# 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.
from typing import Optional
import tensorrt as trt
from ..._common import default_net
from ..._utils import pad_vocab_size, str_dtype_to_trt
from ...functional import (Tensor, gather_last_token_logits,
is_gated_activation, non_gated_version)
from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, GatedMLP, KeyValueCacheParams,
LayerNorm, LoraParams, PositionEmbeddingType,
PromptTuningEmbedding)
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ..generation_mixin import GenerationMixin
def MLPFactory(hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
instance_id: int = 0):
MLPClass = GatedMLP if is_gated_activation(hidden_act) else MLP
hidden_act = non_gated_version(hidden_act)
return MLPClass(hidden_size, ffn_hidden_size, hidden_act, bias, dtype,
tp_group, tp_size, quant_mode, instance_id)
class GPTEmbedding(Module):
def __init__(self,
vocab_size,
hidden_size,
max_position_embeddings,
position_embedding_type=PositionEmbeddingType.learned_absolute,
dtype=None,
use_prompt_tuning=False,
tensor_parallel=1,
tensor_parallel_group=None,
sharding_dim=0,
tp_rank=None,
instance_id: int = 0):
super().__init__()
self.max_position_embeddings = max_position_embeddings
self.position_embedding_type = position_embedding_type
self.use_prompt_tuning = use_prompt_tuning
EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
self.vocab_embedding = EmbeddingCls(vocab_size,
hidden_size,
dtype=dtype,
tp_size=tensor_parallel,
tp_group=tensor_parallel_group,
sharding_dim=sharding_dim,
tp_rank=tp_rank,
instance_id=instance_id)
if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
self.position_embedding = Embedding(max_position_embeddings,
hidden_size,
dtype=dtype)
def forward(self,
input_ids,
position_ids,
prompt_embedding_table=None,
prompt_tasks=None,
prompt_vocab_size=None,
workspace: Optional[Tensor] = None):
args = []
if self.use_prompt_tuning:
args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size]
x = self.vocab_embedding(input_ids, *args, workspace=workspace)
if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
x = x + self.position_embedding(position_ids)
return x
class GPTDecoderLayer(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings,
num_layers,
dtype=None,
apply_query_key_layer_scaling=False,
attention_mask_type=AttentionMaskType.causal,
hidden_act='relu',
position_embedding_type=PositionEmbeddingType.learned_absolute,
quant_mode=QuantMode(0),
rotary_embedding_percentage=1.0,
rotary_base=10000.0,
rotary_scaling=None,
inter_size=None,
bias=True,
num_kv_heads=None,
tp_group=None,
tp_size=1,
tp_rank=0,
instance_id: int = 0):
super().__init__()
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.num_layers = num_layers
self.dtype = dtype
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.attention_mask_type = attention_mask_type
self.hidden_act = hidden_act
self.position_embedding_type = position_embedding_type
self.tp_group = tp_group
self.tp_size = tp_size
self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
self.attention = Attention(
hidden_size,
num_attention_heads,
num_kv_heads,
max_position_embeddings,
num_layers,
apply_query_key_layer_scaling,
dtype=dtype,
attention_mask_type=attention_mask_type,
position_embedding_type=position_embedding_type,
rotary_embedding_percentage=rotary_embedding_percentage,
rotary_embedding_base=rotary_base,
rotary_embedding_scaling=rotary_scaling,
bias=bias,
tp_group=tp_group,
tp_size=tp_size,
tp_rank=tp_rank,
use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
quant_mode=quant_mode,
instance_id=2 * instance_id)
if inter_size is None:
inter_size = hidden_size * 4
self.mlp = MLPFactory(hidden_size=hidden_size,
ffn_hidden_size=inter_size,
hidden_act=hidden_act,
dtype=dtype,
bias=bias,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode,
instance_id=2 * instance_id + 1)
self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
def forward(self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
workspace=None,
lora_params=None):
assert isinstance(hidden_states, Tensor)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attention_output = self.attention(hidden_states,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
workspace=workspace,
lora_params=lora_params)
if use_cache:
attention_output, presents = attention_output
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states, workspace=workspace)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
[docs]
class GPTModel(Module):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
dtype=None,
mapping=Mapping(),
apply_query_key_layer_scaling=False,
position_embedding_type=PositionEmbeddingType.learned_absolute,
rotary_embedding_percentage=1.0,
rotary_base=10000.0,
rotary_scaling=None,
inter_size=None,
bias=True,
quant_mode=QuantMode(0),
num_kv_heads=None,
use_prompt_tuning=False,
use_parallel_embedding=False,
embedding_sharding_dim=0):
super().__init__()
self.mapping = mapping
self.embedding = GPTEmbedding(
vocab_size,
hidden_size,
max_position_embeddings,
position_embedding_type,
dtype=dtype,
use_prompt_tuning=use_prompt_tuning,
tensor_parallel=mapping.tp_size if use_parallel_embedding else 1,
tensor_parallel_group=mapping.tp_group
if use_parallel_embedding else None,
sharding_dim=embedding_sharding_dim,
tp_rank=mapping.tp_rank,
instance_id=2 * num_layers,
)
self.layers = ModuleList([
GPTDecoderLayer(
hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=max_position_embeddings,
num_layers=num_layers,
dtype=dtype,
apply_query_key_layer_scaling=apply_query_key_layer_scaling,
attention_mask_type=AttentionMaskType.causal,
hidden_act=hidden_act,
position_embedding_type=position_embedding_type,
rotary_embedding_percentage=rotary_embedding_percentage,
rotary_base=rotary_base,
rotary_scaling=rotary_scaling,
num_kv_heads=num_kv_heads,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
tp_rank=mapping.tp_rank,
inter_size=inter_size,
bias=bias,
quant_mode=quant_mode,
instance_id=i,
) for i in range(num_layers)
])
self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype)
[docs]
def forward(self,
input_ids,
position_ids,
use_cache=False,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
prompt_embedding_table=None,
prompt_tasks=None,
prompt_vocab_size=None,
workspace=None,
lora_params=None):
hidden_states = self.embedding(input_ids,
position_ids,
prompt_embedding_table,
prompt_tasks,
prompt_vocab_size,
workspace=workspace)
kv_cache_params.fill_none_tensor_list(len(self.layers))
if use_cache:
presents = []
for layer_idx, (layer, past, pointer, max_kv_cache_length) in enumerate(
zip(self.layers, kv_cache_params.past_key_value,
kv_cache_params.kv_cache_block_pointers,
kv_cache_params.host_max_kv_cache_lengths)):
lora_param = None
if lora_params.lora_ranks is not None:
lora_param = LoraParams(
lora_ranks=lora_params.lora_ranks,
lora_weights_pointers_list=[
lora_params.lora_weights_pointers_list[layer_idx]
])
hidden_states = layer(
hidden_states,
use_cache=use_cache,
attention_mask=attention_mask,
kv_cache_params=KeyValueCacheParams(
past_key_value=[past],
host_past_key_value_lengths=kv_cache_params.
host_past_key_value_lengths,
host_max_kv_cache_lengths=max_kv_cache_length,
kv_cache_block_pointers=[pointer],
cache_indirection=kv_cache_params.cache_indirection),
attention_params=attention_params,
workspace=workspace,
lora_params=lora_param)
if use_cache:
presents.append(hidden_states[1])
hidden_states = hidden_states[0]
hidden_states = self.ln_f(hidden_states)
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
[docs]
class GPTLMHeadModel(GPTModel, GenerationMixin):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
dtype,
logits_dtype='float32',
mapping=Mapping(),
apply_query_key_layer_scaling=False,
position_embedding_type=PositionEmbeddingType.learned_absolute,
rotary_embedding_percentage=1.0,
rotary_base=10000.0,
rotary_scaling=None,
inter_size=None,
bias=True,
quant_mode=QuantMode(0),
num_kv_heads=None,
use_prompt_tuning=False,
use_parallel_embedding=False,
embedding_sharding_dim=0,
share_embedding_table=False):
if isinstance(dtype, str):
self._kv_dtype = str_dtype_to_trt(dtype)
else:
assert isinstance(dtype, trt.DataType)
self._kv_dtype = dtype
if share_embedding_table and mapping.tp_size > 1:
if (not use_parallel_embedding) or (use_parallel_embedding and
embedding_sharding_dim == 1):
raise NotImplementedError(
'For multiple-processes cases, sharing the embedding table must set use_parallel_embedding=True and embedding_sharding_dim = 0'
)
self._dtype = self._kv_dtype
self.quant_mode = quant_mode
if quant_mode.has_int8_kv_cache():
self._kv_dtype = str_dtype_to_trt('int8')
elif quant_mode.has_fp8_kv_cache():
self._kv_dtype = str_dtype_to_trt('fp8')
if isinstance(logits_dtype, str):
self._logits_dtype = str_dtype_to_trt(logits_dtype)
else:
assert isinstance(logits_dtype, trt.DataType)
self._logits_dtype = logits_dtype
self._num_layers = num_layers
self._num_heads = num_heads
self._hidden_size = hidden_size
self._vocab_size = vocab_size
self._tp_size = mapping.tp_size
self._num_kv_heads = num_kv_heads if num_kv_heads else num_heads
super().__init__(
num_layers=num_layers,
num_heads=num_heads,
hidden_size=hidden_size,
vocab_size=vocab_size,
hidden_act=hidden_act,
max_position_embeddings=max_position_embeddings,
dtype=dtype,
mapping=mapping,
apply_query_key_layer_scaling=apply_query_key_layer_scaling,
position_embedding_type=position_embedding_type,
rotary_embedding_percentage=rotary_embedding_percentage,
rotary_base=rotary_base,
rotary_scaling=rotary_scaling,
inter_size=inter_size,
bias=bias,
quant_mode=quant_mode,
num_kv_heads=num_kv_heads,
use_prompt_tuning=use_prompt_tuning,
use_parallel_embedding=use_parallel_embedding,
embedding_sharding_dim=embedding_sharding_dim,
)
vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
share_weight = None
if share_embedding_table:
share_weight = self.embedding.vocab_embedding.weight
self.lm_head = ColumnLinear(hidden_size,
vocab_size_padded,
bias=False,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
gather_output=True,
share_weight=share_weight)
[docs]
def forward(self,
input_ids: Tensor,
position_ids=None,
use_cache=False,
last_token_ids=None,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
prompt_embedding_table=None,
prompt_tasks=None,
prompt_vocab_size=None,
workspace=None,
lora_params=None):
hidden_states = super().forward(input_ids, position_ids, use_cache,
attention_mask, kv_cache_params,
attention_params,
prompt_embedding_table, prompt_tasks,
prompt_vocab_size, workspace,
lora_params)
if use_cache:
hidden_states, presents = hidden_states
hidden_states = gather_last_token_logits(
hidden_states, last_token_ids,
default_net().plugin_config.remove_input_padding)
# [batch_size, hidden_size] -> [batch_size, vocab_size]
lm_logits = self.lm_head(hidden_states)
lm_logits.mark_output('logits', self._logits_dtype)
if use_cache:
if default_net().plugin_config.paged_kv_cache == False:
for i, present in enumerate(presents):
present.mark_output(f'present_key_value_{i}',
self._kv_dtype)
return (lm_logits, presents)
return lm_logits