Tinygrad runner (#34171)
* squash * bump tg * bump tg * debump tinygrad * bump tinygrad * bump tg * Skip init iteration * fixes * cleanups * skip first test sample * typos * linter unhappy * update cpu usage * OPENCL just zeros for now * imports * Try printing * Runs again, but slower * unused import * Allow more buffer with tg and all on gpu * bump tinygrad * seems ok * stricter timings for driving looser for dm * try llvm * check nvidia * More timeout for now * make test pass * Revert "try llvm" This reverts commit ef136e478320101fea262bae3579e558da991902. * small fixes * whitespace * revert test timeout * No model runners * Always CPU always fast * No onnx runtime GPU * more cores * cleanup * Is this faster * Is this faster * at least runs * FP32 is faster than 16 * fix deps * whitespace * comment --------- Co-authored-by: Adeeb Shihadeh <adeebshihadeh@gmail.com>pull/34219/head
Harald Schäfer
5 months ago
committed by
GitHub
parent
0cf04af227
commit
7b5a4fbb03
37 changed files with 226 additions and 1505 deletions
@ -1,10 +1,4 @@ |
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#!/usr/bin/env bash |
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|
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DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null && pwd)" |
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cd "$DIR/../../" |
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|
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if [ -f "$DIR/libthneed.so" ]; then |
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export LD_PRELOAD="$DIR/libthneed.so" |
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fi |
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|
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exec "$DIR/dmonitoringmodeld.py" "$@" |
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@ -1,27 +0,0 @@ |
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import os |
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from openpilot.system.hardware import TICI |
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from openpilot.selfdrive.modeld.runners.runmodel_pyx import RunModel, Runtime |
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assert Runtime |
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USE_THNEED = int(os.getenv('USE_THNEED', str(int(TICI)))) |
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USE_SNPE = int(os.getenv('USE_SNPE', str(int(TICI)))) |
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|
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class ModelRunner(RunModel): |
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THNEED = 'THNEED' |
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SNPE = 'SNPE' |
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ONNX = 'ONNX' |
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|
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def __new__(cls, paths, *args, **kwargs): |
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if ModelRunner.THNEED in paths and USE_THNEED: |
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from openpilot.selfdrive.modeld.runners.thneedmodel_pyx import ThneedModel as Runner |
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runner_type = ModelRunner.THNEED |
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elif ModelRunner.SNPE in paths and USE_SNPE: |
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from openpilot.selfdrive.modeld.runners.snpemodel_pyx import SNPEModel as Runner |
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runner_type = ModelRunner.SNPE |
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elif ModelRunner.ONNX in paths: |
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from openpilot.selfdrive.modeld.runners.onnxmodel import ONNXModel as Runner |
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runner_type = ModelRunner.ONNX |
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else: |
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raise Exception("Couldn't select a model runner, make sure to pass at least one valid model path") |
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return Runner(str(paths[runner_type]), *args, **kwargs) |
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@ -1,98 +0,0 @@ |
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import onnx |
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import itertools |
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import os |
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import sys |
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import numpy as np |
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from typing import Any |
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from openpilot.selfdrive.modeld.runners.runmodel_pyx import RunModel |
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ORT_TYPES_TO_NP_TYPES = {'tensor(float16)': np.float16, 'tensor(float)': np.float32, 'tensor(uint8)': np.uint8} |
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def attributeproto_fp16_to_fp32(attr): |
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float32_list = np.frombuffer(attr.raw_data, dtype=np.float16) |
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attr.data_type = 1 |
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attr.raw_data = float32_list.astype(np.float32).tobytes() |
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|
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def convert_fp16_to_fp32(onnx_path_or_bytes): |
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if isinstance(onnx_path_or_bytes, bytes): |
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model = onnx.load_from_string(onnx_path_or_bytes) |
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elif isinstance(onnx_path_or_bytes, str): |
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model = onnx.load(onnx_path_or_bytes) |
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for i in model.graph.initializer: |
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if i.data_type == 10: |
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attributeproto_fp16_to_fp32(i) |
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for i in itertools.chain(model.graph.input, model.graph.output): |
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if i.type.tensor_type.elem_type == 10: |
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i.type.tensor_type.elem_type = 1 |
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for i in model.graph.node: |
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if i.op_type == 'Cast' and i.attribute[0].i == 10: |
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i.attribute[0].i = 1 |
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for a in i.attribute: |
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if hasattr(a, 't'): |
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if a.t.data_type == 10: |
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attributeproto_fp16_to_fp32(a.t) |
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return model.SerializeToString() |
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|
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def create_ort_session(path, fp16_to_fp32): |
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os.environ["OMP_NUM_THREADS"] = "4" |
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os.environ["OMP_WAIT_POLICY"] = "PASSIVE" |
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import onnxruntime as ort |
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print("Onnx available providers: ", ort.get_available_providers(), file=sys.stderr) |
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options = ort.SessionOptions() |
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options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL |
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provider: str | tuple[str, dict[Any, Any]] |
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if 'OpenVINOExecutionProvider' in ort.get_available_providers() and 'ONNXCPU' not in os.environ: |
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provider = 'OpenVINOExecutionProvider' |
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elif 'CUDAExecutionProvider' in ort.get_available_providers() and 'ONNXCPU' not in os.environ: |
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options.intra_op_num_threads = 2 |
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provider = ('CUDAExecutionProvider', {'cudnn_conv_algo_search': 'EXHAUSTIVE'}) |
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else: |
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options.intra_op_num_threads = 2 |
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options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL |
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options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL |
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provider = 'CPUExecutionProvider' |
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model_data = convert_fp16_to_fp32(path) if fp16_to_fp32 else path |
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print("Onnx selected provider: ", [provider], file=sys.stderr) |
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ort_session = ort.InferenceSession(model_data, options, providers=[provider]) |
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print("Onnx using ", ort_session.get_providers(), file=sys.stderr) |
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return ort_session |
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class ONNXModel(RunModel): |
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def __init__(self, path, output, runtime, use_tf8, cl_context): |
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self.inputs = {} |
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self.output = output |
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self.session = create_ort_session(path, fp16_to_fp32=True) |
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self.input_names = [x.name for x in self.session.get_inputs()] |
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self.input_shapes = {x.name: [1, *x.shape[1:]] for x in self.session.get_inputs()} |
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self.input_dtypes = {x.name: ORT_TYPES_TO_NP_TYPES[x.type] for x in self.session.get_inputs()} |
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# run once to initialize CUDA provider |
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if "CUDAExecutionProvider" in self.session.get_providers(): |
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self.session.run(None, {k: np.zeros(self.input_shapes[k], dtype=self.input_dtypes[k]) for k in self.input_names}) |
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print("ready to run onnx model", self.input_shapes, file=sys.stderr) |
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def addInput(self, name, buffer): |
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assert name in self.input_names |
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self.inputs[name] = buffer |
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def setInputBuffer(self, name, buffer): |
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assert name in self.inputs |
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self.inputs[name] = buffer |
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def getCLBuffer(self, name): |
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return None |
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def execute(self): |
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inputs = {k: v.view(self.input_dtypes[k]) for k,v in self.inputs.items()} |
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inputs = {k: v.reshape(self.input_shapes[k]).astype(self.input_dtypes[k]) for k,v in inputs.items()} |
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outputs = self.session.run(None, inputs) |
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assert len(outputs) == 1, "Only single model outputs are supported" |
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self.output[:] = outputs[0] |
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return self.output |
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@ -0,0 +1,38 @@ |
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import onnx |
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import onnxruntime as ort |
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import numpy as np |
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import itertools |
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ORT_TYPES_TO_NP_TYPES = {'tensor(float16)': np.float16, 'tensor(float)': np.float32, 'tensor(uint8)': np.uint8} |
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def attributeproto_fp16_to_fp32(attr): |
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float32_list = np.frombuffer(attr.raw_data, dtype=np.float16) |
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attr.data_type = 1 |
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attr.raw_data = float32_list.astype(np.float32).tobytes() |
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def convert_fp16_to_fp32(onnx_path): |
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model = onnx.load(onnx_path) |
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for i in model.graph.initializer: |
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if i.data_type == 10: |
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attributeproto_fp16_to_fp32(i) |
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for i in itertools.chain(model.graph.input, model.graph.output): |
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if i.type.tensor_type.elem_type == 10: |
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i.type.tensor_type.elem_type = 1 |
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for i in model.graph.node: |
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if i.op_type == 'Cast' and i.attribute[0].i == 10: |
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i.attribute[0].i = 1 |
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for a in i.attribute: |
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if hasattr(a, 't'): |
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if a.t.data_type == 10: |
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attributeproto_fp16_to_fp32(a.t) |
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return model.SerializeToString() |
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def make_onnx_cpu_runner(model_path): |
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options = ort.SessionOptions() |
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options.intra_op_num_threads = 4 |
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options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL |
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options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL |
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model_data = convert_fp16_to_fp32(model_path) |
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return ort.InferenceSession(model_data, options, providers=['CPUExecutionProvider']) |
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@ -1,4 +0,0 @@ |
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#pragma once |
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#include "selfdrive/modeld/runners/runmodel.h" |
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#include "selfdrive/modeld/runners/snpemodel.h" |
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@ -1,49 +0,0 @@ |
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#pragma once |
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#include <string> |
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#include <vector> |
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#include <memory> |
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#include <cassert> |
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#include "common/clutil.h" |
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#include "common/swaglog.h" |
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#define USE_CPU_RUNTIME 0 |
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#define USE_GPU_RUNTIME 1 |
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#define USE_DSP_RUNTIME 2 |
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struct ModelInput { |
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const std::string name; |
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float *buffer; |
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int size; |
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ModelInput(const std::string _name, float *_buffer, int _size) : name(_name), buffer(_buffer), size(_size) {} |
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virtual void setBuffer(float *_buffer, int _size) { |
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assert(size == _size || size == 0); |
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buffer = _buffer; |
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size = _size; |
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} |
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}; |
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class RunModel { |
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public: |
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std::vector<std::unique_ptr<ModelInput>> inputs; |
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virtual ~RunModel() {} |
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virtual void execute() {} |
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virtual void* getCLBuffer(const std::string name) { return nullptr; } |
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virtual void addInput(const std::string name, float *buffer, int size) { |
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inputs.push_back(std::unique_ptr<ModelInput>(new ModelInput(name, buffer, size))); |
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} |
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virtual void setInputBuffer(const std::string name, float *buffer, int size) { |
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for (auto &input : inputs) { |
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if (name == input->name) { |
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input->setBuffer(buffer, size); |
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return; |
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} |
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} |
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LOGE("Tried to update input `%s` but no input with this name exists", name.c_str()); |
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assert(false); |
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} |
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}; |
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@ -1,14 +0,0 @@ |
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# distutils: language = c++ |
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from libcpp.string cimport string |
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cdef extern from "selfdrive/modeld/runners/runmodel.h": |
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cdef int USE_CPU_RUNTIME |
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cdef int USE_GPU_RUNTIME |
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cdef int USE_DSP_RUNTIME |
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cdef cppclass RunModel: |
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void addInput(string, float*, int) |
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void setInputBuffer(string, float*, int) |
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void * getCLBuffer(string) |
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void execute() |
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@ -1,6 +0,0 @@ |
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# distutils: language = c++ |
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from .runmodel cimport RunModel as cppRunModel |
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cdef class RunModel: |
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cdef cppRunModel * model |
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@ -1,37 +0,0 @@ |
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# distutils: language = c++ |
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# cython: c_string_encoding=ascii, language_level=3 |
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from libcpp.string cimport string |
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from .runmodel cimport USE_CPU_RUNTIME, USE_GPU_RUNTIME, USE_DSP_RUNTIME |
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from selfdrive.modeld.models.commonmodel_pyx cimport CLMem |
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class Runtime: |
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CPU = USE_CPU_RUNTIME |
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GPU = USE_GPU_RUNTIME |
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DSP = USE_DSP_RUNTIME |
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cdef class RunModel: |
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def __dealloc__(self): |
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del self.model |
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def addInput(self, string name, float[:] buffer): |
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if buffer is not None: |
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self.model.addInput(name, &buffer[0], len(buffer)) |
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else: |
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self.model.addInput(name, NULL, 0) |
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def setInputBuffer(self, string name, float[:] buffer): |
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if buffer is not None: |
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self.model.setInputBuffer(name, &buffer[0], len(buffer)) |
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else: |
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self.model.setInputBuffer(name, NULL, 0) |
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def getCLBuffer(self, string name): |
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cdef void * cl_buf = self.model.getCLBuffer(name) |
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if not cl_buf: |
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return None |
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return CLMem.create(cl_buf) |
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def execute(self): |
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self.model.execute() |
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@ -1,116 +0,0 @@ |
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#pragma clang diagnostic ignored "-Wexceptions" |
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#include "selfdrive/modeld/runners/snpemodel.h" |
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#include <cstring> |
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#include <memory> |
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#include <string> |
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#include <utility> |
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#include <vector> |
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#include "common/util.h" |
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#include "common/timing.h" |
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void PrintErrorStringAndExit() { |
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std::cerr << zdl::DlSystem::getLastErrorString() << std::endl; |
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std::exit(EXIT_FAILURE); |
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} |
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SNPEModel::SNPEModel(const std::string path, float *_output, size_t _output_size, int runtime, bool _use_tf8, cl_context context) { |
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output = _output; |
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output_size = _output_size; |
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use_tf8 = _use_tf8; |
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#ifdef QCOM2 |
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if (runtime == USE_GPU_RUNTIME) { |
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snpe_runtime = zdl::DlSystem::Runtime_t::GPU; |
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} else if (runtime == USE_DSP_RUNTIME) { |
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snpe_runtime = zdl::DlSystem::Runtime_t::DSP; |
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} else { |
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snpe_runtime = zdl::DlSystem::Runtime_t::CPU; |
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} |
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assert(zdl::SNPE::SNPEFactory::isRuntimeAvailable(snpe_runtime)); |
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#endif |
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model_data = util::read_file(path); |
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assert(model_data.size() > 0); |
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|
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// load model
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std::unique_ptr<zdl::DlContainer::IDlContainer> container = zdl::DlContainer::IDlContainer::open((uint8_t*)model_data.data(), model_data.size()); |
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if (!container) { PrintErrorStringAndExit(); } |
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LOGW("loaded model with size: %lu", model_data.size()); |
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// create model runner
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zdl::SNPE::SNPEBuilder snpe_builder(container.get()); |
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while (!snpe) { |
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#ifdef QCOM2 |
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snpe = snpe_builder.setOutputLayers({}) |
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.setRuntimeProcessor(snpe_runtime) |
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.setUseUserSuppliedBuffers(true) |
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.setPerformanceProfile(zdl::DlSystem::PerformanceProfile_t::HIGH_PERFORMANCE) |
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.build(); |
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#else |
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snpe = snpe_builder.setOutputLayers({}) |
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.setUseUserSuppliedBuffers(true) |
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.setPerformanceProfile(zdl::DlSystem::PerformanceProfile_t::HIGH_PERFORMANCE) |
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.build(); |
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#endif |
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if (!snpe) std::cerr << zdl::DlSystem::getLastErrorString() << std::endl; |
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} |
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|
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// create output buffer
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zdl::DlSystem::UserBufferEncodingFloat ub_encoding_float; |
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zdl::DlSystem::IUserBufferFactory &ub_factory = zdl::SNPE::SNPEFactory::getUserBufferFactory(); |
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const auto &output_tensor_names_opt = snpe->getOutputTensorNames(); |
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if (!output_tensor_names_opt) throw std::runtime_error("Error obtaining output tensor names"); |
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const auto &output_tensor_names = *output_tensor_names_opt; |
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assert(output_tensor_names.size() == 1); |
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const char *output_tensor_name = output_tensor_names.at(0); |
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const zdl::DlSystem::TensorShape &buffer_shape = snpe->getInputOutputBufferAttributes(output_tensor_name)->getDims(); |
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if (output_size != 0) { |
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assert(output_size == buffer_shape[1]); |
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} else { |
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output_size = buffer_shape[1]; |
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} |
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std::vector<size_t> output_strides = {output_size * sizeof(float), sizeof(float)}; |
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output_buffer = ub_factory.createUserBuffer(output, output_size * sizeof(float), output_strides, &ub_encoding_float); |
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output_map.add(output_tensor_name, output_buffer.get()); |
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} |
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void SNPEModel::addInput(const std::string name, float *buffer, int size) { |
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const int idx = inputs.size(); |
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const auto &input_tensor_names_opt = snpe->getInputTensorNames(); |
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if (!input_tensor_names_opt) throw std::runtime_error("Error obtaining input tensor names"); |
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const auto &input_tensor_names = *input_tensor_names_opt; |
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const char *input_tensor_name = input_tensor_names.at(idx); |
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const bool input_tf8 = use_tf8 && strcmp(input_tensor_name, "input_img") == 0; // TODO: This is a terrible hack, get rid of this name check both here and in onnx_runner.py
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LOGW("adding index %d: %s", idx, input_tensor_name); |
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|
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zdl::DlSystem::UserBufferEncodingFloat ub_encoding_float; |
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zdl::DlSystem::UserBufferEncodingTf8 ub_encoding_tf8(0, 1./255); // network takes 0-1
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zdl::DlSystem::IUserBufferFactory &ub_factory = zdl::SNPE::SNPEFactory::getUserBufferFactory(); |
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zdl::DlSystem::UserBufferEncoding *input_encoding = input_tf8 ? (zdl::DlSystem::UserBufferEncoding*)&ub_encoding_tf8 : (zdl::DlSystem::UserBufferEncoding*)&ub_encoding_float; |
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|
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const auto &buffer_shape_opt = snpe->getInputDimensions(input_tensor_name); |
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const zdl::DlSystem::TensorShape &buffer_shape = *buffer_shape_opt; |
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size_t size_of_input = input_tf8 ? sizeof(uint8_t) : sizeof(float); |
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std::vector<size_t> strides(buffer_shape.rank()); |
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strides[strides.size() - 1] = size_of_input; |
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size_t product = 1; |
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for (size_t i = 0; i < buffer_shape.rank(); i++) product *= buffer_shape[i]; |
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size_t stride = strides[strides.size() - 1]; |
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for (size_t i = buffer_shape.rank() - 1; i > 0; i--) { |
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stride *= buffer_shape[i]; |
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strides[i-1] = stride; |
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} |
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auto input_buffer = ub_factory.createUserBuffer(buffer, product*size_of_input, strides, input_encoding); |
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input_map.add(input_tensor_name, input_buffer.get()); |
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inputs.push_back(std::unique_ptr<SNPEModelInput>(new SNPEModelInput(name, buffer, size, std::move(input_buffer)))); |
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} |
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|
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void SNPEModel::execute() { |
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if (!snpe->execute(input_map, output_map)) { |
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PrintErrorStringAndExit(); |
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} |
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} |
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@ -1,52 +0,0 @@ |
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#pragma once |
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#pragma clang diagnostic ignored "-Wdeprecated-declarations" |
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|
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#include <memory> |
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#include <string> |
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#include <utility> |
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|
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#include <DlContainer/IDlContainer.hpp> |
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#include <DlSystem/DlError.hpp> |
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#include <DlSystem/ITensor.hpp> |
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#include <DlSystem/ITensorFactory.hpp> |
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#include <DlSystem/IUserBuffer.hpp> |
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#include <DlSystem/IUserBufferFactory.hpp> |
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#include <SNPE/SNPE.hpp> |
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#include <SNPE/SNPEBuilder.hpp> |
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#include <SNPE/SNPEFactory.hpp> |
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|
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#include "selfdrive/modeld/runners/runmodel.h" |
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|
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struct SNPEModelInput : public ModelInput { |
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std::unique_ptr<zdl::DlSystem::IUserBuffer> snpe_buffer; |
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|
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SNPEModelInput(const std::string _name, float *_buffer, int _size, std::unique_ptr<zdl::DlSystem::IUserBuffer> _snpe_buffer) : ModelInput(_name, _buffer, _size), snpe_buffer(std::move(_snpe_buffer)) {} |
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void setBuffer(float *_buffer, int _size) { |
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ModelInput::setBuffer(_buffer, _size); |
||||
assert(snpe_buffer->setBufferAddress(_buffer) == true); |
||||
} |
||||
}; |
||||
|
||||
class SNPEModel : public RunModel { |
||||
public: |
||||
SNPEModel(const std::string path, float *_output, size_t _output_size, int runtime, bool use_tf8 = false, cl_context context = NULL); |
||||
void addInput(const std::string name, float *buffer, int size); |
||||
void execute(); |
||||
|
||||
private: |
||||
std::string model_data; |
||||
|
||||
#ifdef QCOM2 |
||||
zdl::DlSystem::Runtime_t snpe_runtime; |
||||
#endif |
||||
|
||||
// snpe model stuff
|
||||
std::unique_ptr<zdl::SNPE::SNPE> snpe; |
||||
zdl::DlSystem::UserBufferMap input_map; |
||||
zdl::DlSystem::UserBufferMap output_map; |
||||
std::unique_ptr<zdl::DlSystem::IUserBuffer> output_buffer; |
||||
|
||||
bool use_tf8; |
||||
float *output; |
||||
size_t output_size; |
||||
}; |
||||
@ -1,9 +0,0 @@ |
||||
# distutils: language = c++ |
||||
|
||||
from libcpp.string cimport string |
||||
|
||||
from msgq.visionipc.visionipc cimport cl_context |
||||
|
||||
cdef extern from "selfdrive/modeld/runners/snpemodel.h": |
||||
cdef cppclass SNPEModel: |
||||
SNPEModel(string, float*, size_t, int, bool, cl_context) |
||||
@ -1,17 +0,0 @@ |
||||
# distutils: language = c++ |
||||
# cython: c_string_encoding=ascii, language_level=3 |
||||
|
||||
import os |
||||
from libcpp cimport bool |
||||
from libcpp.string cimport string |
||||
|
||||
from .snpemodel cimport SNPEModel as cppSNPEModel |
||||
from selfdrive.modeld.models.commonmodel_pyx cimport CLContext |
||||
from selfdrive.modeld.runners.runmodel_pyx cimport RunModel |
||||
from selfdrive.modeld.runners.runmodel cimport RunModel as cppRunModel |
||||
|
||||
os.environ['ADSP_LIBRARY_PATH'] = "/data/pythonpath/third_party/snpe/dsp/" |
||||
|
||||
cdef class SNPEModel(RunModel): |
||||
def __cinit__(self, string path, float[:] output, int runtime, bool use_tf8, CLContext context): |
||||
self.model = <cppRunModel *> new cppSNPEModel(path, &output[0], len(output), runtime, use_tf8, context.context) |
||||
@ -1,58 +0,0 @@ |
||||
#include "selfdrive/modeld/runners/thneedmodel.h" |
||||
|
||||
#include <string> |
||||
|
||||
#include "common/swaglog.h" |
||||
|
||||
ThneedModel::ThneedModel(const std::string path, float *_output, size_t _output_size, int runtime, bool luse_tf8, cl_context context) { |
||||
thneed = new Thneed(true, context); |
||||
thneed->load(path.c_str()); |
||||
thneed->clexec(); |
||||
|
||||
recorded = false; |
||||
output = _output; |
||||
} |
||||
|
||||
void* ThneedModel::getCLBuffer(const std::string name) { |
||||
int index = -1; |
||||
for (int i = 0; i < inputs.size(); i++) { |
||||
if (name == inputs[i]->name) { |
||||
index = i; |
||||
break; |
||||
} |
||||
} |
||||
|
||||
if (index == -1) { |
||||
LOGE("Tried to get CL buffer for input `%s` but no input with this name exists", name.c_str()); |
||||
assert(false); |
||||
} |
||||
|
||||
if (thneed->input_clmem.size() >= inputs.size()) { |
||||
return &thneed->input_clmem[inputs.size() - index - 1]; |
||||
} else { |
||||
return nullptr; |
||||
} |
||||
} |
||||
|
||||
void ThneedModel::execute() { |
||||
if (!recorded) { |
||||
thneed->record = true; |
||||
float *input_buffers[inputs.size()]; |
||||
for (int i = 0; i < inputs.size(); i++) { |
||||
input_buffers[inputs.size() - i - 1] = inputs[i]->buffer; |
||||
} |
||||
|
||||
thneed->copy_inputs(input_buffers); |
||||
thneed->clexec(); |
||||
thneed->copy_output(output); |
||||
thneed->stop(); |
||||
|
||||
recorded = true; |
||||
} else { |
||||
float *input_buffers[inputs.size()]; |
||||
for (int i = 0; i < inputs.size(); i++) { |
||||
input_buffers[inputs.size() - i - 1] = inputs[i]->buffer; |
||||
} |
||||
thneed->execute(input_buffers, output); |
||||
} |
||||
} |
||||
@ -1,17 +0,0 @@ |
||||
#pragma once |
||||
|
||||
#include <string> |
||||
|
||||
#include "selfdrive/modeld/runners/runmodel.h" |
||||
#include "selfdrive/modeld/thneed/thneed.h" |
||||
|
||||
class ThneedModel : public RunModel { |
||||
public: |
||||
ThneedModel(const std::string path, float *_output, size_t _output_size, int runtime, bool use_tf8 = false, cl_context context = NULL); |
||||
void *getCLBuffer(const std::string name); |
||||
void execute(); |
||||
private: |
||||
Thneed *thneed = NULL; |
||||
bool recorded; |
||||
float *output; |
||||
}; |
||||
@ -1,9 +0,0 @@ |
||||
# distutils: language = c++ |
||||
|
||||
from libcpp.string cimport string |
||||
|
||||
from msgq.visionipc.visionipc cimport cl_context |
||||
|
||||
cdef extern from "selfdrive/modeld/runners/thneedmodel.h": |
||||
cdef cppclass ThneedModel: |
||||
ThneedModel(string, float*, size_t, int, bool, cl_context) |
||||
@ -1,14 +0,0 @@ |
||||
# distutils: language = c++ |
||||
# cython: c_string_encoding=ascii, language_level=3 |
||||
|
||||
from libcpp cimport bool |
||||
from libcpp.string cimport string |
||||
|
||||
from .thneedmodel cimport ThneedModel as cppThneedModel |
||||
from selfdrive.modeld.models.commonmodel_pyx cimport CLContext |
||||
from selfdrive.modeld.runners.runmodel_pyx cimport RunModel |
||||
from selfdrive.modeld.runners.runmodel cimport RunModel as cppRunModel |
||||
|
||||
cdef class ThneedModel(RunModel): |
||||
def __cinit__(self, string path, float[:] output, int runtime, bool use_tf8, CLContext context): |
||||
self.model = <cppRunModel *> new cppThneedModel(path, &output[0], len(output), runtime, use_tf8, context.context) |
||||
@ -0,0 +1,8 @@ |
||||
|
||||
from tinygrad.tensor import Tensor |
||||
from tinygrad.helpers import to_mv |
||||
|
||||
def qcom_tensor_from_opencl_address(opencl_address, shape, dtype): |
||||
cl_buf_desc_ptr = to_mv(opencl_address, 8).cast('Q')[0] |
||||
rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer. |
||||
return Tensor.from_blob(rawbuf_ptr, shape, dtype=dtype, device='QCOM') |
||||
@ -1,8 +0,0 @@ |
||||
thneed is an SNPE accelerator. I know SNPE is already an accelerator, but sometimes things need to go even faster.. |
||||
|
||||
It runs on the local device, and caches a single model run. Then it replays it, but fast. |
||||
|
||||
thneed slices through abstraction layers like a fish. |
||||
|
||||
You need a thneed. |
||||
|
||||
@ -1,154 +0,0 @@ |
||||
#include <cassert> |
||||
#include <set> |
||||
|
||||
#include "third_party/json11/json11.hpp" |
||||
#include "common/util.h" |
||||
#include "common/clutil.h" |
||||
#include "common/swaglog.h" |
||||
#include "selfdrive/modeld/thneed/thneed.h" |
||||
using namespace json11; |
||||
|
||||
extern map<cl_program, string> g_program_source; |
||||
|
||||
void Thneed::load(const char *filename) { |
||||
LOGD("Thneed::load: loading from %s\n", filename); |
||||
|
||||
string buf = util::read_file(filename); |
||||
int jsz = *(int *)buf.data(); |
||||
string jsonerr; |
||||
string jj(buf.data() + sizeof(int), jsz); |
||||
Json jdat = Json::parse(jj, jsonerr); |
||||
|
||||
map<cl_mem, cl_mem> real_mem; |
||||
real_mem[NULL] = NULL; |
||||
|
||||
int ptr = sizeof(int)+jsz; |
||||
for (auto &obj : jdat["objects"].array_items()) { |
||||
auto mobj = obj.object_items(); |
||||
int sz = mobj["size"].int_value(); |
||||
cl_mem clbuf = NULL; |
||||
|
||||
if (mobj["buffer_id"].string_value().size() > 0) { |
||||
// image buffer must already be allocated
|
||||
clbuf = real_mem[*(cl_mem*)(mobj["buffer_id"].string_value().data())]; |
||||
assert(mobj["needs_load"].bool_value() == false); |
||||
} else { |
||||
if (mobj["needs_load"].bool_value()) { |
||||
clbuf = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR | CL_MEM_READ_WRITE, sz, &buf[ptr], NULL); |
||||
if (debug >= 1) printf("loading %p %d @ 0x%X\n", clbuf, sz, ptr); |
||||
ptr += sz; |
||||
} else { |
||||
// TODO: is there a faster way to init zeroed out buffers?
|
||||
void *host_zeros = calloc(sz, 1); |
||||
clbuf = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR | CL_MEM_READ_WRITE, sz, host_zeros, NULL); |
||||
free(host_zeros); |
||||
} |
||||
} |
||||
assert(clbuf != NULL); |
||||
|
||||
if (mobj["arg_type"] == "image2d_t" || mobj["arg_type"] == "image1d_t") { |
||||
cl_image_desc desc = {0}; |
||||
desc.image_type = (mobj["arg_type"] == "image2d_t") ? CL_MEM_OBJECT_IMAGE2D : CL_MEM_OBJECT_IMAGE1D_BUFFER; |
||||
desc.image_width = mobj["width"].int_value(); |
||||
desc.image_height = mobj["height"].int_value(); |
||||
desc.image_row_pitch = mobj["row_pitch"].int_value(); |
||||
assert(sz == desc.image_height*desc.image_row_pitch); |
||||
#ifdef QCOM2 |
||||
desc.buffer = clbuf; |
||||
#else |
||||
// TODO: we are creating unused buffers on PC
|
||||
clReleaseMemObject(clbuf); |
||||
#endif |
||||
cl_image_format format = {0}; |
||||
format.image_channel_order = CL_RGBA; |
||||
format.image_channel_data_type = mobj["float32"].bool_value() ? CL_FLOAT : CL_HALF_FLOAT; |
||||
|
||||
cl_int errcode; |
||||
|
||||
#ifndef QCOM2 |
||||
if (mobj["needs_load"].bool_value()) { |
||||
clbuf = clCreateImage(context, CL_MEM_COPY_HOST_PTR | CL_MEM_READ_WRITE, &format, &desc, &buf[ptr-sz], &errcode); |
||||
} else { |
||||
clbuf = clCreateImage(context, CL_MEM_READ_WRITE, &format, &desc, NULL, &errcode); |
||||
} |
||||
#else |
||||
clbuf = clCreateImage(context, CL_MEM_READ_WRITE, &format, &desc, NULL, &errcode); |
||||
#endif |
||||
if (clbuf == NULL) { |
||||
LOGE("clError: %s create image %zux%zu rp %zu with buffer %p\n", cl_get_error_string(errcode), |
||||
desc.image_width, desc.image_height, desc.image_row_pitch, desc.buffer); |
||||
} |
||||
assert(clbuf != NULL); |
||||
} |
||||
|
||||
real_mem[*(cl_mem*)(mobj["id"].string_value().data())] = clbuf; |
||||
} |
||||
|
||||
map<string, cl_program> g_programs; |
||||
for (const auto &[name, source] : jdat["programs"].object_items()) { |
||||
if (debug >= 1) printf("building %s with size %zu\n", name.c_str(), source.string_value().size()); |
||||
g_programs[name] = cl_program_from_source(context, device_id, source.string_value()); |
||||
} |
||||
|
||||
for (auto &obj : jdat["inputs"].array_items()) { |
||||
auto mobj = obj.object_items(); |
||||
int sz = mobj["size"].int_value(); |
||||
cl_mem aa = real_mem[*(cl_mem*)(mobj["buffer_id"].string_value().data())]; |
||||
input_clmem.push_back(aa); |
||||
input_sizes.push_back(sz); |
||||
LOGD("Thneed::load: adding input %s with size %d\n", mobj["name"].string_value().data(), sz); |
||||
|
||||
cl_int cl_err; |
||||
void *ret = clEnqueueMapBuffer(command_queue, aa, CL_TRUE, CL_MAP_WRITE, 0, sz, 0, NULL, NULL, &cl_err); |
||||
if (cl_err != CL_SUCCESS) LOGE("clError: %s map %p %d\n", cl_get_error_string(cl_err), aa, sz); |
||||
assert(cl_err == CL_SUCCESS); |
||||
inputs.push_back(ret); |
||||
} |
||||
|
||||
for (auto &obj : jdat["outputs"].array_items()) { |
||||
auto mobj = obj.object_items(); |
||||
int sz = mobj["size"].int_value(); |
||||
LOGD("Thneed::save: adding output with size %d\n", sz); |
||||
// TODO: support multiple outputs
|
||||
output = real_mem[*(cl_mem*)(mobj["buffer_id"].string_value().data())]; |
||||
assert(output != NULL); |
||||
} |
||||
|
||||
for (auto &obj : jdat["binaries"].array_items()) { |
||||
string name = obj["name"].string_value(); |
||||
size_t length = obj["length"].int_value(); |
||||
if (debug >= 1) printf("binary %s with size %zu\n", name.c_str(), length); |
||||
g_programs[name] = cl_program_from_binary(context, device_id, (const uint8_t*)&buf[ptr], length); |
||||
ptr += length; |
||||
} |
||||
|
||||
for (auto &obj : jdat["kernels"].array_items()) { |
||||
auto gws = obj["global_work_size"]; |
||||
auto lws = obj["local_work_size"]; |
||||
auto kk = shared_ptr<CLQueuedKernel>(new CLQueuedKernel(this)); |
||||
|
||||
kk->name = obj["name"].string_value(); |
||||
kk->program = g_programs[kk->name]; |
||||
kk->work_dim = obj["work_dim"].int_value(); |
||||
for (int i = 0; i < kk->work_dim; i++) { |
||||
kk->global_work_size[i] = gws[i].int_value(); |
||||
kk->local_work_size[i] = lws[i].int_value(); |
||||
} |
||||
kk->num_args = obj["num_args"].int_value(); |
||||
for (int i = 0; i < kk->num_args; i++) { |
||||
string arg = obj["args"].array_items()[i].string_value(); |
||||
int arg_size = obj["args_size"].array_items()[i].int_value(); |
||||
kk->args_size.push_back(arg_size); |
||||
if (arg_size == 8) { |
||||
cl_mem val = *(cl_mem*)(arg.data()); |
||||
val = real_mem[val]; |
||||
kk->args.push_back(string((char*)&val, sizeof(val))); |
||||
} else { |
||||
kk->args.push_back(arg); |
||||
} |
||||
} |
||||
kq.push_back(kk); |
||||
} |
||||
|
||||
clFinish(command_queue); |
||||
} |
||||
@ -1,133 +0,0 @@ |
||||
#pragma once |
||||
|
||||
#ifndef __user |
||||
#define __user __attribute__(()) |
||||
#endif |
||||
|
||||
#include <cstdint> |
||||
#include <cstdlib> |
||||
#include <memory> |
||||
#include <string> |
||||
#include <vector> |
||||
|
||||
#include <CL/cl.h> |
||||
|
||||
#include "third_party/linux/include/msm_kgsl.h" |
||||
|
||||
using namespace std; |
||||
|
||||
cl_int thneed_clSetKernelArg(cl_kernel kernel, cl_uint arg_index, size_t arg_size, const void *arg_value); |
||||
|
||||
namespace json11 { |
||||
class Json; |
||||
} |
||||
class Thneed; |
||||
|
||||
class GPUMalloc { |
||||
public: |
||||
GPUMalloc(int size, int fd); |
||||
~GPUMalloc(); |
||||
void *alloc(int size); |
||||
private: |
||||
uint64_t base; |
||||
int remaining; |
||||
}; |
||||
|
||||
class CLQueuedKernel { |
||||
public: |
||||
CLQueuedKernel(Thneed *lthneed) { thneed = lthneed; } |
||||
CLQueuedKernel(Thneed *lthneed, |
||||
cl_kernel _kernel, |
||||
cl_uint _work_dim, |
||||
const size_t *_global_work_size, |
||||
const size_t *_local_work_size); |
||||
cl_int exec(); |
||||
void debug_print(bool verbose); |
||||
int get_arg_num(const char *search_arg_name); |
||||
cl_program program; |
||||
string name; |
||||
cl_uint num_args; |
||||
vector<string> arg_names; |
||||
vector<string> arg_types; |
||||
vector<string> args; |
||||
vector<int> args_size; |
||||
cl_kernel kernel = NULL; |
||||
json11::Json to_json() const; |
||||
|
||||
cl_uint work_dim; |
||||
size_t global_work_size[3] = {0}; |
||||
size_t local_work_size[3] = {0}; |
||||
private: |
||||
Thneed *thneed; |
||||
}; |
||||
|
||||
class CachedIoctl { |
||||
public: |
||||
virtual void exec() {} |
||||
}; |
||||
|
||||
class CachedSync: public CachedIoctl { |
||||
public: |
||||
CachedSync(Thneed *lthneed, string ldata) { thneed = lthneed; data = ldata; } |
||||
void exec(); |
||||
private: |
||||
Thneed *thneed; |
||||
string data; |
||||
}; |
||||
|
||||
class CachedCommand: public CachedIoctl { |
||||
public: |
||||
CachedCommand(Thneed *lthneed, struct kgsl_gpu_command *cmd); |
||||
void exec(); |
||||
private: |
||||
void disassemble(int cmd_index); |
||||
struct kgsl_gpu_command cache; |
||||
unique_ptr<kgsl_command_object[]> cmds; |
||||
unique_ptr<kgsl_command_object[]> objs; |
||||
Thneed *thneed; |
||||
vector<shared_ptr<CLQueuedKernel> > kq; |
||||
}; |
||||
|
||||
class Thneed { |
||||
public: |
||||
Thneed(bool do_clinit=false, cl_context _context = NULL); |
||||
void stop(); |
||||
void execute(float **finputs, float *foutput, bool slow=false); |
||||
void wait(); |
||||
|
||||
vector<cl_mem> input_clmem; |
||||
vector<void *> inputs; |
||||
vector<size_t> input_sizes; |
||||
cl_mem output = NULL; |
||||
|
||||
cl_context context = NULL; |
||||
cl_command_queue command_queue; |
||||
cl_device_id device_id; |
||||
int context_id; |
||||
|
||||
// protected?
|
||||
bool record = false; |
||||
int debug; |
||||
int timestamp; |
||||
|
||||
#ifdef QCOM2 |
||||
unique_ptr<GPUMalloc> ram; |
||||
vector<unique_ptr<CachedIoctl> > cmds; |
||||
int fd; |
||||
#endif |
||||
|
||||
// all CL kernels
|
||||
void copy_inputs(float **finputs, bool internal=false); |
||||
void copy_output(float *foutput); |
||||
cl_int clexec(); |
||||
vector<shared_ptr<CLQueuedKernel> > kq; |
||||
|
||||
// pending CL kernels
|
||||
vector<shared_ptr<CLQueuedKernel> > ckq; |
||||
|
||||
// loading
|
||||
void load(const char *filename); |
||||
private: |
||||
void clinit(); |
||||
}; |
||||
|
||||
@ -1,216 +0,0 @@ |
||||
#include "selfdrive/modeld/thneed/thneed.h" |
||||
|
||||
#include <cassert> |
||||
#include <cstring> |
||||
#include <map> |
||||
|
||||
#include "common/clutil.h" |
||||
#include "common/timing.h" |
||||
|
||||
map<pair<cl_kernel, int>, string> g_args; |
||||
map<pair<cl_kernel, int>, int> g_args_size; |
||||
map<cl_program, string> g_program_source; |
||||
|
||||
void Thneed::stop() { |
||||
//printf("Thneed::stop: recorded %lu commands\n", cmds.size());
|
||||
record = false; |
||||
} |
||||
|
||||
void Thneed::clinit() { |
||||
device_id = cl_get_device_id(CL_DEVICE_TYPE_DEFAULT); |
||||
if (context == NULL) context = CL_CHECK_ERR(clCreateContext(NULL, 1, &device_id, NULL, NULL, &err)); |
||||
//cl_command_queue_properties props[3] = {CL_QUEUE_PROPERTIES, CL_QUEUE_PROFILING_ENABLE, 0};
|
||||
cl_command_queue_properties props[3] = {CL_QUEUE_PROPERTIES, 0, 0}; |
||||
command_queue = CL_CHECK_ERR(clCreateCommandQueueWithProperties(context, device_id, props, &err)); |
||||
printf("Thneed::clinit done\n"); |
||||
} |
||||
|
||||
cl_int Thneed::clexec() { |
||||
if (debug >= 1) printf("Thneed::clexec: running %lu queued kernels\n", kq.size()); |
||||
for (auto &k : kq) { |
||||
if (record) ckq.push_back(k); |
||||
cl_int ret = k->exec(); |
||||
assert(ret == CL_SUCCESS); |
||||
} |
||||
return clFinish(command_queue); |
||||
} |
||||
|
||||
void Thneed::copy_inputs(float **finputs, bool internal) { |
||||
for (int idx = 0; idx < inputs.size(); ++idx) { |
||||
if (debug >= 1) printf("copying %lu -- %p -> %p (cl %p)\n", input_sizes[idx], finputs[idx], inputs[idx], input_clmem[idx]); |
||||
|
||||
if (internal) { |
||||
// if it's internal, using memcpy is fine since the buffer sync is cached in the ioctl layer
|
||||
if (finputs[idx] != NULL) memcpy(inputs[idx], finputs[idx], input_sizes[idx]); |
||||
} else { |
||||
if (finputs[idx] != NULL) CL_CHECK(clEnqueueWriteBuffer(command_queue, input_clmem[idx], CL_TRUE, 0, input_sizes[idx], finputs[idx], 0, NULL, NULL)); |
||||
} |
||||
} |
||||
} |
||||
|
||||
void Thneed::copy_output(float *foutput) { |
||||
if (output != NULL) { |
||||
size_t sz; |
||||
clGetMemObjectInfo(output, CL_MEM_SIZE, sizeof(sz), &sz, NULL); |
||||
if (debug >= 1) printf("copying %lu for output %p -> %p\n", sz, output, foutput); |
||||
CL_CHECK(clEnqueueReadBuffer(command_queue, output, CL_TRUE, 0, sz, foutput, 0, NULL, NULL)); |
||||
} else { |
||||
printf("CAUTION: model output is NULL, does it have no outputs?\n"); |
||||
} |
||||
} |
||||
|
||||
// *********** CLQueuedKernel ***********
|
||||
|
||||
CLQueuedKernel::CLQueuedKernel(Thneed *lthneed, |
||||
cl_kernel _kernel, |
||||
cl_uint _work_dim, |
||||
const size_t *_global_work_size, |
||||
const size_t *_local_work_size) { |
||||
thneed = lthneed; |
||||
kernel = _kernel; |
||||
work_dim = _work_dim; |
||||
assert(work_dim <= 3); |
||||
for (int i = 0; i < work_dim; i++) { |
||||
global_work_size[i] = _global_work_size[i]; |
||||
local_work_size[i] = _local_work_size[i]; |
||||
} |
||||
|
||||
char _name[0x100]; |
||||
clGetKernelInfo(kernel, CL_KERNEL_FUNCTION_NAME, sizeof(_name), _name, NULL); |
||||
name = string(_name); |
||||
clGetKernelInfo(kernel, CL_KERNEL_NUM_ARGS, sizeof(num_args), &num_args, NULL); |
||||
|
||||
// get args
|
||||
for (int i = 0; i < num_args; i++) { |
||||
char arg_name[0x100] = {0}; |
||||
clGetKernelArgInfo(kernel, i, CL_KERNEL_ARG_NAME, sizeof(arg_name), arg_name, NULL); |
||||
arg_names.push_back(string(arg_name)); |
||||
clGetKernelArgInfo(kernel, i, CL_KERNEL_ARG_TYPE_NAME, sizeof(arg_name), arg_name, NULL); |
||||
arg_types.push_back(string(arg_name)); |
||||
|
||||
args.push_back(g_args[make_pair(kernel, i)]); |
||||
args_size.push_back(g_args_size[make_pair(kernel, i)]); |
||||
} |
||||
|
||||
// get program
|
||||
clGetKernelInfo(kernel, CL_KERNEL_PROGRAM, sizeof(program), &program, NULL); |
||||
} |
||||
|
||||
int CLQueuedKernel::get_arg_num(const char *search_arg_name) { |
||||
for (int i = 0; i < num_args; i++) { |
||||
if (arg_names[i] == search_arg_name) return i; |
||||
} |
||||
printf("failed to find %s in %s\n", search_arg_name, name.c_str()); |
||||
assert(false); |
||||
} |
||||
|
||||
cl_int CLQueuedKernel::exec() { |
||||
if (kernel == NULL) { |
||||
kernel = clCreateKernel(program, name.c_str(), NULL); |
||||
arg_names.clear(); |
||||
arg_types.clear(); |
||||
|
||||
for (int j = 0; j < num_args; j++) { |
||||
char arg_name[0x100] = {0}; |
||||
clGetKernelArgInfo(kernel, j, CL_KERNEL_ARG_NAME, sizeof(arg_name), arg_name, NULL); |
||||
arg_names.push_back(string(arg_name)); |
||||
clGetKernelArgInfo(kernel, j, CL_KERNEL_ARG_TYPE_NAME, sizeof(arg_name), arg_name, NULL); |
||||
arg_types.push_back(string(arg_name)); |
||||
|
||||
cl_int ret; |
||||
if (args[j].size() != 0) { |
||||
assert(args[j].size() == args_size[j]); |
||||
ret = thneed_clSetKernelArg(kernel, j, args[j].size(), args[j].data()); |
||||
} else { |
||||
ret = thneed_clSetKernelArg(kernel, j, args_size[j], NULL); |
||||
} |
||||
assert(ret == CL_SUCCESS); |
||||
} |
||||
} |
||||
|
||||
if (thneed->debug >= 1) { |
||||
debug_print(thneed->debug >= 2); |
||||
} |
||||
|
||||
return clEnqueueNDRangeKernel(thneed->command_queue, |
||||
kernel, work_dim, NULL, global_work_size, local_work_size, 0, NULL, NULL); |
||||
} |
||||
|
||||
void CLQueuedKernel::debug_print(bool verbose) { |
||||
printf("%p %56s -- ", kernel, name.c_str()); |
||||
for (int i = 0; i < work_dim; i++) { |
||||
printf("%4zu ", global_work_size[i]); |
||||
} |
||||
printf(" -- "); |
||||
for (int i = 0; i < work_dim; i++) { |
||||
printf("%4zu ", local_work_size[i]); |
||||
} |
||||
printf("\n"); |
||||
|
||||
if (verbose) { |
||||
for (int i = 0; i < num_args; i++) { |
||||
string arg = args[i]; |
||||
printf(" %s %s", arg_types[i].c_str(), arg_names[i].c_str()); |
||||
void *arg_value = (void*)arg.data(); |
||||
int arg_size = arg.size(); |
||||
if (arg_size == 0) { |
||||
printf(" (size) %d", args_size[i]); |
||||
} else if (arg_size == 1) { |
||||
printf(" = %d", *((char*)arg_value)); |
||||
} else if (arg_size == 2) { |
||||
printf(" = %d", *((short*)arg_value)); |
||||
} else if (arg_size == 4) { |
||||
if (arg_types[i] == "float") { |
||||
printf(" = %f", *((float*)arg_value)); |
||||
} else { |
||||
printf(" = %d", *((int*)arg_value)); |
||||
} |
||||
} else if (arg_size == 8) { |
||||
cl_mem val = (cl_mem)(*((uintptr_t*)arg_value)); |
||||
printf(" = %p", val); |
||||
if (val != NULL) { |
||||
cl_mem_object_type obj_type; |
||||
clGetMemObjectInfo(val, CL_MEM_TYPE, sizeof(obj_type), &obj_type, NULL); |
||||
if (arg_types[i] == "image2d_t" || arg_types[i] == "image1d_t" || obj_type == CL_MEM_OBJECT_IMAGE2D) { |
||||
cl_image_format format; |
||||
size_t width, height, depth, array_size, row_pitch, slice_pitch; |
||||
cl_mem buf; |
||||
clGetImageInfo(val, CL_IMAGE_FORMAT, sizeof(format), &format, NULL); |
||||
assert(format.image_channel_order == CL_RGBA); |
||||
assert(format.image_channel_data_type == CL_HALF_FLOAT || format.image_channel_data_type == CL_FLOAT); |
||||
clGetImageInfo(val, CL_IMAGE_WIDTH, sizeof(width), &width, NULL); |
||||
clGetImageInfo(val, CL_IMAGE_HEIGHT, sizeof(height), &height, NULL); |
||||
clGetImageInfo(val, CL_IMAGE_ROW_PITCH, sizeof(row_pitch), &row_pitch, NULL); |
||||
clGetImageInfo(val, CL_IMAGE_DEPTH, sizeof(depth), &depth, NULL); |
||||
clGetImageInfo(val, CL_IMAGE_ARRAY_SIZE, sizeof(array_size), &array_size, NULL); |
||||
clGetImageInfo(val, CL_IMAGE_SLICE_PITCH, sizeof(slice_pitch), &slice_pitch, NULL); |
||||
assert(depth == 0); |
||||
assert(array_size == 0); |
||||
assert(slice_pitch == 0); |
||||
|
||||
clGetImageInfo(val, CL_IMAGE_BUFFER, sizeof(buf), &buf, NULL); |
||||
size_t sz = 0; |
||||
if (buf != NULL) clGetMemObjectInfo(buf, CL_MEM_SIZE, sizeof(sz), &sz, NULL); |
||||
printf(" image %zu x %zu rp %zu @ %p buffer %zu", width, height, row_pitch, buf, sz); |
||||
} else { |
||||
size_t sz; |
||||
clGetMemObjectInfo(val, CL_MEM_SIZE, sizeof(sz), &sz, NULL); |
||||
printf(" buffer %zu", sz); |
||||
} |
||||
} |
||||
} |
||||
printf("\n"); |
||||
} |
||||
} |
||||
} |
||||
|
||||
cl_int thneed_clSetKernelArg(cl_kernel kernel, cl_uint arg_index, size_t arg_size, const void *arg_value) { |
||||
g_args_size[make_pair(kernel, arg_index)] = arg_size; |
||||
if (arg_value != NULL) { |
||||
g_args[make_pair(kernel, arg_index)] = string((char*)arg_value, arg_size); |
||||
} else { |
||||
g_args[make_pair(kernel, arg_index)] = string(""); |
||||
} |
||||
cl_int ret = clSetKernelArg(kernel, arg_index, arg_size, arg_value); |
||||
return ret; |
||||
} |
||||
@ -1,32 +0,0 @@ |
||||
#include "selfdrive/modeld/thneed/thneed.h" |
||||
|
||||
#include <cassert> |
||||
|
||||
#include "common/clutil.h" |
||||
#include "common/timing.h" |
||||
|
||||
Thneed::Thneed(bool do_clinit, cl_context _context) { |
||||
context = _context; |
||||
if (do_clinit) clinit(); |
||||
char *thneed_debug_env = getenv("THNEED_DEBUG"); |
||||
debug = (thneed_debug_env != NULL) ? atoi(thneed_debug_env) : 0; |
||||
} |
||||
|
||||
void Thneed::execute(float **finputs, float *foutput, bool slow) { |
||||
uint64_t tb, te; |
||||
if (debug >= 1) tb = nanos_since_boot(); |
||||
|
||||
// ****** copy inputs
|
||||
copy_inputs(finputs); |
||||
|
||||
// ****** run commands
|
||||
clexec(); |
||||
|
||||
// ****** copy outputs
|
||||
copy_output(foutput); |
||||
|
||||
if (debug >= 1) { |
||||
te = nanos_since_boot(); |
||||
printf("model exec in %lu us\n", (te-tb)/1000); |
||||
} |
||||
} |
||||
@ -1,258 +0,0 @@ |
||||
#include "selfdrive/modeld/thneed/thneed.h" |
||||
|
||||
#include <dlfcn.h> |
||||
#include <sys/mman.h> |
||||
|
||||
#include <cassert> |
||||
#include <cerrno> |
||||
#include <cstring> |
||||
#include <map> |
||||
#include <string> |
||||
|
||||
#include "common/clutil.h" |
||||
#include "common/timing.h" |
||||
|
||||
Thneed *g_thneed = NULL; |
||||
int g_fd = -1; |
||||
|
||||
void hexdump(uint8_t *d, int len) { |
||||
assert((len%4) == 0); |
||||
printf(" dumping %p len 0x%x\n", d, len); |
||||
for (int i = 0; i < len/4; i++) { |
||||
if (i != 0 && (i%0x10) == 0) printf("\n"); |
||||
printf("%8x ", d[i]); |
||||
} |
||||
printf("\n"); |
||||
} |
||||
|
||||
// *********** ioctl interceptor ***********
|
||||
|
||||
extern "C" { |
||||
|
||||
int (*my_ioctl)(int filedes, unsigned long request, void *argp) = NULL; |
||||
#undef ioctl |
||||
int ioctl(int filedes, unsigned long request, void *argp) { |
||||
request &= 0xFFFFFFFF; // needed on QCOM2
|
||||
if (my_ioctl == NULL) my_ioctl = reinterpret_cast<decltype(my_ioctl)>(dlsym(RTLD_NEXT, "ioctl")); |
||||
Thneed *thneed = g_thneed; |
||||
|
||||
// save the fd
|
||||
if (request == IOCTL_KGSL_GPUOBJ_ALLOC) g_fd = filedes; |
||||
|
||||
// note that this runs always, even without a thneed object
|
||||
if (request == IOCTL_KGSL_DRAWCTXT_CREATE) { |
||||
struct kgsl_drawctxt_create *create = (struct kgsl_drawctxt_create *)argp; |
||||
create->flags &= ~KGSL_CONTEXT_PRIORITY_MASK; |
||||
create->flags |= 6 << KGSL_CONTEXT_PRIORITY_SHIFT; // priority from 1-15, 1 is max priority
|
||||
printf("IOCTL_KGSL_DRAWCTXT_CREATE: creating context with flags 0x%x\n", create->flags); |
||||
} |
||||
|
||||
if (thneed != NULL) { |
||||
if (request == IOCTL_KGSL_GPU_COMMAND) { |
||||
struct kgsl_gpu_command *cmd = (struct kgsl_gpu_command *)argp; |
||||
if (thneed->record) { |
||||
thneed->timestamp = cmd->timestamp; |
||||
thneed->context_id = cmd->context_id; |
||||
thneed->cmds.push_back(unique_ptr<CachedCommand>(new CachedCommand(thneed, cmd))); |
||||
} |
||||
if (thneed->debug >= 1) { |
||||
printf("IOCTL_KGSL_GPU_COMMAND(%2zu): flags: 0x%lx context_id: %u timestamp: %u numcmds: %d numobjs: %d\n", |
||||
thneed->cmds.size(), |
||||
cmd->flags, |
||||
cmd->context_id, cmd->timestamp, cmd->numcmds, cmd->numobjs); |
||||
} |
||||
} else if (request == IOCTL_KGSL_GPUOBJ_SYNC) { |
||||
struct kgsl_gpuobj_sync *cmd = (struct kgsl_gpuobj_sync *)argp; |
||||
struct kgsl_gpuobj_sync_obj *objs = (struct kgsl_gpuobj_sync_obj *)(cmd->objs); |
||||
|
||||
if (thneed->debug >= 2) { |
||||
printf("IOCTL_KGSL_GPUOBJ_SYNC count:%d ", cmd->count); |
||||
for (int i = 0; i < cmd->count; i++) { |
||||
printf(" -- offset:0x%lx len:0x%lx id:%d op:%d ", objs[i].offset, objs[i].length, objs[i].id, objs[i].op); |
||||
} |
||||
printf("\n"); |
||||
} |
||||
|
||||
if (thneed->record) { |
||||
thneed->cmds.push_back(unique_ptr<CachedSync>(new |
||||
CachedSync(thneed, string((char *)objs, sizeof(struct kgsl_gpuobj_sync_obj)*cmd->count)))); |
||||
} |
||||
} else if (request == IOCTL_KGSL_DEVICE_WAITTIMESTAMP_CTXTID) { |
||||
struct kgsl_device_waittimestamp_ctxtid *cmd = (struct kgsl_device_waittimestamp_ctxtid *)argp; |
||||
if (thneed->debug >= 1) { |
||||
printf("IOCTL_KGSL_DEVICE_WAITTIMESTAMP_CTXTID: context_id: %d timestamp: %d timeout: %d\n", |
||||
cmd->context_id, cmd->timestamp, cmd->timeout); |
||||
} |
||||
} else if (request == IOCTL_KGSL_SETPROPERTY) { |
||||
if (thneed->debug >= 1) { |
||||
struct kgsl_device_getproperty *prop = (struct kgsl_device_getproperty *)argp; |
||||
printf("IOCTL_KGSL_SETPROPERTY: 0x%x sizebytes:%zu\n", prop->type, prop->sizebytes); |
||||
if (thneed->debug >= 2) { |
||||
hexdump((uint8_t *)prop->value, prop->sizebytes); |
||||
if (prop->type == KGSL_PROP_PWR_CONSTRAINT) { |
||||
struct kgsl_device_constraint *constraint = (struct kgsl_device_constraint *)prop->value; |
||||
hexdump((uint8_t *)constraint->data, constraint->size); |
||||
} |
||||
} |
||||
} |
||||
} else if (request == IOCTL_KGSL_DRAWCTXT_CREATE || request == IOCTL_KGSL_DRAWCTXT_DESTROY) { |
||||
// this happens
|
||||
} else if (request == IOCTL_KGSL_GPUOBJ_ALLOC || request == IOCTL_KGSL_GPUOBJ_FREE) { |
||||
// this happens
|
||||
} else { |
||||
if (thneed->debug >= 1) { |
||||
printf("other ioctl %lx\n", request); |
||||
} |
||||
} |
||||
} |
||||
|
||||
int ret = my_ioctl(filedes, request, argp); |
||||
// NOTE: This error message goes into stdout and messes up pyenv
|
||||
// if (ret != 0) printf("ioctl returned %d with errno %d\n", ret, errno);
|
||||
return ret; |
||||
} |
||||
|
||||
} |
||||
|
||||
// *********** GPUMalloc ***********
|
||||
|
||||
GPUMalloc::GPUMalloc(int size, int fd) { |
||||
struct kgsl_gpuobj_alloc alloc; |
||||
memset(&alloc, 0, sizeof(alloc)); |
||||
alloc.size = size; |
||||
alloc.flags = 0x10000a00; |
||||
ioctl(fd, IOCTL_KGSL_GPUOBJ_ALLOC, &alloc); |
||||
void *addr = mmap64(NULL, alloc.mmapsize, 0x3, 0x1, fd, alloc.id*0x1000); |
||||
assert(addr != MAP_FAILED); |
||||
|
||||
base = (uint64_t)addr; |
||||
remaining = size; |
||||
} |
||||
|
||||
GPUMalloc::~GPUMalloc() { |
||||
// TODO: free the GPU malloced area
|
||||
} |
||||
|
||||
void *GPUMalloc::alloc(int size) { |
||||
void *ret = (void*)base; |
||||
size = (size+0xff) & (~0xFF); |
||||
assert(size <= remaining); |
||||
remaining -= size; |
||||
base += size; |
||||
return ret; |
||||
} |
||||
|
||||
// *********** CachedSync, at the ioctl layer ***********
|
||||
|
||||
void CachedSync::exec() { |
||||
struct kgsl_gpuobj_sync cmd; |
||||
|
||||
cmd.objs = (uint64_t)data.data(); |
||||
cmd.obj_len = data.length(); |
||||
cmd.count = data.length() / sizeof(struct kgsl_gpuobj_sync_obj); |
||||
|
||||
int ret = ioctl(thneed->fd, IOCTL_KGSL_GPUOBJ_SYNC, &cmd); |
||||
assert(ret == 0); |
||||
} |
||||
|
||||
// *********** CachedCommand, at the ioctl layer ***********
|
||||
|
||||
CachedCommand::CachedCommand(Thneed *lthneed, struct kgsl_gpu_command *cmd) { |
||||
thneed = lthneed; |
||||
assert(cmd->numsyncs == 0); |
||||
|
||||
memcpy(&cache, cmd, sizeof(cache)); |
||||
|
||||
if (cmd->numcmds > 0) { |
||||
cmds = make_unique<struct kgsl_command_object[]>(cmd->numcmds); |
||||
memcpy(cmds.get(), (void *)cmd->cmdlist, sizeof(struct kgsl_command_object)*cmd->numcmds); |
||||
cache.cmdlist = (uint64_t)cmds.get(); |
||||
for (int i = 0; i < cmd->numcmds; i++) { |
||||
void *nn = thneed->ram->alloc(cmds[i].size); |
||||
memcpy(nn, (void*)cmds[i].gpuaddr, cmds[i].size); |
||||
cmds[i].gpuaddr = (uint64_t)nn; |
||||
} |
||||
} |
||||
|
||||
if (cmd->numobjs > 0) { |
||||
objs = make_unique<struct kgsl_command_object[]>(cmd->numobjs); |
||||
memcpy(objs.get(), (void *)cmd->objlist, sizeof(struct kgsl_command_object)*cmd->numobjs); |
||||
cache.objlist = (uint64_t)objs.get(); |
||||
for (int i = 0; i < cmd->numobjs; i++) { |
||||
void *nn = thneed->ram->alloc(objs[i].size); |
||||
memset(nn, 0, objs[i].size); |
||||
objs[i].gpuaddr = (uint64_t)nn; |
||||
} |
||||
} |
||||
|
||||
kq = thneed->ckq; |
||||
thneed->ckq.clear(); |
||||
} |
||||
|
||||
void CachedCommand::exec() { |
||||
cache.timestamp = ++thneed->timestamp; |
||||
int ret = ioctl(thneed->fd, IOCTL_KGSL_GPU_COMMAND, &cache); |
||||
|
||||
if (thneed->debug >= 1) printf("CachedCommand::exec got %d\n", ret); |
||||
|
||||
if (thneed->debug >= 2) { |
||||
for (auto &it : kq) { |
||||
it->debug_print(false); |
||||
} |
||||
} |
||||
|
||||
assert(ret == 0); |
||||
} |
||||
|
||||
// *********** Thneed ***********
|
||||
|
||||
Thneed::Thneed(bool do_clinit, cl_context _context) { |
||||
// TODO: QCOM2 actually requires a different context
|
||||
//context = _context;
|
||||
if (do_clinit) clinit(); |
||||
assert(g_fd != -1); |
||||
fd = g_fd; |
||||
ram = make_unique<GPUMalloc>(0x80000, fd); |
||||
timestamp = -1; |
||||
g_thneed = this; |
||||
char *thneed_debug_env = getenv("THNEED_DEBUG"); |
||||
debug = (thneed_debug_env != NULL) ? atoi(thneed_debug_env) : 0; |
||||
} |
||||
|
||||
void Thneed::wait() { |
||||
struct kgsl_device_waittimestamp_ctxtid wait; |
||||
wait.context_id = context_id; |
||||
wait.timestamp = timestamp; |
||||
wait.timeout = -1; |
||||
|
||||
uint64_t tb = nanos_since_boot(); |
||||
int wret = ioctl(fd, IOCTL_KGSL_DEVICE_WAITTIMESTAMP_CTXTID, &wait); |
||||
uint64_t te = nanos_since_boot(); |
||||
|
||||
if (debug >= 1) printf("wait %d after %lu us\n", wret, (te-tb)/1000); |
||||
} |
||||
|
||||
void Thneed::execute(float **finputs, float *foutput, bool slow) { |
||||
uint64_t tb, te; |
||||
if (debug >= 1) tb = nanos_since_boot(); |
||||
|
||||
// ****** copy inputs
|
||||
copy_inputs(finputs, true); |
||||
|
||||
// ****** run commands
|
||||
int i = 0; |
||||
for (auto &it : cmds) { |
||||
++i; |
||||
if (debug >= 1) printf("run %2d @ %7lu us: ", i, (nanos_since_boot()-tb)/1000); |
||||
it->exec(); |
||||
if ((i == cmds.size()) || slow) wait(); |
||||
} |
||||
|
||||
// ****** copy outputs
|
||||
copy_output(foutput); |
||||
|
||||
if (debug >= 1) { |
||||
te = nanos_since_boot(); |
||||
printf("model exec in %lu us\n", (te-tb)/1000); |
||||
} |
||||
} |
||||
@ -1 +1 @@ |
||||
Subproject commit 9dda6d260db0255750bacff61e3cee1e580567e1 |
||||
Subproject commit bfd980da7d2c2bab5b073127442c361922032ba1 |
||||
Loading…
Reference in new issue