# # SPDX-FileCopyrightText: Copyright (c) 1993-2024 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. # import onnx_graphsurgeon as gs import numpy as np import onnx import cupy as cp from numba import cuda 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 @cuda.jit def circ_pad(X, all_pads, orig_dims, Y, Y_shape, Y_len): index = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x stride = cuda.blockDim.x * cuda.gridDim.x for i in range(index, Y_len, stride): i3 = int(i % Y_shape[3]) i2 = int((i // Y_shape[3]) % Y_shape[2]) i1 = int((i // Y_shape[3] // Y_shape[2]) % Y_shape[1]) i0 = int(i // Y_shape[3] // Y_shape[2] // Y_shape[1]) j0 = int((i0 - all_pads[0]) % orig_dims[0]) j1 = int((i1 - all_pads[2]) % orig_dims[1]) j2 = int((i2 - all_pads[4]) % orig_dims[2]) j3 = int((i3 - all_pads[6]) % orig_dims[3]) Y[i] = X[ int( orig_dims[3] * orig_dims[2] * orig_dims[1] * j0 + orig_dims[3] * orig_dims[2] * j1 + orig_dims[3] * j2 + j3 ) ] 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] 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) numba_stream = cuda.external_stream(stream) 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] += pads[i * 2] + pads[i * 2 + 1] all_pads[N * 2 - 2 * i - 2] = pads[i * 2] all_pads[N * 2 - 2 * i - 1] = pads[i * 2 + 1] all_pads_d = cp.asarray(all_pads) orig_dims_d = cp.asarray(orig_dims) Y_shape_d = cp.asarray(out_dims) blockSize = 256 numBlocks = int((np.prod(out_dims) + blockSize - 1) // blockSize) circ_pad[numBlocks, blockSize, numba_stream]( a, all_pads_d, orig_dims_d, c, Y_shape_d, np.prod(out_dims) ) return 0 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 = (10, 3, 32, 32) X = np.random.normal(size=inp_shape).astype(precision) pads = (1, 1, 1, 1) # 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_numba_{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), CreateConfig(fp16=precision == np.float16) ) 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!")