Skip to content

add timing measurements for autoregressive and speculative sampling m… #38

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
jayeshthk wants to merge 1 commit into
base: main
Choose a base branch
from
Open

add timing measurements for autoregressive and speculative sampling m… #38

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 13 additions & 5 deletions main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import contexttimer
from colorama import Fore, Style
from transformers import AutoTokenizer, AutoModelForCausalLM

import time
from sampling import autoregressive_sampling, speculative_sampling, speculative_sampling_v2
from globals import Decoder

Expand Down Expand Up @@ -95,32 +95,40 @@ def generate(input_text, approx_model_name, target_model_name, num_tokens=20, ga
top_p = 0.9

torch.manual_seed(123)
st=time.time()
output = autoregressive_sampling(input_ids, large_model, num_tokens, top_k = top_k, top_p=top_p)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
color_print(f"large (target) model autoregressive_sampling: {generated_text}")
tot=time.time()-st
color_print(f"large (target) model autoregressive_sampling {tot:.4f}s: {generated_text}")

if use_benchmark:
benchmark(autoregressive_sampling, "AS_large", use_profiling,
input_ids, large_model, num_tokens, top_k = top_k, top_p=top_p)

torch.manual_seed(123)
st=time.time()
output = autoregressive_sampling(input_ids, small_model, num_tokens, top_k = top_k, top_p=top_p)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
color_print(f"small (approx) model autoregressive_sampling: {generated_text}")
tot=time.time()-st
color_print(f"small (approx) model autoregressive_sampling {tot:.4f}s: {generated_text}")

if use_benchmark:
benchmark(autoregressive_sampling, "AS_small", use_profiling,
input_ids, small_model, num_tokens, top_k = top_k, top_p=top_p)

torch.manual_seed(123)
st=time.time()
output = speculative_sampling_v2(input_ids, small_model, large_model, num_tokens, top_k = top_k, top_p=top_p, random_seed = random_seed)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
color_print(f"deepmind's speculative_sampling: {generated_text}")
tot=time.time()-st
color_print(f"deepmind's speculative_sampling {tot:.4f}s: {generated_text}")

torch.manual_seed(123)
st=time.time()
output = speculative_sampling(input_ids, small_model, large_model, num_tokens, gamma = gamma, top_k = top_k, top_p=top_p, random_seed = random_seed, verbose = verbose)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
color_print(f"google's speculative_sampling: {generated_text}")
tot=time.time()-st
color_print(f"google's speculative_sampling {tot:.4f}s: {generated_text}")

if use_benchmark:
benchmark(speculative_sampling, "SP", use_profiling,
Expand Down