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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2017 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 * as util from './util';
/**
 * Computes a list of TapeNodes that connect x to y, filtering everything else
 * out and preserving the order of the original tape elements.
 *
 * @param tape The tape elements to filter.
 * @param xs The input Tensors.
 * @param y The output Tensor.
 */
export function getFilteredNodesXToY(tape, xs, y) {
    // Forward pass to compute all the nodes and Tensors that are transitively a
    // function of x.
    const tensorsFromX = {};
    const nodesFromX = {};
    for (let i = 0; i < xs.length; i++) {
        tensorsFromX[xs[i].id] = true;
    }
    for (let i = 0; i < tape.length; i++) {
        const node = tape[i];
        const nodeInputs = node.inputs;
        for (const inputName in nodeInputs) {
            const input = nodeInputs[inputName];
            let anyInputFromX = false;
            for (let j = 0; j < xs.length; j++) {
                if (tensorsFromX[input.id]) {
                    node.outputs.forEach(output => tensorsFromX[output.id] = true);
                    anyInputFromX = true;
                    nodesFromX[node.id] = true;
                    break;
                }
            }
            if (anyInputFromX) {
                break;
            }
        }
    }
    // Backward pass to find all of the nodes and Tensors that lead to y.
    const tensorsLeadToY = {};
    tensorsLeadToY[y.id] = true;
    const nodesToY = {};
    for (let i = tape.length - 1; i >= 0; i--) {
        const node = tape[i];
        const nodeInputs = node.inputs;
        // If any of the outputs lead to y, mark all of the inputs as leading to y.
        for (let j = 0; j < node.outputs.length; j++) {
            if (tensorsLeadToY[node.outputs[j].id]) {
                for (const inputName in nodeInputs) {
                    tensorsLeadToY[nodeInputs[inputName].id] = true;
                    nodesToY[node.id] = true;
                }
                break;
            }
        }
    }
    // Return the paths that come from x and lead to y.
    const filteredTape = [];
    for (let i = 0; i < tape.length; i++) {
        const node = tape[i];
        if (nodesFromX[node.id] && nodesToY[node.id]) {
            // Prune the inputs from the node that aren't a function of x.
            const prunedInputs = {};
            for (const inputName in node.inputs) {
                const nodeInput = node.inputs[inputName];
                if (tensorsFromX[nodeInput.id]) {
                    prunedInputs[inputName] = nodeInput;
                }
            }
            // Copy the node and overwrite inputsAndArgs to the pruned version.
            const prunedNode = Object.assign({}, node);
            prunedNode.inputs = prunedInputs;
            prunedNode.outputs = node.outputs;
            filteredTape.push(prunedNode);
        }
    }
    return filteredTape;
}
/**
 * Backpropagate gradients through the filtered TapeNodes.
 *
 * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map
 * is mutated by this method.
 * @param filteredTape The filtered TapeNodes to backprop through.
 */
export function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {
    // Walk the tape backward and keep a map of Tensor to its gradient.
    for (let i = filteredTape.length - 1; i >= 0; i--) {
        const node = filteredTape[i];
        const dys = [];
        node.outputs.forEach(o => {
            const gradTensor = tensorAccumulatedGradientMap[o.id];
            if (gradTensor != null) {
                dys.push(gradTensor);
            }
            else {
                // This particular output is not in the back-propagation subgraph, so it
                // does not affect the final output, thus we put null for its dy.
                dys.push(null);
            }
        });
        if (node.gradient == null) {
            throw new Error(`Cannot compute gradient: gradient function not found ` +
                `for ${node.kernelName}.`);
        }
        // Backprop dy through this node and accumulate gradients over the inputs.
        const inputGradients = node.gradient(dys);
        for (const inputName in node.inputs) {
            if (!(inputName in inputGradients)) {
                throw new Error(`Cannot backprop through input ${inputName}. ` +
                    `Available gradients found: ${Object.keys(inputGradients)}.`);
            }
            // Call the gradient function.
            const dx = tidy(() => inputGradients[inputName]());
            if (dx.dtype !== 'float32') {
                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +
                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
            }
            const x = node.inputs[inputName];
            if (!util.arraysEqual(dx.shape, x.shape)) {
                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +
                    `'${inputName}' has shape '${dx.shape}', which does not match ` +
                    `the shape of the input '${x.shape}'`);
            }
            if (tensorAccumulatedGradientMap[x.id] == null) {
                tensorAccumulatedGradientMap[x.id] = dx;
            }
            else {
                const curGradient = tensorAccumulatedGradientMap[x.id];
                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);
                curGradient.dispose();
            }
        }
    }
}
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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 {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n  id: number;\n  kernelName: string;\n  outputs: Tensor[];\n  inputs: NamedTensorMap;\n  // Optional params, defined only for ops with gradient impl.\n  gradient?: (dys: Tensor[]) => NamedGradientMap;\n  saved?: Tensor[];\n}\n\nexport type NamedGradientMap = {\n  [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n  // Forward pass to compute all the nodes and Tensors that are transitively a\n  // function of x.\n  const tensorsFromX: {[tensorId: number]: boolean} = {};\n  const nodesFromX: {[nodeId: number]: boolean} = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input = nodeInputs[inputName];\n\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input.id]) {\n          node.outputs.forEach(output => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n\n  // Backward pass to find all of the nodes and Tensors that lead to y.\n  const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY: {[nodeId: number]: boolean} = {};\n\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n\n    // If any of the outputs lead to y, mark all of the inputs as leading to y.\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n\n  // Return the paths that come from x and lead to y.\n  const filteredTape: TapeNode[] = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      // Prune the inputs from the node that aren't a function of x.\n      const prunedInputs: {[inputName: string]: Tensor} = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n\n      // Copy the node and overwrite inputsAndArgs to the pruned version.\n      const prunedNode = Object.assign({}, node);\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n\n      filteredTape.push(prunedNode);\n    }\n  }\n\n  return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n    filteredTape: TapeNode[], tidy: (f: Function) => Tensor,\n    add: (a: Tensor, b: Tensor) => Tensor) {\n  // Walk the tape backward and keep a map of Tensor to its gradient.\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n\n    const dys: Tensor[] = [];\n    node.outputs.forEach(o => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        // This particular output is not in the back-propagation subgraph, so it\n        // does not affect the final output, thus we put null for its dy.\n        dys.push(null);\n      }\n    });\n\n    if (node.gradient == null) {\n      throw new Error(\n          `Cannot compute gradient: gradient function not found ` +\n          `for ${node.kernelName}.`);\n    }\n\n    // Backprop dy through this node and accumulate gradients over the inputs.\n    const inputGradients = node.gradient(dys);\n\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(\n            `Cannot backprop through input ${inputName}. ` +\n            `Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n\n      // Call the gradient function.\n      const dx = tidy(() => inputGradients[inputName]());\n      if (dx.dtype !== 'float32') {\n        throw new Error(\n            `Error in gradient for op ${\n                node.kernelName}. The gradient of input ` +\n            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!util.arraysEqual(dx.shape, x.shape)) {\n        throw new Error(\n            `Error in gradient for op ${\n                node.kernelName}. The gradient of input ` +\n            `'${inputName}' has shape '${dx.shape}', which does not match ` +\n            `the shape of the input '${x.shape}'`);\n      }\n\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n"]}