Merge branch 'transformer_refactoring_from_pretrain_refactoring' into 'master'

Major refactoring, combining gpt2 and bert

See merge request ADLR/megatron-lm!8

docker/Dockerfile

 # ===========
 # base images
 # ===========
-FROM nvcr.io/nvidia/pytorch:19.05-py3
+FROM nvcr.io/nvidia/pytorch:19.09-py3
 
 
 # ===============
@@ -27,12 +27,3 @@ RUN pip install --upgrade pip && \
 COPY requirements.txt /tmp/
 RUN pip install --upgrade --ignore-installed -r /tmp/requirements.txt
 
-
-# ===========
-# latest apex
-# ===========
-RUN pip uninstall -y apex && \
-git clone https://github.com/NVIDIA/apex.git ~/apex && \
-cd ~/apex && \
-pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .
-

docker/README.md

-Note that as of now you need to have PySOL cloned to the directory here before building the container.

evaluate_gpt2.py

@@ -29,7 +29,6 @@ from megatron.fp16 import FP16_Module
 from megatron.fp16 import FP16_Optimizer
 from megatron.learning_rates import AnnealingLR
 from megatron.model import GPT2Model
-from megatron.model import gpt2_get_params_for_weight_decay_optimization
 from megatron.model import DistributedDataParallel as DDP
 from megatron import mpu
 from apex.optimizers import FusedAdam as Adam

generate_samples.py

@@ -26,9 +26,8 @@ import argparse
 import time
 from arguments import get_args
 from megatron.utils import Timers
-from pretrain_gpt2 import initialize_distributed
-from pretrain_gpt2 import set_random_seed
-from pretrain_gpt2 import get_train_val_test_data
+from megatron.utils import initialize_distributed
+from megatron.utils import set_random_seed
 from pretrain_gpt2 import get_masks_and_position_ids
 from megatron.utils import load_checkpoint
 from megatron.data_utils import make_tokenizer
@@ -96,7 +95,8 @@ def get_batch(context_tokens, args):
         tokens,
         args.eod_token,
         args.reset_position_ids,
-        args.reset_attention_mask)
+        args.reset_attention_mask,
+        False)
 
     return tokens, attention_mask, position_ids
 
@@ -361,7 +361,7 @@ def switch(val1, val2, boolean):
     boolean = boolean.type_as(val1)
     return (1-boolean)*val1 + boolean*val2
 
-def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask, position_ids, tokenizer, args, maxlen=None):
+def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask, position_ids, tokenizer, args, maxlen=None, type_ids=None):
     model.eval()
     with torch.no_grad():
         context_length = context_lengths.min().item()
@@ -384,16 +384,21 @@ def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask
         while context_length <= (maxlen):
 
             if args.recompute:
-                logits = model(tokens, position_ids, attention_mask)
+                logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids)
                 logits = logits[:, context_length - 1, :]
             else:
+                types2use = None
                 if counter == 0:
                     tokens2use = tokens[:, :context_length]
                     positions2use = position_ids[:, :context_length]
+                    if type_ids is not None:
+                        types2use = type_ids[:, :context_length]
                 else:
                     tokens2use = tokens[:, context_length - 1].view(batch_size, -1)
                     positions2use = position_ids[:, context_length - 1].view(batch_size, -1)
-                logits, layer_past = model(tokens2use, positions2use, attention_mask, layer_past=layer_past, get_present=True)
+                    if type_ids is not None:
+                        types2use = type_ids[:, context_length - 1].view(batch_size, -1)
+                logits, layer_past = model(tokens2use, positions2use, attention_mask, layer_past=layer_past, get_key_value=True, tokentype_ids=types2use)
                 logits = logits[:, -1].view(batch_size,-1).contiguous()
 
             if args.greedy:

megatron/fp16/fp16.py

@@ -22,6 +22,9 @@ from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
 from .loss_scaler import DynamicLossScaler, LossScaler
 from .fp16util import model_grads_to_master_grads, master_params_to_model_params, clip_grad_norm
 
+
+from megatron.module import MegatronModule
+
 FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
 HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
 
@@ -56,7 +59,7 @@ def fp16_to_fp32(val):
         return val
     return conversion_helper(val, float_conversion)
 
-class FP16_Module(nn.Module):
+class FP16_Module(MegatronModule):
     def __init__(self, module):
         super(FP16_Module, self).__init__()
         self.add_module('module', module.half())
@@ -67,6 +70,11 @@ class FP16_Module(nn.Module):
     def state_dict(self, destination=None, prefix='', keep_vars=False):
         return self.module.state_dict(destination, prefix, keep_vars)
 
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        return self.module.state_dict_for_save_checkpoint(destination, prefix,
+                                                          keep_vars)
+
     def load_state_dict(self, state_dict, strict=True):
         self.module.load_state_dict(state_dict, strict=strict)
 

megatron/model/__init__.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 from .distributed import *
-from .gpt2_modeling import gpt2_get_params_for_weight_decay_optimization
-from .gpt2_modeling import GPT2Model
-from .model import BertModel
-from .model import get_params_for_weight_decay_optimization
+from .bert_model import BertModel
+from .gpt2_model import GPT2Model
+from .utils import get_params_for_weight_decay_optimization

megatron/model/bert_model.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+"""BERT model."""
+
+import torch
+
+from megatron.module import MegatronModule
+
+from .language_model import parallel_lm_logits
+from .language_model import get_language_model
+from .transformer import LayerNorm
+from .utils import gelu
+from .utils import get_linear_layer
+from .utils import init_method_normal
+from .utils import scaled_init_method_normal
+
+
+def bert_attention_mask_func(attention_scores, attention_mask):
+    attention_scores = attention_scores + attention_mask
+    return attention_scores
+
+
+def bert_extended_attention_mask(attention_mask, dtype):
+    # We create a 3D attention mask from a 2D tensor mask.
+    # [b, 1, s]
+    attention_mask_b1s = attention_mask.unsqueeze(1)
+    # [b, s, 1]
+    attention_mask_bs1 = attention_mask.unsqueeze(2)
+    # [b, s, s]
+    attention_mask_bss = attention_mask_b1s * attention_mask_bs1
+    # [b, 1, s, s]
+    extended_attention_mask = attention_mask_bss.unsqueeze(1)
+    # Since attention_mask is 1.0 for positions we want to attend and 0.0
+    # for masked positions, this operation will create a tensor which is
+    # 0.0 for positions we want to attend and -10000.0 for masked positions.
+    # Since we are adding it to the raw scores before the softmax, this is
+    # effectively the same as removing these entirely.
+    # fp16 compatibility
+    extended_attention_mask = extended_attention_mask.to(dtype=dtype)
+    extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+    return extended_attention_mask
+
+
+def bert_position_ids(token_ids):
+    # Create position ids
+    seq_length = token_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long,
+                                device=token_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(token_ids)
+
+    return position_ids
+
+
+
+class BertLMHead(MegatronModule):
+    """Masked LM head for Bert
+
+    Arguments:
+        mpu_vocab_size: model parallel size of vocabulary.
+        hidden_size: hidden size
+        init_method: init method for weight initialization
+        layernorm_epsilon: tolerance for layer norm divisions
+        parallel_output: wether output logits being distributed or not.
+    """
+    def __init__(self, mpu_vocab_size, hidden_size, init_method,
+                 layernorm_epsilon, parallel_output):
+
+        super(BertLMHead, self).__init__()
+
+        self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size))
+        self.bias.model_parallel = True
+        self.parallel_output = parallel_output
+
+        self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
+        self.layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)
+
+
+    def forward(self, hidden_states, word_embeddings_weight):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = gelu(hidden_states)
+        hidden_states = self.layernorm(hidden_states)
+        output = parallel_lm_logits(hidden_states,
+                                    word_embeddings_weight,
+                                    self.parallel_output,
+                                    bias=self.bias)
+        return output
+
+
+
+class BertModel(MegatronModule):
+    """Bert Language model."""
+
+    def __init__(self,
+                 num_layers,
+                 vocab_size,
+                 hidden_size,
+                 num_attention_heads,
+                 embedding_dropout_prob,
+                 attention_dropout_prob,
+                 output_dropout_prob,
+                 max_sequence_length,
+                 checkpoint_activations,
+                 checkpoint_num_layers=1,
+                 add_binary_head=False,
+                 layernorm_epsilon=1.0e-5,
+                 init_method_std=0.02,
+                 num_tokentypes=0,
+                 parallel_output=True):
+
+        super(BertModel, self).__init__()
+
+        self.add_binary_head = add_binary_head
+        self.parallel_output = parallel_output
+        init_method = init_method_normal(init_method_std)
+
+        self.language_model, self._language_model_key = get_language_model(
+            num_layers=num_layers,
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            embedding_dropout_prob=embedding_dropout_prob,
+            attention_dropout_prob=attention_dropout_prob,
+            output_dropout_prob=output_dropout_prob,
+            max_sequence_length=max_sequence_length,
+            num_tokentypes=num_tokentypes,
+            add_pooler=self.add_binary_head,
+            attention_mask_func=bert_attention_mask_func,
+            checkpoint_activations=checkpoint_activations,
+            checkpoint_num_layers=checkpoint_num_layers,
+            layernorm_epsilon=layernorm_epsilon,
+            init_method=init_method,
+            scaled_init_method=scaled_init_method_normal(init_method_std,
+                                                         num_layers),
+            residual_connection_post_layernorm=True)
+
+        self.lm_head = BertLMHead(
+            self.language_model.embedding.word_embeddings.weight.size(0),
+            hidden_size, init_method, layernorm_epsilon, parallel_output)
+        self._lm_head_key = 'lm_head'
+
+        if self.add_binary_head:
+            self.binary_head = get_linear_layer(hidden_size, 2, init_method)
+            self._binary_head_key = 'binary_head'
+
+
+    def forward(self, input_ids, attention_mask,
+                tokentype_ids=None):
+
+        extended_attention_mask = bert_extended_attention_mask(
+            attention_mask, next(self.language_model.parameters()).dtype)
+        position_ids = bert_position_ids(input_ids)
+
+        if self.add_binary_head:
+            lm_output, pooled_output = self.language_model(
+                input_ids,
+                position_ids,
+                extended_attention_mask,
+                tokentype_ids=tokentype_ids)
+        else:
+            lm_output = self.language_model(
+                input_ids,
+                position_ids,
+                extended_attention_mask,
+                tokentype_ids=tokentype_ids)
+
+        # Output.
+        lm_logits = self.lm_head(
+            lm_output, self.language_model.embedding.word_embeddings.weight)
+
+        if self.add_binary_head:
+            binary_logits = self.binary_head(pooled_output)
+            return lm_logits, binary_logits
+
+        return lm_logits, None
+
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        """For easy load when model is combined with other heads,
+        add an extra key."""
+
+        state_dict_ = {}
+        state_dict_[self._language_model_key] \
+            = self.language_model.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+        state_dict_[self._lm_head_key] \
+            = self.lm_head.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+        if self.add_binary_head:
+            state_dict_[self._binary_head_key] \
+                = self.binary_head.state_dict(destination, prefix, keep_vars)
+        return state_dict_
+
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Customized load."""
+
+        self.language_model.load_state_dict(
+            state_dict[self._language_model_key], strict=strict)
+        self.lm_head.load_state_dict(state_dict[self._lm_head_key],
+                                     strict=strict)
+        if self.add_binary_head:
+            self.binary_head.load_state_dict(state_dict[self._binary_head_key],
+                                             strict=strict)

megatron/model/distributed.py

@@ -20,8 +20,10 @@ from torch.nn.modules import Module
 from torch.autograd import Variable
 
 from megatron import mpu
+from megatron.module import MegatronModule
 
-class DistributedDataParallel(Module):
+
+class DistributedDataParallel(MegatronModule):
 
     def __init__(self, module):
         super(DistributedDataParallel, self).__init__()
@@ -86,6 +88,11 @@ class DistributedDataParallel(Module):
 
         return sd
 
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        return self.module.state_dict_for_save_checkpoint(destination, prefix,
+                                                          keep_vars)
+
     def load_state_dict(self, state_dict, strict=True):
         self.module.load_state_dict(state_dict, strict=strict)
 

megatron/model/gpt2_model.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+"""GPT-2 model."""
+
+import torch
+
+from megatron.module import MegatronModule
+
+from .language_model import parallel_lm_logits
+from .language_model import get_language_model
+from .utils import init_method_normal
+from .utils import scaled_init_method_normal
+
+
+def gpt2_attention_mask_func(attention_scores, ltor_mask):
+    attention_scores = torch.mul(attention_scores, ltor_mask) - \
+                       10000.0 * (1.0 - ltor_mask)
+    return attention_scores
+
+
+class GPT2Model(MegatronModule):
+    """GPT-2 Language model."""
+
+    def __init__(self,
+                 num_layers,
+                 vocab_size,
+                 hidden_size,
+                 num_attention_heads,
+                 embedding_dropout_prob,
+                 attention_dropout_prob,
+                 output_dropout_prob,
+                 max_sequence_length,
+                 checkpoint_activations,
+                 checkpoint_num_layers=1,
+                 layernorm_epsilon=1.0e-5,
+                 init_method_std=0.02,
+                 num_tokentypes=0,
+                 parallel_output=True):
+
+        super(GPT2Model, self).__init__()
+
+        self.parallel_output = parallel_output
+
+        self.language_model, self._language_model_key = get_language_model(
+            num_layers=num_layers,
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            embedding_dropout_prob=embedding_dropout_prob,
+            attention_dropout_prob=attention_dropout_prob,
+            output_dropout_prob=output_dropout_prob,
+            max_sequence_length=max_sequence_length,
+            num_tokentypes=num_tokentypes,
+            add_pooler=False,
+            attention_mask_func=gpt2_attention_mask_func,
+            checkpoint_activations=checkpoint_activations,
+            checkpoint_num_layers=checkpoint_num_layers,
+            layernorm_epsilon=layernorm_epsilon,
+            init_method=init_method_normal(init_method_std),
+            scaled_init_method=scaled_init_method_normal(init_method_std,
+                                                         num_layers),
+            residual_connection_post_layernorm=False)
+
+
+    def forward(self, input_ids, position_ids, attention_mask,
+                tokentype_ids=None, layer_past=None, get_key_value=False):
+
+        # Language model.
+        lm_output = self.language_model(input_ids,
+                                        position_ids,
+                                        attention_mask,
+                                        tokentype_ids=tokentype_ids,
+                                        layer_past=layer_past,
+                                        get_key_value=get_key_value)
+
+        if get_key_value:
+            lm_output, presents = lm_output
+
+        # Output.
+        output = parallel_lm_logits(
+            lm_output,
+            self.language_model.embedding.word_embeddings.weight,
+            self.parallel_output)
+
+        if get_key_value:
+            output = [output, presents]
+
+        return output
+
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+
+        state_dict_ = {}
+        state_dict_[self._language_model_key] \
+            = self.language_model.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+        return state_dict_
+
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Customized load."""
+
+        if self._language_model_key in state_dict:
+            state_dict = state_dict[self._language_model_key]
+        self.language_model.load_state_dict(state_dict, strict=strict)

megatron/model/gpt2_modeling.py

-# coding=utf-8
-# Copyright (c) 2019, 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.
-
-"""GPT-2 model."""
-
-import torch
-import torch.nn.functional as F
-
-from megatron import mpu
-
-
-def init_method_normal(std=0.02):
-    """Init method based on normal distribution.
-
-    This is only used for embeddings. The transformer has its
-    own initializer.
-    """
-    def init_(tensor):
-        return torch.nn.init.normal_(tensor, mean=0.0, std=std)
-    return init_
-
-
-class GPT2Model(torch.nn.Module):
-    """GPT-2 Language model.
-
-    The output of the forward method are the logits (parallel or
-    serial depending on the `parallel_output` flag.
-    """
-
-    def __init__(self,
-                 num_layers,
-                 vocab_size,
-                 hidden_size,
-                 num_attention_heads,
-                 embedding_dropout_prob,
-                 attention_dropout_prob,
-                 output_dropout_prob,
-                 max_sequence_length,
-                 checkpoint_activations,
-                 checkpoint_num_layers=1,
-                 parallel_output=True):
-
-        super(GPT2Model, self).__init__()
-
-        self.parallel_output = parallel_output
-
-        init_method = init_method_normal(std=0.02)
-
-        # Word embeddings (parallel).
-        self.word_embeddings = mpu.VocabParallelEmbedding(
-            vocab_size, hidden_size, init_method=init_method)
-
-        # Position embedding (serial).
-        self.position_embeddings = torch.nn.Embedding(max_sequence_length,
-                                                      hidden_size)
-
-        # Token type embedding.
-        # Add this as an optional field that can be added through
-        # method call so we can load a pretrain model without
-        # token types and add them as needed.
-        self.tokentype_embeddings = None
-        self.hidden_size = hidden_size
-
-        # Initialize the position embeddings.
-        init_method(self.position_embeddings.weight)
-
-        # Embeddings dropout
-        self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
-
-        # Transformer
-        self.transformer = mpu.GPT2ParallelTransformer(num_layers,
-                                                       hidden_size,
-                                                       num_attention_heads,
-                                                       attention_dropout_prob,
-                                                       output_dropout_prob,
-                                                       checkpoint_activations,
-                                                       checkpoint_num_layers)
-
-
-    def add_tokentype_embeddings(self, num_tokentypes):
-        if self.tokentype_embeddings is not None:
-            raise Exception('tokentype embeddings is already initialized')
-        if torch.distributed.get_rank() == 0:
-            print('adding embedding for {} tokentypes'.format(num_tokentypes),
-                  flush=True)
-        self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes,
-                                                       self.hidden_size)
-
-
-    def forward(self, input_ids, position_ids, attention_mask,
-                layer_past=None, get_present=False, tokentype_ids=None):
-
-        # Embeddings.
-        words_embeddings = self.word_embeddings(input_ids)
-        position_embeddings = self.position_embeddings(position_ids)
-        embeddings = words_embeddings + position_embeddings
-        if tokentype_ids is not None:
-            assert self.tokentype_embeddings is not None
-            embeddings = embeddings + self.tokentype_embeddings(tokentype_ids)
-        else:
-            assert self.tokentype_embeddings is None
-
-        # Dropout.
-        embeddings = self.embedding_dropout(embeddings)
-
-        # Transformer.
-        transformer_output = self.transformer(embeddings, attention_mask,
-                                              layer_past=layer_past,
-                                              get_present=get_present)
-        if get_present:
-            transformer_output, presents = transformer_output
-
-        # Parallel logits.
-        transformer_output_parallel = mpu.copy_to_model_parallel_region(
-            transformer_output)
-        logits_parallel = F.linear(transformer_output_parallel,
-                                   self.word_embeddings.weight)
-
-        if self.parallel_output:
-            output = logits_parallel
-        else:
-            output = mpu.gather_from_model_parallel_region(logits_parallel)
-        if get_present:
-            output = [output, presents]
-        return output
-
-
-def gpt2_get_params_for_weight_decay_optimization(module):
-
-    weight_decay_params = {'params': []}
-    no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
-    for module_ in module.modules():
-        if isinstance(module_, (mpu.LayerNorm, torch.nn.LayerNorm)):
-            no_weight_decay_params['params'].extend(
-                [p for p in list(module_._parameters.values())
-                 if p is not None])
-        else:
-            weight_decay_params['params'].extend(
-                [p for n, p in list(module_._parameters.items())
-                 if p is not None and n != 'bias'])
-            no_weight_decay_params['params'].extend(
-                [p for n, p in list(module_._parameters.items())
-                 if p is not None and n == 'bias'])
-
-    return weight_decay_params, no_weight_decay_params

megatron/model/language_model.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+"""Transformer based language model."""
+
+import torch
+import torch.nn.functional as F
+
+from megatron import mpu
+from megatron.module import MegatronModule
+
+from .transformer import ParallelTransformer
+from .transformer import TransformerHyperparameters
+from .utils import gelu
+from .utils import get_linear_layer
+
+
+def parallel_lm_logits(input_, word_embeddings_weight, parallel_output,
+                       bias=None):
+    """LM logits using word embedding weights."""
+    # Parallel logits.
+    input_parallel = mpu.copy_to_model_parallel_region(input_)
+    # Matrix multiply.
+    if bias is None:
+        logits_parallel = F.linear(input_parallel, word_embeddings_weight)
+    else:
+        logits_parallel = F.linear(input_parallel, word_embeddings_weight, bias)
+    # Gather if needed.
+    if parallel_output:
+        return logits_parallel
+    else:
+        return mpu.gather_from_model_parallel_region(logits_parallel)
+
+
+def get_language_model(num_layers,
+                       vocab_size,
+                       hidden_size,
+                       num_attention_heads,
+                       embedding_dropout_prob,
+                       attention_dropout_prob,
+                       output_dropout_prob,
+                       max_sequence_length,
+                       num_tokentypes,
+                       attention_mask_func,
+                       add_pooler,
+                       checkpoint_activations,
+                       checkpoint_num_layers,
+                       layernorm_epsilon,
+                       init_method,
+                       scaled_init_method,
+                       residual_connection_post_layernorm):
+    # Transformer hyperparameters.
+    transformer_hparams = TransformerHyperparameters(
+        hidden_size=hidden_size,
+        num_layers=num_layers,
+        num_attention_heads=num_attention_heads,
+        attention_dropout_prob=attention_dropout_prob,
+        output_dropout_prob=output_dropout_prob,
+        mlp_activation_func=gelu,
+        layernorm_epsilon=layernorm_epsilon,
+        init_method=init_method,
+        output_layer_init_method=scaled_init_method,
+        checkpoint_activations=checkpoint_activations,
+        checkpoint_num_layers=checkpoint_num_layers,
+        apply_residual_connection_post_layernorm=residual_connection_post_layernorm)
+    # Language model.
+    language_model = TransformerLanguageModel(
+        transformer_hparams=transformer_hparams,
+        attention_mask_func=attention_mask_func,
+        vocab_size=vocab_size,
+        max_sequence_length=max_sequence_length,
+        embedding_dropout_prob=embedding_dropout_prob,
+        num_tokentypes=num_tokentypes,
+        add_pooler=add_pooler)
+    # key used for checkpoints.
+    language_model_key = 'language_model'
+
+    return language_model, language_model_key
+
+
+
+class Pooler(MegatronModule):
+    """Pooler layer.
+
+    Pool hidden states of a specific token (for example start of the
+    sequence) and add a linear transformation followed by a tanh.
+
+    Arguments:
+        hidden_size: hidden size
+        init_method: weight initialization method for the linear layer.
+            bias is set to zero.
+    """
+    def __init__(self, hidden_size, init_method):
+        super(Pooler, self).__init__()
+        self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
+
+
+    def forward(self, hidden_states, sequence_index=0):
+        # hidden_states: [b, s, h]
+        # sequence_index: index of the token to pool.
+        pooled = hidden_states[:, sequence_index, :]
+        pooled = self.dense(pooled)
+        pooled = torch.tanh(pooled)
+        return pooled
+
+
+class Embedding(MegatronModule):
+    """Language model embeddings.
+
+    Arguments:
+        hidden_size: hidden size
+        vocab_size: vocabulary size
+        max_sequence_length: maximum size of sequence. This
+                             is used for positional embedding
+        embedding_dropout_prob: dropout probability for embeddings
+        init_method: weight initialization method
+        num_tokentypes: size of the token-type embeddings. 0 value
+                        will ignore this embedding
+    """
+    def __init__(self,
+                 hidden_size,
+                 vocab_size,
+                 max_sequence_length,
+                 embedding_dropout_prob,
+                 init_method,
+                 num_tokentypes=0):
+        super(Embedding, self).__init__()
+
+        self.hidden_size = hidden_size
+        self.init_method = init_method
+        self.num_tokentypes = num_tokentypes
+
+        # Word embeddings (parallel).
+        self.word_embeddings = mpu.VocabParallelEmbedding(
+            vocab_size, self.hidden_size, init_method=self.init_method)
+        self._word_embeddings_key = 'word_embeddings'
+
+        # Position embedding (serial).
+        self.position_embeddings = torch.nn.Embedding(
+            max_sequence_length, self.hidden_size)
+        self._position_embeddings_key = 'position_embeddings'
+        # Initialize the position embeddings.
+        self.init_method(self.position_embeddings.weight)
+
+        # Token type embedding.
+        # Add this as an optional field that can be added through
+        # method call so we can load a pretrain model without
+        # token types and add them as needed.
+        self._tokentype_embeddings_key = 'tokentype_embeddings'
+        if self.num_tokentypes > 0:
+            self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes,
+                                                           self.hidden_size)
+            # Initialize the token-type embeddings.
+            self.init_method(self.tokentype_embeddings.weight)
+        else:
+            self.tokentype_embeddings = None
+
+        # Embeddings dropout
+        self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
+
+
+    def add_tokentype_embeddings(self, num_tokentypes):
+        """Add token-type embedding. This function is provided so we can add
+        token-type embeddings in case the pretrained model does not have it.
+        This allows us to load the model normally and then add this embedding.
+        """
+        if self.tokentype_embeddings is not None:
+            raise Exception('tokentype embeddings is already initialized')
+        if torch.distributed.get_rank() == 0:
+            print('adding embedding for {} tokentypes'.format(num_tokentypes),
+                  flush=True)
+        self.num_tokentypes = num_tokentypes
+        self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes,
+                                                       self.hidden_size)
+        # Initialize the token-type embeddings.
+        self.init_method(self.tokentype_embeddings.weight)
+
+
+    def forward(self, input_ids, position_ids, tokentype_ids=None):
+        # Embeddings.
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        embeddings = words_embeddings + position_embeddings
+        if tokentype_ids is not None:
+            assert self.tokentype_embeddings is not None
+            embeddings = embeddings + self.tokentype_embeddings(tokentype_ids)
+        else:
+            assert self.tokentype_embeddings is None
+
+        # Dropout.
+        embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings
+
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        """For easy load."""
+
+        state_dict_ = {}
+        state_dict_[self._word_embeddings_key] \
+            = self.word_embeddings.state_dict(destination, prefix, keep_vars)
+        state_dict_[self._position_embeddings_key] \
+            = self.position_embeddings.state_dict(
+                destination, prefix, keep_vars)
+        if self.num_tokentypes > 0:
+            state_dict_[self._tokentype_embeddings_key] \
+                = self.tokentype_embeddings.state_dict(
+                    destination, prefix, keep_vars)
+
+        return state_dict_
+
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Customized load."""
+
+        # Word embedding.
+        if self._word_embeddings_key in state_dict:
+            state_dict_ = state_dict[self._word_embeddings_key]
+        else:
+            # for backward compatibility.
+            state_dict_ = {}
+            for key in state_dict.keys():
+                if 'word_embeddings' in key:
+                    state_dict_[key.split('word_embeddings.')[1]] \
+                        = state_dict[key]
+        self.word_embeddings.load_state_dict(state_dict_, strict=strict)
+
+        # Position embedding.
+        if self._position_embeddings_key in state_dict:
+            state_dict_ = state_dict[self._position_embeddings_key]
+        else:
+            # for backward compatibility.
+            state_dict_ = {}
+            for key in state_dict.keys():
+                if 'position_embeddings' in key:
+                    state_dict_[key.split('position_embeddings.')[1]] \
+                        = state_dict[key]
+        self.position_embeddings.load_state_dict(state_dict_, strict=strict)
+
+        # Tokentype embedding.
+        if  self.num_tokentypes > 0:
+            state_dict_ = {}
+            if self._tokentype_embeddings_key in state_dict:
+                state_dict_ = state_dict[self._tokentype_embeddings_key]
+            else:
+                # for backward compatibility.
+                for key in state_dict.keys():
+                    if 'tokentype_embeddings' in key:
+                        state_dict_[key.split('tokentype_embeddings.')[1]] \
+                            = state_dict[key]
+            if len(state_dict_.keys()) > 0:
+                self.tokentype_embeddings.load_state_dict(state_dict_,
+                                                          strict=strict)
+            else:
+                print('***WARNING*** expected tokentype embeddings in the '
+                      'checkpoint but could not find it', flush=True)
+
+
+
+class TransformerLanguageModel(MegatronModule):
+    """Transformer language model.
+
+    Arguments:
+        transformer_hparams: transformer hyperparameters
+        attention_mask_func: a function that takes `unmaksed-attention-scores`
+            with size [b, np, s, s] and an `attention-mask` and will apply
+            the masking. The function should return a masked score of the
+            same size [b, np, s, s].
+          masked-attention-scores = attention_mask_func(
+                                     unmaksed-attention-scores, attention-mask)
+        vocab_size: vocabulary size
+        max_sequence_length: maximum size of sequence. This
+                             is used for positional embedding
+        embedding_dropout_prob: dropout probability for embeddings
+        num_tokentypes: size of the token-type embeddings. 0 value
+                        will ignore this embedding
+    """
+    def __init__(self,
+                 transformer_hparams,
+                 attention_mask_func,
+                 vocab_size,
+                 max_sequence_length,
+                 embedding_dropout_prob,
+                 num_tokentypes=0,
+                 add_pooler=False):
+        super(TransformerLanguageModel, self).__init__()
+
+        self.hidden_size = transformer_hparams['hidden_size']
+        self.num_tokentypes = num_tokentypes
+        self.init_method = transformer_hparams['init_method']
+        self.add_pooler = add_pooler
+
+        # Embeddings
+        self.embedding = Embedding(self.hidden_size,
+                                   vocab_size,
+                                   max_sequence_length,
+                                   embedding_dropout_prob,
+                                   self.init_method,
+                                   self.num_tokentypes)
+        self._embedding_key = 'embedding'
+
+        # Transformer
+        self.transformer = ParallelTransformer(
+            transformer_hparams,
+            attention_mask_func)
+        self._transformer_key = 'transformer'
+
+        # Pooler
+        if self.add_pooler:
+            self.pooler = Pooler(self.hidden_size, self.init_method)
+            self._pooler_key = 'pooler'
+
+
+    def forward(self, input_ids, position_ids, attention_mask,
+                tokentype_ids=None, layer_past=None, get_key_value=False,
+                pooling_sequence_index=0):
+
+        # Embeddings.
+        embedding_output = self.embedding(input_ids, position_ids,
+                                          tokentype_ids=tokentype_ids)
+
+        # Transformer.
+        transformer_output = self.transformer(embedding_output,
+                                              attention_mask,
+                                              layer_past=layer_past,
+                                              get_key_value=get_key_value)
+
+        if self.add_pooler:
+            pooled_output = self.pooler(transformer_output,
+                                        pooling_sequence_index)
+            return transformer_output, pooled_output
+
+        return transformer_output
+
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        """For easy load."""
+
+        state_dict_ = {}
+        state_dict_[self._embedding_key] \
+            = self.embedding.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+        state_dict_[self._transformer_key] \
+            = self.transformer.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+        if self.add_pooler:
+            state_dict_[self._pooler_key] \
+                = self.pooler.state_dict_for_save_checkpoint(
+                    destination, prefix, keep_vars)
+
+        return state_dict_
+
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Customized load."""
+
+        # Embedding.
+        if self._embedding_key in state_dict:
+            state_dict_ = state_dict[self._embedding_key]
+        else:
+            # for backward compatibility.
+            state_dict_ = {}
+            for key in state_dict.keys():
+                if '_embeddings' in key:
+                    state_dict_[key] = state_dict[key]
+        self.embedding.load_state_dict(state_dict_, strict=strict)
+
+        # Transformer.
+        if self._transformer_key in state_dict:
+            state_dict_ = state_dict[self._transformer_key]
+        else:
+            # for backward compatibility.
+            state_dict_ = {}
+            for key in state_dict.keys():
+                if 'transformer.' in key:
+                    state_dict_[key.split('transformer.')[1]] = state_dict[key]
+        self.transformer.load_state_dict(state_dict_, strict=strict)
+
+        # Pooler.
+        if self.add_pooler:
+            assert 'pooler' in state_dict, \
+                'could not find data for pooler in the checkpoint'
+            self.pooler.load_state_dict(state_dict[self._pooler_key],
+                                        strict=strict)

megatron/model/transformer.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+"""Transformer."""
+
+import math
+
+import torch
+from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
+
+from megatron import mpu
+from megatron.module import MegatronModule
+
+
+""" We use the following notation throughout this file:
+     h: hidden size
+     n: number of attention heads
+     p: number of model parallel partitions
+     np: n/p
+     hp: h/p
+     hn: h/n
+     b: batch size
+     s: sequence length
+     l: number of layers
+    Transformer takes input of size [b, s, h] and returns a
+    tensor of the same size. We use the following arguments:
+        hyperparameters: transformer hyperparameters
+        attention_mask_func: a function that takes `unmaksed-attention-scores`
+            with size [b, np, s, s] and an `attention-mask` and will apply
+            the masking. The function should return a masked score of the
+            same size [b, np, s, s].
+               masked-attention-scores = attention_mask_func(
+                                     unmaksed-attention-scores, attention-mask)
+"""
+
+
+class TransformerHyperparameters:
+    """Hyperparameters used to build and run the transformer.
+
+    Arguments:
+        hidden_size: hidden size (h)
+        num_layers: number of layers (l)
+        num_attention_heads: number of attention heads (n)
+        attention_dropout_prob: dropout probability for the attention
+                                probabiliies
+        output_dropout_prob: dropout probability for the output
+                             layers (attention output and mlp output)
+        mlp_activation_func: activation function for the mlp layer
+        layernorm_epsilon: tolerance parameters used for layer norm
+                           dividions
+        init_method: init method used for all weights except layer
+                     norm and output weights
+        output_layer_init_method: init method for output weights (
+                                  attention output and mlp output)
+        checkpoint_activations: flag to use activation checkpointing
+        checkpoint_num_layers: number of layers use in each chunk of
+                               activation checkpointing
+        apply_residual_connection_post_layernorm: Take the post layer-norm
+            values for resudual connecton. BERT: True, GPT-2: False
+    """
+    def __init__(self,
+                 hidden_size=None,
+                 num_layers=None,
+                 num_attention_heads=None,
+                 attention_dropout_prob=None,
+                 output_dropout_prob=None,
+                 mlp_activation_func=None,
+                 layernorm_epsilon=None,
+                 init_method=None,
+                 output_layer_init_method=None,
+                 checkpoint_activations=None,
+                 checkpoint_num_layers=None,
+                 apply_residual_connection_post_layernorm=None):
+        self.params_dict = {}
+        self.params_dict['hidden_size'] = hidden_size
+        self.params_dict['num_layers'] = num_layers
+        self.params_dict['num_attention_heads'] = num_attention_heads
+        self.params_dict['attention_dropout_prob'] = attention_dropout_prob
+        self.params_dict['output_dropout_prob'] = output_dropout_prob
+        self.params_dict['mlp_activation_func'] = mlp_activation_func
+        self.params_dict['layernorm_epsilon'] = layernorm_epsilon
+        self.params_dict['init_method'] = init_method
+        self.params_dict['output_layer_init_method'] = output_layer_init_method
+        self.params_dict['checkpoint_activations'] = checkpoint_activations
+        self.params_dict['checkpoint_num_layers'] = checkpoint_num_layers
+        self.params_dict['apply_residual_connection_post_layernorm'] \
+            = apply_residual_connection_post_layernorm
+
+
+    def __getitem__(self, key):
+        """Custom retrieval with error checks."""
+        try:
+            value = self.params_dict[key]
+        except KeyError:
+            raise Exception(
+                'could not find {} in transformer hyperparameters'.format(key))
+        except Exception as e:
+            print('unexpected error in transformer hyperparameters:', e)
+            raise Exception()
+        else:
+            assert value is not None, \
+                'parameter value for {} is not set in transformer '\
+                'hyperparameters'.format(key)
+            return value
+        raise Exception('should not be here')
+
+
+
+class ParallelMLP(MegatronModule):
+    """MLP.
+
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension. At the end, dropout is also
+    applied.
+    """
+
+    def __init__(self, hyperparameters):
+        super(ParallelMLP, self).__init__()
+
+        # Project to 4h.
+        self.dense_h_to_4h = mpu.ColumnParallelLinear(
+            hyperparameters['hidden_size'],
+            4*hyperparameters['hidden_size'],
+            gather_output=False,
+            init_method=hyperparameters['init_method'])
+
+        self.activation_func = hyperparameters['mlp_activation_func']
+
+        # Project back to h.
+        self.dense_4h_to_h = mpu.RowParallelLinear(
+            4*hyperparameters['hidden_size'],
+            hyperparameters['hidden_size'],
+            input_is_parallel=True,
+            init_method=hyperparameters['output_layer_init_method'])
+
+        self.dropout = torch.nn.Dropout(hyperparameters['output_dropout_prob'])
+
+
+    def forward(self, hidden_states):
+
+        # [b, s, 4hp]
+        intermediate_parallel = self.dense_h_to_4h(hidden_states)
+        intermediate_parallel = self.activation_func(intermediate_parallel)
+
+        # [b, s, h]
+        output = self.dense_4h_to_h(intermediate_parallel)
+        output = self.dropout(output)
+        return output
+
+
+
+class ParallelSelfAttention(MegatronModule):
+    """Parallel self-attention layer abstract class.
+
+    Self-attention layer takes input with size [b, s, h]
+    and returns output of the same size.
+    """
+
+    def __init__(self, hyperparameters, attention_mask_func):
+        super(ParallelSelfAttention, self).__init__()
+
+        self.attention_mask_func = attention_mask_func
+
+        # Per attention head and per partition values.
+        world_size = mpu.get_model_parallel_world_size()
+        self.hidden_size_per_partition = mpu.divide(
+            hyperparameters['hidden_size'], world_size)
+        self.hidden_size_per_attention_head = mpu.divide(
+            hyperparameters['hidden_size'],
+            hyperparameters['num_attention_heads'])
+        self.num_attention_heads_per_partition = mpu.divide(
+            hyperparameters['num_attention_heads'], world_size)
+
+        # Strided linear layer.
+        self.query_key_value = mpu.ColumnParallelLinear(
+            hyperparameters['hidden_size'],
+            3*hyperparameters['hidden_size'],
+            stride=3,
+            gather_output=False,
+            init_method=hyperparameters['init_method'])
+
+        # Dropout. Note that for a single iteration, this layer will generate
+        # different outputs on different number of parallel partitions but
+        # on average it should not be partition dependent.
+        self.attention_dropout = torch.nn.Dropout(
+            hyperparameters['attention_dropout_prob'])
+
+        # Output.
+        self.dense = mpu.RowParallelLinear(
+            hyperparameters['hidden_size'],
+            hyperparameters['hidden_size'],
+            input_is_parallel=True,
+            init_method=hyperparameters['output_layer_init_method'])
+        self.output_dropout = torch.nn.Dropout(
+            hyperparameters['output_dropout_prob'])
+
+
+    def _transpose_for_scores(self, tensor):
+        """Transpose a 3D tensor [b, s, np*hn] into a 4D tensor with
+        size [b, np, s, hn].
+        """
+        new_tensor_shape = tensor.size()[:-1] + \
+                           (self.num_attention_heads_per_partition,
+                            self.hidden_size_per_attention_head)
+        tensor = tensor.view(*new_tensor_shape)
+        return tensor.permute(0, 2, 1, 3)
+
+
+    def _get_query_key_value(self, hidden_states):
+        """Get query, key, and value and transpose to
+        get size [b, np, s, hn].
+        """
+        # Attention heads. [b, s, hp]
+        mixed_x_layer = self.query_key_value(hidden_states)
+        (mixed_query_layer,
+         mixed_key_layer,
+         mixed_value_layer) = mpu.split_tensor_along_last_dim(mixed_x_layer, 3)
+
+        # Reshape and transpose [b, np, s, hn]
+        query_layer = self._transpose_for_scores(mixed_query_layer)
+        key_layer = self._transpose_for_scores(mixed_key_layer)
+        value_layer = self._transpose_for_scores(mixed_value_layer)
+
+        return query_layer, key_layer, value_layer
+
+
+    def _get_unmasked_attention_scores(self, query_layer, key_layer):
+        """Unmasked attention scores with size [b, np, s, s]."""
+        norm_factor = math.sqrt(math.sqrt(self.hidden_size_per_attention_head))
+        # Raw attention scores. [b, np, s, s]
+        return torch.matmul(query_layer/norm_factor,
+                            key_layer.transpose(-1, -2)/norm_factor)
+
+
+    def _get_attention_probs(self, attention_scores):
+        """Attention probabilies with dropout. The output has
+        the size [b, np, s, s].
+        """
+        # Attention probabilities. [b, np, s, s]
+        attention_probs = torch.nn.Softmax(dim=-1)(attention_scores)
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        with mpu.get_cuda_rng_tracker().fork():
+            attention_probs = self.attention_dropout(attention_probs)
+
+        return attention_probs
+
+
+    def _get_attended_context(self, attention_probs, value_layer):
+        """Final attended tesnor and transposed back to [b, s, hp]."""
+        # Context layer.
+        # [b, np, s, hn]
+        context_layer = torch.matmul(attention_probs, value_layer)
+        # [b, s, np, hn]
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + \
+                                  (self.hidden_size_per_partition,)
+        # [b, s, hp]
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        return context_layer
+
+
+    def _get_output(self, context_layer):
+        """Output layer with dropout."""
+        # Output. [b, s, h]
+        output = self.dense(context_layer)
+        output = self.output_dropout(output)
+
+        return output
+
+
+    def forward(self, hidden_states, attention_mask, layer_past=None,
+                get_key_value=False):
+        # hidden_states: [b, s, h]
+
+        # Attention heads. [b, np, s, hn]
+        query_layer, key_layer, value_layer = self._get_query_key_value(
+            hidden_states)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            key_layer = torch.cat((past_key.type_as(key_layer),
+                                   key_layer), dim=-2)
+            value_layer = torch.cat((past_value.type_as(value_layer),
+                                     value_layer), dim=-2)
+        if get_key_value:
+            present = (key_layer, value_layer)
+
+        # Raw attention scores. [b, np, s, s]
+        attention_scores = self._get_unmasked_attention_scores(
+            query_layer, key_layer)
+
+        # Apply attention mask. [b, np, s, s]
+        if get_key_value:
+            with torch.no_grad():
+                if layer_past is not None:
+                    attention_mask = attention_mask[
+                        ...,
+                        attention_scores.size(3)-1,
+                        :attention_scores.size(3)].unsqueeze(2)
+                else:
+                    attention_mask = attention_mask[
+                        ...,
+                        :attention_scores.size(3),
+                        :attention_scores.size(3)]
+        attention_scores = self.attention_mask_func(attention_scores,
+                                                    attention_mask)
+
+        # Attention probabilities. [b, np, s, s]
+        attention_probs = self._get_attention_probs(attention_scores)
+
+        # Context layer. [b, s, hp]
+        context_layer = self._get_attended_context(attention_probs, value_layer)
+
+        # Output. [b, s, h]
+        output = self._get_output(context_layer)
+
+        if get_key_value:
+            output = [output, present]
+
+        return output
+
+
+
+class ParallelTransformerLayer(MegatronModule):
+    """A single transformer layer.
+
+    Transformore layer takes input with size [b, s, h] and returns an
+    output of the same size.
+    """
+    def __init__(self, hyperparameters, attention_mask_func):
+
+        super(ParallelTransformerLayer, self).__init__()
+
+        self.apply_residual_connection_post_layernorm \
+            = hyperparameters['apply_residual_connection_post_layernorm']
+
+        # Layernorm on the input data.
+        self.input_layernorm = LayerNorm(
+            hyperparameters['hidden_size'],
+            eps=hyperparameters['layernorm_epsilon'])
+
+        # Self attention.
+        self.attention = ParallelSelfAttention(
+            hyperparameters,
+            attention_mask_func)
+
+        # Layernorm on the input data.
+        self.post_attention_layernorm = LayerNorm(
+            hyperparameters['hidden_size'],
+            eps=hyperparameters['layernorm_epsilon'])
+
+        # MLP
+        self.mlp = ParallelMLP(hyperparameters)
+
+
+    def forward(self, hidden_states, attention_mask, layer_past=None,
+                get_key_value=False):
+        # hidden_states: [b, s, h]
+
+        # Layer norm at the begining of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+        # Self attention.
+        attention_output = self.attention(layernorm_output,
+                                          attention_mask,
+                                          layer_past=layer_past,
+                                          get_key_value=get_key_value)
+        if get_key_value:
+            attention_output, presents = attention_output
+
+        # Residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            layernorm_input = layernorm_output + attention_output
+        else:
+            layernorm_input = hidden_states + attention_output
+        # Layer norm post the self attention.
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+
+        # MLP.
+        mlp_output = self.mlp(layernorm_output)
+        # Second residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            output = layernorm_output + mlp_output
+        else:
+            output = layernorm_input + mlp_output
+
+        if get_key_value:
+            output = [output, presents]
+
+        return output
+
+
+class ParallelTransformer(MegatronModule):
+    """Transformer class."""
+
+    def __init__(self, hyperparameters, attention_mask_func):
+        super(ParallelTransformer, self).__init__()
+
+        # Store activation checkpoiting flag.
+        self.checkpoint_activations = hyperparameters['checkpoint_activations']
+        self.checkpoint_num_layers = hyperparameters['checkpoint_num_layers']
+
+        def get_layer():
+            return ParallelTransformerLayer(
+                hyperparameters,
+                attention_mask_func)
+
+        # Transformer layers.
+        self.layers = torch.nn.ModuleList(
+            [get_layer() for _ in range(hyperparameters['num_layers'])])
+
+        # Final layer norm before output.
+        self.final_layernorm = LayerNorm(
+            hyperparameters['hidden_size'],
+            eps=hyperparameters['layernorm_epsilon'])
+
+
+    def _checkpointed_forward(self, hidden_states, attention_mask):
+        """Forward method with activation checkpointing."""
+        def custom(start, end):
+            def custom_forward(*inputs):
+                layers_ = self.layers[start:end]
+                x_ = inputs[0]
+                for layer in layers_:
+                    x_ = layer(x_, inputs[1])
+                return x_
+            return custom_forward
+
+        l = 0
+        num_layers = len(self.layers)
+        while l < num_layers:
+            hidden_states = mpu.checkpoint(
+                custom(l, l+self.checkpoint_num_layers),
+                hidden_states, attention_mask)
+            l += self.checkpoint_num_layers
+
+        return hidden_states
+
+
+    def forward(self, hidden_states, attention_mask, layer_past=None,
+                get_key_value=False):
+
+        # Checks
+        if layer_past is not None:
+            assert get_key_value, \
+                'for not None values in layer_past, ' \
+                'expected get_key_value to be set'
+        if get_key_value:
+            assert not self.checkpoint_activations, \
+                'get_key_value does not work with ' \
+                'activation checkpointing'
+
+        if self.checkpoint_activations:
+            hidden_states = self._checkpointed_forward(hidden_states,
+                                                       attention_mask)
+        else:
+            if get_key_value:
+                presents = []
+            for i, layer in enumerate(self.layers):
+                past = None
+                if layer_past is not None:
+                    past = layer_past[i]
+                hidden_states = layer(hidden_states,
+                                      attention_mask,
+                                      layer_past=past,
+                                      get_key_value=get_key_value)
+                if get_key_value:
+                    hidden_states, present = hidden_states
+                    presents.append(present)
+
+        # Final layer norm.
+        output = self.final_layernorm(hidden_states)
+        if get_key_value:
+            output = [output, presents]
+
+        return output

megatron/model/model.py → megatron/model/utils.py

@@ -13,21 +13,59 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Utilities for wrapping BertModel."""
+"""Utilities for models."""
+
+import math
 
 import torch
 
-from .modeling import BertConfig
-from .modeling import BertForPreTraining, BertForMaskedLM
-from .modeling import BertLayerNorm
+from .transformer import LayerNorm
 
 
-def get_params_for_weight_decay_optimization(module):
+def init_method_normal(sigma):
+    """Init method based on N(0, sigma)."""
+    def init_(tensor):
+        return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)
+
+    return init_
+
+
+def scaled_init_method_normal(sigma, num_layers):
+    """Init method based on N(0, sigma/sqrt(2*num_layers)."""
+    std = sigma / math.sqrt(2.0 * num_layers)
+    def init_(tensor):
+        return torch.nn.init.normal_(tensor, mean=0.0, std=std)
+
+    return init_
+
+
+def get_linear_layer(rows, columns, init_method):
+    """Simple linear layer with weight initialization."""
+    layer = torch.nn.Linear(rows, columns)
+    init_method(layer.weight)
+    with torch.no_grad():
+        layer.bias.zero_()
+    return layer
+
+
+@torch.jit.script
+def gelu_impl(x):
+    """OpenAI's gelu implementation."""
+    return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
+                                       (1.0 + 0.044715 * x * x)))
 
+def gelu(x):
+    return gelu_impl(x)
+
+
+def get_params_for_weight_decay_optimization(module):
+    """Divide params into with-weight-decay and without-weight-decay groups.
+    Layernorms and baises will have no weight decay but the rest will.
+    """
     weight_decay_params = {'params': []}
     no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
     for module_ in module.modules():
-        if isinstance(module_, (BertLayerNorm, torch.nn.LayerNorm)):
+        if isinstance(module_, LayerNorm):
             no_weight_decay_params['params'].extend(
                 [p for p in list(module_._parameters.values())
                  if p is not None])
@@ -40,51 +78,3 @@ def get_params_for_weight_decay_optimization(module):
                  if p is not None and n == 'bias'])
 
     return weight_decay_params, no_weight_decay_params
-
-
-class BertModel(torch.nn.Module):
-
-    def __init__(self, args):
-        super(BertModel, self).__init__()
-        if args.pretrained_bert:
-            self.model = BertForPreTraining.from_pretrained(
-                args.tokenizer_model_type,
-                cache_dir=args.cache_dir,
-                fp32_layernorm=args.fp32_layernorm,
-                fp32_embedding=args.fp32_embedding,
-                layernorm_epsilon=args.layernorm_epsilon)
-        else:
-            if args.intermediate_size is None:
-                intermediate_size = 4 * args.hidden_size
-            else:
-                intermediate_size = args.intermediate_size
-            self.config = BertConfig(
-                args.tokenizer_num_tokens,
-                hidden_size=args.hidden_size,
-                num_hidden_layers=args.num_layers,
-                num_attention_heads=args.num_attention_heads,
-                intermediate_size=intermediate_size,
-                hidden_dropout_prob=args.hidden_dropout,
-                attention_probs_dropout_prob=args.attention_dropout,
-                max_position_embeddings=args.max_position_embeddings,
-                type_vocab_size=args.tokenizer_num_type_tokens,
-                fp32_layernorm=args.fp32_layernorm,
-                fp32_embedding=args.fp32_embedding,
-                fp32_tokentypes=args.fp32_tokentypes,
-                layernorm_epsilon=args.layernorm_epsilon,
-                deep_init=args.deep_init)
-            self.model = BertForPreTraining(self.config)
-
-    def forward(self, input_tokens, token_type_ids=None,
-                attention_mask=None, checkpoint_activations=False):
-        return self.model(
-            input_tokens, token_type_ids, attention_mask,
-            checkpoint_activations=checkpoint_activations)
-
-    def state_dict(self, destination=None, prefix='', keep_vars=False):
-        return self.model.state_dict(destination=destination, prefix=prefix,
-                                     keep_vars=keep_vars)
-
-    def load_state_dict(self, state_dict, strict=True):
-        return self.model.load_state_dict(state_dict, strict=strict)
-

megatron/module.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+
+"""Megatron Module"""
+
+import torch
+
+
+class MegatronModule(torch.nn.Module):
+    """Megatron specific extentions of torch Module."""
+
+
+    def __init__(self):
+        super(MegatronModule, self).__init__()
+
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        """Use this function to override the state dict for
+        saving checkpoints."""
+        return self.state_dict(destination, prefix, keep_vars)

megatron/mpu/__init__.py

@@ -46,7 +46,5 @@ from .random import checkpoint
 from .random import get_cuda_rng_tracker
 from .random import model_parallel_cuda_manual_seed
 
-from .transformer import BertParallelSelfAttention
-from .transformer import BertParallelTransformerLayer
-from .transformer import GPT2ParallelTransformer
-from .transformer import LayerNorm
+from .utils import divide
+from .utils import split_tensor_along_last_dim

megatron/training.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+"""Pretrain utilities"""
+
+from datetime import datetime
+import math
+
+import torch
+from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
+from apex.optimizers import FusedAdam as Adam
+
+from arguments import get_args
+from megatron import mpu
+from megatron.fp16 import FP16_Module
+from megatron.fp16 import FP16_Optimizer
+from megatron.learning_rates import AnnealingLR
+from megatron.model import DistributedDataParallel as LocalDDP
+from megatron.model import get_params_for_weight_decay_optimization
+from megatron.utils import check_adlr_autoresume_termination
+from megatron.utils import enable_adlr_autoresume
+from megatron.utils import get_tensorboard_writer
+from megatron.utils import initialize_distributed
+from megatron.utils import load_checkpoint
+from megatron.utils import print_args
+from megatron.utils import print_rank_0
+from megatron.utils import report_memory
+from megatron.utils import save_checkpoint
+from megatron.utils import set_random_seed
+from megatron.utils import Timers
+
+
+def run(top_level_message, train_val_test_data_provider,
+        model_provider, forward_step_func):
+    """Main training program.
+
+    This function will run the followings in the order provided:
+        1) get input arguments.
+        2) initialize distributed and seeds.
+        3) call train_val_test_data_provider to get train/val/test datasets.
+        4) setup model, optimizer and lr schedule using the model_provider.
+        5) train the modle using the forward_step_func.
+
+    Arguments:
+        top_level_message: a meesage to print at the top of the run.
+        train_val_test_data_provider: a function that takes `args` as input
+            and returns `train, val, test` dataloaders. Note that args are
+            passed and can be modified in case we need to use some parameters
+            later. For example, we can set vocab size using
+                args.vocab_size = ...
+            and later use this value in `model_provider`.
+        model_provider: a function that takes `args` and returns a vanilla
+            version of the model. By vanilla we mean a simple model on cpu
+            with no fp16 or ddp.
+        forward_step_func: a function that takes a `data iterator`, `model`,
+            `args`, and `timers` and returns a `loss` scalar with a dictionary
+            with key:values being the info we would like to monitor during
+            training, for example `lm-loss: value`. We also require that this
+            function add `batch generator` to the timers class.
+    """
+
+    # Timer.
+    timers = Timers()
+
+    # Arguments.
+    args = get_args()
+
+    # Tensorboard writer
+    writer = get_tensorboard_writer(args)
+
+    # Pytorch distributed.
+    initialize_distributed(args)
+    if torch.distributed.get_rank() == 0:
+        print(top_level_message, flush=True)
+        print_args(args, writer)
+
+    # Autoresume.
+    torch.distributed.barrier()
+    if args.adlr_autoresume:
+        enable_adlr_autoresume(args)
+
+    # Random seeds for reproducability.
+    set_random_seed(args.seed)
+
+    # Data stuff.
+    train_data, val_data, test_data = train_val_test_data_provider(args)
+
+    # Model, optimizer, and learning rate.
+    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider,
+                                                               args)
+
+    # Train, validation, and test data.
+    train_data_iterator, val_data_iterator, \
+        test_data_iterator = get_train_val_test_data_iterators(train_data,
+                                                               val_data,
+                                                               test_data,
+                                                               args)
+
+    iteration = 0
+    if args.train_iters > 0:
+        if args.do_train:
+            iteration, _ = train(forward_step_func, model,
+                                 optimizer, lr_scheduler,
+                                 train_data_iterator, val_data_iterator,
+                                 timers, args, writer)
+
+    if args.do_valid:
+        prefix = 'the end of training for val data'
+        evaluate_and_print_results(prefix, forward_step_func,
+                                   val_data_iterator, model,
+                                   args, writer, iteration,
+                                   timers, False)
+
+    if args.save and iteration != 0:
+        save_checkpoint(iteration, model, optimizer,
+                        lr_scheduler, args)
+
+    if args.do_test:
+        # Run on test data.
+        prefix = 'the end of training for test data'
+        evaluate_and_print_results(prefix, forward_step_func,
+                                   test_data_iterator, model,
+                                   args, None, 0, timers, True)
+
+
+def get_model(model_provider_func, args):
+    """Build the model."""
+
+    # Build model on cpu.
+    model = model_provider_func(args)
+
+    # Print number of parameters.
+    if mpu.get_data_parallel_rank() == 0:
+        print(' > number of parameters on model parallel rank {}: {}'.format(
+            mpu.get_model_parallel_rank(),
+            sum([p.nelement() for p in model.parameters()])), flush=True)
+
+    # GPU allocation.
+    model.cuda(torch.cuda.current_device())
+
+    # Fp16 conversion.
+    if args.fp16:
+        model = FP16_Module(model)
+
+    # Wrap model for distributed training."""
+    if args.DDP_impl == 'torch':
+        i = torch.cuda.current_device()
+        args.DDP_type = torchDDP
+        model = args.DDP_type(model, device_ids=[i], output_device=i,
+                              process_group=mpu.get_data_parallel_group())
+        return model
+    if args.DDP_impl == 'local':
+        args.DDP_type = LocalDDP
+        model = args.DDP_type(model)
+        return model
+
+    print_rank_0('Unknown DDP implementation specified: {}. '
+                 'Exiting.'.format(args.DDP_impl))
+    exit()
+    return model
+
+
+def get_optimizer(model, args):
+    """Set up the optimizer."""
+
+    # Build parameter groups (weight decay and non-decay).
+    while isinstance(model, (args.DDP_type, FP16_Module)):
+        model = model.module
+    param_groups = get_params_for_weight_decay_optimization(model)
+
+    # Add model parallel attribute if it is not set.
+    for param_group in param_groups:
+        for param in param_group['params']:
+            if not hasattr(param, 'model_parallel'):
+                param.model_parallel = False
+
+    # Use Adam.
+    optimizer = Adam(param_groups,
+                     lr=args.lr, weight_decay=args.weight_decay)
+
+    # Wrap into fp16 optimizer.
+    if args.fp16:
+        optimizer = FP16_Optimizer(optimizer,
+                                   static_loss_scale=args.loss_scale,
+                                   dynamic_loss_scale=args.dynamic_loss_scale,
+                                   dynamic_loss_args={
+                                       'scale_window': args.loss_scale_window,
+                                       'min_scale':args.min_scale,
+                                       'delayed_shift': args.hysteresis})
+
+    return optimizer
+
+
+def get_learning_rate_scheduler(optimizer, args):
+    """Build the learning rate scheduler."""
+
+    # Add linear learning rate scheduler.
+    if args.lr_decay_iters is not None:
+        num_iters = args.lr_decay_iters
+    else:
+        num_iters = args.train_iters
+    num_iters = max(1, num_iters)
+    init_step = -1
+    warmup_iter = args.warmup * num_iters
+    lr_scheduler = AnnealingLR(
+        optimizer,
+        start_lr=args.lr,
+        warmup_iter=warmup_iter,
+        num_iters=num_iters,
+        decay_style=args.lr_decay_style,
+        last_iter=init_step,
+        min_lr=args.min_lr,
+        use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler,
+        override_lr_scheduler=args.override_lr_scheduler)
+
+    return lr_scheduler
+
+
+def setup_model_and_optimizer(model_provider_func, args):
+    """Setup model and optimizer."""
+
+    model = get_model(model_provider_func, args)
+    optimizer = get_optimizer(model, args)
+    lr_scheduler = get_learning_rate_scheduler(optimizer, args)
+
+    if args.load is not None:
+        args.iteration = load_checkpoint(model, optimizer, lr_scheduler, args)
+    else:
+        args.iteration = 0
+
+    return model, optimizer, lr_scheduler
+
+
+def backward_step(optimizer, model, loss, args, timers):
+    """Backward step."""
+
+    # Backward pass.
+    optimizer.zero_grad()
+    if args.fp16:
+        optimizer.backward(loss, update_master_grads=False)
+    else:
+        loss.backward()
+
+    # All-reduce if needed.
+    if args.DDP_impl == 'local':
+        timers('allreduce').start()
+        model.allreduce_params(reduce_after=False,
+                               fp32_allreduce=args.fp32_allreduce)
+        timers('allreduce').stop()
+
+    # Update master gradients.
+    if args.fp16:
+        optimizer.update_master_grads()
+
+    # Clipping gradients helps prevent the exploding gradient.
+    if args.clip_grad > 0:
+        if not args.fp16:
+            mpu.clip_grad_norm(model.parameters(), args.clip_grad)
+        else:
+            optimizer.clip_master_grads(args.clip_grad)
+
+
+def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler,
+               args, timers):
+    """Single training step."""
+
+    # Forward model for one step.
+    timers('forward').start()
+    loss, loss_reduced = forward_step_func(data_iterator, model, args, timers)
+    timers('forward').stop()
+
+    # Calculate gradients, reduce across processes, and clip.
+    timers('backward').start()
+    backward_step(optimizer, model, loss, args, timers)
+    timers('backward').stop()
+
+    # Update parameters.
+    timers('optimizer').start()
+    optimizer.step()
+    timers('optimizer').stop()
+
+    # Update learning rate.
+    skipped_iter = 0
+    if not (args.fp16 and optimizer.overflow):
+        lr_scheduler.step()
+    else:
+        skipped_iter = 1
+
+    return loss_reduced, skipped_iter
+
+
+def train(forward_step_func, model, optimizer, lr_scheduler,
+          train_data_iterator, val_data_iterator, timers, args, writer):
+    """Train the model function."""
+
+    # Turn on training mode which enables dropout.
+    model.train()
+
+    # Tracking loss.
+    total_loss_dict = {}
+
+    # Iterations.
+    iteration = args.iteration
+    skipped_iters = 0
+
+    timers('interval time').start()
+    report_memory_flag = True
+    while iteration < args.train_iters:
+
+        loss_dict, skipped_iter = train_step(forward_step_func,
+                                             train_data_iterator,
+                                             model,
+                                             optimizer,
+                                             lr_scheduler,
+                                             args, timers)
+        skipped_iters += skipped_iter
+        iteration += 1
+
+        # Update losses.
+        for key in loss_dict:
+            total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key]
+
+        # Logging.
+        if args.DDP_impl == 'torch':
+            timers_to_log = ['forward', 'backward', 'optimizer',
+                             'batch generator']
+        else:
+            timers_to_log = ['forward', 'backward', 'allreduce', 'optimizer',
+                             'batch generator']
+
+        learning_rate = optimizer.param_groups[0]['lr']
+
+        if writer and torch.distributed.get_rank() == 0:
+            writer.add_scalar('learning_rate', learning_rate, iteration)
+            for key in total_loss_dict:
+                writer.add_scalar(key, total_loss_dict[key], iteration)
+            if args.fp16:
+                writer.add_scalar('loss_scale', optimizer.loss_scale, iteration)
+            normalizer = iteration % args.log_interval
+            if normalizer == 0:
+                normalizer = args.log_interval
+            timers.write(timers_to_log, writer, iteration,
+                         normalizer=normalizer)
+
+        if iteration % args.log_interval == 0:
+            elapsed_time = timers('interval time').elapsed()
+            if writer and torch.distributed.get_rank() == 0:
+                writer.add_scalar('iteration_time',
+                                  elapsed_time / args.log_interval, iteration)
+            log_string = ' iteration {:8d}/{:8d} |'.format(iteration,
+                                                           args.train_iters)
+            log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
+                elapsed_time * 1000.0 / args.log_interval)
+            log_string += ' learning rate: {:.3E} |'.format(learning_rate)
+            for key in total_loss_dict:
+                avg = total_loss_dict[key].item() / args.log_interval
+                log_string += ' {}: {:.6E} |'.format(key, avg)
+                total_loss_dict[key] = 0.0
+            if args.fp16:
+                log_string += ' loss scale: {:.1f} |'.format(
+                    optimizer.loss_scale)
+            print_rank_0(log_string)
+            if report_memory_flag:
+                report_memory('after {} iterations'.format(iteration))
+                report_memory_flag = False
+            timers.log(timers_to_log, normalizer=args.log_interval)
+
+        # Autoresume
+        if (iteration % args.adlr_autoresume_interval == 0) and \
+           args.adlr_autoresume:
+            check_adlr_autoresume_termination(iteration, model, optimizer,
+                                              lr_scheduler, args)
+
+        # Checkpointing
+        if args.save and args.save_interval and \
+           iteration % args.save_interval == 0:
+            save_checkpoint(iteration, model, optimizer, lr_scheduler, args)
+
+        # Evaluation
+        if args.eval_interval and iteration % args.eval_interval == 0 and \
+           args.do_valid:
+            prefix = 'iteration {}'.format(iteration)
+            evaluate_and_print_results(prefix, forward_step_func,
+                                       val_data_iterator, model, args,
+                                       writer, iteration, timers, False)
+
+        if args.exit_interval and iteration % args.exit_interval == 0:
+            torch.distributed.barrier()
+            time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+            rank = torch.distributed.get_rank()
+            print('rank: {} | time: {} | exiting the program at iteration {}'.
+                  format(rank, time_str, iteration), flush=True)
+            exit()
+
+    return iteration, skipped_iters
+
+
+def evaluate(forward_step_func, data_iterator, model,
+             args, timers, verbose=False):
+    """Evaluation."""
+
+    # Turn on evaluation mode which disables dropout.
+    model.eval()
+
+    total_loss_dict = {}
+
+    with torch.no_grad():
+        iteration = 0
+        while iteration < args.eval_iters:
+            iteration += 1
+            if verbose and iteration % args.log_interval == 0:
+                print_rank_0('Evaluating iter {}/{}'.format(iteration,
+                                                            args.eval_iters))
+            # Forward evaluation.
+            _, loss_dict = forward_step_func(data_iterator, model,
+                                             args, timers)
+            # Reduce across processes.
+            for key in loss_dict:
+                total_loss_dict[key] = total_loss_dict.get(key, 0.) + \
+                                       loss_dict[key]
+    # Move model back to the train mode.
+    model.train()
+
+    for key in total_loss_dict:
+        total_loss_dict[key] /= args.eval_iters
+
+    return total_loss_dict
+
+
+def evaluate_and_print_results(prefix, forward_step_func,
+                               data_iterator, model,
+                               args, writer, iteration,
+                               timers, verbose=False):
+    """Helper function to evaluate and dump results on screen."""
+    total_loss_dict = evaluate(forward_step_func, data_iterator, model,
+                               args, timers, verbose)
+    string = ' validation loss at {} | '.format(prefix)
+    for key in total_loss_dict:
+        string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())
+        ppl = math.exp(min(20, total_loss_dict[key].item()))
+        string += '{} PPL: {:.6E} | '.format(key, ppl)
+        if writer and torch.distributed.get_rank() == 0:
+            writer.add_scalar('{} value'.format(key),
+                              total_loss_dict[key].item(),
+                              iteration)
+            writer.add_scalar('{} ppl'.format(key), ppl, iteration)
+
+    length = len(string) + 1
+    print_rank_0('-' * length)
+    print_rank_0(string)
+    print_rank_0('-' * length)
+
+
+def get_train_val_test_data_iterators(train_data, val_data, test_data, args):
+    """Build train/validation/test iterators"""
+
+    # If resume is on, shift the start iterations.
+    if args.resume_dataloader:
+        if train_data is not None:
+            train_data.batch_sampler.start_iter = args.iteration % \
+                                                  len(train_data)
+            print_rank_0('setting training data start iteration to {}'.
+                         format(train_data.batch_sampler.start_iter))
+        if val_data is not None:
+            start_iter_val = (args.iteration // args.eval_interval) * \
+                             args.eval_iters
+            val_data.batch_sampler.start_iter = start_iter_val % \
+                                                len(val_data)
+            print_rank_0('setting validation data start iteration to {}'.
+                         format(val_data.batch_sampler.start_iter))
+
+    if train_data is not None:
+        train_data_iterator = iter(train_data)
+    else:
+        train_data_iterator = None
+
+    if val_data is not None:
+        val_data_iterator = iter(val_data)
+    else:
+        val_data_iterator = None
+
+    if test_data is not None:
+        test_data_iterator = iter(test_data)
+    else:
+        test_data_iterator = None
+
+    return train_data_iterator, val_data_iterator, test_data_iterator

megatron/utils.py

@@ -20,12 +20,29 @@ import random
 import time
 import numpy as np
 import torch
-
 from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
-from megatron.model import DistributedDataParallel as LocalDDP
-from megatron.fp16 import FP16_Optimizer
+
+from apex.optimizers import FusedAdam as Adam
+
 from megatron import mpu
-from megatron import model
+from megatron.fp16 import FP16_Module
+from megatron.fp16 import FP16_Optimizer
+from megatron.model import DistributedDataParallel as LocalDDP
+from megatron.model import get_params_for_weight_decay_optimization
+
+
+def get_tensorboard_writer(args):
+    writer = None
+    if args.tensorboard_dir and args.rank == 0:
+        try:
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter(log_dir=args.tensorboard_dir)
+        except ModuleNotFoundError:
+            print_rank_0('WARNING: TensorBoard writing requested but is not '
+                         'available (are you using PyTorch 1.1.0 or later?), '
+                         'no TensorBoard logs will be written.')
+            writer = None
+    return writer
 
 
 def print_rank_0(message):
@@ -39,12 +56,12 @@ def print_rank_0(message):
 def enable_adlr_autoresume(args):
     print_rank_0('enabling autoresume ...')
     import sys
-    sys.path.append(os.environ.get('SUBMIT_SCRIPTS','.'))
+    sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.'))
     try:
         from userlib.auto_resume import AutoResume
     except:
         print_rank_0('ADLR autoresume is not available, exiting ...')
-        exit(0)
+        exit()
     args.AutoResume = AutoResume
     args.AutoResume.init()
 
@@ -74,6 +91,7 @@ def print_args(args, writer=None):
         if writer:
             writer.add_text(arg, str(getattr(args, arg)))
 
+
 def print_params_min_max_norm(optimizer, iteration):
     """Print min, max, and norm of all parameters."""
     index = 0
@@ -220,24 +238,6 @@ def initialize_distributed(args):
     mpu.initialize_model_parallel(args.model_parallel_size)
 
 
-def wrap_model_for_distributed_training(model, args):
-    """Wrap model for distributed training."""
-    if args.DDP_impl == 'torch':
-        i = torch.cuda.current_device()
-        args.DDP_type = torchDDP
-        model = args.DDP_type(model, device_ids=[i], output_device=i,
-                              process_group=mpu.get_data_parallel_group())
-        return model
-    elif args.DDP_impl == 'local':
-        args.DDP_type = LocalDDP
-        model = args.DDP_type(model)
-        return model
-    else:
-        print_rank_0('Unknown DDP implementation specified: {}. '
-                     'Exiting.'.format(args.DDP_impl))
-        exit()
-
-
 def set_random_seed(seed):
     """Set random seed for reproducability."""
 
@@ -284,7 +284,7 @@ def save_checkpoint(iteration, model, optimizer,
 
         sd = {}
         sd['iteration'] = iteration
-        sd['model'] = model.state_dict()
+        sd['model'] = model.state_dict_for_save_checkpoint()
 
         # Optimizer stuff.
         if not args.no_save_optim:
@@ -378,7 +378,6 @@ def load_checkpoint(model, optimizer, lr_scheduler, args):
                 print_rank_0('A metadata file exists but Unable to load iteration '
                              ' from checkpoint {}, exiting'.format(checkpoint_name))
                 exit()
-
     # Model.
     try:
         model.load_state_dict(sd['model'])
@@ -410,7 +409,7 @@ def load_checkpoint(model, optimizer, lr_scheduler, args):
             torch.cuda.set_rng_state(sd['cuda_rng_state'])
             mpu.get_cuda_rng_tracker().set_states(sd['rng_tracker_states'])
         except KeyError:
-            print_rank_0('Unable to load optimizer from checkpoint {}, exiting. '
+            print_rank_0('Unable to load optimizer from checkpoint {}, exiting.'
                          'Specify --no-load-optim or --finetune to prevent '
                          'attempting to load the optimizer '
                          'state.'.format(checkpoint_name))
@@ -422,6 +421,7 @@ def load_checkpoint(model, optimizer, lr_scheduler, args):
 
     return iteration
 
+
 def load_weights(src, dst, dst2src=False):
     """
     Loads weights from src to dst via in place copy.