chore: import upstream snapshot with attribution
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) 2024-2025 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 os
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import sys
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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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create_network,
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engine_from_network,
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)
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import argparse
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from polygraphy import mod
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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 cuda_call, KernelHelper, UnownedMemory, volume
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cuda = mod.lazy_import("cuda.bindings.driver")
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cudart = mod.lazy_import("cuda.bindings.runtime")
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nvrtc = mod.lazy_import("cuda.bindings.nvrtc")
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torch = mod.lazy_import("torch")
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cp = mod.lazy_import("cupy")
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non_zero_half_kernel = r'''
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#include <cuda_fp16.h>
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extern "C" __global__
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void find_non_zero_indices_half(
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half const* X, int* indices, unsigned long long* count, int R, int C)
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{
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static_assert(sizeof(unsigned long long) == 8U, "unsigned long long must be 8 bytes in NVCC");
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int row = blockIdx.x * blockDim.x + threadIdx.x;
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// Check if the row index is within bounds
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if (row < R)
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{
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for (int col = 0; col < C; ++col)
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{
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half const z = static_cast<half>(0.F);
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if (X[col + C * row] != z)
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{
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// Increment count atomically and get the previous value
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unsigned long long index = atomicAdd(count, 1ULL);
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indices[2 * index] = row;
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indices[2 * index + 1] = col;
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}
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}
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}
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}
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'''
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non_zero_float_kernel = r'''
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extern "C" __global__
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void find_non_zero_indices_float(
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float const* X, int* indices, unsigned long long* count, int R, int C)
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{
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static_assert(sizeof(unsigned long long) == 8U, "unsigned long long must be 8 bytes in NVCC");
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int row = blockIdx.x * blockDim.x + threadIdx.x;
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// Check if the row index is within bounds
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if (row < R)
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{
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for (int col = 0; col < C; ++col)
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{
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if (X[col + C * row] != 0.F)
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{
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// Increment count atomically and get the previous value
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unsigned long long index = atomicAdd(count, 1ULL);
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indices[2 * index] = row;
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indices[2 * index + 1] = col;
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}
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}
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}
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}
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'''
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class NonZeroPlugin(trt.IPluginV3, trt.IPluginV3OneCore, trt.IPluginV3OneBuild, trt.IPluginV3OneRuntime):
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def __init__(self, backend = None):
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trt.IPluginV3.__init__(self)
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trt.IPluginV3OneCore.__init__(self)
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trt.IPluginV3OneBuild.__init__(self)
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trt.IPluginV3OneRuntime.__init__(self)
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self.num_outputs = 2
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self.plugin_namespace = ""
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self.plugin_name = "NonZeroPlugin"
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self.plugin_version = "1"
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if backend is not None:
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self.backend = backend.tobytes().decode("utf-8")
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else:
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self.backend = "cuda_python"
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self.cuDevice = None
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def get_capability_interface(self, type):
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return self
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def get_output_data_types(self, input_types):
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return [trt.DataType.INT32, trt.DataType.INT64]
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def get_output_shapes(self, inputs, shape_inputs, exprBuilder):
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# First output is 2-D
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# Second output is a size tensor, which must be declared a scalar (0-D)
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output_dims = [trt.DimsExprs(2), trt.DimsExprs(0)]
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upper_bound = exprBuilder.operation(trt.DimensionOperation.PROD, inputs[0][0], inputs[0][1])
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opt_value = exprBuilder.operation(trt.DimensionOperation.FLOOR_DIV, upper_bound, exprBuilder.constant(2))
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num_non_zero_size_tensor = exprBuilder.declare_size_tensor(1, opt_value, upper_bound)
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output_dims[0][0] = num_non_zero_size_tensor
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output_dims[0][1] = exprBuilder.constant(2)
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return output_dims
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def get_fields_to_serialize(self):
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return trt.PluginFieldCollection(
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[
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trt.PluginField(
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"backend", self.backend.encode(), trt.PluginFieldType.CHAR
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)
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]
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)
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def configure_plugin(self, inp, out):
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if self.backend == "cuda_python":
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self.cuDevice = cuda_call(cuda.cuDeviceGet(0))
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def on_shape_change(self, inp, out):
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if self.backend == "cuda_python":
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self.cuDevice = cuda_call(cuda.cuDeviceGet(0))
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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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type_ok = False
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# first input should be float16 or float32
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if pos == 0:
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type_ok = in_out[0].desc.type == trt.DataType.FLOAT or in_out[0].desc.type == trt.DataType.HALF
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elif pos == 1:
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type_ok = in_out[1].desc.type == trt.DataType.INT32
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else: # pos == 2
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# size tensor outputs must be NCHW INT64
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type_ok = in_out[2].desc.type == trt.DataType.INT64
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return in_out[pos].desc.format == trt.TensorFormat.LINEAR and type_ok
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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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if self.backend == "cuda_python":
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R = input_desc[0].dims[0]
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C = input_desc[0].dims[1]
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blockSize = 256
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numBlocks = int((C + blockSize - 1) // blockSize)
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d_in = np.array([inputs[0]], dtype=np.uint64)
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d_out_0 = np.array([outputs[0]], dtype=np.uint64)
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d_out_1 = np.array([outputs[1]], dtype=np.uint64)
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args = [d_in, d_out_0, d_out_1, np.array(R, dtype=np.uint32), np.array(C, dtype=np.uint32)]
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kernelArgs = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
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stream_ptr = np.array([stream], dtype=np.uint64)
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if inp_dtype == np.float32:
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kernelHelper = KernelHelper(non_zero_float_kernel, int(self.cuDevice))
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_non_zero_float_kernel = kernelHelper.getFunction(b'find_non_zero_indices_float')
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cuda_call(cuda.cuLaunchKernel(_non_zero_float_kernel,
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numBlocks, 1, 1,
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blockSize, 1, 1,
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0,
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stream_ptr,
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kernelArgs, 0))
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elif inp_dtype == np.float16:
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kernelHelper = KernelHelper(non_zero_half_kernel, int(self.cuDevice))
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_non_zero_half_kernel = kernelHelper.getFunction(b'find_non_zero_indices_half')
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cuda_call(cuda.cuLaunchKernel(_non_zero_half_kernel,
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numBlocks, 1, 1,
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blockSize, 1, 1,
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0,
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stream_ptr,
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kernelArgs, 0))
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else:
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raise ValueError("inp_dtype not valid")
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elif self.backend == "torch":
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inp_mem = UnownedMemory(inputs[0], input_desc[0].dims, inp_dtype)
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out_mem = UnownedMemory(
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outputs[0], 2 * volume(input_desc[0].dims), np.int32
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)
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out_1_mem = UnownedMemory(outputs[1], 1, np.int64)
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a_t = torch.as_tensor(inp_mem.d, device="cuda")
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out = torch.nonzero(a_t)
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out_mem.d[: volume(out.shape)] = cp.reshape(cp.asarray(out), (-1,))
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cp.copyto(out_1_mem.d, cp.reshape(cp.asarray([out.shape[0]]), (-1,)))
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else:
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raise ValueError(f"backend not valid: {self.backend}")
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def attach_to_context(self, context):
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return self.clone()
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def set_tactic(self, tactic):
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pass
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def clone(self):
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cloned_plugin = NonZeroPlugin()
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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 get_valid_tactics(self):
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# return []
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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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class NonZeroPluginCreator(trt.IPluginCreatorV3One):
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def __init__(self):
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trt.IPluginCreatorV3One.__init__(self)
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self.name = "NonZeroPlugin"
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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("backend", np.array([]), trt.PluginFieldType.CHAR)]
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)
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def create_plugin(self, name, fc, phase):
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backend = None
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for f in fc:
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if f.name == "backend":
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backend = f.data[:-1] if f.data[-1] == 0 else f.data
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return NonZeroPlugin(backend)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--precision', type=str, default="fp32", choices=["fp32", "fp16"])
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parser.add_argument("--backend", type=str, default="torch", choices=["cuda_python", "torch"])
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parser.add_argument('--net_type', type=str, default="onnx", choices=["onnx", "inetdef"])
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args = parser.parse_args()
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if args.backend == "cuda_python":
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# Initialize CUDA and create default context
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cuda_call(cudart.cudaFree(0))
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elif args.backend == "torch":
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# Initialize CUDA and create default context
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torch.cuda.init()
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precision = np.float32 if args.precision == "fp32" else np.float16
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inp_shape = (128, 128)
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X = np.random.normal(size=inp_shape).astype(precision)
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# Zero out a random set of indices
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indices = np.random.choice(np.prod(inp_shape), replace=False, size=np.random.randint(0, np.prod(inp_shape) + 1))
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X[np.unravel_index(indices, inp_shape)] = 0
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# Register plugin creator
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plg_registry = trt.get_plugin_registry()
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my_plugin_creator = NonZeroPluginCreator()
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plg_registry.register_creator(my_plugin_creator, "")
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if args.net_type == "onnx":
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# create ONNX model
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onnx_path = "test_NonZeroPlugin.onnx"
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inputX = gs.Variable(name="X", shape=inp_shape, dtype=precision)
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Y = gs.Variable(name="Y", dtype=np.int32)
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Y_num = gs.Variable(name="Y_num", dtype=np.int64)
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nonZeroPluginNode = gs.Node(
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name="NonZeroPlugin",
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op="NonZeroPlugin",
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inputs=[inputX],
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outputs=[Y, Y_num],
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attrs={"backend": args.backend.encode()},
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)
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graph = gs.Graph(nodes=[nonZeroPluginNode], inputs=[inputX], 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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else:
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# Create plugin object
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builder, network = create_network(strongly_typed=True)
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plg_creator = plg_registry.get_creator("NonZeroPlugin", "1", "")
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plugin_fields_list = [
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trt.PluginField("backend", args.backend.encode(), trt.PluginFieldType.CHAR)
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]
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pfc = trt.PluginFieldCollection(plugin_fields_list)
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plugin = plg_creator.create_plugin("NonZeroPlugin", pfc, trt.TensorRTPhase.BUILD)
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# Populate network
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inputX = network.add_input(name="X", dtype=trt.float32 if precision==np.float32 else trt.float16, shape=inp_shape)
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out = network.add_plugin_v3([inputX], [], plugin)
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out.get_output(0).name = "Y"
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network.mark_output(tensor=out.get_output(0))
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build_engine = engine_from_network((builder, network), CreateConfig())
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# Compare against Numpy's nonzero
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Y_ref = np.transpose(np.nonzero(X))
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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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Y = Y[np.lexsort(np.fliplr(Y).T)]
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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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