zero out the weight in bert init run (tinygrad#9076)

`DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 EVAL_BS=6 GPUS=6 MODEL=bert python3 examples/mlperf/model_train.py` no longer oom. I think the buffer of random init weights caused the oom.

examples/mlperf/helpers.py

@@ -208,7 +208,7 @@ def get_mlperf_bert_config():
     "vocab_size": 30522
   }
 
-def get_mlperf_bert_model(checkpoint_path:Optional[str]=None):
+def get_mlperf_bert_model():
   from extra.models import bert
   from examples.mlperf.initializers import LinearBert, EmbeddingBert, LayerNormBert
 
@@ -220,8 +220,7 @@ def get_mlperf_bert_model(checkpoint_path:Optional[str]=None):
   config = get_mlperf_bert_config()
   if getenv("DISABLE_DROPOUT", 0):
     config["hidden_dropout_prob"] = config["attention_probs_dropout_prob"] = 0.0
-  model = BertForPretraining(**config)
-  return model.load_from_pretrained(checkpoint_path) if checkpoint_path else model
+  return BertForPretraining(**config)
 
 def get_data_bert(GPUS:list[str], it):
   data: dict[str, Tensor] = next(it)

examples/mlperf/model_train.py

@@ -683,8 +683,14 @@ def train_bert():
 
   # ** init model **
 
-  model = get_mlperf_bert_model(init_ckpt if RUNMLPERF else None)
-
+  model = get_mlperf_bert_model()
+  if RUNMLPERF:
+    model.load_from_pretrained(init_ckpt)
+  else:
+    # for init, zero out all weights
+    for p in get_parameters(model):
+      p = p.assign(Tensor.zeros_like(p).contiguous()).realize()
+
   parameters = get_parameters(model)
   for p in parameters:
     p.to_(GPUS)