onnx.cpp 77.3 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;
31
32
33
    program prog            = program();
    bool is_pytorch         = false;
    unsigned int batch_size = 1;
Paul's avatar
Paul committed
34
35

    std::unordered_map<std::string, op_func> ops;
36
    std::unordered_map<std::string, operation> map_actv_funcs;
Paul's avatar
Paul committed
37
38
39

    onnx_parser()
    {
40
        // sort onnx operator alphabetically through name
Khalique's avatar
Khalique committed
41
        add_generic_op("Abs", op::abs{});
42
43
44
45
46
47
48
49
50
        add_generic_op("Acos", op::acos{});
        add_generic_op("Acosh", op::acosh{});
        add_generic_op("Asin", op::asin{});
        add_generic_op("Asinh", op::asinh{});
        add_generic_op("Atan", op::atan{});
        add_generic_op("Atanh", op::atanh{});
        add_generic_op("Ceil", op::ceil{});
        add_generic_op("Cos", op::cos{});
        add_generic_op("Cosh", op::cosh{});
Shucai Xiao's avatar
Shucai Xiao committed
51
        add_generic_op("Erf", op::erf{});
52
        add_generic_op("Exp", op::exp{});
Khalique's avatar
Khalique committed
53
        add_generic_op("Dropout", op::identity{});
54
55
        add_generic_op("Log", op::log{});
        add_generic_op("Floor", op::floor{});
Khalique's avatar
Khalique committed
56
        add_generic_op("Identity", op::identity{});
57
58
59
60
        add_generic_op("Relu", op::relu{});
        add_generic_op("Round", op::round{});
        add_generic_op("Sigmoid", op::sigmoid{});
        add_generic_op("Sign", op::sign{});
Shucai Xiao's avatar
Shucai Xiao committed
61
        add_generic_op("Sin", op::sin{});
62
        add_generic_op("Sinh", op::sinh{});
63
        add_generic_op("Sqrt", op::sqrt{});
64
65
        add_generic_op("Tan", op::tan{});
        add_generic_op("Tanh", op::tanh{});
Paul's avatar
Paul committed
66

Khalique's avatar
Khalique committed
67
68
69
        add_binary_op("Add", op::add{});
        add_binary_op("Div", op::div{});
        add_binary_op("Mul", op::mul{});
Shucai Xiao's avatar
Shucai Xiao committed
70
        add_binary_op("Pow", op::pow{});
71
        add_binary_op("Sub", op::sub{});
Khalique's avatar
Khalique committed
72

Khalique's avatar
Khalique committed
73
74
75
        add_variadic_op("Sum", op::add{});
        add_variadic_op("Max", op::max{});
        add_variadic_op("Min", op::min{});
Paul's avatar
Paul committed
76

77
        add_mem_op("AveragePool", &onnx_parser::parse_pooling);
78
79
        add_mem_op("ArgMax", &onnx_parser::parse_arg_op<op::argmax>);
        add_mem_op("ArgMin", &onnx_parser::parse_arg_op<op::argmin>);
80
        add_mem_op("BatchNormalization", &onnx_parser::parse_batchnorm);
81
        add_mem_op("Cast", &onnx_parser::parse_cast);
Khalique's avatar
Khalique committed
82
        add_mem_op("Clip", &onnx_parser::parse_clip);
83
        add_mem_op("Concat", &onnx_parser::parse_concat);
Paul's avatar
Paul committed
84
        add_mem_op("Constant", &onnx_parser::parse_constant);
85
86
87
88
        add_mem_op("ConstantFill", &onnx_parser::parse_constant_fill);
        add_mem_op("ConstantOfShape", &onnx_parser::parse_constant_of_shape);
        add_mem_op("Conv", &onnx_parser::parse_conv<op::convolution>);
        add_mem_op("ConvInteger", &onnx_parser::parse_conv<op::quant_convolution>);
kahmed10's avatar
kahmed10 committed
89
        add_mem_op("ConvTranspose", &onnx_parser::parse_conv_transpose);
90
91
        add_mem_op("Elu", &onnx_parser::parse_elu);
        add_mem_op("Expand", &onnx_parser::parse_expand);
Paul's avatar
Paul committed
92
        add_mem_op("Flatten", &onnx_parser::parse_flatten);
93
        add_mem_op("Gather", &onnx_parser::parse_gather);
Paul's avatar
Paul committed
94
        add_mem_op("Gemm", &onnx_parser::parse_gemm);
95
96
97
98
        add_mem_op("GlobalAveragePool", &onnx_parser::parse_pooling);
        add_mem_op("GlobalMaxPool", &onnx_parser::parse_pooling);
        add_mem_op("GRU", &onnx_parser::parse_gru);
        add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
kahmed10's avatar
kahmed10 committed
99
        add_mem_op("InstanceNormalization", &onnx_parser::parse_instancenorm);
100
        add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu);
101
        add_mem_op("LogSoftmax", &onnx_parser::parse_softmax<op::logsoftmax>);
102
103
104
105
        add_mem_op("LRN", &onnx_parser::parse_lrn);
        add_mem_op("MatMul", &onnx_parser::parse_matmul<op::dot>);
        add_mem_op("MatMulInteger", &onnx_parser::parse_matmul<op::quant_dot>);
        add_mem_op("MaxPool", &onnx_parser::parse_pooling);
Shucai Xiao's avatar
Shucai Xiao committed
106
107
108
109
110
        add_mem_op("ReduceL1", &onnx_parser::parse_reduce_l1);
        add_mem_op("ReduceL2", &onnx_parser::parse_reduce_l2);
        add_mem_op("ReduceLogSum", &onnx_parser::parse_reduce_log_sum);
        add_mem_op("ReduceLogSumExp", &onnx_parser::parse_reduce_log_sum_exp);
        add_mem_op("ReduceMax", &onnx_parser::parse_reduce_oper<op::reduce_max>);
Shucai Xiao's avatar
Shucai Xiao committed
111
        add_mem_op("ReduceMean", &onnx_parser::parse_reduce_oper<op::reduce_mean>);
Shucai Xiao's avatar
Shucai Xiao committed
112
        add_mem_op("ReduceMin", &onnx_parser::parse_reduce_oper<op::reduce_min>);
Shucai Xiao's avatar
Shucai Xiao committed
113
114
115
        add_mem_op("ReduceProd", &onnx_parser::parse_reduce_oper<op::reduce_prod>);
        add_mem_op("ReduceSum", &onnx_parser::parse_reduce_oper<op::reduce_sum>);
        add_mem_op("ReduceSumSquare", &onnx_parser::parse_reduce_sum_square);
116
117
118
119
120
121
122
123
124
125
        add_mem_op("Reshape", &onnx_parser::parse_reshape);
        add_mem_op("RNN", &onnx_parser::parse_rnn);
        add_mem_op("Pad", &onnx_parser::parse_pad);
        add_mem_op("Shape", &onnx_parser::parse_shape);
        add_mem_op("Slice", &onnx_parser::parse_slice);
        add_mem_op("Softmax", &onnx_parser::parse_softmax<op::softmax>);
        add_mem_op("Squeeze", &onnx_parser::parse_squeeze);
        add_mem_op("Transpose", &onnx_parser::parse_transpose);
        add_mem_op("Unsqueeze", &onnx_parser::parse_unsqueeze);
        add_mem_op("LSTM", &onnx_parser::parse_lstm);
126
127
128
129
130
131
132

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

    void init_actv_func()
    {
133
134
135
136
137
138
        // Support name format of all lower case or the first letter capital
        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
139
140
141
142
    }

    template <class F>
    void add_op(std::string name, F f)
Paul's avatar
Paul committed
143
144
145
146
147
148
149
150
151
    {
        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
152
153
154
155
156
157
158
    {
        ops.emplace(name, f);
    }

    template <class F>
    void add_mem_op(std::string name, F f)
    {
Paul's avatar
Paul committed
159
        add_op(name, [=](auto&&... xs) {
Paul's avatar
Paul committed
160
161
162
            return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...);
        });
    }
Khalique's avatar
Khalique committed
163

164
    template <class T>
Khalique's avatar
Khalique committed
165
    void add_binary_op(std::string name, T x)
166
    {
Paul's avatar
Paul committed
167
        add_op(name, [this, x](attribute_map attributes, std::vector<instruction_ref> args) {
Scott Thornton's avatar
Scott Thornton committed
168
            if(args.size() != 2)
Paul's avatar
Paul committed
169
                MIGRAPHX_THROW("binary operators should have 2 operands");
170
            if(contains(attributes, "broadcast") and contains(attributes, "axis"))
171
172
173
174
            {
                uint64_t broadcasted = parse_value(attributes.at("broadcast")).at<uint64_t>();
                if(broadcasted != 0)
                {
175
                    uint64_t axis = parse_value(attributes.at("axis")).at<uint64_t>();
Shucai Xiao's avatar
Shucai Xiao committed
176
177
                    auto l = prog.add_instruction(op::broadcast{axis, args[0]->get_shape().lens()},
                                                  args[1]);
178
179
                    return prog.add_instruction(x, args[0], l);
                }
180
                return prog.add_instruction(x, args);
181
            }
Paul's avatar
Paul committed
182
            else
183
            {
Khalique's avatar
Khalique committed
184
                return add_broadcastable_binary_op(args[0], args[1], x);
185
186
187
188
            }
        });
    }

Shucai Xiao's avatar
Shucai Xiao committed
189
190
    std::vector<std::size_t> compute_broadcasted_lens(std::vector<std::size_t> s0,
                                                      std::vector<std::size_t> s1)
191
192
193
194
195
196
197
198
199
200
201
202
203
    {
        // 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)
Shucai Xiao's avatar
Shucai Xiao committed
204
        if(s0.size() > s1.size())
205
206
207
208
209
210
        {
            s0.swap(s1);
        }

        std::vector<std::size_t> out_lens(s1);
        auto offset = s1.size() - s0.size();
Shucai Xiao's avatar
Shucai Xiao committed
211
212
213
214
        std::transform(s0.begin(),
                       s0.end(),
                       s1.begin() + offset,
                       out_lens.begin() + offset,
215
                       [&](auto a, auto b) {
Shucai Xiao's avatar
Shucai Xiao committed
216
                           if(a != b and a != 1 and b != 1)
217
                           {
Shucai Xiao's avatar
Shucai Xiao committed
218
219
220
221
222
223
                               MIGRAPHX_THROW("COMPUTE_BROADCASTLEN: shape {" +
                                              to_string_range(s0) + "} and {" +
                                              to_string_range(s1) + "} mismatch!");
                           }
                           return std::max(a, b);
                       });
224
225
226
227

        return out_lens;
    }

Shucai Xiao's avatar
Shucai Xiao committed
228
229
    instruction_ref make_contiguous(instruction_ref ins)
    {
Shucai Xiao's avatar
Shucai Xiao committed
230
        if(ins->get_shape().standard())
Shucai Xiao's avatar
Shucai Xiao committed
231
232
233
234
235
236
237
        {
            return ins;
        }

        return prog.add_instruction(op::contiguous{}, ins);
    }

Khalique's avatar
Khalique committed
238
239
240
    template <class T>
    instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
    {
Khalique's avatar
Khalique committed
241
        if(arg0->get_shape().lens() != arg1->get_shape().lens())
Khalique's avatar
Khalique committed
242
243
        {
            // Get lengths for both arguments
Shucai Xiao's avatar
Shucai Xiao committed
244
245
            auto s0       = arg0->get_shape().lens();
            auto s1       = arg1->get_shape().lens();
246
            auto out_lens = compute_broadcasted_lens(s0, s1);
247
248
249
250
251
252
253
254
255

            auto l0 = arg0;
            if(arg0->get_shape().lens() != out_lens)
                l0 = prog.add_instruction(op::multibroadcast{out_lens}, arg0);

            auto l1 = arg1;
            if(arg1->get_shape().lens() != out_lens)
                l1 = prog.add_instruction(op::multibroadcast{out_lens}, arg1);

Khalique's avatar
Khalique committed
256
257
258
259
260
261
            return prog.add_instruction(x, l0, l1);
        }
        else
        {
            return prog.add_instruction(x, {arg0, arg1});
        }
262
263
    }

Paul's avatar
Paul committed
264
    template <class T>
Paul's avatar
Paul committed
265
266
    void add_generic_op(std::string name, T x)
    {
Paul's avatar
Paul committed
267
        add_op(name, [this, x](const attribute_map&, std::vector<instruction_ref> args) {
Paul's avatar
Paul committed
268
269
270
271
            return prog.add_instruction(x, args);
        });
    }

Khalique's avatar
Khalique committed
272
    template <class T>
Khalique's avatar
Khalique committed
273
    void add_variadic_op(std::string name, T x)
Khalique's avatar
Khalique committed
274
    {
Paul's avatar
Paul committed
275
        add_op(name, [this, x](const attribute_map&, std::vector<instruction_ref> args) {
Khalique's avatar
Khalique committed
276
            return std::accumulate(std::next(args.begin()),
Khalique's avatar
Khalique committed
277
278
279
280
281
                                   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
282
        });
Khalique's avatar
Khalique committed
283
284
    }

kahmed10's avatar
kahmed10 committed
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
    template <class T>
    std::vector<int64_t> to_int64_vector(const std::vector<T>& input_vector)
    {
        std::vector<int64_t> output_vector(input_vector.begin(), input_vector.end());
        return output_vector;
    }

    instruction_ref
    add_bias(const std::vector<instruction_ref>& args, instruction_ref curr_ins, uint64_t axis)
    {
        if(args.size() == 3)
        {
            auto bias_bcast =
                prog.add_instruction(op::broadcast{axis, curr_ins->get_shape().lens()}, args[2]);
            return prog.add_instruction(op::add{}, curr_ins, bias_bcast);
        }
        return curr_ins;
    }

Khalique's avatar
Khalique committed
304
305
306
    instruction_ref parse_clip(const std::string&,
                               const attribute_map& attributes,
                               std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
307
308
309
310
311
312
313
314
315
316
317
318
319
    {
        op::clip op;
        if(contains(attributes, "max"))
        {
            op.max_val = parse_value(attributes.at("max")).at<float>();
        }
        if(contains(attributes, "min"))
        {
            op.min_val = parse_value(attributes.at("min")).at<float>();
        }
        return prog.add_instruction(op, std::move(args));
    }

Shucai Xiao's avatar
Shucai Xiao committed
320
    template <class Op>
321
    instruction_ref parse_softmax(const std::string&,
Shucai Xiao's avatar
Shucai Xiao committed
322
323
                                  const attribute_map& attributes,
                                  std::vector<instruction_ref> args)
Paul's avatar
Paul committed
324
    {
325
        int64_t axis = 1;
326
327
328
329
330
        if(contains(attributes, "axis"))
        {
            axis = parse_value(attributes.at("axis")).at<int>();
        }

331
        return prog.add_instruction(Op{axis}, std::move(args));
Shucai Xiao's avatar
Shucai Xiao committed
332
333
    }

Shucai Xiao's avatar
Shucai Xiao committed
334
    template <class Op>
335
    instruction_ref parse_arg_op(const std::string&,
Shucai Xiao's avatar
Shucai Xiao committed
336
337
                                 const attribute_map& attributes,
                                 std::vector<instruction_ref> args)
338
    {
339
        int64_t axis = 0;
340
341
        if(contains(attributes, "axis"))
        {
342
            axis = static_cast<int64_t>(parse_value(attributes.at("axis")).at<int>());
343
344
        }

Shucai Xiao's avatar
Shucai Xiao committed
345
        int keep_dims = 1;
Shucai Xiao's avatar
Shucai Xiao committed
346
        if(contains(attributes, "keepdims"))
Shucai Xiao's avatar
Shucai Xiao committed
347
348
349
350
        {
            keep_dims = parse_value(attributes.at("keepdims")).at<int>();
        }

Shucai Xiao's avatar
Shucai Xiao committed
351
        if(keep_dims == 0)
352
        {
353
            auto ins = prog.add_instruction(Op{axis}, std::move(args));
354
            return prog.add_instruction(op::squeeze{{axis}}, ins);
355
356
357
        }
        else
        {
358
            return prog.add_instruction(Op{axis}, std::move(args));
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
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
    template <class Op>
    instruction_ref process_auto_pad_attribute(instruction_ref ins,
                                               attribute_map& attributes,
                                               Op& op,
                                               const std::vector<std::size_t>& in_lens)
    {
        if(!contains(attributes, "auto_pad"))
        {
            return ins;
        }

        auto auto_pad = attributes["auto_pad"].s();
        if(auto_pad.find("SAME") != std::string::npos)
        {
            // calculate the padding
            std::array<std::size_t, 2> out_lens;
            out_lens[0] = (in_lens[2] + op.stride[0] - 1) / op.stride[0];
            out_lens[1] = (in_lens[3] + op.stride[1] - 1) / op.stride[1];

            std::array<std::size_t, 2> explicit_pads;
            explicit_pads[0] = (out_lens[0] - 1) * op.stride[0] + op.lengths[0] - in_lens[2];
            explicit_pads[1] = (out_lens[1] - 1) * op.stride[1] + op.lengths[1] - in_lens[3];
            op.padding[0]    = explicit_pads[0] / 2;
            op.padding[1]    = explicit_pads[1] / 2;
            explicit_pads[0] -= 2 * op.padding[0];
            explicit_pads[1] -= 2 * op.padding[1];
            std::vector<std::int64_t> pads(8, 0);
            if(explicit_pads[0] != 0 or explicit_pads[1] != 0)
            {
                if(auto_pad == "SAME_UPPER")
                {
                    pads[6] = explicit_pads[0];
                    pads[7] = explicit_pads[1];
                }
                else if(auto_pad == "SAME_LOWER")
                {
                    pads[2] = explicit_pads[0];
                    pads[3] = explicit_pads[1];
                }

                // MaxPool
                if(op.mode == "max")
                {
                    ins = prog.add_instruction(op::pad{pads, std::numeric_limits<float>::lowest()},
                                               ins);
                }
                // AveragePool
                else
                {
                    ins = prog.add_instruction(op::pad{pads}, ins);
                }
            }

            op.padding_mode = op::padding_mode_t::same;
        }

        return ins;
    }

421
    template <class Op>
Paul's avatar
Paul committed
422
    instruction_ref
Paul's avatar
Paul committed
423
    parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
424
    {
425
        Op op;
426
        auto l0 = args[0];
Paul's avatar
Paul committed
427
428
        if(contains(attributes, "pads"))
        {
Scott Thornton's avatar
Scott Thornton committed
429
            if(contains(attributes, "auto_pad"))
430
            {
431
432
433
434
435
                auto s = attributes["auto_pad"].s();
                if(contains(attributes, "pads") and to_upper(s) != "NOTSET")
                {
                    MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
                }
436
            }
437
438
            std::vector<std::int64_t> padding;
            copy(attributes["pads"].ints(), std::back_inserter(padding));
Scott Thornton's avatar
Scott Thornton committed
439
            if(padding.size() != 4)
440
            {
Paul's avatar
Paul committed
441
                MIGRAPHX_THROW("padding should have 4 values");
442
            }
Scott Thornton's avatar
Scott Thornton committed
443
            if(padding[0] != padding[2] || padding[1] != padding[3])
444
            {
445
446
                // 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
447
                l0      = prog.add_instruction(op::pad{padding}, l0);
448
            }
449
450
451
452
            else
            {
                op.padding[0] = padding[0];
                op.padding[1] = padding[1];
453
            }
Paul's avatar
Paul committed
454
        }
Paul's avatar
Paul committed
455
456
457
458
459
460
461
462
        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
463
        if(contains(attributes, "auto_pad"))
464
465
        {
            auto s = attributes["auto_pad"].s();
Scott Thornton's avatar
Scott Thornton committed
466
            if(contains(attributes, "pads") and to_upper(s) != "NOTSET")
467
            {
Paul's avatar
Paul committed
468
                MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
469
470
            }

wsttiger's avatar
fixes  
wsttiger committed
471
            if(s.find("SAME") != std::string::npos)
472
            {
473
                op.padding_mode = op::padding_mode_t::same;
474
475
            }
        }
Khalique's avatar
Khalique committed
476
477
478
479
        if(contains(attributes, "group"))
        {
            op.group = parse_value(attributes.at("group")).at<int>();
        }
kahmed10's avatar
kahmed10 committed
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522

        auto l1 = prog.add_instruction(op, l0, args[1]);
        return add_bias(args, l1, 1);
    }

    instruction_ref parse_conv_transpose(const std::string&,
                                         attribute_map attributes,
                                         std::vector<instruction_ref> args)
    {
        op::deconvolution op;
        auto l0 = args[0];
        std::vector<std::int64_t> padding;
        bool asymm_padding = false;
        if(contains(attributes, "pads"))
        {
            if(contains(attributes, "auto_pad"))
            {
                auto s = attributes["auto_pad"].s();
                if(contains(attributes, "pads") and to_upper(s) != "NOTSET")
                {
                    MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
                }
            }
            copy(attributes["pads"].ints(), std::back_inserter(padding));
            if(padding.size() != 4)
            {
                MIGRAPHX_THROW("padding should have 4 values");
            }
            if(padding[0] != padding[2] || padding[1] != padding[3])
            {
                asymm_padding = true;
            }
            else
            {
                op.padding[0] = padding[0];
                op.padding[1] = padding[1];
            }
        }
        if(contains(attributes, "strides"))
        {
            copy(attributes["strides"].ints(), op.stride.begin());
        }
        if(contains(attributes, "dilations"))
Paul's avatar
Paul committed
523
        {
kahmed10's avatar
kahmed10 committed
524
            copy(attributes["dilations"].ints(), op.dilation.begin());
Paul's avatar
Paul committed
525
        }
kahmed10's avatar
kahmed10 committed
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
        if(contains(attributes, "auto_pad"))
        {
            auto s = attributes["auto_pad"].s();
            if(contains(attributes, "pads") and to_upper(s) != "NOTSET")
            {
                MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
            }

            if(s.find("SAME") != std::string::npos)
            {
                op.padding_mode = op::padding_mode_t::same;
            }
        }

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

        auto l1                   = prog.add_instruction(op, l0, args[1]);
        std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
        std::vector<int64_t> curr_shape{dims[2], dims[3]};
        if(asymm_padding)
        {
            op::slice slice_op;
            slice_op.axes   = {0, 1, 2, 3};
            slice_op.starts = {0, 0, 0 + padding[0], 0 + padding[1]};
            slice_op.ends   = {
                dims[0], dims[1], curr_shape[0] - padding[2], curr_shape[1] - padding[3]};

            l1 = prog.add_instruction(slice_op, l1);
        }

        if(contains(attributes, "output_padding"))
        {
            std::vector<int64_t> output_padding;
            copy(attributes["output_padding"].ints(), std::back_inserter(output_padding));
            output_padding = {0, 0, 0, 0, 0, 0, output_padding[0], output_padding[1]};
            l1             = prog.add_instruction(op::pad{output_padding}, l1);
        }

        if(contains(attributes, "output_shape"))
        {
            std::vector<int64_t> output_shape;
            copy(attributes["output_shape"].ints(), std::back_inserter(output_shape));
            dims       = to_int64_vector(l1->get_shape().lens());
            curr_shape = {dims[2], dims[3]};
            if(curr_shape != output_shape)
            {
                std::vector<int64_t> target_padding = {0,
                                                       0,
                                                       0,
                                                       0,
                                                       0,
                                                       0,
                                                       output_shape[0] - curr_shape[0],
                                                       output_shape[1] - curr_shape[1]};
                l1 = prog.add_instruction(op::pad{target_padding}, l1);
            }
        }

        return add_bias(args, l1, 1);
Paul's avatar
Paul committed
588
    }
Paul's avatar
Paul committed
589

Paul's avatar
Paul committed
590
591
592
    instruction_ref parse_pooling(const std::string& name,
                                  attribute_map attributes,
                                  std::vector<instruction_ref> args)
Paul's avatar
Paul committed
593
    {
Khalique's avatar
Khalique committed
594
        op::pooling op{ends_with(name, "MaxPool") ? "max" : "average"};
595
        auto l0 = args[0];
Khalique's avatar
Khalique committed
596
        if(starts_with(name, "Global"))
597
        {
Khalique's avatar
Khalique committed
598
599
            auto lens  = args.front()->get_shape().lens();
            op.lengths = {lens[2], lens[3]};
600
        }
601

Paul's avatar
Paul committed
602
603
        if(contains(attributes, "pads"))
        {
604
605
606
607
608
609
610
611
612
613
            if(contains(attributes, "auto_pad"))
            {
                auto s = attributes["auto_pad"].s();
                if(to_upper(s) != "NOTSET")
                {
                    MIGRAPHX_THROW(
                        "PARSE_POOLING: auto_pad and padding cannot be specified simultaneously");
                }
            }

614
615
            std::vector<std::int64_t> padding;
            copy(attributes["pads"].ints(), std::back_inserter(padding));
Scott Thornton's avatar
Scott Thornton committed
616
            if(padding.size() != 4)
617
            {
618
                MIGRAPHX_THROW("PARSE_POOLING: padding should have 4 values");
619
            }
Scott Thornton's avatar
Scott Thornton committed
620
            if(padding[0] != padding[2] || padding[1] != padding[3])
621
            {
622
623
                // insert zeros for pad op (args[0] has 4 dims)
                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
624
625
626
627
628
629
630
631
632
633
634
                // MaxPool
                if(op.mode == "max")
                {
                    l0 = prog.add_instruction(
                        op::pad{padding, std::numeric_limits<float>::lowest()}, l0);
                }
                // AveragePool
                else
                {
                    l0 = prog.add_instruction(op::pad{padding}, l0);
                }
635
636
637
638
639
            }
            else
            {
                op.padding[0] = padding[0];
                op.padding[1] = padding[1];
640
            }
Paul's avatar
Paul committed
641
        }
642

Paul's avatar
Paul committed
643
644
645
646
647
648
649
650
        if(contains(attributes, "strides"))
        {
            copy(attributes["strides"].ints(), op.stride.begin());
        }
        if(contains(attributes, "kernel_shape"))
        {
            copy(attributes["kernel_shape"].ints(), op.lengths.begin());
        }
651

Scott Thornton's avatar
Scott Thornton committed
652
        if(contains(attributes, "auto_pad"))
653
        {
654
655
            auto in_lens = args[0]->get_shape().lens();
            l0           = process_auto_pad_attribute(l0, attributes, op, in_lens);
656
657
        }

658
        return prog.add_instruction(op, l0);
Paul's avatar
Paul committed
659
660
    }

Paul's avatar
Paul committed
661
    instruction_ref
Paul's avatar
Paul committed
662
    parse_reshape(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
663
    {
664
        op::reshape op;
Paul's avatar
Paul committed
665
666
        if(args.size() == 1)
        {
667
668
            literal s = parse_value(attributes.at("shape"));
            s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
Paul's avatar
Paul committed
669
670
671
        }
        if(args.size() == 2)
        {
Paul's avatar
Paul committed
672
            auto s = args[1]->eval();
Shucai Xiao's avatar
Shucai Xiao committed
673
            check_arg_empty(s, "Reshape: dynamic shape is not supported");
Paul's avatar
Paul committed
674
            s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
Paul's avatar
Paul committed
675
        }
676

Shucai Xiao's avatar
Shucai Xiao committed
677
        return prog.add_instruction(op, make_contiguous(args[0]));
Paul's avatar
Paul committed
678
679
    }

Paul's avatar
Paul committed
680
    instruction_ref
Paul's avatar
Paul committed
681
    parse_flatten(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
682
    {
683
        int64_t axis = 1;
Paul's avatar
Paul committed
684
685
686
687
        if(contains(attributes, "axis"))
        {
            axis = parse_value(attributes.at("axis")).at<int>();
        }
688
        return prog.add_instruction(op::flatten{axis}, args[0]);
Paul's avatar
Paul committed
689
690
    }

691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
    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
709
710
711
    instruction_ref
    parse_concat(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
Shucai Xiao's avatar
Shucai Xiao committed
712
713
714
715
716
717
718
        // change to hande axis to be negative values
        if(!contains(attributes, "axis"))
        {
            MIGRAPHX_THROW("PARSE_CONCAT: attribute axis is required!");
        }

        int axis = parse_value(attributes.at("axis")).at<int>();
Scott Thornton's avatar
Scott Thornton committed
719
720
721
        op::concat op{axis};
        return prog.add_instruction(op, std::move(args));
    }
722

723
724
725
    instruction_ref
    parse_gather(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
726
        int axis = 0;
727
728
729
730
        if(contains(attributes, "axis"))
        {
            axis = parse_value(attributes.at("axis")).at<int>();
        }
731

732
        op::gather op{axis};
Shucai Xiao's avatar
Shucai Xiao committed
733
        return prog.add_instruction(op, make_contiguous(args[0]), make_contiguous(args[1]));
734
735
    }

736
737
738
739
    instruction_ref
    parse_slice(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::slice op;
Khalique's avatar
Khalique committed
740
        std::vector<size_t> dims = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
741
        size_t num_dims          = dims.size();
742
743
744
745
746
        if(contains(attributes, "axes"))
        {
            literal s = parse_value(attributes.at("axes"));
            s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
        }
Khalique's avatar
Khalique committed
747
748
749
750
751
        else
        {
            op.axes = std::vector<int64_t>(num_dims);
            std::iota(op.axes.begin(), op.axes.end(), 0);
        }
Khalique's avatar
Khalique committed
752

Khalique's avatar
Khalique committed
753
        if(contains(attributes, "ends"))
754
        {
Paul's avatar
Paul committed
755
            op.ends = get_indices(attributes.at("ends"));
756
        }
Khalique's avatar
Khalique committed
757
        if(contains(attributes, "starts"))
758
759
760
761
762
763
764
        {
            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
765
766
767
    instruction_ref parse_constant(const std::string&,
                                   attribute_map attributes,
                                   const std::vector<instruction_ref>&)
Paul's avatar
Paul committed
768
    {
Shucai Xiao's avatar
Shucai Xiao committed
769
        literal v = parse_value(attributes.at("value"));
770
        // return empty literal
Shucai Xiao's avatar
Shucai Xiao committed
771
        if(v.get_shape().elements() == 0)
772
773
774
775
        {
            return prog.add_literal(literal{});
        }

776
777
778
        auto dim_size = attributes.at("value").t().dims_size();
        // if dim_size is 0, it is a scalar
        if(dim_size == 0)
779
        {
780
            migraphx::shape scalar_shape{v.get_shape().type()};
781
782
783
            return prog.add_literal(migraphx::literal{scalar_shape, v.data()});
        }

Paul's avatar
Paul committed
784
785
        return prog.add_literal(v);
    }
Paul's avatar
Paul committed
786

Paul's avatar
Paul committed
787
    instruction_ref
Paul's avatar
Paul committed
788
    parse_gemm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Paul's avatar
Paul committed
789
790
    {
        float alpha = 1.0f;
Khalique's avatar
Khalique committed
791
        float beta  = 1.0f;
Paul's avatar
Paul committed
792
793
794
795
796
797
798
799
        bool transa = false;
        bool transb = false;
        if(contains(attributes, "alpha"))
        {
            alpha = parse_value(attributes.at("alpha")).at<float>();
        }
        if(contains(attributes, "beta"))
        {
800
            beta = parse_value(attributes.at("beta")).at<float>();
Paul's avatar
Paul committed
801
802
803
804
805
806
807
808
809
        }
        if(contains(attributes, "transA"))
        {
            transa = parse_value(attributes.at("transA")).at<bool>();
        }
        if(contains(attributes, "transB"))
        {
            transb = parse_value(attributes.at("transB")).at<bool>();
        }
810
811
812
813
814
815

        std::vector<int64_t> perm(args[0]->get_shape().lens().size());
        std::iota(perm.begin(), perm.end(), int64_t{0});
        // swap the last two elements
        std::swap(*perm.rbegin(), *(perm.rbegin() + 1));

816
817
        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
818
819
        if(args.size() == 3)
        {
820
            if(beta != 0.f && args[2]->get_shape().elements() > 0)
821
            {
Shucai Xiao's avatar
Shucai Xiao committed
822
                auto out_lens   = l1->get_shape().lens();
823
                out_lens.back() = l2->get_shape().lens().back();
Shucai Xiao's avatar
Shucai Xiao committed
824
                auto l3         = args[2];
Shucai Xiao's avatar
Shucai Xiao committed
825
826
                auto l3_lens    = l3->get_shape().lens();
                if(!std::equal(out_lens.begin(), out_lens.end(), l3_lens.begin(), l3_lens.end()))
Khalique's avatar
Khalique committed
827
                {
828
                    l3 = prog.add_instruction(op::multibroadcast{out_lens}, args[2]);
Khalique's avatar
Khalique committed
829
                }
830
                return prog.add_instruction(op::dot{alpha, beta}, l1, l2, l3);
831
            }
Paul's avatar
Paul committed
832
        }
833
834

        return prog.add_instruction(op::dot{alpha, beta}, l1, l2);
Paul's avatar
Paul committed
835
836
    }

837
    template <class Op>
838
    instruction_ref
Shucai Xiao's avatar
Shucai Xiao committed
839
    parse_matmul(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
840
    {
Shucai Xiao's avatar
Shucai Xiao committed
841
842
        auto l0      = args[0];
        auto l1      = args[1];
843
844
845
846
847
        auto l0_lens = l0->get_shape().lens();
        auto l1_lens = l1->get_shape().lens();

        // args[0] is a vector, prepend 1 to the shape
        bool is_a_prepended = false;
Shucai Xiao's avatar
Shucai Xiao committed
848
        if(l0_lens.size() == 1)
849
850
851
852
853
854
855
        {
            is_a_prepended = true;
            l0_lens.insert(l0_lens.begin(), 1);
            l0 = prog.add_instruction(op::unsqueeze{{0}}, args[0]);
        }

        bool is_b_appended = false;
Shucai Xiao's avatar
Shucai Xiao committed
856
        if(l1_lens.size() == 1)
857
858
859
860
861
862
863
864
        {
            is_b_appended = true;
            l1_lens.push_back(1);
            l1 = prog.add_instruction(op::unsqueeze{{1}}, args[1]);
        }

        instruction_ref bl0 = l0;
        instruction_ref bl1 = l1;
Shucai Xiao's avatar
Shucai Xiao committed
865
        if(!std::equal(l0_lens.rbegin() + 2, l0_lens.rend(), l1_lens.rbegin() + 2, l1_lens.rend()))
866
867
868
869
870
871
        {
            auto l0_it = l0_lens.begin() + l0_lens.size() - 2;
            std::vector<std::size_t> l0_broadcasted_lens(l0_lens.begin(), l0_it);
            auto l1_it = l1_lens.begin() + l1_lens.size() - 2;
            std::vector<std::size_t> l1_broadcasted_lens(l1_lens.begin(), l1_it);
            auto output_lens = compute_broadcasted_lens(l0_broadcasted_lens, l1_broadcasted_lens);
872
            l0_broadcasted_lens = output_lens;
873
            l0_broadcasted_lens.insert(l0_broadcasted_lens.end(), l0_it, l0_lens.end());
874
            l1_broadcasted_lens = output_lens;
875
            l1_broadcasted_lens.insert(l1_broadcasted_lens.end(), l1_it, l1_lens.end());
Shucai Xiao's avatar
Shucai Xiao committed
876
            if(l0_lens != l0_broadcasted_lens)
877
878
879
            {
                bl0 = prog.add_instruction(op::multibroadcast{l0_broadcasted_lens}, l0);
            }
Shucai Xiao's avatar
Shucai Xiao committed
880
            if(l1_lens != l1_broadcasted_lens)
881
882
883
884
885
            {
                bl1 = prog.add_instruction(op::multibroadcast{l1_broadcasted_lens}, l1);
            }
        }

886
        auto dot_res     = prog.add_instruction(Op{1, 0}, bl0, bl1);
887
        int64_t num_axis = static_cast<int64_t>(dot_res->get_shape().lens().size());
Shucai Xiao's avatar
Shucai Xiao committed
888
        if(is_a_prepended)
889
890
891
892
        {
            dot_res = prog.add_instruction(op::squeeze{{num_axis - 2}}, dot_res);
            --num_axis;
        }
Shucai Xiao's avatar
Shucai Xiao committed
893
        if(is_b_appended)
894
895
896
        {
            dot_res = prog.add_instruction(op::squeeze{{num_axis - 1}}, dot_res);
        }
Shucai Xiao's avatar
Shucai Xiao committed
897

898
899
900
        return dot_res;
    }

901
    instruction_ref
Paul's avatar
Paul committed
902
    parse_batchnorm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
903
    {
Scott Thornton's avatar
Scott Thornton committed
904
905
        float epsilon                                     = 1e-5f;
        float momentum                                    = 0.9f;
906
        op::batch_norm_inference::bn_infer_mode_t bn_mode = op::batch_norm_inference::spatial;
907
908
909
910
911
912
        if(contains(attributes, "epsilon"))
        {
            epsilon = parse_value(attributes.at("epsilon")).at<float>();
        }
        if(contains(attributes, "momentum"))
        {
913
            momentum = parse_value(attributes.at("momentum")).at<float>();
914
915
916
        }
        if(contains(attributes, "spatial"))
        {
917
            bn_mode = (parse_value(attributes.at("spatial")).at<uint64_t>() > 0)
918
919
                          ? op::batch_norm_inference::spatial
                          : op::batch_norm_inference::per_activation;
920
        }
Paul's avatar
Paul committed
921
        op::batch_norm_inference op{epsilon, momentum, bn_mode};
Paul's avatar
Paul committed
922
        return prog.add_instruction(op, std::move(args));
923
924
    }

kahmed10's avatar
kahmed10 committed
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
    instruction_ref parse_instancenorm(const std::string&,
                                       attribute_map attributes,
                                       std::vector<instruction_ref> args)
    {
        // y = scale * ( x - mean ) / sqrt ( variance + epsilon ) + bias
        // mean = reduce_mean({H, W}, x)
        // variance = reduce_mean({H, W}, (x - mean)^2)

        float epsilon = 1e-5f;
        if(contains(attributes, "epsilon"))
        {
            epsilon = parse_value(attributes.at("epsilon")).at<float>();
        }
        auto x     = args[0];
        auto scale = args[1];
        auto bias  = args[2];
        auto dims  = x->get_shape().lens();

        auto mean            = prog.add_instruction(op::reduce_mean{{2, 3}}, x);
        auto mean_bcast      = prog.add_instruction(op::multibroadcast{dims}, mean);
        auto l0              = prog.add_instruction(op::sqdiff{}, x, mean_bcast);
        auto variance        = prog.add_instruction(op::reduce_mean{{2, 3}}, l0);
        auto l1              = prog.add_instruction(op::sub{}, x, mean_bcast);
        auto epsilon_literal = prog.add_literal(epsilon);
        auto epsilon_bcast   = prog.add_instruction(op::multibroadcast{dims}, epsilon_literal);
        auto variance_bcast  = prog.add_instruction(op::multibroadcast{dims}, variance);
        auto l2              = prog.add_instruction(op::add{}, variance_bcast, epsilon_bcast);
        auto l3              = prog.add_instruction(op::rsqrt{}, l2);
        auto l4              = prog.add_instruction(op::mul{}, l1, l3);
        auto scale_bcast     = prog.add_instruction(op::broadcast{1, dims}, scale);
        ;
        auto bias_bcast = prog.add_instruction(op::broadcast{1, dims}, bias);
        auto l5         = prog.add_instruction(op::mul{}, l4, scale_bcast);
        return prog.add_instruction(op::add{}, l5, bias_bcast);
    }

961
962
963
964
    instruction_ref parse_leaky_relu(const std::string&,
                                     attribute_map attributes,
                                     std::vector<instruction_ref> args)
    {
Khalique's avatar
Khalique committed
965
        float alpha = 0.01; // default alpha val for leaky relu
966
967
968
969
970
971
972
973
        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
974
975
    instruction_ref
    parse_elu(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
976
977
978
979
980
981
982
983
984
985
    {
        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
986
987
    instruction_ref
    parse_lrn(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
988
989
    {
        float alpha = 0.0001;
Khalique's avatar
Khalique committed
990
991
992
        float beta  = 0.75;
        float bias  = 1.0;
        int size    = 1;
Khalique's avatar
Khalique committed
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
        if(contains(attributes, "alpha"))
            alpha = parse_value(attributes.at("alpha")).at<float>();
        if(contains(attributes, "beta"))
            beta = parse_value(attributes.at("beta")).at<float>();
        if(contains(attributes, "bias"))
            bias = parse_value(attributes.at("bias")).at<float>();
        if(contains(attributes, "size"))
            size = parse_value(attributes.at("size")).at<int>();
        op::lrn op{alpha, beta, bias, size};
        return prog.add_instruction(op, args.front());
    }

Khalique's avatar
Khalique committed
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
    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());
        }
Shucai Xiao's avatar
Shucai Xiao committed
1021
1022
1023
        auto input_shape       = args.front()->get_shape();
        auto const& input_lens = input_shape.lens();
        auto input_type        = input_shape.type();
Khalique's avatar
Khalique committed
1024

Shucai Xiao's avatar
Shucai Xiao committed
1025
1026
        auto scale_val = prog.add_literal(literal{shape{input_type}, {scale}});
        auto bias_vals = prog.add_literal(literal{shape{input_type, {bias.size()}}, bias});
Khalique's avatar
Khalique committed
1027

1028
        auto scale_tensor = prog.add_instruction(migraphx::op::scalar{input_lens}, scale_val);
Paul's avatar
Paul committed
1029
        auto img_scaled   = prog.add_instruction(migraphx::op::mul{}, args.front(), scale_tensor);
Shucai Xiao's avatar
Shucai Xiao committed
1030
        auto bias_bcast   = prog.add_instruction(migraphx::op::broadcast{1, input_lens}, bias_vals);
Paul's avatar
Paul committed
1031
        return prog.add_instruction(migraphx::op::add{}, img_scaled, bias_bcast);
Khalique's avatar
Khalique committed
1032
    }
Khalique's avatar
Khalique committed
1033

Khalique's avatar
Khalique committed
1034
1035
    instruction_ref
    parse_transpose(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
1036
1037
1038
1039
1040
1041
1042
    {
        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
1043
        return prog.add_instruction(migraphx::op::transpose{perm}, args.front());
Khalique's avatar
Khalique committed
1044
1045
    }

Khalique's avatar
Khalique committed
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
    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());
        }
1056
        // check if padding is actually being done (at least one value is nonzero)
Khalique's avatar
Khalique committed
1057
        if(std::all_of(pads.begin(), pads.end(), [](const int& i) { return i == 0; }))
1058
1059
1060
        {
            return prog.add_instruction(migraphx::op::identity{}, args.front());
        }
Khalique's avatar
Khalique committed
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
        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());
    }
1073
1074
1075
    // 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
1076
    parse_shape(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
1077
1078
    {
        if(args.size() != 1)
1079
            MIGRAPHX_THROW("Shape: operator should have 1 operand");
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
        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>();
        }
Shucai Xiao's avatar
Shucai Xiao committed
1104
        shape::type_t type = get_type(dtype);
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115

        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
1116
1117
        if(contains(attributes, "extra_shape"))
        {
1118
            MIGRAPHX_THROW("ConstantFill: cannot handle extra shape attribute");
1119
1120
        }

1121
1122
        if(input_as_shape == 1)
        {
Shucai Xiao's avatar
Shucai Xiao committed
1123
            if(args.size() != 1)
1124
            {
1125
                MIGRAPHX_THROW("ConstantFill: need an input argument as output shape");
1126
1127
            }

Shucai Xiao's avatar
Shucai Xiao committed
1128
1129
            if(contains(attributes, "shape"))
            {
1130
                MIGRAPHX_THROW("ConstantFill: cannot set the shape argument and pass in an input "
Shucai Xiao's avatar
Shucai Xiao committed
1131
                               "at the same time");
1132
1133
            }

1134
            migraphx::argument in = args[0]->eval();
Shucai Xiao's avatar
Shucai Xiao committed
1135
            check_arg_empty(in, "ConstantFill: dynamic shape is not supported");
1136

1137
1138
1139
            std::vector<std::size_t> dims;
            in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
            migraphx::shape s(type, dims);
1140
1141
            std::vector<float> values(s.elements(), value);
            return prog.add_literal(migraphx::literal(s, values));
1142
1143
1144
        }
        else if(input_as_shape == 0)
        {
Shucai Xiao's avatar
Shucai Xiao committed
1145
1146
            if(!contains(attributes, "shape"))
            {
1147
                MIGRAPHX_THROW("ConstantFill: attribute output shape is needed");
1148
1149
1150
            }

            literal ls = parse_value(attributes.at("shape"));
1151
            std::vector<std::size_t> dims;
Shucai Xiao's avatar
Shucai Xiao committed
1152
            ls.visit([&](auto s) { dims.assign(s.begin(), s.end()); });
1153
            migraphx::shape s{type, dims};
1154
1155
            std::vector<float> values(s.elements(), value);
            return prog.add_literal(migraphx::literal(s, values));
1156
1157
1158
        }
        else
        {
1159
            MIGRAPHX_THROW("ConstantFill: wrong value of attribute input_as_shape");
1160
1161
1162
        }
    }

Shucai Xiao's avatar
Shucai Xiao committed
1163
1164
1165
    instruction_ref parse_constant_of_shape(const std::string&,
                                            attribute_map attributes,
                                            std::vector<instruction_ref> args)
1166
1167
    {
        literal l_val{};
Shucai Xiao's avatar
Shucai Xiao committed
1168
        if(contains(attributes, "value"))
1169
1170
        {
            l_val = parse_value(attributes.at("value"));
Shucai Xiao's avatar
Shucai Xiao committed
1171
            if(l_val.get_shape().elements() != 1)
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
            {
                MIGRAPHX_THROW("ConstantOfShape: attribute value can contain only 1 elements!");
            }
        }
        else
        {
            l_val = literal({shape::float_type, {1}, {0}}, {0.0f});
        }

        // input is empty, output is a scalar
        auto type = l_val.get_shape().type();
1183

Shucai Xiao's avatar
Shucai Xiao committed
1184
        if(args.empty())
1185
        {
Shucai Xiao's avatar
Shucai Xiao committed
1186
            MIGRAPHX_THROW("ConstantOfShape : must have 1 input!");
1187
1188
1189
        }
        else
        {
1190
1191
            migraphx::shape s;
            // empty input tensor, output is a scalar
Shucai Xiao's avatar
Shucai Xiao committed
1192
            if(args[0]->get_shape().elements() == 0)
1193
            {
1194
                s = migraphx::shape{type, {1}, {0}};
1195
            }
1196
1197
1198
            else
            {
                migraphx::argument in = args[0]->eval();
Shucai Xiao's avatar
Shucai Xiao committed
1199
                check_arg_empty(in, "ConstantOfShape: dynamic shape is not supported");
1200

1201
1202
1203
1204
                std::vector<std::size_t> dims;
                in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
                s = migraphx::shape{type, dims};
            }
1205

Shucai Xiao's avatar
Shucai Xiao committed
1206
            literal l_out{};
1207
            l_val.visit([&](auto val) {
Shucai Xiao's avatar
Shucai Xiao committed
1208
                using val_type = std::remove_cv_t<typename decltype(val)::value_type>;
1209
                // l_val contains only one element
1210
                std::vector<val_type> out_vec(s.elements(), val.front());
1211
1212
1213
1214
1215
1216
1217
                l_out = literal(s, out_vec);
            });

            return prog.add_literal(l_out);
        }
    }

Shucai Xiao's avatar
Shucai Xiao committed
1218
    instruction_ref
Shucai Xiao's avatar
Shucai Xiao committed
1219
    parse_expand(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
1220
    {
Shucai Xiao's avatar
Shucai Xiao committed
1221
        auto in_lens             = args[0]->get_shape().lens();
1222
        migraphx::argument arg_s = args[1]->eval();
Shucai Xiao's avatar
Shucai Xiao committed
1223
        check_arg_empty(arg_s, "Expand: dynamic shape is not supported");
1224
1225
1226
        std::vector<std::size_t> dims;
        arg_s.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
        auto out_lens = compute_broadcasted_lens(in_lens, dims);
Shucai Xiao's avatar
Shucai Xiao committed
1227
        return prog.add_instruction(op::multibroadcast{out_lens}, args[0]);
1228
1229
    }

Shucai Xiao's avatar
Shucai Xiao committed
1230
    std::vector<instruction_ref>
Shucai Xiao's avatar
Shucai Xiao committed
1231
1232
1233
    parse_rnn(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        migraphx::shape input_shape = args[0]->get_shape();
1234
        std::size_t hidden_size     = args[1]->get_shape().lens()[1];
Shucai Xiao's avatar
Shucai Xiao committed
1235
1236
1237

        if(contains(attributes, "hidden_size"))
        {
Shucai Xiao's avatar
Shucai Xiao committed
1238
            std::size_t hidden_size_att = parse_value(attributes.at("hidden_size")).at<int>();
Shucai Xiao's avatar
Shucai Xiao committed
1239
            if(hidden_size != hidden_size_att)
Shucai Xiao's avatar
Shucai Xiao committed
1240
1241
1242
            {
                MIGRAPHX_THROW("RNN: hidden size mismatch in input and attribute");
            }
Shucai Xiao's avatar
Shucai Xiao committed
1243
1244
1245
1246
1247
1248
1249
1250
1251
        }

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

1252
        op::rnn_direction dirct = op::rnn_direction::forward;
Shucai Xiao's avatar
Shucai Xiao committed
1253
1254
        if(direction == "bidirectional")
        {
1255
            dirct = op::rnn_direction::bidirectional;
Shucai Xiao's avatar
Shucai Xiao committed
1256
1257
1258
        }
        else if(direction == "reverse")
        {
1259
            dirct = op::rnn_direction::reverse;
Shucai Xiao's avatar
Shucai Xiao committed
1260
1261
        }

1262
        std::vector<std::string> vec_names{"tanh"};
1263
1264
1265
1266
        if(contains(attributes, "activations"))
        {
            auto names = attributes.at("activations").strings();
            vec_names.clear();
1267
            vec_names.resize(names.size());
Shucai Xiao's avatar
Shucai Xiao committed
1268
1269
1270
            std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
                return to_lower(name);
            });
1271
1272
        }

1273
1274
1275
        auto name_it = std::find_if(vec_names.begin(), vec_names.end(), [&](auto& name) {
            return (map_actv_funcs.count(name) == 0);
        });
Shucai Xiao's avatar
Shucai Xiao committed
1276
        if(name_it != vec_names.end())
1277
1278
1279
        {
            MIGRAPHX_THROW("RNN: activation function " + std::string(*name_it) + " not supported");
        }
1280

Shucai Xiao's avatar
Shucai Xiao committed
1281
        // bidirectional case should have two activation functions.
Shucai Xiao's avatar
Shucai Xiao committed
1282
        // one is for forward, and the other is for reverse.
Shucai Xiao's avatar
Shucai Xiao committed
1283
        // if only one actv function is provided, we use it in both
1284
        // forward and reverse direction
1285
        if(dirct == op::rnn_direction::bidirectional)
1286
        {
Shucai Xiao's avatar
Shucai Xiao committed
1287
            if(vec_names.size() == 1)
1288
1289
1290
1291
1292
            {
                vec_names.push_back(vec_names.at(0));
            }
        }

Shucai Xiao's avatar
Shucai Xiao committed
1293
        std::vector<operation> vec_actv_funcs(vec_names.size());
Paul's avatar
Paul committed
1294
1295
1296
1297
        std::transform(vec_names.begin(),
                       vec_names.end(),
                       vec_actv_funcs.begin(),
                       [&](const auto& fn) { return map_actv_funcs[fn]; });
Shucai Xiao's avatar
Shucai Xiao committed
1298

Shucai Xiao's avatar
Shucai Xiao committed
1299
1300
1301
1302
1303
1304
1305
        // To be added later
        float clip = 0.0;
        if(contains(attributes, "clip"))
        {
            clip = parse_value(attributes.at("clip")).at<float>();
        }

1306
1307
        // if the number of arguments is less than 6, append
        // undefined operator to have 6 arguments
Shucai Xiao's avatar
Shucai Xiao committed
1308
        if(args.size() < 6)
1309
1310
1311
1312
1313
        {
            auto ins = prog.add_instruction(op::undefined{});
            args.insert(args.end(), (6 - args.size()), ins);
        }

Shucai Xiao's avatar
Shucai Xiao committed
1314
1315
        // 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
1316
                                                  std::move(args));
Shucai Xiao's avatar
Shucai Xiao committed
1317

1318
        // second output for the last hidden state
Shucai Xiao's avatar
Shucai Xiao committed
1319
        auto last_output = prog.add_instruction(op::rnn_last_output{}, hidden_states);
Shucai Xiao's avatar
Shucai Xiao committed
1320

Shucai Xiao's avatar
Shucai Xiao committed
1321
        return {hidden_states, last_output};
Shucai Xiao's avatar
Shucai Xiao committed
1322
1323
    }

1324
    std::vector<instruction_ref>
1325
1326
1327
1328
1329
1330
1331
    parse_gru(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        migraphx::shape input_shape = args[0]->get_shape();
        std::size_t hidden_size     = args[2]->get_shape().lens()[2];

        if(contains(attributes, "hidden_size"))
        {
Shucai Xiao's avatar
Shucai Xiao committed
1332
            std::size_t hidden_size_att = parse_value(attributes.at("hidden_size")).at<int>();
Shucai Xiao's avatar
Shucai Xiao committed
1333
            if(hidden_size != hidden_size_att)
Shucai Xiao's avatar
Shucai Xiao committed
1334
1335
1336
            {
                MIGRAPHX_THROW("GRU: hidden size mismatch in input and attribute");
            }
1337
1338
1339
1340
1341
1342
1343
1344
1345
        }

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

1346
        op::rnn_direction dirct = op::rnn_direction::forward;
1347
1348
        if(direction == "bidirectional")
        {
1349
            dirct = op::rnn_direction::bidirectional;
1350
1351
1352
        }
        else if(direction == "reverse")
        {
1353
            dirct = op::rnn_direction::reverse;
1354
1355
        }

1356
        std::vector<std::string> vec_names = {"sigmoid", "tanh"};
1357
1358
        if(contains(attributes, "activations"))
        {
1359
            auto names = attributes.at("activations").strings();
1360
            vec_names.clear();
Shucai Xiao's avatar
Shucai Xiao committed
1361
            vec_names.resize(names.size());
Shucai Xiao's avatar
Shucai Xiao committed
1362
1363
1364
            std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
                return to_lower(name);
            });
1365
1366
        }

1367
        // need 4 activation functions
1368
        if(dirct == op::rnn_direction::bidirectional)
1369
        {
Shucai Xiao's avatar
Shucai Xiao committed
1370
            // 4 activation functions are used in the bidirectional
1371
            // scenario. No spec is provided in onnx::operator. we
Shucai Xiao's avatar
Shucai Xiao committed
1372
1373
            // use the algorithm that: if 1 actv function is provided,
            // repeat 1 four times. If 2 actv functins are provided,
1374
1375
            // assume forward and reverse use the same pair of actv
            // functions. For the case of 3 actv functions provided,
Shucai Xiao's avatar
Shucai Xiao committed
1376
1377
1378
            // assume the 3rd one is repeated once and used by the
            // reverse direction.
            // This may need change later
1379
            if(vec_names.size() == 1)
1380
            {
1381
                vec_names.insert(vec_names.end(), 3, vec_names.at(0));
1382
            }
1383
            else if(vec_names.size() == 2)
1384
            {
1385
1386
1387
                // repeat the activation functions
                vec_names.push_back(vec_names.at(0));
                vec_names.push_back(vec_names.at(1));
1388
            }
1389
            else if(vec_names.size() == 3)
1390
            {
1391
                vec_names.push_back(vec_names.at(2));
1392
1393
            }
        }
Shucai Xiao's avatar
Shucai Xiao committed
1394
        else
1395
        {
1396
            if(vec_names.size() == 1)
1397
            {
1398
                vec_names.push_back(vec_names.at(0));
1399
1400
1401
            }
        }

1402
1403
1404
        auto name_it = std::find_if(vec_names.begin(), vec_names.end(), [&](auto& name) {
            return (map_actv_funcs.count(name) == 0);
        });
Shucai Xiao's avatar
Shucai Xiao committed
1405
        if(name_it != vec_names.end())
1406
1407
1408
        {
            MIGRAPHX_THROW("GRU: activation function " + std::string(*name_it) + " not supported");
        }
1409

Shucai Xiao's avatar
Shucai Xiao committed
1410
        std::vector<operation> vec_actv_funcs(vec_names.size());
Paul's avatar
Paul committed
1411
1412
1413
1414
        std::transform(vec_names.begin(),
                       vec_names.end(),
                       vec_actv_funcs.begin(),
                       [&](const auto& name) { return map_actv_funcs[name]; });
1415
1416
1417
1418
1419
1420
1421
1422

        float clip = 0.0;
        if(contains(attributes, "clip"))
        {
            clip = parse_value(attributes.at("clip")).at<float>();
        }

        int linear_before_reset = 0;
Shucai Xiao's avatar
Shucai Xiao committed
1423
        if(contains(attributes, "linear_before_reset"))
1424
1425
1426
1427
        {
            linear_before_reset = parse_value(attributes.at("linear_before_reset")).at<int>();
        }

Shucai Xiao's avatar
Shucai Xiao committed
1428
        // append undefined opeator to make 6 arguments
Shucai Xiao's avatar
Shucai Xiao committed
1429
        if(args.size() < 6)
Shucai Xiao's avatar
Shucai Xiao committed
1430
1431
1432
1433
1434
        {
            auto ins = prog.add_instruction(op::undefined{});
            args.insert(args.end(), 6 - args.size(), ins);
        }

1435
1436
        // first output for concatenation of hidden states
        auto hidden_states = prog.add_instruction(
Shucai Xiao's avatar
Shucai Xiao committed
1437
            op::gru{hidden_size, vec_actv_funcs, dirct, clip, linear_before_reset},
Shucai Xiao's avatar
Shucai Xiao committed
1438
            std::move(args));
1439
1440

        // second output for last gru output
1441
        auto last_output = prog.add_instruction(op::rnn_last_output{}, hidden_states);
1442

Shucai Xiao's avatar
Shucai Xiao committed
1443
        return {hidden_states, last_output};
1444
1445
    }

Shucai Xiao's avatar
Shucai Xiao committed
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
    std::vector<instruction_ref>
    parse_lstm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        migraphx::shape input_shape = args[0]->get_shape();
        std::size_t hidden_size     = args[2]->get_shape().lens()[2];

        if(contains(attributes, "hidden_size"))
        {
            std::size_t hidden_size_att = parse_value(attributes.at("hidden_size")).at<int>();
            if(hidden_size != hidden_size_att)
            {
                MIGRAPHX_THROW("LSTM: hidden size mismatch in input and attribute");
            }
        }

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

Shucai Xiao's avatar
Shucai Xiao committed
1468
        op::rnn_direction dirct = op::rnn_direction::forward;
Shucai Xiao's avatar
Shucai Xiao committed
1469
1470
        if(direction == "bidirectional")
        {
Shucai Xiao's avatar
Shucai Xiao committed
1471
            dirct = op::rnn_direction::bidirectional;
Shucai Xiao's avatar
Shucai Xiao committed
1472
1473
1474
        }
        else if(direction == "reverse")
        {
Shucai Xiao's avatar
Shucai Xiao committed
1475
            dirct = op::rnn_direction::reverse;
Shucai Xiao's avatar
Shucai Xiao committed
1476
        }
Shucai Xiao's avatar
Shucai Xiao committed
1477
        else if(direction == "forward")
Shucai Xiao's avatar
Shucai Xiao committed
1478
        {
Shucai Xiao's avatar
Shucai Xiao committed
1479
            dirct = op::rnn_direction::forward;
Shucai Xiao's avatar
Shucai Xiao committed
1480
1481
1482
1483
1484
1485
        }
        else
        {
            MIGRAPHX_THROW("LSTM: incorrect direction attribute");
        }

1486
        std::vector<std::string> vec_names = {"sigmoid", "tanh", "tanh"};
Shucai Xiao's avatar
Shucai Xiao committed
1487
1488
1489
1490
1491
        if(contains(attributes, "activations"))
        {
            auto names = attributes.at("activations").strings();
            vec_names.clear();
            vec_names.resize(names.size());
Shucai Xiao's avatar
Shucai Xiao committed
1492
1493
1494
            std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
                return to_lower(name);
            });
Shucai Xiao's avatar
Shucai Xiao committed
1495
1496
1497
        }

        // need 6 activation functions for bidirectional directions
Shucai Xiao's avatar
Shucai Xiao committed
1498
        if(dirct == op::rnn_direction::bidirectional)
Shucai Xiao's avatar
Shucai Xiao committed
1499
1500
1501
1502
1503
1504
        {
            // 6 activation functions are used in the bidirectional
            // scenario. No spec is provided in onnx::operator. we
            // use the algorithm that: if 1 actv function is provided,
            // repeat 1st six times. If 2 actv functins are provided,
            // repeat 2nd once, then repeat all three once
Shucai Xiao's avatar
Shucai Xiao committed
1505
            // if 3 actv funcs are provide, repeat all three once.
Shucai Xiao's avatar
Shucai Xiao committed
1506
1507
1508
1509
            // the same algorithm is used for 4, 5, and 6 actv funcions
            // provided. This may need change later
            switch(vec_names.size())
            {
1510
            case 1:
Shucai Xiao's avatar
Shucai Xiao committed
1511
1512
1513
1514
1515
1516
                vec_names = {vec_names.at(0),
                             vec_names.at(0),
                             vec_names.at(0),
                             vec_names.at(0),
                             vec_names.at(0),
                             vec_names.at(0)};
1517
                break;
Shucai Xiao's avatar
Shucai Xiao committed
1518
1519
1520

            case 2:
                // repeat the 2nd actv func once, then repeat all three another time
Shucai Xiao's avatar
Shucai Xiao committed
1521
1522
1523
1524
1525
1526
                vec_names = {vec_names.at(0),
                             vec_names.at(1),
                             vec_names.at(1),
                             vec_names.at(0),
                             vec_names.at(1),
                             vec_names.at(1)};
Shucai Xiao's avatar
Shucai Xiao committed
1527
1528
1529
1530
                break;

            case 3:
                // repeat all three actv funcs once
Shucai Xiao's avatar
Shucai Xiao committed
1531
1532
1533
1534
1535
1536
                vec_names = {vec_names.at(0),
                             vec_names.at(1),
                             vec_names.at(2),
                             vec_names.at(0),
                             vec_names.at(1),
                             vec_names.at(2)};
Shucai Xiao's avatar
Shucai Xiao committed
1537
1538
                break;

Shucai Xiao's avatar
Shucai Xiao committed
1539
1540
1541
1542
1543
1544
1545
            case 4:
                vec_names = {vec_names.at(0),
                             vec_names.at(1),
                             vec_names.at(2),
                             vec_names.at(3),
                             vec_names.at(3),
                             vec_names.at(3)};
1546
                break;
Shucai Xiao's avatar
Shucai Xiao committed
1547

Shucai Xiao's avatar
Shucai Xiao committed
1548
1549
1550
1551
1552
1553
1554
            case 5:
                vec_names = {vec_names.at(0),
                             vec_names.at(1),
                             vec_names.at(2),
                             vec_names.at(3),
                             vec_names.at(4),
                             vec_names.at(4)};
1555
                break;
Shucai Xiao's avatar
Shucai Xiao committed
1556

Shucai Xiao's avatar
Shucai Xiao committed
1557
            default: break;
Shucai Xiao's avatar
Shucai Xiao committed
1558
1559
1560
1561
1562
1563
            }
        }
        else
        {
            switch(vec_names.size())
            {
Shucai Xiao's avatar
Shucai Xiao committed
1564
            case 1: vec_names = {vec_names.at(0), vec_names.at(0), vec_names.at(0)}; break;
Shucai Xiao's avatar
Shucai Xiao committed
1565
1566
1567

            case 2:
                // repeat the 2nd actv func once, so we have 3 actv funcs
Shucai Xiao's avatar
Shucai Xiao committed
1568
                vec_names = {vec_names.at(0), vec_names.at(1), vec_names.at(1)};
Shucai Xiao's avatar
Shucai Xiao committed
1569
1570
                break;

Shucai Xiao's avatar
Shucai Xiao committed
1571
            default: break;
Shucai Xiao's avatar
Shucai Xiao committed
1572
1573
1574
            }
        }

1575
1576
1577
        auto name_it = std::find_if(vec_names.begin(), vec_names.end(), [&](auto& name) {
            return (map_actv_funcs.count(name) == 0);
        });
Shucai Xiao's avatar
Shucai Xiao committed
1578
        if(name_it != vec_names.end())
1579
1580
1581
        {
            MIGRAPHX_THROW("LSTM: activation function " + std::string(*name_it) + " not supported");
        }
Shucai Xiao's avatar
Shucai Xiao committed
1582
1583

        std::vector<operation> vec_actv_funcs(vec_names.size());
Paul's avatar
Paul committed
1584
1585
1586
1587
        std::transform(vec_names.begin(),
                       vec_names.end(),
                       vec_actv_funcs.begin(),
                       [&](const auto& name) { return map_actv_funcs[name]; });
Shucai Xiao's avatar
Shucai Xiao committed
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604

        float clip = 0.0;
        if(contains(attributes, "clip"))
        {
            clip = parse_value(attributes.at("clip")).at<float>();
        }

        int input_forget = 0;
        if(contains(attributes, "input_forget"))
        {
            input_forget = parse_value(attributes.at("input_forget")).at<int>();
        }

        // append undefined opeator to make 6 arguments
        if(args.size() < 8)
        {
            auto ins = prog.add_instruction(op::undefined{});
Shucai Xiao's avatar
Shucai Xiao committed
1605
            args.insert(args.end(), 8 - args.size(), ins);
Shucai Xiao's avatar
Shucai Xiao committed
1606
1607
1608
1609
        }

        // first output for concatenation of hidden states
        auto hidden_states = prog.add_instruction(
Shucai Xiao's avatar
Shucai Xiao committed
1610
            op::lstm{hidden_size, vec_actv_funcs, dirct, clip, input_forget}, std::move(args));
Shucai Xiao's avatar
Shucai Xiao committed
1611
1612

        // second output for last lstm output
Shucai Xiao's avatar
Shucai Xiao committed
1613
        auto last_output = prog.add_instruction(op::rnn_last_output{}, hidden_states);
Shucai Xiao's avatar
Shucai Xiao committed
1614
1615
1616
1617
1618
1619

        // third output for last cell output
        auto last_cell_output = prog.add_instruction(op::lstm_last_cell_output{}, hidden_states);

        return {hidden_states, last_output, last_cell_output};
    }
1620

Shucai Xiao's avatar
Shucai Xiao committed
1621
    template <class T>
Shucai Xiao's avatar
Shucai Xiao committed
1622
    instruction_ref parse_reduce_oper(const std::string&,
Shucai Xiao's avatar
Shucai Xiao committed
1623
1624
                                      attribute_map attributes,
                                      std::vector<instruction_ref> args)
Shucai Xiao's avatar
Shucai Xiao committed
1625
1626
1627
1628
    {
        std::size_t n_dim = args.front()->get_shape().lens().size();

        // default to reduce over all dimensions
1629
        std::vector<int64_t> axes(n_dim);
Shucai Xiao's avatar
Shucai Xiao committed
1630
1631
1632
1633
1634
        std::iota(axes.begin(), axes.end(), 0);
        if(contains(attributes, "axes"))
        {
            axes.clear();
            auto&& attr_axes = attributes["axes"].ints();
1635
            axes             = std::vector<int64_t>(attr_axes.begin(), attr_axes.end());
Shucai Xiao's avatar
Shucai Xiao committed
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
        }

        int keep_dims = 1;
        if(contains(attributes, "keepdims"))
        {
            keep_dims = parse_value(attributes.at("keepdims")).at<int>();
        }

        if(keep_dims == 1)
        {
Shucai Xiao's avatar
Shucai Xiao committed
1646
            return prog.add_instruction(T{axes}, std::move(args));
Shucai Xiao's avatar
Shucai Xiao committed
1647
1648
1649
        }
        else
        {
Shucai Xiao's avatar
Shucai Xiao committed
1650
            auto ins = prog.add_instruction(T{axes}, std::move(args));
1651
            return prog.add_instruction(op::squeeze{axes}, ins);
1652
1653
        }
    }
1654

Shucai Xiao's avatar
Shucai Xiao committed
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
    instruction_ref
    parse_reduce_l1(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        auto abs_ins = prog.add_instruction(op::abs{}, args[0]);
        return parse_reduce_oper<op::reduce_sum>({}, std::move(attributes), {abs_ins});
    }

    instruction_ref
    parse_reduce_l2(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        auto square_ins = prog.add_instruction(op::mul{}, args[0], args[0]);
        auto sum_ins = parse_reduce_oper<op::reduce_sum>({}, std::move(attributes), {square_ins});
        return prog.add_instruction(op::sqrt{}, sum_ins);
    }

    instruction_ref parse_reduce_log_sum(const std::string&,
                                         attribute_map attributes,
                                         std::vector<instruction_ref> args)
    {
        auto sum_ins =
            parse_reduce_oper<op::reduce_sum>({}, std::move(attributes), std::move(args));
        return prog.add_instruction(op::log{}, sum_ins);
    }

    instruction_ref parse_reduce_log_sum_exp(const std::string&,
                                             attribute_map attributes,
                                             std::vector<instruction_ref> args)
    {
        auto exp_ins = prog.add_instruction(op::exp{}, args[0]);
        auto sum_ins = parse_reduce_oper<op::reduce_sum>({}, std::move(attributes), {exp_ins});
        return prog.add_instruction(op::log{}, sum_ins);
    }

    instruction_ref parse_reduce_sum_square(const std::string&,
                                            attribute_map attributes,
                                            std::vector<instruction_ref> args)
    {
        auto square_ins = prog.add_instruction(op::mul{}, args[0], args[0]);
        return parse_reduce_oper<op::reduce_sum>({}, std::move(attributes), {square_ins});
    }

Shucai Xiao's avatar
Shucai Xiao committed
1696
1697
    instruction_ref
    parse_cast(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
1698
    {
Shucai Xiao's avatar
Shucai Xiao committed
1699
        if(!contains(attributes, "to"))
1700
1701
1702
1703
        {
            MIGRAPHX_THROW("PARSE_CAST: missing to type attribute!");
        }

Shucai Xiao's avatar
Shucai Xiao committed
1704
        int to_type        = parse_value(attributes.at("to")).at<int>();
1705
1706
1707
        shape::type_t type = get_type(to_type);
        return prog.add_instruction(op::convert{type}, std::move(args));
    }
Shucai Xiao's avatar
Shucai Xiao committed
1708

Paul's avatar
Paul committed
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
    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
1721
            MIGRAPHX_THROW("Failed reading onnx file.");
Paul's avatar
Paul committed
1722
1723
1724
        }
    }

Paul Fultz II's avatar
Paul Fultz II committed
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
    void parse_from(const void* data, std::size_t size)
    {
        onnx::ModelProto model;
        if(model.ParseFromArray(data, size))
        {
            if(model.has_graph())
            {
                this->parse_graph(model.graph());
            }
        }
        else
        {
            MIGRAPHX_THROW("Failed reading onnx file.");
        }
    }

Paul's avatar
Paul committed
1741
1742
1743
    void parse_graph(const onnx::GraphProto& graph)
    {
        nodes = get_nodes(graph);
1744
        for(auto&& f : graph.initializer())
1745
1746
            instructions[f.name()] = prog.add_literal(parse_tensor(f));

Paul's avatar
Paul committed
1747
1748
1749
        for(auto&& input : graph.input())
        {
            const std::string& name = input.name();
1750
1751
            // input not in initializer_data, so it is a real input
            if(!contains(instructions, name))
1752
1753
            {
                // TODO: Get shape of input parameter
1754
                shape s            = parse_type(input.type(), batch_size);
1755
1756
                instructions[name] = prog.add_parameter(name, s);
            }
Paul's avatar
Paul committed
1757
        }
Paul's avatar
Paul committed
1758
        for(auto&& output : graph.output())
Paul's avatar
Paul committed
1759
        {
Paul's avatar
Paul committed
1760
            this->parse_node(output.name());
Paul's avatar
Paul committed
1761
        }
Shucai Xiao's avatar
Shucai Xiao committed
1762

1763
        // Find instructions corresponding to the output
Shucai Xiao's avatar
Shucai Xiao committed
1764
        auto prog_output = graph.output();
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
        std::vector<std::string> all_output_names;
        std::vector<std::string> prog_output_names;
        std::transform(prog_output.begin(),
                       prog_output.end(),
                       std::back_inserter(all_output_names),
                       [](auto& node) { return node.name(); });
        std::copy_if(
            all_output_names.begin(),
            all_output_names.end(),
            std::back_inserter(prog_output_names),
            [&](const auto& name) { return !(name.empty() or instructions.count(name) == 0); });

        std::vector<instruction_ref> output_ins;
        std::transform(prog_output_names.begin(),
                       prog_output_names.end(),
                       std::back_inserter(output_ins),
                       [&](const auto& name) { return instructions[name]; });

        // add the return instuction
        prog.add_return(output_ins);
Paul's avatar
Paul committed
1785
1786
    }

Shucai Xiao's avatar
Shucai Xiao committed
1787
    void parse_undefined(const std::string& name)
1788
    {
Shucai Xiao's avatar
Shucai Xiao committed
1789
        auto ins           = prog.add_instruction(op::undefined{});
1790
1791
1792
        instructions[name] = ins;
    }

Paul's avatar
Paul committed
1793
    void parse_node(const std::string& name)
Paul's avatar
Paul committed
1794
    {
Paul's avatar
Paul committed
1795
        if(name.empty())
Paul's avatar
Paul committed
1796
            MIGRAPHX_THROW("Onnx node must have a name");
Paul's avatar
Paul committed
1797
1798
1799
1800
1801
1802
        if(instructions.count(name) == 0)
        {
            auto&& node = nodes.at(name);
            std::vector<instruction_ref> args;
            for(auto&& input : node.input())
            {
Shucai Xiao's avatar
Shucai Xiao committed
1803
                if(input.empty())
Paul's avatar
Paul committed
1804
                {
Shucai Xiao's avatar
Shucai Xiao committed
1805
                    this->parse_undefined(input);
Paul's avatar
Paul committed
1806
                }
Shucai Xiao's avatar
Shucai Xiao committed
1807
                else if(nodes.count(input) > 0)
Paul's avatar
Paul committed
1808
                {
Shucai Xiao's avatar
Shucai Xiao committed
1809
1810
                    assert(name != input);
                    this->parse_node(input);
Paul's avatar
Paul committed
1811
                }
1812
                args.push_back(instructions.at(input));
Paul's avatar
Paul committed
1813
            }
Paul's avatar
Paul committed
1814
            std::vector<instruction_ref> result;
Paul's avatar
Paul committed
1815
1816
            if(ops.count(node.op_type()) == 0)
            {
1817
                result.push_back(prog.add_instruction(op::unknown{node.op_type()}, args));
Paul's avatar
Paul committed
1818
1819
1820
            }
            else
            {
Paul's avatar
Paul committed
1821
                result = ops[node.op_type()](get_attributes(node), args);
Paul's avatar
Paul committed
1822
            }
Paul's avatar
Paul committed
1823
            // Even no output nodes produce output in migraphx
Paul's avatar
Paul committed
1824
            if(node.output().empty() and result.size() == 1)
Paul's avatar
Paul committed
1825
1826
            {
                instructions[name] = result.front();
Paul's avatar
Paul committed
1827
1828
1829
            }
            else
            {
1830
                auto output_num = std::min<std::size_t>(node.output().size(), result.size());
Shucai Xiao's avatar
Shucai Xiao committed
1831
                std::transform(node.output().begin(),
1832
                               node.output().begin() + output_num,
Shucai Xiao's avatar
Shucai Xiao committed
1833
                               result.begin(),
Paul's avatar
Paul committed
1834
                               std::inserter(instructions, instructions.end()),
Shucai Xiao's avatar
Shucai Xiao committed
1835
                               [](auto&& x, auto&& y) { return std::make_pair(x, y); });
Paul's avatar
Paul committed
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
            }
        }
    }

    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
1853
        std::size_t n = 0;
Paul's avatar
Paul committed
1854
1855
        for(auto&& node : graph.node())
        {
Paul's avatar
Paul committed
1856
            if(node.output().empty())
Paul's avatar
Paul committed
1857
            {
Paul's avatar
Paul committed
1858
                if(node.name().empty())
Paul's avatar
Paul committed
1859
1860
1861
1862
1863
1864
1865
1866
1867
                {
                    result["migraphx_unamed_node_" + std::to_string(n)] = node;
                    n++;
                }
                else
                {
                    result[node.name()] = node;
                }
            }
Paul's avatar
Paul committed
1868
1869
1870
1871
1872
1873
1874
1875
            for(auto&& output : node.output())
            {
                result[output] = node;
            }
        }
        return result;
    }

Paul's avatar
Paul committed
1876
1877
1878
1879
1880
1881
    static std::vector<int64_t> get_indices(const onnx::AttributeProto& attr)
    {
        std::vector<int64_t> result;
        literal s = parse_value(attr);
        s.visit([&](auto v) { copy(v, std::back_inserter(result)); });
        // Clamp large indices to -1
Paul's avatar
Paul committed
1882
1883
1884
1885
1886
        std::replace_if(
            result.begin(),
            result.end(),
            [](auto x) { return x > int64_t{std::numeric_limits<std::int32_t>::max()} / 2; },
            -1);
Paul's avatar
Paul committed
1887
1888
1889
        return result;
    }

Paul's avatar
Paul committed
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
    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::FLOAT: return literal{attr.f()};
        case onnx::AttributeProto::INT: return literal{attr.i()};
        case onnx::AttributeProto::TENSOR: return parse_tensor(attr.t());
Paul's avatar
Paul committed
1904
        case onnx::AttributeProto::FLOATS: return from_repeated(shape::float_type, attr.floats());
Paul's avatar
Paul committed
1905
        case onnx::AttributeProto::INTS: return from_repeated(shape::int64_type, attr.ints());
Paul's avatar
Paul committed
1906
1907
1908
1909
1910
        case onnx::AttributeProto::UNDEFINED:
        case onnx::AttributeProto::GRAPH:
        case onnx::AttributeProto::STRING:
        case onnx::AttributeProto::STRINGS:
        case onnx::AttributeProto::TENSORS:
1911
1912
        case onnx::AttributeProto::SPARSE_TENSOR:
        case onnx::AttributeProto::SPARSE_TENSORS:
Paul's avatar
Paul committed
1913
1914
        case onnx::AttributeProto::GRAPHS: return {};
        }
Paul's avatar
Paul committed
1915
        MIGRAPHX_THROW("Invalid attribute type");
Paul's avatar
Paul committed
1916
1917
1918
1919
1920
    }

    static literal parse_tensor(const onnx::TensorProto& t)
    {
        std::vector<std::size_t> dims(t.dims().begin(), t.dims().end());
1921
1922
        if(t.has_raw_data())
        {
wsttiger's avatar
wsttiger committed
1923
            const std::string& s = t.raw_data();
Scott Thornton's avatar
Scott Thornton committed
1924
1925
            switch(t.data_type())
            {
1926
            case onnx::TensorProto::FLOAT: return create_literal(shape::float_type, dims, s.data());
Khalique's avatar
Khalique committed
1927
1928
1929
1930
            case onnx::TensorProto::FLOAT16:
                return create_literal(shape::half_type, dims, s.data());
            case onnx::TensorProto::DOUBLE:
                return create_literal(shape::double_type, dims, s.data());
1931
            case onnx::TensorProto::INT64: return create_literal(shape::int64_type, dims, s.data());
Paul's avatar
Paul committed
1932
1933
1934
1935
            case onnx::TensorProto::INT8:
            case onnx::TensorProto::UINT16:
            case onnx::TensorProto::INT16:
            case onnx::TensorProto::INT32:
1936
            case onnx::TensorProto::BOOL: return create_literal(shape::int32_type, dims, s.data());
Paul's avatar
Paul committed
1937
1938
1939
1940
1941
1942
            case onnx::TensorProto::UINT8:
            case onnx::TensorProto::STRING:
            case onnx::TensorProto::UNDEFINED:
            case onnx::TensorProto::UINT32:
            case onnx::TensorProto::UINT64:
            case onnx::TensorProto::COMPLEX64:
Scott Thornton's avatar
Scott Thornton committed
1943
1944
            case onnx::TensorProto::COMPLEX128: throw std::runtime_error("");
            }
Paul's avatar
Paul committed
1945
            MIGRAPHX_THROW("Invalid tensor type");
1946
        }
Paul's avatar
Paul committed
1947
1948
1949
1950
1951
1952
        switch(t.data_type())
        {
        case onnx::TensorProto::INT8:
        case onnx::TensorProto::UINT16:
        case onnx::TensorProto::INT16:
        case onnx::TensorProto::INT32:
Paul's avatar
Paul committed
1953
        case onnx::TensorProto::BOOL:
Khalique's avatar
Khalique committed
1954
            return create_literal(shape::int32_type, dims, t.int32_data());
Paul's avatar
Paul committed
1955
        case onnx::TensorProto::INT64:
Khalique's avatar
Khalique committed
1956
            return create_literal(shape::int64_type, dims, t.int64_data());
Paul's avatar
Paul committed
1957
1958
1959
1960
        case onnx::TensorProto::DOUBLE:
            return create_literal(shape::double_type, dims, t.double_data());
        case onnx::TensorProto::FLOAT:
            return create_literal(shape::float_type, dims, t.float_data());
Paul's avatar
Paul committed
1961
        case onnx::TensorProto::FLOAT16:
Khalique's avatar
Khalique committed
1962
        {
Khalique's avatar
Khalique committed
1963
            std::vector<uint16_t> data_uint16(t.int32_data().begin(), t.int32_data().end());
1964
            std::vector<half> data_half;
Khalique's avatar
Khalique committed
1965
1966
1967
            std::transform(data_uint16.begin(),
                           data_uint16.end(),
                           std::back_inserter(data_half),
1968
                           [](uint16_t raw_val) { return *reinterpret_cast<half*>(&raw_val); });
1969
            return create_literal(shape::half_type, dims, data_half);
Khalique's avatar
Khalique committed
1970
        }
Paul's avatar
Paul committed
1971
1972
1973
1974
1975
1976
        case onnx::TensorProto::UNDEFINED:
        case onnx::TensorProto::UINT8:
        case onnx::TensorProto::STRING:
        case onnx::TensorProto::UINT32:
        case onnx::TensorProto::UINT64:
        case onnx::TensorProto::COMPLEX64:
Paul's avatar
Paul committed
1977
1978
        case onnx::TensorProto::COMPLEX128: throw std::runtime_error("");
        }
Paul's avatar
Paul committed
1979
        MIGRAPHX_THROW("Invalid tensor type");
Paul's avatar
Paul committed
1980
1981
    }

Khalique's avatar
Khalique committed
1982
    static literal
1983
    create_literal(shape::type_t shape_type, const std::vector<size_t>& dims, const char* data)
1984
    {
Khalique's avatar
Khalique committed
1985
        // in case of scalar constants in onnx file, use dims=1 to fill initializer data
1986
        if(dims.empty())
1987
            return literal{{shape_type}, data};
1988
1989
1990
        return literal{{shape_type, dims}, data};
    }

1991
    template <class T, MIGRAPHX_REQUIRES(not std::is_pointer<T>{})>
Khalique's avatar
Khalique committed
1992
    static literal create_literal(shape::type_t shape_type, const std::vector<size_t>& dims, T data)
1993
1994
    {
        if(dims.empty())
1995
            return literal{{shape_type}, data.begin(), data.end()};
1996
        return literal{{shape_type, dims}, data.begin(), data.end()};
1997
1998
    }

1999
    static shape parse_type(const onnx::TypeProto& t, const unsigned int batch_size)
Paul's avatar
Paul committed
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
    {
        shape::type_t shape_type{};
        switch(t.tensor_type().elem_type())
        {
        case onnx::TensorProto::FLOAT: shape_type = shape::float_type; break;
        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;
Paul's avatar
Paul committed
2010
        case onnx::TensorProto::FLOAT16: shape_type = shape::half_type; break;
Paul's avatar
Paul committed
2011
2012
2013
        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;
Paul's avatar
Paul committed
2014
2015
2016
2017
        case onnx::TensorProto::UINT8:
        case onnx::TensorProto::STRING:
        case onnx::TensorProto::BOOL:
        case onnx::TensorProto::UNDEFINED:
Paul's avatar
Paul committed
2018
2019
        case onnx::TensorProto::COMPLEX64:
        case onnx::TensorProto::COMPLEX128:
Paul's avatar
Paul committed
2020
            break; // throw std::runtime_error("Unsupported type");
Paul's avatar
Paul committed
2021
2022
        }
        std::vector<std::size_t> dims;
Paul's avatar
Paul committed
2023
        auto&& tensor_dims = t.tensor_type().shape().dim();
2024
2025
2026
        std::transform(tensor_dims.begin(),
                       tensor_dims.end(),
                       std::back_inserter(dims),
2027
2028
                       [&](auto&& d) -> std::size_t {
                           if(d.has_dim_value())
2029
                           {
2030
2031
2032
                               if(static_cast<int>(d.dim_value()) <= 0)
                                   return batch_size;
                               return d.dim_value();
2033
                           }
2034
                           return batch_size;
2035
                       });
2036
2037
2038
        if(dims.empty())
            return {shape_type};

Paul's avatar
Paul committed
2039
2040
        return {shape_type, dims};
    }
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062

    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");
        }
        }
    }
Shucai Xiao's avatar
Shucai Xiao committed
2063
2064
2065

    void check_arg_empty(const argument& arg, const std::string& msg)
    {
Shucai Xiao's avatar
Shucai Xiao committed
2066
        if(arg.empty())
Shucai Xiao's avatar
Shucai Xiao committed
2067
2068
2069
2070
        {
            MIGRAPHX_THROW(msg);
        }
    }
Paul's avatar
Paul committed
2071
2072
};

Paul Fultz II's avatar
Paul Fultz II committed
2073
2074
template <class... Ts>
program parse_onnx_from(onnx_options options, Ts&&... xs)
Paul's avatar
Paul committed
2075
2076
{
    onnx_parser parser;
2077
    parser.batch_size = options.batch_size;
Paul's avatar
Paul committed
2078
2079
2080
2081
#ifndef NDEBUG
    // Log the program when it can't be parsed
    try
    {
Paul Fultz II's avatar
Paul Fultz II committed
2082
        parser.parse_from(std::forward<Ts>(xs)...);
Paul's avatar
Paul committed
2083
2084
2085
2086
2087
2088
2089
    }
    catch(...)
    {
        std::cerr << parser.prog << std::endl;
        throw;
    }
#else
Paul Fultz II's avatar
Paul Fultz II committed
2090
    parser.parse_from(std::forward<Ts>(xs)...);
Paul's avatar
Paul committed
2091
2092
2093
2094
#endif
    return std::move(parser.prog);
}

Paul Fultz II's avatar
Paul Fultz II committed
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
program parse_onnx(const std::string& name, onnx_options options)
{
    std::fstream input(name.c_str(), std::ios::in | std::ios::binary);
    return parse_onnx_from(options, input);
}

program parse_onnx_buffer(const std::string& buffer, onnx_options options)
{
    return parse_onnx_from(options, buffer.data(), buffer.size());
}

program parse_onnx_buffer(const void* data, std::size_t size, onnx_options options)
{
    return parse_onnx_from(options, data, size);
}

Paul's avatar
Paul committed
2111
} // namespace MIGRAPHX_INLINE_NS
Paul's avatar
Paul committed
2112
} // namespace migraphx