@tensorflow/tfjs-converter
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Tensorflow model converter for javascript
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JavaScript
/**
* @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 { env, keep, tidy, util } from '@tensorflow/tfjs-core';
import { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContext, parseNodeName } from '../operations/executors/utils';
import { executeOp } from '../operations/operation_executor';
import { ExecutionContext } from './execution_context';
import { getExecutionSubgraph, getNodeLiveUntilMap, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';
export class GraphExecutor {
get weightIds() {
return this.parent ? this.parent.weightIds : this._weightIds;
}
get functionExecutorMap() {
return this.parent ? this.parent.functionExecutorMap :
this._functionExecutorMap;
}
get weightMap() {
return this.parent ? this.parent.weightMap : this._weightMap;
}
set weightMap(weightMap) {
const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));
this._weightIds = [].concat(...weightIds);
this._weightMap = weightMap;
}
/**
* Set `ResourceManager` shared by executors of a model.
* @param resourceManager: `ResourceManager` of the `GraphModel`.
*/
set resourceManager(resourceManager) {
this._resourceManager = resourceManager;
}
get inputs() {
return this._inputs.map(node => {
return {
name: node.name,
shape: node.attrParams['shape'] ?
node.attrParams['shape'].value :
undefined,
dtype: node.attrParams['dtype'] ?
node.attrParams['dtype'].value :
undefined
};
});
}
get outputs() {
return this._outputs.map(node => {
return {
name: node.name,
shape: node.attrParams['shape'] ?
node.attrParams['shape'].value :
undefined,
dtype: node.attrParams['dtype'] ?
node.attrParams['dtype'].value :
undefined
};
});
}
get inputNodes() {
return this._inputs.map(node => node.signatureKey || node.name);
}
get outputNodes() {
return this._outputs.map((node) => {
const name = node.signatureKey || node.name;
return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;
});
}
get functions() {
return Object.keys(this._functions).reduce((map, key) => {
map[key] = this._functions[key].signature;
return map;
}, {});
}
/**
*
* @param graph Graph the model or function graph to be executed.
* @param parent When building function exector you need to set the parent
* executor. Since the weights and function executor maps are set at parant
* level, that function executor can access the function maps and weight maps
* through the parent.
*/
constructor(graph, parent) {
this.graph = graph;
this.parent = parent;
this.compiledMap = new Map();
this.parseNodeNameCache = new Map();
this._weightMap = {};
this.SEPARATOR = ',';
this._functions = {};
this._functionExecutorMap = {};
this.keepIntermediateTensors = false;
this._outputs = graph.outputs;
this._inputs = graph.inputs;
this._initNodes = graph.initNodes;
this._signature = graph.signature;
this._functions = graph.functions;
// create sub-graph executors
if (graph.functions != null) {
Object.keys(graph.functions).forEach(name => {
this._functionExecutorMap[name] =
new GraphExecutor(graph.functions[name], this);
});
}
}
getCompilationKey(inputs, outputs) {
const sortedInputs = inputs.map(node => node.name).sort();
const sortedOutputs = outputs.map(node => node.name).sort();
return sortedInputs.join(this.SEPARATOR) + '--' +
sortedOutputs.join(this.SEPARATOR);
}
/**
* Compiles the inference graph and returns the minimal set of nodes that are
* required for execution, in the correct execution order.
* @returns {Object} compilation The compile result.
* @returns {Node[]} compilation.orderedNodes Nodes in the correct execution
* order.
* @returns {Map<string, Node[]>} compilation.nodeLiveUntilMap A map from node
* to disposable nodes after its execution. That is, for a node `x`,
* `nodeLiveUntilMap[x]` indicates all nodes whose intermediate
* tensors should be disposed after `x` is executed.
*/
compile(inputs, outputs) {
const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
const { missingInputs, dynamicNode, syncInputs } = executionInfo;
if (dynamicNode != null) {
throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +
`the dynamic op '${dynamicNode.op}'. Please use ` +
`model.executeAsync() instead. Alternatively, to avoid the ` +
`dynamic ops, specify the inputs [${syncInputs}]`);
}
if (missingInputs.length > 0) {
const outNames = outputs.map(n => n.name);
const inNames = Object.keys(inputs);
throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +
`[${inNames}]. Missing the following inputs: [${missingInputs}]`);
}
const orderedNodes = getNodesInTopologicalOrder(this.graph, executionInfo);
const nodeLiveUntilMap = getNodeLiveUntilMap(orderedNodes);
return { orderedNodes, nodeLiveUntilMap };
}
cloneAndKeepTensor(tensor) {
if (tensor == null) {
return null;
}
const clone = tensor.clone();
// Keep the clone because`model.execute()` may be called within
// a `tidy()`, but the user may inspect these tensors after the
// tidy.
keep(clone);
return clone;
}
cloneTensorList(tensors) {
if (!tensors) {
return null;
}
const clonedTensor = tensors.map(tensor => {
return this.cloneAndKeepTensor(tensor);
});
return clonedTensor;
}
cloneTensorMap(tensorsMap) {
return Object.fromEntries(Object.entries(tensorsMap).map(([name, tensorsList]) => {
return [name, this.cloneTensorList(tensorsList)];
}));
}
/**
* Executes the inference for given input tensors.
* @param inputs Tensor map for the model inputs, keyed by the input node
* names.
* @param outputs Optional. output node name from the Tensorflow model, if
* no outputs are specified, the default outputs of the model would be used.
* You can inspect intermediate nodes of the model by adding them to the
* outputs array.
*/
execute(inputs, outputs) {
// Dispose any tensors from a prior run to avoid leaking them.
this.disposeIntermediateTensors();
inputs = this.mapInputs(inputs);
const names = Object.keys(inputs).sort();
this.checkInputs(inputs);
this.checkInputShapeAndType(inputs);
outputs = this.mapOutputs(outputs);
this.checkOutputs(outputs);
const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);
const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);
const outputNodeNameSet = new Set(outputNodeNames);
let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);
// If no outputs are specified, then use the default outputs of the model.
if (outputNodes.length === 0) {
outputNodes = this._outputs;
}
const compilationKey = this.getCompilationKey(inputNodes, outputNodes);
// Do nothing if the compiled graph cache contains the input.
let compilation = this.compiledMap.get(compilationKey);
if (compilation == null) {
compilation = this.compile(inputs, outputNodes);
this.compiledMap.set(compilationKey, compilation);
}
// Keep tensors if KEEP_INTERMEDIATE_TENSORS is on.
try {
this.keepIntermediateTensors = env().getBool('KEEP_INTERMEDIATE_TENSORS');
}
catch (e) {
this.keepIntermediateTensors = false;
console.warn(e.message);
}
const tensorArrayMap = {};
const tensorListMap = {};
return tidy(() => {
const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache);
const tensorsMap = Object.assign({}, this.weightMap);
if (this.keepIntermediateTensors) {
this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);
}
Object.keys(inputs).forEach(name => {
const [nodeName, index] = parseNodeName(name, context);
const tensors = [];
tensors[index] = inputs[name];
tensorsMap[nodeName] = tensors;
if (this.keepIntermediateTensors) {
this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);
}
});
const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
const { orderedNodes, nodeLiveUntilMap } = compilation;
for (const node of orderedNodes) {
if (tensorsMap[node.name]) {
continue;
}
const tensors = executeOp(node, tensorsMap, context, this._resourceManager);
if (util.isPromise(tensors)) {
throw new Error(`The execution of the op '${node.op}' returned a promise. ` +
`Please use model.executeAsync() instead.`);
}
tensorsMap[node.name] = tensors;
if (this.keepIntermediateTensors) {
this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors);
}
this.checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorsMap, context, tensorsToKeep, outputNodeNameSet, nodeLiveUntilMap.get(node.name));
}
// dispose the context for the root executor
if (this.parent == null) {
context.dispose(tensorsToKeep);
}
return outputs.map(name => getTensor(name, tensorsMap, context));
});
}
getFrozenTensorIds(tensorMap) {
const ids = [].concat.apply([], Object.keys(tensorMap)
.map(key => tensorMap[key])
.map(tensors => tensors.map(tensor => tensor.id)));
return new Set(ids);
}
checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount) {
// Skip output nodes and any control flow nodes, since its dependency is
// tricky to track correctly.
if (isControlFlow(node) || outputNodeNameSet.has(nodeName)) {
return;
}
for (const tensor of tensorMap[nodeName]) {
if (tensor == null) {
continue;
}
intermediateTensorConsumerCount[tensor.id] =
(intermediateTensorConsumerCount[tensor.id] || 0) +
node.children.length;
}
for (const input of node.inputs) {
// Skip any control flow nodes, since its dependency is tricky to track
// correctly.
if (isControlFlow(input)) {
continue;
}
const tensors = getTensorsForCurrentContext(input.name, tensorMap, context);
if (tensors == null) {
continue;
}
for (const tensor of tensors) {
if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) {
continue;
}
// Only intermediate nodes' tensors have counts set, not marked as
// kept, and not in `tensorsToKeep`.
// Input and weight nodes' tensors should exist in `tensorsToKeep`.
// Output and control flow nodes' tensors should never have count set.
const count = intermediateTensorConsumerCount[tensor.id];
if (count === 1) {
tensor.dispose();
delete intermediateTensorConsumerCount[tensor.id];
}
else if (count != null) {
intermediateTensorConsumerCount[tensor.id]--;
}
}
}
}
checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorMap, context, tensorsToKeep, outputNodeNameSet, liveUntilNodes) {
function isNonDisposableNode(node) {
// Skip output nodes and any control flow nodes, since its dependency is
// tricky to track correctly.
return isControlFlow(node) || outputNodeNameSet.has(node.name);
}
if (isControlFlow(node) || liveUntilNodes == null) {
return;
}
for (const nodeToDispose of liveUntilNodes) {
if (isNonDisposableNode(nodeToDispose)) {
continue;
}
const tensors = getTensorsForCurrentContext(nodeToDispose.name, tensorMap, context);
for (const tensor of tensors) {
if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) {
continue;
}
tensor.dispose();
}
}
}
/**
* Executes the inference for given input tensors in Async fashion.
* @param inputs Tensor map for the model inputs, keyed by the input node
* names.
* @param outputs output node name from the Tensorflow model, if no outputs
* are specified, the default outputs of the model would be used. You can
* inspect intermediate nodes of the model by adding them to the outputs
* array.
*/
async executeAsync(inputs, outputs) {
return this._executeAsync(inputs, outputs);
}
disposeIntermediateTensors() {
if (!this.clonedTensorsMap) {
return;
}
Object.values(this.clonedTensorsMap).forEach(tensorsList => {
for (const tensor of tensorsList) {
if (tensor && !tensor.isDisposed) {
tensor.dispose();
}
}
});
this.clonedTensorsMap = null;
}
getIntermediateTensors() {
return this.clonedTensorsMap;
}
/**
* Executes the inference for given input tensors in Async fashion.
* @param inputs Tensor map for the model inputs, keyed by the input node
* names.
* @param outputs Optional. output node name from the Tensorflow model,
* if no outputs are specified, the default outputs of the model would be
* used. You can inspect intermediate nodes of the model by adding them to
* the outputs array.
* @param isFunctionExecution Optional. Flag for executing a function.
* @param tensorArrayMap Optional, global TensorArray map by id. Used for
* function execution.
* @param tensorArrayMap Optional global TensorList map by id. Used for
* function execution.
*/
async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {
// Dispose any tensors from a prior run to avoid leaking them.
this.disposeIntermediateTensors();
if (!isFunctionExecution) {
inputs = this.mapInputs(inputs);
this.checkInputs(inputs);
this.checkInputShapeAndType(inputs);
outputs = this.mapOutputs(outputs);
this.checkOutputs(outputs);
}
// Keep tensors if KEEP_INTERMEDIATE_TENSORS is on.
try {
this.keepIntermediateTensors = env().getBool('KEEP_INTERMEDIATE_TENSORS');
}
catch (e) {
this.keepIntermediateTensors = false;
console.warn(e.message);
}
const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache);
if (this.keepIntermediateTensors) {
this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);
}
// Graph with control flow op requires runtime evaluation of the execution
// order, while without control flow the execution order is pre-determined
// in the compile method.
const tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
const results = outputs.map(name => getTensor(name, tensorsMap, context));
// dispose all the intermediate tensors
const outputIds = results.map(t => t.id);
const inputIds = Object.keys(inputs).map(name => inputs[name].id);
const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);
Object.values(tensorsMap).forEach(tensorsList => {
tensorsList.forEach(tensor => {
if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {
tensor.dispose();
}
});
});
// dispose the context for the root executor
if (this.parent == null) {
context.dispose(keepIds);
}
return results;
}
async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {
const mappedInputs = inputs.reduce((map, tensor, index) => {
map[this.inputs[index].name] = tensor;
return map;
}, {});
return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
}
/**
* When there are control flow nodes in the graph, the graph execution use
* ExecutionContext to keep track of the frames and loop iterators.
* @param inputs placeholder tensors for the graph.
* @param context the execution context object for current execution.
* @param outputNames Optional. output node name from the Tensorflow model,
* if no outputs are specified, the default outputs of the model would be
* used. You can inspect intermediate nodes of the model by adding them to
* the outputs array.
* @param isFunctionExecution Flag for executing a function.
*/
async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {
const names = Object.keys(inputs);
const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);
const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);
const outputNodeNameSet = new Set(outputNodeNames);
let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);
// If no outputs are specified, then use the default outputs of the model.
if (outputNodes.length === 0) {
outputNodes = this._outputs;
}
const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);
// First nodes to execute include inputNodes, weights, and initNodes.
const stack = [
...inputNodes, ...this.graph.weights, ...(this._initNodes || [])
].map(node => {
return { node, contexts: context.currentContext };
});
const tensorsMap = Object.assign({}, this.weightMap);
Object.keys(inputs).forEach(name => {
const [nodeName, index] = parseNodeName(name);
const tensors = [];
tensors[index] = inputs[name];
tensorsMap[nodeName] = tensors;
});
const intermediateTensorConsumerCount = {};
const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
const added = {};
while (stack.length > 0) {
const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes);
await Promise.all(promises);
}
if (dynamicNode == null && !isFunctionExecution) {
console.warn(`This model execution did not contain any nodes with control flow ` +
`or dynamic output shapes. You can use model.execute() instead.`);
}
const missingOutputs = outputNodes
.filter(node => !isControlFlow(node) &&
!getTensor(node.name, tensorsMap, context))
.map(node => node.name);
if (missingOutputs.length > 0) {
let alternativeMsg = '';
if (dynamicNode != null) {
alternativeMsg =
`Alternatively, to avoid the dynamic ops, use model.execute() ` +
`and specify the inputs [${syncInputs}]`;
}
throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +
`inputs [${names}]. Consider providing the following inputs: ` +
`[${missingInputs}]. ${alternativeMsg}`);
}
return tensorsMap;
}
processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes) {
const promises = [];
while (stack.length > 0) {
const item = stack.pop();
context.currentContext = item.contexts;
let nodeName = '';
// The tensor of the Enter op with isConstant set should be set
// in the parent scope, so it will be available as constant for the
// whole loop.
if (item.node.op === 'Enter' &&
getParamValue('isConstant', item.node, tensorMap, context)) {
[nodeName] = getNodeNameAndIndex(item.node.name, context);
}
// only process nodes that are not in the tensorMap yet, this include
// inputNodes and internal initNodes.
if (tensorMap[item.node.name] == null) {
const tensors = executeOp(item.node, tensorMap, context, this._resourceManager);
if (!nodeName) {
[nodeName] = getNodeNameAndIndex(item.node.name, context);
}
const currentContext = context.currentContext;
if (util.isPromise(tensors)) {
promises.push(tensors.then(t => {
tensorMap[nodeName] = t;
if (this.keepIntermediateTensors) {
this.clonedTensorsMap[nodeName] = this.cloneTensorList(t);
}
context.currentContext = currentContext;
this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount);
this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
return t;
}));
}
else {
tensorMap[nodeName] = tensors;
if (this.keepIntermediateTensors) {
this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);
}
this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount);
this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
}
}
else {
this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
}
}
return promises;
}
processChildNodes(node, stack, context, tensorMap, added, usedNodes) {
node.children.forEach((childNode) => {
const [nodeName,] = getNodeNameAndIndex(childNode.name, context);
if (added[nodeName] || !usedNodes.has(childNode.name)) {
return;
}
// Merge op can be pushed if any of its inputs has value.
if (childNode.op === 'Merge') {
if (childNode.inputNames.some(name => {
return !!getTensor(name, tensorMap, context);
})) {
added[nodeName] = true;
stack.push({ contexts: context.currentContext, node: childNode });
}
}
else // Otherwise all inputs must to have value.
if (childNode.inputNames.every(name => {
return !!getTensor(name, tensorMap, context);
})) {
added[nodeName] = true;
stack.push({ contexts: context.currentContext, node: childNode });
}
});
}
/**
* Releases the memory used by the weight tensors.
*/
dispose() {
Object.keys(this.weightMap)
.forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));
}
checkInputShapeAndType(inputs) {
Object.keys(inputs).forEach(name => {
const input = inputs[name];
const [nodeName,] = parseNodeName(name);
const node = this.graph.nodes[nodeName];
if (node.attrParams['shape'] && node.attrParams['shape'].value) {
const shape = node.attrParams['shape'].value;
const match = shape.length === input.shape.length &&
input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);
util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +
`model.execute(dict) must be [${shape}], but was ` +
`[${input.shape}]`);
}
if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {
util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +
`model.execute(dict) must be ` +
`${node.attrParams['dtype'].value}, but was ${input.dtype}`);
}
});
}
mapInputs(inputs) {
var _a, _b;
const result = {};
for (const inputName in inputs) {
const tensor = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName];
if (tensor != null) {
result[tensor.name] = inputs[inputName];
}
else {
result[inputName] = inputs[inputName];
}
}
return result;
}
checkInputs(inputs) {
const notInGraph = Object.keys(inputs).filter(name => {
const [nodeName] = parseNodeName(name);
return this.graph.nodes[nodeName] == null;
});
if (notInGraph.length > 0) {
throw new Error(`The dict provided in model.execute(dict) has ` +
`keys: [${notInGraph}] that are not part of graph`);
}
}
mapOutputs(outputs) {
return outputs.map(name => {
var _a, _b;
const tensor = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.outputs) === null || _b === void 0 ? void 0 : _b[name];
if (tensor != null) {
return tensor.name;
}
return name;
}, {});
}
checkOutputs(outputs) {
outputs.forEach(name => {
const [normalizedName] = parseNodeName(name);
if (!this.graph.nodes[normalizedName]) {
throw new Error(`The output '${name}' is not found in the graph`);
}
});
}
}
//# 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