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Unified conv3D API + support for all data types. (#133)

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* Convolution ND

* Code unification across dimensions for generating tensor descriptors.
* Example
* Instances

* Move convnd f32 instance file to comply with repo structure.

* Conv 1D tensor layouts.

* Formatting and use ReferenceConv

* Reference ConvFwd supporting 1D and 2D convolution.

* Debug printing TensorLayout name.

* Conv fwd 1D instance f32

* Refactor conv ND example.

Needed to support various conv dimensio.

Needed to support various conv dimensions

* Rename conv nd example director to prevent conflicts.

* Refactor some common utility to single file.

Plus some tests.

* Refactor GetHostTensorDescriptor + UT.

* Add 1D test case.

* Test reference convolution 1d/2d

* Remove some leftovers.

* Fix convolution example error for 1D

* Refactor test check errors utility function.

* Test Conv2D Fwd XDL

* More UT for 1D case.

* Parameterize input & weight initializers.

* Rename example to prevent conflicts.

* Split convnd instance into separate files for 1d/2d

* Address review comments.

* Fix data type for flops/gbytes calculations.

* Assign example number 11.

* 3D cases for convolution utility functions.

* 3D reference convolution.

* Add support for 3D convolution.

* Check for inputs bigger than  2GB.

* Formatting

* Support for bf16/f16/f32/i8 - conv instances + UT.

* Use check_err from test_util.hpp.

* Split convnd test into separate files for each dim.

* Fix data generation and use proper instances.

* Formatting

* Skip tensor initialization if not necessary.

* Fix CMakefiles.

* Remove redundant conv2d_fwd test.

* Lower problem size for conv3D UT.

* 3D case for convnd example.

* Remove leftovers after merge.

* Add Conv Specialization string to GetTypeString

* Skip instance causing numerical errors.

* Small fixes.

* Remove redundant includes.

* Fix namespace name error.

* Script for automatic testing and logging convolution fwd UTs

* Comment out numactl cmd.

* Refine weights initalization and relax rtol for fp16

* Fix weights initialization for int8.

* Add type_convert when store output in ref conv 1D.

* Get back old conv2d_fwd_xdl operation.

* Silence conv debug print.

* format

* clean

* clean

* Fix merge.

* Fix namespace for check_err

Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
This commit is contained in:
Adam Osewski
2022-03-23 16:23:13 +01:00
committed by GitHub
parent 2206136628
commit f91579aab6
39 changed files with 2586 additions and 863 deletions
Download Patch File Download Diff File Expand all files Collapse all files

157
test/reference_conv_fwd/reference_conv_fwd.cpp
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@@ -23,11 +23,16 @@ template <typename T>
struct FillMonotonicSeq
{
T m_init_value{0};
T m_step{1};
template <typename ForwardIter>
void operator()(ForwardIter first, ForwardIter last) const
{
std::iota(first, last, m_init_value);
std::generate(first, last, [=, n = m_init_value]() mutable {
auto tmp = n;
n += m_step;
return tmp;
});
}
};
@@ -53,7 +58,7 @@ template <ck::index_t NDim,
typename FillInputOp = FillMonotonicSeq<InDataType>,
typename FillWeightsOp = FillConstant<WeiDataType>>
Tensor<OutDataType> RunReferenceConv(const ck::conv_util::ConvParams& params,
const FillInputOp& fill_input_op = FillInputOp{0},
const FillInputOp& fill_input_op = FillInputOp{},
const FillWeightsOp& fill_weights_op = FillWeightsOp{0.5f})
{
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
@@ -84,6 +89,9 @@ Tensor<OutDataType> RunReferenceConv(const ck::conv_util::ConvParams& params,
fill_weights_op(weights.begin(), weights.end());
std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
// std::cout <<"input: " << input.mDesc << std::endl << input.mData << std::endl;
// std::cout <<"weight: " << weights.mDesc << std::endl << weights.mData << std::endl;
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
WeiDataType,
OutDataType,
@@ -104,6 +112,7 @@ Tensor<OutDataType> RunReferenceConv(const ck::conv_util::ConvParams& params,
OutElementOp{});
ref_invoker.Run(ref_argument);
// std::cout <<"output: " << host_output.mDesc << std::endl << host_output.mData << std::endl;
return host_output;
}
@@ -139,10 +148,10 @@ bool TestConv2DNHWC()
472.5,
490.5,
508.5};
res = res && test_util::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test_util::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
res = res && test::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
params.N = 1;
params.K = 2;
@@ -162,10 +171,10 @@ bool TestConv2DNHWC()
747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5,
1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5,
1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5};
res = res && test_util::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test_util::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
res = res && test::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
return res;
}
@@ -194,10 +203,10 @@ bool TestConv1DNWC()
ck::tensor_layout::convolution::NWK>(params);
std::vector<std::size_t> ref_dims{1, 1, 4};
std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5};
res = res && test_util::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test_util::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
res = res && test::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
params.num_dim_spatial = 1;
params.N = 1;
@@ -219,10 +228,10 @@ bool TestConv1DNWC()
ck::tensor_layout::convolution::NWK>(params);
ref_dims = std::vector<std::size_t>{1, 2, 5};
ref_data = std::vector<float>{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5};
res = res && test_util::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test_util::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
res = res && test::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test::check_err(out_tensor.mData, ref_data, "Error: incorrect results!");
params.num_dim_spatial = 1;
params.N = 2;
@@ -235,16 +244,14 @@ bool TestConv1DNWC()
params.input_left_pads = std::vector<ck::index_t>{1};
params.input_right_pads = std::vector<ck::index_t>{1};
auto out_tensor2 =
RunReferenceConv<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params, [](auto first, auto last) {
std::generate(first, last, [n = 0]() mutable { return float(n++) * float(0.1f); });
});
auto out_tensor2 = RunReferenceConv<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
params, FillMonotonicSeq<float>{0.f, 0.1f});
ref_dims = std::vector<std::size_t>{2, 16, 16};
ref_data = std::vector<float>{
@@ -312,10 +319,94 @@ bool TestConv1DNWC()
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4};
res = res && test_util::check_err(out_tensor2.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test_util::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!");
res = res && test::check_err(out_tensor2.mDesc.GetLengths(),
ref_dims,
"Error: wrong output tensor dimensions!");
res = res && test::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!");
return res;
}
bool TestConv3DNCDHW()
{
bool res{true};
ck::conv_util::ConvParams params;
params.num_dim_spatial = 3;
params.N = 1;
params.K = 1;
params.C = 2;
params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths = std::vector<ck::index_t>{6, 6, 6};
params.conv_filter_strides = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads = std::vector<ck::index_t>{0, 0, 0};
auto out_tensor = RunReferenceConv<3,
float,
float,
float,
ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>(
params, FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4};
std::vector<float> ref_data{
407.7, 410.40002, 413.09998, 415.80002, 423.90002, 426.6, 429.30002, 432.,
440.1, 442.80002, 445.5, 448.2, 456.30002, 459., 461.7, 464.40002,
504.90002, 507.6, 510.30002, 513., 521.1, 523.8, 526.5, 529.2001,
537.3, 540., 542.7001, 545.4, 553.5, 556.2001, 558.9, 561.6,
602.10004, 604.8, 607.5, 610.2, 618.3, 621., 623.7, 626.4,
634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8,
699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004,
731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.};
res = res && test::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 1]: wrong output tensor dimensions!");
res = res && test::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!");
params.N = 1;
params.K = 2;
params.C = 2;
params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths = std::vector<ck::index_t>{12, 12, 12};
params.conv_filter_strides = std::vector<ck::index_t>{3, 3, 3};
params.conv_filter_dilations = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads = std::vector<ck::index_t>{0, 0, 0};
out_tensor = RunReferenceConv<3,
float,
float,
float,
ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>(
params, FillMonotonicSeq<float>{0.f, 0.1f});
ref_dims = std::vector<std::size_t>{1, 2, 4, 4, 4};
ref_data = std::vector<float>{
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801,
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801};
res = res && test::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 2]: wrong output tensor dimensions!");
res =
res && test::check_err(
out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f);
return res;
}
@@ -329,5 +420,7 @@ int main(void)
std::cout << "TestConv2DNHWC ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = TestConv1DNWC();
std::cout << "TestConv1DNHWC ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = TestConv3DNCDHW();
std::cout << "TestConv3DNCDHW ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return 0;
}