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Add absf and floor to cute.math (#3156)
The ops are already exposed by the underlying dialect.
This commit is contained in:
Nandor Licker
@@ -49,6 +49,33 @@ def _math_op(func: Callable, fastmath: bool, *args, **kwargs):
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@dsl_user_op
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def absf(
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a: Union[TensorSSA, Numeric], fastmath: bool = False, *, loc=None, ip=None
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) -> Union[TensorSSA, Numeric]:
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"""Compute element-wise absolute value of the input tensor.
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:param a: Input tensor
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:type a: Union[TensorSSA, Numeric]
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:param fastmath: Enable fast math optimizations, defaults to False
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:type fastmath: bool, optional
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:param loc: Source location information, defaults to None
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:type loc: Optional[Location]
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:param ip: Insertion point for IR generation, defaults to None
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:type ip: Optional[InsertionPoint]
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:return: Tensor containing the absolute value of each element in input tensor
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:rtype: Union[TensorSSA, Numeric]
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Example:
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.. code-block::
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x = cute.make_rmem_tensor(layout) # Create tensor
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y = x.load() # Load values
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z = absf(y) # Compute absolute value
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"""
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return _math_op(math.absf, fastmath, a, loc=loc, ip=ip)
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def acos(
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a: Union[TensorSSA, Numeric], fastmath: bool = False, *, loc=None, ip=None
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) -> Union[TensorSSA, Numeric]:
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@@ -166,6 +193,43 @@ def atan2(
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return _math_op(math.atan2, fastmath, a, b, loc=loc, ip=ip)
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@dsl_user_op
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def copysign(
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a: Union[TensorSSA, Numeric],
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b: Union[TensorSSA, Numeric],
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fastmath: bool = False,
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*,
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loc=None,
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ip=None,
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) -> Union[TensorSSA, Numeric]:
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"""Compute element-wise copysign of two tensors.
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Returns a value with the magnitude of ``a`` and the sign of ``b``.
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:param a: Input tensor providing magnitude
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:type a: Union[TensorSSA, Numeric]
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:param b: Input tensor providing sign
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:type b: Union[TensorSSA, Numeric]
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:param fastmath: Enable fast math optimizations, defaults to False
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:type fastmath: bool, optional
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:param loc: Source location information, defaults to None
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:type loc: Optional[Location]
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:param ip: Insertion point for IR generation, defaults to None
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:type ip: Optional[InsertionPoint]
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:return: Tensor where each element has the magnitude of ``a`` and the sign of ``b``
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:rtype: Union[TensorSSA, Numeric]
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Example:
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.. code-block::
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mag = cute.make_rmem_tensor(ptr1, layout).load() # magnitudes
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sgn = cute.make_rmem_tensor(ptr2, layout).load() # signs
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result = copysign(mag, sgn) # Combine magnitude and sign
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"""
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return _math_op(math.copysign, fastmath, a, b, loc=loc, ip=ip)
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@dsl_user_op
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def cos(
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a: Union[TensorSSA, Numeric], fastmath: bool = False, *, loc=None, ip=None
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@@ -282,6 +346,33 @@ def exp2(
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@dsl_user_op
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def floor(
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a: Union[TensorSSA, Numeric], fastmath: bool = False, *, loc=None, ip=None
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) -> Union[TensorSSA, Numeric]:
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"""Compute element-wise floor of the input tensor.
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:param a: Input tensor
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:type a: Union[TensorSSA, Numeric]
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:param fastmath: Enable fast math optimizations, defaults to False
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:type fastmath: bool, optional
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:param loc: Source location information, defaults to None
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:type loc: Optional[Location]
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:param ip: Insertion point for IR generation, defaults to None
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:type ip: Optional[InsertionPoint]
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:return: Tensor containing the largest integer less than or equal to each element in input tensor
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:rtype: Union[TensorSSA, Numeric]
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Example:
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.. code-block::
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x = cute.make_rmem_tensor(layout) # Create tensor
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y = x.load() # Load values
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z = floor(y) # Compute floor
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"""
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return _math_op(math.floor, fastmath, a, loc=loc, ip=ip)
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def log(
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a: Union[TensorSSA, Numeric], fastmath: bool = False, *, loc=None, ip=None
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) -> Union[TensorSSA, Numeric]:
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@@ -508,14 +599,17 @@ def tanh(
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__all__ = [
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"absf",
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"acos",
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"asin",
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"atan",
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"atan2",
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"copysign",
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"cos",
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"erf",
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"exp",
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"exp2",
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"floor",
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"log",
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"log10",
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"log2",
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109
test/examples/CuTeDSL/test_math.py
Normal file
109
test/examples/CuTeDSL/test_math.py
Normal file
@@ -0,0 +1,109 @@
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# Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import pytest
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import torch
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import cutlass
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import cutlass.cute as cute
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from cutlass.cute.runtime import from_dlpack
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@cute.kernel
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def _unary_ops_kernel(
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absf_inp: cute.Tensor, absf_out: cute.Tensor,
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floor_inp: cute.Tensor, floor_out: cute.Tensor,
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):
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tidx, _, _ = cute.arch.thread_idx()
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absf_out[tidx] = cute.math.absf(absf_inp[tidx])
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floor_out[tidx] = cute.math.floor(floor_inp[tidx])
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@cute.jit
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def _unary_ops_host(
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absf_inp: cute.Tensor, absf_out: cute.Tensor,
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floor_inp: cute.Tensor, floor_out: cute.Tensor,
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):
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_unary_ops_kernel(absf_inp, absf_out, floor_inp, floor_out).launch(
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grid=[1, 1, 1], block=[absf_inp.shape[0], 1, 1]
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)
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def test_unary_ops():
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absf_inp = torch.tensor([-3.5, 2.0, 0.0], device="cuda", dtype=torch.float32)
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absf_expected = torch.tensor([3.5, 2.0, 0.0], device="cuda", dtype=torch.float32)
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absf_out = torch.zeros(3, device="cuda", dtype=torch.float32)
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floor_inp = torch.tensor([3.7, -2.3, 5.0], device="cuda", dtype=torch.float32)
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floor_expected = torch.tensor([3.0, -3.0, 5.0], device="cuda", dtype=torch.float32)
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floor_out = torch.zeros(3, device="cuda", dtype=torch.float32)
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absf_inp_cute = from_dlpack(absf_inp)
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absf_out_cute = from_dlpack(absf_out)
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floor_inp_cute = from_dlpack(floor_inp)
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floor_out_cute = from_dlpack(floor_out)
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args = (absf_inp_cute, absf_out_cute, floor_inp_cute, floor_out_cute)
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cute.compile(_unary_ops_host, *args)(*args)
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torch.cuda.synchronize()
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assert torch.equal(absf_out, absf_expected)
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assert torch.equal(floor_out, floor_expected)
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@cute.kernel
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def _binary_ops_kernel(
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mag_inp: cute.Tensor, sign_inp: cute.Tensor, out: cute.Tensor,
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):
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tidx, _, _ = cute.arch.thread_idx()
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out[tidx] = cute.math.copysign(mag_inp[tidx], sign_inp[tidx])
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@cute.jit
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def _binary_ops_host(
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mag_inp: cute.Tensor, sign_inp: cute.Tensor, out: cute.Tensor,
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):
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_binary_ops_kernel(mag_inp, sign_inp, out).launch(
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grid=[1, 1, 1], block=[mag_inp.shape[0], 1, 1]
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)
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def test_binary_ops():
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mag_inp = torch.tensor([3.5, -2.0, 0.0, 1.0], device="cuda", dtype=torch.float32)
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sign_inp = torch.tensor([-1.0, 1.0, -1.0, 1.0], device="cuda", dtype=torch.float32)
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expected = torch.tensor([-3.5, 2.0, -0.0, 1.0], device="cuda", dtype=torch.float32)
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out = torch.zeros(4, device="cuda", dtype=torch.float32)
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mag_inp_cute = from_dlpack(mag_inp)
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sign_inp_cute = from_dlpack(sign_inp)
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out_cute = from_dlpack(out)
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args = (mag_inp_cute, sign_inp_cute, out_cute)
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cute.compile(_binary_ops_host, *args)(*args)
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torch.cuda.synchronize()
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assert torch.equal(out, expected)
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