Upload folder xtuner to code/xtuner

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+code/xtuner/model/dynamic_llava/__pycache__/dynamic_qwen.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text

code/xtuner/__init__.py

@@ -0,0 +1,25 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+
+from mmengine.utils import digit_version
+
+from .entry_point import cli
+from .version import __version__, version_info
+
+HF_CEPH_HUB = os.getenv('HF_CEPH_HUB', '')
+HF_USE_CEPH = os.getenv('HF_USE_CEPH', 0) or HF_CEPH_HUB != ''
+DS_CEPH_DIR = os.getenv('DS_CEPH_DIR', None)
+if HF_USE_CEPH:
+    from .utils.fileio import (patch_hf_auto_from_pretrained,
+                               patch_hf_save_pretrained)
+    patch_hf_auto_from_pretrained(HF_CEPH_HUB)
+    patch_hf_save_pretrained()
+
+if DS_CEPH_DIR:
+    from .utils.fileio import patch_deepspeed_engine
+    patch_deepspeed_engine()
+
+__all__ = [
+    '__version__', 'version_info', 'digit_version', 'cli', 'HF_USE_CEPH',
+    'DS_CEPH_DIR'
+]

code/xtuner/_lite/__init__.py

@@ -0,0 +1,77 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import subprocess
+import sys
+
+from loguru import logger
+
+from .device import get_device, get_torch_device_module
+
+_LOGGER = None
+
+
+def log_format(debug=False):
+    formatter = "[XTuner][{time:YYYY-MM-DD HH:mm:ss}][<level>{level}</level>]"
+
+    if debug:
+        formatter += "[<cyan>{name}</cyan>:"
+        formatter += "<cyan>{function}</cyan>:"
+        formatter += "<cyan>{line}</cyan>]"
+
+    formatter += " <level>{message}</level>"
+    return formatter
+
+
+def get_logger(level="INFO"):
+    global _LOGGER
+    if _LOGGER is None:
+        # Remove the original logger in Python to prevent duplicate printing.
+        logger.remove()
+        logger.add(sys.stderr, level=level, format=log_format(debug=level == "DEBUG"))
+        _LOGGER = logger
+    return _LOGGER
+
+
+def get_repo_git_info(repo_path):
+    original_directory = os.getcwd()
+    os.chdir(repo_path)
+
+    try:
+        branch = (
+            subprocess.check_output(
+                ["git", "rev-parse", "--abbrev-ref", "HEAD"], stderr=subprocess.STDOUT
+            )
+            .strip()
+            .decode("utf-8")
+        )
+
+        commit_id = (
+            subprocess.check_output(
+                ["git", "rev-parse", "HEAD"], stderr=subprocess.STDOUT
+            )
+            .strip()
+            .decode("utf-8")
+        )
+
+        remote_url = (
+            subprocess.check_output(
+                ["git", "remote", "get-url", "origin"], stderr=subprocess.STDOUT
+            )
+            .strip()
+            .decode("utf-8")
+        )
+
+        return branch, commit_id, remote_url
+    except subprocess.CalledProcessError:
+        return None, None, None
+    finally:
+        os.chdir(original_directory)
+
+
+__all__ = [
+    "AutoConfig",
+    "AutoModelForCausalLM",
+    "AutoTokenizer",
+    "get_device",
+    "get_torch_device_module",
+]

code/xtuner/_lite/accelerate/__init__.py

@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .lora import LORA_TARGET_MAP
+from .packed import pack_sequence, unpack_sequence
+from .utils import (
+    liger_kernel_is_available,
+    lmdeploy_is_available,
+    mlu_is_available,
+    npu_is_available,
+    profile_time_and_memory,
+    varlen_attn_is_available,
+)
+
+__all__ = [
+    "LORA_TARGET_MAP",
+    "pack_sequence",
+    "packed_sequence",
+    "unpack_sequence",
+    "liger_kernel_is_available",
+    "varlen_attn_is_available",
+    "lmdeploy_is_available",
+    "npu_is_available",
+    "mlu_is_available",
+    "profile_time_and_memory",
+]

code/xtuner/_lite/accelerate/lora.py

@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+LORA_TARGET_MAP = {
+    "InternLM2ForCausalLM": ["wqkv", "wo", "w1", "w2", "w3"],
+    "CLIPVisionModel": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
+}

code/xtuner/_lite/accelerate/ops/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .moe_permute import GROUPED_GEMM_INSTALLED, permute_func, unpermute_func
+
+__all__ = ["GROUPED_GEMM_INSTALLED", "permute_func", "unpermute_func"]

code/xtuner/_lite/accelerate/ops/moe_permute.py

@@ -0,0 +1,200 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Modified from
+https://github.com/fanshiqing/grouped_gemm/blob/v1.1.4/grouped_gemm/ops.py
+Support torch compile."""
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+
+GROUPED_GEMM_INSTALLED = False
+
+try:
+    from grouped_gemm import backend
+
+    GROUPED_GEMM_INSTALLED = True
+except ImportError:
+    # install grouped gemm https://github.com/fanshiqing/grouped_gemm/tree/v1.1.4?tab=readme-ov-file#pip-install
+    grouped_gmm = None
+
+
+@torch.library.custom_op("moe::permute", mutates_args=())
+def permute(input_act: Tensor, indices: Tensor, num_topK: int) -> Tuple[Tensor, Tensor]:
+    input_max_expanded_token_num = input_act.size(0) * num_topK
+    workspace_fw = []
+    permuted_act, row_id_map, _ = backend.permute(
+        input_act, indices, 0, workspace_fw, input_max_expanded_token_num
+    )
+    return permuted_act, row_id_map
+
+
+@permute.register_fake
+def permute_fake(
+    input_act: Tensor,
+    indices: Tensor,
+    num_topK: int,
+):
+    permuted_act = input_act.new_empty(
+        (input_act.shape[0] * num_topK, *input_act.shape[1:])
+    )
+    row_id_map = indices.new_empty((indices.numel(),))
+    return permuted_act, row_id_map
+
+
+@torch.library.custom_op("moe::unpermute", mutates_args=())
+def unpermute(
+    input: Tensor, row_id_map: Tensor, prob: Tensor, max_tokens: int, num_topK: int
+) -> Tensor:
+    if not input.is_contiguous():
+        input = input.contiguous()
+    return backend.unpermute(input, row_id_map, prob, max_tokens, num_topK)
+
+
+@unpermute.register_fake
+def unpermute_fake(
+    input: Tensor, row_id_map: Tensor, prob: Tensor, max_tokens: int, num_topK: int
+) -> Tensor:
+    return input.new_empty((input.shape[0] // num_topK, *input.shape[1:]))
+
+
+@torch.library.custom_op("moe::unpermute_bwd", mutates_args=())
+def unpermute_bwd(
+    input_bwd: Tensor,
+    input_fwd: Tensor,
+    row_id_map: Tensor,
+    prob: Optional[Tensor],
+) -> Tuple[Tensor, Tensor]:
+    if not input_bwd.is_contiguous():
+        input_bwd = input_bwd.contiguous()
+    topk = input_fwd.shape[0] // input_bwd.shape[0]
+    if prob is None:
+        prob = torch.ones(
+            [input_bwd.size(0), topk], dtype=torch.float32, device=input_bwd.device
+        )
+    return backend.unpermute_bwd(input_bwd, input_fwd, row_id_map, prob)
+
+
+@unpermute_bwd.register_fake
+def unpermute_bwd_fake(
+    input_bwd: Tensor,
+    input_fwd: Tensor,
+    row_id_map: Tensor,
+    prob: Optional[Tensor],
+) -> Tuple[Tensor, Tensor]:
+    act_grad = torch.empty_like(input_fwd)
+    topk = input_fwd.shape[0] // input_bwd.shape[0]
+    prob_grad = torch.empty(
+        (input_bwd.size(0), topk), dtype=torch.float32, device=input_bwd.device
+    )
+    return act_grad, prob_grad
+
+
+if torch.__version__ >= "2.4.0":
+    _wrapped_permute = torch.ops.moe.permute
+    _wrapped_unpermute = torch.ops.moe.unpermute
+    _wrapped_unpermute_bwd = torch.ops.moe.unpermute_bwd
+else:
+    _wrapped_permute = permute
+    _wrapped_unpermute = unpermute
+    _wrapped_unpermute_bwd = unpermute_bwd
+
+
+class PermuteMoE_topK(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        input_act: Tensor,
+        indices: Tensor,
+    ):
+        if not input_act.numel():
+            return input_act, None
+
+        if indices.dim() == 1:
+            indices = indices.view(-1, 1)
+        if not input_act.is_contiguous():
+            input_act = input_act.contiguous()
+        if not indices.is_contiguous():
+            indices = indices.contiguous()
+
+        num_topK = indices.size(1)
+
+        permuted_act, row_id_map = _wrapped_permute(
+            input_act,
+            indices,
+            num_topK,
+        )
+
+        ctx.row_id_map = row_id_map
+        ctx.num_tokens = indices.size(0)
+        ctx.num_topK = num_topK
+        return permuted_act, row_id_map
+
+    @staticmethod
+    def backward(ctx, permuted_act_grad, *args):
+        if not permuted_act_grad.numel():
+            return permuted_act_grad, None
+
+        permuted_act_grad = permuted_act_grad.contiguous()
+
+        row_id_map = ctx.row_id_map
+        num_tokens = ctx.num_tokens
+        num_topK = ctx.num_topK
+
+        unpermuted_act_grad = _wrapped_unpermute(
+            permuted_act_grad, row_id_map, torch.tensor([]), num_tokens, num_topK
+        )
+        return unpermuted_act_grad, None
+
+
+class UnpermuteMoE_topK(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input_act: Tensor, row_id_map: Tensor, probs: Tensor = None):
+        if not input_act.numel():
+            ctx.probs = probs
+            return input_act
+
+        if not input_act.is_contiguous():
+            input_act = input_act.contiguous()
+        if not row_id_map.is_contiguous():
+            row_id_map = row_id_map.contiguous()
+        if probs is not None and not probs.is_contiguous():
+            probs = probs.contiguous()
+
+        num_tokens = probs.size(0) if probs is not None else input_act.size(0)
+        num_topK = probs.size(1) if probs is not None else 1
+
+        unpermuted_output = _wrapped_unpermute(
+            input_act,
+            row_id_map,
+            probs if probs is not None else torch.tensor([]),
+            num_tokens,
+            num_topK,
+        )
+
+        ctx.save_for_backward(input_act, row_id_map, probs)
+        return unpermuted_output
+
+    @staticmethod
+    def backward(ctx, unpermuted_act_grad):
+        if not unpermuted_act_grad.numel():
+            return unpermuted_act_grad, None, ctx.probs
+
+        input_act, row_id_map, probs = ctx.saved_tensors
+
+        act_grad = None
+        if ctx.needs_input_grad[0]:
+            act_grad, prob_grad = _wrapped_unpermute_bwd(
+                unpermuted_act_grad, input_act, row_id_map, probs
+            )
+
+        if not ctx.needs_input_grad[2]:
+            prob_grad = None
+        return act_grad, None, prob_grad
+
+
+def permute_func(input_act, indices):
+    return PermuteMoE_topK.apply(input_act, indices)
+
+
+def unpermute_func(input_act, row_id_map, probs=None):
+    return UnpermuteMoE_topK.apply(input_act, row_id_map, probs)

code/xtuner/_lite/accelerate/packed.py

@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List, Union
+
+import torch
+
+
+def unpack_sequence(packed: torch.Tensor, num_tokens: Union[torch.Tensor, List], dim=1):
+    if isinstance(num_tokens, torch.Tensor):
+        num_tokens = num_tokens.tolist()
+    sequences = torch.split(packed, num_tokens, dim=dim)
+    return sequences
+
+
+def pack_sequence(sequences, dim=1):
+    num_tokens = torch.IntTensor([seq.size(dim) for seq in sequences])
+    packed = torch.cat(sequences, dim=dim)
+    return packed, num_tokens.to(packed.device)
+
+
+def packed_cumulative_length(num_tokens: torch.Tensor):
+    device = num_tokens.device
+    _zero_pad = torch.zeros(1, device=device)
+    _pad_length = torch.cat([_zero_pad, num_tokens]).int()
+    return torch.cumsum(_pad_length, 0).int()

code/xtuner/_lite/accelerate/utils.py

@@ -0,0 +1,62 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import time
+from contextlib import contextmanager
+
+from transformers.utils.import_utils import is_flash_attn_2_available
+
+from xtuner._lite import get_device, get_logger, get_torch_device_module
+
+logger = get_logger()
+
+
+def npu_is_available():
+    return get_device() == "npu"
+
+
+def mlu_is_available():
+    return get_device() == "mlu"
+
+
+def varlen_attn_is_available():
+    return is_flash_attn_2_available() or npu_is_available()
+
+
+def lmdeploy_is_available():
+    available = False
+    try:
+        import lmdeploy  # noqa: F401
+
+        available = True
+    except ImportError:
+        available = False
+
+    return available
+
+
+def liger_kernel_is_available():
+    available = False
+    try:
+        import liger_kernel  # noqa: F401
+
+        available = True
+    except ImportError:
+        available = False
+
+    return available
+
+
+@contextmanager
+def profile_time_and_memory(desc):
+    torch_device = get_torch_device_module()
+    start_t = time.time()
+    torch_device.reset_peak_memory_stats()
+
+    yield
+
+    max_memory = torch_device.max_memory_allocated()
+    cost_time = time.time() - start_t
+
+    logger.success(
+        f"{desc} Elapsed time {cost_time:.2f} seconds, "
+        f"peak gpu memory {max_memory/1024**3:.1f}G"
+    )

code/xtuner/_lite/algorithms/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) OpenMMLab. All rights reserved.

code/xtuner/_lite/algorithms/ppo/__init__.py

@@ -0,0 +1,32 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .dataset import (
+    InferDataset,
+    PPOTokenizeFunction,
+    RewardBuffer,
+    RewardBufferCollator,
+)
+from .loss import (
+    CriticLoss,
+    PPOPolicyLoss,
+    compute_advantages_and_returns,
+    compute_kl_rewards,
+    gather_logprobs,
+)
+from .model import build_actor_model, build_reward_model
+
+__all__ = [
+    "InferDataset",
+    "RewardBuffer",
+    "RewardBufferCollator",
+    "PPOCollator",
+    "PPODataset",
+    "PPOTokenizeFunction",
+    "CriticLoss",
+    "PPOPolicyLoss",
+    "compute_advantages_and_returns",
+    "compute_kl_rewards",
+    "compute_rewards",
+    "gather_logprobs",
+    "build_actor_model",
+    "build_reward_model",
+]

code/xtuner/_lite/algorithms/ppo/dataset.py

@@ -0,0 +1,153 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+
+import numpy as np
+import torch
+from torch import nn
+
+from xtuner._lite.chat.messages.chat import ChatMsg
+from xtuner._lite.datasets import OPENAI_CONVERT_MAP
+
+from ..sft import SftCollator, SftTokenizeFunction
+
+
+class InferDataset(torch.utils.data.Dataset):
+    def __init__(self, prompts, responses):
+        super().__init__()
+
+        assert len(prompts) == len(responses)
+        self.prompts = prompts
+        self.responses = responses
+        self.policies = None
+
+    def __len__(self):
+        return len(self.prompts)
+
+    def __getitem__(self, item):
+        prompt = self.prompts[item]
+        response = self.responses[item]
+        num_prefill_tokens = len(prompt)
+
+        input_ids = prompt + response
+        labels = [-100] * (num_prefill_tokens - 1) + response + [-100]
+
+        return {"input_ids": input_ids, "labels": labels, "num_tokens": len(input_ids)}
+
+
+FASTER = False
+
+
+class RewardBuffer(torch.utils.data.Dataset):
+    def __init__(self, clip_min=-5, clip_max=5, normalize=True, faster=False):
+        super().__init__()
+
+        self.clip_min = clip_min
+        self.clip_max = clip_max
+
+        self.normalize = normalize
+
+        if self.normalize:
+            self.bn = nn.BatchNorm1d(1, momentum=None, affine=False)
+        else:
+            self.bn = None
+
+        self._num_action_tokens = 0
+        self._num_total_tokens = 0
+        self._trajectories = []
+
+        self._current_mean = 0
+
+    @property
+    def running_mean(self):
+        return self.bn.running_mean.item()
+
+    @property
+    def current_mean(self):
+        return self._current_mean
+
+    @property
+    def num_action_tokens(self):
+        return self._num_action_tokens.item()
+
+    @property
+    def num_total_tokens(self):
+        return self._num_total_tokens
+
+    def update(self, trajectories):
+        rewards = [data["reward"] for data in trajectories]
+
+        for i in range(len(trajectories)):
+            trajectories[i]["ori_reward"] = trajectories[i]["reward"]
+
+        rewards = torch.tensor(rewards)
+
+        self._current_mean = rewards.mean().item()
+
+        rewards = rewards.clip(self.clip_min, self.clip_max)
+
+        if self.normalize:
+            self.bn.train()
+            _ = self.bn(rewards.unsqueeze(-1))
+            self.bn.eval()
+            rewards = self.bn(rewards.unsqueeze(-1))
+
+        for i in range(len(trajectories)):
+            trajectories[i]["reward"] = rewards[i].item()
+
+        num_total_tokens = 0
+        num_action_tokens = 0
+        for data in trajectories:
+            labels = np.array(data["labels"])
+            num_total_tokens += labels.size
+            num_action_tokens += (labels >= 0).sum()
+
+        self._num_action_tokens = num_action_tokens
+        self._num_total_tokens = num_total_tokens
+
+        self._trajectories = trajectories
+
+    def dump_jsonl(self, path, tokenizer, debug=False):
+        with open(path, "w", encoding="utf8") as f:
+            for data in self._trajectories:
+                json_line = {
+                    "num_tokens": data["num_tokens"],
+                    "reward": data["ori_reward"],
+                    "sequence": tokenizer.decode(data["input_ids"]),
+                }
+
+                if debug:
+                    json_line["input_ids"] = data["input_ids"]
+                    json_line["labels"] = data["labels"]
+
+                json_str = json.dumps(json_line, ensure_ascii=False)
+                f.write(json_str + "\n")
+
+    def __len__(self):
+        return len(self._trajectories)
+
+    def __getitem__(self, item):
+        return self._trajectories[item]
+
+
+class PPOTokenizeFunction(SftTokenizeFunction):
+    def __init__(self, tokenizer, chat_template, raw_format="openai", sys_prompt=None):
+        super().__init__(tokenizer, chat_template, raw_format)
+        self.sys_prompt = sys_prompt
+
+    def __call__(self, item):
+        formatter = OPENAI_CONVERT_MAP[self.raw_format]
+        msg = formatter(item)
+        if self.sys_prompt is not None:
+            sys_msg = ChatMsg(role="system", content=self.sys_prompt)
+            msg.messages = [sys_msg] + msg.messages
+        tokenized = msg.tokenize(self.tokenizer, self.chat_template)
+
+        return tokenized
+
+
+class RewardBufferCollator(SftCollator):
+    def __call__(self, instances):
+        data = super().__call__(instances)
+        data["rewards"] = [item["reward"] for item in instances]
+
+        return data

code/xtuner/_lite/algorithms/ppo/loss.py

@@ -0,0 +1,119 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from torch.nn import functional as F
+
+from xtuner._lite import get_logger
+
+logger = get_logger()
+
+
+def gather_logprobs(logits, labels):
+    log_probs = F.log_softmax(logits, dim=-1)
+    log_probs_labels = log_probs.gather(dim=-1, index=labels.unsqueeze(-1))
+    return log_probs_labels.squeeze(-1)
+
+
+@torch.no_grad()
+def compute_kl_rewards(logprobs, ref_logprobs, reward_score, kl_coef=0.01):
+    assert logprobs.ndim == 1
+    last_mask = torch.zeros_like(logprobs, dtype=torch.int)
+    last_mask[-1] = 1
+
+    kl = ref_logprobs - logprobs
+    kl_reward = kl_coef * kl * (1 - last_mask)
+
+    last_reward = reward_score * last_mask
+
+    rewards = kl_reward + last_reward
+
+    return rewards
+
+
+@torch.no_grad()
+def compute_advantages_and_returns(values, rewards, gamma=1.0, gae_lambda=0.99):
+    # Adopted from https://github.com/CarperAI/trlx/blob/main/trlx/models/modeling_ppo.py#L134  # noqa: E501
+    """Function that computes advantages and returns from rewards and values.
+    Calculated as in the original PPO paper: https://arxiv.org/abs/1707.06347
+    Note that rewards may include a KL divergence loss term.
+
+    Advantages looks like this:
+    Adv1 =  R1 + γ * λ * R2     + γ^2 * λ^2 * R3       + ...
+            - V1 + γ * (1 - λ) V2 + γ^2 * λ * (1 - λ) V3 + ...
+
+    Returns looks like this:
+    Ret1 =  R1 + γ * λ * R2     + γ^2 * λ^2 * R3       + ...
+                + γ * (1 - λ) V2 + γ^2 * λ * (1 - λ) V3 + ...
+    """
+    lastgaelam = 0
+    advantages_reversed = []
+
+    assert values.numel() == rewards.numel(), f"{values.numel()}, {rewards.numel()}"
+    length = rewards.numel()
+
+    for t in reversed(range(0, length)):
+        nextvalues = values[t + 1] if t < length - 1 else 0.0
+        # Since old_rewards and old_values are masked with action_mask,
+        # i.e. they have 0's at pad tokens,
+        # delta will be 0 if current t is at a pad token,
+        # so will lastgaelam
+        delta = rewards[t] + gamma * nextvalues - values[t]
+        lastgaelam = delta + gamma * gae_lambda * lastgaelam
+        advantages_reversed.append(lastgaelam)
+
+    advantages = torch.stack(advantages_reversed[::-1], dim=0)
+    returns = advantages + values
+    return advantages.detach(), returns
+
+
+class CriticLoss(torch.nn.Module):
+    """Loss function for critic model."""
+
+    def __init__(self, cliprange_value: float = 0.5, loss_type: str = "per_seq"):
+        super().__init__()
+        self.cliprange_value = cliprange_value
+        self.loss_type = loss_type
+
+        assert self.loss_type in ["per_token", "per_seq"]
+
+    def critic_loss_fn(self, values, old_values, returns, loss_factor=None):
+        values_clipped = old_values + (values - old_values).clamp(
+            -self.cliprange_value, self.cliprange_value
+        )
+        vf_loss1 = (values_clipped - returns) ** 2
+        vf_loss2 = (values - returns) ** 2
+        if self.loss_type == "per_seq":
+            vf_loss = torch.max(vf_loss1, vf_loss2).mean(-1)
+        elif self.loss_type == "per_token":
+            assert loss_factor is not None
+            vf_loss = torch.sum(torch.max(vf_loss1, vf_loss2) * loss_factor)
+        return 0.5 * vf_loss
+
+    def forward(self, values: torch.Tensor, old_values, returns, loss_factor=None):
+        loss = self.critic_loss_fn(
+            values=values,
+            old_values=old_values,
+            returns=returns,
+            loss_factor=loss_factor,
+        )
+        return loss
+
+
+class PPOPolicyLoss(torch.nn.Module):
+    """Loss function for policy model."""
+
+    def __init__(self, cliprange: float = 0.2, loss_type: str = "per_seq"):
+        super().__init__()
+        self.cliprange = cliprange
+        self.loss_type = loss_type
+        assert self.loss_type in ["per_token", "per_seq"]
+
+    def forward(self, logprobs, old_logprobs, advantages, loss_factor=None):
+        ratio = (logprobs - old_logprobs).exp()
+        pg_loss1 = -ratio * advantages
+        pg_loss2 = -ratio.clamp(1 - self.cliprange, 1 + self.cliprange) * advantages
+        if self.loss_type == "per_seq":
+            pg_loss = torch.max(pg_loss1, pg_loss2).mean(dim=-1)
+        elif self.loss_type == "per_token":
+            assert loss_factor is not None
+            pg_loss = torch.sum(torch.max(pg_loss1, pg_loss2)) * loss_factor
+        return pg_loss

code/xtuner/_lite/algorithms/ppo/model.py

@@ -0,0 +1,49 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
+from transformers.utils.import_utils import (
+    is_flash_attn_2_available,
+    is_torch_sdpa_available,
+)
+
+from xtuner._lite.accelerate import LoadWoInit
+
+
+def build_actor_model(model_path, dtype=torch.float32, trust_remote_code=True):
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+    if is_flash_attn_2_available():
+        config.attn_implementation = "flash_attention_2"
+    elif is_torch_sdpa_available():
+        config.attn_implementation = "sdpa"
+
+    with LoadWoInit():
+        policy = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            attn_implementation="flash_attention_2",
+            torch_dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+
+    return policy
+
+
+def build_reward_model(model_path, dtype=torch.float32, trust_remote_code=True):
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+    if is_flash_attn_2_available():
+        config.attn_implementation = "flash_attention_2"
+    elif is_torch_sdpa_available():
+        config.attn_implementation = "sdpa"
+
+    config.use_cache = False
+    config.torch_dtype = dtype
+    with LoadWoInit():
+        reward = AutoModel.from_pretrained(
+            model_path,
+            attn_implementation="flash_attention_2",
+            torch_dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+
+    reward.model.use_cache = False
+
+    return reward

code/xtuner/_lite/algorithms/sft/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .dataset import SftCollator, SftTokenizeFunction
+
+__all__ = ["SftCollator", "SftTokenizeFunction"]

code/xtuner/_lite/algorithms/sft/dataset.py

@@ -0,0 +1,109 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from torch.nn.utils.rnn import pad_sequence
+
+from xtuner._lite import get_logger
+from xtuner._lite.datasets import OPENAI_CONVERT_MAP
+
+logger = get_logger()
+
+
+class SftTokenizeFunction:
+    def __init__(self, tokenizer, chat_template, raw_format="openai"):
+        self.tokenizer = tokenizer
+        self.chat_template = chat_template
+        self.raw_format = raw_format
+
+    def __call__(self, item):
+        formatter = OPENAI_CONVERT_MAP[self.raw_format]
+        msg = formatter(item)
+        tokenized = msg.tokenize(self.tokenizer, self.chat_template)
+        return tokenized
+
+
+class SftCollator:
+    def __init__(
+        self, pad_token_id=0, ignore_id=-100, pack_batch=False, max_length=None
+    ):
+        self.pack_batch = pack_batch
+        self.pad_token_id = pad_token_id
+        self.ignore_id = ignore_id
+        self.max_length = max_length
+
+    def __call__(self, instances):
+        _instances = []
+        for ins in instances:
+            if isinstance(ins, list):
+                _instances.extend(ins)
+            else:
+                _instances.append(ins)
+
+        instances = _instances
+
+        input_ids = []
+        labels = []
+        num_tokens = []
+
+        for data in instances:
+            _input_ids = data["input_ids"]
+            _labels = data["labels"]
+            _num_tokens = data["num_tokens"]
+
+            # TODO remove list
+            if isinstance(_num_tokens, list):
+                assert len(_num_tokens) == 1
+                _num_tokens = _num_tokens[0]
+
+            assert isinstance(_num_tokens, int)
+
+            if self.max_length:
+                _input_ids = _input_ids[: self.max_length]
+                _labels = _labels[: self.max_length]
+                _num_tokens = min(_num_tokens, self.max_length)
+
+            input_ids.append(torch.LongTensor(_input_ids))
+            labels.append(torch.LongTensor(_labels))
+            num_tokens.append(_num_tokens)
+
+        attention_mask = [torch.ones_like(ids) for ids in input_ids]
+        num_tokens = torch.IntTensor(num_tokens)
+
+        if len(instances) > 1 and self.pack_batch:
+            input_ids = torch.cat(input_ids, dim=0).unsqueeze(0)
+            labels = torch.cat(labels, dim=0).unsqueeze(0)
+            attention_mask = torch.cat(attention_mask, dim=0).unsqueeze(0)
+
+        elif len(instances) > 1 and not self.pack_batch:
+            input_ids = pad_sequence(
+                input_ids, batch_first=True, padding_value=self.pad_token_id
+            )
+            labels = pad_sequence(
+                labels, batch_first=True, padding_value=self.ignore_id
+            )
+            attention_mask = pad_sequence(
+                attention_mask, batch_first=True, padding_value=0
+            )
+        else:
+            input_ids = torch.stack(input_ids)
+            labels = torch.stack(labels)
+            attention_mask = torch.stack(attention_mask)
+
+        if input_ids.shape != labels.shape:
+            logger.error(f"[instances] {instances}")
+            logger.error(f"[num_tokens] {num_tokens}")
+            logger.error(f"[input_ids] {input_ids}")
+            logger.error(f"[labels] {labels}")
+            raise RuntimeError(
+                "The shape of input_ids and labels must be "
+                f"equal, but  found {input_ids.shape} and "
+                f"{labels.shape}."
+            )
+
+        data_dict = {
+            "input_ids": input_ids,
+            "labels": labels,
+            "num_tokens": num_tokens,
+            "attention_mask": attention_mask.bool(),
+        }
+
+        return data_dict

code/xtuner/_lite/chat/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .messages import ChatMessages
+from .templates import CHAT_TEMPLATE_MAP, ChatTemplate, HybridChatTemplate
+
+__all__ = ["ChatMessages", "CHAT_TEMPLATE_MAP", "ChatTemplate", "HybridChatTemplate"]

code/xtuner/_lite/chat/backends/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) OpenMMLab. All rights reserved.

code/xtuner/_lite/chat/messages/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .base import BaseMessages
+from .chat import ChatMessages
+
+__all__ = ["BaseMessages", "ChatMessages"]

code/xtuner/_lite/chat/messages/base.py

@@ -0,0 +1,32 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import abstractclassmethod, abstractmethod
+from typing import Dict
+
+from pydantic import BaseModel
+from transformers import PreTrainedTokenizer
+
+from ..templates import ChatTemplate
+
+
+class BaseMessages(BaseModel):
+    @abstractmethod
+    def add(self, role: str, content):
+        pass
+
+    @abstractmethod
+    def pop(self):
+        pass
+
+    @abstractmethod
+    def get_prompt(self, chat_template: ChatTemplate) -> str:
+        pass
+
+    @abstractmethod
+    def tokenize(
+        self, tokenizer: PreTrainedTokenizer, chat_template: ChatTemplate
+    ) -> Dict:
+        pass
+
+    @abstractclassmethod
+    def from_dict(cls, item: Dict) -> "BaseMessages":
+        pass

code/xtuner/_lite/chat/messages/chat.py

@@ -0,0 +1,202 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+from typing import Dict, List, Literal, Optional, Union
+
+from pydantic import BaseModel
+from transformers import PreTrainedTokenizer
+
+from xtuner._lite import get_logger
+from xtuner.utils import IGNORE_INDEX
+
+from ..templates import ChatTemplate, HybridChatTemplate
+from .base import BaseMessages
+
+logger = get_logger()
+
+
+class TextContentItem(BaseModel):
+    type: Literal["text"] = "text"
+    text: str
+
+    def apply_chat_template(self, chat_template: HybridChatTemplate) -> str:
+        return self.text
+
+
+class ImageContentItem(BaseModel):
+    type: Literal["image_url"] = "image_url"
+    image_url: str
+
+    def apply_chat_template(self, chat_template: HybridChatTemplate) -> str:
+        return chat_template.image_token
+
+
+MultModalContentType = Union[TextContentItem, ImageContentItem]
+ContentType = Union[str, List[MultModalContentType]]
+
+
+class ChatMsg(BaseModel):
+    role: Literal["assistant", "user", "system"]
+    content: ContentType
+    loss: Optional[bool] = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.loss is None:
+            if self.role == "system":
+                self.loss = False
+            elif self.role == "user":
+                self.loss = False
+            elif self.role == "assistant":
+                self.loss = True
+            else:
+                raise NotImplementedError
+
+    def collect_img_urls(self) -> List[str]:
+        img_urls = []
+        if isinstance(self.content, list):
+            for item in self.content:
+                if isinstance(item, ImageContentItem):
+                    img_urls.append(item.image_url)
+        return img_urls
+
+    def get_prompt(self, chat_template: ChatTemplate) -> str:
+        if isinstance(self.content, str):
+            text = self.content
+        elif isinstance(self.content, list):
+            text = ""
+            for i, item in enumerate(self.content):
+                if i == 0:
+                    text += item.apply_chat_template(chat_template)
+                else:
+                    text += "\n" + item.apply_chat_template(chat_template)
+        else:
+            raise NotImplementedError
+
+        if self.role == "system":
+            prompt = chat_template.decorate_system(text)
+        elif self.role == "user":
+            prompt = chat_template.decorate_user(text)
+        elif self.role == "assistant":
+            prompt = chat_template.decorate_assistant(text)
+        else:
+            raise NotImplementedError
+
+        return prompt
+
+    def tokenize(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        chat_template: ChatTemplate,
+    ):
+        decorated = self.get_prompt(chat_template)
+
+        token_ids = tokenizer.encode(decorated, add_special_tokens=False)
+
+        if self.loss:
+            label_ids = copy.deepcopy(token_ids)
+        else:
+            label_ids = [IGNORE_INDEX] * len(token_ids)
+
+        return {
+            "input_ids": token_ids,
+            "labels": label_ids,
+        }
+
+
+class ChatMessages(BaseMessages):
+    messages: List[ChatMsg]
+
+    def add(self, role, content, loss=False):
+        self.messages.append(ChatMsg(role=role, content=content, loss=loss))
+
+    def pop(self):
+        return self.messages.pop()
+
+    def get_prompt(self, chat_template: ChatTemplate) -> str:
+        prompt = ""
+
+        for msg in self.messages:
+            prompt += msg.get_prompt(chat_template)
+            if msg.role == "assistant":
+                prompt += chat_template.sep
+        return prompt
+
+    def tokenize(
+        self, tokenizer: PreTrainedTokenizer, chat_template: ChatTemplate
+    ) -> Dict:
+        input_ids = tokenizer.encode("", add_special_tokens=True)
+        labels = [IGNORE_INDEX for _ in input_ids]
+        image_urls = []
+
+        for msg in self.messages:
+            res = msg.tokenize(tokenizer, chat_template)
+            token_ids, label_ids = res["input_ids"], res["labels"]
+
+            input_ids.extend(token_ids)
+            labels.extend(label_ids)
+
+            image_urls.extend(msg.collect_img_urls())
+
+            if msg.role == "assistant":
+                sep = chat_template.sep
+                sep_tokens = tokenizer.encode(sep, add_special_tokens=False)
+                input_ids.extend(sep_tokens)
+                labels.extend([IGNORE_INDEX] * len(sep_tokens))
+
+        if len(input_ids) != len(labels):
+            logger.error(f"[messages] {self.messages}")
+            logger.error(f"[input_ids] {input_ids}")
+            logger.error(f"[labels] {labels}")
+            raise RuntimeError(
+                "The lengths of input_ids and labels must be "
+                f"equal, but  found {len(input_ids)} and "
+                f"{len(labels)}."
+            )
+
+        training_data = {
+            "input_ids": input_ids,
+            "labels": labels,
+            "num_tokens": len(input_ids),
+        }
+
+        if len(image_urls) > 0:
+            training_data["image_urls"] = image_urls
+
+        return training_data
+
+    @classmethod
+    def from_str(cls, prompt: str) -> "ChatMessages":
+        msg = ChatMsg(role="user", content=prompt)
+        return cls(messages=[msg])
+
+    @classmethod
+    def from_dict(cls, item: dict) -> "ChatMessages":
+        """
+        item
+        {
+            'messages':[
+                {'role':'user', 'content':'hello'},
+                {'role':'assistant', 'content':'hello!'},
+            ],
+        }
+        """
+        return cls(**item)
+
+
+if __name__ == "__main__":
+    data = {
+        "messages": [
+            {"role": "user", "content": "hello"},
+            {"role": "assistant", "content": "hello!"},
+        ]
+    }
+
+    messages = ChatMessages.from_dict(data)
+    chat_template = ChatTemplate(
+        system="<|im_start|>system\n{system}<|im_end|>\n",
+        user="<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n",
+        assistant="{assistant}<|im_end|>\n",
+        stop_words=["<|im_end|>"],
+    )
+
+    print(messages.get_prompt(chat_template))

code/xtuner/_lite/chat/templates/__init__.py

@@ -0,0 +1,30 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .chat import ChatTemplate
+from .hybrid import HybridChatTemplate
+
+CHAT_TEMPLATE_MAP = {
+    "internlm2": HybridChatTemplate(
+        system="<|im_start|>system\n{system}<|im_end|>\n",
+        user="<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n",
+        assistant="{assistant}<|im_end|>",
+        stop_words=["<|im_end|>"],
+    ),
+    "qwen2": HybridChatTemplate(
+        system="<|im_start|>system\n{system}<|im_end|>\n",
+        user="<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n",
+        assistant="{assistant}<|im_end|>",
+        stop_words=["<|im_end|>", "<|endoftext|>"],
+    ),
+    "llama3": HybridChatTemplate(
+        system=("<|start_header_id|>system<|end_header_id|>\n\n{system}" "<|eot_id|>"),
+        user=(
+            "<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|>"
+            "<|start_header_id|>assistant<|end_header_id|>\n\n"
+        ),
+        assistant="{assistant}<|eot_id|>",
+        sep="",
+        stop_words=["<|eot_id|>"],
+    ),
+}
+
+__all__ = ["ChatTemplate", "HybridChatTemplate"]

code/xtuner/_lite/chat/templates/chat.py

@@ -0,0 +1,59 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List
+
+from pydantic import BaseModel, field_validator
+
+
+class ChatTemplate(BaseModel):
+    """Define a Pydantic data model for a hybrid chat with attributes for
+    system, user and assistant chat as well as function and interpreter calls
+    and results."""
+
+    # Normal Chat
+    system: str  # System message format
+    user: str  # User message format
+    assistant: str  # Assistant message format
+    stop_words: List[str]  # List of stop words
+    sep: str = "\n"
+
+    def decorate_system(self, text: str) -> str:
+        """Decorate text with the `system` template."""
+        return self.system.format(system=text)
+
+    def decorate_assistant(self, text: str) -> str:
+        """Decorate text with the `assistant` template."""
+        return self.assistant.format(assistant=text)
+
+    def decorate_user(self, text: str) -> str:
+        """Decorate text with the `user` template."""
+        return self.user.format(user=text)
+
+    @field_validator("system")
+    def check_system(cls, v: str) -> str:
+        """Validate that `system` contains '{system}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{system}" not in v:
+            raise ValueError("system must contain the keyword '{system}'")
+        return v
+
+    @field_validator("user")
+    def check_user(cls, v: str) -> str:
+        """Validate that `user` contains '{user}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{user}" not in v:
+            raise ValueError("user must contain the keyword '{user}'")
+        return v
+
+    @field_validator("assistant")
+    def check_assistant(cls, v: str) -> str:
+        """Validate that `assistant` contains '{assistant}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{assistant}" not in v:
+            raise ValueError("assistant must contain the keyword '{assistant}'")
+        return v

code/xtuner/_lite/chat/templates/hybrid.py

@@ -0,0 +1,206 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict, List, Optional
+
+from pydantic import BaseModel, field_validator
+
+
+class HybridChatTemplate(BaseModel):
+    """Define a Pydantic data model for a hybrid chat with attributes for
+    system, user and assistant chat as well as function and interpreter calls
+    and results."""
+
+    # Normal Chat
+    system: str  # System message format
+    user: str  # User message format
+    assistant: str  # Assistant message format
+    stop_words: List[str]  # List of stop words
+    sep: str = "\n"
+
+    # Multimodal Chat
+    # Predefined token and index for images
+    image_token: str = "<image>"
+    image_token_index: int = -100
+
+    # Agent Chat
+
+    # Interpreter and function related strings
+    files: Optional[str] = None
+
+    functions: Optional[str] = None  # Function description format
+    function_call: Optional[str] = None  # Function call format
+    function_result: Optional[str] = None  # Function result format
+
+    code_interpreter: Optional[str] = None
+    code_interpreter_call: Optional[str] = None  # Interpreter call format
+    code_interpreter_result: Optional[str] = None  # Interpreter result format
+
+    function_token: Optional[str] = None
+    code_interpreter_token: Optional[str] = None
+    action_start_token: Optional[str] = None
+    action_end_token: Optional[str] = None
+
+    @property
+    def mm_token_maps(self) -> Dict[str, int]:
+        """Return a dictionary that maps multimodal tokens to corresponding
+        token indexes."""
+        return {self.image_token: self.image_token_index}
+
+    def decorate_system(self, text: str) -> str:
+        """Decorate text with the `system` template."""
+        return self.system.format(system=text)
+
+    def decorate_assistant(self, text: str) -> str:
+        """Decorate text with the `assistant` template."""
+        return self.assistant.format(assistant=text)
+
+    def decorate_user(self, text: str) -> str:
+        """Decorate text with the `user` template."""
+        return self.user.format(user=text)
+
+    def decorate_files(self, text: str) -> str:
+        """Decorate text with the `functions` template."""
+        return self.files.format(files=text)
+
+    def decorate_functions(self, text: str) -> str:
+        """Decorate text with the `functions` template."""
+        return self.functions.format(functions=text)
+
+    def decorate_function_call(self, text: str, func: str) -> str:
+        """Decorate text with the `function_call` template."""
+        return self.function_call.format(assistant=text, function_call=func)
+
+    def decorate_function_result(self, text: str) -> str:
+        """Decorate text with the `function_result` template."""
+        return self.function_result.format(function_result=text)
+
+    def decorate_code_interpreter(self, text: str) -> str:
+        """Decorate text with the `code_interpreter` template."""
+        return self.code_interpreter.format(code_interpreter=text)
+
+    def decorate_code_interpreter_call(self, text: str, func: str) -> str:
+        """Decorate text with the `code_interpreter_call` template."""
+        return self.code_interpreter_call.format(
+            assistant=text, code_interpreter_call=func
+        )
+
+    def decorate_code_interpreter_result(self, text: str) -> str:
+        """Decorate text with the `code_interpreter_result` template."""
+        return self.code_interpreter_result.format(code_interpreter_result=text)
+
+    @field_validator("system")
+    def check_system(cls, v: str) -> str:
+        """Validate that `system` contains '{system}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{system}" not in v:
+            raise ValueError("system must contain the keyword '{system}'")
+        return v
+
+    @field_validator("user")
+    def check_user(cls, v: str) -> str:
+        """Validate that `user` contains '{user}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{user}" not in v:
+            raise ValueError("user must contain the keyword '{user}'")
+        return v
+
+    @field_validator("assistant")
+    def check_assistant(cls, v: str) -> str:
+        """Validate that `assistant` contains '{assistant}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{assistant}" not in v:
+            raise ValueError("assistant must contain the keyword '{assistant}'")
+        return v
+
+    @field_validator("function_call")
+    def check_function_call(cls, v: str) -> str:
+        """Validate that `function_call` contains '{function_call}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{function_call}" not in v and "{assistant}" not in v:
+            raise ValueError(
+                "function_call must contain the keywords '{function_call}'"
+            )
+        if v is not None and "{assistant}" not in v:
+            raise ValueError(
+                "function_call must contain the keyword '{assistant}' and "
+                "'{function_call}'"
+            )
+        return v
+
+    @field_validator("function_result")
+    def check_function_result(cls, v: str) -> str:
+        """Validate that `function_result` contains '{function_result}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{function_result}" not in v:
+            raise ValueError(
+                "function_result must contain the keyword '{function_result}'"
+            )
+        return v
+
+    @field_validator("functions")
+    def check_functions(cls, v: str) -> str:
+        """Validate that `functions` contains '{functions}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{functions}" not in v:
+            raise ValueError("functions must contain the keyword '{functions}'")
+        return v
+
+    @field_validator("code_interpreter")
+    def check_code_interpreter(cls, v: str) -> str:
+        """Validate that `code_interpreter` contains '{code_interpreter}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{code_interpreter}" not in v:
+            raise ValueError(
+                "code_interpreter must contain the keyword " "'{code_interpreter}'"
+            )
+        return v
+
+    @field_validator("code_interpreter_call")
+    def check_code_interpreter_call(cls, v: str) -> str:
+        """Validate that `code_interpreter_call` contains
+        '{code_interpreter_call}'.
+
+        If not, raises a ValueError.
+        """
+        if (
+            v is not None
+            and "{code_interpreter_call}" not in v
+            and "{assistant}" not in v
+        ):
+            raise ValueError(
+                "code_interpreter_call must contain the keywords "
+                "'{assistant}' and '{code_interpreter_call}'"
+            )
+        if v is not None and "{assistant}" not in v:
+            raise ValueError(
+                "code_interpreter_call must contain the keywords "
+                "'{assistant}' and '{code_interpreter_call}'"
+            )
+        return v
+
+    @field_validator("code_interpreter_result")
+    def check_code_interpreter_result(cls, v: str) -> str:
+        """Validate that `code_interpreter_result` contains
+        '{code_interpreter_result}'.
+
+        If not, raises a ValueError.
+        """
+        if v is not None and "{code_interpreter_result}" not in v:
+            raise ValueError(
+                "code_interpreter_result must contain the keyword "
+                "'{code_interpreter_result}'"
+            )
+        return v

code/xtuner/_lite/datasets/__init__.py

@@ -0,0 +1,14 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .json import JsonDataset
+from .jsonl import JsonlDataset
+from .pack import SoftPackDataset
+from .utils import DATASET_CLS_MAP, OPENAI_CONVERT_MAP, load_datasets
+
+__all__ = [
+    "JsonDataset",
+    "JsonlDataset",
+    "SoftPackDataset",
+    "DATASET_CLS_MAP",
+    "OPENAI_CONVERT_MAP",
+    "load_datasets",
+]

code/xtuner/_lite/datasets/json.py

@@ -0,0 +1,177 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import hashlib
+import inspect
+import json
+import math
+import os
+import random
+from concurrent.futures import ProcessPoolExecutor
+
+import numpy as np
+import torch
+from mmengine import mkdir_or_exist
+from torch import distributed as dist
+from tqdm import tqdm
+
+from xtuner._lite import get_logger
+
+logger = get_logger()
+
+
+def calculate_json_sha256(file_path):
+    with open(file_path, "rb") as f:
+        data = f.read()
+
+    hash_object = hashlib.sha256(data)
+    hash_hex = hash_object.hexdigest()
+    return hash_hex
+
+
+def calculate_tokenize_fn_sha256(tokenize_fn):
+    """Calculate SHA-256 hash for an instance method's source code."""
+    # Get the source code of the method
+    fn_source = inspect.getsource(tokenize_fn.__call__)
+    return hashlib.sha256(fn_source.encode("utf-8")).hexdigest()
+
+
+class JsonDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, path, sample_ratio=1.0, tokenize_fn=None, cache_dir=None, max_length=None
+    ):
+        super().__init__()
+
+        self.tokenize_fn = tokenize_fn
+        self.path = path
+        self.tokenizer_workers = int(os.environ.get("XTUNER_TOKENIZE_WORKERS", 8))
+
+        if cache_dir:
+            if os.path.exists(cache_dir):
+                assert os.path.isdir(cache_dir)
+            else:
+                mkdir_or_exist(cache_dir)
+
+            file_hash = calculate_json_sha256(path)
+            file_cache_dir = os.path.join(cache_dir, file_hash)
+
+            if file_hash not in os.listdir(cache_dir):
+                mkdir_or_exist(file_cache_dir)
+
+            if self.tokenize_fn:
+                tok_hash = calculate_tokenize_fn_sha256(tokenize_fn)
+                tok_cache_dir = os.path.join(file_cache_dir, tok_hash)
+                if tok_hash not in os.listdir(file_cache_dir):
+                    mkdir_or_exist(tok_cache_dir)
+
+                if "num_tokens.npy" in os.listdir(tok_cache_dir):
+                    _cached_file = os.path.join(tok_cache_dir, "num_tokens.npy")
+                    num_tokens = np.load(_cached_file)
+                else:
+                    num_tokens = self.count_tokens(tok_cache_dir)
+            else:
+                num_tokens = None
+
+        else:
+            num_tokens = None
+
+        with open(self.path) as f:
+            dataset = json.load(f)
+
+        _sampled = [i for i in range(len(dataset))]
+
+        if max_length is not None:
+            assert isinstance(max_length, int)
+            _filtered = [
+                x for i, x in enumerate(_sampled) if num_tokens[i] < max_length
+            ]
+
+            if len(_filtered) < len(_sampled):
+                missed_num = len(_sampled) - len(_filtered)
+                logger.warning(
+                    f"{path} has {missed_num} prompt length>{max_length}, discard."
+                )
+
+            _sampled = _filtered
+
+        _target_num_samples = int(len(_sampled) * sample_ratio)
+        self.sampled = _sampled * int(sample_ratio)
+        self.sampled.extend(
+            random.sample(_sampled, _target_num_samples - len(self.sampled))
+        )
+
+        if num_tokens is not None:
+            num_tokens = num_tokens[self.sampled]
+
+        self.num_tokens = num_tokens
+        self.dataset = None
+
+    def count_tokens(self, cache_dir=None):
+        dataset = []
+
+        with open(self.path) as f:
+            dataset = json.load(f)
+
+        num_samples = len(dataset)
+
+        if dist.is_available():
+            world_size = dist.get_world_size()
+            rank = dist.get_rank()
+        else:
+            world_size = 1
+            rank = 0
+
+        num_per_rank = math.ceil(num_samples / world_size)
+
+        start = rank * num_per_rank
+        end = (rank + 1) * num_per_rank
+        dataset_shard = dataset[start:end]
+
+        desc = f"[Rank {rank}] {self.path}"
+        chunk_size = min(1024, max(1, len(dataset_shard) // self.tokenizer_workers))
+        with ProcessPoolExecutor(max_workers=self.tokenizer_workers) as executor:
+            tokenized = list(
+                tqdm(
+                    executor.map(self.tokenize_fn, dataset_shard, chunksize=chunk_size),
+                    desc=desc,
+                    total=len(dataset_shard),
+                )
+            )
+
+        _num_tokens = [data["num_tokens"] for data in tokenized]
+        _num_tokens = np.array(_num_tokens)
+
+        if dist.is_available():
+            num_tokens = [None] * world_size
+            dist.all_gather_object(num_tokens, _num_tokens)
+            num_tokens = np.concatenate(num_tokens, axis=0)
+        else:
+            num_tokens = _num_tokens
+
+        if rank == 0 and cache_dir:
+            save_path = os.path.join(cache_dir, "num_tokens.npy")
+            np.save(save_path, num_tokens)
+
+        return num_tokens
+
+    def __len__(self):
+        return len(self.sampled)
+
+    def __getitem__(self, item):
+        """Returns a dict containing packed data in the given item.
+
+        Args:
+            item: An index to retrieve packed data.
+
+        Returns:
+            A dict including packed input_ids, labels, and cumulative_len.
+        """
+        if self.dataset is None:
+            with open(self.path) as f:
+                self.dataset = json.load(f)
+
+        raw_data = self.dataset[self.sampled[item]]
+
+        if self.tokenize_fn:
+            tokenized_data = self.tokenize_fn(raw_data)
+            return tokenized_data
+        else:
+            return raw_data

code/xtuner/_lite/datasets/jsonl.py

@@ -0,0 +1,220 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import hashlib
+import json
+import math
+import multiprocessing
+import os
+import random
+from abc import ABC, abstractmethod
+from concurrent.futures import ProcessPoolExecutor
+from typing import Any, Callable, TypedDict
+
+import numpy as np
+import torch
+from mmengine import mkdir_or_exist
+from torch import distributed as dist
+from tqdm import tqdm
+
+from xtuner._lite import get_logger
+
+logger = get_logger()
+
+
+def calculate_jsonl_sha256(path):
+    with open(path, "rb") as f:
+        file_hash = hashlib.sha256()
+        file_hash.update(f.read())
+    return file_hash.hexdigest()
+
+
+CacheObj = TypedDict("CachedObj", {"num_tokens": int}, total=False)
+
+
+class CachableTokenizeFunction(ABC):
+    @abstractmethod
+    def __call__(self, item: Any) -> CacheObj:
+        raise NotImplementedError
+
+    @abstractmethod
+    def hash(self) -> str:
+        raise NotImplementedError
+
+
+class JsonlDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        path,
+        sample_ratio: float = 1.0,
+        tokenize_fn: Callable[[Any], CacheObj] | None = None,
+        cache_dir: str | None = None,
+        max_length: int | None = None,
+    ):
+        super().__init__()
+
+        self.tokenize_fn = tokenize_fn
+        self.path = path
+        self.tokenizer_workers = int(os.environ.get("XTUNER_TOKENIZE_WORKERS", 8))
+
+        if cache_dir and isinstance(tokenize_fn, CachableTokenizeFunction):
+            if os.path.exists(cache_dir):
+                assert os.path.isdir(cache_dir)
+            else:
+                mkdir_or_exist(cache_dir)
+
+            file_hash = calculate_jsonl_sha256(path)
+            file_cache_dir = os.path.join(cache_dir, file_hash)
+
+            if file_hash not in os.listdir(cache_dir):
+                mkdir_or_exist(file_cache_dir)
+
+            if "offsets.npy" in os.listdir(file_cache_dir):
+                _cached_file = os.path.join(file_cache_dir, "offsets.npy")
+                offsets = np.load(_cached_file)
+            else:
+                offsets = self.count_offsets(file_cache_dir)
+
+            if self.tokenize_fn:
+                tok_hash = tokenize_fn.hash()
+                tok_cache_dir = os.path.join(file_cache_dir, tok_hash)
+                if tok_hash not in os.listdir(file_cache_dir):
+                    mkdir_or_exist(tok_cache_dir)
+
+                if "num_tokens.npy" in os.listdir(tok_cache_dir):
+                    _cached_file = os.path.join(tok_cache_dir, "num_tokens.npy")
+                    num_tokens = np.load(_cached_file)
+                else:
+                    num_tokens = self.count_tokens(offsets, tok_cache_dir)
+            else:
+                num_tokens = None
+
+            offsets = offsets
+            num_tokens = num_tokens
+
+        else:
+            offsets = self.count_offsets()
+            num_tokens = None
+            if max_length is not None:
+                assert self.tokenize_fn
+                num_tokens = self.count_tokens(offsets)
+
+        _sampled = [i for i in range(len(offsets))]
+
+        if max_length is not None:
+            assert isinstance(max_length, int)
+            _filtered = [
+                x for i, x in enumerate(_sampled) if num_tokens[i] < max_length
+            ]
+
+            if len(_filtered) < len(_sampled):
+                missed_num = len(_sampled) - len(_filtered)
+                logger.warning(
+                    f"{path} has {missed_num} prompt length>{max_length}, discard."
+                )
+
+            _sampled = _filtered
+
+        _target_num_samples = int(len(_sampled) * sample_ratio)
+        self.sampled = _sampled * int(sample_ratio)
+        self.sampled.extend(
+            random.sample(_sampled, _target_num_samples - len(self.sampled))
+        )
+
+        if num_tokens is not None:
+            num_tokens = num_tokens[self.sampled]
+
+        self.num_tokens = num_tokens
+        self.offsets = offsets[self.sampled]
+
+    def count_offsets(self, cache_dir=None):
+        offsets = [0]
+        with open(self.path) as f:
+            lines = f.readlines()
+            for line in lines[:-1]:
+                offsets.append(offsets[-1] + len(line.encode()))
+
+        offsets = np.array(offsets)
+
+        if dist.get_rank() == 0 and cache_dir:
+            save_path = os.path.join(cache_dir, "offsets.npy")
+            np.save(save_path, offsets)
+
+        return offsets
+
+    def _tokenize_by_offset(self, offset):
+        with open(self.path) as f:
+            f.seek(offset)
+            data = json.loads(f.readline())
+        return self.tokenize_fn(data)
+
+    def count_tokens(self, offsets, cache_dir=None):
+        num_samples = len(offsets)
+
+        if dist.is_available():
+            world_size = dist.get_world_size()
+            rank = dist.get_rank()
+        else:
+            world_size = 1
+            rank = 0
+
+        num_per_rank = math.ceil(num_samples / world_size)
+
+        start = rank * num_per_rank
+        end = (rank + 1) * num_per_rank
+        offsets_shard = offsets[start:end]
+
+        desc = f"[Rank {rank}] {self.path}"
+        chunk_size = min(1024, max(1, len(offsets_shard) // self.tokenizer_workers))
+
+        mp_context = multiprocessing.get_context("fork")
+        with ProcessPoolExecutor(
+            max_workers=self.tokenizer_workers, mp_context=mp_context
+        ) as executor:
+            tokenized = list(
+                tqdm(
+                    executor.map(
+                        self._tokenize_by_offset, offsets_shard, chunksize=chunk_size
+                    ),
+                    desc=desc,
+                    total=len(offsets_shard),
+                )
+            )
+
+        _num_tokens = [data["num_tokens"] for data in tokenized]
+        _num_tokens = np.array(_num_tokens)
+
+        if dist.is_available():
+            num_tokens = [None] * world_size
+            dist.all_gather_object(num_tokens, _num_tokens)
+            num_tokens = np.concatenate(num_tokens, axis=0)
+        else:
+            num_tokens = _num_tokens
+
+        if rank == 0 and cache_dir:
+            save_path = os.path.join(cache_dir, "num_tokens.npy")
+            np.save(save_path, num_tokens)
+
+        return num_tokens
+
+    def __len__(self):
+        return len(self.offsets)
+
+    def __getitem__(self, item):
+        """Returns a dict containing packed data in the given item.
+
+        Args:
+            item: An index to retrieve packed data.
+
+        Returns:
+            A dict including packed input_ids, labels, and cumulative_len.
+        """
+        with open(self.path) as f:
+            f.seek(self.offsets[item])
+            line = f.readline()
+
+        raw_data = json.loads(line)
+
+        if self.tokenize_fn:
+            tokenized_data = self.tokenize_fn(raw_data)
+            return tokenized_data
+        else:
+            return raw_data

code/xtuner/_lite/datasets/pack.py

@@ -0,0 +1,257 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import bisect
+import itertools
+import random
+
+import numpy as np
+import torch
+from datasets import Dataset, concatenate_datasets
+from torch.utils.data import ConcatDataset
+
+
+class SoftPackDataset(torch.utils.data.Dataset):
+    def __init__(self, datasets, target=2048, blend=False, sort=False):
+        if blend:
+            num_tokens = [np.concatenate([dset.num_tokens for dset in datasets])]
+            datasets = [ConcatDataset(datasets)]
+        else:
+            num_tokens = [dset.num_tokens for dset in datasets]
+        self.datasets = datasets
+        self.target = target
+
+        pack_infos = []
+        for i, dataset in enumerate(self.datasets):
+            _infos = self.get_pack_infos(dataset, i, num_tokens[i])
+            pack_infos.append(_infos)
+        self.pack_infos = concatenate_datasets(pack_infos)
+
+    @property
+    def longest(self):
+        return self.pack_infos["longest"]
+
+    def get_pack_infos(self, dataset, dataset_id, num_tokens):
+        # _ori_lens = dataset['num_tokens']
+        inds = [i for i in range(len(dataset))]
+        random.shuffle(inds)
+
+        item_buffer = []
+        length_buffer = []
+        longest = 0
+
+        pack_infos = []
+        for shfl_i in inds:
+            if num_tokens[shfl_i] + sum(length_buffer) <= self.target:
+                item_buffer.append(shfl_i)
+                length_buffer.append(num_tokens[shfl_i])
+                longest = max(longest, num_tokens[shfl_i])
+            else:
+                if len(item_buffer) > 0:
+                    info = {
+                        "dataset_id": dataset_id,
+                        "indices": item_buffer,
+                        "longest": int(longest),
+                    }
+                    pack_infos.append(info)
+
+                item_buffer = [shfl_i]
+                length_buffer = [num_tokens[shfl_i]]
+                longest = num_tokens[shfl_i]
+
+        if len(item_buffer) > 0:
+            info = {
+                "dataset_id": dataset_id,
+                "indices": item_buffer,
+                "longest": int(longest),
+            }
+
+            pack_infos.append(info)
+
+        pack_infos = Dataset.from_list(pack_infos)
+
+        return pack_infos
+
+    def __len__(self):
+        return len(self.pack_infos)
+
+    def __getitem__(self, item):
+        indices = self.pack_infos[item]["indices"]
+        dataset_id = self.pack_infos[item]["dataset_id"]
+        return [self.datasets[dataset_id][i] for i in indices]
+
+
+class HardPackDataset(torch.utils.data.Dataset):
+    def __init__(self, datasets, target=2048, blend=True, sort=False):
+        if blend:
+            num_tokens = [np.concatenate([dset.num_tokens for dset in datasets])]
+            datasets = [ConcatDataset(datasets)]
+        else:
+            num_tokens = [dset.num_tokens for dset in datasets]
+        self.datasets = datasets
+        self.target = target
+
+        pack_infos = []
+        for i, dataset in enumerate(self.datasets):
+            _info = self.get_pack_info(dataset, i, num_tokens[i])
+            pack_infos.append(_info)
+
+        _ranges_left = []
+        _ranges_right = []
+        _num_packed_samples = []
+        _indices = []
+        _max_length_per_pack = []
+        _dataset_id = []
+        for info in pack_infos:
+            _ranges_left.extend(info["ranges_left"])
+            _ranges_right.extend(info["ranges_right"])
+            _num_packed_samples.append(info["num_packed_samples"])
+            _indices.extend(info["indices"])
+            _max_length_per_pack.extend(info["max_length_per_pack"])
+            _dataset_id.extend(info["dataset_id"])
+
+        self.pack_infos = {
+            "ranges_left": _ranges_left,
+            "ranges_right": _ranges_right,
+            "num_packed_samples": _num_packed_samples,
+            "indices": _indices,
+            "max_length_per_pack": _max_length_per_pack,
+            "dataset_id": _dataset_id,
+        }
+
+    @classmethod
+    def _cal_max_length(cls, begin, end, shfl_item_rngs_left, shfl_item_rngs_right):
+        left = bisect.bisect(shfl_item_rngs_right, begin)
+        right = bisect.bisect(shfl_item_rngs_left, end)
+        max_length = 0
+        for i in range(left, right):
+            item_begin = shfl_item_rngs_left[i]
+            item_end = shfl_item_rngs_right[i]
+            inner_l = max(begin, item_begin) - item_begin
+            inner_r = min(end, item_end) - item_begin
+            trunc_size = inner_r - inner_l
+            max_length = max(max_length, trunc_size)
+        return max_length
+
+    def get_pack_info(self, dataset, dataset_id, num_tokens):
+        # The number of data items after packing
+        num_packed_samples = int(num_tokens.sum() / self.target)
+
+        # Shuffle the order of the original dataset
+        # The packing will proceed according to the order after shuffle.
+        # Assume the following conditions hold:
+        #   (1) shfl_inds = [3, 1, 2, 0]
+        #   (2) self._ori_lens[3] + self._ori_lens[1] = max_length
+        #   (3) self._ori_lens[2] + self._ori_lens[0] = max_length
+        # Ultimately, dataset[3] and dataset[1] will be combined into a new
+        # data, and dataset[2] and dataset[0] will be combined into a new data.
+        inds = [i for i in range(len(dataset))]
+        # if seed is not None:
+        #     random.seed(seed)
+        random.shuffle(inds)
+        shfl_inds = inds
+
+        # shuffled cumulative lengths
+        shfl_lens = [num_tokens[i] for i in shfl_inds]
+        shfl_acc_lens = list(itertools.accumulate(shfl_lens))
+
+        shfl_item_rngs_left = [0] + shfl_acc_lens[:-1]
+        shfl_item_rngs_right = shfl_acc_lens
+
+        max_length_per_pack = []
+        belong_dataset_ids = []
+        for i in range(num_packed_samples):
+            begin = i * self.target
+            end = (i + 1) * self.target
+            max_length_per_pack.append(
+                self._cal_max_length(
+                    begin, end, shfl_item_rngs_left, shfl_item_rngs_right
+                )
+            )
+            belong_dataset_ids.append(dataset_id)
+
+        pack_infos = {
+            "ranges_left": shfl_item_rngs_left,
+            "ranges_right": shfl_item_rngs_right,
+            "num_packed_samples": num_packed_samples,
+            "indices": shfl_inds,
+            "dataset_id": belong_dataset_ids,
+            "max_length_per_pack": max_length_per_pack,
+        }
+
+        # pack_infos = Dataset.from_list(pack_infos)
+
+        return pack_infos
+
+    def _pack_ids_and_labels_in_range(self, begin: int, end: int):
+        """Packs ids and labels in a given range using bisection method.
+
+        Args:
+            begin: Index indicating the beginning of the range.
+            end: Index indicating the end of the range.
+
+        Returns:
+            A tuple containing packed ids, labels, and cumulative lengths.
+        """
+
+        # Use binary search to find dataset positions that fall within begin
+        # and end range
+        left = bisect.bisect(self.pack_infos["ranges_left"], begin)
+        right = bisect.bisect(self.pack_infos["ranges_right"], end)
+
+        trunc_input_ids = []
+        trunc_labels = []
+        trunc_sizes = []
+
+        for i in range(left, right):
+            # Determine the real range we will cut in current original item
+            item_begin = self.pack_infos["ranges_left"][i]
+            item_end = self.pack_infos["ranges_right"][i]
+
+            # Calculate exact positions within current dataset item
+            inner_l = max(begin, item_begin) - item_begin
+            inner_r = min(end, item_end) - item_begin
+
+            # Get original data and labels
+            ori_idx = self.pack_infos["indices"][i]
+            ori_dataset_id = self.pack_infos["dataset_id"][i]
+            ori_input_ids = self.datasets[ori_dataset_id][ori_idx]["input_ids"]
+            ori_labels = self.datasets[ori_dataset_id][ori_idx]["labels"]
+
+            # Add original data and labels from calculated positions
+            # to trunc_ids and trunc_labels
+            trunc_input_ids.extend(ori_input_ids[inner_l:inner_r])
+            trunc_labels.extend(ori_labels[inner_l:inner_r])
+            trunc_sizes.append(inner_r - inner_l)
+
+        # return populated lists of truncated ids, labels and their cumulative
+        # lengths
+        return trunc_input_ids, trunc_labels, trunc_sizes
+
+    def __len__(self):
+        return len(self.pack_infos["indices"])
+
+    def __getitem__(self, item):
+        """Returns a dict containing packed data in the given item.
+
+        Args:
+            item: An index to retrieve packed data.
+
+        Returns:
+            A dict including packed input_ids, labels, and cumulative_len.
+        """
+        # The cumulative length from the start position of this data
+        begin = item * self.target
+        # The cumulative length from the end position of this data
+        end = (item + 1) * self.target
+
+        # Extract data within the range from the shuffled original dataset.
+        _res = self._pack_ids_and_labels_in_range(begin, end)
+        packed_input_ids, packed_labels, num_tokens = _res
+        assert self.target == len(packed_input_ids) == len(packed_labels)
+
+        packed = {
+            "input_ids": packed_input_ids,
+            "labels": packed_labels,
+            "num_tokens": num_tokens,
+        }
+
+        return packed

code/xtuner/_lite/datasets/streaming.py

@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+
+class Streaming:
+    def __init__(self, file, max_epoch=1):
+        self.file = file
+        self.offset = 0
+        self.epoch = 1
+        self.max_epoch = max_epoch
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        with open(self.file) as f:
+            f.seek(self.offset)
+            line = f.readline()
+
+            if not line and self.epoch < self.max_epoch:
+                self.offset = 0
+                self.epoch += 1
+                return next(self)
+
+            elif not line and self.epoch == self.max_epoch:
+                raise StopIteration
+
+            self.offset = f.tell()
+        return line

code/xtuner/_lite/datasets/utils/__init__.py

@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .convert import OPENAI_CONVERT_MAP
+from .load import DATASET_CLS_MAP, load_datasets
+from .utils import apply_exif_orientation, move_data_to_device
+
+__all__ = [
+    "OPENAI_CONVERT_MAP",
+    "DATASET_CLS_MAP",
+    "load_datasets",
+    "apply_exif_orientation",
+    "move_data_to_device",
+]

code/xtuner/_lite/datasets/utils/convert.py

@@ -0,0 +1,195 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import re
+
+from xtuner._lite.chat import ChatMessages
+
+
+class XTunerFormat2Openai:
+    @classmethod
+    def source_format(cls):
+        data = {
+            "conversation": [
+                {"system": "SYSTEM", "input": "INPUT", "output": "OUTPUT"},
+                {"input": "INPUT", "output": "OUTPUT"},
+            ]
+        }
+        return data
+
+    @classmethod
+    def target_format(cls):
+        data = {
+            "messages": [
+                {"role": "system", "content": "SYSTEM"},
+                {"role": "user", "content": "INPUT"},
+                {"role": "assistant", "content": "OUTPUT"},
+                {"role": "user", "content": "INPUT"},
+                {"role": "assistant", "content": "OUTPUT"},
+            ]
+        }
+        return data
+
+    @staticmethod
+    def convert(data):
+        ROLE_MAPPING = {"system": "system", "input": "user", "output": "assistant"}
+        messages = []
+        for single_turn_conversation in data["conversation"]:
+            for role, content in single_turn_conversation.items():
+                messages.append({"role": ROLE_MAPPING[role], "content": content})
+        return ChatMessages.from_dict({"messages": messages})
+
+
+class Alpaca2Openai:
+    @classmethod
+    def source_format(cls):
+        data = {
+            "instruction": "INSTRUCTION",
+            "input": "INPUT",
+            "output": "OUTPUT",
+        }
+        return data
+
+    @classmethod
+    def target_format(cls):
+        data = {
+            "messages": [
+                {"role": "user", "content": "INSTRUCTION\nINPUT"},
+                {"role": "assistant", "content": "OUTPUT"},
+            ]
+        }
+        return data
+
+    @staticmethod
+    def convert(data):
+        if data.get("output") == "<nooutput>":
+            return ChatMessages.from_dict({"messages": []})
+        else:
+            return ChatMessages.from_dict(
+                {
+                    "messages": [
+                        {
+                            "role": "user",
+                            "content": f"{data['instruction']}\n{data['input']}",
+                        },
+                        {"role": "assistant", "content": f"{data['output']}"},
+                    ]
+                }
+            )
+
+
+def llava_to_openai(data):
+    image_token = "<image>"
+    conversations = data["conversations"]
+    messages = []
+
+    if "image" in data:
+        image_urls = data["image"]
+        if isinstance(image_urls, str):
+            image_urls = [image_urls]
+    else:
+        image_urls = None
+
+    while conversations and conversations[0]["from"] == "gpt":
+        # Skip the first one if it is from gpt
+        conversations = conversations[1:]
+
+    image_id = 0
+    for convs in conversations:
+        if convs["from"] == "human":
+            pattern = f"({image_token})"
+            chunks = re.split(pattern, convs["value"])
+
+            text_content = []
+            img_content = []
+
+            for chunk in chunks:
+                if chunk == image_token:
+                    url = image_urls[image_id]
+                    if not isinstance(url, str):
+                        raise TypeError(data)
+                    # assert , image_url
+                    item = dict(type="image_url", image_url=url)
+                    img_content.append(item)
+                    image_id += 1
+                elif len(chunk.strip()):
+                    item = dict(type="text", text=chunk.strip())
+                    text_content.append(item)
+
+            msg = {"role": "user", "content": img_content + text_content}
+            messages.append(msg)
+
+        elif convs["from"] == "gpt":
+            msg = {"role": "assistant", "content": convs["value"]}
+            messages.append(msg)
+        else:
+            raise NotImplementedError
+
+    return ChatMessages.from_dict({"messages": messages})
+
+
+def llava_to_openai_interleave(data):
+    image_token = "<image>"
+    conversations = data["conversations"]
+    messages = []
+
+    if "image" in data:
+        image_urls = data["image"]
+        if isinstance(image_urls, str):
+            image_urls = [image_urls]
+    else:
+        image_urls = None
+
+    while conversations and conversations[0]["from"] == "gpt":
+        # Skip the first one if it is from gpt
+        conversations = conversations[1:]
+
+    image_id = 0
+    for convs in conversations:
+        if convs["from"] == "human":
+            pattern = f"({image_token})"
+            chunks = re.split(pattern, convs["value"])
+
+            content = []
+
+            for chunk in chunks:
+                if chunk == image_token:
+                    url = image_urls[image_id]
+                    if not isinstance(url, str):
+                        raise TypeError(data)
+                    # assert , image_url
+                    item = dict(type="image_url", image_url=url)
+                    content.append(item)
+                    image_id += 1
+                elif len(chunk.strip()):
+                    item = dict(type="text", text=chunk.strip())
+                    content.append(item)
+
+            msg = {"role": "user", "content": content}
+            messages.append(msg)
+
+        elif convs["from"] == "gpt":
+            msg = {"role": "assistant", "content": convs["value"]}
+            messages.append(msg)
+        else:
+            raise NotImplementedError
+
+    return ChatMessages.from_dict({"messages": messages})
+
+
+def official_openai(data):
+    if "messages" in data:
+        return ChatMessages.from_dict(data)
+    elif "message_data" in data:
+        return ChatMessages.from_dict({"messages": data["message_data"]})
+    elif "dialogs" in data:
+        return ChatMessages.from_dict({"messages": data["dialogs"]})
+    else:
+        return ChatMessages.from_dict({"messages": data})
+
+
+OPENAI_CONVERT_MAP = {
+    "llava": llava_to_openai,
+    "llava_interleave": llava_to_openai_interleave,
+    "alpaca": Alpaca2Openai.convert,
+    "xtuner": XTunerFormat2Openai.convert,
+    "openai": official_openai,
+}

code/xtuner/_lite/datasets/utils/load.py

@@ -0,0 +1,286 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+import math
+import os
+import random
+import re
+
+from torch import distributed as dist
+from tqdm import tqdm
+
+from xtuner._lite import get_logger
+
+from ..json import JsonDataset
+from ..jsonl import JsonlDataset
+
+logger = get_logger()
+
+DATASET_CLS_MAP = {".jsonl": JsonlDataset, ".json": JsonDataset}
+
+
+def load_hf_dataset(path, split="train", sample_ratio=1.0, cache_dir=None, map_fn=None):
+    from datasets import load_dataset
+
+    dataset = load_dataset(path)[split]
+
+    if map_fn:
+        dataset = dataset.map(map_fn, num_proc=8)
+
+    if sample_ratio != 1:
+        ori_samples = len(dataset)
+        target_samples = int(sample_ratio * ori_samples)
+        indices = random.choices([i for i in range(ori_samples)], k=target_samples)
+        dataset = dataset.select(indices)
+
+    dataset = dataset.to_list()
+
+    # if init_fn:
+    #     dataset = init_fn(dataset)
+
+    # if cache_dir and isinstance(dataset, CacheDataset):
+    #     dataset.cache(cache_dir)
+
+    return dataset
+
+
+def load_from_cache(cache_dir, init_fn):
+    if dist.is_available():
+        world_size = dist.get_world_size()
+        rank = dist.get_rank()
+    else:
+        world_size = 1
+        rank = 0
+
+    sub_cache_dirs = []
+    for _path in tqdm(os.listdir(cache_dir)):
+        path = os.path.join(cache_dir, _path)
+        if os.path.isdir(path):
+            sub_cache_dirs.append(path)
+
+    num_dsets = len(sub_cache_dirs)
+    avg_num = math.ceil(num_dsets / world_size)
+    start = rank * avg_num
+    end = min((rank + 1) * avg_num, num_dsets)
+    desc = f"[Rank {rank}] Loading Cached Dataset"
+
+    rank_datasets = []
+    for ind in tqdm(range(start, end), desc=desc):
+        dset = init_fn(sub_cache_dirs[ind])
+        rank_datasets.append(dset)
+
+    if dist.is_available() and world_size > 1:
+        dist.barrier()
+        buffers = [None] * world_size
+        dist.all_gather_object(buffers, rank_datasets)
+        world_datasets = []
+        for dsets_per_rank in buffers:
+            world_datasets.extend(dsets_per_rank)
+
+        assert len(world_datasets) == num_dsets
+    else:
+        world_datasets = rank_datasets
+
+    return world_datasets
+
+
+def load_local_datasets(
+    paths,
+    file_types,
+    file_pattern=None,
+    cache_dir=None,
+    sample_ratios=1.0,
+    map_fns=None,
+    max_length=None,
+):
+    if isinstance(paths, str):
+        paths = [paths]
+
+    if isinstance(sample_ratios, (tuple, list)):
+        if len(sample_ratios) == 1:
+            sample_ratios = list(sample_ratios) * len(paths)
+
+        if len(sample_ratios) != len(paths):
+            raise RuntimeError(
+                f"There are {len(paths)} paths, but only "
+                f"{len(sample_ratios)} sample ratios were set."
+            )
+
+    if map_fns is None:
+        map_fns = [None] * len(paths)
+
+    if isinstance(map_fns, (tuple, list)):
+        if len(map_fns) == 1:
+            map_fns = list(map_fns) * len(paths)
+
+        if len(map_fns) != len(paths):
+            raise RuntimeError(
+                f"There are {len(paths)} paths, but only"
+                f"{len(map_fns)} map fns were set."
+            )
+
+    files = []
+    file_sample_ratios = []
+    file_map_fns = []
+
+    for pid, path in enumerate(paths):
+        if os.path.isdir(path):
+            dir_files = []
+            for root, dirs, _files in os.walk(path, followlinks=True):
+                dirs.sort()
+                for relative_path in sorted(_files):
+                    suffix = os.path.splitext(relative_path)[-1]
+                    absolute_path = os.path.join(root, relative_path)
+                    if file_pattern is not None:
+                        if bool(re.match(file_pattern, absolute_path)):
+                            dir_files.append(absolute_path)
+                    elif suffix in file_types:
+                        dir_files.append(absolute_path)
+
+            _num_dir_files = len(dir_files)
+            if _num_dir_files == 0:
+                raise RuntimeError(
+                    f"There are no files with the suffix {file_types}" f"in `{path}`."
+                )
+
+            logger.info(f"Found {len(dir_files)} files in {path}")
+            files.extend(dir_files)
+            file_sample_ratios.extend([sample_ratios[pid]] * _num_dir_files)
+            file_map_fns.extend([map_fns[pid]] * _num_dir_files)
+
+        elif os.path.isfile(path):
+            files.append(path)
+            file_sample_ratios.append(sample_ratios[pid])
+            file_map_fns.append(map_fns[pid])
+
+        else:
+            raise RuntimeError(f"`{path}` not found.")
+
+    num_files = len(files)
+
+    datasets = []
+    for i in range(num_files):
+        _path = files[i]
+        _ratio = file_sample_ratios[i]
+        _map_fn = file_map_fns[i]
+        _suffix = os.path.splitext(_path)[-1]
+
+        dataset_cls = DATASET_CLS_MAP[_suffix]
+        _dataset = dataset_cls(_path, _ratio, _map_fn, cache_dir, max_length)
+        datasets.append(_dataset)
+
+    return datasets
+
+
+def load_datasets(
+    paths,
+    sources="local",
+    sample_ratios=1.0,
+    file_types=DATASET_CLS_MAP.keys(),
+    file_pattern=None,
+    cache_dir=None,
+    map_fns=None,
+    max_length=None,
+):
+    if isinstance(paths, str):
+        paths = [paths]
+
+    num_paths = len(paths)
+
+    if isinstance(sample_ratios, (float, int)):
+        sample_ratios = [sample_ratios] * num_paths
+
+    if isinstance(sample_ratios, (tuple, list)):
+        if len(sample_ratios) == 1:
+            sample_ratios = list(sample_ratios) * num_paths
+
+        if len(sample_ratios) != num_paths:
+            raise RuntimeError(
+                f"There are {num_paths} paths, but only "
+                f"{len(sample_ratios)} sample ratios were set."
+            )
+
+    if isinstance(sources, str):
+        sources = [sources]
+
+    if isinstance(sources, (tuple, list)):
+        if len(sources) == 1:
+            sources = list(sources) * num_paths
+
+        if len(sources) != num_paths:
+            raise RuntimeError(
+                f"There are {num_paths} paths, but only "
+                f"{len(sources)} sources were set."
+            )
+
+    if not isinstance(map_fns, (tuple, list)):
+        map_fns = [map_fns] * num_paths
+
+    if isinstance(map_fns, (tuple, list)):
+        if len(map_fns) == 1:
+            map_fns = list(map_fns) * num_paths
+
+        if len(map_fns) != num_paths:
+            raise RuntimeError(
+                f"There are {num_paths} paths, but only"
+                f"{len(map_fns)} map fns were set."
+            )
+
+    local_inds = [i for i, src in enumerate(sources) if src == "local"]
+    local_paths = [paths[ind] for ind in local_inds]
+    local_map_fns = [map_fns[ind] for ind in local_inds]
+    local_sample_ratios = [sample_ratios[ind] for ind in local_inds]
+
+    hf_inds = [i for i, src in enumerate(sources) if src == "huggingface"]
+    hf_paths = [paths[ind] for ind in hf_inds]
+    hf_map_fns = [map_fns[ind] for ind in hf_inds]
+    hf_sample_ratios = [sample_ratios[ind] for ind in hf_inds]
+
+    datasets = []
+    if len(local_inds):
+        local_datasets = load_local_datasets(
+            local_paths,
+            file_types,
+            file_pattern,
+            cache_dir,
+            local_sample_ratios,
+            local_map_fns,
+            max_length,
+        )
+        datasets.extend(local_datasets)
+
+    if len(hf_inds):
+        cached_infos = {}
+        for i in range(len(hf_inds)):
+            if cache_dir:
+                digits = len(str(abs(len(hf_inds))))
+                cache_id = f"cache-hf-{i+1:0{digits}}-of-" f"{len(hf_inds):0{digits}}"
+                sub_cache_dir = os.path.join(cache_dir, cache_id)
+            else:
+                sub_cache_dir = None
+            dset = load_hf_dataset(
+                hf_paths[i],
+                sample_ratio=hf_sample_ratios[i],
+                map_fn=hf_map_fns[i],
+                cache_dir=sub_cache_dir,
+                max_length=max_length,
+            )
+            datasets.append(dset)
+            breakpoint()
+            if cache_dir:
+                infos = {
+                    "path": hf_paths[i],
+                    "num_samples": dset.num_samples,
+                    "num_tokens": dset.total_tokens,
+                }
+                cached_infos[cache_id] = infos
+
+        if cache_dir:
+            _path = os.path.join(cache_dir, "hf_infos.json")
+            with open(_path, "w") as f:
+                json.dump(cached_infos, f)
+
+    return datasets
+
+
+def load_ms_dataset():
+    pass

code/xtuner/_lite/datasets/utils/utils.py

@@ -0,0 +1,66 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from collections.abc import Mapping
+
+import torch
+from PIL import Image
+
+_EXIF_ORIENT = 274  # exif 'Orientation' tag
+
+
+def apply_exif_orientation(image):
+    """Applies the exif orientation correctly.
+
+    This code exists per the bug:
+      https://github.com/python-pillow/Pillow/issues/3973
+    with the function `ImageOps.exif_transpose`. The Pillow source raises errors with
+    various methods, especially `tobytes`
+
+    Function based on:
+      https://github.com/wkentaro/labelme/blob/v4.5.4/labelme/utils/image.py#L59
+      https://github.com/python-pillow/Pillow/blob/7.1.2/src/PIL/ImageOps.py#L527
+
+    Args:
+        image (PIL.Image): a PIL image
+
+    Returns:
+        (PIL.Image): the PIL image with exif orientation applied, if applicable
+    """
+    if not hasattr(image, "getexif"):
+        return image
+
+    try:
+        exif = image.getexif()
+    except Exception:  # https://github.com/facebookresearch/detectron2/issues/1885
+        exif = None
+
+    if exif is None:
+        return image
+
+    orientation = exif.get(_EXIF_ORIENT)
+
+    method = {
+        2: Image.FLIP_LEFT_RIGHT,
+        3: Image.ROTATE_180,
+        4: Image.FLIP_TOP_BOTTOM,
+        5: Image.TRANSPOSE,
+        6: Image.ROTATE_270,
+        7: Image.TRANSVERSE,
+        8: Image.ROTATE_90,
+    }.get(orientation)
+
+    if method is not None:
+        return image.transpose(method)
+    return image
+
+
+def move_data_to_device(data, device="cuda"):
+    """Prepares one `data` before feeding it to the model, be it a tensor or a
+    nested list/dictionary of tensors."""
+    if isinstance(data, Mapping):
+        return type(data)({k: move_data_to_device(v) for k, v in data.items()})
+    elif isinstance(data, (tuple, list)):
+        return type(data)(move_data_to_device(v) for v in data)
+    elif isinstance(data, torch.Tensor):
+        kwargs = {"device": device}
+        return data.to(non_blocking=True, **kwargs)
+    return data

code/xtuner/_lite/device.py

@@ -0,0 +1,42 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def get_device():
+    device = None
+    if torch.cuda.is_available():
+        device = "cuda"
+    else:
+        try:
+            import torch_npu  # noqa: F401
+
+            device = "npu"
+        except ImportError:
+            pass
+    try:
+        import torch_mlu  # noqa: F401
+
+        device = "mlu"
+    except ImportError:
+        pass
+
+    if device is None:
+        raise NotImplementedError(
+            "Supports only CUDA or NPU. If your device is CUDA or NPU, "
+            "please make sure that your environmental settings are "
+            "configured correctly."
+        )
+
+    return device
+
+
+def get_torch_device_module():
+    device = get_device()
+    if device == "cuda":
+        return torch.cuda
+    elif device == "npu":
+        return torch.npu
+    elif device == "mlu":
+        return torch.mlu
+    else:
+        raise NotImplementedError

code/xtuner/_lite/modelings/__init__.py

@@ -0,0 +1,17 @@
+from .internlm2 import InternLM2Config, InternLM2ForCausalLM
+from .internlm3 import InternLM3Config, InternLM3ForCausalLM, InternLM3Tokenizer
+from .llava.modeling_llava import LlavaForConditionalGeneration
+from .llava.configuration_llava import EnhancedLlavaConfig
+from .llava.processing_llava import LlavaProcessor
+
+def register_remote_code():
+    from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+    AutoConfig.register('internlm2', InternLM2Config, exist_ok=True)
+    AutoModelForCausalLM.register(
+        InternLM2Config, InternLM2ForCausalLM, exist_ok=True)
+    
+    AutoConfig.register('internlm3', InternLM3Config, exist_ok=True)
+    AutoModelForCausalLM.register(
+        InternLM3Config, InternLM3ForCausalLM, exist_ok=True)
+    AutoTokenizer.register(
+        InternLM3Config, InternLM3Tokenizer, exist_ok=True)

code/xtuner/_lite/modelings/internlm2/__init__.py

@@ -0,0 +1,2 @@
+from .configuration_internlm2 import InternLM2Config
+from .modeling_internlm2 import InternLM2ForCausalLM

code/xtuner/_lite/modelings/internlm2/configuration_internlm2.py

@@ -0,0 +1,175 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" InternLM2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
+            to understand more about it. This value is necessary to ensure exact reproducibility
+            of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+    """
+    _auto_class = 'AutoConfig'
+    model_type = 'internlm2'
+    keys_to_ignore_at_inference = ['past_key_values']
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act='silu',
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.bias = bias
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = 'eager'
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling,
+                          dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
+                f'got {self.rope_scaling}')
+        rope_scaling_type = self.rope_scaling.get('type', None)
+        rope_scaling_factor = self.rope_scaling.get('factor', None)
+        if rope_scaling_type is None or rope_scaling_type not in [
+                'linear', 'dynamic'
+        ]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (rope_scaling_factor is None
+                or not isinstance(rope_scaling_factor,
+                                  (float, int)) or rope_scaling_factor < 1.0):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a number >= 1, got {rope_scaling_factor} "
+                f'of type {type(rope_scaling_factor)}')

code/xtuner/_lite/modelings/internlm2/modeling_internlm2.py

@@ -0,0 +1,1899 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch InternLM2.5 model."""
+import math
+import queue
+import threading
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast,
+                                           QuestionAnsweringModelOutput,
+                                           SequenceClassifierOutputWithPast,
+                                           TokenClassifierOutput)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import (add_start_docstrings,
+                                add_start_docstrings_to_model_forward,
+                                is_flash_attn_greater_or_equal_2_10, logging,
+                                replace_return_docstrings)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except Exception:
+    BaseStreamer = None
+
+from .configuration_internlm2 import InternLM2Config
+
+try:
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import (index_first_axis, pad_input,
+                                         unpad_input)
+except:
+    pass
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = 'InternLM2Config'
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(
+        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))  # pylint: disable=E1102
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class InternLM2RMSNorm(nn.Module):
+    """InternLM2RMSNorm is equivalent to T5LayerNorm."""
+
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance +
+                                                    self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(InternLM2RMSNorm)
+
+
+class InternLM2RotaryEmbedding(nn.Module):
+    """Rotary Position Embedding for the InternLM2 model. Credits to the Reddit user /u/lucidrains."""
+
+    def __init__(self,
+                 dim,
+                 max_position_embeddings=2048,
+                 base=10000,
+                 device=None,
+                 scaling_factor=1.0):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (
+            self.base
+            **(torch.arange(0, self.dim, 2,
+                            dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer('inv_freq', inv_freq, persistent=False)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(
+            position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(
+            device_type, str) and device_type != 'mps' else 'cpu'
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float()
+                     @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: a scaling factor is aplied to the position ids
+        position_ids = position_ids.float() / self.scaling_factor
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
+    Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_position_embeddings:
+            base = self.base * ((self.scaling_factor * seq_len /
+                                 self.max_position_embeddings) -
+                                (self.scaling_factor - 1))**(
+                                    self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base
+                **(torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(
+                    x.device) / self.dim))
+            self.register_buffer(
+                'inv_freq', inv_freq,
+                persistent=False)  # TODO joao: this may break with compilation
+
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., :x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):  # pylint: disable=unused-argument
+    """Applies Rotary Position Embedding to the query and key tensors.
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class InternLM2MLP(nn.Module):
+    """MLP for InternLM2 model."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.w1 = nn.Linear(
+            self.hidden_size, self.intermediate_size, bias=False)
+        self.w3 = nn.Linear(
+            self.hidden_size, self.intermediate_size, bias=False)
+        self.w2 = nn.Linear(
+            self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :,
+                                  None, :, :].expand(batch,
+                                                     num_key_value_heads,
+                                                     n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen,
+                                 head_dim)
+
+
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self,
+                 config: InternLM2Config,
+                 layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f'Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will '
+                'lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` '
+                'when creating this class.')
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
+                f' and `num_heads`: {self.num_heads}).')
+
+        self.wqkv = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+        )
+        self.wo = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling['type']
+            scaling_factor = self.config.rope_scaling['factor']
+            if scaling_type == 'linear':
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == 'dynamic':
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f'Unknown RoPE scaling type {scaling_type}')
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.config.pretraining_tp > 1:
+            # split qkv_states by tp size
+            key_value_slicing = (self.num_key_value_heads *
+                                 self.head_dim) // self.config.pretraining_tp
+            qkv_slices = self.wqkv.weight.split(key_value_slicing, dim=0)
+            qkv_states = torch.cat(
+                [
+                    F.linear(hidden_states, qkv_slice)
+                    for qkv_slice in qkv_slices
+                ],
+                dim=-1  # pylint: disable=E1102
+            )
+        else:
+            qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., :self.num_key_value_groups, :]
+        query_states = rearrange(query_states,
+                                 'b q h gs d -> b q (h gs) d').transpose(1, 2)
+        key_states = qkv_states[..., -2, :].transpose(1, 2)
+        value_states = qkv_states[..., -1, :].transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {
+                'sin': sin,
+                'cos': cos,
+                'cache_position': cache_position
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(
+            2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, :key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
+                f' {attn_output.size()}')
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(
+                self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.wo.weight.split(
+                self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([
+                F.linear(attn_output[i], o_proj_slices[i])  # pylint: disable=E1102
+                for i in range(self.config.pretraining_tp)
+            ])
+        else:
+            attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class InternLM2FlashAttention2(InternLM2Attention):
+    """
+    InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement,
+        #   that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
+        # Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1)
+        #   produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10(
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        if isinstance(past_key_value, StaticCache):
+            raise ValueError(
+                '`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` '
+                'make sure to use `sdpa` in the mean time, and open an issue at '
+                'https://github.com/huggingface/transformers')
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., :self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {
+                'sin': sin,
+                'cos': cos,
+                'cache_position': cache_position
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout
+        # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # dropout_rate = self.attention_dropout if self.training else 0.0
+        dropout_rate = 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (InternLM2RMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, '_pre_quantization_dtype'):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.wqkv.weight.dtype
+
+            logger.warning_once(
+                f'The input hidden states seems to be silently casted in float32, this might be related to'
+                f' the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in'
+                f' {target_dtype}.')
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate)
+
+        attn_output = attn_output.reshape(bsz, q_len,
+                                          self.hidden_size).contiguous()
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value  # pylint: disable=E0606
+
+    def _flash_attention_forward(self,
+                                 query_states,
+                                 key_states,
+                                 value_states,
+                                 attention_mask,
+                                 query_length,
+                                 dropout=0.0,
+                                 softmax_scale=None):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1.
+            # For details, please see the comment in InternLM2FlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask,
+                query_length)
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(  # pylint: disable=E0606
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size,
+                                    query_length)  # pylint: disable=E0606
+        else:
+            attn_output = flash_attn_func(  # pylint: disable=E0606
+                query_states,
+                key_states,
+                value_states,
+                dropout,
+                softmax_scale=softmax_scale,
+                causal=causal)
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask,
+                    query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(
+            attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(  # pylint: disable=E0606
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
+                              head_dim), indices_k)
+        value_layer = index_first_axis(  # pylint: disable=E0606
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
+                                head_dim), indices_k)
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(  # pylint: disable=E0606
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads,
+                                    head_dim), indices_k)
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(  # pylint: disable=E0606
+                query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LllamaSdpaAttention with Llama->InternLM2
+class InternLM2SdpaAttention(InternLM2Attention):
+    """
+    InternLM2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `InternLM2Attention` as the weights of the module stays untouched. The only changes are on the forward pass
+    to adapt to SDPA API.
+    """
+
+    # Adapted from InternLM2Attention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"`
+            # once this is implemented.
+            logger.warning_once(
+                'InternLM2Model uses InternLM2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` '
+                'does not support `output_attentions=True`. Falling back to the manual attention implementation, '
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. '
+                'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., :self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {
+                'sin': sin,
+                'cos': cos,
+                'cache_position': cache_position
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, :key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
+        # custom attn_mask, Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == 'cuda' and causal_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of
+        # an inline conditional assignment in SDPA to support both torch.compile's dynamic shapes and full graph
+        # options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = bool(causal_mask is None and q_len > 1)
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(  # pylint: disable=E1102
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.wo(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+INTERNLM2_ATTENTION_CLASSES = {
+    'eager': InternLM2Attention,
+    'flash_attention_2': InternLM2FlashAttention2,
+    'sdpa': InternLM2SdpaAttention,
+}
+
+
+# Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->InternLM2
+class InternLM2DecoderLayer(nn.Module):
+    """InternLM2 Decoder Layer. This module is a single layer of the InternLM2 model."""
+
+    def __init__(self, config: InternLM2Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.layer_idx = layer_idx
+
+        self.attention = INTERNLM2_ATTENTION_CLASSES[
+            config.attn_implementation](
+                config=config, layer_idx=layer_idx)
+
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor,
+                                                 torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states, )
+
+        if output_attentions:
+            outputs += (self_attn_weights, )
+
+        if use_cache:
+            outputs += (present_key_value, )
+
+        return outputs
+
+
+InternLM2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+    Parameters:
+        config ([`InternLM2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    """
+    InternLM2 pretraiend model's base class.
+    """
+
+    config_class = InternLM2Config
+    base_model_prefix = 'model'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['InternLM2DecoderLayer']
+    _skip_keys_device_placement = ['past_key_values']
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+InternLM2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+            [What are attention masks?](../glossary#attention-mask)
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+# Modified from transformers.models.llama.modeling_llama.LlamaModel with Llama->InternLM2
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = 'AutoModel'
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size,
+                                           config.hidden_size,
+                                           self.padding_idx)
+
+        self.layers = nn.ModuleList([
+            InternLM2DecoderLayer(config, layer_idx)
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+        self.norm = InternLM2RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache,
+                                        List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else
+            self.config.output_hidden_states)
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                'You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one'
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.'
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+
+        return_legacy_cache = False
+        if use_cache and not isinstance(
+                past_key_values,
+                Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+            return_legacy_cache = True
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length(
+            ) if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device)
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds,
+                                               cache_position, past_key_values,
+                                               output_attentions)
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states, )
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[
+                    2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1], )
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states, )
+
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        if not return_dict:
+            return tuple(
+                v for v in
+                [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length
+        # even when the static KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at
+        # each decode steps due to the dynamic shapes. (`recording cudagraph tree for symint key 13`, etc.), which is
+        # VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using `fullgraph=True`.
+        # See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self.config.attn_implementation == 'flash_attention_2':
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length(
+        ) if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config.attn_implementation == 'sdpa' and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                    attention_mask,
+                    inputs_embeds=input_tensor,
+                    past_key_values_length=past_seen_tokens,
+                    is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1] if isinstance(
+                    attention_mask, torch.Tensor) else past_seen_tokens +
+                sequence_length + 1)
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError(
+                    'Custom 4D attention mask should be passed in inverted form with max==0`'
+                )
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full((sequence_length, target_length),
+                                     fill_value=min_dtype,
+                                     dtype=dtype,
+                                     device=device)
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(
+                target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(
+                input_tensor.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone(
+                )  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :
+                                           mask_length] + attention_mask[:,
+                                                                         None,
+                                                                         None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :
+                            mask_length] = causal_mask[:, :, :, :
+                                                       mask_length].masked_fill(
+                                                           padding_mask,
+                                                           min_dtype)
+        if (self.config.attn_implementation == 'sdpa'
+                and attention_mask is not None
+                and attention_mask.device.type == 'cuda'
+                and not output_attentions):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(
+                causal_mask, min_dtype)  # pylint: disable=E1120
+
+        return causal_mask
+
+
+# Modified from transformers.models.llama.modeling_llama.LlamaForCausalLM
+class InternLM2ForCausalLM(InternLM2PreTrainedModel):
+    """Causal language model (CLM) for InternLM2."""
+
+    _auto_class = 'AutoModelForCausalLM'
+    _tied_weights_keys = ['output.weight']
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache,
+                                        List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        Returns:
+        Example:
+        ```python
+        >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
+        >>> model = InternLM2ForCausalLM.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else
+            self.config.output_hidden_states)
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        if self.config.pretraining_tp > 1:
+            output_slices = self.output.weight.split(
+                self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [
+                F.linear(hidden_states, output_slices[i])  # pylint: disable=not-callable
+                for i in range(self.config.pretraining_tp)
+            ]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.output(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits, ) + outputs[1:]
+            return (loss, ) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                past_length = cache_position[
+                    0] if cache_position is not None else past_key_values.get_seq_length(
+                    )
+                max_cache_length = (
+                    torch.tensor(
+                        past_key_values.get_max_length(),
+                        device=input_ids.device)
+                    if past_key_values.get_max_length() is not None else None)
+                cache_length = past_length if max_cache_length is None else torch.min(
+                    max_cache_length, past_length)
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)
+            if attention_mask is not None and attention_mask.shape[
+                    1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] -
+                                           past_length):]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (max_cache_length is not None and attention_mask is not None
+                    and cache_length + input_ids.shape[1] > max_cache_length):
+                attention_mask = attention_mask[:, -max_cache_length:]  # pylint: disable=E1130
+
+        position_ids = kwargs.get('position_ids', None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {'input_ids': input_ids.contiguous()}
+
+        input_length = position_ids.shape[
+            -1] if position_ids is not None else input_ids.shape[-1]
+        if cache_position is None:
+            cache_position = torch.arange(
+                past_length,
+                past_length + input_length,
+                device=input_ids.device)
+        elif use_cache:
+            cache_position = cache_position[-input_length:]
+
+        model_inputs.update({
+            'position_ids': position_ids,
+            'cache_position': cache_position,
+            'past_key_values': past_key_values,
+            'use_cache': use_cache,
+            'attention_mask': attention_mask,
+        })
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(
+                past_state.index_select(0, beam_idx.to(past_state.device))
+                for past_state in layer_past), )
+        return reordered_past
+
+    def build_inputs(self,
+                     tokenizer,
+                     query: str,
+                     history: List[Tuple[str, str]] = None,
+                     meta_instruction=''):
+        if history is None:
+            history = []
+        if tokenizer.add_bos_token:
+            prompt = ''
+        else:
+            prompt = tokenizer.bos_token
+        if meta_instruction:
+            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+        for record in history:
+            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+        return tokenizer([prompt], return_tensors='pt')
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: Optional[List[Tuple[str, str]]] = None,
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        meta_instruction:
+        str = 'You are an AI assistant whose name is InternLM (书生·浦语).\n'
+        '- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory '
+        '(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
+        '- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such '
+        'as English and 中文.',
+        **kwargs,
+    ):
+        if history is None:
+            history = []
+        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
+        inputs = {
+            k: v.to(self.device)
+            for k, v in inputs.items() if torch.is_tensor(v)
+        }
+        # also add end-of-assistant token in eos token id to avoid unnecessary generation
+        eos_token_id = [
+            tokenizer.eos_token_id,
+            tokenizer.convert_tokens_to_ids(['<|im_end|>'])[0]
+        ]
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split('<|im_end|>')[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        if history is None:
+            history = []
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                'The version of `transformers` is too low. Please make sure '
+                'that you have installed `transformers>=4.28.0`.')
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+            """
+            Streamer used in generate to print words one by one.
+            """
+
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ''
+                self.cache = []
+                self.received_inputs = False
+                self.queue.put(
+                    (self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError('ChatStreamer only supports batch size 1')
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                self.cache.extend(value.tolist())
+                token = self.tokenizer.decode(
+                    self.cache, skip_special_tokens=True)
+                if token.strip() != '<|im_end|>':
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+                    self.cache = []
+                else:
+                    self.end()
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
+    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
+    """Sequence Classification Head for InternLM2 Model."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache,
+                                        List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                'Cannot handle batch sizes > 1 if no padding token is defined.'
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(
+                    input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device),
+                               sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = 'regression'
+                elif self.num_labels > 1 and (labels.dtype
+                                              in (torch.long, torch.int)):
+                    self.config.problem_type = 'single_label_classification'
+                else:
+                    self.config.problem_type = 'multi_label_classification'
+
+            if self.config.problem_type == 'regression':
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == 'single_label_classification':
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == 'multi_label_classification':
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits, ) + transformer_outputs[1:]
+            return ((loss, ) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaForQuestionAnswering with Llama->InternLM2
+@add_start_docstrings(
+    """
+The InternLM2 Model transformer with a span classification head on top for extractive question-answering tasks like
+SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForQuestionAnswering(InternLM2PreTrainedModel):
+    """Question Answering model for InternLM2."""
+
+    base_model_prefix = 'transformer'
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = InternLM2Model(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.transformer.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache,
+                                        List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(
+                    start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss, ) +
+                    output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
+    output) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForTokenClassification(InternLM2PreTrainedModel):
+    """Token classification model for InternLM2."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        if getattr(config, 'classifier_dropout', None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, 'hidden_dropout', None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits, ) + outputs[2:]
+            return ((loss, ) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

code/xtuner/_lite/modelings/internlm3/__init__.py

@@ -0,0 +1,3 @@
+from .configuration_internlm3 import InternLM3Config
+from .modeling_internlm3 import InternLM3ForCausalLM
+from .tokenization_internlm3 import InternLM3Tokenizer

code/xtuner/_lite/modelings/internlm3/configuration_internlm3.py

@@ -0,0 +1,197 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" InternLM3 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class InternLM3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the InternLM3 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM3Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key and value projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in o_proj, up_proj, down_proj and gate_proj layers.
+        head_dim (`int`, *optional*):
+            The attention head dimension. If None, it will default to hidden_size // num_heads
+
+    ```python
+    >>> from transformers import InternLM3Model, InternLM3Config
+
+    >>> # Initializing a InternLM3 style configuration
+    >>> configuration = InternLM3Config()
+
+    >>> # Initializing a model from the InternLM3-8B style configuration
+    >>> model = InternLM3Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "internlm3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `InternLM3`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+
+    def __init__(
+        self,
+        vocab_size=128512,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        qkv_bias=False,
+        attention_dropout=0.0,
+        bias=False,
+        head_dim=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.qkv_bias = qkv_bias
+        self.attention_dropout = attention_dropout
+        self.bias = bias
+        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

code/xtuner/_lite/modelings/internlm3/modeling_internlm3.py

@@ -0,0 +1,825 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/internlm3/modular_internlm3.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_internlm3.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from transformers.utils import logging
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import LossKwargs, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from .configuration_internlm3 import InternLM3Config
+
+
+logger = logging.get_logger(__name__)
+_CONFIG_FOR_DOC = "InternLM3Config"
+
+
+class InternLM3MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class InternLM3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternLM3Config, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.qkv_bias)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.qkv_bias)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.qkv_bias)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+                logger.warning_once(
+                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+                )
+            else:
+                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class InternLM3RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        InternLM3RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class InternLM3DecoderLayer(nn.Module):
+    def __init__(self, config: InternLM3Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = InternLM3Attention(config=config, layer_idx=layer_idx)
+        self.mlp = InternLM3MLP(config)
+        self.input_layernorm = InternLM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = InternLM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class InternLM3RotaryEmbedding(nn.Module):
+    def __init__(self, config: InternLM3Config, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            # This .to() is needed if the model has been moved to a device after being initialized (because
+            # the buffer is automatically moved, but not the original copy)
+            self.original_inv_freq = self.original_inv_freq.to(device)
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+
+        # Core RoPE block
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+INTERNLM3_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`InternLM3Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare InternLM3 Model outputting raw hidden-states without any specific head on top.",
+    INTERNLM3_START_DOCSTRING,
+)
+class InternLM3PreTrainedModel(PreTrainedModel):
+    config_class = InternLM3Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["InternLM3DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+INTERNLM3_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance, see our
+            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare InternLM3 Model outputting raw hidden-states without any specific head on top.",
+    INTERNLM3_START_DOCSTRING,
+)
+class InternLM3Model(InternLM3PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM3DecoderLayer`]
+
+    Args:
+        config: InternLM3Config
+    """
+
+    def __init__(self, config: InternLM3Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [InternLM3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = InternLM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = InternLM3RotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(INTERNLM3_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                    **flash_attn_kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        output = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+        return output if return_dict else output.to_tuple()
+
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and (attention_mask == 0.0).any():
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_cache_shape()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_position: torch.Tensor,
+        batch_size: int,
+        **kwargs,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+                `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache,
+                to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            device (`torch.device`):
+                The device to plcae the 4D attention mask on.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+
+        return causal_mask
+
+
+class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
+
+
+class InternLM3ForCausalLM(InternLM3PreTrainedModel, GenerationMixin):
+    _auto_class = 'AutoModelForCausalLM'
+    _tied_weights_keys = ["lm_head.weight"]
+    _tp_plan = {"lm_head": "colwise_rep"}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM3Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(INTERNLM3_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, InternLM3ForCausalLM
+
+        >>> model = InternLM3ForCausalLM.from_pretrained("meta-internlm3/InternLM3-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-internlm3/InternLM3-2-7b-hf")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )