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import sys
import time
from pathlib import Path
from typing import Literal, Optional
import lightning as L
import torch
from lightning.fabric.plugins import BitsandbytesPrecision
from lightning.fabric.strategies import FSDPStrategy
# support running without installing as a package
wd = Path(__file__).parent.parent.resolve()
sys.path.append(str(wd))
#from lit_gpt import GPT, Config, Tokenizer
from config import Config
from tokenizer import Tokenizer
from model import GPT, Block
from utils import (
check_valid_checkpoint_dir,
get_default_supported_precision,
gptq_quantization,
load_checkpoint,
)
def sample(logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None) -> torch.Tensor:
logits = logits[0, -1]
# optionally crop the logits to only the top k options
if top_k is not None:
v, i = torch.topk(logits, min(top_k, logits.size(-1)))
# do not use `torch.where` as in nanogpt because it will repeat top-k collisions
logits = torch.full_like(logits, float("-inf")).scatter_(-1, i, v)
# optionally scale the logits and sample from a probability distribution
if temperature > 0.0:
probs = torch.nn.functional.softmax(logits / temperature, dim=-1)
return torch.multinomial(probs, num_samples=1)
return torch.argmax(logits, dim=-1, keepdim=True)
@torch.inference_mode()
def generate(
model: GPT,
idx: torch.Tensor,
max_returned_tokens: int,
*,
temperature: float = 1.0,
top_k: Optional[int] = None,
eos_id: Optional[int] = None,
) -> torch.Tensor:
"""Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
The implementation of this function is modified from A. Karpathy's nanoGPT.
Args:
model: The model to use.
idx: Tensor of shape (T) with indices of the prompt sequence.
max_returned_tokens: The maximum number of tokens to return (given plus generated).
temperature: Scales the predicted logits by 1 / temperature.
top_k: If specified, only sample among the tokens with the k highest probabilities.
eos_id: If specified, stop generating any more token once the <eos> token is triggered.
"""
T = idx.size(0)
assert max_returned_tokens > T
if model.max_seq_length < max_returned_tokens - 1:
# rolling the kv cache based on the `input_pos` value would be necessary. However, doing so would introduce a
# data dependency on the `input_pos` tensor and impact model compilation. Since this setting is uncommon, we do
# not support it to avoid negatively impacting the overall speed
raise NotImplementedError(f"max_seq_length {model.max_seq_length} needs to be >= {max_returned_tokens - 1}")
device, dtype = idx.device, idx.dtype
# create an empty tensor of the expected final shape and fill in the current tokens
empty = torch.empty(max_returned_tokens, dtype=dtype, device=device)
empty[:T] = idx
idx = empty
input_pos = torch.arange(0, T, device=device)
# generate up to a fixed number of tokens
for _ in range(max_returned_tokens - T):
x = idx.index_select(0, input_pos).view(1, -1)
# forward
logits = model(x, input_pos)
idx_next = sample(logits, temperature, top_k).to(dtype=dtype)
# advance
input_pos = input_pos[-1:] + 1
# concatenate the new generation
idx = idx.index_copy(0, input_pos, idx_next)
# if <eos> token is triggered, return the output (stop generation)
if idx_next == eos_id:
return idx[:input_pos] # include the EOS token
return idx
def main(
prompt: str = "Hello, my name is",
*,
num_samples: int = 1,
max_new_tokens: int = 50,
top_k: Optional[int] = 200,
temperature: float = 0.8,
checkpoint_dir: Path = Path("checkpoints/stabilityai/stablelm-base-alpha-3b"),
quantize: Optional[Literal["bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq", "bnb.int8", "gptq.int4"]] = None,
strategy: str = "auto",
devices: int = 1,
precision: Optional[str] = None,
) -> None:
"""Generates text samples based on a pre-trained model and tokenizer.
Args:
prompt: The prompt string to use for generating the samples.
num_samples: The number of text samples to generate.
max_new_tokens: The number of generation steps to take.
top_k: The number of top most probable tokens to consider in the sampling process.
temperature: A value controlling the randomness of the sampling process. Higher values result in more random
samples.
checkpoint_dir: The checkpoint directory to load.
quantize: Whether to quantize the model and using which method:
- bnb.nf4, bnb.nf4-dq, bnb.fp4, bnb.fp4-dq: 4-bit quantization from bitsandbytes
- bnb.int8: 8-bit quantization from bitsandbytes
- gptq.int4: 4-bit quantization from GPTQ
for more details, see https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials/quantize.md
strategy: Indicates the Fabric strategy setting to use.
devices: How many devices to use.
precision: Indicates the Fabric precision setting to use.
"""
precision = precision or get_default_supported_precision(training=False)
plugins = None
if quantize is not None:
if devices > 1:
raise NotImplementedError(
"Quantization is currently not supported for multi-GPU training. Please set devices=1 when using the"
" --quantize flag."
)
if quantize.startswith("bnb."):
if "mixed" in precision:
raise ValueError("Quantization and mixed precision is not supported.")
dtype = {"16-true": torch.float16, "bf16-true": torch.bfloat16, "32-true": torch.float32}[precision]
plugins = BitsandbytesPrecision(quantize[4:], dtype)
precision = None
if strategy == "fsdp":
strategy = FSDPStrategy(auto_wrap_policy={Block}, cpu_offload=False)
fabric = L.Fabric(devices=devices, precision=precision, strategy=strategy, plugins=plugins)
fabric.launch()
check_valid_checkpoint_dir(checkpoint_dir)
config = Config.from_json(checkpoint_dir / "lit_config.json")
if quantize == "gptq.int4":
model_file = "lit_model_gptq.4bit.pth"
if not (checkpoint_dir / model_file).is_file():
raise ValueError("Please run `python quantize/gptq.py` first")
else:
model_file = "lit_model.pth" #"lit_model.pth"
checkpoint_path = checkpoint_dir / model_file
fabric.print(f"Loading model {str(checkpoint_path)!r} with {config.__dict__}", file=sys.stderr)
t0 = time.perf_counter()
with fabric.init_module(empty_init=True), gptq_quantization(quantize == "gptq.int4"):
model = GPT(config)
fabric.print(f"Time to instantiate model: {time.perf_counter() - t0:.02f} seconds.", file=sys.stderr)
model.eval()
model = fabric.setup_module(model)
t0 = time.perf_counter()
load_checkpoint(fabric, model, checkpoint_path)
fabric.print(f"Time to load the model weights: {time.perf_counter() - t0:.02f} seconds.", file=sys.stderr)
tokenizer = Tokenizer(checkpoint_dir)
encoded = tokenizer.encode(prompt, device=fabric.device)
prompt_length = encoded.size(0)
max_returned_tokens = prompt_length + max_new_tokens
with fabric.init_tensor():
# set the max_seq_length to limit the memory usage to what we need
model.max_seq_length = max_returned_tokens
L.seed_everything(1234)
for i in range(num_samples):
with fabric.init_tensor():
# enable the kv cache
model.set_kv_cache(batch_size=1)
t0 = time.perf_counter()
y = generate(model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k)
t = time.perf_counter() - t0
fabric.print(tokenizer.decode(y))
tokens_generated = y.size(0) - prompt_length
fabric.print(
f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_generated / t:.02f} tokens/sec", file=sys.stderr
)
if fabric.device.type == "cuda":
fabric.print(f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB", file=sys.stderr)
if __name__ == "__main__":
from jsonargparse import CLI
torch.set_float32_matmul_precision("high")
CLI(main) |