From c542eacb0aadcbc49c63db40429fca4e08f807a4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 七月 2023 10:27:35 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/export/models/decoder/xformer_decoder.py | 121 ++++++++++++++++++++++++++++++++++++++++
1 files changed, 121 insertions(+), 0 deletions(-)
diff --git a/funasr/export/models/decoder/xformer_decoder.py b/funasr/export/models/decoder/xformer_decoder.py
new file mode 100644
index 0000000..15199aa
--- /dev/null
+++ b/funasr/export/models/decoder/xformer_decoder.py
@@ -0,0 +1,121 @@
+import os
+
+import torch
+import torch.nn as nn
+
+from funasr.modules.attention import MultiHeadedAttention
+
+from funasr.export.models.modules.decoder_layer import DecoderLayer as OnnxDecoderLayer
+from funasr.export.models.language_models.embed import Embedding
+from funasr.export.models.modules.multihead_att import \
+ OnnxMultiHeadedAttention
+
+from funasr.export.utils.torch_function import MakePadMask, subsequent_mask
+
+class XformerDecoder(nn.Module):
+ def __init__(self,
+ model,
+ max_seq_len = 512,
+ model_name = 'decoder',
+ onnx: bool = True,):
+ super().__init__()
+ self.embed = Embedding(model.embed, max_seq_len)
+ self.model = model
+ if onnx:
+ self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
+ else:
+ self.make_pad_mask = subsequent_mask(max_seq_len, flip=False)
+
+ if isinstance(self.model.decoders[0].self_attn, MultiHeadedAttention):
+ self.num_heads = self.model.decoders[0].self_attn.h
+ self.hidden_size = self.model.decoders[0].self_attn.linear_out.out_features
+
+ # replace multi-head attention module into customized module.
+ for i, d in enumerate(self.model.decoders):
+ # d is DecoderLayer
+ if isinstance(d.self_attn, MultiHeadedAttention):
+ d.self_attn = OnnxMultiHeadedAttention(d.self_attn)
+ if isinstance(d.src_attn, MultiHeadedAttention):
+ d.src_attn = OnnxMultiHeadedAttention(d.src_attn)
+ self.model.decoders[i] = OnnxDecoderLayer(d)
+
+ self.model_name = model_name
+
+ def prepare_mask(self, mask):
+ mask_3d_btd = mask[:, :, None]
+ if len(mask.shape) == 2:
+ mask_4d_bhlt = 1 - mask[:, None, None, :]
+ elif len(mask.shape) == 3:
+ mask_4d_bhlt = 1 - mask[:, None, :]
+
+ mask_4d_bhlt = mask_4d_bhlt * -10000.0
+ return mask_3d_btd, mask_4d_bhlt
+
+ def forward(self,
+ tgt,
+ memory,
+ cache):
+
+ mask = subsequent_mask(tgt.size(-1)).unsqueeze(0) # (B, T)
+
+ x = self.embed(tgt)
+ mask = self.prepare_mask(mask)
+ new_cache = []
+ for c, decoder in zip(cache, self.model.decoders):
+ x, mask = decoder(x, mask, memory, None, c)
+ new_cache.append(x)
+ x = x[:, 1:, :]
+
+ if self.model.normalize_before:
+ y = self.model.after_norm(x[:, -1])
+ else:
+ y = x[:, -1]
+
+ if self.model.output_layer is not None:
+ y = torch.log_softmax(self.model.output_layer(y), dim=-1)
+ return y, new_cache
+
+ def get_dummy_inputs(self, enc_size):
+ tgt = torch.LongTensor([0]).unsqueeze(0)
+ memory = torch.randn(1, 100, enc_size)
+ cache_num = len(self.model.decoders)
+ cache = [
+ torch.zeros((1, 1, self.model.decoders[0].size))
+ for _ in range(cache_num)
+ ]
+ return (tgt, memory, cache)
+
+ def is_optimizable(self):
+ return True
+
+ def get_input_names(self):
+ cache_num = len(self.model.decoders)
+ return ["tgt", "memory"] + [
+ "cache_%d" % i for i in range(cache_num)
+ ]
+
+ def get_output_names(self):
+ cache_num = len(self.model.decoders)
+ return ["y"] + ["out_cache_%d" % i for i in range(cache_num)]
+
+ def get_dynamic_axes(self):
+ ret = {
+ "tgt": {0: "tgt_batch", 1: "tgt_length"},
+ "memory": {0: "memory_batch", 1: "memory_length"},
+ }
+ cache_num = len(self.model.decoders)
+ ret.update(
+ {
+ "cache_%d" % d: {0: "cache_%d_batch" % d, 2: "cache_%d_length" % d}
+ for d in range(cache_num)
+ }
+ )
+ return ret
+
+ def get_model_config(self, path):
+ return {
+ "dec_type": "XformerDecoder",
+ "model_path": os.path.join(path, f"{self.model_name}.onnx"),
+ "n_layers": len(self.model.decoders),
+ "odim": self.model.decoders[0].size,
+ }
--
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