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Commit 31065c7d authored by charlie's avatar charlie
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Merge branch 'dyn_squeeze' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

parents 6bec381f 6acbd4e4
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Showing with 416 additions and 226 deletions
test/tf/gen_tf_pb.py
View file @ 31065c7d
......@@ -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='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')
@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='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')
......@@ -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')
......@@ -495,10 +523,10 @@ def relu6_test(g1):
@tf_test
def relu6_mismatch_test(g1):
def relu6_half_test(g1):
with g1.as_default():
g1_input = tf.compat.v1.placeholder(tf.float16,
shape=(1, 3, 13, 37),
shape=(1, 3, 16, 16),
name='0')
tf.nn.relu6(g1_input, 'relu6')
......@@ -708,7 +736,7 @@ if __name__ == '__main__':
pow_test()
relu_test()
relu6_test()
relu6_mismatch_test()
relu6_half_test()
reshape_test()
rsqrt_test()
shape_test()
......
test/tf/relu6_mismatch_test.pb → test/tf/relu6_half_test.pb
View file @ 31065c7d
......@@ -2,7 +2,7 @@
:
0 Placeholder*
dtype0*
shape: %
shape:

relu6Relu60*
T0"
\ No newline at end of file
test/tf/tf_test.cpp
View file @ 31065c7d
......@@ -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);
}
......@@ -327,10 +371,9 @@ migraphx::program create_conv()
mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 32}}, weight_data);
migraphx::op::convolution op;
op.padding_mode = migraphx::op::padding_mode_t::same;
op.padding = {1, 1, 1, 1};
op.stride = {1, 1};
op.dilation = {1, 1};
op.padding = {1, 1, 1, 1};
op.stride = {1, 1};
op.dilation = {1, 1};
auto l2 =
mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {3, 2, 0, 1}}}), l1);
mm->add_instruction(op, l0, l2);
......@@ -406,11 +449,10 @@ TEST_CASE(depthwiseconv_test)
mm->add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 1}}, weight_data);
migraphx::op::convolution op;
op.padding_mode = migraphx::op::padding_mode_t::same;
op.padding = {1, 1};
op.stride = {1, 1};
op.dilation = {1, 1};
op.group = 3;
op.padding = {1, 1};
op.stride = {1, 1};
op.dilation = {1, 1};
op.group = 3;
auto l3 =
mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {3, 2, 0, 1}}}), l1);
auto l4 = mm->add_instruction(migraphx::make_op("contiguous"), l3);
......@@ -729,27 +771,23 @@ TEST_CASE(relu6_test)
EXPECT(p == prog);
}
TEST_CASE(relu6_mismatch_test)
TEST_CASE(relu6_half_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<size_t> input_lens{1, 3, 13, 37};
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::half_type, input_lens});
auto min_val = mm->add_literal(0.0f);
auto max_val = mm->add_literal(6.0f);
auto l0_convert = mm->add_instruction(
migraphx::make_op("convert", {{"target_type", migraphx::shape::float_type}}), l0);
std::vector<size_t> input_lens{1, 3, 16, 16};
auto l0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::half_type, input_lens});
auto min_val =
mm->add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type}, {0.0f}});
auto max_val =
mm->add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type}, {6.0f}});
min_val = mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
min_val);
max_val = mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
max_val);
mm->add_instruction(migraphx::make_op("clip"), l0_convert, min_val, max_val);
auto prog = optimize_tf("relu6_mismatch_test.pb", false);
mm->add_instruction(migraphx::make_op("clip"), l0, min_val, max_val);
auto prog = optimize_tf("relu6_half_test.pb", false);
EXPECT(p == prog);
}
......
test/verify/gemm_add_broadcast1.cpp 0 → 100644
View file @ 31065c7d
/*
* 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>
#include <migraphx/apply_alpha_beta.hpp>
struct gemm_add_broadcast1 : verify_program<gemm_add_broadcast1>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape m1_shape{migraphx::shape::float_type, {1, 2, 3}};
migraphx::shape m2_shape{migraphx::shape::float_type, {1, 3, 4}};
migraphx::shape m3_shape{migraphx::shape::float_type, {1, 1, 4}};
auto l1 = mm->add_parameter("1", m1_shape);
auto l2 = mm->add_parameter("2", m2_shape);
auto l3 = mm->add_parameter("3", m3_shape);
auto l3_b =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 2, 4}}}), l3);
auto dot = mm->add_instruction(migraphx::make_op("dot"), l1, l2);
mm->add_instruction(migraphx::make_op("add"), dot, l3_b);
return p;
}
};
test/verify/gemm_add_broadcast2.cpp 0 → 100644
View file @ 31065c7d
/*
* 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>
#include <migraphx/apply_alpha_beta.hpp>
struct gemm_add_broadcast2 : verify_program<gemm_add_broadcast2>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape m1_shape{migraphx::shape::float_type, {1, 2, 3}};
migraphx::shape m2_shape{migraphx::shape::float_type, {1, 3, 4}};
migraphx::shape m3_shape{migraphx::shape::float_type, {1, 2, 1}};
auto l1 = mm->add_parameter("1", m1_shape);
auto l2 = mm->add_parameter("2", m2_shape);
auto l3 = mm->add_parameter("3", m3_shape);
auto l3_b =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 2, 4}}}), l3);
auto dot = mm->add_instruction(migraphx::make_op("dot"), l1, l2);
mm->add_instruction(migraphx::make_op("add"), dot, l3_b);
return p;
}
};
test/verify/quant_conv_valid_mode.cpp → test/verify/quant_conv_1d.cpp
View file @ 31065c7d
......@@ -25,20 +25,21 @@
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/op/quant_convolution.hpp>
#include <migraphx/make_op.hpp>
struct quant_conv_valid_mode : verify_program<quant_conv_valid_mode>
struct quant_conv_1d : verify_program<quant_conv_1d>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape a_shape{migraphx::shape::int8_type, {2, 3, 4, 4}};
migraphx::shape a_shape{migraphx::shape::int8_type, {2, 3, 4}};
auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}};
migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3}};
auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction(
migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::valid},
migraphx::make_op("quant_convolution",
{{"padding", {0}}, {"stride", {1}}, {"dilation", {1}}}),
pa,
pc);
return p;
......
test/verify/quant_conv_default_mode.cpp
View file @ 31065c7d
......@@ -37,10 +37,7 @@ struct quant_conv_default_mode : verify_program<quant_conv_default_mode>
auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {2, 3, 3, 3}};
auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction(
migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::same},
pa,
pc);
mm->add_instruction(migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}}, pa, pc);
return p;
}
};
test/verify/quant_conv_int8x4_default.cpp
View file @ 31065c7d
......@@ -37,10 +37,7 @@ struct quant_conv_int8x4_default : verify_program<quant_conv_int8x4_default>
auto pa = mm->add_parameter("a", a_shape);
migraphx::shape c_shape{migraphx::shape::int8_type, {16, 16, 3, 3}};
auto pc = mm->add_parameter("c", c_shape);
mm->add_instruction(
migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}, migraphx::op::same},
pa,
pc);
mm->add_instruction(migraphx::op::quant_convolution{{{0, 0}}, {{1, 1}}, {{1, 1}}}, pa, pc);
return p;
}
};
test/verify/run_verify.cpp
View file @ 31065c7d
......@@ -169,7 +169,7 @@ void run_verify::verify(const std::string& name, const migraphx::program& p) con
for(const auto& tname : migraphx::get_targets())
{
// TODO(varunsh): once verify tests can run, remove fpga
if(tname == "ref" || tname == "fpga")
if(tname == "ref" or tname == "fpga")
continue;
// if tests disabled, skip running it
......
test/verify/test_batchnorm_3d.cpp → test/verify/test_add_gelu_half.cpp
View file @ 31065c7d
......@@ -27,28 +27,30 @@
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_batchnorm_3d : verify_program<test_batchnorm_3d>
struct test_add_gelu_half : verify_program<test_add_gelu_half>
{
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);
std::vector<size_t> input_lens{1, 1, 5};
auto x = mm->add_parameter("x", {migraphx::shape::half_type, input_lens});
auto y = mm->add_parameter("y", {migraphx::shape::half_type, input_lens});
auto half = mm->add_literal(migraphx::literal{{migraphx::shape::half_type}, {0.5f}});
auto one = mm->add_literal(migraphx::literal{{migraphx::shape::half_type}, {1.0f}});
auto sqrt2 = mm->add_literal(migraphx::literal{{migraphx::shape::half_type}, {M_SQRT2}});
auto add = mm->add_instruction(migraphx::make_op("add"), x, y);
auto half_mbcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}), half);
auto mul_half = mm->add_instruction(migraphx::make_op("mul"), add, half_mbcast);
auto sqrt2_mbcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}), sqrt2);
auto div = mm->add_instruction(migraphx::make_op("div"), add, sqrt2_mbcast);
auto erf = mm->add_instruction(migraphx::make_op("erf"), div);
auto one_mbcast = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}), one);
auto add_one = mm->add_instruction(migraphx::make_op("add"), erf, one_mbcast);
mm->add_instruction(migraphx::make_op("mul"), mul_half, add_one);
return p;
}
};
test/verify/test_batchnorm_inference.cpp deleted 100644 → 0
View file @ 6bec381f
/*
* 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 @ 6bec381f
/*
* 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_leaky_relu.cpp → test/verify/test_concat_axis_2.cpp
View file @ 31065c7d
......@@ -27,14 +27,19 @@
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct test_leaky_relu : verify_program<test_leaky_relu>
struct test_concat_axis_2 : verify_program<test_concat_axis_2>
{
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", 0.01}}), x);
migraphx::shape s0{migraphx::shape::int32_type, {3, 2, 1}};
migraphx::shape s1{migraphx::shape::int32_type, {3, 2, 1}};
migraphx::shape s2{migraphx::shape::int32_type, {3, 2, 1}};
auto l0 = mm->add_parameter("x", s0);
auto l1 = mm->add_parameter("y", s1);
auto l2 = mm->add_parameter("z", s2);
mm->add_instruction(migraphx::make_op("concat", {{"axis", 2}}), l0, l1, l2);
return p;
}
};
test/verify/test_conv_bn.cpp
View file @ 31065c7d
......@@ -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 @ 31065c7d
......@@ -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 @ 31065c7d
......@@ -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 @ 31065c7d
......@@ -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",
......
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