Files
pytorch/torch/csrc/autograd/python_variable_indexing.cpp
Jerry Zhang 6ec55c13a9 Enable assignment for QTensor in pytorch frontend (#19676)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19676

Make copy work with QTensor, enable assignment of QTensor in pytorch frontend.

Differential Revision: D15064710

fbshipit-source-id: 04f2dc02a825695d41fa1114bfca49e92108fef3
2019-04-24 16:05:34 -07:00

387 lines
13 KiB
C++

#include <torch/csrc/autograd/python_variable_indexing.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/THP_export.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/autograd/utils/wrap_outputs.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/utils/python_compat.h>
#include <torch/csrc/utils/python_numbers.h>
#include <torch/csrc/utils/tensor_new.h>
#include <torch/csrc/jit/tracer.h>
#include <ATen/DeviceGuard.h>
#include <ATen/ExpandUtils.h>
#include <c10/core/TensorOptions.h>
#include <ATen/core/LegacyTypeDispatch.h>
#include <vector>
#include <tuple>
using namespace at;
using namespace torch::autograd::utils;
namespace torch { namespace autograd {
Py_ssize_t THPVariable_length(PyObject* self) {
HANDLE_TH_ERRORS
auto& self_ = reinterpret_cast<THPVariable*>(self)->cdata;
if (self_.dim() == 0) {
return 0;
}
return (Py_ssize_t)self_.size(0);
END_HANDLE_TH_ERRORS_RET(-1)
}
// We allow indexing by integers, slices, ellipsis, None, Variables,
// and tuples of those types. We also handle bools as if they were a
// Variable[ByteTensor].
static int64_t count_specified_dimensions(PyObject* index) {
// Count the number of indexed dimensions (everything but ellipsis and None)
int64_t count = 0;
auto size = PyTuple_GET_SIZE(index); // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
for (Py_ssize_t i = 0; i < size; i++) {
PyObject* obj = PyTuple_GET_ITEM(index, i); // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
if (THPVariable_Check(obj)) {
auto& var = reinterpret_cast<THPVariable*>(obj)->cdata;
if (var.scalar_type() == kByte) {
count += var.dim();
} else {
count++;
}
} else if (obj != Py_None && obj != Py_Ellipsis && obj != Py_True && obj != Py_False) { // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
count++;
}
}
return count;
}
[[noreturn]]
static void invalid_index(PyObject* obj) {
throw IndexError(
"only integers, slices (`:`), ellipsis (`...`), None and long or byte "
"Variables are valid indices (got %s)", Py_TYPE(obj)->tp_name);
}
static Variable applySlice(const Variable& self, int64_t dim, PyObject* slice, bool ensure_view=false) {
Py_ssize_t start, stop, step;
auto length = self.size(dim);
if (!THPUtils_unpackSlice(slice, &start, &stop, &step)) {
throw python_error();
}
if (step == 0) {
throw ValueError("step cannot be zero");
}
if (step < 0) {
// TODO: implement negative step
throw ValueError("negative step not yet supported");
}
// Skip this optimization if we are tracing, as the trace may be polymorphic
// over the shape of the `self` tensor, and we still want to record
// the slice.
if (!ensure_view && start == 0 && stop == length && step == 1 && !jit::tracer::isTracing()) {
return self;
}
return self.slice(dim, start, stop, step);
}
static Variable applySelect(const Variable& self, int64_t dim, int64_t index, int64_t real_dim=0) {
if (index == 0 && dim == 0 && self.dim() == 0) {
throw IndexError(
"invalid index of a 0-dim tensor. "
"Use tensor.item() to convert a 0-dim tensor to a Python number");
}
int64_t size = self.size(dim);
if (index < -size || index >= size) {
throw IndexError("index %lld is out of bounds for dimension %lld with size %lld",
index, real_dim, size);
}
// if the index is negative, do not normalize it because that would fix the index
// on the current tensor size in the tracer.
// aten::select also works on negative indices
return self.select(dim, index);
}
static Variable sequenceToVariable(const at::Type& type, PyObject* seq) {
auto& idx_type = type.toScalarType(kLong);
return torch::utils::indexing_tensor_from_data(idx_type, kLong, c10::nullopt, seq);
}
static Variable valueToTensor(const at::Type & type, const ScalarType scalar_type, PyObject* value) {
if (THPVariable_Check(value)) {
return reinterpret_cast<THPVariable*>(value)->cdata;
}
if (THPUtils_checkLong(value) || PyBool_Check(value)) {
return at::scalar_tensor(Scalar(THPUtils_unpackLong(value)), type.options(scalar_type));
}
if (PyFloat_Check(value)) {
return at::scalar_tensor(Scalar(THPUtils_unpackDouble(value)), type.options(scalar_type));
}
throw TypeError("can't assign a %s to a %s", Py_TYPE(value)->tp_name, type.toString());
}
static Variable boolToIndexingTensor(const Variable& self, bool value) {
// booleans add a dimension of size 1. true indexes this dimension as if 0:, false as empty.
if (value) {
return at::zeros({1}, self.options().dtype(kLong));
} else {
return at::empty({0}, self.options().dtype(kLong));
}
}
static Variable applySlicing(const Variable& self, PyObject* index, variable_list& outIndices) {
int64_t size = PyTuple_GET_SIZE(index); // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
int64_t dim = 0;
int64_t specified_dims = count_specified_dimensions(index);
auto handle_var = [&](const Variable& var) {
// TODO: check scalarType
outIndices.resize(dim + 1);
outIndices[dim] = var;
dim++;
};
if (specified_dims > self.dim()) {
throw IndexError("too many indices for tensor of dimension %d", (int)self.dim());
}
Variable result = self;
for (int64_t i = 0; i < size; i++) {
PyObject* obj = PyTuple_GET_ITEM(index, i); // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
if (THPUtils_checkLong(obj)) {
result = applySelect(result, dim, THPUtils_unpackLong(obj), i);
} else if (PySlice_Check(obj)) {
result = applySlice(result, dim, obj);
dim++;
} else if (obj == Py_Ellipsis) {
dim += self.dim() - specified_dims;
} else if (obj == Py_None) {
result = result.unsqueeze(dim);
dim++;
} else if (PyBool_Check(obj)) {
result = result.unsqueeze(dim);
handle_var(boolToIndexingTensor(result, obj == Py_True)); // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
} else if (THPVariable_Check(obj)) {
auto& var = THPVariable_Unpack(obj);
auto scalar_type = var.scalar_type();
if (var.dim() == 0 && at::isIntegralType(scalar_type)) {
if (scalar_type != at::kByte) {
result = applySelect(result, dim, THPUtils_unpackLong(obj), i);
} else {
result = result.unsqueeze(dim);
handle_var(boolToIndexingTensor(result, var.item<uint8_t>() != 0));
}
} else {
handle_var(var);
}
} else if (PySequence_Check(obj)) {
handle_var(sequenceToVariable(self.dispatch_type(), obj));
} else {
auto index = THPObjectPtr(PyNumber_Index(obj));
if (!index) {
PyErr_Clear();
invalid_index(obj);
}
result = applySelect(result, dim, THPUtils_unpackLong(index), i);
}
}
return result;
}
static std::vector<Tensor> typeConvertIndices(const Variable& self, const variable_list& indices) {
std::vector<Tensor> converted_inds(indices.size());
for (size_t i = 0; i < indices.size(); ++i) {
const auto &ind = indices[i];
if (ind.defined()) {
converted_inds[i] = ind.to(ind.options().device(self.device()));
} else {
converted_inds[i] = indices[i];
}
}
return converted_inds;
}
static Variable dispatch_index(const Variable& self, const variable_list& indices) {
AutoNoGIL no_gil;
std::vector<Tensor> converted_indices = typeConvertIndices(self, indices);
OptionalDeviceGuard device_guard(device_of(self));
return self.index(converted_indices);
}
static Variable dispatch_index_put_(Variable& self, const variable_list& indices, const Variable& value) {
AutoNoGIL no_gil;
std::vector<Tensor> converted_indices = typeConvertIndices(self, indices);
OptionalDeviceGuard device_guard(device_of(self));
return self.index_put_(converted_indices, value);
}
static bool treatSequenceAsTuple(PyObject* index) {
if (PyTuple_Check(index)) {
return true;
}
if (!PySequence_Check(index)) {
return false;
}
// This uses a heuristics from NumPy for determining whether to treat
// non-tuple sequences as if they were a tuple. From the NumPy code comments:
//
// "At this point, we're left with a non-tuple, non-array, sequence:
// typically, a list. We use some somewhat-arbitrary heuristics from here
// onwards to decided whether to treat that list as a single index, or a
// list of indices. Backwards compatibility only takes effect for short
// sequences - otherwise we treat it like any other scalar."
auto n = PySequence_Size(index);
if (n < 0) {
// Negative size indicates a Python error in the PySequence_Size call.
PyErr_Clear();
return false;
}
if (n >= 32) {
return false;
}
for (Py_ssize_t i = 0; i < n; i++) {
auto obj = THPObjectPtr{PySequence_GetItem(index, i)};
if (!obj.get()) {
PyErr_Clear();
return false;
}
if (THPVariable_Check(obj.get()) || PySequence_Check(obj.get()) || PySlice_Check(obj.get())) {
return true;
}
if (obj.get() == Py_Ellipsis || obj.get() == Py_None) {
return true;
}
}
return false;
}
static THPObjectPtr wrapTuple(PyObject* index) {
THPObjectPtr res;
if (treatSequenceAsTuple(index)) {
res = PySequence_Tuple(index);
} else {
res = PyTuple_Pack(1, index); // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
}
if (!res) throw python_error();
return res;
}
PyObject* THPVariable_getitem(PyObject* self, PyObject* index) {
HANDLE_TH_ERRORS
auto& self_ = reinterpret_cast<THPVariable*>(self)->cdata;
OptionalDeviceGuard device_guard(device_of(self_));
// handle simple types: integers, slices, ellipsis
if (index == Py_None) {
return wrap(self_.unsqueeze(0));
} else if (index == Py_Ellipsis) {
return wrap(at::alias(self_));
} else if (THPUtils_checkLong(index)) {
return wrap(applySelect(self_, 0, THPUtils_unpackLong(index)));
} else if (PySlice_Check(index)) {
return wrap(applySlice(self_, 0, index, true));
}
// wrap index in a tuple if it's not already one
THPObjectPtr holder = wrapTuple(index);
variable_list variableIndices;
Variable sliced = applySlicing(self_, holder.get(), variableIndices);
if (variableIndices.empty()) {
if (sliced.is_same(self_)) {
// ensure we return a shallow copy for things like x[...]
sliced = at::alias(sliced);
}
return wrap(sliced);
}
// indexing by tensors ("advanced" indexing)
return wrap(dispatch_index(sliced, variableIndices));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
// To match numpy semantics:
// As a special case for backwards compatibility,
// strip away unit dimensions from the left of 'src'
static IntArrayRef slicePrefix1sSize(IntArrayRef sizes) {
size_t first_non1_src = sizes.size();
for (size_t i = 0; i < sizes.size(); ++i) {
if (sizes[i] != 1) {
first_non1_src = i;
break;
}
}
return sizes.slice(first_non1_src);
}
static void copy_to(Variable dst, const Variable& src) {
Tensor b_src;
IntArrayRef sliced_src_sizes = slicePrefix1sSize(src.sizes());
std::tie(b_src) = expand_inplace(dst, src.view(sliced_src_sizes), "setitem");
dst.copy_(b_src);
}
int THPVariable_setitem(PyObject* self, PyObject* index, PyObject* py_value) {
HANDLE_TH_ERRORS
if (py_value == nullptr) {
throw TypeError("Tensor does not support deleting items");
}
auto& self_ = reinterpret_cast<THPVariable*>(self)->cdata;
OptionalDeviceGuard device_guard(device_of(self_));
Variable value;
if (isQIntType(self_.scalar_type())) {
value = valueToTensor(at::globalContext().getVariableType(at::Backend::CPU, at::kFloat), at::kFloat, py_value);
} else {
value = valueToTensor(self_.dispatch_type(), self_.scalar_type(), py_value);
}
// handle simple types: integers, slices, ellipsis, bool
if (index == Py_False) { // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
// do nothing for false (technically we should check the size, but we don't have
// real 0-sized shapes.
return 0;
} else if (index == Py_Ellipsis) {
copy_to(self_, value);
return 0;
} else if (index == Py_None || index == Py_True) { // NOLINT(cppcoreguidelines-pro-type-cstyle-cast)
copy_to(self_.unsqueeze(0), value);
return 0;
} else if (THPUtils_checkLong(index)) {
copy_to(applySelect(self_, 0, THPUtils_unpackLong(index)), value);
return 0;
} else if (PySlice_Check(index)) {
copy_to(applySlice(self_, 0, index), value);
return 0;
}
// wrap index in a tuple if it's not already one
THPObjectPtr holder = wrapTuple(index);
variable_list variableIndices;
Variable sliced = applySlicing(self_, holder.get(), variableIndices);
if (variableIndices.empty()) {
copy_to(sliced, value);
return 0;
}
IntArrayRef slicedValueSizes = slicePrefix1sSize(value.sizes());
torch::autograd::Variable valuesSliced;
if (!value.sizes().equals(slicedValueSizes)) {
valuesSliced = value.view(slicedValueSizes);
} else {
valuesSliced = value;
}
dispatch_index_put_(sliced, variableIndices, valuesSliced);
return 0;
END_HANDLE_TH_ERRORS_RET(-1)
}
}} // namespace torch::autograd