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InternLM / lmdeploy

LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
https://lmdeploy.readthedocs.io/en/latest/
Apache License 2.0
4.11k stars 373 forks source link

[Bug] output diff when temperature set zero #1688

Open zhyncs opened 3 months ago

zhyncs commented 3 months ago

Checklist

Describe the bug

I used the latest code from LMDeploy to run the vicuna 13b model. The client used temperature 0 and made two requests, resulting in some differences in the output.

Theoretically, when the temperature is 0, multiple requests should yield consistent results without any differences.

Reproduction

def text_to_sha256(text): text_bytes = text.encode('utf-8') sha256_hash = hashlib.sha256() sha256_hash.update(text_bytes) hash_digest = sha256_hash.hexdigest() return hash_digest


```python3
    def _inference(self, req_queue: Queue, res_queue: Queue, session_id: int,
                   stream_output: bool):

        stats = []
        client = APIClient(self.server_addr, api_key=self.api_key)

        for prompt, input_seqlen, output_seqlen in iter(
                req_queue.get, [None, None, None]):
            timestamps = []
            timestamps.append(time.perf_counter())
            for output in client.chat_completions_v1(
                    model=self.model_name,
                    messages=prompt,
                    temperature=self.temperature,
                    top_p=self.top_p,
                    n=1,
                    max_tokens=output_seqlen,
                    stream=stream_output,
                    session_id=session_id,
                    ignore_eos=True):
                timestamps.append(time.perf_counter())
            print(text_to_sha256(output['choices'][0]['message']['content']))

client first try

python3 benchmark/profile_restful_api.py 127.0.0.1:23333 /workdir/vicuna-13b-v1.3 /workdir/ShareGPT_V3_unfiltered_cleaned_split.json --concurrency 128 --num_prompts 128 --temperature 0 | tee first

client second try

python3 benchmark/profile_restful_api.py 127.0.0.1:23333 /workdir/vicuna-13b-v1.3 /workdir/ShareGPT_V3_unfiltered_cleaned_split.json --concurrency 128 --num_prompts 128 --temperature 0 | tee second

only keep the hash results and sort

sort first -o first_output sort second -o second_output

compare and find the diff

icdiff first_output second_output


### Environment

```Shell
sys.platform: linux
Python: 3.9.16 (main, Aug 15 2023, 19:38:56) [GCC 8.3.1 20190311 (Red Hat 8.3.1-3)]
CUDA available: True
GPU 0,1: NVIDIA A100-SXM4-80GB
GCC: gcc (GCC) 10.2.1 20210130 (Red Hat 10.2.1-11)
PyTorch: 2.3.0+cu118
LMDeploy: 0.4.2+e6468e7
triton: 2.3.0

Error traceback

No response

zhyncs commented 3 months ago 
diff first_output second_output -y -W 196
031a4481d6cac164b685924ec9a83ce7434f9d0b1596cf15185dab47404d89c7                031a4481d6cac164b685924ec9a83ce7434f9d0b1596cf15185dab47404d89c7
07ef14b39226a8d37b147398eb9ffd18b162a5a5b81602f9722ef502245e2012                07ef14b39226a8d37b147398eb9ffd18b162a5a5b81602f9722ef502245e2012
0abff593983bc1b4f2f8bbe6a895dd7f7726d1c9a666fb6589031b7c0c5c3d27                0abff593983bc1b4f2f8bbe6a895dd7f7726d1c9a666fb6589031b7c0c5c3d27
0ba761db6f0f518a72cf7e2d8bca1c507be1b3f28c2e4db00a9ed0f956755fe0                0ba761db6f0f518a72cf7e2d8bca1c507be1b3f28c2e4db00a9ed0f956755fe0
0cb84eda5915f334a959932320ac06b9cc1d4ed65ee1b7edcf0bbf0a44b26329                0cb84eda5915f334a959932320ac06b9cc1d4ed65ee1b7edcf0bbf0a44b26329
                                                  > 0d01b41b147fc77f14dc5e57d4d1f772bc35be7668bc180fcf0ff43c1df00349
149f0fdc3b1e898c5a6598792a0b2315879b1b724237a8f50d9d2381e92e47e0                149f0fdc3b1e898c5a6598792a0b2315879b1b724237a8f50d9d2381e92e47e0
1b04b7179897553c8a092683a19eae08edc762d1d11816a5de7d909e2ebd560a                1b04b7179897553c8a092683a19eae08edc762d1d11816a5de7d909e2ebd560a
1db3da6956a88ff3a7adc029d08cc2855773c7fb8430389d29664bb285e0497f                  <
2048ee609e15ed69cad004d03371405a673599b64b29cc628564a5dc38d50227                2048ee609e15ed69cad004d03371405a673599b64b29cc628564a5dc38d50227
20fde8ec4db734a77110c988e5673cf773abdeb07b784d39aecdd86dc54a5101                  <
230fcac0c49b0051a4a37afb867d0683650b04ce737fb95dc2b6fae338c2a6ea                230fcac0c49b0051a4a37afb867d0683650b04ce737fb95dc2b6fae338c2a6ea
234906550527527b0956205333ed82aab24a5b10beb1c98ea3c7a612dd717a92                234906550527527b0956205333ed82aab24a5b10beb1c98ea3c7a612dd717a92
25857ddbf8aae4b395bc6c131ef7bd1a4ea119007537a89ac7995b00ffb4656a                25857ddbf8aae4b395bc6c131ef7bd1a4ea119007537a89ac7995b00ffb4656a
26b4b5b395c56db3975d4fdb53f9bf8e2258e6a157878286a9a96a52588a5bf0                26b4b5b395c56db3975d4fdb53f9bf8e2258e6a157878286a9a96a52588a5bf0
27191762fbe72e5eef3a8d8c9462329eb0bd4f5799168b1556852d95f3d85382                27191762fbe72e5eef3a8d8c9462329eb0bd4f5799168b1556852d95f3d85382
28088e0e8d1e2e50602c8a09aced7eb8525794a11ec7fcb62a214afbe2772b7d                28088e0e8d1e2e50602c8a09aced7eb8525794a11ec7fcb62a214afbe2772b7d
28b445aa51d40d69a6ad3523efd59a5add3438c9c5c1ebf88162530e74d87df5                28b445aa51d40d69a6ad3523efd59a5add3438c9c5c1ebf88162530e74d87df5
2a57b9202c02e03d4a58e0e87f1b1a5690afd48f06798687c04a45ff791dfce6                2a57b9202c02e03d4a58e0e87f1b1a5690afd48f06798687c04a45ff791dfce6
2ba30d617df39b92aefc59e34c046a505f417d192b8652a408b7200c1891e97f                2ba30d617df39b92aefc59e34c046a505f417d192b8652a408b7200c1891e97f
2bec9d38b2b52085ddd85b1e0305644e067acd5edfd4ced3db599303666fcbc2                2bec9d38b2b52085ddd85b1e0305644e067acd5edfd4ced3db599303666fcbc2
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301c1cb3c4c40f89c7f00cd990e0f08c53a0766d87aac4ff460db5934b091cf0                301c1cb3c4c40f89c7f00cd990e0f08c53a0766d87aac4ff460db5934b091cf0
316f41e12813cd8744dc1e7d5704fe9fe8e8a83b8f77619ec710bba50bdbaf9f                316f41e12813cd8744dc1e7d5704fe9fe8e8a83b8f77619ec710bba50bdbaf9f
377828d8a85cb60f2688816786cdf3ebda2c10f6886838ad3023b0a93da695fd                377828d8a85cb60f2688816786cdf3ebda2c10f6886838ad3023b0a93da695fd
38439161677a63490a1e93b5d61150a7504375cbac4fc528d10acaac87de042f                38439161677a63490a1e93b5d61150a7504375cbac4fc528d10acaac87de042f
3c0c9c04132619f0e6217bc0f4064f88a001ce3b8ba18181b0f8fc4bd9468322                3c0c9c04132619f0e6217bc0f4064f88a001ce3b8ba18181b0f8fc4bd9468322
3d2341b11b6bcb75cb20896af06036768b87887404597ac1a6d1e164f0d9e6b5                  <
3effb4544508a673f6729a25463e8bbd99f72111070619c5d86b7d7187b034cc                3effb4544508a673f6729a25463e8bbd99f72111070619c5d86b7d7187b034cc
40aa32495beefd79b444227fe259c1a5ea72adebc84be01fbbd7ef74068343b9                40aa32495beefd79b444227fe259c1a5ea72adebc84be01fbbd7ef74068343b9
4284bb3d5751309680dd6304d7fa54d2a1f4380f6dbb6523298f67dfd05fcf95                4284bb3d5751309680dd6304d7fa54d2a1f4380f6dbb6523298f67dfd05fcf95
43d1894d8c9cff0259a93d8124f2f43640abe1166dff7e28ff12e1443c7b4a24                43d1894d8c9cff0259a93d8124f2f43640abe1166dff7e28ff12e1443c7b4a24
447864d1ffc54ce7a41056df0d32310c88066cf28c760599f50e5bb0dde0f576                447864d1ffc54ce7a41056df0d32310c88066cf28c760599f50e5bb0dde0f576
468f8f800ef08a899a828bb8f2fe7784b4d37f43335096823c1168fd26995fe8                468f8f800ef08a899a828bb8f2fe7784b4d37f43335096823c1168fd26995fe8
46983e97c22b26deb9c37e6ca5bf373496b0d97339430b94c7cdc8c5218a2f96                46983e97c22b26deb9c37e6ca5bf373496b0d97339430b94c7cdc8c5218a2f96
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49078fcbee4d2616e81d345d873ef815fdc6369b1663e72d7019083a530ccad4                49078fcbee4d2616e81d345d873ef815fdc6369b1663e72d7019083a530ccad4
4a82caf344afdb3f663bbfee990d0b60135b0ccfb8775e130f943017d7a38c3b                4a82caf344afdb3f663bbfee990d0b60135b0ccfb8775e130f943017d7a38c3b
                                                  > 4c7fd937fd29d36aeaa9aa4bdaaa6b0c41635ad46eed937f5215096862f8a78b
4d3e0be8d5cb0e9c641160bb382eb5c4785cd2c28363ba6c6515a308e56ccd20                4d3e0be8d5cb0e9c641160bb382eb5c4785cd2c28363ba6c6515a308e56ccd20
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4e7a13d645d3d3a495b1dc25a58b4fa8c818dd4bf785a99007bad54275182b7e                  <
516c9949eb61f3d2e653641476e662ccecf4d308f11f97abc2552a9582ad5f90                516c9949eb61f3d2e653641476e662ccecf4d308f11f97abc2552a9582ad5f90
5292e987b0c297f3e3455cd7fb718fc68ed0d2b995de43990cc3666f7ee8e082                5292e987b0c297f3e3455cd7fb718fc68ed0d2b995de43990cc3666f7ee8e082
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5939aa03df2bf97bb195e6614fce571abed3e0350c034333e7b2188d3824331c                5939aa03df2bf97bb195e6614fce571abed3e0350c034333e7b2188d3824331c
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                                                  > 88ac8e9198540d095080ebddb90af8e73aa0d8eca4c6619c10ac418d6e3d5c4f
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diff rate: 10/128=7.8%

lzhangzz commented 3 months ago

You may need to set top_k = 1 and a fixed random seed to stop sampling because we don't have an alternative argmax kernel to bypass sampling.

Also, as split-kv is taking effect automatically, variable batch size and sequence length at runtime may result in different split-kv factor. This will lead to differnt accumulation order and thus differnt outcome.

zhyncs commented 3 months ago

You may need to set top_k = 1 and a fixed random seed to stop sampling because we don't have an alternative argmax kernel to bypass sampling.

In the benchmark/profile_restful_api.py, the seed is default 0 and top_p is default 1. https://github.com/InternLM/lmdeploy/blob/21be189628d064931edc5f166ee00f61c8fb1412/benchmark/profile_restful_api.py#L226 https://github.com/InternLM/lmdeploy/blob/21be189628d064931edc5f166ee00f61c8fb1412/benchmark/profile_restful_api.py#L67

Do I still need to set top_k = 1?

Also, as split-kv is taking effect automatically, variable batch size and sequence length at runtime may result in different split-kv factor. This will lead to differnt accumulation order and thus differnt outcome.

In terms of design and implementation, do we have to ensure that when temperature is 0, under batch inference, the results of the two requests are completely consistent?

zhyncs commented 1 month ago

TurboMind will support do_sample sampling parameter ref https://github.com/InternLM/lmdeploy/pull/1966