Add new export LLM config

Pull Request resolved: #11028




@imported-using-ghimport

Differential Revision: [D75263991](https://our.internmc.facebook.com/intern/diff/D75263991/)
ghstack-source-id: 288422344

Author: jackzhxng

examples/models/llama/config/TARGETS

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+# Any targets that should be shared between fbcode and xplat must be defined in
+# targets.bzl. This file can contain fbcode-only targets.
+
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+load(":targets.bzl", "define_common_targets")
+
+oncall("executorch")
+
+define_common_targets()

examples/models/llama/config/llm_config.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# 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.
+"""
+
+import re
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import List, Optional
+
+
+################################################################################
+################################## BaseConfig ##################################
+################################################################################
+
+
+class ModelType(str, Enum):
+    STORIES110M = "stories110m"
+    LLAMA2 = "llama2"
+    LLAMA3 = "llama3"
+    LLAMA3_1 = "llama3_1"
+    LLAMA3_2 = "llama3_2"
+    LLAMA3_2_VISION = "llama3_2_vision"
+    STATIC_LLAMA = "static_llama"
+    QWEN2_5 = "qwen2_5"
+    QWEN3_0_6B = "qwen3-0_6b"
+    QWEN3_1_7B = "qwen3-1_7b"
+    QWEN3_4B = "qwen3-4b"
+    PHI_4_MINI = "phi_4_mini"
+    SMOLLM2 = "smollm2"
+
+
+class PreqMode(str, Enum):
+    """
+    If you are dealing with pre-quantized checkpoints, this used to
+    be the way to specify them. Now you don't need to specify these
+    options if you use a TorchAo-prequantized checkpoint, but they
+    are still around to preservce backward compatibility.
+    """
+
+    PREQ_8DA4W = "8da4w"
+    PREQ_8DA4W_OUT_8DA8W = "8da4w_output_8da8w"
+
+
+@dataclass
+class BaseConfig:
+    """
+    Configurations specific to the model, e.g. whether it’s Qwen3 or Phi-4-mini,
+    and are the minimal set of parameters needed to load the pretrained
+    eager model and its weights.
+    """
+
+    model_class: ModelType = ModelType.LLAMA3
+    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
+    use_lora: bool = False
+    fairseq2: bool = False  # For legacy internal use cases.
+
+    # Legacy pre-quantization options that happen during model weight loading.
+    preq_mode: Optional[PreqMode] = None
+    preq_group_size: int = 32
+    preq_embedding_quantize: str = "8,0"
+
+
+################################################################################
+################################# ModelConfig ##################################
+################################################################################
+
+
+class DtypeOverride(str, Enum):
+    """
+    DType of the model. Highly recommended to use "fp32", unless you want to
+    export without a backend, in which case you can also use "bf16". "fp16"
+    is not recommended.
+    """
+
+    FP32 = "fp32"
+    FP16 = "fp16"
+    BF16 = "bf16"
+
+
+@dataclass
+class ModelConfig:
+    """
+    Configurations 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 multiple models.
+    """
+
+    dtype_override: DtypeOverride = DtypeOverride.FP32
+    enable_dynamic_shape: bool = True
+    use_shared_embedding: bool = False
+    use_sdpa_with_kv_cache: bool = False
+    expand_rope_table: bool = False
+    use_attention_sink: Optional[str] = None
+    output_prune_map: Optional[str] = None
+    input_prune_map: Optional[str] = None
+
+    # Below are config options relating to kv cache.
+    use_kv_cache: bool = False
+    quantize_kv_cache: bool = False
+    local_global_attention: Optional[List[int]] = None
+
+
+################################################################################
+################################ ExportConfig ##################################
+################################################################################
+
+
+@dataclass
+class ExportConfig:
+    """
+    Configures properties relevant to the export process.
+    """
+
+    max_seq_length: int = 128
+    max_context_length: int = 128
+    output_dir: Optional[str] = None
+    output_name: Optional[str] = None
+    so_library: Optional[str] = None
+    export_only: bool = False
+
+
+################################################################################
+################################# DebugConfig ##################################
+################################################################################
+
+
+@dataclass
+class DebugConfig:
+    """
+    Configures options to debug the export process.
+    """
+
+    profile_memory: bool = False
+    profile_path: Optional[str] = None
+    generate_etrecord: bool = False
+    generate_full_logits: bool = False
+    verbose: bool = False
+
+
+################################################################################
+############################# QuantizationConfig ###############################
+################################################################################
+
+
+class Pt2eQuantize(str, Enum):
+    """
+    Type of backend-specific Pt2e quantization strategy to use.
+
+    Pt2e uses a different quantization library that is graph-based
+    compared to `qmode`, which is also specified in the QuantizationConfig
+    and is source transform-based.
+    """
+
+    XNNPACK_DYNAMIC = "xnnpack_dynamic"
+    XNNPACK_DYNAMIC_QC4 = "xnnpack_dynamic_qc4"
+    QNN_8A8W = "qnn_8a8w"
+    QNN_16A16W = "qnn_16a16w"
+    QNN_16A4W = "qnn_16a4w"
+    COREML_C4W = "coreml_c4w"
+    COREML_8A_C8W = "coreml_8a_c8w"
+    COREML_8A_C4W = "coreml_8a_c4w"
+    COREML_BASELINE_8A_C8W = "coreml_baseline_8a_c8w"
+    COREML_BASELINE_8A_C4W = "coreml_baseline_8a_c4w"
+    VULKAN_8W = "vulkan_8w"
+
+
+class SpinQuant(str, Enum):
+    CUDA = "cuda"
+    NATIVE = "native"
+
+
+@dataclass
+class QuantizationConfig:
+    """
+    Configures how the model should be quantized (PTQ).
+    """
+
+    qmode: Optional[str] = None
+    embedding_quantize: Optional[str] = None
+    pt2e_quantize: Optional[Pt2eQuantize] = None
+    group_size: Optional[int] = None
+    use_spin_quant: Optional[SpinQuant] = None
+    use_qat: bool = False
+    calibration_tasks: Optional[List[str]] = None
+    calibration_limit: Optional[int] = None
+    calibration_seq_length: Optional[int] = None
+    calibration_data: str = "Once upon a time"
+
+    def __post_init__(self):
+        if self.qmode:
+            self._validate_qmode()
+
+    def _validate_qmode(self) -> None:
+        choices = ["int8", "8da4w", "8da4w-gptq", "vulkan_4w"]
+        patterns = [r"torchao:8da(\d+)w", r"torchao:fpa(\d+)w"]
+
+        if self.qmode in choices:
+            return
+
+        for pattern in patterns:
+            matches = re.findall(pattern, self.qmode)
+            if len(matches) == 1:
+                return
+
+        raise ValueError(
+            f"Got qmode {self.qmode}, but expected one of {choices}, or one of the regex patterns {patterns}."
+        )
+
+
+################################################################################
+############################### BackendConfig ##################################
+################################################################################
+
+
+@dataclass
+class XNNPackConfig:
+    enabled: bool = False
+    extended_ops: bool = False
+
+
+class CoreMLQuantize(str, Enum):
+    B4W = "b4w"
+    C4W = "c4w"
+
+
+class CoreMLComputeUnit(str, Enum):
+    CPU_ONLY = "cpu_only"
+    CPU_AND_GPU = "cpu_and_gpu"
+    CPU_AND_NE = "cpu_and_ne"
+    ALL = "all"
+
+
+@dataclass
+class CoreMLConfig:
+    enabled: bool = False
+    enable_state: bool = False
+    preserve_sdpa: bool = False
+    quantize: Optional[CoreMLQuantize] = None
+    ios: int = 15
+    compute_units: CoreMLComputeUnit = CoreMLComputeUnit.CPU_ONLY
+
+    def __post_init__(self):
+        if self.ios not in (15, 16, 17, 18):
+            raise ValueError(f"Invalid coreml ios version: {self.ios}")
+
+
+@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: bool = False
+
+
+@dataclass
+class BackendConfig:
+    """
+    Configures which backends should be used and how the backends
+    should be set up.
+    """
+
+    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)
+
+
+################################################################################
+################################## LlmConfig ###################################
+################################################################################
+
+
+@dataclass
+class LlmConfig:
+    """
+    The overall configuration for customizing the LLM export process.
+    """
+
+    base: BaseConfig = field(default_factory=BaseConfig)
+    model: ModelConfig = field(default_factory=ModelConfig)
+    export: ExportConfig = field(default_factory=ExportConfig)
+    debug: DebugConfig = field(default_factory=DebugConfig)
+    quantization: QuantizationConfig = field(default_factory=QuantizationConfig)
+    backend: BackendConfig = field(default_factory=BackendConfig)

examples/models/llama/config/targets.bzl

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+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+
+def define_common_targets():
+    runtime.python_library(
+        name = "llm_config",
+        srcs = [
+            "llm_config.py",
+        ],
+        _is_external_target = True,
+        base_module = "executorch.examples.models.llama.config",
+        visibility = [
+            "//executorch/...",
+            "@EXECUTORCH_CLIENTS",
+        ],
+    )