Open zhyncs opened 3 months ago
diff first_output second_output -y -W 196
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07ef14b39226a8d37b147398eb9ffd18b162a5a5b81602f9722ef502245e2012 07ef14b39226a8d37b147398eb9ffd18b162a5a5b81602f9722ef502245e2012
0abff593983bc1b4f2f8bbe6a895dd7f7726d1c9a666fb6589031b7c0c5c3d27 0abff593983bc1b4f2f8bbe6a895dd7f7726d1c9a666fb6589031b7c0c5c3d27
0ba761db6f0f518a72cf7e2d8bca1c507be1b3f28c2e4db00a9ed0f956755fe0 0ba761db6f0f518a72cf7e2d8bca1c507be1b3f28c2e4db00a9ed0f956755fe0
0cb84eda5915f334a959932320ac06b9cc1d4ed65ee1b7edcf0bbf0a44b26329 0cb84eda5915f334a959932320ac06b9cc1d4ed65ee1b7edcf0bbf0a44b26329
> 0d01b41b147fc77f14dc5e57d4d1f772bc35be7668bc180fcf0ff43c1df00349
149f0fdc3b1e898c5a6598792a0b2315879b1b724237a8f50d9d2381e92e47e0 149f0fdc3b1e898c5a6598792a0b2315879b1b724237a8f50d9d2381e92e47e0
1b04b7179897553c8a092683a19eae08edc762d1d11816a5de7d909e2ebd560a 1b04b7179897553c8a092683a19eae08edc762d1d11816a5de7d909e2ebd560a
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2048ee609e15ed69cad004d03371405a673599b64b29cc628564a5dc38d50227 2048ee609e15ed69cad004d03371405a673599b64b29cc628564a5dc38d50227
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230fcac0c49b0051a4a37afb867d0683650b04ce737fb95dc2b6fae338c2a6ea 230fcac0c49b0051a4a37afb867d0683650b04ce737fb95dc2b6fae338c2a6ea
234906550527527b0956205333ed82aab24a5b10beb1c98ea3c7a612dd717a92 234906550527527b0956205333ed82aab24a5b10beb1c98ea3c7a612dd717a92
25857ddbf8aae4b395bc6c131ef7bd1a4ea119007537a89ac7995b00ffb4656a 25857ddbf8aae4b395bc6c131ef7bd1a4ea119007537a89ac7995b00ffb4656a
26b4b5b395c56db3975d4fdb53f9bf8e2258e6a157878286a9a96a52588a5bf0 26b4b5b395c56db3975d4fdb53f9bf8e2258e6a157878286a9a96a52588a5bf0
27191762fbe72e5eef3a8d8c9462329eb0bd4f5799168b1556852d95f3d85382 27191762fbe72e5eef3a8d8c9462329eb0bd4f5799168b1556852d95f3d85382
28088e0e8d1e2e50602c8a09aced7eb8525794a11ec7fcb62a214afbe2772b7d 28088e0e8d1e2e50602c8a09aced7eb8525794a11ec7fcb62a214afbe2772b7d
28b445aa51d40d69a6ad3523efd59a5add3438c9c5c1ebf88162530e74d87df5 28b445aa51d40d69a6ad3523efd59a5add3438c9c5c1ebf88162530e74d87df5
2a57b9202c02e03d4a58e0e87f1b1a5690afd48f06798687c04a45ff791dfce6 2a57b9202c02e03d4a58e0e87f1b1a5690afd48f06798687c04a45ff791dfce6
2ba30d617df39b92aefc59e34c046a505f417d192b8652a408b7200c1891e97f 2ba30d617df39b92aefc59e34c046a505f417d192b8652a408b7200c1891e97f
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301c1cb3c4c40f89c7f00cd990e0f08c53a0766d87aac4ff460db5934b091cf0 301c1cb3c4c40f89c7f00cd990e0f08c53a0766d87aac4ff460db5934b091cf0
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38439161677a63490a1e93b5d61150a7504375cbac4fc528d10acaac87de042f 38439161677a63490a1e93b5d61150a7504375cbac4fc528d10acaac87de042f
3c0c9c04132619f0e6217bc0f4064f88a001ce3b8ba18181b0f8fc4bd9468322 3c0c9c04132619f0e6217bc0f4064f88a001ce3b8ba18181b0f8fc4bd9468322
3d2341b11b6bcb75cb20896af06036768b87887404597ac1a6d1e164f0d9e6b5 <
3effb4544508a673f6729a25463e8bbd99f72111070619c5d86b7d7187b034cc 3effb4544508a673f6729a25463e8bbd99f72111070619c5d86b7d7187b034cc
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46983e97c22b26deb9c37e6ca5bf373496b0d97339430b94c7cdc8c5218a2f96 46983e97c22b26deb9c37e6ca5bf373496b0d97339430b94c7cdc8c5218a2f96
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> 4c7fd937fd29d36aeaa9aa4bdaaa6b0c41635ad46eed937f5215096862f8a78b
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> 88ac8e9198540d095080ebddb90af8e73aa0d8eca4c6619c10ac418d6e3d5c4f
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diff rate: 10/128=7.8%
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.
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?
TurboMind will support do_sample
sampling parameter ref https://github.com/InternLM/lmdeploy/pull/1966
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
benchmark/profile_restful_api.py
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
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
Error traceback
No response