Output log breakdown
An example of running clika-ace on OpenAI's CLIP Model.
Basic flow
- Parses the input model
- Applies series of transformations to remove nodes from the model until the graph converges.
- Applies quantization, pruning if relevant.
- In-case of quantization, a table will be output to show how the model behaves under different quantization thresholds.
- Exports the model to the selected framework.
Output log
CLIKA: Pre-Compiling 'CLIPVisionModel'
Precompiling: [####################] 100% [34/34][0:00:03<0:00:00, it/s: 2.505]
CLIKA: Done Pre-Compiling 'CLIPVisionModel'
CLIKA: Compiling 'CLIPVisionModel'
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== License is Valid. Time Left: N days X hours Y minutes ==
===============================================================
Created log at: /home/user/.clika/logs/clika_<TIME_STAMP>.log
[2025-04-20 17:52:50] CLIKA ACE Version: 25.4.0
[2025-04-20 17:52:50] 'torch' Version: 2.6.0+cu124
[2025-04-20 17:52:50] Python Version: 3.11.11 (main, Dec 11 2024, 16:28:39) [GCC 11.2.0]
[2025-04-20 17:52:50]
Deployment Settings:
+ target_framework = TensorRT (NVIDIA)
[2025-04-20 17:52:51] Starting to parse the model: 'CLIPVisionModel'
Parsing Model: [####################] 100% [485/485][0:00:00<0:00:00, it/s: 337.262] - Done
[2025-04-20 17:52:52] Discarding given model
[2025-04-20 17:52:53] Removed 201 unnecessary nodes
[2025-04-20 17:53:02] Merged 37 similar nodes
[2025-04-20 17:53:05] Removed 2 nodes
[2025-04-20 17:53:10]
Global Quantization Settings:
+ weights_num_bits = [8, 4]
+ activations_num_bits = [8]
+ prefer_weights_only_quantization = False
+ weights_only_quantization_block_sizes = [0, 32, 64, 128, 256, 512]
+ quantization_sensitivity_threshold = 0.03
+ weights_utilize_full_int_range = True
+ one_extra_bit_for_symmetric_weights = None
Deployment Settings:
+ target_framework = TensorRT (NVIDIA)
Equalization Preparation: [####################] 100% [0:00:01<0:00:00, it/s: 22.240]
Equalizing Model: [####################] 100% [0:00:27<0:00:00, it/s: 26.143]
[2025-04-20 17:53:40] Modified 50 nodes.
Calibrating: [####################] 100% [0:00:07<0:00:00, it/s: 4.482]]
Processing Quantization statistics: [####################] 100% [0:00:05<0:00:00, it/s: 51.551]8]
Measuring Quantization Sensitivity: [####################] 100% [0:01:58<0:00:00, it/s: 0.844]
Applying Quantization: [####################] 100% [0:00:18<0:00:00, it/s: 12.056]]
[2025-04-20 17:56:16]
Quantization Summary:
# of Quantizable layers: 99
# of Layers to be Quantized: 59
* Activations 8 bits: 24
* Weights 4 bits | Activations 8 bits: 35
Threshold | # Q-Layers | # Q-Confs Threshold | # Q-Layers | # Q-Confs
----------|------------|---------- ----------|------------|----------
..... | 99 | 149 ....... | 27 | 42
..... | 90 | 140 ....... | 14 | 20
..... | 84 | 134 ....... | 2 | 2
The table shows how many Layers will be Quantized and
how many Quantization configurations exist for a given Quantization Sensitivity Threshold.
Reminder, the sensitivity given in the Quantization Settings was: 0.03
CLIKA: Done Compiling 'CLIPVisionModel'
[2025-04-20 17:56:20] Serializing Chunk 1/1 345MB to: clip/files/openai_clip_vit_base_patch16_vision_model_trt-clika-ckpt-00001-00001.pompom
[2025-04-20 17:56:20] WARNING Inference may not work as intended.
While trying to deploy dynamic shape model, some warning messages have been generated:
1. flatten: Flatten: in dynamic shape inference shape may not always be the same after Flatten operation.
[2025-04-20 17:56:20] WARNING
===========
Note, you have just used Dynamic-Shape Deployment but the Model is not necessarily Dynamic-Shape Deployment Friendly.
This can be happen since you may have a 'Flatten' layer followed by a 'Linear' layer that expects fixed Input Features.
Or perhaps you may have an AdaptivePooling with output_size != (1, 1, ...)
In-case the Deployment/Inference fails, please try Fixed Shape.
===========
Exporting Model: [####################] 100% [0:00:01<0:00:00, it/s: 389.359]]
[2025-04-20 17:56:24] Overwriting existing model file clip/files/openai_clip_vit_base_patch16_vision_model_trt.onnx
[2025-04-20 17:56:26] Saved model at: clip/files/openai_clip_vit_base_patch16_vision_model_trt.onnx