We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
falcon-rw-1b does not work
### System Info

A Nvidia Tesla T4 GPU in Kubernetes running text-generation-inference version 1.0.0 (official image)

### Information

- [ ] Docker
- [X] The CLI directly

### Tasks

- [X] An officially supported command
- [ ] My own modifications

### Reproduction

Run
```
text-generation-launcher --model-id tiiuae/falcon-rw-1b --trust-remote-code --revision e4b9872bb803165eb22f0a867d4e6a64d34fce19
```
Once the model is loaded, calling the server gives error:
```
$ curl 127.0.0.1:80/generate_stream     -X POST     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}'     -H 'Content-Type: application/json'
data:{"token":{"id":43398,"text":" chapel","logprob":-4.859375,"special":false},"generated_text":null,"details":null}

data:{"error":"Request failed during generation: Server error: not enough values to unpack (expected 4, got 3)","error_type":"generation"}
```
In the server, there is this error:
```
2023-08-11T21:57:42.634325Z ERROR text_generation_launcher: Method Decode encountered an error.
Traceback (most recent call last):
  File "/opt/conda/bin/text-generation-server", line 8, in <module>
    sys.exit(app())
  File "/opt/conda/lib/python3.9/site-packages/typer/main.py", line 311, in __call__
    return get_command(self)(*args, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/click/core.py", line 1130, in __call__
    return self.main(*args, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/typer/core.py", line 778, in main
    return _main(
  File "/opt/conda/lib/python3.9/site-packages/typer/core.py", line 216, in _main
    rv = self.invoke(ctx)
  File "/opt/conda/lib/python3.9/site-packages/click/core.py", line 1657, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/opt/conda/lib/python3.9/site-packages/click/core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/opt/conda/lib/python3.9/site-packages/click/core.py", line 760, in invoke
    return __callback(*args, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/typer/main.py", line 683, in wrapper
    return callback(**use_params)  # type: ignore
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py", line 78, in serve
    server.serve(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 184, in serve
    asyncio.run(
  File "/opt/conda/lib/python3.9/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/opt/conda/lib/python3.9/asyncio/base_events.py", line 634, in run_until_complete
    self.run_forever()
  File "/opt/conda/lib/python3.9/asyncio/base_events.py", line 601, in run_forever
    self._run_once()
  File "/opt/conda/lib/python3.9/asyncio/base_events.py", line 1905, in _run_once
    handle._run()
  File "/opt/conda/lib/python3.9/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/opt/conda/lib/python3.9/site-packages/grpc_interceptor/server.py", line 159, in invoke_intercept_method
    return await self.intercept(
> File "/opt/conda/lib/python3.9/site-packages/text_generation_server/interceptor.py", line 21, in intercept
    return await response
  File "/opt/conda/lib/python3.9/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py", line 82, in _unary_interceptor
    raise error
  File "/opt/conda/lib/python3.9/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py", line 73, in _unary_interceptor
    return await behavior(request_or_iterator, context)
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 98, in Decode
    generations, next_batch = self.model.generate_token(batch)
  File "/opt/conda/lib/python3.9/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/causal_lm.py", line 541, in generate_token
    logits, past = self.forward(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/rw.py", line 82, in forward
    outputs = self.model.forward(
  File "/root/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-rw-1b/e4b9872bb803165eb22f0a867d4e6a64d34fce19/modeling_falcon.py", line 900, in forward
    transformer_outputs = self.transformer(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-rw-1b/e4b9872bb803165eb22f0a867d4e6a64d34fce19/modeling_falcon.py", line 734, in forward
    past_key_values = self._convert_to_rw_cache(past_key_values)
  File "/root/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-rw-1b/e4b9872bb803165eb22f0a867d4e6a64d34fce19/modeling_falcon.py", line 622, in _convert_to_rw_cache
    batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape
ValueError: not enough values to unpack (expected 4, got 3)
```


### Expected behavior

No error

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `server/text_generation_server/models/rw.py`
Content:
```
1 import torch
2 
3 from transformers import AutoTokenizer, AutoModelForCausalLM
4 from typing import List, Optional, Tuple
5 
6 from text_generation_server.models import CausalLM
7 
8 
9 class RW(CausalLM):
10     def __init__(
11         self,
12         model_id: str,
13         revision: Optional[str] = None,
14         quantize: Optional[str] = None,
15         dtype: Optional[torch.dtype] = None,
16         trust_remote_code: bool = False,
17     ):
18         if torch.cuda.is_available():
19             device = torch.device("cuda")
20             dtype = torch.float16 if dtype is None else dtype
21         else:
22             if quantize:
23                 raise ValueError("quantization is not available on CPU")
24 
25             device = torch.device("cpu")
26             dtype = torch.float32
27 
28         tokenizer = AutoTokenizer.from_pretrained(
29             model_id,
30             revision=revision,
31             padding_side="left",
32             truncation_side="left",
33             trust_remote_code=trust_remote_code,
34         )
35         model = AutoModelForCausalLM.from_pretrained(
36             model_id,
37             revision=revision,
38             torch_dtype=dtype,
39             device_map="auto"
40             if torch.cuda.is_available() and torch.cuda.device_count() > 1
41             else None,
42             load_in_8bit=quantize == "bitsandbytes",
43             trust_remote_code=trust_remote_code,
44         )
45         if torch.cuda.is_available() and torch.cuda.device_count() == 1:
46             model = model.cuda()
47 
48         if tokenizer.pad_token_id is None:
49             if model.config.pad_token_id is not None:
50                 tokenizer.pad_token_id = model.config.pad_token_id
51             elif model.config.eos_token_id is not None:
52                 tokenizer.pad_token_id = model.config.eos_token_id
53             elif tokenizer.eos_token_id is not None:
54                 tokenizer.pad_token_id = tokenizer.eos_token_id
55             else:
56                 tokenizer.add_special_tokens({"pad_token": "[PAD]"})
57 
58         super(CausalLM, self).__init__(
59             model=model,
60             tokenizer=tokenizer,
61             requires_padding=True,
62             dtype=dtype,
63             device=device,
64         )
65 
66     def forward(
67         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
68     ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
69         # Model Forward
70         if past_key_values is not None:
71             reshaped_past_key_values = []
72             for layer in past_key_values:
73                 past_keys, past_values = layer
74                 reshaped_past_key_values.append(
75                     (
76                         past_keys.view(-1, *past_keys.shape[-2:]),
77                         past_values.view(-1, *past_values.shape[-2:]),
78                     )
79                 )
80             past_key_values = reshaped_past_key_values
81 
82         outputs = self.model.forward(
83             input_ids=input_ids,
84             attention_mask=attention_mask,
85             past_key_values=past_key_values,
86         )
87         return outputs.logits, outputs.past_key_values
88
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/server/text_generation_server/models/rw.py b/server/text_generation_server/models/rw.py
--- a/server/text_generation_server/models/rw.py
+++ b/server/text_generation_server/models/rw.py
@@ -67,18 +67,6 @@
         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
     ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
         # Model Forward
-        if past_key_values is not None:
-            reshaped_past_key_values = []
-            for layer in past_key_values:
-                past_keys, past_values = layer
-                reshaped_past_key_values.append(
-                    (
-                        past_keys.view(-1, *past_keys.shape[-2:]),
-                        past_values.view(-1, *past_values.shape[-2:]),
-                    )
-                )
-            past_key_values = reshaped_past_key_values
-
         outputs = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,

{"golden_diff": "diff --git a/server/text_generation_server/models/rw.py b/server/text_generation_server/models/rw.py\n--- a/server/text_generation_server/models/rw.py\n+++ b/server/text_generation_server/models/rw.py\n@@ -67,18 +67,6 @@\n         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None\n     ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:\n         # Model Forward\n-        if past_key_values is not None:\n-            reshaped_past_key_values = []\n-            for layer in past_key_values:\n-                past_keys, past_values = layer\n-                reshaped_past_key_values.append(\n-                    (\n-                        past_keys.view(-1, *past_keys.shape[-2:]),\n-                        past_values.view(-1, *past_values.shape[-2:]),\n-                    )\n-                )\n-            past_key_values = reshaped_past_key_values\n-\n         outputs = self.model.forward(\n             input_ids=input_ids,\n             attention_mask=attention_mask,\n", "issue": "falcon-rw-1b does not work\n### System Info\n\nA Nvidia Tesla T4 GPU in Kubernetes running text-generation-inference version 1.0.0 (official image)\n\n### Information\n\n- [ ] Docker\n- [X] The CLI directly\n\n### Tasks\n\n- [X] An officially supported command\n- [ ] My own modifications\n\n### Reproduction\n\nRun\r\n```\r\ntext-generation-launcher --model-id tiiuae/falcon-rw-1b --trust-remote-code --revision e4b9872bb803165eb22f0a867d4e6a64d34fce19\r\n```\r\nOnce the model is loaded, calling the server gives error:\r\n```\r\n$ curl 127.0.0.1:80/generate_stream     -X POST     -d '{\"inputs\":\"What is Deep Learning?\",\"parameters\":{\"max_new_tokens\":20}}'     -H 'Content-Type: application/json'\r\ndata:{\"token\":{\"id\":43398,\"text\":\" chapel\",\"logprob\":-4.859375,\"special\":false},\"generated_text\":null,\"details\":null}\r\n\r\ndata:{\"error\":\"Request failed during generation: Server error: not enough values to unpack (expected 4, got 3)\",\"error_type\":\"generation\"}\r\n```\r\nIn the server, there is this error:\r\n```\r\n2023-08-11T21:57:42.634325Z ERROR text_generation_launcher: Method Decode encountered an error.\r\nTraceback (most recent call last):\r\n  File \"/opt/conda/bin/text-generation-server\", line 8, in <module>\r\n    sys.exit(app())\r\n  File \"/opt/conda/lib/python3.9/site-packages/typer/main.py\", line 311, in __call__\r\n    return get_command(self)(*args, **kwargs)\r\n  File \"/opt/conda/lib/python3.9/site-packages/click/core.py\", line 1130, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"/opt/conda/lib/python3.9/site-packages/typer/core.py\", line 778, in main\r\n    return _main(\r\n  File \"/opt/conda/lib/python3.9/site-packages/typer/core.py\", line 216, in _main\r\n    rv = self.invoke(ctx)\r\n  File \"/opt/conda/lib/python3.9/site-packages/click/core.py\", line 1657, in invoke\r\n    return _process_result(sub_ctx.command.invoke(sub_ctx))\r\n  File \"/opt/conda/lib/python3.9/site-packages/click/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"/opt/conda/lib/python3.9/site-packages/click/core.py\", line 760, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"/opt/conda/lib/python3.9/site-packages/typer/main.py\", line 683, in wrapper\r\n    return callback(**use_params)  # type: ignore\r\n  File \"/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py\", line 78, in serve\r\n    server.serve(\r\n  File \"/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py\", line 184, in serve\r\n    asyncio.run(\r\n  File \"/opt/conda/lib/python3.9/asyncio/runners.py\", line 44, in run\r\n    return loop.run_until_complete(main)\r\n  File \"/opt/conda/lib/python3.9/asyncio/base_events.py\", line 634, in run_until_complete\r\n    self.run_forever()\r\n  File \"/opt/conda/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\r\n    self._run_once()\r\n  File \"/opt/conda/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\r\n    handle._run()\r\n  File \"/opt/conda/lib/python3.9/asyncio/events.py\", line 80, in _run\r\n    self._context.run(self._callback, *self._args)\r\n  File \"/opt/conda/lib/python3.9/site-packages/grpc_interceptor/server.py\", line 159, in invoke_intercept_method\r\n    return await self.intercept(\r\n> File \"/opt/conda/lib/python3.9/site-packages/text_generation_server/interceptor.py\", line 21, in intercept\r\n    return await response\r\n  File \"/opt/conda/lib/python3.9/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py\", line 82, in _unary_interceptor\r\n    raise error\r\n  File \"/opt/conda/lib/python3.9/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py\", line 73, in _unary_interceptor\r\n    return await behavior(request_or_iterator, context)\r\n  File \"/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py\", line 98, in Decode\r\n    generations, next_batch = self.model.generate_token(batch)\r\n  File \"/opt/conda/lib/python3.9/contextlib.py\", line 79, in inner\r\n    return func(*args, **kwds)\r\n  File \"/opt/conda/lib/python3.9/site-packages/text_generation_server/models/causal_lm.py\", line 541, in generate_token\r\n    logits, past = self.forward(\r\n  File \"/opt/conda/lib/python3.9/site-packages/text_generation_server/models/rw.py\", line 82, in forward\r\n    outputs = self.model.forward(\r\n  File \"/root/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-rw-1b/e4b9872bb803165eb22f0a867d4e6a64d34fce19/modeling_falcon.py\", line 900, in forward\r\n    transformer_outputs = self.transformer(\r\n  File \"/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n    return forward_call(*args, **kwargs)\r\n  File \"/root/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-rw-1b/e4b9872bb803165eb22f0a867d4e6a64d34fce19/modeling_falcon.py\", line 734, in forward\r\n    past_key_values = self._convert_to_rw_cache(past_key_values)\r\n  File \"/root/.cache/huggingface/modules/transformers_modules/tiiuae/falcon-rw-1b/e4b9872bb803165eb22f0a867d4e6a64d34fce19/modeling_falcon.py\", line 622, in _convert_to_rw_cache\r\n    batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape\r\nValueError: not enough values to unpack (expected 4, got 3)\r\n```\r\n\n\n### Expected behavior\n\nNo error\n", "before_files": [{"content": "import torch\n\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nfrom typing import List, Optional, Tuple\n\nfrom text_generation_server.models import CausalLM\n\n\nclass RW(CausalLM):\n    def __init__(\n        self,\n        model_id: str,\n        revision: Optional[str] = None,\n        quantize: Optional[str] = None,\n        dtype: Optional[torch.dtype] = None,\n        trust_remote_code: bool = False,\n    ):\n        if torch.cuda.is_available():\n            device = torch.device(\"cuda\")\n            dtype = torch.float16 if dtype is None else dtype\n        else:\n            if quantize:\n                raise ValueError(\"quantization is not available on CPU\")\n\n            device = torch.device(\"cpu\")\n            dtype = torch.float32\n\n        tokenizer = AutoTokenizer.from_pretrained(\n            model_id,\n            revision=revision,\n            padding_side=\"left\",\n            truncation_side=\"left\",\n            trust_remote_code=trust_remote_code,\n        )\n        model = AutoModelForCausalLM.from_pretrained(\n            model_id,\n            revision=revision,\n            torch_dtype=dtype,\n            device_map=\"auto\"\n            if torch.cuda.is_available() and torch.cuda.device_count() > 1\n            else None,\n            load_in_8bit=quantize == \"bitsandbytes\",\n            trust_remote_code=trust_remote_code,\n        )\n        if torch.cuda.is_available() and torch.cuda.device_count() == 1:\n            model = model.cuda()\n\n        if tokenizer.pad_token_id is None:\n            if model.config.pad_token_id is not None:\n                tokenizer.pad_token_id = model.config.pad_token_id\n            elif model.config.eos_token_id is not None:\n                tokenizer.pad_token_id = model.config.eos_token_id\n            elif tokenizer.eos_token_id is not None:\n                tokenizer.pad_token_id = tokenizer.eos_token_id\n            else:\n                tokenizer.add_special_tokens({\"pad_token\": \"[PAD]\"})\n\n        super(CausalLM, self).__init__(\n            model=model,\n            tokenizer=tokenizer,\n            requires_padding=True,\n            dtype=dtype,\n            device=device,\n        )\n\n    def forward(\n        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None\n    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:\n        # Model Forward\n        if past_key_values is not None:\n            reshaped_past_key_values = []\n            for layer in past_key_values:\n                past_keys, past_values = layer\n                reshaped_past_key_values.append(\n                    (\n                        past_keys.view(-1, *past_keys.shape[-2:]),\n                        past_values.view(-1, *past_values.shape[-2:]),\n                    )\n                )\n            past_key_values = reshaped_past_key_values\n\n        outputs = self.model.forward(\n            input_ids=input_ids,\n            attention_mask=attention_mask,\n            past_key_values=past_key_values,\n        )\n        return outputs.logits, outputs.past_key_values\n", "path": "server/text_generation_server/models/rw.py"}], "after_files": [{"content": "import torch\n\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nfrom typing import List, Optional, Tuple\n\nfrom text_generation_server.models import CausalLM\n\n\nclass RW(CausalLM):\n    def __init__(\n        self,\n        model_id: str,\n        revision: Optional[str] = None,\n        quantize: Optional[str] = None,\n        dtype: Optional[torch.dtype] = None,\n        trust_remote_code: bool = False,\n    ):\n        if torch.cuda.is_available():\n            device = torch.device(\"cuda\")\n            dtype = torch.float16 if dtype is None else dtype\n        else:\n            if quantize:\n                raise ValueError(\"quantization is not available on CPU\")\n\n            device = torch.device(\"cpu\")\n            dtype = torch.float32\n\n        tokenizer = AutoTokenizer.from_pretrained(\n            model_id,\n            revision=revision,\n            padding_side=\"left\",\n            truncation_side=\"left\",\n            trust_remote_code=trust_remote_code,\n        )\n        model = AutoModelForCausalLM.from_pretrained(\n            model_id,\n            revision=revision,\n            torch_dtype=dtype,\n            device_map=\"auto\"\n            if torch.cuda.is_available() and torch.cuda.device_count() > 1\n            else None,\n            load_in_8bit=quantize == \"bitsandbytes\",\n            trust_remote_code=trust_remote_code,\n        )\n        if torch.cuda.is_available() and torch.cuda.device_count() == 1:\n            model = model.cuda()\n\n        if tokenizer.pad_token_id is None:\n            if model.config.pad_token_id is not None:\n                tokenizer.pad_token_id = model.config.pad_token_id\n            elif model.config.eos_token_id is not None:\n                tokenizer.pad_token_id = model.config.eos_token_id\n            elif tokenizer.eos_token_id is not None:\n                tokenizer.pad_token_id = tokenizer.eos_token_id\n            else:\n                tokenizer.add_special_tokens({\"pad_token\": \"[PAD]\"})\n\n        super(CausalLM, self).__init__(\n            model=model,\n            tokenizer=tokenizer,\n            requires_padding=True,\n            dtype=dtype,\n            device=device,\n        )\n\n    def forward(\n        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None\n    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:\n        # Model Forward\n        outputs = self.model.forward(\n            input_ids=input_ids,\n            attention_mask=attention_mask,\n            past_key_values=past_key_values,\n        )\n        return outputs.logits, outputs.past_key_values\n", "path": "server/text_generation_server/models/rw.py"}]}