# 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, recv, send
from ...layers import (Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, FusedGatedMLP, GatedMLP,
KeyValueCacheParams, PositionEmbeddingType,
PromptTuningEmbedding, RmsNorm)
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ..generation_mixin import GenerationMixin
class LLaMADecoderLayer(Module):
def __init__(self,
layer_id,
hidden_size,
num_attention_heads,
num_kv_heads=None,
max_position_embeddings=2048,
dtype=None,
attention_mask_type=AttentionMaskType.causal,
hidden_act='silu',
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
rotary_base=10000.0,
rotary_scaling=None,
mlp_hidden_size=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
rms_norm_eps=1e-06,
attn_bias=False,
mlp_bias=False,
use_fused_mlp=False):
super().__init__()
self._layer_id = layer_id # useful for debugging
# used for quantizing model
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_kv_heads = num_kv_heads
self.max_position_embeddings = max_position_embeddings
self.dtype = dtype
self.hidden_act = hidden_act
self.tp_group = tp_group
self.tp_size = tp_size
self.mlp_hidden_size = mlp_hidden_size
self.attention_mask_type = attention_mask_type
self.position_embedding_type = position_embedding_type
self.input_layernorm = RmsNorm(normalized_shape=hidden_size,
eps=rms_norm_eps,
dtype=dtype)
self.attention = Attention(
hidden_size,
num_attention_heads,
num_kv_heads,
max_position_embeddings,
dtype=dtype,
attention_mask_type=AttentionMaskType.causal,
bias=attn_bias,
position_embedding_type=position_embedding_type,
rotary_embedding_base=rotary_base,
rotary_embedding_scaling=rotary_scaling,
tp_group=tp_group,
tp_size=tp_size,
use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
quant_mode=quant_mode,
instance_id=2 * layer_id,
)
if not mlp_hidden_size:
self.mlp_hidden_size = hidden_size * 4
ClsMLP = FusedGatedMLP if use_fused_mlp is True else GatedMLP
self.mlp = ClsMLP(hidden_size=hidden_size,
ffn_hidden_size=self.mlp_hidden_size,
hidden_act=hidden_act,
dtype=dtype,
bias=mlp_bias,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode,
instance_id=2 * layer_id + 1)
self.post_layernorm = RmsNorm(normalized_shape=hidden_size,
eps=rms_norm_eps,
dtype=dtype)
def forward(self,
hidden_states,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
all_reduce_workspace=None):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
if self._layer_id == 0:
self.register_network_output(f"norm0", 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=all_reduce_workspace)
if use_cache:
attention_output, presents = attention_output
if self._layer_id == 0:
self.register_network_output(f"attn", attention_output)
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
if self._layer_id == 0:
self.register_network_output(f"norm1", hidden_states)
hidden_states = self.mlp(hidden_states, all_reduce_workspace)
if self._layer_id == 0:
self.register_network_output(f"mlp", hidden_states)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
[docs]
class LLaMAModel(Module):
def __init__(self,
num_layers,
num_heads,
num_kv_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
dtype,
mlp_hidden_size=None,
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
rotary_base=10000.0,
rotary_scaling=None,
mapping=Mapping(),
quant_mode=QuantMode(0),
use_parallel_embedding=False,
embedding_sharding_dim=0,
rms_norm_eps=1e-06,
use_fused_mlp=False,
attn_bias=False,
mlp_bias=False,
use_prompt_tuning: bool = False):
super().__init__()
self.mapping = mapping
self.use_prompt_tuning = use_prompt_tuning
EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
if self.mapping.is_first_pp_rank():
self.vocab_embedding = EmbeddingCls(
num_embeddings=vocab_size,
embedding_dim=hidden_size,
dtype=dtype,
tp_size=mapping.tp_size if use_parallel_embedding else 1,
tp_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, # ids in [0, 2 * (num_layers - 1) + 1] already used
)
self.layers = ModuleList([
LLaMADecoderLayer(layer_id=i,
hidden_size=hidden_size,
num_attention_heads=num_heads,
num_kv_heads=num_kv_heads,
max_position_embeddings=max_position_embeddings,
dtype=dtype,
hidden_act=hidden_act,
mlp_hidden_size=mlp_hidden_size,
position_embedding_type=position_embedding_type,
rotary_base=rotary_base,
rotary_scaling=rotary_scaling,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
quant_mode=quant_mode,
rms_norm_eps=rms_norm_eps,
attn_bias=attn_bias,
mlp_bias=mlp_bias,
use_fused_mlp=use_fused_mlp)
for i in self.get_transformer_layers(self.mapping, num_layers)
])
if self.mapping.is_last_pp_rank():
self.ln_f = RmsNorm(normalized_shape=hidden_size,
eps=rms_norm_eps,
dtype=dtype)
[docs]
def forward(
self,
input_ids,
position_ids=None,
use_cache=False,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
hidden_states=None,
all_reduce_workspace=None,
prompt_embedding_table: Optional[Tensor] = None,
prompt_tasks: Optional[Tensor] = None,
prompt_vocab_size: Optional[Tensor] = None,
):
kv_cache_params.fill_none_tensor_list(len(self.layers))
if use_cache:
presents = []
ptuning_args = []
if self.use_prompt_tuning:
ptuning_args = [
prompt_embedding_table, prompt_tasks, prompt_vocab_size
]
if self.mapping.is_first_pp_rank():
hidden_states = self.vocab_embedding(input_ids, *ptuning_args,
all_reduce_workspace)
else:
hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
self.register_network_output(f"embd", hidden_states)
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,
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,
all_reduce_workspace=all_reduce_workspace)
if use_cache:
presents.append(hidden_states[1])
hidden_states = hidden_states[0]
if self.mapping.is_last_pp_rank():
hidden_states = self.ln_f(hidden_states)
else:
hidden_states = send(hidden_states, self.mapping.next_pp_rank())
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
[docs]
class LLaMAForCausalLM(LLaMAModel, GenerationMixin):
def __init__(self,
num_layers,
num_heads,
num_kv_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
dtype,
logits_dtype="float32",
mlp_hidden_size=None,
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
rotary_base=10000.0,
rotary_scaling=None,
mapping=Mapping(),
quant_mode=QuantMode(0),
use_parallel_embedding=False,
embedding_sharding_dim=0,
rms_norm_eps=1e-06,
use_fused_mlp=False,
attn_bias=False,
mlp_bias=False,
use_prompt_tuning: bool = False):
if isinstance(dtype, str):
self.dtype = str_dtype_to_trt(dtype)
else:
assert isinstance(dtype, trt.DataType)
self.dtype = dtype
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
if num_kv_heads is None or num_kv_heads <= 0:
num_kv_heads = num_heads
self.num_kv_heads = num_kv_heads
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.tp_size = mapping.tp_size
self.kv_dtype = self.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.quant_mode = quant_mode
self.use_parallel_embedding = use_parallel_embedding
self.embedding_sharding_dim = embedding_sharding_dim
super().__init__(num_layers, num_heads, num_kv_heads, hidden_size,
vocab_size, hidden_act, max_position_embeddings, dtype,
mlp_hidden_size, position_embedding_type, rotary_base,
rotary_scaling, mapping, quant_mode,
use_parallel_embedding, embedding_sharding_dim,
rms_norm_eps, use_fused_mlp, attn_bias, mlp_bias,
use_prompt_tuning)
vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
if self.mapping.is_last_pp_rank():
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)
[docs]
def forward(
self,
input_ids,
position_ids=None,
use_cache=False,
last_token_ids=None,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
hidden_states=None,
all_reduce_workspace=None,
prompt_embedding_table: Optional[Tensor] = None,
prompt_tasks: Optional[Tensor] = None,
prompt_vocab_size: Optional[Tensor] = None,
):
hidden_states = super().forward(input_ids, position_ids, use_cache,
attention_mask, kv_cache_params,
attention_params, hidden_states,
all_reduce_workspace,
prompt_embedding_table, prompt_tasks,
prompt_vocab_size)
if use_cache:
hidden_states, presents = hidden_states
if self.mapping.is_last_pp_rank():
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)
else:
hidden_states.mark_output('hidden_states_output', self.dtype)
if use_cache and default_net().plugin_config.paged_kv_cache == False:
for i, present in zip(
self.get_transformer_layers(self.mapping, self.num_layers),
presents):
present.mark_output(f'present_key_value_{i}', self.kv_dtype)
if self.mapping.is_last_pp_rank():
return (lm_logits, presents)
return (hidden_states, presents)
else:
if self.mapping.is_last_pp_rank():
return lm_logits
return hidden_states