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import torch
import transformers
import torch.nn.functional as F
import accelerate
from accelerate.state import AcceleratorState
from datasets import load_dataset
import datasets
import argparse
import deepspeed
def prepare_deepspeed2(args, ref_policy):
deepspeed_states = AcceleratorState().deepspeed_plugin
deepspeed_states.deepspeed_config["train_micro_batch_size_per_gpu"] = args.batch_size
eval_ds_config = {
"train_micro_batch_size_per_gpu": deepspeed_states.deepspeed_config["train_micro_batch_size_per_gpu"],
"bf16": {"enabled": True},
"prescale_gradients": False,
"wall_clock_breakdown": False,
}
ref_policy, *_ = deepspeed.initialize(model=ref_policy, config=eval_ds_config)
ref_policy.eval()
print("🔥 deepspeed2 is initialized")
return ref_policy
def prepare_deepspeed3(args, model, accelerator):
# Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
# deepspeed_states = AcceleratorState().deepspeed_plugin
# deepspeed_states.deepspeed_config["train_micro_batch_size_per_gpu"] = args.batch_size
deepspeed_plugin = accelerator.state.deepspeed_plugin
config_kwargs = deepspeed_plugin.deepspeed_config
if model is not None:
if hasattr(model, "config"):
hidden_size = (
max(model.config.hidden_sizes)
if getattr(model.config, "hidden_sizes", None)
else getattr(model.config, "hidden_size", None)
)
if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
# Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
# This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
config_kwargs.update(
{
"zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
"zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
"zero_optimization.stage3_prefetch_bucket_size": 0,
}
)
model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
model.eval()
print("🔥 deepspeed3 is initialized")
return model
parser = argparse.ArgumentParser()
parser.add_argument("--deepspeed2", action="store_true")
parser.add_argument("--deepspeed3", action="store_true")
parser.add_argument("--batch_size", type=int, default=64)
parser.add_argument("--micro_batch_size", type=int, default=1)
args = parser.parse_args()
dataset = load_dataset("vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144", split="train")
dataset = dataset.with_format("torch", columns=["query_token", "reference_response_token"])
dummy_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
accelerator = accelerate.Accelerator()
tokenizer = transformers.AutoTokenizer.from_pretrained("EleutherAI/pythia-160m", padding_side="right")
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
accelerator.print("=================")
policy = transformers.AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-160m")
policy.generation_config.eos_token_id = None # disable `pad_token_id` and `eos_token_id` because we just want to
policy.generation_config.pad_token_id = None # generate tokens without truncation / padding
ref_policy = transformers.AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-160m")
for model in [policy, ref_policy]:
for module in model.modules():
if isinstance(module, torch.nn.Dropout):
module.p = 0
dummy_optimizer = torch.optim.Adam(policy.parameters(), lr=0.01)
policy, dummy_dataloader, dummy_optimizer = accelerator.prepare(policy, dummy_dataloader, dummy_optimizer)
accelerator.print({
"transformers": transformers.__version__,
"accelerate": accelerate.__version__,
"deepspeed": deepspeed.__version__,
"datasets": datasets.__version__,
})
if args.deepspeed2:
ref_policy = prepare_deepspeed2(args, ref_policy)
elif args.deepspeed3:
ref_policy = prepare_deepspeed3(args, ref_policy, accelerator)
else:
ref_policy = ref_policy.to(accelerator.device)
for data in dummy_dataloader:
query = data["query_token"]
break
temperature = 0.7
context_length = query.shape[1]
def forward(model, query_responses, tokenizer):
attention_mask = query_responses != tokenizer.pad_token_id
position_ids = attention_mask.cumsum(1) - attention_mask.long()
input_ids = torch.masked_fill(query_responses, ~attention_mask, 0)
return model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
return_dict=True,
output_hidden_states=True,
)
def generate(lm_backbone, queries, tokenizer, generation_config):
"""generate in a way that does not affect padding tokens"""
context_length = queries.shape[1]
attention_mask = queries != tokenizer.pad_token_id
input_ids = torch.masked_fill(queries, ~attention_mask, 0)
output = lm_backbone.generate(
input_ids=input_ids,
attention_mask=attention_mask,
# position_ids=attention_mask.cumsum(1) - attention_mask.long(), # already handled in generation
generation_config=generation_config,
return_dict_in_generate=True,
output_logits=True
)
logits = torch.stack(output.logits, 1)
return torch.cat((queries, output.sequences[:, context_length:]), dim=1), logits
def generate(lm_backbone, queries, tokenizer, generation_config):
"""generate in a way that does not affect padding tokens"""
context_length = queries.shape[1]
attention_mask = queries != tokenizer.pad_token_id
input_ids = torch.masked_fill(queries, ~attention_mask, 0)
output = lm_backbone.generate(
input_ids=input_ids,
attention_mask=attention_mask,
# position_ids=attention_mask.cumsum(1) - attention_mask.long(), # already handled in generation
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True
)
logits = torch.stack(output.scores, 1)
return torch.cat((queries, output.sequences[:, context_length:]), dim=1), logits
generation_config = transformers.GenerationConfig(
max_new_tokens=50,
min_new_tokens=50,
temperature=temperature,
top_k=0.0,
top_p=1.0,
do_sample=True,
)
# rollout
with torch.no_grad():
query_response, logits = generate(accelerator.unwrap_model(policy), query, tokenizer, generation_config)
response = query_response[:, context_length:]
# logits /= temperature + 1e-7
all_logprob = F.log_softmax(logits, dim=-1)
logprob = torch.gather(all_logprob, 2, response.unsqueeze(-1)).squeeze(-1)
# accelerator.print(f"{response=}")
# accelerator.print(f"{logprob=}")
ref_output = forward(ref_policy, query_response, tokenizer)
ref_logits = ref_output.logits[:, context_length - 1 : -1]
ref_logits /= temperature + 1e-7
ref_all_logprob = F.log_softmax(ref_logits, dim=-1)
ref_logprob = torch.gather(ref_all_logprob, 2, response.unsqueeze(-1)).squeeze(-1)
# accelerator.print(f"{ref_logprob=}")
accelerator.print(f"{(ref_logprob-logprob).exp().mean()=}")
torch.testing.assert_close(ref_logprob, logprob, rtol=1e-2, atol=1e-2) # a very generous tolerance
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