def test_sparse_adagrad_empty(self, inputs, lr, epsilon, gc, dc): param, momentum = inputs grad = np.empty(shape=(0, ) + param.shape[1:], dtype=np.float32) ref_using_fp16_values = [False] if gc == hu.gpu_do: ref_using_fp16_values.append(True) for ref_using_fp16 in ref_using_fp16_values: if ref_using_fp16: print( "test_sparse_adagrad_empty with half precision embedding") momentum_i = momentum.astype(np.float16) param_i = param.astype(np.float16) else: print( "test_sparse_adagrad_empty with full precision embedding") momentum_i = momentum.astype(np.float32) param_i = param.astype(np.float32) adagrad_sparse_test_helper( self, [param_i, momentum_i, grad], lr, epsilon, None, ref_adagrad, gc, dc, )
def test_sparse_adagrad(self, inputs, lr, epsilon, weight_decay, gc, dc): adagrad_sparse_test_helper( self, inputs, lr, epsilon, None, ref_adagrad, gc, dc, weight_decay=weight_decay, )
def test_row_wise_sparse_adagrad(self, inputs, lr, epsilon, data_strategy, gc, dc): adagrad_sparse_test_helper( self, inputs, lr, epsilon, None, functools.partial(ref_adagrad, row_wise=True), gc, dc, row_wise=True, )
def test_row_wise_sparse_adagrad_empty(self, inputs, lr, epsilon, gc, dc): param, momentum = inputs grad = np.empty(shape=(0, ) + param.shape[1:], dtype=np.float32) adagrad_sparse_test_helper( self, [param, momentum, grad], lr, epsilon, None, ref_adagrad, gc, dc, row_wise=True, )
def test_row_wise_sparse_adagrad(self, inputs, lr, epsilon, weight_decay, gc, dc): adagrad_sparse_test_helper( self, inputs, lr, epsilon, None, functools.partial(ref_adagrad, row_wise=True), gc, dc, row_wise=True, weight_decay=weight_decay, )
def test_sparse_adagrad(self, inputs, lr, epsilon, gc, dc): return adagrad_sparse_test_helper(self, inputs, lr, epsilon, None, ref_adagrad, gc, dc)