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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>
74 lines
2.1 KiB
Python
74 lines
2.1 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 dataclasses import dataclass
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from enum import Enum
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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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@dataclass
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class QuantizationArgs:
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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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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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if k == "scheme":
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setattr(self, k, QuantizationScheme(v))
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else:
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if hasattr(self, k):
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setattr(self, k, v)
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@dataclass
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class LoRAArgs:
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rank: int
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scale: float
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@dataclass
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class ModelArgs:
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dim: int = 4096
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n_layers: int = 32
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n_heads: int = 32
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n_kv_heads: 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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norm_eps: float = 1e-5
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rope_theta: float = 500000
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use_scaled_rope: bool = False
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max_batch_size: int = 32
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max_seq_len: int = 2048
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# vision model params
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vision_chunk_size: int = -1 # image resolution for image models
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vision_max_num_chunks: int = 4
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vision_num_cross_attention_layers: int = -1
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quantization_args: QuantizationArgs | None = None
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lora_args: LoRAArgs | None = None
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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if k == "lora_args":
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setattr(self, k, LoRAArgs(**v))
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elif k == "quantization_args":
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setattr(self, k, QuantizationArgs(**v))
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else:
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if hasattr(self, k):
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setattr(self, k, v)
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if self.n_kv_heads is None:
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self.n_kv_heads = self.n_heads
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assert self.n_kv_heads <= self.n_heads
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assert self.n_heads % self.n_kv_heads == 0
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assert self.dim % self.n_heads == 0
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