[theano-users] Re: Implementing a GPU op
Does it work if you do not modify the source for GpuEye at all?
If it does, then maybe sharing your new source would get you more help.
On Tuesday, July 11, 2017 at 12:12:03 PM UTC-4, Christopher Bourez wrote:
>
> Hi,
>
> I'm trying to implement a simple GPU op but it always gives me a
> Segmentation fault during compilation, without other message.
>
> For example :
> import theano
> from theano.gpuarray.basic_ops import GpuEye
>
> x = theano.tensor.iscalar('x')
> y = theano.tensor.iscalar('y')
> z = GpuEye(dtype='float32', context_name=None)(x,y,
> theano.tensor.constant(0))
>
> theano.printing.debugprint(z)
> print("Compiling")
> f = theano.function( [x,y], z)
> theano.printing.debugprint(f)
> print("Results")
> print(f(3, 3))
>
> I've also tried with the softmax gpu function. Is there something I'm
> missing ?
>
> I copied the file, created a complete new op, and the segmentation fault
> appears when I'm defining a Kernel in gpu_kernels() method of the op.
>
> Thank you a lot for your help
>
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[theano-users] Re: Unused input error with chained OpFromGraph ops
That error actually comes from the function compilation that happens inside
op2.
One option is to add "inline=True" to the call to OpFromGraph returning
op2, in that case the gradient gets overriden, but you will have the
inlined subgraph in the global one, and OpFromGraph will not build an inner
function.
The other one would be to add "on_unused_input='ignore'" to the call to
OpFromGraph:
op2 = theano.OpFromGraph(
inputs=[z_var, m_var, r_var],
outputs=[z_var],
grad_overrides=grad_op2,
on_unused_input='ignore')
With it, the program worked, but I still got an error in g.eval(...):
TypeError: Cannot convert Type DisconnectedType (of Variable
) into Type TensorType(float64, scalar). You can try to
manually convert into a TensorType(float64, scalar).
I'm not sure where that comes from, please let us know if you do not figure
it out.
On Tuesday, July 11, 2017 at 5:32:50 AM UTC-4, nicolas.grange...@gmail.com
wrote:
>
> Hi,
>
> I am trying to split an computation over two ops in order to avoid
> spurious computations when computing the gradient.
> My current attempt uses a first op which returns the desired result for
> the forward part and extra intermediate results. The second op just
> forwards the desired result, but its grad is overriden to compute the
> gradient based on the intermediate results.
>
> In this configuration, Theano complains about unused inputs in the forward
> computation because the intermediate results are not used for the forward
> method of the second op.
>
> Is this an expected behaviour or a bug?
>
>
>
> import numpy as np
> import theano.tensor as T
> import theano
>
>
> def make_ops():
> x_var = T.vector()
> m_var = T.bvector()
>
> r = m_var.sum().astype('floatX')
> z = x_var * m_var / r
>
>
> def grad_op1(inputs, output_gradients):
> return [
> output_gradients[0], # computation delegated to op2
> theano.gradient.DisconnectedType()()
> ]
>
>
> op1 = theano.OpFromGraph(
> inputs=[x_var, m_var],
> outputs=[z, r],
> grad_overrides=grad_op1)
>
>
> z_var = T.vector()
> r_var = T.scalar()
>
> def grad_op2(inputs, output_gradients):
> _, m_, r_ = inputs
> return [
> m_ * r_,
> theano.gradient.DisconnectedType()(),
> theano.gradient.DisconnectedType()()
> ]
>
> op2 = theano.OpFromGraph(
> inputs=[z_var, m_var, r_var],
> outputs=[z_var],
> grad_overrides=grad_op2)
>
> return op1, op2
>
>
> op1, op2 = make_ops()
> x_var = T.vector()
> m_var = T.bvector()
> z_, r = op1(x_var, m_var)
> z = op2(z_, m_var, r)
>
> print(z_.eval({x_var: np.array([1., .3, .0, .2], dtype=np.float32),
>m_var: np.array([1, 0, 1, 1], dtype=np.int8)}))
>
> f = theano.function([x_var, m_var], [z], on_unused_input='ignore') #
> raises anyway
>
> print(f(np.array([1., .3, .0, .2], dtype=np.float32),
> np.array([1, 0, 1, 1], dtype=np.int8)))
>
> # g = theano.grad(T.sum(z), wrt=x_var)
> # print(g.eval({x_var: np.array([1., .3, .0, .2], dtype=np.float32),
> # m_var: np.array([1, 0, 1, 1], dtype=np.int8)}))
>
>
>
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[theano-users] Implementing a GPU op
Hi,
I'm trying to implement a simple GPU op but it always gives me a
Segmentation fault during compilation, without other message.
For example :
import theano
from theano.gpuarray.basic_ops import GpuEye
x = theano.tensor.iscalar('x')
y = theano.tensor.iscalar('y')
z = GpuEye(dtype='float32', context_name=None)(x,y,
theano.tensor.constant(0))
theano.printing.debugprint(z)
print("Compiling")
f = theano.function( [x,y], z)
theano.printing.debugprint(f)
print("Results")
print(f(3, 3))
I've also tried with the softmax gpu function. Is there something I'm
missing ?
I copied the file, created a complete new op, and the segmentation fault
appears when I'm defining a Kernel in gpu_kernels() method of the op.
Thank you a lot for your help
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[theano-users] Unused input error with chained OpFromGraph ops
Hi,
I am trying to split an computation over two ops in order to avoid spurious
computations when computing the gradient.
My current attempt uses a first op which returns the desired result for the
forward part and extra intermediate results. The second op just forwards
the desired result, but its grad is overriden to compute the gradient based
on the intermediate results.
In this configuration, Theano complains about unused inputs in the forward
computation because the intermediate results are not used for the forward
method of the second op.
Is this an expected behaviour or a bug?
import numpy as np
import theano.tensor as T
import theano
def make_ops():
x_var = T.vector()
m_var = T.bvector()
r = m_var.sum().astype('floatX')
z = x_var * m_var / r
def grad_op1(inputs, output_gradients):
return [
output_gradients[0], # computation delegated to op2
theano.gradient.DisconnectedType()()
]
op1 = theano.OpFromGraph(
inputs=[x_var, m_var],
outputs=[z, r],
grad_overrides=grad_op1)
z_var = T.vector()
r_var = T.scalar()
def grad_op2(inputs, output_gradients):
_, m_, r_ = inputs
return [
m_ * r_,
theano.gradient.DisconnectedType()(),
theano.gradient.DisconnectedType()()
]
op2 = theano.OpFromGraph(
inputs=[z_var, m_var, r_var],
outputs=[z_var],
grad_overrides=grad_op2)
return op1, op2
op1, op2 = make_ops()
x_var = T.vector()
m_var = T.bvector()
z_, r = op1(x_var, m_var)
z = op2(z_, m_var, r)
print(z_.eval({x_var: np.array([1., .3, .0, .2], dtype=np.float32),
m_var: np.array([1, 0, 1, 1], dtype=np.int8)}))
f = theano.function([x_var, m_var], [z], on_unused_input='ignore') #
raises anyway
print(f(np.array([1., .3, .0, .2], dtype=np.float32),
np.array([1, 0, 1, 1], dtype=np.int8)))
# g = theano.grad(T.sum(z), wrt=x_var)
# print(g.eval({x_var: np.array([1., .3, .0, .2], dtype=np.float32),
# m_var: np.array([1, 0, 1, 1], dtype=np.int8)}))
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