Add basic 2D `layer_norm` operator (#588)

src/utils/tensor.js

@@ -762,6 +762,42 @@ export function mean_pooling(last_hidden_state, attention_mask) {
     )
 }
 
+/**
+ * Apply Layer Normalization for last certain number of dimensions.
+ * @param {Tensor} input The input tensor
+ * @param {number[]} normalized_shape input shape from an expected input of size
+ * @param {Object} options The options for the layer normalization
+ * @param {number} [options.eps=1e-5] A value added to the denominator for numerical stability.
+ * @returns {Tensor} The normalized tensor.
+ */
+export function layer_norm(input, normalized_shape, {
+    eps = 1e-5,
+} = {}) {
+    if (input.dims.length !== 2) {
+        throw new Error('`layer_norm` currently only supports 2D input.');
+    }
+
+    const [batchSize, featureDim] = input.dims;
+
+    if (normalized_shape.length !== 1 && normalized_shape[0] !== featureDim) {
+        throw new Error('`normalized_shape` must be a 1D array with shape `[input.dims[1]]`.');
+    }
+
+    const [std, mean] = std_mean(input, 1, 0, true);
+
+    // @ts-ignore
+    const returnedData = new input.data.constructor(input.data.length);
+
+    for (let i = 0; i < batchSize; ++i) {
+        const offset = i * featureDim;
+        for (let j = 0; j < featureDim; ++j) {
+            const offset2 = offset + j;
+            returnedData[offset2] = (input.data[offset2] - mean.data[i]) / (std.data[i] + eps);
+        }
+    }
+    return new Tensor(input.type, returnedData, input.dims);
+}
+
 /**
  * Helper function to calculate new dimensions when performing a squeeze operation.
  * @param {number[]} dims The dimensions of the tensor.

tests/tensor.test.js

@@ -1,7 +1,7 @@
 
 import { Tensor } from '../src/transformers.js';
 import { compare } from './test_utils.js';
-import { cat, mean, stack } from '../src/utils/tensor.js';
+import { cat, mean, stack, layer_norm } from '../src/utils/tensor.js';
 
 describe('Tensor operations', () => {
 
@@ -103,7 +103,6 @@ describe('Tensor operations', () => {
         });
     });
 
-
     describe('mean', () => {
         it('should calculate mean', async () => {
             const t1 = new Tensor('float32', [1, 2, 3, 4, 5, 6], [2, 3, 1]);
@@ -128,4 +127,18 @@ describe('Tensor operations', () => {
 
         })
     });
+
+    describe('layer_norm', () => {
+        it('should calculate layer norm', async () => {
+            const t1 = new Tensor('float32', [1, 2, 3, 4, 5, 6], [2, 3]);
+
+            const target = new Tensor('float32', [
+                -1.2247356176376343, 0.0, 1.2247356176376343,
+                -1.2247357368469238, -1.1920928955078125e-07, 1.2247354984283447,
+            ], [2, 3]);
+
+            const norm = layer_norm(t1, [t1.dims.at(-1)]);
+            compare(norm, target, 1e-3);
+        });
+    });
 });