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/decoder.py | 380 ------------------------------------------------------
1 files changed, 0 insertions(+), 380 deletions(-)
diff --git a/funasr/models/scama/decoder.py b/funasr/models/scama/decoder.py
index 9dcb9da..8257f59 100644
--- a/funasr/models/scama/decoder.py
+++ b/funasr/models/scama/decoder.py
@@ -474,383 +474,3 @@
return y, new_cache
- 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
- embed_tensor_name_prefix_tf = self.embed_tensor_name_prefix_tf if self.embed_tensor_name_prefix_tf is not None else tensor_name_prefix_tf
- map_dict_local = {
-
- ## decoder
- # ffn
- "{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.decoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
-
- # fsmn
- "{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format(
- tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/beta".format(
- tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.self_attn.fsmn_block.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/depth_conv_w".format(
- tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 2, 0),
- }, # (256,1,31),(1,31,256,1)
- # src att
- "{}.decoders.layeridx.norm3.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.norm3.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.src_attn.linear_q.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.decoders.layeridx.src_attn.linear_q.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.src_attn.linear_k_v.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.decoders.layeridx.src_attn.linear_k_v.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.src_attn.linear_out.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.decoders.layeridx.src_attn.linear_out.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- # dnn
- "{}.decoders3.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders3.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders3.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.decoders3.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders3.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders3.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders3.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
-
- # embed_concat_ffn
- "{}.embed_concat_ffn.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.embed_concat_ffn.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.embed_concat_ffn.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.embed_concat_ffn.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.embed_concat_ffn.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.embed_concat_ffn.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm_1/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.embed_concat_ffn.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
-
- # 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,)
-
- # in embed
- "{}.embed.0.weight".format(tensor_name_prefix_torch):
- {"name": "{}/w_embs".format(embed_tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (4235,256),(4235,256)
-
- # out layer
- "{}.output_layer.weight".format(tensor_name_prefix_torch):
- {"name": ["{}/dense/kernel".format(tensor_name_prefix_tf),
- "{}/w_embs".format(embed_tensor_name_prefix_tf)],
- "squeeze": [None, None],
- "transpose": [(1, 0), None],
- }, # (4235,256),(256,4235)
- "{}.output_layer.bias".format(tensor_name_prefix_torch):
- {"name": ["{}/dense/bias".format(tensor_name_prefix_tf),
- "seq2seq/2bias" if tensor_name_prefix_tf == "seq2seq/decoder/inputter_1" else "seq2seq/bias"],
- "squeeze": [None, None],
- "transpose": [None, None],
- }, # (4235,),(4235,)
-
- }
- 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()
- decoder_layeridx_sets = set()
- 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] == "decoders":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
- layeridx_bias = 0
- layeridx += layeridx_bias
- decoder_layeridx_sets.add(layeridx)
- 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] == "decoders2":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
- name_q = name_q.replace("decoders2", "decoders")
- layeridx_bias = len(decoder_layeridx_sets)
-
- layeridx += layeridx_bias
- if "decoders." in name:
- decoder_layeridx_sets.add(layeridx)
- 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] == "decoders3":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
-
- layeridx_bias = 0
- layeridx += layeridx_bias
- if "decoders." in name:
- decoder_layeridx_sets.add(layeridx)
- 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] == "embed" or names[1] == "output_layer":
- name_tf = map_dict[name]["name"]
- if isinstance(name_tf, list):
- idx_list = 0
- if name_tf[idx_list] in var_dict_tf.keys():
- pass
- else:
- idx_list = 1
- data_tf = var_dict_tf[name_tf[idx_list]]
- if map_dict[name]["squeeze"][idx_list] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"][idx_list])
- if map_dict[name]["transpose"][idx_list] is not None:
- data_tf = np.transpose(data_tf, map_dict[name]["transpose"][idx_list])
- 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_tf[idx_list],
- var_dict_tf[name_tf[
- idx_list]].shape))
-
- else:
- data_tf = var_dict_tf[name_tf]
- if map_dict[name]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
- if map_dict[name]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name]["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_tf,
- 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))
-
- elif names[1] == "embed_concat_ffn":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
-
- layeridx_bias = 0
- layeridx += layeridx_bias
- if "decoders." in name:
- decoder_layeridx_sets.add(layeridx)
- 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))
-
- return var_dict_torch_update
-
--
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