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Mar 27, 2021
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numpy,taco: sweep over sparsities in slicing benchmarks #27

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84 changes: 48 additions & 36 deletions numpy/windowing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,11 +32,12 @@ def sliceTensor(tensor, dim, config):
# Benchmark to measure the time it takes to perform the slice.
@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("format", ['csr'])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("config", sizeConfigs[:len(sizeConfigs)-1])
def bench_slice_sparse_window(tacoBench, dim, format, config):
def bench_slice_sparse_window(tacoBench, dim, format, sparsity, config):
loader = RandomScipySparseTensorLoader(format)
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
Expand All @@ -46,11 +47,12 @@ def bench():
# Benchmark to measure the time it takes to perform the addition.
@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("format", ['csr'])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("config", sizeConfigs[:len(sizeConfigs)-1])
def bench_add_sliced_sparse_window(tacoBench, dim, format, config):
def bench_add_sliced_sparse_window(tacoBench, dim, format, sparsity, config):
loader = RandomScipySparseTensorLoader(format)
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
def bench():
Expand All @@ -60,11 +62,12 @@ def bench():
# Benchmark that performs the slice and addition.
@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("format", ['csr'])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("config", sizeConfigs)
def bench_add_sparse_window(tacoBench, dim, format, config):
def bench_add_sparse_window(tacoBench, dim, format, sparsity, config):
loader = RandomScipySparseTensorLoader(format)
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
Expand All @@ -74,35 +77,38 @@ def bench():
tacoBench(bench)

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("config", sizeConfigs[:len(sizeConfigs)-1])
def bench_slice_pydata_sparse_window(tacoBench, dim, config):
def bench_slice_pydata_sparse_window(tacoBench, dim, sparsity, config):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
return (x, x2)
tacoBench(bench)

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("config", sizeConfigs[:len(sizeConfigs)-1])
def bench_add_sliced_pydata_sparse_window(tacoBench, dim, config):
def bench_add_sliced_pydata_sparse_window(tacoBench, dim, sparsity, config):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
def bench():
return x + x2
tacoBench(bench)

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("config", sizeConfigs)
def bench_add_pydata_sparse_window(tacoBench, dim, config):
def bench_add_pydata_sparse_window(tacoBench, dim, sparsity, config):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = sliceTensor(matrix, dim, config)
x2 = sliceTensor(matrix2, dim, config)
Expand All @@ -111,11 +117,12 @@ def bench():

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("format", ['csr'])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("strideWidth", [2, 4, 8])
def bench_slice_strided_window(tacoBench, dim, format, strideWidth):
def bench_slice_strided_window(tacoBench, dim, format, sparsity, strideWidth):
loader = RandomScipySparseTensorLoader(format)
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
Expand All @@ -124,11 +131,12 @@ def bench():

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("format", ['csr'])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("strideWidth", [2, 4, 8])
def bench_add_sliced_sparse_strided_window(tacoBench, dim, format, strideWidth):
def bench_add_sliced_sparse_strided_window(tacoBench, dim, format, sparsity, strideWidth):
loader = RandomScipySparseTensorLoader(format)
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
def bench():
Expand All @@ -137,47 +145,51 @@ def bench():

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("format", ['csr'])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("strideWidth", [2, 4, 8])
def bench_add_sparse_strided_window(tacoBench, dim, format, strideWidth):
def bench_add_sparse_strided_window(tacoBench, dim, format, sparsity, strideWidth):
loader = RandomScipySparseTensorLoader(format)
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
res = x + x2
tacoBench(bench)

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("strideWidth", [2, 4, 8])
def bench_slice_pydata_sparse_strided_window(tacoBench, dim, strideWidth):
def bench_slice_pydata_sparse_strided_window(tacoBench, dim, sparsity, strideWidth):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
return (x, x2)
tacoBench(bench)

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("strideWidth", [2, 4, 8])
def bench_add_sliced_pydata_sparse_strided_window(tacoBench, dim, strideWidth):
def bench_add_sliced_pydata_sparse_strided_window(tacoBench, dim, sparsity, strideWidth):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
def bench():
res = x + x2
tacoBench(bench)

@pytest.mark.parametrize("dim", [5000, 10000, 20000])
@pytest.mark.parametrize("sparsity", [0.01, 0.005, 0.05])
@pytest.mark.parametrize("strideWidth", [2, 4, 8])
def bench_add_pydata_sparse_strided_window(tacoBench, dim, strideWidth):
def bench_add_pydata_sparse_strided_window(tacoBench, dim, sparsity, strideWidth):
loader = RandomPydataSparseTensorLoader()
matrix = loader.random((dim, dim), 0.01).astype('float64')
matrix2 = loader.random((dim, dim), 0.01, variant=1).astype('float64')
matrix = loader.random((dim, dim), sparsity).astype('float64')
matrix2 = loader.random((dim, dim), sparsity, variant=1).astype('float64')
def bench():
x = matrix[0:dim:strideWidth, 0:dim:strideWidth]
x2 = matrix2[0:dim:strideWidth, 0:dim:strideWidth]
Expand Down
15 changes: 8 additions & 7 deletions taco/windowing.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -66,9 +66,8 @@ Tensor<double> windowedTensorOp(Tensor<double> input1, Tensor<double> input2, in
}
}

static void bench_add_sparse_window(benchmark::State& state, const Format& f, WindowConfig config) {
static void bench_add_sparse_window(benchmark::State& state, const Format& f, WindowConfig config, float sparsity) {
int dim = state.range(0);
auto sparsity = 0.01;
Tensor<double> matrix = loadRandomTensor("A", {dim, dim}, sparsity, f);
Tensor<double> matrix2 = loadRandomTensor("A2", {dim, dim}, sparsity, f, 1 /* variant */);
matrix.pack();
Expand All @@ -87,14 +86,15 @@ static void bench_add_sparse_window(benchmark::State& state, const Format& f, Wi
}

#define DECLARE_ADD_SPARSE_WINDOW_BENCH(configName, config) \
TACO_BENCH_ARGS(bench_add_sparse_window, csr/configName, CSR, config)->ArgsProduct({tensorSizes});
TACO_BENCH_ARGS(bench_add_sparse_window, csr/configName/0.01, CSR, config, 0.01)->ArgsProduct({tensorSizes}); \
TACO_BENCH_ARGS(bench_add_sparse_window, csr/configName/0.005, CSR, config, 0.005)->ArgsProduct({tensorSizes}); \
TACO_BENCH_ARGS(bench_add_sparse_window, csr/configName/0.05, CSR, config, 0.05)->ArgsProduct({tensorSizes}); \

FOREACH_WINDOW_CONFIG(DECLARE_ADD_SPARSE_WINDOW_BENCH)

static void bench_add_sparse_strided_window(benchmark::State& state, const Format& f) {
static void bench_add_sparse_strided_window(benchmark::State& state, const Format& f, float sparsity) {
int dim = state.range(0);
int strideWidth = state.range(1);
auto sparsity = 0.01;
Tensor<double> matrix = loadRandomTensor("A", {dim, dim}, sparsity, f);
Tensor<double> matrix2 = loadRandomTensor("A2", {dim, dim}, sparsity, f, 1 /* variant */);
matrix.pack();
Expand All @@ -113,8 +113,9 @@ static void bench_add_sparse_strided_window(benchmark::State& state, const Forma
}
}
std::vector<int64_t> strides({2, 4, 8});
TACO_BENCH_ARG(bench_add_sparse_strided_window, csr, CSR)
->ArgsProduct({tensorSizes, strides});
TACO_BENCH_ARGS(bench_add_sparse_strided_window, csr/0.01, CSR, 0.01)->ArgsProduct({tensorSizes, strides});
TACO_BENCH_ARGS(bench_add_sparse_strided_window, csr/0.005, CSR, 0.005)->ArgsProduct({tensorSizes, strides});
TACO_BENCH_ARGS(bench_add_sparse_strided_window, csr/0.05, CSR, 0.05)->ArgsProduct({tensorSizes, strides});

static void bench_add_sparse_index_set(benchmark::State& state, const Format& f) {
int dim = state.range(0);
Expand Down