# 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 Tensor, gather_last_token_logits
from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm,
PositionEmbeddingType)
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ..generation_mixin import GenerationMixin
class BloomDecoderLayer(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings,
num_layers,
dtype=None,
attention_mask_type=AttentionMaskType.causal,
hidden_act='gelu',
position_embedding_type=PositionEmbeddingType.alibi,
quant_mode=QuantMode(0),
mlp_hidden_size=None,
bias=True,
multi_query_mode=False,
tp_group=None,
tp_size=1,
tp_rank=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.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.tp_rank = tp_rank
self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
self.attention = Attention(
hidden_size,
num_attention_heads,
1 if multi_query_mode else num_attention_heads,
max_position_embeddings,
num_layers,
dtype=dtype,
attention_mask_type=AttentionMaskType.causal,
position_embedding_type=position_embedding_type,
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)
if mlp_hidden_size is None:
mlp_hidden_size = hidden_size * 4
self.mlp = MLP(hidden_size=hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=hidden_act,
dtype=dtype,
bias=bias,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode)
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):
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)
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)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
[docs]
class BloomModel(Module):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
dtype=None,
mapping=Mapping(),
mlp_hidden_size=None,
bias=True,
quant_mode=QuantMode(0),
multi_query_mode=False,
use_parallel_embedding=False,
embedding_sharding_dim=0):
super().__init__()
if use_parallel_embedding:
self.embedding = Embedding(vocab_size,
hidden_size,
dtype=dtype,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
sharding_dim=embedding_sharding_dim,
tp_rank=mapping.tp_rank)
else:
self.embedding = Embedding(vocab_size, hidden_size, dtype=dtype)
self.ln_embed = LayerNorm(normalized_shape=hidden_size, dtype=dtype)
self.layers = ModuleList([
BloomDecoderLayer(hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=max_position_embeddings,
num_layers=num_layers,
dtype=dtype,
attention_mask_type=AttentionMaskType.causal,
hidden_act=hidden_act,
multi_query_mode=multi_query_mode,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
tp_rank=mapping.tp_rank,
mlp_hidden_size=mlp_hidden_size,
bias=bias,
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,
position_ids=None,
use_cache=False,
attention_mask=None,
kv_cache_params=None,
attention_params=None):
hidden_states = self.embedding(input_ids)
hidden_states = self.ln_embed(hidden_states)
kv_cache_params.fill_none_tensor_list(len(self.layers))
if use_cache:
presents = []
for layer, past, max_kv_cache_length in zip(
self.layers, kv_cache_params.past_key_value,
kv_cache_params.host_max_kv_cache_lengths):
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,
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 BloomForCausalLM(BloomModel, GenerationMixin):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
max_position_embeddings,
hidden_act='gelu',
dtype=None,
mapping=Mapping(),
mlp_hidden_size=None,
bias=True,
quant_mode=QuantMode(0),
multi_query_mode=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
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')
self.mapping = mapping
self.quant_mode = quant_mode
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._multi_query_mode = multi_query_mode
super().__init__(num_layers, num_heads, hidden_size, vocab_size,
hidden_act, max_position_embeddings, dtype, mapping,
mlp_hidden_size, bias, quant_mode, multi_query_mode,
use_parallel_embedding, 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.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):
hidden_states = super().forward(input_ids, position_ids, use_cache,
attention_mask, 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._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