[js/webgpu] Add HardSigmoid support (#19215)

### Description
This op is required in mobilenetv3-small-100. With this PR,
mobilenetv3-small-100 model becomes less than 10 ms from over 100 ms on
ADL.

Author: qjia7

js/web/docs/webgpu-operators.md

@@ -52,6 +52,7 @@ Do not modify directly.*
 | GlobalMaxPool | ai.onnx(1+); com.ms.internal.nhwc(1+) |  |
 | Greater | ai.onnx(7-8,9-12,13+) |  |
 | GreaterOrEqual | ai.onnx(12-15,16+) |  |
+| HardSigmoid | ai.onnx(6+) |  |
 | If | ai.onnx(1-10,11-12,13-18,19+) |  |
 | InstanceNormalization | ai.onnx(6+); com.ms.internal.nhwc(6+) |  |
 | LayerNormalization | ai.onnx(17+) |  |

js/web/lib/wasm/jsep/webgpu/op-resolve-rules.ts

@@ -82,6 +82,7 @@ export const WEBGPU_OP_RESOLVE_RULES: Map<string, OperatorImplementation> = new
   ['GlobalMaxPool', [pool.globalMaxPool, pool.parseGlobalMaxPoolAttributes]],
   ['Greater', [binaryOps.greater]],
   ['GreaterOrEqual', [binaryOps.greaterOrEqual]],
+  ['HardSigmoid', [unaryOps.hardSigmoid, unaryOps.parseHardSigmoidAttributes]],
   ['InstanceNormalization', [instanceNorm]],
   ['LayerNormalization', [layerNorm]],
   ['LeakyRelu', [unaryOps.leakyRelu, unaryOps.parseAlphaAttributes]],

js/web/lib/wasm/jsep/webgpu/ops/unary-op.ts

@@ -242,6 +242,26 @@ export const sigmoid = (context: ComputeContext): void => {
   context.compute(createElementwiseProgramInfo(context.inputs[0], 'Sigmoid', a => `(1.0 / (1.0 + exp(-${a})))`));
 };
 
+export interface HardSigmoidAttributes extends AttributeWithCacheKey {
+  readonly alpha: number;
+  readonly beta: number;
+}
+
+export const parseHardSigmoidAttributes = (attributes: Record<string, unknown>): HardSigmoidAttributes =>
+    createAttributeWithCacheKey(attributes as {
+      alpha: number;
+      beta: number;
+    });
+
+export const hardSigmoid = (context: ComputeContext, attributes: HardSigmoidAttributes): void => {
+  const dataType = tensorTypeToWsglValueType(context.inputs[0].dataType);
+  context.compute(createElementwiseProgramInfo(
+      context.inputs[0], 'HardSigmoid',
+      a => `max(vec4<${dataType}>(0.0), min(vec4<${dataType}>(1.0), ${attributes.alpha} * ${a} + vec4<${dataType}>(${
+          attributes.beta})))`,
+      undefined, attributes.cacheKey));
+};
+
 export const sin = (context: ComputeContext): void => {
   context.compute(createElementwiseProgramInfo(context.inputs[0], 'Sin', 'sin'));
 };

js/web/test/suite-test-list.jsonc

@@ -597,9 +597,9 @@
       // // "test_hardmax_example",
       // // "test_hardmax_negative_axis",
       // // "test_hardmax_one_hot",
-      // // "test_hardsigmoid_default",
-      // // "test_hardsigmoid_example",
-      // // "test_hardsigmoid",
+      "test_hardsigmoid_default",
+      "test_hardsigmoid_example",
+      "test_hardsigmoid",
       // // "test_hardswish_expanded",
       // // "test_hardswish",
       "test_if",

onnxruntime/core/providers/js/js_execution_provider.cc

@@ -98,6 +98,7 @@ class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomai
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomain, 13, Erf);
 class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomain, 6, 12, Sigmoid);
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomain, 13, Sigmoid);
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomain, 6, HardSigmoid);
 class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomain, 6, 12, Log);
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kJsExecutionProvider, kOnnxDomain, 13, Log);
 
@@ -392,6 +393,7 @@ std::unique_ptr<KernelRegistry> RegisterKernels() {
       KERNEL_CREATE_INFO(13, Erf),
       KERNEL_CREATE_INFO_VERSIONED(6, 12, Sigmoid),
       KERNEL_CREATE_INFO(13, Sigmoid),
+      KERNEL_CREATE_INFO(6, HardSigmoid),
       KERNEL_CREATE_INFO_VERSIONED(6, 12, Log),
       KERNEL_CREATE_INFO(13, Log),
 

onnxruntime/core/providers/js/operators/unary.cc

@@ -77,6 +77,9 @@ JSEP_KERNEL_IMPL(Sigmoid, Sigmoid)
 JSEP_ELEMENTWISE_VERSIONED_KERNEL(Sigmoid, 6, 12, Sigmoid)
 JSEP_ELEMENTWISE_KERNEL(Sigmoid, 13, Sigmoid)
 
+JSEP_CLASS_IMPL_ATTRIBUTE_FLOAT_2_DEFAULT(HardSigmoid, HardSigmoid, alpha, 0.2, beta, 0.5)
+JSEP_ELEMENTWISE_KERNEL(HardSigmoid, 6, HardSigmoid)
+
 JSEP_KERNEL_IMPL(Log, Log)
 JSEP_ELEMENTWISE_VERSIONED_KERNEL(Log, 6, 12, Log)
 JSEP_ELEMENTWISE_KERNEL(Log, 13, Log)