1 files changed, 386 insertions, 0 deletions
diff --git a/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js b/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js
new file mode 100644
index 0000000000..efa05090ca
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+++ b/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js
@@ -0,0 +1,386 @@
+// META: title=validation tests for WebNN API lstm operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+// META: timeout=long
+
+'use strict';
+
+const steps = 10, batchSize = 5, inputSize = 3, hiddenSize = 8,
+      numDirections = 1;
+
+const tests = [
+  {
+    name: '[lstm] Test with default options',
+    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    outputs: [
+      {dataType: 'float16', dimensions: [numDirections, batchSize, hiddenSize]},
+      {dataType: 'float16', dimensions: [numDirections, batchSize, hiddenSize]}
+    ]
+  },
+  {
+    name: '[lstm] Test with given options',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [/*numDirections=*/ 2, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [/*numDirections=*/ 2, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      bias: {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, 4 * hiddenSize]
+      },
+      recurrentBias: {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, 4 * hiddenSize]
+      },
+      peepholeWeight: {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, 3 * hiddenSize]
+      },
+      initialHiddenState: {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize]
+      },
+      initialCellState: {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize]
+      },
+      returnSequence: true,
+      direction: 'both',
+      layout: 'ifgo',
+      activations: ['sigmoid', 'relu', 'tanh']
+    },
+    outputs: [
+      {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize]
+      },
+      {
+        dataType: 'float32',
+        dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize]
+      },
+      {
+        dataType: 'float32',
+        dimensions: [steps, /*numDirections=*/ 2, batchSize, hiddenSize]
+      }
+    ]
+  },
+  {
+    name: '[lstm] DataError is expected if hiddenSize equals to zero',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: 0
+  },
+  {
+    name: '[lstm] DataError is expected if hiddenSize is too large',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: 4294967295,
+  },
+  {
+    name: '[lstm] DataError is expected if steps equals to zero',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: 0,
+    hiddenSize: hiddenSize,
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the data type is not one of the floating point types',
+    input: {dataType: 'uint32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'uint32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'uint32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the rank of input is not 3',
+    input: {dataType: 'float32', dimensions: [steps, batchSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[lstm] DataError is expected if input.dimensions[0] is not equal to steps',
+    input: {dataType: 'float32', dimensions: [1000, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstm] DataError is expected if the shape of weight is incorrect',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, 1000]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the rank of recurrentWeight is not 3',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [numDirections, 4 * hiddenSize]},
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the size of options.activations is not 3',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {activations: ['sigmoid', 'tanh']}
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the rank of options.bias is not 2',
+    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {bias: {dataType: 'float16', dimensions: [numDirections]}}
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the shape of options.recurrentBias.dimensions is incorrect',
+    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      recurrentBias: {dataType: 'float16', dimensions: [numDirections, 1000]}
+    }
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the dataType of options.peepholeWeight is incorrect',
+    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      peepholeWeight:
+          {dataType: 'float32', dimensions: [numDirections, 3 * hiddenSize]}
+    }
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the dataType of options.initialHiddenState is incorrect',
+    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float16',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      initialHiddenState: {
+        dataType: 'uint64',
+        dimensions: [numDirections, batchSize, hiddenSize]
+      }
+    }
+  },
+  {
+    name:
+        '[lstm] DataError is expected if the shape of options.initialCellState is incorrect',
+    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    weight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, inputSize]
+    },
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
+    },
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      initialCellState:
+          {dataType: 'float32', dimensions: [numDirections, batchSize, 1000]}
+    }
+  }
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      const input = builder.input(
+          'input',
+          {dataType: test.input.dataType, dimensions: test.input.dimensions});
+      const weight = builder.input(
+          'weight',
+          {dataType: test.weight.dataType, dimensions: test.weight.dimensions});
+      const recurrentWeight = builder.input('recurrentWeight', {
+        dataType: test.recurrentWeight.dataType,
+        dimensions: test.recurrentWeight.dimensions
+      });
+
+      const options = {};
+      if (test.options) {
+        if (test.options.bias) {
+          options.bias = builder.input('bias', {
+            dataType: test.options.bias.dataType,
+            dimensions: test.options.bias.dimensions
+          });
+        }
+        if (test.options.recurrentBias) {
+          options.bias = builder.input('recurrentBias', {
+            dataType: test.options.recurrentBias.dataType,
+            dimensions: test.options.recurrentBias.dimensions
+          });
+        }
+        if (test.options.peepholeWeight) {
+          options.peepholeWeight = builder.input('peepholeWeight', {
+            dataType: test.options.peepholeWeight.dataType,
+            dimensions: test.options.peepholeWeight.dimensions
+          });
+        }
+        if (test.options.initialHiddenState) {
+          options.initialHiddenState = builder.input('initialHiddenState', {
+            dataType: test.options.initialHiddenState.dataType,
+            dimensions: test.options.initialHiddenState.dimensions
+          });
+        }
+        if (test.options.initialCellState) {
+          options.initialCellState = builder.input('initialCellState', {
+            dataType: test.options.initialCellState.dataType,
+            dimensions: test.options.initialCellState.dimensions
+          });
+        }
+        if (test.options.returnSequence) {
+          options.returnSequence = test.options.returnSequence;
+        }
+        if (test.options.direction) {
+          options.direction = test.options.direction;
+        }
+        if (test.options.layout) {
+          options.layout = test.options.layout;
+        }
+        if (test.options.activations) {
+          options.activations = [];
+          test.options.activations.forEach(
+              activation => options.activations.push(builder[activation]()));
+        }
+      }
+
+      if (test.outputs) {
+        const outputs = builder.lstm(
+            input, weight, recurrentWeight, test.steps, test.hiddenSize,
+            options);
+        assert_equals(outputs.length, test.outputs.length);
+        for (let i = 0; i < outputs.length; ++i) {
+          assert_equals(outputs[i].dataType(), test.outputs[i].dataType);
+          assert_array_equals(outputs[i].shape(), test.outputs[i].dimensions);
+        }
+      } else {
+        assert_throws_dom(
+            'DataError',
+            () => builder.lstm(
+                input, weight, recurrentWeight, test.steps, test.hiddenSize,
+                options));
+      }
+    }, test.name));