[14de349] | 1 | """ |
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[eafc9fa] | 2 | GPU driver for C kernels |
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[14de349] | 3 | |
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| 4 | There should be a single GPU environment running on the system. This |
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| 5 | environment is constructed on the first call to :func:`env`, and the |
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| 6 | same environment is returned on each call. |
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| 7 | |
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| 8 | After retrieving the environment, the next step is to create the kernel. |
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| 9 | This is done with a call to :meth:`GpuEnvironment.make_kernel`, which |
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| 10 | returns the type of data used by the kernel. |
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| 11 | |
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| 12 | Next a :class:`GpuData` object should be created with the correct kind |
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| 13 | of data. This data object can be used by multiple kernels, for example, |
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| 14 | if the target model is a weighted sum of multiple kernels. The data |
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| 15 | should include any extra evaluation points required to compute the proper |
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| 16 | data smearing. This need not match the square grid for 2D data if there |
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| 17 | is an index saying which q points are active. |
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| 18 | |
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| 19 | Together the GpuData, the program, and a device form a :class:`GpuKernel`. |
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| 20 | This kernel is used during fitting, receiving new sets of parameters and |
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| 21 | evaluating them. The output value is stored in an output buffer on the |
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| 22 | devices, where it can be combined with other structure factors and form |
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| 23 | factors and have instrumental resolution effects applied. |
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[92da231] | 24 | |
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| 25 | In order to use OpenCL for your models, you will need OpenCL drivers for |
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| 26 | your machine. These should be available from your graphics card vendor. |
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| 27 | Intel provides OpenCL drivers for CPUs as well as their integrated HD |
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| 28 | graphics chipsets. AMD also provides drivers for Intel CPUs, but as of |
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| 29 | this writing the performance is lacking compared to the Intel drivers. |
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| 30 | NVidia combines drivers for CUDA and OpenCL in one package. The result |
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| 31 | is a bit messy if you have multiple drivers installed. You can see which |
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| 32 | drivers are available by starting python and running: |
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| 33 | |
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| 34 | import pyopencl as cl |
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| 35 | cl.create_some_context(interactive=True) |
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| 36 | |
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| 37 | Once you have done that, it will show the available drivers which you |
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| 38 | can select. It will then tell you that you can use these drivers |
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| 39 | automatically by setting the PYOPENCL_CTX environment variable. |
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| 40 | |
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| 41 | Some graphics cards have multiple devices on the same card. You cannot |
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| 42 | yet use both of them concurrently to evaluate models, but you can run |
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| 43 | the program twice using a different device for each session. |
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| 44 | |
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| 45 | OpenCL kernels are compiled when needed by the device driver. Some |
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| 46 | drivers produce compiler output even when there is no error. You |
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| 47 | can see the output by setting PYOPENCL_COMPILER_OUTPUT=1. It should be |
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| 48 | harmless, albeit annoying. |
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[14de349] | 49 | """ |
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[ba32cdd] | 50 | from __future__ import print_function |
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[250fa25] | 51 | import os |
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| 52 | import warnings |
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| 53 | |
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[14de349] | 54 | import numpy as np |
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[b3f6bc3] | 55 | |
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[250fa25] | 56 | try: |
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| 57 | import pyopencl as cl |
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[3c56da87] | 58 | # Ask OpenCL for the default context so that we know that one exists |
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| 59 | cl.create_some_context(interactive=False) |
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[9404dd3] | 60 | except Exception as exc: |
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[7841376] | 61 | warnings.warn(str(exc)) |
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[664c8e7] | 62 | raise RuntimeError("OpenCL not available") |
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[7841376] | 63 | |
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[14de349] | 64 | from pyopencl import mem_flags as mf |
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[5d316e9] | 65 | from pyopencl.characterize import get_fast_inaccurate_build_options |
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[14de349] | 66 | |
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[cb6ecf4] | 67 | from . import generate |
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[14de349] | 68 | |
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[ce27e21] | 69 | # The max loops number is limited by the amount of local memory available |
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| 70 | # on the device. You don't want to make this value too big because it will |
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| 71 | # waste resources, nor too small because it may interfere with users trying |
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| 72 | # to do their polydispersity calculations. A value of 1024 should be much |
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| 73 | # larger than necessary given that cost grows as npts^k where k is the number |
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| 74 | # of polydisperse parameters. |
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[5d4777d] | 75 | MAX_LOOPS = 2048 |
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| 76 | |
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[ce27e21] | 77 | |
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[5464d68] | 78 | # Pragmas for enable OpenCL features. Be sure to protect them so that they |
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| 79 | # still compile even if OpenCL is not present. |
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| 80 | _F16_PRAGMA = """\ |
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| 81 | #if defined(__OPENCL_VERSION__) // && !defined(cl_khr_fp16) |
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| 82 | # pragma OPENCL EXTENSION cl_khr_fp16: enable |
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| 83 | #endif |
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| 84 | """ |
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| 85 | |
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| 86 | _F64_PRAGMA = """\ |
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| 87 | #if defined(__OPENCL_VERSION__) // && !defined(cl_khr_fp64) |
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| 88 | # pragma OPENCL EXTENSION cl_khr_fp64: enable |
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| 89 | #endif |
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| 90 | """ |
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| 91 | |
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| 92 | |
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[14de349] | 93 | ENV = None |
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| 94 | def environment(): |
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| 95 | """ |
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| 96 | Returns a singleton :class:`GpuEnvironment`. |
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| 97 | |
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| 98 | This provides an OpenCL context and one queue per device. |
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| 99 | """ |
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| 100 | global ENV |
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| 101 | if ENV is None: |
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| 102 | ENV = GpuEnvironment() |
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| 103 | return ENV |
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| 104 | |
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[5d316e9] | 105 | def has_type(device, dtype): |
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[14de349] | 106 | """ |
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[5d316e9] | 107 | Return true if device supports the requested precision. |
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[14de349] | 108 | """ |
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[5d316e9] | 109 | if dtype == generate.F32: |
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| 110 | return True |
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| 111 | elif dtype == generate.F64: |
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| 112 | return "cl_khr_fp64" in device.extensions |
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| 113 | elif dtype == generate.F16: |
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| 114 | return "cl_khr_fp16" in device.extensions |
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| 115 | else: |
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| 116 | return False |
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[14de349] | 117 | |
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[f5b9a6b] | 118 | def get_warp(kernel, queue): |
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| 119 | """ |
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| 120 | Return the size of an execution batch for *kernel* running on *queue*. |
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| 121 | """ |
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[750ffa5] | 122 | return kernel.get_work_group_info( |
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[63b32bb] | 123 | cl.kernel_work_group_info.PREFERRED_WORK_GROUP_SIZE_MULTIPLE, |
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| 124 | queue.device) |
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[14de349] | 125 | |
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[f5b9a6b] | 126 | def _stretch_input(vector, dtype, extra=1e-3, boundary=32): |
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[14de349] | 127 | """ |
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| 128 | Stretch an input vector to the correct boundary. |
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| 129 | |
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| 130 | Performance on the kernels can drop by a factor of two or more if the |
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| 131 | number of values to compute does not fall on a nice power of two |
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[f5b9a6b] | 132 | boundary. The trailing additional vector elements are given a |
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| 133 | value of *extra*, and so f(*extra*) will be computed for each of |
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| 134 | them. The returned array will thus be a subset of the computed array. |
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| 135 | |
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| 136 | *boundary* should be a power of 2 which is at least 32 for good |
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| 137 | performance on current platforms (as of Jan 2015). It should |
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| 138 | probably be the max of get_warp(kernel,queue) and |
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| 139 | device.min_data_type_align_size//4. |
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| 140 | """ |
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[c85db69] | 141 | remainder = vector.size % boundary |
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[f5b9a6b] | 142 | if remainder != 0: |
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| 143 | size = vector.size + (boundary - remainder) |
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[c85db69] | 144 | vector = np.hstack((vector, [extra] * (size - vector.size))) |
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[14de349] | 145 | return np.ascontiguousarray(vector, dtype=dtype) |
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| 146 | |
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| 147 | |
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[5d316e9] | 148 | def compile_model(context, source, dtype, fast=False): |
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[14de349] | 149 | """ |
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| 150 | Build a model to run on the gpu. |
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| 151 | |
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| 152 | Returns the compiled program and its type. The returned type will |
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| 153 | be float32 even if the desired type is float64 if any of the |
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| 154 | devices in the context do not support the cl_khr_fp64 extension. |
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| 155 | """ |
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| 156 | dtype = np.dtype(dtype) |
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[5d316e9] | 157 | if not all(has_type(d, dtype) for d in context.devices): |
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| 158 | raise RuntimeError("%s not supported for devices"%dtype) |
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[14de349] | 159 | |
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[5464d68] | 160 | source_list = [generate.convert_type(source, dtype)] |
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| 161 | |
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| 162 | if dtype == generate.F16: |
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| 163 | source_list.insert(0, _F16_PRAGMA) |
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| 164 | elif dtype == generate.F64: |
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| 165 | source_list.insert(0, _F64_PRAGMA) |
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| 166 | |
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[14de349] | 167 | # Note: USE_SINCOS makes the intel cpu slower under opencl |
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| 168 | if context.devices[0].type == cl.device_type.GPU: |
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[5464d68] | 169 | source_list.insert(0, "#define USE_SINCOS\n") |
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[5d316e9] | 170 | options = (get_fast_inaccurate_build_options(context.devices[0]) |
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| 171 | if fast else []) |
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[ba32cdd] | 172 | source = "\n".join(source_list) |
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[5d316e9] | 173 | program = cl.Program(context, source).build(options=options) |
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[ce27e21] | 174 | return program |
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[14de349] | 175 | |
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| 176 | |
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| 177 | # for now, this returns one device in the context |
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| 178 | # TODO: create a context that contains all devices on all platforms |
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| 179 | class GpuEnvironment(object): |
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| 180 | """ |
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| 181 | GPU context, with possibly many devices, and one queue per device. |
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| 182 | """ |
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| 183 | def __init__(self): |
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[250fa25] | 184 | # find gpu context |
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| 185 | #self.context = cl.create_some_context() |
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| 186 | |
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| 187 | self.context = None |
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| 188 | if 'PYOPENCL_CTX' in os.environ: |
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| 189 | self._create_some_context() |
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| 190 | |
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| 191 | if not self.context: |
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[3c56da87] | 192 | self.context = _get_default_context() |
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[250fa25] | 193 | |
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[f5b9a6b] | 194 | # Byte boundary for data alignment |
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| 195 | #self.data_boundary = max(d.min_data_type_align_size |
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| 196 | # for d in self.context.devices) |
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[d18582e] | 197 | self.queues = [cl.CommandQueue(context, context.devices[0]) |
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| 198 | for context in self.context] |
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[ce27e21] | 199 | self.compiled = {} |
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| 200 | |
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[5d316e9] | 201 | def has_type(self, dtype): |
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[eafc9fa] | 202 | """ |
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| 203 | Return True if all devices support a given type. |
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| 204 | """ |
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[cde11f0f] | 205 | dtype = generate.F32 if dtype == 'fast' else np.dtype(dtype) |
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[d18582e] | 206 | return any(has_type(d, dtype) |
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| 207 | for context in self.context |
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| 208 | for d in context.devices) |
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| 209 | |
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| 210 | def get_queue(self, dtype): |
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| 211 | """ |
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| 212 | Return a command queue for the kernels of type dtype. |
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| 213 | """ |
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| 214 | for context, queue in zip(self.context, self.queues): |
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| 215 | if all(has_type(d, dtype) for d in context.devices): |
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| 216 | return queue |
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| 217 | |
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| 218 | def get_context(self, dtype): |
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| 219 | """ |
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| 220 | Return a OpenCL context for the kernels of type dtype. |
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| 221 | """ |
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| 222 | for context, queue in zip(self.context, self.queues): |
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| 223 | if all(has_type(d, dtype) for d in context.devices): |
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| 224 | return context |
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[5d316e9] | 225 | |
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[250fa25] | 226 | def _create_some_context(self): |
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[eafc9fa] | 227 | """ |
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| 228 | Protected call to cl.create_some_context without interactivity. Use |
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| 229 | this if PYOPENCL_CTX is set in the environment. Sets the *context* |
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| 230 | attribute. |
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| 231 | """ |
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[250fa25] | 232 | try: |
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[d18582e] | 233 | self.context = [cl.create_some_context(interactive=False)] |
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[9404dd3] | 234 | except Exception as exc: |
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[250fa25] | 235 | warnings.warn(str(exc)) |
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| 236 | warnings.warn("pyopencl.create_some_context() failed") |
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| 237 | warnings.warn("the environment variable 'PYOPENCL_CTX' might not be set correctly") |
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| 238 | |
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[5d316e9] | 239 | def compile_program(self, name, source, dtype, fast=False): |
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[eafc9fa] | 240 | """ |
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| 241 | Compile the program for the device in the given context. |
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| 242 | """ |
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[cde11f0f] | 243 | key = "%s-%s-%s"%(name, dtype, fast) |
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| 244 | if key not in self.compiled: |
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[fec69dd] | 245 | print("compiling",name) |
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[cde11f0f] | 246 | dtype = np.dtype(dtype) |
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[fec69dd] | 247 | program = compile_model(self.get_context(dtype), |
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| 248 | str(source), dtype, fast) |
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[cde11f0f] | 249 | self.compiled[key] = program |
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| 250 | return self.compiled[key] |
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[ce27e21] | 251 | |
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| 252 | def release_program(self, name): |
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[eafc9fa] | 253 | """ |
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| 254 | Free memory associated with the program on the device. |
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| 255 | """ |
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[ce27e21] | 256 | if name in self.compiled: |
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| 257 | self.compiled[name].release() |
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| 258 | del self.compiled[name] |
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[14de349] | 259 | |
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[3c56da87] | 260 | def _get_default_context(): |
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[eafc9fa] | 261 | """ |
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[d18582e] | 262 | Get an OpenCL context, preferring GPU over CPU, and preferring Intel |
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| 263 | drivers over AMD drivers. |
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[eafc9fa] | 264 | """ |
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[d18582e] | 265 | # Note: on mobile devices there is automatic clock scaling if either the |
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| 266 | # CPU or the GPU is underutilized; probably doesn't affect us, but we if |
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| 267 | # it did, it would mean that putting a busy loop on the CPU while the GPU |
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| 268 | # is running may increase throughput. |
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| 269 | # |
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| 270 | # Macbook pro, base install: |
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| 271 | # {'Apple': [Intel CPU, NVIDIA GPU]} |
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| 272 | # Macbook pro, base install: |
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| 273 | # {'Apple': [Intel CPU, Intel GPU]} |
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| 274 | # 2 x nvidia 295 with Intel and NVIDIA opencl drivers installed |
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| 275 | # {'Intel': [CPU], 'NVIDIA': [GPU, GPU, GPU, GPU]} |
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| 276 | gpu, cpu = None, None |
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[3c56da87] | 277 | for platform in cl.get_platforms(): |
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[e6a5556] | 278 | # AMD provides a much weaker CPU driver than Intel/Apple, so avoid it. |
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| 279 | # If someone has bothered to install the AMD/NVIDIA drivers, prefer them over the integrated |
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| 280 | # graphics driver that may have been supplied with the CPU chipset. |
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| 281 | preferred_cpu = platform.vendor.startswith('Intel') or platform.vendor.startswith('Apple') |
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| 282 | preferred_gpu = platform.vendor.startswith('Advanced') or platform.vendor.startswith('NVIDIA') |
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[3c56da87] | 283 | for device in platform.get_devices(): |
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| 284 | if device.type == cl.device_type.GPU: |
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[e6a5556] | 285 | # If the existing type is not GPU then it will be CUSTOM or ACCELERATOR, |
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| 286 | # so don't override it. |
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| 287 | if gpu is None or (preferred_gpu and gpu.type == cl.device_type.GPU): |
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| 288 | gpu = device |
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| 289 | elif device.type == cl.device_type.CPU: |
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| 290 | if cpu is None or preferred_cpu: |
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| 291 | cpu = device |
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[d18582e] | 292 | else: |
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[e6a5556] | 293 | # System has cl.device_type.ACCELERATOR or cl.device_type.CUSTOM |
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| 294 | # Intel Phi for example registers as an accelerator |
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| 295 | # Since the user installed a custom device on their system and went through the |
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| 296 | # pain of sorting out OpenCL drivers for it, lets assume they really do want to |
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| 297 | # use it as their primary compute device. |
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| 298 | gpu = device |
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[199d40d] | 299 | |
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[e6a5556] | 300 | # order the devices by gpu then by cpu; when searching for an available device by data type they |
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| 301 | # will be checked in this order, which means that if the gpu supports double then the cpu will never |
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| 302 | # be used (though we may make it possible to explicitly request the cpu at some point). |
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| 303 | devices = [] |
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| 304 | if gpu is not None: |
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| 305 | devices.append(gpu) |
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| 306 | if cpu is not None: |
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| 307 | devices.append(cpu) |
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| 308 | return [cl.Context([d]) for d in devices] |
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[3c56da87] | 309 | |
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[250fa25] | 310 | |
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[14de349] | 311 | class GpuModel(object): |
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| 312 | """ |
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| 313 | GPU wrapper for a single model. |
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| 314 | |
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[17bbadd] | 315 | *source* and *model_info* are the model source and interface as returned |
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| 316 | from :func:`generate.make_source` and :func:`generate.make_model_info`. |
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[14de349] | 317 | |
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| 318 | *dtype* is the desired model precision. Any numpy dtype for single |
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| 319 | or double precision floats will do, such as 'f', 'float32' or 'single' |
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| 320 | for single and 'd', 'float64' or 'double' for double. Double precision |
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| 321 | is an optional extension which may not be available on all devices. |
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[cde11f0f] | 322 | Half precision ('float16','half') may be available on some devices. |
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| 323 | Fast precision ('fast') is a loose version of single precision, indicating |
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| 324 | that the compiler is allowed to take shortcuts. |
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[14de349] | 325 | """ |
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[17bbadd] | 326 | def __init__(self, source, model_info, dtype=generate.F32): |
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| 327 | self.info = model_info |
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[ce27e21] | 328 | self.source = source |
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[823e620] | 329 | self.dtype = generate.F32 if dtype == 'fast' else np.dtype(dtype) |
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[cde11f0f] | 330 | self.fast = (dtype == 'fast') |
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[ce27e21] | 331 | self.program = None # delay program creation |
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[14de349] | 332 | |
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[ce27e21] | 333 | def __getstate__(self): |
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[eafc9fa] | 334 | return self.info, self.source, self.dtype, self.fast |
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[14de349] | 335 | |
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[ce27e21] | 336 | def __setstate__(self, state): |
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[eafc9fa] | 337 | self.info, self.source, self.dtype, self.fast = state |
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| 338 | self.program = None |
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[ce27e21] | 339 | |
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[48fbd50] | 340 | def make_kernel(self, q_vectors): |
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[ce27e21] | 341 | if self.program is None: |
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[3c56da87] | 342 | compiler = environment().compile_program |
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[c072f83] | 343 | self.program = compiler(self.info['name'], self.source, |
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| 344 | self.dtype, self.fast) |
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[eafc9fa] | 345 | is_2d = len(q_vectors) == 2 |
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| 346 | kernel_name = generate.kernel_name(self.info, is_2d) |
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[14de349] | 347 | kernel = getattr(self.program, kernel_name) |
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[48fbd50] | 348 | return GpuKernel(kernel, self.info, q_vectors, self.dtype) |
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[ce27e21] | 349 | |
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| 350 | def release(self): |
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[eafc9fa] | 351 | """ |
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| 352 | Free the resources associated with the model. |
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| 353 | """ |
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[ce27e21] | 354 | if self.program is not None: |
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| 355 | environment().release_program(self.info['name']) |
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| 356 | self.program = None |
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[14de349] | 357 | |
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[eafc9fa] | 358 | def __del__(self): |
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| 359 | self.release() |
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[14de349] | 360 | |
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| 361 | # TODO: check that we don't need a destructor for buffers which go out of scope |
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| 362 | class GpuInput(object): |
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| 363 | """ |
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| 364 | Make q data available to the gpu. |
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| 365 | |
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| 366 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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| 367 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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| 368 | to get the best performance on OpenCL, which may involve shifting and |
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| 369 | stretching the array to better match the memory architecture. Additional |
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| 370 | points will be evaluated with *q=1e-3*. |
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| 371 | |
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| 372 | *dtype* is the data type for the q vectors. The data type should be |
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| 373 | set to match that of the kernel, which is an attribute of |
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| 374 | :class:`GpuProgram`. Note that not all kernels support double |
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| 375 | precision, so even if the program was created for double precision, |
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| 376 | the *GpuProgram.dtype* may be single precision. |
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| 377 | |
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| 378 | Call :meth:`release` when complete. Even if not called directly, the |
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| 379 | buffer will be released when the data object is freed. |
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| 380 | """ |
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[cb6ecf4] | 381 | def __init__(self, q_vectors, dtype=generate.F32): |
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[17bbadd] | 382 | # TODO: do we ever need double precision q? |
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[14de349] | 383 | env = environment() |
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| 384 | self.nq = q_vectors[0].size |
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| 385 | self.dtype = np.dtype(dtype) |
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[eafc9fa] | 386 | self.is_2d = (len(q_vectors) == 2) |
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[f5b9a6b] | 387 | # TODO: stretch input based on get_warp() |
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| 388 | # not doing it now since warp depends on kernel, which is not known |
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| 389 | # at this point, so instead using 32, which is good on the set of |
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| 390 | # architectures tested so far. |
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[c072f83] | 391 | if self.is_2d: |
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| 392 | # Note: 17 rather than 15 because results is 2 elements |
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| 393 | # longer than input. |
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| 394 | width = ((self.nq+17)//16)*16 |
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| 395 | self.q = np.empty((width, 2), dtype=dtype) |
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| 396 | self.q[:self.nq, 0] = q_vectors[0] |
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| 397 | self.q[:self.nq, 1] = q_vectors[1] |
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| 398 | else: |
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| 399 | # Note: 33 rather than 31 because results is 2 elements |
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| 400 | # longer than input. |
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| 401 | width = ((self.nq+33)//32)*32 |
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| 402 | self.q = np.empty(width, dtype=dtype) |
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| 403 | self.q[:self.nq] = q_vectors[0] |
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| 404 | self.global_size = [self.q.shape[0]] |
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[d18582e] | 405 | context = env.get_context(self.dtype) |
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[c094758] | 406 | #print("creating inputs of size", self.global_size) |
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[c072f83] | 407 | self.q_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, |
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| 408 | hostbuf=self.q) |
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[14de349] | 409 | |
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| 410 | def release(self): |
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[eafc9fa] | 411 | """ |
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| 412 | Free the memory. |
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| 413 | """ |
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[c072f83] | 414 | if self.q is not None: |
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| 415 | self.q.release() |
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| 416 | self.q = None |
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[14de349] | 417 | |
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[eafc9fa] | 418 | def __del__(self): |
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| 419 | self.release() |
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| 420 | |
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[14de349] | 421 | class GpuKernel(object): |
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[ff7119b] | 422 | """ |
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| 423 | Callable SAS kernel. |
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| 424 | |
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[eafc9fa] | 425 | *kernel* is the GpuKernel object to call |
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[ff7119b] | 426 | |
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[17bbadd] | 427 | *model_info* is the module information |
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[ff7119b] | 428 | |
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[eafc9fa] | 429 | *q_vectors* is the q vectors at which the kernel should be evaluated |
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| 430 | |
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| 431 | *dtype* is the kernel precision |
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[ff7119b] | 432 | |
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| 433 | The resulting call method takes the *pars*, a list of values for |
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| 434 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
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| 435 | vectors for the polydisperse parameters. *cutoff* determines the |
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| 436 | integration limits: any points with combined weight less than *cutoff* |
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| 437 | will not be calculated. |
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| 438 | |
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| 439 | Call :meth:`release` when done with the kernel instance. |
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| 440 | """ |
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[48fbd50] | 441 | def __init__(self, kernel, model_info, q_vectors, dtype): |
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[ba32cdd] | 442 | max_pd = model_info['max_pd'] |
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[c072f83] | 443 | npars = len(model_info['parameters'])-2 |
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[eafc9fa] | 444 | q_input = GpuInput(q_vectors, dtype) |
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[c072f83] | 445 | self.dtype = dtype |
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[445d1c0] | 446 | self.dim = '2d' if q_input.is_2d else '1d' |
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[14de349] | 447 | self.kernel = kernel |
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[17bbadd] | 448 | self.info = model_info |
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[c072f83] | 449 | self.pd_stop_index = 4*max_pd-1 |
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| 450 | # plus three for the normalization values |
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| 451 | self.result = np.empty(q_input.nq+3, q_input.dtype) |
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[14de349] | 452 | |
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| 453 | # Inputs and outputs for each kernel call |
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[ce27e21] | 454 | # Note: res may be shorter than res_b if global_size != nq |
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| 455 | env = environment() |
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[d18582e] | 456 | self.queue = env.get_queue(dtype) |
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[c072f83] | 457 | |
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[48fbd50] | 458 | # details is int32 data, padded to an 8 integer boundary |
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| 459 | size = ((max_pd*5 + npars*3 + 2 + 7)//8)*8 |
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[c072f83] | 460 | self.result_b = cl.Buffer(self.queue.context, mf.READ_WRITE, |
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[d18582e] | 461 | q_input.global_size[0] * q_input.dtype.itemsize) |
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[c072f83] | 462 | self.q_input = q_input # allocated by GpuInput above |
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[14de349] | 463 | |
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[48fbd50] | 464 | self._need_release = [ self.result_b, self.q_input ] |
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[d18582e] | 465 | |
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[48fbd50] | 466 | def __call__(self, details, weights, values, cutoff): |
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[5d316e9] | 467 | real = (np.float32 if self.q_input.dtype == generate.F32 |
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| 468 | else np.float64 if self.q_input.dtype == generate.F64 |
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| 469 | else np.float16 if self.q_input.dtype == generate.F16 |
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| 470 | else np.float32) # will never get here, so use np.float32 |
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[48fbd50] | 471 | assert details.dtype == np.int32 |
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| 472 | assert weights.dtype == real and values.dtype == real |
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| 473 | |
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| 474 | context = self.queue.context |
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| 475 | details_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, |
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| 476 | hostbuf=details) |
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| 477 | weights_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, |
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| 478 | hostbuf=weights) |
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| 479 | values_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, |
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| 480 | hostbuf=values) |
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| 481 | |
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| 482 | start, stop = 0, self.details[self.pd_stop_index] |
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[c072f83] | 483 | args = [ |
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[48fbd50] | 484 | np.uint32(self.q_input.nq), np.uint32(start), np.uint32(stop), |
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| 485 | self.details_b, self.weights_b, self.values_b, |
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| 486 | self.q_input.q_b, self.result_b, real(cutoff), |
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[c072f83] | 487 | ] |
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[d18582e] | 488 | self.kernel(self.queue, self.q_input.global_size, None, *args) |
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[c072f83] | 489 | cl.enqueue_copy(self.queue, self.result, self.result_b) |
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[48fbd50] | 490 | [v.release() for v in details_b, weights_b, values_b] |
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[14de349] | 491 | |
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[c072f83] | 492 | return self.result[:self.nq] |
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[14de349] | 493 | |
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| 494 | def release(self): |
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[eafc9fa] | 495 | """ |
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| 496 | Release resources associated with the kernel. |
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| 497 | """ |
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[d18582e] | 498 | for v in self._need_release: |
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| 499 | v.release() |
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| 500 | self._need_release = [] |
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[14de349] | 501 | |
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| 502 | def __del__(self): |
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| 503 | self.release() |
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