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https://github.com/meta-llama/llama-stack.git
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9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from enum import Enum
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from pydantic import BaseModel, model_validator
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class QuantizationScheme(Enum):
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int4_weight_int8_dynamic_activation = "int4_weight_int8_dynamic_activation"
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class QuantizationArgs(BaseModel):
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scheme: QuantizationScheme | None = None
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group_size: int | None = None
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spinquant: bool = False
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class LoRAArgs(BaseModel):
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rank: int
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scale: float
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class MoEArgs(BaseModel):
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num_experts: int = -1
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capacity_factor: float = 1.0 # capacity factor determines how many tokens each expert can choose
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auto_scale_F: bool = ( # noqa: N815
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True # if true, rescales hidden_dim such that number of activated params is same as equivalent dense layer
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)
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top_k: int = 1
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interleave_moe_layer_step: int = 1
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class Size(BaseModel):
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height: int
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width: int
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class VisionArgs(BaseModel):
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image_size: Size
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patch_size: Size
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# parameters for the encoder transformer
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dim: int
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n_layers: int
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n_heads: int
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mlp_ratio: float
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output_dim: int
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pixel_shuffle_ratio: float
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class ModelArgs(BaseModel):
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dim: int = -1
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n_layers: int = -1
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n_heads: int = -1
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n_kv_heads: int | None = None
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head_dim: int | None = None
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vocab_size: int = -1
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multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
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ffn_dim_multiplier: float | None = None
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ffn_exp: float | None = None
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norm_eps: float = 1e-5
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attention_chunk_size: int | None = None
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rope_theta: float = 500000
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use_scaled_rope: bool = False
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rope_scaling_factor: float | None = None
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rope_high_freq_factor: float | None = None
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nope_layer_interval: int | None = None # No position encoding in every n layers
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use_qk_norm: bool = False
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# Set to True to enable inference-time temperature tuning (useful for very long context)
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attn_temperature_tuning: bool = False
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floor_scale: float = 8192.0
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attn_scale: float = 0.1
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vision_args: VisionArgs | None = None
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moe_args: MoEArgs | None = None
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quantization_args: QuantizationArgs | None = None
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lora_args: LoRAArgs | None = None
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max_batch_size: int = 32
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max_seq_len: int = 2048
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@model_validator(mode="after")
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def validate(self) -> "ModelArgs":
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assert self.n_kv_heads <= self.n_heads, f"n_kv_heads ({self.n_kv_heads}) must be <= n_heads ({self.n_heads})"
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assert self.n_heads % self.n_kv_heads == 0, (
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f"n_heads ({self.n_heads}) must be divisible by n_kv_heads ({self.n_kv_heads})"
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)
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assert self.dim % self.n_heads == 0, f"dim ({self.dim}) must be divisible by n_heads ({self.n_heads})"
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if self.use_scaled_rope:
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# NOTE: ideally these values should have come from params.json. However, we have
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# shipped the models everywhere. Only Llama-4-Scout uses scaled rope and needs these
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# specific values.
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if self.rope_scaling_factor is None:
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self.rope_scaling_factor = 16
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if self.rope_high_freq_factor is None:
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self.rope_high_freq_factor = 1
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return self
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