Add OpRunValue

Author: xadupre

_doc/api/reference/torch_ops/access_ops.rst

@@ -0,0 +1,6 @@
+
+onnx_diagnostic.reference.torch_ops.access_ops
+==============================================
+
+.. automodule:: onnx_diagnostic.reference.torch_ops.access_ops
+    :members:

_doc/api/reference/torch_ops/index.rst

@@ -7,6 +7,7 @@ onnx_diagnostic.reference.torch_ops
     :maxdepth: 1
     :caption: modules
 
+    access_ops
     binary_ops
 
 OpRun
@@ -15,6 +16,12 @@ OpRun
 .. autoclass:: onnx_diagnostic.reference.torch_ops.OpRun
     :members:
 
+OpRunValue
+++++++++++
+
+.. autoclass:: onnx_diagnostic.reference.torch_ops.OpRunValue
+    :members:
+
 Other functions
 +++++++++++++++
 

_unittests/ut_reference/test_torch_evaluator.py

@@ -10,6 +10,7 @@
 
 
 TFLOAT = onnx.TensorProto.FLOAT
+TINT64 = onnx.TensorProto.INT64
 
 
 class TestTorchEvaluator(ExtTestCase):
@@ -72,6 +73,31 @@ def test_binary_ops(self):
             else:
                 self.assertEmpty(v.value)
 
+    def test_slice_squeeze(self):
+        X = oh.make_tensor_value_info("X", TFLOAT, [None, None])
+        starts = oh.make_tensor_value_info("starts", TINT64, [None])
+        ends = oh.make_tensor_value_info("ends", TINT64, [None])
+        axes = oh.make_tensor_value_info("axes", TINT64, [None])
+        Y = oh.make_tensor_value_info("Y", TINT64, [None])
+        nodes = [
+            oh.make_node("Slice", ["X", "starts", "ends", "axes"], ["T"]),
+            oh.make_node("Squeeze", ["T", "axes"], ["Y"]),
+        ]
+        graph = oh.make_graph(nodes, "g", [X, starts, ends, axes], [Y])
+        model = oh.make_model(graph, opset_imports=[oh.make_opsetid("", 18)])
+        feeds = {
+            "X": torch.tensor([[0]], dtype=torch.int64),
+            "starts": torch.tensor([0], dtype=torch.int64),
+            "ends": torch.tensor([1], dtype=torch.int64),
+            "axes": torch.tensor([0], dtype=torch.int64),
+        }
+        expected = ExtendedReferenceEvaluator(model).run(
+            None, {k: v.numpy() for k, v in feeds.items()}
+        )
+        rt = TorchEvaluator(model)
+        got = rt.run(None, feeds)
+        self.assertEqualAny(expected, [g.detach().numpy() for g in got])
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

onnx_diagnostic/reference/torch_evaluator.py

@@ -125,7 +125,7 @@ def _build_kernels(self, nodes: Sequence[onnx.NodeProto]):
                 continue
             opset = self.opsets[node.domain]
             key = node.domain, node.op_type, opset
-            while key not in kernels:
+            while key not in kernels and opset > 0:
                 opset -= 1
                 key = node.domain, node.op_type, opset
             assert (
@@ -150,12 +150,20 @@ def run(
         for k, v in self.constants.items():
             r = self.runtime_info[k]
             if not r.has_value:
-                r.set_value(v.to(self.CUDA) if r.is_shape and self.on_cuda else v)
+                r.set_value(
+                    torch_ops.OpRunValue(
+                        v.to(self.CUDA) if r.is_shape and self.on_cuda else v, True
+                    )
+                )
 
         # inputs
         for k, v in feeds.items():
             r = self.runtime_info[k]
-            r.set_value(v.to(self.CUDA) if r.is_shape and self.on_cuda else v)
+            r.set_value(
+                torch_ops.OpRunValue(
+                    v.to(self.CUDA) if r.is_shape and self.on_cuda else v, False
+                )
+            )
 
         # node execution
         for it, kernel in enumerate(self.kernels):
@@ -174,7 +182,7 @@ def run(
                 self.runtime_info[name].clean_value()
 
         # outputs
-        res = [self.runtime_info[o].value for o in outputs]
+        res = [self.runtime_info[o].value.tensor for o in outputs]
 
         # clean previous execution
         for k in feeds:

onnx_diagnostic/reference/torch_ops/__init__.py

@@ -1,3 +1,4 @@
-from ._op_run import OpRun
-
+from ._op_run import OpRun, OpRunValue
+from .access_ops import Slice_13
 from .binary_ops import Add_1, Div_1, Mul_1, Sub_1
+from .shape_ops import Squeeze_13

onnx_diagnostic/reference/torch_ops/_op_run.py

@@ -3,6 +3,44 @@
 import torch
 
 
+class OpRunValue:
+    """
+    Wrapper around the tensor.
+
+    :param tensor: torch.Tensor
+    :param is_constant: is it a constant
+    """
+
+    __slots__ = ("cached", "is_constant", "tensor")
+
+    def __init__(self, tensor: torch.Tensor, is_constant: bool = False):
+        self.tensor = tensor
+        self.is_constant = is_constant
+        self.cached = None
+
+    @property
+    def shape(self):
+        "shape"
+        return self.tensor.shape
+
+    @property
+    def dtype(self):
+        "dtype"
+        return self.tensor.dtype
+
+    def _tensor_as_tuple_int(self) -> Tuple[int, ...]:
+        return tuple(map(int, self.tensor))
+
+    @property
+    def as_tuple_int(self):
+        "value as int"
+        if self.is_constant:
+            if self.cached is not None:
+                self.cached = self._tensor_as_tuple_int()
+            return self.cached
+        return self._tensor_as_tuple_int()
+
+
 class OpRun:
     """
     Main class. Every kernel should inherit from it.

onnx_diagnostic/reference/torch_ops/access_ops.py

@@ -0,0 +1,32 @@
+from typing import Optional
+from . import OpRun, OpRunValue
+
+
+class Slice_13(OpRun):
+    "Slice"
+
+    def run(
+        self,
+        data: OpRunValue,
+        starts: OpRunValue,
+        ends: OpRunValue,
+        axes: Optional[OpRunValue] = None,
+        steps: Optional[OpRunValue] = None,
+    ) -> OpRunValue:
+        if axes is None:
+            if steps is None:
+                slices = [slice(s, e) for s, e in zip(starts.tensor, ends.tensor)]
+            else:
+                slices = [
+                    slice(s, e, d) for s, e, d in zip(starts.tensor, ends.tensor, steps.tensor)
+                ]
+        else:
+            if steps is None:
+                slices = [slice(0, a) for a in data.shape]
+                for s, e, a in zip(starts.tensor, ends.tensor, axes.tensor):
+                    slices[a] = slice(s, e)
+            else:
+                slices = [slice(0, a) for a in data.shape]
+                for s, e, a, d in zip(starts.tensor, ends.tensor, axes.tensor, steps.tensor):
+                    slices[a] = slice(s, e, d)
+        return OpRunValue(data.tensor[tuple(slices)])

onnx_diagnostic/reference/torch_ops/binary_ops.py

@@ -1,29 +1,29 @@
-from . import OpRun
+from . import OpRun, OpRunValue
 
 
 class Add_1(OpRun):
     """Add"""
 
-    def run(self, x, y):
-        return x + y
+    def run(self, x: OpRunValue, y: OpRunValue) -> OpRunValue:
+        return OpRunValue(x.tensor + y.tensor)
 
 
 class Mul_1(OpRun):
     """Mul"""
 
-    def run(self, x, y):
-        return x * y
+    def run(self, x: OpRunValue, y: OpRunValue) -> OpRunValue:
+        return OpRunValue(x.tensor * y.tensor)
 
 
 class Div_1(OpRun):
     """Div"""
 
-    def run(self, x, y):
-        return x / y
+    def run(self, x: OpRunValue, y: OpRunValue) -> OpRunValue:
+        return OpRunValue(x.tensor / y.tensor)
 
 
 class Sub_1(OpRun):
     """Sub"""
 
-    def run(self, x, y):
-        return x - y
+    def run(self, x: OpRunValue, y: OpRunValue) -> OpRunValue:
+        return OpRunValue(x.tensor - y.tensor)

onnx_diagnostic/reference/torch_ops/shape_ops.py

@@ -0,0 +1,8 @@
+from . import OpRun, OpRunValue
+
+
+class Squeeze_13(OpRun):
+    "Squeeze"
+
+    def run(self, data: OpRunValue, axes: OpRunValue) -> OpRunValue:
+        return OpRunValue(data.tensor.squeeze(axes.as_tuple_int))