More robust VRAM calculation

This commit is contained in:
oobabooga 2025-05-16 22:33:32 -07:00
parent 0f77ff9670
commit 4800d1d522

View file

@ -2,7 +2,7 @@ import functools
import json
import re
import subprocess
from math import exp
from math import floor
from pathlib import Path
import gradio as gr
@ -331,8 +331,6 @@ def estimate_vram(gguf_file, gpu_layers, ctx_size, cache_type):
n_layers = None
n_kv_heads = None
embedding_dim = None
context_length = None
feed_forward_dim = None
for key, value in metadata.items():
if key.endswith('.block_count'):
@ -341,10 +339,6 @@ def estimate_vram(gguf_file, gpu_layers, ctx_size, cache_type):
n_kv_heads = value
elif key.endswith('.embedding_length'):
embedding_dim = value
elif key.endswith('.context_length'):
context_length = value
elif key.endswith('.feed_forward_length'):
feed_forward_dim = value
if gpu_layers > n_layers:
gpu_layers = n_layers
@ -359,22 +353,16 @@ def estimate_vram(gguf_file, gpu_layers, ctx_size, cache_type):
# Derived features
size_per_layer = size_in_mb / max(n_layers, 1e-6)
context_per_layer = context_length / max(n_layers, 1e-6)
ffn_per_embedding = feed_forward_dim / max(embedding_dim, 1e-6)
kv_cache_factor = n_kv_heads * cache_type * ctx_size
# Helper function for smaller
def smaller(x, y):
return 1 if x < y else 0
embedding_per_context = embedding_dim / ctx_size
# Calculate VRAM using the model
# Details: https://oobabooga.github.io/blog/posts/gguf-vram-formula/
vram = (
(size_per_layer - 21.19195204848197)
* exp(0.0001047328491557063 * size_in_mb * smaller(ffn_per_embedding, 2.671096993407845))
+ 0.0006621544775632052 * context_per_layer
+ 3.34664386576376e-05 * kv_cache_factor
) * (1.363306170123392 + gpu_layers) + 1255.163594536052
(size_per_layer - 17.99552795246051 + 3.148552680382576e-05 * kv_cache_factor)
* (gpu_layers + max(0.9690636483914102, cache_type - (floor(50.77817218646521 * embedding_per_context) + 9.987899908205632)))
+ 1516.522943869404
)
return vram
@ -485,7 +473,7 @@ def update_gpu_layers_and_vram(loader, model, gpu_layers, ctx_size, cache_type,
return_free = False if (for_ui and shared.model_name not in [None, 'None']) else True
available_vram = get_nvidia_vram(return_free=return_free)
if available_vram > 0:
tolerance = 906
tolerance = 577
while current_layers > 0 and estimate_vram(model, current_layers, ctx_size, cache_type) > available_vram - tolerance:
current_layers -= 1