Truncation of sequences that are beyond the model's maximum length

Hi,
First, I would like to thank you for this library :-) I'm really enjoying it.

I tried to tokenize a sequence with around 4K tokens and then fed it to a RoBERTa-based model (CodeBERT). This led to the following issue,

```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
2 ids = input_sentence.padded_tensor(padding_id=0, pad_left=True)
3 mask = input.attention_mask(pad_left=True)
----> 4 model_output = encoder(piece_ids=ids, attention_mask=mask)

10 frames
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs)
1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1517 else:
-> 1518 return self._call_impl(*args, **kwargs)
1519
1520 def _call_impl(self, *args, **kwargs):

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1525 or _global_backward_pre_hooks or _global_backward_hooks
1526 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527 return forward_call(*args, **kwargs)
1528
1529 try:

[/usr/local/lib/python3.10/dist-packages/curated_transformers/models/transformer.py](https://localhost:8080/#) in forward(self, piece_ids, attention_mask, positions, type_ids)
122 type_ids: Optional[Tensor] = None,
123 ) -> ModelOutput:
--> 124 embeddings = self.embeddings(piece_ids, positions=positions, type_ids=type_ids)
125 layer_output = embeddings
126

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs)
1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1517 else:
-> 1518 return self._call_impl(*args, **kwargs)
1519
1520 def _call_impl(self, *args, **kwargs):

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1525 or _global_backward_pre_hooks or _global_backward_hooks
1526 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527 return forward_call(*args, **kwargs)
1528
1529 try:

[/usr/local/lib/python3.10/dist-packages/curated_transformers/models/roberta/embeddings.py](https://localhost:8080/#) in forward(self, piece_ids, positions, type_ids)
96 if positions is None:
97 positions = self._get_positions(piece_ids)
---> 98 return super().forward(
99 piece_ids,
100 positions=positions,

[/usr/local/lib/python3.10/dist-packages/curated_transformers/layers/transformer.py](https://localhost:8080/#) in forward(self, piece_ids, positions, type_ids)
180 if positions is None:
181 positions = self._get_positions(piece_ids)
--> 182 position_embeddings = self.position_embeddings(positions)
183 embeddings += position_embeddings
184

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs)
1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1517 else:
-> 1518 return self._call_impl(*args, **kwargs)
1519
1520 def _call_impl(self, *args, **kwargs):

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs)
1525 or _global_backward_pre_hooks or _global_backward_hooks
1526 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527 return forward_call(*args, **kwargs)
1528
1529 try:

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py](https://localhost:8080/#) in forward(self, input)
160
161 def forward(self, input: Tensor) -> Tensor:
--> 162 return F.embedding(
163 input, self.weight, self.padding_idx, self.max_norm,
164 self.norm_type, self.scale_grad_by_freq, self.sparse)

[/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py](https://localhost:8080/#) in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
2231 # remove once script supports set_grad_enabled
2232 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2233 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
2234
2235

IndexError: index out of range in self
```
For reference, here was the code that I was using,

```python
MODEL_TAG = "microsoft/codebert-base"
tokenizer = AutoTokenizer.from_hf_hub(name=MODEL_TAG, revision="main")
model = RoBERTaEncoder.from_hf_hub(
name=MODEL_TAG,
revision="main",
)
code = [
'void avcodec_string(char *buf, int buf_size, AVCodecContext *enc, int encode)\n\n{\n\n const char *codec_type;\n\n const char *codec_name;\n\n const char *profile = NULL;\n\n const AVCodec *p;\n\n int64_t bitrate;\n\n int new_line = 0;\n\n AVRational display_aspect_ratio;\n\n const char *separator = enc->dump_separator ? (const char *)enc->dump_separator : ", ";\n\n\n\n if (!buf || buf_size <= 0)\n\n return;\n\n codec_type = av_get_media_type_string(enc->codec_type);\n\n codec_name = avcodec_get_name(enc->codec_id);\n\n if (enc->profile != FF_PROFILE_UNKNOWN) {\n\n if (enc->codec)\n\n p = enc->codec;\n\n else\n\n p = encode ? avcodec_find_encoder(enc->codec_id) :\n\n avcodec_find_decoder(enc->codec_id);\n\n if (p)\n\n profile = av_get_profile_name(p, enc->profile);\n\n }\n\n\n\n snprintf(buf, buf_size, "%s: %s", codec_type ? codec_type : "unknown",\n\n codec_name);\n\n buf[0] ^= \'a\' ^ \'A\'; /* first letter in uppercase */\n\n\n\n if (enc->codec && strcmp(enc->codec->name, codec_name))\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf), " (%s)", enc->codec->name);\n\n\n\n if (profile)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf), " (%s)", profile);\n\n if ( enc->codec_type == AVMEDIA_TYPE_VIDEO\n\n && av_log_get_level() >= AV_LOG_VERBOSE\n\n && enc->refs)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", %d reference frame%s",\n\n enc->refs, enc->refs > 1 ? "s" : "");\n\n\n\n if (enc->codec_tag) {\n\n char tag_buf[32];\n\n av_get_codec_tag_string(tag_buf, sizeof(tag_buf), enc->codec_tag);\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n " (%s / 0x%04X)", tag_buf, enc->codec_tag);\n\n }\n\n\n\n switch (enc->codec_type) {\n\n case AVMEDIA_TYPE_VIDEO:\n\n {\n\n char detail[256] = "(";\n\n\n\n av_strlcat(buf, separator, buf_size);\n\n\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n "%s", enc->pix_fmt == AV_PIX_FMT_NONE ? "none" :\n\n av_get_pix_fmt_name(enc->pix_fmt));\n\n if (enc->bits_per_raw_sample && enc->pix_fmt != AV_PIX_FMT_NONE &&\n\n enc->bits_per_raw_sample < av_pix_fmt_desc_get(enc->pix_fmt)->comp[0].depth)\n\n av_strlcatf(detail, sizeof(detail), "%d bpc, ", enc->bits_per_raw_sample);\n\n if (enc->color_range != AVCOL_RANGE_UNSPECIFIED)\n\n av_strlcatf(detail, sizeof(detail), "%s, ",\n\n av_color_range_name(enc->color_range));\n\n\n\n if (enc->colorspace != AVCOL_SPC_UNSPECIFIED ||\n\n enc->color_primaries != AVCOL_PRI_UNSPECIFIED ||\n\n enc->color_trc != AVCOL_TRC_UNSPECIFIED) {\n\n if (enc->colorspace != (int)enc->color_primaries ||\n\n enc->colorspace != (int)enc->color_trc) {\n\n new_line = 1;\n\n av_strlcatf(detail, sizeof(detail), "%s/%s/%s, ",\n\n av_color_space_name(enc->colorspace),\n\n av_color_primaries_name(enc->color_primaries),\n\n av_color_transfer_name(enc->color_trc));\n\n } else\n\n av_strlcatf(detail, sizeof(detail), "%s, ",\n\n av_get_colorspace_name(enc->colorspace));\n\n }\n\n\n\n if (av_log_get_level() >= AV_LOG_DEBUG &&\n\n enc->chroma_sample_location != AVCHROMA_LOC_UNSPECIFIED)\n\n av_strlcatf(detail, sizeof(detail), "%s, ",\n\n av_chroma_location_name(enc->chroma_sample_location));\n\n\n\n if (strlen(detail) > 1) {\n\n detail[strlen(detail) - 2] = 0;\n\n av_strlcatf(buf, buf_size, "%s)", detail);\n\n }\n\n }\n\n\n\n if (enc->width) {\n\n av_strlcat(buf, new_line ? separator : ", ", buf_size);\n\n\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n "%dx%d",\n\n enc->width, enc->height);\n\n\n\n if (av_log_get_level() >= AV_LOG_VERBOSE &&\n\n (enc->width != enc->coded_width ||\n\n enc->height != enc->coded_height))\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n " (%dx%d)", enc->coded_width, enc->coded_height);\n\n\n\n if (enc->sample_aspect_ratio.num) {\n\n av_reduce(&display_aspect_ratio.num, &display_aspect_ratio.den,\n\n enc->width * enc->sample_aspect_ratio.num,\n\n enc->height * enc->sample_aspect_ratio.den,\n\n 1024 * 1024);\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n " [SAR %d:%d DAR %d:%d]",\n\n enc->sample_aspect_ratio.num, enc->sample_aspect_ratio.den,\n\n display_aspect_ratio.num, display_aspect_ratio.den);\n\n }\n\n if (av_log_get_level() >= AV_LOG_DEBUG) {\n\n int g = av_gcd(enc->time_base.num, enc->time_base.den);\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", %d/%d",\n\n enc->time_base.num / g, enc->time_base.den / g);\n\n }\n\n }\n\n if (encode) {\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", q=%d-%d", enc->qmin, enc->qmax);\n\n } else {\n\n if (enc->properties & FF_CODEC_PROPERTY_CLOSED_CAPTIONS)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", Closed Captions");\n\n if (enc->properties & FF_CODEC_PROPERTY_LOSSLESS)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", lossless");\n\n }\n\n break;\n\n case AVMEDIA_TYPE_AUDIO:\n\n av_strlcat(buf, separator, buf_size);\n\n\n\n if (enc->sample_rate) {\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n "%d Hz, ", enc->sample_rate);\n\n }\n\n av_get_channel_layout_string(buf + strlen(buf), buf_size - strlen(buf), enc->channels, enc->channel_layout);\n\n if (enc->sample_fmt != AV_SAMPLE_FMT_NONE) {\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", %s", av_get_sample_fmt_name(enc->sample_fmt));\n\n }\n\n if ( enc->bits_per_raw_sample > 0\n\n && enc->bits_per_raw_sample != av_get_bytes_per_sample(enc->sample_fmt) * 8)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n " (%d bit)", enc->bits_per_raw_sample);\n\n break;\n\n case AVMEDIA_TYPE_DATA:\n\n if (av_log_get_level() >= AV_LOG_DEBUG) {\n\n int g = av_gcd(enc->time_base.num, enc->time_base.den);\n\n if (g)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", %d/%d",\n\n enc->time_base.num / g, enc->time_base.den / g);\n\n }\n\n break;\n\n case AVMEDIA_TYPE_SUBTITLE:\n\n if (enc->width)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", %dx%d", enc->width, enc->height);\n\n break;\n\n default:\n\n return;\n\n }\n\n if (encode) {\n\n if (enc->flags & AV_CODEC_FLAG_PASS1)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", pass 1");\n\n if (enc->flags & AV_CODEC_FLAG_PASS2)\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", pass 2");\n\n }\n\n bitrate = get_bit_rate(enc);\n\n if (bitrate != 0) {\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", %"PRId64" kb/s", bitrate / 1000);\n\n } else if (enc->rc_max_rate > 0) {\n\n snprintf(buf + strlen(buf), buf_size - strlen(buf),\n\n ", max. %"PRId64" kb/s", (int64_t)enc->rc_max_rate / 1000);\n\n }\n\n}\n',
]
with torch.no_grad():
input_sentence = tokenizer(code)
ids = input_sentence.padded_tensor(padding_id=0, pad_left=False)
mask = input_sentence.attention_mask(pad_left=False)
model_output = model(piece_ids=ids, attention_mask=mask)
```
I went through the API docs and skimmed through source code and it appears that truncation is not supported. Note that when I manually truncated the sequence, I was able to feed it to the RoBERTa encoder.