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/inference/llama/llama_generation.py`
  Content:
  ```
  1 import argparse
  2 
  3 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
  4 
  5 import colossalai
  6 from colossalai.cluster import DistCoordinator
  7 from colossalai.inference.config import InferenceConfig
  8 from colossalai.inference.core.engine import InferenceEngine
  9 from colossalai.inference.modeling.policy.nopadding_llama import NoPaddingLlamaModelInferPolicy
  10 
  11 # For Llama 3, we'll use the following configuration
  12 MODEL_CLS = AutoModelForCausalLM
  13 POLICY_CLS = NoPaddingLlamaModelInferPolicy
  14 
  15 
  16 def infer(args):
  17     # ==============================
  18     # Launch colossalai, setup distributed environment
  19     # ==============================
  20     colossalai.launch_from_torch()
  21     coordinator = DistCoordinator()
  22 
  23     # ==============================
  24     # Load model and tokenizer
  25     # ==============================
  26     model_path_or_name = args.model
  27     model = MODEL_CLS.from_pretrained(model_path_or_name)
  28     tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)
  29     tokenizer.pad_token = tokenizer.eos_token
  30     coordinator.print_on_master(f"Model Config:\n{model.config}")
  31 
  32     # ==============================
  33     # Initialize InferenceEngine
  34     # ==============================
  35     inference_config = InferenceConfig(
  36         dtype=args.dtype,
  37         max_batch_size=args.max_batch_size,
  38         max_input_len=args.max_input_len,
  39         max_output_len=args.max_output_len,
  40         prefill_ratio=1.2,
  41         block_size=16,
  42         tp_size=args.tp_size,
  43         use_cuda_kernel=args.use_cuda_kernel,
  44     )
  45     coordinator.print_on_master(f"Initializing Inference Engine...")
  46     engine = InferenceEngine(model, tokenizer, inference_config, model_policy=POLICY_CLS(), verbose=True)
  47 
  48     # ==============================
  49     # Generation
  50     # ==============================
  51     generation_config = GenerationConfig(
  52         pad_token_id=tokenizer.eos_token_id,
  53         eos_token_id=tokenizer.eos_token_id,
  54         max_length=args.max_length,
  55         do_sample=True,
  56     )
  57     coordinator.print_on_master(f"Generating...")
  58     out = engine.generate(prompts=[args.prompt], generation_config=generation_config)
  59     coordinator.print_on_master(out[0])
  60 
  61 
  62 # colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH
  63 if __name__ == "__main__":
  64     # ==============================
  65     # Parse Arguments
  66     # ==============================
  67     parser = argparse.ArgumentParser()
  68     parser.add_argument("-m", "--model", type=str, help="Path to the model or model name")
  69     parser.add_argument(
  70         "-p", "--prompt", type=str, default="Introduce some landmarks in the United Kingdom, such as", help="Prompt"
  71     )
  72     parser.add_argument("-b", "--max_batch_size", type=int, default=1, help="Max batch size")
  73     parser.add_argument("-i", "--max_input_len", type=int, default=128, help="Max input length")
  74     parser.add_argument("-o", "--max_output_len", type=int, default=128, help="Max output length")
  75     parser.add_argument("-t", "--tp_size", type=int, default=1, help="Tensor Parallelism size")
  76     parser.add_argument("-d", "--dtype", type=str, default="fp16", help="Data type", choices=["fp16", "fp32", "bf16"])
  77     parser.add_argument("--use_cuda_kernel", action="store_true", help="Use CUDA kernel, use Triton by default")
  78     parser.add_argument("--max_length", type=int, default=32, help="Max length for generation")
  79     args = parser.parse_args()
  80 
  81     infer(args)
  82
  ```
  --- 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/inference/llama/llama_generation.py b/examples/inference/llama/llama_generation.py
  --- a/examples/inference/llama/llama_generation.py
  +++ b/examples/inference/llama/llama_generation.py
  @@ -27,7 +27,7 @@
       model = MODEL_CLS.from_pretrained(model_path_or_name)
       tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)
       tokenizer.pad_token = tokenizer.eos_token
  -    coordinator.print_on_master(f"Model Config:\n{model.config}")
  +    # coordinator.print_on_master(f"Model Config:\n{model.config}")
   
       # ==============================
       # Initialize InferenceEngine
  @@ -52,20 +52,39 @@
           pad_token_id=tokenizer.eos_token_id,
           eos_token_id=tokenizer.eos_token_id,
           max_length=args.max_length,
  -        do_sample=True,
  +        do_sample=args.do_sample,
  +        temperature=args.temperature,
  +        top_k=args.top_k,
  +        top_p=args.top_p,
       )
       coordinator.print_on_master(f"Generating...")
       out = engine.generate(prompts=[args.prompt], generation_config=generation_config)
  -    coordinator.print_on_master(out[0])
  +    coordinator.print_on_master(out)
  +
  +    # ==============================
  +    # Optionally, load drafter model and proceed speculative decoding
  +    # ==============================
  +    drafter_model_path_or_name = args.drafter_model
  +    if drafter_model_path_or_name is not None:
  +        drafter_model = AutoModelForCausalLM.from_pretrained(drafter_model_path_or_name)
  +        # turn on speculative decoding with the drafter model
  +        engine.enable_spec_dec(drafter_model)
  +        coordinator.print_on_master(f"Generating...")
  +        out = engine.generate(prompts=[args.prompt], generation_config=generation_config)
  +        coordinator.print_on_master(out)
  +
  +        engine.disable_spec_dec()
   
   
   # colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH
  +# colossalai run --nproc_per_node 2 llama_generation.py -m MODEL_PATH --tp_size 2
   if __name__ == "__main__":
       # ==============================
       # Parse Arguments
       # ==============================
       parser = argparse.ArgumentParser()
       parser.add_argument("-m", "--model", type=str, help="Path to the model or model name")
  +    parser.add_argument("--drafter_model", type=str, help="Path to the drafter model or model name")
       parser.add_argument(
           "-p", "--prompt", type=str, default="Introduce some landmarks in the United Kingdom, such as", help="Prompt"
       )
  @@ -75,7 +94,12 @@
       parser.add_argument("-t", "--tp_size", type=int, default=1, help="Tensor Parallelism size")
       parser.add_argument("-d", "--dtype", type=str, default="fp16", help="Data type", choices=["fp16", "fp32", "bf16"])
       parser.add_argument("--use_cuda_kernel", action="store_true", help="Use CUDA kernel, use Triton by default")
  -    parser.add_argument("--max_length", type=int, default=32, help="Max length for generation")
  +    # Generation configs
  +    parser.add_argument("--max_length", type=int, default=64, help="Max length for generation")
  +    parser.add_argument("--do_sample", action="store_true", help="Use sampling for generation")
  +    parser.add_argument("--temperature", type=float, default=1.0, help="Temperature for generation")
  +    parser.add_argument("--top_k", type=int, default=50, help="Top k for generation")
  +    parser.add_argument("--top_p", type=float, default=1.0, help="Top p for generation")
       args = parser.parse_args()
   
       infer(args)
  
verification_info: {"golden_diff": "diff --git a/examples/inference/llama/llama_generation.py b/examples/inference/llama/llama_generation.py\n--- a/examples/inference/llama/llama_generation.py\n+++ b/examples/inference/llama/llama_generation.py\n@@ -27,7 +27,7 @@\n     model = MODEL_CLS.from_pretrained(model_path_or_name)\n     tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)\n     tokenizer.pad_token = tokenizer.eos_token\n-    coordinator.print_on_master(f\"Model Config:\\n{model.config}\")\n+    # coordinator.print_on_master(f\"Model Config:\\n{model.config}\")\n \n     # ==============================\n     # Initialize InferenceEngine\n@@ -52,20 +52,39 @@\n         pad_token_id=tokenizer.eos_token_id,\n         eos_token_id=tokenizer.eos_token_id,\n         max_length=args.max_length,\n-        do_sample=True,\n+        do_sample=args.do_sample,\n+        temperature=args.temperature,\n+        top_k=args.top_k,\n+        top_p=args.top_p,\n     )\n     coordinator.print_on_master(f\"Generating...\")\n     out = engine.generate(prompts=[args.prompt], generation_config=generation_config)\n-    coordinator.print_on_master(out[0])\n+    coordinator.print_on_master(out)\n+\n+    # ==============================\n+    # Optionally, load drafter model and proceed speculative decoding\n+    # ==============================\n+    drafter_model_path_or_name = args.drafter_model\n+    if drafter_model_path_or_name is not None:\n+        drafter_model = AutoModelForCausalLM.from_pretrained(drafter_model_path_or_name)\n+        # turn on speculative decoding with the drafter model\n+        engine.enable_spec_dec(drafter_model)\n+        coordinator.print_on_master(f\"Generating...\")\n+        out = engine.generate(prompts=[args.prompt], generation_config=generation_config)\n+        coordinator.print_on_master(out)\n+\n+        engine.disable_spec_dec()\n \n \n # colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH\n+# colossalai run --nproc_per_node 2 llama_generation.py -m MODEL_PATH --tp_size 2\n if __name__ == \"__main__\":\n     # ==============================\n     # Parse Arguments\n     # ==============================\n     parser = argparse.ArgumentParser()\n     parser.add_argument(\"-m\", \"--model\", type=str, help=\"Path to the model or model name\")\n+    parser.add_argument(\"--drafter_model\", type=str, help=\"Path to the drafter model or model name\")\n     parser.add_argument(\n         \"-p\", \"--prompt\", type=str, default=\"Introduce some landmarks in the United Kingdom, such as\", help=\"Prompt\"\n     )\n@@ -75,7 +94,12 @@\n     parser.add_argument(\"-t\", \"--tp_size\", type=int, default=1, help=\"Tensor Parallelism size\")\n     parser.add_argument(\"-d\", \"--dtype\", type=str, default=\"fp16\", help=\"Data type\", choices=[\"fp16\", \"fp32\", \"bf16\"])\n     parser.add_argument(\"--use_cuda_kernel\", action=\"store_true\", help=\"Use CUDA kernel, use Triton by default\")\n-    parser.add_argument(\"--max_length\", type=int, default=32, help=\"Max length for generation\")\n+    # Generation configs\n+    parser.add_argument(\"--max_length\", type=int, default=64, help=\"Max length for generation\")\n+    parser.add_argument(\"--do_sample\", action=\"store_true\", help=\"Use sampling for generation\")\n+    parser.add_argument(\"--temperature\", type=float, default=1.0, help=\"Temperature for generation\")\n+    parser.add_argument(\"--top_k\", type=int, default=50, help=\"Top k for generation\")\n+    parser.add_argument(\"--top_p\", type=float, default=1.0, help=\"Top p for generation\")\n     args = parser.parse_args()\n \n     infer(args)\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "import argparse\n\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n\nimport colossalai\nfrom colossalai.cluster import DistCoordinator\nfrom colossalai.inference.config import InferenceConfig\nfrom colossalai.inference.core.engine import InferenceEngine\nfrom colossalai.inference.modeling.policy.nopadding_llama import NoPaddingLlamaModelInferPolicy\n\n# For Llama 3, we'll use the following configuration\nMODEL_CLS = AutoModelForCausalLM\nPOLICY_CLS = NoPaddingLlamaModelInferPolicy\n\n\ndef infer(args):\n    # ==============================\n    # Launch colossalai, setup distributed environment\n    # ==============================\n    colossalai.launch_from_torch()\n    coordinator = DistCoordinator()\n\n    # ==============================\n    # Load model and tokenizer\n    # ==============================\n    model_path_or_name = args.model\n    model = MODEL_CLS.from_pretrained(model_path_or_name)\n    tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)\n    tokenizer.pad_token = tokenizer.eos_token\n    coordinator.print_on_master(f\"Model Config:\\n{model.config}\")\n\n    # ==============================\n    # Initialize InferenceEngine\n    # ==============================\n    inference_config = InferenceConfig(\n        dtype=args.dtype,\n        max_batch_size=args.max_batch_size,\n        max_input_len=args.max_input_len,\n        max_output_len=args.max_output_len,\n        prefill_ratio=1.2,\n        block_size=16,\n        tp_size=args.tp_size,\n        use_cuda_kernel=args.use_cuda_kernel,\n    )\n    coordinator.print_on_master(f\"Initializing Inference Engine...\")\n    engine = InferenceEngine(model, tokenizer, inference_config, model_policy=POLICY_CLS(), verbose=True)\n\n    # ==============================\n    # Generation\n    # ==============================\n    generation_config = GenerationConfig(\n        pad_token_id=tokenizer.eos_token_id,\n        eos_token_id=tokenizer.eos_token_id,\n        max_length=args.max_length,\n        do_sample=True,\n    )\n    coordinator.print_on_master(f\"Generating...\")\n    out = engine.generate(prompts=[args.prompt], generation_config=generation_config)\n    coordinator.print_on_master(out[0])\n\n\n# colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH\nif __name__ == \"__main__\":\n    # ==============================\n    # Parse Arguments\n    # ==============================\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-m\", \"--model\", type=str, help=\"Path to the model or model name\")\n    parser.add_argument(\n        \"-p\", \"--prompt\", type=str, default=\"Introduce some landmarks in the United Kingdom, such as\", help=\"Prompt\"\n    )\n    parser.add_argument(\"-b\", \"--max_batch_size\", type=int, default=1, help=\"Max batch size\")\n    parser.add_argument(\"-i\", \"--max_input_len\", type=int, default=128, help=\"Max input length\")\n    parser.add_argument(\"-o\", \"--max_output_len\", type=int, default=128, help=\"Max output length\")\n    parser.add_argument(\"-t\", \"--tp_size\", type=int, default=1, help=\"Tensor Parallelism size\")\n    parser.add_argument(\"-d\", \"--dtype\", type=str, default=\"fp16\", help=\"Data type\", choices=[\"fp16\", \"fp32\", \"bf16\"])\n    parser.add_argument(\"--use_cuda_kernel\", action=\"store_true\", help=\"Use CUDA kernel, use Triton by default\")\n    parser.add_argument(\"--max_length\", type=int, default=32, help=\"Max length for generation\")\n    args = parser.parse_args()\n\n    infer(args)\n", "path": "examples/inference/llama/llama_generation.py"}], "after_files": [{"content": "import argparse\n\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n\nimport colossalai\nfrom colossalai.cluster import DistCoordinator\nfrom colossalai.inference.config import InferenceConfig\nfrom colossalai.inference.core.engine import InferenceEngine\nfrom colossalai.inference.modeling.policy.nopadding_llama import NoPaddingLlamaModelInferPolicy\n\n# For Llama 3, we'll use the following configuration\nMODEL_CLS = AutoModelForCausalLM\nPOLICY_CLS = NoPaddingLlamaModelInferPolicy\n\n\ndef infer(args):\n    # ==============================\n    # Launch colossalai, setup distributed environment\n    # ==============================\n    colossalai.launch_from_torch()\n    coordinator = DistCoordinator()\n\n    # ==============================\n    # Load model and tokenizer\n    # ==============================\n    model_path_or_name = args.model\n    model = MODEL_CLS.from_pretrained(model_path_or_name)\n    tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)\n    tokenizer.pad_token = tokenizer.eos_token\n    # coordinator.print_on_master(f\"Model Config:\\n{model.config}\")\n\n    # ==============================\n    # Initialize InferenceEngine\n    # ==============================\n    inference_config = InferenceConfig(\n        dtype=args.dtype,\n        max_batch_size=args.max_batch_size,\n        max_input_len=args.max_input_len,\n        max_output_len=args.max_output_len,\n        prefill_ratio=1.2,\n        block_size=16,\n        tp_size=args.tp_size,\n        use_cuda_kernel=args.use_cuda_kernel,\n    )\n    coordinator.print_on_master(f\"Initializing Inference Engine...\")\n    engine = InferenceEngine(model, tokenizer, inference_config, model_policy=POLICY_CLS(), verbose=True)\n\n    # ==============================\n    # Generation\n    # ==============================\n    generation_config = GenerationConfig(\n        pad_token_id=tokenizer.eos_token_id,\n        eos_token_id=tokenizer.eos_token_id,\n        max_length=args.max_length,\n        do_sample=args.do_sample,\n        temperature=args.temperature,\n        top_k=args.top_k,\n        top_p=args.top_p,\n    )\n    coordinator.print_on_master(f\"Generating...\")\n    out = engine.generate(prompts=[args.prompt], generation_config=generation_config)\n    coordinator.print_on_master(out)\n\n    # ==============================\n    # Optionally, load drafter model and proceed speculative decoding\n    # ==============================\n    drafter_model_path_or_name = args.drafter_model\n    if drafter_model_path_or_name is not None:\n        drafter_model = AutoModelForCausalLM.from_pretrained(drafter_model_path_or_name)\n        # turn on speculative decoding with the drafter model\n        engine.enable_spec_dec(drafter_model)\n        coordinator.print_on_master(f\"Generating...\")\n        out = engine.generate(prompts=[args.prompt], generation_config=generation_config)\n        coordinator.print_on_master(out)\n\n        engine.disable_spec_dec()\n\n\n# colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH\n# colossalai run --nproc_per_node 2 llama_generation.py -m MODEL_PATH --tp_size 2\nif __name__ == \"__main__\":\n    # ==============================\n    # Parse Arguments\n    # ==============================\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-m\", \"--model\", type=str, help=\"Path to the model or model name\")\n    parser.add_argument(\"--drafter_model\", type=str, help=\"Path to the drafter model or model name\")\n    parser.add_argument(\n        \"-p\", \"--prompt\", type=str, default=\"Introduce some landmarks in the United Kingdom, such as\", help=\"Prompt\"\n    )\n    parser.add_argument(\"-b\", \"--max_batch_size\", type=int, default=1, help=\"Max batch size\")\n    parser.add_argument(\"-i\", \"--max_input_len\", type=int, default=128, help=\"Max input length\")\n    parser.add_argument(\"-o\", \"--max_output_len\", type=int, default=128, help=\"Max output length\")\n    parser.add_argument(\"-t\", \"--tp_size\", type=int, default=1, help=\"Tensor Parallelism size\")\n    parser.add_argument(\"-d\", \"--dtype\", type=str, default=\"fp16\", help=\"Data type\", choices=[\"fp16\", \"fp32\", \"bf16\"])\n    parser.add_argument(\"--use_cuda_kernel\", action=\"store_true\", help=\"Use CUDA kernel, use Triton by default\")\n    # Generation configs\n    parser.add_argument(\"--max_length\", type=int, default=64, help=\"Max length for generation\")\n    parser.add_argument(\"--do_sample\", action=\"store_true\", help=\"Use sampling for generation\")\n    parser.add_argument(\"--temperature\", type=float, default=1.0, help=\"Temperature for generation\")\n    parser.add_argument(\"--top_k\", type=int, default=50, help=\"Top k for generation\")\n    parser.add_argument(\"--top_p\", type=float, default=1.0, help=\"Top p for generation\")\n    args = parser.parse_args()\n\n    infer(args)\n", "path": "examples/inference/llama/llama_generation.py"}]}