Fix

scripts/deepseek_slice.py

@@ -1,173 +1,3 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from safetensors.torch import safe_open, save_file
-import torch
-import os
-from pathlib import Path
-import json
-import re
-
-model_dir_name = "DeepSeek-V3-bf16"
-model_dir_path = Path(model_dir_name)
-
-output_dir_name = "DeepSeek-V3-slice"
-output_dir_path = Path(output_dir_name)
-os.makedirs(output_dir_name, exist_ok=True)
-
-try:
-    tensor_map_json = json.load(open(model_dir_path / "model.safetensors.index.json"))
-    weight_map = tensor_map_json["weight_map"]
-except FileNotFoundError:
-    print("モデルのインデックスファイルが見つかりません")
-    raise
-
-tensor_files = list(set(weight_map.values()))
-tensor_files.sort()
-print(f"変換対象のファイル数: {len(tensor_files)}")
-
-try:
-    config_json = json.load(open(model_dir_path / "config.json"))
-except FileNotFoundError:
-    print("モデルの設定ファイルが見つかりません")
-    raise
-
-# experts
-n_routed_experts  = int(config_json["n_routed_experts"]) 
-
-# layers
-num_hidden_layers = int(config_json["num_hidden_layers"]) 
-
-# active experts
-num_experts_per_tok = int(config_json["num_experts_per_tok"]) 
-
-# このlayer-idxからdenseレイヤーをMoEにする
-first_k_dense_replace = int(config_json["first_k_dense_replace"])
-
-converted_tensors_size = 0
-
-target_n_routed_experts = 64
-
-
-def print_tensor_info(tensor, key, new_key=None):
-    print(f"key: {key} to {new_key if new_key else key}, shape: {tensor.shape}, size: {tensor.numel() * tensor.element_size() } Byte")
-
-def ensure_tensor_has_data(tensor):
-    try:
-        # テンソルが実際にアクセス可能かテスト
-        tensor[0]
-        return tensor
-    except Exception as e:
-        print(f"テンソルの再構築が必要: {e}")
-        # テンソルを明示的に再構築
-        return torch.tensor(tensor.cpu().numpy(), dtype=tensor.dtype)
-
-with open("layer_topk_idx_distribution.json", "r") as f:
-    layer_topk_idx_distribution = json.load(f)
-
-
-for i, tensor_file_name in enumerate(tensor_files, 1):
-    print(f"\n処理中: {tensor_file_name} ({i}/{len(tensor_files)})")
-    
-    tensor_path = model_dir_path / tensor_file_name
-    tensor_data = safe_open(tensor_path, framework="pt")
-    converted_tensors = {}
-    
-    for key in tensor_data.keys():
-        tensor = tensor_data.get_tensor(key)
-        tensor = ensure_tensor_has_data(tensor)  # テンソルの実データを確保
-
-        # レイヤーidxを取得 model.layers.0.から数値 ない場合もある
-        layer_idx = int(re.search(r'model\.layers\.(\d+)\.', key).group(1)) if re.search(r'model\.layers\.(\d+)\.', key) else -1
-
-        # レイヤーidxがない場合はそのまま保存
-        if layer_idx < first_k_dense_replace:
-            converted_tensors[key] = tensor.clone()
-            converted_tensors_size += tensor.numel() * tensor.element_size()
-            print_tensor_info(tensor, key, key)
-            continue
-
-        if layer_idx >= num_hidden_layers:
-            del tensor_map_json["weight_map"][key]
-            continue
-
-        # layer_topk_idx_distribution から当該レイヤーで使いたい experts idx を取得
-        if str(layer_idx) in layer_topk_idx_distribution:
-            experts_list = layer_topk_idx_distribution[str(layer_idx)]["experts"][:target_n_routed_experts]
-        else:
-            step = n_routed_experts // target_n_routed_experts
-            experts_list = list(range(0, n_routed_experts, step))[:target_n_routed_experts]
-        experts_list.sort()
-        experts_tensor = torch.tensor(experts_list, dtype=torch.long, device=tensor.device)
-
-        # experts
-        if ".mlp.experts." in key:
-            experts_idx = int(re.search(r'\.mlp\.experts\.(\d+)\.', key).group(1))
-            if experts_idx in experts_list:
-                new_key = key.replace(f".mlp.experts.{experts_idx}.", f".mlp.experts.{experts_list.index(experts_idx)}.")
-                converted_tensors[new_key] = tensor.clone()
-                converted_tensors_size += tensor.numel() * tensor.element_size()
-                print_tensor_info(tensor, key, new_key)
-                tensor_map_json["weight_map"][new_key] = tensor_file_name
-            else:
-                print(f"skip experts: {key}")
-            continue
-
-        # shared-experts
-        if ".mlp.shared_experts." in key:
-            # shared-expertsを保存
-            converted_tensors[key] = tensor.clone()
-            converted_tensors_size += tensor.numel() * tensor.element_size()
-            print_tensor_info(tensor, key, key)
-            continue
-
-        if ".mlp.gate.e_score_correction_bias" in key:
-            # Tensor [256]を [target_n_routed_experts]に変換
-            squeezed_tensor = tensor[experts_tensor].clone()
-            converted_tensors[key] = squeezed_tensor
-            converted_tensors_size += squeezed_tensor.numel() * squeezed_tensor.element_size()
-            print_tensor_info(squeezed_tensor, key, key)
-            continue
-
-        if ".mlp.gate.weight" in key:
-            # Tensor [256, 7168]を [target_n_routed_experts, 7168]に変換
-            squeezed_tensor = tensor[experts_tensor, :].clone()
-            converted_tensors[key] = squeezed_tensor
-            converted_tensors_size += squeezed_tensor.numel() * squeezed_tensor.element_size()
-            print_tensor_info(squeezed_tensor, key, key)
-            continue
-        
-        converted_tensors[key] = tensor.clone()
-        converted_tensors_size += tensor.numel() * tensor.element_size()
-        print_tensor_info(tensor, key, key)
-
-    save_file(converted_tensors, output_dir_path / tensor_file_name, metadata={"format": "pt"})
-
-print(f"\n変換完了！")
-print(f"合計サイズ: {converted_tensors_size / (1024**3):.2f} GB")
-
-# model.safetensors.index.json
-
-old_keys = list(tensor_map_json["weight_map"].keys())
-for key in old_keys:
-    if ".mlp.experts." in key:
-        experts_idx = int(re.search(r'\.mlp\.experts\.(\d+)\.', key).group(1))
-        if experts_idx >= target_n_routed_experts:
-            del tensor_map_json["weight_map"][key]
-
-
-tensor_map_json["metadata"]["total_size"] = converted_tensors_size
-with open(output_dir_path / "model.safetensors.index.json", "w") as f:
-    json.dump(tensor_map_json, f, indent=4)
-
-# config.json
-output_config_json = config_json.copy()
-output_config_json["n_routed_experts"] = target_n_routed_experts
-# output_config_json["num_hidden_layers"] = num_hidden_layers
-output_config_json["num_experts_per_tok"] = 4
-# output_config_json["first_k_dense_replace"] = first_k_dense_replace
-# output_config_json["n_shared_experts"] = n_shared_experts
-# output_config_json["topk_group"] = topk_group
-# output_config_json["n_group"] = n_group
-
-
-with open(output_dir_path / "config.json", "w") as f:
-    json.dump(output_config_json, f, indent=4)
+\[ 
+R = P \land Q \land C 
+\]