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[dnn/shift] fixed in leading dimensions for shift-conv operation

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This commit is contained in:
Philippe Tillet
2019-07-05 17:17:22 -07:00
parent c666f71fd6
commit 3e49dbe6ab
3 changed files with 14 additions and 18 deletions
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24
examples/python/tensorflow/run.py
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@@ -56,38 +56,30 @@ def blocksparse_matmul_grad(op, dy):
return (dx, dw) return (dx, dw)
def run_shift(): def run_shift():
B, C, H, W = 1, 16, 4, 4 B, C, H, W = 1, 16, 8, 8
R, S, F = 3, 3, 16 R, S, F = 3, 3, 16
np.random.seed(2) np.random.seed(2)
a = tf.placeholder(tf.float32, shape=[C, H, W, B]) a = tf.placeholder(tf.float32, shape=[C, H, W, B])
b = tf.placeholder(tf.float32, shape=[C, F]) b = tf.placeholder(tf.float32, shape=[C, F])
#hshift_h = np.random.randint(- (R//2), R//2 + 1, size=C, dtype=np.int32) hshift_h = np.random.randint(- (R//2), R//2 + 1, size=C, dtype=np.int32)
#hshift_w = np.random.randint(- (S//2), R//2 + 1, size=C, dtype=np.int32) hshift_w = np.random.randint(- (S//2), R//2 + 1, size=C, dtype=np.int32)
hshift_h = -1*np.ones(C, dtype=np.int32) #hshift_h = np.ones(C, dtype=np.int32)
hshift_w = -1*np.ones(C, dtype=np.int32) #hshift_w = np.ones(C, dtype=np.int32)
print(hshift_h) print(hshift_h)
print(hshift_w) print(hshift_w)
c = module.shift_conv(a, b, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w)) c = module.shift_conv(a, b, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w))
c = tf.math.reduce_sum(c)
# Reference # Reference
ha = np.ones((C, H, W, B), dtype=np.float32) ha = np.random.rand(C, H, W, B)
hb = np.ones((C, F), dtype=np.float32) hb = np.random.rand(C, F)
#ha = np.ones((C, H, W, B), dtype=np.int32) #ha = np.ones((C, H, W, B), dtype=np.int32)
#hb = np.ones((C, F), dtype=np.int32) #hb = np.ones((C, F), dtype=np.int32)
sess = tf.InteractiveSession() sess = tf.InteractiveSession()
grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (1,), grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (F, H, W, B),
extra_feed_dict={a: ha, b: hb}) extra_feed_dict={a: ha, b: hb})
dx_t, dx_n = grads[0] dx_t, dx_n = grads[0]
dw_t, dw_n = grads[1] dw_t, dw_n = grads[1]
#print(dw_t - dw_n)
#np.savetxt('diff.dat', dw_t - dw_n, fmt='%2.4f')
#np.savetxt('theoretical.dat', dw_t, fmt='%2.4f')
#np.savetxt('numerical.dat', dw_n, fmt='%2.4f')
print(np.max(np.abs(dw_t - dw_n))) print(np.max(np.abs(dw_t - dw_n)))
print(np.max(np.abs(dx_t - dx_n))) print(np.max(np.abs(dx_t - dx_n)))
np.savetxt('diff.dat', dx_t - dx_n, fmt='%2.4f')
np.savetxt('theoretical.dat', dx_t, fmt='%2.4f')
np.savetxt('numerical.dat', dx_n, fmt='%2.4f')
# Run # Run
sess.run(tf.global_variables_initializer()) sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {a: ha, result = sess.run([c], feed_dict = {a: ha,

2
examples/python/tensorflow/shift.cpp
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@@ -71,6 +71,8 @@ public:
// checks // checks
OP_REQUIRES(context, Ca == Cb, tensorflow::errors::InvalidArgument("operands must have the same number of channels")); OP_REQUIRES(context, Ca == Cb, tensorflow::errors::InvalidArgument("operands must have the same number of channels"));
C = Ca; C = Ca;
if(OP == triton::dnn::shift::BPROP)
std::swap(C, F);
} }
} }

6
lib/dnn/shift.cpp
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@@ -125,14 +125,16 @@ void shift::init(driver::stream *stream, driver::cu_module *module) {
void shift::enqueue(driver::stream *stream, driver::kernel *kernel, void shift::enqueue(driver::stream *stream, driver::kernel *kernel,
driver::buffer *a, driver::buffer *b, driver::buffer *c, driver::buffer *a, driver::buffer *b, driver::buffer *c,
size_t TM, size_t TN, size_t nthreads) { size_t TM, size_t TN, size_t nthreads) {
int32_t lda = AT_ ? K_ : M_;
int32_t ldb = BT_ ? N_ : K_;
kernel->setArg(0, a); kernel->setArg(0, a);
kernel->setArg(1, b); kernel->setArg(1, b);
kernel->setArg(2, c); kernel->setArg(2, c);
kernel->setArg(3, M_); kernel->setArg(3, M_);
kernel->setArg(4, N_); kernel->setArg(4, N_);
kernel->setArg(5, K_); kernel->setArg(5, K_);
kernel->setArg(6, M_); kernel->setArg(6, lda);
kernel->setArg(7, N_); kernel->setArg(7, ldb);
kernel->setArg(8, B_); kernel->setArg(8, B_);
kernel->setArg(9, AH_); kernel->setArg(9, AH_);
kernel->setArg(10, AW_); kernel->setArg(10, AW_);