microsoft
/

phi-1

@@ -1,855 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-#
-# BSD 3-Clause License
-#
-# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright notice,
-#   this list of conditions and the following disclaimer in the documentation
-#   and/or other materials provided with the distribution.
-#
-# * Neither the name of the copyright holder nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-from __future__ import annotations
-import math
-from typing import Any, Dict, Optional, Tuple, Union
-from dataclasses import dataclass, field
-import torch
-import torch.nn as nn
-from einops import rearrange, repeat
-from transformers.activations import ACT2FN
-from transformers import PretrainedConfig, PreTrainedModel
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from .configuration_mixformer_sequential import MixFormerSequentialConfig
-try:
-    from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
-    from flash_attn.ops.fused_dense import FusedDense
-except:
-    FlashRotaryEmbedding = None
-    FusedDense = None
-@dataclass
-class InferenceParams:
-    """Inference parameters passed to model to efficiently calculate
-    and store context during inference.
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
-    Args:
-        max_seqlen: Maximum sequence length.
-        max_batch_size: Maximum batch size.
-        seqlen_offset: Sequence length offset.
-        batch_size_offset: Batch size offset.
-        key_value_memory_dict: Key value memory dictionary.
-        lengths_per_sample: Lengths per sample.
-    """
-    max_seqlen: int = field(metadata={"help": "Maximum sequence length."})
-    max_batch_size: int = field(metadata={"help": "Maximum batch size."})
-    seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
-    batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
-    key_value_memory_dict: Dict[str, Any] = field(
-        default_factory=dict, metadata={"help": "Key value memory dictionary."}
-    )
-    lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."})
-class Embedding(nn.Module):
-    """Token embedding with dropout."""
-    def __init__(self, config: PretrainedConfig) -> None:
-        super().__init__()
-        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
-        self.drop = nn.Dropout(config.embd_pdrop)
-    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
-        input_shape = input_ids.size()
-        input_ids = input_ids.view(-1, input_shape[-1])
-        hidden_states = self.wte(input_ids)
-        hidden_states = self.drop(hidden_states)
-        return hidden_states
-def _apply_rotary_emb(
-    x: torch.FloatTensor,
-    cos: torch.FloatTensor,
-    sin: torch.FloatTensor,
-) -> torch.FloatTensor:
-    _, seqlen, _, head_dim = x.shape
-    rotary_seqlen, rotary_dim = cos.shape
-    rotary_dim *= 2
-    assert rotary_dim <= head_dim
-    assert seqlen <= rotary_seqlen
-    assert cos.shape == sin.shape == (rotary_seqlen, rotary_dim // 2)
-    x_rot = x[:, :, :, :rotary_dim]
-    x_pass = x[:, :, :, rotary_dim:]
-    x1, x2 = x_rot.chunk(2, dim=-1)
-    c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
-    x1, x2, c, s = [t.to(dtype=torch.float32) for t in [x1, x2, c, s]]
-    x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype)
-    return torch.cat([x_rot, x_pass], axis=-1)
-def _apply_rotary_emb_kv(
-    kv: torch.FloatTensor,
-    cos: torch.FloatTensor,
-    sin: torch.FloatTensor,
-    cos_k: Optional[torch.FloatTensor] = None,
-    sin_k: Optional[torch.FloatTensor] = None,
-) -> torch.FloatTensor:
-    _, seqlen, two, _, head_dim = kv.shape
-    assert two == 2
-    rotary_seqlen, rotary_dim = cos.shape
-    rotary_dim *= 2
-    assert rotary_dim <= head_dim
-    assert seqlen <= rotary_seqlen
-    assert cos.shape == sin.shape == (rotary_seqlen, rotary_dim // 2)
-    k_rot = kv[:, :, 0, :, :rotary_dim]
-    k_pass = kv[:, :, 0, :, rotary_dim:]
-    k1, k2 = k_rot.chunk(2, dim=-1)
-    c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
-    k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]]
-    k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(kv.dtype)
-    return torch.cat(
-        [
-            torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
-            kv[:, :, 1:2, :, :],
-        ],
-        axis=2,
-    )
-def _apply_rotary_emb_qkv(
-    qkv: torch.FloatTensor,
-    cos: torch.FloatTensor,
-    sin: torch.FloatTensor,
-    cos_k: Optional[torch.FloatTensor] = None,
-    sin_k: Optional[torch.FloatTensor] = None,
-) -> torch.FloatTensor:
-    _, seqlen, three, _, head_dim = qkv.shape
-    assert three == 3
-    rotary_seqlen, rotary_dim = cos.shape
-    rotary_dim *= 2
-    assert rotary_dim <= head_dim
-    assert seqlen <= rotary_seqlen
-    assert cos.shape == sin.shape == (rotary_seqlen, rotary_dim // 2)
-    q_rot = qkv[:, :, 0, :, :rotary_dim]
-    q_pass = qkv[:, :, 0, :, rotary_dim:]
-    k_rot = qkv[:, :, 1, :, :rotary_dim]
-    k_pass = qkv[:, :, 1, :, rotary_dim:]
-    q1, q2 = q_rot.chunk(2, dim=-1)
-    k1, k2 = k_rot.chunk(2, dim=-1)
-    c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
-    q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
-    q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
-    k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
-    return torch.cat(
-        [
-            torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
-            torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
-            qkv[:, :, 2:3, :, :],
-        ],
-        axis=2,
-    )
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding (RoPE).
-    Reference:
-        RoFormer: Enhanced Transformer with Rotary Position Embedding.
-        https://arxiv.org/pdf/2104.09864.pdf.
-    """
-    def __init__(
-        self,
-        dim: int,
-        base: int = 10000,
-        scale_base: Optional[float] = None,
-        pos_idx_in_fp32: bool = True,
-        device: Optional[str] = None,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-        if scale_base is not None:
-            raise NotImplementedError
-        self.dim = dim
-        self.base = float(base)
-        self.scale_base = scale_base
-        self.pos_idx_in_fp32 = pos_idx_in_fp32
-        self.device = device
-        # Generate and save the inverse frequency buffer (non-trainable)
-        inv_freq = self._compute_inv_freq(device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        # Generate and save the scale buffer (non-trainable)
-        scale = (
-            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
-            if scale_base is not None
-            else None
-        )
-        self.register_buffer("scale", scale, persistent=False)
-        self._seq_len_cached = 0
-        self._cos_cached = None
-        self._sin_cached = None
-        self._cos_k_cached = None
-        self._sin_k_cached = None
-    def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
-        return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
-    def _update_cos_sin_cache(
-        self, seqlen: int, device: Optional[str] = None, dtype: Optional[torch.dtype] = None
-    ) -> None:
-        # Reset the tables if sequence length has been chaned, if we are on a
-        # new device or if we are switching from inference mode to training
-        if (
-            seqlen > self._seq_len_cached
-            or self._cos_cached is None
-            or self._cos_cached.device != device
-            or self._cos_cached.dtype != dtype
-            or (self.training and self._cos_cached.is_inference())
-        ):
-            self._seq_len_cached = seqlen
-            # fp32 is preferred since the output of `torch.arange` can be quite large
-            # and bf16 would lose a lot of precision
-            if self.pos_idx_in_fp32:
-                t = torch.arange(seqlen, device=device, dtype=torch.float32)
-                if self.inv_freq.dtype != torch.float32:
-                    inv_freq = self._compute_inv_freq(device=device)
-                else:
-                    inv_freq = self.inv_freq
-            else:
-                t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
-                inv_freq = self.inv_freq
-            # `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP
-            freqs = torch.outer(t, inv_freq)
-            if self.scale is None:
-                self._cos_cached = torch.cos(freqs).to(dtype)
-                self._sin_cached = torch.sin(freqs).to(dtype)
-            else:
-                power = (
-                    torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
-                ) / self.scale_base
-                scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
-                # Force the scale multiplication to happen in fp32
-                self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
-                self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
-                self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
-                self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
-    def forward(
-        self,
-        qkv: torch.Tensor,
-        kv: Optional[torch.Tensor] = None,
-        seqlen_offset: int = 0,
-        max_seqlen: Optional[int] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        seqlen = qkv.shape[1]
-        if max_seqlen is not None:
-            self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
-        else:
-            self._update_cos_sin_cache(seqlen + seqlen_offset, device=qkv.device, dtype=qkv.dtype)
-        if kv is None:
-            return _apply_rotary_emb_qkv(qkv, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:])
-        else:
-            q = _apply_rotary_emb(qkv, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:])
-            kv = _apply_rotary_emb_kv(kv, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:])
-            return q, kv
-class MLP(nn.Module):
-    """Multi-Layer Perceptron.
-    Reference:
-        Attention Is All You Need.
-        https://arxiv.org/pdf/1706.03762.pdf.
-    """
-    def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None) -> None:
-        super().__init__()
-        act_fn = config.activation_function if act_fn is None else act_fn
-        assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
-        n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
-        n_inner = n_inner if n_inner is not None else 4 * config.n_embd
-        self.fc1 = nn.Linear(config.n_embd, n_inner)
-        self.fc2 = nn.Linear(n_inner, config.n_embd)
-        self.act = ACT2FN[act_fn]
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-class SelfAttention(nn.Module):
-    """Self-attention layer (compatible with PyTorch).
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
-    """
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ) -> None:
-        super().__init__()
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
-    def forward(
-        self,
-        qkv: torch.FloatTensor,
-        causal: bool = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
-        q, k, v = qkv.unbind(dim=2)
-        causal = self.causal if causal is None else causal
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
-        if attention_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device)
-            padding_mask.masked_fill_(attention_mask, 0.0)
-            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
-        if causal:
-            causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
-            scores = scores + causal_mask.to(dtype=scores.dtype)
-        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
-        attention = self.drop(attention)
-        output = torch.einsum("bhts,bshd->bthd", attention, v)
-        return output
-class CrossAttention(nn.Module):
-    """Cross-attention layer (compatible with PyTorch).
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
-    """
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ) -> None:
-        super().__init__()
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
-    def forward(
-        self,
-        q: torch.FloatTensor,
-        kv: torch.FloatTensor,
-        causal: bool = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
-        seqlen_k = kv.shape[1]
-        assert kv.shape[0] == batch_size and kv.shape[4] == q.shape[3]
-        if kv.shape[3] != q.shape[2]:
-            kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
-        k, v = kv.unbind(dim=2)
-        causal = self.causal if causal is None else causal
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
-        if attention_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen_k), -10000.0, dtype=scores.dtype, device=scores.device)
-            padding_mask.masked_fill_(attention_mask, 0.0)
-            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
-        if causal:
-            rows = rearrange(torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1")
-            cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
-            causal_mask = cols > rows + seqlen_k - seqlen_q
-            scores = scores.masked_fill(causal_mask, -10000.0)
-        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
-        attention = self.drop(attention)
-        output = torch.einsum("bhts,bshd->bthd", attention, v)
-        return output
-def _find_mha_dims(
-    config: PretrainedConfig,
-    n_head: Optional[int] = None,
-    n_head_kv: Optional[int] = None,
-    head_dim: Optional[int] = None,
-) -> Tuple[int, int]:
-    assert all(
-        hasattr(config, attr) for attr in ["n_embd", "n_head"]
-    ), "`config` must have `n_embd` and `n_head` attributes."
-    if head_dim is None:
-        assert (
-            config.n_embd % config.n_head == 0
-        ), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
-    if n_head is None and head_dim is None:
-        head_dim = config.n_embd // config.n_head
-        n_head = config.n_head
-    elif n_head is None or head_dim is None:
-        raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
-    if n_head_kv is None:
-        n_head_kv = getattr(config, "n_head_kv", None) or n_head
-    assert n_head % n_head_kv == 0, "`n_head` must be divisible by `n_head_kv`."
-    return n_head, n_head_kv, head_dim
-def _update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
-    num_heads, head_dim = kv.shape[-2:]
-    if layer_idx not in inference_params.key_value_memory_dict:
-        kv_cache = torch.empty(
-            inference_params.max_batch_size,
-            inference_params.max_seqlen,
-            2,
-            num_heads,
-            head_dim,
-            dtype=kv.dtype,
-            device=kv.device,
-        )
-        inference_params.key_value_memory_dict[layer_idx] = kv_cache
-    else:
-        kv_cache = inference_params.key_value_memory_dict[layer_idx]
-    batch_start = inference_params.batch_size_offset
-    batch_end = batch_start + kv.shape[0]
-    assert batch_end <= kv_cache.shape[0]
-    sequence_start = inference_params.seqlen_offset
-    sequence_end = sequence_start + kv.shape[1]
-    assert sequence_end <= kv_cache.shape[1]
-    assert kv_cache is not None
-    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
-    kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
-    return kv
-class MHA(nn.Module):
-    """Multi-head attention layer."""
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[str] = None,
-        rotary_dim: Optional[int] = None,
-        rotary_emb_scale_base: Optional[float] = None,
-        n_head: Optional[int] = None,
-        n_head_kv: Optional[int] = None,
-        head_dim: Optional[int] = None,
-        bias: bool = True,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        layer_idx: Optional[int] = None,
-        return_residual: bool = False,
-        checkpointing: bool = False,
-    ) -> None:
-        super().__init__()
-        # Rotary embedding
-        self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
-        if self.rotary_emb_dim > 0:
-            rotary_kwargs = {"device": device}
-            if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
-                rotary_kwargs["scale_base"] = rotary_emb_scale_base
-            rotary_cls = FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding
-            if rotary_cls is None:
-                rotary_cls = RotaryEmbedding
-            self.rotary_emb = rotary_cls(self.rotary_emb_dim, **rotary_kwargs)
-        # MLP
-        self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim)
-        op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
-        hidden_size = config.n_embd
-        linear_cls = FusedDense if config.fused_dense else nn.Linear
-        if linear_cls is None:
-            linear_cls = nn.Linear
-        self.Wqkv = linear_cls(hidden_size, op_size, bias=bias, device=device, dtype=dtype)
-        self.out_proj = linear_cls(hidden_size, hidden_size, bias=bias, device=device, dtype=dtype)
-        # Attention
-        self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=config.attn_pdrop)
-        self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=config.attn_pdrop)
-        self.layer_idx = layer_idx
-        self.return_residual = return_residual
-        self.checkpointing = checkpointing
-    def _forward_self_attn(
-        self, x: torch.FloatTensor, attention_mask: Optional[torch.BoolTensor]
-    ) -> torch.FloatTensor:
-        qkv = self.Wqkv(x)
-        qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
-        if self.rotary_emb_dim > 0:
-            qkv = self.rotary_emb(qkv)
-        if self.checkpointing:
-            return torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, attention_mask=attention_mask)
-        return self.inner_attn(qkv, attention_mask=attention_mask)
-    def _forward_cross_attn(
-        self,
-        x: torch.FloatTensor,
-        past_key_values: Optional[InferenceParams],
-        attention_mask: Optional[torch.BoolTensor],
-    ) -> torch.FloatTensor:
-        qkv = self.Wqkv(x)
-        q = qkv[..., : self.n_head * self.head_dim]
-        q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
-        kv = qkv[..., self.n_head * self.head_dim :]
-        kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
-        seqlen_offset = past_key_values.seqlen_offset if past_key_values is not None else 0
-        causal = None if seqlen_offset == 0 else False
-        if self.rotary_emb_dim > 0:
-            q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
-        if past_key_values is not None:
-            kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
-        if self.checkpointing:
-            return torch.utils.checkpoint.checkpoint(
-                self.inner_cross_attn, q, kv, attention_mask=attention_mask, causal=causal
-            )
-        return self.inner_cross_attn(q, kv, attention_mask=attention_mask, causal=causal)
-    def forward(
-        self,
-        x: torch.FloatTensor,
-        past_key_values: Optional[InferenceParams] = None,
-        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        if attention_mask is not None and torch.any(~attention_mask.bool()):
-            attention_mask = attention_mask.bool()
-        else:
-            attention_mask = None
-        # MHA
-        if self.n_head == self.n_head_kv:
-            if past_key_values is None:
-                # If `past_key_values` are not supplied, we run self-attention
-                attn_output = self._forward_self_attn(x, attention_mask)
-            else:
-                # If `past_key_values` are supplied, it means that we might have cached values and
-                # could take advantage of cross-attention
-                attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
-        # MQA / GQA
-        else:
-            # Regardless of `past_key_values` being supplied or not, it always use cross-attention
-            # because `q` and `kv` lengths might be different
-            attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
-        output = rearrange(attn_output, "... h d -> ... (h d)")
-        output = self.out_proj(output)
-        return output if not self.return_residual else (output, x)
-class ParallelBlock(nn.Module):
-    """Parallel block.
-    This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
-    """
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        block_idx: Optional[int] = None,
-    ) -> None:
-        super().__init__()
-        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.resid_dropout = nn.Dropout(config.resid_pdrop)
-        self.block_idx = block_idx
-        self.mixer = MHA(config, layer_idx=block_idx)
-        self.mlp = MLP(config)
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        residual = hidden_states
-        hidden_states = self.ln(hidden_states)
-        attn_outputs = self.mixer(hidden_states, past_key_values=past_key_values, attention_mask=attention_mask)
-        if isinstance(attn_outputs, tuple):
-            attn_outputs = attn_outputs[0]
-        attn_outputs = self.resid_dropout(attn_outputs)
-        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
-        hidden_states = attn_outputs + feed_forward_hidden_states + residual
-        return hidden_states
-class CausalLMHead(nn.Module):
-    """Causal Language Modeling head.
-    Reference:
-        Improving Language Understanding by Generative Pre-Training.
-        https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
-    """
-    def __init__(self, config: PretrainedConfig) -> None:
-        super().__init__()
-        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.linear = nn.Linear(config.n_embd, config.vocab_size)
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        hidden_states = self.ln(hidden_states)
-        logits = self.linear(hidden_states).to(torch.float32)
-        return logits
-class CausalLMLoss(nn.Module):
-    """Causal Language Modeling loss.
-    Reference:
-        Improving Language Understanding by Generative Pre-Training.
-        https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
-    """
-    def __init__(self, shift_labels: bool = True) -> None:
-        super().__init__()
-        self.shift_labels = shift_labels
-        self.loss_fct = nn.CrossEntropyLoss()
-    def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor:
-        if self.shift_labels:
-            logits = logits[..., :-1, :].contiguous()
-            labels = labels[..., 1:].contiguous()
-        loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
-        return loss
-class MixFormerSequentialPreTrainedModel(PreTrainedModel):
-    """MixFormer (sequential for DeepSpeed) pre-trained model."""
-    config_class = MixFormerSequentialConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    def __init__(self, *inputs, **kwargs) -> None:
-        super().__init__(*inputs, **kwargs)
-    def _init_weights(self, module: nn.Module) -> None:
-        if isinstance(module, (nn.Linear,)):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            if module.bias is not None:
-                module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-    def prepare_inputs_for_generation(
-        self,
-        input_ids: torch.LongTensor,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
-        **kwargs,
-    ) -> Dict[str, Any]:
-        if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
-            past_key_values = InferenceParams(
-                max_seqlen=self.config.n_positions,
-                max_batch_size=input_ids.shape[0],
-                seqlen_offset=0,
-                batch_size_offset=0,
-                key_value_memory_dict={},
-                lengths_per_sample=None,
-            )
-        else:
-            # Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
-            past_key_values.seqlen_offset = len(input_ids[0]) - 1
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-        return {
-            "input_ids": input_ids,
-            "past_key_values": past_key_values,
-            "attention_mask": attention_mask,
-        }
-    def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False) -> None:
-        if isinstance(module, MixFormerSequentialPreTrainedModel):
-            module.gradient_checkpointing = value
-class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
-    """MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
-    _keys_to_ignore_on_load_missing = [""]
-    _keys_to_ignore_on_load_unexpected = [r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
-    _no_split_modules = ["ParallelBlock"]
-    def __init__(self, config: MixFormerSequentialConfig) -> None:
-        super().__init__(config)
-        modules = [Embedding(config)]
-        modules += [ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
-        modules.append(CausalLMHead(config))
-        self.layers = nn.Sequential(*modules)
-        self.loss = CausalLMLoss()
-        self.post_init()
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.layers[0].wte
-    def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
-        self.layers[0].wte = new_embeddings
-    def get_output_embeddings(self) -> nn.Linear:
-        return self.layers[-1].linear
-    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
-        self.layers[-1].linear = new_embeddings
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> CausalLMOutputWithPast:
-        hidden_layer = self.layers[0](input_ids)
-        for module in self.layers[1:-1]:
-            hidden_layer = module(hidden_layer, past_key_values=past_key_values, attention_mask=attention_mask)
-        lm_logits = self.layers[-1](hidden_layer)
-        loss = None
-        if labels is not None:
-            loss = self.loss(lm_logits, labels)
-        return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)