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mkl_simplernn_op.py
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import theano
from theano import tensor, gof
from theano.tensor.blas import ldflags
from mkl_simplernn_backward import SimpleRNNGrad
from theano.gradient import DisconnectedType
class SimpleRNN(gof.Op):
__props__ = ('return_sequences',)
def __init__(self, return_sequences=False):
self.return_sequences = return_sequences
super(SimpleRNN, self).__init__()
def make_node(self, *inputs):
inp = list(map(tensor.as_tensor_variable, inputs))
#x = tensor.as_tensor_variable(x)
#h_init = tensor.as_tensor_variable(h_init)
#w_x = tensor.as_tensor_variable(w_x)
#w_h = tensor.as_tensor_variable(w_h)
#b = tensor.as_tensor_variable(b)
assert inp[0].ndim is 3 #x
assert inp[1].ndim is 2 #w_x
assert inp[2].ndim is 2 #w_h
assert inp[3].ndim is 2 #b
assert inp[4].ndim is 2 #h_init
if self.return_sequences:
out = [inp[0].type()]
else:
out = [inp[1].type()]
return gof.Apply(self, inp, out)
def c_headers(self):
headers = ['<mkl.h>']
return headers
def c_libraries(self):
return ldflags()
def c_support_code_struct(self, node, name):
if node.inputs[0].type.dtype is 'float32':
dtype = 'float'
elif node.inputs[0].type.dtype is 'float64':
dtype = 'double'
else:
raise TypeError('Gemm: dtype %s is not supported.'
% (node.inputs[0].type.dtype))
ccode = """
size_t timesteps;
size_t input_dim;
size_t batch_size;
size_t units;
%(dtype)s** A;
%(dtype)s** B;
%(dtype)s** C;
%(dtype)s* h_tm1;
%(dtype)s* b_3d;
MKL_INT m_g[1];
MKL_INT k_g[1];
MKL_INT n_g[1];
MKL_INT lda_g[1];
MKL_INT ldb_g[1];
MKL_INT ldc_g[1];
CBLAS_TRANSPOSE transA_g[1];
CBLAS_TRANSPOSE transB_g[1];
%(dtype)s alpha_g[1];
%(dtype)s beta_g[1];
MKL_INT size_per_grp[1];
""" % locals()
return ccode
def c_init_code_struct(self, node, name, sub):
ccode = """
timesteps = 0;
input_dim = 0;
batch_size = 0;
units = 0;
A = NULL;
B = NULL;
C = NULL;
h_tm1 = NULL;
b_3d = NULL;
m_g[0] = 0;
k_g[0] = 0;
n_g[0] = 0;
lda_g[0] = 0;
ldb_g[0] = 0;
ldc_g[0] = 0;
transA_g[0] = CblasNoTrans;
transB_g[0] = CblasNoTrans;
alpha_g[0] = 1.0;
beta_g[0] = 1.0;
size_per_grp[0] = 1;
""" % locals()
return ccode
def c_cleanup_code_struct(self, node, name):
ccode = """
if (A) {
mkl_free (A);
A = NULL;
}
if (B) {
mkl_free (B);
B = NULL;
}
if (C) {
mkl_free (C);
C = NULL;
}
if (h_tm1) {
mkl_free (h_tm1);
h_tm1 = NULL;
}
if (b_3d) {
mkl_free (b_3d);
b_3d = NULL;
}
"""
return ccode
def c_code(self, node, name, inputs, outputs, sub):
x, w_x, w_h, b, h_init = inputs
if self.return_sequences:
return_sequences = 1
else:
return_sequences = 0
o, = outputs
if node.inputs[0].type.dtype is 'float32':
dtype = 's'
d = 'float'
elif node.inputs[0].type.dtype is 'float64':
dtype = 'd'
d = 'double'
else:
raise TypeError('Gemm: dtype %s is not supported.'
% (node.inputs[0].type.dtype))
#print locals()
ccode = """
int i = 0;
timesteps = PyArray_DIMS(%(x)s)[0];
input_dim = PyArray_DIMS(%(x)s)[2];
batch_size = PyArray_DIMS(%(x)s)[1];
units = PyArray_DIMS(%(h_init)s)[1];
%(d)s* b_ptr = NULL;
PyArrayObject* b_src = NULL;
if (!PyArray_IS_C_CONTIGUOUS(%(b)s)) {
printf(\"Warning: Need convert bias to C-Contiguous\\n\");
b_src = (PyArrayObject*)PyArray_ContiguousFromAny((PyObject*)%(b)s,
PyArray_TYPE(%(b)s),
PyArray_NDIM(%(b)s),
PyArray_NDIM(%(b)s));
if (!b_src) {
PyErr_SetString(PyExc_RuntimeError, \"GRU: fail to case bias to contiguous array\");
goto simplernn_fail;
}
b_ptr = (%(d)s*) PyArray_DATA(b_src);
} else {
b_ptr = (%(d)s*) PyArray_DATA(%(b)s);
}
// check Dot(x, w_x)
if (PyArray_DIMS(%(w_x)s)[0] != PyArray_DIMS(%(x)s)[2]) {
PyErr_SetString(PyExc_RuntimeError, \"GRU: dot x,w_x size error\");
goto simplernn_fail;
}
// check Dot(h_init, w_h)
if (PyArray_DIMS(%(w_h)s)[0] != PyArray_DIMS(%(h_init)s)[1]) {
PyErr_SetString(PyExc_RuntimeError, \"GRU: dot h_init,w_h size error\");
goto simplernn_fail;
}
// check b
if (PyArray_DIMS(%(b)s)[1] != PyArray_DIMS(%(w_h)s)[1]) {
PyErr_SetString(PyExc_RuntimeError, \"GRU: b size error\");
goto simplernn_fail;
}
if (NULL == b_3d) {
b_3d = (%(d)s*) mkl_malloc(timesteps * batch_size * units * sizeof (%(d)s), 64);
}
for (i = 0; i < timesteps; i++){
memcpy((void*)(b_3d) + i*batch_size*units* sizeof (%(d)s), (void*)b_ptr, batch_size*units* sizeof (%(d)s));
}
m_g[0] = batch_size;
k_g[0] = input_dim;
n_g[0] = units;
lda_g[0] = k_g[0];
ldb_g[0] = n_g[0];
ldc_g[0] = n_g[0];
size_per_grp[0] = timesteps;
if (A == NULL)
A = (%(d)s**)mkl_malloc(timesteps * sizeof (%(d)s*), 64);
if (B == NULL)
B = (%(d)s**)mkl_malloc(timesteps * sizeof (%(d)s*), 64);
if (C == NULL)
C = (%(d)s**)mkl_malloc(timesteps * sizeof (%(d)s*), 64);
if (A == NULL || B == NULL || C == NULL) {
printf( "\\n ERROR: Can't allocate memory for matrices. Aborting... \\n");
mkl_free(A);
mkl_free(B);
mkl_free(C);
return 1;
}
for (i = 0 ; i < timesteps; i ++) {
A[i] = (%(d)s*) PyArray_DATA(%(x)s) + i * batch_size * input_dim;
B[i] = (%(d)s*) PyArray_DATA(%(w_x)s);
C[i] = (%(d)s*) b_3d + i * batch_size * units;
}
//xW+b
cblas_%(dtype)sgemm_batch (
CblasRowMajor,
transA_g,
transB_g,
m_g,
n_g,
k_g,
alpha_g,
A,
lda_g,
B,
ldb_g,
beta_g,
C,
ldc_g,
1,
size_per_grp);
int sz = units * batch_size;
//%(d)s *h_tm1 = NULL;
npy_intp dims[3] = {0, 0, 0};
if (NULL == %(o)s) {
if (%(return_sequences)s) {
dims[0] = timesteps;
dims[1] = batch_size;
dims[2] = units;
%(o)s = (PyArrayObject*) PyArray_ZEROS(3, dims, PyArray_TYPE(%(x)s), 0);
} else {
dims[0] = batch_size;
dims[1] = units;
%(o)s = (PyArrayObject*) PyArray_ZEROS(2, dims, PyArray_TYPE(%(x)s), 0);
}
}
//h_tm1 = (%(d)s*)PyArray_DATA(%(o)s);
if (NULL == h_tm1) {
h_tm1 = (%(d)s*) mkl_malloc(batch_size * units * sizeof (%(d)s), 64);
}
if (NULL == h_tm1) {
PyErr_SetString(PyExc_MemoryError, \"SimpleRNN: create buffer for hidden state failed\");
goto simplernn_fail;
}
for(i = 0; i < sz; i++){
h_tm1[i] = ((%(d)s*) PyArray_DATA(%(h_init)s))[i];
}
for (i = 0; i < timesteps; i ++) {
cblas_%(dtype)sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
batch_size, units, units, 1.0, h_tm1, units, (%(d)s*) PyArray_DATA(%(w_h)s), units, 1.0, C[i], units);
v%(dtype)sTanh(sz, C[i], h_tm1);
if (%(return_sequences)s) {
memcpy(((char*)PyArray_DATA(%(o)s)) + i * sz * sizeof (%(d)s),
(char*)h_tm1,
sz * sizeof (%(d)s));
} else {
if (i == timesteps - 1) {
memcpy(((char*)PyArray_DATA(%(o)s)),
(char*)h_tm1,
sz * sizeof (%(d)s));
}
}
}
simplernn_fail:
Py_XDECREF(b_src);
""" % locals()
return ccode
def grad(self, inp, grads):
x, wx, wh, b, h_init, = inp[0:5]
gz, = grads
#disc = [DisconnectedType()() for i in inp[4:]]
h = SimpleRNN(self.return_sequences)(x, wx, wh, b, h_init)
gradX, gradW, gradU, gradB, gradHinit = SimpleRNNGrad(self.return_sequences)(x, h_init, h, wx, wh, gz)
return [gradX, gradW, gradU, gradB, gradHinit]
#return [gradX, gradW, gradU, gradB] + disc
def c_code_cache_version(self):
return (1, 0, 0)
#def connect_pattern(self, node):
# return [[1], [1], [1], [1], [0]]