-{{MLGraph/compute()|MLGraph.compute()}}:
+{{MLGraph/compute(inputs, outputs)|MLGraph.compute(inputs, outputs)}}:
- 1. User issues a compute request by calling {{MLGraph/compute()|MLGraph.compute()}} on the [=Content timeline=] and gets a promise in return.
+ 1. User issues a compute request by calling {{MLGraph/compute(inputs, outputs)|MLGraph.compute(inputs, outputs)}} on the [=Content timeline=] and gets a promise in return.
2. User agent processes the compute request on the [=Device timeline=] by calling the OS ML API.
3. After the ML device operating on [=Queue timeline=] is done, the user agent makes the results ready to be consumed by user and [=resolves=] the promise.
@@ -1701,23 +1701,30 @@ partial interface MLGraphBuilder {
The {{MLGraph}} interface represents a compiled computational graph. A compiled graph once constructed is immutable and cannot be subsequently changed.
@@ -1742,22 +1749,85 @@ interface MLGraph {
+ : compute(inputs)
+ ::
+ Issue a compute request of the {{MLGraph}} given {{MLNamedInputs}}. Return {{MLNamedTensors}}.
+
+
+ **Called on:** {{MLGraph}} |this|.
+
+ **Arguments:**
+
+ |inputs|: an {{MLNamedInputs}}. The inputs for the compute request.
+
+
+ **Returns:** {{MLNamedTensors}}.
+
+ 1. Let |outputs| be a new {{MLNamedTensors}}.
+
+
+ 1. If any of the following requirements are unmet, then throw a {{TypeError}} and stop.
+
+
+ 1. For each |key| -> |value| of |inputs|:
+ 1. |this|.{{MLGraph/[[inputOperands]]}}[|key|] must exist.
+ 1. Let |inputOperand| be |this|.{{MLGraph/[[inputOperands]]}}[|key|].
+ 1. If |value| is an {{MLInput}}, then:
+ 1. If |value|.{{MLInput/data}} is an {{ArrayBufferView}}, then:
+ 1. The kind of |value|.{{MLInput/data}} must be compatible to |inputOperand|.{{MLOperandDescriptor/type}} according to [this table](#appendices-mloperandtype-arraybufferview-compatibility).
+ 1. If |value|.{{MLInput/dimensions}} was given, then:
+ 1. The length of |value|.{{MLInput/dimensions}} must be the same as the length of |inputOperand|.{{MLOperandDescriptor/dimensions}}.
+ 1. Let |i| be 0.
+ 1. While true:
+ 1. Let |dimension| be |value|.{{MLInput/dimensions}}[|i|].
+ 1. |dimension| must be greater than 0.
+ 1. If |inputOperand|.{{MLOperandDescriptor/dimensions}}[|i|] is greater than 0, then |dimension| must be equal to |inputOperand|.{{MLOperandDescriptor/dimensions}}[|i|].
+ 1. Increment |i| by 1.
+ 1. If |i| if equal to the length of |value|.{{MLInput/dimensions}}, then break.
+ 1. Else:
+ 1. For each |dimension| of |inputOperand|.{{MLOperandDescriptor/dimensions}}:
+ 1. The value of |dimension| must be greater than 0.
+
+
+
+ 1. Issue the following steps on the [=Device timeline=] of |this|.{{MLGraph/[[implementation]]}}:
+
+ 1. For each |key| -> |value| of |inputs|:
+ 1. Let |inputTensor| be the input tensor of |this|.{{MLGraph/[[implementation]]}} for |key|.
+ 1. Set the dimensions of |inputTensor| to |value|.{{MLInput/dimensions}}.
+ 1. Set the content of |inputTensor| to the content of |value|.{{MLInput/data}}.
+ 1. For each |key| -> |value| of |this|.{{MLGraph/[[outputOperands]]}}:
+ 1. Issue a compute request of |this|.{{MLGraph/[[implementation]]}} for |key|.
+ 1. If there is an error returned by |this|.{{MLGraph/[[implementation]]}}, then:
+ 1. Throw an {{OperationError}} and stop.
+ 1. Else:
+ 1. Let |outputTensor| be the output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
+ 1. Let |output| be a new {{MLTensor}}.
+ 1. Associate |output| with |outputTensor|.
+ 1. Set |outputs|[|key|] be |output|.
+
+
+ 1. Return |outputs|.
+
+
+
**Called on:** {{MLGraph}} |this|.
**Arguments:**
- |inputs|: a {{MLNamedInputs}}. The data and optional dimensions of inputs for the compute request.
- |outputs|: an optional {{MLNamedOutputs}}. The names and pre-allocated resources of required outputs for the compute request. Default to be an empty [=record=] which means that the compute request is for all outputs.
+ |inputs|: an {{MLNamedInputs}}. The inputs for the compute request.
+ |outputs|: an {{MLNamedOutputs}}. The pre-allocated outputs for the compute request.
- **Returns:** {{Promise}}<{{MLNamedOutputs}}>. The dimensions and data of outputs returned by the compute request.
+ **Returns:** {{Promise}}<{{undefined}}>.
1. Let |promise| be [=a new promise=].
+
1. If any of the following requirements are unmet, then [=reject=] |promise| with a {{TypeError}} and stop.
@@ -1765,85 +1835,59 @@ interface MLGraph {
1. For each |key| -> |value| of |inputs|:
1. |this|.{{MLGraph/[[inputOperands]]}}[|key|] must exist.
1. Let |inputOperand| be |this|.{{MLGraph/[[inputOperands]]}}[|key|].
- 1. If |value|.{{MLInput/data}} is an {{ArrayBufferView}}, then:
- 1. The kind of |value|.{{MLInput/data}} must be compatible to |inputOperand|.{{MLOperandDescriptor/type}} according to [this table](#appendices-mloperandtype-arraybufferview-compatibility).
- 1. If |value|.{{MLInput/dimensions}} was given, then:
- 1. The length of |value|.{{MLInput/dimensions}} must be the same as the length of |inputOperand|.{{MLOperandDescriptor/dimensions}}.
- 1. Let |i| be 0.
- 1. While true:
- 1. Let |dimension| be |value|.{{MLInput/dimensions}}[|i|].
- 1. |dimension| must be greater than 0.
- 1. If |inputOperand|.{{MLOperandDescriptor/dimensions}}[|i|] is greater than 0, then |dimension| must be equal to |inputOperand|.{{MLOperandDescriptor/dimensions}}[|i|].
- 1. Set |i| to |i| + 1.
- 1. If |i| if equal to the length of |value|.{{MLInput/dimensions}}, then break.
- 1. Else:
- 1. For each |dimension| of |inputOperand|.{{MLOperandDescriptor/dimensions}}:
- 1. The value of |dimension| must be greater than 0.
-
- 1. If |outputs| was not an empty [=record=], then:
- 1. For each |key| -> |value| of |outputs|:
- 1. |this|.{{MLGraph/[[outputOperands]]}}[|key|] must exist.
- 1. If |value|.{{MLOutput/data}} was given, then the kind of |value|.{{MLOutput/data}} must be compatible to |this|.{{MLGraph/[[outputOperands]]}}[|key|] according to [this table](#appendices-mloperandtype-arraybufferview-compatibility).
+ 1. If |value| is an {{MLInput}}, then:
+ 1. If |value|.{{MLInput/data}} is an {{ArrayBufferView}}, then:
+ 1. The kind of |value|.{{MLInput/data}} must be compatible to |inputOperand|.{{MLOperandDescriptor/type}} according to [this table](#appendices-mloperandtype-arraybufferview-compatibility).
+ 1. If |value|.{{MLInput/dimensions}} was given, then:
+ 1. The length of |value|.{{MLInput/dimensions}} must be the same as the length of |inputOperand|.{{MLOperandDescriptor/dimensions}}.
+ 1. Let |i| be 0.
+ 1. While true:
+ 1. Let |dimension| be |value|.{{MLInput/dimensions}}[|i|].
+ 1. |dimension| must be greater than 0.
+ 1. If |inputOperand|.{{MLOperandDescriptor/dimensions}}[|i|] is greater than 0, then |dimension| must be equal to |inputOperand|.{{MLOperandDescriptor/dimensions}}[|i|].
+ 1. Increment |i| by 1.
+ 1. If |i| if equal to the length of |value|.{{MLInput/dimensions}}, then break.
+ 1. Else:
+ 1. For each |dimension| of |inputOperand|.{{MLOperandDescriptor/dimensions}}:
+ 1. The value of |dimension| must be greater than 0.
+
+ 1. For each |key| -> |value| of |outputs|:
+ 1. |this|.{{MLGraph/[[outputOperands]]}}[|key|] must exist.
+ 1. If |value|.{{MLOutput/data}} is an {{ArrayBufferView}}, then:
+ 1. The kind of |value|.{{MLOutput/data}} must be compatible to |this|.{{MLGraph/[[outputOperands]]}}[|key|] according to [this table](#appendices-mloperandtype-arraybufferview-compatibility).
-
- 1. Let |requiredOutputNames| be a new [=ordered set=]<{{DOMString}}>.
- 1. If |outputs| was not an empty [=record=], then:
- 1. For each |key| -> |value| of |outputs|:
- 1. Append |key| to |requiredOutputNames|.
- 1. Else:
- 1. For each |key| -> |value| of |this|.{{MLGraph/[[outputOperands]]}}:
- 1. Append |key| to |requiredOutputNames|.
-
- 1. Let |copiedInputs| be a new {{MLNamedInputs}}.
- 1. For each |key| -> |value| of |inputs|:
- 1. Let |copiedInputs| be a new {{MLInput}}.
- 1. Let |copiedInputs|.{{MLInput/data}} be a new {{ArrayBufferView}} that has the same kind and length as |value|.{{MLInput/data}}'s.
- 1. Set the content of |copiedInputs|.{{MLInput/data}} to the content of |value|.{{MLInput/data}}.
- 1. Let |copiedInputs|.{{MLInput/dimensions}} be a new [=sequence=]<{{long}}> that has the same length of |value|.{{MLInput/dimensions}}'s.
- 1. Set the content of |copiedInputs|.{{MLInput/dimensions}} to the content of |value|.{{MLInput/dimensions}}.
- 1. Set |copiedInputs|[key] to |copiedInputs|.
+
- 1. Let |results| be a new {{MLNamedOutputs}}.
1. Let |remainingOutputNames| be a new [=ordered set=]<{{DOMString}}>.
- 1. Set the content of |remainingOutputNames| to the content of |requiredOutputNames|.
+ 1. For each |key| -> |value| of |outputs|:
+ 1. Append |key| to |remainingOutputNames|.
1. Issue the following steps on the [=Device timeline=] of |this|.{{MLGraph/[[implementation]]}}:
- 1. For each |outputName| of |requiredOutputNames|:
- 1. Issue a compute request of |this|.{{MLGraph/[[implementation]]}} for output whose name is |outputName| with given |copiedInputs|.
- 1. When the compute request is completed, issue the following steps on the appropriate [=Queue timeline=]:
-
- 1. If there is an error returned by |this|.{{MLGraph/[[implementation]]}}, then:
- 1. [=reject=] |promise| with an {{OperationError}} and stop.
- 1. Else:
- 1. Let |outputRank| be a {{unsigned long}}.
- 1. Set |outputRank| to the rank of output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
- 1. Let |outputDemisions| be a new [=sequence=]<{{long}}> of size |outputRank|.
- 1. Let |i| be 0.
- 1. Let |outputSize| to 1.
- 1. While true:
- 1. Set |outputDimensions|[|i|] to the dimension at |i|th axis of output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
- 1. Set |outputSize| to |outputSize| * |outputDimensions|[|i|].
- 1. Set |i| to |i| + 1.
- 1. If |i| is equal to |outputRank|, then break.
- 1. Set |results|[|outputName|].{{MLOutput/dimensions}} to |outputDemisions|.
- 1. If |this|.{{MLGraph/[[context]]}} is created from {{MLContextOptions}}, then:
- 1. If |outputs|[|outputName|].{{MLOutput/data}} was given, then:
- 1. If outputs|[|outputName|].{{MLOutput/data}} is not an {{ArrayBufferView}}, then [=reject=] |promise| with an {{TypeError}} and stop.
- 1. If the kind of |outputs|[|outputName|].{{MLOutput/data}} is not compatible to output tensor according to [this table](#appendices-mloperandtype-arraybufferview-compatibility), then [=reject=] |promise| with a {{TypeError}} and stop.
- 1. If the length of |outputs|[|outputName|].{{MLOutput/data}} is less than |outputSize|, then [=reject=] |promise| with a {{TypeError}} and stop.
- 1. Set the content of |outputs|[|outputName|].{{MLOutput/data}} to the content of output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
- 1. Else:
- 1. Let |results|[|outputName|].{{MLOutput/data}} be a new {{ArrayBufferView}} of size |outputSize| and kind that is compatible to output tensor according to [this table](#appendices-mloperandtype-arraybufferview-compatibility).
- 1. Set the content of |results|[|outputName|].{{MLOutput/data}} to the content of output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
- 1. Remove |outputName| from |remainingOutputNames|.
- 1. If |remainingOutputNames| is empty, then resolve |promise| with |results| and stop.
-
+ 1. For each |key| -> |value| of |inputs|:
+ 1. Let |inputTensor| be the input tensor of |this|.{{MLGraph/[[implementation]]}} for |key|.
+ 1. Set the content of |inputTensor| to the content of |value|.{{MLInput/data}}.
+ 1. Set the dimensions of |inputTensor| to |value|.{{MLInput/dimensions}}.
+ 1. For each |key| -> |value| of |outputs|:
+ 1. Issue a compute request of |this|.{{MLGraph/[[implementation]]}} for |key|.
+ 1. If there is an error returned by |this|.{{MLGraph/[[implementation]]}}, then:
+ 1. [=reject=] |promise| with an {{OperationError}} and stop.
+ 1. Else:
+ 1. Let |outputTensor| be the output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
+ 1. Issue the following steps on the appropriate [=Queue timeline=]:
+
+ 1. If the length of |outputs|[|key|].{{MLOutput/data}} is less than the size of |outputTensor|, then [=reject=] |promise| with a {{TypeError}} and stop.
+ 1. Set the content of |outputs|[|key|].{{MLOutput/data}} to the content of output tensor returned by |this|.{{MLGraph/[[implementation]]}}.
+ 1. Remove |key| from |remainingOutputNames|.
+ 1. If |remainingOutputNames| is empty, then resolve |promise| with {{undefined}} and stop.
+
1. Return |promise|.
-
- Issue: Describe the algorithm steps for |this|.{{MLGraph/[[context]]}} created from {{WebGLRenderingContext}} and {{GPUDevice}}.
### Examples ### {#compilation-examples}
@@ -1860,7 +1904,7 @@ const a = builder.input('a', descA);
const descB = {type: 'float32', dimensions: [4, -1]};
const b = builder.input('b', descB);
const c = builder.matmul(a, b);
-const graph = await builder.build({c});
+const graph = await builder.build({'c': c});
async function compute(shapeA, shapeB) {
const bufferA = new Float32Array(sizeOfShape(shapeA)).fill(0.5);
@@ -1871,8 +1915,8 @@ async function compute(shapeA, shapeB) {
'a': {data: bufferA, dimensions: shapeA},
'b': {data: bufferB, dimensions: shapeB},
};
- const outputs = await graph.compute(inputs);
- console.log(`shape: [${outputs.c.dimensions}], values: ${outputs.c.data}`);
+ const outputs = graph.compute(inputs);
+ console.log(`shape: [${outputs.c.dimensions()}], values: ${await outputs.c.data()}`);
}
await compute([3, 4], [4, 3]);
@@ -1907,40 +1951,30 @@ console.log(`values: ${outputs.c.data}`);
-The following code showcases the computation with optional outputs.
+The following code showcases the computation of multiple graphs without accessing the intermediate results.
const context = navigator.ml.createContext();
-
-// Build a graph with two outputs.
const builder = new MLGraphBuilder(context);
-const descA = {type: 'float32', dimensions: [3, 4]};
-const a = builder.input('a', descA);
-const descB = {type: 'float32', dimensions: [4, 3]};
-const bufferB = new Float32Array(sizeOfShape(descB.dimensions)).fill(0.5);
-const b = builder.constant(descB, bufferB);
-const descC = {type: 'float32', dimensions: [3, 3]};
-const bufferC = new Float32Array(sizeOfShape(descC.dimensions)).fill(1);
-const c = builder.constant(descC, bufferC);
-const d = builder.matmul(a, b);
-const e = builder.add(d, c);
-const graph = await builder.build({d, e});
-
-const bufferA = new Float32Array(sizeOfShape(descA.dimensions)).fill(0.5);
-const inputs = {'a': {data: bufferA}};
-
-// Compute both d and e.
-let outputs = await graph.compute(inputs);
-console.log(`outputs include ${Object.keys(outputs)}`);
-// Compute d.
-outputs = await graph.compute(inputs, {d});
-console.log(`outputs include ${Object.keys(outputs)}`);
-console.log(`shape: [${outputs.d.dimensions}], values: ${outputs.d.data}`);
+async function buildConv2d(inputShape, filterShape) {
+ const input = builder.input('input', {type: 'float32', dimensions: inputShape});
+ const filter = builder.constant({type: 'float32', dimensions: filterShape},
+ new Float32Array(sizeOfShape(filterShape)).fill(0.5));
+ const output = builder.conv2d(input, filter);
+ return await builder.build({'output': output});
+}
-// Compute e.
-outputs = await graph.compute(inputs, {e});
-console.log(`outputs include ${Object.keys(outputs)}`);
-console.log(`shape: [${outputs.e.dimensions}], values: ${outputs.e.data}`);
+// Build three graphs that each one contains a conv2d op.
+const conv2dOp1 = await buildConv2d([1, 1, 9, 9], [1, 1, 3, 3]);
+const conv2dOp2 = await buildConv2d([1, 1, 7, 7], [1, 1, 3, 3]);
+const conv2dOp3 = await buildConv2d([1, 1, 5, 5], [1, 1, 3, 3]);
+
+// Compute the graphs and access the final result.
+const inputBuffer = new Float32Array(9*9).fill(0.5);
+const output1 = conv2dOp1.compute({'input': {data: inputBuffer}).output;
+const output2 = conv2dOp2.compute({'input': output1}).output;
+const output3 = conv2dOp3.compute({'input': output2}).output;
+console.log(`shape: [${output3.dimensions()}], values: ${await output3.data()}`);