# 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.
from collections import OrderedDict
from typing import List
import tensorrt as trt
from ..._utils import str_dtype_to_trt
from ...functional import Tensor, cast, gather_last_token_logits
from ...layers import (Embedding, LayerNorm, Linear, Mamba, MambaParameters,
RmsNorm)
from ...module import Module, ModuleList
from ..generation_mixin import GenerationMixin
from ..modeling_utils import PretrainedConfig, PretrainedModel
class MambaLayer(Module):
def __init__(self, config: PretrainedConfig, last_layer=False):
super().__init__()
self.dtype = config.dtype
self.residual_in_fp32 = config.residual_in_fp32
self.last_layer = last_layer
self.ssm = Mamba(config.hidden_size,
**config.ssm_cfg,
dtype=config.dtype)
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):
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)
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__()
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, last_layer=i == n_layer - 1)
for i in range(n_layer)
])
if config.rms_norm:
self.norm_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
else:
self.norm_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):
hidden_states = self.vocab_embedding(input_ids)
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)
present_convs.append(hidden_values[2])
present_ssms.append(hidden_values[3])
hidden_states = hidden_values[0]
hidden_states = self.norm_f(hidden_states)
return hidden_states, tuple(present_convs), tuple(present_ssms)
[docs]
class MambaLMHeadModel(PretrainedModel):
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.ssm_cfg = MambaParameters(**config.ssm_cfg)
self.d_inner = self.ssm_cfg.expand * config.hidden_size
self.d_conv = self.ssm_cfg.d_conv
self.d_state = self.ssm_cfg.d_state
self.config = config
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 = Linear(config.hidden_size,
config.vocab_size,
bias=False,
dtype=dtype,
gather_output=False)
def __post_init__(self):
return
[docs]
def forward(self, input_ids, conv_states, ssm_states, host_request_types,
last_token_ids):
hidden_states, present_convs, present_ssms = self.backbone(
input_ids, conv_states, ssm_states, host_request_types)
hidden_states = gather_last_token_logits(hidden_states, last_token_ids,
False)
lm_logits = self.lm_head(hidden_states)
lm_logits.mark_output('logits', self._logits_dtype)
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_ssm_state_{i}',
str_dtype_to_trt('float32'))
return (lm_logits, present_convs, present_ssms)