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[STABLE ABI] Port forced_align #4079

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[STABLE ABI] Port forced_align #4079

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154 changes: 92 additions & 62 deletions src/libtorchaudio/forced_align/cpu/compute.cpp
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Original file line number Diff line number Diff line change
@@ -1,26 +1,24 @@
#include <torch/script.h>
#include <torch/torch.h>
#include <libtorchaudio/utils.h>
#include <torch/csrc/stable/library.h>
#include <torch/csrc/stable/tensor.h>
#include <torch/csrc/stable/ops.h>
#include <torch/csrc/inductor/aoti_torch/c/shim.h>

using namespace std;

namespace torchaudio {
namespace alignment {
namespace cpu {

using torch::stable::Tensor;
using torch::headeronly::ScalarType;

// Inspired from
// https://github.com/flashlight/sequence/blob/main/flashlight/lib/sequence/criterion/cpu/ConnectionistTemporalClassificationCriterion.cpp
template <typename scalar_t, at::ScalarType target_scalar_type>
template <typename scalar_t, ScalarType target_scalar_type>
void forced_align_impl(
const torch::Tensor& logProbs,
const torch::Tensor& targets,
const Tensor& logProbs,
const Tensor& targets,
const int64_t blank,
torch::Tensor& paths) {
Tensor& paths) {
const scalar_t kNegInfinity = -std::numeric_limits<scalar_t>::infinity();
using target_t = typename std::
conditional<target_scalar_type == torch::kInt, int, int64_t>::type;
conditional<target_scalar_type == ScalarType::Int, int, int64_t>::type;
const auto batchIndex =
0; // TODO: support batch version and use the real batch index
const auto T = logProbs.size(1);
Expand Down Expand Up @@ -132,79 +130,111 @@ void forced_align_impl(
delete[] backPtr_a;
}

std::tuple<torch::Tensor, torch::Tensor> compute(
const torch::Tensor& logProbs,
const torch::Tensor& targets,
const torch::Tensor& inputLengths,
const torch::Tensor& targetLengths,
std::tuple<Tensor, Tensor> compute(
const Tensor& logProbs,
const Tensor& targets,
const Tensor& inputLengths,
const Tensor& targetLengths,
const int64_t blank) {
TORCH_CHECK(logProbs.is_cpu(), "log_probs must be a CPU tensor");
TORCH_CHECK(targets.is_cpu(), "targets must be a CPU tensor");
TORCH_CHECK(
logProbs.device() == targets.device(),
"log_probs and targets need to be on the same device");
TORCH_CHECK(
logProbs.dtype() == torch::kFloat64 ||
logProbs.dtype() == torch::kFloat32 ||
logProbs.dtype() == torch::kFloat16,
STD_TORCH_CHECK(logProbs.is_cpu(), "log_probs must be a CPU tensor");
STD_TORCH_CHECK(targets.is_cpu(), "targets must be a CPU tensor");
STD_TORCH_CHECK(inputLengths.is_cpu(), "input_lengths must be a CPU tensor");
STD_TORCH_CHECK(targetLengths.is_cpu(), "target_lengths must be a CPU tensor");
STD_TORCH_CHECK(
logProbs.scalar_type() == ScalarType::Double ||
logProbs.scalar_type() == ScalarType::Float ||
logProbs.scalar_type() == ScalarType::Half,
"log_probs must be float64, float32 or float16 (half) type");
TORCH_CHECK(
targets.dtype() == torch::kInt32 || targets.dtype() == torch::kInt64,
STD_TORCH_CHECK(
targets.scalar_type() == ScalarType::Int || targets.scalar_type() == ScalarType::Long,
"targets must be int32 or int64 type");
TORCH_CHECK(logProbs.is_contiguous(), "log_probs must be contiguous");
TORCH_CHECK(targets.is_contiguous(), "targets must be contiguous");
TORCH_CHECK(
STD_TORCH_CHECK(logProbs.is_contiguous(), "log_probs must be contiguous");
STD_TORCH_CHECK(targets.is_contiguous(), "targets must be contiguous");
STD_TORCH_CHECK(
logProbs.dim() == 3,
"log_probs must be 3-D (batch_size, input length, num classes)");
TORCH_CHECK(
STD_TORCH_CHECK(
targets.dim() == 2, "targets must be 2-D (batch_size, target length,)");
TORCH_CHECK(
STD_TORCH_CHECK(
inputLengths.dim() == 1, "input_lengths must be 1-D (batch_size,)");
TORCH_CHECK(
STD_TORCH_CHECK(
targetLengths.dim() == 1, "target_lengths must be 1-D (batch_size,)");
TORCH_CHECK(
STD_TORCH_CHECK(
logProbs.size(0) == 1,
"The batch dimension for log_probs must be 1 at the current version.")
TORCH_CHECK(
STD_TORCH_CHECK(
targets.size(0) == 1,
"The batch dimension for targets must be 1 at the current version.")
TORCH_CHECK(
STD_TORCH_CHECK(
blank >= 0 && blank < logProbs.size(-1),
"blank must be within [0, num classes)");

TORCH_CHECK(
logProbs.size(1) == at::max(inputLengths).item().toInt(),
STD_TORCH_CHECK(
logProbs.size(1) == torchaudio::util::max<int>(inputLengths),
"input length mismatch");
TORCH_CHECK(
targets.size(1) == at::max(targetLengths).item().toInt(),
STD_TORCH_CHECK(
targets.size(1) == torchaudio::util::max<int>(targetLengths),
"target length mismatch");

const auto B = logProbs.size(0);
const auto T = logProbs.size(1);
auto paths = torch::zeros(
{B, T},
torch::TensorOptions().device(targets.device()).dtype(targets.dtype()));
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
logProbs.scalar_type(), "forced_align_impl", [&] {
if (targets.scalar_type() == torch::kInt64) {
forced_align_impl<scalar_t, torch::kInt64>(
logProbs, targets, blank, paths);
} else {
forced_align_impl<scalar_t, torch::kInt32>(
logProbs, targets, blank, paths);
}
});
return std::make_tuple(
paths,
logProbs
);
}

Tensor paths = torch::stable::new_empty(targets, {B, T});
torch::stable::zero_(paths);

switch (logProbs.scalar_type()) {
case ScalarType::Double: {
if (targets.scalar_type() == ScalarType::Long) {
forced_align_impl<double, ScalarType::Long>(logProbs, targets, blank, paths);
} else if (targets.scalar_type() == ScalarType::Int) {
forced_align_impl<double, ScalarType::Int>(logProbs, targets, blank, paths);
} else {
STD_TORCH_CHECK(false, "unreachable");
}
break;
}
case ScalarType::Float: {
if (targets.scalar_type() == ScalarType::Long) {
forced_align_impl<float, ScalarType::Long>(logProbs, targets, blank, paths);
} else if (targets.scalar_type() == ScalarType::Int) {
forced_align_impl<float, ScalarType::Int>(logProbs, targets, blank, paths);
} else {
STD_TORCH_CHECK(false, "unreachable");
}
break;
}
case ScalarType::Half: {
if (targets.scalar_type() == ScalarType::Long) {
forced_align_impl<c10::Half, ScalarType::Long>(logProbs, targets, blank, paths);
} else if (targets.scalar_type() == ScalarType::Int) {
forced_align_impl<c10::Half, ScalarType::Int>(logProbs, targets, blank, paths);
} else {
STD_TORCH_CHECK(false, "unreachable");
}
break;
}
default: {
STD_TORCH_CHECK(false, "unreachable");
}
};

return std::make_tuple(paths, logProbs);
}

void boxed_forced_align_cpu(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
STD_TORCH_CHECK(num_args == 5, "num_args must be 5");
STD_TORCH_CHECK(num_outputs == 2, "num_outputs must be 2");
std::tuple<Tensor, Tensor> res = compute(
/*logProbs*/to<Tensor>(stack[0]),
/*targets*/to<Tensor>(stack[1]),
/*logit_lengths*/to<Tensor>(stack[2]),
/*target_lengths*/to<Tensor>(stack[3]),
/*blank*/float(to<int64_t>(stack[4])));
stack[0] = from(std::get<0>(res));
stack[1] = from(std::get<1>(res));
}

TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
m.impl("forced_align", &compute);
STABLE_TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
m.impl("forced_align", &boxed_forced_align_cpu);
}

} // namespace cpu
Expand Down
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