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提交 79153fde authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Type context for nnet.py

上级 9eeafd89
隐藏空白字符变更
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正在显示 包含 53 行增加 和 35 行删除
theano/sandbox/gpuarray/nnet.py
浏览文件 @ 79153fde
......@@ -10,7 +10,8 @@ try:
except ImportError:
pass
from .basic_ops import (as_gpuarray_variable, GpuKernelBase, Kernel)
from .basic_ops import (as_gpuarray_variable, GpuKernelBase, Kernel,
infer_context_name)
from .type import GpuArrayType
from .kernel_codegen import (nvcc_kernel,
inline_softmax,
......@@ -23,23 +24,26 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
Implement CrossentropySoftmaxArgmax1HotWithBias on the gpu.
"""
nin = 3
nout = 3
__props__ = ()
_f16_ok = True
def make_node(self, x, b, y_idx):
# N.B. won't work when we don't cast y_idx to float anymore
x = as_gpuarray_variable(x)
b = as_gpuarray_variable(b)
y_idx = as_gpuarray_variable(y_idx)
ctx_name = infer_context_name(x, b, y_idx)
x = as_gpuarray_variable(x, ctx_name)
b = as_gpuarray_variable(b, ctx_name)
y_idx = as_gpuarray_variable(y_idx, ctx_name)
nll = GpuArrayType(x.type.dtype,
y_idx.type.broadcastable)()
y_idx.type.broadcastable,
context_name=ctx_name)()
sm = x.type()
am = y_idx.type()
return Apply(self, [x, b, y_idx], [nll, sm, am])
def get_context(self, node):
return node.inputs[0].type.context
def c_headers(self):
return ['<numpy_compat.h>', '<gpuarray/types.h>']
......@@ -144,6 +148,8 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
flags=flags, objvar=k_var)]
def c_code(self, node, nodename, inp, out, sub):
if node.inputs[0].type.context.kind != 'cuda':
raise NotImplementedError('cuda only')
typecode_x = pygpu.gpuarray.dtype_to_typecode(node.inputs[0].dtype)
typecode_b = pygpu.gpuarray.dtype_to_typecode(node.inputs[1].dtype)
typecode_y_idx = pygpu.gpuarray.dtype_to_typecode(node.inputs[2].dtype)
......@@ -163,6 +169,7 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
dtype_am = node.outputs[2].dtype
classname = self.__class__.__name__
fail = sub['fail']
ctx = sub['context']
k_var = "k_xent_sm_1hot_bias_%(nodename)s" % locals()
err_check = """
if (err != GA_NO_ERROR) {
......@@ -214,9 +221,8 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
{
Py_XDECREF(%(nll)s);
%(nll)s = pygpu_empty(1, PyGpuArray_DIMS(%(y_idx)s),
%(typecode_x)s,
GA_C_ORDER,
pygpu_default_context(), Py_None);
%(typecode_x)s, GA_C_ORDER, %(ctx)s,
Py_None);
if (!%(nll)s) {
%(fail)s
}
......@@ -229,9 +235,8 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
{
Py_XDECREF(%(sm)s);
%(sm)s = pygpu_empty(2, PyGpuArray_DIMS(%(x)s),
%(typecode_b)s,
GA_C_ORDER,
pygpu_default_context(), Py_None);
%(typecode_b)s, GA_C_ORDER,
%(ctx)s, Py_None);
if(!%(sm)s)
{
PyErr_SetString(PyExc_MemoryError,
......@@ -246,9 +251,8 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
{
Py_XDECREF(%(am)s);
%(am)s = pygpu_empty(1, PyGpuArray_DIMS(%(y_idx)s),
%(typecode_y_idx)s,
GA_C_ORDER,
pygpu_default_context(), Py_None);
%(typecode_y_idx)s, GA_C_ORDER,
%(ctx)s, Py_None);
if(!%(am)s)
{
PyErr_SetString(PyExc_MemoryError,
......@@ -306,18 +310,21 @@ class GpuCrossentropySoftmax1HotWithBiasDx(GpuKernelBase, Op):
Gradient wrt x of the CrossentropySoftmax1Hot Op.
"""
nin = 3
nout = 1
__props__ = ()
_f16_ok = True
def make_node(self, dnll, sm, y_idx):
dnll = as_gpuarray_variable(dnll)
sm = as_gpuarray_variable(sm)
y_idx = as_gpuarray_variable(y_idx)
ctx_name = infer_context_name(dnll, sm, y_idx)
dnll = as_gpuarray_variable(dnll, ctx_name)
sm = as_gpuarray_variable(sm, ctx_name)
y_idx = as_gpuarray_variable(y_idx, ctx_name)
return Apply(self, [dnll, sm, y_idx], [sm.type()])
def get_context(self, node):
return node.inputs[0].type.context
def c_code_cache_version(self):
return (11,)
......@@ -325,6 +332,8 @@ class GpuCrossentropySoftmax1HotWithBiasDx(GpuKernelBase, Op):
return ['<numpy_compat.h>', '<gpuarray/types.h>']
def c_code(self, node, nodename, inp, out, sub):
if node.inputs[0].type.context.kind != 'cuda':
raise NotImplementedError("cuda only")
typecode_dx = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
itemsize_dnll = numpy.dtype(node.inputs[0].dtype).itemsize
itemsize_sm = numpy.dtype(node.inputs[1].dtype).itemsize
......@@ -338,6 +347,7 @@ class GpuCrossentropySoftmax1HotWithBiasDx(GpuKernelBase, Op):
dnll, sm, y_idx = inp
dx, = out
fail = sub['fail']
ctx = sub['context']
k_var = "kCrossEntropySoftmax1HotWithBiasDx_" + nodename
err_check = """
if (err != GA_NO_ERROR) {
......@@ -403,9 +413,8 @@ class GpuCrossentropySoftmax1HotWithBiasDx(GpuKernelBase, Op):
{
Py_XDECREF(%(dx)s);
%(dx)s = pygpu_empty(2, PyGpuArray_DIMS(%(sm)s),
%(typecode_dx)s,
GA_C_ORDER,
pygpu_default_context(), Py_None);
%(typecode_dx)s, GA_C_ORDER,
%(ctx)s, Py_None);
if (!%(dx)s) {
%(fail)s
}
......@@ -512,14 +521,16 @@ class GpuSoftmax(GpuKernelBase, Op):
Implement Softmax on the gpu.
"""
__props__ = ()
_f16_ok = True
def make_node(self, x):
x = as_gpuarray_variable(x)
x = as_gpuarray_variable(x, infer_context_name(x))
return Apply(self, [x], [x.type()])
def get_context(self, node):
return node.inputs[0].type.context
def infer_shape(self, node, shape):
return shape
......@@ -530,6 +541,8 @@ class GpuSoftmax(GpuKernelBase, Op):
return ['<numpy_compat.h>', '<gpuarray/types.h>']
def c_code(self, node, nodename, inp, out, sub):
if node.inputs[0].type.context.kind != 'cuda':
raise NotImplementedError("cuda only")
dtype_x = node.inputs[0].dtype
work_x = work_dtype(dtype_x)
dtype_z = node.outputs[0].dtype
......@@ -539,6 +552,7 @@ class GpuSoftmax(GpuKernelBase, Op):
x, = inp
z, = out
fail = sub['fail']
ctx = sub['context']
err_check = """
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError, fmt_str, msg);
......@@ -568,9 +582,8 @@ class GpuSoftmax(GpuKernelBase, Op):
{
Py_XDECREF(%(z)s);
%(z)s = pygpu_empty(2, PyGpuArray_DIMS(%(x)s),
%(typecode)s,
GA_C_ORDER,
pygpu_default_context(), Py_None);
%(typecode)s, GA_C_ORDER,
%(ctx)s, Py_None);
if (!%(z)s) {
%(fail)s
}
......@@ -698,22 +711,25 @@ class GpuSoftmax(GpuKernelBase, Op):
gpu_softmax = GpuSoftmax()
class GpuSoftmaxWithBias (GpuKernelBase, Op):
class GpuSoftmaxWithBias(GpuKernelBase, Op):
"""
Implement SoftmaxWithBias on the gpu.
"""
nin = 2
nout = 1
__props__ = ()
_f16_ok = True
def make_node(self, x, b):
x = as_gpuarray_variable(x)
b = as_gpuarray_variable(b)
ctx_name = infer_context_name(x, b)
x = as_gpuarray_variable(x, ctx_name)
b = as_gpuarray_variable(b, ctx_name)
return Apply(self, [x, b], [x.type()])
def get_context(self, node):
return node.inputs[0].type.context
def infer_shape(self, node, shape):
return [shape[0]]
......@@ -724,6 +740,8 @@ class GpuSoftmaxWithBias (GpuKernelBase, Op):
return ['<numpy_compat.h>', '<gpuarray/types.h>']
def c_code(self, node, nodename, inp, out, sub):
if node.inputs[0].type.context.kind != 'cuda':
raise NotImplementedError('cuda only')
dtype_x = node.inputs[0].dtype
dtype_b = node.inputs[1].dtype
dtype_z = node.outputs[0].dtype
......@@ -735,6 +753,7 @@ class GpuSoftmaxWithBias (GpuKernelBase, Op):
x, b = inp
z, = out
fail = sub['fail']
ctx = sub['context']
err_check = """
if (err != GA_NO_ERROR) {
PyErr_Format(PyExc_RuntimeError, fmt_str, msg);
......@@ -777,9 +796,8 @@ class GpuSoftmaxWithBias (GpuKernelBase, Op):
{
Py_XDECREF(%(z)s);
%(z)s = pygpu_empty(2, PyGpuArray_DIMS(%(x)s),
%(typecode)s,
GA_C_ORDER,
pygpu_default_context(), Py_None);
%(typecode)s, GA_C_ORDER,
%(ctx)s, Py_None);
if (!%(z)s) {
%(fail)s
}
......
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