From aa8547c54c3f58ddb26b6cb18181f84e21870fcf Mon Sep 17 00:00:00 2001 From: Altan Haan Date: Mon, 25 Jan 2021 21:11:21 -0800 Subject: [PATCH] fix tanh gradient and update tests to use downstream gradient (#7340) --- python/tvm/relay/op/_tensor_grad.py | 2 +- tests/python/relay/test_op_grad_level1.py | 52 ++++++++++++----------- 2 files changed, 28 insertions(+), 26 deletions(-) diff --git a/python/tvm/relay/op/_tensor_grad.py b/python/tvm/relay/op/_tensor_grad.py index c9a20a3b2989..90120d64c2ac 100644 --- a/python/tvm/relay/op/_tensor_grad.py +++ b/python/tvm/relay/op/_tensor_grad.py @@ -198,7 +198,7 @@ def sigmoid_grad(orig, grad): @register_gradient("tanh") def tanh_grad(orig, grad): """Returns grad * (1 - tanh(x) * tanh(x)).""" - return [grad * ones_like(orig) - orig * orig] + return [grad * (ones_like(orig) - orig * orig)] @register_gradient("nn.relu") diff --git a/tests/python/relay/test_op_grad_level1.py b/tests/python/relay/test_op_grad_level1.py index a79be8684b20..0ac604c6bca1 100644 --- a/tests/python/relay/test_op_grad_level1.py +++ b/tests/python/relay/test_op_grad_level1.py @@ -42,42 +42,44 @@ def check_single_op(opfunc, ref, dtype): shape = (10, 4) tp = relay.TensorType(shape, dtype) x = relay.var("x", tp) - y = opfunc(x) + g = relay.var("g", tp) + y = opfunc(x) * g if ref is not None: data = np.random.rand(*shape).astype(dtype) - ref_grad = ref(data) - fwd_func = relay.Function([x], y) + grad_in = np.random.rand(*shape).astype(dtype) + ref_grad = ref(data, grad_in) + fwd_func = relay.Function([x, g], y) fwd_func = run_infer_type(fwd_func) bwd_func = run_infer_type(gradient(fwd_func)) for target, ctx in tvm.testing.enabled_targets(): intrp = relay.create_executor(ctx=ctx, target=target) - op_res, (op_grad,) = intrp.evaluate(bwd_func)(data) + op_res, (op_grad, _) = intrp.evaluate(bwd_func)(data, grad_in) np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01) for opfunc, ref in [ - (tvm.relay.log, lambda x: 1 / x), - (tvm.relay.exp, np.exp), - (tvm.relay.sigmoid, lambda x: sigmoid(x) * (1 - sigmoid(x))), - (tvm.relay.tanh, lambda x: 1 - np.tanh(x) * np.tanh(x)), - (tvm.relay.sqrt, lambda x: 0.5 * np.power(x, -0.5)), - (tvm.relay.abs, lambda x: np.where(x < 0, -np.ones_like(x), np.ones_like(x))), - (relay.nn.relu, lambda x: np.where(x < 0, np.zeros_like(x), np.ones_like(x))), - (tvm.relay.erf, lambda x: 2.0 / (np.pi ** (0.5)) * np.exp(-x * x)), - (tvm.relay.cos, lambda x: -1.0 * np.sin(x)), - (tvm.relay.sin, lambda x: np.cos(x)), - (tvm.relay.tan, lambda x: 1.0 / (np.cos(x) ** 2)), - (tvm.relay.atan, lambda x: 1 / (1 + np.power(x, 2.0))), - (tvm.relay.log2, lambda x: 1 / (np.log(2) * x)), - (tvm.relay.log10, lambda x: 1 / (np.log(10) * x)), - (tvm.relay.cosh, lambda x: np.sinh(x)), - (tvm.relay.sinh, lambda x: np.cosh(x)), - (tvm.relay.asin, lambda x: 1.0 / (1.0 - x ** 2) ** (1.0 / 2.0)), - (tvm.relay.acos, lambda x: -1.0 / (1.0 - x ** 2.0) ** (1.0 / 2.0)), - (tvm.relay.acosh, lambda x: 1.0 / (x ** 2 - 1.0) ** (1.0 / 2.0)), - (tvm.relay.asinh, lambda x: 1.0 / (x ** 2 + 1.0) ** (1.0 / 2.0)), - (tvm.relay.atanh, lambda x: -1.0 / (x ** 2 - 1.0)), + (tvm.relay.log, lambda x, g: g * (1 / x)), + (tvm.relay.exp, lambda x, g: g * np.exp(x)), + (tvm.relay.sigmoid, lambda x, g: g * sigmoid(x) * (1 - sigmoid(x))), + (tvm.relay.tanh, lambda x, g: g * (1 - np.tanh(x) * np.tanh(x))), + (tvm.relay.sqrt, lambda x, g: g * 0.5 * np.power(x, -0.5)), + (tvm.relay.abs, lambda x, g: np.where(x < 0, -g, g)), + (relay.nn.relu, lambda x, g: np.where(x < 0, np.zeros_like(x), g)), + (tvm.relay.erf, lambda x, g: g * (2.0 / (np.pi ** (0.5)) * np.exp(-x * x))), + (tvm.relay.cos, lambda x, g: g * -1.0 * np.sin(x)), + (tvm.relay.sin, lambda x, g: g * np.cos(x)), + (tvm.relay.tan, lambda x, g: g * (1.0 / (np.cos(x) ** 2))), + (tvm.relay.atan, lambda x, g: g * (1 / (1 + np.power(x, 2.0)))), + (tvm.relay.log2, lambda x, g: g * (1 / (np.log(2) * x))), + (tvm.relay.log10, lambda x, g: g * (1 / (np.log(10) * x))), + (tvm.relay.cosh, lambda x, g: g * (np.sinh(x))), + (tvm.relay.sinh, lambda x, g: g * (np.cosh(x))), + (tvm.relay.asin, lambda x, g: g * (1.0 / (1.0 - x ** 2) ** (1.0 / 2.0))), + (tvm.relay.acos, lambda x, g: g * (-1.0 / (1.0 - x ** 2.0) ** (1.0 / 2.0))), + (tvm.relay.acosh, lambda x, g: g * (1.0 / (x ** 2 - 1.0) ** (1.0 / 2.0))), + (tvm.relay.asinh, lambda x, g: g * (1.0 / (x ** 2 + 1.0) ** (1.0 / 2.0))), + (tvm.relay.atanh, lambda x, g: g * (-1.0 / (x ** 2 - 1.0))), ]: for dtype in ("float32", "float64"): check_single_op(opfunc, ref, dtype)