File size: 15,197 Bytes
c6e7238 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 |
import mesh_tensorflow as mtf
import tensorflow.compat.v1 as tf
import math
import mesh_tensorflow.transformer as mtf_transformer
from models.activations import get_activation_fn
# --------------------------------------------------------------------------------
# LAYERS:
sentinel = object()
def exists(x):
return x is not None
def identity(x, *args, **kwargs):
return x
def is_incremental_inference(context):
return exists(context) and context.mode == "incremental"
def norm(x, axis, epsilon=1e-8):
x -= mtf.reduce_mean(x, reduced_dim=axis, name="norm_reduce_mean_u")
s = mtf.reduce_mean(mtf.square(x), reduced_dim=axis, name="norm_reduce_mean_s")
return x * mtf.rsqrt(s + epsilon)
def rezero(x, scope, dtype):
with tf.variable_scope(scope):
g = mtf.get_variable(x.mesh, "g", [], initializer=tf.constant_initializer(0), dtype=dtype)
return x * g
def scale_norm(x, scope, *, variable_dtype, axis=sentinel, epsilon=1e-5, params=None):
if axis is sentinel:
axis = x.shape[-1]
with tf.variable_scope(scope):
g = mtf.get_variable(x.mesh, "g", [], initializer=tf.constant_initializer(1),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
x = norm(x, axis, epsilon)
x = x * g
return x
def layer_norm(x, scope, *, variable_dtype, axis=sentinel, epsilon=1e-5, params=None):
"""Normalize to mean = 0, std = 1, then do a diagonal affine transform."""
if axis is sentinel:
axis = x.shape[-1]
with tf.variable_scope(scope):
n_state = x.shape[-1]
g = mtf.get_variable(x.mesh, "g", [n_state], initializer=tf.constant_initializer(1),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
b = mtf.get_variable(x.mesh, "b", [n_state], initializer=tf.constant_initializer(0),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
x = norm(x, axis, epsilon)
x = x * g + b
return x
def linear_attention(q, k, v):
batch_dim, seq_dim, head_dim, dim_out = (v.shape[0], v.shape[1], v.shape[2], v.shape[3])
q = mtf.rename_dimension(q, "features_per_head", "features_per_head_in")
k = mtf.rename_dimension(k, "features_per_head", "features_per_head_in")
dim_in = k.shape[-1]
q = mtf.softmax(q, dim_in)
k = mtf.softmax(k, seq_dim)
context = mtf.einsum([k, v], output_shape=[batch_dim, head_dim, dim_in, dim_out])
attn = mtf.einsum([q, context], output_shape=[batch_dim, seq_dim, head_dim, dim_out])
return attn
def causal_linear_attention(q, k, v, eps = 1e-6):
batch_dim, seq_dim, head_dim, dim_out = (v.shape[0], v.shape[1], v.shape[2], v.shape[3])
q = mtf.rename_dimension(q, "features_per_head", "features_per_head_in")
k = mtf.rename_dimension(k, "features_per_head", "features_per_head_in")
dim_in = k.shape[-1]
q = mtf.softmax(q, dim_in)
k = mtf.exp(k)
cumulative_k = mtf.cumsum(k, seq_dim) + eps
D_inv = 1. / mtf.einsum([q, cumulative_k], output_shape=[batch_dim, seq_dim, head_dim])
context = mtf.einsum([k, v], output_shape=[batch_dim, seq_dim, head_dim, dim_in, dim_out])
cumulative_context = mtf.cumsum(context, seq_dim)
attn = mtf.einsum([q, cumulative_context, D_inv], output_shape=[batch_dim, seq_dim, head_dim, dim_out])
return attn
def linear(x, scope, nf, *, w_init_stdev=0.02, variable_dtype, params=None, scale=False):
# nf = number of features
if params["scale_by_depth"] and scale:
# Scale by sqrt(num_layers), only happens at the final projection before a res block output
w_init_stdev = w_init_stdev * (1. / math.sqrt(params["n_layer"]))
if params["scale_by_in"]: # Scale by sqrt(num_input_features)
w_init_stdev = w_init_stdev * (1. / math.sqrt(x.shape[-1].size)) # Dimension is a namedtuple of (name, size)
# Not in the variable_scope because mtf already has a variable_scope in it
with tf.variable_scope("conv1d_main"):
c = mtf.layers.dense(x, new_dims=[nf], reduced_dims=[x.shape[-1]], name=scope, use_bias=True,
kernel_initializer=tf.random_normal_initializer(stddev=w_init_stdev),
variable_dtype=variable_dtype,
)
return c
def memory_key_values(k, v, num_mem_kv, dim_batch, dim_heads, variable_dtype, mesh):
"""memory / key values from all attention paper"""
dim_mem_kv = mtf.Dimension("mem_kv_sequence", num_mem_kv)
emb_dim = k.shape[-1]
mem_std = 1 / math.sqrt(emb_dim.size)
mem_k = mtf.get_variable(mesh, "mem_k", mtf.Shape([dim_mem_kv, dim_heads, emb_dim]),
initializer=tf.random_normal_initializer(stddev=mem_std),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype,
)
mem_v = mtf.get_variable(mesh, "mem_v", mtf.Shape([dim_mem_kv, dim_heads, emb_dim]),
initializer=tf.random_normal_initializer(stddev=mem_std),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
mem_k, mem_v = map(lambda t: mtf.broadcast(t, [dim_batch, dim_mem_kv, dim_heads, emb_dim]),
(mem_k, mem_v))
mem_k, mem_v = map(lambda t: mtf.rename_dimension(t, "mem_kv_sequence", "sequence"),
(mem_k, mem_v))
k = mtf.concat([mem_k, k], "sequence")
v = mtf.concat([mem_v, v], "sequence")
return k, v
def attn(x, scope, n_state, *, attention_type, params, bias, dim_seq, memory_length_dim, variable_dtype, context=None, pos_emb=None):
# x :: [batch, seq, n_embd]
x_shape, dim_batch, *_, dim_embd, mesh = x.shape, *x.shape, x.mesh
# n_state is the same as config["n_embd"], which is also the same as dim_embd.
assert n_state.size % params["n_head"] == 0
dim_heads = mtf.Dimension("heads", params["n_head"])
num_mem_kv = params.get("num_mem_kv", 0)
use_num_mem_kv = num_mem_kv > 0
with tf.variable_scope(scope):
# Compute attention inputs
dim_kv = mtf.Dimension("features_per_head", params["n_embd"] // params["n_head"])
mtfparams = mtf.transformer.attention.attention_params_simple(
x.mesh,
io_dim=dim_embd,
kv_dim=dim_kv,
heads_dim=dim_heads,
variable_dtype=variable_dtype
)
q = mtfparams.compute_q(x)
k = mtfparams.compute_k(x)
v = mtfparams.compute_v(x)
if is_incremental_inference(context):
one_hot = mtf.one_hot(context.position - 1, dim_seq, dtype=variable_dtype.master_dtype)
inv_one_hot = 1.0 - one_hot
old_k, old_v = context.get_states(2)
k = old_k * inv_one_hot + k * one_hot
v = old_v * inv_one_hot + v * one_hot
if exists(context):
context.record_new_states([k, v])
if exists(pos_emb):
cos, sin = pos_emb
k = apply_rotary_emb(k, cos, sin)
if is_incremental_inference(context):
seq_dim = cos.shape.get_dim_by_name('sequence')
cos = mtf.gather(cos, context.position - 1, seq_dim)
sin = mtf.gather(sin, context.position - 1, seq_dim)
q = apply_rotary_emb(q, cos, sin)
with tf.variable_scope("attention"):
if attention_type == "local":
# `local_attention_1d` has built in autoregressive masking, so we don't need mask_attn_weights.
radius = params.get("local_attention_radius", 256)
if is_incremental_inference(context):
q *= one_hot
a = mtf_transformer.attention.local_attention_1d(
q, k, v,
length_dim=k.shape[1],
key_dim=dim_kv,
value_dim=dim_kv,
radius=radius,
length_dim_num_splits=1,
fully_autoregressive=params["causal"],
attention_kwargs={},
)
if is_incremental_inference(context):
a = mtf.gather(a, context.position - 1, dim_seq)
elif attention_type == "global":
# TODO: pass in fake context
# Broadcast mask bias across batch and heads
if exists(bias):
if not is_incremental_inference(context):
broadcasted_bias = mtf.broadcast(bias, [dim_batch, dim_heads, bias.shape[-2], bias.shape[-1]])
else:
# In the incremental case, a custom mask needs to be built that masks out all key/values that are greater than the current position
bias = mtf.gather(bias, context.position - 1, dim_seq)
broadcasted_bias = mtf.broadcast(bias, [dim_batch, dim_heads, bias.shape[-1]])
# memory key / values, from all-attention paper
if use_num_mem_kv:
k, v = memory_key_values(k, v, num_mem_kv, dim_batch, dim_heads, variable_dtype, mesh)
k = mtf.replace_dimensions(k, k.shape[1], memory_length_dim)
v = mtf.replace_dimensions(v, v.shape[1], memory_length_dim)
attn_dropout_rate = params["attn_dropout"] if params["mode"] == "train" else 0
a = mtf_transformer.attention.attention(
q, k, v,
memory_length_dim=memory_length_dim,
key_dim=dim_kv,
value_dim=dim_kv,
bias=broadcasted_bias,
dropout_rate=attn_dropout_rate
)
elif attention_type == "linear":
linear_attn_fn = causal_linear_attention if params["causal"] else linear_attention
a = linear_attn_fn(q, k, v)
else:
raise NotImplementedError("Unknown attention type {}!".format(attention_type))
with tf.variable_scope("compute_output"):
a = mtfparams.compute_output(a, x_shape)
with tf.variable_scope("compute_output_bias"):
b = mtf.get_variable(x.mesh, "o_b", [dim_embd], initializer=tf.constant_initializer(0),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
a += b
if params["mode"] == "train" and params["res_dropout"] > 0:
a = mtf.dropout(a, rate=params["res_dropout"], name="res_dropout")
return a
def mlp(x, scope, n_state, *, variable_dtype, params):
activation_fn = get_activation_fn(params)
with tf.variable_scope(scope):
nx = x.shape[-1]
h = activation_fn(linear(x, "c_fc", n_state, variable_dtype=variable_dtype, params=params))
h2 = linear(h, "c_proj", nx, variable_dtype=variable_dtype, params=params, scale=True)
if params["mode"] == "train" and params["res_dropout"] > 0:
h2 = mtf.dropout(h2, rate=params["res_dropout"], name="mlp_dropout")
return h2
def mlp_glu(x, scope, n_state, *, variable_dtype, params):
activation_fn = get_activation_fn(params)
with tf.variable_scope(scope):
nx = x.shape[-1]
h = linear(x, "c_fc", n_state, params=params)
h, gate = mtf.split(h, h.shape[-1], 2)
h *= activation_fn(gate)
h2 = linear(h, "c_proj", nx, variable_dtype=variable_dtype, params=params, scale=True)
if params["mode"] == "train" and params["res_dropout"] > 0:
h2 = mtf.dropout(h2, rate=params["res_dropout"], name="mlp_dropout")
return h2
def axial_positional_emb(embd_dim, mesh, params, variable_dtype):
# Use axial position encoding
axial_dim_1, axial_dim_2 = params["axial_pos_emb"]
axial_dim = mtf.Dimension("axial_dim", axial_dim_1 * axial_dim_2)
dim_axials = [mtf.Dimension(f"axial_dim_{i}", t) for i, t in enumerate((axial_dim_1, axial_dim_2))]
axial_wpe_1 = mtf.get_variable(mesh, "axial_wpe_1", mtf.Shape([dim_axials[0], embd_dim]),
initializer=tf.random_normal_initializer(stddev=0.01),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
axial_wpe_2 = mtf.get_variable(mesh, "axial_wpe_2", mtf.Shape([dim_axials[1], embd_dim]),
initializer=tf.random_normal_initializer(stddev=0.01),
master_dtype=variable_dtype.master_dtype,
slice_dtype=variable_dtype.slice_dtype,
activation_dtype=variable_dtype.activation_dtype)
axial_wpe_1, axial_wpe_2 = map(lambda t: mtf.broadcast(t, [dim_axials[0], dim_axials[1], embd_dim]),
(axial_wpe_1, axial_wpe_2))
wpe = (axial_wpe_1 + axial_wpe_2) / 2
wpe = mtf.reshape(wpe, [axial_dim, embd_dim])
return wpe
def rotary_positional_emb(mesh, sequence_dim, params, variable_dtype):
dtype = variable_dtype.master_dtype
dim_head = params["n_embd"] // params["n_head"]
dim_head = mtf.Dimension("features_per_head", dim_head)
half_dim_head = mtf.Dimension("half_features_per_head", dim_head.size // 2)
dim_range = mtf.range(mesh, half_dim_head, dtype) * 2 / dim_head.size
half_freqs = 1. / mtf.pow(mtf.constant(mesh, 10000, dtype = dtype), dim_range)
seq = mtf.range(mesh, sequence_dim, dtype)
half_freqs = mtf.einsum([half_freqs, seq], [sequence_dim, half_dim_head])
freqs = mtf.concat((half_freqs, half_freqs), half_dim_head.name)
freqs = mtf.rename_dimension(freqs, half_dim_head.name, dim_head.name)
return mtf.cos(freqs), mtf.sin(freqs)
def rotate_half(x):
dim_head_name = "features_per_head"
dim_head = x.shape.get_dim_by_name(dim_head_name)
half_dim_head_size = dim_head.size // 2
x1 = mtf.slice(x, 0, half_dim_head_size, dim_head_name)
x2 = mtf.slice(x, half_dim_head_size, half_dim_head_size, dim_head_name)
return mtf.concat((-x2, x1), dim_head.name)
def apply_rotary_emb(x, cos, sin):
rotated_x = rotate_half(x)
return x * cos + rotated_x * sin
|