forked from phoenix-oss/llama-stack-mirror
530d4bdfe1
# What does this PR do? Move around bits. This makes the copies from llama-models _much_ easier to maintain and ensures we don't entangle meta-reference specific tidbits into llama-models code even by accident. Also, kills the meta-reference-quantized-gpu distro and rolls quantization deps into meta-reference-gpu. ## Test Plan ``` LLAMA_MODELS_DEBUG=1 \ with-proxy llama stack run meta-reference-gpu \ --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct \ --env INFERENCE_CHECKPOINT_DIR=<DIR> \ --env MODEL_PARALLEL_SIZE=4 \ --env QUANTIZATION_TYPE=fp8_mixed ``` Start a server with and without quantization. Point integration tests to it using: ``` pytest -s -v tests/integration/inference/test_text_inference.py \ --stack-config http://localhost:8321 --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct ```
75 lines
2.1 KiB
Python
75 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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from typing import Optional
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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: Optional[QuantizationScheme] = None
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group_size: Optional[int] = 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: Optional[int] = 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: Optional[float] = 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: Optional[QuantizationArgs] = None
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lora_args: Optional[LoRAArgs] = 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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