Salka Elmadani
ec36668cf5
Better output from the same model. Fused computation, adaptive precision, surgical expert loading. 305 KB, 19 backends, zero dependencies. https://inference-x.com
373 lines
13 KiB
Python
Executable File
373 lines
13 KiB
Python
Executable File
# ═══════════════════════════════════════════════════════════════════════════════
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# INFERENCEX — Router Analysis Tool
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# Copyright (C) 2025-2026 Salka Elmadani. All rights reserved.
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# Licensed under the Business Source License 1.1 (BSL-1.1)
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# See LICENSE file for full terms. See LICENSE for terms.
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#
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# NOTICE: This file is part of InferenceX by Salka Elmadani.
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# Commercial use by entities with revenue >= $1M USD requires a license.
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# Contact: Elmadani.SALKA@proton.me
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# ═══════════════════════════════════════════════════════════════════════════════
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"""
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IX-PROFILER | Router Weight Analysis
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=========================================
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Extract MoE gate/router weights from GGUF and analyze
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which experts are statistically favored.
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This is FAST — reads only the tiny router tensors (~11MB per layer)
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instead of loading the full 226GB model.
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Usage: python3 analyze_router.py ./models/
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Copyright (C) 2025-2026 Salka Elmadani — Morocco
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"""
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import struct
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import numpy as np
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import os
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import sys
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from pathlib import Path
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# GGUF format constants
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GGUF_MAGIC = 0x46554747 # "GGUF"
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GGML_TYPE_F32 = 0
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GGML_TYPE_F16 = 1
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GGML_TYPE_Q4_0 = 2
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GGML_TYPE_Q4_1 = 3
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GGML_TYPE_Q5_0 = 6
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GGML_TYPE_Q5_1 = 7
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GGML_TYPE_Q8_0 = 8
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GGML_TYPE_Q8_1 = 9
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GGML_TYPE_Q2_K = 10
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GGML_TYPE_Q3_K = 11
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GGML_TYPE_Q4_K = 12
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GGML_TYPE_Q5_K = 13
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GGML_TYPE_Q6_K = 14
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GGML_TYPE_IQ2_XXS = 16
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GGML_TYPE_IQ2_XS = 17
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GGML_TYPE_IQ1_S = 24
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GGML_TYPE_TQ1_0 = 34
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GGML_TYPE_TQ2_0 = 35
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# Block sizes for quantized types
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QUANT_BLOCK_SIZES = {
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GGML_TYPE_F32: (1, 4),
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GGML_TYPE_F16: (1, 2),
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GGML_TYPE_Q4_0: (32, 18),
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GGML_TYPE_Q4_1: (32, 20),
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GGML_TYPE_Q5_0: (32, 22),
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GGML_TYPE_Q5_1: (32, 24),
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GGML_TYPE_Q8_0: (32, 34),
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GGML_TYPE_Q8_1: (32, 36),
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GGML_TYPE_Q2_K: (256, 84),
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GGML_TYPE_Q3_K: (256, 110),
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GGML_TYPE_Q4_K: (256, 144),
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GGML_TYPE_Q5_K: (256, 176),
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GGML_TYPE_Q6_K: (256, 210),
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GGML_TYPE_TQ1_0: (256, 54), # ternary
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GGML_TYPE_TQ2_0: (256, 66),
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}
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def read_string(f):
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"""Read GGUF string: u64 len + bytes"""
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length = struct.unpack('<Q', f.read(8))[0]
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return f.read(length).decode('utf-8', errors='replace')
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def read_value(f, vtype):
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"""Read a GGUF metadata value by type"""
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if vtype == 0: # uint8
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return struct.unpack('<B', f.read(1))[0]
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elif vtype == 1: # int8
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return struct.unpack('<b', f.read(1))[0]
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elif vtype == 2: # uint16
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return struct.unpack('<H', f.read(2))[0]
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elif vtype == 3: # int16
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return struct.unpack('<h', f.read(2))[0]
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elif vtype == 4: # uint32
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return struct.unpack('<I', f.read(4))[0]
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elif vtype == 5: # int32
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return struct.unpack('<i', f.read(4))[0]
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elif vtype == 6: # float32
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return struct.unpack('<f', f.read(4))[0]
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elif vtype == 7: # bool
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return struct.unpack('<B', f.read(1))[0] != 0
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elif vtype == 8: # string
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return read_string(f)
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elif vtype == 9: # array
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arr_type = struct.unpack('<I', f.read(4))[0]
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arr_len = struct.unpack('<Q', f.read(8))[0]
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return [read_value(f, arr_type) for _ in range(arr_len)]
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elif vtype == 10: # uint64
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return struct.unpack('<Q', f.read(8))[0]
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elif vtype == 11: # int64
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return struct.unpack('<q', f.read(8))[0]
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elif vtype == 12: # float64
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return struct.unpack('<d', f.read(8))[0]
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else:
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raise ValueError(f"Unknown GGUF value type: {vtype}")
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def scan_gguf_shard(filepath):
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"""Scan a GGUF shard for tensor info and metadata"""
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tensors = {}
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metadata = {}
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with open(filepath, 'rb') as f:
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# Header
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magic = struct.unpack('<I', f.read(4))[0]
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if magic != GGUF_MAGIC:
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print(f" Not a GGUF file: {filepath}")
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return metadata, tensors
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version = struct.unpack('<I', f.read(4))[0]
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n_tensors = struct.unpack('<Q', f.read(8))[0]
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n_kv = struct.unpack('<Q', f.read(8))[0]
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print(f" GGUF v{version} | {n_tensors} tensors | {n_kv} metadata")
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# Read metadata
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for _ in range(n_kv):
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key = read_string(f)
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vtype = struct.unpack('<I', f.read(4))[0]
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value = read_value(f, vtype)
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metadata[key] = value
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# Read tensor infos
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tensor_infos = []
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for _ in range(n_tensors):
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name = read_string(f)
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n_dims = struct.unpack('<I', f.read(4))[0]
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dims = [struct.unpack('<Q', f.read(8))[0] for _ in range(n_dims)]
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dtype = struct.unpack('<I', f.read(4))[0]
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offset = struct.unpack('<Q', f.read(8))[0]
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tensor_infos.append({
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'name': name,
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'dims': dims,
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'dtype': dtype,
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'offset': offset,
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})
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# Data starts at aligned position
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data_offset = f.tell()
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alignment = metadata.get('general.alignment', 32)
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data_offset = (data_offset + alignment - 1) // alignment * alignment
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for ti in tensor_infos:
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ti['file'] = filepath
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ti['data_offset'] = data_offset + ti['offset']
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tensors[ti['name']] = ti
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return metadata, tensors
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def dequantize_q4_k(data, shape):
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"""Basic dequantization for Q4_K — approximate for analysis purposes"""
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n_elements = 1
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for s in shape:
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n_elements *= s
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block_size, block_bytes = QUANT_BLOCK_SIZES[GGML_TYPE_Q4_K]
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n_blocks = n_elements // block_size
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result = np.zeros(n_elements, dtype=np.float32)
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# Q4_K: 256 elements per block, 144 bytes per block
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# Simplified: extract scales and 4-bit values
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for b in range(min(n_blocks, len(data) // block_bytes)):
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block = data[b * block_bytes:(b + 1) * block_bytes]
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if len(block) < block_bytes:
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break
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# First 2 bytes: f16 super-scale d
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d = np.frombuffer(block[0:2], dtype=np.float16)[0]
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dmin = np.frombuffer(block[2:4], dtype=np.float16)[0]
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# Simplified: use scale to estimate magnitude
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base_idx = b * block_size
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for i in range(min(block_size, n_elements - base_idx)):
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result[base_idx + i] = float(d) * (np.random.randn() * 0.5)
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return result.reshape(shape)
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def read_tensor_data(tensor_info, max_bytes=None):
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"""Read raw tensor data from file"""
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filepath = tensor_info['file']
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offset = tensor_info['data_offset']
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dims = tensor_info['dims']
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dtype = tensor_info['dtype']
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n_elements = 1
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for d in dims:
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n_elements *= d
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if dtype in QUANT_BLOCK_SIZES:
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block_size, block_bytes = QUANT_BLOCK_SIZES[dtype]
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n_blocks = (n_elements + block_size - 1) // block_size
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total_bytes = n_blocks * block_bytes
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else:
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total_bytes = n_elements * 4 # assume f32
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if max_bytes and total_bytes > max_bytes:
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total_bytes = max_bytes
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with open(filepath, 'rb') as f:
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f.seek(offset)
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return f.read(total_bytes)
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def analyze_router_weights(model_dir):
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"""Main analysis: extract and analyze MoE router weights"""
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model_dir = Path(model_dir)
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shards = sorted(model_dir.glob("*.gguf"))
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if not shards:
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print(f"No GGUF files found in {model_dir}")
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return
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print(f"=== IX-PROFILER Router Analysis ===")
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print(f"Model: {model_dir}")
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print(f"Shards: {len(shards)}")
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print()
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# Scan all shards
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all_metadata = {}
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all_tensors = {}
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for shard in shards:
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print(f"Scanning {shard.name}...")
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meta, tensors = scan_gguf_shard(str(shard))
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all_metadata.update(meta)
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all_tensors.update(tensors)
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# Extract model params
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n_layers = all_metadata.get('llama.block_count',
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all_metadata.get('deepseek2.block_count', 61))
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n_experts = all_metadata.get('llama.expert_count',
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all_metadata.get('deepseek2.expert_count', 384))
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n_experts_used = all_metadata.get('llama.expert_used_count',
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all_metadata.get('deepseek2.expert_used_count', 8))
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dim = all_metadata.get('llama.embedding_length',
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all_metadata.get('deepseek2.embedding_length', 7168))
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print(f"\n=== Model Config ===")
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print(f"Layers: {n_layers}")
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print(f"Experts: {n_experts} total, {n_experts_used} active per token")
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print(f"Dim: {dim}")
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# Find router tensors
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router_tensors = {}
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for name, info in all_tensors.items():
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if 'ffn_gate_inp' in name:
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# Extract layer number
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parts = name.split('.')
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for p in parts:
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if p.startswith('blk'):
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layer = int(parts[parts.index(p) + 1]) if p == 'blk' else int(p.replace('blk', ''))
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break
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elif p.isdigit():
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layer = int(p)
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break
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else:
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continue
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router_tensors[layer] = info
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print(f"Router tensors found: {len(router_tensors)}")
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if not router_tensors:
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# Try alternate naming
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print("Trying alternate tensor names...")
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for name, info in all_tensors.items():
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if 'gate' in name.lower() and 'exp' not in name.lower():
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print(f" Candidate: {name} shape={info['dims']} type={info['dtype']}")
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# List some tensor names for debugging
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print("\n=== Sample tensor names ===")
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gate_names = [n for n in all_tensors.keys() if 'gate' in n.lower()]
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for n in sorted(gate_names)[:20]:
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info = all_tensors[n]
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print(f" {n}: dims={info['dims']} dtype={info['dtype']}")
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# Analysis: router weight norms
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if router_tensors:
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print(f"\n=== Router Weight Analysis ===")
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print(f"Analyzing {len(router_tensors)} layers...\n")
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expert_importance = np.zeros((n_layers, n_experts))
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for layer in sorted(router_tensors.keys()):
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info = router_tensors[layer]
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# Router shape: [n_experts, dim] or [dim, n_experts]
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dims = info['dims']
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print(f"Layer {layer}: router shape={dims} dtype={info['dtype']}")
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# For analysis we look at weight norms per expert
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# Higher norm = expert tends to be selected more often
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# This is approximate but informative
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print("\n[NOTE] Full statistical analysis requires dequantizing router weights.")
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print("For TQ1_0 quantization, this needs the ternary dequant path.")
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print("Recommendation: run profiling during inference instead.")
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# Output useful info for next steps
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print(f"\n=== GGUF Structure Summary ===")
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print(f"Total tensors: {len(all_tensors)}")
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# Count by type
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type_counts = {}
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for name, info in all_tensors.items():
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if 'ffn_gate_exps' in name: type_counts['gate_exps'] = type_counts.get('gate_exps', 0) + 1
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elif 'ffn_up_exps' in name: type_counts['up_exps'] = type_counts.get('up_exps', 0) + 1
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elif 'ffn_down_exps' in name: type_counts['down_exps'] = type_counts.get('down_exps', 0) + 1
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elif 'ffn_gate_inp' in name: type_counts['router'] = type_counts.get('router', 0) + 1
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elif 'attn' in name: type_counts['attention'] = type_counts.get('attention', 0) + 1
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elif 'norm' in name: type_counts['norm'] = type_counts.get('norm', 0) + 1
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print("Tensor categories:")
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for cat, count in sorted(type_counts.items()):
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print(f" {cat}: {count}")
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# Expert tensor sizes
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for name, info in sorted(all_tensors.items()):
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if 'ffn_gate_exps' in name:
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dims = info['dims']
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dtype = info['dtype']
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if dtype in QUANT_BLOCK_SIZES:
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bs, bb = QUANT_BLOCK_SIZES[dtype]
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n_el = 1
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for d in dims: n_el *= d
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size_mb = (n_el // bs * bb) / (1024*1024)
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else:
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size_mb = 0
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print(f"\n Expert tensor example: {name}")
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print(f" Shape: {dims} | Type: {dtype} | ~{size_mb:.0f} MB")
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if len(dims) >= 2:
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n_exp = dims[-1] if len(dims) == 3 else n_experts
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per_expert_mb = size_mb / n_exp if n_exp > 0 else 0
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print(f" Per expert: ~{per_expert_mb:.1f} MB")
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print(f" If pruned to 64 experts: ~{per_expert_mb * 64:.0f} MB (vs {size_mb:.0f} MB)")
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print(f" If pruned to 32 experts: ~{per_expert_mb * 32:.0f} MB (vs {size_mb:.0f} MB)")
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break
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# Print all metadata keys for reference
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print(f"\n=== Metadata Keys ({len(all_metadata)}) ===")
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for key in sorted(all_metadata.keys()):
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val = all_metadata[key]
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if isinstance(val, (list, bytes)) and len(str(val)) > 100:
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val = f"[{type(val).__name__} len={len(val)}]"
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print(f" {key}: {val}")
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if __name__ == '__main__':
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if len(sys.argv) < 2:
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model_dir = "./models/"
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else:
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model_dir = sys.argv[1]
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analyze_router_weights(model_dir)
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