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tensor_methods.cpp
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#include "torch_xla/csrc/tensor_methods.h"
#include <ATen/OpMathType.h>
#include <ATen/core/Reduction.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/lazy/core/helpers.h>
#include <torch/csrc/lazy/core/util.h>
#include <algorithm>
#include <functional>
#include "absl/strings/str_cat.h"
#include "absl/strings/str_split.h"
#include "torch_xla/csrc/LazyIr.h"
#include "torch_xla/csrc/aten_xla_bridge.h"
#include "torch_xla/csrc/data_ops.h"
#include "torch_xla/csrc/dtype.h"
#include "torch_xla/csrc/helpers.h"
#include "torch_xla/csrc/layout_manager.h"
#include "torch_xla/csrc/lowering_context.h"
#include "torch_xla/csrc/ops/adam_optimizer_step.h"
#include "torch_xla/csrc/ops/adaptive_max_pool2d.h"
#include "torch_xla/csrc/ops/all_gather.h"
#include "torch_xla/csrc/ops/all_reduce.h"
#include "torch_xla/csrc/ops/all_to_all.h"
#include "torch_xla/csrc/ops/amp_foreach_non_finite_check_and_unscale.h"
#include "torch_xla/csrc/ops/amp_update_scale.h"
#include "torch_xla/csrc/ops/arithmetic_ir_ops.h"
#include "torch_xla/csrc/ops/as_strided.h"
#include "torch_xla/csrc/ops/avg_pool_nd.h"
#include "torch_xla/csrc/ops/avg_pool_nd_backward.h"
#include "torch_xla/csrc/ops/bernoulli.h"
#include "torch_xla/csrc/ops/cast.h"
#include "torch_xla/csrc/ops/cast_int4.h"
#include "torch_xla/csrc/ops/cat.h"
#include "torch_xla/csrc/ops/cdist.h"
#include "torch_xla/csrc/ops/collective_permute.h"
#include "torch_xla/csrc/ops/constant.h"
#include "torch_xla/csrc/ops/constant_pad_nd.h"
#include "torch_xla/csrc/ops/convolution_backward_overrideable.h"
#include "torch_xla/csrc/ops/convolution_overrideable.h"
#include "torch_xla/csrc/ops/count_nonzero.h"
#include "torch_xla/csrc/ops/cummax.h"
#include "torch_xla/csrc/ops/cummin.h"
#include "torch_xla/csrc/ops/cumprod.h"
#include "torch_xla/csrc/ops/cumsum.h"
#include "torch_xla/csrc/ops/custom_call.h"
#include "torch_xla/csrc/ops/dequant_tensor.h"
#include "torch_xla/csrc/ops/device_data.h"
#include "torch_xla/csrc/ops/diagonal.h"
#include "torch_xla/csrc/ops/discrete_uniform.h"
#include "torch_xla/csrc/ops/dot_general.h"
#include "torch_xla/csrc/ops/dynamic_expand.h"
#include "torch_xla/csrc/ops/dynamic_view.h"
#include "torch_xla/csrc/ops/eigh.h"
#include "torch_xla/csrc/ops/einsum.h"
#include "torch_xla/csrc/ops/einsum_backward.h"
#include "torch_xla/csrc/ops/embedding_bag.h"
#include "torch_xla/csrc/ops/expand.h"
#include "torch_xla/csrc/ops/expand_symint.h"
#include "torch_xla/csrc/ops/exponential.h"
#include "torch_xla/csrc/ops/flip.h"
#include "torch_xla/csrc/ops/gather.h"
#include "torch_xla/csrc/ops/generic.h"
#include "torch_xla/csrc/ops/generic_slice.h"
#include "torch_xla/csrc/ops/get_dimensions_size.h"
#include "torch_xla/csrc/ops/gpu_custom_call.h"
#include "torch_xla/csrc/ops/hardtanh_backward.h"
#include "torch_xla/csrc/ops/index_ops.h"
#include "torch_xla/csrc/ops/index_select.h"
#include "torch_xla/csrc/ops/infer_output_shape.h"
#include "torch_xla/csrc/ops/kth_value.h"
#include "torch_xla/csrc/ops/linear_interpolation.h"
#include "torch_xla/csrc/ops/linspace.h"
#include "torch_xla/csrc/ops/log_softmax.h"
#include "torch_xla/csrc/ops/logsumexp.h"
#include "torch_xla/csrc/ops/mark_tensor.h"
#include "torch_xla/csrc/ops/masked_scatter.h"
#include "torch_xla/csrc/ops/masked_select.h"
#include "torch_xla/csrc/ops/max_in_dim.h"
#include "torch_xla/csrc/ops/max_pool_nd.h"
#include "torch_xla/csrc/ops/max_pool_nd_backward.h"
#include "torch_xla/csrc/ops/max_unpool_nd.h"
#include "torch_xla/csrc/ops/mean.h"
#include "torch_xla/csrc/ops/min_in_dim.h"
#include "torch_xla/csrc/ops/mse_loss.h"
#include "torch_xla/csrc/ops/mse_loss_backward.h"
#include "torch_xla/csrc/ops/multinomial.h"
#include "torch_xla/csrc/ops/native_batch_norm_backward.h"
#include "torch_xla/csrc/ops/native_batch_norm_forward.h"
#include "torch_xla/csrc/ops/native_dropout.h"
#include "torch_xla/csrc/ops/nll_loss.h"
#include "torch_xla/csrc/ops/nll_loss2d.h"
#include "torch_xla/csrc/ops/nll_loss2d_backward.h"
#include "torch_xla/csrc/ops/nll_loss_backward.h"
#include "torch_xla/csrc/ops/nms.h"
#include "torch_xla/csrc/ops/nonzero.h"
#include "torch_xla/csrc/ops/normal.h"
#include "torch_xla/csrc/ops/not_supported.h"
#include "torch_xla/csrc/ops/ops.h"
#include "torch_xla/csrc/ops/optimization_barrier.h"
#include "torch_xla/csrc/ops/permute.h"
#include "torch_xla/csrc/ops/prod.h"
#include "torch_xla/csrc/ops/put.h"
#include "torch_xla/csrc/ops/qr.h"
#include "torch_xla/csrc/ops/quant_tensor.h"
#include "torch_xla/csrc/ops/randperm.h"
#include "torch_xla/csrc/ops/recv.h"
#include "torch_xla/csrc/ops/reduce_scatter.h"
#include "torch_xla/csrc/ops/reflection_pad2d.h"
#include "torch_xla/csrc/ops/reflection_pad2d_backward.h"
#include "torch_xla/csrc/ops/replication_pad.h"
#include "torch_xla/csrc/ops/replication_pad_backward.h"
#include "torch_xla/csrc/ops/resize.h"
#include "torch_xla/csrc/ops/roll.h"
#include "torch_xla/csrc/ops/rrelu_with_noise.h"
#include "torch_xla/csrc/ops/rrelu_with_noise_backward.h"
#include "torch_xla/csrc/ops/scalar.h"
#include "torch_xla/csrc/ops/scatter.h"
#include "torch_xla/csrc/ops/scatter_add.h"
#include "torch_xla/csrc/ops/scatter_reduce.h"
#include "torch_xla/csrc/ops/select.h"
#include "torch_xla/csrc/ops/send.h"
#include "torch_xla/csrc/ops/sgd_optimizer_step.h"
#include "torch_xla/csrc/ops/softmax.h"
#include "torch_xla/csrc/ops/split.h"
#include "torch_xla/csrc/ops/squeeze.h"
#include "torch_xla/csrc/ops/stack.h"
#include "torch_xla/csrc/ops/std.h"
#include "torch_xla/csrc/ops/std_mean.h"
#include "torch_xla/csrc/ops/sum.h"
#include "torch_xla/csrc/ops/svd.h"
#include "torch_xla/csrc/ops/threshold.h"
#include "torch_xla/csrc/ops/threshold_backward.h"
#include "torch_xla/csrc/ops/topk.h"
#include "torch_xla/csrc/ops/tpu_custom_call.h"
#include "torch_xla/csrc/ops/triangular_solve.h"
#include "torch_xla/csrc/ops/uniform.h"
#include "torch_xla/csrc/ops/unsqueeze.h"
#include "torch_xla/csrc/ops/upsample_bilinear2d.h"
#include "torch_xla/csrc/ops/upsample_bilinear2d_backward.h"
#include "torch_xla/csrc/ops/upsample_nearest2d.h"
#include "torch_xla/csrc/ops/upsample_nearest2d_backward.h"
#include "torch_xla/csrc/ops/user_computation.h"
#include "torch_xla/csrc/ops/var.h"
#include "torch_xla/csrc/ops/var_mean.h"
#include "torch_xla/csrc/ops/view.h"
#include "torch_xla/csrc/runtime/computation_client.h"
#include "torch_xla/csrc/runtime/debug_macros.h"
#include "torch_xla/csrc/runtime/metrics.h"
#include "torch_xla/csrc/runtime/sys_util.h"
#include "torch_xla/csrc/runtime/util.h"
#include "torch_xla/csrc/runtime/xla_util.h"
#include "torch_xla/csrc/shape_builder.h"
#include "torch_xla/csrc/tensor.h"
#include "torch_xla/csrc/tensor_ops.h"
#include "torch_xla/csrc/tensor_util.h"
#include "torch_xla/csrc/xla_graph_executor.h"
#include "xla/literal_util.h"
namespace torch_xla {
namespace tensor_methods {
namespace {
struct MinMaxValues {
torch::lazy::Value min;
torch::lazy::Value max;
};
torch::lazy::Value MaybeExpand(const torch::lazy::Value& input,
const xla::Shape& target_shape) {
if (GetXlaShape(input).dimensions() == target_shape.dimensions()) {
return input;
}
return torch_xla::MakeNode<Expand>(
input, torch::lazy::ToVector<int64_t>(target_shape.dimensions()));
}
MinMaxValues GetMinMaxValues(const XLATensorPtr& tensor,
const std::optional<at::Scalar>& min,
const std::optional<at::Scalar>& max) {
XLA_CHECK(min || max)
<< "At least one of \'min\' or \'max\' must not be None";
xla::PrimitiveType raw_element_type = XlaTypeFromTorchType(tensor->dtype());
XlaHelpers::MinMax min_max = XlaHelpers::MinMaxValues(raw_element_type);
auto shape = tensor->shape();
return {XLAGraphExecutor::Get()->GetIrValueForScalar(
min ? *min : min_max.min, shape.get().element_type(),
tensor->GetDevice()),
XLAGraphExecutor::Get()->GetIrValueForScalar(
max ? *max : min_max.max, shape.get().element_type(),
tensor->GetDevice())};
}
void CheckRank(const XLATensorPtr& t, int64_t expected_rank,
const std::string& tag, const std::string& arg_name,
int arg_number) {
int64_t actual_rank = t->shape().get().rank();
XLA_CHECK_EQ(actual_rank, expected_rank)
<< "Expected " << expected_rank << "-dimensional tensor, but got "
<< actual_rank << "-dimensional tensor for "
<< "argument #" << arg_number << " '" << arg_name << "'"
<< " (while checking arguments for " << tag << ")";
}
template <typename T>
void CheckShapeDimensions(const T& size) {
XLA_CHECK(std::all_of(size.begin(), size.end(), [](int64_t dim) {
return dim >= 0;
})) << "Dimensions cannot be negative numbers";
}
void CheckDimensionSize(const XLATensorPtr& t, int64_t dim,
int64_t expected_size, const std::string& tag,
const std::string& arg_name, int arg_number) {
int64_t dim_size = t->size(dim);
XLA_CHECK_EQ(t->size(dim), expected_size)
<< "Expected tensor to have size " << expected_size << " at dimension "
<< dim << ", but got size " << dim_size << " for "
<< "argument #" << arg_number << " '" << arg_name << "'"
<< " (while checking arguments for " << tag << ")";
}
void CheckBmmDimension(const std::string& tag, const XLATensorPtr& batch1,
const XLATensorPtr& batch2) {
// Consistent with the checks in bmm_out_or_baddbmm_.
CheckRank(batch1, 3, tag, "batch1", 1);
CheckRank(batch2, 3, tag, "batch2", 2);
CheckDimensionSize(batch2, 0, /*batch_size=*/batch1->size(0), tag, "batch2",
2);
CheckDimensionSize(batch2, 1, /*contraction_size=*/batch1->size(2), tag,
"batch2", 2);
}
std::vector<int64_t> GetExpandDimensions(const xla::Shape& shape,
std::vector<int64_t> dimensions) {
XLA_CHECK_GE(dimensions.size(), shape.rank()) << shape;
int64_t base = dimensions.size() - shape.rank();
for (size_t i = 0; i < shape.rank(); ++i) {
if (dimensions[base + i] == -1) {
dimensions[base + i] = shape.dimensions(i);
}
}
return dimensions;
}
// Resizes and / or checks whether a list is of the given size. The list is only
// resized if its size is 1. If it's empty, it's replaced with the provided
// default first.
std::vector<int64_t> CheckIntList(absl::Span<const int64_t> list, size_t length,
const std::string& name,
std::vector<int64_t> def = {}) {
std::vector<int64_t> result;
if (list.empty()) {
result = std::move(def);
} else {
result = torch::lazy::ToVector<int64_t>(list);
}
if (result.size() == 1 && length > 1) {
result.resize(length, result[0]);
return result;
}
XLA_CHECK_EQ(result.size(), length)
<< "Invalid length for the '" << name << "' attribute";
return result;
}
// Returns a 1-D shape for batch norm weight or bias based on the input shape.
xla::Shape BatchNormFeaturesShape(const XLATensorPtr& input) {
xla::PrimitiveType input_element_type =
MakeXlaPrimitiveType(input->dtype(), &input->GetDevice());
auto input_shape = input->shape();
return ShapeBuilder(input_element_type).Add(input_shape.get(), 1).Build();
}
// Returns the IR for the given input or the provided default value broadcasted
// to the default shape, if the input is undefined.
torch::lazy::Value GetIrValueOrDefault(
const XLATensorPtr& input, const at::Scalar& default_value,
const xla::Shape& default_shape, const torch::lazy::BackendDevice& device) {
return input ? input->GetIrValue()
: XLAGraphExecutor::Get()->GetIrValueForScalar(
default_value, default_shape, device);
}
// Returns the IR for the given input. If the IR is not a floating point value,
// cast it to the float_type.
torch::lazy::Value GetFloatingIrValue(const XLATensorPtr& input,
at::ScalarType float_type) {
torch::lazy::Value input_value = input->GetIrValue();
xla::PrimitiveType input_type = GetXlaShape(input_value).element_type();
if (xla::primitive_util::IsIntegralType(input_type) ||
input_type == xla::PRED) {
input_value = torch_xla::MakeNode<Cast>(input_value, float_type);
}
return input_value;
}
torch::lazy::Value GetBooleanIrValue(torch::lazy::Value input_value) {
if (GetXlaShape(input_value).element_type() != xla::PrimitiveType::PRED) {
input_value =
torch_xla::MakeNode<Cast>(input_value, xla::PrimitiveType::PRED);
}
return input_value;
}
absl::optional<torch::lazy::Value> GetOptionalIrValue(
const XLATensorPtr& tensor) {
absl::optional<torch::lazy::Value> value;
if (tensor) {
value = tensor->GetIrValue();
}
return value;
}
ViewInfo CreateAsStridedViewInfo(const xla::Shape& input_shape,
std::vector<int64_t> size,
std::vector<int64_t> stride,
std::optional<int64_t> storage_offset) {
xla::Shape result_shape = XlaHelpers::GetDynamicReshape(input_shape, size);
AsStridedInfo as_strided_info;
as_strided_info.stride = std::move(stride);
if (storage_offset) {
as_strided_info.offset = *storage_offset;
}
return ViewInfo(ViewInfo::Type::kAsStrided, std::move(result_shape),
input_shape, std::move(as_strided_info));
}
// Dispatches a comparison operator, setting the logical type of the result
// appropriately.
XLATensorPtr DispatchComparisonOp(c10::Symbol kind, const XLATensorPtr& input,
const at::Scalar& other) {
torch::lazy::NodePtr node = ComparisonOp(
kind, input->GetIrValue(),
XLAGraphExecutor::Get()->GetIrValueForScalar(other, input->GetDevice()));
return XLATensor::Create(node, input->GetDevice(), at::ScalarType::Bool);
}
// Same as above, with the second input a tensor as well.
XLATensorPtr DispatchComparisonOp(c10::Symbol kind, const XLATensorPtr& input,
const XLATensorPtr& other) {
torch::lazy::NodePtr node =
ComparisonOp(kind, input->GetIrValue(), other->GetIrValue());
return XLATensor::Create(node, input->GetDevice(), at::ScalarType::Bool);
}
} // namespace
//////////////////////////////////////////////////////////////////////////////
// XLA dedicated operators follows here, listed in alphabetical order.
//////////////////////////////////////////////////////////////////////////////
XLATensorPtr all_reduce(const XLATensorPtr& input, AllReduceType reduce_type,
double scale, std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
std::vector<torch::lazy::Value> input_values({input->GetIrValue()});
torch::lazy::NodePtr node = torch_xla::MakeNode<AllReduce>(
reduce_type, input_values, GetAllReduceToken(input->GetDevice()), scale,
std::move(groups), pin_layout);
SetAllReduceToken(input->GetDevice(),
std::make_shared<torch::lazy::Value>(node, 1));
return input->CreateFrom(torch::lazy::Value(node, 0));
}
void all_reduce(const std::vector<XLATensorPtr>& inputs,
AllReduceType reduce_type, double scale,
std::vector<std::vector<int64_t>> groups, bool pin_layout) {
std::vector<torch::lazy::Value> input_values;
input_values.reserve(inputs.size());
for (auto& input : inputs) {
input_values.push_back(input->GetIrValue());
}
torch::lazy::NodePtr node = torch_xla::MakeNode<AllReduce>(
reduce_type, input_values, GetAllReduceToken(inputs.front()->GetDevice()),
scale, std::move(groups), pin_layout);
for (size_t i = 0; i < inputs.size(); ++i) {
// In eager mode we don't want to execute the IR for each tensor because
// that will execute the `all_reduce` x times.
inputs[i]->SetInPlaceIrValue(torch::lazy::Value(node, i),
/*delay_eager_executation=*/true);
}
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `all_reduce` and in place update all
// tensors in one graph.
graph_executor->ApplyEagerSync(
const_cast<std::vector<XLATensorPtr>&>(inputs));
} else {
// all_reduce_token is to enforce the order of the cc ops. There is no point
// of setting it for eager mode since each cc op will be executed
// independently.
SetAllReduceToken(
inputs.front()->GetDevice(),
std::make_shared<torch::lazy::Value>(node, inputs.size()));
}
}
XLATensorPtr all_reduce(const XLATensorPtr& input, AllReduceType reduce_type,
double scale,
std::vector<std::vector<int64_t>> groups) {
return input->CreateFrom(torch_xla::MakeNode<AllReduce>(
reduce_type, input->GetIrValue(), scale, std::move(groups)));
}
std::pair<XLATensorPtr, torch::lazy::Value> reduce_scatter(
const XLATensorPtr& input, const torch::lazy::Value& token,
AllReduceType reduce_type, double scale, int64_t scatter_dim,
int64_t shard_count, std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
torch::lazy::NodePtr node = torch_xla::MakeNode<ReduceScatter>(
reduce_type, input->GetIrValue(), token, scale, scatter_dim, shard_count,
std::move(groups), pin_layout);
return {input->CreateFrom(torch::lazy::Value(node, 0)),
torch::lazy::Value(node, 1)};
}
XLATensorPtr reduce_scatter(const XLATensorPtr& input,
AllReduceType reduce_type, double scale,
int64_t scatter_dim, int64_t shard_count,
std::vector<std::vector<int64_t>> groups) {
auto canonical_scatter_dim = torch::lazy::GetCanonicalDimensionIndex(
scatter_dim, input->shape().get().rank());
return input->CreateFrom(torch_xla::MakeNode<ReduceScatter>(
reduce_type, input->GetIrValue(), scale, canonical_scatter_dim,
shard_count, std::move(groups)));
}
torch::lazy::Value reduce_scatter_out(XLATensorPtr& output,
const XLATensorPtr& input,
const torch::lazy::Value& token,
AllReduceType reduce_type, double scale,
int64_t scatter_dim, int64_t shard_count,
std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
torch::lazy::NodePtr node = torch_xla::MakeNode<ReduceScatter>(
reduce_type, input->GetIrValue(), token, scale, scatter_dim, shard_count,
std::move(groups), pin_layout);
output->SetIrValue(torch::lazy::Value(node, 0));
return torch::lazy::Value(node, 1);
}
std::pair<std::vector<XLATensorPtr>, torch::lazy::Value>
reduce_scatter_coalesced(const std::vector<XLATensorPtr>& inputs,
const torch::lazy::Value& token,
AllReduceType reduce_type, double scale,
int64_t scatter_dim, int64_t shard_count,
std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
std::vector<torch::lazy::Value> input_values;
input_values.reserve(inputs.size());
for (auto& input : inputs) {
input_values.push_back(input->GetIrValue());
}
torch::lazy::NodePtr node = torch_xla::MakeNode<ReduceScatterCoalesced>(
reduce_type, input_values, token, scale, scatter_dim, shard_count,
std::move(groups), pin_layout);
std::vector<XLATensorPtr> result;
for (size_t i = 0; i < inputs.size(); ++i) {
result.emplace_back(inputs[i]->CreateFrom(torch::lazy::Value(node, i)));
}
return {result, torch::lazy::Value(node, inputs.size())};
}
torch::lazy::Value reduce_scatter_coalesced_out(
const std::vector<XLATensorPtr>& outputs,
const std::vector<XLATensorPtr>& inputs, const torch::lazy::Value& token,
AllReduceType reduce_type, double scale, int64_t scatter_dim,
int64_t shard_count, std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
std::vector<torch::lazy::Value> input_values;
input_values.reserve(inputs.size());
for (auto& input : inputs) {
input_values.push_back(input->GetIrValue());
}
torch::lazy::NodePtr node = torch_xla::MakeNode<ReduceScatterCoalesced>(
reduce_type, input_values, token, scale, scatter_dim, shard_count,
std::move(groups), pin_layout);
for (size_t i = 0; i < inputs.size(); ++i) {
outputs[i]->SetIrValue(torch::lazy::Value(node, i));
}
return torch::lazy::Value(node, inputs.size());
}
std::pair<XLATensorPtr, torch::lazy::Value> all_to_all(
const XLATensorPtr& input, const torch::lazy::Value& token,
int64_t split_dimension, int64_t concat_dimension, int64_t split_count,
std::vector<std::vector<int64_t>> groups, bool pin_layout) {
torch::lazy::NodePtr node = torch_xla::MakeNode<AllToAll>(
input->GetIrValue(), token, split_dimension, concat_dimension,
split_count, std::move(groups), pin_layout);
return {input->CreateFrom(torch::lazy::Value(node, 0)),
torch::lazy::Value(node, 1)};
}
XLATensorPtr all_gather(const XLATensorPtr& input, int64_t dim,
int64_t shard_count,
std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
torch::lazy::NodePtr node = torch_xla::MakeNode<AllGather>(
input->GetIrValue(), GetAllReduceToken(input->GetDevice()), dim,
shard_count, std::move(groups), pin_layout);
SetAllReduceToken(input->GetDevice(),
std::make_shared<torch::lazy::Value>(node, 1));
return input->CreateFrom(torch::lazy::Value(node, 0));
}
torch::lazy::Value all_gather_out(XLATensorPtr& output,
const XLATensorPtr& input,
const torch::lazy::Value& token, int64_t dim,
int64_t shard_count,
std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
torch::lazy::NodePtr node = torch_xla::MakeNode<AllGather>(
input->GetIrValue(), token, dim, shard_count, std::move(groups),
pin_layout);
output->SetIrValue(torch::lazy::Value(node, 0));
return torch::lazy::Value(node, 1);
}
std::pair<std::vector<XLATensorPtr>, torch::lazy::Value> all_gather_coalesced(
const std::vector<XLATensorPtr>& inputs, const torch::lazy::Value& token,
int64_t dim, int64_t shard_count, std::vector<std::vector<int64_t>> groups,
bool pin_layout) {
std::vector<torch::lazy::Value> input_values;
input_values.reserve(inputs.size());
for (auto& input : inputs) {
input_values.push_back(input->GetIrValue());
}
torch::lazy::NodePtr node = torch_xla::MakeNode<AllGatherCoalesced>(
input_values, token, dim, shard_count, std::move(groups), pin_layout);
std::vector<XLATensorPtr> result;
for (size_t i = 0; i < inputs.size(); ++i) {
result.emplace_back(inputs[i]->CreateFrom(torch::lazy::Value(node, i)));
}
return {result, torch::lazy::Value(node, inputs.size())};
}
torch::lazy::Value all_gather_coalesced_out(
std::vector<XLATensorPtr>& outputs, const std::vector<XLATensorPtr>& inputs,
const torch::lazy::Value& token, int64_t dim, int64_t shard_count,
std::vector<std::vector<int64_t>> groups, bool pin_layout) {
std::vector<torch::lazy::Value> input_values;
input_values.reserve(inputs.size());
for (auto& input : inputs) {
input_values.push_back(input->GetIrValue());
}
torch::lazy::NodePtr node = torch_xla::MakeNode<AllGatherCoalesced>(
input_values, token, dim, shard_count, std::move(groups), pin_layout);
for (size_t i = 0; i < inputs.size(); ++i) {
outputs[i]->SetIrValue(torch::lazy::Value(node, i));
}
return torch::lazy::Value(node, inputs.size());
}
std::pair<XLATensorPtr, torch::lazy::Value> collective_permute(
const XLATensorPtr& input, const torch::lazy::Value& token,
std::vector<std::pair<int64_t, int64_t>> source_target_pairs) {
torch::lazy::NodePtr node = torch_xla::MakeNode<CollectivePermute>(
input->GetIrValue(), token, std::move(source_target_pairs));
return {input->CreateFrom(torch::lazy::Value(node, 0)),
torch::lazy::Value(node, 1)};
}
std::vector<XLATensorPtr> custom_call(
const std::vector<XLATensorPtr>& inputs, const std::string& target,
const std::vector<std::vector<int64_t>>& output_shapes,
const std::vector<at::ScalarType>& output_dtypes, bool has_side_effect,
const std::string& backend_config, const int api_version,
const std::unordered_map<std::string, std::string>& frontend_attributes) {
XLA_CHECK(inputs.size() > 0) << "inputs are empty";
std::vector<torch::lazy::Value> values;
values.reserve(inputs.size());
for (const auto& input : inputs) {
values.push_back(input->GetIrValue());
}
XLA_CHECK_EQ(output_shapes.size(), output_dtypes.size());
std::vector<xla::Shape> output_xla_shapes;
output_xla_shapes.reserve(output_shapes.size());
for (size_t i = 0; i < output_shapes.size(); ++i) {
output_xla_shapes.push_back(xla::ShapeUtil::MakeShape(
MakeXlaPrimitiveType(output_dtypes[i], &(inputs[0]->GetDevice())),
output_shapes[i]));
}
auto node = torch_xla::MakeNode<CustomCall>(
values, target, xla::ShapeUtil::MakeTupleShape(output_xla_shapes),
has_side_effect, backend_config, api_version, frontend_attributes);
std::vector<XLATensorPtr> outputs;
outputs.reserve(output_shapes.size());
for (size_t i = 0; i < output_shapes.size(); ++i) {
outputs.push_back(inputs[0]->CreateFrom(torch::lazy::Value(node, i),
output_dtypes[i],
/*delay_eager_executation=*/true));
}
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `customcall` and in one graph
graph_executor->ApplyEagerSync(outputs);
}
return outputs;
}
void custom_sharding_(
const XLATensorPtr& input,
const std::shared_ptr<XLATensor::ShardingSpec>& sharding_spec,
const CustomSharding::Type& type) {
input->SetInPlaceIrValue(torch_xla::MakeNode<CustomSharding>(
input->GetIrValue(), input->shape().get(), type));
input->SetShardingSpec(*sharding_spec);
}
std::vector<XLATensorPtr> gpu_custom_call(
const std::vector<XLATensorPtr>& inputs, const std::string& payload,
const std::vector<std::vector<int64_t>>& output_shapes,
const std::vector<at::ScalarType>& output_dtypes) {
XLA_CHECK(inputs.size() > 0) << "inputs are empty";
std::vector<torch::lazy::Value> values;
values.reserve(inputs.size());
for (const auto& input : inputs) {
values.push_back(input->GetIrValue());
}
XLA_CHECK_EQ(output_shapes.size(), output_dtypes.size());
std::vector<xla::Shape> output_xla_shapes;
output_xla_shapes.reserve(output_shapes.size());
for (size_t i = 0; i < output_shapes.size(); ++i) {
output_xla_shapes.push_back(xla::ShapeUtil::MakeShape(
MakeXlaPrimitiveType(output_dtypes[i], &(inputs[0]->GetDevice())),
output_shapes[i]));
}
auto node = torch_xla::MakeNode<GpuCustomCall>(
values, xla::ShapeUtil::MakeTupleShape(output_xla_shapes), payload);
std::vector<XLATensorPtr> outputs;
outputs.reserve(output_shapes.size());
for (size_t i = 0; i < output_shapes.size(); ++i) {
outputs.push_back(inputs[0]->CreateFrom(torch::lazy::Value(node, i),
output_dtypes[i],
/*delay_eager_executation=*/true));
}
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `custom` and in one hlo
graph_executor->ApplyEagerSync(outputs);
}
return outputs;
}
std::vector<XLATensorPtr> tpu_custom_call(
const std::vector<XLATensorPtr>& inputs, const std::string& payload,
const std::vector<std::vector<int64_t>>& output_shapes,
const std::vector<at::ScalarType>& output_dtypes) {
XLA_CHECK(inputs.size() > 0) << "inputs are empty";
std::vector<torch::lazy::Value> values;
values.reserve(inputs.size());
for (const auto& input : inputs) {
values.push_back(input->GetIrValue());
}
XLA_CHECK_EQ(output_shapes.size(), output_dtypes.size());
std::vector<xla::Shape> output_xla_shapes;
output_xla_shapes.reserve(output_shapes.size());
for (size_t i = 0; i < output_shapes.size(); ++i) {
output_xla_shapes.push_back(xla::ShapeUtil::MakeShape(
MakeXlaPrimitiveType(output_dtypes[i], &(inputs[0]->GetDevice())),
output_shapes[i]));
}
auto node = torch_xla::MakeNode<TpuCustomCall>(
values, xla::ShapeUtil::MakeTupleShape(output_xla_shapes), payload);
std::vector<XLATensorPtr> outputs;
outputs.reserve(output_shapes.size());
for (size_t i = 0; i < output_shapes.size(); ++i) {
outputs.push_back(inputs[0]->CreateFrom(torch::lazy::Value(node, i),
output_dtypes[i],
/*delay_eager_executation=*/true));
}
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `custom` and in one hlo
graph_executor->ApplyEagerSync(outputs);
}
return outputs;
}
XLATensorPtr get_dimensions_size(const XLATensorPtr& input,
std::vector<int64_t> dimensions) {
return input->CreateFrom(torch_xla::MakeNode<GetDimensionsSize>(
input->GetIrValue(), std::move(dimensions)),
at::ScalarType::Int);
}
std::pair<XLATensorPtr, torch::lazy::Value> recv(
XLATensorPtr& output, const torch::lazy::Value& token, int64_t channel_id) {
torch::lazy::NodePtr node = torch_xla::MakeNode<ir::ops::Recv>(
token, GetXlaShape(output->GetIrValue()), channel_id);
output->SetIrValue(torch::lazy::Value(node, 0));
return {output->CreateFrom(torch::lazy::Value(node, 0)),
torch::lazy::Value(node, 1)};
}
std::pair<XLATensorPtr, torch::lazy::Value> send(
const XLATensorPtr& input, const torch::lazy::Value& token,
int64_t channel_id) {
torch::lazy::NodePtr node = torch_xla::MakeNode<ir::ops::Send>(
input->GetIrValue(), token, channel_id);
return {input->CreateFrom(torch::lazy::Value(node, 0)),
torch::lazy::Value(node, 1)};
}
void sgd_optimizer_step_(const XLATensorPtr& found_inf, XLATensorPtr& step,
XLATensorPtr& param, XLATensorPtr& buf,
const XLATensorPtr& d_p, double weight_decay,
double momentum, double lr, double dampening,
bool nesterov, bool maximize) {
torch::lazy::Value weight_decay_value =
XLAGraphExecutor::Get()->GetIrValueForScalar(weight_decay, param->shape(),
param->GetDevice());
torch::lazy::Value momentum_value =
XLAGraphExecutor::Get()->GetIrValueForScalar(momentum, param->shape(),
param->GetDevice());
torch::lazy::Value lr_value = XLAGraphExecutor::Get()->GetIrValueForScalar(
maximize ? -lr : lr, param->shape(), param->GetDevice());
torch::lazy::Value dampening_value =
XLAGraphExecutor::Get()->GetIrValueForScalar(dampening, param->shape(),
param->GetDevice());
torch::lazy::NodePtr node = torch_xla::MakeNode<SgdOptimizerStep>(
found_inf->GetIrValue(), step->GetIrValue(), param->GetIrValue(),
buf->GetIrValue(), d_p->GetIrValue(), weight_decay_value, momentum_value,
lr_value, dampening_value,
/*use_weight_decay=*/weight_decay != 0,
/*use_momentum=*/momentum != 0, /*use_nesterov=*/nesterov);
step->SetInPlaceIrValue(torch::lazy::Value(node, 0),
/*delay_eager_executation=*/true);
param->SetInPlaceIrValue(torch::lazy::Value(node, 1),
/*delay_eager_executation=*/true);
buf->SetInPlaceIrValue(torch::lazy::Value(node, 2),
/*delay_eager_executation=*/true);
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `sgd_optimizer_step_` and in one hlo
std::vector<XLATensorPtr> tensors_to_sync = {step, param, buf};
graph_executor->ApplyEagerSync(tensors_to_sync);
}
}
void adam_optimizer_step_(const XLATensorPtr& found_inf, XLATensorPtr& step,
XLATensorPtr& param, const XLATensorPtr& grad,
XLATensorPtr& exp_avg, XLATensorPtr& exp_avg_sq,
XLATensorPtr& max_exp_avg_sq, double beta1,
double beta2, double lr, double weight_decay,
double eps, bool amsgrad, bool maximize,
bool use_adamw) {
torch::lazy::Value grad_value =
maximize ? mul(grad, -1)->GetIrValue() : grad->GetIrValue();
torch::lazy::Value beta1_value = XLAGraphExecutor::Get()->GetIrValueForScalar(
beta1, found_inf->shape(), found_inf->GetDevice());
torch::lazy::Value beta2_value = XLAGraphExecutor::Get()->GetIrValueForScalar(
beta2, found_inf->shape(), found_inf->GetDevice());
torch::lazy::Value lr_value = XLAGraphExecutor::Get()->GetIrValueForScalar(
lr, found_inf->shape(), found_inf->GetDevice());
torch::lazy::Value weight_decay_value =
XLAGraphExecutor::Get()->GetIrValueForScalar(weight_decay, param->shape(),
param->GetDevice());
torch::lazy::Value eps_value = XLAGraphExecutor::Get()->GetIrValueForScalar(
eps, param->shape(), param->GetDevice());
torch::lazy::NodePtr node = torch_xla::MakeNode<AdamOptimizerStep>(
found_inf->GetIrValue(), step->GetIrValue(), param->GetIrValue(),
grad_value, exp_avg->GetIrValue(), exp_avg_sq->GetIrValue(),
max_exp_avg_sq->GetIrValue(), beta1_value, beta2_value, lr_value,
weight_decay_value, eps_value,
/*use_weight_decay=*/weight_decay != 0,
/*use_amsgrad=*/amsgrad, /*use_adamw=*/use_adamw);
step->SetInPlaceIrValue(torch::lazy::Value(node, 0),
/*delay_eager_executation=*/true);
param->SetInPlaceIrValue(torch::lazy::Value(node, 1),
/*delay_eager_executation=*/true);
exp_avg->SetInPlaceIrValue(torch::lazy::Value(node, 2),
/*delay_eager_executation=*/true);
exp_avg_sq->SetInPlaceIrValue(torch::lazy::Value(node, 3),
/*delay_eager_executation=*/true);
if (amsgrad) {
max_exp_avg_sq->SetInPlaceIrValue(torch::lazy::Value(node, 4),
/*delay_eager_executation=*/true);
}
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `adam_optimizer_step_` and in one hlo
std::vector<XLATensorPtr> tensors_to_sync = {step, param, exp_avg,
exp_avg_sq};
if (amsgrad) {
tensors_to_sync.push_back(max_exp_avg_sq);
}
graph_executor->ApplyEagerSync(tensors_to_sync);
}
}
std::vector<XLATensorPtr> user_computation(
const std::string& opname, absl::Span<const XLATensorPtr> inputs,
runtime::ComputationClient::ComputationPtr computation) {
XLA_CHECK(!inputs.empty());
std::vector<torch::lazy::Value> input_values;
for (auto& input : inputs) {
input_values.push_back(input->GetIrValue());
}
torch::lazy::NodePtr node = torch_xla::MakeNode<UserComputation>(
torch::lazy::OpKind::Get(opname), input_values, std::move(computation));
// Cast can be one of the user computation and we don't want to inherit the
// logical_element_type in this case
return inputs.front()->MakeOutputTensors(node,
/*inherit_logical_type=*/false);
}
//////////////////////////////////////////////////////////////////////////////
// ATEN operators follows here, listed in alphabetical order.
//////////////////////////////////////////////////////////////////////////////
void __ilshift__(XLATensorPtr& input, const at::Scalar& other) {
input->SetInPlaceIrValue(Lshift(input->GetIrValue(), other));
}
void __ilshift__(XLATensorPtr& input, const XLATensorPtr& other) {
input->SetInPlaceIrValue(Lshift(input->GetIrValue(), other->GetIrValue()));
}
void __irshift__(XLATensorPtr& input, const at::Scalar& other) {
input->SetInPlaceIrValue(Rshift(input->GetIrValue(), other));
}
void __irshift__(XLATensorPtr& input, const XLATensorPtr& other) {
input->SetInPlaceIrValue(Rshift(input->GetIrValue(), other->GetIrValue()));
}
XLATensorPtr __lshift__(const XLATensorPtr& input, const at::Scalar& other,
std::optional<at::ScalarType> logical_element_type) {
return input->CreateFrom(Lshift(input->GetIrValue(), other),
logical_element_type);
}
XLATensorPtr __lshift__(const XLATensorPtr& input, const XLATensorPtr& other,
std::optional<at::ScalarType> logical_element_type) {
return input->CreateFrom(Lshift(input->GetIrValue(), other->GetIrValue()),
logical_element_type);
}
XLATensorPtr __rshift__(const XLATensorPtr& input, const at::Scalar& other,
std::optional<at::ScalarType> logical_element_type) {
return input->CreateFrom(Rshift(input->GetIrValue(), other),
logical_element_type);
}
XLATensorPtr __rshift__(const XLATensorPtr& input, const XLATensorPtr& other,
std::optional<at::ScalarType> logical_element_type) {
return input->CreateFrom(Rshift(input->GetIrValue(), other->GetIrValue()),
logical_element_type);
}
std::tuple<XLATensorPtr, XLATensorPtr> adaptive_max_pool2d(
const XLATensorPtr& input, std::vector<int64_t> output_size) {
torch::lazy::NodePtr node =
torch_xla::MakeNode<AdaptiveMaxPool2d>(input->GetIrValue(), output_size);
XLATensorPtr out = input->CreateFrom(torch::lazy::Value(node, 0),
/*delay_eager_executation=*/true);
XLATensorPtr indices =
input->CreateFrom(torch::lazy::Value(node, 1), at::ScalarType::Long,
/*delay_eager_executation=*/true);
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `adaptive_max_pool2d` and in one hlo
std::vector<XLATensorPtr> tensors_to_sync = {out, indices};
graph_executor->ApplyEagerSync(tensors_to_sync);
}
return std::make_tuple(std::move(out), std::move(indices));
}
XLATensorPtr adaptive_max_pool2d_backward(const XLATensorPtr& grad_output,
const XLATensorPtr& input) {
return input->CreateFrom(AdaptiveMaxPool2dBackward(grad_output->GetIrValue(),
input->GetIrValue()));
}
XLATensorPtr _adaptive_avg_pool2d(const XLATensorPtr& input,
std::vector<int64_t> output_size) {
return input->CreateFrom(torch_xla::MakeNode<AdaptiveAvgPool2d>(
input->GetIrValue(), std::move(output_size)));
}
XLATensorPtr _adaptive_avg_pool2d_backward(const XLATensorPtr& grad_output,
const XLATensorPtr& input) {
return input->CreateFrom(torch_xla::MakeNode<AdaptiveAvgPool2dBackward>(
grad_output->GetIrValue(), input->GetIrValue()));
}
void _amp_foreach_non_finite_check_and_unscale_(std::vector<XLATensorPtr> self,
XLATensorPtr& found_inf,
const XLATensorPtr& inv_scale) {
std::vector<torch::lazy::Value> inputs;
XLATensorPtr new_inv_scale = max(inv_scale);
for (const auto& x : self) {
inputs.push_back(x->GetIrValue());
}
torch::lazy::NodePtr node =
torch_xla::MakeNode<AmpForachNonFiniteCheckAndUnscale>(
inputs, found_inf->GetIrValue(), new_inv_scale->GetIrValue());
for (size_t i = 0; i < self.size(); ++i) {
self[i]->SetInPlaceIrValue(torch::lazy::Value(node, i),
/*delay_eager_executation=*/true);
}
found_inf->SetInPlaceIrValue(torch::lazy::Value(node, self.size()),
/*delay_eager_executation=*/true);
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the
// `_amp_foreach_non_finite_check_and_unscale_` and in one hlo
std::vector<XLATensorPtr> tensors_to_sync = self;
tensors_to_sync.push_back(found_inf);
graph_executor->ApplyEagerSync(tensors_to_sync);
}
}
void _amp_update_scale_(XLATensorPtr& current_scale,
XLATensorPtr& growth_tracker,
const XLATensorPtr& found_inf,
double scale_growth_factor, double scale_backoff_factor,
int growth_interval) {
torch::lazy::NodePtr node = torch_xla::MakeNode<AmpUpdateScale>(
growth_tracker->GetIrValue(), current_scale->GetIrValue(),
found_inf->GetIrValue(), scale_growth_factor, scale_backoff_factor,
growth_interval);
growth_tracker->SetInPlaceIrValue(torch::lazy::Value(node, 1),
/*delay_eager_executation=*/true);
current_scale->SetInPlaceIrValue(torch::lazy::Value(node, 0),
/*delay_eager_executation=*/true);
XLAGraphExecutor* graph_executor = XLAGraphExecutor::Get();
if (graph_executor->UseEagerMode()) {
// Execute the HLO that will run the `_amp_update_scale_` and in one hlo
std::vector<XLATensorPtr> tensors_to_sync = {growth_tracker, current_scale};
graph_executor->ApplyEagerSync(tensors_to_sync);
}
}
XLATensorPtr abs(const XLATensorPtr& input) {
return input->CreateFrom(torch_xla::MakeNode<Abs>(input->GetIrValue()));
}
XLATensorPtr add(const XLATensorPtr& input, const XLATensorPtr& other,
const at::Scalar& alpha,
std::optional<at::ScalarType> logical_element_type) {
xla::Shape input_shape = input->shape().get();
xla::Shape other_shape = other->shape().get();
torch::lazy::Value constant;
const torch::lazy::BackendDevice& device = input->GetDevice();
if (!input_shape.is_dynamic() && !other_shape.is_dynamic()) {
constant = XLAGraphExecutor::Get()->GetIrValueForScalar(
alpha,
xla::ShapeUtil::MakeScalarShape(
MakeXlaPrimitiveType(other->dtype(), &device)),
logical_element_type, device);
} else {
SymIntElements sym_int_elements(other->GetIrValue());
constant = XLAGraphExecutor::Get()->GetIrValueForScalar(
alpha,
xla::ShapeUtil::MakeScalarShape(
MakeXlaPrimitiveType(other->dtype(), &device)),
sym_int_elements, logical_element_type, device);
}
return input->CreateFrom(
Add(input->GetIrValue(), other->GetIrValue(), constant),
logical_element_type);
}
XLATensorPtr add(const XLATensorPtr& input, const at::Scalar& other,
const at::Scalar& alpha,
std::optional<at::ScalarType> logical_element_type) {
const torch::lazy::BackendDevice& device = input->GetDevice();
torch::lazy::Value other_constant =
XLAGraphExecutor::Get()->GetIrValueForScalar(
other,
xla::ShapeUtil::MakeScalarShape(
MakeXlaPrimitiveType(input->dtype(), &device)),
logical_element_type, device);
torch::lazy::Value alpha_constant =
XLAGraphExecutor::Get()->GetIrValueForScalar(
alpha,
xla::ShapeUtil::MakeScalarShape(
MakeXlaPrimitiveType(input->dtype(), &device)),
logical_element_type, device);
return input->CreateFrom(
Add(input->GetIrValue(), other_constant, alpha_constant),
logical_element_type);