Comments (7)
owulveryck
commented on June 19, 2024
I just made a quick test by "hacking" the tensor package.
I've made a "lazy initialization" of the array; the value is populated on a call to Data() which is not often.
The results look promising:
Normal bench:
➜ onnx-go git:(benchmarks) ✗ go test -bench=. -benchmem -memprofile memprofile.out -cpuprofile profile.out -benchtime=10s
goos: darwin
goarch: amd64
pkg: github.com/owulveryck/onnx-go
BenchmarkUnmarshalBinary-4 2000 10688594 ns/op 3906741 B/op 67107 allocs/op
PASS
ok github.com/owulveryck/onnx-go 22.620s
bench with the hack:
➜ onnx-go git:(benchmarks) ✗ go test -bench=. -benchmem -memprofile memprofile.out -cpuprofile profile.out -benchtime=10s
goos: darwin
goarch: amd64
pkg: github.com/owulveryck/onnx-go
BenchmarkUnmarshalBinary-4 3000 6136003 ns/op 2642474 B/op 27664 allocs/op
PASS
ok github.com/owulveryck/onnx-go 19.169s
from onnx-go.
owulveryck
commented on June 19, 2024
This commit from then tensor.Tensor package drastically enhances performances and memory consumption.
However, I will keep this issue open for now to do a further investigation with the broadcasting mechanism.
from onnx-go.
owulveryck
commented on June 19, 2024
In Gorgonia, the broadcast mechanism is based on the repeatOp, which itself triggers a call to Repeat(...) in the Dense implementation of the Tensor package.
This mechanism is calling copyDenseSliced many times in two embedded for loops. A single call to copyDenseSlice is creating two new objects here
d := dst.arr().slice(dstart, dend)
s := src.arr().slice(sstart, send)Within this loop, we are creating i x j x 2 strides.
We could reduce the number of creation and garbage collection by extracting the s and d slices from the copyDenseFunction.
from onnx-go.
owulveryck
commented on June 19, 2024
With the PR 43 from the tensor package, the results are now:
➜ onnx-go git:(benchmarks) ✗ go test -bench=. -benchmem -memprofile memprofile.out -cpuprofile profile.out -benchtime=10s
goos: darwin
goarch: amd64
pkg: github.com/owulveryck/onnx-go
BenchmarkUnmarshalBinary-4 3000 4208506 ns/op 2042788 B/op 18273 allocs/op
PASS
ok github.com/owulveryck/onnx-go 13.320s
Comparing with the initial investigation of the issue, the performance comparison will be:
benchmark old ns/op new ns/op delta
BenchmarkUnmarshalBinary-4 9457554 4528740 -52.12%
benchmark old allocs new allocs delta
BenchmarkUnmarshalBinary-4 67120 18272 -72.78%
benchmark old bytes new bytes delta
BenchmarkUnmarshalBinary-4 3910542 2042637 -47.77%
Once the PR is merged, that will be enough to close this issue
from onnx-go.
owulveryck
commented on June 19, 2024
Closed thanks to PR #43 of the tensor package
from onnx-go.
owulveryck
commented on June 19, 2024
I reopen this issue because on NN involving small tensors, broadcasting is ok, but on bigger tensor it's still too slow.
from onnx-go.
owulveryck
commented on June 19, 2024
PR 299 from Gorgonia should improve things
from onnx-go.
Related Issues (20)
- Implement operator PRelu for backend Gorgonia
- Implement operator pRelu for backend Gorgonia
- Failed to infer shape. Op: A × Bᵀ: Inner dimensions do not match up HOT 2
- Implement operator `Gather` for backend `Gorgonia` HOT 3
- Cannot model.UnmarshalBinary - says 'No data found' HOT 1
- Can't import onnx model, converted from BigGAN-PyTorch HOT 2
- Implement operator LSTM,Clip for backend Gorgonia HOT 1
- Will this project be maintained further and are contributions still welcomed?
- Implement operator `LinearRegressor` for backend `gorgonia`
- "Asymmetric padding" error
- panic: negative dimension size does not make sense
- ../../go/src/gorgonia.org/tensor/dense_compat.go:442:23: undefined: array.Interface HOT 2
- Updated depens
- poor performance (run model)
- run() function calls newMachine() everytime HOT 1
- Question: unsqueeze: axes in not an []int64 HOT 3
- Support for empty tensors
- Tape machine does not reset properly for some models HOT 2
- Implement operator `PReLU` for backend `Gorgonia`
- Implement operator `Cast` for backend `Gorgonia`
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