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[QualcommQnn] add tanh, unsqueeze, equal, slice, cast #9296

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Jul 29, 2022
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[QualcommQnn] add tanh, unsqueeze, equal, slice, cast #9296

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3 changes: 3 additions & 0 deletions ...ackends/nnadapter/nnadapter/include/nnadapter/optimizer/convert_datalayout_nchw_to_nhwc.h
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@ class NCHW2NHWCDataLayoutConverter {
void ConvertBatchNormalization(core::Operation* operation);
void ConvertCast(core::Operation* operation);
void ConvertClip(core::Operation* operation);
void ConvertComparisons(core::Operation* operation);
void ConvertConv2DTranspose(core::Operation* operation);
void ConvertCumSum(core::Operation* operation);
void ConvertElementwise(core::Operation* operation);
Expand All @@ -59,12 +60,14 @@ class NCHW2NHWCDataLayoutConverter {
void ConvertResizeNearest(core::Operation* operation);
void ConvertResizeLinear(core::Operation* operation);
void ConvertShape(core::Operation* operation);
void ConvertSlice(core::Operation* operation);
void ConvertSoftmax(core::Operation* operation);
void ConvertSplit(core::Operation* operation);
void ConvertSqueeze(core::Operation* operation);
void ConvertStack(core::Operation* operation);
void ConvertTranspose(core::Operation* operation);
void ConvertMatMul(core::Operation* operation);
void ConvertUnsqueeze(core::Operation* operation);

private:
core::Model* model_{nullptr};
Expand Down
135 changes: 135 additions & 0 deletions lite/backends/nnadapter/nnadapter/src/optimizer/convert_datalayout_nchw_to_nhwc.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -421,6 +421,81 @@ void NCHW2NHWCDataLayoutConverter::ConvertConv2DTranspose(
SetOperationLayout(operation, 3);
}

void NCHW2NHWCDataLayoutConverter::ConvertComparisons(
core::Operation* operation) {
auto& input_operands = operation->input_operands;
auto& output_operands = operation->output_operands;
auto input_count = input_operands.size();
auto output_count = output_operands.size();
NNADAPTER_CHECK_EQ(input_count, 2);
NNADAPTER_CHECK_EQ(output_count, 1);
auto output_operand = output_operands[0];
auto output_dimensions_count = output_operand->type.dimensions.count;
// Force to align the dimorder vector of all of input operands
std::vector<int32_t> reference_permutation;
core::Operand* reference_operand = nullptr;
for (size_t i = 0; i < input_count; i++) {
auto input_operand = input_operands[i];
if (!IsConstantOperand(input_operand)) {
auto input_permutation = GetPermutation(input_operand);
if (input_permutation.size() > reference_permutation.size()) {
reference_permutation = input_permutation;
reference_operand = input_operand;
}
}
}
if (reference_permutation.empty()) {
// All of input operands are constant
SetPermutation(output_operand,
IdentityPermutation(output_dimensions_count));
} else {
auto reference_dimensions_count = reference_operand->type.dimensions.count;
for (size_t i = 0; i < input_count; i++) {
auto input_operand = input_operands[i];
auto input_dimensions_count = input_operand->type.dimensions.count;
if (!IsConstantOperand(input_operand)) {
auto input_permutation = GetPermutation(input_operand);
auto transpose_input_permutation = MultiplyPermutation(
InversePermutation(input_permutation), reference_permutation);
if (!IsIdentityPermutation(transpose_input_permutation)) {
auto transpose_input_operand = AppendTransposeOperation(
model_, input_operand, transpose_input_permutation);
UpdateOperationInputOperands(
{operation}, input_operand, transpose_input_operand);
SetPermutation(transpose_input_operand, reference_permutation);
}
} else {
if (IsIdentityPermutation(reference_permutation)) {
// Ignore
} else if (input_dimensions_count == reference_permutation.size()) {
TransposeOperand(input_operand, reference_permutation);
} else {
// Expand shape with 1
std::vector<int32_t> origin_reference_dimensions(
reference_dimensions_count);
TransposeDimensions(reference_operand->type.dimensions.data,
InversePermutation(reference_permutation),
&origin_reference_dimensions[0]);
std::vector<int32_t> expanded_input_dimensions;
for (uint32_t j = 0, k = 0; j < reference_dimensions_count; j++) {
if (origin_reference_dimensions[j] ==
input_operand->type.dimensions.data[k] &&
k < input_dimensions_count) {
expanded_input_dimensions.push_back(
input_operand->type.dimensions.data[k]);
++k;
} else {
expanded_input_dimensions.push_back(1);
}
}
}
}
}
TransposeOperand(output_operand, reference_permutation);
SetPermutation(output_operand, reference_permutation);
}
}

void NCHW2NHWCDataLayoutConverter::ConvertCumSum(core::Operation* operation) {
auto& input_operands = operation->input_operands;
auto& output_operands = operation->output_operands;
Expand Down Expand Up @@ -679,6 +754,26 @@ void NCHW2NHWCDataLayoutConverter::ConvertShape(core::Operation* operation) {
SetPermutation(output_operand, IdentityPermutation(output_dimensions_count));
}

void NCHW2NHWCDataLayoutConverter::ConvertSlice(core::Operation* operation) {
auto& input_operands = operation->input_operands;
auto& output_operands = operation->output_operands;
auto input_count = input_operands.size();
auto output_count = output_operands.size();
NNADAPTER_CHECK_EQ(input_count, 5);
NNADAPTER_CHECK_EQ(output_count, 1);
// Recalculate the axis according to the dimorder vector of the input operand
auto axes_operand = input_operands[1];
int axes_count = axes_operand->length / sizeof(int32_t);
int* axes = reinterpret_cast<int32_t*>(axes_operand->buffer);
auto input_permutation = GetPermutation(input_operands[0]);
for (int i = 0; i < axes_count; i++) {
axes[i] = TransposeAxis(axes[i], input_permutation);
}
auto output_operand = output_operands[0];
TransposeOperand(output_operand, input_permutation);
SetPermutation(output_operand, input_permutation);
}

void NCHW2NHWCDataLayoutConverter::ConvertFill(core::Operation* operation) {
auto& input_operands = operation->input_operands;
auto& output_operands = operation->output_operands;
Expand Down Expand Up @@ -937,6 +1032,32 @@ void NCHW2NHWCDataLayoutConverter::ConvertTranspose(
SetPermutation(output_operand, input_permutation);
}

void NCHW2NHWCDataLayoutConverter::ConvertUnsqueeze(
core::Operation* operation) {
auto& input_operands = operation->input_operands;
auto& output_operands = operation->output_operands;
auto input_count = input_operands.size();
auto output_count = output_operands.size();
NNADAPTER_CHECK_EQ(input_count, 2);
NNADAPTER_CHECK_EQ(output_count, 1);
auto input_operand = input_operands[0];
int input_dimensions_count = input_operand->type.dimensions.count;
auto output_operand = output_operands[0];
auto output_dimensions_count = output_operand->type.dimensions.count;
// Force to restore the dimorder vector of the input operand
auto input_permutation = GetPermutation(input_operand);
auto transpose_input_permutation = InversePermutation(input_permutation);
if (!IsIdentityPermutation(transpose_input_permutation)) {
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先这样简单处理，后面再优化下，看看是否能类似squeeze一样处理，目的还是尽可能避免插入 transpose算子而带来的性能下降。

auto transpose_input_operand = AppendTransposeOperation(
model_, input_operand, transpose_input_permutation);
UpdateOperationInputOperands(
{operation}, input_operand, transpose_input_operand);
SetPermutation(transpose_input_operand,
IdentityPermutation(input_dimensions_count));
}
SetPermutation(output_operand, IdentityPermutation(output_dimensions_count));
}

void NCHW2NHWCDataLayoutConverter::Apply(core::Model* model) {
model_ = model;
// Initialize the permutation of model input operands
Expand Down Expand Up @@ -987,6 +1108,14 @@ void NCHW2NHWCDataLayoutConverter::Apply(core::Model* model) {
case NNADAPTER_CUM_SUM:
ConvertCumSum(operation);
break;
case NNADAPTER_EQUAL:
case NNADAPTER_GREATER:
case NNADAPTER_GREATER_EQUAL:
case NNADAPTER_LESS:
case NNADAPTER_LESS_EQUAL:
case NNADAPTER_NOT_EQUAL:
ConvertComparisons(operation);
break;
case NNADAPTER_FILL:
ConvertFill(operation);
break;
Expand Down Expand Up @@ -1045,6 +1174,9 @@ void NCHW2NHWCDataLayoutConverter::Apply(core::Model* model) {
case NNADAPTER_SHAPE:
ConvertShape(operation);
break;
case NNADAPTER_SLICE:
ConvertSlice(operation);
break;
case NNADAPTER_SOFTMAX:
ConvertSoftmax(operation);
break;
Expand All @@ -1060,6 +1192,9 @@ void NCHW2NHWCDataLayoutConverter::Apply(core::Model* model) {
case NNADAPTER_TRANSPOSE:
ConvertTranspose(operation);
break;
case NNADAPTER_UNSQUEEZE:
ConvertUnsqueeze(operation);
break;
default:
NNADAPTER_LOG(FATAL)
<< "Missing the processing of "
Expand Down
26 changes: 17 additions & 9 deletions lite/backends/nnadapter/nnadapter/src/optimizer/convert_quantization_symm_to_asymm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -82,12 +82,18 @@ NNADAPTER_EXPORT void ConvertQuantizationSymmToAsymm(core::Model* model) {
switch (operation->type) {
case NNADAPTER_ADD:
case NNADAPTER_DIV:
case NNADAPTER_EQUAL:
case NNADAPTER_FULLY_CONNECTED:
case NNADAPTER_GATHER:
case NNADAPTER_GREATER:
case NNADAPTER_GREATER_EQUAL:
case NNADAPTER_LESS:
case NNADAPTER_LESS_EQUAL:
case NNADAPTER_MAT_MUL:
case NNADAPTER_MAX:
case NNADAPTER_MIN:
case NNADAPTER_MUL:
case NNADAPTER_NOT_EQUAL:
case NNADAPTER_POW:
case NNADAPTER_SUB: {
ConvertOperandSymmToAsymm(input_operands[0], 128);
Expand All @@ -96,25 +102,27 @@ NNADAPTER_EXPORT void ConvertQuantizationSymmToAsymm(core::Model* model) {
} break;
case NNADAPTER_AVERAGE_POOL_2D:
case NNADAPTER_BATCH_NORMALIZATION:
case NNADAPTER_CAST:
case NNADAPTER_CHANNEL_SHUFFLE:
case NNADAPTER_CLIP:
case NNADAPTER_CUM_SUM:
case NNADAPTER_FILL_LIKE:
case NNADAPTER_FLATTEN:
case NNADAPTER_HARD_SIGMOID:
case NNADAPTER_HARD_SWISH:
case NNADAPTER_LEAKY_RELU:
case NNADAPTER_MAX_POOL_2D:
case NNADAPTER_RELU:
case NNADAPTER_RELU6:
case NNADAPTER_RESHAPE:
case NNADAPTER_RESIZE_NEAREST:
case NNADAPTER_RESIZE_LINEAR:
case NNADAPTER_SLICE:
case NNADAPTER_SQUEEZE:
case NNADAPTER_SWISH:
case NNADAPTER_TANH:
case NNADAPTER_FLATTEN:
case NNADAPTER_TRANSPOSE:
case NNADAPTER_HARD_SIGMOID:
case NNADAPTER_HARD_SWISH:
case NNADAPTER_LEAKY_RELU:
case NNADAPTER_SQUEEZE:
case NNADAPTER_CLIP:
case NNADAPTER_CHANNEL_SHUFFLE:
case NNADAPTER_SLICE:
case NNADAPTER_FILL_LIKE: {
case NNADAPTER_UNSQUEEZE: {
ConvertOperandSymmToAsymm(input_operands[0], 128);
ConvertOperandSymmToAsymm(output_operands[0], 128);
PropagateAsymmZeroPoint(input_operands[0], output_operands[0]);
Expand Down
49 changes: 25 additions & 24 deletions lite/kernels/nnadapter/converter/all.h
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ REGISTER_CONVERTER(bmm, ConvertBmm, "intel_openvino");
REGISTER_CONVERTER(cast,
ConvertCast,
"huawei_ascend_npu,cambricon_mlu,huawei_kirin_npu,nvidia_"
"tensorrt,intel_openvino");
"tensorrt,intel_openvino,qualcomm_qnn");
REGISTER_CONVERTER(clip,
ConvertClip,
"huawei_ascend_npu,cambricon_mlu,verisilicon_timvx,huawei_"
Expand Down Expand Up @@ -92,10 +92,10 @@ REGISTER_CONVERTER(reshape2,
"npu,amlogic_npu,imagination_nna,verisilicon_timvx,"
"kunlunxin_xtcl,cambricon_mlu,android_nnapi,nvidia_tensorrt,"
"intel_openvino,qualcomm_qnn,google_xnnpack");
REGISTER_CONVERTER(
unsqueeze,
ConvertUnsqueeze,
"huawei_ascend_npu,cambricon_mlu,nvidia_tensorrt,intel_openvino");
REGISTER_CONVERTER(unsqueeze,
ConvertUnsqueeze,
"huawei_ascend_npu,cambricon_mlu,nvidia_tensorrt,intel_"
"openvino,qualcomm_qnn");
REGISTER_CONVERTER(
unsqueeze2,
ConvertUnsqueeze,
Expand Down Expand Up @@ -215,7 +215,7 @@ REGISTER_CONVERTER(tanh,
"rockchip_npu,mediatek_apu,huawei_kirin_npu,huawei_ascend_"
"npu,amlogic_npu,cambricon_mlu,verisilicon_timvx,kunlunxin_"
"xtcl,android_nnapi,intel_openvino,nvidia_tensorrt,"
"eeasytech_npu");
"eeasytech_npu,qualcomm_qnn");
REGISTER_CONVERTER(abs,
ConvertUnaryActivations,
"huawei_ascend_npu,huawei_kirin_npu,intel_openvino");
Expand Down Expand Up @@ -262,29 +262,30 @@ REGISTER_CONVERTER(
REGISTER_CONVERTER(equal,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,intel_"
"openvino,nvidia_tensorrt");
"openvino,nvidia_tensorrt,qualcomm_qnn");
REGISTER_CONVERTER(expand_v2,
ConvertExpandV2,
"huawei_ascend_npu,cambricon_mlu,intel_openvino");
REGISTER_CONVERTER(not_equal,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu");
REGISTER_CONVERTER(greater_than,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu");
REGISTER_CONVERTER(
greater_equal,
not_equal,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,intel_openvino");
REGISTER_CONVERTER(less_than,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu");
REGISTER_CONVERTER(less_equal,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu");
REGISTER_CONVERTER(less_than,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,qualcomm_qnn");
REGISTER_CONVERTER(
greater_than,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,qualcomm_qnn");
REGISTER_CONVERTER(greater_equal,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu");
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,intel_"
"openvino,qualcomm_qnn");
REGISTER_CONVERTER(
less_than,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,qualcomm_qnn");
REGISTER_CONVERTER(
less_equal,
ConvertComparisons,
"huawei_ascend_npu,huawei_kirin_npu,cambricon_mlu,qualcomm_qnn");
REGISTER_CONVERTER(
reduce_mean,
ConvertReduce,
Expand Down Expand Up @@ -321,7 +322,7 @@ REGISTER_CONVERTER(
REGISTER_CONVERTER(slice,
ConvertSlice,
"huawei_ascend_npu,verisilicon_timvx,cambricon_mlu,nvidia_"
"tensorrt,intel_openvino");
"tensorrt,intel_openvino,qualcomm_qnn");
REGISTER_CONVERTER(strided_slice,
ConvertStridedSlice,
"huawei_ascend_npu,huawei_kirin_npu,nvidia_tensorrt");
Expand Down
2 changes: 2 additions & 0 deletions lite/tests/kernels/activation_compute_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -697,6 +697,8 @@ TEST(Activation_tanh, precision) {
abs_error = 2e-5;
#elif defined(NNADAPTER_WITH_NVIDIA_TENSORRT)
abs_error = 2e-5;
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
abs_error = 2e-5;
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
abs_error = 1e-5;
for (auto& dims : test_dims) {
Expand Down
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