Document corrected ERNIE qmm runtime profile
Browse filesAdds runtime_config.json and corrected explicit quantized-matmul/default allocator metrics. Updates the model card to distinguish serialized config-path measurements from explicit qmm runtime, and documents the PYTORCH_CUDA_ALLOC_CONF allocator pitfall.
README.md
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@@ -18,8 +18,8 @@ tags:
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# ERNIE-Image-Turbo SDNQ UINT4 Static
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This is a 4-bit SDNQ static quantization of [baidu/ERNIE-Image-Turbo](https://huggingface.co/baidu/ERNIE-Image-Turbo).
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-
The published SDNQ configs
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-
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## Recipe
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- Runtime validation: `use_quantized_matmul=true`
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- Validation GPU: NVIDIA RTX 6000 Ada Generation
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- Validation settings: 10 fixed prompt/seed pairs, 8 inference steps, guidance scale 1.0, `use_pe=False`
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`use_pe=False` is used for the headline validation table to compare the image models directly. Stage-level debugging showed that `use_pe=True` can dominate latency: on the `1200x896` technical-diagram prompt, `pe.forward` accounted for most of the runtime, while the denoising transformer was much smaller.
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| Model | PE | Load s | Load peak VRAM MiB | Cold inference s | Cold peak VRAM MiB | Hot mean s/img | Hot median s/img | Hot peak VRAM MiB |
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|---|---:|---:|---:|---:|---:|---:|---:|---:|
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| Original BF16 | off | 91.84 | 29692 | 7.67 | 34840 | 7.69 | 7.67 | 34932 |
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| SDNQ UINT4 static
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-
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## Visual Comparison
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@@ -53,12 +67,18 @@ Individual prompt pairs are stored in `comparison/`, and full metrics are stored
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import torch
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import sdnq # registers SDNQ support
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from diffusers import ErnieImagePipeline
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pipe = ErnieImagePipeline.from_pretrained(
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"WaveCut/ERNIE-Image-Turbo-SDNQ-uint4-static",
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torch_dtype=torch.bfloat16,
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).to("cuda")
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image = pipe(
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prompt="A clean modern poster with readable Cyrillic typography",
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width=1024,
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).images[0]
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```
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If
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-
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from sdnq.loader import apply_sdnq_options_to_model
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for name in ("pe", "text_encoder", "transformer"):
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setattr(pipe, name, apply_sdnq_options_to_model(component, use_quantized_matmul=True))
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```
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## Prompt Set
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- The comparison uses the same prompts, dimensions, seeds, 8 inference steps, and guidance scale for both original and quantized runs.
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- `use_pe=True` remains supported by the pipeline, but it measures prompt-enhancer behavior in addition to image generation.
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- This is an independent quantized artifact; see the original Baidu model card for upstream model details, benchmarks, and license terms.
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# ERNIE-Image-Turbo SDNQ UINT4 Static
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This is a 4-bit SDNQ static quantization of [baidu/ERNIE-Image-Turbo](https://huggingface.co/baidu/ERNIE-Image-Turbo).
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The published SDNQ configs set `use_quantized_matmul=true` for `pe`, `text_encoder`, `transformer`, and the pipeline-level config.
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For current SDNQ/Diffusers builds, enable quantized matmul explicitly after loading with `apply_sdnq_options_to_model`; the serialized flag is retained in metadata, but may not be applied automatically by `from_pretrained()`.
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## Recipe
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- Runtime validation: `use_quantized_matmul=true`
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- Validation GPU: NVIDIA RTX 6000 Ada Generation
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- Validation settings: 10 fixed prompt/seed pairs, 8 inference steps, guidance scale 1.0, `use_pe=False`
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- Runtime note: do not set `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True,max_split_size_mb:32` for this pipeline; it caused allocator over-reservation and much slower denoising in validation.
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- Machine-readable runtime recommendations are stored in `runtime_config.json`.
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`use_pe=False` is used for the headline validation table to compare the image models directly. Stage-level debugging showed that `use_pe=True` can dominate latency: on the `1200x896` technical-diagram prompt, `pe.forward` accounted for most of the runtime, while the denoising transformer was much smaller.
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| Model | PE | Load s | Load peak VRAM MiB | Cold inference s | Cold peak VRAM MiB | Hot mean s/img | Hot median s/img | Hot peak VRAM MiB |
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|---|---:|---:|---:|---:|---:|---:|---:|---:|
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| Original BF16 | off | 91.84 | 29692 | 7.67 | 34840 | 7.69 | 7.67 | 34932 |
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| SDNQ UINT4 static, serialized config path | off | 71.84 | 10172 | 16.10 | 15254 | 11.15 | 12.26 | 15390 |
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The row above is preserved for reproducibility of the original validation run. A follow-up profiling pass found that current loaders may leave quantized matmul disabled unless it is applied explicitly after loading.
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### Explicit Quantized-Matmul Runtime
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With explicit `apply_sdnq_options_to_model(..., use_quantized_matmul=True)`, default PyTorch CUDA allocator settings, and no `torch.cuda.empty_cache()` between hot generations:
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| Runtime | PE | Cold s | Hot mean s/img | Hot median s/img | Hot range s/img | Hot peak torch reserved MiB | Hot peak torch allocated MiB |
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|---|---:|---:|---:|---:|---:|---:|---:|
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| SDNQ UINT4 static + explicit qmm | off | 8.34 | 6.08 | 5.81 | 5.55-6.94 | 19540 | 19391 |
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The slow component with PE disabled is the denoising transformer. In the corrected qmm profile, `transformer.forward` accounts for roughly `5.0-5.4s` of a `5.8-7.0s` hot generation on RTX 6000 Ada. `text_encoder.forward` is about `0.55-0.65s` after warmup, and `vae.decode` is usually about `0.15s`.
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The allocator pitfall is large: with `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True,max_split_size_mb:32`, the same explicit-qmm runtime reserved about `48 GiB` and measured `25.88s` hot median with `empty_cache=True`, or `15.86s` without `empty_cache`.
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## Visual Comparison
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import torch
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import sdnq # registers SDNQ support
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from diffusers import ErnieImagePipeline
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from sdnq.loader import apply_sdnq_options_to_model
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pipe = ErnieImagePipeline.from_pretrained(
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"WaveCut/ERNIE-Image-Turbo-SDNQ-uint4-static",
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torch_dtype=torch.bfloat16,
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).to("cuda")
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for name in ("pe", "text_encoder", "transformer"):
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component = getattr(pipe, name, None)
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if component is not None:
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setattr(pipe, name, apply_sdnq_options_to_model(component, use_quantized_matmul=True))
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image = pipe(
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prompt="A clean modern poster with readable Cyrillic typography",
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width=1024,
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).images[0]
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```
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If you need maximum throughput, keep the model resident and avoid calling `torch.cuda.empty_cache()` between requests.
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You can confirm the runtime state after loading:
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```python
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for name in ("pe", "text_encoder", "transformer"):
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qcfg = getattr(getattr(pipe, name, None), "quantization_config", None)
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print(name, getattr(qcfg, "use_quantized_matmul", None))
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```
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## Prompt Set
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- The comparison uses the same prompts, dimensions, seeds, 8 inference steps, and guidance scale for both original and quantized runs.
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- `use_pe=True` remains supported by the pipeline, but it measures prompt-enhancer behavior in addition to image generation.
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- Corrected qmm runtime metrics are stored in `metrics/ernie_uint4_qmm_explicit_default_allocator_8step_metrics.json`; allocator-debug metrics are stored in `metrics/runtime_allocator_debug_metrics.json`.
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- This is an independent quantized artifact; see the original Baidu model card for upstream model details, benchmarks, and license terms.
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metrics/ernie_qmm_peoff_vs_flux2_klein_qmm_summary.json
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}
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],
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"hot_speed_ratio_ernie_over_flux": 5.11579273478586,
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"note": "Hot mean excludes the first generation after loading; cold is prompt 00."
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}
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}
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],
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"hot_speed_ratio_ernie_over_flux": 5.11579273478586,
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"note": "Hot mean excludes the first generation after loading; cold is prompt 00. The ERNIE timing in this comparison was later found to be allocator-affected: PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True,max_split_size_mb:32 caused about 48 GiB torch reservation and slow transformer.forward. For corrected ERNIE explicit-qmm/default-allocator timings, see metrics/ernie_uint4_qmm_explicit_default_allocator_8step_metrics.json."
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}
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metrics/ernie_uint4_qmm_explicit_default_allocator_8step_metrics.json
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{
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"label": "ernie_uint4_qmm_explicit_default_allocator_8step_repeat2",
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"device": "NVIDIA RTX 6000 Ada Generation",
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"torch": "2.8.0+cu128",
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"sdnq": "0.1.9",
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"env": {
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"PYTORCH_CUDA_ALLOC_CONF": null
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},
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"settings": {
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"num_inference_steps": 8,
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"guidance_scale": 1.0,
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"use_pe": false,
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"explicit_apply_qmm": true,
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"qmm_states": {
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"pe": true,
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"text_encoder": true,
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"transformer": true
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}
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},
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"load": {
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"seconds": 63.15932087600231,
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"gpu_start_mib": 436,
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"gpu_end_mib": 10172,
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"torch_peak_allocated_mib": 9724,
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"torch_peak_reserved_mib": 9738
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},
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"generations": [
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{
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"prompt_id": "00-cyrillic-poster",
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"title": "Cyrillic event poster",
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"seed": 41001,
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"width": 1024,
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"height": 1024,
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"seconds": 8.344464145600796,
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"gpu_start_mib": 10172,
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"gpu_end_mib": 11070,
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"torch_peak_allocated_mib": 19150,
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"torch_peak_reserved_mib": 19296
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},
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{
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"prompt_id": "01-long-text-bakery-ad",
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"title": "Long text product ad",
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"seed": 41002,
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"width": 896,
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"height": 1200,
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"seconds": 6.855839736759663,
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"gpu_start_mib": 11070,
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"gpu_end_mib": 11086,
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"torch_peak_allocated_mib": 19391,
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"torch_peak_reserved_mib": 19540
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},
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{
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"prompt_id": "02-technical-diagram",
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"title": "Technical diagram",
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"seed": 41003,
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"width": 1200,
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"height": 896,
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"seconds": 6.9446728229522705,
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"gpu_start_mib": 11086,
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"gpu_end_mib": 11086,
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"torch_peak_allocated_mib": 19391,
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"torch_peak_reserved_mib": 19540
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},
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{
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"prompt_id": "03-four-panel-comic",
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"title": "Four-panel comic",
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"seed": 41004,
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"width": 1024,
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"height": 1024,
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"seconds": 5.54518087208271,
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"gpu_start_mib": 11086,
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"gpu_end_mib": 13474,
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+
"torch_peak_allocated_mib": 12172,
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"torch_peak_reserved_mib": 12954
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},
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{
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"prompt_id": "04-public-domain-painter-fusion",
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"title": "Painterly style fusion",
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"seed": 41005,
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"width": 1024,
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"height": 1024,
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"seconds": 5.585576198995113,
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"gpu_start_mib": 13474,
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"gpu_end_mib": 13474,
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| 85 |
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"torch_peak_allocated_mib": 12171,
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"torch_peak_reserved_mib": 12954
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},
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{
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"prompt_id": "05-dashboard-ui",
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"title": "Dense UI dashboard",
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"seed": 41006,
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"width": 1376,
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"height": 768,
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"seconds": 6.736225217580795,
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"gpu_start_mib": 13474,
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+
"gpu_end_mib": 11140,
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+
"torch_peak_allocated_mib": 19226,
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"torch_peak_reserved_mib": 19308
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},
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{
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"prompt_id": "06-glass-still-life",
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"title": "Glass and reflections",
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"seed": 41007,
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"width": 1024,
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"height": 1024,
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"seconds": 5.988675691187382,
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"gpu_start_mib": 11140,
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| 108 |
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"gpu_end_mib": 13506,
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| 109 |
+
"torch_peak_allocated_mib": 12171,
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| 110 |
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"torch_peak_reserved_mib": 12986
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},
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{
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"prompt_id": "07-botanical-field-guide",
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"title": "Field guide plate",
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"seed": 41008,
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"width": 896,
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"height": 1200,
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"seconds": 5.80891427397728,
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| 119 |
+
"gpu_start_mib": 13506,
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| 120 |
+
"gpu_end_mib": 15086,
|
| 121 |
+
"torch_peak_allocated_mib": 12236,
|
| 122 |
+
"torch_peak_reserved_mib": 14564
|
| 123 |
+
},
|
| 124 |
+
{
|
| 125 |
+
"prompt_id": "08-restaurant-menu-board",
|
| 126 |
+
"title": "Menu board text",
|
| 127 |
+
"seed": 41009,
|
| 128 |
+
"width": 1024,
|
| 129 |
+
"height": 1024,
|
| 130 |
+
"seconds": 5.700445763766766,
|
| 131 |
+
"gpu_start_mib": 15086,
|
| 132 |
+
"gpu_end_mib": 15098,
|
| 133 |
+
"torch_peak_allocated_mib": 12172,
|
| 134 |
+
"torch_peak_reserved_mib": 14576
|
| 135 |
+
},
|
| 136 |
+
{
|
| 137 |
+
"prompt_id": "09-isometric-city-map",
|
| 138 |
+
"title": "Isometric map",
|
| 139 |
+
"seed": 41010,
|
| 140 |
+
"width": 1200,
|
| 141 |
+
"height": 896,
|
| 142 |
+
"seconds": 5.566534325480461,
|
| 143 |
+
"gpu_start_mib": 15098,
|
| 144 |
+
"gpu_end_mib": 15888,
|
| 145 |
+
"torch_peak_allocated_mib": 12236,
|
| 146 |
+
"torch_peak_reserved_mib": 15366
|
| 147 |
+
}
|
| 148 |
+
],
|
| 149 |
+
"summary": {
|
| 150 |
+
"cold_seconds": 8.344464145600796,
|
| 151 |
+
"hot_mean_seconds": 6.081340544753605,
|
| 152 |
+
"hot_median_seconds": 5.80891427397728,
|
| 153 |
+
"hot_min_seconds": 5.54518087208271,
|
| 154 |
+
"hot_max_seconds": 6.9446728229522705,
|
| 155 |
+
"hot_max_reserved_mib": 19540,
|
| 156 |
+
"hot_max_allocated_mib": 19391
|
| 157 |
+
}
|
| 158 |
+
}
|
metrics/runtime_allocator_debug_metrics.json
ADDED
|
@@ -0,0 +1,740 @@
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|
| 1 |
+
{
|
| 2 |
+
"device": "NVIDIA RTX 6000 Ada Generation",
|
| 3 |
+
"torch": "2.8.0+cu128",
|
| 4 |
+
"cases": [
|
| 5 |
+
{
|
| 6 |
+
"name": "qmm_all_empty_true",
|
| 7 |
+
"enabled_qmm_components": [
|
| 8 |
+
"pe",
|
| 9 |
+
"text_encoder",
|
| 10 |
+
"transformer"
|
| 11 |
+
],
|
| 12 |
+
"empty_cache": true,
|
| 13 |
+
"load": {
|
| 14 |
+
"seconds": 59.13287413865328,
|
| 15 |
+
"gpu_start_mib": 434,
|
| 16 |
+
"gpu_end_mib": 10006,
|
| 17 |
+
"gpu_peak_mib": 10006,
|
| 18 |
+
"torch_peak_allocated_mib": 9555,
|
| 19 |
+
"torch_peak_reserved_mib": 9572,
|
| 20 |
+
"qmm_states": {
|
| 21 |
+
"pe": {
|
| 22 |
+
"requested": true,
|
| 23 |
+
"actual": true
|
| 24 |
+
},
|
| 25 |
+
"text_encoder": {
|
| 26 |
+
"requested": true,
|
| 27 |
+
"actual": true
|
| 28 |
+
},
|
| 29 |
+
"transformer": {
|
| 30 |
+
"requested": true,
|
| 31 |
+
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|
| 695 |
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| 696 |
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| 697 |
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|
| 698 |
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|
| 699 |
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|
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|
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|
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|
| 708 |
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|
| 709 |
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|
| 710 |
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|
| 711 |
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{
|
| 712 |
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| 713 |
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| 714 |
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| 715 |
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| 718 |
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|
| 719 |
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| 720 |
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|
| 721 |
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|
| 722 |
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|
| 723 |
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|
| 724 |
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|
| 725 |
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}
|
| 726 |
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],
|
| 727 |
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|
| 728 |
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|
| 729 |
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|
| 730 |
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|
| 731 |
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|
| 732 |
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|
| 733 |
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|
| 734 |
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|
| 735 |
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|
| 736 |
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}
|
| 737 |
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}
|
| 738 |
+
],
|
| 739 |
+
"sdnq": "0.1.9"
|
| 740 |
+
}
|
runtime_config.json
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"runtime": {
|
| 3 |
+
"recommended_torch_cuda_alloc_conf": null,
|
| 4 |
+
"avoid_torch_cuda_alloc_conf": [
|
| 5 |
+
"expandable_segments:True,max_split_size_mb:32"
|
| 6 |
+
],
|
| 7 |
+
"keep_model_resident": true,
|
| 8 |
+
"avoid_empty_cache_between_generations": true,
|
| 9 |
+
"use_pe_for_image_benchmarks": false
|
| 10 |
+
},
|
| 11 |
+
"sdnq": {
|
| 12 |
+
"requires_explicit_apply_quantized_matmul": true,
|
| 13 |
+
"apply_quantized_matmul_components": [
|
| 14 |
+
"pe",
|
| 15 |
+
"text_encoder",
|
| 16 |
+
"transformer"
|
| 17 |
+
],
|
| 18 |
+
"apply_quantized_matmul_function": "sdnq.loader.apply_sdnq_options_to_model(component, use_quantized_matmul=True)"
|
| 19 |
+
},
|
| 20 |
+
"validated": {
|
| 21 |
+
"device": "NVIDIA RTX 6000 Ada Generation",
|
| 22 |
+
"torch": "2.8.0+cu128",
|
| 23 |
+
"sdnq": "0.1.9",
|
| 24 |
+
"num_inference_steps": 8,
|
| 25 |
+
"guidance_scale": 1.0,
|
| 26 |
+
"use_pe": false
|
| 27 |
+
},
|
| 28 |
+
"metrics": {
|
| 29 |
+
"explicit_quantized_matmul_default_allocator": "metrics/ernie_uint4_qmm_explicit_default_allocator_8step_metrics.json",
|
| 30 |
+
"allocator_debug": "metrics/runtime_allocator_debug_metrics.json"
|
| 31 |
+
}
|
| 32 |
+
}
|