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from exllamav2 import(
ExLlamaV2,
ExLlamaV2Config,
ExLlamaV2Cache,
ExLlamaV2Tokenizer,
model_init,
)
import argparse, os, math, time
import pandas, fastparquet
import torch
import torch.nn.functional as F
from conversion.tokenize import get_tokens
from conversion.quantize import list_live_tensors
import sys
import json
torch.cuda._lazy_init()
torch.set_printoptions(precision = 10)
# torch.backends.cuda.matmul.allow_tf32 = True
# torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
# torch.set_float32_matmul_precision("medium")
parser = argparse.ArgumentParser(description = "Test inference on ExLlamaV2 model")
parser.add_argument("-ed", "--eval_dataset", type = str, help = "Perplexity evaluation dataset (.parquet file)")
parser.add_argument("-er", "--eval_rows", type = int, default = 128, help = "Number of rows to apply from dataset")
parser.add_argument("-el", "--eval_length", type = int, default = 2048, help = "Max no. tokens per sample")
parser.add_argument("-p", "--prompt", type = str, help = "Generate from prompt")
parser.add_argument("-t", "--tokens", type = int, default = 128, help = "Max no. tokens")
parser.add_argument("-ps", "--prompt_speed", action = "store_true", help = "Test prompt processing (batch) speed over context length")
parser.add_argument("-s", "--speed", action = "store_true", help = "Test raw generation speed over context length")
# Initialize model and tokenizer
model_init.add_args(parser)
args = parser.parse_args()
model_init.check_args(args)
model_init.print_options(args)
model, tokenizer = model_init.init(args)
# Test generation
if args.prompt:
with torch.inference_mode():
cache = ExLlamaV2Cache(model)
ids = tokenizer.encode(args.prompt)
tokens_prompt = ids.shape[-1]
print(f" -- Warmup...")
model.forward(ids[:, -1:])
print(f" -- Generating (greedy sampling)...")
print()
print(args.prompt, end = "")
sys.stdout.flush()
time_begin = time.time()
if ids.shape[-1] > 1: model.forward(ids[:, :-1], cache, preprocess_only = True)
torch.cuda.synchronize()
time_prompt = time.time()
for i in range(args.tokens):
text1 = tokenizer.decode(ids[:, -2:])[0]
logits = model.forward(ids[:, -1:], cache)
sample = torch.argmax(logits[0, -1]).cpu().unsqueeze(0).unsqueeze(0)
ids = torch.cat((ids, sample), dim = -1)
text2 = tokenizer.decode(ids[:, -3:])[0]
text2 = text2[len(text1):]
print (text2, end = "")
# sys.stdout.flush()
time_end = time.time()
print()
print()
total_prompt = time_prompt - time_begin
total_gen = time_end - time_prompt
print(f"Prompt processed in {total_prompt:.2f} seconds, {tokens_prompt} tokens, {tokens_prompt / total_prompt:.2f} tokens/second")
print(f"Response generated in {total_gen:.2f} seconds, {args.tokens} tokens, {args.tokens / total_gen:.2f} tokens/second")
cache = None
# Test perplexity
if args.eval_dataset:
with torch.inference_mode():
eval_dataset = args.eval_dataset
eval_rows = args.eval_rows
eval_length = args.eval_length
print(f" -- Running perplexity test")
print(f" -- Dataset: {eval_dataset}")
print(f" -- Tokenizing eval data, {eval_rows} rows x {eval_length} tokens...")
eval_tokens = get_tokens(eval_rows, eval_length, eval_dataset, tokenizer)
print(f" -- Inference", end = "")
sys.stdout.flush()
logprob_sum = 0.0
logprob_count = 0
for i in range(eval_tokens.shape[0]):
#for i in range(126, 127):
if i % 10 == 0: print(".", end = "")
sys.stdout.flush()
input_ids = eval_tokens[i:i+1, :]
input_ids = input_ids[:, :-1]
logits = model.forward(input_ids)
# print (tokenizer.decode(input_ids))
target_ids = input_ids[:, 1:].to(logits.device)
log_probs = F.log_softmax(logits, dim=-1)
token_log_probs = log_probs.gather(-1, target_ids.unsqueeze(-1)).squeeze(-1)
logprob_sum += token_log_probs.sum().item()
logprob_count += target_ids.numel()
print()
mean_log_prob = logprob_sum / logprob_count
perplexity = math.exp(-mean_log_prob)
print(f" -- Evaluation perplexity: {perplexity:.4f}")
xx = 0
# Test prompt speed
if args.prompt_speed:
with torch.inference_mode():
cache = ExLlamaV2Cache(model)
ids = torch.randint(0, model.config.vocab_size - 1, (1, model.config.max_seq_len))
print(f" -- Warmup...")
model.forward(ids[:, -1:])
print(f" -- Measuring prompt speed...")
current_len = 128
while True:
time_begin = time.time()
cache.current_seq_len = 0
model.forward(ids[:, :current_len], cache, preprocess_only = True)
torch.cuda.synchronize()
time_end = time.time()
tps = current_len / (time_end - time_begin)
print(f" ** Length {current_len:>5} tokens: {tps:>11.4f} t/s")
current_len_ = current_len
current_len = min(current_len + 128, model.config.max_seq_len)
if current_len == current_len_: break
cache = None
# Test token speed
if args.speed:
with torch.inference_mode():
cache = ExLlamaV2Cache(model)
print(f" -- Measuring token speed...")
ids = tokenizer.encode("X")
model.forward(ids[:, :])
current_idx = ids.shape[-1]
next_stop = 128
while True:
time_begin = time.time()
tokens = next_stop - current_idx
for i in range(tokens):
logits = model.forward(ids[:, -1:], cache)
sample = torch.argmax(logits[0, -1]).cpu().unsqueeze(0).unsqueeze(0)
ids = torch.cat((ids, sample), dim=-1)
time_end = time.time()
tps = tokens / (time_end - time_begin)
print(f" ** Position {current_idx:>5} + {tokens:>3} tokens: {tps:>9.4f} t/s")
current_idx = next_stop
next_stop = min(next_stop + 128, model.config.max_seq_len)
if next_stop == current_idx: break
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