5a086353ab
Add end-to-end embedding/sentence-transformer training pipeline using FastSentenceTransformer, SentenceTransformerTrainer, and MultipleNegativesRankingLoss with BatchSamplers.NO_DUPLICATES. Backend: - Add is_embedding_model() detection via HF tags + pipeline_tag - Add /check-embedding/ API route and EmbeddingCheckResponse - Extend derive_model_type() to return "embeddings" - Add _run_embedding_training() in worker.py with progress callbacks, stop handling, LoRA (task_type=FEATURE_EXTRACTION), and model saving - Add is_embedding field to TrainingStartRequest and ModelDetails - Add YAML configs for 5 models: all-MiniLM-L6-v2, bge-m3, embeddinggemma-300m, gte-modernbert-base, Qwen3-Embedding-0.6B Frontend: - Wire isEmbeddingModel flag through store, API types, and mappers - Force packing=false, train_on_completions=false, warmup_ratio=0.03 - Hide packing and train_on_completions checkboxes for embedding models - Auto-set modelType to "embeddings" from backend model_type response
737 lines
28 KiB
Python
737 lines
28 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only - See /studio/LICENSE.AGPL-3.0
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# Copyright © 2025 Unsloth AI
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"""
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Training subprocess entry point.
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Each training job runs in a fresh subprocess (mp.get_context("spawn")).
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This gives us a clean Python interpreter with no stale module state —
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solving the transformers version-switching problem completely.
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Pattern follows core/data_recipe/jobs/worker.py.
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"""
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from __future__ import annotations
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import logging
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import os
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import sys
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import time
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import traceback
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from pathlib import Path
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from typing import Any
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logger = logging.getLogger(__name__)
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def _activate_transformers_version(model_name: str, project_root: str) -> None:
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"""Activate the correct transformers version BEFORE any ML imports.
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If the model needs transformers 5.x, prepend the pre-installed .venv_t5/
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directory to sys.path. Otherwise do nothing (default 4.57.x in .venv/).
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"""
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# Ensure backend is on path for utils imports
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backend_path = os.path.join(project_root, "studio", "backend")
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if backend_path not in sys.path:
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sys.path.insert(0, backend_path)
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from utils.transformers_version import needs_transformers_5, _resolve_base_model
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resolved = _resolve_base_model(model_name)
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if needs_transformers_5(resolved):
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venv_t5 = os.path.join(project_root, ".venv_t5")
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if os.path.isdir(venv_t5):
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sys.path.insert(0, venv_t5)
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logger.info("Activated transformers 5.x from %s", venv_t5)
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else:
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# Fallback: pip install at runtime (slower, ~10-15s)
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logger.warning(".venv_t5 not found at %s — installing at runtime", venv_t5)
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import subprocess as sp
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os.makedirs(venv_t5, exist_ok=True)
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r1 = sp.run(
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[sys.executable, "-m", "pip", "install", "--target", venv_t5,
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"--no-deps", "transformers==5.2.0"],
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stdout=sp.PIPE, stderr=sp.STDOUT,
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)
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r2 = sp.run(
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[sys.executable, "-m", "pip", "install", "--target", venv_t5,
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"--no-deps", "huggingface_hub==1.3.0"],
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stdout=sp.PIPE, stderr=sp.STDOUT,
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)
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if r1.returncode != 0 or r2.returncode != 0:
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raise RuntimeError(
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f"Failed to install transformers 5.x into {venv_t5}. "
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f"pip returncode: transformers={r1.returncode}, huggingface_hub={r2.returncode}"
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)
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sys.path.insert(0, venv_t5)
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# Propagate to child subprocesses (e.g. GGUF converter)
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_pp = os.environ.get("PYTHONPATH", "")
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os.environ["PYTHONPATH"] = venv_t5 + (os.pathsep + _pp if _pp else "")
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else:
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logger.info("Using default transformers (4.57.x) for %s", model_name)
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def run_training_process(
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*,
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event_queue: Any,
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stop_queue: Any,
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config: dict,
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) -> None:
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"""Subprocess entrypoint. Fresh Python — no stale module state.
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Args:
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event_queue: mp.Queue for sending progress/status/error events to parent.
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stop_queue: mp.Queue for receiving stop commands from parent.
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config: Training configuration dict with all parameters.
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"""
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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project_root = config["project_root"]
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model_name = config["model_name"]
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# ── 1. Activate correct transformers version BEFORE any ML imports ──
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try:
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_activate_transformers_version(model_name, project_root)
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except Exception as exc:
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event_queue.put({
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"type": "error",
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"error": f"Failed to activate transformers version: {exc}",
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"stack": traceback.format_exc(limit=20),
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"ts": time.time(),
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})
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return
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# ── 1b. On Windows, check Triton availability (must be before import torch) ──
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if sys.platform == "win32":
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try:
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import triton # noqa: F401
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logger.info("Triton available — torch.compile enabled")
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except ImportError:
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os.environ["TORCHDYNAMO_DISABLE"] = "1"
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logger.warning(
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"Triton not found on Windows — torch.compile disabled. "
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'Install for better performance: pip install "triton-windows<3.7"'
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)
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# ── 2. Now import ML libraries (fresh in this clean process) ──
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try:
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_send_status(event_queue, "Importing ML libraries...")
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backend_path = os.path.join(project_root, "studio", "backend")
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if backend_path not in sys.path:
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sys.path.insert(0, backend_path)
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from core.training.trainer import UnslothTrainer, TrainingProgress
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import transformers
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logger.info("Subprocess loaded transformers %s", transformers.__version__)
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except Exception as exc:
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event_queue.put({
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"type": "error",
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"error": f"Failed to import ML libraries: {exc}",
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"stack": traceback.format_exc(limit=20),
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"ts": time.time(),
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})
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return
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# ── 2b. EMBEDDING MODEL FAST-PATH ──
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# Embedding models use a completely different pipeline (FastSentenceTransformer
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# + SentenceTransformerTrainer + MultipleNegativesRankingLoss) so we branch
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# early and handle the entire flow in a self-contained function.
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if config.get("is_embedding", False):
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try:
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_run_embedding_training(event_queue, stop_queue, config)
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except Exception as exc:
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event_queue.put({
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"type": "error",
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"error": str(exc),
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"stack": traceback.format_exc(limit=20),
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"ts": time.time(),
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})
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return
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# ── 3. Create a fresh trainer instance ──
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trainer = UnslothTrainer()
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# Wire up progress callback → event_queue
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def _on_progress(progress: TrainingProgress):
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has_train_loss = progress.step >= 0 and progress.loss > 0
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has_eval_loss = progress.eval_loss is not None
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if has_train_loss or has_eval_loss:
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event_queue.put({
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"type": "progress",
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"step": progress.step,
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"epoch": progress.epoch,
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"loss": progress.loss,
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"learning_rate": progress.learning_rate,
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"total_steps": progress.total_steps,
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"elapsed_seconds": progress.elapsed_seconds,
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"eta_seconds": progress.eta_seconds,
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"grad_norm": progress.grad_norm,
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"num_tokens": progress.num_tokens,
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"eval_loss": progress.eval_loss,
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"status_message": progress.status_message,
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"ts": time.time(),
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})
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if progress.status_message:
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_send_status(event_queue, progress.status_message)
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trainer.add_progress_callback(_on_progress)
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# Wire up stop_queue polling to trainer.should_stop
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import threading
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import queue as _queue
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def _poll_stop():
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while True:
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try:
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msg = stop_queue.get(timeout=1.0)
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if msg and msg.get("type") == "stop":
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save = msg.get("save", True)
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trainer.should_stop = True
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trainer.save_on_stop = save
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logger.info("Stop signal received (save=%s)", save)
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return
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except _queue.Empty:
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continue
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except (EOFError, OSError):
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return
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stop_thread = threading.Thread(target=_poll_stop, daemon=True)
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stop_thread.start()
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# ── 4. Execute the training pipeline ──
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try:
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hf_token = config.get("hf_token", "")
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hf_token = hf_token if hf_token and hf_token.strip() else None
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# Load model
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_send_status(event_queue, "Loading model...")
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success = trainer.load_model(
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model_name=model_name,
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max_seq_length=config["max_seq_length"],
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load_in_4bit=config["load_in_4bit"],
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hf_token=hf_token,
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is_dataset_image=config.get("is_dataset_image", False),
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is_dataset_audio=config.get("is_dataset_audio", False),
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trust_remote_code=config.get("trust_remote_code", False),
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)
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if not success or trainer.should_stop:
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if trainer.should_stop:
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event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
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else:
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error_msg = trainer.training_progress.error or "Failed to load model"
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event_queue.put({
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"type": "error",
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"error": error_msg,
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"stack": "", "ts": time.time(),
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})
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return
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# Prepare model (LoRA or full finetuning)
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training_type = config.get("training_type", "LoRA/QLoRA")
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use_lora = (training_type == "LoRA/QLoRA")
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if use_lora:
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_send_status(event_queue, "Configuring LoRA adapters...")
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success = trainer.prepare_model_for_training(
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use_lora=True,
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finetune_vision_layers=config.get("finetune_vision_layers", True),
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finetune_language_layers=config.get("finetune_language_layers", True),
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finetune_attention_modules=config.get("finetune_attention_modules", True),
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finetune_mlp_modules=config.get("finetune_mlp_modules", True),
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target_modules=config.get("target_modules"),
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lora_r=config.get("lora_r", 16),
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lora_alpha=config.get("lora_alpha", 16),
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lora_dropout=config.get("lora_dropout", 0.0),
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use_gradient_checkpointing=config.get("gradient_checkpointing", "unsloth"),
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use_rslora=config.get("use_rslora", False),
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use_loftq=config.get("use_loftq", False),
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)
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else:
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_send_status(event_queue, "Preparing model for full finetuning...")
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success = trainer.prepare_model_for_training(use_lora=False)
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if not success or trainer.should_stop:
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if trainer.should_stop:
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event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
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else:
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event_queue.put({
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"type": "error",
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"error": trainer.training_progress.error or "Failed to prepare model",
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"stack": "", "ts": time.time(),
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})
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return
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# Load dataset
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_send_status(event_queue, "Loading and formatting dataset...")
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hf_dataset = config.get("hf_dataset", "")
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dataset_result = trainer.load_and_format_dataset(
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dataset_source=hf_dataset if hf_dataset and hf_dataset.strip() else None,
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format_type=config.get("format_type", ""),
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local_datasets=config.get("local_datasets") or None,
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custom_format_mapping=config.get("custom_format_mapping"),
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subset=config.get("subset"),
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train_split=config.get("train_split", "train"),
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eval_split=config.get("eval_split"),
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eval_steps=config.get("eval_steps", 0.00),
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dataset_slice_start=config.get("dataset_slice_start"),
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dataset_slice_end=config.get("dataset_slice_end"),
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)
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if isinstance(dataset_result, tuple):
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dataset, eval_dataset = dataset_result
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else:
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dataset = dataset_result
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eval_dataset = None
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# Disable eval if eval_steps <= 0
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eval_steps = config.get("eval_steps", 0.00)
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if eval_steps is not None and float(eval_steps) <= 0:
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eval_dataset = None
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# Tell the parent process that eval is configured so the frontend
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# shows "Waiting for first evaluation step..." instead of "not configured"
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if eval_dataset is not None:
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event_queue.put({
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"type": "eval_configured",
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"ts": time.time(),
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})
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if dataset is None or trainer.should_stop:
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if trainer.should_stop:
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event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
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else:
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event_queue.put({
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"type": "error",
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"error": trainer.training_progress.error or "Failed to load dataset",
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"stack": "", "ts": time.time(),
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})
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return
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# Convert learning rate
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try:
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lr_value = float(config.get("learning_rate", "2e-4"))
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except ValueError:
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event_queue.put({
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"type": "error",
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"error": f"Invalid learning rate: {config.get('learning_rate')}",
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"stack": "", "ts": time.time(),
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})
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return
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# Generate output dir
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output_dir = config.get("output_dir")
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if not output_dir:
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output_dir = f"./outputs/{model_name.replace('/', '_')}_{int(time.time())}"
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# Start training (directly — no inner thread, we ARE the subprocess)
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_send_status(event_queue, "Starting training...")
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max_steps = config.get("max_steps", 0)
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save_steps = config.get("save_steps", 0)
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trainer._train_worker(
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dataset,
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output_dir=output_dir,
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num_epochs=config.get("num_epochs", 3),
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learning_rate=lr_value,
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batch_size=config.get("batch_size", 2),
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gradient_accumulation_steps=config.get("gradient_accumulation_steps", 4),
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warmup_steps=config.get("warmup_steps"),
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warmup_ratio=config.get("warmup_ratio"),
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max_steps=max_steps if max_steps and max_steps > 0 else 0,
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save_steps=save_steps if save_steps and save_steps > 0 else 0,
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weight_decay=config.get("weight_decay", 0.01),
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random_seed=config.get("random_seed", 3407),
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packing=config.get("packing", False),
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train_on_completions=config.get("train_on_completions", False),
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enable_wandb=config.get("enable_wandb", False),
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wandb_project=config.get("wandb_project", "unsloth-training"),
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wandb_token=config.get("wandb_token"),
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enable_tensorboard=config.get("enable_tensorboard", False),
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tensorboard_dir=config.get("tensorboard_dir", "runs"),
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eval_dataset=eval_dataset,
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eval_steps=eval_steps,
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max_seq_length=config.get("max_seq_length", 2048),
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optim=config.get("optim", "adamw_8bit"),
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lr_scheduler_type=config.get("lr_scheduler_type", "linear"),
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)
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# Check final state
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progress = trainer.get_training_progress()
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if progress.error:
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event_queue.put({
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"type": "error",
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"error": progress.error,
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"stack": "",
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"ts": time.time(),
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})
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else:
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event_queue.put({
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"type": "complete",
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"output_dir": output_dir,
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"status_message": progress.status_message or "Training completed",
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"ts": time.time(),
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})
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except Exception as exc:
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event_queue.put({
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"type": "error",
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"error": str(exc),
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"stack": traceback.format_exc(limit=20),
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"ts": time.time(),
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})
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def _send_status(event_queue: Any, message: str) -> None:
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"""Send a status update to the parent process."""
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event_queue.put({
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"type": "status",
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"message": message,
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"ts": time.time(),
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})
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def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) -> None:
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"""Self-contained embedding model training pipeline.
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Uses FastSentenceTransformer + SentenceTransformerTrainer +
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MultipleNegativesRankingLoss — completely separate from the
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LLM/VLM/audio paths in UnslothTrainer.
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Mirrors the pattern from the reference embedding notebooks:
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All_MiniLM_L6_v2.py, BGE_M3.py, EmbeddingGemma_300M.py,
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ModernBert.py, Qwen3_Embedding_0_6B.py
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"""
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import math
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import queue as _queue
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import threading
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model_name = config["model_name"]
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training_start_time = time.time()
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# ── 1. Import embedding-specific libraries ──
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_send_status(event_queue, "Importing embedding libraries...")
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try:
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from unsloth import FastSentenceTransformer, is_bfloat16_supported
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from sentence_transformers import (
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SentenceTransformerTrainer,
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SentenceTransformerTrainingArguments,
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)
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from sentence_transformers.losses import MultipleNegativesRankingLoss
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from sentence_transformers.training_args import BatchSamplers
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from datasets import load_dataset, Dataset
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from transformers import TrainerCallback
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except ImportError as e:
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event_queue.put({
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"type": "error",
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"error": f"Failed to import embedding libraries: {e}. "
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"Ensure 'sentence_transformers' and 'unsloth' are installed.",
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"stack": traceback.format_exc(limit=20),
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"ts": time.time(),
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})
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return
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# ── Stop signal handling ──
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_should_stop = False
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_save_on_stop = True
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def _poll_stop():
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nonlocal _should_stop, _save_on_stop
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while True:
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try:
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msg = stop_queue.get(timeout=1.0)
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if msg and msg.get("type") == "stop":
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_save_on_stop = msg.get("save", True)
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_should_stop = True
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logger.info("Embedding training: stop signal received (save=%s)", _save_on_stop)
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return
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except _queue.Empty:
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continue
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except (EOFError, OSError):
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return
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stop_thread = threading.Thread(target=_poll_stop, daemon=True)
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stop_thread.start()
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# ── 2. Load model ──
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_send_status(event_queue, "Loading embedding model...")
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try:
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max_seq_length = config.get("max_seq_length", 512)
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training_type = config.get("training_type", "LoRA/QLoRA")
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|
use_lora = (training_type == "LoRA/QLoRA")
|
|
|
|
model = FastSentenceTransformer.from_pretrained(
|
|
model_name=model_name,
|
|
max_seq_length=max_seq_length,
|
|
full_finetuning=not use_lora,
|
|
)
|
|
except Exception as e:
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": f"Failed to load embedding model '{model_name}': {e}",
|
|
"stack": traceback.format_exc(limit=20),
|
|
"ts": time.time(),
|
|
})
|
|
return
|
|
|
|
if _should_stop:
|
|
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
|
return
|
|
|
|
# ── 3. Apply LoRA ──
|
|
if use_lora:
|
|
_send_status(event_queue, "Configuring LoRA adapters (FEATURE_EXTRACTION)...")
|
|
try:
|
|
gradient_checkpointing = config.get("gradient_checkpointing", False)
|
|
# Normalize: "none" or empty → False
|
|
if gradient_checkpointing in ("none", "", None):
|
|
gradient_checkpointing = False
|
|
|
|
model = FastSentenceTransformer.get_peft_model(
|
|
model,
|
|
r=config.get("lora_r", 32),
|
|
target_modules=config.get("target_modules") or ["q_proj", "k_proj", "v_proj", "o_proj"],
|
|
lora_alpha=config.get("lora_alpha", 64),
|
|
lora_dropout=config.get("lora_dropout", 0.0),
|
|
bias="none",
|
|
use_gradient_checkpointing=gradient_checkpointing,
|
|
random_state=config.get("random_seed", 3407),
|
|
use_rslora=config.get("use_rslora", False),
|
|
loftq_config={"loftq_bits": 4, "loftq_iter": 1} if config.get("use_loftq") else None,
|
|
task_type="FEATURE_EXTRACTION",
|
|
)
|
|
except Exception as e:
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": f"Failed to configure LoRA for embedding model: {e}",
|
|
"stack": traceback.format_exc(limit=20),
|
|
"ts": time.time(),
|
|
})
|
|
return
|
|
|
|
if _should_stop:
|
|
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
|
return
|
|
|
|
# ── 4. Load dataset ──
|
|
_send_status(event_queue, "Loading dataset...")
|
|
try:
|
|
hf_dataset = config.get("hf_dataset", "")
|
|
local_datasets = config.get("local_datasets") or []
|
|
subset = config.get("subset") or None
|
|
train_split = config.get("train_split", "train") or "train"
|
|
|
|
if hf_dataset and hf_dataset.strip():
|
|
hf_token = config.get("hf_token", "")
|
|
hf_token = hf_token if hf_token and hf_token.strip() else None
|
|
dataset = load_dataset(
|
|
hf_dataset.strip(),
|
|
subset,
|
|
split=train_split,
|
|
token=hf_token,
|
|
)
|
|
elif local_datasets:
|
|
# Load from local file(s)
|
|
local_path = local_datasets[0]
|
|
if local_path.endswith(".csv"):
|
|
dataset = load_dataset("csv", data_files=local_path, split="train")
|
|
elif local_path.endswith(".json") or local_path.endswith(".jsonl"):
|
|
dataset = load_dataset("json", data_files=local_path, split="train")
|
|
elif local_path.endswith(".parquet"):
|
|
dataset = load_dataset("parquet", data_files=local_path, split="train")
|
|
else:
|
|
dataset = load_dataset(local_path, split="train")
|
|
else:
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": "No dataset specified for embedding training.",
|
|
"stack": "", "ts": time.time(),
|
|
})
|
|
return
|
|
|
|
# Apply dataset slicing if specified
|
|
slice_start = config.get("dataset_slice_start")
|
|
slice_end = config.get("dataset_slice_end")
|
|
if slice_start is not None or slice_end is not None:
|
|
start = slice_start or 0
|
|
end = slice_end or len(dataset)
|
|
dataset = dataset.select(range(start, min(end + 1, len(dataset))))
|
|
|
|
logger.info(f"Embedding dataset loaded: {len(dataset)} samples")
|
|
except Exception as e:
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": f"Failed to load dataset: {e}",
|
|
"stack": traceback.format_exc(limit=20),
|
|
"ts": time.time(),
|
|
})
|
|
return
|
|
|
|
if _should_stop:
|
|
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
|
return
|
|
|
|
# ── 5. Create loss function ──
|
|
loss = MultipleNegativesRankingLoss(model)
|
|
|
|
# ── 6. Build training arguments ──
|
|
_send_status(event_queue, "Configuring training...")
|
|
try:
|
|
lr_value = float(config.get("learning_rate", "2e-4"))
|
|
except ValueError:
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": f"Invalid learning rate: {config.get('learning_rate')}",
|
|
"stack": "", "ts": time.time(),
|
|
})
|
|
return
|
|
|
|
output_dir = config.get("output_dir")
|
|
if not output_dir:
|
|
output_dir = f"./outputs/{model_name.replace('/', '_')}_{int(time.time())}"
|
|
|
|
num_epochs = config.get("num_epochs", 2)
|
|
batch_size = config.get("batch_size", 256)
|
|
gradient_accumulation_steps = config.get("gradient_accumulation_steps", 1)
|
|
max_steps_val = config.get("max_steps", 0)
|
|
save_steps_val = config.get("save_steps", 0)
|
|
warmup_ratio = config.get("warmup_ratio", 0.03)
|
|
warmup_steps_val = config.get("warmup_steps")
|
|
log_frequency = config.get("log_frequency", 50)
|
|
|
|
# Build args dict
|
|
training_args_kwargs = {
|
|
"output_dir": output_dir,
|
|
"per_device_train_batch_size": batch_size,
|
|
"gradient_accumulation_steps": gradient_accumulation_steps,
|
|
"learning_rate": lr_value,
|
|
"fp16": not is_bfloat16_supported(),
|
|
"bf16": is_bfloat16_supported(),
|
|
"logging_steps": max(1, log_frequency) if log_frequency else 1,
|
|
"report_to": ["wandb"] if config.get("enable_wandb") else "none",
|
|
"lr_scheduler_type": config.get("lr_scheduler_type", "linear"),
|
|
"batch_sampler": BatchSamplers.NO_DUPLICATES,
|
|
"optim": config.get("optim", "adamw_8bit"),
|
|
"weight_decay": config.get("weight_decay", 0.01),
|
|
"seed": config.get("random_seed", 3407),
|
|
}
|
|
|
|
# max_steps vs epochs
|
|
if max_steps_val and max_steps_val > 0:
|
|
training_args_kwargs["max_steps"] = max_steps_val
|
|
else:
|
|
training_args_kwargs["num_train_epochs"] = num_epochs if num_epochs > 0 else 2
|
|
|
|
# warmup: prefer warmup_ratio (standard for embedding scripts), fallback to steps
|
|
if warmup_ratio is not None and warmup_ratio > 0:
|
|
training_args_kwargs["warmup_ratio"] = warmup_ratio
|
|
elif warmup_steps_val is not None and warmup_steps_val > 0:
|
|
training_args_kwargs["warmup_steps"] = warmup_steps_val
|
|
|
|
# save_steps
|
|
if save_steps_val and save_steps_val > 0:
|
|
training_args_kwargs["save_steps"] = save_steps_val
|
|
training_args_kwargs["save_strategy"] = "steps"
|
|
|
|
args = SentenceTransformerTrainingArguments(**training_args_kwargs)
|
|
|
|
# ── 7. Calculate total steps for progress tracking ──
|
|
if max_steps_val and max_steps_val > 0:
|
|
total_steps = max_steps_val
|
|
else:
|
|
effective_epochs = num_epochs if num_epochs > 0 else 2
|
|
len_dataloader = math.ceil(len(dataset) / batch_size)
|
|
steps_per_epoch = max(len_dataloader // gradient_accumulation_steps, 1)
|
|
total_steps = steps_per_epoch * effective_epochs
|
|
|
|
# ── 8. Create progress callback ──
|
|
class _EmbeddingProgressCallback(TrainerCallback):
|
|
"""Sends training progress events to the parent process via event_queue."""
|
|
|
|
def on_log(self, args, state, control, logs=None, **kwargs):
|
|
if not logs:
|
|
return
|
|
loss_value = logs.get("loss", logs.get("train_loss", 0.0))
|
|
current_step = state.global_step
|
|
|
|
elapsed = time.time() - training_start_time
|
|
eta = None
|
|
if current_step > 0 and total_steps > 0:
|
|
remaining = total_steps - current_step
|
|
if remaining > 0:
|
|
eta = (elapsed / current_step) * remaining
|
|
|
|
event_queue.put({
|
|
"type": "progress",
|
|
"step": current_step,
|
|
"epoch": round(state.epoch, 2) if state.epoch else 0,
|
|
"loss": loss_value,
|
|
"learning_rate": logs.get("learning_rate", 0.0),
|
|
"total_steps": total_steps,
|
|
"elapsed_seconds": elapsed,
|
|
"eta_seconds": eta,
|
|
"grad_norm": logs.get("grad_norm"),
|
|
"num_tokens": getattr(state, "num_input_tokens_seen", None),
|
|
"eval_loss": logs.get("eval_loss"),
|
|
"status_message": "",
|
|
"ts": time.time(),
|
|
})
|
|
|
|
def on_step_end(self, args, state, control, **kwargs):
|
|
if _should_stop:
|
|
logger.info("Embedding training: stop at step %d", state.global_step)
|
|
control.should_training_stop = True
|
|
return control
|
|
|
|
# ── 9. Create trainer and train ──
|
|
_send_status(event_queue, "Starting embedding training...")
|
|
try:
|
|
trainer = SentenceTransformerTrainer(
|
|
model=model,
|
|
train_dataset=dataset,
|
|
loss=loss,
|
|
args=args,
|
|
callbacks=[_EmbeddingProgressCallback()],
|
|
)
|
|
|
|
trainer.train()
|
|
except Exception as e:
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": f"Embedding training failed: {e}",
|
|
"stack": traceback.format_exc(limit=20),
|
|
"ts": time.time(),
|
|
})
|
|
return
|
|
|
|
# ── 10. Save model ──
|
|
if _should_stop and not _save_on_stop:
|
|
event_queue.put({
|
|
"type": "complete",
|
|
"output_dir": None,
|
|
"status_message": "Training cancelled",
|
|
"ts": time.time(),
|
|
})
|
|
return
|
|
|
|
_send_status(event_queue, "Saving model...")
|
|
try:
|
|
model.save_pretrained(output_dir)
|
|
model.tokenizer.save_pretrained(output_dir)
|
|
logger.info("Embedding model saved to %s", output_dir)
|
|
except Exception as e:
|
|
logger.error("Failed to save embedding model: %s", e)
|
|
event_queue.put({
|
|
"type": "error",
|
|
"error": f"Training completed but failed to save: {e}",
|
|
"stack": traceback.format_exc(limit=20),
|
|
"ts": time.time(),
|
|
})
|
|
return
|
|
|
|
# ── 11. Done ──
|
|
event_queue.put({
|
|
"type": "complete",
|
|
"output_dir": output_dir,
|
|
"status_message": "Embedding training completed",
|
|
"ts": time.time(),
|
|
})
|