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Commit c6aa379d authored by qiyuxinlin's avatar qiyuxinlin
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support safetensor load, delete architectures argument

parent 900a7f7c
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Showing with 875 additions and 255 deletions
ktransformers/server/balance_serve/inference/forward_batch.py
View file @ c6aa379d
......@@ -200,7 +200,7 @@ class ForwardBatchInput:
device=None,
tokens: torch.Tensor = None,
num_mini_batches: int = 1,
max_seq_length: int = 1024, # TODO: add to yaml
max_seq_length: int = 4096, # TODO: add to yaml
prefill_query_length: int = (Config().chunk_size - Config().max_decode_batch_size) // Config().max_prefill_batch_size, # TODO: use config
prefill_active_length: int = (Config().chunk_size - Config().max_decode_batch_size) // Config().max_prefill_batch_size,
gen_prefill: bool = True,
......@@ -223,12 +223,12 @@ class ForwardBatchInput:
decode_querys_info = []
for i in range(min(decode_batch_size, cuda_lens)):
query_info = QueryInfo(i+Config().max_prefill_batch_size, prefill_query_length, max_seq_length, page_size, device, is_prefill=False, offset=offset)
query_info = QueryInfo(i+Config().max_prefill_batch_size, prefill_query_length, 256, page_size, device, is_prefill=False, offset=offset)
offset += max_seq_length // page_size
if tokens is not None:
query_info.query_tokens[prefill_active_length:prefill_active_length + 1].copy_(tokens)
if decode_active_position is None:
query_info.active_position = prefill_active_length
query_info.active_position = 255
else:
query_info.active_position = decode_active_position[i]
......
ktransformers/server/balance_serve/inference/model_runner.py
View file @ c6aa379d
......@@ -39,6 +39,17 @@ def pad_num_tokens(num_tokens):
def deduplicate_and_sort(lst):
return sorted(set(lst))
def generate_cuda_graphs(chunk_size: int) -> list:
# 如果输入不符合要求，assert掉
assert chunk_size <= 1024 or chunk_size % 1024 == 0, "chunk_size must <= 1024 or a multiple of 1024"
base_list = [1, 2, 3, Config().max_batch_size, 64, 256, 512, chunk_size]
if chunk_size <= 1024:
return base_list
multiples = [i for i in range(1024, chunk_size + 1, 1024)]
return deduplicate_and_sort(base_list + multiples)
class ModelRunner:
"""A CudaGraphRunner runs the forward pass of a model with CUDA graph and torch.compile."""
......@@ -56,7 +67,7 @@ class ModelRunner:
self.features_buf = None
self.output = None
self.graph_memory_pool = None
self.cuda_graphs = deduplicate_and_sort([1, 2, 3, Config().max_batch_size, 64, Config().chunk_size])
self.cuda_graphs = generate_cuda_graphs(Config().chunk_size)
self.use_cuda_graph = use_cuda_graph
self.model_time = 0
self.page_size = page_size
......
ktransformers/tests/dequant_gpu.py
View file @ c6aa379d
......@@ -7,7 +7,7 @@ sys.path.append(current_path+"/../..")
import numpy as np
# from ktransformers.operators.linear import KTransformersLinear, KLinearMarlin
# from ktransformers.operators.experts import KTransformersExperts, KExpertsTorch
from ktransformers.util.custom_gguf import GGUFLoader
from ktransformers.util.custom_loader import GGUFLoader
import torch
import KTransformersOps
torch.set_default_dtype(torch.bfloat16)
......
ktransformers/tests/dequant_gpu_t.py
View file @ c6aa379d
......@@ -9,7 +9,7 @@ from pycuda.compiler import SourceModule
import numpy as np
from ktransformers.operators.linear import KTransformersLinear, KLinearMarlin
from ktransformers.operators.experts import KTransformersExperts, KExpertsTorch
from ktransformers.util.custom_gguf import GGUFLoader, dequantize_q4_k_gpu, dequantize_q4_k
from ktransformers.util.custom_loader import GGUFLoader, dequantize_q4_k_gpu, dequantize_q4_k
import torch
import KTransformersOps
torch.set_default_dtype(torch.bfloat16)
......
ktransformers/tests/test_speed.py
View file @ c6aa379d
......@@ -159,5 +159,7 @@ if __name__ == "__main__":
prompt = ktansformer_prompt1024
elif args.prompt_lens == 2048:
prompt = ktansformer_prompt1024 * 2
elif args.prompt_lens == 4096:
prompt = ktansformer_prompt1024 * 4
asyncio.run(main(args.concurrent, prompt, max_tokens, model))
ktransformers/util/custom_gguf.py
View file @ c6aa379d
......@@ -25,7 +25,6 @@ import os
from enum import IntEnum
import torch
import KTransformersOps
from .custom_loader import SafeTensorLoader
import ctypes
import math
......@@ -166,238 +165,6 @@ DATA_TYPES = {
"FP8": 13,
}
class GGUFLoader:
tensor_info: dict
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):
raise FileNotFoundError(f"GGUF dir not found: {gguf_path}")
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 = {}
self.file_data_map = {}
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):
for file in files:
if file.endswith(".gguf"):
found_gguf = True
file_name = os.path.join(root, file)
with open(file_name, "rb") as f:
self.load_gguf(f)
if file_name not in self.file_data_map:
self.file_data_map[file_name] = np.memmap(file_name, mode = 'r')
if not found_gguf:
raise FileNotFoundError(f"Cannot find any .gguf files in: {gguf_path}")
def load_gguf(self, f):
f.seek(0)
assert f.read(4) == b'GGUF'
values = struct.unpack("<IQQ", f.read(4+8+8))
version, n_tensors, n_kv = values
if version != 3:
warnings.warn(f"Version {version} has never been tested, might not work")
info = {}
for _ in range(n_kv):
name = read_value(f, DATA_TYPES["string"])
data_type = struct.unpack("<I", f.read(4))[0]
info[name] = read_value(f, data_type)
tensor_info = {}
for _ in range(n_tensors):
name = read_value(f, DATA_TYPES["string"])
shape_len = read_value(f, DATA_TYPES["uint32"])
shape = [read_value(f, DATA_TYPES["uint64"]) for _ in range(shape_len)]
ggml_type = read_value(f, DATA_TYPES["uint32"])
bad_offset = read_value(f, DATA_TYPES["uint64"])
n_elems = int(math.prod(shape))
block_size, type_size = GGML_QUANT_SIZES[ggml_type]
n_bytes = n_elems * type_size // block_size
np_dims = tuple(reversed(shape))
item_type: npt.DTypeLike
if ggml_type == GGMLQuantizationType.F16:
item_count = n_elems
item_type = np.float16
elif ggml_type == GGMLQuantizationType.F32:
item_count = n_elems
item_type = np.float32
elif ggml_type == GGMLQuantizationType.F64:
item_count = n_elems
item_type = np.float64
elif ggml_type == GGMLQuantizationType.I8:
item_count = n_elems
item_type = np.int8
elif ggml_type == GGMLQuantizationType.I16:
item_count = n_elems
item_type = np.int16
elif ggml_type == GGMLQuantizationType.I32:
item_count = n_elems
item_type = np.int32
elif ggml_type == GGMLQuantizationType.I64:
item_count = n_elems
item_type = np.int64
else:
item_count = n_bytes
item_type = np.uint8
np_dims = quant_shape_to_byte_shape(np_dims, ggml_type)
tensor_info[name] = {
"ggml_type": ggml_type,
"shape": shape,
"bad_offset": bad_offset,
"item_type": item_type,
"item_count": item_count,
"np_dims": np_dims
}
start = f.tell()
# Alignment is 32 by default.
# https://github.com/ggerganov/ggml/blob/e1daebbf9d38d510ba456c4d50b4500a73ac2b14/docs/gguf.md?plain=1#L253
alignment = info.get("general.alignment", 32)
# Inconveniently, the offset defined in gguf files is relative to the
# end of the header and is unaligned.
# We need to compute the absolute file offset ourselves instead.
for t in tensor_info.values():
offset = start + t["bad_offset"]
offset += (alignment - offset % alignment) % alignment
t["offset"] = offset
for name in tensor_info:
self.tensor_file_map[name] = f.name
self.tensor_info.update(tensor_info)
self.gguf_file_meta.update(info)
def get_mmap_tensor(self, name):
t = self.tensor_info[name]
mmap_data = self.file_data_map[ self.tensor_file_map[name] ]
offset = t["offset"]
item_type = t["item_type"]
item_count = t["item_count"]
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 = "cuda", target_dtype = torch.get_default_dtype())->torch.Tensor:
t = self.tensor_info[name]
if device.lower() == "cpu":
print(f"loading expert {expert_id} of {name} with CPU")
shape = t["shape"]
ggml_type = t["ggml_type"]
if ggml_type not in GGML_NAMES:
raise NotImplementedError(f"ggml_type {ggml_type} not implemented")
ggml_name = GGML_NAMES[ggml_type]
# TODO: experts may fused in quant block, split it
assert elements_per_expert % GGML_ELEMENTS_PER_BLOCK[ggml_name] == 0, "experts may fused in quant block, please use CPU dequant"
blocks_per_experts = elements_per_expert // GGML_ELEMENTS_PER_BLOCK[ggml_name]
block_size = GGML_BLOCK_SIZES[ggml_name]
offset = expert_id * block_size * blocks_per_experts
data = data[offset: offset + block_size * blocks_per_experts]
if "cuda" in device.lower():
values = GGML_DEQUANTIZE_GPU[ggml_name](data, device, target_dtype)
else:
values = GGML_DEQUANTIZE[ggml_name](data)
values = torch.from_numpy(values.copy())
if ggml_name == "BF16":
values = values.view(torch.bfloat16)
values = values.view(shape[-2::-1])
return values
def load_gguf_tensor(self, name: str, device:str = "cpu", target_dtype = None)->torch.Tensor:
t = self.tensor_info[name]
if device.lower() == "cpu":
print(f"loading {name} with CPU")
if target_dtype == None:
target_dtype = torch.get_default_dtype()
shape = t["shape"]
ggml_type = t["ggml_type"]
if ggml_type not in GGML_NAMES:
raise NotImplementedError(f"ggml_type {ggml_type} not implemented")
ggml_name = GGML_NAMES[ggml_type]
data = self.get_mmap_tensor(name)
block_size = GGML_BLOCK_SIZES[ggml_name]
elements_per_block = GGML_ELEMENTS_PER_BLOCK[ggml_name]
num_elements = int(np.prod(shape))
num_blocks = num_elements // elements_per_block
blocks_per_iter = 16384
if num_blocks > blocks_per_iter: # dequant large tensor
values = torch.empty((num_blocks, elements_per_block), dtype=target_dtype, device=device)
for i in range( (num_blocks + blocks_per_iter - 1) // blocks_per_iter):
blocks_begin = i * blocks_per_iter
blocks_end = min(blocks_begin + blocks_per_iter, num_blocks)
if "cuda" in device.lower():
cur_values = GGML_DEQUANTIZE_GPU[ggml_name](data[blocks_begin*block_size : blocks_end*block_size], device, target_dtype)
else:
cur_values = GGML_DEQUANTIZE[ggml_name](data[blocks_begin*block_size : blocks_end*block_size])
cur_values = torch.from_numpy(cur_values.copy())
cur_values = cur_values.view(-1, elements_per_block)
if ggml_name == "BF16":
cur_values = cur_values.view(torch.bfloat16)
values[blocks_begin : blocks_end] = cur_values
else:
if "cuda" in device.lower():
values = GGML_DEQUANTIZE_GPU[ggml_name](data, device)
else:
values = GGML_DEQUANTIZE[ggml_name](data)
values = torch.from_numpy(values)
if ggml_name == "BF16":
values = values.view(torch.bfloat16)
values = values.view(shape[::-1])
if "attn_q" in name and self.gguf_file_meta['general.architecture'] in ["llama"]:
n_head = self.gguf_file_meta['llama.attention.head_count']
values = (values.reshape(n_head, values.shape[0] // n_head // 2, 2, *values.shape[1:])
.swapaxes(1, 2)
.reshape(values.shape))
elif "attn_k" in name and self.gguf_file_meta['general.architecture'] in ["llama"]:
n_head = self.gguf_file_meta['llama.attention.head_count_kv']
values = (values.reshape(n_head, values.shape[0] // n_head // 2, 2, *values.shape[1:])
.swapaxes(1, 2)
.reshape(values.shape))
return values
def read_value(f, data_type):
if data_type == DATA_TYPES["string"]:
......@@ -921,6 +688,7 @@ def translate_name_to_gguf(name):
name = name.replace(".gate_up_proj.", ".up_proj")
name = name.replace(".mlp.shared_experts.down_proj", ".ffn_down_shexp")
name = name.replace(".mlp.gate.e_score_correction_bias", ".exp_probs_b.bias")
name = name.replace(".mlp.gate", ".ffn_gate_inp")
name = name.replace(".mlp.shared_experts.gate_proj", ".ffn_gate_shexp")
name = name.replace(".mlp.shared_experts.up_proj", ".ffn_up_shexp")
......
ktransformers/util/custom_loader.py
View file @ c6aa379d
This diff is collapsed.
ktransformers/util/utils.py
View file @ c6aa379d
......@@ -22,8 +22,7 @@ from transformers import (
EtaLogitsWarper,
)
from ktransformers.util.custom_gguf import translate_name_to_gguf
from ktransformers.util.custom_gguf import GGUFLoader
from ktransformers.util.custom_loader import ModelLoaderFactory, ModelLoader, SafeTensorLoader, GGUFLoader, translate_name_to_gguf
from ktransformers.operators import base_operator
from ktransformers.models.custom_cache import StaticCache
from ktransformers.util.cuda_graph_runner import CUDAGraphRunner
......@@ -98,25 +97,24 @@ def get_all_used_cuda_device(device_map:dict):
all_device_list = list(all_device_list)
return all_device_list
def load_cur_state_dict(module: nn.Module, gguf_loader: GGUFLoader, prefix: str = ""):
def load_cur_state_dict(module: nn.Module, gguf_loader: ModelLoader, prefix: str = ""):
prefix = prefix.replace("orig_module.", "")
persistent_buffers = {k: v for k, v in module._buffers.items() if k not in module._non_persistent_buffers_set}
local_name_params = itertools.chain(module._parameters.items(), persistent_buffers.items())
local_state = {k: v for k, v in local_name_params if v is not None}
for name, param in local_state.items():
key = prefix + name
translated_key = translate_name_to_gguf(key)
translated_key = key
# 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
if isinstance(gguf_loader, SafeTensorLoader):
load_dequantized_tensor = gguf_loader.load_dequantized_tensor
else:
load_dequantized_tensor = gguf_loader.load_gguf_tensor
tensor_file_map = gguf_loader.tensor_file_map
if translated_key in tensor_file_map:
if gguf_loader.has_tensor(translated_key):
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}")
......@@ -128,7 +126,7 @@ def load_cur_state_dict(module: nn.Module, gguf_loader: GGUFLoader, prefix: str
#print(load_config.tensor_file_map.keys())
raise Exception(f"can't find {translated_key} in GGUF file!")
def load_weights(module:nn.Module, gguf_loader:GGUFLoader, prefix=''):
def load_weights(module:nn.Module, gguf_loader:ModelLoader, prefix=''):
#print(f"recursively loading weights {prefix}")
if not isinstance(module, base_operator.BaseInjectedModule):
load_cur_state_dict(module, gguf_loader, prefix)
......
ktransformers/util/weight_loader.py 0 → 100644
View file @ c6aa379d
from abc import ABC, abstractmethod
import os
import torch
import numpy as np
from safetensors import safe_open
from typing import Dict, Any, Optional, Union
class ModelLoader(ABC):
"""
Abstract base class for model loaders.
Defines the interface that all model loaders must implement.
"""
@abstractmethod
def load_tensor(self, name: str, device: str = "cpu") -> torch.Tensor:
"""
Load a tensor by name.
Args:
name: Name of the tensor to load
device: Device to load the tensor to
Returns:
The loaded tensor
"""
pass
@classmethod
@abstractmethod
def supports_format(cls, path: str) -> bool:
"""
Check if this loader supports the given path format.
Args:
path: Path to check
Returns:
True if this loader supports the given path, False otherwise
"""
pass
class SafeTensorLoader(ModelLoader):
"""
Loader for SafeTensor format models.
"""
def __init__(self, path: str):
"""
Initialize the SafeTensor loader.
Args:
path: Path to the model directory or file
"""
self.tensor_file_map = {} # Maps tensor names to file paths
self.file_handle_map = {} # Maps file names to file handles
self._load_tensor_file_map(path)
def _load_tensor_file_map(self, path: str) -> None:
"""
Load the tensor file map from the given path.
Args:
path: Path to the model directory or file
"""
# Normalize path to directory
if not os.path.exists(path):
raise FileNotFoundError(f"Path not found: {path}")
if os.path.isfile(path):
folder_path = os.path.dirname(path)
else:
folder_path = 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:
# Not raising an error here allows for the factory to try other loaders
print(f"No Safetensor files found in {folder_path}")
def load_tensor(self, name: str, device: str = "cpu") -> torch.Tensor:
"""
Load a tensor by name.
Args:
name: Name of the tensor to load
device: Device to load the tensor to
Returns:
The loaded tensor
"""
if name not in self.tensor_file_map:
raise KeyError(f"Key {name} not found in Safetensor files")
file = self.tensor_file_map[name]
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(name)
return tensor.to(device)
def load_dequantized_tensor(self, name: str, device: str = "cpu") -> torch.Tensor:
"""
Load and dequantize a tensor.
Args:
name: Name of the tensor to load
device: Device to load the tensor to
Returns:
The dequantized tensor
"""
if name not in self.tensor_file_map:
raise KeyError(f"Key {name} not found in Safetensor files")
file = self.tensor_file_map[name]
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(name).to(device)
if name.endswith(".weight"):
if name[:-7] + ".weight_scale_inv" in self.tensor_file_map:
weight_scale_inv = f.get_tensor(name[:-7] + ".weight_scale_inv").to(device)
# Assuming weight_dequant function is imported
from ktransformers.ktransformers_ext.triton.fp8gemm import weight_dequant
tensor = weight_dequant(tensor, weight_scale_inv)
return tensor.to(device)
def close_all_handles(self) -> None:
"""
Close all file handles.
"""
for handle in self.file_handle_map.values():
handle.close()
self.file_handle_map.clear()
@classmethod
def supports_format(cls, path: str) -> bool:
"""
Check if this loader supports the given path format.
Args:
path: Path to check
Returns:
True if safetensor files are found in the path, False otherwise
"""
# Normalize path to directory
if not os.path.exists(path):
return False
if os.path.isfile(path):
if path.endswith(".safetensors"):
return True
folder_path = os.path.dirname(path)
else:
folder_path = path
# Check if any safetensor files exist in the folder
for root, _, files in os.walk(folder_path):
for file in files:
if file.endswith(".safetensors"):
return True
return False
class GGUFLoader(ModelLoader):
"""
Loader for GGUF format models.
"""
def __init__(self, path: str):
"""
Initialize the GGUF loader.
Args:
path: Path to the model directory or file
"""
# Check if path exists
if not os.path.exists(path):
raise FileNotFoundError(f"GGUF dir not found: {path}")
if os.path.isfile(path):
self.gguf_path = os.path.dirname(path)
else:
self.gguf_path = path
self.tensor_info = {} # Stores tensor metadata
self.tensor_file_map = {} # Maps tensor names to file paths
self.file_data_map = {} # Maps file paths to memory-mapped data
self.gguf_file_meta = {} # Stores GGUF metadata
# For compatibility with the factory pattern
self.safetensor_loader = None
# Scan all GGUF files in the directory
found_gguf = False
for root, _, files in os.walk(self.gguf_path):
for file in files:
if file.endswith(".gguf"):
found_gguf = True
file_path = os.path.join(root, file)
with open(file_path, "rb") as f:
self._load_gguf(f)
if file_path not in self.file_data_map:
self.file_data_map[file_path] = np.memmap(file_path, mode='r')
if not found_gguf:
raise FileNotFoundError(f"Cannot find any .gguf files in: {self.gguf_path}")
def _load_gguf(self, f) -> None:
"""
Load GGUF file metadata and tensor info.
Args:
f: File handle of the GGUF file
"""
# Implementation should follow the original GGUFLoader._load_gguf
# This is a simplified version for illustration
f.seek(0)
assert f.read(4) == b'GGUF'
# Read header
values = struct.unpack("<IQQ", f.read(4+8+8))
version, n_tensors, n_kv = values
if version != 3:
warnings.warn(f"Version {version} has never been tested, might not work")
# Read key-value pairs
info = {}
for _ in range(n_kv):
name = self._read_value(f, 8) # DATA_TYPES["string"]
data_type = struct.unpack("<I", f.read(4))[0]
info[name] = self._read_value(f, data_type)
# Read tensor info
tensor_info = {}
for _ in range(n_tensors):
name = self._read_value(f, 8) # DATA_TYPES["string"]
shape_len = self._read_value(f, 4) # DATA_TYPES["uint32"]
shape = [self._read_value(f, 10) for _ in range(shape_len)] # DATA_TYPES["uint64"]
ggml_type = self._read_value(f, 4) # DATA_TYPES["uint32"]
offset = self._read_value(f, 10) # DATA_TYPES["uint64"]
# Additional tensor metadata would be calculated here
# For brevity, we're omitting the detailed tensor metadata calculation
tensor_info[name] = {
"ggml_type": ggml_type,
"shape": shape,
"offset": offset,
# ... other tensor metadata
}
start = f.tell()
alignment = info.get("general.alignment", 32)
# Calculate actual file offsets
for t in tensor_info.values():
offset = start + t["offset"]
offset += (alignment - offset % alignment) % alignment
t["offset"] = offset
# Update file maps
for name in tensor_info:
self.tensor_file_map[name] = f.name
self.tensor_info.update(tensor_info)
self.gguf_file_meta.update(info)
def _read_value(self, f, data_type) -> Any:
"""
Read a value from the file according to its data type.
Args:
f: File handle
data_type: Type of data to read
Returns:
The read value
"""
# Simplified implementation
# In a complete implementation, this would handle all data types
if data_type == 8: # DATA_TYPES["string"]
length = struct.unpack("<Q", f.read(8))[0]
return f.read(length).decode("utf-8")
elif data_type == 4: # DATA_TYPES["uint32"]
return struct.unpack("<I", f.read(4))[0]
elif data_type == 10: # DATA_TYPES["uint64"]
return struct.unpack("<Q", f.read(8))[0]
# ... handling for other data types
return None
def load_tensor(self, name: str, device: str = "cpu") -> torch.Tensor:
"""
Load a tensor by name.
Args:
name: Name of the tensor to load
device: Device to load the tensor to
Returns:
The loaded tensor
"""
# This should call load_gguf_tensor with the appropriate parameters
return self.load_gguf_tensor(name, device)
def load_gguf_tensor(self, name: str, device: str = "cpu", target_dtype = None) -> torch.Tensor:
"""
Load a GGUF tensor by name.
Args:
name: Name of the tensor to load
device: Device to load the tensor to
target_dtype: Target data type for the tensor
Returns:
The loaded tensor
"""
# Implementation would follow the original GGUFLoader.load_gguf_tensor
# This is a placeholder for illustration
if name not in self.tensor_info:
raise KeyError(f"Tensor {name} not found")
# Actual implementation would dequantize the tensor data
# and return a torch.Tensor
return torch.zeros(1, device=device) # Placeholder
@classmethod
def supports_format(cls, path: str) -> bool:
"""
Check if this loader supports the given path format.
Args:
path: Path to check
Returns:
True if GGUF files are found in the path, False otherwise
"""
# Normalize path to directory
if not os.path.exists(path):
return False
if os.path.isfile(path):
return path.endswith(".gguf")
# Check if any GGUF files exist in the folder
for root, _, files in os.walk(path):
for file in files:
if file.endswith(".gguf"):
return True
return False
\ No newline at end of file
merge_tensors/merge_safetensor_gguf.py
View file @ c6aa379d
......@@ -6,7 +6,7 @@ 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 ktransformers.util.custom_loader import GGUFLoader, translate_name_to_gguf
from safetensors import safe_open
from safetensors.torch import save_file
import re
......
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