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Fix kt-kernel for new wrapper (#1588)

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* update README for kt-kernel

* style: format C++ and Python code in kt-kernel

  - Format C++ files: task_queue, ext_bindings, and MoE operators
  - Format Python utility modules: amx, llamafile, and loader
  - Improve code readability and consistency
This commit is contained in:
Jiaqi Liao
2025-11-10 21:47:34 +08:00
committed by GitHub
parent 9bc00e587b
commit 94c25626dc
10 changed files with 219 additions and 179 deletions
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11
kt-kernel/python/__init__.py
View File

@@ -6,8 +6,8 @@ KT-Kernel provides high-performance kernel operations for KTransformers,
including CPU-optimized MoE inference with AMX, AVX, and KML support.
Example usage:
>>> from kt_kernel import AMXMoEWrapper
>>> wrapper = AMXMoEWrapper(
>>> from kt_kernel import KTMoEWrapper
>>> wrapper = KTMoEWrapper(
... layer_idx=0,
... num_experts=8,
... num_experts_per_tok=2,
@@ -15,9 +15,10 @@ Example usage:
... moe_intermediate_size=14336,
... num_gpu_experts=2,
... cpuinfer_threads=32,
... subpool_count=2,
... amx_weight_path="/path/to/weights",
... chunked_prefill_size=512
... threadpool_count=2,
... weight_path="/path/to/weights",
... chunked_prefill_size=512,
... method="AMXINT4"
... )
"""

10
kt-kernel/python/experts_base.py
View File

@@ -18,13 +18,13 @@ import ctypes
import kt_kernel_ext
class KExpertsCPUBuffer:
"""
CPU buffer management for expert computation.
Manages pinned memory buffers for efficient GPU-CPU data transfer.
"""
capture_bs: List = list()
capture_buffers: Dict = dict()
temp_bs: int = 0
@@ -62,8 +62,7 @@ class KExpertsCPUBuffer:
for _ in range(cls.buffer_depth)
]
bsz_tensor_cpu = [
torch.zeros((1,), device="cpu", dtype=torch.int32, pin_memory=True)
for _ in range(cls.buffer_depth)
torch.zeros((1,), device="cpu", dtype=torch.int32, pin_memory=True) for _ in range(cls.buffer_depth)
]
output_gpu = [
torch.zeros((batch_size, hidden_size), device=hidden_states.device, dtype=hidden_states.dtype)
@@ -129,7 +128,6 @@ class BaseMoEWrapper(ABC):
max_deferred_experts_per_token: Number of experts per token to defer on this layer. Defaults to 0 (no defer).
method: Backend method string
"""
print(f"Init {self.__class__.__name__}")
self.layer_idx = layer_idx
self.num_experts = num_experts
self.num_experts_per_tok = num_experts_per_tok
@@ -139,7 +137,9 @@ class BaseMoEWrapper(ABC):
self.weight_path = weight_path
self.chunked_prefill_size = chunked_prefill_size
self.cpu_save = cpu_save
self.max_deferred_experts_per_token = int(max_deferred_experts_per_token) if max_deferred_experts_per_token is not None else 0
self.max_deferred_experts_per_token = (
int(max_deferred_experts_per_token) if max_deferred_experts_per_token is not None else 0
)
BaseMoEWrapper._layer_has_pending_deferred[self.layer_idx] = False
self.method = method

4
kt-kernel/python/utils/amx.py
View File

@@ -6,15 +6,17 @@ import ctypes
from ..experts_base import BaseMoEWrapper
from .loader import SafeTensorLoader
from kt_kernel_ext.moe import MOEConfig
try:
from kt_kernel_ext.moe import AMXInt4_MOE, AMXInt8_MOE
_HAS_AMX_SUPPORT = True
except (ImportError, AttributeError):
_HAS_AMX_SUPPORT = False
AMXInt4_MOE, AMXInt8_MOE = None, None
from typing import Optional
class AMXMoEWrapper(BaseMoEWrapper):
"""

22
kt-kernel/python/utils/llamafile.py
View File

@@ -1,12 +1,15 @@
import torch
from typing import Optional
import os
# Use relative imports for package structure
from ..experts_base import BaseMoEWrapper
from .loader import GGUFLoader
from kt_kernel_ext.moe import MOEConfig
try:
from kt_kernel_ext.moe import MOE
_HAS_LLAMAFILE_SUPPORT = True
except (ImportError, AttributeError):
_HAS_LLAMAFILE_SUPPORT = False
@@ -14,6 +17,7 @@ except (ImportError, AttributeError):
from kt_kernel_ext.kvcache import ggml_type
class LlamafileMoEWrapper(BaseMoEWrapper):
"""
Llamafile-based MoE wrapper implementation.
@@ -162,27 +166,17 @@ class LlamafileMoEWrapper(BaseMoEWrapper):
)
if physical_to_logical_map_cpu is None:
physical_to_logical_map_cpu = torch.arange(
self.num_experts,
dtype=torch.int32,
device="cpu"
)
physical_to_logical_map_cpu = torch.arange(self.num_experts, dtype=torch.int32, device="cpu")
print(f" Using default identity mapping for {self.num_experts} experts")
base_key = f"blk.{self.layer_idx}"
# Load quantized tensors from GGUF
gate_data, gate_type = self.gguf_loader.get_undequanted_tensor_and_ggml_type(
f"{base_key}.ffn_gate_exps.weight"
)
gate_data, gate_type = self.gguf_loader.get_undequanted_tensor_and_ggml_type(f"{base_key}.ffn_gate_exps.weight")
up_data, up_type = self.gguf_loader.get_undequanted_tensor_and_ggml_type(
f"{base_key}.ffn_up_exps.weight"
)
up_data, up_type = self.gguf_loader.get_undequanted_tensor_and_ggml_type(f"{base_key}.ffn_up_exps.weight")
down_data, down_type = self.gguf_loader.get_undequanted_tensor_and_ggml_type(
f"{base_key}.ffn_down_exps.weight"
)
down_data, down_type = self.gguf_loader.get_undequanted_tensor_and_ggml_type(f"{base_key}.ffn_down_exps.weight")
# Keep tensors alive
self.weights_to_keep = (gate_data, up_data, down_data)

153
kt-kernel/python/utils/loader.py
View File

@@ -18,35 +18,36 @@ from gguf.gguf_reader import GGUFReader
class GGMLQuantizationType(IntEnum):
"""GGML quantization type enumeration"""
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
IQ2_XXS = 16
IQ2_XS = 17
IQ2_XS = 17
IQ3_XXS = 18
IQ1_S = 19
IQ4_NL = 20
IQ3_S = 21
IQ2_S = 22
IQ4_XS = 23
I8 = 24
I16 = 25
I32 = 26
I64 = 27
F64 = 28
IQ1_M = 29
BF16 = 30
IQ1_S = 19
IQ4_NL = 20
IQ3_S = 21
IQ2_S = 22
IQ4_XS = 23
I8 = 24
I16 = 25
I32 = 26
I64 = 27
F64 = 28
IQ1_M = 29
BF16 = 30
def translate_name_to_gguf(name):
@@ -104,6 +105,7 @@ class SafeTensorLoader:
Supports loading tensors from .safetensors files with NUMA-sharded expert weights.
"""
tensor_file_map: dict
tensor_type_map: dict
file_handle_map: dict
@@ -257,7 +259,7 @@ class GGUFLoader:
self.tensor_file_map = {}
self.file_data_map = {}
if os.path.isfile(gguf_path) and gguf_path.endswith('.gguf'):
if os.path.isfile(gguf_path) and gguf_path.endswith(".gguf"):
print(f"\n[GGUFLoader] Loading single GGUF file : {os.path.basename(gguf_path)}")
self._load_single_file(gguf_path)
elif os.path.isdir(gguf_path):
@@ -283,24 +285,24 @@ class GGUFLoader:
for key, field in reader.fields.items():
value = field.parts[field.data[0]]
if isinstance(value, bytes):
value = value.decode('utf-8')
value = value.decode("utf-8")
elif isinstance(value, np.ndarray) and value.dtype == np.uint8:
try:
value = bytes(value).decode('utf-8')
value = bytes(value).decode("utf-8")
except:
pass
self.metadata[key] = value
for tensor in reader.tensors:
self.tensor_info[tensor.name] = {
'shape': list(reversed(tensor.shape)), # Reverse to match PyTorch order
'dtype': tensor.tensor_type,
'offset': tensor.data_offset,
'n_elements': tensor.n_elements,
"shape": list(reversed(tensor.shape)), # Reverse to match PyTorch order
"dtype": tensor.tensor_type,
"offset": tensor.data_offset,
"n_elements": tensor.n_elements,
}
self.tensor_file_map[tensor.name] = file_path
self.file_data_map[file_path] = np.memmap(file_path, mode='r')
self.file_data_map[file_path] = np.memmap(file_path, mode="r")
def _load_directory(self, dir_path: str):
"""Load all GGUF files from a directory (non-recursive)"""
@@ -317,24 +319,24 @@ class GGUFLoader:
for key, field in reader.fields.items():
value = field.parts[field.data[0]]
if isinstance(value, bytes):
value = value.decode('utf-8')
value = value.decode("utf-8")
elif isinstance(value, np.ndarray) and value.dtype == np.uint8:
try:
value = bytes(value).decode('utf-8')
value = bytes(value).decode("utf-8")
except:
pass
self.metadata[key] = value
for tensor in reader.tensors:
self.tensor_info[tensor.name] = {
'shape': list(reversed(tensor.shape)),
'dtype': tensor.tensor_type,
'offset': tensor.data_offset,
'n_elements': tensor.n_elements,
"shape": list(reversed(tensor.shape)),
"dtype": tensor.tensor_type,
"offset": tensor.data_offset,
"n_elements": tensor.n_elements,
}
self.tensor_file_map[tensor.name] = file_path
self.file_data_map[file_path] = np.memmap(file_path, mode='r')
self.file_data_map[file_path] = np.memmap(file_path, mode="r")
if not found_gguf:
raise FileNotFoundError(f"No .gguf files found in directory: {dir_path}")
@@ -407,7 +409,7 @@ class GGUFLoader:
base_key = f"blk.{layer_idx}.ffn_gate_exps.weight"
if base_key in self.tensor_info:
gate_shape = self.tensor_info[base_key]['shape']
gate_shape = self.tensor_info[base_key]["shape"]
print(f" Found tensor '{base_key}' with shape: {gate_shape}")
if len(gate_shape) >= 3:
@@ -438,8 +440,9 @@ class GGUFLoader:
print(f" Total metadata entries: {len(self.metadata)}")
if filter_keywords:
filtered = {k: v for k, v in self.metadata.items()
if any(kw.lower() in k.lower() for kw in filter_keywords)}
filtered = {
k: v for k, v in self.metadata.items() if any(kw.lower() in k.lower() for kw in filter_keywords)
}
for k, v in sorted(filtered.items()):
print(f" {k}: {v}")
else:
@@ -477,40 +480,40 @@ class GGUFLoader:
file_path = self.tensor_file_map[name]
mmap_data = self.file_data_map[file_path]
offset = info['offset']
n_elements = info['n_elements']
ggml_type = info['dtype']
offset = info["offset"]
n_elements = info["n_elements"]
ggml_type = info["dtype"]
GGML_QUANT_SIZES = {
GGMLQuantizationType.F32: (1, 4),
GGMLQuantizationType.F16: (1, 2),
GGMLQuantizationType.BF16: (1, 2),
GGMLQuantizationType.Q4_0: (32, 2 + 16),
GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
GGMLQuantizationType.Q8_0: (32, 2 + 32),
GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
GGMLQuantizationType.Q2_K: (256, 2 + 2 + 256 // 16 + 256 // 4),
GGMLQuantizationType.Q3_K: (256, 2 + 256 // 4 + 256 // 8 + 12),
GGMLQuantizationType.Q4_K: (256, 2 + 2 + 256 // 2 + 12),
GGMLQuantizationType.Q5_K: (256, 2 + 2 + 256 // 2 + 256 // 8 + 12),
GGMLQuantizationType.Q6_K: (256, 2 + 256 // 2 + 256 // 4 + 256 // 16),
GGMLQuantizationType.Q8_K: (256, 4 + 256 + 256 // 8),
GGMLQuantizationType.F32: (1, 4),
GGMLQuantizationType.F16: (1, 2),
GGMLQuantizationType.BF16: (1, 2),
GGMLQuantizationType.Q4_0: (32, 2 + 16),
GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
GGMLQuantizationType.Q8_0: (32, 2 + 32),
GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
GGMLQuantizationType.Q2_K: (256, 2 + 2 + 256 // 16 + 256 // 4),
GGMLQuantizationType.Q3_K: (256, 2 + 256 // 4 + 256 // 8 + 12),
GGMLQuantizationType.Q4_K: (256, 2 + 2 + 256 // 2 + 12),
GGMLQuantizationType.Q5_K: (256, 2 + 2 + 256 // 2 + 256 // 8 + 12),
GGMLQuantizationType.Q6_K: (256, 2 + 256 // 2 + 256 // 4 + 256 // 16),
GGMLQuantizationType.Q8_K: (256, 4 + 256 + 256 // 8),
GGMLQuantizationType.IQ2_XXS: (256, 2 + 256 // 4),
GGMLQuantizationType.IQ2_XS: (256, 2 + 256 // 4 + 256 // 32),
GGMLQuantizationType.IQ2_XS: (256, 2 + 256 // 4 + 256 // 32),
GGMLQuantizationType.IQ3_XXS: (256, 2 + 256 // 4 + 256 // 8),
GGMLQuantizationType.IQ1_S: (256, 2 + 256 // 8 + 256 // 16),
GGMLQuantizationType.IQ4_NL: (32, 2 + 16),
GGMLQuantizationType.IQ3_S: (256, 2 + 256 // 4 + 256 // 8 + 256 // 32 + 4),
GGMLQuantizationType.IQ2_S: (256, 2 + 256 // 4 + 256 // 16),
GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + 256 // 2 + 256 // 64),
GGMLQuantizationType.I8: (1, 1),
GGMLQuantizationType.I16: (1, 2),
GGMLQuantizationType.I32: (1, 4),
GGMLQuantizationType.I64: (1, 8),
GGMLQuantizationType.F64: (1, 8),
GGMLQuantizationType.IQ1_M: (256, 256 // 8 + 256 // 16 + 256 // 32),
GGMLQuantizationType.IQ1_S: (256, 2 + 256 // 8 + 256 // 16),
GGMLQuantizationType.IQ4_NL: (32, 2 + 16),
GGMLQuantizationType.IQ3_S: (256, 2 + 256 // 4 + 256 // 8 + 256 // 32 + 4),
GGMLQuantizationType.IQ2_S: (256, 2 + 256 // 4 + 256 // 16),
GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + 256 // 2 + 256 // 64),
GGMLQuantizationType.I8: (1, 1),
GGMLQuantizationType.I16: (1, 2),
GGMLQuantizationType.I32: (1, 4),
GGMLQuantizationType.I64: (1, 8),
GGMLQuantizationType.F64: (1, 8),
GGMLQuantizationType.IQ1_M: (256, 256 // 8 + 256 // 16 + 256 // 32),
}
block_size, type_size = GGML_QUANT_SIZES[ggml_type]