File size: 4,502 Bytes
83ddd7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | import sys, os, math
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from exllamav2 import(
ExLlamaV2,
ExLlamaV2Config,
ExLlamaV2Cache,
ExLlamaV2Tokenizer,
)
from exllamav2.generator import (
ExLlamaV2BaseGenerator,
ExLlamaV2Sampler
)
import time
import torch
# The allocation to test
# model_directory = "/mnt/str/models/_exl2/openllama-3b-3.0bpw-h6-exl2/"
# model_directory = "/mnt/str/models/_exl2/llama-7b-3.0bpw-h6-exl2/"
# model_directory = "/mnt/str/models/_exl2/llama2-70b-chat-2.5bpw-h6-exl2/"
model_directory = "/mnt/str/models/_exl2/codellama-34b-instruct-4.0bpw-h6-exl2/"
allocation = [18, 24]
# Prime CUDA and initialize mem measurement
torch_devices = [f"cuda:{i}" for i in range(torch.cuda.device_count())]
torch.cuda.init()
temp = [torch.randn((1024, 1024), dtype = torch.float, device = x) for x in torch_devices]
temp2 = [x * 2 for x in temp]
temp = []
temp2 = []
torch.cuda.empty_cache()
mem_base = {}
for dev in torch_devices:
torch.cuda.reset_peak_memory_stats(dev)
mem_base[dev] = torch.cuda.max_memory_allocated(dev)
# Initialize and load model
config = ExLlamaV2Config()
config.model_dir = model_directory
config.prepare()
config.max_seq_len = 8192
model = ExLlamaV2(config)
print("Loading model: " + model_directory)
_, stats = model.load(allocation, stats = True)
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats(dev)
# Load tokenizer
tokenizer = ExLlamaV2Tokenizer(config)
# Initialize and measure cache
cache = ExLlamaV2Cache(model)
cache_fp = cache.footprint()
expected = [(ab - rb) for (ab, rb) in zip(allocation, stats)]
expected_with_cache = [e for e in expected]
for idx, c in enumerate(cache_fp): expected_with_cache[idx] += c / 1024**3
# Initialize generator
generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)
# Generate some text
settings = ExLlamaV2Sampler.Settings()
settings.temperature = 0.85
settings.top_k = 50
settings.top_p = 0.8
settings.token_repetition_penalty = 1.15
settings.disallow_tokens(tokenizer, [tokenizer.eos_token_id])
prompt = "Our story begins in the Scottish town of Auchtermuchty, where once"
max_new_tokens = 150
generator.warmup()
time_begin = time.time()
output = generator.generate_simple(prompt, settings, max_new_tokens, seed = 1234)
time_end = time.time()
time_total = time_end - time_begin
print(output)
print()
print(f"Response generated in {time_total:.2f} seconds, {max_new_tokens} tokens, {max_new_tokens / time_total:.2f} tokens/second")
print()
print(f"Prompt processing, {model.config.max_seq_len - 1} tokens...")
cache.current_seq_len = 0
time_begin = time.time()
input_ids = torch.randint(0, model.config.vocab_size - 1, (1, model.config.max_seq_len - 1))
model.forward(input_ids, cache, preprocess_only = True)
torch.cuda.synchronize()
time_end = time.time()
time_total = time_end - time_begin
print(f"Prompt processed in {time_total:.2f} seconds, {(model.config.max_seq_len - 1) / time_total:.2f} tokens/second")
print()
# Report
res1 = f" ** VRAM reported by Torch : "
res2 = f" ** VRAM expected : "
res3 = f" ** VRAM expected (with cache) : "
res4 = f" ** VRAM allocated (max) : "
res5 = f" ** Cache size : "
first = True
mem_total = 0
mem_exp = 0
for idx, device in enumerate(torch_devices):
mem_this = torch.cuda.max_memory_allocated(device) - mem_base[device]
mem_total += mem_this
mem_exp += expected_with_cache[idx] * 1024 ** 3
if not first: res1 += " - "
if not first: res2 += " - "
if not first: res3 += " - "
if not first: res4 += " - "
if not first: res5 += " - "
first = False
res1 += f"[{device}] {mem_this / (1024 ** 2):,.2f} MB"
res2 += f"[{device}] {expected[idx] * 1024:,.2f} MB"
res3 += f"[{device}] {expected_with_cache[idx] * 1024:,.2f} MB"
res4 += f"[{device}] {allocation[idx] * 1024:,.2f} MB"
res5 += f"[{device}] {cache_fp[idx] / (1024 ** 2) if idx < len(cache_fp) else 0:,.2f} MB"
print(res4)
print(res2)
print(res5)
print(res3)
print(res1)
print()
print(f"Max sequence length: {config.max_seq_len}")
print(f"Hidden size: {config.hidden_size}")
print(f"Attention heads: {config.num_attention_heads}")
print(f"Key/value heads: {config.num_key_value_heads}")
print(f"Max attention size: {math.sqrt(config.max_attention_size)} ** 2")
print(f"Max input len: {config.max_input_len}")
# print(f"Correction amount: {mem_total - mem_exp:,.2f} B")
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