diff --git a/paddle/fluid/pybind/eager_math_op_patch.cc b/paddle/fluid/pybind/eager_math_op_patch.cc index fa38a40f8fba1b5c8c8dcc6bf13cbcfcba69b13a..19afd128f568eb75885b3b1e70a026eb559b3c37 100644 --- a/paddle/fluid/pybind/eager_math_op_patch.cc +++ b/paddle/fluid/pybind/eager_math_op_patch.cc @@ -539,8 +539,8 @@ static PyObject* tensor__mul__method(TensorObject* self, CastPyArg2Scalar(other_obj, "__mul__", 0); if (PyComplex_Check(other_obj)) { eager_gil_scoped_release guard; - other_tensor = - full_ad_func({1}, value, DataType::COMPLEX64, self_tensor.place()); + other_tensor = full_ad_func( + self_tensor.shape(), value, DataType::COMPLEX64, self_tensor.place()); } else { eager_gil_scoped_release guard; other_tensor = full_ad_func( diff --git a/python/paddle/fluid/layers/math_op_patch.py b/python/paddle/fluid/layers/math_op_patch.py index feed6641af889d3f8f6a8b049e1528b7d2e9fc4d..136670bf27c9c1d24185c13258dd9502cd6e80b6 100644 --- a/python/paddle/fluid/layers/math_op_patch.py +++ b/python/paddle/fluid/layers/math_op_patch.py @@ -98,8 +98,7 @@ def monkey_patch_variable(): return var def create_scalar(block, value, dtype): - # TODO(zhouwei): will change to [] which is 0-D Tensor - return create_tensor(block, value, dtype, shape=[1]) + return create_tensor(block, value, dtype, shape=[]) def create_tensor_with_batchsize(ref_var, value, dtype): assert isinstance(ref_var, Variable) diff --git a/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py b/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py index 4c6f26f0e552b2bbf640aac49970e1eca4e6a4be..b1ba68be2b6571dd739e62828ed3f2f96d7456fa 100644 --- a/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py +++ b/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py @@ -139,12 +139,7 @@ class TestUnaryAPI(unittest.TestCase): x = paddle.rand([]) x.stop_gradient = False out = api(x) - # TODO(zhouwei): - # ScaleLossGradOp / append_backward set grad shape to [1] - # after output 0D, may change it to [] - # use out.sum() to avoid this two problem now - loss = out.sum() - paddle.static.append_backward(loss) + paddle.static.append_backward(out) fetch_list = [x, out] if block.has_var(x.grad_name): @@ -156,12 +151,10 @@ class TestUnaryAPI(unittest.TestCase): self.assertEqual(item.shape, ()) # 2) Test CompiledProgram Program - expect_shape = () compile_prog = paddle.static.CompiledProgram(main_prog) - res = exe.run(compile_prog, fetch_list=fetch_list) for item in res: - self.assertEqual(item.shape, expect_shape) + self.assertEqual(item.shape, ()) paddle.disable_static() @@ -229,7 +222,7 @@ class TestReduceAPI(unittest.TestCase): x = paddle.rand([]) x.stop_gradient = False out = api(x, None) - paddle.static.append_backward(out.sum()) + paddle.static.append_backward(out) out_empty_list = api(x, None) self.assertEqual(out_empty_list.shape, ()) @@ -437,7 +430,7 @@ class TestBinaryAPI(unittest.TestCase): self.assertEqual(out.shape, out_cls.shape) else: out = api(x, y) - paddle.static.append_backward(out.sum()) + paddle.static.append_backward(out) self.assertEqual(x.shape, ()) self.assertEqual(y.shape, ()) @@ -464,7 +457,7 @@ class TestBinaryAPI(unittest.TestCase): self.assertEqual(out.shape, out_cls.shape) else: out = api(x, y) - paddle.static.append_backward(out.sum()) + paddle.static.append_backward(out) self.assertEqual(x.shape, ()) self.assertEqual(y.shape, (2, 3, 4)) @@ -491,7 +484,7 @@ class TestBinaryAPI(unittest.TestCase): self.assertEqual(out.shape, out_cls.shape) else: out = api(x, y) - paddle.static.append_backward(out.sum()) + paddle.static.append_backward(out) self.assertEqual(x.shape, (2, 3, 4)) self.assertEqual(y.shape, ()) @@ -505,9 +498,6 @@ class TestBinaryAPI(unittest.TestCase): self.assertEqual(y_grad.shape, ()) self.assertEqual(out_grad.shape, (2, 3, 4)) - # TODO(zhouwei25): - # will open this UT after fix create_scalar in static graph - ''' # 4) x is 0D , y is scalar x = paddle.rand([]) x.stop_gradient = False @@ -516,17 +506,16 @@ class TestBinaryAPI(unittest.TestCase): out = getattr(paddle.static.Variable, api['cls_method'])( x, y ) - paddle.static.append_backward(out.sum()) + paddle.static.append_backward(out) self.assertEqual(x.shape, ()) self.assertEqual(out.shape, ()) - if block.has_var(x.name): + if block.has_var(x.grad_name): out_grad = block.var(out.grad_name) x_grad = block.var(x.grad_name) self.assertEqual(out_grad.shape, ()) self.assertEqual(x_grad.shape, ()) - ''' for api in binary_int_api_list: main_prog = paddle.static.Program() @@ -2154,10 +2143,10 @@ class TestSundryAPIStatic(unittest.TestCase): @prog_scope() def test_std(self): x = paddle.rand([]) + x.stop_gradient = False out1 = paddle.std(x) out2 = paddle.std(x, []) paddle.static.append_backward(out1) - paddle.static.append_backward(out2) prog = paddle.static.default_main_program() res = self.exe.run( @@ -2166,19 +2155,23 @@ class TestSundryAPIStatic(unittest.TestCase): x, out1, out2, + x.grad_name, + out1.grad_name, ], ) self.assertEqual(res[0].shape, ()) self.assertEqual(res[1].shape, ()) self.assertEqual(res[2].shape, ()) + self.assertEqual(res[3].shape, ()) + self.assertEqual(res[4].shape, ()) @prog_scope() def test_var(self): x = paddle.rand([]) + x.stop_gradient = False out1 = paddle.var(x) out2 = paddle.var(x, []) paddle.static.append_backward(out1) - paddle.static.append_backward(out2) prog = paddle.static.default_main_program() res = self.exe.run( @@ -2187,11 +2180,15 @@ class TestSundryAPIStatic(unittest.TestCase): x, out1, out2, + x.grad_name, + out1.grad_name, ], ) self.assertEqual(res[0].shape, ()) self.assertEqual(res[1].shape, ()) self.assertEqual(res[2].shape, ()) + self.assertEqual(res[3].shape, ()) + self.assertEqual(res[4].shape, ()) @prog_scope() def test_quantile(self): @@ -3651,7 +3648,7 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase): def test_dygraph_unary(self): paddle.disable_static() for api in unary_apis_with_complex_input: - x = paddle.to_tensor(2.0 + 3.0j).squeeze() + x = paddle.rand([]) + 1j * paddle.rand([]) x.stop_gradient = False x.retain_grads() out = api(x) @@ -3668,7 +3665,6 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase): def test_static_unary(self): paddle.enable_static() - for api in unary_apis_with_complex_input: main_prog = paddle.static.Program() block = main_prog.global_block() @@ -3676,18 +3672,10 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase): with paddle.static.program_guard( main_prog, paddle.static.Program() ): - # before full support for complex, we cannot create complex tensor with the same code as in dynamic graph - x = paddle.complex( - paddle.to_tensor(2.0), paddle.to_tensor(2.0) - ).squeeze() + x = paddle.complex(paddle.rand([]), paddle.rand([])) x.stop_gradient = False out = api(x) - # TODO(zhouwei): - # ScaleLossGradOp / append_backward set grad shape to [1] - # after output 0D, may change it to [] - # use out.sum() to avoid this two problem now - loss = out.sum() - paddle.static.append_backward(loss) + paddle.static.append_backward(out) fetch_list = [x, out] if block.has_var(x.grad_name): @@ -3699,12 +3687,10 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase): self.assertEqual(item.shape, ()) # 2) Test CompiledProgram Program - expect_shape = () compile_prog = paddle.static.CompiledProgram(main_prog) - res = exe.run(compile_prog, fetch_list=fetch_list) for item in res: - self.assertEqual(item.shape, expect_shape) + self.assertEqual(item.shape, ()) paddle.disable_static() @@ -3712,66 +3698,88 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase): class TestAsReal(unittest.TestCase): def test_dygraph(self): paddle.disable_static() - for api in unary_apis_with_complex_input: - x = paddle.to_tensor(2.0 + 3.0j).squeeze() - x.stop_gradient = False - x.retain_grads() - out = paddle.as_real(x) - out.retain_grads() - out.backward() + x = paddle.rand([]) + 1j * paddle.rand([]) + x.stop_gradient = False + x.retain_grads() + out = paddle.as_real(x) + out.retain_grads() + out.backward() - self.assertEqual(x.shape, []) - self.assertEqual(out.shape, [2]) - if x.grad is not None: - self.assertEqual(x.grad.shape, []) - self.assertEqual(out.grad.shape, [2]) + self.assertEqual(x.shape, []) + self.assertEqual(out.shape, [2]) + if x.grad is not None: + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.grad.shape, [2]) paddle.enable_static() def test_static(self): paddle.enable_static() - for api in unary_apis_with_complex_input: - main_prog = paddle.static.Program() - block = main_prog.global_block() - exe = paddle.static.Executor() - with paddle.static.program_guard( - main_prog, paddle.static.Program() - ): - # before full support for complex, we cannot create complex tensor with the same code as in dynamic graph - x = paddle.complex( - paddle.to_tensor(2.0), paddle.to_tensor(2.0) - ).squeeze() - x.stop_gradient = False - out = paddle.as_real(x) - self.assertEqual(x.shape, ()) - self.assertEqual(out.shape, (2,)) - # TODO(zhouwei): - # ScaleLossGradOp / append_backward set grad shape to [1] - # after output 0D, may change it to [] - # use out.sum() to avoid this two problem now - loss = out.abs().sum() - paddle.static.append_backward(loss) + main_prog = paddle.static.Program() + block = main_prog.global_block() + exe = paddle.static.Executor() + with paddle.static.program_guard(main_prog, paddle.static.Program()): + x = paddle.complex(paddle.rand([]), paddle.rand([])) + x.stop_gradient = False + out = paddle.as_real(x) + self.assertEqual(x.shape, ()) + self.assertEqual(out.shape, (2,)) + paddle.static.append_backward(out.sum()) - fetch_list = [x, out] - if block.has_var(x.grad_name): - fetch_list.extend([x.grad_name, out.grad_name]) + fetch_list = [x, out] + if block.has_var(x.grad_name): + fetch_list.extend([x.grad_name, out.grad_name]) - # 1) Test Program - res = exe.run(main_prog, fetch_list=fetch_list) - self.assertEqual(res[0].shape, ()) - self.assertEqual(res[1].shape, (2,)) - self.assertEqual(res[2].shape, ()) - self.assertEqual(res[3].shape, (2,)) + res = exe.run(main_prog, fetch_list=fetch_list) + self.assertEqual(res[0].shape, ()) + self.assertEqual(res[1].shape, (2,)) + self.assertEqual(res[2].shape, ()) + self.assertEqual(res[3].shape, (2,)) - # 2) Test CompiledProgram Program - expect_shapes = (), (2,), (), (2,) - compile_prog = paddle.static.CompiledProgram(main_prog) + paddle.disable_static() - res = exe.run(compile_prog, fetch_list=fetch_list) - print(res) - for actual, expect in zip(res, expect_shapes): - self.assertEqual(actual.shape, expect) + +class TestAsComplex(unittest.TestCase): + def test_dygraph(self): + paddle.disable_static() + x = paddle.rand([2]) + x.stop_gradient = False + x.retain_grads() + out = paddle.as_complex(x) + out.retain_grads() + out.backward() + + self.assertEqual(x.shape, [2]) + self.assertEqual(out.shape, []) + if x.grad is not None: + self.assertEqual(x.grad.shape, [2]) + self.assertEqual(out.grad.shape, []) + + paddle.enable_static() + + def test_static(self): + paddle.enable_static() + main_prog = paddle.static.Program() + block = main_prog.global_block() + exe = paddle.static.Executor() + with paddle.static.program_guard(main_prog, paddle.static.Program()): + x = paddle.rand([2]) + x.stop_gradient = False + out = paddle.as_complex(x) + self.assertEqual(x.shape, (2,)) + self.assertEqual(out.shape, ()) + paddle.static.append_backward(out.sum()) + + fetch_list = [x, out] + if block.has_var(x.grad_name): + fetch_list.extend([x.grad_name, out.grad_name]) + + res = exe.run(main_prog, fetch_list=fetch_list) + self.assertEqual(res[0].shape, (2,)) + self.assertEqual(res[1].shape, ()) + self.assertEqual(res[2].shape, (2,)) + self.assertEqual(res[3].shape, ()) paddle.disable_static() diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index 9667626c053f498efef2944eaa5228587cfd46f1..9e7c04be8fca90ea9f83237d7e826c70e4154d1f 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -4469,19 +4469,15 @@ def gcd(x, y, name=None): y = paddle.broadcast_to(y, shape) x = paddle.abs(x) y = paddle.abs(y) - # TODO(zhouwei25): Support 0D for not_equal tensor with scalar - zero = paddle.full([], 0) def _gcd_cond_fn(x, y): - # return paddle.any(y != 0) - return paddle.any(y != zero) + return paddle.any(y != 0) def _gcd_body_fn(x, y): # paddle.mod will raise an error when any element of y is 0. To avoid # that, we change those zeros to ones. Their values don't matter because # they won't be used. - # y_not_equal_0 = y != 0 - y_not_equal_0 = y != zero + y_not_equal_0 = y != 0 y_safe = paddle.where(y_not_equal_0, y, paddle.ones(y.shape, y.dtype)) x, y = ( paddle.where(y_not_equal_0, y, x),