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Initial drafts for TorchTensorAlgebra

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This commit is contained in:
rgrit91 2021-01-08 08:27:39 +00:00
parent cfe93886ac
commit 9b1a958491
11 changed files with 220 additions and 57 deletions
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4
kmath-torch/ctorch/include/ctorch.h
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@ -58,6 +58,10 @@ extern "C"
TorchTensorHandle copy_to_cpu(TorchTensorHandle tensor_handle);
TorchTensorHandle copy_to_gpu(TorchTensorHandle tensor_handle, int device);
TorchTensorHandle randn_float(int* shape, int shape_size);
TorchTensorHandle matmul(TorchTensorHandle lhs, TorchTensorHandle rhs);
#ifdef __cplusplus
}
#endif

22
kmath-torch/ctorch/include/utils.hh
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@ -33,13 +33,17 @@ namespace ctorch
return *static_cast<torch::Tensor *>(tensor_handle);
}
template <typename Dtype>
inline torch::Tensor copy_from_blob(Dtype *data, int *shape, int dim, torch::Device device)
inline std::vector<int64_t> to_vec_int(int *arr, int arr_size)
{
auto shape_vec = std::vector<int64_t>(dim);
shape_vec.assign(shape, shape + dim);
return torch::from_blob(data, shape_vec, dtype<Dtype>()).to(
torch::TensorOptions().layout(torch::kStrided).device(device), false, true);
auto vec = std::vector<int64_t>(arr_size);
vec.assign(arr, arr + arr_size);
return vec;
}
template <typename Dtype>
inline torch::Tensor copy_from_blob(Dtype *data, std::vector<int64_t> shape, torch::Device device)
{
return torch::from_blob(data, shape, dtype<Dtype>()).to(torch::TensorOptions().layout(torch::kStrided).device(device), false, true);
}
inline int *to_dynamic_ints(const c10::IntArrayRef &arr)
@ -78,4 +82,10 @@ namespace ctorch
ten.index(offset_to_index(offset, ten.strides())) = value;
}
template <typename Dtype>
inline torch::Tensor randn(std::vector<int64_t> shape, torch::Device device)
{
return torch::randn(shape, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
}
} // namespace ctorch

24
kmath-torch/ctorch/src/ctorch.cc
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@ -27,36 +27,36 @@ void set_seed(int seed)
TorchTensorHandle copy_from_blob_double(double *data, int *shape, int dim)
{
return new torch::Tensor(ctorch::copy_from_blob<double>(data, shape, dim, torch::kCPU));
return new torch::Tensor(ctorch::copy_from_blob<double>(data, ctorch::to_vec_int(shape, dim), torch::kCPU));
}
TorchTensorHandle copy_from_blob_float(float *data, int *shape, int dim)
{
return new torch::Tensor(ctorch::copy_from_blob<float>(data, shape, dim, torch::kCPU));
return new torch::Tensor(ctorch::copy_from_blob<float>(data, ctorch::to_vec_int(shape, dim), torch::kCPU));
}
TorchTensorHandle copy_from_blob_long(long *data, int *shape, int dim)
{
return new torch::Tensor(ctorch::copy_from_blob<long>(data, shape, dim, torch::kCPU));
return new torch::Tensor(ctorch::copy_from_blob<long>(data, ctorch::to_vec_int(shape, dim), torch::kCPU));
}
TorchTensorHandle copy_from_blob_int(int *data, int *shape, int dim)
{
return new torch::Tensor(ctorch::copy_from_blob<int>(data, shape, dim, torch::kCPU));
return new torch::Tensor(ctorch::copy_from_blob<int>(data, ctorch::to_vec_int(shape, dim), torch::kCPU));
}
TorchTensorHandle copy_from_blob_to_gpu_double(double *data, int *shape, int dim, int device)
{
return new torch::Tensor(ctorch::copy_from_blob<double>(data, shape, dim, torch::Device(torch::kCUDA, device)));
return new torch::Tensor(ctorch::copy_from_blob<double>(data, ctorch::to_vec_int(shape, dim), torch::Device(torch::kCUDA, device)));
}
TorchTensorHandle copy_from_blob_to_gpu_float(float *data, int *shape, int dim, int device)
{
return new torch::Tensor(ctorch::copy_from_blob<float>(data, shape, dim, torch::Device(torch::kCUDA, device)));
return new torch::Tensor(ctorch::copy_from_blob<float>(data, ctorch::to_vec_int(shape, dim), torch::Device(torch::kCUDA, device)));
}
TorchTensorHandle copy_from_blob_to_gpu_long(long *data, int *shape, int dim, int device)
{
return new torch::Tensor(ctorch::copy_from_blob<long>(data, shape, dim, torch::Device(torch::kCUDA, device)));
return new torch::Tensor(ctorch::copy_from_blob<long>(data, ctorch::to_vec_int(shape, dim), torch::Device(torch::kCUDA, device)));
}
TorchTensorHandle copy_from_blob_to_gpu_int(int *data, int *shape, int dim, int device)
{
return new torch::Tensor(ctorch::copy_from_blob<int>(data, shape, dim, torch::Device(torch::kCUDA, device)));
return new torch::Tensor(ctorch::copy_from_blob<int>(data, ctorch::to_vec_int(shape, dim), torch::Device(torch::kCUDA, device)));
}
TorchTensorHandle copy_tensor(TorchTensorHandle tensor_handle)
@ -167,3 +167,11 @@ TorchTensorHandle copy_to_gpu(TorchTensorHandle tensor_handle, int device)
{
return new torch::Tensor(ctorch::cast(tensor_handle).to(torch::Device(torch::kCUDA, device),false, true));
}
TorchTensorHandle randn_float(int* shape, int shape_size){
return new torch::Tensor(ctorch::randn<float>(ctorch::to_vec_int(shape, shape_size), torch::kCPU));
}
TorchTensorHandle matmul(TorchTensorHandle lhs, TorchTensorHandle rhs){
return new torch::Tensor(torch::matmul(ctorch::cast(lhs), ctorch::cast(rhs)));
}

2
kmath-torch/src/nativeGPUTest/kotlin/kscience/kmath/torch/TestTorchTensorGPU.kt
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@ -20,6 +20,6 @@ class TestTorchTensorGPU {
tensor.elements().forEach {
assertEquals(tensor[it.first], it.second)
}
assertTrue(tensor.buffer.contentEquals(array.asBuffer()))
assertTrue(tensor.asBuffer().contentEquals(array.asBuffer()))
}
}

18
kmath-torch/src/nativeMain/kotlin/kscience/kmath/torch/TorchMemoryHolder.kt Normal file
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@ -0,0 +1,18 @@
package kscience.kmath.torch
import kotlinx.cinterop.*
import ctorch.*
public abstract class TorchMemoryHolder internal constructor(
internal val scope: DeferScope,
internal var tensorHandle: COpaquePointer?
){
init {
scope.defer(::close)
}
protected fun close() {
dispose_tensor(tensorHandle)
tensorHandle = null
}
}

74
kmath-torch/src/nativeMain/kotlin/kscience/kmath/torch/TorchTensor.kt
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@ -5,22 +5,32 @@ import kscience.kmath.structures.*
import kotlinx.cinterop.*
import ctorch.*
public abstract class TorchTensor<T, out TorchTensorBufferImpl : TorchTensorBuffer<T>> :
public sealed class TorchTensor<T, out TorchTensorBufferImpl : TorchTensorBuffer<T>> :
MutableNDBufferTrait<T, TorchTensorBufferImpl, TorchTensorStrides>() {
public fun asBuffer(): MutableBuffer<T> = buffer
public companion object {
public fun copyFromFloatArray(scope: DeferScope, array: FloatArray, shape: IntArray): TorchTensorFloat {
val tensorHandle: COpaquePointer = copy_from_blob_float(
array.toCValues(), shape.toCValues(), shape.size
)!!
return TorchTensorFloat(populateStridesFromNative(tensorHandle, rawShape = shape), scope, tensorHandle)
return TorchTensorFloat(
scope = scope,
tensorHandle = tensorHandle,
strides = populateStridesFromNative(tensorHandle, rawShape = shape)
)
}
public fun copyFromIntArray(scope: DeferScope, array: IntArray, shape: IntArray): TorchTensorInt {
val tensorHandle: COpaquePointer = copy_from_blob_int(
array.toCValues(), shape.toCValues(), shape.size
)!!
return TorchTensorInt(populateStridesFromNative(tensorHandle, rawShape = shape), scope, tensorHandle)
return TorchTensorInt(
scope = scope,
tensorHandle = tensorHandle,
strides = populateStridesFromNative(tensorHandle, rawShape = shape)
)
}
public fun copyFromFloatArrayToGPU(
@ -32,7 +42,11 @@ public abstract class TorchTensor<T, out TorchTensorBufferImpl : TorchTensorBuff
val tensorHandle: COpaquePointer = copy_from_blob_to_gpu_float(
array.toCValues(), shape.toCValues(), shape.size, device
)!!
return TorchTensorFloatGPU(populateStridesFromNative(tensorHandle, rawShape = shape), scope, tensorHandle)
return TorchTensorFloatGPU(
scope = scope,
tensorHandle = tensorHandle,
strides = populateStridesFromNative(tensorHandle, rawShape = shape)
)
}
}
@ -43,53 +57,63 @@ public abstract class TorchTensor<T, out TorchTensorBufferImpl : TorchTensorBuff
return stringRepresentation
}
internal abstract fun wrap(
outStrides: TorchTensorStrides,
protected abstract fun wrap(
outScope: DeferScope,
outTensorHandle: COpaquePointer
outTensorHandle: COpaquePointer,
outStrides: TorchTensorStrides
): TorchTensor<T, TorchTensorBufferImpl>
public fun copy(): TorchTensor<T, TorchTensorBufferImpl> = wrap(
outStrides = strides,
outScope = buffer.scope,
outTensorHandle = copy_tensor(buffer.tensorHandle!!)!!
outTensorHandle = copy_tensor(buffer.tensorHandle!!)!!,
outStrides = strides
)
}
public class TorchTensorFloat internal constructor(
override val strides: TorchTensorStrides,
scope: DeferScope,
tensorHandle: COpaquePointer
tensorHandle: COpaquePointer,
override val strides: TorchTensorStrides
) : TorchTensor<Float, TorchTensorBufferFloat>() {
override val buffer: TorchTensorBufferFloat = TorchTensorBufferFloat(scope, tensorHandle)
override fun wrap(outStrides: TorchTensorStrides, outScope: DeferScope, outTensorHandle: COpaquePointer) =
TorchTensorFloat(
strides = outStrides, scope = outScope, tensorHandle = outTensorHandle
)
override fun wrap(
outScope: DeferScope,
outTensorHandle: COpaquePointer,
outStrides: TorchTensorStrides
): TorchTensorFloat = TorchTensorFloat(
scope = outScope, tensorHandle = outTensorHandle, strides = outStrides
)
}
public class TorchTensorInt internal constructor(
override val strides: TorchTensorStrides,
scope: DeferScope,
tensorHandle: COpaquePointer
tensorHandle: COpaquePointer,
override val strides: TorchTensorStrides
) : TorchTensor<Int, TorchTensorBufferInt>() {
override val buffer: TorchTensorBufferInt = TorchTensorBufferInt(scope, tensorHandle)
override fun wrap(outStrides: TorchTensorStrides, outScope: DeferScope, outTensorHandle: COpaquePointer) =
TorchTensorInt(
strides = outStrides, scope = outScope, tensorHandle = outTensorHandle
)
override fun wrap(
outScope: DeferScope,
outTensorHandle: COpaquePointer,
outStrides: TorchTensorStrides
): TorchTensorInt = TorchTensorInt(
scope = outScope, tensorHandle = outTensorHandle, strides = outStrides
)
}
public class TorchTensorFloatGPU internal constructor(
override val strides: TorchTensorStrides,
scope: DeferScope,
tensorHandle: COpaquePointer
tensorHandle: COpaquePointer,
override val strides: TorchTensorStrides
) : TorchTensor<Float, TorchTensorBufferFloatGPU>() {
override val buffer: TorchTensorBufferFloatGPU = TorchTensorBufferFloatGPU(scope, tensorHandle)
override fun wrap(outStrides: TorchTensorStrides, outScope: DeferScope, outTensorHandle: COpaquePointer) =
override fun wrap(
outScope: DeferScope,
outTensorHandle: COpaquePointer,
outStrides: TorchTensorStrides
): TorchTensorFloatGPU =
TorchTensorFloatGPU(
strides = outStrides, scope = outScope, tensorHandle = outTensorHandle
scope = outScope, tensorHandle = outTensorHandle, strides = outStrides
)
}

65
kmath-torch/src/nativeMain/kotlin/kscience/kmath/torch/TorchTensorAlgebra.kt Normal file
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@ -0,0 +1,65 @@
package kscience.kmath.torch
import kotlinx.cinterop.*
import ctorch.*
public sealed class TorchTensorAlgebra<
T,
TorchTensorBufferImpl : TorchTensorBuffer<T>,
PrimitiveArrayType>
constructor(
internal val scope: DeferScope
) {
protected abstract fun wrap(
outTensorHandle: COpaquePointer,
outStrides: TorchTensorStrides
): TorchTensor<T, TorchTensorBufferImpl>
public infix fun TorchTensor<T, TorchTensorBufferImpl>.swap(other: TorchTensor<T, TorchTensorBufferImpl>): Unit {
check(this.shape contentEquals other.shape) {
"Attempt to swap tensors with different shapes"
}
this.buffer.tensorHandle = other.buffer.tensorHandle.also {
other.buffer.tensorHandle = this.buffer.tensorHandle
}
}
public abstract fun copyFromArray(array: PrimitiveArrayType, shape: IntArray): TorchTensor<T, TorchTensorBufferImpl>
public infix fun TorchTensor<T, TorchTensorBufferImpl>.dot(other: TorchTensor<T, TorchTensorBufferImpl>):
TorchTensor<T, TorchTensorBufferImpl> {
val resultHandle = matmul(this.buffer.tensorHandle, other.buffer.tensorHandle)!!
val strides = populateStridesFromNative(tensorHandle = resultHandle)
return wrap(resultHandle, strides)
}
}
public sealed class TorchTensorField<T, TorchTensorBufferImpl : TorchTensorBuffer<T>, PrimitiveArrayType>
constructor(scope: DeferScope) : TorchTensorAlgebra<T, TorchTensorBufferImpl, PrimitiveArrayType>(scope) {
public abstract fun randn(shape: IntArray): TorchTensor<T, TorchTensorBufferImpl>
}
public class TorchTensorFloatAlgebra(scope: DeferScope) :
TorchTensorField<Float, TorchTensorBufferFloat, FloatArray>(scope) {
override fun wrap(
outTensorHandle: COpaquePointer,
outStrides: TorchTensorStrides
): TorchTensorFloat = TorchTensorFloat(scope = scope, tensorHandle = outTensorHandle, strides = outStrides)
override fun randn(shape: IntArray): TorchTensor<Float, TorchTensorBufferFloat> {
val tensorHandle = randn_float(shape.toCValues(), shape.size)!!
val strides = populateStridesFromNative(tensorHandle = tensorHandle, rawShape = shape)
return wrap(tensorHandle, strides)
}
override fun copyFromArray(array: FloatArray, shape: IntArray): TorchTensorFloat =
TorchTensor.copyFromFloatArray(scope, array, shape)
}
public fun <R> TorchTensorFloatAlgebra(block: TorchTensorFloatAlgebra.() -> R): R =
memScoped { TorchTensorFloatAlgebra(this).block() }

17
kmath-torch/src/nativeMain/kotlin/kscience/kmath/torch/TorchTensorBuffer.kt
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@ -5,25 +5,16 @@ import kscience.kmath.structures.MutableBuffer
import kotlinx.cinterop.*
import ctorch.*
public abstract class TorchTensorBuffer<T> internal constructor(
internal val scope: DeferScope,
internal var tensorHandle: COpaquePointer?
) : MutableBuffer<T> {
public sealed class TorchTensorBuffer<T> constructor(
scope: DeferScope,
tensorHandle: COpaquePointer?
) : MutableBuffer<T>, TorchMemoryHolder(scope, tensorHandle) {
override val size: Int
get(){
return get_numel(tensorHandle!!)
}
init {
scope.defer(::close)
}
protected fun close() {
dispose_tensor(tensorHandle)
tensorHandle = null
}
internal abstract fun wrap(outScope: DeferScope, outTensorHandle: COpaquePointer): TorchTensorBuffer<T>
override fun copy(): TorchTensorBuffer<T> = wrap(

4
kmath-torch/src/nativeTest/kotlin/kscience/kmath/torch/TestTorchTensor.kt
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@ -16,7 +16,7 @@ internal class TestTorchTensor {
tensor.elements().forEach {
assertEquals(tensor[it.first], it.second)
}
assertTrue(tensor.buffer.contentEquals(array.asBuffer()))
assertTrue(tensor.asBuffer().contentEquals(array.asBuffer()))
}
@Test
@ -27,7 +27,7 @@ internal class TestTorchTensor {
tensor.elements().forEach {
assertEquals(tensor[it.first], it.second)
}
assertTrue(tensor.buffer.contentEquals(array.asBuffer()))
assertTrue(tensor.asBuffer().contentEquals(array.asBuffer()))
}
@Test

36
kmath-torch/src/nativeTest/kotlin/kscience/kmath/torch/TestTorchTensorAlgebra.kt Normal file
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@ -0,0 +1,36 @@
package kscience.kmath.torch
import kotlin.test.*
import kotlin.time.measureTime
class TestTorchTensorAlgebra {
@Test
fun swappingTensors() = TorchTensorFloatAlgebra {
val tensorA = copyFromArray(floatArrayOf(1f, 2f, 3f), intArrayOf(3))
val tensorB = tensorA.copy()
val tensorC = copyFromArray(floatArrayOf(4f, 5f, 6f), intArrayOf(3))
tensorA swap tensorC
assertTrue(tensorB.asBuffer().contentEquals(tensorC.asBuffer()))
}
@Test
fun dotOperation() = TorchTensorFloatAlgebra {
setSeed(987654)
var tensorA = randn(intArrayOf(1000, 1000))
val tensorB = randn(intArrayOf(1000, 1000))
measureTime {
repeat(100) {
TorchTensorFloatAlgebra {
tensorA swap (tensorA dot tensorB)
}
}
}.also(::println)
assertTrue(tensorA.shape contentEquals tensorB.shape)
}
}

11
kmath-torch/src/nativeTest/kotlin/kscience/kmath/torch/TestUtils.kt
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@ -1,7 +1,6 @@
package kscience.kmath.torch
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.*
internal class TestUtils {
@ -11,4 +10,12 @@ internal class TestUtils {
setNumThreads(numThreads)
assertEquals(numThreads, getNumThreads())
}
@Test
fun seedSetting() = TorchTensorFloatAlgebra {
setSeed(987654)
val tensorA = randn(intArrayOf(2,3))
setSeed(987654)
val tensorB = randn(intArrayOf(2,3))
assertTrue(tensorA.asBuffer().contentEquals(tensorB.asBuffer()))
}
}