/**
 * @license
 * Copyright 2019 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 { ENGINE } from '../../engine';
import { customGrad } from '../../gradients';
import { _FusedMatMul } from '../../kernel_names';
import { makeTypesMatch } from '../../tensor_util';
import { convertToTensor } from '../../tensor_util_env';
import * as util from '../../util';
import { add } from '../add';
import * as broadcast_util from '../broadcast_util';
import { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';
import { matMul as unfusedMatMul } from '../mat_mul';
import { op } from '../operation';
import { reshape } from '../reshape';
/**
 * Computes the dot product of two matrices with optional activation and bias.
 *
 * ```js
 * const a = tf.tensor2d([-1, -2], [1, 2]);
 * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const bias = tf.tensor2d([1, 2], [1, 2]);
 *
 * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();
 * ```
 *
 * @param obj An object with the following properties:
 * - `a` First matrix in dot product operation.
 * - `b` Second matrix in dot product operation.
 * - `transposeA` If true, `a` is transposed before multiplication.
 * - `transposeB` If true, `b` is transposed before multiplication.
 * - `bias` Matrix to be added to the result.
 * - `activation` Name of activation kernel (defaults to `linear`).
 * - `preluActivationWeights` Tensor of prelu weights.
 * - `leakyreluAlpha` Alpha of leakyrelu.
 */
function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha = 0.2, }) {
    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
        let result = unfusedMatMul(a, b, transposeA, transposeB);
        if (bias != null) {
            result = add(result, bias);
        }
        return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
    }
    let $a = convertToTensor(a, 'a', 'fused matMul');
    let $b = convertToTensor(b, 'b', 'fused matMul');
    [$a, $b] = makeTypesMatch($a, $b);
    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
    const outerDimsA = $a.shape.slice(0, -2);
    const outerDimsB = $b.shape.slice(0, -2);
    const batchDimA = util.sizeFromShape(outerDimsA);
    const batchDimB = util.sizeFromShape(outerDimsB);
    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +
        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +
        `${$b.shape} and transposeA=${transposeA}` +
        ` and transposeB=${transposeB} must match.`);
    const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
    const a3D = transposeA ?
        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :
        reshape($a, [batchDimA, outerShapeA, innerShapeA]);
    const b3D = transposeB ?
        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :
        reshape($b, [batchDimB, innerShapeB, outerShapeB]);
    let $bias;
    if (bias != null) {
        $bias = convertToTensor(bias, 'bias', 'fused matMul');
        [$bias] = makeTypesMatch($bias, $a);
        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) {
        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');
    }
    const grad = (dy, saved) => {
        const [a3D, b3D, y, $bias] = saved;
        // we reshape dy because the result of the forward is not
        // necessarily going to be a 3d tensor due to a reshape done at the end of
        // the customOp.
        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
        let aDer;
        let bDer;
        if (!transposeA && !transposeB) {
            aDer = unfusedMatMul(dyActivation, b3D, false, true);
            bDer = unfusedMatMul(a3D, dyActivation, true, false);
        }
        else if (!transposeA && transposeB) {
            aDer = unfusedMatMul(dyActivation, b3D, false, false);
            bDer = unfusedMatMul(dyActivation, a3D, true, false);
        }
        else if (transposeA && !transposeB) {
            aDer = unfusedMatMul(b3D, dyActivation, false, true);
            bDer = unfusedMatMul(a3D, dyActivation, false, false);
        }
        else {
            aDer = unfusedMatMul(b3D, dyActivation, true, true);
            bDer = unfusedMatMul(dyActivation, a3D, true, true);
        }
        if (bias != null) {
            const biasDer = getFusedBiasGradient($bias, dyActivation);
            return [aDer, bDer, biasDer];
        }
        else {
            return [aDer, bDer];
        }
    };
    const inputs = {
        a: a3D,
        b: b3D,
        bias: $bias,
        preluActivationWeights: $preluActivationWeights
    };
    const attrs = { transposeA, transposeB, activation, leakyreluAlpha };
    // Depending on the the params passed in we will have different number of
    // inputs and thus a a different number of elements in the gradient.
    if (bias == null) {
        const customOp = customGrad((a3D, b3D, save) => {
            const res = 
            // tslint:disable-next-line: no-unnecessary-type-assertion
            ENGINE.runKernel(_FusedMatMul, inputs, attrs);
            save([a3D, b3D, res]);
            return { value: reshape(res, outShape), gradFunc: grad };
        });
        return customOp(a3D, b3D);
    }
    else {
        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {
            const res = 
            // tslint:disable-next-line: no-unnecessary-type-assertion
            ENGINE.runKernel(_FusedMatMul, inputs, attrs);
            save([a3D, b3D, res, $bias]);
            return { value: reshape(res, outShape), gradFunc: grad };
        });
        return customOpWithBias(a3D, b3D, $bias);
    }
}
export const matMul = /* @__PURE__ */ op({ fusedMatMul_ });
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* @license\n * Copyright 2019 Google LLC. 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 {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {matMul as unfusedMatMul} from '../mat_mul';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n * - `leakyreluAlpha` Alpha of leakyrelu.\n */\nfunction fusedMatMul_({\n  a,\n  b,\n  transposeA = false,\n  transposeB = false,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha = 0.2,\n}: {\n  a: Tensor|TensorLike,\n  b: Tensor|TensorLike,\n  transposeA?: boolean,\n  transposeB?: boolean,\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor\n  leakyreluAlpha?: number\n}): Tensor {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n      let result = unfusedMatMul(a, b, transposeA, transposeB);\n      if (bias != null) {\n        result = add(result, bias);\n      }\n\n      return applyActivation(\n                 result, activation, preluActivationWeights, leakyreluAlpha);\n    }\n\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n\n    const innerShapeA =\n        transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB =\n        transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n    const outerShapeA =\n        transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB =\n        transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n\n    util.assert(\n        innerShapeA === innerShapeB,\n        () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n\n    const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(\n        $a.shape.slice(0, -2), $b.shape.slice(0, -2));\n    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n\n    const a3D: Tensor3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D: Tensor3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n\n    let $bias: Tensor;\n    if (bias != null) {\n      $bias = convertToTensor(bias, 'bias', 'fused matMul');\n      [$bias] = makeTypesMatch($bias, $a);\n\n      broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n\n    let $preluActivationWeights: Tensor;\n    if (preluActivationWeights != null) {\n      $preluActivationWeights = convertToTensor(\n          preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n\n    const grad = (dy: Tensor3D, saved: Tensor[]) => {\n      const [a3D, b3D, y, $bias] = saved;\n      // we reshape dy because the result of the forward is not\n      // necessarily going to be a 3d tensor due to a reshape done at the end of\n      // the customOp.\n      const dyActivation =\n          getFusedDyActivation(reshape(dy, y.shape), y, activation);\n      let aDer: Tensor;\n      let bDer: Tensor;\n\n      if (!transposeA && !transposeB) {\n        aDer = unfusedMatMul(dyActivation, b3D, false, true);\n        bDer = unfusedMatMul(a3D, dyActivation, true, false);\n      } else if (!transposeA && transposeB) {\n        aDer = unfusedMatMul(dyActivation, b3D, false, false);\n        bDer = unfusedMatMul(dyActivation, a3D, true, false);\n      } else if (transposeA && !transposeB) {\n        aDer = unfusedMatMul(b3D, dyActivation, false, true);\n        bDer = unfusedMatMul(a3D, dyActivation, false, false);\n      } else {\n        aDer = unfusedMatMul(b3D, dyActivation, true, true);\n        bDer = unfusedMatMul(dyActivation, a3D, true, true);\n      }\n\n      if (bias != null) {\n        const biasDer = getFusedBiasGradient($bias, dyActivation);\n        return [aDer, bDer, biasDer];\n      } else {\n        return [aDer, bDer];\n      }\n    };\n\n    const inputs: _FusedMatMulInputs = {\n      a: a3D,\n      b: b3D,\n      bias: $bias,\n      preluActivationWeights: $preluActivationWeights\n    };\n    const attrs: _FusedMatMulAttrs =\n        {transposeA, transposeB, activation, leakyreluAlpha};\n\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n      const customOp =\n          customGrad((a3D: Tensor3D, b3D: Tensor3D, save: GradSaveFunc) => {\n            const res =\n                // tslint:disable-next-line: no-unnecessary-type-assertion\n                ENGINE.runKernel(\n                    _FusedMatMul, inputs as unknown as NamedTensorMap,\n                    attrs as unknown as NamedAttrMap) as Tensor;\n\n            save([a3D, b3D, res]);\n\n            return {value: reshape(res, outShape), gradFunc: grad};\n          });\n      return customOp(a3D, b3D);\n    } else {\n      const customOpWithBias = customGrad(\n          (a3D: Tensor3D, b3D: Tensor3D, $bias: Tensor, save: GradSaveFunc) => {\n            const res =\n                // tslint:disable-next-line: no-unnecessary-type-assertion\n                ENGINE.runKernel(\n                    _FusedMatMul, inputs as unknown as NamedTensorMap,\n                    attrs as unknown as NamedAttrMap) as Tensor;\n\n            save([a3D, b3D, res, $bias]);\n\n            return {value: reshape(res, outShape), gradFunc: grad};\n          });\n\n      return customOpWithBias(a3D, b3D, $bias);\n    }\n  }\n\n  export const matMul = /* @__PURE__ */ op({fusedMatMul_});\n"]}