Add data loader to read special weights for fp8; Add special weight process script

ktransformers/ktransformers_ext/triton/fp8gemm.py

@@ -1,3 +1,4 @@
+# Adopted from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py
 from typing import Tuple
 
 import torch

ktransformers/operators/experts.py

@@ -245,7 +245,16 @@ class KExpertsCPU(KExpertsBase):
         down_type = None
 
         for key in keys:
-            if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
+            if self.gguf_loader.safetensor_loader is not None:
+                # using a temp ugly way to temprary load the tensor
+                gate = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_gate_exps.weight").numpy()
+                up = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_up_exps.weight").numpy()
+                down = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_down_exps.weight").numpy()
+                gate_type = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_gate_exps.ggml_type").item()
+                up_type = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_up_exps.ggml_type").item()
+                down_type = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_down_exps.ggml_type").item()
+            
+            elif key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
                 gate = self.gguf_loader.get_mmap_tensor(key + ".ffn_gate_exps.weight")
                 up = self.gguf_loader.get_mmap_tensor(key + ".ffn_up_exps.weight")
                 down = self.gguf_loader.get_mmap_tensor(key + ".ffn_down_exps.weight")

ktransformers/operators/gate.py

@@ -67,7 +67,14 @@ class KMoEGateBase(ABC):
 
         for key in keys:
             key = ".".join(key.split(".")[:-1])
-            if key + ".ffn_gate_inp.weight" in self.gguf_loader.tensor_info:
+            if self.gguf_loader.safetensor_loader is not None:
+                targets = [".ffn_gate_inp.weight", ".exp_probs_b.bias"]
+                weight = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_gate_inp.weight") 
+                e_score_correction_bias = self.gguf_loader.safetensor_loader.load_tensor(key + ".exp_probs_b.bias")
+                weight_type = weight.dtype
+                e_score_correction_bias_type = e_score_correction_bias.dtype
+                res = {"weight": weight, "e_score_correction_bias": e_score_correction_bias,  "weight_type": weight_type, "e_score_correction_bias_type": e_score_correction_bias_type}
+            elif key + ".ffn_gate_inp.weight" in self.gguf_loader.tensor_info:
                 targets = [".ffn_gate_inp.weight", ".exp_probs_b.bias"]
                 tensors = self.load_multi(key, targets, device=device)
                 weight = tensors[".ffn_gate_inp.weight"]
@@ -116,8 +123,8 @@ class KMoEGate(BaseInjectedModule, KMoEGateBase):
             self.orig_module.e_score_correction_bias = nn.Parameter(w["e_score_correction_bias"])
         else:
             raise ValueError("Invalid weight type")
-        self.orig_module.weight = self.orig_module.weight.to(device)
-        self.orig_module.e_score_correction_bias = self.orig_module.e_score_correction_bias.to(device)
+        self.orig_module.weight = nn.Parameter(self.orig_module.weight.to(device))
+        self.orig_module.e_score_correction_bias = nn.Parameter(self.orig_module.e_score_correction_bias.to(device))
 
     def unload(self):
         if self.weight is not None:

ktransformers/operators/linear.py

@@ -76,7 +76,13 @@ class KLinearBase(ABC):
             keys = [self.key]
 
         for key in keys:
-            if key + ".weight" in self.gguf_loader.tensor_file_map:
+            if self.gguf_loader.safetensor_loader is not None:
+                # using safetensor_loader
+                tensor = self.gguf_loader.safetensor_loader.load_tensor(key+'.weight')
+                weight_scale_inv = self.gguf_loader.safetensor_loader.load_tensor(key+'.weight_scale_inv')
+                return nn.Parameter(tensor), nn.Parameter(weight_scale_inv)
+                
+            elif key + ".weight" in self.gguf_loader.tensor_file_map:
                 if key + ".bias" in self.gguf_loader.tensor_file_map:
                     tensors = self.load_multi(key, ["weight", "bias"], device=device)
                     tensor = tensors["weight"]
@@ -166,6 +172,8 @@ class KLinearTorch(KLinearBase):
             self.bias = None
 
 class KLinearFP8(KLinearBase):
+    # this kernel requires special handling for weight
+    # Please load the weight file downloaded from KVCache.AI
     marlin_q_w: torch.Tensor
     marlin_s: torch.Tensor
     g_idx: torch.Tensor
@@ -191,26 +199,20 @@ class KLinearFP8(KLinearBase):
     
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = x.to(self.device)
-        orig_shape = list(x.shape)
         orig_dtype = x.dtype        
-        x = x.reshape(-1, orig_shape[-1])
         x_quantized, scale_x = act_quant(x, self.block_size)
-        y = fp8_gemm(x_quantized, scale_x, self.weight, self.weight.scale)
-        if self.bias is not None:
-            y += self.bias
-        return y.to(orig_dtype).reshape(orig_shape)
+        y = fp8_gemm(x_quantized, scale_x, self.weight, self.weight_scale_inv)
+        return y.to(dtype=orig_dtype)
     
     def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
         if device is None: device = self.device
         if w is None: 
             w = self.load_weight(device=device) 
-        if isinstance(w, nn.Parameter):
-            self.weight = w.to(device)
-            self.has_bias = False
-        elif isinstance(w, tuple):
+        ### TODO fit weight_inv format
+        if isinstance(w, tuple):
             self.weight = w[0].to(device)
-            self.bias = w[1].to(device)
-            self.has_bias = True
+            self.weight_scale_inv = w[1].to(device)
+            self.has_bias = False
         else:
             raise ValueError("Invalid weight type")
         self.weight = self.weight.to(device)
@@ -425,7 +427,8 @@ class KLinearCPUInfer(KLinearBase):
 LINEAR_MAP = {
     "KLinearMarlin": KLinearMarlin,
     "KLinearTorch": KLinearTorch,
-    "KLinearCPUInfer": KLinearCPUInfer
+    "KLinearCPUInfer": KLinearCPUInfer,
+    "KLinearFP8": KLinearFP8,
 }
 
 class KTransformersLinear(BaseInjectedModule, KLinearBase):
@@ -472,10 +475,11 @@ class KTransformersLinear(BaseInjectedModule, KLinearBase):
     def forward(self, x):
         if self.mode == InferenceState.PREFILL:
             assert self.prefill_linear is not None, "cpu linear is not initialized"
-            return self.prefill_linear.forward(x)
+            y = self.prefill_linear.forward(x)
         else:
             assert self.generate_linear is not None, "gpu linear is not initialized"
-            return self.generate_linear.forward(x)
+            y = self.generate_linear.forward(x)
+        return y
 
     def load(self, w: dict | nn.Parameter | tuple | None = None, mode: InferenceState = InferenceState.GENERATE):
         if not mode:

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml

@@ -0,0 +1,63 @@
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearFP8"
+      prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"

ktransformers/tests/triton_fp8gemm_test.py

@@ -3,7 +3,7 @@ import torch.nn.functional as F
 from typing import Optional
 import pytest
 from typing import Tuple, Optional, Literal
-
+import time
 # use dir path
 import os
 import sys
@@ -56,18 +56,61 @@ def test_fp8_gemm_vs_torch_matmul_load():
     print(f"weight_dequantized: {weight_dequantized.shape}")
     N, K = weight_dequantized.shape
     M = 64
-    x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
+    x = torch.randn(2 ,M, K, dtype=torch.bfloat16, device='cuda')
     x_quantized, scale_x = act_quant(x, block_size)
     
     # Test case 1: quantized x matmal with undequantized weight
     result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
     print(f"result_fp8_gemm:\n {result_fp8_gemm}")
+    print(f"dtype {result_fp8_gemm.dtype}")
 
     # Perform torch.matmul using the original floating point tensors
     result_torch_matmul = torch.matmul(x, weight_dequantized.to(torch.bfloat16).T)
     print(f"result_torch_matmul:\n {result_torch_matmul}")
 
+def test_fp8_gemm_tplops():
+    file_path = "/mnt/data/model/DeepSeek-V3/model-00001-of-000163.safetensors"
+    with safe_open(file_path, framework="pt", device=0) as f:
+        weight = f.get_tensor("model.layers.0.mlp.down_proj.weight")
+        scale = f.get_tensor("model.layers.0.mlp.down_proj.weight_scale_inv")
+
+    # weight_dequant
+    weight_dequantized = weight_dequant(weight, scale)
+    print(f"weight_dequantized: {weight_dequantized.shape}")
+    N, K = weight_dequantized.shape
+    M = 6400
+    x = torch.randn(2 ,M, K, dtype=torch.bfloat16, device='cuda')
+    # x_quantized, scale_x = act_quant(x, block_size)
+    
+    # Calculate time for 1000 fp8_gemm
+    i = 10
+    flops_per_gemm = 2 * M * N * K
+    total_flops = i * flops_per_gemm
+    
+    x_quantized, scale_x = act_quant(x, block_size)
+    result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+    x_quantized, scale_x = act_quant(x, block_size)
+    result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+
+    
+    t0 = time.time()
+    torch.cuda.synchronize()
+    for i in range(i):
+        x_quantized, scale_x = act_quant(x, block_size)
+        result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+    torch.cuda.synchronize()
+    t1 = time.time()
+    
+    total_time = t1 - t0
+    tflops = total_flops / total_time / 1e12
+    print(f"total_time: {total_time}")
+    print(f"tflops: {tflops}")
+    
+
+    
+    
 if __name__ == "__main__":
     test_fp8_gemm_vs_torch_matmul()
     test_fp8_gemm_vs_torch_matmul_load()
+    test_fp8_gemm_tplops()
     

ktransformers/util/custom_gguf.py

@@ -25,6 +25,7 @@ import os
 from enum import IntEnum
 import torch
 import KTransformersOps
+from .custom_loader import SafeTensorLoader
 
 class GGMLQuantizationType(IntEnum):
     F32     = 0
@@ -168,6 +169,7 @@ class GGUFLoader:
     gguf_path: str
     tensor_file_map: dict # {tensor_name: tensor_file_path}
     gguf_file_meta: dict
+    safetensor_loader: SafeTensorLoader
     def __init__(self, gguf_path: str):
         # Check dir exist
         if not os.path.exists(gguf_path):
@@ -175,6 +177,8 @@ class GGUFLoader:
         if os.path.isfile(gguf_path):
             gguf_path = os.path.dirname(gguf_path)
 
+        self.safetensor_loader = None
+        
         self.tensor_info = {}
         self.gguf_path = gguf_path
         self.tensor_file_map = {}
@@ -182,6 +186,12 @@ class GGUFLoader:
         self.gguf_file_meta = {}
         self.tensor_device_map = {}
 
+        # I know this is ugly, but I don't want to change the original code too much
+        # TODO: merge gguf load and other loads.
+        safetensor_loader = SafeTensorLoader(gguf_path)
+        if safetensor_loader.tensor_file_map:
+            self.safetensor_loader = safetensor_loader
+            return
         # Walk through all the .gguf files in the directory
         found_gguf = False
         for root, dirs, files in os.walk(gguf_path):
@@ -288,6 +298,13 @@ class GGUFLoader:
         itemsize = int(np.empty([], dtype = item_type).itemsize)
         return mmap_data[offset : offset + itemsize * item_count]
     
+    def get_undequanted_tensor_and_ggml_type(self, name):
+        t = self.tensor_info[name]
+        data = self.get_mmap_tensor(name)
+        ggml_type = t["ggml_type"]
+        data = torch.from_numpy(data)
+        return data, ggml_type
+
     def load_expert_tensor(self, name, data, expert_id, elements_per_expert, device = "gpu")->torch.Tensor:
         t = self.tensor_info[name]
         if device.lower() == "cpu":

ktransformers/util/custom_loader.py

@@ -0,0 +1,86 @@
+import struct
+import warnings
+import numpy as np
+import re
+import numpy.typing as npt
+from typing import Sequence
+import os
+from enum import IntEnum
+import torch
+import KTransformersOps
+from safetensors import safe_open
+from ktransformers.ktransformers_ext.triton.fp8gemm import fp8_gemm, act_quant, weight_dequant
+from safetensors.torch import save_file
+
+class SafeTensorLoader:
+    tensor_file_map = {}
+    tensor_type_map = {}
+    file_handle_map = {}
+    
+    def __init__(self, file_path: str):
+        self.__load_tensor_file_map(file_path)
+
+    def __load_tensor_file_map(self, file_path: str):
+        # 处理传入路径，确保是文件夹路径
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"Path not found: {file_path}")
+        if os.path.isfile(file_path):
+            folder_path = os.path.dirname(file_path)
+        else:
+            folder_path = file_path
+
+        found_safetensor = False
+        for root, _, files in os.walk(folder_path):
+            files = sorted(files)
+            for file in files:
+                if file.endswith(".safetensors"):
+                    found_safetensor = True
+                    file_path = os.path.join(root, file)
+                    if file not in self.file_handle_map:
+                        try:
+                            handle = safe_open(file_path, framework="pt")
+                            self.file_handle_map[file] = handle
+                        except Exception as e:
+                            print(f"Error opening Safetensor file {file_path}: {e}")
+                            continue
+
+                    f = self.file_handle_map.get(file)
+                    if f is None:
+                        continue
+                    try:
+                        for key in f.keys():
+                            self.tensor_file_map[key] = file
+                    except Exception as e:
+                        print(f"Error reading Safetensor file {file_path}: {e}")
+
+        # if not found_safetensor:
+        #     raise FileNotFoundError(f"No Safetensor files found in {folder_path}")
+
+    def load_tensor(self, key: str, device: str="cpu"):
+        if key not in self.tensor_file_map:
+            raise KeyError(f"Key {key} not found in Safetensor files")
+        file = self.tensor_file_map[key]
+        f = self.file_handle_map.get(file)
+        if f is None:
+            raise FileNotFoundError(f"File {file} not found in Safetensor files")
+        tensor = f.get_tensor(key)
+        return tensor.to(device)
+
+    def close_all_handles(self):
+        for handle in self.file_handle_map.values():
+            handle.close()
+        self.file_handle_map.clear()
+
+    def load_dequantized_tensor(self, key:str, device: str="cpu"):
+        if key not in self.tensor_file_map:
+            raise KeyError(f"Key {key} not found in Safetensor files")
+        file = self.tensor_file_map[key]
+        f = self.file_handle_map.get(file)
+        if f is None:
+            raise FileNotFoundError(f"File {file} not found in Safetensor files")
+        tensor = f.get_tensor(key).to(device)
+        if key.endswith(".weight"):
+            if key[:-7] + ".weight_scale_inv" in self.tensor_file_map:
+                weight_scale_inv = f.get_tensor(key[:-7] + ".weight_scale_inv").to(device)
+                tensor = weight_dequant(tensor, weight_scale_inv)
+        return tensor.to(device)

ktransformers/util/utils.py

@@ -66,12 +66,23 @@ def load_cur_state_dict(module: nn.Module, gguf_loader: GGUFLoader, prefix: str
     for name, param in local_state.items():
         key = prefix + name
         translated_key = translate_name_to_gguf(key)
-        if translated_key in gguf_loader.tensor_file_map:
+        
+        # TODO: Merge all loader.
+        # I know this is ugly but lets do it for now.
+        if gguf_loader.safetensor_loader is not None:
+            load_dequantized_tensor = gguf_loader.safetensor_loader.load_dequantized_tensor
+            tensor_file_map = gguf_loader.safetensor_loader.tensor_file_map
+        else:
+            load_dequantized_tensor = gguf_loader.load_gguf_tensor
+            tensor_file_map = gguf_loader.tensor_file_map
+        
+        if translated_key in tensor_file_map:
             target_dtype = torch.get_default_dtype()
             device = get_device(translated_key[:translated_key.rfind(".")], gguf_loader.tensor_device_map)
             print(f"loading {translated_key} to {device}")
             torch.cuda.empty_cache() # To fit in 16G VRAM. By "wkGCaSS - 知乎 https://zhuanlan.zhihu.com/p/25491611225"
-            weights = gguf_loader.load_gguf_tensor(translated_key, device = device).to(dtype = target_dtype)
+            # weights = gguf_loader.load_gguf_tensor(translated_key, device = device).to(dtype = target_dtype)
+            weights = load_dequantized_tensor(translated_key, device=device).to(dtype=target_dtype)
             set_param(module, name, weights)
             del weights
         else:

merge_tensors/merge_safetensor_gguf.py

@@ -0,0 +1,214 @@
+# this script targets to merge the fp8 safe tensor and the gguf quantized tensors.
+
+import os
+# insert the path of the project
+import sys
+sys.path.insert(0, "/home/azure/ktransformers")
+import argparse
+import torch
+from ktransformers.util.custom_gguf import GGUFLoader, translate_name_to_gguf
+from safetensors import safe_open
+from safetensors.torch import save_file
+import re
+from collections import defaultdict
+
+def read_safetensor_keys_from_folder(folder_path)->dict:
+    """    
+    :param folder_path: folder path
+    :return: key_to_file_map
+    """
+    # check if the folder path is exist
+    if not os.path.exists(folder_path):
+        raise FileNotFoundError(f"GGUF dir not found: {folder_path}")
+    if os.path.isfile(folder_path):
+        folder_path = os.path.dirname(folder_path)
+    
+    key_to_file_map = {}
+
+    found_safetensor = False
+    for root, dirs, files in os.walk(folder_path):
+        # sort files
+        files = sorted(files)
+        for file in files:
+            if file.endswith(".safetensors"):
+                found_safetensor = True
+                file_path = os.path.join(root, file)
+                try:
+                    with safe_open(file_path, framework="pt") as f:
+                        for key in f.keys():
+                            if "model.layers.61" in key:
+                                # skip MTP layer
+                                continue
+                            # try:
+                            #     if int(key.split('.')[2]) > 4:
+                            #         continue
+                            # except:
+                            #     pass
+                            key_to_file_map[key] = file_path
+                except Exception as e:
+                    print(f"Error reading Safetensor file {file_path}: {e}")
+    
+    if not found_safetensor:
+        raise FileNotFoundError(f"No Safetensor files found in {folder_path}")
+    
+    return key_to_file_map
+
+tensor_from_gguf = [] # todo: add keys in gguf that should be used in the final tensor
+
+def translate_name(name:str)->str:
+    """
+    :param name: name of the tensor
+    :return: translated name
+    """
+    name = translate_name_to_gguf(name)
+    name = name.replace(".up_proj.", ".ffn_up_exps.")
+    name = name.replace(".down_proj.", ".ffn_down_exps.")
+    name = name.replace(".gate_proj.", ".ffn_gate_exps.")
+    name = name.replace(".ffn_gate_inp.e_score_correction_bias", ".exp_probs_b.bias") 
+    return name
+    
+
+def combine_tensor_sources(safetensor_path:str, gguf_path:str):
+    gguf_loader = GGUFLoader(gguf_path)
+    gguf_tensor_file_map = gguf_loader.tensor_file_map
+    safetensor_tensor_file_map = read_safetensor_keys_from_folder(safetensor_path)
+    
+    # build a map for the key to the tensor
+    # according to the key, we can get the tensor from the file
+    
+    target_tensor_map = {}
+    for key in safetensor_tensor_file_map.keys():
+        # for all experts, we use the gguf tensor
+        if ".mlp.experts." in key:
+            if '.weight_scale_inv' in key:
+                continue
+            key = '.'.join(key.split('.')[:5]+key.split('.')[-2:])
+            translated_key = translate_name(key)
+            target_tensor_map[key] = gguf_tensor_file_map[translated_key]
+            continue
+        
+        if any(target_key in key for target_key in tensor_from_gguf):
+            target_tensor_map[key] = gguf_tensor_file_map[translate_name(key)]
+        else:
+            target_tensor_map[key] = safetensor_tensor_file_map[key]
+    
+    return target_tensor_map, gguf_loader
+
+def write_combined_tensor(target_tensor_map: dict, output_path: str, gguf_loader: GGUFLoader):
+    # Ensure output directory exists
+    os.makedirs(output_path, exist_ok=True)
+    
+    # Cache for safetensor file handles and GGUF loaders
+    safetensors_cache = {}
+    gguf_cache = {}
+    
+    # Group tensors by layer
+    layer_groups = defaultdict(list)
+    non_layer_keys = []
+    layer_pattern = re.compile(r'\.layers\.(\d+)\.')
+    
+    for key in target_tensor_map:
+        match = layer_pattern.search(key)
+        if match:
+            layer_num = int(match.group(1))
+            layer_groups[layer_num].append(key)
+        else:
+            non_layer_keys.append(key)
+    
+    # Calculate total shards
+    total_shards = len(layer_groups) + (1 if non_layer_keys else 0) - 1
+    if total_shards == 0:
+        raise ValueError("No tensors to save")
+    
+    shard_idx = 0
+    
+    # Save non-layer tensors to the first shard if they exist
+    if non_layer_keys:
+        tensors = {}
+        for key in non_layer_keys:
+            file_path = target_tensor_map[key]
+            tensor = None
+            ggml_type = None
+            if file_path.endswith('.safetensors'):
+                if file_path not in safetensors_cache:
+                    safetensors_cache[file_path] = safe_open(file_path, framework='pt')
+                f = safetensors_cache[file_path]
+                tensor = f.get_tensor(key)
+            elif file_path.endswith('.gguf'):
+                gguf_name = translate_name(key)
+                tensor, ggml_type = gguf_loader.get_undequanted_tensor_and_ggml_type(gguf_name)
+            else:
+                raise ValueError(f"Unsupported file format: {file_path}")
+            tensors[translate_name(key)] = tensor
+            if ggml_type:
+                ggml_type = torch.tensor(ggml_type)
+                ggml_key = translate_name(key)[:-7] + ".ggml_type" if translate_name(key).endswith(".weight") else translate_name(key) + ".ggml_type"
+                tensors[ggml_key] = ggml_type
+        
+        output_file = os.path.join(output_path, f"model-{shard_idx:05}-of-{total_shards:05}.safetensors")
+        print(f"Saving non-layer tensors to {output_file}")
+        save_file(tensors, output_file)
+        print(tensors.keys())
+
+        shard_idx += 1
+    
+    # Save each layer's tensors to subsequent shards
+    for layer_num in sorted(layer_groups.keys()):
+        layer_keys = layer_groups[layer_num]
+        tensors = {}
+        for key in layer_keys:
+            file_path = target_tensor_map[key]
+            tensor = None
+            ggml_type = None
+            if file_path.endswith('.safetensors'):
+                if file_path not in safetensors_cache:
+                    safetensors_cache[file_path] = safe_open(file_path, framework='pt')
+                f = safetensors_cache[file_path]
+                tensor = f.get_tensor(key)
+                tensor_info = tensor.shape
+            elif file_path.endswith('.gguf'):
+                gguf_name = translate_name(key)
+                tensor, ggml_type = gguf_loader.get_undequanted_tensor_and_ggml_type(gguf_name)
+                # tensor_info = gguf_loader.tensor_info[gguf_name]
+                # ggml_type = gguf_loader.tensor_info[gguf_name]['ggml_type']
+            else:
+                raise ValueError(f"Unsupported file format: {file_path}")
+            tensors[translate_name(key)] = tensor
+            if ggml_type:
+                ggml_type = torch.tensor(ggml_type)
+                ggml_key = translate_name(key)[:-7] + ".ggml_type" if translate_name(key).endswith(".weight") else translate_name(key) + ".ggml_type"
+                tensors[ggml_key] = ggml_type
+        
+        output_file = os.path.join(output_path, f"model-{shard_idx:05}-of-{total_shards:05}.safetensors")
+        print(f"Saving layer {layer_num} to {output_file}")
+        print(tensors.keys())
+        save_file(tensors, output_file)
+        shard_idx += 1
+    
+    return
+    
+def main():
+    # 创建命令行参数解析器
+    parser = argparse.ArgumentParser(description="Read parameters from Safetensor and GGUF files")
+    parser.add_argument("--safetensor_path", type=str, help="Path to the Safetensor file", default="/mnt/data/model/DeepSeek-V3")
+    parser.add_argument("--gguf_path", type=str, help="Path to the GGUF file", default="/mnt/data/model/DeepseekV3-q4km-gguf")
+    parser.add_argument("--output_path", type=str, help="Path to the output file", default="/mnt/data/model/ktrans-safetensors/DeepSeek-V3-q4km-fp8")
+    
+    # print all the arguments
+    print("All the arguments:")
+    print(parser.parse_args())
+    
+    # 解析命令行参数
+    args = parser.parse_args()
+
+    safetensor_path = args.safetensor_path
+    gguf_path = args.gguf_path
+    output_path = args.output_path
+    
+    target_tensor_map, gguf_loader = combine_tensor_sources(safetensor_path, gguf_path)
+    write_combined_tensor(target_tensor_map, output_path, gguf_loader)
+    
+    return
+
+if __name__ == "__main__":
+    main()