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Histogram.cpp
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Histogram.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/Dispatch.h>
#include <ATen/native/Histogram.h>
#include <ATen/native/Resize.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/_histogramdd_bin_edges.h>
#include <ATen/ops/_histogramdd_bin_edges_native.h>
#include <ATen/ops/_histogramdd_from_bin_cts.h>
#include <ATen/ops/_histogramdd_from_bin_cts_native.h>
#include <ATen/ops/_histogramdd_from_bin_tensors.h>
#include <ATen/ops/_histogramdd_from_bin_tensors_native.h>
#include <ATen/ops/aminmax.h>
#include <ATen/ops/empty.h>
#include <ATen/ops/histc_native.h>
#include <ATen/ops/histogram_native.h>
#include <ATen/ops/histogramdd_native.h>
#include <ATen/ops/linspace_native.h>
#endif
#include <numeric>
#include <tuple>
#include <vector>
#include <functional>
#include <c10/util/ArrayRef.h>
#include <c10/core/ScalarType.h>
#include <c10/core/DefaultDtype.h>
#include <c10/util/irange.h>
/* Implements a numpy-like histogramdd function running on cpu
* https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html
*
* See the docstr for torch.histogramdd in torch/functional.py for further explanation.
*
* - torch.histogramdd(input, bins, range=None, weight=None, density=False)
* input - tensor with shape (M, N). input is interpreted as M coordinates in N-dimensional space.
* If a tensor with more than 2 dimensions is passed, all but the last dimension will be flattened.
* bins - int[] of length N or tensor list of length N. If int[], defines the number of equal-width bins
* in each dimension. If tensor list, defines the sequences of bin edges, including rightmost edges,
* for each dimension.
* range - float[] of length 2 * N, optional. If specified, defines the leftmost and rightmost bin edges
* for each dimension.
* weight - tensor, optional. If provided, weight should have the same shape as input excluding its last dimension.
* Each N-dimensional value in input contributes its associated weight towards its bin's result.
* If weight is not specified, each value has weight 1 by default.
* density - bool, optional. If false (default), the result will contain the total count (weight) in each bin.
* If True, each count (weight) is divided by the total count (total weight), then divided by the
* volume of its associated bin.
*
* Returns:
* hist - N-dimensional tensor containing the values of the histogram.
* bin_edges - tensor list of length N containing the edges of the histogram bins in each dimension.
* Bins include their left edge and exclude their right edge, with the exception of the
* rightmost bin in each dimension which includes both of its edges.
*
* Restrictions are defined in histogram_check_inputs() and in select_outer_bin_edges().
*/
namespace at { namespace native {
DEFINE_DISPATCH(histogramdd_stub);
DEFINE_DISPATCH(histogramdd_linear_stub);
namespace {
/* Checks properties of input tensors input, bins, and weight.
*/
void histogramdd_check_inputs(const Tensor& input, const TensorList& bins, const c10::optional<Tensor>& weight) {
TORCH_CHECK(input.dim() >= 2, "torch.histogramdd: input tensor should have at least 2 dimensions, but got ",
input.dim());
const int64_t N = input.size(-1);
TORCH_CHECK(static_cast<int64_t>(bins.size()) == N, "torch.histogramdd: expected ", N, " sequences of bin edges for a ", N,
"-dimensional histogram but got ", bins.size());
auto input_dtype = input.dtype();
for (const auto dim : c10::irange(N)) {
const Tensor& dim_bins = bins[dim];
auto bins_dtype = dim_bins.dtype();
TORCH_CHECK(input_dtype == bins_dtype, "torch.histogramdd: input tensor and bins tensors should",
" have the same dtype, but got input with dtype ", input_dtype,
" and bins for dimension ", dim, " with dtype ", bins_dtype);
const int64_t dim_bins_dim = dim_bins.dim();
TORCH_CHECK(dim_bins_dim == 1, "torch.histogramdd: bins tensor should have one dimension,",
" but got ", dim_bins_dim, " dimensions in the bins tensor for dimension ", dim);
const int64_t numel = dim_bins.numel();
TORCH_CHECK(numel > 0, "torch.histogramdd: bins tensor should have at least 1 element,",
" but got ", numel, " elements in the bins tensor for dimension ", dim);
}
if (weight.has_value()) {
TORCH_CHECK(input.dtype() == weight.value().dtype(), "torch.histogramdd: if weight tensor is provided,"
" input tensor and weight tensor should have the same dtype, but got input(", input.dtype(), ")",
", and weight(", weight.value().dtype(), ")");
/* If a weight tensor is provided, we expect its shape to match that of
* the input tensor excluding its innermost dimension N.
*/
auto input_sizes = input.sizes().vec();
input_sizes.pop_back();
auto weight_sizes = weight.value().sizes().vec();
if (weight_sizes.empty()) {
// correctly handle scalars
weight_sizes = {1};
}
TORCH_CHECK(input_sizes == weight_sizes, "torch.histogramdd: if weight tensor is provided it should have"
" the same shape as the input tensor excluding its innermost dimension, but got input with shape ",
input.sizes(), " and weight with shape ", weight.value().sizes());
}
}
/* Checks properties of output tensors hist and bin_edges, then resizes them.
*/
void histogramdd_prepare_out(const Tensor& input, const std::vector<int64_t>& bin_ct,
const Tensor& hist, const TensorList& bin_edges) {
const int64_t N = input.size(-1);
TORCH_INTERNAL_ASSERT((int64_t)bin_ct.size() == N);
TORCH_INTERNAL_ASSERT((int64_t)bin_edges.size() == N);
TORCH_CHECK(input.dtype() == hist.dtype(), "torch.histogram: input tensor and hist tensor should",
" have the same dtype, but got input ", input.dtype(), " and hist ", hist.dtype());
for (const auto dim : c10::irange(N)) {
TORCH_CHECK(input.dtype() == bin_edges[dim].dtype(), "torch.histogram: input tensor and bin_edges tensor should",
" have the same dtype, but got input ", input.dtype(), " and bin_edges ", bin_edges[dim].dtype(),
" for dimension ", dim);
TORCH_CHECK(bin_ct[dim] > 0,
"torch.histogram(): bins must be > 0, but got ", bin_ct[dim], " for dimension ", dim);
at::native::resize_output(bin_edges[dim], bin_ct[dim] + 1);
}
at::native::resize_output(hist, bin_ct);
}
void histogramdd_prepare_out(const Tensor& input, TensorList bins,
const Tensor& hist, const TensorList& bin_edges) {
std::vector<int64_t> bin_ct(bins.size());
std::transform(bins.begin(), bins.end(), bin_ct.begin(), [](Tensor t) { return t.numel() - 1; });
histogramdd_prepare_out(input, bin_ct, hist, bin_edges);
}
template<typename scalar_t>
void infer_bin_edges_from_input(const Tensor& input, const int64_t N,
std::vector<double> &leftmost_edges, std::vector<double> &rightmost_edges) {
// Calls aminmax on input with dim=0, reducing all but the innermost dimension of input.
Tensor min, max;
std::tie(min, max) = aminmax(input, 0);
TORCH_INTERNAL_ASSERT(min.is_contiguous() && max.is_contiguous());
const scalar_t *min_data = min.data_ptr<scalar_t>();
std::copy(min_data, min_data + N, leftmost_edges.begin());
const scalar_t *max_data = max.data_ptr<scalar_t>();
std::copy(max_data, max_data + N, rightmost_edges.begin());
}
/* Determines the outermost bin edges. For simplicity when calling into aminmax,
* assumes that input has already been reshaped to (M, N).
*/
std::pair<std::vector<double>, std::vector<double>>
select_outer_bin_edges(const Tensor& input, c10::optional<c10::ArrayRef<double>> range) {
TORCH_INTERNAL_ASSERT(input.dim() == 2, "expected input to have shape (M, N)");
const int64_t N = input.size(-1);
// Default ranges for empty input matching numpy.histogram's default
std::vector<double> leftmost_edges(N, 0.);
std::vector<double> rightmost_edges(N, 1.);
if (range.has_value()) {
// range is specified
TORCH_CHECK((int64_t)range.value().size() == 2 * N, "torch.histogramdd: for a ", N, "-dimensional histogram",
" range should have ", 2 * N, " elements, but got ", range.value().size());
for (const auto dim : c10::irange(N)) {
leftmost_edges[dim] = range.value()[2 * dim];
rightmost_edges[dim] = range.value()[2 * dim + 1];
}
} else if (input.numel() > 0) {
// non-empty input
AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "histogramdd", [&]() {
infer_bin_edges_from_input<scalar_t>(input, N, leftmost_edges, rightmost_edges);
});
}
for (const auto dim : c10::irange(N)) {
double leftmost_edge = leftmost_edges[dim];
double rightmost_edge = rightmost_edges[dim];
TORCH_CHECK(std::isfinite(leftmost_edge) && std::isfinite(rightmost_edge),
"torch.histogramdd: dimension ", dim, "'s range [",
leftmost_edge, ", ", rightmost_edge, "] is not finite");
TORCH_CHECK(leftmost_edge <= rightmost_edge, "torch.histogramdd: min should not exceed max, but got",
" min ", leftmost_edge, " max ", rightmost_edge, " for dimension ", dim);
// Expand empty range to match numpy behavior and avoid division by 0 in normalization
if (leftmost_edge == rightmost_edge) {
leftmost_edges[dim] -= 0.5;
rightmost_edges[dim] += 0.5;
}
}
return std::make_pair(leftmost_edges, rightmost_edges);
}
/* histc's version of the logic for outermost bin edges.
*/
std::pair<double, double> histc_select_outer_bin_edges(const Tensor& input,
const Scalar& min, const Scalar& max) {
double leftmost_edge = min.to<double>();
double rightmost_edge = max.to<double>();
if (leftmost_edge == rightmost_edge && input.numel() > 0) {
auto extrema = aminmax(input);
leftmost_edge = std::get<0>(extrema).item<double>();
rightmost_edge = std::get<1>(extrema).item<double>();
}
if (leftmost_edge == rightmost_edge) {
leftmost_edge -= 1;
rightmost_edge += 1;
}
TORCH_CHECK(!(std::isinf(leftmost_edge) || std::isinf(rightmost_edge) ||
std::isnan(leftmost_edge) || std::isnan(rightmost_edge)),
"torch.histc: range of [", leftmost_edge, ", ", rightmost_edge, "] is not finite");
TORCH_CHECK(leftmost_edge < rightmost_edge, "torch.histc: max must be larger than min");
return std::make_pair(leftmost_edge, rightmost_edge);
}
} // namespace
std::vector<Tensor> allocate_bin_edges_tensors(const Tensor& self) {
TORCH_CHECK(self.dim() >= 2, "torch.histogramdd: input tensor should have at least 2 dimensions");
const int64_t N = self.size(-1);
std::vector<Tensor> bin_edges_out(N);
for (const auto dim : c10::irange(N)) {
bin_edges_out[dim] = at::empty({0}, self.options(), MemoryFormat::Contiguous);
}
return bin_edges_out;
}
/* Versions of histogramdd in which bins is a Tensor[] defining the sequences of bin edges.
*/
Tensor& histogramdd_out_cpu(const Tensor& self, TensorList bins,
const c10::optional<Tensor>& weight, bool density,
Tensor& hist, TensorList& bin_edges) {
histogramdd_check_inputs(self, bins, weight);
histogramdd_prepare_out(self, bins, hist, bin_edges);
for (const auto dim : c10::irange(bins.size())) {
bin_edges[dim].copy_(bins[dim]);
}
histogramdd_stub(self.device().type(), self, weight, density, hist, bin_edges);
return hist;
}
Tensor histogramdd_cpu(const Tensor& self, TensorList bins,
const c10::optional<Tensor>& weight, bool density) {
Tensor hist = at::empty({0}, self.options(), MemoryFormat::Contiguous);
std::vector<Tensor> bin_edges_out = allocate_bin_edges_tensors(self);
TensorList bin_edges_out_tl(bin_edges_out);
histogramdd_out_cpu(self, bins, weight, density, hist, bin_edges_out_tl);
return hist;
}
/* Versions of histogramdd in which bins is an int[]
* defining the number of bins in each dimension.
*/
std::vector<Tensor>& histogramdd_bin_edges_out_cpu(const Tensor& self, IntArrayRef bin_ct,
c10::optional<c10::ArrayRef<double>> range,
const c10::optional<Tensor>& weight, bool density,
std::vector<Tensor>& bin_edges_out) {
TensorList bin_edges_out_tl(bin_edges_out);
const int64_t N = self.size(-1);
const int64_t M = std::accumulate(self.sizes().begin(), self.sizes().end() - 1,
(int64_t)1, std::multiplies<int64_t>());
Tensor reshaped_self = self.reshape({ M, N });
auto outer_bin_edges = select_outer_bin_edges(reshaped_self, range);
for (const auto dim : c10::irange(N)) {
linspace_out(outer_bin_edges.first[dim], outer_bin_edges.second[dim],
bin_ct[dim] + 1, bin_edges_out[dim]);
}
return bin_edges_out;
}
std::vector<Tensor> histogramdd_bin_edges_cpu(const Tensor& self, IntArrayRef bin_ct,
c10::optional<c10::ArrayRef<double>> range,
const c10::optional<Tensor>& weight, bool density) {
std::vector<Tensor> bin_edges_out = allocate_bin_edges_tensors(self);
return histogramdd_bin_edges_out_cpu(self, bin_ct, range, weight, density, bin_edges_out);
}
Tensor& histogramdd_out_cpu(const Tensor& self, IntArrayRef bin_ct,
c10::optional<c10::ArrayRef<double>> range,
const c10::optional<Tensor>& weight, bool density,
Tensor& hist, TensorList& bin_edges) {
std::vector<Tensor> bins = histogramdd_bin_edges_cpu(self, bin_ct, range, weight, density);
histogramdd_check_inputs(self, bins, weight);
histogramdd_prepare_out(self, bins, hist, bin_edges);
for (const auto dim : c10::irange(bins.size())) {
bin_edges[dim].copy_(bins[dim]);
}
histogramdd_linear_stub(self.device().type(), self, weight, density, hist, bin_edges, true);
return hist;
}
Tensor histogramdd_cpu(const Tensor& self, IntArrayRef bin_ct,
c10::optional<c10::ArrayRef<double>> range,
const c10::optional<Tensor>& weight, bool density) {
Tensor hist = at::empty({0}, self.options(), MemoryFormat::Contiguous);
std::vector<Tensor> bin_edges_out = allocate_bin_edges_tensors(self);
TensorList bin_edges_out_tl(bin_edges_out);
histogramdd_out_cpu(self, bin_ct, range, weight, density, hist, bin_edges_out_tl);
return hist;
}
/* Versions of histogram in which bins is a Tensor defining the sequence of bin edges.
*/
std::tuple<Tensor&, Tensor&>
histogram_out_cpu(const Tensor& self, const Tensor& bins,
const c10::optional<Tensor>& weight, bool density,
Tensor& hist, Tensor& bin_edges) {
Tensor reshaped_self = self.reshape({ self.numel(), 1 });
c10::optional<Tensor> reshaped_weight = weight.has_value()
? weight.value().reshape({ weight.value().numel() }) : weight;
TensorList bins_in = bins;
TensorList bins_out = bin_edges;
histogramdd_out_cpu(reshaped_self, bins_in, reshaped_weight, density, hist, bins_out);
return std::forward_as_tuple(hist, bin_edges);
}
std::tuple<Tensor, Tensor>
histogram_cpu(const Tensor& self, const Tensor& bins,
const c10::optional<Tensor>& weight, bool density) {
Tensor hist = at::empty({0}, self.options(), MemoryFormat::Contiguous);
Tensor bin_edges = at::empty({0}, bins.options(), MemoryFormat::Contiguous);
return histogram_out_cpu(self, bins, weight, density, hist, bin_edges);
}
/* Versions of histogram in which bins is an integer specifying the number of equal-width bins.
*/
std::tuple<Tensor&, Tensor&>
histogram_out_cpu(const Tensor& self, int64_t bin_ct, c10::optional<c10::ArrayRef<double>> range,
const c10::optional<Tensor>& weight, bool density,
Tensor& hist, Tensor& bin_edges) {
Tensor reshaped_self = self.reshape({ self.numel(), 1 });
c10::optional<Tensor> reshaped_weight = weight.has_value()
? weight.value().reshape({ weight.value().numel() }) : weight;
TensorList bins_in = bin_edges;
TensorList bins_out = bin_edges;
histogramdd_prepare_out(reshaped_self, std::vector<int64_t>{bin_ct}, hist, bins_out);
auto outer_bin_edges = select_outer_bin_edges(reshaped_self, range);
linspace_out(outer_bin_edges.first[0], outer_bin_edges.second[0], bin_ct + 1, bin_edges);
histogramdd_check_inputs(reshaped_self, bins_in, reshaped_weight);
histogramdd_linear_stub(reshaped_self.device().type(), reshaped_self, reshaped_weight, density, hist, bin_edges, true);
return std::forward_as_tuple(hist, bin_edges);
}
std::tuple<Tensor, Tensor>
histogram_cpu(const Tensor& self, int64_t bin_ct, c10::optional<c10::ArrayRef<double>> range,
const c10::optional<Tensor>& weight, bool density) {
Tensor hist = at::empty({0}, self.options(), MemoryFormat::Contiguous);
Tensor bin_edges_out = at::empty({0}, self.options());
return histogram_out_cpu(self, bin_ct, range, weight, density, hist, bin_edges_out);
}
/* Narrowed interface for the legacy torch.histc function.
*/
Tensor& histogram_histc_cpu_out(const Tensor& self, int64_t bin_ct,
const Scalar& min, const Scalar& max, Tensor& hist) {
Tensor bin_edges = at::empty({0}, self.options());
Tensor reshaped = self.reshape({ self.numel(), 1 });
TensorList bins_in = bin_edges;
TensorList bins_out = bin_edges;
histogramdd_prepare_out(reshaped, std::vector<int64_t>{bin_ct}, hist, bins_out);
auto outer_bin_edges = histc_select_outer_bin_edges(self, min, max);
linspace_out(outer_bin_edges.first, outer_bin_edges.second, bin_ct + 1, bin_edges);
histogramdd_check_inputs(reshaped, bins_in, {});
histogramdd_linear_stub(reshaped.device().type(), reshaped,
c10::optional<Tensor>(), false, hist, bin_edges, false);
return hist;
}
Tensor histogram_histc_cpu(const Tensor& self, int64_t bin_ct,
const Scalar& min, const Scalar& max) {
Tensor hist = at::empty({0}, self.options(), MemoryFormat::Contiguous);
return histogram_histc_cpu_out(self, bin_ct, min, max, hist);
}
std::tuple<Tensor, std::vector<Tensor>> histogramdd(
const Tensor &self, TensorList bins, c10::optional<ArrayRef<double>> /*range*/,
const c10::optional<Tensor> &weight, bool density) {
auto hist = at::_histogramdd_from_bin_tensors(self, bins, weight, density);
return std::tuple<Tensor, std::vector<Tensor>>{
std::move(hist), bins.vec()};
}
std::tuple<Tensor, std::vector<Tensor>> histogramdd(
const Tensor &self, IntArrayRef bins, c10::optional<ArrayRef<double>> range,
const c10::optional<Tensor> &weight, bool density) {
auto bin_edges = at::_histogramdd_bin_edges(self, bins, range, weight, density);
auto hist = at::_histogramdd_from_bin_cts(self, bins, range, weight, density);
return std::tuple<Tensor, std::vector<Tensor>>{
std::move(hist), std::move(bin_edges)};
}
std::tuple<Tensor, std::vector<Tensor>> histogramdd(
const Tensor &self, int64_t bins, c10::optional<ArrayRef<double>> range,
const c10::optional<Tensor> &weight, bool density) {
DimVector bins_v(self.size(-1), bins);
return at::native::histogramdd(self, bins_v, range, weight, density);
}
}} // namespace at::native