delete Non-approximate option, recommend to use
the approximate option Signed-off-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
Chen Xin
Sven
parent
6f2e92ffa6
commit
633075f689
|
|
@ -48,7 +48,7 @@ namespace ops {
|
|||
* Sigmoid(x) : 1/(1 + e^{-x})
|
||||
*
|
||||
* Swish(x) : x * sigmoid(x)
|
||||
*
|
||||
*
|
||||
* HardSwish(x) : 0 if x <= -3; x(x + 3)/6 if -3 < x < 3; x if x >= 3
|
||||
*
|
||||
* Mish(x) : x if x >= 0 else alpha * x
|
||||
|
|
@ -122,9 +122,13 @@ class Linear : public Operation {
|
|||
};
|
||||
|
||||
class Gelu : public Operation {
|
||||
public:
|
||||
explicit Gelu(Graph* graph, bool approximate = false);
|
||||
std::shared_ptr<Operation> Clone(
|
||||
public:
|
||||
/****************************************************************************
|
||||
*Non-approximate calculations will also have errors when the data type is
|
||||
*fp32, it is recommended to use the approximate option.
|
||||
****************************************************************************/
|
||||
explicit Gelu(Graph* graph, bool approximate = true);
|
||||
std::shared_ptr<Operation> Clone(
|
||||
std::shared_ptr<Graph>& graph) const override;
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -29,181 +29,152 @@
|
|||
#include "src/tim/vx/test_utils.h"
|
||||
|
||||
TEST(Linear, shape_5_1_fp32) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType io_shape({5, 1});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
|
||||
io_shape, tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
|
||||
io_shape, tim::vx::TensorAttribute::OUTPUT);
|
||||
tim::vx::ShapeType io_shape({5, 1});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = { -2.5, -0.1, 0, 0.55, std::numeric_limits<float>::infinity() };
|
||||
std::vector<float> golden = {-0.5, 1.9, 2, 2.55, std::numeric_limits<float>::infinity() };
|
||||
std::vector<float> in_data = {-2.5, -0.1, 0, 0.55,
|
||||
std::numeric_limits<float>::infinity()};
|
||||
std::vector<float> golden = {-0.5, 1.9, 2, 2.55,
|
||||
std::numeric_limits<float>::infinity()};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
|
||||
EXPECT_TRUE(
|
||||
input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Linear>(1, 2);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Linear>(1, 2);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<float> output(5, 0);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<float> output(5, 0);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Linear, shape_5_1_fp32_omit_b) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType io_shape({5, 1});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
|
||||
io_shape, tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
|
||||
io_shape, tim::vx::TensorAttribute::OUTPUT);
|
||||
tim::vx::ShapeType io_shape({5, 1});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = { -2.5, -0.1, 0, 0.55, std::numeric_limits<float>::infinity() };
|
||||
std::vector<float> golden = {-5.0, -0.2, 0, 1.1, std::numeric_limits<float>::infinity() };
|
||||
std::vector<float> in_data = {-2.5, -0.1, 0, 0.55,
|
||||
std::numeric_limits<float>::infinity()};
|
||||
std::vector<float> golden = {-5.0, -0.2, 0, 1.1,
|
||||
std::numeric_limits<float>::infinity()};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
|
||||
EXPECT_TRUE(
|
||||
input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Linear>(2);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Linear>(2);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<float> output(5, 0);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Gelu, shape_5_1_fp32) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType in_shape({5, 1});
|
||||
tim::vx::ShapeType out_shape({5, 1});
|
||||
tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32,
|
||||
in_shape, tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32,
|
||||
out_shape, tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto in_tensor = graph->CreateTensor(in_spec);
|
||||
auto out_tensor = graph->CreateTensor(out_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
-3, -1, 0, 1, 3
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
-0.00404951, -0.15865529, 0, 0.8413447, 2.9959507
|
||||
};
|
||||
|
||||
EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Gelu>(false);
|
||||
(*op).BindInput(in_tensor).BindOutput(out_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
std::vector<float> output(golden.size());
|
||||
EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<float> output(5, 0);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(Gelu, shape_5_1_fp32_approximate) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType in_shape({5, 1});
|
||||
tim::vx::ShapeType out_shape({5, 1});
|
||||
tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32,
|
||||
in_shape, tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32,
|
||||
out_shape, tim::vx::TensorAttribute::OUTPUT);
|
||||
tim::vx::ShapeType in_shape({5, 1});
|
||||
tim::vx::ShapeType out_shape({5, 1});
|
||||
tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32, in_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32, out_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto in_tensor = graph->CreateTensor(in_spec);
|
||||
auto out_tensor = graph->CreateTensor(out_spec);
|
||||
auto in_tensor = graph->CreateTensor(in_spec);
|
||||
auto out_tensor = graph->CreateTensor(out_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
-3, -1, 0, 1, 3
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
-0.00363752, -0.15880796, 0, 0.841192, 2.9963627
|
||||
};
|
||||
std::vector<float> in_data = {-3, -1, 0, 1, 3};
|
||||
std::vector<float> golden = {-0.00363752, -0.15880796, 0, 0.841192,
|
||||
2.9963627};
|
||||
|
||||
EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Gelu>(true);
|
||||
(*op).BindInput(in_tensor).BindOutput(out_tensor);
|
||||
EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Gelu>(true);
|
||||
(*op).BindInput(in_tensor).BindOutput(out_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
std::vector<float> output(golden.size());
|
||||
EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
|
||||
std::vector<float> output(golden.size());
|
||||
EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
|
||||
}
|
||||
|
||||
TEST(Gelu, shape_5_1_uint8_Quantized) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType in_shape({5, 1});
|
||||
tim::vx::ShapeType out_shape({5, 1});
|
||||
tim::vx::ShapeType in_shape({5, 1});
|
||||
tim::vx::ShapeType out_shape({5, 1});
|
||||
|
||||
const float InputMin = -127, InputMax = 128, OutputMin = -127, OutputMax = 128;
|
||||
const float InputMin = -127, InputMax = 128, OutputMin = -127,
|
||||
OutputMax = 128;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<uint8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first}; //scale
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
|
||||
scalesAndZp = QuantizationParams<uint8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first}; //scale
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
|
||||
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, in_shape,
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, in_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT, quantOutput);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_float_data = {
|
||||
-3, -1, 0, 1, 3
|
||||
};
|
||||
std::vector<float> golden_float = {
|
||||
-0.00404951, -0.15865529, 0, 0.8413447, 2.9959507
|
||||
};
|
||||
std::vector<float> in_float_data = {-3, -1, 0, 1, 3};
|
||||
std::vector<float> golden_float = {-0.00404951, -0.15865529, 0, 0.8413447,
|
||||
2.9959507};
|
||||
|
||||
std::vector<uint8_t> input_data =
|
||||
Quantize<uint8_t>(in_float_data, scalesInput[0], zeroPointsInput[0]); //Quantification process
|
||||
std::vector<uint8_t> golden =
|
||||
std::vector<uint8_t> input_data =
|
||||
Quantize<uint8_t>(in_float_data, scalesInput[0],
|
||||
zeroPointsInput[0]); //Quantification process
|
||||
std::vector<uint8_t> golden =
|
||||
Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()*4));
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Gelu>(false);
|
||||
(*op).BindInput(input_tensor).BindOutput(output_tensor);
|
||||
EXPECT_TRUE(
|
||||
input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Gelu>(false);
|
||||
(*op).BindInput(input_tensor).BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<uint8_t> output(golden.size());
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<uint8_t> output(golden.size());
|
||||
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, (uint8_t)1));
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, (uint8_t)1));
|
||||
}
|
||||
Loading…
Reference in New Issue