d38e6f954e
Removes print regarding attention_mask to prevent excessive information from being logged.
772 lines
28 KiB
Python
772 lines
28 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
|
|
import copy
|
|
from typing import Any, Dict, Optional, Tuple, Union
|
|
from dataclasses import dataclass, field
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from einops import rearrange
|
|
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_sequence_len: Maximum sequence length.
|
|
max_batch_size: Maximum batch size.
|
|
sequence_len_offset: Sequence length offset.
|
|
batch_size_offset: Batch size offset.
|
|
key_value_memory_dict: Key value memory dictionary.
|
|
fused_ft_kernel: Whether to use fused kernel for fast inference.
|
|
lengths_per_sample: Lengths per sample.
|
|
|
|
"""
|
|
|
|
max_sequence_len: int = field(metadata={"help": "Maximum sequence length."})
|
|
|
|
max_batch_size: int = field(metadata={"help": "Maximum batch size."})
|
|
|
|
sequence_len_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."}
|
|
)
|
|
|
|
fused_ft_kernel: bool = field(default=False, metadata={"help": "Whether to use fused kernel for fast inference."})
|
|
|
|
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
|
|
|
|
|
|
class RotaryEmbedding(nn.Module):
|
|
"""Rotary embeddings.
|
|
|
|
Reference:
|
|
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py.
|
|
|
|
"""
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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, x: torch.FloatTensor, seqlen_offset: int = 0) -> None:
|
|
# Reset the tables if the sequence length has changed,
|
|
# or if we're on a new device (possibly due to tracing for instance)
|
|
seqlen = x.shape[1] + seqlen_offset
|
|
|
|
# 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=self.device, dtype=torch.float32) / self.dim)
|
|
)
|
|
|
|
if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
|
|
self._seq_len_cached = seqlen
|
|
t = torch.arange(seqlen, device=x.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(x.dtype)
|
|
self._sin_cached = torch.sin(freqs).to(x.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(x.dtype)
|
|
self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
|
|
self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
|
|
self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
|
|
|
|
def _apply_rotary_emb_qkv(
|
|
self,
|
|
qkv: torch.FloatTensor,
|
|
sin: torch.FloatTensor,
|
|
cos: torch.FloatTensor,
|
|
sin_k: Optional[torch.FloatTensor] = None,
|
|
cos_k: Optional[torch.FloatTensor] = None,
|
|
) -> torch.FloatTensor:
|
|
_, seqlen, three, _, headdim = qkv.shape
|
|
assert three == 3
|
|
|
|
rotary_seqlen, rotary_dim = cos.shape
|
|
rotary_dim *= 2
|
|
assert rotary_dim <= headdim
|
|
assert seqlen <= rotary_seqlen
|
|
|
|
cos_k = cos if cos_k is None else cos_k
|
|
sin_k = sin if sin_k is None else sin_k
|
|
assert sin.shape == cos_k.shape == sin_k.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:]
|
|
|
|
# Splits the queries and keys in half
|
|
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")
|
|
|
|
# Casts to fp32 are necessary to prevent fp16 overflow issues
|
|
q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
|
|
|
|
# Computes the new keys and queries, recasting to original dtype
|
|
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,
|
|
)
|
|
|
|
def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# `qkv` is of shape (batch, seqlen, 3, nheads, headdim)
|
|
self._update_cos_sin_cache(qkv, seqlen_offset)
|
|
return self._apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
|
|
|
|
|
|
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:
|
|
causal = self.causal if causal is None else causal
|
|
batch_size, seq_len = qkv.shape[0], qkv.shape[1]
|
|
q, k, v = qkv.unbind(dim=2)
|
|
|
|
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, seq_len), -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((seq_len, seq_len), -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:
|
|
causal = self.causal if causal is None else causal
|
|
batch_size, seq_len_q = q.shape[0], q.shape[1]
|
|
assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
|
|
|
|
seq_len_k = kv.shape[1]
|
|
k, v = kv.unbind(dim=2)
|
|
|
|
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, seq_len_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:
|
|
causal_mask = torch.triu(torch.full((seq_len_q, seq_len_k), -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
|
|
|
|
|
|
def find_mha_dims(
|
|
config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
|
|
) -> Tuple[int, int]:
|
|
"""Validate and return the number of heads and head dimension for multi-head attention.
|
|
|
|
Args:
|
|
config: Model configuration.
|
|
n_head: Number of heads.
|
|
head_dim: Head dimension.
|
|
|
|
Returns:
|
|
Number of heads and head dimension.
|
|
|
|
"""
|
|
|
|
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`.")
|
|
|
|
return n_head, head_dim
|
|
|
|
|
|
def update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
|
|
"""Update the key-value cache for inference.
|
|
|
|
Reference:
|
|
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
|
|
|
|
Args:
|
|
kv: Key-value tensor.
|
|
inference_params: Inference parameters.
|
|
layer_idx: Layer index.
|
|
|
|
Returns:
|
|
Updated key-value tensor.
|
|
|
|
"""
|
|
|
|
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_sequence_len,
|
|
2,
|
|
num_heads,
|
|
head_dim,
|
|
dtype=kv.dtype,
|
|
device=kv.device,
|
|
)
|
|
inference_params.key_value_memory_dict[layer_idx] = kv_cache
|
|
else:
|
|
if not inference_params.fused_ft_kernel:
|
|
kv_cache = inference_params.key_value_memory_dict[layer_idx]
|
|
else:
|
|
k_cache, v_cache = inference_params.key_value_memory_dict[layer_idx]
|
|
kv_cache = None
|
|
|
|
batch_start = inference_params.batch_size_offset
|
|
batch_end = batch_start + kv.shape[0]
|
|
assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
|
|
|
|
sequence_start = inference_params.sequence_len_offset
|
|
sequence_end = sequence_start + kv.shape[1]
|
|
assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
|
|
|
|
if not inference_params.fused_ft_kernel:
|
|
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
|
|
|
|
assert inference_params.sequence_len_offset == 0
|
|
assert kv.dtype in [torch.float16, torch.bfloat16, torch.float32]
|
|
|
|
packsize = 4 if kv.dtype == torch.float32 else 8
|
|
|
|
if kv_cache is not None:
|
|
kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
|
|
k_cache = rearrange(kv_cache[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize).contiguous()
|
|
v_cache = rearrange(kv_cache[:, :, 1], "b s h d -> b h s d").contiguous()
|
|
inference_params.key_value_memory_dict[layer_idx] = (k_cache, v_cache)
|
|
else:
|
|
k_cache[batch_start:batch_end, :, :, :sequence_end, :] = rearrange(
|
|
kv[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize
|
|
)
|
|
v_cache[batch_start:batch_end, :, :sequence_end, :] = rearrange(kv[:, :, 1], "b s h d -> b h s d")
|
|
|
|
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,
|
|
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.head_dim = find_mha_dims(config, n_head, head_dim)
|
|
op_size = self.n_head * self.head_dim
|
|
hidden_size = config.n_embd
|
|
|
|
self.Wqkv = nn.Linear(hidden_size, 3 * op_size, bias=bias, device=device, dtype=dtype)
|
|
self.out_proj = nn.Linear(op_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,
|
|
x: torch.FloatTensor,
|
|
past_key_values: Optional[InferenceParams] = None,
|
|
attention_mask: Optional[torch.BoolTensor] = None,
|
|
cu_seqlens: Optional[torch.LongTensor] = None,
|
|
max_seqlen: Optional[int] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
|
qkv = self.Wqkv(x)
|
|
qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
|
|
|
|
seqlen_offset = past_key_values.sequence_len_offset if past_key_values is not None else 0
|
|
if self.rotary_emb_dim > 0:
|
|
qkv = self.rotary_emb(qkv, seqlen_offset=seqlen_offset)
|
|
|
|
if past_key_values is not None:
|
|
kv = update_kv_cache(qkv[:, :, 1:], past_key_values, self.layer_idx)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask[0] if isinstance(attention_mask, tuple) else attention_mask
|
|
attention_mask = attention_mask.bool().to(qkv.device)
|
|
|
|
attention_kwargs = {"attention_mask": attention_mask}
|
|
|
|
if past_key_values is None or seqlen_offset == 0:
|
|
if self.checkpointing:
|
|
attn_output = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **attention_kwargs)
|
|
else:
|
|
attn_output = self.inner_attn(qkv, **attention_kwargs)
|
|
else:
|
|
q = qkv[:, :, 0]
|
|
causal = None if past_key_values.sequence_len_offset == 0 else False
|
|
attn_output = self.inner_cross_attn(q, kv, causal=causal, **attention_kwargs)
|
|
|
|
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):
|
|
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[torch.BoolTensor] = None,
|
|
**kwargs,
|
|
) -> Dict[str, Any]:
|
|
if attention_mask is not None and torch.any(~attention_mask.bool()):
|
|
total_seq_len = torch.sum(attention_mask, dim=1)
|
|
max_seq_len = torch.max(total_seq_len)
|
|
|
|
total_seq_len = torch.cat((torch.tensor([0], device=attention_mask.device), total_seq_len)).unsqueeze(1)
|
|
cumulative_seq_len = torch.cumsum(total_seq_len, dim=0).squeeze(1).to(torch.int32)
|
|
attention_mask = (attention_mask.bool(), cumulative_seq_len, max_seq_len.item())
|
|
else:
|
|
attention_mask = None
|
|
|
|
if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
|
|
past_key_values = InferenceParams(
|
|
max_batch_size=input_ids.shape[0],
|
|
max_sequence_len=self.config.n_positions,
|
|
sequence_len_offset=0,
|
|
batch_size_offset=0,
|
|
fused_ft_kernel=False,
|
|
key_value_memory_dict={},
|
|
)
|
|
else:
|
|
# Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
|
|
past_key_values.sequence_len_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, value=False):
|
|
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:
|
|
if past_key_values is None and attention_mask is None:
|
|
lm_logits = self.layers(input_ids)
|
|
else:
|
|
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)
|