# Converting a PyTorch model into Tensorflow **URL:** \ **Category:** Beginners\ **Created:** [May 3, 2022, 5:00am UTC](https://discuss.huggingface.co/t/converting-a-pytorch-model-into-tensorflow/17456 "2022-05-03T05:00:24Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![yulgm](https://avatars.discourse-cdn.com/v4/letter/y/41988e/32.png) [@yulgm](https://discuss.huggingface.co/u/yulgm)\ **Post date:** [May 3, 2022, 5:00am UTC](https://discuss.huggingface.co/t/converting-a-pytorch-model-into-tensorflow/17456/1 "2022-05-03T05:00:24Z")
Hi everyone! I am trying to load a model with instructions for PyTorch into a Tensorflow framework. [tuner007/pegasus\_paraphrase · Hugging Face](https://huggingface.co/tuner007/pegasus_paraphrase) (I have M1 with accelerators for tensorflow and so far none for PyTorch, so most of my work is tensorflow) I tested the code provided in the card in torch and it works without issues: ```auto import torch from transformers import PegasusForConditionalGeneration, PegasusTokenizer model_name = 'tuner007/pegasus_paraphrase' torch_device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = PegasusTokenizer.from_pretrained(model_name) model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device) def get_response(input_text,num_return_sequences,num_beams): batch = tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device) translated = model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5) tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True) return tgt_text num_beams = 10 num_return_sequences = 5 context = "The ultimate test of your knowledge is your capacity to convey it to another." get_response(context,num_return_sequences,num_beams) ``` So I’m trying to modify all of this for Tensorflow and got this far: ```auto from transformers import TFPegasusForConditionalGeneration, PegasusTokenizer tf_model_name = 'tuner007/pegasus_paraphrase' tf_tokenizer = PegasusTokenizer.from_pretrained(tf_model_name, return_tensors='tf', max_length=512, truncation=True) tf_model = TFPegasusForConditionalGeneration.from_pretrained(tf_model_name, from_pt=True) def get_response_tf(input_text,num_return_sequences,num_beams): batch = tf_tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="tf") translated = tf_model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5) tgt_text = tf_tokenizer.batch_decode(translated, skip_special_tokens=True) return tgt_text num_beams = 10 num_return_sequences = 5 context = "The ultimate test of your knowledge is your capacity to convey it to another." get_response_tf(context,num_return_sequences,num_beams) ``` However when I run it I get this error: `InvalidArgumentError: slice index -22336 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/` Am I missing some step in conversion? FULL ERROR TEXT: ```nohighlight --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) Input In [80], in () 2 num_return_sequences = 5 3 context = "The ultimate test of your knowledge is your capacity to convey it to another." ----> 4 get_response_tf(context,num_return_sequences,num_beams) Input In [79], in get_response_tf(input_text, num_return_sequences, num_beams) 1 def get_response_tf(input_text,num_return_sequences,num_beams): 2 batch = tf_tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="tf") ----> 3 translated = tf_model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5) 4 tgt_text = tf_tokenizer.batch_decode(translated, skip_special_tokens=True) 5 return tgt_text File ~/miniforge3/envs/ml1/lib/python3.9/site-packages/transformers/generation_tf_utils.py:736, in TFGenerationMixin.generate(self, input_ids, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, num_return_sequences, attention_mask, decoder_start_token_id, use_cache, output_scores, output_attentions, output_hidden_states, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, **model_kwargs) 731 assert ( 732 cur_len < max_length 733 ), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`" 735 if num_beams > 1: --> 736 output = self._generate_beam_search( 737 input_ids, 738 cur_len=cur_len, 739 max_length=max_length, 740 min_length=min_length, 741 do_sample=do_sample, 742 early_stopping=early_stopping, 743 temperature=temperature, 744 top_k=top_k, 745 top_p=top_p, 746 repetition_penalty=repetition_penalty, 747 no_repeat_ngram_size=no_repeat_ngram_size, 748 bad_words_ids=bad_words_ids, 749 pad_token_id=pad_token_id, 750 eos_token_id=eos_token_id, 751 batch_size=effective_batch_size, 752 num_return_sequences=num_return_sequences, 753 length_penalty=length_penalty, 754 num_beams=num_beams, 755 vocab_size=vocab_size, 756 encoder_outputs=encoder_outputs, 757 attention_mask=attention_mask, 758 use_cache=use_cache, 759 forced_bos_token_id=forced_bos_token_id, 760 forced_eos_token_id=forced_eos_token_id, 761 return_dict_in_generate=return_dict_in_generate, 762 **model_kwargs, 763 ) 764 else: 765 output = self._generate_no_beam_search( 766 input_ids, 767 cur_len=cur_len, (...) 785 **model_kwargs, 786 ) File ~/miniforge3/envs/ml1/lib/python3.9/site-packages/transformers/generation_tf_utils.py:1307, in TFGenerationMixin._generate_beam_search(self, input_ids, cur_len, max_length, min_length, do_sample, early_stopping, temperature, top_k, top_p, repetition_penalty, no_repeat_ngram_size, bad_words_ids, pad_token_id, eos_token_id, batch_size, num_return_sequences, length_penalty, num_beams, vocab_size, encoder_outputs, attention_mask, use_cache, forced_bos_token_id, forced_eos_token_id, return_dict_in_generate, **kwargs) 1304 beam_idx = tf.convert_to_tensor([x[2] for x in next_batch_beam], dtype=tf.int32) 1306 # re-order batch and update current length -> 1307 input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx]) 1308 input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1) 1309 cur_len = cur_len + 1 File ~/miniforge3/envs/ml1/lib/python3.9/site-packages/transformers/generation_tf_utils.py:1307, in (.0) 1304 beam_idx = tf.convert_to_tensor([x[2] for x in next_batch_beam], dtype=tf.int32) 1306 # re-order batch and update current length -> 1307 input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx]) 1308 input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1) 1309 cur_len = cur_len + 1 File ~/miniforge3/envs/ml1/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:153, in filter_traceback..error_handler(*args, **kwargs) 151 except Exception as e: 152 filtered_tb = _process_traceback_frames(e. __traceback__ ) --> 153 raise e.with_traceback(filtered_tb) from None 154 finally: 155 del filtered_tb File ~/miniforge3/envs/ml1/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:7107, in raise_from_not_ok_status(e, name) 7105 def raise_from_not_ok_status(e, name): 7106 e.message += (" name: " + name if name is not None else "") -> 7107 raise core._status_to_exception(e) from None InvalidArgumentError: slice index -22336 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/ ```