def single_state_reshape_same_state(inp: dace.float64[9],
target_shape: dace.int64[2]):
reshaped = dace.define_local([3, 3], dace.float64)
donnx.ONNXReshape(data=inp, shape=target_shape, reshaped=reshaped)
Zl = dace.elementwise(lambda x: log(x + 1), reshaped)
S = np.sum(Zl)
return S
def middle_sqrt_with_intermediate(Y: dace.float32[3, 3]):
intermediate = dace.define_local([3, 3], dace.float32)
W = dace.define_local([3, 3], dace.float32)
intermediate[:] = dace.elementwise(lambda x: sqrt(x), Y)
inner_sdfg_with_intermediate(intermediate, W)
Z = np.sum(W)
return Z
def outer_sqrt_with_intermediate(Y: dace.float32[3, 3]):
intermediate = dace.define_local([3, 3], dace.float32)
W = dace.define_local([3, 3], dace.float32)
intermediate[:] = dace.elementwise(lambda x: sqrt(x), Y)
W[:] = middle_sqrt_no_sum(intermediate)
Z = np.sum(W)
return Z
def dace_func(X: dace.float64[4, 5], Y: dace.float64[4, 3],
W: dace.float64[7, 4, 3]):
Xt[:] = np.transpose(X)
YW[:] = np.min(W, axis=0) * Y
Z[:] = Xt @ YW
Zl = dace.elementwise(lambda x: log(x + 1), Z)
S = np.sum(Zl)
return S
def add_reshape_grad_test_nested(inp: dace.float64[9],
bias: dace.float64[3],
target_shape: dace.int64[2],
result: dace.float64):
reshaped = dace.define_local([3, 3], dace.float64)
added = inp + 1
donnx.ONNXReshape(data=added, shape=target_shape, reshaped=reshaped)
Z = reshaped * bias
Zl = dace.elementwise(lambda x: log(x + 1), Z)
result[:] = np.sum(Zl)
def reduce_node_1_axis_and_none_axis(X: dace.float32[4, 5],
Y: dace.float32[4, 3],
W: dace.float32[7, 4, 3]):
Xt[:] = np.transpose(X)
YW[:] = np.sum(W, axis=0) * Y
Z[:] = Xt @ YW
Zl = dace.elementwise(lambda x: log(x + 1), Z)
S = np.sum(Zl)
return S
def prog(A: dace.float64[5, 3], B: dace.float64[5, 3]):
dace.elementwise(lambda x: log(x), A, B)
def elementwise_cast(A: dace.float32[5, 3], B: dace.float64[5, 3]):
dace.elementwise(lambda x: x, A, B)
def elementise_none(A: dace.float64[5, 3], B: dace.float64[5, 3]):
B[:] = dace.elementwise(lambda x: log(x), A)
def prog(A: dace.float32[5, 3], B: dace.float64[5, 3]):
dace.elementwise(lambda x: x, A, B)
def inner_sdfg_with_intermediate(Z: dace.float32[3, 3], W: dace.float32[3, 3]):
intermediate = dace.define_local([3, 3], dace.float32)
intermediate[:] = dace.elementwise(lambda x: sqrt(x), Z)
W[:] = dace.elementwise(lambda x: log(x), intermediate)
def inner_sdfg(Z: dace.float32[3, 3], W: dace.float32[3, 3]):
W[:] = dace.elementwise(lambda x: log(x), Z)
def prog(input, output):
output[:] = dace.elementwise(lambda x: log(x), input)
def prog(X, Y):
Y[:] = dace.elementwise(cast_lambda, X)
def prog(X, Y):
Y[:] = dace.elementwise(tanh_lambda, X)
def prog(X, Y):
Y[:] = dace.elementwise(lambda x: sqrt(x), X)
