File: kernel_codegen.py

package info (click to toggle)
theano 1.0.3+dfsg-1
  • links: PTS, VCS
  • area: main
  • in suites: bullseye, buster, sid
  • size: 30,752 kB
  • sloc: python: 141,182; ansic: 9,505; makefile: 259; sh: 214; pascal: 81
file content (391 lines) | stat: -rw-r--r-- 12,011 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
from __future__ import absolute_import, print_function, division

"""
Helper routines for generating gpu kernels for nvcc.

"""

try:
    from pygpu import gpuarray
except ImportError:
    pass


def nvcc_kernel(name, params, body):
    """
    Return the c code of a kernel function.

    Parameters
    ----------
    params
        The parameters to the function as one or more strings.
    body
        The [nested] list of statements for the body of the function.
        These will be separated by ';' characters.

    """
    paramstr = ', '.join(params)

    def flatbody():
        for b in body:
            if isinstance(b, (list, tuple)):
                for bb in b:
                    yield bb
            else:
                yield b
    bodystr = ';\n'.join(flatbody())
    return """#include "cluda.h"

    KERNEL void %(name)s (%(paramstr)s)
    {
        %(bodystr)s;
    }
    """ % locals()


def code_version(version):
    """
    Decorator to support version-based cache mechanism.

    """
    if not isinstance(version, tuple):
        raise TypeError('version must be tuple', version)

    def deco(f):
        f.code_version = version
        return f
    return deco

UNVERSIONED = ()


@code_version((2,))
def inline_reduce(N, buf, pos, count, manner_fn):
    """
    Return C++ code for a function that reduces a contiguous buffer.

    Parameters
    ----------
    N
        Length of the buffer.
    buf
        buffer pointer.
    pos
        Index of executing thread.
    count
        Number of executing threads.
    manner_fn
        A function that accepts strings of arguments a and b, and
        returns c code for their reduction.

          return "%(a)s + %(b)s"

        for a sum reduction.

    Notes
    -----
    `buf` should be in gpu shared memory, we access it many times.

    This function leaves the answer in position 0 of the buffer. The
    rest of the buffer is trashed by this function.

    """
    loop_line = manner_fn("%s[%s]" % (buf, pos), "%s[i]" % (buf))
    r_n = manner_fn("%s[%s]" % (buf, pos), "%s[%s+_n]" % (buf, pos))

    return """
    {
        // This function trashes buf[1..warpSize],
        // leaving the reduction result in buf[0].

        if (%(pos)s < warpSize) {
            for (int i = %(pos)s + warpSize; i < %(N)s; i += warpSize)
            {
                %(buf)s[%(pos)s] = %(loop_line)s;
            }
        }
        __syncthreads();
        //reduce so that %(pos)s 0 has the reduction of everything
        for (unsigned int _n = warpSize / 2; _n > 0; _n /= 2) {
          if (%(pos)s < _n && %(pos)s + _n < %(N)s)
            %(buf)s[%(pos)s] = %(r_n)s;
          __syncthreads();
        }
    }
    """ % locals()


@code_version(inline_reduce.code_version)
def inline_reduce_max(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count,
                         lambda a, b: "max(%s, %s)" % (a, b))


@code_version(inline_reduce.code_version)
def inline_reduce_sum(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count,
                         lambda a, b: "%s + %s" % (a, b))


@code_version(inline_reduce.code_version)
def inline_reduce_min(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count,
                         lambda a, b: "min(%s, %s)" % (a, b))


@code_version(inline_reduce.code_version)
def inline_reduce_prod(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count,
                         lambda a, b: "%s * %s" % (a, b))


@code_version((2,) + inline_reduce_max.code_version +
              inline_reduce_sum.code_version)
def inline_softmax(N, buf, buf2, threadPos, threadCount, dtype="float32"):
    """
    Generate code for a softmax.

    On entry, `buf` and `buf2` must contain two identical copies of
    the input to softmax.

    After the code returns `buf` contains the softmax, `buf2` contains
    un-normalized softmax.

    Parameters
    ----------
    N
        Length of the buffer.
    threadPos
        Index of executing thread.
    threadCount
        Number of executing threads.
    dtype
        Dtype of the softmax's output.

    Notes
    -----
    `buf` and `buf2` should be in gpu shared memory, we access it many
    times.

    We use __i as an int variable in a loop.

    """
    ctype = gpuarray.dtype_to_ctype(dtype)
    # get max of buf (trashing all but buf[0])
    return [inline_reduce_max(N, buf, threadPos, threadCount),
            '__syncthreads()',
            ('%s row_max = ' + buf + '[0]') % ctype,
            '__syncthreads()',
            'for(int __i=' + threadPos + '; __i<' + N +
            '; __i+=' + threadCount + '){',
            buf + '[__i] = exp(' + buf2 + '[__i] - row_max)',
            buf2 + '[__i] = ' + buf + '[__i]',
            '}',
            '__syncthreads()',
            inline_reduce_sum(N, buf, threadPos, threadCount),
            '__syncthreads()',
            ('%s row_sum = ' + buf + '[0]') % ctype,
            '__syncthreads()',
            # divide each exp() result by the sum to complete the job.
            'for(int __i=' + threadPos + '; __i<' + N +
            '; __i+=' + threadCount + '){',
            buf + '[__i] = ' + buf2 + '[__i] / row_sum',
            '}',
            '__syncthreads()',
            ]


@code_version((3,))
def inline_reduce_fixed_shared(N, buf, x, stride_x, load_x, pos, count,
                               manner_fn, manner_init,
                               b='', stride_b='', load_b='', dtype='float32'):
    """
    Return C++ code for a function that reduces a contiguous buffer.

    This function leaves the answer in position 0 of the buffer. The
    rest of the buffer is trashed by this function.

    Parameters
    ----------
    N
        Length of the buffer.
    buf
        Buffer pointer of size warpSize * sizeof(dtype).
    x
        Input data.
    stride_x
        Input data stride.
    load_x
        Wrapper to read from x.
    pos
        Index of executing thread.
    count
        Number of executing threads.
    manner_fn
        A function that accepts strings of arguments a and b, and
        returns c code for their reduction.

          return "%(a)s + %(b)s"

        for a sum reduction.
    manner_init
        A function that accepts strings of arguments a and return c
        code for its initialization.
    b
        Optional, pointer to the bias.
    stride_b
        Optional, the stride of b if b is provided.
    load_b
        Optional, wrapper to read from b if b is provided.
    dtype
        Optional, the dtype of the output.

    Notes
    -----
    `buf` should be in gpu shared memory, we access it many times.

    """
    if b:
        init = manner_init("%(load_x)s(%(x)s[%(pos)s * %(stride_x)s]) +"
                           " %(load_b)s(%(b)s[%(pos)s * %(stride_b)s])" % locals())
        loop_line = manner_fn("red",
                              manner_init("%(load_x)s(%(x)s[i * %(stride_x)s]) + "
                                          "%(load_b)s(%(b)s[i * %(stride_b)s])" %
                                          locals()))
    else:
        init = manner_init("%(load_x)s(%(x)s[%(pos)s * %(stride_x)s])" % locals())
        loop_line = manner_fn("red", manner_init("%(load_x)s(%(x)s[i * %(stride_x)s])" %
                                                 locals()))
    loop_line2 = manner_fn("%s[%s]" % (buf, pos),
                           "%s[i]" % buf)
    r_n = manner_fn("%s[%s]" % (buf, pos), "%s[%s+_n]" % (buf, pos))

    ctype = gpuarray.dtype_to_ctype(dtype)
    return """
    {
        // This function trashes buf[1..n_threads],
        // leaving the reduction result in buf[0].
        %(ctype)s red = %(init)s;
        #pragma unroll 16
        for (int i = %(pos)s + %(count)s; i<%(N)s; i += %(count)s) {
          red = %(loop_line)s;
        }
        buf[%(pos)s] = red;
        __syncthreads();
        if (%(pos)s < warpSize) {
            for (int i = %(pos)s + warpSize; i < %(count)s; i += warpSize) {
                %(buf)s[%(pos)s] = %(loop_line2)s;
            }
        }
        __syncthreads();
        //reduce so that %(pos)s 0 has the reduction of everything
        for (unsigned int _n = warpSize / 2; _n > 0; _n /= 2) {
          if (%(pos)s < _n && %(pos)s + _n < %(N)s)
            %(buf)s[%(pos)s] = %(r_n)s;
          __syncthreads();
        }
    }
    """ % locals()


@code_version(inline_reduce_fixed_shared.code_version)
def inline_reduce_fixed_shared_max(N, buf, x, stride_x, load_x, pos, count,
                                   b='', stride_b='', load_b='',
                                   dtype='float32'):
    return inline_reduce_fixed_shared(N, buf, x, stride_x, load_x, pos, count,
                                      lambda a, b: "max(%s, %s)" % (a, b),
                                      lambda a: a,
                                      b, stride_b, load_b, dtype)


@code_version((2,) + inline_reduce_max.code_version +
              inline_reduce_sum.code_version)
def inline_softmax_fixed_shared(N, buf, x, stride_x, load_x,
                                sm, sm_stride, write_sm,
                                threadPos, threadCount,
                                b='', stride_b='', load_b='',
                                dtype="float32"):
    """
    Generate code to perform softmax with a fixed amount of shared
    memory.

    On entry, `buf` is assumed to be empty.

    On exit, `buf[0]` contains the softmax, `buf2` contains
    un-normalized softmax.

    Parameters
    ----------
    N
        Length of the buffer, atleast waprSize(32).
    buf
        A shared memory buffer of size warpSize * sizeof(dtype).
    x
        A ptr to the gpu memory where the row is stored.
    stride_x
        The stride between each element in x.
    load_x
        Wrapper to read from x.
    sm
        A ptr to the gpu memory to store the result.
    sm_stride
        The stride between each sm element.
    write_sm
        Wrapper before writing to sm.
    threadPos
        Index of executing thread.
    threadCount
        Number of executing threads.
    b
        Optional, pointer to the bias.
    stride_b
        Optional, the stride of b if b is provided.
    load_b
        Optional, wrapper to read from b if b is provided.
    dtype
        Optional, the dtype of the softmax's output if not float32.

    Notes
    -----
    `buf` should be in gpu shared memory, we access it many times.

    We use tx as an int variable in a loop.

    """
    ctype = gpuarray.dtype_to_ctype(dtype)
    ret = [
        # get max of buf (trashing all but buf[0])
        inline_reduce_fixed_shared_max(N, buf, x, stride_x, load_x,
                                       threadPos, threadCount,
                                       b, stride_b, load_b,
                                       dtype),
        '__syncthreads()',
        ('%s row_max = ' + buf + '[0]') % ctype,
        '__syncthreads()',
        inline_reduce_fixed_shared(N, buf, x, stride_x, load_x,
                                   threadPos, threadCount,
                                   lambda a, b: "%s + %s" % (a, b),
                                   lambda a: "exp(%s - row_max)" % a,
                                   b, stride_b, load_b, dtype),
        '__syncthreads()',
        ('%s row_sum = ' + buf + '[0]') % ctype,
        '__syncthreads()',
        "for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
        ]
    # This set all value correctly
    if b:
        ret += [
            "%(sm)s[tx * %(sm_stride)s] = "
            "  %(write_sm)s(exp(%(load_x)s(%(x)s[tx * %(stride_x)s]) +"
            "            %(load_b)s(%(b)s[tx * %(stride_b)s]) - row_max)"
            " / row_sum)" % locals()]
    else:
        ret += [
            "%(sm)s[tx * %(sm_stride)s] = "
            "%(write_sm)s(exp(%(load_x)s(%(x)s[tx * %(stride_x)s]) - row_max)"
            " / row_sum)" % locals()]
    ret += [
        "}",
        '__syncthreads()',
    ]
    return ret