824 lines
29 KiB
Python
824 lines
29 KiB
Python
# 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
|
|
|
|
|
|
@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,
|
|
device: Optional[str] = None,
|
|
**kwargs,
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
if scale_base is not None:
|
|
raise NotImplementedError
|
|
|
|
# Generate and save the inverse frequency buffer (non-trainable)
|
|
self.dim = dim
|
|
self.base = base
|
|
self.scale_base = scale_base
|
|
self.device = device
|
|
|
|
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
|
|
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 _update_cos_sin_cache(
|
|
self, seqlen: int, device: Optional[str] = None, dtype: Optional[torch.dtype] = None
|
|
) -> None:
|
|
# Re-generate the inverse frequency buffer if it's not fp32
|
|
# (for instance if model.half() was called)
|
|
if self.inv_freq.dtype != "torch.float32":
|
|
self.inv_freq = 1.0 / (
|
|
self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim)
|
|
)
|
|
|
|
if seqlen > self._seq_len_cached or self._cos_cached.device != device or self._cos_cached.dtype != dtype:
|
|
self._seq_len_cached = seqlen
|
|
t = torch.arange(seqlen, device=device, dtype=torch.float32)
|
|
|
|
# Don't do einsum, it converts fp32 to fp16
|
|
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
|
freqs = torch.outer(t, self.inv_freq.to(device=t.device, dtype=torch.float32))
|
|
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")
|
|
|
|
# We want the multiplication by scale 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,
|
|
dropout: float = 0.0,
|
|
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
|
|
self.rotary_emb = RotaryEmbedding(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
|
|
|
|
self.Wqkv = nn.Linear(hidden_size, op_size, bias=bias, device=device, dtype=dtype)
|
|
self.out_proj = nn.Linear(hidden_size, hidden_size, bias=bias, device=device, dtype=dtype)
|
|
|
|
# Attention
|
|
self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
|
|
self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
|
|
|
|
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)
|