It seems that the problem is related to the shape of the “embed_positions” layer of the decoder.
This code snippet:

works fine with a max_length of 60 (or less) in ‘generate’ (throws no error)
and
also works fine with max_length=200 and a different Pegasus model, e.g.: “sshleifer/distill-pegasus-xsum-16-4”
(examples below)

The two models differ in the shape of this layer:
tuner007/pegasus_paraphrase:
(decoder): PegasusDecoder(
(embed_tokens): Embedding(96103, 1024, padding_idx=0)
(embed_positions): PegasusSinusoidalPositionalEmbedding(60, 1024)
sshleifer/distill-pegasus-xsum-16-4:
(decoder): PegasusDecoder(
(embed_tokens): Embedding(96103, 1024, padding_idx=0)
(embed_positions): PegasusSinusoidalPositionalEmbedding(1024, 1024)
tuner007/pegasus_paraphrase, max_length=60, config.max_position_embeddings=60 :
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer

para_model_name = 'tuner007/pegasus_paraphrase'
para_tokenizer = PegasusTokenizer.from_pretrained(para_model_name)
para_model = PegasusForConditionalGeneration.from_pretrained(para_model_name)

text = [' (Chng et al']
batch = para_tokenizer(text, truncation=True, padding='longest', max_length=200, 
return_tensors="pt")
translated = para_model.generate(**batch, 
                                 #max_length=200, 
                                 max_length=60,
                                 num_beams=10, 
                                 num_return_sequences=1, 
                                 temperature=1.5)

print(para_model.config.max_position_embeddings)

sshleifer/distill-pegasus-xsum-16-4, max_length=200, config.max_position_embeddings=1024 :
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer

para_model_name = "sshleifer/distill-pegasus-xsum-16-4" 
para_tokenizer_s = PegasusTokenizer.from_pretrained(para_model_name)
para_model_s = PegasusForConditionalGeneration.from_pretrained(para_model_name)

text = [' (Chng et al']
batch = para_tokenizer_s(text, truncation=True, padding='longest', max_length=200, 
return_tensors="pt")
translated = para_model_s.generate(**batch, max_length=200, num_beams=10, 
num_return_sequences=1, temperature=1.5)

print(para_model_s.config.max_position_embeddings)