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[NhwcTransformerTests] Fix linker error due to explicit template inst…
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…antiation of ModelBuilder methods (#19980)

Currently, the nhwc_transformer_test.cc compilation unit defines
explicit FP16 versions of `ModelTestBuilder::MakeInput<MLFloat16>` and
`ModelTestBuilder::MakeInitializer<MLFloat16>` outside of the
ModelTestBuilder class's header file.

These explicit template instantiations cause linker errors when other
compilation units also instantiate these functions due to duplicate
definitions. Additionally, the versions defined in
nhwc_transformer_test.cc do not really conform to the expected behavior
in the original ModelTestBuilder class, which is to make random
input/initializer values. Instead, the versions in
nhwc_transformer_test.cc create a range of values.

The solution is to edit nhwc_transformer_test.cc to use stand-alone
static functions that do not change the ModelTestBuilder class.

**Note**: This linker error cannot currently be replicated in our CIs
because it requires a QNN-HTP-enabled Windows ARM64 environment with
`MLAS_F16VEC_INTRINSICS_SUPPORTED` defined. I can replicate on a local
build. The linker error/conflict happens with with this new FP16 QNN
test:

https://github.com/microsoft/onnxruntime/blob/d4c8bc359e321cdabdd87b70b392dd0e7a14502e/onnxruntime/test/providers/qnn/clip_op_test.cc#L186
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@adrianlizarraga
adrianlizarraga authored Mar 19, 2024
1 parent 01c7aaf commit 18a7f34
Showing 1 changed file with 17 additions and 17 deletions.
34 changes: 17 additions & 17 deletions onnxruntime/test/optimizer/nhwc_transformer_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -518,7 +518,7 @@ TEST(NhwcTransformerTests, ConvMixTensorRanks) {

#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED

std::vector<MLFloat16> randomfp16(const std::vector<int64_t>& shape, MLFloat16 min, MLFloat16 max) {
static std::vector<MLFloat16> ARangeOfFP16Values(const std::vector<int64_t>& shape, MLFloat16 min, MLFloat16 max) {
std::vector<MLFloat16> val(detail::SizeFromDims(shape));
float start = min.ToFloat();
float end = max.ToFloat();
Expand All @@ -534,22 +534,22 @@ std::vector<MLFloat16> randomfp16(const std::vector<int64_t>& shape, MLFloat16 m
return val;
}

template <>
NodeArg* ModelTestBuilder::MakeInput<MLFloat16>(const std::vector<int64_t>& shape, MLFloat16 min, MLFloat16 max) {
return MakeInput<MLFloat16>(shape, randomfp16(shape, min, max));
static NodeArg* MakeInputARangeFP16(ModelTestBuilder& builder, const std::vector<int64_t>& shape,
MLFloat16 min, MLFloat16 max) {
return builder.MakeInput<MLFloat16>(shape, ARangeOfFP16Values(shape, min, max));
}

template <>
NodeArg* ModelTestBuilder::MakeInitializer(const std::vector<int64_t>& shape, MLFloat16 min, MLFloat16 max) {
return MakeInitializer(shape, randomfp16(shape, min, max));
static NodeArg* MakeInitializerARangeFP16(ModelTestBuilder& builder, const std::vector<int64_t>& shape,
MLFloat16 min, MLFloat16 max) {
return builder.MakeInitializer<MLFloat16>(shape, ARangeOfFP16Values(shape, min, max));
}

TEST(NhwcTransformerTests, ConvFp16) {
auto test_case = [&](const std::vector<int64_t>& input_shape, const std::vector<int64_t>& weights_shape) {
auto build_test_case = [&](ModelTestBuilder& builder) {
auto* input_arg = builder.MakeInput<MLFloat16>(input_shape, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* input_arg = MakeInputARangeFP16(builder, input_shape, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* output_arg = builder.MakeOutput();
auto* weight_arg = builder.MakeInitializer(weights_shape, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* weight_arg = MakeInitializerARangeFP16(builder, weights_shape, MLFloat16(-1.5f), MLFloat16(1.5f));

builder.AddConvNode(input_arg, weight_arg, output_arg);
};
Expand All @@ -575,10 +575,10 @@ TEST(NhwcTransformerTests, ConvFp16) {
TEST(NhwcTransformerTests, ConvMaxPoolFp16) {
auto test_case = [&](const std::vector<int64_t>& input_shape, const std::vector<int64_t>& weights_shape) {
auto build_test_case = [&](ModelTestBuilder& builder) {
auto* input_arg = builder.MakeInput<MLFloat16>(input_shape, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* input_arg = MakeInputARangeFP16(builder, input_shape, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv_output_arg = builder.MakeIntermediate();
auto* output_arg = builder.MakeOutput();
auto* conv_weight_arg = builder.MakeInitializer(weights_shape, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv_weight_arg = MakeInitializerARangeFP16(builder, weights_shape, MLFloat16(-1.5f), MLFloat16(1.5f));

builder.AddConvNode(input_arg, conv_weight_arg, conv_output_arg);
Node& pool_node = builder.AddNode("MaxPool", {conv_output_arg}, {output_arg});
Expand Down Expand Up @@ -609,13 +609,13 @@ TEST(NhwcTransformerTests, ConvMaxPoolFp16) {

TEST(NhwcTransformerTests, ConvGlobalAveragePoolFp16) {
auto build_test_case = [&](ModelTestBuilder& builder) {
auto* input_arg = builder.MakeInput<MLFloat16>({1, 23, 13, 13}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* input_arg = MakeInputARangeFP16(builder, {1, 23, 13, 13}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv1_output_arg = builder.MakeIntermediate();
auto* conv2_output_arg = builder.MakeIntermediate();
auto* gavgpool1_output_arg = builder.MakeIntermediate();
auto* output_arg = builder.MakeOutput();
auto* conv1_weight_arg = builder.MakeInitializer<MLFloat16>({30, 23, 3, 3}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv2_weight_arg = builder.MakeInitializer<MLFloat16>({16, 30, 1, 1}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv1_weight_arg = MakeInitializerARangeFP16(builder, {30, 23, 3, 3}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv2_weight_arg = MakeInitializerARangeFP16(builder, {16, 30, 1, 1}, MLFloat16(-1.5f), MLFloat16(1.5f));

Node& conv1_node = builder.AddConvNode(input_arg, conv1_weight_arg, conv1_output_arg);
conv1_node.AddAttribute("pads", std::vector<int64_t>{1, 1, 1, 1});
Expand All @@ -640,13 +640,13 @@ TEST(NhwcTransformerTests, ConvGlobalAveragePoolFp16) {

TEST(NhwcTransformerTests, ConvAveragePoolFp16) {
auto build_test_case = [&](ModelTestBuilder& builder) {
auto* input_arg = builder.MakeInput<MLFloat16>({1, 23, 13, 13}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* input_arg = MakeInputARangeFP16(builder, {1, 23, 13, 13}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv1_output_arg = builder.MakeIntermediate();
auto* conv2_output_arg = builder.MakeIntermediate();
auto* avgpool1_output_arg = builder.MakeIntermediate();
auto* output_arg = builder.MakeOutput();
auto* conv1_weight_arg = builder.MakeInitializer<MLFloat16>({30, 23, 3, 3}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv2_weight_arg = builder.MakeInitializer<MLFloat16>({16, 30, 3, 3}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv1_weight_arg = MakeInitializerARangeFP16(builder, {30, 23, 3, 3}, MLFloat16(-1.5f), MLFloat16(1.5f));
auto* conv2_weight_arg = MakeInitializerARangeFP16(builder, {16, 30, 3, 3}, MLFloat16(-1.5f), MLFloat16(1.5f));

Node& conv1_node = builder.AddConvNode(input_arg, conv1_weight_arg, conv1_output_arg);
conv1_node.AddAttribute("pads", std::vector<int64_t>{1, 1, 1, 1});
Expand Down

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