Launch MLP in C++ Args: mlp_sizes (list of int): MLP sizes. Example: [1024,1024,1024] will create 2 MLP layers with shape 1024x1024

Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via ``python setup.py install --cuda_ext --cpp_ext``. It has be

r"""Applies Layer Normalization over a mini-batch of inputs as described in the paper `Layer Normalization`_ . Currently only runs on cuda()

r"""Applies RMS Normalization over a mini-batch of inputs Currently only runs on cuda() tensors. .. math:: y = \frac{x}{\mathrm{RMS}

Optimized GroupNorm for NHWC layout with optional Swish/SiLU fusion. There are two version of CUDA kernels under the hood: one pass and two p

Computes and stores the average and current value

Computes and stores the average and current value

Symbolic representation of CUDA Events in the Inductor scheduling phase. Args: factory: The CUDAEventFactory that generate this event.

AlphaFold SWA (Stochastic Weight Averaging) module wrapper.

A pool managing reusable CUDA streams to optimize GPU operations. Attributes: pool_size (int): The maximum number of CUDA streams managed

CuDNN fronted context manager. Notice: CuDNN handle must be created after distributed process group initialization.

A factory that managements CUDA event creations and materializations. This factory maintains internal states to ensure that created cuda events g

A wrapper class for handling decompositions in model compilation. This class extends the `_TorchCompileInductorWrapper` to include additional

r"""Adam optimizer with ZeRO algorithm. Currently GPU-only. Requires Apex to be installed via ``python setup.py install --cuda_ext --cpp_ext

Implements LAMB algorithm. Currently GPU-only. Requires Apex to be installed via ``pip install -v --no-cache-dir --global-option="--cpp_ext"

Enter a context executed by respective CUDA Stream and insert necessary syncs. Attributes: wrapper: The code-gen wrapper of the current c

Enter a CUDA device context and allocate required side streams. Note: - The number of allocated streams is controlled by :attr:`torchsche

Generate code to exit the current stream context. Note: Most attributes and checking logics of this class have been moved to :met

Exit a CUDA device context and release allocated streams.

MultiheadAttention modules are unique, we need to check permutations for input and ouput projections

Wrapper code generator for graph scheduling.

Transducer joint Detail of this loss function can be found in: Sequence Transduction with Recurrent Neural Networks Arguments:

Transducer loss Detail of this loss function can be found in: Sequence Transduction with Recurrent Neural Networks Arguments:

if weights are already sparse, permutations should be skipped

1D convolutions in isolation and with siblings

A single coparent that cannot permute along K poisons all other coparents in its group

The child of a depthwise convolution should act as a sibling

Convolutions with different group sizes need to use the GCD of the input channel counts if siblings

Stack of 2d convolutions with different types of grouped convolutions

Attributes of some module must be permuted if they feed some operation that is permuted

If only one of two siblings is sparse, both need to be permuted

A single sibling that cannot permute along C poisons all other siblings in its group

Stack of 2d convolutions with different normalization and activation functions

Some simple residual connections to test collecting parameters into a single group. Four sections: input, blocka + residual, blockb + blockc, output

Attributes with multiple dimensions matching the permutation length should only be permuted along the correct dimension

Averaging function for EMA with configurable decay rate (Supplementary '1.11.7 Evaluator setup').

If concats are along the channel dimension (dim1 of NCHW), downstream layers can still be permuted despite C!=parentK

flatten modules may change the effective channel count, typically by collapsing N,C,H,W into N,C*H*W before a classifier

flatten ops may change the effective channel count, typically by collapsing N,C,H,W into N,C*H*W before a classifier

make sure tracing failures are handled gracefully

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:class:`FP16_Optimizer` A cutdown version of apex.fp16_utils.FP16_Optimizer. Designed only to wrap apex.contrib.optimizers.FusedAdam, FusedSG