# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import List, Optional
import numpy as np
import tensorrt as trt
from .._common import default_net, precision
from .._utils import numpy_fp32_to_bf16, trt_dtype_to_np
from ..functional import (AttentionMaskType, PositionEmbeddingType,
RotaryScalingType, Tensor, bert_attention, cast, clip,
concat, constant, embedding, expand_dims, expand_mask,
generate_alibi_biases, generate_alibi_slopes,
gpt_attention, matmul, repeat_interleave, round,
shape, slice, softmax, split, view, where)
from ..module import Module
from ..parameter import Parameter
from ..quantization import QuantMode
from ..quantization.layers import FP8Linear, FP8RowLinear
from .linear import ColumnLinear, RowLinear
from .lora import Lora
[docs]
class RopeEmbeddingUtils:
[docs]
@staticmethod
def create_sinusoidal_positions(num_pos: int,
dim: int,
theta: float = 10000.0,
dtype=np.float32):
inv_freq = 1.0 / (theta**(np.arange(0, dim, 2) / dim)).astype(dtype)
sinusoid_inp = np.einsum("i , j -> i j",
np.arange(num_pos, dtype=dtype),
inv_freq,
dtype=dtype)
concat = np.concatenate((np.sin(sinusoid_inp), np.cos(sinusoid_inp)),
axis=1)
return np.expand_dims(concat, axis=0).astype(np.float32)
[docs]
@staticmethod
def rotate_every_two(tensor: Tensor) -> Tensor:
assert tensor.ndim() == 4
shape_tensor = concat([
shape(tensor, i) / 2 if i == (tensor.ndim() -
1) else shape(tensor, i)
for i in range(tensor.ndim())
])
x1 = slice(tensor, [0, 0, 0, 0], shape_tensor, [1, 1, 1, 2])
x2 = slice(tensor, [0, 0, 0, 1], shape_tensor, [1, 1, 1, 2])
x1 = expand_dims(x1, 4)
x2 = expand_dims(x2, 4)
zero = constant(
np.ascontiguousarray(np.zeros([1],
dtype=trt_dtype_to_np(x2.dtype))))
x2 = zero - x2
x = concat([x2, x1], 4)
return view(
x, concat([shape(x, 0),
shape(x, 1),
shape(x, 2),
shape(x, 3) * 2]))
[docs]
@staticmethod
def rotate_half(tensor: Tensor) -> Tensor:
# [bs, num_attention_kv_heads, seqlen, attention_head_size]
assert tensor.ndim() == 4
shape_tensor = concat([
shape(tensor, i) / 2 if i == (tensor.ndim() -
1) else shape(tensor, i)
for i in range(tensor.ndim())
])
last_dim = shape(tensor, tensor.ndim() - 1) / 2
x1 = slice(tensor, [0, 0, 0, 0], shape_tensor, [1, 1, 1, 1])
x2 = slice(tensor, concat([0, 0, 0, last_dim]), shape_tensor,
[1, 1, 1, 1])
zero = constant(
np.ascontiguousarray(np.zeros([1],
dtype=trt_dtype_to_np(x2.dtype))))
x2 = zero - x2
x = concat([x2, x1], 3)
return x
[docs]
@staticmethod
def apply_rotary_pos_emb(
tensor: Tensor,
position_embedding: List[Tensor] = None,
pos_emb_type: PositionEmbeddingType = PositionEmbeddingType.rope_gptj
) -> Tensor:
rotate_func = None
if pos_emb_type == PositionEmbeddingType.rope_gpt_neox:
assert len(position_embedding) == 2
cos, sin = position_embedding
sin = expand_dims(sin, 2)
cos = expand_dims(cos, 2)
sin = concat([sin, sin], 3)
cos = concat([cos, cos], 3)
rotate_func = RopeEmbeddingUtils.rotate_half
elif pos_emb_type == PositionEmbeddingType.rope_gptj:
assert len(position_embedding) == 2
cos, sin = position_embedding
sin = expand_dims(sin, 2)
cos = expand_dims(cos, 2)
sin = repeat_interleave(sin, 2, 3)
cos = repeat_interleave(cos, 2, 3)
rotate_func = RopeEmbeddingUtils.rotate_every_two
elif pos_emb_type == PositionEmbeddingType.chatglm:
assert len(position_embedding) == 4
cos0, cos1, sin0, sin1 = position_embedding
shape_tensor = concat([
shape(tensor, i) / 2 if i == (tensor.ndim() -
1) else shape(tensor, i)
for i in range(tensor.ndim())
])
last_dim = shape(tensor, tensor.ndim() - 1) / 2
x_part0 = slice(tensor, [0, 0, 0, 0], shape_tensor, [1, 1, 1, 1])
x_part1 = slice(tensor, concat([0, 0, 0, last_dim]), shape_tensor,
[1, 1, 1, 1])
y_part0 = (x_part0 *
cos0) + (RopeEmbeddingUtils.rotate_half(x_part0) * sin0)
y_part1 = (x_part1 *
cos1) + (RopeEmbeddingUtils.rotate_half(x_part1) * sin1)
result = concat([y_part0, y_part1], dim=3)
return result.view(shape(tensor))
else:
raise ValueError('The PositionEmbeddingType is not RoPE')
return (tensor * cos) + (rotate_func(tensor) * sin)
[docs]
@staticmethod
def apply_rotary_pos_emb_chatglm(
qkv,
position_embedding,
num_attention_heads,
attention_head_size,
max_position_embeddings,
rotary_embedding_scale,
) -> Tensor:
half_head_size = attention_head_size // 2
qkv_shape = shape(qkv)
qkv = qkv.view(
concat([
shape(qkv, 0),
shape(qkv, 1),
num_attention_heads,
3,
attention_head_size,
]))
query, key, value = split(qkv, 1, dim=3)
q_shape = concat([
shape(qkv, 0),
shape(qkv, 1),
num_attention_heads,
attention_head_size,
])
query = query.view(q_shape)
key = key.view(q_shape)
value = value.view(q_shape)
embedding_weight = RopeEmbeddingUtils.create_sinusoidal_positions(
max_position_embeddings, half_head_size)
embedding_weight /= rotary_embedding_scale
embedding_weight = np.split(embedding_weight.squeeze(0), 2, axis=1)
embedding_weight = np.concatenate(
[
embedding_weight[0],
embedding_weight[0],
embedding_weight[1],
embedding_weight[1],
],
axis=1,
)
embedding_weight = constant(embedding_weight)
position_embedding = embedding(position_embedding, embedding_weight)
position_embedding, block_embedding = split(
position_embedding,
1,
dim=1,
)
sin0, cos0 = split(position_embedding, half_head_size, dim=3)
sin1, cos1 = split(block_embedding, half_head_size, dim=3)
new_shape = concat([
shape(qkv, 0),
shape(qkv, 1),
1,
half_head_size,
])
position_embedding = [
tensor.view(new_shape) for tensor in [cos0, cos1, sin0, sin1]
]
query = RopeEmbeddingUtils.apply_rotary_pos_emb(
tensor=query,
position_embedding=position_embedding,
pos_emb_type=PositionEmbeddingType.chatglm)
key = RopeEmbeddingUtils.apply_rotary_pos_emb(
tensor=key,
position_embedding=position_embedding,
pos_emb_type=PositionEmbeddingType.chatglm)
qkv = concat([query, key, value], dim=2)
qkv = qkv.view(qkv_shape)
return qkv
[docs]
class AttentionParams(object):
def __init__(self,
sequence_length: Tensor = None,
context_lengths: Tensor = None,
host_context_lengths: Tensor = None,
max_context_length: int = None,
host_request_types: Tensor = None,
encoder_input_lengths: Tensor = None,
encoder_max_input_length: Tensor = None):
self.sequence_length = sequence_length
self.context_lengths = context_lengths
self.host_context_lengths = host_context_lengths
# max allowed context length. Required to
# compute scratch memory size.
self.max_context_length = max_context_length
self.host_request_types = host_request_types
self.encoder_input_lengths = encoder_input_lengths
self.encoder_max_input_length = encoder_max_input_length
[docs]
def is_valid_cross_attn(self, do_cross_attention):
if do_cross_attention:
if self.encoder_input_lengths is None:
return False
if self.encoder_max_input_length is None:
return False
return True
[docs]
def is_valid(self, gpt_attention_plugin, remove_input_padding):
if gpt_attention_plugin:
if self.sequence_length is None:
return False
if self.context_lengths is None:
return False
if self.host_request_types is None:
return False
if self.max_context_length is None:
return False
if remove_input_padding:
if self.host_context_lengths is None:
return False
if not gpt_attention_plugin:
return False
return True
[docs]
class KeyValueCacheParams:
def __init__(self,
past_key_value: List[Tensor] = None,
host_past_key_value_lengths: Tensor = None,
host_max_kv_cache_lengths: List[Tensor] = None,
kv_cache_block_pointers: List[Tensor] = None,
cache_indirection: Tensor = None,
past_key_value_length: Tensor = None):
self.past_key_value = past_key_value
self.host_past_key_value_lengths = host_past_key_value_lengths
self.host_max_kv_cache_lengths = host_max_kv_cache_lengths
self.kv_cache_block_pointers = kv_cache_block_pointers
self.cache_indirection = cache_indirection
# self.past_key_value_length = past_key_value_length
[docs]
def get_first_past_key_value(self):
if self.past_key_value is None:
return None
return self.past_key_value[0]
[docs]
def get_first_kv_cache_block_pointers(self):
if self.kv_cache_block_pointers is None:
return None
return self.kv_cache_block_pointers[0]
[docs]
def fill_none_tensor_list(self, list_size):
if self.past_key_value is None:
self.past_key_value = tuple([None] * list_size)
if self.host_max_kv_cache_lengths is None:
self.host_max_kv_cache_lengths = tuple([None] * list_size)
[docs]
def is_valid(self, gpt_attention_plugin):
if gpt_attention_plugin:
if self.host_past_key_value_lengths is None:
return False
if self.host_max_kv_cache_lengths is None:
return False
if self.cache_indirection is None:
return False
return True
[docs]
class Attention(Module):
def __init__(
self,
hidden_size,
num_attention_heads,
num_kv_heads=None,
max_position_embeddings=1024,
num_layers=1,
apply_query_key_layer_scaling=False,
attention_head_size=None,
attention_mask_type=AttentionMaskType.padding,
bias=True,
dtype=None,
position_embedding_type=PositionEmbeddingType.learned_absolute,
rotary_embedding_base=10000.0,
rotary_embedding_scaling=None,
use_int8_kv_cache=False,
rotary_embedding_percentage=1.0,
tp_group=None,
tp_size=1,
tp_rank=0,
quant_mode: QuantMode = QuantMode(0),
q_scaling=1.0,
cross_attention=False,
relative_attention=False,
max_distance=0,
num_buckets=0,
instance_id: int = 0,
dense_bias=None,
):
super().__init__()
self.cross_attention = cross_attention
self.attention_mask_type = attention_mask_type
self.attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
assert num_attention_heads % tp_size == 0, \
"num_attention_heads must be divisible by tp_size"
self.num_attention_heads = num_attention_heads // tp_size
self.num_attention_kv_heads = (
num_kv_heads + tp_size - 1
) // tp_size if num_kv_heads is not None else self.num_attention_heads
self.hidden_size = hidden_size // tp_size
self.max_position_embeddings = max_position_embeddings
self.tp_size = tp_size
self.tp_rank = tp_rank
self.dtype = dtype
if dense_bias is None:
dense_bias = bias
self.num_layers = num_layers
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.norm_factor = math.sqrt(self.attention_head_size)
self.q_scaling = q_scaling
if self.apply_query_key_layer_scaling:
self.norm_factor *= self.num_layers
self.q_scaling *= self.num_layers
# Whether to scale ALiBi bias. Mathematically, it's equivalent to
# normalizing QK after adding bias.
# - False, inv_sqrt_Dh * Q*K^T + alibi_bias
# - True, inv_sqrt_Dh * Q*K^T + inv_sqrt_Dh * alibi_bias
self.scale_alibi_bias = position_embedding_type == PositionEmbeddingType.alibi_with_scale
self.position_embedding_type = position_embedding_type
self.relative_attention = relative_attention
self.max_distance = max_distance
self.rotary_embedding_base = rotary_embedding_base
self.rotary_embedding_scale_type = RotaryScalingType.none
self.rotary_embedding_scale = 1.0
if rotary_embedding_scaling is not None:
assert rotary_embedding_scaling["type"] in ["linear", "dynamic"]
self.rotary_embedding_scale_type = RotaryScalingType.linear if rotary_embedding_scaling[
"type"] == "linear" else RotaryScalingType.dynamic
self.rotary_embedding_scale = rotary_embedding_scaling["factor"]
assert self.rotary_embedding_scale > 1.0
self.embed_positions = None
self.rotary_enabled = False
self.rotary_embedding_dim = 0
if self.position_embedding_type.is_rope():
self.rotary_embedding_dim = int(self.attention_head_size *
rotary_embedding_percentage)
self.rotary_enabled = True
self.embed_positions = RopeEmbeddingUtils.create_sinusoidal_positions(
self.max_position_embeddings,
self.rotary_embedding_dim,
)
self.quant_mode = quant_mode
if use_int8_kv_cache:
# TODO: remove use_int8_kv_cache as can be replaced by quant_mode.has_kv_cache_quant()
# Merge int8 setting into quant_mode
self.quant_mode = self.quant_mode.set_int8_kv_cache()
self.use_int8_kv_cache = use_int8_kv_cache
if self.quant_mode.has_kv_cache_quant():
self.kv_orig_quant_scale = Parameter(shape=(1, ), dtype='float32')
self.kv_quant_orig_scale = Parameter(shape=(1, ), dtype='float32')
else:
self.register_parameter('kv_orig_quant_scale', None)
self.register_parameter('kv_quant_orig_scale', None)
# The output feature size is therefore (h/tp + 2*kvh/tp) * d, where h is num_heads,
# d is head_size, kvh is the num_kv_heads and tp is tensor_parallel_size.
# In ColumnLinear op, the output dim is calculated by (h + 2*kvh) * d / tp,
# which matches the desired output size (h/tp + 2*kvh/tp) * d after splitting
self.use_fp8_qdq = self.quant_mode.has_fp8_qdq()
if self.use_fp8_qdq:
self.qkv = FP8Linear(
hidden_size,
tp_size * self.num_attention_heads * self.attention_head_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False)
self.dense = FP8RowLinear(hidden_size,
hidden_size,
bias=dense_bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
instance_id=instance_id)
else:
# out dim is not necessarily hidden_size + kv specific size (in MQA/GQA), but num_heads * heads_size
# example: d_model != num_heads * head_size in Flan-T5
self.qkv = ColumnLinear(
hidden_size,
tp_size * self.num_attention_heads * self.attention_head_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False)
self.dense = RowLinear(tp_size * self.num_attention_heads *
self.attention_head_size,
hidden_size,
bias=dense_bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
instance_id=instance_id)
# per-layer relative attention table
if relative_attention:
self.rel_attn_table = Parameter(shape=(num_attention_heads //
tp_size, num_buckets),
dtype=dtype)
self.qkv_lora = Lora(
in_hidden_size=hidden_size,
out_hidden_size=hidden_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
max_low_rank=hidden_size,
)
[docs]
def forward(self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
encoder_output: Optional[Tensor] = None,
workspace=None,
position_embedding=None,
norm_before_bmm1=False,
lora_params=None):
assert isinstance(hidden_states, Tensor)
alibi_slopes = None
if self.position_embedding_type.is_alibi():
dtype = trt.float32
if default_net().plugin_config.gpt_attention_plugin:
dtype = hidden_states.dtype
alibi_scale = 1. / self.norm_factor if self.scale_alibi_bias else 1.
alibi_slopes = generate_alibi_slopes(self.num_attention_heads *
self.tp_size,
dtype=dtype,
tp_size=self.tp_size,
tp_rank=self.tp_rank,
alibi_scale=alibi_scale)
qkv = self.qkv(hidden_states)
if default_net().plugin_config.lora_plugin:
qkv = qkv + self.qkv_lora(
hidden_states,
host_request_types=attention_params.host_request_types,
host_context_lengths=attention_params.host_context_lengths,
max_context_length=attention_params.max_context_length,
lora_ranks=lora_params.lora_ranks,
lora_weights_pointers=lora_params.lora_weights_pointers_list[0])
if self.position_embedding_type == PositionEmbeddingType.chatglm:
qkv = RopeEmbeddingUtils.apply_rotary_pos_emb_chatglm(
qkv,
position_embedding,
self.num_attention_heads,
self.attention_head_size,
self.max_position_embeddings,
self.rotary_embedding_scale,
)
self.rotary_embedding_scale_type = RotaryScalingType.none
self.rotary_embedding_scale = 1.0
paged_kv_cache = default_net().plugin_config.paged_kv_cache
assert attention_params is None or attention_params.is_valid(
default_net().plugin_config.gpt_attention_plugin,
default_net().plugin_config.remove_input_padding)
assert kv_cache_params is None or kv_cache_params.is_valid(
default_net().plugin_config.gpt_attention_plugin)
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
)
if self.cross_attention and (past_key_value is not None):
past_key_value = kv_cache_params.past_key_value[1]
# if cross attention, cross QKV only needs to be calculated once in the
# 1st decoding step --> write to cross KV cache --> remains constant
# during the entire decoding. 1st and >1 steps are distinguished by
# whether past_key_value exists or not
# also, cross KV cache max length is set from encoder output seqlen,
# this maps to the max context length concept in decoder-only models
cross_qkv = None
# get length data in every run
if encoder_output:
assert isinstance(encoder_output, Tensor)
# but only do projection once at 1st decoding step
if self.cross_attention and encoder_output:
cross_qkv = self.qkv(encoder_output)
if default_net().plugin_config.gpt_attention_plugin:
assert self.attention_mask_type in [
AttentionMaskType.causal, AttentionMaskType.bidirectional,
AttentionMaskType.bidirectionalglm
], 'Plugin only support masked MHA.'
kv_orig_quant_scale = self.kv_orig_quant_scale.value if self.quant_mode.has_kv_cache_quant(
) else None
kv_quant_orig_scale = self.kv_quant_orig_scale.value if self.quant_mode.has_kv_cache_quant(
) else None
context, past_key_value = gpt_attention(
tensor=qkv,
past_key_value=past_key_value,
sequence_length=attention_params.sequence_length,
host_past_key_value_lengths=kv_cache_params.
host_past_key_value_lengths,
host_max_kv_cache_lengths=kv_cache_params.
host_max_kv_cache_lengths,
context_lengths=attention_params.context_lengths,
cache_indirection=kv_cache_params.cache_indirection,
host_request_types=attention_params.host_request_types,
num_heads=self.num_attention_heads,
num_kv_heads=self.num_attention_kv_heads,
hidden_size_per_head=self.attention_head_size,
q_scaling=self.q_scaling,
rotary_embedding_dim=self.rotary_embedding_dim,
rotary_embedding_base=self.rotary_embedding_base,
rotary_embedding_scale_type=self.rotary_embedding_scale_type,
rotary_embedding_scale=self.rotary_embedding_scale,
rotary_embedding_max_positions=self.max_position_embeddings,
position_embedding_type=self.position_embedding_type,
kv_orig_quant_scale=kv_orig_quant_scale,
kv_quant_orig_scale=kv_quant_orig_scale,
kv_cache_quant_mode=self.quant_mode,
max_context_length=attention_params.max_context_length,
mask_type=self.attention_mask_type,
alibi_slopes=alibi_slopes,
tp_size=self.tp_size,
tp_rank=self.tp_rank,
kv_cache_block_pointers=kv_cache_params.
get_first_kv_cache_block_pointers(),
do_cross_attention=self.cross_attention,
cross_qkv=cross_qkv,
cross_qkv_length=attention_params.encoder_max_input_length,
encoder_input_lengths=attention_params.encoder_input_lengths,
relative_attention_bias=self.rel_attn_table.value
if self.relative_attention else None,
max_distance=self.max_distance,
host_context_lengths=attention_params.host_context_lengths,
)
else:
# plain TensorRT mode
assert paged_kv_cache == False
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
)
def transpose_for_scores(x,
rotary: bool = False,
is_kv: bool = False):
_num_attention_heads = self.num_attention_kv_heads if is_kv else self.num_attention_heads
new_x_shape = concat([
shape(x, 0),
shape(x, 1), _num_attention_heads, self.attention_head_size
])
if rotary:
return x.view(new_x_shape)
else:
return x.view(new_x_shape).permute([0, 2, 1, 3])
# qkv after projection is of shape
# [bs, seqlen, (num_attention_heads + 2 * num_attention_kv_heads), attention_head_size].
# The projected and split qkv after transpose_for_scores():
# Q[bs, num_attention_heads, seqlen, attention_head_size]
# K[bs, num_attention_kv_heads, seqlen, attention_head_size]
# V[bs, num_attention_kv_heads, seqlen, attention_head_size]
kv_size = self.attention_head_size * self.num_attention_kv_heads
query, key, value = split(qkv, [self.hidden_size, kv_size, kv_size],
dim=2)
# in cross attention mode, replace kv by encoder_output
if self.cross_attention and encoder_output is not None:
encoder_qkv = self.qkv(encoder_output)
_, key, value = split(encoder_qkv,
[self.hidden_size, kv_size, kv_size],
dim=2)
query = transpose_for_scores(query, rotary=self.rotary_enabled)
key = transpose_for_scores(key,
is_kv=True,
rotary=self.rotary_enabled)
value = transpose_for_scores(value, is_kv=True)
if self.rotary_enabled:
if self.dtype == trt.bfloat16:
embed_positions = numpy_fp32_to_bf16(
self.embed_positions.astype(np.float32))
embed_positions = constant(embed_positions)
else:
embed_positions = constant(self.embed_positions)
if default_net().strongly_typed and (embed_positions.dtype !=
value.dtype):
embed_positions = cast(embed_positions, value.dtype)
if self.rotary_embedding_dim is not None:
# When shape(hidden_states, 1) > 1(Context phase), the embedding start from 0,
# otherwise (Generation phase) move start to position
start = where(
shape(hidden_states, 1) > 1, 0,
shape(past_key_value, 3))
size = where(
shape(hidden_states, 1) > 1, shape(hidden_states, 1), 1)
sincos = slice(embed_positions, concat([0, start, 0]),
concat([1, size, self.rotary_embedding_dim]))
sin, cos = split(sincos,
self.rotary_embedding_dim // 2,
dim=-1)
key_rot_size = concat([
shape(key, 0),
shape(key, 1),
shape(key, 2), self.rotary_embedding_dim
])
query_rot_size = concat([
shape(query, 0),
shape(query, 1),
shape(query, 2), self.rotary_embedding_dim
])
remaining = shape(key, 3) - self.rotary_embedding_dim
key_pass_size = concat([
shape(key, 0),
shape(key, 1),
shape(key, 2), remaining
])
query_pass_size = concat([
shape(query, 0),
shape(query, 1),
shape(query, 2), remaining
])
k_rot = slice(key, [0, 0, 0, 0], key_rot_size)
k_pass = slice(key, [0, 0, 0, self.rotary_embedding_dim],
key_pass_size)
q_rot = slice(query, [0, 0, 0, 0], query_rot_size)
q_pass = slice(query, [0, 0, 0, self.rotary_embedding_dim],
query_pass_size)
k_rot = RopeEmbeddingUtils.apply_rotary_pos_emb(
k_rot, [cos, sin], self.position_embedding_type)
q_rot = RopeEmbeddingUtils.apply_rotary_pos_emb(
q_rot, [cos, sin], self.position_embedding_type)
key = concat([k_rot, k_pass], dim=3)
query = concat([q_rot, q_pass], dim=3)
else:
key = RopeEmbeddingUtils.apply_rotary_pos_emb(
key, [cos, sin], self.position_embedding_type)
query = RopeEmbeddingUtils.apply_rotary_pos_emb(
query, [cos, sin], self.position_embedding_type)
key = key.permute([0, 2, 1, 3])
query = query.permute([0, 2, 1, 3])
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
)
if past_key_value is not None:
def dequantize_tensor(x, scale):
# Cast from int8 to dtype
casted_x = cast(x, self.dtype)
return casted_x * scale
if self.use_int8_kv_cache:
past_key_value = dequantize_tensor(
past_key_value, self.kv_quant_orig_scale.value)
# past_key_value [bs, 2, num_heads, max_seq_len, head_dim]
past_key, past_value = split(past_key_value, 1, dim=1)
key_shape = concat([
shape(past_key, 0),
shape(past_key, 2),
shape(past_key, 3),
shape(past_key, 4)
])
past_key = past_key.view(key_shape, zero_is_placeholder=False)
past_value = past_value.view(key_shape,
zero_is_placeholder=False)
key = concat([past_key, key], dim=2).cast(self.dtype)
value = concat([past_value, value], dim=2).cast(self.dtype)
if use_cache:
key_inflated_shape = concat([
shape(key, 0), 1,
shape(key, 1),
shape(key, 2),
shape(key, 3)
])
inflated_key = key.view(key_inflated_shape,
zero_is_placeholder=False)
inflated_value = value.view(key_inflated_shape,
zero_is_placeholder=False)
past_key_value = concat([inflated_key, inflated_value], dim=1)
if self.use_int8_kv_cache:
def quantize_tensor(x, scale):
scaled = x * scale
rounded = round(scaled)
clipped = clip(rounded, -128, 127)
quantized = cast(clipped, 'int8')
return quantized
past_key_value = quantize_tensor(
past_key_value, self.kv_orig_quant_scale.value)
# MQA broadcast
if self.num_attention_heads // self.num_attention_kv_heads > 1:
key = repeat_interleave(
key,
self.num_attention_heads // self.num_attention_kv_heads, 1)
value = repeat_interleave(
value,
self.num_attention_heads // self.num_attention_kv_heads, 1)
key_length = shape(key, 2)
# The following code creates a 2D tensor with 0s in the lower triangular (including the diagonal) and
# +INF in the upper triangular parts. This bias tensor will be added to the output of the Q*K^T matrix
# multiplication (BMM1). The +INF elements will be transformed to 0s by the Softmax operator that
# follows. The elements that corresponds to 0s in the bias are unaffected by the bias tensor.
#
# Note that when we added to another bias tensor B (for example, with AliBi), the values in the lower-
# triangular part of the B tensor are not affected and the upper-triangular ones are set to +INF.
if self.attention_mask_type == AttentionMaskType.causal:
query_length = shape(query, 2)
starts = concat([0, 0, key_length - query_length, 0])
sizes = concat([1, 1, query_length, key_length])
select_buf = np.expand_dims(
np.tril(
np.ones((self.max_position_embeddings,
self.max_position_embeddings))).astype(bool),
(0, 1))
select_buf = np.logical_not(select_buf)
mask_buf = np.zeros_like(select_buf, np.float32)
mask_buf[select_buf] = float('-inf')
buffer = constant(mask_buf)
generated_mask = slice(buffer, starts, sizes)
elif self.attention_mask_type == AttentionMaskType.bidirectional:
query_length = shape(query, 2)
zero_buf = np.expand_dims(
np.zeros((self.max_position_embeddings,
self.max_position_embeddings),
dtype=np.float32), (0, 1))
zero_buf[:, :, :-1, -1] = 1
zero_buf *= -10000
mask = constant(zero_buf)
# context phase, query_length
mask_size = where(query_length > 1, query_length, 1)
mask_start = where(query_length > 1,
self.max_position_embeddings - mask_size, 1)
start = concat([0, 0, mask_start, mask_start])
size = concat([1, 1, mask_size, mask_size])
generated_mask = slice(mask, start, size)
if attention_mask is not None:
attention_mask = expand_mask(attention_mask, shape(query, 2))
bias = attention_mask
if self.position_embedding_type.is_alibi():
alibi_biases = generate_alibi_biases(alibi_slopes, key_length)
bias = alibi_biases if bias is None else bias + alibi_biases
key = key.permute([0, 1, 3, 2])
with precision('float32'):
if norm_before_bmm1:
# Apply norm on query earlier to prevent matmul fp16 overflow.
query /= self.norm_factor
attention_scores = matmul(cast(query, 'float32'),
cast(key, 'float32'))
if not norm_before_bmm1:
attention_scores = attention_scores / self.norm_factor
if self.attention_mask_type in [
AttentionMaskType.causal,
AttentionMaskType.bidirectional
]:
bias = generated_mask if bias is None else bias + generated_mask
if bias is not None and not self.cross_attention:
attention_scores = attention_scores + bias
attention_probs = softmax(attention_scores, dim=-1)
if default_net().strongly_typed and (attention_probs.dtype !=
value.dtype):
attention_probs = cast(attention_probs, value.dtype)
context = matmul(attention_probs, value).permute([0, 2, 1, 3])
context = context.view(
concat([shape(context, 0),
shape(context, 1), self.hidden_size]))
context = self.dense(context, workspace)
if use_cache:
return (context, past_key_value)
else:
return context
[docs]
class BertAttention(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings=1024,
num_layers=1,
attention_head_size=None,
num_kv_heads=None,
q_scaling=1.0,
apply_query_key_layer_scaling=False,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
tp_rank=0,
relative_attention=False,
max_distance=0,
num_buckets=0):
super().__init__()
self.attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
self.num_attention_heads = num_attention_heads // tp_size
self.num_attention_kv_heads = (
num_kv_heads + tp_size - 1
) // tp_size if num_kv_heads is not None else self.num_attention_heads
self.hidden_size = hidden_size // tp_size
self.max_position_embeddings = max_position_embeddings
self.norm_factor = math.sqrt(self.attention_head_size)
self.tp_size = tp_size
self.tp_rank = tp_rank
self.num_layers = num_layers
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.norm_factor = math.sqrt(self.attention_head_size)
self.q_scaling = q_scaling
if self.apply_query_key_layer_scaling:
self.norm_factor *= self.num_layers
self.q_scaling *= self.num_layers
self.dtype = dtype
self.relative_attention = relative_attention
self.max_distance = max_distance
# out dim is not necessarily hidden_size + kv specific size (in MQA/GQA), but num_heads * heads_size
# example: d_model != num_heads * head_size in Flan-T5
self.qkv = ColumnLinear(
hidden_size,
tp_size * self.num_attention_heads * self.attention_head_size +
(2 * tp_size * self.num_attention_kv_heads *
self.attention_head_size),
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False)
self.dense = RowLinear(tp_size * self.num_attention_heads *
self.attention_head_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size)
# per-layer relative attention table
if relative_attention:
self.rel_attn_table = Parameter(shape=(num_attention_heads //
tp_size, num_buckets),
dtype=dtype)
[docs]
def forward(self,
hidden_states: Tensor,
attention_mask=None,
input_lengths=None,
workspace=None,
max_input_length=None):
assert isinstance(hidden_states, Tensor)
qkv = self.qkv(hidden_states)
if default_net().plugin_config.bert_attention_plugin:
# TRT plugin mode
assert input_lengths is not None
context = bert_attention(
qkv,
input_lengths,
self.num_attention_heads,
self.attention_head_size,
q_scaling=self.q_scaling,
relative_attention=self.relative_attention,
max_distance=self.max_distance,
relative_attention_bias=self.rel_attn_table.value
if self.relative_attention else None,
max_input_length=max_input_length)
else:
# plain TRT mode
def transpose_for_scores(x):
new_x_shape = concat([
shape(x, 0),
shape(x, 1), self.num_attention_heads,
self.attention_head_size
])
return x.view(new_x_shape).permute([0, 2, 1, 3])
query, key, value = split(qkv, self.hidden_size, dim=2)
query = transpose_for_scores(query)
key = transpose_for_scores(key)
value = transpose_for_scores(value)
key = key.permute([0, 1, 3, 2])
attention_scores = matmul(query, key)
attention_scores = attention_scores / self.norm_factor
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
attention_probs = softmax(attention_scores, dim=-1)
context = matmul(attention_probs, value).permute([0, 2, 1, 3])
context = context.view(
concat([shape(context, 0),
shape(context, 1), self.hidden_size]))
context = self.dense(context, workspace)
return context