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InfiniTensor
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Copyout numpy接口 (#135) 

* Add copy out numpy interface, delete returning buffer directly, add api test

* Add dtype interface
This commit is contained in:
PanZezhong1725 2023-09-15 16:40:44 +08:00 committed by GitHub
parent 4c321c8a91
commit c6b82cfda0
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5 changed files with 125 additions and 91 deletions
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4
Makefile
View File

@ -54,6 +54,10 @@ test-onnx:
@echo
python3 pyinfinitensor/tests/test_onnx.py
test-api:
@echo
python3 pyinfinitensor/tests/test_api.py
docker-build:
docker build -f scripts/dockerfile/$(DOCKER_FILE) -t $(DOCKER_NAME) .

1
include/core/tensor_base.h
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@ -44,6 +44,7 @@ class TensorBaseObj : public Object {
}
DataType getDType() const { return dtype; }
int getDTypeIndex() const { return dtype.getIndex(); }
Runtime getRuntime() const { return runtime; }
// std::pair<Operator *, int> getOutputOfWithIndex();

65
pyinfinitensor/tests/test_api.py Normal file
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@ -0,0 +1,65 @@
import os, unittest
from onnx import TensorProto
from pyinfinitensor import backend
import numpy as np
class TestPythonAPI(unittest.TestCase):
def test_copyin_numpy(self):
dims = [2, 3, 5, 4]
np_array = np.random.random(dims).astype(np.float32)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.FLOAT)
tensor2 = handler.tensor(dims, TensorProto.FLOAT)
handler.data_malloc()
tensor1.copyin_numpy(np_array)
tensor2.copyin_float(np_array.flatten().tolist())
array1 = tensor1.copyout_float()
array2 = tensor2.copyout_float()
self.assertEqual(array1, array2)
self.assertTrue(np.array_equal(np.array(array1).reshape(dims), np_array))
np_array = np.random.random(dims).astype(np.int64)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.INT64)
tensor2 = handler.tensor(dims, TensorProto.INT64)
handler.data_malloc()
tensor1.copyin_numpy(np_array)
tensor2.copyin_int64(np_array.flatten().tolist())
array1 = tensor1.copyout_int64()
array2 = tensor2.copyout_int64()
self.assertEqual(array1, array2)
self.assertTrue(np.array_equal(np.array(array1).reshape(dims), np_array))
def test_copyout_numpy(self):
dims = [2, 3, 5, 4]
np_array = np.random.random(dims).astype(np.float32)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.FLOAT)
tensor2 = handler.tensor(dims, TensorProto.FLOAT)
handler.data_malloc()
tensor1.copyin_float(np_array.flatten().tolist())
tensor2.copyin_float(np_array.flatten().tolist())
array1 = np.array(tensor1.copyout_float()).reshape(dims)
array2 = tensor2.copyout_numpy()
self.assertTrue(np.array_equal(array2, np_array))
self.assertTrue(np.array_equal(array1, array2))
np_array = np.random.random(dims).astype(np.float16)
np_array[0, 0, 0, 0] = .1
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.FLOAT16)
handler.data_malloc()
tensor1.copyin_numpy(np_array)
array1 = tensor1.copyout_numpy()
# Copy should be the same as original array
self.assertTrue(np.array_equal(array1, np_array))
# Modify the value so that tensorObj value changes
np_array[0, 0, 0, 0] = 0.
tensor1.copyin_numpy(np_array)
# The copied-out array should not change
self.assertFalse(np.array_equal(array1, np_array))
if __name__ == "__main__":
unittest.main()

48
pyinfinitensor/tests/test_onnx.py
View File

@ -458,54 +458,6 @@ class TestStringMethods(unittest.TestCase):
where = make_node("Where", ["x", "y", "con"], ["output"], name="where")
make_and_import_model(make_graph([where], "where", [x, y, con], [output]))
def test_copyin(self):
dims = [2, 3, 5, 4]
np_array = np.random.random(dims).astype(np.float32)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.FLOAT)
tensor2 = handler.tensor(dims, TensorProto.FLOAT)
handler.data_malloc()
tensor1.copyin_numpy(np_array)
tensor2.copyin_float(np_array.flatten().tolist())
array1 = tensor1.copyout_float()
array2 = tensor2.copyout_float()
self.assertEqual(array1, array2)
self.assertTrue(np.array_equal(np.array(array1).reshape(dims), np_array))
np_array = np.random.random(dims).astype(np.int64)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.INT64)
tensor2 = handler.tensor(dims, TensorProto.INT64)
handler.data_malloc()
tensor1.copyin_numpy(np_array)
tensor2.copyin_int64(np_array.flatten().tolist())
array1 = tensor1.copyout_int64()
array2 = tensor2.copyout_int64()
self.assertEqual(array1, array2)
self.assertTrue(np.array_equal(np.array(array1).reshape(dims), np_array))
def test_to_numpy(self):
dims = [2, 3, 5, 4]
np_array = np.random.random(dims).astype(np.float32)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.FLOAT)
tensor2 = handler.tensor(dims, TensorProto.FLOAT)
handler.data_malloc()
tensor1.copyin_float(np_array.flatten().tolist())
tensor2.copyin_float(np_array.flatten().tolist())
array1 = np.array(tensor1.copyout_float()).reshape(dims)
array2 = np.array(tensor2)
self.assertTrue(np.array_equal(array2, np_array))
self.assertTrue(np.array_equal(array1, array2))
np_array = np.random.random(dims).astype(np.float16)
handler = backend.GraphHandler(backend.cpu_runtime())
tensor1 = handler.tensor(dims, TensorProto.FLOAT16)
handler.data_malloc()
tensor1.copyin_numpy(np_array)
array1 = np.array(tensor1, copy=False)
self.assertTrue(np.array_equal(array1, np_array))
if __name__ == "__main__":
unittest.main()

98
src/ffi/ffi_infinitensor.cc
View File

@ -306,6 +306,44 @@ void export_functions(py::module &m) {
#undef FUNCTION
}
// A helper function that converts DataType to python format string
static std::string getFormat(DataType type) {
std::string format;
if (type == DataType::Float32) {
format = py::format_descriptor<float>::format();
} else if (type == DataType::Double) {
format = py::format_descriptor<double>::format();
} else if (type == DataType::Int32) {
format = py::format_descriptor<int>::format();
} else if (type == DataType::UInt32) {
format = py::format_descriptor<uint32_t>::format();
} else if (type == DataType::Int64) {
format = py::format_descriptor<int64_t>::format();
} else if (type == DataType::UInt64) {
format = py::format_descriptor<uint64_t>::format();
} else if (type == DataType::Int16) {
format = py::format_descriptor<int16_t>::format();
} else if (type == DataType::UInt16) {
format = py::format_descriptor<uint16_t>::format();
} else if (type == DataType::Int8) {
format = py::format_descriptor<int8_t>::format();
} else if (type == DataType::UInt8) {
format = py::format_descriptor<uint8_t>::format();
} else if (type == DataType::Bool) {
format = py::format_descriptor<bool>::format();
} else if (type == DataType::Float16 || type == DataType::BFloat16) {
// Python uses "e" for half precision float type code.
// Check the following link for more information.
// https://docs.python.org/3/library/struct.html#format-characters
format = "e";
} else {
throw std::runtime_error("Error converting TensorObj to "
"Numpy: unsupported datatype.\n");
}
return format;
}
void init_graph_builder(py::module &m) {
using Handler = GraphHandlerObj;
@ -326,6 +364,7 @@ void init_graph_builder(py::module &m) {
py::buffer_protocol())
.def("fuid", &TensorObj::getFuid, policy::automatic)
.def("shape", &TensorObj::getDims, policy::move)
.def("dtype", &TensorObj::getDTypeIndex, policy::automatic)
.def("copyin_float", &TensorObj::copyin<float>, policy::move)
.def("copyin_int32", &TensorObj::copyin<int32_t>, policy::move)
.def("copyin_int64", &TensorObj::copyin<int64_t>, policy::move)
@ -352,51 +391,24 @@ void init_graph_builder(py::module &m) {
}
self.copyin(data_np, self.getBytes());
})
// A buffer can be used to convert a TensorObj directly to Numpy array
// without copy
.def_buffer([](TensorObj &self) -> py::buffer_info {
vector<size_t> stride_byte;
for (int s : self.getStride()) {
stride_byte.push_back(s * self.getDType().getSize());
}
// Return a Numpy array which copies the values of this tensor
.def("copyout_numpy",
[](TensorObj &self) -> py::array {
vector<size_t> stride_byte;
for (int s : self.getStride()) {
stride_byte.push_back(s * self.getDType().getSize());
}
std::string format = getFormat(self.getDType());
std::string format;
if (self.getDType() == DataType::Float32) {
format = py::format_descriptor<float>::format();
} else if (self.getDType() == DataType::Double) {
format = py::format_descriptor<double>::format();
} else if (self.getDType() == DataType::Int32) {
format = py::format_descriptor<int>::format();
} else if (self.getDType() == DataType::UInt32) {
format = py::format_descriptor<uint32_t>::format();
} else if (self.getDType() == DataType::Int64) {
format = py::format_descriptor<int64_t>::format();
} else if (self.getDType() == DataType::UInt64) {
format = py::format_descriptor<uint64_t>::format();
} else if (self.getDType() == DataType::Int16) {
format = py::format_descriptor<int16_t>::format();
} else if (self.getDType() == DataType::UInt16) {
format = py::format_descriptor<uint16_t>::format();
} else if (self.getDType() == DataType::Int8) {
format = py::format_descriptor<int8_t>::format();
} else if (self.getDType() == DataType::UInt8) {
format = py::format_descriptor<uint8_t>::format();
} else if (self.getDType() == DataType::Float16 ||
self.getDType() == DataType::BFloat16) {
// Python uses "e" for half precision float type code.
// Check the following link for more information.
// https://docs.python.org/3/library/struct.html#format-characters
format = "e";
} else {
throw std::runtime_error("Error converting TensorObj to "
"Numpy: unsupported datatype.\n");
}
py::array numpy_array(py::dtype(format), self.getDims(),
nullptr);
return py::buffer_info(self.getRawDataPtr<void *>(),
self.getDType().getSize(), format,
self.getRank(), self.getDims(), stride_byte,
true); // Read-only = true
})
// Copy data to the numpy array
auto ptr = numpy_array.mutable_data();
self.copyout(ptr, self.getBytes());
return numpy_array;
})
.def("has_target", &TensorObj::hasTarget, policy::automatic)
.def("src", &TensorObj::getSource, policy::move)
.def("printData", &TensorObj::printData, policy::automatic);