def forward_gpu(self, x):
i_len, j_len = array.as_mat(x[0]).shape
k_len = array.as_mat(x[1]).shape[1]
l_len = self.W.shape[2]
# When indices are enclosed with [], they are 'flatten'
# (i.e. linealized as 1-D array)
# ij->[ij]
e1 = array.as_vec(x[0])
# ik->[ik]
e2 = array.as_vec(x[1])
e1e2 = cuda.empty(i_len * j_len * k_len, dtype=numpy.float32)
# '[ij],[ik]->[ijk]'
cuda.elementwise(
'float* y, float* e1, float* e2, int e1c, int e2c',
'''
int I = i / e1c / e2c;
int J = (i - I * e1c * e2c) / e2c;
int K = i % e2c;
y[i] = e1[I * e1c + J] * e2[I * e2c + K];
''',
'row_wise_outer_product')(
e1e2, e1, e2, j_len, k_len)
# [ijk]->i[jk]
e1e2 = e1e2.reshape(i_len, j_len * k_len)
# jkl->[jk]l
W_mat = self.W.reshape(
self.W.shape[0] * self.W.shape[1], self.W.shape[2])
y = cuda.empty((i_len, l_len), dtype=numpy.float32)
with cuda.using_cumisc():
# 'i[jk],[jk]l->il'
cuda.culinalg.dot(e1e2, W_mat, out=y)
if not self.nobias:
e1 = array.as_mat(x[0])
e2 = array.as_mat(x[1])
with cuda.using_cumisc():
# ij,jl->il
cuda.culinalg.add_dot(e1, self.V1, y)
# ik,kl->il
cuda.culinalg.add_dot(e2, self.V2, y)
cuda.elementwise(
'float* y, float* b, int n_channel',
'y[i] += b[i % n_channel]',
'linear_bias')(y, self.b, self.b.size)
return y,
def forward_gpu(self, x):
i_len, j_len = array.as_mat(x[0]).shape
k_len = array.as_mat(x[1]).shape[1]
l_len = self.W.shape[2]
# When indices are enclosed with [], they are 'flatten'
# (i.e. linealized as 1-D array)
# ij->[ij]
e1 = array.as_vec(x[0])
# ik->[ik]
e2 = array.as_vec(x[1])
e1e2 = cuda.empty(i_len * j_len * k_len, dtype=numpy.float32)
# '[ij],[ik]->[ijk]'
cuda.elementwise(
'float* y, float* e1, float* e2, int e1c, int e2c', '''
int I = i / e1c / e2c;
int J = (i - I * e1c * e2c) / e2c;
int K = i % e2c;
y[i] = e1[I * e1c + J] * e2[I * e2c + K];
''', 'row_wise_outer_product')(e1e2, e1, e2, j_len, k_len)
# [ijk]->i[jk]
e1e2 = e1e2.reshape(i_len, j_len * k_len)
# jkl->[jk]l
W_mat = self.W.reshape(self.W.shape[0] * self.W.shape[1],
self.W.shape[2])
y = cuda.empty((i_len, l_len), dtype=numpy.float32)
with cuda.using_cumisc():
# 'i[jk],[jk]l->il'
cuda.culinalg.dot(e1e2, W_mat, out=y)
if not self.nobias:
e1 = array.as_mat(x[0])
e2 = array.as_mat(x[1])
with cuda.using_cumisc():
# ij,jl->il
cuda.culinalg.add_dot(e1, self.V1, y)
# ik,kl->il
cuda.culinalg.add_dot(e2, self.V2, y)
cuda.elementwise('float* y, float* b, int n_channel',
'y[i] += b[i % n_channel]',
'linear_bias')(y, self.b, self.b.size)
return y,
def backward_gpu(self, x, gy):
i_len, j_len = array.as_mat(x[0]).shape
k_len = array.as_mat(x[1]).shape[1]
l_len = gy[0].shape[1]
# ij->[ij]
e1 = array.as_vec(x[0])
# ik->[ik]
e2 = array.as_vec(x[1])
gy, = gy
# il->[il]
gy_vec = array.as_vec(gy)
# jkl->[jkl]
W_vec = array.as_vec(self.W)
dgW = cuda.empty((j_len * k_len * l_len,), dtype=numpy.float32)
# '[ij],[ik],[il]->[jkl]'
cuda.elementwise(
'''
float* y, float* e1, float* e2, float* gy,
int r, int e1c, int e2c, int gyc
''',
'''
int J = i / e2c / gyc;
int K = (i - J * e2c * gyc) / gyc;
int L = i % gyc;
float yval = 0;
for (int I = 0; I < r; ++I) {
int e1idx = I * e1c + J;
int e2idx = I * e2c + K;
int gyidx = I * gyc + L;
yval += e1[e1idx] * e2[e2idx] * gy[gyidx];
}
y[i] = yval;
''',
'sum_of_three_ary_tensor_product')(
dgW, e1, e2, gy_vec, i_len, j_len, k_len, l_len)
# [jkl]->jkl
self.gW += dgW.reshape((j_len, k_len, l_len))
if not self.nobias:
e1 = array.as_mat(x[0])
e2 = array.as_mat(x[1])
with cuda.using_cumisc():
# ij,il->jl
cuda.culinalg.add_dot(e1, gy, self.gV1, transa='T')
# ik,il->kl
cuda.culinalg.add_dot(e2, gy, self.gV2, transa='T')
self.gb += cuda.cumisc.sum(gy, 0)
ge1 = cuda.empty((i_len * j_len,), dtype=numpy.float32)
# '[ik],[jkl],[il]->[ij]'
cuda.elementwise(
'''
float* y, float* e, float* W, float* gy,
int ec, int gyc, int gec
''',
'''
int I = i / gec;
int J = i % gec;
float yval = 0;
for (int K = 0; K < ec; ++K) {
for (int L = 0; L < gyc; ++L) {
int eidx = I * ec + K;
int Widx = J * ec * gyc + K * gyc + L;
int gyidx = I * gyc + L;
yval += e[eidx] * W[Widx] * gy[gyidx];
}
}
y[i] = yval;
''',
'ge_kernel')(ge1, e2, W_vec, gy_vec, k_len, l_len, j_len)
# [ij]->ij
ge1 = ge1.reshape(i_len, j_len)
ge2 = cuda.empty((i_len * k_len,), dtype=numpy.float32)
# '[ij],[jkl],[il]->[ik]'
cuda.elementwise(
'''
float* y, float* e, float* W, float* gy,
int ec, int gyc, int gec
''',
'''
int I = i / gec;
int K = i % gec;
float yval = 0;
for (int J = 0; J < ec; ++J) {
for (int L = 0; L < gyc; ++L) {
int eidx = I * ec + J;
int Widx = J * gec * gyc + K * gyc + L;
int gyidx = I * gyc + L;
yval += e[eidx] * W[Widx] * gy[gyidx];
}
}
y[i] = yval;
''',
'ge_kernel2')(ge2, e1, W_vec, gy_vec, j_len, l_len, k_len)
# [ik]->ik
ge2 = ge2.reshape(i_len, k_len)
if not self.nobias:
with cuda.using_cumisc():
# il,jl->ij
cuda.culinalg.add_dot(gy, self.V1, ge1, transb='T')
# il,kl->ik
cuda.culinalg.add_dot(gy, self.V2, ge2, transb='T')
return (ge1.reshape(x[0].shape), ge2.reshape(x[1].shape))
def backward_gpu(self, x, gy):
i_len, j_len = array.as_mat(x[0]).shape
k_len = array.as_mat(x[1]).shape[1]
l_len = gy[0].shape[1]
# ij->[ij]
e1 = array.as_vec(x[0])
# ik->[ik]
e2 = array.as_vec(x[1])
gy, = gy
# il->[il]
gy_vec = array.as_vec(gy)
# jkl->[jkl]
W_vec = array.as_vec(self.W)
dgW = cuda.empty((j_len * k_len * l_len, ), dtype=numpy.float32)
# '[ij],[ik],[il]->[jkl]'
cuda.elementwise(
'''
float* y, float* e1, float* e2, float* gy,
int r, int e1c, int e2c, int gyc
''', '''
int J = i / e2c / gyc;
int K = (i - J * e2c * gyc) / gyc;
int L = i % gyc;
float yval = 0;
for (int I = 0; I < r; ++I) {
int e1idx = I * e1c + J;
int e2idx = I * e2c + K;
int gyidx = I * gyc + L;
yval += e1[e1idx] * e2[e2idx] * gy[gyidx];
}
y[i] = yval;
''', 'sum_of_three_ary_tensor_product')(dgW, e1, e2, gy_vec, i_len,
j_len, k_len, l_len)
# [jkl]->jkl
self.gW += dgW.reshape((j_len, k_len, l_len))
if not self.nobias:
e1 = array.as_mat(x[0])
e2 = array.as_mat(x[1])
with cuda.using_cumisc():
# ij,il->jl
cuda.culinalg.add_dot(e1, gy, self.gV1, transa='T')
# ik,il->kl
cuda.culinalg.add_dot(e2, gy, self.gV2, transa='T')
self.gb += cuda.cumisc.sum(gy, 0)
ge1 = cuda.empty((i_len * j_len, ), dtype=numpy.float32)
# '[ik],[jkl],[il]->[ij]'
cuda.elementwise(
'''
float* y, float* e, float* W, float* gy,
int ec, int gyc, int gec
''', '''
int I = i / gec;
int J = i % gec;
float yval = 0;
for (int K = 0; K < ec; ++K) {
for (int L = 0; L < gyc; ++L) {
int eidx = I * ec + K;
int Widx = J * ec * gyc + K * gyc + L;
int gyidx = I * gyc + L;
yval += e[eidx] * W[Widx] * gy[gyidx];
}
}
y[i] = yval;
''', 'ge_kernel')(ge1, e2, W_vec, gy_vec, k_len, l_len, j_len)
# [ij]->ij
ge1 = ge1.reshape(i_len, j_len)
ge2 = cuda.empty((i_len * k_len, ), dtype=numpy.float32)
# '[ij],[jkl],[il]->[ik]'
cuda.elementwise(
'''
float* y, float* e, float* W, float* gy,
int ec, int gyc, int gec
''', '''
int I = i / gec;
int K = i % gec;
float yval = 0;
for (int J = 0; J < ec; ++J) {
for (int L = 0; L < gyc; ++L) {
int eidx = I * ec + J;
int Widx = J * gec * gyc + K * gyc + L;
int gyidx = I * gyc + L;
yval += e[eidx] * W[Widx] * gy[gyidx];
}
}
y[i] = yval;
''', 'ge_kernel2')(ge2, e1, W_vec, gy_vec, j_len, l_len, k_len)
# [ik]->ik
ge2 = ge2.reshape(i_len, k_len)
if not self.nobias:
with cuda.using_cumisc():
# il,jl->ij
cuda.culinalg.add_dot(gy, self.V1, ge1, transb='T')
# il,kl->ik
cuda.culinalg.add_dot(gy, self.V2, ge2, transb='T')
return (ge1.reshape(x[0].shape), ge2.reshape(x[1].shape))
