Delete code

code/xtuner/__init__.py

@@ -1,25 +0,0 @@
-# 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

@@ -1,77 +0,0 @@
-# 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

@@ -1,24 +0,0 @@
-# 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

@@ -1,5 +0,0 @@
-# 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

@@ -1,4 +0,0 @@
-# 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

@@ -1,200 +0,0 @@
-# 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

@@ -1,24 +0,0 @@
-# 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

@@ -1,62 +0,0 @@
-# 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

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

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

@@ -1,32 +0,0 @@
-# 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

@@ -1,153 +0,0 @@
-# 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

@@ -1,119 +0,0 @@
-# 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

@@ -1,49 +0,0 @@
-# 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

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

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

@@ -1,109 +0,0 @@
-# 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

@@ -1,5 +0,0 @@
-# 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

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

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

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

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

@@ -1,32 +0,0 @@
-# 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

@@ -1,202 +0,0 @@
-# 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

@@ -1,30 +0,0 @@
-# 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

@@ -1,59 +0,0 @@
-# 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

@@ -1,206 +0,0 @@
-# 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

@@ -1,14 +0,0 @@
-# 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

@@ -1,177 +0,0 @@
-# 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

@@ -1,220 +0,0 @@
-# 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

@@ -1,257 +0,0 @@
-# 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

@@ -1,28 +0,0 @@
-# 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

@@ -1,12 +0,0 @@
-# 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

@@ -1,195 +0,0 @@
-# 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

@@ -1,286 +0,0 @@
-# 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

@@ -1,66 +0,0 @@
-# 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

@@ -1,42 +0,0 @@
-# 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

@@ -1,17 +0,0 @@
-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

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

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

@@ -1,175 +0,0 @@
-# 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

@@ -1,1899 +0,0 @@
-# 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

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

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

@@ -1,197 +0,0 @@
-# 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

@@ -1,825 +0,0 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           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,
-        )

code/xtuner/_lite/modelings/internlm3/tokenization_internlm3.py

@@ -1,295 +0,0 @@
-import os
-from shutil import copyfile
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
-
-import sentencepiece as spm
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-if TYPE_CHECKING:
-    from transformers.tokenization_utils_base import TextInput
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-
-SPIECE_UNDERLINE = "▁"
-
-
-class InternLM3Tokenizer(PreTrainedTokenizer):
-    """
-    Construct a InternLM3 tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
-    no padding token in the original model.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
-            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
-            attention mechanisms or loss computation.
-        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-
-        add_bos_token (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an `bos_token` at the start of sequences.
-        add_eos_token (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an `eos_token` at the end of sequences.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
-            extra spaces.
-        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not the default system prompt for InternLM3 should be used.
-        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
-            Whether or not to add spaces between special tokens.
-        spaces_for_interleaved_special_tokens (`bool`, *optional*, defaults to `False`):
-           Whether or not to add spaces between special tokens that are interleaved with normal tokens.
-        add_prefix_space (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask"]
-    _auto_class = "AutoTokenizer"
-    
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token="<s>",
-        eos_token="</s>",
-        pad_token=None,
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=True,
-        add_eos_token=False,
-        clean_up_tokenization_spaces=False,
-        use_default_system_prompt=False,
-        spaces_between_special_tokens=False,
-        spaces_for_interleaved_special_tokens=False,
-        add_prefix_space=True,
-        **kwargs,
-    ):
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
-
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.use_default_system_prompt = use_default_system_prompt
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
-        self.add_prefix_space = add_prefix_space
-        self.spaces_for_interleaved_special_tokens = spaces_for_interleaved_special_tokens
-
-        vocab_size = self.sp_model.get_piece_size()
-        self.decoder = {i: self.sp_model.id_to_piece(i) for i in range(vocab_size)}
-
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            sp_model_kwargs=sp_model_kwargs,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            use_default_system_prompt=use_default_system_prompt,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-            add_prefix_space=add_prefix_space,
-            **kwargs,
-        )
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__.update(d)
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
-
-    @property
-    def vocab_size(self):
-        """Returns vocab size"""
-        return self.sp_model.get_piece_size()
-
-    def get_vocab(self):
-        """Returns vocab as a dict"""
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
-        """
-        Args:
-            text: TextInput
-        Simply calls PreTrainedTokenizer's method
-        """
-        return super().tokenize(text, **kwargs)
-
-    def _tokenize(self, text, **kwargs):
-        """
-        Args:
-            text: TextInput
-        Returns a tokenized string. The Gemma tokenizer never adds a prefix space.
-        """
-        return self.sp_model.encode(text, out_type=str)
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.sp_model.piece_to_id(token)
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index, "")
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        # since we manually add the prefix space, we have to remove it when decoding
-        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
-            tokens[0] = tokens[0][1:]
-
-        current_sub_tokens = []
-        out_string = ""
-        prev_is_special = False
-        for i, token in enumerate(tokens):
-            # make sure that special tokens are not decoded using sentencepiece model
-            if token in self.all_special_tokens:
-                if not prev_is_special and i != 0 and self.spaces_for_interleaved_special_tokens:
-                    out_string += " "
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                prev_is_special = True
-                current_sub_tokens = []
-            else:
-                if (
-                    prev_is_special
-                    and i == 1
-                    and self.add_prefix_space
-                    and not token.startswith(SPIECE_UNDERLINE)
-                    and self.spaces_for_interleaved_special_tokens
-                ):
-                    out_string += " "
-                current_sub_tokens.append(token)
-                prev_is_special = False
-        out_string += self.sp_model.decode(current_sub_tokens)
-        return out_string
-
-    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        """
-        Save the vocabulary and special tokens file to a directory.
-
-        Args:
-            save_directory (`str`):
-                The directory in which to save the vocabulary.
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        return (out_vocab_file,)
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
-
-        bos_token_id = [1] if self.add_bos_token else []
-        eos_token_id = [1] if self.add_eos_token else []
-
-        if token_ids_1 is None:
-            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
-        return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id
-
-    def create_token_type_ids_from_sequences(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]:
-        """
-        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
-        sequence pair mask has the following format:
-
-        ```
-        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
-        | first sequence    | second sequence |
-        ```
-
-        if token_ids_1 is None, only returns the first portion of the mask (0s).
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of ids.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
-        """
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
-
-        if token_ids_1 is not None:
-            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
-
-        return output