Megatron KERPLE positional embeddings (#6478) (#6480)

* [TTS] FastPitch adapter fine-tune and conditional layer normalization (#6416) [TTS] FastPitch adapter fine-tune and conditional layer normalization (#6416) --------- * [TTS] whitelist broken path fix. (#6412) * [TTS] whitelist broken path fix. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- * [TTS] FastPitch speaker encoder (#6417) * Add initial codes * Remove wemb * Fix import * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Restore aligner loss * Add ConditionalInput * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix error and support pre-trained config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Follow comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Rename config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Change copyright and random weight test * Add initial codes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix import error * Add initial codes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix dataset error * Remove reference speaker embedding * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove SV encoder * Follow comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix length type * Fix append * Move error msg * Add look-up into speaker encoder * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add valueerror msg * Move lookup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove unused * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix error * Rebase and Fix error * Fix spk encoder * Rename n_speakers * Follow comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix n_speakers None error --------- * Sharded manifests for tarred datasets (#6395) * testing sharded manifests * compatibility * proper fixes * adding flag tot convert_to_tarred_audio_dataset * shard_manifests conf param * propagating the shard_manifests param * propagating the shard_manifests param * distributed checks * typo * typo * fixes * fixes * fixes * fixes * fixes * fixes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixes based on PR comments and tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixes to convert_to_tarred_audio_dataset.py * reversing manifest shards flag * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * tests * excluding manifests from webdataset url expansion * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * expand manifest paths before attempting to cache from datastore * explicit use of UTF-8 for manifest i/o --------- * Update wfst_text_normalization.rst (#6374) Add Hungarian (incoming in NeMo-text-processing) * Support Swiglu in TP PP Conversion (#6437) (#6451) * Support Swiglu in TP PP Conversion * Guard activation * Guard activation --------- * Update NeMo_TTS_Primer.ipynb (#6436) * Update NeMo_TTS_Primer.ipynb Changed a mistake in line 782. Instead of frequency band (ie. pitch) we should write frequency bin. Note that frequency bins in FFT are not related to pitch. * Update NeMo_TTS_Primer.ipynb Corrected the description of spectrogram and mel spectrogram calculations in lines 782 & 783 and added a fourth point to the description and added a reference for more mathematical details at the end of this point. --------- * add rampup batch size support for Megatron GPT (#6424) * added rampup batch size support * added tests for rampup batch size * fixed the typos * added assertions * changed assertion rules * deleted unused imports * changed tests for rampup batch size * updated rampup batch size tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixed styling * rampup batch size tests changes --------- * Meagtron encoder decoder fix for empty validation outputs (#6459) (#6461) * 1. Meagtron encoder decoder fix for empty validation outputs. * 1. Debugging. --------- * Code-Switching dataset creation - upgrading to aggregate tokenizer manifest format (#6448) * added functionality to create agg tokenizer compatible manifest for CS, flag to use this mode by default * updated README with the new agg_tokenizer_manifest flag * fixed typo in scripts/speech_recognition/code_switching/README.md * changed agg_tokenizer_manifest to is_lid_manifest --------- * Added/updated new Conformer configs (#6426) (#6467) * Update script for ngram rnnt and hat beam search decoding (#6370) * add rnnt ngram beamsearch script * add return encoding embedding option * update script * add rnnt and hat ngram decoding script * add some parameters * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add return_encoder_embeddings parameter to RNNTDecodingConfig * replace return_encoder_embeddings parameter * generalization of scipt behavior * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove return_encoder_embeddings parameter * remove return_encoder_embeddings parameter * add manual encoder_embeddings calculation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix beam_width value to 8 * fix rescoring description --------- * BERT pre-training mp fork to spawn (#6442) (#6454) * change bert fork to spawn * num_workers=0 fix --------- * fix replace_bos_with_pad not found (#6443) (#6450) * reduce workers on NMT CI (#6472) (#6474) * 1. Added KERPLE positional embeddings to encoder-decoder. * 1. Added a missing file. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 1. Fixing commits. * 1. Debugging. * 1. Debugging. * 1. Debugging. * 1. Debugging. --------- Signed-off-by: hsiehjackson <c2hsieh@ucsd.edu> Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Dima Rekesh <bmwshop@gmail.com> Signed-off-by: Jim O’Regan <jaoregan@tcd.ie> Signed-off-by: smajumdar <titu1994@gmail.com> Signed-off-by: Mostafa Ghorbandoost <mos.ghorbandoost@gmail.com> Signed-off-by: Dmytro Pykhtar <dpykhtar@nvidia.com> Signed-off-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Signed-off-by: Micha Livne <mlivne@nvidia.com> Signed-off-by: Kunal Dhawan <kunaldhawan97@gmail.com> Signed-off-by: andrusenkoau <andrusenkoau@gmail.com> Signed-off-by: Andrei Andrusenko <52885736+andrusenkoau@users.noreply.github.com> Signed-off-by: Abhinav Khattar <aklife97@gmail.com> Co-authored-by: Micha Livne <michalivne@users.noreply.github.com> Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Dima Rekesh <bmwshop@gmail.com> Co-authored-by: Jim O’Regan <jaoregan@tcd.ie> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Somshubra Majumdar <titu1994@gmail.com> Co-authored-by: Mostafa Ghorbandoost <mos.ghorbandoost@gmail.com> Co-authored-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Co-authored-by: Dmytro Pykhtar <dpykhtar@nvidia.com> Co-authored-by: Eric Harper <complex451@gmail.com> Co-authored-by: Micha Livne <mlivne@nvidia.com> Co-authored-by: Kunal Dhawan <kunaldhawan97@gmail.com> Co-authored-by: Andrei Andrusenko <52885736+andrusenkoau@users.noreply.github.com> Co-authored-by: Abhinav Khattar <aklife97@gmail.com>
NVIDIA · May 26, 2023 · 543cf55 · 543cf55
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diff --git a/Jenkinsfile b/Jenkinsfile
@@ -3806,6 +3806,102 @@ assert_frame_equal(training_curve, gt_curve, rtol=1e-3, atol=1e-3)"'''
         sh "rm -rf examples/nlp/language_modeling/t5_index_mappings"
       }
     }
+    stage('L2: Megatron T5 with KERPLE Pretraining and Resume Training TP=2') {
+      when {
+        anyOf {
+          branch 'r1.18.0'
+          changeRequest target: 'r1.18.0'
+        }
+      }
+      failFast true
+      steps {
+        sh "python examples/nlp/language_modeling/megatron_t5_pretraining.py \
+        trainer.devices=2 \
+        trainer.accelerator=gpu \
+        trainer.log_every_n_steps=1 \
+        trainer.val_check_interval=10 \
+        trainer.limit_val_batches=2 \
+        trainer.accumulate_grad_batches=1 \
+        trainer.max_steps=10 \
+        trainer.precision=16 \
+        trainer.gradient_clip_val=1.0 \
+        exp_manager.exp_dir=examples/nlp/language_modeling/t5_pretrain_results \
+        model.tensor_model_parallel_size=2 \
+        model.seq_length=128 \
+        model.encoder.num_layers=4 \
+        model.encoder.hidden_size=64 \
+        model.encoder.num_attention_heads=8 \
+        model.encoder.activation='swiglu' \
+        model.encoder.masked_softmax_fusion=False \
+        model.encoder.bias_activation_fusion=False \
+        model.encoder.activations_checkpoint_method='block' \
+        model.encoder.activations_checkpoint_num_layers=1 \
+        model.encoder.position_embedding_type=kerple \
+        model.decoder.num_layers=2 \
+        model.decoder.hidden_size=64 \
+        model.decoder.num_attention_heads=8 \
+        model.decoder.activation='swiglu' \
+        model.decoder.masked_softmax_fusion=False \
+        model.decoder.bias_activation_fusion=False \
+        model.decoder.activations_checkpoint_method='block' \
+        model.decoder.activations_checkpoint_num_layers=1 \
+        model.encoder.transformer_block_type='pre_ln' \
+        model.decoder.transformer_block_type='pre_ln' \
+        model.data.data_prefix=[.5,/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src,.5,/home/TestData/nlp/nmt/toy_data/wmt14-de-en.ref] \
+        model.data.index_mapping_dir=examples/nlp/language_modeling/t5_index_mappings \
+        model.data.data_impl=text_mmap \
+        +model.data.data_impl_kwargs.newline_int=10 \
+        +model.data.data_impl_kwargs.header_lines=0 \
+        +model.data.data_impl_kwargs.workers=null \
+        +model.data.data_impl_kwargs.sort_dataset_paths=False \
+        model.share_token_embeddings=False \
+        model.share_decoder_tokens_head_embeddings=False"
+        sh "python examples/nlp/language_modeling/megatron_t5_pretraining.py \
+        trainer.devices=2 \
+        trainer.accelerator=gpu \
+        trainer.log_every_n_steps=1 \
+        trainer.val_check_interval=10 \
+        trainer.limit_val_batches=2 \
+        trainer.accumulate_grad_batches=1 \
+        trainer.max_steps=10 \
+        trainer.precision=16 \
+        trainer.gradient_clip_val=1.0 \
+        exp_manager.exp_dir=examples/nlp/language_modeling/t5_pretrain_results \
+        exp_manager.resume_if_exists=True \
+        model.tensor_model_parallel_size=2 \
+        model.seq_length=128 \
+        model.encoder.num_layers=4 \
+        model.encoder.hidden_size=64 \
+        model.encoder.num_attention_heads=8 \
+        model.encoder.activation='swiglu' \
+        model.encoder.masked_softmax_fusion=False \
+        model.encoder.bias_activation_fusion=False \
+        model.encoder.activations_checkpoint_method='block' \
+        model.encoder.activations_checkpoint_num_layers=1 \
+        model.encoder.position_embedding_type=kerple \
+        model.decoder.num_layers=2 \
+        model.decoder.hidden_size=64 \
+        model.decoder.num_attention_heads=8 \
+        model.decoder.activation='swiglu' \
+        model.decoder.masked_softmax_fusion=False \
+        model.decoder.bias_activation_fusion=False \
+        model.decoder.activations_checkpoint_method='block' \
+        model.decoder.activations_checkpoint_num_layers=1 \
+        model.encoder.transformer_block_type='pre_ln' \
+        model.decoder.transformer_block_type='pre_ln' \
+        model.data.data_prefix=[.5,/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src,.5,/home/TestData/nlp/nmt/toy_data/wmt14-de-en.ref] \
+        model.data.index_mapping_dir=examples/nlp/language_modeling/t5_index_mappings \
+        model.data.data_impl=text_mmap \
+        +model.data.data_impl_kwargs.newline_int=10 \
+        +model.data.data_impl_kwargs.header_lines=0 \
+        +model.data.data_impl_kwargs.workers=null \
+        +model.data.data_impl_kwargs.sort_dataset_paths=False \
+        model.share_token_embeddings=False \
+        model.share_decoder_tokens_head_embeddings=False"
+        sh "rm -rf examples/nlp/language_modeling/t5_pretrain_results"
+        sh "rm -rf examples/nlp/language_modeling/t5_index_mappings"
+      }
+    }
     stage('L2: Megatron T5 Pretraining and Resume Training PP=2') {
       when {
         anyOf {

diff --git a/examples/nlp/language_modeling/conf/megatron_model_base_config.yaml b/examples/nlp/language_modeling/conf/megatron_model_base_config.yaml
@@ -6,7 +6,7 @@ init_method_std: 0.02 # Standard deviation of the zero mean normal distribution
 hidden_dropout: 0.1 # Dropout probability for hidden state transformer.
 attention_dropout: 0.1 # Dropout probability in the attention layer.
 ffn_dropout: 0.0 # Dropout probability in the feed-forward layer.
-position_embedding_type: 'learned_absolute' # Position embedding type. Options ['learned_absolute', 'relative', 'alibi']
+position_embedding_type: 'learned_absolute' # Position embedding type. Options ['learned_absolute', 'relative', 'alibi', 'kerple']
 relative_attention_num_buckets: 32 # Relative position number of buckets for computing the bias
 relative_attention_max_distance: 128 # max_distance to keep relative distance in the attention_num_buckets.
 relative_position_bias_self_attention_only: True # whether to only use relative position bias for self attention only.

diff --git a/nemo/collections/nlp/modules/common/megatron/kerple_relative_position_embedding.py b/nemo/collections/nlp/modules/common/megatron/kerple_relative_position_embedding.py
@@ -0,0 +1,88 @@
+# coding=utf-8
+# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import torch
+
+from nemo.collections.nlp.modules.common.megatron.alibi_relative_position_embedding import (
+    build_relative_position,
+    build_slopes,
+)
+
+__all__ = ['KERPLERelativePositionEmbedding']
+
+
+class KERPLERelativePositionEmbedding(torch.nn.Module):
+    """
+    kerple (Attention with Linear Biases) relative position embedding for auto-regressive decoder
+    and joint encoder (symmetric for forward and backward distance).
+    Based on https://arxiv.org/bas/2108.12409
+    """
+
+    def __init__(
+        self, bidirectional, num_attention_heads, layer_type, num_attention_heads_kerple=None, max_seq_len=512
+    ):
+        """
+        Args:
+            bidirectional: Whether to use bidirectional relative position embedding
+            num_attention_heads: Number of attention heads
+            layer_type: Layer type. Can be one of [LayerType.encoder or LayerType.decoder]. Willdetermine the bias construction
+            num_attention_heads_kerple: Number of attention heads for which kerple bias will be used
+            max_seq_len: Maximum sequence length for precomputed relative positions. Larger sizes will result in more memory usage by computing kerple mask on-the-fly.
+        """
+        super().__init__()
+
+        if (num_attention_heads_kerple is None) or (num_attention_heads_kerple <= 0):
+            num_attention_heads_kerple = num_attention_heads
+
+        if num_attention_heads_kerple > num_attention_heads:
+            raise ValueError(
+                f"num_attention_heads_kerple ({num_attention_heads_kerple}) cannot be larger than num_attention_heads ({num_attention_heads})"
+            )
+
+        self.bidirectional = bidirectional
+        self.num_attention_heads = num_attention_heads
+        # LayerType.encoder or LayerType.decoder. Is only needed to determine the group for the all_reduce
+        self.layer_type = layer_type
+        # define the size of pre-computed relative position slopes.
+        # define the number of attention heads for which kerple mask will be pre-computed (the rest are disabled).
+        self.num_attention_heads_kerple = num_attention_heads_kerple
+        # Larger sizes will result in more memory usage by computing kerple mask on-the-fly.
+        self.max_seq_len = max_seq_len
+
+        # initialize the slopes
+        self.kerple_b = torch.nn.Parameter(build_slopes(num_attention_heads, num_attention_heads_kerple))
+        self.kerple_a = torch.zeros_like(self.kerple_b)
+        self.kerple_p = torch.ones_like(self.kerple_b)
+
+        # cache the relative position bias. shape (num_attention_heads, max_seq_len, max_seq_len)
+        self.relative_position = build_relative_position(max_seq_len, max_seq_len, num_attention_heads)
+
+    def forward(self, query_seq_length, key_seq_length):
+        # used cached relative position if possible
+        max_seq_len = max(query_seq_length, key_seq_length)
+        if max_seq_len > self.max_seq_len:
+            relative_position = build_relative_position(max_seq_len, max_seq_len, self.num_attention_heads)
+        else:
+            relative_position = self.relative_position
+        # shape (num_attention_heads, query_seq_length, key_seq_length)
+        relative_position = relative_position[:, :query_seq_length, :key_seq_length]
+        # if not bidirectional, mask out the future positions
+        if not self.bidirectional:
+            relative_position = torch.tril(relative_position)
+
+        # shape (1, num_heads, query_length, key_length)
+        return -self.kerple_b * torch.log(1 + self.kerple_a * relative_position.unsqueeze(0).pow(self.kerple_p))
diff --git a/nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py b/nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py
@@ -18,6 +18,9 @@
 from nemo.collections.nlp.modules.common.megatron.alibi_relative_position_embedding import (
     ALiBiRelativePositionEmbedding,
 )
+from nemo.collections.nlp.modules.common.megatron.kerple_relative_position_embedding import (
+    KERPLERelativePositionEmbedding,
+)
 from nemo.collections.nlp.modules.common.megatron.language_model import Embedding
 from nemo.collections.nlp.modules.common.megatron.layer_type import LayerType
 from nemo.collections.nlp.modules.common.megatron.megatron_decoders import get_decoder_model
@@ -176,7 +179,16 @@ def __init__(
                     num_attention_heads_alibi=None,
                     max_seq_len=max_position_embeddings,
                 )
-                self._encoder_relative_position_embedding_key = "encoder_relative_position_embedding"
+                self._encoder_relative_position_embedding_key = "encoder_alibi_position_embedding"
+            elif self.encoder_cfg.get('position_embedding_type', 'learned_absolute') == 'kerple':
+                self.encoder_relative_position_embedding = KERPLERelativePositionEmbedding(
+                    bidirectional=True,
+                    num_attention_heads=encoder_cfg.num_attention_heads,
+                    layer_type=LayerType.encoder,
+                    num_attention_heads_kerple=None,
+                    max_seq_len=max_position_embeddings,
+                )
+                self._encoder_relative_position_embedding_key = "encoder_kerple_position_embedding"
             else:
                 self.encoder_relative_position_embedding = None
 
@@ -296,7 +308,16 @@ def __init__(
                     num_attention_heads_alibi=None,
                     max_seq_len=max_position_embeddings,
                 )
-                self._decoder_relative_position_embedding_key = "decoder_relative_position_embedding"
+                self._decoder_relative_position_embedding_key = "decoder_alibi_position_embedding"
+            elif self.decoder_cfg.get('position_embedding_type', 'learned_absolute') == 'kerple':
+                self.decoder_relative_position_embedding = KERPLERelativePositionEmbedding(
+                    bidirectional=False,
+                    num_attention_heads=decoder_cfg.num_attention_heads,
+                    layer_type=LayerType.decoder,
+                    num_attention_heads_kerple=None,
+                    max_seq_len=max_position_embeddings,
+                )
+                self._decoder_relative_position_embedding_key = "decoder_kerple_position_embedding"
             else:
                 self.decoder_relative_position_embedding = None