More work done on torch
This commit is contained in:
parent
28707b3f1b
commit
703fea46f2
@ -2,7 +2,7 @@ FROM docker.io/nvidia/cuda:11.8.0-devel-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update
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RUN apt-get install -y wget sudo pkg-config libopencv-dev unzip python3-pip
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RUN apt-get install -y wget sudo pkg-config libopencv-dev unzip python3-pip vim
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RUN pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0
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@ -25,30 +25,36 @@ RUN go install || true
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WORKDIR /root
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RUN wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-libtorch.sh
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RUN wget https://git.andr3h3nriqu3s.com/andr3/gotch/raw/commit/22e75becf0432cda41a7c055a4d60ea435f76599/setup-libtorch.sh
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RUN chmod +x setup-libtorch.sh
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ENV CUDA_VER=11.8
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ENV GOTCH_VER=v0.9.1
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ENV GOTCH_VER=v0.9.2
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RUN bash setup-libtorch.sh
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ENV GOTCH_LIBTORCH="/usr/local/lib/libtorch"
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ENV REFRESH_SETUP=0
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ENV LIBRARY_PATH="$LIBRARY_PATH:$GOTCH_LIBTORCH/lib"
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ENV export CPATH="$CPATH:$GOTCH_LIBTORCH/lib:$GOTCH_LIBTORCH/include:$GOTCH_LIBTORCH/include/torch/csrc/api/include"
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ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$GOTCH_LIBTORCH/lib:/usr/lib64-nvidia:/usr/local/cuda-${CUDA_VERSION}/lib64"
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RUN wget https://github.com/sugarme/gotch/releases/download/v0.9.0/setup-gotch.sh
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RUN wget https://git.andr3h3nriqu3s.com/andr3/gotch/raw/branch/master/setup-gotch.sh
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RUN chmod +x setup-gotch.sh
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RUN echo 'root ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
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RUN bash setup-gotch.sh
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RUN ln -s /usr/local/lib/libtorch/include/torch/csrc /usr/local/lib/libtorch/include/torch/csrc/api/include/torch
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RUN mkdir -p /go/pkg/mod/github.com/sugarme/gotch@v0.9.1/libtch/libtorch/include/torch/csrc/api
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RUN find /usr/local/lib/libtorch/include -maxdepth 1 -type d | tail -n +2 | grep -ve 'torch$' | xargs -I{} ln -s {} /go/pkg/mod/github.com/sugarme/gotch@v0.9.1/libtch/libtorch/include
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RUN ln -s /usr/local/lib/libtorch/include/torch/csrc/api/include /go/pkg/mod/github.com/sugarme/gotch@v0.9.1/libtch/libtorch/include/torch/csrc/api/include
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RUN find /usr/local/lib/libtorch/include/torch -maxdepth 1 -type f | xargs -I{} ln -s {} /go/pkg/mod/github.com/sugarme/gotch@v0.9.1/libtch/libtorch/include/torch
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RUN mkdir -p /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include/torch/csrc/api
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RUN find /usr/local/lib/libtorch/include -maxdepth 1 -type d | tail -n +2 | grep -ve 'torch$' | xargs -I{} ln -s {} /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include
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RUN ln -s /usr/local/lib/libtorch/include/torch/csrc/api/include /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include/torch/csrc/api/include
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RUN find /usr/local/lib/libtorch/include/torch -maxdepth 1 -type f | xargs -I{} ln -s {} /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include/torch
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RUN ln -s /usr/local/lib/libtorch/lib/libcudnn.so.8 /usr/local/lib/libcudnn.so
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WORKDIR /app
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ENV CGO_CXXFLAGS="-I/usr/local/lib/libtorch/include/torch/csrc/api/include/ -I/usr/local/lib/libtorch/include"
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ENV CGO_CFLAGS="-I/usr/local/lib/libtorch/include/torch/csrc/api/include/ -I/usr/local/lib/libtorch/include"
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ADD . .
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RUN go install || true
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RUN go build -x || true
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CMD ["bash", "-c", "go run ."]
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2
go.mod
2
go.mod
@ -7,6 +7,7 @@ require (
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github.com/google/uuid v1.6.0
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github.com/lib/pq v1.10.9
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golang.org/x/crypto v0.19.0
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git.andr3h3nriqu3s.com/andr3/gotch v0.9.2
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)
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require (
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@ -32,7 +33,6 @@ require (
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github.com/muesli/termenv v0.15.2 // indirect
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github.com/pkg/errors v0.9.1 // indirect
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github.com/rivo/uniseg v0.4.6 // indirect
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github.com/sugarme/gotch v0.9.1 // indirect
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golang.org/x/exp v0.0.0-20240119083558-1b970713d09a // indirect
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golang.org/x/net v0.21.0 // indirect
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golang.org/x/sync v0.1.0 // indirect
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8
go.sum
8
go.sum
@ -1,3 +1,7 @@
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git.andr3h3nriqu3s.com/andr3/gotch v0.9.1 h1:1q34JKV8cX80n7LXbJswlXCiRtNbzcvJ/vbgb6an1tA=
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git.andr3h3nriqu3s.com/andr3/gotch v0.9.1/go.mod h1:FXusE3CHt8NLf5wynUGaHtIbToRuYifsZaC5EZH0pJY=
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git.andr3h3nriqu3s.com/andr3/gotch v0.9.2 h1:aZcsPgDVGVhrEFoer0upSkzPqJWNMxdUHRktP4s6MSc=
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git.andr3h3nriqu3s.com/andr3/gotch v0.9.2/go.mod h1:FXusE3CHt8NLf5wynUGaHtIbToRuYifsZaC5EZH0pJY=
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github.com/BurntSushi/toml v1.3.2 h1:o7IhLm0Msx3BaB+n3Ag7L8EVlByGnpq14C4YWiu/gL8=
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github.com/BurntSushi/toml v1.3.2/go.mod h1:CxXYINrC8qIiEnFrOxCa7Jy5BFHlXnUU2pbicEuybxQ=
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github.com/aymanbagabas/go-osc52/v2 v2.0.1 h1:HwpRHbFMcZLEVr42D4p7XBqjyuxQH5SMiErDT4WkJ2k=
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@ -70,10 +74,6 @@ github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+
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github.com/stretchr/testify v1.3.0/go.mod h1:M5WIy9Dh21IEIfnGCwXGc5bZfKNJtfHm1UVUgZn+9EI=
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github.com/stretchr/testify v1.6.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
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github.com/stretchr/testify v1.7.0/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
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github.com/sugarme/gotch v0.9.1 h1:J6JCE1C2AfPmM1xk0p46LdzWtfNvbvZZnWdkj9v54jo=
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github.com/sugarme/gotch v0.9.1/go.mod h1:dien16KQcZPg/g+YiEH3q3ldHlKO2//2I2i2Gp5OQcI=
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github.com/wangkuiyi/gotorch v0.0.0-20201028015551-9afed2f3ad7b h1:oJfm5gCGdy9k2Yb+qmMR+HMRQ89CbVDsDi6DD9AZSTk=
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github.com/wangkuiyi/gotorch v0.0.0-20201028015551-9afed2f3ad7b/go.mod h1:WC7g+ojb7tPOZhHI2+ZI7ZXTW7uzF9uFOZfZgIX+SjI=
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github.com/x448/float16 v0.8.4/go.mod h1:14CWIYCyZA/cWjXOioeEpHeN/83MdbZDRQHoFcYsOfg=
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golang.org/x/crypto v0.13.0 h1:mvySKfSWJ+UKUii46M40LOvyWfN0s2U+46/jDd0e6Ck=
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golang.org/x/crypto v0.13.0/go.mod h1:y6Z2r+Rw4iayiXXAIxJIDAJ1zMW4yaTpebo8fPOliYc=
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@ -3,9 +3,9 @@ package imageloader
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import (
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"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
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types "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
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"github.com/sugarme/gotch"
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torch "github.com/sugarme/gotch/ts"
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"github.com/sugarme/gotch/vision"
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"git.andr3h3nriqu3s.com/andr3/gotch"
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torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
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"git.andr3h3nriqu3s.com/andr3/gotch/vision"
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)
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type Dataset struct {
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168
logic/models/train/torch/nn/linear.go
Normal file
168
logic/models/train/torch/nn/linear.go
Normal file
@ -0,0 +1,168 @@
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package my_nn
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// linear is a fully-connected layer
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import (
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"math"
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"git.andr3h3nriqu3s.com/andr3/gotch/nn"
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"git.andr3h3nriqu3s.com/andr3/gotch/ts"
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)
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// LinearConfig is a configuration for a linear layer
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type LinearConfig struct {
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WsInit nn.Init // iniital weights
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BsInit nn.Init // optional initial bias
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Bias bool
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}
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// DefaultLinearConfig creates default LinearConfig with
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// weights initiated using KaimingUniform and Bias is set to true
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func DefaultLinearConfig() *LinearConfig {
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negSlope := math.Sqrt(5)
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return &LinearConfig{
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// NOTE. KaimingUniform cause mem leak due to ts.Uniform()!!!
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// Avoid using it now.
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WsInit: nn.NewKaimingUniformInit(nn.WithKaimingNegativeSlope(negSlope)),
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BsInit: nil,
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Bias: true,
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}
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}
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// Linear is a linear fully-connected layer
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type Linear struct {
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Ws *ts.Tensor
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weight_name string
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Bs *ts.Tensor
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bias_name string
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}
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// NewLinear creates a new linear layer
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// y = x*wT + b
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// inDim - input dimension (x) [input features - columns]
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// outDim - output dimension (y) [output features - columns]
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// NOTE: w will have shape{outDim, inDim}; b will have shape{outDim}
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func NewLinear(vs *Path, inDim, outDim int64, c *LinearConfig) *Linear {
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var bias_name string
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var bs *ts.Tensor
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var err error
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if c.Bias {
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switch {
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case c.BsInit == nil:
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shape := []int64{inDim, outDim}
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fanIn, _, err := nn.CalculateFans(shape)
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or_panic(err)
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bound := 0.0
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if fanIn > 0 {
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bound = 1 / math.Sqrt(float64(fanIn))
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}
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bsInit := nn.NewUniformInit(-bound, bound)
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bs, bias_name, err = vs.NewVarNamed("bias", []int64{outDim}, bsInit)
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or_panic(err)
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// Find better way to do this
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bs, err = bs.T(true)
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or_panic(err)
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bs, err = bs.T(true)
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or_panic(err)
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bs, err = bs.SetRequiresGrad(true, true)
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or_panic(err)
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err = bs.RetainGrad(false)
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or_panic(err)
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vs.varstore.UpdateVarTensor(bias_name, bs, true)
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case c.BsInit != nil:
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bs, bias_name, err = vs.NewVarNamed("bias", []int64{outDim}, c.BsInit)
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or_panic(err)
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}
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}
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ws, weight_name, err := vs.NewVarNamed("weight", []int64{outDim, inDim}, c.WsInit)
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or_panic(err)
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ws, err = ws.T(true)
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or_panic(err)
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ws, err = ws.SetRequiresGrad(true, true)
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or_panic(err)
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err = ws.RetainGrad(false)
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or_panic(err)
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vs.varstore.UpdateVarTensor(weight_name, ws, true)
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return &Linear{
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Ws: ws,
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weight_name: weight_name,
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Bs: bs,
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bias_name: bias_name,
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}
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}
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func (l *Linear) ExtractFromVarstore(vs *VarStore) {
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l.Ws = vs.GetTensorOfVar(l.weight_name)
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l.Bs = vs.GetTensorOfVar(l.bias_name)
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}
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// Implement `Module` for `Linear` struct:
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// =======================================
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// Forward proceeds input node through linear layer.
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// NOTE:
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// - It assumes that node has dimensions of 2 (matrix).
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// To make it work for matrix multiplication, input node should
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// has same number of **column** as number of **column** in
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// `LinearLayer` `Ws` property as weights matrix will be
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// transposed before multiplied to input node. (They are all used `inDim`)
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// - Input node should have shape of `shape{batch size, input features}`.
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// (shape{batchSize, inDim}). The input features is `inDim` while the
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// output feature is `outDim` in `LinearConfig` struct.
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//
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// Example:
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//
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// inDim := 3
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// outDim := 2
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// batchSize := 4
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// weights: 2x3
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// [ 1 1 1
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// 1 1 1 ]
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//
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// input node: 3x4
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// [ 1 1 1
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// 1 1 1
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// 1 1 1
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// 1 1 1 ]
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func (l *Linear) Forward(xs *ts.Tensor) (retVal *ts.Tensor) {
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mul, err := xs.Matmul(l.Ws, false)
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or_panic(err)
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if l.Bs != nil {
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mul, err = mul.Add(l.Bs, false)
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or_panic(err)
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}
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out, err := mul.Relu(false)
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or_panic(err)
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return out
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}
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// ForwardT implements ModuleT interface for Linear layer.
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//
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// NOTE: train param will not be used.
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func (l *Linear) ForwardT(xs *ts.Tensor, train bool) (retVal *ts.Tensor) {
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mul, err := xs.Matmul(l.Ws, true)
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or_panic(err)
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mul, err = mul.Add(l.Bs, true)
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or_panic(err)
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out, err := mul.Relu(true)
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or_panic(err)
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return out
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}
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603
logic/models/train/torch/nn/optimizer.go
Normal file
603
logic/models/train/torch/nn/optimizer.go
Normal file
@ -0,0 +1,603 @@
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package my_nn
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// Optimizers to be used for gradient-descent based training.
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import (
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"fmt"
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"math"
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"github.com/charmbracelet/log"
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"git.andr3h3nriqu3s.com/andr3/gotch/ts"
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)
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// Optimizer is a struct object to run gradient descent.
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type Optimizer struct {
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varstore *VarStore
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opt *ts.COptimizer
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// variablesInOptimizer uint8
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variablesInOptimizer map[string]struct{}
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config OptimizerConfig //interface{}
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stepCount int
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lr float64
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}
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func (o *Optimizer) Debug() {
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for n, _ := range o.variablesInOptimizer {
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v := o.varstore.GetVarOfName(n)
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leaf, err := v.Tensor.IsLeaf(false)
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or_panic(err)
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retains, err := v.Tensor.RetainsGrad(false)
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or_panic(err)
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log.Info("[opt] var test", "n", n, "leaf", leaf, "retains", retains)
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}
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}
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func (o *Optimizer) RefreshValues() (err error) {
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opt, err := o.config.buildCOpt(o.lr)
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if err != nil {
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return
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}
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for name := range o.variablesInOptimizer {
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v := o.varstore.GetVarOfName(name)
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if v.Trainable {
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if err = opt.AddParameter(v.Tensor, v.Group); err != nil {
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err = fmt.Errorf("Optimizer defaultBuild - AddParameter failed: %w\n", err)
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return
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}
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}
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}
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o.opt = opt
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return
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}
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// OptimizerConfig defines Optimizer configurations. These configs can be used to build optimizer.
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type OptimizerConfig interface {
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buildCOpt(lr float64) (*ts.COptimizer, error)
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// Build builds an optimizer with the specified learning rate handling variables stored in `vs`.
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//
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// NOTE: Build is a 'default' method. It can be called by wrapping
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// 'DefaultBuild' function
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// E.g. AdamOptimizerConfig struct have a method to fullfil `Build` method of
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// OptimizerConfig by wrapping `DefaultBuild` like
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// (config AdamOptimizerConfig) Build(vs VarStore, lr float64) (retVal Optimizer, err error){
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// return defaultBuild(config, vs, lr)
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// }
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Build(vs *VarStore, lr float64) (*Optimizer, error)
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}
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// defaultBuild is `default` Build method for OptimizerConfig interface
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func defaultBuild(config OptimizerConfig, vs *VarStore, lr float64) (*Optimizer, error) {
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opt, err := config.buildCOpt(lr)
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if err != nil {
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return nil, err
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}
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names := make(map[string]struct{})
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for name, v := range vs.vars {
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if v.Trainable {
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log.Info("Adding parameter", "name", name, "g", v.Group)
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if err = opt.AddParameter(v.Tensor, v.Group); err != nil {
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err = fmt.Errorf("Optimizer defaultBuild - AddParameter failed: %w\n", err)
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return nil, err
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}
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}
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names[name] = struct{}{}
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}
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return &Optimizer{
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varstore: vs,
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opt: opt,
|
||||
variablesInOptimizer: names,
|
||||
config: config,
|
||||
stepCount: 0,
|
||||
lr: 0,
|
||||
}, nil
|
||||
}
|
||||
|
||||
// SGD Optimizer:
|
||||
//===============
|
||||
|
||||
// SGDConfig holds parameters for building the SGD (Stochastic Gradient Descent) optimizer.
|
||||
type SGDConfig struct {
|
||||
Momentum float64
|
||||
Dampening float64
|
||||
Wd float64
|
||||
Nesterov bool
|
||||
}
|
||||
|
||||
// DefaultSGDConfig creates SGDConfig with default values.
|
||||
func DefaultSGDConfig() *SGDConfig {
|
||||
return &SGDConfig{
|
||||
Momentum: 0.0,
|
||||
Dampening: 0.0,
|
||||
Wd: 0.0,
|
||||
Nesterov: false,
|
||||
}
|
||||
}
|
||||
|
||||
// NewSGD creates the configuration for a SGD optimizer with specified values
|
||||
func NewSGDConfig(momentum, dampening, wd float64, nesterov bool) *SGDConfig {
|
||||
return &SGDConfig{
|
||||
Momentum: momentum,
|
||||
Dampening: dampening,
|
||||
Wd: wd,
|
||||
Nesterov: nesterov,
|
||||
}
|
||||
}
|
||||
|
||||
// Implement OptimizerConfig interface for SGDConfig
|
||||
func (c *SGDConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
|
||||
return ts.Sgd(lr, c.Momentum, c.Dampening, c.Wd, c.Nesterov)
|
||||
}
|
||||
|
||||
func (c *SGDConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
|
||||
return defaultBuild(c, vs, lr)
|
||||
}
|
||||
|
||||
// Adam optimizer:
|
||||
// ===============
|
||||
|
||||
type AdamConfig struct {
|
||||
Beta1 float64
|
||||
Beta2 float64
|
||||
Wd float64
|
||||
}
|
||||
|
||||
// DefaultAdamConfig creates AdamConfig with default values
|
||||
func DefaultAdamConfig() *AdamConfig {
|
||||
return &AdamConfig{
|
||||
Beta1: 0.9,
|
||||
Beta2: 0.999,
|
||||
Wd: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
// NewAdamConfig creates AdamConfig with specified values
|
||||
func NewAdamConfig(beta1, beta2, wd float64) *AdamConfig {
|
||||
return &AdamConfig{
|
||||
Beta1: beta1,
|
||||
Beta2: beta2,
|
||||
Wd: wd,
|
||||
}
|
||||
}
|
||||
|
||||
// Implement OptimizerConfig interface for AdamConfig
|
||||
func (c *AdamConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
|
||||
return ts.Adam(lr, c.Beta1, c.Beta2, c.Wd)
|
||||
}
|
||||
|
||||
func (c *AdamConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
|
||||
return defaultBuild(c, vs, lr)
|
||||
}
|
||||
|
||||
// AdamW optimizer:
|
||||
// ===============
|
||||
|
||||
type AdamWConfig struct {
|
||||
Beta1 float64
|
||||
Beta2 float64
|
||||
Wd float64
|
||||
}
|
||||
|
||||
// DefaultAdamWConfig creates AdamWConfig with default values
|
||||
func DefaultAdamWConfig() *AdamWConfig {
|
||||
return &AdamWConfig{
|
||||
Beta1: 0.9,
|
||||
Beta2: 0.999,
|
||||
Wd: 0.01,
|
||||
}
|
||||
}
|
||||
|
||||
// NewAdamWConfig creates AdamWConfig with specified values
|
||||
func NewAdamWConfig(beta1, beta2, wd float64) *AdamWConfig {
|
||||
return &AdamWConfig{
|
||||
Beta1: beta1,
|
||||
Beta2: beta2,
|
||||
Wd: wd,
|
||||
}
|
||||
}
|
||||
|
||||
// Implement OptimizerConfig interface for AdamWConfig
|
||||
func (c *AdamWConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
|
||||
return ts.AdamW(lr, c.Beta1, c.Beta2, c.Wd)
|
||||
}
|
||||
|
||||
// Build builds AdamW optimizer
|
||||
func (c *AdamWConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
|
||||
return defaultBuild(c, vs, lr)
|
||||
}
|
||||
|
||||
// RMSProp optimizer:
|
||||
// ===============
|
||||
|
||||
type RMSPropConfig struct {
|
||||
Alpha float64
|
||||
Eps float64
|
||||
Wd float64
|
||||
Momentum float64
|
||||
Centered bool
|
||||
}
|
||||
|
||||
// DefaultAdamConfig creates AdamConfig with default values
|
||||
func DefaultRMSPropConfig() *RMSPropConfig {
|
||||
return &RMSPropConfig{
|
||||
Alpha: 0.99,
|
||||
Eps: 1e-8,
|
||||
Wd: 0.0,
|
||||
Momentum: 0.0,
|
||||
Centered: false,
|
||||
}
|
||||
}
|
||||
|
||||
// NewRMSPropConfig creates RMSPropConfig with specified values
|
||||
func NewRMSPropConfig(alpha, eps, wd, momentum float64, centered bool) *RMSPropConfig {
|
||||
return &RMSPropConfig{
|
||||
Alpha: alpha,
|
||||
Eps: eps,
|
||||
Wd: wd,
|
||||
Momentum: momentum,
|
||||
Centered: centered,
|
||||
}
|
||||
}
|
||||
|
||||
// Implement OptimizerConfig interface for RMSPropConfig
|
||||
func (c *RMSPropConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
|
||||
return ts.RmsProp(lr, c.Alpha, c.Eps, c.Wd, c.Momentum, c.Centered)
|
||||
}
|
||||
|
||||
func (c *RMSPropConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
|
||||
return defaultBuild(c, vs, lr)
|
||||
}
|
||||
|
||||
// Optimizer methods:
|
||||
// ==================
|
||||
|
||||
func (opt *Optimizer) addMissingVariables() {
|
||||
type param struct {
|
||||
tensor *ts.Tensor
|
||||
group uint
|
||||
}
|
||||
trainables := make(map[string]param)
|
||||
for name, v := range opt.varstore.vars {
|
||||
if v.Trainable {
|
||||
trainables[name] = param{tensor: v.Tensor, group: v.Group}
|
||||
}
|
||||
}
|
||||
missingVariables := len(trainables) - len(opt.variablesInOptimizer)
|
||||
if missingVariables > 0 {
|
||||
log.Info("INFO: Optimizer.addMissingVariables()...")
|
||||
for name, x := range trainables {
|
||||
if _, ok := opt.variablesInOptimizer[name]; !ok {
|
||||
opt.opt.AddParameter(x.tensor, x.group)
|
||||
opt.variablesInOptimizer[name] = struct{}{}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ZeroGrad zeroes the gradient for the tensors tracked by this optimizer.
|
||||
func (opt *Optimizer) ZeroGrad() error {
|
||||
if err := opt.opt.ZeroGrad(); err != nil {
|
||||
err = fmt.Errorf("Optimizer.ZeroGrad() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
// MustZeroGrad zeroes the gradient for the tensors tracked by this optimizer.
|
||||
func (opt *Optimizer) MustZeroGrad() {
|
||||
err := opt.ZeroGrad()
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
}
|
||||
|
||||
// Clips gradient value at some specified maximum value.
|
||||
func (opt *Optimizer) ClipGradValue(max float64) {
|
||||
opt.varstore.Lock()
|
||||
defer opt.varstore.Unlock()
|
||||
|
||||
for _, v := range opt.varstore.vars {
|
||||
if v.Trainable {
|
||||
// v.Tensor.MustGrad().Clamp_(ts.FloatScalar(-max), ts.FloatScalar(max))
|
||||
gradTs := v.Tensor.MustGrad(false)
|
||||
gradTs.Clamp_(ts.FloatScalar(-max), ts.FloatScalar(max))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Step performs an optimization step, updating the tracked tensors based on their gradients.
|
||||
func (opt *Optimizer) Step() error {
|
||||
err := opt.opt.Step()
|
||||
if err != nil {
|
||||
err = fmt.Errorf("Optimizer.Step() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
opt.stepCount += 1
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
// MustStep performs an optimization step, updating the tracked tensors based on their gradients.
|
||||
func (opt *Optimizer) MustStep() {
|
||||
err := opt.Step()
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
}
|
||||
|
||||
// ResetStepCount set step count to zero.
|
||||
func (opt *Optimizer) ResetStepCount() {
|
||||
opt.stepCount = 0
|
||||
}
|
||||
|
||||
// StepCount get current step count.
|
||||
func (opt *Optimizer) StepCount() int {
|
||||
return opt.stepCount
|
||||
}
|
||||
|
||||
// BackwardStep applies a backward step pass, update the gradients, and performs an optimization step.
|
||||
func (opt *Optimizer) BackwardStep(loss *ts.Tensor) error {
|
||||
err := opt.opt.ZeroGrad()
|
||||
if err != nil {
|
||||
err = fmt.Errorf("Optimizer.BackwardStep() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
|
||||
loss.MustBackward()
|
||||
err = opt.opt.Step()
|
||||
if err != nil {
|
||||
err = fmt.Errorf("Optimizer.BackwardStep() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
// MustBackwardStep applies a backward step pass, update the gradients, and performs an optimization step.
|
||||
func (opt *Optimizer) MustBackwardStep(loss *ts.Tensor) {
|
||||
err := opt.BackwardStep(loss)
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
}
|
||||
|
||||
// BackwardStepClip applies a backward step pass, update the gradients, and performs an optimization step.
|
||||
//
|
||||
// The gradients are clipped based on `max` before being applied.
|
||||
func (opt *Optimizer) BackwardStepClip(loss *ts.Tensor, max float64) error {
|
||||
err := opt.opt.ZeroGrad()
|
||||
if err != nil {
|
||||
err = fmt.Errorf("Optimizer.BackwardStepClip() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
loss.MustBackward()
|
||||
opt.ClipGradValue(max)
|
||||
err = opt.opt.Step()
|
||||
if err != nil {
|
||||
err = fmt.Errorf("Optimizer.BackwardStepClip() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
// MustBackwardStepClip applies a backward step pass, update the gradients, and performs an optimization step.
|
||||
//
|
||||
// The gradients are clipped based on `max` before being applied.
|
||||
func (opt *Optimizer) MustBackwardStepClip(loss *ts.Tensor, max float64) {
|
||||
err := opt.BackwardStepClip(loss, max)
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
}
|
||||
|
||||
type ClipOpts struct {
|
||||
NormType float64
|
||||
ErrorIfNonFinite bool
|
||||
}
|
||||
|
||||
type ClipOpt func(*ClipOpts)
|
||||
|
||||
func defaultClipOpts() *ClipOpts {
|
||||
return &ClipOpts{
|
||||
NormType: 2.0,
|
||||
ErrorIfNonFinite: false, // will switch to "true" in the future.
|
||||
}
|
||||
}
|
||||
|
||||
func WithNormType(v float64) ClipOpt {
|
||||
return func(o *ClipOpts) {
|
||||
o.NormType = v
|
||||
}
|
||||
}
|
||||
|
||||
func WithErrorIfNonFinite(v bool) ClipOpt {
|
||||
return func(o *ClipOpts) {
|
||||
o.ErrorIfNonFinite = v
|
||||
}
|
||||
}
|
||||
|
||||
// / Clips gradient L2 norm over all trainable parameters.
|
||||
//
|
||||
// The norm is computed over all gradients together, as if they were
|
||||
// concatenated into a single vector.
|
||||
//
|
||||
// / Args:
|
||||
// - max: max norm of the gradient
|
||||
// - o.NormType. Type of the used p-norm, can be "inf" for infinity norm. Default= 2.0
|
||||
// - o.ErrorIfNonFinite bool. If true, throw error if total norm of the gradients from paramters is "nan", "inf" or "-inf". Default=false
|
||||
// Returns: total norm of the parameters (viewed as a single vector)
|
||||
// ref. https://github.com/pytorch/pytorch/blob/cb4aeff7d8e4c70bb638cf159878c5204d0cc2da/torch/nn/utils/clip_grad.py#L59
|
||||
func (opt *Optimizer) ClipGradNorm(max float64, opts ...ClipOpt) error {
|
||||
o := defaultClipOpts()
|
||||
for _, option := range opts {
|
||||
option(o)
|
||||
}
|
||||
|
||||
opt.varstore.Lock()
|
||||
defer opt.varstore.Unlock()
|
||||
parameters := opt.varstore.TrainableVariables()
|
||||
if len(parameters) == 0 {
|
||||
// return ts.MustOfSlice([]float64{0.0}), nil
|
||||
return nil
|
||||
}
|
||||
|
||||
var (
|
||||
norms []*ts.Tensor
|
||||
totalNorm *ts.Tensor
|
||||
)
|
||||
|
||||
device := opt.varstore.device
|
||||
|
||||
// FIXME. What about mixed-precision?
|
||||
dtype := parameters[0].DType()
|
||||
|
||||
if o.NormType == math.Inf(1) {
|
||||
for _, v := range opt.varstore.vars {
|
||||
n := v.Tensor.MustGrad(false).MustDetach(true).MustAbs(true).MustMax(true).MustTo(device, true)
|
||||
norms = append(norms, n)
|
||||
}
|
||||
// total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
|
||||
totalNorm = ts.MustStack(norms, 0).MustMax(true)
|
||||
} else {
|
||||
for _, v := range opt.varstore.vars {
|
||||
// x := v.Tensor.MustGrad(false).MustNorm(true)
|
||||
|
||||
// NOTE. tensor.Norm() is going to be deprecated. So use linalg_norm
|
||||
// Ref. https://pytorch.org/docs/stable/generated/torch.linalg.norm.html#torch.linalg.norm
|
||||
x := v.Tensor.MustGrad(false).MustDetach(true).MustLinalgNorm(ts.FloatScalar(o.NormType), nil, false, dtype, true)
|
||||
norms = append(norms, x)
|
||||
}
|
||||
}
|
||||
|
||||
// totalNorm = ts.MustStack(norms, 0).MustNorm(true).MustAddScalar(ts.FloatScalar(1e-6), true)
|
||||
// total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
|
||||
totalNorm = ts.MustStack(norms, 0).MustLinalgNorm(ts.FloatScalar(o.NormType), nil, false, dtype, true)
|
||||
for _, x := range norms {
|
||||
x.MustDrop()
|
||||
}
|
||||
|
||||
totalNormVal := totalNorm.Float64Values(true)[0]
|
||||
// if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
|
||||
if o.ErrorIfNonFinite && (math.IsNaN(totalNormVal) || math.IsInf(totalNormVal, 1)) {
|
||||
err := fmt.Errorf("The total norm of order (%v) for gradients from 'parameters' is non-finite, so it cannot be clipped. To disable this error and scale the gradients by the non-finite norm anyway, set option.ErrorIfNonFinite= false", o.NormType)
|
||||
return err
|
||||
}
|
||||
|
||||
// clip_coef = max_norm / (total_norm + 1e-6)
|
||||
// clipCoefTs := ts.TensorFrom([]float64{max}).MustDiv(totalNorm, true)
|
||||
clipCoef := max / (totalNormVal + 1e-6)
|
||||
// NOTE: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
|
||||
// avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
|
||||
// when the gradients do not reside in CPU memory.
|
||||
// clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
|
||||
if clipCoef > 1.0 {
|
||||
clipCoef = 1.0
|
||||
}
|
||||
for _, v := range opt.varstore.vars {
|
||||
if v.Trainable {
|
||||
// p.grad.detach().mul_(clip_coef_clamped.to(p.grad.device))
|
||||
// v.Tensor.MustGrad(false).MustDetach(true).MustMulScalar_(ts.FloatScalar(clipCoef))
|
||||
v.Tensor.MustGrad(false).MustMulScalar_(ts.FloatScalar(clipCoef))
|
||||
}
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
// BackwardStepClipNorm applies a backward step pass, update the gradients, and performs an optimization step.
|
||||
//
|
||||
// The gradients L2 norm is clipped based on `max`.
|
||||
func (opt *Optimizer) BackwardStepClipNorm(loss *ts.Tensor, max float64, opts ...ClipOpt) error {
|
||||
err := opt.opt.ZeroGrad()
|
||||
if err != nil {
|
||||
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
err = loss.Backward()
|
||||
if err != nil {
|
||||
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
|
||||
err = opt.ClipGradNorm(max, opts...)
|
||||
if err != nil {
|
||||
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
|
||||
err = opt.Step()
|
||||
if err != nil {
|
||||
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
|
||||
return err
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
// MustBackwardStepClipNorm applies a backward step pass, update the gradients, and performs an optimization step.
|
||||
//
|
||||
// The gradients L2 norm is clipped based on `max`.
|
||||
func (opt *Optimizer) MustBackwardStepClipNorm(loss *ts.Tensor, max float64, opts ...ClipOpt) {
|
||||
err := opt.BackwardStepClipNorm(loss, max, opts...)
|
||||
if err != nil {
|
||||
log.Fatal(err)
|
||||
}
|
||||
}
|
||||
|
||||
// SetLR sets the optimizer learning rate.
|
||||
//
|
||||
// NOTE. it sets a SINGLE value of learning rate for all parameter groups.
|
||||
// Most of the time, there's one parameter group.
|
||||
func (opt *Optimizer) SetLR(lr float64) {
|
||||
err := opt.opt.SetLearningRate(lr)
|
||||
if err != nil {
|
||||
log.Fatalf("Optimizer - SetLR method call error: %v\n", err)
|
||||
}
|
||||
}
|
||||
|
||||
func (opt *Optimizer) GetLRs() []float64 {
|
||||
lrs, err := opt.opt.GetLearningRates()
|
||||
if err != nil {
|
||||
log.Fatalf("Optimizer - GetLRs method call error: %v\n", err)
|
||||
}
|
||||
|
||||
return lrs
|
||||
}
|
||||
|
||||
// SetLRs sets learning rates for ALL parameter groups respectively.
|
||||
func (opt *Optimizer) SetLRs(lrs []float64) {
|
||||
err := opt.opt.SetLearningRates(lrs)
|
||||
if err != nil {
|
||||
log.Fatalf("Optimizer - SetLRs method call error: %v\n", err)
|
||||
}
|
||||
}
|
||||
|
||||
// SetMomentum sets the optimizer momentum.
|
||||
func (opt *Optimizer) SetMomentum(m float64) {
|
||||
err := opt.opt.SetMomentum(m)
|
||||
if err != nil {
|
||||
log.Fatalf("Optimizer - SetMomentum method call error: %v\n", err)
|
||||
}
|
||||
}
|
||||
|
||||
func (opt *Optimizer) ParamGroupNum() int {
|
||||
ngroup, err := opt.opt.ParamGroupNum()
|
||||
if err != nil {
|
||||
log.Fatalf("Optimizer - ParamGroupNum method call error: %v\n", err)
|
||||
}
|
||||
|
||||
return int(ngroup)
|
||||
}
|
||||
|
||||
func (opt *Optimizer) AddParamGroup(tensors []*ts.Tensor) {
|
||||
err := opt.opt.AddParamGroup(tensors)
|
||||
if err != nil {
|
||||
log.Fatalf("Optimizer - ParamGroupNum method call error: %v\n", err)
|
||||
}
|
||||
}
|
||||
17
logic/models/train/torch/nn/utils.go
Normal file
17
logic/models/train/torch/nn/utils.go
Normal file
@ -0,0 +1,17 @@
|
||||
package my_nn
|
||||
|
||||
import (
|
||||
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
|
||||
)
|
||||
|
||||
func or_panic(err error) {
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
}
|
||||
|
||||
type MyLayer interface {
|
||||
torch.ModuleT
|
||||
|
||||
ExtractFromVarstore(vs *VarStore)
|
||||
}
|
||||
1359
logic/models/train/torch/nn/varstore.go
Normal file
1359
logic/models/train/torch/nn/varstore.go
Normal file
File diff suppressed because it is too large
Load Diff
@ -2,14 +2,12 @@ package train
|
||||
|
||||
import (
|
||||
types "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
|
||||
my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
|
||||
|
||||
"git.andr3h3nriqu3s.com/andr3/gotch"
|
||||
"github.com/charmbracelet/log"
|
||||
"github.com/sugarme/gotch"
|
||||
"github.com/sugarme/gotch/nn"
|
||||
|
||||
//"github.com/sugarme/gotch"
|
||||
//"github.com/sugarme/gotch/vision"
|
||||
torch "github.com/sugarme/gotch/ts"
|
||||
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
|
||||
)
|
||||
|
||||
type IForwardable interface {
|
||||
@ -18,23 +16,55 @@ type IForwardable interface {
|
||||
|
||||
// Container for a model
|
||||
type ContainerModel struct {
|
||||
Seq *nn.SequentialT
|
||||
Vs *nn.VarStore
|
||||
Layers []my_nn.MyLayer
|
||||
Vs *my_nn.VarStore
|
||||
path *my_nn.Path
|
||||
}
|
||||
|
||||
func (n *ContainerModel) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
|
||||
return n.Seq.ForwardT(x, train)
|
||||
if len(n.Layers) == 0 {
|
||||
return x.MustShallowClone()
|
||||
}
|
||||
|
||||
if len(n.Layers) == 1 {
|
||||
return n.Layers[0].ForwardT(x, train)
|
||||
}
|
||||
|
||||
// forward sequentially
|
||||
outs := make([]*torch.Tensor, len(n.Layers))
|
||||
for i := 0; i < len(n.Layers); i++ {
|
||||
if i == 0 {
|
||||
outs[0] = n.Layers[i].ForwardT(x, train)
|
||||
defer outs[0].MustDrop()
|
||||
} else if i == len(n.Layers)-1 {
|
||||
return n.Layers[i].ForwardT(outs[i-1], train)
|
||||
} else {
|
||||
outs[i] = n.Layers[i].ForwardT(outs[i-1], train)
|
||||
defer outs[i].MustDrop()
|
||||
}
|
||||
}
|
||||
panic("Do not reach here")
|
||||
}
|
||||
|
||||
func (n *ContainerModel) To(device gotch.Device) {
|
||||
n.Vs.ToDevice(device)
|
||||
for _, layer := range n.Layers {
|
||||
layer.ExtractFromVarstore(n.Vs)
|
||||
}
|
||||
}
|
||||
|
||||
func (n *ContainerModel) Refresh() {
|
||||
for _, layer := range n.Layers {
|
||||
layer.ExtractFromVarstore(n.Vs)
|
||||
}
|
||||
}
|
||||
|
||||
func BuildModel(layers []*types.Layer, _lastLinearSize int64, addSigmoid bool) *ContainerModel {
|
||||
|
||||
base_vs := nn.NewVarStore(gotch.CPU)
|
||||
base_vs := my_nn.NewVarStore(gotch.CPU)
|
||||
vs := base_vs.Root()
|
||||
seq := nn.SeqT()
|
||||
|
||||
m_layers := []my_nn.MyLayer{}
|
||||
|
||||
var lastLinearSize int64 = _lastLinearSize
|
||||
lastLinearConv := []int64{}
|
||||
@ -46,18 +76,19 @@ func BuildModel(layers []*types.Layer, _lastLinearSize int64, addSigmoid bool) *
|
||||
} else if layer.LayerType == types.LAYER_DENSE {
|
||||
shape := layer.GetShape()
|
||||
log.Info("New Dense: ", "In:", lastLinearSize, "out:", shape[0])
|
||||
seq.Add(NewLinear(vs, lastLinearSize, shape[0]))
|
||||
m_layers = append(m_layers, NewLinear(vs, lastLinearSize, shape[0]))
|
||||
lastLinearSize = shape[0]
|
||||
} else if layer.LayerType == types.LAYER_FLATTEN {
|
||||
seq.Add(NewFlatten())
|
||||
m_layers = append(m_layers, NewFlatten())
|
||||
lastLinearSize = 1
|
||||
for _, i := range lastLinearConv {
|
||||
lastLinearSize *= i
|
||||
}
|
||||
log.Info("Flatten: ", "In:", lastLinearConv, "out:", lastLinearSize)
|
||||
} else if layer.LayerType == types.LAYER_SIMPLE_BLOCK {
|
||||
panic("TODO")
|
||||
log.Info("New Block: ", "In:", lastLinearConv, "out:", []int64{lastLinearConv[1] / 2, lastLinearConv[2] / 2, 128})
|
||||
seq.Add(NewSimpleBlock(vs, lastLinearConv[0]))
|
||||
//m_layers = append(m_layers, NewSimpleBlock(vs, lastLinearConv[0]))
|
||||
lastLinearConv[0] = 128
|
||||
lastLinearConv[1] /= 2
|
||||
lastLinearConv[2] /= 2
|
||||
@ -65,12 +96,13 @@ func BuildModel(layers []*types.Layer, _lastLinearSize int64, addSigmoid bool) *
|
||||
}
|
||||
|
||||
if addSigmoid {
|
||||
seq.Add(NewSigmoid())
|
||||
m_layers = append(m_layers, NewSigmoid())
|
||||
}
|
||||
|
||||
b := &ContainerModel{
|
||||
Seq: seq,
|
||||
Layers: m_layers,
|
||||
Vs: base_vs,
|
||||
path: vs,
|
||||
}
|
||||
return b
|
||||
}
|
||||
|
||||
@ -1,10 +1,14 @@
|
||||
package train
|
||||
|
||||
import (
|
||||
"unsafe"
|
||||
|
||||
my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
|
||||
|
||||
"github.com/charmbracelet/log"
|
||||
|
||||
"github.com/sugarme/gotch/nn"
|
||||
torch "github.com/sugarme/gotch/ts"
|
||||
"git.andr3h3nriqu3s.com/andr3/gotch/nn"
|
||||
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
|
||||
)
|
||||
|
||||
func or_panic(err error) {
|
||||
@ -19,7 +23,9 @@ type SimpleBlock struct {
|
||||
}
|
||||
|
||||
// BasicBlock returns a BasicBlockModule instance
|
||||
func NewSimpleBlock(vs *nn.Path, inplanes int64) *SimpleBlock {
|
||||
func NewSimpleBlock(_vs *my_nn.Path, inplanes int64) *SimpleBlock {
|
||||
vs := (*nn.Path)(unsafe.Pointer(_vs))
|
||||
|
||||
conf1 := nn.DefaultConv2DConfig()
|
||||
conf1.Stride = []int64{2, 2}
|
||||
|
||||
@ -85,40 +91,11 @@ func (b *SimpleBlock) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
|
||||
return out
|
||||
}
|
||||
|
||||
type MyLinear struct {
|
||||
FC1 *nn.Linear
|
||||
}
|
||||
|
||||
// BasicBlock returns a BasicBlockModule instance
|
||||
func NewLinear(vs *nn.Path, in, out int64) *MyLinear {
|
||||
config := nn.DefaultLinearConfig()
|
||||
b := &MyLinear{
|
||||
FC1: nn.NewLinear(vs, in, out, config),
|
||||
}
|
||||
return b
|
||||
}
|
||||
|
||||
// Forward method
|
||||
func (b *MyLinear) Forward(x *torch.Tensor) *torch.Tensor {
|
||||
var err error
|
||||
|
||||
out := b.FC1.Forward(x)
|
||||
|
||||
out, err = out.Relu(false)
|
||||
or_panic(err)
|
||||
|
||||
return out
|
||||
}
|
||||
|
||||
func (b *MyLinear) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
|
||||
var err error
|
||||
|
||||
out := b.FC1.ForwardT(x, train)
|
||||
|
||||
out, err = out.Relu(false)
|
||||
or_panic(err)
|
||||
|
||||
return out
|
||||
func NewLinear(vs *my_nn.Path, in, out int64) *my_nn.Linear {
|
||||
config := my_nn.DefaultLinearConfig()
|
||||
return my_nn.NewLinear(vs, in, out, config)
|
||||
}
|
||||
|
||||
type Flatten struct{}
|
||||
@ -128,6 +105,9 @@ func NewFlatten() *Flatten {
|
||||
return &Flatten{}
|
||||
}
|
||||
|
||||
// The flatten layer does not to move anything to the device
|
||||
func (b *Flatten) ExtractFromVarstore(vs *my_nn.VarStore) {}
|
||||
|
||||
// Forward method
|
||||
func (b *Flatten) Forward(x *torch.Tensor) *torch.Tensor {
|
||||
|
||||
@ -151,6 +131,9 @@ func NewSigmoid() *Sigmoid {
|
||||
return &Sigmoid{}
|
||||
}
|
||||
|
||||
// The sigmoid layer does not need to move anything to another device
|
||||
func (b *Sigmoid) ExtractFromVarstore(vs *my_nn.VarStore) {}
|
||||
|
||||
func (b *Sigmoid) Forward(x *torch.Tensor) *torch.Tensor {
|
||||
out, err := x.Sigmoid(false)
|
||||
or_panic(err)
|
||||
|
||||
@ -16,16 +16,17 @@ import (
|
||||
|
||||
"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
|
||||
. "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
|
||||
|
||||
my_torch "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
|
||||
modelloader "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/modelloader"
|
||||
my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
|
||||
. "git.andr3h3nriqu3s.com/andr3/fyp/logic/tasks/utils"
|
||||
. "git.andr3h3nriqu3s.com/andr3/fyp/logic/utils"
|
||||
|
||||
"git.andr3h3nriqu3s.com/andr3/gotch"
|
||||
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
|
||||
"github.com/charmbracelet/log"
|
||||
"github.com/goccy/go-json"
|
||||
"github.com/sugarme/gotch"
|
||||
"github.com/sugarme/gotch/nn"
|
||||
torch "github.com/sugarme/gotch/ts"
|
||||
)
|
||||
|
||||
const EPOCH_PER_RUN = 20
|
||||
@ -132,11 +133,12 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
|
||||
}
|
||||
|
||||
model = my_torch.BuildModel(layers, 0, true)
|
||||
|
||||
}
|
||||
|
||||
// TODO Make the runner provide this
|
||||
// device := gotch.CudaIfAvailable()
|
||||
device := gotch.CPU
|
||||
device := gotch.CudaIfAvailable()
|
||||
// device := gotch.CPU
|
||||
|
||||
result_path := path.Join(getDir(), "savedData", m.Id, "defs", def.Id)
|
||||
err = os.MkdirAll(result_path, os.ModePerm)
|
||||
@ -144,6 +146,16 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
|
||||
return
|
||||
}
|
||||
|
||||
/* opt1, err := my_nn.DefaultAdamConfig().Build(model.Vs, 0.001)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
opt1.Debug() */
|
||||
|
||||
//log.Info("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
|
||||
|
||||
// TODO remove this
|
||||
model.To(device)
|
||||
defer model.To(gotch.CPU)
|
||||
|
||||
@ -153,19 +165,14 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
|
||||
return
|
||||
}
|
||||
|
||||
err = ds.To(device)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
opt, err := nn.DefaultAdamConfig().Build(model.Vs, 0.001)
|
||||
opt, err := my_nn.DefaultAdamConfig().Build(model.Vs, 0.001)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
for epoch := 0; epoch < EPOCH_PER_RUN; epoch++ {
|
||||
var trainIter *torch.Iter2
|
||||
trainIter, err = ds.TrainIter(64)
|
||||
trainIter, err = ds.TrainIter(32)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
@ -184,15 +191,45 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
|
||||
continue
|
||||
}
|
||||
|
||||
pred := model.ForwardT(item.Data, true)
|
||||
|
||||
// Calculate loss
|
||||
|
||||
loss, err = pred.BinaryCrossEntropyWithLogits(item.Label, &torch.Tensor{}, &torch.Tensor{}, 1, false)
|
||||
data := item.Data
|
||||
data, err = data.ToDevice(device, gotch.Float, false, true, false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
data, err = data.SetRequiresGrad(true, true)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
err = data.RetainGrad(false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
pred := model.ForwardT(data, true)
|
||||
pred, err = pred.SetRequiresGrad(true, true)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
pred.RetainGrad(false)
|
||||
|
||||
label := item.Label
|
||||
label, err = label.ToDevice(device, gotch.Float, false, true, false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
label, err = label.SetRequiresGrad(true, true)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
label.RetainGrad(false)
|
||||
|
||||
// Calculate loss
|
||||
loss, err = pred.BinaryCrossEntropyWithLogits(label, &torch.Tensor{}, &torch.Tensor{}, 1, false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
loss, err = loss.SetRequiresGrad(true, false)
|
||||
if err != nil {
|
||||
return
|
||||
@ -213,11 +250,32 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
|
||||
return
|
||||
}
|
||||
|
||||
vars := model.Vs.Variables()
|
||||
|
||||
for k, v := range vars {
|
||||
var grad *torch.Tensor
|
||||
grad, err = v.Grad(false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
grad, err = grad.Abs(false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
grad, err = grad.Max(false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
log.Info("[grad check]", "k", k, "grad", grad.Float64Values())
|
||||
}
|
||||
|
||||
trainLoss = loss.Float64Values()[0]
|
||||
|
||||
// Calculate accuracy
|
||||
|
||||
var p_pred, p_labels *torch.Tensor
|
||||
/*var p_pred, p_labels *torch.Tensor
|
||||
p_pred, err = pred.Argmax([]int64{1}, true, false)
|
||||
if err != nil {
|
||||
return
|
||||
@ -235,8 +293,12 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
|
||||
if floats[i] == floats_labels[i] {
|
||||
trainCorrect += 1
|
||||
}
|
||||
} */
|
||||
|
||||
// panic("fornow")
|
||||
}
|
||||
}
|
||||
|
||||
//v := []float64{}
|
||||
|
||||
log.Info("model training epoch done loss", "loss", trainLoss, "correct", trainCorrect, "out", ds.TrainImagesSize, "accuracy", trainCorrect/float64(ds.TrainImagesSize))
|
||||
|
||||
|
||||
1
run.sh
Normal file → Executable file
1
run.sh
Normal file → Executable file
@ -1,2 +1,3 @@
|
||||
#!/bin/fish
|
||||
podman run --rm --network host --gpus all -ti -v (pwd):/app -e "TERM=xterm-256color" fyp-server bash
|
||||
|
||||
|
||||
108
test.go
Normal file
108
test.go
Normal file
@ -0,0 +1,108 @@
|
||||
package main
|
||||
|
||||
import (
|
||||
"git.andr3h3nriqu3s.com/andr3/gotch"
|
||||
|
||||
dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
|
||||
"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
|
||||
my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
|
||||
|
||||
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
|
||||
"github.com/charmbracelet/log"
|
||||
)
|
||||
|
||||
func main_() {
|
||||
|
||||
log.Info("Hello world")
|
||||
|
||||
m := train.BuildModel([]*dbtypes.Layer{
|
||||
&dbtypes.Layer{
|
||||
LayerType: dbtypes.LAYER_INPUT,
|
||||
Shape: "[ 2, 3, 3 ]",
|
||||
},
|
||||
&dbtypes.Layer{
|
||||
LayerType: dbtypes.LAYER_FLATTEN,
|
||||
},
|
||||
&dbtypes.Layer{
|
||||
LayerType: dbtypes.LAYER_DENSE,
|
||||
Shape: "[ 10 ]",
|
||||
},
|
||||
}, 0, true)
|
||||
|
||||
var err error
|
||||
|
||||
d := gotch.CudaIfAvailable()
|
||||
|
||||
log.Info("device", "d", d)
|
||||
|
||||
m.To(d)
|
||||
|
||||
|
||||
opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
|
||||
ones := torch.MustOnes([]int64{1, 2, 3, 3}, gotch.Float, d)
|
||||
ones = ones.MustSetRequiresGrad(true, true)
|
||||
ones.RetainGrad(false)
|
||||
|
||||
res := m.ForwardT(ones, true)
|
||||
res = res.MustSetRequiresGrad(true, true)
|
||||
res.RetainGrad(false)
|
||||
|
||||
outs := torch.MustOnes([]int64{1, 10}, gotch.Float, d)
|
||||
outs = outs.MustSetRequiresGrad(true, true)
|
||||
outs.RetainsGrad(false)
|
||||
|
||||
|
||||
loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 1, false)
|
||||
if err != nil {
|
||||
return
|
||||
}
|
||||
loss = loss.MustSetRequiresGrad(true, false)
|
||||
|
||||
opt.ZeroGrad()
|
||||
|
||||
|
||||
log.Info("loss", "loss", loss.Float64Values())
|
||||
|
||||
loss.MustBackward()
|
||||
|
||||
|
||||
opt.Step()
|
||||
|
||||
// log.Info(mean.MustGrad(false).Float64Values())
|
||||
log.Info(res.MustGrad(false).Float64Values())
|
||||
log.Info(ones.MustGrad(false).Float64Values())
|
||||
log.Info(outs.MustGrad(false).Float64Values())
|
||||
|
||||
vars := m.Vs.Variables()
|
||||
|
||||
for k, v := range vars {
|
||||
|
||||
log.Info("[grad check]", "k", k)
|
||||
|
||||
var grad *torch.Tensor
|
||||
grad, err = v.Grad(false)
|
||||
if err != nil {
|
||||
log.Error(err)
|
||||
return
|
||||
}
|
||||
|
||||
grad, err = grad.Abs(false)
|
||||
if err != nil {
|
||||
log.Error(err)
|
||||
return
|
||||
}
|
||||
|
||||
grad, err = grad.Max(false)
|
||||
if err != nil {
|
||||
log.Error(err)
|
||||
return
|
||||
}
|
||||
|
||||
log.Info("[grad check]", "k", k, "grad", grad.Float64Values())
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user