Partially implement performers in transformer_builders

codes/models/gpt_voice/transformer_builders.py

@@ -12,7 +12,9 @@ Every function contains the following arguments:
         checkpointing: Whether or not the underlying implementation should support gradient checkpointing.
 """
 import functools
+from time import time
 import torch
+from tqdm import tqdm
 
 
 def null_position_embeddings(range, dim):
@@ -36,6 +38,8 @@ def build_hf_gpt_transformer(layers, model_dim, heads, num_tokens, max_seq_len,
     # Override the built in positional embeddings
     del gpt.wpe
     gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
+    # Built-in token embeddings are unused.
+    del gpt.wte
     return gpt
 
 
@@ -43,7 +47,10 @@ def build_lr_performer(layers, model_dim, heads, num_tokens, max_seq_len, checkp
     """
     lucidrains Performer implementation, https://github.com/lucidrains/performer-pytorch
     """
-    pass
+    from models.lucidrains.performer.performer_pytorch import PerformerLM
+    model = PerformerLM(dim=model_dim, depth=layers, heads=heads, dim_head=model_dim, causal=True,
+                        num_tokens=num_tokens, max_seq_len=max_seq_len)
+    return model
 
 
 def build_lr_reformer(layers, model_dim, heads, num_tokens, max_seq_len, checkpointing):
@@ -61,10 +68,19 @@ def build_lr_xformer(layers, model_dim, heads, num_tokens, max_seq_len, checkpoi
 
 
 def test_all_performance(**kwargs):
-    transformer_builders = [build_hf_gpt_transformer, build_lr_performer, build_lr_reformer, build_lr_xformer]
+    transformer_builders = [#build_hf_gpt_transformer,
+                            build_lr_performer,]
+                            # build_lr_reformer,
+                            # build_lr_xformer]
     for builder in transformer_builders:
         model = builder(**kwargs)
+        start = time()
+        args = torch.randint(0, 8192, (16,450))
+        for k in tqdm(range(10)):
+            model(args)
+        stop = time()
+        print(f"Model: {str(builder)}; Elapsed: {stop-start}")
 
 
 if __name__ == '__main__':
-    test_all_performance(12, 512, 8, 8192, 1000, False)
+    test_all_performance(layers=12, model_dim=512, heads=8, num_tokens=8192, max_seq_len=1000, checkpointing=False)