wehub-resource-sync
353 lines
12 KiB
Python
353 lines
12 KiB
Python
#
|
|
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 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 os
|
|
import sys
|
|
|
|
import tensorrt as trt
|
|
from polygraphy.backend.trt import (
|
|
CreateConfig,
|
|
EngineFromNetwork,
|
|
NetworkFromOnnxPath,
|
|
TrtRunner,
|
|
create_network,
|
|
engine_from_network,
|
|
)
|
|
|
|
import argparse
|
|
|
|
from polygraphy import mod
|
|
|
|
sys.path.insert(1, os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
|
|
from plugin_utils import cuda_call, KernelHelper, UnownedMemory, volume
|
|
|
|
|
|
cuda = mod.lazy_import("cuda.bindings.driver")
|
|
cudart = mod.lazy_import("cuda.bindings.runtime")
|
|
nvrtc = mod.lazy_import("cuda.bindings.nvrtc")
|
|
|
|
torch = mod.lazy_import("torch")
|
|
cp = mod.lazy_import("cupy")
|
|
|
|
non_zero_half_kernel = r'''
|
|
#include <cuda_fp16.h>
|
|
extern "C" __global__
|
|
void find_non_zero_indices_half(
|
|
half const* X, int* indices, unsigned long long* count, int R, int C)
|
|
{
|
|
static_assert(sizeof(unsigned long long) == 8U, "unsigned long long must be 8 bytes in NVCC");
|
|
int row = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
// Check if the row index is within bounds
|
|
if (row < R)
|
|
{
|
|
for (int col = 0; col < C; ++col)
|
|
{
|
|
half const z = static_cast<half>(0.F);
|
|
if (X[col + C * row] != z)
|
|
{
|
|
// Increment count atomically and get the previous value
|
|
unsigned long long index = atomicAdd(count, 1ULL);
|
|
indices[2 * index] = row;
|
|
indices[2 * index + 1] = col;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
'''
|
|
|
|
non_zero_float_kernel = r'''
|
|
extern "C" __global__
|
|
void find_non_zero_indices_float(
|
|
float const* X, int* indices, unsigned long long* count, int R, int C)
|
|
{
|
|
static_assert(sizeof(unsigned long long) == 8U, "unsigned long long must be 8 bytes in NVCC");
|
|
int row = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
// Check if the row index is within bounds
|
|
if (row < R)
|
|
{
|
|
for (int col = 0; col < C; ++col)
|
|
{
|
|
if (X[col + C * row] != 0.F)
|
|
{
|
|
// Increment count atomically and get the previous value
|
|
unsigned long long index = atomicAdd(count, 1ULL);
|
|
indices[2 * index] = row;
|
|
indices[2 * index + 1] = col;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
'''
|
|
|
|
class NonZeroPlugin(trt.IPluginV3, trt.IPluginV3OneCore, trt.IPluginV3OneBuild, trt.IPluginV3OneRuntime):
|
|
def __init__(self, backend = None):
|
|
trt.IPluginV3.__init__(self)
|
|
trt.IPluginV3OneCore.__init__(self)
|
|
trt.IPluginV3OneBuild.__init__(self)
|
|
trt.IPluginV3OneRuntime.__init__(self)
|
|
|
|
self.num_outputs = 2
|
|
self.plugin_namespace = ""
|
|
self.plugin_name = "NonZeroPlugin"
|
|
self.plugin_version = "1"
|
|
|
|
if backend is not None:
|
|
self.backend = backend.tobytes().decode("utf-8")
|
|
else:
|
|
self.backend = "cuda_python"
|
|
|
|
self.cuDevice = None
|
|
|
|
def get_capability_interface(self, type):
|
|
return self
|
|
|
|
def get_output_data_types(self, input_types):
|
|
return [trt.DataType.INT32, trt.DataType.INT64]
|
|
|
|
def get_output_shapes(self, inputs, shape_inputs, exprBuilder):
|
|
# First output is 2-D
|
|
# Second output is a size tensor, which must be declared a scalar (0-D)
|
|
output_dims = [trt.DimsExprs(2), trt.DimsExprs(0)]
|
|
|
|
upper_bound = exprBuilder.operation(trt.DimensionOperation.PROD, inputs[0][0], inputs[0][1])
|
|
opt_value = exprBuilder.operation(trt.DimensionOperation.FLOOR_DIV, upper_bound, exprBuilder.constant(2))
|
|
num_non_zero_size_tensor = exprBuilder.declare_size_tensor(1, opt_value, upper_bound)
|
|
|
|
output_dims[0][0] = num_non_zero_size_tensor
|
|
output_dims[0][1] = exprBuilder.constant(2)
|
|
|
|
return output_dims
|
|
|
|
def get_fields_to_serialize(self):
|
|
return trt.PluginFieldCollection(
|
|
[
|
|
trt.PluginField(
|
|
"backend", self.backend.encode(), trt.PluginFieldType.CHAR
|
|
)
|
|
]
|
|
)
|
|
|
|
def configure_plugin(self, inp, out):
|
|
if self.backend == "cuda_python":
|
|
self.cuDevice = cuda_call(cuda.cuDeviceGet(0))
|
|
|
|
def on_shape_change(self, inp, out):
|
|
if self.backend == "cuda_python":
|
|
self.cuDevice = cuda_call(cuda.cuDeviceGet(0))
|
|
|
|
def supports_format_combination(self, pos, in_out, num_inputs):
|
|
assert num_inputs == 1
|
|
assert pos < len(in_out)
|
|
|
|
type_ok = False
|
|
|
|
# first input should be float16 or float32
|
|
if pos == 0:
|
|
type_ok = in_out[0].desc.type == trt.DataType.FLOAT or in_out[0].desc.type == trt.DataType.HALF
|
|
elif pos == 1:
|
|
type_ok = in_out[1].desc.type == trt.DataType.INT32
|
|
else: # pos == 2
|
|
# size tensor outputs must be NCHW INT64
|
|
type_ok = in_out[2].desc.type == trt.DataType.INT64
|
|
|
|
return in_out[pos].desc.format == trt.TensorFormat.LINEAR and type_ok
|
|
|
|
def enqueue(self, input_desc, output_desc, inputs, outputs, workspace, stream):
|
|
inp_dtype = trt.nptype(input_desc[0].type)
|
|
|
|
if self.backend == "cuda_python":
|
|
R = input_desc[0].dims[0]
|
|
C = input_desc[0].dims[1]
|
|
|
|
blockSize = 256
|
|
numBlocks = int((C + blockSize - 1) // blockSize)
|
|
|
|
d_in = np.array([inputs[0]], dtype=np.uint64)
|
|
d_out_0 = np.array([outputs[0]], dtype=np.uint64)
|
|
d_out_1 = np.array([outputs[1]], dtype=np.uint64)
|
|
|
|
args = [d_in, d_out_0, d_out_1, np.array(R, dtype=np.uint32), np.array(C, dtype=np.uint32)]
|
|
kernelArgs = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
|
|
|
|
stream_ptr = np.array([stream], dtype=np.uint64)
|
|
|
|
if inp_dtype == np.float32:
|
|
kernelHelper = KernelHelper(non_zero_float_kernel, int(self.cuDevice))
|
|
_non_zero_float_kernel = kernelHelper.getFunction(b'find_non_zero_indices_float')
|
|
cuda_call(cuda.cuLaunchKernel(_non_zero_float_kernel,
|
|
numBlocks, 1, 1,
|
|
blockSize, 1, 1,
|
|
0,
|
|
stream_ptr,
|
|
kernelArgs, 0))
|
|
elif inp_dtype == np.float16:
|
|
kernelHelper = KernelHelper(non_zero_half_kernel, int(self.cuDevice))
|
|
_non_zero_half_kernel = kernelHelper.getFunction(b'find_non_zero_indices_half')
|
|
cuda_call(cuda.cuLaunchKernel(_non_zero_half_kernel,
|
|
numBlocks, 1, 1,
|
|
blockSize, 1, 1,
|
|
0,
|
|
stream_ptr,
|
|
kernelArgs, 0))
|
|
else:
|
|
raise ValueError("inp_dtype not valid")
|
|
|
|
elif self.backend == "torch":
|
|
inp_mem = UnownedMemory(inputs[0], input_desc[0].dims, inp_dtype)
|
|
|
|
out_mem = UnownedMemory(
|
|
outputs[0], 2 * volume(input_desc[0].dims), np.int32
|
|
)
|
|
|
|
out_1_mem = UnownedMemory(outputs[1], 1, np.int64)
|
|
|
|
a_t = torch.as_tensor(inp_mem.d, device="cuda")
|
|
out = torch.nonzero(a_t)
|
|
|
|
out_mem.d[: volume(out.shape)] = cp.reshape(cp.asarray(out), (-1,))
|
|
cp.copyto(out_1_mem.d, cp.reshape(cp.asarray([out.shape[0]]), (-1,)))
|
|
|
|
else:
|
|
raise ValueError(f"backend not valid: {self.backend}")
|
|
|
|
def attach_to_context(self, context):
|
|
return self.clone()
|
|
|
|
def set_tactic(self, tactic):
|
|
pass
|
|
|
|
def clone(self):
|
|
cloned_plugin = NonZeroPlugin()
|
|
cloned_plugin.__dict__.update(self.__dict__)
|
|
return cloned_plugin
|
|
|
|
#
|
|
# The following defaults take effect since the respective methods are not overriden
|
|
#
|
|
|
|
# def get_valid_tactics(self):
|
|
# return []
|
|
|
|
# def get_workspace_size(self, input_desc, output_desc):
|
|
# return 0
|
|
|
|
# def destroy(self):
|
|
# pass
|
|
|
|
|
|
class NonZeroPluginCreator(trt.IPluginCreatorV3One):
|
|
def __init__(self):
|
|
trt.IPluginCreatorV3One.__init__(self)
|
|
self.name = "NonZeroPlugin"
|
|
self.plugin_namespace = ""
|
|
self.plugin_version = "1"
|
|
self.field_names = trt.PluginFieldCollection(
|
|
[trt.PluginField("backend", np.array([]), trt.PluginFieldType.CHAR)]
|
|
)
|
|
|
|
def create_plugin(self, name, fc, phase):
|
|
backend = None
|
|
for f in fc:
|
|
if f.name == "backend":
|
|
backend = f.data[:-1] if f.data[-1] == 0 else f.data
|
|
return NonZeroPlugin(backend)
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--precision', type=str, default="fp32", choices=["fp32", "fp16"])
|
|
parser.add_argument("--backend", type=str, default="torch", choices=["cuda_python", "torch"])
|
|
parser.add_argument('--net_type', type=str, default="onnx", choices=["onnx", "inetdef"])
|
|
|
|
args = parser.parse_args()
|
|
|
|
if args.backend == "cuda_python":
|
|
# Initialize CUDA and create default context
|
|
cuda_call(cudart.cudaFree(0))
|
|
|
|
elif args.backend == "torch":
|
|
# Initialize CUDA and create default context
|
|
torch.cuda.init()
|
|
|
|
precision = np.float32 if args.precision == "fp32" else np.float16
|
|
|
|
inp_shape = (128, 128)
|
|
X = np.random.normal(size=inp_shape).astype(precision)
|
|
# Zero out a random set of indices
|
|
indices = np.random.choice(np.prod(inp_shape), replace=False, size=np.random.randint(0, np.prod(inp_shape) + 1))
|
|
X[np.unravel_index(indices, inp_shape)] = 0
|
|
|
|
# Register plugin creator
|
|
plg_registry = trt.get_plugin_registry()
|
|
my_plugin_creator = NonZeroPluginCreator()
|
|
plg_registry.register_creator(my_plugin_creator, "")
|
|
|
|
if args.net_type == "onnx":
|
|
# create ONNX model
|
|
onnx_path = "test_NonZeroPlugin.onnx"
|
|
inputX = gs.Variable(name="X", shape=inp_shape, dtype=precision)
|
|
Y = gs.Variable(name="Y", dtype=np.int32)
|
|
Y_num = gs.Variable(name="Y_num", dtype=np.int64)
|
|
nonZeroPluginNode = gs.Node(
|
|
name="NonZeroPlugin",
|
|
op="NonZeroPlugin",
|
|
inputs=[inputX],
|
|
outputs=[Y, Y_num],
|
|
attrs={"backend": args.backend.encode()},
|
|
)
|
|
graph = gs.Graph(nodes=[nonZeroPluginNode], inputs=[inputX], outputs=[Y], opset=16)
|
|
onnx.save(gs.export_onnx(graph), onnx_path)
|
|
|
|
# build engine
|
|
build_engine = EngineFromNetwork(
|
|
NetworkFromOnnxPath(onnx_path, strongly_typed=True), CreateConfig()
|
|
)
|
|
else:
|
|
# Create plugin object
|
|
builder, network = create_network(strongly_typed=True)
|
|
plg_creator = plg_registry.get_creator("NonZeroPlugin", "1", "")
|
|
plugin_fields_list = [
|
|
trt.PluginField("backend", args.backend.encode(), trt.PluginFieldType.CHAR)
|
|
]
|
|
pfc = trt.PluginFieldCollection(plugin_fields_list)
|
|
plugin = plg_creator.create_plugin("NonZeroPlugin", pfc, trt.TensorRTPhase.BUILD)
|
|
|
|
# Populate network
|
|
inputX = network.add_input(name="X", dtype=trt.float32 if precision==np.float32 else trt.float16, shape=inp_shape)
|
|
out = network.add_plugin_v3([inputX], [], plugin)
|
|
out.get_output(0).name = "Y"
|
|
network.mark_output(tensor=out.get_output(0))
|
|
build_engine = engine_from_network((builder, network), CreateConfig())
|
|
|
|
# Compare against Numpy's nonzero
|
|
Y_ref = np.transpose(np.nonzero(X))
|
|
|
|
# Run
|
|
with TrtRunner(build_engine, "trt_runner") as runner:
|
|
outputs = runner.infer({"X": X})
|
|
Y = outputs["Y"]
|
|
Y = Y[np.lexsort(np.fliplr(Y).T)]
|
|
|
|
if np.allclose(Y, Y_ref):
|
|
print("Inference result correct!")
|
|
else:
|
|
print("Inference result incorrect!")
|