List of gpuarray Ops implemented

Normally you should not call directly those Ops! Theano should automatically transform CPU ops to their GPU equivalent. So this list is just useful to let people know what is implemented on the GPU.

Basic Op

class theano.gpuarray.basic_ops.CGpuKernelBase(func_files: Union[str, List[str]], func_name: Optional[str] = None)

Class to combine GpuKernelBase and ExternalCOp.

It adds a new section type ‘kernels’ where you can define kernels with the ‘#kernel’ tag

c_code_cache_version_apply(node)

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version

Notes

This function overrides c_code_cache_version unless it explicitly calls c_code_cache_version. The default implementation simply calls c_code_cache_version and ignores the node argument.

gpu_kernels(node, name)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

class theano.gpuarray.basic_ops.GpuAlloc(context_name, memset_0=False)

Allocate initialized memory on the GPU.

Parameters:

• context_name (str) – The name of the context in which to allocate memory
• memset_0 (bool) – It’s only an optimized version. True, it means the value is always 0, so the c code call memset as it is faster.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

do_constant_folding(fgraph, node)

Determine whether or not constant folding should be performed for the given node.

This allows each Op to determine if it wants to be constant folded when all its inputs are constant. This allows it to choose where it puts its memory/speed trade-off. Also, it could make things faster as constants can’t be used for in-place operations (see *IncSubtensor).

Parameters:node (Apply) – The node for which the constant folding determination is made. Returns:res Return type:bool

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

make_node(value, *shape)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, outs, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuAllocEmpty(dtype, context_name)

Allocate uninitialized memory on the GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

do_constant_folding(fgraph, node)

Determine whether or not constant folding should be performed for the given node.

This allows each Op to determine if it wants to be constant folded when all its inputs are constant. This allows it to choose where it puts its memory/speed trade-off. Also, it could make things faster as constants can’t be used for in-place operations (see *IncSubtensor).

Parameters:node (Apply) – The node for which the constant folding determination is made. Returns:res Return type:bool

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

grad(*args)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(*shape)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, out_, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuContiguous

Return a C contiguous version of the input.

This may either pass the object as-is (if already C contiguous) or make a copy.

grad(inputs, dout)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(input)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out_)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuEye(dtype=None, context_name=None)

Eye for GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

gpu_kernels(node, name)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(n, m, k)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.basic_ops.GpuFromHost(context_name)

Transfer data to GPU.

R_op(inputs, eval_points)

Construct a graph for the R-operator.

This method is primarily used by Rop

Suppose the op outputs

[ f_1(inputs), …, f_n(inputs) ]

Parameters:

• inputs (a Variable or list of Variables) –
• eval_points – A Variable or list of Variables with the same length as inputs. Each element of eval_points specifies the value of the corresponding input at the point where the R op is to be evaluated.

Returns:

rval[i] should be Rop(f=f_i(inputs),

wrt=inputs, eval_points=eval_points)

Return type:

list of n elements

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

grad(inputs, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(x)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out, ctx)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuJoin(view=- 1)

Join for GPU.

c_code(node, name, inputs, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_support_code(**kwargs)

Return utility code for use by a Variable or Op.

This is included at global scope prior to the rest of the code for this class.

Question: How many times will this support code be emitted for a graph with many instances of the same type?

Returns: Return type:str

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

make_node(axis, *tensors)

Parameters:

• axis (an Int or integer-valued Variable) –
• tensors – A variable number (but not zero) of tensors to concatenate along the specified axis. These tensors must have the same shape along all dimensions other than this axis.

Returns:

It has the same ndim as the input tensors, and the most inclusive dtype.

Return type:

A symbolic Variable

perform(node, axis_and_tensors, out_, ctx)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuKernelBase

Base class for operations that need to compile kernels.

It is not mandatory to use this class, but it helps with a lot of the small things that you have to pay attention to.

gpu_kernels(node, name)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

kernel_version(node)

If you override c_code_cache_version_apply(), call this method to have the version of the kernel support code.

Parameters:node (apply node) – The node that we need the cache version for.

class theano.gpuarray.basic_ops.GpuKernelBaseCOp

class theano.gpuarray.basic_ops.GpuKernelBaseExternalCOp(func_files: Union[str, List[str]], func_name: Optional[str] = None)

class theano.gpuarray.basic_ops.GpuReshape(ndim, name=None)

Reshape for GPU variables.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

make_node(x, shp)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out_, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuSplit(len_splits)

Split for GPU.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

make_node(x, axis, splits)

WRITEME

class theano.gpuarray.basic_ops.GpuToGpu(context_name)

Transfer data between GPUs.

R_op(inputs, eval_points)

Construct a graph for the R-operator.

This method is primarily used by Rop

Suppose the op outputs

[ f_1(inputs), …, f_n(inputs) ]

Parameters:

• inputs (a Variable or list of Variables) –
• eval_points – A Variable or list of Variables with the same length as inputs. Each element of eval_points specifies the value of the corresponding input at the point where the R op is to be evaluated.

Returns:

rval[i] should be Rop(f=f_i(inputs),

wrt=inputs, eval_points=eval_points)

Return type:

list of n elements

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

grad(inputs, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(x)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out, ctx)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.GpuTri(dtype=None, context_name=None)

Tri for GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

gpu_kernels(node, name)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(n, m, k)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.basic_ops.HostFromGpu

Transfer data to CPU.

R_op(inputs, eval_points)

Construct a graph for the R-operator.

This method is primarily used by Rop

Suppose the op outputs

[ f_1(inputs), …, f_n(inputs) ]

Parameters:

• inputs (a Variable or list of Variables) –
• eval_points – A Variable or list of Variables with the same length as inputs. Each element of eval_points specifies the value of the corresponding input at the point where the R op is to be evaluated.

Returns:

rval[i] should be Rop(f=f_i(inputs),

wrt=inputs, eval_points=eval_points)

Return type:

list of n elements

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

grad(inputs, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(x)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.basic_ops.Kernel(code, params, name, flags, codevar=None, objvar=None, fname=None, sname=None)

This class groups together all the attributes of a gpu kernel.

params should contain the data type for each argument. Buffer arguments should use the GpuArray class as the data type and scalar should use their equivalent numpy dtype. For ga_size and ga_ssize, use gpuarray.SIZE and gpuarray.SSIZE.

If the ctypes flags is set to True then it should be a C string which represent the typecode to use.

flags can contain the following keys whose values are booleans:

have_double

the kernel uses double-typed variables somewhere

have_small

the kernel uses variables whose type takes less than 4 bytes somewhere

have_complex

the kernel uses complex values somewhere

have_half

the kernel uses half-floats somewhere

ctypes

the params list consists of C typecodes

It can also have the key cflags which is a string of C flag values like this “GA_USE_DOUBLE|GA_USE_SMALL”.

Parameters:

• code (str) – The source code of the kernel.
• params (list) – list of parameter types.
• name (str) – the name of the kernel function in the source.
• flags (dict) – dictionary of flags
• codevar (str) – the name of the variable for the code object. (defaults to kcode_ + name)
• objvar (str) – the name of the variable for the kernel object. (defaults to k_ + name)
• fname (str) – the name of the function wrapper. (defaults to name + _call)
• sname (str) – the name of the scheduled call function (defaults to name _ _scall)

theano.gpuarray.basic_ops.as_gpuarray_variable(x, context_name)

This will attempt to convert x into a variable on the GPU.

It can take either a value of another variable. If x is already suitable, it will be returned as-is.

Parameters:

• x – Object to convert
• context_name (str or None) – target context name for the result

theano.gpuarray.basic_ops.infer_context_name(*vars)

Infer the context name to use from the inputs given

Blas Op

class theano.gpuarray.blas.BaseGpuCorr3dMM(border_mode='valid', subsample=(1, 1, 1), filter_dilation=(1, 1, 1), num_groups=1)

Base class for GpuCorr3dMM, GpuCorr3dMM_gradWeights and GpuCorr3dMM_gradInputs. Cannot be used directly.

Parameters:

• border_mode ({'valid', 'full', 'half'}) – Additionally, the padding size could be directly specified by an integer or a pair of integers
• subsample – Perform subsampling of the output (default: (1, 1, 1)).
• filter_dilation – Perform subsampling of the input, also known as dilation (default: (1, 1, 1)).
• num_groups – Divides the image, kernel and output tensors into num_groups separate groups. Each which carry out convolutions separately (default : 1).

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_code_helper(bottom, weights, top, direction, sub, height=None, width=None, depth=None)

This generates the C code for GpuCorr3dMM (direction=”forward”), GpuCorr3dMM_gradWeights (direction=”backprop weights”), and GpuCorr3dMM_gradInputs (direction=”backprop inputs”). Depending on the direction, one of bottom, weights, top will receive the output, while the other two serve as inputs.

Parameters:

• bottom – Variable name of the input images in the forward pass, or the gradient of the input images in backprop wrt. inputs
• weights – Variable name of the filters in the forward pass, or the gradient of the filters in backprop wrt. weights
• top – Variable name of the output images / feature maps in the forward pass, or the gradient of the outputs in the backprop passes
• direction ({'forward', 'backprop weights', 'backprop inputs'}) – “forward” to correlate bottom with weights and store results in top, “backprop weights” to do a valid convolution of bottom with top (swapping the first two dimensions) and store results in weights, and “backprop inputs” to do a full convolution of top with weights (swapping the first two dimensions) and store results in bottom.
• sub – Dictionary of substitutions useable to help generating the C code.
• height – Required if self.subsample[0] != 1, a variable giving the height of the filters for direction=”backprop weights” or the height of the input images for direction=”backprop inputs”. Required if self.border_mode == ‘half’, a variable giving the height of the filters for direction=”backprop weights”. Not required otherwise, but if a value is given this will be checked.
• width – Required if self.subsample[1] != 1, a variable giving the width of the filters for direction=”backprop weights” or the width of the input images for direction=”backprop inputs”. Required if self.border_mode == ‘half’, a variable giving the width of the filters for direction=”backprop weights”. Not required otherwise, but if a value is given this will be checked.
• depth – Required if self.subsample[2] != 1, a variable giving the depth of the filters for direction=”backprop weights” or the depth of the input images for direction=”backprop inputs”. Required if self.border_mode == ‘half’, a variable giving the depth of the filters for direction=”backprop weights”. Not required otherwise, but if a value is given this will be checked.

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

flops(inp, outp)

Useful with the hack in profilemode to print the MFlops.

class theano.gpuarray.blas.BaseGpuCorrMM(border_mode='valid', subsample=(1, 1), filter_dilation=(1, 1), num_groups=1, unshared=False)

Base class for GpuCorrMM, GpuCorrMM_gradWeights and GpuCorrMM_gradInputs. Cannot be used directly.

Parameters:

• border_mode ({'valid', 'full', 'half'}) – Additionally, the padding size could be directly specified by an integer, a pair of integers, or two pairs of integers.
• subsample – Perform subsampling of the output (default: (1, 1)).
• filter_dilation – Perform subsampling of the input, also known as dilation (default: (1, 1)).
• num_groups – Divides the image, kernel and output tensors into num_groups separate groups. Each which carry out convolutions separately (default : 1).
• unshared – Perform unshared correlation (default: False)

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_code_helper(bottom, weights, top, direction, sub, height=None, width=None)

This generates the C code for GpuCorrMM (direction=”forward”), GpuCorrMM_gradWeights (direction=”backprop weights”), and GpuCorrMM_gradInputs (direction=”backprop inputs”). Depending on the direction, one of bottom, weights, top will receive the output, while the other two serve as inputs.

Parameters:

• bottom – Variable name of the input images in the forward pass, or the gradient of the input images in backprop wrt. inputs
• weights – Variable name of the filters in the forward pass, or the gradient of the filters in backprop wrt. weights
• top – Variable name of the output images / feature maps in the forward pass, or the gradient of the outputs in the backprop passes
• direction ({'forward', 'backprop weights', 'backprop inputs'}) – “forward” to correlate bottom with weights and store results in top, “backprop weights” to do a valid convolution of bottom with top (swapping the first two dimensions) and store results in weights, and “backprop inputs” to do a full convolution of top with weights (swapping the first two dimensions) and store results in bottom.
• sub – Dictionary of substitutions useable to help generating the C code.
• height – Required if self.subsample[0] != 1, a variable giving the height of the filters for direction=”backprop weights” or the height of the input images for direction=”backprop inputs”. Required if self.border_mode == ‘half’, a variable giving the height of the filters for direction=”backprop weights”. Not required otherwise, but if a value is given this will be checked.
• width – Required if self.subsample[1] != 1, a variable giving the width of the filters for direction=”backprop weights” or the width of the input images for direction=”backprop inputs”. Required if self.border_mode == ‘half’, a variable giving the width of the filters for direction=”backprop weights”. Not required otherwise, but if a value is given this will be checked.

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

flops(inp, outp)

Useful with the hack in profilemode to print the MFlops.

class theano.gpuarray.blas.BlasOp

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_init_code(**kwargs)

Return a list of code snippets to be inserted in module initialization.

class theano.gpuarray.blas.GpuCorr3dMM(border_mode='valid', subsample=(1, 1, 1), filter_dilation=(1, 1, 1), num_groups=1)

GPU correlation implementation using Matrix Multiplication.

Parameters:

• border_mode – The width of a border of implicit zeros to pad the input with. Must be a tuple with 3 elements giving the width of the padding on each side, or a single integer to pad the same on all sides, or a string shortcut setting the padding at runtime: 'valid' for (0, 0, 0) (valid convolution, no padding), 'full' for (kernel_rows - 1, kernel_columns - 1, kernel_depth - 1) (full convolution), 'half' for (kernel_rows // 2, kernel_columns // 2, kernel_depth // 2) (same convolution for odd-sized kernels). Note that the three widths are each applied twice, once per side (left and right, top and bottom, front and back).
• subsample – The subsample operation applied to each output image. Should be a tuple with 3 elements. (sv, sh, sl) is equivalent to GpuCorrMM(…)(…)[:,:,::sv, ::sh, ::sl], but faster. Set to (1, 1, 1) to disable subsampling.
• filter_dilation – The filter dilation operation applied to each input image. Should be a tuple with 3 elements. Set to (1, 1, 1) to disable filter dilation.
• num_groups – The number of distinct groups the image and kernel must be divided into. should be an int set to 1 to disable grouped convolution

Notes

Currently, the Op requires the inputs, filters and outputs to be C-contiguous. Use gpu_contiguous on these arguments if needed.

You can either enable the Theano flag optimizer_including=conv_gemm to automatically replace all convolution operations with GpuCorr3dMM or one of its gradients, or you can use it as a replacement for conv2d, called as GpuCorr3dMM(subsample=…)(image, filters). The latter is currently faster, but note that it computes a correlation – if you need to compute a convolution, flip the filters as filters[:,:,::-1,::-1,::-1].

c_code(node, nodename, inp, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(img, kern)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuCorr3dMM_gradInputs(border_mode='valid', subsample=(1, 1, 1), filter_dilation=(1, 1, 1), num_groups=1)

Gradient wrt. inputs for GpuCorr3dMM.

Notes

You will not want to use this directly, but rely on Theano’s automatic differentiation or graph optimization to use it as needed.

c_code(node, nodename, inp, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(kern, topgrad, shape=None)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuCorr3dMM_gradWeights(border_mode='valid', subsample=(1, 1, 1), filter_dilation=(1, 1, 1), num_groups=1)

Gradient wrt. filters for GpuCorr3dMM.

Notes

You will not want to use this directly, but rely on Theano’s automatic differentiation or graph optimization to use it as needed.

c_code(node, nodename, inp, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(img, topgrad, shape=None)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuCorrMM(border_mode='valid', subsample=(1, 1), filter_dilation=(1, 1), num_groups=1, unshared=False)

GPU correlation implementation using Matrix Multiplication.

Parameters:

• border_mode – The width of a border of implicit zeros to pad the input with. Must be a tuple with 2 elements giving the numbers of rows and columns to pad on each side, or a single integer to pad the same on all sides, or a string shortcut setting the padding at runtime: 'valid' for (0, 0) (valid convolution, no padding), 'full' for (kernel_rows - 1, kernel_columns - 1) (full convolution), 'half' for (kernel_rows // 2, kernel_columns // 2) (same convolution for odd-sized kernels). If it is a tuple containing 2 pairs of integers, then these specify the padding to be applied on each side ((left, right), (top, bottom)). Otherwise, each width is applied twice, once per side (left and right, top and bottom).
• subsample – The subsample operation applied to each output image. Should be a tuple with 2 elements. (sv, sh) is equivalent to GpuCorrMM(…)(…)[:,:,::sv, ::sh], but faster. Set to (1, 1) to disable subsampling.
• filter_dilation – The filter dilation operation applied to each input image. Should be a tuple with 2 elements. Set to (1, 1) to disable filter dilation.
• num_groups – The number of distinct groups the image and kernel must be divided into. should be an int set to 1 to disable grouped convolution
• unshared – Perform unshared correlation (default: False)

Notes

Currently, the Op requires the inputs, filters and outputs to be C-contiguous. Use gpu_contiguous on these arguments if needed.

You can either enable the Theano flag optimizer_including=conv_gemm to automatically replace all convolution operations with GpuCorrMM or one of its gradients, or you can use it as a replacement for conv2d, called as GpuCorrMM(subsample=…)(image, filters). The latter is currently faster, but note that it computes a correlation – if you need to compute a convolution, flip the filters as filters[:,:,::-1,::-1].

c_code(node, nodename, inp, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(img, kern)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuCorrMM_gradInputs(border_mode='valid', subsample=(1, 1), filter_dilation=(1, 1), num_groups=1, unshared=False)

Gradient wrt. inputs for GpuCorrMM.

Notes

You will not want to use this directly, but rely on Theano’s automatic differentiation or graph optimization to use it as needed.

c_code(node, nodename, inp, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(kern, topgrad, shape=None)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuCorrMM_gradWeights(border_mode='valid', subsample=(1, 1), filter_dilation=(1, 1), num_groups=1, unshared=False)

Gradient wrt. filters for GpuCorrMM.

Notes

You will not want to use this directly, but rely on Theano’s automatic differentiation or graph optimization to use it as needed.

c_code(node, nodename, inp, out_, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

grad(inp, grads)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(img, topgrad, shape=None)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuDot22

Dot22 on the GPU.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

make_node(x, y)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, outputs)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.blas.GpuGemm(inplace=False)

Gemm on the GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

make_node(C, alpha, A, B, beta)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, outputs, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.blas.GpuGemmBatch(inplace=False)

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

make_node(C, alpha, A, B, beta)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.blas.GpuGemv(inplace=False)

Gemv on the GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

make_node(y, alpha, A, x, beta)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, out_storage, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.blas.GpuGer(inplace=False)

Ger on the GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

make_node(A, alpha, x, y)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

Elemwise Op

theano.gpuarray.elemwise.GpuCAReduce

alias of theano.gpuarray.elemwise.GpuCAReduceCPY

class theano.gpuarray.elemwise.GpuCAReduceCPY(scalar_op, axis=None, dtype=None, acc_dtype=None)

CAReduce that reuse the python code from gpuarray.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version_apply(node)

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version

Notes

This function overrides c_code_cache_version unless it explicitly calls c_code_cache_version. The default implementation simply calls c_code_cache_version and ignores the node argument.

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

gpu_kernels(node, name)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(input)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out, ctx)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

prepare_node(node, storage_map, compute_map, impl)

Make any special modifications that the Op needs before doing Op.make_thunk.

This can modify the node inplace and should return nothing.

It can be called multiple time with different impl. It is the op responsibility to don’t re-prepare the node when it isn’t good to do so.

class theano.gpuarray.elemwise.GpuCAReduceCuda(scalar_op, axis=None, reduce_mask=None, dtype=None, acc_dtype=None, pre_scalar_op=None)

GpuCAReduceCuda is a Reduction along some dimensions by a scalar op.

Parameters:

• reduce_mask – The dimensions along which to reduce. The reduce_mask is a tuple of booleans (actually integers 0 or 1) that specify for each input dimension, whether to reduce it (1) or not (0).
• pre_scalar_op – If present, must be a scalar op with only 1 input. We will execute it on the input value before reduction.

Examples

When scalar_op is a theano.scalar.basic.Add instance:

• reduce_mask == (1,) sums a vector to a scalar
• reduce_mask == (1,0) computes the sum of each column in a matrix
• reduce_mask == (0,1) computes the sum of each row in a matrix
• reduce_mask == (1,1,1) computes the sum of all elements in a 3-tensor.

Notes

Any reduce_mask of all zeros is a sort of ‘copy’, and may be removed during graph optimization.

This Op is a work in progress.

This op was recently upgraded from just GpuSum a general CAReduce. Not many code cases are supported for scalar_op being anything other than scalar.Add instances yet.

Important note: if you implement new cases for this op, be sure to benchmark them and make sure that they actually result in a speedup. GPUs are not especially well-suited to reduction operations so it is quite possible that the GPU might be slower for some cases.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version_apply(node)

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version

Notes

This function overrides c_code_cache_version unless it explicitly calls c_code_cache_version. The default implementation simply calls c_code_cache_version and ignores the node argument.

c_code_reduce_01X(sio, node, name, x, z, fail, N)

Parameters:N – The number of 1 in the pattern N=1 -> 01, N=2 -> 011 N=3 ->0111 Work for N=1,2,3.

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_support_code(**kwargs)

Return utility code for use by a Variable or Op.

This is included at global scope prior to the rest of the code for this class.

Question: How many times will this support code be emitted for a graph with many instances of the same type?

Returns: Return type:str

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(x)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

supports_c_code(inputs)

Returns True if the current op and reduce pattern has functioning C code.

class theano.gpuarray.elemwise.GpuDimShuffle(input_broadcastable, new_order, inplace=True)

DimShuffle on the GPU.

make_node(input)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.elemwise.GpuElemwise(scalar_op, inplace_pattern=None, name=None, nfunc_spec=None, openmp=None)

Elemwise on the GPU.

c_cleanup_code_struct(node, name)

Return an Apply-specific code string to be inserted in the struct cleanup code.

Parameters:

• node (Apply) – The node in the graph being compiled
• name (str) – A unique name to distinguish variables from those of other nodes.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return the header file name “omp.h” if openMP is supported

c_init_code_struct(node, name, sub)

Return an Apply-specific code string to be inserted in the struct initialization code.

Parameters:

• node (Apply) – The node in the graph being compiled.
• name (str) – A unique name to distinguish variables from those of other nodes.
• sub (dict of str) – A dictionary of values to substitute in the code. Most notably it contains a 'fail' entry that you should place in your code after setting a Python exception to indicate an error.

c_support_code_struct(node, name)

Return Apply-specific utility code for use by an Op that will be inserted at struct scope.

Parameters:

• node (Apply) – The node in the graph being compiled
• name (str) – A unique name to distinguish you variables from those of other nodes.

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

make_node(*inputs)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

python_constant_folding(node)

Return True if we do not want to compile c code when doing constant folding of this node.

class theano.gpuarray.elemwise.GpuErfcinv(output_types_preference=None, name=None)

Inverse complementary error function for GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

class theano.gpuarray.elemwise.GpuErfinv(output_types_preference=None, name=None)

Inverse error function for GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

exception theano.gpuarray.elemwise.SupportCodeError

We do not support certain things (such as the C++ complex struct).

theano.gpuarray.elemwise.max_inputs_to_GpuElemwise(node_or_outputs)

Compute the maximum number of inputs that fit in a kernel call.

Subtensor Op

class theano.gpuarray.subtensor.GpuAdvancedIncSubtensor(inplace=False, set_instead_of_inc=False)

Implement AdvancedIncSubtensor on the gpu.

make_node(x, y, *inputs)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.subtensor.GpuAdvancedIncSubtensor1(inplace=False, set_instead_of_inc=False)

Implement AdvancedIncSubtensor1 on the gpu.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_init_code_struct(node, name, sub)

Return an Apply-specific code string to be inserted in the struct initialization code.

Parameters:

• node (Apply) – The node in the graph being compiled.
• name (str) – A unique name to distinguish variables from those of other nodes.
• sub (dict of str) – A dictionary of values to substitute in the code. Most notably it contains a 'fail' entry that you should place in your code after setting a Python exception to indicate an error.

c_support_code_struct(node, nodename)

Return Apply-specific utility code for use by an Op that will be inserted at struct scope.

Parameters:

• node (Apply) – The node in the graph being compiled
• name (str) – A unique name to distinguish you variables from those of other nodes.

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

make_node(x, y, ilist)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out_, params=None)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.subtensor.GpuAdvancedIncSubtensor1_dev20(inplace=False, set_instead_of_inc=False)

Implement AdvancedIncSubtensor1 on the gpu with atomics

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_support_code_struct(node, nodename)

Return Apply-specific utility code for use by an Op that will be inserted at struct scope.

Parameters:

• node (Apply) – The node in the graph being compiled
• name (str) – A unique name to distinguish you variables from those of other nodes.

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(x, y, ilist)

It differs from GpuAdvancedIncSubtensor1 in that it makes sure the indexes are of type long.

perform(node, inp, out, params)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.subtensor.GpuAdvancedSubtensor

AdvancedSubtensor on the GPU.

make_node(x, *inputs)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.subtensor.GpuAdvancedSubtensor1(sparse_grad=False)

AdvancedSubrensor1 on the GPU.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_support_code(**kwargs)

Return utility code for use by a Variable or Op.

This is included at global scope prior to the rest of the code for this class.

Question: How many times will this support code be emitted for a graph with many instances of the same type?

Returns: Return type:str

make_node(x, ilist)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inp, out_)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.subtensor.GpuAllocDiag(offset=0, axis1=0, axis2=1)

grad(inputs, gout)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(diag)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, outputs)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.subtensor.GpuExtractDiag(offset=0, axis1=0, axis2=1, view=False)

grad(inputs, gout)

Construct a graph for the gradient with respect to each input variable.

Each returned Variable represents the gradient with respect to that input computed based on the symbolic gradients with respect to each output. If the output is not differentiable with respect to an input, then this method should return an instance of type NullType for that input.

Parameters:

• inputs (list of Variable) – The input variables.
• output_grads (list of Variable) – The gradients of the output variables.

Returns:

grads – The gradients with respect to each Variable in inputs.

Return type:

list of Variable

make_node(_x)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

perform(node, inputs, outputs)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.subtensor.GpuIncSubtensor(idx_list, inplace=False, set_instead_of_inc=False, destroyhandler_tolerate_aliased=None)

Implement IncSubtensor on the gpu.

Notes

The optimization to make this inplace is in tensor/opt. The same optimization handles IncSubtensor and GpuIncSubtensor. This Op has c_code too; it inherits IncSubtensor’s c_code. The helper methods like do_type_checking(), copy_of_x(), etc. specialize the c_code for this Op.

add_to_zview(nodename, x, fail)

Return C code to add x to zview. Should DECREF zview if the add fails.

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_init_code_struct(node, name, sub)

Return an Apply-specific code string to be inserted in the struct initialization code.

Parameters:

• node (Apply) – The node in the graph being compiled.
• name (str) – A unique name to distinguish variables from those of other nodes.
• sub (dict of str) – A dictionary of values to substitute in the code. Most notably it contains a 'fail' entry that you should place in your code after setting a Python exception to indicate an error.

c_support_code(**kwargs)

Return utility code for use by a Variable or Op.

This is included at global scope prior to the rest of the code for this class.

Question: How many times will this support code be emitted for a graph with many instances of the same type?

Returns: Return type:str

c_support_code_struct(node, nodename)

Return Apply-specific utility code for use by an Op that will be inserted at struct scope.

Parameters:

• node (Apply) – The node in the graph being compiled
• name (str) – A unique name to distinguish you variables from those of other nodes.

copy_into(view, source)

Parameters:

• view (string) – C code expression for an array.
• source (string) – C code expression for an array.

Returns:

C code expression to copy source into view, and 0 on success.

Return type:

str

copy_of_x(x)

Parameters:x – A string giving the name of a C variable pointing to an array. Returns:C code expression to make a copy of x. Return type:str

Notes

Base class uses PyArrayObject *, subclasses may override for different types of arrays.

do_type_checking(node)

Should raise NotImplementedError if c_code does not support the types involved in this node.

get_helper_c_code_args()

Return a dictionary of arguments to use with helper_c_code.

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

make_node(x, y, *inputs)

Parameters:

• x – The tensor to increment.
• y – The value to increment by.
• inputs (TODO WRITEME) –

make_view_array(x, view_ndim)

//TODO

Parameters:

• x – A string identifying an array to be viewed.
• view_ndim – A string specifying the number of dimensions to have in the view. This doesn’t need to actually set up the view with the right indexing; we’ll do that manually later.

perform(node, inputs, out_, ctx)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

class theano.gpuarray.subtensor.GpuSubtensor(idx_list)

Subtensor on the GPU.

c_code(node, name, inputs, outputs, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_support_code(**kwargs)

Return utility code for use by a Variable or Op.

This is included at global scope prior to the rest of the code for this class.

Question: How many times will this support code be emitted for a graph with many instances of the same type?

Returns: Return type:str

make_node(x, *inputs)

Parameters:

• x – The tensor to take a subtensor of.
• inputs – A list of theano Scalars.

perform(node, inputs, out_)

Calculate the function on the inputs and put the variables in the output storage.

Parameters:

• node (Apply) – The symbolic Apply node that represents this computation.
• inputs (Sequence) – Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in node.inputs.
• output_storage (list of list) – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in node.outputs. The primary purpose of this method is to set the values of these sub-lists.
• params (tuple) – A tuple containing the values of each entry in __props__.

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy ndarray with the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this Op.perform; they could’ve been allocated by another Op’s perform method. A Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.

theano.gpuarray.subtensor.check_and_convert_boolean_masks(input, idx_list)

This function checks if the boolean mask arrays in the index have the right shape and converts them to index arrays by calling nonzero. For each boolean mask, we check if the mask has the same shape as the input. This is enforced in NumPy 0.13.0 and newer, but not by earlier versions. If the size is not the same, this method raises an IndexError.

Nnet Op

class theano.gpuarray.nnet.GpuCrossentropySoftmax1HotWithBiasDx

Implement CrossentropySoftmax1HotWithBiasDx on the gpu.

Gradient wrt x of the CrossentropySoftmax1Hot Op.

c_code(node, nodename, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(dnll, sm, y_idx)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.nnet.GpuCrossentropySoftmaxArgmax1HotWithBias

Implement CrossentropySoftmaxArgmax1HotWithBias on the gpu.

c_code(node, nodename, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_header_dirs(**kwargs)

Return a list of header search paths required by code returned by this class.

Provides search paths for headers, in addition to those in any relevant environment variables.

Note: for Unix compilers, these are the things that get -I prefixed in the compiler command line arguments.

Examples

def c_header_dirs(self, **kwargs):

return [‘/usr/local/include’, ‘/opt/weirdpath/src/include’]

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(x, b, y_idx)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.nnet.GpuSoftmax

Implement Softmax on the gpu.

c_code(node, nodename, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(x)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.nnet.GpuSoftmaxWithBias

Implement SoftmaxWithBias on the gpu.

c_code(node, nodename, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(x, b)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply

class theano.gpuarray.neighbours.GpuImages2Neibs(mode='valid')

Images2Neibs for the GPU.

c_code(node, name, inp, out, sub)

Return the C implementation of an Op.

Returns C code that does the computation associated to this Op, given names for the inputs and outputs.

Parameters:

• node (Apply instance) – The node for which we are compiling the current c_code. The same Op may be used in more than one node.
• name (str) – A name that is automatically assigned and guaranteed to be unique.
• inputs (list of strings) – There is a string for each input of the function, and the string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list.
• outputs (list of strings) – Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that can be accessed by prepending “py_” to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent.
• sub (dict of strings) – Extra symbols defined in CLinker sub symbols (such as ‘fail’). WRITEME

c_code_cache_version()

Return a tuple of integers indicating the version of this Op.

An empty tuple indicates an ‘unversioned’ Op that will not be cached between processes.

The cache mechanism may erase cached modules that have been superceded by newer versions. See ModuleCache for details.

See also

c_code_cache_version_apply

c_headers(**kwargs)

Return a list of header files required by code returned by this class.

These strings will be prefixed with #include and inserted at the beginning of the C source code.

Strings in this list that start neither with < nor " will be enclosed in double-quotes.

Examples

def c_headers(self, **kwargs):

return [‘<iostream>’, ‘<math.h>’, ‘/full/path/to/header.h’]

c_support_code(**kwargs)

Return utility code for use by a Variable or Op.

This is included at global scope prior to the rest of the code for this class.

Question: How many times will this support code be emitted for a graph with many instances of the same type?

Returns: Return type:str

get_params(node)

Try to detect params from the op if Op.params_type is set to a ParamsType.

gpu_kernels(node, nodename)

This is the method to override. This should return an iterable of Kernel objects that describe the kernels this op will need.

make_node(ten4, neib_shape, neib_step=None)

Construct an Apply node that represent the application of this operation to the given inputs.

This must be implemented by sub-classes.

Returns:node – The constructed Apply node. Return type:Apply