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#include <test/cpp/lazy/test_lazy_ops_util.h>
#include <torch/csrc/lazy/backend/lowering_context.h>
#include <torch/csrc/lazy/core/ir_builder.h>
#include <torch/csrc/lazy/core/ir_dump_util.h>
#include <torch/csrc/lazy/core/tensor_impl.h>
#include <iostream>
#include <string>
namespace torch {
namespace lazy {
namespace {
bool IsLtcTensor(const at::Tensor& tensor) {
return dynamic_cast<torch::lazy::LTCTensorImpl*>(
tensor.unsafeGetTensorImpl());
}
std::unordered_set<std::string>* CreateIgnoredCounters() {
std::unordered_set<std::string>* icounters =
new std::unordered_set<std::string>();
// Add below the counters whose name need to be ignored when doing
// is-any-counter-changed assertins.
icounters->insert("aten::rand");
return icounters;
}
} // namespace
const std::unordered_set<std::string>* GetIgnoredCounters() {
static const std::unordered_set<std::string>* icounters =
CreateIgnoredCounters();
return icounters;
}
at::Tensor ToCpuTensor(const at::Tensor& tensor) {
// tensor.to() implicitly triggers a sync if t.device=torch::kLazy.
return tensor.to(torch::kCPU);
}
torch::Tensor CopyToDevice(
const torch::Tensor& tensor,
const torch::Device& device) {
return tensor.clone().to(device, /*non_blocking=*/false, /*copy=*/true);
}
bool EqualValues(at::Tensor tensor1, at::Tensor tensor2) {
tensor1 = ToCpuTensor(tensor1);
tensor2 = ToCpuTensor(tensor2);
if (torch::isnan(tensor1).any().item<bool>()) {
EXPECT_TRUE(EqualValues(torch::isnan(tensor1), torch::isnan(tensor2)));
tensor1.nan_to_num_();
tensor2.nan_to_num_();
}
if (tensor1.sizes() != tensor2.sizes() ||
tensor1.dtype() != tensor2.dtype()) {
std::cerr << "Different shape:\n"
<< tensor1.dtype() << " " << tensor1.sizes() << "\n-vs-\n"
<< tensor2.dtype() << " " << tensor2.sizes() << "\n";
return false;
}
at::ScalarType type1 = tensor1.scalar_type();
at::ScalarType type2 = tensor2.scalar_type();
if (type1 != type2) {
tensor1 = tensor1.toType(type2);
}
bool equal = tensor1.equal(tensor2);
return equal;
}
bool EqualValuesNoElementTypeCheck(at::Tensor tensor1, at::Tensor tensor2) {
tensor1 = ToCpuTensor(tensor1);
tensor2 = ToCpuTensor(tensor2);
if (tensor1.sizes() != tensor2.sizes()) {
std::cerr << "Different shape:\n"
<< tensor1.dtype() << " " << tensor1.sizes() << "\n-vs-\n"
<< tensor2.dtype() << " " << tensor2.sizes() << "\n";
return false;
}
at::ScalarType type1 = tensor1.scalar_type();
at::ScalarType type2 = tensor2.scalar_type();
if (type1 != type2) {
tensor1 = tensor1.toType(type2);
}
bool equal = tensor1.equal(tensor2);
return equal;
}
void ForEachDevice(const std::function<void(const torch::Device&)>& devfn) {
// Currently TorchScript backend only supports one type of hardware per
// process, which is set by env. And the ordinal is always 0 given distributed
// training/ multi-device is not supported yet.
auto device = torch::lazy::BackendDevice();
torch::Device torch_device = torch::lazy::backendDeviceToAtenDevice(device);
devfn(torch_device);
}
bool CloseValues(
at::Tensor tensor1,
at::Tensor tensor2,
double rtol,
double atol) {
tensor1 = ToCpuTensor(tensor1);
tensor2 = ToCpuTensor(tensor2);
if (torch::isnan(tensor1).any().item<bool>()) {
EXPECT_TRUE(EqualValues(torch::isnan(tensor1), torch::isnan(tensor2)));
tensor1.nan_to_num_();
tensor2.nan_to_num_();
}
if (tensor1.sizes() != tensor2.sizes() ||
tensor1.dtype() != tensor2.dtype()) {
std::cerr << "Different shape:\n"
<< tensor1.dtype() << " " << tensor1.sizes() << "\n-vs-\n"
<< tensor2.dtype() << " " << tensor2.sizes() << "\n";
return false;
}
bool equal = tensor1.allclose(tensor2, rtol, atol);
return equal;
}
std::string GetTensorTextGraph(at::Tensor tensor) {
torch::lazy::LazyTensorPtr lazy_tensor = torch::lazy::TryGetLtcTensor(tensor);
return torch::lazy::DumpUtil::ToText({lazy_tensor->GetIrValue().node.get()});
}
std::string GetTensorDotGraph(at::Tensor tensor) {
torch::lazy::LazyTensorPtr lazy_tensor = torch::lazy::TryGetLtcTensor(tensor);
return torch::lazy::DumpUtil::ToDot({lazy_tensor->GetIrValue().node.get()});
}
void TestBackward(
const std::vector<torch::Tensor>& inputs,
const torch::Device& device,
const std::function<torch::Tensor(const std::vector<torch::Tensor>&)>&
testfn,
double rtol,
double atol,
int derivative_level) {
std::vector<torch::Tensor> input_vars;
std::vector<torch::Tensor> xinput_vars;
std::vector<torch::Tensor> inputs_w_grad;
std::vector<torch::Tensor> xinputs_w_grad;
for (size_t i = 0; i < inputs.size(); ++i) {
const torch::Tensor& input = inputs[i];
if (input.defined()) {
torch::Tensor oinput =
input.clone().detach().set_requires_grad(input.requires_grad());
input_vars.push_back(oinput);
torch::Tensor xinput = CopyToDevice(input, device)
.detach()
.set_requires_grad(input.requires_grad());
xinput_vars.push_back(xinput);
if (input.requires_grad()) {
inputs_w_grad.push_back(oinput);
xinputs_w_grad.push_back(xinput);
}
} else {
input_vars.emplace_back();
xinput_vars.emplace_back();
}
}
torch::Tensor output = testfn(input_vars);
torch::Tensor xoutput = testfn(xinput_vars);
torch::lazy::AllClose(output, xoutput, rtol, atol);
std::vector<torch::Tensor> outs = {output};
std::vector<torch::Tensor> xouts = {xoutput};
for (int d = 1; d <= derivative_level; ++d) {
// Check grad of sum(outs) w.r.t inputs_w_grad.
torch::Tensor sum = torch::zeros_like(outs[0]).sum();
torch::Tensor xsum = torch::zeros_like(xouts[0]).sum();
for (size_t i = 0; i < outs.size(); ++i) {
if (outs[i].requires_grad()) {
sum += outs[i].sum();
xsum += xouts[i].sum();
}
}
// Calculating higher order derivative requires create_graph=true
bool create_graph = d != derivative_level;
outs = torch::autograd::grad(
{sum},
inputs_w_grad,
/*grad_outputs=*/{},
/*retain_graph=*/c10::nullopt,
/*create_graph=*/create_graph,
/*allow_unused=*/true);
xouts = torch::autograd::grad(
{xsum},
xinputs_w_grad,
/*grad_outputs=*/{},
/*retain_graph=*/c10::nullopt,
/*create_graph=*/create_graph,
/*allow_unused=*/true);
for (size_t i = 0; i < outs.size(); ++i) {
ASSERT_EQ(outs[i].defined(), xouts[i].defined());
if (outs[i].defined()) {
AllClose(outs[i], xouts[i], rtol, atol);
}
}
}
}
} // namespace lazy
} // namespace torch
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