# 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 tensorrt as trt
from ..._common import default_net
from ..._utils import pad_vocab_size, str_dtype_to_trt
from ...functional import (PositionEmbeddingType, Tensor,
gather_last_token_logits)
from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm)
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ..generation_mixin import GenerationMixin
class GPTJDecoderLayer(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings,
rotary_dim,
dtype=None,
hidden_act='relu',
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0)):
super().__init__()
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.rotary_dim = rotary_dim
self.dtype = dtype
self.hidden_act = hidden_act
self.tp_group = tp_group
self.tp_size = tp_size
self.quant_mode = quant_mode
self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
self.attention = Attention(
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
rotary_embedding_percentage=rotary_dim /
(hidden_size // num_attention_heads),
position_embedding_type=PositionEmbeddingType.rope_gptj,
max_position_embeddings=max_position_embeddings,
dtype=dtype,
attention_mask_type=AttentionMaskType.causal,
bias=False,
tp_group=tp_group,
tp_size=tp_size,
use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
quant_mode=quant_mode)
self.mlp = MLP(hidden_size=hidden_size,
ffn_hidden_size=hidden_size * 4,
hidden_act=hidden_act,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode)
def forward(self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None):
if not default_net(
).plugin_config.layernorm_plugin and trt.__version__[:3] == '8.6':
raise AssertionError(
"You need to enable the LayerNorm plugin for GPT-J with TensorRT 8.6. Please set plugin_config.layernorm_plugin"
)
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)
if use_cache:
attention_output, presents = attention_output
attention_output = attention_output
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = attention_output + feed_forward_hidden_states + residual
if use_cache:
return (hidden_states, presents)
return hidden_states
[docs]
class GPTJModel(Module):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
rotary_dim,
dtype=None,
mapping=Mapping(),
quant_mode=QuantMode(0)):
super().__init__()
self.mapping = mapping
self.embedding = Embedding(vocab_size, hidden_size, dtype=dtype)
self.layers = ModuleList([
GPTJDecoderLayer(hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=max_position_embeddings,
rotary_dim=rotary_dim,
dtype=dtype,
hidden_act=hidden_act,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
quant_mode=quant_mode) for _ in range(num_layers)
])
self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype)
[docs]
def forward(self,
input_ids: Tensor,
use_cache=False,
kv_cache_params=None,
attention_params=None):
hidden_states = self.embedding(input_ids)
kv_cache_params.fill_none_tensor_list(len(self.layers))
if use_cache:
presents = []
for layer, past, pointer, max_kv_cache_length in zip(
self.layers, kv_cache_params.past_key_value,
kv_cache_params.kv_cache_block_pointers,
kv_cache_params.host_max_kv_cache_lengths):
hidden_states = layer(
hidden_states,
use_cache=use_cache,
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)
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 GPTJForCausalLM(GPTJModel, GenerationMixin):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
rotary_dim,
dtype,
logits_dtype='float32',
mapping=Mapping(),
quant_mode=QuantMode(0)):
if isinstance(dtype, str):
self._dtype = str_dtype_to_trt(dtype)
else:
assert isinstance(dtype, trt.DataType)
self._dtype = dtype
self._kv_dtype = 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
super().__init__(num_layers, num_heads, hidden_size, vocab_size,
hidden_act, max_position_embeddings, rotary_dim, dtype,
mapping, quant_mode)
self._vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
self.lm_head = ColumnLinear(hidden_size,
self._vocab_size_padded,
bias=True,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
gather_output=True)
[docs]
def forward(self,
input_ids: Tensor,
position_ids=None,
use_cache=False,
last_token_ids=None,
kv_cache_params=None,
attention_params=None):
hidden_states = super().forward(input_ids, use_cache, kv_cache_params,
attention_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 and 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