Merge branch 'develop' into pr/223

docs/SUMMARY.md

@@ -80,6 +80,7 @@
     * [tensor.quantize\_linear](framework/operators/tensor/tensor.quantize\_linear.md)
     * [tensor.dequantize\_linear](framework/operators/tensor/tensor.dequantize\_linear.md)
     * [tensor.nonzero](framework/operators/tensor/tensor.nonzero.md)
+    * [tensor.unsqueeze](framework/operators/tensor/tensor.unsqueeze.md)
   * [Neural Network](framework/operators/neural-network/README.md)
     * [nn.relu](framework/operators/neural-network/nn.relu.md)
     * [nn.leaky\_relu](framework/operators/neural-network/nn.leaky\_relu.md)

docs/framework/compatibility.md

@@ -54,5 +54,6 @@ You can see below the list of current supported ONNX Operators:
 | [DequantizeLinear](operators/tensor/tensor.quantize\_linear.md) | :white\_check\_mark: |
 |          [Nonzero](operators/tensor/tensor.nonzero.md)          | :white\_check\_mark: |
 |          [Squeeze](operators/tensor/tensor.squeeze.md)          | :white\_check\_mark: |
+|        [Unsqueeze](operators/tensor/tensor.unsqueeze.md)        | :white\_check\_mark: |
 
 Current Operators support: **48/156 (30%)**

docs/framework/operators/tensor/README.md

@@ -79,6 +79,7 @@ use orion::operators::tensor::TensorTrait;
 | [`tensor.gather`](tensor.gather.md) | Gather entries of the axis dimension of data. |
 | [`tensor.nonzero`](tensor.nonzero.md) | Produces indices of the elements that are non-zero (in row-major order - by dimension). |
 | [`tensor.squeeze`](tensor.squeeze.md) | Removes dimensions of size 1 from the shape of a tensor. |
+| [`tensor.unsqueeze`](tensor.unsqueeze.md) | Inserts single-dimensional entries to the shape of an input tensor. |
 
 ## Arithmetic Operations
 

docs/framework/operators/tensor/tensor.unsqueeze.md

@@ -0,0 +1,51 @@
+# tensor.unsqueeze
+
+```rust 
+   fn unsqueeze(self: @Tensor<T>, axes: Span<usize>) -> Tensor<T>;
+```
+
+Insert single-dimensional entries to the shape of an input tensor (data). Takes one required input axes -
+which contains a list of dimension indices and this operator will insert a dimension of value 1 into the
+corresponding index of the output tensor (expanded).
+
+## Args
+
+* `self`(`@Tensor<T>`) - Tensor of data to unsquezee.
+* `axes`(`Span<usize>`) - List of integers indicating the dimensions to be inserted. 
+
+## Panics
+
+* Panics if the given axes have duplicate elements.
+* Panics if one of the given axes is invalid.
+
+## Returns 
+
+Reshaped `Tensor<T>` with same data as input.
+
+## Example
+
+```rust
+use array::{ArrayTrait, SpanTrait};
+
+use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+
+fn unsqueeze_example() -> Tensor<u32> {
+    let tensor = TensorTrait::<u32>::new(
+        shape: array![2, 4].span(), 
+        data: array![0, 1, 2, 3, 4, 5, 6, 7].span(), 
+    );
+
+    return tensor.unsqueeze(
+        axes: array![0, 3].span(), 
+    );
+}
+>>> [[[[0]
+       [1]
+       [2]
+       [3]]
+
+      [[4]
+       [5]
+       [6]
+       [7]]]]
+```

nodegen/node/unsqueeze.py

@@ -0,0 +1,175 @@
+import numpy as np
+from nodegen.node import RunAll
+from ..helpers import make_node, make_test, to_fp, Tensor, Dtype, FixedImpl
+
+
+class Unsqueeze(RunAll):
+    @staticmethod
+    def unsqueeze_u32():
+        def unsqueeze_2D():
+            x = np.random.randint(0, 255, (2, 4)).astype(np.uint32)
+            y = np.expand_dims(x, axis=0)
+            y = np.expand_dims(y, axis=1)
+            y = np.expand_dims(y, axis=4)
+
+            x = Tensor(Dtype.U32, x.shape, x.flatten())
+            y = Tensor(Dtype.U32, y.shape, y.flatten())
+
+            name = "unsqueeze_u32_2d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![1, 4, 0].span())", name)
+
+        def unsqueeze_3D():
+            x = np.random.randint(0, 255, (20, 10, 5)).astype(np.uint32)
+            y = np.expand_dims(x, axis=2)
+            y = np.expand_dims(y, axis=4)
+            y = np.expand_dims(y, axis=5)
+
+            x = Tensor(Dtype.U32, x.shape, x.flatten())
+            y = Tensor(Dtype.U32, y.shape, y.flatten())
+
+            name = "unsqueeze_u32_3d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![5, 4, 2].span())", name)
+
+        unsqueeze_2D()
+        unsqueeze_3D()
+
+    @staticmethod
+    def unsqueeze_i32():
+        def unsqueeze_2D():
+            x = np.random.randint(-127, 127, (2, 4)).astype(np.int32)
+            y = np.expand_dims(x, axis=0)
+            y = np.expand_dims(y, axis=1)
+            y = np.expand_dims(y, axis=4)
+
+            x = Tensor(Dtype.I32, x.shape, x.flatten())
+            y = Tensor(Dtype.I32, y.shape, y.flatten())
+
+            name = "unsqueeze_i32_2d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![1, 4, 0].span())", name)
+
+        def unsqueeze_3D():
+            x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int32)
+            y = np.expand_dims(x, axis=2)
+            y = np.expand_dims(y, axis=4)
+            y = np.expand_dims(y, axis=5)
+
+            x = Tensor(Dtype.I32, x.shape, x.flatten())
+            y = Tensor(Dtype.I32, y.shape, y.flatten())
+
+            name = "unsqueeze_i32_3d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![5, 4, 2].span())", name)
+
+
+        unsqueeze_2D()
+        unsqueeze_3D()
+
+    @staticmethod
+    def unsqueeze_i8():
+        def unsqueeze_2D():
+            x = np.random.randint(-127, 127, (2, 4)).astype(np.int8)
+            y = np.expand_dims(x, axis=0)
+            y = np.expand_dims(y, axis=1)
+            y = np.expand_dims(y, axis=4)
+
+            x = Tensor(Dtype.I8, x.shape, x.flatten())
+            y = Tensor(Dtype.I8, y.shape, y.flatten())
+
+            name = "unsqueeze_i8_2d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![1, 4, 0].span())", name)
+
+        def unsqueeze_3D():
+            x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int8)
+            y = np.expand_dims(x, axis=2)
+            y = np.expand_dims(y, axis=4)
+            y = np.expand_dims(y, axis=5)
+
+            x = Tensor(Dtype.I8, x.shape, x.flatten())
+            y = Tensor(Dtype.I8, y.shape, y.flatten())
+
+            name = "unsqueeze_i8_3d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![5, 4, 2].span())", name)
+
+        unsqueeze_2D()
+        unsqueeze_3D()
+
+    @staticmethod
+    def unsqueeze_fp8x23():
+        def unsqueeze_2D():
+            x = to_fp(np.random.randint(-127, 127, (2, 4)
+                                        ).astype(np.int64), FixedImpl.FP8x23)
+            y = np.expand_dims(x, axis=0)
+            y = np.expand_dims(y, axis=1)
+            y = np.expand_dims(y, axis=4)
+
+            x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
+            y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
+
+            name = "unsqueeze_fp8x23_2d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![1, 4, 0].span())", name)
+
+        def unsqueeze_3D():
+            x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
+                                        ).astype(np.int64), FixedImpl.FP8x23)
+            y = np.expand_dims(x, axis=2)
+            y = np.expand_dims(y, axis=4)
+            y = np.expand_dims(y, axis=5)
+
+            x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
+            y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
+
+            name = "unsqueeze_fp8x23_3d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![5, 4, 2].span())", name)
+
+        unsqueeze_2D()
+        unsqueeze_3D()
+
+    @staticmethod
+    def unsqueeze_fp16x16():
+        def unsqueeze_2D():
+            x = to_fp(np.random.randint(-127, 127, (2, 4)
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = np.expand_dims(x, axis=0)
+            y = np.expand_dims(y, axis=1)
+            y = np.expand_dims(y, axis=4)
+
+            x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
+
+            name = "unsqueeze_fp16x16_2d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![1, 4, 0].span())", name)
+
+        def unsqueeze_3D():
+            x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = np.expand_dims(x, axis=2)
+            y = np.expand_dims(y, axis=4)
+            y = np.expand_dims(y, axis=5)
+
+            x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
+
+            name = "unsqueeze_fp16x16_3d"
+            make_node([x], [y], name)
+            make_test(
+                [x], y, "input_0.unsqueeze(array![5, 4, 2].span())", name)
+
+        unsqueeze_2D()
+        unsqueeze_3D()

src/operators/tensor/core.cairo

@@ -75,7 +75,8 @@ impl TensorSerde<T, impl TSerde: Serde<T>, impl TDrop: Drop<T>> of Serde<Tensor<
 /// gather - Gather entries of the axis dimension of data.
 /// nonzero - Produces indices of the elements that are non-zero (in row-major order - by dimension).
 /// squeeze - Removes dimensions of size 1 from the shape of a tensor.
-/// 
+/// unsqueeze - Inserts single-dimensional entries to the shape of an input tensor.
+///
 trait TensorTrait<T> {
     /// # tensor.new
     ///
@@ -2503,6 +2504,59 @@ trait TensorTrait<T> {
     fn gather(
     self: @Tensor<T>, indices: Tensor<usize>, axis: Option<usize>
     ) -> Tensor<T> ;
+    /// # tensor.unsqueeze
+    ///
+    /// ```rust 
+    ///    fn unsqueeze(self: @Tensor<T>, axes: Span<usize>) -> Tensor<T>;
+    /// ```
+    ///
+    /// Insert single-dimensional entries to the shape of an input tensor (data). Takes one required input axes -
+    /// which contains a list of dimension indices and this operator will insert a dimension of value 1 into the
+    /// corresponding index of the output tensor (expanded).
+    ///
+    /// ## Args
+    ///
+    /// * `self`(`@Tensor<T>`) - Tensor of data to unsquezee.
+    /// * `axes`(`Span<usize>`) - List of integers indicating the dimensions to be inserted. 
+    ///
+    /// ## Panics
+    ///
+    /// * Panics if the given axes have duplicate elements.
+    /// * Panics if one of the given axes is invalid.
+    ///
+    /// ## Returns 
+    ///
+    /// Reshaped `Tensor<T>` with same data as input.
+    ///
+    /// ## Example
+    ///
+    /// ```rust
+    /// use array::{ArrayTrait, SpanTrait};
+    /// 
+    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+    /// 
+    /// fn unsqueeze_example() -> Tensor<u32> {
+    ///     let tensor = TensorTrait::<u32>::new(
+    ///         shape: array![2, 4].span(), 
+    ///         data: array![0, 1, 2, 3, 4, 5, 6, 7].span(), 
+    ///     );
+    /// 
+    ///     return tensor.unsqueeze(
+    ///         axes: array![0, 3].span(), 
+    ///     );
+    /// }
+    /// >>> [[[[0]
+    ///        [1]
+    ///        [2]
+    ///        [3]]
+    ///
+    ///       [[4]
+    ///        [5]
+    ///        [6]
+    ///        [7]]]]
+    /// ```
+    ///
+    fn unsqueeze(self: @Tensor<T>, axes: Span<usize>) -> Tensor<T>;
     /// # tensor.squeeze
     ///
     /// ```rust 
@@ -2985,4 +3039,43 @@ fn squeeze<T>(self: @Tensor<T>, axes: Option<Span<i32>>) -> Tensor<T> {
     };
 
     return Tensor::<T>{ shape: target_shape, data: *self.data };
-}
+}
+/// Cf: TensorTrait::unsqueeze docstring
+fn unsqueeze<T>(self: @Tensor<T>, axes: Span<usize>) -> Tensor<T> {
+    let dedupped_array = axes.dedup();
+    assert(dedupped_array.len() == axes.len(), 'Duplicated input axes');
+
+    let mut self_shape_copy = *self.shape;
+    let mut i: usize = 0;
+    let mut added_axes_count: usize = 0;
+    let mut output_shape: Array<usize> = ArrayTrait::new();
+    loop {
+        if axes.contains(i + added_axes_count) {
+            output_shape.append(1);
+            added_axes_count += 1;
+        } else {
+            match self_shape_copy.pop_front() {
+                Option::Some(val) => {
+                    output_shape.append(*val);
+                    i += 1;
+                },
+                Option::None(_) => {
+                    break ();
+                }
+            };
+        };
+    };
+
+    let mut j: usize = output_shape.len();
+    loop {
+        if axes.contains(j) {
+            output_shape.append(1);
+        } else {
+            break ();
+        }
+        j += 1;
+    };
+    assert(output_shape.len() == axes.len() + (*self.shape).len(), 'Invalid input axes');
+
+    return Tensor::<T> { shape: output_shape.span(), data: *self.data };
+}

src/operators/tensor/implementations/tensor_fp16x16.cairo

@@ -217,6 +217,10 @@ impl FP16x16Tensor of TensorTrait<FP16x16> {
     fn squeeze(self: @Tensor<FP16x16>, axes: Option<Span<i32>>) -> Tensor<FP16x16> {
         core::squeeze(self, axes)
     }
+
+    fn unsqueeze(self: @Tensor<FP16x16>, axes: Span<usize>) -> Tensor<FP16x16> {
+        core::unsqueeze(self, axes)
+    }
 }
 
 /// Implements addition for `Tensor<FP16x16>` using the `Add` trait.

src/operators/tensor/implementations/tensor_fp32x32.cairo

@@ -210,14 +210,18 @@ impl FP32x32Tensor of TensorTrait<FP32x32> {
     ) -> Tensor<FP32x32> {
         math::gather::gather(self, indices, axis)
     }
-    
+
     fn nonzero(self: @Tensor<FP32x32>) -> Tensor<usize> {
         core::nonzero(self)
     }
 
     fn squeeze(self: @Tensor<FP32x32>, axes: Option<Span<i32>>) -> Tensor<FP32x32> {
         core::squeeze(self, axes)
     }
+
+    fn unsqueeze(self: @Tensor<FP32x32>, axes: Span<usize>) -> Tensor<FP32x32> {
+        core::unsqueeze(self, axes)
+    }
 }
 
 /// Implements addition for `Tensor<FP32x32>` using the `Add` trait.