modeling_plamo.py

import enum
import math
import warnings
from typing import Any, Dict, List, Literal, NamedTuple, Optional, Tuple, Union

try:
    # It is difficult to install mamba_ssm in login node because
    # it requires GPU for installation
    import mamba_ssm
except ModuleNotFoundError:
    warnings.warn("mamba_ssm could not be imported", stacklevel=2)
try:
    # It is difficult to install causal_conv1d in login node because
    # it requires GPU for installation
    import causal_conv1d.causal_conv1d_interface as causal_conv1d
except ModuleNotFoundError:
    warnings.warn("causal_conv1d could not be imported", stacklevel=2)
import torch
from torch import nn
from torch.nn import functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast


def _is_first_token(mask: torch.Tensor) -> torch.Tensor:
    assert mask.dtype == torch.bool
    B, Nh, q_len, kv_len = mask.shape
    mask = mask[:, :, :, -q_len:]
    cont = q_len != kv_len
    v = False if cont else True
    out = torch.logical_not(torch.diagonal(mask, offset=-1, dim1=-2, dim2=-1).bool())
    out = torch.cat(
        [
            torch.full(size=(B, Nh, 1), dtype=torch.bool, device=out.device, fill_value=v),
            out,
        ],
        dim=-1,
    )
    return out


def _swiglu(h: torch.Tensor) -> torch.Tensor:
    h0, h1 = h.chunk(2, dim=-1)
    return torch.nn.functional.silu(h0) * h1


class RotaryEmbedding(torch.nn.Module):
    def __init__(
        self, dim: int, max_position_embeddings: int = 2048, base: int = 10000, device: Optional[torch.device] = None
    ) -> None:
        super().__init__()

        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).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len: int, device: Any, dtype: Any) -> None:
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)  # type: ignore

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

    def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        return (
            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),  # type: ignore
            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),  # type: ignore
        )


def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    """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 _rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor:
    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    x_embed = (x * cos) + (_rotate_half(x) * sin)
    return x_embed


class LinearType(str, enum.Enum):
    Normal = "normal"
    Fp8 = "fp8"
    Fp8Retain = "fp8-retain"


class PlamoConfig(PretrainedConfig):  # type: ignore
    model_type: str = "plamo"

    def __init__(
        self,
        hidden_size: int = 4096,
        num_hidden_layers: int = 32,
        rms_norm_eps: float = 1e-6,
        tie_word_embeddings: bool = True,
        # Attention
        num_attention_heads: int = 32,
        num_key_value_heads: int = 4,
        hidden_size_per_head: int = 128,
        max_position_embeddings: int = 2048,
        attention_window_size: int = 2048,
        full_attention_idx: list[int] | None = None,
        # Mamba
        mamba_d_state: int = 64,
        mamba_d_conv: int = 4,
        mamba_num_heads: int = 64,
        mamba_step: int = 2,
        mamba_chunk_size: int = 256,
        mamba_enabled: bool = True,
        # MLP
        intermediate_size: int = 13312,
        # Tokenizer
        vocab_size: int = 32000,
        tokenizer_class: str = "PlamoTokenizer",
        pad_token_id: Optional[int] = None,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        # Multimodal
        image_token_id: Optional[int] = None,
        image_feature_size: Optional[int] = None,
        image_proj_type: Literal["linear", "mlp"] = "linear",
        # FP8
        linear_type: LinearType = LinearType.Normal,
        fp8_accum_dtype: Optional[str] = None,
        # Evaluation
        eval_attention_n_bit: Optional[int] = None,
        eval_mlp_n_bit: Optional[int] = None,
        use_cache: bool = True,
        **kwargs: Any,
    ) -> None:
        # max_position_embeddings is often used to determine the max length during inference,
        # but samba should have extrapolation abilities
        self.max_position_embeddings = max(10 * 1024 * 1024, max_position_embeddings)
        self.hidden_size = hidden_size
        self.rms_norm_eps = rms_norm_eps

        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_size_per_head = hidden_size_per_head
        self.num_key_value_heads = num_key_value_heads
        self.attention_window_size = attention_window_size
        self.full_attention_idx = full_attention_idx if full_attention_idx is not None else []

        self.mamba_d_state = mamba_d_state
        self.mamba_d_conv = mamba_d_conv
        self.mamba_num_heads = mamba_num_heads
        self.mamba_step = mamba_step
        self.mamba_chunk_size = mamba_chunk_size
        self.mamba_enabled = mamba_enabled

        self.intermediate_size = intermediate_size

        self.vocab_size = vocab_size

        self.image_token_id = image_token_id
        self.image_feature_size = image_feature_size
        self.image_proj_type = image_proj_type

        self.linear_type = linear_type
        self.fp8_accum_dtype = fp8_accum_dtype

        self.eval_attention_n_bit = eval_attention_n_bit
        self.eval_mlp_n_bit = eval_mlp_n_bit
        self.use_cache = use_cache

        # fields for vLLM
        self.sliding_window = attention_window_size

        super().__init__(
            tokenizer_class=tokenizer_class,
            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,
        )


class PlamoAttentionCache(torch.nn.Module):
    def __init__(self, key: torch.Tensor, value: torch.Tensor) -> None:
        super().__init__()
        B, nh, L, c = key.shape
        assert len(value.shape) == 4
        assert value.shape[0] == B
        assert value.shape[2] == L
        self.register_parameter("key", torch.nn.Parameter(key, requires_grad=False))
        self.register_parameter("value", torch.nn.Parameter(value, requires_grad=False))


class PlamoMambaCache(torch.nn.Module):
    def __init__(self, conv_state: torch.Tensor, ssm_state: torch.Tensor) -> None:
        super().__init__()
        # conv_state: [B, C, d_conv]
        # ssm_state: [B, nhead, nchanel_per_head, d_state]
        assert len(conv_state.shape) == 3
        assert len(ssm_state.shape) == 4
        assert conv_state.shape[0] == ssm_state.shape[0]
        self.register_parameter("conv_state", torch.nn.Parameter(conv_state, requires_grad=False))
        self.register_parameter("ssm_state", torch.nn.Parameter(ssm_state, requires_grad=False))


PlamoLayerCache = PlamoAttentionCache | PlamoMambaCache


class PlamoCache(torch.nn.Module):
    """
    stores states of the model for fast decoding.
    `transformers` uses `transformers.Cache` for this purpose, but the interface and variable names are
    deeply dependent on Transformers architecture (e.g., `key_states`) and it is difficult to use
    other architectures (e.g., Mamba).
    This class provides a similar interface to `transformers.Cache`, but is designed to also handle
    the state of Mamba properly.
    """

    def __init__(self, config: PlamoConfig) -> None:
        super().__init__()
        self.config = config
        self.cache = torch.nn.ModuleList([None for _ in range(config.num_hidden_layers)])  # type: ignore

    def append_kv(self, key: torch.Tensor, value: torch.Tensor, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        c = self.cache[layer_idx]
        if c is None:
            return key, value
        assert isinstance(c, PlamoAttentionCache)

        def _validate(cache: torch.Tensor, new_tensor: torch.Tensor) -> None:
            assert len(cache.shape) == 4
            assert len(new_tensor.shape) == 4
            assert cache.shape[0] == new_tensor.shape[0]
            assert cache.shape[1] == new_tensor.shape[1]
            assert cache.shape[3] == new_tensor.shape[3]

        _validate(c.key, key)
        _validate(c.value, value)
        assert key.shape[2] == value.shape[2]
        return torch.cat([c.key, key], dim=2), torch.cat([c.value, value], dim=2)

    def update_attention(
        self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int
    ) -> PlamoAttentionCache:
        full_attn = layer_idx in self.config.full_attention_idx
        window_size = self.config.attention_window_size

        if self.cache[layer_idx] is None:
            if full_attn:
                self.cache[layer_idx] = PlamoAttentionCache(key_states, value_states)
            else:
                self.cache[layer_idx] = PlamoAttentionCache(
                    key_states[:, :, -window_size:, :], value_states[:, :, -window_size:, :]
                )
        else:
            c = self.cache[layer_idx]
            assert isinstance(c, PlamoAttentionCache)
            k, v = self.append_kv(key_states, value_states, layer_idx)
            if full_attn:
                c.key.data = k
                c.value.data = v
            else:
                c.key.data = k[:, :, -window_size:, :]
                c.value.data = v[:, :, -window_size:, :]
        return self.cache[layer_idx]  # type: ignore

    def update_mamba(self, conv_state: torch.Tensor, ssm_state: torch.Tensor, layer_idx: int) -> PlamoMambaCache:
        if self.cache[layer_idx] is None:
            self.cache[layer_idx] = PlamoMambaCache(conv_state, ssm_state)
        else:
            c = self.cache[layer_idx]
            assert isinstance(c, PlamoMambaCache)
            assert c.conv_state.shape == conv_state.shape
            assert c.ssm_state.shape == ssm_state.shape
            c.conv_state.data = conv_state
            c.ssm_state.data = ssm_state
        return self.cache[layer_idx]  # type: ignore

    def __getitem__(self, layer_idx: int) -> PlamoLayerCache | None:
        assert layer_idx < len(self.cache)
        layer_cache = self.cache[layer_idx]
        return layer_cache  # type: ignore

    def __len__(self) -> int:
        return len(self.cache)

    def get_seq_length(self, layer_idx: Optional[int] = None) -> int:
        if layer_idx is not None:
            c = self.cache[layer_idx]
            assert isinstance(c, PlamoAttentionCache)
            return c.key.shape[2]  # type: ignore

        sequence_length: int | None = None
        for layer_cache in self.cache:
            if isinstance(layer_cache, PlamoAttentionCache):
                sequence_length = (
                    max(layer_cache.key.shape[2], sequence_length)
                    if sequence_length is not None
                    else layer_cache.key.shape[2]
                )
        assert sequence_length is not None
        return sequence_length

    def get_max_length(self) -> int | None:
        return None

    def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -> int:
        """Given the sequence length of the new inputs, returns the usable length of the cache."""
        # Cache without size limit -> all cache is usable
        # Cache with size limit -> if the length cache plus the length of the new inputs is larger the maximum cache
        #   length, we will need to evict part of the cache (and thus not all cache is usable)
        max_length = self.get_max_length()
        previous_seq_length = self.get_seq_length(layer_idx)
        if max_length is not None and previous_seq_length + new_seq_length > max_length:
            return max_length - new_seq_length
        return previous_seq_length

    def reorder_cache(self, beam_idx: torch.Tensor) -> None:
        def _mamba(cache: PlamoMambaCache) -> PlamoMambaCache:
            return PlamoMambaCache(
                conv_state=cache.conv_state.index_select(0, beam_idx),
                ssm_state=cache.ssm_state.index_select(0, beam_idx),
            )

        def _attention(cache: PlamoAttentionCache) -> PlamoAttentionCache:
            return PlamoAttentionCache(
                key=cache.key.index_select(0, beam_idx),
                value=cache.value.index_select(0, beam_idx),
            )

        for i in range(len(self.cache)):
            if self.cache[i] is None:
                continue
            layer_cache = self.cache[i]
            if isinstance(layer_cache, PlamoMambaCache):
                self.cache[i] = _mamba(layer_cache)
            else:
                assert isinstance(layer_cache, PlamoAttentionCache)
                self.cache[i] = _attention(layer_cache)

    @property
    def seen_tokens(self) -> int | None:
        return None


class DecoderInput(NamedTuple):
    hidden_states: torch.Tensor
    attention_mask: Optional[torch.Tensor] = None
    past_states: Optional[PlamoCache] = None
    output_hidden_states: Optional[bool] = False
    output_attentions: Optional[bool] = False
    gradient_checkpointing: bool = False
    input_ids: Optional[torch.Tensor] = None


class DecoderOutput(NamedTuple):
    hidden_states: torch.Tensor
    all_hidden_states: Optional[Tuple[torch.Tensor, ...]]
    all_self_attns: Optional[Tuple[torch.Tensor, ...]]


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
    input_ids_shape: Tuple[int, int], dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
) -> torch.Tensor:
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), float("-inf"), device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None) -> torch.Tensor:
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), float("-inf"))  # type: ignore


def _rms_norm(
    hidden_states: torch.Tensor, weight: Optional[torch.Tensor], eps: float, offset: float = 1.0
) -> torch.Tensor:
    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 + eps)
    hidden_states = hidden_states.to(input_dtype)
    if weight is not None:
        hidden_states = (offset + weight) * hidden_states
    return hidden_states


class RMSNorm(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
        offset: float = 1.0,
        device: Optional[Union[torch.device, str]] = None,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(hidden_size, device=device))
        self.variance_epsilon = eps
        self.offset = offset

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return _rms_norm(hidden_states, self.weight, self.variance_epsilon, offset=self.offset)


def get_initial_dt_bias(num_heads: int) -> torch.Tensor:
    dt_min = 0.001
    dt_max = 0.1
    dt = torch.exp(torch.rand(num_heads) * (math.log(dt_max) - math.log(dt_min)) + math.log(dt_min))
    dt = torch.clamp(dt, 1e-4)
    inv_dt = dt + torch.log(-torch.expm1(-dt))
    return inv_dt


def get_initial_A(num_heads: int) -> torch.Tensor:
    A = torch.arange(1, num_heads + 1, dtype=torch.float32)
    return torch.log(A)


def _bf16_supported_in_triton() -> bool:
    # newer torch (2.2.0 and later?) supports bfloat16 even when using Voltas
    # but triton cannot compile bf16 kernels for Volta
    major, _ = torch.cuda.get_device_capability()
    return major >= 8


def _get_trition_dtype(dtype: torch.dtype) -> torch.dtype:
    if dtype != torch.bfloat16:
        return dtype
    if _bf16_supported_in_triton():
        return dtype
    return torch.float32


def ssd_update_state(
    ssm_state: torch.Tensor,
    x: torch.Tensor,
    dt: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
    D: torch.Tensor,
    z: torch.Tensor,
    dt_bias: torch.Tensor,
    dt_softplus: bool,
) -> torch.Tensor:
    assert ssm_state.dtype == torch.float32
    if dt.is_cuda:
        dtype = _get_trition_dtype(x.dtype)
    else:
        dtype = x.dtype
    if dt.is_cuda:
        f = mamba_ssm.ops.triton.selective_state_update.selective_state_update
    else:
        f = mamba_ssm.ops.triton.selective_state_update.selective_state_update_ref

    hidden_size_per_head = x.shape[-1]
    d_state = B.shape[-1]
    A = A[:, None, None].expand(-1, hidden_size_per_head, d_state).float()
    dt = dt[..., None].expand(-1, -1, hidden_size_per_head)
    dt_bias = dt_bias[:, None].expand(-1, hidden_size_per_head)
    D = D[:, None].expand(-1, hidden_size_per_head)
    assert ssm_state.dtype == torch.float32
    out = f(
        ssm_state,
        x.to(dtype),
        dt.to(dtype),
        A.float(),
        B.to(dtype),
        C.to(dtype),
        D.float(),
        z.to(dtype),
        dt_bias.float(),
        dt_softplus=dt_softplus,
    )
    return out[:, None]  # type: ignore


def _ssd_chunk_scan_combined_naive(
    x: torch.Tensor,
    dt: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
    D: torch.Tensor,
    z: torch.Tensor,
    dt_bias: torch.Tensor,
    dt_softplus: bool,
    seq_idx: torch.Tensor | None,
    ssm_state: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    assert ssm_state.dtype == torch.float32
    length = x.shape[1]
    ys = []
    for i in range(length):
        if i != 0 and seq_idx is not None:
            ssm_state = torch.where(
                (seq_idx[:, i - 1] != seq_idx[:, i])[:, None, None, None],
                torch.zeros_like(ssm_state),
                ssm_state,
            )
        y = ssd_update_state(
            ssm_state,
            x[:, i],
            dt[:, i],
            A,
            B[:, i],
            C[:, i],
            D,
            z=z[:, i],
            dt_bias=dt_bias,
            dt_softplus=dt_softplus,
        )
        ys.append(y)
    return torch.cat(ys, dim=1), ssm_state


def _ssd_chunk_scan_combined_cpu(
    x: torch.Tensor,
    dt: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
    chunk_size: int,
    D: torch.Tensor,
    z: torch.Tensor,
    dt_bias: torch.Tensor,
    dt_softplus: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
    # (bsize, nhead, nchunk, chunk_size)
    dt = dt.float()  # We want high precision for this before cumsum
    dt = dt.permute(0, 2, 1).unflatten(2, (-1, chunk_size))  # type: ignore
    if dt_bias is not None:
        dt = dt + dt_bias[None, :, None, None]
    if dt_softplus:
        dt = F.softplus(dt)
    dA = dt * A[None, :, None, None]
    dA_cumsum = torch.cumsum(dA, dim=-1)

    _, _, nheads, _ = x.shape
    dstate = B.shape[-1]
    _ = dt.shape[2]

    with torch.profiler.record_function("ssd_chunk_scan_combined_cpu_chunk_state"):
        # Following is equivalent to `mamba_ssm.ops.triton.ssd_combined.chunk_state_ref(B, x, dt, dA_cumsum)`
        # But `einsum` in the above function is too slow in CPU.
        x_ = torch.unflatten(x, 1, (-1, chunk_size))
        assert B.shape[2] == nheads  # B should be already expanded
        B_ = torch.unflatten(B, 1, (-1, chunk_size)).to(x.dtype)  # (bsize, nchunk, chunk_size, nheads, dstate)
        decay_states = torch.exp((dA_cumsum[:, :, :, -1:] - dA_cumsum)).to(x.dtype)
        dt_ = dt.to(x.dtype)

        # einsum("bclhn,bhcl,bhcl,bclhp->bchpn", B_, decay_states, dt_, x_)
        B_ = B_.permute(0, 1, 3, 4, 2)  # bchnl
        tmp = dt_ * decay_states  # bhcl
        tmp = tmp.permute(0, 2, 1, 3)[:, :, :, None]  # bch1l
        tmp = B_ * tmp  # bchnl
        x_ = x_.permute(0, 1, 3, 2, 4)  # bchlp
        tmp = tmp @ x_  # bchnp
        states = tmp.permute(0, 1, 2, 4, 3)  # bchpn

    states_dtype = states.dtype
    if states.dtype not in [torch.float32, torch.float64]:
        states = states.to(torch.float32)
    with torch.profiler.record_function("ssd_chunk_scan_combined_cpu_state_passing"):
        out, last_state = mamba_ssm.ops.triton.ssd_combined.state_passing_ref(
            states.flatten(start_dim=-2, end_dim=-1),
            dA_cumsum[:, :, :, -1],
        )
    states = torch.unflatten(out, -1, (-1, dstate))
    last_state = torch.unflatten(last_state, -1, (-1, dstate))
    states = states.to(states_dtype)
    with torch.profiler.record_function("ssd_chunk_scan_combined_cpu_chunk_scan"):
        out = mamba_ssm.ops.triton.ssd_combined.chunk_scan_ref(B, C, x, dt, dA_cumsum, states, D=D, z=z)

    return out, last_state


@torch.profiler.record_function("ssd_chunk_scan_combined")
def ssd_chunk_scan_combined(
    x: torch.Tensor,
    dt: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
    chunk_size: int,
    D: torch.Tensor,
    z: torch.Tensor,
    dt_bias: torch.Tensor,
    dt_softplus: bool,
    return_final_states: bool,
    seq_idx: torch.Tensor | None,
    ssm_state: torch.Tensor | None,
) -> tuple[torch.Tensor, torch.Tensor] | torch.Tensor:
    if seq_idx is not None:
        assert seq_idx.dtype == torch.int32
        assert ssm_state is None
        assert not return_final_states
    if ssm_state is not None:
        assert ssm_state.dtype == torch.float32
        assert seq_idx is None

    length = x.shape[1]

    """
    state will be updates by following:
    ```
    dt = softplus(dt)
    dA = exp(dt * A)
    state_next = state * dA + dB * x
    ```

    To avoid updating state, we set dt to -inf and x to 0
    because `softplus(-inf) = 0` and `exp(0) = 1`
    """
    pad = (chunk_size - length % chunk_size) % chunk_size
    x = torch.nn.functional.pad(x, pad=[0, 0, 0, 0, pad, 0], value=0.0)
    dt = torch.nn.functional.pad(dt, pad=[0, 0, pad, 0], value=float("-inf"))
    B = torch.nn.functional.pad(B, pad=[0, 0, 0, 0, pad, 0], value=0.0)
    C = torch.nn.functional.pad(C, pad=[0, 0, 0, 0, pad, 0], value=0.0)
    z = torch.nn.functional.pad(z, pad=[0, 0, 0, 0, pad, 0], value=0.0)
    if seq_idx is not None:
        seq_idx = torch.nn.functional.pad(seq_idx, pad=[pad, 0], value=0)

    length = x.shape[1]
    assert length % chunk_size == 0, (length, chunk_size)

    if dt.is_cuda:
        dtype = _get_trition_dtype(x.dtype)
        out = mamba_ssm.ops.triton.ssd_combined.mamba_chunk_scan_combined(  # type: ignore
            x.to(dtype),
            dt.to(dtype),
            A.float(),
            B.to(dtype),
            C.to(dtype),
            chunk_size,
            D=D.float(),
            z=z.to(dtype),
            initial_states=ssm_state,
            dt_bias=dt_bias.float(),
            dt_softplus=dt_softplus,
            seq_idx=seq_idx,
            return_final_states=return_final_states,
        )
        if return_final_states:
            return out[0][:, pad:], out[1]
        else:
            assert isinstance(out, torch.Tensor)
            return out[:, pad:]
    else:
        if ssm_state is None and seq_idx is None:
            tmp = _ssd_chunk_scan_combined_cpu(
                x,
                dt,
                A,
                B,
                C,
                chunk_size,
                D=D,
                z=z,
                dt_bias=dt_bias.float(),
                dt_softplus=dt_softplus,
            )
        else:
            if ssm_state is None:
                bsize, _, num_heads, channel = x.shape
                state = B.shape[-1]
                ssm_state = torch.zeros(bsize, num_heads, channel, state, dtype=torch.float32, device=x.device)
            tmp = _ssd_chunk_scan_combined_naive(
                x, dt, A, B, C, D, z=z, dt_bias=dt_bias, dt_softplus=dt_softplus, seq_idx=seq_idx, ssm_state=ssm_state
            )
        tmp = (tmp[0][:, pad:], tmp[1])
        if return_final_states:
            return tmp
        else:
            return tmp[0]


def _causal_conv1d_update(
    conv_state: torch.Tensor, weight: torch.Tensor, xBC: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    dtype = conv_state.dtype
    xBC = xBC.to(dtype)
    weight = weight.to(dtype)
    if conv_state.is_cuda:
        x = causal_conv1d.causal_conv1d_update(
            x=xBC,
            conv_state=conv_state,
            weight=weight[:, 0, :],
            activation="silu",
        )
        return x, conv_state
    else:
        x = causal_conv1d.causal_conv1d_update_ref(
            x=xBC,
            conv_state=conv_state,
            weight=weight[:, 0, :],
            activation="silu",
        )
        return x, conv_state


def _causal_conv1d_naive(
    conv_state: torch.Tensor, weight: torch.Tensor, x: torch.Tensor, seq_idx: torch.Tensor | None
) -> tuple[torch.Tensor, torch.Tensor]:
    length = x.shape[-1]
    out = torch.zeros_like(x)
    for i in range(length):
        if i != 0 and seq_idx is not None:
            conv_state = torch.where(
                (seq_idx[:, i - 1] != seq_idx[:, i])[:, None, None],
                torch.zeros_like(conv_state),
                conv_state,
            )
        out[:, :, i : i + 1], conv_state = _causal_conv1d_update(conv_state, weight, x[:, :, i : i + 1])
    return out, conv_state


@torch.profiler.record_function("causal_conv1d")
def _causal_conv1d(
    conv_state: torch.Tensor | None, weight: torch.Tensor, x: torch.Tensor, seq_idx: torch.Tensor | None
) -> tuple[torch.Tensor, torch.Tensor | None]:
    dtype = x.dtype
    if conv_state is not None:
        dtype = conv_state.dtype
        assert seq_idx is None
    if seq_idx is not None:
        assert seq_idx.dtype == torch.int32
        assert conv_state is None
    weight = weight.to(dtype)
    x = x.to(dtype)

    return_final_states = conv_state is not None
    if weight.is_cuda:
        if x.stride(1) != 1:
            # to channel-last format
            x = x.transpose(-1, -2).contiguous().transpose(-1, -2)
        if conv_state is not None:
            if conv_state.stride(1) != 1:
                # to channel-last format
                conv_state = conv_state.transpose(-1, -2).contiguous().transpose(-1, -2)
        tmp = causal_conv1d.causal_conv1d_fn(
            x=x,
            weight=weight[:, 0, :],
            initial_states=conv_state,
            return_final_states=conv_state is not None,
            activation="silu",
            seq_idx=seq_idx,
        )
        if conv_state is not None:
            x, conv_state = tmp
        else:
            x = tmp
    else:
        if seq_idx is None:
            x, conv_state = causal_conv1d.causal_conv1d_ref(
                x=x,
                initial_states=conv_state,
                return_final_states=True,
                weight=weight[:, 0, :],
                activation="silu",
            )
        else:
            if conv_state is None:
                bsize = x.shape[0]
                dim = weight.shape[0]
                d_conv = weight.shape[-1]
                conv_state = torch.zeros(bsize, dim, d_conv - 1, dtype=x.dtype, device=x.device)
            x, conv_state = _causal_conv1d_naive(conv_state, weight, x, seq_idx)
    if return_final_states:
        return x, conv_state
    else:
        return x, None


class Mamba(torch.nn.Module):
    def __init__(self, config: PlamoConfig, layer_idx: int) -> None:
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.d_state = config.mamba_d_state
        self.d_conv = config.mamba_d_conv
        self.chunk_size = config.mamba_chunk_size
        self.num_heads = config.mamba_num_heads
        # TODO add mamba_hidden_size_per_head config (?)
        self.hidden_size_per_head = config.hidden_size_per_head

        self.intermediate_size = self.num_heads * self.hidden_size_per_head

        self.in_proj = torch.nn.Linear(self.hidden_size, 2 * self.intermediate_size, bias=False)
        self.conv1d = torch.nn.Conv1d(
            in_channels=self.intermediate_size,
            out_channels=self.intermediate_size,
            bias=False,  # TODO the original implementation uses bias
            kernel_size=self.d_conv,
            groups=self.intermediate_size,
            padding=0,
        )
        self.dt_dim = max(64, self.hidden_size // 16)
        # Notes:
        # Mamba2 removes this linear projection for simplicity (Figure 6 in the paper),
        # but it may degrade the ability of content-length extrapolation.
        self.bcdt_proj = torch.nn.Linear(
            self.intermediate_size,
            self.dt_dim + 2 * self.d_state,
            bias=False,
        )
        self.dt_proj = torch.nn.Linear(self.dt_dim, self.num_heads, bias=False)

        self.dt_bias = torch.nn.Parameter(get_initial_dt_bias(self.num_heads))
        self.A_log = torch.nn.Parameter(get_initial_A(self.num_heads))
        self.D = torch.nn.Parameter(torch.ones(self.num_heads))

        # TODO norm weight before gating like Mamba2
        self.dt_norm_weight = torch.nn.Parameter(torch.ones(self.dt_dim))
        self.B_norm_weight = torch.nn.Parameter(torch.ones(self.d_state))
        self.C_norm_weight = torch.nn.Parameter(torch.ones(self.d_state))

        self.out_proj = torch.nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

    def _no_weight_decay_param_names(self) -> set[str]:
        return set(["D", "dt_bias", "A_log"])

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_states: Optional[PlamoCache] = None,
    ) -> Tuple[torch.Tensor, Optional[PlamoCache]]:
        bsize, length, _ = hidden_states.shape
        is_update = length == 1 and past_states is not None

        bool_mask: torch.Tensor | None = None
        seq_idx: torch.Tensor | None = None
        if attention_mask is not None:
            if len(attention_mask.shape) == 2:
                attention_mask = attention_mask[None, None].expand(bsize, 1, -1, -1)
            assert len(attention_mask.shape) == 4

            if past_states is None:
                # TODO: support seq_idx with cache
                bool_mask_4d = attention_mask == 0
                is_first_token = _is_first_token(bool_mask_4d)[:, 0, :]
                seq_idx = torch.cumsum(is_first_token, dim=-1) - 1
                seq_idx = seq_idx.to(torch.int32)

            # `generate` function creates attention mask that contains past tokens,
            # but mamba does not use them
            attention_mask = attention_mask[:, 0, -length:, -length:]
            bool_mask = torch.diagonal(attention_mask, dim1=-2, dim2=-1) == 0

        conv_state: torch.Tensor | None
        ssm_state: torch.Tensor | None
        if past_states is None:
            conv_state = None
            ssm_state = None
        elif past_states[self.layer_idx] is None:
            conv_state = torch.zeros(
                bsize, self.intermediate_size, self.d_conv - 1, dtype=hidden_states.dtype, device=hidden_states.device
            )
            ssm_state = torch.zeros(
                bsize,
                self.num_heads,
                self.hidden_size_per_head,
                self.d_state,
                dtype=torch.float32,
                device=hidden_states.device,
            )
        else:
            c = past_states[self.layer_idx]
            assert isinstance(c, PlamoMambaCache)
            conv_state = c.conv_state
            ssm_state = c.ssm_state

        zx = self.in_proj(hidden_states)
        zx = zx.reshape(bsize, length, self.num_heads, -1)
        # z: (bsize, length, num_heads, hidden_size_per_head)
        # x: (bsize, length, num_heads, hidden_size_per_head)
        z, x = torch.split(zx, [self.hidden_size_per_head, self.hidden_size_per_head], dim=-1)

        # conv
        x = x.reshape(bsize, length, -1).transpose(1, 2)  # (bsize, intermediate_size, length)
        if bool_mask is not None:
            x = torch.where(bool_mask[:, None, :], x, 0.0)
        if is_update:
            assert conv_state is not None
            x, conv_state = _causal_conv1d_update(conv_state, self.conv1d.weight, x)
        else:
            x, conv_state = _causal_conv1d(conv_state, self.conv1d.weight, x, seq_idx=seq_idx)
        x = x.to(dtype=hidden_states.dtype)
        x = x.transpose(1, 2)  # (bsize, length, intermediate_size)
        x = x.reshape(bsize, length, -1)
        # x: (bsize, length, num_heads, hidden_size_per_head)
        # B: (bsize, length, 1, d_state)
        # C: (bsize, length, 1, d_state)
        # dt: (bsize, length, dt_dim)
        BCdt = self.bcdt_proj(x)
        x = x.reshape(bsize, length, self.num_heads, -1)
        B, C, dt = torch.split(BCdt, [self.d_state, self.d_state, self.dt_dim], dim=-1)
        B = B[:, :, None, :]
        C = C[:, :, None, :]

        A = -torch.exp(self.A_log.float())  # (num_heads,)
        dt = _rms_norm(dt, None, self.config.rms_norm_eps) * self.dt_norm_weight[None, None, :]
        B = _rms_norm(B, None, self.config.rms_norm_eps) * self.B_norm_weight[None, None, None, :]
        C = _rms_norm(C, None, self.config.rms_norm_eps) * self.C_norm_weight[None, None, None, :]

        # (bsize, length, num_heads, 1)
        dt = self.dt_proj(dt)[..., None]

        # TODO it may not be required
        B = B.expand(-1, -1, self.num_heads, -1)
        C = C.expand(-1, -1, self.num_heads, -1)

        if bool_mask is not None:
            """
            state will be updates by following:
            ```
            dt = softplus(dt)
            dA = exp(dt * A)
            state_next = state * dA + dB * x
            ```

            To avoid updating state, we set dt to -inf and x to 0
            because `softplus(-inf) = 0` and `exp(0) = 1`
            """
            dt = torch.where(bool_mask[:, :, None, None], dt, float("-inf"))
            x = torch.where(bool_mask[:, :, None, None], x, 0.0)

        # ssm
        if is_update:
            assert ssm_state is not None
            out = ssd_update_state(
                ssm_state,
                x[:, 0],
                dt[:, 0].reshape(bsize, -1),
                A,
                B[:, 0],
                C[:, 0],
                D=self.D,
                z=z[:, 0],
                dt_bias=self.dt_bias,
                dt_softplus=True,
            )
        else:
            tmp = ssd_chunk_scan_combined(
                x,
                dt.reshape(bsize, length, -1),
                A,
                B,
                C,
                self.chunk_size,
                D=self.D,
                z=z,
                dt_bias=self.dt_bias,
                dt_softplus=True,
                return_final_states=past_states is not None,
                seq_idx=seq_idx,
                ssm_state=ssm_state,
            )
            if past_states is not None:
                out, ssm_state = tmp
            else:
                assert isinstance(tmp, torch.Tensor)
                out = tmp

        y = self.out_proj(out.reshape(bsize, length, -1))

        if past_states is not None:
            assert ssm_state is not None
            assert conv_state is not None
            past_states.update_mamba(conv_state, ssm_state, self.layer_idx)

        return y, past_states


def swa_mask(q_len: int, kv_len: int, device: torch.device, window_size: int) -> torch.Tensor:
    max_len = max(q_len, kv_len)
    mask = (
        torch.ones(max_len, max_len, dtype=torch.bool, device=device)
        .triu(diagonal=-window_size)
        .tril(diagonal=window_size)
    )
    return mask[-q_len:, -kv_len:]


class Attention(torch.nn.Module):
    def __init__(self, config: PlamoConfig, layer_idx: int) -> None:
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        head_dim = config.hidden_size_per_head
        self.max_position_embeddings = config.max_position_embeddings

        self.q_num_heads = config.num_attention_heads
        self.qk_dim = self.v_dim = head_dim
        self.k_num_heads = self.v_num_heads = config.num_key_value_heads
        assert self.q_num_heads % self.k_num_heads == 0
        self.n_group = self.q_num_heads // self.k_num_heads

        self.q_proj_dim = self.q_num_heads * self.qk_dim
        self.k_proj_dim = self.k_num_heads * self.qk_dim
        self.v_proj_dim = self.k_num_heads * self.v_dim
        self.qkv_proj = nn.Linear(self.hidden_size, self.q_proj_dim + self.k_proj_dim + self.v_proj_dim, bias=False)
        self.o_proj = nn.Linear(self.q_num_heads * self.v_dim, self.hidden_size, bias=False)

        self.q_weight = torch.nn.Parameter(torch.ones((self.q_num_heads, self.qk_dim)))
        self.k_weight = torch.nn.Parameter(torch.ones((self.k_num_heads, self.qk_dim)))

        self.rotary_emb = RotaryEmbedding(self.qk_dim, max_position_embeddings=self.config.attention_window_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_states: Optional[PlamoCache] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[PlamoCache]]:
        bsz, q_len, _ = hidden_states.size()

        qkv = self.qkv_proj(hidden_states)
        query_states, key_states, value_states = torch.split(
            qkv, [self.q_proj_dim, self.k_proj_dim, self.v_proj_dim], dim=-1
        )
        query_states = query_states.view(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.v_num_heads, self.v_dim).transpose(1, 2)

        attn_dtype = query_states.dtype

        query_states = _rms_norm(query_states, None, 1e-6) * self.q_weight[None, :, None]
        key_states = _rms_norm(key_states, None, 1e-6) * self.k_weight[None, :, None]

        if past_states is not None:
            # reuse k, v, self_attention
            key_states_new = key_states
            value_states_new = value_states
            key_states, value_states = past_states.append_kv(key_states, value_states, self.layer_idx)  # type: ignore
            past_states.update_attention(key_states_new, value_states_new, self.layer_idx)

        kv_seq_len = key_states.shape[-2]
        device = hidden_states.device
        position_ids = torch.arange(kv_seq_len, dtype=torch.long, device=device)[None]
        q_position_ids = position_ids[:, -query_states.shape[2] :]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states = _rotary_pos_emb(query_states, cos, sin, q_position_ids)
        key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
        # [bsz, nh, t, hd]

        def _expand_kv(t: torch.Tensor, repeat: int, target: int) -> torch.Tensor:
            t = torch.repeat_interleave(t, repeat, dim=1)
            return t[:, :target]

        # expand shared kv
        assert self.k_num_heads == self.v_num_heads
        key_states = _expand_kv(key_states, self.n_group, self.q_num_heads)
        value_states = _expand_kv(value_states, self.n_group, self.q_num_heads)

        full_attn = self.layer_idx in self.config.full_attention_idx

        query_states = query_states.to(attn_dtype)
        key_states = key_states.to(attn_dtype)
        value_states = value_states.to(attn_dtype)
        if attention_mask is not None and attention_mask.dtype != torch.bool:
            attention_mask = attention_mask.to(attn_dtype)
        if attention_mask is None:
            if not full_attn:
                assert key_states.shape[2] <= self.config.attention_window_size + 1
            attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=True)
        else:
            if attention_mask.dtype == torch.bool:
                attention_mask = torch.where(attention_mask, torch.tensor(0.0, dtype=torch.float), float("-inf"))
            if len(attention_mask.shape) == 2:
                attention_mask = attention_mask[None, None]
            assert len(attention_mask.shape) == 4

            if not full_attn:
                m_swa = swa_mask(
                    query_states.shape[2], key_states.shape[2], query_states.device, self.config.attention_window_size
                )
                # `generate` function creates attention mask that does not consider sliding window
                m_swa = m_swa[None, None]
                attention_mask = attention_mask[:, :, -query_states.shape[2] :, -key_states.shape[2] :]
                attention_mask = torch.where(m_swa, attention_mask, float("-inf"))

            # like AttentionMaskConverter._unmask_unattended in huggingface.transfoermers,
            # we need to attend to all tokens in masked rows for `scaled_dot_product_attention`
            bool_mask = torch.logical_not(torch.isneginf(attention_mask))
            valid_tokens = torch.sum(bool_mask, dim=-1).bool()  # (..., q_len)
            attention_mask = torch.where(valid_tokens[..., None], attention_mask, float(0.0))
            attn_output = F.scaled_dot_product_attention(
                query_states, key_states, value_states, attn_mask=attention_mask
            )

        attn_output = attn_output.transpose(1, 2)

        attn_output = attn_output.reshape(bsz, q_len, self.q_num_heads * self.v_dim)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_states


class MLP(nn.Module):
    def __init__(self, config: PlamoConfig) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_up_proj = torch.nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False)
        self.down_proj = torch.nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        h = self.gate_up_proj(x)
        h = _swiglu(h)
        return self.down_proj(h)  # type: ignore


class PlamoDecoderLayer(torch.nn.Module):
    def __init__(self, config: PlamoConfig, is_mamba: bool, layer_idx: int) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.is_mamba = is_mamba
        self.mixer: torch.nn.Module
        if is_mamba:
            self.mixer = Mamba(config, layer_idx)
        else:
            self.mixer = Attention(config, layer_idx)
        self.mlp = MLP(config)
        """
        Notes: The model performance was degraded when setting all offsets to 1.
        """
        self.pre_mixer_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, offset=1.0)
        self.post_mixer_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, offset=1.0 / 5)
        self.pre_mlp_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, offset=1.0)
        self.post_mlp_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, offset=1.0 / (5**1.5))

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_state: Optional[PlamoCache] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[Any, ...]:
        # from LlamaDecoder
        residual = hidden_states
        hidden_states = self.pre_mixer_norm(hidden_states)

        # Self Attention
        if self.is_mamba:
            hidden_states_sa, present_key_value = self.mixer(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                past_states=past_state,
            )
            self_attn_weights = None
        else:
            hidden_states_sa, self_attn_weights, present_key_value = self.mixer(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                past_states=past_state,
                output_attentions=output_attentions,
            )

        hidden_states_sa = self.post_mixer_norm(hidden_states_sa)
        hidden_states = residual + hidden_states_sa

        residual = hidden_states
        hidden_states = self.pre_mlp_norm(hidden_states)

        # Fully Connected
        hidden_states_mlp = self.mlp(hidden_states)

        # Residual
        hidden_states_mlp = self.post_mlp_norm(hidden_states_mlp)
        hidden_states = residual + hidden_states_mlp

        outputs: Any = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs  # type: ignore


def is_mamba(config: PlamoConfig, i: int) -> bool:
    if not config.mamba_enabled:
        return False
    assert config.mamba_step > 1
    assert i < config.num_hidden_layers

    if config.num_hidden_layers <= (config.mamba_step // 2):
        # use attention in last layer
        return i != config.num_hidden_layers - 1
    return (i % config.mamba_step) != (config.mamba_step // 2)


class PlamoDecoder(torch.nn.Module):
    def __init__(self, config: PlamoConfig) -> None:
        super().__init__()

        self.layers = torch.nn.ModuleList(
            [
                PlamoDecoderLayer(config, is_mamba=is_mamba(config, i), layer_idx=i)
                for i in range(config.num_hidden_layers)
            ]
        )
        self.gradient_checkpointing = False

    def forward(self, x: DecoderInput) -> DecoderOutput:
        all_hidden_states: Optional[Tuple[torch.Tensor, ...]] = () if x.output_hidden_states else None
        all_self_attns: Optional[Tuple[torch.Tensor, ...]] = () if x.output_attentions else None
        hidden_states = x.hidden_states

        for decoder_layer in self.layers:
            if x.output_hidden_states:
                assert all_hidden_states is not None
                all_hidden_states += (hidden_states,)

            if self.training and x.gradient_checkpointing:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    x.attention_mask,
                    x.past_states,
                    x.output_attentions,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=x.attention_mask,
                    past_state=x.past_states,
                    output_attentions=x.output_attentions,
                )

            hidden_states = layer_outputs[0]

            if x.output_attentions:
                assert layer_outputs[1] is not None
                assert all_self_attns is not None
                all_self_attns += (layer_outputs[1],)
        return DecoderOutput(hidden_states, all_hidden_states, all_self_attns)


class PlamoPreTrainedModel(PreTrainedModel):  # type: ignore
    config_class = PlamoConfig
    _no_split_modules: List[str]
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["PlamoDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

    def _init_weights(self, module: torch.nn.Module) -> None:
        std = 0.02
        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_()


class PlamoModel(PlamoPreTrainedModel):
    def __init__(self, config: PlamoConfig):
        super().__init__(config)
        assert config.eval_attention_n_bit is None
        assert config.eval_mlp_n_bit is None

        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)
        if config.image_feature_size is not None:
            if config.image_proj_type == "mlp":
                self.image_proj = MLPImageProjector(config)  # type: ignore
            elif config.image_proj_type == "linear":
                self.image_proj = nn.Linear(config.image_feature_size, config.hidden_size, bias=False)  # type: ignore
            else:
                raise ValueError(f"Unknown image_proj_type: {config.image_proj_type}")
        self.layers = PlamoDecoder(config)  # type: ignore
        self.norm = RMSNorm(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) -> torch.nn.Embedding:
        return self.embed_tokens

    def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(
        self,
        attention_mask: torch.Tensor,
        input_shape: Tuple[int, int],
        inputs_embeds: Optional[torch.Tensor],
        past_key_values_length: int,
    ) -> Optional[torch.Tensor]:
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask: Optional[torch.Tensor] = None
        if input_shape[-1] > 1:
            assert inputs_embeds is not None
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                device=inputs_embeds.device,
                past_key_values_length=past_key_values_length,
            )
            input_shape = (input_shape[0], combined_attention_mask.shape[2])

        if attention_mask is not None:
            if attention_mask.dim() == 4:
                # Custom 4D attention mask
                expanded_attn_mask = attention_mask
            else:
                # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
                assert inputs_embeds is not None
                expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
                    inputs_embeds.device
                )
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_values: Optional[PlamoCache] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        image_features: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        assert input_ids is not None
        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

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            past_key_values_length = past_key_values.get_seq_length()
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if image_features is not None:
            assert self.config.image_token_id is not None
            image_embeds = self.image_proj(image_features)
            assert image_embeds.shape == inputs_embeds.shape, (image_embeds.shape, inputs_embeds.shape)
            mask = input_ids == self.config.image_token_id
            inputs_embeds[mask] = image_embeds[mask]

        # embed positions
        require_attn_mask = False
        if not self.training or past_key_values is not None:
            require_attn_mask = True
        if seq_length_with_past >= self.config.attention_window_size:
            require_attn_mask = True
        if require_attn_mask and attention_mask is None:
            attention_mask = torch.ones(
                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
            )
        if attention_mask is not None:
            attention_mask = self._prepare_decoder_attention_mask(
                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
            )

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                use_cache = False

        if use_cache and past_key_values is None:
            past_key_values = PlamoCache(self.config)

        # decoder layers
        out = self.layers(
            DecoderInput(
                hidden_states,
                attention_mask,
                past_key_values,
                output_hidden_states,
                output_attentions,
                self.gradient_checkpointing,
            )
        )
        assert isinstance(out, DecoderOutput)
        hidden_states = out.hidden_states
        all_hidden_states = out.all_hidden_states
        all_self_attns = out.all_self_attns

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            assert all_hidden_states is not None
            all_hidden_states += (hidden_states,)

        if not return_dict:
            return tuple(
                v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
            )
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class PlamoForCausalLM(PlamoPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    # Without this, the model cannot be loaded into a meta device.
    # Relevant code:
    # https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/modeling_utils.py#L4376-L4381
    # https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/modeling_utils.py#L356
    # https://github.com/pytorch/pytorch/blob/v2.4.1/torch/nn/modules/module.py#L2068
    _supports_param_buffer_assignment = False

    def __init__(self, config: PlamoConfig) -> None:
        super().__init__(config)
        self.model = PlamoModel(config)

        self.vocab_size = config.vocab_size
        vocab_size = ((self.vocab_size + 15) // 16) * 16
        self.lm_head: torch.nn.Module = nn.Linear(config.hidden_size, vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> torch.nn.Embedding:
        return self.model.embed_tokens

    def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
        self.model.embed_tokens = value

    def get_output_embeddings(self) -> torch.nn.Module:
        return self.lm_head

    def set_output_embeddings(self, new_embeddings: torch.nn.Module) -> None:
        self.lm_head = new_embeddings

    def set_decoder(self, decoder: PlamoModel) -> None:
        self.model = decoder

    def get_decoder(self) -> PlamoModel:
        return self.model

    def forward(  # type: ignore
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_values: Optional[PlamoCache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        image_features: Optional[torch.Tensor] = 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, 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, LlamaForCausalLM

        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> prompt = "Hey, are you consciours? 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 consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
        ```"""
        assert input_ids is not None

        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,
            image_features=image_features,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits[..., : self.vocab_size]

        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 = nn.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: torch.Tensor,
        past_key_values: Optional[PlamoCache] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        image_features: Optional[torch.Tensor] = None,
        **kwargs: Any,
    ) -> Dict[str, Any]:
        if past_key_values:
            input_ids = input_ids[:, -1:]
            if image_features is not None:
                image_features = image_features[:, -1:, :]

        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[:, -1].unsqueeze(-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: Dict[str, Any] = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "image_features": image_features,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values: PlamoCache, beam_idx: torch.Tensor) -> PlamoCache:
        past_key_values.reorder_cache(beam_idx)
        return past_key_values


class MLPImageProjector(nn.Module):
    def __init__(self, config: PlamoConfig) -> None:
        super().__init__()
        self.config = config

        assert config.image_feature_size is not None  # for typing

        # nn.LayerNorm is not supported by PFVM, so use RMSNorm + Bias instead to approximate this.
        self.norm0 = RMSNorm(config.image_feature_size, eps=config.rms_norm_eps)
        self.bias0 = Bias(config.image_feature_size)

        # PFVM doesn't support Linear with bias, so add bias manually afterwards.
        self.linear1 = nn.Linear(config.image_feature_size, config.hidden_size, bias=False)
        self.bias1 = Bias(config.hidden_size)
        self.act1 = nn.GELU()

        self.linear2 = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.bias2 = Bias(config.hidden_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        hidden_states = self.norm0(hidden_states)
        hidden_states = self.bias0(hidden_states)

        hidden_states = self.linear1(hidden_states)
        hidden_states = self.bias1(hidden_states)
        hidden_states = self.act1(hidden_states)

        hidden_states = self.linear2(hidden_states)
        hidden_states = self.bias2(hidden_states)

        return hidden_states


class Bias(nn.Module):
    def __init__(self, num_features: int) -> None:
        super().__init__()
        self._bias = nn.Parameter(torch.zeros((num_features,)))

    def forward(
        self,
        x: torch.Tensor,
    ) -> torch.Tensor:
        return x + self._bias