885 lines
37 KiB
Python
885 lines
37 KiB
Python
import math
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from typing import List, Optional, Tuple
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import torch
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__all__ = ["Emformer"]
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def _lengths_to_padding_mask(lengths: torch.Tensor) -> torch.Tensor:
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batch_size = lengths.shape[0]
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max_length = int(torch.max(lengths).item())
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padding_mask = torch.arange(max_length, device=lengths.device, dtype=lengths.dtype).expand(
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batch_size, max_length
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) >= lengths.unsqueeze(1)
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return padding_mask
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def _gen_padding_mask(
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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lengths: torch.Tensor,
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mems: torch.Tensor,
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left_context_key: Optional[torch.Tensor] = None,
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) -> Optional[torch.Tensor]:
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T = right_context.size(0) + utterance.size(0) + summary.size(0)
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B = right_context.size(1)
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if B == 1:
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padding_mask = None
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else:
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right_context_blocks_length = T - torch.max(lengths).int() - summary.size(0)
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left_context_blocks_length = left_context_key.size(0) if left_context_key is not None else 0
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klengths = lengths + mems.size(0) + right_context_blocks_length + left_context_blocks_length
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padding_mask = _lengths_to_padding_mask(lengths=klengths)
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return padding_mask
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def _get_activation_module(activation: str) -> torch.nn.Module:
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if activation == "relu":
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return torch.nn.ReLU()
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elif activation == "gelu":
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return torch.nn.GELU()
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elif activation == "silu":
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return torch.nn.SiLU()
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else:
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raise ValueError(f"Unsupported activation {activation}")
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def _get_weight_init_gains(weight_init_scale_strategy: Optional[str], num_layers: int) -> List[Optional[float]]:
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if weight_init_scale_strategy is None:
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return [None for _ in range(num_layers)]
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elif weight_init_scale_strategy == "depthwise":
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return [1.0 / math.sqrt(layer_idx + 1) for layer_idx in range(num_layers)]
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elif weight_init_scale_strategy == "constant":
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return [1.0 / math.sqrt(2) for layer_idx in range(num_layers)]
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else:
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raise ValueError(f"Unsupported weight_init_scale_strategy value {weight_init_scale_strategy}")
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def _gen_attention_mask_block(
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col_widths: List[int], col_mask: List[bool], num_rows: int, device: torch.device
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) -> torch.Tensor:
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if len(col_widths) != len(col_mask):
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raise ValueError("Length of col_widths must match that of col_mask")
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mask_block = [
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torch.ones(num_rows, col_width, device=device)
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if is_ones_col
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else torch.zeros(num_rows, col_width, device=device)
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for col_width, is_ones_col in zip(col_widths, col_mask)
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]
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return torch.cat(mask_block, dim=1)
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class _EmformerAttention(torch.nn.Module):
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r"""Emformer layer attention module.
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Args:
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input_dim (int): input dimension.
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num_heads (int): number of attention heads in each Emformer layer.
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dropout (float, optional): dropout probability. (Default: 0.0)
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weight_init_gain (float or None, optional): scale factor to apply when initializing
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attention module parameters. (Default: ``None``)
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tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
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negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
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"""
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def __init__(
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self,
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input_dim: int,
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num_heads: int,
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dropout: float = 0.0,
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weight_init_gain: Optional[float] = None,
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tanh_on_mem: bool = False,
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negative_inf: float = -1e8,
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):
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super().__init__()
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if input_dim % num_heads != 0:
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raise ValueError(f"input_dim ({input_dim}) is not a multiple of num_heads ({num_heads}).")
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self.input_dim = input_dim
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self.num_heads = num_heads
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self.dropout = dropout
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self.tanh_on_mem = tanh_on_mem
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self.negative_inf = negative_inf
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self.scaling = (self.input_dim // self.num_heads) ** -0.5
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self.emb_to_key_value = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
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self.emb_to_query = torch.nn.Linear(input_dim, input_dim, bias=True)
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self.out_proj = torch.nn.Linear(input_dim, input_dim, bias=True)
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if weight_init_gain:
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torch.nn.init.xavier_uniform_(self.emb_to_key_value.weight, gain=weight_init_gain)
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torch.nn.init.xavier_uniform_(self.emb_to_query.weight, gain=weight_init_gain)
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def _gen_key_value(self, input: torch.Tensor, mems: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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T, _, _ = input.shape
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summary_length = mems.size(0) + 1
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right_ctx_utterance_block = input[: T - summary_length]
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mems_right_ctx_utterance_block = torch.cat([mems, right_ctx_utterance_block])
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key, value = self.emb_to_key_value(mems_right_ctx_utterance_block).chunk(chunks=2, dim=2)
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return key, value
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def _gen_attention_probs(
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self,
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attention_weights: torch.Tensor,
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attention_mask: torch.Tensor,
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padding_mask: Optional[torch.Tensor],
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) -> torch.Tensor:
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attention_weights_float = attention_weights.float()
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attention_weights_float = attention_weights_float.masked_fill(attention_mask.unsqueeze(0), self.negative_inf)
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T = attention_weights.size(1)
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B = attention_weights.size(0) // self.num_heads
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if padding_mask is not None:
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attention_weights_float = attention_weights_float.view(B, self.num_heads, T, -1)
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attention_weights_float = attention_weights_float.masked_fill(
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padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), self.negative_inf
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)
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attention_weights_float = attention_weights_float.view(B * self.num_heads, T, -1)
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attention_probs = torch.nn.functional.softmax(attention_weights_float, dim=-1).type_as(attention_weights)
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return torch.nn.functional.dropout(attention_probs, p=float(self.dropout), training=self.training)
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def _forward_impl(
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self,
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utterance: torch.Tensor,
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lengths: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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mems: torch.Tensor,
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attention_mask: torch.Tensor,
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left_context_key: Optional[torch.Tensor] = None,
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left_context_val: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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B = utterance.size(1)
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T = right_context.size(0) + utterance.size(0) + summary.size(0)
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# Compute query with [right context, utterance, summary].
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query = self.emb_to_query(torch.cat([right_context, utterance, summary]))
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# Compute key and value with [mems, right context, utterance].
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key, value = self.emb_to_key_value(torch.cat([mems, right_context, utterance])).chunk(chunks=2, dim=2)
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if left_context_key is not None and left_context_val is not None:
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right_context_blocks_length = T - torch.max(lengths).int() - summary.size(0)
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key = torch.cat(
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[
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key[: mems.size(0) + right_context_blocks_length],
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left_context_key,
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key[mems.size(0) + right_context_blocks_length :],
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],
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)
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value = torch.cat(
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[
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value[: mems.size(0) + right_context_blocks_length],
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left_context_val,
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value[mems.size(0) + right_context_blocks_length :],
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],
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)
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# Compute attention weights from query, key, and value.
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reshaped_query, reshaped_key, reshaped_value = [
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tensor.contiguous().view(-1, B * self.num_heads, self.input_dim // self.num_heads).transpose(0, 1)
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for tensor in [query, key, value]
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]
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attention_weights = torch.bmm(reshaped_query * self.scaling, reshaped_key.transpose(1, 2))
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# Compute padding mask.
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padding_mask = _gen_padding_mask(utterance, right_context, summary, lengths, mems, left_context_key)
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# Compute attention probabilities.
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attention_probs = self._gen_attention_probs(attention_weights, attention_mask, padding_mask)
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# Compute attention.
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attention = torch.bmm(attention_probs, reshaped_value)
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if attention.shape != (
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B * self.num_heads,
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T,
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self.input_dim // self.num_heads,
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):
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raise AssertionError("Computed attention has incorrect dimensions")
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attention = attention.transpose(0, 1).contiguous().view(T, B, self.input_dim)
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# Apply output projection.
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output_right_context_mems = self.out_proj(attention)
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summary_length = summary.size(0)
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output_right_context = output_right_context_mems[: T - summary_length]
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output_mems = output_right_context_mems[T - summary_length :]
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if self.tanh_on_mem:
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output_mems = torch.tanh(output_mems)
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else:
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output_mems = torch.clamp(output_mems, min=-10, max=10)
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return output_right_context, output_mems, key, value
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def forward(
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self,
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utterance: torch.Tensor,
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lengths: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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mems: torch.Tensor,
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attention_mask: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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r"""Forward pass for training.
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B: batch size;
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D: feature dimension of each frame;
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T: number of utterance frames;
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R: number of right context frames;
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S: number of summary elements;
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M: number of memory elements.
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Args:
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utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
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lengths (torch.Tensor): with shape `(B,)` and i-th element representing
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number of valid frames for i-th batch element in ``utterance``.
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right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
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summary (torch.Tensor): summary elements, with shape `(S, B, D)`.
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mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
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attention_mask (torch.Tensor): attention mask for underlying attention module.
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Returns:
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(Tensor, Tensor):
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Tensor
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output frames corresponding to utterance and right_context, with shape `(T + R, B, D)`.
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Tensor
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updated memory elements, with shape `(M, B, D)`.
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"""
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output, output_mems, _, _ = self._forward_impl(utterance, lengths, right_context, summary, mems, attention_mask)
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return output, output_mems[:-1]
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@torch.jit.export
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def infer(
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self,
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utterance: torch.Tensor,
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lengths: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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mems: torch.Tensor,
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left_context_key: torch.Tensor,
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left_context_val: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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r"""Forward pass for inference.
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B: batch size;
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D: feature dimension of each frame;
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T: number of utterance frames;
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R: number of right context frames;
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S: number of summary elements;
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M: number of memory elements.
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Args:
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utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
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lengths (torch.Tensor): with shape `(B,)` and i-th element representing
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number of valid frames for i-th batch element in ``utterance``.
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right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
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summary (torch.Tensor): summary elements, with shape `(S, B, D)`.
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mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
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left_context_key (torch.Tensor): left context attention key computed from preceding invocation.
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left_context_val (torch.Tensor): left context attention value computed from preceding invocation.
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Returns:
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(Tensor, Tensor, Tensor, and Tensor):
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Tensor
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output frames corresponding to utterance and right_context, with shape `(T + R, B, D)`.
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Tensor
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updated memory elements, with shape `(M, B, D)`.
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Tensor
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attention key computed for left context and utterance.
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Tensor
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attention value computed for left context and utterance.
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"""
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query_dim = right_context.size(0) + utterance.size(0) + summary.size(0)
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key_dim = right_context.size(0) + utterance.size(0) + mems.size(0) + left_context_key.size(0)
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attention_mask = torch.zeros(query_dim, key_dim).to(dtype=torch.bool, device=utterance.device)
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attention_mask[-1, : mems.size(0)] = True
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output, output_mems, key, value = self._forward_impl(
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utterance,
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lengths,
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right_context,
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summary,
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mems,
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attention_mask,
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left_context_key=left_context_key,
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left_context_val=left_context_val,
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)
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return (
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output,
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output_mems,
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key[mems.size(0) + right_context.size(0) :],
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value[mems.size(0) + right_context.size(0) :],
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)
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class _EmformerLayer(torch.nn.Module):
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r"""Emformer layer that constitutes Emformer.
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Args:
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input_dim (int): input dimension.
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num_heads (int): number of attention heads.
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ffn_dim: (int): hidden layer dimension of feedforward network.
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segment_length (int): length of each input segment.
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dropout (float, optional): dropout probability. (Default: 0.0)
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activation (str, optional): activation function to use in feedforward network.
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Must be one of ("relu", "gelu", "silu"). (Default: "relu")
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left_context_length (int, optional): length of left context. (Default: 0)
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max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
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weight_init_gain (float or None, optional): scale factor to apply when initializing
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attention module parameters. (Default: ``None``)
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tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
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negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
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"""
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def __init__(
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self,
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input_dim: int,
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num_heads: int,
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ffn_dim: int,
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segment_length: int,
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dropout: float = 0.0,
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activation: str = "relu",
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left_context_length: int = 0,
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max_memory_size: int = 0,
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weight_init_gain: Optional[float] = None,
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tanh_on_mem: bool = False,
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negative_inf: float = -1e8,
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):
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super().__init__()
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self.attention = _EmformerAttention(
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input_dim=input_dim,
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num_heads=num_heads,
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dropout=dropout,
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weight_init_gain=weight_init_gain,
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tanh_on_mem=tanh_on_mem,
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negative_inf=negative_inf,
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)
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self.dropout = torch.nn.Dropout(dropout)
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self.memory_op = torch.nn.AvgPool1d(kernel_size=segment_length, stride=segment_length, ceil_mode=True)
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activation_module = _get_activation_module(activation)
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self.pos_ff = torch.nn.Sequential(
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torch.nn.LayerNorm(input_dim),
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torch.nn.Linear(input_dim, ffn_dim),
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activation_module,
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torch.nn.Dropout(dropout),
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torch.nn.Linear(ffn_dim, input_dim),
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torch.nn.Dropout(dropout),
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)
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self.layer_norm_input = torch.nn.LayerNorm(input_dim)
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self.layer_norm_output = torch.nn.LayerNorm(input_dim)
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self.left_context_length = left_context_length
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self.segment_length = segment_length
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self.max_memory_size = max_memory_size
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self.input_dim = input_dim
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self.use_mem = max_memory_size > 0
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def _init_state(self, batch_size: int, device: Optional[torch.device]) -> List[torch.Tensor]:
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empty_memory = torch.zeros(self.max_memory_size, batch_size, self.input_dim, device=device)
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left_context_key = torch.zeros(self.left_context_length, batch_size, self.input_dim, device=device)
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left_context_val = torch.zeros(self.left_context_length, batch_size, self.input_dim, device=device)
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past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
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return [empty_memory, left_context_key, left_context_val, past_length]
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def _unpack_state(self, state: List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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past_length = state[3][0][0].item()
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past_left_context_length = min(self.left_context_length, past_length)
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past_mem_length = min(self.max_memory_size, math.ceil(past_length / self.segment_length))
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pre_mems = state[0][self.max_memory_size - past_mem_length :]
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lc_key = state[1][self.left_context_length - past_left_context_length :]
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lc_val = state[2][self.left_context_length - past_left_context_length :]
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return pre_mems, lc_key, lc_val
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def _pack_state(
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self,
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next_k: torch.Tensor,
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next_v: torch.Tensor,
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update_length: int,
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mems: torch.Tensor,
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state: List[torch.Tensor],
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) -> List[torch.Tensor]:
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new_k = torch.cat([state[1], next_k])
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new_v = torch.cat([state[2], next_v])
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state[0] = torch.cat([state[0], mems])[-self.max_memory_size :]
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state[1] = new_k[new_k.shape[0] - self.left_context_length :]
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state[2] = new_v[new_v.shape[0] - self.left_context_length :]
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state[3] = state[3] + update_length
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return state
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def _process_attention_output(
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self,
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rc_output: torch.Tensor,
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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) -> torch.Tensor:
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result = self.dropout(rc_output) + torch.cat([right_context, utterance])
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result = self.pos_ff(result) + result
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result = self.layer_norm_output(result)
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return result
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def _apply_pre_attention_layer_norm(
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self, utterance: torch.Tensor, right_context: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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layer_norm_input = self.layer_norm_input(torch.cat([right_context, utterance]))
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return (
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layer_norm_input[right_context.size(0) :],
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layer_norm_input[: right_context.size(0)],
|
|
)
|
|
|
|
def _apply_post_attention_ffn(
|
|
self, rc_output: torch.Tensor, utterance: torch.Tensor, right_context: torch.Tensor
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
rc_output = self._process_attention_output(rc_output, utterance, right_context)
|
|
return rc_output[right_context.size(0) :], rc_output[: right_context.size(0)]
|
|
|
|
def _apply_attention_forward(
|
|
self,
|
|
utterance: torch.Tensor,
|
|
lengths: torch.Tensor,
|
|
right_context: torch.Tensor,
|
|
mems: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if attention_mask is None:
|
|
raise ValueError("attention_mask must be not None when for_inference is False")
|
|
|
|
if self.use_mem:
|
|
summary = self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
|
|
else:
|
|
summary = torch.empty(0).to(dtype=utterance.dtype, device=utterance.device)
|
|
rc_output, next_m = self.attention(
|
|
utterance=utterance,
|
|
lengths=lengths,
|
|
right_context=right_context,
|
|
summary=summary,
|
|
mems=mems,
|
|
attention_mask=attention_mask,
|
|
)
|
|
return rc_output, next_m
|
|
|
|
def _apply_attention_infer(
|
|
self,
|
|
utterance: torch.Tensor,
|
|
lengths: torch.Tensor,
|
|
right_context: torch.Tensor,
|
|
mems: torch.Tensor,
|
|
state: Optional[List[torch.Tensor]],
|
|
) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
|
|
if state is None:
|
|
state = self._init_state(utterance.size(1), device=utterance.device)
|
|
pre_mems, lc_key, lc_val = self._unpack_state(state)
|
|
if self.use_mem:
|
|
summary = self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
|
|
summary = summary[:1]
|
|
else:
|
|
summary = torch.empty(0).to(dtype=utterance.dtype, device=utterance.device)
|
|
rc_output, next_m, next_k, next_v = self.attention.infer(
|
|
utterance=utterance,
|
|
lengths=lengths,
|
|
right_context=right_context,
|
|
summary=summary,
|
|
mems=pre_mems,
|
|
left_context_key=lc_key,
|
|
left_context_val=lc_val,
|
|
)
|
|
state = self._pack_state(next_k, next_v, utterance.size(0), mems, state)
|
|
return rc_output, next_m, state
|
|
|
|
def forward(
|
|
self,
|
|
utterance: torch.Tensor,
|
|
lengths: torch.Tensor,
|
|
right_context: torch.Tensor,
|
|
mems: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
r"""Forward pass for training.
|
|
|
|
B: batch size;
|
|
D: feature dimension of each frame;
|
|
T: number of utterance frames;
|
|
R: number of right context frames;
|
|
M: number of memory elements.
|
|
|
|
Args:
|
|
utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
|
|
lengths (torch.Tensor): with shape `(B,)` and i-th element representing
|
|
number of valid frames for i-th batch element in ``utterance``.
|
|
right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
|
|
mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
|
|
attention_mask (torch.Tensor): attention mask for underlying attention module.
|
|
|
|
Returns:
|
|
(Tensor, Tensor, Tensor):
|
|
Tensor
|
|
encoded utterance frames, with shape `(T, B, D)`.
|
|
Tensor
|
|
updated right context frames, with shape `(R, B, D)`.
|
|
Tensor
|
|
updated memory elements, with shape `(M, B, D)`.
|
|
"""
|
|
(
|
|
layer_norm_utterance,
|
|
layer_norm_right_context,
|
|
) = self._apply_pre_attention_layer_norm(utterance, right_context)
|
|
rc_output, output_mems = self._apply_attention_forward(
|
|
layer_norm_utterance,
|
|
lengths,
|
|
layer_norm_right_context,
|
|
mems,
|
|
attention_mask,
|
|
)
|
|
output_utterance, output_right_context = self._apply_post_attention_ffn(rc_output, utterance, right_context)
|
|
return output_utterance, output_right_context, output_mems
|
|
|
|
@torch.jit.export
|
|
def infer(
|
|
self,
|
|
utterance: torch.Tensor,
|
|
lengths: torch.Tensor,
|
|
right_context: torch.Tensor,
|
|
state: Optional[List[torch.Tensor]],
|
|
mems: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor], torch.Tensor]:
|
|
r"""Forward pass for inference.
|
|
|
|
B: batch size;
|
|
D: feature dimension of each frame;
|
|
T: number of utterance frames;
|
|
R: number of right context frames;
|
|
M: number of memory elements.
|
|
|
|
Args:
|
|
utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
|
|
lengths (torch.Tensor): with shape `(B,)` and i-th element representing
|
|
number of valid frames for i-th batch element in ``utterance``.
|
|
right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
|
|
state (List[torch.Tensor] or None): list of tensors representing layer internal state
|
|
generated in preceding invocation of ``infer``.
|
|
mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
|
|
|
|
Returns:
|
|
(Tensor, Tensor, List[torch.Tensor], Tensor):
|
|
Tensor
|
|
encoded utterance frames, with shape `(T, B, D)`.
|
|
Tensor
|
|
updated right context frames, with shape `(R, B, D)`.
|
|
List[Tensor]
|
|
list of tensors representing layer internal state
|
|
generated in current invocation of ``infer``.
|
|
Tensor
|
|
updated memory elements, with shape `(M, B, D)`.
|
|
"""
|
|
(
|
|
layer_norm_utterance,
|
|
layer_norm_right_context,
|
|
) = self._apply_pre_attention_layer_norm(utterance, right_context)
|
|
rc_output, output_mems, output_state = self._apply_attention_infer(
|
|
layer_norm_utterance, lengths, layer_norm_right_context, mems, state
|
|
)
|
|
output_utterance, output_right_context = self._apply_post_attention_ffn(rc_output, utterance, right_context)
|
|
return output_utterance, output_right_context, output_state, output_mems
|
|
|
|
|
|
class _EmformerImpl(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
emformer_layers: torch.nn.ModuleList,
|
|
segment_length: int,
|
|
left_context_length: int = 0,
|
|
right_context_length: int = 0,
|
|
max_memory_size: int = 0,
|
|
):
|
|
super().__init__()
|
|
|
|
self.use_mem = max_memory_size > 0
|
|
self.memory_op = torch.nn.AvgPool1d(
|
|
kernel_size=segment_length,
|
|
stride=segment_length,
|
|
ceil_mode=True,
|
|
)
|
|
self.emformer_layers = emformer_layers
|
|
self.left_context_length = left_context_length
|
|
self.right_context_length = right_context_length
|
|
self.segment_length = segment_length
|
|
self.max_memory_size = max_memory_size
|
|
|
|
def _gen_right_context(self, input: torch.Tensor) -> torch.Tensor:
|
|
T = input.shape[0]
|
|
num_segs = math.ceil((T - self.right_context_length) / self.segment_length)
|
|
right_context_blocks = []
|
|
for seg_idx in range(num_segs - 1):
|
|
start = (seg_idx + 1) * self.segment_length
|
|
end = start + self.right_context_length
|
|
right_context_blocks.append(input[start:end])
|
|
right_context_blocks.append(input[T - self.right_context_length :])
|
|
return torch.cat(right_context_blocks)
|
|
|
|
def _gen_attention_mask_col_widths(self, seg_idx: int, utterance_length: int) -> List[int]:
|
|
num_segs = math.ceil(utterance_length / self.segment_length)
|
|
rc = self.right_context_length
|
|
lc = self.left_context_length
|
|
rc_start = seg_idx * rc
|
|
rc_end = rc_start + rc
|
|
seg_start = max(seg_idx * self.segment_length - lc, 0)
|
|
seg_end = min((seg_idx + 1) * self.segment_length, utterance_length)
|
|
rc_length = self.right_context_length * num_segs
|
|
|
|
if self.use_mem:
|
|
m_start = max(seg_idx - self.max_memory_size, 0)
|
|
mem_length = num_segs - 1
|
|
col_widths = [
|
|
m_start, # before memory
|
|
seg_idx - m_start, # memory
|
|
mem_length - seg_idx, # after memory
|
|
rc_start, # before right context
|
|
rc, # right context
|
|
rc_length - rc_end, # after right context
|
|
seg_start, # before query segment
|
|
seg_end - seg_start, # query segment
|
|
utterance_length - seg_end, # after query segment
|
|
]
|
|
else:
|
|
col_widths = [
|
|
rc_start, # before right context
|
|
rc, # right context
|
|
rc_length - rc_end, # after right context
|
|
seg_start, # before query segment
|
|
seg_end - seg_start, # query segment
|
|
utterance_length - seg_end, # after query segment
|
|
]
|
|
|
|
return col_widths
|
|
|
|
def _gen_attention_mask(self, input: torch.Tensor) -> torch.Tensor:
|
|
utterance_length = input.size(0)
|
|
num_segs = math.ceil(utterance_length / self.segment_length)
|
|
|
|
rc_mask = []
|
|
query_mask = []
|
|
summary_mask = []
|
|
|
|
if self.use_mem:
|
|
num_cols = 9
|
|
# memory, right context, query segment
|
|
rc_q_cols_mask = [idx in [1, 4, 7] for idx in range(num_cols)]
|
|
# right context, query segment
|
|
s_cols_mask = [idx in [4, 7] for idx in range(num_cols)]
|
|
masks_to_concat = [rc_mask, query_mask, summary_mask]
|
|
else:
|
|
num_cols = 6
|
|
# right context, query segment
|
|
rc_q_cols_mask = [idx in [1, 4] for idx in range(num_cols)]
|
|
s_cols_mask = None
|
|
masks_to_concat = [rc_mask, query_mask]
|
|
|
|
for seg_idx in range(num_segs):
|
|
col_widths = self._gen_attention_mask_col_widths(seg_idx, utterance_length)
|
|
|
|
rc_mask_block = _gen_attention_mask_block(
|
|
col_widths, rc_q_cols_mask, self.right_context_length, input.device
|
|
)
|
|
rc_mask.append(rc_mask_block)
|
|
|
|
query_mask_block = _gen_attention_mask_block(
|
|
col_widths,
|
|
rc_q_cols_mask,
|
|
min(
|
|
self.segment_length,
|
|
utterance_length - seg_idx * self.segment_length,
|
|
),
|
|
input.device,
|
|
)
|
|
query_mask.append(query_mask_block)
|
|
|
|
if s_cols_mask is not None:
|
|
summary_mask_block = _gen_attention_mask_block(col_widths, s_cols_mask, 1, input.device)
|
|
summary_mask.append(summary_mask_block)
|
|
|
|
attention_mask = (1 - torch.cat([torch.cat(mask) for mask in masks_to_concat])).to(torch.bool)
|
|
return attention_mask
|
|
|
|
def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
r"""Forward pass for training and non-streaming inference.
|
|
|
|
B: batch size;
|
|
T: max number of input frames in batch;
|
|
D: feature dimension of each frame.
|
|
|
|
Args:
|
|
input (torch.Tensor): utterance frames right-padded with right context frames, with
|
|
shape `(B, T + right_context_length, D)`.
|
|
lengths (torch.Tensor): with shape `(B,)` and i-th element representing
|
|
number of valid utterance frames for i-th batch element in ``input``.
|
|
|
|
Returns:
|
|
(Tensor, Tensor):
|
|
Tensor
|
|
output frames, with shape `(B, T, D)`.
|
|
Tensor
|
|
output lengths, with shape `(B,)` and i-th element representing
|
|
number of valid frames for i-th batch element in output frames.
|
|
"""
|
|
input = input.permute(1, 0, 2)
|
|
right_context = self._gen_right_context(input)
|
|
utterance = input[: input.size(0) - self.right_context_length]
|
|
attention_mask = self._gen_attention_mask(utterance)
|
|
mems = (
|
|
self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[:-1]
|
|
if self.use_mem
|
|
else torch.empty(0).to(dtype=input.dtype, device=input.device)
|
|
)
|
|
output = utterance
|
|
for layer in self.emformer_layers:
|
|
output, right_context, mems = layer(output, lengths, right_context, mems, attention_mask)
|
|
return output.permute(1, 0, 2), lengths
|
|
|
|
@torch.jit.export
|
|
def infer(
|
|
self,
|
|
input: torch.Tensor,
|
|
lengths: torch.Tensor,
|
|
states: Optional[List[List[torch.Tensor]]] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
|
|
r"""Forward pass for streaming inference.
|
|
|
|
B: batch size;
|
|
D: feature dimension of each frame.
|
|
|
|
Args:
|
|
input (torch.Tensor): utterance frames right-padded with right context frames, with
|
|
shape `(B, segment_length + right_context_length, D)`.
|
|
lengths (torch.Tensor): with shape `(B,)` and i-th element representing
|
|
number of valid frames for i-th batch element in ``input``.
|
|
states (List[List[torch.Tensor]] or None, optional): list of lists of tensors
|
|
representing internal state generated in preceding invocation of ``infer``. (Default: ``None``)
|
|
|
|
Returns:
|
|
(Tensor, Tensor, List[List[Tensor]]):
|
|
Tensor
|
|
output frames, with shape `(B, segment_length, D)`.
|
|
Tensor
|
|
output lengths, with shape `(B,)` and i-th element representing
|
|
number of valid frames for i-th batch element in output frames.
|
|
List[List[Tensor]]
|
|
output states; list of lists of tensors representing internal state
|
|
generated in current invocation of ``infer``.
|
|
"""
|
|
if input.size(1) != self.segment_length + self.right_context_length:
|
|
raise ValueError(
|
|
"Per configured segment_length and right_context_length"
|
|
f", expected size of {self.segment_length + self.right_context_length} for dimension 1 of input"
|
|
f", but got {input.size(1)}."
|
|
)
|
|
input = input.permute(1, 0, 2)
|
|
right_context_start_idx = input.size(0) - self.right_context_length
|
|
right_context = input[right_context_start_idx:]
|
|
utterance = input[:right_context_start_idx]
|
|
output_lengths = torch.clamp(lengths - self.right_context_length, min=0)
|
|
mems = (
|
|
self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
|
|
if self.use_mem
|
|
else torch.empty(0).to(dtype=input.dtype, device=input.device)
|
|
)
|
|
output = utterance
|
|
output_states: List[List[torch.Tensor]] = []
|
|
for layer_idx, layer in enumerate(self.emformer_layers):
|
|
output, right_context, output_state, mems = layer.infer(
|
|
output,
|
|
output_lengths,
|
|
right_context,
|
|
None if states is None else states[layer_idx],
|
|
mems,
|
|
)
|
|
output_states.append(output_state)
|
|
|
|
return output.permute(1, 0, 2), output_lengths, output_states
|
|
|
|
|
|
class Emformer(_EmformerImpl):
|
|
r"""Emformer architecture introduced in
|
|
*Emformer: Efficient Memory Transformer Based Acoustic Model for Low Latency Streaming Speech Recognition*
|
|
:cite:`shi2021emformer`.
|
|
|
|
See Also:
|
|
* :func:`~torchaudio.models.emformer_rnnt_model`,
|
|
:func:`~torchaudio.models.emformer_rnnt_base`: factory functions.
|
|
* :class:`torchaudio.pipelines.RNNTBundle`: ASR pipelines with pretrained model.
|
|
|
|
Args:
|
|
input_dim (int): input dimension.
|
|
num_heads (int): number of attention heads in each Emformer layer.
|
|
ffn_dim (int): hidden layer dimension of each Emformer layer's feedforward network.
|
|
num_layers (int): number of Emformer layers to instantiate.
|
|
segment_length (int): length of each input segment.
|
|
dropout (float, optional): dropout probability. (Default: 0.0)
|
|
activation (str, optional): activation function to use in each Emformer layer's
|
|
feedforward network. Must be one of ("relu", "gelu", "silu"). (Default: "relu")
|
|
left_context_length (int, optional): length of left context. (Default: 0)
|
|
right_context_length (int, optional): length of right context. (Default: 0)
|
|
max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
|
|
weight_init_scale_strategy (str or None, optional): per-layer weight initialization scaling
|
|
strategy. Must be one of ("depthwise", "constant", ``None``). (Default: "depthwise")
|
|
tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
|
|
negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
|
|
|
|
Examples:
|
|
>>> emformer = Emformer(512, 8, 2048, 20, 4, right_context_length=1)
|
|
>>> input = torch.rand(128, 400, 512) # batch, num_frames, feature_dim
|
|
>>> lengths = torch.randint(1, 200, (128,)) # batch
|
|
>>> output, lengths = emformer(input, lengths)
|
|
>>> input = torch.rand(128, 5, 512)
|
|
>>> lengths = torch.ones(128) * 5
|
|
>>> output, lengths, states = emformer.infer(input, lengths, None)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
input_dim: int,
|
|
num_heads: int,
|
|
ffn_dim: int,
|
|
num_layers: int,
|
|
segment_length: int,
|
|
dropout: float = 0.0,
|
|
activation: str = "relu",
|
|
left_context_length: int = 0,
|
|
right_context_length: int = 0,
|
|
max_memory_size: int = 0,
|
|
weight_init_scale_strategy: Optional[str] = "depthwise",
|
|
tanh_on_mem: bool = False,
|
|
negative_inf: float = -1e8,
|
|
):
|
|
weight_init_gains = _get_weight_init_gains(weight_init_scale_strategy, num_layers)
|
|
emformer_layers = torch.nn.ModuleList(
|
|
[
|
|
_EmformerLayer(
|
|
input_dim,
|
|
num_heads,
|
|
ffn_dim,
|
|
segment_length,
|
|
dropout=dropout,
|
|
activation=activation,
|
|
left_context_length=left_context_length,
|
|
max_memory_size=max_memory_size,
|
|
weight_init_gain=weight_init_gains[layer_idx],
|
|
tanh_on_mem=tanh_on_mem,
|
|
negative_inf=negative_inf,
|
|
)
|
|
for layer_idx in range(num_layers)
|
|
]
|
|
)
|
|
super().__init__(
|
|
emformer_layers,
|
|
segment_length,
|
|
left_context_length=left_context_length,
|
|
right_context_length=right_context_length,
|
|
max_memory_size=max_memory_size,
|
|
)
|