olmoe memory usage cleanups

examples/olmoe.py

@@ -1,4 +1,5 @@
 # https://arxiv.org/pdf/2409.02060
+import time
 import numpy as np
 np.set_printoptions(suppress=True, linewidth=1000)
 import functools
@@ -53,13 +54,17 @@ if __name__ == "__main__":
     model_state_dict = nn.state.get_state_dict(model)
     del model_state_dict['freqs_cis']
 
-  with Timing("fetch and load weights: "):
-    state = fetch_weights()
-    nhf_state = convert_from_huggingface(state, model, 16, 16)
+  with Timing("load weights to GPU: "):
+    nhf_state = convert_from_huggingface(fetch_weights(), model, 16, 16)
     # NOTE: i'm not sure this actually needs float32, it may just change the type of things downstream from it. but doesn't match torch w/o this
     for needs_float32 in ['tok_embeddings.weight']: nhf_state[needs_float32] = nhf_state[needs_float32].float()
+  print(f"ram used: {GlobalCounters.mem_used/1e9:.2f} GB")
+
+  with Timing("unpack weights: "):
     nn.state.load_state_dict(model, nhf_state, verbose=False, strict=False, consume=True, realize=False)
     assert len(nhf_state) == 0
+    Tensor.realize(*list(nn.state.get_state_dict(model).values()))
+  print(f"ram used: {GlobalCounters.mem_used/1e9:.2f} GB")
 
   count = 30
   temperature = 0
@@ -70,13 +75,17 @@ if __name__ == "__main__":
 
   toks = [12092]
   start_pos = 0
+  timings = []
   for i in range(count):
     GlobalCounters.reset()
+    st = time.perf_counter()
     tok = model(Tensor([toks[start_pos:]]), start_pos, temperature).item()
+    timings.append(time.perf_counter()-st)
     toks.append(tok)
     start_pos += 1
     print(toks)
     print(tokenizer.decode(toks))
+  print(f"fastest token {min(timings)*1e3:.2f} ms, {1/min(timings):.1f} tok/s")
 
   if temperature == 0:
     # Hello, I am a newbie to this forum and I am trying to get a better understanding of the different types of data that can be stored in a