source: sasmodels/sasmodels/kernelcl.py @ b0de252

core_shell_microgelsmagnetic_modelticket-1257-vesicle-productticket_1156ticket_1265_superballticket_822_more_unit_tests
Last change on this file since b0de252 was b0de252, checked in by pkienzle, 5 years ago

improve control over cuda context

  • Property mode set to 100644
File size: 22.9 KB
Line 
1"""
2GPU driver for C kernels
3
4TODO: docs are out of date
5
6There should be a single GPU environment running on the system.  This
7environment is constructed on the first call to :func:`env`, and the
8same environment is returned on each call.
9
10After retrieving the environment, the next step is to create the kernel.
11This is done with a call to :meth:`GpuEnvironment.make_kernel`, which
12returns the type of data used by the kernel.
13
14Next a :class:`GpuData` object should be created with the correct kind
15of data.  This data object can be used by multiple kernels, for example,
16if the target model is a weighted sum of multiple kernels.  The data
17should include any extra evaluation points required to compute the proper
18data smearing.  This need not match the square grid for 2D data if there
19is an index saying which q points are active.
20
21Together the GpuData, the program, and a device form a :class:`GpuKernel`.
22This kernel is used during fitting, receiving new sets of parameters and
23evaluating them.  The output value is stored in an output buffer on the
24devices, where it can be combined with other structure factors and form
25factors and have instrumental resolution effects applied.
26
27In order to use OpenCL for your models, you will need OpenCL drivers for
28your machine.  These should be available from your graphics card vendor.
29Intel provides OpenCL drivers for CPUs as well as their integrated HD
30graphics chipsets.  AMD also provides drivers for Intel CPUs, but as of
31this writing the performance is lacking compared to the Intel drivers.
32NVidia combines drivers for CUDA and OpenCL in one package.  The result
33is a bit messy if you have multiple drivers installed.  You can see which
34drivers are available by starting python and running:
35
36    import pyopencl as cl
37    cl.create_some_context(interactive=True)
38
39Once you have done that, it will show the available drivers which you
40can select.  It will then tell you that you can use these drivers
41automatically by setting the SAS_OPENCL environment variable, which is
42PYOPENCL_CTX equivalent but not conflicting with other pyopnecl programs.
43
44Some graphics cards have multiple devices on the same card.  You cannot
45yet use both of them concurrently to evaluate models, but you can run
46the program twice using a different device for each session.
47
48OpenCL kernels are compiled when needed by the device driver.  Some
49drivers produce compiler output even when there is no error.  You
50can see the output by setting PYOPENCL_COMPILER_OUTPUT=1.  It should be
51harmless, albeit annoying.
52"""
53from __future__ import print_function
54
55import os
56import warnings
57import logging
58import time
59
60import numpy as np  # type: ignore
61
62
63# Attempt to setup opencl. This may fail if the pyopencl package is not
64# installed or if it is installed but there are no devices available.
65try:
66    import pyopencl as cl  # type: ignore
67    from pyopencl import mem_flags as mf
68    from pyopencl.characterize import get_fast_inaccurate_build_options
69    # Ask OpenCL for the default context so that we know that one exists
70    cl.create_some_context(interactive=False)
71    HAVE_OPENCL = True
72    OPENCL_ERROR = ""
73except Exception as exc:
74    HAVE_OPENCL = False
75    OPENCL_ERROR = str(exc)
76
77from . import generate
78from .kernel import KernelModel, Kernel
79
80# pylint: disable=unused-import
81try:
82    from typing import Tuple, Callable, Any
83    from .modelinfo import ModelInfo
84    from .details import CallDetails
85except ImportError:
86    pass
87# pylint: enable=unused-import
88
89# CRUFT: pyopencl < 2017.1  (as of June 2016 needs quotes around include path)
90def quote_path(v):
91    """
92    Quote the path if it is not already quoted.
93
94    If v starts with '-', then assume that it is a -I option or similar
95    and do not quote it.  This is fragile:  -Ipath with space needs to
96    be quoted.
97    """
98    return '"'+v+'"' if v and ' ' in v and not v[0] in "\"'-" else v
99
100def fix_pyopencl_include():
101    """
102    Monkey patch pyopencl to allow spaces in include file path.
103    """
104    import pyopencl as cl
105    if hasattr(cl, '_DEFAULT_INCLUDE_OPTIONS'):
106        cl._DEFAULT_INCLUDE_OPTIONS = [quote_path(v) for v in cl._DEFAULT_INCLUDE_OPTIONS]
107
108if HAVE_OPENCL:
109    fix_pyopencl_include()
110
111# The max loops number is limited by the amount of local memory available
112# on the device.  You don't want to make this value too big because it will
113# waste resources, nor too small because it may interfere with users trying
114# to do their polydispersity calculations.  A value of 1024 should be much
115# larger than necessary given that cost grows as npts^k where k is the number
116# of polydisperse parameters.
117MAX_LOOPS = 2048
118
119
120# Pragmas for enable OpenCL features.  Be sure to protect them so that they
121# still compile even if OpenCL is not present.
122_F16_PRAGMA = """\
123#if defined(__OPENCL_VERSION__) // && !defined(cl_khr_fp16)
124#  pragma OPENCL EXTENSION cl_khr_fp16: enable
125#endif
126"""
127
128_F64_PRAGMA = """\
129#if defined(__OPENCL_VERSION__) // && !defined(cl_khr_fp64)
130#  pragma OPENCL EXTENSION cl_khr_fp64: enable
131#endif
132"""
133
134def use_opencl():
135    env = os.environ.get("SAS_OPENCL", "").lower()
136    return HAVE_OPENCL and env != "none" and not env.startswith("cuda")
137
138ENV = None
139def reset_environment():
140    """
141    Call to create a new OpenCL context, such as after a change to SAS_OPENCL.
142    """
143    global ENV
144    ENV = GpuEnvironment() if use_opencl() else None
145
146def environment():
147    # type: () -> "GpuEnvironment"
148    """
149    Returns a singleton :class:`GpuEnvironment`.
150
151    This provides an OpenCL context and one queue per device.
152    """
153    if ENV is None:
154        if not HAVE_OPENCL:
155            raise RuntimeError("OpenCL startup failed with ***"
156                               + OPENCL_ERROR + "***; using C compiler instead")
157        reset_environment()
158        if ENV is None:
159            raise RuntimeError("SAS_OPENCL=None in environment")
160    return ENV
161
162def has_type(device, dtype):
163    # type: (cl.Device, np.dtype) -> bool
164    """
165    Return true if device supports the requested precision.
166    """
167    if dtype == generate.F32:
168        return True
169    elif dtype == generate.F64:
170        return "cl_khr_fp64" in device.extensions
171    elif dtype == generate.F16:
172        return "cl_khr_fp16" in device.extensions
173    else:
174        return False
175
176def get_warp(kernel, queue):
177    # type: (cl.Kernel, cl.CommandQueue) -> int
178    """
179    Return the size of an execution batch for *kernel* running on *queue*.
180    """
181    return kernel.get_work_group_info(
182        cl.kernel_work_group_info.PREFERRED_WORK_GROUP_SIZE_MULTIPLE,
183        queue.device)
184
185def compile_model(context, source, dtype, fast=False):
186    # type: (cl.Context, str, np.dtype, bool) -> cl.Program
187    """
188    Build a model to run on the gpu.
189
190    Returns the compiled program and its type.
191
192    Raises an error if the desired precision is not available.
193    """
194    dtype = np.dtype(dtype)
195    if not all(has_type(d, dtype) for d in context.devices):
196        raise RuntimeError("%s not supported for devices"%dtype)
197
198    source_list = [generate.convert_type(source, dtype)]
199
200    if dtype == generate.F16:
201        source_list.insert(0, _F16_PRAGMA)
202    elif dtype == generate.F64:
203        source_list.insert(0, _F64_PRAGMA)
204
205    # Note: USE_SINCOS makes the intel cpu slower under opencl
206    if context.devices[0].type == cl.device_type.GPU:
207        source_list.insert(0, "#define USE_SINCOS\n")
208    options = (get_fast_inaccurate_build_options(context.devices[0])
209               if fast else [])
210    source = "\n".join(source_list)
211    program = cl.Program(context, source).build(options=options)
212    #print("done with "+program)
213    return program
214
215
216# for now, this returns one device in the context
217# TODO: create a context that contains all devices on all platforms
218class GpuEnvironment(object):
219    """
220    GPU context, with possibly many devices, and one queue per device.
221    """
222    def __init__(self):
223        # type: () -> None
224        # find gpu context
225        #self.context = cl.create_some_context()
226
227        self.context = None
228        if 'SAS_OPENCL' in os.environ:
229            #Setting PYOPENCL_CTX as a SAS_OPENCL to create cl context
230            os.environ["PYOPENCL_CTX"] = os.environ["SAS_OPENCL"]
231        if 'PYOPENCL_CTX' in os.environ:
232            self._create_some_context()
233
234        if not self.context:
235            self.context = _get_default_context()
236
237        # Byte boundary for data alignment
238        #self.data_boundary = max(d.min_data_type_align_size
239        #                         for d in self.context.devices)
240        self.queues = [cl.CommandQueue(context, context.devices[0])
241                       for context in self.context]
242        self.compiled = {}
243
244    def has_type(self, dtype):
245        # type: (np.dtype) -> bool
246        """
247        Return True if all devices support a given type.
248        """
249        return any(has_type(d, dtype)
250                   for context in self.context
251                   for d in context.devices)
252
253    def get_queue(self, dtype):
254        # type: (np.dtype) -> cl.CommandQueue
255        """
256        Return a command queue for the kernels of type dtype.
257        """
258        for context, queue in zip(self.context, self.queues):
259            if all(has_type(d, dtype) for d in context.devices):
260                return queue
261
262    def get_context(self, dtype):
263        # type: (np.dtype) -> cl.Context
264        """
265        Return a OpenCL context for the kernels of type dtype.
266        """
267        for context in self.context:
268            if all(has_type(d, dtype) for d in context.devices):
269                return context
270
271    def _create_some_context(self):
272        # type: () -> cl.Context
273        """
274        Protected call to cl.create_some_context without interactivity.  Use
275        this if SAS_OPENCL is set in the environment.  Sets the *context*
276        attribute.
277        """
278        try:
279            self.context = [cl.create_some_context(interactive=False)]
280        except Exception as exc:
281            warnings.warn(str(exc))
282            warnings.warn("pyopencl.create_some_context() failed")
283            warnings.warn("the environment variable 'SAS_OPENCL' might not be set correctly")
284
285    def compile_program(self, name, source, dtype, fast, timestamp):
286        # type: (str, str, np.dtype, bool, float) -> cl.Program
287        """
288        Compile the program for the device in the given context.
289        """
290        # Note: PyOpenCL caches based on md5 hash of source, options and device
291        # so we don't really need to cache things for ourselves.  I'll do so
292        # anyway just to save some data munging time.
293        tag = generate.tag_source(source)
294        key = "%s-%s-%s%s"%(name, dtype, tag, ("-fast" if fast else ""))
295        # Check timestamp on program
296        program, program_timestamp = self.compiled.get(key, (None, np.inf))
297        if program_timestamp < timestamp:
298            del self.compiled[key]
299        if key not in self.compiled:
300            context = self.get_context(dtype)
301            logging.info("building %s for OpenCL %s", key,
302                         context.devices[0].name.strip())
303            program = compile_model(self.get_context(dtype),
304                                    str(source), dtype, fast)
305            self.compiled[key] = (program, timestamp)
306        return program
307
308def _get_default_context():
309    # type: () -> List[cl.Context]
310    """
311    Get an OpenCL context, preferring GPU over CPU, and preferring Intel
312    drivers over AMD drivers.
313    """
314    # Note: on mobile devices there is automatic clock scaling if either the
315    # CPU or the GPU is underutilized; probably doesn't affect us, but we if
316    # it did, it would mean that putting a busy loop on the CPU while the GPU
317    # is running may increase throughput.
318    #
319    # Macbook pro, base install:
320    #     {'Apple': [Intel CPU, NVIDIA GPU]}
321    # Macbook pro, base install:
322    #     {'Apple': [Intel CPU, Intel GPU]}
323    # 2 x nvidia 295 with Intel and NVIDIA opencl drivers installed
324    #     {'Intel': [CPU], 'NVIDIA': [GPU, GPU, GPU, GPU]}
325    gpu, cpu = None, None
326    for platform in cl.get_platforms():
327        # AMD provides a much weaker CPU driver than Intel/Apple, so avoid it.
328        # If someone has bothered to install the AMD/NVIDIA drivers, prefer
329        # them over the integrated graphics driver that may have been supplied
330        # with the CPU chipset.
331        preferred_cpu = (platform.vendor.startswith('Intel')
332                         or platform.vendor.startswith('Apple'))
333        preferred_gpu = (platform.vendor.startswith('Advanced')
334                         or platform.vendor.startswith('NVIDIA'))
335        for device in platform.get_devices():
336            if device.type == cl.device_type.GPU:
337                # If the existing type is not GPU then it will be CUSTOM
338                # or ACCELERATOR so don't override it.
339                if gpu is None or (preferred_gpu and gpu.type == cl.device_type.GPU):
340                    gpu = device
341            elif device.type == cl.device_type.CPU:
342                if cpu is None or preferred_cpu:
343                    cpu = device
344            else:
345                # System has cl.device_type.ACCELERATOR or cl.device_type.CUSTOM
346                # Intel Phi for example registers as an accelerator
347                # Since the user installed a custom device on their system
348                # and went through the pain of sorting out OpenCL drivers for
349                # it, lets assume they really do want to use it as their
350                # primary compute device.
351                gpu = device
352
353    # order the devices by gpu then by cpu; when searching for an available
354    # device by data type they will be checked in this order, which means
355    # that if the gpu supports double then the cpu will never be used (though
356    # we may make it possible to explicitly request the cpu at some point).
357    devices = []
358    if gpu is not None:
359        devices.append(gpu)
360    if cpu is not None:
361        devices.append(cpu)
362    return [cl.Context([d]) for d in devices]
363
364
365class GpuModel(KernelModel):
366    """
367    GPU wrapper for a single model.
368
369    *source* and *model_info* are the model source and interface as returned
370    from :func:`generate.make_source` and :func:`generate.make_model_info`.
371
372    *dtype* is the desired model precision.  Any numpy dtype for single
373    or double precision floats will do, such as 'f', 'float32' or 'single'
374    for single and 'd', 'float64' or 'double' for double.  Double precision
375    is an optional extension which may not be available on all devices.
376    Half precision ('float16','half') may be available on some devices.
377    Fast precision ('fast') is a loose version of single precision, indicating
378    that the compiler is allowed to take shortcuts.
379    """
380    def __init__(self, source, model_info, dtype=generate.F32, fast=False):
381        # type: (Dict[str,str], ModelInfo, np.dtype, bool) -> None
382        self.info = model_info
383        self.source = source
384        self.dtype = dtype
385        self.fast = fast
386        self.program = None # delay program creation
387        self._kernels = None
388
389    def __getstate__(self):
390        # type: () -> Tuple[ModelInfo, str, np.dtype, bool]
391        return self.info, self.source, self.dtype, self.fast
392
393    def __setstate__(self, state):
394        # type: (Tuple[ModelInfo, str, np.dtype, bool]) -> None
395        self.info, self.source, self.dtype, self.fast = state
396        self.program = None
397
398    def make_kernel(self, q_vectors):
399        # type: (List[np.ndarray]) -> "GpuKernel"
400        if self.program is None:
401            compile_program = environment().compile_program
402            timestamp = generate.ocl_timestamp(self.info)
403            self.program = compile_program(
404                self.info.name,
405                self.source['opencl'],
406                self.dtype,
407                self.fast,
408                timestamp)
409            variants = ['Iq', 'Iqxy', 'Imagnetic']
410            names = [generate.kernel_name(self.info, k) for k in variants]
411            kernels = [getattr(self.program, k) for k in names]
412            self._kernels = dict((k, v) for k, v in zip(variants, kernels))
413        is_2d = len(q_vectors) == 2
414        if is_2d:
415            kernel = [self._kernels['Iqxy'], self._kernels['Imagnetic']]
416        else:
417            kernel = [self._kernels['Iq']]*2
418        return GpuKernel(kernel, self.dtype, self.info, q_vectors)
419
420    def release(self):
421        # type: () -> None
422        """
423        Free the resources associated with the model.
424        """
425        if self.program is not None:
426            self.program = None
427
428    def __del__(self):
429        # type: () -> None
430        self.release()
431
432# TODO: check that we don't need a destructor for buffers which go out of scope
433class GpuInput(object):
434    """
435    Make q data available to the gpu.
436
437    *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data,
438    and *[qx, qy]* for 2-D data.  Internally, the vectors will be reallocated
439    to get the best performance on OpenCL, which may involve shifting and
440    stretching the array to better match the memory architecture.  Additional
441    points will be evaluated with *q=1e-3*.
442
443    *dtype* is the data type for the q vectors. The data type should be
444    set to match that of the kernel, which is an attribute of
445    :class:`GpuProgram`.  Note that not all kernels support double
446    precision, so even if the program was created for double precision,
447    the *GpuProgram.dtype* may be single precision.
448
449    Call :meth:`release` when complete.  Even if not called directly, the
450    buffer will be released when the data object is freed.
451    """
452    def __init__(self, q_vectors, dtype=generate.F32):
453        # type: (List[np.ndarray], np.dtype) -> None
454        # TODO: do we ever need double precision q?
455        env = environment()
456        self.nq = q_vectors[0].size
457        self.dtype = np.dtype(dtype)
458        self.is_2d = (len(q_vectors) == 2)
459        # TODO: stretch input based on get_warp()
460        # not doing it now since warp depends on kernel, which is not known
461        # at this point, so instead using 32, which is good on the set of
462        # architectures tested so far.
463        if self.is_2d:
464            # Note: 16 rather than 15 because result is 1 longer than input.
465            width = ((self.nq+16)//16)*16
466            self.q = np.empty((width, 2), dtype=dtype)
467            self.q[:self.nq, 0] = q_vectors[0]
468            self.q[:self.nq, 1] = q_vectors[1]
469        else:
470            # Note: 32 rather than 31 because result is 1 longer than input.
471            width = ((self.nq+32)//32)*32
472            self.q = np.empty(width, dtype=dtype)
473            self.q[:self.nq] = q_vectors[0]
474        self.global_size = [self.q.shape[0]]
475        context = env.get_context(self.dtype)
476        #print("creating inputs of size", self.global_size)
477        self.q_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR,
478                             hostbuf=self.q)
479
480    def release(self):
481        # type: () -> None
482        """
483        Free the memory.
484        """
485        if self.q_b is not None:
486            self.q_b.release()
487            self.q_b = None
488
489    def __del__(self):
490        # type: () -> None
491        self.release()
492
493class GpuKernel(Kernel):
494    """
495    Callable SAS kernel.
496
497    *kernel* is the GpuKernel object to call
498
499    *model_info* is the module information
500
501    *q_vectors* is the q vectors at which the kernel should be evaluated
502
503    *dtype* is the kernel precision
504
505    The resulting call method takes the *pars*, a list of values for
506    the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight)
507    vectors for the polydisperse parameters.  *cutoff* determines the
508    integration limits: any points with combined weight less than *cutoff*
509    will not be calculated.
510
511    Call :meth:`release` when done with the kernel instance.
512    """
513    def __init__(self, kernel, dtype, model_info, q_vectors):
514        # type: (cl.Kernel, np.dtype, ModelInfo, List[np.ndarray]) -> None
515        q_input = GpuInput(q_vectors, dtype)
516        self.kernel = kernel
517        self.info = model_info
518        self.dtype = dtype
519        self.dim = '2d' if q_input.is_2d else '1d'
520        # plus three for the normalization values
521        self.result = np.empty(q_input.nq+1, dtype)
522
523        # Inputs and outputs for each kernel call
524        # Note: res may be shorter than res_b if global_size != nq
525        env = environment()
526        self.queue = env.get_queue(dtype)
527
528        self.result_b = cl.Buffer(self.queue.context, mf.READ_WRITE,
529                                  q_input.global_size[0] * dtype.itemsize)
530        self.q_input = q_input # allocated by GpuInput above
531
532        self._need_release = [self.result_b, self.q_input]
533        self.real = (np.float32 if dtype == generate.F32
534                     else np.float64 if dtype == generate.F64
535                     else np.float16 if dtype == generate.F16
536                     else np.float32)  # will never get here, so use np.float32
537
538    def __call__(self, call_details, values, cutoff, magnetic):
539        # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray
540        context = self.queue.context
541        # Arrange data transfer to card
542        details_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR,
543                              hostbuf=call_details.buffer)
544        values_b = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR,
545                             hostbuf=values)
546
547        kernel = self.kernel[1 if magnetic else 0]
548        args = [
549            np.uint32(self.q_input.nq), None, None,
550            details_b, values_b, self.q_input.q_b, self.result_b,
551            self.real(cutoff),
552        ]
553        #print("Calling OpenCL")
554        #call_details.show(values)
555        # Call kernel and retrieve results
556        wait_for = None
557        last_nap = time.clock()
558        step = 1000000//self.q_input.nq + 1
559        for start in range(0, call_details.num_eval, step):
560            stop = min(start + step, call_details.num_eval)
561            #print("queuing",start,stop)
562            args[1:3] = [np.int32(start), np.int32(stop)]
563            wait_for = [kernel(self.queue, self.q_input.global_size, None,
564                               *args, wait_for=wait_for)]
565            if stop < call_details.num_eval:
566                # Allow other processes to run
567                wait_for[0].wait()
568                current_time = time.clock()
569                if current_time - last_nap > 0.5:
570                    time.sleep(0.05)
571                    last_nap = current_time
572        cl.enqueue_copy(self.queue, self.result, self.result_b)
573        #print("result", self.result)
574
575        # Free buffers
576        for v in (details_b, values_b):
577            if v is not None:
578                v.release()
579
580        pd_norm = self.result[self.q_input.nq]
581        scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
582        background = values[1]
583        #print("scale",scale,values[0],self.result[self.q_input.nq],background)
584        return scale*self.result[:self.q_input.nq] + background
585        # return self.result[:self.q_input.nq]
586
587    def release(self):
588        # type: () -> None
589        """
590        Release resources associated with the kernel.
591        """
592        for v in self._need_release:
593            v.release()
594        self._need_release = []
595
596    def __del__(self):
597        # type: () -> None
598        self.release()
Note: See TracBrowser for help on using the repository browser.