wehub-resource-sync
351 lines
12 KiB
Python
351 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Shared Conformer encoder components for FireRedASR2 and FireRedLID.
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Both models use the same Conformer-based audio encoder architecture
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(Conv2dSubsampling → RelPositionalEncoding → N × RelPosEmbConformerBlock).
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This module factors out the common building blocks to avoid duplication.
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"""
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import torch
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import torch.nn.functional as F
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from torch import nn
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from vllm.model_executor.layers.linear import ReplicatedLinear
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class Conv2dSubsampling(nn.Module):
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def __init__(self, idim: int, d_model: int, out_channels: int = 32):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(1, out_channels, 3, 2),
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nn.ReLU(),
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nn.Conv2d(out_channels, out_channels, 3, 2),
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nn.ReLU(),
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)
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subsample_idim = ((idim - 1) // 2 - 1) // 2
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self.out = ReplicatedLinear(
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input_size=out_channels * subsample_idim,
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output_size=d_model,
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bias=True,
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)
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self.subsampling = 4
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left_context = right_context = 3 # both exclude current frame
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self.context = left_context + 1 + right_context # 7
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def forward(
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self, x: torch.Tensor, x_mask: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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x = x.unsqueeze(1)
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x = self.conv(x)
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N, C, T, D = x.size()
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x, _ = self.out(x.transpose(1, 2).contiguous().view(N, T, C * D))
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mask = x_mask[:, :, :-2:2][:, :, :-2:2]
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input_lengths = mask[:, -1, :].sum(dim=-1)
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return x, input_lengths, mask
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class Swish(nn.Module):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return x * torch.sigmoid(x)
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class RelPositionalEncoding(nn.Module):
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def __init__(self, d_model: int, max_len: int = 5000):
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super().__init__()
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pe_positive = torch.zeros(max_len, d_model, requires_grad=False)
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pe_negative = torch.zeros(max_len, d_model, requires_grad=False)
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position = torch.arange(0, max_len).unsqueeze(1).float()
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div_term = torch.exp(
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torch.arange(0, d_model, 2).float()
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* -(torch.log(torch.tensor(10000.0)).item() / d_model)
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)
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pe_positive[:, 0::2] = torch.sin(position * div_term)
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pe_positive[:, 1::2] = torch.cos(position * div_term)
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pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
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pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
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pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
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pe_negative = pe_negative[1:].unsqueeze(0)
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self.pe = torch.cat([pe_positive, pe_negative], dim=1)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# Tmax = 2 * max_len - 1
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Tmax, T = self.pe.size(1), x.size(1)
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pos_emb = self.pe[:, Tmax // 2 - T + 1 : Tmax // 2 + T].clone().detach()
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return pos_emb
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class ConformerFeedForward(nn.Module):
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def __init__(self, d_model: int):
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super().__init__()
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self.pre_layer_norm = nn.LayerNorm(d_model)
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self.linear_expand = ReplicatedLinear(
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input_size=d_model,
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output_size=d_model * 4,
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bias=True,
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)
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self.nonlinear = Swish()
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self.linear_project = ReplicatedLinear(
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input_size=d_model * 4,
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output_size=d_model,
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bias=True,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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residual = x
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x = self.pre_layer_norm(x)
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x, _ = self.linear_expand(x)
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x = self.nonlinear(x)
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x, _ = self.linear_project(x)
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return x + residual
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class EncoderMultiHeadAttention(nn.Module):
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def __init__(self, n_head: int, d_model: int):
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super().__init__()
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assert d_model % n_head == 0
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self.n_head = n_head
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self.d_k = d_model // n_head
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self.d_v = self.d_k
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self.w_qs = ReplicatedLinear(d_model, n_head * self.d_k, bias=False)
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self.w_ks = ReplicatedLinear(d_model, n_head * self.d_k, bias=False)
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self.w_vs = ReplicatedLinear(d_model, n_head * self.d_v, bias=False)
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self.layer_norm_q = nn.LayerNorm(d_model)
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self.layer_norm_k = nn.LayerNorm(d_model)
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self.layer_norm_v = nn.LayerNorm(d_model)
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self.fc = ReplicatedLinear(n_head * self.d_v, d_model, bias=False)
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def forward_qkv(
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self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
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sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
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q = self.layer_norm_q(q)
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k = self.layer_norm_k(k)
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v = self.layer_norm_v(v)
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q = self.w_qs(q)[0].view(sz_b, len_q, n_head, d_k)
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k = self.w_ks(k)[0].view(sz_b, len_k, n_head, d_k)
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v = self.w_vs(v)[0].view(sz_b, len_v, n_head, d_v)
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q = q.transpose(1, 2)
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k = k.transpose(1, 2)
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v = v.transpose(1, 2)
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return q, k, v
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def forward_output(
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self,
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output: torch.Tensor,
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residual: torch.Tensor,
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sz_b: int,
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len_q: int,
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) -> torch.Tensor:
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output = output.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
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fc_out, _ = self.fc(output)
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return fc_out + residual
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def forward_attention(
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self,
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attn: torch.Tensor,
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v: torch.Tensor,
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mask: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if mask is not None:
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mask = mask.unsqueeze(1)
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mask = mask.eq(0)
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attn = attn.masked_fill(mask, -float("inf"))
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attn = torch.softmax(attn, dim=-1).masked_fill(mask, 0.0)
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else:
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attn = torch.softmax(attn, dim=-1)
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output = torch.matmul(attn, v)
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return output, attn
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class RelPosMultiHeadAttention(EncoderMultiHeadAttention):
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def __init__(self, n_head: int, d_model: int):
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super().__init__(n_head, d_model)
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d_k = d_model // n_head
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self.scale = 1.0 / (d_k**0.5)
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self.linear_pos = ReplicatedLinear(d_model, n_head * d_k, bias=False)
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self.pos_bias_u = nn.Parameter(torch.empty([n_head, d_k]))
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self.pos_bias_v = nn.Parameter(torch.empty([n_head, d_k]))
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def _rel_shift(self, x):
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N, H, T1, T2 = x.size()
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zero_pad = torch.zeros((N, H, T1, 1), device=x.device, dtype=x.dtype)
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x_padded = torch.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(N, H, T2 + 1, T1)
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x = x_padded[:, :, 1:].view_as(x)
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x = x[:, :, :, : x.size(-1) // 2 + 1]
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return x
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def forward(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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pos_emb: torch.Tensor,
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mask: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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sz_b, len_q = q.size(0), q.size(1)
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residual = q
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q, k, v = self.forward_qkv(q, k, v)
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q = q.transpose(1, 2)
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n_batch_pos = pos_emb.size(0)
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p = self.linear_pos(pos_emb)[0].view(n_batch_pos, -1, self.n_head, self.d_k)
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p = p.transpose(1, 2)
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q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
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q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
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matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
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matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
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matrix_bd = self._rel_shift(matrix_bd)
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attn_scores = matrix_ac + matrix_bd
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attn_scores.mul_(self.scale)
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output, attn = self.forward_attention(attn_scores, v, mask=mask)
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output = self.forward_output(output, residual, sz_b, len_q)
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return output, attn
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class ConformerConvolution(nn.Module):
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def __init__(self, d_model: int, kernel_size: int = 33):
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super().__init__()
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assert kernel_size % 2 == 1
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self.pre_layer_norm = nn.LayerNorm(d_model)
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self.pointwise_conv1 = nn.Conv1d(
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d_model, d_model * 4, kernel_size=1, bias=False
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)
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self.padding = (kernel_size - 1) // 2
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self.depthwise_conv = nn.Conv1d(
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d_model * 2,
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d_model * 2,
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kernel_size,
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stride=1,
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padding=self.padding,
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groups=d_model * 2,
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bias=False,
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)
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self.batch_norm = nn.LayerNorm(d_model * 2)
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self.swish = Swish()
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self.pointwise_conv2 = nn.Conv1d(
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d_model * 2, d_model, kernel_size=1, bias=False
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)
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def forward(
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self, x: torch.Tensor, mask: torch.Tensor | None = None
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) -> torch.Tensor:
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residual = x
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out = self.pre_layer_norm(x)
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out = out.transpose(1, 2)
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if mask is not None:
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out.masked_fill_(mask.ne(1), 0.0)
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out = self.pointwise_conv1(out)
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out = F.glu(out, dim=1)
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out = self.depthwise_conv(out)
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out = out.transpose(1, 2)
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out = self.swish(self.batch_norm(out))
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out = out.transpose(1, 2)
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out = self.pointwise_conv2(out)
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if mask is not None:
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out.masked_fill_(mask.ne(1), 0.0)
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out = out.transpose(1, 2)
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return out + residual
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class RelPosEmbConformerBlock(nn.Module):
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def __init__(self, d_model: int, n_head: int, kernel_size: int = 33):
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super().__init__()
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self.ffn1 = ConformerFeedForward(d_model)
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self.mhsa = RelPosMultiHeadAttention(n_head, d_model)
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self.conv = ConformerConvolution(d_model, kernel_size)
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self.ffn2 = ConformerFeedForward(d_model)
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self.layer_norm = nn.LayerNorm(d_model)
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def forward(
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self,
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x: torch.Tensor,
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pos_emb: torch.Tensor,
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slf_attn_mask: torch.Tensor | None = None,
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pad_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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out = 0.5 * x + 0.5 * self.ffn1(x)
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out = self.mhsa(out, out, out, pos_emb, mask=slf_attn_mask)[0]
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out = self.conv(out, pad_mask)
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out = 0.5 * out + 0.5 * self.ffn2(out)
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out = self.layer_norm(out)
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return out
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class ConformerEncoder(nn.Module):
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"""
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Conformer encoder shared by FireRedASR2 and FireRedLID.
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"""
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def __init__(
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self,
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idim: int,
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n_layers_enc: int,
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n_head: int,
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d_model: int,
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kernel_size: int = 33,
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pe_maxlen: int = 5000,
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):
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super().__init__()
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self.odim = d_model
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self.input_preprocessor = Conv2dSubsampling(idim, d_model)
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self.positional_encoding = RelPositionalEncoding(d_model, max_len=pe_maxlen)
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self.layer_stack = nn.ModuleList()
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for _ in range(n_layers_enc):
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block = RelPosEmbConformerBlock(d_model, n_head, kernel_size)
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self.layer_stack.append(block)
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def forward(
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self,
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padded_input: torch.Tensor,
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input_lengths: torch.Tensor,
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pad: bool = True,
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):
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if pad:
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padded_input = F.pad(
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padded_input,
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(0, 0, 0, self.input_preprocessor.context - 1),
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"constant",
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0.0,
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)
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src_mask = self.padding_position_is_0(padded_input, input_lengths)
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embed_output, input_lengths, src_mask = self.input_preprocessor(
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padded_input, src_mask
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)
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enc_output = embed_output
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pos_emb = self.positional_encoding(embed_output)
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for enc_layer in self.layer_stack:
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enc_output = enc_layer(
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enc_output, pos_emb, slf_attn_mask=src_mask, pad_mask=src_mask
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)
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return enc_output, input_lengths, src_mask
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def padding_position_is_0(
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self, padded_input: torch.Tensor, input_lengths: torch.Tensor
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) -> torch.Tensor:
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N, T = padded_input.size()[:2]
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# Use broadcasting instead of a Python loop for efficiency.
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positions = torch.arange(T, device=padded_input.device).unsqueeze(0)
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mask = (positions < input_lengths.unsqueeze(1)).to(torch.uint8)
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return mask.unsqueeze(1)
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