Merge branch 'refactor_gpt2' into 'master'

Refactor gpt2

See merge request ADLR/megatron-lm!15

generate_samples.py

@@ -28,7 +28,7 @@ from arguments import get_args
 from megatron.utils import Timers
 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 get_ltor_masks_and_position_ids
 from megatron.utils import load_checkpoint
 from megatron.data_utils import make_tokenizer
 from configure_data import configure_data
@@ -91,7 +91,7 @@ def get_batch(context_tokens, args):
     tokens = tokens.to(device)
 
     # Get the masks and postition ids.
-    attention_mask, loss_mask, position_ids = get_masks_and_position_ids(
+    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
         tokens,
         args.eod_token,
         args.reset_position_ids,

megatron/utils.py

@@ -31,6 +31,63 @@ from megatron.model import DistributedDataParallel as LocalDDP
 from megatron.model import get_params_for_weight_decay_optimization
 
 
+def get_ltor_masks_and_position_ids(data,
+                                    eod_token,
+                                    reset_position_ids,
+                                    reset_attention_mask,
+                                    eod_mask_loss):
+    """Build masks and position id for left to right model."""
+
+    # Extract batch size and sequence length.
+    batch_size, seq_length = data.size()
+
+    # Attention mask (lower triangular).
+    if reset_attention_mask:
+        att_mask_batch = batch_size
+    else:
+        att_mask_batch = 1
+    attention_mask = torch.tril(torch.ones(
+        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
+            att_mask_batch, 1, seq_length, seq_length)
+
+    # Loss mask.
+    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
+    if eod_mask_loss:
+        loss_mask[data == eod_token] = 0.0
+
+    # Position ids.
+    position_ids = torch.arange(seq_length, dtype=torch.long,
+                                device=data.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(data)
+    # We need to clone as the ids will be modifed based on batch index.
+    if reset_position_ids:
+        position_ids = position_ids.clone()
+
+    if reset_position_ids or reset_attention_mask:
+        # Loop through the batches:
+        for b in range(batch_size):
+
+            # Find indecies where EOD token is.
+            eod_index = position_ids[b, data[b] == eod_token]
+            # Detach indecies from positions if going to modify positions.
+            if reset_position_ids:
+                eod_index = eod_index.clone()
+
+            # Loop through EOD indecies:
+            prev_index = 0
+            for j in range(eod_index.size()[0]):
+                i = eod_index[j]
+                # Mask attention loss.
+                if reset_attention_mask:
+                    attention_mask[b, 0, (i+1):, :(i+1)] = 0
+                # Reset positions.
+                if reset_position_ids:
+                    position_ids[b, (i+1):] -= (i + 1 - prev_index)
+                    prev_index = i + 1
+
+    return attention_mask, loss_mask, position_ids
+
+
 def reduce_losses(losses):
     reduced_losses = torch.cat(
         [loss.clone().detach().view(1) for loss in losses])

pretrain_gpt2.py

@@ -21,6 +21,7 @@ from configure_data import configure_data
 from gpt2_data_loader import make_gpt2_dataloaders
 from megatron import mpu
 from megatron.model import GPT2Model
+from megatron.utils import get_ltor_masks_and_position_ids
 from megatron.utils import print_rank_0
 from megatron.utils import reduce_losses
 from megatron.utils import vocab_size_with_padding
@@ -47,63 +48,6 @@ def model_provider(args):
     return model
 
 
-def get_masks_and_position_ids(data,
-                               eod_token,
-                               reset_position_ids,
-                               reset_attention_mask,
-                               eod_mask_loss):
-    """Build masks and position id."""
-
-    # Extract batch size and sequence length.
-    batch_size, seq_length = data.size()
-
-    # Attention mask (lower triangular).
-    if reset_attention_mask:
-        att_mask_batch = batch_size
-    else:
-        att_mask_batch = 1
-    attention_mask = torch.tril(torch.ones(
-        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
-            att_mask_batch, 1, seq_length, seq_length)
-
-    # Loss mask.
-    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
-    if eod_mask_loss:
-        loss_mask[data == eod_token] = 0.0
-
-    # Position ids.
-    position_ids = torch.arange(seq_length, dtype=torch.long,
-                                device=data.device)
-    position_ids = position_ids.unsqueeze(0).expand_as(data)
-    # We need to clone as the ids will be modifed based on batch index.
-    if reset_position_ids:
-        position_ids = position_ids.clone()
-
-    if reset_position_ids or reset_attention_mask:
-        # Loop through the batches:
-        for b in range(batch_size):
-
-            # Find indecies where EOD token is.
-            eod_index = position_ids[b, data[b] == eod_token]
-            # Detach indecies from positions if going to modify positions.
-            if reset_position_ids:
-                eod_index = eod_index.clone()
-
-            # Loop through EOD indecies:
-            prev_index = 0
-            for j in range(eod_index.size()[0]):
-                i = eod_index[j]
-                # Mask attention loss.
-                if reset_attention_mask:
-                    attention_mask[b, 0, (i+1):, :(i+1)] = 0
-                # Reset positions.
-                if reset_position_ids:
-                    position_ids[b, (i+1):] -= (i + 1 - prev_index)
-                    prev_index = i + 1
-
-    return attention_mask, loss_mask, position_ids
-
-
 def get_batch(data_iterator, args, timers):
     """Generate a batch"""
 
@@ -126,7 +70,7 @@ def get_batch(data_iterator, args, timers):
     tokens = tokens_[:, :-1].contiguous()
 
     # Get the masks and postition ids.
-    attention_mask, loss_mask, position_ids = get_masks_and_position_ids(
+    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
         tokens,
         args.eod_token,
         args.reset_position_ids,