Add new export LLM config

examples/models/llama/config/llm_config.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+"""
+Configurations for exporting Llama.
+
+Uses dataclases, which integrate with OmegaConf and Hydra.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+
+@dataclass
+class BaseConfig:
+    """
+    These are specific to the specific model, e.g. whether it’s Qwen3 0.6B or Phi-4-mini.
+    for each of these different models, you can expect each of these fields to change.
+    """
+
+    model_class: str = "llama"
+    params: Optional[str] = None
+    checkpoint: Optional[str] = None
+    checkpoint_dir: Optional[str] = None  # For sharded checkpoint
+    tokenizer_path: Optional[str] = None
+    metadata: Optional[str] = None
+    fairseq2: bool = False  # For legacy internal use cases
+
+
+@dataclass
+class ModelConfig:
+    """
+    These are not necessarily specific to the model, but are needed to finish off
+    the rest of the model configuration in eager. You can think of these like
+    optimizations / actual configurations. The same ModelConfig can be applied
+    to different models.
+    """
+
+    dtype_override: str = "fp32"
+    enable_dynamic_shape: bool = True
+    use_shared_embedding: bool = False
+    use_lora: bool = False
+    use_sdpa_with_kv_cache: bool = False
+    expand_rope_table: bool = False
+    output_prune_map: Optional[str] = None
+    input_prune_map: Optional[str] = None
+
+    # Below are config options relating to kv cache.
+    use_kv_cache: Optional[bool] = None
+    quantize_kv_cache: Optional[bool] = None
+    local_global_attention: List[int] = None
+
+
+@dataclass
+class ExportConfig:
+    max_seq_length: Optional[int] = None
+    max_context_length: Optional[int] = None
+    output_dir: Optional[str] = None
+    output_name: Optional[str] = None
+    so_library: Optional[str] = None
+    export_only: Optional[bool] = None
+
+
+@dataclass
+class DebugConfig:
+    profile_memory: bool = False
+    profile_path: Optional[str] = None
+    generate_etrecord: bool = False
+    generate_full_logits: bool = False
+    verbose: bool = False  # Would be good to remove this from the config eventually
+
+
+########################################################################
+#### The below config can eventually be replaced by export recipes #####
+########################################################################
+
+
+@dataclass
+class QuantizationConfig:
+    qmode: Optional[str] = None
+    embedding_quantize: Optional[bool] = None
+    pt2e_quantize: Optional[bool] = None
+    group_size: Optional[int] = None
+    use_spin_quant: Optional[bool] = None
+    use_qat: Optional[bool] = None
+    preq_mode: Optional[str] = None
+    preq_group_size: Optional[int] = None
+    preq_embedding_quantize: Optional[bool] = None
+    calibration_tasks: Optional[str] = None
+    calibration_limit: Optional[int] = None
+    calibration_seq_length: Optional[int] = None
+    calibration_data: Optional[str] = None
+
+
+@dataclass
+class XNNPackConfig:
+    enabled: Optional[bool] = None
+    extended_ops: Optional[bool] = None
+
+
+@dataclass
+class CoreMLConfig:  # coreML recipe?
+    enabled: Optional[bool] = None
+    enable_state: Optional[bool] = None
+    preserve_sdpa: Optional[bool] = None
+    quantize: Optional[bool] = None
+    ios: Optional[bool] = None
+    compute_units: Optional[str] = None
+
+
+@dataclass
+class VulkanConfig:
+    enabled: bool = False
+
+
+@dataclass
+class QNNConfig:
+    enabled: bool = False
+    use_sha: bool = False
+    soc_model: str = "SM8650"
+    use_qnn_sha: bool = False
+    optimized_rotation_path: Optional[str] = None
+    num_sharding: int = 0
+
+
+@dataclass
+class MPSConfig:
+    enabled: Optional[bool] = False
+
+
+@dataclass
+class BackendConfig:
+    xnnpack: XNNPackConfig = field(default_factory=XNNPackConfig)
+    coreml: CoreMLConfig = field(default_factory=CoreMLConfig)
+    vulkan: VulkanConfig = field(default_factory=VulkanConfig)
+    qnn: QNNConfig = field(default_factory=QNNConfig)
+    mps: MPSConfig = field(default_factory=MPSConfig)
+
+
+@dataclass
+class LlmConfig:
+    base: BaseConfig = field(default_factory=BaseConfig)
+    model: ModelConfig = field(default_factory=ModelConfig)
+    quantization: QuantizationConfig = field(default_factory=QuantizationConfig)
+    backend: BackendConfig = field(default_factory=BackendConfig)