fix gpt attention mask (#461)

model_zoo/gpt/gpt.py

@@ -21,6 +21,7 @@ __all__ = [
 
 @LAYERS.register_module
 class GPTEmbedding(nn.Module):
+
     def __init__(self,
                  embedding_dim: int,
                  vocab_size: int,
@@ -56,6 +57,7 @@ class GPTEmbedding(nn.Module):
 
 @LAYERS.register_module
 class GPTSelfAttention(nn.Module):
+
     def __init__(self,
                  dim: int,
                  num_heads: int,
@@ -70,7 +72,8 @@ class GPTSelfAttention(nn.Module):
         self.query_key_value = col_nn.Linear(dim, 3 * dim, dtype=dtype, bias=bias)
         if fuse_scale_mask_softmax:
             from colossalai.kernel import FusedScaleMaskSoftmax
-            from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
+            from colossalai.kernel.cuda_native.scaled_softmax import \
+                AttnMaskType
             self.softmax = FusedScaleMaskSoftmax(input_in_fp16=True,
                                                  input_in_bf16=False,
                                                  attn_mask_type=AttnMaskType.causal,
@@ -113,7 +116,7 @@ class GPTSelfAttention(nn.Module):
 
         x = torch.matmul(x, v)
         x = x.transpose(1, 2)
-        new_context_layer_shape = x.size()[:-2] + (all_head_size, )
+        new_context_layer_shape = x.size()[:-2] + (all_head_size,)
         x = x.reshape(new_context_layer_shape)
 
         x = self.dense(x)
@@ -124,6 +127,7 @@ class GPTSelfAttention(nn.Module):
 
 @LAYERS.register_module
 class GPTMLP(nn.Module):
+
     def __init__(self,
                  dim: int,
                  mlp_ratio: float,
@@ -148,6 +152,7 @@ class GPTMLP(nn.Module):
 
 @LAYERS.register_module
 class GPTBlock(CheckpointModule):
+
     def __init__(self,
                  dim: int,
                  num_heads: int,
@@ -194,6 +199,7 @@ class GPTBlock(CheckpointModule):
 
 @LAYERS.register_module
 class GPTLMHead(nn.Module):
+
     def __init__(self,
                  dim: int,
                  vocab_size: int,
@@ -214,6 +220,7 @@ class GPTLMHead(nn.Module):
 
 @LOSSES.register_module
 class GPTLMLoss(nn.Module):
+
     def __init__(self):
         super().__init__()
         self.loss = col_nn.CrossEntropyLoss()
@@ -227,6 +234,7 @@ class GPTLMLoss(nn.Module):
 
 @MODELS.register_module
 class GPT(nn.Module):
+
     def __init__(self,
                  vocab_size: int = 50304,
                  max_position_embeddings: int = 1024,
@@ -279,6 +287,18 @@ class GPT(nn.Module):
     def forward(self, input_ids, attention_mask=None):
         x, attention_mask = self.embed(input_ids, attention_mask)
 
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # Adapted from huggingface
+        if attention_mask is not None:
+            batch_size = x.shape[0]
+            attention_mask = attention_mask.view(batch_size, -1)
+            attention_mask = col_nn.partition_batch(attention_mask)
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = attention_mask.to(dtype=x.dtype)    # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * -10000.0
+
         for block in self.blocks:
             x, attention_mask = block(x, attention_mask)
 
@@ -288,6 +308,7 @@ class GPT(nn.Module):
 
 
 class PipelineGPT(nn.Module):
+
     def __init__(self,
                  vocab_size: int = 50304,
                  max_position_embeddings: int = 1024,
@@ -355,7 +376,7 @@ class PipelineGPT(nn.Module):
             attention_mask = attention_mask.view(batch_size, -1)
             attention_mask = col_nn.partition_batch(attention_mask)
             attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-            attention_mask = attention_mask.to(dtype=x.dtype)  # fp16 compatibility
+            attention_mask = attention_mask.to(dtype=x.dtype)    # fp16 compatibility
             attention_mask = (1.0 - attention_mask) * -10000.0
 
         for block in self.blocks: