Merging megatron with ICT

megatron/model/biencoder_model.py

@@ -3,17 +3,17 @@ import torch
 import sys
 
 from megatron import get_args, print_rank_0
-from megatron.checkpointing import get_checkpoint_tracker_filename, get_checkpoint_name
-from megatron.module import MegatronModule
+from megatron.checkpointing import fix_query_key_value_ordering
+from megatron.checkpointing import get_checkpoint_tracker_filename
+from megatron.checkpointing import get_checkpoint_name
 from megatron import mpu, get_tokenizer
-from megatron.model.bert_model import bert_attention_mask_func
-from megatron.model.bert_model import bert_extended_attention_mask
 from megatron.model.bert_model import bert_position_ids
+from megatron.model.enums import AttnMaskType
 from megatron.model.language_model import get_language_model
 from megatron.model.utils import get_linear_layer
 from megatron.model.utils import init_method_normal
 from megatron.model.utils import scaled_init_method_normal
-
+from .module import MegatronModule
 
 def biencoder_model_provider(only_query_model=False,
                              only_context_model=False,
@@ -165,16 +165,32 @@ class BiEncoderModel(MegatronModule):
             print('global rank {} is loading BERT checkpoint {}'.format(
                 torch.distributed.get_rank(), checkpoint_name))
 
+        # Load the checkpoint.
         try:
             state_dict = torch.load(checkpoint_name, map_location='cpu')
+        except ModuleNotFoundError:
+            from megatron.fp16_deprecated import loss_scaler
+            # For backward compatibility.
+            print_rank_0(' > deserializing using the old code structure ...')
+            sys.modules['fp16.loss_scaler'] = sys.modules[
+                'megatron.fp16_deprecated.loss_scaler']
+            sys.modules['megatron.fp16.loss_scaler'] = sys.modules[
+                'megatron.fp16_deprecated.loss_scaler']
+            state_dict = torch.load(checkpoint_name, map_location='cpu')
+            sys.modules.pop('fp16.loss_scaler', None)
+            sys.modules.pop('megatron.fp16.loss_scaler', None)
         except BaseException:
-            raise ValueError("Could not load BERT checkpoint")
+            print_rank_0('could not load the BERT checkpoint')
+            sys.exit()
+
+        checkpoint_version = state_dict.get('checkpoint_version', 0)
 
         # load the LM state dict into each model
         model_dict = state_dict['model']['language_model']
 
         if self.shared_query_context_model:
             self.model.language_model.load_state_dict(model_dict)
+            fix_query_key_value_ordering(self.model, checkpoint_version)
         else:
             if self.use_query_model:
                 self.query_model.language_model.load_state_dict(model_dict)
@@ -183,11 +199,14 @@ class BiEncoderModel(MegatronModule):
                     query_proj_state_dict = \
                         self.state_dict_for_save_checkpoint()\
                         [self._query_key]['projection_enc']
+                fix_query_key_value_ordering(self.query_model, checkpoint_version)
+
             if self.use_context_model:
                 self.context_model.language_model.load_state_dict(model_dict)
                 if self.query_model is not None and self.projection_dim > 0:
                     self.context_model.projection_enc.load_state_dict\
                         (query_proj_state_dict)
+                fix_query_key_value_ordering(self.context_model, checkpoint_version)
 
 
 class PretrainedBertModel(MegatronModule):
@@ -209,9 +228,9 @@ class PretrainedBertModel(MegatronModule):
             args.init_method_std, args.num_layers)
 
         self.language_model, self._language_model_key = get_language_model(
-            attention_mask_func=bert_attention_mask_func,
             num_tokentypes=num_tokentypes,
             add_pooler=False,
+            encoder_attn_mask_type=AttnMaskType.padding,
             init_method=init_method,
             scaled_init_method=scaled_init_method)
 

megatron/model/classification.py

@@ -19,15 +19,16 @@ import torch
 
 from megatron import get_args, print_rank_last
 from megatron import mpu
-from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids
+from megatron.model.enums import AttnMaskType
+from megatron.model.bert_model import bert_extended_attention_mask, bert_position_ids
 from megatron.model.language_model import get_language_model
 from megatron.model.utils import get_linear_layer
 from megatron.model.utils import init_method_normal
 from megatron.model.utils import scaled_init_method_normal
-from megatron.module import PipelinedMegatronModule
+from .module import MegatronModule
 
 
-class ClassificationBase(PipelinedMegatronModule):
+class ClassificationBase(MegatronModule):
 
     def __init__(self, num_classes, num_tokentypes=2):
         super(ClassificationBase, self).__init__(share_word_embeddings=False)
@@ -37,9 +38,9 @@ class ClassificationBase(PipelinedMegatronModule):
         init_method = init_method_normal(args.init_method_std)
 
         self.language_model, self._language_model_key = get_language_model(
-            attention_mask_func=bert_attention_mask_func,
             num_tokentypes=num_tokentypes,
             add_pooler=True,
+            encoder_attn_mask_type=AttnMaskType.padding,
             init_method=init_method,
             scaled_init_method=scaled_init_method_normal(args.init_method_std,
                                                          args.num_layers))

megatron/model/distributed.py

@@ -20,7 +20,7 @@ from torch.nn.modules import Module
 from torch.autograd import Variable
 
 from megatron import mpu
-from megatron.module import MegatronModule
+from .module import MegatronModule
 
 
 class DistributedDataParallel(MegatronModule):

megatron/fp16/__init__.py → megatron/model/enums.py

@@ -12,19 +12,17 @@
 # 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.
-from .fp16util import (
-    BN_convert_float,
-    network_to_half,
-    prep_param_lists,
-    model_grads_to_master_grads,
-    master_params_to_model_params,
-    tofp16,
-    to_python_float,
-    clip_grad_norm,
-    convert_module,
-    convert_network,
-    FP16Model,
-)
 
-from .fp16 import *
-from .loss_scaler import *
+import enum
+
+class LayerType(enum.Enum):
+    encoder = 1
+    decoder = 2
+ 
+class AttnType(enum.Enum):
+    self_attn = 1
+    cross_attn = 2
+
+class AttnMaskType(enum.Enum):
+    padding = 1
+    causal = 2

megatron/model/fused_softmax.py

@@ -14,103 +14,127 @@
 # limitations under the License.
 
 import torch
+from megatron.model.enums import AttnMaskType
 
-class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function) :
+
+class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
     """
     Fused operation which performs following three operations in sequence
     1. Scale the tensor.
     2. Apply upper triangular mask (typically used in gpt models).
     3. Perform softmax.
     """
+
     @staticmethod
     def forward(ctx, inputs, scale):
         import scaled_upper_triang_masked_softmax_cuda
+
         scale_t = torch.tensor([scale])
 
-        softmax_results =  \
-            scaled_upper_triang_masked_softmax_cuda.forward(inputs, scale_t[0])
+        softmax_results = scaled_upper_triang_masked_softmax_cuda.forward(
+            inputs, scale_t[0]
+        )
         ctx.save_for_backward(softmax_results, scale_t)
         return softmax_results
 
     @staticmethod
     def backward(ctx, output_grads):
         import scaled_upper_triang_masked_softmax_cuda
+
         softmax_results, scale_t = ctx.saved_tensors
 
-        input_grads =   \
-            scaled_upper_triang_masked_softmax_cuda.backward(output_grads,                             
-                                                 softmax_results,                          
-                                                 scale_t[0])
+        input_grads = scaled_upper_triang_masked_softmax_cuda.backward(
+            output_grads, softmax_results, scale_t[0]
+        )
         return input_grads, None
 
-class ScaledMaskedSoftmax(torch.autograd.Function) :
+
+class ScaledMaskedSoftmax(torch.autograd.Function):
     """
     Fused operation which performs following three operations in sequence
     1. Scale the tensor.
     2. Apply the mask.
     3. Perform softmax.
     """
+
     @staticmethod
     def forward(ctx, inputs, mask, scale):
         import scaled_masked_softmax_cuda
+
         scale_t = torch.tensor([scale])
 
-        softmax_results =  \
-            scaled_masked_softmax_cuda.forward(inputs, mask, scale_t[0])
+        softmax_results = scaled_masked_softmax_cuda.forward(
+            inputs, mask, scale_t[0]
+        )
         ctx.save_for_backward(softmax_results, scale_t)
         return softmax_results
 
     @staticmethod
     def backward(ctx, output_grads):
         import scaled_masked_softmax_cuda
+
         softmax_results, scale_t = ctx.saved_tensors
 
-        input_grads =   \
-            scaled_masked_softmax_cuda.backward(output_grads,
-                                                softmax_results,
-                                                scale_t[0])
+        input_grads = scaled_masked_softmax_cuda.backward(
+            output_grads, softmax_results, scale_t[0]
+        )
         return input_grads, None, None
 
+
 class FusedScaleMaskSoftmax(torch.nn.Module):
     """
     fused operation: scaling + mask + softmax
     Arguments:
         input_in_fp16: flag to indicate if input in fp16 data format.
-           upper_triang_mask: if true, apply upper triangular masking.
-                              (used in gpt family networks)
+        attn_mask_type: attention mask type (pad or causal)
         mask_func: mask function to be applied.
         softmax_in_fp32: if true, softmax in performed at fp32 precision.
         scale: scaling factor used in input tensor scaling.
 
     """
-    def __init__(self, input_in_fp16, upper_triang_mask_fusion, 
-                 general_mask_fusion, mask_func, softmax_in_fp32, scale):
+
+    def __init__(
+        self,
+        input_in_fp16,
+        attn_mask_type,
+        scaled_masked_softmax_fusion,
+        mask_func,
+        softmax_in_fp32,
+        scale,
+    ):
         super(FusedScaleMaskSoftmax, self).__init__()
         self.input_in_fp16 = input_in_fp16
-        self.upper_triang_mask_fusion = upper_triang_mask_fusion
-        self.general_mask_fusion = general_mask_fusion
+        self.attn_mask_type = attn_mask_type
+        self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
         self.mask_func = mask_func
         self.softmax_in_fp32 = softmax_in_fp32
         self.scale = scale
 
-        assert self.scale is None or softmax_in_fp32, \
-            'softmax should be in fp32 when scaled'
+        assert (
+            self.scale is None or softmax_in_fp32
+        ), "softmax should be in fp32 when scaled"
 
     def forward(self, input, mask):
-        # [b, np, s, s]
+        # [b, np, sq, sk]
         data_size = input.size()
+        query_seq_len = data_size[-2]
+        key_seq_len = data_size[-1]
         assert input.dim() == 4
 
         # invoke custom kernel
-        if self.input_in_fp16 and data_size[-1] <= 2048 and \
-            (self.upper_triang_mask_fusion or self.general_mask_fusion) and \
-            input.size()[2] == input.size()[3]:
+        if self.input_in_fp16 and key_seq_len <= 2048 and mask is not None and \
+           query_seq_len % 4 == 0 and self.scaled_masked_softmax_fusion:
+
             scale = self.scale if self.scale is not None else 1.0
-            if self.upper_triang_mask_fusion:
-                input = input.view(-1, data_size[2], data_size[3])
+
+            if self.attn_mask_type == AttnMaskType.causal:
+                assert query_seq_len == key_seq_len, \
+                    "causal mask is only for self attention"
+                input = input.view(-1, query_seq_len, key_seq_len)
                 probs = ScaledUpperTriangMaskedSoftmax.apply(input, scale)
                 probs = probs.view(*data_size)
             else:
+                assert self.attn_mask_type == AttnMaskType.padding
                 probs = ScaledMaskedSoftmax.apply(input, mask, scale)
         else:
             if self.input_in_fp16 and self.softmax_in_fp32:
@@ -118,7 +142,7 @@ class FusedScaleMaskSoftmax(torch.nn.Module):
 
             if self.scale is not None:
                 input = input * self.scale
-            mask_output = self.mask_func(input, mask)
+            mask_output = self.mask_func(input, mask) if mask is not None else input
             probs = torch.nn.Softmax(dim=-1)(mask_output)
 
             if self.input_in_fp16 and self.softmax_in_fp32:

megatron/model/gpt2_model.py → megatron/model/gpt_model.py

@@ -19,19 +19,15 @@ import torch
 
 from megatron import get_args
 from megatron import mpu
-from megatron.module import PipelinedMegatronModule
+from .module import MegatronModule
 
+from .enums import AttnMaskType
 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.masked_fill_(ltor_mask, -10000.0)
-    return attention_scores
-
-
 def post_language_model_processing(lm_output, labels, logit_weights,
                                    get_key_value, parallel_output,
                                    forward_method_parallel_output,
@@ -61,37 +57,37 @@ def post_language_model_processing(lm_output, labels, logit_weights,
         return loss
 
 
-class GPT2ModelBase(PipelinedMegatronModule):
+class GPTModelBase(MegatronModule):
     """GPT-2 Language model."""
 
     def __init__(self, num_tokentypes=0, parallel_output=True):
-        super(GPT2ModelBase, self).__init__()
+        super(GPTModelBase, self).__init__()
         args = get_args()
 
         self.parallel_output = parallel_output
         self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
 
         self.language_model, self._language_model_key = get_language_model(
-            attention_mask_func=gpt2_attention_mask_func,
             num_tokentypes=num_tokentypes,
             add_pooler=False,
+            encoder_attn_mask_type=AttnMaskType.causal,
             init_method=init_method_normal(args.init_method_std),
             scaled_init_method=scaled_init_method_normal(args.init_method_std,
                                                          args.num_layers))
 
         self.initialize_word_embeddings(init_method_normal)
 
-    def forward(self, gpt2_model_input, attention_mask, labels=None,
+    def forward(self, gpt_model_input, attention_mask, labels=None,
                 tokentype_ids=None, layer_past=None, get_key_value=False,
                 forward_method_parallel_output=None):
 
         kwargs = {'layer_past': layer_past, 'get_key_value': get_key_value}
         if mpu.is_pipeline_first_stage():
-            (input_ids, position_ids) = gpt2_model_input
+            (input_ids, position_ids) = gpt_model_input
             args = [input_ids, position_ids, attention_mask]
             kwargs['tokentype_ids'] = tokentype_ids
         else:
-            args = [gpt2_model_input, attention_mask]
+            args = [gpt_model_input, attention_mask]
         lm_output = self.language_model(*args, **kwargs)
 
         if mpu.is_pipeline_last_stage():
@@ -130,17 +126,17 @@ class GPT2ModelBase(PipelinedMegatronModule):
         self.language_model.load_state_dict(state_dict, strict=strict)
 
 
-class GPT2Model(GPT2ModelBase):
+class GPTModel(GPTModelBase):
 
     def __init__(self, num_tokentypes=0, parallel_output=True):
-        super(GPT2Model, self).__init__(
+        super(GPTModel, self).__init__(
             num_tokentypes=num_tokentypes,
             parallel_output=parallel_output)
 
     def forward(self, input_ids, position_ids, attention_mask, labels=None,
                 tokentype_ids=None, layer_past=None, get_key_value=False,
                 forward_method_parallel_output=None):
-        return super(GPT2Model, self).forward(
+        return super(GPTModel, self).forward(
             (input_ids, position_ids),
             attention_mask,
             labels=labels,
@@ -150,15 +146,15 @@ class GPT2Model(GPT2ModelBase):
             forward_method_parallel_output=forward_method_parallel_output)
 
 
-class GPT2ModelFirstStage(GPT2ModelBase):
+class GPTModelFirstStage(GPTModelBase):
 
     def __init__(self, num_tokentypes=0):
-        super(GPT2ModelFirstStage, self).__init__(
+        super(GPTModelFirstStage, self).__init__(
             num_tokentypes=num_tokentypes)
 
     def forward(self, input_ids, position_ids, attention_mask,
                 tokentype_ids=None, layer_past=None, get_key_value=False):
-        return super(GPT2ModelFirstStage, self).forward(
+        return super(GPTModelFirstStage, self).forward(
             (input_ids, position_ids),
             attention_mask,
             tokentype_ids=tokentype_ids,
@@ -166,32 +162,32 @@ class GPT2ModelFirstStage(GPT2ModelBase):
             get_key_value=get_key_value)
 
 
-class GPT2ModelIntermediateStage(GPT2ModelBase):
+class GPTModelIntermediateStage(GPTModelBase):
 
     def __init__(self, num_tokentypes=0):
-        super(GPT2ModelIntermediateStage, self).__init__(
+        super(GPTModelIntermediateStage, self).__init__(
             num_tokentypes=num_tokentypes)
 
     def forward(self, hidden_state, attention_mask,
                 layer_past=None, get_key_value=False):
-        return super(GPT2ModelIntermediateStage, self).forward(
+        return super(GPTModelIntermediateStage, self).forward(
             hidden_state,
             attention_mask,
             layer_past=layer_past,
             get_key_value=get_key_value)
 
 
-class GPT2ModelLastStage(GPT2ModelBase):
+class GPTModelLastStage(GPTModelBase):
 
     def __init__(self, num_tokentypes=0, parallel_output=True):
-        super(GPT2ModelLastStage, self).__init__(
+        super(GPTModelLastStage, self).__init__(
             num_tokentypes=num_tokentypes,
             parallel_output=parallel_output)
 
     def forward(self, hidden_state, attention_mask, labels=None,
                 layer_past=None, get_key_value=False,
                 forward_method_parallel_output=None):
-        return super(GPT2ModelLastStage, self).forward(
+        return super(GPTModelLastStage, self).forward(
             hidden_state,
             attention_mask,
             labels=labels,

megatron/model/language_model.py

@@ -20,7 +20,8 @@ import torch.nn.functional as F
 
 from megatron import get_args
 from megatron import mpu
-from megatron.module import MegatronModule
+from .module import MegatronModule
+from megatron.model.enums import LayerType, AttnMaskType
 from megatron.model.transformer import ParallelTransformer
 from megatron.model.utils import get_linear_layer
 from megatron.model.utils import init_method_normal, scaled_init_method_normal
@@ -42,8 +43,10 @@ def parallel_lm_logits(input_, word_embeddings_weight, parallel_output,
     return mpu.gather_from_tensor_model_parallel_region(logits_parallel)
 
 
-def get_language_model(attention_mask_func, num_tokentypes, add_pooler,
-                       init_method=None, scaled_init_method=None):
+def get_language_model(num_tokentypes, add_pooler,
+                       encoder_attn_mask_type, init_method=None,
+                       scaled_init_method=None, add_decoder=False,
+                       decoder_attn_mask_type=AttnMaskType.causal):
     """Build language model and return along with the key to save."""
     args = get_args()
 
@@ -51,15 +54,18 @@ def get_language_model(attention_mask_func, num_tokentypes, add_pooler,
         init_method = init_method_normal(args.init_method_std)
 
     if scaled_init_method is None:
-        scaled_init_method = scaled_init_method_normal(args.init_method_std, args.num_layers)
+        scaled_init_method = scaled_init_method_normal(args.init_method_std,
+                                                       args.num_layers)
 
     # Language model.
-    args = [attention_mask_func, init_method, scaled_init_method]
+    args = [init_method, scaled_init_method, encoder_attn_mask_type]
     kwargs = {}
     cls = None
     if mpu.is_pipeline_first_stage() and mpu.is_pipeline_last_stage():
         cls = TransformerLanguageModel
         kwargs['num_tokentypes'] = num_tokentypes
+        kwargs['add_decoder'] = add_decoder
+        kwargs['decoder_attn_mask_type'] = decoder_attn_mask_type
         kwargs['add_pooler'] = add_pooler
     elif mpu.is_pipeline_first_stage() and not mpu.is_pipeline_last_stage():
         cls = TransformerLanguageModelFirstStage
@@ -262,12 +268,6 @@ class TransformerLanguageModelBase(MegatronModule):
 
     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
@@ -277,10 +277,12 @@ class TransformerLanguageModelBase(MegatronModule):
     """
 
     def __init__(self,
-                 attention_mask_func,
                  init_method,
                  output_layer_init_method,
+                 encoder_attn_mask_type,
                  num_tokentypes=0,
+                 add_decoder=False,
+                 decoder_attn_mask_type=AttnMaskType.causal,
                  add_pooler=False):
         super(TransformerLanguageModelBase, self).__init__()
         args = get_args()
@@ -288,6 +290,9 @@ class TransformerLanguageModelBase(MegatronModule):
         self.hidden_size = args.hidden_size
         self.num_tokentypes = num_tokentypes
         self.init_method = init_method
+        self.encoder_attn_mask_type = encoder_attn_mask_type
+        self.add_decoder = add_decoder
+        self.decoder_attn_mask_type = decoder_attn_mask_type
         self.add_pooler = add_pooler
 
         # Embeddings.
@@ -301,41 +306,83 @@ class TransformerLanguageModelBase(MegatronModule):
             self._embedding_key = 'embedding'
 
         # Transformer.
-        self.transformer = ParallelTransformer(
-            attention_mask_func, self.init_method, 
-            output_layer_init_method)
-        self._transformer_key = 'transformer'
+        self.encoder = ParallelTransformer(
+            self.init_method,
+            output_layer_init_method,
+            self_attn_mask_type=self.encoder_attn_mask_type)
+        self._encoder_key = 'encoder'
+
+        # Decoder
+        if self.add_decoder:
+            assert args.pipeline_model_parallel_size == 1, \
+                'pipeline parallelism is not supported in the presence of decoder'
+            self.decoder = ParallelTransformer(
+                self.init_method,
+                output_layer_init_method,
+                layer_type=LayerType.decoder,
+                self_attn_mask_type=self.decoder_attn_mask_type)
+            self._decoder_key = 'decoder'
 
+        if mpu.is_pipeline_last_stage():
             # Pooler.
-        if mpu.is_pipeline_last_stage() and self.add_pooler:
+            if self.add_pooler:
                 self.pooler = Pooler(self.hidden_size, self.init_method)
                 self._pooler_key = 'pooler'
 
-    def forward(self, language_model_input, attention_mask,
-                tokentype_ids=None, layer_past=None, get_key_value=False,
-                pooling_sequence_index=0):
+    def forward(self, enc_language_model_input, enc_attn_mask,
+                dec_language_model_input=None, dec_attn_mask=None,
+                enc_dec_attn_mask=None, tokentype_ids=None, layer_past=None,
+                get_key_value=False, pooling_sequence_index=0,
+                enc_hidden_states=None, output_enc_hidden=False):
 
         # Embeddings.
         if mpu.is_pipeline_first_stage():
-            (input_ids, position_ids) = language_model_input
+            (input_ids, position_ids) = enc_language_model_input
             embedding_output = self.embedding(input_ids, position_ids,
                                               tokentype_ids=tokentype_ids)
-            transformer_input = embedding_output
+            encoder_input = embedding_output
         else:
-            transformer_input = language_model_input
+            encoder_input = enc_language_model_input
 
-        # Transformer.
-        transformer_output = self.transformer(transformer_input,
-                                              attention_mask,
+        # encoder.
+        if enc_hidden_states is None:
+            encoder_output = self.encoder(encoder_input,
+                                          enc_attn_mask,
                                           layer_past=layer_past,
                                           get_key_value=get_key_value)
+        else:
+            encoder_output = enc_hidden_states.to(encoder_input.dtype)
 
-        if mpu.is_pipeline_last_stage() and self.add_pooler:
-            pooled_output = self.pooler(transformer_output,
+        if mpu.is_pipeline_last_stage():
+            if self.add_pooler:
+                pooled_output = self.pooler(encoder_output,
                                             pooling_sequence_index)
-            return transformer_output, pooled_output
 
-        return transformer_output
+        # output_enc_hidden refers to when we just need the encoder's
+        # output. For example, it is helpful to compute
+        # similarity between two sequences by average pooling
+        if not self.add_decoder or output_enc_hidden:
+            if self.add_pooler and mpu.is_pipeline_last_stage():
+                return encoder_output, pooled_output
+            else:
+                return encoder_output
+
+        # Decoder Embedding
+        (dec_input_ids, dec_position_ids) = dec_language_model_input
+        dec_embedding_output = self.embedding(dec_input_ids,
+                                              dec_position_ids)
+        # decoder
+        decoder_output = self.decoder(dec_embedding_output,
+                                      dec_attn_mask,
+                                      layer_past=layer_past,
+                                      get_key_value=get_key_value,
+                                      encoder_output=encoder_output,
+                                      enc_dec_attn_mask=enc_dec_attn_mask)
+
+        if self.add_pooler and mpu.is_pipeline_last_stage():
+            return decoder_output, encoder_output, pooled_output
+        else:
+            return decoder_output, encoder_output
 
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
@@ -346,13 +393,18 @@ class TransformerLanguageModelBase(MegatronModule):
             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(
+        state_dict_[self._encoder_key] \
+            = self.encoder.state_dict_for_save_checkpoint(
                 destination, prefix, keep_vars)
-        if mpu.is_pipeline_last_stage() and self.add_pooler:
+        if mpu.is_pipeline_last_stage():
+            if self.add_pooler:
                 state_dict_[self._pooler_key] \
                     = self.pooler.state_dict_for_save_checkpoint(
                         destination, prefix, keep_vars)
+        if self.add_decoder:
+            state_dict_[self._decoder_key] \
+                = self.decoder.state_dict_for_save_checkpoint(
+                    destination, prefix, keep_vars)
 
         return state_dict_
 
@@ -371,36 +423,44 @@ class TransformerLanguageModelBase(MegatronModule):
                         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]
-        # for compatiability with t5 architecture
-        # this is temporary unless t5_main is merged
-        elif 'encoder' in state_dict:
-            state_dict_ = state_dict['encoder']
-            # for forward compatibility for t5 architecture
-            state_dict_attention = {}
-            for key in state_dict_.keys():
-                if '.self_attention.' in key:
-                    state_dict_attention[key.replace(".self_attention.",
-                        ".attention.")] = state_dict_[key]
-                else:
-                    state_dict_attention[key] = state_dict_[key]
-            state_dict_ = state_dict_attention
+        # Encoder.
+        if self._encoder_key in state_dict:
+            state_dict_ = state_dict[self._encoder_key]
+        # for backward compatibility.
+        elif 'transformer' in state_dict:
+            state_dict_ = state_dict['transformer']
         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 mpu.is_pipeline_last_stage() and self.add_pooler:
+        # for backward compatibility.
+        state_dict_self_attention = {}
+        for key in state_dict_.keys():
+            if '.attention.' in key:
+                state_dict_self_attention[key.replace(".attention.",
+                    ".self_attention.")] = state_dict_[key]
+            else:
+                state_dict_self_attention[key] = state_dict_[key]
+        state_dict_ = state_dict_self_attention
+
+        self.encoder.load_state_dict(state_dict_, strict=strict)
+
+        if mpu.is_pipeline_last_stage():
+            # 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)
+        # decoder
+        if self.add_decoder:
+            assert 'decoder' in state_dict, \
+                'could not find data for pooler in the checkpoint'
+            self.decoder.load_state_dict(state_dict[self._decoder_key],
+                                         strict=strict)
 
 
 class TransformerLanguageModel(TransformerLanguageModelBase):
@@ -409,28 +469,39 @@ class TransformerLanguageModel(TransformerLanguageModelBase):
     """
 
     def __init__(self,
-                 attention_mask_func,
                  init_method,
                  output_layer_init_method,
+                 encoder_attn_mask_type,
                  num_tokentypes=0,
+                 decoder_attn_mask_type=AttnMaskType.causal,
+                 add_decoder=False,
                  add_pooler=False):
         super(TransformerLanguageModel, self).__init__(
-            attention_mask_func,
             init_method,
             output_layer_init_method,
+            encoder_attn_mask_type,
             num_tokentypes=num_tokentypes,
+            add_decoder=add_decoder,
+            decoder_attn_mask_type=decoder_attn_mask_type,
             add_pooler=add_pooler)
 
-    def forward(self, input_ids, position_ids, attention_mask,
-                tokentype_ids=None, layer_past=None, get_key_value=False,
-                pooling_sequence_index=0):
+    def forward(self, enc_input_ids, enc_position_ids, enc_attn_mask,
+                dec_input_ids=None, dec_position_ids=None, dec_attn_mask=None,
+                enc_dec_attn_mask=None, tokentype_ids=None, layer_past=None,
+                get_key_value=False, pooling_sequence_index=0,
+                enc_hidden_states=None, output_enc_hidden=False):
         return super(TransformerLanguageModel, self).forward(
-            (input_ids, position_ids),
-            attention_mask,
+            (enc_input_ids, enc_position_ids),
+            enc_attn_mask,
+            dec_language_model_input=(dec_input_ids, dec_position_ids),
+            dec_attn_mask=dec_attn_mask,
+            enc_dec_attn_mask=enc_dec_attn_mask,
             tokentype_ids=tokentype_ids,
             layer_past=layer_past,
             get_key_value=get_key_value,
-            pooling_sequence_index=pooling_sequence_index
+            pooling_sequence_index=pooling_sequence_index,
+            enc_hidden_states=enc_hidden_states,
+            output_enc_hidden=output_enc_hidden
         )
 
 
@@ -440,14 +511,14 @@ class TransformerLanguageModelFirstStage(TransformerLanguageModelBase):
     """
 
     def __init__(self,
-                 attention_mask_func,
                  init_method,
                  output_layer_init_method,
+                 encoder_attn_mask_type,
                  num_tokentypes=0):
         super(TransformerLanguageModelFirstStage, self).__init__(
-            attention_mask_func,
             init_method,
             output_layer_init_method,
+            encoder_attn_mask_type,
             num_tokentypes=num_tokentypes)
 
     def forward(self, input_ids, position_ids, attention_mask,
@@ -467,13 +538,13 @@ class TransformerLanguageModelIntermediateStage(TransformerLanguageModelBase):
     """
 
     def __init__(self,
-                 attention_mask_func,
                  init_method,
-                 output_layer_init_method):
+                 output_layer_init_method,
+                 encoder_attn_mask_type):
         super(TransformerLanguageModelIntermediateStage, self).__init__(
-            attention_mask_func,
             init_method,
-            output_layer_init_method)
+            output_layer_init_method,
+            encoder_attn_mask_type)
 
     def forward(self, hidden_states, attention_mask,
                 layer_past=None, get_key_value=False):
@@ -491,14 +562,14 @@ class TransformerLanguageModelLastStage(TransformerLanguageModelBase):
     """
 
     def __init__(self,
-                 attention_mask_func,
                  init_method,
                  output_layer_init_method,
+                 encoder_attn_mask_type,
                  add_pooler=False):
         super(TransformerLanguageModelLastStage, self).__init__(
-            attention_mask_func,
             init_method,
             output_layer_init_method,
+            encoder_attn_mask_type,
             add_pooler=add_pooler)
 
     def forward(self, hidden_states, attention_mask,
@@ -509,5 +580,5 @@ class TransformerLanguageModelLastStage(TransformerLanguageModelBase):
             attention_mask,
             layer_past=layer_past,
             get_key_value=get_key_value,
-            pooling_sequence_index=pooling_sequence_index
+            pooling_sequence_index=pooling_sequence_index,
         )

megatron/module.py → megatron/model/module.py

@@ -16,16 +16,31 @@
 """Megatron Module"""
 
 import torch
+from torch.autograd import Variable
+from torch.nn.parameter import Parameter
 
 from megatron import get_args
 from megatron import mpu
 
 
+_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
+_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
+
+
+
+def param_is_not_shared(param):
+    return not hasattr(param, 'shared') or not param.shared
+
+
+
 class MegatronModule(torch.nn.Module):
-    """Megatron specific extensions of torch Module."""
+    """Megatron specific extensions of torch Module with support
+    for pipelining."""
 
-    def __init__(self):
+    def __init__(self, share_word_embeddings=True):
         super(MegatronModule, self).__init__()
+        self.share_word_embeddings = share_word_embeddings
+
 
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
@@ -34,52 +49,127 @@ class MegatronModule(torch.nn.Module):
         return self.state_dict(destination, prefix, keep_vars)
 
 
-class PipelinedMegatronModule(MegatronModule):
-    """Pipelining specific extensions of MegatronModule."""
-
-    def __init__(self, share_word_embeddings=True):
-        super(PipelinedMegatronModule, self).__init__()
-        args = get_args()
-        self.share_word_embeddings = share_word_embeddings
-
     def word_embeddings_weight(self):
         if mpu.is_pipeline_first_stage():
             return self.language_model.embedding.word_embeddings.weight
         if mpu.is_pipeline_last_stage():
             if not self.share_word_embeddings:
-                raise Exception('word_embeddings_weight() called for last stage, '
-                                'but share_word_embeddings is false')
+                raise Exception('word_embeddings_weight() called for last '
+                                'stage, but share_word_embeddings is false')
             return self.word_embeddings.weight
         raise Exception('word_embeddings_weight() should be '
                         'called for first and last stage only')
 
+
     def initialize_word_embeddings(self, init_method_normal):
         args = get_args()
         if not self.share_word_embeddings:
             raise Exception('initialize_word_embeddings() was called but '
                             'share_word_embeddings is false')
-        # Parameters are shared between the word embeddings layer, and the heads at
-        # the end of the model. In a pipelined setup with more than one stage, the
-        # initial embedding layer and the head are on different workers, so we do
-        # the following:
-        # 1. Create a second copy of word_embeddings on the last stage, with initial
-        #    parameters of 0.0.
-        # 2. Do an all-reduce between the first and last stage to ensure that the
-        #    two copies of word_embeddings start off with the same parameter values.
-        # 3. In the training loop, before an all-reduce between the grads of the two
-        #    word_embeddings layers to ensure that every applied weight update is the
-        #    same on both stages.
+
+        # This function just initializes the word embeddings in the final stage
+        # when we are using pipeline parallelism. If we aren't using pipeline
+        # parallelism there is nothing to do.
+        if args.pipeline_model_parallel_size == 1:
+            return
+
+        # Parameters are shared between the word embeddings layer, and the
+        # heads at the end of the model. In a pipelined setup with more than
+        # one stage, the initial embedding layer and the head are on different
+        # workers, so we do the following:
+        # 1. Create a second copy of word_embeddings on the last stage, with
+        #    initial parameters of 0.0.
+        # 2. Do an all-reduce between the first and last stage to ensure that
+        #    the two copies of word_embeddings start off with the same
+        #    parameter values.
+        # 3. In the training loop, before an all-reduce between the grads of
+        #    the two word_embeddings layers to ensure that every applied weight
+        #    update is the same on both stages.
         if mpu.is_pipeline_last_stage():
-            if not mpu.is_pipeline_first_stage():
+            assert not mpu.is_pipeline_first_stage()
             self._word_embeddings_for_head_key = 'word_embeddings_for_head'
-                # If first and last stages are different, set word_embeddings
-                # weights to 0 here, then copy first stage's weights using all_reduce
-                # below.
+            # set word_embeddings weights to 0 here, then copy first
+            # stage's weights using all_reduce below.
             self.word_embeddings = mpu.VocabParallelEmbedding(
                 args.padded_vocab_size, args.hidden_size,
                 init_method=init_method_normal(args.init_method_std))
             self.word_embeddings.weight.data.fill_(0)
-        # Ensure that first and last stages have the same initial parameter values.
+            self.word_embeddings.weight.shared = True
+
+        # Ensure that first and last stages have the same initial parameter
+        # values.
+        if torch.distributed.is_initialized():
             if mpu.is_pipeline_first_stage() or mpu.is_pipeline_last_stage():
                 torch.distributed.all_reduce(self.word_embeddings_weight().data,
                                              group=mpu.get_embedding_group())
+        else:
+            print("WARNING! Distributed processes aren't initialized, so "
+                  "word embeddings in the last layer are not initialized. "
+                  "If you are just manipulating a model this is fine, but "
+                  "this needs to be handled manually. If you are training "
+                  "something is definitely wrong.")
+
+def conversion_helper(val, conversion):
+    """Apply conversion to val. Recursively apply conversion if `val`
+    #is a nested tuple/list structure."""
+    if not isinstance(val, (tuple, list)):
+        return conversion(val)
+    rtn = [conversion_helper(v, conversion) for v in val]
+    if isinstance(val, tuple):
+        rtn = tuple(rtn)
+    return rtn
+
+
+def fp32_to_fp16(val):
+    """Convert fp32 `val` to fp16"""
+    def half_conversion(val):
+        val_typecheck = val
+        if isinstance(val_typecheck, (Parameter, Variable)):
+            val_typecheck = val.data
+        if isinstance(val_typecheck, _FLOAT_TYPES):
+            val = val.half()
+        return val
+    return conversion_helper(val, half_conversion)
+
+
+def fp16_to_fp32(val):
+    """Convert fp16 `val` to fp32"""
+    def float_conversion(val):
+        val_typecheck = val
+        if isinstance(val_typecheck, (Parameter, Variable)):
+            val_typecheck = val.data
+        if isinstance(val_typecheck, _HALF_TYPES):
+            val = val.float()
+        return val
+    return conversion_helper(val, float_conversion)
+
+
+
+class FP16Module(MegatronModule):
+
+    def __init__(self, module):
+        super(FP16Module, self).__init__()
+        self.add_module('module', module.half())
+
+
+    def forward(self, *inputs, **kwargs):
+        if mpu.is_pipeline_first_stage():
+            inputs = fp32_to_fp16(inputs)
+        outputs = self.module(*inputs, **kwargs)
+        if mpu.is_pipeline_last_stage():
+            outputs = fp16_to_fp32(outputs)
+        return outputs
+
+
+    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/multiple_choice.py

@@ -19,15 +19,16 @@ import torch
 
 from megatron import get_args, print_rank_last
 from megatron import mpu
-from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids
+from megatron.model.enums import AttnMaskType
+from megatron.model.bert_model import bert_extended_attention_mask, bert_position_ids
 from megatron.model.language_model import get_language_model
 from megatron.model.utils import get_linear_layer
 from megatron.model.utils import init_method_normal
 from megatron.model.utils import scaled_init_method_normal
-from megatron.module import PipelinedMegatronModule
+from .module import MegatronModule
 
 
-class MultipleChoiceBase(PipelinedMegatronModule):
+class MultipleChoiceBase(MegatronModule):
 
     def __init__(self, num_tokentypes=2):
         super(MultipleChoiceBase, self).__init__(share_word_embeddings=False)
@@ -36,9 +37,9 @@ class MultipleChoiceBase(PipelinedMegatronModule):
         init_method = init_method_normal(args.init_method_std)
 
         self.language_model, self._language_model_key = get_language_model(
-            attention_mask_func=bert_attention_mask_func,
             num_tokentypes=num_tokentypes,
             add_pooler=True,
+            encoder_attn_mask_type=AttnMaskType.padding,
             init_method=init_method,
             scaled_init_method=scaled_init_method_normal(args.init_method_std,
                                                          args.num_layers))

megatron/model/realm_model.py

@@ -4,13 +4,14 @@ import torch
 from megatron import get_args, print_rank_0
 from megatron.checkpointing import get_checkpoint_tracker_filename, get_checkpoint_name
 from megatron.model import BertModel
-from megatron.module import MegatronModule
+from .module import MegatronModule
 from megatron import mpu
+from megatron.model.enums import AttnMaskType
 from megatron.model.utils import get_linear_layer
 from megatron.model.utils import init_method_normal
 from megatron.model.language_model import get_language_model
 from megatron.model.utils import scaled_init_method_normal
-from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids
+from megatron.model.bert_model import bert_extended_attention_mask, bert_position_ids
 
 
 def general_ict_model_provider(only_query_model=False, only_block_model=False):
@@ -156,9 +157,9 @@ class IREncoderBertModel(MegatronModule):
                                                        args.num_layers)
 
         self.language_model, self._language_model_key = get_language_model(
-            attention_mask_func=bert_attention_mask_func,
             num_tokentypes=num_tokentypes,
             add_pooler=True,
+            encoder_attn_mask_type=AttnMaskType.padding,
             init_method=init_method,
             scaled_init_method=scaled_init_method)
 

megatron/model/utils.py

@@ -20,7 +20,6 @@ import math
 import torch
 
 from megatron import get_args
-from megatron.model import import_layernorm
 
 def init_method_normal(sigma):
     """Init method based on N(0, sigma)."""
@@ -40,6 +39,11 @@ def scaled_init_method_normal(sigma, num_layers):
     return init_
 
 
+def attention_mask_func(attention_scores, attention_mask):
+    attention_scores.masked_fill_(attention_mask, -10000.0)
+    return attention_scores
+
+
 def get_linear_layer(rows, columns, init_method):
     """Simple linear layer with weight initialization."""
     layer = torch.nn.Linear(rows, columns)
@@ -60,28 +64,3 @@ def openai_gelu(x):
 @torch.jit.script
 def erf_gelu(x):
     return x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype)+torch.ones_like(x).to(dtype=x.dtype))
-
-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.
-    """
-
-    args = get_args()
-    LayerNorm = import_layernorm(args.fp32_residual_connection)
-    
-    weight_decay_params = {'params': []}
-    no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
-    for module_ in module.modules():
-        if isinstance(module_, 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/vit_model.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Vision Transformer(VIT) model."""
+
+import math
+import einops
+import torch
+import torch.nn.functional as F
+from megatron import get_args
+from megatron.model.transformer import ParallelTransformer
+from megatron.model.utils import (
+    get_linear_layer,
+    init_method_normal,
+    scaled_init_method_normal,
+)
+from .module import MegatronModule
+
+
+class VitMlpHead(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, num_classes):
+        super(VitMlpHead, self).__init__()
+        self.dense_in = torch.nn.Linear(hidden_size, hidden_size)
+        self.dense_out = torch.nn.Linear(hidden_size, num_classes)
+        torch.nn.init.constant_(self.dense_out.bias, -10)
+
+    def forward(self, hidden_states, sequence_index=0):
+        # hidden_states: [b, s, h]
+        # sequence_index: index of the token to pool.
+        x = hidden_states[:, sequence_index, :]
+        x = self.dense_in(x)
+        x = torch.tanh(x)
+        x = self.dense_out(x)
+        return x
+
+
+def twod_interpolate_position_embeddings_hook(
+    state_dict,
+    prefix,
+    local_metadata,
+    strict,
+    missing_keys,
+    unexpected_keys,
+    error_msgs,
+):
+
+    args = get_args()
+    num_patches_per_dim = args.img_dim // args.patch_dim
+    num_patches = num_patches_per_dim ** 2
+    seq_length = num_patches + 1
+    hidden_size = args.hidden_size
+
+    key = prefix + "weight"
+    # import pdb
+    # pdb.set_trace()
+    assert key in state_dict
+    if key in state_dict:
+        input_param = state_dict[key]
+
+        assert input_param.shape[1] == hidden_size
+        if input_param.shape[0] != seq_length:
+            # update input_param and load it to state_dict[key]
+
+            num_tok_input = input_param.shape[0] - 1
+            num_tok_new = seq_length - 1
+            input_param_tok, input_param_grid = (
+                input_param[:1, :],
+                input_param[1:, :],
+            )
+
+            gs_input = int(math.sqrt(num_tok_input))
+            gs_new = int(math.sqrt(num_tok_new))
+
+            input_param_grid = input_param_grid.transpose(0, 1).contiguous()
+            input_param_grid = input_param_grid.reshape(
+                (1, -1, gs_input, gs_input)
+            )
+            input_param_grid = input_param_grid.float()
+            scale_factor = gs_new / gs_input
+
+            input_param_grid = F.interpolate(
+                input_param_grid, scale_factor=scale_factor, mode="bilinear"
+            )
+
+            input_param_grid = input_param_grid.half()
+            input_param_grid = input_param_grid.reshape((-1, gs_new * gs_new))
+            input_param_grid = input_param_grid.transpose(0, 1).contiguous()
+
+            assert input_param_grid.shape[1] == hidden_size
+            input_param = torch.cat((input_param_tok, input_param_grid), dim=0)
+            assert (
+                input_param.shape[0] == seq_length
+                and input_param.shape[1] == hidden_size
+            )
+
+            state_dict[key] = input_param
+
+
+class VitModel(MegatronModule):
+    """Vision Transformer Model."""
+
+    def __init__(self, num_classes, finetune=False):
+        super(VitModel, self).__init__()
+        args = get_args()
+
+        self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
+        if args.init_method_xavier_uniform:
+            self.init_method = torch.nn.init.xavier_uniform_
+            self.scaled_init_method = torch.nn.init.xavier_uniform_
+        else:
+            self.init_method = init_method_normal(args.init_method_std)
+            self.scaled_init_method = scaled_init_method_normal(
+                args.init_method_std, args.num_layers
+            )
+
+        self.hidden_size = args.hidden_size
+        self.num_classes = num_classes
+        self.patch_dim = args.patch_dim
+        self.img_dim = args.img_dim
+        self.finetune = finetune
+
+        assert self.img_dim % self.patch_dim == 0
+        self.num_patches_per_dim = self.img_dim // self.patch_dim
+        self.num_patches = self.num_patches_per_dim ** 2
+        self.seq_length = self.num_patches + 1
+        self.flatten_dim = self.patch_dim * self.patch_dim * args.num_channels
+
+        # cls_token
+        self.cls_token = torch.nn.Parameter(torch.randn(1, 1, self.hidden_size))
+        torch.nn.init.zeros_(self.cls_token)
+
+        # Linear encoder
+        self.linear_encoder = torch.nn.Linear(
+            self.flatten_dim, self.hidden_size
+        )
+
+        # embedding
+        self.position_embeddings = torch.nn.Embedding(
+            self.seq_length, self.hidden_size
+        )
+        init_method_normal(args.init_method_std)(
+            self.position_embeddings.weight
+        )
+        self.position_ids = torch.arange(self.seq_length).expand(1, -1).cuda()
+
+        self.position_embeddings._register_load_state_dict_pre_hook(
+            twod_interpolate_position_embeddings_hook
+        )
+
+        self.embedding_dropout = torch.nn.Dropout(args.hidden_dropout)
+
+        # Transformer
+        self.transformer = ParallelTransformer(
+            self.init_method, self.scaled_init_method
+        )
+
+        # MLP head
+        if not self.finetune:
+            self.mlp_head = VitMlpHead(self.hidden_size, self.num_classes)
+        else:
+            self.class_head = get_linear_layer(
+                self.hidden_size, num_classes, torch.nn.init.zeros_
+            )
+
+    def forward(self, x):
+        x = einops.rearrange(
+            x,
+            "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
+            p1=self.patch_dim,
+            p2=self.patch_dim,
+        )
+
+        assert x.dtype == torch.half
+        x = self.linear_encoder(x)
+        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        x = x + self.position_embeddings(self.position_ids)
+        x = self.embedding_dropout(x)
+        x = self.transformer(x, None)
+
+        if not self.finetune:
+            x = self.mlp_head(x)
+        else:
+            x = self.class_head(x[:, 0, :])
+
+        return x

megatron/mpu/__init__.py

@@ -19,8 +19,6 @@ from .cross_entropy import vocab_parallel_cross_entropy
 
 from .data import broadcast_data
 
-from .grads import clip_grad_norm
-
 from .initialize import is_unitialized
 from .initialize import destroy_model_parallel
 from .initialize import get_data_parallel_group
@@ -46,6 +44,9 @@ from .initialize import model_parallel_is_initialized
 from .layers import ColumnParallelLinear
 from .layers import RowParallelLinear
 from .layers import VocabParallelEmbedding
+from .layers import (set_tensor_model_parallel_attributes,
+                     set_defaults_if_not_set_tensor_model_parallel_attributes,
+                     copy_tensor_model_parallel_attributes)
                      
 from .mappings import copy_to_tensor_model_parallel_region
 from .mappings import gather_from_tensor_model_parallel_region

megatron/mpu/data.py

@@ -20,7 +20,7 @@ from .initialize import get_tensor_model_parallel_rank
 from .initialize import get_tensor_model_parallel_src_rank
 
 
-_MAX_DATA_DIM = 4
+_MAX_DATA_DIM = 5
 
 
 def _check_data_types(keys, data, target_dtype):