[STABLE ABI] Port rnnt.

src/libtorchaudio/CMakeLists.txt

@@ -22,19 +22,13 @@ if(BUILD_RNNT)
   list(
     APPEND
     sources
-    rnnt/cpu/compute_alphas.cpp
-    rnnt/cpu/compute_betas.cpp
     rnnt/cpu/compute.cpp
-    rnnt/compute_alphas.cpp
-    rnnt/compute_betas.cpp
     rnnt/compute.cpp
     )
   if (USE_CUDA)
     list(
       APPEND
       sources
-      rnnt/gpu/compute_alphas.cu
-      rnnt/gpu/compute_betas.cu
       rnnt/gpu/compute.cu
       )
   endif()

src/libtorchaudio/forced_align/cpu/compute.cpp

@@ -1,8 +1,5 @@
 #include <torch/script.h>
 #include <torch/torch.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;

src/libtorchaudio/rnnt/compute.cpp

@@ -1,34 +1,12 @@
-#include <libtorchaudio/rnnt/compute.h>
+#include <torch/csrc/stable/library.h>
 
-std::tuple<torch::Tensor, std::optional<torch::Tensor>> rnnt_loss(
-    torch::Tensor& logits,
-    const torch::Tensor& targets,
-    const torch::Tensor& logit_lengths,
-    const torch::Tensor& target_lengths,
-    int64_t blank,
-    double clamp,
-    bool fused_log_softmax = true) {
-  static auto op = torch::Dispatcher::singleton()
-                       .findSchemaOrThrow("torchaudio::rnnt_loss", "")
-                       .typed<decltype(rnnt_loss)>();
-  return op.call(
-      logits,
-      targets,
-      logit_lengths,
-      target_lengths,
-      blank,
-      clamp,
-      fused_log_softmax);
-}
-
-TORCH_LIBRARY_FRAGMENT(torchaudio, m) {
+STABLE_TORCH_LIBRARY_FRAGMENT(torchaudio, m) {
   m.def(
-      "rnnt_loss(Tensor logits,"
+      "rnnt_loss_forward(Tensor logits,"
       "Tensor targets,"
       "Tensor logit_lengths,"
       "Tensor target_lengths,"
       "int blank,"
       "float clamp,"
-      "bool fused_log_softmax) -> (Tensor, Tensor?)");
-  m.def("torchaudio::rnnt_loss_forward", &rnnt_loss);
+      "bool fused_log_softmax) -> (Tensor, Tensor)");
 }

src/libtorchaudio/rnnt/cpu/compute.cpp

@@ -1,76 +1,89 @@
 #include <libtorchaudio/rnnt/cpu/cpu_transducer.h>
-#include <torch/script.h>
+#include <torch/csrc/stable/library.h>
+#include <torch/csrc/stable/ops.h>
 
 namespace torchaudio {
 namespace rnnt {
 namespace cpu {
 
+using torch::stable::Tensor;
+using torch::headeronly::ScalarType;
+
 // Entry point into RNNT Loss
-std::tuple<torch::Tensor, std::optional<torch::Tensor>> compute(
-    torch::Tensor& logits,
-    const torch::Tensor& targets,
-    const torch::Tensor& logit_lengths,
-    const torch::Tensor& target_lengths,
+std::tuple<Tensor, Tensor> compute(
+    const Tensor& logits,
+    const Tensor& targets,
+    const Tensor& logit_lengths,
+    const Tensor& target_lengths,
     int64_t blank,
     double clamp,
     bool fused_log_softmax = true) {
-  TORCH_CHECK(
-      logits.device().type() == targets.device().type(),
+  STD_TORCH_CHECK(logits.is_cpu(), "logits must be on CPU");
+
+  STD_TORCH_CHECK(
+      targets.is_cpu(),
       "logits and targets must be on the same device");
-  TORCH_CHECK(
-      logits.device().type() == logit_lengths.device().type(),
+  STD_TORCH_CHECK(
+      logit_lengths.is_cpu(),
       "logits and logit_lengths must be on the same device");
-  TORCH_CHECK(
-      logits.device().type() == target_lengths.device().type(),
+  STD_TORCH_CHECK(
+      target_lengths.is_cpu(),
       "logits and target_lengths must be on the same device");
 
-  TORCH_CHECK(
-      logits.dtype() == torch::kFloat32 || logits.dtype() == torch::kFloat16,
+  STD_TORCH_CHECK(
+      logits.scalar_type() == ScalarType::Float || logits.scalar_type() == ScalarType::Half,
       "logits must be float32 or float16 (half) type");
-  TORCH_CHECK(targets.dtype() == torch::kInt32, "targets must be int32 type");
-  TORCH_CHECK(
-      logit_lengths.dtype() == torch::kInt32,
+
+  STD_TORCH_CHECK(targets.scalar_type() == ScalarType::Int, "targets must be int32 type");
+
+  STD_TORCH_CHECK(
+      logit_lengths.scalar_type() == ScalarType::Int,
       "logit_lengths must be int32 type");
-  TORCH_CHECK(
-      target_lengths.dtype() == torch::kInt32,
+  STD_TORCH_CHECK(
+      target_lengths.scalar_type() == ScalarType::Int,
       "target_lengths must be int32 type");
 
-  TORCH_CHECK(logits.is_contiguous(), "logits must be contiguous");
-  TORCH_CHECK(targets.is_contiguous(), "targets must be contiguous");
-  TORCH_CHECK(
+  STD_TORCH_CHECK(logits.is_contiguous(), "logits must be contiguous");
+  STD_TORCH_CHECK(targets.is_contiguous(), "targets must be contiguous");
+  STD_TORCH_CHECK(
       logit_lengths.is_contiguous(), "logit_lengths must be contiguous");
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       target_lengths.is_contiguous(), "target_lengths must be contiguous");
 
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       logits.dim() == 4, "logits must be 4-D (batch, time, target, class)");
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       targets.dim() == 2, "targets must be 2-D (batch, max target length)");
-  TORCH_CHECK(logit_lengths.dim() == 1, "logit_lengths must be 1-D");
-  TORCH_CHECK(target_lengths.dim() == 1, "target_lengths must be 1-D");
+  STD_TORCH_CHECK(logit_lengths.dim() == 1, "logit_lengths must be 1-D");
+  STD_TORCH_CHECK(target_lengths.dim() == 1, "target_lengths must be 1-D");
 
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       logit_lengths.size(0) == logits.size(0),
       "batch dimension mismatch between logits and logit_lengths");
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       target_lengths.size(0) == logits.size(0),
       "batch dimension mismatch between logits and target_lengths");
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       targets.size(0) == logits.size(0),
       "batch dimension mismatch between logits and targets");
 
-  TORCH_CHECK(
+  STD_TORCH_CHECK(
       blank >= 0 && blank < logits.size(-1),
       "blank must be within [0, logits.shape[-1])");
 
-  TORCH_CHECK(
-      logits.size(1) == at::max(logit_lengths).item().toInt(),
+  auto max_ivalue = [](const Tensor& t) {
+    // TODO: eliminate const_cast after pytorch/pytorch#161826 is fixed
+    return reinterpret_cast<int32_t*>(torch::stable::amax(const_cast<Tensor&>(t), {}).data_ptr())[0];
+  };
+
+  STD_TORCH_CHECK(
+      logits.size(1) == max_ivalue(logit_lengths),
       "input length mismatch");
-  TORCH_CHECK(
-      logits.size(2) == at::max(target_lengths).item().toInt() + 1,
+  STD_TORCH_CHECK(
+      logits.size(2) == max_ivalue(target_lengths) + 1,
       "output length mismatch");
-  TORCH_CHECK(
-      targets.size(1) == at::max(target_lengths).item().toInt(),
+  STD_TORCH_CHECK(
+      targets.size(1) + 1 == logits.size(2),
       "target length mismatch");
 
   Options options;
@@ -82,67 +95,70 @@ std::tuple<torch::Tensor, std::optional<torch::Tensor>> compute(
   options.blank_ = blank;
   options.clamp_ = clamp;
   options.fusedLogSmax_ = fused_log_softmax;
-
-  TORCH_CHECK_EQ(logits.device().type(), torch::DeviceType::CPU);
   options.device_ = CPU;
 
-  torch::Tensor costs = torch::empty(
-      options.batchSize_ * options.nHypos_,
-      torch::TensorOptions().device(logits.device()).dtype(logits.dtype()));
-  std::optional<torch::Tensor> gradients = torch::zeros_like(logits);
-
-  torch::Tensor int_workspace = torch::empty(
-      IntWorkspace::ComputeSizeFromOptions(options),
-      torch::TensorOptions()
-          .device(logits.device())
-          .dtype(torch::ScalarType::Int));
+  Tensor costs = torch::stable::new_empty(logits, {options.batchSize_ * options.nHypos_});
+  Tensor gradients = torch::stable::empty_like(logits);
+  torch::stable::fill_(gradients, 0.0);
 
-  torch::Tensor float_workspace = torch::empty(
-      DtypeWorkspace<float>::ComputeSizeFromOptions(options),
-      torch::TensorOptions()
-          .device(logits.device())
-          .dtype(torch::ScalarType::Float));
+  Tensor int_workspace = torch::stable::new_empty(logits, {IntWorkspace::ComputeSizeFromOptions(options)}, ScalarType::Int);
+  Tensor float_workspace = torch::stable::new_empty(logits, {DtypeWorkspace<float>::ComputeSizeFromOptions(options)}, ScalarType::Float);
 
   Workspace<float> workspace(
       /*options=*/options,
-      /*dtype_data=*/float_workspace.data_ptr<float>(),
+      /*dtype_data=*/reinterpret_cast<float*>(float_workspace.data_ptr()),
       /*dtype_size=*/float_workspace.numel(),
-      /*int_data=*/int_workspace.data_ptr<int>(),
+      /*int_data=*/reinterpret_cast<int*>(int_workspace.data_ptr()),
       /*int_size=*/int_workspace.numel());
 
   switch (logits.scalar_type()) {
-    case torch::ScalarType::Float: {
+    case ScalarType::Float: {
       Compute</*DTYPE=*/float, /*CAST_DTYPE=*/float>(
           /*workspace=*/workspace,
-          /*logits=*/logits.data_ptr<float>(),
-          /*targets=*/targets.data_ptr<int>(),
-          /*srcLengths=*/logit_lengths.data_ptr<int>(),
-          /*tgtLengths=*/target_lengths.data_ptr<int>(),
-          /*costs=*/costs.data_ptr<float>(),
-          /*gradients=*/gradients->data_ptr<float>());
+          /*logits=*/reinterpret_cast<float*>(logits.data_ptr()),
+          /*targets=*/reinterpret_cast<int*>(targets.data_ptr()),
+          /*srcLengths=*/reinterpret_cast<int*>(logit_lengths.data_ptr()),
+          /*tgtLengths=*/reinterpret_cast<int*>(target_lengths.data_ptr()),
+          /*costs=*/reinterpret_cast<float*>(costs.data_ptr()),
+          /*gradients=*/reinterpret_cast<float*>(gradients.data_ptr()));
       break;
     }
-    case torch::ScalarType::Half: {
+    case ScalarType::Half: {
       Compute</*DTYPE=*/c10::Half, /*CAST_DTYPE=*/float>(
           /*workspace=*/workspace,
-          /*logits=*/logits.data_ptr<c10::Half>(),
-          /*targets=*/targets.data_ptr<int>(),
-          /*srcLengths=*/logit_lengths.data_ptr<int>(),
-          /*tgtLengths=*/target_lengths.data_ptr<int>(),
-          /*costs=*/costs.data_ptr<c10::Half>(),
-          /*gradients=*/gradients->data_ptr<c10::Half>());
+          /*logits=*/reinterpret_cast<c10::Half*>(logits.data_ptr()),
+          /*targets=*/reinterpret_cast<int*>(targets.data_ptr()),
+          /*srcLengths=*/reinterpret_cast<int*>(logit_lengths.data_ptr()),
+          /*tgtLengths=*/reinterpret_cast<int*>(target_lengths.data_ptr()),
+          /*costs=*/reinterpret_cast<c10::Half*>(costs.data_ptr()),
+          /*gradients=*/reinterpret_cast<c10::Half*>(gradients.data_ptr()));
       break;
     }
     default: {
-      break;
+      STD_TORCH_CHECK(false, "unreachable");
     }
   };
 
   return std::make_tuple(costs, gradients);
 }
 
-TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
-  m.impl("rnnt_loss", &compute);
+void boxed_rnnt_loss(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
+  STD_TORCH_CHECK(num_args == 7, "num_args must be 7");
+  STD_TORCH_CHECK(num_outputs == 2, "num_outputs must be 2");
+  std::tuple<Tensor, Tensor> res = compute(
+      /*logits*/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])),
+      /*clamp*/to<double>(stack[5]),
+      /*fused_log_softmax*/to<bool>(stack[6]));
+  stack[0] = from(std::get<0>(res));
+  stack[1] = from(std::get<1>(res));
+}
+
+STABLE_TORCH_LIBRARY_IMPL(torchaudio, CPU, m) {
+  m.impl("rnnt_loss_forward", &boxed_rnnt_loss);
 }
 
 } // namespace cpu