8bf551ae7a
Run: ``` isort ./python autopep8 -i --ignore E501,E701,E731 $(find ./python/ -name '*.py') ``` with an `.isort.cfg` and then clean up a few warts. This PR should be a no-op; the idea is that this is all boring whitespace changes, and any config file changes will be in a different change to make it easier to review.
925 lines
31 KiB
Python
925 lines
31 KiB
Python
import copy
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import itertools
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import re
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from typing import Optional, Union
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import numpy as np
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import pytest
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import torch
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from numpy.random import RandomState
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import triton
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import triton.language as tl
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from triton.code_gen import TensorWrapper, reinterpret
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int_dtypes = ['int8', 'int16', 'int32', 'int64']
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uint_dtypes = ['uint8', 'uint16', 'uint32', 'uint64']
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float_dtypes = ['float16', 'float32', 'float64']
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dtypes = int_dtypes + uint_dtypes + float_dtypes
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def _bitwidth(dtype: str) -> int:
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# ex.: "int64" -> 64
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return int(re.search(r'(\d+)$', dtype).group(1))
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def numpy_random(shape, dtype_str, rs: Optional[RandomState] = None, low=None, high=None):
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"""
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Override `rs` if you're calling this function twice and don't want the same
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result for both calls.
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"""
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if isinstance(shape, int):
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shape = (shape, )
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if rs is None:
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rs = RandomState(seed=17)
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dtype = getattr(np, dtype_str)
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if dtype_str in int_dtypes + uint_dtypes:
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iinfo = np.iinfo(getattr(np, dtype_str))
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low = iinfo.min if low is None else max(low, iinfo.min)
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high = iinfo.max if high is None else min(high, iinfo.max)
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x = rs.randint(low, high, shape, dtype=dtype)
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x[x == 0] = 1 # Hack. Never return zero so tests of division don't error out.
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return x
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elif dtype_str in float_dtypes:
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return rs.normal(0, 1, shape).astype(dtype)
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else:
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raise RuntimeError(f'Unknown dtype {dtype_str}')
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def to_triton(x: np.ndarray, device='cuda') -> Union[TensorWrapper, torch.Tensor]:
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t = x.dtype.name
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if t in uint_dtypes:
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signed_type_name = t.lstrip('u') # e.g. "uint16" -> "int16"
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x_signed = x.astype(getattr(np, signed_type_name))
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return reinterpret(torch.tensor(x_signed, device=device), getattr(tl, t))
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else:
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return torch.tensor(x, device=device)
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def torch_dtype_name(dtype) -> str:
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if isinstance(dtype, triton.language.dtype):
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return dtype.name
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elif isinstance(dtype, torch.dtype):
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# 'torch.int64' -> 'int64'
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m = re.match(r'^torch\.(\w+)$', str(dtype))
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return m.group(1)
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else:
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raise TypeError(f'not a triton or torch dtype: {type(dtype)}')
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def to_numpy(x):
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if isinstance(x, TensorWrapper):
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return x.base.cpu().numpy().astype(getattr(np, torch_dtype_name(x.dtype)))
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elif isinstance(x, torch.Tensor):
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return x.cpu().numpy()
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else:
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raise ValueError(f"Not a triton-compatible tensor: {x}")
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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", [dtype_x for dtype_x in dtypes])
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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, SIZE: tl.constexpr):
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pass
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x = to_triton(numpy_random(SIZE, dtype_str=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, numpy_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, SIZE: tl.constexpr):
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off = tl.arange(0, 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 = numpy_random(SIZE, dtype_str=dtype_x)
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if 'log' in expr:
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x = np.abs(x) + 0.01
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# reference result
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z_ref = eval(expr if numpy_expr is None else numpy_expr)
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# triton result
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x_tri = to_triton(x, device=device)
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z_tri = to_triton(np.empty_like(z_ref), device=device)
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kernel[(1, )](z_tri, x_tri, SIZE=SIZE, num_warps=4)
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# compare
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np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01)
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def _binary_op_dtype_override(a: str, b: str) -> Optional[np.dtype]:
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"""
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Given two dtype strings, returns the numpy dtype Triton thinks binary
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operations on the two types should return. Returns None if the return value
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matches numpy. This is generally needed because Triton and pytorch return
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narrower floating point types than numpy in mixed operations, and because
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Triton follows C/C++ semantics around mixed signed/unsigned operations, and
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numpy/pytorch do not.
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"""
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overrides = {
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('float16', 'int16'): np.float16,
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('float16', 'int32'): np.float16,
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('float16', 'int64'): np.float16,
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('float16', 'uint16'): np.float16,
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('float16', 'uint32'): np.float16,
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('float16', 'uint64'): np.float16,
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('int8', 'uint8'): np.uint8,
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('int8', 'uint16'): np.uint16,
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('int8', 'uint32'): np.uint32,
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('int8', 'uint64'): np.uint64,
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('int16', 'uint16'): np.uint16,
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('int16', 'uint32'): np.uint32,
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('int16', 'uint64'): np.uint64,
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('int32', 'uint32'): np.uint32,
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('int32', 'uint64'): np.uint64,
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('int64', 'uint64'): np.uint64,
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}
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key = (a, b) if a < b else (b, a)
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return overrides.get(key)
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def _test_binary(dtype_x, dtype_y, expr, numpy_expr=None, mode_x='real', mode_y='real', device='cuda', y_low=None, y_high=None):
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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, SIZE: tl.constexpr):
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off = tl.arange(0, 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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rs = RandomState(17)
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x = numpy_random(SIZE, dtype_str=dtype_x, rs=rs)
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y = numpy_random(SIZE, dtype_str=dtype_y, rs=rs, low=y_low, high=y_high)
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if mode_x == 'nan':
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x[:] = float('nan')
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if mode_y == 'nan':
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y[:] = float('nan')
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# reference result
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z_ref = eval(expr if numpy_expr is None else numpy_expr)
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dtype_z = _binary_op_dtype_override(dtype_x, dtype_y)
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if dtype_z is not None:
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z_ref = z_ref.astype(dtype_z)
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# triton result
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x_tri = to_triton(x, device=device)
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y_tri = to_triton(y, device=device)
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z_tri = to_triton(np.empty(SIZE, dtype=z_ref.dtype), device=device)
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kernel[(1, )](z_tri, x_tri, y_tri, SIZE=SIZE, num_warps=4)
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np.testing.assert_allclose(z_ref, to_numpy(z_tri), err_msg=expr, rtol=0.01)
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def _mod_operation_ill_conditioned(dtype_x, dtype_y) -> bool:
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# The result of x % y is ill-conditioned if x % y is much smaller than x.
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# pytorch/CUDA has slightly different (probably better) rounding on
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# remainders than stock LLVM. We currently don't expect to match it
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# bit-for-bit.
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return (dtype_x, dtype_y) in [
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('int32', 'float16'),
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('int32', 'float32'),
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('int64', 'float16'),
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('int64', 'float32'),
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('int64', 'float64'),
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('uint16', 'float16'),
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('uint16', 'float32'),
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('uint32', 'float16'),
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('uint32', 'float32'),
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('uint64', 'float16'),
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('uint64', 'float32'),
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('uint64', 'float64'),
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]
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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, op", [
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(dtype_x, dtype_y, op)
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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, op, device='cuda'):
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expr = f' x {op} y'
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if op == '%' and dtype_x in int_dtypes + uint_dtypes and dtype_y in int_dtypes + uint_dtypes:
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# LLVM has 'numpy.fmod', not 'numpy.remainder', semantics on integer remainders.
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numpy_expr = 'np.fmod(x, y)'
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elif op in ('/', '%') and dtype_x in ('int16', 'float16') and dtype_y in ('int16', 'float16'):
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# Triton promotes 16-bit floating-point / and % to 32-bit because there
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# are no native div or FRem operations on float16. Since we have to
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# convert anyway, we may as well take the accuracy bump.
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numpy_expr = f'x.astype(np.float32) {op} y.astype(np.float32)'
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elif (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)):
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numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})'
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elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)):
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numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})'
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else:
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numpy_expr = None
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if op == '%' and _mod_operation_ill_conditioned(dtype_x, dtype_y):
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with pytest.raises(AssertionError, match='Not equal to tolerance'):
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_test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device)
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elif (op in ('%', '/') and
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((dtype_x in int_dtypes and dtype_y in uint_dtypes) or
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(dtype_x in uint_dtypes and dtype_y in int_dtypes))):
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with pytest.raises(triton.code_gen.CompilationError) as exc_info:
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_test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device)
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assert re.match('Cannot use .* because they have different signedness', str(exc_info.value.__cause__))
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else:
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_test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device)
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@pytest.mark.parametrize("dtype_x, dtype_y",
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[(dtype_x, dtype_y) for dtype_x in int_dtypes for dtype_y in int_dtypes] +
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[(dtype_x, dtype_y) for dtype_x in uint_dtypes for dtype_y in uint_dtypes]
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)
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def test_floordiv(dtype_x, dtype_y, device='cuda'):
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# Triton has IEEE, not numpy/torch, semantics for %, and those carry
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# through to //, so we have to use a nonstandard expression to get a
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# reference result for //.
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expr = 'x // y'
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numpy_expr = '((x - np.fmod(x, y)) / y)'
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_test_binary(dtype_x, dtype_y, expr, numpy_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, op", [
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(dtype_x, dtype_y, op)
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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, op, device='cuda'):
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expr = f'x {op} y'
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if (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)):
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numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})'
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elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)):
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numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})'
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else:
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numpy_expr = None
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if 'float' in dtype_x + dtype_y:
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with pytest.raises(triton.code_gen.CompilationError) as exc_info:
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_test_binary(dtype_x, dtype_y, expr, numpy_expr='np.array([])', device=device)
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# The CompilationError must have been caused by a C++ exception with this text.
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assert re.match('invalid operands of type', str(exc_info.value.__cause__))
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else:
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_test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device)
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@pytest.mark.parametrize("dtype_x, dtype_y, op", [
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(dtype_x, dtype_y, op)
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for op in ['<<', '>>']
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for dtype_x in int_dtypes + uint_dtypes
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for dtype_y in int_dtypes + uint_dtypes
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])
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def test_shift_op(dtype_x, dtype_y, op, device='cuda'):
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expr = f'x {op} y'
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bw = max(_bitwidth(dtype_x), _bitwidth(dtype_y))
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dtype_z = f'uint{bw}'
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numpy_expr = f'x.astype(np.{dtype_z}) {op} y.astype(np.{dtype_z})'
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_test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device, y_low=0, y_high=65)
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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, op, mode_x, mode_y",
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# real
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[
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(dtype_x, dtype_y, op, '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', op, 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, op, mode_x, mode_y, device='cuda'):
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expr = f'x {op} y'
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if (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)):
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numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})'
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elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)):
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numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})'
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else:
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numpy_expr = None
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_test_binary(dtype_x, dtype_y, expr, numpy_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, ' -x') for dtype_x in dtypes
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] + [
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(dtype_x, ' ~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'np.{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, dtype_str", [
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(f'x[{s}]', d)
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for s in ['None, :', ':, None', 'None, :, :', ':, :, None']
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for d in ['int32', 'uint32', 'uint16']
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])
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def test_index1d(expr, dtype_str, device='cuda'):
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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, SIZE: tl.constexpr):
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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 = numpy_random(shape_x, dtype_str=dtype_str)
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y = np.zeros(shape_z, dtype=getattr(np, dtype_str))
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z_ref = eval(expr) + y
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# triton result
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z_tri = to_triton(np.empty_like(z_ref), device=device)
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x_tri = to_triton(x)
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kernel[(1, )](z_tri, x_tri, num_warps=1, SIZE=shape_x[0])
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# compare
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assert (z_ref == to_numpy(z_tri)).all()
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# ---------------
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# test tuples
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# ---------------
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@triton.jit
|
|
def fn(a, b):
|
|
return a + b, \
|
|
a - b, \
|
|
a * b
|
|
|
|
|
|
def test_tuples():
|
|
device = 'cuda'
|
|
|
|
@triton.jit
|
|
def with_fn(X, Y, A, B, C):
|
|
x = tl.load(X)
|
|
y = tl.load(Y)
|
|
a, b, c = fn(x, y)
|
|
tl.store(A, a)
|
|
tl.store(B, b)
|
|
tl.store(C, c)
|
|
|
|
@triton.jit
|
|
def without_fn(X, Y, A, B, C):
|
|
x = tl.load(X)
|
|
y = tl.load(Y)
|
|
a, b, c = x + y, x - y, x * y
|
|
tl.store(A, a)
|
|
tl.store(B, b)
|
|
tl.store(C, c)
|
|
|
|
x = torch.tensor([1.3], device=device, dtype=torch.float32)
|
|
y = torch.tensor([1.9], device=device, dtype=torch.float32)
|
|
a_tri = torch.tensor([0], device=device, dtype=torch.float32)
|
|
b_tri = torch.tensor([0], device=device, dtype=torch.float32)
|
|
c_tri = torch.tensor([0], device=device, dtype=torch.float32)
|
|
for kernel in [with_fn, without_fn]:
|
|
kernel[(1, )](x, y, a_tri, b_tri, c_tri, num_warps=1)
|
|
a_ref, b_ref, c_ref = x + y, x - y, x * y
|
|
assert a_tri == a_ref
|
|
assert b_tri == b_ref
|
|
assert c_tri == c_ref
|
|
|
|
|
|
# ---------------
|
|
# test atomics
|
|
# ---------------
|
|
@pytest.mark.parametrize("op, dtype_x_str, mode", itertools.chain.from_iterable([
|
|
[
|
|
('add', 'float16', mode),
|
|
('add', 'uint32', mode), ('add', 'int32', mode), ('add', 'float32', mode),
|
|
('max', 'uint32', mode), ('max', 'int32', mode), ('max', 'float32', mode),
|
|
('min', 'uint32', mode), ('min', 'int32', mode), ('min', 'float32', mode),
|
|
]
|
|
for mode in ['all_neg', 'all_pos', 'min_neg', 'max_pos']]))
|
|
def test_atomic_rmw(op, dtype_x_str, mode, device='cuda'):
|
|
n_programs = 5
|
|
|
|
# triton kernel
|
|
@triton.jit
|
|
def kernel(X, Z):
|
|
pid = tl.program_id(0)
|
|
x = tl.load(X + pid)
|
|
old = GENERATE_TEST_HERE
|
|
|
|
kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.atomic_{op}(Z, x)'})
|
|
numpy_op = {'add': np.sum, 'max': np.max, 'min': np.min}[op]
|
|
max_neutral = float('-inf') if dtype_x_str in float_dtypes else np.iinfo(getattr(np, dtype_x_str)).min
|
|
min_neutral = float('inf') if dtype_x_str in float_dtypes else np.iinfo(getattr(np, dtype_x_str)).max
|
|
neutral = {'add': 0, 'max': max_neutral, 'min': min_neutral}[op]
|
|
|
|
# triton result
|
|
rs = RandomState(17)
|
|
x = numpy_random((n_programs, ), dtype_str=dtype_x_str, rs=rs)
|
|
if mode == 'all_neg':
|
|
x = -np.abs(x)
|
|
if mode == 'all_pos':
|
|
x = np.abs(x)
|
|
if mode == 'min_neg':
|
|
idx = rs.randint(n_programs, size=(1, )).item()
|
|
x[idx] = -np.max(np.abs(x)) - 1
|
|
if mode == 'max_pos':
|
|
idx = rs.randint(n_programs, size=(1, )).item()
|
|
x[idx] = np.max(np.abs(x)) + 1
|
|
x_tri = to_triton(x, device=device)
|
|
|
|
z_tri = to_triton(np.array([neutral], dtype=getattr(np, dtype_x_str)), device=device)
|
|
kernel[(n_programs, )](x_tri, z_tri)
|
|
# torch result
|
|
z_ref = numpy_op(x).astype(getattr(np, dtype_x_str))
|
|
# compare
|
|
exact = op not in ['add']
|
|
if exact:
|
|
assert z_ref.item() == to_numpy(z_tri).item()
|
|
else:
|
|
np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01)
|
|
|
|
|
|
# ---------------
|
|
# test cast
|
|
# ---------------
|
|
@pytest.mark.parametrize("dtype_x, dtype_z, bitcast", [
|
|
(dtype_x, dtype_z, False)
|
|
for dtype_x in dtypes
|
|
for dtype_z in dtypes
|
|
] + [
|
|
('float32', 'bfloat16', False),
|
|
('bfloat16', 'float32', False),
|
|
('float32', 'int32', True),
|
|
] + [
|
|
(f'uint{x}', f'int{x}', True) for x in [8, 16, 32, 64]
|
|
] + [
|
|
(f'int{x}', f'uint{x}', True) for x in [8, 16, 32, 64]
|
|
])
|
|
def test_cast(dtype_x, dtype_z, bitcast, device='cuda'):
|
|
# This is tricky because numpy doesn't have bfloat, and torch doesn't have uints.
|
|
x0 = 43 if dtype_x in int_dtypes else 43.5
|
|
if dtype_x.startswith('bfloat'):
|
|
x_tri = torch.tensor([x0], dtype=getattr(torch, dtype_x), device=device)
|
|
else:
|
|
x = np.array([x0], dtype=getattr(np, dtype_x))
|
|
x_tri = to_triton(x)
|
|
|
|
# triton kernel
|
|
@triton.jit
|
|
def kernel(X, Z, BITCAST: tl.constexpr):
|
|
x = tl.load(X)
|
|
z = x.to(Z.dtype.element_ty, bitcast=BITCAST)
|
|
tl.store(Z, z)
|
|
|
|
# triton result
|
|
if dtype_z.startswith('bfloat'):
|
|
z_tri = torch.empty((1,), dtype=getattr(torch, dtype_z), device=device)
|
|
else:
|
|
z_tri = to_triton(np.empty((1, ), dtype=getattr(np, dtype_z)), device=device)
|
|
kernel[(1, )](x_tri, z_tri, BITCAST=bitcast)
|
|
# torch result
|
|
if dtype_z.startswith('bfloat') or dtype_x.startswith('bfloat'):
|
|
assert bitcast is False
|
|
z_ref = x_tri.to(z_tri.dtype)
|
|
assert z_tri == z_ref
|
|
else:
|
|
if bitcast:
|
|
z_ref = x.view(getattr(np, dtype_z))
|
|
else:
|
|
z_ref = x.astype(getattr(np, dtype_z))
|
|
assert to_numpy(z_tri) == z_ref
|
|
|
|
# ---------------
|
|
# test reduce
|
|
# ---------------
|
|
|
|
|
|
@pytest.mark.parametrize("dtype_str, shape",
|
|
[(dtype, shape)
|
|
for dtype in dtypes
|
|
for shape in [128, 512]])
|
|
def test_reduce1d(dtype_str, shape, device='cuda'):
|
|
|
|
# triton kernel
|
|
@triton.jit
|
|
def kernel(X, Z, BLOCK: tl.constexpr):
|
|
x = tl.load(X + tl.arange(0, BLOCK))
|
|
tl.store(Z, tl.sum(x, axis=0))
|
|
|
|
rs = RandomState(17)
|
|
x = numpy_random((shape,), dtype_str=dtype_str, rs=rs)
|
|
# numpy result
|
|
z_ref = np.sum(x).astype(getattr(np, dtype_str))
|
|
# triton result
|
|
x_tri = to_triton(x, device=device)
|
|
z_tri = to_triton(numpy_random((1,), dtype_str=dtype_str, rs=rs), device=device)
|
|
kernel[(1,)](x_tri, z_tri, BLOCK=shape)
|
|
# compare
|
|
np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01)
|
|
|
|
|
|
@pytest.mark.parametrize("dtype_str, shape, axis", [
|
|
(dtype, (1, 1024), 1) for dtype in ['float32', 'uint32']
|
|
])
|
|
def test_reduce2d(dtype_str, shape, axis, device='cuda'):
|
|
# triton kernel
|
|
@triton.jit
|
|
def kernel(X, Z, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, AXIS: tl.constexpr):
|
|
range_m = tl.arange(0, BLOCK_M)
|
|
range_n = tl.arange(0, BLOCK_N)
|
|
x = tl.load(X + range_m[:, None] * BLOCK_N + range_n[None, :])
|
|
z = tl.sum(x, axis=AXIS)
|
|
tl.store(Z + range_m, z)
|
|
# input
|
|
x = numpy_random(shape, dtype_str=dtype_str)
|
|
# triton result
|
|
x_tri = to_triton(x)
|
|
z_tri = to_triton(np.empty((shape[0],), dtype=getattr(np, dtype_str)), device=device)
|
|
kernel[(1,)](x_tri, z_tri, BLOCK_M=shape[0], BLOCK_N=shape[1], AXIS=axis)
|
|
# numpy reference result
|
|
z_ref = np.sum(x, axis=axis).astype(x.dtype)
|
|
# compare
|
|
np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01)
|
|
|
|
# ---------------
|
|
# test permute
|
|
# ---------------
|
|
|
|
|
|
@pytest.mark.parametrize("dtype_str, shape, perm",
|
|
[(dtype, shape, perm)
|
|
for dtype in ['float32']
|
|
for shape in [(128, 128)]
|
|
for perm in [(1, 0)]])
|
|
def test_permute(dtype_str, shape, perm, device='cuda'):
|
|
|
|
# triton kernel
|
|
@triton.jit
|
|
def kernel(X, stride_xm, stride_xn,
|
|
Z, stride_zm, stride_zn,
|
|
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
|
off_m = tl.arange(0, BLOCK_M)
|
|
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 = numpy_random(shape, dtype_str=dtype_str)
|
|
# triton result
|
|
z_tri = to_triton(np.empty_like(x), device=device)
|
|
x_tri = to_triton(x, device=device)
|
|
pgm = kernel[(1, 1)](x_tri, x_tri.stride(0), x_tri.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.transpose(*perm)
|
|
# 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', 'trans', 'add-matrix', 'add-rows', 'add-cols'])
|
|
def test_dot(epilogue, device='cuda'):
|
|
# triton kernel
|
|
@triton.jit
|
|
def kernel(X, stride_xm, stride_xk,
|
|
Y, stride_yk, stride_yn,
|
|
Z, stride_zm, stride_zn,
|
|
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
|
|
ADD_MATRIX: tl.constexpr, ADD_ROWS: tl.constexpr, ADD_COLS: tl.constexpr):
|
|
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 ADD_MATRIX:
|
|
z += tl.load(Zs)
|
|
if ADD_ROWS:
|
|
ZRs = Z + off_m * stride_zm
|
|
z += tl.load(ZRs)[:, None]
|
|
if ADD_COLS:
|
|
ZCs = Z + off_n * stride_zn
|
|
z += tl.load(ZCs)[None, :]
|
|
tl.store(Zs, z)
|
|
# input
|
|
M, N, K = 64, 64, 32
|
|
rs = RandomState(17)
|
|
x = numpy_random((M, K), dtype_str='float32', rs=rs)
|
|
y = numpy_random((K, N), dtype_str='float32', rs=rs)
|
|
x_tri = to_triton(x, device=device)
|
|
y_tri = to_triton(y, device=device)
|
|
# triton result
|
|
z = numpy_random((M, N), dtype_str='float32', rs=rs)
|
|
z_tri = to_triton(z, device=device)
|
|
if epilogue == 'trans':
|
|
z_tri = torch.as_strided(z_tri, (M, N), z_tri.stride()[::-1])
|
|
pgm = kernel[(1, 1)](x_tri, x_tri.stride(0), x_tri.stride(1),
|
|
y_tri, y_tri.stride(0), y_tri.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 = np.matmul(x, y)
|
|
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, :]
|
|
# compare
|
|
np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01)
|
|
# make sure ld/st are vectorized
|
|
ptx = pgm.asm['ptx']
|
|
assert 'ld.global.v4' in ptx
|
|
assert 'st.global.v4' in ptx
|
|
|
|
|
|
def test_dot_without_load():
|
|
@triton.jit
|
|
def kernel(out):
|
|
pid = tl.program_id(axis=0)
|
|
a = tl.zeros((32, 32), tl.float32)
|
|
b = tl.zeros((32, 32), tl.float32)
|
|
c = tl.zeros((32, 32), tl.float32)
|
|
c = tl.dot(a, b)
|
|
pout = out + tl.arange(0, 32)[:, None] * 32 + tl.arange(0, 32)[None, :]
|
|
tl.store(pout, c)
|
|
|
|
out = torch.ones((32, 32), dtype=torch.float32, device="cuda")
|
|
kernel[(1,)](out)
|
|
|
|
# ---------------
|
|
# test arange
|
|
# ---------------
|
|
|
|
|
|
@pytest.mark.parametrize("start", [0, 1, 7, 16])
|
|
def test_arange(start, device='cuda'):
|
|
BLOCK = 128
|
|
z_tri = torch.empty(BLOCK, dtype=torch.int32, device=device)
|
|
|
|
@triton.jit
|
|
def _kernel(z, BLOCK: tl.constexpr,
|
|
START: tl.constexpr, END: tl.constexpr):
|
|
off = tl.arange(0, BLOCK)
|
|
val = tl.arange(START, END)
|
|
tl.store(z + off, val)
|
|
_kernel[(1,)](z_tri, START=start, END=start + BLOCK, BLOCK=BLOCK)
|
|
z_ref = torch.arange(start, BLOCK + start, dtype=torch.int32, device=device)
|
|
triton.testing.assert_almost_equal(z_tri, z_ref)
|
|
|
|
# ---------------
|
|
# test load
|
|
# ---------------
|
|
# 'bfloat16': torch.bfloat16,
|
|
# Testing masked loads with an intermate copy to shared memory run.
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", [torch.float16, torch.float32])
|
|
def test_masked_load_shared_memory(dtype, device='cuda'):
|
|
M = 32
|
|
N = 32
|
|
K = 8
|
|
|
|
in1 = torch.rand((M, K), dtype=dtype, device=device)
|
|
in2 = torch.rand((K, N), dtype=dtype, device=device)
|
|
out = torch.zeros((M, N), dtype=dtype, device=device)
|
|
|
|
@triton.jit
|
|
def _kernel(in1_ptr, in2_ptr, output_ptr,
|
|
in_stride, in2_stride, out_stride,
|
|
in_numel, in2_numel, out_numel,
|
|
M: tl.constexpr, N: tl.constexpr, K: tl.constexpr):
|
|
|
|
M_offsets = tl.arange(0, M)
|
|
N_offsets = tl.arange(0, N)
|
|
K_offsets = tl.arange(0, K)
|
|
|
|
in_offsets = M_offsets[:, None] * in_stride + K_offsets[None, :]
|
|
in2_offsets = K_offsets[:, None] * in2_stride + N_offsets[None, :]
|
|
|
|
# Load inputs.
|
|
x = tl.load(in1_ptr + in_offsets, mask=in_offsets < in_numel)
|
|
w = tl.load(in2_ptr + in2_offsets, mask=in2_offsets < in2_numel)
|
|
|
|
# Without a dot product the memory doesn't get promoted to shared.
|
|
o = tl.dot(x, w)
|
|
|
|
# Store output
|
|
output_offsets = M_offsets[:, None] * out_stride + N_offsets[None, :]
|
|
tl.store(output_ptr + output_offsets, o, mask=output_offsets < in2_numel)
|
|
|
|
pgm = _kernel[(1,)](in1, in2, out,
|
|
in1.stride()[0],
|
|
in2.stride()[0],
|
|
out.stride()[0],
|
|
in1.numel(),
|
|
in2.numel(),
|
|
out.numel(),
|
|
M=M, N=N, K=K)
|
|
|
|
reference_out = torch.matmul(in1, in2)
|
|
triton.testing.allclose(out, reference_out)
|
|
|
|
|
|
@pytest.mark.parametrize("cache", ["", ".ca", ".cg"])
|
|
def test_load_cache_modifier(cache):
|
|
src = torch.empty(128, device='cuda')
|
|
dst = torch.empty(128, device='cuda')
|
|
|
|
@triton.jit
|
|
def _kernel(dst, src, CACHE: tl.constexpr):
|
|
offsets = tl.arange(0, 128)
|
|
x = tl.load(src + offsets, cache_modifier=CACHE)
|
|
tl.store(dst + offsets, x)
|
|
|
|
pgm = _kernel[(1,)](dst, src, CACHE=cache)
|
|
ptx = pgm.asm['ptx']
|
|
if cache == '':
|
|
assert 'ld.global.ca' not in ptx
|
|
assert 'ld.global.cg' not in ptx
|
|
if cache == '.cg':
|
|
assert 'ld.global.cg' in ptx
|
|
assert 'ld.global.ca' not in ptx
|
|
if cache == '.ca':
|
|
assert 'ld.global.ca' in ptx
|
|
assert 'ld.global.cg' not in ptx
|
|
|
|
# ---------------
|
|
# test store
|
|
# ---------------
|
|
|
|
# ---------------
|
|
# test if
|
|
# ---------------
|
|
|
|
# ---------------
|
|
# test for
|
|
# ---------------
|
|
|
|
# ---------------
|
|
# test while
|
|
# ---------------
|
|
|
|
# ---------------
|
|
# test default
|
|
# ---------------
|
|
# TODO: can't be local to test_default
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@triton.jit
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def _impl(value=10):
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return value
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|
|
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def test_default():
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value = 5
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ret0 = torch.zeros(1, dtype=torch.int32, device='cuda')
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ret1 = torch.zeros(1, dtype=torch.int32, device='cuda')
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@triton.jit
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def _kernel(ret0, ret1, value):
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tl.store(ret0, _impl())
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tl.store(ret1, _impl(value))
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_kernel[(1,)](ret0, ret1, value)
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assert ret0.item() == 10
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assert ret1.item() == value
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# ---------------
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# test noop
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# ----------------
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|
|
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def test_noop(device='cuda'):
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@triton.jit
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def kernel(x):
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pass
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x = to_triton(numpy_random((1,), dtype_str='int32'), device=device)
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kernel[(1, )](x)
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|
|
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@pytest.mark.parametrize("value, value_type", [
|
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(-1, 'i32'), (0, 'i32'), (1, None), (-2**31, 'i32'), (2**31 - 1, 'i32'),
|
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(2**31, 'u32'), (2**32 - 1, 'u32'), (2**32, 'i64'), (2**63 - 1, 'i64'),
|
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(-2**63, 'i64'), (2**63, 'u64'), (2**64 - 1, 'u64')
|
|
])
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def test_value_specialization(value: int, value_type: str, device='cuda') -> None:
|
|
|
|
@triton.jit
|
|
def kernel(VALUE, X):
|
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pass
|
|
|
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x = torch.tensor([3.14159], device='cuda')
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pgm = kernel[(1, )](value, x)
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|
|
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# Parse out the type of the 'VALUE' parameter from the Triton IR.
|
|
triton_ir = pgm.asm['ttir']
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|
ir_value_match = re.match(r'\s*def void kernel\((\w+) VALUE ', triton_ir)
|
|
ir_value_type = None if ir_value_match is None else ir_value_match.group(1)
|
|
assert ir_value_type == value_type
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"value, overflow",
|
|
[(2**64 - 1, False), (2**64, True), (-2**63, False), (-2**63 - 1, True)]
|
|
)
|
|
def test_value_specialization_overflow(value: int, overflow: bool, device='cuda') -> None:
|
|
|
|
@triton.jit
|
|
def kernel(VALUE, X):
|
|
pass
|
|
|
|
x = torch.tensor([3.14159], device='cuda')
|
|
|
|
if overflow:
|
|
with pytest.raises(RuntimeError, match='integer overflow'):
|
|
kernel[(1, )](value, x)
|
|
else:
|
|
kernel[(1, )](value, x)
|