Not able to get good performance for diffusion models when doing single image inference with batch size 1

[basantaxpatra]

System Info

System Configuration: Single node Habana Gaudi setup
Firmware Version: hl-1.15.0-fw-48.2.1.1
Software Stack: Synapse AI 1.15

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

$ docker pull vault.habana.ai/gaudi-docker/1.15.0/ubuntu22.04/habanalabs/pytorch-installer-2.2.0:latest
$ docker run --rm -it vault.habana.ai/gaudi-docker/1.15.0/ubuntu22.04/habanalabs/pytorch-installer-2.2.0:latest bash
$ git clone [email protected]:huggingface/optimum-habana.git
$ optimum-habana
$ pip install .
$ cd examples/stable-diffusion
$ pip install -r requirements.txt
$ python text_to_image_generation.py
--model_name_or_path stabilityai/stable-diffusion-xl-base-1.0
--prompts "Sailing ship painting by Van Gogh" "A shiny flying horse taking off"
--num_images_per_prompt 20
--batch_size 8
--image_save_dir /tmp/stable_diffusion_xl_images
--scheduler euler_discrete
--use_habana
--use_hpu_graphs
--gaudi_config Habana/stable-diffusion
--bf16

Logs for reference:
2 prompt(s) received, 20 generation(s) per prompt, 8 sample(s) per batch, 5 total batch(es).
{'generation_runtime': 470.2324, 'generation_samples_per_second': 0.219, 'generation_steps_per_second': 0.068}

initial compilation took 170 seconds, so if we disregard that, it'd be like 300 second for 32 images which is ~9.2 seconds per image on SDXL (H100s are around 2-3seconds depending on sampling params)
[{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:17:58.694850", "statistics": {"TotalNumber": 1, "TotalTime": 2406683, "AvgTime": 2406683.0}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:19:04.525621", "statistics": {"TotalNumber": 2, "TotalTime": 66733949, "AvgTime": 33366974.5}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:19:05.394485", "statistics": {"TotalNumber": 3, "TotalTime": 66871477, "AvgTime": 22290492.333333332}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:20:08.701577", "statistics": {"TotalNumber": 4, "TotalTime": 130001484, "AvgTime": 32500371.0}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:20:09.602500", "statistics": {"TotalNumber": 5, "TotalTime": 130138275, "AvgTime": 26027655.0}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:20:58.849669", "statistics": {"TotalNumber": 6, "TotalTime": 144735532, "AvgTime": 24122588.666666668}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:22:02.477322", "statistics": {"TotalNumber": 7, "TotalTime": 207751639, "AvgTime": 29678805.57142857}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:22:03.371944", "statistics": {"TotalNumber": 8, "TotalTime": 207892568, "AvgTime": 25986571.0}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:23:06.978577", "statistics": {"TotalNumber": 9, "TotalTime": 271316124, "AvgTime": 30146236.0}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:23:56.499370", "statistics": {"TotalNumber": 10, "TotalTime": 285510855, "AvgTime": 28551085.5}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:23:57.930979", "statistics": {"TotalNumber": 11, "TotalTime": 285652606, "AvgTime": 25968418.727272727}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:23:58.791526", "statistics": {"TotalNumber": 12, "TotalTime": 285788064, "AvgTime": 23815672.0}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:26:00.652013", "statistics": {"TotalNumber": 13, "TotalTime": 299983406, "AvgTime": 23075646.615384616}},
{"metric_name": "graph_compilation", "triggered_by": "metric_change", "generated_on": "2024-06-10T19:26:01.511422", "statistics": {"TotalNumber": 14, "TotalTime": 300058888, "AvgTime": 21432777.714285713}},
{"metric_name": "graph_compilation", "triggered_by": "process_exit", "generated_on": "2024-06-10T19:26:15.341419", "statistics": {"TotalNumber": 14, "TotalTime": 300058888, "AvgTime": 21432777.714285713}},
{"metric_name": "cpu_fallback", "triggered_by": "process_exit", "generated_on": "2024-06-10T19:26:15.341498", "statistics": {"TotalNumber": 0, "FallbackOps": {}}},
{"metric_name": "memory_defragmentation", "triggered_by": "process_exit", "generated_on": "2024-06-10T19:26:15.341520", "statistics": {"TotalNumber": 0, "TotalSuccessful": 0, "AvgTime": 0, "MaxTime": 0}}]

Expected behavior

initial compilation took 170 seconds, so if we disregard that, it'd be like 300 second for 32 images which is ~9.2 seconds per image on SDXL. Expecting performance ~ 2-3seconds

[regisss]

@basantaxpatra Are you still seeing this issue on newer versions of the lib?