From c2e4e3c2e9be855277d9f4fa9cd0544892ff829a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 30 八月 2023 09:57:30 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/models/decoder/sanm_decoder.py | 15 ++++++++-------
1 files changed, 8 insertions(+), 7 deletions(-)
diff --git a/funasr/models/decoder/sanm_decoder.py b/funasr/models/decoder/sanm_decoder.py
index 508eb73..c12e098 100644
--- a/funasr/models/decoder/sanm_decoder.py
+++ b/funasr/models/decoder/sanm_decoder.py
@@ -7,7 +7,6 @@
from funasr.modules.streaming_utils import utils as myutils
from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
-from typeguard import check_argument_types
from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funasr.modules.embedding import PositionalEncoding
@@ -181,7 +180,6 @@
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
embed_tensor_name_prefix_tf: str = None,
):
- assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -835,10 +833,13 @@
att_layer_num: int = 6,
kernel_size: int = 21,
sanm_shfit: int = 0,
+ lora_list: List[str] = None,
+ lora_rank: int = 8,
+ lora_alpha: int = 16,
+ lora_dropout: float = 0.1,
tf2torch_tensor_name_prefix_torch: str = "decoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
):
- assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -888,7 +889,7 @@
attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
),
MultiHeadedAttentionCrossAtt(
- attention_heads, attention_dim, src_attention_dropout_rate
+ attention_heads, attention_dim, src_attention_dropout_rate, lora_list, lora_rank, lora_alpha, lora_dropout
),
PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
dropout_rate,
@@ -956,13 +957,13 @@
"""
tgt = ys_in_pad
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
+
+ memory = hs_pad
+ memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
if chunk_mask is not None:
memory_mask = memory_mask * chunk_mask
if tgt_mask.size(1) != memory_mask.size(1):
memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)
-
- memory = hs_pad
- memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
x = tgt
x, tgt_mask, memory, memory_mask, _ = self.decoders(
--
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