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linear.py
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# 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 numpy as np
import tensorrt as trt
from .._common import default_net, default_trtnet
from .._utils import int32_array, str_dtype_to_trt
from ..functional import (Tensor, _create_tensor, allgather, allreduce, cast,
concat, constant, matmul, shape, slice)
from ..module import Module
from ..parameter import Parameter
from ..plugin import TRT_LLM_PLUGIN_NAMESPACE
def _gemm_plugin(input: Tensor,
mat2: Tensor,
transa: bool = False,
transb: bool = False,
use_fp8: bool = False) -> Tensor:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'Gemm', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
transa = 1 if transa else 0
transa = trt.PluginField("transa", np.array(transa, dtype=np.int32),
trt.PluginFieldType.INT32)
transb = 1 if transb else 0
transb = trt.PluginField("transb", np.array(transb, dtype=np.int32),
trt.PluginFieldType.INT32)
use_fp8 = 1 if use_fp8 else 0
use_fp8 = trt.PluginField("use_fp8", np.array(use_fp8, dtype=np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.gemm_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection([transa, transb, pf_type, use_fp8])
gemm_plug = plg_creator.create_plugin("gemm", pfc)
plug_inputs = [input.trt_tensor, mat2.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, gemm_plug)
return _create_tensor(layer.get_output(0), layer)
class Linear(Module):
def __init__(self,
in_features,
out_features,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
gather_output=True,
share_weight=None):
super().__init__()
self.in_features = in_features
self.out_features = out_features // tp_size
self.dtype = dtype
if not share_weight:
self.weight = Parameter(shape=(self.out_features, self.in_features),
dtype=dtype)
else:
self.weight = share_weight
self.tp_size = tp_size
self.tp_group = tp_group
self.gather_output = gather_output
if bias:
self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
else:
self.register_parameter('bias', None)
def multiply_gather(self, x, weight, gemm_plugin, use_fp8=False):
if gemm_plugin:
x = _gemm_plugin(x, weight, transb=True, use_fp8=use_fp8)
else:
x = matmul(x, weight, transb=True)
if self.bias is not None:
if x.dtype != self.bias.value.dtype:
x = cast(x, self.bias.value.dtype)
x = x + self.bias.value
if self.gather_output and self.tp_size > 1 and self.tp_group is not None:
# 1. [dim0, local_dim] -> [dim0 * tp_size, local_dim]
x = allgather(x, self.tp_group)
# 2. [dim0 * tp_size, local_dim] -> [dim0, local_dim * tp_size]
# 2.1 split
split_size = shape(x, dim=0) / self.tp_size
ndim = x.ndim()
starts = [constant(int32_array([0])) for _ in range(ndim)]
sizes = [shape(x, dim=d) for d in range(ndim)]
sizes[0] = split_size
sections = []
for i in range(self.tp_size):
starts[0] = split_size * i
sections.append(slice(x, concat(starts), concat(sizes)))
# 2.2 concat
x = concat(sections, dim=1)
return x
def forward(self, x):
return self.multiply_gather(x, self.weight.value,
default_net().plugin_config.gemm_plugin)
ColumnLinear = Linear
class RowLinear(Module):
def __init__(self,
in_features,
out_features,
bias=True,
dtype=None,
tp_group=None,
tp_size=1,
instance_id: int = 0):
super().__init__()
self.in_features = in_features // tp_size
self.out_features = out_features
self.dtype = dtype
self.weight = Parameter(shape=(self.out_features, self.in_features),
dtype=dtype)
if bias:
self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
else:
self.register_parameter('bias', None)
self.tp_group = tp_group
self.tp_size = tp_size
self.instance_id = instance_id
def multiply_reduce(self,
x,
weight,
gemm_plugin,
use_fp8=False,
workspace=None):
if gemm_plugin:
x = _gemm_plugin(x, weight, transb=True, use_fp8=use_fp8)
else:
x = matmul(x, weight, transb=True)
if self.tp_size > 1 and self.tp_group is not None:
x = allreduce(x, self.tp_group, workspace, self.instance_id)
if self.bias is not None:
if x.dtype != self.bias.value.dtype:
x = cast(x, self.bias.value.dtype)
x = x + self.bias.value
return x
def forward(self, x, workspace=None):
return self.multiply_reduce(x,
self.weight.value,
default_net().plugin_config.gemm_plugin,
workspace=workspace)