def test_optimized_plated_einsum_adjoint(equation, plates, backend):
inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(
equation)
with interpretation(reflect):
fwd_expr = einsum(equation,
*funsor_operands,
plates=plates,
backend=backend)
actuals = adjoint(fwd_expr, funsor_operands)
for operand in operands:
pyro_require_backward(operand)
expected_out = pyro_einsum(equation,
*operands,
modulo_total=False,
plates=plates,
backend=backend)[0]
expected_out._pyro_backward()
for i, (inp, tv, fv) in enumerate(zip(inputs, operands, funsor_operands)):
actual = actuals[fv]
expected = tv._pyro_backward_result
if inp:
actual = actual.align(tuple(inp))
assert isinstance(actual, funsor.Tensor)
assert expected.shape == actual.data.shape
assert torch.allclose(expected, actual.data, atol=1e-7)
def test_einsum_adjoint(einsum_impl, equation, backend):
inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(
equation)
sum_op, prod_op = BACKEND_ADJOINT_OPS[backend]
with AdjointTape() as tape: # interpretation(reflect):
fwd_expr = einsum_impl(equation, *funsor_operands, backend=backend)
actuals = tape.adjoint(sum_op, prod_op, fwd_expr, funsor_operands)
for operand in operands:
pyro_require_backward(operand)
expected_out = pyro_einsum(equation,
*operands,
modulo_total=True,
backend=backend)[0]
expected_out._pyro_backward()
for i, (inp, tv, fv) in enumerate(zip(inputs, operands, funsor_operands)):
actual = actuals[fv]
expected = tv._pyro_backward_result
if inp:
actual = actual.align(tuple(inp))
assert isinstance(actual, funsor.Tensor)
assert expected.shape == actual.data.shape
assert torch.allclose(expected, actual.data, atol=1e-7)
