[Tutorial] LLM integration

sota-implementations/post-training/grpo.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from argparse import ArgumentParser
+
+import torch
+from datasets import load_dataset
+from grpo_utils import (
+    HF2vLLMLocalWeightUpdater,
+    PrepareQuestion,
+    ShapedCorrectnessReward,
+)
+from tensordict import TensorDict
+from torch.utils._pytree import tree_map
+from torch.utils.data import DataLoader
+from torchrl.collectors import SyncDataCollector
+from torchrl.data import LazyStackStorage, RayReplayBuffer, ReplayBuffer, SamplerWithoutReplacement
+from torchrl.envs import DataLoadingPrimer, KLRewardTransform, LLMEnv, StepCounter
+from torchrl.modules import from_hf_transformers, from_vllm
+from torchrl.objectives import ClipPPOLoss
+from transformers import AutoTokenizer, GPT2LMHeadModel
+from vllm import LLM
+
+parser = ArgumentParser()
+parser.add_argument("--dataset", type=str, default="gsm8k")
+parser.add_argument("--batch_size", type=int, default=4)
+parser.add_argument("--epochs", type=int, default=10)
+parser.add_argument("--repeats", type=int, default=10)
+parser.add_argument("--steps_per_batch", type=int, default=16)
+parser.add_argument("--optim_batch_size", type=int, default=4)
+
+
+def compute_mc_advantage(trajectories):
+    # Get the question
+    answer = trajectories["answer"]
+    # Identify indices where the answers match
+    answer_ids = tree_map(lambda string: hash(string), answer)
+    answer_ids = torch.tensor(answer_ids)
+    unique_qs = answer_ids.view(-1).unique()
+    trajectories["advantage"] = trajectories["next", "reward"] * 0
+    for u in unique_qs:
+        idx = answer_ids == u
+        rewards = trajectories[idx]["next", "reward"]
+        rewards = (rewards - rewards.mean()) / rewards.std().clamp(min=1e-4)
+        trajectories.set_at_("advantage", rewards, idx)
+    return trajectories
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    # Create env instance:
+    #  - Load the gsm8k dataset
+    dataset = load_dataset(args.dataset, "main")
+    train_dataset = dataset["train"]
+
+    def collate_fn(batch):
+        batch = torch.stack([TensorDict.from_dict(_batch) for _batch in batch])
+        batch.rename_key_("question", "text")
+        return batch
+
+    # LLM
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    # inference_model = GPT2LMHeadModel(GPT2Config())
+    inference_model = LLM("gpt2")
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "left"
+
+    # Env
+    dataloader = DataLoader(  # noqa: TOR401
+        train_dataset, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn
+    )
+    env = LLMEnv.from_dataloader(
+        dataloader=dataloader,
+        tokenizer=tokenizer,
+        str2str=True,
+        batch_size=(args.batch_size * args.repeats,),
+        repeats=args.repeats,
+    )
+    for i, trsf in enumerate(env.transform):
+        if isinstance(trsf, DataLoadingPrimer):
+            env.insert_transform(i, PrepareQuestion())
+            break
+
+    # Finally, we want the env to stop after the first step
+    env.append_transform(StepCounter(max_steps=1))
+
+    policy = from_vllm(
+        inference_model,
+        tokenizer=tokenizer,
+        from_text=False,
+        generate=True,
+        return_log_probs=True,
+    )
+
+    # Reward transform
+    env.append_transform(ShapedCorrectnessReward(tokenizer=tokenizer))
+
+    # Ref model
+    ref_model = GPT2LMHeadModel.from_pretrained("gpt2").eval()
+    TensorDict.from_module(ref_model).data.to_module(ref_model)
+    ref_model = from_hf_transformers(
+        ref_model,
+        tokenizer=tokenizer,
+        from_text=False,
+        generate=False,
+        return_log_probs=True,
+    )
+    env.append_transform(
+        KLRewardTransform(actor=ref_model, coef=0.1, log_prob_key="log_probs")
+    )
+
+    # replay buffer
+    rb = ReplayBuffer(
+        storage=LazyStackStorage(args.steps_per_batch),
+        sampler=SamplerWithoutReplacement(),
+        batch_size=args.optim_batch_size,
+    )
+
+    # Collector
+    train_model = GPT2LMHeadModel.from_pretrained("gpt2").eval()
+    collector = SyncDataCollector(
+        env,
+        policy,
+        frames_per_batch=args.steps_per_batch,
+        total_frames=1_000_000,
+        local_weights_updater=HF2vLLMLocalWeightUpdater(
+            hf_model=train_model, vllm_model=inference_model
+        ),
+    )
+
+    # Loss module
+    policy_training = from_hf_transformers(
+        train_model,
+        tokenizer=tokenizer,
+        from_text=False,
+        generate=False,
+        return_log_probs=True,
+    )
+    loss_fn = ClipPPOLoss(
+        actor_network=policy_training,
+        critic_network=None,
+        critic_coef=0.0,
+        functional=False,
+    )
+    loss_fn.set_keys(sample_log_prob="log_probs")
+    loss_fn._set_in_keys()
+    optim = torch.optim.Adam(loss_fn.parameters())
+
+    # loss_fn = ReinforceLoss(
+    #     actor_network=policy,
+    #     critic_network=None,
+    #     critic_coef=0.0,
+    # )
+
+    for trajs in collector:
+        trajs = trajs.reshape(-1)
+        trajs = compute_mc_advantage(trajs)
+        rb.extend(trajs)
+        for _ in range(args.epochs):
+            for batch in rb:
+                loss = loss_fn(batch)
+                loss_val = loss.mean(reduce=True)
+                loss_val.backward()
+                optim.step()
+                optim.zero_grad()
+        collector.update_policy_weights_()