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| 1 | +# ------------------------------------------------------------------------- |
| 2 | +# Copyright (c) Qualcomm Technologies, Inc. and/or its subsidiaries. |
| 3 | +# SPDX-License-Identifier: MIT |
| 4 | +# -------------------------------------------------------------------------- |
| 5 | + |
| 6 | +import logging |
| 7 | +import shutil |
| 8 | +from pathlib import Path |
| 9 | + |
| 10 | +from olive.common.config_utils import ParamCategory |
| 11 | +from olive.hardware.accelerator import AcceleratorSpec |
| 12 | +from olive.model import HfModelHandler, QairtModelHandler |
| 13 | +from olive.passes import Pass |
| 14 | +from olive.passes.pass_config import BasePassConfig, PassConfigParam |
| 15 | +from olive.passes.qairt.utils import QairtLogLevel |
| 16 | + |
| 17 | +logger = logging.getLogger(__name__) |
| 18 | + |
| 19 | + |
| 20 | +class QairtPipelinePass(Pass): |
| 21 | + """Run a QairtPipeline from a YAML recipe on a HuggingFace model. |
| 22 | +
|
| 23 | + Executes the full LLMPipeline workflow (model loading, quantization, compilation) |
| 24 | + defined by the recipe and exports the result as a QairtModelHandler. This pass |
| 25 | + is intended to replace the QairtPreparation -> QairtGenAIBuilder workflow. |
| 26 | +
|
| 27 | + The input HfModelHandler is the authoritative source for the model identity. |
| 28 | + If the recipe also specifies model_id_or_path and it differs from the handler's |
| 29 | + path, an error is raised. If the recipe omits model_id_or_path, the handler's |
| 30 | + path is used. |
| 31 | + """ |
| 32 | + |
| 33 | + @classmethod |
| 34 | + def _default_config(cls, accelerator_spec: AcceleratorSpec) -> dict[str, PassConfigParam]: |
| 35 | + return { |
| 36 | + "recipe": PassConfigParam( |
| 37 | + type_=str, |
| 38 | + required=True, |
| 39 | + category=ParamCategory.PATH, |
| 40 | + description="Path to the YAML recipe file that defines the LLM pipeline stages " |
| 41 | + "(model loading, quantization, genai_builder, etc.).", |
| 42 | + ), |
| 43 | + "cache_dir": PassConfigParam( |
| 44 | + type_=str, |
| 45 | + required=False, |
| 46 | + default_value=None, |
| 47 | + description="Directory for pipeline intermediate artifacts. " |
| 48 | + "Overrides the recipe's cache_dir field when set.", |
| 49 | + ), |
| 50 | + "log_level": PassConfigParam( |
| 51 | + type_=QairtLogLevel, |
| 52 | + required=False, |
| 53 | + default_value=None, |
| 54 | + description="Log level for underlying QAIRT pipeline components. " |
| 55 | + "Valid values: DEBUG, INFO, WARNING, ERROR, TRACE. " |
| 56 | + "Overrides the recipe's log_level field when set.", |
| 57 | + ), |
| 58 | + } |
| 59 | + |
| 60 | + @classmethod |
| 61 | + def validate_config( |
| 62 | + cls, |
| 63 | + config: type[BasePassConfig], |
| 64 | + accelerator_spec: AcceleratorSpec, |
| 65 | + ) -> bool: |
| 66 | + # Only validates the top-level qairt import. The qairt.experimental.pipeline.* |
| 67 | + # sub-modules are not checked here; if they are absent (e.g. older SDK), the |
| 68 | + # error surfaces in _run_for_config instead. |
| 69 | + try: |
| 70 | + import qairt # noqa: F401 # pylint: disable=unused-import |
| 71 | + except ImportError as exc: |
| 72 | + raise ImportError( |
| 73 | + "Failed to import QAIRT SDK - please install olive-ai[qairt] to use QAIRT passes. " |
| 74 | + "If already installed, please run `qairt-vm -i` for help troubleshooting issues." |
| 75 | + ) from exc |
| 76 | + |
| 77 | + return True |
| 78 | + |
| 79 | + def _run_for_config( |
| 80 | + self, |
| 81 | + model: HfModelHandler, |
| 82 | + config: type[BasePassConfig], |
| 83 | + output_model_path: str, |
| 84 | + ) -> QairtModelHandler: |
| 85 | + try: |
| 86 | + import qairt # noqa: F401 # pylint: disable=unused-import |
| 87 | + from qairt.experimental.pipeline.torch.common.recipe import Recipe |
| 88 | + from qairt.experimental.pipeline.torch.llm.pipeline import LLMPipeline |
| 89 | + except ImportError as exc: |
| 90 | + raise ImportError( |
| 91 | + "Failed to import QAIRT Pipeline API - please install olive-ai[qairt] to use QAIRT passes. " |
| 92 | + "If already installed, please run `qairt-vm -i` for help troubleshooting issues." |
| 93 | + ) from exc |
| 94 | + |
| 95 | + if not isinstance(model, HfModelHandler): |
| 96 | + raise ValueError(f"QairtPipelinePass requires HfModelHandler as input, got {type(model).__name__}") |
| 97 | + |
| 98 | + recipe_path = Path(config.recipe).resolve() |
| 99 | + if not recipe_path.exists(): |
| 100 | + raise ValueError(f"Recipe file not found at: {recipe_path}") |
| 101 | + |
| 102 | + recipe_data = dict(Recipe.from_file(recipe_path)) |
| 103 | + |
| 104 | + recipe_model_id = recipe_data.get("model_id_or_path") |
| 105 | + if recipe_model_id and recipe_model_id != model.model_path: |
| 106 | + raise ValueError( |
| 107 | + f"Conflict between recipe model_id_or_path '{recipe_model_id}' and input model " |
| 108 | + f"path '{model.model_path}'. Remove model_id_or_path from the recipe or ensure " |
| 109 | + "it matches the input model path." |
| 110 | + ) |
| 111 | + |
| 112 | + if config.cache_dir is not None: |
| 113 | + recipe_data["cache_dir"] = config.cache_dir |
| 114 | + if config.log_level is not None: |
| 115 | + recipe_data["log_level"] = config.log_level |
| 116 | + |
| 117 | + pipe = LLMPipeline.from_pretrained(model.model_path, recipe=recipe_data) |
| 118 | + pipe.construct() |
| 119 | + |
| 120 | + Path(output_model_path).mkdir(parents=True, exist_ok=True) |
| 121 | + pipe.export(output_model_path) |
| 122 | + |
| 123 | + # QairtEncapsulation needs config.json and generation_config.json to generate |
| 124 | + # genai_config.json. Resolve the local HF cache path (model.model_path may be a |
| 125 | + # HuggingFace repo ID rather than a local directory) and copy if not already present. |
| 126 | + try: |
| 127 | + from huggingface_hub import snapshot_download |
| 128 | + |
| 129 | + local_model_path = snapshot_download( |
| 130 | + model.model_path, |
| 131 | + local_files_only=True, |
| 132 | + ignore_patterns=["*.pt", "*.bin", "*.safetensors"], |
| 133 | + ) |
| 134 | + except Exception as e: |
| 135 | + logger.warning( |
| 136 | + "Failed to resolve local HF cache for '%s': %s. File copy will be skipped.", |
| 137 | + model.model_path, |
| 138 | + e, |
| 139 | + ) |
| 140 | + local_model_path = model.model_path |
| 141 | + |
| 142 | + for fname in ("config.json", "generation_config.json"): |
| 143 | + src = Path(local_model_path) / fname |
| 144 | + dst = Path(output_model_path) / fname |
| 145 | + if src.exists() and not dst.exists(): |
| 146 | + shutil.copy2(src, dst) |
| 147 | + |
| 148 | + # The pipeline exports chat_template files into a chat_template/ subdirectory. |
| 149 | + # QairtEncapsulation expects these as flat files in the model root. |
| 150 | + chat_template_dir = Path(output_model_path) / "chat_template" |
| 151 | + for fname in ("chat_template.jinja", "tokenizer_config.json"): |
| 152 | + src = chat_template_dir / fname |
| 153 | + dst = Path(output_model_path) / fname |
| 154 | + if src.exists() and not dst.exists(): |
| 155 | + shutil.copy2(src, dst) |
| 156 | + |
| 157 | + return QairtModelHandler(model_path=output_model_path) |
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