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2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 geohot
|
f150e27ad1 | ||
|
geohot
|
81d597ebbc |
@@ -68,10 +68,8 @@ jobs:
|
||||
run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test AMX tensor cores
|
||||
run: |
|
||||
DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
DEBUG=2 CPU=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
|
||||
DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
DEBUG=2 LLVM=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
|
||||
DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
|
||||
DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
|
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- name: Run Tensor Core GEMM (float)
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run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt
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- name: Run Tensor Core GEMM (half)
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||||
|
||||
+76
-77
@@ -7,7 +7,6 @@ env:
|
||||
BUILD_CACHE_VERSION: '1'
|
||||
CAPTURE_PROCESS_REPLAY: 1
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||||
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
PYTHONPATH: ${{ github.workspace }}
|
||||
|
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on:
|
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push:
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@@ -80,7 +79,7 @@ jobs:
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python docs/abstractions2.py
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python docs/abstractions3.py
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- name: Test Quickstart
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run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py && python quickstart.py
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run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py && PYTHONPATH=. python quickstart.py
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- name: Test DEBUG
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run: DEBUG=100 python3 -c "from tinygrad import Tensor; N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N); c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2); print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
|
||||
- name: Compile EfficientNet to C and test it
|
||||
@@ -183,19 +182,19 @@ jobs:
|
||||
pip3 install --upgrade --force-reinstall ruff==0.11.0
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python3 -m ruff check extra/torch_backend/backend.py
|
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- name: Test one op
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run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
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run: PYTHONPATH=. FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
|
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- name: Test ResNet-18
|
||||
run: DEBUG=2 python3 extra/torch_backend/example.py
|
||||
run: PYTHONPATH=. DEBUG=2 python3 extra/torch_backend/example.py
|
||||
- name: My (custom) tests
|
||||
run: python3 extra/torch_backend/test.py
|
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run: PYTHONPATH=. python3 extra/torch_backend/test.py
|
||||
- name: Test one op in torch tests
|
||||
run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
|
||||
run: PYTHONPATH=. DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
|
||||
- name: Test Ops with TINY_BACKEND
|
||||
run: LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
|
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run: PYTHONPATH=. LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
|
||||
- name: Test in-place operations on views
|
||||
run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
|
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run: PYTHONPATH=. TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
|
||||
- name: Test multi-gpu
|
||||
run: LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
|
||||
run: PYTHONPATH=. LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
|
||||
|
||||
torchbackendmore:
|
||||
name: Torch Backend Tests More
|
||||
@@ -217,9 +216,9 @@ jobs:
|
||||
sudo apt update || true
|
||||
sudo apt install -y --no-install-recommends ninja-build
|
||||
- name: Test beautiful_mnist in torch with TINY_BACKEND
|
||||
run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
|
||||
run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 PYTHONPATH=. LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
|
||||
- name: Test some torch tests (expect failure)
|
||||
run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
|
||||
run: PYTHONPATH=. python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
|
||||
|
||||
tc:
|
||||
name: Tensor Core tests
|
||||
@@ -241,55 +240,55 @@ jobs:
|
||||
IMAGE=2 PYTHON=1 python3 test/test_ops.py TestOps.test_simple_conv2d
|
||||
- name: Test emulated METAL tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
|
||||
DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated AMX tensor cores
|
||||
run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
run: PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
- name: Test emulated AMD tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated AMD MFMA tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated AMD RDNA4 tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated CUDA tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=CUDA FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
DEBUG=2 EMULATE=CUDA_SM75 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
DEBUG=2 EMULATE_CUDA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
|
||||
DEBUG=2 EMULATE_CUDA_SM75=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
|
||||
PYTHONPATH="." DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH="." DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
|
||||
- name: Test emulated INTEL OpenCL tensor cores
|
||||
run: DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
|
||||
run: DEBUG=2 EMULATE_INTEL=1 FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
|
||||
- name: Full test tensor cores
|
||||
run: |
|
||||
DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_INTEL=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
|
||||
- name: Test device flop counts
|
||||
run: |
|
||||
DEBUG=2 EMULATE=METAL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 EMULATE=AMD PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 EMULATE=CUDA PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 EMULATE=INTEL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
DEBUG=2 AMX=1 EMULATE=AMX PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_CUDA=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 EMULATE_INTEL=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
|
||||
PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
|
||||
|
||||
bepython:
|
||||
name: Python Backend
|
||||
@@ -306,15 +305,15 @@ jobs:
|
||||
key: be-minimal
|
||||
deps: testing_minimal
|
||||
- name: Test dtype with Python emulator
|
||||
run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
|
||||
run: DEBUG=1 PYTHONPATH=. PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
|
||||
- name: Test ops with Python emulator
|
||||
run: DEBUG=2 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py -k "not (test_split or test_simple_cumsum or test_cumsum or test_einsum or test_dot or test_dot_1d or test_big_gemm or test_broadcastdot or test_multidot or test_var_axis or test_std_axis or test_broadcast_full or test_broadcast_partial or test_simple_conv3d or test_dilated_conv_transpose2d or test_simple_conv_transpose3d or test_large_input_conv2d or test_max_pool2d or test_max_pool2d_simple or test_max_pool2d_bigger_stride or test_avg_pool2d or test_cat or test_scaled_product_attention or test_scaled_product_attention_causal or test_slice_fancy_indexing_dim_inject_none or test_slice_fancy_indexing_list_indices or test_slice_fancy_indexing_no_dim_collapse or test_slice_fancy_indexing_tuple_indices or test_slice_fancy_indexing_list_with_tensors or test_slice_fancy_indexing_dim_collapse_int or test_interpolate_bilinear or test_interpolate_bilinear_corners_aligned or test_scaled_dot_product_attention or test_cummax or test_simple_cummax or test_logcumsumexp or test_sort or test_cumprod)" --durations=20
|
||||
- name: Test uops with Python emulator
|
||||
run: PYTHON=1 python3 -m pytest test/test_uops.py --durations=20
|
||||
- name: Test symbolic with Python emulator
|
||||
run: PYTHON=1 python3 test/test_symbolic_ops.py
|
||||
run: PYTHONPATH=. PYTHON=1 python3 test/test_symbolic_ops.py
|
||||
- name: test_renderer_failures with Python emulator
|
||||
run: PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
|
||||
run: PYTHONPATH=. PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
|
||||
|
||||
linter:
|
||||
name: Linters
|
||||
@@ -362,7 +361,7 @@ jobs:
|
||||
pydeps: "pillow"
|
||||
deps: testing_unit
|
||||
- name: Test README
|
||||
run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py && python README.py
|
||||
run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py && PYTHONPATH=. python README.py
|
||||
- name: Run unit tests
|
||||
run: PYTHONPATH="." python -m pytest -n=auto test/unit/ --durations=20
|
||||
- name: Run targetted tests on NULL backend
|
||||
@@ -380,11 +379,11 @@ jobs:
|
||||
run: |
|
||||
test/external/process_replay/reset.py
|
||||
CAPTURE_PROCESS_REPLAY=1 python test/test_tiny.py TestTiny.test_plus
|
||||
python extra/optimization/extract_dataset.py
|
||||
PYTHONPATH=. python extra/optimization/extract_dataset.py
|
||||
gzip -c /tmp/sops > extra/datasets/sops.gz
|
||||
DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
|
||||
- name: Repo line count < 18000 lines
|
||||
run: MAX_LINE_COUNT=18000 python sz.py
|
||||
DEBUG=1 MIN_ASTS=1 PYTHONPATH=. python extra/optimization/get_action_space.py
|
||||
- name: Repo line count < 17500 lines
|
||||
run: MAX_LINE_COUNT=17500 python sz.py
|
||||
|
||||
fuzzing:
|
||||
name: Fuzzing
|
||||
@@ -514,11 +513,11 @@ jobs:
|
||||
- name: Test ONNX (LLVM)
|
||||
run: LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test ONNX Runner (CPU)
|
||||
run: CPU=1 python3 test/external/external_test_onnx_runner.py
|
||||
run: CPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
|
||||
- name: Test Additional ONNX Ops (CPU)
|
||||
run: CPU=1 python3 test/external/external_test_onnx_ops.py
|
||||
run: CPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_ops.py
|
||||
- name: Test Quantize ONNX
|
||||
run: CPU=1 python3 test/test_quantize_onnx.py
|
||||
run: CPU=1 PYTHONPATH=. python3 test/test_quantize_onnx.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
@@ -543,15 +542,15 @@ jobs:
|
||||
- name: Test ONNX (GPU)
|
||||
run: GPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
|
||||
- name: Test Optimization Helpers
|
||||
run: DEBUG=1 python3 extra/optimization/test_helpers.py
|
||||
run: PYTHONPATH="." DEBUG=1 python3 extra/optimization/test_helpers.py
|
||||
#- name: Test Action Space
|
||||
# run: DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
|
||||
# run: PYTHONPATH="." DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
|
||||
- name: Test Beam Search
|
||||
run: GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
run: PYTHONPATH="." GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
- name: Test MLPerf stuff
|
||||
run: GPU=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
|
||||
- name: Test llama 3 training
|
||||
run: MAX_BUFFER_SIZE=0 DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
|
||||
run: MAX_BUFFER_SIZE=0 PYTHONPATH="." DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
|
||||
- name: Run process replay tests
|
||||
uses: ./.github/actions/process-replay
|
||||
|
||||
@@ -640,7 +639,7 @@ jobs:
|
||||
- name: Test LLVM=1 DEVECTORIZE=0
|
||||
run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
|
||||
- name: Test LLVM=1 DEVECTORIZE=0 for model
|
||||
run: LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
|
||||
run: PYTHONPATH="." LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
|
||||
- name: Test CPU=1 DEVECTORIZE=0
|
||||
run: CPU=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
|
||||
|
||||
@@ -676,9 +675,9 @@ jobs:
|
||||
- name: Run test_tiny on DSP
|
||||
run: DEBUG=2 DSP=1 python test/test_tiny.py
|
||||
- name: Test transcendentals
|
||||
run: CC=clang-20 DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
|
||||
run: CC=clang-20 PYTHONPATH="." DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
|
||||
- name: Test quantize onnx
|
||||
run: DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
|
||||
run: PYTHONPATH="." DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
|
||||
|
||||
testwebgpu:
|
||||
name: Linux (WebGPU)
|
||||
@@ -721,6 +720,7 @@ jobs:
|
||||
MOCKGPU: 1
|
||||
FORWARD_ONLY: 1
|
||||
AMD_LLVM: ${{ matrix.backend == 'amdllvm' && '1' || matrix.backend != 'amdllvm' && '0' }}
|
||||
PYTHONPATH: ${{ github.workspace }}
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
@@ -760,9 +760,7 @@ jobs:
|
||||
name: Linux (${{ matrix.backend }})
|
||||
runs-on: ubuntu-22.04
|
||||
timeout-minutes: 20
|
||||
env:
|
||||
MOCKGPU: 1
|
||||
FORWARD_ONLY: 1
|
||||
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
@@ -774,11 +772,11 @@ jobs:
|
||||
cuda: 'true'
|
||||
ocelot: 'true'
|
||||
- name: Set env
|
||||
run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nPTX=1' || matrix.backend == 'nv' && 'NV=1' }}" >> $GITHUB_ENV
|
||||
run: printf "${{ matrix.backend == 'PTX' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nCUDA=1\nPTX=1\nMOCKGPU=1' || matrix.backend == 'nv' && 'NV=1\nMOCKGPU=1\nFORWARD_ONLY=1' }}" >> $GITHUB_ENV
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
|
||||
DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
|
||||
DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
- name: Run pytest (cuda)
|
||||
# skip multitensor because it's slow
|
||||
run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
|
||||
@@ -811,8 +809,8 @@ jobs:
|
||||
run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'gpu' && 'GPU=1' }}" >> $GITHUB_ENV
|
||||
- name: Check Device.DEFAULT and print some source
|
||||
run: |
|
||||
python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
|
||||
DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
|
||||
DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
|
||||
- name: Run pytest (not cuda)
|
||||
run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
|
||||
- name: Run TRANSCENDENTAL math
|
||||
@@ -853,11 +851,11 @@ jobs:
|
||||
- name: Test tensor core ops (real)
|
||||
run: METAL=1 DEBUG=3 python test/test_ops.py TestOps.test_big_gemm
|
||||
- name: Test LLaMA compile speed
|
||||
run: METAL=1 python test/external/external_test_speed_llama.py
|
||||
run: PYTHONPATH="." METAL=1 python test/external/external_test_speed_llama.py
|
||||
- name: Test Beam Search
|
||||
run: METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
run: PYTHONPATH="." METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
|
||||
#- name: Fuzz Test linearizer
|
||||
# run: METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
|
||||
# run: PYTHONPATH="." METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
|
||||
- name: Run TRANSCENDENTAL math
|
||||
run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
|
||||
- name: Run pytest (amd)
|
||||
@@ -920,7 +918,7 @@ jobs:
|
||||
# cp $GITHUB_WORKSPACE/test/web/test_viz.js .
|
||||
# node test_viz.js
|
||||
- name: Test ONNX Runner (WEBGPU)
|
||||
run: WEBGPU=1 python3 test/external/external_test_onnx_runner.py
|
||||
run: WEBGPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
|
||||
|
||||
osxremote:
|
||||
name: MacOS (remote metal)
|
||||
@@ -952,6 +950,7 @@ jobs:
|
||||
timeout-minutes: 20
|
||||
env:
|
||||
REMOTE: 1
|
||||
PYTHONPATH: ${{ github.workspace }}
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v4
|
||||
@@ -1072,7 +1071,7 @@ jobs:
|
||||
run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
|
||||
- name: Run unit tests
|
||||
if: matrix.backend=='llvm'
|
||||
run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --durations=20
|
||||
run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py
|
||||
- name: Run pytest (${{ matrix.backend }})
|
||||
shell: bash
|
||||
run: |
|
||||
|
||||
@@ -22,6 +22,12 @@ Group UOps into kernels.
|
||||
|
||||
Transforms the ast into an optimized ast. This is where BEAM search and heuristics live.
|
||||
|
||||
::: tinygrad.codegen.opt.get_optimized_ast
|
||||
options:
|
||||
members: false
|
||||
show_labels: false
|
||||
show_source: false
|
||||
|
||||
---
|
||||
|
||||
## tinygrad/codegen
|
||||
|
||||
@@ -4,7 +4,7 @@ import multiprocessing
|
||||
|
||||
from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
|
||||
from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW, Profiling
|
||||
from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
|
||||
from tinygrad.nn.state import get_parameters, get_state_dict, safe_load, safe_save
|
||||
from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW
|
||||
|
||||
from extra.lr_scheduler import LRSchedulerGroup
|
||||
@@ -1356,15 +1356,6 @@ def train_llama3():
|
||||
b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
|
||||
scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
|
||||
|
||||
if resume_ckpt := getenv("RESUME_CKPT"):
|
||||
fn = f"./ckpts/llama3_{resume_ckpt}.safe"
|
||||
print(f"loading initial checkpoint from {fn}")
|
||||
load_state_dict(model, safe_load(fn), realize=False)
|
||||
|
||||
fn = f"./ckpts/llama3_{resume_ckpt}_optim.safe"
|
||||
print(f"loading optim checkpoint from {fn}")
|
||||
load_state_dict(scheduler, safe_load(fn), realize=False)
|
||||
|
||||
@TinyJit
|
||||
@Tensor.train()
|
||||
def train_step(model, tokens:Tensor, grad_acc:int):
|
||||
@@ -1440,30 +1431,26 @@ def train_llama3():
|
||||
return batch_load_llama3(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
|
||||
|
||||
iter = get_train_iter()
|
||||
i, sequences_seen = resume_ckpt, 0
|
||||
i, sequences_seen = 0, 0
|
||||
for tokens in tqdm(iter, total=SAMPLES//GBS):
|
||||
t = time.perf_counter()
|
||||
GlobalCounters.reset()
|
||||
loss, lr = train_step(model, tokens, grad_acc)
|
||||
loss = loss.float().item()
|
||||
|
||||
i += 1
|
||||
sequences_seen += tokens.shape[0]
|
||||
|
||||
tqdm.write(f"{loss:.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
|
||||
if (fname:=getenv("LOSS_FILE", "")):
|
||||
with open(fname, "a") as f:
|
||||
f.write(f"{i} {loss:.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
|
||||
|
||||
if (ckpt_freq := getenv("CKPT")) and (i % ckpt_freq == 0 and (i != 1 or ckpt_freq == 1)):
|
||||
if getenv("CKPT") and (i % 200 == 0 or i == 10):
|
||||
tqdm.write("saving checkpoint")
|
||||
if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
|
||||
fn = f"{ckpt_dir}/llama3_{i}.safe"
|
||||
safe_save(get_state_dict(model), fn)
|
||||
|
||||
tqdm.write("saving optim checkpoint")
|
||||
fn = f"{ckpt_dir}/llama3_{i}_optim.safe"
|
||||
safe_save(get_state_dict(scheduler), fn)
|
||||
i += 1
|
||||
sequences_seen += tokens.shape[0]
|
||||
|
||||
if sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1):
|
||||
tqdm.write(f"evaluating after {sequences_seen} sequences")
|
||||
|
||||
@@ -29,10 +29,10 @@ def uops_to_rdna(function_name:str, uops:UOpGraph) -> str:
|
||||
r: Dict[UOp, str] = {}
|
||||
for u in uops:
|
||||
if u.uop == UOps.SPECIAL:
|
||||
if u.arg.startswith("lidx"):
|
||||
r[u] = f'v{u.src[0].arg}'
|
||||
elif u.arg.startswith("gidx"):
|
||||
r[u] = f's{2+u.src[0].arg}'
|
||||
if u.arg[1].startswith("lidx"):
|
||||
r[u] = f'v{u.arg[0]}'
|
||||
elif u.arg[1].startswith("gidx"):
|
||||
r[u] = f's{2+u.arg[0]}'
|
||||
else:
|
||||
raise NotImplementedError
|
||||
elif u.uop == UOps.CONST:
|
||||
|
||||
@@ -1,22 +0,0 @@
|
||||
from extra.optimization.helpers import load_worlds, ast_str_to_lin
|
||||
from tinygrad.codegen.lowerer import pm_lowerer, get_index
|
||||
from tinygrad.uop.ops import graph_rewrite
|
||||
from tinygrad.codegen.opt.postrange import Scheduler
|
||||
from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
|
||||
|
||||
if __name__ == "__main__":
|
||||
ast_strs = load_worlds()
|
||||
for i, ast_str in enumerate(ast_strs):
|
||||
lin = ast_str_to_lin(ast_str)
|
||||
opt1 = hand_coded_optimizations(lin)
|
||||
|
||||
lowered = graph_rewrite(lin.ast, pm_lowerer, ctx=get_index(lin.ast), bottom_up=True)
|
||||
sch = Scheduler(lowered, lin.opts)
|
||||
opt2 = hand_coded_optimizations(sch)
|
||||
|
||||
if opt1 != opt2:
|
||||
print("*******")
|
||||
print("Kernel: ", opt1)
|
||||
print("Scheduler: ", opt2)
|
||||
else:
|
||||
print("******* MATCH")
|
||||
-56
@@ -1,56 +0,0 @@
|
||||
# ruff: noqa: E501
|
||||
from tinygrad import dtypes
|
||||
from tinygrad.helpers import Timing, getenv
|
||||
from tinygrad.codegen.opt.kernel import Opt, OptOps
|
||||
from tinygrad.engine.realize import get_program, CompiledRunner
|
||||
from tinygrad.uop.ops import UOp, Ops, AxisType
|
||||
|
||||
if __name__ == "__main__":
|
||||
if getenv("TC", 0) == 0:
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1179648), arg=0, src=())
|
||||
c1 = UOp.range(UOp.const(dtypes.int, 512), 0, AxisType.GLOBAL)
|
||||
c2 = UOp.range(UOp.const(dtypes.int, 64), 1, AxisType.GLOBAL)
|
||||
c3 = UOp.range(UOp.const(dtypes.int, 6), 2, AxisType.GLOBAL)
|
||||
c4 = UOp.range(UOp.const(dtypes.int, 6), 3, AxisType.GLOBAL)
|
||||
c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(2097152), arg=1, src=())
|
||||
c6 = UOp.range(UOp.const(dtypes.int, 64), 1004, AxisType.REDUCE)
|
||||
c7 = UOp.range(UOp.const(dtypes.int, 3), 1005, AxisType.REDUCE)
|
||||
c8 = UOp.range(UOp.const(dtypes.int, 3), 1006, AxisType.REDUCE)
|
||||
c9 = c5.index(((((((c1*UOp.const(dtypes.int, 4096))+(c3*UOp.const(dtypes.int, 8)))+c4)+(c6*UOp.const(dtypes.int, 64)))+(c7*UOp.const(dtypes.int, 8)))+c8), UOp.const(dtypes.bool, True)).load()
|
||||
c10 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(36864), arg=2, src=())
|
||||
c11 = c10.index(((((c2*UOp.const(dtypes.int, 576))+(c6*UOp.const(dtypes.int, 9)))+(c7*UOp.const(dtypes.int, 3)))+c8), UOp.const(dtypes.bool, True)).load()
|
||||
c12 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=3, src=())
|
||||
c13 = c12.index(c2, UOp.const(dtypes.bool, True)).load()
|
||||
c14 = ((c9*c11).reduce(c6, c7, c8, arg=Ops.ADD)+c13)
|
||||
c15 = c0.index(((((c1*UOp.const(dtypes.int, 2304))+(c2*UOp.const(dtypes.int, 36)))+(c3*UOp.const(dtypes.int, 6)))+c4), UOp.const(dtypes.bool, True)).store(c14, c1, c2, c3, c4)
|
||||
ast = c15.sink()
|
||||
|
||||
# this does have tons of locals
|
||||
opts = [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=3, arg=0),
|
||||
Opt(op=OptOps.LOCAL, axis=0, arg=16), Opt(op=OptOps.UPCAST, axis=3, arg=2),
|
||||
Opt(op=OptOps.GROUPTOP, axis=0, arg=16)]
|
||||
else:
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(10616832), arg=0, src=())
|
||||
c1 = UOp.range(UOp.const(dtypes.int, 512), 0, AxisType.GLOBAL)
|
||||
c2 = UOp.range(UOp.const(dtypes.int, 64), 1, AxisType.GLOBAL)
|
||||
c3 = UOp.range(UOp.const(dtypes.int, 36), 2, AxisType.GLOBAL)
|
||||
c4 = UOp.range(UOp.const(dtypes.int, 9), 3, AxisType.GLOBAL)
|
||||
c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(36864), arg=1, src=())
|
||||
c6 = UOp.range(UOp.const(dtypes.int, 64), 1004, AxisType.REDUCE)
|
||||
c7 = c5.index((((c2*UOp.const(dtypes.int, 9))+c4)+(c6*UOp.const(dtypes.int, 576))), UOp.const(dtypes.bool, True)).load()
|
||||
c8 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1179648), arg=2, src=())
|
||||
c9 = c8.index((((c1*UOp.const(dtypes.int, 2304))+c3)+(c6*UOp.const(dtypes.int, 36))), UOp.const(dtypes.bool, True)).load()
|
||||
c10 = (c7*c9).reduce(c6, arg=Ops.ADD)
|
||||
c11 = c0.index(((((c1*UOp.const(dtypes.int, 20736))+(c2*UOp.const(dtypes.int, 324)))+(c3*UOp.const(dtypes.int, 9)))+c4), UOp.const(dtypes.bool, True)).store(c10, c1, c2, c3, c4)
|
||||
ast = c11.sink()
|
||||
|
||||
opts = [Opt(op=OptOps.TC, axis=0, arg=(0, 0, 1)), Opt(op=OptOps.UPCAST, axis=2, arg=4),
|
||||
Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=0)]
|
||||
|
||||
prg = get_program(ast, opts=opts)
|
||||
print(prg.src)
|
||||
for i in range(10):
|
||||
with Timing(f"try {i}: "):
|
||||
# NOTE: this doesn't even run the kernel
|
||||
try: CompiledRunner(prg)
|
||||
except RuntimeError: pass
|
||||
Vendored
+41
@@ -0,0 +1,41 @@
|
||||
from tinygrad import Device
|
||||
from tinygrad.helpers import getenv, DEBUG, BEAM
|
||||
from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
|
||||
from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
|
||||
from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
|
||||
|
||||
if __name__ == "__main__":
|
||||
filter_reduce = bool(getenv("FILTER_REDUCE"))
|
||||
ast_strs = load_worlds(filter_reduce=filter_reduce, filter_novariable=True)
|
||||
dev = Device[Device.DEFAULT]
|
||||
|
||||
test_n = getenv("TEST_N", 10)
|
||||
single = getenv("NUM", -1)
|
||||
if single != -1: ast_strs = ast_strs[single:single+1]
|
||||
|
||||
beam_won, tested = 0, 0
|
||||
|
||||
for num, ast in enumerate(ast_strs[:test_n]):
|
||||
def new_lin(): return ast_str_to_lin(ast, opts=dev.renderer)
|
||||
|
||||
k = new_lin()
|
||||
|
||||
if not (used_tensor_cores:=k.apply_tensor_cores(getenv("TC", 1))): k.apply_opts(hand_coded_optimizations(k))
|
||||
|
||||
assert BEAM > 0
|
||||
|
||||
lins = [(("tc" if used_tensor_cores else "hc"), k)]
|
||||
if used_tensor_cores:
|
||||
lins.append(("hc", new_lin()))
|
||||
lins[-1][1].apply_opts(hand_coded_optimizations(lins[-1][1]))
|
||||
kb = new_lin()
|
||||
test_rawbuffers = bufs_from_lin(kb) # allocate scratch buffers for optimization
|
||||
lins.append((f"beam{BEAM.value}", beam_search(kb, test_rawbuffers, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))))
|
||||
timed = sorted([(nm, tk, time_linearizer(tk, test_rawbuffers, allow_test_size=False, clear_l2=True)) for nm, tk in lins], key=lambda x: x[2])
|
||||
if DEBUG >= 1: print(" < ".join(f"{nm:6s} : {lin.colored_shape(30, dense=True)} : {tm*1e6:8.2f} us" for nm, lin, tm in timed))
|
||||
|
||||
tested += 1
|
||||
if timed[0][0].startswith("beam"):
|
||||
beam_won += 1
|
||||
|
||||
print(f"{beam_won=} / {tested=} = {beam_won/tested:.3f}")
|
||||
@@ -1,229 +0,0 @@
|
||||
import unittest
|
||||
from tinygrad import Device, Tensor, dtypes
|
||||
from tinygrad.uop.ops import UOp, Ops
|
||||
from tinygrad.codegen.opt import Opt, OptOps
|
||||
from tinygrad.shape.shapetracker import ShapeTracker, View
|
||||
from tinygrad.engine.realize import get_program
|
||||
from tinygrad.helpers import AMX
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need backends that support float4")
|
||||
class TestFloat4(unittest.TestCase):
|
||||
@staticmethod
|
||||
def count_float4(uops: list[UOp], n=4):
|
||||
return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float.vec(n)]),
|
||||
len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.float.vec(n)]))
|
||||
@staticmethod
|
||||
def count_half4(uops: list[UOp]):
|
||||
return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half.vec(4)]),
|
||||
len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.half.vec(4)]))
|
||||
|
||||
def test_float4_basic(self):
|
||||
a = Tensor.empty(2, 8).realize()
|
||||
b = Tensor.empty(2, 8).realize()
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
realized_ast = s.ast
|
||||
opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
|
||||
program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts_to_apply)
|
||||
|
||||
assert TestFloat4.count_float4(program.uops) == (2, 1)
|
||||
|
||||
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim(self):
|
||||
a = Tensor.empty(2, 8).realize()
|
||||
b = Tensor.empty(2, 8).realize()
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]).uops
|
||||
assert TestFloat4.count_float4(uops) == (4, 2)
|
||||
|
||||
@unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim_amx(self):
|
||||
def kernel_for_shape(size, shift):
|
||||
a = Tensor.empty(2, size).realize()
|
||||
b = Tensor.empty(2, size).realize()
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=shift)]).uops
|
||||
|
||||
sizes = [12, 8, 16]
|
||||
shifts = [3, 2, 4]
|
||||
expected_upcast_size = [4, 8, 16]
|
||||
expected_output = [(6,3), (2,1), (2,1)]
|
||||
|
||||
for i in range(len(sizes)):
|
||||
assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
|
||||
|
||||
def test_float4_unaligned_load(self):
|
||||
a = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
b = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
realized_ast = s.ast
|
||||
opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
|
||||
program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts_to_apply)
|
||||
|
||||
assert TestFloat4.count_float4(program.uops) == (0, 1)
|
||||
|
||||
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim_unaligned_load(self):
|
||||
a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
|
||||
b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (0, 2)
|
||||
|
||||
@unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim_unaligned_load_amx(self):
|
||||
def kernel_for_shape(size, shift):
|
||||
a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
|
||||
b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=shift)]).uops
|
||||
|
||||
sizes = [13, 9, 17]
|
||||
shifts = [3, 2, 4]
|
||||
expected_upcast_size = [4, 8, 16]
|
||||
expected_output = [(0,3), (0,1), (0,1)]
|
||||
|
||||
for i in range(len(sizes)):
|
||||
assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
|
||||
|
||||
def test_float4_sometimes_unaligned(self):
|
||||
a = Tensor.empty(1, 1, 8).realize()
|
||||
b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
|
||||
c = a.conv2d(b)
|
||||
# only the first and last conv dot products are aligned in a, and b is never aligned, so no
|
||||
# float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UNROLL, axis=0, arg=4)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (0, 0)
|
||||
|
||||
def test_float4_multidim_sometimes_unaligned(self):
|
||||
a = Tensor.empty(1, 1, 7).realize()
|
||||
b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
|
||||
c = a.conv2d(b)
|
||||
# the first conv dot product is aligned in a. If we upcast the output and reduce
|
||||
# dimension, then we could do float4 for only that one set of loads, but we currently
|
||||
# don't.
|
||||
# UPDATE: now we do this fusion
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) in {(0,1), (1,1)}
|
||||
|
||||
def test_float4_expand(self):
|
||||
a = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
|
||||
c = a + b
|
||||
|
||||
# we will upcast the top axis of sz 4. they should not be coalesced into float4,
|
||||
# since the top axis is not contiguous.
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (0, 1)
|
||||
|
||||
def test_float4_heterogeneous(self):
|
||||
a = Tensor.empty(8).realize()
|
||||
b = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
c = a + b
|
||||
|
||||
# should float4 b but not a
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (1, 1)
|
||||
|
||||
def test_half4_load_unrolled(self):
|
||||
# from llama 7B shard 4 gpus
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(96000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1), strides=(0, 32000, 1, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(96000), arg=0, src=()),)),
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
|
||||
UOp(Ops.CAST, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.MUL, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(9216), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 4096, 0, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(9216), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(32768000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 0, 1024, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(32768000), arg=2, src=()),)),)),)),)),)),)),))
|
||||
|
||||
# TODO: fix this, expected might change but should be positive
|
||||
for expected, opts in [
|
||||
((7, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=3), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
|
||||
((5, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
|
||||
((2, 0), [Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
|
||||
]:
|
||||
program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
|
||||
|
||||
count = TestFloat4.count_half4(program.uops)
|
||||
assert count == expected, f"{count=}, {expected=}"
|
||||
|
||||
@unittest.skip("this doesn't happen anymore")
|
||||
def test_float4_acc(self):
|
||||
# from float32 stable diffusion red tinybox
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(33554432), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 262144, 512, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),)),
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
|
||||
UOp(Ops.MUL, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(67108864), arg=ShapeTracker(views=(View(shape=(1, 1, 1, 256, 4, 514, 4, 514), strides=(0, 0, 0, 262144, 0, 512, 0, 1), offset=-513, mask=((0, 1), (0, 1), (0, 1), (0, 256), (0, 4), (1, 513), (0, 4), (1, 513)), contiguous=False), View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 0, 2056, 1, 4227136, 1058840, 515), offset=0, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(67108864), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(294912), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 2304, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(294912), arg=2, src=()),)),)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(128), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), arg=3, src=()),)),)),)),)),))
|
||||
|
||||
for expected, opts in [
|
||||
(1, [Opt(op=OptOps.UPCAST, axis=2, arg=4)]),
|
||||
(4, [Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]),
|
||||
]:
|
||||
program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
|
||||
count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(4)])
|
||||
assert count == expected, f"{count=}, {expected=}"
|
||||
|
||||
@unittest.skip("this doesn't happen anymore")
|
||||
def test_float2_acc(self):
|
||||
# from resnet
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(212926464), arg=ShapeTracker(views=(View(shape=(1, 256, 1, 64, 1, 114, 1, 114), strides=(0, 831744, 0, 12996, 0, 114, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(212926464), arg=0, src=()),)),
|
||||
UOp(Ops.CAST, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4, 6)), src=(
|
||||
UOp(Ops.CAST, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(462422016), arg=ShapeTracker(views=(View(shape=(256, 64, 3, 56, 2, 3, 56, 2), strides=(1806336, 28224, 3, 504, 0, 1, 9, 0), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 56), (0, 1), (0, 3), (0, 56), (0, 1)), contiguous=False), View(shape=(256, 64, 3, 115, 3, 115), strides=(7225344, 112896, 37632, 336, 112, 1), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 112), (0, 3), (0, 112)), contiguous=False), View(shape=(256, 64, 456, 456), strides=(7617600, 119025, 345, 1), offset=0, mask=((0, 256), (0, 64), (0, 345), (0, 345)), contiguous=False), View(shape=(1, 256, 1, 64, 4, 114, 4, 114), strides=(0, 13307904, 0, 207936, 51984, 456, 114, 1), offset=0, mask=None, contiguous=True))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(462422016), arg=1, src=()),)),)),)),)),)),)),))
|
||||
for expected, opts in [
|
||||
(16, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=2, arg=3), Opt(op=OptOps.UPCAST, axis=3, arg=4)]), # noqa: E501
|
||||
(4, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2)]),
|
||||
]:
|
||||
program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
|
||||
count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(2)])
|
||||
assert count == expected, f"{count=}, {expected=}"
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -1,326 +0,0 @@
|
||||
import unittest
|
||||
from tinygrad import Device, Tensor, dtypes
|
||||
from tinygrad.helpers import CI
|
||||
from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
|
||||
|
||||
# TODO: write a clean version of this
|
||||
from test.test_linearizer import helper_linearizer_opt
|
||||
|
||||
class TestKernelOpts(unittest.TestCase):
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_local_and_grouped_reduce(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1882)
|
||||
a = Tensor.rand(4, 4, N, N)
|
||||
b = Tensor.rand(4, 4, N)
|
||||
r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.LOCAL, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 8)],
|
||||
[Opt(OptOps.LOCAL, 0, 16)], # Checking how it works with locals
|
||||
[Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with grouped reduce
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.GROUPTOP, 0, 16)],
|
||||
[Opt(OptOps.LOCAL, 0, 32), Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
# Checking how it works with locals + grouped reduce
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 64)],
|
||||
# Checking how it works with locals + grouped reduce + upcasts
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.UPCAST, 0, 8), Opt(OptOps.UNROLL, 1, 4)],
|
||||
# many local + many group
|
||||
[Opt(OptOps.GROUP, 0, 2)] * 4,
|
||||
[Opt(OptOps.LOCAL, 0, 2)] * 4,
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)] * 4,
|
||||
])
|
||||
|
||||
def test_upcasts(self):
|
||||
N = 16
|
||||
Tensor.manual_seed(1772)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
r = (a+b).sqrt() * ((a+1).exp())
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UPCAST, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 8)], # Checking how it works with upcasts
|
||||
])
|
||||
|
||||
def test_full_upcast(self):
|
||||
Tensor.manual_seed(1772)
|
||||
a = Tensor.rand(4)
|
||||
b = Tensor.rand(4)
|
||||
r = (a+b).sqrt() * ((a+1).exp())
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UPCAST, 0, 4)], # Checking how it works with upcasts
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_matmul(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
r = a@b
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UPCAST, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # Checking how it works with upcasts
|
||||
[Opt(OptOps.LOCAL, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 1, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.LOCAL, 1, 8)], # Checking how it works with locals
|
||||
[Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 32), Opt(OptOps.UNROLL, 0, 4)], # Checking how it works with grouped_reduce
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 4)], # Checking how it works with local+grouped_reduce
|
||||
# Checking all together
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4),
|
||||
Opt(OptOps.UPCAST, 1, 2)],
|
||||
# Full global upcast + local
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 8)],
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_double_reduce(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(8, N, 8, N)
|
||||
r = a.sum(axis=(1,3))
|
||||
helper_linearizer_opt(r, [
|
||||
# openCL / GPU=1 is 256 max threads
|
||||
[Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce.
|
||||
[Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 64)], # Checking how it works with 2 grouped_reduces.
|
||||
[Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2), Opt(OptOps.UNROLL, 0, 4)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 2, 4)], # Checking how it works with 2 grouped_reduces + upcasts.
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4)],
|
||||
# Checking how it works with 2 grouped_reduces + upcasts + locals.
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 1, 4)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
|
||||
Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals.
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
|
||||
Opt(OptOps.UPCAST, 0, 2)], # No globals
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
|
||||
"test requires tensor cores with accumulation in half") # testing with half suffices.
|
||||
def test_tensor_core_opts(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
|
||||
r = a.matmul(b, dtype=dtypes.half)
|
||||
atol, rtol = 0.25, 0.01
|
||||
helper_linearizer_opt(r, [
|
||||
[],
|
||||
[Opt(OptOps.UPCAST, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 1, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # check upcasts
|
||||
[Opt(OptOps.UNROLL, 0, 2)], # check unroll
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2)], # check combo of unroll and local
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4)], # check permutations
|
||||
[Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4)],
|
||||
[Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)],
|
||||
], apply_tc=True, atol=atol, rtol=rtol)
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
|
||||
"test requires tensor cores with accumulation in half") # testing with half suffices.
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
def test_tensor_core_opts_locals(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
|
||||
r = a.matmul(b, dtype=dtypes.half)
|
||||
atol, rtol = 0.25, 0.01
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UNROLL, 0, 0)], # check full unroll of reduce with locals
|
||||
[Opt(OptOps.LOCAL, 0, 4)], # check local
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.LOCAL, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
|
||||
], apply_tc=True, atol=atol, rtol=rtol)
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared memory")
|
||||
@unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
|
||||
"test requires tensor cores with accumulation in half") # testing with half suffices.
|
||||
# NOTE: the METAL test is broken, likely due to a compiler bug. passes on CI with -O0 and with default opt level locally on M3
|
||||
@unittest.skipIf(Device.DEFAULT == "METAL", "broken for METAL")
|
||||
@unittest.skip("feature was removed")
|
||||
def test_tensor_core_opts_group(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
|
||||
r = a.matmul(b, dtype=dtypes.half)
|
||||
atol, rtol = 0.25, 0.01
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 2)],
|
||||
], apply_tc=True, atol=atol, rtol=rtol)
|
||||
|
||||
def test_padto_matmul(self):
|
||||
if (CI and Device.DEFAULT in ["AMD", "NV", "CUDA"]):
|
||||
self.skipTest("super slow on CUDA and AMD because of the big grid dims")
|
||||
N = 17 * 17
|
||||
Tensor.manual_seed(289)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
helper_linearizer_opt(a@b, [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 1, 32)],
|
||||
[Opt(OptOps.PADTO, 2, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.PADTO, 2, 32)],
|
||||
# can optimize further post PADTO
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 2),],
|
||||
])
|
||||
|
||||
def test_padto_upcasted_not_ok(self):
|
||||
N = 4
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
helper_linearizer_opt(a@b, [
|
||||
[Opt(OptOps.UPCAST, 0, 0)],
|
||||
[Opt(OptOps.UPCAST, 1, 0)],
|
||||
[Opt(OptOps.UNROLL, 0, 0)],
|
||||
[Opt(OptOps.PADTO, 0, 8)],
|
||||
[Opt(OptOps.PADTO, 1, 8)],
|
||||
[Opt(OptOps.PADTO, 2, 8)],
|
||||
])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 0, 0), Opt(OptOps.PADTO, 1, 8)]])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 1, 0), Opt(OptOps.PADTO, 1, 8)]])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a@b, [[Opt(OptOps.UNROLL, 0, 0), Opt(OptOps.PADTO, 2, 8)]])
|
||||
|
||||
def test_padto_sum_ok(self):
|
||||
N = 18 * 18
|
||||
# NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
|
||||
a = Tensor.rand(N, N).realize().shrink(((0, 17), (0, 17))) * 100
|
||||
b = (Tensor.rand(N, N) < 0.5).realize().shrink(((0, 17), (0, 17)))
|
||||
|
||||
helper_linearizer_opt(a.sum(0), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
helper_linearizer_opt(a.sum(1), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
# can pad sum reduce axis if there's no unsafe ops prior to sum
|
||||
for axis in (0, 1):
|
||||
helper_linearizer_opt(a.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(a.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
# TODO: why?
|
||||
if Device.DEFAULT != "WEBGPU":
|
||||
helper_linearizer_opt(b.sum(0, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(1, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
|
||||
# having unsafe ops after sum is fine
|
||||
helper_linearizer_opt(a.sum().exp(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
def test_padto_sum_not_ok(self):
|
||||
N = 18 * 18
|
||||
# NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
|
||||
a = Tensor.rand(N, N).shrink(((0, 17), (0, 17))).exp()
|
||||
# exp is not safe to pad
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.exp().sum(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.exp().sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
b = a < 1
|
||||
# lt is not safe to pad
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
def test_padto_max(self):
|
||||
N = 18 * 18
|
||||
# NOTE: this setup prevents 17 * 17 contiguous merged into one axis
|
||||
a = -Tensor.rand(N, N).shrink(((0, 17), (0, 17))) * 100
|
||||
|
||||
helper_linearizer_opt(a.max(0), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
helper_linearizer_opt(a.max(1), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
# cannot pad max kernel on reduce
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.max(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.max(0), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
def test_padto_where(self):
|
||||
Tensor.manual_seed(0)
|
||||
N = 17 * 17
|
||||
a = (Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1).where(1, 0)
|
||||
helper_linearizer_opt(a.max(0), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
def test_padto_where_multioutput(self):
|
||||
Tensor.manual_seed(0)
|
||||
N = 17 * 17
|
||||
r = Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1
|
||||
a0 = r.where(1, 0)
|
||||
a1 = r.where(2, 0)
|
||||
helper_linearizer_opt([a0.max(0), a1.max(0)], [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_color_shapes_with_local(self):
|
||||
N = 32
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
r = a@b
|
||||
opts_shapes = [
|
||||
([Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("red",32)]),
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",2),("red",16)]),
|
||||
# check to ensure local_dims are stable for full UNROLL of the first reduce
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
([Opt(OptOps.UNROLL, 0, 0),Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
# check behavior for full UNROLL on an existing GROUP
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",16),("magenta",2)]),
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
([Opt(OptOps.GROUP, 0, 0),Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
([Opt(OptOps.GROUP, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",32),("blue",32),("red",16),("magenta",2)]),
|
||||
]
|
||||
helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes])
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -102,6 +102,7 @@ class TestIndexing(unittest.TestCase):
|
||||
run_schedule(sched)
|
||||
self.assertEqual(out.item(), 1337)
|
||||
|
||||
@unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
|
||||
def test_manual_index(self):
|
||||
dataset = Tensor.rand(DSET, DDIM).realize()
|
||||
idxs = Tensor([0,3,5,6]).realize()
|
||||
@@ -171,6 +172,7 @@ class TestIndexing(unittest.TestCase):
|
||||
X = dataset[idxs]
|
||||
np.testing.assert_equal(X.numpy(), 0)
|
||||
|
||||
@unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
|
||||
def test_index_mnist(self, noopt=1, op_limit=512*784*13, split_reduceop=0):
|
||||
# WEBGPU generates more ops due to bitpacking of < 4-byte dtypes
|
||||
if Device.DEFAULT == "WEBGPU": op_limit *= 15
|
||||
@@ -189,6 +191,7 @@ class TestIndexing(unittest.TestCase):
|
||||
def test_index_mnist_split(self): self.test_index_mnist(1, split_reduceop=1)
|
||||
def test_index_mnist_opt_split(self): self.test_index_mnist(0, split_reduceop=1)
|
||||
|
||||
@unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
|
||||
def test_llama_embedding(self, noopt=1, op_limit=65536):
|
||||
# llama3 is 128256
|
||||
vocab_size, embed_size = (10, 3) if CI else (32000, 4096)
|
||||
|
||||
@@ -73,7 +73,6 @@ def universal_test_unary(a, dtype, op):
|
||||
ta = Tensor([a], dtype=dtype)
|
||||
# TODO: cos does not match for large input
|
||||
if op[0] == Tensor.cos and abs(a) > 100: return
|
||||
if op[0] == Tensor.log and a <= 0: return
|
||||
out: Tensor = op[0](ta)
|
||||
tensor_value = out.numpy()
|
||||
numpy_value = op[1](ta.numpy())
|
||||
|
||||
+572
-14
@@ -11,7 +11,7 @@ from tinygrad.shape.view import View
|
||||
from tinygrad.tensor import Tensor, _to_np_dtype
|
||||
from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program
|
||||
from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
|
||||
from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM, TC_SELECT, TC_OPT
|
||||
from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM
|
||||
from tinygrad.dtype import DType, dtypes, PtrDType, AddrSpace
|
||||
from tinygrad.codegen import apply_rewrites, rewrites_for_views
|
||||
|
||||
@@ -337,7 +337,7 @@ class TestLinearizer(unittest.TestCase):
|
||||
else:
|
||||
assert "__WMMA_" in prg.src
|
||||
|
||||
@unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "broken for AMD")
|
||||
@unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and getenv("EMULATE_AMD")), "broken for AMD")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
def test_tensor_cores_padded(self):
|
||||
for tc in Device[Device.DEFAULT].renderer.tensor_cores:
|
||||
@@ -346,7 +346,7 @@ class TestLinearizer(unittest.TestCase):
|
||||
|
||||
# AMD compiler bug: AMD miscompiles non-zero padded tc kernels with -O3, producing wrong results, nans or hang (see #9606)
|
||||
# Internal bug: zero-stride dimensions combined with a mask may produce wrong index/valid for pad == 1 on AMD
|
||||
@unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "test for AMD's tc")
|
||||
@unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and getenv("EMULATE_AMD")), "test for AMD's tc")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skip("warp elements not duplicated properly across lanes")
|
||||
def test_tensor_cores_padded_amd(self):
|
||||
@@ -380,7 +380,6 @@ class TestLinearizer(unittest.TestCase):
|
||||
def test_tensor_cores_multi_reduce(self):
|
||||
for tc in Device[Device.DEFAULT].renderer.tensor_cores:
|
||||
if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
|
||||
if tc.dtype_in is dtypes.bfloat16: continue # <-- broken with numpy
|
||||
# this will be a M=G16, N=G32, M=G16, M=G16, K=R16, K=R16, K=R16 with 9 choices of TC MNK axes
|
||||
golden_result = None
|
||||
for axis in range(9):
|
||||
@@ -473,8 +472,8 @@ class TestLinearizer(unittest.TestCase):
|
||||
def _assert_grouped_dims(prefix, dims, max_sizes, reverse_dims, expected_sizes, assert_same_length = True):
|
||||
idxs = get_grouped_dims(prefix, dims, max_sizes, reverse_dims)
|
||||
loop_idxs = dedup(flatten([[y for y in x.toposort() if y.op is Ops.SPECIAL] for x in idxs]))
|
||||
loop_idxs = sorted(loop_idxs, key=lambda uop: uop.arg)
|
||||
sizes = [x.src[0].arg for x in loop_idxs]
|
||||
loop_idxs = sorted(loop_idxs, key=lambda uop: uop.arg[0])
|
||||
sizes = [x.arg[1] for x in loop_idxs]
|
||||
assert len(idxs) == len(dims), f"expected idxs to have same length as dims {len(dims)}, got {len(idxs)}"
|
||||
if assert_same_length:
|
||||
assert len(loop_idxs) == min(len(sizes), len(dims)), f"expected idxs to have length {min(len(sizes), len(dims))}, got {len(loop_idxs)}"
|
||||
@@ -547,10 +546,10 @@ class TestLinearizer(unittest.TestCase):
|
||||
k = helper_linearizer_opt(t+1)[0]
|
||||
uops = get_program(k.ast, k.opts, k.applied_opts).uops
|
||||
idxs = dedup([uop for uop in uops if uop.op is Ops.SPECIAL])
|
||||
idxs = sorted(idxs, key=lambda uop: uop.arg)
|
||||
assert (idxs[0].arg, idxs[0].src[0].arg) == ('gidx0', 6), idxs[0]
|
||||
assert (idxs[1].arg, idxs[1].src[0].arg) == ('gidx1', 5), idxs[1].arg
|
||||
assert (idxs[2].arg, idxs[2].src[0].arg) == ('gidx2', 4), idxs[2].arg
|
||||
idxs = sorted(idxs, key=lambda uop: uop.arg[0])
|
||||
assert idxs[0].arg == ('gidx0', 6), idxs[0].arg
|
||||
assert idxs[1].arg == ('gidx1', 5), idxs[1].arg
|
||||
assert idxs[2].arg == ('gidx2', 4), idxs[2].arg
|
||||
|
||||
def test_sum_collapse(self):
|
||||
t = Tensor([2]).reshape(1, 1).expand(256, 256).sum()
|
||||
@@ -616,8 +615,7 @@ class TestLinearizer(unittest.TestCase):
|
||||
"""
|
||||
x, y = Tensor.randn(64,64), Tensor.randn(64,64)
|
||||
out = x.matmul(y)
|
||||
with Context(TC=0):
|
||||
k = helper_linearizer_opt(out)[-1]
|
||||
k = helper_linearizer_opt(out)[-1]
|
||||
uops = get_program(k.ast, k.opts, k.applied_opts).uops
|
||||
# check that the float4 cast collapses
|
||||
store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
|
||||
@@ -723,6 +721,230 @@ class TestLinearizer(unittest.TestCase):
|
||||
out = [u for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
|
||||
assert out.src[1].op is Ops.VECTORIZE and out.src[1].dtype.count != 1
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need backends that support float4")
|
||||
class TestFloat4(unittest.TestCase):
|
||||
@staticmethod
|
||||
def count_float4(uops: list[UOp], n=4):
|
||||
return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float.vec(n)]),
|
||||
len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.float.vec(n)]))
|
||||
@staticmethod
|
||||
def count_half4(uops: list[UOp]):
|
||||
return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half.vec(4)]),
|
||||
len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.half.vec(4)]))
|
||||
|
||||
def test_float4_basic(self):
|
||||
a = Tensor.empty(2, 8).realize()
|
||||
b = Tensor.empty(2, 8).realize()
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
realized_ast = s.ast
|
||||
opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
|
||||
realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
|
||||
program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
|
||||
|
||||
assert TestFloat4.count_float4(program.uops) == (2, 1)
|
||||
|
||||
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim(self):
|
||||
a = Tensor.empty(2, 8).realize()
|
||||
b = Tensor.empty(2, 8).realize()
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]).uops
|
||||
assert TestFloat4.count_float4(uops) == (4, 2)
|
||||
|
||||
@unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim_amx(self):
|
||||
def kernel_for_shape(size, shift):
|
||||
a = Tensor.empty(2, size).realize()
|
||||
b = Tensor.empty(2, size).realize()
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=shift)]).uops
|
||||
|
||||
sizes = [12, 8, 16]
|
||||
shifts = [3, 2, 4]
|
||||
expected_upcast_size = [4, 8, 16]
|
||||
expected_output = [(6,3), (2,1), (2,1)]
|
||||
|
||||
for i in range(len(sizes)):
|
||||
assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
|
||||
|
||||
def test_float4_unaligned_load(self):
|
||||
a = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
b = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
realized_ast = s.ast
|
||||
opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
|
||||
realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
|
||||
program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
|
||||
|
||||
assert TestFloat4.count_float4(program.uops) == (0, 1)
|
||||
|
||||
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim_unaligned_load(self):
|
||||
a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
|
||||
b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (0, 2)
|
||||
|
||||
@unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
|
||||
def test_float4_multidim_unaligned_load_amx(self):
|
||||
def kernel_for_shape(size, shift):
|
||||
a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
|
||||
b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
|
||||
c = a + b
|
||||
|
||||
s = c.schedule()[0]
|
||||
return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=shift)]).uops
|
||||
|
||||
sizes = [13, 9, 17]
|
||||
shifts = [3, 2, 4]
|
||||
expected_upcast_size = [4, 8, 16]
|
||||
expected_output = [(0,3), (0,1), (0,1)]
|
||||
|
||||
for i in range(len(sizes)):
|
||||
assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
|
||||
|
||||
def test_float4_sometimes_unaligned(self):
|
||||
a = Tensor.empty(1, 1, 8).realize()
|
||||
b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
|
||||
c = a.conv2d(b)
|
||||
# only the first and last conv dot products are aligned in a, and b is never aligned, so no
|
||||
# float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UNROLL, axis=0, arg=4)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (0, 0)
|
||||
|
||||
def test_float4_multidim_sometimes_unaligned(self):
|
||||
a = Tensor.empty(1, 1, 7).realize()
|
||||
b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
|
||||
c = a.conv2d(b)
|
||||
# the first conv dot product is aligned in a. If we upcast the output and reduce
|
||||
# dimension, then we could do float4 for only that one set of loads, but we currently
|
||||
# don't.
|
||||
# UPDATE: now we do this fusion
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) in {(0,1), (1,1)}
|
||||
|
||||
def test_float4_expand(self):
|
||||
a = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
|
||||
c = a + b
|
||||
|
||||
# we will upcast the top axis of sz 4. they should not be coalesced into float4,
|
||||
# since the top axis is not contiguous.
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (0, 1)
|
||||
|
||||
def test_float4_heterogeneous(self):
|
||||
a = Tensor.empty(8).realize()
|
||||
b = Tensor.empty(9).realize().shrink(((1, 9),))
|
||||
c = a + b
|
||||
|
||||
# should float4 b but not a
|
||||
|
||||
s = c.schedule()[0]
|
||||
uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
|
||||
|
||||
assert TestFloat4.count_float4(uops) == (1, 1)
|
||||
|
||||
def test_half4_load_unrolled(self):
|
||||
# from llama 7B shard 4 gpus
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(96000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1), strides=(0, 32000, 1, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(96000), arg=0, src=()),)),
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
|
||||
UOp(Ops.CAST, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.MUL, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(9216), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 4096, 0, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(9216), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(32768000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 0, 1024, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(32768000), arg=2, src=()),)),)),)),)),)),)),))
|
||||
|
||||
# TODO: fix this, expected might change but should be positive
|
||||
for expected, opts in [
|
||||
((7, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=3), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
|
||||
((5, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
|
||||
((2, 0), [Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
|
||||
]:
|
||||
ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
|
||||
program = get_program(ast, Device[Device.DEFAULT].renderer)
|
||||
|
||||
count = TestFloat4.count_half4(program.uops)
|
||||
assert count == expected, f"{count=}, {expected=}"
|
||||
|
||||
@unittest.skip("this doesn't happen anymore")
|
||||
def test_float4_acc(self):
|
||||
# from float32 stable diffusion red tinybox
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(33554432), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 262144, 512, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),)),
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
|
||||
UOp(Ops.MUL, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(67108864), arg=ShapeTracker(views=(View(shape=(1, 1, 1, 256, 4, 514, 4, 514), strides=(0, 0, 0, 262144, 0, 512, 0, 1), offset=-513, mask=((0, 1), (0, 1), (0, 1), (0, 256), (0, 4), (1, 513), (0, 4), (1, 513)), contiguous=False), View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 0, 2056, 1, 4227136, 1058840, 515), offset=0, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(67108864), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(294912), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 2304, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(294912), arg=2, src=()),)),)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(128), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), arg=3, src=()),)),)),)),)),))
|
||||
|
||||
for expected, opts in [
|
||||
(1, [Opt(op=OptOps.UPCAST, axis=2, arg=4)]),
|
||||
(4, [Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]),
|
||||
]:
|
||||
ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
|
||||
program = get_program(ast, Device[Device.DEFAULT].renderer)
|
||||
count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(4)])
|
||||
assert count == expected, f"{count=}, {expected=}"
|
||||
|
||||
@unittest.skip("this doesn't happen anymore")
|
||||
def test_float2_acc(self):
|
||||
# from resnet
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(212926464), arg=ShapeTracker(views=(View(shape=(1, 256, 1, 64, 1, 114, 1, 114), strides=(0, 831744, 0, 12996, 0, 114, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(212926464), arg=0, src=()),)),
|
||||
UOp(Ops.CAST, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4, 6)), src=(
|
||||
UOp(Ops.CAST, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(462422016), arg=ShapeTracker(views=(View(shape=(256, 64, 3, 56, 2, 3, 56, 2), strides=(1806336, 28224, 3, 504, 0, 1, 9, 0), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 56), (0, 1), (0, 3), (0, 56), (0, 1)), contiguous=False), View(shape=(256, 64, 3, 115, 3, 115), strides=(7225344, 112896, 37632, 336, 112, 1), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 112), (0, 3), (0, 112)), contiguous=False), View(shape=(256, 64, 456, 456), strides=(7617600, 119025, 345, 1), offset=0, mask=((0, 256), (0, 64), (0, 345), (0, 345)), contiguous=False), View(shape=(1, 256, 1, 64, 4, 114, 4, 114), strides=(0, 13307904, 0, 207936, 51984, 456, 114, 1), offset=0, mask=None, contiguous=True))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(462422016), arg=1, src=()),)),)),)),)),)),)),))
|
||||
for expected, opts in [
|
||||
(16, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=2, arg=3), Opt(op=OptOps.UPCAST, axis=3, arg=4)]), # noqa: E501
|
||||
(4, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2)]),
|
||||
]:
|
||||
ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
|
||||
program = get_program(ast, Device[Device.DEFAULT].renderer)
|
||||
count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(2)])
|
||||
assert count == expected, f"{count=}, {expected=}"
|
||||
|
||||
class TestHandCodedOpts(unittest.TestCase):
|
||||
def test_masked_upcast(self):
|
||||
layer_1 = Tensor.cat(*[Tensor.empty(5) for _ in range(4)])
|
||||
@@ -832,8 +1054,10 @@ def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[]
|
||||
def check_opt(opts, create_k, expected_color_size):
|
||||
k = create_k()
|
||||
lins.append(k)
|
||||
if apply_tc: k.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, 1)))
|
||||
k.apply_opts(opts)
|
||||
if apply_tc:
|
||||
assert k.apply_tensor_cores(1, extra_opts=opts), "no tensor core triggered"
|
||||
else:
|
||||
k.apply_opts(opts)
|
||||
if expected_color_size is not None:
|
||||
cs = list(zip(k.colors(), k.full_shape))
|
||||
assert cs == expected_color_size, f"expected={expected_color_size} got={cs}"
|
||||
@@ -864,5 +1088,339 @@ def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[]
|
||||
check_opt(x, lambda: Kernel(realized_ast), color_sizes[i] if i < len(color_sizes) else None)
|
||||
return lins
|
||||
|
||||
class TestKernelOpts(unittest.TestCase):
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_local_and_grouped_reduce(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1882)
|
||||
a = Tensor.rand(4, 4, N, N)
|
||||
b = Tensor.rand(4, 4, N)
|
||||
r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.LOCAL, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 8)],
|
||||
[Opt(OptOps.LOCAL, 0, 16)], # Checking how it works with locals
|
||||
[Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with grouped reduce
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.GROUPTOP, 0, 16)],
|
||||
[Opt(OptOps.LOCAL, 0, 32), Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
# Checking how it works with locals + grouped reduce
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 64)],
|
||||
# Checking how it works with locals + grouped reduce + upcasts
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.UPCAST, 0, 8), Opt(OptOps.UNROLL, 1, 4)],
|
||||
# many local + many group
|
||||
[Opt(OptOps.GROUP, 0, 2)] * 4,
|
||||
[Opt(OptOps.LOCAL, 0, 2)] * 4,
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)] * 4,
|
||||
])
|
||||
|
||||
def test_upcasts(self):
|
||||
N = 16
|
||||
Tensor.manual_seed(1772)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
r = (a+b).sqrt() * ((a+1).exp())
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UPCAST, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 8)], # Checking how it works with upcasts
|
||||
])
|
||||
|
||||
def test_full_upcast(self):
|
||||
Tensor.manual_seed(1772)
|
||||
a = Tensor.rand(4)
|
||||
b = Tensor.rand(4)
|
||||
r = (a+b).sqrt() * ((a+1).exp())
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UPCAST, 0, 4)], # Checking how it works with upcasts
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_matmul(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
r = a@b
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UPCAST, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # Checking how it works with upcasts
|
||||
[Opt(OptOps.LOCAL, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 1, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.LOCAL, 1, 8)], # Checking how it works with locals
|
||||
[Opt(OptOps.GROUPTOP, 0, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 32), Opt(OptOps.UNROLL, 0, 4)], # Checking how it works with grouped_reduce
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 4)], # Checking how it works with local+grouped_reduce
|
||||
# Checking all together
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4),
|
||||
Opt(OptOps.UPCAST, 1, 2)],
|
||||
# Full global upcast + local
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 8)],
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_double_reduce(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(8, N, 8, N)
|
||||
r = a.sum(axis=(1,3))
|
||||
helper_linearizer_opt(r, [
|
||||
# openCL / GPU=1 is 256 max threads
|
||||
[Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)],
|
||||
[Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce.
|
||||
[Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 64)], # Checking how it works with 2 grouped_reduces.
|
||||
[Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2), Opt(OptOps.UNROLL, 0, 4)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 2, 4)], # Checking how it works with 2 grouped_reduces + upcasts.
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4)],
|
||||
# Checking how it works with 2 grouped_reduces + upcasts + locals.
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 1, 4)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
|
||||
Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals.
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
|
||||
Opt(OptOps.UPCAST, 0, 2)], # No globals
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
def test_invalid_tensor_core_extra_opts(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
realized_ast, _ = helper_realized_ast(a@b)
|
||||
invalid_opts = [
|
||||
[Opt(OptOps.LOCAL, 2, 2)],
|
||||
[Opt(OptOps.UPCAST, 2, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 2, 2)],
|
||||
]
|
||||
for x in invalid_opts:
|
||||
k = Kernel(realized_ast)
|
||||
with self.assertRaises(AssertionError):
|
||||
assert k.apply_tensor_cores(use_tensor_cores=1, extra_opts=x), "no valid tensor core" # for METAL in runners
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
|
||||
"test requires tensor cores with accumulation in half") # testing with half suffices.
|
||||
def test_tensor_core_opts(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
|
||||
r = a.matmul(b, dtype=dtypes.half)
|
||||
atol, rtol = 0.25, 0.01
|
||||
helper_linearizer_opt(r, [
|
||||
[],
|
||||
[Opt(OptOps.UPCAST, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 1, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # check upcasts
|
||||
[Opt(OptOps.UNROLL, 0, 2)], # check unroll
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2)], # check combo of unroll and local
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4)], # check permutations
|
||||
[Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4)],
|
||||
[Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)],
|
||||
], apply_tc=True, atol=atol, rtol=rtol)
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
|
||||
"test requires tensor cores with accumulation in half") # testing with half suffices.
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
def test_tensor_core_opts_locals(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
|
||||
r = a.matmul(b, dtype=dtypes.half)
|
||||
atol, rtol = 0.25, 0.01
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.UNROLL, 0, 0)], # check full unroll of reduce with locals
|
||||
[Opt(OptOps.LOCAL, 0, 4)], # check local
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.LOCAL, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
|
||||
], apply_tc=True, atol=atol, rtol=rtol)
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared memory")
|
||||
@unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
|
||||
"test requires tensor cores with accumulation in half") # testing with half suffices.
|
||||
# NOTE: the METAL test is broken, likely due to a compiler bug. passes on CI with -O0 and with default opt level locally on M3
|
||||
@unittest.skipIf(Device.DEFAULT == "METAL", "broken for METAL")
|
||||
@unittest.skip("feature was removed")
|
||||
def test_tensor_core_opts_group(self):
|
||||
N = 128
|
||||
Tensor.manual_seed(1552)
|
||||
a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
|
||||
r = a.matmul(b, dtype=dtypes.half)
|
||||
atol, rtol = 0.25, 0.01
|
||||
helper_linearizer_opt(r, [
|
||||
[Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.GROUPTOP, 0, 4)],
|
||||
[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)],
|
||||
[Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 2)],
|
||||
], apply_tc=True, atol=atol, rtol=rtol)
|
||||
|
||||
def test_padto_matmul(self):
|
||||
if (CI and Device.DEFAULT in ["AMD", "NV", "CUDA"]):
|
||||
self.skipTest("super slow on CUDA and AMD because of the big grid dims")
|
||||
N = 17 * 17
|
||||
Tensor.manual_seed(289)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
helper_linearizer_opt(a@b, [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 1, 32)],
|
||||
[Opt(OptOps.PADTO, 2, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.PADTO, 2, 32)],
|
||||
# can optimize further post PADTO
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 2),],
|
||||
])
|
||||
|
||||
def test_padto_upcasted_not_ok(self):
|
||||
N = 4
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
helper_linearizer_opt(a@b, [
|
||||
[Opt(OptOps.UPCAST, 0, 0)],
|
||||
[Opt(OptOps.UPCAST, 1, 0)],
|
||||
[Opt(OptOps.UNROLL, 0, 0)],
|
||||
[Opt(OptOps.PADTO, 0, 8)],
|
||||
[Opt(OptOps.PADTO, 1, 8)],
|
||||
[Opt(OptOps.PADTO, 2, 8)],
|
||||
])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 0, 0), Opt(OptOps.PADTO, 1, 8)]])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 1, 0), Opt(OptOps.PADTO, 1, 8)]])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a@b, [[Opt(OptOps.UNROLL, 0, 0), Opt(OptOps.PADTO, 2, 8)]])
|
||||
|
||||
def test_padto_sum_ok(self):
|
||||
N = 18 * 18
|
||||
# NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
|
||||
a = Tensor.rand(N, N).realize().shrink(((0, 17), (0, 17))) * 100
|
||||
b = (Tensor.rand(N, N) < 0.5).realize().shrink(((0, 17), (0, 17)))
|
||||
|
||||
helper_linearizer_opt(a.sum(0), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
helper_linearizer_opt(a.sum(1), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
# can pad sum reduce axis if there's no unsafe ops prior to sum
|
||||
for axis in (0, 1):
|
||||
helper_linearizer_opt(a.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(a.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
# TODO: why?
|
||||
if Device.DEFAULT != "WEBGPU":
|
||||
helper_linearizer_opt(b.sum(0, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
helper_linearizer_opt(b.sum(1, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
|
||||
|
||||
# having unsafe ops after sum is fine
|
||||
helper_linearizer_opt(a.sum().exp(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
def test_padto_sum_not_ok(self):
|
||||
N = 18 * 18
|
||||
# NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
|
||||
a = Tensor.rand(N, N).shrink(((0, 17), (0, 17))).exp()
|
||||
# exp is not safe to pad
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.exp().sum(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.exp().sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
b = a < 1
|
||||
# lt is not safe to pad
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
def test_padto_max(self):
|
||||
N = 18 * 18
|
||||
# NOTE: this setup prevents 17 * 17 contiguous merged into one axis
|
||||
a = -Tensor.rand(N, N).shrink(((0, 17), (0, 17))) * 100
|
||||
|
||||
helper_linearizer_opt(a.max(0), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
helper_linearizer_opt(a.max(1), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
# cannot pad max kernel on reduce
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.max(), [[Opt(OptOps.PADTO, 0, 32)],])
|
||||
with self.assertRaises(KernelOptError):
|
||||
helper_linearizer_opt(a.max(0), [[Opt(OptOps.PADTO, 1, 32)],])
|
||||
|
||||
def test_padto_where(self):
|
||||
Tensor.manual_seed(0)
|
||||
N = 17 * 17
|
||||
a = (Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1).where(1, 0)
|
||||
helper_linearizer_opt(a.max(0), [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
def test_padto_where_multioutput(self):
|
||||
Tensor.manual_seed(0)
|
||||
N = 17 * 17
|
||||
r = Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1
|
||||
a0 = r.where(1, 0)
|
||||
a1 = r.where(2, 0)
|
||||
helper_linearizer_opt([a0.max(0), a1.max(0)], [
|
||||
[Opt(OptOps.PADTO, 0, 32)],
|
||||
[Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
|
||||
])
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
|
||||
def test_color_shapes_with_local(self):
|
||||
N = 32
|
||||
Tensor.manual_seed(1552)
|
||||
a = Tensor.rand(N, N)
|
||||
b = Tensor.rand(N, N)
|
||||
r = a@b
|
||||
opts_shapes = [
|
||||
([Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("red",32)]),
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",2),("red",16)]),
|
||||
# check to ensure local_dims are stable for full UNROLL of the first reduce
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
([Opt(OptOps.UNROLL, 0, 0),Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
# check behavior for full UNROLL on an existing GROUP
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",16),("magenta",2)]),
|
||||
([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
([Opt(OptOps.GROUP, 0, 0),Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
|
||||
([Opt(OptOps.GROUP, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",32),("blue",32),("red",16),("magenta",2)]),
|
||||
]
|
||||
helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes])
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
+128
-58
@@ -14,17 +14,26 @@ from tinygrad.engine.realize import get_program
|
||||
class TestLinearizerDumb(unittest.TestCase):
|
||||
@unittest.skipUnless(Device.DEFAULT == "METAL", "only tested on METAL")
|
||||
def test_unmerged_ifs(self):
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=0, src=())
|
||||
c1 = c0.view(ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(25088, 0, 49, 7, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)))
|
||||
c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=1, src=())
|
||||
c3 = c2.view(ShapeTracker(views=(View(shape=(1, 64, 1, 512, 4, 9, 4, 9), strides=(0, 25088, 0, 49, 0, 7, 0, 1), offset=-8, mask=((0, 1), (0, 64), (0, 1), (0, 512), (0, 4), (1, 8), (0, 4), (1, 8)), contiguous=False), View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(663552, 0, 0, 36, 1, 1296, 360, 10), offset=0, mask=None, contiguous=False))))
|
||||
c4 = c3.load()
|
||||
c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(2359296), arg=2, src=())
|
||||
c6 = c5.view(ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(0, 0, 4608, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)))
|
||||
c7 = c6.load()
|
||||
c8 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(0, 0, 0, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
|
||||
c9 = c1.store(((c4*c7).cast(dtypes.float).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (5, 6, 7))).cast(dtypes.half)*UOp.const(dtypes.half, 0.9999950000374996, src=c8)).alu(Ops.MAX, UOp.const(dtypes.half, 0.0, src=c8)))
|
||||
ast = c9.sink()
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(1605632), arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(25088, 0, 49, 7, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=0, src=()),)),
|
||||
UOp(Ops.MAX, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.MUL, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.CAST, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
|
||||
UOp(Ops.CAST, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.MUL, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(1605632), arg=ShapeTracker(views=(View(shape=(1, 64, 1, 512, 4, 9, 4, 9), strides=(0, 25088, 0, 49, 0, 7, 0, 1), offset=-8, mask=((0, 1), (0, 64), (0, 1), (0, 512), (0, 4), (1, 8), (0, 4), (1, 8)), contiguous=False), View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(663552, 0, 0, 36, 1, 1296, 360, 10), offset=0, mask=None, contiguous=False))), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(2359296), arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(0, 0, 4608, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(2359296), arg=2, src=()),)),)),)),)),)),)),
|
||||
UOp(Ops.CONST, dtypes.half, arg=0.9999950000374996, src=(
|
||||
x16:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(0, 0, 0, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
|
||||
UOp(Ops.CONST, dtypes.half, arg=0.0, src=(
|
||||
x16,)),)),)),))
|
||||
opts = [Opt(op=OptOps.TC, axis=2, arg=(-1, 2, 1)), Opt(op=OptOps.UPCAST, axis=2, arg=0), Opt(op=OptOps.UNROLL, axis=1, arg=0)]
|
||||
prg = get_program(ast, Device["METAL"].renderer, opts)
|
||||
print(prg.src)
|
||||
@@ -35,57 +44,116 @@ class TestLinearizerDumb(unittest.TestCase):
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
|
||||
def test_max_simplify_and_cancel(self):
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1000), arg=0, src=())
|
||||
c1 = c0.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)))
|
||||
c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1000), arg=1, src=())
|
||||
c3 = c2.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)))
|
||||
c4 = c3.load()
|
||||
c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=2, src=())
|
||||
c6 = c5.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)))
|
||||
c7 = c6.load()
|
||||
c8 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
|
||||
c9 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1001, 1999), strides=(0, 0), offset=0, mask=((0, 1001), (999, 1999)), contiguous=False), View(shape=(1000, 1000), strides=(1, 2000), offset=0, mask=None, contiguous=False))), src=())
|
||||
c10 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1000), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
|
||||
c11 = c1.store((c4.alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c8)).cast(dtypes.int)*(c9.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, -1, src=c10), UOp.const(dtypes.int, 0, src=c10)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,)))+UOp.const(dtypes.int, 1000, src=c8))))
|
||||
ast = c11.sink()
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.int.ptr(1000), arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1000), arg=0, src=()),)),
|
||||
UOp(Ops.MUL, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.CAST, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(1000), arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1000), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(1), arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=2, src=()),)),)),)),
|
||||
UOp(Ops.CONST, dtypes.bool, arg=True, src=(
|
||||
x14:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),
|
||||
UOp(Ops.ADD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.int, arg=(Ops.ADD, (1,)), src=(
|
||||
UOp(Ops.WHERE, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.VALID, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1001, 1999), strides=(0, 0), offset=0, mask=((0, 1001), (999, 1999)), contiguous=False), View(shape=(1000, 1000), strides=(1, 2000), offset=0, mask=None, contiguous=False))), src=()),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=-1, src=(
|
||||
x21:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1000), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=0, src=(
|
||||
x21,)),)),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=1000, src=(
|
||||
x14,)),)),)),)),))
|
||||
opts = [Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8)]
|
||||
prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
|
||||
print(prg.src)
|
||||
assert prg.uops is not None and not any(uop.op is Ops.MAX for uop in prg.uops), "leftover MAX"
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
|
||||
@unittest.skip("not applicable")
|
||||
def test_expander_new_srcs(self):
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(25), arg=ShapeTracker(views=(View(shape=(25, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(25), arg=0, src=()),)),
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (1,)), src=(
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(25), arg=ShapeTracker(views=(View(shape=(26, 49), strides=(0, -1), offset=48, mask=((0, 26), (24, 49)), contiguous=False), View(shape=(25, 25), strides=(1, 50), offset=0, mask=None, contiguous=False))), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(25), arg=1, src=()),)),)),)),)),))
|
||||
opts = [Opt(op=OptOps.GROUP, axis=0, arg=0), Opt(op=OptOps.PADTO, axis=0, arg=32), Opt(op=OptOps.LOCAL, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=0)]
|
||||
prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
|
||||
print(prg.src)
|
||||
if_uops = [u for u in prg.uops if u.op is Ops.IF]
|
||||
self.assertIn(len(if_uops), {1,2,3})
|
||||
conditions = if_uops[0].src[0].toposort()
|
||||
self.assertLessEqual(len(conditions), 9)
|
||||
|
||||
# this was a bug in embedding, someday we should fold this anyway
|
||||
@unittest.skipUnless(is_dtype_supported(dtypes.half), f"half dtype not supported on {Device.DEFAULT}")
|
||||
def test_llama_embedding(self):
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(4096), arg=0, src=())
|
||||
c1 = c0.view(ShapeTracker(views=(View(shape=(4096, 1, 1), strides=(1, 0, 0), offset=0, mask=None, contiguous=True),)))
|
||||
c2 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(32001, 63999), strides=(0, 0), offset=0, mask=((0, 32001), (31999, 63999)), contiguous=False), View(shape=(4096, 32000, 32000), strides=(0, 1, 64000), offset=0, mask=None, contiguous=False))), src=())
|
||||
c3 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 32000), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
|
||||
c4 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
|
||||
c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=())
|
||||
c6 = c5.view(ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)))
|
||||
c7 = c6.load()
|
||||
c8 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(131072000), arg=2, src=())
|
||||
c9 = c8.view(ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(1, 4096, 0), offset=0, mask=None, contiguous=False),)))
|
||||
c10 = c9.load()
|
||||
c11 = c1.store(((c2.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, 1, src=c3), UOp.const(dtypes.int, 0, src=c3)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (2,)))+UOp.const(dtypes.int, -1, src=c4)).alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c4)).cast(dtypes.half)*c10).cast(dtypes.float).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,))).cast(dtypes.half))
|
||||
ast = c11.sink()
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(4096), arg=ShapeTracker(views=(View(shape=(4096, 1, 1), strides=(1, 0, 0), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(4096), arg=0, src=()),)),
|
||||
UOp(Ops.CAST, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (1,)), src=(
|
||||
UOp(Ops.CAST, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.MUL, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.CAST, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.int, arg=(Ops.ADD, (2,)), src=(
|
||||
UOp(Ops.WHERE, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.VALID, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(32001, 63999), strides=(0, 0), offset=0, mask=((0, 32001), (31999, 63999)), contiguous=False), View(shape=(4096, 32000, 32000), strides=(0, 1, 64000), offset=0, mask=None, contiguous=False))), src=()),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=1, src=(
|
||||
x16:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 32000), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=0, src=(
|
||||
x16,)),)),)),
|
||||
UOp(Ops.CONST, dtypes.int, arg=-1, src=(
|
||||
x19:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.int, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.int.ptr(1), arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=()),)),)),)),
|
||||
UOp(Ops.CONST, dtypes.bool, arg=True, src=(
|
||||
x19,)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.half, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.half.ptr(131072000), arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(1, 4096, 0), offset=0, mask=None, contiguous=False),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(131072000), arg=2, src=()),)),)),)),)),)),)),)),))
|
||||
prg = get_program(ast, Device[Device.DEFAULT].renderer)
|
||||
print(prg.src)
|
||||
|
||||
@unittest.expectedFailure
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
|
||||
def test_unrolled_float4_align(self):
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=0, src=())
|
||||
c1 = c0.view(ShapeTracker(views=(View(shape=(1, 1), strides=(0, 0), offset=0, mask=None, contiguous=True),)))
|
||||
c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(18), arg=1, src=())
|
||||
c3 = c2.view(ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)))
|
||||
c4 = c3.load()
|
||||
c5 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(3, 6), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
|
||||
c6 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(18), arg=2, src=())
|
||||
c7 = c6.view(ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)))
|
||||
c8 = c7.load()
|
||||
c9 = c1.store(c4.alu(Ops.CMPNE, UOp.const(dtypes.long, -1, src=c5)).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c5)).where(UOp.const(dtypes.float, 0.0, src=c5), c8).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (0, 1))))
|
||||
ast = c9.sink()
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(1), arg=ShapeTracker(views=(View(shape=(1, 1), strides=(0, 0), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=0, src=()),)),
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (0, 1)), src=(
|
||||
UOp(Ops.WHERE, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.long, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.long.ptr(18), arg=ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(18), arg=1, src=()),)),)),
|
||||
UOp(Ops.CONST, dtypes.long, arg=-1, src=(
|
||||
x11:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(3, 6), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
|
||||
UOp(Ops.CONST, dtypes.bool, arg=True, src=(
|
||||
x11,)),)),
|
||||
UOp(Ops.CONST, dtypes.float, arg=0.0, src=(
|
||||
x11,)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(18), arg=ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(18), arg=2, src=()),)),)),)),)),)),))
|
||||
opts = [Opt(op=OptOps.UNROLL, axis=0, arg=0)]
|
||||
prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
|
||||
print(prg.src)
|
||||
@@ -96,16 +164,18 @@ class TestLinearizerDumb(unittest.TestCase):
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
|
||||
@unittest.skipIf(getenv("PTX"), "this is somehow correct in PTX")
|
||||
def test_upcasted_stores_out_of_order(self):
|
||||
c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9360), arg=0, src=())
|
||||
c1 = c0.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 1, 1, 4, 3, 3), strides=(2340, 468, 36, 0, 0, 0, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=True),)))
|
||||
c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), arg=1, src=())
|
||||
c3 = c2.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(0, 0, 0, 0, 0, 0, 1, 0, 4, 48, 16), offset=0, mask=None, contiguous=False),)))
|
||||
c4 = c3.load()
|
||||
c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1040), arg=2, src=())
|
||||
c6 = c5.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(260, 13, 1, 0, 0, 0, 65, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)))
|
||||
c7 = c6.load()
|
||||
c8 = c1.store((c4*c7).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (6,))))
|
||||
ast = c8.sink()
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(9360), arg=ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 1, 1, 4, 3, 3), strides=(2340, 468, 36, 0, 0, 0, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=True),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9360), arg=0, src=()),)),
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (6,)), src=(
|
||||
UOp(Ops.MUL, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(144), arg=ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(0, 0, 0, 0, 0, 0, 1, 0, 4, 48, 16), offset=0, mask=None, contiguous=False),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(1040), arg=ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(260, 13, 1, 0, 0, 0, 65, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=(
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1040), arg=2, src=()),)),)),)),)),)),))
|
||||
opts = [Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=0)]
|
||||
prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
|
||||
print(prg.src)
|
||||
|
||||
+2
-2
@@ -2,7 +2,7 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
import torch
|
||||
from tinygrad import Tensor, Device, TinyJit, dtypes
|
||||
from tinygrad import Tensor, Device, TinyJit
|
||||
from tinygrad.uop.ops import Ops
|
||||
from tinygrad.helpers import GlobalCounters, CI, Context
|
||||
from tinygrad.nn import Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, Embedding
|
||||
@@ -465,7 +465,7 @@ class TestNN(unittest.TestCase):
|
||||
# used to fail bounds check
|
||||
with Context(FUSE_ARANGE=1):
|
||||
embedding = Embedding(100, 1024)
|
||||
input_ids = Tensor.empty(16, 16, dtype=dtypes.int)
|
||||
input_ids = Tensor.empty(16, 16)
|
||||
embedding(input_ids).realize()
|
||||
|
||||
def test_load_state_dict(self):
|
||||
|
||||
+20
-12
@@ -1209,24 +1209,32 @@ class TestOps(unittest.TestCase):
|
||||
# match torch ellipsis handling
|
||||
helper_test_op([(32, 7, 24, 24, 24), (32, 7, 24, 24, 24)], lambda a, b: torch.einsum('ij...,ij...->ij', [a, b]),
|
||||
lambda a, b: Tensor.einsum('ij...,ij...->ij', [a, b]))
|
||||
# multiple ellipsis in one operand are not allowed
|
||||
self.helper_test_exception([(2, 3, 4), (2, 3, 4)], lambda a, b: torch.einsum('...ik..., ...jk ->', [a, b]),
|
||||
lambda a, b: Tensor.einsum('...ik..., ...jk ->', [a, b]), expected=(RuntimeError, IndexError))
|
||||
# multiple ellipsis must broadcast together
|
||||
self.helper_test_exception([(2, 3, 4), (2, 3, 4)], lambda a, b: torch.einsum('i...j,ji...->...', [a, b]),
|
||||
lambda a, b: Tensor.einsum('i...j,ji...->...', [a, b]), expected=RuntimeError)
|
||||
# multiple ellipsis in one operand are not allowed. This test shall raise an exception.
|
||||
with self.assertRaises(RuntimeError):
|
||||
helper_test_op([(2, 3, 4), (2, 3, 4)], lambda a, b: torch.einsum('...ik..., ...jk ->', [a, b]),
|
||||
lambda a, b: Tensor.einsum('...ik..., ...jk ->', [a, b]))
|
||||
# multiple ellipsis must broadcast together. This test shall raise an exception.
|
||||
with self.assertRaises(RuntimeError):
|
||||
helper_test_op([(2, 3, 4, 5), (5, 2, 7)], lambda a, b: torch.einsum('i...j,ji...->...', [a, b]),
|
||||
lambda a, b: Tensor.einsum('i...j,ji...->...', [a, b]))
|
||||
|
||||
def test_einsum_shape_check(self):
|
||||
self.helper_test_exception([(3,8,10,5), (11,5,13,16,8)], lambda a, b: torch.einsum('pqrs,tuqvr->pstuv', [a, b]),
|
||||
lambda a, b: Tensor.einsum('pqrs,tuqvr->pstuv', [a, b]), expected=RuntimeError)
|
||||
a = Tensor.zeros(3,8,10,5)
|
||||
b = Tensor.zeros(11,5,13,16,8)
|
||||
with self.assertRaises(AssertionError):
|
||||
Tensor.einsum('pqrs,tuqvr->pstuv',a,b)
|
||||
|
||||
def test_einsum_arity_check1(self):
|
||||
self.helper_test_exception([(10,15), (15,20), (20,10)], lambda a, b, c: torch.einsum('ij,jk->ij', [a, b, c]),
|
||||
lambda a, b, c: Tensor.einsum('ij,jk->ij', [a, b, c]), expected=(ValueError, RuntimeError))
|
||||
a = Tensor.zeros(10,15)
|
||||
b = Tensor.zeros(15,20)
|
||||
c = Tensor.zeros(20,10)
|
||||
with self.assertRaises(AssertionError):
|
||||
Tensor.einsum('ij,jk->ij', a,b,c)
|
||||
|
||||
def test_einsum_arity_check2(self):
|
||||
self.helper_test_exception([(10,10)], lambda a: torch.einsum('ij,jk->ij', a),
|
||||
lambda a: Tensor.einsum('ij,jk->ij', a), expected=(ValueError, RuntimeError))
|
||||
a = Tensor.zeros(10,10)
|
||||
with self.assertRaises(AssertionError):
|
||||
Tensor.einsum('ij,jk->ij', a)
|
||||
|
||||
@unittest.skipIf(IMAGE>0, "no 1d dot for images")
|
||||
def test_dot_1d(self):
|
||||
|
||||
@@ -180,7 +180,6 @@ class TestOuterworld(unittest.TestCase):
|
||||
out.realize()
|
||||
print(out.numpy())
|
||||
|
||||
@unittest.skip("opts don't work")
|
||||
def test_triple_gemm(self):
|
||||
x = Tensor.rand(1, 16).realize()
|
||||
W = Tensor.rand(3, 16, 16).realize()
|
||||
|
||||
@@ -46,7 +46,7 @@ class TestRendererFailures(unittest.TestCase):
|
||||
@unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, PythonRenderer)), "test is for ptx or python renderer")
|
||||
def test_gated_store_with_alu(self):
|
||||
a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
|
||||
gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
|
||||
gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
|
||||
gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0, gate_alu), UOp.const(dtypes.int, 1)))
|
||||
sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
|
||||
uops = full_rewrite(sink, Device[Device.DEFAULT].renderer)
|
||||
@@ -56,8 +56,8 @@ class TestRendererFailures(unittest.TestCase):
|
||||
@unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, PythonRenderer)), "test is for ptx or python renderer")
|
||||
def test_gated_store_with_alu_2d(self):
|
||||
a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
|
||||
gate_alu_0 = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
|
||||
gate_alu_1 = (lidx1:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 2),), 'lidx1')).ne(0)
|
||||
gate_alu_0 = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
|
||||
gate_alu_1 = (lidx1:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx1', 2))).ne(0)
|
||||
gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0+lidx1*4, gate_alu_0&gate_alu_1), UOp.const(dtypes.int, 1)))
|
||||
sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
|
||||
uops = full_rewrite(sink, Device[Device.DEFAULT].renderer)
|
||||
@@ -101,7 +101,7 @@ class TestPTXFailures(unittest.TestCase):
|
||||
@unittest.skip("INDEX can only have a gate ALU parent, not an IF")
|
||||
def test_gated_store_with_if(self):
|
||||
a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
|
||||
gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
|
||||
gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
|
||||
val = UOp.const(dtypes.int, 1)
|
||||
if_uop = UOp(Ops.IF, dtypes.void, (gate_alu,))
|
||||
gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0, if_uop), val))
|
||||
|
||||
@@ -1,10 +1,16 @@
|
||||
import unittest
|
||||
|
||||
from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel
|
||||
from tinygrad.uop.ops import UOp, Ops
|
||||
from tinygrad.codegen.opt.search import bufs_from_lin, actions, beam_search
|
||||
from tinygrad.device import Device
|
||||
from tinygrad.tensor import Tensor
|
||||
from tinygrad.dtype import dtypes
|
||||
from tinygrad.helpers import Context, GlobalCounters
|
||||
from tinygrad.engine.realize import capturing
|
||||
from tinygrad.shape.shapetracker import ShapeTracker
|
||||
from tinygrad.shape.view import View
|
||||
from extra.optimization.helpers import time_linearizer
|
||||
|
||||
class TestBEAM(unittest.TestCase):
|
||||
def test_dynamic_beam(self):
|
||||
@@ -52,6 +58,24 @@ class TestBEAM(unittest.TestCase):
|
||||
assert Opt(OptOps.GROUP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUP"
|
||||
assert Opt(OptOps.GROUPTOP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUPTOP"
|
||||
|
||||
@unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
|
||||
def test_search_over_shape(self):
|
||||
from test.test_linearizer import helper_realized_ast
|
||||
from tinygrad.codegen.opt.search import get_kernel_actions
|
||||
|
||||
dtype_pairs = [(tc.dtype_in, tc.dtype_out) for tc in Device[Device.DEFAULT].renderer.tensor_cores]
|
||||
multi_shape_dtype_pairs = [dts for dts in dtype_pairs if dtype_pairs.count(dts) > 1]
|
||||
|
||||
if len(multi_shape_dtype_pairs) == 0: raise unittest.SkipTest("only one tc available per dtype pair to search over")
|
||||
|
||||
for (dtype_in, dtype_out) in multi_shape_dtype_pairs:
|
||||
a = Tensor.rand(16, 16, dtype=dtype_in)
|
||||
b = Tensor.rand(16, 16, dtype=dtype_in)
|
||||
realized_ast, _ = helper_realized_ast(a.matmul(b, dtype=dtype_out))
|
||||
|
||||
lins = get_kernel_actions(Kernel(realized_ast)).values()
|
||||
assert len(set(lin.tensor_core.dims for lin in lins if lin.tensor_core is not None)) > 1
|
||||
|
||||
def test_get_kernel_actions_preserves_actions_state(self):
|
||||
from test.test_linearizer import helper_realized_ast
|
||||
from tinygrad.codegen.opt.search import get_kernel_actions
|
||||
@@ -63,6 +87,49 @@ class TestBEAM(unittest.TestCase):
|
||||
actions_after = actions.copy()
|
||||
assert actions_after == actions_before, "actions state was not preserved"
|
||||
|
||||
@unittest.skip("invalid reduce now")
|
||||
def test_filter_global_buffer(self):
|
||||
# taken from https://github.com/tinygrad/tinygrad/issues/4612
|
||||
ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.STORE, dtypes.void, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(256), arg=ShapeTracker(views=(View(shape=(1, 1, 256), strides=(0, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=0, src=()),)),
|
||||
UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.MAX, (1,)), src=(
|
||||
UOp(Ops.MUL, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.ADD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=0, mask=((0, 64128),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=1, src=()),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-64128, mask=((64128, 128256),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=2, src=()),)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-128256, mask=((128256, 192384),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=3, src=()),)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-192384, mask=((192384, 256512),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=4, src=()),)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-256512, mask=((256512, 320640),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=5, src=()),)),)),)),
|
||||
UOp(Ops.LOAD, dtypes.float, arg=None, src=(
|
||||
UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-320640, mask=((320640, 384768),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
|
||||
UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=6, src=()),)),)),)),
|
||||
UOp(Ops.CONST, dtypes.float, arg=1.4285714285714286, src=(
|
||||
UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 501, 256), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),)) # noqa: E501
|
||||
lin = Kernel(ast)
|
||||
|
||||
bufs = bufs_from_lin(lin)
|
||||
best_lin = beam_search(lin, bufs, 2)
|
||||
assert best_lin
|
||||
# need disable_cache to trigger.
|
||||
tm = time_linearizer(best_lin, bufs, allow_test_size=False, cnt=2, disable_cache=True)
|
||||
assert tm
|
||||
|
||||
def test_beam_unnamed_kernels(self):
|
||||
from test.test_linearizer import push_views
|
||||
a = Tensor.rand(100)
|
||||
|
||||
+11
-11
@@ -458,8 +458,8 @@ class TestUOpGraph(unittest.TestCase):
|
||||
sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.uint.ptr(8, addrspace=AddrSpace.LOCAL), (), "temp0")
|
||||
|
||||
# Define indices, valids and barrier
|
||||
gidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 416),), "gidx0")
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "lidx0")
|
||||
gidx = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 416))
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 10))
|
||||
|
||||
gate = (gidx<400) & (lidx<8)
|
||||
|
||||
@@ -512,7 +512,7 @@ class TestUOpGraph(unittest.TestCase):
|
||||
def test_in_out_bounds_access_with_mask(self):
|
||||
with Context(IGNORE_OOB=0):
|
||||
glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 42),), "gidx0")
|
||||
gidx0 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx0", 42))
|
||||
ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, (5<gidx0)&(gidx0<16)),))
|
||||
ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<16),))
|
||||
to_uops_list([ld0, ld1])
|
||||
@@ -536,7 +536,7 @@ class TestUOpGraph(unittest.TestCase):
|
||||
with Context(IGNORE_OOB=0):
|
||||
glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
|
||||
glbl1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(64), (), 0)
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 42),), "gidx0")
|
||||
gidx0 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx0", 42))
|
||||
ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<8),))
|
||||
ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<32)),))
|
||||
to_uops_list([ld1])
|
||||
@@ -559,7 +559,7 @@ class TestUOpGraph(unittest.TestCase):
|
||||
def test_fold_gated_load_local(self):
|
||||
glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
|
||||
smem = UOp(Ops.DEFINE_LOCAL, dtypes.int.ptr(size=18, addrspace=AddrSpace.LOCAL), (), "temp")
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 16))
|
||||
st = UOp(Ops.STORE, dtypes.void, (smem.index(lidx), UOp.load(glbl0.index(lidx), dtype=dtypes.int)))
|
||||
barrier = UOp(Ops.BARRIER, dtypes.void, (st, ))
|
||||
ld0 = UOp(Ops.LOAD, dtypes.int, (smem.index(lidx+1, UOp.const(dtypes.bool, False)), barrier))
|
||||
@@ -756,8 +756,8 @@ class TestIFUOps(unittest.TestCase):
|
||||
def test_create_ifs(self):
|
||||
gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
|
||||
sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(size=4, addrspace=AddrSpace.LOCAL), (), "smem")
|
||||
valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "gidx0")<5
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "lidx0")
|
||||
valid = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 10))<5
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 4))
|
||||
gate = valid&(lidx.ne(2))
|
||||
idx = UOp.const(dtypes.int, 0)
|
||||
st = UOp(Ops.STORE, dtypes.void, (sbuf.index(idx), UOp.const(dtypes.float, 42)))
|
||||
@@ -775,8 +775,8 @@ class TestIFUOps(unittest.TestCase):
|
||||
def test_expand_ifs_one_gate(self):
|
||||
gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
|
||||
sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(size=16, addrspace=AddrSpace.LOCAL), (), "smem")
|
||||
valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "gidx0")<1
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
|
||||
valid = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 4))<1
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 16))
|
||||
gate = valid&(lidx.ne(2))
|
||||
st = UOp(Ops.STORE, dtypes.void, (sbuf, lidx, UOp.const(dtypes.float, 42)))
|
||||
barrier = UOp(Ops.BARRIER, dtypes.void, (st,))
|
||||
@@ -794,8 +794,8 @@ class TestIFUOps(unittest.TestCase):
|
||||
@unittest.expectedFailure
|
||||
def test_expand_ifs_dumb(self):
|
||||
buf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
|
||||
valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "gidx0")<5
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "lidx0")
|
||||
valid = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 10))<5
|
||||
lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 4))
|
||||
gate = valid&(lidx.ne(2))
|
||||
stores = [UOp(Ops.STORE, dtypes.void, (buf, UOp.const(dtypes.int, i), UOp.const(dtypes.float, i), gate)) for i in range(4)]
|
||||
sink = UOp(Ops.SINK, dtypes.void, tuple(stores))
|
||||
|
||||
+4
-5
@@ -201,7 +201,6 @@ class TestSafeCast(TestUOps):
|
||||
self.assertEqual(a.cast(dtypes.int16).cast(dtypes.int).simplify(), a.cast(dtypes.int))
|
||||
a = UOp.variable("a", -10, 10, dtype=dtypes.int32)
|
||||
self.assertEqual(a.cast(dtypes.int8).cast(dtypes.int64).simplify(), a.cast(dtypes.int64))
|
||||
self.assertEqual(a.cast(dtypes.int8).cast(dtypes.float).simplify(), a.cast(dtypes.float))
|
||||
|
||||
class TestExecALU(TestUOps):
|
||||
def test_sqrt(self):
|
||||
@@ -270,7 +269,7 @@ class TestConstantFolding(unittest.TestCase):
|
||||
class TestGatedStoreRewrite(unittest.TestCase):
|
||||
def test_tiny_gate_store(self):
|
||||
gmem = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 4))
|
||||
gate = gidx0<UOp.const(dtypes.int, 1)
|
||||
idx = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem, gidx0 * UOp.const(dtypes.int, 2), gate))
|
||||
val = UOp.const(dtypes.float, 42.0)
|
||||
@@ -287,7 +286,7 @@ class TestGatedStoreRewrite(unittest.TestCase):
|
||||
def test_gate_some_stores(self):
|
||||
gmem0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
|
||||
gmem1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 4))
|
||||
idx = gidx0 * UOp.const(dtypes.int, 2)
|
||||
idx0 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem0, idx, gidx0<UOp.const(dtypes.int, 1)))
|
||||
idx1 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem1, idx))
|
||||
@@ -306,7 +305,7 @@ class TestGatedStoreRewrite(unittest.TestCase):
|
||||
def test_merge_ifs_alt(self):
|
||||
gmem0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
|
||||
gmem1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 4))
|
||||
idx = gidx0*UOp.const(dtypes.int, 2)
|
||||
gate = gidx0<UOp.const(dtypes.int, 1)
|
||||
idx0 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem0, idx, gate))
|
||||
@@ -479,7 +478,7 @@ class TestUOpMethod(unittest.TestCase):
|
||||
self.assertEqual(list(var_vals)[0], a)
|
||||
|
||||
def test_const_factor(self):
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 8),), 'gidx0')
|
||||
gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 8))
|
||||
self.assertEqual(UOp(Ops.CONST, dtypes.int, (), 17).const_factor(), 17)
|
||||
self.assertEqual(gidx0.const_factor(), 1)
|
||||
self.assertEqual((gidx0*3).const_factor(), 3)
|
||||
|
||||
@@ -98,6 +98,7 @@ class TestUOpsStatsMatmulHalf(unittest.TestCase):
|
||||
self.assertEqual(expected_ops, GlobalCounters.global_ops)
|
||||
|
||||
class TestUOpsStats(unittest.TestCase):
|
||||
@unittest.skipIf(getenv("PTX"), "wrong in PTX")
|
||||
def test_simple_add(self):
|
||||
a = Tensor.empty(100,100)
|
||||
b = Tensor.empty(100,100)
|
||||
@@ -109,6 +110,7 @@ class TestUOpsStats(unittest.TestCase):
|
||||
# NOTE; ops also include indexing ops
|
||||
assert expected_ops <= ops and ops <= expected_ops * 2
|
||||
|
||||
@unittest.skipIf(getenv("PTX"), "wrong in PTX")
|
||||
def test_simple_add_sq(self):
|
||||
a = Tensor.empty(100,100)
|
||||
b = Tensor.empty(100,100)
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
from tinygrad import Tensor, GlobalCounters, dtypes, Context, nn
|
||||
from tinygrad.helpers import CI, Profiling, WINO
|
||||
from tinygrad.helpers import CI, Profiling, WINO, getenv
|
||||
|
||||
class TestWinogradClose(unittest.TestCase):
|
||||
def test_close(self):
|
||||
@@ -38,6 +38,7 @@ class TestWinograd(unittest.TestCase):
|
||||
assert GlobalCounters.kernel_count == 4
|
||||
out.numpy()
|
||||
|
||||
@unittest.skipIf(getenv("PTX"), "winograd uses too much in PTX")
|
||||
def test_counters(self):
|
||||
IC, OC, X, Y = 4,4,9,9
|
||||
#OC, IC, X, Y = 512, 256, 8, 8
|
||||
@@ -37,8 +37,8 @@ class TestBlockReorder(unittest.TestCase):
|
||||
a = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=0)
|
||||
b = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=1)
|
||||
c = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=2)
|
||||
v1 = UOp(Ops.SPECIAL, dtype=dtypes.int, src=(UOp.const(dtypes.int, 4),), arg="gidx0")
|
||||
v2 = UOp(Ops.SPECIAL, dtype=dtypes.int, src=(UOp.const(dtypes.int, 4),), arg="gidx1")
|
||||
v1 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx0", 4))
|
||||
v2 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx1", 4))
|
||||
v1 = v1*27
|
||||
v2 = v2*4
|
||||
loads = [
|
||||
|
||||
@@ -21,10 +21,6 @@ class TestEqStrDType(unittest.TestCase):
|
||||
def test_ptr_eq(self):
|
||||
assert dtypes.float32.ptr() == dtypes.float32.ptr()
|
||||
assert not (dtypes.float32.ptr() != dtypes.float32.ptr())
|
||||
def test_ptr_nbytes(self):
|
||||
assert dtypes.float16.ptr(32).nbytes() == 32 * dtypes.float16.itemsize
|
||||
def test_ptr_nbytes_unlimited(self):
|
||||
self.assertRaises(RuntimeError, lambda: dtypes.float32.ptr().nbytes())
|
||||
def test_strs(self):
|
||||
if PtrDType is None: raise unittest.SkipTest("no PtrDType support")
|
||||
self.assertEqual(str(dtypes.imagef((1,2,4))), "dtypes.imagef((1, 2, 4))")
|
||||
|
||||
@@ -116,7 +116,7 @@ class TestModuloAndDivisionFolding(unittest.TestCase):
|
||||
|
||||
def test_graph_rewrite_div_folding_bug(self):
|
||||
lhs = UOp(Ops.ADD, dtypes.int.vec(4), src=(
|
||||
UOp(Ops.VECTORIZE, dtypes.int.vec(4), arg=None, src=(UOp(Ops.SPECIAL, dtypes.int, arg='lidx0', src=(UOp.const(dtypes.int, 32),)),)*4),
|
||||
UOp(Ops.VECTORIZE, dtypes.int.vec(4), arg=None, src=(UOp(Ops.SPECIAL, dtypes.int, arg=('lidx0', 32), src=()),)*4),
|
||||
UOp(Ops.VCONST, dtypes.int.vec(4), arg=(0, 256, 512, 768), src=())))
|
||||
rhs = UOp.const(dtypes.int.vec(4), 2)
|
||||
unopt = lhs<rhs
|
||||
|
||||
@@ -93,7 +93,7 @@ class TestMergeDicts(unittest.TestCase):
|
||||
assert merge_dicts([a, b]) == {"a": 1, "b": 2, "c": 3}
|
||||
assert merge_dicts([a, c]) == a
|
||||
assert merge_dicts([a, b, c]) == {"a": 1, "b": 2, "c": 3}
|
||||
with self.assertRaises(RuntimeError):
|
||||
with self.assertRaises(AssertionError):
|
||||
merge_dicts([a, d])
|
||||
|
||||
class TestStripParens(unittest.TestCase):
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import unittest
|
||||
from tinygrad import Tensor, Context, Device
|
||||
from tinygrad.engine.realize import get_program
|
||||
from tinygrad.codegen.opt import Opt, OptOps
|
||||
from tinygrad.codegen.opt.kernel import Opt, OptOps
|
||||
from tinygrad.uop.ops import KernelInfo
|
||||
|
||||
class TestLinearizerRewrite(unittest.TestCase):
|
||||
|
||||
@@ -1,28 +0,0 @@
|
||||
import unittest
|
||||
from tinygrad.uop.ops import PatternMatcher, UOp, graph_rewrite, Ops, UPat, BottomUpGate
|
||||
|
||||
def assert_not_reached(): assert False, "This function should not be reached"
|
||||
def gate(): raise BottomUpGate
|
||||
|
||||
class TestBottomUpGate(unittest.TestCase):
|
||||
def test_basic_bottom_up_gate(self):
|
||||
"""Test that BottomUpGate stops bottom-up"""
|
||||
pm = PatternMatcher([
|
||||
(UPat(Ops.ADD), gate),
|
||||
(UPat(Ops.MUL), assert_not_reached)
|
||||
])
|
||||
|
||||
a,b,c = UOp.variable("a",0,10), UOp.variable("b",0,10), UOp.variable("c",0,10)
|
||||
graph_rewrite((a*a)+(b*c), pm, bottom_up=True)
|
||||
|
||||
def test_bottom_up_gate_with_rewriting(self):
|
||||
pm = PatternMatcher([
|
||||
(UPat.var("a")+UPat.var("a"), lambda a: 2*a),
|
||||
(UPat(Ops.MUL), gate),
|
||||
(UPat(Ops.CONST), assert_not_reached)
|
||||
])
|
||||
a = UOp.variable("a",0,10)
|
||||
graph_rewrite(a+a, pm, bottom_up=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -4,7 +4,6 @@ from tinygrad.codegen import full_rewrite_to_sink
|
||||
from tinygrad.dtype import dtypes
|
||||
from tinygrad.uop.ops import UOp, Ops
|
||||
from tinygrad.uop.symbolic import simplify_valid
|
||||
from tinygrad.helpers import Context
|
||||
|
||||
def get_gated_load_uop(valid:UOp, idx:UOp):
|
||||
return UOp(Ops.LOAD, dtypes.float, (
|
||||
@@ -18,7 +17,7 @@ def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UO
|
||||
UOp(Ops.VECTORIZE, dtypes.float.vec(4), src=(UOp.const(dtypes.float, 0.0),) * 4)
|
||||
))
|
||||
|
||||
def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.int32, (UOp.const(dtypes.int, nmax),), expr)
|
||||
def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.int, (), (expr, nmax))
|
||||
def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
|
||||
def Range(n, nmax): return UOp.range(nmax, n)
|
||||
|
||||
@@ -46,8 +45,7 @@ class TestHelpers(unittest.TestCase):
|
||||
|
||||
class TestValidIdxSimplification(unittest.TestCase):
|
||||
def check(self, load, sidx, svalid):
|
||||
with Context(NOOPT=1):
|
||||
load = full_rewrite_to_sink(load.sink()).src[0]
|
||||
load = full_rewrite_to_sink(load.sink()).src[0]
|
||||
idx, valid = load.src[0].src[1], load.src[0].src[2]
|
||||
self.assertEqual(idx.render(simplify=False), sidx)
|
||||
self.assertEqual(valid.render(simplify=False), svalid)
|
||||
@@ -197,21 +195,9 @@ class TestValidIdxSimplification(unittest.TestCase):
|
||||
"1",
|
||||
"((((ridx0+ridx1)<1)!=True)&(((ridx2+ridx3)<1)!=True))")
|
||||
|
||||
def test_valid_with_non_const_rhs(self):
|
||||
ridx0 = Range(0, 2**16)
|
||||
ridx1 = Range(1, 4)
|
||||
ridx2 = Range(2, 4)
|
||||
valid = (ridx0<(ridx1*4 + ridx2))&(ridx0<-1).ne(True)
|
||||
idx = ridx0%1024
|
||||
load = get_gated_load_uop(valid, idx)
|
||||
self.check(load,
|
||||
"ridx0",
|
||||
"(ridx0<((ridx1*4)+ridx2))")
|
||||
|
||||
class TestImageSimplification(unittest.TestCase):
|
||||
def check(self, load, svalid, sidx0, sidx1):
|
||||
with Context(NOOPT=1):
|
||||
load = full_rewrite_to_sink(load.sink()).src[0]
|
||||
load = full_rewrite_to_sink(load.sink()).src[0]
|
||||
idx = load.src[0].src[1]
|
||||
self.assertEqual(idx.op, Ops.VECTORIZE)
|
||||
self.assertEqual(len(idx.src), 2)
|
||||
|
||||
@@ -732,12 +732,6 @@ class TestSymbolic(unittest.TestCase):
|
||||
a = Variable("a", 1, 10, dtypes.int)
|
||||
self.helper_test_variable(a.trunc(), 1, 10, "a", test_z3=False)
|
||||
|
||||
def test_do_math_in_int32(self):
|
||||
a = Variable("a", 1, 10)
|
||||
b = Variable("b", 1, 10)
|
||||
self.helper_test_variable(a.cast(dtypes.long)+b.cast(dtypes.long), 2, 20, "(long)((a+b))")
|
||||
self.helper_test_variable(a.cast(dtypes.long)*b.cast(dtypes.long), 1, 100, "(long)((a*b))")
|
||||
|
||||
class TestSymbolicNumeric(unittest.TestCase):
|
||||
def helper_test_numeric(self, f):
|
||||
MIN, MAX = 0, 10
|
||||
|
||||
@@ -58,7 +58,7 @@ class TestVminVmaxProperties(unittest.TestCase):
|
||||
self.assertEqual(uop.vmax, 8)
|
||||
|
||||
def test_vmin_vmax_variable_inside_special(self):
|
||||
uop = UOp(Ops.SPECIAL, dtypes.int, arg='gidx0', src=(UOp(Ops.DEFINE_VAR, dtypes.int, arg=('i', 1, 10)),))
|
||||
uop = UOp(Ops.SPECIAL, dtypes.int, arg=('gidx0', UOp(Ops.DEFINE_VAR, dtypes.int, arg=('i', 1, 10))))
|
||||
self.assertEqual(uop.vmin, 0)
|
||||
self.assertEqual(uop.vmax, 9)
|
||||
|
||||
@@ -251,15 +251,6 @@ class TestVminVmaxVConst(unittest.TestCase):
|
||||
self.assertIs(uop.vmin, False)
|
||||
self.assertIs(uop.vmax, True)
|
||||
|
||||
def test_vmin_vmax_vector_with_gep(self):
|
||||
# vmin and vmax for a vector constant of bool values
|
||||
d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 1)
|
||||
idx = UOp.const(dtypes.int, 0)
|
||||
val = UOp(Ops.LOAD, dtypes.int.vec(2), (d1.index(idx),))
|
||||
uop = (val // 32).gep(0)
|
||||
self.assertEqual(uop.vmin, -67108864)
|
||||
self.assertEqual(uop.vmax, 67108863)
|
||||
|
||||
class TestConstFactor(unittest.TestCase):
|
||||
def test_const_factor_constant(self):
|
||||
# const_factor for a constant
|
||||
|
||||
+3
-18
@@ -5,7 +5,7 @@ from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatch
|
||||
from tinygrad.uop.ops import graph_rewrite, track_rewrites, TRACK_MATCH_STATS
|
||||
from tinygrad.uop.symbolic import sym
|
||||
from tinygrad.dtype import dtypes
|
||||
from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context, cpu_events, profile_marker
|
||||
from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context
|
||||
from tinygrad.device import Buffer
|
||||
|
||||
@track_rewrites(name=True)
|
||||
@@ -267,7 +267,7 @@ def load_profile(lst:list[ProfileEvent]) -> dict:
|
||||
ret = get_profile(lst)
|
||||
u = TinyUnpacker(ret)
|
||||
total_dur, global_peak, index_len, layout_len = u("<IQII")
|
||||
strings, dtypes, markers = json.loads(ret[u.offset:u.offset+index_len]).values()
|
||||
strings, dtypes = json.loads(ret[u.offset:u.offset+index_len]).values()
|
||||
u.offset += index_len
|
||||
layout:dict[str, dict] = {}
|
||||
for _ in range(layout_len):
|
||||
@@ -286,7 +286,7 @@ def load_profile(lst:list[ProfileEvent]) -> dict:
|
||||
alloc, ts, key = u("<BII")
|
||||
if alloc: v["events"].append({"event":"alloc", "ts":ts, "key":key, "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
|
||||
else: v["events"].append({"event":"free", "ts":ts, "key":key})
|
||||
return {"dur":total_dur, "peak":global_peak, "layout":layout, "markers":markers}
|
||||
return {"dur":total_dur, "peak":global_peak, "layout":layout}
|
||||
|
||||
class TestVizProfiler(unittest.TestCase):
|
||||
def test_perfetto_node(self):
|
||||
@@ -367,21 +367,6 @@ class TestVizProfiler(unittest.TestCase):
|
||||
with self.assertRaises(struct.error):
|
||||
get_profile(prof)
|
||||
|
||||
def test_python_marker(self):
|
||||
with Context(PROFILE=1):
|
||||
a = Tensor.empty(1, device="NULL")
|
||||
b = Tensor.empty(1, device="NULL")
|
||||
(a+b).realize()
|
||||
profile_marker("test 1")
|
||||
(a*b).realize()
|
||||
profile_marker("test 2")
|
||||
profile_ret = load_profile(cpu_events)
|
||||
markers = profile_ret["markers"]
|
||||
kernels = profile_ret["layout"]["NULL"]["events"]
|
||||
self.assertEqual(len(markers), 2)
|
||||
assert kernels[0]["st"] <= markers[0]["ts"] <= kernels[1]["st"]
|
||||
assert markers[1]["ts"] >= kernels[1]["st"]+kernels[1]["dur"]
|
||||
|
||||
def _alloc(b:int):
|
||||
a = Tensor.empty(b, device="NULL", dtype=dtypes.char)
|
||||
a.uop.buffer.allocate()
|
||||
|
||||
@@ -16,7 +16,7 @@ from tinygrad.codegen.late.expander import migrate_indexing, expander, pm_pre_ex
|
||||
from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_indexing, devectorize, pm_reduce, \
|
||||
ReduceContext, correct_load_store, pm_render
|
||||
from tinygrad.codegen.late.linearize import block_create, pm_blockend_merge, block_merge, pm_finalize, BlockContext
|
||||
from tinygrad.codegen.opt.kernel import pm_get_optimization, pm_do_optimize
|
||||
from tinygrad.codegen.opt import pm_get_optimization, pm_do_optimize
|
||||
from tinygrad.codegen.opt.swizzler import view_left, view_right, fix_kernel_ops
|
||||
from tinygrad.codegen.opt.postrange import pm_postrange_opt
|
||||
from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen
|
||||
@@ -57,7 +57,7 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
|
||||
ret.extend(rewrites_for_views)
|
||||
|
||||
# this is kernel.py
|
||||
if _POSTOPT <= 1 and not _RANGEIFY: ret.append(RewriteStep(pm_get_optimization, ctx=lambda _: opts, name="get optimization"))
|
||||
if not _RANGEIFY: ret.append(RewriteStep(pm_get_optimization, ctx=lambda _: opts, name="get optimization"))
|
||||
if not _POSTOPT and not _RANGEIFY: ret.append(RewriteStep(pm_do_optimize, ctx=lambda _: opts, name="optimize ast"))
|
||||
|
||||
if _QUANTIZE and opts.device in {"CPU", "DSP"}: ret.append(RewriteStep(pm_quant, name="quantize"))
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import math
|
||||
from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, sint_to_uop
|
||||
from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType
|
||||
from tinygrad.helpers import all_int, dedup
|
||||
from tinygrad.dtype import dtypes
|
||||
from tinygrad.shape.view import get_contraction
|
||||
@@ -34,7 +34,7 @@ def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|No
|
||||
if max_sizes is not None and len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
|
||||
# try to split up dims: (a,) -> (b, c)
|
||||
if limited == dims: limited = _split_dims(dims, max_sizes) if max_sizes is not None else dims
|
||||
ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.int, (sint_to_uop(s),), (f"{prefix}{i}")) for i,s in enumerate(limited)]
|
||||
ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.int, (), (f"{prefix}{i}", s)) for i,s in enumerate(limited)]
|
||||
if len(limited) < len(dims):
|
||||
ret = []
|
||||
if (contraction:=get_contraction(dims, limited)) is None: raise AssertionError(f"get_contraction should not be None {dims=} {limited=}")
|
||||
|
||||
@@ -4,7 +4,7 @@ from collections import defaultdict
|
||||
from dataclasses import dataclass
|
||||
from tinygrad.dtype import dtypes, ImageDType, DType, AddrSpace
|
||||
from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, graph_rewrite, GroupOp, identity_element
|
||||
from tinygrad.uop.symbolic import uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
|
||||
from tinygrad.uop.symbolic import split_uop, uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
|
||||
from tinygrad.helpers import getenv, flatten, AMX, prod, partition
|
||||
from tinygrad.renderer import Renderer
|
||||
|
||||
@@ -19,13 +19,13 @@ def simplify_valid_load(buf:UOp, start_idx:UOp, valid:UOp) -> UOp|None:
|
||||
|
||||
# can drop valid if idx is out of bound when valid is False
|
||||
drop_stmt = []
|
||||
for stmt in valid.split_uop(Ops.AND):
|
||||
for stmt in split_uop(valid, Ops.AND):
|
||||
try: X, is_upper_bound, c = parse_valid(stmt)
|
||||
except ValueError: return None
|
||||
|
||||
# for X0 + X1 + ... >= 1, check if it's out of bound when Xi = 0 for all i
|
||||
if not is_upper_bound and c == 1 and all(u.op in GroupOp.Irreducible and u.vmin == 0 for u in X.split_uop(Ops.ADD)):
|
||||
testidx = functools.reduce(lambda nowidx,u: nowidx.substitute({u:u.const_like(0)}), X.split_uop(Ops.ADD), idx)
|
||||
if not is_upper_bound and c == 1 and all(u.op in GroupOp.Irreducible and u.vmin == 0 for u in split_uop(X, Ops.ADD)):
|
||||
testidx = functools.reduce(lambda nowidx,u: nowidx.substitute({u:u.const_like(0)}), split_uop(X, Ops.ADD), idx)
|
||||
testidx = testidx.simplify()
|
||||
if testidx.gep(0).vmax < 0 or testidx.gep(1).vmax < 0:
|
||||
drop_stmt.append(stmt)
|
||||
@@ -42,7 +42,7 @@ def simplify_valid_load(buf:UOp, start_idx:UOp, valid:UOp) -> UOp|None:
|
||||
break
|
||||
|
||||
if not drop_stmt and idx is start_idx: return None
|
||||
new_valid = functools.reduce(operator.and_, ss) if (ss:=[s for s in valid.split_uop(Ops.AND) if s not in drop_stmt]) else None
|
||||
new_valid = functools.reduce(operator.and_, ss) if (ss:=[s for s in split_uop(valid, Ops.AND) if s not in drop_stmt]) else None
|
||||
return buf.index(idx, new_valid)
|
||||
|
||||
def delete_redundant_gates(store:UOp, buf:UOp, idx:UOp, val:UOp, store_gate:UOp, cast:UOp|None=None) -> UOp|None:
|
||||
|
||||
@@ -2,7 +2,7 @@ from __future__ import annotations
|
||||
import heapq
|
||||
from collections import defaultdict
|
||||
from dataclasses import dataclass, replace
|
||||
from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, BottomUpGate
|
||||
from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp
|
||||
from tinygrad.helpers import dedup, all_same, flatten, BLOCK_REORDER
|
||||
|
||||
# NOTE: any toposort should be valid here, unlike last time this isn't required, it's just for speed
|
||||
@@ -76,13 +76,12 @@ class BlockContext:
|
||||
def from_sink(sink:UOp) -> BlockContext:
|
||||
# get children and all block contexts
|
||||
ctx = BlockContext({}, {}, {})
|
||||
for u in sink.toposort(gate=lambda u:u.op is not Ops.SPECIAL):
|
||||
for u in sink.toposort():
|
||||
this_block_ctx: list[UOp] = []
|
||||
ctx.child_count[u] = 0
|
||||
|
||||
# get children and accumulate the last_ctx
|
||||
for s in u.src:
|
||||
if s.op is Ops.SPECIAL: continue
|
||||
# NOTE: if a parent appears multiple times in the src, it counts multiple times as a child
|
||||
ctx.child_count[s] += 1
|
||||
this_block_ctx += ctx.last_ctx(s)
|
||||
@@ -143,7 +142,7 @@ def make_block_bottom_up(ctx:BlockContext, x:UOp):
|
||||
|
||||
# add unmergables to sources
|
||||
srcs = []
|
||||
for u,cnt in unmergable.items(): srcs += [add_blockends(u, ctx.block_ctxs.get(u,()), current_ctx, cnt=cnt)]*cnt
|
||||
for u,cnt in unmergable.items(): srcs += [add_blockends(u, ctx.block_ctxs[u], current_ctx, cnt=cnt)]*cnt
|
||||
|
||||
# add blockseeds, with blockends as needed
|
||||
for (new_ctx, new_child_ctx), v in blockseeds.items():
|
||||
@@ -155,12 +154,8 @@ def make_block_bottom_up(ctx:BlockContext, x:UOp):
|
||||
bb = BasicBlock(tuple(lst), ctx=current_ctx, cnt=child_count, child_ctx=child_ctx)
|
||||
return UOp(Ops.BLOCK, src=tuple(srcs), arg=bb)
|
||||
|
||||
# we prevent the source of the SPECIAL from being linearized since its not part of the kernel
|
||||
def raise_bottom_up_gate(): raise BottomUpGate()
|
||||
|
||||
block_create = PatternMatcher([
|
||||
(UPat(GroupOp.All-DONT_PLACE_IN_BLOCK.union({Ops.BLOCK, Ops.BLOCKEND}), name="x"), make_block_bottom_up),
|
||||
(UPat(Ops.SPECIAL), raise_bottom_up_gate)
|
||||
])
|
||||
|
||||
# ***** blockend merging ****
|
||||
|
||||
@@ -1,26 +1,51 @@
|
||||
# opt opinionatedly transforms an ast into an optimized ast using either heuristics or beam search
|
||||
from __future__ import annotations
|
||||
from enum import Enum, auto
|
||||
from dataclasses import dataclass
|
||||
from tinygrad.uop.ops import AxisType
|
||||
|
||||
class OptOps(Enum):
|
||||
TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
|
||||
GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
|
||||
def __lt__(self, x:OptOps): return self.value < x.value
|
||||
from tinygrad.codegen.opt.kernel import Kernel
|
||||
from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
|
||||
from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, KernelInfo
|
||||
from tinygrad.helpers import NOOPT, BEAM, USE_TC, getenv
|
||||
from tinygrad.renderer import Renderer
|
||||
from tinygrad.uop.spec import type_verify
|
||||
|
||||
@dataclass(frozen=True, order=True)
|
||||
class Opt:
|
||||
op: OptOps
|
||||
axis: int|None = None
|
||||
arg: int|tuple|None = None
|
||||
def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
|
||||
def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp|None:
|
||||
"""
|
||||
Optimize an AST based on heuristics or BEAM search.
|
||||
|
||||
axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.LOOP: "L", AxisType.UPCAST: "u",
|
||||
AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
|
||||
axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow",
|
||||
AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
|
||||
Args:
|
||||
ast: The Ops.SINK rooted AST
|
||||
renderer: The renderer used to generate the code
|
||||
|
||||
class KernelOptError(Exception): pass
|
||||
def check(cond:bool, msg:str=""):
|
||||
if not cond: raise KernelOptError(msg)
|
||||
Returns:
|
||||
The Ops.SINK rooted AST transformed to apply the opts and with a KernelInfo in the arg.
|
||||
"""
|
||||
|
||||
# no shape, no opt
|
||||
if ast.src[0].st is None: return None
|
||||
new_arg = ast.arg
|
||||
if new_arg is None:
|
||||
k = Kernel(ast, opts=renderer)
|
||||
if not NOOPT:
|
||||
if not k.apply_tensor_cores(USE_TC.value): k.apply_opts(hand_coded_optimizations(k))
|
||||
if BEAM >= 1:
|
||||
from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
|
||||
kb = Kernel(ast, opts=renderer)
|
||||
rawbufs = bufs_from_lin(kb, allocate=False)
|
||||
k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
|
||||
new_arg = KernelInfo(opts_to_apply=tuple(k.applied_opts))
|
||||
elif len(new_arg.applied_opts): return None
|
||||
return Kernel(ast.replace(arg=None), opts=renderer).get_optimized_ast().replace(arg=new_arg)
|
||||
|
||||
pm_get_optimization = PatternMatcher([
|
||||
(UPat(Ops.SINK, name="ast"), lambda ctx,ast: get_optimized_ast(ast, ctx)),
|
||||
])
|
||||
|
||||
def apply_opt(ast:UOp, renderer:Renderer):
|
||||
k = Kernel(ast, opts=renderer)
|
||||
k.apply_opts(ast.arg.opts_to_apply)
|
||||
ret = k.get_optimized_ast()
|
||||
if __debug__: type_verify(list(ret.toposort()))
|
||||
return ret
|
||||
|
||||
pm_do_optimize = PatternMatcher([
|
||||
(UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_opt(ast, ctx) if ast.arg is not None and ast.arg.opts_to_apply is not None else None),
|
||||
])
|
||||
|
||||
@@ -1,50 +1,10 @@
|
||||
import itertools
|
||||
from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
|
||||
from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS, TC_OPT, TC_SELECT, USE_TC, AMX
|
||||
from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps, KernelOptError, AxisType
|
||||
from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS
|
||||
from tinygrad.dtype import ImageDType
|
||||
from tinygrad.uop.ops import Ops, resolve, AxisType
|
||||
|
||||
# both versions
|
||||
from tinygrad.codegen.opt.kernel import Kernel
|
||||
from tinygrad.codegen.opt.postrange import Scheduler
|
||||
|
||||
def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
|
||||
# first try the tensor cores
|
||||
""" Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
|
||||
Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).
|
||||
|
||||
Keyword arguments:
|
||||
use_tensor_cores -- controls how tensor cores are applied (default 1)
|
||||
0: will disable any tensor core matching
|
||||
1: enable tensor cores
|
||||
2: apply tensor core shape but don't use UOp.WMMA
|
||||
extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None)
|
||||
tc_select -- specifies which tensor core(s) to use for optimization (default -1)
|
||||
-1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes)
|
||||
[0-N]: uses only the n'th tensor core available; useful for search
|
||||
tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise)
|
||||
0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL
|
||||
1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
|
||||
2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
|
||||
"""
|
||||
if USE_TC > 0:
|
||||
try: # check TC first and apply hand-coded opts if successful
|
||||
tk = k.copy()
|
||||
tk.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, USE_TC.value)))
|
||||
|
||||
# skip hand-coded TC opts if AMX, upcasting will make kernel slower
|
||||
if isinstance(k, Kernel) and (tc_opts:=tk.tensor_core_opts) is not None and not AMX:
|
||||
# hand-coded TC opts
|
||||
for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N
|
||||
szs = [sz for sz in [5,4,3,2] if tk.full_shape[tc_opts.axes[tc_dim]] % sz == 0]
|
||||
if szs: tk.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0]))
|
||||
|
||||
if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if tk.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N
|
||||
tk.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0]))
|
||||
return tk.applied_opts
|
||||
except KernelOptError:
|
||||
pass
|
||||
from tinygrad.uop.ops import Ops, resolve
|
||||
|
||||
def hand_coded_optimizations(k:Kernel) -> list[Opt]:
|
||||
# make a copy so it does not mutate the input
|
||||
k = k.copy()
|
||||
|
||||
@@ -53,20 +13,19 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
|
||||
if k.opts.has_local and getenv("MV",1) != 0 and (MV_BLOCKSIZE > 1 or MV_THREADS_PER_ROW > 1 or MV_ROWS_PER_THREAD > 1) and \
|
||||
k.reduceop is not None and k.reduceop.arg[0] is Ops.ADD and len(k.full_shape) >= 2 and k.opts.has_shared and \
|
||||
(mulop:=k.reduceop.src[0]).op is Ops.MUL and mulop.src[0].op is Ops.LOAD and mulop.src[1].op is Ops.LOAD:
|
||||
if isinstance(k, Kernel):
|
||||
st0, st1 = k.sts[k.bufs.index(mulop.src[0])], k.sts[k.bufs.index(mulop.src[1])]
|
||||
strides0, strides1 = st0.real_strides(), st1.real_strides()
|
||||
def has_expanded_axis(shape, strides): return any(resolve(s > 1) and not resolve(st != 0) for s,st in zip(shape,strides))
|
||||
if strides0[first_reduce:=(k.axes_of(AxisType.REDUCE)[0])] == 1 and \
|
||||
not (has_expanded_axis(st0.shape, strides0) and has_expanded_axis(st1.shape, strides1)):
|
||||
for global_idx in k.axes_of(AxisType.GLOBAL):
|
||||
if k.full_shape[first_reduce]%MV_THREADS_PER_ROW == 0 and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
|
||||
if DEBUG >= 3:
|
||||
print(f"MATVEC: {k.full_shape=} {first_reduce=} {strides0=} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
|
||||
if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
|
||||
if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
|
||||
if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
|
||||
return k.applied_opts
|
||||
st0, st1 = k.sts[k.bufs.index(mulop.src[0])], k.sts[k.bufs.index(mulop.src[1])]
|
||||
strides0, strides1 = st0.real_strides(), st1.real_strides()
|
||||
def has_expanded_axis(shape, strides): return any(resolve(s > 1) and not resolve(st != 0) for s,st in zip(shape,strides))
|
||||
if strides0[first_reduce:=(k.axes_of(AxisType.REDUCE)[0])] == 1 and \
|
||||
not (has_expanded_axis(st0.shape, strides0) and has_expanded_axis(st1.shape, strides1)):
|
||||
for global_idx in k.axes_of(AxisType.GLOBAL):
|
||||
if k.full_shape[first_reduce]%MV_THREADS_PER_ROW == 0 and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
|
||||
if DEBUG >= 3:
|
||||
print(f"MATVEC: {k.full_shape=} {first_reduce=} {strides0=} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
|
||||
if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
|
||||
if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
|
||||
if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
|
||||
return k.applied_opts
|
||||
|
||||
# are we grouping? (requires local shape support)
|
||||
if resolve(prod(k.output_shape[i] for i in k.upcastable_dims) <= 2048, False):
|
||||
@@ -79,12 +38,7 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
|
||||
# upcast float4 images
|
||||
for buf_index,buf in enumerate(k.bufs):
|
||||
if isinstance(buf.src[0].dtype, ImageDType):
|
||||
if hasattr(k, "sts"):
|
||||
unit_stride_axes_mul_4 = [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]
|
||||
else:
|
||||
# part of real_strides
|
||||
unit_stride_axes_mul_4 = [k.rngs.index(c) for c in k.bufs[buf_index].src[1].split_uop(Ops.ADD) if c.op is Ops.RANGE and (c.vmax+1)%4 == 0]
|
||||
if len(unit_stride_axes_mul_4):
|
||||
if (unit_stride_axes_mul_4 := [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]):
|
||||
if (axis:=unit_stride_axes_mul_4[0]) in k.upcastable_dims:
|
||||
k.apply_opt(Opt(OptOps.UPCAST, axis, 4))
|
||||
elif axis in k.unrollable_dims:
|
||||
@@ -99,9 +53,8 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
|
||||
to_upcast: list[int] = []
|
||||
# upcast leading axes first (hack-ish for winograd; we actually want to upcast masked axes with low stride first)
|
||||
for axis in k.upcastable_dims:
|
||||
if isinstance(k, Kernel): is_masked = any(st.axis_is_masked(axis) for st in k.sts)
|
||||
else: is_masked = any(len(st.src) > 2 and k.rngs[axis] in st.src[2].parents for st in k.bufs)
|
||||
if k.full_shape[axis] <= 7 and is_masked and prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
|
||||
if k.full_shape[axis] <= 7 and any(st.axis_is_masked(axis) for st in k.sts) and \
|
||||
prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
|
||||
if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
|
||||
to_upcast.append(axis)
|
||||
for axis in to_upcast[::-1]: k.apply_opt(Opt(OptOps.UPCAST, axis, 0))
|
||||
@@ -115,24 +68,10 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
|
||||
for axis, upcast_amount in itertools.product(k.upcastable_dims, ([128] if not len(upcasted_axis) else []) if is_dsp else [3,4]):
|
||||
# if we haven't upcasted it, it mods, and buffer has stride 0 on axis while having no stride 0 in the upcasted axis already
|
||||
if axis in upcasted_axis or k.full_shape[axis]%upcast_amount != 0: continue
|
||||
if isinstance(k, Kernel):
|
||||
# must have stride 0 on a view
|
||||
# must have all non stride 0 on what's upcasted before
|
||||
if any(st.views[-1].strides[axis] == 0 and \
|
||||
all(x != 0 for t,x in zip(k.axis_types, st.real_strides()) if t in (AxisType.UPCAST, AxisType.UNROLL)) for st in k.sts):
|
||||
xb_choices.append((sum(st.views[-1].strides[axis]>0 for st in k.sts),
|
||||
sum(st.views[-1].strides[axis] for st in k.sts), axis, upcast_amount))
|
||||
else:
|
||||
rng = k.rngs[axis]
|
||||
if any(rng not in b.src[1].parents and all(r2 in b.src[1].parents for r2 in k.ranges_of(AxisType.UPCAST, AxisType.UNROLL)) for b in k.bufs):
|
||||
num_strides, sum_strides = 0, 0
|
||||
for b in k.bufs:
|
||||
if rng in b.src[1].parents: num_strides += 1
|
||||
for c in b.src[1].split_uop(Ops.ADD):
|
||||
if c is rng: sum_strides += 1
|
||||
if c.op is Ops.MUL and c.src[0] is rng and c.src[1].op is Ops.CONST: sum_strides += c.src[1].arg
|
||||
if c.op is Ops.MUL and c.src[1] is rng and c.src[0].op is Ops.CONST: sum_strides += c.src[0].arg
|
||||
xb_choices.append((num_strides, sum_strides, axis, upcast_amount))
|
||||
if any(st.views[-1].strides[axis] == 0 and \
|
||||
all(x != 0 for t,x in zip(k.axis_types, st.real_strides()) if t in (AxisType.UPCAST, AxisType.UNROLL)) for st in k.sts):
|
||||
xb_choices.append((sum(st.views[-1].strides[axis]>0 for st in k.sts),
|
||||
sum(st.views[-1].strides[axis] for st in k.sts), axis, upcast_amount))
|
||||
if xb_choices:
|
||||
xb_choices = sorted(xb_choices)
|
||||
if DEBUG >= 4: print(f"more upcast axis : {xb_choices}")
|
||||
@@ -170,11 +109,7 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
|
||||
k.apply_opt(Opt(OptOps.NOLOCALS))
|
||||
else:
|
||||
# prioritize making expand axes local
|
||||
if isinstance(k, Kernel):
|
||||
local_axis_ranking = [(any(st.views[-1].strides[axis] == 0 for st in k.sts), axis) for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP)]
|
||||
else:
|
||||
local_axis_ranking = [(any(k.rngs[axis] not in b.src[1].parents for b in k.bufs), axis) \
|
||||
for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP) if k.rngs[axis].src[0].op is Ops.CONST]
|
||||
local_axis_ranking = [(any(st.views[-1].strides[axis] == 0 for st in k.sts), axis) for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP)]
|
||||
to_local: list[tuple[int, int]] = []
|
||||
for _, axis in sorted(local_axis_ranking, key=lambda x: (-x[0], -x[1])):
|
||||
local_size = prod(sz for _, sz in to_local)
|
||||
|
||||
@@ -3,18 +3,40 @@ import itertools, functools, math
|
||||
from dataclasses import dataclass
|
||||
from collections import defaultdict
|
||||
from typing import cast, Final, Callable, Sequence
|
||||
from tinygrad.codegen.opt import OptOps, Opt, KernelOptError, check, axis_letters, axis_colors
|
||||
from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType, PatternMatcher, UPat
|
||||
from enum import Enum, auto
|
||||
|
||||
from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType
|
||||
from tinygrad.uop.spec import type_verify, ast_spec
|
||||
from tinygrad.device import Device
|
||||
from tinygrad.codegen.opt.tc import TensorCore
|
||||
from tinygrad.renderer import Renderer
|
||||
from tinygrad.dtype import ImageDType
|
||||
from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, NOOPT, BEAM, getenv, POSTOPT
|
||||
from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, TC_SELECT, TC_OPT, AMX
|
||||
from tinygrad.shape.shapetracker import ShapeTracker
|
||||
from tinygrad.shape.view import strides_for_shape, get_contraction
|
||||
from tinygrad.codegen.opt.swizzler import view_left, view_left_through_load
|
||||
|
||||
class OptOps(Enum):
|
||||
TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
|
||||
GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
|
||||
def __lt__(self, x:OptOps): return self.value < x.value
|
||||
|
||||
@dataclass(frozen=True, order=True)
|
||||
class Opt:
|
||||
op: OptOps
|
||||
axis: int|None = None
|
||||
arg: int|tuple|None = None
|
||||
def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
|
||||
|
||||
axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.LOOP: "L", AxisType.UPCAST: "u",
|
||||
AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
|
||||
axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow",
|
||||
AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
|
||||
|
||||
class KernelOptError(Exception): pass
|
||||
def check(cond:bool, msg:str=""):
|
||||
if not cond: raise KernelOptError(msg)
|
||||
|
||||
@dataclass
|
||||
class TensorCoreOptions:
|
||||
axes: tuple[int, ...] # the location of the original N and M axes if still in the shape
|
||||
@@ -377,6 +399,45 @@ class Kernel:
|
||||
return True
|
||||
return False
|
||||
|
||||
def apply_tensor_cores(self, use_tensor_cores=1, extra_opts:list[Opt]|None=None, axis:int=0, tc_select:int|None=None, tc_opt:int|None=None) -> bool:
|
||||
""" Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
|
||||
Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).
|
||||
|
||||
Keyword arguments:
|
||||
use_tensor_cores -- controls how tensor cores are applied (default 1)
|
||||
0: will disable any tensor core matching
|
||||
1: enable tensor cores
|
||||
2: apply tensor core shape but don't use UOp.WMMA
|
||||
extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None)
|
||||
tc_select -- specifies which tensor core(s) to use for optimization (default -1)
|
||||
-1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes)
|
||||
[0-N]: uses only the n'th tensor core available; useful for search
|
||||
tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise)
|
||||
0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL
|
||||
1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
|
||||
2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
|
||||
"""
|
||||
if tc_select is None: tc_select = TC_SELECT.value
|
||||
if tc_opt is None: tc_opt = TC_OPT.value
|
||||
if not self.opts.tensor_cores: return False
|
||||
try: # check TC first and apply hand-coded opts if successful
|
||||
self.apply_opt(Opt(OptOps.TC, axis, (tc_select, tc_opt, use_tensor_cores)))
|
||||
|
||||
if (tc_opts:=self.tensor_core_opts) is not None:
|
||||
if extra_opts is not None: self.apply_opts(extra_opts)
|
||||
else:
|
||||
if AMX: return True # skip hand-coded TC opts if AMX, upcasting will make kernel slower
|
||||
# hand-coded TC opts
|
||||
for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N
|
||||
szs = [sz for sz in [5,4,3,2] if self.full_shape[tc_opts.axes[tc_dim]] % sz == 0]
|
||||
if szs: self.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0]))
|
||||
|
||||
if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if self.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N
|
||||
self.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0]))
|
||||
return True
|
||||
except KernelOptError:
|
||||
return False
|
||||
|
||||
# strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
|
||||
def shape_str(self) -> list[str]:
|
||||
ret: list[str] = []
|
||||
@@ -433,47 +494,3 @@ class Kernel:
|
||||
fixed_ast = fixup_ast(self.ast)
|
||||
del fixup_ast
|
||||
return graph_rewrite(fixed_ast, view_left+view_left_through_load, name="fixup optimized AST")
|
||||
|
||||
def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp|None:
|
||||
"""
|
||||
Optimize an AST based on heuristics or BEAM search.
|
||||
|
||||
Args:
|
||||
ast: The Ops.SINK rooted AST
|
||||
renderer: The renderer used to generate the code
|
||||
|
||||
Returns:
|
||||
The Ops.SINK rooted AST transformed to apply the opts and with a KernelInfo in the arg.
|
||||
"""
|
||||
|
||||
# no shape, no opt
|
||||
if ast.src[0].st is None: return None
|
||||
new_arg = ast.arg
|
||||
if new_arg is None:
|
||||
k = Kernel(ast, opts=renderer)
|
||||
if not NOOPT:
|
||||
from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
|
||||
k.apply_opts(hand_coded_optimizations(k))
|
||||
if not POSTOPT and BEAM >= 1:
|
||||
from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
|
||||
kb = Kernel(ast, opts=renderer)
|
||||
rawbufs = bufs_from_lin(kb, allocate=False)
|
||||
k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
|
||||
new_arg = KernelInfo(opts_to_apply=tuple(k.applied_opts))
|
||||
elif len(new_arg.applied_opts): return None
|
||||
return Kernel(ast.replace(arg=None), opts=renderer).get_optimized_ast().replace(arg=new_arg)
|
||||
|
||||
pm_get_optimization = PatternMatcher([
|
||||
(UPat(Ops.SINK, name="ast"), lambda ctx,ast: get_optimized_ast(ast, ctx)),
|
||||
])
|
||||
|
||||
def apply_opt(ast:UOp, renderer:Renderer):
|
||||
k = Kernel(ast, opts=renderer)
|
||||
k.apply_opts(ast.arg.opts_to_apply)
|
||||
ret = k.get_optimized_ast()
|
||||
if __debug__: type_verify(list(ret.toposort()))
|
||||
return ret
|
||||
|
||||
pm_do_optimize = PatternMatcher([
|
||||
(UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_opt(ast, ctx) if ast.arg is not None and ast.arg.opts_to_apply is not None else None),
|
||||
])
|
||||
@@ -1,332 +1,18 @@
|
||||
import math, itertools
|
||||
from collections import defaultdict
|
||||
from typing import cast, Final
|
||||
from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo, graph_rewrite, _substitute, AxisType
|
||||
from tinygrad.uop.symbolic import symbolic
|
||||
from tinygrad.device import Buffer
|
||||
from tinygrad.dtype import AddrSpace, dtypes
|
||||
from tinygrad.helpers import colored, BEAM, getenv, DEBUG, to_function_name, NOOPT, argsort, round_up
|
||||
from tinygrad.codegen.opt import axis_colors, Opt, OptOps, KernelOptError, check, axis_letters
|
||||
from tinygrad.renderer import Renderer
|
||||
from tinygrad.schedule.rangeify import remove_tags
|
||||
from dataclasses import replace
|
||||
from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo
|
||||
from tinygrad.helpers import colored
|
||||
from tinygrad.codegen.opt.kernel import axis_colors
|
||||
|
||||
# NOTE: LOCAL and GROUP_REDUCE have the same priority. the order here matters
|
||||
axis_to_pos = {AxisType.LOOP: -1, AxisType.GLOBAL: 0, AxisType.LOCAL: 1, AxisType.UPCAST: 2,
|
||||
AxisType.GROUP_REDUCE: 1, AxisType.REDUCE: 3, AxisType.UNROLL: 4}
|
||||
def rename_sink(s:UOp):
|
||||
if s.arg is not None and s.arg.name != "test": return None
|
||||
|
||||
def flatten_range(r:UOp):
|
||||
off = 2 if r.op is Ops.STORE else 1
|
||||
rngs = r.src[off:]
|
||||
if not len(rngs): return None
|
||||
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))
|
||||
# get all ranges (sorted)
|
||||
rngs = sorted([u for u in s.parents if u.op is Ops.RANGE], key=lambda x: x.arg[0:-1])
|
||||
|
||||
pm_flatten_range = PatternMatcher([
|
||||
# real ranges only
|
||||
(UPat((Ops.REDUCE, Ops.STORE), name="r"), flatten_range),
|
||||
])
|
||||
|
||||
def count_divmod(x:UOp): return len([u for u in x.toposort() if u.op in {Ops.IDIV, Ops.MOD}])
|
||||
|
||||
class Scheduler:
|
||||
def __init__(self, ast:UOp, opts:Renderer):
|
||||
self.ast, self.opts = ast, opts
|
||||
self.dont_use_locals = self.ast.arg.dont_use_locals if self.ast.arg is not None else False
|
||||
self.applied_opts = list(self.ast.arg.applied_opts) if self.ast.arg is not None else []
|
||||
|
||||
@property
|
||||
def rngs(self):
|
||||
# always in order by axistype
|
||||
return sorted([u for u in self.ast.parents if u.op is Ops.RANGE and u.vmax > 0], key=lambda x: (axis_to_pos[x.arg[-1]],) + x.arg[0:-1])
|
||||
@property
|
||||
def shape_len(self): return len(self.rngs)
|
||||
@property
|
||||
def full_shape(self): return [x.vmax+1 for x in self.rngs]
|
||||
@property
|
||||
def axis_types(self): return [x.arg[-1] for x in self.rngs]
|
||||
@property
|
||||
def maxarg(self): return max([x.arg[0] for x in self.rngs], default=0)
|
||||
|
||||
# strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
|
||||
def shape_str(self) -> list[str]:
|
||||
ret: list[str] = []
|
||||
cnt: dict[AxisType, int] = {}
|
||||
for x in self.axis_types:
|
||||
cnt[x] = (cnt[x] + 1) if x in cnt else 0
|
||||
ret.append(f"{axis_letters[x]}{cnt[x]}")
|
||||
return ret
|
||||
def shape_str_to_axis(self, nms:list[str]) -> tuple[int, ...]: return tuple([self.shape_str().index(x) for x in nms])
|
||||
|
||||
@property
|
||||
def termination(self):
|
||||
terminators = [u for u in self.ast.parents if u.op in {Ops.REDUCE, Ops.STORE}]
|
||||
termination = {}
|
||||
for t in terminators:
|
||||
# works without pm_flatten_range
|
||||
for u in UOp.sink(*t.src[1 if t.op is Ops.REDUCE else 2:]).parents:
|
||||
if u.op is Ops.RANGE: termination[u] = t
|
||||
return termination
|
||||
|
||||
def copy(self): return Scheduler(self.get_optimized_ast(), self.opts)
|
||||
|
||||
kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int)
|
||||
def get_optimized_ast(self, name_override:str|None=None):
|
||||
if name_override is not None: name = name_override
|
||||
else:
|
||||
name = "k" + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
|
||||
Scheduler.kernel_cnt[(function_name := to_function_name(name))] += 1
|
||||
num = f"n{Scheduler.kernel_cnt[function_name]-1}" if Scheduler.kernel_cnt[function_name] > 1 else ""
|
||||
name += colored(num, 'BLACK')
|
||||
self.ast = graph_rewrite(self.ast, pm_flatten_range, name="flatten range")
|
||||
return self.ast.replace(arg=KernelInfo(name=name, applied_opts=tuple(self.applied_opts), dont_use_locals=self.dont_use_locals), tag=1)
|
||||
|
||||
def convert_loop_to_global(self):
|
||||
if not self.opts.has_local: return None
|
||||
store_rngs = self.ast.src[0].src[2:]
|
||||
|
||||
# filter any not in local stores
|
||||
local_store_rngs = [x.ranges for x in self.ast.toposort() if (x.op is Ops.STORE and x.src[0].ptrdtype.addrspace == AddrSpace.LOCAL) \
|
||||
or (x.op is Ops.BUFFERIZE and x.arg == AddrSpace.LOCAL)]
|
||||
for ls in local_store_rngs: store_rngs = tuple([x for x in store_rngs if x in ls])
|
||||
|
||||
store_rng = [x for x in UOp.sink(*store_rngs).toposort() if x.op is Ops.RANGE] if store_rngs else []
|
||||
rng = [x.replace(arg=(x.arg[0], AxisType.GLOBAL)) if x.arg[1] == AxisType.LOOP and x in store_rng else x for x in self.rngs]
|
||||
|
||||
self.ast = self.ast.substitute(dict(zip(self.rngs, rng)))
|
||||
|
||||
def simplify_merge_adjacent(self):
|
||||
i = 0
|
||||
while i < len(self.rngs)-1:
|
||||
r0, r1 = self.rngs[i], self.rngs[i+1]
|
||||
# same axistype and same termination
|
||||
termination = self.termination
|
||||
if r0.arg[1] == r1.arg[1] and r0 in termination and r1 in termination and termination[r0] == termination[r1]:
|
||||
s0, s1 = r0.src[0], r1.src[0]
|
||||
new_range = r0.replace(src=(s0*s1,)).simplify()
|
||||
# this checks the legality of a merge
|
||||
oidx = self.ast.simplify()
|
||||
nidx = graph_rewrite(oidx, _substitute+symbolic+pm_flatten_range, ctx={r0:new_range//s1, r1:new_range%s1}, name=f"check_merge_{i}_{i+1}")
|
||||
# it simplifies
|
||||
if count_divmod(nidx) <= count_divmod(oidx):
|
||||
# it is correct
|
||||
midx = graph_rewrite(nidx, _substitute+symbolic+pm_flatten_range, ctx={new_range:r0*s1+r1}, name=f"correct_merge_{i}_{i+1}")
|
||||
if oidx is midx:
|
||||
self.ast = nidx
|
||||
continue
|
||||
i += 1
|
||||
|
||||
def colors(self) -> list[str]: return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
|
||||
def colored_shape(self) -> str: return ' '.join([colored(f'{x.src[0].render():4s}', color) for x,color in zip(self.rngs, self.colors())])
|
||||
|
||||
def shift_to(self, rng:UOp, amount:int, new_type:AxisType, top:bool=False):
|
||||
if (old_sz:=rng.src[0].divides(amount)) is None:
|
||||
raise KernelOptError(f"{amount} can't divide {rng.src[0]} in {self.colored_shape()}")
|
||||
new_rng = UOp.range(amount, self.maxarg+1, new_type)
|
||||
replaced_rng = rng.replace(src=(UOp.const(dtypes.int, old_sz),))
|
||||
sub_axis = (new_rng * old_sz + replaced_rng) if top else (replaced_rng * amount + new_rng)
|
||||
self.ast = self.ast.substitute({rng:sub_axis}, name=f"shift {rng.arg[0]} {amount}")
|
||||
return replaced_rng, new_rng
|
||||
|
||||
def ranges_of(self, *axis_type:AxisType) -> list[UOp]: return [r for r in self.rngs if r.arg[-1] in axis_type]
|
||||
def axes_of(self, *axis_type:AxisType) -> list[int]: return [i for i,t in enumerate(self.axis_types) if t in axis_type]
|
||||
@property
|
||||
def upcastable_dims(self): return self.axes_of(AxisType.GLOBAL, AxisType.LOCAL)
|
||||
@property
|
||||
def unrollable_dims(self): return self.axes_of(AxisType.REDUCE, AxisType.GROUP_REDUCE)
|
||||
|
||||
def real_axis(self, op:OptOps, axis:int|None):
|
||||
try:
|
||||
if axis is None: return -1
|
||||
if op is OptOps.UNROLL: return self.unrollable_dims[axis]
|
||||
if op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.axes_of(AxisType.REDUCE)[axis]
|
||||
check(axis < self.shape_len, f"invalid axis on {axis=} {op=} {self.shape_len=}")
|
||||
return axis
|
||||
except IndexError as e: raise KernelOptError from e
|
||||
|
||||
def apply_opt(self, opt:Opt, append_opt:bool=True):
|
||||
if opt.op is OptOps.NOLOCALS:
|
||||
check(all(x not in {AxisType.LOCAL, AxisType.GROUP_REDUCE} for x in self.axis_types), "no locals can't have locals")
|
||||
self.dont_use_locals = True
|
||||
self.applied_opts.append(opt)
|
||||
return
|
||||
|
||||
if opt.op in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}:
|
||||
check(self.opts.has_local, "locals needed for opt")
|
||||
|
||||
rng = self.rngs[self.real_axis(opt.op, opt.axis)]
|
||||
|
||||
opt_to_at = {
|
||||
OptOps.LOCAL: AxisType.LOCAL, OptOps.UPCAST: AxisType.UPCAST,
|
||||
OptOps.UNROLL: AxisType.UNROLL, OptOps.GROUP: AxisType.GROUP_REDUCE,
|
||||
OptOps.GROUPTOP: AxisType.GROUP_REDUCE}
|
||||
|
||||
if opt.op in opt_to_at:
|
||||
amt:int = (rng.vmax+1) if opt.arg == 0 else cast(int, opt.arg)
|
||||
if opt.op is OptOps.UNROLL:
|
||||
check(amt <= 32, "don't unroll more than 32")
|
||||
check(rng.arg[-1] in {AxisType.GROUP_REDUCE, AxisType.REDUCE}, "unroll is for GROUP_REDUCE/REDUCE")
|
||||
if opt.op is OptOps.UPCAST:
|
||||
check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16")
|
||||
check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP}, "upcast is for GLOBAL/LOCAL/LOOP")
|
||||
if opt.op is OptOps.LOCAL:
|
||||
check(not self.dont_use_locals, "can't use locals")
|
||||
check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOOP}, "local is for globals")
|
||||
if opt.op in {OptOps.GROUP, OptOps.GROUPTOP}:
|
||||
check(not self.dont_use_locals, "can't use locals")
|
||||
check(rng.arg[-1] == AxisType.REDUCE, "group is for reduce")
|
||||
self.shift_to(rng, amt, opt_to_at[opt.op], top=opt.op==OptOps.GROUPTOP)
|
||||
elif opt.op is OptOps.TC:
|
||||
check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: remove the need for this by having warps
|
||||
check(opt.axis is not None, "tensor core opts must have an axis")
|
||||
check(opt.arg is not None and isinstance(opt.arg, tuple) and len(opt.arg) == 3, "tensor core opts must have valid arg")
|
||||
check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select")
|
||||
check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt")
|
||||
check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid")
|
||||
check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available")
|
||||
elif opt.op is OptOps.PADTO:
|
||||
check(rng.src[0].op is Ops.CONST, "only pad const")
|
||||
replaced_rng = UOp.range(round_up(rng.vmax+1, cast(int, opt.arg)), *rng.arg)
|
||||
replaces = {rng:replaced_rng}
|
||||
for b in self.bufs:
|
||||
if rng in b.src[1].sparents:
|
||||
valid = replaced_rng < rng.vmax+1
|
||||
if len(b.src) > 2: valid = b.src[2] & valid
|
||||
replaces[b] = b.replace(src=b.src[0:2]+(valid,))
|
||||
self.ast = self.ast.substitute(replaces, f"padto {rng.arg[:-1]} {opt.arg}")
|
||||
elif opt.op is OptOps.SWAP:
|
||||
try:
|
||||
altrng = self.rngs[opt.arg]
|
||||
except IndexError:
|
||||
raise KernelOptError
|
||||
check(rng.arg[-1] == AxisType.GLOBAL and altrng.arg[-1] == AxisType.GLOBAL, "swap only for globals")
|
||||
self.ast = self.ast.substitute({rng:rng.replace(arg=(*altrng.arg[0:-1], rng.arg[-1]), tag=1),
|
||||
altrng:altrng.replace(arg=(*rng.arg[0:-1], altrng.arg[-1]), tag=1)})
|
||||
self.ast = graph_rewrite(self.ast, remove_tags)
|
||||
else:
|
||||
raise KernelOptError(f"unsupported opt {opt.op}")
|
||||
if append_opt:
|
||||
self.applied_opts.append(opt)
|
||||
|
||||
def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> bool:
|
||||
reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE]
|
||||
if not len(reduceops): raise KernelOptError("no reduce ops for TensorCore")
|
||||
reduceop = reduceops[0]
|
||||
if use_tensor_cores and reduceop is not None and reduceop.arg is Ops.ADD:
|
||||
mul = reduceop.src[0] if reduceop.src[0].op is not Ops.CAST else reduceop.src[0].src[0]
|
||||
if mul.op is not Ops.MUL: return False
|
||||
in0, in1 = mul.src
|
||||
try:
|
||||
tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
|
||||
except IndexError:
|
||||
raise KernelOptError(f"invalid tensor core choice {tc_select}")
|
||||
for tc in tensor_cores:
|
||||
if tc.dtype_in == in0.dtype.scalar() and tc.dtype_in == in1.dtype.scalar() and tc.dtype_out == reduceop.dtype.scalar():
|
||||
# tensor cores have three ranges. X, Y, and REDUCE
|
||||
in0_ranges = sorted([u for u in in0.ranges if u not in in1.ranges], key=lambda x: x.arg[0])
|
||||
in1_ranges = sorted([u for u in in1.ranges if u not in in0.ranges], key=lambda x: x.arg[0])
|
||||
red_ranges = sorted(reduceop.src[1:], key=lambda x: x.arg[0])
|
||||
if DEBUG >= 3:
|
||||
print(f"TC({axis}): {[(x.arg[0],x.vmax+1) for x in in0_ranges]}",
|
||||
f"{[(x.arg[0],x.vmax+1) for x in in1_ranges]} {[(x.arg[0],x.vmax+1) for x in red_ranges]}")
|
||||
if not len(in0_ranges) or not len(in1_ranges) or not len(red_ranges): continue
|
||||
|
||||
# pick ranges
|
||||
# NOTE: why are in1 and in0 switched?
|
||||
axis_choices = list(itertools.product(in1_ranges, in0_ranges, red_ranges))
|
||||
if not (axis < len(axis_choices)): continue
|
||||
axes = list(axis_choices[axis])
|
||||
|
||||
# do optimizations and save the ranges
|
||||
try:
|
||||
for i,a in enumerate(axes):
|
||||
# apply_opt should return the updated range?
|
||||
idx = self.rngs.index(a)
|
||||
self.apply_opt(Opt(OptOps.PADTO, idx, tc.dims[i]), append_opt=False) # PADTO might fail
|
||||
axes[i] = self.rngs[idx]
|
||||
except KernelOptError: continue
|
||||
|
||||
ne: list[UOp] = []
|
||||
for opt in tc.opts:
|
||||
axes[int(opt[1])], new_range = self.shift_to(axes[int(opt[1])], 2, {"u":AxisType.UPCAST, "l":AxisType.LOCAL}[opt[0]])
|
||||
ne.append(new_range)
|
||||
for _, amt in tc.get_reduce_axes():
|
||||
axes[2], new_range = self.shift_to(axes[2], amt, AxisType.UNROLL)
|
||||
ne.append(new_range)
|
||||
|
||||
if use_tensor_cores != 2:
|
||||
# fix the srcs
|
||||
reduceop = [x for x in self.ast.toposort() if x.op is Ops.REDUCE][0]
|
||||
tne = [x.replace(tag=1) for x in ne]
|
||||
ret = reduceop.substitute(dict(zip(ne, tne)))
|
||||
srcs = list((ret.src[0] if ret.src[0].op is not Ops.CAST else ret.src[0].src[0]).src)
|
||||
srcs = [x.substitute(dict(zip(tne, [ne[i] for i in argsort(p)]))) for x,p in zip(srcs, tc.permutes_for_shape_str(tc.base_shape_str()))]
|
||||
|
||||
# get reduce/upcast axes for the tensor cores
|
||||
tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))])
|
||||
base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis(tc.base_upcast_axes())])
|
||||
tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
|
||||
|
||||
# axes to range number (was done in lowerer)
|
||||
tc_upcast_axes = tuple([tuple([(self.rngs[a].arg[0], sz) for a,sz in v]) for v in tc_upcast_axes])
|
||||
tc_reduce_axes = tuple([self.rngs[a].arg[0] for a in tc_reduce_axes])
|
||||
|
||||
# construct the op
|
||||
# TODO: remove tc_upcast_axes from the arg
|
||||
# do the reduce_axes always disappear? i think they don't
|
||||
# they need to be moved into the WMMA srcs
|
||||
wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, self.opts.device, tc.threads, tc_upcast_axes, ()) #, tc_reduce_axes)
|
||||
wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=(
|
||||
UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0], tag=1),
|
||||
UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1], tag=1),
|
||||
UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg, tag=1)
|
||||
tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2], tag=1)
|
||||
|
||||
# preserve extra reduces
|
||||
reduce_ranges = [x for x in UOp.sink(*reduceop.src[1:]).toposort() if x.op is Ops.RANGE and x.arg[0] not in tc_reduce_axes]
|
||||
if len(reduce_ranges): tc_uop = UOp(Ops.REDUCE, tc_uop.dtype, (tc_uop,)+tuple(reduce_ranges), Ops.ADD)
|
||||
self.ast = self.ast.substitute({reduceop: tc_uop})
|
||||
return True
|
||||
return False
|
||||
|
||||
# helpers for hand_coded_optimizations
|
||||
@property
|
||||
def reduceop(self) -> UOp|None:
|
||||
red = [x for x in self.ast.parents if x.op is Ops.REDUCE]
|
||||
if not len(red): return None
|
||||
return UOp(Ops.REDUCE_AXIS, red[0].dtype, red[0].src, (red[0].arg, ()))
|
||||
@property
|
||||
def bufs(self) -> list[UOp]: return [x for x in self.ast.toposort() if x.op is Ops.INDEX][::-1]
|
||||
@property
|
||||
def output_shape(self):
|
||||
return [s if at not in {AxisType.REDUCE, AxisType.UNROLL, AxisType.GROUP_REDUCE} else 1 for s,at in zip(self.full_shape, self.axis_types)]
|
||||
@property
|
||||
def upcasted(self) -> int: return len(self.axes_of(AxisType.UPCAST, AxisType.UNROLL))
|
||||
@property
|
||||
def group_for_reduces(self) -> int: return len(self.axes_of(AxisType.GROUP_REDUCE))
|
||||
|
||||
def bufs_from_ast(ast:UOp, dname:str) -> list[Buffer]:
|
||||
glbls = sorted([x for x in ast.parents if x.op is Ops.DEFINE_GLOBAL], key=lambda x: x.arg)
|
||||
return [Buffer(dname, x.ptrdtype.size, x.dtype.base) for x in glbls]
|
||||
|
||||
def apply_opts(ctx:Renderer, ast:UOp):
|
||||
if ast.tag is not None: return None
|
||||
k = Scheduler(ast, ctx)
|
||||
k.convert_loop_to_global()
|
||||
if BEAM >= 1:
|
||||
k.simplify_merge_adjacent()
|
||||
from tinygrad.codegen.opt.search import beam_search
|
||||
rawbufs = bufs_from_ast(ast, ctx.device)
|
||||
k = beam_search(k, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
|
||||
elif ast.arg is not None and ast.arg.opts_to_apply is not None:
|
||||
for opt in ast.arg.opts_to_apply: k.apply_opt(opt)
|
||||
elif not NOOPT:
|
||||
k.simplify_merge_adjacent()
|
||||
from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
|
||||
# NOTE: hand_coded_optimizations doesn't support multiblock opts yet
|
||||
if all(len(u.src) == 1 for u in ast.parents if u.op is Ops.LOAD):
|
||||
for opt in hand_coded_optimizations(k): k.apply_opt(opt)
|
||||
return k.get_optimized_ast(name_override=ast.arg.name if ast.arg is not None and ast.arg.name != "test" else None)
|
||||
# add name to kernel
|
||||
name = "k" + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), axis_colors[x.arg[-1]]) for x in rngs])
|
||||
return s.replace(arg=KernelInfo(name=name) if s.arg is None else replace(s.arg, name=name))
|
||||
|
||||
pm_postrange_opt = PatternMatcher([
|
||||
(UPat(Ops.SINK, name="ast"), apply_opts),
|
||||
(UPat(Ops.SINK, name="s"), rename_sink),
|
||||
])
|
||||
|
||||
@@ -2,20 +2,16 @@ from typing import cast
|
||||
import functools, math, time, multiprocessing, traceback, signal, atexit
|
||||
from collections import defaultdict
|
||||
from dataclasses import replace
|
||||
from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType, pyrender
|
||||
from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType
|
||||
from tinygrad.device import Device, Buffer, Compiler
|
||||
from tinygrad.helpers import prod, flatten, DEBUG, CACHELEVEL, diskcache_get, diskcache_put, getenv, Context, colored, time_to_str
|
||||
from tinygrad.helpers import IGNORE_BEAM_CACHE
|
||||
from tinygrad.helpers import IGNORE_BEAM_CACHE, TC_SEARCH_OVER_SHAPE
|
||||
from tinygrad.dtype import ImageDType, PtrDType
|
||||
from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
|
||||
from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps, KernelOptError
|
||||
from tinygrad.tensor import Tensor
|
||||
from tinygrad.engine.realize import CompiledRunner, get_program
|
||||
from tinygrad.renderer import ProgramSpec
|
||||
|
||||
# both versions
|
||||
from tinygrad.codegen.opt.kernel import Kernel
|
||||
from tinygrad.codegen.opt.postrange import Scheduler
|
||||
|
||||
actions = [Opt(op=OptOps.UPCAST, axis=axis, arg=amt) for amt in [0,2,3,4,5,7] for axis in range(8)]
|
||||
actions += [Opt(op=OptOps.UNROLL, axis=axis, arg=amt) for amt in [0,4,7] for axis in range(5)]
|
||||
actions += [Opt(op=OptOps.LOCAL, axis=axis, arg=amt) for amt in [2,3,4,8,13,16,29] for axis in range(6)]
|
||||
@@ -59,9 +55,7 @@ def _time_program(p:ProgramSpec, lib:bytes, var_vals:dict[Variable, int], rawbuf
|
||||
return tms
|
||||
|
||||
class TimeoutException(Exception): pass
|
||||
def timeout_handler(signum, frame):
|
||||
if DEBUG >= 2: print("*** BEAM COMPILE TIMEOUT")
|
||||
raise TimeoutException()
|
||||
def timeout_handler(signum, frame): raise TimeoutException()
|
||||
|
||||
def _try_compile_linearized_w_idx(x:tuple[int,Kernel], compiler:Compiler) -> tuple[int, tuple[ProgramSpec, bytes, float]|None]:
|
||||
if hasattr(signal, "alarm"):
|
||||
@@ -97,7 +91,6 @@ def _ensure_buffer_alloc(bufs:list[Buffer]) -> list[Buffer]: return [buf.ensure_
|
||||
# *** external API ***
|
||||
|
||||
# get (scrap) buffers for timing the linearizer
|
||||
# NOTE: there's also bufs_from_ast in postrange
|
||||
def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
|
||||
bufsts: defaultdict[int, list[UOp]] = defaultdict(list)
|
||||
for x in lin.bufs:
|
||||
@@ -115,10 +108,17 @@ def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
|
||||
return cast(list[Buffer], rawbufs)
|
||||
|
||||
# get dictionary of all possible actions
|
||||
def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel|Scheduler]:
|
||||
def get_kernel_actions(lin:Kernel, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel]:
|
||||
acted_lins, max_up, max_lcl = {0:lin} if include_0 else {}, getenv("BEAM_UPCAST_MAX", 256), getenv("BEAM_LOCAL_MAX", 1024)
|
||||
kernel_actions = (actions if candidates is None else candidates).copy()
|
||||
|
||||
if TC_SEARCH_OVER_SHAPE and len(lin.applied_opts) == 0: # tensor core opts must be first
|
||||
for i, action in enumerate(kernel_actions):
|
||||
if action.op == OptOps.TC and (tc_arg := cast(tuple, action.arg))[0] == -1:
|
||||
# replace every tc_action with default tc with one tc_action for each available tc
|
||||
kernel_actions[i:i+1] = \
|
||||
[Opt(op=OptOps.TC, axis=action.axis, arg=(tc_select, tc_arg[1], tc_arg[2])) for tc_select,_ in enumerate(lin.opts.tensor_cores)]
|
||||
|
||||
for i,a in enumerate(kernel_actions):
|
||||
if a.axis is not None and a.op is not OptOps.TC:
|
||||
try: ax = lin.real_axis(a.op, a.axis)
|
||||
@@ -127,7 +127,7 @@ def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt
|
||||
lin2 = lin.copy()
|
||||
try:
|
||||
lin2.apply_opt(a)
|
||||
up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if hasattr(lin2, 'tensor_core') and (tc:=lin2.tensor_core) else 1
|
||||
up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if (tc:=lin2.tensor_core) else 1
|
||||
for s,c in zip(lin2.full_shape, lin2.axis_types):
|
||||
if c in (AxisType.UPCAST, AxisType.UNROLL): up *= s
|
||||
elif c in (AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
|
||||
@@ -139,7 +139,7 @@ def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt
|
||||
return acted_lins
|
||||
|
||||
beam_pool, BEAM_DEBUG = None, getenv("BEAM_DEBUG")
|
||||
def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value):
|
||||
def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value) -> Kernel:
|
||||
global beam_pool
|
||||
key = {"ast": lin.ast.key, "amt": amt, "allow_test_size": allow_test_size, "device": lin.opts.device, "suffix": lin.opts.suffix}
|
||||
if not disable_cache and CACHELEVEL >= 1 and (val:=diskcache_get("beam_search", key)) is not None:
|
||||
@@ -147,7 +147,7 @@ def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_
|
||||
for o in val[len(lin.applied_opts):]: ret.apply_opt(o)
|
||||
return ret
|
||||
|
||||
beam: list[tuple[Kernel|Scheduler, float]] = [(lin, float("inf"))]
|
||||
beam: list[tuple[Kernel, float]] = [(lin, float("inf"))]
|
||||
seen_libs = set()
|
||||
|
||||
default_parallel = multiprocessing.cpu_count() if lin.opts.device in {"CUDA", "AMD", "NV", "METAL", "HIP"} else 0
|
||||
@@ -157,9 +157,7 @@ def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_
|
||||
def close_pool(): beam_pool.close()
|
||||
|
||||
min_progress = getenv("BEAM_MIN_PROGRESS", 0.01)/1e6
|
||||
if BEAM_DEBUG:
|
||||
print("BEAM_SEARCH:")
|
||||
print('\n'.join(pyrender(lin.ast.replace(arg=None))))
|
||||
if BEAM_DEBUG: print(f"BEAM_SEARCH:\n{lin.ast}")
|
||||
if DEBUG >= 2: print(f" 0.00s: from 1 -> 1 actions {lin.colored_shape()}")
|
||||
|
||||
try:
|
||||
@@ -168,8 +166,8 @@ def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_
|
||||
exiting, st = False, time.perf_counter()
|
||||
dev = Device[lin.opts.device]
|
||||
while not exiting:
|
||||
acted_lins: list[Kernel|Scheduler] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
|
||||
timed_lins: list[tuple[Kernel|Scheduler, float]] = []
|
||||
acted_lins: list[Kernel] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
|
||||
timed_lins: list[tuple[Kernel, float]] = []
|
||||
_compile_fn = functools.partial(_try_compile_linearized_w_idx, compiler=dev.compiler)
|
||||
least_compute_ops = math.inf
|
||||
for i,proc in (map(_compile_fn, enumerate(acted_lins)) if beam_pool is None else beam_pool.imap_unordered(_compile_fn, enumerate(acted_lins))):
|
||||
|
||||
+1
-1
@@ -67,7 +67,7 @@ class PtrDType(DType):
|
||||
return type(self)(self.priority, self.itemsize, self.name, self.fmt, self.count, self, self._base, self.addrspace, sz, self.size)
|
||||
def ptr(self, size=-1, addrspace=AddrSpace.GLOBAL): raise RuntimeError("can't make a pointer from a pointer")
|
||||
def nbytes(self) -> int:
|
||||
if self.size == -1: raise RuntimeError("can't get nbytes of a pointer with unlimited size")
|
||||
if self.size == -1: return 0 # TODO: this should be an exception
|
||||
return self.size*self.itemsize
|
||||
@property
|
||||
def vcount(self): return self.v
|
||||
|
||||
@@ -3,7 +3,7 @@ import time, pprint, random, itertools, math
|
||||
from dataclasses import dataclass, replace, field
|
||||
from tinygrad.helpers import all_same, colored, DEBUG, GlobalCounters, ansilen, BEAM, NOOPT, all_int, CAPTURING, Metadata, TRACEMETA, TracingKey
|
||||
from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile, PROFILE, ProfilePointEvent, cpu_events, prod
|
||||
from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites, KernelInfo, pyrender
|
||||
from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites, KernelInfo
|
||||
from tinygrad.device import Device, Buffer
|
||||
from tinygrad.renderer import Renderer, ProgramSpec, Estimates
|
||||
from tinygrad.engine.schedule import ScheduleItem
|
||||
@@ -26,7 +26,6 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
|
||||
"""
|
||||
|
||||
if getenv("VIZ"): graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
|
||||
if DEBUG >= 5: print('\n'.join(pyrender(ast)))
|
||||
|
||||
# linearize
|
||||
if renderer is None: renderer = Device.default.renderer
|
||||
@@ -38,7 +37,7 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
|
||||
except RuntimeError as e:
|
||||
print("***** LINEARIZE FAILURE *****")
|
||||
print(e)
|
||||
print('\n'.join(pyrender(ast)))
|
||||
print(f"ast = {ast}")
|
||||
raise
|
||||
assert uops[-1].op is Ops.SINK, "last uop must be sink"
|
||||
|
||||
|
||||
+2
-6
@@ -56,7 +56,7 @@ def i2u(bits: int, value: int): return value if value >= 0 else (1<<bits)+value
|
||||
def is_numpy_ndarray(x) -> bool: return str(type(x)) == "<class 'numpy.ndarray'>"
|
||||
def merge_dicts(ds:Iterable[dict[T,U]]) -> dict[T,U]:
|
||||
kvs = set([(k,v) for d in ds for k,v in d.items()])
|
||||
if len(kvs) != len(set(kv[0] for kv in kvs)): raise RuntimeError(f"{kvs} contains different values for the same key")
|
||||
assert len(kvs) == len(set(kv[0] for kv in kvs)), f"cannot merge, {kvs} contains different values for the same key"
|
||||
return {k:v for d in ds for k,v in d.items()}
|
||||
def partition(itr:Iterable[T], fxn:Callable[[T],bool]) -> tuple[list[T], list[T]]:
|
||||
ret:tuple[list[T], list[T]] = ([], [])
|
||||
@@ -130,7 +130,7 @@ JIT = ContextVar("JIT", 2 if platform.system() == 'Darwin' and ('Intel' in platf
|
||||
JIT_BATCH_SIZE = ContextVar("JIT_BATCH_SIZE", 32)
|
||||
WINO, CAPTURING, TRACEMETA = ContextVar("WINO", 0), ContextVar("CAPTURING", 1), ContextVar("TRACEMETA", 1)
|
||||
USE_TC, TC_SELECT, TC_OPT, AMX = ContextVar("TC", 1), ContextVar("TC_SELECT", -1), ContextVar("TC_OPT", 0), ContextVar("AMX", 0)
|
||||
TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS", 0)
|
||||
TRANSCENDENTAL, TC_SEARCH_OVER_SHAPE, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("TC_SEARCH_OVER_SHAPE", 1), ContextVar("NOLOCALS", 0)
|
||||
FUSE_ARANGE, FUSE_CONV_BW = ContextVar("FUSE_ARANGE", 1), ContextVar("FUSE_CONV_BW", 0)
|
||||
SPLIT_REDUCEOP, NO_MEMORY_PLANNER, RING = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("RING", 1)
|
||||
PICKLE_BUFFERS, PROFILE, LRU = ContextVar("PICKLE_BUFFERS", 1), ContextVar("PROFILE", getenv("VIZ")), ContextVar("LRU", 1)
|
||||
@@ -141,7 +141,6 @@ QUANTIZE, VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("QUANTIZE", 0), Cont
|
||||
CORRECT_DIVMOD_FOLDING, FUSE_OPTIM = ContextVar("CORRECT_DIVMOD_FOLDING", 0), ContextVar("FUSE_OPTIM", 0)
|
||||
ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, AMD_LLVM = ContextVar("ALLOW_DEVICE_USAGE", 1), ContextVar("MAX_BUFFER_SIZE", 0), ContextVar("AMD_LLVM", 1)
|
||||
RANGEIFY, POSTOPT, FUSE_ATTENTION = ContextVar("RANGEIFY", 0), ContextVar("POSTOPT", 0), ContextVar("FUSE_ATTENTION", 0)
|
||||
EMULATE = ContextVar("EMULATE", "")
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Metadata:
|
||||
@@ -219,9 +218,6 @@ def cpu_profile(name:str|TracingKey, device="CPU", is_copy=False, display=True)
|
||||
res.en = perf_counter_us()
|
||||
if PROFILE and display: cpu_events.append(res)
|
||||
|
||||
def profile_marker(name:str, color="gray") -> None:
|
||||
cpu_events.append(ProfilePointEvent("TINY", "marker", None, {"name":name, "color":color}))
|
||||
|
||||
# *** universal database cache ***
|
||||
|
||||
cache_dir: str = os.path.join(getenv("XDG_CACHE_HOME", os.path.expanduser("~/Library/Caches" if OSX else "~/.cache")), "tinygrad")
|
||||
|
||||
@@ -320,7 +320,6 @@ class Embedding:
|
||||
|
||||
def __call__(self, idx:Tensor) -> Tensor:
|
||||
if not hasattr(self, 'arange'): self.arange = Tensor.arange(self.vocab_sz, requires_grad=False, device=self.weight.device).unsqueeze(-1)
|
||||
if not dtypes.is_int(idx.dtype): raise TypeError(f"Expected integer dtype for index in embedding, got {idx.dtype}")
|
||||
big_shp = idx.shape+(self.vocab_sz, self.embed_sz)
|
||||
arange, idx, vals = self.arange.expand(big_shp), idx.reshape(idx.shape+(1, 1)).expand(big_shp), self.weight.expand(big_shp)
|
||||
return (arange == idx).mul(vals).sum(-2, dtype=vals.dtype)
|
||||
|
||||
@@ -46,7 +46,7 @@ class Estimates:
|
||||
# SPECIAL are already counted in mults
|
||||
mults = mults.substitute({x:x.const_like(0) for x in mults.toposort() if x.op is Ops.SPECIAL}) if isinstance(mults, UOp) else mults
|
||||
elif u.op is Ops.ENDRANGE: mults = mult_stack.pop(-1)
|
||||
elif u.op is Ops.SPECIAL: mults *= cast(sint, u.src[0].ssimplify()) # NOTE: we don't push to the mult_stack here, you can't end these
|
||||
elif u.op is Ops.SPECIAL: mults *= u.arg[1] # NOTE: we don't push to the mult_stack here, you can't end these
|
||||
elif u.op is Ops.LOAD and (not isinstance(u.src[0].dtype, PtrDType) or u.src[0].dtype.addrspace != AddrSpace.REG):
|
||||
lds += u.dtype.itemsize * mults
|
||||
elif u.op is Ops.STORE and (not isinstance(u.src[0].dtype, PtrDType) or u.src[0].dtype.addrspace != AddrSpace.REG):
|
||||
@@ -82,9 +82,9 @@ class ProgramSpec:
|
||||
if u.op is Ops.LOAD: self.ins.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
|
||||
if u.op is Ops.SPECIAL:
|
||||
# NOTE: you have to set local_size and global_size to the base [1,1,1] outside this
|
||||
special_size = self.local_size if u.arg[0] == 'l' else self.global_size
|
||||
assert special_size is not None, f"special_size is None but found SPECIAL in uops {u}"
|
||||
special_size[int(u.arg[-1])] = cast(int, u.src[0].ssimplify()) # TODO: the type here should be sint
|
||||
if u.arg[0][0] == 'i': self.local_size = None
|
||||
special_size = self.local_size if u.arg[0][0] == 'l' else self.global_size
|
||||
if special_size is not None: special_size[int(u.arg[0][-1])] = u.arg[1]
|
||||
self.vars = sorted(self.vars, key=lambda v: v.arg)
|
||||
self.outs = sorted(dedup(self.outs))
|
||||
self.ins = sorted(dedup(self.ins))
|
||||
|
||||
@@ -26,7 +26,7 @@ base_rewrite = PatternMatcher([
|
||||
(UPat(Ops.DEFINE_LOCAL, name="x"), lambda ctx,x: f"{ctx.smem_align}{ctx.smem_prefix}{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}];"),
|
||||
(UPat(Ops.BARRIER), lambda ctx: ctx.barrier),
|
||||
(UPat(Ops.PRECAST, name="x"), lambda ctx,x: ctx[x.src[0]]),
|
||||
(UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0]](x.arg[-1])}; /* {(x.src[0]).render()} */"),
|
||||
(UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0][0]](x.arg[0][-1])}; /* {sint_to_uop(x.arg[1]).render()} */"),
|
||||
# const
|
||||
(UPat(Ops.CONST, arg=math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, ctx.infinity)})"),
|
||||
(UPat(Ops.CONST, arg=-math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, f'-{ctx.infinity}')})"),
|
||||
@@ -111,8 +111,7 @@ class CStyleLanguage(Renderer):
|
||||
tmp = "const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;\n" if any(isinstance(dtype, ImageDType) for _,(dtype,_) in bufs) else "" # noqa: E501
|
||||
buftypes = [(name, self.render_dtype(dtype, mutable)+self.buffer_suffix if isinstance(dtype, (ImageDType, PtrDType)) else
|
||||
self.arg_int_prefix if dtype == dtypes.int else None) for name,(dtype,mutable) in bufs]
|
||||
local_dims = [u.src[0] for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]
|
||||
launch_bounds = sint_to_uop(prod(local_dims)).vmax
|
||||
launch_bounds = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
|
||||
prg = ''.join([f"{self.kernel_typedef.format(launch_bounds=launch_bounds)} {function_name}(",] +
|
||||
[', '.join([f'{t} {name}' for name,t in buftypes] + self.extra_args)] +
|
||||
[") {\n" + tmp] + ['\n'.join(kernel), "\n}"])
|
||||
@@ -157,7 +156,7 @@ class CStyleLanguage(Renderer):
|
||||
|
||||
# naming
|
||||
prefix = None
|
||||
if u.op is Ops.SPECIAL: r[u] = u.arg
|
||||
if u.op is Ops.SPECIAL: r[u] = u.arg[0]
|
||||
elif u.op is Ops.RANGE: r[u] = "ridx"+'_'.join([str(x) if x >= 0 else "m"+str(-x) for x in u.arg[0:-1]])
|
||||
else:
|
||||
prefix = {Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
|
||||
|
||||
@@ -3,7 +3,7 @@ import math, struct, sys
|
||||
from tinygrad.codegen.opt import tc
|
||||
from tinygrad.renderer import Renderer
|
||||
from tinygrad.renderer.cstyle import AMDRenderer
|
||||
from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, GroupOp, sint_to_uop
|
||||
from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, GroupOp
|
||||
from tinygrad.dtype import dtypes, DType, PtrDType, truncate
|
||||
from tinygrad.helpers import prod, AMX
|
||||
|
||||
@@ -207,7 +207,7 @@ class AMDLLVMRenderer(LLVMRenderer):
|
||||
abi = "amdgpu_kernel"
|
||||
code_for_op = {**LLVMRenderer.code_for_op, **{op: lambda: None for op in llvm_intrinsics}}
|
||||
string_rewrite = PatternMatcher([
|
||||
(UPat(Ops.SPECIAL, name="x"), lambda ctx, x: f" {ctx[x]} = " + f"{ code_for_workitem[x.arg[0]](x.arg[-1])}; "),
|
||||
(UPat(Ops.SPECIAL, name="x"), lambda ctx, x: f" {ctx[x]} = " + f"{ code_for_workitem[x.arg[0][0]](x.arg[0][-1])}; "),
|
||||
(UPat(tuple(llvm_intrinsics), name="x"),
|
||||
lambda ctx, x: f" {ctx[x]} = call {ldt(x.dtype)} @llvm.{llvm_intrinsics[x.op]}.{ldt(x.dtype.scalar())}({ldt(x.src[0].dtype)} {ctx[x.src[0]]})"),
|
||||
(UPat(Ops.BARRIER), lambda ctx: barrier),
|
||||
@@ -220,8 +220,7 @@ class AMDLLVMRenderer(LLVMRenderer):
|
||||
])
|
||||
def _render_footer(self, uops: list[UOp]) -> str:
|
||||
# TODO: this is copied from cstyle
|
||||
local_dims = [u.src[0] for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]
|
||||
requiredMaxThreadsPerBlock = sint_to_uop(prod(local_dims)).vmax
|
||||
requiredMaxThreadsPerBlock = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
|
||||
attributes = ["alwaysinline", "nounwind", '"no-builtins"',
|
||||
f'"amdgpu-flat-work-group-size"="1,{requiredMaxThreadsPerBlock}"', '"no-trapping-math"="true"']
|
||||
return 'attributes #0 = { ' + ' '.join(attributes) + ' }'
|
||||
|
||||
@@ -2,7 +2,7 @@ from typing import cast, Callable
|
||||
import struct
|
||||
from collections import defaultdict
|
||||
from tinygrad.codegen.opt import tc
|
||||
from tinygrad.uop.ops import Ops, UOp, PatternMatcher, UPat, GroupOp, sint_to_uop
|
||||
from tinygrad.uop.ops import Ops, UOp, PatternMatcher, UPat, GroupOp
|
||||
from tinygrad.dtype import dtypes, DType, PtrDType, AddrSpace
|
||||
from tinygrad.renderer import Renderer
|
||||
from tinygrad.renderer.cstyle import CUDARenderer
|
||||
@@ -91,7 +91,7 @@ string_rewrite = PatternMatcher([
|
||||
(UPat(Ops.STORE, name="x", src=(UPat.var('bidx'), UPat.var("var")), allow_any_len=True), lambda ctx, x, bidx, var: f"st.{mem_type(bidx)}" + \
|
||||
f"{f'.v{cnt}' if ((cnt:=var.dtype.count)>1) else ''}.{ctx.mem_types[var.dtype.scalar()]} " + \
|
||||
f"[{ctx.r[bidx]}+0], {('{' + ', '.join(ctx.r[var]) + '}') if var.dtype.count > 1 else ctx.r[var]};"),
|
||||
(UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"mov.u32 %{x.arg}, %{'ctaid' if x.arg[0] == 'g' else 'tid'}.{chr(120+int(x.arg[-1]))};"),
|
||||
(UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"mov.u32 %{x.arg[0]}, %{'ctaid' if x.arg[0][0] == 'g' else 'tid'}.{chr(120+int(x.arg[0][-1]))};"),
|
||||
(UPat(Ops.DEFINE_GLOBAL, name="x"), lambda ctx, x: f"ld.param.{ctx.types[dtypes.ulong]} {ctx.r[x]}, [data{x.arg}+0];"),
|
||||
(UPat((Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ), name="x", allow_any_len=True, src=(UPat.var("src0"),)),
|
||||
lambda ctx, x, src0: ctx.code_for_op[x.op](ctx.r[x], *[ctx.r[v] for v in x.src], src0.dtype, ctx.types[src0.dtype])),
|
||||
@@ -155,8 +155,7 @@ class PTXRenderer(Renderer):
|
||||
def render_kernel(self, kernel, function_name, bufs, regs, uops) -> str:
|
||||
def fmt(line): return line if line[0]=="$" else "\t" + line.replace(" ", "\t" if len(line.split(" ")[0]) > 7 else "\t\t", 1)
|
||||
kernel = '\n'.join(map(fmt, [f".reg .{reg.split('_')[-2]} %{reg}<{cnt}>;" for reg,cnt in regs] + kernel + ["ret;"]))
|
||||
local_dims = [u.src[0] for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]
|
||||
launch_bounds = sint_to_uop(prod(local_dims)).vmax
|
||||
launch_bounds = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
|
||||
params = ',\n\t'.join([f".param .{'u64' if dtype.__class__ == PtrDType else self.types[dtype]} {name}" for name,dtype in bufs])
|
||||
return f"{self.kernel_prefix.format(launch_bounds=launch_bounds)} {function_name} (\n\t{params}\n)\n.maxntid {launch_bounds}\n{{\n{kernel}\n}}"
|
||||
|
||||
@@ -203,7 +202,7 @@ class PTXRenderer(Renderer):
|
||||
typ = "pred" if u.src[1].dtype == dtypes.bool else ("b"+self.types[u.src[1].dtype][1:])
|
||||
kernel.append(f"mov.{typ} {self.r[u.src[0]]}, {self.r[u.src[1]]};")
|
||||
continue
|
||||
if u.op is Ops.SPECIAL: r[u] = "%" + u.arg
|
||||
if u.op is Ops.SPECIAL: r[u] = "%" + u.arg[0]
|
||||
elif u.op is Ops.DEFINE_VAR: bufs.append((u.arg[0], u.dtype))
|
||||
elif u.op is Ops.LOAD:
|
||||
assert u.src[0].dtype == dtypes.int64, "load isn't int64"
|
||||
@@ -224,5 +223,5 @@ class PTXRenderer(Renderer):
|
||||
raise RuntimeError(f"failed to render {u.op} with {u.dtype} srcs {[x.dtype for x in u.src]}")
|
||||
kernel.extend([l] if isinstance(l, str) else l)
|
||||
|
||||
if u.op is Ops.SPECIAL: kernel = [f".reg .u32 %{u.arg};"] + kernel
|
||||
if u.op is Ops.SPECIAL: kernel = [f".reg .u32 %{u.arg[0]};"] + kernel
|
||||
return self.render_kernel(kernel, name, bufs, c.items(), uops)
|
||||
|
||||
@@ -84,7 +84,7 @@ class WGSLRenderer(CStyleLanguage):
|
||||
def render_load(self, x:str, dt:DType) -> str: return f"atomicLoad(&{x})" if is_packed(dt) else x
|
||||
def buf_map(self, dt:DType) -> str: return "atomic<u32>" if is_packed(dt) else self.type_map[dt.base]
|
||||
def render_kernel(self, function_name:str, kernel:list[str], bufs:list[tuple[str,tuple[DType,bool]]], uops:list[UOp], prefix=None) -> str:
|
||||
local_size = [u.src[0].ssimplify() for u in sorted([u for u in uops if u.op is Ops.SPECIAL and u.arg[0] == 'l'], key=lambda u: u.arg)]
|
||||
local_size = [num for _, num in sorted([u.arg for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == 'l'], key=lambda x: x[0])]
|
||||
if not local_size: local_size = [1]
|
||||
bind_it = iter(range(len(bufs)))
|
||||
external_local_bufs = [line.lstrip() for line in kernel if "var<workgroup>" in line]
|
||||
|
||||
@@ -28,9 +28,9 @@ class ClangJITCompiler(Compiler):
|
||||
def disassemble(self, lib:bytes): return capstone_flatdump(lib)
|
||||
|
||||
class CPUWorker(threading.Thread):
|
||||
def __init__(self, dev, tasks, thread_id):
|
||||
def __init__(self, dev):
|
||||
super().__init__()
|
||||
self.dev, self.tasks, self.thread_id, self.daemon = dev, tasks, thread_id, True
|
||||
self.dev, self.tasks, self.daemon = dev, dev.tasks, True
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
@@ -121,5 +121,5 @@ class CPUAllocator(HCQAllocatorBase):
|
||||
class CPUDevice(HCQCompiled):
|
||||
def __init__(self, device:str=""):
|
||||
self.tasks:queue.Queue = queue.Queue()
|
||||
CPUWorker(self, self.tasks, thread_id=0).start()
|
||||
CPUWorker(self).start()
|
||||
super().__init__(device, CPUAllocator(self), ClangRenderer(), ClangJITCompiler(), functools.partial(CPUProgram, self), CPUSignal, CPUComputeQueue)
|
||||
|
||||
@@ -74,5 +74,5 @@ class HostLLVMCompiler(LLVMCompiler):
|
||||
class LLVMDevice(HCQCompiled):
|
||||
def __init__(self, device:str=""):
|
||||
self.tasks:queue.Queue = queue.Queue()
|
||||
CPUWorker(self, self.tasks, thread_id=0).start()
|
||||
CPUWorker(self).start()
|
||||
super().__init__(device, CPUAllocator(self), LLVMRenderer(), HostLLVMCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue)
|
||||
|
||||
@@ -2,10 +2,10 @@
|
||||
# a python uops emulator
|
||||
# works to test the tensor cores, and all the uops in general
|
||||
# this is the (living) definition of uops
|
||||
from typing import Any, TYPE_CHECKING, cast
|
||||
from typing import Any, TYPE_CHECKING
|
||||
import pickle, base64, itertools, time, struct, sys
|
||||
from tinygrad.dtype import DType, dtypes, ImageDType, PtrDType, truncate, float_to_bf16
|
||||
from tinygrad.helpers import all_same, getenv, flatten, get_single_element, EMULATE
|
||||
from tinygrad.helpers import all_same, getenv, flatten, get_single_element
|
||||
from tinygrad.device import Compiled, Compiler, Allocator
|
||||
from tinygrad.codegen.opt import tc
|
||||
from tinygrad.uop.ops import exec_alu, python_alu, Ops, UOp, GroupOp
|
||||
@@ -84,8 +84,8 @@ class PythonProgram:
|
||||
elif uop is Ops.DEFINE_VAR:
|
||||
ul[i] = [pvals.pop(0)] * warp_size
|
||||
elif uop is Ops.SPECIAL:
|
||||
if arg[0] == 'g': ul[i] = [idxs[2-int(arg[-1])]] * warp_size
|
||||
elif arg[0] == 'l': ul[i] = [x[2-int(arg[-1])] for x in warp]
|
||||
if arg[0][0] == 'g': ul[i] = [idxs[2-int(arg[0][-1])]] * warp_size
|
||||
elif arg[0][0] == 'l': ul[i] = [x[2-int(arg[0][-1])] for x in warp]
|
||||
elif uop is Ops.CONST: ul[i] = [arg] * warp_size
|
||||
elif uop is Ops.INDEX:
|
||||
ret:list = []
|
||||
@@ -210,21 +210,17 @@ class PythonRenderer(Renderer):
|
||||
device = "PYTHON"
|
||||
code_for_op = python_alu
|
||||
def __init__(self):
|
||||
match cast(str, EMULATE.value):
|
||||
case "METAL": self.device, self.tensor_cores = "METAL", tc.metal
|
||||
case "AMD": self.device, self.tensor_cores = "AMD", tc.amd_rdna3
|
||||
case "AMD_MFMA": self.device, self.tensor_cores = "AMD", tc.amd_cdna
|
||||
case "AMD_RDNA4": self.device, self.tensor_cores = "AMD", tc.amd_rdna4
|
||||
case "CUDA": self.device, self.tensor_cores = "CUDA", tc.cuda_sm80
|
||||
case "CUDA_SM75": self.device, self.tensor_cores = "CUDA", tc.cuda_sm75
|
||||
case "INTEL": self.device, self.suffix, self.tensor_cores = "INTEL", "INTEL", tc.intel
|
||||
case "AMX": self.device, self.tensor_cores = "CPU", tc.amx
|
||||
case "": pass
|
||||
case _: raise RuntimeError(f"can't EMULATE device: {EMULATE.value}")
|
||||
if getenv("EMULATE_METAL"): self.device, self.tensor_cores = "METAL", tc.metal
|
||||
if getenv("EMULATE_AMD"): self.device, self.tensor_cores = "AMD", tc.amd_rdna3
|
||||
if getenv("EMULATE_AMD_MFMA"): self.device, self.tensor_cores = "AMD", tc.amd_cdna
|
||||
if getenv("EMULATE_AMD_RDNA4"): self.device, self.tensor_cores = "AMD", tc.amd_rdna4
|
||||
if getenv("EMULATE_CUDA"): self.device, self.tensor_cores = "CUDA", tc.cuda_sm80
|
||||
if getenv("EMULATE_CUDA_SM75"): self.device, self.tensor_cores = "CUDA", tc.cuda_sm75
|
||||
if getenv("EMULATE_INTEL"): self.device, self.suffix, self.tensor_cores = "INTEL", "INTEL", tc.intel
|
||||
if getenv("EMULATE_AMX"): self.device, self.tensor_cores = "CPU", tc.amx
|
||||
|
||||
def render(self, uops:list[UOp]) -> str:
|
||||
# the value of SPECIAL comes from local/global_size, not form its source
|
||||
lops = [(u.op, u.dtype, [uops.index(v) for v in u.src if u.op is not Ops.SPECIAL], u.arg) for u in uops]
|
||||
lops = [(u.op, u.dtype, [uops.index(v) for v in u.src], u.arg) for u in uops]
|
||||
return base64.b64encode(pickle.dumps(lops)).decode()
|
||||
|
||||
class PythonCompiler(Compiler):
|
||||
|
||||
@@ -8,7 +8,7 @@ from tinygrad.dtype import ImageDType
|
||||
from tinygrad.schedule.multi import multi_pm
|
||||
from tinygrad.schedule.grouper import group_realizes, ALWAYS_CONTIGUOUS
|
||||
from tinygrad.codegen.opt.swizzler import merge_views, apply_swizzle, swizzle_reduceop
|
||||
from tinygrad.codegen.opt import Opt
|
||||
from tinygrad.codegen.opt.kernel import Opt
|
||||
|
||||
# creation can recurse a lot
|
||||
import sys
|
||||
|
||||
@@ -7,7 +7,7 @@ from tinygrad.helpers import merge_dicts, getenv
|
||||
from tinygrad.shape.view import View, unravel
|
||||
from tinygrad.dtype import dtypes
|
||||
from tinygrad.uop.ops import UOp, Ops, graph_rewrite, Variable, sint, sint_to_uop, Context, PatternMatcher, UPat, GroupOp
|
||||
from tinygrad.uop.symbolic import symbolic_flat, uop_given_valid, simplify_valid
|
||||
from tinygrad.uop.symbolic import split_uop, symbolic_flat, uop_given_valid, simplify_valid
|
||||
|
||||
# If a node overflow, its srcs need to be checked to see if this overflow is the result of an ALU operation,
|
||||
# or that the node simply inherits the dtype from srcs. Upcast is either `Ops.CAST`+`replace` or just `replace`.
|
||||
@@ -43,7 +43,7 @@ def views_to_real_strides(views: tuple[View, ...], ignore_valid=False) -> tuple[
|
||||
if len(views) == 1 and views[-1].mask is None: return views[-1].strides
|
||||
ret: list[sint|None] = [None] * len(views[-1].shape)
|
||||
idx, valid = views_to_indexed_uops(views)
|
||||
for c in idx.split_uop(Ops.ADD):
|
||||
for c in split_uop(idx, Ops.ADD):
|
||||
if c.op is Ops.RANGE: ret[c.arg[0]] = 1
|
||||
if c.op is Ops.MUL and c.src[0].op is Ops.RANGE and c.src[1].op is Ops.CONST: ret[c.src[0].arg[0]] = c.src[1].arg
|
||||
if c.op is Ops.MUL and c.src[1].op is Ops.RANGE and c.src[0].op is Ops.CONST: ret[c.src[1].arg[0]] = c.src[0].arg
|
||||
|
||||
+1
-1
@@ -2255,7 +2255,7 @@ class Tensor(MathTrait):
|
||||
xs:tuple[Tensor, ...] = argfix(*operands)
|
||||
inputs_str, output = parse_formula(formula, *xs)
|
||||
inputs = inputs_str.split(",")
|
||||
if len(xs)!=len(inputs): raise ValueError(f"number of inputs doesn't match number of operands in formula, expected {len(inputs)}, got {len(xs)}")
|
||||
assert len(xs) == len(inputs), f"number of inputs doesn't match number of operands in formula, expected {len(inputs)}, got {len(xs)}"
|
||||
|
||||
# map the value of each letter in the formula
|
||||
letter_val = sorted(merge_dicts([dict(zip(letters, tensor.shape)) for letters, tensor in zip(inputs, xs)]).items())
|
||||
|
||||
+20
-71
@@ -102,8 +102,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
def argstr(self): return f'({", ".join(map(str, self.arg))})' if self.op is Ops.REDUCE_AXIS else repr(self.arg)
|
||||
def tagstr(self): return f", tag={self.tag}" if self.tag is not None else ""
|
||||
|
||||
def f(self, op, **kwargs): return UOp(op, dtype=kwargs.pop("dtype", self.dtype), src=(self,), **kwargs)
|
||||
|
||||
@functools.cached_property
|
||||
def parents(self:UOp) -> dict[UOp, None]:
|
||||
ret = {s:None for s in self.src}
|
||||
@@ -292,10 +290,10 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
if op in {Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
|
||||
return UOp(op, out_dtype, (self,)+src, **kwargs)
|
||||
@staticmethod
|
||||
def const(dtype:DType, b:ConstLike, device:str|tuple[str, ...]|None=None, shape:tuple[sint, ...]|None=None, src=None):
|
||||
def const(dtype:DType, b:ConstLike, device:str|tuple[str, ...]|None=None, shape:tuple[sint, ...]|None=None):
|
||||
if isinstance(b, UOp): return b.unbind()[0] if b.op is Ops.BIND else b
|
||||
if isinstance(b, tuple) and all_same(b): b = b[0] # doesn't have to be a VCONST if they are all the same
|
||||
ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype), src=() if src is None else (src,))
|
||||
ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype))
|
||||
if shape is not None:
|
||||
from tinygrad.shape.shapetracker import ShapeTracker
|
||||
ret = ret.replace(src=(UOp(Ops.VIEW, dtypes.void, (), ShapeTracker.from_shape(shape, (0,)*len(shape))),))
|
||||
@@ -327,14 +325,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
def allreduce(self, op, device:str|tuple[str, ...]|UOp):
|
||||
assert isinstance(self.device, tuple), f"allreduce must be on tuple {self.device} isn't"
|
||||
return UOp(Ops.ALLREDUCE, self.dtype, (self, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device), op)
|
||||
def overflows(self, dtype:DType) -> bool: return self.vmin < dtype.min or dtype.max < self.vmax
|
||||
|
||||
# *** ShapeTracker helpers ***
|
||||
|
||||
def split_uop(self:UOp, sep:Ops):
|
||||
if self.op is sep:
|
||||
for s in self.src: yield from s.split_uop(sep)
|
||||
else: yield self
|
||||
|
||||
# *** from MultiLazyBuffer ***
|
||||
|
||||
@@ -566,12 +556,13 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
|
||||
if self.op is Ops.WHERE and dtypes.is_int(self.dtype): return min(self.src[1].vmin, self.src[2].vmin), max(self.src[1].vmax, self.src[2].vmax)
|
||||
# NOTE: returned UOp is assumed to be CONST
|
||||
if self.op is Ops.DEFINE_VAR and self.arg: return self.arg[1], self.arg[2]
|
||||
if self.op in (Ops.RANGE, Ops.SPECIAL): return 0, (self.src[0]-1).vmax
|
||||
if self.op is Ops.RANGE: return 0, (self.src[0]-1).vmax
|
||||
if self.op is Ops.BIND: return self.src[0]._min_max # ignore the bound value
|
||||
if self.op in {Ops.UNROLL, Ops.VECTORIZE}: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)
|
||||
# TODO: Ops.SPECIAL is Ops.DEFINE_VAR
|
||||
if self.op is Ops.SPECIAL: return 0, self.arg[1]-1 if isinstance(self.arg[1], int) else self.arg[1].vmax-1
|
||||
if self.op is Ops.CONST: return self.arg, self.arg
|
||||
if self.op is Ops.VCONST: return (min(self.arg), max(self.arg))
|
||||
if self.op is Ops.GEP: return self.src[0]._min_max
|
||||
# TODO: CAST to bool/unsigned is not monotone, still some case can be simplified
|
||||
if self.op is Ops.CAST and self.dtype in (dtypes.floats+dtypes.sints):
|
||||
return max(dtypes.min(self.dtype), self.src[0].vmin), min(self.src[0].vmax, dtypes.max(self.dtype))
|
||||
@@ -715,8 +706,7 @@ class UPat(MathTrait):
|
||||
def assign(self, x:UPat, **kwargs): return UPat(Ops.ASSIGN, self.dtype, (self,x), **kwargs)
|
||||
def reduce(self, *src:UPat, **kwargs): return UPat(Ops.REDUCE, self.dtype, src=(self,)+src, **kwargs)
|
||||
def fuse(self): return self.alu(Ops.FUSE)
|
||||
def broadcast(self, **kwargs): return UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs)
|
||||
def or_broadcasted(self, **kwargs): return UPat.any(self, self.broadcast(**kwargs))
|
||||
def or_broadcasted(self, **kwargs): return UPat.any(self, UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs))
|
||||
def contiguous(self, *args, **kwargs): return UPat(Ops.CONTIGUOUS, dtype=self.dtype, src=(self,)+args, **kwargs)
|
||||
|
||||
def const_like(self, b:ConstLike): return UPat.const(self.dtype, cast(ConstType, b))
|
||||
@@ -940,7 +930,6 @@ if TRACK_MATCH_STATS or PROFILE:
|
||||
# *** simple graph rewrite engine ***
|
||||
|
||||
class RewriteNotReady(Exception): pass
|
||||
class BottomUpGate(Exception): pass
|
||||
class RewriteContext:
|
||||
def __init__(self, pm, bpm, ctx=None):
|
||||
self.pm: PatternMatcher|None = pm
|
||||
@@ -968,20 +957,17 @@ class RewriteContext:
|
||||
if n in self.replace: continue # skip any nodes we have seen
|
||||
try:
|
||||
if stage == 0:
|
||||
try:
|
||||
# if bottom up, we rewrite this node early. in both cases, we add its parents to the stack
|
||||
if self.bpm is not None:
|
||||
# apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match
|
||||
test_n: UOp|None = n
|
||||
seen = set()
|
||||
while test_n is not None:
|
||||
if test_n in seen: raise RuntimeError("infinite loop in fixed_point_rewrite")
|
||||
seen.add(test_n)
|
||||
new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
|
||||
stack.append((n, 1, new_n))
|
||||
for x in reversed(new_n.src): stack.append((x, 0, x))
|
||||
# if the bpm matching raised a gate, we are done with this node and dont continue down the srcs
|
||||
except BottomUpGate: self.replace[n] = new_n
|
||||
# if bottom up, we rewrite this node early. in both cases, we add its parents to the stack
|
||||
if self.bpm is not None:
|
||||
# apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match
|
||||
test_n: UOp|None = n
|
||||
seen = set()
|
||||
while test_n is not None:
|
||||
if test_n in seen: raise RuntimeError("infinite loop in fixed_point_rewrite")
|
||||
seen.add(test_n)
|
||||
new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
|
||||
stack.append((n, 1, new_n))
|
||||
for x in reversed(new_n.src): stack.append((x, 0, x))
|
||||
elif stage == 1:
|
||||
try: new_src = tuple([self.replace[x] for x in new_n.src])
|
||||
except KeyError: raise RewriteNotReady
|
||||
@@ -1030,12 +1016,12 @@ _substitute = PatternMatcher([(UPat(tuple(Ops), name="x"), lambda ctx,x: ctx.get
|
||||
syms = { Ops.ADD: "+", Ops.SUB: "-", Ops.IDIV: "//", Ops.MOD: "%", Ops.SHL: "<<", Ops.SHR: ">>",
|
||||
Ops.MUL: "*", Ops.CMPLT: "<", Ops.CMPNE: "!=", Ops.AND: "&", Ops.OR: "|", Ops.XOR: "^"}
|
||||
renderer = PatternMatcher([
|
||||
(UPat((Ops.DEFINE_VAR,), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
|
||||
(UPat((Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg)),
|
||||
(UPat((Ops.DEFINE_VAR, Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
|
||||
(UPat(Ops.RANGE, name="x"), lambda x: UOp(Ops.NOOP, arg=f"ridx{x.arg[0]}" if x.arg[0] >= 0 else f"ridxm{-x.arg[0]}")),
|
||||
(UPat((Ops.CONST, Ops.VCONST), name="x"), lambda x: UOp(Ops.NOOP, arg=str(x.arg))),
|
||||
(UPat(Ops.UNROLL, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UNROLL({x.src[0].arg}, {x.arg})")),
|
||||
(UPat(Ops.CAST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"({str(x.dtype)[7:]})({x.src[0].arg})")),
|
||||
(UPat(Ops.LOAD), lambda: UOp(Ops.NOOP, arg="load")),
|
||||
(UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
|
||||
#(UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}[={x.src[1].arg}]")),
|
||||
(UPat(Ops.NEG, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(-{x.src[0].arg})")),
|
||||
@@ -1043,8 +1029,7 @@ renderer = PatternMatcher([
|
||||
(UPat(Ops.MAX, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"max({x.src[0].arg}, {x.src[1].arg})")),
|
||||
(UPat(Ops.MULACC, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}*{x.src[1].arg}+{x.src[2].arg})")),
|
||||
(UPat(Ops.WHERE, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[1].arg} if {x.src[0].arg} else {x.src[2].arg})")),
|
||||
(UPat(set(syms.keys()), src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}{syms[x.op]}{x.src[1].arg})")),
|
||||
(UPat(Ops.VIEW, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.view({x.arg})")),
|
||||
(UPat(GroupOp.ALU, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}{syms[x.op]}{x.src[1].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})")),
|
||||
@@ -1052,42 +1037,6 @@ renderer_infer = PatternMatcher([
|
||||
*renderer.patterns
|
||||
])
|
||||
|
||||
sugar = { Ops.SINK: "sink", Ops.STORE: "store", Ops.LOAD: "load", Ops.SQRT: "sqrt", Ops.INDEX: "index", Ops.REDUCE: "reduce",
|
||||
Ops.WHERE: "where", Ops.RECIP: "reciprocal", Ops.EXP2: "exp2", Ops.LOG2: "log2"}
|
||||
pm_pyrender = PatternMatcher([
|
||||
(UPat(Ops.CONST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg}, src={x.src[0].arg})")),
|
||||
(UPat(Ops.CONST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg})")),
|
||||
(UPat(Ops.CAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.cast({x.dtype})")),
|
||||
(UPat(Ops.BITCAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.bitcast({x.dtype})")),
|
||||
(UPat({Ops.MAX, Ops.THREEFRY, Ops.CMPLT, Ops.CMPNE}, src=UPat(Ops.NOOP), name="x"),
|
||||
lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.alu({x.op}, {x.src[1].arg})")),
|
||||
(UPat(Ops.RANGE, src=(UPat(Ops.NOOP),), name="x"), lambda x:
|
||||
UOp(Ops.NOOP, arg=f"UOp.range({x.src[0].arg}, {str(x.arg[0])}, {str(x.arg[1])})")),
|
||||
(UPat(set(sugar.keys()), src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP,
|
||||
arg=f"{x.src[0].arg}.{sugar[x.op]}({', '.join([y.arg for y in x.src[1:]] + ([f'arg={str(x.arg)}'] if x.arg is not None else []))})")),
|
||||
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.NOOP),), name="x"),
|
||||
lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.f({x.op}, arg=({', '.join([str(y) for y in x.arg])}))")),
|
||||
(UPat(Ops.VALID, src=(UPat(Ops.NOOP),), name="x"),
|
||||
lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.f({x.op}, dtype=dtypes.bool)")),
|
||||
])
|
||||
|
||||
def pyrender(ast:UOp) -> list[str]:
|
||||
cmap = ast.get_children_map()
|
||||
to_render = set()
|
||||
for u in ast.toposort():
|
||||
if u.op is Ops.STORE: to_render.add(u.src[1])
|
||||
if len(cmap[u]) == 1 and u.op not in {Ops.DEFINE_GLOBAL, Ops.VIEW, Ops.LOAD} or u.op in {Ops.CONST}: continue
|
||||
if u.op in {Ops.SINK, Ops.VIEW}:
|
||||
for s in u.src: to_render.add(s)
|
||||
to_render.add(u)
|
||||
ret: list[str] = []
|
||||
rep: dict[UOp, UOp] = {}
|
||||
for u in ast.toposort():
|
||||
if u not in to_render: continue
|
||||
ret.append(f"c{len(ret)} = {u.substitute(rep).render(simplify=False, pm=pm_pyrender+renderer)}")
|
||||
rep[u] = UOp(Ops.NOOP, arg=f"c{len(ret)-1}")
|
||||
return ret[0:-1] + ["ast ="+ret[-1].split("=", 1)[1]]
|
||||
|
||||
# *** what was symbolic.py ***
|
||||
|
||||
sint = int|UOp
|
||||
|
||||
@@ -20,6 +20,8 @@ try:
|
||||
# each uop gets rewritten to NOOP(arg=(solver, z3_object)), the arg has the solver first due to UOpMetaClass caching. z3 objects from different
|
||||
# contexts can have the same hash but error on comparison
|
||||
z3_renderer = PatternMatcher([
|
||||
# Ops.SPECIAL can have symbolic arg but it wont be in the toposort beacuse its not a src, we need to add it manually
|
||||
(UPat(Ops.SPECIAL, src=(), name="x"), lambda x: UOp(Ops.SPECIAL, arg=x.arg[0], src=(x.ufix(x.arg[1]),))),
|
||||
(UPat(Ops.SPECIAL, src=UPat(Ops.NOOP), name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0],create_bounded(x.arg, 0, x.src[0].arg[1]-1, ctx[0])))),
|
||||
(UPat(Ops.DEFINE_VAR, name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0],create_bounded(x.arg[0], x.arg[1], x.arg[2], ctx[0])))),
|
||||
(UPat(Ops.RANGE, name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0],create_bounded(f"ridx{x.arg}", 0, x.src[0].arg[1]-1, ctx[0])))),
|
||||
@@ -155,7 +157,7 @@ spec = PatternMatcher([
|
||||
(UPat(Ops.DEFINE_VAR, name="x"), lambda x: isinstance(x.arg[1], int) and isinstance(x.arg[2], int)),
|
||||
|
||||
(UPat(Ops.RANGE, src=(UPat.var("x"),), name="rng"), lambda rng,x: rng.dtype == x.dtype and isinstance(rng.arg, tuple)),
|
||||
(UPat(Ops.SPECIAL, src=(UPat.var("x"),), name="s"), lambda s,x: s.dtype == x.dtype == dtypes.int32 and isinstance(s.arg, str)),
|
||||
(UPat(Ops.SPECIAL, src=()), lambda: True),
|
||||
|
||||
(UPat(Ops.VIEW, dtypes.void, src=(), name="x"), lambda x: isinstance(x.arg, ShapeTracker)),
|
||||
(UPat(Ops.VIEW, src=(UPat.var("src"),), name="x"),
|
||||
|
||||
+22
-19
@@ -93,11 +93,16 @@ symbolic_simple = PatternMatcher([
|
||||
|
||||
# ******** phase 2 builds on phase 1, it includes the old "symbolic", rules that match deeper ********
|
||||
|
||||
def split_uop(x:UOp, sep:Ops):
|
||||
if x.op is sep:
|
||||
for s in x.src: yield from split_uop(s, sep)
|
||||
else: yield x
|
||||
|
||||
def fold_unrolled_divs(divs:UOp, denominator: int, fac=1) -> UOp|None:
|
||||
# div pattern in unrolled arange
|
||||
# example: (x//4+(x+1)//4+(x+2)//4+(x+3)//4 -> x
|
||||
seen_const, ans = [], None
|
||||
for u in divs.split_uop(Ops.ADD):
|
||||
for u in split_uop(divs, Ops.ADD):
|
||||
if fac!=1:
|
||||
if u.op is not Ops.MUL or u.src[1].op is not Ops.CONST or u.src[1].arg != fac: return None
|
||||
u = u.src[0]
|
||||
@@ -120,7 +125,7 @@ def fold_unrolled_divs(divs:UOp, denominator: int, fac=1) -> UOp|None:
|
||||
return None
|
||||
|
||||
def lt_folding(x:UOp, c:int) -> UOp|None:
|
||||
p, np = partition(x.split_uop(Ops.ADD), lambda u: u.const_factor() == 1)
|
||||
p, np = partition(split_uop(x, Ops.ADD), lambda u: u.const_factor() == 1)
|
||||
if np and (d:=math.gcd(*[u.const_factor() for u in np], c)) > 1 and 0 <= sum(u.vmin for u in p) and sum(u.vmax for u in p) < d:
|
||||
return cast(UOp, functools.reduce(operator.add, np).divides(d))<(c//d)
|
||||
return None
|
||||
@@ -129,7 +134,7 @@ def canonicalize_simplex(X:UOp) -> UOp|None:
|
||||
# (X := a0*x0 + a1*x1 + ...) > 0 is equivalent to x0 + x1 + ... > 0 if xi >= 0 and ai > 0 for ints.
|
||||
# returns x0 + x1 + ... in such case, or None if not
|
||||
changed, ret = False, []
|
||||
for u in X.split_uop(Ops.ADD):
|
||||
for u in split_uop(X, Ops.ADD):
|
||||
# assumed the const is the last src of MUL
|
||||
if u.op is Ops.MUL and u.src[1].op is Ops.CONST and u.src[1].arg > 0:
|
||||
changed = True
|
||||
@@ -153,7 +158,7 @@ def remove_nested_mod(m: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
if ((c := y.arg) < 0) or x.vmin<0: return None
|
||||
new_xs = []
|
||||
something_changed = False
|
||||
for u in x.split_uop(Ops.ADD):
|
||||
for u in split_uop(x, Ops.ADD):
|
||||
if u.op is Ops.MOD:
|
||||
if u.src[1].divides(c) is not None:
|
||||
something_changed = True
|
||||
@@ -167,7 +172,7 @@ def fold_binary_numerator(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
# we can fold if the expression has only one non-constant term and this term can only take on two values
|
||||
if ((c := y.arg) < 0) or (x.dtype.count > 1): return None
|
||||
x,const = x.pop_const()
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in x.split_uop(Ops.ADD)])
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in split_uop(x, Ops.ADD)])
|
||||
if len(terms)==1 and (v:=terms[0]).vmax-v.vmin == 1:
|
||||
y1 = cmod(factors[0]*v.vmin+const, c) if d.op is Ops.MOD else cdiv(factors[0]*v.vmin+const, c) # type: ignore
|
||||
y2 = cmod(factors[0]*v.vmax+const, c) if d.op is Ops.MOD else cdiv(factors[0]*v.vmax+const, c) # type: ignore
|
||||
@@ -178,7 +183,7 @@ def fold_divmod_congruence(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
# within a mod we can freely subtract multiples of c, we use this to see if a is congruent to an expression whose vmin/vmax are between 0 and c
|
||||
if (x.vmin<0 and CORRECT_DIVMOD_FOLDING) or ((c := y.arg) < 0) or (x.dtype.count > 1): return None
|
||||
x,const = x.pop_const()
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in x.split_uop(Ops.ADD)])
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in split_uop(x, Ops.ADD)])
|
||||
# a//c = (a-a%c)/c, if we can fold a%c, we can fold a//c
|
||||
rems = [min((r:=f%c), r-c, key=abs) for f in factors]
|
||||
if (rem:=sum(r*v for r,v in zip(rems,terms))+const%c).vmin//c!=rem.vmax//c: return None
|
||||
@@ -187,7 +192,7 @@ def fold_divmod_congruence(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
|
||||
def divide_by_gcd(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
# x//y -> (x//gcd)//(y//gcd) or x%y -> gcd*(x//gcd)%(y//gcd)
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in x.split_uop(Ops.ADD)])
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in split_uop(x, Ops.ADD)])
|
||||
if (gcd := math.gcd(y.arg, *factors)) == 1: return None
|
||||
ret = sum(f//gcd * v for f,v in zip(factors, terms)).alu(d.op, y.const_like(y.arg//gcd))
|
||||
return ret*gcd if d.op is Ops.MOD else ret
|
||||
@@ -195,7 +200,7 @@ def divide_by_gcd(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
def nest_div_by_smallest_factor(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
# we try and nest the div and see if it allows the numerator to be simplified
|
||||
if ((c := y.arg) < 0) or (x.dtype.count > 1): return None
|
||||
factors = [u.const_factor() for u in x.pop_const()[0].split_uop(Ops.ADD)]
|
||||
factors = [u.const_factor() for u in split_uop(x.pop_const()[0], Ops.ADD)]
|
||||
# div is the smallest factor of the denominator (greater than 1) out of all "factors"
|
||||
# TODO: there are better ways to pick `div`, this sometimes adds extra divisions
|
||||
# TODO: add same optimization for mod
|
||||
@@ -207,7 +212,7 @@ def simplify_remainder(d: UOp, x: UOp, y: UOp) -> UOp|None:
|
||||
# we try and take out the quotient and see if it allows the numerator to be simplified
|
||||
if ((c := y.arg) < 0) or (x.dtype.count > 1): return None
|
||||
x_no_const,const = x.pop_const()
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in x_no_const.split_uop(Ops.ADD)])
|
||||
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in split_uop(x_no_const, Ops.ADD)])
|
||||
quotients, remainders = zip(*[divmod(f, c) for f in factors])
|
||||
gcd = math.gcd(c, *remainders) # gcd without const!
|
||||
if const%c==const and gcd==1 and not any(r==0 or (r!=f and d.op is Ops.MOD) for r,f in zip(remainders, factors)): return None
|
||||
@@ -282,10 +287,8 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
|
||||
((UPat.var("y") + UPat.var("x")) + UPat.var("x"), lambda y,x: y+x*2),
|
||||
((UPat.var("x") / UPat.var("x2")) / UPat.var("x3"), lambda x,x2,x3: x/(x2*x3) if x2 is not x3 else None), # (x/x2)/x3 -> x/(x2*x3)
|
||||
(-1 * (UPat.var("x") + UPat.cvar("c")), lambda x,c: (-x)+(-c)), # -(x+c) -> -x + -c
|
||||
(UPat.var('x', dtypes.ints).cast(dtypes.ints, name="a").cast(name="b"),
|
||||
(UPat.var('x', dtypes.ints).cast(dtypes.ints, name="a").cast(dtypes.ints, name="b"),
|
||||
lambda x,a,b: x.cast(b.dtype) if a.dtype.min<=x.vmin and x.vmax<=a.dtype.max else None),
|
||||
(UPat(GroupOp.Binary, src=(UPat.var("x",dtypes.long), UPat.var("y", dtypes.long)), name="u"), lambda u,x,y:
|
||||
x.cast(dtypes.int).alu(u.op, y.cast(dtypes.int)).cast(u.dtype) if not any(v.overflows(dtypes.int) for v in (u,x,y)) else None),
|
||||
# a conditional with the same results either way is a noop, also fold const conditionals
|
||||
(UPat.var().where(UPat.var("val"), UPat.var("val")), lambda val: val),
|
||||
(UPat.cvar("gate", vec=False).where(UPat.var("c0"), UPat.var("c1")), lambda gate, c0, c1: c0 if gate.arg else c1),
|
||||
@@ -370,9 +373,9 @@ def parse_valid(valid:UOp) -> tuple[UOp, bool, int]:
|
||||
|
||||
# (X < c).ne(True) -> X >= c
|
||||
if valid.op is Ops.CMPNE and valid.src[1].op is Ops.CONST and valid.src[1].arg == 1 and \
|
||||
(s0:=valid.src[0]).op is Ops.CMPLT and dtypes.is_int(s0.src[0].dtype): return s0.src[0], False, int(s0.src[1].vmin)
|
||||
(s0:=valid.src[0]).op is Ops.CMPLT and s0.src[1].op is Ops.CONST: return s0.src[0], False, s0.src[1].arg
|
||||
# X < c -> X <= c-1
|
||||
if valid.op is Ops.CMPLT and dtypes.is_int(valid.src[0].dtype): return valid.src[0], True, int((valid.src[1]).vmax)-1
|
||||
if valid.op is Ops.CMPLT and valid.src[1].op is Ops.CONST and dtypes.is_int(valid.src[0].dtype): return valid.src[0], True, valid.src[1].arg-1
|
||||
raise ValueError(f"not able to parse {valid=}")
|
||||
|
||||
def uop_given_valid(valid:UOp, uop:UOp) -> UOp|None:
|
||||
@@ -380,7 +383,7 @@ def uop_given_valid(valid:UOp, uop:UOp) -> UOp|None:
|
||||
|
||||
# first, parse valid into {expr: (lower_bound, upper_bound)}
|
||||
bounds:defaultdict[UOp, list[ConstType|None]] = defaultdict(lambda: [None, None])
|
||||
for stmt in valid.split_uop(Ops.AND):
|
||||
for stmt in split_uop(valid, Ops.AND):
|
||||
try: expr, is_upper, c = parse_valid(stmt)
|
||||
except ValueError: return uop # give up if we cannot parse the valid
|
||||
bounds[expr][int(is_upper)] = c
|
||||
@@ -399,9 +402,9 @@ def uop_given_valid(valid:UOp, uop:UOp) -> UOp|None:
|
||||
continue
|
||||
# every candidate is a set of constrained UOp based on valid, and if every item in a set simplifies the uop into a same output, we rewrite uop
|
||||
candidates = []
|
||||
if expr.op is Ops.ADD and v0 == 1 and all(u.op in GroupOp.Irreducible for u in expr.split_uop(Ops.ADD)):
|
||||
if expr.op is Ops.ADD and v0 == 1 and all(u.op in GroupOp.Irreducible for u in split_uop(expr, Ops.ADD)):
|
||||
# if the constraint is a simplex: X0 + X1 + ... > 0, we can check if all Xi > 0 simplify into the same output
|
||||
candidates.append([(Xi, UOp.variable("fake", 1, Xi.vmax, Xi.dtype)) for Xi in expr.split_uop(Ops.ADD)])
|
||||
candidates.append([(Xi, UOp.variable("fake", 1, Xi.vmax, Xi.dtype)) for Xi in split_uop(expr, Ops.ADD)])
|
||||
# try checking the whole clause
|
||||
if expr in uop.toposort(): candidates.append([(expr, UOp.variable("fake", v0, v1, expr.dtype))])
|
||||
|
||||
@@ -425,7 +428,7 @@ def _valid_priority(v: UOp, valids:list[UOp]):
|
||||
def simplify_valid(valid:UOp) -> UOp|None:
|
||||
ret:list[UOp] = []
|
||||
something_changed = False
|
||||
valids = list(valid.split_uop(Ops.AND))
|
||||
valids = list(split_uop(valid, Ops.AND))
|
||||
for stmt in sorted(valids, key=lambda v: _valid_priority(v, valids)):
|
||||
# TODO: root cause this and test_simplify_valid_from_div
|
||||
if stmt.op is Ops.CAST: return None
|
||||
@@ -439,7 +442,7 @@ def reduce_mul_chain(r:UOp):
|
||||
if r.arg not in {Ops.ADD, Ops.MAX}: return None
|
||||
if r.dtype != r.src[0].dtype: return None
|
||||
inside, outside = [], []
|
||||
for m in r.src[0].split_uop(Ops.MUL):
|
||||
for m in split_uop(r.src[0], Ops.MUL):
|
||||
m_parents = m.toposort()
|
||||
if all(r not in m_parents for r in r.src[1:]) and (r.arg != Ops.MAX or m.vmin >= 0): outside.append(m)
|
||||
else: inside.append(m)
|
||||
|
||||
@@ -157,11 +157,11 @@ const rescaleTrack = (source, tid, k) => {
|
||||
return change;
|
||||
}
|
||||
|
||||
const drawLine = (ctx, x, y, opts) => {
|
||||
const drawLine = (ctx, x, y) => {
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(x[0], y[0]);
|
||||
ctx.lineTo(x[1], y[1]);
|
||||
ctx.fillStyle = ctx.strokeStyle = opts?.color || "#f0f0f5";
|
||||
ctx.fillStyle = ctx.strokeStyle = "#f0f0f5";
|
||||
ctx.stroke();
|
||||
}
|
||||
|
||||
@@ -182,7 +182,7 @@ async function renderProfiler() {
|
||||
const optional = (i) => i === 0 ? null : i-1;
|
||||
const dur = u32(), peak = u64(), indexLen = u32(), layoutsLen = u32();
|
||||
const textDecoder = new TextDecoder("utf-8");
|
||||
const { strings, dtypeSize, markers } = JSON.parse(textDecoder.decode(new Uint8Array(buf, offset, indexLen))); offset += indexLen;
|
||||
const { strings, dtypeSize } = JSON.parse(textDecoder.decode(new Uint8Array(buf, offset, indexLen))); offset += indexLen;
|
||||
// place devices on the y axis and set vertical positions
|
||||
const [tickSize, padding] = [10, 8];
|
||||
const deviceList = profiler.append("div").attr("id", "device-list").style("padding-top", tickSize+padding+"px");
|
||||
@@ -295,6 +295,7 @@ async function renderProfiler() {
|
||||
function render(transform) {
|
||||
zoomLevel = transform;
|
||||
rectLst.length = 0;
|
||||
ctx.save();
|
||||
ctx.clearRect(0, 0, canvas.clientWidth, canvas.clientHeight);
|
||||
// rescale to match current zoom
|
||||
const xscale = d3.scaleLinear().domain([0, dur]).range([0, canvas.clientWidth]);
|
||||
@@ -358,6 +359,7 @@ async function renderProfiler() {
|
||||
drawLine(ctx, [x, x], [0, tickSize])
|
||||
// tick label
|
||||
ctx.textBaseline = "top";
|
||||
ctx.textAlign = "left";
|
||||
ctx.fillText(formatTime(tick, dur), x+ctx.lineWidth+2, tickSize);
|
||||
}
|
||||
if (yscale != null) {
|
||||
@@ -365,16 +367,12 @@ async function renderProfiler() {
|
||||
for (const tick of yscale.ticks()) {
|
||||
const y = yscale(tick);
|
||||
drawLine(ctx, [0, tickSize], [y, y]);
|
||||
ctx.textAlign = "left";
|
||||
ctx.textBaseline = "middle";
|
||||
ctx.fillText(formatUnit(tick, data.axes.y.fmt), tickSize+2, y);
|
||||
}
|
||||
}
|
||||
// draw markers
|
||||
for (const m of markers) {
|
||||
const x = xscale(m.ts);
|
||||
drawLine(ctx, [x, x], [0, canvas.clientHeight], { color:m.color });
|
||||
ctx.fillText(m.name, x+2, 1);
|
||||
}
|
||||
ctx.restore();
|
||||
}
|
||||
|
||||
function resize() {
|
||||
|
||||
@@ -179,14 +179,12 @@ def get_profile(profile:list[ProfileEvent]) -> bytes|None:
|
||||
if isinstance(ev,ProfileDeviceEvent): device_ts_diffs[ev.device] = (ev.comp_tdiff, ev.copy_tdiff if ev.copy_tdiff is not None else ev.comp_tdiff)
|
||||
# map events per device
|
||||
dev_events:dict[str, list[tuple[int, int, float, DevEvent]]] = {}
|
||||
markers:list[ProfilePointEvent] = []
|
||||
start_ts:int|None = None
|
||||
end_ts:int|None = None
|
||||
for ts,en,e in flatten_events(profile):
|
||||
dev_events.setdefault(e.device,[]).append((st:=int(ts), et:=int(en), float(en-ts), e))
|
||||
if start_ts is None or st < start_ts: start_ts = st
|
||||
if end_ts is None or et > end_ts: end_ts = et
|
||||
if isinstance(e, ProfilePointEvent) and e.name == "marker": markers.append(e)
|
||||
if start_ts is None: return None
|
||||
# return layout of per device events
|
||||
layout:dict[str, bytes|None] = {}
|
||||
@@ -198,7 +196,7 @@ def get_profile(profile:list[ProfileEvent]) -> bytes|None:
|
||||
layout[k] = timeline_layout(v, start_ts, scache)
|
||||
layout[f"{k} Memory"] = mem_layout(v, start_ts, unwrap(end_ts), peaks, dtype_size, scache)
|
||||
ret = [b"".join([struct.pack("<B", len(k)), k.encode(), v]) for k,v in layout.items() if v is not None]
|
||||
index = json.dumps({"strings":list(scache), "dtypeSize":dtype_size, "markers":[{"ts":int(e.ts-start_ts), **e.arg} for e in markers]}).encode()
|
||||
index = json.dumps({"strings":list(scache), "dtypeSize":dtype_size}).encode()
|
||||
return struct.pack("<IQII", unwrap(end_ts)-start_ts, max(peaks,default=0), len(index), len(ret))+index+b"".join(ret)
|
||||
|
||||
def get_runtime_stats(key) -> list[dict]:
|
||||
|
||||
Reference in New Issue
Block a user