def _fwd_grad(self, wrt, valuation, cache):
q = pl.qs[0]
lhs = cache[id(self.ops[0])]
rhs = cache[id(self.ops[1])]
a = linalg.dot(q, self.ops[0]._fwd_grad(wrt, valuation, cache), rhs)
b = linalg.dot(q, lhs, self.ops[1]._fwd_grad(wrt, valuation, cache))
return a + b
def _rev_grad(self, valuation, adjoint, gradient, cache):
q = pl.qs[0]
lhs = cache[id(self.ops[0])]
rhs = cache[id(self.ops[1])]
adj1 = linalg.dot(q, adjoint, rhs, transB=not self.transB)
adj2 = linalg.dot(q, lhs, adjoint, transA=not self.transA)
self.ops[0]._rev_grad(valuation, adj1, gradient, cache)
self.ops[1]._rev_grad(valuation, adj2, gradient, cache)
def _evaluate(self, valuation, cache):
q = pl.qs[0]
if id(self) not in cache:
X = self.ops[0]._evaluate(valuation, cache)
W = self.ops[1]._evaluate(valuation, cache)
b = self.ops[2]._evaluate(valuation, cache)
out_c, _, kh, kw = W.shape
n, c, h, w = X.shape
out_h = conv.get_conv_outsize(h,
kh,
self.sy,
self.ph,
cover_all=self.cover_all)
out_w = conv.get_conv_outsize(w,
kw,
self.sx,
self.pw,
cover_all=self.cover_all)
y = clarray.empty(q, (n, out_c, out_h, out_w), dtype=X.dtype)
self.col, ev1 = conv.im2col(q, X, kh, kw, self.sy, self.sx,
self.ph, self.pw, self.cover_all)
W_mat = W.reshape(out_c, -1)
ev1.wait() # TODO asynchronize
col_mats = self.col.reshape(n, -1, out_h * out_w)
y_mats = y.reshape(n, out_c, -1)
for i in xrange(n):
y_mats[i] = linalg.dot(q, W_mat, col_mats[i])
if b is not None:
# y += b[:, None, None]
_, ev3 = conv.bcast_add(q, y, b, y)
ev3.wait() # TODO asynchronize
cache[id(self)] = y
return cache[id(self)]
def _evaluate(self, valuation, cache):
q = pl.qs[0]
if id(self) not in cache:
e1, e2 = self.ops[0]._evaluate, self.ops[1]._evaluate
cache[id(self)] = linalg.dot(q, e1(valuation, cache),
e2(valuation, cache))
return cache[id(self)]
def _evaluate(self, valuation, cache):
if id(self) not in cache:
q = pl.qs[0]
o1 = self.ops[0]._evaluate(valuation, cache)
o2 = self.ops[1]._evaluate(valuation, cache)
self.diff = o1 - o2
self.diffr = self.diff.ravel()
dop = linalg.dot(q, self.diffr, self.diffr)
cache[id(self)] = dop / (2.0 * self.diff.size)
return cache[id(self)]
def test_dot(self):
q = clplatf.qs[0]
X = np.random.uniform(0, 1, (50000, )).astype(np.float32)
Y = np.random.uniform(0, 1, (50000, )).astype(np.float32)
expected = np.dot(X, Y)
gX = clarray.to_device(q, X)
gY = clarray.to_device(q, Y)
gR = linalg.dot(q, gX, gY)
R = gR.get()
self.assertTrue(np.allclose(R, expected))
A = np.random.uniform(0, 1, (512, 512)).astype(np.float32)
B = np.random.uniform(0, 1, (512, 512)).astype(np.float32)
expected = np.dot(A, B)
gA = clarray.to_device(q, A)
gB = clarray.to_device(q, B)
gC = linalg.dot(q, gA, gB)
C = gC.get()
self.assertTrue(np.allclose(C, expected))
def _rev_grad(self, valuation, adjoint, gradient, cache):
q = pl.qs[0]
X = cache[id(self.ops[0])]
W = cache[id(self.ops[1])]
b = cache[id(self.ops[2])]
gy = adjoint
_, out_c, out_h, out_w = gy.shape
n, c, h, w = X.shape
kh, kw = W.shape[2:]
gW = clarray.zeros_like(W)
gW_mat = gW.reshape(out_c, c * kh * kw)
col_mats = self.col.reshape(n, c * kh * kw, out_h * out_w)
gy_mats = gy.reshape(n, out_c, out_h * out_w)
for i in xrange(n):
gwmat = linalg.dot(q, gy_mats[i], col_mats[i], transB=True)
gW_mat += gwmat
W_mat = W.reshape(out_c, -1)
gcol = clarray.empty_like(self.col)
gcol_mats = gcol.reshape(n, c * kh * kw, out_h * out_w)
for i in xrange(n):
gcol_mats[i] = linalg.dot(q, W_mat, gy_mats[i], transA=True)
gx, ev = conv.col2im(q, gcol, self.sy, self.sx, self.ph, self.pw, h, w)
ev.wait()
gb = None
if b is not None:
gb, ev = conv.bgrads_sum(q, gy)
ev.wait()
# TODO bias... sum along multiple axes of gy?
# TODO set gW, gx and gb in gradient dict
self.ops[0]._rev_grad(valuation, gx, gradient, cache)
self.ops[1]._rev_grad(valuation, gW, gradient, cache)
if gb is not None:
self.ops[2]._rev_grad(valuation, gb, gradient, cache)
def test_dot_offentdingvectors(self):
q = clplatf.qs[0]
X = np.loadtxt(open('test/gymat.txt', 'r'),
delimiter=',').astype(np.float32)
Y = np.loadtxt(open('test/colmat.txt', 'r'),
delimiter=',').astype(np.float32)
gX = clarray.to_device(q, X)
gY = clarray.to_device(q, Y)
expected = X.dot(Y.T)
gR = linalg.dot(q, gX, gY, transB=True)
R = gR.get()
print >> sys.stderr, '\nReal:\n', R
print >> sys.stderr, 'expected:\n', expected
print >> sys.stderr, 'shapes: r:', R.shape, 'e:', expected.shape
print >> sys.stderr, 'mean diff:', np.mean(R - expected)
self.assertTrue(np.allclose(R, expected))
def test_dot_again(self):
q = clplatf.qs[0]
X = np.random.uniform(0, 1, (128, 64, 1024)).astype(np.float32)
Y = np.random.uniform(0, 1, (128, 27, 1024)).astype(np.float32)
gX = clarray.to_device(q, X)
gY = clarray.to_device(q, Y)
for i in range(128):
expected = X[i].dot(Y[i].T)
gR = linalg.dot(q, gX[i], gY[i], transB=True)
R = gR.get()
if not np.allclose(R, expected):
print >> sys.stderr, '\nReal:\n', R
print >> sys.stderr, 'expected:\n', expected
print >> sys.stderr, 'shapes: r:', R.shape, 'e:', expected.shape
print >> sys.stderr, 'mean diff:', np.mean(R - expected)
break
self.assertTrue(np.allclose(R, expected))
def train(self, X, Y, learning_rate=0.01):
val = pl.valuation()
val['X'] = X
val['Y'] = Y
for name, value in self.params:
val[name] = value
grad = self.cost.rev_grad(val)
debatch_help_vector = clarray.zeros(pl.qs[0], (Y.shape[0], 1),
dtype=np.float32) + 1
for name, value in self.params:
if name.startswith('b'):
dbh = linalg.dot(pl.qs[0],
grad[name],
debatch_help_vector,
transA=True)
value -= learning_rate * dbh.ravel()
else:
value -= learning_rate * grad[name]
