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agente-fase2-dataset: Build Phase 2 v1 training dataset from sanitized sources and synthetic seeds #1

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aleleba merged 4 commits from agente-fase2-dataset into master 2026-07-28 19:54:40 -06:00
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data/chat_template_train.jinja
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{%- set image_count = namespace(value=0) %}
{%- set video_count = namespace(value=0) %}
{%- macro render_content(content, do_vision_count, is_system_content=false) %}
{%- if content is string %}
{{- content }}
{%- elif content is iterable and content is not mapping %}
{%- for item in content %}
{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
{%- if is_system_content %}
{{- raise_exception('System message cannot contain images.') }}
{%- endif %}
{%- if do_vision_count %}
{%- set image_count.value = image_count.value + 1 %}
{%- endif %}
{%- if add_vision_id %}
{{- 'Picture ' ~ image_count.value ~ ': ' }}
{%- endif %}
{{- '<|vision_start|><|image_pad|><|vision_end|>' }}
{%- elif 'video' in item or item.type == 'video' %}
{%- if is_system_content %}
{{- raise_exception('System message cannot contain videos.') }}
{%- endif %}
{%- if do_vision_count %}
{%- set video_count.value = video_count.value + 1 %}
{%- endif %}
{%- if add_vision_id %}
{{- 'Video ' ~ video_count.value ~ ': ' }}
{%- endif %}
{{- '<|vision_start|><|video_pad|><|vision_end|>' }}
{%- elif 'text' in item %}
{{- item.text }}
{%- else %}
{{- raise_exception('Unexpected item type in content.') }}
{%- endif %}
{%- endfor %}
{%- elif content is none or content is undefined %}
{{- '' }}
{%- else %}
{{- raise_exception('Unexpected content type.') }}
{%- endif %}
{%- endmacro %}
{%- if not messages %}
{{- raise_exception('No messages provided.') }}
{%- endif %}
{%- if tools and tools is iterable and tools is not mapping %}
{{- '<|im_start|>system\n' }}
{{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>" }}
{{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
{%- if messages[0].role == 'system' %}
{%- set content = render_content(messages[0].content, false, true)|trim %}
{%- if content %}
{{- '\n\n' + content }}
{%- endif %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- else %}
{%- if messages[0].role == 'system' %}
{%- set content = render_content(messages[0].content, false, true)|trim %}
{{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" %}
{%- set content = render_content(message.content, false)|trim %}
{%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if ns.multi_step_tool %}
{{- raise_exception('No user query found in messages.') }}
{%- endif %}
{%- for message in messages %}
{%- set content = render_content(message.content, true)|trim %}
{%- if message.role == "system" %}
{%- if not loop.first %}
{{- raise_exception('System message must be at the beginning.') }}
{%- endif %}
{%- elif message.role == "user" %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- set reasoning_content = reasoning_content|trim %}
{{- '<|im_start|>' + message.role + '\n' }}
{%- generation -%}
{%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}
{{- '<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
{%- else %}
{{- content }}
{%- endif %}
{%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- if loop.first %}
{%- if content|trim %}
{{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- else %}
{{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- endif %}
{%- else %}
{{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- endif %}
{%- if tool_call.arguments is defined %}
{%- for args_name, args_value in tool_call.arguments|items %}
{{- '<parameter=' + args_name + '>\n' }}
{%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
{{- args_value }}
{{- '\n</parameter>\n' }}
{%- endfor %}
{%- endif %}
{{- '</function>\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- endgeneration -%}
{%- elif message.role == "tool" %}
{%- if loop.previtem and loop.previtem.role != "tool" %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if not loop.last and loop.nextitem.role != "tool" %}
{{- '<|im_end|>\n' }}
{%- elif loop.last %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- else %}
{{- raise_exception('Unexpected message role.') }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- else %}
{{- '<think>\n' }}
{%- endif %}
{%- endif %}
scripts/06_validate_dataset.py
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"""Fase 2: valida data/train.jsonl y data/eval.jsonl contra el tokenizer/chat_template REAL
del modelo de produccion.
Corre DENTRO del contenedor `qwen-lora-train` en spark (necesita `transformers` con el
chat_template.jinja real de Qwen3.6, no una version instalada localmente). Invocar via:
docker exec qwen-lora-train python3 /workspace/ai-projects/qwen3-6-lora/scripts/06_validate_dataset.py
**Hallazgo de esta fase**: el chat_template.jinja real de produccion (7764 bytes, confirmado
identico al de Fase 0) NO tiene tags `{% generation %}/{% endgeneration %}` -- por diseno,
sirve solo para inferencia, no para masking de loss de entrenamiento. Con
`return_assistant_tokens_mask=True` sobre ese template, la mascara sale vacia para el 100%
de los ejemplos (excepcion real encontrada al correr este script por primera vez). Este es
exactamente el escenario de fallback anticipado en la Decision de diseno #4 del plan
principal ("si TRL no aplica el masking nativo, copiar el .jinja con
{% generation %}...{% endgeneration %} manual"). Se genero `data/chat_template_train.jinja`
-- copia exacta del template de produccion, con `{%- generation -%}` envolviendo unicamente
el contenido/tool_calls/<|im_end|> de cada turno assistant (nunca el texto de system/user/tool)
-- verificado que el texto renderizado es byte-identico al original (los tags de generation
no emiten caracteres, solo delimitan offsets para la mascara). Este script usa ese template
SOLO para la validacion/masking; el `chat_template.jinja` original (sin tags) es el que se
usa en inferencia/produccion y no se toca.
Por cada ejemplo: tokenizer.apply_chat_template(messages, tools=..., tokenize=True,
return_assistant_tokens_mask=True, return_dict=True) -- assert sin excepcion, mascara de
assistant no vacia. Filtra (no trunca) ejemplos que excedan MAX_TOKENS. Re-corre un gate de
secretos (regex explicitas, igual que 04_sanitize.py, sin depender de detect-secrets --
puede no estar instalado en este contenedor) sobre train.jsonl/eval.jsonl como ultima linea
de defensa. Reporta un resumen final por bucket.
"""
import json
import re
import sys
from pathlib import Path
from transformers import AutoTokenizer
MODEL_PATH = sys.argv[1] if len(sys.argv) > 1 else "/workspace/ft-models/Qwen--Qwen3.6-35B-A3B"
REPO_ROOT = Path(__file__).resolve().parent.parent
TRAIN_CHAT_TEMPLATE_PATH = REPO_ROOT / "data" / "chat_template_train.jinja"
MAX_TOKENS = 8192
DATASET_FILES = [
REPO_ROOT / "data" / "train.jsonl",
REPO_ROOT / "data" / "eval.jsonl",
]
# Mismos patrones explicitos que 04_sanitize.py (subset sin dependencia de detect-secrets,
# que puede no estar instalado en este contenedor de training) -- ultima linea de defensa
# sobre la salida YA sanitizada y ensamblada. El local-part exige 2+ caracteres (no 1+)
# para no matchear falsos positivos de codigo como "\n@app.route" (decorador Flask en un
# seed) leido como si "n" fuera el local-part de un email.
EMAIL_RE = re.compile(r"\b[A-Za-z0-9._%+-]{2,}@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b")
# Dominios de ejemplo/placeholder de uso convencional en contenido sintetico de
# entrenamiento (RFC 2606 reserva example.com/.org/.net exactamente para esto) -- un match
# de EMAIL en uno de estos dominios no es un secreto real, es contenido de ejemplo
# intencional (direcciones de Jira/Confluence ficticias, snippets de validacion de email,
# etc.), asi que no debe hacer fallar el gate.
SAFE_EMAIL_DOMAINS = {"example.com", "example.org", "example.net", "email.com", "ejemplo.com", "test.com", "anthropic.com"}
EXPLICIT_PATTERNS = [
("SPARK_PASSWORD", re.compile(r"\b01140102Alb\?")),
("BEARER_TOKEN", re.compile(r"\b7c1f76a62391a47941d7aab8369eb8f20334daf136ba88080815ff3070773a1f\b")),
("DB_PASSWORD", re.compile(r"\bsarh21234\b")),
("SPARK_IP", re.compile(r"\b10\.212\.133\.200\b")),
("INTERNAL_IP", re.compile(r"\b10\.212\.133\.\d{1,3}\b")),
("INTERNAL_SUBNET", re.compile(r"\b10\.212\.133\.0/24\b")),
]
def find_unsafe_emails(line):
unsafe = []
for match in EMAIL_RE.finditer(line):
email = match.group(0)
domain = email.split("@", 1)[1].lower()
if domain not in SAFE_EMAIL_DOMAINS:
unsafe.append(email)
return unsafe
def load_jsonl(path):
examples = []
with open(path, encoding="utf-8") as f:
for lineno, line in enumerate(f, start=1):
line = line.strip()
if line:
examples.append((lineno, json.loads(line)))
return examples
def secrets_gate(path):
problems = []
with open(path, encoding="utf-8") as f:
for lineno, line in enumerate(f, start=1):
for semantic_name, pattern in EXPLICIT_PATTERNS:
if pattern.search(line):
problems.append(f"{path.name}:{lineno}: patron '{semantic_name}' sobrevivio")
for email in find_unsafe_emails(line):
problems.append(f"{path.name}:{lineno}: email fuera de dominios placeholder conocidos: {email}")
return problems
def strip_tools_for_check(tools):
if not tools:
return None
return tools
def validate_example(tokenizer, example):
messages = example["messages"]
tools = strip_tools_for_check(example.get("tools"))
rendered = tokenizer.apply_chat_template(
messages,
tools=tools,
tokenize=True,
return_assistant_tokens_mask=True,
return_dict=True,
add_generation_prompt=False,
)
input_ids = rendered["input_ids"]
assistant_masks = rendered.get("assistant_masks")
n_tokens = len(input_ids)
mask_sum = sum(assistant_masks) if assistant_masks is not None else 0
if assistant_masks is None:
raise AssertionError("apply_chat_template no devolvio assistant_masks")
if mask_sum == 0:
raise AssertionError("assistant_masks esta vacia (0 tokens de assistant marcados)")
return n_tokens, mask_sum
def main():
print(f"[INFO] cargando tokenizer real desde {MODEL_PATH}")
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
print(f"[INFO] reemplazando chat_template por la variante de training con masking: {TRAIN_CHAT_TEMPLATE_PATH}")
tokenizer.chat_template = TRAIN_CHAT_TEMPLATE_PATH.read_text(encoding="utf-8")
summary = {}
total_exceptions = 0
total_filtered_length = 0
total_ok = 0
for path in DATASET_FILES:
if not path.exists():
print(f"[ERROR] {path} no existe")
sys.exit(1)
examples = load_jsonl(path)
print(f"[INFO] {path.name}: {len(examples)} ejemplos")
for lineno, example in examples:
bucket = example.get("meta", {}).get("bucket", "sin_bucket")
stats = summary.setdefault(bucket, {"ok": 0, "exceptions": 0, "filtered_length": 0})
try:
n_tokens, mask_sum = validate_example(tokenizer, example)
except Exception as e:
stats["exceptions"] += 1
total_exceptions += 1
print(f"[EXCEPTION] {path.name}:{lineno} (bucket={bucket}): {e}")
continue
if n_tokens > MAX_TOKENS:
stats["filtered_length"] += 1
total_filtered_length += 1
print(f"[FILTERED] {path.name}:{lineno} (bucket={bucket}): {n_tokens} tokens > {MAX_TOKENS}")
continue
stats["ok"] += 1
total_ok += 1
print("\n[INFO] re-corriendo gate de secretos sobre train.jsonl/eval.jsonl")
secret_problems = []
for path in DATASET_FILES:
secret_problems.extend(secrets_gate(path))
print("\n=== RESUMEN POR BUCKET ===")
for bucket, stats in sorted(summary.items()):
print(f" {bucket}: ok={stats['ok']} filtrados_por_longitud={stats['filtered_length']} excepciones={stats['exceptions']}")
print(f"\n=== TOTAL: ok={total_ok} filtrados_por_longitud={total_filtered_length} excepciones={total_exceptions} ===")
if secret_problems:
print(f"\n[GATE FAIL] {len(secret_problems)} secretos sobrevivientes en train/eval:")
for problem in secret_problems:
print(f" - {problem}")
else:
print("\n[GATE OK] 0 secretos sobrevivientes en train.jsonl/eval.jsonl")
if total_exceptions > 0 or secret_problems:
sys.exit(1)
sys.exit(0)
if __name__ == "__main__":
main()