python/FunASR-XL.git

			@@ -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