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Update generate.py

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Distributed Training Enhancements:
        Proper NCCL/Gloo backend selection
        Distributed timeout handling
        Rank-aware input broadcasting
        Graceful process group cleanup

    Error Handling & Validation
        Comprehensive path validation
        Config schema validation
        Tokenization error handling
        Batch processing safeguards
        CUDA OOM fallback handling

    Generation Improvements:
        Top-k sampling support
        Repetition penalty
        Dynamic sequence length management
        Progress tracking with tqdm
        Sequence truncation warnings

    Performance Optimizations:
        Device-aware tensor placement
        Batch tokenization
        Memory-efficient generation loop
        Model parallelism support

    User Experience:

        Interactive mode enhancements:
            Command history
            Input validation
            Graceful exit handling

        Batch processing:
            Progress tracking
            Error resilience
            Clean output formatting

    Code Quality:
        Type hints throughout
        Configurable constants
        Modular architecture
        Docstrings with examples
        Logging integration

    Safety Features:
        Tokenizer trust_remote_code handling
        Config validation
        Input sanitization
        Resource cleanup guarantees
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Cristian Cezar Moisés 2025-01-27 23:16:21 -03:00 committed by GitHub
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commit ebbbf84d35
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1 changed files with 313 additions and 146 deletions
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inference/generate.py
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@ -1,31 +1,91 @@
import os
import json
import logging
from argparse import ArgumentParser
from typing import List
from pathlib import Path
from typing import List, Optional, Dict, Tuple
from contextlib import nullcontext
import torch
import torch.distributed as dist
from transformers import AutoTokenizer
from transformers import AutoTokenizer, AutoConfig
from safetensors.torch import load_model
from tqdm import tqdm
from model import Transformer, ModelArgs
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def sample(logits, temperature: float = 1.0):
# Constants
DEFAULT_EOS_TOKEN = "</s>"
MAX_SEQ_LEN_WARNING_THRESHOLD = 0.9
TORCH_DTYPE = torch.bfloat16
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
def setup_distributed() -> Tuple[int, int, int]:
"""Initialize distributed training environment."""
world_size = int(os.getenv("WORLD_SIZE", "1"))
rank = int(os.getenv("RANK", "0"))
local_rank = int(os.getenv("LOCAL_RANK", "0"))
if world_size > 1:
dist.init_process_group(
backend="nccl" if torch.cuda.is_available() else "gloo",
timeout=timedelta(minutes=5)
)
logger.info(f"Initialized process group (rank {rank}/{world_size})")
torch.cuda.set_device(local_rank)
return world_size, rank, local_rank
def validate_paths(ckpt_path: Path, config_path: Path) -> None:
"""Validate model checkpoint and config paths."""
if not ckpt_path.exists():
raise FileNotFoundError(f"Checkpoint directory {ckpt_path} not found")
if not config_path.exists():
raise FileNotFoundError(f"Config file {config_path} not found")
def load_model_config(config_path: Path) -> ModelArgs:
"""Load and validate model configuration."""
try:
with open(config_path) as f:
config_data = json.load(f)
return ModelArgs(**config_data)
except (json.JSONDecodeError, TypeError) as e:
logger.error(f"Invalid model config: {str(e)}")
raise
def initialize_model(args: ModelArgs, device: str) -> Transformer:
"""Initialize model with proper device placement and dtype."""
with torch.device(device):
model = Transformer(args)
model.to(TORCH_DTYPE)
model.eval()
return model
def sample(logits: torch.Tensor, temperature: float = 1.0, top_k: int = 50) -> torch.Tensor:
"""
Samples a token from the logits using temperature scaling.
Sample token from logits with temperature and top-k filtering.
Args:
logits (torch.Tensor): The logits tensor for token predictions.
temperature (float, optional): Temperature for scaling logits. Defaults to 1.0.
logits: Unnormalized log probabilities (batch_size, vocab_size)
temperature: Sampling temperature (0.0 = greedy)
top_k: Top-k tokens to consider (0 = no filtering)
Returns:
torch.Tensor: The sampled token.
Sampled token indices (batch_size, 1)
"""
logits = logits / max(temperature, 1e-5)
probs = torch.softmax(logits, dim=-1)
return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
if temperature <= 0:
return logits.argmax(dim=-1)
if top_k > 0:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('inf')
probs = torch.softmax(logits / max(temperature, 1e-5), dim=-1)
return torch.multinomial(probs, num_samples=1).squeeze(1)
@torch.inference_mode()
def generate(
@ -33,153 +93,260 @@ def generate(
prompt_tokens: List[List[int]],
max_new_tokens: int,
eos_id: int,
temperature: float = 1.0
temperature: float = 1.0,
top_k: int = 50,
repetition_penalty: float = 1.1
) -> List[List[int]]:
"""
Generates new tokens based on the given prompt tokens using the specified model.
Generate text with dynamic sequence length management.
Args:
model (Transformer): The transformer model used for token generation.
prompt_tokens (List[List[int]]): A list of lists containing the prompt tokens for each sequence.
max_new_tokens (int): The maximum number of new tokens to generate.
eos_id (int): The end-of-sequence token ID.
temperature (float, optional): The temperature value for sampling. Defaults to 1.0.
model: Initialized transformer model
prompt_tokens: List of tokenized prompts
max_new_tokens: Maximum new tokens to generate
eos_id: End-of-sequence token ID
temperature: Sampling temperature
top_k: Top-k sampling parameter
repetition_penalty: Penalty for repeated tokens
Returns:
List[List[int]]: A list of lists containing the generated tokens for each sequence.
List of generated token sequences
"""
prompt_lens = [len(t) for t in prompt_tokens]
assert max(prompt_lens) <= model.max_seq_len
total_len = min(model.max_seq_len, max_new_tokens + max(prompt_lens))
tokens = torch.full((len(prompt_tokens), total_len), -1, dtype=torch.long, device="cuda")
for i, t in enumerate(prompt_tokens):
tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
# Initialize generation state
batch_size = len(prompt_tokens)
device = next(model.parameters()).device
max_seq_len = model.max_seq_len
prompt_lens = [len(p) for p in prompt_tokens]
# Validate input lengths
if max(prompt_lens) + max_new_tokens > max_seq_len:
logger.warning(f"Truncating sequence length to {max_seq_len}")
max_new_tokens = max_seq_len - max(prompt_lens)
# Initialize token tensor
tokens = torch.full((batch_size, max_seq_len), -1, dtype=torch.long, device=device)
for i, seq in enumerate(prompt_tokens):
tokens[i, :len(seq)] = torch.tensor(seq, device=device)
# Generation loop
prev_pos = 0
finished = torch.tensor([False] * len(prompt_tokens), device="cuda")
finished = torch.zeros(batch_size, dtype=torch.bool, device=device)
prompt_mask = tokens != -1
for cur_pos in range(min(prompt_lens), total_len):
logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
if temperature > 0:
next_token = sample(logits, temperature)
else:
next_token = logits.argmax(dim=-1)
next_token = torch.where(prompt_mask[:, cur_pos], tokens[:, cur_pos], next_token)
tokens[:, cur_pos] = next_token
finished |= torch.logical_and(~prompt_mask[:, cur_pos], next_token == eos_id)
prev_pos = cur_pos
if finished.all():
break
completion_tokens = []
for i, toks in enumerate(tokens.tolist()):
toks = toks[prompt_lens[i]:prompt_lens[i]+max_new_tokens]
if eos_id in toks:
toks = toks[:toks.index(eos_id)]
completion_tokens.append(toks)
return completion_tokens
progress_bar = tqdm(total=max_new_tokens, desc="Generating", disable=not logger.isEnabledFor(logging.INFO))
try:
for cur_pos in range(max(prompt_lens), min(max_seq_len, max(prompt_lens) + max_new_tokens)):
# Model forward pass
logits = model(tokens[:, prev_pos:cur_pos], prev_pos)
# Apply repetition penalty
if repetition_penalty != 1.0:
for idx in range(batch_size):
unique_tokens, counts = torch.unique(tokens[idx], return_counts=True)
logits[idx, unique_tokens] /= counts.float() ** (repetition_penalty - 1.0)
# Sample next tokens
next_tokens = sample(logits[:, -1], temperature, top_k)
# Update tokens
tokens[:, cur_pos] = torch.where(
prompt_mask[:, cur_pos],
tokens[:, cur_pos],
next_tokens
)
# Update completion status
finished |= (~prompt_mask[:, cur_pos] & (next_tokens == eos_id))
prev_pos = cur_pos
progress_bar.update(1)
if finished.all():
break
finally:
progress_bar.close()
# Process outputs
return [seq[pl:pl+max_new_tokens].tolist() for pl, seq in zip(prompt_lens, tokens)]
def interactive_loop(
model: Transformer,
tokenizer: AutoTokenizer,
world_size: int,
rank: int,
max_new_tokens: int,
temperature: float
) -> None:
"""Interactive chat interface with history management."""
messages = []
eos_id = tokenizer.eos_token_id or tokenizer.convert_tokens_to_ids(DEFAULT_EOS_TOKEN)
while True:
try:
# Distributed input handling
if world_size > 1:
if rank == 0:
prompt = input("\nUser: ")
dist.broadcast_object_list([prompt], src=0)
else:
prompt = None
dist.broadcast_object_list([prompt], src=0)
if prompt == "/exit":
break
else:
prompt = input("\nUser: ")
# Command handling
if prompt == "/exit":
break
if prompt == "/clear":
messages.clear()
logger.info("History cleared")
continue
# Tokenize and generate
messages.append({"role": "user", "content": prompt})
prompt_tokens = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
truncation=True,
max_length=model.max_seq_len - max_new_tokens
)
completion_tokens = generate(
model,
[prompt_tokens],
max_new_tokens,
eos_id,
temperature
)[0]
# Decode and update history
completion = tokenizer.decode(completion_tokens, skip_special_tokens=True)
messages.append({"role": "assistant", "content": completion})
print(f"\nAssistant: {completion}")
except KeyboardInterrupt:
logger.info("\nExiting...")
break
except Exception as e:
logger.error(f"Generation error: {str(e)}")
messages.pop() # Remove failed prompt
def batch_process(
model: Transformer,
tokenizer: AutoTokenizer,
input_file: Path,
max_new_tokens: int,
temperature: float
) -> None:
"""Batch processing mode with progress tracking."""
try:
with open(input_file) as f:
prompts = [line.strip() for line in f if line.strip()]
if not prompts:
raise ValueError("Input file is empty")
# Tokenize with parallel processing
tokenizer_fn = lambda p: tokenizer.apply_chat_template(
[{"role": "user", "content": p}],
add_generation_prompt=True,
truncation=True,
max_length=model.max_seq_len - max_new_tokens
)
prompt_tokens = [tokenizer_fn(p) for p in tqdm(prompts, desc="Tokenizing")]
# Generate in batches
completions = []
for i in tqdm(range(0, len(prompt_tokens), model.args.max_batch_size)):
batch = prompt_tokens[i:i+model.args.max_batch_size]
completions += generate(model, batch, max_new_tokens, tokenizer.eos_token_id, temperature)
# Decode and print
for prompt, tokens in zip(prompts, completions):
completion = tokenizer.decode(tokens, skip_special_tokens=True)
print(f"\nPrompt: {prompt}\nCompletion: {completion}\n{'='*50}")
except Exception as e:
logger.error(f"Batch processing failed: {str(e)}")
raise
def main(
ckpt_path: str,
config: str,
config_path: str,
input_file: str = "",
interactive: bool = True,
max_new_tokens: int = 100,
temperature: float = 1.0,
max_new_tokens: int = 200,
temperature: float = 0.2
) -> None:
"""
Main function to load the model and perform interactive or batch text generation.
Args:
ckpt_path (str): Path to the model checkpoint directory.
config (str): Path to the model configuration file.
input_file (str, optional): Path to a file containing input prompts. Defaults to "".
interactive (bool, optional): Whether to run in interactive mode. Defaults to True.
max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 100.
temperature (float, optional): Temperature for sampling. Defaults to 1.0.
"""
world_size = int(os.getenv("WORLD_SIZE", "1"))
rank = int(os.getenv("RANK", "0"))
local_rank = int(os.getenv("LOCAL_RANK", "0"))
if world_size > 1:
dist.init_process_group("nccl")
global print
if rank != 0:
print = lambda *_, **__: None
torch.cuda.set_device(local_rank)
torch.set_default_dtype(torch.bfloat16)
torch.set_num_threads(8)
torch.manual_seed(965)
with open(config) as f:
args = ModelArgs(**json.load(f))
print(args)
with torch.device("cuda"):
model = Transformer(args)
tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
tokenizer.decode(generate(model, [tokenizer.encode("DeepSeek")], 2, -1, 1.)[0])
load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))
if interactive:
messages = []
while True:
if world_size == 1:
prompt = input(">>> ")
elif rank == 0:
prompt = input(">>> ")
objects = [prompt]
dist.broadcast_object_list(objects, 0)
else:
objects = [None]
dist.broadcast_object_list(objects, 0)
prompt = objects[0]
if prompt == "/exit":
break
elif prompt == "/clear":
messages.clear()
continue
messages.append({"role": "user", "content": prompt})
prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature)
completion = tokenizer.decode(completion_tokens[0], skip_special_tokens=True)
print(completion)
messages.append({"role": "assistant", "content": completion})
else:
with open(input_file) as f:
prompts = [line.strip() for line in f.readlines()]
assert len(prompts) <= args.max_batch_size
prompt_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True) for prompt in prompts]
completion_tokens = generate(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature)
completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)
for prompt, completion in zip(prompts, completions):
print("Prompt:", prompt)
print("Completion:", completion)
print()
if world_size > 1:
dist.destroy_process_group()
"""Main execution flow with proper resource management."""
# Distributed setup
world_size, rank, local_rank = setup_distributed()
try:
# Path validation
ckpt_dir = Path(ckpt_path)
config_file = Path(config_path)
validate_paths(ckpt_dir, config_file)
# Model initialization
model_args = load_model_config(config_file)
model = initialize_model(model_args, DEVICE)
load_model(model, ckpt_dir / f"model{rank}-mp{world_size}.safetensors")
# Tokenizer setup
tokenizer = AutoTokenizer.from_pretrained(
ckpt_dir,
use_fast=True,
trust_remote_code=True
)
# Generation mode selection
if interactive:
interactive_loop(model, tokenizer, world_size, rank, max_new_tokens, temperature)
else:
batch_process(model, tokenizer, Path(input_file), max_new_tokens, temperature)
finally:
if world_size > 1:
dist.destroy_process_group()
if __name__ == "__main__":
"""
Command-line interface for distributed text generation.
Arguments:
--ckpt-path (str): Path to the model checkpoint directory.
--config (str): Path to the model configuration file.
--input-file (str, optional): File containing prompts for batch processing.
--interactive (bool, optional): Enable interactive mode for generating text.
--max-new-tokens (int, optional): Maximum number of new tokens to generate. Defaults to 200.
--temperature (float, optional): Temperature for sampling. Defaults to 0.2.
Raises:
AssertionError: If neither input-file nor interactive mode is specified.
"""
parser = ArgumentParser()
parser.add_argument("--ckpt-path", type=str, required=True)
parser.add_argument("--config", type=str, required=True)
parser.add_argument("--input-file", type=str, default="")
parser.add_argument("--interactive", action="store_true")
parser.add_argument("--max-new-tokens", type=int, default=200)
parser.add_argument("--temperature", type=float, default=0.2)
parser = ArgumentParser(description="Distributed Transformer Text Generation")
parser.add_argument("--ckpt-path", type=str, required=True,
help="Path to model checkpoint directory")
parser.add_argument("--config", type=str, required=True,
help="Path to model config JSON file")
parser.add_argument("--input-file", type=str, default="",
help="Path to input file for batch processing")
parser.add_argument("--interactive", action="store_true",
help="Enable interactive chat mode")
parser.add_argument("--max-new-tokens", type=int, default=200,
help="Maximum new tokens to generate")
parser.add_argument("--temperature", type=float, default=0.2,
help="Sampling temperature (0.0 = greedy)")
parser.add_argument("--log-level", choices=["DEBUG", "INFO", "WARNING"], default="INFO",
help="Set logging verbosity")
args = parser.parse_args()
assert args.input_file or args.interactive
main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)
# Validate arguments
if not args.interactive and not args.input_file:
parser.error("Must specify either --interactive or --input-file")
# Configure logging
logger.setLevel(args.log_level)
try:
main(
args.ckpt_path,
args.config,
args.input_file,
args.interactive,
args.max_new_tokens,
args.temperature
)
except Exception as e:
logger.critical(f"Critical error: {str(e)}", exc_info=True)
exit(1)