Commit
•
0f4ae0e
1
Parent(s):
47c069f
Upload MixFormerSequentialForCausalLM
Browse files- config.json +35 -0
- configuration_mixformer_sequential.py +59 -0
- generation_config.json +4 -0
- modeling_mixformer_sequential.py +742 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "phi-1",
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"activation_function": "gelu_new",
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"architecture": {
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"block_cls": "parallel",
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"mixer": {},
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"mlp": {
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"mlp_cls": "mlp"
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}
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},
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"architectures": [
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"MixFormerSequentialForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_mixformer_sequential.MixFormerSequentialConfig",
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"AutoModelForCausalLM": "modeling_mixformer_sequential.MixFormerSequentialForCausalLM"
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},
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"embd_layer": "default",
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"embd_pdrop": 0.0,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "mixformer-sequential",
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"n_embd": 2048,
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"n_head": 32,
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"n_inner": null,
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"n_layer": 24,
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"n_positions": 2048,
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"phyagi_version": "0.0.4.dev",
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"resid_pdrop": 0.0,
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"rotary_dim": 32,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.32.1",
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"vocab_size": 51200
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}
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configuration_mixformer_sequential.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT license.
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import math
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from typing import Any, Dict, List, Optional, Union
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from transformers import PretrainedConfig
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class MixFormerSequentialConfig(PretrainedConfig):
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"""MixFormer (sequential for DeepSpeed) configuration."""
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model_type = "mixformer-sequential"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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"input_emb_layer": "embd_layer", # `input_emb_layer` key is for backward compatibility
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"blocks": "architecture", # `blocks` key is for backward compatibility
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}
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def __init__(
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self,
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vocab_size: Optional[int] = 50304,
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n_positions: Optional[int] = 2048,
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n_embd: Optional[int] = 1024,
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n_layer: Optional[int] = 20,
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n_inner: Optional[int] = None,
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n_head: Optional[int] = 16,
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rotary_dim: Optional[int] = 32,
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activation_function: Optional[str] = "gelu_new",
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embd_layer: Optional[str] = "default",
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architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
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embd_pdrop: Optional[float] = 0.0,
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resid_pdrop: Optional[float] = 0.0,
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layer_norm_epsilon: Optional[float] = 1e-5,
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initializer_range: Optional[float] = 0.02,
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tie_word_embeddings: Optional[bool] = False,
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pad_vocab_size_multiple: Optional[int] = 64,
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**kwargs
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) -> None:
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self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_inner = n_inner
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self.n_head = n_head
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self.rotary_dim = min(rotary_dim, n_embd // n_head)
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self.activation_function = activation_function
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self.embd_layer = embd_layer
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self.architecture = architecture
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self.embd_pdrop = embd_pdrop
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self.resid_pdrop = resid_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "4.32.1"
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}
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modeling_mixformer_sequential.py
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1 |
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# Copyright (c) Microsoft Corporation.
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2 |
+
# Licensed under the MIT license.
|
3 |
+
|
4 |
+
from __future__ import annotations
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5 |
+
|
6 |
+
import math
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7 |
+
import copy
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8 |
+
from typing import Any, Dict, Optional, Tuple
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9 |
+
from dataclasses import dataclass, field
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10 |
+
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11 |
+
import torch
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12 |
+
import torch.nn as nn
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13 |
+
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14 |
+
from einops import rearrange
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15 |
+
from transformers.activations import ACT2FN
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16 |
+
from transformers import PretrainedConfig, PreTrainedModel
|
17 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
18 |
+
|
19 |
+
from .configuration_mixformer_sequential import MixFormerSequentialConfig
|
20 |
+
|
21 |
+
@dataclass
|
22 |
+
class InferenceParams:
|
23 |
+
"""Inference parameters that are passed to the main model in order
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24 |
+
to efficienly calculate and store the context during inference."""
|
25 |
+
max_sequence_len: int
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26 |
+
max_batch_size: int
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27 |
+
sequence_len_offset: int = 0
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28 |
+
batch_size_offset: int = 0
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29 |
+
key_value_memory_dict: dict = field(default_factory=dict)
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30 |
+
fused_ft_kernel: bool = False
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31 |
+
lengths_per_sample: Optional[torch.Tensor] = None
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32 |
+
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33 |
+
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34 |
+
class Embedding(nn.Module):
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35 |
+
"""Token embedding with dropout."""
|
36 |
+
|
37 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
38 |
+
super().__init__()
|
39 |
+
|
40 |
+
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
|
41 |
+
self.drop = nn.Dropout(config.embd_pdrop)
|
42 |
+
|
43 |
+
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
|
44 |
+
input_shape = input_ids.size()
|
45 |
+
input_ids = input_ids.view(-1, input_shape[-1])
|
46 |
+
|
47 |
+
hidden_states = self.wte(input_ids)
|
48 |
+
hidden_states = self.drop(hidden_states)
|
49 |
+
|
50 |
+
return hidden_states
|
51 |
+
|
52 |
+
class RotaryEmbedding(nn.Module):
|
53 |
+
"""PyTorch implementation of `flash-attn` RotaryEmbedding layer."""
|
54 |
+
|
55 |
+
def __init__(
|
56 |
+
self,
|
57 |
+
dim: int,
|
58 |
+
base: Optional[int] = 10000,
|
59 |
+
scale_base: Optional[float] = None,
|
60 |
+
device: Optional[str] = None,
|
61 |
+
**kwargs,
|
62 |
+
) -> None:
|
63 |
+
super().__init__()
|
64 |
+
|
65 |
+
if scale_base is not None:
|
66 |
+
raise NotImplementedError
|
67 |
+
|
68 |
+
# Generate and save the inverse frequency buffer (non-trainable)
|
69 |
+
self.dim = dim
|
70 |
+
self.base = base
|
71 |
+
self.scale_base = scale_base
|
72 |
+
self.device = device
|
73 |
+
|
74 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
|
75 |
+
self.register_buffer("inv_freq", inv_freq)
|
76 |
+
|
77 |
+
scale = (
|
78 |
+
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
|
79 |
+
if scale_base is not None
|
80 |
+
else None
|
81 |
+
)
|
82 |
+
self.register_buffer("scale", scale)
|
83 |
+
|
84 |
+
self._seq_len_cached = 0
|
85 |
+
self._cos_cached = None
|
86 |
+
self._sin_cached = None
|
87 |
+
self._cos_k_cached = None
|
88 |
+
self._sin_k_cached = None
|
89 |
+
|
90 |
+
def _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0) -> None:
|
91 |
+
# Reset the tables if the sequence length has changed,
|
92 |
+
# or if we're on a new device (possibly due to tracing for instance)
|
93 |
+
seqlen = x.shape[1] + seqlen_offset
|
94 |
+
|
95 |
+
# Re-generate the inverse frequency buffer if it's not fp32
|
96 |
+
# (for instance if model.half() was called)
|
97 |
+
if self.inv_freq.dtype != "torch.float32":
|
98 |
+
self.inv_freq = 1.0 / (
|
99 |
+
self.base ** (torch.arange(0, self.dim, 2, device=self.device, dtype=torch.float32) / self.dim)
|
100 |
+
)
|
101 |
+
|
102 |
+
if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
|
103 |
+
self._seq_len_cached = seqlen
|
104 |
+
t = torch.arange(seqlen, device=x.device, dtype=torch.float32)
|
105 |
+
|
106 |
+
# Don't do einsum, it converts fp32 to fp16
|
107 |
+
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
108 |
+
freqs = torch.outer(t, self.inv_freq.to(device=t.device, dtype=torch.float32))
|
109 |
+
if self.scale is None:
|
110 |
+
self._cos_cached = torch.cos(freqs).to(x.dtype)
|
111 |
+
self._sin_cached = torch.sin(freqs).to(x.dtype)
|
112 |
+
else:
|
113 |
+
power = (
|
114 |
+
torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
|
115 |
+
) / self.scale_base
|
116 |
+
scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
|
117 |
+
|
118 |
+
# We want the multiplication by scale to happen in fp32
|
119 |
+
self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
|
120 |
+
self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
|
121 |
+
self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
|
122 |
+
self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
|
123 |
+
|
124 |
+
def apply_rotary_emb_qkv(
|
125 |
+
self,
|
126 |
+
qkv: torch.FloatTensor,
|
127 |
+
sin: torch.FloatTensor,
|
128 |
+
cos: torch.FloatTensor,
|
129 |
+
sin_k: Optional[torch.FloatTensor] = None,
|
130 |
+
cos_k: Optional[torch.FloatTensor] = None,
|
131 |
+
) -> torch.FloatTensor:
|
132 |
+
_, seqlen, three, _, headdim = qkv.shape
|
133 |
+
assert three == 3
|
134 |
+
|
135 |
+
rotary_seqlen, rotary_dim = cos.shape
|
136 |
+
rotary_dim *= 2
|
137 |
+
assert rotary_dim <= headdim
|
138 |
+
assert seqlen <= rotary_seqlen
|
139 |
+
|
140 |
+
cos_k = cos if cos_k is None else cos_k
|
141 |
+
sin_k = sin if sin_k is None else sin_k
|
142 |
+
assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
|
143 |
+
|
144 |
+
q_rot = qkv[:, :, 0, :, :rotary_dim]
|
145 |
+
q_pass = qkv[:, :, 0, :, rotary_dim:]
|
146 |
+
|
147 |
+
k_rot = qkv[:, :, 1, :, :rotary_dim]
|
148 |
+
k_pass = qkv[:, :, 1, :, rotary_dim:]
|
149 |
+
|
150 |
+
# Splits the queries and keys in half
|
151 |
+
q1, q2 = q_rot.chunk(2, dim=-1)
|
152 |
+
k1, k2 = k_rot.chunk(2, dim=-1)
|
153 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
|
154 |
+
|
155 |
+
# Casts to fp32 are necessary to prevent fp16 overflow issues
|
156 |
+
q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
|
157 |
+
|
158 |
+
# Computes the new keys and queries, recasting to original dtype
|
159 |
+
q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
|
160 |
+
|
161 |
+
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
|
162 |
+
|
163 |
+
return torch.cat(
|
164 |
+
[
|
165 |
+
torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
|
166 |
+
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
|
167 |
+
qkv[:, :, 2:3, :, :],
|
168 |
+
],
|
169 |
+
axis=2,
|
170 |
+
)
|
171 |
+
|
172 |
+
def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
|
173 |
+
"""Perform the forward pass.
|
174 |
+
|
175 |
+
Args:
|
176 |
+
qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
|
177 |
+
seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.
|
178 |
+
|
179 |
+
Returns:
|
180 |
+
New `qkv` and the cached sinusoids.
|
181 |
+
|
182 |
+
"""
|
183 |
+
|
184 |
+
self._update_cos_sin_cache(qkv, seqlen_offset)
|
185 |
+
|
186 |
+
return self.apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
|
187 |
+
|
188 |
+
def _update_kv_cache(kv, inference_params, layer_idx):
|
189 |
+
"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
|
190 |
+
"""
|
191 |
+
# Pre-allocate memory for key-values for inference.
|
192 |
+
num_heads, head_dim = kv.shape[-2:]
|
193 |
+
if layer_idx not in inference_params.key_value_memory_dict:
|
194 |
+
kv_cache = torch.empty(
|
195 |
+
inference_params.max_batch_size, inference_params.max_sequence_len, 2,
|
196 |
+
num_heads, head_dim, dtype=kv.dtype, device=kv.device
|
197 |
+
)
|
198 |
+
inference_params.key_value_memory_dict[layer_idx] = kv_cache
|
199 |
+
else:
|
200 |
+
kv_cache = inference_params.key_value_memory_dict[layer_idx]
|
201 |
+
|
202 |
+
# Adjust key and value for inference
|
203 |
+
batch_start = inference_params.batch_size_offset
|
204 |
+
batch_end = batch_start + kv.shape[0]
|
205 |
+
sequence_start = inference_params.sequence_len_offset
|
206 |
+
sequence_end = sequence_start + kv.shape[1]
|
207 |
+
assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
|
208 |
+
assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
|
209 |
+
|
210 |
+
assert kv_cache is not None
|
211 |
+
kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
|
212 |
+
kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
|
213 |
+
return kv
|
214 |
+
|
215 |
+
|
216 |
+
class MLP(nn.Module):
|
217 |
+
"""Multi-Layer Perceptron.
|
218 |
+
|
219 |
+
Reference:
|
220 |
+
Attention Is All You Need.
|
221 |
+
https://arxiv.org/pdf/1706.03762.pdf.
|
222 |
+
|
223 |
+
"""
|
224 |
+
|
225 |
+
def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None) -> None:
|
226 |
+
super().__init__()
|
227 |
+
|
228 |
+
act_fn = config.activation_function if act_fn is None else act_fn
|
229 |
+
assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
|
230 |
+
|
231 |
+
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
|
232 |
+
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
|
233 |
+
|
234 |
+
self.fc1 = nn.Linear(config.n_embd, n_inner)
|
235 |
+
self.fc2 = nn.Linear(n_inner, config.n_embd)
|
236 |
+
self.act = ACT2FN[act_fn]
|
237 |
+
|
238 |
+
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
|
239 |
+
old_keys = [prefix + "fc_in.weight", prefix + "fc_out.weight", prefix + "fc_in.bias", prefix + "fc_out.bias"]
|
240 |
+
new_keys = [prefix + "fc1.weight", prefix + "fc2.weight", prefix + "fc1.bias", prefix + "fc2.bias"]
|
241 |
+
|
242 |
+
if all(k in state_dict for k in old_keys) and not all(k in state_dict for k in new_keys):
|
243 |
+
# Older version of `MLP` saved with different key names.
|
244 |
+
for old_key, new_key in zip(old_keys, new_keys):
|
245 |
+
state_dict[new_key] = state_dict.pop(old_key)
|
246 |
+
|
247 |
+
return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
|
248 |
+
|
249 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
250 |
+
hidden_states = self.fc1(hidden_states)
|
251 |
+
hidden_states = self.act(hidden_states)
|
252 |
+
hidden_states = self.fc2(hidden_states)
|
253 |
+
|
254 |
+
return hidden_states
|
255 |
+
|
256 |
+
|
257 |
+
class FusedMLP(nn.Module):
|
258 |
+
"""Fused Multi-Layer Perceptron from `flash-attn`.
|
259 |
+
|
260 |
+
Reference:
|
261 |
+
https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.
|
262 |
+
|
263 |
+
"""
|
264 |
+
def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None,
|
265 |
+
raise_on_missing: bool = False) -> None:
|
266 |
+
super().__init__()
|
267 |
+
|
268 |
+
act_fn = config.activation_function if act_fn is None else act_fn
|
269 |
+
assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
|
270 |
+
|
271 |
+
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
|
272 |
+
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
|
273 |
+
|
274 |
+
gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"]
|
275 |
+
activation = "gelu_approx" if act_fn in gelu_activations else "relu"
|
276 |
+
|
277 |
+
self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)
|
278 |
+
|
279 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
280 |
+
return self.mlp(hidden_states)
|
281 |
+
|
282 |
+
class SelfAttention(nn.Module):
|
283 |
+
"""Implement the scaled dot product attention with softmax.
|
284 |
+
Arguments
|
285 |
+
---------
|
286 |
+
softmax_scale: The temperature to use for the softmax attention.
|
287 |
+
(default: 1/sqrt(d_keys) where d_keys is computed at
|
288 |
+
runtime)
|
289 |
+
attention_dropout: The dropout rate to apply to the attention
|
290 |
+
(default: 0.0)
|
291 |
+
"""
|
292 |
+
def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
|
293 |
+
super().__init__()
|
294 |
+
self.causal = causal
|
295 |
+
self.softmax_scale = softmax_scale
|
296 |
+
self.drop = nn.Dropout(attention_dropout)
|
297 |
+
|
298 |
+
def forward(self, qkv, causal=None, key_padding_mask=None):
|
299 |
+
"""Implements the multihead softmax attention.
|
300 |
+
Arguments
|
301 |
+
---------
|
302 |
+
qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
|
303 |
+
causal: if passed, will override self.causal
|
304 |
+
key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
|
305 |
+
False means to mask out. (B, S)
|
306 |
+
"""
|
307 |
+
batch_size, seqlen = qkv.shape[0], qkv.shape[1]
|
308 |
+
causal = self.causal if causal is None else causal
|
309 |
+
q, k, v = qkv.unbind(dim=2)
|
310 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
311 |
+
scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
|
312 |
+
if key_padding_mask is not None:
|
313 |
+
padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype,
|
314 |
+
device=scores.device)
|
315 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
316 |
+
# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
|
317 |
+
scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
|
318 |
+
if causal:
|
319 |
+
# "triu_tril_cuda_template" not implemented for 'BFloat16'
|
320 |
+
# So we have to construct the mask in float
|
321 |
+
causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
|
322 |
+
# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
|
323 |
+
scores = scores + causal_mask.to(dtype=scores.dtype)
|
324 |
+
attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
|
325 |
+
attention_drop = self.drop(attention)
|
326 |
+
output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
|
327 |
+
return output
|
328 |
+
|
329 |
+
|
330 |
+
class CrossAttention(nn.Module):
|
331 |
+
"""Implement the scaled dot product attention with softmax.
|
332 |
+
Arguments
|
333 |
+
---------
|
334 |
+
softmax_scale: The temperature to use for the softmax attention.
|
335 |
+
(default: 1/sqrt(d_keys) where d_keys is computed at
|
336 |
+
runtime)
|
337 |
+
attention_dropout: The dropout rate to apply to the attention
|
338 |
+
(default: 0.0)
|
339 |
+
"""
|
340 |
+
def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
|
341 |
+
super().__init__()
|
342 |
+
self.causal = causal
|
343 |
+
self.softmax_scale = softmax_scale
|
344 |
+
self.drop = nn.Dropout(attention_dropout)
|
345 |
+
|
346 |
+
def forward(self, q, kv, causal=None, key_padding_mask=None):
|
347 |
+
"""Implements the multihead softmax attention.
|
348 |
+
Arguments
|
349 |
+
---------
|
350 |
+
q: The tensor containing the query. (B, Sq, H, D)
|
351 |
+
kv: The tensor containing the key and value. (B, Sk, 2, H, D)
|
352 |
+
causal: if passed, will override self.causal
|
353 |
+
key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
|
354 |
+
False means to mask out. (B, Sk)
|
355 |
+
"""
|
356 |
+
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
357 |
+
causal = self.causal if causal is None else causal
|
358 |
+
seqlen_k = kv.shape[1]
|
359 |
+
assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
|
360 |
+
k, v = kv.unbind(dim=2)
|
361 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
362 |
+
scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
|
363 |
+
if key_padding_mask is not None:
|
364 |
+
padding_mask = torch.full((batch_size, seqlen_k), -10000.0, dtype=scores.dtype,
|
365 |
+
device=scores.device)
|
366 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
367 |
+
# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
|
368 |
+
scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
|
369 |
+
if causal:
|
370 |
+
# "triu_tril_cuda_template" not implemented for 'BFloat16'
|
371 |
+
# So we have to construct the mask in float
|
372 |
+
causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0,
|
373 |
+
device=scores.device), 1)
|
374 |
+
# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
|
375 |
+
scores = scores + causal_mask.to(dtype=scores.dtype)
|
376 |
+
attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
|
377 |
+
attention_drop = self.drop(attention)
|
378 |
+
output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
|
379 |
+
return output
|
380 |
+
|
381 |
+
def find_mha_dims(
|
382 |
+
config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
|
383 |
+
) -> Tuple[int, int]:
|
384 |
+
"""Validate and return the number of heads and head dimension for multi-head attention.
|
385 |
+
|
386 |
+
Args:
|
387 |
+
config: Model configuration.
|
388 |
+
n_head: Number of heads.
|
389 |
+
head_dim: Head dimension.
|
390 |
+
|
391 |
+
Returns:
|
392 |
+
Number of heads and head dimension.
|
393 |
+
|
394 |
+
"""
|
395 |
+
|
396 |
+
assert all(
|
397 |
+
hasattr(config, attr) for attr in ["n_embd", "n_head"]
|
398 |
+
), "`config` must have `n_embd` and `n_head` attributes."
|
399 |
+
|
400 |
+
if head_dim is None:
|
401 |
+
assert (
|
402 |
+
config.n_embd % config.n_head == 0
|
403 |
+
), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
|
404 |
+
|
405 |
+
if n_head is None and head_dim is None:
|
406 |
+
head_dim = config.n_embd // config.n_head
|
407 |
+
n_head = config.n_head
|
408 |
+
elif n_head is None or head_dim is None:
|
409 |
+
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
|
410 |
+
|
411 |
+
return n_head, head_dim
|
412 |
+
|
413 |
+
|
414 |
+
class MHA(nn.Module):
|
415 |
+
"""Multi-head attention layer."""
|
416 |
+
|
417 |
+
def __init__(
|
418 |
+
self,
|
419 |
+
config: PretrainedConfig,
|
420 |
+
rotary_dim: Optional[int] = None,
|
421 |
+
n_head: Optional[int] = None,
|
422 |
+
head_dim: Optional[int] = None,
|
423 |
+
bias: Optional[bool] = True,
|
424 |
+
dropout: Optional[float] = 0.0,
|
425 |
+
softmax_scale: Optional[float] = None,
|
426 |
+
causal: Optional[bool] = True,
|
427 |
+
layer_idx: Optional[int] = None,
|
428 |
+
rotary_emb_scale_base: Optional[float] = None,
|
429 |
+
return_residual: Optional[bool] = False,
|
430 |
+
checkpointing: Optional[bool] = False,
|
431 |
+
device: Optional[str] = None,
|
432 |
+
dtype: Optional[torch.dtype] = None,
|
433 |
+
fused_dense: Optional[bool] = True,
|
434 |
+
flash_attn: Optional[bool] = True,
|
435 |
+
cutlass_attn: Optional[bool] = False,
|
436 |
+
flash_rotary: Optional[bool] = True,
|
437 |
+
raise_on_missing: Optional[bool] = False
|
438 |
+
) -> None:
|
439 |
+
super().__init__()
|
440 |
+
|
441 |
+
factory_kwargs = {"device": device, "dtype": dtype}
|
442 |
+
n_head, head_dim = find_mha_dims(config, n_head, head_dim)
|
443 |
+
|
444 |
+
self.hidden_size = config.n_embd
|
445 |
+
self.n_head = n_head
|
446 |
+
self.head_dim = head_dim
|
447 |
+
self.op_size = n_head * head_dim
|
448 |
+
|
449 |
+
self.causal = causal
|
450 |
+
self.layer_idx = layer_idx
|
451 |
+
self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
|
452 |
+
self.fused_dense = fused_dense
|
453 |
+
self.flash_attn = flash_attn
|
454 |
+
self.cutlass_attn = cutlass_attn
|
455 |
+
self.flash_rotary = flash_rotary
|
456 |
+
self.return_residual = return_residual
|
457 |
+
self.checkpointing = checkpointing
|
458 |
+
|
459 |
+
if self.rotary_emb_dim > 0:
|
460 |
+
rotary_kwargs = {"device": device}
|
461 |
+
if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
|
462 |
+
rotary_kwargs["scale_base"] = rotary_emb_scale_base
|
463 |
+
|
464 |
+
self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
|
465 |
+
else:
|
466 |
+
pass
|
467 |
+
|
468 |
+
self.Wqkv = nn.Linear(self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs)
|
469 |
+
self.out_proj = nn.Linear(self.op_size, self.hidden_size, bias=bias, **factory_kwargs)
|
470 |
+
|
471 |
+
self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
|
472 |
+
self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
|
473 |
+
|
474 |
+
def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
|
475 |
+
"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
|
476 |
+
|
477 |
+
assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
|
478 |
+
|
479 |
+
return _update_kv_cache(kv, inference_params, self.layer_idx)
|
480 |
+
|
481 |
+
def forward(
|
482 |
+
self,
|
483 |
+
x: torch.FloatTensor,
|
484 |
+
x_kv: Optional[torch.FloatTensor] = None,
|
485 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
486 |
+
cu_seqlens: Optional[torch.LongTensor] = None,
|
487 |
+
max_seqlen: Optional[int] = None,
|
488 |
+
mixer_subset: Optional[torch.LongTensor] = None,
|
489 |
+
past_cache: Optional[InferenceParams] = None,
|
490 |
+
**kwargs
|
491 |
+
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
492 |
+
"""Perform the forward pass.
|
493 |
+
|
494 |
+
Args:
|
495 |
+
x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
|
496 |
+
cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
|
497 |
+
is the is the sum of the sequence lengths in the batch.
|
498 |
+
x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
|
499 |
+
key_padding_mask: boolean mask, True means to keep, False means to mask out.
|
500 |
+
(batch, seqlen). Only applicable when not using FlashAttention.
|
501 |
+
cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
|
502 |
+
of the sequences in the batch, used to index into x. Only applicable when using
|
503 |
+
FlashAttention.
|
504 |
+
max_seqlen: int. Maximum sequence length in the batch.
|
505 |
+
mixer_subset: for cross-attention only. If not None, will take a subset of x
|
506 |
+
before applying the query projection. Useful for e.g., ViT where we only care
|
507 |
+
about the CLS token in the last layer.
|
508 |
+
past_cache: For generation only.
|
509 |
+
|
510 |
+
Returns:
|
511 |
+
(batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
|
512 |
+
else (total, hidden_dim) where total is the is the sum of the sequence lengths
|
513 |
+
in the batch.
|
514 |
+
|
515 |
+
"""
|
516 |
+
|
517 |
+
if cu_seqlens is not None:
|
518 |
+
assert max_seqlen is not None
|
519 |
+
assert key_padding_mask is None
|
520 |
+
assert self.flash_attn
|
521 |
+
assert self.rotary_emb_dim == 0
|
522 |
+
|
523 |
+
if key_padding_mask is not None:
|
524 |
+
assert cu_seqlens is None
|
525 |
+
assert max_seqlen is None
|
526 |
+
assert not self.flash_attn
|
527 |
+
|
528 |
+
if past_cache is not None:
|
529 |
+
assert key_padding_mask is None
|
530 |
+
assert cu_seqlens is None and max_seqlen is None
|
531 |
+
|
532 |
+
attn_kwargs = {"key_padding_mask": key_padding_mask}
|
533 |
+
|
534 |
+
assert x_kv is None and mixer_subset is None
|
535 |
+
|
536 |
+
qkv = self.Wqkv(x)
|
537 |
+
qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
|
538 |
+
|
539 |
+
if past_cache is None:
|
540 |
+
if self.rotary_emb_dim > 0:
|
541 |
+
qkv = self.rotary_emb(qkv)
|
542 |
+
context = self.inner_attn(qkv, **attn_kwargs)
|
543 |
+
|
544 |
+
else:
|
545 |
+
if self.rotary_emb_dim > 0:
|
546 |
+
qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
|
547 |
+
q = qkv[:, :, 0]
|
548 |
+
kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
|
549 |
+
# If we're processing the prompt, causal=None (use self.causal).
|
550 |
+
# If we're decoding, then causal=False.
|
551 |
+
causal = None if past_cache.sequence_len_offset == 0 else False
|
552 |
+
context = self.inner_cross_attn(q, kv, causal=causal)
|
553 |
+
|
554 |
+
out = rearrange(context, "... h d -> ... (h d)")
|
555 |
+
out = self.out_proj(out)
|
556 |
+
|
557 |
+
return out if not self.return_residual else (out, x)
|
558 |
+
|
559 |
+
class ParallelBlock(nn.Module):
|
560 |
+
"""Parallel block.
|
561 |
+
|
562 |
+
This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
|
563 |
+
|
564 |
+
"""
|
565 |
+
|
566 |
+
def __init__(
|
567 |
+
self,
|
568 |
+
config: PretrainedConfig,
|
569 |
+
mixer: Optional[Dict[str, Any]] = None,
|
570 |
+
mlp: Optional[Dict[str, Any]] = None,
|
571 |
+
block_idx: Optional[int] = None,
|
572 |
+
) -> None:
|
573 |
+
super().__init__()
|
574 |
+
|
575 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
576 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
577 |
+
self.block_idx = block_idx
|
578 |
+
|
579 |
+
self.mixer = MHA(config=config, **mixer, layer_idx=block_idx)
|
580 |
+
mlp_cls = mlp.pop('mlp_cls')
|
581 |
+
if mlp_cls == 'fused_mlp':
|
582 |
+
self.mlp = FusedMLP(config=config, **mlp)
|
583 |
+
else:
|
584 |
+
self.mlp = MLP(config=config, **mlp)
|
585 |
+
|
586 |
+
def forward(self, hidden_states: torch.FloatTensor,
|
587 |
+
past_cache: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
|
588 |
+
residual = hidden_states
|
589 |
+
hidden_states = self.ln(hidden_states)
|
590 |
+
|
591 |
+
attn_outputs = self.mixer(hidden_states, past_cache=past_cache)
|
592 |
+
if isinstance(attn_outputs, tuple):
|
593 |
+
attn_outputs = attn_outputs[0]
|
594 |
+
|
595 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
596 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
597 |
+
|
598 |
+
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
599 |
+
|
600 |
+
return hidden_states
|
601 |
+
|
602 |
+
class CausalLMHead(nn.Module):
|
603 |
+
"""Causal Language Modeling head.
|
604 |
+
|
605 |
+
Reference:
|
606 |
+
Improving Language Understanding by Generative Pre-Training.
|
607 |
+
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
608 |
+
|
609 |
+
"""
|
610 |
+
|
611 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
612 |
+
super().__init__()
|
613 |
+
|
614 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
615 |
+
self.linear = nn.Linear(config.n_embd, config.vocab_size)
|
616 |
+
|
617 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
618 |
+
hidden_states = self.ln(hidden_states)
|
619 |
+
logits = self.linear(hidden_states).to(torch.float32)
|
620 |
+
|
621 |
+
return logits
|
622 |
+
|
623 |
+
|
624 |
+
class CausalLMLoss(nn.Module):
|
625 |
+
"""Causal Language Modeling loss.
|
626 |
+
|
627 |
+
Reference:
|
628 |
+
Improving Language Understanding by Generative Pre-Training.
|
629 |
+
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
630 |
+
|
631 |
+
"""
|
632 |
+
|
633 |
+
def __init__(self, shift_labels: Optional[bool] = True) -> None:
|
634 |
+
super().__init__()
|
635 |
+
|
636 |
+
self.shift_labels = shift_labels
|
637 |
+
self.loss_fct = nn.CrossEntropyLoss()
|
638 |
+
|
639 |
+
def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor:
|
640 |
+
if self.shift_labels:
|
641 |
+
logits = logits[..., :-1, :].contiguous()
|
642 |
+
labels = labels[..., 1:].contiguous()
|
643 |
+
|
644 |
+
loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
|
645 |
+
|
646 |
+
return loss
|
647 |
+
|
648 |
+
class MixFormerSequentialPreTrainedModel(PreTrainedModel):
|
649 |
+
"""MixFormer (sequential for DeepSpeed) pre-trained model."""
|
650 |
+
|
651 |
+
config_class = MixFormerSequentialConfig
|
652 |
+
base_model_prefix = "transformer"
|
653 |
+
supports_gradient_checkpointing = True
|
654 |
+
|
655 |
+
def __init__(self, *inputs, **kwargs) -> None:
|
656 |
+
super().__init__(*inputs, **kwargs)
|
657 |
+
|
658 |
+
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs) -> Dict[str, Any]:
|
659 |
+
if "use_cache" in kwargs and not kwargs["use_cache"]:
|
660 |
+
return {"input_ids": input_ids}
|
661 |
+
|
662 |
+
if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
|
663 |
+
past_key_values = InferenceParams(
|
664 |
+
max_batch_size=input_ids.shape[0],
|
665 |
+
max_sequence_len=self.config.n_positions,
|
666 |
+
sequence_len_offset=0,
|
667 |
+
batch_size_offset=0,
|
668 |
+
fused_ft_kernel=False,
|
669 |
+
key_value_memory_dict={},
|
670 |
+
)
|
671 |
+
else:
|
672 |
+
# assume past_key_values has cached all but last token in input_ids
|
673 |
+
past_key_values.sequence_len_offset = len(input_ids[0]) - 1
|
674 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
675 |
+
|
676 |
+
return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}
|
677 |
+
|
678 |
+
|
679 |
+
class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
|
680 |
+
"""MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
|
681 |
+
|
682 |
+
_keys_to_ignore_on_load_missing = [""]
|
683 |
+
_keys_to_ignore_on_load_unexpected = [r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
|
684 |
+
|
685 |
+
def __init__(self, config: MixFormerSequentialConfig) -> None:
|
686 |
+
super().__init__(config)
|
687 |
+
|
688 |
+
modules = [Embedding(config)]
|
689 |
+
block_config = config.architecture
|
690 |
+
|
691 |
+
if not isinstance(block_config, list):
|
692 |
+
block_config = [block_config for _ in range(config.n_layer)]
|
693 |
+
|
694 |
+
if config.n_layer != len(block_config):
|
695 |
+
config.n_layer = len(block_config)
|
696 |
+
|
697 |
+
for block_idx, block in enumerate(block_config):
|
698 |
+
# `block_cls` with `legacy` value is for backward compatibility
|
699 |
+
# `path` key is for backward compatibility
|
700 |
+
block = copy.deepcopy(block) or {"block_cls": "parallel"}
|
701 |
+
block_cls = block.pop("path", None) or block.pop("block_cls", None)
|
702 |
+
|
703 |
+
block["block_idx"] = block_idx
|
704 |
+
modules.append(ParallelBlock(config, **block))
|
705 |
+
|
706 |
+
modules.append(CausalLMHead(config))
|
707 |
+
|
708 |
+
self.layers = nn.Sequential(*modules)
|
709 |
+
self.loss = CausalLMLoss()
|
710 |
+
|
711 |
+
self.post_init()
|
712 |
+
|
713 |
+
def get_input_embeddings(self) -> nn.Embedding:
|
714 |
+
return self.layers[0].wte
|
715 |
+
|
716 |
+
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
717 |
+
self.layers[0].wte = new_embeddings
|
718 |
+
|
719 |
+
def get_output_embeddings(self) -> nn.Linear:
|
720 |
+
return self.layers[-1].linear
|
721 |
+
|
722 |
+
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
|
723 |
+
self.layers[-1].linear = new_embeddings
|
724 |
+
|
725 |
+
def forward(
|
726 |
+
self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None,
|
727 |
+
past_key_values: Optional[torch.FloatTensor] = None, **kwargs
|
728 |
+
) -> CausalLMOutputWithPast:
|
729 |
+
|
730 |
+
if not past_key_values:
|
731 |
+
lm_logits = self.layers(input_ids)
|
732 |
+
else:
|
733 |
+
hidden_layer = self.layers[0](input_ids)
|
734 |
+
for module in self.layers[1:-1]:
|
735 |
+
hidden_layer = module(hidden_layer, past_cache=past_key_values)
|
736 |
+
lm_logits = self.layers[-1](hidden_layer)
|
737 |
+
|
738 |
+
loss = None
|
739 |
+
if labels is not None:
|
740 |
+
loss = self.loss(lm_logits, labels)
|
741 |
+
|
742 |
+
return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0bd82d6ec4fc74e2800a71d20c6ef8ea85724fdb41e34711858377ef5b46b4e2
|
3 |
+
size 5673167489
|