Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[TOPI] Fast tanh #3255

Merged
merged 1 commit into from
Jun 5, 2019
Merged

[TOPI] Fast tanh #3255

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
74 changes: 71 additions & 3 deletions topi/include/topi/elemwise.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,9 +6,9 @@
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
*
* http://www.apache.org/licenses/LICENSE-2.0
*
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
Expand All @@ -31,6 +31,7 @@
#include "tvm/tvm.h"
#include "tvm/ir.h"
#include "tvm/ir_pass.h"
#include "broadcast.h"

namespace topi {
using namespace tvm;
Expand All @@ -46,7 +47,6 @@ using namespace tvm;
}

TOPI_DECLARE_UNARY_OP(exp);
TOPI_DECLARE_UNARY_OP(tanh);
TOPI_DECLARE_UNARY_OP(sigmoid);
TOPI_DECLARE_UNARY_OP(sqrt);
TOPI_DECLARE_UNARY_OP(log);
Expand All @@ -56,6 +56,74 @@ TOPI_DECLARE_UNARY_OP(round);
TOPI_DECLARE_UNARY_OP(trunc);
TOPI_DECLARE_UNARY_OP(abs);

/*
* \brief Fast_tanh_float implementation from Eigen
* https://github.com/eigenteam/eigen-git-mirror/blob/master/Eigen/src/Core/MathFunctionsImpl.h#L26
*/
inline Tensor fast_tanh_float(const Tensor& in,
std::string name,
std::string tag) {
// Clamp the inputs to the range [-9, 9] since anything outside
// this range is +/-1.0f in single-precision.
auto x = maximum(minimum(in, make_const(in->dtype, 9.0)), make_const(in->dtype, -9.0));

// The monomial coefficients of the numerator polynomial (odd).
auto alpha_1 = make_const(in->dtype, 4.89352455891786e-03);
auto alpha_3 = make_const(in->dtype, 6.37261928875436e-04);
auto alpha_5 = make_const(in->dtype, 1.48572235717979e-05);
auto alpha_7 = make_const(in->dtype, 5.12229709037114e-08);
auto alpha_9 = make_const(in->dtype, -8.60467152213735e-11);
auto alpha_11 = make_const(in->dtype, 2.00018790482477e-13);
auto alpha_13 = make_const(in->dtype, -2.76076847742355e-16);

// The monomial coefficients of the denominator polynomial (even).
auto beta_0 = make_const(in->dtype, 4.89352518554385e-03);
auto beta_2 = make_const(in->dtype, 2.26843463243900e-03);
auto beta_4 = make_const(in->dtype, 1.18534705686654e-04);
auto beta_6 = make_const(in->dtype, 1.19825839466702e-06);

return compute(x->shape,
[&](const Array<Var>& i) {
auto x2 = x(i) * x(i);
auto p = x2 * alpha_13 + alpha_11;
p = x2 * p + alpha_9;
p = x2 * p + alpha_7;
p = x2 * p + alpha_5;
p = x2 * p + alpha_3;
p = x2 * p + alpha_1;
p = x(i) * p;

auto q = x2 * beta_6 + beta_4;
q = x2 * q + beta_2;
q = x2 * q + beta_0;
return p / q;
},
name, tag);
}

/*!
* \brief Creates an operation that returns hyperbolic tanh of a given tensor
*
* \param x The input tensor
* \param name The name of the operation
* \param tag The tag to mark the operation
*
* \return A Tensor whose op member is tanh
*/
inline Tensor tanh(const Tensor& x,
std::string name = "T_tanh",
std::string tag = kElementWise) {
if (x->dtype == Float(32)) {
// invoke fast_tanh_float implementation
return fast_tanh_float(x, name, tag);
} else {
// fallback to default implementation
return compute(x->shape, [&](const Array<Var>& i) {
return ::tvm::tanh(x(i));
}, name, tag);
}
}

/*!
* \brief Creates an operation that returns identity of a given tensor
*
Expand Down
30 changes: 19 additions & 11 deletions topi/tests/python/test_topi_math.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,13 +29,21 @@ def test_util():


def test_ewise():
m = tvm.var('m')
l = tvm.var('l')
A = tvm.placeholder((m, l), name='A')
def test_apply(
func,
name,
f_numpy,
low,
high,
shape=(20, 3),
dtype=tvm.float32,
check_round=False,
skip_name_check=False,
):
m = tvm.var("m")
l = tvm.var("l")
A = tvm.placeholder((m, l), dtype=dtype, name="A")

shape = (20, 3)

def test_apply(func, name, f_numpy, low, high, check_round=False, skip_name_check=False):
B = func(A)
assert tuple(B.shape) == tuple(A.shape)
if not skip_name_check:
Expand Down Expand Up @@ -63,19 +71,19 @@ def check_device(device):
for device in get_all_backend():
check_device(device)


test_apply(topi.floor, "floor", np.floor, -100, 100)
test_apply(topi.ceil, "ceil", np.ceil, -100, 100)
test_apply(topi.sign, "sign", np.sign, -100, 100, skip_name_check=True)
test_apply(topi.trunc, "trunc", np.trunc, -100, 100)
test_apply(topi.abs, "fabs", np.abs, -100, 100)
test_apply(topi.round, "round", np.round, -100, 100, check_round=True)
test_apply(topi.exp, "exp", np.exp, -1, 1)
test_apply(topi.tanh, "tanh", np.tanh, -10, 10)
test_apply(topi.sigmoid, "sigmoid", lambda x:1/(1+np.exp(-x)), -1, 1)
test_apply(topi.tanh, "tanh", np.tanh, -10, 10, shape=(128, 128))
test_apply(topi.tanh, "tanh", np.tanh, -10, 10, shape=(128, 128), dtype="float64")
test_apply(topi.sigmoid, "sigmoid", lambda x: 1 / (1 + np.exp(-x)), -1, 1)
test_apply(topi.log, "log", np.log, 0, 100)
test_apply(topi.sqrt, "sqrt", np.sqrt, 0, 100)
test_apply(topi.rsqrt, "rsqrt", lambda x:np.ones_like(x)/np.sqrt(x), 0, 100, skip_name_check=True)
test_apply(topi.rsqrt, "rsqrt", lambda x: np.ones_like(x) / np.sqrt(x), 0, 100, skip_name_check=True)


def test_cast():
Expand All @@ -93,7 +101,7 @@ def verify(from_dtype, to_dtype, low=-100, high=100):
b_np = a_np.astype(to_dtype)

for device in get_all_backend():
ctx = tvm.context(device, 0)
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
continue
Expand Down