espnet2.asr.transducer.rnnt_multi_blank.utils.cuda_utils.gpu_rnnt_kernel.compute_alphas_kernel

source

espnet2.asr.transducer.rnnt_multi_blank.utils.cuda_utils.gpu_rnnt_kernel.compute_alphas_kernel(acts: Tensor, denom: Tensor, alphas: Tensor, llForward: Tensor, xlen: Tensor, ylen: Tensor, mlabels: Tensor, minibatch: int, maxT: int, maxU: int, alphabet_size: int, blank_: int)

Compute alpha (forward variable) probabilities over the transduction step.

• Parameters:
  • acts – Tensor of shape [B, T, U, V+1] flattened. Represents the logprobs activation tensor.
  • denom – Tensor of shape [B, T, U] flattened. Represents the denominator of the logprobs activation tensor across entire vocabulary.
  • alphas – Zero tensor of shape [B, T, U]. Will be updated inside the kernel with the forward variable probabilities.
  • llForward – Zero tensor of shape [B]. Represents the log-likelihood of the forward pass. Returned as the forward pass loss that is reduced by the optimizer.
  • xlen – Vector of length B which contains the actual acoustic sequence lengths in the padded activation tensor.
  • ylen – Vector of length B which contains the actual target sequence lengths in the padded activation tensor.
  • mlabels – Matrix of shape [B, U+1] (+1 here is due to <SOS> token
    • usually the RNNT blank). The matrix contains the padded target transcription that must be predicted.
  • minibatch – Int representing the batch size.
  • maxT – The maximum possible acoustic sequence length. Represents T in the logprobs tensor.
  • maxU – The maximum possible target sequence length. Represents U in the logprobs tensor.
  • alphabet_size – The vocabulary dimension V+1 (inclusive of RNNT blank).
  • blank – Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab.

Updates: : Kernel inplace updates the following inputs:

• alphas: forward variable scores.
• llForward: log-likelihood of forward variable.