diff --git a/studio/backend/core/inference/worker.py b/studio/backend/core/inference/worker.py index ad1f7c38ec..4efcb5f70c 100644 --- a/studio/backend/core/inference/worker.py +++ b/studio/backend/core/inference/worker.py @@ -34,13 +34,50 @@ from utils.hardware import apply_gpu_ids def _activate_transformers_version(model_name: str) -> None: - """Activate the correct transformers version BEFORE any ML imports.""" + """Activate the correct transformers version BEFORE any ML imports. + + Uses get_transformers_tier() to decide between .venv_t5_550/ (5.5.0), + .venv_t5_530/ (5.3.0), or the default 4.57.x. + """ + # Ensure backend is on path for utils imports + backend_path = str(Path(__file__).resolve().parent.parent.parent) if backend_path not in sys.path: sys.path.insert(0, backend_path) - from utils.transformers_version import activate_transformers_for_subprocess + from utils.transformers_version import ( + get_transformers_tier, + _resolve_base_model, + _ensure_venv_t5_530_exists, + _ensure_venv_t5_550_exists, + _VENV_T5_530_DIR, + _VENV_T5_550_DIR, + ) - activate_transformers_for_subprocess(model_name) + resolved = _resolve_base_model(model_name) + tier = get_transformers_tier(resolved) + + if tier == "550": + if not _ensure_venv_t5_550_exists(): + raise RuntimeError( + f"Cannot activate transformers 5.5.0: .venv_t5_550 missing at {_VENV_T5_550_DIR}" + ) + if _VENV_T5_550_DIR not in sys.path: + sys.path.insert(0, _VENV_T5_550_DIR) + logger.info("Activated transformers 5.5.0 from %s", _VENV_T5_550_DIR) + _pp = os.environ.get("PYTHONPATH", "") + os.environ["PYTHONPATH"] = _VENV_T5_550_DIR + (os.pathsep + _pp if _pp else "") + elif tier == "530": + if not _ensure_venv_t5_530_exists(): + raise RuntimeError( + f"Cannot activate transformers 5.3.0: .venv_t5_530 missing at {_VENV_T5_530_DIR}" + ) + if _VENV_T5_530_DIR not in sys.path: + sys.path.insert(0, _VENV_T5_530_DIR) + logger.info("Activated transformers 5.3.0 from %s", _VENV_T5_530_DIR) + _pp = os.environ.get("PYTHONPATH", "") + os.environ["PYTHONPATH"] = _VENV_T5_530_DIR + (os.pathsep + _pp if _pp else "") + else: + logger.info("Using default transformers (4.57.x) for %s", model_name) def _decode_image(image_base64: str): @@ -285,18 +322,13 @@ def _handle_load(backend, config: dict, resp_queue: Any) -> None: except Exception as e: logger.warning("Could not read adapter_config.json: %s", e) - # Auto-enable trust_remote_code for NemotronH/Nano models only. + # Auto-enable trust_remote_code for Nemotron models only. # NemotronH has config parsing bugs requiring trust_remote_code=True. # Other transformers 5.x models are native and do NOT need it. - # NOTE: Must NOT match Llama-Nemotron (standard Llama architecture). - _NEMOTRON_TRUST_SUBSTRINGS = ("nemotron_h", "nemotron-h", "nemotron-3-nano") trust_remote_code = config.get("trust_remote_code", False) if not trust_remote_code: model_name = config["model_name"] - _mn_lower = model_name.lower() - if any(sub in _mn_lower for sub in _NEMOTRON_TRUST_SUBSTRINGS) and ( - _mn_lower.startswith("unsloth/") or _mn_lower.startswith("nvidia/") - ): + if "nemotron" in model_name.lower(): trust_remote_code = True logger.info( "Auto-enabled trust_remote_code for Nemotron model: %s", diff --git a/studio/backend/core/training/worker.py b/studio/backend/core/training/worker.py index f47a6bd599..dcb0025f48 100644 --- a/studio/backend/core/training/worker.py +++ b/studio/backend/core/training/worker.py @@ -126,6 +126,97 @@ def _hipcc_gcc_install_dir() -> str | None: return None +def _causal_conv1d_platform_tag() -> str | None: + machine = platform.machine().lower() + if sys.platform.startswith("linux"): + if machine in {"x86_64", "amd64"}: + return "linux_x86_64" + if machine in {"aarch64", "arm64"}: + return "linux_aarch64" + return None + # No prebuilt wheels published for macOS or Windows + return None + + +def _probe_causal_conv1d_env() -> dict[str, str] | None: + try: + probe = _sp.run( + [ + sys.executable, + "-c", + ( + "import json, sys, re, torch; " + "parts = torch.__version__.split('+', 1)[0].split('.')[:2]; " + "minor = re.sub(r'[^0-9].*', '', parts[1]) if len(parts) > 1 else '0'; " + "torch_mm = parts[0] + '.' + minor; " + "print(json.dumps({" + "'python_tag': f'cp{sys.version_info.major}{sys.version_info.minor}', " + "'torch_mm': torch_mm, " + "'cuda_major': str(int(str(torch.version.cuda).split('.', 1)[0])) if torch.version.cuda else '', " + "'cxx11abi': str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()" + "}))" + ), + ], + stdout = _sp.PIPE, + stderr = _sp.PIPE, + text = True, + timeout = 30, + ) + except _sp.TimeoutExpired: + logger.warning("Torch environment probe timed out after 30s") + return None + if probe.returncode != 0: + logger.warning( + "Failed to probe torch environment for causal-conv1d wheel:\n%s", + probe.stdout, + ) + return None + + try: + return json.loads(probe.stdout.strip()) + except json.JSONDecodeError: + logger.warning( + "Failed to parse torch environment probe output: %s", probe.stdout + ) + return None + + +def _direct_wheel_url( + *, + filename_prefix: str, + package_version: str, + release_tag: str, + release_base_url: str, + env: dict[str, str] | None = None, +) -> str | None: + env = env or _probe_causal_conv1d_env() + platform_tag = _causal_conv1d_platform_tag() + if env is None or platform_tag is None or not env.get("cuda_major"): + return None + + filename = ( + f"{filename_prefix}-{package_version}" + f"+cu{env['cuda_major']}torch{env['torch_mm']}" + f"cxx11abi{env['cxx11abi']}-{env['python_tag']}-{env['python_tag']}-{platform_tag}.whl" + ) + return f"{release_base_url}/{release_tag}/{filename}" + + +def _url_exists(url: str) -> bool: + try: + request = urllib.request.Request(url, method = "HEAD") + with urllib.request.urlopen(request, timeout = 10): + return True + except urllib.error.HTTPError as exc: + if exc.code == 404: + return False + logger.warning("Unexpected HTTP error while probing %s: %s", url, exc) + return False + except Exception as exc: + logger.warning("Failed to probe %s: %s", url, exc) + return False + + def _install_package_wheel_first( *, event_queue: Any, @@ -948,15 +1039,50 @@ def _ensure_flash_attn_for_long_context(event_queue: Any, max_seq_length: int) - def _activate_transformers_version(model_name: str) -> None: - """Activate the correct transformers version BEFORE any ML imports.""" + """Activate the correct transformers version BEFORE any ML imports. + + Uses get_transformers_tier() to decide between .venv_t5_550/ (5.5.0), + .venv_t5_530/ (5.3.0), or the default 4.57.x. + """ # Ensure backend is on path for utils imports backend_path = str(Path(__file__).resolve().parent.parent.parent) if backend_path not in sys.path: sys.path.insert(0, backend_path) - from utils.transformers_version import activate_transformers_for_subprocess + from utils.transformers_version import ( + get_transformers_tier, + _resolve_base_model, + _ensure_venv_t5_530_exists, + _ensure_venv_t5_550_exists, + _VENV_T5_530_DIR, + _VENV_T5_550_DIR, + ) - activate_transformers_for_subprocess(model_name) + resolved = _resolve_base_model(model_name) + tier = get_transformers_tier(resolved) + + if tier == "550": + if not _ensure_venv_t5_550_exists(): + raise RuntimeError( + f"Cannot activate transformers 5.5.0: .venv_t5_550 missing at {_VENV_T5_550_DIR}" + ) + if _VENV_T5_550_DIR not in sys.path: + sys.path.insert(0, _VENV_T5_550_DIR) + logger.info("Activated transformers 5.5.0 from %s", _VENV_T5_550_DIR) + _pp = os.environ.get("PYTHONPATH", "") + os.environ["PYTHONPATH"] = _VENV_T5_550_DIR + (os.pathsep + _pp if _pp else "") + elif tier == "530": + if not _ensure_venv_t5_530_exists(): + raise RuntimeError( + f"Cannot activate transformers 5.3.0: .venv_t5_530 missing at {_VENV_T5_530_DIR}" + ) + if _VENV_T5_530_DIR not in sys.path: + sys.path.insert(0, _VENV_T5_530_DIR) + logger.info("Activated transformers 5.3.0 from %s", _VENV_T5_530_DIR) + _pp = os.environ.get("PYTHONPATH", "") + os.environ["PYTHONPATH"] = _VENV_T5_530_DIR + (os.pathsep + _pp if _pp else "") + else: + logger.info("Using default transformers (4.57.x) for %s", model_name) def _adapt_for_mlx_vlm(items): @@ -1737,17 +1863,14 @@ def run_training_process( ) return - # ── 1a. Auto-enable trust_remote_code for NemotronH/Nano models ── + # ── 1a. Auto-enable trust_remote_code for Nemotron models ── # NemotronH has config parsing bugs in transformers that require # trust_remote_code=True as a workaround. Other transformers 5.x models # (Qwen3.5, Gemma 4, etc.) are native and do NOT need it — enabling it # bypasses the compiler (disabling fused CE). - # NOTE: Must NOT match Llama-Nemotron (standard Llama architecture). - _NEMOTRON_TRUST_SUBSTRINGS = ("nemotron_h", "nemotron-h", "nemotron-3-nano") _lowered = model_name.lower() if ( - any(sub in _lowered for sub in _NEMOTRON_TRUST_SUBSTRINGS) - and (_lowered.startswith("unsloth/") or _lowered.startswith("nvidia/")) + "nemotron" in _lowered and not config.get("trust_remote_code", False) ): config["trust_remote_code"] = True