Skip to content

GitLab

Unverified Commit 3685906c authored by Charlie Lin's avatar Charlie Lin Committed by GitHub
Browse files

Merge branch 'develop' into refactor_auto_pad_conv

parents 2b936b13 e19f78ae
Hide whitespace changes
Inline Side-by-side
Showing with 629 additions and 127 deletions
test/onnx/batchnorm_1d_test.onnx deleted 100644 → 0
View file @ 2b936b13
batchnorm_1d_test:
M
0
1
2
3
45"BatchNormalization*
epsilon75*
momentumfff?batchnorm_1d_testZ
0



Z
1

Z
2

Z
3

Z
4

b
5



B
\ No newline at end of file
test/onnx/batchnorm_3d_test.onnx deleted 100644 → 0
View file @ 2b936b13
batchnorm_3d_test:
M
0
1
2
3
45"BatchNormalization*
epsilon75*
momentumfff?batchnorm_3d_testZ
0





Z
1

Z
2

Z
3

Z
4

b
5





B
\ No newline at end of file
test/onnx/gen_onnx.py
View file @ 3685906c
......@@ -314,38 +314,107 @@ def averagepool_same_upper_test():
@onnx_test
def batchnorm_1d_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
mean = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
var = helper.make_tensor_value_info('4', TensorProto.FLOAT, [3])
out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 3, 5])
node = onnx.helper.make_node('BatchNormalization',
inputs=['0', '1', '2', '3', '4'],
outputs=['5'],
epsilon=1e-6,
momentum=0.9)
def batch_norm_flat_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [1])
bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [1])
var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [1])
out = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 'scale', 'bias', 'mean', 'variance'],
outputs=['y'],
epsilon=1e-6)
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
def batchnorm_3d_test():
x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5])
scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
mean = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
var = helper.make_tensor_value_info('4', TensorProto.FLOAT, [3])
out = helper.make_tensor_value_info('5', TensorProto.FLOAT,
[1, 3, 5, 5, 5])
node = onnx.helper.make_node('BatchNormalization',
inputs=['0', '1', '2', '3', '4'],
outputs=['5'],
epsilon=1e-6,
momentum=0.9)
def batch_norm_1d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [2, 3, 4])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [3])
bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [3])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [3])
var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [3])
out = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [2, 3, 4])
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 'scale', 'bias', 'mean', 'variance'],
outputs=['y'])
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
def batch_norm_2d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4, 4])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [3])
bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [3])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [3])
var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [3])
out = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 4])
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 'scale', 'bias', 'mean', 'variance'],
outputs=['y'])
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
def batch_norm_3d_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT16,
[2, 2, 2, 2, 2])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT16, [2])
bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT16, [2])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT16, [2])
var = helper.make_tensor_value_info('variance', TensorProto.FLOAT16, [2])
out = helper.make_tensor_value_info('y', TensorProto.FLOAT16,
[2, 2, 2, 2, 2])
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 'scale', 'bias', 'mean', 'variance'],
outputs=['y'],
epsilon=1e-6)
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
def batch_norm_invalid_rank_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [8, 8])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [8])
bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [8])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [8])
var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [8])
out = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8, 8])
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 'scale', 'bias', 'mean', 'variance'],
outputs=['y'])
return ([node], [x, scale, bias, mean, var], [out])
@onnx_test
def batch_norm_invalid_bias_rank_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4, 4])
scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [3])
bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [3, 1])
mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [3])
var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [3])
out = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 4])
node = onnx.helper.make_node(
'BatchNormalization',
inputs=['x', 'scale', 'bias', 'mean', 'variance'],
outputs=['y'])
return ([node], [x, scale, bias, mean, var], [out])
......
test/onnx/onnx_test.cpp
View file @ 3685906c
......@@ -369,36 +369,134 @@ TEST_CASE(averagepool_same_upper_test)
EXPECT(p == prog);
}
TEST_CASE(batchnorm_1d_test)
TEST_CASE(batch_norm_flat_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("0", {migraphx::shape::float_type, {1, 3, 5}});
auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {3}});
auto l2 = mm->add_parameter("2", {migraphx::shape::float_type, {3}});
auto l3 = mm->add_parameter("3", {migraphx::shape::float_type, {3}});
auto l4 = mm->add_parameter("4", {migraphx::shape::float_type, {3}});
mm->add_instruction(migraphx::make_op("batch_norm_inference"), l0, l1, l2, l3, l4);
auto prog = optimize_onnx("batchnorm_1d_test.onnx");
auto x = mm->add_parameter("x", {migraphx::shape::float_type, {10}});
auto scale = mm->add_parameter("scale", {migraphx::shape::float_type, {1}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {1}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {1}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {1}});
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 numer = add_common_op(*mm, migraphx::make_op("sub"), {x, mean});
auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {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, scale});
add_common_op(*mm, migraphx::make_op("add"), {r0, bias});
auto prog = optimize_onnx("batch_norm_flat_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(batchnorm_3d_test)
TEST_CASE(batch_norm_1d_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter("0", {migraphx::shape::float_type, {1, 3, 5, 5, 5}});
auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {3}});
auto l2 = mm->add_parameter("2", {migraphx::shape::float_type, {3}});
auto l3 = mm->add_parameter("3", {migraphx::shape::float_type, {3}});
auto l4 = mm->add_parameter("4", {migraphx::shape::float_type, {3}});
mm->add_instruction(migraphx::make_op("batch_norm_inference"), l0, l1, l2, l3, l4);
auto prog = optimize_onnx("batchnorm_3d_test.onnx");
auto x = mm->add_parameter("x", {migraphx::shape::half_type, {2, 3, 4}});
auto scale = mm->add_parameter("scale", {migraphx::shape::float_type, {3}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {3}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {3}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {3}});
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-5f}});
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), scale);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), bias);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), 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_onnx("batch_norm_1d_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(batch_norm_2d_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", {migraphx::shape::float_type, {2, 3, 4, 4}});
auto scale = mm->add_parameter("scale", {migraphx::shape::float_type, {3}});
auto bias = mm->add_parameter("bias", {migraphx::shape::float_type, {3}});
auto mean = mm->add_parameter("mean", {migraphx::shape::float_type, {3}});
auto var = mm->add_parameter("variance", {migraphx::shape::float_type, {3}});
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}}}), 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_onnx("batch_norm_2d_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(batch_norm_3d_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
auto x = mm->add_parameter("x", {migraphx::shape::half_type, {2, 2, 2, 2, 2}});
auto scale = mm->add_parameter("scale", {migraphx::shape::half_type, {2}});
auto bias = mm->add_parameter("bias", {migraphx::shape::half_type, {2}});
auto mean = mm->add_parameter("mean", {migraphx::shape::half_type, {2}});
auto var = mm->add_parameter("variance", {migraphx::shape::half_type, {2}});
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-6f}});
auto usq_scale =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2, 3}}}), scale);
auto usq_bias =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2, 3}}}), bias);
auto usq_mean =
mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2, 3}}}), mean);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2, 3}}}), 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_onnx("batch_norm_3d_test.onnx");
EXPECT(p == prog);
}
TEST_CASE(batch_norm_invalid_rank)
{
EXPECT(test::throws([&] { migraphx::parse_onnx("batch_norm_invalid_rank.onnx"); }));
}
TEST_CASE(batch_norm_invalid_bias_rank)
{
EXPECT(test::throws([&] { migraphx::parse_onnx("batch_norm_invalid_bias_rank.onnx"); }));
}
TEST_CASE(cast_test)
{
migraphx::program p;
......@@ -790,18 +888,46 @@ TEST_CASE(conv_bn_relu_maxpool_test)
auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
auto l2 = mm->add_parameter("2", {migraphx::shape::float_type, {1}});
auto p3 = mm->add_parameter("3", {migraphx::shape::float_type, {1}});
auto p4 = mm->add_parameter("4", {migraphx::shape::float_type, {1}});
auto p5 = mm->add_parameter("5", {migraphx::shape::float_type, {1}});
auto p6 = mm->add_parameter("6", {migraphx::shape::float_type, {1}});
auto p3 = mm->add_parameter("3", {migraphx::shape::float_type, {1}});
auto p4 = mm->add_parameter("4", {migraphx::shape::float_type, {1}});
auto p5 = mm->add_parameter("5", {migraphx::shape::float_type, {1}});
auto p6 = mm->add_parameter("6", {migraphx::shape::float_type, {1}});
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}});
uint64_t axis = 1;
auto l3 =
mm->add_instruction(migraphx::make_op("convolution", {{"padding", {0, 0, 0, 0}}}), l0, l1);
auto l4 = mm->add_instruction(
migraphx::make_op("broadcast", {{"axis", axis}, {"out_lens", l3->get_shape().lens()}}), l2);
auto l5 = mm->add_instruction(migraphx::make_op("add"), l3, l4);
auto l6 = mm->add_instruction(
migraphx::make_op("batch_norm_inference", {{"epsilon", 1.0e-5f}}), l5, p3, p4, p5, p6);
auto usq_scale = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), p3);
auto usq_bias = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), p4);
auto usq_mean = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), p5);
auto usq_var = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), p6);
auto mb_mean = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 28, 28}}}), usq_mean);
auto numer = mm->add_instruction(migraphx::make_op("sub"), l5, mb_mean);
auto mb_eps =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1}}}), eps);
auto var_eps = mm->add_instruction(migraphx::make_op("add"), usq_var, mb_eps);
auto mb_rt =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 1}}}), rt);
auto denom = mm->add_instruction(migraphx::make_op("pow"), var_eps, mb_rt);
auto mb_denom = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 28, 28}}}), denom);
auto div0 = mm->add_instruction(migraphx::make_op("div"), numer, mb_denom);
auto mb_scale = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 28, 28}}}), usq_scale);
auto r0 = mm->add_instruction(migraphx::make_op("mul"), div0, mb_scale);
auto mb_bias = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 1, 28, 28}}}), usq_bias);
auto l6 = mm->add_instruction(migraphx::make_op("add"), r0, mb_bias);
auto l7 = mm->add_instruction(migraphx::make_op("relu"), l6);
mm->add_instruction(migraphx::make_op("pooling",
{{"mode", migraphx::op::pooling_mode::max},
......
test/onnx/verify_onnx.cpp
View file @ 3685906c
......@@ -30,6 +30,7 @@
#include <migraphx/pass_manager.hpp>
#include <migraphx/verify.hpp>
#include <migraphx/onnx.hpp>
#include <migraphx/half.hpp>
#include "test.hpp"
TEST_CASE(averagepool_notset_test)
......@@ -68,6 +69,196 @@ TEST_CASE(averagepool_nt_cip_test)
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_flat_test)
{
migraphx::program p = migraphx::parse_onnx("batch_norm_flat_test.onnx");
p.compile(migraphx::ref::target{});
migraphx::shape x_shape{migraphx::shape::float_type, {10}};
migraphx::shape c_shape(migraphx::shape::float_type, {1});
std::vector<float> x_data = {1.6524342,
-0.51048076,
0.32543048,
2.4410043,
2.0833702,
0.44981122,
1.0044622,
-0.24006313,
-0.43065986,
0.07626268};
std::vector<float> scale_data = {-0.02927135};
std::vector<float> bias_data = {0.42347777};
std::vector<float> mean_data = {-0.00449735};
std::vector<float> variance_data = {0.5184545};
migraphx::parameter_map params;
params["x"] = migraphx::argument(x_shape, x_data.data());
params["scale"] = migraphx::argument(c_shape, scale_data.data());
params["bias"] = migraphx::argument(c_shape, bias_data.data());
params["mean"] = migraphx::argument(c_shape, mean_data.data());
params["variance"] = migraphx::argument(c_shape, variance_data.data());
auto result = p.eval(params).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0.35612,
0.44404706,
0.4100655,
0.32406294,
0.33860153,
0.40500915,
0.38246143,
0.43305403,
0.4408022,
0.42019472};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_1d_test)
{
migraphx::program p = migraphx::parse_onnx("batch_norm_1d_test.onnx");
p.compile(migraphx::ref::target{});
migraphx::shape x_shape{migraphx::shape::half_type, {2, 3, 4}};
migraphx::shape c_shape(migraphx::shape::float_type, {3});
std::vector<float> tmp = {1.652, -0.5103, 0.3254, 2.441, 2.084, 0.4497,
1.005, -0.2401, -0.4307, 0.07623, -0.02927, 0.4236,
-0.004498, -0.4282, -0.5527, 0.02205, -1.472, -1.7295,
0.796, 0.9507, 0.2312, 0.664, -0.06964, 1.035};
std::vector<migraphx::half> x_data{tmp.cbegin(), tmp.cend()};
std::vector<float> scale_data = {-1.336926, -1.0679098, 0.10368501};
std::vector<float> bias_data = {0.20240043, -0.70175606, -0.8859727};
std::vector<float> mean_data = {0.30854642, -0.36574763, -0.9463552};
std::vector<float> variance_data = {0.43428132, 0.97773486, 0.30332062};
migraphx::parameter_map params;
params["x"] = migraphx::argument(x_shape, x_data.data());
params["scale"] = migraphx::argument(c_shape, scale_data.data());
params["bias"] = migraphx::argument(c_shape, bias_data.data());
params["mean"] = migraphx::argument(c_shape, mean_data.data());
params["variance"] = migraphx::argument(c_shape, variance_data.data());
auto result = p.eval(params).back();
std::vector<migraphx::half> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
tmp = {-2.523, 1.863, 0.1681, -4.125, -3.348, -1.582, -2.182, -0.8374,
-0.789, -0.6934, -0.7134, -0.628, 0.8374, 1.697, 1.949, 0.7837,
0.4927, 0.771, -1.956, -2.123, -0.664, -0.583, -0.7207, -0.5127};
std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_2d_test)
{
migraphx::program p = migraphx::parse_onnx("batch_norm_2d_test.onnx");
p.compile(migraphx::ref::target{});
migraphx::shape x_shape{migraphx::shape::float_type, {2, 3, 4, 4}};
migraphx::shape c_shape(migraphx::shape::float_type, {3});
std::vector<float> x_data = {
1.6524342, -0.51048076, 0.32543048, 2.4410043, 2.0833702, 0.44981122, 1.0044622,
-0.24006313, -0.43065986, 0.07626268, -0.02927135, 0.42347777, -0.00449735, -0.4281568,
-0.5527635, 0.02204161, -1.4719028, -1.7298799, 0.79596406, 0.9505461, 0.23115851,
0.6639593, -0.06963254, 1.0348768, -1.336926, -1.0679098, 0.10368501, 0.20240043,
-0.70175606, -0.8859727, 0.30854642, -0.36574763, -0.9463552, 0.9476916, 0.37686515,
-0.05184272, -0.7151244, -0.37341377, 0.59440356, 0.10051094, -0.20755945, 0.9098465,
1.1664004, 1.4075205, -1.1522529, -0.34607422, 0.32027543, -0.6885485, 0.5404544,
0.10012514, 0.8767704, 1.0032021, -1.2755303, 0.23577735, 0.74239916, 1.0146079,
0.60875916, -0.29163074, 1.4872868, 0.20466477, -0.26367408, -0.56394804, -0.56043875,
0.7763664, -0.9626441, 0.29653943, -3.2231965, 0.03322164, 0.03402911, 0.77308357,
-0.0654009, -0.30463725, 0.22182712, -0.22594836, -0.5807543, -0.22390617, -0.24484141,
-2.0761833, 1.8459716, 0.2455878, 0.99913245, -0.9266217, -0.1938893, 0.6417983,
-1.0880078, 0.49565446, 2.1584804, 1.2276239, 3.3091128, 0.14217089, 0.9425477,
0.07578196, 0.4067431, 0.71984154, -0.20796849, 0.90003085};
std::vector<float> scale_data = {0.658487, 0.03700604, 2.463201};
std::vector<float> bias_data = {0.03497279, 0.17080553, 0.5636415};
std::vector<float> mean_data = {0.1954783, 0.6203974, 0.8116831};
std::vector<float> variance_data = {0.30558077, 0.04536599, 0.05461315};
migraphx::parameter_map params;
params["x"] = migraphx::argument(x_shape, x_data.data());
params["scale"] = migraphx::argument(c_shape, scale_data.data());
params["bias"] = migraphx::argument(c_shape, bias_data.data());
params["mean"] = migraphx::argument(c_shape, mean_data.data());
params["variance"] = migraphx::argument(c_shape, variance_data.data());
auto result = p.eval(params).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {
1.77046824e+00, -8.05950999e-01, 1.89769119e-01, 2.70979643e+00, 2.28379035e+00,
3.37928861e-01, 9.98617530e-01, -4.83835101e-01, -7.10869908e-01, -1.07034385e-01,
-2.32744321e-01, 3.06560963e-01, -2.03234047e-01, -7.07888365e-01, -8.56317282e-01,
-1.71621382e-01, -1.92677066e-01, -2.37493858e-01, 2.01305658e-01, 2.28160262e-01,
1.03185430e-01, 1.78373277e-01, 5.09308279e-02, 2.42810518e-01, -1.69228360e-01,
-1.22493818e-01, 8.10402334e-02, 9.81894583e-02, -5.88841513e-02, -9.08869803e-02,
1.16629556e-01, -5.11445105e-04, -1.79648399e+01, 1.99707508e+00, -4.01903248e+00,
-8.53731060e+00, -1.55278311e+01, -1.19264421e+01, -1.72633123e+00, -6.93161058e+00,
-1.01784554e+01, 1.59821415e+00, 4.30211163e+00, 6.84334660e+00, -2.01348572e+01,
-1.16383028e+01, -4.61544800e+00, -1.52477398e+01, 4.45901126e-01, -7.86099210e-02,
8.46513629e-01, 9.97116446e-01, -1.71726203e+00, 8.29761624e-02, 6.86453462e-01,
1.01070285e+00, 5.27264357e-01, -5.45261383e-01, 1.57374811e+00, 4.59154993e-02,
-5.11959970e-01, -8.69639993e-01, -8.65459919e-01, 7.26914644e-01, -1.04206637e-01,
1.14543661e-01, -4.96918678e-01, 6.87990561e-02, 6.89393356e-02, 1.97330773e-01,
5.16659655e-02, 1.01048872e-02, 1.01564340e-01, 2.37750299e-02, -3.78632471e-02,
2.41298079e-02, 2.04928555e-02, -2.97655046e-01, 3.83717060e-01, 1.05692141e-01,
2.53922558e+00, -1.77568626e+01, -1.00343809e+01, -1.22682428e+00, -1.94577579e+01,
-2.76707697e+00, 1.47579327e+01, 4.94736385e+00, 2.68847847e+01, -6.49254417e+00,
1.94286156e+00, -7.19223642e+00, -3.70413971e+00, -4.04303551e-01, -1.01827660e+01,
1.49476433e+00};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(batch_norm_3d_test)
{
migraphx::program p = migraphx::parse_onnx("batch_norm_3d_test.onnx");
p.compile(migraphx::ref::target{});
migraphx::shape x_shape{migraphx::shape::half_type, {2, 2, 2, 2, 2}};
migraphx::shape c_shape(migraphx::shape::half_type, {2});
// using migraphx::half copy conversion since it doesn't have initializer_list constructor
std::vector<float> tmp = {5., 5., 8., 7., 3., 4., 1., 7., 5., 5., 9., 4., 7., 2., 2., 2.,
6., 1., 4., 9., 2., 8., 0., 2., 1., 4., 8., 8., 3., 3., 0., 8.};
std::vector<migraphx::half> x_data{tmp.cbegin(), tmp.cend()};
tmp = {1., 1.};
std::vector<migraphx::half> scale_data{tmp.cbegin(), tmp.cend()};
tmp = {
0.,
0.,
};
std::vector<migraphx::half> bias_data{tmp.cbegin(), tmp.cend()};
tmp = {-0.75, 0.29};
std::vector<migraphx::half> mean_data{tmp.cbegin(), tmp.cend()};
tmp = {0.31, 0.37};
std::vector<migraphx::half> variance_data{tmp.cbegin(), tmp.cend()};
migraphx::parameter_map params;
params["x"] = migraphx::argument(x_shape, x_data.data());
params["scale"] = migraphx::argument(c_shape, scale_data.data());
params["bias"] = migraphx::argument(c_shape, bias_data.data());
params["mean"] = migraphx::argument(c_shape, mean_data.data());
params["variance"] = migraphx::argument(c_shape, variance_data.data());
auto result = p.eval(params).back();
std::vector<migraphx::half> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
tmp = {10.33, 10.33, 15.71, 13.914, 6.734, 8.53, 3.143, 13.914, 7.742, 7.742, 14.32,
6.098, 11.03, 2.81, 2.81, 2.81, 12.125, 3.143, 8.53, 17.52, 4.938, 15.71,
1.347, 4.938, 1.167, 6.098, 12.67, 12.67, 4.453, 4.453, -0.4768, 12.67};
std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
EXPECT(migraphx::verify_range(result_vector, gold));
}
TEST_CASE(celu_verify_test)
{
migraphx::program p = migraphx::parse_onnx("celu_verify_test.onnx");
......
test/py/onnx_backend_test.py
View file @ 3685906c
......@@ -138,6 +138,7 @@ def create_backend_test(testname=None, target_device=None):
backend_test.include(r'.*test_eyelike.*')
backend_test.include(r'.*test_flatten.*')
backend_test.include(r'.*test_floor.*')
backend_test.include(r'.*test_fmod.*')
backend_test.include(r'.*test_gather.*')
backend_test.include(r'.*test_gemm.*')
backend_test.include(r'.*test_globalaveragepool.*')
......@@ -162,6 +163,7 @@ def create_backend_test(testname=None, target_device=None):
backend_test.include(r'.*test_MaxPool[1-9]d.*')
backend_test.include(r'.*test_mean.*')
backend_test.include(r'.*test_min.*')
backend_test.include(r' .*test_mod.*')
backend_test.include(r'.*test_mul.*')
backend_test.include(r'.*test_multinomial.*')
backend_test.include(r'.*test_Multinomial.*')
......@@ -179,6 +181,7 @@ def create_backend_test(testname=None, target_device=None):
backend_test.include(r'.*test_operator_max_.*')
backend_test.include(r'.*test_operator_maxpool.*')
backend_test.include(r'.*test_operator_min.*')
backend_test.include(r'.*test_operator_mod.*')
backend_test.include(r'.*test_operator_mm.*')
backend_test.include(r'.*test_operator_non_float_params.*')
backend_test.include(r'.*test_operator_params.*')
......
test/ref_ops_test.cpp
View file @ 3685906c
......@@ -3663,7 +3663,7 @@ TEST_CASE(multinomial_test)
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
std::vector<int> res_dist(5, 0);
for(auto& r : result_vec)
for(const auto& r : result_vec)
res_dist[r]++;
auto dist_sum = std::accumulate(dist.begin(), dist.end(), 0);
auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
......
test/simplify_algebra_test.cpp
View file @ 3685906c
......@@ -236,6 +236,105 @@ TEST_CASE(simplify_mul_conv1)
EXPECT(new_conv->outputs().front()->name() != "mul");
}
TEST_CASE(simplify_mul_conv2)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256}}));
auto unsq_a = m.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1, 2}}}), a);
auto b = m.add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", {1, 256, 14, 14}}}), unsq_a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() != "mul");
}
// len = 1 case
TEST_CASE(simplify_mul_conv3)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(
migraphx::generate_literal({migraphx::shape::int32_type, {256, 1, 1}, {1, 18, 1}}));
auto b =
m.add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 256, 14, 14}}}), a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() != "mul");
}
// Previously broadcasted literal case, should skip
TEST_CASE(simplify_mul_conv_skip1)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(
migraphx::generate_literal({migraphx::shape::int32_type, {256, 14, 14}, {1, 0, 0}}));
auto b = m.add_instruction(
migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", {1, 256, 14, 14}}}), a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() == "mul");
}
// Another previously broadcasted literal case, should skip
TEST_CASE(simplify_mul_conv_skip2)
{
migraphx::module m;
auto x = m.add_parameter("x", {migraphx::shape::int32_type, {1, 128, 28, 28}});
auto w =
m.add_literal(migraphx::generate_literal({migraphx::shape::int32_type, {256, 128, 3, 3}}));
auto conv = m.add_instruction(
migraphx::make_op("convolution",
{{"padding", {1, 1}}, {"stride", {2, 2}}, {"dilation", {1, 1}}}),
x,
w);
auto a = m.add_literal(
migraphx::generate_literal({migraphx::shape::int32_type, {256, 14, 14}, {1, 0, 0}}));
auto b =
m.add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 256, 14, 14}}}), a);
auto mul = m.add_instruction(migraphx::make_op("mul"), conv, b);
m.add_instruction(pass_op{}, mul);
EXPECT(conv->outputs().front()->name() == "mul");
run_pass(m);
auto new_conv =
std::find_if(m.begin(), m.end(), [](auto&& ins) { return ins.name() == "convolution"; });
EXPECT(new_conv->outputs().front()->name() == "mul");
}
TEST_CASE(simplify_mul_slice_conv1)
{
migraphx::module m1;
......
src/targets/gpu/include/migraphx/gpu/device/softmax.hpp → test/verify/test_fmod_mod.cpp
View file @ 3685906c
......@@ -21,23 +21,53 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_SOFTMAX_HPP
#define MIGRAPHX_GUARD_RTGLIB_DEVICE_SOFTMAX_HPP
#include <migraphx/argument.hpp>
#include <migraphx/config.hpp>
#include <hip/hip_runtime_api.h>
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/common.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {
/*
Checking for y == 0 ? eps : y
void softmax(hipStream_t stream, const argument& result, const argument& arg, int64_t axis);
Adding this because HIP fmod sign changes when y = 0 resulting in nan and -nan not beign
consistent between ref and gpu implementations.
*/
migraphx::instruction_ref add_epsilon(migraphx::module& m, migraphx::instruction_ref y)
{
auto zero = m.add_literal(0.0f);
auto eps = m.add_literal(1e-3f);
auto op_y = add_common_op(m, migraphx::make_op("equal"), {y, zero});
return add_common_op(m, migraphx::make_op("where"), {op_y, eps, y});
}
} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
struct test_fmod : verify_program<test_fmod>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {64}};
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto op_where = add_epsilon(*mm, y);
mm->add_instruction(migraphx::make_op("fmod"), x, op_where);
return p;
}
};
#endif
struct test_mod : verify_program<test_mod>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {64}};
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto op_where = add_epsilon(*mm, y);
mm->add_instruction(migraphx::make_op("mod"), x, op_where);
return p;
}
};
test/verify/test_layernorm.cpp
View file @ 3685906c
......@@ -29,14 +29,16 @@
#include <migraphx/op/reduce_mean.hpp>
migraphx::instruction_ref
add_layernorm(migraphx::module& m, migraphx::instruction_ref x, std::vector<size_t> dims)
migraphx::instruction_ref add_layernorm(migraphx::module& m,
migraphx::instruction_ref x,
std::vector<size_t> dims,
float eps = 1e-12f)
{
auto scale =
m.add_parameter("scale", migraphx::shape{migraphx::shape::float_type, {dims.back()}});
auto bias =
m.add_parameter("bias", migraphx::shape{migraphx::shape::float_type, {dims.back()}});
auto epsilon = m.add_literal(1e-12f);
auto epsilon = m.add_literal(eps);
auto exponent = m.add_literal(2.0f);
auto mean = m.add_instruction(migraphx::op::reduce_mean({2}), x);
......@@ -88,6 +90,19 @@ struct test_layernorm2 : verify_program<test_layernorm2>
}
};
struct test_layernorm_eps : verify_program<test_layernorm_eps>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<size_t> dims = {1, 2, 5};
auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, dims});
add_layernorm(*mm, x, dims, 1e-5f);
return p;
}
};
struct test_layernorm_triadd : verify_program<test_layernorm_triadd>
{
migraphx::program create_program() const
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment