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9bd422a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | /**
* Unit tests for SafeTensorsParser
* Validates: Requirements 37.1, 37.2, 37.3, 37.4, 37.5, 37.6, 37.7
*/
import { describe, it, expect, beforeEach } from 'vitest';
// ─── Re-implement the pure logic from SafeTensorsParser for testability ──
const BYTES_PER_ELEMENT = {
'BOOL': 1, 'U8': 1, 'I8': 1,
'U16': 2, 'I16': 2, 'F16': 2, 'BF16': 2,
'I32': 4, 'U32': 4, 'F32': 4,
'F64': 8, 'I64': 8, 'U64': 8
};
function computeElementCount(shape) {
if (!shape || shape.length === 0) return 1;
return shape.reduce((acc, dim) => acc * dim, 1);
}
/**
* Build a fake .safetensors ArrayBuffer from a header object.
*/
function buildSafeTensorsBuffer(headerObj) {
const headerStr = JSON.stringify(headerObj);
const encoder = new TextEncoder();
const headerBytes = encoder.encode(headerStr);
const headerSize = headerBytes.byteLength;
// 8 bytes for header size (little-endian uint64) + header bytes
const totalSize = 8 + headerSize;
const buffer = new ArrayBuffer(totalSize);
const view = new DataView(buffer);
// Write header size as little-endian uint32 (low 32 bits)
view.setUint32(0, headerSize, true);
// High 32 bits = 0
view.setUint32(4, 0, true);
const dest = new Uint8Array(buffer, 8, headerSize);
dest.set(headerBytes);
return buffer;
}
/**
* Minimal parse function mirroring SafeTensorsParser.parse()
*/
function parse(buffer) {
try {
if (!buffer || buffer.byteLength < 8) {
return { success: false, error: 'Tệp không hợp lệ: không đủ dữ liệu để đọc header size' };
}
const view = new DataView(buffer);
const headerSize = view.getUint32(0, true);
if (headerSize > buffer.byteLength - 8) {
return { success: false, error: 'Tệp không hợp lệ: header size lớn hơn dữ liệu có sẵn' };
}
const headerBytes = new Uint8Array(buffer, 8, headerSize);
const headerString = new TextDecoder('utf-8').decode(headerBytes);
let headerObj;
try {
headerObj = JSON.parse(headerString);
} catch (_e) {
return { success: false, error: 'Tệp không hợp lệ: header không phải JSON hợp lệ' };
}
const metadata = headerObj.__metadata__ || null;
const tensors = [];
for (const [name, info] of Object.entries(headerObj)) {
if (name === '__metadata__') continue;
const dtype = info.dtype || '';
const shape = info.shape || [];
const dataOffsets = info.data_offsets || [0, 0];
const elementCount = computeElementCount(shape);
const bytesPerEl = BYTES_PER_ELEMENT[dtype] || 1;
const byteSize = elementCount * bytesPerEl;
tensors.push({ name, dtype, shape, data_offsets: dataOffsets, elementCount, byteSize });
}
return { success: true, data: { tensors, metadata, headerSize } };
} catch (err) {
return { success: false, error: 'Tệp không hợp lệ: ' + (err.message || 'lỗi không xác định') };
}
}
// ─── Tests ──────────────────────────────────────────────────────────────
describe('SafeTensorsParser - parse', () => {
describe('Error handling', () => {
it('should return error for null buffer (Req 37.4)', () => {
const result = parse(null);
expect(result.success).toBe(false);
expect(result.error).toContain('không đủ dữ liệu để đọc header size');
});
it('should return error for buffer smaller than 8 bytes (Req 37.4)', () => {
const buffer = new ArrayBuffer(4);
const result = parse(buffer);
expect(result.success).toBe(false);
expect(result.error).toContain('không đủ dữ liệu để đọc header size');
});
it('should return error when header size exceeds remaining data (Req 37.5)', () => {
// Create buffer with 8 bytes header size pointing to 1000 bytes, but only 16 bytes total
const buffer = new ArrayBuffer(16);
const view = new DataView(buffer);
view.setUint32(0, 1000, true); // header size = 1000
view.setUint32(4, 0, true);
const result = parse(buffer);
expect(result.success).toBe(false);
expect(result.error).toContain('header size lớn hơn dữ liệu có sẵn');
});
it('should return error for invalid JSON header (Req 37.6)', () => {
// Build buffer with non-JSON content
const invalidJson = 'this is not json{{{';
const encoder = new TextEncoder();
const headerBytes = encoder.encode(invalidJson);
const buffer = new ArrayBuffer(8 + headerBytes.byteLength);
const view = new DataView(buffer);
view.setUint32(0, headerBytes.byteLength, true);
view.setUint32(4, 0, true);
new Uint8Array(buffer, 8).set(headerBytes);
const result = parse(buffer);
expect(result.success).toBe(false);
expect(result.error).toContain('header không phải JSON hợp lệ');
});
});
describe('Successful parsing', () => {
it('should parse a valid safetensors buffer with tensors (Req 37.1, 37.2, 37.3)', () => {
const header = {
'weight': { dtype: 'F32', shape: [768, 768], data_offsets: [0, 2359296] },
'bias': { dtype: 'F32', shape: [768], data_offsets: [2359296, 2362368] }
};
const buffer = buildSafeTensorsBuffer(header);
const result = parse(buffer);
expect(result.success).toBe(true);
expect(result.data.tensors).toHaveLength(2);
expect(result.data.metadata).toBeNull();
const weight = result.data.tensors.find(t => t.name === 'weight');
expect(weight.dtype).toBe('F32');
expect(weight.shape).toEqual([768, 768]);
expect(weight.elementCount).toBe(768 * 768);
expect(weight.byteSize).toBe(768 * 768 * 4);
});
it('should separate __metadata__ from tensors (Req 37.7)', () => {
const header = {
'__metadata__': { format: 'pt', framework: 'pytorch' },
'layer.weight': { dtype: 'F16', shape: [512, 256], data_offsets: [0, 262144] }
};
const buffer = buildSafeTensorsBuffer(header);
const result = parse(buffer);
expect(result.success).toBe(true);
expect(result.data.tensors).toHaveLength(1);
expect(result.data.tensors[0].name).toBe('layer.weight');
expect(result.data.metadata).toEqual({ format: 'pt', framework: 'pytorch' });
});
it('should handle empty header (no tensors, no metadata)', () => {
const buffer = buildSafeTensorsBuffer({});
const result = parse(buffer);
expect(result.success).toBe(true);
expect(result.data.tensors).toHaveLength(0);
expect(result.data.metadata).toBeNull();
});
it('should return correct headerSize', () => {
const header = { 'x': { dtype: 'I8', shape: [10], data_offsets: [0, 10] } };
const buffer = buildSafeTensorsBuffer(header);
const result = parse(buffer);
const expectedHeaderSize = new TextEncoder().encode(JSON.stringify(header)).byteLength;
expect(result.data.headerSize).toBe(expectedHeaderSize);
});
});
describe('Element count and byte size calculation', () => {
it('should compute elementCount as product of shape', () => {
expect(computeElementCount([3, 4, 5])).toBe(60);
expect(computeElementCount([1])).toBe(1);
expect(computeElementCount([])).toBe(1); // scalar
});
it('should compute correct byteSize for each dtype', () => {
const dtypes = { 'F32': 4, 'F16': 2, 'BF16': 2, 'I8': 1, 'I64': 8, 'BOOL': 1, 'U32': 4, 'F64': 8 };
const shape = [10, 20]; // 200 elements
for (const [dtype, bpe] of Object.entries(dtypes)) {
const header = { 't': { dtype, shape, data_offsets: [0, 200 * bpe] } };
const buffer = buildSafeTensorsBuffer(header);
const result = parse(buffer);
expect(result.success).toBe(true);
expect(result.data.tensors[0].elementCount).toBe(200);
expect(result.data.tensors[0].byteSize).toBe(200 * bpe);
}
});
});
});
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