Two factors will be taken into account when scoring an entry:
1. Parameter Storage:
Definition & Counting: The number of parameters that are required to perform inference. In addition to trainable parameters, any values that are needed to perform inference should be counted (e.g., indices for sparse matrix formats).
For the purpose of counting storage overhead, sparse matrices are assumed to be stored as a set of nonzero values and a bitmask of the full tensor shape that denotes the location of the nonzero values. For block sparsity, a single bit can be used to denote a block of values. For example, a 512x128 matrix with 4x4 block sparsity would require a bitmask with 4096 bits.
Entries that do not perform any quantization are allowed to assume that their models are quantized to 16-bits at no accuracy penalty. That is to say, entrants can calculate parameter storage for their models as if it were quantized to 16-bits without actually doing any quantization. A 32-bit parameter counts as one parameter. Quantized parameters of less than 32-bits will be counted as a fraction of one parameter. For example, an 8-bit parameter counts as 1/4th a parameter towards the model’s parameter storage requirements. If an entry quantizes any part of their model to less than 16-bits, then no “free” quantization to 16-bits is allowed.
Rationale: When designing hardware, the number of bits of parameters in a model dictates the number of values that must be moved to and from arithmetic units, how much fast, on-chip memory would be needed to store the model, and the power consumption and chip area requirements for multiplication and addition circuits.
Quantization can significantly reduce parameter storage and math requirements, but can be non-trivial to perform in popular deep learning frameworks. Our goal with the system explained above is to balance between rewarding quantization approaches while mitigating the complexity of entering the competition. Without the above rule, it’s likely that all entries would be required to perform some kind of quantization to be competitive.
Parameter efficiency is also an important metric from a modeling perspective, as it captures a models ability use it’s weights effectively.
2. Math Operations:
Definition & Counting: The mean number of arithmetic operations per example required to perform inference on the test set. Multiplies and additions count separately. Transcendental function evaluations and bitwise operations count as one op. Dynamic activation sparsity (e.g., from ReLU activation functions) should not be taken into account.
Entries that do not perform any quantization are allowed to assume that their models are quantized to 16-bits at no accuracy penalty. That is to say, entrants can calculate math operations for their models as if it were quantized to 16-bits without actually doing any quantization. With this “freebie” quantization, all addition operations are still considered to be calculated in full 32-bit precision. A 32-bit operation counts as one operation. If quantization is performed, an operation on data of less than 32-bits will be counted as a fraction of one operation, where the numerator is the maximum number of bits in the inputs of the operation and the denominator is 32. For example, a multiplication operation with one 3-bit and one 5-bit input, with a 7-bit output, will count as 5/32nd of an operation. A multiplication operation where one input is 32-bits, the other input is 8-bits and the output is 8-bits will count as one whole op, as the first input is 32-bits. If an entry quantizes any part of their model, then no “free” quantization to 16-bits is allowed.
The standard rule for scoring quantized math operations considers the resolution of an operation to be the maximum bit-width of the operation’s inputs. However, multiplication of an n-bit value (stored in a numerical format with a standalone sign-bit) by a binary weight where the binary values are taken to represent -1 or +1 only needs to modify the sign-bit of the n-bit input. Because of this, we allow multiplication of a value with a standalone sign-bit by a binary value in this format to be counted as a 1/32nd of an operation regardless of the resolution of the other input. Note that by “standalone sign-bit” we refer to numerical formats where the sign can be inverted by changing a single bit, such as IEEE 754 floating point. Two’s-complement integers do not satisfy this criterion.
For fixed-point and floating-point formats that can be exactly represented by a 32-bit IEEE 754 floating point number (INT8, FP8, INT4, etc.) we do not require conversions to/from reduced precision formats to be tallied. For example, if an FP32 addition is followed by a FP16 multiplication the conversion of the output of the addition to FP16 to perform the multiplication does not need to be taken into account. For more exotic formats or fixed/floating-point formats that cannot be directly converted to FP32 without rounding, all conversions should be taken into account.
For WikiText-103, numbers can be calculated on the tokenized version of the test set, and will be averaged per-token. Numbers with WikiText-103 should be calculated with a batch size of 1 sequence, such that no padding is needed to handle variable sequence lengths. Numbers with WikiText-103 should also be calculated as if inference is performed on-line, i.e. tokens are fed sequentially to the model and it must predict the next token prior to receiving it. This is important for architectures that do not have any state (e.g., Transformer).
Rationale: The number and resolution (bit-width of operands) of mathematical operations dictates how much work has to be done at inference time. Reduced precision can be exploited to save power and chip area.
We count transcendental functions as a single operation under the assumption that they can be accelerated by hardware if necessary.
We do not take dynamic activation sparsity into account because we do not take activation storage size into account. Thus, we cannot incorporate the storage and bandwidth cost of storing activations as sparse tensors into a submission’s score. In future iterations of the competition, we plan to include a metric for maximum transient activation size (see appendix) and will allow entries to take dynamic activation sparsity into account.
We allow static activation sparsity (e.g., sparse attention) because the sparsity pattern is known ahead of time and we can avoid expensive dynamic compression of layer outputs and potentially mitigate load-balancing issues.
Note that this restriction on dynamic activation sparsity is not intended to rule out mixture-of-experts (MoE) approaches, which could be viewed as a form of dynamic activation sparsity. If you’re interested in entering the competition with MoE model, reach out to the organizers at firstname.lastname@example.org.
Similar to the quantization scoring procedure for parameter storage, we allow “freebie” quantization of math operations to 16-bits to strike a balance between rewarding quantization approaches and minimizing the complexity of entering the competition. However, it is typically necessary to perform full 32-bit accumulation to maintain model quality. Thus, we do not allow “freebie” quantization for addition operations.
Entries that finish in the top 10% of one of these metrics will earn the distinction of a “highly storage-efficient solution” or “highly compute-efficient solution” for parameter storage and math operations respectively. To select overall winners for each task, we will rank entries based on the sum of these two values, with each value normalized by a baseline state-of-the-art model for the task. The entry with the lowest overall score for each task will be declared the winner.
For ImageNet, parameter storage and compute requirements will be normalized relative to MobileNetV2 with width 1.4 (6.9M parameters, 1170M math operations). For example, an ImageNet submission that has 3 million parameters and 500 million math operations will have a final score of 3M / 6.9M + 500M / 1170M = .862.
For CIFAR-100, parameter storage and compute requirements will be normalized relative to WideResNet-28-10, which has 36.5M parameters and 10.49B math operations.
For WikiText-103, these metrics will be normalized relative to the LSTM model presented here. This model uses a single-layer LSTM with 2048 hidden units, tied embedding and softmax parameters with dimension 512, and a linear projection to the embedding/softmax width of 512 from the LSTM width of 2048. In total, it has 159M parameters, and 318M math operations.
When evaluating a quantized model, inputs to operations that are to be calculated using reduced precision arithmetic should be properly stored in the reduced precision format. For fixed-point and floating-point formats that can be exactly represented by a 32-bit IEEE 754 floating point number (INT8, FP8, INT4, etc.) we allow these operations to be simulated by converting the inputs to FP32 and executing standard floating-point math operations.
This procedure is an approximation of reduced precision calculation, but it enables simpler evaluation of quantized models for participants and avoids issues that could arise from the lack of software support for reduced precision calculation in standard software packages. Note that an entry following this “fake quantization” procedure that performs accumulation in less than 32-bits would need to write custom kernels for matmul/convolution to ensure the inputs to addition operations are properly rounded during evaluation.
For more exotic formats or fixed/floating-point formats that cannot be directly converted to FP32 without rounding, true reduced precision calculation must be performed to verify model quality.
Participants must submit their code, final checkpoint, and a writeup on their approach through a private GitHub repository. The repository must be made public after the submission deadline passes. The writeup must include details on the approach as well as documentation on how to reproduce the results using the provided code. Test set performance with the provided checkpoint will be verified to ensure reproducibility. Test set performance should be easily verifiable with the provided code and checkpoint. When submitting an entry, contestants are required to calculate the two scoring metrics for their entries and thoroughly document their calculation. Calculations for top entries will be manually verified by the organizers.
Am I allowed to use data augmentation when training my model?
Data augmentation (e.g., random mirroring, color transforms, random cropping, etc.) is allowed. The only constraint on the training data is that it must only come from the training dataset of the task that the model will be entered in. This means that AutoAugment and similar approaches are allowed, but the policies should not be learned on data that is not the training data for the task that the model will be entered in (e.g., you should not use the CIFAR10 AutoAugment policy for CIFAR100).
How should I count permutations of data like those used in ShuffleNet?
Arbitrary permutations of data in a tensor can be expressed through the product of the tensor with a permutation matrix. Permutation matrices are massively sparse, and thus an arbitrary permutation can be expressed as a sparse matrix-matrix product. For math operations, an arbitrary permutation should be counted towards a model’s score as the product of a dense matrix and a sparse permutation matrix. For parameter storage, the cost of storing the sparse permutation matrix should be taken into account.
Are the Quality Targets for Each Task Hard Thresholds?
Yes, the quality targets listed for each task are hard thresholds, i.e. entries must achieve at or above the specified accuracy without rounding. For ImageNet, this entails correctly classifying 37,500 out of the 50,000 validation images. For CIFAR100, this entails correctly classifying 8,000 out of the 10,000 validation images. For WikiText-103, models must achieve a test set perplexity of 35 or below.
Maximum Transient Activation Size: When performing inference, our ability to keep activations and weights in fast, on-chip memory can be a key factor in the runtime of our model. While the number of parameters that must be stored is captured by our above metrics, we do not currently take activation storage requirements into account.
Our ability to keep activations on chip is governed by the maximum amount of activations that must be kept around at any point during inference, which we refer to as maximum transient activation size (MTAS). Depending on the model, and batching choices, transient activations storage can exceed parameters storage, and thus have as much, if not more, impact than parameters.
While we would like to include this as a metric for this competition, it is difficult to calculate the exact maximum transient activation size on complex computational graphs with large numbers of branches. While we explored approximations, we decided that we should favor simplicity in our metrics for the first iteration of the competition. In future iterations of this competition, we would like to include MTAS, and to provide tooling to calculate it automatically for models written in popular frameworks.