mirror of
https://github.com/kvcache-ai/ktransformers.git
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698 lines
26 KiB
Python
698 lines
26 KiB
Python
# Wrapper for AMX MoE CPU inference operations
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# This module encapsulates CPU inference engine, weight loading, and buffer management
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# SPDX-License-Identifier: Apache-2.0
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"""
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Expert wrappers for CPU-based MoE inference.
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This module provides high-level Python wrappers around the low-level C++ kernel
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implementations, handling weight loading, buffer management, and forward inference.
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"""
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from __future__ import annotations
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import torch
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from typing import Dict, List, Optional, Tuple
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from safetensors import safe_open
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import os
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import ctypes
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# Import the C++ extension module (compiled as cpuinfer_ext)
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import cpuinfer_ext
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from cpuinfer_ext.moe import MOEConfig, AMXInt4_MOE, AMXInt8_MOE
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class SafeTensorLoader:
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tensor_file_map: dict
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tensor_type_map: dict
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file_handle_map: dict
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tensor_device_map: dict
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def __init__(self, file_path: str):
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self.__load_tensor_file_map(file_path)
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def __load_tensor_file_map(self, file_path: str):
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"Path not found: {file_path}")
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if os.path.isfile(file_path):
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folder_path = os.path.dirname(file_path)
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else:
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folder_path = file_path
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self.file_handle_map = {}
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self.tensor_file_map = {}
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self.tensor_type_map = {}
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self.tensor_device_map = {}
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found_safetensor = False
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for root, _, files in os.walk(folder_path):
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files = sorted(files)
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for file in files:
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if file.endswith(".safetensors"):
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found_safetensor = True
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file_path = os.path.join(root, file)
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if file not in self.file_handle_map:
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try:
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handle = safe_open(file_path, framework="pt")
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self.file_handle_map[file] = handle
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except Exception as e:
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print(f"Error opening Safetensor file {file_path}: {e}")
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continue
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f = self.file_handle_map.get(file)
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if f is None:
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continue
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try:
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for key in f.keys():
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self.tensor_file_map[key] = file
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except Exception as e:
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print(f"Error reading Safetensor file {file_path}: {e}")
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if not found_safetensor:
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raise FileNotFoundError(f"No Safetensor files found in {folder_path}")
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def load_tensor(self, key: str, device: str = "cpu"):
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if key not in self.tensor_file_map:
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raise KeyError(f"Key {key} not found in Safetensor files")
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file = self.tensor_file_map[key]
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f = self.file_handle_map.get(file)
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if f is None:
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raise FileNotFoundError(f"File {file} not found in Safetensor files")
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tensor = f.get_tensor(key)
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return tensor.to(device)
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def close_all_handles(self):
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for handle in self.file_handle_map.values():
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handle.close()
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self.file_handle_map.clear()
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def load_experts(self, base_key: str, device: str = "cpu"):
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# base_key: blk.{layer_index}
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# blk.{layer_index}.ffn_[up, down, gate]_exps.{expert_id}.numa.{numa_id}.weight
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up_base_key = f"{base_key}.ffn_up_exps"
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gate_base_key = f"{base_key}.ffn_gate_exps"
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down_base_key = f"{base_key}.ffn_down_exps"
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max_numa_id = -1
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max_experts_count = -1
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while self.has_tensor(f"{up_base_key}.{max_experts_count+1}.numa.{0}.weight"):
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max_experts_count += 1
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if max_experts_count == 0:
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raise ValueError(f"No experts found for key {base_key}")
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while self.has_tensor(f"{up_base_key}.{0}.numa.{max_numa_id+1}.weight"):
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max_numa_id += 1
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# Initialize empty lists to store tensors for each projection type
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up_weights = [[] for _ in range(max_numa_id + 1)]
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gate_weights = [[] for _ in range(max_numa_id + 1)]
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down_weights = [[] for _ in range(max_numa_id + 1)]
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up_scales = [[] for _ in range(max_numa_id + 1)]
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gate_scales = [[] for _ in range(max_numa_id + 1)]
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down_scales = [[] for _ in range(max_numa_id + 1)]
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for numa_id in range(max_numa_id + 1):
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for expert_id in range(max_experts_count + 1):
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up_key = f"{up_base_key}.{expert_id}.numa.{numa_id}.weight"
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gate_key = f"{gate_base_key}.{expert_id}.numa.{numa_id}.weight"
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down_key = f"{down_base_key}.{expert_id}.numa.{numa_id}.weight"
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up_scale_key = f"{up_base_key}.{expert_id}.numa.{numa_id}.scale"
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gate_scale_key = f"{gate_base_key}.{expert_id}.numa.{numa_id}.scale"
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down_scale_key = f"{down_base_key}.{expert_id}.numa.{numa_id}.scale"
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# make sure contiguous
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up_tensor = self.load_tensor(up_key, device).numpy()
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gate_tensor = self.load_tensor(gate_key, device).numpy()
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down_tensor = self.load_tensor(down_key, device).numpy()
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up_scale_tensor = self.load_tensor(up_scale_key, device).numpy()
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gate_scale_tensor = self.load_tensor(gate_scale_key, device).numpy()
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down_scale_tensor = self.load_tensor(down_scale_key, device).numpy()
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up_weights[numa_id].append(up_tensor)
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gate_weights[numa_id].append(gate_tensor)
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down_weights[numa_id].append(down_tensor)
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up_scales[numa_id].append(up_scale_tensor)
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gate_scales[numa_id].append(gate_scale_tensor)
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down_scales[numa_id].append(down_scale_tensor)
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return {
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"up": up_weights,
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"gate": gate_weights,
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"down": down_weights,
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"up_scale": up_scales,
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"gate_scale": gate_scales,
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"down_scale": down_scales,
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}
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def has_tensor(self, name: str):
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return name in self.tensor_file_map
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class KExpertsCPUBuffer:
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capture_bs: List = list()
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capture_buffers: Dict = dict()
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temp_bs: int = 0
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temp_buffer: tuple = tuple()
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buffer_depth: int = 2
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@classmethod
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def get_buffer(cls, hidden_states: torch.Tensor, num_experts_per_tok):
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hidden_size = hidden_states.shape[-1]
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batch_size = hidden_states.shape[0]
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if batch_size in cls.capture_buffers:
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return cls.capture_buffers[batch_size]
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if batch_size == cls.temp_bs:
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return cls.temp_buffer
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input_tensor_cpu = [
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torch.zeros((batch_size, hidden_size), device="cpu", pin_memory=True, dtype=torch.bfloat16)
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for _ in range(cls.buffer_depth)
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]
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immediate_experts_ids_cpu = [
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torch.zeros((batch_size, num_experts_per_tok), device="cpu", dtype=torch.long, pin_memory=True)
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for _ in range(cls.buffer_depth)
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]
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deferred_experts_ids_cpu = [
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torch.full((batch_size, num_experts_per_tok), -1, device="cpu", dtype=torch.long, pin_memory=True)
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for _ in range(cls.buffer_depth)
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]
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weights_cpu = [
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torch.zeros((batch_size, num_experts_per_tok), device="cpu", dtype=torch.float32, pin_memory=True)
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for _ in range(cls.buffer_depth)
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]
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output_cpu = [
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torch.zeros((batch_size, hidden_size), device="cpu", pin_memory=True, dtype=torch.bfloat16)
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for _ in range(cls.buffer_depth)
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]
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bsz_tensor_cpu = [
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torch.zeros((1,), device="cpu", dtype=torch.int32, pin_memory=True)
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for _ in range(cls.buffer_depth)
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]
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output_gpu = [
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torch.zeros((batch_size, hidden_size), device=hidden_states.device, dtype=hidden_states.dtype)
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for _ in range(cls.buffer_depth)
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]
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cur_buffer = (
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input_tensor_cpu,
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immediate_experts_ids_cpu,
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deferred_experts_ids_cpu,
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weights_cpu,
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output_cpu,
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bsz_tensor_cpu,
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output_gpu,
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)
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if batch_size in cls.capture_bs:
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cls.capture_buffers[batch_size] = cur_buffer
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cls.temp_bs = batch_size
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cls.temp_buffer = cur_buffer
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return cur_buffer
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class AMXMoEWrapper:
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"""
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Wrapper for AMX MoE CPU inference operations.
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Manages CPU inference engine, weight loading, and buffer management.
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"""
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_cpu_infer_instance = None
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_safetensor_loader_instance = None
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_layer_has_pending_deferred: Dict[int, bool] = {}
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def __init__(
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self,
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layer_idx: int,
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num_experts: int,
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num_experts_per_tok: int,
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hidden_size: int,
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moe_intermediate_size: int,
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num_gpu_experts: int,
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cpuinfer_threads: int,
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threadpool_count: int,
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amx_weight_path: str,
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chunked_prefill_size: int,
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cpu_save: bool = False,
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max_deferred_experts_per_token: Optional[int] = None,
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):
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"""
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Initialize AMX MoE Wrapper.
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Args:
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layer_idx: Layer index
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num_experts: Total number of experts
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num_experts_per_tok: Number of experts per token (top-k)
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hidden_size: Hidden dimension size
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moe_intermediate_size: MoE intermediate size
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num_gpu_experts: Number of experts to run on GPU
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cpuinfer_threads: Number of CPU inference threads
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threadpool_count: Number of NUMA subpools
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amx_weight_path: Path to AMX weights
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chunked_prefill_size: Maximum prefill chunk size
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cpu_save: Whether to save weights to CPU memory
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max_deferred_experts_per_token: Number of experts per token to defer on this layer. Defaults to 0 (no defer).
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"""
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self.layer_idx = layer_idx
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self.num_experts = num_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.hidden_size = hidden_size
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self.moe_intermediate_size = moe_intermediate_size
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self.num_gpu_experts = num_gpu_experts
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self.amx_weight_path = amx_weight_path
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self.chunked_prefill_size = chunked_prefill_size
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self.cpu_save = cpu_save
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self.max_deferred_experts_per_token = int(max_deferred_experts_per_token) if max_deferred_experts_per_token is not None else 0
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AMXMoEWrapper._layer_has_pending_deferred[self.layer_idx] = False
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# Initialize CPU inference engine (singleton)
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if AMXMoEWrapper._cpu_infer_instance is None:
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worker_config = cpuinfer_ext.WorkerPoolConfig()
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subpool_numa_map = list(range(threadpool_count))
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subpool_thread_count = [
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cpuinfer_threads // threadpool_count + (1 if i < cpuinfer_threads % threadpool_count else 0)
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for i in range(threadpool_count)
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]
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worker_config.subpool_count = threadpool_count
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worker_config.subpool_numa_map = subpool_numa_map
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worker_config.subpool_thread_count = subpool_thread_count
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AMXMoEWrapper._cpu_infer_instance = cpuinfer_ext.CPUInfer(worker_config)
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self.cpu_infer = AMXMoEWrapper._cpu_infer_instance
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# Check if we should load merged safetensor weights
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self.load_merged_weight = False
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import glob
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if glob.glob(os.path.join(amx_weight_path, "*.safetensors")):
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self.load_merged_weight = True
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# Initialize SafeTensor loader (singleton)
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if self.load_merged_weight:
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if AMXMoEWrapper._safetensor_loader_instance is None:
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AMXMoEWrapper._safetensor_loader_instance = SafeTensorLoader(amx_weight_path)
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self.safetensor_loader = AMXMoEWrapper._safetensor_loader_instance
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self.moe = None
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self.gate_weights = None
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self.up_weights = None
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self.down_weights = None
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self.gate_scales = None
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self.up_scales = None
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self.down_scales = None
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def load_weights_from_tensors(
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self,
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gate_proj: torch.Tensor,
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up_proj: torch.Tensor,
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down_proj: torch.Tensor,
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physical_to_logical_map_cpu: torch.Tensor,
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):
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"""
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Load and quantize weights from BF16/FP16 tensors (online quantization).
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Args:
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gate_proj: Gate projection weights [num_experts, intermediate_size, hidden_size]
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up_proj: Up projection weights [num_experts, intermediate_size, hidden_size]
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down_proj: Down projection weights [num_experts, hidden_size, intermediate_size]
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physical_to_logical_map_cpu: Mapping from physical to logical expert IDs
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"""
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# Store tensors as instance variables to keep them alive
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self.gate_proj = gate_proj.contiguous()
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self.up_proj = up_proj.contiguous()
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self.down_proj = down_proj.contiguous()
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# Configure MoE with online quantization (cpu_save mode)
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moe_config = MOEConfig(
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self.num_experts,
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self.num_experts_per_tok,
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self.hidden_size,
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self.moe_intermediate_size,
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self.num_gpu_experts,
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)
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moe_config.layer_idx = self.layer_idx
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moe_config.pool = self.cpu_infer.backend_
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moe_config.max_len = self.chunked_prefill_size
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# Enable save mode for online quantization
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moe_config.save = True
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moe_config.load = False
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# Set weight pointers
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moe_config.gate_proj = self.gate_proj.data_ptr()
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moe_config.up_proj = self.up_proj.data_ptr()
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moe_config.down_proj = self.down_proj.data_ptr()
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# Set output path for quantized weights
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moe_config.path = self.amx_weight_path
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# Create MoE module based on AMX method
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amx_method = os.environ.get("AMX_METHOD", "AMXINT4")
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if amx_method == "AMXINT4":
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self.moe = AMXInt4_MOE(moe_config)
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elif amx_method == "AMXINT8":
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self.moe = AMXInt8_MOE(moe_config)
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else:
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raise NotImplementedError(f"Unsupported AMX method: {amx_method}")
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# Submit quantization and save task
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self.cpu_infer.submit(self.moe.load_weights_task(physical_to_logical_map_cpu.data_ptr()))
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self.cpu_infer.sync()
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def load_weights(self, physical_to_logical_map_cpu: torch.Tensor):
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"""
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Load weights for this layer and initialize the MoE module.
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Args:
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physical_to_logical_map_cpu: Mapping from physical to logical expert IDs
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"""
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gate_ptr = 0
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up_ptr = 0
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down_ptr = 0
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gate_ptrs = []
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up_ptrs = []
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down_ptrs = []
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gate_scale_ptrs = []
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up_scale_ptrs = []
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down_scale_ptrs = []
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if self.load_merged_weight:
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base_key = f"blk.{self.layer_idx}"
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w = self.safetensor_loader.load_experts(base_key)
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self.gate_weights = w["gate"]
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self.up_weights = w["up"]
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self.down_weights = w["down"]
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self.gate_scales = w["gate_scale"]
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self.up_scales = w["up_scale"]
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self.down_scales = w["down_scale"]
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# Get pointers to weight arrays
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gate_ptrs = [
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[
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ctypes.addressof(ctypes.cast(et.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents)
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for et in numa_array
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]
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for numa_array in self.gate_weights
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]
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up_ptrs = [
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[
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ctypes.addressof(ctypes.cast(et.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents)
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for et in numa_array
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]
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for numa_array in self.up_weights
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]
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down_ptrs = [
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[
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ctypes.addressof(ctypes.cast(et.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents)
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for et in numa_array
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]
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for numa_array in self.down_weights
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]
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gate_scale_ptrs = [
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[
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ctypes.addressof(ctypes.cast(et.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents)
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for et in numa_array
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]
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for numa_array in self.gate_scales
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]
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up_scale_ptrs = [
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[
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ctypes.addressof(ctypes.cast(et.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents)
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for et in numa_array
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]
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for numa_array in self.up_scales
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]
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down_scale_ptrs = [
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[
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ctypes.addressof(ctypes.cast(et.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents)
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for et in numa_array
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]
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for numa_array in self.down_scales
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]
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# Configure MoE
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moe_config = MOEConfig(
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self.num_experts,
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self.num_experts_per_tok,
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self.hidden_size,
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self.moe_intermediate_size,
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self.num_gpu_experts,
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)
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moe_config.layer_idx = self.layer_idx
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moe_config.pool = self.cpu_infer.backend_
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moe_config.max_len = self.chunked_prefill_size
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moe_config.gate_proj = gate_ptr
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moe_config.up_proj = up_ptr
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moe_config.down_proj = down_ptr
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moe_config.gate_projs = gate_ptrs
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moe_config.up_projs = up_ptrs
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moe_config.down_projs = down_ptrs
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moe_config.gate_scales = gate_scale_ptrs
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moe_config.up_scales = up_scale_ptrs
|
|
moe_config.down_scales = down_scale_ptrs
|
|
|
|
if self.cpu_save:
|
|
moe_config.save = True
|
|
moe_config.load = False
|
|
base_key = f"model.layers.{self.layer_idx}"
|
|
w = self.safetensor_loader.load_experts(base_key)
|
|
|
|
self.gate_proj = torch.cat(w["gate_weight"], dim=0).contiguous()
|
|
self.up_proj = torch.cat(w["up_weight"], dim=0).contiguous()
|
|
self.down_proj = torch.cat(w["down_weight"], dim=0).contiguous()
|
|
|
|
moe_config.gate_proj = self.gate_proj.data_ptr()
|
|
moe_config.up_proj = self.up_proj.data_ptr()
|
|
moe_config.down_proj = self.down_proj.data_ptr()
|
|
else:
|
|
moe_config.load = True
|
|
|
|
if not self.load_merged_weight:
|
|
moe_config.path = self.amx_weight_path
|
|
|
|
# Create MoE module based on AMX method
|
|
amx_method = os.environ.get("AMX_METHOD", "AMXINT4")
|
|
if amx_method == "AMXINT4":
|
|
self.moe = AMXInt4_MOE(moe_config)
|
|
elif amx_method == "AMXINT8":
|
|
self.moe = AMXInt8_MOE(moe_config)
|
|
else:
|
|
raise NotImplementedError(f"Unsupported AMX method: {amx_method}")
|
|
|
|
# Load weights
|
|
self.cpu_infer.submit(self.moe.load_weights_task(physical_to_logical_map_cpu.data_ptr()))
|
|
self.cpu_infer.sync()
|
|
|
|
# Clean up temporary weight storage if using merged weights
|
|
if self.load_merged_weight:
|
|
del self.gate_weights
|
|
del self.up_weights
|
|
del self.down_weights
|
|
del self.gate_scales
|
|
del self.up_scales
|
|
del self.down_scales
|
|
|
|
def select_deferred_experts(
|
|
self,
|
|
expert_ids: torch.Tensor,
|
|
expert_scores: torch.Tensor,
|
|
protected_k: int,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
batch, topk = expert_ids.shape
|
|
device = expert_ids.device
|
|
|
|
protected_k = max(0, min(int(protected_k), topk))
|
|
if protected_k == 0:
|
|
deferred_ids = expert_ids.clone()
|
|
immediate_ids = torch.full_like(expert_ids, -1)
|
|
return immediate_ids, deferred_ids
|
|
|
|
topk_result = torch.topk(expert_scores, k=protected_k, dim=-1, largest=True, sorted=False)
|
|
protected_indices = topk_result.indices
|
|
protected_ids = torch.gather(expert_ids, -1, protected_indices)
|
|
|
|
protected_flag = torch.zeros((self.num_experts,), dtype=torch.int32, device=device)
|
|
protected_flag.scatter_(0, protected_ids.reshape(-1), 1)
|
|
|
|
protected_mask_flat = torch.gather(protected_flag, 0, expert_ids.reshape(-1)).ne(0)
|
|
protected_mask = protected_mask_flat.view(batch, topk)
|
|
|
|
immediate_ids = expert_ids.clone().masked_fill(~protected_mask, -1)
|
|
deferred_ids = expert_ids.clone().masked_fill(protected_mask, -1)
|
|
|
|
return immediate_ids, deferred_ids
|
|
|
|
def submit_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
cuda_stream,
|
|
):
|
|
"""
|
|
Submit forward inference task to CPU (non-blocking).
|
|
|
|
Args:
|
|
hidden_states: Input hidden states [batch_size, hidden_size]
|
|
topk_ids: Top-k expert IDs [batch_size, num_experts_per_tok]
|
|
topk_weights: Top-k expert weights [batch_size, num_experts_per_tok]
|
|
cuda_stream: CUDA stream for synchronization
|
|
"""
|
|
flat_hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
|
batch_size = flat_hidden_states.shape[0]
|
|
|
|
(
|
|
input_tensor_cpu,
|
|
immediate_experts_ids_cpu,
|
|
deferred_experts_ids_cpu,
|
|
weights_cpu,
|
|
output_cpu,
|
|
bsz_tensor_cpu,
|
|
_output_gpu,
|
|
) = KExpertsCPUBuffer.get_buffer(flat_hidden_states, self.num_experts_per_tok)
|
|
|
|
current_slot = self.layer_idx % KExpertsCPUBuffer.buffer_depth
|
|
next_slot = (current_slot + 1) % KExpertsCPUBuffer.buffer_depth
|
|
|
|
bsz_slot_tensor = bsz_tensor_cpu[current_slot]
|
|
bsz_slot_tensor.fill_(batch_size)
|
|
deferred_experts_ids_cpu[current_slot].fill_(-1)
|
|
|
|
topk_ids_long = topk_ids.to(torch.long)
|
|
immediate_ids: torch.Tensor
|
|
deferred_ids: Optional[torch.Tensor]
|
|
if self.max_deferred_experts_per_token > 0:
|
|
protected_k = self.num_experts_per_tok - self.max_deferred_experts_per_token
|
|
|
|
immediate_ids, deferred_ids = self.select_deferred_experts(topk_ids_long, topk_weights, protected_k)
|
|
else:
|
|
immediate_ids = topk_ids_long
|
|
deferred_ids = None
|
|
|
|
input_tensor_cpu[current_slot].copy_(flat_hidden_states, non_blocking=True)
|
|
weights_cpu[current_slot].copy_(topk_weights, non_blocking=True)
|
|
immediate_experts_ids_cpu[current_slot].copy_(immediate_ids, non_blocking=True)
|
|
|
|
incremental = AMXMoEWrapper._layer_has_pending_deferred.get(self.layer_idx - 1, False)
|
|
self.cpu_infer.submit_with_cuda_stream(
|
|
cuda_stream,
|
|
self.moe.forward_task(
|
|
bsz_slot_tensor.data_ptr(),
|
|
immediate_experts_ids_cpu[current_slot].size(-1),
|
|
immediate_experts_ids_cpu[current_slot].data_ptr(),
|
|
weights_cpu[current_slot].data_ptr(),
|
|
input_tensor_cpu[current_slot].data_ptr(),
|
|
output_cpu[current_slot].data_ptr(),
|
|
incremental,
|
|
),
|
|
)
|
|
|
|
AMXMoEWrapper._layer_has_pending_deferred[self.layer_idx] = False
|
|
if deferred_ids is not None:
|
|
deferred_experts_ids_cpu[current_slot].copy_(deferred_ids, non_blocking=True)
|
|
self.cpu_infer.submit_with_cuda_stream(
|
|
cuda_stream,
|
|
self.moe.forward_task(
|
|
bsz_slot_tensor.data_ptr(),
|
|
deferred_experts_ids_cpu[current_slot].size(-1),
|
|
deferred_experts_ids_cpu[current_slot].data_ptr(),
|
|
weights_cpu[current_slot].data_ptr(),
|
|
input_tensor_cpu[current_slot].data_ptr(),
|
|
output_cpu[next_slot].data_ptr(),
|
|
False,
|
|
),
|
|
)
|
|
AMXMoEWrapper._layer_has_pending_deferred[self.layer_idx] = True
|
|
|
|
def sync_forward(self, hidden_states: torch.Tensor, cuda_stream) -> torch.Tensor:
|
|
"""
|
|
Synchronize and retrieve forward inference results.
|
|
|
|
Args:
|
|
hidden_states: Original input hidden states (for getting buffer)
|
|
cuda_stream: CUDA stream for synchronization
|
|
|
|
Returns:
|
|
output_gpu: Output tensor on GPU
|
|
"""
|
|
flat_hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
|
(
|
|
input_tensor_cpu,
|
|
immediate_experts_ids_cpu,
|
|
_deferred_experts_ids_cpu,
|
|
weights_cpu,
|
|
output_cpu,
|
|
_bsz_tensor_cpu,
|
|
output_gpu,
|
|
) = KExpertsCPUBuffer.get_buffer(flat_hidden_states, self.num_experts_per_tok)
|
|
|
|
current_slot = self.layer_idx % KExpertsCPUBuffer.buffer_depth
|
|
allow_pending = 1 if AMXMoEWrapper._layer_has_pending_deferred.get(self.layer_idx, False) else 0
|
|
self.cpu_infer.sync_with_cuda_stream(cuda_stream, allow_pending)
|
|
output_gpu[current_slot].copy_(output_cpu[current_slot], non_blocking=True)
|
|
return output_gpu[current_slot]
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
cuda_stream,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Execute forward inference synchronously (submit + sync).
|
|
|
|
Args:
|
|
hidden_states: Input hidden states [batch_size, hidden_size]
|
|
topk_ids: Top-k expert IDs [batch_size, num_experts_per_tok]
|
|
topk_weights: Top-k expert weights [batch_size, num_experts_per_tok]
|
|
cuda_stream: CUDA stream for synchronization
|
|
|
|
Returns:
|
|
Output tensor on GPU
|
|
"""
|
|
self.submit_forward(hidden_states, topk_ids, topk_weights, cuda_stream)
|
|
return self.sync_forward(hidden_states, cuda_stream)
|
|
|
|
@staticmethod
|
|
def set_capture_batch_sizes(capture_bs: List[int]):
|
|
"""
|
|
Set batch sizes to capture and cache buffers for.
|
|
|
|
This allows pre-allocation of CPU buffers for specific batch sizes,
|
|
improving performance by avoiding buffer re-allocation during inference.
|
|
|
|
Args:
|
|
capture_bs: List of batch sizes to capture (e.g., [1, 2, 4, 8, 16])
|
|
|
|
Example:
|
|
>>> AMXMoEWrapper.set_capture_batch_sizes([1, 2, 4, 8, 16])
|
|
"""
|
|
KExpertsCPUBuffer.capture_bs = capture_bs
|
|
|
|
@staticmethod
|
|
def get_capture_batch_sizes() -> List[int]:
|
|
"""
|
|
Get currently configured capture batch sizes.
|
|
|
|
Returns:
|
|
List of batch sizes that are being captured
|
|
"""
|
|
return KExpertsCPUBuffer.capture_bs
|
|
|
|
@staticmethod
|
|
def clear_buffer_cache():
|
|
"""
|
|
Clear all cached buffers.
|
|
|
|
This frees up memory by clearing the buffer cache. Useful when you want
|
|
to reset the buffer state or free memory.
|
|
"""
|
|
KExpertsCPUBuffer.capture_buffers.clear()
|
|
KExpertsCPUBuffer.temp_bs = 0
|
|
KExpertsCPUBuffer.temp_buffer = tuple()
|