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Add HardSigmoid and HardSwish (#156) 

* Add HardSigmoid and HardSwish

* fix format
This commit is contained in:
PanZezhong1725 2023-10-10 22:41:06 +08:00 committed by GitHub
parent 1151101fb9
commit ed3034f878
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11 changed files with 90 additions and 0 deletions
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2
include/core/graph_handler.h
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@ -47,6 +47,8 @@ class GraphHandlerObj {
Tensor relu(Tensor x, Tensor y);
Tensor gelu(Tensor x, Tensor y);
Tensor sigmoid(Tensor x, Tensor y);
Tensor hardSigmoid(Tensor x, Tensor y);
Tensor hardSwish(Tensor x, Tensor y);
Tensor tanh(Tensor x, Tensor y);
Tensor erf(Tensor x, Tensor y);
Tensor softmax(Tensor x, Tensor y, int axis);

6
include/cuda/cuda_unary.h
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@ -12,6 +12,8 @@ void sqrt_kernel(float *input, float *output, size_t num);
void neg_kernel(float *input, float *output, size_t num);
void gelu_kernel(float *input, float *output, size_t num);
void erf_kernel(float *input, float *output, size_t num);
void hard_sigmoid_kernel(float *input, float *output, size_t num);
void hard_swish_kernel(float *input, float *output, size_t num);
void unary_kernel(const Operator &_op) {
auto op = as<UnaryObj>(_op);
@ -25,6 +27,10 @@ void unary_kernel(const Operator &_op) {
relu_kernel(inputData, outputData, num);
else if (op->getOpType() == OpType::Sigmoid)
sigmoid_kernel(inputData, outputData, num);
else if (op->getOpType() == OpType::HardSigmoid)
hard_sigmoid_kernel(inputData, outputData, num);
else if (op->getOpType() == OpType::HardSwish)
hard_swish_kernel(inputData, outputData, num);
else if (op->getOpType() == OpType::Tanh)
tanh_kernel(inputData, outputData, num);
else if (op->getOpType() == OpType::Abs)

2
include/operators/unary.h
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@ -263,6 +263,8 @@ DEFINE_UNARY_OBJ(Sigmoid, OpType::Sigmoid)
DEFINE_UNARY_OBJ(Tanh, OpType::Tanh)
// DEFINE_UNARY_OBJ(Softmax, OpType::Softmax)
DEFINE_UNARY_OBJ(Abs, OpType::Abs)
DEFINE_UNARY_OBJ(HardSigmoid, OpType::HardSigmoid)
DEFINE_UNARY_OBJ(HardSwish, OpType::HardSwish)
DEFINE_UNARY_OBJ(Sin, OpType::Sin)
DEFINE_UNARY_OBJ(Cos, OpType::Cos)

12
pyinfinitensor/src/pyinfinitensor/onnx.py
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@ -395,6 +395,16 @@ class OnnxStub:
tensors[node.input[0]],
tensors.get(node.output[0]),
)
elif node.op_type == "HardSigmoid":
tensors[node.output[0]] = self.handler.hardSigmoid(
tensors[node.input[0]],
tensors.get(node.output[0]),
)
elif node.op_type == "HardSwish":
tensors[node.output[0]] = self.handler.hardSwish(
tensors[node.input[0]],
tensors.get(node.output[0]),
)
elif node.op_type == "Tanh":
tensors[node.output[0]] = self.handler.tanh(
tensors[node.input[0]],
@ -931,6 +941,8 @@ class OnnxStub:
backend.OpTypeId.Relu,
backend.OpTypeId.Gelu,
backend.OpTypeId.Sigmoid,
backend.OpTypeId.HardSigmoid,
backend.OpTypeId.HardSwish,
backend.OpTypeId.Tanh,
backend.OpTypeId.Softmax,
backend.OpTypeId.Abs,

12
pyinfinitensor/tests/test_onnx.py
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@ -239,6 +239,18 @@ class TestStringMethods(unittest.TestCase):
y = make_tensor_value_info("y", TensorProto.FLOAT, [1, 3, 5, 7])
tanh = make_node("Tanh", ["x"], ["y"], name="tanh")
make_and_import_model(make_graph([tanh], "tanh", [x], [y]))
def test_hard_sigmoid(self):
x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 3, 5, 7])
y = make_tensor_value_info("y", TensorProto.FLOAT, [1, 3, 5, 7])
hardSigmoid = make_node("HardSigmoid", ["x"], ["y"], name="hardSigmoid")
make_and_import_model(make_graph([hardSigmoid], "hardSigmoid", [x], [y]))
def test_hard_swish(self):
x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 3, 5, 7])
y = make_tensor_value_info("y", TensorProto.FLOAT, [1, 3, 5, 7])
hardSwish = make_node("HardSwish", ["x"], ["y"], name="hardSwish")
make_and_import_model(make_graph([hardSwish], "hardSwish", [x], [y]))
def test_softmax(self):
x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 3, 5, 7])

2
src/core/graph_handler.cc
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@ -158,6 +158,8 @@ DEFINE_UNARY_METHOD(relu, Relu)
DEFINE_UNARY_METHOD(gelu, Gelu)
DEFINE_UNARY_METHOD(sigmoid, Sigmoid)
DEFINE_UNARY_METHOD(tanh, Tanh)
DEFINE_UNARY_METHOD(hardSigmoid, HardSigmoid)
DEFINE_UNARY_METHOD(hardSwish, HardSwish)
DEFINE_UNARY_METHOD(abs, Abs)
DEFINE_UNARY_METHOD(sqrt, Sqrt)
DEFINE_UNARY_METHOD(neg, Neg)

4
src/ffi/ffi_infinitensor.cc
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@ -96,6 +96,8 @@ void export_values(py::module &m) {
.VALUE(OpType, PRelu)
.VALUE(OpType, Sigmoid)
.VALUE(OpType, Tanh)
.VALUE(OpType, HardSigmoid)
.VALUE(OpType, HardSwish)
.VALUE(OpType, Abs)
.VALUE(OpType, Resize)
.VALUE(OpType, Dropout)
@ -444,6 +446,8 @@ void init_graph_builder(py::module &m) {
.def("gelu", &Handler::gelu, policy::move)
.def("sigmoid", &Handler::sigmoid, policy::move)
.def("tanh", &Handler::tanh, policy::move)
.def("hardSigmoid", &Handler::hardSigmoid, policy::move)
.def("hardSwish", &Handler::hardSwish, policy::move)
.def("softmax", &Handler::softmax, policy::move)
.def("abs", &Handler::abs, policy::move)
.def("sqrt", &Handler::sqrt, policy::move)

15
src/kernels/cpu/unary.cc
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@ -46,6 +46,17 @@ template <typename T> class NaiveSigmoid : public NativeUnary<T> {
return 1 / (1 + pow(E_CONSTANT, -val));
}
};
template <typename T> class NaiveHardSigmoid : public NativeUnary<T> {
T doCompute(T val) const override {
return std::max(T(0), std::min(T(1), T(0.2) * val + T(0.5)));
}
};
template <typename T> class NaiveHardSwish : public NativeUnary<T> {
T doCompute(T val) const override {
return val *
std::max(T(0), std::min(T(1), val * T(1.0 / 6.0) + T(0.5)));
}
};
template <typename T> class NaiveTanh : public NativeUnary<T> {
T doCompute(T val) const override {
return (pow(E_CONSTANT, val) - pow(E_CONSTANT, -val)) /
@ -105,6 +116,10 @@ REGISTER_KERNEL(Device::CPU, OpType::Sigmoid, DataType::UInt32,
NaiveSigmoid<uint32_t>, "sigmoidNaive_CPU_uint32");
REGISTER_KERNEL(Device::CPU, OpType::Sigmoid, DataType::Float32,
NaiveSigmoid<float>, "sigmoidNaive_CPU_float32");
REGISTER_KERNEL(Device::CPU, OpType::HardSigmoid, DataType::Float32,
NaiveHardSigmoid<float>, "hardSigmoidNaive_CPU_float32");
REGISTER_KERNEL(Device::CPU, OpType::HardSwish, DataType::Float32,
NaiveHardSwish<float>, "hardSwishNaive_CPU_float32");
REGISTER_KERNEL(Device::CPU, OpType::Tanh, DataType::UInt32,
NaiveTanh<uint32_t>, "tanhNaive_CPU_uint32");
REGISTER_KERNEL(Device::CPU, OpType::Tanh, DataType::Float32, NaiveTanh<float>,

4
src/kernels/cuda/unary.cc
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@ -134,6 +134,10 @@ REGISTER_KERNEL(Device::CUDA, OpType::Relu, DataType::Float32, ReluCudnn,
"Relu_CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::Sigmoid, DataType::Float32, SigmoidCudnn,
"Sigmoid_CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::HardSigmoid, DataType::Float32, UnaryCuda,
"Hard_Sigmoid_CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::HardSwish, DataType::Float32, UnaryCuda,
"Hard_Swish_CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::Tanh, DataType::Float32, TanhCudnn,
"Tanh_CUDA_Float32");
REGISTER_KERNEL(Device::CUDA, OpType::Abs, DataType::Float32, UnaryCuda,

29
src/kernels/cuda/unary.cu
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@ -41,6 +41,23 @@ __global__ void _sigmoid_kernel(float *input, float *output, size_t n) {
}
}
__global__ void _hard_sigmoid_kernel(float *input, float *output, size_t n) {
size_t index = threadIdx.x + blockIdx.x * blockDim.x;
size_t stride = blockDim.x * gridDim.x;
for (size_t i = index; i < n; i += stride) {
output[i] = max(0.0f, min(1.0f, 0.2f * input[i] + 0.5f));
}
}
__global__ void _hard_swish_kernel(float *input, float *output, size_t n) {
size_t index = threadIdx.x + blockIdx.x * blockDim.x;
size_t stride = blockDim.x * gridDim.x;
for (size_t i = index; i < n; i += stride) {
output[i] =
input[i] * max(0.f, min(1.f, (1.f / 6.f) * input[i] + 0.5f));
}
}
__global__ void _tanh_kernel(float *input, float *output, size_t n) {
size_t index = threadIdx.x + blockIdx.x * blockDim.x;
size_t stride = blockDim.x * gridDim.x;
@ -112,6 +129,18 @@ void sigmoid_kernel(float *input, float *output, size_t num) {
int gridsize = (num + block_work_size() - 1) / block_work_size();
_sigmoid_kernel<<<gridsize, blocksize>>>(input, output, num);
}
void hard_sigmoid_kernel(float *input, float *output, size_t num) {
int blocksize = block_work_size();
int gridsize = (num + block_work_size() - 1) / block_work_size();
_hard_sigmoid_kernel<<<gridsize, blocksize>>>(input, output, num);
}
void hard_swish_kernel(float *input, float *output, size_t num) {
int blocksize = block_work_size();
int gridsize = (num + block_work_size() - 1) / block_work_size();
_hard_swish_kernel<<<gridsize, blocksize>>>(input, output, num);
}
void tanh_kernel(float *input, float *output, size_t num) {
int blocksize = block_work_size();

2
test/kernels/cuda/test_cuda_unary.cc
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@ -45,6 +45,8 @@ TEST(cuDNN_Unary, run) {
testUnary<AbsObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<SigmoidObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<TanhObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<HardSigmoidObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<HardSwishObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<SqrtObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<NegObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<ErfObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});