a644350589
Tacotron2+Waveglow/PyT * AMP support * Data preprocessing for Tacotron 2 training * Fixed dropouts on LSTMCells SSD/PyT * script and notebook for inference * AMP support * README update * updates to examples/* BERT/PyT * initial release GNMT/PyT * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP * Added inference throughput and latency results on NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line NCF/PyT * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs Transformer/PyT * new README * jit support added UNet Medical/TF * inference example scripts added * inference benchmark measuring latency added * TRT/TF-TRT support added * README updated GNMT/TF * Performance improvements Small updates (mostly README) for other models.
122 lines
5 KiB
Python
122 lines
5 KiB
Python
# Copyright (c) 2018, deepakn94, codyaustun, robieta. 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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#
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# -----------------------------------------------------------------------
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#
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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import time
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import torch
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def create_test_data(test_ratings, test_negs, args):
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test_users = test_ratings[:,0]
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test_pos = test_ratings[:,1].reshape(-1,1)
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# create items with real sample at last position
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num_valid_negative = test_negs.shape[1]
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test_users = test_users.reshape(-1,1).repeat(1, 1 + num_valid_negative)
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test_items = torch.cat((test_negs, test_pos), dim=1)
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del test_ratings, test_negs
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# generate dup mask and real indices for exact same behavior on duplication compare to reference
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# here we need a sort that is stable(keep order of duplicates)
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sorted_items, indices = torch.sort(test_items) # [1,1,1,2], [3,1,0,2]
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sum_item_indices = sorted_items.float()+indices.float()/len(indices[0]) #[1.75,1.25,1.0,2.5]
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indices_order = torch.sort(sum_item_indices)[1] #[2,1,0,3]
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stable_indices = torch.gather(indices, 1, indices_order) #[0,1,3,2]
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# produce -1 mask
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dup_mask = (sorted_items[:,0:-1] == sorted_items[:,1:])
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dup_mask = torch.cat((torch.zeros_like(test_pos, dtype=torch.uint8), dup_mask),dim=1)
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dup_mask = torch.gather(dup_mask,1,stable_indices.sort()[1])
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# produce real sample indices to later check in topk
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sorted_items, indices = (test_items != test_pos).sort()
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sum_item_indices = sorted_items.float()+indices.float()/len(indices[0])
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indices_order = torch.sort(sum_item_indices)[1]
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stable_indices = torch.gather(indices, 1, indices_order)
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real_indices = stable_indices[:,0]
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if args.distributed:
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test_users = torch.chunk(test_users, args.world_size)[args.local_rank]
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test_items = torch.chunk(test_items, args.world_size)[args.local_rank]
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dup_mask = torch.chunk(dup_mask, args.world_size)[args.local_rank]
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real_indices = torch.chunk(real_indices, args.world_size)[args.local_rank]
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test_users = test_users.view(-1).split(args.valid_batch_size)
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test_items = test_items.view(-1).split(args.valid_batch_size)
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return test_users, test_items, dup_mask, real_indices
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def prepare_epoch_train_data(train_ratings, nb_items, args):
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# create label
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train_label = torch.ones_like(train_ratings[:,0], dtype=torch.float32)
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neg_label = torch.zeros_like(train_label, dtype=torch.float32)
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neg_label = neg_label.repeat(args.negative_samples)
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train_label = torch.cat((train_label,neg_label))
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del neg_label
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train_users = train_ratings[:,0]
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train_items = train_ratings[:,1]
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train_users_per_worker = len(train_label) / args.world_size
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train_users_begin = int(train_users_per_worker * args.local_rank)
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train_users_end = int(train_users_per_worker * (args.local_rank + 1))
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# prepare data for epoch
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neg_users = train_users.repeat(args.negative_samples)
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neg_items = torch.empty_like(neg_users, dtype=torch.int64).random_(0, nb_items)
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epoch_users = torch.cat((train_users, neg_users))
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epoch_items = torch.cat((train_items, neg_items))
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del neg_users, neg_items
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# shuffle prepared data and split into batches
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epoch_indices = torch.randperm(train_users_end - train_users_begin, device='cuda:{}'.format(args.local_rank))
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epoch_indices += train_users_begin
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epoch_users = epoch_users[epoch_indices]
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epoch_items = epoch_items[epoch_indices]
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epoch_label = train_label[epoch_indices]
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if args.distributed:
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local_batch = args.batch_size // args.world_size
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else:
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local_batch = args.batch_size
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epoch_users = epoch_users.split(local_batch)
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epoch_items = epoch_items.split(local_batch)
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epoch_label = epoch_label.split(local_batch)
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# the last batch will almost certainly be smaller, drop it
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epoch_users = epoch_users[:-1]
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epoch_items = epoch_items[:-1]
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epoch_label = epoch_label[:-1]
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return epoch_users, epoch_items, epoch_label
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