Added Bitcast layer & extended MatMul and DFT layers support - #29594
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Dynamic KV-cache support - #29642
The core idea is: reserveKVCache() API to pre-allocate memory for attention caches upfront, which eliminates allocation overhead during token decoding. For LLM inference, simply call reserveKVCache(prompt_len + max_new_tokens) before the prefill stage so the decode loop runs without page allocations, significantly reducing per-token latency for models like Gemma3 and Qwen.
Speedups after this PR on AMD Ryzen 9 9950X 16-Core Processor device:
Qwen2.5-0.5B-Instruct, fp32, CPU, tok/s:
```
Tokens Before After Speedup
64 12.49 23.72 1.90×
128 10.37 23.14 2.23×
256 7.20 22.40 3.11×
512 4.25 21.03 4.95×
```
Gemma 3 1B-it, fp32, CPU, 512 tokens :
```
Tokens Before After Speedup
64 6.99 11.84 1.69×
128 5.84 11.72 2.01×
256 4.17 11.50 2.76×
512 2.47 11.15 4.51×
```
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Caffe importer cleanup #28678
Merge with: https://github.com/opencv/opencv_extra/pull/1324
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Added SDPA layer (Scaled Dot Product Attention) #29104
Merge with: https://github.com/opencv/opencv_extra/pull/1374
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Add KV cache with paged attention and prefetch #29127
Closes: https://github.com/opencv/opencv/issues/27159
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FP32 KV Cache #28840
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1348
## This PR introduces basic (Paged ) KV-Cache to use on CPU
### Summary:
1. To ensure proper gemm-prepacking,
1.1. The Page Size of Key Cache is currently hardcoded as `FAST_GEMM_F32_NR`(which is 8, 12 or 16 depending on CPU architecture)
1.2. The Page Size of Values Cache is hardcoded as `FAST_GEMM_F32_PACKED_STRIDE_K`
2. there are two phases supported - prefill & generate.
2.1. prefill grows cache by `N` tokens and is allowed **only** for empty cache
2.2. generate grows cache by 1 token.
2.3. **Improtant**: it is currently not allowed to grow non-empty cache by more than one token at a time (thisbehaviour is sufficient for normal LLM querying, but should be extended if we want to implement speculative decoding)
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Fixed subgraph name scoping in new DNN engine #28971
closes: https://github.com/opencv/opencv/issues/23663, https://github.com/opencv/opencv/issues/19977
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1362
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Fix dnn NaN bugs in GELU SIMD and softmax for large inputs #28836
### Bug Description
- **GELU SIMD bug:** `exp(2*inner)` overflows to `inf` for large inputs (e.g. `x=10.6` → `inner≈51`), causing `inf/inf = NaN`; fixed by clamping `inner` to [-9, 9] as `tanh` already saturates to ±1.0 beyond this range.
- **Softmax bug:** All `-inf` inputs (masked attention rows) produce `sum=0`, then `1/0 = inf` and `0*inf = NaN`;
fixed by outputting zeros when `sum == 0`.
- Add regression tests for both fixes
Depends on : https://github.com/opencv/opencv/pull/28837
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Extended fusion for activation functions in new DNN engine #28750
After this fusion, we see following improvements in YOLO models:
| Model | Before (`ENGINE_NEW`) | After (`ENGINE_NEW`) | `ENGINE_ORT` | % Improvement (Before v/s After) |
| :--- | :--- | :--- | :--- | :--- |
| **YOLOv8n** | 18.89 ms| 12.06 ms| 12.15 ms| 36.16% |
| **YOLOv5n** | 17.12 ms| 9.29 ms| 9.23 ms| 45.73% |
| **YOLOX-S** | 38.78 ms| 25.56 ms| 25.16 ms| 34.09% |
Device details:
- Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
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resolves https://github.com/opencv/opencv/issues/27580
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Use MatShape instead of MatSize inside cv::Mat/cv::UMat #27757
**Merge together with https://github.com/opencv/opencv_contrib/pull/3996**
---
This PR continues cv::Mat/cv::UMat refactoring. See #26056, where `MatShape` was introduced. Now it's put inside cv::Mat/cv::UMat instead of a weird `MatSize`. MatSize is now an alias for MatShape:
**before:**
```
struct MatShape { ... };
struct MatSize { ... };
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { ... /* constructs MatShape out of MatSize and returns it;
layout is always 'unknown', because we don't store it */ }
MatSize size; // size is not valid without the parent cv::Mat,
// because size.p may point to Mat::rows or to Mat::cols,
// depending on the dimensionality, and dims() returns Mat::dims.
MatStep step; // may allocate memory, depending on the dimensionality.
...
};
```
**after:**
```
struct MatShape { ... };
typedef MatShape MatSize; // they are now synonyms
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { return size; } // just return the embedded shape (including the proper layout information)
MatSize size; // size is self-contained data structure that can be used without the parent cv::Mat.
// size.dims is now a copy of dims; size.p[*] contains copies of Mat::rows and Mat::cols when dims <= 2.
MatStep step; // does not allocate extra memory buffers.
...
};
```
There are several reasons to do that:
1. the main reason is to be able to store data layout (MatShape::layout) inside each cv::Mat/cv::UMat. This is necessary for the proper shape inference in DNN module. In particular, it's necessary for the next step of DNN inference optimization where we introduce block-layout-optimized convolution and other operations. Later on, we can use layout information to support non-interleaved images (e.g. RRR...GGG...BBB...) or even batches of such images in core/imgproc modules.
2. the other reason is to represent 3D/4D/5D etc. tensors as cv::Mat/cv::UMat instances more conveniently, without extra dynamic memory allocation. Before this patch we allocated some memory buffers dynamically to store shape & steps for more than 2D arrays. Now the whole cv::Mat/cv::UMat header can be stored completely on stack/in a container. Creating another copy of Mat/UMat header is now done more efficiently.
3. the third reason is to introduce the new coding pattern: `dst.create(src.size, <dst_type>);`. The pattern is suitable for most of element-wise (including cloning) and filtering operations. This pattern does not only look crisp and self-documenting, it will also automatically copy shape (including layout) from the source tensor into the destination matrix/tensor.
4. in the future we might add `colorspace` member to MatShape that will allow to distinguish RGB from BGR or NV12. `dst.create(src.size, <dst_type>);` will then copy the colorspace information as well.
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Added Size layer to new DNN engine #27656
Merge with https://github.com/opencv/opencv_extra/pull/1274
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IfLayer add to new DNN engine #27508
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LSTM layer for new graph engine. #26391
Merge with extra: https://github.com/opencv/opencv_extra/pull/1218
This PR updates/creates LSTM layer compatible with new graph engine. It is based on previous LSTM implementation with some modification on how initializers blobs are processed.
Note: Following tests are currently are disabled
Two following two tests are disbled since ONNNRuntime does not support `layout=1` attiribute inference. See a detailed issue #26456 on this.
- `LSTM_layout_seq`
- `LSTM_layout_batch`
Following test fails with the new engine as it is not able to deal with shapes of the form [?, C, H, W]
- `LSTM_Activations`
Works:
- [x] One directional case any batch type
- [x] Fix directional case when batch size large than 1
- [x] Add peepholes attribute
TODO with the next PRs:
- [ ] Activation support
Note:
> Currently `LSTM_layout_seq`, `LSTM_layout_batch` are disabled as the tests are incorrect. They do not comply with the ONNX standard. Particularly test outputs are of incorrect dimensionality. They produce 3-dimentinal output instead of 4-dimentional.
------
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Modified Caffe parser to support the new dnn engine #26208
Now the Caffe parser supports both the old and the new engine. It can be selected using newEngine argument in PopulateNet.
All cpu Caffe tests work fine except:
- Test_Caffe_nets.Colorization
- Test_Caffe_layers.FasterRCNN_Proposal
Both these tests doesn't work because of the bug in the new net.forward function. The function takes the name of the desired target last layer, but uses this name as the name of the desired output tensor.
Also Colorization test contains a strange model with a Silence layer in the end, so it doesn't have outputs. The old parser just ignored it. I think, the proper solution is to run this model until the (number_of_layers - 2) layer using proper net.forward arguments in the test.
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New dnn engine #26056
This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794
---
**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.
---
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Disabled conversion to float of model's input #25555
In dnn 4.x usually any model's input is converted to float32 or float16 (except quantized models). Also mean and scale can be applied. In current dnn 5.x there is the same conversion except int32 and int64 types. I removed this conversion.
Here is how the pipeline works now:
- if input Mat type is float32, the pipeline applies mean and scale and may convert it to float16.
- if input Mat type is not float32, the pipeline preserves the input type and doesn't apply mean and scale
There was a conflict in protobuf parser between ONNX importer and tests. In ONNX importer any uint8 weight was handled as quantized weight and x = int8(x_uint8 - 128) conversion was used inside the protobuf parser. ONNX conformance tests used the same protobuf reader, so tests with uint8 inputs couldn't read the input values properly. I've made this conversion optional.
These ONNX conformance tests are enabled:
- test_add_uint8
- test_div_uint8
- test_mul_uint8
- test_sub_uint8
- test_max_int8
- test_max_uint8
- test_min_int8
- test_min_uint8
- test_mod_mixed_sign_int8
- test_mod_uint8
These tests were removed:
- Test_two_inputs.basic (when input is uint8)
- setInput.normalization (when input is uint8)
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Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
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Fixed ReduceMean layer behaviour #25120
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a93c31e3c9/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc (L433-L443)
Added int32, int64 support and type inference to dnn #24411
**Added a type inference to dnn similar to the shape inference, added int32 and int64 support.**
- Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type
- Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types
- All layers output blobs are now allocated using the calculated types from the type inference.
- Inputs and constants with int32 and int64 types are not automatically converted into float32 now.
- Added int32 and int64 support for all the layers with indexing and for all the layers required in tests.
Added int32 and int64 support for CUDA:
- Added host<->device data moving for int32 and int64
- Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates)
Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model)
**CURRENT PROBLEMS**:
- ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102)
- I didn't add type inference and int support to VULCAN, so it doesn't work at all now.
- Some layers don't support int yet, so some unknown models may not work.
**CURRENT WORKAROUNDS**:
- CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion)
- CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion
- CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion)
**DISABLED TESTS**:
- RAFT model
**REMOVED TESTS**:
- Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant)
**TODO IN NEXT PULL REQUESTS**:
- Add int64 support for ONNX parser
- Add int support for more layers
- Add int support for OCL (currently int layers just run on CPU)
- Add int tests
- Add int support for other backends
Primitive 1D Tests #24977
This PR is designed to add tests for 1D inputs for layer, which is required after introducing 1d support in 5.x. Currently tests are written for following layers:
- [x] `Add`, `Sub`
- [x] `Product`, `Div`
- [x] `Min`, `Max`
- [x] `Argmin`, `Argmax`
- [x] `Gather`
This list is to be extended for more layer such `gemm`, `conv` etc.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Supporting protobuf v22 and later(with abseil-cpp/C++17) #24372
fix https://github.com/opencv/opencv/issues/24369
related https://github.com/opencv/opencv/issues/23791
1. This patch supports external protobuf v22 and later, it required abseil-cpp and c++17.
Even if the built-in protobuf is upgraded to v22 or later,
the dependency on abseil-cpp and the requirement for C++17 will continue.
2. Some test for caffe required patched protobuf, so this patch disable them.
This patch is tested by following libraries.
- Protobuf: /usr/local/lib/libprotobuf.so (4.24.4)
- abseil-cpp: YES (20230125)
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
dnn: cleanup of halide backend for 5.x #24231
Merge with https://github.com/opencv/opencv_extra/pull/1092.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
TFLite models on different backends (tests and improvements) #24039
### Pull Request Readiness Checklist
* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Switch to new OpenVINO API after 2022.1 release
* Pass Layer_Test_Convolution_DLDT.Accuracy/0 test
* Pass test Test_Caffe_layers.Softmax
* Failed 136 tests
* Fix Concat. Failed 120 tests
* Custom nGraph ops. 19 failed tests
* Set and get properties from Core
* Read model from buffer
* Change MaxPooling layer output names. Restore reshape
* Cosmetic changes
* Cosmetic changes
* Override getOutputsInfo
* Fixes for OpenVINO < 2022.1
* Async inference for 2021.4 and less
* Compile model with config
* Fix serialize for 2022.1
* Asynchronous inference with 2022.1
* Handle 1d outputs
* Work with model with dynamic output shape
* Fixes with 1d output for old API
* Control outputs by nGraph function for all OpenVINO versions
* Refer inputs in PrePostProcessor by indices
* Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet.
Add InferRequest callback only for async inference. Do not capture InferRequest object.
* Fix tests thresholds
* Fix HETERO:GPU,CPU plugin issues with unsupported layer
