Seems yes.
Yes: you can feed your 512-pretrained T5 inputs of length 1024 (and more) at fine-tuning and inference time, as long as:
- your tokenizer + collator + Trainer are configured to actually produce 1024-token sequences, and
- your GPU memory can handle the quadratic attention cost.
It will run without positional-embedding errors. The only real caveat is that 1024 is longer than what you pretrained on, so it’s “length extrapolation”: it’s safe but may change quality, and may benefit from some extra training at that length.
Below is the reasoning, step-by-step, tied to your exact setup.
1. Why T5 doesn’t have a hard 512-token limit
1.1 Absolute vs relative positions
Many models (BERT, GPT-2, classic BART) use an absolute positional embedding matrix with a fixed size max_position_embeddings. If you go past that, you get an “index out of range in positional embedding matrix” error.
T5 is different:
- It uses relative positional bias via a small bucketed table:
parameters like relative_attention_num_buckets and relative_attention_max_distance define how query–key distance is mapped to a bucket, and each bucket has a learned scalar bias. (Hugging Face)
- Distances larger than
relative_attention_max_distance (typically 128) are all mapped into one “far” bucket.
Because of that:
- There is no absolute position embedding indexed 0…511 which would overflow at 512+.
- The attention bias for each (query position, key position) pair is computed on the fly from the distance; that code works for any sequence length the GPU can hold. (GitLab)
The Hugging Face forum thread you quoted says exactly this: T5 was trained mostly with 512-token inputs, but “thanks to its use of relative attention it can handle much longer sequences; the real limit is GPU memory.” (Hugging Face Forums)
1.2 So what did “512” do in practice for you?
In your code, 512 comes from:
max_length = 512DataCollatorForT5MLM(... input_length=max_length, ...)
So:
-
Architecturally, the model does not know “512 is my max”; there’s no max_position_embeddings=512 being enforced for T5.
-
The 512 cap is enforced by your data pipeline:
- the collator produces encoder inputs of length 512;
expanded_inputs_length=568 is just internal math for span corruption; it shrinks back to 512 before going into the model.
Conclusion:
- 512 is a training window size, not a hard architectural ceiling.
- The model’s weights have only seen sequences up to 512 so far, but the machinery can process longer sequences.
2. Will 1024-token inputs work technically?
Yes.
2.1 No positional-embedding crash
For Hugging Face’s T5ForConditionalGeneration / T5Model:
- The forward pass accepts
input_ids shaped [batch_size, seq_len] with arbitrary seq_len.
- Relative attention biases are computed for that
seq_len × seq_len shape; there’s no index into a [max_len, dim] position-embedding table that would overflow at >512. (GitLab)
- HF maintainers explicitly confirm in that “Does T5 truncate input longer than 512 internally?” thread that T5 does not internally truncate or error at >512; the limit is memory. (Hugging Face Forums)
So if you construct input_ids with length 1024, the model will run.
2.2 The real constraint: GPU memory and n2 attention
T5 uses standard full self-attention:
- Every encoder layer builds an attention matrix of shape
seq_len Ă— seq_len.
- Memory and compute therefore scale as O(n2) in sequence length. (Hugging Face Forums)
If you go from 512 → 1024:
- The attention matrix becomes 4Ă— larger.
- Roughly, memory and time for the attention part also grow by ~4Ă— (plus some overhead).
Practically, when you move to 1024:
3. How to actually get 1024 tokens through HF today
One subtle part is the tooling: recent HF versions changed some defaults around tokenizers and truncation. If you don’t wire things explicitly, you may think you are using 1024 but still be capped at 512 (or something else) by the tokenizer/collator.
3.1 Tokenizer: always set max_length + truncation
The tokenizer controls how long your input sequences are before they reach the model.
- Padding/truncation docs show that
padding and truncation combine with max_length to produce fixed-size batches. (Hugging Face)
- The tokenizer API docs say: if
max_length is not set, it uses an internal “predefined model maximum length” if the model specifies one; if not, no max-length truncation happens. (Hugging Face)
Because you have a custom T5 config/tokenizer, that “model max length” may be missing or not set to 512. You should therefore always be explicit:
enc = tokenizer(
texts,
padding="max_length",
truncation=True,
max_length=1024, # <- your new window size
return_tensors="pt",
)
assert enc.input_ids.shape[1] == 1024
Key point:
- If you don’t pass
max_length=1024 (or don’t set tokenizer.model_max_length = 1024 yourself), you can’t rely on the tokenizer using 1024 for you; it might stay at some default 512, or use no truncation at all. (Hugging Face)
3.2 Your pretraining collator: bump input_length to 1024
Your current collator:
data_collator = DataCollatorForT5MLM(
tokenizer=tokenizer,
noise_density=args.mlm_probability,
mean_noise_span_length=args.mean_noise_span_length,
input_length=max_length, # 512 now
target_length=targets_length,
pad_token_id=model.config.pad_token_id,
decoder_start_token_id=model.config.decoder_start_token_id,
)
To continue pretraining or fine-tuning at 1024:
- Recompute
expanded_inputs_length, targets_length with inputs_length=1024.
- Recreate the collator with
input_length=1024.
That way:
- The encoder sees inputs of length 1024, not 512.
- The decoder still sees span-corrupted targets of whatever
target_length your helper calculates for 1024.
If you keep input_length=512, the collator will keep cropping/padding back to 512 even if the tokenizer produced 1024-token sequences.
3.3 Trainer / generation: don’t forget output lengths
Two separate “lengths” to care about:
- Encoder input length: controlled via
max_length at tokenization / input_length in your collator.
- Decoder output length: controlled via
max_new_tokens or max_length in model.generate.
There’s a common gotcha where outputs look “oddly short” because generate defaults are small; StackOverflow answers show that you need to raise max_length in generate explicitly when you want longer outputs. (Stack Overflow)
Example for generation:
outputs = model.generate(
**enc,
max_new_tokens=128, # or max_length for total sequence length
)
This generation limit is independent of your encoder input length (512 vs 1024).
4. What changes semantically when you go from 512 → 1024?
T5’s relative positional bias scheme:
- Uses ~32 buckets (
relative_attention_num_buckets) to encode relative distances.
- Distances are exact for small offsets and merged into logarithmic ranges up to
relative_attention_max_distance (usually 128); beyond that, all distances share the final “far” bucket. (Hugging Face)
In your training at 512:
When you switch to 1024:
- No new distance buckets are introduced; farther tokens still land in the same “far” bucket.
- From the perspective of the relative position module, 1024 isn’t qualitatively new; it just has more “far” relationships.
However, length extrapolation work shows that:
- Even with relatively robust positional encodings like T5’s RPE, performance can degrade as you go far beyond the training length, because the model never learned how to use very distant context in that regime. (arXiv)
So:
- 512 → 1024 is usually moderate extrapolation (reasonably safe).
- 512 → 4096+ is aggressive and often needs special architectures like LongT5 (which modifies attention to handle 4k–16k efficiently). (Hugging Face)
5. Best-practice strategies for your situation
5.1 If you “just want to try 1024”
This is often enough if you mainly want more context, and your tasks are not hyper-sensitive to the very longest spans.
- Change tokenizer + collator to 1024 (as above).
- Reduce batch size and enable memory-saving tricks.
- Fine-tune directly on your downstream task with 1024 inputs.
You’ll likely see:
- No crashes.
- Usable performance, often close to 512-based performance, sometimes better for tasks that genuinely benefit from extra context.
5.2 If long context is core: do a short “continued pretraining”
If long range is central (e.g. long-doc summarization, retrieval-augmented generation with big concatenated passages):
-
Run continued pretraining (same span-corruption objective) at longer lengths, with your existing weights:
- e.g. a few thousand steps at 768, then at 1024.
-
Use a lower LR than initial pretraining (you’re refining, not starting from scratch).
-
Then fine-tune on your task at 1024.
This is analogous to context-extension techniques for other LLMs: a small amount of training at the new length helps the model adapt its attention patterns to the bigger window. (arXiv)
5.3 When to switch to LongT5 or other long-context models
If you start needing 4k–16k inputs consistently, vanilla T5’s full attention becomes:
- Very expensive (n2).
- Less effective, because everything beyond 128 positions is in that single “far” bucket.
LongT5 re-architects attention (local + transient-global) to handle up to 16k tokens more efficiently and comes with configs that explicitly expect long contexts. (Hugging Face)
For 1024, you are still firmly in “vanilla T5 is okay” territory; for 8k, something like LongT5 is much more attractive.
6. Direct answer to your question
Since I pretrained the model with a maximum input length of 512, does this mean I can still pass longer sequences (e.g., 1024 tokens) during fine-tuning and inference, as long as the GPU memory allows it?
Summarizing:
-
Yes, you can.
- T5’s relative positional attention does not impose a hard 512 limit.
- Feeding 1024 tokens is architecturally safe; the main limit is GPU memory. (Hugging Face Forums)
-
You must configure HF to actually use 1024.
- Set
max_length=1024, truncation=True in the tokenizer. (Hugging Face)
- Recreate your
DataCollatorForT5MLM with input_length=1024 and recomputed target_length.
- Set appropriate max lengths in your Trainer / generation code.
-
Quality-wise, 1024 is length extrapolation, not magic.
- T5’s RPE tends to generalize beyond training length reasonably well, but performance can still change or degrade as you go longer. (arXiv)
- For important long-context use cases, a bit of continued pretraining (or at least fine-tuning) at 1024 is recommended.
So as long as you handle the tokenizer/collator/Trainer lengths explicitly and watch your GPU memory, you are free to use 1024-token inputs with your 512-pretrained T5.