forked from tinygrad/tinygrad
Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 geohot
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7e4cf39929 | ||
 George HotzandGitHub
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75ce11593c | ||
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chenyuandGitHub
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fe774a4319 | ||
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chenyuandGitHub
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8ad5f9e74f | ||
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George HotzandGitHub
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ea7672931f |
@@ -52,14 +52,16 @@ jobs:
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- name: reset process replay
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run: python3.11 test/external/process_replay/reset.py
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- name: Run Stable Diffusion
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run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=500 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
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run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=1000 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
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- name: Run Stable Diffusion without fp16
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run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=700 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
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run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=1000 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
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- name: Run Stable Diffusion v2
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run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=1600 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
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# TODO: very slow step time
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run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=100000 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
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# process replay can't capture this, the graph is too large
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- name: Run SDXL
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run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
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# TODO: too slow
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# - name: Run SDXL
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# run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=5000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
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- name: Run model inference benchmark
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run: METAL=1 python3.11 test/external/external_model_benchmark.py
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- name: Test speed vs torch
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@@ -99,7 +101,7 @@ jobs:
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- name: Run GPT2
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run: |
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BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
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BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=8 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
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BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=16 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
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- name: Run GPT2 w HALF
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run: BENCHMARK_LOG=gpt2_half HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
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- name: Run GPT2 w HALF/BEAM
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@@ -109,13 +111,14 @@ jobs:
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- name: Train MNIST
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run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py | tee beautiful_mnist.txt
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- name: Run 10 CIFAR training steps
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run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=330 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
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run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=3000 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
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- name: Run 10 CIFAR training steps w HALF
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run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=385 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
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run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=3000 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
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#- name: Run 10 CIFAR training steps w BF16
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# run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
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- name: Run 10 CIFAR training steps w winograd
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run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
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# TODO: too slow
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# - name: Run 10 CIFAR training steps w winograd
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# run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
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- name: UsbGPU boot time
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run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
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- name: UsbGPU tiny tests
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@@ -213,8 +216,9 @@ jobs:
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run: DEBUG=2 CUDA=1 python -m pytest -rA test/test_tiny.py
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- name: Run Stable Diffusion
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run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
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- name: Run SDXL
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run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
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# TODO: too slow
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# - name: Run SDXL
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# run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
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- name: Run LLaMA
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run: |
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BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
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@@ -238,9 +242,9 @@ jobs:
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- name: Run GPT2
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run: |
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BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
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BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
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BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=10 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
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- name: Run GPT2 w HALF
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run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
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run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=10 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
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- name: Run GPT2 w HALF/BEAM
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run: BENCHMARK_LOG=gpt2_half_beam NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
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- uses: actions/upload-artifact@v4
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@@ -299,22 +303,25 @@ jobs:
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rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
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- name: reset process replay
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run: test/external/process_replay/reset.py
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- name: Fuzz Padded Tensor Core GEMM (NV)
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run: NV=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
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- name: Fuzz Padded Tensor Core GEMM (PTX)
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run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
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# TODO: too slow
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# - name: Fuzz Padded Tensor Core GEMM (NV)
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# run: NV=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
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# TODO: too slow
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# - name: Fuzz Padded Tensor Core GEMM (PTX)
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# run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
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- name: Train MNIST
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run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
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- name: Run 10 CIFAR training steps
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run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=85 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
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run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=850 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
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- name: Run 10 CIFAR training steps w HALF
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run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=68 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
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run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=680 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
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- name: Run 10 CIFAR training steps w BF16
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run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=75 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
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- name: Run 10 CIFAR training steps w winograd
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run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=35 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
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run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=750 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
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# TODO: too slow
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# - name: Run 10 CIFAR training steps w winograd
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# run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
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- name: Run full CIFAR training w 1 GPU
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run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
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run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
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- name: Run full CIFAR training steps w 6 GPUS
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run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
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- name: Run MLPerf resnet eval on training data
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@@ -415,9 +422,10 @@ jobs:
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- name: Test AM warm start time
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run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
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- name: Run Stable Diffusion
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run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=450 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
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- name: Run SDXL
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run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=1400 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
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run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=900 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
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# TODO: too slow
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# - name: Run SDXL
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# run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3200 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
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- name: Run LLaMA 7B
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run: |
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BENCHMARK_LOG=llama_nojit AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
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@@ -508,15 +516,16 @@ jobs:
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- name: Train MNIST
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run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
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- name: Run 10 CIFAR training steps
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run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=85 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
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run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=400 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
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- name: Run 10 CIFAR training steps w HALF
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run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=188 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
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run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=500 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
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# - name: Run 10 CIFAR training steps w BF16
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# run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
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- name: Run 10 CIFAR training steps w winograd
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run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
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# TODO: too slow
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# - name: Run 10 CIFAR training steps w winograd
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# run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
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- name: Run full CIFAR training w 1 GPU
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run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
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run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
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#- name: Run full CIFAR training steps w 6 GPUS
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# run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
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#- name: Run full CIFAR training steps w 6 GPUS (REMOTE)
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@@ -695,7 +704,7 @@ jobs:
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AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
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AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
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- name: Run full CIFAR training w 1 GPU
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run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
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run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
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# TODO: enable
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# - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
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# run: BENCHMARK_LOG=resnet_10steps AMD=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee am_train_resnet_one_gpu.txt
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@@ -758,7 +767,7 @@ jobs:
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- name: Test LLAMA-3
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run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
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- name: Run full CIFAR training w 1 GPU
|
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run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
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run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
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#- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
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# run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
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- name: Run 10 MLPerf Bert training steps (1 gpu)
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@@ -267,10 +267,13 @@ jobs:
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run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
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- name: Run unit tests
|
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run: CPU=1 python -m pytest -n=auto test/unit/ --durations=20
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- name: Check SPEC=1
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run: SPEC=1 python3 test/test_tiny.py
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- name: Run targetted tests on NULL backend
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run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step test/device/test_null.py
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- name: Run SDXL on NULL backend
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run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
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# TODO: too slow
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# - name: Run SDXL on NULL backend
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# run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
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- name: Run Clip tests for SD MLPerf on NULL backend
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run: NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
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# TODO: support fake weights
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@@ -1,109 +0,0 @@
|
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from tinygrad import Device, Tensor, Context
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from tinygrad.uop.ops import UOp, Ops, graph_rewrite, AxisType, PatternMatcher, UPat, pm_lower_index_dtype, GroupOp, KernelInfo
|
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from tinygrad.dtype import dtypes, AddrSpace
|
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from tinygrad.helpers import prod
|
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from tinygrad.schedule.rangeify import pm_mops
|
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from tinygrad.codegen.simplify import pm_flatten_range
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|
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TILE_DIM = 8
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N_BLOCK = 4
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K_BLOCK = 2
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M_BLOCK = 4
|
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|
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#M = N = K = 4096
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M = N = K = 1024
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|
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range_num = 0
|
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def rng(x, typ=AxisType.LOOP) -> UOp:
|
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global range_num
|
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range_num += 1
|
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return UOp.range(x, range_num-1, typ)
|
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|
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def glbl(nm, dtype, sz): return UOp(Ops.DEFINE_GLOBAL, dtype.ptr(prod(sz), AddrSpace.GLOBAL), arg=nm).reshape(sz)
|
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def rt(nm, dtype, sz): return UOp(Ops.DEFINE_REG, dtype.ptr(prod(sz), AddrSpace.REG), arg=nm).reshape(sz)
|
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|
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def zero(reg:UOp, *endrngs):
|
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rngs = [rng(s//TILE_DIM)*TILE_DIM for s in reg.shape]
|
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rngs = [x+rng(TILE_DIM) for x in rngs]
|
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|
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return reg[*rngs].store(UOp.const(reg.dtype.base, 0.0), *rngs, *endrngs, dtype=reg.dtype).reshape(reg.shape)
|
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|
||||
def load(reg:UOp, gl:UOp, *idxs):
|
||||
rngs = [rng(s//TILE_DIM)*TILE_DIM for s in reg.shape]
|
||||
rngs = [x+rng(TILE_DIM) for x in rngs]
|
||||
|
||||
grngs = [i*(r.vmax+1)+r for i,r in zip(idxs,rngs)]
|
||||
return reg[*rngs].store(gl[*grngs].load(), *rngs, dtype=reg.dtype).reshape(reg.shape)
|
||||
|
||||
def store(gl:UOp, reg:UOp, *idxs):
|
||||
rngs = [rng(s//TILE_DIM)*TILE_DIM for s in reg.shape]
|
||||
rngs = [x+rng(TILE_DIM) for x in rngs]
|
||||
|
||||
# TODO: why does this not have shape?
|
||||
#rngs = [rng(s) for s in (N_BLOCK*TILE_DIM, M_BLOCK*TILE_DIM)]
|
||||
grngs = [i*(r.vmax+1)+r for i,r in zip(idxs,rngs)]
|
||||
return gl[*grngs].store(reg[*rngs].load(), *rngs)
|
||||
|
||||
def mma_AB(outacc:UOp, a:UOp, b:UOp, *endrngs):
|
||||
assert a.shape[1] == b.shape[0]
|
||||
# meta::unroll_i_j_in_range -- split on TILE_DIM
|
||||
rngs = [rng(s//TILE_DIM)*TILE_DIM for s in outacc.shape]
|
||||
red = rng(a.shape[1]//TILE_DIM, AxisType.REDUCE)*TILE_DIM
|
||||
# meta::unroll_i_in_range -- split reduce on TILE_DIM
|
||||
rngs = [x+rng(TILE_DIM) for x in rngs]
|
||||
red = red + rng(TILE_DIM, AxisType.REDUCE)
|
||||
acc = outacc[*rngs].load(red) + a[rngs[0],red].load() * b[red,rngs[1]].load()
|
||||
return outacc[*rngs].store(acc, *rngs, red, *endrngs, dtype=outacc.dtype).reshape(outacc.shape)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# TODO: support string ranges
|
||||
tg_id_y = UOp.range(M // (M_BLOCK * TILE_DIM), -3, AxisType.GLOBAL if Device.DEFAULT != "CPU" else AxisType.LOOP)
|
||||
tg_id_x = UOp.range(N // (N_BLOCK * TILE_DIM), -2, AxisType.GLOBAL if Device.DEFAULT != "CPU" else AxisType.LOOP)
|
||||
|
||||
gl_d = glbl("gl0_d", dtypes.float, (N, M))
|
||||
gl_a = glbl("gl1_a", dtypes.float, (N, K))
|
||||
gl_b = glbl("gl2_b", dtypes.float, (K, M))
|
||||
|
||||
a_reg = rt("a_reg", dtypes.float, (N_BLOCK*TILE_DIM, K_BLOCK*TILE_DIM))
|
||||
b_reg = rt("b_reg", dtypes.float, (K_BLOCK*TILE_DIM, M_BLOCK*TILE_DIM))
|
||||
d_reg = rt("d_reg", dtypes.float, (N_BLOCK*TILE_DIM, M_BLOCK*TILE_DIM))
|
||||
d_reg = zero(d_reg, UOp(Ops.NOOP, src=(tg_id_y, tg_id_x)))
|
||||
|
||||
k = UOp.range(K // (K_BLOCK * TILE_DIM), -1, AxisType.REDUCE)
|
||||
a_reg = load(a_reg, gl_a, tg_id_y, k)
|
||||
b_reg = load(b_reg, gl_b, k, tg_id_x)
|
||||
d_reg = mma_AB(d_reg, a_reg, b_reg, k)
|
||||
sink = store(gl_d, d_reg, tg_id_y, tg_id_x).sink(arg=KernelInfo())
|
||||
|
||||
sink = graph_rewrite(sink, pm_mops+pm_flatten_range, name="pm_mops")
|
||||
|
||||
from tinygrad.codegen.gpudims import pm_add_gpudims
|
||||
sink = graph_rewrite(sink, pm_add_gpudims, ctx=Device.default.renderer, name="gpudims")
|
||||
|
||||
pm_lower_index_dtype_simple = PatternMatcher([
|
||||
(UPat(GroupOp.All, dtype=dtypes.index, name="x"), lambda x: x.replace(dtype=dtypes.int))
|
||||
])
|
||||
sink = graph_rewrite(sink, pm_lower_index_dtype_simple, name="index_dtype")
|
||||
|
||||
from tinygrad.codegen import rewrites_for_linearizer, apply_rewrites
|
||||
lin = apply_rewrites(sink, rewrites_for_linearizer)
|
||||
src = Device.default.renderer.render(lin.arg.lst)
|
||||
print(src)
|
||||
#exit(0)
|
||||
|
||||
from tinygrad.engine.realize import CompiledRunner, ExecItem
|
||||
from tinygrad.renderer import ProgramSpec
|
||||
|
||||
ps = ProgramSpec("test", src, Device.DEFAULT, sink, lin.arg.lst, [1,1,1], [1,1,1])
|
||||
run = CompiledRunner(ps)
|
||||
|
||||
a = Tensor.randn(N, N)
|
||||
b = Tensor.randn(N, N)
|
||||
c = Tensor.empty(N, N)
|
||||
Tensor.realize(a, b, c)
|
||||
|
||||
ei = ExecItem(run, [x.uop.buffer.ensure_allocated() for x in (c,a,b)])
|
||||
with Context(DEBUG=2):
|
||||
for i in range(5): ei.run()
|
||||
for i in range(5): ref = (a@b).realize()
|
||||
print((ref-c).mean().item())
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
[pytest]
|
||||
norecursedirs = extra
|
||||
timeout = 240
|
||||
timeout = 300
|
||||
timeout_method = thread
|
||||
timeout_func_only = true
|
||||
testpaths = test
|
||||
|
||||
+16
-3
@@ -1,8 +1,7 @@
|
||||
import unittest
|
||||
from tinygrad import Tensor, nn
|
||||
from tinygrad.helpers import RANGEIFY, Context, GlobalCounters
|
||||
from tinygrad.uop.ops import UOp
|
||||
from test.helpers import expect_rangeify_fails
|
||||
from tinygrad.uop.ops import UOp, graph_rewrite, PatternMatcher, UPat, Ops
|
||||
|
||||
@unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
|
||||
class TestRangeifyAssign(unittest.TestCase):
|
||||
@@ -301,8 +300,22 @@ class TestOuterworld(unittest.TestCase):
|
||||
o.contiguous(i).realize()
|
||||
self.assertTrue((t==o).all().item())
|
||||
|
||||
from tinygrad.schedule.rangeify import pm_rangeify, RangeifyContext
|
||||
class TestRangeifyPM(unittest.TestCase):
|
||||
@unittest.expectedFailure
|
||||
def test_reshape_match(self):
|
||||
def proc(a:Tensor):
|
||||
sink = a.uop.sink()
|
||||
pm_realize = PatternMatcher([(UPat(Ops.CONTIGUOUS, name="x"), lambda x: x.replace(op=Ops.REALIZE))])
|
||||
sink = graph_rewrite(sink, pm_realize)
|
||||
return graph_rewrite(sink, pm_rangeify, ctx=RangeifyContext())
|
||||
a = Tensor.empty(10*10).reshape(10, 10).contiguous().pad(((0,0),(0,1))).contiguous()
|
||||
b = Tensor.empty(10*10).reshape(10, 10).contiguous().reshape(100).reshape(10, 10).pad(((0,0),(0,1))).contiguous()
|
||||
sink1 = proc(a)
|
||||
sink2 = proc(b)
|
||||
self.assertIs(sink1, sink2)
|
||||
|
||||
class TestRangeifyEdgeCase(unittest.TestCase):
|
||||
@expect_rangeify_fails # TODO: fix
|
||||
def test_matmul_relu_cat(self):
|
||||
a = Tensor.ones(100, 512).contiguous().realize()
|
||||
c = Tensor.ones(1, 512).contiguous().realize()
|
||||
|
||||
@@ -568,5 +568,13 @@ class TestUOpChildren(unittest.TestCase):
|
||||
del c
|
||||
self.assertEqual(len(a.children), 0)
|
||||
|
||||
class TestUOpRender(unittest.TestCase):
|
||||
def test_render_vectorize_same(self):
|
||||
u = UOp(Ops.VECTORIZE, src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0)))
|
||||
self.assertEqual(u.render(), "{0, ...}")
|
||||
def test_render_vectorize_different(self):
|
||||
u = UOp(Ops.VECTORIZE, src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1), UOp.const(dtypes.int, 2)))
|
||||
self.assertEqual(u.render(), "{0,1,2}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main(verbosity=2)
|
||||
|
||||
@@ -5,10 +5,10 @@ from tinygrad.dtype import dtypes
|
||||
|
||||
def flatten_range(r:UOp):
|
||||
off = range_start[r.op]
|
||||
rngs, noops = partition(r.src[off:], lambda x: x.op is not Ops.NOOP)
|
||||
rngs = r.src[off:]
|
||||
if not len(rngs): return None
|
||||
new_rngs = [x for x in UOp.sink(*rngs).toposort(lambda x: x.op is not Ops.NOOP) if x.op is Ops.RANGE]
|
||||
return r.replace(src=r.src[:off]+tuple(new_rngs)+tuple(noops))
|
||||
new_rngs = [x for x in UOp.sink(*rngs).toposort() if x.op is Ops.RANGE]
|
||||
return r.replace(src=r.src[:off]+tuple(new_rngs))
|
||||
|
||||
pm_flatten_range = PatternMatcher([
|
||||
# real ranges only
|
||||
|
||||
@@ -159,7 +159,6 @@ class CStyleLanguage(Renderer):
|
||||
prefix = None
|
||||
if u.op is Ops.SPECIAL: r[u] = u.arg
|
||||
elif u.op is Ops.RANGE: r[u] = "ridx"+range_str(u)
|
||||
elif u.op is Ops.STORE: r[u] = r[u.src[0].src[0]]
|
||||
else:
|
||||
prefix = {Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
|
||||
Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.GEP: "gep", Ops.VECTORIZE: "cast", Ops.PRECAST: "precast",
|
||||
|
||||
@@ -175,8 +175,7 @@ class RangeifyContext:
|
||||
def map_reshape(idx:UOp, r:UOp):
|
||||
acc = 1
|
||||
to_sum = []
|
||||
idx_shape = [x.vmax+1 for x in idx.src[1:]]
|
||||
for s,src in list(zip(idx_shape, idx.src[1:]))[::-1]:
|
||||
for s,src in list(zip(idx.shape, idx.src[1:]))[::-1]:
|
||||
to_sum.append(acc*src)
|
||||
acc *= s
|
||||
mish = sum(to_sum, start=UOp.const(dtypes.index, 0))
|
||||
|
||||
+5
-9
@@ -182,7 +182,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
Ops.MSELECT, Ops.BUFFER, Ops.BUFFERIZE, Ops.VECTORIZE, Ops.STORE}:
|
||||
return None
|
||||
if self.op is Ops.INDEX and self.src[0].op is Ops.ASSIGN and self.src[0].src[1].op is Ops.KERNEL: return None
|
||||
if self.op is Ops.INDEX: return None
|
||||
if self.op is Ops.BARRIER: return None
|
||||
if self.op in GroupOp.Block: return None
|
||||
from tinygrad.shape.shapetracker import ShapeTracker
|
||||
@@ -195,9 +194,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
# CONST with a DEVICE has a shape of ()
|
||||
if self.op is Ops.CONST and len(self.src) and self.src[0].op is Ops.DEVICE: return ShapeTracker.from_shape(())
|
||||
if self.op is Ops.STORE and isinstance(self.dtype, PtrDType): return ShapeTracker.from_shape((self.dtype.size,))
|
||||
#if self.op is Ops.LOAD: return ShapeTracker.from_shape((self.dtype.count,))
|
||||
|
||||
# skip the INDEX
|
||||
if self.op is Ops.STORE and self.dtype is not dtypes.void: return self.src[0].src[0].st
|
||||
# BufferOps and ASSIGN flow ShapeTracker from a direct edge
|
||||
if self.op in {Ops.STORE, Ops.ASSIGN, Ops.LOAD}: return self.src[0].st
|
||||
@@ -312,7 +308,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
def detach(self): return UOp(Ops.DETACH, self.dtype, (self,))
|
||||
def index(self, *srcs:UOp|None, **kwargs):
|
||||
return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
|
||||
def __getitem__(self, idx): return self.index(*idx)
|
||||
def __getitem__(self, idx): return self.index(idx)
|
||||
def const_like(self, b:ConstLike):
|
||||
# constants can optionally have a DEVICE source
|
||||
return UOp.const(self.dtype, b, device=self._device, shape=self.shape if self.st is not None else None)
|
||||
@@ -336,9 +332,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
i = (i,)
|
||||
return UOp(Ops.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)
|
||||
def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, dtype=kwargs.pop("dtype", self.dtype.base), src=(self,)+src, **kwargs)
|
||||
def store(self, *src:UOp, **kwargs):
|
||||
return UOp(Ops.STORE, kwargs.pop("dtype", dtypes.void), (self,)+src, **kwargs)
|
||||
#return UOp(Ops.STORE, self.dtype, (self,)+src, **kwargs)
|
||||
def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, kwargs.pop("dtype", dtypes.void), (self,)+src, **kwargs)
|
||||
def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))
|
||||
def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
|
||||
def alu(self, op, *src:UOp, **kwargs):
|
||||
@@ -1048,7 +1042,7 @@ if TRACK_MATCH_STATS or PROFILE:
|
||||
|
||||
# *** simple graph rewrite engine ***
|
||||
|
||||
SENTINEL = UOp(Ops.SENTINEL)
|
||||
with Context(SPEC=0): SENTINEL = UOp(Ops.SENTINEL)
|
||||
class RewriteNotReady(Exception): pass
|
||||
class BottomUpGate(Exception): pass
|
||||
class RewriteContext:
|
||||
@@ -1202,6 +1196,8 @@ renderer = PatternMatcher([
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.view({x.arg})")),
|
||||
(UPat((Ops.INDEX, Ops.BUFFERIZE), name="x"), lambda x:
|
||||
UOp(Ops.NOOP, arg=''.join([f"[{strip_parens(y.arg)}]" for y in x.src[1:]])) if all(y.op is Ops.NOOP for y in x.src[1:]) else None),
|
||||
(UPat(Ops.VECTORIZE, src=UPat(Ops.NOOP), name="x"),
|
||||
lambda x: UOp(Ops.NOOP, arg=f"{{{','.join([y.arg for y in x.src])}}}" if not all_same(x.src) else f"{{{x.src[0].arg}, ...}}")),
|
||||
])
|
||||
renderer_infer = PatternMatcher([
|
||||
(UPat(Ops.MOD, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"cmod({x.src[0].arg}, {x.src[1].arg})")),
|
||||
|
||||
@@ -258,6 +258,9 @@ full_non_rangeify_spec = PatternMatcher([]) if RANGEIFY else PatternMatcher([
|
||||
])
|
||||
|
||||
full_spec = PatternMatcher([
|
||||
# SENTINEL should never be in the graph
|
||||
(UPat(Ops.SENTINEL), lambda: False),
|
||||
|
||||
# Invalid must have type Index
|
||||
(UPat(Ops.CONST, arg=Invalid, name="x"), lambda x: x.dtype.scalar() == dtypes.index),
|
||||
# where on index in rhs position is fine
|
||||
|
||||
@@ -455,6 +455,7 @@ def _valid_priority(v: UOp, valids:list[UOp]):
|
||||
except ValueError: return 0
|
||||
|
||||
def simplify_valid(valid:UOp) -> UOp|None:
|
||||
if valid.op_in_parents(Ops.LOAD): return None # this should only be for indexing, skip if there's a LOAD
|
||||
ret:list[UOp] = []
|
||||
something_changed = False
|
||||
valids = list(valid.split_uop(Ops.AND))
|
||||
|
||||
Reference in New Issue
Block a user