The bug happens when merging following shapes:
input0: [1, 1, 'Min(1024, input1_dynamic_axes_3)', 'Min(1024, input1_dynamic_axes_3)']
input1: ['input1_dynamic_axes_1*input1_dynamic_axes_2', 12, 'input1_dynamic_axes_3', 'input1_dynamic_axes_3']
input2: []
The fix is to avoid broadcasting merge on input2
* Symbolic shape inference: fix a case when concat requires merge multiple dims
* Fix a bug triggered in newer version of sympy
Fix a bug in output data type guessing
* remove shape inference and fix save large model problem
* remove unnecessary import
* refine code and add external format for quantize_qat
* remove initializers in tensors_to_calibrate
* small refine
Co-authored-by: t-yguo <t-yguo@microsoft.com>
* Initialize tensorrt perf script
* Add bert-squad dependencies
* Modified code to make ort inference with CUDA/Tensorrt
* Add get CUDA/TRT version
* uncomment bert-squad
* Add BERT-SQUAD inputs.json
* Add FastRCNN
* Make preprocess/validation in to common functions
* Add MaskRCNN and SSD and consolidate the code
* Add dependencies for MaskRCNN
* following modifications are made:
- create common fetch function to get inputs/outputs of model from ONNX model zoo.
- create common validation function to compare inference outputs with reference outputs from ONNX model zoo.
- move run/repeat time to argument list. (still working on other arguments, like fp16 or fp32, latency percentile).
- generate table in csv file to show the latency comparison (TRT vs CUDA) side by side.
* Add approache to analyze profling file and also update model related
settings
* Add models
* Add most of models from ONNX model zoo
* Add model input name and print all the model names at the end of run
* Add system info
* Add TRT fp16 support
* Refine the code
* Handle TRT fall back and modify the way to get input data
* Refine code
* Modify code
* Add more precise approach to measure inference
* Add io-binding
* Add YoLoV4
* Refine the code
* Refine the code
* Add models
* Add yolov4 notebook for jetson device
* Update notebook
* Update notebook
* Add CVS models
* Add missing model
* Add support of float16
* Add new way to get trt version
* Add "validate" and "benchmark" mode
* Add randomly generated input
* Refine perf script
* Refine the code.
* Add README
* Refine the code
* Update README.md
* Refine code
* Update README.md
* Remove all the model related python and instead using model_list.json as
models configuration.
Refine the benchmark.py
* Refine the code
Co-authored-by: Chi Lo <lochi@microsoft.com>
(1) Save gpt2 test data during test generation.
(2) Use torch fp32 model as baseline when onnx model is fp16.
(3) Refine logic to compose onnx model path
Improve quantization tools:
1. Support QAT
2. Make quantization tool to register Operators.
3. Make the API clear to use
Co-authored-by: t-yguo <t-yguo@microsoft.com>
* Export GPT-2 ONNX model without postion_ids and attention_mask inputs
* allow benchmark_gpt2 on user's model
* refactor: get_dummy_inputs returns a data class.
