# 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 ..functional import ACT2FN, concat
from ..module import Module
from ..quantization import QuantMode
from ..quantization.layers import FP8Linear, FP8RowLinear
from .linear import ColumnLinear, RowLinear
from .lora import Lora, LoraRuntimeParams
[docs]
class MLP(Module):
def __init__(
self,
hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
max_lora_rank=None,
):
super().__init__()
if hidden_act not in ACT2FN:
raise ValueError(
'unsupported activation function: {}'.format(hidden_act))
fc_output_size = 2 * ffn_hidden_size if hidden_act == 'swiglu' else ffn_hidden_size
self.use_fp8_qdq = quant_mode.has_fp8_qdq()
if self.use_fp8_qdq:
self.fc = FP8Linear(hidden_size,
fc_output_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
max_lora_rank=max_lora_rank)
self.proj = FP8RowLinear(ffn_hidden_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
max_lora_rank=max_lora_rank)
else:
self.fc = ColumnLinear(hidden_size,
fc_output_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
max_lora_rank=max_lora_rank)
self.proj = RowLinear(ffn_hidden_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
max_lora_rank=max_lora_rank)
self.hidden_act = hidden_act
self.dtype = dtype
self.bias = bias
[docs]
def forward(self, hidden_states, lora_layer_params=None):
mlp_fc_lora_params = None
if lora_layer_params is not None:
mlp_fc_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_h_to_4h")
mlp_proj_lora_params = None
if lora_layer_params is not None:
mlp_proj_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_4h_to_h")
inter = self.fc(hidden_states, mlp_fc_lora_params)
inter = ACT2FN[self.hidden_act](inter)
output = self.proj(inter, lora_runtime_params=mlp_proj_lora_params)
return output
[docs]
class GatedMLP(MLP):
def __init__(
self,
hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
max_lora_rank=None,
):
super().__init__(hidden_size,
ffn_hidden_size,
hidden_act,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode)
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.hidden_act = hidden_act
self.bias = bias
self.dtype = dtype
self.tp_group = tp_group
self.tp_size = tp_size
self.quant_mode = quant_mode
self.max_lora_rank = max_lora_rank
if self.use_fp8_qdq:
self.gate = FP8Linear(hidden_size,
ffn_hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
max_lora_rank=max_lora_rank)
else:
self.gate = ColumnLinear(hidden_size,
ffn_hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False,
max_lora_rank=max_lora_rank)
[docs]
def forward(self, hidden_states, lora_layer_params=None):
mlp_fc_lora_params = None
if lora_layer_params is not None:
mlp_fc_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_h_to_4h")
mlp_gate_lora_params = None
if lora_layer_params is not None:
mlp_gate_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_gate")
mlp_proj_lora_params = None
if lora_layer_params is not None:
mlp_proj_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_4h_to_h")
inter = self.fc(hidden_states, mlp_fc_lora_params)
inter = ACT2FN[self.hidden_act](inter)
gate = self.gate(hidden_states, mlp_gate_lora_params)
intermediate = inter * gate
output = self.proj(intermediate,
lora_runtime_params=mlp_proj_lora_params)
return output
[docs]
class FusedGatedMLP(Module):
def __init__(
self,
hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
quant_mode=QuantMode(0),
max_lora_rank=None,
):
super().__init__()
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.hidden_act = hidden_act
self.bias = bias
self.dtype = dtype
self.tp_group = tp_group
self.tp_size = tp_size
self.quant_mode = quant_mode
self.use_fp8_qdq = quant_mode.has_fp8_qdq()
if self.use_fp8_qdq:
self.fused_fc = FP8Linear(
self.hidden_size,
self.ffn_hidden_size * 2,
bias=self.bias,
dtype=self.dtype,
tp_group=self.tp_group,
tp_size=self.tp_size,
gather_output=False,
)
self.proj = FP8RowLinear(ffn_hidden_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
max_lora_rank=max_lora_rank)
else:
self.fused_fc = ColumnLinear(
self.hidden_size,
self.ffn_hidden_size * 2,
bias=self.bias,
dtype=self.dtype,
tp_group=self.tp_group,
tp_size=self.tp_size,
gather_output=False,
)
self.proj = RowLinear(ffn_hidden_size,
hidden_size,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
max_lora_rank=max_lora_rank)
if max_lora_rank is None:
max_lora_rank = min(hidden_size, ffn_hidden_size // tp_size)
self.mlp_in_lora = Lora(
in_hidden_size=hidden_size,
out_hidden_sizes=[
ffn_hidden_size // tp_size, ffn_hidden_size // tp_size
],
max_low_rank=max_lora_rank,
)
[docs]
def forward(self, hidden_states, lora_layer_params=None):
# Combine the following pattern
#
# SiLU(FC(x)) + Gate(x)
#
# into:
#
# SwiGLU(FusedFC(x))
#
# Upside is we don't need to modify 4 different weight loading paths just to concat weights
inter = self.fused_fc(hidden_states)
if lora_layer_params is not None:
mlp_fc_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_h_to_4h")
mlp_gate_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_gate")
if mlp_fc_lora_params is not None and mlp_gate_lora_params is not None:
mlp_in_lora_params = LoraRuntimeParams(
lora_ranks=[
mlp_fc_lora_params.lora_ranks[0],
mlp_gate_lora_params.lora_ranks[0]
],
lora_weights_pointers=[
mlp_fc_lora_params.lora_weights_pointers[0],
mlp_gate_lora_params.lora_weights_pointers[0]
],
host_request_types=mlp_fc_lora_params.host_request_types,
host_context_lengths=mlp_fc_lora_params.
host_context_lengths,
max_context_length=mlp_fc_lora_params.max_context_length)
mlp_fc_lora, mlp_gate_lora = self.mlp_in_lora(
hidden_states, mlp_in_lora_params)
mlp_in_result = concat([mlp_gate_lora, mlp_fc_lora],
dim=mlp_fc_lora.rank() - 1)
inter = inter + mlp_in_result
if self.hidden_act == 'silu':
inter = ACT2FN['swiglu'](inter)
else:
raise NotImplementedError(
f"Activation {self.hidden_act} not yet implemented for FusedGatedMLP"
)
mlp_proj_lora_params = None
if lora_layer_params is not None:
mlp_proj_lora_params = lora_layer_params.get_runtime_params(
0, "mlp_4h_to_h")
output = self.proj(inter, lora_runtime_params=mlp_proj_lora_params)
return output