Merge pull request #20 from jemc-savi/add/op

Delay errors until session compute.

spec/Tensor.Comp.TensorDot.Outer.Spec.savi

@@ -5,12 +5,12 @@
   :it "is equivalent to matrix multiplication for rank-2 tensors"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.tensordot_outer!("example"
-          g.const!("A", Tensor(F64).from_array([
+        g.tensordot_outer("example"
+          g.const("A", Tensor(F64).from_array([
             1.0, 2.0
             3.0, 4.0
           ]).try_reshape(Tensor.Shape.new([2, 2])))
-          g.const!("B", Tensor(F64).from_array([
+          g.const("B", Tensor(F64).from_array([
             5.0, 6.0
             7.0, 8.0
           ]).try_reshape(Tensor.Shape.new([2, 2])))
@@ -26,15 +26,15 @@
   :it "handles larger-rank tensors by applying to the outer axes"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.tensordot_outer!("example"
-          g.const!("A", Tensor(F64).from_array([
+        g.tensordot_outer("example"
+          g.const("A", Tensor(F64).from_array([
             1,  2,  3
             4,  5,  6
 
             7,  8,  9
             10, 11, 12
           ]).try_reshape(Tensor.Shape.new([2, 2, 3])))
-          g.const!("B", Tensor(F64).from_array([
+          g.const("B", Tensor(F64).from_array([
             13, 14
             15, 16
 

spec/Tensor.Gen.Random.Spec.savi

@@ -3,16 +3,16 @@
   :const describes: "Tensor.Gen.Random"
 
   :it "raises its internal counter with each graph node that uses it"
-    _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
-      random = g.gen_random!("random"
-        g.const!("seed", Tensor(U32).from_array([2, 3]))
+    _WithGraphHelper.run(@env) -> (g, session |
+      random = g.gen_random("random"
+        g.const("seed", Tensor(U32).from_array([2, 3]))
       )
       shape = Tensor.Shape.scalar
-      example1 = g.random_uniform!("example1", random, Tensor(F64), shape)
-      example2 = g.random_uniform!("example2", random, Tensor(F64), shape)
-      example3 = g.random_uniform!("example3", random, Tensor(F64), shape)
-      example4 = g.random_uniform!("example4", random, Tensor(F64), shape)
-      example5 = g.random_uniform!("example5", random, Tensor(F64), shape)
+      example1 = g.random_uniform("example1", random, Tensor(F64), shape)
+      example2 = g.random_uniform("example2", random, Tensor(F64), shape)
+      example3 = g.random_uniform("example3", random, Tensor(F64), shape)
+      example4 = g.random_uniform("example4", random, Tensor(F64), shape)
+      example5 = g.random_uniform("example5", random, Tensor(F64), shape)
 
       assert: [
         session.compute!(example1).as!(Tensor(F64)).into_array.first!
@@ -27,4 +27,4 @@
         0.913356627721398900
         0.007108289495397546
       ]
-    ))
+    )

spec/Tensor.Graph.Spec.savi

@@ -8,38 +8,38 @@
 
     graph = Tensor.Graph.new
     session = Tensor.Graph.Session.new(graph)
-    try (
-      a = graph.new_operation("Const", "a") -> (builder |
-        builder
-          .set_attr_type("dtype", Tensor(F64).element_type_code)
-          .set_attr_tensor!("value", a_value)
-          .finish!
-      )
 
-      assert: a.output(0).shape.rank == 2
-      assert: a.output(0).shape.into_array == [2, 2]
+    a = graph.new_operation("Const", "a") -> (builder |
+      builder
+        .set_attr_type("dtype", Tensor(F64).element_type_code)
+        .set_attr_tensor("value", a_value)
+        .finish
+    )
 
-      b = graph.new_operation("Const", "b") -> (builder |
-        builder
-          .set_attr_type("dtype", Tensor(F64).element_type_code)
-          .set_attr_tensor!("value", b_value)
-          .finish!
-      )
-      product1 = graph.new_operation("MatMul", "product1") -> (builder |
-        builder
-          .add_input(a.output(0))
-          .add_input(b.output(0))
-          .finish!
-      )
-      product2 = graph.new_operation("MatMul", "product2") -> (builder |
-        builder
-          .add_input(a.output(0))
-          .add_input(b.output(0))
-          .set_attr_bool("transpose_a", True)
-          .finish!
-      )
+    assert: a.output(0).shape.rank == 2
+    assert: a.output(0).shape.into_array == [2, 2]
 
+    b = graph.new_operation("Const", "b") -> (builder |
+      builder
+        .set_attr_type("dtype", Tensor(F64).element_type_code)
+        .set_attr_tensor("value", b_value)
+        .finish
+    )
+    product1 = graph.new_operation("MatMul", "product1") -> (builder |
+      builder
+        .add_input(a.output(0))
+        .add_input(b.output(0))
+        .finish
+    )
+    product2 = graph.new_operation("MatMul", "product2") -> (builder |
+      builder
+        .add_input(a.output(0))
+        .add_input(b.output(0))
+        .set_attr_bool("transpose_a", True)
+        .finish
+    )
 
+    try (
       result = session.compute!(product1.output(0))
       assert: result.as!(Tensor(F64)).into_array == [
         1.0 * 5.0 + 2.0 * 7.0, 1.0 * 6.0 + 2.0 * 8.0 // row1⋅col1, row1⋅col2
@@ -61,25 +61,27 @@
       assert no_error: error!
     )
 
-  :it "complains when creating an operation with an invalid type"
+  :it "complains when evaluating an operation with an invalid type"
     g = Tensor.Graph.new
-    assert error: (
+    session = Tensor.Graph.Session.new(g)
+
+    assert error: session.compute!(
       g.new_operation("Bogus", "example") -> (builder |
-        builder.finish!
-      )
+        builder.finish
+      ).output(0)
     )
     assert: g.errors.first!.code == Tensor.Graph.Error.Code.InvalidArgument
     assert: g.errors.first!.message.includes("Op type not registered 'Bogus'")
 
   :it "optimizes to minimize a loss function with gradient descent"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
-      learning_rate = g.const!("learning_rate", Tensor(F64).scalar(0.25))
+      learning_rate = g.const("learning_rate", Tensor(F64).scalar(0.25))
 
-      x = g.variable!("x", Tensor(F64), [])
-      loss = g.square!("square", x)
+      x = g.variable("x", Tensor(F64), [])
+      loss = g.square("square", x)
       grad = g.graph.add_gradients!([loss], [x]).first!
 
-      x2 = g.apply_gradient_descent!("apply_grad", grad, x, learning_rate)
+      x2 = g.apply_gradient_descent("apply_grad", grad, x, learning_rate)
 
       result Tensor.Any = Tensor(F64).scalar(5)
       [

spec/Tensor.Op.Add.Spec.savi

@@ -5,9 +5,9 @@
   :it "computes arithmetic addition"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.add!("example"
-          g.const!("x", Tensor(I32).from_array([1, 2, 3, 4]))
-          g.const!("y", Tensor(I32).from_array([5, 6, 7, 8]))
+        g.add("example"
+          g.const("x", Tensor(I32).from_array([1, 2, 3, 4]))
+          g.const("y", Tensor(I32).from_array([5, 6, 7, 8]))
         )
       )
 
@@ -17,9 +17,9 @@
   :it "can broadcast smaller sizes/shapes across larger sizes/shapes"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.add!("example"
-          g.const!("x", Tensor(I32).from_array([-1, -3, -5]))
-          g.const!("y", Tensor(I32).from_array([
+        g.add("example"
+          g.const("x", Tensor(I32).from_array([-1, -3, -5]))
+          g.const("y", Tensor(I32).from_array([
             1, 2, 3
             4, 5, 6
             7, 8, 9

spec/Tensor.Op.Bitcast.Spec.savi

@@ -5,8 +5,8 @@
   :it "distributes bits into a larger number of narrower elements"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.bitcast!("example"
-          g.const!("input", Tensor(U16).from_array([0x0246, 0x8ace]))
+        g.bitcast("example"
+          g.const("input", Tensor(U16).from_array([0x0246, 0x8ace]))
           Tensor(U8)
         )
       )
@@ -18,8 +18,8 @@
   :it "consolidates bits into a smaller number of wider elements"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.bitcast!("example"
-          g.const!("input", Tensor(U8).from_array([0x46, 0x02, 0xce, 0x8a])
+        g.bitcast("example"
+          g.const("input", Tensor(U8).from_array([0x46, 0x02, 0xce, 0x8a])
               .try_reshape(Tensor.Shape.new([2, 2]))
           )
           Tensor(U16)
@@ -31,10 +31,12 @@
 
   :it "complains on narrow to wide with more than one wide result per row"
     _WithGraphHelper.run(@env, False) -> (g, session |
-      assert error: g.bitcast!("example"
-        g.const!("input", Tensor(U8).from_array([0x46, 0x02, 0xce, 0x8a])
-          // this would work if we did a reshape like: [2, 2]
+      assert error: session.compute!(
+        g.bitcast("example"
+          g.const("input", Tensor(U8).from_array([0x46, 0x02, 0xce, 0x8a])
+            // this would work if we did a reshape like: [2, 2]
+          )
+          Tensor(U16)
         )
-        Tensor(U16)
       )
     )

spec/Tensor.Op.Cast.Spec.savi

@@ -5,8 +5,8 @@
   :it "does bounds-wrapping when converting to a narrower integer type"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.cast!("example"
-          g.const!("input", Tensor(I16).from_array([0, 1, 0xffff, 0x7890]))
+        g.cast("example"
+          g.const("input", Tensor(I16).from_array([0, 1, 0xffff, 0x7890]))
           Tensor(I8)
         )
       )
@@ -17,8 +17,8 @@
   :it "rounds a floating-point value to its nearest integer value"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.cast!("example"
-          g.const!("input", Tensor(F64).from_array([2.4, 2.5, 2.6, -2.5]))
+        g.cast("example"
+          g.const("input", Tensor(F64).from_array([2.4, 2.5, 2.6, -2.5]))
           Tensor(I32)
         )
       )
@@ -29,8 +29,8 @@
   :it "rounds to the nearest representable less-precise floating-point value"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.cast!("example"
-          g.const!("input", Tensor(F64).from_array([-1e26]))
+        g.cast("example"
+          g.const("input", Tensor(F64).from_array([-1e26]))
           Tensor(F32)
         )
       )
@@ -41,8 +41,8 @@
   :it "can be set to round with floating-point truncation (toward zero)"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.cast_with_floating_point_truncation!("example"
-          g.const!("input", Tensor(F64).from_array([-1e26]))
+        g.cast_with_floating_point_truncation("example"
+          g.const("input", Tensor(F64).from_array([-1e26]))
           Tensor(F32)
         )
       )

spec/Tensor.Op.Concat.Spec.savi

@@ -8,10 +8,10 @@
   :it "combines the list of tensors into one new tensor"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.concat!("example"
+        g.concat("example"
           [
-            g.const!("input_a", @f64_2x2(1, 2, 3, 4))
-            g.const!("input_b", @f64_2x2(5, 6, 7, 8))
+            g.const("input_a", @f64_2x2(1, 2, 3, 4))
+            g.const("input_b", @f64_2x2(5, 6, 7, 8))
           ]
         )
       )
@@ -28,10 +28,10 @@
   :it "can combine along a different axis"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.concat!("example"
+        g.concat("example"
           [
-            g.const!("input_a", @f64_2x2(1, 2, 3, 4))
-            g.const!("input_b", @f64_2x2(5, 6, 7, 8))
+            g.const("input_a", @f64_2x2(1, 2, 3, 4))
+            g.const("input_b", @f64_2x2(5, 6, 7, 8))
           ]
           1 // axis
         )
@@ -46,31 +46,37 @@
 
   :it "complains when the inputs are of different types"
     _WithGraphHelper.run(@env, False) -> (g, session |
-      assert error: g.concat!("example"
-        [
-          g.const!("input_a", Tensor(F64).from_array([1, 2, 3, 4]))
-          g.const!("input_b", Tensor(F32).from_array([5, 6, 7, 8]))
-        ]
+      assert error: session.compute!(
+        g.concat("example"
+          [
+            g.const("input_a", Tensor(F64).from_array([1, 2, 3, 4]))
+            g.const("input_b", Tensor(F32).from_array([5, 6, 7, 8]))
+          ]
+        )
       )
     )
 
   :it "complains when the inputs are of different shapes"
     _WithGraphHelper.run(@env, False) -> (g, session |
-      assert error: g.concat!("example"
-        [
-          g.const!("input_a", @f64_2x2(1, 2, 3, 4))
-          g.const!("input_b", @f64_2x2(5, 6, 7, 8).try_reshape(Tensor.Shape.new([1, 4])))
-        ]
+      assert error: session.compute!(
+        g.concat("example"
+          [
+            g.const("input_a", @f64_2x2(1, 2, 3, 4))
+            g.const("input_b", @f64_2x2(5, 6, 7, 8).try_reshape(Tensor.Shape.new([1, 4])))
+          ]
+        )
       )
     )
 
   :it "complains when the given axis is greater-or-equal to the inputs' rank"
     _WithGraphHelper.run(@env, False) -> (g, session |
-      assert error: g.concat!("example"
-        [
-          g.const!("input_a", @f64_2x2(1, 2, 3, 4))
-          g.const!("input_b", @f64_2x2(5, 6, 7, 8))
-        ]
-        2
+      assert error: session.compute!(
+        g.concat("example"
+          [
+            g.const("input_a", @f64_2x2(1, 2, 3, 4))
+            g.const("input_b", @f64_2x2(5, 6, 7, 8))
+          ]
+          2
+        )
       )
     )

spec/Tensor.Op.Const.Spec.savi

@@ -5,7 +5,7 @@
   :it "emits a constant tensor value"
     _WithGraphHelper.run(@env) -> (g, session | assert no_error: (
       result = session.compute!(
-        g.const!("example"
+        g.const("example"
           Tensor(F64).from_array([1, 2, 3, 4])
         )
       )