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 ```
95 lines
2.8 KiB
Python
95 lines
2.8 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 typing import Optional
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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: Optional[QuantizationScheme] = None
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group_size: Optional[int] = 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: Optional[int] = None
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head_dim: 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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ffn_exp: Optional[float] = None
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norm_eps: float = 1e-5
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attention_chunk_size: Optional[int] = None
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rope_theta: float = 500000
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use_scaled_rope: bool = False
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nope_layer_interval: Optional[int] = 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: Optional[VisionArgs] = None
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moe_args: Optional[MoEArgs] = None
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quantization_args: Optional[QuantizationArgs] = None
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lora_args: Optional[LoRAArgs] = 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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return self
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