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generation_mixin.py
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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 collections import OrderedDict
from typing import List
import tensorrt as trt
from ..functional import Tensor
from ..mapping import Mapping
from ..plugin import current_all_reduce_helper
class GenerationMixin:
@staticmethod
def has_ctx_gen_opt_profiles(use_gpt_attention_plugin: bool,
use_gemm_plugin: bool,
remove_input_padding: bool,
paged_kv_cache: bool) -> bool:
res = False
if not use_gpt_attention_plugin or not use_gemm_plugin:
use_in_flight_batching = use_gpt_attention_plugin and remove_input_padding and paged_kv_cache
res = not use_in_flight_batching
return res
@staticmethod
def default_range(max_range, offset=0):
result = [1, (max_range + 1) // 2, max_range]
return [elem + offset for elem in result]
def prepare_attention_inputs(self,
*,
max_batch_size,
max_beam_width,
max_input_len,
max_seq_len,
num_kv_heads,
head_size,
num_layers,
kv_dtype,
num_profiles=1,
enable_ctx_gen_opt_profiles=False,
remove_input_padding=False,
use_gpt_attention_plugin=False,
paged_kv_cache=False,
tokens_per_block=64,
mapping=Mapping(),
use_cache=True,
streamingllm=False):
default_range = GenerationMixin.default_range
bb_range_cxt = default_range(max_batch_size)
bb_range_gen = default_range(max_batch_size * max_beam_width)
_bs_range = default_range(max_batch_size)
_beam_width_range = default_range(max_beam_width)
_max_len_range = default_range(max_seq_len)
_mask_len_ctx = default_range(max_input_len)
_kv_cache_range_ctx = [0, 0, 0]
_kv_cache_range_gen = default_range(max_seq_len, -1)
if not paged_kv_cache:
_kv_cache_range = default_range(max_seq_len)
else:
kv_max_seq_len = max_seq_len
if streamingllm:
# add the max bubble length
kv_max_seq_len += tokens_per_block - 1
if max_beam_width > 1:
# support cyclic kv cache cases that use one more block
kv_max_seq_len += tokens_per_block
_kv_cache_range = default_range(kv_max_seq_len)
if enable_ctx_gen_opt_profiles:
assert num_profiles == 2
bb_range = [bb_range_cxt, bb_range_gen]
mask_len_range = [_mask_len_ctx, _max_len_range]
if use_gpt_attention_plugin:
kv_cache_range = [_kv_cache_range, _kv_cache_range]
else:
kv_cache_range = [_kv_cache_range_ctx, _kv_cache_range_gen]
else:
bb_range = [bb_range_gen] * num_profiles
mask_len_range = [_max_len_range] * num_profiles
kv_cache_range = [_kv_cache_range] * num_profiles
bs_range = [_bs_range] * num_profiles
beam_width_range = [_beam_width_range] * num_profiles
max_len_range = [_max_len_range] * num_profiles
num_kv_heads = (num_kv_heads + mapping.tp_size - 1) // mapping.tp_size
layers_range = mapping.pp_layers(num_layers)
num_pp_layers = len(layers_range)
past_key_value = []
kv_cache_block_pointers = None
host_kv_cache_block_pointers = None
if use_cache:
if not paged_kv_cache:
for i in layers_range:
kv_dim_range = OrderedDict([
('batch_size_beam_width', bb_range),
('kv', [2] * num_profiles),
('num_heads', [num_kv_heads] * num_profiles),
('past_key_len', kv_cache_range),
('head_size', [head_size] * num_profiles),
])
kv = Tensor(name=f'past_key_value_{i}',
dtype=kv_dtype,
shape=[-1, 2, num_kv_heads, -1, head_size],
dim_range=kv_dim_range)
past_key_value.append(kv)
else:
if enable_ctx_gen_opt_profiles:
max_blocks_per_seq_range = [
[
math.ceil(kv_cache_range[0][0] / tokens_per_block),
math.ceil(kv_cache_range[0][1] / tokens_per_block),
math.ceil(kv_cache_range[0][2] / tokens_per_block)
],
[
math.ceil(kv_cache_range[1][0] / tokens_per_block),
math.ceil(kv_cache_range[1][1] / tokens_per_block),
math.ceil(kv_cache_range[1][2] / tokens_per_block)
]
]
else:
max_blocks_per_seq_range = [[
math.ceil(kv_cache_range[0][0] / tokens_per_block),
math.ceil(kv_cache_range[0][1] / tokens_per_block),
math.ceil(kv_cache_range[0][2] / tokens_per_block)
]] * num_profiles
kv_cache_block_pointers = Tensor(
name=f'kv_cache_block_pointers',
dtype=trt.int64,
shape=[num_pp_layers, -1, 2, -1],
dim_range=OrderedDict([
('num_layers', [num_pp_layers] * num_profiles),
('batch_size_beam_width', bb_range),
('kv', [2] * num_profiles),
('max_blocks_per_seq', max_blocks_per_seq_range),
]))
host_kv_cache_block_pointers = Tensor(
name=f'host_kv_cache_block_pointers',
dtype=trt.int64,
shape=[num_pp_layers, -1, 2, -1],
dim_range=OrderedDict([
('num_layers', [num_pp_layers] * num_profiles),
('batch_size_beam_width', bb_range),
('kv', [2] * num_profiles),
('max_blocks_per_seq', max_blocks_per_seq_range),
]))
for i in layers_range:
past_key_value.append(None)
sequence_length = None
context_lengths = None
host_context_lengths = None
host_past_key_value_lengths = None
host_max_attention_window_sizes = None
host_sink_token_length = None
attention_mask = None
cache_indirection = None
host_request_types = None
if use_gpt_attention_plugin:
if use_cache:
sequence_length = Tensor(
name='sequence_length',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size_beam_width', bb_range)
]),
)
host_request_types = Tensor(
name='host_request_types',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size_beam_width', bb_range)]),
)
if use_cache:
host_past_key_value_lengths = Tensor(
name='host_past_key_value_lengths',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size_beam_width', bb_range)
]),
)
context_lengths = Tensor(
name='context_lengths',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size_beam_width', bb_range)]),
)
else:
attention_mask = Tensor(
name='attention_mask',
dtype=trt.int32,
shape=[-1, -1],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('mask_len', mask_len_range),
]),
)
if use_gpt_attention_plugin and remove_input_padding:
host_context_lengths = Tensor(
name='host_context_lengths',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size_beam_width', bb_range)]),
)
if use_gpt_attention_plugin:
host_max_attention_window_sizes = Tensor(
name=f'host_max_attention_window_sizes',
dtype=trt.int32,
shape=[num_pp_layers],
dim_range=OrderedDict([('num_layers',
[num_pp_layers] * num_profiles)]))
host_sink_token_length = Tensor(name='host_sink_token_length',
dtype=trt.int32,
shape=[1],
dim_range=OrderedDict([
('scalar', [1] * num_profiles)
]))
if use_cache:
cache_indirection = Tensor(
name='cache_indirection',
dtype=trt.int32,
shape=[-1, -1, -1],
dim_range=OrderedDict([
('batch_size_cache', bs_range),
('beam_width', beam_width_range),
('max_seq_len', max_len_range),
]),
)
return {
'attention_mask': attention_mask,
'sequence_length': sequence_length,
'host_past_key_value_lengths': host_past_key_value_lengths,
'host_max_attention_window_sizes': host_max_attention_window_sizes,
'host_sink_token_length': host_sink_token_length,
'past_key_value': past_key_value,
'cache_indirection': cache_indirection,
'kv_cache_block_pointers': kv_cache_block_pointers,
'host_kv_cache_block_pointers': host_kv_cache_block_pointers,
'context_lengths': context_lengths,
'host_context_lengths': host_context_lengths,
'host_request_types': host_request_types,
}
def prepare_basic_inputs(self,
*,
max_batch_size,
max_beam_width,
max_input_len,
max_seq_len,
num_kv_heads,
head_size,
num_layers,
kv_dtype,
remove_input_padding=False,
use_gpt_attention_plugin=False,
use_gemm_plugin=False,
use_custom_all_reduce=False,
paged_kv_cache=False,
tokens_per_block=64,
gather_context_logits=False,
gather_generation_logits=False,
dtype=None,
num_heads=None,
mapping=Mapping(),
max_num_tokens=None,
opt_num_tokens=None,
prompt_embedding_table_size: int = 0,
position_encoding_2d=False,
use_lora_plugin: bool = False,
lora_target_modules: List[str] = None,
max_draft_len=0,
multiple_profiles: bool = False,
streamingllm: bool = False):
default_range = GenerationMixin.default_range
last_token_range = [1, max_draft_len + 1, max_draft_len + 1]
bb_range_cxt = default_range(max_batch_size)
bb_range_gen = default_range(max_batch_size * max_beam_width)
bbd_range_ctx = [
bb_range_cxt[i] * ((max_draft_len + 1) if i != 0 else 1)
for i in range(len(bb_range_cxt))
]
bbd_range_gen = [
bb_range_gen[i] * ((max_draft_len + 1) if i != 0 else 1)
for i in range(len(bb_range_gen))
]
inlen_range_cxt = default_range(max_input_len)
inlen_range_gen = [1, 1, max_draft_len + 1]
enable_ctx_gen_opt_profiles = GenerationMixin.has_ctx_gen_opt_profiles(
use_gpt_attention_plugin, use_gemm_plugin, remove_input_padding,
paged_kv_cache)
if max_num_tokens is None:
max_num_tokens = max(
max_input_len * max_batch_size,
max_beam_width * (max_draft_len + 1) * max_batch_size)
if enable_ctx_gen_opt_profiles:
num_profiles = 2
bb_range = [bb_range_cxt, bb_range_gen]
bbd_range = [bbd_range_ctx, bbd_range_gen]
inlen_range = [inlen_range_cxt, inlen_range_gen]
position_ids_inlen_range = [inlen_range_cxt, [1, 1, 1]]
num_tokens_range_ctx = default_range(max_num_tokens)
num_tokens_range_gen = default_range(
max_batch_size * (max_draft_len + 1) * max_beam_width)
num_tokens_range = [num_tokens_range_ctx, num_tokens_range_gen]
else:
max_bs_x_max_bw = max_batch_size * max_beam_width
if opt_num_tokens is None:
opt_num_tokens = max_bs_x_max_bw
if multiple_profiles:
if max_num_tokens > max_bs_x_max_bw:
num_tokens_range = [[1, max_bs_x_max_bw, max_bs_x_max_bw],
[
max_bs_x_max_bw, max_num_tokens,
max_num_tokens
]]
else:
num_tokens_range = [[1, max_num_tokens, max_num_tokens]]
else:
num_tokens_range = [[1, opt_num_tokens, max_num_tokens]]
num_profiles = len(num_tokens_range)
bb_range = [bb_range_gen] * num_profiles
bbd_range = [bbd_range_gen] * num_profiles
inlen_range = [[1, 1, max_input_len]] * num_profiles
position_ids_inlen_range = [[1, 1, max_input_len]] * num_profiles
last_token_range = [last_token_range] * num_profiles
position_ids_num_tokens_range = num_tokens_range
input_ids = None
position_ids = None
hidden_states = None
if remove_input_padding:
if mapping.is_first_pp_rank():
input_ids = Tensor(name='input_ids',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('num_tokens', num_tokens_range),
]))
if position_encoding_2d:
position_ids = Tensor(
name='position_ids',
dtype=trt.int32,
shape=[2, -1],
dim_range=OrderedDict([
('2', [2] * num_profiles),
('position_ids_num_tokens_range',
position_ids_num_tokens_range),
]),
)
else:
position_ids = Tensor(
name='position_ids',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('position_ids_num_tokens_range',
position_ids_num_tokens_range),
]),
)
else:
assert dtype is not None
assert num_heads is not None
hidden_states = Tensor(
name='hidden_states_input',
dtype=dtype,
shape=[-1, head_size * num_heads],
dim_range=OrderedDict([
('num_tokens', num_tokens_range),
('hidden_size', [head_size * num_heads] * num_profiles),
]))
else:
if mapping.is_first_pp_rank():
input_ids = Tensor(name='input_ids',
dtype=trt.int32,
shape=[-1, -1],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('input_len', inlen_range),
]))
if position_encoding_2d:
position_ids = Tensor(
name='position_ids',
dtype=trt.int32,
shape=[-1, 2, -1],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('2', [2] * num_profiles),
('position_ids_inlen_range',
position_ids_inlen_range),
]),
)
else:
position_ids = Tensor(
name='position_ids',
dtype=trt.int32,
shape=[-1, -1],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('position_ids_inlen_range',
position_ids_inlen_range),
]),
)
else:
assert dtype is not None
assert num_heads is not None
hidden_states = Tensor(
name='hidden_states_input',
dtype=dtype,
shape=[-1, -1, head_size * num_heads],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('input_len', inlen_range),
('hidden_size', [head_size * num_heads] * num_profiles),
]))
if use_custom_all_reduce and mapping.tp_size > 1:
current_all_reduce_helper().set_workspace_tensor(
mapping, num_profiles)
prompt_embedding_table = None
tasks = None
prompt_vocab_size = None
if prompt_embedding_table_size > 0:
assert num_heads is not None
hidden_size = num_heads * head_size
_p_embedding_range = [
1, prompt_embedding_table_size // 2, prompt_embedding_table_size
]
p_embedding_range = [_p_embedding_range] * num_profiles
prompt_embedding_table = Tensor(name='prompt_embedding_table',
dtype=dtype,
shape=[-1, hidden_size],
dim_range=OrderedDict([
('prompt_embedding_table_size',
p_embedding_range),
('hidden_size',
[hidden_size] * num_profiles),
]))
if remove_input_padding:
tasks = Tensor(name='tasks',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('input_len_task', num_tokens_range),
]))
else:
tasks = Tensor(name='tasks',
dtype=trt.int32,
shape=[-1, 1],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('broadcast_dim', [1] * num_profiles),
]))
prompt_vocab_size = Tensor(name='prompt_vocab_size',
dtype=trt.int32,
shape=[1],
dim_range=OrderedDict([
('size', [1] * num_profiles)
]))
lora_weights_pointers = None
lora_ranks = None
if use_lora_plugin:
lora_weights_pointers = []
lora_ranks = []
layers_range = mapping.pp_layers(num_layers)
for i in layers_range:
lora_weight_pointer_dict = {}
lora_rank_dict = {}
for lora_module in lora_target_modules:
lora_weight_pointer = Tensor(
name=f'{lora_module}_lora_weights_pointers_{i}',
dtype=trt.int64,
shape=[-1, 2],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('in_out', [2] * num_profiles),
]))
lora_weight_pointer_dict.update({
f"{lora_module}_lora_weights_pointers":
lora_weight_pointer
})
lora_rank = Tensor(
name=f'{lora_module}_lora_ranks_{i}',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size_beam_width',
bb_range)]),
)
lora_rank_dict.update(
{f"{lora_module}_lora_ranks": lora_rank})
lora_weights_pointers.append(lora_weight_pointer_dict)
lora_ranks.append(lora_rank_dict)
last_token_ids = None
if mapping.is_last_pp_rank() and not gather_context_logits:
if not remove_input_padding and max_draft_len > 0:
last_token_ids = Tensor(
name='last_token_ids',
dtype=trt.int32,
shape=[-1, -1],
dim_range=OrderedDict([
('batch_size_beam_width', bb_range),
('last_token_ids', last_token_range),
]),
)
else:
last_token_ids = Tensor(
name='last_token_ids',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('batch_size_last_token_ids', bbd_range),
]),
)
basic_inputs = {
'input_ids': input_ids,
'hidden_states_input': hidden_states,
'position_ids': position_ids,
'last_token_ids': last_token_ids,
'prompt_embedding_table': prompt_embedding_table,
'tasks': tasks,
'prompt_vocab_size': prompt_vocab_size,
'lora_ranks': lora_ranks,
'lora_weights_pointers': lora_weights_pointers,
}
attention_inputs = self.prepare_attention_inputs(
max_batch_size=max_batch_size,
max_beam_width=max_beam_width,
max_input_len=max_input_len,
max_seq_len=max_seq_len,
num_kv_heads=num_kv_heads,
head_size=head_size,
num_layers=num_layers,
kv_dtype=kv_dtype,
num_profiles=num_profiles,
enable_ctx_gen_opt_profiles=enable_ctx_gen_opt_profiles,
remove_input_padding=remove_input_padding,
use_gpt_attention_plugin=use_gpt_attention_plugin,
paged_kv_cache=paged_kv_cache,
tokens_per_block=tokens_per_block,
mapping=mapping,
streamingllm=streamingllm)
for key, value in attention_inputs.items():
basic_inputs[key] = value
return basic_inputs