Source code for tensorrt_llm.models.mamba.model
# 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 os
from collections import OrderedDict
from typing import List, Optional, Union
import tensorrt as trt
from transformers import AutoModelForCausalLM
from ..._common import default_net
from ..._utils import str_dtype_to_trt
from ...functional import (Tensor, arange, cast, concat, expand,
gather_last_token_logits, shape, unsqueeze)
from ...layers import ColumnLinear, Embedding, LayerNorm, Mamba, Mamba2, RmsNorm
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...plugin import current_all_reduce_helper
from ..generation_mixin import GenerationMixin
from ..modeling_utils import PretrainedConfig, PretrainedModel, QuantConfig
from .config import MambaConfig
from .convert import convert_from_hf_checkpoint, convert_hf_mamba
class MambaLayer(Module):
def __init__(self, config: PretrainedConfig, layer_idx: int):
super().__init__()
self.dtype = config.dtype
self.residual_in_fp32 = config.residual_in_fp32
n_layer = config.num_hidden_layers
self.last_layer = layer_idx == n_layer - 1
if config.mamba_version == 'Mamba1':
assert config.mapping.tp_size == 1, "Mamba1 can not support tensor parallelism."
self.ssm = Mamba(config.hidden_size,
config.rnn_hidden_size,
d_state=config.state_size,
d_conv=config.conv_kernel,
bias=config.use_bias,
dtype=config.dtype)
elif config.mamba_version == 'Mamba2':
self.ssm = Mamba2(config.hidden_size,
config.rnn_hidden_size,
d_state=config.state_size,
d_conv=config.conv_kernel,
headdim=config.rnn_head_size,
ngroups=config.ngroups,
chunk_size=config.chunk_size,
bias=config.use_bias,
rmsnorm=config.ssm_rmsnorm,
dtype=config.dtype,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size)
if config.rms_norm:
self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
else:
self.input_layernorm = LayerNorm(
normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(self,
hidden_states: Tensor,
residual: Tensor,
conv_state: Tensor,
ssm_state: Tensor,
host_request_types: Tensor,
last_token_ids: Tensor,
host_context_lengths: Optional[Tensor] = None,
slot_mapping: Optional[Tensor] = None,
conv_indices: Optional[Tensor] = None):
hidden_states = self.input_layernorm(hidden_states)
ssm_out, present_conv, present_ssm = self.ssm(
hidden_states,
conv_state=conv_state,
ssm_state=ssm_state,
host_request_types=host_request_types,
last_token_ids=last_token_ids,
host_context_lengths=host_context_lengths,
slot_mapping=slot_mapping,
conv_indices=conv_indices)
if self.residual_in_fp32:
residual = residual + cast(ssm_out, 'float32')
hidden_states = cast(residual, self.dtype)
else:
residual = residual + ssm_out
hidden_states = residual
if self.last_layer:
return hidden_states, None, present_conv, present_ssm
else:
return hidden_states, residual, present_conv, present_ssm
class MambaModel(Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.d_conv = config.conv_kernel
self.d_inner = config.rnn_hidden_size // config.mapping.tp_size
n_layer = config.num_hidden_layers
self.residual_in_fp32 = config.residual_in_fp32
if config.vocab_size % config.pad_vocab_size_multiple != 0:
config.vocab_size += config.pad_vocab_size_multiple - (
config.vocab_size % config.pad_vocab_size_multiple)
self.vocab_embedding = Embedding(config.vocab_size,
config.hidden_size,
dtype=config.dtype)
self.layers = ModuleList(
[MambaLayer(config, i) for i in range(n_layer)])
if config.rms_norm:
self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
else:
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(self,
input_ids,
conv_states,
ssm_states,
host_request_types,
last_token_ids,
host_context_lengths,
slot_mapping: Optional[Tensor] = None):
hidden_states = self.vocab_embedding(input_ids)
# Get conv state indices
indices = None
if not default_net().plugin_config.mamba_conv1d_plugin:
batch_size = shape(input_ids, 0)
indices = expand(
unsqueeze(arange(0, self.d_conv - 1, dtype='int32'), 0),
concat([batch_size, self.d_conv - 1]))
offsets = expand(unsqueeze(last_token_ids, 1),
concat([batch_size, self.d_conv - 1]))
indices = unsqueeze(indices + offsets, 1)
indices = expand(
indices, concat([batch_size, self.d_inner, self.d_conv - 1]))
residual = cast(hidden_states,
'float32') if self.residual_in_fp32 else hidden_states
hidden_values = [hidden_states, residual]
present_convs, present_ssms = [], []
for layer, past_conv, past_ssm in zip(self.layers, conv_states,
ssm_states):
hidden_values = layer(hidden_values[0], hidden_values[1], past_conv,
past_ssm, host_request_types, last_token_ids,
host_context_lengths, slot_mapping, indices)
present_convs.append(hidden_values[2])
present_ssms.append(hidden_values[3])
hidden_states = hidden_values[0]
hidden_states = self.ln_f(hidden_states)
return hidden_states, tuple(present_convs), tuple(present_ssms)
[docs]
class MambaForCausalLM(PretrainedModel):
config_class = MambaConfig
def __init__(self, config: PretrainedConfig):
super().__init__(config)
dtype = config.dtype
logits_dtype = config.logits_dtype
if isinstance(dtype, str):
self.dtype = str_dtype_to_trt(dtype)
else:
assert isinstance(dtype, trt.DataType)
self.dtype = dtype
self.config = config
self.mamba_version = config.mamba_version
self.d_inner = config.rnn_hidden_size // config.mapping.tp_size
self.d_conv = config.conv_kernel
self.d_state = config.state_size
self.conv_dim = config.rnn_conv_dim_size // config.mapping.tp_size
self.gather_context_logits = False
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.backbone = MambaModel(config)
self.lm_head = ColumnLinear(config.hidden_size,
config.vocab_size,
bias=False,
dtype=dtype,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
gather_output=True)
def __post_init__(self):
return
[docs]
def forward(self,
input_ids,
conv_states,
ssm_states,
host_request_types,
last_token_ids,
last_token_ids_for_logits,
host_context_lengths,
slot_mapping: Optional[Tensor] = None):
hidden_states, present_convs, present_ssms = self.backbone(
input_ids, conv_states, ssm_states, host_request_types,
last_token_ids, host_context_lengths, slot_mapping)
if not self.gather_context_logits:
hidden_states = gather_last_token_logits(
hidden_states, last_token_ids_for_logits,
default_net().plugin_config.remove_input_padding)
lm_logits = self.lm_head(hidden_states)
lm_logits.mark_output('logits', self._logits_dtype)
if not default_net().plugin_config.paged_state:
for i, present_conv in enumerate(present_convs):
present_conv.mark_output(f'present_conv_state_{i}', self.dtype)
for i, present_ssm in enumerate(present_ssms):
present_ssm.mark_output(f'present_rnn_state_{i}', self.dtype)
return (lm_logits, present_convs, present_ssms)
[docs]
def prepare_inputs(
self,
max_batch_size,
max_input_len,
max_seq_len,
max_num_tokens,
use_cache,
max_beam_width: int = 1,
opt_num_tokens: int = None,
opt_batch_size: int = 0,
prompt_embedding_table_size: int = 0,
max_draft_len: int = 0,
gather_context_logits: bool = False,
gather_generation_logits: bool = False,
lora_target_modules: List[str] = None,
speculative_decoding_draft_tokens_external: bool = False):
'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
ranges of the dimensions of when using TRT dynamic shapes.
@return: a list contains values which can be fed into the self.forward()
'''
assert speculative_decoding_draft_tokens_external == False, "Speculative decoding is not supported in Mamba"
assert max_beam_width == 1, "We don't support beam search for the Mamba model."
remove_input_padding = default_net().plugin_config.remove_input_padding
use_gemm_plugin = default_net().plugin_config.gemm_plugin
paged_state = default_net().plugin_config.paged_state
multiple_profiles = default_net().plugin_config.multiple_profiles
use_mamba_conv1d_plugin = default_net(
).plugin_config.mamba_conv1d_plugin
self.gather_context_logits = gather_context_logits
mapping = self.config.mapping
# basic inputs
enable_ctx_gen_opt_profiles = GenerationMixin.has_ctx_gen_opt_profiles(
use_gemm_plugin=use_gemm_plugin,
use_mamba_conv1d_plugin=use_mamba_conv1d_plugin,
remove_input_padding=remove_input_padding,
paged_state=paged_state)
num_profiles, ranges = GenerationMixin.get_profiles_ranges(
max_batch_size=max_batch_size,
max_beam_width=max_beam_width,
max_input_len=max_input_len,
max_num_tokens=max_num_tokens,
max_draft_len=max_draft_len,
opt_batch_size=opt_batch_size,
opt_num_tokens=opt_num_tokens,
enable_ctx_gen_opt_profiles=enable_ctx_gen_opt_profiles,
multiple_profiles=multiple_profiles)
if remove_input_padding:
assert use_mamba_conv1d_plugin, "mamba_conv1d_plugin is needed to support remove_input_padding"
input_ids = Tensor(name='input_ids',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('num_tokens', ranges['num_tokens_range']),
]))
else:
input_ids = Tensor(name='input_ids',
dtype=trt.int32,
shape=[-1, -1],
dim_range=OrderedDict([
('batch_size_beam_width',
ranges['bb_range']),
('input_len', ranges['inlen_range']),
]))
if mapping.tp_size > 1:
current_all_reduce_helper().set_workspace_tensor(
mapping, num_profiles)
# recurrent inputs
conv_states = []
ssm_states = []
if use_mamba_conv1d_plugin:
conv_state_dim_range = OrderedDict([
('batch_size', ranges['bb_range']),
('kernel_size', [self.d_conv - 1] * num_profiles),
('dim_size', [self.conv_dim] * num_profiles),
])
else:
conv_state_dim_range = OrderedDict([
('batch_size', ranges['bb_range']),
('dim_size', [self.conv_dim] * num_profiles),
('kernel_size', [self.d_conv - 1] * num_profiles),
])
if self.mamba_version == 'Mamba2':
headdim = self.config.rnn_head_size
nheads = self.d_inner // headdim
ssm_state_dim_range = OrderedDict([
('batch_size', ranges['bb_range']),
('head_size', [nheads] * num_profiles),
('state_size', [self.d_state] * num_profiles),
('headdim_size', [headdim] * num_profiles),
])
ssm_state_shape = [-1, nheads, self.d_state, headdim]
else:
ssm_state_dim_range = OrderedDict([
('batch_size', ranges['bb_range']),
('state_size', [self.d_state] * num_profiles),
('dim_size', [self.d_inner] * num_profiles),
])
ssm_state_shape = [-1, self.d_state, self.d_inner]
one_dim_range = OrderedDict([
('buffer_count', [1] * num_profiles),
])
for i in range(self.config.num_hidden_layers):
if default_net().plugin_config.paged_state:
conv_state = Tensor(name=f'conv_state_ptr_{i}',
dtype=str_dtype_to_trt('int64'),
shape=[1],
dim_range=one_dim_range)
ssm_state = Tensor(name=f'rnn_state_ptr_{i}',
dtype=str_dtype_to_trt('int64'),
shape=[1],
dim_range=one_dim_range)
else:
if use_mamba_conv1d_plugin:
conv_state = Tensor(
name=f'past_conv_state_{i}',
dtype=self.dtype,
shape=[-1, self.d_conv - 1, self.conv_dim],
dim_range=conv_state_dim_range)
else:
conv_state = Tensor(
name=f'past_conv_state_{i}',
dtype=self.dtype,
shape=[-1, self.conv_dim, self.d_conv - 1],
dim_range=conv_state_dim_range)
ssm_state = Tensor(name=f'past_rnn_state_{i}',
dtype=self.dtype,
shape=ssm_state_shape,
dim_range=ssm_state_dim_range)
conv_states.append(conv_state)
ssm_states.append(ssm_state)
host_request_types = Tensor(
name='host_request_types',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size', ranges['bb_range'])]),
)
if remove_input_padding:
host_context_lengths = Tensor(
name='host_context_lengths',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size', ranges['bb_range'])]),
)
else:
host_context_lengths = None
last_token_ids = Tensor(
name='last_token_ids',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('batch_size', ranges['bbd_range']),
]),
)
last_token_ids_for_logits = None
if not gather_context_logits:
last_token_ids_for_logits = last_token_ids
return_dict = {
'input_ids': input_ids,
'conv_states': conv_states,
'ssm_states': ssm_states,
'host_request_types': host_request_types,
'last_token_ids': last_token_ids,
'last_token_ids_for_logits': last_token_ids_for_logits,
'host_context_lengths': host_context_lengths,
}
if default_net().plugin_config.paged_state:
slot_mapping = Tensor(
name='slot_mapping',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([('batch_size', ranges['bb_range'])]),
)
return_dict['slot_mapping'] = slot_mapping
return return_dict
[docs]
@classmethod
def from_hugging_face(
cls,
hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'],
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
import transformers
assert hf_model_or_dir is not None
use_preloading = isinstance(hf_model_or_dir,
transformers.PreTrainedModel)
if use_preloading:
hf_model = hf_model_or_dir
hf_config_or_dir = hf_model.config
else:
hf_model_dir = hf_model_or_dir
hf_config_or_dir = hf_model_or_dir
config = MambaConfig.from_hugging_face(hf_config_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if not os.path.exists(hf_model_dir):
hf_model = AutoModelForCausalLM.from_pretrained(
hf_model_dir, torch_dtype="auto", trust_remote_code=True)
assert isinstance(hf_model, transformers.PreTrainedModel)
weights = convert_hf_mamba(hf_model, dtype)
else:
weights = convert_from_hf_checkpoint(config, hf_model_dir)
model = cls(config)
model.load(weights)
return model