diff --git a/dev/bench/data.js b/dev/bench/data.js index f20782b..3ffa047 100644 --- a/dev/bench/data.js +++ b/dev/bench/data.js @@ -1,54 +1,8 @@ window.BENCHMARK_DATA = { - "lastUpdate": 1732673167029, + "lastUpdate": 1732673302314, "repoUrl": "https://github.com/neuralmagic/nm-vllm-ent", "entries": { "smaller_is_better": [ - { - "commit": { - "author": { - "name": "Andy Linfoot", - "username": "andy-neuma", - "email": "78757007+andy-neuma@users.noreply.github.com" - }, - "committer": { - "name": "GitHub", - "username": "web-flow", - "email": "noreply@github.com" - }, - "id": "7c992e25436127a48dfb9a168bbcd28470ddb986", - "message": "initial test fan out (#117)\n\nSUMMARY:\r\n* update python script that parses and processes \"buildkite\" yaml\r\n* added `partitions` outputs to \"nm build\" workflow\r\n* add Neural Magic partition files\r\n* update helper scripts\r\n* adjust workflows and add actions for updated style\r\n\r\nNOTES: this generates a json data structure which can be used to iterate\r\nover to run tests. some benefits of this approach are:\r\n* programmatically fanning out\r\n* setting ENV's locally to the partitions where they are necessary\r\n* localizing pip installs to the partitions where they are necessary\r\n* running any other commands necessary for correct test execution\r\n\r\nTESTPLAN:\r\ndogfooding\r\n\r\n---------\r\n\r\nCo-authored-by: andy-neuma ", - "timestamp": "2024-11-15T18:31:15Z", - "url": "https://github.com/neuralmagic/nm-vllm-ent/commit/7c992e25436127a48dfb9a168bbcd28470ddb986" - }, - "date": 1731810261605, - "tool": "customSmallerIsBetter", - "benches": [ - { - "name": "{\"name\": \"mean_ttft_ms\", \"description\": \"VLLM Serving - Dense\\nmodel - meta-llama/Meta-Llama-3-8B-Instruct\\nmax-model-len - 4096\\nsparsity - None\\nbenchmark_serving {\\n \\\"nr-qps-pair_\\\": \\\"300,1\\\",\\n \\\"dataset\\\": \\\"sharegpt\\\"\\n}\", \"gpu_description\": \"NVIDIA A100-SXM4-80GB x 1\", \"vllm_version\": \"0.6.3.0.20241117\", \"python_version\": \"3.10.12 (main, Sep 30 2024, 21:31:31) [GCC 11.4.0]\", \"torch_version\": \"2.4.0+cu121\"}", - "value": 61.327604623511434, - "unit": "ms", - "extra": "{\n \"description\": \"VLLM Serving - Dense\\nmodel - meta-llama/Meta-Llama-3-8B-Instruct\\nmax-model-len - 4096\\nsparsity - None\\nbenchmark_serving {\\n \\\"nr-qps-pair_\\\": \\\"300,1\\\",\\n \\\"dataset\\\": \\\"sharegpt\\\"\\n}\",\n \"benchmarking_context\": {\n \"vllm_version\": \"0.6.3.0.20241117\",\n \"python_version\": \"3.10.12 (main, Sep 30 2024, 21:31:31) [GCC 11.4.0]\",\n \"torch_version\": \"2.4.0+cu121\",\n \"torch_cuda_version\": \"12.1\",\n \"cuda_devices\": \"[_CudaDeviceProperties(name='NVIDIA A100-SXM4-80GB', major=8, minor=0, total_memory=81049MB, multi_processor_count=108)]\",\n \"cuda_device_names\": [\n \"NVIDIA A100-SXM4-80GB\"\n ]\n },\n \"gpu_description\": \"NVIDIA A100-SXM4-80GB x 1\",\n \"script_name\": \"benchmark_serving.py\",\n \"script_args\": {\n \"description\": \"VLLM Serving - 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}, - { - "name": "{\"name\": \"mean_tpot_ms\", \"description\": \"VLLM Serving - Dense\\nmodel - meta-llama/Meta-Llama-3-8B-Instruct\\nmax-model-len - 4096\\nsparsity - None\\nbenchmark_serving {\\n \\\"nr-qps-pair_\\\": \\\"300,1\\\",\\n \\\"dataset\\\": \\\"sharegpt\\\"\\n}\", \"gpu_description\": \"NVIDIA A100-SXM4-80GB x 1\", \"vllm_version\": \"0.6.3.0.20241117\", \"python_version\": \"3.10.12 (main, Sep 30 2024, 21:31:31) [GCC 11.4.0]\", \"torch_version\": \"2.4.0+cu121\"}", - "value": 14.410766984397478, - "unit": "ms", - "extra": "{\n \"description\": \"VLLM Serving - Dense\\nmodel - meta-llama/Meta-Llama-3-8B-Instruct\\nmax-model-len - 4096\\nsparsity - None\\nbenchmark_serving {\\n \\\"nr-qps-pair_\\\": \\\"300,1\\\",\\n \\\"dataset\\\": \\\"sharegpt\\\"\\n}\",\n \"benchmarking_context\": {\n \"vllm_version\": \"0.6.3.0.20241117\",\n \"python_version\": \"3.10.12 (main, Sep 30 2024, 21:31:31) [GCC 11.4.0]\",\n \"torch_version\": \"2.4.0+cu121\",\n \"torch_cuda_version\": \"12.1\",\n \"cuda_devices\": \"[_CudaDeviceProperties(name='NVIDIA A100-SXM4-80GB', major=8, minor=0, total_memory=81049MB, multi_processor_count=108)]\",\n \"cuda_device_names\": [\n \"NVIDIA A100-SXM4-80GB\"\n ]\n },\n \"gpu_description\": \"NVIDIA A100-SXM4-80GB x 1\",\n \"script_name\": \"benchmark_serving.py\",\n \"script_args\": {\n \"description\": \"VLLM Serving - 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} - ] - }, { "commit": { "author": { @@ -2302,6 +2256,52 @@ window.BENCHMARK_DATA = { "extra": "{\n \"description\": \"VLLM Serving - Dense\\nmodel - meta-llama/Meta-Llama-3-8B-Instruct\\nmax-model-len - 4096\\nsparsity - None\\nbenchmark_serving {\\n \\\"nr-qps-pair_\\\": \\\"300,1\\\",\\n \\\"dataset\\\": \\\"sharegpt\\\"\\n}\",\n \"benchmarking_context\": {\n \"vllm_version\": \"0.6.3.0.20241127\",\n \"python_version\": \"3.10.12 (main, Sep 30 2024, 21:31:31) [GCC 11.4.0]\",\n \"torch_version\": \"2.4.0+cu121\",\n \"torch_cuda_version\": \"12.1\",\n \"cuda_devices\": \"[_CudaDeviceProperties(name='NVIDIA H100 80GB HBM3', major=9, minor=0, total_memory=80994MB, multi_processor_count=132)]\",\n \"cuda_device_names\": [\n \"NVIDIA H100 80GB HBM3\"\n ]\n },\n \"gpu_description\": \"NVIDIA H100 80GB HBM3 x 1\",\n \"script_name\": \"benchmark_serving.py\",\n \"script_args\": {\n \"description\": \"VLLM Serving - Dense\\nmodel - meta-llama/Meta-Llama-3-8B-Instruct\\nmax-model-len - 4096\\nsparsity - 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