diff --git a/python/tvm/relay/op/_tensor_grad.py b/python/tvm/relay/op/_tensor_grad.py index 173e97a00496..0e796294e96c 100644 --- a/python/tvm/relay/op/_tensor_grad.py +++ b/python/tvm/relay/op/_tensor_grad.py @@ -3,7 +3,7 @@ from __future__ import absolute_import from ..expr import const from .op import register_gradient -from .transform import collapse_sum_like, where +from .transform import collapse_sum_like, broadcast_to_like, where from .tensor import exp, negative, power, less from .tensor import zeros_like, ones_like @@ -77,3 +77,20 @@ def divide_grad(orig, grad): x, y = orig.args return [collapse_sum_like(grad / y, x), collapse_sum_like(- (grad * orig / y), y)] + + +@register_gradient("zeros_like") +def zeros_like_grad(orig, grad): + """Returns [0]""" + return [orig] + +@register_gradient("ones_like") +def ones_like_grad(orig, grad): + """Returns [0]""" + return [zeros_like(orig.args[0])] + +@register_gradient("collapse_sum_like") +def collapse_sum_like_grad(orig, grad): + """Returns [broadcast_to_like(grad, x), 0]""" + x, y = orig.args + return [broadcast_to_like(grad, x), zeros_like(y)] diff --git a/src/relay/pass/fuse_ops.cc b/src/relay/pass/fuse_ops.cc index c7b16da9036c..12c1826ff4e8 100644 --- a/src/relay/pass/fuse_ops.cc +++ b/src/relay/pass/fuse_ops.cc @@ -263,21 +263,19 @@ class IndexedForwardGraph::Creator : private ExprVisitor { void VisitExpr_(const TupleGetItemNode* op) final { auto tuple_type = op->tuple->checked_type().as(); CHECK(tuple_type); - // If this tuple contain a reference type, and we fuse TupleGetItem and - // the reference, a fused function will have a tuple containing a reference - // in its parameters. But when TVM lowers a fused function, it expects all - // arguments to be a Tensor or a tuple containing only Tensors. - // To avoid modifying codegen logic, we do not allow fusing through a reference. - // The reference itself will be recursively visited via call to ExprVisitor::VisitExpr_(op) - // below and corresponding visitor methods - bool has_reference = false; + // when TVM lowers a fused function, it expects all arguments to be a Tensor or + // a tuple containing only Tensors. But this tuple may contain a reference or + // another tuple. To avoid modifying codegen logic, we do not allow fusing through this node + // if the tuple contains such non Tensor fields. However, all fields will be recursively + // visited via call to ExprVisitor::VisitExpr_(op) below and corresponding visitor methods. + bool has_non_tensor = false; for (auto ty : tuple_type->fields) { - if (ty.as()) { - has_reference = true; + if (!ty.as()) { + has_non_tensor = true; break; } } - if (has_reference) { + if (has_non_tensor) { this->Update(op->tuple, nullptr, kOpaque); } else { CHECK(graph_.node_map.count(op)); diff --git a/tests/python/relay/test_pass_gradient.py b/tests/python/relay/test_pass_gradient.py index 400941f12617..690c82e5febe 100644 --- a/tests/python/relay/test_pass_gradient.py +++ b/tests/python/relay/test_pass_gradient.py @@ -20,8 +20,8 @@ def test_id(): ex = create_executor() x = rand(dtype, *shape) forward, (grad,) = ex.evaluate(back_func)(x) - np.testing.assert_allclose(forward.asnumpy(), x.asnumpy()) - np.testing.assert_allclose(grad.asnumpy(), np.ones_like(x.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), x.asnumpy()) + tvm.testing.assert_allclose(grad.asnumpy(), np.ones_like(x.asnumpy())) def test_add(): @@ -35,8 +35,8 @@ def test_add(): ex = create_executor() x = rand(dtype, *shape) forward, (grad,) = ex.evaluate(back_func)(x) - np.testing.assert_allclose(forward.asnumpy(), 2 * x.asnumpy()) - np.testing.assert_allclose(grad.asnumpy(), 2 * np.ones_like(x.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), 2 * x.asnumpy()) + tvm.testing.assert_allclose(grad.asnumpy(), 2 * np.ones_like(x.asnumpy())) def test_temp_add(): @@ -51,8 +51,8 @@ def test_temp_add(): ex = create_executor() x = rand(dtype, *shape) forward, (grad,) = ex.evaluate(back_func)(x) - np.testing.assert_allclose(forward.asnumpy(), 4 * x.asnumpy()) - np.testing.assert_allclose(grad.asnumpy(), 4 * np.ones_like(x.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), 4 * x.asnumpy()) + tvm.testing.assert_allclose(grad.asnumpy(), 4 * np.ones_like(x.asnumpy())) def test_sub(): @@ -66,8 +66,8 @@ def test_sub(): ex = create_executor() x = rand(dtype, *shape) forward, (grad,) = ex.evaluate(back_func)(x) - np.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy())) - np.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy())) + tvm.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy())) def test_broadcast_add(): @@ -90,11 +90,11 @@ def test_broadcast_add(): relay.TupleType([t1, t2])])) ex = create_executor() forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd) - np.testing.assert_allclose(forward.asnumpy(), expected_forward) - np.testing.assert_allclose(grad_x.asnumpy(), - np.ones_like(expected_forward).sum(axis=2, keepdims=True)) - np.testing.assert_allclose(grad_y.asnumpy(), - np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0)) + tvm.testing.assert_allclose(forward.asnumpy(), expected_forward) + tvm.testing.assert_allclose(grad_x.asnumpy(), + np.ones_like(expected_forward).sum(axis=2, keepdims=True)) + tvm.testing.assert_allclose(grad_y.asnumpy(), + np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0)) def test_broadcast_subtract(): @@ -117,11 +117,11 @@ def test_broadcast_subtract(): relay.TupleType([t1, t2])])) ex = create_executor() forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd) - np.testing.assert_allclose(forward.asnumpy(), expected_forward) - np.testing.assert_allclose(grad_x.asnumpy(), - np.ones_like(expected_forward).sum(axis=2, keepdims=True)) - np.testing.assert_allclose(grad_y.asnumpy(), - -np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0)) + tvm.testing.assert_allclose(forward.asnumpy(), expected_forward) + tvm.testing.assert_allclose(grad_x.asnumpy(), + np.ones_like(expected_forward).sum(axis=2, keepdims=True)) + tvm.testing.assert_allclose(grad_y.asnumpy(), + -np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0)) def test_tuple(): @@ -147,10 +147,10 @@ def test_tuple(): expected_forward = x_np + y_np - z_np ex = create_executor() forward, (grad_x, grad_y, grad_z) = ex.evaluate(back_func)(x_nd, y_nd, z_nd) - np.testing.assert_allclose(forward.asnumpy(), expected_forward) - np.testing.assert_allclose(grad_x.asnumpy(), np.ones_like(grad_x.asnumpy())) - np.testing.assert_allclose(grad_y.asnumpy(), np.ones_like(grad_y.asnumpy())) - np.testing.assert_allclose(grad_z.asnumpy(), -1 * np.ones_like(grad_z.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), expected_forward) + tvm.testing.assert_allclose(grad_x.asnumpy(), np.ones_like(grad_x.asnumpy())) + tvm.testing.assert_allclose(grad_y.asnumpy(), np.ones_like(grad_y.asnumpy())) + tvm.testing.assert_allclose(grad_z.asnumpy(), -1 * np.ones_like(grad_z.asnumpy())) def test_pow(): @@ -168,8 +168,9 @@ def test_pow(): i_nd = rand(dtype, *shape) ex = create_executor(mod=mod) forward, (grad_i,) = ex.evaluate(back_func)(i_nd) - np.testing.assert_allclose(forward.asnumpy(), 8 * i_nd.asnumpy()) - np.testing.assert_allclose(grad_i.asnumpy(), 8 * np.ones_like(grad_i.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), 8 * i_nd.asnumpy()) + tvm.testing.assert_allclose(grad_i.asnumpy(), 8 * np.ones_like(grad_i.asnumpy())) + def test_ref(): shape = (10, 10) @@ -187,8 +188,28 @@ def test_ref(): x_nd = rand(dtype, *shape) ex = create_executor() forward, (grad_x,) = ex.evaluate(back_func)(x_nd) - np.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy()) - np.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy())) + tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy()) + tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy())) + + +def test_square_second_order(): + shape = (10, 10) + dtype = 'float32' + t = relay.TensorType(shape, dtype) + x = relay.var("x", t) + func = relay.Function([x], x * x) + back_func = relay.ir_pass.infer_type(gradient(func)) + y = relay.var("y", t) + back_func_adjusted = relay.Function([y], relay.TupleGetItem(relay.TupleGetItem(back_func(y), 1), 0)) + back_func_adjusted = relay.ir_pass.infer_type(back_func_adjusted) + back_back_func = relay.ir_pass.infer_type(gradient(back_func_adjusted)) + assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])])) + x_nd = rand(dtype, *shape) + ex = create_executor() + forward, (grad_x,) = ex.evaluate(back_back_func)(x_nd) + tvm.testing.assert_allclose(forward.asnumpy(), 2 * x_nd.asnumpy()) + tvm.testing.assert_allclose(grad_x.asnumpy(), 2 * np.ones_like(grad_x.asnumpy())) + if __name__ == "__main__": test_id() @@ -200,3 +221,4 @@ def test_ref(): test_tuple() test_pow() test_ref() + test_square_second_order()