chore: import upstream snapshot with attribution

tests/python/relax/test_frontend_nn_jit.py

@@ -0,0 +1,208 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import pytest
+import torch
+
+import tvm
+import tvm.testing
+from tvm import tirx
+from tvm.relax.frontend import nn
+from tvm.relax.frontend.nn import spec
+
+
+@pytest.mark.parametrize("debug", [True, False])
+def test_jit(debug):
+    class Layer(nn.Module):
+        def __init__(self):
+            pass
+
+        def forward(self, x: nn.Tensor):
+            y = nn.add(x, x)
+            return y
+
+    forward_spec = {"forward": {"x": spec.Tensor([10, 5], dtype="float32")}}
+    mod = Layer()
+
+    model = mod.jit(spec=forward_spec, debug=debug)
+
+    x = torch.rand((10, 5), dtype=torch.float32)
+    y = model["forward"](x)
+    assert isinstance(y, torch.Tensor)
+    assert torch.allclose(x + x, y)
+
+
+@pytest.mark.parametrize("debug", [True, False])
+def test_jit_int_input(debug):
+    class Layer(nn.Module):
+        def __init__(self):
+            pass
+
+        def forward(self, x: nn.Tensor, i: tirx.Var):
+            y = nn.add(x, x)
+            y = nn.reshape(y, (i, 5, 5))
+            return y
+
+    forward_spec = {"forward": {"x": spec.Tensor([10, 5], dtype="float32"), "i": int}}
+    mod = Layer()
+
+    model = mod.jit(spec=forward_spec, debug=debug)
+
+    x = torch.rand((10, 5), dtype=torch.float32)
+    y = model["forward"](x, 2)
+    assert isinstance(y, torch.Tensor)
+    assert torch.allclose(torch.reshape(x + x, (2, 5, 5)), y)
+
+
+@pytest.mark.parametrize("debug", [True, False])
+def test_jit_with_effect(debug):
+    class Layer(nn.Module):
+        def __init__(self):
+            self.cache = nn.KVCache(10, [10, 5])
+
+        def forward(self, x: nn.Tensor, total_seq_len: tirx.Var):
+            self.cache.append(x)
+            y = self.cache.view(total_seq_len)
+            return y
+
+    forward_spec = {
+        "forward": {"x": spec.Tensor([1, 10, 5], dtype="float32"), "total_seq_len": int}
+    }
+    mod = Layer()
+
+    with tvm.transform.PassContext(opt_level=3):
+        model = mod.jit(spec=forward_spec, debug=debug)
+
+    x0 = torch.rand((1, 10, 5), dtype=torch.float32)
+    y = model["forward"](x0, 1)
+    assert isinstance(y, torch.Tensor)
+    assert torch.allclose(x0, y)
+
+    x1 = torch.rand((1, 10, 5), dtype=torch.float32)
+    y = model["forward"](x1, 2)
+    assert torch.allclose(torch.concat([x0, x1], dim=0), y)
+
+    x2 = torch.rand((1, 10, 5), dtype=torch.float32)
+    y = model["forward"](x2, 3)
+    assert torch.allclose(torch.concat([x0, x1, x2], dim=0), y)
+
+
+@pytest.mark.parametrize("debug", [True, False])
+def test_jit_tuple_input(debug):
+    class Layer(nn.Module):
+        def __init__(self):
+            pass
+
+        def forward(self, x: tuple[nn.Tensor, nn.Tensor]):
+            assert isinstance(x, tuple)
+            x0 = x[0]
+            x1 = x[1]
+            y0 = nn.add(x0, x1)
+            y1 = nn.subtract(x0, x1)
+            return (y0, y1)
+
+    forward_spec = {
+        "forward": {
+            "x": (
+                spec.Tensor([10, 5], dtype="float32"),
+                spec.Tensor([10, 5], dtype="float32"),
+            )
+        }
+    }
+    mod = Layer()
+
+    model = mod.jit(spec=forward_spec, debug=debug)
+
+    x0 = torch.rand((10, 5), dtype=torch.float32)
+    x1 = torch.rand((10, 5), dtype=torch.float32)
+    x = (x0, x1)
+    y = model["forward"](x)
+
+    assert torch.allclose(x0 + x1, y[0])
+    assert torch.allclose(x0 - x1, y[1])
+
+
+@pytest.mark.parametrize("debug", [True, False])
+def test_jit_list_input(debug):
+    class Layer(nn.Module):
+        def __init__(self):
+            pass
+
+        def forward(self, x: list[nn.Tensor]):
+            assert isinstance(x, list)
+            x0 = x[0]
+            x1 = x[1]
+            y0 = nn.add(x0, x1)
+            y1 = nn.subtract(x0, x1)
+            return (y0, y1)
+
+    forward_spec = {
+        "forward": {
+            "x": [
+                spec.Tensor([10, 5], dtype="float32"),
+                spec.Tensor([10, 5], dtype="float32"),
+            ]
+        }
+    }
+    mod = Layer()
+
+    model = mod.jit(spec=forward_spec, debug=debug)
+
+    x0 = torch.rand((10, 5), dtype=torch.float32)
+    x1 = torch.rand((10, 5), dtype=torch.float32)
+    x = (x0, x1)
+    y = model["forward"](x)
+
+    assert torch.allclose(x0 + x1, y[0])
+    assert torch.allclose(x0 - x1, y[1])
+
+
+@pytest.mark.parametrize("debug", [True, False])
+def test_jit_tuple_input_with_int(debug):
+    class Layer(nn.Module):
+        def __init__(self):
+            pass
+
+        def forward(self, x: tuple[nn.Tensor, nn.Tensor, int]):
+            x0 = x[0]
+            x1 = x[1]
+            y0 = nn.add(x0, x1)
+            y1 = nn.subtract(x0, x1)
+            y2 = nn.reshape(x0, (5, x[2], 5))
+            return (y0, y1, y2)
+
+    forward_spec = {
+        "forward": {
+            "x": (spec.Tensor([10, 5], dtype="float32"), spec.Tensor([10, 5], dtype="float32"), int)
+        }
+    }
+    mod = Layer()
+
+    model = mod.jit(spec=forward_spec, debug=debug)
+
+    x0 = torch.rand((10, 5), dtype=torch.float32)
+    x1 = torch.rand((10, 5), dtype=torch.float32)
+    x = (x0, x1, 2)
+    y0, y1, y2 = model["forward"](x)
+
+    assert torch.allclose(x0 + x1, y0)
+    assert torch.allclose(x0 - x1, y1)
+    assert torch.allclose(torch.reshape(x0, (5, 2, 5)), y2)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()