[LANG] Fixed semantics of NaN in float comparisons (#281)
This commit is contained in:
Philippe Tillet
GitHub
547
python/test/unit/language/test_core.py
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547
python/test/unit/language/test_core.py
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@@ -0,0 +1,547 @@
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import torch
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import triton
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import triton.language as tl
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import copy
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import pytest
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import ast
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import itertools
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torch.manual_seed(0)
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# convert from string to torch.dtype
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# Necessary because doesn't print torch.dtype properly
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cvt = {
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'bool': torch.bool,
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'int8': torch.int8,
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'int16': torch.int16,
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'int32': torch.int32,
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'int64': torch.int64,
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'bfloat16': torch.bfloat16,
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'float16': torch.float16,
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'float32': torch.float32,
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'float64': torch.float64,
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}
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int_dtypes = ['int8', 'int16', 'int32', 'int64']
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float_dtypes = ['float16', 'float32', 'float64']
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dtypes = int_dtypes + float_dtypes
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def patch_kernel(template, to_replace):
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kernel = copy.deepcopy(template)
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for key, value in to_replace.items():
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kernel.src = kernel.src.replace(key, value)
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return kernel
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@pytest.mark.parametrize("dtype_x", [
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(dtype_x) for dtype_x in dtypes
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])
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def test_empty_kernel(dtype_x, device='cuda'):
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SIZE = 128
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@triton.jit
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def kernel(X, **meta):
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pass
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x = triton.testing.random(SIZE, dtype=cvt[dtype_x], device=device)
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kernel[(1, )](x, SIZE=SIZE, num_warps=4)
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# generic test functions
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def _test_unary(dtype_x, expr, torch_expr=None, device='cuda'):
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SIZE = 128
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# define the kernel / launch-grid
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@triton.jit
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def kernel(Z, X, **meta):
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off = tl.arange(0, meta['SIZE'])
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x = tl.load(X + off)
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z = GENERATE_TEST_HERE
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tl.store(Z + off, z)
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kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': expr})
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# inputs
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x = triton.testing.random(SIZE, dtype=cvt[dtype_x], device=device)
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if 'log' in expr: x = torch.abs(x) + 0.01
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# reference result
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z_ref = eval(expr if torch_expr is None else torch_expr)
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# triton result
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z_tri = torch.empty_like(z_ref)
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kernel[(1, )](z_tri, x, SIZE=SIZE, num_warps=4)
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# compare
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triton.testing.assert_almost_equal(z_ref, z_tri)
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def _test_binary(dtype_x, dtype_y, expr, mode_x='real', mode_y='real', device='cuda'):
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SIZE = 128
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# define the kernel / launch-grid
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@triton.jit
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def kernel(Z, X, Y, **meta):
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off = tl.arange(0, meta['SIZE'])
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x = tl.load(X + off)
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y = tl.load(Y + off)
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z = GENERATE_TEST_HERE
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tl.store(Z + off, z)
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kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': expr})
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# inputs
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x = triton.testing.random(SIZE, dtype=cvt[dtype_x], device=device)
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y = triton.testing.random(SIZE, dtype=cvt[dtype_y], device=device)
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if mode_x == 'nan': x[:] = float('nan')
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if mode_y == 'nan': y[:] = float('nan')
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# reference result
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z_ref = eval(expr)
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# triton result
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z_tri = torch.empty(SIZE, dtype=z_ref.dtype, device=device)
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kernel[(1, )](z_tri, x, y, SIZE=SIZE, num_warps=4)
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# compare
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triton.testing.assert_almost_equal(z_ref, z_tri, err_msg=expr)
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# ---------------
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# test binary ops
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# ---------------
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@pytest.mark.parametrize("dtype_x, dtype_y, expr", [
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(dtype_x, dtype_y, f' x {op} y') \
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for op in ['+', '-', '*', '/', '%'] \
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for dtype_x in dtypes \
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for dtype_y in dtypes
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])
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def test_bin_op(dtype_x, dtype_y, expr, device='cuda'):
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_test_binary(dtype_x, dtype_y, expr, device=device)
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# ---------------
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# test bitwise ops
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# ---------------
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@pytest.mark.parametrize("dtype_x, dtype_y, expr", [
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(dtype_x, dtype_y, f' x {op} y') \
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for op in ['&', '|', '^'] \
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for dtype_x in dtypes \
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for dtype_y in dtypes
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])
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def test_bitwise_op(dtype_x, dtype_y, expr, device='cuda'):
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if 'float' in dtype_x + dtype_y:
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with pytest.raises(RuntimeError):
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_test_binary(dtype_x, dtype_y, expr, device=device)
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else:
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_test_binary(dtype_x, dtype_y, expr, device=device)
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# ---------------
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# test compare ops
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# ---------------
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ops = ['==', '!=', '>', '<', '>=', '<=']
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@pytest.mark.parametrize("dtype_x, dtype_y, expr, mode_x, mode_y", \
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# real
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[
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(dtype_x, dtype_y, f' x {op} y', 'real', 'real') \
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for op in ops \
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for dtype_x in dtypes \
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for dtype_y in dtypes
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] + \
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# NaNs
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[('float32', 'float32', f' x {op} y', mode_x, mode_y) \
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for op in ops
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for mode_x, mode_y in [('nan' , 'real'),
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('real', 'nan'),
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('nan' , 'nan')]
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])
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def test_compare_op(dtype_x, dtype_y, expr, mode_x, mode_y, device='cuda'):
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_test_binary(dtype_x, dtype_y, expr, mode_x=mode_x, mode_y=mode_y, device=device)
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# ---------------
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# test unary ops
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# ---------------
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@pytest.mark.parametrize("dtype_x, expr", [
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(dtype_x, f' -x') for dtype_x in float_dtypes
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] + [\
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(dtype_x, f' ~x') for dtype_x in int_dtypes
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])
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def test_unary_op(dtype_x, expr, device='cuda'):
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_test_unary(dtype_x, expr, device=device)
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# ----------------
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# test math ops
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# ----------------
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# @pytest.mark.paramterize("expr", [
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# 'exp', 'log', 'cos', 'sin'
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# ])
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@pytest.mark.parametrize("expr", [
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'exp', 'log', 'cos', 'sin'
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])
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def test_math_op(expr, device='cuda'):
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_test_unary('float32', f'tl.{expr}(x)', f'torch.{expr}(x) ', device=device)
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# ----------------
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# test indexing
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# ----------------
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def make_ptr_str(name, shape):
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rank = len(shape)
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offsets = []
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stride = 1
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for i in reversed(range(rank)):
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idx = ', '.join([':' if ii == i else 'None' for ii in range(rank)])
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offsets += [f'tl.arange(0, {shape[i]})[{idx}]*{stride}']
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stride *= shape[i]
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return f"{name} + {' + '.join(offsets)}"
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@pytest.mark.parametrize("expr", [f'x[{s}]' for s in
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['None, :', ':, None',\
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'None, :, :', ':, :, None']\
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])
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def test_index1d(expr, device='cuda'):
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dtype = torch.int32
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rank_x = expr.count(':')
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rank_y = expr.count(',') + 1
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shape_x = [32 for _ in range(rank_x)]
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shape_z = [32 for _ in range(rank_y)]
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# Triton kernel
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@triton.jit
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def kernel(Z, X, **meta):
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SIZE = meta['SIZE']
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m = tl.arange(0, SIZE)
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n = tl.arange(0, SIZE)
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x = tl.load(X_PTR_EXPR)
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z = GENERATE_TEST_HERE
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tl.store(Z_PTR_EXPR, z)
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to_replace = {
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'X_PTR_EXPR': make_ptr_str('X', shape_x),
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'Z_PTR_EXPR': make_ptr_str('Z', shape_z),
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'GENERATE_TEST_HERE': expr,
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}
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kernel = patch_kernel(kernel, to_replace)
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# torch result
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x = triton.testing.random(shape_x, dtype=dtype, device=device)
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y = torch.zeros(shape_z, dtype=dtype, device=device)
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z_ref = eval(expr) + y
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# triton result
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z_tri = torch.empty_like(z_ref)
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kernel[(1, )](z_tri, x, num_warps=1, SIZE=shape_x[0])
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# compare
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triton.testing.assert_almost_equal(z_ref, z_tri)
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# ---------------
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# test tuples
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# ---------------
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@triton.jit
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def fn(a, b):
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return a + b, \
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a - b, \
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a * b
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def test_tuples():
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device = 'cuda'
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@triton.jit
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def with_fn(X, Y, A, B, C):
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x = tl.load(X)
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y = tl.load(Y)
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a, b, c = fn(x, y)
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tl.store(A, a)
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tl.store(B, b)
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tl.store(C, c)
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@triton.jit
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def without_fn(X, Y, A, B, C):
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x = tl.load(X)
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y = tl.load(Y)
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a, b, c = x + y, x - y, x * y
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tl.store(A, a)
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tl.store(B, b)
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tl.store(C, c)
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x = torch.tensor([1.3], device=device, dtype=torch.float32)
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y = torch.tensor([1.9], device=device, dtype=torch.float32)
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a_tri = torch.tensor([0], device=device, dtype=torch.float32)
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b_tri = torch.tensor([0], device=device, dtype=torch.float32)
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c_tri = torch.tensor([0], device=device, dtype=torch.float32)
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for kernel in [with_fn, without_fn]:
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kernel[(1, )](x, y, a_tri, b_tri, c_tri, num_warps=1)
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a_ref, b_ref, c_ref = x + y, x - y, x * y
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assert a_tri == a_ref
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assert b_tri == b_ref
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assert c_tri == c_ref
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# ---------------
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# test atomics
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# ---------------
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@pytest.mark.parametrize("op, dtype_x, mode", itertools.chain.from_iterable([
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[('add', 'int32', mode), ('add', 'float16', mode), ('add', 'float32', mode), \
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('max', 'int32', mode), ('max', 'float32', mode),\
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('min', 'int32', mode), ('min', 'float32', mode),\
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]
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for mode in ['all_neg', 'all_pos', 'min_neg', 'max_pos']]))
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def test_atomic_rmw(op, dtype_x, mode, device='cuda'):
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dtype_x = cvt[dtype_x]
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n_programs = 5
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# triton kernel
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@triton.jit
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def kernel(X, Z, **meta):
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pid = tl.program_id(0)
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x = tl.load(X + pid)
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old = GENERATE_TEST_HERE
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kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.atomic_{op}(Z, x)'})
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torch_op = {'add': torch.sum, 'max': torch.max, 'min': torch.min}[op]
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max_neutral = float('-inf') if dtype_x.is_floating_point else torch.iinfo(dtype_x).min
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min_neutral = float('inf') if dtype_x.is_floating_point else torch.iinfo(dtype_x).max
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neutral = {'add': 0, 'max': max_neutral, 'min': min_neutral}[op]
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# triton result
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x_tri = triton.testing.random((n_programs, ), dtype=dtype_x, device=device)
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if mode == 'all_neg':
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x_tri = -torch.abs(x_tri)
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if mode == 'all_pos':
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x_tri = torch.abs(x_tri)
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if mode == 'min_neg':
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idx = torch.randint(n_programs, size=(1, )).item()
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x_tri[idx] = -torch.max(torch.abs(x_tri)) - 1
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if mode == 'max_pos':
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idx = torch.randint(n_programs, size=(1, )).item()
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x_tri[idx] = torch.max(torch.abs(x_tri)) + 1
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z_tri = torch.empty([], dtype=dtype_x, device=device)
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z_tri.fill_(neutral)
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kernel[(n_programs, )](x_tri, z_tri)
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# torch result
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z_ref = torch_op(x_tri).to(dtype_x)
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# compare
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exact = op not in ['add']
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if exact:
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assert z_ref.item() == z_tri.item()
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else:
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triton.testing.assert_almost_equal(z_ref, z_tri)
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# ---------------
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# test cast
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# ---------------
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@pytest.mark.parametrize("dtype_x, dtype_z, bitcast", [
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(dtype_x, dtype_z, False) \
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for dtype_x in dtypes\
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for dtype_z in dtypes
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] + [
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('float32', 'bfloat16', False),
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('bfloat16', 'float32', False),
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('float32', 'int32', True)
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])
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def test_cast(dtype_x, dtype_z, bitcast, device='cuda'):
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x = torch.tensor([43.5], dtype=cvt[dtype_x], device=device)
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# triton kernel
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@triton.jit
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def kernel(X, Z, **meta):
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x = tl.load(X)
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z = x.to(Z.dtype.element_ty, bitcast=meta['BITCAST'])
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tl.store(Z, z)
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# triton result
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z_tri = torch.empty((1, ), dtype=cvt[dtype_z], device=device)
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kernel[(1, )](x, z_tri, BITCAST=bitcast)
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# torch result
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if bitcast:
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import numpy as np
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z_ref = x.detach().cpu().numpy().view(getattr(np, dtype_z))
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z_ref = torch.from_numpy(z_ref).to(device)
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else:
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z_ref = x.to(z_tri.dtype)
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assert z_tri == z_ref
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# ---------------
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# test reduce
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# ---------------
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@pytest.mark.parametrize("dtype, shape",
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[(dtype, shape) \
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for dtype in dtypes\
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for shape in [128, 512]])
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def test_reduce1d(dtype, shape, device='cuda'):
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dtype = cvt[dtype]
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# triton kernel
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@triton.jit
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def kernel(X, Z, **meta):
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x = tl.load(X + tl.arange(0, meta['BLOCK']))
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tl.store(Z, tl.sum(x, axis=0))
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x = triton.testing.random((shape,), dtype=dtype, device=device)
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# triton result
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z_tri = triton.testing.random((1,), dtype=dtype, device=device)
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kernel[(1,)](x, z_tri, BLOCK=shape)
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# torch result
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z_ref = torch.sum(x).to(dtype)
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# compare
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triton.testing.assert_almost_equal(z_tri, z_ref)
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@pytest.mark.parametrize("dtype, shape, axis",
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[(dtype, shape, 1) \
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for dtype in ['float32']\
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for shape in [(1, 1024)]])
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def test_reduce2d(dtype, shape, axis, device='cuda'):
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dtype = cvt[dtype]
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# triton kernel
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@triton.jit
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def kernel(X, Z, **meta):
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range_m = tl.arange(0, meta['BLOCK_M'])
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range_n = tl.arange(0, meta['BLOCK_N'])
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x = tl.load(X + range_m[:, None]*meta['BLOCK_N'] + range_n[None, :])
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z = tl.sum(x, axis=meta['AXIS'])
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tl.store(Z + range_m, z)
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# input
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x = triton.testing.random(shape, dtype=dtype, device=device)
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# triton result
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z_tri = torch.empty((shape[0],), dtype=dtype, device=device)
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kernel[(1,)](x, z_tri, BLOCK_M=shape[0], BLOCK_N=shape[1], AXIS=axis)
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# torch result
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z_ref = torch.sum(x, axis=axis).to(dtype)
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# compare
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triton.testing.assert_almost_equal(z_tri, z_ref)
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# ---------------
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# test permute
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# ---------------
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# ---------------
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# test permute
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# ---------------
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@pytest.mark.parametrize("dtype, shape, perm",
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[(dtype, shape, perm) \
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for dtype in ['float32']\
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for shape in [(128, 128)]\
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for perm in [(1, 0)]])
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def test_permute(dtype, shape, perm, device='cuda'):
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dtype = cvt[dtype]
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# triton kernel
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@triton.jit
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def kernel(X, stride_xm, stride_xn,
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Z, stride_zm, stride_zn, **meta):
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BLOCK_M = meta['BLOCK_M']
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BLOCK_N = meta['BLOCK_N']
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off_m = tl.arange(0, BLOCK_M)
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||||
off_n = tl.arange(0, BLOCK_N)
|
||||
Xs = X + off_m[:, None] * stride_xm + off_n[None, :] * stride_xn
|
||||
Zs = Z + off_m[:, None] * stride_zm + off_n[None, :] * stride_zn
|
||||
tl.store(Zs, tl.load(Xs))
|
||||
# input
|
||||
x = triton.testing.random(shape, dtype=dtype, device=device)
|
||||
# triton result
|
||||
z_tri = torch.empty_like(x)
|
||||
pgm = kernel[(1, 1)](x, x.stride(0), x.stride(1),
|
||||
z_tri, z_tri.stride(1), z_tri.stride(0),
|
||||
BLOCK_M=shape[0], BLOCK_N=shape[1])
|
||||
# torch result
|
||||
z_ref = x.permute(*perm).contiguous()
|
||||
# compare
|
||||
triton.testing.assert_almost_equal(z_tri, z_ref)
|
||||
# parse ptx to make sure ld/st are vectorized
|
||||
ptx = pgm.asm['ptx']
|
||||
assert 'ld.global.v4' in ptx
|
||||
assert 'st.global.v4' in ptx
|
||||
|
||||
# ---------------
|
||||
# test dot
|
||||
# ---------------
|
||||
|
||||
@pytest.mark.parametrize("epilogue", ['none', 'add-matrix', 'add-rows', 'add-cols'])
|
||||
def test_dot(epilogue, device='cuda'):
|
||||
torch.manual_seed(0)
|
||||
# triton kernel
|
||||
@triton.jit
|
||||
def kernel(X, stride_xm, stride_xk,
|
||||
Y, stride_yk, stride_yn,
|
||||
Z, stride_zm, stride_zn, **meta):
|
||||
BLOCK_M = meta['BLOCK_M']
|
||||
BLOCK_K = meta['BLOCK_K']
|
||||
BLOCK_N = meta['BLOCK_N']
|
||||
off_m = tl.arange(0, BLOCK_M)
|
||||
off_n = tl.arange(0, BLOCK_N)
|
||||
off_k = tl.arange(0, BLOCK_K)
|
||||
Xs = X + off_m[:, None] * stride_xm + off_k[None, :] * stride_xk
|
||||
Ys = Y + off_k[:, None] * stride_yk + off_n[None, :] * stride_yn
|
||||
Zs = Z + off_m[:, None] * stride_zm + off_n[None, :] * stride_zn
|
||||
z = tl.dot(tl.load(Xs), tl.load(Ys))
|
||||
if meta['ADD_MATRIX']:
|
||||
z += tl.load(Zs)
|
||||
if meta['ADD_ROWS']:
|
||||
ZRs = Z + off_m * stride_zm
|
||||
z += tl.load(ZRs)[:, None]
|
||||
if meta['ADD_COLS']:
|
||||
ZCs = Z + off_n * stride_zn
|
||||
z += tl.load(ZCs)[None, :]
|
||||
tl.store(Zs, z)
|
||||
# input
|
||||
M, N, K = 64, 64, 32
|
||||
x = triton.testing.random((M, K), dtype=torch.float16, device=device)
|
||||
y = triton.testing.random((K, N), dtype=torch.float16, device=device)
|
||||
# triton result
|
||||
z = triton.testing.random((M, N), dtype=torch.float16, device=device)
|
||||
z_tri = z.clone()
|
||||
pgm = kernel[(1, 1)](x, x.stride(0), x.stride(1),
|
||||
y, y.stride(0), y.stride(1),
|
||||
z_tri, z_tri.stride(0), z_tri.stride(1),
|
||||
BLOCK_M=M, BLOCK_K=K, BLOCK_N=N,
|
||||
ADD_MATRIX = epilogue=='add-matrix',
|
||||
ADD_ROWS = epilogue=='add-rows',
|
||||
ADD_COLS = epilogue=='add-cols')
|
||||
# torch result
|
||||
z_ref = torch.matmul(x.float(), y.float())
|
||||
if epilogue == 'add-matrix':
|
||||
z_ref += z
|
||||
if epilogue == 'add-rows':
|
||||
z_ref += z[:,0][:, None]
|
||||
if epilogue == 'add-cols':
|
||||
z_ref += z[0,:][None, :]
|
||||
z_ref = z_ref.to(torch.float16)
|
||||
# compare
|
||||
ptx = pgm.asm['ptx']
|
||||
# print(ptx)
|
||||
triton.testing.assert_almost_equal(z_tri, z_ref)
|
||||
# make sure ld/st are vectorized
|
||||
assert 'ld.global.v4' in ptx
|
||||
assert 'st.global.v4' in ptx
|
||||
|
||||
|
||||
# ---------------
|
||||
# test load
|
||||
# ---------------
|
||||
|
||||
# ---------------
|
||||
# test store
|
||||
# ---------------
|
||||
|
||||
# ---------------
|
||||
# test if
|
||||
# ---------------
|
||||
|
||||
# ---------------
|
||||
# test for
|
||||
# ---------------
|
||||
|
||||
# ---------------
|
||||
# test while
|
||||
# ---------------
|
||||
|
||||
# ---------------
|
||||
# test noop
|
||||
#----------------
|
||||
def test_noop(device='cuda'):
|
||||
@triton.jit
|
||||
def kernel(**meta):
|
||||
pass
|
||||
x = triton.testing.random((1,), dtype=torch.int32, device=device)
|
||||
kernel[(1, )](x)
|
||||
198
python/test/unit/language/test_random.py
Normal file
198
python/test/unit/language/test_random.py
Normal file
@@ -0,0 +1,198 @@
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
import pytest
|
||||
import scipy.stats
|
||||
import numpy as np
|
||||
|
||||
from numpy.random import Philox
|
||||
|
||||
#####################################
|
||||
## Reference Philox Implementation
|
||||
#####################################
|
||||
|
||||
class PhiloxConfig:
|
||||
def __init__(self, PHILOX_ROUND_A, PHILOX_ROUND_B, PHILOX_KEY_A, PHILOX_KEY_B, DTYPE):
|
||||
self.PHILOX_ROUND_A = np.array(PHILOX_ROUND_A, dtype=DTYPE)
|
||||
self.PHILOX_ROUND_B = np.array(PHILOX_ROUND_B, dtype=DTYPE)
|
||||
self.PHILOX_KEY_A = np.array(PHILOX_KEY_A, dtype=DTYPE)
|
||||
self.PHILOX_KEY_B = np.array(PHILOX_KEY_B, dtype=DTYPE)
|
||||
self.DTYPE = DTYPE
|
||||
|
||||
|
||||
# This is better for GPU
|
||||
PHILOX_32 = PhiloxConfig(
|
||||
PHILOX_KEY_A=0x9E3779B9,
|
||||
PHILOX_KEY_B=0xBB67AE85,
|
||||
PHILOX_ROUND_A=0xD2511F53,
|
||||
PHILOX_ROUND_B=0xCD9E8D57,
|
||||
DTYPE=np.uint32,
|
||||
)
|
||||
|
||||
# This is what numpy implements
|
||||
PHILOX_64 = PhiloxConfig(
|
||||
PHILOX_KEY_A=0x9E3779B97F4A7C15,
|
||||
PHILOX_KEY_B=0xBB67AE8584CAA73B,
|
||||
PHILOX_ROUND_A=0xD2E7470EE14C6C93,
|
||||
PHILOX_ROUND_B=0xCA5A826395121157,
|
||||
DTYPE=np.uint64,
|
||||
)
|
||||
|
||||
|
||||
class CustomPhilox4x:
|
||||
def __init__(self, seed, config):
|
||||
self._config = config
|
||||
seed = self._into_pieces(seed)
|
||||
self._key = np.array(seed[:2], dtype=self._dtype)
|
||||
self._counter = np.array((0, 0) + seed[2:], dtype=self._dtype)
|
||||
|
||||
@property
|
||||
def _dtype(self):
|
||||
return self._config.DTYPE
|
||||
|
||||
def _into_pieces(self, n, pad=4):
|
||||
res = []
|
||||
while len(res) < pad:
|
||||
res.append(np.array(n, dtype=self._dtype))
|
||||
n >>= (np.dtype(self._dtype).itemsize * 8)
|
||||
assert n == 0
|
||||
return tuple(res)
|
||||
|
||||
def _multiply_low_high(self, a, b):
|
||||
low = a * b
|
||||
high = int(a) * int(b)
|
||||
high = np.array(high >> (np.dtype(self._dtype).itemsize * 8), dtype=self._dtype)
|
||||
return low, high
|
||||
|
||||
def _single_round(self, counter, key):
|
||||
lo0, hi0 = self._multiply_low_high(self._config.PHILOX_ROUND_A, counter[0])
|
||||
lo1, hi1 = self._multiply_low_high(self._config.PHILOX_ROUND_B, counter[2])
|
||||
ret0 = hi1 ^ counter[1] ^ key[0]
|
||||
ret1 = lo1
|
||||
ret2 = hi0 ^ counter[3] ^ key[1]
|
||||
ret3 = lo0
|
||||
return np.array([ret0, ret1, ret2, ret3], dtype=self._dtype)
|
||||
|
||||
def _raise_key(self, key):
|
||||
ret0 = key[0] + self._config.PHILOX_KEY_A
|
||||
ret1 = key[1] + self._config.PHILOX_KEY_B
|
||||
return np.array([ret0, ret1], dtype=self._dtype)
|
||||
|
||||
def random_raw(self):
|
||||
counter = self._counter
|
||||
key = self._key
|
||||
for _ in range(10):
|
||||
counter = self._single_round(counter, key)
|
||||
key = self._raise_key(key)
|
||||
self.advance(1)
|
||||
return counter
|
||||
|
||||
def advance(self, n_steps):
|
||||
self._counter[0] += n_steps
|
||||
assert self._counter[0] < 2**32, "FIXME: doesn't work for large offsets"
|
||||
|
||||
|
||||
class CustomPhilox(CustomPhilox4x):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.buffer = []
|
||||
|
||||
def random_raw(self):
|
||||
if len(self.buffer) == 0:
|
||||
self.buffer = list(super().random_raw())[::-1]
|
||||
return int(self.buffer.pop())
|
||||
|
||||
|
||||
#####################################
|
||||
## Unit Tests
|
||||
#####################################
|
||||
|
||||
BLOCK = 1024
|
||||
|
||||
# test generation of random uint32
|
||||
@pytest.mark.parametrize('size, seed',
|
||||
[(size, seed) for size in ['10', '4,53', '10000']\
|
||||
for seed in [0, 42, 124, 54]]
|
||||
)
|
||||
def test_randint(size, seed, device='cuda'):
|
||||
size = list(map(int, size.split(',')))
|
||||
@triton.jit
|
||||
def kernel(X, N, seed):
|
||||
offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
|
||||
rand = tl.randint(seed, offset)
|
||||
tl.store(X + offset, rand, mask=offset < N)
|
||||
# triton result
|
||||
x = torch.empty(size, dtype=torch.int32, device=device)
|
||||
N = x.numel()
|
||||
grid = (triton.cdiv(N, BLOCK),)
|
||||
kernel[grid](x, N, seed)
|
||||
out_tri = x.cpu().numpy().astype(np.uint32).flatten().tolist()
|
||||
# reference result
|
||||
gen = CustomPhilox4x(seed, config=PHILOX_32)
|
||||
out_ref = [gen.random_raw()[0] for _ in out_tri]
|
||||
assert out_tri == out_ref
|
||||
|
||||
# test conversion of random uint32 into random float in [0, 1]
|
||||
def test_uint32_to_uniform_float():
|
||||
@triton.jit
|
||||
def kernel(SRC, TGT, N, **meta):
|
||||
pid = tl.program_id(0)
|
||||
offset = pid * BLOCK + tl.arange(0, BLOCK)
|
||||
src = tl.load(SRC + offset)
|
||||
tgt = tl.random.uint32_to_uniform_float(src)
|
||||
tl.store(TGT + offset, tgt, mask=offset < N)
|
||||
|
||||
def run(source):
|
||||
target = -torch.ones(source.shape, dtype=torch.float32, device=source.device)
|
||||
N = source.numel()
|
||||
grid = lambda meta: (triton.cdiv(N, BLOCK),)
|
||||
kernel[grid](source, target, N)
|
||||
return target
|
||||
|
||||
# check range of edge values
|
||||
n = 100
|
||||
source = torch.tensor(list(range(n)) + list(range(-n, 0)), dtype=torch.int32).cuda()
|
||||
target = run(source).tolist()
|
||||
assert target == sorted(target)
|
||||
assert all(0.0 <= num < 1.0 for num in target)
|
||||
# check distribution is uniform
|
||||
source = torch.randint(-2**31, 2**31 - 1, dtype=torch.int32, size=(100000,)).cuda()
|
||||
target = run(source).tolist()
|
||||
assert scipy.stats.kstest(target, 'uniform', args=(0, 1)).statistic < 0.01
|
||||
|
||||
# test uniform PRNG
|
||||
@pytest.mark.parametrize('size, seed',
|
||||
[(size, seed) for size in [1000000]\
|
||||
for seed in [0, 42, 124, 54]]
|
||||
)
|
||||
def test_rand(size, seed, device='cuda'):
|
||||
@triton.jit
|
||||
def kernel(X, N, seed):
|
||||
offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
|
||||
rand = tl.rand(seed, offset)
|
||||
tl.store(X + offset, rand, mask=offset < N)
|
||||
# triton result
|
||||
x = torch.empty(size, dtype=torch.float32, device=device)
|
||||
N = x.numel()
|
||||
grid = (triton.cdiv(N, BLOCK),)
|
||||
kernel[grid](x, N, seed)
|
||||
assert scipy.stats.kstest(x.tolist(), 'uniform', args=(0, 1)).statistic < 0.01
|
||||
|
||||
# test normal PRNG
|
||||
@pytest.mark.parametrize('size, seed',
|
||||
[(size, seed) for size in [1000000]\
|
||||
for seed in [0, 42, 124, 54]]
|
||||
)
|
||||
def test_randn(size, seed, device='cuda'):
|
||||
@triton.jit
|
||||
def kernel(X, N, seed):
|
||||
offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
|
||||
rand = tl.randn(seed, offset)
|
||||
tl.store(X + offset, rand, mask=offset < N)
|
||||
# triton result
|
||||
x = torch.empty(size, dtype=torch.float32, device=device)
|
||||
N = x.numel()
|
||||
grid = (triton.cdiv(N, BLOCK),)
|
||||
kernel[grid](x, N, seed)
|
||||
assert abs(x.mean()) < 1e-2
|
||||
assert abs(x.std() - 1) < 1e-2
|
||||
Reference in New Issue
Block a user