[tests]'
## 4. Test
pytest test/unit
```
### Environment
```sh
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 520.61.05 Driver Version: 520.61.05 CUDA Version: 11.8 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla P100-PCIE... On | 00000000:00:04.0 Off | 0 |
| N/A 36C P0 25W / 250W | 0MiB / 16384MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
```
</p></details>
Fix two problems in libdevice and external dispatch:
1. Use static triton types (e.g., tl.int32) instead of creating new
types. Otherwise, `tl.int32` and `tl.dtype('int32')` are not the same
thing.
2. The name of an extern inst should be empty but not the symbol name of
the inst. TTIR generator will assign names automatically. Otherwise, we
have the same variable name when there are multiple same extern insts.
Before the PR:
```bash
__nv_exp = extern_elementwise f64<1024> %11;
__nv_exp = extern_elementwise f64<1024> %11;
```
After the PR:
```bash
%12 = extern_elementwise f64<1024> %11;
%13 = extern_elementwise f64<1024> %11;
```
It is currently necessary for optimal performance in quantized workloads to add a special-purpose instruction in the IR. Backward compatibility with this instruction is *NOT* guaranteed.
This PR completely rewrites the runtime of Triton to be more lean and
clearly separate the compilation step from the just-in-time caching logic.
This should substantially reduce launch overhead.
This PR adds several optimization capabilities in the compiler backend:
- Now using inline PTX for `tl.store`, making it possible to use things like evict_last
- For A100, mma layout can be directly converted to shared memory
- For A100, an additional "transpose" argument in `dot` allows tensors to be loaded once and used both row- and col- major.
- Fixed liveness analysis; this was broken.
- Now can load/store directly mma layout without converting. Useful for when tl.dot accumulator is initialized with DRAM data inside of an inner loop.
- `tl.dot` can now take LHS inputs in registers when it comes from a previous `tl.dot` instruction. Useful for e.g. fused attention.
This is a more stable commit that produce bitwise identical code to earlier
versions. Using commits after this one may lead to slightly different numerics
Significantly improves the performance of `triton.ops.matmul` in memory-bound settings via the use of many more block configs coupled with a performance model to drive the auto-tuning process.
