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import json
import os
import warnings
from pathlib import Path
from typing import Optional
#import llama
import torch
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
from torch import nn
from transformers import (
AutoModelForCausalLM,
LlamaForCausalLM,
LlamaTokenizer,
MistralForCausalLM,
GemmaTokenizer,
GemmaForCausalLM
)
from finetune import data_provider, extra_args, get_batch, loss_func, model_provider
from megatron import get_args, update_num_microbatches
from megatron.arguments import parse_args
from megatron.initialize import initialize_megatron, set_jit_fusion_options
from megatron.training import (
_setup_model_and_optimizer,
build_train_valid_test_data_iterators,
)
class Llama2Wrapper(nn.Module):
def __init__(self, cache_dir):
super().__init__()
initialize_model_parallel(1)
cache_dir = Path(cache_dir)
checkpoints = sorted(cache_dir.glob("*.pth"))
assert (
len(checkpoints) == 1
), "Currently, only llama2 unsharded models implemented"
with open(cache_dir / "params.json", "r") as f:
params = json.loads(f.read())
params["vocab_size"] = 32000
self.model = llama.Transformer(
llama.ModelArgs(max_seq_len=4096, max_batch_size=1, **params)
)
self.model.load_state_dict(torch.load(checkpoints[0]), strict=False)
def forward(self, input_ids, position_ids=None, attention_mask=None, labels=None):
if labels is not None:
warnings.warn("Llama2 does not compute loss")
logits = self.model(input_ids, 0)
loss = torch.tensor(0.0).to(logits.device, logits.dtype)
return {"logits": logits, "loss": loss}
def is_meta_llama2_path(path: Optional[Path]) -> bool:
return path is not None and len(list(path.glob("*.pth"))) > 0
def hf_provider(
name: str, cache_dir: Optional[Path], device: str, size: int = 7, bf16: bool = False
):
print("Getting huggingface model...")
extra_kwargs = {}
if bf16:
extra_kwargs = {"torch_dtype": torch.bfloat16}
if name == "falcon":
model = AutoModelForCausalLM.from_pretrained(
f"tiiuae/falcon-{size}b",
cache_dir=cache_dir,
trust_remote_code=True,
**extra_kwargs,
)
elif name == "llama":
try:
model = LlamaForCausalLM.from_pretrained(cache_dir, **extra_kwargs)
except OSError:
print(
f"Cache dir {cache_dir} does not look like a huggingface "
"checkpoint, assuming cache_dir instead"
)
model = LlamaForCausalLM.from_pretrained(
f"decapoda-research/llama-{size}b-hf",
cache_dir=cache_dir,
**extra_kwargs,
)
elif name == "llama2" and is_meta_llama2_path(cache_dir):
print(
f"baseline path {cache_dir} does not look like a huggingface, "
"assuming it's raw llama2 weights instead"
)
model = Llama2Wrapper(cache_dir)
elif name == "llama2":
model = LlamaForCausalLM.from_pretrained(cache_dir, **extra_kwargs)
elif name == "mistral":
assert size == 7, "Mistral only supports 7B model"
try:
model = MistralForCausalLM.from_pretrained(cache_dir, **extra_kwargs)
except OSError:
print(
f"Cache dir {cache_dir} does not look like a huggingface "
"checkpoint, assuming cache_dir instead"
)
model = MistralForCausalLM.from_pretrained(
f"mistralai/Mistral-{size}B-v0.1", cache_dir=cache_dir, **extra_kwargs
)
elif name == "gemma":
model = GemmaForCausalLM.from_pretrained(cache_dir, **extra_kwargs)
else:
raise KeyError(f"Model {name} not implemented")
return model.eval().requires_grad_(False).to(device)
def hf_our_provider(name: str, data_dir: Path, device: str, size: int = 7):
if name in {"llama", "llama2"}:
model = LlamaForCausalLM.from_pretrained(data_dir)
else:
raise NotImplementedError("Testing custom checkpoints supported for llama")
return model.eval().requires_grad_(False).to(device)
def hf_forward(model, batch):
device = next(param.device for param in model.parameters())
batch = [tensor.to(device) for tensor in batch]
tokens, labels, loss_mask, attention_mask, position_ids = batch
output = model(input_ids=tokens, position_ids=position_ids, labels=tokens)
return output["logits"], output["loss"]
def mega_provider(name: str):
print("Getting megatron model...")
model, _, _ = _setup_model_and_optimizer(model_provider, name, args=get_args())
assert (
len(model) == 1
), "correctness verification only supported with unsharded models"
model = model[0].eval().requires_grad_(False)
return model
def mega_forward(model, batch):
tokens, labels, loss_mask, attention_mask, position_ids = batch
assert torch.all(loss_mask)
# we need to do two forward passes to get both the logits and the loss
_, logits = out = model(tokens, position_ids, attention_mask, labels=labels)
loss, _ = loss_func(model.training, batch, out)
return logits, loss
def verify_step(our_forward, our_model, base_forward, base_model, batch):
our_logits, our_loss = our_forward(our_model, batch)
base_logits, base_loss = base_forward(base_model, batch)
assert (
our_logits.size() == base_logits.size()
), f"ours={our_logits.size()}, true={base_logits.size()}"
our_logits = our_logits.cpu()
base_logits = base_logits.cpu()
abs_error = torch.abs(our_logits - base_logits)
print(
"Max absoulute error in the logits:",
f"max={torch.max(abs_error):.6f}, avg={torch.mean(abs_error):.6f}",
)
assert our_loss.size() == base_loss.size()
our_loss = our_loss.cpu()
base_loss = base_loss.cpu()
loss_error = torch.abs(our_loss - base_loss)
print(
f"Abs loss error: {loss_error:.6f} "
f"Our loss: {our_loss:.3f}, theirs: {base_loss:.3f}"
)
def is_megatron_path(path):
path = Path(path) if isinstance(path, str) else path
return (path / "latest_checkpointed_iteration.txt").exists()
def main():
# Misc initializations
print("Starting megatron vs huggingface verification")
args = get_args()
set_jit_fusion_options(args)
# Determine if the provided weight is a megatron checkpoint or huggingface checkpoint
print("Loading our model!")
if is_megatron_path(args.load):
our_model = mega_provider(args.model_name)
our_forward = mega_forward
else:
print(
"NOTE: The given path does not look like a megatron checkpoint, "
f"assuming it's a huggingface checkpoint instead (path={args.load})"
)
our_model = hf_our_provider(
args.model_name, args.load, "cuda:0"
)
our_forward = hf_forward
args.iteration = 0
# Load baseline model
print("Loading baseline model!")
base_model = hf_provider(
args.model_name, args.cache_dir, args.baseline_device, size=args.model_size
)
base_forward = hf_forward
# Load dataset iterator
print("Loading dataset!")
data_iterator, _, _ = build_train_valid_test_data_iterators(data_provider, args)
# Now we can start the verifications
for iteration in range(0, 10):
print(f"Iteration {iteration}...")
update_num_microbatches(args.consumed_train_samples)
args.curr_iteration = iteration
verify_step(
our_forward, our_model, base_forward, base_model, get_batch(data_iterator)
)
def extra_extra_args(parser):
parser = extra_args(parser)
group = parser.add_argument_group(title="huggingface")
group.add_argument(
"--huggingface_cache",
type=Path,
default=None,
dest="cache_dir",
help=(
"If falcon, optional: path to huggingface cache. "
"If llama2, optional: either the huggingface cache path, or "
"the raw weight directory given by meta. "
"If llama, optional: either the path to converted huggingface weights "
"(use convert_llama_weights_to_hf.py) or the huggingface cache dir."
),
)
group.add_argument(
"--huggingface_device",
default="cuda:1",
dest="baseline_device",
help="Device to use for the baseline model",
)
group.add_argument("--model_size", type=int, default=7)
return parser
if __name__ == "__main__":
defaults = {
"micro_batch_size": 1,
"use_checkpoint_args": True,
"train_iters": 10,
"lr": 1.0,
}
# if not is_megatron_path(parse_args(extra_extra_args).load):
# defaults.update(
# {
# "encoder_num_layers": 1,
# "hidden_size": 1,
# "num_attention_heads": 1,
# "seq_length": 2048,
# "max_position_embeddings": 2048,
# }
# )
initialize_megatron(extra_extra_args, args_defaults=defaults)
main()
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