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提交 573ccea7 authored 作者: Mehdi Mirza's avatar Mehdi Mirza 提交者： memimo
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typo and better example of profile output

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doc/cifarSC2011/advanced_theano.txt
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...@@ -184,170 +184,182 @@ Profiling ...@@ -184,170 +184,182 @@ Profiling
- To enable the memory profiling use the flags ``profile=True,profile_memory=True`` - To enable the memory profiling use the flags ``profile=True,profile_memory=True``
Theano output: Theano output for running the train function of logistic regression
example from :doc:`here <../tutorial/examples>` for one epoch:
.. code-block:: python .. code-block:: python
""" """
Function profiling Function profiling
================== ==================
Message: train.py:17 Message: train.py:47
Time in 1 calls to Function.__call__: 5.440712e-04s Time in 1 calls to Function.__call__: 5.981922e-03s
Time in Function.fn.__call__: 4.799366e-04s (88.212%) Time in Function.fn.__call__: 5.180120e-03s (86.596%)
Time in thunks: 7.891655e-05s (14.505%) Time in thunks: 4.213095e-03s (70.430%)
Total compile time: 5.701292e-01s Total compile time: 3.739440e-01s
Number of Apply nodes: 20 Number of Apply nodes: 21
Theano Optimizer time: 2.405829e-01s Theano Optimizer time: 3.258998e-01s
Theano validate time: 1.702785e-03s Theano validate time: 5.632162e-03s
Theano Linker time (includes C, CUDA code generation/compiling): 1.597619e-02s Theano Linker time (includes C, CUDA code generation/compiling): 3.185582e-02s
Import time 1.968861e-03s Import time 3.157377e-03s
Time in all call to theano.grad() 0.000000e+00s Time in all call to theano.grad() 2.997899e-02s
Time since theano import 1.436s Time since theano import 3.616s
Class Class
--- ---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name> <% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>
54.4% 54.4% 0.000s 3.90e-06s C 11 11 theano.tensor.elemwise.Elemwise 50.6% 50.6% 0.002s 1.07e-03s Py 2 2 theano.tensor.basic.Dot
17.8% 72.2% 0.000s 1.41e-05s C 1 1 theano.compile.ops.Shape_i 27.2% 77.8% 0.001s 5.74e-04s C 2 2 theano.sandbox.cuda.basic_ops.HostFromGpu
11.5% 83.7% 0.000s 2.26e-06s C 4 4 theano.tensor.basic.ScalarFromTensor 18.1% 95.9% 0.001s 3.81e-04s C 2 2 theano.sandbox.cuda.basic_ops.GpuFromHost
9.1% 92.7% 0.000s 3.58e-06s C 2 2 theano.tensor.subtensor.Subtensor 2.6% 98.6% 0.000s 1.23e-05s C 9 9 theano.tensor.elemwise.Elemwise
3.6% 96.4% 0.000s 2.86e-06s C 1 1 theano.tensor.elemwise.DimShuffle 0.8% 99.3% 0.000s 3.29e-05s C 1 1 theano.sandbox.cuda.basic_ops.GpuElemwise
3.6% 100.0% 0.000s 2.86e-06s C 1 1 theano.tensor.elemwise.Sum 0.3% 99.6% 0.000s 5.60e-06s C 2 2 theano.tensor.elemwise.DimShuffle
0.2% 99.8% 0.000s 6.91e-06s C 1 1 theano.sandbox.cuda.basic_ops.GpuDimShuffle
0.1% 99.9% 0.000s 5.01e-06s C 1 1 theano.compile.ops.Shape_i
0.1% 100.0% 0.000s 5.01e-06s C 1 1 theano.tensor.elemwise.Sum
... (remaining 0 Classes account for 0.00%(0.00s) of the runtime) ... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)
Ops Ops
--- ---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name> <% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>
17.8% 17.8% 0.000s 1.41e-05s C 1 1 Shape_i{0} 50.6% 50.6% 0.002s 1.07e-03s Py 2 2 dot
15.1% 32.9% 0.000s 1.19e-05s C 1 1 Elemwise{Composite{(i0 * (i1 ** i2))}} 27.2% 77.8% 0.001s 5.74e-04s C 2 2 HostFromGpu
11.5% 44.4% 0.000s 2.26e-06s C 4 4 ScalarFromTensor 18.1% 95.9% 0.001s 3.81e-04s C 2 2 GpuFromHost
9.1% 53.5% 0.000s 3.58e-06s C 2 2 Subtensor{int64:int64:int8} 1.0% 97.0% 0.000s 4.39e-05s C 1 1 Elemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}
8.8% 62.2% 0.000s 3.46e-06s C 2 2 Elemwise{switch,no_inplace} 0.8% 97.7% 0.000s 3.29e-05s C 1 1 GpuElemwise{Sub}[(0, 1)]
6.3% 68.6% 0.000s 2.50e-06s C 2 2 Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)] 0.4% 98.1% 0.000s 1.50e-05s C 1 1 Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)]
6.0% 74.6% 0.000s 2.38e-06s C 2 2 Elemwise{le,no_inplace} 0.3% 98.4% 0.000s 5.60e-06s C 2 2 InplaceDimShuffle{x}
5.1% 79.8% 0.000s 4.05e-06s C 1 1 Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i2, i1), i2, i1))}}[(0, 2)] 0.3% 98.6% 0.000s 1.10e-05s C 1 1 Elemwise{ScalarSigmoid}[(0, 0)]
5.1% 84.9% 0.000s 4.05e-06s C 1 1 Elemwise{minimum,no_inplace} 0.2% 98.8% 0.000s 9.06e-06s C 1 1 Elemwise{Composite{(i0 - (i1 * (i2 + (i3 * i0))))}}[(0, 0)]
3.9% 88.8% 0.000s 3.10e-06s C 1 1 Elemwise{lt,no_inplace} 0.2% 99.0% 0.000s 7.15e-06s C 1 1 Elemwise{gt,no_inplace}
3.9% 92.7% 0.000s 3.10e-06s C 1 1 Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}} 0.2% 99.2% 0.000s 6.91e-06s C 1 1 Elemwise{Composite{(i0 - (i1 * i2))}}[(0, 0)]
3.6% 96.4% 0.000s 2.86e-06s C 1 1 Sum{acc_dtype=float64} 0.2% 99.3% 0.000s 6.91e-06s C 1 1 GpuDimShuffle{1,0}
3.6% 100.0% 0.000s 2.86e-06s C 1 1 InplaceDimShuffle{x} 0.2% 99.5% 0.000s 6.91e-06s C 1 1 Elemwise{neg,no_inplace}
0.1% 99.6% 0.000s 5.96e-06s C 1 1 Elemwise{Composite{((-i0) - i1)}}[(0, 0)]
0.1% 99.8% 0.000s 5.01e-06s C 1 1 Elemwise{Cast{float64}}
0.1% 99.9% 0.000s 5.01e-06s C 1 1 Shape_i{0}
0.1% 100.0% 0.000s 5.01e-06s C 1 1 Sum{acc_dtype=float64}
... (remaining 0 Ops account for 0.00%(0.00s) of the runtime) ... (remaining 0 Ops account for 0.00%(0.00s) of the runtime)
Apply Apply
------ ------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Mflops> <Gflops/s> <Apply name> <% time> <sum %> <apply time> <time per call> <#call> <id> <Mflops> <Gflops/s> <Apply name>
17.8% 17.8% 0.000s 1.41e-05s 1 0 Shape_i{0}(coefficients) 26.8% 26.8% 0.001s 1.13e-03s 1 1 dot(x, w)
input 0: dtype=float32, shape=(3,), strides=c input 0: dtype=float32, shape=(400, 784), strides=c
output 0: dtype=int64, shape=(), strides=c input 1: dtype=float64, shape=(784,), strides=c
15.1% 32.9% 0.000s 1.19e-05s 1 18 Elemwise{Composite{(i0 * (i1 ** i2))}}(Subtensor{int64:int64:int8}.0, InplaceDimShuffle{x}.0, Subtensor{int64:int64:int8}.0) output 0: dtype=float64, shape=(400,), strides=c
input 0: dtype=float32, shape=(3,), strides=c 26.5% 53.4% 0.001s 1.12e-03s 1 10 HostFromGpu(GpuDimShuffle{1,0}.0)
input 0: dtype=float32, shape=(784, 400), strides=(1, 784)
output 0: dtype=float32, shape=(784, 400), strides=c
23.8% 77.1% 0.001s 1.00e-03s 1 18 dot(x.T, Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)].0)
input 0: dtype=float32, shape=(784, 400), strides=c
input 1: dtype=float64, shape=(400,), strides=c
output 0: dtype=float64, shape=(784,), strides=c
9.6% 86.7% 0.000s 4.04e-04s 1 3 GpuFromHost(y)
input 0: dtype=float32, shape=(400,), strides=c
output 0: dtype=float32, shape=(400,), strides=(1,)
8.5% 95.2% 0.000s 3.58e-04s 1 2 GpuFromHost(x)
input 0: dtype=float32, shape=(400, 784), strides=c
output 0: dtype=float32, shape=(400, 784), strides=(784, 1)
1.0% 96.3% 0.000s 4.39e-05s 1 13 Elemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}(y, Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0, TensorConstant{(1,) of -1.0}, HostFromGpu.0, Elemwise{neg,no_inplace}.0)
input 0: dtype=float32, shape=(400,), strides=c
input 1: dtype=float64, shape=(400,), strides=c
input 2: dtype=float64, shape=(1,), strides=c
input 3: dtype=float32, shape=(400,), strides=c
input 4: dtype=float64, shape=(400,), strides=c
output 0: dtype=float64, shape=(400,), strides=c
0.8% 97.1% 0.000s 3.29e-05s 1 7 GpuElemwise{Sub}[(0, 1)](CudaNdarrayConstant{[ 1.]}, GpuFromHost.0)
input 0: dtype=float32, shape=(1,), strides=c
input 1: dtype=float32, shape=(400,), strides=(1,)
output 0: dtype=float32, shape=(400,), strides=c
0.7% 97.7% 0.000s 2.91e-05s 1 11 HostFromGpu(GpuElemwise{Sub}[(0, 1)].0)
input 0: dtype=float32, shape=(400,), strides=c
output 0: dtype=float32, shape=(400,), strides=c
0.4% 98.1% 0.000s 1.50e-05s 1 15 Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)](Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0, TensorConstant{(1,) of -1.0}, y, Elemwise{Cast{float64}}.0, Elemwise{ScalarSigmoid}[(0, 0)].0, HostFromGpu.0)
input 0: dtype=float64, shape=(400,), strides=c
input 1: dtype=float64, shape=(1,), strides=c
input 2: dtype=float32, shape=(400,), strides=c
input 3: dtype=float64, shape=(1,), strides=c
input 4: dtype=float64, shape=(400,), strides=c
input 5: dtype=float32, shape=(400,), strides=c
output 0: dtype=float64, shape=(400,), strides=c
0.3% 98.4% 0.000s 1.10e-05s 1 14 Elemwise{ScalarSigmoid}[(0, 0)](Elemwise{neg,no_inplace}.0)
input 0: dtype=float64, shape=(400,), strides=c
output 0: dtype=float64, shape=(400,), strides=c
0.2% 98.6% 0.000s 9.06e-06s 1 20 Elemwise{Composite{(i0 - (i1 * (i2 + (i3 * i0))))}}[(0, 0)](w, TensorConstant{(1,) of 0...0000000149}, dot.0, TensorConstant{(1,) of 0...9999999553})
input 0: dtype=float64, shape=(784,), strides=c
input 1: dtype=float64, shape=(1,), strides=c
input 2: dtype=float64, shape=(784,), strides=c
input 3: dtype=float64, shape=(1,), strides=c
output 0: dtype=float64, shape=(784,), strides=c
0.2% 98.7% 0.000s 7.15e-06s 1 16 Elemwise{gt,no_inplace}(Elemwise{ScalarSigmoid}[(0, 0)].0, TensorConstant{(1,) of 0.5})
input 0: dtype=float64, shape=(400,), strides=c
input 1: dtype=float32, shape=(1,), strides=c input 1: dtype=float32, shape=(1,), strides=c
input 2: dtype=int64, shape=(3,), strides=c output 0: dtype=int8, shape=(400,), strides=c
output 0: dtype=float64, shape=(3,), strides=c 0.2% 98.9% 0.000s 7.15e-06s 1 0 InplaceDimShuffle{x}(b)
5.1% 38.1% 0.000s 4.05e-06s 1 17 Subtensor{int64:int64:int8}(TensorConstant{[ 0 1..9998 9999]}, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1}) input 0: dtype=float64, shape=(), strides=c
input 0: dtype=int64, shape=(10000,), strides=c output 0: dtype=float64, shape=(1,), strides=c
input 1: dtype=int64, shape=8, strides=c 0.2% 99.1% 0.000s 6.91e-06s 1 19 Elemwise{Composite{(i0 - (i1 * i2))}}[(0, 0)](b, TensorConstant{0.10000000149}, Sum{acc_dtype=float64}.0)
input 2: dtype=int64, shape=8, strides=c input 0: dtype=float64, shape=(), strides=c
input 3: dtype=int8, shape=1, strides=c input 1: dtype=float64, shape=(), strides=c
output 0: dtype=int64, shape=(3,), strides=c input 2: dtype=float64, shape=(), strides=c
5.1% 43.2% 0.000s 4.05e-06s 1 11 Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0})
input 0: dtype=int8, shape=(), strides=c
input 1: dtype=int8, shape=(), strides=c
input 2: dtype=int64, shape=(), strides=c
output 0: dtype=int64, shape=(), strides=c
5.1% 48.3% 0.000s 4.05e-06s 1 5 Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i2, i1), i2, i1))}}[(0, 2)](Elemwise{lt,no_inplace}.0, TensorConstant{10000}, Elemwise{minimum,no_inplace}.0, TensorConstant{0})
input 0: dtype=int8, shape=(), strides=c
input 1: dtype=int64, shape=(), strides=c
input 2: dtype=int64, shape=(), strides=c
input 3: dtype=int8, shape=(), strides=c
output 0: dtype=int64, shape=(), strides=c
5.1% 53.5% 0.000s 4.05e-06s 1 2 Elemwise{minimum,no_inplace}(Shape_i{0}.0, TensorConstant{10000})
input 0: dtype=int64, shape=(), strides=c
input 1: dtype=int64, shape=(), strides=c
output 0: dtype=int64, shape=(), strides=c
3.9% 57.4% 0.000s 3.10e-06s 1 16 Subtensor{int64:int64:int8}(coefficients, ScalarFromTensor.0, ScalarFromTensor.0, Constant{1})
input 0: dtype=float32, shape=(3,), strides=c
input 1: dtype=int64, shape=8, strides=c
input 2: dtype=int64, shape=8, strides=c
input 3: dtype=int8, shape=1, strides=c
output 0: dtype=float32, shape=(3,), strides=c
3.9% 61.3% 0.000s 3.10e-06s 1 14 ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0)
input 0: dtype=int64, shape=(), strides=c
output 0: dtype=int64, shape=8, strides=c
3.9% 65.3% 0.000s 3.10e-06s 1 10 Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i2, i1), i2, i1))}}[(0, 2)].0, TensorConstant{10000})
input 0: dtype=int8, shape=(), strides=c
input 1: dtype=int8, shape=(), strides=c
input 2: dtype=int64, shape=(), strides=c
input 3: dtype=int64, shape=(), strides=c
output 0: dtype=int64, shape=(), strides=c
3.9% 69.2% 0.000s 3.10e-06s 1 4 Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}(Elemwise{lt,no_inplace}.0, Elemwise{minimum,no_inplace}.0, Shape_i{0}.0, TensorConstant{0})
input 0: dtype=int8, shape=(), strides=c
input 1: dtype=int64, shape=(), strides=c
input 2: dtype=int64, shape=(), strides=c
input 3: dtype=int8, shape=(), strides=c
output 0: dtype=int64, shape=(), strides=c
3.9% 73.1% 0.000s 3.10e-06s 1 3 Elemwise{lt,no_inplace}(Elemwise{minimum,no_inplace}.0, TensorConstant{0})
input 0: dtype=int64, shape=(), strides=c
input 1: dtype=int8, shape=(), strides=c
output 0: dtype=int8, shape=(), strides=c
3.6% 76.7% 0.000s 2.86e-06s 1 19 Sum{acc_dtype=float64}(Elemwise{Composite{(i0 * (i1 ** i2))}}.0)
input 0: dtype=float64, shape=(3,), strides=c
output 0: dtype=float64, shape=(), strides=c output 0: dtype=float64, shape=(), strides=c
3.6% 80.4% 0.000s 2.86e-06s 1 9 Elemwise{switch,no_inplace}(Elemwise{le,no_inplace}.0, TensorConstant{0}, TensorConstant{0}) 0.2% 99.2% 0.000s 6.91e-06s 1 9 Elemwise{neg,no_inplace}(Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0)
input 0: dtype=int8, shape=(), strides=c input 0: dtype=float64, shape=(400,), strides=c
input 1: dtype=int8, shape=(), strides=c output 0: dtype=float64, shape=(400,), strides=c
input 2: dtype=int64, shape=(), strides=c 0.2% 99.4% 0.000s 6.91e-06s 1 6 GpuDimShuffle{1,0}(GpuFromHost.0)
output 0: dtype=int64, shape=(), strides=c input 0: dtype=float32, shape=(400, 784), strides=(784, 1)
3.6% 84.0% 0.000s 2.86e-06s 1 7 Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i2, i1), i2, i1))}}[(0, 2)].0, TensorConstant{0}) output 0: dtype=float32, shape=(784, 400), strides=(1, 784)
input 0: dtype=int64, shape=(), strides=c 0.1% 99.5% 0.000s 5.96e-06s 1 5 Elemwise{Composite{((-i0) - i1)}}[(0, 0)](dot.0, InplaceDimShuffle{x}.0)
input 1: dtype=int8, shape=(), strides=c input 0: dtype=float64, shape=(400,), strides=c
output 0: dtype=int8, shape=(), strides=c input 1: dtype=float64, shape=(1,), strides=c
3.6% 87.6% 0.000s 2.86e-06s 1 1 InplaceDimShuffle{x}(x) output 0: dtype=float64, shape=(400,), strides=c
input 0: dtype=float32, shape=(), strides=c 0.1% 99.7% 0.000s 5.01e-06s 1 17 Sum{acc_dtype=float64}(Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)].0)
output 0: dtype=float32, shape=(1,), strides=c input 0: dtype=float64, shape=(400,), strides=c
2.7% 90.3% 0.000s 2.15e-06s 1 12 ScalarFromTensor(Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)].0) output 0: dtype=float64, shape=(), strides=c
input 0: dtype=int64, shape=(), strides=c 0.1% 99.8% 0.000s 5.01e-06s 1 12 Elemwise{Cast{float64}}(InplaceDimShuffle{x}.0)
output 0: dtype=int64, shape=8, strides=c input 0: dtype=int64, shape=(1,), strides=c
2.4% 92.7% 0.000s 1.91e-06s 1 15 ScalarFromTensor(Elemwise{switch,no_inplace}.0) output 0: dtype=float64, shape=(1,), strides=c
input 0: dtype=int64, shape=(), strides=c 0.1% 99.9% 0.000s 5.01e-06s 1 4 Shape_i{0}(y)
output 0: dtype=int64, shape=8, strides=c input 0: dtype=float32, shape=(400,), strides=c
2.4% 95.2% 0.000s 1.91e-06s 1 13 ScalarFromTensor(Elemwise{switch,no_inplace}.0)
input 0: dtype=int64, shape=(), strides=c
output 0: dtype=int64, shape=8, strides=c
2.4% 97.6% 0.000s 1.91e-06s 1 8 Elemwise{Composite{Switch(i0, i1, minimum(i2, i3))}}[(0, 2)](Elemwise{le,no_inplace}.0, TensorConstant{0}, Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, Shape_i{0}.0)
input 0: dtype=int8, shape=(), strides=c
input 1: dtype=int8, shape=(), strides=c
input 2: dtype=int64, shape=(), strides=c
input 3: dtype=int64, shape=(), strides=c
output 0: dtype=int64, shape=(), strides=c output 0: dtype=int64, shape=(), strides=c
2.4% 100.0% 0.000s 1.91e-06s 1 6 Elemwise{le,no_inplace}(Elemwise{Composite{Switch(i0, Switch(LT((i1 + i2), i3), i3, (i1 + i2)), Switch(LT(i1, i2), i1, i2))}}.0, TensorConstant{0}) ... (remaining 1 Apply instances account for 0.10%(0.00s) of the runtime)
input 0: dtype=int64, shape=(), strides=c
input 1: dtype=int8, shape=(), strides=c
output 0: dtype=int8, shape=(), strides=c
... (remaining 0 Apply instances account for 0.00%(0.00s) of the runtime)
Memory Profile Memory Profile
(Sparse variables are ignored) (Sparse variables are ignored)
(For values in brackets, it's for linker = c|py (For values in brackets, it's for linker = c|py
--- ---
Max if no gc (allow_gc=False): 0KB (0KB) Max if no gc (allow_gc=False): 2469KB (2469KB)
CPU: 0KB (0KB) CPU: 1242KB (1242KB)
GPU: 0KB (0KB) GPU: 1227KB (1227KB)
--- ---
Max if linker=cvm(default): 0KB (0KB) Max if linker=cvm(default): 2466KB (2464KB)
CPU: 0KB (0KB) CPU: 1241KB (1238KB)
GPU: 0KB (0KB) GPU: 1225KB (1227KB)
--- ---
Memory saved if views are used: 0KB (0KB) Memory saved if views are used: 1225KB (1225KB)
Memory saved if inplace ops are used: 0KB (0KB) Memory saved if inplace ops are used: 17KB (17KB)
Memory saved if gc is enabled: 0KB (0KB) Memory saved if gc is enabled: 3KB (4KB)
--- ---
<Sum apply outputs (bytes)> <Apply outputs shape> <created/inplace/view> <Apply node> <Sum apply outputs (bytes)> <Apply outputs shape> <created/inplace/view> <Apply node>
... (remaining 20 Apply account for 171B/171B ((100.00%)) of the Apply with dense outputs sizes) 1254400B [(400, 784)] c GpuFromHost(x)
1254400B [(784, 400)] v GpuDimShuffle{1,0}(GpuFromHost.0)
1254400B [(784, 400)] c HostFromGpu(GpuDimShuffle{1,0}.0)
6272B [(784,)] c dot(x.T, Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)].0)
6272B [(784,)] i Elemwise{Composite{(i0 - (i1 * (i2 + (i3 * i0))))}}[(0, 0)](w, TensorConstant{(1,) of 0...0000000149}, dot.0, TensorConstant{(1,) of 0...9999999553})
3200B [(400,)] c dot(x, w)
3200B [(400,)] i Elemwise{Composite{((-i0) - i1)}}[(0, 0)](dot.0, InplaceDimShuffle{x}.0)
3200B [(400,)] i Elemwise{ScalarSigmoid}[(0, 0)](Elemwise{neg,no_inplace}.0)
3200B [(400,)] c Elemwise{neg,no_inplace}(Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0)
3200B [(400,)] i Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)](Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0, TensorConstant{(1,) of -1.0}, y, Elemwise{Cast{float64}}.0, Elemwise{ScalarSigmoid}[(0, 0)].0, HostFromGpu.0)
3200B [(400,)] c Elemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}(y, Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0, TensorConstant{(1,) of -1.0}, HostFromGpu.0, Elemwise{neg,no_inplace}.0)
1600B [(400,)] i GpuElemwise{Sub}[(0, 1)](CudaNdarrayConstant{[ 1.]}, GpuFromHost.0)
1600B [(400,)] c HostFromGpu(GpuElemwise{Sub}[(0, 1)].0)
1600B [(400,)] c GpuFromHost(y)
... (remaining 7 Apply account for 448B/3800192B ((0.01%)) of the Apply with dense outputs sizes)
All Apply nodes have output sizes that take less than 1024B.
<created/inplace/view> is taken from the Op's declaration. <created/inplace/view> is taken from the Op's declaration.
Apply nodes marked 'inplace' or 'view' may actually allocate memory, this is not reported here. If you use DebugMode, warnings will be emitted in those cases. Apply nodes marked 'inplace' or 'view' may actually allocate memory, this is not reported here. If you use DebugMode, warnings will be emitted in those cases.
...@@ -355,7 +367,6 @@ Theano output: ...@@ -355,7 +367,6 @@ Theano output:
(if you think of new ones, suggest them on the mailing list). (if you think of new ones, suggest them on the mailing list).
Test them first, as they are not guaranteed to always provide a speedup. Test them first, as they are not guaranteed to always provide a speedup.
Sorry, no tip for today. Sorry, no tip for today.
""" """
Exercise 5 Exercise 5
......
doc/tutorial/using_gpu.txt
浏览文件 @ 573ccea7
...@@ -214,7 +214,7 @@ Tips for Improving Performance on GPU ...@@ -214,7 +214,7 @@ Tips for Improving Performance on GPU
the GPU, *float32* tensor ``shared`` variables are stored on the GPU by default to the GPU, *float32* tensor ``shared`` variables are stored on the GPU by default to
eliminate transfer time for GPU ops using those variables. eliminate transfer time for GPU ops using those variables.
* If you aren't happy with the performance you see, try running your script with * If you aren't happy with the performance you see, try running your script with
``profil=True`` flag. This should print some timing information at program ``profile=True`` flag. This should print some timing information at program
termination. Is time being used sensibly? If an op or Apply is termination. Is time being used sensibly? If an op or Apply is
taking more time than its share, then if you know something about GPU taking more time than its share, then if you know something about GPU
programming, have a look at how it's implemented in theano.sandbox.cuda. programming, have a look at how it's implemented in theano.sandbox.cuda.
......
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