prompt: 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()
  
  ```
  
golden_diff: 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(),
  
verification_info: {"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"}]}