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deploy: 958dbb3a1d02ecced9ff62624625892afb2206c3
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csukuangfj
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a172087ed0
@ -204,7 +204,7 @@ Next, we use the following code to export our model:
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.. literalinclude:: ./code/export-conv-emformer-transducer-for-ncnn-output.txt
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The log shows the model has ``75490012`` number of parameters, i.e., ``~75 M``.
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The log shows the model has ``75490012`` parameters, i.e., ``~75 M``.
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.. code-block::
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@ -213,7 +213,7 @@ Next, we use the following code to export our model:
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-rw-r--r-- 1 kuangfangjun root 289M Jan 11 12:05 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/pretrained-epoch-30-avg-10-averaged.pt
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You can see that the file size of the pre-trained model is ``289 MB``, which
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is roughly ``4 x 75 M``.
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is roughly ``75490012*4/1024/1024 = 287.97 MB``.
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After running ``conv_emformer_transducer_stateless2/export-for-ncnn.py``,
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we will get the following files:
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@ -286,8 +286,8 @@ We compare the file sizes of the models below before and after converting via ``
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| joiner_jit_trace-pnnx.ncnn.bin | 1.5 MB |
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+----------------------------------+------------+
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You can see that the file size of the models after converting is about one half
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of the models before converting:
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You can see that the file sizes of the models after conversion are about one half
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of the models before conversion:
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- encoder: 283 MB vs 142 MB
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- decoder: 1010 KB vs 503 KB
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@ -338,6 +338,8 @@ The output is given below:
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Congratulations! You have successfully exported a model from PyTorch to `ncnn`_!
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.. _conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn:
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5. Modify the exported encoder for sherpa-ncnn
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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@ -356,14 +358,15 @@ Let us have a look at the first few lines of ``encoder_jit_trace-pnnx.ncnn.param
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1. ``7767517``, it is a magic number and should not be changed.
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2. ``1060 1342``, the first number ``1060`` specifies the number of layers
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in this file, while ``1342`` specifies the number intermediate outputs of
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this file
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in this file, while ``1342`` specifies the number of intermediate outputs
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of this file
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3. ``Input in0 0 1 in0``, ``Input`` is the layer type of this layer; ``in0``
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is the layer name of this layer; ``0`` means this layer has no input;
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``1`` means this layer has one output. ``in0`` is the output name of
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``1`` means this layer has one output; ``in0`` is the output name of
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this layer.
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We need to add 1 extra line and the result looks like below:
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We need to add 1 extra line and also increment the number of layers.
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The result looks like below:
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.. code-block:: bash
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@ -376,13 +379,13 @@ We need to add 1 extra line and the result looks like below:
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1. ``7767517``, it is still the same
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2. ``1061 1342``, we have added an extra layer, so we need to update ``1060`` to ``1061``.
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We don't need to change ``1342`` since the newly added layer has no inputs and outputs.
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We don't need to change ``1342`` since the newly added layer has no inputs or outputs.
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3. ``SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512``
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This line is newly added. Its explanation is given below:
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- ``SherpaMetaData`` is the type of this layer. Must be ``SherpaMetaData``.
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- ``sherpa_meta_data1`` is the name of this layer. Must be ``sherpa_meta_data1``.
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- ``0 0`` means this layer has no inputs and output. Must be ``0 0``
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- ``0 0`` means this layer has no inputs or output. Must be ``0 0``
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- ``0=1``, 0 is the key and 1 is the value. MUST be ``0=1``
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- ``1=12``, 1 is the key and 12 is the value of the
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parameter ``--num-encoder-layers`` that you provided when running
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@ -483,10 +486,286 @@ disable ``fp16`` when using ``pnnx``:
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.. note::
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We add ``fp16=0`` when exporting the encoder and joiner. ``ncnn`` does not
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We add ``fp16=0`` when exporting the encoder and joiner. `ncnn`_ does not
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support quantizing the decoder model yet. We will update this documentation
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once ``ncnn`` supports it. (Maybe in this year, 2023).
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once `ncnn`_ supports it. (Maybe in this year, 2023).
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TODO(fangjun): Finish it.
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It will generate the following files
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Have fun with `sherpa-ncnn`_!
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.. code-block:: bash
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ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/*_jit_trace-pnnx.ncnn.{param,bin}
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-rw-r--r-- 1 kuangfangjun root 503K Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin
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-rw-r--r-- 1 kuangfangjun root 437 Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param
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-rw-r--r-- 1 kuangfangjun root 283M Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin
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-rw-r--r-- 1 kuangfangjun root 79K Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param
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-rw-r--r-- 1 kuangfangjun root 3.0M Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin
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-rw-r--r-- 1 kuangfangjun root 488 Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.param
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Let us compare again the file sizes:
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+----------------------------------------+------------+
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| File name | File size |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.pt | 283 MB |
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+----------------------------------------+------------+
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| decoder_jit_trace-pnnx.pt | 1010 KB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.pt | 3.0 MB |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 142 MB |
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+----------------------------------------+------------+
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| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 283 MB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
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+----------------------------------------+------------+
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You can see that the file sizes are doubled when we disable ``fp16``.
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.. note::
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You can again use ``streaming-ncnn-decode.py`` to test the exported models.
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Next, follow :ref:`conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn`
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to modify ``encoder_jit_trace-pnnx.ncnn.param``.
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Change
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.. code-block:: bash
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7767517
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1060 1342
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Input in0 0 1 in0
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to
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.. code-block:: bash
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7767517
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1061 1342
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SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512
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Input in0 0 1 in0
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.. caution::
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Please follow :ref:`conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn`
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to change the values for ``SherpaMetaData`` if your model uses a different setting.
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Next, let us compile `sherpa-ncnn`_ since we will quantize our models within
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`sherpa-ncnn`_.
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.. code-block:: bash
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# We will download sherpa-ncnn to $HOME/open-source/
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# You can change it to anywhere you like.
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cd $HOME
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mkdir -p open-source
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cd open-source
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git clone https://github.com/k2-fsa/sherpa-ncnn
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cd sherpa-ncnn
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mkdir build
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cd build
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cmake ..
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make -j 4
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./bin/generate-int8-scale-table
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export PATH=$HOME/open-source/sherpa-ncnn/build/bin:$PATH
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The output of the above commands are:
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.. code-block:: bash
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(py38) kuangfangjun:build$ generate-int8-scale-table
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Please provide 10 arg. Currently given: 1
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Usage:
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generate-int8-scale-table encoder.param encoder.bin decoder.param decoder.bin joiner.param joiner.bin encoder-scale-table.txt joiner-scale-table.txt wave_filenames.txt
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Each line in wave_filenames.txt is a path to some 16k Hz mono wave file.
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We need to create a file ``wave_filenames.txt``, in which we need to put
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some calibration wave files. For testing purpose, we put the ``test_wavs``
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from the pre-trained model repository `<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_
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.. code-block:: bash
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cd egs/librispeech/ASR
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cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
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cat <<EOF > wave_filenames.txt
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../test_wavs/1089-134686-0001.wav
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../test_wavs/1221-135766-0001.wav
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../test_wavs/1221-135766-0002.wav
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EOF
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Now we can calculate the scales needed for quantization with the calibration data:
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.. code-block:: bash
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cd egs/librispeech/ASR
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cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
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generate-int8-scale-table \
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./encoder_jit_trace-pnnx.ncnn.param \
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./encoder_jit_trace-pnnx.ncnn.bin \
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./decoder_jit_trace-pnnx.ncnn.param \
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./decoder_jit_trace-pnnx.ncnn.bin \
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./joiner_jit_trace-pnnx.ncnn.param \
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./joiner_jit_trace-pnnx.ncnn.bin \
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./encoder-scale-table.txt \
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./joiner-scale-table.txt \
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./wave_filenames.txt
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The output logs are in the following:
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.. literalinclude:: ./code/generate-int-8-scale-table-for-conv-emformer.txt
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It generates the following two files:
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.. code-block:: bash
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$ ls -lh encoder-scale-table.txt joiner-scale-table.txt
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-rw-r--r-- 1 kuangfangjun root 955K Jan 11 17:28 encoder-scale-table.txt
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-rw-r--r-- 1 kuangfangjun root 18K Jan 11 17:28 joiner-scale-table.txt
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.. caution::
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Definitely, you need more calibration data to compute the scale table.
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Finally, let us use the scale table to quantize our models into ``int8``.
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.. code-block:: bash
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ncnn2int8
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usage: ncnn2int8 [inparam] [inbin] [outparam] [outbin] [calibration table]
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First, we quantize the encoder model:
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.. code-block:: bash
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cd egs/librispeech/ASR
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cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
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ncnn2int8 \
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./encoder_jit_trace-pnnx.ncnn.param \
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./encoder_jit_trace-pnnx.ncnn.bin \
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./encoder_jit_trace-pnnx.ncnn.int8.param \
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./encoder_jit_trace-pnnx.ncnn.int8.bin \
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./encoder-scale-table.txt
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Next, we quantize the joiner model:
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.. code-block:: bash
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ncnn2int8 \
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./joiner_jit_trace-pnnx.ncnn.param \
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./joiner_jit_trace-pnnx.ncnn.bin \
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./joiner_jit_trace-pnnx.ncnn.int8.param \
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./joiner_jit_trace-pnnx.ncnn.int8.bin \
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./joiner-scale-table.txt
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The above two commands generate the following 4 files:
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.. code-block:: bash
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-rw-r--r-- 1 kuangfangjun root 99M Jan 11 17:34 encoder_jit_trace-pnnx.ncnn.int8.bin
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-rw-r--r-- 1 kuangfangjun root 78K Jan 11 17:34 encoder_jit_trace-pnnx.ncnn.int8.param
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-rw-r--r-- 1 kuangfangjun root 774K Jan 11 17:35 joiner_jit_trace-pnnx.ncnn.int8.bin
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-rw-r--r-- 1 kuangfangjun root 496 Jan 11 17:35 joiner_jit_trace-pnnx.ncnn.int8.param
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Congratulations! You have successfully quantized your model from ``float32`` to ``int8``.
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.. caution::
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``ncnn.int8.param`` and ``ncnn.int8.bin`` must be used in pairs.
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You can replace ``ncnn.param`` and ``ncnn.bin`` with ``ncnn.int8.param``
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and ``ncnn.int8.bin`` in `sherpa-ncnn`_ if you like.
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For instance, to use only the ``int8`` encoder in ``sherpa-ncnn``, you can
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replace the following invocation:
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.. code-block::
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cd egs/librispeech/ASR
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cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
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sherpa-ncnn \
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../data/lang_bpe_500/tokens.txt \
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./encoder_jit_trace-pnnx.ncnn.param \
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./encoder_jit_trace-pnnx.ncnn.bin \
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./decoder_jit_trace-pnnx.ncnn.param \
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./decoder_jit_trace-pnnx.ncnn.bin \
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./joiner_jit_trace-pnnx.ncnn.param \
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./joiner_jit_trace-pnnx.ncnn.bin \
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../test_wavs/1089-134686-0001.wav
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with
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.. code-block::
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cd egs/librispeech/ASR
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cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
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sherpa-ncnn \
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../data/lang_bpe_500/tokens.txt \
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./encoder_jit_trace-pnnx.ncnn.int8.param \
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./encoder_jit_trace-pnnx.ncnn.int8.bin \
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./decoder_jit_trace-pnnx.ncnn.param \
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./decoder_jit_trace-pnnx.ncnn.bin \
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./joiner_jit_trace-pnnx.ncnn.param \
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./joiner_jit_trace-pnnx.ncnn.bin \
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../test_wavs/1089-134686-0001.wav
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The following table compares again the file sizes:
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+----------------------------------------+------------+
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| File name | File size |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.pt | 283 MB |
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+----------------------------------------+------------+
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| decoder_jit_trace-pnnx.pt | 1010 KB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.pt | 3.0 MB |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 142 MB |
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+----------------------------------------+------------+
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| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 283 MB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
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+----------------------------------------+------------+
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| encoder_jit_trace-pnnx.ncnn.int8.bin | 99 MB |
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+----------------------------------------+------------+
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| joiner_jit_trace-pnnx.ncnn.int8.bin | 774 KB |
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+----------------------------------------+------------+
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You can see that the file sizes of the model after ``int8`` quantization
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are much smaller.
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.. hint::
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Currently, only linear layers and convolutional layers are quantized
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with ``int8``, so you don't see an exact ``4x`` reduction in file sizes.
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.. note::
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You need to test the recognition accuracy after ``int8`` quantization.
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You can find the speed comparison at `<https://github.com/k2-fsa/sherpa-ncnn/issues/44>`_.
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That's it! Have fun with `sherpa-ncnn`_!
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@ -308,14 +308,14 @@ and select the best combination with with <code class="docutils literal notransl
|
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<span class="mi">2023</span><span class="o">-</span><span class="mi">01</span><span class="o">-</span><span class="mi">11</span> <span class="mi">12</span><span class="p">:</span><span class="mi">15</span><span class="p">:</span><span class="mi">41</span><span class="p">,</span><span class="mi">682</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">149</span><span class="p">]</span> <span class="n">chunk_length</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="n">right_context_length</span><span class="p">:</span> <span class="mi">8</span>
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</pre></div>
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</div>
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<p>The log shows the model has <code class="docutils literal notranslate"><span class="pre">75490012</span></code> number of parameters, i.e., <code class="docutils literal notranslate"><span class="pre">~75</span> <span class="pre">M</span></code>.</p>
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<p>The log shows the model has <code class="docutils literal notranslate"><span class="pre">75490012</span></code> parameters, i.e., <code class="docutils literal notranslate"><span class="pre">~75</span> <span class="pre">M</span></code>.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ls</span> <span class="o">-</span><span class="n">lh</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">conv</span><span class="o">-</span><span class="n">emformer</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">07</span><span class="o">-</span><span class="mi">05</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">pretrained</span><span class="o">-</span><span class="n">epoch</span><span class="o">-</span><span class="mi">30</span><span class="o">-</span><span class="n">avg</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">averaged</span><span class="o">.</span><span class="n">pt</span>
|
||||
|
||||
<span class="o">-</span><span class="n">rw</span><span class="o">-</span><span class="n">r</span><span class="o">--</span><span class="n">r</span><span class="o">--</span> <span class="mi">1</span> <span class="n">kuangfangjun</span> <span class="n">root</span> <span class="mi">289</span><span class="n">M</span> <span class="n">Jan</span> <span class="mi">11</span> <span class="mi">12</span><span class="p">:</span><span class="mi">05</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">conv</span><span class="o">-</span><span class="n">emformer</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">07</span><span class="o">-</span><span class="mi">05</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">pretrained</span><span class="o">-</span><span class="n">epoch</span><span class="o">-</span><span class="mi">30</span><span class="o">-</span><span class="n">avg</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">averaged</span><span class="o">.</span><span class="n">pt</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>You can see that the file size of the pre-trained model is <code class="docutils literal notranslate"><span class="pre">289</span> <span class="pre">MB</span></code>, which
|
||||
is roughly <code class="docutils literal notranslate"><span class="pre">4</span> <span class="pre">x</span> <span class="pre">75</span> <span class="pre">M</span></code>.</p>
|
||||
is roughly <code class="docutils literal notranslate"><span class="pre">75490012*4/1024/1024</span> <span class="pre">=</span> <span class="pre">287.97</span> <span class="pre">MB</span></code>.</p>
|
||||
</div>
|
||||
<p>After running <code class="docutils literal notranslate"><span class="pre">conv_emformer_transducer_stateless2/export-for-ncnn.py</span></code>,
|
||||
we will get the following files:</p>
|
||||
@ -391,8 +391,8 @@ use a text editor to view its content.</p></li>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>You can see that the file size of the models after converting is about one half
|
||||
of the models before converting:</p>
|
||||
<p>You can see that the file sizes of the models after conversion are about one half
|
||||
of the models before conversion:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p>encoder: 283 MB vs 142 MB</p></li>
|
||||
@ -448,7 +448,7 @@ only 1 wave file as input.</p>
|
||||
<p>Congratulations! You have successfully exported a model from PyTorch to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>!</p>
|
||||
</section>
|
||||
<section id="modify-the-exported-encoder-for-sherpa-ncnn">
|
||||
<h3>5. Modify the exported encoder for sherpa-ncnn<a class="headerlink" href="#modify-the-exported-encoder-for-sherpa-ncnn" title="Permalink to this heading"></a></h3>
|
||||
<span id="conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn"></span><h3>5. Modify the exported encoder for sherpa-ncnn<a class="headerlink" href="#modify-the-exported-encoder-for-sherpa-ncnn" title="Permalink to this heading"></a></h3>
|
||||
<p>In order to use the exported models in <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>, we have to modify
|
||||
<code class="docutils literal notranslate"><span class="pre">encoder_jit_trace-pnnx.ncnn.param</span></code>.</p>
|
||||
<p>Let us have a look at the first few lines of <code class="docutils literal notranslate"><span class="pre">encoder_jit_trace-pnnx.ncnn.param</span></code>:</p>
|
||||
@ -462,15 +462,16 @@ only 1 wave file as input.</p>
|
||||
<div><ol class="arabic simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">7767517</span></code>, it is a magic number and should not be changed.</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">1060</span> <span class="pre">1342</span></code>, the first number <code class="docutils literal notranslate"><span class="pre">1060</span></code> specifies the number of layers
|
||||
in this file, while <code class="docutils literal notranslate"><span class="pre">1342</span></code> specifies the number intermediate outputs of
|
||||
this file</p></li>
|
||||
in this file, while <code class="docutils literal notranslate"><span class="pre">1342</span></code> specifies the number of intermediate outputs
|
||||
of this file</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">Input</span> <span class="pre">in0</span> <span class="pre">0</span> <span class="pre">1</span> <span class="pre">in0</span></code>, <code class="docutils literal notranslate"><span class="pre">Input</span></code> is the layer type of this layer; <code class="docutils literal notranslate"><span class="pre">in0</span></code>
|
||||
is the layer name of this layer; <code class="docutils literal notranslate"><span class="pre">0</span></code> means this layer has no input;
|
||||
<code class="docutils literal notranslate"><span class="pre">1</span></code> means this layer has one output. <code class="docutils literal notranslate"><span class="pre">in0</span></code> is the output name of
|
||||
<code class="docutils literal notranslate"><span class="pre">1</span></code> means this layer has one output; <code class="docutils literal notranslate"><span class="pre">in0</span></code> is the output name of
|
||||
this layer.</p></li>
|
||||
</ol>
|
||||
</div></blockquote>
|
||||
<p>We need to add 1 extra line and the result looks like below:</p>
|
||||
<p>We need to add 1 extra line and also increment the number of layers.
|
||||
The result looks like below:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">7767517</span>
|
||||
<span class="m">1061</span><span class="w"> </span><span class="m">1342</span>
|
||||
SherpaMetaData<span class="w"> </span>sherpa_meta_data1<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="nv">0</span><span class="o">=</span><span class="m">1</span><span class="w"> </span><span class="nv">1</span><span class="o">=</span><span class="m">12</span><span class="w"> </span><span class="nv">2</span><span class="o">=</span><span class="m">32</span><span class="w"> </span><span class="nv">3</span><span class="o">=</span><span class="m">31</span><span class="w"> </span><span class="nv">4</span><span class="o">=</span><span class="m">8</span><span class="w"> </span><span class="nv">5</span><span class="o">=</span><span class="m">32</span><span class="w"> </span><span class="nv">6</span><span class="o">=</span><span class="m">8</span><span class="w"> </span><span class="nv">7</span><span class="o">=</span><span class="m">512</span>
|
||||
@ -482,14 +483,14 @@ Input<span class="w"> </span>in0<span class="w">
|
||||
<div><ol class="arabic">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">7767517</span></code>, it is still the same</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">1061</span> <span class="pre">1342</span></code>, we have added an extra layer, so we need to update <code class="docutils literal notranslate"><span class="pre">1060</span></code> to <code class="docutils literal notranslate"><span class="pre">1061</span></code>.
|
||||
We don’t need to change <code class="docutils literal notranslate"><span class="pre">1342</span></code> since the newly added layer has no inputs and outputs.</p></li>
|
||||
We don’t need to change <code class="docutils literal notranslate"><span class="pre">1342</span></code> since the newly added layer has no inputs or outputs.</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span>  <span class="pre">sherpa_meta_data1</span>  <span class="pre">0</span> <span class="pre">0</span> <span class="pre">0=1</span> <span class="pre">1=12</span> <span class="pre">2=32</span> <span class="pre">3=31</span> <span class="pre">4=8</span> <span class="pre">5=32</span> <span class="pre">6=8</span> <span class="pre">7=512</span></code>
|
||||
This line is newly added. Its explanation is given below:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code> is the type of this layer. Must be <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code>.</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">sherpa_meta_data1</span></code> is the name of this layer. Must be <code class="docutils literal notranslate"><span class="pre">sherpa_meta_data1</span></code>.</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">0</span></code> means this layer has no inputs and output. Must be <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">0</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">0</span></code> means this layer has no inputs or output. Must be <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">0</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">0=1</span></code>, 0 is the key and 1 is the value. MUST be <code class="docutils literal notranslate"><span class="pre">0=1</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">1=12</span></code>, 1 is the key and 12 is the value of the
|
||||
parameter <code class="docutils literal notranslate"><span class="pre">--num-encoder-layers</span></code> that you provided when running
|
||||
@ -611,12 +612,388 @@ disable <code class="docutils literal notranslate"><span class="pre">fp16</span>
|
||||
</div>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>We add <code class="docutils literal notranslate"><span class="pre">fp16=0</span></code> when exporting the encoder and joiner. <code class="docutils literal notranslate"><span class="pre">ncnn</span></code> does not
|
||||
<p>We add <code class="docutils literal notranslate"><span class="pre">fp16=0</span></code> when exporting the encoder and joiner. <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> does not
|
||||
support quantizing the decoder model yet. We will update this documentation
|
||||
once <code class="docutils literal notranslate"><span class="pre">ncnn</span></code> supports it. (Maybe in this year, 2023).</p>
|
||||
once <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> supports it. (Maybe in this year, 2023).</p>
|
||||
</div>
|
||||
<p>TODO(fangjun): Finish it.</p>
|
||||
<p>Have fun with <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>!</p>
|
||||
<p>It will generate the following files</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ls<span class="w"> </span>-lh<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/*_jit_trace-pnnx.ncnn.<span class="o">{</span>param,bin<span class="o">}</span>
|
||||
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>503K<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">15</span>:56<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">437</span><span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">15</span>:56<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>283M<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">15</span>:56<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>79K<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">15</span>:56<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">3</span>.0M<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">15</span>:56<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">488</span><span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">15</span>:56<span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.param
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Let us compare again the file sizes:</p>
|
||||
<table class="docutils align-default">
|
||||
<colgroup>
|
||||
<col style="width: 77%" />
|
||||
<col style="width: 23%" />
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p>File name</p></td>
|
||||
<td><p>File size</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.pt</p></td>
|
||||
<td><p>283 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>decoder_jit_trace-pnnx.pt</p></td>
|
||||
<td><p>1010 KB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>joiner_jit_trace-pnnx.pt</p></td>
|
||||
<td><p>3.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
|
||||
<td><p>142 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>decoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
|
||||
<td><p>503 KB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
|
||||
<td><p>1.5 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
|
||||
<td><p>283 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
|
||||
<td><p>3.0 MB</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>You can see that the file sizes are doubled when we disable <code class="docutils literal notranslate"><span class="pre">fp16</span></code>.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>You can again use <code class="docutils literal notranslate"><span class="pre">streaming-ncnn-decode.py</span></code> to test the exported models.</p>
|
||||
</div>
|
||||
<p>Next, follow <a class="reference internal" href="#conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn"><span class="std std-ref">5. Modify the exported encoder for sherpa-ncnn</span></a>
|
||||
to modify <code class="docutils literal notranslate"><span class="pre">encoder_jit_trace-pnnx.ncnn.param</span></code>.</p>
|
||||
<p>Change</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">7767517</span>
|
||||
<span class="m">1060</span><span class="w"> </span><span class="m">1342</span>
|
||||
Input<span class="w"> </span>in0<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">1</span><span class="w"> </span>in0
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>to</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">7767517</span>
|
||||
<span class="m">1061</span><span class="w"> </span><span class="m">1342</span>
|
||||
SherpaMetaData<span class="w"> </span>sherpa_meta_data1<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="nv">0</span><span class="o">=</span><span class="m">1</span><span class="w"> </span><span class="nv">1</span><span class="o">=</span><span class="m">12</span><span class="w"> </span><span class="nv">2</span><span class="o">=</span><span class="m">32</span><span class="w"> </span><span class="nv">3</span><span class="o">=</span><span class="m">31</span><span class="w"> </span><span class="nv">4</span><span class="o">=</span><span class="m">8</span><span class="w"> </span><span class="nv">5</span><span class="o">=</span><span class="m">32</span><span class="w"> </span><span class="nv">6</span><span class="o">=</span><span class="m">8</span><span class="w"> </span><span class="nv">7</span><span class="o">=</span><span class="m">512</span>
|
||||
Input<span class="w"> </span>in0<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">1</span><span class="w"> </span>in0
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="admonition caution">
|
||||
<p class="admonition-title">Caution</p>
|
||||
<p>Please follow <a class="reference internal" href="#conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn"><span class="std std-ref">5. Modify the exported encoder for sherpa-ncnn</span></a>
|
||||
to change the values for <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code> if your model uses a different setting.</p>
|
||||
</div>
|
||||
<p>Next, let us compile <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a> since we will quantize our models within
|
||||
<a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># We will download sherpa-ncnn to $HOME/open-source/</span>
|
||||
<span class="c1"># You can change it to anywhere you like.</span>
|
||||
<span class="nb">cd</span><span class="w"> </span><span class="nv">$HOME</span>
|
||||
mkdir<span class="w"> </span>-p<span class="w"> </span>open-source
|
||||
|
||||
<span class="nb">cd</span><span class="w"> </span>open-source
|
||||
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/k2-fsa/sherpa-ncnn
|
||||
<span class="nb">cd</span><span class="w"> </span>sherpa-ncnn
|
||||
mkdir<span class="w"> </span>build
|
||||
<span class="nb">cd</span><span class="w"> </span>build
|
||||
cmake<span class="w"> </span>..
|
||||
make<span class="w"> </span>-j<span class="w"> </span><span class="m">4</span>
|
||||
|
||||
./bin/generate-int8-scale-table
|
||||
|
||||
<span class="nb">export</span><span class="w"> </span><span class="nv">PATH</span><span class="o">=</span><span class="nv">$HOME</span>/open-source/sherpa-ncnn/build/bin:<span class="nv">$PATH</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The output of the above commands are:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">(</span>py38<span class="o">)</span><span class="w"> </span>kuangfangjun:build$<span class="w"> </span>generate-int8-scale-table
|
||||
Please<span class="w"> </span>provide<span class="w"> </span><span class="m">10</span><span class="w"> </span>arg.<span class="w"> </span>Currently<span class="w"> </span>given:<span class="w"> </span><span class="m">1</span>
|
||||
Usage:
|
||||
generate-int8-scale-table<span class="w"> </span>encoder.param<span class="w"> </span>encoder.bin<span class="w"> </span>decoder.param<span class="w"> </span>decoder.bin<span class="w"> </span>joiner.param<span class="w"> </span>joiner.bin<span class="w"> </span>encoder-scale-table.txt<span class="w"> </span>joiner-scale-table.txt<span class="w"> </span>wave_filenames.txt
|
||||
|
||||
Each<span class="w"> </span>line<span class="w"> </span><span class="k">in</span><span class="w"> </span>wave_filenames.txt<span class="w"> </span>is<span class="w"> </span>a<span class="w"> </span>path<span class="w"> </span>to<span class="w"> </span>some<span class="w"> </span>16k<span class="w"> </span>Hz<span class="w"> </span>mono<span class="w"> </span>wave<span class="w"> </span>file.
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>We need to create a file <code class="docutils literal notranslate"><span class="pre">wave_filenames.txt</span></code>, in which we need to put
|
||||
some calibration wave files. For testing purpose, we put the <code class="docutils literal notranslate"><span class="pre">test_wavs</span></code>
|
||||
from the pre-trained model repository <a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05">https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05</a></p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
|
||||
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
|
||||
|
||||
cat<span class="w"> </span><span class="s"><<EOF > wave_filenames.txt</span>
|
||||
<span class="s">../test_wavs/1089-134686-0001.wav</span>
|
||||
<span class="s">../test_wavs/1221-135766-0001.wav</span>
|
||||
<span class="s">../test_wavs/1221-135766-0002.wav</span>
|
||||
<span class="s">EOF</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Now we can calculate the scales needed for quantization with the calibration data:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
|
||||
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
|
||||
|
||||
generate-int8-scale-table<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./decoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./decoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder-scale-table.txt<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner-scale-table.txt<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./wave_filenames.txt
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The output logs are in the following:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Don</span><span class="s1">'t Use GPU. has_gpu: 0, config.use_vulkan_compute: 1</span>
|
||||
<span class="n">num</span> <span class="n">encoder</span> <span class="n">conv</span> <span class="n">layers</span><span class="p">:</span> <span class="mi">88</span>
|
||||
<span class="n">num</span> <span class="n">joiner</span> <span class="n">conv</span> <span class="n">layers</span><span class="p">:</span> <span class="mi">3</span>
|
||||
<span class="n">num</span> <span class="n">files</span><span class="p">:</span> <span class="mi">3</span>
|
||||
<span class="n">Processing</span> <span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1089</span><span class="o">-</span><span class="mi">134686</span><span class="o">-</span><span class="mf">0001.</span><span class="n">wav</span>
|
||||
<span class="n">Processing</span> <span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0001.</span><span class="n">wav</span>
|
||||
<span class="n">Processing</span> <span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0002.</span><span class="n">wav</span>
|
||||
<span class="n">Processing</span> <span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1089</span><span class="o">-</span><span class="mi">134686</span><span class="o">-</span><span class="mf">0001.</span><span class="n">wav</span>
|
||||
<span class="n">Processing</span> <span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0001.</span><span class="n">wav</span>
|
||||
<span class="n">Processing</span> <span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0002.</span><span class="n">wav</span>
|
||||
<span class="o">----------</span><span class="n">encoder</span><span class="o">----------</span>
|
||||
<span class="n">conv_87</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">15.942385</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">15.938493</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">7.968131</span>
|
||||
<span class="n">conv_88</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">35.442448</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">15.549335</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">8.167552</span>
|
||||
<span class="n">conv_89</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">23.228289</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">8.001738</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">15.871552</span>
|
||||
<span class="n">linear_90</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.976146</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.101789</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">115.267128</span>
|
||||
<span class="n">linear_91</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.962030</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.162033</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">24.602713</span>
|
||||
<span class="n">linear_92</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.323041</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.853959</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.953129</span>
|
||||
<span class="n">linear_94</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.905416</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.648006</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">27.323545</span>
|
||||
<span class="n">linear_93</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.905416</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.474093</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.200188</span>
|
||||
<span class="n">linear_95</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">1.888012</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.403563</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">90.483986</span>
|
||||
<span class="n">linear_96</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.856741</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.398679</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.524273</span>
|
||||
<span class="n">linear_97</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.635942</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.613655</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">48.590950</span>
|
||||
<span class="n">linear_98</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.460340</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.670146</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.398010</span>
|
||||
<span class="n">linear_99</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.532276</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.585537</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">49.119396</span>
|
||||
<span class="n">linear_101</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.585871</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.719224</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.205809</span>
|
||||
<span class="n">linear_100</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.585871</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.751382</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.081648</span>
|
||||
<span class="n">linear_102</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">1.593344</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.450581</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">87.551147</span>
|
||||
<span class="n">linear_103</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.592681</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.705824</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.257959</span>
|
||||
<span class="n">linear_104</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.752957</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.980955</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">64.110489</span>
|
||||
<span class="n">linear_105</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.696240</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.877193</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">21.608953</span>
|
||||
<span class="n">linear_106</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.059659</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.643138</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">48.048950</span>
|
||||
<span class="n">linear_108</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.975461</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.589567</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">27.671457</span>
|
||||
<span class="n">linear_107</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.975461</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">6.190381</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">20.515701</span>
|
||||
<span class="n">linear_109</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.710759</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.305635</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">55.082436</span>
|
||||
<span class="n">linear_110</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">7.531228</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.731162</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.159557</span>
|
||||
<span class="n">linear_111</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.528083</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.259322</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">56.211544</span>
|
||||
<span class="n">linear_112</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.148807</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.500842</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.087374</span>
|
||||
<span class="n">linear_113</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.592566</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.948851</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">65.166611</span>
|
||||
<span class="n">linear_115</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.437109</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.608947</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.642395</span>
|
||||
<span class="n">linear_114</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.437109</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">6.193942</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">20.503904</span>
|
||||
<span class="n">linear_116</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.966980</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.200896</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">39.676392</span>
|
||||
<span class="n">linear_117</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.451303</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">6.061664</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">20.951344</span>
|
||||
<span class="n">linear_118</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.077262</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.965800</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.023804</span>
|
||||
<span class="n">linear_119</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.671615</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.847613</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">26.198460</span>
|
||||
<span class="n">linear_120</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.625638</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.131427</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.556595</span>
|
||||
<span class="n">linear_122</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.274080</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.888716</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">25.978189</span>
|
||||
<span class="n">linear_121</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.274080</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.420480</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.429659</span>
|
||||
<span class="n">linear_123</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">4.826197</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.599617</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">35.281532</span>
|
||||
<span class="n">linear_124</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.396383</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.325849</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">17.335875</span>
|
||||
<span class="n">linear_125</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.337198</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.941410</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.221970</span>
|
||||
<span class="n">linear_126</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.699965</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.842878</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">26.224073</span>
|
||||
<span class="n">linear_127</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.775370</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.884215</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.696438</span>
|
||||
<span class="n">linear_129</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.872276</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.837319</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">26.254213</span>
|
||||
<span class="n">linear_128</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.872276</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.180057</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">17.687883</span>
|
||||
<span class="n">linear_130</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">4.150427</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.454298</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">36.765789</span>
|
||||
<span class="n">linear_131</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.112692</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.924847</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">16.025545</span>
|
||||
<span class="n">linear_132</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.852893</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.116593</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.749626</span>
|
||||
<span class="n">linear_133</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.517084</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.024665</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">25.275314</span>
|
||||
<span class="n">linear_134</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.683807</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.878618</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.743618</span>
|
||||
<span class="n">linear_136</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.421055</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">6.322729</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">20.086264</span>
|
||||
<span class="n">linear_135</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.421055</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.309880</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.917679</span>
|
||||
<span class="n">linear_137</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">4.827781</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.744595</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">33.915554</span>
|
||||
<span class="n">linear_138</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">14.422395</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.742882</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">16.402161</span>
|
||||
<span class="n">linear_139</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.527538</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.866123</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.849449</span>
|
||||
<span class="n">linear_140</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.128619</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.657793</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">27.266134</span>
|
||||
<span class="n">linear_141</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.839593</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.845993</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">33.021378</span>
|
||||
<span class="n">linear_143</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.442304</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.099039</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">17.889746</span>
|
||||
<span class="n">linear_142</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.442304</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.325038</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.849592</span>
|
||||
<span class="n">linear_144</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">5.929444</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.618206</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.605080</span>
|
||||
<span class="n">linear_145</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">13.382126</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">9.321095</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">13.625010</span>
|
||||
<span class="n">linear_146</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.894987</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.867645</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.836517</span>
|
||||
<span class="n">linear_147</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.915313</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.906028</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">25.886522</span>
|
||||
<span class="n">linear_148</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.614287</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.908151</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.496181</span>
|
||||
<span class="n">linear_150</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.724932</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.485588</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">28.312899</span>
|
||||
<span class="n">linear_149</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.724932</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.161146</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">24.606939</span>
|
||||
<span class="n">linear_151</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">7.164453</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.847355</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">21.719223</span>
|
||||
<span class="n">linear_152</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">13.086471</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.984121</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">21.222834</span>
|
||||
<span class="n">linear_153</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.099524</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.991601</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">31.816805</span>
|
||||
<span class="n">linear_154</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.054585</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.489706</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">28.286930</span>
|
||||
<span class="n">linear_155</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.389185</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.100321</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.963501</span>
|
||||
<span class="n">linear_157</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.982999</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.154796</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">24.637253</span>
|
||||
<span class="n">linear_156</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.982999</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">8.537706</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">14.875190</span>
|
||||
<span class="n">linear_158</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">8.420287</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">6.502287</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">19.531588</span>
|
||||
<span class="n">linear_159</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">25.014746</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">9.423280</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">13.477261</span>
|
||||
<span class="n">linear_160</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">45.633553</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.715335</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.220921</span>
|
||||
<span class="n">linear_161</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">20.371849</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.117830</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">24.815203</span>
|
||||
<span class="n">linear_162</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.492933</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.126283</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.623318</span>
|
||||
<span class="n">linear_164</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">20.697504</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.825712</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">26.317358</span>
|
||||
<span class="n">linear_163</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">20.697504</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.078367</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">25.008038</span>
|
||||
<span class="n">linear_165</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.023975</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">6.836278</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">18.577358</span>
|
||||
<span class="n">linear_166</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">34.860619</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.259792</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">17.493614</span>
|
||||
<span class="n">linear_167</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">30.380934</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.496160</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.107042</span>
|
||||
<span class="n">linear_168</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">20.691216</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.733317</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">26.831076</span>
|
||||
<span class="n">linear_169</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.723948</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.952728</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.129707</span>
|
||||
<span class="n">linear_171</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">21.034811</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.366547</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.665123</span>
|
||||
<span class="n">linear_170</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">21.034811</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.356277</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.710501</span>
|
||||
<span class="n">linear_172</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.556884</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.729481</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">22.166058</span>
|
||||
<span class="n">linear_173</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">20.033039</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">10.207264</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">12.442120</span>
|
||||
<span class="n">linear_174</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.597379</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.658676</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">47.768131</span>
|
||||
<span class="o">----------</span><span class="n">joiner</span><span class="o">----------</span>
|
||||
<span class="n">linear_2</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">19.293503</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">14.305265</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">8.877850</span>
|
||||
<span class="n">linear_1</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.812222</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">8.766452</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">14.487047</span>
|
||||
<span class="n">linear_3</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">0.999999</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">0.999755</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">127.031174</span>
|
||||
<span class="n">ncnn</span> <span class="n">int8</span> <span class="n">calibration</span> <span class="n">table</span> <span class="n">create</span> <span class="n">success</span><span class="p">,</span> <span class="n">best</span> <span class="n">wish</span> <span class="k">for</span> <span class="n">your</span> <span class="n">int8</span> <span class="n">inference</span> <span class="n">has</span> <span class="n">a</span> <span class="n">low</span> <span class="n">accuracy</span> <span class="n">loss</span><span class="o">...</span>\<span class="p">(</span><span class="o">^</span><span class="mi">0</span><span class="o">^</span><span class="p">)</span><span class="o">/..</span><span class="mf">.233</span><span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>It generates the following two files:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>ls<span class="w"> </span>-lh<span class="w"> </span>encoder-scale-table.txt<span class="w"> </span>joiner-scale-table.txt
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>955K<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">17</span>:28<span class="w"> </span>encoder-scale-table.txt
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>18K<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">17</span>:28<span class="w"> </span>joiner-scale-table.txt
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="admonition caution">
|
||||
<p class="admonition-title">Caution</p>
|
||||
<p>Definitely, you need more calibration data to compute the scale table.</p>
|
||||
</div>
|
||||
<p>Finally, let us use the scale table to quantize our models into <code class="docutils literal notranslate"><span class="pre">int8</span></code>.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ncnn2int8
|
||||
|
||||
usage:<span class="w"> </span>ncnn2int8<span class="w"> </span><span class="o">[</span>inparam<span class="o">]</span><span class="w"> </span><span class="o">[</span>inbin<span class="o">]</span><span class="w"> </span><span class="o">[</span>outparam<span class="o">]</span><span class="w"> </span><span class="o">[</span>outbin<span class="o">]</span><span class="w"> </span><span class="o">[</span>calibration<span class="w"> </span>table<span class="o">]</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>First, we quantize the encoder model:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
|
||||
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
|
||||
|
||||
ncnn2int8<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.int8.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.int8.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./encoder-scale-table.txt
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Next, we quantize the joiner model:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ncnn2int8<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.int8.param<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.int8.bin<span class="w"> </span><span class="se">\</span>
|
||||
<span class="w"> </span>./joiner-scale-table.txt
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The above two commands generate the following 4 files:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>99M<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">17</span>:34<span class="w"> </span>encoder_jit_trace-pnnx.ncnn.int8.bin
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>78K<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">17</span>:34<span class="w"> </span>encoder_jit_trace-pnnx.ncnn.int8.param
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>774K<span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">17</span>:35<span class="w"> </span>joiner_jit_trace-pnnx.ncnn.int8.bin
|
||||
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">496</span><span class="w"> </span>Jan<span class="w"> </span><span class="m">11</span><span class="w"> </span><span class="m">17</span>:35<span class="w"> </span>joiner_jit_trace-pnnx.ncnn.int8.param
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Congratulations! You have successfully quantized your model from <code class="docutils literal notranslate"><span class="pre">float32</span></code> to <code class="docutils literal notranslate"><span class="pre">int8</span></code>.</p>
|
||||
<div class="admonition caution">
|
||||
<p class="admonition-title">Caution</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">ncnn.int8.param</span></code> and <code class="docutils literal notranslate"><span class="pre">ncnn.int8.bin</span></code> must be used in pairs.</p>
|
||||
<p>You can replace <code class="docutils literal notranslate"><span class="pre">ncnn.param</span></code> and <code class="docutils literal notranslate"><span class="pre">ncnn.bin</span></code> with <code class="docutils literal notranslate"><span class="pre">ncnn.int8.param</span></code>
|
||||
and <code class="docutils literal notranslate"><span class="pre">ncnn.int8.bin</span></code> in <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a> if you like.</p>
|
||||
<p>For instance, to use only the <code class="docutils literal notranslate"><span class="pre">int8</span></code> encoder in <code class="docutils literal notranslate"><span class="pre">sherpa-ncnn</span></code>, you can
|
||||
replace the following invocation:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">egs</span><span class="o">/</span><span class="n">librispeech</span><span class="o">/</span><span class="n">ASR</span>
|
||||
<span class="n">cd</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">conv</span><span class="o">-</span><span class="n">emformer</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">07</span><span class="o">-</span><span class="mi">05</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span>
|
||||
|
||||
<span class="n">sherpa</span><span class="o">-</span><span class="n">ncnn</span> \
|
||||
<span class="o">../</span><span class="n">data</span><span class="o">/</span><span class="n">lang_bpe_500</span><span class="o">/</span><span class="n">tokens</span><span class="o">.</span><span class="n">txt</span> \
|
||||
<span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
|
||||
<span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
|
||||
<span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
|
||||
<span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
|
||||
<span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
|
||||
<span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
|
||||
<span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1089</span><span class="o">-</span><span class="mi">134686</span><span class="o">-</span><span class="mf">0001.</span><span class="n">wav</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>with</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">egs</span><span class="o">/</span><span class="n">librispeech</span><span class="o">/</span><span class="n">ASR</span>
|
||||
<span class="n">cd</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">conv</span><span class="o">-</span><span class="n">emformer</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">07</span><span class="o">-</span><span class="mi">05</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span>
|
||||
|
||||
<span class="n">sherpa</span><span class="o">-</span><span class="n">ncnn</span> \
|
||||
<span class="o">../</span><span class="n">data</span><span class="o">/</span><span class="n">lang_bpe_500</span><span class="o">/</span><span class="n">tokens</span><span class="o">.</span><span class="n">txt</span> \
|
||||
<span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">int8</span><span class="o">.</span><span class="n">param</span> \
|
||||
<span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">int8</span><span class="o">.</span><span class="n">bin</span> \
|
||||
<span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
|
||||
<span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
|
||||
<span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
|
||||
<span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
|
||||
<span class="o">../</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1089</span><span class="o">-</span><span class="mi">134686</span><span class="o">-</span><span class="mf">0001.</span><span class="n">wav</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
</div>
|
||||
<p>The following table compares again the file sizes:</p>
|
||||
<table class="docutils align-default">
|
||||
<colgroup>
|
||||
<col style="width: 77%" />
|
||||
<col style="width: 23%" />
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p>File name</p></td>
|
||||
<td><p>File size</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.pt</p></td>
|
||||
<td><p>283 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>decoder_jit_trace-pnnx.pt</p></td>
|
||||
<td><p>1010 KB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>joiner_jit_trace-pnnx.pt</p></td>
|
||||
<td><p>3.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
|
||||
<td><p>142 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>decoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
|
||||
<td><p>503 KB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
|
||||
<td><p>1.5 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
|
||||
<td><p>283 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
|
||||
<td><p>3.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.ncnn.int8.bin</p></td>
|
||||
<td><p>99 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.int8.bin</p></td>
|
||||
<td><p>774 KB</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>You can see that the file sizes of the model after <code class="docutils literal notranslate"><span class="pre">int8</span></code> quantization
|
||||
are much smaller.</p>
|
||||
<div class="admonition hint">
|
||||
<p class="admonition-title">Hint</p>
|
||||
<p>Currently, only linear layers and convolutional layers are quantized
|
||||
with <code class="docutils literal notranslate"><span class="pre">int8</span></code>, so you don’t see an exact <code class="docutils literal notranslate"><span class="pre">4x</span></code> reduction in file sizes.</p>
|
||||
</div>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>You need to test the recognition accuracy after <code class="docutils literal notranslate"><span class="pre">int8</span></code> quantization.</p>
|
||||
</div>
|
||||
<p>You can find the speed comparison at <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn/issues/44">https://github.com/k2-fsa/sherpa-ncnn/issues/44</a>.</p>
|
||||
<p>That’s it! Have fun with <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>!</p>
|
||||
</section>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
BIN
objects.inv
BIN
objects.inv
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Loading…
x
Reference in New Issue
Block a user