We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[tensor] fix some unittests

[tensor] fix some unittests

[tensor] fix some unittests


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/language/grok-1/inference_tp.py`
Content:
```
1 import time
2 
3 import torch
4 from grok1_policy import Grok1ForCausalLMPolicy
5 from transformers import AutoModelForCausalLM, LlamaTokenizerFast
6 from utils import get_defualt_parser, inference, print_output
7 
8 import colossalai
9 from colossalai.booster import Booster
10 from colossalai.booster.plugin import HybridParallelPlugin
11 from colossalai.cluster import DistCoordinator
12 from colossalai.lazy import LazyInitContext
13 from colossalai.utils import get_current_device
14 
15 if __name__ == "__main__":
16     parser = get_defualt_parser()
17     args = parser.parse_args()
18     start = time.time()
19     colossalai.launch_from_torch({})
20     coordinator = DistCoordinator()
21     plugin = HybridParallelPlugin(
22         tp_size=coordinator.world_size,
23         pp_size=1,
24         precision="bf16",
25         parallel_output=False,
26         custom_policy=Grok1ForCausalLMPolicy(),
27     )
28     booster = Booster(plugin=plugin)
29     torch.set_default_dtype(torch.bfloat16)
30     with LazyInitContext(default_device=get_current_device()):
31         model = AutoModelForCausalLM.from_pretrained(
32             args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16
33         )
34     model, *_ = booster.boost(model)
35     model.eval()
36     init_time = time.time() - start
37 
38     # A transformers-compatible version of the grok-1 tokenizer by Xenova
39     # https://huggingface.co/Xenova/grok-1-tokenizer
40     tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
41 
42     for text in args.text:
43         output = inference(
44             model.unwrap(),
45             tokenizer,
46             text,
47             max_new_tokens=args.max_new_tokens,
48             do_sample=args.do_sample,
49             temperature=args.temperature,
50             top_k=args.top_k,
51             top_p=args.top_p,
52         )
53         if coordinator.is_master():
54             print_output(text, tokenizer.decode(output))
55 
56     overall_time = time.time() - start
57     gen_latency = overall_time - init_time
58     avg_gen_latency = gen_latency / len(args.text)
59     coordinator.print_on_master(
60         f"Initializing time: {init_time:.2f} seconds.\n"
61         f"Overall time: {overall_time:.2f} seconds. \n"
62         f"Generation latency: {gen_latency:.2f} seconds. \n"
63         f"Average generation latency: {avg_gen_latency:.2f} seconds. \n"
64     )
65
```

Path: `examples/language/grok-1/inference.py`
Content:
```
1 import time
2 
3 import torch
4 from transformers import AutoModelForCausalLM, LlamaTokenizerFast
5 from utils import get_defualt_parser, inference, print_output
6 
7 if __name__ == "__main__":
8     parser = get_defualt_parser()
9     args = parser.parse_args()
10     start = time.time()
11     torch.set_default_dtype(torch.bfloat16)
12     model = AutoModelForCausalLM.from_pretrained(
13         args.pretrained,
14         trust_remote_code=True,
15         device_map="auto",
16         torch_dtype=torch.bfloat16,
17     )
18     model.eval()
19     init_time = time.time() - start
20 
21     # A transformers-compatible version of the grok-1 tokenizer by Xenova
22     # https://huggingface.co/Xenova/grok-1-tokenizer
23     tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
24 
25     for text in args.text:
26         output = inference(
27             model,
28             tokenizer,
29             text,
30             max_new_tokens=args.max_new_tokens,
31             do_sample=args.do_sample,
32             temperature=args.temperature,
33             top_k=args.top_k,
34             top_p=args.top_p,
35         )
36         print_output(text, tokenizer.decode(output))
37 
38     overall_time = time.time() - start
39     gen_latency = overall_time - init_time
40     avg_gen_latency = gen_latency / len(args.text)
41     print(
42         f"Initializing time: {init_time:.2f} seconds.\n"
43         f"Overall time: {overall_time:.2f} seconds. \n"
44         f"Generation latency: {gen_latency:.2f} seconds. \n"
45         f"Average generation latency: {avg_gen_latency:.2f} seconds. \n"
46     )
47
```
--- 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/examples/language/grok-1/inference.py b/examples/language/grok-1/inference.py
--- a/examples/language/grok-1/inference.py
+++ b/examples/language/grok-1/inference.py
@@ -1,7 +1,7 @@
 import time
 
 import torch
-from transformers import AutoModelForCausalLM, LlamaTokenizerFast
+from transformers import AutoModelForCausalLM, AutoTokenizer
 from utils import get_defualt_parser, inference, print_output
 
 if __name__ == "__main__":
@@ -9,6 +9,9 @@
     args = parser.parse_args()
     start = time.time()
     torch.set_default_dtype(torch.bfloat16)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)
+
     model = AutoModelForCausalLM.from_pretrained(
         args.pretrained,
         trust_remote_code=True,
@@ -18,10 +21,6 @@
     model.eval()
     init_time = time.time() - start
 
-    # A transformers-compatible version of the grok-1 tokenizer by Xenova
-    # https://huggingface.co/Xenova/grok-1-tokenizer
-    tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
-
     for text in args.text:
         output = inference(
             model,
diff --git a/examples/language/grok-1/inference_tp.py b/examples/language/grok-1/inference_tp.py
--- a/examples/language/grok-1/inference_tp.py
+++ b/examples/language/grok-1/inference_tp.py
@@ -2,7 +2,7 @@
 
 import torch
 from grok1_policy import Grok1ForCausalLMPolicy
-from transformers import AutoModelForCausalLM, LlamaTokenizerFast
+from transformers import AutoModelForCausalLM, AutoTokenizer
 from utils import get_defualt_parser, inference, print_output
 
 import colossalai
@@ -27,6 +27,9 @@
     )
     booster = Booster(plugin=plugin)
     torch.set_default_dtype(torch.bfloat16)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)
+
     with LazyInitContext(default_device=get_current_device()):
         model = AutoModelForCausalLM.from_pretrained(
             args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16
@@ -35,10 +38,6 @@
     model.eval()
     init_time = time.time() - start
 
-    # A transformers-compatible version of the grok-1 tokenizer by Xenova
-    # https://huggingface.co/Xenova/grok-1-tokenizer
-    tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
-
     for text in args.text:
         output = inference(
             model.unwrap(),

{"golden_diff": "diff --git a/examples/language/grok-1/inference.py b/examples/language/grok-1/inference.py\n--- a/examples/language/grok-1/inference.py\n+++ b/examples/language/grok-1/inference.py\n@@ -1,7 +1,7 @@\n import time\n \n import torch\n-from transformers import AutoModelForCausalLM, LlamaTokenizerFast\n+from transformers import AutoModelForCausalLM, AutoTokenizer\n from utils import get_defualt_parser, inference, print_output\n \n if __name__ == \"__main__\":\n@@ -9,6 +9,9 @@\n     args = parser.parse_args()\n     start = time.time()\n     torch.set_default_dtype(torch.bfloat16)\n+\n+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)\n+\n     model = AutoModelForCausalLM.from_pretrained(\n         args.pretrained,\n         trust_remote_code=True,\n@@ -18,10 +21,6 @@\n     model.eval()\n     init_time = time.time() - start\n \n-    # A transformers-compatible version of the grok-1 tokenizer by Xenova\n-    # https://huggingface.co/Xenova/grok-1-tokenizer\n-    tokenizer = LlamaTokenizerFast.from_pretrained(\"Xenova/grok-1-tokenizer\")\n-\n     for text in args.text:\n         output = inference(\n             model,\ndiff --git a/examples/language/grok-1/inference_tp.py b/examples/language/grok-1/inference_tp.py\n--- a/examples/language/grok-1/inference_tp.py\n+++ b/examples/language/grok-1/inference_tp.py\n@@ -2,7 +2,7 @@\n \n import torch\n from grok1_policy import Grok1ForCausalLMPolicy\n-from transformers import AutoModelForCausalLM, LlamaTokenizerFast\n+from transformers import AutoModelForCausalLM, AutoTokenizer\n from utils import get_defualt_parser, inference, print_output\n \n import colossalai\n@@ -27,6 +27,9 @@\n     )\n     booster = Booster(plugin=plugin)\n     torch.set_default_dtype(torch.bfloat16)\n+\n+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)\n+\n     with LazyInitContext(default_device=get_current_device()):\n         model = AutoModelForCausalLM.from_pretrained(\n             args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16\n@@ -35,10 +38,6 @@\n     model.eval()\n     init_time = time.time() - start\n \n-    # A transformers-compatible version of the grok-1 tokenizer by Xenova\n-    # https://huggingface.co/Xenova/grok-1-tokenizer\n-    tokenizer = LlamaTokenizerFast.from_pretrained(\"Xenova/grok-1-tokenizer\")\n-\n     for text in args.text:\n         output = inference(\n             model.unwrap(),\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "import time\n\nimport torch\nfrom grok1_policy import Grok1ForCausalLMPolicy\nfrom transformers import AutoModelForCausalLM, LlamaTokenizerFast\nfrom utils import get_defualt_parser, inference, print_output\n\nimport colossalai\nfrom colossalai.booster import Booster\nfrom colossalai.booster.plugin import HybridParallelPlugin\nfrom colossalai.cluster import DistCoordinator\nfrom colossalai.lazy import LazyInitContext\nfrom colossalai.utils import get_current_device\n\nif __name__ == \"__main__\":\n    parser = get_defualt_parser()\n    args = parser.parse_args()\n    start = time.time()\n    colossalai.launch_from_torch({})\n    coordinator = DistCoordinator()\n    plugin = HybridParallelPlugin(\n        tp_size=coordinator.world_size,\n        pp_size=1,\n        precision=\"bf16\",\n        parallel_output=False,\n        custom_policy=Grok1ForCausalLMPolicy(),\n    )\n    booster = Booster(plugin=plugin)\n    torch.set_default_dtype(torch.bfloat16)\n    with LazyInitContext(default_device=get_current_device()):\n        model = AutoModelForCausalLM.from_pretrained(\n            args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16\n        )\n    model, *_ = booster.boost(model)\n    model.eval()\n    init_time = time.time() - start\n\n    # A transformers-compatible version of the grok-1 tokenizer by Xenova\n    # https://huggingface.co/Xenova/grok-1-tokenizer\n    tokenizer = LlamaTokenizerFast.from_pretrained(\"Xenova/grok-1-tokenizer\")\n\n    for text in args.text:\n        output = inference(\n            model.unwrap(),\n            tokenizer,\n            text,\n            max_new_tokens=args.max_new_tokens,\n            do_sample=args.do_sample,\n            temperature=args.temperature,\n            top_k=args.top_k,\n            top_p=args.top_p,\n        )\n        if coordinator.is_master():\n            print_output(text, tokenizer.decode(output))\n\n    overall_time = time.time() - start\n    gen_latency = overall_time - init_time\n    avg_gen_latency = gen_latency / len(args.text)\n    coordinator.print_on_master(\n        f\"Initializing time: {init_time:.2f} seconds.\\n\"\n        f\"Overall time: {overall_time:.2f} seconds. \\n\"\n        f\"Generation latency: {gen_latency:.2f} seconds. \\n\"\n        f\"Average generation latency: {avg_gen_latency:.2f} seconds. \\n\"\n    )\n", "path": "examples/language/grok-1/inference_tp.py"}, {"content": "import time\n\nimport torch\nfrom transformers import AutoModelForCausalLM, LlamaTokenizerFast\nfrom utils import get_defualt_parser, inference, print_output\n\nif __name__ == \"__main__\":\n    parser = get_defualt_parser()\n    args = parser.parse_args()\n    start = time.time()\n    torch.set_default_dtype(torch.bfloat16)\n    model = AutoModelForCausalLM.from_pretrained(\n        args.pretrained,\n        trust_remote_code=True,\n        device_map=\"auto\",\n        torch_dtype=torch.bfloat16,\n    )\n    model.eval()\n    init_time = time.time() - start\n\n    # A transformers-compatible version of the grok-1 tokenizer by Xenova\n    # https://huggingface.co/Xenova/grok-1-tokenizer\n    tokenizer = LlamaTokenizerFast.from_pretrained(\"Xenova/grok-1-tokenizer\")\n\n    for text in args.text:\n        output = inference(\n            model,\n            tokenizer,\n            text,\n            max_new_tokens=args.max_new_tokens,\n            do_sample=args.do_sample,\n            temperature=args.temperature,\n            top_k=args.top_k,\n            top_p=args.top_p,\n        )\n        print_output(text, tokenizer.decode(output))\n\n    overall_time = time.time() - start\n    gen_latency = overall_time - init_time\n    avg_gen_latency = gen_latency / len(args.text)\n    print(\n        f\"Initializing time: {init_time:.2f} seconds.\\n\"\n        f\"Overall time: {overall_time:.2f} seconds. \\n\"\n        f\"Generation latency: {gen_latency:.2f} seconds. \\n\"\n        f\"Average generation latency: {avg_gen_latency:.2f} seconds. \\n\"\n    )\n", "path": "examples/language/grok-1/inference.py"}], "after_files": [{"content": "import time\n\nimport torch\nfrom grok1_policy import Grok1ForCausalLMPolicy\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom utils import get_defualt_parser, inference, print_output\n\nimport colossalai\nfrom colossalai.booster import Booster\nfrom colossalai.booster.plugin import HybridParallelPlugin\nfrom colossalai.cluster import DistCoordinator\nfrom colossalai.lazy import LazyInitContext\nfrom colossalai.utils import get_current_device\n\nif __name__ == \"__main__\":\n    parser = get_defualt_parser()\n    args = parser.parse_args()\n    start = time.time()\n    colossalai.launch_from_torch({})\n    coordinator = DistCoordinator()\n    plugin = HybridParallelPlugin(\n        tp_size=coordinator.world_size,\n        pp_size=1,\n        precision=\"bf16\",\n        parallel_output=False,\n        custom_policy=Grok1ForCausalLMPolicy(),\n    )\n    booster = Booster(plugin=plugin)\n    torch.set_default_dtype(torch.bfloat16)\n\n    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)\n\n    with LazyInitContext(default_device=get_current_device()):\n        model = AutoModelForCausalLM.from_pretrained(\n            args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16\n        )\n    model, *_ = booster.boost(model)\n    model.eval()\n    init_time = time.time() - start\n\n    for text in args.text:\n        output = inference(\n            model.unwrap(),\n            tokenizer,\n            text,\n            max_new_tokens=args.max_new_tokens,\n            do_sample=args.do_sample,\n            temperature=args.temperature,\n            top_k=args.top_k,\n            top_p=args.top_p,\n        )\n        if coordinator.is_master():\n            print_output(text, tokenizer.decode(output))\n\n    overall_time = time.time() - start\n    gen_latency = overall_time - init_time\n    avg_gen_latency = gen_latency / len(args.text)\n    coordinator.print_on_master(\n        f\"Initializing time: {init_time:.2f} seconds.\\n\"\n        f\"Overall time: {overall_time:.2f} seconds. \\n\"\n        f\"Generation latency: {gen_latency:.2f} seconds. \\n\"\n        f\"Average generation latency: {avg_gen_latency:.2f} seconds. \\n\"\n    )\n", "path": "examples/language/grok-1/inference_tp.py"}, {"content": "import time\n\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom utils import get_defualt_parser, inference, print_output\n\nif __name__ == \"__main__\":\n    parser = get_defualt_parser()\n    args = parser.parse_args()\n    start = time.time()\n    torch.set_default_dtype(torch.bfloat16)\n\n    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)\n\n    model = AutoModelForCausalLM.from_pretrained(\n        args.pretrained,\n        trust_remote_code=True,\n        device_map=\"auto\",\n        torch_dtype=torch.bfloat16,\n    )\n    model.eval()\n    init_time = time.time() - start\n\n    for text in args.text:\n        output = inference(\n            model,\n            tokenizer,\n            text,\n            max_new_tokens=args.max_new_tokens,\n            do_sample=args.do_sample,\n            temperature=args.temperature,\n            top_k=args.top_k,\n            top_p=args.top_p,\n        )\n        print_output(text, tokenizer.decode(output))\n\n    overall_time = time.time() - start\n    gen_latency = overall_time - init_time\n    avg_gen_latency = gen_latency / len(args.text)\n    print(\n        f\"Initializing time: {init_time:.2f} seconds.\\n\"\n        f\"Overall time: {overall_time:.2f} seconds. \\n\"\n        f\"Generation latency: {gen_latency:.2f} seconds. \\n\"\n        f\"Average generation latency: {avg_gen_latency:.2f} seconds. \\n\"\n    )\n", "path": "examples/language/grok-1/inference.py"}]}