/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { useShapeUniforms } from './gpgpu_math';
export class MatMulPackedProgram {
    constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false, hasLeakyreluActivation = false) {
        this.variableNames = ['matrixA', 'matrixB'];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = outputShape;
        this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
        const sharedDim = transposeA ? aShape[1] : aShape[2];
        const sharedDimensionPacked = Math.ceil(sharedDim / 2);
        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';
        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';
        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];
        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];
        let activationSnippet = '', applyActivationSnippet = '';
        if (activation) {
            if (hasPreluActivation) {
                activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation}
        }`;
            }
            else if (hasLeakyreluActivation) {
                activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${activation}
        }`;
            }
            else {
                activationSnippet = `vec4 activation(vec4 x) {
          ${activation}
        }`;
            }
            applyActivationSnippet = `result = activation(result);`;
        }
        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
        if (addBias) {
            this.variableNames.push('bias');
        }
        if (hasPreluActivation) {
            this.variableNames.push('preluActivationWeights');
        }
        if (hasLeakyreluActivation) {
            this.variableNames.push('leakyreluAlpha');
        }
        let batchASnippet = 'rc.x';
        let batchBSnippet = 'rc.x';
        if (aShape[0] < bShape[0]) {
            batchASnippet = `imod(rc.x, ${aShape[0]})`;
        }
        else if (bShape[0] < aShape[0]) {
            batchBSnippet = `imod(rc.x, ${bShape[0]})`;
        }
        this.userCode = `
      ${activationSnippet}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${sharedDimensionPacked}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        int batchA = ${batchASnippet};
        int batchB = ${batchBSnippet};
        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
          vec4 a = getMatrixA(batchA, ${aSample});
          vec4 b = getMatrixB(batchB, ${bSample});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${aSwizzle[0]} * ${bSwizzle[0]});
          result += (${aSwizzle[1]} * ${bSwizzle[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${addBiasSnippet}

        ${applyActivationSnippet}

        setOutput(result);
      }
    `;
    }
}
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram, useShapeUniforms} from './gpgpu_math';\n\nexport class MatMulPackedProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  packedInputs = true;\n  packedOutput = true;\n  outputShape: number[];\n  userCode: string;\n  enableShapeUniforms: boolean;\n\n  constructor(\n      aShape: [number, number, number], bShape: [number, number, number],\n      outputShape: [number, number, number], transposeA = false,\n      transposeB = false, addBias = false, activation: string = null,\n      hasPreluActivation = false, hasLeakyreluActivation = false) {\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n\n    const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n    const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n    const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n    const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n\n    let activationSnippet = '', applyActivationSnippet = '';\n    if (activation) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n      } else if (hasLeakyreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n      }\n\n      applyActivationSnippet = `result = activation(result);`;\n    }\n\n    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n    if (addBias) {\n      this.variableNames.push('bias');\n    }\n\n    if (hasPreluActivation) {\n      this.variableNames.push('preluActivationWeights');\n    }\n\n    if (hasLeakyreluActivation) {\n      this.variableNames.push('leakyreluAlpha');\n    }\n\n    let batchASnippet = 'rc.x';\n    let batchBSnippet = 'rc.x';\n    if (aShape[0] < bShape[0]) {\n      batchASnippet = `imod(rc.x, ${aShape[0]})`;\n    } else if (bShape[0] < aShape[0]) {\n      batchBSnippet = `imod(rc.x, ${bShape[0]})`;\n    }\n\n    this.userCode = `\n      ${activationSnippet}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        int batchA = ${batchASnippet};\n        int batchB = ${batchBSnippet};\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n"]}