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Fix Tensor.mean to compute the mean correctly when 0-length axes are … #12

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Fix Tensor.mean to compute the mean correctly when 0-length axes are …

Fix Tensor.mean to compute the mean correctly when 0-length axes are … #12

Workflow file for this run

.github/workflows/benchmark.yml at ee25f73
name: Benchmarks
on:
push:
branches:
- master
- update_benchmark
jobs:
testmacbenchmark:
name: Mac Benchmark
runs-on: [self-hosted, macOS]
defaults:
run:
shell: bash -o pipefail {0}
if: github.repository_owner == 'tinygrad'
env:
PYTHONPATH: .
steps:
- name: Checkout Code
uses: actions/checkout@v4
- name: Symlink models and datasets
run: |
mkdir -p weights
ln -s ~/tinygrad/disassemblers/applegpu disassemblers/applegpu
ln -s ~/tinygrad/weights/sd-v1-4.ckpt weights/sd-v1-4.ckpt
ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
# TODO: why is this test not reliable?
#- name: Run Stable Diffusion
# run: python3 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd.txt
- name: Run model inference benchmark
run: METAL=1 python3 test/external/external_model_benchmark.py
- name: Test speed vs torch
run: BIG=2 MPS=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
- name: Run Tensor Core GEMM
run: |
DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
DEBUG=2 HALF=1 python3 extra/gemm/simple_matmul.py | tee matmul_half.txt
- name: Run LLaMA
run: |
JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
- name: Run GPT2
run: |
JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
- name: Run 10 CIFAR training steps
run: STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
# TODO: this is flaky too
# - name: Run 10 CIFAR training steps w winograd
# run: WINO=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
- uses: actions/upload-artifact@v4
with:
name: Speed (Mac)
path: |
onnx_inference_speed.csv
torch_speed.txt
train_cifar.txt
train_cifar_wino.txt
llama_unjitted.txt
llama_jitted.txt
gpt2_unjitted.txt
gpt2_jitted.txt
matmul.txt
matmul_half.txt
sd.txt
testnvidiabenchmark:
name: NVIDIA Benchmark
runs-on: [self-hosted, Linux, CUDA]
defaults:
run:
shell: bash -o pipefail {0}
if: github.repository_owner == 'tinygrad'
env:
PYTHONPATH: .
steps:
- name: Checkout Code
uses: actions/checkout@v4
- name: Run model inference benchmark
run: CUDA=1 python3 test/external/external_model_benchmark.py
- name: Test speed vs torch
run: CUDA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
- name: Run GPT2
run: |
CUDA=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
CUDA=1 JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
- name: Run GPT2 w HALF
run: CUDA=1 JIT=1 HALF=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing
- name: Run GPT2 w HALF/BEAM
run: CUDA=1 JIT=1 HALF=1 BEAM=2 CACHELEVEL=0 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
- uses: actions/upload-artifact@v4
with:
name: Speed (NVIDIA)
path: |
onnx_inference_speed.csv
torch_speed.txt
gpt2_unjitted.txt
gpt2_jitted.txt
gpt2_half_beam.txt
testamdbenchmark:
name: tinybox Benchmark
runs-on: [self-hosted, Linux, tinybox]
defaults:
run:
shell: bash -o pipefail {0}
if: github.repository_owner == 'tinygrad'
env:
PYTHONPATH: .
steps:
- name: Checkout Code
uses: actions/checkout@v4
- name: Show off tinybox
run: /opt/rocm/bin/rocm-bandwidth-test
- name: Symlink models and datasets
run: |
mkdir -p weights
ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
- name: Run model inference benchmark
run: HIP=1 NOCLANG=1 python3 test/external/external_model_benchmark.py
- name: Test speed vs torch
run: BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
- name: Run Tensor Core GEMM
run: HIP=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
- name: Run Stable Diffusion
run: python3 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd.txt
- name: Run LLaMA (with HIP)
run: |
HIP=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
HIP=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
- name: Run GPT2 (with HIP)
run: |
HIP=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
HIP=1 JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
- name: Run 10 CIFAR training steps
run: HIP=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
- name: Run 10 CIFAR training steps w HALF
run: HIP=1 STEPS=10 HALF=1 python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
# # TODO: enable this. it took 3 minutes in CI and made the full training one more than 5 minutes
# - name: Run 10 CIFAR training steps w 6 GPUS
# run: time HALF=1 STEPS=10 BS=1536 GPUS=6 python3 examples/hlb_cifar10.py
- name: Run full CIFAR training
run: time HIP=1 HALF=1 LATEWINO=1 STEPS=1000 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.py
- uses: actions/upload-artifact@v4
with:
name: Speed (AMD)
path: |
onnx_inference_speed.csv
torch_speed.txt
train_cifar.txt
train_cifar_half.txt
train_cifar_wino.txt
train_cifar_wino_half_hip.txt
llama_unjitted.txt
llama_jitted.txt
gpt2_unjitted.txt
gpt2_jitted.txt
matmul.txt
sd.txt