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import logging
import os
import torch
from bytelatent.args import EvalArgs
from bytelatent.config_parser import parse_args_to_pydantic_model
from bytelatent.data.file_util import get_fs
from bytelatent.data.patcher import Patcher
from bytelatent.distributed import (
DistributedArgs,
dist_max,
dist_min,
dist_sum,
get_device_mesh,
setup_torch_distributed,
)
from bytelatent.generate import load_consolidated_model_and_tokenizer
from bytelatent.model.blt import ByteLatentTransformer
from bytelatent.tokenizers.blt_tokenizer import BltTokenizer
logger = logging.getLogger()
def get_max_length(input_tokens: list[list[int]] | None) -> int:
# reduce max length prompt over all processes to have an equal number of call on each process with fsdp
if input_tokens is None:
max_length = 0
else:
max_length = max([len(t) for t in input_tokens])
if torch.distributed.is_initialized():
max_length = int(dist_max(max_length))
return max_length
def get_min_length(input_tokens: list[list[int]] | None) -> int:
# reduce min length prompt over all processes to have an equal number of call on each process with fsdp
if input_tokens is None:
# TODO: Double check this change from int(1e9) is correct
min_length = 0
else:
min_length = min([len(t) for t in input_tokens])
if torch.distributed.is_initialized():
min_length = int(dist_min(min_length))
return min_length
def get_generation_range(
prompt_tokens: list[list[int]] | None, max_gen_len: int
) -> tuple[int, int]:
batch_min_prompt_length = get_min_length(prompt_tokens)
batch_max_prompt_length = get_max_length(prompt_tokens)
return batch_min_prompt_length, batch_max_prompt_length + max_gen_len
def sample_top_k(probs, k):
topk_value, _ = torch.topk(probs, k) # batch_sz x topk
min_value_top_k = topk_value[:, [-1]]
probs[probs < min_value_top_k] = 0.0
probs.div_(probs.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs, num_samples=1)
return next_token
def sample_top_p(probs, p):
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
return next_token
@torch.inference_mode()
def generate_nocache(
prompts: list[str] | None,
*,
model: ByteLatentTransformer,
tokenizer: BltTokenizer,
patcher: Patcher,
max_prompt_len: int = 256,
max_gen_len: int = 256,
use_sampling: bool = False,
temp: float = 1.0,
top_k: int = 0,
top_p: float = 0.0,
remove_prompts: bool = True,
) -> list[list[int]]:
assert (
patcher.realtime_patching
), "generate_nocache requires patcher.realtime_patching=True"
model.eval()
if prompts is None:
prompt_tokens = None
n_truncated_prompts = 0
total_truncated_prompts = 0
else:
prompt_tokens = [tokenizer.encode(t, add_eos=False) for t in prompts]
n_truncated_prompts = sum([max_prompt_len < len(t) for t in prompt_tokens])
total_truncated_prompts = dist_sum(n_truncated_prompts)
# Truncation
prompt_tokens = [
t if len(t) < max_prompt_len else t[len(t) - max_prompt_len :]
for t in prompt_tokens
]
if total_truncated_prompts > 0:
logger.info(
f"There are {total_truncated_prompts} prompts that are truncated on the left, "
f"length greater than max_prompt_len = {max_prompt_len}, "
f"maximum prompt length = {get_max_length(prompt_tokens)} across all gpus."
)
if prompt_tokens is None:
prompt_tokens = [[tokenizer.bos_id] for _ in range(end_pos)]
start_pos, end_pos = get_generation_range(prompt_tokens, max_gen_len)
batch_size = len(prompt_tokens)
tokens = torch.full((batch_size, end_pos), tokenizer.pad_id).cuda().long()
# Copy inputs to tensor for generated tokens
for i, row_tokens in enumerate(prompt_tokens):
tokens[i, : len(row_tokens)] = torch.tensor(row_tokens).long()
input_text_mask = tokens != tokenizer.pad_id
for i, curr_pos in enumerate(range(start_pos, end_pos)):
current_tokens = tokens[:, :curr_pos]
patch_lengths, _ = patcher.patch(current_tokens, include_next_token=True)
logits = model(current_tokens, patch_lengths=patch_lengths)[:, -1]
if use_sampling:
probs = torch.softmax(logits / temp, dim=-1)
if top_p > 0.0:
next_token = sample_top_p(probs, top_p)
elif top_k > 0:
next_token = sample_top_k(probs, top_k)
else:
next_token = torch.multinomial(probs, num_samples=1)
else:
next_token = torch.argmax(logits, dim=-1)
next_token = torch.where(
input_text_mask[:, curr_pos], tokens[:, curr_pos], next_token
)
tokens[:, curr_pos] = next_token
if remove_prompts:
generated_tokens = [
t[len(prompt_tokens[i]) : len(prompt_tokens[i]) + max_gen_len].tolist()
for i, t in enumerate(tokens)
]
else:
generated_tokens = [
t[: len(prompt_tokens[i]) + max_gen_len].tolist()
for i, t in enumerate(tokens)
]
return generated_tokens
def launch_generate(eval_args: EvalArgs):
assert eval_args.dump_dir is not None
assert eval_args.ckpt_dir is not None
distributed_args = DistributedArgs()
distributed_args.configure_world()
if not torch.distributed.is_initialized():
setup_torch_distributed(distributed_args)
world_mesh = get_device_mesh(distributed_args)
dp_mesh = world_mesh["dp_replicate"]
assert distributed_args.dp_shard == 1
world_size = dp_mesh.size()
world_rank = dp_mesh.get_local_rank()
fs = get_fs(eval_args.ckpt_dir, s3_profile=eval_args.s3_profile)
if (
fs.exists(eval_args.ckpt_dir)
and fs.exists(os.path.join(eval_args.ckpt_dir, "params.json"))
and len(fs.glob(os.path.join(eval_args.ckpt_dir, "*.pth"))) != 0
):
consolidate_path = eval_args.ckpt_dir
else:
raise ValueError("Did not find a consolidated checkpoint in the ckpt_dir")
model, tokenizer, train_cfg = load_consolidated_model_and_tokenizer(
consolidate_path,
)
patcher_args = train_cfg.data.patcher_args.model_copy(deep=True)
patcher_args.realtime_patching = True
patcher_args.entropy_model_checkpoint_dir = eval_args.entropy_ckpt_dir
patcher = patcher_args.build()
outputs = generate_nocache(
eval_args.prompts, model=model, tokenizer=tokenizer, patcher=patcher
)
text_outputs = [tokenizer.decode(t) for t in outputs]
for p, t in zip(eval_args.prompts, text_outputs):
print(f'Prompt: "{p}" Completion: "{t}"')
print()
def main():
eval_args = parse_args_to_pydantic_model(EvalArgs)
launch_generate(eval_args)
if __name__ == "__main__":
main()
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