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@jerryzh168 jerryzh168 commented Dec 4, 2024

Summary:
Previously we need to apply_torchao_config_ to each model manually, this PR changes it to run on the entire model, we can also add autoquant in the future

Test Plan:

llama:

python3 -m sglang.bench_one_batch --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --json-model-override-arg s '{"architectures": ["TorchNativeLlamaForCausalLM"]}' --enable-torch-compile

Benchmark ...
Prefill. latency: 0.03361 s, throughput: 3808.05 token/s
Decode. latency: 0.01227 s, throughput: 81.50 token/s
Decode. latency: 0.01195 s, throughput: 83.70 token/s
Decode. latency: 0.01181 s, throughput: 84.65 token/s
Decode. latency: 0.01176 s, throughput: 85.05 token/s
Decode. latency: 0.01133 s, throughput: 88.25 token/s
Decode. median latency: 0.01176 s, median throughput: 85.05 token/s
Total. latency: 0.115 s, throughput: 1179.56 token/s

python3 -m sglang.bench_one_batch --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --json-model-override-arg s '{"architectures": ["TorchNativeLlamaForCausalLM"]}' --enable-torch-compile —torchao-config int4wo-128 Benchmark ...
Prefill. latency: 0.11769 s, throughput: 1087.60 token/s
Decode. latency: 0.00687 s, throughput: 145.47 token/s
Decode. latency: 0.00648 s, throughput: 154.25 token/s
Decode. latency: 0.00641 s, throughput: 156.01 token/s
Decode. latency: 0.00635 s, throughput: 157.53 token/s
Decode. latency: 0.00634 s, throughput: 157.74 token/s
Decode. median latency: 0.00644 s, median throughput: 155.28 token/s
Total. latency: 0.163 s, throughput: 834.21 token/s

qwen:

python3 -m sglang.bench_one_batch --model Qwen/Qwen1.5-MoE-A2.7B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config int4wo-128 
original:
Benchmark ...
Prefill. latency: 0.06101 s, throughput: 2097.86 token/s
Decode. latency: 0.00532 s, throughput: 187.93 token/s
Decode. latency: 0.00524 s, throughput: 190.88 token/s
Decode. latency: 0.00520 s, throughput: 192.43 token/s
Decode. latency: 0.00513 s, throughput: 194.97 token/s
Decode. latency: 0.00507 s, throughput: 197.26 token/s
Decode. median latency: 0.00513 s, median throughput: 194.97 token/s
Total. latency: 0.097 s, throughput: 1400.16 token/s

after change:

Benchmark ...
Prefill. latency: 0.05830 s, throughput: 2195.38 token/s
Decode. latency: 0.00517 s, throughput: 193.50 token/s
Decode. latency: 0.00508 s, throughput: 196.71 token/s
Decode. latency: 0.00512 s, throughput: 195.36 token/s
Decode. latency: 0.00508 s, throughput: 196.97 token/s
Decode. latency: 0.00504 s, throughput: 198.44 token/s
Decode. median latency: 0.00508 s, median throughput: 196.97 token/s
Total. latency: 0.094 s, throughput: 1449.19 token/s
Reviewers:

Subscribers:

Tasks:

Tags:

Summary:
Previously we need to apply_torchao_config_ to each model manually, this PR
changes it to run on the entire model, we can also add autoquant in the future

Test Plan:

llama:

python3 -m sglang.bench_one_batch --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --json-model-override-arg
s '{"architectures": ["TorchNativeLlamaForCausalLM"]}' --enable-torch-compile

Benchmark ...
Prefill. latency: 0.03361 s, throughput:   3808.05 token/s
Decode.  latency: 0.01227 s, throughput:     81.50 token/s
Decode.  latency: 0.01195 s, throughput:     83.70 token/s
Decode.  latency: 0.01181 s, throughput:     84.65 token/s
Decode.  latency: 0.01176 s, throughput:     85.05 token/s
Decode.  latency: 0.01133 s, throughput:     88.25 token/s
Decode.  median latency: 0.01176 s, median throughput:     85.05 token/s
Total. latency:  0.115 s, throughput:   1179.56 token/s

python3 -m sglang.bench_one_batch --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --json-model-override-arg
s '{"architectures": ["TorchNativeLlamaForCausalLM"]}' --enable-torch-compile —torchao-config int4wo-128
Benchmark ...
Prefill. latency: 0.11769 s, throughput:   1087.60 token/s
Decode.  latency: 0.00687 s, throughput:    145.47 token/s
Decode.  latency: 0.00648 s, throughput:    154.25 token/s
Decode.  latency: 0.00641 s, throughput:    156.01 token/s
Decode.  latency: 0.00635 s, throughput:    157.53 token/s
Decode.  latency: 0.00634 s, throughput:    157.74 token/s
Decode.  median latency: 0.00644 s, median throughput:    155.28 token/s
Total. latency:  0.163 s, throughput:    834.21 token/s

qwen:

python3 -m sglang.bench_one_batch --model Qwen/Qwen1.5-MoE-A2.7B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config int4wo-128

original:
Benchmark ...
Prefill. latency: 0.06101 s, throughput:   2097.86 token/s
Decode.  latency: 0.00532 s, throughput:    187.93 token/s
Decode.  latency: 0.00524 s, throughput:    190.88 token/s
Decode.  latency: 0.00520 s, throughput:    192.43 token/s
Decode.  latency: 0.00513 s, throughput:    194.97 token/s
Decode.  latency: 0.00507 s, throughput:    197.26 token/s
Decode.  median latency: 0.00513 s, median throughput:    194.97 token/s
Total. latency:  0.097 s, throughput:   1400.16 token/s

after change:

Benchmark ...
Prefill. latency: 0.05830 s, throughput:   2195.38 token/s
Decode.  latency: 0.00517 s, throughput:    193.50 token/s
Decode.  latency: 0.00508 s, throughput:    196.71 token/s
Decode.  latency: 0.00512 s, throughput:    195.36 token/s
Decode.  latency: 0.00508 s, throughput:    196.97 token/s
Decode.  latency: 0.00504 s, throughput:    198.44 token/s
Decode.  median latency: 0.00508 s, median throughput:    196.97 token/s
Total. latency:  0.094 s, throughput:   1449.19 token/s
Reviewers:

Subscribers:

Tasks:

Tags:
@merrymercy
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Could you fix the CI error?

@merrymercy merrymercy merged commit 9cc733b into sgl-project:main Dec 5, 2024
14 of 15 checks passed
timethink pushed a commit to timethink/sglang that referenced this pull request Mar 9, 2025
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2 participants