chore: import upstream snapshot with attribution
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Docker Image CI / build-ubuntu2004 (push) Has been cancelled
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wehub-resource-sync
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import onnx_graphsurgeon as gs
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import numpy as np
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import onnx
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import cupy as cp
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import time
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import pickle
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import sys
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import os
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import tensorrt as trt
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from polygraphy.backend.trt import (
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CreateConfig,
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EngineFromNetwork,
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NetworkFromOnnxPath,
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TrtRunner,
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)
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from polygraphy.json import to_json, from_json
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sys.path.insert(1, os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
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from plugin_utils import volume, parseArgs
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circ_pad_half_kernel = cp.RawKernel(
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r"""
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#include <cuda_fp16.h>
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extern "C" __global__
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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) {
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int index = blockIdx.x * blockDim.x + threadIdx.x;
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int stride = blockDim.x * gridDim.x;
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for(int i = index; i < *Y_len; i += stride)
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{
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int i3 = i % Y_shape[3];
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int i2 = (i / Y_shape[3]) % Y_shape[2];
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int i1 = (i / Y_shape[3] / Y_shape[2]) % Y_shape[1];
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int i0 = i / Y_shape[3] / Y_shape[2] / Y_shape[1];
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int j0 = (i0 - all_pads[0] + orig_dims[0]) % orig_dims[0];
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int j1 = (i1 - all_pads[2] + orig_dims[1]) % orig_dims[1];
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int j2 = (i2 - all_pads[4] + orig_dims[2]) % orig_dims[2];
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int j3 = (i3 - all_pads[6] + orig_dims[3]) % orig_dims[3];
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Y[i] = X[
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orig_dims[3] * orig_dims[2] * orig_dims[1] * j0
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+ orig_dims[3] * orig_dims[2] * j1
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+ orig_dims[3] * j2
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+ j3
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];
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}
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}
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""",
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"circ_pad_half",
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)
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circ_pad_float_kernel = cp.RawKernel(
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r"""
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extern "C" __global__
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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) {
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int index = blockIdx.x * blockDim.x + threadIdx.x;
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int stride = blockDim.x * gridDim.x;
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for(int i = index; i < *Y_len; i += stride)
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{
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int i3 = i % Y_shape[3];
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int i2 = (i / Y_shape[3]) % Y_shape[2];
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int i1 = (i / Y_shape[3] / Y_shape[2]) % Y_shape[1];
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int i0 = i / Y_shape[3] / Y_shape[2] / Y_shape[1];
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int j0 = (i0 - all_pads[0] + orig_dims[0]) % orig_dims[0];
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int j1 = (i1 - all_pads[2] + orig_dims[1]) % orig_dims[1];
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int j2 = (i2 - all_pads[4] + orig_dims[2]) % orig_dims[2];
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int j3 = (i3 - all_pads[6] + orig_dims[3]) % orig_dims[3];
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Y[i] = X[
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orig_dims[3] * orig_dims[2] * orig_dims[1] * j0
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+ orig_dims[3] * orig_dims[2] * j1
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+ orig_dims[3] * j2
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+ j3
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];
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}
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}
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""",
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"circ_pad_float",
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)
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class CircPadPlugin(trt.IPluginV2DynamicExt):
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def __init__(self, fc=None):
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trt.IPluginV2DynamicExt.__init__(self)
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self.pads = []
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self.X_shape = []
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self.num_outputs = 1
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self.plugin_namespace = ""
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self.plugin_type = "CircPadPlugin"
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self.plugin_version = "1"
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if fc is not None:
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assert fc[0].name == "pads"
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self.pads = fc[0].data
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def get_output_datatype(self, index, input_types):
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return input_types[0]
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def get_output_dimensions(self, output_index, inputs, exprBuilder):
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output_dims = trt.DimsExprs(inputs[0])
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for i in range(np.size(self.pads) // 2):
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output_dims[len(output_dims) - i - 1] = exprBuilder.operation(
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trt.DimensionOperation.SUM,
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inputs[0][len(output_dims) - i - 1],
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exprBuilder.constant(self.pads[i * 2] + self.pads[i * 2 + 1]),
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)
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return output_dims
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def serialize(self):
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return to_json({"pads": self.pads})
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def configure_plugin(self, inp, out):
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X_dims = inp[0].desc.dims
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self.X_shape = np.zeros((len(X_dims),))
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for i in range(len(X_dims)):
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self.X_shape[i] = X_dims[i]
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N = len(self.X_shape)
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all_pads = np.zeros((N * 2,))
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orig_dims = np.array(self.X_shape)
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out_dims = np.array(self.X_shape)
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for i in range(np.size(pads) // 2):
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out_dims[N - i - 1] += self.pads[i * 2] + self.pads[i * 2 + 1]
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all_pads[N * 2 - 2 * i - 2] = self.pads[i * 2]
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all_pads[N * 2 - 2 * i - 1] = self.pads[i * 2 + 1]
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self.all_pads_d = cp.asarray(all_pads, dtype=cp.int32)
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self.orig_dims_d = cp.asarray(orig_dims, dtype=cp.int32)
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self.Y_shape_d = cp.asarray(out_dims, dtype=cp.int32)
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self.Y_len_d = cp.array([np.prod(out_dims)], dtype=cp.int32)
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def supports_format_combination(self, pos, in_out, num_inputs):
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assert num_inputs == 1
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assert pos < len(in_out)
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desc = in_out[pos]
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if desc.format != trt.TensorFormat.LINEAR:
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return False
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# first input should be float16 or float32
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if pos == 0:
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return desc.type == trt.DataType.FLOAT or desc.type == trt.DataType.HALF
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# output should have the same type as the input
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if pos == 1:
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return in_out[0].type == desc.type
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assert False
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def enqueue(self, input_desc, output_desc, inputs, outputs, workspace, stream):
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inp_dtype = trt.nptype(input_desc[0].type)
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a_mem = cp.cuda.UnownedMemory(
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inputs[0], volume(input_desc[0].dims) * cp.dtype(inp_dtype).itemsize, self
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)
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c_mem = cp.cuda.UnownedMemory(
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outputs[0],
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volume(output_desc[0].dims) * cp.dtype(inp_dtype).itemsize,
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self,
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)
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a_ptr = cp.cuda.MemoryPointer(a_mem, 0)
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c_ptr = cp.cuda.MemoryPointer(c_mem, 0)
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a = cp.ndarray((volume(input_desc[0].dims)), dtype=inp_dtype, memptr=a_ptr)
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c = cp.ndarray((volume(output_desc[0].dims)), dtype=inp_dtype, memptr=c_ptr)
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cuda_stream = cp.cuda.ExternalStream(stream)
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blockSize = 256
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numBlocks = int((np.prod(np.array(self.X_shape)) + blockSize - 1) // blockSize)
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with cuda_stream:
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if inp_dtype == np.float32:
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circ_pad_float_kernel(
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(numBlocks,),
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(blockSize,),
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(
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a,
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self.all_pads_d,
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self.orig_dims_d,
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c,
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self.Y_shape_d,
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self.Y_len_d,
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),
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)
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elif inp_dtype == np.float16:
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circ_pad_half_kernel(
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(numBlocks,),
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(blockSize,),
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(
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a,
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self.all_pads_d,
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self.orig_dims_d,
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c,
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self.Y_shape_d,
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self.Y_len_d,
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),
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)
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else:
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raise ValueError("inp_dtype not valid")
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def clone(self):
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cloned_plugin = CircPadPlugin()
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cloned_plugin.__dict__.update(self.__dict__)
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return cloned_plugin
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#
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# The following defaults take effect since the respective methods are not overriden
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#
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# def initialize(self):
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# pass
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# def get_serialization_size(self):
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# return len(to_json({"pads": self.pads}))
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# def get_workspace_size(self, input_desc, output_desc):
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# return 0
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# def destroy(self):
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# pass
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# def terminate(self):
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# pass
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class CircPadPluginCreator(trt.IPluginCreator):
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def __init__(self):
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trt.IPluginCreator.__init__(self)
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self.name = "CircPadPlugin"
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self.plugin_namespace = ""
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self.plugin_version = "1"
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self.field_names = trt.PluginFieldCollection(
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[trt.PluginField("pads", np.array([]), trt.PluginFieldType.INT32)]
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)
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def create_plugin(self, name, fc):
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return CircPadPlugin(fc)
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def deserialize_plugin(self, name, data):
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j = dict(from_json(data.decode("utf-8")))
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deserialized = CircPadPlugin()
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deserialized.__dict__.update(j)
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return deserialized
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if __name__ == "__main__":
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args = parseArgs()
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precision = np.float32 if args.precision == "fp32" else np.float16
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inp_shape = (100, 2, 32, 32)
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X = np.random.normal(size=inp_shape).astype(precision)
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pads = (1, 1, 1, 1)
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# Load standard plugins
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TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
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trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="")
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# Register plugin creator
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plg_registry = trt.get_plugin_registry()
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my_plugin_creator = CircPadPluginCreator()
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plg_registry.register_creator(my_plugin_creator, "")
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# create ONNX model
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onnx_path = f"test_CircPadPlugin_cupy_{args.precision}.onnx"
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inputA = gs.Variable(name="X", shape=inp_shape, dtype=precision)
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Y = gs.Variable(name="Y", dtype=precision)
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myPluginNode = gs.Node(
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name="CircPadPlugin",
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op="CircPadPlugin",
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inputs=[inputA],
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outputs=[Y],
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attrs={"pads": pads},
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)
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graph = gs.Graph(nodes=[myPluginNode], inputs=[inputA], outputs=[Y], opset=16)
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onnx.save(gs.export_onnx(graph), onnx_path)
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# build engine
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build_engine = EngineFromNetwork(
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NetworkFromOnnxPath(onnx_path, strongly_typed=True), CreateConfig()
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)
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Y_ref = np.pad(X, [[0, 0], [0, 0], [pads[0], pads[1]], [pads[2], pads[3]]], "wrap")
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# Run
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with TrtRunner(build_engine, "trt_runner") as runner:
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outputs = runner.infer({"X": X})
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Y = outputs["Y"]
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if np.allclose(Y, Y_ref):
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print("Inference result correct!")
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else:
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print("Inference result incorrect!")
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