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does the chatglm2-6B example support codegeex2-6b's building and running? #93
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Can you share the expected results and the steps you use to generate the results of TRT-LLM? |
expected result is something like this: print(bubble_sort([5, 2, 1, 8, 4]))` my steps as follows: |
The TRT-LLM results you shared at the beginning is not very different to the HF's result. Can you try building the engine with FP32 (because chatglm2 does not support BF16 now) first? |
I take a try and find that chatglm2 only supports FP16 now. So, you cannot run it on FP32. We will fix it soon. |
okkk thx |
This issue is fixed by this MR #148, you can try on latest main branch. Close this bug. Feel free to reopen if needed. |
i tried codegeex2-6b's building and running with chatglm2-6B example, but it resulted incorrectly.
the result listed as follows:
root@***:/app/tensorrt_llm/examples/chatglm2-6b# python3 run.py --input_text '# language: Python\n# write a bubble sort function\n'
[10/24/2023-08:24:41] [TRT] [I] Loaded engine size: 11921 MiB
[10/24/2023-08:24:43] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 12584, GPU 12164 (MiB)
[10/24/2023-08:24:43] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +2, GPU +10, now: CPU 12586, GPU 12174 (MiB)
[10/24/2023-08:24:43] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +11912, now: CPU 0, GPU 11912 (MiB)
[10/24/2023-08:24:43] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 12585, GPU 15910 (MiB)
[10/24/2023-08:24:43] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 12586, GPU 15918 (MiB)
[10/24/2023-08:24:44] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 11912 (MiB)
[10/24/2023-08:24:44] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 12680, GPU 15940 (MiB)
[10/24/2023-08:24:44] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 12680, GPU 15950 (MiB)
[10/24/2023-08:24:44] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 11912 (MiB)
Input --->
language: Python\n# write a bubble sort function\n
Output --->
\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\nnumbers[j],numbers[j+1]=numbers[j+1],numbers[j]\nreturnnumbers\n\nprint(bubble_sort([5,2,1,8,4]))\n\n#bubblesort\n#takesalistofnumbers\n#returnsasortedlist\n\ndefbubble_sort(numbers):\nforiinrange(len(numbers)):\nforjinrange(len(numbers)-1):\nifnumbers[j]>numbers[j+1]:\n
Finished!
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