Upload split.py

convert/sg/mixtral-8x7b-32kseqlen/split.py

@@ -0,0 +1,144 @@
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+
+import torch
+from pathlib import Path
+
+Path("./converted").mkdir(exist_ok=True)
+
+ori = torch.load("consolidated.00.pth", map_location="cpu")
+ori = {"llma." + key: val for key, val in ori.items()}
+
+
+def func(rank=0):
+    shard_split_to = 8
+    split_ckpt = {}
+    for key, ori_param in ori.items():
+        if key in weight_parallel_dim:
+            split_ckpt[key] = torch.chunk(ori_param, shard_split_to, weight_parallel_dim[key])[
+                rank % shard_split_to].clone()
+            if rank == 0:
+                print(f"chunk {key}")
+        else:
+            if "experts." in key and int(key.split("experts.")[1].split(".")[0]) != rank:
+                continue
+            else:
+                split_ckpt[key] = ori_param
+                if rank == 0:
+                    print(f"inherit {key}")
+    torch.save({"model": split_ckpt}, f"converted/consolidated.{rank:02d}-of-08.model.pth")
+
+
+for r in range(8):
+    func(r)