Add support for CB with native transformers #3471
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
PR is merged to transformers! |
|
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
|
I'm trying CB right now. Currently, when running (from here): import time
import datasets
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
torch.set_float32_matmul_precision("high")
model_id = "meta-llama/Llama-3.2-3b-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_id, attn_implementation="sdpa_paged", torch_dtype=torch.bfloat16, device_map="auto"
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
generation_config = GenerationConfig(
max_new_tokens=512,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
use_cache=False,
num_blocks=2048,
block_size=128,
do_sample=True,
max_batch_tokens=1024, # Maximum number of tokens to process in a single batch
scheduler="prefill_first",
)
train_dataset = datasets.load_dataset("openai/gsm8k", "socratic", split="test")
# --- Example 1: Simple Version using generate_batch ---
print("--- Running CB Generation Example ---")
def tokenize_function(examples):
return tokenizer(examples["question"])
tokenized_datasets = train_dataset.map(tokenize_function, batched=True)
simple_batch_inputs = [item["input_ids"] for item in tokenized_datasets]
start_time_simple = time.time()
# model.forward = torch.compile(model.forward, mode="max-autotune-no-cudagraphs", fullgraph=True)
batch_outputs = model.generate_batch(
inputs=simple_batch_inputs,
generation_config=generation_config,
)
end_time_simple = time.time()
for request in batch_outputs:
input_text = tokenizer.decode(batch_outputs[request].prompt_ids, skip_special_tokens=False)
try:
output_text = tokenizer.decode(batch_outputs[request].generated_tokens, skip_special_tokens=False)
except Exception as e:
print(f"Decoding failed for request {request}: {e}")
output_text = tokenizer.decode(batch_outputs[request].generated_tokens[1:], skip_special_tokens=False)
if len(output_text) > 0:
print("-" * 20)
print(f"{request} Input: {input_text}")
print(f"{request} Output: {output_text}")
else:
print("", end="\r\r\r\r")
print("-" * 20)
print("--- Finished CB Generation Example ---\n\n")
print(f"CB generation took: {end_time_simple - start_time_simple:.2f} seconds")I'm getting any idea why? |
|
Yes! That's open telemetry having issues with the cluster. cc @McPatate let's fix this! |
|
(TLDR if you do |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
What does this PR do?
Testing this with: