[Tripy] Do not implicitly convert external input formats in convert_inputs_to_tensors decorator

tripy/examples/nanogpt/weight_loader.py

@@ -1,4 +1,3 @@
-
 #
 # SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0

tripy/tests/backend/mlir/test_compiler.py

@@ -53,8 +53,8 @@ def test_reason_context(self):
         with FlatIRTensor.context(["This is the first level of context"]):
             with FlatIRTensor.context(["This is the second level of context"]):
                 # We need to emit an error from one of the internally created `FlatIRTensor`s to see the context
-                a = tp.ones(1)
-                b = tp.ones(1)
+                a = tp.ones((1,))
+                b = tp.ones((1,))
                 trace = Trace([a + b])
                 flat_ir = trace.to_flat_ir()
                 producer = flat_ir.outputs[0].producer.inputs[0]

tripy/tests/frontend/test_shape.py

@@ -29,11 +29,10 @@ def values(request):
 
 
 @pytest.fixture(
-    params=[[4, 5], tp.Tensor([4, 5], dtype=tp.int32), np.array([4, 5], dtype=np.int32)],
+    params=[[4, 5], tp.Tensor([4, 5], dtype=tp.int32)],
     ids=[
         "python_list",
         "tripy_tensor",
-        "numpy_array",
     ],
 )
 def other_values(request):
@@ -95,7 +94,7 @@ def test_plus_override(self, values, other_values):
         appended = [4, 5]
         s = tp.Shape(values)
 
-        # conversion is implicit except for tp.Tensor
+        # conversion must be explicit for tp.Tensor
         lhs_shape = other_values if not isinstance(other_values, tp.Tensor) else tp.Shape(other_values)
         new_shape = s + lhs_shape
         assert isinstance(new_shape, tp.Shape)
@@ -308,7 +307,7 @@ def test_right_addition(self, other_values):
         appended = [4, 5]
         s = tp.Shape(values)
 
-        # conversion is implicit except for tp.Tensor
+        # conversion must be explicit for tp.Tensor
         rhs_shape = other_values if not isinstance(other_values, tp.Tensor) else tp.Shape(other_values)
 
         new_shape = rhs_shape + s
@@ -418,19 +417,29 @@ def test_invalid_input_rank_tensor(self):
         with raises(tp.TripyException, match="Shape tensors must be of rank 1, but input tensor is rank 2"):
             _ = tp.Shape(tp.ones((3, 2), dtype=tp.int32))
 
+    def test_invalid_mul_sequence(self, values):
+        s = tp.Shape(values)
+        with raises(tp.TripyException, match="Attempting to multiply a Tripy Shape by a sequence, which is undefined"):
+            _ = s * values
+
     def test_invalid_mul_rank(self, values):
         s = tp.Shape(values)
+        t = tp.Tensor(values)
         with raises(
             tp.TripyException, match="Attempting to multiply a Tripy Shape by a tensor of rank >= 1, which is undefined"
         ):
-            _ = s * values
+            _ = s * t
 
     def test_invalid_plus_type(self, values):
         s = tp.Shape(values)
         t = tp.Tensor(values, dtype=tp.int32)
         with raises(
             tp.TripyException,
-            match="Attempting to add a Tripy Tensor to a Tripy Shape, which is not allowed. Consider calling tp.Shape explicitly",
+            match=(
+                "Invalid types for addition with a Tripy Shape."
+                r"\s*Implicit conversions are done only for sequences of Python ints. "
+                "Consider calling tp.Shape for an explicit conversion."
+            ),
         ):
             s + t
 
@@ -439,7 +448,11 @@ def test_invalid_right_addition_type(self, values):
         t = tp.Tensor(values, dtype=tp.int32)
         with raises(
             tp.TripyException,
-            match="Attempting to add a Tripy Tensor to a Tripy Shape, which is not allowed. Consider calling tp.Shape explicitly",
+            match=(
+                "Invalid types for addition with a Tripy Shape."
+                r"\s*Implicit conversions are done only for sequences of Python ints. "
+                "Consider calling tp.Shape for an explicit conversion."
+            ),
         ):
             t + s
 
@@ -469,8 +482,6 @@ def test_unary_elementwise_fails_at_run_time(self, values):
 
     def test_shape_equality(self, other_values):
         a = tp.Shape([4, 5])
-        if isinstance(other_values, np.ndarray):
-            pytest.skip("numpy array cannot be implicitly cast to Shape type")
         eq = a == other_values
         assert isinstance(eq, bool)
         assert eq

tripy/tests/frontend/test_utils.py

@@ -16,6 +16,7 @@
 #
 
 import cupy as cp
+import numpy as np
 
 import tripy as tp
 from tripy.frontend.utils import convert_inputs_to_tensors
@@ -291,3 +292,29 @@ def test_sync_arg_type_invalid(self):
             match=r"At least one of the arguments: \('a', 'b', 'c'\) must be a \`tripy.Tensor\`.",
         ):
             x, y, z = sync_arg_types(3.0, 3, 4)
+
+    def test_seq_arg_invalid(self):
+        with helper.raises(
+            tp.TripyException,
+            match=r"Encountered non-number of type str in sequence: hello",
+        ):
+            _ = func([1, 2, "hello"])
+
+    def test_nested_seq_inconsistent_len(self):
+        with helper.raises(
+            tp.TripyException,
+            match=r"Expected a sequence of length 3 but got length 4: \[7, 8, 9, 10\]",
+        ):
+            _ = func([[1, 2, 3], [4, 5, 6], [7, 8, 9, 10]])
+
+    def test_nested_seq_inconsistent_types(self):
+        with helper.raises(
+            tp.TripyException,
+            match=r"Expected a sequence but got str: hi",
+        ):
+            _ = func([[1, 2, 3], [4, 5, 6], "hi"])
+
+    def test_invalid_argument_type_not_converted(self):
+        a = np.array([1, 2, 3])
+        b = func(np.array([1, 2, 3]))
+        assert (a == b).all()

tripy/tests/frontend/trace/ops/test_binary_elementwise.py

@@ -59,10 +59,8 @@ class TestBinaryElementwise:
         "lhs, rhs, left_side_is_non_tensor",
         [
             (tp.Tensor([1.0]), tp.Tensor([2.0]), False),
-            (tp.Tensor([1.0]), np.array([2.0], dtype=np.float32), False),
             # shape of (0,) is broadcastable with (1,)
             (tp.Tensor([], dtype=tp.float32), tp.Tensor([1.0], dtype=tp.float32), False),
-            (np.array([1.0], dtype=np.float32), tp.Tensor([2.0]), True),
             (tp.Tensor([1.0]), 2.0, False),
             (1.0, tp.Tensor([2.0]), True),
         ],
@@ -86,8 +84,6 @@ def test_op_funcs(self, kind, func, lhs, rhs, left_side_is_non_tensor):
         "lhs, rhs, expected_rank",
         [
             (tp.Tensor([1.0]), tp.Tensor([2.0]), 1),
-            (tp.Tensor([1.0]), np.array([2.0], dtype=np.float32), 1),
-            (np.array([1.0], dtype=np.float32), tp.Tensor([2.0]), 1),
             (tp.Tensor([1.0]), 2.0, 1),
             (1.0, tp.Tensor([2.0]), 1),
             (tp.ones((2, 3)), 2.0, 2),

tripy/tests/integration/test_functional.py

@@ -130,36 +130,3 @@ def test_multiple_copy_2(self):
         out = tp.copy(a, tp.device("gpu"))
         assert out.tolist() == [1, 2]
         assert out.device.kind == "gpu"
-
-
-class TestConversionToTripyType:
-    @pytest.mark.parametrize(
-        "reverse_direction",
-        [False, True],
-    )
-    @pytest.mark.parametrize(
-        "input0",
-        [cp.ones((2, 3), dtype=cp.float32), cp.ones((3,), dtype=np.float32)],
-    )
-    @pytest.mark.parametrize(
-        "input1",
-        [
-            [
-                4.0,
-            ],
-            (5.0,),
-            cp.array([4.0], dtype=cp.float32),
-            cp.ones((1, 3), dtype=cp.float32),
-            torch.Tensor([[4.0]]),
-        ],
-    )
-    def test_element_wise_prod(self, reverse_direction, input0, input1):
-        a = tp.Tensor(input0)
-        if isinstance(input1, torch.Tensor):
-            input1 = input1.to("cuda")
-        if reverse_direction:
-            out = input1 * a
-            input0, input1 = input1, input0
-        else:
-            out = a * input1
-        assert cp.array_equal(cp.from_dlpack(out), cp.array(input0) * cp.array(input1))