From dcb92f13eddbf3032ce363b35f13f80afa8f94d1 Mon Sep 17 00:00:00 2001
From: haoneng.lhn <haoneng.lhn@alibaba-inc.com>
Date: 星期四, 14 九月 2023 16:46:30 +0800
Subject: [PATCH] add paraformer online opt infer code
---
funasr/models/decoder/sanm_decoder.py | 146 ++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 127 insertions(+), 19 deletions(-)
diff --git a/funasr/models/decoder/sanm_decoder.py b/funasr/models/decoder/sanm_decoder.py
index c12e098..3e4e554 100644
--- a/funasr/models/decoder/sanm_decoder.py
+++ b/funasr/models/decoder/sanm_decoder.py
@@ -105,7 +105,50 @@
return x, tgt_mask, memory, memory_mask, cache
- def forward_chunk(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
+ #def forward_chunk(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
+ # """Compute decoded features.
+
+ # Args:
+ # tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
+ # tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
+ # memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
+ # memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
+ # cache (List[torch.Tensor]): List of cached tensors.
+ # Each tensor shape should be (#batch, maxlen_out - 1, size).
+
+ # Returns:
+ # torch.Tensor: Output tensor(#batch, maxlen_out, size).
+ # torch.Tensor: Mask for output tensor (#batch, maxlen_out).
+ # torch.Tensor: Encoded memory (#batch, maxlen_in, size).
+ # torch.Tensor: Encoded memory mask (#batch, maxlen_in).
+
+ # """
+ # # tgt = self.dropout(tgt)
+ # residual = tgt
+ # if self.normalize_before:
+ # tgt = self.norm1(tgt)
+ # tgt = self.feed_forward(tgt)
+
+ # x = tgt
+ # if self.self_attn:
+ # if self.normalize_before:
+ # tgt = self.norm2(tgt)
+ # if self.training:
+ # cache = None
+ # x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
+ # x = residual + self.dropout(x)
+
+ # if self.src_attn is not None:
+ # residual = x
+ # if self.normalize_before:
+ # x = self.norm3(x)
+
+ # x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
+
+
+ # return x, tgt_mask, memory, memory_mask, cache
+
+ def forward_chunk(self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0):
"""Compute decoded features.
Args:
@@ -123,7 +166,6 @@
torch.Tensor: Encoded memory mask (#batch, maxlen_in).
"""
- # tgt = self.dropout(tgt)
residual = tgt
if self.normalize_before:
tgt = self.norm1(tgt)
@@ -133,9 +175,7 @@
if self.self_attn:
if self.normalize_before:
tgt = self.norm2(tgt)
- if self.training:
- cache = None
- x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
+ x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache)
x = residual + self.dropout(x)
if self.src_attn is not None:
@@ -143,10 +183,11 @@
if self.normalize_before:
x = self.norm3(x)
- x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
+ x, opt_cache = self.src_attn.forward_chunk(x, memory, opt_cache, chunk_size, look_back)
+ x = residual + x
+ return x, memory, fsmn_cache, opt_cache
- return x, tgt_mask, memory, memory_mask, cache
class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
"""
@@ -992,6 +1033,65 @@
)
return logp.squeeze(0), state
+ #def forward_chunk(
+ # self,
+ # memory: torch.Tensor,
+ # tgt: torch.Tensor,
+ # cache: dict = None,
+ #) -> Tuple[torch.Tensor, torch.Tensor]:
+ # """Forward decoder.
+
+ # Args:
+ # hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
+ # hlens: (batch)
+ # ys_in_pad:
+ # input token ids, int64 (batch, maxlen_out)
+ # if input_layer == "embed"
+ # input tensor (batch, maxlen_out, #mels) in the other cases
+ # ys_in_lens: (batch)
+ # Returns:
+ # (tuple): tuple containing:
+
+ # x: decoded token score before softmax (batch, maxlen_out, token)
+ # if use_output_layer is True,
+ # olens: (batch, )
+ # """
+ # x = tgt
+ # if cache["decode_fsmn"] is None:
+ # cache_layer_num = len(self.decoders)
+ # if self.decoders2 is not None:
+ # cache_layer_num += len(self.decoders2)
+ # new_cache = [None] * cache_layer_num
+ # else:
+ # new_cache = cache["decode_fsmn"]
+ # for i in range(self.att_layer_num):
+ # decoder = self.decoders[i]
+ # x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
+ # x, None, memory, None, cache=new_cache[i]
+ # )
+ # new_cache[i] = c_ret
+
+ # if self.num_blocks - self.att_layer_num > 1:
+ # for i in range(self.num_blocks - self.att_layer_num):
+ # j = i + self.att_layer_num
+ # decoder = self.decoders2[i]
+ # x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
+ # x, None, memory, None, cache=new_cache[j]
+ # )
+ # new_cache[j] = c_ret
+
+ # for decoder in self.decoders3:
+
+ # x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
+ # x, None, memory, None, cache=None
+ # )
+ # if self.normalize_before:
+ # x = self.after_norm(x)
+ # if self.output_layer is not None:
+ # x = self.output_layer(x)
+ # cache["decode_fsmn"] = new_cache
+ # return x
+
def forward_chunk(
self,
memory: torch.Tensor,
@@ -1020,35 +1120,43 @@
cache_layer_num = len(self.decoders)
if self.decoders2 is not None:
cache_layer_num += len(self.decoders2)
- new_cache = [None] * cache_layer_num
+ fsmn_cache = [None] * cache_layer_num
else:
- new_cache = cache["decode_fsmn"]
+ fsmn_cache = cache["decode_fsmn"]
+
+ if cache["opt"] is None:
+ cache_layer_num = len(self.decoders)
+ opt_cache = [None] * cache_layer_num
+ else:
+ opt_cache = cache["opt"]
+
for i in range(self.att_layer_num):
decoder = self.decoders[i]
- x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
- x, None, memory, None, cache=new_cache[i]
+ x, memory, fsmn_cache[i], opt_cache[i] = decoder.forward_chunk(
+ x, memory, fsmn_cache=fsmn_cache[i], opt_cache=opt_cache[i],
+ chunk_size=cache["chunk_size"], look_back=cache["decoder_chunk_look_back"]
)
- new_cache[i] = c_ret
if self.num_blocks - self.att_layer_num > 1:
for i in range(self.num_blocks - self.att_layer_num):
j = i + self.att_layer_num
decoder = self.decoders2[i]
- x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
- x, None, memory, None, cache=new_cache[j]
+ x, memory, fsmn_cache[j], _ = decoder.forward_chunk(
+ x, memory, fsmn_cache=fsmn_cache[j]
)
- new_cache[j] = c_ret
for decoder in self.decoders3:
-
- x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
- x, None, memory, None, cache=None
+ x, memory, _, _ = decoder.forward_chunk(
+ x, memory
)
if self.normalize_before:
x = self.after_norm(x)
if self.output_layer is not None:
x = self.output_layer(x)
- cache["decode_fsmn"] = new_cache
+
+ cache["decode_fsmn"] = fsmn_cache
+ if cache["decoder_chunk_look_back"] > 0 or cache["decoder_chunk_look_back"] == -1:
+ cache["opt"] = opt_cache
return x
def forward_one_step(
--
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