def test_softmax():
ctx = ndarray.gpu(0)
shape = (400, 1000)
x = np.random.uniform(-5, 5, shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.empty(shape, ctx=ctx)
gpu_op.softmax(arr_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(autodiff.softmax_func(x), y, rtol=1e-5)
def test_softmax():
shape = (400, 1000)
x = np.random.uniform(-5, 5, shape).astype(dtype)
y = np.zeros(shape).astype(dtype)
arr_x = tvm.nd.array(x, ctx=ctx)
arr_y = tvm.nd.array(y, ctx=ctx)
matrix_softmax = tvm_op.make_matrix_softmax(shape, tgt, tgt_host, "matrix_softmax")
matrix_softmax(arr_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(autodiff.softmax_func(x), y, rtol=1e-5)
def test_softmax():
shape = (400, 1000)
x = np.random.uniform(-5, 5, shape).astype(dtype)
y = np.zeros(shape).astype(dtype)
arr_x = tvm.nd.array(x, ctx=ctx)
arr_y = tvm.nd.array(y, ctx=ctx)
matrix_softmax = tvm_op.make_matrix_softmax(shape, tgt, tgt_host, "matrix_softmax")
matrix_softmax(arr_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(autodiff.softmax_func(x), y, rtol=1e-5)
def test_softmax_cross_entropy():
ctx = ndarray.gpu(0)
shape = (400, 1000)
y = np.random.uniform(-5, 5, shape).astype(np.float32)
y_ = np.random.uniform(-5, 5, shape).astype(np.float32)
arr_y = ndarray.array(y, ctx=ctx)
arr_y_ = ndarray.array(y_, ctx=ctx)
arr_out = ndarray.empty((1, ), ctx=ctx)
gpu_op.softmax_cross_entropy(arr_y, arr_y_, arr_out)
out = arr_out.asnumpy()
# numpy calculation
cross_entropy = np.mean(
-np.sum(y_ * np.log(autodiff.softmax_func(y)), axis=1), keepdims=True)
np.testing.assert_allclose(cross_entropy, out, rtol=1e-5)
def test_softmax_cross_entropy():
shape = (400, 1000)
y = np.random.uniform(-5, 5, shape).astype(dtype)
y_ = np.random.uniform(-5, 5, shape).astype(dtype)
out = np.zeros((1,)).astype(dtype)
arr_y = tvm.nd.array(y, ctx=ctx)
arr_y_ = tvm.nd.array(y_, ctx=ctx)
arr_out = tvm.nd.array(out, ctx=ctx)
matrix_softmax_cross_entropy = tvm_op.make_matrix_softmax_cross_entropy(shape, tgt, tgt_host, "softmax_cross_entropy")
matrix_softmax_cross_entropy(arr_y, arr_y_, arr_out)
out = arr_out.asnumpy()
# numpy calculation
cross_entropy = np.mean(
-np.sum(y_ * np.log(autodiff.softmax_func(y)), axis=1), keepdims=True)
np.testing.assert_allclose(cross_entropy, out, rtol=1e-5)
def test_softmax_cross_entropy():
shape = (400, 1000)
y = np.random.uniform(-5, 5, shape).astype(dtype)
y_ = np.random.uniform(-5, 5, shape).astype(dtype)
out = np.zeros((1,)).astype(dtype)
arr_y = tvm.nd.array(y, ctx=ctx)
arr_y_ = tvm.nd.array(y_, ctx=ctx)
arr_out = tvm.nd.array(out, ctx=ctx)
matrix_softmax_cross_entropy = tvm_op.make_matrix_softmax_cross_entropy(shape, tgt, tgt_host, "softmax_cross_entropy")
matrix_softmax_cross_entropy(arr_y, arr_y_, arr_out)
out = arr_out.asnumpy()
# numpy calculation
cross_entropy = np.mean(
-np.sum(y_ * np.log(autodiff.softmax_func(y)), axis=1), keepdims=True)
np.testing.assert_allclose(cross_entropy, out, rtol=1e-5)
