wehub-resource-sync
298 lines
8.3 KiB
Python
298 lines
8.3 KiB
Python
#
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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 sys
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import os
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import triton
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import triton.language as tl
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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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import torch
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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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@triton.jit
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def circ_pad(
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X,
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all_pads_0,
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all_pads_2,
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all_pads_4,
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all_pads_6,
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orig_dims_0,
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orig_dims_1,
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orig_dims_2,
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orig_dims_3,
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Y,
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Y_shape_1,
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Y_shape_2,
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Y_shape_3,
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X_len,
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Y_len,
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BLOCK_SIZE: tl.constexpr,
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):
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pid = tl.program_id(0)
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i = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
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mask_y = i < Y_len
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i3 = i % Y_shape_3
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i2 = (i // Y_shape_3) % Y_shape_2
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i1 = (i // Y_shape_3 // Y_shape_2) % Y_shape_1
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i0 = i // Y_shape_3 // Y_shape_2 // Y_shape_1
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j0 = (i0 - all_pads_0 + orig_dims_0) % orig_dims_0
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j1 = (i1 - all_pads_2 + orig_dims_1) % orig_dims_1
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j2 = (i2 - all_pads_4 + orig_dims_2) % orig_dims_2
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j3 = (i3 - all_pads_6 + orig_dims_3) % orig_dims_3
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load_idx = (
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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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mask_x = load_idx < X_len
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x = tl.load(X + load_idx, mask=mask_x)
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tl.store(Y + i, x, mask=mask_y)
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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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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_d = cp.ndarray((volume(input_desc[0].dims)), dtype=inp_dtype, memptr=a_ptr)
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c_d = cp.ndarray((volume(output_desc[0].dims)), dtype=inp_dtype, memptr=c_ptr)
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a_t = torch.as_tensor(a_d, device="cuda")
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c_t = torch.as_tensor(c_d, device="cuda")
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N = len(self.X_shape)
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all_pads = np.zeros((N * 2,), dtype=np.int32)
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orig_dims = np.array(self.X_shape, dtype=np.int32)
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out_dims = np.array(self.X_shape, dtype=np.int32)
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for i in range(np.size(pads) // 2):
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out_dims[N - i - 1] += pads[i * 2] + pads[i * 2 + 1]
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all_pads[N * 2 - 2 * i - 2] = pads[i * 2]
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all_pads[N * 2 - 2 * i - 1] = pads[i * 2 + 1]
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all_pads = all_pads.tolist()
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orig_dims = orig_dims.tolist()
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out_dims = out_dims.tolist()
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blockSize = 256
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numBlocks = (int((np.prod(out_dims) + blockSize - 1) // blockSize),)
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circ_pad[numBlocks](
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a_t,
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all_pads[0],
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all_pads[2],
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all_pads[4],
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all_pads[6],
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orig_dims[0],
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orig_dims[1],
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orig_dims[2],
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orig_dims[3],
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c_t,
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out_dims[1],
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out_dims[2],
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out_dims[3],
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int(np.prod(orig_dims)),
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int(np.prod(out_dims)),
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BLOCK_SIZE=256,
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)
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return 0
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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 = (10, 3, 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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# 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_triton_{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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