diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py index 123b0299839e..cb5392fa16ab 100644 --- a/python/tvm/relay/frontend/pytorch.py +++ b/python/tvm/relay/frontend/pytorch.py @@ -319,6 +319,31 @@ def square(self, inputs, input_types): (dtype,) = input_types return _op.power(inputs[0], _expr.const(2, dtype)) + def tril(self, inputs, input_types): + data = inputs[0] + if len(inputs) == 2: + k_value = inputs[1] + else: + k_value = 0 + input_shape = self.infer_shape(data) + k1, k2 = input_shape[-2:] + k1 = k_value + 1 + diag_input = _op.zeros(input_shape, dtype=input_types[0]) + return _op.matrix_set_diag(data, diag_input, k=(k1, k2)) + + def triu(self, inputs, input_types): + data = inputs[0] + if len(inputs) == 2: + k_value = inputs[1] + else: + k_value = 0 + input_shape = self.infer_shape(data) + k1, k2 = input_shape[-2:] + k1 = (k1 * -1) - 1 + k2 = k_value - 1 + diag_input = _op.zeros(input_shape, dtype=input_types[0]) + return _op.matrix_set_diag(data, diag_input, k=(k1, k2)) + def arange(self, inputs, input_types): def _get_value(val, dtype): # dtype is a tvm dtype @@ -3328,6 +3353,8 @@ def create_convert_map(self): "aten::sqrt": self.make_unary("sqrt"), "aten::rsqrt": self.make_unary("rsqrt"), "aten::square": self.square, + "aten::tril": self.tril, + "aten::triu": self.triu, "aten::ceil": self.make_unary("ceil"), "aten::floor": self.make_unary("floor"), "aten::round": self.make_unary("round"), diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py index 4f42c183b66a..80a5cd07f7b6 100644 --- a/tests/python/frontend/pytorch/test_forward.py +++ b/tests/python/frontend/pytorch/test_forward.py @@ -199,12 +199,21 @@ def visit(op): torch.cuda.empty_cache() -def verify_model_with_input(test_func, input_data, input_dict={}): +def verify_model_with_input( + test_func, + input_data, + *, + input_dict={}, + custom_convert_map={}, + rtol=1e-5, + atol=1e-5, + assert_shape_only=False, +): baseline_outputs = test_func(*input_data) trace = torch.jit.trace(test_func, [input.clone() for input in input_data]) input_names = ["input{}".format(idx) for idx, inp in enumerate(input_data)] input_shapes = list(zip(input_names, [inp.shape for inp in input_data])) - mod, params = relay.frontend.from_pytorch(trace, input_shapes, {}) + mod, params = relay.frontend.from_pytorch(trace, input_shapes, custom_convert_map) with tvm.transform.PassContext(opt_level=3): for target in ["llvm", "cuda"]: if not tvm.runtime.enabled(target): @@ -218,7 +227,8 @@ def verify_model_with_input(test_func, input_data, input_dict={}): compiled_output = relay_model.get_output(0).numpy() assert_shapes_match(baseline_outputs, compiled_output) - tvm.testing.assert_allclose(baseline_outputs, compiled_output, rtol=1e-5, atol=1e-5) + if assert_shape_only == False: + tvm.testing.assert_allclose(baseline_outputs, compiled_output, rtol=rtol, atol=atol) # Single operator tests @@ -1304,7 +1314,7 @@ def test_func(input_tensor, other_tensor): input_data = [torch.rand([2, 1, 10, 1, 10]), torch.rand([2, 1, 10, 10])] - verify_model_with_input(test_func, input_data, {"input0": input_data[0]}) + verify_model_with_input(test_func, input_data, input_dict={"input0": input_data[0]}) @tvm.testing.uses_gpu @@ -3423,6 +3433,64 @@ def forward(self, *args): verify_model(Neg1().float().eval(), input_data=input_data) +@tvm.testing.uses_gpu +def test_forward_tril(): + torch.set_grad_enabled(False) + + def test_func(input_data): + return torch.tril(input_data) + + input_data = torch.rand([3, 3]).float() + verify_model(test_func, input_data=input_data) + input_data = torch.rand([1, 3, 10, 10]).float() + verify_model(test_func, input_data=input_data) + + def test_func1(input_data): + return torch.tril(input_data, 1) + + input_data = torch.rand([3, 3]).float() + verify_model(test_func1, input_data=input_data) + input_data = torch.rand([1, 3, 10, 10]).float() + verify_model(test_func1, input_data=input_data) + + def test_func2(input_data): + return torch.tril(input_data, -1) + + input_data = torch.rand([3, 3]).float() + verify_model(test_func2, input_data=input_data) + input_data = torch.rand([1, 3, 10, 10]).float() + verify_model(test_func2, input_data=input_data) + + +@tvm.testing.uses_gpu +def test_forward_triu(): + torch.set_grad_enabled(False) + + def test_func(input_data): + return torch.triu(input_data) + + input_data = torch.rand([3, 3]).float() + verify_model(test_func, input_data=input_data) + input_data = torch.rand([1, 3, 10, 10]).float() + verify_model(test_func, input_data=input_data) + + def test_func1(input_data): + return torch.triu(input_data, 1) + + input_data = torch.rand([3, 3]).float() + verify_model(test_func1, input_data=input_data) + input_data = torch.rand([1, 3, 10, 10]).float() + verify_model(test_func1, input_data=input_data) + + def test_func2(input_data): + return torch.triu(input_data, -1) + + input_data = torch.rand([3, 3]).float() + verify_model(test_func2, input_data=input_data) + input_data = torch.rand([1, 3, 10, 10]).float() + verify_model(test_func2, input_data=input_data) + + @tvm.testing.uses_gpu def test_forward_where(): torch.set_grad_enabled(False) @@ -3817,15 +3885,14 @@ def test_empty(): def test_func(): return torch.empty([1, 3, 10, 10]) - verify_model_with_input(test_func, []) + verify_model_with_input(test_func, [], assert_shape_only=True) -@pytest.mark.skip(reason="See https://github.com/apache/tvm/issues/11967") def test_empty_like(): def test_func(data): return torch.empty_like(data) - verify_model_with_input(test_func, [torch.rand([1, 3, 10, 10]).float()]) + verify_model_with_input(test_func, [torch.rand([1, 3, 10, 10]).float()], assert_shape_only=True) def test_forward_pretrained_bert_base_uncased():