[Wait for #2568][Mixed] Mixed Precision Layer update #2579
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We will add Var32 Tensor if the Variable Weight is not Full precision (FP32). This eables the Weight Update with full precision and only Apply Gradient Process ueses this Tensor. Therefore, the lifespan of this tensor should be "ApplyGradient". . Modify TensorPool to generate Weigth considering Mixed Precsion. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
This pr create the variable fp32 tensor when we create the Weight and Optimizer Weight. . update the manager to create Weight with var32 tensor which requested to weight pool. . update the weight requests with Weight Spec and var, grad and var32 tensors which created already. . add clone Tensor with specific type in tensor.h Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
This PR enables the FP16 support for the layers below: . input layer . mse loss layer Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
This PR includes the mixed precision test case. . Input - FC - MSE : "batch_size=2", "model_tensor_type=FP16-FP16", "loss_scale=128" **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
This commit modify apply gradient in optimizer. We do not need to save optimizer variables in weight type. Only Optimizer needs the optimizer variables and we should update the weight with full precision to maintain the accuracy. Therefore, remove the var32 tensors for optimizer variables. Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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📝 TAOS-CI Version: 1.5.20200925. Thank you for submitting PR #2579. Please a submit 1commit/1PR (one commit per one PR) policy to get comments quickly from reviewers. Your PR must pass all verificiation processes of cibot before starting a review process from reviewers. If you are new member to join this project, please read manuals in documentation folder and wiki page. In order to monitor a progress status of your PR in more detail, visit http://ci.nnstreamer.ai/. |
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…8x8 kernel - Apply similar change made in commit#52a3c734 but in 8x8 kernel **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <ss.kong@samsung.com>
- To avoid the constraint of 4-8 divisibilty w.r.t. K, loop for adaptive K direction. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <ss.kong@samsung.com>
- I found there was a repeated usage of matrix initialization before mul-add fused operations. - With separate initialization code, we can enjoy: 1. Cleaner code that is reusable for both f16 & f16-f32 kernel 2. Redundant init process is minimized for f16 kernel. Better latency with the SAME accuracy. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <ss.kong@samsung.com>
- Due to adaptive macro kernel usage, previous comment is no longer needed. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <ss.kong@samsung.com>
Added naive version of OpenCl implementation for FC Layer. Incorporated separate kernels for ops used. Added unit test for fc_layer_cl. Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
Added incremental forwarding as an option for unit testing layers Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
Added blas_kernels to enhance resuability of the common blas kernels. Used FullyConnected interface for both CPU and GPU calls. Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
Renamed global variables in unittest_layers_fully_connected_cl.cpp to fix duplicate declaration error Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
Fixed kernel argument bug for dot_cl kernel Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
Used proper size while creating OpenCL buffers. Optimized SGEMM kernel with 2D global work size. Modified function docs. Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
update yolo v2 modeling part of yolo v2. (update some hyper param values) - update yolo v2 pytorch(python) script - update yolo v2 nntrainer(c++) script * issue - activation function(in this case, leaky relu) of nntrainer needs to support setting negative slope via parameter... **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: Seungbaek Hong <sb92.hong@samsung.com>
Added code stub to generate Swiglu layer's golden test data. Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>
It adds tests for conv2d fp16 test. Signed-off-by: Jiho Chu <jiho.chu@samsung.com>
It is assumed that activations and weight are fully compotaible, so it's unnecessary to be converted to. input layer and loss layres are different, cause input data and label data is assumed to be always float 32 type now. Signed-off-by: Jiho Chu <jiho.chu@samsung.com>
This PR is to update the mixed precision layer. - integrate nnstreamer#2568 & nnstreamer#2455 - will update more test **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: Donghak PARK <donghak.park@samsung.com>
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will update layers with new PR. |
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR #2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR update the conv2D Layer to support Mixed Precision (FP16). It is based on the PR nnstreamer#2579 Resolves: **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>
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This PR is to update the mixed precision layer.
Self evaluation:
Signed-off-by: Donghak PARK donghak.park@samsung.com