From e04489ce4c0fd0095d0c79ef8f504f425e0435a8 Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期三, 13 三月 2024 16:34:42 +0800
Subject: [PATCH] contextual&seaco ONNX export (#1481)
---
funasr/models/scama/encoder.py | 157 ----------------------------------------------------
1 files changed, 0 insertions(+), 157 deletions(-)
diff --git a/funasr/models/scama/encoder.py b/funasr/models/scama/encoder.py
index 3651e61..2c676b2 100644
--- a/funasr/models/scama/encoder.py
+++ b/funasr/models/scama/encoder.py
@@ -460,160 +460,3 @@
return xs_pad, ilens, None
- def gen_tf2torch_map_dict(self):
- tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
- tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
- map_dict_local = {
- ## encoder
- # cicd
- "{}.encoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.encoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.encoders.layeridx.self_attn.linear_q_k_v.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (768,256),(1,256,768)
- "{}.encoders.layeridx.self_attn.linear_q_k_v.bias".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (768,),(768,)
- "{}.encoders.layeridx.self_attn.fsmn_block.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/depth_conv_w".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 2, 0),
- }, # (256,1,31),(1,31,256,1)
- "{}.encoders.layeridx.self_attn.linear_out.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.encoders.layeridx.self_attn.linear_out.bias".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- # ffn
- "{}.encoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.encoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.encoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/ffn/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.encoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/ffn/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.encoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
- "{}.encoders.layeridx.feed_forward.w_2.bias".format(tensor_name_prefix_torch):
- {"name": "{}/layer_layeridx/ffn/conv1d_1/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- # out norm
- "{}.after_norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.after_norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
-
- }
-
- return map_dict_local
-
- def convert_tf2torch(self,
- var_dict_tf,
- var_dict_torch,
- ):
-
- map_dict = self.gen_tf2torch_map_dict()
-
- var_dict_torch_update = dict()
- for name in sorted(var_dict_torch.keys(), reverse=False):
- names = name.split('.')
- if names[0] == self.tf2torch_tensor_name_prefix_torch:
- if names[1] == "encoders0":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
-
- name_q = name_q.replace("encoders0", "encoders")
- layeridx_bias = 0
- layeridx += layeridx_bias
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
- elif names[1] == "encoders":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
- layeridx_bias = 1
- layeridx += layeridx_bias
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
-
- elif names[1] == "after_norm":
- name_tf = map_dict[name]["name"]
- data_tf = var_dict_tf[name_tf]
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
- var_dict_tf[name_tf].shape))
-
- return var_dict_torch_update
-
--
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