aws-cdk-neuronx-patterns
Version:
> [!WARNING] > This library is experimental module.
61 lines • 12 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.calcMemoryFootprint = calcMemoryFootprint;
exports.calcTensorParallel = calcTensorParallel;
exports.inferMemoryFootprintFromParameters = inferMemoryFootprintFromParameters;
const aws_cdk_lib_1 = require("aws-cdk-lib");
const model_1 = require("./model");
function calcDataSize(dataTypeBits) {
return dataTypeBits / 8;
}
function calcWeightMemoryFootprint(layers, embeddingDimension, dataTypeBits) {
const dataSize = calcDataSize(dataTypeBits);
return aws_cdk_lib_1.Size.bytes(12 * layers * embeddingDimension ** 2 * dataSize);
}
function calcKvCacheMemoryFootprint(layers, embeddingDimension, dataTypeBits, maxSequenceLength, batchSize) {
const dataSize = calcDataSize(dataTypeBits);
return aws_cdk_lib_1.Size.bytes(2 * layers * embeddingDimension * dataSize * maxSequenceLength * batchSize);
}
function calcMemoryFootprint(embeddingDimension, layers, dataTypeBits, maxSequenceLength, batchSize) {
// # OPT-66B example (BF16, Inf2)
// # n_layer=64, n_ctx=2048, n_embd=9216, batch=16
// weight_mem_footprint = 12 x 64 x 9216^2 x 2 = 121.5 GiB
// KV_cache_mem_footprint = 16 x 64 x 2048 x 9216 x 2 x 2 = 72 GiB
// mem_footprint = 121.5GiB + 72GiB = 193.5 GiB
// num_neuron_cores = ceil_to_closest_supported_size (193.5GiB / 16GiB, Inf2)
// = ceil_to_closest_supported_size (12.1) = 24
// ## Currently, the Neuron runtime supports tensor-parallelism degrees 2, 8, and 32 on Trn1
// ## and supports tensor-parallelism degrees 2, 4, 8, 12 and 24 on Inf2.
const weightMemFootprint = calcWeightMemoryFootprint(layers, embeddingDimension, dataTypeBits);
const kvCacheMemFootprint = calcKvCacheMemoryFootprint(layers, embeddingDimension, dataTypeBits, maxSequenceLength, batchSize);
const bytes = aws_cdk_lib_1.Size.bytes(weightMemFootprint.toBytes() + kvCacheMemFootprint.toBytes());
return aws_cdk_lib_1.Size.gibibytes(Math.ceil(bytes.toGibibytes({ rounding: aws_cdk_lib_1.SizeRoundingBehavior.NONE })));
}
function calcTensorParallel(neuronxInstanceType, memoryFootprint, attentionHeads) {
const toGibibytesCeil = (size) => Math.ceil(size.toGibibytes({
rounding: aws_cdk_lib_1.SizeRoundingBehavior.NONE,
}));
return (neuronxInstanceType.supportedTensorParallelism
// Attention heads need to be divisible by TP
.filter((tp) => !attentionHeads || attentionHeads % tp === 0)
// Fits into Neuron Core's memory capacity
.filter((tp) => tp *
toGibibytesCeil(neuronxInstanceType.acceleratorChips.acceleratorMemory) >=
toGibibytesCeil(memoryFootprint))
.map((tp) => {
const workers = Math.floor(neuronxInstanceType.acceleratorChips.neuronxCores / tp);
return {
tp,
workers,
usage: (tp * workers) / neuronxInstanceType.acceleratorChips.neuronxCores,
};
})
.sort((a, b) => b.usage - a.usage));
}
function inferMemoryFootprintFromParameters(parameters, maxSequenceLength, batchSize) {
const dataSize = calcDataSize(model_1.DataTypeBits.BF16_OR_FP16);
const base = aws_cdk_lib_1.Size.gibibytes(parameters.toBillion() * dataSize * 1.2);
const additional = aws_cdk_lib_1.Size.gibibytes(((base.toGibibytes() * (maxSequenceLength / 1000)) / 10) * batchSize);
return aws_cdk_lib_1.Size.gibibytes(base.toGibibytes() + additional.toGibibytes());
}
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Currently, the Neuron runtime supports tensor-parallelism degrees 2, 8, and 32 on Trn1\n  //                  ## and supports tensor-parallelism degrees 2, 4, 8, 12 and 24 on Inf2.\n  const weightMemFootprint = calcWeightMemoryFootprint(\n    layers,\n    embeddingDimension,\n    dataTypeBits,\n  );\n  const kvCacheMemFootprint = calcKvCacheMemoryFootprint(\n    layers,\n    embeddingDimension,\n    dataTypeBits,\n    maxSequenceLength,\n    batchSize,\n  );\n  const bytes = Size.bytes(\n    weightMemFootprint.toBytes() + kvCacheMemFootprint.toBytes(),\n  );\n  return Size.gibibytes(\n    Math.ceil(bytes.toGibibytes({ rounding: SizeRoundingBehavior.NONE })),\n  );\n}\n\nexport function calcTensorParallel(\n  neuronxInstanceType: INeuronxInstanceType,\n  memoryFootprint: Size,\n  attentionHeads?: number,\n) {\n  const toGibibytesCeil = (size: Size) =>\n    Math.ceil(\n      size.toGibibytes({\n        rounding: SizeRoundingBehavior.NONE,\n      }),\n    );\n  return (\n    neuronxInstanceType.supportedTensorParallelism\n      // Attention heads need to be divisible by TP\n      .filter((tp) => !attentionHeads || attentionHeads % tp === 0)\n      // Fits into Neuron Core's memory capacity\n      .filter(\n        (tp) =>\n          tp *\n            toGibibytesCeil(\n              neuronxInstanceType.acceleratorChips.acceleratorMemory,\n            ) >=\n          toGibibytesCeil(memoryFootprint),\n      )\n      .map((tp) => {\n        const workers = Math.floor(\n          neuronxInstanceType.acceleratorChips.neuronxCores / tp,\n        );\n        return {\n          tp,\n          workers,\n          usage:\n            (tp * workers) / neuronxInstanceType.acceleratorChips.neuronxCores,\n        };\n      })\n      .sort((a, b) => b.usage - a.usage)\n  );\n}\n\nexport function inferMemoryFootprintFromParameters(\n  parameters: Parameters,\n  maxSequenceLength: number,\n  batchSize: number,\n) {\n  const dataSize = calcDataSize(DataTypeBits.BF16_OR_FP16);\n  const base = Size.gibibytes(parameters.toBillion() * dataSize * 1.2);\n  const additional = Size.gibibytes(\n    ((base.toGibibytes() * (maxSequenceLength / 1000)) / 10) * batchSize,\n  );\n  return Size.gibibytes(base.toGibibytes() + additional.toGibibytes());\n}\n"]}