Added more input validation.

js/web/lib/wasm/jsep/webgpu/ops/quantize_linear.ts

@@ -14,21 +14,54 @@ export interface DequantizeLinerAttributes extends AttributeWithCacheKey {
   blockSize: number;
 }
 
-const validateInputs = (inputs: readonly TensorView[]): void => {
+const validateInputs = (inputs: readonly TensorView[], attributes: DequantizeLinerAttributes): void => {
   if (inputs.length < 2 || inputs.length > 3) {
     throw new Error('DequantizeLinear requires 2 or 3 inputs.');
   }
   if (inputs.length === 3 && inputs[1].dims === inputs[2].dims) {
-    throw new Error('x-scale and x-zero-point must have the same shape');
+    throw new Error('x-scale and x-zero-point must have the same shape.');
   }
   if (inputs.length === 3 && inputs[0].dataType !== inputs[2].dataType) {
-    throw new Error('x and x-zero-point must have the same data type');
+    throw new Error('x and x-zero-point must have the same data type.');
   }
   if (inputs[0].dataType === DataType.int32 && inputs.length > 2) {
     throw new Error('In the case of dequantizing int32 there is no zero point.');
   }
   if (inputs[1].dims.length !== 0 && inputs[1].dims.length !== 1 && inputs[1].dims.length !== inputs[0].dims.length) {
-    throw new Error('scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor');
+    throw new Error('scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.');
+  }
+  // validate scale and zero-point input shapes
+  if (inputs.length > 2) {
+    // zero-point input type should be the same as input data type.
+    if (inputs[0].dataType !== inputs[2].dataType) {
+      throw new Error('x and x-zero-point must have the same data type.');
+    }
+    // Scale and zero-point inputs must have the same shape
+    if (inputs[1].dims.length !== inputs[2].dims.length) {
+      throw new Error('scale and zero-point inputs must have the same rank.');
+    }
+    if (!inputs[1].dims.map((d, i) => d === inputs[2].dims[i]).reduce((a, b) => a && b, true)) {
+      throw new Error('scale and zero-point inputs must have the same shape.');
+    }
+  }
+  // Validate blockSize
+  if (attributes.blockSize > 0) {
+    // Block qunatization
+    if (inputs[1].dims.length === 0 || (inputs[1].dims.length === 1 && inputs[1].dims[0] === 1)) {
+      throw new Error('blockSize must be set only for block quantization.');
+    }
+    if (!inputs[1].dims.map((d, i) => i === attributes.axis || d === inputs[0].dims[i]).reduce((a, b) => a && b, true)) {
+      throw new Error('For block qunatization, scale input shape to match the input shape except for the axis')
+    }
+    // Scale input rank should be same as the input rank
+    if (inputs[1].dims.length != inputs[0].dims.length) {
+      throw new Error('For block qunatization the scale input rank must be the same as the x rank.');
+    }
+    const dI = inputs[0].dims[attributes.axis];
+    const si = inputs[1].dims[attributes.axis];
+    if (attributes.blockSize < Math.ceil(dI / si) || attributes.blockSize > Math.ceil(dI / (si - 1) - 1)) {
+      throw new Error('blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].');
+    }
   }
 };
 
@@ -164,7 +197,7 @@ const createDequantizeLinearProgramInfo =
     };
 
 export const dequantizeLinear = (context: ComputeContext, attributes: DequantizeLinerAttributes): void => {
-  validateInputs(context.inputs);
+  validateInputs(context.inputs, attributes);
   context.compute(createDequantizeLinearProgramInfo(context.inputs, attributes));
 };