Incompatible shapes when

I tried to modify the code in https://github.com/huggingface/candle/blob/main/candle-examples/examples/llama/main.rs to become a chatbot where each new prompt considers the history of all previous prompts. This is my code:

```rs
use anyhow::{Error, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::{LogitsProcessor, Sampling};
use candle_transformers::models::llama::Cache;
use candle_transformers::models::llama::Llama;
use candle_transformers::models::llama::LlamaConfig;
use candle_transformers::models::llama::LlamaEosToks;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;

const EOS_TOKEN: &str = "";
const REPEAT_PENALTY: f32 = 1.1;
const REPEAT_LAST_N: usize = 128;
const SEED: u64 = 299792458;
const SAMPLE_LEN: usize = 10000;
const ADD_SPECIAL_TOKENS: bool = true;
const SKIP_SPECIAL_TOKENS: bool = true;
const USE_KV_CACHE: bool = true;
const USE_FLASH_ATTENTION: bool = false;

pub struct Chat {
model: Llama,
logits_processor: LogitsProcessor,
cache: Cache,
tokenizer: Tokenizer,
device: Device,
eos_token_id: Option,
tokens: Vec,
index: usize,
}

impl Chat {
pub fn new() -> Result {
let device = Device::new_metal(0)?;
let dtype = DType::F16;
let api = Api::new()?;
let api = api.repo(Repo::with_revision(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0".to_string(),
RepoType::Model,
"main".to_string(),
));

let tokenizer_filename = api.get("tokenizer.json")?;
let config_filename = api.get("config.json")?;
let config: LlamaConfig = serde_json::from_slice(&std::fs::read(config_filename)?)?;
let config = config.into_config(USE_FLASH_ATTENTION);
let filenames = vec![api.get("model.safetensors")?];
let cache = Cache::new(USE_KV_CACHE, dtype, &config, &device)?;

let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = Llama::load(vb, &config)?;

let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(Error::msg)?;
let eos_token_id = config
.eos_token_id
.or_else(|| tokenizer.token_to_id(EOS_TOKEN).map(LlamaEosToks::Single));
let logits_processor = LogitsProcessor::from_sampling(SEED, Sampling::ArgMax);

Ok(Self {
model,
tokenizer,
logits_processor,
eos_token_id,
cache,
device,
tokens: Vec::new(),
index: 0,
})
}

pub fn run(&mut self, prompt: &str) -> Result {
self.tokens.extend(
self.tokenizer
.encode(prompt, ADD_SPECIAL_TOKENS)
.map_err(Error::msg)?
.get_ids(),
);

for _ in 0..SAMPLE_LEN {
let tokens_slice = &self.tokens[self.index..];
let input = Tensor::new(tokens_slice, &self.device)?.unsqueeze(0)?;
let logits = self
.model
.forward(&input, self.index, &mut self.cache)?
.squeeze(0)?;
let logits = candle_transformers::utils::apply_repeat_penalty(
&logits,
REPEAT_PENALTY,
&self.tokens[self.tokens.len().saturating_sub(REPEAT_LAST_N)..],
)?;
self.index += tokens_slice.len();

let next_token = self.logits_processor.sample(&logits)?;
self.tokens.push(next_token);

if self.is_eos_token(next_token) {
break;
}
}
let output = self
.tokenizer
.decode(&self.tokens, SKIP_SPECIAL_TOKENS)
.map_err(Error::msg)?;
Ok(output)
}

fn is_eos_token(&self, token: u32) -> bool {
matches!(self.eos_token_id, Some(LlamaEosToks::Single(id)) if token == id)
|| matches!(self.eos_token_id, Some(LlamaEosToks::Multiple(ref ids)) if ids.contains(&token))
}
}

fn main() {
let mut chat = Chat::new().unwrap();
println!("{}", chat.run("Hello my name is").unwrap());
println!("{}", chat.run("Today").unwrap());
}
```
When I run, I get this error:
```
called `Result::unwrap()` on an `Err` value: BroadcastIncompatibleShapes { src_shape: [3, 3], dst_shape: [1, 32, 3, 349] }
```
The error happens the second time I call `Chat::run` in `main` and is thrown from this statement.
```
// ...
let logits = self
.model
.forward(&input, self.index, &mut self.cache)?
// ...
```

The first time I run the chat in main, the shape of `input` is `[1,5]`. After producing an output token, the next shape of `input` is `[1,1]` since I use key-value caching.

When I later enter a new prompt and run the chat, the `input` shape is `[1,3]` (which includes the EOS token from the previous run). The error disappears if drop some tokens so the shape becomes `[1,1]`. Is there something that says the shape must be `[1,1]` when we use key-value caching?

Start with candle-examples/examples/llama/main.rs, especially Chat::run and the model.forward call using Cache. Reproduce the second Chat::run invocation and inspect how the cached position and newly encoded prompt shape interact. Done means determining whether multi-token input is supported with the existing KV cache and documenting or correcting the behavior so the repeated prompt call no longer produces incompatible shapes.

Written by the indexing model from the issue text.