1 | """ |
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2 | GPU driver for C kernels (with CUDA) |
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3 | |
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4 | To select cuda, use SAS_OPENCL=cuda, or SAS_OPENCL=cuda:n for a particular |
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5 | device number. If no device number is specified, then look for CUDA_DEVICE=n |
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6 | or a file ~/.cuda-device containing n for the device number. Otherwise, try |
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7 | all available device numbers. |
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8 | |
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9 | TODO: docs are out of date |
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10 | |
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11 | There should be a single GPU environment running on the system. This |
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12 | environment is constructed on the first call to :func:`env`, and the |
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13 | same environment is returned on each call. |
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14 | |
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15 | After retrieving the environment, the next step is to create the kernel. |
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16 | This is done with a call to :meth:`GpuEnvironment.make_kernel`, which |
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17 | returns the type of data used by the kernel. |
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18 | |
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19 | Next a :class:`GpuData` object should be created with the correct kind |
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20 | of data. This data object can be used by multiple kernels, for example, |
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21 | if the target model is a weighted sum of multiple kernels. The data |
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22 | should include any extra evaluation points required to compute the proper |
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23 | data smearing. This need not match the square grid for 2D data if there |
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24 | is an index saying which q points are active. |
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25 | |
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26 | Together the GpuData, the program, and a device form a :class:`GpuKernel`. |
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27 | This kernel is used during fitting, receiving new sets of parameters and |
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28 | evaluating them. The output value is stored in an output buffer on the |
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29 | devices, where it can be combined with other structure factors and form |
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30 | factors and have instrumental resolution effects applied. |
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31 | |
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32 | In order to use OpenCL for your models, you will need OpenCL drivers for |
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33 | your machine. These should be available from your graphics card vendor. |
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34 | Intel provides OpenCL drivers for CPUs as well as their integrated HD |
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35 | graphics chipsets. AMD also provides drivers for Intel CPUs, but as of |
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36 | this writing the performance is lacking compared to the Intel drivers. |
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37 | NVidia combines drivers for CUDA and OpenCL in one package. The result |
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38 | is a bit messy if you have multiple drivers installed. You can see which |
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39 | drivers are available by starting python and running: |
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40 | |
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41 | import pyopencl as cl |
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42 | cl.create_some_context(interactive=True) |
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43 | |
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44 | Once you have done that, it will show the available drivers which you |
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45 | can select. It will then tell you that you can use these drivers |
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46 | automatically by setting the SAS_OPENCL environment variable, which is |
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47 | PYOPENCL_CTX equivalent but not conflicting with other pyopnecl programs. |
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48 | |
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49 | Some graphics cards have multiple devices on the same card. You cannot |
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50 | yet use both of them concurrently to evaluate models, but you can run |
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51 | the program twice using a different device for each session. |
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52 | |
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53 | OpenCL kernels are compiled when needed by the device driver. Some |
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54 | drivers produce compiler output even when there is no error. You |
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55 | can see the output by setting PYOPENCL_COMPILER_OUTPUT=1. It should be |
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56 | harmless, albeit annoying. |
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57 | """ |
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58 | from __future__ import print_function |
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59 | |
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60 | import os |
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61 | import logging |
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62 | import time |
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63 | import re |
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64 | import atexit |
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65 | |
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66 | import numpy as np # type: ignore |
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67 | |
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68 | |
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69 | # Attempt to setup CUDA. This may fail if the pycuda package is not |
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70 | # installed or if it is installed but there are no devices available. |
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71 | try: |
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72 | import pycuda.driver as cuda # type: ignore |
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73 | from pycuda.compiler import SourceModule |
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74 | from pycuda.tools import make_default_context, clear_context_caches |
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75 | # Ask CUDA for the default context (so that we know that one exists) |
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76 | # then immediately throw it away in case the user doesn't want it. |
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77 | # Note: cribbed from pycuda.autoinit |
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78 | cuda.init() |
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79 | context = make_default_context() |
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80 | context.pop() |
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81 | clear_context_caches() |
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82 | del context |
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83 | HAVE_CUDA = True |
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84 | CUDA_ERROR = "" |
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85 | except Exception as exc: |
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86 | HAVE_CUDA = False |
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87 | CUDA_ERROR = str(exc) |
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88 | |
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89 | from . import generate |
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90 | from .kernel import KernelModel, Kernel |
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91 | |
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92 | # pylint: disable=unused-import |
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93 | try: |
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94 | from typing import Tuple, Callable, Any |
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95 | from .modelinfo import ModelInfo |
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96 | from .details import CallDetails |
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97 | except ImportError: |
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98 | pass |
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99 | # pylint: enable=unused-import |
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100 | |
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101 | # The max loops number is limited by the amount of local memory available |
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102 | # on the device. You don't want to make this value too big because it will |
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103 | # waste resources, nor too small because it may interfere with users trying |
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104 | # to do their polydispersity calculations. A value of 1024 should be much |
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105 | # larger than necessary given that cost grows as npts^k where k is the number |
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106 | # of polydisperse parameters. |
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107 | MAX_LOOPS = 2048 |
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108 | |
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109 | |
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110 | def use_cuda(): |
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111 | # type: None -> bool |
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112 | """Returns True if CUDA is the default compute engine.""" |
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113 | sas_opencl = os.environ.get("SAS_OPENCL", "CUDA").lower() |
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114 | return HAVE_CUDA and sas_opencl.startswith("cuda") |
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115 | |
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116 | |
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117 | ENV = None |
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118 | def reset_environment(): |
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119 | """ |
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120 | Call to create a new OpenCL context, such as after a change to SAS_OPENCL. |
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121 | """ |
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122 | global ENV |
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123 | # Free any previous allocated context. |
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124 | if ENV is not None and ENV.context is not None: |
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125 | ENV.release() |
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126 | ENV = GpuEnvironment() if use_cuda() else None |
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127 | |
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128 | |
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129 | def environment(): |
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130 | # type: () -> "GpuEnvironment" |
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131 | """ |
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132 | Returns a singleton :class:`GpuEnvironment`. |
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133 | |
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134 | This provides an OpenCL context and one queue per device. |
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135 | """ |
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136 | if ENV is None: |
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137 | if not HAVE_CUDA: |
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138 | raise RuntimeError("CUDA startup failed with ***" |
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139 | + CUDA_ERROR + "***; using C compiler instead") |
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140 | reset_environment() |
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141 | if ENV is None: |
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142 | raise RuntimeError("SAS_OPENCL=None in environment") |
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143 | return ENV |
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144 | |
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145 | |
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146 | # PyTest is not freeing ENV, so make sure it gets freed. |
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147 | atexit.register(lambda: ENV.release() if ENV is not None else None) |
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148 | |
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149 | |
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150 | def has_type(dtype): |
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151 | # type: (np.dtype) -> bool |
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152 | """ |
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153 | Return true if device supports the requested precision. |
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154 | """ |
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155 | # Assume the NVIDIA card supports 32-bit and 64-bit floats. |
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156 | # TODO: Check if pycuda support F16. |
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157 | return dtype in (generate.F32, generate.F64) |
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158 | |
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159 | |
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160 | FUNCTION_PATTERN = re.compile(r"""^ |
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161 | (?P<space>\s*) # Initial space. |
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162 | (?P<qualifiers>^(?:\s*\b\w+\b\s*)+) # One or more qualifiers before function. |
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163 | (?P<function>\s*\b\w+\b\s*[(]) # Function name plus open parens. |
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164 | """, re.VERBOSE|re.MULTILINE) |
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165 | |
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166 | MARKED_PATTERN = re.compile(r""" |
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167 | \b(return|else|kernel|device|__device__)\b |
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168 | """, re.VERBOSE|re.MULTILINE) |
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169 | |
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170 | |
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171 | def _add_device_tag(match): |
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172 | # type: (None) -> str |
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173 | # Note: Should be re.Match, but that isn't a simple type. |
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174 | """ |
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175 | replace qualifiers with __device__ qualifiers if needed |
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176 | """ |
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177 | qualifiers = match.group("qualifiers") |
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178 | if MARKED_PATTERN.search(qualifiers): |
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179 | start, end = match.span() |
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180 | return match.string[start:end] |
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181 | else: |
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182 | function = match.group("function") |
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183 | space = match.group("space") |
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184 | return "".join((space, "__device__ ", qualifiers, function)) |
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185 | |
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186 | |
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187 | def mark_device_functions(source): |
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188 | # type: (str) -> str |
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189 | """ |
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190 | Mark all function declarations as __device__ functions (except kernel). |
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191 | """ |
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192 | return FUNCTION_PATTERN.sub(_add_device_tag, source) |
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193 | |
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194 | |
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195 | def show_device_functions(source): |
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196 | # type: (str) -> str |
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197 | """ |
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198 | Show all discovered function declarations, but don't change any. |
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199 | """ |
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200 | for match in FUNCTION_PATTERN.finditer(source): |
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201 | print(match.group('qualifiers').replace('\n', r'\n'), |
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202 | match.group('function'), '(') |
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203 | return source |
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204 | |
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205 | |
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206 | def compile_model(source, dtype, fast=False): |
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207 | # type: (str, np.dtype, bool) -> SourceModule |
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208 | """ |
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209 | Build a model to run on the gpu. |
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210 | |
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211 | Returns the compiled program and its type. The returned type will |
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212 | be float32 even if the desired type is float64 if any of the |
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213 | devices in the context do not support the cl_khr_fp64 extension. |
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214 | """ |
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215 | dtype = np.dtype(dtype) |
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216 | if not has_type(dtype): |
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217 | raise RuntimeError("%s not supported for devices"%dtype) |
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218 | |
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219 | source_list = [generate.convert_type(source, dtype)] |
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220 | |
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221 | source_list.insert(0, "#define USE_SINCOS\n") |
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222 | source = "\n".join(source_list) |
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223 | #source = show_device_functions(source) |
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224 | source = mark_device_functions(source) |
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225 | #with open('/tmp/kernel.cu', 'w') as fd: fd.write(source) |
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226 | #print(source) |
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227 | #options = ['--verbose', '-E'] |
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228 | options = ['--use_fast_math'] if fast else None |
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229 | program = SourceModule(source, no_extern_c=True, options=options) #, include_dirs=[...]) |
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230 | |
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231 | #print("done with "+program) |
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232 | return program |
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233 | |
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234 | |
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235 | # For now, this returns one device in the context. |
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236 | # TODO: Create a context that contains all devices on all platforms. |
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237 | class GpuEnvironment(object): |
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238 | """ |
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239 | GPU context for CUDA. |
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240 | """ |
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241 | context = None # type: cuda.Context |
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242 | def __init__(self, devnum=None): |
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243 | # type: (int) -> None |
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244 | env = os.environ.get("SAS_OPENCL", "").lower() |
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245 | if devnum is None and env.startswith("cuda:"): |
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246 | devnum = int(env[5:]) |
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247 | |
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248 | # Set the global context to the particular device number if one is |
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249 | # given, otherwise use the default context. Perhaps this will be set |
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250 | # by an environment variable within autoinit. |
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251 | if devnum is not None: |
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252 | self.context = cuda.Device(devnum).make_context() |
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253 | else: |
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254 | self.context = make_default_context() |
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255 | |
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256 | ## Byte boundary for data alignment. |
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257 | #self.data_boundary = max(d.min_data_type_align_size |
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258 | # for d in self.context.devices) |
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259 | |
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260 | # Cache for compiled programs, and for items in context. |
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261 | self.compiled = {} |
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262 | |
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263 | def release(self): |
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264 | """Free the CUDA device associated with this context.""" |
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265 | if self.context is not None: |
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266 | self.context.pop() |
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267 | self.context = None |
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268 | |
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269 | def __del__(self): |
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270 | self.release() |
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271 | |
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272 | def has_type(self, dtype): |
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273 | # type: (np.dtype) -> bool |
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274 | """ |
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275 | Return True if all devices support a given type. |
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276 | """ |
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277 | return has_type(dtype) |
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278 | |
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279 | def compile_program(self, name, source, dtype, fast, timestamp): |
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280 | # type: (str, str, np.dtype, bool, float) -> cl.Program |
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281 | """ |
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282 | Compile the program for the device in the given context. |
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283 | """ |
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284 | # Note: PyCuda (probably) caches but I'll do so as well just to |
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285 | # save some data munging time. |
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286 | tag = generate.tag_source(source) |
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287 | key = "%s-%s-%s%s"%(name, dtype, tag, ("-fast" if fast else "")) |
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288 | # Check timestamp on program. |
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289 | program, program_timestamp = self.compiled.get(key, (None, np.inf)) |
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290 | if program_timestamp < timestamp: |
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291 | del self.compiled[key] |
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292 | if key not in self.compiled: |
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293 | logging.info("building %s for CUDA", key) |
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294 | program = compile_model(str(source), dtype, fast) |
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295 | self.compiled[key] = (program, timestamp) |
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296 | return program |
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297 | |
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298 | |
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299 | class GpuModel(KernelModel): |
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300 | """ |
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301 | GPU wrapper for a single model. |
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302 | |
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303 | *source* and *model_info* are the model source and interface as returned |
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304 | from :func:`generate.make_source` and :func:`generate.make_model_info`. |
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305 | |
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306 | *dtype* is the desired model precision. Any numpy dtype for single |
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307 | or double precision floats will do, such as 'f', 'float32' or 'single' |
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308 | for single and 'd', 'float64' or 'double' for double. Double precision |
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309 | is an optional extension which may not be available on all devices. |
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310 | Half precision ('float16','half') may be available on some devices. |
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311 | Fast precision ('fast') is a loose version of single precision, indicating |
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312 | that the compiler is allowed to take shortcuts. |
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313 | """ |
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314 | info = None # type: ModelInfo |
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315 | source = "" # type: str |
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316 | dtype = None # type: np.dtype |
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317 | fast = False # type: bool |
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318 | _program = None # type: SourceModule |
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319 | _kernels = None # type: Dict[str, cuda.Function] |
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320 | |
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321 | def __init__(self, source, model_info, dtype=generate.F32, fast=False): |
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322 | # type: (Dict[str,str], ModelInfo, np.dtype, bool) -> None |
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323 | self.info = model_info |
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324 | self.source = source |
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325 | self.dtype = dtype |
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326 | self.fast = fast |
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327 | |
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328 | def __getstate__(self): |
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329 | # type: () -> Tuple[ModelInfo, str, np.dtype, bool] |
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330 | return self.info, self.source, self.dtype, self.fast |
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331 | |
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332 | def __setstate__(self, state): |
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333 | # type: (Tuple[ModelInfo, str, np.dtype, bool]) -> None |
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334 | self.info, self.source, self.dtype, self.fast = state |
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335 | self._program = self._kernels = None |
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336 | |
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337 | def make_kernel(self, q_vectors): |
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338 | # type: (List[np.ndarray]) -> "GpuKernel" |
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339 | return GpuKernel(self, q_vectors) |
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340 | |
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341 | def get_function(self, name): |
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342 | # type: (str) -> cuda.Function |
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343 | """ |
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344 | Fetch the kernel from the environment by name, compiling it if it |
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345 | does not already exist. |
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346 | """ |
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347 | if self._program is None: |
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348 | self._prepare_program() |
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349 | return self._kernels[name] |
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350 | |
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351 | def _prepare_program(self): |
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352 | # type: (str) -> None |
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353 | env = environment() |
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354 | timestamp = generate.ocl_timestamp(self.info) |
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355 | program = env.compile_program( |
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356 | self.info.name, |
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357 | self.source['opencl'], |
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358 | self.dtype, |
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359 | self.fast, |
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360 | timestamp) |
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361 | variants = ['Iq', 'Iqxy', 'Imagnetic'] |
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362 | names = [generate.kernel_name(self.info, k) for k in variants] |
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363 | functions = [program.get_function(k) for k in names] |
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364 | self._kernels = {k: v for k, v in zip(variants, functions)} |
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365 | # Keep a handle to program so GC doesn't collect. |
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366 | self._program = program |
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367 | |
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368 | |
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369 | # TODO: Check that we don't need a destructor for buffers which go out of scope. |
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370 | class GpuInput(object): |
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371 | """ |
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372 | Make q data available to the gpu. |
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373 | |
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374 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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375 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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376 | to get the best performance on OpenCL, which may involve shifting and |
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377 | stretching the array to better match the memory architecture. Additional |
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378 | points will be evaluated with *q=1e-3*. |
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379 | |
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380 | *dtype* is the data type for the q vectors. The data type should be |
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381 | set to match that of the kernel, which is an attribute of |
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382 | :class:`GpuProgram`. Note that not all kernels support double |
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383 | precision, so even if the program was created for double precision, |
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384 | the *GpuProgram.dtype* may be single precision. |
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385 | |
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386 | Call :meth:`release` when complete. Even if not called directly, the |
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387 | buffer will be released when the data object is freed. |
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388 | """ |
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389 | def __init__(self, q_vectors, dtype=generate.F32): |
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390 | # type: (List[np.ndarray], np.dtype) -> None |
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391 | # TODO: Do we ever need double precision q? |
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392 | self.nq = q_vectors[0].size |
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393 | self.dtype = np.dtype(dtype) |
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394 | self.is_2d = (len(q_vectors) == 2) |
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395 | # TODO: Stretch input based on get_warp(). |
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396 | # Not doing it now since warp depends on kernel, which is not known |
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397 | # at this point, so instead using 32, which is good on the set of |
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398 | # architectures tested so far. |
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399 | if self.is_2d: |
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400 | width = ((self.nq+15)//16)*16 |
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401 | self.q = np.empty((width, 2), dtype=dtype) |
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402 | self.q[:self.nq, 0] = q_vectors[0] |
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403 | self.q[:self.nq, 1] = q_vectors[1] |
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404 | else: |
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405 | width = ((self.nq+31)//32)*32 |
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406 | self.q = np.empty(width, dtype=dtype) |
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407 | self.q[:self.nq] = q_vectors[0] |
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408 | self.global_size = [self.q.shape[0]] |
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409 | #print("creating inputs of size", self.global_size) |
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410 | |
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411 | # Transfer input value to GPU. |
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412 | self.q_b = cuda.to_device(self.q) |
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413 | |
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414 | def release(self): |
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415 | # type: () -> None |
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416 | """ |
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417 | Free the buffer associated with the q value. |
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418 | """ |
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419 | if self.q_b is not None: |
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420 | self.q_b.free() |
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421 | self.q_b = None |
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422 | |
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423 | def __del__(self): |
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424 | # type: () -> None |
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425 | self.release() |
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426 | |
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427 | |
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428 | class GpuKernel(Kernel): |
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429 | """ |
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430 | Callable SAS kernel. |
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431 | |
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432 | *model* is the GpuModel object to call |
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433 | |
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434 | The kernel is derived from :class:`Kernel`, providing the |
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435 | :meth:`call_kernel` method to evaluate the kernel for a given set of |
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436 | parameters. Because of the need to move the q values to the GPU before |
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437 | evaluation, the kernel is instantiated for a particular set of q vectors, |
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438 | and can be called many times without transfering q each time. |
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439 | |
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440 | Call :meth:`release` when done with the kernel instance. |
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441 | """ |
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442 | #: SAS model information structure. |
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443 | info = None # type: ModelInfo |
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444 | #: Kernel precision. |
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445 | dtype = None # type: np.dtype |
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446 | #: Kernel dimensions (1d or 2d). |
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447 | dim = "" # type: str |
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448 | #: Calculation results, updated after each call to :meth:`_call_kernel`. |
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449 | result = None # type: np.ndarray |
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450 | |
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451 | def __init__(self, model, q_vectors): |
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452 | # type: (GpuModel, List[np.ndarray]) -> None |
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453 | dtype = model.dtype |
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454 | self.q_input = GpuInput(q_vectors, dtype) |
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455 | self._model = model |
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456 | |
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457 | # Attributes accessed from the outside. |
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458 | self.dim = '2d' if self.q_input.is_2d else '1d' |
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459 | self.info = model.info |
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460 | self.dtype = dtype |
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461 | |
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462 | # Converter to translate input to target type. |
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463 | self._as_dtype = np.float64 if dtype == generate.F64 else np.float32 |
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464 | |
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465 | # Holding place for the returned value. |
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466 | nout = 2 if self.info.have_Fq and self.dim == '1d' else 1 |
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467 | extra_q = 4 # Total weight, form volume, shell volume and R_eff. |
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468 | self.result = np.empty(self.q_input.nq*nout + extra_q, dtype) |
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469 | |
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470 | # Allocate result value on GPU. |
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471 | width = ((self.result.size+31)//32)*32 * self.dtype.itemsize |
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472 | self._result_b = cuda.mem_alloc(width) |
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473 | |
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474 | def _call_kernel(self, call_details, values, cutoff, magnetic, |
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475 | effective_radius_type): |
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476 | # type: (CallDetails, np.ndarray, float, bool, int) -> np.ndarray |
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477 | |
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478 | # Arrange data transfer to card. |
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479 | details_b = cuda.to_device(call_details.buffer) |
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480 | values_b = cuda.to_device(values) |
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481 | |
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482 | # Setup kernel function and arguments. |
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483 | name = 'Iq' if self.dim == '1d' else 'Imagnetic' if magnetic else 'Iqxy' |
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484 | kernel = self._model.get_function(name) |
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485 | kernel_args = [ |
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486 | np.uint32(self.q_input.nq), # Number of inputs. |
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487 | None, # Placeholder for pd_start. |
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488 | None, # Placeholder for pd_stop. |
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489 | details_b, # Problem definition. |
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490 | values_b, # Parameter values. |
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491 | self.q_input.q_b, # Q values. |
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492 | self._result_b, # Result storage. |
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493 | self._as_dtype(cutoff), # Probability cutoff. |
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494 | np.uint32(effective_radius_type), # R_eff mode. |
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495 | ] |
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496 | grid = partition(self.q_input.nq) |
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497 | |
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498 | # Call kernel and retrieve results. |
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499 | #print("Calling CUDA") |
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500 | #call_details.show(values) |
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501 | last_nap = time.clock() |
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502 | step = 100000000//self.q_input.nq + 1 |
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503 | #step = 1000000000 |
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504 | for start in range(0, call_details.num_eval, step): |
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505 | stop = min(start + step, call_details.num_eval) |
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506 | #print("queuing",start,stop) |
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507 | kernel_args[1:3] = [np.int32(start), np.int32(stop)] |
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508 | kernel(*kernel_args, **grid) |
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509 | if stop < call_details.num_eval: |
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510 | sync() |
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511 | # Allow other processes to run. |
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512 | current_time = time.clock() |
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513 | if current_time - last_nap > 0.5: |
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514 | time.sleep(0.001) |
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515 | last_nap = current_time |
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516 | sync() |
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517 | cuda.memcpy_dtoh(self.result, self._result_b) |
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518 | #print("result", self.result) |
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519 | |
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520 | details_b.free() |
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521 | values_b.free() |
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522 | |
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523 | def release(self): |
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524 | # type: () -> None |
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525 | """ |
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526 | Release resources associated with the kernel. |
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527 | """ |
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528 | self.q_input.release() |
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529 | if self._result_b is not None: |
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530 | self._result_b.free() |
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531 | self._result_b = None |
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532 | |
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533 | def __del__(self): |
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534 | # type: () -> None |
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535 | self.release() |
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536 | |
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537 | |
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538 | def sync(): |
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539 | """ |
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540 | Overview: |
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541 | Waits for operation in the current context to complete. |
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542 | |
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543 | Note: Maybe context.synchronize() is sufficient. |
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544 | """ |
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545 | # Create an event with which to synchronize. |
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546 | done = cuda.Event() |
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547 | |
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548 | # Schedule an event trigger on the GPU. |
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549 | done.record() |
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550 | |
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551 | # Make sure we don't hog resource while waiting to sync. |
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552 | while not done.query(): |
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553 | time.sleep(0.01) |
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554 | |
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555 | # Block until the GPU executes the kernel. |
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556 | done.synchronize() |
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557 | |
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558 | # Clean up the event; I don't think they can be reused. |
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559 | del done |
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560 | |
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561 | |
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562 | def partition(n): |
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563 | ''' |
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564 | Overview: |
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565 | Auto grids the thread blocks to achieve some level of calculation |
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566 | efficiency. |
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567 | ''' |
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568 | max_gx, max_gy = 65535, 65535 |
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569 | blocksize = 32 |
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570 | #max_gx, max_gy = 5, 65536 |
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571 | #blocksize = 3 |
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572 | block = (blocksize, 1, 1) |
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573 | num_blocks = int((n+blocksize-1)/blocksize) |
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574 | if num_blocks < max_gx: |
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575 | grid = (num_blocks, 1) |
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576 | else: |
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577 | gx = max_gx |
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578 | gy = (num_blocks + max_gx - 1) / max_gx |
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579 | if gy >= max_gy: |
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580 | raise ValueError("vector is too large") |
---|
581 | grid = (gx, gy) |
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582 | #print("block", block, "grid", grid) |
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583 | #print("waste", block[0]*block[1]*block[2]*grid[0]*grid[1] - n) |
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584 | return dict(block=block, grid=grid) |
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