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fix: correct moe OSL in inference_rules.adoc #296

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merged 2 commits into from
Nov 26, 2024
Merged

fix: correct moe OSL in inference_rules.adoc #296

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65a94f5
correct moe OSL in inference_rules.adoc
viraatc Jun 28, 2024
ab8f221
Merge branch 'master' into patch-1
pgmpablo157321 Nov 26, 2024
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4 changes: 2 additions & 2 deletions inference_rules.adoc
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Expand Up @@ -257,7 +257,7 @@ The Datacenter suite includes the following benchmarks:
|Language |Language processing |BERT |SQuAD v1.1 (max_seq_len=384) | 10833 | 99% of FP32 and 99.9% of FP32 (f1_score=90.874%) | 130 ms
|Language |Summarization |GPT-J |CNN Dailymail (v3.0.0, max_seq_len=2048) | 13368 | 99% of FP32 and 99.9% of FP32 (rouge1=42.9865, rouge2=20.1235, rougeL=29.9881). Additionally, for both cases the total generation length of the texts should be more than 90% of the reference (gen_len=4016878)| 20 s
|Language |Question Answering |Llama2 |OpenOrca (GPT-4 split, max_seq_len=1024) | 24576 | 99% of FP32 and 99.9% of FP32 (rouge1=44.4312, rouge2=22.0352, rougeL=28.6162). Additionally, for both cases the generation length of the tokens per sample should be more than 90% of the reference (tokens_per_sample=294.45)| TTFT/TPOTfootnote:[For Llama2, 2 latency metrics are collected - time to first token (TTFT) which measures the latency of the first token, and time per output token (TPOT) which measures the average interval between all the tokens generated.]: 2000 ms/200 ms
|Language |Text Generation (Question Answering, Math and Code Generation) |Mixtral-8x7B |OpenOrca (5k samples of the GPT-4 split, max_seq_len=2048), GSM8K (5k samples of the validation split, max_seq_len=2048), MBXP (5k samples of the validation split, max_seq_len=2048) | 15000 | 99% of FP32 and 99.9% of FP32 (rouge1=45.4911, rouge2=23.2829, rougeL=30.3615, (gsm8k)Accuracy=73.78, (mbxp)Accuracy=60.16). Additionally, for both cases the tokens per sample should be between than 90% and 110% of the reference (tokens_per_sample=294.45)| TTFT/TPOTfootnote:[For Mixtral-8x7B, 2 latency metrics are collected - time to first token (TTFT) which measures the latency of the first token, and time per output token (TPOT) which measures the average interval between all the tokens generated.]: 2000 ms/200 ms
|Language |Text Generation (Question Answering, Math and Code Generation) |Mixtral-8x7B |OpenOrca (5k samples of the GPT-4 split, max_seq_len=1024), GSM8K (5k samples of the validation split, max_seq_len=1024), MBXP (5k samples of the validation split, max_seq_len=1024) | 15000 | 99% of FP32 and 99.9% of FP32 (rouge1=45.4911, rouge2=23.2829, rougeL=30.3615, (gsm8k)Accuracy=73.78, (mbxp)Accuracy=60.16). Additionally, for both cases the tokens per sample should be between than 90% and 110% of the reference (tokens_per_sample=294.45)| TTFT/TPOTfootnote:[For Mixtral-8x7B, 2 latency metrics are collected - time to first token (TTFT) which measures the latency of the first token, and time per output token (TPOT) which measures the average interval between all the tokens generated.]: 2000 ms/200 ms
|Commerce |Recommendation |DLRMv2 |Synthetic Multihot Criteo Dataset | 204800 |99% of FP32 and 99.9% of FP32 (AUC=80.31%) | 60 ms
|Generative |Text to image |SDXL |Subset of coco-2014 val | 5000 |FID range: [23.01085758, 23.95007626] and CLIP range: [31.68631873, 31.81331801] | 20 s
|===
Expand Down Expand Up @@ -355,7 +355,7 @@ For each of the following benchmarks it is necessary to use the following infere
|Summarization (GPT-J) |max_new_tokens |128 | Maximum number of new tokens to generate
|Summarization (GPT-J) |early_stopping |True | Use the EOS token to stop generating tokens
|Summarization (Llama2) |max_new_tokens |1024 | Maximum number of new tokens to generate
|Text Generation (Mixtral-8x7B) |max_new_tokens |2048 | Maximum number of new tokens to generate
|Text Generation (Mixtral-8x7B) |max_new_tokens |1024 | Maximum number of new tokens to generate
|===

== Load Generator
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