From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/models/llm_asr/adaptor.py | 127 ++++++++++++++++++++++++++++++++++++++++++
1 files changed, 126 insertions(+), 1 deletions(-)
diff --git a/funasr/models/llm_asr/adaptor.py b/funasr/models/llm_asr/adaptor.py
index 0676e7d..4348213 100644
--- a/funasr/models/llm_asr/adaptor.py
+++ b/funasr/models/llm_asr/adaptor.py
@@ -1,7 +1,10 @@
import torch
import torch.nn as nn
+import torch.nn.functional as F
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.register import tables
+
@tables.register("adaptor_classes", "Linear")
class Linear(nn.Module):
@@ -20,10 +23,132 @@
if num_frames_to_discard > 0:
x = x[:, :-num_frames_to_discard, :]
seq_len = x.size(1)
-
+
x = x.contiguous()
x = x.view(batch_size, seq_len // self.k, dim * self.k)
x = self.linear1(x)
x = self.relu(x)
x = self.linear2(x)
return x
+
+
+@tables.register("adaptor_classes", "QFormer")
+class EncoderProjectorQFormer(nn.Module):
+ def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
+ super().__init__()
+ self.encoder_dim = encoder_dim
+ self.llm_dim = llm_dim
+ from transformers import Blip2QFormerConfig, Blip2QFormerModel
+
+ configuration = Blip2QFormerConfig()
+ configuration.encoder_hidden_size = self.encoder_dim
+ configuration.num_hidden_layers = 2
+
+ self.query_len = 64
+ self.query = nn.Parameter(torch.zeros(1, self.query_len, configuration.hidden_size))
+ self.query.data.normal_(mean=0.0, std=1.0)
+ self.qformer = Blip2QFormerModel(configuration)
+
+ self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
+ self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)
+
+ self.second_per_frame = 0.333333
+ self.second_stride = 0.333333
+
+ def split_frames(self, speech_embeds):
+ B, T, C = speech_embeds.shape
+ kernel = round(T * self.second_per_frame / 30.0)
+ stride = round(T * self.second_stride / 30.0)
+ kernel = (1, kernel)
+ stride = (1, stride)
+ speech_embeds_tr = speech_embeds.transpose(1, 2).unsqueeze(2)
+ speech_embeds_overlap = torch.nn.functional.unfold(speech_embeds_tr, kernel_size=kernel, dilation=1, padding=0, stride=stride)
+ _, _, L = speech_embeds_overlap.shape
+ speech_embeds_overlap = speech_embeds_overlap.view(B, -1, kernel[1], L)
+ speech_embeds_overlap = torch.permute(speech_embeds_overlap, [0, 3, 2, 1])
+ speech_embeds = speech_embeds_overlap.reshape(-1, kernel[1], C)
+ speech_atts = torch.ones(speech_embeds.size()[:-1], dtype=torch.long, device=speech_embeds.device)
+ return speech_embeds, speech_atts
+
+ def forward(self, x):
+ B, T, C = x.size()
+ encoder_out_feat, attention_mask = self.split_frames(x)
+ query = self.query.expand(encoder_out_feat.shape[0], -1, -1)
+
+
+ query_output = self.qformer(
+ query_embeds=query,
+ encoder_hidden_states=encoder_out_feat,
+ encoder_attention_mask=attention_mask,
+ return_dict=True,
+ )
+
+ query_proj = self.norm(self.linear(query_output.last_hidden_state))
+ query_proj = query_proj.view(B, -1, query_proj.size(2)).contiguous()
+
+ return query_proj
+
+
+@tables.register("adaptor_classes", "Transformer")
+class Transformer(nn.Module):
+ def __init__(
+ self, downsample_rate=2, encoder_dim=1280, llm_dim=4096, ffn_dim: int = 2048, **kwargs
+ ):
+ super().__init__()
+ self.k = downsample_rate
+ self.encoder_dim = encoder_dim
+ self.llm_dim = llm_dim
+ self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
+ self.relu = nn.ReLU()
+ self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
+ from funasr.models.transformer.encoder import EncoderLayer
+ from funasr.models.transformer.attention import MultiHeadedAttention
+ from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
+
+ self.blocks = None
+ if kwargs.get("n_layer", 2) > 0:
+ self.blocks = nn.ModuleList(
+ [
+ EncoderLayer(
+ llm_dim,
+ MultiHeadedAttention(
+ kwargs.get("attention_heads", 8),
+ llm_dim,
+ kwargs.get("attention_dropout_rate", 0.0),
+ ),
+ PositionwiseFeedForward(
+ llm_dim,
+ llm_dim // 4,
+ kwargs.get("dropout_rate", 0.0),
+ ),
+ kwargs.get("dropout_rate", 0.0),
+ )
+ for i in range(kwargs.get("n_layer", 2))
+ ]
+ )
+
+ def forward(self, x, ilens=None):
+
+ batch_size, seq_len, dim = x.size()
+ # num_frames_to_discard = seq_len % self.k
+ chunk_num = (seq_len - 1) // self.k + 1
+ pad_num = chunk_num * self.k - seq_len
+ x = F.pad(x, (0, 0, 0, pad_num, 0, 0), value=0.0)
+ # if num_frames_to_discard > 0:
+ # x = x[:, :-num_frames_to_discard, :]
+ seq_len = x.size(1)
+
+ x = x.contiguous()
+ x = x.view(batch_size, chunk_num, dim * self.k)
+ x = self.linear1(x)
+ x = self.relu(x)
+ x = self.linear2(x)
+
+ olens = None
+ olens = (ilens - 1) // self.k + 1
+ masks = (~make_pad_mask(olens)[:, None, :]).to(x.device)
+
+ if self.blocks is not None:
+ for layer, block in enumerate(self.blocks):
+ x, masks = block(x, masks)
+ return x, olens
--
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