Feat: GlobalAveragePool

gizatechxyz · Mar 12, 2024 · 0a8d00a · 0a8d00a
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diff --git a/docs/framework/operators/neural-network/README.md b/docs/framework/operators/neural-network/README.md
@@ -39,4 +39,5 @@ Orion supports currently these `NN` types.
 | [`nn.col2im`](nn.col2im.md) | Rearranges column blocks back into a multidimensional image |
 | [`nn.conv_transpose`](nn.conv\_transpose.md) | Performs the convolution transpose of the input data tensor and weight tensor. |
 | [`nn.conv`](nn.conv.md) | Performs the convolution of the input data tensor and weight tensor. |
+| [`nn.global_average_pool`](nn.global\_average\_pool.md) | GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. |
 
diff --git a/docs/framework/operators/neural-network/nn.global_average_pool.md b/docs/framework/operators/neural-network/nn.global_average_pool.md
@@ -0,0 +1,75 @@
+# NNTrait::global_average_pool
+
+```rust
+        fn global_average_pool(tensor: @Tensor<T>) -> Tensor<T>;
+```
+
+GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. 
+This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor.
+
+## Args
+
+* `tensor`(`@Tensor<T>`) - Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
+
+## Returns
+
+* Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
+
+## Examples
+
+```rust
+use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{FixedTrait, FP8x23};
+use orion::operators::nn::NNTrait;
+use orion::operators::nn::FP8x23NN;
+
+fn example() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(4);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 85392644, sign: false });
+    data.append(FP8x23 { mag: 61594092, sign: false });
+    data.append(FP8x23 { mag: 163676643, sign: true });
+    data.append(FP8x23 { mag: 180530738, sign: false });
+    data.append(FP8x23 { mag: 168048412, sign: true });
+    data.append(FP8x23 { mag: 5915510, sign: false });
+    data.append(FP8x23 { mag: 9047009, sign: true });
+    data.append(FP8x23 { mag: 46030420, sign: false });
+    data.append(FP8x23 { mag: 184797857, sign: false });
+    data.append(FP8x23 { mag: 129370611, sign: false });
+    data.append(FP8x23 { mag: 174006060, sign: true });
+    data.append(FP8x23 { mag: 162252480, sign: false });
+    data.append(FP8x23 { mag: 139240444, sign: true });
+    data.append(FP8x23 { mag: 168836878, sign: true });
+    data.append(FP8x23 { mag: 246913333, sign: true });
+    data.append(FP8x23 { mag: 1047194, sign: true });
+    data.append(FP8x23 { mag: 238599466, sign: true });
+    data.append(FP8x23 { mag: 216763643, sign: true });
+    data.append(FP8x23 { mag: 40581779, sign: true });
+    data.append(FP8x23 { mag: 209811161, sign: true });
+    data.append(FP8x23 { mag: 250078311, sign: false });
+    data.append(FP8x23 { mag: 31811183, sign: true });
+    data.append(FP8x23 { mag: 36411415, sign: true });
+    data.append(FP8x23 { mag: 107986324, sign: false });
+    data.append(FP8x23 { mag: 69727339, sign: false });
+    data.append(FP8x23 { mag: 223159880, sign: true });
+    data.append(FP8x23 { mag: 184932087, sign: true });
+    data.append(FP8x23 { mag: 118617436, sign: false });
+    data.append(FP8x23 { mag: 134825391, sign: true });
+    data.append(FP8x23 { mag: 217861279, sign: false });
+    data.append(FP8x23 { mag: 199069387, sign: false });
+    data.append(FP8x23 { mag: 192925915, sign: true });
+    let tensor1 = TensorTrait::new(shape.span(), data.span());
+
+    return NNTrait::global_average_pool(@tensor1);
+}
+>>> [{ mag: 40960207, sign: true } { mag: 31287372, sign: false } { mag: 75603722, sign: true } { mag: 139009462, sign: false } { mag: 176439012, sign: false } { mag: 72460509, sign: true } { mag: 54936798, sign: false } { mag: 22294840, sign: true } ]
+```
diff --git a/nodegen/node/global_average_pool.py b/nodegen/node/global_average_pool.py
@@ -0,0 +1,110 @@
+import numpy as np
+from nodegen.node import RunAll
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
+
+def global_average_pool(x: np.ndarray) -> np.ndarray:
+    axis = tuple(range(2, np.ndim(x)))
+    y = np.average(x, axis=axis)
+    for _ in axis:
+        y = np.expand_dims(y, -1)
+    return y  # type: ignore
+
+class Global_average_pool(RunAll):
+
+    @staticmethod
+    def fp8x23_2D():
+        x = np.random.uniform(-30, 30, (2, 4)).astype(np.float64)
+        y = global_average_pool(x)
+
+        x = Tensor(Dtype.FP8x23, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP8x23))
+        y = Tensor(Dtype.FP8x23, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP8x23))
+
+        name = "global_average_pool_fp8x23_2D"
+        make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+                    name, Trait.NN)
+
+    @staticmethod
+    def fp16x16_2D():
+        x = np.random.uniform(-30, 30, (3, 2)).astype(np.float16)
+        y = global_average_pool(x)
+
+        x = Tensor(Dtype.FP16x16, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP16x16))
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP16x16))
+
+        name = "global_average_pool_fp16x16_2D"
+        make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+                    name, Trait.NN)
+
+    @staticmethod
+    def fp8x23_3D():
+        x = np.random.uniform(-30, 30, (2, 4, 2)).astype(np.float64)
+        y = global_average_pool(x)
+
+        x = Tensor(Dtype.FP8x23, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP8x23))
+        y = Tensor(Dtype.FP8x23, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP8x23))
+
+        name = "global_average_pool_fp8x23_3D"
+        make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+                    name, Trait.NN)
+
+    @staticmethod
+    def fp16x16_3D():
+        x = np.random.uniform(-30, 30, (3, 2, 2)).astype(np.float16)
+        y = global_average_pool(x)
+
+        x = Tensor(Dtype.FP16x16, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP16x16))
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP16x16))
+
+        name = "global_average_pool_fp16x16_3D"
+        make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+                    name, Trait.NN)
+
+    @staticmethod
+    def fp8x23_4D():
+        x = np.random.uniform(-30, 30, (2, 4, 2, 2)).astype(np.float64)
+        y = global_average_pool(x)
+
+        x = Tensor(Dtype.FP8x23, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP8x23))
+        y = Tensor(Dtype.FP8x23, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP8x23))
+
+        name = "global_average_pool_fp8x23_4D"
+        make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+                    name, Trait.NN)
+
+    @staticmethod
+    def fp16x16_4D():
+        x = np.random.uniform(-30, 30, (3, 2, 2, 3)).astype(np.float16)
+        y = global_average_pool(x)
+
+        x = Tensor(Dtype.FP16x16, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP16x16))
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP16x16))
+
+        name = "global_average_pool_fp16x16_4D"
+        make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+                    name, Trait.NN)
+
+    # @staticmethod
+    # def fp32x32():
+    #     x = np.random.uniform(-30, 30, (2, 4)).astype(np.float64)
+    #     y = global_average_pool(x)
+
+    #     x = Tensor(Dtype.FP32x32, x.shape, to_fp(
+    #         x.flatten(), FixedImpl.FP32x32))
+    #     y = Tensor(Dtype.FP32x32, y.shape, to_fp(
+    #         y.flatten(), FixedImpl.FP32x32))
+
+    #     name = "global_average_pool_fp32x32"
+    #     make_test([x], y, "NNTrait::global_average_pool(@input_0)",
+    #                 name, Trait.NN)
diff --git a/src/operators/nn/core.cairo b/src/operators/nn/core.cairo
@@ -18,6 +18,7 @@ use orion::operators::tensor::core::Tensor;
 /// col2im - Rearranges column blocks back into a multidimensional image
 /// conv_transpose - Performs the convolution transpose of the input data tensor and weight tensor.
 /// conv - Performs the convolution of the input data tensor and weight tensor.
+/// global_average_pool - GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel.
 trait NNTrait<T> {
     /// # NNTrait::relu
     ///
@@ -1304,4 +1305,81 @@ trait NNTrait<T> {
         mode: Option<orion::operators::nn::functional::grid_sample::MODE>,
         padding_mode: Option<orion::operators::nn::functional::grid_sample::PADDING_MODE>,
     ) -> Tensor<T>;
+    /// # NNTrait::global_average_pool
+    ///
+    /// ```rust
+    ///         fn global_average_pool(tensor: @Tensor<T>) -> Tensor<T>;
+    /// ```
+    ///
+    /// GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. 
+    /// This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor.
+    ///
+    /// ## Args
+    ///
+    /// * `tensor`(`@Tensor<T>`) - Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
+    ///
+    /// ## Returns
+    ///
+    /// * Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
+    ///
+    /// ## Examples
+    ///
+    /// ```rust
+    /// use orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd};
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// use orion::operators::tensor::{TensorTrait, Tensor};
+    /// use orion::utils::{assert_eq, assert_seq_eq};
+    /// use orion::operators::tensor::FP8x23TensorPartialEq;
+    /// use orion::numbers::{FixedTrait, FP8x23};
+    /// use orion::operators::nn::NNTrait;
+    /// use orion::operators::nn::FP8x23NN;
+    /// 
+    /// fn example() -> Tensor<FP8x23> {
+    ///     let mut shape = ArrayTrait::<usize>::new();
+    ///     shape.append(2);
+    ///     shape.append(4);
+    ///     shape.append(2);
+    ///     shape.append(2);
+    /// 
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(FP8x23 { mag: 85392644, sign: false });
+    ///     data.append(FP8x23 { mag: 61594092, sign: false });
+    ///     data.append(FP8x23 { mag: 163676643, sign: true });
+    ///     data.append(FP8x23 { mag: 180530738, sign: false });
+    ///     data.append(FP8x23 { mag: 168048412, sign: true });
+    ///     data.append(FP8x23 { mag: 5915510, sign: false });
+    ///     data.append(FP8x23 { mag: 9047009, sign: true });
+    ///     data.append(FP8x23 { mag: 46030420, sign: false });
+    ///     data.append(FP8x23 { mag: 184797857, sign: false });
+    ///     data.append(FP8x23 { mag: 129370611, sign: false });
+    ///     data.append(FP8x23 { mag: 174006060, sign: true });
+    ///     data.append(FP8x23 { mag: 162252480, sign: false });
+    ///     data.append(FP8x23 { mag: 139240444, sign: true });
+    ///     data.append(FP8x23 { mag: 168836878, sign: true });
+    ///     data.append(FP8x23 { mag: 246913333, sign: true });
+    ///     data.append(FP8x23 { mag: 1047194, sign: true });
+    ///     data.append(FP8x23 { mag: 238599466, sign: true });
+    ///     data.append(FP8x23 { mag: 216763643, sign: true });
+    ///     data.append(FP8x23 { mag: 40581779, sign: true });
+    ///     data.append(FP8x23 { mag: 209811161, sign: true });
+    ///     data.append(FP8x23 { mag: 250078311, sign: false });
+    ///     data.append(FP8x23 { mag: 31811183, sign: true });
+    ///     data.append(FP8x23 { mag: 36411415, sign: true });
+    ///     data.append(FP8x23 { mag: 107986324, sign: false });
+    ///     data.append(FP8x23 { mag: 69727339, sign: false });
+    ///     data.append(FP8x23 { mag: 223159880, sign: true });
+    ///     data.append(FP8x23 { mag: 184932087, sign: true });
+    ///     data.append(FP8x23 { mag: 118617436, sign: false });
+    ///     data.append(FP8x23 { mag: 134825391, sign: true });
+    ///     data.append(FP8x23 { mag: 217861279, sign: false });
+    ///     data.append(FP8x23 { mag: 199069387, sign: false });
+    ///     data.append(FP8x23 { mag: 192925915, sign: true });
+    ///     let tensor1 = TensorTrait::new(shape.span(), data.span());
+    /// 
+    ///     return NNTrait::global_average_pool(@tensor1);
+    /// }
+    /// >>> [{ mag: 40960207, sign: true } { mag: 31287372, sign: false } { mag: 75603722, sign: true } { mag: 139009462, sign: false } { mag: 176439012, sign: false } { mag: 72460509, sign: true } { mag: 54936798, sign: false } { mag: 22294840, sign: true } ]
+    /// ```
+    ///
+    fn global_average_pool(tensor: @Tensor<T>) -> Tensor<T>;
 }
diff --git a/src/operators/nn/functional.cairo b/src/operators/nn/functional.cairo
@@ -16,3 +16,4 @@ mod conv_transpose;
 mod depth_to_space;
 mod space_to_depth;
 mod conv;
+mod global_average_pool;
diff --git a/src/operators/nn/functional/global_average_pool.cairo b/src/operators/nn/functional/global_average_pool.cairo
@@ -0,0 +1,63 @@
+use orion::numbers::fixed_point::core::FixedTrait;
+use orion::numbers::NumberTrait;
+use orion::operators::tensor::core::{Tensor, TensorTrait};
+use orion::operators::tensor::helpers::{reduce_output_shape, len_from_shape, combine_indices};
+use orion::operators::tensor::math::{reduce_sum::accumulate_sum, arithmetic::div_downcast};
+
+
+fn global_average_pool<
+    T,
+    MAG,
+    impl TTensor: TensorTrait<T>,
+    impl TNumber: NumberTrait<T, MAG>,
+    impl TAdd: Add<T>,
+    impl TSub: Sub<T>,
+    impl TMul: Mul<T>,
+    impl TDiv: Div<T>,
+    impl TTensorAdd: Add<Tensor<T>>,
+    impl TPartialOrd: PartialOrd<T>,
+    impl TPartialEq: PartialEq<T>,
+    impl TAddEq: AddEq<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
+>(
+    tensor: Tensor<T>
+) -> Tensor<T> {
+    assert((tensor.shape).len() >= 2, 'Unexpected shape.');
+    let N = *(tensor.shape).at(0);
+    let C = *(tensor.shape).at(1);
+    let mut shape = array![N, C];
+    let mut i:usize = 2;
+    let one: T = NumberTrait::one();
+    let len = (tensor.shape).len();
+    let mut num: usize = 1;
+    let mut num_t: T = one;
+    while i < len {
+        shape.append(1);
+        let v = *(tensor.shape).at(i);
+        let mut v_t: T =  one;
+        let mut j: usize = 1;
+        while j != v {
+            v_t = v_t + one;
+            j += 1;
+        };
+        num *= v;
+        num_t = num_t * v_t;
+        i += 1;
+    };
+    let mut arr: Array<T> = array![];
+    i = 0;
+    let tensor_len = (tensor.data).len();
+    while i < tensor_len {
+        let mut j:usize = 0;
+        let mut r: T = NumberTrait::zero();
+        while j != num {
+            r += *(tensor.data).at(i);
+            j += 1;
+            i += 1;
+        };
+        arr.append(r / num_t);
+    };
+
+    TensorTrait::<T>::new(shape.span(), arr.span())
+}
diff --git a/src/operators/nn/implementations/nn_fp16x16.cairo b/src/operators/nn/implementations/nn_fp16x16.cairo
@@ -143,4 +143,8 @@ impl FP16x16NN of NNTrait<FP16x16> {
     ) -> Tensor<FP16x16> {
         functional::conv::conv(X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides)
     }
+
+    fn global_average_pool(tensor: @Tensor<FP16x16>) -> Tensor<FP16x16> {
+        functional::global_average_pool::global_average_pool(*tensor)
+    }
 }
diff --git a/src/operators/nn/implementations/nn_fp32x32.cairo b/src/operators/nn/implementations/nn_fp32x32.cairo
@@ -137,4 +137,8 @@ impl FP32x32NN of NNTrait<FP32x32> {
     ) -> Tensor<FP32x32> {
         functional::conv::conv(X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides)
     }
+
+    fn global_average_pool(tensor: @Tensor<FP32x32>) -> Tensor<FP32x32> {
+        functional::global_average_pool::global_average_pool(*tensor)
+    }
 }
diff --git a/src/operators/nn/implementations/nn_fp64x64.cairo b/src/operators/nn/implementations/nn_fp64x64.cairo
@@ -137,4 +137,8 @@ impl FP64x64NN of NNTrait<FP64x64> {
     ) -> Tensor<FP64x64> {
         functional::conv::conv(X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides)
     }
+
+    fn global_average_pool(tensor: @Tensor<FP64x64>) -> Tensor<FP64x64> {
+        functional::global_average_pool::global_average_pool(*tensor)
+    }
 }
diff --git a/src/operators/nn/implementations/nn_fp8x23.cairo b/src/operators/nn/implementations/nn_fp8x23.cairo
@@ -139,4 +139,8 @@ impl FP8x23NN of NNTrait<FP8x23> {
     ) -> Tensor<FP8x23> {
         functional::conv::conv(X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides)
     }
+
+    fn global_average_pool(tensor: @Tensor<FP8x23>) -> Tensor<FP8x23> {
+        functional::global_average_pool::global_average_pool(*tensor)
+    }
 }
diff --git a/src/operators/nn/implementations/nn_i32.cairo b/src/operators/nn/implementations/nn_i32.cairo
@@ -130,4 +130,8 @@ impl I32NN of NNTrait<i32> {
     ) -> Tensor<i32> {
         functional::conv::conv(X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides)
     }
+
+    fn global_average_pool(tensor: @Tensor<i32>) -> Tensor<i32> {
+        panic(array!['not supported!'])
+    }
 }