Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 wehub-resource-sync
|
12c7410bab | ||
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wehub-resource-sync
|
3fe054ae00 | ||
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wehub-resource-sync
|
1612ff5875 |
@@ -1,6 +1,7 @@
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name: Build and test
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|
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on:
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create:
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workflow_dispatch:
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||||
push:
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branches:
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@@ -138,6 +139,33 @@ jobs:
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call "C:\\Program Files\\Microsoft Visual Studio\\2022\\Enterprise\\VC\\Auxiliary\\Build\\vcvars64.bat"
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make-4.4.1\dist\make -j WIN_CI_BUILD=1 train_gpt2fp32cu test_gpt2fp32cu test_gpt2cu train_gpt2cu profile_gpt2cu
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build-ubuntu20-04:
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runs-on: ubuntu-20.04
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container:
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image: nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: System Info
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run: |
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nvcc --version
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g++ --version
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- name: Install cudnn frontend
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run: |
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apt-get update && apt-get install -y git
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git clone https://github.com/NVIDIA/cudnn-frontend.git
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- name: Build FP32 checkpoint
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run: make train_gpt2fp32cu test_gpt2fp32cu
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- name: Build FP32 precision
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run: PRECISION=FP32 make train_gpt2cu test_gpt2cu profile_gpt2cu
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- name: Build with CUDNN
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run: PRECISION=BF16 USE_CUDNN=1 make train_gpt2cu test_gpt2cu profile_gpt2cu
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build-cuda-fp32:
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runs-on: ubuntu-latest
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container:
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@@ -0,0 +1,128 @@
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name: GPU Builds and Tests
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|
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on:
|
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create:
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workflow_dispatch:
|
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push:
|
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branches:
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- master
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||||
pull_request:
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branches:
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- master
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jobs:
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build-and-test-gpu:
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runs-on: ubicloud-gpu-standard-1-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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|
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- name: Install OpenMP
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run: sudo apt-get update && sudo apt-get install -y libomp-dev
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|
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- name: Install dependencies
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run: pip install -r requirements.txt
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- name: Run preprocessing
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run: python dev/data/tinyshakespeare.py
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- name: Train model
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run: python train_gpt2.py
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- name: Compile training and testing program
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run: make test_gpt2cu train_gpt2cu test_gpt2fp32cu train_gpt2fp32cu
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- name: Train model (With OpenMP)
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run: OMP_NUM_THREADS=8 ./train_gpt2cu
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- name: Train model (FP32) with gpt2_124M.bin
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run: |
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PRECISION=FP32 make train_gpt2cu
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./train_gpt2cu -b 1 -t 64 -d 256 -l 0.0001 -v 200 -s 200 -a 1 -x 10 -r 0 -f 0 -e "gpt2_124M.bin"
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- name: Test for percent loss differential for FP32
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run: |
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PRECISION=FP32 make train_gpt2cu
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./train_gpt2cu -b 1 -t 64 -d 256 -l 0.0001 -v 200 -s 200 -a 1 -x 10 -r 0 -f 0 -e "gpt2_124M.bin" > train_gpt2cu_fp32_precision.txt
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python dev/loss_checker_ci.py -f train_gpt2cu_fp32_precision.txt -s 20 -e 28 -a 5.0
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- name: Build FP32 precision
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run: PRECISION=FP32 make test_gpt2cu profile_gpt2cu
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|
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- name: Run default
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run: ./test_gpt2cu
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- name: Run no recompute GeLU
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run: ./test_gpt2cu -r 0
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- name: Run recompute LN
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run: ./test_gpt2cu -r 2
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|
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- name: Build BF16 precision
|
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run: PRECISION=BF16 make train_gpt2cu test_gpt2cu profile_gpt2cu
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|
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- name: Run default
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run: ./test_gpt2cu
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|
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- name: Run no recompute GeLU
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run: ./test_gpt2cu -r 0
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- name: Run no master weights
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run: ./test_gpt2cu -w 0
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|
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- name: Run recompute LN
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run: ./test_gpt2cu -r 2
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|
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- name: Train model fp32 (With OpenMP)
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run: OMP_NUM_THREADS=8 ./train_gpt2fp32cu
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|
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- name: Execute testing program (With OpenMP)
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run: OMP_NUM_THREADS=8 ./test_gpt2cu
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|
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- name: Execute testing program fp32 (With OpenMP)
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run: OMP_NUM_THREADS=8 ./test_gpt2fp32cu
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|
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- name: Compile training and testing program without OpenMP
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run: NO_OMP=1 make test_gpt2cu train_gpt2cu test_gpt2fp32cu train_gpt2fp32cu
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|
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- name: Train model (No OpenMP)
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run: NO_OMP=1 ./train_gpt2cu
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|
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- name: Train model fp32 (No OpenMP)
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run: NO_OMP=1 ./train_gpt2fp32cu
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|
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- name: Execute testing program (No OpenMP)
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run: ./test_gpt2cu -b 32
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|
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- name: Execute testing program fp32 (No OpenMP)
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run: ./test_gpt2fp32cu
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||||
|
||||
- name: Install cuDNN-frontend
|
||||
run:
|
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git clone https://github.com/NVIDIA/cudnn-frontend.git
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|
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- name: Build with cuDNN
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run: USE_CUDNN=1 make test_gpt2cu train_gpt2cu test_gpt2fp32cu train_gpt2fp32cu
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- name: Train model with cuDNN
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run: ./train_gpt2cu
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|
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- name: Train model fp32 with cuDNN
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run: ./train_gpt2fp32cu
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|
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- name: Execute testing program with cuDNN
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run: ./test_gpt2cu
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|
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- name: Execute testing program fp32 with cuDNN
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run: ./test_gpt2fp32cu
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|
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unit-tests-gpu:
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runs-on: ubicloud-gpu-standard-1-latest
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||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
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|
||||
- name: Test Device<->File IO
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run: cd dev/test && nvcc -o device_file_io device_file_io.cu && ./device_file_io
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@@ -0,0 +1,100 @@
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name: Unit, Static and other Tests
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||||
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||||
on:
|
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create:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
branches:
|
||||
- master
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||||
pull_request:
|
||||
branches:
|
||||
- master
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||||
|
||||
jobs:
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dataloader_test:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: test the dataloader without / with sanitize address
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run: |
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cd dev/test
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make PRECISION=BF16 test_dataloader
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./test_dataloader
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make clean
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make PRECISION=BF16 TEST_CFLAGS="-fsanitize=address -fno-omit-frame-pointer" test_dataloader
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./test_dataloader
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ptx_and_sass_files:
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runs-on: ubuntu-latest
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||||
container:
|
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image: nvidia/cuda:12.4.1-devel-ubuntu22.04
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Install OpenMP and OpenMPI
|
||||
run: apt-get update && apt-get install -y libomp-dev libopenmpi-dev
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||||
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- name: Generate ptx/sass files and upload them to persistent storage
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run: |
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mkdir -p dev/cuda/ptx_sass_logs
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make train_gpt2cu
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cuobjdump --dump-ptx train_gpt2cu > dev/cuda/train_gpt2cu.ptx
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cuobjdump --dump-sass train_gpt2cu > dev/cuda/train_gpt2cu.sass
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cd dev/cuda
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make -j all_ptx
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make -j all_sass
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cp *.ptx ptx_sass_logs/
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cp *.sass ptx_sass_logs/
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ls ptx_sass_logs/
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- name: Generate ptx/sass files for A100 and upload them to persistent storage
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run: |
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mkdir -p dev/cuda/ptx_sass_logs_A100
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make train_gpt2cu GPU_COMPUTE_CAPABILITY=80
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cuobjdump --dump-ptx train_gpt2cu > dev/cuda/train_gpt2cu.ptx
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cuobjdump --dump-sass train_gpt2cu > dev/cuda/train_gpt2cu.sass
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cd dev/cuda
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make -j GPU_COMPUTE_CAPABILITY=80 all_ptx
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make -j GPU_COMPUTE_CAPABILITY=80 all_sass
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cp *.ptx ptx_sass_logs_A100/
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cp *.sass ptx_sass_logs_A100/
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ls ptx_sass_logs_A100/
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- name: Generate ptx/sass files for H100 and upload them to persistent storage
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run: |
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mkdir -p dev/cuda/ptx_sass_logs_H100
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make train_gpt2cu GPU_COMPUTE_CAPABILITY=90
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cuobjdump --dump-ptx train_gpt2cu > dev/cuda/train_gpt2cu.ptx
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cuobjdump --dump-sass train_gpt2cu > dev/cuda/train_gpt2cu.sass
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cd dev/cuda
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make -j GPU_COMPUTE_CAPABILITY=90 all_ptx
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make -j GPU_COMPUTE_CAPABILITY=90 all_sass
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cp *.ptx ptx_sass_logs_H100/
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cp *.sass ptx_sass_logs_H100/
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ls ptx_sass_logs_H100/
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||||
- name: Upload ptx/sass files
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||||
uses: actions/upload-artifact@v4
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||||
with:
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||||
name: ptx_sass_files
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||||
path: dev/cuda/ptx_sass_logs/
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retention-days: 30 # days to retain
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||||
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||||
- name: Upload ptx/sass files for A100
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||||
uses: actions/upload-artifact@v4
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||||
with:
|
||||
name: ptx_sass_files_A100
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||||
path: dev/cuda/ptx_sass_logs_A100/
|
||||
retention-days: 30 # days to retain
|
||||
|
||||
- name: Upload ptx/sass files for H100
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: ptx_sass_files_H100
|
||||
path: dev/cuda/ptx_sass_logs_H100/
|
||||
retention-days: 30 # days to retain
|
||||
@@ -11,9 +11,12 @@ REMOVE_FILES = rm -f
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OUTPUT_FILE = -o $@
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CUDA_OUTPUT_FILE = -o $@
|
||||
|
||||
# Default O3 CPU optimization level for NVCC (0 for fastest compile time)
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FORCE_NVCC_O ?= 3
|
||||
|
||||
# NVCC flags
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||||
# -t=0 is short for --threads, 0 = number of CPUs on the machine
|
||||
NVCC_FLAGS = -O3 -t=0 --use_fast_math
|
||||
NVCC_FLAGS = --threads=0 -t=0 --use_fast_math -std=c++17 -O$(FORCE_NVCC_O)
|
||||
NVCC_LDFLAGS = -lcublas -lcublasLt
|
||||
NVCC_INCLUDES =
|
||||
NVCC_LDLIBS =
|
||||
@@ -26,8 +29,10 @@ USE_CUDNN ?= 0
|
||||
BUILD_DIR = build
|
||||
ifeq ($(OS), Windows_NT)
|
||||
$(shell if not exist $(BUILD_DIR) mkdir $(BUILD_DIR))
|
||||
REMOVE_BUILD_OBJECT_FILES := del $(BUILD_DIR)\*.obj
|
||||
else
|
||||
$(shell mkdir -p $(BUILD_DIR))
|
||||
REMOVE_BUILD_OBJECT_FILES := rm -f $(BUILD_DIR)/*.o
|
||||
endif
|
||||
|
||||
# Function to check if a file exists in the PATH
|
||||
@@ -43,8 +48,10 @@ endif
|
||||
|
||||
ifneq ($(CI),true) # if not in CI, then use the GPU query
|
||||
ifndef GPU_COMPUTE_CAPABILITY # set to defaults if: make GPU_COMPUTE_CAPABILITY=
|
||||
ifneq ($(call file_exists_in_path, __nvcc_device_query),)
|
||||
GPU_COMPUTE_CAPABILITY = $(shell __nvcc_device_query)
|
||||
ifneq ($(call file_exists_in_path, nvidia-smi),)
|
||||
# Get the compute capabilities of all GPUs
|
||||
# Remove decimal points, sort numerically in ascending order, and select the first (lowest) value
|
||||
GPU_COMPUTE_CAPABILITY=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader | sed 's/\.//g' | sort -n | head -n 1)
|
||||
GPU_COMPUTE_CAPABILITY := $(strip $(GPU_COMPUTE_CAPABILITY))
|
||||
endif
|
||||
endif
|
||||
@@ -60,6 +67,7 @@ $(info ---------------------------------------------)
|
||||
|
||||
ifneq ($(OS), Windows_NT)
|
||||
NVCC := $(shell which nvcc 2>/dev/null)
|
||||
NVCC_LDFLAGS += -lnvidia-ml
|
||||
|
||||
# Function to test if the compiler accepts a given flag.
|
||||
define check_and_add_flag
|
||||
@@ -81,7 +89,7 @@ else
|
||||
NVCC :=
|
||||
endif
|
||||
CC := cl
|
||||
CFLAGS = /Idev /Zi /nologo /Wall /WX- /diagnostics:column /sdl /O2 /Oi /Ot /GL /D _DEBUG /D _CONSOLE /D _UNICODE /D UNICODE /Gm- /EHsc /MD /GS /Gy /fp:fast /Zc:wchar_t /Zc:forScope /Zc:inline /permissive- \
|
||||
CFLAGS = /Idev /Zi /nologo /W4 /WX- /diagnostics:column /sdl /O2 /Oi /Ot /GL /D _DEBUG /D _CONSOLE /D _UNICODE /D UNICODE /Gm- /EHsc /MD /GS /Gy /fp:fast /Zc:wchar_t /Zc:forScope /Zc:inline /permissive- \
|
||||
/external:W3 /Gd /TP /wd4996 /Fd$@.pdb /FC /openmp:llvm
|
||||
LDFLAGS :=
|
||||
LDLIBS :=
|
||||
@@ -186,27 +194,41 @@ else
|
||||
endif
|
||||
endif
|
||||
|
||||
# Check if OpenMPI and NCCL are available, include them if so, for multi-GPU training
|
||||
# Check if NCCL is available, include if so, for multi-GPU training
|
||||
ifeq ($(NO_MULTI_GPU), 1)
|
||||
$(info → Multi-GPU (OpenMPI + NCCL) is manually disabled)
|
||||
$(info → Multi-GPU (NCCL) is manually disabled)
|
||||
else
|
||||
ifneq ($(OS), Windows_NT)
|
||||
# Detect if running on macOS or Linux
|
||||
ifeq ($(SHELL_UNAME), Darwin)
|
||||
$(info ✗ Multi-GPU on CUDA on Darwin is not supported, skipping OpenMPI + NCCL support)
|
||||
else ifeq ($(shell [ -d /usr/lib/x86_64-linux-gnu/openmpi/lib/ ] && [ -d /usr/lib/x86_64-linux-gnu/openmpi/include/ ] && echo "exists"), exists)
|
||||
$(info ✓ OpenMPI found, OK to train with multiple GPUs)
|
||||
NVCC_INCLUDES += -I/usr/lib/x86_64-linux-gnu/openmpi/include
|
||||
NVCC_LDFLAGS += -L/usr/lib/x86_64-linux-gnu/openmpi/lib/
|
||||
NVCC_LDLIBS += -lmpi -lnccl
|
||||
$(info ✗ Multi-GPU on CUDA on Darwin is not supported, skipping NCCL support)
|
||||
else ifeq ($(shell dpkg -l | grep -q nccl && echo "exists"), exists)
|
||||
$(info ✓ NCCL found, OK to train with multiple GPUs)
|
||||
NVCC_FLAGS += -DMULTI_GPU
|
||||
NVCC_LDLIBS += -lnccl
|
||||
else
|
||||
$(info ✗ OpenMPI is not found, disabling multi-GPU support)
|
||||
$(info ---> On Linux you can try install OpenMPI with `sudo apt install openmpi-bin openmpi-doc libopenmpi-dev`)
|
||||
$(info ✗ NCCL is not found, disabling multi-GPU support)
|
||||
$(info ---> On Linux you can try install NCCL with `sudo apt install libnccl2 libnccl-dev`)
|
||||
endif
|
||||
endif
|
||||
endif
|
||||
|
||||
# Attempt to find and include OpenMPI on the system
|
||||
OPENMPI_DIR ?= /usr/lib/x86_64-linux-gnu/openmpi
|
||||
OPENMPI_LIB_PATH = $(OPENMPI_DIR)/lib/
|
||||
OPENMPI_INCLUDE_PATH = $(OPENMPI_DIR)/include/
|
||||
ifeq ($(NO_USE_MPI), 1)
|
||||
$(info → MPI is manually disabled)
|
||||
else ifeq ($(shell [ -d $(OPENMPI_LIB_PATH) ] && [ -d $(OPENMPI_INCLUDE_PATH) ] && echo "exists"), exists)
|
||||
$(info ✓ MPI enabled)
|
||||
NVCC_INCLUDES += -I$(OPENMPI_INCLUDE_PATH)
|
||||
NVCC_LDFLAGS += -L$(OPENMPI_LIB_PATH)
|
||||
NVCC_LDLIBS += -lmpi
|
||||
NVCC_FLAGS += -DUSE_MPI
|
||||
else
|
||||
$(info ✗ MPI not found)
|
||||
endif
|
||||
|
||||
# Precision settings, default to bf16 but ability to override
|
||||
PRECISION ?= BF16
|
||||
VALID_PRECISIONS := FP32 FP16 BF16
|
||||
@@ -246,7 +268,7 @@ test_gpt2: test_gpt2.c
|
||||
$(CC) $(CFLAGS) $(INCLUDES) $(LDFLAGS) $^ $(LDLIBS) $(OUTPUT_FILE)
|
||||
|
||||
$(NVCC_CUDNN): llmc/cudnn_att.cpp
|
||||
$(NVCC) -c $(NVCC_FLAGS) $(PFLAGS) $^ $(NVCC_INCLUDES) $(CUDA_OUTPUT_FILE)
|
||||
$(NVCC) -c $(NVCC_FLAGS) $(PFLAGS) $^ $(NVCC_INCLUDES) -o $@
|
||||
|
||||
train_gpt2cu: train_gpt2.cu $(NVCC_CUDNN)
|
||||
$(NVCC) $(NVCC_FLAGS) $(PFLAGS) $^ $(NVCC_LDFLAGS) $(NVCC_INCLUDES) $(NVCC_LDLIBS) $(CUDA_OUTPUT_FILE)
|
||||
@@ -264,4 +286,5 @@ profile_gpt2cu: profile_gpt2.cu $(NVCC_CUDNN)
|
||||
$(NVCC) $(NVCC_FLAGS) $(PFLAGS) -lineinfo $^ $(NVCC_LDFLAGS) $(NVCC_INCLUDES) $(NVCC_LDLIBS) $(CUDA_OUTPUT_FILE)
|
||||
|
||||
clean:
|
||||
$(REMOVE_FILES) $(TARGETS) $(NVCC_CUDNN)
|
||||
$(REMOVE_FILES) $(TARGETS)
|
||||
$(REMOVE_BUILD_OBJECT_FILES)
|
||||
|
||||
+267
@@ -0,0 +1,267 @@
|
||||
# llm.c
|
||||
|
||||
LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the [GPT-2](https://github.com/openai/gpt-2) and [GPT-3](https://arxiv.org/abs/2005.14165) miniseries, along with a parallel PyTorch reference implementation in [train_gpt2.py](train_gpt2.py). You'll recognize this file as a slightly tweaked [nanoGPT](https://github.com/karpathy/nanoGPT), an earlier project of mine. Currently, llm.c is a bit faster than PyTorch Nightly (by about 7%). In addition to the bleeding edge mainline code in [train_gpt2.cu](train_gpt2.cu), we have a simple reference CPU fp32 implementation in ~1,000 lines of clean code in one file [train_gpt2.c](train_gpt2.c). I'd like this repo to only maintain C and CUDA code. Ports to other languages or repos are very welcome, but should be done in separate repos, and I am happy to link to them below in the "notable forks" section. Developer coordination happens in the [Discussions](https://github.com/karpathy/llm.c/discussions) and on Discord, either the `#llmc` channel on the [Zero to Hero](https://discord.gg/3zy8kqD9Cp) channel, or on `#llmdotc` on [GPU MODE](https://discord.gg/gpumode) Discord.
|
||||
|
||||
## quick start
|
||||
|
||||
The best introduction to the llm.c repo today is reproducing the GPT-2 (124M) model. [Discussion #481](https://github.com/karpathy/llm.c/discussions/481) steps through this in detail. We can reproduce other models from the GPT-2 and GPT-3 series in both llm.c and in the parallel implementation of PyTorch. Have a look at the [scripts README](scripts/README.md).
|
||||
|
||||
debugging tip: when you run the `make` command to build the binary, modify it by replacing `-O3` with `-g` so you can step through the code in your favorite IDE (e.g. vscode).
|
||||
|
||||
## quick start (1 GPU, fp32 only)
|
||||
|
||||
If you won't be training on multiple nodes, aren't interested in mixed precision, and are interested in learning CUDA, the fp32 (legacy) files might be of interest to you. These are files that were "checkpointed" early in the history of llm.c and frozen in time. They are simpler, more portable, and possibly easier to understand. Run the 1 GPU, fp32 code like this:
|
||||
|
||||
```bash
|
||||
chmod u+x ./dev/download_starter_pack.sh
|
||||
./dev/download_starter_pack.sh
|
||||
make train_gpt2fp32cu
|
||||
./train_gpt2fp32cu
|
||||
```
|
||||
|
||||
The download_starter_pack.sh script is a quick & easy way to get started and it downloads a bunch of .bin files that help get you off the ground. These contain: 1) the GPT-2 124M model saved in fp32, in bfloat16, 2) a "debug state" used in unit testing (a small batch of data, and target activations and gradients), 3) the GPT-2 tokenizer, and 3) the tokenized [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset. Alternatively, instead of running the .sh script, you can re-create these artifacts manually as follows:
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
python dev/data/tinyshakespeare.py
|
||||
python train_gpt2.py
|
||||
```
|
||||
|
||||
## quick start (CPU)
|
||||
|
||||
The "I am so GPU poor that I don't even have one GPU" section. You can still enjoy seeing llm.c train! But you won't go too far. Just like the fp32 version above, the CPU version is an even earlier checkpoint in the history of llm.c, back when it was just a simple reference implementation in C. For example, instead of training from scratch, you can finetune a GPT-2 small (124M) to output Shakespeare-like text, as an example:
|
||||
|
||||
```bash
|
||||
chmod u+x ./dev/download_starter_pack.sh
|
||||
./dev/download_starter_pack.sh
|
||||
make train_gpt2
|
||||
OMP_NUM_THREADS=8 ./train_gpt2
|
||||
```
|
||||
|
||||
If you'd prefer to avoid running the starter pack script, then as mentioned in the previous section you can reproduce the exact same .bin files and artifacts by running `python dev/data/tinyshakespeare.py` and then `python train_gpt2.py`.
|
||||
|
||||
The above lines (1) download an already tokenized [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset and download the GPT-2 (124M) weights, (3) init from them in C and train for 40 steps on tineshakespeare with AdamW (using batch size 4, context length only 64), evaluate validation loss, and sample some text. Honestly, unless you have a beefy CPU (and can crank up the number of OMP threads in the launch command), you're not going to get that far on CPU training LLMs, but it might be a good demo/reference. The output looks like this on my MacBook Pro (Apple Silicon M3 Max):
|
||||
|
||||
```
|
||||
[GPT-2]
|
||||
max_seq_len: 1024
|
||||
vocab_size: 50257
|
||||
num_layers: 12
|
||||
num_heads: 12
|
||||
channels: 768
|
||||
num_parameters: 124439808
|
||||
train dataset num_batches: 1192
|
||||
val dataset num_batches: 128
|
||||
num_activations: 73323776
|
||||
val loss 5.252026
|
||||
step 0: train loss 5.356189 (took 1452.121000 ms)
|
||||
step 1: train loss 4.301069 (took 1288.673000 ms)
|
||||
step 2: train loss 4.623322 (took 1369.394000 ms)
|
||||
step 3: train loss 4.600470 (took 1290.761000 ms)
|
||||
... (trunctated) ...
|
||||
step 39: train loss 3.970751 (took 1323.779000 ms)
|
||||
val loss 4.107781
|
||||
generating:
|
||||
---
|
||||
Come Running Away,
|
||||
Greater conquer
|
||||
With the Imperial blood
|
||||
the heaviest host of the gods
|
||||
into this wondrous world beyond.
|
||||
I will not back thee, for how sweet after birth
|
||||
Netflix against repounder,
|
||||
will not
|
||||
flourish against the earlocks of
|
||||
Allay
|
||||
---
|
||||
```
|
||||
|
||||
## datasets
|
||||
|
||||
The data files inside `/dev/data/(dataset).py` are responsible for downloading, tokenizing and saving the tokens to .bin files, readable easily from C. So for example when you run:
|
||||
|
||||
```bash
|
||||
python dev/data/tinyshakespeare.py
|
||||
```
|
||||
|
||||
We download and tokenize the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset. The output of this looks like this:
|
||||
|
||||
```
|
||||
writing 32,768 tokens to ./dev/data/tinyshakespeare/tiny_shakespeare_val.bin
|
||||
writing 305,260 tokens to ./dev/data/tinyshakespeare/tiny_shakespeare_train.bin
|
||||
```
|
||||
|
||||
The .bin files contain a short header (1024 bytes) and then a stream of tokens in uint16, indicating the token ids with the GPT-2 tokenizer. More datasets are available in `/dev/data`.
|
||||
|
||||
## test
|
||||
|
||||
I am also attaching a simple unit test for making sure our C code agrees with the PyTorch code. On the CPU as an example, compile and run with:
|
||||
|
||||
```bash
|
||||
make test_gpt2
|
||||
./test_gpt2
|
||||
```
|
||||
|
||||
This now loads the `gpt2_124M_debug_state.bin` file that gets written by train_gpt2.py, runs a forward pass, compares the logits and loss with the PyTorch reference implementation, then it does 10 iterations of training with Adam and makes sure the losses match PyTorch. To test the GPU version we run:
|
||||
|
||||
```bash
|
||||
# fp32 test (cudnn not supported)
|
||||
make test_gpt2cu PRECISION=FP32 && ./test_gpt2cu
|
||||
# mixed precision cudnn test
|
||||
make test_gpt2cu USE_CUDNN=1 && ./test_gpt2cu
|
||||
```
|
||||
|
||||
This tests both the fp32 path and the mixed precision path. The test should pass and print `overall okay: 1`.
|
||||
|
||||
## tutorial
|
||||
|
||||
I attached a very small tutorial here, in [doc/layernorm/layernorm.md](doc/layernorm/layernorm.md). It's a simple, step-by-step guide to implementing a single layer of the GPT-2 model, the layernorm layer. This is a good starting point to understand how the layers are implemented in C.
|
||||
|
||||
**flash attention**. As of May 1, 2024 we use the Flash Attention from cuDNN. Because cuDNN bloats the compile time from a few seconds to ~minute and this code path is right now very new, this is disabled by default. You can enable it by compiling like this:
|
||||
|
||||
```bash
|
||||
make train_gpt2cu USE_CUDNN=1
|
||||
```
|
||||
|
||||
This will try to compile with cudnn and run it. You have to have cuDNN installed on your system. The [cuDNN installation instructions](https://developer.nvidia.com/cudnn) with apt-get will grab the default set of cuDNN packages. For a minimal setup, the cuDNN dev package is sufficient, e.g. on Ubuntu 22.04 for CUDA 12.x:
|
||||
|
||||
```bash
|
||||
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
|
||||
sudo dpkg -i cuda-keyring_1.1-1_all.deb
|
||||
sudo apt-get update
|
||||
sudo apt-get -y install libcudnn9-dev-cuda-12
|
||||
```
|
||||
|
||||
On top of this you need the [cuDNN frontend](https://github.com/NVIDIA/cudnn-frontend/tree/main), but this is just header files. Simply clone the repo to your disk. The Makefile currently looks for it in either your home directory or the current directory. If you have put it elsewhere, add `CUDNN_FRONTEND_PATH=/path/to/your/cudnn-frontend/include` to the `make` command-line.
|
||||
|
||||
## multi-GPU training
|
||||
|
||||
Make sure you install MPI and NCCL, e.g. on Linux:
|
||||
|
||||
```bash
|
||||
sudo apt install openmpi-bin openmpi-doc libopenmpi-dev
|
||||
```
|
||||
|
||||
For NCCL follow the instructions from the [official website](https://developer.nvidia.com/nccl/nccl-download) (e.g. network installer)
|
||||
|
||||
and then:
|
||||
|
||||
```bash
|
||||
make train_gpt2cu
|
||||
mpirun -np <number of GPUs> ./train_gpt2cu
|
||||
```
|
||||
|
||||
or simply run one of our scripts under `./scripts/`.
|
||||
|
||||
## multi-node training
|
||||
|
||||
Make sure you've installed `NCCL` following instructions from [multi-GPU](#multi-gpu-training) section.
|
||||
|
||||
There are 3 ways we currently support that allow you to run multi-node training:
|
||||
1) Use OpenMPI to exchange nccl id and initialize NCCL. See e.g. `./scripts/multi_node/run_gpt2_124M_mpi.sh` script for details.
|
||||
2) Use shared file system to init NCCL. See `./scripts/multi_node/run_gpt2_124M_fs.sbatch` script for details.
|
||||
3) Use TCP sockets to init NCCL. See `./scripts/multi_node/run_gpt2_124M_tcp.sbatch` script for details.
|
||||
|
||||
Note:
|
||||
* If you're running in a slurm environment and your slurm doesn't support PMIx (which we assume will be a common situation given that `slurm-wlm` dropped PMIx support) you will have to use FS (2) or TCP (3) approach. To test whether your slurm supports PMIx run: `srun --mpi=list` and see whether you get `pmix` in the output.
|
||||
* If you don't have slurm set up, you can kick off a multi-node run using `mpirun` - MPI (1).
|
||||
|
||||
None of these 3 methods is superior, we just offer you options so that you can run in your specific environment.
|
||||
|
||||
## experiments / sweeps
|
||||
|
||||
Just as an example process to sweep learning rates on a machine with 4 GPUs on TinyStories. Run a shell script `sweep.sh` (after you of course `chmod u+x sweep.sh`):
|
||||
|
||||
```bash
|
||||
#!/bin/bash
|
||||
|
||||
learning_rates=(3e-5 1e-4 3e-4 1e-3)
|
||||
|
||||
for i in {0..3}; do
|
||||
export CUDA_VISIBLE_DEVICES=$i
|
||||
screen -dmS "tr$i" bash -c "./train_gpt2cu -i data/TinyStories -v 250 -s 250 -g 144 -l ${learning_rates[$i]} -o stories$i.log"
|
||||
done
|
||||
|
||||
# you can bring these down with
|
||||
# screen -ls | grep -E "tr[0-3]" | cut -d. -f1 | xargs -I {} screen -X -S {} quit
|
||||
```
|
||||
|
||||
This example opens up 4 screen sessions and runs the four commands with different LRs. This writes the log files `stories$i.log` with all the losses, which you can plot as you wish in Python. A quick example of how to parse and plot these logfiles is in [dev/vislog.ipynb](dev/vislog.ipynb).
|
||||
|
||||
## repo
|
||||
|
||||
A few more words on what I want this repo to be:
|
||||
|
||||
First, I want `llm.c` to be a place for education. E.g. our `dev/cuda` folder is a place for a library of kernels for all the layers that are manually hand-written and very well documented, starting from very simple kernels all the way to more complex / faster kernels. If you have a new kernel with various different tradeoffs, please feel free to contribute it here.
|
||||
|
||||
That said, I also want `llm.c` to be very fast too, even practically useful to train networks. E.g. to start, we should be able to reproduce the big GPT-2 (1.6B) training run. This requires that we incorporate whatever fastest kernels there are, including the use of libraries such as cuBLAS, cuBLASLt, CUTLASS, cuDNN, etc. I also think doing so serves an educational purpose to establish an expert upper bound, and a unit of measurement, e.g. you could say that your manually written kernels are 80% of cuBLAS speed, etc. Then you can choose to do a super fast run, or you can choose to "drag and drop" whatever manual kernels you wish to use, and run with those.
|
||||
|
||||
However, as a constraint, I want to keep the mainline `llm.c` in the root folder simple and readable. If there is a PR that e.g. improves performance by 2% but it "costs" 500 lines of complex C code, and maybe an exotic 3rd party dependency, I may reject the PR because the complexity is not worth it. As a concrete example - making cuBLAS for matmuls the default in the root training loop is a no-brainer: it makes the mainline code much faster, it is a single line of interpretable code, and it is a very common dependency. On the side of this, we can have manual implementations that can compete with cuBLAS in `dev/cuda`.
|
||||
|
||||
Lastly, I will be a lot more sensitive to complexity in the root folder of the project, which contains the main / default files of the project. In comparison, the `dev/` folder is a bit more of a scratch space for us to develop a library of kernels or classes and share useful or related or educational code, and some of this code could be ok to be (locally) complex.
|
||||
|
||||
## notable forks
|
||||
|
||||
- AMD support
|
||||
- [llm.c](https://github.com/anthonix/llm.c) by @[anthonix](https://github.com/anthonix): support for AMD devices, such as the 7900 XTX
|
||||
|
||||
- C#
|
||||
- [llm.cs](https://github.com/azret/llm.cs) by @[azret](https://github.com/azret): a C# port of this project
|
||||
- [Llm.cs](https://github.com/nietras/Llm.cs) by @[nietras](https://github.com/nietras): a C# port of this project with focus on easy to get started on any platform. Clone and run ✅
|
||||
|
||||
- CUDA C++
|
||||
- [llm.cpp](https://github.com/gevtushenko/llm.c) by @[gevtushenko](https://github.com/gevtushenko): a port of this project using the [CUDA C++ Core Libraries](https://github.com/NVIDIA/cccl)
|
||||
- A presentation this fork was covered in [this lecture](https://www.youtube.com/watch?v=WiB_3Csfj_Q) in the [GPU MODE Discord Server](https://discord.gg/cudamode)
|
||||
|
||||
- C++/CUDA
|
||||
- [llm.cpp](https://github.com/zhangpiu/llm.cpp/tree/master/llmcpp) by @[zhangpiu](https://github.com/zhangpiu): a port of this project using the [Eigen](https://gitlab.com/libeigen/eigen), supporting CPU/CUDA.
|
||||
|
||||
- WebGPU C++
|
||||
- [gpu.cpp](https://github.com/AnswerDotAI/gpu.cpp) by @[austinvhuang](https://github.com/austinvhuang): a library for portable GPU compute in C++ using native WebGPU. Aims to be a general-purpose library, but also porting llm.c kernels to WGSL.
|
||||
|
||||
- C++
|
||||
- [llm.cpp](https://github.com/GaoYusong/llm.cpp) by @[GaoYusong](https://github.com/GaoYusong): a port of this project featuring a C++ single-header [tinytorch.hpp](https://github.com/GaoYusong/llm.cpp/blob/main/tinytorch.hpp) library
|
||||
|
||||
- Go
|
||||
- [llm.go](https://github.com/joshcarp/llm.go) by @[joshcarp](https://github.com/joshcarp): a Go port of this project
|
||||
|
||||
- Java
|
||||
- [llm.java](https://github.com/harryjackson/llm.java) by @[harryjackson](https://github.com/harryjackson): a Java port of this project
|
||||
|
||||
- Metal
|
||||
- [llm.metal](https://github.com/regrettable-username/llm.metal) by @[regrettable-username](https://github.com/regrettable-username): LLM training in simple, raw C/Metal Shading Language
|
||||
|
||||
- Mojo
|
||||
- [llm.🔥](https://github.com/dorjeduck/llm.mojo) by @[dorjeduck](https://github.com/dorjeduck): a Mojo port of this project
|
||||
|
||||
- OpenCL
|
||||
- [llm.c](https://github.com/krrishnarraj/llm.c) by @[krrishnarraj](https://github.com/krrishnarraj): an OpenCL port of this project
|
||||
|
||||
- Rust
|
||||
- [llm.rs](https://github.com/yijunyu/llm.rs) by @[Yijun Yu](https://github.com/yijunyu): a Rust rewrite with the aim to have same performance
|
||||
- [llm.rs](https://github.com/ToJen/llm.rs) by @[ToJen](https://github.com/ToJen): a Rust port of this project
|
||||
|
||||
- Swift
|
||||
- [llm.swift](https://github.com/otabuzzman/llm.swift) by @[otabuzzman](https://github.com/otabuzzman): a Swift port of this project
|
||||
|
||||
- Zig
|
||||
- [llm.zig](https://github.com/Saimirbaci/llm.zig) by @[saimirbaci](https://github.com/Saimirbaci): a Zig port of this project
|
||||
|
||||
- Habana Gaudi2
|
||||
- [llm.tpc](https://github.com/abhilash1910/llm.tpc) by @[abhilash1910](https://github.com/abhilash1910): a Habana Gaudi2 port of this project
|
||||
|
||||
- Nim
|
||||
- [llm.nim](https://github.com/Vindaar/llm.nim) by @[Vindaar](https://github.com/Vindaar): a Nim port of this project
|
||||
|
||||
## discussions
|
||||
|
||||
Ways of organizing development:
|
||||
|
||||
- Experiencing a concrete issue with the repo? Use [Issues](https://github.com/karpathy/llm.c/issues).
|
||||
- Have some code to contribute? Open a [PR](https://github.com/karpathy/llm.c/pulls)
|
||||
- Chat about the repo, ask questions, etc.? Look at [Discussions](https://github.com/karpathy/llm.c/discussions).
|
||||
- Something faster? I created a new `#llmc` channel on my [Zero to Hero Discord channel](https://discord.gg/3zy8kqD9Cp).
|
||||
|
||||
## license
|
||||
|
||||
MIT
|
||||
@@ -1,38 +1,52 @@
|
||||
<!-- WEHUB_ZH_README -->
|
||||
> [!NOTE]
|
||||
> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
|
||||
> [English](./README.en.md) · [原始项目](https://github.com/karpathy/llm.c) · [上游 README](https://github.com/karpathy/llm.c/blob/HEAD/README.md)
|
||||
> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
|
||||
|
||||
# llm.c
|
||||
|
||||
LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the [GPT-2](https://github.com/openai/gpt-2) and [GPT-3](https://arxiv.org/abs/2005.14165) miniseries, along with a parallel PyTorch reference implementation in [train_gpt2.py](train_gpt2.py). You'll recognize this file as a slightly tweaked [nanoGPT](https://github.com/karpathy/nanoGPT), an earlier project of mine. Currently, llm.c is a bit faster than PyTorch Nightly (by about 7%). In addition to the bleeding edge mainline code in [train_gpt2.cu](train_gpt2.cu), we have a simple reference CPU fp32 implementation in ~1,000 lines of clean code in one file [train_gpt2.c](train_gpt2.c). I'd like this repo to only maintain C and CUDA code. Ports to other languages or repos are very welcome, but should be done in separate repos, and I am happy to link to them below in the "notable forks" section. Developer coordination happens in the [Discussions](https://github.com/karpathy/llm.c/discussions) and on Discord, either the `#llmc` channel on the [Zero to Hero](https://discord.gg/3zy8kqD9Cp) channel, or on `#llmdotc` on CUDA MODE Discord.
|
||||
用简洁、纯粹的 C/CUDA 实现 LLM，无需 245MB 的 PyTorch 或 107MB 的 cPython。当前重点是预训练（pretraining），尤其是复现 [GPT-2](https://github.com/openai/gpt-2) 和 [GPT-3](https://arxiv.org/abs/2005.14165) 迷你系列，同时在 [train_gpt2.py](train_gpt2.py) 中提供并行的 PyTorch 参考实现。你会认出该文件是我早期项目 [nanoGPT](https://github.com/karpathy/nanoGPT), 的稍加改动版本。目前，llm.c 比 PyTorch Nightly 略快（约 7%）。除了 [train_gpt2.cu](train_gpt2.cu) 中的前沿主线代码外，我们还在单文件 [train_gpt2.c](train_gpt2.c) 中提供了一个约 1,000 行整洁代码的简单 CPU fp32 参考实现。我希望本仓库仅维护 C 和 CUDA 代码。欢迎移植到其他语言或仓库，但应在独立仓库中进行，我很乐意在下方「notable forks」部分链接它们。开发者协作在 [Discussions](https://github.com/karpathy/llm.c/discussions) 以及 Discord 上进行，可在 [Zero to Hero](https://discord.gg/3zy8kqD9Cp) 频道的 `#llmc` 频道，或在 [GPU MODE](https://discord.gg/gpumode) Discord 的 `#llmdotc` 上交流。
|
||||
|
||||
## quick start
|
||||
|
||||
The best introduction to the llm.c repo today is reproducing the GPT-2 (124M) model. [Discussion #481](https://github.com/karpathy/llm.c/discussions/481) steps through this in detail. We can reproduce other models from the GPT-2 and GPT-3 series in both llm.c and in the parallel implementation of PyTorch. Have a look at the [scripts README](scripts/README.md).
|
||||
当前入门 llm.c 仓库的最佳方式是复现 GPT-2 (124M) 模型。[Discussion #481](https://github.com/karpathy/llm.c/discussions/481) 详细介绍了这一过程。我们可以在 llm.c 及并行的 PyTorch 实现中复现 GPT-2 和 GPT-3 系列的其他模型。请查看 [scripts README](scripts/README.md)。
|
||||
|
||||
debugging tip: 运行 `make` 命令构建二进制文件时，将其中的 `-O3` 替换为 `-g`，即可在你喜爱的 IDE（例如 vscode）中单步调试代码。
|
||||
|
||||
## quick start (1 GPU, fp32 only)
|
||||
|
||||
If you won't be training on multiple nodes, aren't interested in mixed precision, and are interested in learning CUDA, the fp32 (legacy) files might be of interest to you. These are files that were "checkpointed" early in the history of llm.c and frozen in time. They are simpler, more portable, and possibly easier to understand. Run the 1 GPU, fp32 code like this:
|
||||
如果你不会在多节点上训练、对混合精度不感兴趣，并且想学习 CUDA，fp32（legacy）文件可能适合你。这些文件是 llm.c 早期历史中的「checkpoint」版本，被定格在那一刻。它们更简单、更可移植，也可能更容易理解。单 GPU fp32 代码可按如下方式运行：
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
python dev/data/tinyshakespeare.py
|
||||
python train_gpt2.py
|
||||
chmod u+x ./dev/download_starter_pack.sh
|
||||
./dev/download_starter_pack.sh
|
||||
make train_gpt2fp32cu
|
||||
./train_gpt2fp32cu
|
||||
```
|
||||
|
||||
The above lines (1) download the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset, tokenize it with the GPT-2 Tokenizer, (2) download and save the GPT-2 (124M) weights, (3) init from them in C/CUDA and train for one epoch on tineshakespeare with AdamW (using batch size 4, context length 1024, total of 74 steps), evaluate validation loss, and sample some text.
|
||||
|
||||
## quick start (CPU)
|
||||
|
||||
The "I am so GPU poor that I don't even have one GPU" section. You can still enjoy seeing llm.c train! But you won't go too far. Just like the fp32 version above, the CPU version is an even earlier checkpoint in the history of llm.c, back when it was just a simple reference implementation in C. For example, instead of training from scratch, you can finetune a GPT-2 small (124M) to output Shakespeare-like text, as an example:
|
||||
`download_starter_pack.sh` 脚本是快速上手的简便方式，会下载一批有助于入门的 .bin 文件，其中包含：1) 以 fp32、bfloat16 保存的 GPT-2 124M 模型，2) 单元测试中使用的「debug state」（小批量数据及目标激活值和梯度），3) GPT-2 分词器，以及 3) 已分词化的 [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) 数据集。或者，不运行 .sh 脚本，也可按以下步骤手动重新创建这些产物：
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
python dev/data/tinyshakespeare.py
|
||||
python train_gpt2.py
|
||||
```
|
||||
|
||||
## quick start (CPU)
|
||||
|
||||
「穷到连一块 GPU 都没有」专区。你仍然可以欣赏 llm.c 的训练过程！但走不了太远。与上述 fp32 版本类似，CPU 版本是 llm.c 历史上更早的一个 checkpoint，当时它还只是 C 语言的简单参考实现。例如，你可以不从头训练，而是将 GPT-2 small (124M) 微调为输出类莎士比亚文本，示例如下：
|
||||
|
||||
```bash
|
||||
chmod u+x ./dev/download_starter_pack.sh
|
||||
./dev/download_starter_pack.sh
|
||||
make train_gpt2
|
||||
OMP_NUM_THREADS=8 ./train_gpt2
|
||||
```
|
||||
|
||||
The above lines (1) download the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset, tokenize it with the GPT-2 Tokenizer, (2) download and save the GPT-2 (124M) weights, (3) init from them in C and train for 40 steps on tineshakespeare with AdamW (using batch size 4, context length only 64), evaluate validation loss, and sample some text. Honestly, unless you have a beefy CPU (and can crank up the number of OMP threads in the launch command), you're not going to get that far on CPU training LLMs, but it might be a good demo/reference. The output looks like this on my MacBook Pro (Apple Silicon M3 Max):
|
||||
如果你更希望不运行 starter pack 脚本，如上一节所述，可运行 `python dev/data/tinyshakespeare.py` 再运行 `python train_gpt2.py`，复现完全相同的 .bin 文件和产物。
|
||||
|
||||
上述命令（1）下载已分词化的 [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) 数据集并下载 GPT-2 (124M) 权重，（3）在 C 中从它们初始化，在 tineshakespeare 上用 AdamW 训练 40 步（batch size 4，context length 仅 64），评估验证集损失并采样部分文本。坦白说，除非你有一块强劲的 CPU（并能在启动命令中调高 OMP 线程数），否则在 CPU 上训练 LLM 走不了太远，但这可能是个不错的演示/参考。在我的 MacBook Pro（Apple Silicon M3 Max）上，输出如下：
|
||||
|
||||
```
|
||||
[GPT-2]
|
||||
@@ -70,31 +84,31 @@ Allay
|
||||
|
||||
## datasets
|
||||
|
||||
The data files inside `/dev/data/(dataset).py` are responsible for downloading, tokenizing and saving the tokens to .bin files, readable easily from C. So for example when you run:
|
||||
`/dev/data/(dataset).py` 内的数据文件负责下载、分词并将 token 保存为可从 C 轻松读取的 .bin 文件。例如运行：
|
||||
|
||||
```bash
|
||||
python dev/data/tinyshakespeare.py
|
||||
```
|
||||
|
||||
We download and tokenize the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset. The output of this looks like this:
|
||||
我们会下载并对 [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) 数据集进行分词。输出如下：
|
||||
|
||||
```
|
||||
writing 32,768 tokens to ./dev/data/tinyshakespeare/tiny_shakespeare_val.bin
|
||||
writing 305,260 tokens to ./dev/data/tinyshakespeare/tiny_shakespeare_train.bin
|
||||
```
|
||||
|
||||
The .bin files contain a short header (1024 bytes) and then a stream of tokens in uint16, indicating the token ids with the GPT-2 tokenizer. More datasets are available in `/dev/data`.
|
||||
.bin 文件包含一个短头部（1024 字节），随后是以 uint16 存储的 token 流，表示 GPT-2 分词器的 token id。更多数据集可在 `/dev/data` 中找到。
|
||||
|
||||
## test
|
||||
|
||||
I am also attaching a simple unit test for making sure our C code agrees with the PyTorch code. On the CPU as an example, compile and run with:
|
||||
我还附带了一个简单的单元测试，用于确保 C 代码与 PyTorch 代码一致。以 CPU 为例，编译并运行：
|
||||
|
||||
```bash
|
||||
make test_gpt2
|
||||
./test_gpt2
|
||||
```
|
||||
|
||||
This now loads the `gpt2_124M_debug_state.bin` file that gets written by train_gpt2.py, runs a forward pass, compares the logits and loss with the PyTorch reference implementation, then it does 10 iterations of training with Adam and makes sure the losses match PyTorch. To test the GPU version we run:
|
||||
该测试会加载由 train_gpt2.py 写入的 `gpt2_124M_debug_state.bin` 文件，执行前向传播，将 logits 和 loss 与 PyTorch 参考实现对比，然后进行 10 次 Adam 训练迭代并确保 loss 与 PyTorch 一致。要测试 GPU 版本，运行：
|
||||
|
||||
```bash
|
||||
# fp32 test (cudnn not supported)
|
||||
@@ -103,19 +117,19 @@ make test_gpt2cu PRECISION=FP32 && ./test_gpt2cu
|
||||
make test_gpt2cu USE_CUDNN=1 && ./test_gpt2cu
|
||||
```
|
||||
|
||||
This tests both the fp32 path and the mixed precision path. The test should pass and print `overall okay: 1`.
|
||||
这会同时测试 fp32 路径和混合精度路径。测试应通过并打印 `overall okay: 1`。
|
||||
|
||||
## tutorial
|
||||
|
||||
I attached a very small tutorial here, in [doc/layernorm/layernorm.md](doc/layernorm/layernorm.md). It's a simple, step-by-step guide to implementing a single layer of the GPT-2 model, the layernorm layer. This is a good starting point to understand how the layers are implemented in C.
|
||||
我在此附上了一份很短的教程：[doc/layernorm/layernorm.md](doc/layernorm/layernorm.md)。这是一份简单、分步的指南，讲解如何实现 GPT-2 模型的单层——layernorm 层。这是理解 C 中如何实现各层的好起点。
|
||||
|
||||
**flash attention**. As of May 1, 2024 we use the Flash Attention from cuDNN. Because cuDNN bloats the compile time from a few seconds to ~minute and this code path is right now very new, this is disabled by default. You can enable it by compiling like this:
|
||||
**flash attention**。截至 2024 年 5 月 1 日，我们使用 cuDNN 的 Flash Attention。由于 cuDNN 会将编译时间从几秒延长到约一分钟，且该代码路径目前非常新，因此默认禁用。可按如下方式编译以启用：
|
||||
|
||||
```bash
|
||||
make train_gpt2cu USE_CUDNN=1
|
||||
```
|
||||
|
||||
This will try to compile with cudnn and run it. You have to have cuDNN installed on your system. The [cuDNN installation instructions](https://developer.nvidia.com/cudnn) with apt-get will grab the default set of cuDNN packages. For a minimal setup, the cuDNN dev package is sufficient, e.g. on Ubuntu 22.04 for CUDA 12.x:
|
||||
这将尝试使用 cudnn 编译并运行。你的系统上必须已安装 cuDNN。[cuDNN installation instructions](https://developer.nvidia.com/cudnn) 中通过 apt-get 安装会获取默认的 cuDNN 包集。最小化安装时，cuDNN 开发包即可，例如在 Ubuntu 22.04、CUDA 12.x 上：
|
||||
|
||||
```bash
|
||||
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
|
||||
@@ -124,24 +138,45 @@ sudo apt-get update
|
||||
sudo apt-get -y install libcudnn9-dev-cuda-12
|
||||
```
|
||||
|
||||
On top of this you need the [cuDNN frontend](https://github.com/NVIDIA/cudnn-frontend/tree/main), but this is just header files. Simply clone the repo to your disk. The Makefile currently looks for it in either your home directory or the current directory. If you have put it elsewhere, add `CUDNN_FRONTEND_PATH=/path/to/your/cudnn-frontend/include` to the `make` command-line.
|
||||
此外还需要 [cuDNN frontend](https://github.com/NVIDIA/cudnn-frontend/tree/main),，但这只是头文件。将仓库克隆到本地即可。Makefile 目前会在你的主目录或当前目录中查找它。若放在其他位置，请在 `make` 命令行中添加 `CUDNN_FRONTEND_PATH=/path/to/your/cudnn-frontend/include`。
|
||||
|
||||
**multi-GPU training**. As of April 26, 2024 there is now also support for multi-GPU training using MPI and NCCL. Make sure you install MPI, e.g. on Linux:
|
||||
## 多 GPU 训练
|
||||
|
||||
请确保已安装 MPI 和 NCCL，例如在 Linux 上：
|
||||
|
||||
```bash
|
||||
sudo apt install openmpi-bin openmpi-doc libopenmpi-dev
|
||||
```
|
||||
|
||||
and then:
|
||||
对于 NCCL，请按照[官方网站](https://developer.nvidia.com/nccl/nccl-download)（例如网络安装器）中的说明操作
|
||||
|
||||
然后：
|
||||
|
||||
```bash
|
||||
make train_gpt2cu
|
||||
mpirun -np <number of GPUs> ./train_gpt2cu
|
||||
```
|
||||
|
||||
## experiments / sweeps
|
||||
或者直接运行 `./scripts/` 下的某个脚本。
|
||||
|
||||
Just as an example process to sweep learning rates on a machine with 4 GPUs on TinyStories. Run a shell script `sweep.sh` (after you of course `chmod u+x sweep.sh`):
|
||||
## 多节点训练
|
||||
|
||||
请确保已按照 [multi-GPU](#multi-gpu-training) 章节的说明安装 `NCCL`。
|
||||
|
||||
目前我们支持 3 种方式来进行多节点训练：
|
||||
1) 使用 OpenMPI 交换 nccl id 并初始化 NCCL。详见例如 `./scripts/multi_node/run_gpt2_124M_mpi.sh` 脚本。
|
||||
2) 使用共享文件系统初始化 NCCL。详见 `./scripts/multi_node/run_gpt2_124M_fs.sbatch` 脚本。
|
||||
3) 使用 TCP 套接字初始化 NCCL。详见 `./scripts/multi_node/run_gpt2_124M_tcp.sbatch` 脚本。
|
||||
|
||||
注意：
|
||||
* 如果你在 slurm 环境中运行，且你的 slurm 不支持 PMIx（考虑到 `slurm-wlm` 已移除 PMIx 支持，我们认为这会是常见情况），则必须使用 FS（2）或 TCP（3）方式。要测试你的 slurm 是否支持 PMIx，请运行：`srun --mpi=list`，并查看输出中是否出现 `pmix`。
|
||||
* 如果你没有配置 slurm，可以使用 `mpirun` - MPI（1）方式启动多节点运行。
|
||||
|
||||
这 3 种方法并无优劣之分，我们只是提供多种选项，以便你在特定环境中运行。
|
||||
|
||||
## 实验 / 参数扫描（sweeps）
|
||||
|
||||
下面是一个示例流程：在一台配备 4 个 GPU 的机器上，对 TinyStories 进行学习率扫描。运行 shell 脚本 `sweep.sh`（当然，在此之前你需要先 `chmod u+x sweep.sh`）：
|
||||
|
||||
```bash
|
||||
#!/bin/bash
|
||||
@@ -157,64 +192,82 @@ done
|
||||
# screen -ls | grep -E "tr[0-3]" | cut -d. -f1 | xargs -I {} screen -X -S {} quit
|
||||
```
|
||||
|
||||
This example opens up 4 screen sessions and runs the four commands with different LRs. This writes the log files `stories$i.log` with all the losses, which you can plot as you wish in Python. A quick example of how to parse and plot these logfiles is in [dev/vislog.ipynb](dev/vislog.ipynb).
|
||||
此示例会开启 4 个 screen 会话，并以不同学习率运行四条命令。这会写出日志文件 `stories$i.log`，其中包含所有 loss，你可以用 Python 按需绘制。解析并绘制这些日志文件的快速示例见 [dev/vislog.ipynb](dev/vislog.ipynb)。
|
||||
|
||||
## repo
|
||||
## 仓库
|
||||
|
||||
A few more words on what I want this repo to be:
|
||||
关于我希望这个仓库成为什么样子，再多说几句：
|
||||
|
||||
First, I want `llm.c` to be a place for education. E.g. our `dev/cuda` folder is a place for a library of kernels for all the layers that are manually hand-written and very well documented, starting from very simple kernels all the way to more complex / faster kernels. If you have a new kernel with various different tradeoffs, please feel free to contribute it here.
|
||||
首先，我希望 `llm.c` 成为一个教学场所。例如，我们的 `dev/cuda` 文件夹是一个内核库，涵盖各层的手工编写、文档详尽的内核，从非常简单的内核一直到更复杂/更快的内核。如果你有具有不同权衡（tradeoffs）的新内核，欢迎在此贡献。
|
||||
|
||||
That said, I also want `llm.c` to be very fast too, even practically useful to train networks. E.g. to start, we should be able to reproduce the big GPT-2 (1.6B) training run. This requires that we incorporate whatever fastest kernels there are, including the use of libraries such as cuBLAS, cuBLASLt, CUTLASS, cuDNN, etc. I also think doing so serves an educational purpose to establish an expert upper bound, and a unit of measurement, e.g. you could say that your manually written kernels are 80% of cuBLAS speed, etc. Then you can choose to do a super fast run, or you can choose to "drag and drop" whatever manual kernels you wish to use, and run with those.
|
||||
话虽如此，我也希望 `llm.c` 非常快，甚至能实际用于训练网络。例如，起步时我们应该能够复现大型 GPT-2（1.6B）训练运行。这要求纳入最快的内核，包括使用 cuBLAS、cuBLASLt、CUTLASS、cuDNN 等库。我认为这样做也有教学意义：建立一个专家级上限和度量单位，例如你可以说你的手写内核达到 cuBLAS 速度的 80% 等。然后你可以选择超快运行，也可以选择“拖放”你想使用的任意手工内核来运行。
|
||||
|
||||
However, as a constraint, I want to keep the mainline `llm.c` in the root folder simple and readable. If there is a PR that e.g. improves performance by 2% but it "costs" 500 lines of complex C code, and maybe an exotic 3rd party dependency, I may reject the PR because the complexity is not worth it. As a concrete example - making cuBLAS for matmuls the default in the root training loop is a no-brainer: it makes the mainline code much faster, it is a single line of interpretable code, and it is a very common dependency. On the side of this, we can have manual implementations that can compete with cuBLAS in `dev/cuda`.
|
||||
不过，作为约束，我希望保持根目录中的主线 `llm.c` 简单且可读。如果某个 PR 例如将性能提升 2%，但“代价”是 500 行复杂 C 代码，也许还有冷门第三方依赖，我可能会拒绝该 PR，因为复杂度不值得。举个具体例子——在根训练循环中将 cuBLAS 用于 matmul 设为默认是显而易见的：它让主线代码快得多，只是一行可理解的代码，且是非常常见的依赖。与此同时，我们可以在 `dev/cuda` 中提供能与 cuBLAS 竞争的手工实现。
|
||||
|
||||
Lastly, I will be a lot more sensitive to complexity in the root folder of the project, which contains the main / default files of the project. In comparison, the `dev/` folder is a bit more of a scratch space for us to develop a library of kernels or classes and share useful or related or educational code, and some of this code could be ok to be (locally) complex.
|
||||
最后，我对项目根文件夹中的复杂度会敏感得多，根目录包含项目的主/默认文件。相比之下，`dev/` 文件夹更像是我们开发内核或类库、分享有用/相关/教学代码的草稿空间，其中部分代码可以（在局部）复杂一些。
|
||||
|
||||
## notable forks
|
||||
## 值得关注的分支（fork）
|
||||
|
||||
- AMD support
|
||||
- [llm.c](https://github.com/anthonix/llm.c) by @[anthonix](https://github.com/anthonix): support for AMD devices, such as the 7900 XTX
|
||||
- AMD 支持
|
||||
- [llm.c](https://github.com/anthonix/llm.c) by @[anthonix](https://github.com/anthonix): 支持 AMD 设备，例如 7900 XTX
|
||||
|
||||
- C#
|
||||
- [llm.cs](https://github.com/azret/llm.cs) by @[azret](https://github.com/azret): a C# port of this project
|
||||
- [Llm.cs](https://github.com/nietras/Llm.cs) by @[nietras](https://github.com/nietras): a C# port of this project with focus on easy to get started on any platform. Clone and run ✅
|
||||
- [llm.cs](https://github.com/azret/llm.cs) by @[azret](https://github.com/azret): 本项目的 C# 移植版
|
||||
- [Llm.cs](https://github.com/nietras/Llm.cs) by @[nietras](https://github.com/nietras): 本项目的 C# 移植版，侧重于在任何平台上易于上手。克隆即可运行 ✅
|
||||
|
||||
- CUDA C++
|
||||
- [llm.cpp](https://github.com/gevtushenko/llm.c) by @[gevtushenko](https://github.com/gevtushenko): a port of this project using the [CUDA C++ Core Libraries](https://github.com/NVIDIA/cccl)
|
||||
- A presentation this fork was covered in [this lecture](https://www.youtube.com/watch?v=WiB_3Csfj_Q) in the [CUDA MODE Discord Server](https://discord.gg/cudamode)
|
||||
- [llm.cpp](https://github.com/gevtushenko/llm.c) by @[gevtushenko](https://github.com/gevtushenko): 使用 [CUDA C++ Core Libraries](https://github.com/NVIDIA/cccl) 对本项目的移植
|
||||
- 该分支曾在 [GPU MODE Discord Server](https://discord.gg/cudamode) 的[这场讲座](https://www.youtube.com/watch?v=WiB_3Csfj_Q) 中介绍
|
||||
|
||||
- C++/CUDA
|
||||
- [llm.cpp](https://github.com/zhangpiu/llm.cpp/tree/master/llmcpp) by @[zhangpiu](https://github.com/zhangpiu): 使用 [Eigen](https://gitlab.com/libeigen/eigen), 对本项目的移植，支持 CPU/CUDA。
|
||||
|
||||
- WebGPU C++
|
||||
- [gpu.cpp](https://github.com/AnswerDotAI/gpu.cpp) by @[austinvhuang](https://github.com/austinvhuang): 使用原生 WebGPU 在 C++ 中进行可移植 GPU 计算的库。目标是成为通用库，同时也将 llm.c 内核移植到 WGSL。
|
||||
|
||||
- C++
|
||||
- [llm.cpp](https://github.com/GaoYusong/llm.cpp) by @[GaoYusong](https://github.com/GaoYusong): 本项目的 C++ 移植版，提供 C++ 单头文件库 [tinytorch.hpp](https://github.com/GaoYusong/llm.cpp/blob/main/tinytorch.hpp)
|
||||
|
||||
- Go
|
||||
- [llm.go](https://github.com/joshcarp/llm.go) by @[joshcarp](https://github.com/joshcarp): a Go port of this project
|
||||
- [llm.go](https://github.com/joshcarp/llm.go) by @[joshcarp](https://github.com/joshcarp): 本项目的 Go 移植版
|
||||
|
||||
- Java
|
||||
- [llm.java](https://github.com/harryjackson/llm.java) by @[harryjackson](https://github.com/harryjackson): a Java port of this project
|
||||
- [llm.java](https://github.com/harryjackson/llm.java) by @[harryjackson](https://github.com/harryjackson): 本项目的 Java 移植版
|
||||
|
||||
- Metal
|
||||
- [llm.metal](https://github.com/regrettable-username/llm.metal) by @[regrettable-username](https://github.com/regrettable-username): LLM training in simple, raw C/Metal Shading Language
|
||||
- [llm.metal](https://github.com/regrettable-username/llm.metal) by @[regrettable-username](https://github.com/regrettable-username): 使用简洁、原生 C/Metal Shading Language 进行 LLM 训练
|
||||
|
||||
- Mojo
|
||||
- [llm.🔥](https://github.com/dorjeduck/llm.mojo) by @[dorjeduck](https://github.com/dorjeduck): a Mojo port of this project
|
||||
- [llm.🔥](https://github.com/dorjeduck/llm.mojo) by @[dorjeduck](https://github.com/dorjeduck): 本项目的 Mojo 移植版
|
||||
|
||||
- OpenCL
|
||||
- [llm.c](https://github.com/krrishnarraj/llm.c) by @[krrishnarraj](https://github.com/krrishnarraj): 本项目的 OpenCL 移植版
|
||||
|
||||
- Rust
|
||||
- [llm.rs](https://github.com/yijunyu/llm.rs) by @[Yijun Yu](https://github.com/yijunyu): a Rust rewrite with the aim to have same performance
|
||||
- [llm.rs](https://github.com/ToJen/llm.rs) by @[ToJen](https://github.com/ToJen): a Rust port of this project
|
||||
- [llm.rs](https://github.com/yijunyu/llm.rs) by @[Yijun Yu](https://github.com/yijunyu): Rust 重写版，目标是达到相同性能
|
||||
- [llm.rs](https://github.com/ToJen/llm.rs) by @[ToJen](https://github.com/ToJen): 本项目的 Rust 移植版
|
||||
|
||||
- Swift
|
||||
- [llm.swift](https://github.com/otabuzzman/llm.swift) by @[otabuzzman](https://github.com/otabuzzman): a Swift port of this project
|
||||
- [llm.swift](https://github.com/otabuzzman/llm.swift) by @[otabuzzman](https://github.com/otabuzzman): 本项目的 Swift 移植版
|
||||
|
||||
- Zig
|
||||
- [llm.zig](https://github.com/Saimirbaci/llm.zig) by @[saimirbaci](https://github.com/Saimirbaci): a Zig port of this project
|
||||
- [llm.zig](https://github.com/Saimirbaci/llm.zig) by @[saimirbaci](https://github.com/Saimirbaci): 本项目的 Zig 移植版
|
||||
|
||||
- Habana Gaudi2
|
||||
- [llm.tpc](https://github.com/abhilash1910/llm.tpc) by @[abhilash1910](https://github.com/abhilash1910): 本项目的 Habana Gaudi2 移植版
|
||||
|
||||
## discussions
|
||||
- Nim
|
||||
- [llm.nim](https://github.com/Vindaar/llm.nim) by @[Vindaar](https://github.com/Vindaar): 本项目的 Nim 移植版
|
||||
|
||||
Ways of organizing development:
|
||||
## 讨论
|
||||
|
||||
- Experiencing a concrete issue with the repo? Use [Issues](https://github.com/karpathy/llm.c/issues).
|
||||
- Have some code to contribute? Open a [PR](https://github.com/karpathy/llm.c/pulls)
|
||||
- Chat about the repo, ask questions, etc.? Look at [Discussions](https://github.com/karpathy/llm.c/discussions).
|
||||
- Something faster? I created a new `#llmc` channel on my [Zero to Hero Discord channel](https://discord.gg/3zy8kqD9Cp).
|
||||
组织开发的方式：
|
||||
|
||||
## license
|
||||
- 在使用仓库时遇到具体问题？请使用 [Issues](https://github.com/karpathy/llm.c/issues).
|
||||
- 有代码要贡献？请提交 [PR](https://github.com/karpathy/llm.c/pulls)
|
||||
- 想聊聊仓库、提问等？请查看 [Discussions](https://github.com/karpathy/llm.c/discussions).
|
||||
- 需要更快捷的沟通？我在我的 [Zero to Hero Discord 频道](https://discord.gg/3zy8kqD9Cp). 上新建了 `#llmc` 频道
|
||||
|
||||
## 许可证
|
||||
|
||||
MIT
|
||||
|
||||
@@ -0,0 +1,7 @@
|
||||
# WeHub 来源说明
|
||||
|
||||
- 原始项目：`karpathy/llm.c`
|
||||
- 原始仓库：https://github.com/karpathy/llm.c
|
||||
- 导入方式：上游默认分支的最新快照
|
||||
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
|
||||
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
|
||||
+29
-4
@@ -8,9 +8,21 @@ ifeq ($(NVCC),)
|
||||
$(error nvcc not found.)
|
||||
endif
|
||||
|
||||
ifneq ($(CI),true) # if not in CI, then use the GPU query
|
||||
ifndef GPU_COMPUTE_CAPABILITY # set to defaults if: make GPU_COMPUTE_CAPABILITY=
|
||||
GPU_COMPUTE_CAPABILITY = $(shell __nvcc_device_query) # assume if NVCC is present, then this likely is too
|
||||
GPU_COMPUTE_CAPABILITY := $(strip $(GPU_COMPUTE_CAPABILITY))
|
||||
endif
|
||||
endif
|
||||
|
||||
# Compiler flags
|
||||
CFLAGS = -O3 --use_fast_math
|
||||
NVCCFLAGS = -lcublas -lcublasLt
|
||||
ifeq ($(GPU_COMPUTE_CAPABILITY),) # set to defaults if: make GPU_COMPUTE_CAPABILITY=
|
||||
CFLAGS = -O3 --use_fast_math
|
||||
else
|
||||
CFLAGS = -O3 --use_fast_math --generate-code arch=compute_$(GPU_COMPUTE_CAPABILITY),code=[compute_$(GPU_COMPUTE_CAPABILITY),sm_$(GPU_COMPUTE_CAPABILITY)]
|
||||
endif
|
||||
|
||||
NVCCFLAGS = -lcublas -lcublasLt -std=c++17
|
||||
MPI_PATHS = -I/usr/lib/x86_64-linux-gnu/openmpi/include -L/usr/lib/x86_64-linux-gnu/openmpi/lib/
|
||||
|
||||
# Default rule for our CUDA files
|
||||
@@ -18,8 +30,11 @@ MPI_PATHS = -I/usr/lib/x86_64-linux-gnu/openmpi/include -L/usr/lib/x86_64-linux-
|
||||
$(NVCC) $(CFLAGS) $(NVCCFLAGS) $< -o $@
|
||||
|
||||
# Build all targets
|
||||
TARGETS = adamw attention_backward attention_forward classifier_fused crossentropy_forward crossentropy_softmax_backward encoder_backward encoder_forward gelu_backward gelu_forward layernorm_backward layernorm_forward matmul_backward matmul_backward_bias matmul_forward nccl_all_reduce residual_forward softmax_forward trimat_forward fused_residual_forward global_norm
|
||||
TARGETS = adamw attention_backward attention_forward classifier_fused crossentropy_forward crossentropy_softmax_backward encoder_backward encoder_forward gelu_backward gelu_forward layernorm_backward layernorm_forward matmul_backward matmul_backward_bias matmul_forward nccl_all_reduce residual_forward softmax_forward trimat_forward fused_residual_forward global_norm permute
|
||||
|
||||
all: $(TARGETS)
|
||||
all_ptx: $(TARGETS:%=%.ptx)
|
||||
all_sass: $(TARGETS:%=%.sass)
|
||||
|
||||
# Individual targets: forward pass
|
||||
attention_forward: attention_forward.cu
|
||||
@@ -50,10 +65,20 @@ matmul_backward: matmul_backward.cu
|
||||
adamw: adamw.cu
|
||||
global_norm: global_norm.cu
|
||||
|
||||
permute: permute.cu
|
||||
|
||||
# NCCL communication kernels
|
||||
nccl_all_reduce: nccl_all_reduce.cu
|
||||
$(NVCC) -lmpi -lnccl $(NVCCFLAGS) $(MPI_PATHS) nccl_all_reduce.cu -o nccl_all_reduce
|
||||
|
||||
# Generate PTX using cuobjdump
|
||||
%.ptx: %
|
||||
cuobjdump --dump-ptx $< > $@
|
||||
|
||||
# Generate SASS using cuobjdump
|
||||
%.sass: %
|
||||
cuobjdump --dump-sass $< > $@
|
||||
|
||||
# Run all targets
|
||||
run_all: all
|
||||
@for target in $(TARGETS); do \
|
||||
@@ -65,4 +90,4 @@ run_all: all
|
||||
|
||||
# Clean up
|
||||
clean:
|
||||
rm -f $(TARGETS)
|
||||
rm -f $(TARGETS) *.ptx *.sass
|
||||
|
||||
+1
-1
@@ -159,7 +159,7 @@ int main(int argc, char **argv) {
|
||||
// create random data on host (to be used for the CPU reference implementation)
|
||||
float* params_memory = make_random_float(num_parameters);
|
||||
float* grads_memory = make_random_float(num_parameters);
|
||||
float* m_memory = make_random_float_01(num_parameters);
|
||||
float* m_memory = make_random_float(num_parameters);
|
||||
float* v_memory = make_random_float_01(num_parameters);
|
||||
|
||||
// move to GPU
|
||||
|
||||
@@ -68,7 +68,7 @@ void attention_forward_cpu(float* out, float* preatt, float* att,
|
||||
float* att_bth = att + b*NH*T*T + h*T*T + t*T;
|
||||
|
||||
// pass 1: calculate query dot key and maxval
|
||||
float maxval = -10000.0f; // TODO something better
|
||||
float maxval = -FLT_MAX;
|
||||
for (int t2 = 0; t2 < T; t2++) { // used to be t2 <= t
|
||||
float* key_t2 = inp + b * T * C3 + t2 * C3 + h * hs + C; // +C because it's key
|
||||
|
||||
@@ -1137,6 +1137,7 @@ int main(int argc, char **argv) {
|
||||
free(dinp);
|
||||
free(dpreatt);
|
||||
free(datt);
|
||||
free(h_dinp);
|
||||
cudaCheck(cudaFree(d_inp));
|
||||
cudaCheck(cudaFree(d_qkvr));
|
||||
cudaCheck(cudaFree(d_preatt));
|
||||
|
||||
@@ -98,7 +98,7 @@ void attention_forward_cpu(float* out, float* preatt, float* att,
|
||||
float* att_bth = att + b*NH*T*T + h*T*T + t*T;
|
||||
|
||||
// pass 1: calculate query dot key and maxval
|
||||
float maxval = -10000.0f; // TODO something better
|
||||
float maxval = -FLT_MAX;
|
||||
for (int t2 = 0; t2 <= t; t2++) {
|
||||
const float* key_t2 = inp + b * T * C3 + t2 * C3 + h * hs + C; // +C because it's key
|
||||
|
||||
@@ -203,7 +203,7 @@ __global__ void attention_softmax_kernel1(float* att, const float* preatt,
|
||||
float* att_bth = att + b*NH*T*T + h*T*T + t*T;
|
||||
|
||||
// find maxval
|
||||
float maxval = -10000.0f; // TODO something better
|
||||
float maxval = -FLT_MAX;
|
||||
for (int t2 = 0; t2 <= t; t2++) {
|
||||
if (preatt_bth[t2] > maxval) {
|
||||
maxval = preatt_bth[t2];
|
||||
@@ -1377,6 +1377,7 @@ int main(int argc, char **argv) {
|
||||
cudaCheck(cudaFree(d_preatt));
|
||||
cudaCheck(cudaFree(d_att));
|
||||
cudaCheck(cudaFree(d_inp));
|
||||
cudaCheck(cudaFree(d_stats));
|
||||
cublasDestroy(cublas_handle);
|
||||
|
||||
#ifdef ENABLE_CUDNN
|
||||
|
||||
@@ -1,46 +1,53 @@
|
||||
"""
|
||||
Script for running benchmarks on the Modal platform.
|
||||
This is useful for folks who do not have access to expensive GPUs locally.
|
||||
|
||||
Example usage:
|
||||
Example usage for cuda kernels:
|
||||
GPU_MEM=80 modal run benchmark_on_modal.py \
|
||||
--compile-command "nvcc -O3 --use_fast_math attention_forward.cu -o attention_forward -lcublas" \
|
||||
--run-command "./attention_forward 1"
|
||||
OR if you want to use cuDNN etc.
|
||||
|
||||
This will mount the contents of the current directory to the remote container on modal,
|
||||
compile the `attention_forward.cu` file with `nvcc`, and run the resulting binary on a A100 GPU with 80GB of memory.
|
||||
|
||||
For training the gpt2 model with cuDNN use:
|
||||
GPU_MEM=80 modal run dev/cuda/benchmark_on_modal.py \
|
||||
--compile-command "make train_gpt2cu USE_CUDNN=1"
|
||||
--run-command "./train_gpt2cu -i dev/data/tinyshakespeare/tiny_shakespeare_train.bin -j dev/data/tinyshakespeare/tiny_shakespeare_val.bin -v 250 -s 250 -g 144 -f shakespeare.log -b 4"
|
||||
|
||||
|
||||
For profiling using nsight system:
|
||||
GPU_MEM=80 modal run dev/cuda/benchmark_on_modal.py \
|
||||
--compile-command "make train_gpt2cu USE_CUDNN=1" \
|
||||
--run-command "nsys profile --cuda-graph-trace=graph --python-backtrace=cuda --cuda-memory-usage=true \
|
||||
./train_gpt2cu -i dev/data/tinyshakespeare/tiny_shakespeare_train.bin \
|
||||
-j dev/data/tinyshakespeare/tiny_shakespeare_val.bin -v 250 -s 250 -g 144 -f shakespeare.log -b 4"
|
||||
|
||||
For more nsys profiling specifics and command options, take a look at: https://docs.nvidia.com/nsight-systems/2024.2/UserGuide/
|
||||
-> To profile the report using a GUI, download NVIDIA NSight System GUI version (this software can run on all OS, so you download it locally)
|
||||
|
||||
NOTE: Currently there is a bug in the profiling using nsight system which produces a unrecognized GPU UUId error on the command line but it
|
||||
does not actually interfere with the model training and validation. The report (that you download) is still generated and can be viewed from Nsight Systems
|
||||
"""
|
||||
|
||||
import subprocess
|
||||
import os
|
||||
import sys
|
||||
import datetime
|
||||
|
||||
import modal
|
||||
from modal import Image, Stub
|
||||
|
||||
GPU_NAME_TO_MODAL_CLASS_MAP = {
|
||||
"H100": modal.gpu.H100,
|
||||
"A100": modal.gpu.A100,
|
||||
"A10G": modal.gpu.A10G,
|
||||
}
|
||||
|
||||
N_GPUS = int(os.environ.get("N_GPUS", 1))
|
||||
GPU_MEM = int(os.environ.get("GPU_MEM", 40))
|
||||
GPU_NAME = os.environ.get("GPU_NAME", "A100")
|
||||
GPU_CONFIG = GPU_NAME_TO_MODAL_CLASS_MAP[GPU_NAME](count=N_GPUS, size=str(GPU_MEM)+'GB')
|
||||
GPU_CONFIG = GPU_NAME_TO_MODAL_CLASS_MAP[GPU_NAME](count=N_GPUS, size=str(GPU_MEM) + 'GB')
|
||||
|
||||
APP_NAME = "llm.c benchmark run"
|
||||
|
||||
# We don't actually need to use the Axolotl image here, but it's reliable
|
||||
AXOLOTL_REGISTRY_SHA = (
|
||||
"d5b941ba2293534c01c23202c8fc459fd2a169871fa5e6c45cb00f363d474b6a"
|
||||
)
|
||||
axolotl_image = (
|
||||
Image.from_registry(f"winglian/axolotl@sha256:{AXOLOTL_REGISTRY_SHA}")
|
||||
.run_commands(
|
||||
"git clone https://github.com/OpenAccess-AI-Collective/axolotl /root/axolotl",
|
||||
"cd /root/axolotl && git checkout v0.4.0",
|
||||
)
|
||||
image = (
|
||||
Image.from_registry("totallyvyom/cuda-env:latest-2")
|
||||
.pip_install("huggingface_hub==0.20.3", "hf-transfer==0.1.5")
|
||||
.env(
|
||||
dict(
|
||||
@@ -49,34 +56,66 @@ axolotl_image = (
|
||||
TQDM_DISABLE="true",
|
||||
)
|
||||
)
|
||||
.run_commands(
|
||||
"wget -q https://github.com/Kitware/CMake/releases/download/v3.28.1/cmake-3.28.1-Linux-x86_64.sh",
|
||||
"bash cmake-3.28.1-Linux-x86_64.sh --skip-license --prefix=/usr/local",
|
||||
"rm cmake-3.28.1-Linux-x86_64.sh",
|
||||
"ln -s /usr/local/bin/cmake /usr/bin/cmake",)
|
||||
.run_commands(
|
||||
"apt-get install -y --allow-change-held-packages libcudnn8 libcudnn8-dev",
|
||||
"apt-get install -y openmpi-bin openmpi-doc libopenmpi-dev kmod sudo",
|
||||
"git clone https://github.com/NVIDIA/cudnn-frontend.git /root/cudnn-frontend",
|
||||
"cd /root/cudnn-frontend && mkdir build && cd build && cmake .. && make"
|
||||
)
|
||||
.run_commands(
|
||||
"wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin && \
|
||||
mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \
|
||||
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub && \
|
||||
add-apt-repository \"deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /\" && \
|
||||
apt-get update"
|
||||
).run_commands(
|
||||
"apt-get install -y nsight-systems-2023.3.3"
|
||||
)
|
||||
)
|
||||
|
||||
stub = modal.App(APP_NAME)
|
||||
|
||||
|
||||
def execute_command(command: str):
|
||||
command_args = command.split(" ")
|
||||
print(f"{command_args = }")
|
||||
subprocess.run(command_args, stdout=sys.stdout, stderr=subprocess.STDOUT)
|
||||
|
||||
|
||||
@stub.function(
|
||||
gpu=GPU_CONFIG,
|
||||
image=axolotl_image,
|
||||
image=image,
|
||||
allow_concurrent_inputs=4,
|
||||
container_idle_timeout=900,
|
||||
# This copies everything in this folder to the remote root folder
|
||||
mounts=[modal.Mount.from_local_dir("./", remote_path="/root/")]
|
||||
mounts=[modal.Mount.from_local_dir("./", remote_path="/root/")],
|
||||
# Instead of 'cuda-env' put your volume name that you create from 'modal volume create {volume-name}'
|
||||
# This enables the profiling reports to be saved on the volume that you can download by using:
|
||||
# 'modal volume get {volume-name} {/output_file_name}
|
||||
# For example right now, when profiling using this command "nsys profile --trace=cuda,nvtx --cuda-graph-trace=graph --python-backtrace=cuda --cuda-memory-usage=true" you would get your report
|
||||
# using in a directory in your volume, where the name contains the timestamp unique id.
|
||||
# This script will generate a "report1_{timestamp} folder in volume"
|
||||
# and you can download it with 'modal volume get {volume-name} report1_{timestamp}
|
||||
volumes={"/cuda-env": modal.Volume.from_name("cuda-env")},
|
||||
)
|
||||
def run_benchmark(compile_command: str, run_command: str):
|
||||
execute_command("pwd")
|
||||
execute_command("ls")
|
||||
execute_command(compile_command)
|
||||
execute_command(run_command)
|
||||
return None
|
||||
# Use this section if you want to profile using nsight system and install the reports on your volume to be locally downloaded
|
||||
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
|
||||
|
||||
execute_command("mkdir report1_" + timestamp)
|
||||
execute_command("mv /root/report1.nsys-rep /root/report1_" + timestamp + "/")
|
||||
execute_command("mv /root/report1.qdstrm /root/report1_" + timestamp + "/")
|
||||
execute_command("mv /root/report1_" + timestamp + "/" + " /cuda-env/")
|
||||
|
||||
return None
|
||||
|
||||
@stub.local_entrypoint()
|
||||
def inference_main(compile_command: str, run_command: str):
|
||||
results = run_benchmark.remote(compile_command, run_command)
|
||||
return results
|
||||
return results
|
||||
@@ -114,7 +114,7 @@ __device__ SoftmaxParams prepare_softmax(cg::thread_block_tile<32>& warp,
|
||||
int64_t idx, const float* inp, int V, int P) {
|
||||
// this warp (of 32) threads processes one row of inp, i.e. inp[idx, :] of shape (V,)
|
||||
// note that inp is actually (B * T, P) but we only use the first V elements
|
||||
// this function tehen calculates:
|
||||
// this function then calculates:
|
||||
// 1) the max value to subtract for numerical stability and
|
||||
// 2) the sum normalization factor
|
||||
const float* x = inp + idx * P;
|
||||
@@ -481,33 +481,6 @@ __global__ void fused_classifier_kernel4(floatX* dlogits, floatX* losses, floatX
|
||||
}
|
||||
}
|
||||
|
||||
// todo - move to common.h - or ideally somewhere it's not duplicated between train & common?
|
||||
// requires all 32 threads in the warp to be active, but should work for any block size
|
||||
// uses non-dynamic shared memory so every call increases shared memory requirements by 128 bytes
|
||||
// the fact it's unique shared memory allows us to avoid an extra __syncthreads() call at the end
|
||||
// but if called inside a loop, the shared memory will be implicitly reused, so set final_sync to 1
|
||||
using reduction_func_t = float (*) (float);
|
||||
template<reduction_func_t warp_reduction>
|
||||
__device__ float blockReduce(float val, bool final_sync=false, float out_of_bounds=0.0f) {
|
||||
// two reductions of up to 1024 threads:
|
||||
// 1) inside warp (shuffle), 2) cross-warp (shared memory), 3) inside warp (shuffle)
|
||||
__shared__ float shared_val[32];
|
||||
const int lane_id = threadIdx.x % 32;
|
||||
const int warp_id = threadIdx.x / 32;
|
||||
const int num_warps = blockDim.x / 32;
|
||||
|
||||
float warp_val = warp_reduction(val);
|
||||
if (lane_id == 0) { shared_val[warp_id] = warp_val; }
|
||||
__syncthreads();
|
||||
warp_val = (lane_id < num_warps) ? shared_val[lane_id] : out_of_bounds;
|
||||
float block_val = warp_reduction(warp_val);
|
||||
|
||||
if (final_sync) {
|
||||
__syncthreads(); // only needed in loops when effectively reusing shared memory etc.
|
||||
}
|
||||
return block_val;
|
||||
}
|
||||
|
||||
__device__ SoftmaxParams prepare_softmax_blockwide3(int64_t idx, const floatX* inp, int V, int P) {
|
||||
// same but not float4
|
||||
// one row of inp, i.e. inp[idx, :] of shape (V,)
|
||||
@@ -707,8 +680,8 @@ int main(int argc, char **argv) {
|
||||
cudaCheck(cudaSetDevice(deviceIdx));
|
||||
|
||||
// create host memory of random numbers
|
||||
float* logits = make_random_float_01(B * T * V);
|
||||
float* probs = (float*)malloc(B * T * V * sizeof(float));
|
||||
float* logits = make_random_float(B * T * V);
|
||||
float* probs = make_random_float_01(B * T * V);
|
||||
float* dlogits = (float*)malloc(B * T * V * sizeof(float));
|
||||
float* losses = (float*)malloc(B * T * sizeof(float));
|
||||
float* dlosses = make_random_float(B * T);
|
||||
@@ -787,11 +760,13 @@ int main(int argc, char **argv) {
|
||||
free(losses);
|
||||
free(dlosses);
|
||||
free(targets);
|
||||
free(outliers);
|
||||
cudaCheck(cudaFree(d_dlogits));
|
||||
cudaCheck(cudaFree(d_losses));
|
||||
cudaCheck(cudaFree(d_logits));
|
||||
cudaCheck(cudaFree(d_dlosses));
|
||||
cudaCheck(cudaFree(d_targets));
|
||||
cudaCheck(cudaFree(d_dlogits_no_pad));
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -5,6 +5,8 @@
|
||||
#include <cublasLt.h>
|
||||
#include <float.h>
|
||||
|
||||
#define WARP_SIZE 32U
|
||||
extern cudaDeviceProp deviceProp;
|
||||
|
||||
template<class T>
|
||||
__host__ __device__ T ceil_div(T dividend, T divisor) {
|
||||
@@ -18,6 +20,39 @@ __device__ float warpReduceSum(float val) {
|
||||
return val;
|
||||
}
|
||||
|
||||
// requires all 32 threads in the warp to be active, but should work for any block size
|
||||
// uses non-dynamic shared memory so every call increases shared memory requirements by 128 bytes
|
||||
// the fact it's unique shared memory allows us to avoid an extra __syncthreads() call at the end
|
||||
// but if called inside a loop, the shared memory will be implicitly reused, so set final_sync to 1
|
||||
using reduction_func_t = float (*) (float);
|
||||
|
||||
template<reduction_func_t warp_reduction>
|
||||
__device__ inline float blockReduce(float val, bool final_sync, float out_of_bounds) {
|
||||
// two reductions of up to 1024 threads:
|
||||
// 1) inside warp (shuffle), 2) cross-warp (shared memory), 3) inside warp (shuffle)
|
||||
__shared__ float shared_val[WARP_SIZE];
|
||||
const int lane_id = threadIdx.x % WARP_SIZE;
|
||||
const int warp_id = threadIdx.x / WARP_SIZE;
|
||||
const int num_warps = blockDim.x / WARP_SIZE;
|
||||
|
||||
float warp_val = warp_reduction(val);
|
||||
if (lane_id == 0) { shared_val[warp_id] = warp_val; }
|
||||
__syncthreads();
|
||||
warp_val = (lane_id < num_warps) ? shared_val[lane_id] : out_of_bounds;
|
||||
float block_val = warp_reduction(warp_val);
|
||||
|
||||
if (final_sync) {
|
||||
__syncthreads(); // only needed in loops when effectively reusing shared memory etc.
|
||||
}
|
||||
return block_val;
|
||||
}
|
||||
|
||||
// Helper function to call blockReduce with default arguments
|
||||
template<reduction_func_t warp_reduction>
|
||||
__device__ inline float blockReduce(float val) {
|
||||
return blockReduce<warp_reduction>(val, false, 0.0f);
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// checking utils
|
||||
|
||||
|
||||
@@ -99,7 +99,7 @@ int main(int argc, char **argv) {
|
||||
cudaCheck(cudaSetDevice(deviceIdx));
|
||||
|
||||
// create host memory of random numbers
|
||||
float* probs = make_random_float(B * T * V);
|
||||
float* probs = make_random_float_01(B * T * V);
|
||||
int* targets = make_random_int(B * T, V);
|
||||
float* dlosses = make_random_float(B * T);
|
||||
float* dlogits = make_zeros_float(B * T * V);
|
||||
|
||||
@@ -133,7 +133,7 @@ __global__ void fused_residual_forward2(floatX* residual, floatX* normed, floatX
|
||||
for(int c = 0; c < C; ++c) {
|
||||
float out = (float)inp1[c] + (float)inp2[c];
|
||||
m += out;
|
||||
residual[c] = out;
|
||||
residual[c] = (floatX)out;
|
||||
}
|
||||
|
||||
m = m / C;
|
||||
@@ -149,11 +149,11 @@ __global__ void fused_residual_forward2(floatX* residual, floatX* normed, floatX
|
||||
for (int c = 0; c < C; c++) {
|
||||
float n = (s * ((float)residual[c] - m)); // normalized output
|
||||
float o = n * (float)weight[c] + (float)bias[c]; // scale and shift it
|
||||
normed[c] = o; // write
|
||||
normed[c] = (floatX)o; // write
|
||||
}
|
||||
// cache the mean and rstd for the backward pass later
|
||||
mean[idx] = m;
|
||||
rstd[idx] = s;
|
||||
mean[idx] = (floatX)m;
|
||||
rstd[idx] = (floatX)s;
|
||||
}
|
||||
|
||||
// handle one token per warp for coalesced access
|
||||
@@ -232,7 +232,7 @@ __global__ void fused_residual_forward_kernel4(floatX* residual, floatX* normed,
|
||||
const x128 in2 = load128cs(inp2 + c);
|
||||
x128 out;
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
out[k] = (float)in1[k] + (float)in2[k];
|
||||
out[k] = (floatX)((float)in1[k] + (float)in2[k]);
|
||||
sum += (float)out[k];
|
||||
sum_sq += (float)out[k] * (float)out[k];
|
||||
}
|
||||
@@ -309,7 +309,7 @@ __global__ void fused_residual_forward_kernel5(floatX* residual, floatX* normed,
|
||||
const x128 in2 = load128cs(inp2 + c);
|
||||
x128 out;
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
out[k] = (float)in1[k] + (float)in2[k];
|
||||
out[k] = (floatX)((float)in1[k] + (float)in2[k]);
|
||||
sum += (float)out[k];
|
||||
}
|
||||
store128cs(residual + c, out);
|
||||
@@ -372,8 +372,8 @@ __global__ void fused_residual_forward_kernel6(floatX* residual, floatX* normed,
|
||||
// weights and biases are shared among all tokens
|
||||
x128* s_weight = reinterpret_cast<x128*>(params);
|
||||
x128* s_bias = reinterpret_cast<x128*>(params + C * sizeof(floatX));
|
||||
// residual output (input to layernorm) is indpendent for each sub-block indicates by threadIdx.z
|
||||
x128* s_res = reinterpret_cast<x128*>(params + (2 + threadIdx.z) * C * sizeof(floatX) );
|
||||
// residual output (input to layernorm) is independent for each sub-block indicates by threadIdx.z
|
||||
x128* s_res = reinterpret_cast<x128*>(params + (2 + threadIdx.z) * C * sizeof(floatX));
|
||||
// similarly, each sub-block needs its own reduction buffers
|
||||
float* s_mean = reinterpret_cast<float*>(params + (2 + blockDim.z) * C * sizeof(floatX) + threadIdx.z * 32 * sizeof(float));
|
||||
float* s_var = reinterpret_cast<float*>(params + (2 + blockDim.z) * C * sizeof(floatX) + 32 * sizeof(float) * (blockDim.z + threadIdx.z));
|
||||
@@ -385,10 +385,10 @@ __global__ void fused_residual_forward_kernel6(floatX* residual, floatX* normed,
|
||||
s_weight[c / x128::size] = load128(weight + c);
|
||||
s_bias[c / x128::size] = load128(bias + c);
|
||||
}
|
||||
|
||||
// the block-level reductions will cause sync before the first time we read these
|
||||
// => no syncthreads needed here
|
||||
|
||||
|
||||
// loop over all tokens
|
||||
for(int tidx = blockIdx.x * blockDim.z + threadIdx.z; tidx < N; tidx += gridDim.x * blockDim.z) {
|
||||
// adjust pointers to current token
|
||||
|
||||
+87
-1
@@ -16,6 +16,7 @@ nvcc -O3 --use_fast_math global_norm.cu -o global_norm
|
||||
#define ENABLE_BF16
|
||||
#include "common.h"
|
||||
|
||||
cudaDeviceProp deviceProp;
|
||||
|
||||
float global_norm_cpu(const float* data, size_t count) {
|
||||
// accumulate in double so we have an accurate numerical reference
|
||||
@@ -89,6 +90,54 @@ __global__ void norm_kernel2(float* out, const T* data, size_t count) {
|
||||
}
|
||||
}
|
||||
|
||||
template<class T>
|
||||
__global__ void norm_kernel3(float* out, const T* data, size_t count) {
|
||||
size_t index = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
size_t grid_width = blockDim.x * gridDim.x;
|
||||
float accumulator = 0.f;
|
||||
for(size_t i = index; i < count; i += grid_width) {
|
||||
accumulator += (float)data[i] * (float)data[i];
|
||||
}
|
||||
// block-level reduce
|
||||
float block_sum = blockReduce<warpReduceSum>(accumulator);
|
||||
if(threadIdx.x == 0) {
|
||||
atomicAdd(out, block_sum);
|
||||
}
|
||||
}
|
||||
|
||||
// Same as kernel3 but without atomic adds -> this allows us to have determinism due to the
|
||||
// non associativity of floating point operations. Roughly same performance as kernel3.
|
||||
template<class T>
|
||||
__global__ void norm_kernel4(float* out, const T* data, size_t count) {
|
||||
size_t index = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
size_t grid_width = blockDim.x * gridDim.x;
|
||||
float accumulator = 0.f;
|
||||
for(size_t i = index; i < count; i += grid_width) {
|
||||
accumulator += (float)data[i] * (float)data[i];
|
||||
}
|
||||
// block-level reduce
|
||||
float block_sum = blockReduce<warpReduceSum>(accumulator);
|
||||
// each block accumulates its partial sum to out[blockIdx.x]
|
||||
// we want to avoid using atomic add here so we combine this kernel with the aggregate kernel call
|
||||
// that sums up the partial block sums
|
||||
if(threadIdx.x == 0) {
|
||||
out[blockIdx.x] = block_sum;
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void global_norm_aggregate_kernel(float* out, size_t count) {
|
||||
size_t index = threadIdx.x;
|
||||
// grab block sums from the previous kernel, use 0. as the neutral sum element
|
||||
float block_sum = (index < count) ? out[index] : 0.f;
|
||||
float sum = blockReduce<warpReduceSum>(block_sum);
|
||||
if(threadIdx.x == 0) {
|
||||
out[0] = sum; // out[0] ends up with the final norm squared
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
template<typename T>
|
||||
void global_norm1(float* out, const T* values, size_t count, int block_size) {
|
||||
// launch just enough blocks to fill the grid. deliberately no DIV_CEIL.
|
||||
@@ -111,17 +160,54 @@ void global_norm2(float* out, const T* values, size_t count, int block_size) {
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
void global_norm3(float* out, const T* values, size_t count, int block_size) {
|
||||
// launch just enough blocks to fill the grid. deliberately no DIV_CEIL.
|
||||
// having one block less than possible is a tiny performance hit, having
|
||||
// one block too many is catastrophic, since it only can start once all the other
|
||||
// blocks finish. anyway, I think cuda_threads_per_SM should be a multiple of 512
|
||||
// on all gpus, so the division really is going to be exact.
|
||||
const int grid_size = deviceProp.maxThreadsPerMultiProcessor * deviceProp.multiProcessorCount / block_size;
|
||||
assert(grid_size > 0); // gives a better error than letting the call below fail
|
||||
norm_kernel3<<<grid_size, block_size>>>(out, values, count);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
void global_norm4(float* out, const T* values, size_t count, int block_size) {
|
||||
if (block_size <= 64) {
|
||||
block_size = 128; // to avoid triggering the assert below
|
||||
}
|
||||
// launch just enough blocks to fill the grid. deliberately no DIV_CEIL.
|
||||
// having one block less than possible is a tiny performance hit, having
|
||||
// one block too many is catastrophic, since it only can start once all the other
|
||||
// blocks finish. anyway, I think cuda_threads_per_SM should be a multiple of 512
|
||||
// on all gpus, so the division really is going to be exact.
|
||||
const int grid_size = deviceProp.maxThreadsPerMultiProcessor * deviceProp.multiProcessorCount / block_size;
|
||||
assert(grid_size > 0); // gives a better error than letting the call below fail
|
||||
assert(grid_size < 1024); // we want to later accumulate the block sums in a single block
|
||||
norm_kernel4<<<grid_size, block_size>>>(out, values, count);
|
||||
cudaCheck(cudaGetLastError());
|
||||
global_norm_aggregate_kernel<<<1, 1024>>>(out, grid_size);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void global_norm(int kernel_num, float* out, const floatX* values, size_t count, int block_size) {
|
||||
switch (kernel_num) {
|
||||
case 1:
|
||||
return global_norm1(out, values, count, block_size);
|
||||
case 2:
|
||||
return global_norm2(out, values, count, block_size);
|
||||
case 3:
|
||||
return global_norm3(out, values, count, block_size);
|
||||
case 4:
|
||||
return global_norm4(out, values, count, block_size);
|
||||
}
|
||||
}
|
||||
|
||||
int main(int argc, const char **argv) {
|
||||
setup_main();
|
||||
cudaGetDeviceProperties(&deviceProp, 0);
|
||||
|
||||
int C = 768;
|
||||
int L = 12;
|
||||
@@ -148,7 +234,7 @@ int main(int argc, const char **argv) {
|
||||
// move to GPU
|
||||
float* d_out;
|
||||
floatX* d_inp;
|
||||
cudaCheck(cudaMalloc(&d_out, sizeof(float)));
|
||||
cudaCheck(cudaMalloc(&d_out, 1024 * sizeof(float))); // 1024 needed for kernel 4
|
||||
cudaCheck(cudaMalloc(&d_inp, num_params * sizeof(floatX)));
|
||||
cudaCheck(memcpy_convert(d_inp, inp, num_params));
|
||||
|
||||
|
||||
@@ -874,7 +874,6 @@ __global__ void layernorm_backward_kernel9(floatX* dinp, floatX* dweight, floatX
|
||||
}
|
||||
__trap(); // prefer to crash here than run into a deadlock later on
|
||||
}
|
||||
constexpr int WARP_SIZE = 32;
|
||||
int BLOCK_SIZE = blockDim.x;
|
||||
int warpsInBlock = BLOCK_SIZE / WARP_SIZE; //number of warps in block
|
||||
extern __shared__ float shared[]; // size = 2 * C + 1
|
||||
@@ -1059,7 +1058,6 @@ layernorm_backward_kernel10(floatX* dinp, floatX* dweight, floatX* dbias, float*
|
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const floatX* dout, const floatX* inp, const floatX* weight,
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const floatX* mean, const floatX* rstd,
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int B, int T, int C) {
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constexpr int WARP_SIZE = 32;
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int BLOCK_SIZE = blockDim.x;
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int warpsInBlock = BLOCK_SIZE / WARP_SIZE; //number of warps in block
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extern __shared__ float shared[]; // size = 2 * C + 1
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@@ -28,7 +28,6 @@ verstion 5 allocates blocks per row instead of warps per row, same alg as 4 othe
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#include <cooperative_groups.h>
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#include <cooperative_groups/reduce.h>
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#include "common.h"
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// ----------------------------------------------------------------------------
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// CPU code reference
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@@ -290,7 +289,7 @@ __global__ void layernorm_forward_kernel5(float* __restrict__ out, float* __rest
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int num_warps = blockDim.x / 32;
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int warp_id = threadIdx.x / 32;
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int lane_id = threadIdx.x % 32;
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int idx = blockIdx.x; // simpoy one block per row
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int idx = blockIdx.x; // simply one block per row
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// the row of input that this group of threads is responsible for
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const float* x = inp + idx * C;
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// thread coarsening through the row, reduce the sum in series
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@@ -337,6 +336,82 @@ __global__ void layernorm_forward_kernel5(float* __restrict__ out, float* __rest
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}
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}
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// Inspired by `fused_residual_forward_kernel5` in fused_residual_forward.cu
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__global__ void layernorm_forward_kernel6(float* __restrict__ out, float* __restrict__ mean, float* __restrict__ rstd,
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const float* __restrict__ inp, const float* __restrict__ weight,
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const float* __restrict__ bias, int N, int C) {
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assert(blockDim.x == WARP_SIZE);
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// load weights and biases into shared memory
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// do this before we allow any threads to exit!
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extern __shared__ char params[];
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// load128/store128 sometimes generated multiple instructions when the types here were floatX*, so
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// let's keep everything as x128
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x128* s_weight = reinterpret_cast<x128*>(params);
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x128* s_bias = reinterpret_cast<x128*>(params) + (C / x128::size);
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x128* s_in = reinterpret_cast<x128*>(params) + ((2 + threadIdx.y) * C / x128::size);
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int sidx = (threadIdx.x + WARP_SIZE * threadIdx.y) * x128::size;
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for(int i = sidx; i < C; i += blockDim.y * WARP_SIZE * x128::size) {
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s_weight[i/x128::size] = load128(weight + i);
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s_bias[i/x128::size] = load128(bias + i);
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}
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__syncthreads();
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int idx = blockIdx.x * blockDim.y + threadIdx.y;
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if(idx >= N) { return; } // guard
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// adjust pointers to current token
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inp += idx * C;
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out += idx * C;
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const float eps = 1e-5f;
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float sum = 0.0f;
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for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
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const x128 in_data = load128cs(inp + c);
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for(int k = 0; k < x128::size; ++k) {
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sum += (float)in_data[k];
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}
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s_in[c / x128::size] = in_data;
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}
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sum = warpReduceSum(sum);
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float m = sum / C;
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float v = 0.f;
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for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
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const x128 in_data = s_in[c / x128::size];
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for(int k = 0; k < x128::size; ++k) {
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v += ((float)in_data[k] - m) * ((float)in_data[k] - m);
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}
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}
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v = warpReduceSum(v) / C;
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float s = rsqrtf(v + eps);
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for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
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const x128 in_data = s_in[c / x128::size];
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const x128 w = s_weight[c / x128::size];
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const x128 b = s_bias[c / x128::size];
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x128 out_data;
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for(int k = 0; k < x128::size; ++k) {
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float n = s * ((float)in_data[k] - m); // normalized output
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float o = n * (float)w[k] + (float)b[k]; // scale and shift it
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out_data[k] = o;
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}
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store128cs(out + c, out_data);
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}
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// cache the mean and rstd for the backward pass later
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if(threadIdx.x == 0 && mean != nullptr) {
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__stcs(mean + idx, m);
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}
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// store the rstd, no need to cache it
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if(threadIdx.x == 0 && rstd != nullptr) {
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__stcs(rstd + idx, s);
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}
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}
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// ----------------------------------------------------------------------------
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// kernel launcher
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@@ -356,9 +431,9 @@ void layernorm_forward2(float* out, float* mean, float* rstd,
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const int block_size) {
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int N = B * T;
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// in mean and rstd, threads cooperate within blocks via reductions
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mean_kernel<<<B * T, block_size, block_size * sizeof(float)>>>(mean, inp, N, C, block_size);
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mean_kernel<<<N, block_size, block_size * sizeof(float)>>>(mean, inp, N, C, block_size);
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cudaCheck(cudaGetLastError());
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rstd_kernel<<<B * T, block_size, block_size * sizeof(float)>>>(rstd, inp, mean, N, C, block_size);
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rstd_kernel<<<N, block_size, block_size * sizeof(float)>>>(rstd, inp, mean, N, C, block_size);
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cudaCheck(cudaGetLastError());
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// in the normalization, everything just gets flattened out
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const int block_size2 = 256;
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@@ -394,12 +469,38 @@ void layernorm_forward5(float* out, float* mean, float* rstd,
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int B, int T, int C,
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const int block_size) {
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assert(block_size % 32 == 0);
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assert(block_size <= 1024);
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const int N = B * T;
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const int grid_size = N;
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layernorm_forward_kernel5<<<grid_size, block_size>>>(out, mean, rstd, inp, weight, bias, N, C);
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cudaCheck(cudaGetLastError());
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}
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void layernorm_forward6(float* out, float* mean, float* rstd,
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const float* inp, const float* weight, const float* bias,
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int B, int T, int C,
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int block_size) {
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assert(block_size % 32 == 0);
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const int N = B * T;
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int block_y = block_size / WARP_SIZE;
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const int grid_size = ceil_div(N, block_y);
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size_t smem = (2 + block_y) * C * sizeof(float);
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// in order to use more than 48 KiB of smem, need to call cudaFuncSetAttribute
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// this may fail, in which case we fall back to the smem free implementation.
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cudaCheck(cudaGetLastError());
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auto status = cudaFuncSetAttribute(layernorm_forward_kernel6, cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
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cudaGetLastError();
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if (status == cudaSuccess) {
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layernorm_forward_kernel6<<<grid_size, dim3(32, block_y), smem>>>(out, mean, rstd, inp, weight, bias, N, C);
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} else {
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const int grid_size = N;
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// fall back to the version without shared memory
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layernorm_forward_kernel5<<<grid_size, block_size>>>(out, mean, rstd, inp, weight, bias, N, C);
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}
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cudaCheck(cudaGetLastError());
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}
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// kernel version dispatch
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void layernorm_forward(int kernel_num,
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float* out, float* mean, float* rstd,
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@@ -422,6 +523,9 @@ void layernorm_forward(int kernel_num,
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case 5:
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layernorm_forward5(out, mean, rstd, inp, weight, bias, B, T, C, block_size);
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break;
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case 6:
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layernorm_forward6(out, mean, rstd, inp, weight, bias, B, T, C, block_size);
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break;
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default:
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printf("Invalid kernel number\n");
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exit(1);
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@@ -473,9 +577,6 @@ int main(int argc, char **argv) {
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printf("Using kernel %d\n", kernel_num);
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int block_sizes[] = {32, 64, 128, 256, 512, 1024};
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float* out_gpu = (float*)malloc(B * T * C * sizeof(float));
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float* mean_gpu = (float*)malloc(B * T * sizeof(float));
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float* rstd_gpu = (float*)malloc(B * T * sizeof(float));
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layernorm_forward_cpu(out, mean, rstd, inp, weight, bias, B, T, C);
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@@ -268,12 +268,14 @@ int main(int argc, char **argv) {
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free(dout);
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free(inp);
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free(weight);
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free(ones);
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cudaCheck(cudaFree(d_dinp));
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cudaCheck(cudaFree(d_dweight));
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cudaCheck(cudaFree(d_dbias));
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cudaCheck(cudaFree(d_dout));
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cudaCheck(cudaFree(d_inp));
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cudaCheck(cudaFree(d_weight));
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cudaCheck(cudaFree(d_ones));
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cublasCheck(cublasDestroy(cublas_handle));
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return 0;
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@@ -2,7 +2,7 @@
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Kernels for matmul backward pass bias only.
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Compile example:
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nvcc -O3 -lcublas -lcublasLt matmul_backward_bias.cu -lineinfo -o matmul_backward_bias
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nvcc -O3 -lcublas -lcublasLt -std=c++17 matmul_backward_bias.cu -lineinfo -o matmul_backward_bias
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./matmul_backward_bias 1
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./matmul_backward_bias 2
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@@ -92,7 +92,7 @@ __global__ void matmul_backward_bias_kernel1(floatX* dbias, const floatX* dout,
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}
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// write the final result (at thread 0) to global memory
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if (tid == 0) {
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dbias[o] = (float)dbias[o] + shared[0];
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dbias[o] = (floatX)((float)dbias[o] + shared[0]);
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}
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}
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@@ -116,7 +116,7 @@ __global__ void matmul_backward_bias_kernel2(floatX* dbias, const floatX* dout,
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sum = cg::reduce(warp, sum, cg::plus<float>{});
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// write the result to output (global memory)
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if(warp.thread_rank() == 0) {
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dbias[idx] += sum;
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dbias[idx] = (float)dbias[idx] + sum;
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}
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}
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@@ -132,12 +132,13 @@ __global__ void matmul_backward_bias_kernel3(floatX* dbias, const floatX* dout,
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int warp_id = threadIdx.x / 32;
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int lane_id = threadIdx.x % 32;
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int idx = blockIdx.x; // simply one block per row
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// round 1: thread coarsening to reduce the problem size from B*T to 32
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// round 1: thread coarsening to reduce the problem size from B*T to block_size
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float thread_sum = 0.0f;
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for(int i = threadIdx.x; i < BT; i += blockDim.x) {
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thread_sum += (float)dout[i * OC + idx];
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}
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// now do a warp-level reduce to get the sum across the 32 threads in each warp
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// reduce the problem size from block_size to block_size/32 i.e. `num_warps`
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float warp_sum = cg::reduce(warp, thread_sum, cg::plus<float>{});
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// store the warp sum in shared memory (we could have lane_id == 0 guard but not needed)
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shared_sum[warp_id] = warp_sum;
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@@ -148,7 +149,7 @@ __global__ void matmul_backward_bias_kernel3(floatX* dbias, const floatX* dout,
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float block_sum = cg::reduce(warp, warp_sum, cg::plus<float>{}); // sum(x)
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// write the result to output (global memory)
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if(threadIdx.x == 0) {
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dbias[idx] += block_sum;
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dbias[idx] = (float)dbias[idx] + block_sum;
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}
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}
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@@ -167,7 +168,7 @@ __global__ void matmul_backward_bias_kernel4(floatX* dbias, const floatX* dout,
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const int vstep = blockDim.x / warpSize; // number of warps in a block, e.g. 4
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// pointer to the start of the column for one lane of threads
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// so e.g. 4 threads (of the same lane_id) will reduce this one column
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// so e.g. 4 (`vstep`) threads (of the same lane_id) will reduce this one column
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const floatX* dout_col = dout + tl + lane_id;
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// column reductions by looping through the rows
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@@ -188,7 +189,7 @@ __global__ void matmul_backward_bias_kernel4(floatX* dbias, const floatX* dout,
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for (int j = 0; j < vstep; j++) {
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dout_sum += smem[lane_id + j * warpSize];
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||||
}
|
||||
dbias[tl + lane_id] += dout_sum;
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||||
dbias[tl + lane_id] = (float)dbias[tl + lane_id] + dout_sum;
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}
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||||
}
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@@ -503,7 +504,7 @@ void matmul_backward_bias7(floatX* dbias, const floatX* dout,
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assert(block_size_y >= x128::size); // part of the kernel assumes this is large enough to avoid loops
|
||||
|
||||
cudaCheck(cudaMemsetAsync(dbias_buffer, 0, OC * sizeof(float)));
|
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cudaCheck(cudaMemset(dbias_buffer, 0, OC * sizeof(float)));
|
||||
matmul_backward_bias_kernel7<<<dim3(grid_size_x, grid_size_y),
|
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dim3(block_size_x, block_size_y), OC_per_warp * sizeof(float)>>>(dbias_buffer, dout, B, T, OC, block_size);
|
||||
cudaCheck(cudaGetLastError());
|
||||
@@ -524,7 +525,7 @@ void matmul_backward_bias8(floatX* dbias, const floatX* dout,
|
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matmul_backward_bias_kernel8<<<dim3(grid_size_x, grid_size_y), block_dim>>>(dbias, dout, B, T, OC, std::bool_constant<false>{});
|
||||
cudaCheck(cudaGetLastError());
|
||||
} else {
|
||||
cudaCheck(cudaMemsetAsync(dbias_buffer, 0, OC * sizeof(float)));
|
||||
cudaCheck(cudaMemset(dbias_buffer, 0, OC * sizeof(float)));
|
||||
matmul_backward_bias_kernel8<<<dim3(grid_size_x, grid_size_y), block_dim>>>(dbias_buffer, dout, B, T, OC, std::bool_constant<true>{});
|
||||
cudaCheck(cudaGetLastError());
|
||||
cast_and_add_kernel<<<ceil_div(OC, 256), 256, 0>>>(dbias, dbias_buffer, OC);
|
||||
@@ -661,6 +662,7 @@ int main(int argc, char **argv) {
|
||||
// cleanups
|
||||
free(dbias);
|
||||
free(dout);
|
||||
cudaCheck(cudaFree(dbias_buffer));
|
||||
cudaCheck(cudaFree(d_dbias));
|
||||
cudaCheck(cudaFree(d_dout));
|
||||
|
||||
|
||||
+101
-1
@@ -84,6 +84,88 @@ __global__ void add_bias(float* out, const float* bias, int B, int T, int OC) {
|
||||
}
|
||||
}
|
||||
|
||||
// kernel 4: semi-efficient handwritten kernel
|
||||
// see trimat_forward.cu for some intermediate development steps
|
||||
__device__ float4 ld_vec(const float* address) {
|
||||
return *reinterpret_cast<const float4*>(address);
|
||||
}
|
||||
|
||||
__device__ void st_vec(float* address, float4 val) {
|
||||
*reinterpret_cast<float4*>(address) = val;
|
||||
}
|
||||
|
||||
__global__ void __launch_bounds__(16*16) matmul_forward_kernel4(float* out,
|
||||
const float* inp, const float* weight, const float* bias,
|
||||
int C, int OC) {
|
||||
// out is (B,T,OC). OC is short for "output channels", e.g. OC = 4 * C
|
||||
// inp is (B,T,C), weight is (OC, C), bias is (OC)
|
||||
// each thread handles 8x8 elements; each block 128 by 128 elements.
|
||||
int oc = 8*(blockIdx.y * blockDim.y + threadIdx.y);
|
||||
|
||||
// buffers to cache chunks of the input matrices
|
||||
__shared__ float lhs_s[128][32];
|
||||
__shared__ float rhs_s[128][32];
|
||||
|
||||
// adjust our pointers for the current block
|
||||
inp += 128 * blockIdx.x * C;
|
||||
weight += 128 * blockIdx.y * C;
|
||||
out += 128 * blockIdx.x * OC + 128 * blockIdx.y;
|
||||
|
||||
float vals[8][8] = {};
|
||||
if(bias != NULL) {
|
||||
for (int i = 0; i < 8; i++) {
|
||||
for (int j = 0; j < 8; j += 4) {
|
||||
float4 b = ld_vec(bias + oc + j);
|
||||
vals[i][j+0] = b.x;
|
||||
vals[i][j+1] = b.y;
|
||||
vals[i][j+2] = b.z;
|
||||
vals[i][j+3] = b.w;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
int si_start = 4*(16 * threadIdx.y + threadIdx.x);
|
||||
for (int so = 0; so < C; so += 32) {
|
||||
__syncthreads();
|
||||
int xmod8 = threadIdx.x % 8;
|
||||
int xby8 = threadIdx.x / 8;
|
||||
int xo = 4 * xmod8;
|
||||
for(int y = 2 * threadIdx.y + xby8; y < 128; y += 32) {
|
||||
st_vec(&lhs_s[y][xo], ld_vec(inp + y * C + so + xo));
|
||||
st_vec(&rhs_s[y][xo], ld_vec(weight + y * C + so + xo));
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
for (int si = si_start; si < si_start + 32; si += 4) {
|
||||
float4 rhs[8];
|
||||
for (int u = 0; u < 8; ++u) {
|
||||
rhs[u] = ld_vec(&rhs_s[u + 8 * threadIdx.y][si % 32]);
|
||||
}
|
||||
|
||||
for (int ii = 0; ii < 8; ++ii) {
|
||||
float4 lhs = ld_vec(&lhs_s[ii + 8 * threadIdx.x][si % 32]);
|
||||
for (int ji = 0; ji < 8; ++ji) {
|
||||
vals[ii][ji] += lhs.x * rhs[ji].x;
|
||||
vals[ii][ji] += lhs.y * rhs[ji].y;
|
||||
vals[ii][ji] += lhs.z * rhs[ji].z;
|
||||
vals[ii][ji] += lhs.w * rhs[ji].w;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
for (int j = 0; j < 8; j += 4) {
|
||||
float4 result;
|
||||
result.x = vals[i][j + 0];
|
||||
result.y = vals[i][j + 1];
|
||||
result.z = vals[i][j + 2];
|
||||
result.w = vals[i][j + 3];
|
||||
st_vec(out + (8*threadIdx.x+i) * OC + 8*threadIdx.y + j, result);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launcher
|
||||
|
||||
@@ -218,6 +300,21 @@ void matmul_forward3(float* out,
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(biasLayout));
|
||||
}
|
||||
|
||||
// handwritten, relatively efficient non-tensorcore matmul kernel
|
||||
void matmul_forward4(float* out,
|
||||
const float* inp, const float* weight, const float* bias,
|
||||
int B, int T, int C, int OC,
|
||||
int sqrt_block_size) {
|
||||
// out is (B,T,OC). OC is short for "output channels", e.g. OC = 4 * C
|
||||
// inp is (B,T,C), weight is (OC, C), bias is (OC)
|
||||
sqrt_block_size = 16;
|
||||
|
||||
dim3 gridDim(ceil_div(B * T, 8*sqrt_block_size), ceil_div(OC, 8*sqrt_block_size));
|
||||
dim3 blockDim(sqrt_block_size, sqrt_block_size);
|
||||
matmul_forward_kernel4<<<gridDim, blockDim>>>(out, inp, weight, bias, C, OC);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
// kernel version dispatch
|
||||
void matmul_forward(int kernel_num,
|
||||
float* out,
|
||||
@@ -234,6 +331,9 @@ void matmul_forward(int kernel_num,
|
||||
case 3:
|
||||
matmul_forward3(out, inp, weight, bias, B, T, C, OC);
|
||||
break;
|
||||
case 4:
|
||||
matmul_forward4(out, inp, weight, bias, B, T, C, OC, sqrt_block_size);
|
||||
break;
|
||||
default:
|
||||
printf("Invalid kernel number\n");
|
||||
exit(1);
|
||||
@@ -245,7 +345,7 @@ void matmul_forward(int kernel_num,
|
||||
int main(int argc, char **argv) {
|
||||
srand(0);
|
||||
|
||||
int B = 8;
|
||||
int B = 32;
|
||||
int T = 1024;
|
||||
int C = 768;
|
||||
int OC = 768 * 4; // expansion of 4, e.g. in the MLP
|
||||
|
||||
@@ -193,5 +193,6 @@ int main(int argc, char **argv) {
|
||||
|
||||
free(all_reduce_buffer_host);
|
||||
cudaCheck(cudaFree(all_reduce_buffer));
|
||||
cudaCheck(cudaFree(all_reduce_buffer_recv));
|
||||
multi_gpu_config_free(&multi_gpu_config);
|
||||
}
|
||||
|
||||
@@ -0,0 +1,181 @@
|
||||
/*
|
||||
Kernels to demonstrate permute operation.
|
||||
|
||||
Compile example:
|
||||
nvcc -O3 permute.cu -o permute
|
||||
|
||||
The goal is to permute a 4D matrix from its original shape (dim1, dim2, dim3, dim4) to a new shape (dim4, dim3, dim1, dim2).
|
||||
|
||||
Before permutation, we need to understand how to access elements in a flattened (linear) form of the matrix.
|
||||
|
||||
Given:
|
||||
|
||||
dim1 = size of the 1st dimension
|
||||
dim2 = size of the 2nd dimension
|
||||
dim3 = size of the 3rd dimension
|
||||
dim4 = size of the 4th dimension
|
||||
|
||||
For any element in a 4D matrix at position (i1, i2, i3, i4), where:
|
||||
|
||||
i1 is the index in dimension 1
|
||||
i2 is the index in dimension 2
|
||||
i3 is the index in dimension 3
|
||||
i4 is the index in dimension 4
|
||||
|
||||
If you find it challenging to calculate the indices i1, i2, i3, and i4, observe the pattern in the index calculations.
|
||||
Initially, it might take some time to grasp, but with practice, you'll develop a mental model for it.
|
||||
|
||||
To calculate the indices, use the following formulas:
|
||||
|
||||
i1 = (idx / (dim2 * dim3 * dim4)) % dim1;
|
||||
i2 = (idx / (dim3 * dim4)) % dim2;
|
||||
i3 = (idx / dim4) % dim3;
|
||||
i4 = idx % dim4;
|
||||
|
||||
Pattern Explanation:
|
||||
To find the index for any dimension, divide the thread ID (idx) by the product of all subsequent dimensions.
|
||||
Then, perform modulo operation with the current dimension.
|
||||
|
||||
|
||||
|
||||
The linear index in a flattened 1D array is calculated as:
|
||||
linear_idx = i1 × ( dim2 × dim3 × dim4 ) + i2 × ( dim3 × dim4 ) + i3 × dim4 + i4
|
||||
This linear index uniquely identifies the position of the element in the 1D array.
|
||||
|
||||
To permute the matrix, we need to rearrange the indices according to the new shape.
|
||||
In this case, we are permuting from (dim1, dim2, dim3, dim4) to (dim4, dim3, dim1, dim2).
|
||||
|
||||
The new dimension post permutation will be as follows:
|
||||
|
||||
dim1 becomes the new 3rd dimension.
|
||||
dim2 becomes the new 4th dimension.
|
||||
dim3 becomes the new 2nd dimension.
|
||||
dim4 becomes the new 1st dimension.
|
||||
|
||||
permuted_idx = i4 * (dim3 * dim1 * dim2) + i3 * (dim1 * dim2) + i1 * dim2 + i2;
|
||||
|
||||
Here's how this works:
|
||||
|
||||
i4 * (dim3 * dim1 * dim2): This accounts for how many complete dim3 × dim1 × dim2 blocks fit before the current i4 block.
|
||||
i3 * (dim1 * dim2): This accounts for the offset within the current i4 block, specifying which i3 block we are in.
|
||||
i1 * dim2: This accounts for the offset within the current i3 block, specifying which i1 block we are in.
|
||||
i2: This gives the offset within the current i1 block.
|
||||
|
||||
Lastly at the end we store the current value at idx index of the original value to the permuted index in the permuted_matrix.
|
||||
|
||||
|
||||
--------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Similarly we can follow the above approach to permute matrices of any dimensions.
|
||||
|
||||
*/
|
||||
|
||||
|
||||
#include <cuda_runtime.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <cmath>
|
||||
|
||||
#include "common.h"
|
||||
|
||||
// CPU function to permute a 4D matrix
|
||||
void permute_cpu(const float* matrix, float* out_matrix, int dim1, int dim2, int dim3, int dim4) {
|
||||
int total_threads = dim1 * dim2 * dim3 * dim4;
|
||||
|
||||
for (int idx = 0; idx < total_threads; idx++) {
|
||||
// Calculate the 4D indices from the linear index
|
||||
int i1 = (idx / (dim2 * dim3 * dim4)) % dim1;
|
||||
int i2 = (idx / (dim3 * dim4)) % dim2;
|
||||
int i3 = (idx / dim4) % dim3;
|
||||
int i4 = idx % dim4;
|
||||
|
||||
// Compute the new index for the permuted matrix
|
||||
// Transpose from (dim1, dim2, dim3, dim4) to (dim4, dim3, dim1, dim2)
|
||||
int permuted_idx = i4 * (dim3 * dim1 * dim2) + i3 * (dim1 * dim2) + i1 * dim2 + i2;
|
||||
out_matrix[permuted_idx] = matrix[idx];
|
||||
}
|
||||
}
|
||||
|
||||
// CUDA kernel to permute a 4D matrix
|
||||
__global__ void permute_kernel(const float* matrix, float* out_matrix, int dim1, int dim2, int dim3, int dim4) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
|
||||
// Ensure index is within bounds
|
||||
if (idx < dim1 * dim2 * dim3 * dim4) {
|
||||
// Calculate the 4D indices from the linear index
|
||||
int i1 = (idx / (dim2 * dim3 * dim4)) % dim1;
|
||||
int i2 = (idx / (dim3 * dim4)) % dim2;
|
||||
int i3 = (idx / dim4) % dim3;
|
||||
int i4 = idx % dim4;
|
||||
|
||||
// Compute the new index for the permuted matrix
|
||||
// Transpose from (dim1, dim2, dim3, dim4) to (dim4, dim3, dim1, dim2)
|
||||
int permuted_idx = i4 * (dim3 * dim1 * dim2) + i3 * (dim1 * dim2) + i1 * dim2 + i2;
|
||||
out_matrix[permuted_idx] = matrix[idx];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
int main() {
|
||||
int dim_1 = 24;
|
||||
int dim_2 = 42;
|
||||
int dim_3 = 20;
|
||||
int dim_4 = 32;
|
||||
|
||||
// Set up the device
|
||||
int deviceIdx = 0;
|
||||
cudaSetDevice(deviceIdx);
|
||||
cudaDeviceProp deviceProp;
|
||||
cudaGetDeviceProperties(&deviceProp, deviceIdx);
|
||||
printf("Device %d: %s\n", deviceIdx, deviceProp.name);
|
||||
|
||||
// Allocate host memory
|
||||
float* matrix = make_random_float(dim_1 * dim_2 * dim_3 * dim_4);
|
||||
float* permuted_matrix = (float*)malloc(dim_1 * dim_2 * dim_3 * dim_4 * sizeof(float));
|
||||
|
||||
// Initialize the matrix with random values
|
||||
|
||||
// Allocate device memory
|
||||
float *d_matrix, *d_permuted_matrix;
|
||||
cudaMalloc(&d_matrix, dim_1 * dim_2 * dim_3 * dim_4 * sizeof(float));
|
||||
cudaMalloc(&d_permuted_matrix, dim_1 * dim_2 * dim_3 * dim_4 * sizeof(float));
|
||||
|
||||
// Copy matrix from host to device
|
||||
cudaMemcpy(d_matrix, matrix, dim_1 * dim_2 * dim_3 * dim_4 * sizeof(float), cudaMemcpyHostToDevice);
|
||||
|
||||
// Perform permutation on CPU
|
||||
clock_t start = clock();
|
||||
permute_cpu(matrix, permuted_matrix, dim_1, dim_2, dim_3, dim_4);
|
||||
clock_t end = clock();
|
||||
double elapsed_time_cpu = (double)(end - start) / CLOCKS_PER_SEC;
|
||||
|
||||
// Define block and grid sizes
|
||||
dim3 blockSize(256);
|
||||
int totalThreads = dim_1 * dim_2 * dim_3 * dim_4;
|
||||
int gridSize = (totalThreads + blockSize.x - 1) / blockSize.x; // Compute grid size
|
||||
|
||||
// Launch CUDA kernel to perform permutation
|
||||
permute_kernel<<<gridSize, blockSize>>>(d_matrix, d_permuted_matrix, dim_1, dim_2, dim_3, dim_4);
|
||||
cudaDeviceSynchronize(); // Ensure kernel execution is complete
|
||||
|
||||
// Verify results
|
||||
printf("Checking correctness...\n");
|
||||
validate_result(d_permuted_matrix, permuted_matrix, "permuted_matrix", dim_1 * dim_2 * dim_3 * dim_4, 1e-5f);
|
||||
|
||||
printf("All results match.\n\n");
|
||||
// benchmark kernel
|
||||
int repeat_times = 1000;
|
||||
float elapsed_time = benchmark_kernel(repeat_times, permute_kernel,
|
||||
d_matrix, d_permuted_matrix, dim_1, dim_2, dim_3, dim_4
|
||||
);
|
||||
printf("time gpu %.4f ms\n", elapsed_time);
|
||||
printf("time cpu %.4f ms\n", elapsed_time_cpu);
|
||||
|
||||
// Free allocated memory
|
||||
free(matrix);
|
||||
free(permuted_matrix);
|
||||
cudaFree(d_matrix);
|
||||
cudaFree(d_permuted_matrix);
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -99,7 +99,7 @@ int main(int argc, char **argv) {
|
||||
float* out = (float*)malloc(B * T * C * sizeof(float));
|
||||
float* inp1 = make_random_float(B * T * C);
|
||||
float* inp2 = make_random_float(B * T * C);
|
||||
|
||||
|
||||
// move to GPU
|
||||
floatX* d_out;
|
||||
floatX* d_inp1;
|
||||
|
||||
+26
-28
@@ -135,7 +135,6 @@ __global__ void softmax_forward_kernel2(float* out, const float* inp, int N, int
|
||||
maxval = fmaxf(maxval, x[i]);
|
||||
}
|
||||
shared[tid] = maxval;
|
||||
__syncthreads();
|
||||
// reductions
|
||||
for (int stride = block_size / 2; stride >= 1; stride /= 2) {
|
||||
__syncthreads();
|
||||
@@ -157,7 +156,6 @@ __global__ void softmax_forward_kernel2(float* out, const float* inp, int N, int
|
||||
sumval += x[i];
|
||||
}
|
||||
shared[tid] = sumval;
|
||||
__syncthreads();
|
||||
// reductions
|
||||
for (int stride = block_size / 2; stride >= 1; stride /= 2) {
|
||||
__syncthreads();
|
||||
@@ -210,14 +208,13 @@ __global__ void softmax_forward_kernel3(float* out, const float* inp, int N, int
|
||||
for (int i = tid; i < C; i += blockDim.x) {
|
||||
sumval += x[i];
|
||||
}
|
||||
// No need to broadcast sumval since all threads in the warp will have the same value
|
||||
// (due to the fact that we're using __shfl_xor_sync)
|
||||
sumval = warpReduceSum(sumval);
|
||||
|
||||
// Broadcast sumval within the warp
|
||||
float sum = __shfl_sync(0xFFFFFFFF, sumval, 0);
|
||||
|
||||
// Divide the input values by the sum
|
||||
for (int i = tid; i < C; i += blockDim.x) {
|
||||
out[idx * C + i] = x[i] / sum;
|
||||
out[idx * C + i] = x[i] / sumval;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -238,10 +235,9 @@ __global__ void softmax_forward_kernel4(float* out, const float* inp, int N, int
|
||||
// the number of warps per block. recall that blockDim.x is block_size
|
||||
int warpsPerBlock = blockDim.x / 32;
|
||||
|
||||
// shared[] must be allocated to have 2 * warpsPerBlock elements
|
||||
// first half for max values, the second half for sum values
|
||||
float* maxvals = shared;
|
||||
float* sumvals = &shared[warpsPerBlock];
|
||||
// shared[] must be allocated to have warpsPerBlock elements
|
||||
// those will be used for max and sum values
|
||||
float* max_or_sum_storage = shared;
|
||||
|
||||
// one row of inp, i.e. inp[idx, :] of shape (C,)
|
||||
const float* x = inp + idx * C;
|
||||
@@ -255,21 +251,21 @@ __global__ void softmax_forward_kernel4(float* out, const float* inp, int N, int
|
||||
maxval = warpReduceMax(maxval);
|
||||
|
||||
// the 0th thread of each warp writes the maxval of that warp to shared memory
|
||||
if (laneId == 0) maxvals[warpId] = maxval;
|
||||
if (laneId == 0) max_or_sum_storage[warpId] = maxval;
|
||||
__syncthreads();
|
||||
|
||||
// now the 0th thread reduces the maxvals in shared memory, i.e. across warps
|
||||
// now the 0th thread of the block reduces the max values in shared memory, i.e. across warps
|
||||
if (tid == 0) {
|
||||
float val = maxvals[tid];
|
||||
float val = max_or_sum_storage[tid];
|
||||
for (int i = 1; i < warpsPerBlock; i++) {
|
||||
val = fmaxf(val, maxvals[i]);
|
||||
val = fmaxf(val, max_or_sum_storage[i]);
|
||||
}
|
||||
// store the final max in the first position
|
||||
maxvals[0] = val;
|
||||
max_or_sum_storage[0] = val;
|
||||
}
|
||||
__syncthreads();
|
||||
// broadcast the max to all threads
|
||||
float offset = maxvals[0];
|
||||
float offset = max_or_sum_storage[0];
|
||||
|
||||
// compute expf and write the result to global memory
|
||||
for (int i = tid; i < C; i += blockDim.x) {
|
||||
@@ -289,20 +285,20 @@ __global__ void softmax_forward_kernel4(float* out, const float* inp, int N, int
|
||||
sumval = warpReduceSum(sumval);
|
||||
|
||||
// write sumval to shared memory
|
||||
if (laneId == 0) sumvals[warpId] = sumval;
|
||||
if (laneId == 0) max_or_sum_storage[warpId] = sumval;
|
||||
__syncthreads();
|
||||
|
||||
// inter-thread reduction of sum
|
||||
if (tid == 0) {
|
||||
float val = sumvals[tid];
|
||||
float val = max_or_sum_storage[tid];
|
||||
for (int i = 1; i < warpsPerBlock; ++i) {
|
||||
val += sumvals[i];
|
||||
val += max_or_sum_storage[i];
|
||||
}
|
||||
sumvals[0] = val;
|
||||
max_or_sum_storage[0] = val;
|
||||
}
|
||||
__syncthreads();
|
||||
// broadcast the sum to all threads
|
||||
float sum = sumvals[0];
|
||||
float sum = max_or_sum_storage[0];
|
||||
|
||||
// divide the whole row by the sum
|
||||
for (int i = tid; i < C; i += blockDim.x) {
|
||||
@@ -322,12 +318,13 @@ __global__ void softmax_forward_online_kernel1(float* out, const float* inp, int
|
||||
double sum = 0.0;
|
||||
for (int j = 0; j < C; j++) {
|
||||
float maxval_prev = maxval;
|
||||
if (inp_row[j] > maxval) {
|
||||
maxval = inp_row[j];
|
||||
sum = sum * expf(maxval_prev - maxval) + expf(inp_row[j] - maxval);
|
||||
float current_val = inp_row[j];
|
||||
if (current_val > maxval) {
|
||||
maxval = current_val;
|
||||
sum = sum * expf(maxval_prev - maxval) + expf(current_val - maxval);
|
||||
}
|
||||
else {
|
||||
sum += expf(inp_row[j] - maxval);
|
||||
sum += expf(current_val - maxval);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -590,7 +587,8 @@ void softmax_forward3(float* out, const float* inp, int N, int C, int block_size
|
||||
|
||||
void softmax_forward4(float* out, const float* inp, int N, int C, int block_size) {
|
||||
int grid_size = N;
|
||||
size_t shared_mem_size = 2 * block_size / 32 * sizeof(float);
|
||||
// for each warp in the block we need a float that will be used for both maxval and sumval
|
||||
size_t shared_mem_size = block_size / 32 * sizeof(float);
|
||||
softmax_forward_kernel4<<<grid_size, block_size, shared_mem_size>>>(out, inp, N, C);
|
||||
}
|
||||
|
||||
@@ -672,11 +670,10 @@ int main(int argc, char **argv) {
|
||||
const int* outliers = make_random_int(B * T * 3, V);
|
||||
for(int k = 0; k < 3; ++k) {
|
||||
for(int j = 0; j < B * T; ++j) {
|
||||
inp[j * V + outliers[j*3 + k]] *= 20;
|
||||
inp[j * V + outliers[j*3 + k]] *= 20;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// move to GPU
|
||||
float* d_out;
|
||||
float* d_inp;
|
||||
@@ -728,6 +725,7 @@ int main(int argc, char **argv) {
|
||||
// free memory
|
||||
free(out);
|
||||
free(inp);
|
||||
free((void*)outliers);
|
||||
cudaCheck(cudaFree(d_out));
|
||||
cudaCheck(cudaFree(d_inp));
|
||||
|
||||
|
||||
+125
-66
@@ -65,30 +65,38 @@ static float* d_qkvr; // scratch for the cublas kernel
|
||||
// taken from then attention forward pass
|
||||
void trimul_cpu(float* out, const float* inp,
|
||||
int B, int T, int C, int NH) {
|
||||
// inp shape: (B, T, 3, NH, HS)
|
||||
// out shape: (B, NH, T, T)
|
||||
int C3 = C*3;
|
||||
int hs = C / NH; // head size
|
||||
float scale = 1.0 / sqrtf(hs);
|
||||
int HS = C / NH; // head size
|
||||
float scale = 1.0 / sqrtf(HS);
|
||||
|
||||
for (int b = 0; b < B; b++) {
|
||||
for (int t = 0; t < T; t++) {
|
||||
for (int h = 0; h < NH; h++) {
|
||||
const float* query_t = inp + b * T * C3 + t * C3 + h * hs;
|
||||
float* out_bth = out + b * NH * T * T + h * T * T + t * T;
|
||||
for (int nh = 0; nh < NH; nh++) {
|
||||
// Q[b][nh][t][:] = inp[b][t][0][nh][:] (where : is the slice operator for hs)
|
||||
const float* query_t = inp + b * T * C3 + t * C3 + nh * HS;
|
||||
// out[b][nh][t][:]
|
||||
float* out_bth = out + b * NH * T * T + nh * T * T + t * T;
|
||||
|
||||
// pass 1: calculate query dot key and maxval
|
||||
for (int t2 = 0; t2 <= t; t2++) {
|
||||
const float* key_t2 = inp + b * T * C3 + t2 * C3 + h * hs + C; // +C because it's key
|
||||
// K[b][nh][t2][:] = inp[b][t2][1][nh][:]
|
||||
const float* key_t2 = inp + b * T * C3 + t2 * C3 + nh * HS + C; // +C because it's key
|
||||
|
||||
// (query_t) dot (key_t2)
|
||||
// Q[b][nh][t][:] dot K[b][nh][t2][:]
|
||||
float val = 0.0f;
|
||||
for (int i = 0; i < hs; i++) {
|
||||
for (int i = 0; i < HS; i++) {
|
||||
val += query_t[i] * key_t2[i];
|
||||
}
|
||||
val *= scale;
|
||||
|
||||
// out[b][nh][t][t2] = val
|
||||
out_bth[t2] = val;
|
||||
}
|
||||
for(int t2 = t + 1; t2 < T; ++t2) {
|
||||
// causal mask, using NAN to supress warnings -> it could be -inf
|
||||
// but it doesn't matter because in validate_result we ignore infinities/NANs
|
||||
out_bth[t2] = NAN;
|
||||
}
|
||||
}
|
||||
@@ -98,31 +106,31 @@ void trimul_cpu(float* out, const float* inp,
|
||||
|
||||
__global__ void permute_kernel(float* q, float* k, float* v,
|
||||
const float* inp,
|
||||
int B, int N, int NH, int d) {
|
||||
// okay so now, this kernel wants Q,K,V to all be of shape (B, NH, N, d)
|
||||
// but instead, we have a single tensor QKV (inp) of shape (B, N, 3, NH, d)
|
||||
int B, int T, int NH, int HS) {
|
||||
// okay so now, this kernel wants Q,K,V to all be of shape (B, NH, T, HS)
|
||||
// but instead, we have a single tensor QKV (inp) of shape (B, T, 3, NH, HS)
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
|
||||
// Q[b][nh_][n][d_] = inp[b][n][0][nh_][d_]
|
||||
// Q[b][nh][t][hs] = inp[b][t][0][nh][hs]
|
||||
|
||||
if (idx < B * NH * N * d) {
|
||||
int b = idx / (NH * N * d);
|
||||
int rest = idx % (NH * N * d);
|
||||
int nh_ = rest / (N * d);
|
||||
rest = rest % (N * d);
|
||||
int n = rest / d;
|
||||
int d_ = rest % d;
|
||||
if (idx < B * NH * T * HS) {
|
||||
int b = idx / (NH * T * HS);
|
||||
int rest = idx % (NH * T * HS);
|
||||
int nh = rest / (T * HS);
|
||||
rest = rest % (T * HS);
|
||||
int t = rest / HS;
|
||||
int hs = rest % HS;
|
||||
|
||||
int inp_idx = \
|
||||
(b * N * 3 * NH * d)
|
||||
+ (n * 3 * NH * d)
|
||||
+ (0 * NH * d)
|
||||
+ (nh_ * d)
|
||||
+ d_;
|
||||
(b * T * 3 * NH * HS)
|
||||
+ (t * 3 * NH * HS)
|
||||
+ (0 * NH * HS)
|
||||
+ (nh * HS)
|
||||
+ hs;
|
||||
|
||||
q[idx] = inp[inp_idx];
|
||||
k[idx] = inp[inp_idx + NH * d];
|
||||
v[idx] = inp[inp_idx + 2 * (NH * d)];
|
||||
k[idx] = inp[inp_idx + NH * HS];
|
||||
v[idx] = inp[inp_idx + 2 * (NH * HS)];
|
||||
}
|
||||
}
|
||||
|
||||
@@ -145,6 +153,35 @@ void trimul_cublas(float* preatt,
|
||||
// batched matrix multiply with cuBLAS
|
||||
const float alpha = 1.0f / sqrtf(HS);
|
||||
const float beta = 0.0f;
|
||||
// This schedules in parallel B*NH matmuls of shape q@k^t = (T, HS) @ (HS, T) = (T, T).
|
||||
// IMPORTANT NOTE: Cublas uses a column-major (and we use row-major in our codebase) representation,
|
||||
// so this call might look confusing to you if you look at the `cublasSgemmStridedBatched` signature.
|
||||
//
|
||||
// In order to avoid having to do an additional transpose operation after this func call,
|
||||
// we need to pass in K as the first argument and Q as the second argument, which might make you think we're computing K^T @ Q.
|
||||
// That combined with the shapes we got after the permute kernel - (B, NH, T, HS) (I'll omit B, NH for brevity going forward)
|
||||
// and you might think we end up with (HS, T) @ (T, HS) = (HS, HS).
|
||||
// This is not the case. :)
|
||||
//
|
||||
// Cublas sees our row-major matrix (T, HS) as (HS, T), hence we set the lead dimensions to HS (see function signature).
|
||||
// We transpose K and end up computing K^T @ Q = (T, HS) @ (HS, T) = (T, T).
|
||||
// If you were to interpret the above formula K^T @ Q you might think we end up with:
|
||||
// -----------------------------------
|
||||
// k1.dot(q1) k1.dot(q2) ... k1.dot(qT)
|
||||
// k2.dot(q1) k2.dot(q2) ... k2.dot(qT)
|
||||
// ...
|
||||
// kT.dot(q1) kT.dot(q2) ... kT.dot(qT)
|
||||
// -----------------------------------
|
||||
// But as I mentioned, Cublas is column-major!
|
||||
// So given that the dot product is symmetric we can write k1.dot(q1) as q1.dot(k1) and transposing the above
|
||||
// representation we can see what we actually end up with in the row-major format:
|
||||
// -----------------------------------
|
||||
// q1.dot(k1) q1.dot(k2) ... q1.dot(kT)
|
||||
// q2.dot(k1) q2.dot(k2) ... q2.dot(kT)
|
||||
// ...
|
||||
// qT.dot(k1) qT.dot(k2) ... qT.dot(kT)
|
||||
// -----------------------------------
|
||||
// which is exactly what we wanted! :)
|
||||
cublasCheck(cublasSgemmStridedBatched(cublas_handle,
|
||||
CUBLAS_OP_T, CUBLAS_OP_N,
|
||||
T, T, HS,
|
||||
@@ -173,7 +210,7 @@ void trimul_cublas(float* preatt,
|
||||
*/
|
||||
|
||||
// using creates an alias for a function pointer
|
||||
using matmul_fn_ptr = void(*)(float* p, int ps, const float* k, int ks, const float* q, int qs, int T, int hs, float alpha);
|
||||
using matmul_fn_ptr = void(*)(float* p, int PS, const float* k, int KS, const float* q, int QS, int T, int HS, float alpha);
|
||||
|
||||
template<matmul_fn_ptr matmul_tri>
|
||||
__global__ void __launch_bounds__(256, 2) trimul_global(float* out, const float* inp, int T, int C, int NH) {
|
||||
@@ -183,20 +220,21 @@ __global__ void __launch_bounds__(256, 2) trimul_global(float* out, const float*
|
||||
|
||||
// set up indices
|
||||
int C3 = C*3;
|
||||
int hs = C / NH; // head size
|
||||
float scale = 1.0 / sqrtf(hs);
|
||||
int HS = C / NH; // head size
|
||||
float scale = 1.0 / sqrtf(HS);
|
||||
|
||||
// we put the "batch x head" dimension into the z block index.
|
||||
int h = blockIdx.z % NH;
|
||||
int b = blockIdx.z / NH;
|
||||
int nh = blockIdx.z % NH;
|
||||
|
||||
// Get the base address for the current batch and head
|
||||
const float* q = inp + b * T * C3 + h * hs;
|
||||
const float* k = inp + b * T * C3 + h * hs + C;
|
||||
float* r = out + (b*NH + h)*T*T;
|
||||
// shapes -> inp (B, T, 3, NH, HS), Q (B, NH, T, HS), K (B, NH, T, HS)
|
||||
const float* q = inp + b * T * C3 + nh * HS; // Q[b][nh][:][:] = inp[b][:][0][nh][:]
|
||||
const float* k = inp + b * T * C3 + nh * HS + C; // K[b][nh][:][:] = inp[b][:][1][nh][:]
|
||||
float* r = out + (b*NH + nh)*T*T; // out[b][nh][:][:]
|
||||
|
||||
// start the multiplication
|
||||
matmul_tri(r, T, q, C3, k, C3, T, hs, scale);
|
||||
matmul_tri(r, T, k, C3, q, C3, T, HS, scale);
|
||||
}
|
||||
|
||||
template<matmul_fn_ptr matmul_tri>
|
||||
@@ -239,12 +277,22 @@ void trimul_launcher(float* out, const float* inp, int B, int T, int C, int NH)
|
||||
*/
|
||||
|
||||
// baseline implementation: 20 ms
|
||||
__device__ void matmul_tri_naive(float* p, int ps, const float* k, int ks, const float* q, int qs, int T, int hs, float alpha) {
|
||||
// get coordinates of our block
|
||||
__device__ void matmul_tri_naive(float* p, int PS, const float* k, int KS, const float* q, int QS, int T, int HS, float alpha) {
|
||||
// coordinate system:
|
||||
// | - - - - - > j
|
||||
// |
|
||||
// |
|
||||
// v
|
||||
// i
|
||||
// get coordinates of our block - each thread is responsible for a single 8x8 block.
|
||||
int i_base = 128 * blockIdx.x + 8 * threadIdx.x;
|
||||
int j_base = 128 * blockIdx.y + 8 * threadIdx.y;
|
||||
|
||||
// one more check to skip the upper diagonal in blocks that are on the diagonal.
|
||||
// One more check to skip the upper diagonal in blocks that are on the diagonal.
|
||||
// Note: we deliberately waste some compute on the jagged diagonal i.e. elements that belong
|
||||
// to the upper triangle that should be masked out. This will be ignored due to the causal mask
|
||||
// in the reference CPU implementation when used in the `validate_result` function.
|
||||
// Alternatively this check should be done in the nested for loop below -> if (i > j) return.
|
||||
if(j_base > i_base)
|
||||
return;
|
||||
|
||||
@@ -254,17 +302,17 @@ __device__ void matmul_tri_naive(float* p, int ps, const float* k, int ks, const
|
||||
for(int jo = 0; jo < 8; ++jo) {
|
||||
int j = j_base + jo;
|
||||
float val = 0;
|
||||
for (int s = 0; s < hs; ++s) {
|
||||
val += k[i * ks + s] * q[j * qs + s];
|
||||
for (int s = 0; s < HS; ++s) {
|
||||
val += q[i * QS + s] * k[j * KS + s];
|
||||
}
|
||||
p[i * ps + j] = val * alpha;
|
||||
p[i * PS + j] = val * alpha;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/* ** Chapter IV - ... **
|
||||
*
|
||||
* Each worker is producing 64 combined cookies from 8 animals and 8 landscapes. They send there runners of 64 times
|
||||
* Each worker is producing 64 combined cookies from 8 animals and 8 landscapes. They send their runners 64 times
|
||||
* to fetch the corresponding shapes. This is terribly inefficient; The runners need a minute or so for each trip,
|
||||
* but making a cookie can be done in just a second.
|
||||
*
|
||||
@@ -292,7 +340,7 @@ __device__ void matmul_tri_naive(float* p, int ps, const float* k, int ks, const
|
||||
*/
|
||||
|
||||
// reorganize loops to enable data reuse: 3.5 ms
|
||||
__device__ void matmul_tri_registers(float* p, int ps, const float* k, int ks, const float* q, int qs, int T, int hs, float alpha) {
|
||||
__device__ void matmul_tri_registers(float* p, int PS, const float* k, int KS, const float* q, int QS, int T, int HS, float alpha) {
|
||||
int i_base = 128 * blockIdx.x + 8 * threadIdx.x;
|
||||
int j_base = 128 * blockIdx.y + 8 * threadIdx.y;
|
||||
|
||||
@@ -300,17 +348,17 @@ __device__ void matmul_tri_registers(float* p, int ps, const float* k, int ks, c
|
||||
return;
|
||||
|
||||
// shift our pointers to the sub-block this thread is responsible for
|
||||
k += i_base * ks;
|
||||
q += j_base * qs;
|
||||
p += i_base * ps + j_base;
|
||||
q += i_base * QS;
|
||||
k += j_base * KS;
|
||||
p += i_base * PS + j_base;
|
||||
|
||||
float vals[8][8] = {};
|
||||
for (int s = 0; s < hs; ++s) {
|
||||
for (int hs = 0; hs < HS; ++hs) {
|
||||
float lhs[8];
|
||||
float rhs[8];
|
||||
for (int u = 0; u < 8; ++u) {
|
||||
lhs[u] = k[u * ks + s];
|
||||
rhs[u] = q[u * qs + s];
|
||||
lhs[u] = q[u * QS + hs];
|
||||
rhs[u] = k[u * KS + hs];
|
||||
}
|
||||
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
@@ -322,7 +370,7 @@ __device__ void matmul_tri_registers(float* p, int ps, const float* k, int ks, c
|
||||
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
for (int j = 0; j < 8; ++j) {
|
||||
p[i * ps + j] = vals[i][j] * alpha;
|
||||
p[i * PS + j] = vals[i][j] * alpha;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -334,7 +382,7 @@ __device__ void matmul_tri_registers(float* p, int ps, const float* k, int ks, c
|
||||
* "Of course", the runner answers, "but they've asked me for an elephant, a lion, a zebra, and a goldfish. These
|
||||
* are all over the place, I can't just pick them up at one spot (_strided acccess_).
|
||||
* "But the lion is right next to the palm tree. You could bring those two together?", you confirm.
|
||||
* "Yes", he says, "if the just asked for the different categories at the same time, that would make things
|
||||
* "Yes", he says, "if they just asked for the different categories at the same time, that would make things
|
||||
* so much easier. See, I have this bucket, I could carry lots of things in one go if I could just scoop them up
|
||||
* from the same place (_coalesced access_).
|
||||
*
|
||||
@@ -364,7 +412,8 @@ __device__ void st_vec(float* address, float4 val) {
|
||||
}
|
||||
|
||||
// vector instructions for coalesced memory access: 1.7 ms
|
||||
__device__ void matmul_tri3(float* p, int ps, const float* k, int ks, const float* q, int qs, int T, int hs, float alpha) {
|
||||
__device__ void matmul_tri3(float* p, int PS, const float* k, int KS, const float* q, int QS, int T, int HS, float alpha) {
|
||||
// Same logic as previous kernel we just load in float4 to improve coalescing
|
||||
int i_base = 128 * blockIdx.x + 8 * threadIdx.x;
|
||||
int j_base = 128 * blockIdx.y + 8 * threadIdx.y;
|
||||
|
||||
@@ -372,21 +421,21 @@ __device__ void matmul_tri3(float* p, int ps, const float* k, int ks, const floa
|
||||
return;
|
||||
|
||||
// shift our pointers to the sub-block this thread is responsible for
|
||||
k += i_base * ks;
|
||||
q += j_base * qs;
|
||||
p += i_base * ps + j_base;
|
||||
q += i_base * QS;
|
||||
k += j_base * KS;
|
||||
p += i_base * PS + j_base;
|
||||
|
||||
float vals[8][8] = {};
|
||||
for (int s = 0; s < hs; s += 4) {
|
||||
for (int hs = 0; hs < HS; hs += 4) {
|
||||
// load in float4 to improve coalescing
|
||||
float4 rhs[8];
|
||||
for (int u = 0; u < 8; ++u) {
|
||||
rhs[u] = ld_vec(q + u * qs + s);
|
||||
rhs[u] = ld_vec(k + u * KS + hs);
|
||||
}
|
||||
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
// no need to keep lhs around for the i loop, its only reused in the j loop anyway.
|
||||
float4 lhs = ld_vec(k + i * ks + s);
|
||||
// no need to keep lhs around for the i loop, it's only reused in the j loop anyway.
|
||||
float4 lhs = ld_vec(q + i * QS + hs);
|
||||
for (int j = 0; j < 8; ++j) {
|
||||
vals[i][j] += lhs.x * rhs[j].x;
|
||||
vals[i][j] += lhs.y * rhs[j].y;
|
||||
@@ -403,7 +452,7 @@ __device__ void matmul_tri3(float* p, int ps, const float* k, int ks, const floa
|
||||
result.y = vals[i][j + 1] * alpha;
|
||||
result.z = vals[i][j + 2] * alpha;
|
||||
result.w = vals[i][j + 3] * alpha;
|
||||
st_vec(p + i * ps + j, result);
|
||||
st_vec(p + i * PS + j, result);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -424,7 +473,7 @@ __device__ void matmul_tri3(float* p, int ps, const float* k, int ks, const floa
|
||||
* details.]
|
||||
*
|
||||
*/
|
||||
__device__ void matmul_tri4(float* p, int ps, const float* k, int ks, const float* q, int qs, int T, int hs, float alpha) {
|
||||
__device__ void matmul_tri4(float* p, int PS, const float* k, int KS, const float* q, int QS, int T, int HS, float alpha) {
|
||||
int i_base = 128 * blockIdx.x + 8 * threadIdx.x;
|
||||
int j_base = 128 * blockIdx.y + 8 * threadIdx.y;
|
||||
|
||||
@@ -433,14 +482,14 @@ __device__ void matmul_tri4(float* p, int ps, const float* k, int ks, const floa
|
||||
if (blockIdx.y > blockIdx.x)
|
||||
return;
|
||||
|
||||
k += 128 * blockIdx.x * ks;
|
||||
q += 128 * blockIdx.y * qs;
|
||||
q += 128 * blockIdx.x * QS;
|
||||
k += 128 * blockIdx.y * KS;
|
||||
|
||||
__shared__ float lhs_s[128][32];
|
||||
__shared__ float rhs_s[128][32];
|
||||
|
||||
float vals[8][8] = {};
|
||||
for (int so = 0; so < hs; so += 32) {
|
||||
for (int so = 0; so < HS; so += 32) {
|
||||
// Read a large slice of the input, worked on together by all threads.
|
||||
// They are organized differently for this part. We want to ensure
|
||||
// fully coalesced loads, so we let a single warp handle consecutive
|
||||
@@ -448,14 +497,23 @@ __device__ void matmul_tri4(float* p, int ps, const float* k, int ks, const floa
|
||||
// in one read operation.
|
||||
// note: threads may read data here that they don't need themselves.
|
||||
// this really is a block-level operation.
|
||||
// note2: 16x16 threads (i.e. the block) will, through this for loop, fetch 32 dims from 128 keys and 128 queries
|
||||
// i.e. from Q/K, of shape (T, HS) take q[:128, so*32:(so+1)*32] and k[:128, so*32:(so+1)*32]
|
||||
__syncthreads();
|
||||
for(int y = threadIdx.y / 2; y < 128; y += 8) {
|
||||
int xo = (threadIdx.y % 2) * 16;
|
||||
lhs_s[y][threadIdx.x + xo] = k[y * ks + so + threadIdx.x + xo];
|
||||
rhs_s[y][threadIdx.x + xo] = q[y * qs + so + threadIdx.x + xo];
|
||||
lhs_s[y][threadIdx.x + xo] = q[y * QS + so + threadIdx.x + xo];
|
||||
rhs_s[y][threadIdx.x + xo] = k[y * KS + so + threadIdx.x + xo];
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// Now we compute a partial dot product (only 32 dims) for all combinations of keys and queries (128x128).
|
||||
// Each thread does 8x8 of these partial dot products.
|
||||
// E.g. thread (0,0) covers queries 0-7 and keys 0-7. More generally first row of threads
|
||||
// (0,:) covers queries 0-7 with keys 0-127 and so on.
|
||||
// In the next iterations of the outer (`so`) loop we'll be accumulating values to `vals` until we
|
||||
// get the full dot product. We then later deposit it into the output matrix for all 8x8 blocks
|
||||
// that are below the diagonal.
|
||||
for (int si = 0; si < 32; ++si) {
|
||||
float rhs[8];
|
||||
for (int u = 0; u < 8; ++u) {
|
||||
@@ -484,7 +542,7 @@ __device__ void matmul_tri4(float* p, int ps, const float* k, int ks, const floa
|
||||
result.y = vals[ii][ji + 1] * alpha;
|
||||
result.z = vals[ii][ji + 2] * alpha;
|
||||
result.w = vals[ii][ji + 3] * alpha;
|
||||
st_vec(p + i * ps + j, result);
|
||||
st_vec(p + i * PS + j, result);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -585,6 +643,7 @@ int main(int argc, char **argv) {
|
||||
free(inp);
|
||||
cudaCheck(cudaFree(d_out));
|
||||
cudaCheck(cudaFree(d_inp));
|
||||
cudaCheck(cudaFree(d_qkvr));
|
||||
cublasDestroy(cublas_handle);
|
||||
|
||||
return 0;
|
||||
|
||||
@@ -6,3 +6,5 @@ The idea is that each dataset has a .py file here in the root of `dev/data`, and
|
||||
- running `python tinyshakespeare.py` will create a directory `tinyshakespeare` with its .bin files inside it
|
||||
|
||||
And so on. This way we can nicely organize multiple datasets here, share common utilities between them, and then point the .py/.c code in the root of the project accordingly to these.
|
||||
|
||||
Note: we support "gpt-2" and "llama" (llama 3 in particular) models and the above scripts will tokenize gpt-2 by default.
|
||||
|
||||
+25
-15
@@ -23,28 +23,38 @@ def download_file(url: str, fname: str, chunk_size=1024):
|
||||
bar.update(size)
|
||||
|
||||
|
||||
def write_datafile(filename, toks):
|
||||
HEADERS_INFO = {
|
||||
"gpt-2": {
|
||||
"magic": 20240520,
|
||||
"version": 1,
|
||||
"token_dtype": np.uint16,
|
||||
},
|
||||
"llama-3": {
|
||||
"magic": 20240801,
|
||||
"version": 7,
|
||||
"token_dtype": np.uint32,
|
||||
},
|
||||
}
|
||||
|
||||
def write_datafile(filename, toks, model_desc="gpt-2"):
|
||||
"""
|
||||
Saves token data as a .bin file, for reading in C.
|
||||
- First comes a header with 256 int32s
|
||||
- The tokens follow, each as a uint16
|
||||
- The tokens follow, each as uint16 (gpt-2) or uint32 (llama)
|
||||
"""
|
||||
assert len(toks) < 2**31, "token count too large" # ~2.1B tokens
|
||||
assert model_desc in ["gpt-2", "llama-3"], f"unknown model descriptor {model_desc}"
|
||||
info = HEADERS_INFO[model_desc]
|
||||
# construct the header
|
||||
header = np.zeros(256, dtype=np.int32)
|
||||
header[0] = 20240520 # magic
|
||||
header[1] = 1 # version
|
||||
header[2] = len(toks) # number of tokens after the 256*4 bytes of header (each 2 bytes as uint16)
|
||||
# construct the tokens numpy array, if not already
|
||||
if not isinstance(toks, np.ndarray) or not toks.dtype == np.uint16:
|
||||
# validate that no token exceeds a uint16
|
||||
maxtok = 2**16
|
||||
assert all(0 <= t < maxtok for t in toks), "token dictionary too large for uint16"
|
||||
toks_np = np.array(toks, dtype=np.uint16)
|
||||
else:
|
||||
toks_np = toks
|
||||
header = np.zeros(256, dtype=np.int32) # header is always 256 int32 values
|
||||
header[0] = info["magic"]
|
||||
header[1] = info["version"]
|
||||
header[2] = len(toks) # number of tokens after the 256*4 bytes of header
|
||||
# construct the data (numpy array of tokens)
|
||||
toks_np = np.array(toks, dtype=info["token_dtype"])
|
||||
# write to file
|
||||
print(f"writing {len(toks):,} tokens to {filename}")
|
||||
num_bytes = (256 * 4) + (len(toks) * toks_np.itemsize)
|
||||
print(f"writing {len(toks):,} tokens to {filename} ({num_bytes:,} bytes) in the {model_desc} format")
|
||||
with open(filename, "wb") as f:
|
||||
f.write(header.tobytes())
|
||||
f.write(toks_np.tobytes())
|
||||
|
||||
Executable
+73
@@ -0,0 +1,73 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Downloads the FineWeb-Edu 100B dataset, but in an already tokenized format in .bin files
|
||||
# Example: ./edu_fineweb.sh 100
|
||||
# would download 100 shards
|
||||
# Default is all shards
|
||||
# Make sure to run this from current directory, i.e. inside ./dev/data!
|
||||
|
||||
# Check if MAX_SHARDS is provided as positional first arg, otherwise default to 1024
|
||||
if [ $# -eq 0 ]; then
|
||||
MAX_SHARDS=1001
|
||||
else
|
||||
MAX_SHARDS=$1
|
||||
fi
|
||||
|
||||
if [ $MAX_SHARDS -gt 1001 ]; then
|
||||
MAX_SHARDS=1001
|
||||
fi
|
||||
|
||||
# Base URLs
|
||||
TRAIN_BASE_URL="https://huggingface.co/datasets/karpathy/fineweb-edu-100B-gpt2-token-shards/resolve/main/edu_fineweb_train_"
|
||||
VAL_URL="https://huggingface.co/datasets/karpathy/fineweb-edu-100B-gpt2-token-shards/resolve/main/edu_fineweb_val_000000.bin"
|
||||
|
||||
# Directory to save files
|
||||
SAVE_DIR="edu_fineweb100B"
|
||||
|
||||
# Create the directory if it doesn't exist
|
||||
mkdir -p "$SAVE_DIR"
|
||||
|
||||
download() {
|
||||
local FILE_URL=$1
|
||||
local FILE_NAME=$(basename $FILE_URL | cut -d'?' -f1)
|
||||
local FILE_PATH="${SAVE_DIR}/${FILE_NAME}"
|
||||
curl -s -L -o "$FILE_PATH" "$FILE_URL"
|
||||
echo "Downloaded $FILE_NAME to $SAVE_DIR"
|
||||
}
|
||||
|
||||
# Function to manage parallel jobs
|
||||
run_in_parallel() {
|
||||
local max_jobs=$1
|
||||
shift
|
||||
local commands=("$@")
|
||||
local job_count=0
|
||||
|
||||
for cmd in "${commands[@]}"; do
|
||||
eval "$cmd" &
|
||||
((job_count++))
|
||||
if (( job_count >= max_jobs )); then
|
||||
wait -n
|
||||
((job_count--))
|
||||
fi
|
||||
done
|
||||
|
||||
# Wait for any remaining jobs to finish
|
||||
wait
|
||||
}
|
||||
|
||||
# Export the function so it's available in subshells
|
||||
export -f download
|
||||
|
||||
# Download the validation shard
|
||||
download "$VAL_URL" &
|
||||
|
||||
# Generate train file shard download commands
|
||||
train_commands=()
|
||||
for i in $(seq -f "%06g" 1 $MAX_SHARDS); do
|
||||
FILE_URL="${TRAIN_BASE_URL}${i}.bin?download=true"
|
||||
train_commands+=("download \"$FILE_URL\"")
|
||||
done
|
||||
|
||||
# Run the train file commands in parallel
|
||||
run_in_parallel 40 "${train_commands[@]}"
|
||||
echo "The val shard and first $MAX_SHARDS train shards of FineWebEdu100B files downloaded in $SAVE_DIR"
|
||||
+67
-26
@@ -14,61 +14,102 @@ example doc to highlight the structure of the dataset:
|
||||
"language_score": 0.9185474514961243,
|
||||
"token_count": 594
|
||||
}
|
||||
|
||||
Example of downloading the 100B dataset of FineWebEDU, from root directory:
|
||||
python dev/data/fineweb.py -t edu -v 100B
|
||||
100B runs for small few hours, depending on your internet and computer.
|
||||
"""
|
||||
import os
|
||||
import argparse
|
||||
import multiprocessing as mp
|
||||
|
||||
import numpy as np
|
||||
import tiktoken
|
||||
# from huggingface_hub import snapshot_download
|
||||
from datasets import load_dataset
|
||||
from tqdm import tqdm
|
||||
import argparse
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
|
||||
from data_common import write_datafile
|
||||
# ------------------------------------------
|
||||
|
||||
parser = argparse.ArgumentParser(description="FineWeb dataset preprocessing")
|
||||
parser.add_argument("-v", "--version", type=str, default="10B", help="Which version of fineweb to use 10B|100B")
|
||||
parser.add_argument("-s", "--shard_size", type=int, default=10**8, help="Size of each shard in tokens")
|
||||
parser = argparse.ArgumentParser(description="FineWeb and Edu-FineWeb dataset preprocessing")
|
||||
parser.add_argument("-t", "--type", type=str, default="classic", help="Fineweb type, edu|classic")
|
||||
parser.add_argument("-v", "--version", type=str, default="10B", help="Fineweb data sample size, 10B|100B")
|
||||
parser.add_argument("-m", "--model_desc", type=str, default="gpt-2", help="Model descriptor, gpt-2|llama-3")
|
||||
parser.add_argument("-s", "--shard_size", type=int, default=10**8, help="Size of each data shard in the output .bin files, in tokens")
|
||||
args = parser.parse_args()
|
||||
|
||||
# FineWeb has a few possible subsamples available
|
||||
assert args.version in ["10B", "100B"], "version must be one of 10B, 100B"
|
||||
if args.version == "10B":
|
||||
local_dir = "fineweb10B"
|
||||
remote_name = "sample-10BT"
|
||||
elif args.version == "100B":
|
||||
local_dir = "fineweb100B"
|
||||
remote_name = "sample-100BT"
|
||||
assert args.version in {"10B", "100B"}, "version must be one of: 10B, 100B"
|
||||
assert args.type in {"edu", "classic"}, "type must be one of: edu, classic"
|
||||
directories = {
|
||||
("classic", "10B"): ("fineweb10B", "sample-10BT"),
|
||||
("classic", "100B"): ("fineweb100B", "sample-100BT"),
|
||||
("edu", "10B"): ("edu_fineweb10B", "sample-10BT"),
|
||||
("edu", "100B"): ("edu_fineweb100B", "sample-100BT")
|
||||
}
|
||||
local_dir, remote_name = directories[(args.type, args.version)]
|
||||
|
||||
# create the cache the local directory if it doesn't exist yet
|
||||
DATA_CACHE_DIR = os.path.join(os.path.dirname(__file__), local_dir)
|
||||
os.makedirs(DATA_CACHE_DIR, exist_ok=True)
|
||||
|
||||
# download the dataset
|
||||
fw = load_dataset("HuggingFaceFW/fineweb", name=remote_name, split="train")
|
||||
if args.type == "classic":
|
||||
fw = load_dataset("HuggingFaceFW/fineweb", name=remote_name, split="train")
|
||||
name = "fineweb"
|
||||
elif args.type =="edu":
|
||||
fw = load_dataset("HuggingFaceFW/fineweb-edu", name=remote_name, split="train")
|
||||
name = "edu_fineweb"
|
||||
|
||||
# init the tokenizer
|
||||
enc = tiktoken.get_encoding("gpt2")
|
||||
eot = enc._special_tokens['<|endoftext|>'] # end of text token
|
||||
def tokenize(doc):
|
||||
# tokenizes a single document and returns a numpy array of uint16 tokens
|
||||
def tokenize_llama(doc):
|
||||
# tokenizes a single document and returns a numpy array of uint32 tokens
|
||||
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
|
||||
encode = lambda s: tokenizer.encode(s, add_special_tokens=False, verbose=False, split_special_tokens=True)
|
||||
eot = tokenizer.encode('')[0] # by default the tokenizer adds the EOT token (128000)
|
||||
tokens = [eot] # the special <|endoftext|> token delimits all documents
|
||||
tokens.extend(enc.encode_ordinary(doc["text"]))
|
||||
tokens.extend(encode(doc["text"]))
|
||||
tokens_np = np.array(tokens)
|
||||
assert (0 <= tokens_np).all() and (tokens_np < 2**32).all(), "token dictionary too large for uint32"
|
||||
tokens_np_uint = tokens_np.astype(np.uint32)
|
||||
return tokens_np_uint
|
||||
|
||||
def tokenize_gpt2(doc):
|
||||
# tokenizes a single document and returns a numpy array of uint16 tokens
|
||||
enc = tiktoken.get_encoding("gpt2")
|
||||
encode = lambda s: enc.encode_ordinary(s)
|
||||
eot = enc._special_tokens['<|endoftext|>'] # end of text token
|
||||
tokens = [eot] # the special <|endoftext|> token delimits all documents
|
||||
tokens.extend(encode(doc["text"]))
|
||||
tokens_np = np.array(tokens)
|
||||
assert (0 <= tokens_np).all() and (tokens_np < 2**16).all(), "token dictionary too large for uint16"
|
||||
tokens_np_uint16 = tokens_np.astype(np.uint16)
|
||||
return tokens_np_uint16
|
||||
tokens_np_uint = tokens_np.astype(np.uint16)
|
||||
return tokens_np_uint
|
||||
|
||||
token_dtype = {
|
||||
"gpt-2": np.uint16,
|
||||
"llama-3": np.uint32
|
||||
}[args.model_desc]
|
||||
|
||||
# tokenize all documents and write output shards, each of shard_size tokens (last shard has remainder)
|
||||
nprocs = max(1, os.cpu_count() - 2) # don't hog the entire system
|
||||
with mp.Pool(nprocs) as pool:
|
||||
shard_index = 0
|
||||
# preallocate buffer to hold current shard
|
||||
all_tokens_np = np.empty((args.shard_size,), dtype=np.uint16)
|
||||
all_tokens_np = np.empty((args.shard_size,), dtype=token_dtype)
|
||||
token_count = 0
|
||||
progress_bar = None
|
||||
|
||||
tokenize = lambda x: None
|
||||
if args.model_desc == "gpt-2":
|
||||
tokenize = tokenize_gpt2
|
||||
elif args.model_desc == "llama-3":
|
||||
tokenize = tokenize_llama
|
||||
else:
|
||||
raise ValueError(f"unknown model {args.model_desc}")
|
||||
|
||||
for tokens in pool.imap(tokenize, fw, chunksize=16):
|
||||
|
||||
# is there enough space in the current shard for the new tokens?
|
||||
@@ -83,12 +124,12 @@ with mp.Pool(nprocs) as pool:
|
||||
else:
|
||||
# write the current shard and start a new one
|
||||
split = "val" if shard_index == 0 else "train"
|
||||
filename = os.path.join(DATA_CACHE_DIR, f"fineweb_{split}_{shard_index:06d}.bin")
|
||||
filename = os.path.join(DATA_CACHE_DIR, f"{name}_{split}_{shard_index:06d}.bin")
|
||||
# split the document into whatever fits in this shard; the remainder goes to next one
|
||||
remainder = args.shard_size - token_count
|
||||
progress_bar.update(remainder)
|
||||
all_tokens_np[token_count:token_count+remainder] = tokens[:remainder]
|
||||
write_datafile(filename, all_tokens_np)
|
||||
write_datafile(filename, all_tokens_np.tolist(), args.model_desc)
|
||||
shard_index += 1
|
||||
progress_bar = None
|
||||
# populate the next shard with the leftovers of the current doc
|
||||
@@ -98,5 +139,5 @@ with mp.Pool(nprocs) as pool:
|
||||
# write any remaining tokens as the last shard
|
||||
if token_count != 0:
|
||||
split = "val" if shard_index == 0 else "train"
|
||||
filename = os.path.join(DATA_CACHE_DIR, f"fineweb_{split}_{shard_index:06d}.bin")
|
||||
write_datafile(filename, all_tokens_np[:token_count])
|
||||
filename = os.path.join(DATA_CACHE_DIR, f"{name}_{split}_{shard_index:06d}.bin")
|
||||
write_datafile(filename, (all_tokens_np[:token_count]).tolist(), args.model_desc)
|
||||
|
||||
Executable
+77
@@ -0,0 +1,77 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Downloads the FineWeb100B dataset, but in an already tokenized format in .bin files
|
||||
# Example: ./fineweb.sh 100
|
||||
# would download 100 shards
|
||||
# Default is all shards
|
||||
|
||||
# Check if MAX_SHARDS is provided as positional first arg, otherwise default to 1024
|
||||
if [ $# -eq 0 ]; then
|
||||
MAX_SHARDS=1028
|
||||
else
|
||||
MAX_SHARDS=$1
|
||||
fi
|
||||
|
||||
# Ensure MAX_SHARDS is not greater than 1028
|
||||
if [ $MAX_SHARDS -gt 1028 ]; then
|
||||
MAX_SHARDS=1028
|
||||
fi
|
||||
|
||||
# Base URLs
|
||||
TRAIN_BASE_URL="https://huggingface.co/datasets/chrisdryden/FineWebTokenizedGPT2/resolve/main/fineweb_train_"
|
||||
VAL_URL="https://huggingface.co/datasets/chrisdryden/FineWebTokenizedGPT2/resolve/main/fineweb_val_000000.bin?download=true"
|
||||
|
||||
# Directory to save files
|
||||
SAVE_DIR="fineweb100B"
|
||||
|
||||
# Create the directory if it doesn't exist
|
||||
mkdir -p "$SAVE_DIR"
|
||||
|
||||
# Function to download, decompress, and delete files
|
||||
download() {
|
||||
local FILE_URL=$1
|
||||
local FILE_NAME=$(basename $FILE_URL | cut -d'?' -f1)
|
||||
local FILE_PATH="${SAVE_DIR}/${FILE_NAME}"
|
||||
|
||||
# Download the file
|
||||
curl -s -L -o "$FILE_PATH" "$FILE_URL"
|
||||
echo "Downloaded $FILE_NAME to $SAVE_DIR"
|
||||
}
|
||||
|
||||
# Function to manage parallel jobs
|
||||
run_in_parallel() {
|
||||
local max_jobs=$1
|
||||
shift
|
||||
local commands=("$@")
|
||||
local job_count=0
|
||||
|
||||
for cmd in "${commands[@]}"; do
|
||||
eval "$cmd" &
|
||||
((job_count++))
|
||||
if (( job_count >= max_jobs )); then
|
||||
wait -n
|
||||
((job_count--))
|
||||
fi
|
||||
done
|
||||
|
||||
# Wait for any remaining jobs to finish
|
||||
wait
|
||||
}
|
||||
|
||||
# Export the function so it's available in subshells
|
||||
export -f download
|
||||
|
||||
# Download
|
||||
download "$VAL_URL" &
|
||||
|
||||
# Generate train file commands
|
||||
train_commands=()
|
||||
for i in $(seq -f "%06g" 1 $MAX_SHARDS); do
|
||||
FILE_URL="${TRAIN_BASE_URL}${i}.bin?download=true"
|
||||
train_commands+=("download \"$FILE_URL\"")
|
||||
done
|
||||
|
||||
# Run the train file commands in parallel
|
||||
run_in_parallel 40 "${train_commands[@]}"
|
||||
|
||||
echo "The val shard and first $MAX_SHARDS train shards of FineWeb100B files downloaded in $SAVE_DIR"
|
||||
+41
-16
@@ -6,25 +6,32 @@ Downloads and tokenizes the TinyShakespeare dataset.
|
||||
The output is written to a newly created tinyshakespeare/ folder.
|
||||
The script prints:
|
||||
|
||||
Saved 32768 tokens to tinyshakespeare/tiny_shakespeare_val.bin
|
||||
Saved 305260 tokens to tinyshakespeare/tiny_shakespeare_train.bin
|
||||
For GPT-2:
|
||||
$ python dev/data/tinyshakespeare.py --model=gpt-2
|
||||
writing 32,768 tokens to /home/ubuntu/llm.c/dev/data/tinyshakespeare/tiny_shakespeare_val.bin (66,560 bytes) in the gpt-2 format
|
||||
writing 305,260 tokens to /home/ubuntu/llm.c/dev/data/tinyshakespeare/tiny_shakespeare_train.bin (611,544 bytes) in the gpt-2 format
|
||||
|
||||
For LLaMA 3:
|
||||
$ python dev/data/tinyshakespeare.py --model=llama-3
|
||||
writing 32,768 tokens to /home/ubuntu/llm.c/dev/data/tinyshakespeare/tiny_shakespeare_val.bin (132,096 bytes) in the llama-3 format
|
||||
writing 276,224 tokens to /home/ubuntu/llm.c/dev/data/tinyshakespeare/tiny_shakespeare_train.bin (1,105,920 bytes) in the llama-3 format
|
||||
|
||||
And runs in a few seconds depending on your internet
|
||||
connection and computer. The .bin files are raw byte
|
||||
streams of int32 numbers indicating the token ids.
|
||||
streams of uint16 (gpt-2) or uint32 (llama) numbers indicating the token ids.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import tiktoken
|
||||
import numpy as np
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
from data_common import download_file, write_datafile
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
DATA_CACHE_DIR = os.path.join(os.path.dirname(__file__), "tinyshakespeare")
|
||||
|
||||
enc = tiktoken.get_encoding("gpt2")
|
||||
encode = lambda s: enc.encode(s, allowed_special={'<|endoftext|>'})
|
||||
|
||||
def download():
|
||||
"""Downloads the TinyShakespeare dataset to DATA_CACHE_DIR"""
|
||||
os.makedirs(DATA_CACHE_DIR, exist_ok=True)
|
||||
@@ -37,23 +44,41 @@ def download():
|
||||
else:
|
||||
print(f"{data_filename} already exists, skipping download...")
|
||||
|
||||
def tokenize():
|
||||
def tokenize(model_desc):
|
||||
if model_desc == "gpt-2":
|
||||
enc = tiktoken.get_encoding("gpt2")
|
||||
encode = lambda s: enc.encode_ordinary(s)
|
||||
eot = enc._special_tokens['<|endoftext|>'] # end of text token
|
||||
elif model_desc == "llama-3":
|
||||
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
|
||||
encode = lambda s: tokenizer.encode(s, add_special_tokens=False, verbose=False, split_special_tokens=True)
|
||||
eot = tokenizer.encode('')[0] # by default the tokenizer adds the EOT token (128000)
|
||||
else:
|
||||
raise ValueError(f"unknown model descriptor {model_desc}")
|
||||
data_filename = os.path.join(DATA_CACHE_DIR, "tiny_shakespeare.txt")
|
||||
text = open(data_filename, 'r').read()
|
||||
# let's treat every person's statement in the dialog as a separate document
|
||||
text = "<|endoftext|>" + text
|
||||
text = text.replace('\n\n', '\n\n<|endoftext|>')
|
||||
# encode the text
|
||||
tokens = encode(text)
|
||||
# let's treat every individual chunk of text as a separate "document"
|
||||
sections = text.split("\n\n")
|
||||
tokens = []
|
||||
for i, s in enumerate(sections):
|
||||
tokens.append(eot)
|
||||
# there was a mild bug where I originally intended to remove \n\n, but instead just added
|
||||
# the EOT right after each \n\n, so I'm keeping that behavior for backwards compatibility
|
||||
# therefore we have to here add an extra \n\n at the end of each section, except the last
|
||||
spad = s + "\n\n" if i != len(sections) - 1 else s
|
||||
tokens.extend(encode(spad))
|
||||
# let's take the first 32,768 tokens as the validation split (~10%)
|
||||
val_tokens = tokens[:32768]
|
||||
train_tokens = tokens[32768:]
|
||||
# save to file
|
||||
val_filename = os.path.join(DATA_CACHE_DIR, "tiny_shakespeare_val.bin")
|
||||
train_filename = os.path.join(DATA_CACHE_DIR, "tiny_shakespeare_train.bin")
|
||||
write_datafile(val_filename, val_tokens)
|
||||
write_datafile(train_filename, train_tokens)
|
||||
write_datafile(val_filename, val_tokens, model_desc)
|
||||
write_datafile(train_filename, train_tokens, model_desc)
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Tiny Shakespeare dataset preprocessing")
|
||||
parser.add_argument("-m", "--model_desc", type=str, default="gpt-2", choices=["gpt-2", "llama-3"], help="Model type, gpt-2|llama-3")
|
||||
args = parser.parse_args()
|
||||
download()
|
||||
tokenize()
|
||||
tokenize(args.model_desc)
|
||||
|
||||
+37
-23
@@ -1,38 +1,45 @@
|
||||
"""
|
||||
Downloads and tokenizes the TinyStories dataset.
|
||||
- The download is from HuggingFace datasets.
|
||||
- The tokenization is GPT-2 tokenizer with tiktoken
|
||||
- The tokenization is using either GPT-2 or LLaMA 3 tokenizer.
|
||||
|
||||
The output is written to a newly created tinystories/ folder.
|
||||
The script prints:
|
||||
|
||||
For GPT-2:
|
||||
Number of shards: 50
|
||||
Tokenizing val split...
|
||||
Saved 19043638 tokens to tinystories/TinyStories_val.bin
|
||||
writing 19,043,638 tokens to tinystories/TinyStories_val.bin
|
||||
Tokenizing train split...
|
||||
Saved 925653391 tokens to tinystories/TinyStories_train.bin
|
||||
writing 925,653,391 tokens to tinystories/TinyStories_train.bin
|
||||
|
||||
And runs in 1-2 minutes two depending on your internet
|
||||
For LLaMA 3:
|
||||
Number of shards: 50
|
||||
Tokenizing val split...
|
||||
writing 18,660,516 tokens to tinystories/TinyStories_val.bin
|
||||
Tokenizing train split...
|
||||
writing 907,021,844 tokens to tinystories/TinyStories_train.bin
|
||||
|
||||
And runs in few minutes two depending on your internet
|
||||
connection and computer. The .bin files are raw byte
|
||||
streams of int32 numbers indicating the token ids.
|
||||
streams of uint16 (gpt-2) or uint32 (llama) numbers indicating the token ids.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import glob
|
||||
import json
|
||||
import random
|
||||
import requests
|
||||
from tqdm import tqdm
|
||||
from concurrent.futures import ProcessPoolExecutor, as_completed
|
||||
|
||||
import tiktoken
|
||||
import numpy as np
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
from data_common import download_file, write_datafile
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
DATA_CACHE_DIR = os.path.join(os.path.dirname(__file__), "tinystories")
|
||||
|
||||
enc = tiktoken.get_encoding("gpt2")
|
||||
encode = lambda s: enc.encode_ordinary(s)
|
||||
|
||||
def download():
|
||||
"""Downloads the TinyStories dataset to DATA_CACHE_DIR"""
|
||||
os.makedirs(DATA_CACHE_DIR, exist_ok=True)
|
||||
@@ -63,10 +70,20 @@ def download():
|
||||
# data = json.load(f)
|
||||
# print(f"Example story:\n{data[0]}")
|
||||
|
||||
def process_shard(shard_index, shard_filename):
|
||||
def process_shard(shard_index, shard_filename, model_desc):
|
||||
if model_desc == "gpt-2":
|
||||
enc = tiktoken.get_encoding("gpt2")
|
||||
encode = lambda s: enc.encode_ordinary(s)
|
||||
eot = enc._special_tokens['<|endoftext|>'] # end of text token
|
||||
elif model_desc == "llama-3":
|
||||
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
|
||||
encode = lambda s: tokenizer.encode(s, add_special_tokens=False, verbose=False, split_special_tokens=True)
|
||||
eot = tokenizer.encode('')[0] # by default the tokenizer adds the EOT token (128000)
|
||||
else:
|
||||
raise ValueError(f"unknown model descriptor {model_desc}")
|
||||
|
||||
with open(shard_filename, "r") as f:
|
||||
data = json.load(f)
|
||||
eot = enc._special_tokens['<|endoftext|>'] # end of text token
|
||||
rng = random.Random(1337 + shard_index)
|
||||
rng.shuffle(data)
|
||||
all_tokens = []
|
||||
@@ -78,7 +95,7 @@ def process_shard(shard_index, shard_filename):
|
||||
all_tokens.extend(tokens)
|
||||
return all_tokens
|
||||
|
||||
def tokenize():
|
||||
def tokenize(model_desc):
|
||||
# shard 0 will be the val split, rest is train
|
||||
data_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
|
||||
shard_filenames = sorted(glob.glob(os.path.join(data_dir, "*.json")))
|
||||
@@ -89,20 +106,17 @@ def tokenize():
|
||||
print(f"Tokenizing {split_name} split...")
|
||||
all_tokens = []
|
||||
with ProcessPoolExecutor() as executor:
|
||||
futures = [executor.submit(process_shard, shard_index, shard_filename)
|
||||
futures = [executor.submit(process_shard, shard_index, shard_filename, model_desc)
|
||||
for shard_index, shard_filename in enumerate(split_shards)]
|
||||
for future in as_completed(futures):
|
||||
all_tokens.extend(future.result())
|
||||
|
||||
split_filename = os.path.join(DATA_CACHE_DIR, f"TinyStories_{split_name}.bin")
|
||||
write_datafile(split_filename, all_tokens)
|
||||
write_datafile(split_filename, all_tokens, model_desc)
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Tiny Stories dataset preprocessing")
|
||||
parser.add_argument("-m", "--model_desc", type=str, default="gpt-2", choices=["gpt-2", "llama-3"], help="Model type, gpt-2|llama-3")
|
||||
args = parser.parse_args()
|
||||
download()
|
||||
tokenize()
|
||||
|
||||
# Prints:
|
||||
# Tokenizing val split...
|
||||
# Saved 19043638 tokens to data/TinyStories_val.bin
|
||||
# Tokenizing train split...
|
||||
# Saved 925653391 tokens to data/TinyStories_train.bin
|
||||
tokenize(args.model_desc)
|
||||
Executable
+80
@@ -0,0 +1,80 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Get the directory of the script
|
||||
SCRIPT_DIR=$(dirname "$(realpath "$0")")
|
||||
|
||||
# Base URL
|
||||
BASE_URL="https://huggingface.co/datasets/karpathy/llmc-starter-pack/resolve/main/"
|
||||
|
||||
# Directory paths based on script location
|
||||
SAVE_DIR_PARENT="$SCRIPT_DIR/.."
|
||||
SAVE_DIR_TINY="$SCRIPT_DIR/data/tinyshakespeare"
|
||||
SAVE_DIR_HELLA="$SCRIPT_DIR/data/hellaswag"
|
||||
|
||||
# Create the directories if they don't exist
|
||||
mkdir -p "$SAVE_DIR_TINY"
|
||||
mkdir -p "$SAVE_DIR_HELLA"
|
||||
|
||||
# Files to download
|
||||
FILES=(
|
||||
"gpt2_124M.bin"
|
||||
"gpt2_124M_bf16.bin"
|
||||
"gpt2_124M_debug_state.bin"
|
||||
"gpt2_tokenizer.bin"
|
||||
"tiny_shakespeare_train.bin"
|
||||
"tiny_shakespeare_val.bin"
|
||||
"hellaswag_val.bin"
|
||||
)
|
||||
|
||||
# Function to download files to the appropriate directory
|
||||
download_file() {
|
||||
local FILE_NAME=$1
|
||||
local FILE_URL="${BASE_URL}${FILE_NAME}?download=true"
|
||||
local FILE_PATH
|
||||
|
||||
# Determine the save directory based on the file name
|
||||
if [[ "$FILE_NAME" == tiny_shakespeare* ]]; then
|
||||
FILE_PATH="${SAVE_DIR_TINY}/${FILE_NAME}"
|
||||
elif [[ "$FILE_NAME" == hellaswag* ]]; then
|
||||
FILE_PATH="${SAVE_DIR_HELLA}/${FILE_NAME}"
|
||||
else
|
||||
FILE_PATH="${SAVE_DIR_PARENT}/${FILE_NAME}"
|
||||
fi
|
||||
|
||||
# Download the file
|
||||
curl -s -L -o "$FILE_PATH" "$FILE_URL"
|
||||
echo "Downloaded $FILE_NAME to $FILE_PATH"
|
||||
}
|
||||
|
||||
# Export the function so it's available in subshells
|
||||
export -f download_file
|
||||
|
||||
# Generate download commands
|
||||
download_commands=()
|
||||
for FILE in "${FILES[@]}"; do
|
||||
download_commands+=("download_file \"$FILE\"")
|
||||
done
|
||||
|
||||
# Function to manage parallel jobs in increments of a given size
|
||||
run_in_parallel() {
|
||||
local batch_size=$1
|
||||
shift
|
||||
local i=0
|
||||
local command
|
||||
|
||||
for command; do
|
||||
eval "$command" &
|
||||
((i = (i + 1) % batch_size))
|
||||
if [ "$i" -eq 0 ]; then
|
||||
wait
|
||||
fi
|
||||
done
|
||||
|
||||
# Wait for any remaining jobs to finish
|
||||
wait
|
||||
}
|
||||
|
||||
# Run the download commands in parallel in batches of 2
|
||||
run_in_parallel 6 "${download_commands[@]}"
|
||||
|
||||
echo "All files downloaded and saved in their respective directories"
|
||||
@@ -0,0 +1,59 @@
|
||||
# eleuther eval readme
|
||||
|
||||
The goal here is to run the Eleuther Eval harness exactly in the same way as that used in the [huggingface LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
|
||||
|
||||
The starting point is a `.bin` file trained by llm.c. We now have to export it to a huggingface model and then evaluate it.
|
||||
|
||||
To export the model, use [export_hf.py](export_hf.py). See its documentation up top. Eample usage, from this directory:
|
||||
|
||||
```bash
|
||||
cd dev/eval
|
||||
python export_hf.py --input model.bin --output output_dir
|
||||
```
|
||||
|
||||
Where you point to your model .bin file, and huggingface files get written to output_dir. The script can optionally also upload to huggingface hub. One more post-processing that is advisable is to go into the `output_dir`, open up the `config.json` there and add one more entry into the json object:
|
||||
|
||||
```
|
||||
"_attn_implementation": "flash_attention_2"
|
||||
```
|
||||
|
||||
To use FlashAttention 2. We had trouble evaluating in bfloat16 without using FlashAttention 2 (the scores are much lower, and this was never fully resolved). This is a temporary hack/workaround.
|
||||
|
||||
Now that we have the model in huggingface format, we download the Eleuther Eval Harness repo and run it. Head over to the parent/root directory of the llm.c repo and:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/EleutherAI/lm-evaluation-harness/
|
||||
cd lm-evaluation-harness
|
||||
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
And then run the run_eval.sh script:
|
||||
|
||||
```bash
|
||||
./dev/eval/run_eval.sh output_dir result_dir
|
||||
```
|
||||
|
||||
Where output_dir can either be local output dir (above), or a huggingface repo name.This will write eval json objects to `./lm-evaluation-harness/results/results_dir`. It will print the results into console, e.g. for a 774M model we see:
|
||||
|
||||
```
|
||||
----------------------------------------
|
||||
arc_challenge_25shot.json : 30.4608
|
||||
gsm8k_5shot.json : 0.1516
|
||||
hellaswag_10shot.json : 57.8072
|
||||
mmlu_5shot.json : 25.8682
|
||||
truthfulqa_0shot.json : 35.7830
|
||||
winogrande_5shot.json : 59.3528
|
||||
----------------------------------------
|
||||
Average Score : 34.9039
|
||||
```
|
||||
|
||||
But you can additionally get these results later by running `summarize_eval.py`:
|
||||
|
||||
```bash
|
||||
python dev/eval/summarize_eval.py lm-evaluation-harness/results/results_dir
|
||||
```
|
||||
|
||||
The same information will be printed again.
|
||||
|
||||
For some reason, the evaluation is quite expensive and runs for somewhere around 1-3 hours, even though it should be a few minutes at most. This has not been satisfyingly resolved so far.
|
||||
@@ -0,0 +1,173 @@
|
||||
"""
|
||||
Script to convert GPT2 models from llm.c binary format to Hugging Face
|
||||
|
||||
It can optinally upload to your account on Hugging Face if you have the CLI:
|
||||
pip install -U "huggingface_hub[cli]"
|
||||
huggingface-cli login
|
||||
|
||||
Export to a local HF model:
|
||||
python export_hf.py --input input_file.bin --output output_dir
|
||||
|
||||
Export to a local HF model and also push to your account on Hugging Face:
|
||||
python export_hf.py --input input_file.bin --output output_dir --push true
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import argparse, sys
|
||||
from transformers import GPT2Config, GPT2Tokenizer, GPT2LMHeadModel
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Tensor functions for both bfloat16 (from int16) and normal float32
|
||||
# Both return float32 tensors
|
||||
|
||||
def tensor_bf16(data_int16, transpose=False):
|
||||
if transpose:
|
||||
data_int16 = data_int16.transpose(1,0)
|
||||
return torch.tensor(data_int16).view(torch.bfloat16).to(torch.float32)
|
||||
|
||||
def tensor_fp32(data_float32, transpose=False):
|
||||
if transpose:
|
||||
data_float32 = data_float32.transpose(1,0)
|
||||
return torch.tensor(data_float32).view(torch.float32)
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Main conversion function
|
||||
|
||||
def convert(filepath, output, push_to_hub=False, out_dtype="bfloat16"):
|
||||
print(f"Converting model {filepath} to {output} in {out_dtype} format and pushing to Hugging Face: {push_to_hub}")
|
||||
|
||||
f = open(filepath, 'rb')
|
||||
# Read in our header, checking the magic number and version
|
||||
# version 3 = fp32, padded vocab
|
||||
# version 5 = bf16, padded vocab
|
||||
model_header = np.frombuffer(f.read(256*4), dtype=np.int32)
|
||||
if model_header[0] != 20240326:
|
||||
print("ERROR: magic number mismatch in the data .bin file!")
|
||||
exit(1)
|
||||
version = model_header[1]
|
||||
if not version in [3, 5]:
|
||||
print("Bad version in model file")
|
||||
exit(1)
|
||||
|
||||
# Load in our model parameters
|
||||
maxT = model_header[2].item() # max sequence length
|
||||
V = model_header[3].item() # vocab size
|
||||
L = model_header[4].item() # num layers
|
||||
H = model_header[5].item() # num heads
|
||||
C = model_header[6].item() # channels
|
||||
Vp = model_header[7].item() # padded vocab size
|
||||
|
||||
print(f"{version=}, {maxT=}, {V=}, {Vp=}, {L=}, {H=}, {C=}")
|
||||
|
||||
# Define the shapes of our parameters
|
||||
shapes = {
|
||||
'wte': (Vp, C),
|
||||
'wpe': (maxT, C),
|
||||
'ln1w': (L, C),
|
||||
'ln1b': (L, C),
|
||||
'qkvw': (L, 3 * C, C),
|
||||
'qkvb': (L, 3 * C),
|
||||
'attprojw': (L, C, C),
|
||||
'attprojb': (L, C),
|
||||
'ln2w': (L, C),
|
||||
'ln2b': (L, C),
|
||||
'fcw': (L, 4 * C, C),
|
||||
'fcb': (L, 4 * C),
|
||||
'fcprojw': (L, C, 4 * C),
|
||||
'fcprojb': (L, C),
|
||||
'lnfw': (C,),
|
||||
'lnfb': (C,),
|
||||
}
|
||||
|
||||
# Load in our weights given our parameter shapes
|
||||
dtype = np.float32 if version == 3 else np.int16
|
||||
w = {}
|
||||
for key, shape in shapes.items():
|
||||
num_elements = np.prod(shape)
|
||||
data = np.frombuffer(f.read(num_elements * np.dtype(dtype).itemsize), dtype=dtype)
|
||||
w[key] = data.reshape(shape)
|
||||
# The binary file saves the padded vocab - drop the padding back to GPT2 size
|
||||
if shape[0] == Vp:
|
||||
w[key] = w[key].reshape(shape)[:(V-Vp), :]
|
||||
# Ensure the file is fully read and then close
|
||||
assert f.read() == b''
|
||||
f.close()
|
||||
|
||||
# Map to our model dict, the tensors at this stage are always fp32
|
||||
mk_tensor = {
|
||||
3 : tensor_fp32,
|
||||
5 : tensor_bf16,
|
||||
}[version]
|
||||
model_dict = {}
|
||||
model_dict['transformer.wte.weight'] = mk_tensor(w['wte'])
|
||||
model_dict['transformer.wpe.weight'] = mk_tensor(w['wpe'])
|
||||
model_dict['lm_head.weight'] = model_dict['transformer.wte.weight'] # Tie weights
|
||||
for i in range(L):
|
||||
model_dict[f'transformer.h.{i}.ln_1.weight'] = mk_tensor(w['ln1w'][i])
|
||||
model_dict[f'transformer.h.{i}.ln_1.bias'] = mk_tensor(w['ln1b'][i])
|
||||
model_dict[f'transformer.h.{i}.attn.c_attn.weight'] = mk_tensor(w['qkvw'][i], True)
|
||||
model_dict[f'transformer.h.{i}.attn.c_attn.bias'] = mk_tensor(w['qkvb'][i])
|
||||
model_dict[f'transformer.h.{i}.attn.c_proj.weight'] = mk_tensor(w['attprojw'][i], True)
|
||||
model_dict[f'transformer.h.{i}.attn.c_proj.bias'] = mk_tensor(w['attprojb'][i])
|
||||
model_dict[f'transformer.h.{i}.ln_2.weight'] = mk_tensor(w['ln2w'][i])
|
||||
model_dict[f'transformer.h.{i}.ln_2.bias'] = mk_tensor(w['ln2b'][i])
|
||||
model_dict[f'transformer.h.{i}.mlp.c_fc.weight'] = mk_tensor(w['fcw'][i], True)
|
||||
model_dict[f'transformer.h.{i}.mlp.c_fc.bias'] = mk_tensor(w['fcb'][i])
|
||||
model_dict[f'transformer.h.{i}.mlp.c_proj.weight'] = mk_tensor(w['fcprojw'][i], True)
|
||||
model_dict[f'transformer.h.{i}.mlp.c_proj.bias'] = mk_tensor(w['fcprojb'][i])
|
||||
model_dict['transformer.ln_f.weight'] = mk_tensor(w['lnfw'])
|
||||
model_dict['transformer.ln_f.bias'] = mk_tensor(w['lnfb'])
|
||||
|
||||
# Create a GPT-2 model instance, in the requested dtype
|
||||
config = GPT2Config(vocab_size = V,
|
||||
n_positions = maxT,
|
||||
n_ctx = maxT,
|
||||
n_embd = C,
|
||||
n_layer = L,
|
||||
n_head = H)
|
||||
model = GPT2LMHeadModel(config)
|
||||
if out_dtype == "bfloat16":
|
||||
model = model.to(torch.bfloat16)
|
||||
|
||||
# Set the model dict and save
|
||||
model.load_state_dict(model_dict)
|
||||
model.save_pretrained(output, max_shard_size="5GB", safe_serialization=True)
|
||||
|
||||
# Copy over a standard gpt2 tokenizer
|
||||
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
|
||||
tokenizer.save_pretrained(output)
|
||||
|
||||
if push_to_hub:
|
||||
print(f"Uploading {output} to Hugging Face")
|
||||
model.push_to_hub(output)
|
||||
tokenizer.push_to_hub(output)
|
||||
|
||||
def spin(output):
|
||||
print("Taking the exported model for a spin...")
|
||||
print('-'*80)
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(output)
|
||||
model = AutoModelForCausalLM.from_pretrained(output, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map='cuda')
|
||||
model.eval()
|
||||
tokens = tokenizer.encode("During photosynthesis in green plants", return_tensors="pt")
|
||||
tokens = tokens.to('cuda')
|
||||
output = model.generate(tokens, max_new_tokens=64, repetition_penalty=1.3)
|
||||
samples = tokenizer.batch_decode(output)
|
||||
for sample in samples:
|
||||
print('-'*30)
|
||||
print(sample)
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
if __name__== '__main__':
|
||||
parser=argparse.ArgumentParser()
|
||||
parser.add_argument("--input", "-i", help="The name of the llm.c model.bin file", type=str, required=True)
|
||||
parser.add_argument("--output","-o", help="The Hugging Face output model directory", type=str, required=True)
|
||||
parser.add_argument("--dtype", "-d", help="Output as either float32 or bfloat16 (default)", type=str, default="bfloat16")
|
||||
parser.add_argument("--push", "-p", help="Push the model to your Hugging Face account", type=bool, default=False)
|
||||
parser.add_argument("--spin", "-s", help="Take the model for a spin at the end?", type=bool, default=True)
|
||||
args = parser.parse_args()
|
||||
convert(args.input, args.output, args.push, args.dtype)
|
||||
if args.spin:
|
||||
spin(args.output)
|
||||
Executable
+52
@@ -0,0 +1,52 @@
|
||||
# https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
|
||||
# (See About tab -> REPRODUCIBILITY)
|
||||
|
||||
# This script is intended to be run from the parent/root directory of llm.c repo.
|
||||
|
||||
# Clone the evaluation harness:
|
||||
|
||||
# git clone https://github.com/EleutherAI/lm-evaluation-harness/
|
||||
# cd lm-evaluation-harness
|
||||
# git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
|
||||
# pip install -e .
|
||||
|
||||
# Then return to the parent directory and run this script
|
||||
|
||||
# cd ..
|
||||
# ./dev/eval/run_eval.sh [model_name] [result_name]
|
||||
|
||||
# where model_name is either a HF model such as openai-community/gpt2 or a local path such as ./gpt2-124M-run1
|
||||
# and result_name is the name of the folder under lm-evaluation-harness/results to store the evaluations
|
||||
|
||||
# Since the evals can take a couple of hours to run, depending on the model size, you may wish to
|
||||
# run within a "screen" session or by using nohup to run the script:
|
||||
|
||||
# nohup ./dev/eval/run_eval.sh [model_name] [result_name] > run.txt 2> err.txt &
|
||||
|
||||
if [ -z "$1" ]; then
|
||||
echo "Error: missing HuggingFace model name or path to local model"
|
||||
echo "./run_eval.sh hf_account/model_name my_result"
|
||||
exit 1
|
||||
fi
|
||||
if [ -z "$2" ]; then
|
||||
echo "Error: missing output name for results"
|
||||
echo "./run_eval.sh hf_account/model_name my_result"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
export MODEL="$(realpath -s "$1")"
|
||||
export RESULT="$2"
|
||||
echo "Evaluating model $MODEL"
|
||||
echo "Saving results to ./lm-evaluation-harness/results/$RESULT"
|
||||
|
||||
cd lm-evaluation-harness
|
||||
|
||||
python main.py --model hf-causal-experimental --model_args pretrained=$MODEL,use_accelerate=True,trust_remote_code=True --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/$RESULT/truthfulqa_0shot.json --device cuda
|
||||
python main.py --model hf-causal-experimental --model_args pretrained=$MODEL,use_accelerate=True,trust_remote_code=True --tasks winogrande --batch_size 1 --no_cache --write_out --output_path results/$RESULT/winogrande_5shot.json --device cuda --num_fewshot 5
|
||||
python main.py --model hf-causal-experimental --model_args pretrained=$MODEL,use_accelerate=True,trust_remote_code=True --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/$RESULT/arc_challenge_25shot.json --device cuda --num_fewshot 25
|
||||
python main.py --model hf-causal-experimental --model_args pretrained=$MODEL,use_accelerate=True,trust_remote_code=True --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/$RESULT/hellaswag_10shot.json --device cuda --num_fewshot 10
|
||||
python main.py --model hf-causal-experimental --model_args pretrained=$MODEL,use_accelerate=True,trust_remote_code=True --tasks gsm8k --batch_size 1 --no_cache --write_out --output_path results/$RESULT/gsm8k_5shot.json --device cuda --num_fewshot 5
|
||||
python main.py --model hf-causal-experimental --model_args pretrained=$MODEL,use_accelerate=True,trust_remote_code=True --tasks hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions --batch_size 1 --no_cache --write_out --output_path results/$RESULT/mmlu_5shot.json --device cuda --num_fewshot 5
|
||||
|
||||
cd ..
|
||||
python dev/eval/summarize_eval.py lm-evaluation-harness/results/$RESULT
|
||||
@@ -0,0 +1,32 @@
|
||||
# example run command
|
||||
# python dev/eval/summarize_eval.py lm-evaluation-harness/results/result774M
|
||||
# this script is optional, the run_eval.sh should already print these
|
||||
# but this script can be used to re-print them
|
||||
|
||||
import json, sys
|
||||
|
||||
RESULT = sys.argv[1]
|
||||
print("-"*40)
|
||||
|
||||
key = {"arc_challenge_25shot.json": "acc_norm",
|
||||
"gsm8k_5shot.json": "acc",
|
||||
"hellaswag_10shot.json": "acc_norm",
|
||||
"mmlu_5shot.json": "acc",
|
||||
"truthfulqa_0shot.json": "mc2",
|
||||
"winogrande_5shot.json": "acc"
|
||||
}
|
||||
|
||||
total = 0
|
||||
for test in ["arc_challenge_25shot.json", "gsm8k_5shot.json", "hellaswag_10shot.json", "mmlu_5shot.json", "truthfulqa_0shot.json", "winogrande_5shot.json"]:
|
||||
data = json.loads(open("./%s/%s"%(RESULT, test)).read())
|
||||
r_count = 0
|
||||
r_total = 0
|
||||
for test_name in data['results']:
|
||||
r_count += 1
|
||||
r_total += data['results'][test_name][key[test]]
|
||||
score = (r_total*100)/r_count
|
||||
print(f"{test:<30} : {score:.4f}")
|
||||
total += score
|
||||
average = total / 6.0
|
||||
print("-"*40)
|
||||
print(f"Average Score : {average:.4f}")
|
||||
@@ -0,0 +1,66 @@
|
||||
# Description: A script to compare numbers in a file with fixed values and check for accuracy within a specified percent difference.
|
||||
# Usage: python loss_checker_ci.py -f <file_path> -s <col_start> -e <col_end> -a <percent_accuracy>
|
||||
# Example: python dev/loss_checker_ci.py -f train_gpt2cu_fp32_precision.txt -s 20 -e 28 -a 10.0
|
||||
import sys
|
||||
import argparse
|
||||
|
||||
def read_numbers_from_file(file_path, col_start, col_end):
|
||||
try:
|
||||
numbers = []
|
||||
with open(file_path, 'r') as file:
|
||||
lines = file.readlines()
|
||||
start_index = None
|
||||
for i, line in enumerate(lines):
|
||||
if "step 1/10" in line:
|
||||
start_index = i
|
||||
break
|
||||
|
||||
if start_index is None:
|
||||
print("Error: Could not find the string 'step 1/10' in the file.")
|
||||
return None
|
||||
|
||||
# Read 10 rows starting from the identified start row
|
||||
for line in lines[start_index:start_index + 10]:
|
||||
# Extracting the specified columns
|
||||
number = float(line[col_start:col_end].strip())
|
||||
numbers.append(number)
|
||||
return numbers
|
||||
except Exception as e:
|
||||
print(f"Error reading the file: {e}")
|
||||
return None
|
||||
|
||||
def compare_numbers(read_values, fixed_values, percent_accuracy):
|
||||
for i in range(len(read_values)):
|
||||
read_value = read_values[i]
|
||||
fixed_value = fixed_values[i]
|
||||
percent_difference = ((read_value - fixed_value) / fixed_value) * 100
|
||||
print(f"Fixed Value: {fixed_value}, Read Value: {read_value}, Percent Difference: {percent_difference:.2f}%")
|
||||
if abs(percent_difference) > percent_accuracy:
|
||||
print(f"Error: Percent difference {percent_difference:.2f}% exceeds the allowed accuracy of {percent_accuracy}%")
|
||||
return 1
|
||||
print("Success: All values are within the allowed accuracy.")
|
||||
return 0
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='Compare numbers in a file with fixed values.')
|
||||
parser.add_argument('-f', '--file', required=True, help='Path to the input file')
|
||||
parser.add_argument('-s', '--col_start', type=int, required=True, help='Starting column index (0-based)')
|
||||
parser.add_argument('-e', '--col_end', type=int, required=True, help='Ending column index (0-based)')
|
||||
parser.add_argument('-a', '--percent_accuracy', type=float, required=True, help='Allowed percent accuracy for comparison')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Read numbers from file
|
||||
read_values = read_numbers_from_file(args.file, args.col_start, args.col_end)
|
||||
if read_values is None:
|
||||
return 1
|
||||
|
||||
# Use values from test_gpt2.cu for fp32 precision
|
||||
fixed_values = [5.270009,4.060681,3.320085,2.717550,2.181066,1.653923,1.168050,0.736873,0.401021,0.187493];
|
||||
|
||||
# Compare the numbers and check accuracy
|
||||
result = compare_numbers(read_values, fixed_values, args.percent_accuracy)
|
||||
return result
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,166 @@
|
||||
CC ?= gcc
|
||||
# example: make test_dataloader TEST_CFLAGS=-fsanitize=address -fno-omit-frame-pointer
|
||||
CFLAGS = -Ofast -Wno-unused-result -Wno-ignored-pragmas -Wno-unknown-attributes -g
|
||||
CFLAGS += $(TEST_CFLAGS)
|
||||
LDFLAGS =
|
||||
LDLIBS = -lm
|
||||
INCLUDES =
|
||||
CFLAGS_COND = -march=native
|
||||
|
||||
# Find nvcc
|
||||
SHELL_UNAME = $(shell uname)
|
||||
REMOVE_FILES = rm -f
|
||||
OUTPUT_FILE = -o $@
|
||||
CUDA_OUTPUT_FILE = -o $@
|
||||
|
||||
# NVCC flags
|
||||
# -t=0 is short for --threads, 0 = number of CPUs on the machine
|
||||
NVCC_FLAGS = -O3 -t=0 --use_fast_math -std=c++17
|
||||
NVCC_LDFLAGS = -lcublas -lcublasLt
|
||||
NVCC_INCLUDES =
|
||||
NVCC_LDLIBS =
|
||||
NVCC_CUDNN =
|
||||
# By default we don't build with cudnn because it blows up compile time from a few seconds to ~minute
|
||||
USE_CUDNN ?= 0
|
||||
|
||||
# We will place .o files in the `build` directory (create it if it doesn't exist)
|
||||
BUILD_DIR = build
|
||||
$(shell mkdir -p $(BUILD_DIR))
|
||||
REMOVE_BUILD_OBJECT_FILES := rm -f $(BUILD_DIR)/*.o
|
||||
|
||||
# Function to check if a file exists in the PATH
|
||||
define file_exists_in_path
|
||||
$(which $(1) 2>/dev/null)
|
||||
endef
|
||||
|
||||
ifneq ($(CI),true) # if not in CI, then use the GPU query
|
||||
ifndef GPU_COMPUTE_CAPABILITY # set to defaults if: make GPU_COMPUTE_CAPABILITY=
|
||||
ifneq ($(call file_exists_in_path, __nvcc_device_query),)
|
||||
GPU_COMPUTE_CAPABILITY = $(shell __nvcc_device_query)
|
||||
GPU_COMPUTE_CAPABILITY := $(strip $(GPU_COMPUTE_CAPABILITY))
|
||||
endif
|
||||
endif
|
||||
endif
|
||||
|
||||
# set to defaults if - make GPU_COMPUTE_CAPABILITY= otherwise use the compute capability detected above
|
||||
ifneq ($(GPU_COMPUTE_CAPABILITY),)
|
||||
NVCC_FLAGS += --generate-code arch=compute_$(GPU_COMPUTE_CAPABILITY),code=[compute_$(GPU_COMPUTE_CAPABILITY),sm_$(GPU_COMPUTE_CAPABILITY)]
|
||||
endif
|
||||
|
||||
# autodect a lot of various supports on current platform
|
||||
$(info ---------------------------------------------)
|
||||
|
||||
NVCC := $(shell which nvcc 2>/dev/null)
|
||||
|
||||
# Check and include cudnn if available
|
||||
# You can override the path to cudnn frontend by setting CUDNN_FRONTEND_PATH on the make command line
|
||||
# By default, we look for it in HOME/cudnn-frontend/include and ./cudnn-frontend/include
|
||||
# Refer to the README for cuDNN install instructions
|
||||
ifeq ($(USE_CUDNN), 1)
|
||||
ifeq ($(shell [ -d $(HOME)/cudnn-frontend/include ] && echo "exists"), exists)
|
||||
$(info ✓ cuDNN found, will run with flash-attention)
|
||||
CUDNN_FRONTEND_PATH ?= $(HOME)/cudnn-frontend/include
|
||||
else ifeq ($(shell [ -d cudnn-frontend/include ] && echo "exists"), exists)
|
||||
$(info ✓ cuDNN found, will run with flash-attention)
|
||||
CUDNN_FRONTEND_PATH ?= cudnn-frontend/include
|
||||
else
|
||||
$(error ✗ cuDNN not found. See the README for install instructions and the Makefile for hard-coded paths)
|
||||
endif
|
||||
NVCC_INCLUDES += -I$(CUDNN_FRONTEND_PATH)
|
||||
NVCC_LDFLAGS += -lcudnn
|
||||
NVCC_FLAGS += -DENABLE_CUDNN
|
||||
NVCC_CUDNN = $(BUILD_DIR)/cudnn_att.o
|
||||
else
|
||||
$(info → cuDNN is manually disabled by default, run make with `USE_CUDNN=1` to try to enable)
|
||||
endif
|
||||
|
||||
# Check if OpenMP is available
|
||||
# This is done by attempting to compile an empty file with OpenMP flags
|
||||
# OpenMP makes the code a lot faster so I advise installing it
|
||||
# e.g. on MacOS: brew install libomp
|
||||
# e.g. on Ubuntu: sudo apt-get install libomp-dev
|
||||
# later, run the program by prepending the number of threads, e.g.: OMP_NUM_THREADS=8 ./gpt2
|
||||
# First, check if NO_OMP is set to 1, if not, proceed with the OpenMP checks
|
||||
ifeq ($(NO_OMP), 1)
|
||||
$(info OpenMP is manually disabled)
|
||||
else
|
||||
ifneq ($(OS), Windows_NT)
|
||||
# Check for OpenMP support in GCC or Clang on Linux
|
||||
ifeq ($(shell echo | $(CC) -fopenmp -x c -E - > /dev/null 2>&1; echo $$?), 0)
|
||||
CFLAGS += -fopenmp -DOMP
|
||||
LDLIBS += -lgomp
|
||||
$(info ✓ OpenMP found)
|
||||
else
|
||||
$(info ✗ OpenMP not found)
|
||||
endif
|
||||
endif
|
||||
endif
|
||||
|
||||
# Check if OpenMPI and NCCL are available, include them if so, for multi-GPU training
|
||||
ifeq ($(NO_MULTI_GPU), 1)
|
||||
$(info → Multi-GPU (OpenMPI + NCCL) is manually disabled)
|
||||
else
|
||||
ifeq ($(shell [ -d /usr/lib/x86_64-linux-gnu/openmpi/lib/ ] && [ -d /usr/lib/x86_64-linux-gnu/openmpi/include/ ] && echo "exists"), exists)
|
||||
$(info ✓ OpenMPI found, OK to train with multiple GPUs)
|
||||
NVCC_INCLUDES += -I/usr/lib/x86_64-linux-gnu/openmpi/include
|
||||
NVCC_LDFLAGS += -L/usr/lib/x86_64-linux-gnu/openmpi/lib/
|
||||
NVCC_LDLIBS += -lmpi -lnccl
|
||||
NVCC_FLAGS += -DMULTI_GPU
|
||||
else
|
||||
$(info ✗ OpenMPI is not found, disabling multi-GPU support)
|
||||
$(info ---> On Linux you can try install OpenMPI with `sudo apt install openmpi-bin openmpi-doc libopenmpi-dev`)
|
||||
endif
|
||||
endif
|
||||
|
||||
# Precision settings, default to bf16 but ability to override
|
||||
ifeq ($(MAKECMDGOALS), clean)
|
||||
PRECISION=BF16
|
||||
endif
|
||||
|
||||
VALID_PRECISIONS := FP32 FP16 BF16
|
||||
ifeq ($(filter $(PRECISION),$(VALID_PRECISIONS)),)
|
||||
$(error Invalid precision $(PRECISION), valid precisions are $(VALID_PRECISIONS))
|
||||
endif
|
||||
ifeq ($(PRECISION), FP32)
|
||||
PFLAGS = -DENABLE_FP32
|
||||
else ifeq ($(PRECISION), FP16)
|
||||
PFLAGS = -DENABLE_FP16
|
||||
else
|
||||
PFLAGS = -DENABLE_BF16
|
||||
endif
|
||||
|
||||
# PHONY means these targets will always be executed
|
||||
.PHONY: all clean
|
||||
|
||||
# Add targets
|
||||
TARGETS = test_dataloader
|
||||
|
||||
# Dependency files
|
||||
test_dataloader_dependencies = test_dataloader.d
|
||||
HEADER_DEPENDENCIES = $(test_dataloader_dependencies)
|
||||
|
||||
# Conditional inclusion of CUDA targets
|
||||
ifeq ($(NVCC),)
|
||||
$(info ✗ nvcc not found, skipping GPU/CUDA builds)
|
||||
else
|
||||
$(info ✓ nvcc found, including GPU/CUDA support)
|
||||
TARGETS +=
|
||||
endif
|
||||
|
||||
$(info ---------Build Configuration Complete - Build Targets -------------------------)
|
||||
|
||||
all: $(TARGETS)
|
||||
|
||||
# Generate dependency files
|
||||
%.d: %.c
|
||||
$(CC) $(CFLAGS) -MMD -MP -MF $@ -c $<
|
||||
|
||||
# Include the dependency files
|
||||
-include test_dataloader.d
|
||||
|
||||
test_dataloader: test_dataloader.c
|
||||
$(CC) $(CFLAGS) $(INCLUDES) $(LDFLAGS) -MMD -MP $^ $(LDLIBS) $(OUTPUT_FILE)
|
||||
|
||||
clean:
|
||||
$(REMOVE_FILES) $(TARGETS) *.d *.o
|
||||
$(REMOVE_BUILD_OBJECT_FILES)
|
||||
@@ -0,0 +1,64 @@
|
||||
/*
|
||||
Tests device <-> file IO functions
|
||||
|
||||
compile and run as (from dev/test directory)
|
||||
nvcc -o device_file_io device_file_io.cu && ./device_file_io
|
||||
*/
|
||||
|
||||
|
||||
#include "../../llmc/cuda_common.h"
|
||||
#include <vector>
|
||||
#include <random>
|
||||
#include <cstdio>
|
||||
#include <algorithm>
|
||||
|
||||
void test(size_t nelem, size_t wt_buf_size, size_t rd_buf_size) {
|
||||
|
||||
float* data;
|
||||
cudaCheck(cudaMalloc(&data, nelem*sizeof(float)));
|
||||
|
||||
// generate random array
|
||||
std::vector<float> random_data(nelem);
|
||||
std::mt19937 rng(42);
|
||||
std::uniform_real_distribution<float> dist(-100.f, 100.f);
|
||||
std::generate(random_data.begin(), random_data.end(), [&](){ return dist(rng); });
|
||||
|
||||
cudaCheck(cudaMemcpy(data, random_data.data(), random_data.size()*sizeof(float), cudaMemcpyHostToDevice));
|
||||
|
||||
cudaStream_t stream;
|
||||
cudaStreamCreate(&stream);
|
||||
|
||||
FILE* tmp = fopenCheck("tmp.bin", "w");
|
||||
device_to_file(tmp, data, nelem * sizeof(float), wt_buf_size, stream);
|
||||
fcloseCheck(tmp);
|
||||
|
||||
|
||||
float* reload;
|
||||
cudaCheck(cudaMalloc(&reload, nelem*sizeof(float)));
|
||||
|
||||
tmp = fopenCheck("tmp.bin", "r");
|
||||
file_to_device(reload, tmp, nelem * sizeof(float), rd_buf_size, stream);
|
||||
fcloseCheck(tmp);
|
||||
|
||||
std::vector<float> cmp(nelem);
|
||||
cudaCheck(cudaMemcpy(cmp.data(), reload, nelem * sizeof(float), cudaMemcpyDeviceToHost));
|
||||
for(int i = 0; i < nelem; ++i) {
|
||||
if(random_data[i] != cmp[i]) {
|
||||
fprintf(stderr, "FAIL: Mismatch at position %d: %f vs %f\n", i, random_data[i], cmp[i]);
|
||||
remove("tmp.bin");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
cudaCheck(cudaFree(reload));
|
||||
cudaCheck(cudaFree(data));
|
||||
remove("tmp.bin");
|
||||
}
|
||||
|
||||
int main() {
|
||||
test(1025, 10000, 10000); // buffers larger than data
|
||||
test(1025, 1024, 513); // different and smaller
|
||||
test(500, 500*sizeof(float),
|
||||
500*sizeof(float)); // exact match
|
||||
test(125'000, 10000, 10000); // large array
|
||||
}
|
||||
@@ -0,0 +1,304 @@
|
||||
/*
|
||||
Tests our DataLoader
|
||||
|
||||
compile and run as (from dev/test directory)
|
||||
gcc -O3 -I../../llmc -o test_dataloader test_dataloader.c -lm && ./test_dataloader
|
||||
|
||||
TODOs:
|
||||
- test load/save state of DataLoader
|
||||
*/
|
||||
#include <unistd.h>
|
||||
#include "../../llmc/dataloader.h"
|
||||
|
||||
#define SHARD_NAME_LEN 64
|
||||
char shard_name[SHARD_NAME_LEN];
|
||||
const int num_tokens = 140;
|
||||
int num_shards = 4;
|
||||
|
||||
void check_range(const int *tokens, const int start, const int end, const char *file, int line) {
|
||||
// checks that the tokens[0, ... end-start] are the range [start, end)
|
||||
int n = end - start;
|
||||
for (int i = 0; i < n; i++) {
|
||||
int token = tokens[i];
|
||||
if (token != start + i) {
|
||||
fprintf(stderr, "Error: tokens[%d] = %d, expected %d\n", i, token, start + i);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
fprintf(stderr, " File: %s\n", file);
|
||||
fprintf(stderr, " Line: %d\n", line);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
// printf("tokens in range [%d, %d) OK\n", start, end);
|
||||
}
|
||||
#define checkRange(tokens, start, end) check_range(tokens, start, end, __FILE__, __LINE__)
|
||||
|
||||
void check_equals(const int *tokens, const int n, const int expected, const char *file, int line) {
|
||||
// checks that the tokens[0, ... n] are all equal to expected
|
||||
for (int i = 0; i < n; i++) {
|
||||
int token = tokens[i];
|
||||
if (token != expected) {
|
||||
fprintf(stderr, "Error: tokens[%d] = %d, expected %d\n", i, token, expected);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
fprintf(stderr, " File: %s\n", file);
|
||||
fprintf(stderr, " Line: %d\n", line);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
// printf("tokens all equal to %d OK\n", expected);
|
||||
}
|
||||
#define checkEquals(tokens, n, expected) check_equals(tokens, n, expected, __FILE__, __LINE__)
|
||||
|
||||
void test_simple(void) {
|
||||
/*
|
||||
Tests the simplest DataLoader functionality:
|
||||
- multi-shard
|
||||
- single-process
|
||||
- not shuffled
|
||||
DataLoader should just return all the tokens in order
|
||||
*/
|
||||
printf("test_simple... ");
|
||||
int B = 4;
|
||||
int T = 8;
|
||||
int process_rank = 0;
|
||||
int num_processes = 1;
|
||||
int should_shuffle = 0;
|
||||
snprintf(shard_name, SHARD_NAME_LEN, "shard_????.bin");
|
||||
DataLoader loader;
|
||||
dataloader_init(&loader, shard_name, B, T, process_rank, num_processes, should_shuffle);
|
||||
|
||||
int batches_fit = num_tokens / (B * T); // number of batches that fit per shard
|
||||
int BT = B * T;
|
||||
int num_epochs = 4;
|
||||
for (int e = 0; e < num_epochs; e++) { // epoch
|
||||
for (int s = 0; s < num_shards; s++) { // shard
|
||||
int start = s * num_tokens;
|
||||
for (int b = 0; b < batches_fit; b++) { // batch
|
||||
dataloader_next_batch(&loader);
|
||||
checkRange(loader.inputs, start, start + BT);
|
||||
checkRange(loader.targets, start + 1, start + BT + 1);
|
||||
start += BT;
|
||||
}
|
||||
}
|
||||
}
|
||||
dataloader_free(&loader);
|
||||
printf("OK\n");
|
||||
}
|
||||
|
||||
void test_multiprocess_simple(void) {
|
||||
/*
|
||||
Same as simple above, but using 2 processes.
|
||||
(which we of course use in a serial, single process way here)
|
||||
The DataLoaders simply pull chunks of consecutive tokens, so
|
||||
we expect them to alternate in the "token space".
|
||||
*/
|
||||
printf("test_multiprocess_simple... ");
|
||||
int B = 4;
|
||||
int T = 8;
|
||||
int num_processes = 2;
|
||||
int should_shuffle = 0;
|
||||
snprintf(shard_name, SHARD_NAME_LEN, "shard_????.bin");
|
||||
DataLoader loader0, loader1;
|
||||
dataloader_init(&loader0, shard_name, B, T, 0, num_processes, should_shuffle);
|
||||
dataloader_init(&loader1, shard_name, B, T, 1, num_processes, should_shuffle);
|
||||
|
||||
int batches_fit = num_tokens / (B * T * num_processes); // number of batches that fit per shard
|
||||
int BT = B * T;
|
||||
int num_epochs = 4;
|
||||
for (int e = 0; e < num_epochs; e++) { // epoch
|
||||
for (int s = 0; s < num_shards; s++) { // shard
|
||||
int start = s * num_tokens;
|
||||
for (int b = 0; b < batches_fit; b++) { // batch
|
||||
dataloader_next_batch(&loader0);
|
||||
dataloader_next_batch(&loader1);
|
||||
checkRange(loader0.inputs, start, start + BT);
|
||||
checkRange(loader1.inputs, start + BT, start + 2*BT);
|
||||
checkRange(loader0.targets, start + 1, start + BT + 1);
|
||||
checkRange(loader1.targets, start + BT + 1, start + 2*BT + 1);
|
||||
start += 2*BT;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
dataloader_free(&loader0);
|
||||
dataloader_free(&loader1);
|
||||
printf("OK\n");
|
||||
}
|
||||
|
||||
void test_shuffled(void) {
|
||||
/*
|
||||
Tests the DataLoader when using shuffled:
|
||||
- multi-shard
|
||||
- single-process
|
||||
- shuffled!
|
||||
DataLoader should return all the tokens, but in randperm order.
|
||||
So all we check is that we see all the tokens we expect to see,
|
||||
the correct number of times.
|
||||
*/
|
||||
printf("test_shuffled... ");
|
||||
int B = 4;
|
||||
int T = 8;
|
||||
int process_rank = 0;
|
||||
int num_processes = 1;
|
||||
int should_shuffle = 1; // should shuffle bit turn on
|
||||
snprintf(shard_name, 64, "shard_????.bin");
|
||||
DataLoader loader;
|
||||
dataloader_init(&loader, shard_name, B, T, process_rank, num_processes, should_shuffle);
|
||||
|
||||
// get batches from the dataloader and keep stats on what tokens we see
|
||||
int total_tokens = num_shards * num_tokens;
|
||||
int *num_seen_inputs = (int *)calloc(total_tokens, sizeof(int));
|
||||
int *num_seen_targets = (int *)calloc(total_tokens, sizeof(int));
|
||||
int batches_fit = num_tokens / (B * T); // number of batches that fit per shard
|
||||
int BT = B * T;
|
||||
int num_epochs = 4;
|
||||
for (int e = 0; e < num_epochs; e ++) { // epoch
|
||||
for (int s = 0; s < num_shards; s++) { // shard
|
||||
int start = s * num_tokens;
|
||||
for (int b = 0; b < batches_fit; b++) { // batch
|
||||
dataloader_next_batch(&loader);
|
||||
// count up the tokens we see
|
||||
for (int i = 0; i < BT; i++) {
|
||||
int input_token = loader.inputs[i];
|
||||
int target_token = loader.targets[i];
|
||||
assert(input_token >= 0 && input_token < total_tokens);
|
||||
assert(target_token >= 0 && target_token < total_tokens);
|
||||
num_seen_inputs[input_token]++;
|
||||
num_seen_targets[target_token]++;
|
||||
}
|
||||
start += BT;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// verify that we saw all the tokens the correct number of times
|
||||
int tokens_fit = batches_fit * BT; // number of tokens that fit per shard
|
||||
for (int s = 0; s < num_shards; s++) {
|
||||
int start = s * num_tokens;
|
||||
// verify the inputs counts for this shard:
|
||||
// - the first tokens_fit should have been seen num_epochs times
|
||||
// - the rest of the tokens in that should should have been seen zero times
|
||||
checkEquals(num_seen_inputs + start, tokens_fit, num_epochs);
|
||||
checkEquals(num_seen_inputs + start + tokens_fit, num_tokens - tokens_fit, 0);
|
||||
// verify the target counts. same thing but offset by 1
|
||||
checkEquals(num_seen_targets + start + 1, tokens_fit, num_epochs);
|
||||
checkEquals(num_seen_targets + start + 1 + tokens_fit,
|
||||
(s == (num_shards - 1)) ? num_tokens - tokens_fit - 1 : num_tokens - tokens_fit,0);
|
||||
}
|
||||
|
||||
dataloader_free(&loader);
|
||||
free(num_seen_inputs);
|
||||
free(num_seen_targets);
|
||||
printf("OK\n");
|
||||
}
|
||||
|
||||
void test_multiprocess_shuffled(void) {
|
||||
/*
|
||||
Tests the DataLoader when using both multiprocess and shuffled:
|
||||
- multi-shard
|
||||
- multi-process
|
||||
- shuffled!
|
||||
DataLoaders should return all the tokens, but in randperm order.
|
||||
So all we check is that we see all the tokens we expect to see,
|
||||
the correct number of times, over multiple epochs.
|
||||
*/
|
||||
|
||||
printf("test_multiprocess_shuffled... ");
|
||||
int B = 4;
|
||||
int T = 8;
|
||||
const int num_processes = 2;
|
||||
int should_shuffle = 0;
|
||||
snprintf(shard_name, SHARD_NAME_LEN, "shard_????.bin");
|
||||
DataLoader loaders[num_processes];
|
||||
for (int i = 0; i < num_processes; i++) {
|
||||
dataloader_init(&loaders[i], shard_name, B, T, i, num_processes, should_shuffle);
|
||||
}
|
||||
|
||||
// get batches from the dataloader and keep stats on what tokens we see
|
||||
int total_tokens = num_shards * num_tokens;
|
||||
int *num_seen_inputs = (int *)calloc(total_tokens, sizeof(int));
|
||||
int *num_seen_targets = (int *)calloc(total_tokens, sizeof(int));
|
||||
int batches_fit = num_tokens / (B * T * num_processes); // number of batches that fit per shard
|
||||
int BT = B * T;
|
||||
int num_epochs = 4;
|
||||
for (int e = 0; e < num_epochs; e ++) { // epoch
|
||||
for (int s = 0; s < num_shards; s++) { // shard
|
||||
int start = s * num_tokens;
|
||||
for (int b = 0; b < batches_fit; b++) { // batch
|
||||
for (int n = 0; n < num_processes; n++) { // dataloader
|
||||
DataLoader *loader = &loaders[n];
|
||||
dataloader_next_batch(loader);
|
||||
// count up the tokens we see
|
||||
for (int i = 0; i < BT; i++) {
|
||||
int input_token = loader->inputs[i];
|
||||
int target_token = loader->targets[i];
|
||||
assert(input_token >= 0 && input_token < total_tokens);
|
||||
assert(target_token >= 0 && target_token < total_tokens);
|
||||
num_seen_inputs[input_token]++;
|
||||
num_seen_targets[target_token]++;
|
||||
}
|
||||
start += BT;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// verify that we saw all the tokens the correct number of times
|
||||
int tokens_fit = batches_fit * (B * T * num_processes); // number of tokens that fit per shard
|
||||
for (int s = 0; s < num_shards; s++) {
|
||||
int start = s * num_tokens; // token id that starts this shard
|
||||
// verify the inputs counts for this shard:
|
||||
// - the first tokens_fit should have been seen num_epochs times
|
||||
// - the rest of the tokens in that should should have been seen zero times
|
||||
checkEquals(num_seen_inputs + start, tokens_fit, num_epochs);
|
||||
checkEquals(num_seen_inputs + start + tokens_fit, num_tokens - tokens_fit, 0);
|
||||
// verify the target counts. same thing but offset by 1
|
||||
checkEquals(num_seen_targets + start + 1, tokens_fit, num_epochs);
|
||||
checkEquals(num_seen_targets + start + 1 + tokens_fit,
|
||||
(s == (num_shards - 1)) ? num_tokens - tokens_fit - 1 : num_tokens - tokens_fit,0);
|
||||
}
|
||||
|
||||
// cleanup
|
||||
for (int i = 0; i < num_processes; i++) {
|
||||
dataloader_free(&loaders[i]);
|
||||
}
|
||||
free(num_seen_inputs);
|
||||
free(num_seen_targets);
|
||||
printf("OK\n");
|
||||
}
|
||||
|
||||
int main(void) {
|
||||
|
||||
// generate a few dummy shards of data with incrementing tokens
|
||||
int header[HEADER_SIZE];
|
||||
uint16_t tokens[num_tokens];
|
||||
for (int shard_id = 0; shard_id < num_shards; shard_id++) {
|
||||
// ensure unique tokens across the shards for ez accounting below
|
||||
int token_offset = shard_id * num_tokens;
|
||||
for (int i = 0; i < num_tokens; i++) {
|
||||
tokens[i] = token_offset + i;
|
||||
}
|
||||
// write the shard
|
||||
snprintf(shard_name, SHARD_NAME_LEN, "shard_%04d.bin", shard_id);
|
||||
header[0] = 20240520; // magic
|
||||
header[1] = 1; // version
|
||||
header[2] = num_tokens; // number of tokens within
|
||||
FILE* shard_file = fopenCheck(shard_name, "wb");
|
||||
fwrite(header, sizeof(int), HEADER_SIZE, shard_file);
|
||||
fwrite(tokens, sizeof(uint16_t), num_tokens, shard_file);
|
||||
fcloseCheck(shard_file);
|
||||
printf("Wrote shard %s\n", shard_name);
|
||||
}
|
||||
|
||||
test_simple();
|
||||
test_multiprocess_simple();
|
||||
test_shuffled();
|
||||
test_multiprocess_shuffled();
|
||||
|
||||
// clean up the shards
|
||||
for (int shard_id = 0; shard_id < num_shards; shard_id++) {
|
||||
snprintf(shard_name, SHARD_NAME_LEN, "shard_%04d.bin", shard_id);
|
||||
remove(shard_name);
|
||||
}
|
||||
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
@@ -0,0 +1,52 @@
|
||||
/*
|
||||
Tests our OutlierDetector
|
||||
|
||||
compile and run as (from dev/test directory)
|
||||
gcc -O3 -I../../llmc -o test_outlier_detector test_outlier_detector.c -lm && ./test_outlier_detector
|
||||
*/
|
||||
|
||||
#include <stdlib.h>
|
||||
#include "../../llmc/outlier_detector.h"
|
||||
|
||||
int main(void) {
|
||||
OutlierDetector detector;
|
||||
init_detector(&detector);
|
||||
|
||||
srand(1337); // init rng
|
||||
|
||||
// generate OUTLIER_DETECTOR_WINDOW_SIZE * 2 random numbers between -1 and 1
|
||||
for (int i = 0; i < OUTLIER_DETECTOR_WINDOW_SIZE * 2; i++) {
|
||||
double val = (double)rand() / RAND_MAX * 2 - 1; // Random number between -1 and 1
|
||||
double zscore = update_detector(&detector, val);
|
||||
|
||||
printf("Step %d: Value = %.4f, zscore = %.4f\n", i, val, zscore);
|
||||
|
||||
// check that the first OUTLIER_DETECTOR_WINDOW_SIZE values return nan
|
||||
if (i < OUTLIER_DETECTOR_WINDOW_SIZE) {
|
||||
if (!isnan(zscore)) {
|
||||
printf("Error: Expected nan, got %.4f\n", zscore);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
} else {
|
||||
// check that the zscore is within reasonable bounds
|
||||
if (zscore < -3.0 || zscore > 3.0) {
|
||||
printf("Error: Z-score %.4f is outside of expected range\n", zscore);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// simulate an outlier
|
||||
double outlier = 10.0; // <--- loss spike
|
||||
double zscore = update_detector(&detector, outlier);
|
||||
printf("Outlier Step: Value = %.4f, zscore = %.4f\n", outlier, zscore);
|
||||
|
||||
// check that the z-score here is large
|
||||
if (zscore < 5.0) {
|
||||
printf("Error: Z-score %.4f is not large enough for an outlier\n", zscore);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
printf("OK\n");
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
+8
-4
@@ -4,11 +4,17 @@
|
||||
|
||||
#define _CRT_SECURE_NO_WARNINGS
|
||||
#define _USE_MATH_DEFINES
|
||||
#define WIN32_LEAN_AND_MEAN
|
||||
|
||||
#include <stdio.h>
|
||||
#include <math.h>
|
||||
//#define gen_max_length 64 // compile as C++ to skip this VLA issue
|
||||
#include <time.h>
|
||||
#include <stdlib.h> // for malloc and free
|
||||
#include <string.h>
|
||||
#include <direct.h> // for _mkdir and _stat
|
||||
#include <io.h> // needed for _access below and _findfirst, _findnext, _findclose
|
||||
#pragma comment(lib, "Ws2_32.lib") // Link Ws2_32.lib for socket functions
|
||||
#include <winsock2.h>
|
||||
|
||||
#define CLOCK_MONOTONIC 0
|
||||
static inline int clock_gettime(int ignore_variable, struct timespec* tv)
|
||||
@@ -17,14 +23,12 @@ static inline int clock_gettime(int ignore_variable, struct timespec* tv)
|
||||
}
|
||||
|
||||
#define OMP /* turn it on */
|
||||
#include <io.h> /* needed for access below */
|
||||
#define F_OK 0
|
||||
#define access _access
|
||||
|
||||
#define TURN_OFF_FP_FAST __pragma(float_control( precise, on, push )) // Save current setting and turn on /fp:precise
|
||||
#define TURN_ON_FP_FAST __pragma(float_control(pop)) // Restore file's default settings
|
||||
|
||||
#include <direct.h> /* for _mkdir and _stat */
|
||||
#define mkdir(path, mode) _mkdir(path) /* sketchy way to get mkdir to work on windows */
|
||||
#define stat _stat
|
||||
|
||||
@@ -59,7 +63,7 @@ static inline int glob(const char* pattern, int ignored_flags, int (*ignored_err
|
||||
|
||||
replace_forward_slashes (pattern_copy); // Replace forward slashes with backslashes
|
||||
|
||||
if (strchr(pattern_copy, '\\') != NULL) {
|
||||
if (strchr(pattern_copy, '\\') != (void*) NULL) {
|
||||
strncpy_s(directory_path, sizeof(directory_path) - 1, pattern_copy, strrchr(pattern_copy, '\\') - pattern_copy + 1);
|
||||
directory_path[strrchr(pattern_copy, '\\') - pattern_copy + 1] = '\0';
|
||||
}
|
||||
|
||||
+45
-16
@@ -35,7 +35,6 @@
|
||||
" with open(logfile, \"r\") as f:\n",
|
||||
" for line in f:\n",
|
||||
" parts = line.split()\n",
|
||||
" assert len(parts) == 2\n",
|
||||
" step = int(parts[0].split(\":\")[1])\n",
|
||||
" stream = parts[1].split(\":\")[0]\n",
|
||||
" val = float(parts[1].split(\":\")[1])\n",
|
||||
@@ -53,7 +52,7 @@
|
||||
" # return the xs, ys lists\n",
|
||||
" return streams_xy\n",
|
||||
"\n",
|
||||
"# parse_logfile(\"../log124M/main.log\")"
|
||||
"parse_logfile(\"../log124M/main.log\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -62,55 +61,85 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sz = \"350M\"\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"sz = \"124M\"\n",
|
||||
"loss_baseline = {\n",
|
||||
" \"124M\": 3.424958,\n",
|
||||
" \"350M\": 3.083089,\n",
|
||||
" \"774M\": 3.000580,\n",
|
||||
" \"1558M\": 2.831273,\n",
|
||||
"}[sz]\n",
|
||||
"hella_baseline = {\n",
|
||||
"hella2_baseline = { # for GPT-2\n",
|
||||
" \"124M\": 0.294463,\n",
|
||||
" \"350M\": 0.375224,\n",
|
||||
" \"774M\": 0.431986,\n",
|
||||
" \"1558M\": 0.488946,\n",
|
||||
"}[sz]\n",
|
||||
"\n",
|
||||
"hella3_baseline = { # for GPT-3\n",
|
||||
" \"124M\": 0.337,\n",
|
||||
" \"350M\": 0.436,\n",
|
||||
" \"774M\": 0.510,\n",
|
||||
" \"1558M\": 0.547,\n",
|
||||
"}[sz]\n",
|
||||
"# assumes each model run is stored in this way\n",
|
||||
"logfile = f\"../log{sz}/main.log\"\n",
|
||||
"logfile = f\"../log_gpt2_{sz}/main.log\"\n",
|
||||
"streams = parse_logfile(logfile)\n",
|
||||
"\n",
|
||||
"# optional function that smooths out the loss some\n",
|
||||
"def smooth_moving_average(signal, window_size):\n",
|
||||
" if signal.ndim != 1:\n",
|
||||
" raise ValueError(\"smooth_moving_average only accepts 1D arrays.\")\n",
|
||||
" if signal.size < window_size:\n",
|
||||
" raise ValueError(\"Input vector needs to be bigger than window size.\")\n",
|
||||
" if window_size < 3:\n",
|
||||
" return signal\n",
|
||||
"\n",
|
||||
" s = np.pad(signal, (window_size//2, window_size-1-window_size//2), mode='edge')\n",
|
||||
" w = np.ones(window_size) / window_size\n",
|
||||
" smoothed_signal = np.convolve(s, w, mode='valid')\n",
|
||||
" return smoothed_signal\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(16, 6))\n",
|
||||
"\n",
|
||||
"# Panel 1: losses: both train and val\n",
|
||||
"plt.subplot(121)\n",
|
||||
"xs, ys = streams[\"trl\"] # training loss\n",
|
||||
"ys = np.array(ys)\n",
|
||||
"# smooth out ys using a rolling window\n",
|
||||
"# ys = smooth_moving_average(ys, 21) # optional\n",
|
||||
"plt.plot(xs, ys, label=f'llm.c ({sz}) train loss')\n",
|
||||
"print(\"Min Train Loss:\", min(ys))\n",
|
||||
"xs, ys = streams[\"tel\"] # validation loss\n",
|
||||
"plt.plot(xs, ys, label=f'llm.c ({sz}) val loss')\n",
|
||||
"# horizontal line at GPT-2 baseline\n",
|
||||
"# we don't have GPT-3 loss on this dataset because the weights were never released\n",
|
||||
"if loss_baseline is not None:\n",
|
||||
" plt.axhline(y=loss_baseline, color='r', linestyle='--', label=f\"OpenAI GPT-2 ({sz}) checkpoint val loss\")\n",
|
||||
"plt.xlabel(\"steps\")\n",
|
||||
"plt.ylabel(\"loss\")\n",
|
||||
"plt.yscale('log')\n",
|
||||
"plt.ylim(top=4.0)\n",
|
||||
"plt.legend()\n",
|
||||
"plt.title(\"Loss\")\n",
|
||||
"print(\"Min Validation Loss:\", min(ys))\n",
|
||||
"\n",
|
||||
"# Panel 2: HellaSwag eval\n",
|
||||
"plt.subplot(122)\n",
|
||||
"xs, ys = streams[\"eval\"] # HellaSwag eval\n",
|
||||
"plt.plot(xs, ys, label=f\"llm.c ({sz})\")\n",
|
||||
"# horizontal line at GPT-2 baseline\n",
|
||||
"if hella_baseline:\n",
|
||||
" plt.axhline(y=hella_baseline, color='r', linestyle='--', label=f\"OpenAI GPT-2 ({sz}) checkpoint\")\n",
|
||||
"plt.xlabel(\"steps\")\n",
|
||||
"plt.ylabel(\"accuracy\")\n",
|
||||
"plt.legend()\n",
|
||||
"plt.title(\"HellaSwag eval\")\n",
|
||||
"print(\"Max Hellaswag eval:\", max(ys))"
|
||||
"if \"eval\" in streams:\n",
|
||||
" xs, ys = streams[\"eval\"] # HellaSwag eval\n",
|
||||
" ys = np.array(ys)\n",
|
||||
" plt.plot(xs, ys, label=f\"llm.c ({sz})\")\n",
|
||||
" # horizontal line at GPT-2/3 baselines\n",
|
||||
" if hella2_baseline:\n",
|
||||
" plt.axhline(y=hella2_baseline, color='r', linestyle='--', label=f\"OpenAI GPT-2 ({sz}) checkpoint\")\n",
|
||||
" if hella3_baseline:\n",
|
||||
" plt.axhline(y=hella3_baseline, color='g', linestyle='--', label=f\"OpenAI GPT-3 ({sz}) checkpoint\")\n",
|
||||
" plt.xlabel(\"steps\")\n",
|
||||
" plt.ylabel(\"accuracy\")\n",
|
||||
" plt.legend()\n",
|
||||
" plt.title(\"HellaSwag eval\")\n",
|
||||
" print(\"Max Hellaswag eval:\", max(ys))\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -0,0 +1,98 @@
|
||||
/*
|
||||
AdamW kernel
|
||||
*/
|
||||
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
// Implements linear interpolation using only two floating-point operations (as opposed to three in a naive implementation).
|
||||
// Reference: https://developer.nvidia.com/blog/lerp-faster-cuda
|
||||
__device__ float lerp(float start, float end, float weight) {
|
||||
return fma(weight, end, fma(-weight, start, start));
|
||||
}
|
||||
|
||||
template <typename Tp, typename Tg>
|
||||
__device__ void adamw_update(Tp* params_memory, float* master_params_memory, Tg* grads_memory, float* m_memory, float* v_memory, size_t num_parameters,
|
||||
float learning_rate, float beta1, float beta2, float beta1_correction, float beta2_correction, float eps, float weight_decay,
|
||||
float grad_scale, unsigned int seed) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx >= num_parameters) { return; } // guard
|
||||
|
||||
// get the gradient, m, and v for this parameter
|
||||
float grad = grad_scale * (float)grads_memory[idx];
|
||||
float m = m_memory[idx];
|
||||
float v = v_memory[idx];
|
||||
// update the first moment (momentum)
|
||||
m = lerp(grad, m, beta1);
|
||||
m_memory[idx] = m;
|
||||
// update the second moment (RMSprop)
|
||||
v = lerp(grad * grad, v, beta2);
|
||||
v_memory[idx] = v;
|
||||
m /= beta1_correction; // m_hat
|
||||
v /= beta2_correction; // v_hat
|
||||
// fetch the old value of this parameter as a float, from either source
|
||||
float old_param = (master_params_memory != NULL) ? master_params_memory[idx] : (float)params_memory[idx];
|
||||
// update this parameter
|
||||
float param = old_param - (learning_rate * (m / (sqrtf(v) + eps) + weight_decay * old_param));
|
||||
// update our low precision version of the parameters using stochastic rounding
|
||||
// this will be used in the next forward pass
|
||||
stochastic_rounding(param, ¶ms_memory[idx], seed);
|
||||
// write the full, float version of the param into our master copy, if we maintain one
|
||||
// this will be used in the next update
|
||||
if (master_params_memory != NULL) { master_params_memory[idx] = param; }
|
||||
}
|
||||
|
||||
template <typename Tp, typename Tg>
|
||||
__global__ void adamw_kernel3(Tp* params_memory, float* master_params_memory, Tg* grads_memory, float* m_memory, float* v_memory, size_t num_parameters,
|
||||
ptrdiff_t w_stride, ptrdiff_t g_stride, ptrdiff_t s_stride,
|
||||
float learning_rate, float beta1, float beta2, float beta1_correction, float beta2_correction, float eps, float weight_decay,
|
||||
float grad_scale, unsigned int seed) {
|
||||
adamw_update(params_memory + blockIdx.y * w_stride,
|
||||
master_params_memory ? master_params_memory + blockIdx.y * s_stride : NULL,
|
||||
grads_memory + blockIdx.y * g_stride,
|
||||
m_memory + blockIdx.y * s_stride,
|
||||
v_memory + blockIdx.y * s_stride,
|
||||
num_parameters, learning_rate, beta1, beta2, beta1_correction, beta2_correction, eps, weight_decay, grad_scale,
|
||||
seed
|
||||
);
|
||||
}
|
||||
|
||||
template <typename Tp>
|
||||
__global__ void init_from_master_kernel(Tp* params_memory, float* master_params_memory, size_t num_parameters,
|
||||
ptrdiff_t w_stride, ptrdiff_t s_stride, unsigned int seed) {
|
||||
size_t idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx >= num_parameters) { return; }
|
||||
params_memory += blockIdx.y * w_stride; // adjust for layer offset
|
||||
master_params_memory += blockIdx.y * s_stride;
|
||||
stochastic_rounding(master_params_memory[idx], ¶ms_memory[idx], seed);
|
||||
}
|
||||
|
||||
template <typename Tp, typename Tg>
|
||||
void adamw_update(Tp* params_memory, float* master_params_memory, Tg* grads_memory, float* m_memory, float* v_memory, size_t num_parameters,
|
||||
ptrdiff_t w_stride, ptrdiff_t g_stride, ptrdiff_t s_stride, int num_slices, float learning_rate, float beta1, float beta2, int t, float eps, float weight_decay,
|
||||
float grad_scale, unsigned int seed, cudaStream_t stream) {
|
||||
// AdamW update
|
||||
int block_size = 512;
|
||||
int num_blocks = CEIL_DIV(num_parameters, block_size);
|
||||
float beta1_correction = 1.0f - powf(beta1, t);
|
||||
float beta2_correction = 1.0f - powf(beta2, t);
|
||||
adamw_kernel3<<<dim3(num_blocks, num_slices), block_size, 0, stream>>>(params_memory, master_params_memory, grads_memory,
|
||||
m_memory, v_memory, num_parameters, w_stride, g_stride, s_stride,
|
||||
learning_rate, beta1, beta2, beta1_correction, beta2_correction, eps, weight_decay,
|
||||
grad_scale, seed);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
template <typename Tp>
|
||||
void init_from_master(Tp* params_memory, float* master_params_memory, size_t num_parameters,
|
||||
ptrdiff_t w_stride, ptrdiff_t s_stride, int num_slices, unsigned int seed, cudaStream_t stream) {
|
||||
int block_size = 512; // must match block size of adamw_update so that RNG also matches
|
||||
int num_blocks = CEIL_DIV(num_parameters, block_size);
|
||||
init_from_master_kernel<<<dim3(num_blocks, num_slices), block_size, 0, stream>>>
|
||||
(params_memory, master_params_memory, num_parameters, w_stride, s_stride, seed);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
@@ -0,0 +1,276 @@
|
||||
/*
|
||||
Attention, as a fallback when we do not use the Flash Attention from cuDNN
|
||||
*/
|
||||
#include <assert.h>
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
#include "cublas_common.h"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
// inputs floatX, outputs FP32 (for current FP32-only activation path for this WIP)
|
||||
__global__ void permute_kernel(floatX* q, floatX* k, floatX* v,
|
||||
const floatX* inp,
|
||||
int B, int N, int NH, int d) {
|
||||
// okay so now, this kernel wants Q,K,V to all be of shape (B, NH, N, d)
|
||||
// but instead, we have a single tensor QKV (inp) of shape (B, N, 3, NH, d)
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx >= B * NH * N * d) { return; }
|
||||
|
||||
// Q[b][nh_][n][d_] = inp[b][n][0][nh_][d_]
|
||||
int b = idx / (NH * N * d);
|
||||
int rest = idx % (NH * N * d);
|
||||
int nh_ = rest / (N * d);
|
||||
rest = rest % (N * d);
|
||||
int n = rest / d;
|
||||
int d_ = rest % d;
|
||||
int inp_idx = (b * N * 3 * NH * d) + (n * 3 * NH * d) + (0 * NH * d) + (nh_ * d) + d_;
|
||||
q[idx] = __ldcs(&inp[inp_idx]);
|
||||
k[idx] = __ldcs(&inp[inp_idx + NH * d]);
|
||||
v[idx] = __ldcs(&inp[inp_idx + 2 * (NH * d)]);
|
||||
}
|
||||
|
||||
__global__ void permute_kernel_backward(floatX* dinp,
|
||||
const floatX* dq, const floatX* dk, const floatX* dv,
|
||||
int B, int N, int NH, int d) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx >= B * NH * N * d) { return; }
|
||||
|
||||
int b = idx / (NH * N * d);
|
||||
int rest = idx % (NH * N * d);
|
||||
int nh_ = rest / (N * d);
|
||||
rest = rest % (N * d);
|
||||
int n = rest / d;
|
||||
int d_ = rest % d;
|
||||
|
||||
int inp_idx = (b * N * 3 * NH * d) + (n * 3 * NH * d) + (0 * NH * d) + (nh_ * d) + d_;
|
||||
dinp[inp_idx] = dq[idx];
|
||||
dinp[inp_idx + NH * d] = dk[idx];
|
||||
dinp[inp_idx + 2 * (NH * d)] = dv[idx];
|
||||
}
|
||||
|
||||
__global__ void unpermute_kernel(floatX* inp, floatX *out, int B, int N, int NH, int d) {
|
||||
// out has shape (B, nh, N, d) but we need to unpermute it to (B, N, nh, d)
|
||||
|
||||
int idx = (blockIdx.x * blockDim.x + threadIdx.x);
|
||||
// out[b][n][nh_][d_] <- inp[b][nh_][n][d_]
|
||||
if (idx >= B * NH * N * d) { return; }
|
||||
|
||||
int b = idx / (NH * N * d);
|
||||
int rest = idx % (NH * N * d);
|
||||
int nh_ = rest / (N * d);
|
||||
rest = rest % (N * d);
|
||||
int n = rest / d;
|
||||
int d_ = rest % d;
|
||||
int other_idx = (b * NH * N * d) + (n * NH * d) + (nh_ * d) + d_;
|
||||
out[other_idx] = __ldcs(&inp[idx]);
|
||||
}
|
||||
|
||||
__global__ void unpermute_kernel_backward(floatX* dinp, const floatX *dout, int B, int N, int NH, int d) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx >= B * NH * N * d) { return; }
|
||||
|
||||
int b = idx / (NH * N * d);
|
||||
int rest = idx % (NH * N * d);
|
||||
int nh_ = rest / (N * d);
|
||||
rest = rest % (N * d);
|
||||
int n = rest / d;
|
||||
int d_ = rest % d;
|
||||
int other_idx = (b * NH * N * d) + (n * NH * d) + (nh_ * d) + d_;
|
||||
dinp[idx] = (floatX)dout[other_idx];
|
||||
}
|
||||
|
||||
__global__ void softmax_forward_kernel5(floatX* out, float inv_temperature, const floatX* inp, int N, int T) {
|
||||
// inp, out shape: (N, T, T), where N = B * NH
|
||||
// fuses the multiplication by scale inside attention
|
||||
// directly autoregressive, so we only compute the lower triangular part
|
||||
// uses the online softmax algorithm
|
||||
assert(T % 4 == 0);
|
||||
int lane_id = threadIdx.x % WARP_SIZE;
|
||||
int warp_id = threadIdx.x / WARP_SIZE;
|
||||
int num_warps = blockDim.x / WARP_SIZE;
|
||||
|
||||
// micro-optimization: we iterate backwards so that
|
||||
// after the softmax backward operation completes, the cache retains the
|
||||
// part of the matrix close to the upper left corner, which benefits the
|
||||
// matmul operation that immediately follows.
|
||||
// int idx = blockIdx.x * warp.meta_group_size() + warp.meta_group_rank(); // forward order
|
||||
int idx = (gridDim.x - blockIdx.x - 1) * num_warps + warp_id; // backward order
|
||||
if(idx >= N * T) {
|
||||
return;
|
||||
}
|
||||
int own_pos = idx % T;
|
||||
int pos_by_4 = own_pos / 4;
|
||||
|
||||
// one row of inp, i.e. inp[idx, :] of shape (T,)
|
||||
const floatX* x = inp + idx * T;
|
||||
|
||||
// not INF, so we don't get NaNs accidentally when subtracting two values.
|
||||
const float flt_max = 340282346638528859811704183484516925440.0f; // to avoid including float.h
|
||||
float maxval = -flt_max;
|
||||
float sumval = 0.0f;
|
||||
|
||||
const floatX* x_aligned = reinterpret_cast<const floatX*>(__builtin_assume_aligned(x, 16));
|
||||
for (int i = lane_id; i < pos_by_4; i += WARP_SIZE) {
|
||||
float regarray[4];
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
regarray[k] = (float)x_aligned[4*i + k];
|
||||
}
|
||||
float old_maxval = maxval;
|
||||
for(int k = 0; k < 4; ++k) {
|
||||
maxval = fmaxf(maxval, regarray[k]);
|
||||
}
|
||||
sumval *= expf(inv_temperature * (old_maxval - maxval));
|
||||
for(int k = 0; k < 4; ++k) {
|
||||
sumval += expf(inv_temperature * (regarray[k] - maxval));
|
||||
}
|
||||
}
|
||||
|
||||
if(4*pos_by_4 + lane_id <= own_pos) {
|
||||
float old_maxval = maxval;
|
||||
maxval = fmaxf(maxval, (float)x[4*pos_by_4 + lane_id]);
|
||||
sumval *= expf(inv_temperature * (old_maxval - maxval));
|
||||
sumval += expf(inv_temperature * ((float)x[4*pos_by_4 + lane_id] - maxval));
|
||||
}
|
||||
|
||||
float global_maxval = warpReduceMax(maxval);
|
||||
sumval *= expf(inv_temperature * (maxval - global_maxval));
|
||||
|
||||
float sum = warpReduceSum(sumval);
|
||||
float norm = 1.f / sum;
|
||||
|
||||
// divide the whole row by the sum
|
||||
for (int i = lane_id; i <= own_pos; i += WARP_SIZE) {
|
||||
// recalculation is faster than doing the round-trip through memory.
|
||||
float ev = expf(inv_temperature * ((float)__ldcs(x + i) - global_maxval));
|
||||
__stcs(out + idx * T + i, (floatX)(ev * norm));
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void softmax_autoregressive_backward_inplace_kernel(floatX* datt, const floatX* att,
|
||||
int B, int T, int C, float scale) {
|
||||
constexpr const int BlockSize = 256;
|
||||
constexpr int T_per_block = 4;
|
||||
|
||||
// go through blocks in reverse order, so the slowest block starts first
|
||||
int t0 = T - 1 - T_per_block*blockIdx.x;
|
||||
int idx = blockIdx.y;
|
||||
|
||||
att += idx * T * T;
|
||||
datt += idx * T * T;
|
||||
|
||||
for(int to = 0; to < T_per_block; ++to) {
|
||||
int t = t0 - to;
|
||||
if(t < 0) return;
|
||||
const floatX* att_bth = att + t * T;
|
||||
const floatX* datt_bth = datt + t * T;
|
||||
floatX* dpreatt_bth = datt + t * T;
|
||||
|
||||
float local_sum = 0;
|
||||
for (int t2 = threadIdx.x; t2 <= t; t2 += BlockSize) {
|
||||
local_sum += (float)att_bth[t2] * (float)datt_bth[t2];
|
||||
}
|
||||
|
||||
local_sum = blockReduce<warpReduceSum>(local_sum);
|
||||
|
||||
for (int t3 = threadIdx.x; t3 < T; t3 += BlockSize) {
|
||||
// don't touch the cache. Some parts will still be here from the previous loop, and
|
||||
// we want to exploit those.
|
||||
if(t3 <= t) {
|
||||
float acc = (float) __ldcs(att_bth + t3) * ((float) __ldcs(datt_bth + t3) - local_sum);
|
||||
__stcs(dpreatt_bth + t3, (floatX) (scale * acc));
|
||||
} else {
|
||||
// explicitly set non-causal elements to zero
|
||||
__stcs(dpreatt_bth + t3, (floatX)0.f);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
void attention_forward(floatX* out, floatX* qkvr, floatX* att,
|
||||
floatX* inp,
|
||||
int B, int T, int C, int NH, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
// Note: `inp` is not needed for backward pass, so we re-use it as a scratch buffer.
|
||||
// Its contents will be overwritten by this function.
|
||||
const int block_size = 256;
|
||||
|
||||
// inp is (B, T, 3C) QKV
|
||||
// preatt, att are (B, NH, T, T)
|
||||
// output is (B, T, C)
|
||||
const int HS = C / NH; // head size
|
||||
|
||||
// permute and separate inp from (B, T, 3, NH, HS) to 3X (B, NH, T, HS)
|
||||
floatX *q, *k, *v;
|
||||
q = qkvr + 0 * B * T * C;
|
||||
k = qkvr + 1 * B * T * C;
|
||||
v = qkvr + 2 * B * T * C;
|
||||
int total_threads = B * NH * T * HS;
|
||||
int num_blocks = CEIL_DIV(total_threads, block_size);
|
||||
permute_kernel<<<num_blocks, block_size, 0, stream>>>(q, k, v, inp, B, T, NH, HS);
|
||||
|
||||
floatX* preatt = inp; // reuse inp as scratch buffer
|
||||
matmul_cublaslt(preatt, k, q, nullptr, T, T, HS, stream, true, false, B * NH, T * HS, T * HS, T * T);
|
||||
|
||||
// multiply all elements of preatt elementwise by scale
|
||||
float scale = 1.f / sqrtf(HS);
|
||||
int grid_size = CEIL_DIV(B * NH * T * WARP_SIZE, block_size);
|
||||
softmax_forward_kernel5<<<grid_size, block_size, 0, stream>>>(att, scale, preatt, B * NH, T);
|
||||
|
||||
// new approach: first cuBLAS another batched matmul
|
||||
floatX* vaccum = inp;
|
||||
// y = att @ v # (B, nh, T, T) @ (B, nh, T, hs) -> (B, nh, T, hs)
|
||||
matmul_cublaslt(vaccum, v, att, nullptr, HS, T, T, stream, false, false, B * NH, T * HS, T * T, T * HS);
|
||||
|
||||
// now unpermute
|
||||
// y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
|
||||
num_blocks = CEIL_DIV(B * T * C, block_size);
|
||||
unpermute_kernel<<<num_blocks, block_size, 0, stream>>>(vaccum, out, B, T, NH, HS);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
// the sequence of transformations in this compound op is:
|
||||
// inp (B,T,3C) -> qkvr (B,T,3C) -> preatt (B,NH,T,T) -> att (B,NH,T,T) -> vaccum (B,T,C) -> out (B,T,C)
|
||||
void attention_backward(floatX* dinp, floatX* dqkvr, floatX* datt, floatX* scratch,
|
||||
const floatX* dout,
|
||||
const floatX* qkvr, const floatX* att,
|
||||
int B, int T, int C, int NH, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 256;
|
||||
const int HS = C / NH; // head size
|
||||
|
||||
// unpack convenience pointers into q, k, v
|
||||
const floatX *q, *k, *v;
|
||||
q = qkvr + 0 * B * T * C;
|
||||
k = qkvr + 1 * B * T * C;
|
||||
v = qkvr + 2 * B * T * C;
|
||||
floatX *dq, *dk, *dv;
|
||||
dq = dqkvr + 0 * B * T * C;
|
||||
dk = dqkvr + 1 * B * T * C;
|
||||
dv = dqkvr + 2 * B * T * C;
|
||||
|
||||
// backward through the unpermute operation
|
||||
int num_blocks = CEIL_DIV(B * T * C, block_size);
|
||||
unpermute_kernel_backward<<<num_blocks, block_size, 0, stream>>>(scratch, dout, B, T, NH, HS);
|
||||
// backward into datt
|
||||
matmul_cublaslt(datt, v, scratch, nullptr, T, T, HS, stream, true, false, B * NH, T * HS, T * HS, T * T);
|
||||
// backward into dv
|
||||
matmul_cublaslt(dv, scratch, att, nullptr, HS, T, T, stream, false, true, B * NH, T * HS, T * T, T * HS);
|
||||
const float scale = 1.0f / sqrtf((float)HS);
|
||||
// backward into preatt. this is an in-place operation; datt turns into dpreatt here
|
||||
softmax_autoregressive_backward_inplace_kernel<<<dim3(T / 4, B * NH), 256>>>(datt, att, B, T, C, scale);
|
||||
const floatX* dpreatt = datt;
|
||||
// backward into q
|
||||
matmul_cublaslt(dq, k, dpreatt, nullptr, HS, T, T, stream, false, false, B * NH, T * HS, T * T, T * HS);
|
||||
// backward into k
|
||||
matmul_cublaslt(dk, q, dpreatt, nullptr, HS, T, T, stream, false, true, B * NH, T * HS, T * T, T * HS);
|
||||
// backward into inp
|
||||
num_blocks = CEIL_DIV(B * NH * T * HS, block_size);
|
||||
permute_kernel_backward<<<num_blocks, block_size, 0, stream>>>(dinp, dq, dk, dv, B, T, NH, HS);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
@@ -4,6 +4,9 @@ cuBLAS related utils
|
||||
#ifndef CUBLAS_COMMON_H
|
||||
#define CUBLAS_COMMON_H
|
||||
|
||||
#include <stddef.h>
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <cublas_v2.h>
|
||||
#include <cublasLt.h>
|
||||
|
||||
@@ -11,13 +14,10 @@ cuBLAS related utils
|
||||
// cuBLAS Precision settings
|
||||
|
||||
#if defined(ENABLE_FP32)
|
||||
typedef float floatX;
|
||||
#define CUBLAS_LOWP CUDA_R_32F
|
||||
#elif defined(ENABLE_FP16)
|
||||
typedef half floatX;
|
||||
#define CUBLAS_LOWP CUDA_R_16F
|
||||
#else // default to bfloat16
|
||||
typedef __nv_bfloat16 floatX;
|
||||
#define CUBLAS_LOWP CUDA_R_16BF
|
||||
#endif
|
||||
|
||||
@@ -29,7 +29,6 @@ const size_t cublaslt_workspace_size = 32 * 1024 * 1024;
|
||||
void* cublaslt_workspace = NULL;
|
||||
cublasComputeType_t cublas_compute = CUBLAS_COMPUTE_32F;
|
||||
cublasLtHandle_t cublaslt_handle;
|
||||
cublasHandle_t cublas_handle;
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Error checking
|
||||
|
||||
+114
-3
@@ -4,16 +4,28 @@ Common utilities for CUDA code.
|
||||
#ifndef CUDA_COMMON_H
|
||||
#define CUDA_COMMON_H
|
||||
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <math.h>
|
||||
#include <string>
|
||||
#include <type_traits> // std::bool_constant
|
||||
#include <cuda_runtime.h>
|
||||
#include <nvtx3/nvToolsExt.h>
|
||||
#include <nvtx3/nvToolsExtCudaRt.h>
|
||||
#include <cuda_profiler_api.h>
|
||||
#include <cuda_bf16.h>
|
||||
#include <cuda_fp16.h>
|
||||
|
||||
#include "utils.h"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Global defines and settings
|
||||
|
||||
// Device properties of the CUDA device used in this process
|
||||
// defined as extern here because the individual kernels wish to use it
|
||||
// but it is actually created and instantiated in the main program file
|
||||
extern cudaDeviceProp deviceProp;
|
||||
|
||||
// WarpSize is not a compile time constant
|
||||
// Defining here like this possibly allows the compiler to optimize better
|
||||
#define WARP_SIZE 32U
|
||||
@@ -29,17 +41,33 @@ Common utilities for CUDA code.
|
||||
// convenience macro for calculating grid/block dimensions for kernels
|
||||
#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
|
||||
|
||||
// short-cuts for compile-time boolean values that can be used as function arguments
|
||||
constexpr std::bool_constant<true> True;
|
||||
constexpr std::bool_constant<true> False;
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Error checking
|
||||
|
||||
// CUDA error checking
|
||||
void inline cudaCheck(cudaError_t error, const char *file, int line) {
|
||||
// CUDA error checking. Underscore added so this function can be called directly not just via macro
|
||||
inline void cudaCheck_(cudaError_t error, const char *file, int line) {
|
||||
if (error != cudaSuccess) {
|
||||
printf("[CUDA ERROR] at file %s:%d:\n%s\n", file, line, cudaGetErrorString(error));
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
};
|
||||
#define cudaCheck(err) (cudaCheck(err, __FILE__, __LINE__))
|
||||
#define cudaCheck(err) (cudaCheck_(err, __FILE__, __LINE__))
|
||||
|
||||
// like cudaFree, but checks for errors _and_ resets the pointer.
|
||||
template<class T>
|
||||
inline void cudaFreeCheck(T** ptr, const char *file, int line) {
|
||||
cudaError_t error = cudaFree(*ptr);
|
||||
if (error != cudaSuccess) {
|
||||
printf("[CUDA ERROR] at file %s:%d:\n%s\n", file, line, cudaGetErrorString(error));
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
*ptr = nullptr;
|
||||
}
|
||||
#define cudaFreeCheck(ptr) (cudaFreeCheck(ptr, __FILE__, __LINE__))
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA Precision settings and defines
|
||||
@@ -95,4 +123,87 @@ class NvtxRange {
|
||||
};
|
||||
#define NVTX_RANGE_FN() NvtxRange nvtx_range(__FUNCTION__)
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Utilities to Read & Write between CUDA memory <-> files
|
||||
|
||||
// copy num_bytes from device pointer src into file dest, using double buffering running on the given stream.
|
||||
inline void device_to_file(FILE* dest, void* src, size_t num_bytes, size_t buffer_size, cudaStream_t stream) {
|
||||
// allocate pinned buffer for faster, async transfer
|
||||
char* buffer_space;
|
||||
cudaCheck(cudaMallocHost(&buffer_space, 2*buffer_size));
|
||||
// split allocation in two
|
||||
void* read_buffer = buffer_space;
|
||||
void* write_buffer = buffer_space + buffer_size;
|
||||
|
||||
// prime the read buffer; first copy means we have to wait
|
||||
char* gpu_read_ptr = (char*)src;
|
||||
size_t copy_amount = std::min(buffer_size, num_bytes);
|
||||
cudaCheck(cudaMemcpyAsync(read_buffer, gpu_read_ptr, copy_amount, cudaMemcpyDeviceToHost, stream));
|
||||
cudaCheck(cudaStreamSynchronize(stream));
|
||||
size_t rest_bytes = num_bytes - copy_amount;
|
||||
size_t write_buffer_size = copy_amount;
|
||||
gpu_read_ptr += copy_amount;
|
||||
|
||||
std::swap(read_buffer, write_buffer);
|
||||
// now the main loop; as long as there are bytes left
|
||||
while(rest_bytes > 0) {
|
||||
// initiate next read
|
||||
copy_amount = std::min(buffer_size, rest_bytes);
|
||||
cudaCheck(cudaMemcpyAsync(read_buffer, gpu_read_ptr, copy_amount, cudaMemcpyDeviceToHost, stream));
|
||||
// while this is going on, transfer the write buffer to disk
|
||||
fwriteCheck(write_buffer, 1, write_buffer_size, dest);
|
||||
cudaCheck(cudaStreamSynchronize(stream)); // wait for both buffers to be ready.
|
||||
|
||||
std::swap(read_buffer, write_buffer);
|
||||
rest_bytes -= copy_amount;
|
||||
write_buffer_size = copy_amount;
|
||||
gpu_read_ptr += copy_amount;
|
||||
}
|
||||
|
||||
// make sure to write the last remaining write buffer
|
||||
fwriteCheck(write_buffer, 1, write_buffer_size, dest);
|
||||
cudaCheck(cudaFreeHost(buffer_space));
|
||||
}
|
||||
|
||||
// copy num_bytes from file src into device pointer dest, using double buffering running on the given stream.
|
||||
inline void file_to_device(void* dest, FILE* src, size_t num_bytes, size_t buffer_size, cudaStream_t stream) {
|
||||
// allocate pinned buffer for faster, async transfer
|
||||
// from the docs (https://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/html/group__CUDART__HIGHLEVEL_ge439496de696b166ba457dab5dd4f356.html)
|
||||
// WC memory is a good option for buffers that will be written by the CPU and read by the device via mapped pinned memory or host->device transfers.
|
||||
char* buffer_space;
|
||||
cudaCheck(cudaMallocHost(&buffer_space, 2*buffer_size, cudaHostAllocWriteCombined));
|
||||
// split allocation in two
|
||||
void* read_buffer = buffer_space;
|
||||
void* write_buffer = buffer_space + buffer_size;
|
||||
|
||||
// prime the read buffer;
|
||||
char* gpu_write_ptr = (char*)dest;
|
||||
size_t copy_amount = std::min(buffer_size, num_bytes);
|
||||
freadCheck(read_buffer, 1, copy_amount, src);
|
||||
|
||||
size_t rest_bytes = num_bytes - copy_amount;
|
||||
size_t write_buffer_size = copy_amount;
|
||||
std::swap(read_buffer, write_buffer);
|
||||
|
||||
// now the main loop; as long as there are bytes left
|
||||
while(rest_bytes > 0) {
|
||||
// initiate next read
|
||||
copy_amount = std::min(buffer_size, rest_bytes);
|
||||
cudaCheck(cudaMemcpyAsync(gpu_write_ptr, write_buffer, write_buffer_size, cudaMemcpyHostToDevice, stream));
|
||||
gpu_write_ptr += write_buffer_size;
|
||||
// while this is going on, read from disk
|
||||
freadCheck(read_buffer, 1, copy_amount, src);
|
||||
cudaCheck(cudaStreamSynchronize(stream)); // wait for both buffers to be ready.
|
||||
|
||||
std::swap(read_buffer, write_buffer);
|
||||
rest_bytes -= copy_amount;
|
||||
write_buffer_size = copy_amount;
|
||||
}
|
||||
|
||||
// copy the last remaining write buffer to gpu
|
||||
cudaCheck(cudaMemcpyAsync(gpu_write_ptr, write_buffer, write_buffer_size, cudaMemcpyHostToDevice, stream));
|
||||
cudaCheck(cudaStreamSynchronize(stream));
|
||||
cudaCheck(cudaFreeHost(buffer_space));
|
||||
}
|
||||
|
||||
#endif // CUDA_COMMON_H
|
||||
+115
-5
@@ -79,6 +79,67 @@ __device__ void store128cg(ElementType* target, Packed128<ElementType> value) {
|
||||
typedef Packed128<float> f128;
|
||||
typedef Packed128<floatX> x128;
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// DType support
|
||||
|
||||
// enumerator to indentify the datatype of a tensor.
|
||||
enum class DType : uint8_t {
|
||||
FP32, FP16, BF16
|
||||
};
|
||||
|
||||
// Given a datatype enum, returns the underlying number of bytes
|
||||
// for a scalar of that type
|
||||
size_t sizeof_dtype(DType type) {
|
||||
switch (type) {
|
||||
case DType::FP32:
|
||||
return sizeof(float);
|
||||
case DType::FP16:
|
||||
return sizeof(half);
|
||||
case DType::BF16:
|
||||
return sizeof(nv_bfloat16);
|
||||
default: // handle or get compiler warning
|
||||
fprintf(stderr, "Unknown datatype\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
DType dtype_of(float* f) { return DType::FP32; }
|
||||
DType dtype_of(nv_bfloat16 * f) { return DType::BF16; }
|
||||
DType dtype_of(half * f) { return DType::FP16; }
|
||||
|
||||
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Copy, cast functions
|
||||
|
||||
// device functions and the kernel to cast data between types
|
||||
template<typename Td, typename Ts>
|
||||
__device__ Td cast_value(Ts val);
|
||||
|
||||
template<>
|
||||
__device__ float cast_value<float, float>(float val) {
|
||||
return val;
|
||||
}
|
||||
|
||||
template<>
|
||||
__device__ float cast_value<float, half>(half val) {
|
||||
return __half2float(val);
|
||||
}
|
||||
|
||||
template<>
|
||||
__device__ float cast_value<float, __nv_bfloat16>(__nv_bfloat16 val) {
|
||||
return __bfloat162float(val);
|
||||
}
|
||||
|
||||
template<typename Td, typename Ts>
|
||||
__global__ void copy_and_cast_kernel(Td* dst, const Ts* src, size_t n, ptrdiff_t stride_dst, ptrdiff_t stride_src) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
// need to try grid stride looping for more perf later
|
||||
if (idx < n) {
|
||||
dst[idx + stride_dst * blockIdx.y] = cast_value<Td, Ts>(src[idx + stride_src * blockIdx.y]);
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Warp/Block communication primitives
|
||||
|
||||
@@ -122,6 +183,51 @@ __device__ inline float blockReduce(float val, bool final_sync=false, float out_
|
||||
return block_val;
|
||||
}
|
||||
|
||||
// Performs a _deterministic_ sum reduction. determinism is achieved by requiring that only
|
||||
// a single block be used.
|
||||
template<class Float>
|
||||
__global__ void global_sum_single_block_kernel(float* result, const Float* values, size_t count) {
|
||||
assert(gridDim.x == 1); // only a single block!
|
||||
float thread_sum = 0;
|
||||
for(size_t index = threadIdx.x; index < count; index += blockDim.x) {
|
||||
thread_sum += (float)values[index];
|
||||
}
|
||||
|
||||
float reduction = blockReduce<warpReduceSum>(thread_sum, true);
|
||||
if(threadIdx.x == 0) {
|
||||
*result = reduction;
|
||||
}
|
||||
}
|
||||
|
||||
template<class Float>
|
||||
void global_sum_deterministic(float* result, const Float* values, int count, cudaStream_t stream) {
|
||||
global_sum_single_block_kernel<<<1, 1024, 0, stream>>>(result, values, count);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// memory management
|
||||
|
||||
// allocate memory, preferrably on the device
|
||||
// returns a status code. 0 = OK, 1 = fell back to managed memory
|
||||
int cudaMallocConditionallyManaged(void** out, size_t bytes, const char *file, int line) {
|
||||
// try to allocate
|
||||
cudaError_t err = cudaMalloc(out, bytes);
|
||||
if(err == cudaErrorMemoryAllocation) {
|
||||
// if we OOM, fallback to a managed allocation. slower but at least won't crash.
|
||||
cudaGetLastError(); // reset the error before the next API call
|
||||
cudaCheck_(cudaMallocManaged(out, bytes), file, line);
|
||||
cudaCheck_(cudaMemAdvise(*out, bytes, cudaMemAdviseSetPreferredLocation, cudaCpuDeviceId), file, line);
|
||||
return 1;
|
||||
} else {
|
||||
cudaCheck_(err, file, line);
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
#define cudaMallocConditionallyManaged(out, bytes)\
|
||||
(cudaMallocConditionallyManaged((void**)out, bytes, __FILE__, __LINE__))
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Random Number Generation used in Stochastic Rounding
|
||||
|
||||
@@ -129,14 +235,14 @@ __device__ inline float blockReduce(float val, bool final_sync=false, float out_
|
||||
// This gives us a random number from threadIdx/blockIdx + a single seed for the entire GPU
|
||||
// todo - possibly overkill and we don't need such high quality random numbers? (tbd)
|
||||
// http://eiserloh.net/noise/SquirrelNoise5.hpp
|
||||
__device__ __host__ constexpr unsigned int SquirrelNoise5(int positionX, unsigned int seed)
|
||||
__device__ __host__ constexpr unsigned int SquirrelNoise5(unsigned int positionX, unsigned int seed)
|
||||
{
|
||||
constexpr unsigned int SQ5_BIT_NOISE1 = 0xd2a80a3f; // 11010010101010000000101000111111
|
||||
constexpr unsigned int SQ5_BIT_NOISE2 = 0xa884f197; // 10101000100001001111000110010111
|
||||
constexpr unsigned int SQ5_BIT_NOISE3 = 0x6C736F4B; // 01101100011100110110111101001011
|
||||
constexpr unsigned int SQ5_BIT_NOISE4 = 0xB79F3ABB; // 10110111100111110011101010111011
|
||||
constexpr unsigned int SQ5_BIT_NOISE5 = 0x1b56c4f5; // 00011011010101101100010011110101
|
||||
unsigned int mangledBits = (unsigned int) positionX;
|
||||
unsigned int mangledBits = positionX;
|
||||
mangledBits *= SQ5_BIT_NOISE1;
|
||||
mangledBits += seed;
|
||||
mangledBits ^= (mangledBits >> 9);
|
||||
@@ -152,14 +258,18 @@ __device__ __host__ constexpr unsigned int SquirrelNoise5(int positionX, unsigne
|
||||
}
|
||||
__device__ __host__ constexpr unsigned int Get2dNoiseUint(int indexX, int indexY, unsigned int seed)
|
||||
{
|
||||
constexpr int PRIME_NUMBER = 198491317; // Large prime number with non-boring bits
|
||||
return SquirrelNoise5(indexX + (PRIME_NUMBER * indexY), seed);
|
||||
constexpr unsigned int PRIME_NUMBER = 198491317u; // Large prime number with non-boring bits
|
||||
unsigned int x = static_cast<unsigned int>(indexX);
|
||||
unsigned int y = static_cast<unsigned int>(indexY);
|
||||
|
||||
return SquirrelNoise5(x + (PRIME_NUMBER * y), seed);
|
||||
}
|
||||
|
||||
// stochastic rounding built on top of Squirel Noise above (with seed updated per step via xorshift)
|
||||
__device__ __forceinline__ void stochastic_rounding(float in, __nv_bfloat16 *out, unsigned int seed) {
|
||||
// todo - is this stochastic rounding *too good*? can we cut any corners?
|
||||
unsigned int random = Get2dNoiseUint(threadIdx.x, blockIdx.x, seed);
|
||||
// makes sure each thread gets a different random number
|
||||
unsigned int random = Get2dNoiseUint(threadIdx.x, blockIdx.x * blockDim.x + blockIdx.y, seed);
|
||||
unsigned int threshold = random & 0xFFFF;
|
||||
unsigned int float_bits = __float_as_uint(in);
|
||||
unsigned int rounded_bits = float_bits & 0x0000FFFF;
|
||||
|
||||
+21
-6
@@ -2,6 +2,8 @@
|
||||
// we change some unrelated piece of the code.
|
||||
// TODO this currently duplicates some of the utilities from the main file
|
||||
|
||||
#define NOMINMAX
|
||||
#include <unistd.h>
|
||||
#include "cudnn_att.h"
|
||||
#include <cudnn_frontend.h>
|
||||
|
||||
@@ -20,9 +22,16 @@ static_assert(false, "cuDNN is not supported in FP32 mode.")
|
||||
static cudnnHandle_t cudnn_handle;
|
||||
static size_t cudnn_workspace_size = 0; // dynamically allocated as needed (up to 256MiB!)
|
||||
static void* cudnn_workspace = NULL;
|
||||
#define checkCudnnErr(err) assert((int)err == 0);
|
||||
|
||||
static void checkCudnnFE(fe::error_object e, const char *file, int line) {
|
||||
static void cuDNNCheck(cudnnStatus_t error, const char *file, int line) {
|
||||
if (error != CUDNN_STATUS_SUCCESS) {
|
||||
printf("[CUDNN ERROR] at file %s:%d:\n%s\n", file, line, cudnnGetErrorString(error));
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
};
|
||||
#define cuDNNCheck(err) (cuDNNCheck(err, __FILE__, __LINE__))
|
||||
|
||||
static void checkCudnnFE(const fe::error_object& e, const char *file, int line) {
|
||||
if(!e.is_good()) {
|
||||
printf("[CUDNN ERROR] at file %s:%d:\n%s\n", file, line, e.err_msg.c_str());
|
||||
exit(EXIT_FAILURE);
|
||||
@@ -175,6 +184,9 @@ auto lookup_cache_or_build_graph_bwd(int B, int NH, int T, int HS) {
|
||||
.set_uid(Attn_scale_UID)
|
||||
.set_data_type(fe::DataType_t::FLOAT));
|
||||
auto sdpa_backward_options = fe::graph::SDPA_backward_attributes().set_name("flash_attention_backward")
|
||||
#if CUDNN_FRONTEND_MAJOR_VERSION > 1 || CUDNN_FRONTEND_MINOR_VERSION >= 5
|
||||
.set_deterministic_algorithm(true) // 1.5+ needs this for determinism
|
||||
#endif
|
||||
.set_causal_mask(true)
|
||||
.set_attn_scale(attn_scale);
|
||||
|
||||
@@ -210,11 +222,13 @@ auto lookup_cache_or_build_graph_bwd(int B, int NH, int T, int HS) {
|
||||
void attention_forward_cudnn(floatX* out, // output: (B, T, NH, HS)
|
||||
float* stats, // output for backward pass: (B, NH, T)
|
||||
floatX* inp, // input: (B, T, 3, NH, HS) QKV
|
||||
int B, int T, int NH, int C) {
|
||||
int B, int T, int NH, int C, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
int HS = C / NH; // number of features per head
|
||||
bool is_inference_only = (stats == nullptr);
|
||||
|
||||
cuDNNCheck(cudnnSetStream(cudnn_handle, stream));
|
||||
|
||||
// Get graph and tensors from cache (or generate it on first use)
|
||||
auto graph = lookup_cache_or_build_graph_fwd(B, NH, T, HS, is_inference_only);
|
||||
|
||||
@@ -241,7 +255,7 @@ void attention_forward_cudnn(floatX* out, // output: (B, T, NH, HS)
|
||||
|
||||
void attention_backward_cudnn(floatX* dqkvr, // output
|
||||
floatX* dout, floatX* qkvr, floatX* o, float* stats, // inputs
|
||||
int B, int T, int NH, int C) {
|
||||
int B, int T, int NH, int C, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
int HS = C / NH; // number of features per head
|
||||
|
||||
@@ -268,15 +282,16 @@ void attention_backward_cudnn(floatX* dqkvr,
|
||||
{Attn_scale_UID, &attn_scale_cpu}};
|
||||
|
||||
// Execute graph
|
||||
cuDNNCheck(cudnnSetStream(cudnn_handle, stream));
|
||||
checkCudnnFE(graph->execute(cudnn_handle, variant_pack, cudnn_workspace));
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void create_cudnn() {
|
||||
checkCudnnErr(cudnnCreate(&cudnn_handle));
|
||||
cuDNNCheck(cudnnCreate(&cudnn_handle));
|
||||
}
|
||||
|
||||
void destroy_cudnn() {
|
||||
if (cudnn_workspace != NULL) { cudaCheck(cudaFree(cudnn_workspace)); }
|
||||
checkCudnnErr(cudnnDestroy(cudnn_handle));
|
||||
cuDNNCheck(cudnnDestroy(cudnn_handle));
|
||||
}
|
||||
+2
-2
@@ -12,10 +12,10 @@ void destroy_cudnn();
|
||||
void attention_forward_cudnn(floatX* out, // output: (B, T, NH, HS)
|
||||
float* stats, // output for backward pass: (B, NH, T)
|
||||
floatX* inp, // input: (B, T, 3, NH, HS) QKV
|
||||
int B, int T, int NH, int C);
|
||||
int B, int T, int NH, int C, cudaStream_t stream);
|
||||
|
||||
void attention_backward_cudnn(floatX* dqkvr, // output
|
||||
floatX* dout, floatX* qkvr, floatX* o, float* stats, // inputs
|
||||
int B, int T, int NH, int C);
|
||||
int B, int T, int NH, int C, cudaStream_t stream);
|
||||
|
||||
#endif // CUDNN_ATT_H
|
||||
+123
-53
@@ -15,6 +15,7 @@ Implements:
|
||||
// defines: fopenCheck, freadCheck, fcloseCheck, fseekCheck
|
||||
// defines: mallocCheck
|
||||
#include "utils.h"
|
||||
#include "rand.h"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// implementation of glob for Windows is in dev/unistd.h
|
||||
@@ -30,23 +31,37 @@ typedef struct {
|
||||
// each process/worker has to access different parts of the data
|
||||
int process_rank;
|
||||
int num_processes;
|
||||
// hyperparameters. use size_t to prevent overflow
|
||||
// batch and token information
|
||||
size_t B;
|
||||
size_t T;
|
||||
// input handling and its state
|
||||
glob_t glob_result; // stores the result of glob, for all shards we want to iterate
|
||||
int current_shard; // the current shard we are reading from
|
||||
FILE* tokens_file;
|
||||
int64_t file_size;
|
||||
int64_t current_position;
|
||||
uint16_t* buffer; // we fread data from file into this buffer
|
||||
// public variables that could be accessed from outside
|
||||
size_t num_tokens; // total number of tokens
|
||||
size_t shard_num_samples; // total number of samples in the current shard per process
|
||||
// shards and current position
|
||||
glob_t glob_result; // stores the result of glob, for all shards we want to iterate
|
||||
size_t current_shard_idx; // the current shard we are reading from
|
||||
size_t current_sample_idx; // the current sample we are reading from
|
||||
// file handle
|
||||
FILE* tokens_file;
|
||||
// data buffers
|
||||
uint16_t* buffer; // we fread data from file into this buffer
|
||||
int* inputs; // input tokens into transformer
|
||||
int* targets; // target tokens for the transformer
|
||||
// random shuffle related variables
|
||||
mt19937_state shuffle_rng;
|
||||
int should_shuffle;
|
||||
int* shard_indices;
|
||||
int* intra_shard_indices;
|
||||
// sizes in bytes
|
||||
size_t total_batch_size_bytes; // total across all processes
|
||||
size_t local_batch_offset_bytes; // inner-sample offset for this process
|
||||
size_t header_bytes; // header size in bytes
|
||||
int64_t file_size_bytes;
|
||||
} DataLoader;
|
||||
|
||||
int64_t dataloader_load_shard_(DataLoader *loader, int shard_index) {
|
||||
if (loader->should_shuffle) {
|
||||
shard_index = loader->shard_indices[shard_index];
|
||||
}
|
||||
// use the first glob match as the filename for now
|
||||
const char* filename = loader->glob_result.gl_pathv[shard_index];
|
||||
// open the input file for reading. also only a single file can be opened at a time
|
||||
@@ -68,44 +83,60 @@ int64_t dataloader_load_shard_(DataLoader *loader, int shard_index) {
|
||||
assert(ntok > 0); // we expect some tokens in the file. this should never trip, right?
|
||||
// determine the file size and make sure it is consistent with the number of tokens
|
||||
fseekCheck(loader->tokens_file, 0, SEEK_END); // seek to end of file
|
||||
loader->file_size = ftell(loader->tokens_file); // read the offset, i.e. file size
|
||||
loader->file_size_bytes = ftell(loader->tokens_file); // read the offset, i.e. file size
|
||||
fseekCheck(loader->tokens_file, 0, SEEK_SET); // seek back to the beginning
|
||||
// we expect ntok in the file to be consistent with filesize, assert that is the case
|
||||
int64_t expected_file_size = HEADER_SIZE * sizeof(int) + ntok * sizeof(uint16_t);
|
||||
if (loader->file_size != expected_file_size) {
|
||||
if (loader->file_size_bytes != expected_file_size) {
|
||||
printf("Error: file size is not as expected\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
// -1 uint16_t due to us taking B*T+1 tokens but moving by B*T tokens
|
||||
loader->shard_num_samples = (ntok * sizeof(uint16_t) - sizeof(uint16_t)) / loader->total_batch_size_bytes;
|
||||
return ntok;
|
||||
}
|
||||
|
||||
void dataloader_resume(DataLoader *loader, int current_shard, int64_t current_position) {
|
||||
// used during model resumption (-y 1) flag
|
||||
loader->current_shard = current_shard;
|
||||
loader->current_position = current_position;
|
||||
dataloader_load_shard_(loader, loader->current_shard);
|
||||
void prepare_intra_shard_indices_(DataLoader *loader) {
|
||||
// shuffle the examples inside the shards
|
||||
if (loader->intra_shard_indices != NULL) {
|
||||
// in case shards have different number of samples / sizes
|
||||
free(loader->intra_shard_indices);
|
||||
}
|
||||
loader->intra_shard_indices = (int*)mallocCheck(loader->shard_num_samples * sizeof(int));
|
||||
init_identity_permutation(loader->intra_shard_indices, (int) loader->shard_num_samples);
|
||||
random_permutation(loader->intra_shard_indices, (int) loader->shard_num_samples, &loader->shuffle_rng);
|
||||
}
|
||||
|
||||
void dataloader_reset(DataLoader *loader) {
|
||||
// fully resets the DataLoader object to init configuration
|
||||
// each process starts at a different offset in the file
|
||||
int64_t header_bytes = HEADER_SIZE * sizeof(int);
|
||||
int64_t token_bytes_offset = loader->process_rank * loader->B * loader->T * sizeof(uint16_t);
|
||||
loader->current_shard = 0;
|
||||
loader->current_position = header_bytes + token_bytes_offset;
|
||||
dataloader_load_shard_(loader, loader->current_shard);
|
||||
loader->current_shard_idx = 0;
|
||||
loader->current_sample_idx = 0;
|
||||
|
||||
if (loader->should_shuffle) { // shuffle the shards
|
||||
random_permutation(loader->shard_indices, (int) loader->glob_result.gl_pathc, &loader->shuffle_rng);
|
||||
}
|
||||
|
||||
dataloader_load_shard_(loader, (int) loader->current_shard_idx);
|
||||
|
||||
if (loader->should_shuffle) {
|
||||
prepare_intra_shard_indices_(loader);
|
||||
}
|
||||
}
|
||||
|
||||
void dataloader_advance_(DataLoader *loader) {
|
||||
// advance the loader by loading the next data shard and resetting the position
|
||||
if (loader->glob_result.gl_pathc > 1) {
|
||||
// if we have more than one shard, advance to the next one
|
||||
loader->current_shard = (loader->current_shard + 1) % loader->glob_result.gl_pathc;
|
||||
dataloader_load_shard_(loader, loader->current_shard);
|
||||
if (loader->current_shard_idx == loader->glob_result.gl_pathc - 1) {
|
||||
// if we are at the last shard, we reset the loader and start a new epoch
|
||||
dataloader_reset(loader);
|
||||
return;
|
||||
}
|
||||
|
||||
// advance the loader by loading the next data shard and resetting the position
|
||||
loader->current_shard_idx = (loader->current_shard_idx + 1) % loader->glob_result.gl_pathc;
|
||||
loader->current_sample_idx = 0;
|
||||
dataloader_load_shard_(loader, (int) loader->current_shard_idx);
|
||||
|
||||
if (loader->should_shuffle) {
|
||||
prepare_intra_shard_indices_(loader);
|
||||
}
|
||||
int64_t header_bytes = HEADER_SIZE * sizeof(int);
|
||||
int64_t token_bytes_offset = loader->process_rank * loader->B * loader->T * sizeof(uint16_t);
|
||||
loader->current_position = header_bytes + token_bytes_offset;
|
||||
}
|
||||
|
||||
void dataloader_init(DataLoader *loader,
|
||||
@@ -113,12 +144,17 @@ void dataloader_init(DataLoader *loader,
|
||||
size_t B,
|
||||
size_t T,
|
||||
int process_rank,
|
||||
int num_processes) {
|
||||
int num_processes,
|
||||
int should_shuffle) {
|
||||
loader->process_rank = process_rank;
|
||||
loader->num_processes = num_processes;
|
||||
loader->B = B;
|
||||
loader->T = T;
|
||||
loader->tokens_file = NULL;
|
||||
loader->should_shuffle = should_shuffle;
|
||||
loader->header_bytes = HEADER_SIZE * sizeof(int);
|
||||
loader->total_batch_size_bytes = ((loader->num_processes * (loader->B * loader->T)) * sizeof(uint16_t));
|
||||
loader->local_batch_offset_bytes = loader->process_rank * loader->B * loader->T * sizeof(uint16_t);
|
||||
|
||||
// glob to get the list of files matching the pattern, these are our data shards
|
||||
int glob_status = glob(filename_pattern, 0, NULL, &loader->glob_result);
|
||||
@@ -131,6 +167,15 @@ void dataloader_init(DataLoader *loader,
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
if (should_shuffle) {
|
||||
mt19937_state shuffle_rng;
|
||||
manual_seed(&shuffle_rng, 42 + process_rank);
|
||||
loader->shuffle_rng = shuffle_rng;
|
||||
loader->shard_indices = (int*)mallocCheck(loader->glob_result.gl_pathc * sizeof(int));
|
||||
init_identity_permutation(loader->shard_indices, (int) loader->glob_result.gl_pathc);
|
||||
loader->intra_shard_indices = NULL; // dynamically allocated allowing different shard sizes
|
||||
}
|
||||
|
||||
// inspect and validate all shards so we don't get any runtime errors later
|
||||
// if too slow / too many shards, may wish to revisit later
|
||||
int64_t ntok_total = 0;
|
||||
@@ -138,7 +183,7 @@ void dataloader_init(DataLoader *loader,
|
||||
int64_t shard_ntok = dataloader_load_shard_(loader, shard_index);
|
||||
// we need at least one batch/shard, the way things are written right now.
|
||||
// can be relaxed a lot later.
|
||||
assert(shard_ntok >= num_processes * B * T + 1);
|
||||
assert(shard_ntok >= (int64_t) (num_processes * B * T + 1));
|
||||
ntok_total += shard_ntok;
|
||||
}
|
||||
// debugging prints
|
||||
@@ -146,40 +191,59 @@ void dataloader_init(DataLoader *loader,
|
||||
// printf("DataLoader: Found %ld tokens across %zu shards\n", ntok_total, loader->glob_result.gl_pathc);
|
||||
|
||||
// allocate all the space we'll need
|
||||
loader->buffer = (uint16_t*)malloc((B * T + 1) * sizeof(uint16_t));
|
||||
loader->inputs = (int*)malloc(B * T * sizeof(int));
|
||||
loader->targets = (int*)malloc(B * T * sizeof(int));
|
||||
loader->buffer = (uint16_t*)mallocCheck((B * T + 1) * sizeof(uint16_t));
|
||||
loader->inputs = (int*)mallocCheck(B * T * sizeof(int));
|
||||
loader->targets = (int*)mallocCheck(B * T * sizeof(int));
|
||||
loader->num_tokens = ntok_total;
|
||||
|
||||
// reset the loader, to initialize it
|
||||
dataloader_reset(loader);
|
||||
}
|
||||
|
||||
void dataloader_next_batch(DataLoader *loader) {
|
||||
void dataloader_load_batch(DataLoader* loader) {
|
||||
assert(!loader->should_shuffle || (loader->should_shuffle && loader->intra_shard_indices != NULL));
|
||||
assert(loader->current_sample_idx < loader->shard_num_samples);
|
||||
size_t idx = loader->should_shuffle ? loader->intra_shard_indices[loader->current_sample_idx] : loader->current_sample_idx;
|
||||
size_t global_batch_offset_bytes = idx * loader->total_batch_size_bytes;
|
||||
int64_t current_offset = loader->header_bytes + global_batch_offset_bytes + loader->local_batch_offset_bytes;
|
||||
|
||||
size_t B = loader->B;
|
||||
size_t T = loader->T;
|
||||
// read B*T+1 uint16_t tokens from the file into buffer
|
||||
fseekCheck(loader->tokens_file, loader->current_position, SEEK_SET);
|
||||
fseekCheck(loader->tokens_file, (int) current_offset, SEEK_SET);
|
||||
freadCheck(loader->buffer, sizeof(uint16_t), B*T+1, loader->tokens_file);
|
||||
// decode the buffer into inputs and targets (cast to int)
|
||||
for (int i = 0; i < B*T; i++) {
|
||||
loader->inputs[i] = (int)loader->buffer[i];
|
||||
loader->targets[i] = (int)loader->buffer[i+1];
|
||||
}
|
||||
// advance the current position by B*T*num_processes integers
|
||||
// note: the "stride" of tokens by which we move each time is definitely B * T
|
||||
// we only load B * T + 1 tokens at each iteration because the targets are offset by 1
|
||||
loader->current_position += loader->num_processes * B * T * sizeof(uint16_t);
|
||||
}
|
||||
|
||||
void dataloader_next_batch(DataLoader *loader) {
|
||||
// if the next batch would go past the end of the file, advance the loader
|
||||
if (loader->current_position + (loader->num_processes * B * T + 1) * sizeof(uint16_t) > loader->file_size) {
|
||||
if (loader->current_sample_idx >= loader->shard_num_samples) {
|
||||
dataloader_advance_(loader);
|
||||
}
|
||||
dataloader_load_batch(loader);
|
||||
loader->current_sample_idx += 1;
|
||||
}
|
||||
|
||||
|
||||
void dataloader_resume(DataLoader *loader, size_t current_shard_idx, size_t current_sample_idx) {
|
||||
// used during model resumption (-y 1) flag
|
||||
loader->current_shard_idx = current_shard_idx;
|
||||
loader->current_sample_idx = current_sample_idx;
|
||||
dataloader_load_shard_(loader, (int) loader->current_shard_idx);
|
||||
}
|
||||
|
||||
void dataloader_free(DataLoader *loader) {
|
||||
free(loader->buffer);
|
||||
free(loader->inputs);
|
||||
free(loader->targets);
|
||||
if (loader->should_shuffle) {
|
||||
free(loader->shard_indices);
|
||||
free(loader->intra_shard_indices);
|
||||
}
|
||||
fcloseCheck(loader->tokens_file);
|
||||
globfree(&loader->glob_result);
|
||||
}
|
||||
@@ -237,7 +301,13 @@ void evalloader_reset(EvalLoader *loader) {
|
||||
// then process 0 should start at 0, process 1 at N/4, process 2 at N/2, etc.
|
||||
// determine how much work there is for all processes
|
||||
int examples_per_process = CEIL_DIV(loader->num_examples, loader->num_processes);
|
||||
int can_fit_examples = loader->B / ASSUMED_NUM_COMPLETIONS;
|
||||
int can_fit_examples = (int) (loader->B / ASSUMED_NUM_COMPLETIONS);
|
||||
if (can_fit_examples == 0) {
|
||||
// this could be fixed in the future, but for now keeping it simple and throw error when B too low
|
||||
printf("HellaSwag EvalLoader: batch size %zu is < %d\n", loader->B, ASSUMED_NUM_COMPLETIONS);
|
||||
printf("---> HINT: Disable HellaSwag eval with -h 0, or increase batch size with -b\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
loader->num_batches = CEIL_DIV(examples_per_process, can_fit_examples);
|
||||
// determine the start and end example indices for this process
|
||||
loader->start_example_index = examples_per_process * loader->process_rank;
|
||||
@@ -249,7 +319,7 @@ void evalloader_reset(EvalLoader *loader) {
|
||||
// now seek through the file to the start of that example
|
||||
// utilize <EXAMPLE_BYTES> for efficiency
|
||||
int64_t header_bytes = HEADER_SIZE * sizeof(int);
|
||||
fseekCheck(loader->eval_file, header_bytes, SEEK_SET);
|
||||
fseekCheck(loader->eval_file, (int) header_bytes, SEEK_SET);
|
||||
for (int i = 0; i < loader->start_example_index; i++) {
|
||||
uint16_t example_header[3];
|
||||
// read 3 uint16_t values: <START_EXAMPLE>, <EXAMPLE_BYTES>, <EXAMPLE_INDEX>
|
||||
@@ -261,7 +331,7 @@ void evalloader_reset(EvalLoader *loader) {
|
||||
// skip to the next example, keeping in mind that we already read the header
|
||||
size_t remaining_bytes = example_header[1] - sizeof(uint16_t) * 3;
|
||||
assert(remaining_bytes > 0); // we expect some bytes in the example
|
||||
fseekCheck(loader->eval_file, remaining_bytes, SEEK_CUR);
|
||||
fseekCheck(loader->eval_file, (int) remaining_bytes, SEEK_CUR);
|
||||
}
|
||||
// now we are at the start of the example we want to start at, pointing at <START_EXAMPLE>
|
||||
loader->current_example_index = loader->start_example_index;
|
||||
@@ -296,12 +366,12 @@ void evalloader_init(EvalLoader *loader,
|
||||
assert(longest_example_bytes > 0 && longest_example_bytes < (1+ASSUMED_NUM_COMPLETIONS)*T*2);
|
||||
|
||||
// allocate all the space we'll need
|
||||
int can_fit_examples = B / ASSUMED_NUM_COMPLETIONS;
|
||||
loader->buffer = (uint16_t*)malloc(longest_example_bytes);
|
||||
int can_fit_examples = (int) (B / ASSUMED_NUM_COMPLETIONS);
|
||||
loader->buffer = (uint16_t*)mallocCheck(longest_example_bytes);
|
||||
loader->inputs = (int*)calloc(B * T, sizeof(int));
|
||||
loader->targets = (int*)calloc(B * T, sizeof(int));
|
||||
loader->mask = (char*)malloc(B * T * sizeof(char));
|
||||
loader->label = (int*)malloc(can_fit_examples * sizeof(int));
|
||||
loader->mask = (char*)mallocCheck(B * T * sizeof(char));
|
||||
loader->label = (int*)mallocCheck(can_fit_examples * sizeof(int));
|
||||
|
||||
// reset the loader, to initialize it
|
||||
evalloader_reset(loader);
|
||||
@@ -329,7 +399,7 @@ void evalloader_next_example_(EvalLoader *loader, int example_batch_index) {
|
||||
freadCheck(loader->buffer, sizeof(char), example_bytes, loader->eval_file);
|
||||
// process the example label
|
||||
int label = (int)loader->buffer[0];
|
||||
int can_fit_examples = loader->B / ASSUMED_NUM_COMPLETIONS;
|
||||
int can_fit_examples = (int) (loader->B / ASSUMED_NUM_COMPLETIONS);
|
||||
assert(label >= 0 && label < ASSUMED_NUM_COMPLETIONS); // we expect the label to be in [0, 4) for right now
|
||||
assert(example_batch_index >= 0 && example_batch_index < can_fit_examples);
|
||||
loader->label[example_batch_index] = label; // store for output
|
||||
@@ -386,7 +456,7 @@ void evalloader_next_batch(EvalLoader *loader) {
|
||||
// we have a batch dimension of B, which we want to take full advantage of
|
||||
// each example has some number of completions (usually 4)
|
||||
// so we want to pack as many examples into rows of B as we can fit
|
||||
int can_fit_examples = B / ASSUMED_NUM_COMPLETIONS; // how many examples can we fit in the batch?
|
||||
int can_fit_examples = (int) (B / ASSUMED_NUM_COMPLETIONS); // how many examples can we fit in the batch?
|
||||
for (int i = 0; i < can_fit_examples; i++) {
|
||||
if (loader->current_example_index >= loader->end_example_index) {
|
||||
break; // this process has exhausted its work, noop from here on
|
||||
@@ -405,7 +475,7 @@ int evalloader_stat_losses(EvalLoader *loader, float* losses) {
|
||||
size_t B = loader->B;
|
||||
size_t T = loader->T;
|
||||
// iterate the examples in this batch
|
||||
int can_fit_examples = B / ASSUMED_NUM_COMPLETIONS;
|
||||
int can_fit_examples = (int) (B / ASSUMED_NUM_COMPLETIONS);
|
||||
for (int i = 0; i < can_fit_examples; i++) {
|
||||
float min_loss = 0.0f;
|
||||
int min_loss_index = -1;
|
||||
|
||||
+19
-12
@@ -4,9 +4,10 @@ In the forward pass, both encodings are added together
|
||||
In the backward pass, the gradients flow to both, handled by different kernels
|
||||
*/
|
||||
#include <assert.h>
|
||||
#include <stdint.h>
|
||||
#include <utility> // std::pair
|
||||
#include <vector>
|
||||
#include <algorithm>
|
||||
#include <functional>
|
||||
#include <unordered_map>
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
@@ -106,8 +107,11 @@ __global__ void wte_backward_kernel(floatX* dwte,
|
||||
|
||||
// Add the result to dwte and write back to global memory (read-modify-write)
|
||||
for (unsigned int k = 0; k < x128::size; k++) {
|
||||
// We use stochastic rounding to go from FP32 to BF16 but the seed should be deterministic
|
||||
stochastic_rounding(accum[k] + (float)packed_in_out[k], &packed_in_out[k], seed + k);
|
||||
// We use stochastic rounding to go from FP32 to BF16
|
||||
// The seed is deterministic and unique for each parameter to guarantee we have determinism AND
|
||||
// to avoid **potential** issues with positionX int SquirrelNoise5 argument overflowing which is UB
|
||||
// and that somehow messing the quality of random numbers
|
||||
stochastic_rounding(accum[k] + (float)packed_in_out[k], &packed_in_out[k], seed + bucket * WARP_SIZE + threadIdx.x + k);
|
||||
}
|
||||
store128(dwte_ix, packed_in_out);
|
||||
}
|
||||
@@ -138,8 +142,11 @@ __global__ void wpe_backward_kernel(floatX* dwpe,
|
||||
floatX* dwpe_tc = dwpe + (t * C) + c;
|
||||
x128 packed_dwpe = load128(dwpe_tc);
|
||||
for (unsigned int k = 0; k < x128::size; k++) {
|
||||
// We use stochastic rounding to go from FP32 to BF16 but the seed should be deterministic
|
||||
stochastic_rounding(accum[k] + (float)packed_dwpe[k], &packed_dwpe[k], seed + k);
|
||||
// We use stochastic rounding to go from FP32 to BF16
|
||||
// The seed is deterministic and unique for each parameter to guarantee we have determinism AND
|
||||
// to avoid **potential** issues with positionX int SquirrelNoise5 argument overflowing which is UB
|
||||
// and that somehow messing the quality of random numbers
|
||||
stochastic_rounding(accum[k] + (float)packed_dwpe[k], &packed_dwpe[k], seed + idx + k);
|
||||
}
|
||||
store128(dwpe_tc, packed_dwpe);
|
||||
}
|
||||
@@ -149,12 +156,12 @@ __global__ void wpe_backward_kernel(floatX* dwpe,
|
||||
|
||||
void encoder_forward(floatX* out,
|
||||
const int* inp, const floatX* wte, const floatX* wpe,
|
||||
int B, int T, int C) {
|
||||
int B, int T, int C, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 256;
|
||||
const int N = B * T * C;
|
||||
const int grid_size = CEIL_DIV(N, (int)(block_size * x128::size));
|
||||
encoder_forward_kernel3<<<grid_size, block_size>>>(out, inp, wte, wpe, B, T, C);
|
||||
encoder_forward_kernel3<<<grid_size, block_size, 0, stream>>>(out, inp, wte, wpe, B, T, C);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
@@ -162,14 +169,14 @@ void encoder_forward(floatX* out,
|
||||
void encoder_backward(floatX* dwte, floatX* dwpe, floatX* scratch, // gpu outputs & scratch
|
||||
int* workload_indices, int4* bucket_info, // cpu scratch buffers
|
||||
const floatX* dout, const int* inp, const int* inputs_cpu, // cpu/gpu inputs
|
||||
int B, int T, int C, unsigned int seed) {
|
||||
int B, int T, int C, unsigned int seed, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
|
||||
// Launch wpe kernel first (so it runs on the GPU in parallel with the CPU pre-processing for wte)
|
||||
const int block_size = 256;
|
||||
const int N = T * C / x128::size;
|
||||
const int grid_size = CEIL_DIV(N, block_size);
|
||||
wpe_backward_kernel<<<grid_size, block_size, 0>>>(dwpe, dout, inp, B, T, C, seed);
|
||||
wpe_backward_kernel<<<grid_size, block_size, 0, stream>>>(dwpe, dout, inp, B, T, C, seed);
|
||||
cudaCheck(cudaGetLastError());
|
||||
|
||||
// check the GPU scratch buffer is large enough to hold the bucket info and workload indices
|
||||
@@ -217,11 +224,11 @@ void encoder_backward(floatX* dwte, floatX* dwpe, floatX* scratch, // gpu output
|
||||
// todo - could use CUDA events (even without streams) to avoid CPU/GPU synchronisation completely
|
||||
int4* d_bucket_info = (int4*)scratch;
|
||||
int* d_workload_indices = (int*)(scratch + B*T*num_c_groups * sizeof(int4));
|
||||
cudaCheck(cudaMemcpyAsync(d_bucket_info, bucket_info, num_buckets * sizeof(int4), cudaMemcpyHostToDevice));
|
||||
cudaCheck(cudaMemcpy(d_workload_indices, workload_indices, total_items * sizeof(int), cudaMemcpyHostToDevice));
|
||||
cudaCheck(cudaMemcpyAsync(d_bucket_info, bucket_info, num_buckets * sizeof(int4), cudaMemcpyHostToDevice, stream));
|
||||
cudaCheck(cudaMemcpyAsync(d_workload_indices, workload_indices, total_items * sizeof(int), cudaMemcpyHostToDevice, stream));
|
||||
|
||||
// Launch wte kernel
|
||||
// todo - profile block sizes on more content (depends on number of buckets and on GPU?)
|
||||
wte_backward_kernel<256><<<num_buckets, 256>>>(dwte, d_bucket_info, d_workload_indices, dout, inp, seed, B, T, C);
|
||||
wte_backward_kernel<256><<<num_buckets, 256, 0, stream>>>(dwte, d_bucket_info, d_workload_indices, dout, inp, seed, B, T, C);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
@@ -0,0 +1,149 @@
|
||||
/*
|
||||
Fused Classifier:
|
||||
- Forwards the Cross Entropy Loss
|
||||
- Never materializes the full normalized logits, only at the target label
|
||||
- (fusion) Also kicks off the backward pass, because everything is already loaded
|
||||
*/
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
struct SoftmaxParams {
|
||||
float Scale;
|
||||
float Offset;
|
||||
};
|
||||
|
||||
__device__ SoftmaxParams prepare_softmax_blockwide3(int64_t idx, const floatX* inp, int V, int P) {
|
||||
// same but not float4
|
||||
// one row of inp, i.e. inp[idx, :] of shape (V,)
|
||||
|
||||
const floatX* x = inp + idx * P;
|
||||
float thread_maxval = -INFINITY;
|
||||
float thread_sumval = 0.0f;
|
||||
int i = (V+x128::size-1)/x128::size + threadIdx.x - blockDim.x;
|
||||
|
||||
// special-case loop to handle the unaligned elements at the end of the array
|
||||
// this lets us skip the bounds check in the main loop below, which improves performance
|
||||
while ((i+1)*x128::size > V) {
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
if (i*x128::size+k >= V) {
|
||||
break; // bounds checking against real V (rather than padded P)
|
||||
}
|
||||
float v = (float)x[i*x128::size+k];
|
||||
float old_maxval = thread_maxval;
|
||||
thread_maxval = fmaxf(thread_maxval, v);
|
||||
thread_sumval *= expf((old_maxval - thread_maxval));
|
||||
thread_sumval += expf(v - thread_maxval);
|
||||
}
|
||||
i -= blockDim.x;
|
||||
}
|
||||
|
||||
// main loop for the bulk of the iterations (no bounds checking required!)
|
||||
for (; i >= 0; i -= blockDim.x) {
|
||||
x128 packed_x = load128(x + i * x128::size); // load and keep in cache until fused_classifier loop
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
float v = (float)packed_x[k];
|
||||
float old_maxval = thread_maxval;
|
||||
thread_maxval = fmaxf(thread_maxval, v);
|
||||
thread_sumval *= expf((old_maxval - thread_maxval));
|
||||
thread_sumval += expf(v - thread_maxval);
|
||||
}
|
||||
}
|
||||
|
||||
// Block Max Reduction -> Maths -> Block Sum Reduction
|
||||
float block_maxval = blockReduce<warpReduceMax>(thread_maxval, false, -INFINITY);
|
||||
thread_sumval *= expf(thread_maxval - block_maxval);
|
||||
float block_sumval = blockReduce<warpReduceSum>(thread_sumval);
|
||||
|
||||
// return the softmax parameters
|
||||
return SoftmaxParams{1.f / block_sumval, block_maxval};
|
||||
}
|
||||
|
||||
// will _update_ logits to logit gradients
|
||||
// uses template to decide whether to write logits and probs
|
||||
// split both loops in "multiple-of-x128-size" and "bounds-checked remainder" parts
|
||||
template <bool WriteDLogits = true, bool WriteProbs = false>
|
||||
__global__ void __launch_bounds__(1024, MAX_1024_THREADS_BLOCKS)
|
||||
fused_classifier_kernel5(floatX* logits, float* losses, floatX* probs,
|
||||
const float dloss, const int* targets,
|
||||
int B, int T, int V, int P, std::bool_constant<WriteDLogits>) {
|
||||
// note: idx is small enough that it easily fits into 32 bit;
|
||||
// by making it a long here, we ensure that any offsets calculated with it (e.g., idx * P)
|
||||
// are done is 64 bit
|
||||
int64_t idx = gridDim.x - (blockIdx.x+1); // reverse order for cache hits on matmul data
|
||||
int ix = targets[idx];
|
||||
|
||||
// softmax (reading B * T * V, same logits read again below, hopefully still in cache)
|
||||
SoftmaxParams sp = prepare_softmax_blockwide3(idx, logits, V, P);
|
||||
|
||||
// calculate the probability needed for the loss and update (single-threaded)
|
||||
if(threadIdx.x == 0) {
|
||||
float prob = expf((float)logits[idx * P + ix] - sp.Offset) * sp.Scale;
|
||||
losses[idx] -= logf(prob);
|
||||
}
|
||||
|
||||
// without this synchronization point we have a race condition:
|
||||
// the logits used above to compute the loss are concurrently (race) modified to carry backward pass grads.
|
||||
// since the "logits" are overwritten to be in the [-1, 1] range and sp.Offset is sometimes smaller than -90
|
||||
// we errouneously end up computing exp^(90+) which gives us infinities in the loss! this is the fix.
|
||||
__syncthreads();
|
||||
|
||||
// calculate the gradients directly, saves bandwidth from probs during training
|
||||
// but also supports writing probs for inference-only and debugging
|
||||
const floatX* logits_vec = logits + idx * P;
|
||||
for (int i = threadIdx.x; i < V/x128::size; i += blockDim.x) {
|
||||
// this is the 2nd read of logits after the one in prepare_softmax2
|
||||
// it will be overwritten by the logits gradients which is when we reduce cache persistence
|
||||
x128 packed_logits_vec = load128(logits_vec + i * x128::size); // rely on cs of store128cs
|
||||
x128 packed_probs;
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
int element = i*x128::size + k;
|
||||
float prob = expf((float)packed_logits_vec[k] - sp.Offset) * sp.Scale;
|
||||
packed_probs[k] = (floatX)prob;
|
||||
float indicator = (element == ix) ? 1.0f : 0.0f;
|
||||
packed_logits_vec[k] = (floatX)((prob - indicator) * dloss);
|
||||
}
|
||||
if (WriteDLogits){
|
||||
// reduce cache persistence for the overwritten logits
|
||||
// to maximise probability that logits remain in cache between prepare_softmax and here
|
||||
store128cs(logits + idx * P + i * x128::size, packed_logits_vec);
|
||||
}
|
||||
if (WriteProbs) {
|
||||
store128(probs + idx * P + i * x128::size, packed_probs);
|
||||
}
|
||||
}
|
||||
|
||||
// handle remaining elements after the last multiple of x128::size
|
||||
// e.g. if V = 8003, and x128::size = 8, we need to handle the last 3 elements
|
||||
int unaligned_start = V & ~(x128::size - 1); // round down to multiple of x128::size
|
||||
for (int i = threadIdx.x + unaligned_start; i < V; i++) {
|
||||
float prob = expf((float)logits_vec[i] - sp.Offset) * sp.Scale;
|
||||
float indicator = (i == ix) ? 1.0f : 0.0f;
|
||||
float dlogit = (prob - indicator) * dloss;
|
||||
if (WriteDLogits){
|
||||
__stcs(logits + idx * P + i, (floatX)dlogit);
|
||||
}
|
||||
if (WriteProbs) {
|
||||
probs[idx * P + i] = (floatX)prob;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
// replaces logits with logit gradients
|
||||
template <typename Type, bool WriteDLogits>
|
||||
void fused_classifier(Type* logits, float* losses,
|
||||
const float dloss, const int* targets,
|
||||
int B, int T, int V, int P, std::bool_constant<WriteDLogits> write_dlogits, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 1024;
|
||||
const int N = B * T;
|
||||
const int grid_size = N;
|
||||
fused_classifier_kernel5<<<grid_size, block_size, 0, stream>>>(logits, losses, (floatX*)NULL, dloss, targets, B, T, V, P, write_dlogits);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
@@ -0,0 +1,66 @@
|
||||
/*
|
||||
(Approximate) GeLU non-linearity layer
|
||||
*/
|
||||
#include <assert.h>
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
#define GELU_SCALING_FACTOR sqrtf(2.0f / M_PI)
|
||||
__global__ void gelu_forward_kernel2(floatX* out, const floatX* inp) {
|
||||
int idx = (blockIdx.x * blockDim.x + threadIdx.x) * x128::size;
|
||||
|
||||
x128 packed_out;
|
||||
x128 packed_inp = load128cs(inp + idx); // load and do not keep in cache
|
||||
for(int k = 0; k < packed_inp.size; ++k) {
|
||||
float xi = (float)packed_inp[k];
|
||||
float cube = 0.044715f * xi * xi * xi;
|
||||
packed_out[k] = (floatX)(0.5f * xi * (1.0f + tanhf(GELU_SCALING_FACTOR * (xi + cube))));
|
||||
}
|
||||
// store instead of storecs (without cache streaming) in case it is useful for the
|
||||
// data to be in the cache for the next operation after this GeLU
|
||||
store128(out + idx, packed_out);
|
||||
}
|
||||
|
||||
__global__ void gelu_backward_inplace_kernel(floatX* d_in_out, const floatX* inp) {
|
||||
int idx = (blockIdx.x * blockDim.x + threadIdx.x) * x128::size;
|
||||
|
||||
x128 packed_dinp;
|
||||
x128 packed_inp = load128cs(inp + idx);
|
||||
x128 packed_dout = load128(d_in_out + idx);
|
||||
for (int k = 0; k < packed_inp.size; ++k) {
|
||||
float x = (float)packed_inp[k];
|
||||
float cube = 0.044715f * x * x * x;
|
||||
float tanh_arg = GELU_SCALING_FACTOR * (x + cube);
|
||||
float tanh_out = tanhf(tanh_arg);
|
||||
float coshf_out = coshf(tanh_arg);
|
||||
float sech_out = 1.0f / (coshf_out * coshf_out);
|
||||
float local_grad = 0.5f * (1.0f + tanh_out) + x * 0.5f * sech_out * GELU_SCALING_FACTOR * (1.0f + 3.0f * 0.044715f * x * x);
|
||||
packed_dinp[k] = (floatX)(local_grad * (float)packed_dout[k]);
|
||||
}
|
||||
store128(d_in_out + idx, packed_dinp);
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
void gelu_forward(floatX* out, const floatX* inp, int N, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 512;
|
||||
assert(N % (block_size * x128::size) == 0);
|
||||
const int grid_size = CEIL_DIV(N, block_size * x128::size);
|
||||
gelu_forward_kernel2<<<grid_size, block_size, 0, stream>>>(out, inp);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void gelu_backward_inplace(floatX* d_in_out, const floatX* inp, const int N, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 128;
|
||||
assert(N % (block_size * x128::size) == 0);
|
||||
const int grid_size = CEIL_DIV(N, block_size * x128::size);
|
||||
gelu_backward_inplace_kernel<<<grid_size, block_size, 0, stream>>>(d_in_out, inp);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
/*
|
||||
Global norm, used in gradient clipping
|
||||
*/
|
||||
#include <assert.h>
|
||||
#include <stddef.h>
|
||||
#include <cuda_runtime_api.h>
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
template<class T>
|
||||
__device__ float global_norm_squared_for_range(const T* data, size_t count) {
|
||||
size_t index = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
size_t grid_width = blockDim.x * gridDim.x;
|
||||
float accumulator = 0.f;
|
||||
for(size_t i = index; i < count; i += grid_width) {
|
||||
accumulator += (float)data[i] * (float)data[i];
|
||||
}
|
||||
// block-level reduce
|
||||
return blockReduce<warpReduceSum>(accumulator);
|
||||
}
|
||||
|
||||
template<class T>
|
||||
__global__ void global_norm_squared_kernel(float* out, const T* data, size_t count, ptrdiff_t stride) {
|
||||
float block_sum = global_norm_squared_for_range(data + blockIdx.y * stride, count);
|
||||
// each block accumulates its partial sum to out[out_index]
|
||||
// we want to avoid using atomic add here so we combine this kernel with another kernel call
|
||||
// that sums up the partial block sums
|
||||
if(threadIdx.x == 0) {
|
||||
size_t out_index = blockIdx.y * gridDim.x + blockIdx.x;
|
||||
out[out_index] = out[out_index] + block_sum;
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void global_norm_aggregate_kernel(float* out, size_t grid_size) {
|
||||
size_t index = threadIdx.x;
|
||||
// grab block sums from the previous kernel, use 0. as the neutral sum element
|
||||
float block_sum = (index < grid_size) ? out[index] : 0.f;
|
||||
float sum = blockReduce<warpReduceSum>(block_sum);
|
||||
if(threadIdx.x == 0) {
|
||||
out[0] = sum; // out[0] ends up with the final norm squared
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launcher
|
||||
|
||||
// Helper function determines the maximum number of block sums
|
||||
int get_max_num_block_sums(int* num_slices_all, int numel) {
|
||||
// NOTE: this needs to be kept in sync with `global_norm_squared` below.
|
||||
const int block_size = 512;
|
||||
const int grid_size = deviceProp.maxThreadsPerMultiProcessor * deviceProp.multiProcessorCount / block_size;
|
||||
assert(grid_size > 0);
|
||||
int max_num_block_sums = 0;
|
||||
for (int i = 0; i < numel; i++) {
|
||||
int num_slices = num_slices_all[i];
|
||||
const int gx = CEIL_DIV(grid_size, num_slices);
|
||||
const int gy = num_slices;
|
||||
max_num_block_sums = max(max_num_block_sums, gx * gy);
|
||||
}
|
||||
|
||||
return max_num_block_sums;
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
void global_norm_squared(float* out, const T* values, size_t count, ptrdiff_t stride, int num_slices, int max_num_block_sums, bool reset, cudaStream_t stream) {
|
||||
const int block_size = 512;
|
||||
// launch just enough blocks to fill the grid. deliberately no DIV_CEIL.
|
||||
// having one block less than possible is a tiny performance hit, having
|
||||
// one block too many is catastrophic, since it only can start once all the other
|
||||
// blocks finish. anyway, I think cuda_threads_per_SM should be a multiple of 512
|
||||
// on all gpus, so the division really is going to be exact.
|
||||
const int grid_size = deviceProp.maxThreadsPerMultiProcessor * deviceProp.multiProcessorCount / block_size;
|
||||
assert(grid_size > 0); // gives a better error than letting the call below fail
|
||||
|
||||
const int gx = CEIL_DIV(grid_size, num_slices);
|
||||
const int gy = num_slices;
|
||||
|
||||
assert(gx * gy < 1024); // we want to later accumulate the block sums in a single block
|
||||
|
||||
if (reset) {
|
||||
cudaCheck(cudaMemsetAsync(out, 0, max_num_block_sums * sizeof(float), stream));
|
||||
}
|
||||
global_norm_squared_kernel<<<dim3(gx, gy), block_size, 0, stream>>>(out, values, count, stride);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
@@ -0,0 +1,505 @@
|
||||
/*
|
||||
LayerNorm CUDA kernel, and also Residual, because sometimes they are fused
|
||||
|
||||
Note in llm.c we try to be clever in the backward pass to conserve memory.
|
||||
All parameters use a += in the backward pass, so we can do gradient accumulation.
|
||||
But all activations have = instead of += because these are faster (just read, no write).
|
||||
This is okay for all activations except for those in the residual stream, where the
|
||||
gradients have to add. We make sure that we do a += as necessary.
|
||||
E.g., the layernorms are connected to the residuals so we += in layernorm backward.
|
||||
*/
|
||||
|
||||
#include <assert.h>
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
__global__ void layernorm_forward_kernel3(floatX* __restrict__ out, float* __restrict__ mean, float* __restrict__ rstd,
|
||||
const floatX* __restrict__ inp, const floatX* __restrict__ weight,
|
||||
const floatX* __restrict__ bias, int N, int C) {
|
||||
int lane_id = threadIdx.x % WARP_SIZE;
|
||||
int warp_id = threadIdx.x / WARP_SIZE;
|
||||
int num_warps = blockDim.x / WARP_SIZE;
|
||||
|
||||
int idx = blockIdx.x * num_warps + warp_id;
|
||||
if(idx >= N) { return; } // guard
|
||||
|
||||
// the row of input that this group of threads is responsible for
|
||||
const floatX* x = inp + idx * C;
|
||||
|
||||
// mean
|
||||
float sum = 0.0f;
|
||||
for (int i = lane_id; i < C; i += WARP_SIZE) {
|
||||
sum += (float)x[i];
|
||||
}
|
||||
sum = warpReduceSum(sum);
|
||||
float m = sum / C;
|
||||
if(lane_id == 0 && mean != nullptr) {
|
||||
__stcs(mean + idx, m);
|
||||
}
|
||||
|
||||
// rstd
|
||||
sum = 0.0f;
|
||||
for (int i = lane_id; i < C; i += WARP_SIZE) {
|
||||
float diff = (float)x[i] - m;
|
||||
sum += diff * diff;
|
||||
}
|
||||
sum = warpReduceSum(sum);
|
||||
float s = rsqrtf(sum / C + 1e-5f);
|
||||
if(lane_id == 0 && rstd != nullptr) {
|
||||
__stcs(rstd + idx, s);
|
||||
}
|
||||
|
||||
// final normalization and scaling by weight/bias
|
||||
floatX* o = out + idx * C;
|
||||
for (int c = lane_id; c < C; c += WARP_SIZE) {
|
||||
// load and store using the .cs "streaming" hint to the compiler,
|
||||
// indicating that this data will not be reused soon, and can be streamed through the caches
|
||||
// this allows the threads to get more cache-hits for the (shared) weight and bias parameters
|
||||
float n = s * ((float)__ldcs(x+c) - m);
|
||||
__stcs(o+c, (floatX)(n * (float)weight[c] + (float)bias[c]));
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void layernorm_forward_kernel6(floatX* __restrict__ out, float* __restrict__ mean, float* __restrict__ rstd,
|
||||
const floatX* __restrict__ inp, const floatX* __restrict__ weight,
|
||||
const floatX* __restrict__ bias, int N, int C) {
|
||||
assert(blockDim.x == WARP_SIZE);
|
||||
|
||||
// load weights and biases into shared memory
|
||||
// do this before we allow any threads to exit!
|
||||
extern __shared__ char* params[];
|
||||
// load128/store128 sometimes generated multiple instructions when the types here were floatX*, so
|
||||
// let's keep everything as x128
|
||||
x128* s_weight = reinterpret_cast<x128*>(params);
|
||||
x128* s_bias = reinterpret_cast<x128*>(params) + (C / x128::size);
|
||||
x128* s_in = reinterpret_cast<x128*>(params) + ((2 + threadIdx.y) * C / x128::size);
|
||||
|
||||
int sidx = (threadIdx.x + WARP_SIZE * threadIdx.y) * x128::size;
|
||||
for(int i = sidx; i < C; i += blockDim.y * WARP_SIZE * x128::size) {
|
||||
s_weight[i/x128::size] = load128(weight + i);
|
||||
s_bias[i/x128::size] = load128(bias + i);
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
int idx = blockIdx.x * blockDim.y + threadIdx.y;
|
||||
if(idx >= N) { return; } // guard
|
||||
|
||||
// adjust pointers to current token
|
||||
inp += idx * C;
|
||||
out += idx * C;
|
||||
|
||||
const float eps = 1e-5f;
|
||||
float sum = 0.0f;
|
||||
for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
|
||||
const x128 in_data = load128cs(inp + c);
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
sum += (float)in_data[k];
|
||||
}
|
||||
s_in[c / x128::size] = in_data;
|
||||
}
|
||||
|
||||
sum = warpReduceSum(sum);
|
||||
float m = sum / C;
|
||||
float v = 0.f;
|
||||
|
||||
for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
|
||||
const x128 in_data = s_in[c / x128::size];
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
v += ((float)in_data[k] - m) * ((float)in_data[k] - m);
|
||||
}
|
||||
}
|
||||
|
||||
v = warpReduceSum(v) / C;
|
||||
float s = rsqrtf(v + eps);
|
||||
|
||||
for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
|
||||
const x128 in_data = s_in[c / x128::size];
|
||||
const x128 w = s_weight[c / x128::size];
|
||||
const x128 b = s_bias[c / x128::size];
|
||||
x128 out_data;
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
float n = s * ((float)in_data[k] - m); // normalized output
|
||||
float o = n * (float)w[k] + (float)b[k]; // scale and shift it
|
||||
out_data[k] = (floatX)o;
|
||||
}
|
||||
|
||||
store128cs(out + c, out_data);
|
||||
}
|
||||
// cache the mean and rstd for the backward pass later
|
||||
if(threadIdx.x == 0 && mean != nullptr) {
|
||||
__stcs(mean + idx, m);
|
||||
}
|
||||
// store the rstd, no need to cache it
|
||||
if(threadIdx.x == 0 && rstd != nullptr) {
|
||||
__stcs(rstd + idx, s);
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void fused_residual_forward_kernel5(floatX* residual, floatX* normed, float* mean, float* rstd,
|
||||
const floatX* inp1, const floatX* inp2,
|
||||
const floatX* weight, const floatX* bias,
|
||||
int N, int C) {
|
||||
assert(blockDim.x == WARP_SIZE);
|
||||
|
||||
// load weights and biases into shared memory
|
||||
// do this before we allow any threads to exit!
|
||||
extern __shared__ char* params[];
|
||||
// load128/store128 sometimes generated multiple instructions when the types here were floatX*, so
|
||||
// let's keep everything as x128
|
||||
x128* s_weight = reinterpret_cast<x128*>(params);
|
||||
x128* s_bias = reinterpret_cast<x128*>(params) + (C / x128::size);
|
||||
x128* s_res = reinterpret_cast<x128*>(params) + ((2 + threadIdx.y) * C / x128::size);
|
||||
|
||||
int sidx = (threadIdx.x + WARP_SIZE * threadIdx.y) * x128::size;
|
||||
for(int i = sidx; i < C; i += blockDim.y * WARP_SIZE * x128::size) {
|
||||
s_weight[i/x128::size] = load128(weight + i);
|
||||
s_bias[i/x128::size] = load128(bias + i);
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
int idx = blockIdx.x * blockDim.y + threadIdx.y;
|
||||
if(idx > N) return;
|
||||
|
||||
// adjust pointers to current token
|
||||
residual += C * idx;
|
||||
normed += C * idx;
|
||||
inp1 += C * idx;
|
||||
inp2 += C * idx;
|
||||
|
||||
const float eps = 1e-5f;
|
||||
float sum = 0.0f;
|
||||
for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
|
||||
const x128 in1 = load128cs(inp1 + c);
|
||||
const x128 in2 = load128cs(inp2 + c);
|
||||
x128 out;
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
out[k] = (float)in1[k] + (float)in2[k];
|
||||
sum += (float)out[k];
|
||||
}
|
||||
store128cs(residual + c, out);
|
||||
s_res[c / x128::size] = out;
|
||||
}
|
||||
|
||||
sum = warpReduceSum(sum);
|
||||
float m = sum / C;
|
||||
float v = 0.f;
|
||||
|
||||
for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
|
||||
const x128 res = s_res[c / x128::size];
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
v += ((float)res[k] - m) * ((float)res[k] - m);
|
||||
}
|
||||
}
|
||||
|
||||
v = warpReduceSum(v) / C;
|
||||
float s = rsqrtf(v + eps);
|
||||
|
||||
for(int c = threadIdx.x * x128::size; c < C; c += WARP_SIZE * x128::size) {
|
||||
const x128 res = s_res[c / x128::size];
|
||||
const x128 w = s_weight[c / x128::size];
|
||||
const x128 b = s_bias[c / x128::size];
|
||||
x128 out;
|
||||
for(int k = 0; k < x128::size; ++k) {
|
||||
float n = s * ((float)res[k] - m); // normalized output
|
||||
float o = n * (float)w[k] + (float)b[k]; // scale and shift it
|
||||
out[k] = o;
|
||||
}
|
||||
|
||||
store128cs(normed + c, out);
|
||||
}
|
||||
// cache the mean and rstd for the backward pass later
|
||||
if(threadIdx.x == 0) {
|
||||
mean[idx] = m;
|
||||
rstd[idx] = s;
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void residual_forward_kernel(floatX* out, const floatX* inp1, const floatX* inp2) {
|
||||
int idx = (blockIdx.x * blockDim.x + threadIdx.x) * x128::size;
|
||||
|
||||
x128 packed_out;
|
||||
x128 packed_inp1 = load128cs(inp1 + idx);
|
||||
x128 packed_inp2 = load128cs(inp2 + idx);
|
||||
for (int k = 0; k < packed_inp1.size; k++) {
|
||||
packed_out[k] = (floatX)((float)packed_inp1[k] + (float)packed_inp2[k]);
|
||||
}
|
||||
store128(out + idx, packed_out);
|
||||
}
|
||||
|
||||
__global__ void __launch_bounds__(512, 2) // todo - any warnings on Turing with only 1024 threads?
|
||||
layernorm_backward_kernel10(floatX* dinp, floatX* dweight, floatX* dbias, float* scratch,
|
||||
const floatX* dout, const floatX* inp, const floatX* weight,
|
||||
const float* mean, const float* rstd,
|
||||
int B, int T, int C) {
|
||||
int BLOCK_SIZE = blockDim.x;
|
||||
int warpsInBlock = BLOCK_SIZE / WARP_SIZE; //number of warps in block
|
||||
extern __shared__ float shared[];
|
||||
|
||||
int warpId = threadIdx.x / WARP_SIZE; // warp index within a block
|
||||
int baseIdx = blockIdx.x * warpsInBlock + warpId;
|
||||
int warpThreadIdx = threadIdx.x % WARP_SIZE; // Thread index within the warp
|
||||
int warpsInGrid = gridDim.x * warpsInBlock;
|
||||
int C_per_iteration = WARP_SIZE * x128::size;
|
||||
int iterations_C = CEIL_DIV(C, C_per_iteration); // + 2;
|
||||
|
||||
// the first half of shared memory is bias, second is weight
|
||||
size_t rounded_C = CEIL_DIV(C, (32 * x128::size)) * (32 * x128::size);
|
||||
float* dbias_shared = shared;
|
||||
float* dweight_shared = shared + rounded_C;
|
||||
// warp zero doesn't actually write to the _tmp_shared memory locations, so we don't need to reserve memory
|
||||
// the obvious solution is to change the addressing below to use (threadId.x-32) as offset, but that causes
|
||||
// register spills, so instead we mess with the base pointer here, which doesn't increase register usage.
|
||||
float* dbias_tmp_shared = shared + 2 * rounded_C - WARP_SIZE * f128::size;
|
||||
float* dweight_tmp_shared = shared + 2 * rounded_C + f128::size * BLOCK_SIZE - 2 * WARP_SIZE * f128::size;
|
||||
|
||||
// init shared memory to zero
|
||||
for(int i = threadIdx.x * f128::size; i < rounded_C; i += BLOCK_SIZE * f128::size) {
|
||||
store128(dbias_shared + i, f128::zeros());
|
||||
store128(dweight_shared + i, f128::zeros());
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
for (int bt = baseIdx; bt < B * T; bt += warpsInGrid) {
|
||||
const floatX* dout_bt = dout + bt * C;
|
||||
const floatX* inp_bt = inp +bt * C;
|
||||
floatX* dinp_bt = dinp + bt * C;
|
||||
|
||||
// first: two reduce operations
|
||||
float dnorm_mean = 0.0f;
|
||||
float dnorm_norm_mean = 0.0f;
|
||||
for (int i = warpThreadIdx * x128::size; i < C; i += WARP_SIZE * x128::size) {
|
||||
x128 dout128_i = load128(dout_bt + i);
|
||||
x128 inp128_i = load128(inp_bt + i);
|
||||
x128 weight128_i = load128(weight + i);
|
||||
for (int k = 0; k < x128::size; k++) {
|
||||
float dnorm_i = (float)weight128_i[k] * (float)dout128_i[k];
|
||||
dnorm_mean += dnorm_i;
|
||||
dnorm_norm_mean += dnorm_i * (float)inp128_i[k];
|
||||
}
|
||||
}
|
||||
|
||||
const float mean_bt = mean[bt];
|
||||
const float rstd_bt = rstd[bt];
|
||||
dnorm_mean = warpReduceSum(dnorm_mean) / C;
|
||||
dnorm_norm_mean = warpReduceSum(dnorm_norm_mean) / C * rstd_bt - dnorm_mean * mean_bt * rstd_bt;
|
||||
|
||||
for (int c = 0; c < iterations_C; c++) {
|
||||
int global_index = (warpThreadIdx * x128::size) + (c * C_per_iteration);
|
||||
|
||||
x128 dout128 = x128::zeros();
|
||||
x128 inp128 = x128::zeros();
|
||||
x128 dinp128 = x128::zeros();
|
||||
x128 weight128 = x128::zeros();
|
||||
|
||||
if(global_index < C) {
|
||||
dout128 = load128cs(dout_bt + global_index);
|
||||
inp128 = load128cs(inp_bt + global_index);
|
||||
dinp128 = load128(dinp_bt + global_index);
|
||||
weight128 = load128(weight + global_index);
|
||||
}
|
||||
|
||||
for(int o = 0; o < x128::size / f128::size; ++o) {
|
||||
f128 dbias_f;
|
||||
f128 dweight_f;
|
||||
for(int i = 0; i < f128::size; ++i) {
|
||||
int x = o * f128::size + i;
|
||||
float dout_i = (float)dout128[x];
|
||||
float norm_bti = ((float)inp128[x] - mean_bt) * rstd_bt;
|
||||
dbias_f[i] = dout_i;
|
||||
dweight_f[i] = norm_bti * dout_i;
|
||||
|
||||
float dval = 0.0f;
|
||||
dval += (float) weight128[x] * (float)dout128[x]; // term 1
|
||||
dval -= dnorm_mean; // term 2
|
||||
dval -= norm_bti * dnorm_norm_mean; // term 3
|
||||
dval *= rstd_bt; // final scale
|
||||
dinp128[x] = (floatX) ((float) dinp128[x] + dval);
|
||||
}
|
||||
|
||||
if (warpId != 0) {
|
||||
store128(dbias_tmp_shared + threadIdx.x * f128::size, dbias_f);
|
||||
// this seems to generate a 64-bit store, instead of 128-bit.
|
||||
// however, forcing 128-bit (e.g., using inline ptx), results in register
|
||||
// spilling and much worse performance, so we'll keep it like this for now
|
||||
// but ideally, we could reduce the register pressure a little.
|
||||
store128(dweight_tmp_shared + threadIdx.x * f128::size, dweight_f);
|
||||
}
|
||||
__syncthreads();
|
||||
if (warpId == 0) {
|
||||
for (int j = 1; j < warpsInBlock; j++) {
|
||||
f128 dbias_tmp = load128(dbias_tmp_shared + f128::size * (threadIdx.x + j * WARP_SIZE));
|
||||
f128 dweight_tmp = load128(dweight_tmp_shared + f128::size * (threadIdx.x + j * WARP_SIZE));
|
||||
for(int i = 0; i < f128::size; ++i) {
|
||||
dbias_f[i] += dbias_tmp[i];
|
||||
dweight_f[i] += dweight_tmp[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
if (warpId == 0) {
|
||||
f128 db_old = load128(dbias_shared + global_index + f128::size * o);
|
||||
f128 dw_old = load128(dweight_shared + global_index + f128::size * o);
|
||||
for(int i = 0; i < f128::size; ++i) {
|
||||
dbias_f[i] += db_old[i];
|
||||
dweight_f[i] += dw_old[i];
|
||||
}
|
||||
store128(dbias_shared + global_index + f128::size * o, dbias_f);
|
||||
store128(dweight_shared + global_index + f128::size * o, dweight_f);
|
||||
}
|
||||
}
|
||||
if(global_index < C) {
|
||||
// cache in L2 as this is read by the next kernel, but bypass L1 to minimise thrashing
|
||||
store128cg(dinp_bt + global_index, dinp128);
|
||||
}
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
// Each block writes its partial sum to global memory
|
||||
// The last block to finish becomes responsible for summing up all the partial sums
|
||||
// This is done by atomically incrementing a flag (cleared to 0 before launching the kernel)
|
||||
unsigned int* scratchFlag = (unsigned int*)(scratch);
|
||||
// Increment scratch pointer by a full cacheline so that everything remains cacheline aligned
|
||||
scratch += 32;
|
||||
float* scratch_dbias = scratch;
|
||||
float* scratch_dweight = scratch + C;
|
||||
for(int i = threadIdx.x * f128::size; i < C; i += BLOCK_SIZE * f128::size) {
|
||||
// Write to global memory in the same "shared memory banking friendly" order
|
||||
store128(scratch_dbias + i + 2*C*blockIdx.x, load128(dbias_shared + i));
|
||||
store128(scratch_dweight + i + 2*C*blockIdx.x, load128(dweight_shared + i));
|
||||
}
|
||||
__syncthreads();
|
||||
// that portion of shared memory is no longer used, so we can repurpose it for the scratch flag.
|
||||
unsigned int *tmp_flag = (unsigned int*)(shared + 2*rounded_C);
|
||||
if (threadIdx.x == 0) {
|
||||
*tmp_flag = atomicInc(scratchFlag, gridDim.x);
|
||||
}
|
||||
__syncthreads();
|
||||
if (*tmp_flag == gridDim.x-1) {
|
||||
// Reduction of the partial sums by the final block
|
||||
// todo - there isn't enough parallelism even inside that single SM...
|
||||
// ==> so could maybe split into another kernel with YET ANOTHER level of reduction?!
|
||||
for(int i = threadIdx.x * f128::size; i < C; i += BLOCK_SIZE * f128::size) {
|
||||
f128 dbias_accum = f128::zeros();
|
||||
f128 dweight_accum = f128::zeros();
|
||||
|
||||
for (int read_block_idx = 0; read_block_idx < gridDim.x; read_block_idx++) {
|
||||
int offset = i + 2*C*read_block_idx;
|
||||
f128 dbias128 = load128(scratch_dbias + offset);
|
||||
f128 dweight128 = load128(scratch_dweight + offset);
|
||||
for(int k = 0; k < f128::size; k++) {
|
||||
dbias_accum[k] += dbias128[k];
|
||||
dweight_accum[k] += dweight128[k];
|
||||
}
|
||||
}
|
||||
store128(dbias_shared + i, dbias_accum);
|
||||
store128(dweight_shared + i, dweight_accum);
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// convert from float/FP32 to floatX/BF16 for the final write
|
||||
// this is separate because it cannot use as many warps as the above (f128 vs x128)
|
||||
// todo - if we split this code into another kernel, we could maybe do it at the same time?
|
||||
for (int c = warpId; c < iterations_C; c += warpsInBlock) {
|
||||
int global_index = (warpThreadIdx * x128::size) + (c * C_per_iteration);
|
||||
if (global_index >= C) {
|
||||
break;
|
||||
}
|
||||
|
||||
x128 dbias128 = load128(dbias + global_index);
|
||||
x128 dweight128 = load128(dweight + global_index);
|
||||
for(int o = 0; o < x128::size / f128::size; ++o) {
|
||||
f128 s_db = load128(dbias_shared + global_index + o * f128::size);
|
||||
f128 s_dw = load128(dweight_shared + global_index + o * f128::size);
|
||||
for(int i = 0; i < f128::size; ++i) {
|
||||
int x = o * f128::size + i;
|
||||
dbias128[x] = (floatX)(s_db[i] + (float)dbias128[x]);
|
||||
dweight128[x] = (floatX)(s_dw[i] + (float)dweight128[x]);
|
||||
}
|
||||
}
|
||||
store128(dbias + global_index, dbias128);
|
||||
store128(dweight + global_index, dweight128);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
// similar to `fused_residual_forward5`
|
||||
void layernorm_forward(floatX* out, float* mean, float* rstd,
|
||||
floatX* inp, const floatX* weight, const floatX* bias,
|
||||
int B, int T, int C, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 256;
|
||||
int block_y = block_size / WARP_SIZE;
|
||||
const int N = B * T;
|
||||
const int grid_size = CEIL_DIV(N, block_y);
|
||||
size_t smem = (2 + block_y) * C * sizeof(floatX);
|
||||
|
||||
// in order to use more than 48 KiB of smem, need to call cudaFuncSetAttribute
|
||||
// this may fail, in which case we fall back to the smem free implementation.
|
||||
cudaCheck(cudaGetLastError());
|
||||
auto status = cudaFuncSetAttribute(layernorm_forward_kernel6, cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
|
||||
cudaCheck(cudaGetLastError());
|
||||
if (status == cudaSuccess) {
|
||||
layernorm_forward_kernel6<<<grid_size, dim3(WARP_SIZE, block_y), smem, stream>>>(out, mean, rstd, inp, weight, bias, N, C);
|
||||
} else {
|
||||
// fall back to the version without shared memory
|
||||
const int grid_size_fb = CEIL_DIV(N * WARP_SIZE, block_size);
|
||||
layernorm_forward_kernel3<<<grid_size_fb, block_size, 0, stream>>>(out, mean, rstd, inp, weight, bias, N, C);
|
||||
}
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void residual_forward(floatX* out, const floatX* inp1, const floatX* inp2, int N, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 256;
|
||||
assert(N % (block_size * x128::size) == 0);
|
||||
const int grid_size = CEIL_DIV(N, block_size * x128::size);
|
||||
residual_forward_kernel<<<grid_size, block_size, 0, stream>>>(out, inp1, inp2);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void fused_residual_forward5(floatX* residual, floatX* normed, float* mean, float* rstd,
|
||||
const floatX* inp1, const floatX* inp2,
|
||||
const floatX* weight, const floatX* bias,
|
||||
int N, int C, cudaStream_t stream) {
|
||||
const int block_size = 256;
|
||||
int block_y = block_size / WARP_SIZE;
|
||||
const int grid_size = CEIL_DIV(N, block_y);
|
||||
size_t smem = (2 + block_y) * C * sizeof(floatX);
|
||||
|
||||
// in order to use more than 48 KiB of smem, need to call cudaFuncSetAttribute
|
||||
// this may fail, in which case we fall back to the smem free implementation.
|
||||
cudaCheck(cudaGetLastError());
|
||||
auto status = cudaFuncSetAttribute(fused_residual_forward_kernel5, cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
|
||||
cudaCheck(cudaGetLastError());
|
||||
if(status == cudaSuccess) {
|
||||
fused_residual_forward_kernel5<<<grid_size, dim3(WARP_SIZE, block_y), smem, stream>>>(residual, normed,
|
||||
mean, rstd, inp1, inp2,
|
||||
weight, bias, N, C);
|
||||
} else {
|
||||
residual_forward(residual, inp1, inp2, N*C, stream);
|
||||
layernorm_forward(normed, mean, rstd, residual, weight, bias, N, 1, C, stream);
|
||||
}
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void layernorm_backward(floatX* dinp, floatX* dweight, floatX* dbias, float* scratch,
|
||||
const floatX* dout, const floatX* inp, const floatX* weight, const float* mean, const float* rstd,
|
||||
int B, int T, int C, cudaStream_t stream) {
|
||||
NVTX_RANGE_FN();
|
||||
const int block_size = 512;
|
||||
const int blocks_per_sm = 2; // supported on every architecture and less cache thrashing than 3
|
||||
const int grid_size = blocks_per_sm * deviceProp.multiProcessorCount;
|
||||
size_t rounded_C = CEIL_DIV(C, (32 * x128::size)) * (32 * x128::size);
|
||||
size_t shared_mem_size = (2 * rounded_C + 2 * (block_size - 32) * f128::size) * sizeof(float);
|
||||
|
||||
cudaCheck(cudaMemsetAsync(scratch, 0, 1 * sizeof(float), stream)); // only need to reset the flag to 0
|
||||
layernorm_backward_kernel10<<<grid_size, block_size, shared_mem_size, stream>>>(dinp, dweight, dbias, scratch, dout, inp, weight, mean, rstd, B, T, C);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
+3
-2
@@ -6,6 +6,7 @@ The Logger object is stateless and uses append mode to write to log files.
|
||||
#define LOGGER_H
|
||||
|
||||
#include <assert.h>
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
// defines: fopenCheck, freadCheck, fcloseCheck, fseekCheck, mallocCheck
|
||||
#include "utils.h"
|
||||
@@ -46,10 +47,10 @@ void logger_log_val(Logger *logger, int step, float val_loss) {
|
||||
}
|
||||
}
|
||||
|
||||
void logger_log_train(Logger *logger, int step, float train_loss) {
|
||||
void logger_log_train(Logger *logger, int step, float train_loss, float learning_rate, float grad_norm) {
|
||||
if (logger->active == 1) {
|
||||
FILE *logfile = fopenCheck(logger->output_log_file, "a");
|
||||
fprintf(logfile, "s:%d trl:%.4f\n", step, train_loss);
|
||||
fprintf(logfile, "s:%d trl:%.4f lr:%.6f norm:%.2f\n", step, train_loss, learning_rate, grad_norm);
|
||||
fclose(logfile);
|
||||
}
|
||||
}
|
||||
|
||||
+290
@@ -0,0 +1,290 @@
|
||||
/*
|
||||
Matrix Multiplication, with help from cuBLASLt
|
||||
*/
|
||||
#include <assert.h>
|
||||
#include <type_traits> // std::bool_constant
|
||||
// llmc internal imports
|
||||
#include "cuda_common.h"
|
||||
#include "cuda_utils.cuh"
|
||||
#include "cublas_common.h"
|
||||
// GELU can be either fused (cublasLt) or non-fused (gelu.h)
|
||||
#include "gelu.cuh"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// CUDA kernels
|
||||
|
||||
template<typename OutFloat, bool UseAuxBuffer>
|
||||
__global__ void matmul_backward_bias_kernel9(OutFloat* dbias, const floatX* dout, int B, int T, int OC,
|
||||
std::bool_constant<UseAuxBuffer>) {
|
||||
constexpr const int bdx = 4;
|
||||
constexpr const int bdy = WARP_SIZE / bdx;
|
||||
assert(blockDim.x == bdx);
|
||||
assert(blockDim.y == bdy);
|
||||
|
||||
int warp_d = (int)threadIdx.x;
|
||||
int warp_c = (int)threadIdx.y;
|
||||
int block_d = (int)threadIdx.z;
|
||||
|
||||
const int OC_per_warp = bdy * x128::size; // 64 at BF16
|
||||
|
||||
int local_oc = warp_c * x128::size;
|
||||
int global_oc = blockIdx.x * OC_per_warp + local_oc;
|
||||
|
||||
int local_bt = warp_d + bdx * block_d;
|
||||
int bt_per_block = bdx * blockDim.z;
|
||||
|
||||
float accumulators[x128::size];
|
||||
for (int k = 0; k < x128::size; k++) {
|
||||
accumulators[k] = 0.0f;
|
||||
}
|
||||
|
||||
if(global_oc < OC) {
|
||||
// sum up over all bt within registers
|
||||
for (int idx = blockIdx.y * bt_per_block + local_bt; idx < B * T; idx += gridDim.y * bt_per_block) {
|
||||
x128 packed_dout = load128(dout + global_oc + idx*OC);
|
||||
for (int k = 0; k < x128::size; k++) {
|
||||
accumulators[k] += (float)packed_dout[k];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__shared__ float sub_results[x128::size][WARP_SIZE][bdy];
|
||||
|
||||
// reduce within-warp results
|
||||
for (int k = 0; k < x128::size; k++) {
|
||||
float v = accumulators[k];
|
||||
v += __shfl_down_sync(0xffffffff, v, 1, 4);
|
||||
v += __shfl_down_sync(0xffffffff, v, 2, 4);
|
||||
if(warp_d == 0) {
|
||||
sub_results[k][block_d][warp_c] = v;
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// block-wide reductions
|
||||
for (int k = block_d; k < x128::size; k += blockDim.z) {
|
||||
float a = 0.f;
|
||||
for (int r = warp_d; r < blockDim.z; r += bdx) {
|
||||
float v = sub_results[k][r][warp_c];
|
||||
v += __shfl_down_sync(0xffffffff, v, 1, 4);
|
||||
v += __shfl_down_sync(0xffffffff, v, 2, 4);
|
||||
a += v;
|
||||
}
|
||||
if(warp_d == 0 && global_oc < OC) {
|
||||
if constexpr (!UseAuxBuffer) {
|
||||
dbias[global_oc + k] = (OutFloat)(a + (float)dbias[global_oc + k]);
|
||||
} else {
|
||||
dbias[global_oc + k + blockIdx.y * OC] = a;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void reduce_add_sum_kernel(floatX* dst, const float* src, size_t n, size_t m) {
|
||||
const size_t idx = (blockIdx.x * blockDim.x + threadIdx.x) * f128::size;
|
||||
assert(n % x128::size == 0);
|
||||
if (idx < n) {
|
||||
f128 acc;
|
||||
for(int k = 0; k < f128::size; ++k) {
|
||||
acc[k] = 0.f;
|
||||
}
|
||||
|
||||
for(int l = 0; l < m; ++l) {
|
||||
f128 s = load128(src + idx + n * l);
|
||||
for(int k = 0; k < f128::size; ++k) {
|
||||
acc[k] += s[k];
|
||||
}
|
||||
}
|
||||
for(int k = 0; k < f128::size; ++k) {
|
||||
dst[idx + k] = (floatX) ((float)dst[idx + k] + acc[k]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
// Wrapper around cublasLtMatmul that is meant to support everything we need in llm.c
|
||||
// https://docs.nvidia.com/cuda/cublas/#cublasltmatmul
|
||||
void matmul_cublaslt(floatX* d, const floatX* a, const floatX* b, const floatX* bias,
|
||||
int m, int n, int k, cudaStream_t stream=0, bool transA=true, bool transB=false,
|
||||
int batch_count=0, size_t strideA=0, size_t strideB=0, size_t strideOut=0,
|
||||
bool accumulate=false, floatX* pre_gelu=NULL, bool backward=false)
|
||||
{
|
||||
NVTX_RANGE_FN();
|
||||
bool has_bias = (bias != NULL);
|
||||
bool has_gelu = (pre_gelu != NULL);
|
||||
|
||||
// check alignment (some modes work unaligned but it always best to be aligned for performance)
|
||||
if(((uintptr_t)a % 16) != 0 || ((uintptr_t)b % 16) != 0 || ((uintptr_t)d % 16) != 0 || ((uintptr_t)bias % 16) != 0) {
|
||||
printf("All cuBLASLt pointers must be aligned!\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// create the operation descriptor
|
||||
cublasLtMatmulDesc_t operationDesc;
|
||||
cublasCheck(cublasLtMatmulDescCreate(&operationDesc, cublas_compute, CUDA_R_32F));
|
||||
|
||||
int returnedResults = 0;
|
||||
cublasLtMatmulPreference_t preference;
|
||||
cublasLtMatmulHeuristicResult_t heuristic;
|
||||
|
||||
cublasOperation_t opNoTranspose = CUBLAS_OP_N;
|
||||
cublasOperation_t opTranspose = CUBLAS_OP_T;
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, (transA) ? &opTranspose : &opNoTranspose, sizeof(opTranspose)));
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, (transB) ? &opTranspose : &opNoTranspose, sizeof(opNoTranspose)));
|
||||
|
||||
// define matrix layouts
|
||||
cublasLtMatrixLayout_t ALayout;
|
||||
cublasLtMatrixLayout_t BLayout;
|
||||
cublasLtMatrixLayout_t DLayout;
|
||||
cublasLtMatrixLayout_t CLayout;
|
||||
if (transA) {
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&ALayout, CUBLAS_LOWP, k, m, k));
|
||||
} else {
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&ALayout, CUBLAS_LOWP, m, k, m));
|
||||
}
|
||||
if (transB) {
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&BLayout, CUBLAS_LOWP, n, k, n));
|
||||
} else {
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&BLayout, CUBLAS_LOWP, k, n, k));
|
||||
}
|
||||
// cuBLASLt requires C in FP8 mode to be BF16 or FP32... (sigh)
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&CLayout, (sizeof(floatX) == 1) ? CUDA_R_16BF : CUBLAS_LOWP, m, n, m));
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&DLayout, CUBLAS_LOWP, m, n, m));
|
||||
|
||||
// Strided Batched GEMM (used for non-flash attention, equivalent to cublasGemmStridedBatchedEx)
|
||||
if (batch_count) {
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(ALayout, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(batch_count)));
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(BLayout, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(batch_count)));
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(CLayout, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(batch_count)));
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(DLayout, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(batch_count)));
|
||||
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(ALayout, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideA, sizeof(strideA)));
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(BLayout, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideB, sizeof(strideB)));
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(CLayout, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideOut, sizeof(strideOut)));
|
||||
cublasCheck(cublasLtMatrixLayoutSetAttribute(DLayout, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideOut, sizeof(strideOut)));
|
||||
}
|
||||
|
||||
// create a preference handle with specified max workspace
|
||||
cublasCheck(cublasLtMatmulPreferenceCreate(&preference));
|
||||
cublasCheck(cublasLtMatmulPreferenceSetAttribute(preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
|
||||
&cublaslt_workspace_size, sizeof(cublaslt_workspace_size)));
|
||||
|
||||
// setup epilogue and associated pointers for bias & gelu
|
||||
cublasLtEpilogue_t epilogue;
|
||||
if (has_gelu) {
|
||||
int64_t gelu_ld = m; // todo - is this affected by anything else?
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &gelu_ld, sizeof(gelu_ld)));
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER, &pre_gelu, sizeof(pre_gelu)));
|
||||
if (backward) {
|
||||
assert(!has_bias); // we shouldn't have any backward matmuls that use both GELU and bias
|
||||
epilogue = CUBLASLT_EPILOGUE_DGELU;
|
||||
} else {
|
||||
epilogue = has_bias ? CUBLASLT_EPILOGUE_GELU_AUX_BIAS : CUBLASLT_EPILOGUE_GELU_AUX;
|
||||
}
|
||||
} else if(has_bias){
|
||||
epilogue = backward ? CUBLASLT_EPILOGUE_BGRADB : CUBLASLT_EPILOGUE_BIAS;
|
||||
} else {
|
||||
epilogue = CUBLASLT_EPILOGUE_DEFAULT;
|
||||
}
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue)));
|
||||
|
||||
if (has_bias) {
|
||||
// cuBLASLt requires bias in FP8 mode to be BF16... (sigh)
|
||||
cublasDataType_t bias_data_type = (sizeof(floatX) == 1) ? CUDA_R_16BF : CUBLAS_LOWP; // force BF16 bias for FP8 mode
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE, &bias_data_type, sizeof(bias_data_type)));
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(bias)));
|
||||
}
|
||||
|
||||
// set scale type to FP32 (needs to be FP16 if and only if using CUBLAS_COMPUTE_16F, so it's FP32 even for FP8!)
|
||||
cublasDataType_t scale_type = CUDA_R_32F;
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_SCALE_TYPE, &scale_type, sizeof(scale_type)));
|
||||
|
||||
// find a suitable algorithm (cached internally so shouldn't take much CPU time in practice)
|
||||
cublasLtMatmulAlgoGetHeuristic(cublaslt_handle, operationDesc, ALayout, BLayout, CLayout, DLayout,
|
||||
preference, 1, &heuristic, &returnedResults);
|
||||
if (returnedResults == 0) {
|
||||
printf("No cuBLASLt algorithm: m: %d, n: %d, k: %d, bias: %d\n", n, m, k, has_bias);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// set whether to accumulate (i.e. D += C) or not - note this isn't considered in algorithm selection (?!)
|
||||
const float alpha = 1.0f, beta = accumulate ? 1.0f : 0.0f;
|
||||
|
||||
// call the matmul
|
||||
cublasCheck(cublasLtMatmul(cublaslt_handle, operationDesc,
|
||||
&alpha, a, ALayout, b, BLayout, &beta, d, CLayout, d, DLayout,
|
||||
&heuristic.algo, cublaslt_workspace, cublaslt_workspace_size, stream));
|
||||
|
||||
// cleanups
|
||||
cublasCheck(cublasLtMatmulPreferenceDestroy(preference));
|
||||
cublasCheck(cublasLtMatmulDescDestroy(operationDesc));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(ALayout));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(BLayout));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(CLayout));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(DLayout));
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
// small wrapper around matmul_cublaslt for the forward pass (keeping historical order of arguments)
|
||||
void matmul_forward_cublaslt(floatX* out,
|
||||
floatX* inp, floatX* weight, floatX* bias,
|
||||
int B, int T, int C, int OC, cudaStream_t stream,
|
||||
floatX* pre_gelu=NULL, int gelu_fusion=1) {
|
||||
// By default only fuse GELU for H100+ as cuBLAS seems to be inefficient for fused GELU on Ada/Ampere (?)
|
||||
if (gelu_fusion < 1 && pre_gelu) {
|
||||
matmul_cublaslt(pre_gelu, weight, inp, bias, OC, B*T, C, stream, true, false, 0, 0, 0, 0, false, NULL, false);
|
||||
gelu_forward(out, pre_gelu, B*T*OC, stream);
|
||||
} else {
|
||||
matmul_cublaslt(out, weight, inp, bias, OC, B*T, C, stream, true, false, 0, 0, 0, 0, false, pre_gelu, false);
|
||||
}
|
||||
}
|
||||
|
||||
void matmul_backward(floatX* dinp, floatX* dweight, floatX* dbias,
|
||||
floatX* dout, floatX* inp, floatX* weight,
|
||||
float* dbias_buffer,
|
||||
int B, int T, int C, int OC, cudaStream_t stream,
|
||||
floatX* pre_gelu=NULL, int gelu_fusion=1) {
|
||||
NVTX_RANGE_FN();
|
||||
|
||||
// backward to bias, if given, does a +=
|
||||
if (dbias != NULL) {
|
||||
// Each warp is responsible for 8 * "x128::size" = 64 OCs at BF16 (OC must be a multiple of 64!)
|
||||
// Block size is 1024 | 768 threads (32|24 warps) and we reduce those values into 1 at the end
|
||||
|
||||
const int block_size = deviceProp.maxThreadsPerMultiProcessor == 1536 ? 768 : 1024;
|
||||
|
||||
dim3 block_dim = {4, 8, (unsigned)block_size/WARP_SIZE};
|
||||
const int OC_per_warp = block_dim.y * x128::size; // 64 at BF16
|
||||
const int grid_size_x = CEIL_DIV(OC, OC_per_warp); // e.g. 12 horizontal blocks for 768 OCs at BF16
|
||||
const int grid_size_y = max(1, deviceProp.maxThreadsPerMultiProcessor * deviceProp.multiProcessorCount / (block_size * grid_size_x)); // full GPU!
|
||||
|
||||
// If we have enough OC that we don't need cross-block reductions, we can skip the bias_buffer accumulation
|
||||
// and write results directly to the output.
|
||||
if(grid_size_y == 1) {
|
||||
matmul_backward_bias_kernel9<<<dim3(grid_size_x, grid_size_y), block_dim, 0, stream>>>(dbias, dout, B, T, OC, False);
|
||||
cudaCheck(cudaGetLastError());
|
||||
} else {
|
||||
// kernel 9 overwrites temp buffer, so no need to memset
|
||||
matmul_backward_bias_kernel9<<<dim3(grid_size_x, grid_size_y), block_dim, 0, stream>>>(dbias_buffer, dout, B, T, OC, True);
|
||||
cudaCheck(cudaGetLastError());
|
||||
reduce_add_sum_kernel<<<CEIL_DIV(OC, 256 * f128::size), 256, 0, stream>>>(dbias, dbias_buffer, OC, grid_size_y);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
dbias = NULL; // prevent dbias calculation from also being fused in matmul_cublaslt below (if we enabled fusion)
|
||||
}
|
||||
|
||||
// backward to input, uses = in the backward pass (set the gradient)
|
||||
matmul_cublaslt(dinp, weight, dout, NULL, C, B*T, OC, stream, false, false, 0, 0, 0, 0, false,
|
||||
gelu_fusion >= 2 ? pre_gelu : NULL, true);
|
||||
|
||||
// backward GELU (if it wasn't fused into the matmul above)
|
||||
if (gelu_fusion < 2 && pre_gelu) {
|
||||
gelu_backward_inplace(dinp, pre_gelu, B*T*C, stream);
|
||||
}
|
||||
|
||||
// backward to weight, uses += in the backward pass (accumulate the gradient) by setting alpha=one
|
||||
matmul_cublaslt(dweight, inp, dout, NULL /*dbias*/, C, OC, B*T, stream, false, true, 0, 0, 0, 0,
|
||||
true /* accumulate */, NULL, true);
|
||||
}
|
||||
+108
@@ -4,12 +4,29 @@
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#if __has_include(<nvml.h>)
|
||||
#define USE_NVML 1
|
||||
#include <nvml.h>
|
||||
#else
|
||||
#define USE_NVML 0
|
||||
#endif
|
||||
|
||||
// tied to enum PrecisionMode, in a future refactor make them the same
|
||||
#define MFUH_PRECISION_FP32 0
|
||||
#define MFUH_PRECISION_FP16 1
|
||||
#define MFUH_PRECISION_BF16 2
|
||||
|
||||
#if USE_NVML
|
||||
inline void nvml_check(nvmlReturn_t status, const char *file, int line) {
|
||||
if (status != NVML_SUCCESS) {
|
||||
printf("[NVML ERROR] at file %s:%d:\n%s\n", file, line, nvmlErrorString(status));
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
};
|
||||
#define nvmlCheck(err) (nvml_check(err, __FILE__, __LINE__))
|
||||
#endif
|
||||
|
||||
|
||||
typedef struct {
|
||||
float TF_32; // tensor-core performance 32 bit
|
||||
float BF_16_32; // bf16 with 32 bit accumulate
|
||||
@@ -73,6 +90,7 @@ static GPUEntry gpu_db[] = {
|
||||
{"NVIDIA GeForce RTX 4070", &ADA, 184, 2475},
|
||||
{"NVIDIA GeForce RTX 4060 Ti", &ADA, 136, 2535},
|
||||
{"NVIDIA GeForce RTX 4060", &ADA, 96, 2460},
|
||||
{"NVIDIA H100 PCIe", &HOPPER, 456, 1620},
|
||||
{"NVIDIA H100 80GB HBM3", &HOPPER, 528, 1830}, // HBM3 = SXM5
|
||||
};
|
||||
|
||||
@@ -133,4 +151,94 @@ float get_flops_promised(const char* device, int precision_mode) {
|
||||
return -1.0f; // ¯\_(ツ)_/¯
|
||||
}
|
||||
|
||||
struct GPUUtilInfo {
|
||||
unsigned int clock;
|
||||
unsigned int max_clock;
|
||||
unsigned int power;
|
||||
unsigned int power_limit;
|
||||
unsigned int fan;
|
||||
unsigned int temperature;
|
||||
unsigned int temp_slowdown;
|
||||
|
||||
float gpu_utilization;
|
||||
float mem_utilization;
|
||||
const char* throttle_reason;
|
||||
};
|
||||
|
||||
// lazily initialize nvml and generate a handle to the GPU
|
||||
#if USE_NVML
|
||||
nvmlDevice_t nvml_get_device() {
|
||||
static bool needs_init = true;
|
||||
static nvmlDevice_t device;
|
||||
if(needs_init) {
|
||||
needs_init = false;
|
||||
nvmlCheck(nvmlInit());
|
||||
nvmlCheck(nvmlDeviceGetHandleByIndex_v2(0, &device));
|
||||
}
|
||||
return device;
|
||||
}
|
||||
|
||||
// convert throttle reason bitfield into a text reason.
|
||||
// this is a lossy conversion; we just want to give some idea of what is happening
|
||||
const char* get_throttle_reason(unsigned long long bits) {
|
||||
if(bits & (nvmlClocksThrottleReasonSwPowerCap | nvmlClocksThrottleReasonHwPowerBrakeSlowdown)) {
|
||||
return "power cap";
|
||||
} else if (bits & (nvmlClocksThrottleReasonSwThermalSlowdown | nvmlClocksThrottleReasonHwThermalSlowdown)) {
|
||||
return "thermal cap";
|
||||
} else if (bits & (nvmlClocksThrottleReasonAll)) {
|
||||
return "other cap";
|
||||
} else {
|
||||
return "no cap";
|
||||
}
|
||||
}
|
||||
|
||||
// gather data for a GPUUtilInfo object
|
||||
GPUUtilInfo get_gpu_utilization_info() {
|
||||
GPUUtilInfo info;
|
||||
nvmlDevice_t device = nvml_get_device();
|
||||
// query different infos directly
|
||||
nvmlCheck(nvmlDeviceGetClockInfo(device, NVML_CLOCK_SM, &info.clock));
|
||||
nvmlCheck(nvmlDeviceGetMaxClockInfo(device, NVML_CLOCK_SM, &info.max_clock));
|
||||
nvmlCheck(nvmlDeviceGetPowerManagementLimit(device, &info.power_limit));
|
||||
nvmlCheck(nvmlDeviceGetPowerUsage(device, &info.power));
|
||||
nvmlCheck(nvmlDeviceGetTemperature(device, NVML_TEMPERATURE_GPU, &info.temperature));
|
||||
nvmlCheck(nvmlDeviceGetTemperatureThreshold(device, NVML_TEMPERATURE_THRESHOLD_SLOWDOWN, &info.temp_slowdown));
|
||||
unsigned long long throttle;
|
||||
nvmlCheck(nvmlDeviceGetCurrentClocksThrottleReasons(device, &throttle));
|
||||
info.throttle_reason = get_throttle_reason(throttle);
|
||||
nvmlCheck(nvmlDeviceGetFanSpeed(device, &info.fan));
|
||||
|
||||
// for "utilization", we look at recorded samples. In principle, we could query the driver for how many samples
|
||||
// to request, but then we'd need to dynamically allocate sufficient space. Let's just hard-code a limit of 128,
|
||||
// and have no memory management required
|
||||
constexpr const int BUFFER_LIMIT = 128;
|
||||
nvmlSample_t buffer[BUFFER_LIMIT];
|
||||
nvmlValueType_t v_type;
|
||||
unsigned int sample_count = BUFFER_LIMIT;
|
||||
nvmlCheck(nvmlDeviceGetSamples(device, NVML_GPU_UTILIZATION_SAMPLES, 0, &v_type, &sample_count, buffer));
|
||||
float gpu_utilization = 0.f;
|
||||
for(unsigned i = 0; i < sample_count; ++i) {
|
||||
gpu_utilization += (float)buffer[i].sampleValue.uiVal;
|
||||
}
|
||||
gpu_utilization /= (float)sample_count;
|
||||
|
||||
// sample count may have been modified by the query above; reset back to buffer size
|
||||
sample_count = BUFFER_LIMIT;
|
||||
nvmlCheck(nvmlDeviceGetSamples(device, NVML_MEMORY_UTILIZATION_SAMPLES, 0, &v_type, &sample_count, buffer));
|
||||
float mem_utilization = 0.f;
|
||||
for(unsigned i = 0; i < sample_count; ++i) {
|
||||
mem_utilization += (float)buffer[i].sampleValue.uiVal;
|
||||
}
|
||||
mem_utilization /= (float)sample_count;
|
||||
|
||||
info.gpu_utilization = gpu_utilization;
|
||||
info.mem_utilization = mem_utilization;
|
||||
return info;
|
||||
}
|
||||
#else
|
||||
GPUUtilInfo get_gpu_utilization_info() {
|
||||
fprintf(stderr, "Error: Compiled without nvml support. Cannot perform additional GPU state tracking.");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
#endif
|
||||
#endif // MFU_H
|
||||
|
||||
@@ -0,0 +1,70 @@
|
||||
/*
|
||||
Simple OutlierDetector that we can use to monitor the loss and grad norm
|
||||
Internally, it keeps track of a window of measurements and each time we
|
||||
add a measurement, it returns the z-score of the new value with respect to
|
||||
the window of measurements. This can be used to detect outliers in the data.
|
||||
|
||||
We use double so that the detector doesn't drift too much, because we
|
||||
update the mean and variance with += on each step for efficiency. We could
|
||||
reconsider this choice in the future, as the compute cost here is minimal.
|
||||
*/
|
||||
|
||||
#include <stdio.h>
|
||||
#include <math.h>
|
||||
|
||||
// use compile-time constant for window size to avoid dynamic memory allocations
|
||||
#define OUTLIER_DETECTOR_WINDOW_SIZE 128
|
||||
|
||||
typedef struct {
|
||||
double buffer[OUTLIER_DETECTOR_WINDOW_SIZE];
|
||||
int count;
|
||||
int index;
|
||||
double sum;
|
||||
double sum_sq;
|
||||
} OutlierDetector;
|
||||
|
||||
void init_detector(OutlierDetector *detector) {
|
||||
for (int i = 0; i < OUTLIER_DETECTOR_WINDOW_SIZE; i++) {
|
||||
detector->buffer[i] = 0.0;
|
||||
}
|
||||
detector->count = 0;
|
||||
detector->index = 0;
|
||||
detector->sum = 0.0;
|
||||
detector->sum_sq = 0.0;
|
||||
}
|
||||
|
||||
double update_detector(OutlierDetector *detector, double new_value) {
|
||||
|
||||
if (detector->count < OUTLIER_DETECTOR_WINDOW_SIZE) {
|
||||
// here we are still building up a window of observations
|
||||
detector->buffer[detector->count] = new_value;
|
||||
detector->sum += new_value;
|
||||
detector->sum_sq += new_value * new_value;
|
||||
detector->count++;
|
||||
return nan(""); // not enough data yet
|
||||
|
||||
} else {
|
||||
// we've filled the window, so now we can start detecting outliers
|
||||
|
||||
// pop the oldest value from the window
|
||||
double old_value = detector->buffer[detector->index];
|
||||
detector->sum -= old_value;
|
||||
detector->sum_sq -= old_value * old_value;
|
||||
// push the new value into the window
|
||||
detector->buffer[detector->index] = new_value;
|
||||
detector->sum += new_value;
|
||||
detector->sum_sq += new_value * new_value;
|
||||
// move the index to the next position
|
||||
detector->index = (detector->index + 1) % OUTLIER_DETECTOR_WINDOW_SIZE;
|
||||
// calculate the z-score of the new value
|
||||
double mean = detector->sum / OUTLIER_DETECTOR_WINDOW_SIZE;
|
||||
double variance = (detector->sum_sq / OUTLIER_DETECTOR_WINDOW_SIZE) - (mean * mean);
|
||||
double std_dev = sqrt(variance);
|
||||
if (std_dev == 0.0) {
|
||||
return 0.0;
|
||||
}
|
||||
double z = (new_value - mean) / std_dev;
|
||||
|
||||
return z;
|
||||
}
|
||||
}
|
||||
+26
-9
@@ -165,13 +165,13 @@ void uniform_(float* data, unsigned int numel, float from, float to, mt19937_sta
|
||||
|
||||
// Box-Muller transform: maps uniform random numbers to Gaussian distributed numbers
|
||||
// https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
|
||||
void normal_fill_16(float* data, float mean, float std, mt19937_state* state) {
|
||||
#define EPSILONE 1e-12
|
||||
void normal_fill_16(float* data, float mean, float std) {
|
||||
#define EPSILONE 1e-12f
|
||||
for (unsigned int t = 0; t < 8; t++) {
|
||||
float u1 = 1 - data[t];
|
||||
float u2 = data[t + 8];
|
||||
float radius = sqrtf(-2 * logf(u1 + EPSILONE));
|
||||
float theta = 2.0 * M_PI * u2;
|
||||
float theta = (float) (2.0 * M_PI * u2);
|
||||
data[t] = (radius * cosf(theta) * std + mean);
|
||||
data[t + 8] = (radius * sinf(theta) * std + mean);
|
||||
}
|
||||
@@ -182,7 +182,7 @@ void normal_fill(float* data, unsigned int numel, float mean, float std, mt19937
|
||||
data[t] = randfloat32(state);
|
||||
}
|
||||
for (unsigned int i = 0; i < numel - 15; i += 16) {
|
||||
normal_fill_16(data + i, mean, std, state);
|
||||
normal_fill_16(data + i, mean, std);
|
||||
}
|
||||
if (numel % 16 != 0) {
|
||||
// recompute the last 16 values
|
||||
@@ -190,12 +190,12 @@ void normal_fill(float* data, unsigned int numel, float mean, float std, mt19937
|
||||
for (unsigned int i = 0; i < 16; i++) {
|
||||
data[i] = randfloat32(state);
|
||||
}
|
||||
normal_fill_16(data, mean, std, state);
|
||||
normal_fill_16(data, mean, std);
|
||||
}
|
||||
}
|
||||
|
||||
void normal_(float* data, unsigned int numel, float mean, float std, mt19937_state* state) {
|
||||
#define EPSILONE 1e-12
|
||||
#define EPSILONE 1e-12f
|
||||
if (numel >= 16) {
|
||||
normal_fill(data, numel, mean, std, state);
|
||||
}
|
||||
@@ -209,10 +209,10 @@ void normal_(float* data, unsigned int numel, float mean, float std, mt19937_sta
|
||||
continue;
|
||||
}
|
||||
// for numel < 16 we draw a double (float64)
|
||||
float u1 = randfloat64(state);
|
||||
float u2 = randfloat64(state);
|
||||
float u1 = (float) randfloat64(state);
|
||||
float u2 = (float) randfloat64(state);
|
||||
float radius = sqrtf(-2 * logf(1 - u2 + EPSILONE));
|
||||
float theta = 2.0 * M_PI * u1;
|
||||
float theta = (float) (2.0 * M_PI * u1);
|
||||
next_double_normal_sample = radius * sinf(theta);
|
||||
has_next_double_normal_sample = 1;
|
||||
data[t] = (radius * cosf(theta) * std + mean);
|
||||
@@ -220,4 +220,21 @@ void normal_(float* data, unsigned int numel, float mean, float std, mt19937_sta
|
||||
}
|
||||
}
|
||||
|
||||
void init_identity_permutation(int *data, int numel) {
|
||||
for (int i = 0; i < numel; i++) {
|
||||
data[i] = i;
|
||||
}
|
||||
}
|
||||
|
||||
void random_permutation(int* data, int numel, mt19937_state* state) {
|
||||
for (int i = numel - 1; i > 0; i--) {
|
||||
// pick an index j in [0, i] with equal probability
|
||||
int j = randint32(state) % (i + 1);
|
||||
// swap i <-> j
|
||||
int tmp = data[i];
|
||||
data[i] = data[j];
|
||||
data[j] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,100 @@
|
||||
/*
|
||||
Implements various learning rate schedulers.
|
||||
*/
|
||||
#ifndef SCHEDULERS_H
|
||||
#define SCHEDULERS_H
|
||||
|
||||
#include <assert.h>
|
||||
#include <math.h>
|
||||
#include <string.h>
|
||||
|
||||
typedef struct {
|
||||
const char* type;
|
||||
float learning_rate;
|
||||
int warmup_iterations;
|
||||
int train_num_batches;
|
||||
float final_learning_rate_frac;
|
||||
} LearningRateScheduler;
|
||||
|
||||
void lr_scheduler_init(LearningRateScheduler *scheduler, const char* scheduler_type, float learning_rate, int warmup_iterations, int train_num_batches, float final_learning_rate_frac) {
|
||||
scheduler->type = scheduler_type;
|
||||
scheduler->learning_rate = learning_rate;
|
||||
scheduler->warmup_iterations = warmup_iterations;
|
||||
scheduler->train_num_batches = train_num_batches;
|
||||
scheduler->final_learning_rate_frac = final_learning_rate_frac;
|
||||
}
|
||||
|
||||
// cosine: warmup linearly to max LR, then cosine decay to LR * final_learning_rate_frac
|
||||
float get_learning_rate_cosine(LearningRateScheduler *scheduler, int step) {
|
||||
float lr = scheduler->learning_rate;
|
||||
if (step < scheduler->warmup_iterations) {
|
||||
lr = scheduler->learning_rate * ((float)(step + 1)) / scheduler->warmup_iterations;
|
||||
} else {
|
||||
float decay_ratio = ((float)(step - scheduler->warmup_iterations)) / (scheduler->train_num_batches - scheduler->warmup_iterations);
|
||||
assert(0.0f <= decay_ratio && decay_ratio <= 1.0f);
|
||||
float coeff = 0.5f * (1.0f + cosf(M_PI * decay_ratio)); // coeff starts at 1 and goes to 0
|
||||
assert(0.0f <= coeff && coeff <= 1.0f);
|
||||
float min_lr = scheduler->learning_rate * scheduler->final_learning_rate_frac;
|
||||
lr = min_lr + coeff * (scheduler->learning_rate - min_lr);
|
||||
}
|
||||
return lr;
|
||||
}
|
||||
|
||||
// linear: warmup linearly to max LR, then decay linearly to LR * final_learning_rate_frac
|
||||
float get_learning_rate_linear(LearningRateScheduler *scheduler, int step) {
|
||||
float lr = scheduler->learning_rate;
|
||||
if (step < scheduler->warmup_iterations) {
|
||||
lr = scheduler->learning_rate * ((float)(step + 1)) / scheduler->warmup_iterations;
|
||||
} else {
|
||||
float decay_ratio = ((float)(step - scheduler->warmup_iterations)) / (scheduler->train_num_batches - scheduler->warmup_iterations);
|
||||
assert(0.0f <= decay_ratio && decay_ratio <= 1.0f);
|
||||
float min_lr = scheduler->learning_rate * scheduler->final_learning_rate_frac;
|
||||
lr = scheduler->learning_rate - decay_ratio * (scheduler->learning_rate - min_lr);
|
||||
}
|
||||
return lr;
|
||||
}
|
||||
|
||||
// constant
|
||||
float get_learning_rate_constant(LearningRateScheduler *scheduler, int step) {
|
||||
return scheduler->learning_rate;
|
||||
}
|
||||
|
||||
// wsd schedule: warmup linearly, keep constant, last 20% decay using 1 - sqrt decay to final_frac (should be 0.0)
|
||||
// https://arxiv.org/abs/2405.18392
|
||||
float get_learning_rate_wsd(LearningRateScheduler *scheduler, int step) {
|
||||
int decay_point = (int)(0.8f * scheduler->train_num_batches);
|
||||
float max_lr = scheduler->learning_rate;
|
||||
float lr = max_lr;
|
||||
if (step < scheduler->warmup_iterations) {
|
||||
float decay_ratio = ((float)(step + 1)) / scheduler->warmup_iterations;
|
||||
lr = max_lr * decay_ratio;
|
||||
} else if (step < decay_point) {
|
||||
// noop, keep lr constant
|
||||
} else {
|
||||
float decay_ratio = ((float)(step - decay_point)) / (scheduler->train_num_batches - decay_point);
|
||||
assert(0.0f <= decay_ratio && decay_ratio <= 1.0f);
|
||||
float min_lr = max_lr * scheduler->final_learning_rate_frac;
|
||||
return min_lr + (1.0f - sqrtf(decay_ratio)) * (max_lr - min_lr);
|
||||
}
|
||||
return lr;
|
||||
}
|
||||
|
||||
// return the learning rate at a given step
|
||||
float get_learning_rate(LearningRateScheduler *scheduler, int step) {
|
||||
float step_learning_rate;
|
||||
if (strcmp(scheduler->type, "cosine") == 0) {
|
||||
step_learning_rate = get_learning_rate_cosine(scheduler, step);
|
||||
} else if (strcmp(scheduler->type, "linear") == 0) {
|
||||
step_learning_rate = get_learning_rate_linear(scheduler, step);
|
||||
} else if (strcmp(scheduler->type, "constant") == 0) {
|
||||
step_learning_rate = get_learning_rate_constant(scheduler, step);
|
||||
} else if (strcmp(scheduler->type, "wsd") == 0) {
|
||||
step_learning_rate = get_learning_rate_wsd(scheduler, step);
|
||||
} else {
|
||||
fprintf(stderr, "Unknown learning rate scheduler type: %s\n", scheduler->type);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
return step_learning_rate;
|
||||
}
|
||||
|
||||
#endif // SCHEDULERS_H
|
||||
+1
-1
@@ -90,7 +90,7 @@ const char *tokenizer_decode(Tokenizer *tokenizer, uint32_t token_id) {
|
||||
if (token_id < tokenizer->vocab_size) {
|
||||
return tokenizer->token_table[token_id];
|
||||
} else {
|
||||
printf("invalid token id %d!\n", token_id);
|
||||
printf("invalid token id %u!\n", token_id);
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
|
||||
+88
-7
@@ -7,12 +7,15 @@
|
||||
#ifndef UTILS_H
|
||||
#define UTILS_H
|
||||
|
||||
#include <unistd.h>
|
||||
#include <string.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <sys/stat.h>
|
||||
// implementation of dirent for Windows is in dev/unistd.h
|
||||
#ifndef _WIN32
|
||||
#include <dirent.h>
|
||||
#include <arpa/inet.h>
|
||||
#endif
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
@@ -20,7 +23,7 @@
|
||||
// simple replace fopen, fread, fclose, fseek
|
||||
// with fopenCheck, freadCheck, fcloseCheck, fseekCheck
|
||||
|
||||
FILE *fopen_check(const char *path, const char *mode, const char *file, int line) {
|
||||
extern inline FILE *fopen_check(const char *path, const char *mode, const char *file, int line) {
|
||||
FILE *fp = fopen(path, mode);
|
||||
if (fp == NULL) {
|
||||
fprintf(stderr, "Error: Failed to open file '%s' at %s:%d\n", path, file, line);
|
||||
@@ -38,7 +41,7 @@ FILE *fopen_check(const char *path, const char *mode, const char *file, int line
|
||||
|
||||
#define fopenCheck(path, mode) fopen_check(path, mode, __FILE__, __LINE__)
|
||||
|
||||
void fread_check(void *ptr, size_t size, size_t nmemb, FILE *stream, const char *file, int line) {
|
||||
extern inline void fread_check(void *ptr, size_t size, size_t nmemb, FILE *stream, const char *file, int line) {
|
||||
size_t result = fread(ptr, size, nmemb, stream);
|
||||
if (result != nmemb) {
|
||||
if (feof(stream)) {
|
||||
@@ -60,7 +63,7 @@ void fread_check(void *ptr, size_t size, size_t nmemb, FILE *stream, const char
|
||||
|
||||
#define freadCheck(ptr, size, nmemb, stream) fread_check(ptr, size, nmemb, stream, __FILE__, __LINE__)
|
||||
|
||||
void fclose_check(FILE *fp, const char *file, int line) {
|
||||
extern inline void fclose_check(FILE *fp, const char *file, int line) {
|
||||
if (fclose(fp) != 0) {
|
||||
fprintf(stderr, "Error: Failed to close file at %s:%d\n", file, line);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
@@ -72,7 +75,33 @@ void fclose_check(FILE *fp, const char *file, int line) {
|
||||
|
||||
#define fcloseCheck(fp) fclose_check(fp, __FILE__, __LINE__)
|
||||
|
||||
void fseek_check(FILE *fp, long off, int whence, const char *file, int line) {
|
||||
extern inline void sclose_check(int sockfd, const char *file, int line) {
|
||||
if (close(sockfd) != 0) {
|
||||
fprintf(stderr, "Error: Failed to close socket at %s:%d\n", file, line);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
fprintf(stderr, " File: %s\n", file);
|
||||
fprintf(stderr, " Line: %d\n", line);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
#define scloseCheck(sockfd) sclose_check(sockfd, __FILE__, __LINE__)
|
||||
|
||||
#ifdef _WIN32
|
||||
extern inline void closesocket_check(int sockfd, const char *file, int line) {
|
||||
if (closesocket(sockfd) != 0) {
|
||||
fprintf(stderr, "Error: Failed to close socket at %s:%d\n", file, line);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
fprintf(stderr, " File: %s\n", file);
|
||||
fprintf(stderr, " Line: %d\n", line);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
#define closesocketCheck(sockfd) closesocket_check(sockfd, __FILE__, __LINE__)
|
||||
#endif
|
||||
|
||||
extern inline void fseek_check(FILE *fp, long off, int whence, const char *file, int line) {
|
||||
if (fseek(fp, off, whence) != 0) {
|
||||
fprintf(stderr, "Error: Failed to seek in file at %s:%d\n", file, line);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
@@ -86,10 +115,32 @@ void fseek_check(FILE *fp, long off, int whence, const char *file, int line) {
|
||||
|
||||
#define fseekCheck(fp, off, whence) fseek_check(fp, off, whence, __FILE__, __LINE__)
|
||||
|
||||
extern inline void fwrite_check(void *ptr, size_t size, size_t nmemb, FILE *stream, const char *file, int line) {
|
||||
size_t result = fwrite(ptr, size, nmemb, stream);
|
||||
if (result != nmemb) {
|
||||
if (feof(stream)) {
|
||||
fprintf(stderr, "Error: Unexpected end of file at %s:%d\n", file, line);
|
||||
} else if (ferror(stream)) {
|
||||
fprintf(stderr, "Error: File write error at %s:%d\n", file, line);
|
||||
} else {
|
||||
fprintf(stderr, "Error: Partial write at %s:%d. Expected %zu elements, wrote %zu\n",
|
||||
file, line, nmemb, result);
|
||||
}
|
||||
fprintf(stderr, "Error details:\n");
|
||||
fprintf(stderr, " File: %s\n", file);
|
||||
fprintf(stderr, " Line: %d\n", line);
|
||||
fprintf(stderr, " Expected elements: %zu\n", nmemb);
|
||||
fprintf(stderr, " Written elements: %zu\n", result);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
#define fwriteCheck(ptr, size, nmemb, stream) fwrite_check(ptr, size, nmemb, stream, __FILE__, __LINE__)
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// malloc error-handling wrapper util
|
||||
|
||||
void *malloc_check(size_t size, const char *file, int line) {
|
||||
extern inline void *malloc_check(size_t size, const char *file, int line) {
|
||||
void *ptr = malloc(size);
|
||||
if (ptr == NULL) {
|
||||
fprintf(stderr, "Error: Memory allocation failed at %s:%d\n", file, line);
|
||||
@@ -104,10 +155,29 @@ void *malloc_check(size_t size, const char *file, int line) {
|
||||
|
||||
#define mallocCheck(size) malloc_check(size, __FILE__, __LINE__)
|
||||
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// check that all tokens are within range
|
||||
extern inline void token_check(const int* tokens, int token_count, int vocab_size, const char *file, int line) {
|
||||
for(int i = 0; i < token_count; i++) {
|
||||
if(!(0 <= tokens[i] && tokens[i] < vocab_size)) {
|
||||
fprintf(stderr, "Error: Token out of vocabulary at %s:%d\n", file, line);
|
||||
fprintf(stderr, "Error details:\n");
|
||||
fprintf(stderr, " File: %s\n", file);
|
||||
fprintf(stderr, " Line: %d\n", line);
|
||||
fprintf(stderr, " Token: %d\n", tokens[i]);
|
||||
fprintf(stderr, " Position: %d\n", i);
|
||||
fprintf(stderr, " Vocab: %d\n", vocab_size);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
}
|
||||
#define tokenCheck(tokens, count, vocab) token_check(tokens, count, vocab, __FILE__, __LINE__)
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// I/O ops
|
||||
|
||||
void create_dir_if_not_exists(const char *dir) {
|
||||
extern inline void create_dir_if_not_exists(const char *dir) {
|
||||
if (dir == NULL) { return; }
|
||||
struct stat st = {0};
|
||||
if (stat(dir, &st) == -1) {
|
||||
@@ -119,7 +189,7 @@ void create_dir_if_not_exists(const char *dir) {
|
||||
}
|
||||
}
|
||||
|
||||
int find_max_step(const char* output_log_dir) {
|
||||
extern inline int find_max_step(const char* output_log_dir) {
|
||||
// find the DONE file in the log dir with highest step count
|
||||
if (output_log_dir == NULL) { return -1; }
|
||||
DIR* dir;
|
||||
@@ -139,4 +209,15 @@ int find_max_step(const char* output_log_dir) {
|
||||
return max_step;
|
||||
}
|
||||
|
||||
extern inline int ends_with_bin(const char* str) {
|
||||
// checks if str ends with ".bin". could be generalized in the future.
|
||||
if (str == NULL) { return 0; }
|
||||
size_t len = strlen(str);
|
||||
const char* suffix = ".bin";
|
||||
size_t suffix_len = strlen(suffix);
|
||||
if (len < suffix_len) { return 0; }
|
||||
int suffix_matches = strncmp(str + len - suffix_len, suffix, suffix_len) == 0;
|
||||
return suffix_matches;
|
||||
}
|
||||
|
||||
#endif
|
||||
+597
@@ -0,0 +1,597 @@
|
||||
/*
|
||||
Utilities for ZeRO sharding
|
||||
*/
|
||||
|
||||
#ifndef LLMC_ZERO_CUH
|
||||
#define LLMC_ZERO_CUH
|
||||
|
||||
#include <cuda_runtime_api.h>
|
||||
#include <stdint.h>
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <stddef.h>
|
||||
|
||||
#ifdef MULTI_GPU
|
||||
#include <nccl.h>
|
||||
#ifdef USE_MPI
|
||||
#include <mpi.h>
|
||||
#endif
|
||||
#endif
|
||||
|
||||
// defines: fcloseCheck, fwriteCheck, scloseCheck, sclosesocketCheck
|
||||
#include "utils.h"
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Multi-GPU related
|
||||
#ifdef MULTI_GPU
|
||||
|
||||
#if defined(ENABLE_FP32)
|
||||
const ncclDataType_t ncclFloatX = ncclFloat;
|
||||
#elif defined(ENABLE_FP16)
|
||||
const ncclDataType_t ncclFloatX = ncclHalf;
|
||||
#else // Default to bfloat16
|
||||
const ncclDataType_t ncclFloatX = ncclBfloat16;
|
||||
#endif
|
||||
|
||||
void nccl_check(ncclResult_t status, const char *file, int line) {
|
||||
if (status != ncclSuccess) {
|
||||
printf("[NCCL ERROR] at file %s:%d:\n%s\n", file, line, ncclGetErrorString(status));
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
#define ncclCheck(err) (nccl_check(err, __FILE__, __LINE__))
|
||||
|
||||
#ifdef USE_MPI
|
||||
void mpi_check(int status, const char *file, int line) {
|
||||
if (status != MPI_SUCCESS) {
|
||||
char mpi_error[4096];
|
||||
int mpi_error_len = 0;
|
||||
assert(MPI_Error_string(status, &mpi_error[0], &mpi_error_len) == MPI_SUCCESS);
|
||||
printf("[MPI ERROR] at file %s:%d:\n%.*s\n", file, line, mpi_error_len, mpi_error);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
#define mpiCheck(err) (mpi_check(err, __FILE__, __LINE__))
|
||||
#endif
|
||||
|
||||
#endif // MULTI_GPU
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// Parameters specific to training on multiple GPUs.
|
||||
typedef struct {
|
||||
int process_rank; // Rank of this process among all processes. 0 if no multi-GPU.
|
||||
int num_processes; // Total number of processes. 1 if no multi-GPU.
|
||||
int local_device_idx; // This process GPU index on current machine. 0 if no multi-GPU.
|
||||
|
||||
// Zero Redundancy Optimizer stage - https://fairscale.readthedocs.io/en/stable/deep_dive/oss_sdp_fsdp.html
|
||||
// 0-Disabled
|
||||
// 1-Optimizer State Sharding (OSS)
|
||||
// 2-Optimizer + Gradient State Sharding (SDP)
|
||||
// 3-Optimizer + Gradient + Horizontal Model Sharding (FSDP)
|
||||
int zero_stage;
|
||||
size_t shard_num_parameters;
|
||||
#ifdef MULTI_GPU
|
||||
ncclComm_t nccl_comm; // NCCL communication primitive, used for collective multi-GPU work.
|
||||
cudaStream_t nccl_stream; // CUDA Stream to perform NCCL operations.
|
||||
cudaEvent_t compute_nccl_sync; // Event used to synchronize NCCL with the compute
|
||||
float* unified_buffer;
|
||||
#endif
|
||||
} MultiGpuConfig;
|
||||
|
||||
// one global variable to hold the multi-GPU configuration for this process
|
||||
// inline, so we can include this header multiple times without getting multiple definitions
|
||||
inline MultiGpuConfig multi_gpu_config;
|
||||
|
||||
#ifdef MULTI_GPU
|
||||
|
||||
#ifdef _WIN32
|
||||
void send_nccl_id_to_clients_windows(ncclUniqueId *nccl_id, SOCKET client_sockets[], int num_clients) {
|
||||
for (int i = 0; i < num_clients; ++i) {
|
||||
if (send(client_sockets[i], (const char *)nccl_id, sizeof(*nccl_id), 0) == SOCKET_ERROR) {
|
||||
printf("Failed to send nccl_id");
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
closesocketCheck(client_sockets[i]);
|
||||
}
|
||||
}
|
||||
#else
|
||||
void send_nccl_id_to_clients(ncclUniqueId *nccl_id, int client_sockets[], int num_clients) {
|
||||
for (int i = 0; i < num_clients; ++i) {
|
||||
if (send(client_sockets[i], nccl_id, sizeof(*nccl_id), 0) == -1) {
|
||||
printf("Failed to send nccl_id");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
scloseCheck(client_sockets[i]);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
#ifdef _WIN32
|
||||
// Same as get_nccl_id_via_tcp but for Windows
|
||||
ncclUniqueId get_nccl_id_via_tcp_windows(MultiGpuConfig* result, const char* server_ip) {
|
||||
ncclUniqueId nccl_id;
|
||||
|
||||
int SERVER_PORT = 12345; // hardcoded an arbitrary port number between 1024 and 49151 (registered ports)
|
||||
WSADATA wsaData;
|
||||
if (WSAStartup(MAKEWORD(2, 2), &wsaData) != 0) {
|
||||
printf("WSAStartup failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
if (result->process_rank == 0) {
|
||||
ncclCheck(ncclGetUniqueId(&nccl_id));
|
||||
|
||||
int MAX_CLIENTS = result->num_processes - 1;
|
||||
SOCKET client_sockets[MAX_CLIENTS];
|
||||
int num_clients = 0;
|
||||
SOCKET server_socket, new_socket;
|
||||
struct sockaddr_in address;
|
||||
int addrlen = sizeof(address);
|
||||
|
||||
// Step 1) create a server TCP socket
|
||||
if ((server_socket = socket(AF_INET, SOCK_STREAM, 0)) == INVALID_SOCKET) {
|
||||
printf("Socket failed");
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 2) set the server address and port
|
||||
address.sin_family = AF_INET; // IPv4
|
||||
address.sin_addr.s_addr = inet_addr(server_ip);
|
||||
address.sin_port = htons(SERVER_PORT);
|
||||
|
||||
// Step 3) bind the socket to the address and port
|
||||
if (bind(server_socket, (struct sockaddr *)&address, sizeof(address)) == SOCKET_ERROR) {
|
||||
printf("Bind failed");
|
||||
closesocketCheck(server_socket);
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 4) MAX_CLIENTS specifies the maximum number of clients that can be queued for this server
|
||||
if (listen(server_socket, MAX_CLIENTS) == SOCKET_ERROR) {
|
||||
printf("Listen failed");
|
||||
closesocketCheck(server_socket);
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 5) accept connections from clients
|
||||
printf("Waiting for clients to connect...\n");
|
||||
while (num_clients < MAX_CLIENTS) {
|
||||
if ((new_socket = accept(server_socket, (struct sockaddr *)&address, &addrlen)) == INVALID_SOCKET) {
|
||||
printf("Accept failed");
|
||||
closesocketCheck(server_socket);
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
client_sockets[num_clients++] = new_socket;
|
||||
printf("Client %d connected\n", num_clients);
|
||||
}
|
||||
|
||||
// Step 6) send the NCCL ID to all clients
|
||||
send_nccl_id_to_clients_windows(&nccl_id, client_sockets, num_clients);
|
||||
printf("NCCL ID sent to all clients\n");
|
||||
|
||||
closesocketCheck(server_socket);
|
||||
} else {
|
||||
int num_connection_attempts = 5;
|
||||
int time_to_sleep = 2;
|
||||
SOCKET client_socket;
|
||||
struct sockaddr_in serv_addr;
|
||||
|
||||
// Step 1) create a client TCP socket
|
||||
if ((client_socket = socket(AF_INET, SOCK_STREAM, 0)) == INVALID_SOCKET) {
|
||||
printf("Socket creation error");
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 2) set the server address and port
|
||||
serv_addr.sin_family = AF_INET;
|
||||
serv_addr.sin_port = htons(SERVER_PORT);
|
||||
if (inet_pton(AF_INET, server_ip, &serv_addr.sin_addr) <= 0) {
|
||||
printf("Invalid address or address not supported");
|
||||
closesocketCheck(client_socket);
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 3) Try to connect to the server - retry up to `num_connection_attempts` times if the connection fails
|
||||
while (connect(client_socket, (struct sockaddr *)&serv_addr, sizeof(serv_addr)) == SOCKET_ERROR) {
|
||||
printf("%d Connection failed, retrying in %d seconds\n", result->process_rank, time_to_sleep);
|
||||
if (--num_connection_attempts == 0) {
|
||||
printf("Failed to connect to the server\n");
|
||||
closesocketCheck(client_socket);
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
Sleep(time_to_sleep * 1000);
|
||||
}
|
||||
|
||||
// Step 4) receive the NCCL ID from the server
|
||||
if (recv(client_socket, (char *)&nccl_id, sizeof(nccl_id), 0) <= 0) {
|
||||
printf("Failed to receive nccl_id");
|
||||
closesocketCheck(client_socket);
|
||||
WSACleanup();
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
printf("Received NCCL ID\n");
|
||||
closesocketCheck(client_socket);
|
||||
}
|
||||
|
||||
WSACleanup();
|
||||
return nccl_id;
|
||||
}
|
||||
#else
|
||||
ncclUniqueId get_nccl_id_via_tcp(MultiGpuConfig* result, const char* server_ip) {
|
||||
ncclUniqueId nccl_id;
|
||||
|
||||
int SERVER_PORT = 12345; // hardcoded an arbitrary port number between 1024 and 49151 (registered ports)
|
||||
if (result->process_rank == 0) {
|
||||
ncclCheck(ncclGetUniqueId(&nccl_id));
|
||||
|
||||
int MAX_CLIENTS = result->num_processes - 1;
|
||||
int client_sockets[MAX_CLIENTS];
|
||||
int num_clients = 0;
|
||||
int server_socket, new_socket;
|
||||
struct sockaddr_in address;
|
||||
int addrlen = sizeof(address);
|
||||
int opt = 1;
|
||||
|
||||
// Step 1) create a server TCP socket
|
||||
if ((server_socket = socket(AF_INET, SOCK_STREAM, 0)) < 0) {
|
||||
printf("Socket failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 2) set socket options
|
||||
// SOL_SOCKET - means that option is configured at socket level
|
||||
// SO_REUSEADDR - allows to bind to an address which is in a TIME_WAIT state (already used by another socket) - useful when restarting the server
|
||||
// SO_REUSEPORT - allows to bind to the same port multiple times
|
||||
if (setsockopt(server_socket, SOL_SOCKET, SO_REUSEADDR | SO_REUSEPORT, &opt, sizeof(opt)) < 0) {
|
||||
printf("Setsockopt failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 3) set the server address and port
|
||||
address.sin_family = AF_INET; // IPv4
|
||||
address.sin_addr.s_addr = inet_addr(server_ip); // alternatively use INADDR_ANY to bind to all interfaces, currently we only allow ethernet
|
||||
address.sin_port = htons(SERVER_PORT);
|
||||
|
||||
// Step 4) bind the socket to the address and port
|
||||
if (bind(server_socket, (struct sockaddr *)&address, sizeof(address)) < 0) {
|
||||
printf("Bind failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 5) MAX_CLIENTS specifies the maximum number of clients that can be queued for this server
|
||||
if (listen(server_socket, MAX_CLIENTS) < 0) {
|
||||
printf("Listen failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 6) accept connections from clients
|
||||
printf("Waiting for clients to connect...\n");
|
||||
while (num_clients < MAX_CLIENTS) {
|
||||
if ((new_socket = accept(server_socket, (struct sockaddr *)&address, (socklen_t*)&addrlen)) < 0) {
|
||||
printf("Accept failed");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
client_sockets[num_clients++] = new_socket;
|
||||
printf("Client %d connected\n", num_clients);
|
||||
}
|
||||
|
||||
// Step 7) send the NCCL ID to all clients
|
||||
send_nccl_id_to_clients(&nccl_id, client_sockets, num_clients);
|
||||
printf("NCCL ID sent to all clients\n");
|
||||
|
||||
scloseCheck(server_socket);
|
||||
} else {
|
||||
int num_connection_attempts = 5;
|
||||
int time_to_sleep = 2;
|
||||
int client_socket;
|
||||
struct sockaddr_in serv_addr;
|
||||
|
||||
// Step 1) create a client TCP socket
|
||||
if ((client_socket = socket(AF_INET, SOCK_STREAM, 0)) < 0) {
|
||||
printf("Socket creation error");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 2) set the server address and port
|
||||
serv_addr.sin_family = AF_INET;
|
||||
serv_addr.sin_port = htons(SERVER_PORT);
|
||||
if (inet_pton(AF_INET, server_ip, &serv_addr.sin_addr) <= 0) {
|
||||
printf("Invalid address or address not supported");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// Step 3) Try to connect to the server - retry up to `num_connection_attempts` times if the connection fails
|
||||
while (connect(client_socket, (struct sockaddr *)&serv_addr, sizeof(serv_addr)) < 0) {
|
||||
printf("%d Connection failed, retrying in %d seconds\n", result->process_rank, time_to_sleep);
|
||||
if (--num_connection_attempts == 0) {
|
||||
printf("Failed to connect to the server\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
sleep(time_to_sleep);
|
||||
}
|
||||
|
||||
// Step 4) receive the NCCL ID from the server
|
||||
if (recv(client_socket, &nccl_id, sizeof(nccl_id), 0) <= 0) {
|
||||
printf("Failed to receive nccl_id");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
printf("Received NCCL ID\n");
|
||||
scloseCheck(client_socket);
|
||||
}
|
||||
|
||||
return nccl_id;
|
||||
}
|
||||
#endif
|
||||
|
||||
ncclUniqueId get_nccl_id_via_fs(MultiGpuConfig* result, char* fs_path) {
|
||||
// Works assuming that the filesystem is shared among all processes
|
||||
ncclUniqueId nccl_id;
|
||||
FILE* idFile;
|
||||
static char filename[1024];
|
||||
snprintf(filename, sizeof(filename), "%s/ncclUniqueId.sync", fs_path);
|
||||
|
||||
if (result->process_rank != 0) { // client processse should wait for the server to write to the file
|
||||
// This is a naive and not 100% robust way to synchronize the processes but it should work almost always
|
||||
sleep(2);
|
||||
}
|
||||
|
||||
if (result->process_rank == 0) {
|
||||
ncclCheck(ncclGetUniqueId(&nccl_id));
|
||||
idFile = fopen(filename, "wb");
|
||||
assert(idFile != NULL);
|
||||
fwriteCheck(&nccl_id, sizeof(nccl_id), 1, idFile);
|
||||
fcloseCheck(idFile);
|
||||
} else {
|
||||
// Other ranks wait until the file is available and read the unique ID
|
||||
do {
|
||||
sleep(1); // 1 second
|
||||
idFile = fopen(filename, "rb");
|
||||
if (idFile != NULL) break;
|
||||
} while (idFile == NULL);
|
||||
freadCheck(&nccl_id, sizeof(nccl_id), 1, idFile);
|
||||
fcloseCheck(idFile);
|
||||
}
|
||||
|
||||
return nccl_id;
|
||||
}
|
||||
|
||||
#ifdef USE_MPI
|
||||
// Determine which GPU this process should use.
|
||||
// Processes on the same machines use different GPU indicies. Processes on other machines don't.
|
||||
// Copied from NCCL examples: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/examples.html#example-2-one-device-per-process-or-thread
|
||||
int multi_gpu_get_local_device_idx(int process_rank, int num_processes) {
|
||||
char hostname[1024];
|
||||
hostname[1023] = '\0';
|
||||
// All processes on the same machine will share the same hostname.
|
||||
gethostname(hostname, 1023);
|
||||
for (int i=0; i < 1024; i++) {
|
||||
if (hostname[i] == '.') {
|
||||
hostname[i] = '\0';
|
||||
break;
|
||||
}
|
||||
}
|
||||
uint64_t hostname_hash = 5381u;
|
||||
for (int c = 0; hostname[c] != '\0'; c++){ hostname_hash = ((hostname_hash << 5u) + hostname_hash) ^ hostname[c]; }
|
||||
|
||||
// Distribute all hostname hashes to all processes.
|
||||
uint64_t* all_hostsname_hashes = (uint64_t*)malloc(num_processes * sizeof(uint64_t));
|
||||
all_hostsname_hashes[process_rank] = hostname_hash;
|
||||
mpiCheck(MPI_Allgather(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, all_hostsname_hashes, sizeof(uint64_t), MPI_BYTE, MPI_COMM_WORLD));
|
||||
|
||||
// Identify which GPU we need to use.
|
||||
int local_device_idx = 0;
|
||||
for (int current_process = 0; current_process < num_processes; ++current_process) {
|
||||
if (current_process == process_rank) {
|
||||
// Found my gpu, local_device_idx now has my target GPU index.
|
||||
break;
|
||||
}
|
||||
if (all_hostsname_hashes[current_process] == all_hostsname_hashes[process_rank]) {
|
||||
// This process ID runs on the same machine, but it's not me, skip this GPU
|
||||
local_device_idx++;
|
||||
}
|
||||
}
|
||||
|
||||
free(all_hostsname_hashes);
|
||||
return local_device_idx;
|
||||
}
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
||||
MultiGpuConfig multi_gpu_config_init(int num_processes, int process_rank, int gpus_per_node, char* server_ip, char* fs_path, char* init_method) {
|
||||
#ifdef MULTI_GPU
|
||||
MultiGpuConfig result;
|
||||
ncclUniqueId nccl_id;
|
||||
// Get nccl_id using MPI, TCP, or FS (file system synchronization) methods
|
||||
// On newer slurm versions (slurm-wlm package) PMIx is disabled so we can not use MPI for NCCL init in multi node setup
|
||||
if (strcmp(init_method, "mpi") == 0) {
|
||||
#ifdef USE_MPI
|
||||
mpiCheck(MPI_Init(NULL, NULL));
|
||||
mpiCheck(MPI_Comm_rank(MPI_COMM_WORLD, &result.process_rank));
|
||||
mpiCheck(MPI_Comm_size(MPI_COMM_WORLD, &result.num_processes));
|
||||
result.local_device_idx = multi_gpu_get_local_device_idx(result.process_rank, result.num_processes);
|
||||
if (result.process_rank == 0) {
|
||||
ncclCheck(ncclGetUniqueId(&nccl_id));
|
||||
}
|
||||
mpiCheck(MPI_Bcast(&nccl_id, sizeof(nccl_id), MPI_BYTE, 0, MPI_COMM_WORLD));
|
||||
#else
|
||||
printf("MPI support is disabled. Please enable MPI support to use MPI-based NCCL-init method.\n");
|
||||
exit(EXIT_FAILURE);
|
||||
#endif
|
||||
} else {
|
||||
result.process_rank = process_rank;
|
||||
result.num_processes = num_processes;
|
||||
result.local_device_idx = process_rank % gpus_per_node;
|
||||
if (strcmp(init_method, "tcp") == 0) {
|
||||
#ifdef _WIN32
|
||||
nccl_id = get_nccl_id_via_tcp_windows(&result, server_ip);
|
||||
#else
|
||||
nccl_id = get_nccl_id_via_tcp(&result, server_ip);
|
||||
#endif
|
||||
} else if (strcmp(init_method, "fs") == 0) {
|
||||
nccl_id = get_nccl_id_via_fs(&result, fs_path);
|
||||
} else {
|
||||
printf("Invalid NCCL-init method\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
cudaCheck(cudaSetDevice(result.local_device_idx));
|
||||
ncclCheck(ncclCommInitRank(&result.nccl_comm, result.num_processes, nccl_id, result.process_rank));
|
||||
cudaCheck(cudaStreamCreate(&result.nccl_stream));
|
||||
// event without timing for maximum performance
|
||||
cudaCheck(cudaEventCreate(&result.compute_nccl_sync, cudaEventDisableTiming));
|
||||
nvtxNameCudaStreamA(result.nccl_stream, "nccl stream");
|
||||
nvtxNameCudaEventA(result.compute_nccl_sync, "nccl compute sync");
|
||||
cudaCheck(cudaMallocManaged(&result.unified_buffer, sizeof(float)));
|
||||
return result;
|
||||
#else
|
||||
printf("Multi-GPU support is disabled. Using a single GPU.\n");
|
||||
cudaCheck(cudaSetDevice(0));
|
||||
MultiGpuConfig result;
|
||||
result.process_rank = 0;
|
||||
result.num_processes = 1;
|
||||
result.local_device_idx = 0;
|
||||
return result;
|
||||
#endif
|
||||
}
|
||||
|
||||
void multi_gpu_config_free(MultiGpuConfig* config) {
|
||||
#ifdef MULTI_GPU
|
||||
ncclCheck(ncclCommDestroy(config->nccl_comm));
|
||||
cudaCheck(cudaStreamDestroy(config->nccl_stream));
|
||||
cudaCheck(cudaEventDestroy(config->compute_nccl_sync));
|
||||
cudaCheck(cudaFree(config->unified_buffer));
|
||||
#ifdef USE_MPI
|
||||
mpiCheck(MPI_Finalize());
|
||||
#endif
|
||||
#endif
|
||||
}
|
||||
|
||||
void multi_gpu_barrier(const MultiGpuConfig* config) {
|
||||
#ifdef MULTI_GPU
|
||||
if (config->num_processes > 1) {
|
||||
ncclCheck(ncclAllReduce(config->unified_buffer, config->unified_buffer, sizeof(float), ncclFloat, ncclSum, config->nccl_comm, config->nccl_stream));
|
||||
}
|
||||
cudaCheck(cudaDeviceSynchronize());
|
||||
#endif
|
||||
}
|
||||
|
||||
// Offset and size of a tensor shard
|
||||
typedef struct {
|
||||
ptrdiff_t offset;
|
||||
size_t size;
|
||||
} ShardInfo;
|
||||
|
||||
// Get info about sharding for a tensor of elements many numbers
|
||||
ShardInfo multi_gpu_get_shard_offset(size_t elements, const MultiGpuConfig* config, int shard_at_stage) {
|
||||
const int nproc = config->num_processes;
|
||||
if(config->zero_stage >= shard_at_stage) {
|
||||
if (elements % nproc != 0) {
|
||||
fprintf(stderr, "Number of elements %zu must be a multiple of the number of processes %d\n", elements, nproc);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
return {(ptrdiff_t) (config->process_rank * (elements / nproc)), elements / nproc};
|
||||
} else {
|
||||
return {0, elements};
|
||||
}
|
||||
}
|
||||
|
||||
// Block NCCL stream until computations on compute_stream are done, then aggregate multiple pointers in an NCCL group.
|
||||
// This can work either as an all-reduce (i.e., no ZeRo), or a reduce-scatter (ZeRO 1).
|
||||
// The awkward `(&pointers)[N]` syntax ensures we are capturing the parameters as sized arrays, so that it becomes impossible
|
||||
// to call this function if pointers and pointers_sizes do not match.
|
||||
template<int N>
|
||||
void multi_gpu_async_reduce_gradient(
|
||||
floatX* const (&pointers)[N], const size_t (&pointers_sizes)[N],
|
||||
MultiGpuConfig* config, cudaStream_t compute_stream) {
|
||||
if (config->num_processes == 1) {
|
||||
return; // no multi-GPU, just exit.
|
||||
}
|
||||
|
||||
#ifdef MULTI_GPU
|
||||
NVTX_RANGE_FN();
|
||||
// mark an event on the compute stream, and immediately wait on this in the nccl stream
|
||||
// this means that the nccl stream won't start executing before all compute kernels that
|
||||
// have been submitted before this point have finished.
|
||||
// by using an event instead of cudaSyncStream, we avoid having to synchronize the host, and
|
||||
// can enqueue new work to the GPU right away.
|
||||
cudaCheck(cudaEventRecord(config->compute_nccl_sync, compute_stream));
|
||||
cudaCheck(cudaStreamWaitEvent(config->nccl_stream, config->compute_nccl_sync));
|
||||
ncclCheck(ncclGroupStart()); // NCCL group: aggregate all pointers in a single NCCL GPU kernel.
|
||||
for (int i = 0; i < N; ++i) {
|
||||
if(config->zero_stage == 0) {
|
||||
ncclCheck(ncclAllReduce(
|
||||
pointers[i], pointers[i],
|
||||
pointers_sizes[i],
|
||||
ncclFloatX, ncclAvg,
|
||||
config->nccl_comm, config->nccl_stream
|
||||
));
|
||||
} else if(config->zero_stage == 1) {
|
||||
assert(pointers_sizes[i] % config->num_processes == 0);
|
||||
size_t shard_size = pointers_sizes[i] / config->num_processes;
|
||||
ptrdiff_t shard_offset = (ptrdiff_t)shard_size * config->process_rank;
|
||||
ncclCheck(ncclReduceScatter(
|
||||
pointers[i], pointers[i] + shard_offset,
|
||||
shard_size,
|
||||
ncclFloatX, ncclAvg,
|
||||
config->nccl_comm, config->nccl_stream
|
||||
));
|
||||
}
|
||||
}
|
||||
ncclCheck(ncclGroupEnd());
|
||||
#endif
|
||||
}
|
||||
|
||||
// convenience macro that only prints if the rank of process is zero
|
||||
#define printf0(...) if (::multi_gpu_config.process_rank == 0) { printf(__VA_ARGS__); }
|
||||
|
||||
void set_zero_configs(MultiGpuConfig* config, int zero_stage, size_t total_parameters) {
|
||||
config->zero_stage = 0;
|
||||
config->shard_num_parameters = total_parameters;
|
||||
// Check the Zero Stage and define sharding parameters
|
||||
if (zero_stage == 0) {
|
||||
printf0("| Zero Optimization is disabled |\n");
|
||||
}
|
||||
else if (zero_stage == 1) {
|
||||
if (total_parameters % config->num_processes != 0) {
|
||||
printf0("| Zero Optimization is disabled, Can't equally partition parameters |\n");
|
||||
config->zero_stage = 0;
|
||||
}
|
||||
else {
|
||||
config->zero_stage = 1;
|
||||
config->shard_num_parameters = total_parameters / config->num_processes;
|
||||
}
|
||||
}
|
||||
else{
|
||||
printf0("| Disabling Zero Optimization, Zero Stage2 and Stage3 are not yet supported |\n");
|
||||
config->zero_stage = 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Compute sum of a single CPU value across all GPU processes. No-op when multi-GPU is disabled.
|
||||
float multi_gpu_cpu_float_sum(float value, MultiGpuConfig* config) {
|
||||
#ifdef MULTI_GPU
|
||||
if (config->num_processes == 1) return value;
|
||||
|
||||
float* unified_buffer = config->unified_buffer;
|
||||
*unified_buffer = value;
|
||||
ncclCheck(ncclAllReduce(unified_buffer, unified_buffer, sizeof(float), ncclFloat, ncclSum, config->nccl_comm, config->nccl_stream));
|
||||
cudaCheck(cudaDeviceSynchronize());
|
||||
return *unified_buffer;
|
||||
#else
|
||||
return value;
|
||||
#endif
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
+14
-5
@@ -28,11 +28,18 @@ the profile.ncu-rep from a cloud box to local to pretty view.
|
||||
#include "train_gpt2.cu"
|
||||
|
||||
int main(int argc, char *argv[]) {
|
||||
multi_gpu_config = multi_gpu_config_init(&argc, &argv);
|
||||
char nccl_init_method[256] = "mpi"; // "tcp" or "fs" or "mpi"
|
||||
int num_processes = -1; // doesn't matter when using MPI
|
||||
int process_rank = -1; // doesn't matter when using MPI
|
||||
int gpus_per_node = -1; // doesn't matter when using MPI
|
||||
char server_ip[256] = ""; // doesn't matter when using MPI
|
||||
char fs_path[256] = ""; // doesn't matter when using MPI
|
||||
multi_gpu_config = multi_gpu_config_init(num_processes, process_rank, gpus_per_node, server_ip, fs_path, nccl_init_method);
|
||||
common_start(true, true);
|
||||
|
||||
// build the GPT-2 model from a checkpoint
|
||||
GPT2 model;
|
||||
gpt2_init_common(&model);
|
||||
gpt2_build_from_checkpoint(&model, "gpt2_124M_bf16.bin");
|
||||
|
||||
int B = 24; // if program OOMs decrease this number, e.g. all the way down to 4 or etc
|
||||
@@ -51,11 +58,13 @@ int main(int argc, char *argv[]) {
|
||||
model.config.num_layers = 1;
|
||||
set_zero_configs(&multi_gpu_config, 0, model.num_parameters);
|
||||
|
||||
gpt2_allocate_state(&model, B, T);
|
||||
// do a training step
|
||||
gpt2_forward(&model, x, y, B, T);
|
||||
gpt2_zero_grad(&model);
|
||||
gpt2_backward(&model, x);
|
||||
gpt2_update(&model, 1e-4f, 0.9f, 0.999f, 1e-8f, 0.0f, 1.f, 1, &multi_gpu_config);
|
||||
gpt2_forward(&model, x, B, T);
|
||||
gpt2_backward_and_reduce(&model, x, y, 1, 0);
|
||||
float grad_norm = gpt2_calculate_grad_norm(&model, &multi_gpu_config);
|
||||
float grad_scale = (grad_norm > 1.0f) ? 1.0f / grad_norm : 1.0f;
|
||||
gpt2_update(&model, 1e-4f, 0.9f, 0.999f, 1e-8f, 0.0f, grad_scale, 1, &multi_gpu_config);
|
||||
cudaCheck(cudaDeviceSynchronize()); // finish all CUDA work to get correct precise timings
|
||||
|
||||
// free
|
||||
|
||||
+2
-2
@@ -38,7 +38,7 @@ metrics = [
|
||||
"dram__bytes_read.sum", # DRAM reads
|
||||
"dram__bytes_write.sum", # DRAM writes
|
||||
"lts__t_sectors_srcunit_tex_op_read.sum", # L2 reads (sectors -- 32B)
|
||||
"lts__t_sectors_srcunit_tex_op_write.sum", # L2 reads (sectors -- 32B)
|
||||
"lts__t_sectors_srcunit_tex_op_write.sum", # L2 writes (sectors -- 32B)
|
||||
"sm__pipe_tensor_op_hmma_cycles_active.avg.pct_of_peak_sustained_active", # % of peak tensor core utilization
|
||||
"smsp__inst_executed.sum", # instructions
|
||||
]
|
||||
@@ -130,7 +130,7 @@ for rid, row in kernel_profile_data:
|
||||
# the classifier part, counts only once
|
||||
pass_name = "cls"
|
||||
phase = "bwd"
|
||||
elif "adamw" in kernel or "global_norm" in kernel:
|
||||
elif "adamw" in kernel or "global_norm" in kernel or "copy_and_cast" in kernel:
|
||||
# encoder layer or adam
|
||||
pass_name = "opt"
|
||||
# before the first optimizer run, we create weight copies.
|
||||
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
tqdm
|
||||
numpy
|
||||
numpy<2
|
||||
torch
|
||||
tiktoken
|
||||
transformers
|
||||
|
||||
Executable
+85
@@ -0,0 +1,85 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --job-name=llmc-multinode # job name
|
||||
#SBATCH --output=/home/ubuntu/llm.c/scripts/multi_node/%x_%j_%t.log # output file
|
||||
#SBATCH --error=/home/ubuntu/llm.c/scripts/multi_node/%x_%j_%t.err # error file
|
||||
#SBATCH --partition=llmc # Specify the GPU partition
|
||||
#SBATCH --ntasks=16 # total number of processes to launch on all nodes
|
||||
#SBATCH --nodes=2 # total number of nodes
|
||||
#SBATCH --ntasks-per-node=8 # assuming each node has 8 gpus
|
||||
#SBATCH --gres=gpu:8 # request 8 gpus from each node
|
||||
|
||||
# NOTE: change the above slurm arguments to match your system!
|
||||
# Run with `sbatch <path_to_this_script.sh>`
|
||||
|
||||
make train_gpt2cu USE_CUDNN=1 NO_USE_MPI=1
|
||||
|
||||
# NOTE: change the following to match your system
|
||||
binary_path="/home/ubuntu/llm.c/train_gpt2cu"
|
||||
out_dir="/ephemeral/data/fineweb/log_gpt2_124M_multi"
|
||||
train_data_path='/ephemeral/data/fineweb/bin_10B/fineweb_train_*.bin'
|
||||
val_data_path='/ephemeral/data/fineweb/bin_10B/fineweb_val_*.bin'
|
||||
sync_fs_path=$out_dir # needs to be a shared filesystem path that all nodes can access
|
||||
|
||||
# In case the file system is shared this is a no-op.
|
||||
# Otherwise, we need to copy the binary to all nodes.
|
||||
current_user=$USER
|
||||
hosts=$(scontrol show hostnames $SLURM_JOB_NODELIST) # get the hostnames of the allocated nodes
|
||||
current_host=$(hostname)
|
||||
for host in $hosts; do
|
||||
if [ $host == $current_host ]; then
|
||||
continue
|
||||
fi
|
||||
echo "copying $binary_path to $current_user@$host"
|
||||
scp -r $binary_path $current_user@$host:$binary_path
|
||||
done
|
||||
|
||||
# Use this for NCCL debugging if you run into issues
|
||||
# export NCCL_DEBUG=INFO
|
||||
# export NCCL_DEBUG_SUBSYS=ALL
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
|
||||
|
||||
# Optimization flags
|
||||
export NCCL_NET_GDR_LEVEL=2 # use GPUDirect RDMA - allows for direct memory access between GPUs across different nodes by bypassing the CPU
|
||||
export NCCL_IB_DISABLE=0 # use InfiniBand if available
|
||||
|
||||
# NOTE: change the following environment variables to match your system - or comment them out if you don't need them
|
||||
export NCCL_SOCKET_IFNAME=ens17
|
||||
export OMPI_MCA_btl_tcp_if_include=ens17
|
||||
export NCCL_P2P_LEVEL=PXB
|
||||
|
||||
if [ -z "$SLURM_JOB_ID" ]; then
|
||||
echo "Make sure you're running in a SLURM environment. Did you forget to run with sbatch? Aborting."
|
||||
exit 1
|
||||
else
|
||||
DATESTRING=`date "+%Y-%m-%dT%H:%M:%S"`
|
||||
echo "Running in a SLURM environment (job ID: $SLURM_JOB_ID, user: $current_user)"
|
||||
echo "Running on hosts: $(echo $(scontrol show hostname))"
|
||||
echo "$DATESTRING"
|
||||
fi
|
||||
|
||||
srun -l -u bash -c "
|
||||
$binary_path \
|
||||
-i '$train_data_path' \
|
||||
-j '$val_data_path' \
|
||||
-o $out_dir \
|
||||
-v 250 -s 20000 -g 144 \
|
||||
-h 1 \
|
||||
-b 64 -t 1024 \
|
||||
-d 2097152 \
|
||||
-r 0 \
|
||||
-z 1 \
|
||||
-c 0.1 \
|
||||
-l 0.0006 \
|
||||
-q 0.0 \
|
||||
-u 700 \
|
||||
-n 5000 \
|
||||
-y 1 \
|
||||
-e d12 \
|
||||
-pn \$SLURM_NTASKS \
|
||||
-pr \$SLURM_PROCID \
|
||||
-pg \$SLURM_NTASKS_PER_NODE \
|
||||
-pf $sync_fs_path \
|
||||
-pi "fs" \
|
||||
"
|
||||
|
||||
echo "$DATESTRING"
|
||||
Executable
+49
@@ -0,0 +1,49 @@
|
||||
|
||||
make train_gpt2cu USE_CUDNN=1
|
||||
|
||||
# NOTE: change the following to match your system
|
||||
binary_path="/home/ubuntu/llm.c/train_gpt2cu"
|
||||
out_dir="/ephemeral/data/fineweb/log_gpt2_124M_multi"
|
||||
train_data_path='/ephemeral/data/fineweb/bin_10B/fineweb_train_*.bin'
|
||||
val_data_path='/ephemeral/data/fineweb/bin_10B/fineweb_val_*.bin'
|
||||
# You can find these names either in `/etc/hosts`` file or in the terminal (user@host:~$).
|
||||
host1="h100-node-1-0" # master and worker node
|
||||
host2="h100-node-1-1" # worker node
|
||||
|
||||
# In case the file system is shared this is a no-op.
|
||||
# Otherwise, we need to copy the binary to all nodes.
|
||||
scp -r $binary_path $USER@$host2:$binary_path
|
||||
|
||||
# Use this for NCCL debugging if you run into issues
|
||||
# export NCCL_DEBUG=INFO
|
||||
# export NCCL_DEBUG_SUBSYS=ALL
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
|
||||
|
||||
# Optimization flags
|
||||
export NCCL_NET_GDR_LEVEL=2 # use GPUDirect RDMA - allows for direct memory access between GPUs across different nodes by bypassing the CPU
|
||||
export NCCL_IB_DISABLE=0 # use InfiniBand if available
|
||||
|
||||
# NOTE: change the following environment variables to match your system - or comment them out if you don't need them
|
||||
export NCCL_SOCKET_IFNAME=ens17
|
||||
export OMPI_MCA_btl_tcp_if_include=ens17
|
||||
export NCCL_P2P_LEVEL=PXB
|
||||
|
||||
mpirun -np 16 --host $host1:8,$host2:8 \
|
||||
$binary_path \
|
||||
-i "$train_data_path" \
|
||||
-j "$val_data_path" \
|
||||
-o $out_dir \
|
||||
-v 250 -s 20000 -g 144 \
|
||||
-h 1 \
|
||||
-b 64 -t 1024 \
|
||||
-d 2097152 \
|
||||
-r 0 \
|
||||
-z 1 \
|
||||
-c 0.1 \
|
||||
-l 0.0006 \
|
||||
-q 0.1 \
|
||||
-u 700 \
|
||||
-n 1000 \
|
||||
-y 0 \
|
||||
-e d12 \
|
||||
-pi "mpi" \
|
||||
Executable
+86
@@ -0,0 +1,86 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --job-name=llmc-multinode # job name
|
||||
#SBATCH --output=/home/ubuntu/llm.c/scripts/multi_node/%x_%j_%t.log # output file
|
||||
#SBATCH --error=/home/ubuntu/llm.c/scripts/multi_node/%x_%j_%t.err # error file
|
||||
#SBATCH --partition=llmc # Specify the GPU partition
|
||||
#SBATCH --ntasks=16 # total number of processes to launch on all nodes
|
||||
#SBATCH --nodes=2 # total number of nodes
|
||||
#SBATCH --ntasks-per-node=8 # assuming each node has 8 gpus
|
||||
#SBATCH --gres=gpu:8 # request 8 gpus from each node
|
||||
|
||||
# NOTE: change the above slurm arguments to match your system!
|
||||
# Run with `sbatch <path_to_this_script.sh>`
|
||||
|
||||
make train_gpt2cu USE_CUDNN=1 NO_USE_MPI=1
|
||||
|
||||
# NOTE: change the following to match your system
|
||||
binary_path="/home/ubuntu/llm.c/train_gpt2cu"
|
||||
out_dir="/ephemeral/data/fineweb/log_gpt2_124M_multi"
|
||||
train_data_path='/ephemeral/data/fineweb/bin_10B/fineweb_train_*.bin'
|
||||
val_data_path='/ephemeral/data/fineweb/bin_10B/fineweb_val_*.bin'
|
||||
# NOTE: change the server_ip to the IP address of the machine that is running process zero
|
||||
server_ip="10.0.1.220"
|
||||
|
||||
# In case the file system is shared this is a no-op.
|
||||
# Otherwise, we need to copy the binary to all nodes.
|
||||
current_user=$USER
|
||||
hosts=$(scontrol show hostnames $SLURM_JOB_NODELIST) # get the hostnames of the allocated nodes
|
||||
current_host=$(hostname)
|
||||
for host in $hosts; do
|
||||
if [ $host == $current_host ]; then
|
||||
continue
|
||||
fi
|
||||
echo "copying $binary_path to $current_user@$host"
|
||||
scp -r $binary_path $current_user@$host:$binary_path
|
||||
done
|
||||
|
||||
# Use this for NCCL debugging if you run into issues
|
||||
# export NCCL_DEBUG=INFO
|
||||
# export NCCL_DEBUG_SUBSYS=ALL
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
|
||||
|
||||
# Optimization flags
|
||||
export NCCL_NET_GDR_LEVEL=2 # use GPUDirect RDMA - allows for direct memory access between GPUs across different nodes by bypassing the CPU
|
||||
export NCCL_IB_DISABLE=0 # use InfiniBand if available
|
||||
|
||||
# NOTE: change the following environment variables to match your system - or comment them out if you don't need them
|
||||
export NCCL_SOCKET_IFNAME=ens17
|
||||
export OMPI_MCA_btl_tcp_if_include=ens17
|
||||
export NCCL_P2P_LEVEL=PXB
|
||||
|
||||
if [ -z "$SLURM_JOB_ID" ]; then
|
||||
echo "Make sure you're running in a SLURM environment. Did you forget to run with sbatch? Aborting."
|
||||
exit 1
|
||||
else
|
||||
DATESTRING=`date "+%Y-%m-%dT%H:%M:%S"`
|
||||
echo "Running in a SLURM environment (job ID: $SLURM_JOB_ID, user: $current_user)"
|
||||
echo "Running on hosts: $(echo $(scontrol show hostname))"
|
||||
echo "$DATESTRING"
|
||||
fi
|
||||
|
||||
srun -l -u bash -c "
|
||||
$binary_path \
|
||||
-i '$train_data_path' \
|
||||
-j '$val_data_path' \
|
||||
-o $out_dir \
|
||||
-v 250 -s 20000 -g 144 \
|
||||
-h 1 \
|
||||
-b 64 -t 1024 \
|
||||
-d 2097152 \
|
||||
-r 0 \
|
||||
-z 1 \
|
||||
-c 0.1 \
|
||||
-l 0.0006 \
|
||||
-q 0.0 \
|
||||
-u 700 \
|
||||
-n 5000 \
|
||||
-y 1 \
|
||||
-e d12 \
|
||||
-pn \$SLURM_NTASKS \
|
||||
-pr \$SLURM_PROCID \
|
||||
-pg \$SLURM_NTASKS_PER_NODE \
|
||||
-ps $server_ip \
|
||||
-pi "tcp" \
|
||||
"
|
||||
|
||||
echo "$DATESTRING"
|
||||
@@ -0,0 +1,43 @@
|
||||
# GPT-2 (1558M) repro on FineWeb-EDU
|
||||
# 1558M parameter model on 32B tokens
|
||||
# => 6 * 1558e6 * 32e9 = 6.966e20 ~= 3e20 capability model
|
||||
# 32,000 steps on ~1M tokens/step (1,048,576 to be precise)
|
||||
# on 8X H100 80GB SXM ($28/hr) steps in 2.80s/iter
|
||||
# => training time 32,000 steps * 2.7s => 24 hours ~= 1 day ~= $672
|
||||
|
||||
make train_gpt2cu USE_CUDNN=1
|
||||
out_dir="log_gpt2_1558M"
|
||||
done_file="$out_dir/DONE_00032000"
|
||||
|
||||
# in case the training stalls or crashes, loop to resume (-y 1)
|
||||
while true; do
|
||||
|
||||
# exit condition is that optimization has finished
|
||||
if [ -f "$done_file" ]; then
|
||||
echo "File $done_file exists. Exiting the loop."
|
||||
break
|
||||
fi
|
||||
|
||||
mpirun -np 8 ./train_gpt2cu \
|
||||
-i "dev/data/edu_fineweb100B/edu_fineweb_train_*.bin" \
|
||||
-j "dev/data/edu_fineweb100B/edu_fineweb_val_*.bin" \
|
||||
-o $out_dir \
|
||||
-v 250 -s 300000 -g 384 \
|
||||
-h 1 \
|
||||
-b 16 -t 1024 \
|
||||
-d 1048576 \
|
||||
-r 0 \
|
||||
-z 1 \
|
||||
-c 0.1 \
|
||||
-k "cosine" \
|
||||
-l 0.0006 \
|
||||
-q 0.1 \
|
||||
-u 700 \
|
||||
-n 2000 \
|
||||
-x 32000 \
|
||||
-ge 1 \
|
||||
-y 1 \
|
||||
-e "d48"
|
||||
|
||||
sleep 1
|
||||
done
|
||||
Executable
+43
@@ -0,0 +1,43 @@
|
||||
# GPT-2 (774M) repro on FineWeb
|
||||
# 774M parameter model on ~150B tokens
|
||||
# => 6 * 774e6 * 150e9 = 6.966e20 ~= 7e20 capability model (10X 350M)
|
||||
# => 286,102 steps on 524,288 tokens/step
|
||||
# on 8X A100 80GB SXM ($14/hr) steps in ~1.7s/iter
|
||||
# => training time 286,102 steps * 1.7s = 135 hours ~= 5.6 days ~= $2000 (10X 124M)
|
||||
|
||||
make train_gpt2cu USE_CUDNN=1
|
||||
out_dir="log_gpt2_774M"
|
||||
done_file="$out_dir/DONE_00286102"
|
||||
|
||||
# in case the training stalls or crashes, loop to resume (-y 1)
|
||||
while true; do
|
||||
|
||||
# exit condition is that optimization has finished
|
||||
if [ -f "$done_file" ]; then
|
||||
echo "File $done_file exists. Exiting the loop."
|
||||
break
|
||||
fi
|
||||
|
||||
# run python dev/data/fineweb.py --version 100B to prepro data
|
||||
# run python dev/data/hellaswag.py to prepro hellaswag eval
|
||||
mpirun -np 8 ./train_gpt2cu \
|
||||
-i "dev/data/fineweb100B/fineweb_train_*.bin" \
|
||||
-j "dev/data/fineweb100B/fineweb_val_*.bin" \
|
||||
-o $out_dir \
|
||||
-v 250 -s 300000 -g 144 \
|
||||
-h 1 \
|
||||
-b 32 -t 1024 \
|
||||
-d 524288 \
|
||||
-r 0 \
|
||||
-z 1 \
|
||||
-c 0.1 \
|
||||
-l 0.00025 \
|
||||
-q 0.0 \
|
||||
-u 700 \
|
||||
-n 4000 \
|
||||
-x 286102 \
|
||||
-y 1 \
|
||||
-e "d36"
|
||||
|
||||
sleep 1
|
||||
done
|
||||
@@ -1,14 +1,14 @@
|
||||
# GPT-3 (124M) repro on FineWeb
|
||||
# 124M parameter model on 300B tokens
|
||||
# GPT-3 (125M) repro, but using FineWeb
|
||||
# 125M parameter model on 300B tokens
|
||||
# note context length: 1024 -> 2048 for GPT-3
|
||||
# => 6 * 124e6 * 300e9 = 7.44e18 ~= 2.2e20 capability model
|
||||
# 565,950 steps of 524,288 tokens/step
|
||||
# on 8X A100 80GB SXM ($14/hr) steps in ~300ms/iter
|
||||
# => training time 565,950 * 300ms ~= 47 hours ~= $658
|
||||
# => 6 * 125e6 * 300e9 = ~= 2.25e20 capability model
|
||||
# 572,204 steps of 524,288 tokens/step => 300B
|
||||
# on 8X A100 80GB SXM ($14/hr) steps in ~150ms/iter
|
||||
# => training time 572,204 * 150ms ~= 24 hours ~= $336
|
||||
|
||||
make train_gpt2cu USE_CUDNN=1
|
||||
out_dir="log_gpt3_124M"
|
||||
done_file="$out_dir/DONE_00565950"
|
||||
out_dir="log_gpt3_125M"
|
||||
done_file="$out_dir/DONE_00572204"
|
||||
|
||||
while true; do
|
||||
|
||||
@@ -18,8 +18,6 @@ while true; do
|
||||
break
|
||||
fi
|
||||
|
||||
# run python dev/data/fineweb.py --version 10B to prepro data
|
||||
# run python dev/data/hellaswag.py to prepro hellaswag eval
|
||||
mpirun -np 8 ./train_gpt2cu \
|
||||
-i "dev/data/fineweb100B/fineweb_train_*.bin" \
|
||||
-j "dev/data/fineweb100B/fineweb_val_*.bin" \
|
||||
@@ -32,12 +30,17 @@ while true; do
|
||||
-z 1 \
|
||||
-c 0.1 \
|
||||
-l 0.0006 \
|
||||
-q 0.0 \
|
||||
-q 0.1 \
|
||||
-u 700 \
|
||||
-n 10000 \
|
||||
-nk 5 \
|
||||
-nm 50000 \
|
||||
-ge 1 \
|
||||
-sl 7.0 \
|
||||
-sg 7.0 \
|
||||
-y 1 \
|
||||
-x 565950 \
|
||||
-e "d12"
|
||||
-x 572204 \
|
||||
-e "gpt3:c768"
|
||||
|
||||
sleep 1
|
||||
done
|
||||
+18
-18
@@ -6,7 +6,7 @@ int check_tensor(float *a, float *b, int n, const char* label) {
|
||||
int print_upto = 5;
|
||||
int ok = 1;
|
||||
float maxdiff = 0.0f;
|
||||
float tol = 2e-2;
|
||||
float tol = 2e-2f;
|
||||
printf("%s\n", label);
|
||||
for (int i = 0; i < n; i++) {
|
||||
// look at the diffence at position i of these two tensors
|
||||
@@ -52,7 +52,7 @@ int main(int argc, char *argv[]) {
|
||||
FILE *state_file = fopen("gpt2_124M_debug_state.bin", "rb");
|
||||
if (state_file == NULL) { printf("Error opening state file\n"); return 1; }
|
||||
int state_header[256];
|
||||
fread(state_header, sizeof(int), 256, state_file);
|
||||
freadCheck(state_header, sizeof(int), 256, state_file);
|
||||
if (state_header[0] != 20240327) { printf("Bad magic state file\n"); return 1; }
|
||||
if (state_header[1] != 2) {
|
||||
printf("Bad version in state file\n");
|
||||
@@ -75,28 +75,28 @@ int main(int argc, char *argv[]) {
|
||||
float* expected_loss = (float*) malloc(1 * sizeof(float));
|
||||
|
||||
// read reference information from Python
|
||||
fread(x, sizeof(int), B*T, state_file);
|
||||
fread(y, sizeof(int), B*T, state_file);
|
||||
fread(expected_logits, sizeof(float), B*T*V, state_file);
|
||||
fread(expected_loss, sizeof(float), 1, state_file);
|
||||
fread(expected_grads_memory, sizeof(float), model.num_parameters, state_file);
|
||||
fclose(state_file);
|
||||
freadCheck(x, sizeof(int), B*T, state_file);
|
||||
freadCheck(y, sizeof(int), B*T, state_file);
|
||||
freadCheck(expected_logits, sizeof(float), B*T*V, state_file);
|
||||
freadCheck(expected_loss, sizeof(float), 1, state_file);
|
||||
freadCheck(expected_grads_memory, sizeof(float), model.num_parameters, state_file);
|
||||
fcloseCheck(state_file);
|
||||
|
||||
// overall OK signal for the test
|
||||
int allok = 1;
|
||||
|
||||
// let's do 10 training iterations, following the pytorch code
|
||||
float expected_losses[10] = {
|
||||
5.270007133483887,
|
||||
4.059706687927246,
|
||||
3.3751230239868164,
|
||||
2.8007826805114746,
|
||||
2.315382242202759,
|
||||
1.8490285873413086,
|
||||
1.3946564197540283,
|
||||
0.9991465210914612,
|
||||
0.6240804195404053,
|
||||
0.37651097774505615
|
||||
5.270007133483887f,
|
||||
4.059706687927246f,
|
||||
3.3751230239868164f,
|
||||
2.8007826805114746f,
|
||||
2.315382242202759f,
|
||||
1.8490285873413086f,
|
||||
1.3946564197540283f,
|
||||
0.9991465210914612f,
|
||||
0.6240804195404053f,
|
||||
0.37651097774505615f
|
||||
};
|
||||
for (int step = 0; step < 10; step++) {
|
||||
|
||||
|
||||
+114
-48
@@ -89,7 +89,13 @@ float* float_cpu_malloc_and_point_parameters(FloatParameterTensors* params, size
|
||||
}
|
||||
|
||||
int main(int argc, char *argv[]) {
|
||||
multi_gpu_config = multi_gpu_config_init(&argc, &argv);
|
||||
char nccl_init_method[256] = "mpi"; // "tcp" or "fs" or "mpi"
|
||||
int num_processes = -1; // doesn't matter when using MPI
|
||||
int process_rank = -1; // doesn't matter when using MPI
|
||||
int gpus_per_node = -1; // doesn't matter when using MPI
|
||||
char server_ip[256] = ""; // doesn't matter when using MPI
|
||||
char fs_path[256] = ""; // doesn't matter when using MPI
|
||||
multi_gpu_config = multi_gpu_config_init(num_processes, process_rank, gpus_per_node, server_ip, fs_path, nccl_init_method);
|
||||
common_start(false, true);
|
||||
|
||||
// set the right paths
|
||||
@@ -101,13 +107,21 @@ int main(int argc, char *argv[]) {
|
||||
|
||||
// build the GPT-2 model from a checkpoint
|
||||
GPT2 model;
|
||||
gpt2_init_common(&model);
|
||||
|
||||
gpt2_build_from_checkpoint(&model, load_filename);
|
||||
size_t V = model.config.vocab_size;
|
||||
size_t Vp = model.config.padded_vocab_size;
|
||||
size_t maxT = model.config.max_seq_len;
|
||||
size_t L = model.config.num_layers;
|
||||
size_t C = model.config.channels;
|
||||
|
||||
for (int i = 1; i < argc; i+=2) {
|
||||
if (i + 1 >= argc) { exit(EXIT_FAILURE); } // must have arg after flag
|
||||
if (!(strlen(argv[i]) == 2 || strlen(argv[i]) == 3)) { exit(EXIT_FAILURE); } // must be -x[y] (one dash, one or two letters)
|
||||
if (argv[i][0] != '-') { exit(EXIT_FAILURE); } // must start with dash
|
||||
if (argv[i][1] == 'w') { model.use_master_weights = atoi(argv[i+1]); }
|
||||
else if (argv[i][1] == 'r') { model.recompute = atoi(argv[i+1]); }
|
||||
else if (argv[i][1] == 'g' && argv[i][2] == 'e') { model.gelu_fusion = atoi(argv[i+1]); }
|
||||
}
|
||||
|
||||
// load additional information that we will use for debugging and error checking
|
||||
FILE *state_file = fopenCheck("gpt2_124M_debug_state.bin", "rb");
|
||||
@@ -152,22 +166,25 @@ int main(int argc, char *argv[]) {
|
||||
// overall OK signal for the test
|
||||
int allok = 1;
|
||||
|
||||
gpt2_allocate_state(&model, B, T);
|
||||
|
||||
// First, do target-free forward pass to validate logits
|
||||
gpt2_forward(&model, x, NULL, B, T);
|
||||
gpt2_forward(&model, x, B, T);
|
||||
// at this point, target should be equal to expected_logits, let's compare
|
||||
// copy logits to CPU so we can compare them
|
||||
floatX* logits_cpu_raw = (floatX*)mallocCheck(B * T * Vp * sizeof(floatX));
|
||||
float* logits_cpu = (float*)mallocCheck(B * T * Vp * sizeof(float));
|
||||
cudaMemcpy(logits_cpu_raw, model.acts.output, B * T * Vp * sizeof(floatX), cudaMemcpyDeviceToHost);
|
||||
cudaCheck(cudaMemcpy(logits_cpu_raw, model.acts.output, B * T * Vp * sizeof(floatX), cudaMemcpyDeviceToHost));
|
||||
for (int i = 0; i < B * T * Vp; i++) {
|
||||
logits_cpu[i] = (float)logits_cpu_raw[i];
|
||||
}
|
||||
|
||||
float logit_accuracy_threshold = 1e-3f;
|
||||
float loss_diff_threshold = 1e-5f;
|
||||
// FP16 and lower require very high tolerances unfortunately. TODO look into more
|
||||
float logit_accuracy_threshold = 1e-2f;
|
||||
float loss_diff_threshold = 0.05f;
|
||||
#if defined(ENABLE_BF16) || defined(ENABLE_F16)
|
||||
logit_accuracy_threshold = 25.0f; // 15.0f was too low even without cuDNN?! :(
|
||||
loss_diff_threshold = 0.05f;
|
||||
#endif
|
||||
|
||||
// compare the output logits from the forward pass
|
||||
@@ -201,25 +218,16 @@ int main(int argc, char *argv[]) {
|
||||
for (int step = 0; step < 10; step++) {
|
||||
struct timespec start, end;
|
||||
clock_gettime(CLOCK_MONOTONIC, &start);
|
||||
gpt2_forward(&model, x, y, B, T);
|
||||
gpt2_zero_grad(&model);
|
||||
gpt2_backward(&model, x);
|
||||
gpt2_forward(&model, x, B, T);
|
||||
gpt2_backward_and_reduce(&model, x, y, 1, 0);
|
||||
clock_gettime(CLOCK_MONOTONIC, &end);
|
||||
double time_elapsed_s = (end.tv_sec - start.tv_sec) + (end.tv_nsec - start.tv_nsec) / 1e9;
|
||||
|
||||
if (step == 0) {
|
||||
// error checking at step 0 for reference activations
|
||||
|
||||
// compare the achieved loss
|
||||
if (fabsf(model.mean_loss - *expected_loss) >= loss_diff_threshold) {
|
||||
printf("LOSS MISMATCH: %f %f\n", model.mean_loss, *expected_loss);
|
||||
allok = 0;
|
||||
} else {
|
||||
printf("LOSS OK: %f %f\n", model.mean_loss, *expected_loss);
|
||||
}
|
||||
|
||||
// move the (mixed precision) grads from GPU to CPU
|
||||
cudaMemcpy(grads_memory_cpu, model.grads_memory, model.num_parameters_bytes, cudaMemcpyDeviceToHost);
|
||||
cudaCheck(cudaMemcpy(grads_memory_cpu, model.grads_memory, model.num_parameters_bytes, cudaMemcpyDeviceToHost));
|
||||
|
||||
// convert all gradients to float on the CPU
|
||||
char* src_iterator = (char*)grads_memory_cpu; // can be lower precision, so we use char*
|
||||
@@ -256,43 +264,53 @@ int main(int argc, char *argv[]) {
|
||||
// In that case it's ok to extend the tolerance by a bit, after a manual review.
|
||||
// Also, different GPUs may use different matrix multiplication algorithms, so the
|
||||
// actual errors can be hardware specific.
|
||||
allok = allok & check_tensor(tensors1[0], tensors2[0], V * C, "wte", 6e-1f); // hmm a bit high
|
||||
allok = allok & check_tensor(tensors1[1], tensors2[1], maxT * C, "wpe", 4e-3f);
|
||||
allok = allok & check_tensor(tensors1[2], tensors2[2], L * 3*C * C, "qkvw", 1e-1); // hmm a bit high
|
||||
allok = allok & check_tensor(tensors1[3], tensors2[3], L * 3*C, "qkvb", 3.5e-2f);
|
||||
allok = allok & check_tensor(tensors1[4], tensors2[4], L * C * C, "attprojw", 2e-2f);
|
||||
allok = allok & check_tensor(tensors1[5], tensors2[5], L * C, "attprojb", 3e-2f);
|
||||
allok = allok & check_tensor(tensors1[6], tensors2[6], L * 4*C * C, "fcw", 5e-2f); // hmm a bit high
|
||||
allok = allok & check_tensor(tensors1[7], tensors2[7], L * 4*C, "fcb", 5e-2f); // hmm a bit high
|
||||
allok = allok & check_tensor(tensors1[8], tensors2[8], L * C * 4*C, "fcprojw", 5e-2f); // hmm a bit high
|
||||
allok = allok & check_tensor(tensors1[9], tensors2[9], L * C, "fcprojb", 1.5e-2f);
|
||||
allok = allok & check_tensor(tensors1[10], tensors2[10], L * C, "ln1w", 6e-4f);
|
||||
allok = allok & check_tensor(tensors1[11], tensors2[11], L * C, "ln1b", 9e-3f);
|
||||
allok = allok & check_tensor(tensors1[12], tensors2[12], L * C, "ln2w", 2e-3f);
|
||||
allok = allok & check_tensor(tensors1[13], tensors2[13], L * C, "ln2b", 2.5e-3f);
|
||||
allok = allok & check_tensor(tensors1[14], tensors2[14], C, "lnfw", 0.12f); // hmm bit higher
|
||||
allok = allok & check_tensor(tensors1[15], tensors2[15], C, "lnfb", 2e-2f);
|
||||
|
||||
float grad_thresholds[NUM_PARAMETER_TENSORS] = {
|
||||
5e-1f, 4e-3f, 1e-1f, 4e-2f,
|
||||
5e-2f, 3.5e-2f, 2e-2f, 3e-2f,
|
||||
5e-2f, 3e-2f, 3e-2f, 3e-2f,
|
||||
2e-2f, 1e-2f,1e-1f,2e-2f};
|
||||
|
||||
#if defined(ENABLE_FP32)
|
||||
for (int i = 0; i < NUM_PARAMETER_TENSORS; i++) {
|
||||
grad_thresholds[i] = 1e-6f; // we can be much more precise in FP32
|
||||
}
|
||||
#endif
|
||||
const char* names[NUM_PARAMETER_TENSORS] = {
|
||||
"wte", "wpe", "ln1w", "ln1b", "qkvw", "qkvb", "attrpojw",
|
||||
"attprojb", "ln2w", "ln2b", "fcw", "fcb", "fcprojw", "fcprojb",
|
||||
"lnfw", "lnfb"
|
||||
};
|
||||
size_t* count = model.param_elements;
|
||||
for(int i = 0; i < NUM_PARAMETER_TENSORS; ++i) {
|
||||
allok = allok & check_tensor(tensors1[i], tensors2[i], count[i], names[i], grad_thresholds[i]);
|
||||
}
|
||||
}
|
||||
|
||||
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, 1.0f, step+1, &multi_gpu_config);
|
||||
float grad_norm = gpt2_calculate_grad_norm(&model, &multi_gpu_config);
|
||||
float grad_scale = (grad_norm > 1.0f) ? 1.0f / grad_norm : 1.0f;
|
||||
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, grad_scale, step+1, &multi_gpu_config);
|
||||
|
||||
// print the timing information at the end
|
||||
printf("step %d: loss %f (took %f ms)\n", step+1, model.mean_loss, time_elapsed_s * 1000);
|
||||
losses[step] = model.mean_loss;
|
||||
// the expected losses from PyTorch were copied over after the print formatting rounded
|
||||
// them to 6 decimal places, so we do the same here
|
||||
float rounded_loss = roundf(model.mean_loss * 1000000) / 1000000;
|
||||
losses[step] = rounded_loss;
|
||||
}
|
||||
|
||||
// expected losses are as follows, from Python
|
||||
float expected_losses[10] = {
|
||||
5.2700,
|
||||
4.0607,
|
||||
3.3202,
|
||||
2.7176,
|
||||
2.1811,
|
||||
1.6538,
|
||||
1.1680,
|
||||
0.7367,
|
||||
0.4008,
|
||||
0.1874
|
||||
5.270009f,
|
||||
4.060681f,
|
||||
3.320085f,
|
||||
2.717550f,
|
||||
2.181066f,
|
||||
1.653923f,
|
||||
1.168050f,
|
||||
0.736873f,
|
||||
0.401021f,
|
||||
0.187493f
|
||||
};
|
||||
|
||||
// compare
|
||||
@@ -305,10 +323,58 @@ int main(int argc, char *argv[]) {
|
||||
}
|
||||
}
|
||||
|
||||
// Finally, let's check determinism
|
||||
gpt2_write_to_checkpoint(&model, "test_gpt2cu_model.ckpt");
|
||||
|
||||
DataLoader loader;
|
||||
dataloader_init(&loader, "dev/data/tinyshakespeare/tiny_shakespeare_val.bin", B, T, multi_gpu_config.process_rank, multi_gpu_config.num_processes, 1);
|
||||
save_state("test_gpt2cu_state.ckpt", 10, &model, &loader);
|
||||
int tokens[10];
|
||||
for (int step = 0; step < 10; step++) {
|
||||
dataloader_next_batch(&loader);
|
||||
gpt2_forward(&model, loader.inputs, B, T);
|
||||
gpt2_backward_and_reduce(&model, loader.inputs, loader.targets, 1, 0);
|
||||
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, 1.0f, step+11, &multi_gpu_config);
|
||||
losses[step] = model.mean_loss;
|
||||
tokens[step] = loader.inputs[0];
|
||||
}
|
||||
|
||||
// reload
|
||||
gpt2_free(&model);
|
||||
gpt2_build_from_checkpoint(&model, "test_gpt2cu_model.ckpt");
|
||||
int ld_step;
|
||||
gpt2_allocate_state(&model, B, T);
|
||||
load_state(&ld_step, &model, &loader, "test_gpt2cu_state.ckpt");
|
||||
for (int step = 0; step < 10; step++) {
|
||||
dataloader_next_batch(&loader);
|
||||
gpt2_forward(&model, loader.inputs, B, T);
|
||||
gpt2_backward_and_reduce(&model, loader.inputs, loader.targets, 1, 0);
|
||||
gpt2_update(&model, 1e-4f, 0.9f, 0.95f, 1e-8f, 0.0f, 1.0f, step+11, &multi_gpu_config);
|
||||
|
||||
if(loader.inputs[0] != tokens[step]) {
|
||||
printf("Nondeterminism! Token mismatch at step %d: %d vs %d\n", step, tokens[step], loader.inputs[0]);
|
||||
allok = false;
|
||||
break;
|
||||
}
|
||||
|
||||
if(losses[step] != model.mean_loss) {
|
||||
printf("Nondeterminism! Loss mismatch at step %d: %.15f vs %.15f\n", step, losses[step], model.mean_loss);
|
||||
allok = false;
|
||||
break;
|
||||
} else {
|
||||
printf("loss ok at step %d: %f %f\n", step, losses[step], model.mean_loss);
|
||||
}
|
||||
}
|
||||
|
||||
// final approval
|
||||
printf("overall okay: %d\n", allok);
|
||||
|
||||
// delete intermediate test files
|
||||
remove("test_gpt2cu_model.ckpt");
|
||||
remove("test_gpt2cu_state.ckpt");
|
||||
|
||||
// free everything
|
||||
dataloader_free(&loader);
|
||||
gpt2_free(&model);
|
||||
common_free(model);
|
||||
free(x);
|
||||
@@ -320,5 +386,5 @@ int main(int argc, char *argv[]) {
|
||||
free(expected_grads_memory);
|
||||
free(grads_memory_cpu);
|
||||
free(grads_memory_cpu_float);
|
||||
return 0;
|
||||
return allok ? EXIT_SUCCESS : EXIT_FAILURE;
|
||||
}
|
||||
|
||||
+11
-15
@@ -36,7 +36,6 @@ int main(int argc, char *argv[]) {
|
||||
|
||||
// setup cuBLAS and cuBLASLt
|
||||
cublasCheck(cublasCreate(&cublas_handle));
|
||||
cublasCheck(cublasLtCreate(&cublaslt_handle));
|
||||
// TF32 precision is equivalent to torch.set_float32_matmul_precision('high')
|
||||
int enable_tf32 = deviceProp.major >= 8 ? 1 : 0;
|
||||
enable_tf32 = 0; // NOTE: disable TF32 for testing!!!
|
||||
@@ -44,7 +43,6 @@ int main(int argc, char *argv[]) {
|
||||
cublas_compute_type = enable_tf32 ? CUBLAS_COMPUTE_32F_FAST_TF32 : CUBLAS_COMPUTE_32F;
|
||||
cublasMath_t cublas_math_mode = enable_tf32 ? CUBLAS_TF32_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH;
|
||||
cublasCheck(cublasSetMathMode(cublas_handle, cublas_math_mode));
|
||||
cudaCheck(cudaMalloc(&cublaslt_workspace, cublaslt_workspace_size));
|
||||
|
||||
// build the GPT-2 model from a checkpoint
|
||||
GPT2 model;
|
||||
@@ -100,7 +98,7 @@ int main(int argc, char *argv[]) {
|
||||
// at this point, target should be equal to expected_logits, let's compare
|
||||
// copy logits to CPU so we can compare them
|
||||
float* logits_cpu = (float*)mallocCheck(B * T * Vp * sizeof(float));
|
||||
cudaMemcpy(logits_cpu, model.acts.output, B * T * Vp * sizeof(float), cudaMemcpyDeviceToHost);
|
||||
cudaCheck(cudaMemcpy(logits_cpu, model.acts.output, B * T * Vp * sizeof(float), cudaMemcpyDeviceToHost));
|
||||
|
||||
// compare the output logits from the forward pass
|
||||
// also careful that we don't access and compare the padded columns of logits
|
||||
@@ -198,16 +196,16 @@ int main(int argc, char *argv[]) {
|
||||
|
||||
// expected losses are as follows, from Python
|
||||
float expected_losses[10] = {
|
||||
5.270007133483887,
|
||||
4.059706687927246,
|
||||
3.3751230239868164,
|
||||
2.8007826805114746,
|
||||
2.315382242202759,
|
||||
1.8490285873413086,
|
||||
1.3946564197540283,
|
||||
0.9991465210914612,
|
||||
0.6240804195404053,
|
||||
0.37651097774505615
|
||||
5.270007133483887f,
|
||||
4.059706687927246f,
|
||||
3.3751230239868164f,
|
||||
2.8007826805114746f,
|
||||
2.315382242202759f,
|
||||
1.8490285873413086f,
|
||||
1.3946564197540283f,
|
||||
0.9991465210914612f,
|
||||
0.6240804195404053f,
|
||||
0.37651097774505615f
|
||||
};
|
||||
|
||||
// compare
|
||||
@@ -231,9 +229,7 @@ int main(int argc, char *argv[]) {
|
||||
free(expected_grads_memory);
|
||||
free(calculated_grads_memory);
|
||||
gpt2_free(&model);
|
||||
cudaCheck(cudaFree(cublaslt_workspace));
|
||||
cublasCheck(cublasDestroy(cublas_handle));
|
||||
cublasCheck(cublasLtDestroy(cublaslt_handle));
|
||||
|
||||
return 0;
|
||||
}
|
||||
+37
-30
@@ -352,7 +352,7 @@ void attention_backward(float* dinp, float* dpreatt, float* datt,
|
||||
// dout is (B, T, C)
|
||||
int C3 = C*3;
|
||||
int hs = C / NH; // head size
|
||||
float scale = 1.0 / sqrtf(hs);
|
||||
float scale = 1.f / sqrtf(hs);
|
||||
|
||||
for (int b = 0; b < B; b++) {
|
||||
for (int t = 0; t < T; t++) {
|
||||
@@ -625,6 +625,36 @@ typedef struct {
|
||||
float* losses; // (B, T)
|
||||
} ActivationTensors;
|
||||
|
||||
void fill_in_activation_sizes(size_t* act_sizes, GPT2Config config, int B, int T) {
|
||||
size_t C = config.channels;
|
||||
size_t NH = config.num_heads;
|
||||
size_t L = config.num_layers;
|
||||
size_t Vp = config.padded_vocab_size;
|
||||
act_sizes[0] = B * T * C; // encoded
|
||||
act_sizes[1] = L * B * T * C; // ln1
|
||||
act_sizes[2] = L * B * T; // ln1_mean
|
||||
act_sizes[3] = L * B * T; // ln1_rstd
|
||||
act_sizes[4] = L * B * T * 3 * C; // qkv
|
||||
act_sizes[5] = L * B * T * C; // atty
|
||||
act_sizes[6] = L * B * NH * T * T; // preatt
|
||||
act_sizes[7] = L * B * NH * T * T; // att
|
||||
act_sizes[8] = L * B * T * C; // attproj
|
||||
act_sizes[9] = L * B * T * C; // residual2
|
||||
act_sizes[10] = L * B * T * C; // ln2
|
||||
act_sizes[11] = L * B * T; // ln2_mean
|
||||
act_sizes[12] = L * B * T; // ln2_rstd
|
||||
act_sizes[13] = L * B * T * 4 * C; // fch
|
||||
act_sizes[14] = L * B * T * 4 * C; // fch_gelu
|
||||
act_sizes[15] = L * B * T * C; // fcproj
|
||||
act_sizes[16] = L * B * T * C; // residual3
|
||||
act_sizes[17] = B * T * C; // lnf
|
||||
act_sizes[18] = B * T; // lnf_mean
|
||||
act_sizes[19] = B * T; // lnf_rstd
|
||||
act_sizes[20] = B * T * Vp; // logits
|
||||
act_sizes[21] = B * T * Vp; // probs
|
||||
act_sizes[22] = B * T; // losses
|
||||
}
|
||||
|
||||
float* malloc_and_point_activations(ActivationTensors* acts, size_t* act_sizes) {
|
||||
size_t num_activations = 0;
|
||||
for (size_t i = 0; i < NUM_ACTIVATION_TENSORS; i++) {
|
||||
@@ -678,7 +708,6 @@ void gpt2_build_from_checkpoint(GPT2 *model, const char* checkpoint_path) {
|
||||
|
||||
// read in model from a checkpoint file
|
||||
FILE *model_file = fopenCheck(checkpoint_path, "rb");
|
||||
if (model_file == NULL) { printf("Error opening model file\n"); exit(1); }
|
||||
int model_header[256];
|
||||
freadCheck(model_header, sizeof(int), 256, model_file);
|
||||
if (model_header[0] != 20240326) { printf("Bad magic model file\n"); exit(1); }
|
||||
@@ -763,29 +792,7 @@ void gpt2_forward(GPT2 *model, int* inputs, int* targets, size_t B, size_t T) {
|
||||
model->batch_size = B;
|
||||
model->seq_len = T;
|
||||
// and now allocate the space
|
||||
model->act_sizes[0] = B * T * C; // encoded
|
||||
model->act_sizes[1] = L * B * T * C; // ln1
|
||||
model->act_sizes[2] = L * B * T; // ln1_mean
|
||||
model->act_sizes[3] = L * B * T; // ln1_rstd
|
||||
model->act_sizes[4] = L * B * T * 3*C; // qkv
|
||||
model->act_sizes[5] = L * B * T * C; // atty
|
||||
model->act_sizes[6] = L * B * NH * T * T; // preatt
|
||||
model->act_sizes[7] = L * B * NH * T * T; // att
|
||||
model->act_sizes[8] = L * B * T * C; // attproj
|
||||
model->act_sizes[9] = L * B * T * C; // residual2
|
||||
model->act_sizes[10] = L * B * T * C; // ln2
|
||||
model->act_sizes[11] = L * B * T; // ln2_mean
|
||||
model->act_sizes[12] = L * B * T; // ln2_rstd
|
||||
model->act_sizes[13] = L * B * T * 4*C; // fch
|
||||
model->act_sizes[14] = L * B * T * 4*C; // fch_gelu
|
||||
model->act_sizes[15] = L * B * T * C; // fcproj
|
||||
model->act_sizes[16] = L * B * T * C; // residual3
|
||||
model->act_sizes[17] = B * T * C; // lnf
|
||||
model->act_sizes[18] = B * T; // lnf_mean
|
||||
model->act_sizes[19] = B * T; // lnf_rstd
|
||||
model->act_sizes[20] = B * T * Vp; // logits
|
||||
model->act_sizes[21] = B * T * Vp; // probs
|
||||
model->act_sizes[22] = B * T; // losses
|
||||
fill_in_activation_sizes(model->act_sizes, model->config, B, T);
|
||||
size_t num_activations = 0;
|
||||
for (size_t i = 0; i < NUM_ACTIVATION_TENSORS; i++) {
|
||||
num_activations += model->act_sizes[i];
|
||||
@@ -1041,14 +1048,14 @@ void gpt2_free(GPT2 *model) {
|
||||
// ----------------------------------------------------------------------------
|
||||
// sampler
|
||||
|
||||
unsigned int random_u32(unsigned long long *state) {
|
||||
unsigned int random_u32(uint64_t *state) {
|
||||
// xorshift rng: https://en.wikipedia.org/wiki/Xorshift#xorshift.2A
|
||||
*state ^= *state >> 12;
|
||||
*state ^= *state << 25;
|
||||
*state ^= *state >> 27;
|
||||
return (*state * 0x2545F4914F6CDD1Dull) >> 32;
|
||||
}
|
||||
float random_f32(unsigned long long *state) { // random float32 in [0,1)
|
||||
float random_f32(uint64_t *state) { // random float32 in [0,1)
|
||||
return (random_u32(state) >> 8) / 16777216.0f;
|
||||
}
|
||||
|
||||
@@ -1083,8 +1090,8 @@ int main() {
|
||||
int B = 4; // batch size 4 (i.e. 4 independent token sequences will be trained on)
|
||||
int T = 64; // sequence length 64 (i.e. each sequence is 64 tokens long). must be <= maxT, which is 1024 for GPT-2
|
||||
DataLoader train_loader, val_loader;
|
||||
dataloader_init(&train_loader, train_tokens, B, T, 0, 1);
|
||||
dataloader_init(&val_loader, val_tokens, B, T, 0, 1);
|
||||
dataloader_init(&train_loader, train_tokens, B, T, 0, 1, 1);
|
||||
dataloader_init(&val_loader, val_tokens, B, T, 0, 1, 0);
|
||||
printf("train dataset num_batches: %zu\n", train_loader.num_tokens / (B*T));
|
||||
printf("val dataset num_batches: %zu\n", val_loader.num_tokens / (B*T));
|
||||
int val_num_batches = 5;
|
||||
@@ -1094,7 +1101,7 @@ int main() {
|
||||
tokenizer_init(&tokenizer, "gpt2_tokenizer.bin");
|
||||
|
||||
// some memory for generating samples from the model
|
||||
unsigned long long rng_state = 1337;
|
||||
uint64_t rng_state = 1337;
|
||||
int* gen_tokens = (int*)mallocCheck(B * T * sizeof(int));
|
||||
const int genT = 64; // number of steps of inference we will do
|
||||
|
||||
|
||||
+899
-2011
File diff suppressed because it is too large
Load Diff
+28
-26
@@ -32,6 +32,7 @@ import torch._inductor.config as config
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.distributed import init_process_group, destroy_process_group
|
||||
from torch.distributed.optim import ZeroRedundancyOptimizer
|
||||
import torch.distributed as dist
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# PyTorch nn.Module definitions for the GPT-2 model
|
||||
@@ -767,21 +768,18 @@ if __name__ == "__main__":
|
||||
if (args.sample_every > 0 \
|
||||
and (step % args.sample_every == 0 or last_step)) \
|
||||
and master_process:
|
||||
# TODO I'm not sure why this sampling code (which worked fine)
|
||||
# doesn't work anymore when placed here debug later
|
||||
if False:
|
||||
model.eval()
|
||||
# before we end, let's also do one round of inference
|
||||
# we'll kick off the generation with "<|endoftext|>", which designates the start of a new sequence
|
||||
start_ids = [enc.eot_token]
|
||||
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
|
||||
max_new_tokens = 32
|
||||
temperature = 1.0
|
||||
top_k = 40
|
||||
y = raw_model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
|
||||
print0('---------------')
|
||||
print0(enc.decode(y[0].tolist()))
|
||||
print0('---------------')
|
||||
model.eval()
|
||||
# before we end, let's also do one round of inference
|
||||
# we'll kick off the generation with "<|endoftext|>", which designates the start of a new sequence
|
||||
start_ids = [enc.eot_token]
|
||||
xg = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
|
||||
max_new_tokens = 32
|
||||
temperature = 1.0
|
||||
top_k = 40
|
||||
yg = raw_model.generate(xg, max_new_tokens, temperature=temperature, top_k=top_k)
|
||||
print0('---------------')
|
||||
print0(enc.decode(yg[0].tolist()))
|
||||
print0('---------------')
|
||||
|
||||
# bit confusing: we want to make sure to eval and sample on 0th iteration
|
||||
# but also after the very last iteration. so we loop for step <= num_iterations
|
||||
@@ -792,14 +790,21 @@ if __name__ == "__main__":
|
||||
|
||||
# --------------- TRAINING SECTION BEGIN -----------------
|
||||
model.train()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
# if we are trying to overfit a single batch, we reset the loader here
|
||||
if args.overfit_single_batch:
|
||||
train_loader.reset()
|
||||
# micro-batch loop where we do gradient accumulation to reach desired total batch size
|
||||
lossf = 0.0 # for getting the mean loss (as simple float) over the accumulation steps
|
||||
for micro_step in range(grad_accum_steps):
|
||||
# fetch a batch
|
||||
if not args.overfit_single_batch \
|
||||
or (args.overfit_single_batch and step == 0 and micro_step == 0):
|
||||
x, y = train_loader.next_batch()
|
||||
x, y = x.to(device), y.to(device)
|
||||
x, y = train_loader.next_batch()
|
||||
x, y = x.to(device), y.to(device)
|
||||
if ddp:
|
||||
# we want only the last micro-step to sync grads in a DDP model
|
||||
# the official way to do this is with model.no_sync(), but that is a
|
||||
# context manager that bloats the code, so we just toggle this variable
|
||||
model.require_backward_grad_sync = (micro_step == grad_accum_steps - 1)
|
||||
# forward pass
|
||||
with ctx:
|
||||
_, loss = model(x, y, return_logits=False)
|
||||
@@ -808,15 +813,13 @@ if __name__ == "__main__":
|
||||
# addition of gradients corresponds to a SUM in the objective, but
|
||||
# instead of a SUM we want MEAN, so we scale the loss here
|
||||
loss = loss / grad_accum_steps
|
||||
lossf += loss.item() # keep track of the mean loss
|
||||
lossf += loss.detach() # keep track of the mean loss
|
||||
# backward pass
|
||||
if ddp:
|
||||
# we want only the last micro-step to sync grads in a DDP model
|
||||
# the official way to do this is with model.no_sync(), but that is a
|
||||
# context manager that bloats the code, so we just toggle this variable
|
||||
model.require_backward_grad_sync = (micro_step == grad_accum_steps - 1)
|
||||
if not args.inference_only:
|
||||
loss.backward()
|
||||
if ddp:
|
||||
dist.all_reduce(lossf, op=dist.ReduceOp.AVG)
|
||||
lossf = lossf.item()
|
||||
norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
|
||||
# determine and set the learning rate for this iteration
|
||||
lr = get_lr(step)
|
||||
@@ -824,7 +827,6 @@ if __name__ == "__main__":
|
||||
param_group['lr'] = lr
|
||||
# step the optimizer
|
||||
optimizer.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
# --------------- TRAINING SECTION END -------------------
|
||||
# everything that follows now is just diagnostics, prints, logging, etc.
|
||||
|
||||
|
||||
+99
-86
@@ -23,7 +23,6 @@ the layernorms are connected to the residuals so we += in layernorm backward.
|
||||
// GPU / CUDA related
|
||||
#include <cublas_v2.h>
|
||||
#include <cuda_runtime.h>
|
||||
#include <cublasLt.h>
|
||||
#include <cooperative_groups.h>
|
||||
#include <cooperative_groups/reduce.h>
|
||||
// our own utilities
|
||||
@@ -60,12 +59,8 @@ void cublasCheck(cublasStatus_t status, const char *file, int line)
|
||||
}
|
||||
#define cublasCheck(status) { cublasCheck((status), __FILE__, __LINE__); }
|
||||
|
||||
// cuBLAS workspace. Hardcoding to 32MiB but only Hopper needs 32, for others 4 is OK
|
||||
static size_t cublaslt_workspace_size = 32 * 1024 * 1024;
|
||||
static void* cublaslt_workspace = NULL;
|
||||
static cublasComputeType_t cublas_compute_type;
|
||||
cublasHandle_t cublas_handle;
|
||||
cublasLtHandle_t cublaslt_handle;
|
||||
|
||||
namespace cg = cooperative_groups;
|
||||
|
||||
@@ -611,6 +606,87 @@ __global__ void fused_classifier_kernel3(float* logits, float* losses, float* pr
|
||||
}
|
||||
}
|
||||
|
||||
__device__ float4 ld_vec(const float* address) {
|
||||
return *reinterpret_cast<const float4*>(address);
|
||||
}
|
||||
|
||||
__device__ void st_vec(float* address, float4 val) {
|
||||
*reinterpret_cast<float4*>(address) = val;
|
||||
}
|
||||
|
||||
__global__ void __launch_bounds__(16*16, 2) matmul_forward_kernel4(float* out,
|
||||
const float* inp, const float* weight, const float* bias,
|
||||
int C, int OC) {
|
||||
// out is (B,T,OC). OC is short for "output channels", e.g. OC = 4 * C
|
||||
// inp is (B,T,C), weight is (OC, C), bias is (OC)
|
||||
// each thread handles 8x8 elements; each block 128 by 128 elements.
|
||||
int oc = 8*(blockIdx.y * blockDim.y + threadIdx.y);
|
||||
|
||||
// buffers to cache chunks of the input matrices
|
||||
__shared__ float lhs_s[128][32];
|
||||
__shared__ float rhs_s[128][32];
|
||||
|
||||
// adjust our pointers for the current block
|
||||
inp += 128 * blockIdx.x * C;
|
||||
weight += 128 * blockIdx.y * C;
|
||||
out += 128 * blockIdx.x * OC + 128 * blockIdx.y;
|
||||
|
||||
float vals[8][8] = {};
|
||||
if(bias != NULL) {
|
||||
for (int i = 0; i < 8; i++) {
|
||||
for (int j = 0; j < 8; j += 4) {
|
||||
float4 b = ld_vec(bias + oc + j);
|
||||
vals[i][j+0] = b.x;
|
||||
vals[i][j+1] = b.y;
|
||||
vals[i][j+2] = b.z;
|
||||
vals[i][j+3] = b.w;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
int si_start = 4*(16 * threadIdx.y + threadIdx.x);
|
||||
for (int so = 0; so < C; so += 32) {
|
||||
__syncthreads();
|
||||
int xmod8 = threadIdx.x % 8;
|
||||
int xby8 = threadIdx.x / 8;
|
||||
int xo = 4 * xmod8;
|
||||
for(int y = 2 * threadIdx.y + xby8; y < 128; y += 32) {
|
||||
st_vec(&lhs_s[y][xo], ld_vec(inp + y * C + so + xo));
|
||||
st_vec(&rhs_s[y][xo], ld_vec(weight + y * C + so + xo));
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
for (int si = si_start; si < si_start + 32; si += 4) {
|
||||
float4 rhs[8];
|
||||
for (int u = 0; u < 8; ++u) {
|
||||
rhs[u] = ld_vec(&rhs_s[u + 8 * threadIdx.y][si % 32]);
|
||||
}
|
||||
|
||||
for (int ii = 0; ii < 8; ++ii) {
|
||||
float4 lhs = ld_vec(&lhs_s[ii + 8 * threadIdx.x][si % 32]);
|
||||
for (int ji = 0; ji < 8; ++ji) {
|
||||
vals[ii][ji] += lhs.x * rhs[ji].x;
|
||||
vals[ii][ji] += lhs.y * rhs[ji].y;
|
||||
vals[ii][ji] += lhs.z * rhs[ji].z;
|
||||
vals[ii][ji] += lhs.w * rhs[ji].w;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
for (int j = 0; j < 8; j += 4) {
|
||||
float4 result;
|
||||
result.x = vals[i][j + 0];
|
||||
result.y = vals[i][j + 1];
|
||||
result.z = vals[i][j + 2];
|
||||
result.w = vals[i][j + 3];
|
||||
st_vec(out + (8*threadIdx.x+i) * OC + 8*threadIdx.y + j, result);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// ----------------------------------------------------------------------------
|
||||
// kernel launchers
|
||||
|
||||
@@ -645,77 +721,18 @@ void layernorm_forward(float* out, float* mean, float* rstd,
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
// uses cuBLASLt to fuse the bias and gelu. does not work with OC = 50257 (last layer)
|
||||
// https://docs.nvidia.com/cuda/cublas/#cublasltmatmul
|
||||
// https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuBLASLt/LtSgemm/sample_cublasLt_LtSgemm.cu
|
||||
void matmul_forward_cublaslt(float* out,
|
||||
float* inp, float* weight, float* bias,
|
||||
int B, int T, int C, int OC) {
|
||||
int has_bias = (bias != NULL);
|
||||
// kernel 1 is the most naive matmul kernel
|
||||
void matmul_forward(float* out,
|
||||
const float* inp, const float* weight, const float* bias,
|
||||
int B, int T, int C, int OC) {
|
||||
// out is (B,T,OC). OC is short for "output channels", e.g. OC = 4 * C
|
||||
// inp is (B,T,C), weight is (OC, C), bias is (OC)
|
||||
int sqrt_block_size = 16;
|
||||
|
||||
// check bias alignment
|
||||
if(((uintptr_t)bias % 16) != 0) {
|
||||
printf("Bias pointer is not aligned (cuBLASLt requirement)!\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
int returnedResults = 0;
|
||||
cublasLtMatmulDesc_t operationDesc;
|
||||
cublasLtMatmulPreference_t preference;
|
||||
cublasLtMatrixLayout_t weightLayout;
|
||||
cublasLtMatrixLayout_t inputLayout;
|
||||
cublasLtMatrixLayout_t outputLayout;
|
||||
cublasLtMatrixLayout_t biasLayout;
|
||||
cublasLtMatmulHeuristicResult_t heuristic;
|
||||
|
||||
// create the operation descriptor
|
||||
cublasOperation_t opNoTranspose = CUBLAS_OP_N;
|
||||
cublasOperation_t opTranspose = CUBLAS_OP_T;
|
||||
cublasLtEpilogue_t epilogueBias = CUBLASLT_EPILOGUE_BIAS;
|
||||
cublasCheck(cublasLtMatmulDescCreate(&operationDesc, cublas_compute_type, CUDA_R_32F));
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &opTranspose, sizeof(opTranspose)));
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opNoTranspose, sizeof(opNoTranspose)));
|
||||
if(has_bias) {
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogueBias,
|
||||
sizeof(epilogueBias)));
|
||||
}
|
||||
cublasCheck(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(bias)));
|
||||
|
||||
// define matrix layouts
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&weightLayout, CUDA_R_32F, C, OC, C));
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&inputLayout, CUDA_R_32F, C, B*T, C));
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&outputLayout, CUDA_R_32F, OC, B*T, OC));
|
||||
cublasCheck(cublasLtMatrixLayoutCreate(&biasLayout, CUDA_R_32F, OC, 1, OC));
|
||||
|
||||
// create a preference handle with specified max workspace
|
||||
cublasCheck(cublasLtMatmulPreferenceCreate(&preference));
|
||||
cublasCheck(cublasLtMatmulPreferenceSetAttribute(preference,
|
||||
CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
|
||||
&cublaslt_workspace_size, sizeof(cublaslt_workspace_size)));
|
||||
|
||||
// find a suitable algorithm
|
||||
cublasCheck(cublasLtMatmulAlgoGetHeuristic(cublaslt_handle, operationDesc,
|
||||
weightLayout, inputLayout, outputLayout, outputLayout,
|
||||
preference, 1, &heuristic, &returnedResults));
|
||||
if (returnedResults == 0) {
|
||||
printf("No cuBLASLt algorithm: B: %d, T: %d, C: %d, OC: %d, bias: %d\n", B, T, C, OC, has_bias);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
// call the matmul
|
||||
const float alpha = 1.0f, beta = 0.0f;
|
||||
cublasCheck(cublasLtMatmul(cublaslt_handle, operationDesc,
|
||||
&alpha, weight, weightLayout, inp, inputLayout, &beta,
|
||||
out, outputLayout, out, outputLayout, &heuristic.algo,
|
||||
cublaslt_workspace, cublaslt_workspace_size, 0));
|
||||
|
||||
// cleanups
|
||||
cublasCheck(cublasLtMatmulPreferenceDestroy(preference));
|
||||
cublasCheck(cublasLtMatmulDescDestroy(operationDesc));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(weightLayout));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(inputLayout));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(outputLayout));
|
||||
cublasCheck(cublasLtMatrixLayoutDestroy(biasLayout));
|
||||
dim3 gridDim(CEIL_DIV(B * T, 8*sqrt_block_size), CEIL_DIV(OC, 8*sqrt_block_size));
|
||||
dim3 blockDim(sqrt_block_size, sqrt_block_size);
|
||||
matmul_forward_kernel4<<<gridDim, blockDim>>>(out, inp, weight, bias, C, OC);
|
||||
cudaCheck(cudaGetLastError());
|
||||
}
|
||||
|
||||
void attention_forward(float* out, float* qkvr, float* att,
|
||||
@@ -1255,20 +1272,20 @@ void gpt2_forward(GPT2 *model, int* inputs, int* targets, int B, int T) {
|
||||
|
||||
// now do the forward pass
|
||||
layernorm_forward(l_ln1, l_ln1_mean, l_ln1_rstd, residual, l_ln1w, l_ln1b, B, T, C);
|
||||
matmul_forward_cublaslt(scratch, l_ln1, l_qkvw, l_qkvb, B, T, C, 3*C);
|
||||
matmul_forward(scratch, l_ln1, l_qkvw, l_qkvb, B, T, C, 3*C);
|
||||
attention_forward(l_atty, l_qkvr, l_att, scratch, B, T, C, NH);
|
||||
matmul_forward_cublaslt(l_attproj, l_atty, l_attprojw, l_attprojb, B, T, C, C);
|
||||
matmul_forward(l_attproj, l_atty, l_attprojw, l_attprojb, B, T, C, C);
|
||||
residual_forward(l_residual2, residual, l_attproj, B*T*C);
|
||||
layernorm_forward(l_ln2, l_ln2_mean, l_ln2_rstd, l_residual2, l_ln2w, l_ln2b, B, T, C);
|
||||
matmul_forward_cublaslt(l_fch, l_ln2, l_fcw, l_fcb, B, T, C, 4*C);
|
||||
matmul_forward(l_fch, l_ln2, l_fcw, l_fcb, B, T, C, 4*C);
|
||||
gelu_forward(l_fch_gelu, l_fch, B*T*4*C);
|
||||
matmul_forward_cublaslt(l_fcproj, l_fch_gelu, l_fcprojw, l_fcprojb, B, T, 4*C, C);
|
||||
matmul_forward(l_fcproj, l_fch_gelu, l_fcprojw, l_fcprojb, B, T, 4*C, C);
|
||||
residual_forward(l_residual3, l_residual2, l_fcproj, B*T*C);
|
||||
}
|
||||
|
||||
residual = acts.residual3 + (L-1) * B * T * C; // last residual is in residual3
|
||||
layernorm_forward(acts.lnf, acts.lnf_mean, acts.lnf_rstd, residual, params.lnfw, params.lnfb, B, T, C);
|
||||
matmul_forward_cublaslt(acts.output, acts.lnf, params.wte, NULL, B, T, C, Vp);
|
||||
matmul_forward(acts.output, acts.lnf, params.wte, NULL, B, T, C, Vp);
|
||||
|
||||
// also forward the cross-entropy loss function if we have the targets
|
||||
if (targets != NULL) {
|
||||
@@ -1594,13 +1611,11 @@ int main(int argc, char *argv[]) {
|
||||
cudaGetDeviceProperties(&deviceProp, deviceIdx);
|
||||
// setup cuBLAS and cuBLASLt
|
||||
cublasCheck(cublasCreate(&cublas_handle));
|
||||
cublasCheck(cublasLtCreate(&cublaslt_handle));
|
||||
// TF32 precision is equivalent to torch.set_float32_matmul_precision('high')
|
||||
int enable_tf32 = deviceProp.major >= 8 ? 1 : 0;
|
||||
cublas_compute_type = enable_tf32 ? CUBLAS_COMPUTE_32F_FAST_TF32 : CUBLAS_COMPUTE_32F;
|
||||
cublasMath_t cublas_math_mode = enable_tf32 ? CUBLAS_TF32_TENSOR_OP_MATH : CUBLAS_DEFAULT_MATH;
|
||||
cublasCheck(cublasSetMathMode(cublas_handle, cublas_math_mode));
|
||||
cudaCheck(cudaMalloc(&cublaslt_workspace, cublaslt_workspace_size));
|
||||
printf("| device | %-50s |\n", deviceProp.name);
|
||||
printf("| TF32 | %-50s |\n", enable_tf32 ? "enabled" : "disabled");
|
||||
printf("+-----------------------+----------------------------------------------------+\n");
|
||||
@@ -1619,8 +1634,8 @@ int main(int argc, char *argv[]) {
|
||||
|
||||
// build DataLoaders for both train and val
|
||||
DataLoader train_loader, val_loader;
|
||||
dataloader_init(&train_loader, train_data_pattern, B, T, 0, 1);
|
||||
dataloader_init(&val_loader, val_data_pattern, B, T, 0, 1);
|
||||
dataloader_init(&train_loader, train_data_pattern, B, T, 0, 1, 1);
|
||||
dataloader_init(&val_loader, val_data_pattern, B, T, 0, 1, 0);
|
||||
int train_num_batches = train_loader.num_tokens / (B*T); // let's do 1 epoch by default for now
|
||||
int val_num_batches = val_loader.num_tokens / (B*T);
|
||||
if (val_num_batches > val_max_steps) { val_num_batches = val_max_steps; }
|
||||
@@ -1732,9 +1747,7 @@ int main(int argc, char *argv[]) {
|
||||
gpt2_free(&model);
|
||||
free(cpu_logits);
|
||||
free(gen_tokens);
|
||||
cudaCheck(cudaFree(cublaslt_workspace));
|
||||
cublasCheck(cublasDestroy(cublas_handle));
|
||||
cublasCheck(cublasLtDestroy(cublaslt_handle));
|
||||
logger_free(&logger);
|
||||
|
||||
return 0;
|
||||
|
||||
+1255
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user