pytorch/torch/csrc/autograd/python_variable_indexing.cpp

#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 <torch/csrc/utils/tensor_types.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 || var.scalar_type() == kBool) {
        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(c10::TensorTypeId type_id, PyObject* seq) {
  return torch::utils::indexing_tensor_from_data(type_id, kLong, c10::nullopt, seq);
}

static Variable valueToTensor(c10::TensorTypeId type_id, ScalarType scalar_type, PyObject* value) {
  if (THPVariable_Check(value)) {
    return reinterpret_cast<THPVariable*>(value)->cdata;
  }
  auto options = TensorOptions(scalar_type)
      .device(computeDeviceType(type_id))
      .layout(layout_from_backend(tensorTypeIdToBackend(type_id)))
      .is_variable(true);
  if (THPUtils_checkLong(value) || PyBool_Check(value)) {
    return at::scalar_tensor(Scalar(THPUtils_unpackLong(value)), options);
  }
  if (PyFloat_Check(value)) {
    return at::scalar_tensor(Scalar(THPUtils_unpackDouble(value)), options);
  }
  throw TypeError(
    "can't assign a %s to a %s",
    Py_TYPE(value)->tp_name,
    torch::utils::type_to_string(getNonVariableDeprecatedTypeProperties(tensorTypeIdToBackend(type_id), scalar_type)).c_str());
}

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, /*includeBool=*/true)) {
        if (scalar_type != at::kByte && scalar_type != at::kBool) {
          result = applySelect(result, dim, THPUtils_unpackLong(obj), i);
        } else {
          result = result.unsqueeze(dim);
          if(scalar_type == at::kBool) {
            handle_var(boolToIndexingTensor(result, var.item<bool>() != 0));
          } else {
            handle_var(boolToIndexingTensor(result, var.item<uint8_t>() != 0));
          }
        }
      } else {
        handle_var(var);
      }
    } else if (PySequence_Check(obj)) {
      handle_var(sequenceToVariable(self.type_id(), 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(TensorTypeId::CPUTensorId, kFloat, py_value);
  } else {
    value = valueToTensor(self_.type_id(), 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