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Copy pathcirc_pad_plugin_cupy.py
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executable file
·324 lines (262 loc) · 10 KB
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#
# SPDX-FileCopyrightText: Copyright (c) 1993-2025 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 onnx_graphsurgeon as gs
import numpy as np
import onnx
import cupy as cp
import time
import pickle
import sys
import os
import tensorrt as trt
from polygraphy.backend.trt import (
CreateConfig,
EngineFromNetwork,
NetworkFromOnnxPath,
TrtRunner,
)
from polygraphy.json import to_json, from_json
sys.path.insert(1, os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
from plugin_utils import volume, parseArgs
circ_pad_half_kernel = cp.RawKernel(
r"""
#include <cuda_fp16.h>
extern "C" __global__
void circ_pad_half(half const* X, int const* all_pads, int const* orig_dims, half* Y, int const* Y_shape, int const* Y_len) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for(int i = index; i < *Y_len; i += stride)
{
int i3 = i % Y_shape[3];
int i2 = (i / Y_shape[3]) % Y_shape[2];
int i1 = (i / Y_shape[3] / Y_shape[2]) % Y_shape[1];
int i0 = i / Y_shape[3] / Y_shape[2] / Y_shape[1];
int j0 = (i0 - all_pads[0] + orig_dims[0]) % orig_dims[0];
int j1 = (i1 - all_pads[2] + orig_dims[1]) % orig_dims[1];
int j2 = (i2 - all_pads[4] + orig_dims[2]) % orig_dims[2];
int j3 = (i3 - all_pads[6] + orig_dims[3]) % orig_dims[3];
Y[i] = X[
orig_dims[3] * orig_dims[2] * orig_dims[1] * j0
+ orig_dims[3] * orig_dims[2] * j1
+ orig_dims[3] * j2
+ j3
];
}
}
""",
"circ_pad_half",
)
circ_pad_float_kernel = cp.RawKernel(
r"""
extern "C" __global__
void circ_pad_float(float const* X, int const* all_pads, int const* orig_dims, float* Y, int const* Y_shape, int const* Y_len) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for(int i = index; i < *Y_len; i += stride)
{
int i3 = i % Y_shape[3];
int i2 = (i / Y_shape[3]) % Y_shape[2];
int i1 = (i / Y_shape[3] / Y_shape[2]) % Y_shape[1];
int i0 = i / Y_shape[3] / Y_shape[2] / Y_shape[1];
int j0 = (i0 - all_pads[0] + orig_dims[0]) % orig_dims[0];
int j1 = (i1 - all_pads[2] + orig_dims[1]) % orig_dims[1];
int j2 = (i2 - all_pads[4] + orig_dims[2]) % orig_dims[2];
int j3 = (i3 - all_pads[6] + orig_dims[3]) % orig_dims[3];
Y[i] = X[
orig_dims[3] * orig_dims[2] * orig_dims[1] * j0
+ orig_dims[3] * orig_dims[2] * j1
+ orig_dims[3] * j2
+ j3
];
}
}
""",
"circ_pad_float",
)
class CircPadPlugin(trt.IPluginV2DynamicExt):
def __init__(self, fc=None):
trt.IPluginV2DynamicExt.__init__(self)
self.pads = []
self.X_shape = []
self.num_outputs = 1
self.plugin_namespace = ""
self.plugin_type = "CircPadPlugin"
self.plugin_version = "1"
if fc is not None:
assert fc[0].name == "pads"
self.pads = fc[0].data
def get_output_datatype(self, index, input_types):
return input_types[0]
def get_output_dimensions(self, output_index, inputs, exprBuilder):
output_dims = trt.DimsExprs(inputs[0])
for i in range(np.size(self.pads) // 2):
output_dims[len(output_dims) - i - 1] = exprBuilder.operation(
trt.DimensionOperation.SUM,
inputs[0][len(output_dims) - i - 1],
exprBuilder.constant(self.pads[i * 2] + self.pads[i * 2 + 1]),
)
return output_dims
def serialize(self):
return to_json({"pads": self.pads})
def configure_plugin(self, inp, out):
X_dims = inp[0].desc.dims
self.X_shape = np.zeros((len(X_dims),))
for i in range(len(X_dims)):
self.X_shape[i] = X_dims[i]
N = len(self.X_shape)
all_pads = np.zeros((N * 2,))
orig_dims = np.array(self.X_shape)
out_dims = np.array(self.X_shape)
for i in range(np.size(pads) // 2):
out_dims[N - i - 1] += self.pads[i * 2] + self.pads[i * 2 + 1]
all_pads[N * 2 - 2 * i - 2] = self.pads[i * 2]
all_pads[N * 2 - 2 * i - 1] = self.pads[i * 2 + 1]
self.all_pads_d = cp.asarray(all_pads, dtype=cp.int32)
self.orig_dims_d = cp.asarray(orig_dims, dtype=cp.int32)
self.Y_shape_d = cp.asarray(out_dims, dtype=cp.int32)
self.Y_len_d = cp.array([np.prod(out_dims)], dtype=cp.int32)
def supports_format_combination(self, pos, in_out, num_inputs):
assert num_inputs == 1
assert pos < len(in_out)
desc = in_out[pos]
if desc.format != trt.TensorFormat.LINEAR:
return False
# first input should be float16 or float32
if pos == 0:
return desc.type == trt.DataType.FLOAT or desc.type == trt.DataType.HALF
# output should have the same type as the input
if pos == 1:
return in_out[0].type == desc.type
assert False
def enqueue(self, input_desc, output_desc, inputs, outputs, workspace, stream):
inp_dtype = trt.nptype(input_desc[0].type)
a_mem = cp.cuda.UnownedMemory(
inputs[0], volume(input_desc[0].dims) * cp.dtype(inp_dtype).itemsize, self
)
c_mem = cp.cuda.UnownedMemory(
outputs[0],
volume(output_desc[0].dims) * cp.dtype(inp_dtype).itemsize,
self,
)
a_ptr = cp.cuda.MemoryPointer(a_mem, 0)
c_ptr = cp.cuda.MemoryPointer(c_mem, 0)
a = cp.ndarray((volume(input_desc[0].dims)), dtype=inp_dtype, memptr=a_ptr)
c = cp.ndarray((volume(output_desc[0].dims)), dtype=inp_dtype, memptr=c_ptr)
cuda_stream = cp.cuda.ExternalStream(stream)
blockSize = 256
numBlocks = int((np.prod(np.array(self.X_shape)) + blockSize - 1) // blockSize)
with cuda_stream:
if inp_dtype == np.float32:
circ_pad_float_kernel(
(numBlocks,),
(blockSize,),
(
a,
self.all_pads_d,
self.orig_dims_d,
c,
self.Y_shape_d,
self.Y_len_d,
),
)
elif inp_dtype == np.float16:
circ_pad_half_kernel(
(numBlocks,),
(blockSize,),
(
a,
self.all_pads_d,
self.orig_dims_d,
c,
self.Y_shape_d,
self.Y_len_d,
),
)
else:
raise ValueError("inp_dtype not valid")
def clone(self):
cloned_plugin = CircPadPlugin()
cloned_plugin.__dict__.update(self.__dict__)
return cloned_plugin
#
# The following defaults take effect since the respective methods are not overriden
#
# def initialize(self):
# pass
# def get_serialization_size(self):
# return len(to_json({"pads": self.pads}))
# def get_workspace_size(self, input_desc, output_desc):
# return 0
# def destroy(self):
# pass
# def terminate(self):
# pass
class CircPadPluginCreator(trt.IPluginCreator):
def __init__(self):
trt.IPluginCreator.__init__(self)
self.name = "CircPadPlugin"
self.plugin_namespace = ""
self.plugin_version = "1"
self.field_names = trt.PluginFieldCollection(
[trt.PluginField("pads", np.array([]), trt.PluginFieldType.INT32)]
)
def create_plugin(self, name, fc):
return CircPadPlugin(fc)
def deserialize_plugin(self, name, data):
j = dict(from_json(data.decode("utf-8")))
deserialized = CircPadPlugin()
deserialized.__dict__.update(j)
return deserialized
if __name__ == "__main__":
args = parseArgs()
precision = np.float32 if args.precision == "fp32" else np.float16
inp_shape = (100, 2, 32, 32)
X = np.random.normal(size=inp_shape).astype(precision)
pads = (1, 1, 1, 1)
# Load standard plugins
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="")
# Register plugin creator
plg_registry = trt.get_plugin_registry()
my_plugin_creator = CircPadPluginCreator()
plg_registry.register_creator(my_plugin_creator, "")
# create ONNX model
onnx_path = f"test_CircPadPlugin_cupy_{args.precision}.onnx"
inputA = gs.Variable(name="X", shape=inp_shape, dtype=precision)
Y = gs.Variable(name="Y", dtype=precision)
myPluginNode = gs.Node(
name="CircPadPlugin",
op="CircPadPlugin",
inputs=[inputA],
outputs=[Y],
attrs={"pads": pads},
)
graph = gs.Graph(nodes=[myPluginNode], inputs=[inputA], outputs=[Y], opset=16)
onnx.save(gs.export_onnx(graph), onnx_path)
# build engine
build_engine = EngineFromNetwork(
NetworkFromOnnxPath(onnx_path, strongly_typed=True), CreateConfig()
)
Y_ref = np.pad(X, [[0, 0], [0, 0], [pads[0], pads[1]], [pads[2], pads[3]]], "wrap")
# Run
with TrtRunner(build_engine, "trt_runner") as runner:
outputs = runner.infer({"X": X})
Y = outputs["Y"]
if np.allclose(Y, Y_ref):
print("Inference result correct!")
else:
print("Inference result incorrect!")
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