aws-cdk-neuronx-patterns/lib/base/neuronx/calculator.test.js

> [!WARNING] > This library is experimental module.

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const aws_cdk_lib_1 = require("aws-cdk-lib");
const calculator_1 = require("./calculator");
const model_1 = require("./model");
const neuronx_instance_type_1 = require("./neuronx-instance-type");
describe("When the attention head is divisible", () => {
    test("Inf2", () => {
        const tensorParallel = (0, calculator_1.calcTensorParallel)(neuronx_instance_type_1.NeuronxInstanceType.INF2_48XLARGE, aws_cdk_lib_1.Size.gibibytes(16), 32);
        expect(tensorParallel).toEqual([
            {
                tp: 1,
                workers: 24,
                usage: 1,
            },
            {
                tp: 2,
                workers: 12,
                usage: 1,
            },
            {
                tp: 4,
                workers: 6,
                usage: 1,
            },
            {
                tp: 8,
                workers: 3,
                usage: 1,
            },
        ]);
    });
    test("Trn1", () => {
        const tensorParallel = (0, calculator_1.calcTensorParallel)(neuronx_instance_type_1.NeuronxInstanceType.TRN1_32XLARGE, aws_cdk_lib_1.Size.gibibytes(16), 48);
        expect(tensorParallel).toEqual([
            {
                tp: 1,
                workers: 32,
                usage: 1,
            },
            {
                tp: 2,
                workers: 16,
                usage: 1,
            },
            {
                tp: 8,
                workers: 4,
                usage: 1,
            },
            {
                tp: 16,
                workers: 2,
                usage: 1,
            },
        ]);
    });
});
describe("The one with low usage is shown last", () => {
    test("Inf2", () => {
        const tensorParallel = (0, calculator_1.calcTensorParallel)(neuronx_instance_type_1.NeuronxInstanceType.INF2_24XLARGE, aws_cdk_lib_1.Size.gibibytes(16), 32);
        expect(tensorParallel).toEqual([
            {
                tp: 1,
                workers: 12,
                usage: 1,
            },
            {
                tp: 2,
                workers: 6,
                usage: 1,
            },
            {
                tp: 4,
                workers: 3,
                usage: 1,
            },
            {
                tp: 8,
                workers: 1,
                usage: 0.6666666666666666,
            },
        ]);
    });
    test("Trn1", () => {
        const tensorParallel = (0, calculator_1.calcTensorParallel)(neuronx_instance_type_1.NeuronxInstanceType.TRN1_32XLARGE, aws_cdk_lib_1.Size.gibibytes(16), 32);
        expect(tensorParallel).toEqual([
            {
                tp: 1,
                workers: 32,
                usage: 1,
            },
            {
                tp: 2,
                workers: 16,
                usage: 1,
            },
            {
                tp: 8,
                workers: 4,
                usage: 1,
            },
            {
                tp: 16,
                workers: 2,
                usage: 1,
            },
            {
                tp: 32,
                workers: 1,
                usage: 1,
            },
        ]);
    });
});
describe("When the memory footprint is calculated", () => {
    test("Million size models are calculatable", () => {
        const memoryFootprint = (0, calculator_1.calcMemoryFootprint)(576, 30, model_1.DataTypeBits.BF16_OR_FP16, 128, 1);
        expect(memoryFootprint.toGibibytes()).toEqual(1);
    });
    test("Billion size models are calculatable", () => {
        const memoryFootprint = (0, calculator_1.calcMemoryFootprint)(6144, 48, model_1.DataTypeBits.BF16_OR_FP16, 1024, 1);
        expect(memoryFootprint.toGibibytes()).toEqual(42);
    });
});
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{ Size } from \"aws-cdk-lib\";\nimport { calcMemoryFootprint, calcTensorParallel } from \"./calculator\";\nimport { DataTypeBits } from \"./model\";\nimport { NeuronxInstanceType } from \"./neuronx-instance-type\";\n\ndescribe(\"When the attention head is divisible\", () => {\n  test(\"Inf2\", () => {\n    const tensorParallel = calcTensorParallel(\n      NeuronxInstanceType.INF2_48XLARGE,\n      Size.gibibytes(16),\n      32,\n    );\n    expect(tensorParallel).toEqual([\n      {\n        tp: 1,\n        workers: 24,\n        usage: 1,\n      },\n      {\n        tp: 2,\n        workers: 12,\n        usage: 1,\n      },\n      {\n        tp: 4,\n        workers: 6,\n        usage: 1,\n      },\n      {\n        tp: 8,\n        workers: 3,\n        usage: 1,\n      },\n    ]);\n  });\n  test(\"Trn1\", () => {\n    const tensorParallel = calcTensorParallel(\n      NeuronxInstanceType.TRN1_32XLARGE,\n      Size.gibibytes(16),\n      48,\n    );\n    expect(tensorParallel).toEqual([\n      {\n        tp: 1,\n        workers: 32,\n        usage: 1,\n      },\n      {\n        tp: 2,\n        workers: 16,\n        usage: 1,\n      },\n      {\n        tp: 8,\n        workers: 4,\n        usage: 1,\n      },\n      {\n        tp: 16,\n        workers: 2,\n        usage: 1,\n      },\n    ]);\n  });\n});\ndescribe(\"The one with low usage is shown last\", () => {\n  test(\"Inf2\", () => {\n    const tensorParallel = calcTensorParallel(\n      NeuronxInstanceType.INF2_24XLARGE,\n      Size.gibibytes(16),\n      32,\n    );\n    expect(tensorParallel).toEqual([\n      {\n        tp: 1,\n        workers: 12,\n        usage: 1,\n      },\n      {\n        tp: 2,\n        workers: 6,\n        usage: 1,\n      },\n      {\n        tp: 4,\n        workers: 3,\n        usage: 1,\n      },\n      {\n        tp: 8,\n        workers: 1,\n        usage: 0.6666666666666666,\n      },\n    ]);\n  });\n  test(\"Trn1\", () => {\n    const tensorParallel = calcTensorParallel(\n      NeuronxInstanceType.TRN1_32XLARGE,\n      Size.gibibytes(16),\n      32,\n    );\n    expect(tensorParallel).toEqual([\n      {\n        tp: 1,\n        workers: 32,\n        usage: 1,\n      },\n      {\n        tp: 2,\n        workers: 16,\n        usage: 1,\n      },\n      {\n        tp: 8,\n        workers: 4,\n        usage: 1,\n      },\n      {\n        tp: 16,\n        workers: 2,\n        usage: 1,\n      },\n      {\n        tp: 32,\n        workers: 1,\n        usage: 1,\n      },\n    ]);\n  });\n});\n\ndescribe(\"When the memory footprint is calculated\", () => {\n  test(\"Million size models are calculatable\", () => {\n    const memoryFootprint = calcMemoryFootprint(\n      576,\n      30,\n      DataTypeBits.BF16_OR_FP16,\n      128,\n      1,\n    );\n    expect(memoryFootprint.toGibibytes()).toEqual(1);\n  });\n  test(\"Billion size models are calculatable\", () => {\n    const memoryFootprint = calcMemoryFootprint(\n      6144,\n      48,\n      DataTypeBits.BF16_OR_FP16,\n      1024,\n      1,\n    );\n    expect(memoryFootprint.toGibibytes()).toEqual(42);\n  });\n});\n"]}