chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: import all neural network related api under this directory,
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# including layers, linear, conv, rnn etc.
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from .activation import (
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celu,
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elu,
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elu_,
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gelu,
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glu,
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gumbel_softmax,
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hardshrink,
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hardsigmoid,
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hardswish,
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hardtanh,
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hardtanh_,
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leaky_relu,
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leaky_relu_,
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log_sigmoid,
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log_softmax,
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maxout,
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mish,
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prelu,
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relu,
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relu6,
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relu_,
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rrelu,
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selu,
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sigmoid,
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silu,
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softmax,
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softmax_,
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softplus,
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softshrink,
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softsign,
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swiglu,
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swish,
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tanh,
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tanh_,
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tanhshrink,
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thresholded_relu,
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thresholded_relu_,
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)
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from .common import (
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alpha_dropout,
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bilinear,
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class_center_sample,
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cosine_similarity,
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dropout,
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dropout1d,
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dropout2d,
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dropout3d,
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feature_alpha_dropout,
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fold,
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interpolate,
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label_smooth,
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linear,
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pad,
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unfold,
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upsample,
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zeropad2d,
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)
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from .conv import (
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conv1d,
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conv1d_transpose,
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conv2d,
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conv2d_transpose,
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conv3d,
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conv3d_transpose,
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)
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from .distance import pairwise_distance, pdist # noqa: F401
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from .extension import (
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diag_embed, # noqa: F401
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gather_tree,
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sequence_mask,
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temporal_shift,
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)
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from .flash_attention import (
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flash_attention_v3_varlen,
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flash_attn_qkvpacked,
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flash_attn_varlen_qkvpacked,
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flashmask_attention,
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flashmask_get_unique_id,
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sdp_kernel, # noqa: F401
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)
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from .input import (
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embedding,
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embedding_renorm_, # noqa: F401
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one_hot,
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)
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from .loss import (
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adaptive_log_softmax_with_loss,
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binary_cross_entropy,
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binary_cross_entropy_with_logits,
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cosine_embedding_loss,
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cross_entropy,
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ctc_loss,
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dice_loss,
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gaussian_nll_loss,
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hinge_embedding_loss,
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hsigmoid_loss,
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kl_div,
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l1_loss,
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log_loss,
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margin_cross_entropy,
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margin_ranking_loss,
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mse_loss,
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multi_label_margin_loss,
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multi_label_soft_margin_loss,
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multi_margin_loss,
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nll_loss,
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npair_loss,
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poisson_nll_loss,
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rnnt_loss,
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sigmoid_focal_loss,
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smooth_l1_loss,
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soft_margin_loss,
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softmax_with_cross_entropy,
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square_error_cost,
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triplet_margin_loss,
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triplet_margin_with_distance_loss,
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)
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from .moe_permute import moe_permute
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from .moe_unpermute import moe_unpermute
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from .norm import (
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batch_norm,
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group_norm,
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instance_norm,
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layer_norm,
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local_response_norm,
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normalize,
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rms_norm,
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)
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from .pooling import (
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adaptive_avg_pool1d,
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adaptive_avg_pool2d,
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adaptive_avg_pool3d,
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adaptive_max_pool1d,
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adaptive_max_pool2d,
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adaptive_max_pool3d,
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avg_pool1d,
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avg_pool2d,
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avg_pool3d,
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fractional_max_pool2d,
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fractional_max_pool3d,
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lp_pool1d,
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lp_pool2d,
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max_pool1d,
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max_pool2d,
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max_pool3d,
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max_unpool1d,
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max_unpool2d,
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max_unpool3d,
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)
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from .sdpa import scaled_dot_product_attention
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from .sparse_attention import sparse_attention
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from .vision import (
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affine_grid,
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channel_shuffle,
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grid_sample,
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pixel_shuffle,
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pixel_unshuffle,
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)
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logsigmoid = log_sigmoid
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conv_transpose1d = conv1d_transpose
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conv_transpose2d = conv2d_transpose
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conv_transpose3d = conv3d_transpose
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huber_loss = smooth_l1_loss
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multilabel_margin_loss = multi_label_margin_loss
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multilabel_soft_margin_loss = multi_label_soft_margin_loss
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__all__ = [
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'celu',
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'conv1d',
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'conv1d_transpose',
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'conv2d',
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'conv2d_transpose',
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'conv3d',
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'conv3d_transpose',
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'conv_transpose1d',
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'conv_transpose2d',
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'conv_transpose3d',
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'pairwise_distance',
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'elu',
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'elu_',
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'gelu',
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'hardshrink',
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'hardtanh',
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'hardtanh_',
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'hardsigmoid',
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'hardswish',
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'leaky_relu',
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'leaky_relu_',
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'log_sigmoid',
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'logsigmoid',
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'maxout',
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'prelu',
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'relu',
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'relu_',
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'relu6',
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'selu',
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'softmax',
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'softmax_',
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'softplus',
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'softshrink',
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'softsign',
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'sigmoid',
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'silu',
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'swiglu',
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'swish',
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'mish',
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'tanh',
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'tanh_',
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'tanhshrink',
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'thresholded_relu',
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'thresholded_relu_',
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'log_softmax',
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'glu',
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'gumbel_softmax',
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'sequence_mask',
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'dropout',
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'dropout1d',
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'dropout2d',
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'dropout3d',
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'alpha_dropout',
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'feature_alpha_dropout',
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'label_smooth',
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'linear',
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'pad',
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'zeropad2d',
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'unfold',
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'interpolate',
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'upsample',
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'bilinear',
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'cosine_similarity',
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'avg_pool1d',
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'avg_pool2d',
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'avg_pool3d',
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'lp_pool1d',
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'lp_pool2d',
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'max_pool1d',
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'max_pool2d',
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'max_pool3d',
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'max_unpool1d',
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'max_unpool2d',
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'max_unpool3d',
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'moe_permute',
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'moe_unpermute',
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'adaptive_avg_pool1d',
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'adaptive_avg_pool2d',
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'adaptive_avg_pool3d',
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'adaptive_max_pool1d',
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'adaptive_max_pool2d',
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'adaptive_max_pool3d',
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'fractional_max_pool2d',
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'fractional_max_pool3d',
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'binary_cross_entropy',
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'binary_cross_entropy_with_logits',
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'cross_entropy',
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'dice_loss',
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'hsigmoid_loss',
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'kl_div',
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'l1_loss',
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'log_loss',
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'mse_loss',
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'margin_ranking_loss',
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'multi_label_soft_margin_loss',
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'nll_loss',
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'poisson_nll_loss',
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'npair_loss',
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'sigmoid_focal_loss',
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'smooth_l1_loss',
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'softmax_with_cross_entropy',
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'margin_cross_entropy',
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'square_error_cost',
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'ctc_loss',
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'rnnt_loss',
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'hinge_embedding_loss',
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'affine_grid',
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'grid_sample',
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'local_response_norm',
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'pixel_shuffle',
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'pixel_unshuffle',
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'channel_shuffle',
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'embedding',
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'gather_tree',
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'one_hot',
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'normalize',
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'temporal_shift',
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'batch_norm',
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'layer_norm',
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'rms_norm',
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'instance_norm',
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'class_center_sample',
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'sparse_attention',
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'fold',
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'cosine_embedding_loss',
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'rrelu',
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'triplet_margin_with_distance_loss',
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'triplet_margin_loss',
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'adaptive_log_softmax_with_loss',
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'multi_margin_loss',
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'multi_label_margin_loss',
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'multilabel_margin_loss',
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'multilabel_soft_margin_loss',
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'soft_margin_loss',
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'gaussian_nll_loss',
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'scaled_dot_product_attention',
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'flashmask_attention',
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'flashmask_get_unique_id',
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'flash_attn_qkvpacked',
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"flash_attention_v3_varlen",
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'flash_attn_varlen_qkvpacked',
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'group_norm',
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'huber_loss',
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]
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