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This repository has been archived by the owner on Nov 17, 2023. It is now read-only.
When using MKLDNN, concatenating sometimes fails and results in error. Please see the code.
Error Message
MXNetError: The size of NDArray doesn't match the requested MKLDNN memory desc. MKLDNN memory requests for 41943040 bytes, but got 34603008 bytes from NDArray
To Reproduce
x = mx.sym.Variable('x')
w = mx.sym.Variable('w')
c = mx.sym.Convolution(data=x, weight=w, num_filter=32, kernel=(3, 3), pad=(1, 1), no_bias=True)
n = mx.sym.concat(c, x, dim=1)
context = mx.cpu()
ex = n.simple_bind(context, x=(1, 1, 512, 512))
data = mx.nd.ones(ex.arg_arrays[0].shape, ctx=context)
weight = mx.nd.ones(ex.arg_arrays[1].shape, ctx=context)
If variable x is first tiled to have at least 8 channels (dimension 1), then it works:
c = mx.sym.Convolution(data=x, weight=w, num_filter=32, kernel=(3, 3), pad=(1, 1), no_bias=True)
x = mx.sym.tile(x, (1, 8, 1, 1))
n = mx.sym.concat(c, x, dim=1)
Another way is to reverse the order of concatenation:
n = mx.sym.concat(x, c, dim=1)
Environment
MXNet 1.8.0.rc2, Windows 10, build with MKLDNN
The text was updated successfully, but these errors were encountered:
Simillar error on linux:
MXNetError: The size of NDArray doesn't match the requested MKLDNN memory desc. MKLDNN memory requests for 50331648 bytes, but got 34603008 bytes from NDArray
Description
When using MKLDNN, concatenating sometimes fails and results in error. Please see the code.
Error Message
MXNetError: The size of NDArray doesn't match the requested MKLDNN memory desc. MKLDNN memory requests for 41943040 bytes, but got 34603008 bytes from NDArray
To Reproduce
x = mx.sym.Variable('x')
w = mx.sym.Variable('w')
c = mx.sym.Convolution(data=x, weight=w, num_filter=32, kernel=(3, 3), pad=(1, 1), no_bias=True)
n = mx.sym.concat(c, x, dim=1)
context = mx.cpu()
ex = n.simple_bind(context, x=(1, 1, 512, 512))
data = mx.nd.ones(ex.arg_arrays[0].shape, ctx=context)
weight = mx.nd.ones(ex.arg_arrays[1].shape, ctx=context)
data.copyto(ex.arg_arrays[0])
weight.copyto(ex.arg_arrays[1])
res = ex.forward()
print(res)
Steps to reproduce
Simply run the provided script
What have you tried to solve it?
If variable x is first tiled to have at least 8 channels (dimension 1), then it works:
c = mx.sym.Convolution(data=x, weight=w, num_filter=32, kernel=(3, 3), pad=(1, 1), no_bias=True)
x = mx.sym.tile(x, (1, 8, 1, 1))
n = mx.sym.concat(c, x, dim=1)
Another way is to reverse the order of concatenation:
n = mx.sym.concat(x, c, dim=1)
Environment
MXNet 1.8.0.rc2, Windows 10, build with MKLDNN
The text was updated successfully, but these errors were encountered: