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"ts/nni_manager/common/utils.ts" did not exist on "06413f1af5d1d1a302ab833716e8add1bc0d5eb6"
Commit 5fc6afe6 authored by charlie's avatar charlie
Browse files

Merge branch 'refactor_dynamic_compute' of... 

Merge branch 'refactor_dynamic_compute' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_ref_broadcast
parents a67e6327 55b9438c
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Showing with 226 additions and 610 deletions
test/tf/gen_tf_pb.py
View file @ 5fc6afe6
......@@ -120,19 +120,45 @@ def batchnorm_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 32),
name='0')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='1')
g1_offset = tf.compat.v1.placeholder(tf.float32, shape=(32), name='2')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='3')
name='x')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='scale')
g1_offset = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='bias')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='mean')
g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='4')
name='variance')
tf.compat.v1.nn.fused_batch_norm(x=g1_input,
scale=g1_scale,
offset=g1_offset,
mean=g1_mean,
variance=g1_variance,
epsilon=0.00001,
epsilon=1e-4,
is_training=False,
name='batchnorm1')
@tf_test
def batchnorm_half_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float16,
shape=(1, 16, 16, 32),
name='x')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='scale')
g1_offset = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='bias')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='mean')
g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='variance')
tf.compat.v1.nn.fused_batch_norm(x=g1_input,
scale=g1_scale,
offset=g1_offset,
mean=g1_mean,
variance=g1_variance,
epsilon=1e-4,
is_training=False,
name='batchnorm1')
......@@ -142,19 +168,21 @@ def batchnormv3_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float32,
shape=(1, 16, 16, 32),
name='0')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='1')
g1_offset = tf.compat.v1.placeholder(tf.float32, shape=(32), name='2')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='3')
name='x')
g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name='scale')
g1_offset = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='bias')
g1_mean = tf.compat.v1.placeholder(tf.float32, shape=(32), name='mean')
g1_variance = tf.compat.v1.placeholder(tf.float32,
shape=(32),
name='4')
name='variance')
tf.raw_ops.FusedBatchNormV3(x=g1_input,
scale=g1_scale,
offset=g1_offset,
mean=g1_mean,
variance=g1_variance,
epsilon=0.00001,
epsilon=1e-6,
is_training=False,
name='batchnorm1')
......
test/tf/tf_test.cpp
View file @ 5fc6afe6
......@@ -24,6 +24,7 @@
#include <iostream>
#include <vector>
#include <unordered_map>
#include <migraphx/common.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/pass_manager.hpp>
#include <migraphx/simplify_reshapes.hpp>
......@@ -33,7 +34,6 @@
#include <migraphx/instruction.hpp>
#include <migraphx/tf.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/reduce_mean.hpp>
#include <migraphx/op/pooling.hpp>
......@@ -186,50 +186,94 @@ TEST_CASE(batchmatmul_test)
TEST_CASE(batchnorm_test)
{
float epsilon = 1.001e-5f;
float momentum = 0.9f;
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::op::batch_norm_inference op{
epsilon, momentum, migraphx::op::batch_norm_inference::spatial};
migraphx::shape s0{migraphx::shape::float_type, {32}};
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 32, 16, 16}});
std::vector<float> const_vals(32);
std::fill(const_vals.begin(), const_vals.end(), 1.0f);
auto l2 = mm->add_parameter("2", s0);
auto l3 = mm->add_parameter("3", s0);
auto l4 = mm->add_parameter("4", s0);
auto l1 = mm->add_literal(migraphx::literal{s0, const_vals});
mm->add_instruction(op, l0, l1, l2, l3, l4);
auto x = mm->add_parameter("x", {migraphx::shape::float_type, {1, 32, 16, 16}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {32}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {32}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {32}});
std::vector<float> scale_data(32, 1.0);
auto scale = mm->add_literal(migraphx::shape{migraphx::shape::float_type, {32}}, scale_data);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-4f}});
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), var);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto prog = optimize_tf("batchnorm_test.pb", true);
EXPECT(p == prog);
}
TEST_CASE(batchnorm_half_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", {migraphx::shape::half_type, {1, 32, 16, 16}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {32}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {32}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {32}});
std::vector<float> scale_data(32, 1.0);
auto scale = mm->add_literal(migraphx::shape{migraphx::shape::float_type, {32}}, scale_data);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::half_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::half_type, {1e-4f}});
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), var);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto prog = optimize_tf("batchnorm_half_test.pb", true);
EXPECT(p == prog);
}
TEST_CASE(batchnormv3_test)
{
float epsilon = 1.0e-5f;
float momentum = 0.9f;
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::op::batch_norm_inference op{
epsilon, momentum, migraphx::op::batch_norm_inference::spatial};
migraphx::shape s0{migraphx::shape::float_type, {32}};
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 32, 16, 16}});
std::vector<float> const_vals(32);
std::fill(const_vals.begin(), const_vals.end(), 1.0f);
auto l2 = mm->add_parameter("2", s0);
auto l3 = mm->add_parameter("3", s0);
auto l4 = mm->add_parameter("4", s0);
auto l1 = mm->add_literal(migraphx::literal{s0, const_vals});
mm->add_instruction(op, l0, l1, l2, l3, l4);
auto prog = optimize_tf("batchnormv3_test.pb", true);
auto x = mm->add_parameter("x", {migraphx::shape::float_type, {1, 32, 16, 16}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {32}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {32}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {32}});
std::vector<float> scale_data(32, 1.0);
auto scale = mm->add_literal(migraphx::shape{migraphx::shape::float_type, {32}}, scale_data);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-6f}});
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), var);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto prog = optimize_tf("batchnormv3_test.pb", true);
EXPECT(p == prog);
}
......
test/verify/test_batchnorm_1d.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_batchnorm_1d : verify_program<test_batchnorm_1d>
{
const size_t size = 3;
const size_t channels = 3;
const size_t batches = 4;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, size}};
migraphx::shape vars{migraphx::shape::float_type, {channels}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
return p;
}
};
test/verify/test_batchnorm_1d_per_actv.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/serialize.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
struct test_batchnorm_1d_per_actv : verify_program<test_batchnorm_1d_per_actv>
{
const size_t d1 = 5;
const size_t channels = 2;
const size_t batches = 3;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, d1}};
migraphx::shape vars{migraphx::shape::float_type, {channels, d1}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op(
"batch_norm_inference",
{{"epsilon", 1.0e-5},
{"momentum", 0.96f},
{"bn_mode",
migraphx::to_value(migraphx::op::batch_norm_inference::per_activation)}}),
x,
scale,
bias,
mean,
variance);
return p;
}
};
test/verify/test_batchnorm_2d_per_actv.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/serialize.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
struct test_batchnorm_2d_per_actv : verify_program<test_batchnorm_2d_per_actv>
{
const size_t d1 = 2;
const size_t d2 = 4;
const size_t channels = 2;
const size_t batches = 3;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, d1, d2}};
migraphx::shape vars{migraphx::shape::float_type, {channels, d1, d2}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op(
"batch_norm_inference",
{{"epsilon", 1.0e-6},
{"momentum", 0.9f},
{"bn_mode",
migraphx::to_value(migraphx::op::batch_norm_inference::per_activation)}}),
x,
scale,
bias,
mean,
variance);
return p;
}
};
test/verify/test_batchnorm_3d.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_batchnorm_3d : verify_program<test_batchnorm_3d>
{
const size_t d1 = 2;
const size_t d2 = 2;
const size_t d3 = 2;
const size_t channels = 2;
const size_t batches = 2;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, d1, d2, d3}};
migraphx::shape vars{migraphx::shape::float_type, {channels}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
return p;
}
};
test/verify/test_batchnorm_3d_per_actv.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/serialize.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/batch_norm_inference.hpp>
struct test_batchnorm_3d_per_actv : verify_program<test_batchnorm_3d_per_actv>
{
const size_t d1 = 2;
const size_t d2 = 4;
const size_t d3 = 5;
const size_t channels = 2;
const size_t batches = 3;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, d1, d2, d3}};
migraphx::shape vars{migraphx::shape::float_type, {channels, d1, d2, d3}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op(
"batch_norm_inference",
{{"epsilon", 1.0e-6},
{"momentum", 0.8f},
{"bn_mode",
migraphx::to_value(migraphx::op::batch_norm_inference::per_activation)}}),
x,
scale,
bias,
mean,
variance);
return p;
}
};
test/verify/test_batchnorm_inference.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_batchnorm_inference : verify_program<test_batchnorm_inference>
{
const size_t width = 3;
const size_t height = 3;
const size_t channels = 3;
const size_t batches = 4;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, height, width}};
migraphx::shape vars{migraphx::shape::float_type, {channels}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
return p;
}
};
test/verify/test_batchnorm_inference_2.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_batchnorm_inference_2 : verify_program<test_batchnorm_inference_2>
{
const size_t width = 14;
const size_t height = 14;
const size_t channels = 256;
const size_t batches = 1;
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {batches, channels, height, width}};
migraphx::shape vars{migraphx::shape::float_type, {channels}};
auto x = mm->add_parameter("x", s);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
return p;
}
};
test/verify/test_conv_bn.cpp
View file @ 5fc6afe6
......@@ -26,6 +26,8 @@
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/common.hpp>
struct test_conv_bn : verify_program<test_conv_bn>
{
......@@ -37,19 +39,38 @@ struct test_conv_bn : verify_program<test_conv_bn>
migraphx::shape xs{migraphx::shape::float_type, {1, 3, 224, 224}};
migraphx::shape ws{migraphx::shape::float_type, {64, 3, 7, 7}};
migraphx::shape vars{migraphx::shape::float_type, {64}};
auto x = mm->add_parameter("x", xs);
auto w = mm->add_parameter("w", ws);
auto x = mm->add_parameter("x", xs);
auto w = mm->add_parameter("w", ws);
// non-symmetrical tiling
auto conv = mm->add_instruction(
migraphx::make_op("convolution",
{{"padding", {3, 3}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-5f}});
auto usq_scale =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), variance);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {conv, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
return p;
}
};
test/verify/test_conv_bn_add.cpp
View file @ 5fc6afe6
......@@ -26,21 +26,38 @@
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/common.hpp>
struct test_conv_bn_add : verify_program<test_conv_bn_add>
{
static migraphx::instruction_ref add_bn(migraphx::module& m,
migraphx::instruction_ref x,
std::size_t channels,
std::size_t seed = 1)
static migraphx::instruction_ref add_bn(migraphx::module& m, migraphx::instruction_ref x)
{
migraphx::shape vars{migraphx::shape::float_type, {channels}};
auto scale = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 1 + seed)));
auto bias = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 2 + seed)));
auto mean = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 3 + seed)));
auto variance = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 4 + seed)));
return m.add_instruction(
migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
auto bn_lens = x->get_shape().lens();
auto c_len = bn_lens.at(1);
migraphx::shape vars{migraphx::shape::float_type, {c_len}};
auto scale = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 1 + c_len)));
auto bias = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 2 + c_len)));
auto mean = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 3 + c_len)));
auto variance = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 4 + c_len)));
auto rt = m.add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = m.add_literal(migraphx::literal{migraphx::shape::float_type, {1e-5f}});
auto usq_scale =
m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var =
m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), variance);
auto numer = add_common_op(m, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(m, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(m, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(m, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(m, migraphx::make_op("mul"), {div0, usq_scale});
return add_common_op(m, migraphx::make_op("add"), {r0, usq_bias});
}
migraphx::program create_program() const
......@@ -57,10 +74,10 @@ struct test_conv_bn_add : verify_program<test_conv_bn_add>
{migraphx::shape::float_type, {ochannels, ichannels, 1, 1}}, 2));
auto relu1 = mm->add_instruction(migraphx::make_op("relu"), x);
auto conv1 = mm->add_instruction(migraphx::make_op("convolution"), relu1, w);
auto bn1 = add_bn(*mm, conv1, ochannels, 1);
auto bn1 = add_bn(*mm, conv1);
auto relu2 = mm->add_instruction(migraphx::make_op("relu"), y);
auto conv2 = mm->add_instruction(migraphx::make_op("convolution"), relu2, v);
auto bn2 = add_bn(*mm, conv2, ochannels, 1);
auto bn2 = add_bn(*mm, conv2);
auto sum = mm->add_instruction(migraphx::make_op("add"), bn1, bn2);
mm->add_instruction(migraphx::make_op("relu"), sum);
return p;
......
test/verify/test_conv_bn_relu_pooling.cpp
View file @ 5fc6afe6
......@@ -27,6 +27,8 @@
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/common.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/common.hpp>
struct test_conv_bn_relu_pooling : verify_program<test_conv_bn_relu_pooling>
{
......@@ -49,8 +51,26 @@ struct test_conv_bn_relu_pooling : verify_program<test_conv_bn_relu_pooling>
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
auto bn = mm->add_instruction(
migraphx::make_op("batch_norm_inference"), conv, scale, bias, mean, variance);
auto rt = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-5f}});
auto usq_scale =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), variance);
auto numer = add_common_op(*mm, migraphx::make_op("sub"), {conv, usq_mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(*mm, migraphx::make_op("mul"), {div0, usq_scale});
auto bn = add_common_op(*mm, migraphx::make_op("add"), {r0, usq_bias});
auto relu = mm->add_instruction(migraphx::make_op("relu"), bn);
mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::average},
......
test/verify/test_conv_bn_relu_pooling2.cpp
View file @ 5fc6afe6
......@@ -27,22 +27,40 @@
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/op/common.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/common.hpp>
struct test_conv_bn_relu_pooling2 : verify_program<test_conv_bn_relu_pooling2>
{
static migraphx::instruction_ref
add_bn(migraphx::program& p, migraphx::instruction_ref x, std::size_t channels)
static migraphx::instruction_ref add_bn(migraphx::module& m, migraphx::instruction_ref x)
{
auto* mm = p.get_main_module();
migraphx::shape vars{migraphx::shape::float_type, {channels}};
auto scale = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1 + channels)));
auto bias = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2 + channels)));
auto mean = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3 + channels)));
auto variance =
mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4 + channels)));
return mm->add_instruction(
migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
auto bn_lens = x->get_shape().lens();
auto c_len = bn_lens.at(1);
migraphx::shape vars{migraphx::shape::float_type, {c_len}};
auto scale = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 1 + c_len)));
auto bias = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 2 + c_len)));
auto mean = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 3 + c_len)));
auto variance = m.add_literal(migraphx::abs(migraphx::generate_literal(vars, 4 + c_len)));
auto rt = m.add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
auto eps = m.add_literal(migraphx::literal{migraphx::shape::float_type, {1e-5f}});
auto usq_scale =
m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), scale);
auto usq_bias = m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), bias);
auto usq_mean = m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), mean);
auto usq_var =
m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), variance);
auto numer = add_common_op(m, migraphx::make_op("sub"), {x, usq_mean});
auto var_eps = add_common_op(m, migraphx::make_op("add"), {usq_var, eps});
auto denom = add_common_op(m, migraphx::make_op("pow"), {var_eps, rt});
auto div0 = add_common_op(m, migraphx::make_op("div"), {numer, denom});
auto r0 = add_common_op(m, migraphx::make_op("mul"), {div0, usq_scale});
return add_common_op(m, migraphx::make_op("add"), {r0, usq_bias});
}
migraphx::program create_program() const
{
migraphx::program p;
......@@ -59,7 +77,7 @@ struct test_conv_bn_relu_pooling2 : verify_program<test_conv_bn_relu_pooling2>
{{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
x1,
w1);
auto bn1 = add_bn(p, conv1, 2048);
auto bn1 = add_bn(*mm, conv1);
auto x2 = mm->add_parameter("x2", xs2);
auto w2 = mm->add_parameter("w2", ws2);
auto conv2 = mm->add_instruction(
......@@ -67,7 +85,7 @@ struct test_conv_bn_relu_pooling2 : verify_program<test_conv_bn_relu_pooling2>
{{"padding", {0, 0}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x2,
w2);
auto bn2 = add_bn(p, conv2, 2048);
auto bn2 = add_bn(*mm, conv2);
auto add = mm->add_instruction(migraphx::make_op("add"), bn1, bn2);
auto relu = mm->add_instruction(migraphx::make_op("relu"), add);
mm->add_instruction(migraphx::make_op("pooling",
......
test/verify/test_elu.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_elu : verify_program<test_elu>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {4, 3, 3, 3}});
mm->add_instruction(migraphx::make_op("elu", {{"alpha", 0.8}}), x);
return p;
}
};
test/verify/test_leaky_relu.cpp deleted 100644 → 0
View file @ a67e6327
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_leaky_relu : verify_program<test_leaky_relu>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {4, 3, 3, 3}});
mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 0.41}}), x);
return p;
}
};
tools/include/operation.hpp
View file @ 5fc6afe6
......@@ -32,6 +32,7 @@
#include <utility>
#include <unordered_map>
#include <migraphx/reflect.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/functional.hpp>
#include <migraphx/streamutils.hpp>
#include <migraphx/normalize_attributes.hpp>
......@@ -95,46 +96,6 @@ bool has_finalize(const operation& x);
#else
struct dyn_output
{
// original shape from the instruction
shape ins_shape;
// shape computed at eval time using input arguments
shape computed_shape;
};
/**
* Handle dynamic and static shape at evaluation time.
* If converted to shape type, returns original ins_shape.
* If converted to dyn_output type, will compute an output shape using the input arguments.
*/
template <class F>
struct compute_output_shape
{
F ins_inputs;
operator dyn_output() const
{
return ins_inputs([](const auto& x, shape ins_shape, const std::vector<argument>& inputs) {
if(ins_shape.dynamic())
return dyn_output{ins_shape, compute_shape(x, to_shapes(inputs))};
return dyn_output{ins_shape, ins_shape};
});
}
operator shape() const
{
return ins_inputs(
[](const auto&, shape ins_shape, const std::vector<argument>&) { return ins_shape; });
}
};
template <class F>
compute_output_shape<F> make_compute_output_shape(F f)
{
return {f};
}
namespace detail {
namespace operation_operators {
......
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