def check_log(): flow = myelin.Flow() f = flow.define("log") x = f.var("x", dtype, shape) y = f.log(x, "y") gradcheck(f, [x], [y], 0.0, 10.0)
def tanh_test(n): flow = myelin.Flow() f = flow.define("tanh") x = f.var("x", dt, [n]) y = f.tanh(x) check(flow, n)
def acosh_test(n): flow = myelin.Flow() f = flow.define("acosh") x = f.var("x", dt, [n]) y = f.acosh(x) check(flow, n, 1.0, 10.0, atol=1e-6)
def asin_test(n): flow = myelin.Flow() f = flow.define("asin") x = f.var("x", dt, [n]) y = f.asin(x) check(flow, n, -1.0, 1.0)
def atan_test(n): flow = myelin.Flow() f = flow.define("atan") x = f.var("x", dt, [n]) y = f.atan(x) check(flow, n)
def round_test(n): flow = myelin.Flow() f = flow.define("round") x = f.var("x", dt, [n]) y = f.round(x) check(flow, n)
def exp_test(n): flow = myelin.Flow() f = flow.define("exp") x = f.var("x", dt, [n]) y = f.exp(x) check(flow, n)
def check_normalize(): flow = myelin.Flow() f = flow.define("normalize") x = f.var("x", dtype, shape) y = f.normalize(x, "y") gradcheck(f, [x], [y], tol=1e-2)
def check_softmax(): flow = myelin.Flow() f = flow.define("softmax") x = f.var("x", dtype, shape) y = f.softmax(x, "y") gradcheck(f, [x], [y])
def check_relu(): flow = myelin.Flow() f = flow.define("relu") x = f.var("x", dtype, shape) y = f.relu(x, "y") gradcheck(f, [x], [y], lo=-1.0, hi=1.0)
def check_norm(): flow = myelin.Flow() f = flow.define("norm") x = f.var("x", dtype, shape) y = f.norm(x, "y") gradcheck(f, [x], [y], eps=1e-2)
def check_erf(): flow = myelin.Flow() f = flow.define("erf") x = f.var("x", dtype, shape) y = f.erf(x, "y") gradcheck(f, [x], [y])
def check_sigmoid(): flow = myelin.Flow() f = flow.define("sigmoid") x = f.var("x", dtype, shape) y = f.sigmoid(x, "y") gradcheck(f, [x], [y])
def check_tanh(): flow = myelin.Flow() f = flow.define("tanh") x = f.var("x", dtype, shape) y = f.tanh(x, "y") gradcheck(f, [x], [y])
def floor_test(n): flow = myelin.Flow() f = flow.define("floor") x = f.var("x", dt, [n]) y = f.floor(x) check(flow, n)
def check_sum(): flow = myelin.Flow() f = flow.define("sum") x = f.var("x", dtype, shape) y = f.sum(x, "y") gradcheck(f, [x], [y], tol=1e-3)
def ceil_test(n): flow = myelin.Flow() f = flow.define("ceil") x = f.var("x", dt, [n]) y = f.ceil(x) check(flow, n)
def check_min(): flow = myelin.Flow() f = flow.define("min") x = f.var("x", dtype, shape) y = f.min(x, "y") gradcheck(f, [x], [y])
def trunc_test(n): flow = myelin.Flow() f = flow.define("trunc") x = f.var("x", dt, [n]) y = f.trunc(x) check(flow, n)
def transpose_test(m, k, n, perm): flow = myelin.Flow() f = flow.define("transpose") x = f.var("x", dt, [m, k, n]) y = f.transpose(x, perm=perm) check(flow, (m, k, n, perm))
def erf_test(n): flow = myelin.Flow() f = flow.define("erf") x = f.var("x", dt, [n]) y = f.erf(x) check(flow, n, -2.0, 2.0, 1e-6, 1e-4)
def neg_test(n): flow = myelin.Flow() f = flow.define("neg") x = f.var("x", dt, [n]) y = f.neg(x) check(flow, n)
def acos_test(n): flow = myelin.Flow() f = flow.define("acos") x = f.var("x", dt, [n]) y = f.acos(x) check(flow, n, -1.0, 1.0)
def rcp_test(n): flow = myelin.Flow() f = flow.define("rcp") x = f.var("x", dt, [n]) y = f.rcp(x) check(flow, n)
def cosh_test(n): flow = myelin.Flow() f = flow.define("cosh") x = f.var("x", dt, [n]) y = f.cosh(x) check(flow, n)
def abs_test(n): flow = myelin.Flow() f = flow.define("abs") x = f.var("x", dt, [n]) y = f.abs(x) check(flow, n, -10.0, 10.0)
def asinh_test(n): flow = myelin.Flow() f = flow.define("asinh") x = f.var("x", dt, [n]) y = f.asinh(x) check(flow, n, -1.0, 1.0, rtol=1e-3, atol=1e-6)
def sign_test(n): flow = myelin.Flow() f = flow.define("sign") x = f.var("x", dt, [n]) y = f.sign(x) check(flow, n, -10.0, 10.0)
def sqrt_test(n): flow = myelin.Flow() f = flow.define("sqrt") x = f.var("x", dt, [n]) y = f.sqrt(x) check(flow, n, 0.1, 10.0)
def check_exp(): flow = myelin.Flow() f = flow.define("exp") x = f.var("x", dtype, shape) y = f.exp(x, "y") gradcheck(f, [x], [y], -3.0, 3.0)