onnx.cpp 40.9 KB
Newer Older
Paul's avatar
Paul committed
1
2
3
4
5
6
7
8
#include <google/protobuf/text_format.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <onnx.pb.h>
#include <iostream>
#include <fstream>
#include <unordered_map>
#include <functional>
#include <array>
Paul's avatar
Paul committed
9
#include <utility>
10
#include <vector>
Paul's avatar
Paul committed
11

Paul's avatar
Paul committed
12
13
14
15
16
17
#include <migraphx/fallthrough.hpp>
#include <migraphx/program.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/config.hpp>
18
#include <migraphx/onnx.hpp>
Paul's avatar
Paul committed
19
20

namespace migraphx {
Paul's avatar
Paul committed
21
inline namespace MIGRAPHX_INLINE_NS {
Paul's avatar
Paul committed
22
23
24
25
26

struct onnx_parser
{
    using attribute_map = std::unordered_map<std::string, onnx::AttributeProto>;
    using node_map      = std::unordered_map<std::string, onnx::NodeProto>;
Paul's avatar
Paul committed
27
28
    using op_func =
        std::function<std::vector<instruction_ref>(attribute_map, std::vector<instruction_ref>)>;
Paul's avatar
Paul committed
29
30
    node_map nodes;
    std::unordered_map<std::string, instruction_ref> instructions;
Scott Thornton's avatar
Scott Thornton committed
31
    program prog    = program();
32
    bool is_pytorch = false;
Paul's avatar
Paul committed
33
34

    std::unordered_map<std::string, op_func> ops;
35
    std::unordered_map<std::string, operation> map_actv_funcs;
Paul's avatar
Paul committed
36
37
38

    onnx_parser()
    {
Shucai Xiao's avatar
Shucai Xiao committed
39
        add_generic_op("MatMul", op::dot{});
Khalique's avatar
Khalique committed
40
        add_generic_op("Relu", op::relu{});
Khalique's avatar
Khalique committed
41
42
        add_generic_op("Sigmoid", op::sigmoid{});
        add_generic_op("Abs", op::abs{});
Shucai Xiao's avatar
Shucai Xiao committed
43
44
        add_generic_op("Exp", op::exp{});
        add_generic_op("Log", op::log{});
Khalique's avatar
Khalique committed
45
46
        // disable dropout for inference
        add_generic_op("Dropout", op::identity{});
Khalique's avatar
Khalique committed
47
        add_generic_op("Identity", op::identity{});
Shucai Xiao's avatar
Shucai Xiao committed
48
49
50
        add_generic_op("Sin", op::sin{});
        add_generic_op("Cos", op::cos{});
        add_generic_op("Tan", op::tan{});
51
52
        add_generic_op("Sinh", op::sinh{});
        add_generic_op("Cosh", op::cosh{});
53
        add_generic_op("Tanh", op::tanh{});
54
55
56
        add_generic_op("Asin", op::asin{});
        add_generic_op("Acos", op::acos{});
        add_generic_op("Atan", op::atan{});
Paul's avatar
Paul committed
57

Khalique's avatar
Khalique committed
58
59
60
61
62
        add_binary_op("Add", op::add{});
        add_binary_op("Div", op::div{});
        add_binary_op("Mul", op::mul{});
        add_binary_op("Sub", op::sub{});

Khalique's avatar
Khalique committed
63
64
65
        add_variadic_op("Sum", op::add{});
        add_variadic_op("Max", op::max{});
        add_variadic_op("Min", op::min{});
Paul's avatar
Paul committed
66

Khalique's avatar
Khalique committed
67
        add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
68
        add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu);
Khalique's avatar
Khalique committed
69
        add_mem_op("Elu", &onnx_parser::parse_elu);
Paul's avatar
Paul committed
70
71
        add_mem_op("Constant", &onnx_parser::parse_constant);
        add_mem_op("Conv", &onnx_parser::parse_conv);
Paul's avatar
Paul committed
72
73
        add_mem_op("MaxPool", &onnx_parser::parse_pooling);
        add_mem_op("AveragePool", &onnx_parser::parse_pooling);
74
75
        add_mem_op("GlobalMaxPool", &onnx_parser::parse_pooling);
        add_mem_op("GlobalAveragePool", &onnx_parser::parse_pooling);
Paul's avatar
Paul committed
76
        add_mem_op("Reshape", &onnx_parser::parse_reshape);
Paul's avatar
Paul committed
77
78
        add_mem_op("Flatten", &onnx_parser::parse_flatten);
        add_mem_op("Gemm", &onnx_parser::parse_gemm);
79
        add_mem_op("BatchNormalization", &onnx_parser::parse_batchnorm);
Paul's avatar
Paul committed
80
        add_mem_op("Softmax", &onnx_parser::parse_softmax);
81
82
83
        add_mem_op("Squeeze", &onnx_parser::parse_squeeze);
        add_mem_op("Unsqueeze", &onnx_parser::parse_unsqueeze);
        add_mem_op("Slice", &onnx_parser::parse_slice);
Scott Thornton's avatar
Scott Thornton committed
84
        add_mem_op("Concat", &onnx_parser::parse_concat);
85
86
87
        add_mem_op("Gather", &onnx_parser::parse_gather);
        add_mem_op("Shape", &onnx_parser::parse_shape);
        add_mem_op("ConstantFill", &onnx_parser::parse_constant_fill);
Khalique's avatar
Khalique committed
88
        add_mem_op("Transpose", &onnx_parser::parse_transpose);
Shucai Xiao's avatar
Shucai Xiao committed
89
        add_mem_op("RNN", &onnx_parser::parse_rnn);
Khalique's avatar
Khalique committed
90
        add_mem_op("Pad", &onnx_parser::parse_pad);
91
92
93
94
95
96
97

        // init the activation function map
        init_actv_func();
    }

    void init_actv_func()
    {
98
99
100
101
102
        map_actv_funcs.insert(std::make_pair("tanh", op::tanh{}));
        map_actv_funcs.insert(std::make_pair("relu", op::relu{}));
        map_actv_funcs.insert(std::make_pair("sigmoid", op::sigmoid{}));
        map_actv_funcs.insert(std::make_pair("leakyrelu", op::leaky_relu{}));
        map_actv_funcs.insert(std::make_pair("elu", op::elu{}));
Paul's avatar
Paul committed
103
104
105
106
    }

    template <class F>
    void add_op(std::string name, F f)
Paul's avatar
Paul committed
107
108
109
110
111
112
113
114
115
    {
        ops.emplace(name, [=](auto&&... xs) {
            return std::vector<instruction_ref>{f(std::forward<decltype(xs)>(xs)...)};
        });
    }

    // Multi output op
    template <class F>
    void add_multi_op(std::string name, F f)
Paul's avatar
Paul committed
116
117
118
119
120
121
122
    {
        ops.emplace(name, f);
    }

    template <class F>
    void add_mem_op(std::string name, F f)
    {
Paul's avatar
Paul committed
123
        add_op(name, [=](auto&&... xs) {
Paul's avatar
Paul committed
124
125
126
            return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...);
        });
    }
Khalique's avatar
Khalique committed
127

128
    template <class T>
Khalique's avatar
Khalique committed
129
    void add_binary_op(std::string name, T x)
130
    {
Paul's avatar
Paul committed
131
        add_op(name, [this, x](attribute_map attributes, std::vector<instruction_ref> args) {
Scott Thornton's avatar
Scott Thornton committed
132
            if(args.size() != 2)
Paul's avatar
Paul committed
133
                MIGRAPHX_THROW("binary operators should have 2 operands");
134
            if(contains(attributes, "broadcast") and contains(attributes, "axis"))
135
136
137
138
            {
                uint64_t broadcasted = parse_value(attributes.at("broadcast")).at<uint64_t>();
                if(broadcasted != 0)
                {
139
                    uint64_t axis = parse_value(attributes.at("axis")).at<uint64_t>();
140
141
142
143
                    auto l =
                        prog.add_instruction(op::broadcast{axis, args[0]->get_shape()}, args[1]);
                    return prog.add_instruction(x, args[0], l);
                }
144
                return prog.add_instruction(x, args);
145
            }
Paul's avatar
Paul committed
146
            else
147
            {
Khalique's avatar
Khalique committed
148
                return add_broadcastable_binary_op(args[0], args[1], x);
149
150
151
152
            }
        });
    }

Khalique's avatar
Khalique committed
153
154
155
156
157
    template <class T>
    instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
    {
        if(arg0->get_shape() != arg1->get_shape())
        {
Khalique's avatar
Khalique committed
158
159
160
161
162
163
164
165
166
167
168
169
170
            // Example:
            // s0 = (3,2,4,5) and s1 = (2,1,1)
            //
            // In this case we need to broadcast (:,1,1) portion of
            // s1 plus broadcast the 1st dimension of s1
            // giving output_lens = (3,2,4,5)
            //
            // Another example:
            // s0 = (3,2,1,5) and s1 = (2,7,5)
            // In this case we need to broadcast the (:,:,1:,:) axis
            // of s0 plus the 1st dimension of s1 giving
            // output_lens = (3,2,7,5)
            //
Khalique's avatar
Khalique committed
171
172
173
174
175
176
177
178
            // Get lengths for both arguments
            const std::vector<std::size_t>* s0 = &arg0->get_shape().lens();
            const std::vector<std::size_t>* s1 = &arg1->get_shape().lens();

            // Make sure s0 is the smaller size
            if(s0->size() > s1->size())
                std::swap(s0, s1);

Khalique's avatar
Khalique committed
179
            std::vector<std::size_t> output_lens(*s1);
Khalique's avatar
Khalique committed
180
181
            auto offset = s1->size() - s0->size();
            std::transform(s0->begin(),
Khalique's avatar
Khalique committed
182
183
184
185
                           s0->end(),
                           s1->begin() + offset,
                           output_lens.begin() + offset,
                           [](auto a, auto b) { return std::max(a, b); });
Khalique's avatar
Khalique committed
186
187
188
189
190
191
192
193
194

            auto l0 = prog.add_instruction(op::multibroadcast{output_lens}, arg0);
            auto l1 = prog.add_instruction(op::multibroadcast{output_lens}, arg1);
            return prog.add_instruction(x, l0, l1);
        }
        else
        {
            return prog.add_instruction(x, {arg0, arg1});
        }
195
196
    }

Paul's avatar
Paul committed
197
    template <class T>
Paul's avatar
Paul committed
198
199
    void add_generic_op(std::string name, T x)
    {
Paul's avatar
Paul committed
200
        add_op(name, [this, x](attribute_map, std::vector<instruction_ref> args) {
Paul's avatar
Paul committed
201
202
203
204
            return prog.add_instruction(x, args);
        });
    }

Khalique's avatar
Khalique committed
205
    template <class T>
Khalique's avatar
Khalique committed
206
    void add_variadic_op(std::string name, T x)
Khalique's avatar
Khalique committed
207
    {
Paul's avatar
Paul committed
208
        add_op(name, [this, x](attribute_map, std::vector<instruction_ref> args) {
Khalique's avatar
Khalique committed
209
            return std::accumulate(std::next(args.begin()),
Khalique's avatar
Khalique committed
210
211
212
213
214
                                   args.end(),
                                   args.front(),
                                   [this, x](instruction_ref a, instruction_ref b) {
                                       return add_broadcastable_binary_op(a, b, x);
                                   });
Khalique's avatar
Khalique committed
215
        });
Khalique's avatar
Khalique committed
216
217
    }

Paul's avatar
Paul committed
218
    instruction_ref
Paul's avatar
Paul committed
219
    parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
220
221
    {
        auto dims = args.front()->get_shape().lens();
Scott Thornton's avatar
Scott Thornton committed
222
223
        auto r =
            prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1]), 1, 1}}, args.front());
224
225
        auto s = prog.add_instruction(op::softmax{}, r);
        return prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1])}}, s);
Paul's avatar
Paul committed
226
227
    }

Paul's avatar
Paul committed
228
    instruction_ref
Paul's avatar
Paul committed
229
    parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
230
    {
231
        op::convolution op;
232
        auto l0 = args[0];
Paul's avatar
Paul committed
233
234
        if(contains(attributes, "pads"))
        {
Scott Thornton's avatar
Scott Thornton committed
235
            if(contains(attributes, "auto_pad"))
236
            {
Paul's avatar
Paul committed
237
                MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
238
            }
239
240
            std::vector<std::int64_t> padding;
            copy(attributes["pads"].ints(), std::back_inserter(padding));
Scott Thornton's avatar
Scott Thornton committed
241
            if(padding.size() != 4)
242
            {
Paul's avatar
Paul committed
243
                MIGRAPHX_THROW("padding should have 4 values");
244
            }
Scott Thornton's avatar
Scott Thornton committed
245
            if(padding[0] != padding[2] || padding[1] != padding[3])
246
            {
247
248
                // insert zeros for pad op (args[0] has 4 dims)
                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
Khalique's avatar
Khalique committed
249
                l0      = prog.add_instruction(op::pad{padding}, l0);
250
            }
251
252
253
254
            else
            {
                op.padding[0] = padding[0];
                op.padding[1] = padding[1];
255
            }
Paul's avatar
Paul committed
256
        }
Paul's avatar
Paul committed
257
258
259
260
261
262
263
264
        if(contains(attributes, "strides"))
        {
            copy(attributes["strides"].ints(), op.stride.begin());
        }
        if(contains(attributes, "dilations"))
        {
            copy(attributes["dilations"].ints(), op.dilation.begin());
        }
Scott Thornton's avatar
Scott Thornton committed
265
        if(contains(attributes, "auto_pad"))
266
267
        {
            auto s = attributes["auto_pad"].s();
Scott Thornton's avatar
Scott Thornton committed
268
            if(contains(attributes, "pads") and to_upper(s) != "NOTSET")
269
            {
Paul's avatar
Paul committed
270
                MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
271
272
            }

wsttiger's avatar
fixes  
wsttiger committed
273
            if(s.find("SAME") != std::string::npos)
274
            {
275
                op.padding_mode = op::padding_mode_t::same;
276
277
            }
        }
Khalique's avatar
Khalique committed
278
279
280
281
        if(contains(attributes, "group"))
        {
            op.group = parse_value(attributes.at("group")).at<int>();
        }
Paul's avatar
Paul committed
282
283
284
285
        if(args.size() == 3)
        {
            uint64_t axis = 1;
            auto l1       = prog.add_instruction(op, args[0], args[1]);
Scott Thornton's avatar
Scott Thornton committed
286
            auto l2       = prog.add_instruction(op::broadcast{axis, l1->get_shape()}, args[2]);
287
            return prog.add_instruction(op::add{}, l1, l2);
Paul's avatar
Paul committed
288
        }
289
        return prog.add_instruction(op, l0, args[1]);
Paul's avatar
Paul committed
290
    }
Paul's avatar
Paul committed
291

Paul's avatar
Paul committed
292
293
294
    instruction_ref parse_pooling(const std::string& name,
                                  attribute_map attributes,
                                  std::vector<instruction_ref> args)
Paul's avatar
Paul committed
295
    {
Khalique's avatar
Khalique committed
296
        op::pooling op{ends_with(name, "MaxPool") ? "max" : "average"};
297
        auto l0 = args[0];
Khalique's avatar
Khalique committed
298
        if(starts_with(name, "Global"))
299
        {
Khalique's avatar
Khalique committed
300
301
            auto lens  = args.front()->get_shape().lens();
            op.lengths = {lens[2], lens[3]};
302
        }
Paul's avatar
Paul committed
303
304
        if(contains(attributes, "pads"))
        {
305
306
            std::vector<std::int64_t> padding;
            copy(attributes["pads"].ints(), std::back_inserter(padding));
Scott Thornton's avatar
Scott Thornton committed
307
            if(padding.size() != 4)
308
            {
Paul's avatar
Paul committed
309
                MIGRAPHX_THROW("padding should have 4 values");
310
            }
Scott Thornton's avatar
Scott Thornton committed
311
            if(padding[0] != padding[2] || padding[1] != padding[3])
312
            {
313
314
                // insert zeros for pad op (args[0] has 4 dims)
                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
Khalique's avatar
Khalique committed
315
                l0      = prog.add_instruction(op::pad{padding}, l0);
316
317
318
319
320
            }
            else
            {
                op.padding[0] = padding[0];
                op.padding[1] = padding[1];
321
            }
Paul's avatar
Paul committed
322
323
324
325
326
327
328
329
330
        }
        if(contains(attributes, "strides"))
        {
            copy(attributes["strides"].ints(), op.stride.begin());
        }
        if(contains(attributes, "kernel_shape"))
        {
            copy(attributes["kernel_shape"].ints(), op.lengths.begin());
        }
Scott Thornton's avatar
Scott Thornton committed
331
        if(contains(attributes, "auto_pad"))
332
333
        {
            auto s = attributes["auto_pad"].s();
334
            if(s.find("SAME_UPPER") == std::string::npos)
335
            {
336
                MIGRAPHX_THROW("auto_pad only supports SAME_UPPER for pooling");
337
            }
338
            op.padding_mode = op::padding_mode_t::same;
339
340
        }

341
        return prog.add_instruction(op, l0);
Paul's avatar
Paul committed
342
343
    }

Paul's avatar
Paul committed
344
    instruction_ref
Paul's avatar
Paul committed
345
    parse_reshape(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
346
    {
347
        op::reshape op;
Paul's avatar
Paul committed
348
349
350
351
352
353
354
        if(args.size() == 1)
        {
            literal s = parse_value(attributes.at("shape"));
            s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
        }
        if(args.size() == 2)
        {
Paul's avatar
Paul committed
355
            literal s = args[1]->get_literal();
Paul's avatar
Paul committed
356
            s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
Paul's avatar
Paul committed
357
        }
Paul's avatar
Paul committed
358
359
360
        return prog.add_instruction(op, args[0]);
    }

Paul's avatar
Paul committed
361
    instruction_ref
Paul's avatar
Paul committed
362
    parse_flatten(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
363
    {
364
        uint64_t axis = 1;
Paul's avatar
Paul committed
365
366
367
368
        if(contains(attributes, "axis"))
        {
            axis = parse_value(attributes.at("axis")).at<int>();
        }
369
        return prog.add_instruction(op::flatten{axis}, args[0]);
Paul's avatar
Paul committed
370
371
    }

372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
    instruction_ref
    parse_squeeze(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::squeeze op;
        literal s = parse_value(attributes.at("axes"));
        s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
        return prog.add_instruction(op, args[0]);
    }

    instruction_ref
    parse_unsqueeze(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::unsqueeze op;
        literal s = parse_value(attributes.at("axes"));
        s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
        return prog.add_instruction(op, args[0]);
    }

Scott Thornton's avatar
Scott Thornton committed
390
391
392
393
394
395
396
    instruction_ref
    parse_concat(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        std::size_t axis = parse_value(attributes.at("axis")).at<int>();
        op::concat op{axis};
        return prog.add_instruction(op, std::move(args));
    }
397

398
399
400
    instruction_ref
    parse_gather(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
401
        int axis = 0;
402
403
404
405
        if(contains(attributes, "axis"))
        {
            axis = parse_value(attributes.at("axis")).at<int>();
        }
406
        op::gather op{axis};
407
408
409
        return prog.add_instruction(op, std::move(args));
    }

410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
    instruction_ref
    parse_slice(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::slice op;
        if(contains(attributes, "axes"))
        {
            literal s = parse_value(attributes.at("axes"));
            s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
        }
        {
            literal s = parse_value(attributes.at("ends"));
            s.visit([&](auto v) { copy(v, std::back_inserter(op.ends)); });
        }
        {
            literal s = parse_value(attributes.at("starts"));
            s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); });
        }
        return prog.add_instruction(op, args[0]);
    }

Paul's avatar
Paul committed
430
431
432
    instruction_ref parse_constant(const std::string&,
                                   attribute_map attributes,
                                   const std::vector<instruction_ref>&)
Paul's avatar
Paul committed
433
434
435
436
    {
        literal v = parse_value(attributes.at("value"));
        return prog.add_literal(v);
    }
Paul's avatar
Paul committed
437

Paul's avatar
Paul committed
438
    instruction_ref
Paul's avatar
Paul committed
439
    parse_gemm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
440
441
    {
        float alpha = 1.0f;
Khalique's avatar
Khalique committed
442
        float beta  = 1.0f;
Paul's avatar
Paul committed
443
444
445
446
447
448
449
450
        bool transa = false;
        bool transb = false;
        if(contains(attributes, "alpha"))
        {
            alpha = parse_value(attributes.at("alpha")).at<float>();
        }
        if(contains(attributes, "beta"))
        {
451
            beta = parse_value(attributes.at("beta")).at<float>();
Paul's avatar
Paul committed
452
453
454
455
456
457
458
459
460
461
        }
        if(contains(attributes, "transA"))
        {
            transa = parse_value(attributes.at("transA")).at<bool>();
        }
        if(contains(attributes, "transB"))
        {
            transb = parse_value(attributes.at("transB")).at<bool>();
        }
        std::vector<int64_t> perm = {1, 0};
462
463
        auto l1 = (transa) ? prog.add_instruction(op::transpose{perm}, args[0]) : args[0];
        auto l2 = (transb) ? prog.add_instruction(op::transpose{perm}, args[1]) : args[1];
Paul's avatar
Paul committed
464
465
        if(args.size() == 3)
        {
Khalique's avatar
Khalique committed
466
            if(beta != 0.f)
467
            {
Khalique's avatar
Khalique committed
468
                auto l3 = prog.add_instruction(op::dot{alpha}, l1, l2);
Khalique's avatar
Khalique committed
469
                auto l4 = args[2];
Khalique's avatar
Khalique committed
470
                if(l4->get_shape().scalar()) // ignore args[2] (no C value added to alpha*A*B)
Khalique's avatar
Khalique committed
471
                    return l3;
Khalique's avatar
Khalique committed
472
                if(beta != 1.f)
Khalique's avatar
Khalique committed
473
474
                {
                    auto beta_val = prog.add_literal(beta);
Khalique's avatar
Khalique committed
475
476
                    auto l5 = prog.add_instruction(op::scalar{args[2]->get_shape()}, beta_val);
                    l4      = prog.add_instruction(op::mul{}, args[2], l5);
Khalique's avatar
Khalique committed
477
478
                }
                return add_broadcastable_binary_op(l3, l4, op::add{});
479
            }
Paul's avatar
Paul committed
480
        }
Shucai Xiao's avatar
Shucai Xiao committed
481
        return prog.add_instruction(op::dot{alpha, beta}, l1, l2);
Paul's avatar
Paul committed
482
483
    }

484
    instruction_ref
Paul's avatar
Paul committed
485
    parse_batchnorm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
486
    {
Scott Thornton's avatar
Scott Thornton committed
487
488
        float epsilon                                     = 1e-5f;
        float momentum                                    = 0.9f;
489
        op::batch_norm_inference::bn_infer_mode_t bn_mode = op::batch_norm_inference::spatial;
Scott Thornton's avatar
Scott Thornton committed
490
        bool is_test                                      = false;
491
492
493
494
495
496
        if(contains(attributes, "epsilon"))
        {
            epsilon = parse_value(attributes.at("epsilon")).at<float>();
        }
        if(contains(attributes, "momentum"))
        {
497
            momentum = parse_value(attributes.at("momentum")).at<float>();
498
499
500
        }
        if(contains(attributes, "is_test"))
        {
wsttiger's avatar
wsttiger committed
501
            is_test = parse_value(attributes.at("is_test")).at<uint64_t>() > 0;
502
503
504
        }
        if(contains(attributes, "spatial"))
        {
505
            bn_mode = (parse_value(attributes.at("spatial")).at<uint64_t>() > 0)
506
507
                          ? op::batch_norm_inference::spatial
                          : op::batch_norm_inference::per_activation;
508
        }
Paul's avatar
Paul committed
509
        (void)is_test;
Paul's avatar
Paul committed
510
        op::batch_norm_inference op{epsilon, momentum, bn_mode};
Paul's avatar
Paul committed
511
        return prog.add_instruction(op, std::move(args));
512
513
    }

514
515
516
517
    instruction_ref parse_leaky_relu(const std::string&,
                                     attribute_map attributes,
                                     std::vector<instruction_ref> args)
    {
Khalique's avatar
Khalique committed
518
        float alpha = 0.01; // default alpha val for leaky relu
519
520
521
522
523
524
525
526
        if(contains(attributes, "alpha"))
        {
            alpha = parse_value(attributes.at("alpha")).at<float>();
        }
        op::leaky_relu op{alpha};
        return prog.add_instruction(op, args.front());
    }

Khalique's avatar
Khalique committed
527
528
    instruction_ref
    parse_elu(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
529
530
531
532
533
534
535
536
537
538
    {
        float alpha = 1.0; // default alpha val for elu
        if(contains(attributes, "alpha"))
        {
            alpha = parse_value(attributes.at("alpha")).at<float>();
        }
        op::elu op{alpha};
        return prog.add_instruction(op, args.front());
    }

Khalique's avatar
Khalique committed
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
    instruction_ref parse_imagescaler(const std::string&,
                                      attribute_map attributes,
                                      std::vector<instruction_ref> args)
    {
        float scale = 1.0;
        std::vector<float> bias{};
        if(contains(attributes, "scale"))
        {
            scale = parse_value(attributes.at("scale")).at<float>();
        }

        if(contains(attributes, "bias"))
        {
            auto&& bias_floats = attributes["bias"].floats();
            bias               = std::vector<float>(bias_floats.begin(), bias_floats.end());
        }
        auto input_shape = args.front()->get_shape();
Khalique's avatar
Khalique committed
556

Khalique's avatar
Khalique committed
557
558
        auto scale_val = prog.add_literal(scale);
        auto bias_vals = prog.add_literal(
Paul's avatar
Paul committed
559
            migraphx::literal{migraphx::shape{migraphx::shape::float_type, {bias.size()}}, bias});
Khalique's avatar
Khalique committed
560

Paul's avatar
Paul committed
561
562
        auto scale_tensor = prog.add_instruction(migraphx::op::scalar{input_shape}, scale_val);
        auto img_scaled   = prog.add_instruction(migraphx::op::mul{}, args.front(), scale_tensor);
Paul's avatar
Paul committed
563
        auto bias_bcast = prog.add_instruction(migraphx::op::broadcast{1, input_shape}, bias_vals);
Paul's avatar
Paul committed
564
        return prog.add_instruction(migraphx::op::add{}, img_scaled, bias_bcast);
Khalique's avatar
Khalique committed
565
    }
Khalique's avatar
Khalique committed
566

Khalique's avatar
Khalique committed
567
568
    instruction_ref
    parse_transpose(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
569
570
571
572
573
574
575
    {
        std::vector<int64_t> perm{};
        if(contains(attributes, "perm"))
        {
            auto&& perm_vals = attributes["perm"].ints();
            perm             = std::vector<int64_t>(perm_vals.begin(), perm_vals.end());
        }
Paul's avatar
Paul committed
576
        return prog.add_instruction(migraphx::op::transpose{perm}, args.front());
Khalique's avatar
Khalique committed
577
578
    }

Khalique's avatar
Khalique committed
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
    instruction_ref
    parse_pad(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        std::vector<int64_t> pads{};
        float value = 0.0f;
        if(contains(attributes, "pads"))
        {
            auto&& pad_vals = attributes["pads"].ints();
            pads            = std::vector<int64_t>(pad_vals.begin(), pad_vals.end());
        }
        if(contains(attributes, "value"))
        {
            value = parse_value(attributes.at("value")).at<float>();
        }
        if(contains(attributes, "mode"))
        {
            auto mode = attributes.at("mode").s();
            if(mode != "constant")
                MIGRAPHX_THROW("migraphx currently only supports constant padding");
        }
        return prog.add_instruction(migraphx::op::pad{pads, value}, args.front());
    }
601
602
603
    // Use a literal instruction to replace the shape since, output of
    // shape operator are literals in migraphx
    instruction_ref
Shucai Xiao's avatar
Shucai Xiao committed
604
    parse_shape(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
605
606
    {
        if(args.size() != 1)
607
            MIGRAPHX_THROW("Shape: operator should have 1 operand");
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
        std::vector<std::size_t> arg_shape = args[0]->get_shape().lens();
        std::vector<int64_t> vec_shape(arg_shape.size());
        migraphx::shape s(migraphx::shape::int64_type, {arg_shape.size()});
        std::transform(arg_shape.begin(), arg_shape.end(), vec_shape.begin(), [](auto i) {
            return int64_t(i);
        });
        return prog.add_literal(migraphx::literal{s, vec_shape});
    }

    // Use a literal instruction to replace the constantFill operator. In RNN, input shape
    // and value are fixed, so no need to do the actual computation for the constantFill
    // operator
    instruction_ref parse_constant_fill(const std::string&,
                                        attribute_map attributes,
                                        std::vector<instruction_ref> args)
    {
        int input_as_shape = 0;
        int dtype          = 1;
        float value        = 0.0f;

        if(contains(attributes, "dtype"))
        {
            dtype = parse_value(attributes.at("dtype")).at<int>();
        }
        migraphx::shape::type_t type = get_type(dtype);

        if(contains(attributes, "input_as_shape"))
        {
            input_as_shape = parse_value(attributes.at("input_as_shape")).at<int>();
        }

        if(contains(attributes, "value"))
        {
            value = parse_value(attributes.at("value")).at<float>();
        }

Shucai Xiao's avatar
Shucai Xiao committed
644
645
        if(contains(attributes, "extra_shape"))
        {
646
            MIGRAPHX_THROW("ConstantFill: cannot handle extra shape attribute");
647
648
        }

649
650
        if(input_as_shape == 1)
        {
Shucai Xiao's avatar
Shucai Xiao committed
651
            if(args.size() != 1)
652
            {
653
                MIGRAPHX_THROW("ConstantFill: need an input argument as output shape");
654
655
            }

Shucai Xiao's avatar
Shucai Xiao committed
656
657
            if(contains(attributes, "shape"))
            {
658
                MIGRAPHX_THROW("ConstantFill: cannot set the shape argument and pass in an input "
Shucai Xiao's avatar
Shucai Xiao committed
659
                               "at the same time");
660
661
            }

662
663
664
            migraphx::argument in = args[0]->eval();
            if(in.empty())
            {
665
                MIGRAPHX_THROW("ConstantFill: cannot handle dynamic shape as input");
666
            }
667

668
669
670
            std::vector<std::size_t> dims;
            in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
            migraphx::shape s(type, dims);
671
672
            std::vector<float> values(s.elements(), value);
            return prog.add_literal(migraphx::literal(s, values));
673
674
675
        }
        else if(input_as_shape == 0)
        {
Shucai Xiao's avatar
Shucai Xiao committed
676
677
            if(!contains(attributes, "shape"))
            {
678
                MIGRAPHX_THROW("ConstantFill: attribute output shape is needed");
679
680
681
            }

            literal ls = parse_value(attributes.at("shape"));
682
            std::vector<std::size_t> dims;
Shucai Xiao's avatar
Shucai Xiao committed
683
            ls.visit([&](auto s) { dims.assign(s.begin(), s.end()); });
684
            migraphx::shape s{type, dims};
685
686
            std::vector<float> values(s.elements(), value);
            return prog.add_literal(migraphx::literal(s, values));
687
688
689
        }
        else
        {
690
            MIGRAPHX_THROW("ConstantFill: wrong value of attribute input_as_shape");
691
692
693
        }
    }

Shucai Xiao's avatar
Shucai Xiao committed
694
    std::vector<instruction_ref>
Shucai Xiao's avatar
Shucai Xiao committed
695
696
697
698
699
700
701
702
    parse_rnn(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        migraphx::shape input_shape = args[0]->get_shape();
        migraphx::shape w_shape     = args[1]->get_shape();
        std::size_t hidden_size     = w_shape.lens()[1];

        if(contains(attributes, "hidden_size"))
        {
Shucai Xiao's avatar
Shucai Xiao committed
703
            std::size_t hidden_size_att = parse_value(attributes.at("hidden_size")).at<int>();
Shucai Xiao's avatar
Shucai Xiao committed
704
            if(hidden_size != hidden_size_att)
Shucai Xiao's avatar
Shucai Xiao committed
705
706
707
            {
                MIGRAPHX_THROW("RNN: hidden size mismatch in input and attribute");
            }
Shucai Xiao's avatar
Shucai Xiao committed
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
        }

        // Handling of direction to be added later
        std::string direction{"forward"};
        if(contains(attributes, "direction"))
        {
            direction = attributes.at("direction").s();
        }

        op::rnn::rnn_direction_t dirct = op::rnn::forward;
        if(direction == "bidirectional")
        {
            dirct = op::rnn::bidirectional;
        }
        else if(direction == "reverse")
        {
            dirct = op::rnn::reverse;
        }

727
728
729
730
731
        std::vector<std::string> vec_names{"tanh"};
        if(contains(attributes, "activations"))
        {
            auto names = attributes.at("activations").strings();
            vec_names.clear();
Shucai Xiao's avatar
Shucai Xiao committed
732
            for_each(names.begin(), names.end(), [&](auto& fn) { vec_names.push_back(fn); });
733
734
        }

Shucai Xiao's avatar
Shucai Xiao committed
735
        for_each(vec_names.begin(), vec_names.end(), [&](auto& fn) {
736
737
            if(map_actv_funcs.count(fn) == 0)
            {
Shucai Xiao's avatar
Shucai Xiao committed
738
                MIGRAPHX_THROW("RNN: activation function " + std::string(fn) + " not supported");
739
740
741
            }
        });

Shucai Xiao's avatar
Shucai Xiao committed
742
        // bidirectional case should have two activation functions.
Shucai Xiao's avatar
Shucai Xiao committed
743
        // one is for forward, and the other is for reverse.
Shucai Xiao's avatar
Shucai Xiao committed
744
        // if only one actv function is provided, we use it in both
745
        // forward and reverse direction
Shucai Xiao's avatar
Shucai Xiao committed
746
        if(dirct == op::rnn::bidirectional)
747
        {
Shucai Xiao's avatar
Shucai Xiao committed
748
            if(vec_names.size() == 1)
749
750
751
752
753
            {
                vec_names.push_back(vec_names.at(0));
            }
        }

Shucai Xiao's avatar
Shucai Xiao committed
754
755
756
        std::vector<operation> vec_actv_funcs(vec_names.size());
        std::transform(vec_names.begin(), vec_names.end(), vec_actv_funcs.begin(), [&](auto& fn) {
            return map_actv_funcs[fn];
757
        });
Shucai Xiao's avatar
Shucai Xiao committed
758

Shucai Xiao's avatar
Shucai Xiao committed
759
760
761
762
763
764
765
        // To be added later
        float clip = 0.0;
        if(contains(attributes, "clip"))
        {
            clip = parse_value(attributes.at("clip")).at<float>();
        }

766
767
        // if the number of arguments is less than 6, append
        // undefined operator to have 6 arguments
Shucai Xiao's avatar
Shucai Xiao committed
768
        if(args.size() < 6)
769
770
771
772
773
        {
            auto ins = prog.add_instruction(op::undefined{});
            args.insert(args.end(), (6 - args.size()), ins);
        }

Shucai Xiao's avatar
Shucai Xiao committed
774
775
776
        std::vector<instruction_ref> result;
        // first output for the concatenation of hidden states
        auto hidden_states = prog.add_instruction(op::rnn{hidden_size, vec_actv_funcs, dirct, clip},
Shucai Xiao's avatar
Shucai Xiao committed
777
                                                  std::move(args));
Shucai Xiao's avatar
Shucai Xiao committed
778
779
        result.push_back(hidden_states);

780
        // second output for the last hidden state
Shucai Xiao's avatar
Shucai Xiao committed
781
782
        auto last_output = prog.add_instruction(op::rnn_last_output{}, hidden_states);
        result.push_back(last_output);
Shucai Xiao's avatar
Shucai Xiao committed
783
784

        return result;
Shucai Xiao's avatar
Shucai Xiao committed
785
786
    }

Paul's avatar
Paul committed
787
788
789
790
791
792
793
794
795
796
797
798
    void parse_from(std::istream& is)
    {
        onnx::ModelProto model;
        if(model.ParseFromIstream(&is))
        {
            if(model.has_graph())
            {
                this->parse_graph(model.graph());
            }
        }
        else
        {
Paul's avatar
Paul committed
799
            MIGRAPHX_THROW("Failed reading onnx file.");
Paul's avatar
Paul committed
800
801
802
803
804
805
        }
    }

    void parse_graph(const onnx::GraphProto& graph)
    {
        nodes = get_nodes(graph);
806
807
808
809
810
        std::unordered_map<std::string, onnx::TensorProto> initializer_data;
        for(auto&& f : graph.initializer())
        {
            initializer_data[f.name()] = f;
        }
Paul's avatar
Paul committed
811
812
813
        for(auto&& input : graph.input())
        {
            const std::string& name = input.name();
814
815
816
817
818
819
820
821
822
823
824
825
            // Does the input have an initializer?
            if(contains(initializer_data, name))
            {
                auto t             = initializer_data[name];
                instructions[name] = prog.add_literal(parse_tensor(t));
            }
            else
            {
                // TODO: Get shape of input parameter
                shape s            = parse_type(input.type());
                instructions[name] = prog.add_parameter(name, s);
            }
Paul's avatar
Paul committed
826
827
828
        }
        for(auto&& p : nodes)
        {
Paul's avatar
Paul committed
829
            this->parse_node(p.first);
Paul's avatar
Paul committed
830
831
832
        }
    }

Shucai Xiao's avatar
Shucai Xiao committed
833
    void parse_undefined(const std::string& name)
834
    {
Shucai Xiao's avatar
Shucai Xiao committed
835
        auto ins           = prog.add_instruction(op::undefined{});
836
837
838
        instructions[name] = ins;
    }

Paul's avatar
Paul committed
839
    void parse_node(const std::string& name)
Paul's avatar
Paul committed
840
    {
Paul's avatar
Paul committed
841
        if(name.empty())
Paul's avatar
Paul committed
842
            MIGRAPHX_THROW("Onnx node must have a name");
Paul's avatar
Paul committed
843
844
845
846
847
848
849
850
        if(instructions.count(name) == 0)
        {
            auto&& node = nodes.at(name);
            std::vector<instruction_ref> args;
            for(auto&& input : node.input())
            {
                if(nodes.count(input) > 0)
                {
Paul's avatar
Paul committed
851
852
                    assert(name != input);
                    this->parse_node(input);
Paul's avatar
Paul committed
853
                }
Shucai Xiao's avatar
Shucai Xiao committed
854
                else if(input.empty())
Paul's avatar
Paul committed
855
                {
856
                    this->parse_undefined(input);
Paul's avatar
Paul committed
857
                }
858
                args.push_back(instructions.at(input));
Paul's avatar
Paul committed
859
            }
Paul's avatar
Paul committed
860
            std::vector<instruction_ref> result;
Paul's avatar
Paul committed
861
862
            if(ops.count(node.op_type()) == 0)
            {
Paul's avatar
Paul committed
863
                result.push_back(prog.add_instruction(unknown{node.op_type()}, args));
Paul's avatar
Paul committed
864
865
866
            }
            else
            {
Paul's avatar
Paul committed
867
                result = ops[node.op_type()](get_attributes(node), args);
Paul's avatar
Paul committed
868
            }
Paul's avatar
Paul committed
869
            // Even no output nodes produce output in migraphx
Paul's avatar
Paul committed
870
            if(node.output().empty() and result.size() == 1)
Paul's avatar
Paul committed
871
872
            {
                instructions[name] = result.front();
Paul's avatar
Paul committed
873
874
875
            }
            else
            {
Paul's avatar
Paul committed
876
877
878
879
880
881
                assert(node.output().size() >= result.size());
                std::transform(result.begin(),
                               result.end(),
                               node.output().begin(),
                               std::inserter(instructions, instructions.end()),
                               [](auto&& x, auto&& y) { return std::make_pair(y, x); });
Paul's avatar
Paul committed
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
            }
        }
    }

    static attribute_map get_attributes(const onnx::NodeProto& node)
    {
        std::unordered_map<std::string, onnx::AttributeProto> result;
        for(auto&& attr : node.attribute())
        {
            result[attr.name()] = attr;
        }
        return result;
    }

    static node_map get_nodes(const onnx::GraphProto& graph)
    {
        std::unordered_map<std::string, onnx::NodeProto> result;
Paul's avatar
Paul committed
899
        std::size_t n = 0;
Paul's avatar
Paul committed
900
901
        for(auto&& node : graph.node())
        {
Paul's avatar
Paul committed
902
            if(node.output().empty())
Paul's avatar
Paul committed
903
            {
Paul's avatar
Paul committed
904
                if(node.name().empty())
Paul's avatar
Paul committed
905
906
907
908
909
910
911
912
913
                {
                    result["migraphx_unamed_node_" + std::to_string(n)] = node;
                    n++;
                }
                else
                {
                    result[node.name()] = node;
                }
            }
Paul's avatar
Paul committed
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
            for(auto&& output : node.output())
            {
                result[output] = node;
            }
        }
        return result;
    }

    template <class T>
    static literal from_repeated(shape::type_t t, const T& r)
    {
        std::size_t size = r.size();
        return literal{{t, {size}}, r.begin(), r.end()};
    }

    static literal parse_value(const onnx::AttributeProto& attr)
    {
        switch(attr.type())
        {
        case onnx::AttributeProto::UNDEFINED: return {};
        case onnx::AttributeProto::FLOAT: return literal{attr.f()};
        case onnx::AttributeProto::INT: return literal{attr.i()};
        case onnx::AttributeProto::STRING: return {};
        case onnx::AttributeProto::TENSOR: return parse_tensor(attr.t());
        case onnx::AttributeProto::GRAPH: return {};
Paul's avatar
Paul committed
939
        case onnx::AttributeProto::FLOATS: return from_repeated(shape::float_type, attr.floats());
Paul's avatar
Paul committed
940
941
942
943
944
        case onnx::AttributeProto::INTS: return from_repeated(shape::int64_type, attr.ints());
        case onnx::AttributeProto::STRINGS: return {};
        case onnx::AttributeProto::TENSORS: return {};
        case onnx::AttributeProto::GRAPHS: return {};
        }
Paul's avatar
Paul committed
945
        MIGRAPHX_THROW("Invalid attribute type");
Paul's avatar
Paul committed
946
947
948
949
950
    }

    static literal parse_tensor(const onnx::TensorProto& t)
    {
        std::vector<std::size_t> dims(t.dims().begin(), t.dims().end());
Khalique's avatar
Khalique committed
951
        // in case of scalar constants in onnx file, use dims=1 to fill initializer data
952
        if(dims.empty())
Khalique's avatar
Khalique committed
953
954
955
        {
            dims = {1};
        }
956
957
        if(t.has_raw_data())
        {
wsttiger's avatar
wsttiger committed
958
            const std::string& s = t.raw_data();
Scott Thornton's avatar
Scott Thornton committed
959
960
961
962
963
964
965
966
967
968
969
970
            switch(t.data_type())
            {
            case onnx::TensorProto::UNDEFINED: throw std::runtime_error("");
            case onnx::TensorProto::FLOAT: return literal{{shape::float_type, dims}, s.data()};
            case onnx::TensorProto::UINT8: throw std::runtime_error("");
            case onnx::TensorProto::INT8: return literal{{shape::int32_type, dims}, s.data()};
            case onnx::TensorProto::UINT16: return literal{{shape::int32_type, dims}, s.data()};
            case onnx::TensorProto::INT16: return literal{{shape::int32_type, dims}, s.data()};
            case onnx::TensorProto::INT32: return literal{{shape::int32_type, dims}, s.data()};
            case onnx::TensorProto::INT64: return literal{{shape::int64_type, dims}, s.data()};
            case onnx::TensorProto::STRING: throw std::runtime_error("");
            case onnx::TensorProto::BOOL: return literal{{shape::int32_type, dims}, s.data()};
Paul's avatar
Paul committed
971
            case onnx::TensorProto::FLOAT16: return literal{{shape::half_type, dims}, s.data()};
Scott Thornton's avatar
Scott Thornton committed
972
973
974
975
976
977
            case onnx::TensorProto::DOUBLE: return literal{{shape::double_type, dims}, s.data()};
            case onnx::TensorProto::UINT32: throw std::runtime_error("");
            case onnx::TensorProto::UINT64: throw std::runtime_error("");
            case onnx::TensorProto::COMPLEX64: throw std::runtime_error("");
            case onnx::TensorProto::COMPLEX128: throw std::runtime_error("");
            }
Paul's avatar
Paul committed
978
            MIGRAPHX_THROW("Invalid tensor type");
979
        }
Paul's avatar
Paul committed
980
981
982
983
        switch(t.data_type())
        {
        case onnx::TensorProto::UNDEFINED: throw std::runtime_error("");
        case onnx::TensorProto::FLOAT:
Paul's avatar
Paul committed
984
            return literal{{shape::float_type, dims}, t.float_data().begin(), t.float_data().end()};
Paul's avatar
Paul committed
985
986
        case onnx::TensorProto::UINT8: throw std::runtime_error("");
        case onnx::TensorProto::INT8:
Paul's avatar
Paul committed
987
            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
Paul's avatar
Paul committed
988
        case onnx::TensorProto::UINT16:
Paul's avatar
Paul committed
989
            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
Paul's avatar
Paul committed
990
        case onnx::TensorProto::INT16:
Paul's avatar
Paul committed
991
            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
Paul's avatar
Paul committed
992
        case onnx::TensorProto::INT32:
Paul's avatar
Paul committed
993
            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
Paul's avatar
Paul committed
994
        case onnx::TensorProto::INT64:
Paul's avatar
Paul committed
995
            return literal{{shape::int64_type, dims}, t.int64_data().begin(), t.int64_data().end()};
Paul's avatar
Paul committed
996
997
        case onnx::TensorProto::STRING: throw std::runtime_error("");
        case onnx::TensorProto::BOOL:
Paul's avatar
Paul committed
998
            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
Paul's avatar
Paul committed
999
1000
        case onnx::TensorProto::FLOAT16:
            return literal{{shape::half_type, dims}, t.float_data().begin(), t.float_data().end()};
Paul's avatar
Paul committed
1001
1002
1003
1004
1005
1006
1007
1008
        case onnx::TensorProto::DOUBLE:
            return literal{
                {shape::double_type, dims}, t.double_data().begin(), t.double_data().end()};
        case onnx::TensorProto::UINT32: throw std::runtime_error("");
        case onnx::TensorProto::UINT64: throw std::runtime_error("");
        case onnx::TensorProto::COMPLEX64: throw std::runtime_error("");
        case onnx::TensorProto::COMPLEX128: throw std::runtime_error("");
        }
Paul's avatar
Paul committed
1009
        MIGRAPHX_THROW("Invalid tensor type");
Paul's avatar
Paul committed
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
    }

    static shape parse_type(const onnx::TypeProto& t)
    {
        shape::type_t shape_type{};
        switch(t.tensor_type().elem_type())
        {
        case onnx::TensorProto::UNDEFINED:
            break; // throw std::runtime_error("Unsupported type UNDEFINED");
        case onnx::TensorProto::FLOAT: shape_type = shape::float_type; break;
        case onnx::TensorProto::UINT8:
            break; // throw std::runtime_error("Unsupported type UINT8");
        case onnx::TensorProto::INT8: shape_type = shape::int8_type; break;
        case onnx::TensorProto::UINT16: shape_type = shape::uint16_type; break;
        case onnx::TensorProto::INT16: shape_type = shape::int16_type; break;
        case onnx::TensorProto::INT32: shape_type = shape::int32_type; break;
        case onnx::TensorProto::INT64: shape_type = shape::int64_type; break;
        case onnx::TensorProto::STRING:
            break; // throw std::runtime_error("Unsupported type STRING");
        case onnx::TensorProto::BOOL:
            break; // throw std::runtime_error("Unsupported type BOOL");
Paul's avatar
Paul committed
1031
        case onnx::TensorProto::FLOAT16: shape_type = shape::half_type; break;
Paul's avatar
Paul committed
1032
1033
1034
1035
1036
1037
1038
1039
1040
        case onnx::TensorProto::DOUBLE: shape_type = shape::double_type; break;
        case onnx::TensorProto::UINT32: shape_type = shape::uint32_type; break;
        case onnx::TensorProto::UINT64: shape_type = shape::uint64_type; break;
        case onnx::TensorProto::COMPLEX64:
            break; // throw std::runtime_error("Unsupported type COMPLEX64");
        case onnx::TensorProto::COMPLEX128:
            break; // throw std::runtime_error("Unsupported type COMPLEX128");
        }
        std::vector<std::size_t> dims;
Paul's avatar
Paul committed
1041
        auto&& tensor_dims = t.tensor_type().shape().dim();
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
        std::transform(tensor_dims.begin(),
                       tensor_dims.end(),
                       std::back_inserter(dims),
                       [](auto&& d) -> std::size_t {
                           if(not d.has_dim_value())
                           {
                               long default_batch_size = 1; // FIXME
                               return default_batch_size;
                           }
                           return d.dim_value();
                       });
Paul's avatar
Paul committed
1053
1054
        return {shape_type, dims};
    }
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076

    shape::type_t get_type(int dtype)
    {
        switch(dtype)
        {
        case 1: return shape::float_type;
        case 2: return shape::uint8_type;
        case 3: return shape::int8_type;
        case 4: return shape::uint16_type;
        case 5: return shape::int16_type;
        case 6: return shape::int32_type;
        case 7: return shape::int64_type;
        case 10: return shape::half_type;
        case 11: return shape::double_type;
        case 12: return shape::uint32_type;
        case 13: return shape::uint64_type;
        default:
        {
            MIGRAPHX_THROW("Prototensor data type " + std::to_string(dtype) + " not supported");
        }
        }
    }
Paul's avatar
Paul committed
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
};

program parse_onnx(const std::string& name)
{
    std::fstream input(name.c_str(), std::ios::in | std::ios::binary);
    onnx_parser parser;
#ifndef NDEBUG
    // Log the program when it can't be parsed
    try
    {
        parser.parse_from(input);
    }
    catch(...)
    {
        std::cerr << parser.prog << std::endl;
        throw;
    }
#else
    parser.parse_from(input);
#endif
    return std::move(parser.prog);
}

Paul's avatar
Paul committed
1100
} // namespace MIGRAPHX_INLINE_NS
Paul's avatar
Paul committed
1101
} // namespace migraphx