def test_nested_einsum(eqn1, eqn2, optimize1, optimize2, backend1, backend2, einsum_impl):
inputs1, outputs1, sizes1, operands1, _ = make_einsum_example(eqn1, sizes=(3,))
inputs2, outputs2, sizes2, operands2, funsor_operands2 = make_einsum_example(eqn2, sizes=(3,))
# normalize the probs for ground-truth comparison
operands1 = [operand.abs() / operand.abs().sum(-1, keepdim=True)
for operand in operands1]
expected1 = pyro_einsum(eqn1, *operands1, backend=backend1, modulo_total=True)[0]
expected2 = pyro_einsum(outputs1[0] + "," + eqn2, *([expected1] + operands2),
backend=backend2, modulo_total=True)[0]
with interpretation(normalize):
funsor_operands1 = [
Categorical(probs=Tensor(
operand,
inputs=OrderedDict([(d, bint(sizes1[d])) for d in inp[:-1]])
))(value=Variable(inp[-1], bint(sizes1[inp[-1]]))).exp()
for inp, operand in zip(inputs1, operands1)
]
output1_naive = einsum_impl(eqn1, *funsor_operands1, backend=backend1)
with interpretation(reflect):
output1 = apply_optimizer(output1_naive) if optimize1 else output1_naive
output2_naive = einsum_impl(outputs1[0] + "," + eqn2, *([output1] + funsor_operands2), backend=backend2)
with interpretation(reflect):
output2 = apply_optimizer(output2_naive) if optimize2 else output2_naive
actual1 = reinterpret(output1)
actual2 = reinterpret(output2)
assert torch.allclose(expected1, actual1.data)
assert torch.allclose(expected2, actual2.data)
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_plated_einsum(equation, plates, backend):
inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(
equation)
expected = pyro_einsum(equation,
*operands,
plates=plates,
backend=backend,
modulo_total=False)[0]
with interpretation(reflect):
naive_ast = naive_plated_einsum(equation,
*funsor_operands,
plates=plates,
backend=backend)
optimized_ast = apply_optimizer(naive_ast)
actual_optimized = reinterpret(optimized_ast) # eager by default
actual = naive_plated_einsum(equation,
*funsor_operands,
plates=plates,
backend=backend)
if len(outputs[0]) > 0:
actual = actual.align(tuple(outputs[0]))
actual_optimized = actual_optimized.align(tuple(outputs[0]))
assert_close(actual,
actual_optimized,
atol=1e-3 if backend == 'torch' else 1e-4)
assert expected.shape == actual.data.shape
assert torch.allclose(expected, actual.data)
for output in outputs:
for i, output_dim in enumerate(output):
assert output_dim in actual.inputs
assert actual.inputs[output_dim].dtype == sizes[output_dim]
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)
def test_optimized_einsum(equation, backend, einsum_impl):
inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(equation)
expected = pyro_einsum(equation, *operands, backend=backend)[0]
with interpretation(normalize):
naive_ast = einsum_impl(equation, *funsor_operands, backend=backend)
optimized_ast = apply_optimizer(naive_ast)
actual = reinterpret(optimized_ast) # eager by default
assert isinstance(actual, funsor.Tensor) and len(outputs) == 1
if len(outputs[0]) > 0:
actual = actual.align(tuple(outputs[0]))
assert expected.shape == actual.data.shape
assert torch.allclose(expected, actual.data)
for output in outputs:
for i, output_dim in enumerate(output):
assert output_dim in actual.inputs
assert actual.inputs[output_dim].dtype == sizes[output_dim]
def test_optimized_plated_einsum(equation, plates, backend):
inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(equation)
expected = pyro_einsum(equation, *operands, plates=plates, backend=backend)[0]
actual = einsum(equation, *funsor_operands, plates=plates, backend=backend)
if len(equation) < 10:
actual_naive = naive_plated_einsum(equation, *funsor_operands, plates=plates, backend=backend)
assert_close(actual, actual_naive)
assert isinstance(actual, funsor.Tensor) and len(outputs) == 1
if len(outputs[0]) > 0:
actual = actual.align(tuple(outputs[0]))
assert expected.shape == actual.data.shape
assert torch.allclose(expected, actual.data)
for output in outputs:
for i, output_dim in enumerate(output):
assert output_dim in actual.inputs
assert actual.inputs[output_dim].dtype == sizes[output_dim]
