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kernels-bot commited on 17 days ago

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1 Parent(s): 8cd32bb

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build/torch-stable-abi210-cu126-aarch64-linux/_ops.py +3 -3
build/torch-stable-abi210-cu126-aarch64-linux/{_tdt_loss_cuda_8bdd234.abi3.so → _tdt_loss_cuda_fe2bb66.abi3.so} +1 -1
build/torch-stable-abi210-cu126-aarch64-linux/metadata.json +7 -6
build/torch-stable-abi210-cu126-aarch64-linux/metadata.json.sigstore +1 -1
build/torch-stable-abi210-cu126-aarch64-linux/symbols.json +73 -0
build/torch-stable-abi210-cu128-aarch64-linux/__init__.py +6 -0
build/torch-stable-abi210-cu128-aarch64-linux/_ops.py +9 -0
build/torch-stable-abi210-cu128-aarch64-linux/_tdt_loss_cuda_fe2bb66.abi3.so +3 -0
build/torch-stable-abi210-cu128-aarch64-linux/layers.py +40 -0
build/torch-stable-abi210-cu128-aarch64-linux/loss.py +254 -0
build/torch-stable-abi210-cu128-aarch64-linux/metadata.json +47 -0
build/torch-stable-abi210-cu128-aarch64-linux/metadata.json.sigstore +1 -0
build/torch-stable-abi210-cu128-aarch64-linux/symbols.json +73 -0
build/torch-stable-abi210-cu130-aarch64-linux/_ops.py +3 -3
build/torch-stable-abi210-cu130-aarch64-linux/{_tdt_loss_cuda_8bdd234.abi3.so → _tdt_loss_cuda_fe2bb66.abi3.so} +1 -1
build/torch-stable-abi210-cu130-aarch64-linux/metadata.json +7 -6
build/torch-stable-abi210-cu130-aarch64-linux/metadata.json.sigstore +1 -1
build/torch-stable-abi210-cu130-aarch64-linux/symbols.json +73 -0
build/torch-stable-abi210-cu132-aarch64-linux/_ops.py +3 -3
build/torch-stable-abi210-cu132-aarch64-linux/{_tdt_loss_cuda_8bdd234.abi3.so → _tdt_loss_cuda_fe2bb66.abi3.so} +1 -1
build/torch-stable-abi210-cu132-aarch64-linux/metadata.json +7 -6
build/torch-stable-abi210-cu132-aarch64-linux/metadata.json.sigstore +1 -1
build/torch-stable-abi210-cu132-aarch64-linux/symbols.json +73 -0
build/torch-stable-abi210-cu134-aarch64-linux/__init__.py +6 -0
build/torch-stable-abi210-cu134-aarch64-linux/_ops.py +9 -0
build/torch-stable-abi210-cu134-aarch64-linux/_tdt_loss_cuda_fe2bb66.abi3.so +3 -0
build/torch-stable-abi210-cu134-aarch64-linux/layers.py +40 -0
build/torch-stable-abi210-cu134-aarch64-linux/loss.py +254 -0
build/torch-stable-abi210-cu134-aarch64-linux/metadata.json +47 -0
build/torch-stable-abi210-cu134-aarch64-linux/metadata.json.sigstore +1 -0
build/torch-stable-abi210-cu134-aarch64-linux/symbols.json +73 -0

build/torch-stable-abi210-cu126-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _tdt_loss_cuda_8bdd234
-ops = torch.ops._tdt_loss_cuda_8bdd234
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_tdt_loss_cuda_8bdd234::{op_name}"

 import torch
+from . import _tdt_loss_cuda_fe2bb66
+ops = torch.ops._tdt_loss_cuda_fe2bb66
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_tdt_loss_cuda_fe2bb66::{op_name}"

build/torch-stable-abi210-cu126-aarch64-linux/{_tdt_loss_cuda_8bdd234.abi3.so → _tdt_loss_cuda_fe2bb66.abi3.so} RENAMED Viewed

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build/torch-stable-abi210-cu126-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "tdt-loss",
-  "id": "_tdt_loss_cuda_8bdd234",
   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "Apache-2.0",
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       "layers.py": "76Xr2iKOW3v6xtD8gct2jH6LMwVbqJSD8txMe5r0Nlk=",
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 {
   "name": "tdt-loss",
+  "id": "_tdt_loss_cuda_fe2bb66",
   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "Apache-2.0",
     "algorithm": "sha256",
     "files": {
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+      "_ops.py": "OrFGElR/Z4ojQBmY0Ij0VyxBZHlO4uCyCD9TW8uuAdQ=",
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       "dirty": false
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     "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
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build/torch-stable-abi210-cu126-aarch64-linux/metadata.json.sigstore CHANGED Viewed

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build/torch-stable-abi210-cu126-aarch64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,73 @@

+{
+  "schema_version": 1,
+  "module": "_tdt_loss_cuda_fe2bb66",
+  "functions": [
+    {
+      "name": "tdt_loss",
+      "qualname": "tdt_loss",
+      "module": "_tdt_loss_cuda_fe2bb66.loss",
+      "kind": "function",
+      "doc": "Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).\n\nArgs:\n    token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.\n        Only the last dimension needs to be contiguous.\n    duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.\n    targets: Target labels of shape `(batch, U)`.\n    logit_lengths: Encoder output lengths of shape `(batch,)`.\n    target_lengths: Target lengths of shape `(batch,)`.\n    blank_token_id: Blank token id.\n    durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.\n    sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.\n    reduction: One of `\"mean_volume\"`, `\"mean_batch\"`, `\"mean\"`, `\"sum\"` or `\"none\"`, mirroring NeMo's\n        `RNNTLoss`. `\"mean\"` divides each loss by its target length before averaging over the batch.\n\nReturns:\n    Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction=\"none\"`). Samples without a\n    valid alignment get an infinite loss and a zero gradient.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "token_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "duration_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "targets",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "logit_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "target_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "blank_token_id",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "durations",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "Sequence[int] | torch.Tensor"
+          },
+          {
+            "name": "sigma",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "0.0",
+            "annotation": "float"
+          },
+          {
+            "name": "reduction",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "'mean'",
+            "annotation": "str"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch-stable-abi210-cu128-aarch64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,6 @@

+"""TDT (Token-and-Duration Transducer) loss CUDA kernel."""
+from . import layers
+from .loss import tdt_loss
+__all__ = ["layers", "tdt_loss"]

build/torch-stable-abi210-cu128-aarch64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _tdt_loss_cuda_fe2bb66
+ops = torch.ops._tdt_loss_cuda_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_tdt_loss_cuda_fe2bb66::{op_name}"

build/torch-stable-abi210-cu128-aarch64-linux/_tdt_loss_cuda_fe2bb66.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:dcab968eef1ea592b57b6ad306adf2328e5df9f8af53d524081f7b5847aff855
+size 2384360

build/torch-stable-abi210-cu128-aarch64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,40 @@

+from typing import Sequence, Union
+import torch
+import torch.nn as nn
+from .loss import tdt_loss
+class TDTLoss(nn.Module):
+    """
+    Stateless layer computing the TDT loss with the CUDA kernels. Its forward has the same signature as
+    `transformers.loss.loss_tdt.tdt_loss`, so it can replace it through `kernels.kernelize`.
+    """
+    has_backward = True
+    can_torch_compile = True
+    def forward(
+        self,
+        token_logits: torch.Tensor,
+        duration_logits: torch.Tensor,
+        targets: torch.Tensor,
+        logit_lengths: torch.Tensor,
+        target_lengths: torch.Tensor,
+        blank_token_id: int,
+        durations: Union[Sequence[int], torch.Tensor],
+        sigma: float = 0.0,
+        reduction: str = "mean",
+    ) -> torch.Tensor:
+        return tdt_loss(
+            token_logits,
+            duration_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            blank_token_id,
+            durations,
+            sigma=sigma,
+            reduction=reduction,
+        )

build/torch-stable-abi210-cu128-aarch64-linux/loss.py ADDED Viewed

	@@ -0,0 +1,254 @@

+"""TDT (Token-and-Duration Transducer) loss: autograd function and functional API."""
+from typing import Dict, Sequence, Tuple, Union
+import torch
+from ._ops import add_op_namespace_prefix, ops
+_REDUCTIONS = ("mean_volume", "mean_batch", "mean", "sum", "none")
+_DURATIONS_CACHE: Dict[Tuple[Tuple[int, ...], torch.device], torch.Tensor] = {}
+def _durations_tensor(durations: Union[Sequence[int], torch.Tensor], device: torch.device) -> torch.Tensor:
+    """Validated int32 tensor of durations on `device`, cached in eager mode since they are fixed for a model."""
+    if isinstance(durations, torch.Tensor):
+        durations = durations.tolist()
+    key = (tuple(int(d) for d in durations), device)
+    if any(d < 0 for d in key[0]):
+        raise ValueError(f"Durations must be non-negative, got {list(key[0])}.")
+    if torch.compiler.is_compiling():
+        return torch.tensor(key[0], device=device, dtype=torch.int32)
+    if key not in _DURATIONS_CACHE:
+        _DURATIONS_CACHE[key] = torch.tensor(key[0], device=device, dtype=torch.int32)
+    return _DURATIONS_CACHE[key]
+def _last_dim_contiguous(x: torch.Tensor) -> torch.Tensor:
+    # The kernels accept arbitrary strides for the (batch, T, U) dims, so slices of a
+    # joint `(..., vocab_size + num_durations)` output can be passed without a copy.
+    return x if x.stride(-1) == 1 else x.contiguous()
+# The ops only write to preallocated outputs, so their fake (meta) implementations have nothing to do. They let
+# `torch.compile` trace through the ops.
+@torch.library.register_fake(add_op_namespace_prefix("tdt_logprobs_fwd"))
+def _(
+    token_logits,
+    duration_logits,
+    targets,
+    logit_lengths,
+    target_lengths,
+    blank_id,
+    sigma,
+    blank_lp,
+    label_lp,
+    dur_lp,
+    token_lse,
+):
+    return None
+@torch.library.register_fake(add_op_namespace_prefix("tdt_loss_fwd"))
+def _(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, alphas, log_ll):
+    return None
+@torch.library.register_fake(add_op_namespace_prefix("tdt_loss_bwd"))
+def _(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, betas):
+    return None
+@torch.library.register_fake(add_op_namespace_prefix("tdt_logits_grad"))
+def _(
+    token_logits,
+    targets,
+    logit_lengths,
+    target_lengths,
+    durations,
+    blank_lp,
+    label_lp,
+    dur_lp,
+    token_lse,
+    alphas,
+    betas,
+    log_ll,
+    grad_loss,
+    blank_id,
+    grad_token_logits,
+    grad_duration_logits,
+):
+    return None
+class _TDTLossFunction(torch.autograd.Function):
+    """Per-sample TDT loss (negative log-likelihood) with a CUDA forward and backward."""
+    @staticmethod
+    def forward(
+        ctx,
+        token_logits: torch.Tensor,
+        duration_logits: torch.Tensor,
+        targets: torch.Tensor,
+        logit_lengths: torch.Tensor,
+        target_lengths: torch.Tensor,
+        durations: torch.Tensor,
+        blank_id: int,
+        sigma: float,
+    ) -> torch.Tensor:
+        token_logits = _last_dim_contiguous(token_logits)
+        duration_logits = _last_dim_contiguous(duration_logits)
+        B, max_T, max_U, _ = token_logits.shape
+        D = duration_logits.shape[-1]
+        f32 = dict(device=token_logits.device, dtype=torch.float32)
+        blank_lp = torch.empty(B, max_T, max_U, **f32)
+        label_lp = torch.empty(B, max_T, max_U, **f32)
+        dur_lp = torch.empty(B, max_T, max_U, D, **f32)
+        token_lse = torch.empty(B, max_T, max_U, **f32)
+        ops.tdt_logprobs_fwd(
+            token_logits,
+            duration_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            blank_id,
+            sigma,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+        )
+        # The lattice recursions run in float64, see lattice.cu.
+        f64 = dict(device=token_logits.device, dtype=torch.float64)
+        alphas = torch.empty(B, max_T, max_U, **f64)
+        log_ll = torch.empty(B, **f64)
+        ops.tdt_loss_fwd(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, alphas, log_ll)
+        ctx.save_for_backward(
+            token_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+            alphas,
+            log_ll,
+        )
+        ctx.blank_id = blank_id
+        ctx.duration_shape = duration_logits.shape
+        ctx.duration_dtype = duration_logits.dtype
+        return -log_ll.float()
+    @staticmethod
+    def backward(ctx, grad_loss: torch.Tensor):
+        (
+            token_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+            alphas,
+            log_ll,
+        ) = ctx.saved_tensors
+        if not (ctx.needs_input_grad[0] or ctx.needs_input_grad[1]):
+            return (None,) * 8
+        betas = torch.empty_like(alphas)
+        ops.tdt_loss_bwd(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, betas)
+        grad_token_logits = torch.empty(token_logits.shape, device=token_logits.device, dtype=token_logits.dtype)
+        grad_duration_logits = torch.empty(ctx.duration_shape, device=token_logits.device, dtype=ctx.duration_dtype)
+        ops.tdt_logits_grad(
+            token_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+            alphas,
+            betas,
+            log_ll,
+            grad_loss.float().contiguous(),
+            ctx.blank_id,
+            grad_token_logits,
+            grad_duration_logits,
+        )
+        return grad_token_logits, grad_duration_logits, None, None, None, None, None, None
+def tdt_loss(
+    token_logits: torch.Tensor,
+    duration_logits: torch.Tensor,
+    targets: torch.Tensor,
+    logit_lengths: torch.Tensor,
+    target_lengths: torch.Tensor,
+    blank_token_id: int,
+    durations: Union[Sequence[int], torch.Tensor],
+    sigma: float = 0.0,
+    reduction: str = "mean",
+) -> torch.Tensor:
+    """Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).
+    Args:
+        token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.
+            Only the last dimension needs to be contiguous.
+        duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.
+        targets: Target labels of shape `(batch, U)`.
+        logit_lengths: Encoder output lengths of shape `(batch,)`.
+        target_lengths: Target lengths of shape `(batch,)`.
+        blank_token_id: Blank token id.
+        durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.
+        sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.
+        reduction: One of `"mean_volume"`, `"mean_batch"`, `"mean"`, `"sum"` or `"none"`, mirroring NeMo's
+            `RNNTLoss`. `"mean"` divides each loss by its target length before averaging over the batch.
+    Returns:
+        Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction="none"`). Samples without a
+        valid alignment get an infinite loss and a zero gradient.
+    """
+    if reduction not in _REDUCTIONS:
+        raise ValueError(
+            f'Invalid reduction mode "{reduction}". Expected one of {", ".join(repr(r) for r in _REDUCTIONS)}.'
+        )
+    device = token_logits.device
+    durations = _durations_tensor(durations, device)
+    targets = targets.to(device=device, dtype=torch.int32).contiguous()
+    logit_lengths = logit_lengths.to(device=device, dtype=torch.int32).contiguous()
+    target_lengths = target_lengths.to(device=device, dtype=torch.int32).contiguous()
+    losses = _TDTLossFunction.apply(
+        token_logits,
+        duration_logits,
+        targets,
+        logit_lengths,
+        target_lengths,
+        durations,
+        int(blank_token_id),
+        float(sigma),
+    )
+    if reduction == "mean_volume":
+        return losses.sum() / target_lengths.sum().float()
+    if reduction == "mean_batch":
+        return losses.mean()
+    if reduction == "mean":
+        return (losses / target_lengths.float()).mean()
+    if reduction == "sum":
+        return losses.sum()
+    return losses

build/torch-stable-abi210-cu128-aarch64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,47 @@

+{
+  "name": "tdt-loss",
+  "id": "_tdt_loss_cuda_fe2bb66",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "10.1",
+      "12.0+PTX",
+      "7.0",
+      "7.2",
+      "7.5",
+      "8.0",
+      "8.6",
+      "8.7",
+      "8.9",
+      "9.0"
+    ]
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "lJBeJMzaqMSucC/K3266DuYA0JHw96ji1i/gcrHN/ik=",
+      "_ops.py": "OrFGElR/Z4ojQBmY0Ij0VyxBZHlO4uCyCD9TW8uuAdQ=",
+      "_tdt_loss_cuda_fe2bb66.abi3.so": "3KuWju8epZK1e2rTBq3yMo5d+fivU9UkCB97WEev+FU=",
+      "layers.py": "76Xr2iKOW3v6xtD8gct2jH6LMwVbqJSD8txMe5r0Nlk=",
+      "loss.py": "wxR68dHg9dUSiG/YOrtbNDQ5CSxHgkWAETZe1VE1FQM=",
+      "symbols.json": "Uu88dl5tWmpC533wKqo0ZaZ8dXHuzmevhNOI/bXFxbc="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch-stable-abi210-cu128-aarch64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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build/torch-stable-abi210-cu128-aarch64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,73 @@

+{
+  "schema_version": 1,
+  "module": "_tdt_loss_cuda_fe2bb66",
+  "functions": [
+    {
+      "name": "tdt_loss",
+      "qualname": "tdt_loss",
+      "module": "_tdt_loss_cuda_fe2bb66.loss",
+      "kind": "function",
+      "doc": "Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).\n\nArgs:\n    token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.\n        Only the last dimension needs to be contiguous.\n    duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.\n    targets: Target labels of shape `(batch, U)`.\n    logit_lengths: Encoder output lengths of shape `(batch,)`.\n    target_lengths: Target lengths of shape `(batch,)`.\n    blank_token_id: Blank token id.\n    durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.\n    sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.\n    reduction: One of `\"mean_volume\"`, `\"mean_batch\"`, `\"mean\"`, `\"sum\"` or `\"none\"`, mirroring NeMo's\n        `RNNTLoss`. `\"mean\"` divides each loss by its target length before averaging over the batch.\n\nReturns:\n    Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction=\"none\"`). Samples without a\n    valid alignment get an infinite loss and a zero gradient.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "token_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "duration_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "targets",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "logit_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "target_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "blank_token_id",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "durations",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "Sequence[int] | torch.Tensor"
+          },
+          {
+            "name": "sigma",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "0.0",
+            "annotation": "float"
+          },
+          {
+            "name": "reduction",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "'mean'",
+            "annotation": "str"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch-stable-abi210-cu130-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _tdt_loss_cuda_8bdd234
-ops = torch.ops._tdt_loss_cuda_8bdd234
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_tdt_loss_cuda_8bdd234::{op_name}"

 import torch
+from . import _tdt_loss_cuda_fe2bb66
+ops = torch.ops._tdt_loss_cuda_fe2bb66
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_tdt_loss_cuda_fe2bb66::{op_name}"

build/torch-stable-abi210-cu130-aarch64-linux/{_tdt_loss_cuda_8bdd234.abi3.so → _tdt_loss_cuda_fe2bb66.abi3.so} RENAMED Viewed

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build/torch-stable-abi210-cu130-aarch64-linux/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "tdt-loss",
-  "id": "_tdt_loss_cuda_8bdd234",
   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "Apache-2.0",
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     "algorithm": "sha256",
     "files": {
       "__init__.py": "lJBeJMzaqMSucC/K3266DuYA0JHw96ji1i/gcrHN/ik=",
-      "_ops.py": "cH0xzOWosVtZ2ezM/hTe5ql5CGAnZ5/YPobUSLaDVKs=",
-      "_tdt_loss_cuda_8bdd234.abi3.so": "jmFuRuIwh5FXMzUyX/qpitDvcp+Rf7xgKebZhgaLAGM=",
       "layers.py": "76Xr2iKOW3v6xtD8gct2jH6LMwVbqJSD8txMe5r0Nlk=",
-      "loss.py": "wxR68dHg9dUSiG/YOrtbNDQ5CSxHgkWAETZe1VE1FQM="
     }
   },
   "provenance": {
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       "dirty": false
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-      "commit": "8bdd23441c0aad1e36878aec98bd0f05d69eaa6a",
       "dirty": false
     }
   }

 {
   "name": "tdt-loss",
+  "id": "_tdt_loss_cuda_fe2bb66",
   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "Apache-2.0",
     "algorithm": "sha256",
     "files": {
       "__init__.py": "lJBeJMzaqMSucC/K3266DuYA0JHw96ji1i/gcrHN/ik=",
+      "_ops.py": "OrFGElR/Z4ojQBmY0Ij0VyxBZHlO4uCyCD9TW8uuAdQ=",
+      "_tdt_loss_cuda_fe2bb66.abi3.so": "EJ947SYKsq9pSA9FwpfeoIXC8qjrZu54MWh3jlozC+Y=",
       "layers.py": "76Xr2iKOW3v6xtD8gct2jH6LMwVbqJSD8txMe5r0Nlk=",
+      "loss.py": "wxR68dHg9dUSiG/YOrtbNDQ5CSxHgkWAETZe1VE1FQM=",
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     }
   },
   "provenance": {
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       "dirty": false
     },
     "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
       "dirty": false
     }
   }

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build/torch-stable-abi210-cu130-aarch64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,73 @@

+{
+  "schema_version": 1,
+  "module": "_tdt_loss_cuda_fe2bb66",
+  "functions": [
+    {
+      "name": "tdt_loss",
+      "qualname": "tdt_loss",
+      "module": "_tdt_loss_cuda_fe2bb66.loss",
+      "kind": "function",
+      "doc": "Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).\n\nArgs:\n    token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.\n        Only the last dimension needs to be contiguous.\n    duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.\n    targets: Target labels of shape `(batch, U)`.\n    logit_lengths: Encoder output lengths of shape `(batch,)`.\n    target_lengths: Target lengths of shape `(batch,)`.\n    blank_token_id: Blank token id.\n    durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.\n    sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.\n    reduction: One of `\"mean_volume\"`, `\"mean_batch\"`, `\"mean\"`, `\"sum\"` or `\"none\"`, mirroring NeMo's\n        `RNNTLoss`. `\"mean\"` divides each loss by its target length before averaging over the batch.\n\nReturns:\n    Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction=\"none\"`). Samples without a\n    valid alignment get an infinite loss and a zero gradient.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "token_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "duration_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "targets",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "logit_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
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+}

build/torch-stable-abi210-cu132-aarch64-linux/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _tdt_loss_cuda_8bdd234
-ops = torch.ops._tdt_loss_cuda_8bdd234
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_tdt_loss_cuda_8bdd234::{op_name}"

 import torch
+from . import _tdt_loss_cuda_fe2bb66
+ops = torch.ops._tdt_loss_cuda_fe2bb66
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_tdt_loss_cuda_fe2bb66::{op_name}"

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   "license": "Apache-2.0",
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 {
   "name": "tdt-loss",
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   "version": 1,
   "kernels-minver": "0.14.0",
   "license": "Apache-2.0",
     "algorithm": "sha256",
     "files": {
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+      "_ops.py": "OrFGElR/Z4ojQBmY0Ij0VyxBZHlO4uCyCD9TW8uuAdQ=",
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build/torch-stable-abi210-cu132-aarch64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,73 @@

+{
+  "schema_version": 1,
+  "module": "_tdt_loss_cuda_fe2bb66",
+  "functions": [
+    {
+      "name": "tdt_loss",
+      "qualname": "tdt_loss",
+      "module": "_tdt_loss_cuda_fe2bb66.loss",
+      "kind": "function",
+      "doc": "Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).\n\nArgs:\n    token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.\n        Only the last dimension needs to be contiguous.\n    duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.\n    targets: Target labels of shape `(batch, U)`.\n    logit_lengths: Encoder output lengths of shape `(batch,)`.\n    target_lengths: Target lengths of shape `(batch,)`.\n    blank_token_id: Blank token id.\n    durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.\n    sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.\n    reduction: One of `\"mean_volume\"`, `\"mean_batch\"`, `\"mean\"`, `\"sum\"` or `\"none\"`, mirroring NeMo's\n        `RNNTLoss`. `\"mean\"` divides each loss by its target length before averaging over the batch.\n\nReturns:\n    Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction=\"none\"`). Samples without a\n    valid alignment get an infinite loss and a zero gradient.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "token_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "duration_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "targets",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "logit_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "target_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "blank_token_id",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "durations",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "Sequence[int] | torch.Tensor"
+          },
+          {
+            "name": "sigma",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "0.0",
+            "annotation": "float"
+          },
+          {
+            "name": "reduction",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "'mean'",
+            "annotation": "str"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch-stable-abi210-cu134-aarch64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,6 @@

+"""TDT (Token-and-Duration Transducer) loss CUDA kernel."""
+from . import layers
+from .loss import tdt_loss
+__all__ = ["layers", "tdt_loss"]

build/torch-stable-abi210-cu134-aarch64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _tdt_loss_cuda_fe2bb66
+ops = torch.ops._tdt_loss_cuda_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_tdt_loss_cuda_fe2bb66::{op_name}"

build/torch-stable-abi210-cu134-aarch64-linux/_tdt_loss_cuda_fe2bb66.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:12c269f4140d30928f4102a6f270d896dba50b09ecca1df9399cfd556e4a9618
+size 2591112

build/torch-stable-abi210-cu134-aarch64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,40 @@

+from typing import Sequence, Union
+import torch
+import torch.nn as nn
+from .loss import tdt_loss
+class TDTLoss(nn.Module):
+    """
+    Stateless layer computing the TDT loss with the CUDA kernels. Its forward has the same signature as
+    `transformers.loss.loss_tdt.tdt_loss`, so it can replace it through `kernels.kernelize`.
+    """
+    has_backward = True
+    can_torch_compile = True
+    def forward(
+        self,
+        token_logits: torch.Tensor,
+        duration_logits: torch.Tensor,
+        targets: torch.Tensor,
+        logit_lengths: torch.Tensor,
+        target_lengths: torch.Tensor,
+        blank_token_id: int,
+        durations: Union[Sequence[int], torch.Tensor],
+        sigma: float = 0.0,
+        reduction: str = "mean",
+    ) -> torch.Tensor:
+        return tdt_loss(
+            token_logits,
+            duration_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            blank_token_id,
+            durations,
+            sigma=sigma,
+            reduction=reduction,
+        )

build/torch-stable-abi210-cu134-aarch64-linux/loss.py ADDED Viewed

	@@ -0,0 +1,254 @@

+"""TDT (Token-and-Duration Transducer) loss: autograd function and functional API."""
+from typing import Dict, Sequence, Tuple, Union
+import torch
+from ._ops import add_op_namespace_prefix, ops
+_REDUCTIONS = ("mean_volume", "mean_batch", "mean", "sum", "none")
+_DURATIONS_CACHE: Dict[Tuple[Tuple[int, ...], torch.device], torch.Tensor] = {}
+def _durations_tensor(durations: Union[Sequence[int], torch.Tensor], device: torch.device) -> torch.Tensor:
+    """Validated int32 tensor of durations on `device`, cached in eager mode since they are fixed for a model."""
+    if isinstance(durations, torch.Tensor):
+        durations = durations.tolist()
+    key = (tuple(int(d) for d in durations), device)
+    if any(d < 0 for d in key[0]):
+        raise ValueError(f"Durations must be non-negative, got {list(key[0])}.")
+    if torch.compiler.is_compiling():
+        return torch.tensor(key[0], device=device, dtype=torch.int32)
+    if key not in _DURATIONS_CACHE:
+        _DURATIONS_CACHE[key] = torch.tensor(key[0], device=device, dtype=torch.int32)
+    return _DURATIONS_CACHE[key]
+def _last_dim_contiguous(x: torch.Tensor) -> torch.Tensor:
+    # The kernels accept arbitrary strides for the (batch, T, U) dims, so slices of a
+    # joint `(..., vocab_size + num_durations)` output can be passed without a copy.
+    return x if x.stride(-1) == 1 else x.contiguous()
+# The ops only write to preallocated outputs, so their fake (meta) implementations have nothing to do. They let
+# `torch.compile` trace through the ops.
+@torch.library.register_fake(add_op_namespace_prefix("tdt_logprobs_fwd"))
+def _(
+    token_logits,
+    duration_logits,
+    targets,
+    logit_lengths,
+    target_lengths,
+    blank_id,
+    sigma,
+    blank_lp,
+    label_lp,
+    dur_lp,
+    token_lse,
+):
+    return None
+@torch.library.register_fake(add_op_namespace_prefix("tdt_loss_fwd"))
+def _(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, alphas, log_ll):
+    return None
+@torch.library.register_fake(add_op_namespace_prefix("tdt_loss_bwd"))
+def _(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, betas):
+    return None
+@torch.library.register_fake(add_op_namespace_prefix("tdt_logits_grad"))
+def _(
+    token_logits,
+    targets,
+    logit_lengths,
+    target_lengths,
+    durations,
+    blank_lp,
+    label_lp,
+    dur_lp,
+    token_lse,
+    alphas,
+    betas,
+    log_ll,
+    grad_loss,
+    blank_id,
+    grad_token_logits,
+    grad_duration_logits,
+):
+    return None
+class _TDTLossFunction(torch.autograd.Function):
+    """Per-sample TDT loss (negative log-likelihood) with a CUDA forward and backward."""
+    @staticmethod
+    def forward(
+        ctx,
+        token_logits: torch.Tensor,
+        duration_logits: torch.Tensor,
+        targets: torch.Tensor,
+        logit_lengths: torch.Tensor,
+        target_lengths: torch.Tensor,
+        durations: torch.Tensor,
+        blank_id: int,
+        sigma: float,
+    ) -> torch.Tensor:
+        token_logits = _last_dim_contiguous(token_logits)
+        duration_logits = _last_dim_contiguous(duration_logits)
+        B, max_T, max_U, _ = token_logits.shape
+        D = duration_logits.shape[-1]
+        f32 = dict(device=token_logits.device, dtype=torch.float32)
+        blank_lp = torch.empty(B, max_T, max_U, **f32)
+        label_lp = torch.empty(B, max_T, max_U, **f32)
+        dur_lp = torch.empty(B, max_T, max_U, D, **f32)
+        token_lse = torch.empty(B, max_T, max_U, **f32)
+        ops.tdt_logprobs_fwd(
+            token_logits,
+            duration_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            blank_id,
+            sigma,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+        )
+        # The lattice recursions run in float64, see lattice.cu.
+        f64 = dict(device=token_logits.device, dtype=torch.float64)
+        alphas = torch.empty(B, max_T, max_U, **f64)
+        log_ll = torch.empty(B, **f64)
+        ops.tdt_loss_fwd(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, alphas, log_ll)
+        ctx.save_for_backward(
+            token_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+            alphas,
+            log_ll,
+        )
+        ctx.blank_id = blank_id
+        ctx.duration_shape = duration_logits.shape
+        ctx.duration_dtype = duration_logits.dtype
+        return -log_ll.float()
+    @staticmethod
+    def backward(ctx, grad_loss: torch.Tensor):
+        (
+            token_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+            alphas,
+            log_ll,
+        ) = ctx.saved_tensors
+        if not (ctx.needs_input_grad[0] or ctx.needs_input_grad[1]):
+            return (None,) * 8
+        betas = torch.empty_like(alphas)
+        ops.tdt_loss_bwd(blank_lp, label_lp, dur_lp, logit_lengths, target_lengths, durations, betas)
+        grad_token_logits = torch.empty(token_logits.shape, device=token_logits.device, dtype=token_logits.dtype)
+        grad_duration_logits = torch.empty(ctx.duration_shape, device=token_logits.device, dtype=ctx.duration_dtype)
+        ops.tdt_logits_grad(
+            token_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations,
+            blank_lp,
+            label_lp,
+            dur_lp,
+            token_lse,
+            alphas,
+            betas,
+            log_ll,
+            grad_loss.float().contiguous(),
+            ctx.blank_id,
+            grad_token_logits,
+            grad_duration_logits,
+        )
+        return grad_token_logits, grad_duration_logits, None, None, None, None, None, None
+def tdt_loss(
+    token_logits: torch.Tensor,
+    duration_logits: torch.Tensor,
+    targets: torch.Tensor,
+    logit_lengths: torch.Tensor,
+    target_lengths: torch.Tensor,
+    blank_token_id: int,
+    durations: Union[Sequence[int], torch.Tensor],
+    sigma: float = 0.0,
+    reduction: str = "mean",
+) -> torch.Tensor:
+    """Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).
+    Args:
+        token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.
+            Only the last dimension needs to be contiguous.
+        duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.
+        targets: Target labels of shape `(batch, U)`.
+        logit_lengths: Encoder output lengths of shape `(batch,)`.
+        target_lengths: Target lengths of shape `(batch,)`.
+        blank_token_id: Blank token id.
+        durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.
+        sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.
+        reduction: One of `"mean_volume"`, `"mean_batch"`, `"mean"`, `"sum"` or `"none"`, mirroring NeMo's
+            `RNNTLoss`. `"mean"` divides each loss by its target length before averaging over the batch.
+    Returns:
+        Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction="none"`). Samples without a
+        valid alignment get an infinite loss and a zero gradient.
+    """
+    if reduction not in _REDUCTIONS:
+        raise ValueError(
+            f'Invalid reduction mode "{reduction}". Expected one of {", ".join(repr(r) for r in _REDUCTIONS)}.'
+        )
+    device = token_logits.device
+    durations = _durations_tensor(durations, device)
+    targets = targets.to(device=device, dtype=torch.int32).contiguous()
+    logit_lengths = logit_lengths.to(device=device, dtype=torch.int32).contiguous()
+    target_lengths = target_lengths.to(device=device, dtype=torch.int32).contiguous()
+    losses = _TDTLossFunction.apply(
+        token_logits,
+        duration_logits,
+        targets,
+        logit_lengths,
+        target_lengths,
+        durations,
+        int(blank_token_id),
+        float(sigma),
+    )
+    if reduction == "mean_volume":
+        return losses.sum() / target_lengths.sum().float()
+    if reduction == "mean_batch":
+        return losses.mean()
+    if reduction == "mean":
+        return (losses / target_lengths.float()).mean()
+    if reduction == "sum":
+        return losses.sum()
+    return losses

build/torch-stable-abi210-cu134-aarch64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,47 @@

+{
+  "name": "tdt-loss",
+  "id": "_tdt_loss_cuda_fe2bb66",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "10.3",
+      "11.0",
+      "12.0",
+      "12.1+PTX",
+      "7.5",
+      "8.0",
+      "8.6",
+      "8.7",
+      "8.9",
+      "9.0"
+    ]
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "lJBeJMzaqMSucC/K3266DuYA0JHw96ji1i/gcrHN/ik=",
+      "_ops.py": "OrFGElR/Z4ojQBmY0Ij0VyxBZHlO4uCyCD9TW8uuAdQ=",
+      "_tdt_loss_cuda_fe2bb66.abi3.so": "EsJp9BQNMJKPQQKm8nDYltulCwnsyh35OZz9VW5Klhg=",
+      "layers.py": "76Xr2iKOW3v6xtD8gct2jH6LMwVbqJSD8txMe5r0Nlk=",
+      "loss.py": "wxR68dHg9dUSiG/YOrtbNDQ5CSxHgkWAETZe1VE1FQM=",
+      "symbols.json": "Uu88dl5tWmpC533wKqo0ZaZ8dXHuzmevhNOI/bXFxbc="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

build/torch-stable-abi210-cu134-aarch64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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build/torch-stable-abi210-cu134-aarch64-linux/symbols.json ADDED Viewed

	@@ -0,0 +1,73 @@

+{
+  "schema_version": 1,
+  "module": "_tdt_loss_cuda_fe2bb66",
+  "functions": [
+    {
+      "name": "tdt_loss",
+      "qualname": "tdt_loss",
+      "module": "_tdt_loss_cuda_fe2bb66.loss",
+      "kind": "function",
+      "doc": "Compute the TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).\n\nArgs:\n    token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`, in float32, float16 or bfloat16.\n        Only the last dimension needs to be contiguous.\n    duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`, same dtype as `token_logits`.\n    targets: Target labels of shape `(batch, U)`.\n    logit_lengths: Encoder output lengths of shape `(batch,)`.\n    target_lengths: Target lengths of shape `(batch,)`.\n    blank_token_id: Blank token id.\n    durations: Non-negative duration values, e.g. `[0, 1, 2, 3, 4]`.\n    sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.\n    reduction: One of `\"mean_volume\"`, `\"mean_batch\"`, `\"mean\"`, `\"sum\"` or `\"none\"`, mirroring NeMo's\n        `RNNTLoss`. `\"mean\"` divides each loss by its target length before averaging over the batch.\n\nReturns:\n    Scalar loss tensor (or per-example losses of shape `(batch,)` if `reduction=\"none\"`). Samples without a\n    valid alignment get an infinite loss and a zero gradient.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "token_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "duration_logits",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "targets",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "logit_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "target_lengths",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "blank_token_id",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "durations",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "Sequence[int] | torch.Tensor"
+          },
+          {
+            "name": "sigma",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "0.0",
+            "annotation": "float"
+          },
+          {
+            "name": "reduction",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": "'mean'",
+            "annotation": "str"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}