From 675b4605e8d1d9a406f5e6fc3bc989ddc932b04b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 15 三月 2024 21:14:08 +0800
Subject: [PATCH] Dev gzf llm (#1506)
---
funasr/models/ct_transformer_streaming/model.py | 67 ++-------------------------------
1 files changed, 4 insertions(+), 63 deletions(-)
diff --git a/funasr/models/ct_transformer_streaming/model.py b/funasr/models/ct_transformer_streaming/model.py
index 129cc95..e6977ad 100644
--- a/funasr/models/ct_transformer_streaming/model.py
+++ b/funasr/models/ct_transformer_streaming/model.py
@@ -173,68 +173,9 @@
return results, meta_data
- def export(
- self,
- **kwargs,
- ):
+ def export(self, **kwargs):
- is_onnx = kwargs.get("type", "onnx") == "onnx"
- encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
- self.encoder = encoder_class(self.encoder, onnx=is_onnx)
-
- self.forward = self.export_forward
-
- return self
+ from .export_meta import export_rebuild_model
+ models = export_rebuild_model(model=self, **kwargs)
+ return models
- def export_forward(self, inputs: torch.Tensor,
- text_lengths: torch.Tensor,
- vad_indexes: torch.Tensor,
- sub_masks: torch.Tensor,
- ):
- """Compute loss value from buffer sequences.
-
- Args:
- input (torch.Tensor): Input ids. (batch, len)
- hidden (torch.Tensor): Target ids. (batch, len)
-
- """
- x = self.embed(inputs)
- # mask = self._target_mask(input)
- h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
- y = self.decoder(h)
- return y
-
- def export_dummy_inputs(self):
- length = 120
- text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)).type(torch.int32)
- text_lengths = torch.tensor([length], dtype=torch.int32)
- vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
- sub_masks = torch.ones(length, length, dtype=torch.float32)
- sub_masks = torch.tril(sub_masks).type(torch.float32)
- return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
-
- def export_input_names(self):
- return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']
-
- def export_output_names(self):
- return ['logits']
-
- def export_dynamic_axes(self):
- return {
- 'inputs': {
- 1: 'feats_length'
- },
- 'vad_masks': {
- 2: 'feats_length1',
- 3: 'feats_length2'
- },
- 'sub_masks': {
- 2: 'feats_length1',
- 3: 'feats_length2'
- },
- 'logits': {
- 1: 'logits_length'
- },
- }
- def export_name(self):
- return "model.onnx"
--
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