tf.cpp 45.8 KB
Newer Older
Khalique's avatar
Khalique committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
#include <google/protobuf/text_format.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <graph.pb.h>
#include <iostream>
#include <fstream>
#include <unordered_map>
#include <unordered_set>
#include <functional>
#include <array>
#include <utility>
#include <vector>

#include <migraphx/fallthrough.hpp>
#include <migraphx/program.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/config.hpp>
#include <migraphx/tf.hpp>
Khalique's avatar
Khalique committed
20
#include <migraphx/pad_calc.hpp>
Khalique's avatar
Khalique committed
21
22
23
24
25
26
27

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {

struct tf_parser
{
    using attribute_map = std::unordered_map<std::string, tensorflow::AttrValue>;
Paul's avatar
Paul committed
28
    using node_map      = std::map<std::string, tensorflow::NodeDef>;
Khalique's avatar
Khalique committed
29
30
    // using input_node_map = std::unordered_map<std::string, std::unordered_set<std::string>>;
    using op_func = std::function<instruction_ref(attribute_map, std::vector<instruction_ref>)>;
Khalique's avatar
Khalique committed
31

Khalique's avatar
Khalique committed
32
33
34
35
36
37
38
39
    node_map nodes;
    std::vector<tensorflow::NodeDef> input_nodes;
    std::unordered_map<std::string, instruction_ref> instructions;
    program prog = program();
    bool is_nhwc = true;

    std::unordered_map<std::string, op_func> ops;

Paul's avatar
Paul committed
40
    bool should_transpose(instruction_ref ins) const
Paul's avatar
Paul committed
41
42
43
44
45
46
    {
        return is_nhwc and ins->get_shape().lens().size() == 4;
    }

    instruction_ref to_nhwc(instruction_ref ins)
    {
Paul's avatar
Paul committed
47
        if(should_transpose(ins))
Paul's avatar
Paul committed
48
49
50
51
52
53
            return prog.add_instruction(op::transpose{{0, 2, 3, 1}}, ins);
        return ins;
    }

    instruction_ref to_nchw(instruction_ref ins)
    {
Paul's avatar
Paul committed
54
        if(should_transpose(ins))
Paul's avatar
Paul committed
55
56
57
58
59
60
            return prog.add_instruction(op::transpose{{0, 3, 1, 2}}, ins);
        return ins;
    }

    instruction_ref to_kcxy(instruction_ref ins)
    {
Paul's avatar
Paul committed
61
        if(should_transpose(ins))
Paul's avatar
Paul committed
62
63
64
65
66
67
            return prog.add_instruction(op::transpose{{3, 2, 0, 1}}, ins);
        return ins;
    }

    instruction_ref make_contiguous(instruction_ref ins)
    {
Paul's avatar
Paul committed
68
        if(ins->get_shape().standard())
Paul's avatar
Paul committed
69
70
71
72
73
74
75
76
            return ins;
        else
            return prog.add_instruction(op::contiguous{}, ins);
    }

    std::vector<instruction_ref> to_nchw(const std::vector<instruction_ref>& args)
    {
        std::vector<instruction_ref> result(args.size());
Paul's avatar
Paul committed
77
        std::transform(
Paul's avatar
Paul committed
78
            args.begin(), args.end(), result.begin(), [&](auto ins) { return this->to_nchw(ins); });
Paul's avatar
Paul committed
79
80
81
        return result;
    }

Khalique's avatar
Khalique committed
82
    std::vector<size_t> parse_axes(const attribute_map& attributes, const std::string& s) const
83
    {
84
85
86
        auto attrs = attributes.at(s).list().i();
        std::vector<size_t> axes;
        copy(attrs.begin(), attrs.end(), std::back_inserter(axes));
Khalique's avatar
Khalique committed
87
        if(is_nhwc)
88
        {
Khalique's avatar
Khalique committed
89
90
91
            std::transform(axes.begin(), axes.end(), axes.begin(), [&](size_t axis) {
                return parse_axis(axis);
            });
92
93
94
95
        }
        return axes;
    }

Khalique's avatar
Khalique committed
96
97
98
99
100
    template <class T>
    std::vector<T> parse_axes(std::vector<T> axes) const
    {
        if(is_nhwc)
        {
101
            std::vector<T> new_axes;
Khalique's avatar
Khalique committed
102
103
104
105
            std::transform(axes.begin(),
                           axes.end(),
                           std::back_inserter(new_axes),
                           [&](size_t axis) { return parse_axis(axis); });
106
            return new_axes;
Khalique's avatar
Khalique committed
107
        }
108
        return axes;
Khalique's avatar
Khalique committed
109
110
    }

Khalique's avatar
Khalique committed
111
112
113
    // tf stores certain attributes such as strides, dilations, as a 4D input.
    // The first and last dims are equal to 1, and the relevant data is in dims 2 and 3.
    // This helper function reorders the data to store for the respective operator member variables.
114
    template <class T>
115
    void reorder_data(std::vector<T>& prev_data) const
116
117
    {
        std::vector<T> new_data(prev_data.size());
118
        for(size_t i = 0; i < new_data.size(); i++)
119
        {
Khalique's avatar
Khalique committed
120
            auto new_idx         = parse_axis(i);
121
            new_data.at(new_idx) = prev_data.at(i);
122
        }
123
124
125
126
        prev_data = new_data;
    }

    template <class T>
Khalique's avatar
Khalique committed
127
    T parse_axis(const T& dim) const
128
    {
Khalique's avatar
Khalique committed
129
        T new_dim = dim;
130
131
132
133
        if(is_nhwc)
        {
            switch(dim)
            {
Khalique's avatar
Khalique committed
134
135
136
137
138
            case 0: new_dim = 0; break;
            case 1: new_dim = 2; break;
            case 2: new_dim = 3; break;
            case 3: new_dim = 1; break;
            default: break;
139
140
            }
        }
Khalique's avatar
Khalique committed
141
        return new_dim;
142
143
    }

144
145
146
147
148
149
150
    std::vector<int64_t> get_axes(size_t num_axes) const
    {
        std::vector<int64_t> axes(num_axes);
        std::iota(axes.begin(), axes.end(), 0);
        return axes;
    }

Khalique's avatar
Khalique committed
151
152
153
154
    tf_parser()
    {
        add_generic_op("Identity", op::identity{});
        add_generic_op("Relu", op::relu{});
Khalique's avatar
Khalique committed
155
        add_generic_op("Relu6", op::clip{6.0, 0.0});
Khalique's avatar
Khalique committed
156
        add_generic_op("Rsqrt", op::rsqrt{});
Khalique's avatar
Khalique committed
157
        add_generic_op("Tanh", op::tanh{});
Khalique's avatar
Khalique committed
158

159
        add_binary_op("Add", op::add{});
Khalique's avatar
Khalique committed
160
        add_binary_op("Mul", op::mul{});
Khalique's avatar
Khalique committed
161
        add_binary_op("Sub", op::sub{});
Khalique's avatar
Khalique committed
162

163
        add_mem_op("AvgPool", &tf_parser::parse_pooling);
164
        add_mem_op("BiasAdd", &tf_parser::parse_biasadd);
Paul's avatar
Paul committed
165
        add_mem_op("ConcatV2", &tf_parser::parse_concat, false);
Khalique's avatar
Khalique committed
166
        add_mem_op("Const", &tf_parser::parse_constant);
Paul's avatar
Paul committed
167
        add_mem_op("Conv2D", &tf_parser::parse_conv);
Paul's avatar
Paul committed
168
        add_mem_op("DepthwiseConv2dNative", &tf_parser::parse_depthwiseconv);
Khalique's avatar
Khalique committed
169
        add_mem_op("FusedBatchNorm", &tf_parser::parse_batchnorm);
Paul's avatar
Paul committed
170
        add_mem_op("MatMul", &tf_parser::parse_matmul, false);
171
        add_mem_op("MaxPool", &tf_parser::parse_pooling);
Khalique's avatar
Khalique committed
172
        add_mem_op("Mean", &tf_parser::parse_mean);
Paul's avatar
Paul committed
173
        add_mem_op("Pack", &tf_parser::parse_pack, false);
Paul's avatar
Paul committed
174
        add_mem_op("Pad", &tf_parser::parse_pad);
Paul's avatar
Paul committed
175
        add_mem_op("Reshape", &tf_parser::parse_reshape, false);
176
        add_mem_op("Softmax", &tf_parser::parse_softmax);
Paul's avatar
Paul committed
177
        add_mem_op("Squeeze", &tf_parser::parse_squeeze, false);
178
        add_mem_op("StridedSlice", &tf_parser::parse_stridedslice);
Khalique's avatar
Khalique committed
179
180
    }

181
    template <class F>
Paul's avatar
Paul committed
182
    void add_op(std::string name, F f, bool transpose = true)
183
    {
Paul's avatar
Paul committed
184
        if(transpose)
Paul's avatar
Paul committed
185
        {
Paul's avatar
Paul committed
186
187
            ops.emplace(name,
                        op_func{[=](const attribute_map& attributes,
Paul's avatar
Paul committed
188
                                    const std::vector<instruction_ref>& args) -> instruction_ref {
Paul's avatar
Paul committed
189
190
                            return to_nhwc(f(attributes, to_nchw(args)));
                        }});
Paul's avatar
Paul committed
191
192
193
194
195
        }
        else
        {
            ops.emplace(name, f);
        }
196
197
    }

Khalique's avatar
Khalique committed
198
    template <class F>
Paul's avatar
Paul committed
199
    void add_mem_op(std::string name, F f, bool transpose = true)
Khalique's avatar
Khalique committed
200
    {
Paul's avatar
Paul committed
201
202
203
204
205
        add_op(name,
               [=](auto&&... xs) {
                   return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...);
               },
               transpose);
Khalique's avatar
Khalique committed
206
207
208
209
210
    }

    template <class T>
    void add_binary_op(std::string name, T x)
    {
Paul's avatar
Paul committed
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
        add_op(name,
               [this, x](const attribute_map&, std::vector<instruction_ref> args) {
                   if(args.size() != 2)
                       MIGRAPHX_THROW("binary operators should have 2 operands");
                   // TODO
                   // if(contains(attributes, "data_format"))
                   // {
                   //     if(is_nhwc)
                   //     {
                   //         l0 = prog.add_instruction(op::transpose{{0, 3, 1, 2}}, args[1]);
                   //     }
                   // }
                   return add_broadcastable_binary_op(args[0], args[1], x);
               },
               false);
Khalique's avatar
Khalique committed
226
227
228
229
230
    }

    template <class T>
    instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
    {
Khalique's avatar
Khalique committed
231
        if(arg0->get_shape().lens() != arg1->get_shape().lens())
Khalique's avatar
Khalique committed
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
        {
            // 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)
            //
            // Get lengths for both arguments
247
248
            const std::vector<size_t>* s0 = &arg0->get_shape().lens();
            const std::vector<size_t>* s1 = &arg1->get_shape().lens();
Khalique's avatar
Khalique committed
249
250
251
252
253

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

254
            std::vector<size_t> output_lens(*s1);
Khalique's avatar
Khalique committed
255
256
257
258
259
260
261
262
263
            auto offset = s1->size() - s0->size();
            std::transform(s0->begin(),
                           s0->end(),
                           s1->begin() + offset,
                           output_lens.begin() + offset,
                           [](auto a, auto b) { return std::max(a, b); });

            auto l0 = prog.add_instruction(op::multibroadcast{output_lens}, arg0);
            auto l1 = prog.add_instruction(op::multibroadcast{output_lens}, arg1);
Paul's avatar
Paul committed
264
            return to_nhwc(prog.add_instruction(x, to_nchw(l0), to_nchw(l1)));
Khalique's avatar
Khalique committed
265
266
267
        }
        else
        {
Paul's avatar
Paul committed
268
            return to_nhwc(prog.add_instruction(x, {to_nchw(arg0), to_nchw(arg1)}));
Khalique's avatar
Khalique committed
269
270
271
272
        }
    }

    template <class T>
Paul's avatar
Paul committed
273
    void add_generic_op(std::string name, T x, bool transpose = true)
Khalique's avatar
Khalique committed
274
    {
Paul's avatar
Paul committed
275
276
277
278
279
        add_op(name,
               [this, x](const attribute_map&, std::vector<instruction_ref> args) {
                   return prog.add_instruction(x, args);
               },
               transpose);
Khalique's avatar
Khalique committed
280
281
282
283
284
    }

    instruction_ref
    parse_batchnorm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
Khalique's avatar
Khalique committed
285
286
287
        float epsilon                                     = 1e-5f;
        float momentum                                    = 0.9f;
        op::batch_norm_inference::bn_infer_mode_t bn_mode = op::batch_norm_inference::spatial;
Khalique's avatar
Khalique committed
288
289
290
291
292
293
294
295
        if(contains(attributes, "epsilon"))
        {
            epsilon = attributes.at("epsilon").f();
        }
        op::batch_norm_inference op{epsilon, momentum, bn_mode};
        return prog.add_instruction(op, std::move(args));
    }

296
    instruction_ref
Khalique's avatar
Khalique committed
297
    parse_biasadd(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
298
    {
299
        uint64_t axis = 1; // assume output of previous layer is in NCHW (broadcast on channel)
Shucai Xiao's avatar
Shucai Xiao committed
300
        auto l0 = prog.add_instruction(op::broadcast{axis, args[0]->get_shape().lens()}, args[1]);
301
        return prog.add_instruction(op::add{}, args[0], l0);
302
303
    }

Khalique's avatar
Khalique committed
304
305
306
307
    instruction_ref
    parse_concat(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        // get index for axis within args
308
        size_t axis_idx = attributes.at("N").i();
Paul's avatar
Paul committed
309
        size_t axis     = args[axis_idx]->eval().at<int64_t>();
Khalique's avatar
Khalique committed
310
        op::concat op{axis};
311
        // return only first N arguments (assuming last index is the axis value)
Paul's avatar
Paul committed
312
313
        return prog.add_instruction(
            op, std::vector<instruction_ref>(args.begin(), args.begin() + args.size() - 1));
Khalique's avatar
Khalique committed
314
315
316
317
318
319
    }

    instruction_ref parse_constant(const std::string&,
                                   attribute_map attributes,
                                   const std::vector<instruction_ref>&)
    {
Paul's avatar
Paul committed
320
        literal v = parse_tensor(attributes.at("value").tensor());
Paul's avatar
Paul committed
321
        return prog.add_literal(v);
Khalique's avatar
Khalique committed
322
323
324
325
326
327
328
329
    }

    instruction_ref
    parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::convolution op;
        if(contains(attributes, "strides"))
        {
330
            std::vector<size_t> stride;
331
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
332
            reorder_data(stride);
333
334
            if(stride.size() != 4)
            {
335
                MIGRAPHX_THROW("strides should have 4 values");
336
            }
337
338
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
Khalique's avatar
Khalique committed
339
340
341
        }
        if(contains(attributes, "dilations"))
        {
342
            std::vector<size_t> dilation;
343
            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
344
            reorder_data(dilation);
345
346
347
348
            if(dilation.size() != 4)
            {
                MIGRAPHX_THROW("dilation should have 4 values");
            }
349
350
            op.dilation[0] = dilation[2];
            op.dilation[1] = dilation[3];
Khalique's avatar
Khalique committed
351
        }
Khalique's avatar
Khalique committed
352

Paul's avatar
Paul committed
353
        auto weights = to_kcxy(args[1]);
Paul's avatar
Paul committed
354
        auto l0      = args[0];
Khalique's avatar
Khalique committed
355
356
357
358
359
        if(contains(attributes, "padding"))
        {
            const std::string& pad_mode = attributes.at("padding").s();
            if(pad_mode.find("SAME") != std::string::npos)
            {
Khalique's avatar
Khalique committed
360
                op.padding_mode                 = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
361
362
363
                std::vector<size_t> weight_dims = weights->get_shape().lens();
                size_t weight_h                 = weight_dims[2];
                size_t weight_w                 = weight_dims[3];
Khalique's avatar
Khalique committed
364
365

                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
366
367
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
368
369
370
371
372
373
374
375
376
377
378
                std::vector<int64_t> pads(input_dims.size());
                calculate_padding(0, pads, input_h, op.stride[0], op.dilation[0], weight_h);
                calculate_padding(1, pads, input_w, op.stride[1], op.dilation[1], weight_w);

                if(pads[0] != pads[2] || pads[1] != pads[3])
                {
                    std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
                    l0 = prog.add_instruction(migraphx::op::pad{padding}, l0);
                }
                else
                {
Khalique's avatar
Khalique committed
379
380
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
381
                }
382
383
384
            }
            else if(pad_mode.find("VALID") != std::string::npos)
            {
385
                op.padding_mode = op::padding_mode_t::valid;
Khalique's avatar
Khalique committed
386
            }
Khalique's avatar
Khalique committed
387
            else if(pad_mode.find("EXPLICIT") != std::string::npos)
Khalique's avatar
Khalique committed
388
            {
389
                std::vector<size_t> padding;
390
                copy(attributes.at("explicit_paddings").list().i(), std::back_inserter(padding));
Khalique's avatar
Khalique committed
391
392
393
394
395
396
397
398
399
400
401
402
                if(padding.size() != 4)
                {
                    MIGRAPHX_THROW("padding should have 4 values");
                }
                if(padding[0] != padding[2] || padding[1] != padding[3])
                {
                    MIGRAPHX_THROW("migraphx does not support asymetric padding");
                }
                op.padding[0] = padding[0];
                op.padding[1] = padding[1];
            }
        }
Paul's avatar
Paul committed
403
        return prog.add_instruction(op, {l0, to_kcxy(args[1])});
Khalique's avatar
Khalique committed
404
405
    }

Khalique's avatar
Khalique committed
406
407
408
    instruction_ref parse_depthwiseconv(const std::string&,
                                        attribute_map attributes,
                                        std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
409
410
411
    {
        op::convolution op;
        size_t num_channels = args[0]->get_shape().lens()[1];
Khalique's avatar
Khalique committed
412
        op.group            = num_channels;
Khalique's avatar
Khalique committed
413

Khalique's avatar
Khalique committed
414
415
        if(contains(attributes, "strides"))
        {
416
            std::vector<size_t> stride;
417
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
418
            reorder_data(stride);
419
420
            if(stride.size() != 4)
            {
421
                MIGRAPHX_THROW("strides should have 4 values");
422
            }
423
424
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
Khalique's avatar
Khalique committed
425
        }
Paul's avatar
Paul committed
426
427

        auto weights = to_kcxy(args[1]);
Khalique's avatar
Khalique committed
428
429
        if(contains(attributes, "dilations"))
        {
430
            std::vector<size_t> dilation;
431
            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
432
            reorder_data(dilation);
433
434
435
436
            if(dilation.size() != 4)
            {
                MIGRAPHX_THROW("dilation should have 4 values");
            }
437
438
            op.dilation[0] = dilation[2];
            op.dilation[1] = dilation[3];
Khalique's avatar
Khalique committed
439
440
        }

Khalique's avatar
Khalique committed
441
        auto l0 = args[0];
Khalique's avatar
Khalique committed
442
443
444
        if(contains(attributes, "padding"))
        {
            const std::string& pad_mode = attributes.at("padding").s();
Khalique's avatar
Khalique committed
445

Khalique's avatar
Khalique committed
446
447
            if(pad_mode.find("SAME") != std::string::npos)
            {
Khalique's avatar
Khalique committed
448
                op.padding_mode                 = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
449
450
451
452
453
                std::vector<size_t> weight_dims = weights->get_shape().lens();
                size_t weight_h                 = weight_dims[2];
                size_t weight_w                 = weight_dims[3];

                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
454
455
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
456
457
458
459
460
461
462
463
464
465
466
                std::vector<int64_t> pads(input_dims.size());
                calculate_padding(0, pads, input_h, op.stride[0], op.dilation[0], weight_h);
                calculate_padding(1, pads, input_w, op.stride[1], op.dilation[1], weight_w);

                if(pads[0] != pads[2] || pads[1] != pads[3])
                {
                    std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
                    l0 = prog.add_instruction(migraphx::op::pad{padding}, l0);
                }
                else
                {
Khalique's avatar
Khalique committed
467
468
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
469
                }
Khalique's avatar
Khalique committed
470
            }
Khalique's avatar
Khalique committed
471
            else if(pad_mode.find("VALID") != std::string::npos)
Khalique's avatar
Khalique committed
472
            {
Khalique's avatar
Khalique committed
473
                op.padding_mode = op::padding_mode_t::valid;
Khalique's avatar
Khalique committed
474
475
            }
        }
Khalique's avatar
Khalique committed
476

Khalique's avatar
Khalique committed
477
478
        std::vector<int64_t> new_weights_shape;
        copy(weights->get_shape().lens(), std::back_inserter(new_weights_shape));
Khalique's avatar
Khalique committed
479
480
481
482

        // weight format is (out_channels, in_channels, h, w), but in depthwise_conv,
        // out_channels is equal to the multiplier. Adjust by inserting a reshape and
        // setting in_channels to 1
Khalique's avatar
Khalique committed
483
        int64_t multiplier   = new_weights_shape[0];
Khalique's avatar
Khalique committed
484
485
486
        int64_t out_channels = num_channels * multiplier;
        new_weights_shape[0] = out_channels;
        new_weights_shape[1] = 1;
Paul's avatar
Paul committed
487
        // Make sure weights are contiguous before doing reshape
Paul's avatar
Paul committed
488
489
        auto new_weights =
            prog.add_instruction(op::reshape{new_weights_shape}, make_contiguous(weights));
Khalique's avatar
Khalique committed
490

Khalique's avatar
Khalique committed
491
        return prog.add_instruction(op, {l0, new_weights});
Khalique's avatar
Khalique committed
492
493
    }

Khalique's avatar
Khalique committed
494
495
    instruction_ref
    parse_matmul(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
496
497
498
    {
        bool transa = false;
        bool transb = false;
Khalique's avatar
Khalique committed
499

500
501
502
503
504
505
506
507
508
509
510
511
        if(contains(attributes, "transpose_a"))
        {
            transa = attributes.at("transpose_a").b();
        }
        if(contains(attributes, "transpose_b"))
        {
            transb = attributes.at("transpose_a").b();
        }

        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
Khalique's avatar
Khalique committed
512
        std::iter_swap(perm.end() - 1, perm.end() - 2);
513
514
515
516
517
518
519

        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];

        return prog.add_instruction(op::dot{}, l1, l2);
    }

Khalique's avatar
Khalique committed
520
521
    instruction_ref
    parse_mean(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
522
    {
Paul's avatar
Paul committed
523
        auto axes      = parse_axes(args[1]->eval().get<int32_t>().to_vector());
Khalique's avatar
Khalique committed
524
        bool keep_dims = attributes.at("keep_dims").b();
Paul's avatar
Paul committed
525
        std::vector<int32_t> hw_axes{2, 3};
Khalique's avatar
Khalique committed
526
        // check if conditions for GlobalAvgPool are met
Khalique's avatar
Khalique committed
527
528
        auto lens = args[0]->get_shape().lens();
        if(axes == hw_axes and lens.size() == 4)
Khalique's avatar
Khalique committed
529
530
        {
            op::pooling op{"average"};
Khalique's avatar
Khalique committed
531
532
            op.lengths[0] = lens[2];
            op.lengths[1] = lens[3];
Khalique's avatar
Khalique committed
533
534
535
536
537
            auto l0       = prog.add_instruction(op, args.front());
            if(keep_dims)
                return l0;
            return prog.add_instruction(
                op::squeeze{std::vector<int64_t>(hw_axes.begin(), hw_axes.end())}, l0);
Khalique's avatar
Khalique committed
538
539
540
541
        }
        MIGRAPHX_THROW("MIGraphX does not support mean outside of GlobalAvgPool transformation");
    }

Khalique's avatar
Khalique committed
542
543
544
    instruction_ref parse_pack(const std::string&,
                               const attribute_map& attributes,
                               std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
545
546
547
548
549
550
    {
        // reinterpret as unsqueeze with concat
        std::vector<instruction_ref> unsqueezed_args;
        int64_t axis = 0;
        if(contains(attributes, "axis"))
            axis = attributes.at("axis").i();
551
552
553
        size_t input_size = args.front()->get_shape().lens().size();
        if(axis > input_size)
        {
Khalique's avatar
Khalique committed
554
555
            MIGRAPHX_THROW("TF_PARSER: axis value of " + to_string(axis) +
                           " must be smaller than input size " + to_string(input_size));
556
557
        }

Khalique's avatar
Khalique committed
558
559
560
561
562
        std::transform(
            args.begin(),
            args.end(),
            std::back_inserter(unsqueezed_args),
            [&](instruction_ref arg) { return prog.add_instruction(op::unsqueeze{{axis}}, arg); });
Paul's avatar
Paul committed
563
564
        return to_nhwc(
            prog.add_instruction(op::concat{static_cast<size_t>(axis)}, unsqueezed_args));
Khalique's avatar
Khalique committed
565
566
    }

Khalique's avatar
Khalique committed
567
568
569
570
571
    instruction_ref
    parse_pad(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
    {
        size_t ndims = args.front()->get_shape().lens().size();

Khalique's avatar
Khalique committed
572
573
        // in tf, the paddings are arranged as a 2d shape (ndims, 2),
        // the last dim contains the left padding and right padding respectively
Khalique's avatar
Khalique committed
574
        std::vector<std::pair<int32_t, int32_t>> pad_per_dim(ndims);
Paul's avatar
Paul committed
575
        auto tf_padding = args[1]->eval().get<int32_t>().to_vector();
Khalique's avatar
Khalique committed
576
        for(size_t i = 0; i < 2 * ndims; i += 2)
Khalique's avatar
Khalique committed
577
        {
Khalique's avatar
Khalique committed
578
579
            pad_per_dim[i / 2].first  = tf_padding[i];
            pad_per_dim[i / 2].second = tf_padding[i + 1];
Khalique's avatar
Khalique committed
580
581
582
583
        }
        reorder_data(pad_per_dim);

        op::pad op;
Khalique's avatar
Khalique committed
584
585
        std::vector<int64_t> pads(ndims * 2);
        for(size_t i = 0; i < ndims; i++)
Khalique's avatar
Khalique committed
586
        {
Khalique's avatar
Khalique committed
587
588
            pads[i]         = pad_per_dim[i].first;
            pads[i + ndims] = pad_per_dim[i].second;
Khalique's avatar
Khalique committed
589
590
        }
        op.pads = pads;
Paul's avatar
Paul committed
591
        return prog.add_instruction(op, args.front());
Khalique's avatar
Khalique committed
592
593
    }

594
595
596
597
598
    instruction_ref parse_pooling(const std::string& name,
                                  attribute_map attributes,
                                  std::vector<instruction_ref> args)
    {
        op::pooling op{starts_with(name, "Max") ? "max" : "average"};
Khalique's avatar
Khalique committed
599

600
601
        if(contains(attributes, "strides"))
        {
602
            std::vector<size_t> stride;
603
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
604
            reorder_data(stride);
605
606
607
608
            if(stride.size() != 4)
            {
                MIGRAPHX_THROW("strides should have 4 values");
            }
609
610
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
611
612
613
        }
        if(contains(attributes, "ksize"))
        {
614
            std::vector<size_t> ksize;
615
            copy(attributes.at("ksize").list().i(), std::back_inserter(ksize));
616
            reorder_data(ksize);
617
618
619
            if(ksize.size() != 4)
            {
                MIGRAPHX_THROW("ksize should have 4 values");
Khalique's avatar
Khalique committed
620
            }
621
622
            op.lengths[0] = ksize[2];
            op.lengths[1] = ksize[3];
623
        }
Khalique's avatar
Khalique committed
624
625

        auto l0 = args[0];
Khalique's avatar
Khalique committed
626
627
628
629
630
        if(contains(attributes, "padding"))
        {
            const std::string& pad_mode = attributes.at("padding").s();
            if(pad_mode.find("SAME") != std::string::npos)
            {
Khalique's avatar
Khalique committed
631
                op.padding_mode = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
632
                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
633
634
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
635
636
637
638
639
640
641
                std::vector<int64_t> pads(input_dims.size());
                calculate_padding(0, pads, input_h, op.stride[0], 1, op.lengths[0]);
                calculate_padding(1, pads, input_w, op.stride[1], 1, op.lengths[1]);

                if(pads[0] != pads[2] || pads[1] != pads[3])
                {
                    std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
Khalique's avatar
Khalique committed
642
643
                    l0                           = prog.add_instruction(
                        migraphx::op::pad{padding, std::numeric_limits<float>::lowest()}, l0);
Khalique's avatar
Khalique committed
644
645
646
                }
                else
                {
Khalique's avatar
Khalique committed
647
648
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
649
                }
Khalique's avatar
Khalique committed
650
651
652
653
654
655
            }
            else if(pad_mode.find("VALID") != std::string::npos)
            {
                op.padding_mode = op::padding_mode_t::valid;
            }
        }
Khalique's avatar
Khalique committed
656
        return prog.add_instruction(op, l0);
657
    }
Khalique's avatar
Khalique committed
658

659
    instruction_ref
Khalique's avatar
Khalique committed
660
    parse_reshape(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
661
662
663
664
    {
        op::reshape op;
        if(args.size() != 2)
            MIGRAPHX_THROW("reshape needs 2 arguments (input, new_shape)");
Khalique's avatar
Khalique committed
665
        auto s = args[1]->eval();
666
        s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
Paul's avatar
Paul committed
667
        return prog.add_instruction(op, make_contiguous(args[0]));
668
669
    }

Khalique's avatar
Khalique committed
670
671
672
673
674
675
676
677
678
    void parse_from(std::istream& is)
    {
        tensorflow::GraphDef graph;
        if(graph.ParseFromIstream(&is))
        {
            this->parse_graph(graph);
        }
        else
        {
679
            throw std::runtime_error("Failed reading tf file");
Khalique's avatar
Khalique committed
680
681
682
        }
    }

683
684
685
686
687
688
689
690
691
692
    instruction_ref
    parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
    {
        auto dims = args.front()->get_shape().lens();
        auto r =
            prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1]), 1, 1}}, args.front());
        auto s = prog.add_instruction(op::softmax{}, r);
        return prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1])}}, s);
    }

Khalique's avatar
Khalique committed
693
694
695
    instruction_ref parse_squeeze(const std::string&,
                                  const attribute_map& attributes,
                                  std::vector<instruction_ref> args)
696
697
    {
        op::squeeze op;
Paul's avatar
Paul committed
698
        auto axes = attributes.at("squeeze_dims").list().i();
699
        copy(axes, std::back_inserter(op.axes));
700
        auto args0_dims = args[0]->get_shape().lens();
701
702
        if(op.axes.empty()) // no squeeze_dims provided, remove any dim that equals 1
        {
703
            for(size_t i = 0; i < args0_dims.size(); i++)
704
            {
705
                if(args0_dims.at(i) == 1)
706
707
708
709
                {
                    op.axes.push_back(i);
                }
            }
710
        }
Paul's avatar
Paul committed
711
        return prog.add_instruction(op, make_contiguous(args[0]));
712
713
    }

Khalique's avatar
Khalique committed
714
715
716
    instruction_ref parse_stridedslice(const std::string&,
                                       const attribute_map& attributes,
                                       std::vector<instruction_ref> args)
717
718
    {
        op::slice op;
Khalique's avatar
Khalique committed
719
720
721
        auto starts     = args[1]->eval().get<int32_t>().to_vector();
        auto ends       = args[2]->eval().get<int32_t>().to_vector();
        size_t num_axes = args[0]->get_shape().lens().size();
722

Khalique's avatar
Khalique committed
723
724
725
726
        op.starts = std::vector<int64_t>(starts.begin(), starts.end());
        op.ends   = std::vector<int64_t>(ends.begin(), ends.end());
        op.axes   = std::vector<int64_t>(num_axes);
        std::iota(op.axes.begin(), op.axes.end(), 0);
727
        uint32_t shrink_axis_mask = 0;
Khalique's avatar
Khalique committed
728
        uint32_t bitwise_compare  = 1;
729
730
731
        std::vector<int64_t> squeeze_axes;

        if(contains(attributes, "shrink_axis_mask"))
732
            shrink_axis_mask = static_cast<uint32_t>(attributes.at("shrink_axis_mask").i());
733

Khalique's avatar
Khalique committed
734
        for(size_t i = 0; i < num_axes; i++)
735
        {
736
            // the LSB corresponds to axis 0 when determining which axes to squeeze
Khalique's avatar
Khalique committed
737
            if(((shrink_axis_mask >> i) & bitwise_compare) == 1)
738
739
                squeeze_axes.push_back(i);
        }
Khalique's avatar
Khalique committed
740

Paul's avatar
Paul committed
741
742
        auto l0 = prog.add_instruction(op, make_contiguous(args[0]));
        return to_nhwc(prog.add_instruction(op::squeeze{squeeze_axes}, l0));
743
744
    }

Khalique's avatar
Khalique committed
745
746
747
748
749
    void parse_graph(const tensorflow::GraphDef& graph)
    {
        nodes = get_nodes(graph, input_nodes);
        for(auto&& input : input_nodes)
        {
Khalique's avatar
Khalique committed
750
            const std::string& name   = input.name();
Khalique's avatar
Khalique committed
751
            attribute_map input_attrs = get_attributes(input);
Khalique's avatar
Khalique committed
752
753
            shape::type_t shape_type  = parse_type(input_attrs.at("dtype").type());
            std::vector<size_t> dims  = parse_dims(input_attrs.at("shape").shape());
754
            if(is_nhwc and dims.size() >= 4)
755
            {
756
                reorder_data(dims);
757
            }
Khalique's avatar
Khalique committed
758
            shape s            = shape{shape_type, dims};
Paul's avatar
Paul committed
759
            instructions[name] = to_nhwc(prog.add_parameter(name, s));
Khalique's avatar
Khalique committed
760
761
762
        }
        for(auto&& p : nodes)
        {
763
            this->parse_node(p.first);
Khalique's avatar
Khalique committed
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
        }
    }

    void parse_node(const std::string& name)
    {
        if(instructions.count(name) == 0)
        {
            auto&& node = nodes.at(name);
            std::vector<instruction_ref> args;

            for(auto&& input : node.input())
            {
                if(nodes.count(input) > 0)
                {
                    auto&& iname = get_name(nodes.at(input));
                    assert(name != iname);
                    this->parse_node(iname);
                    args.push_back(instructions.at(iname));
                }
                else
                {
                    args.push_back(instructions.at(input));
                }
            }
            if(ops.count(node.op()) == 0)
            {
790
                instructions[name] = prog.add_instruction(op::unknown{node.op()}, args);
Khalique's avatar
Khalique committed
791
792
793
794
795
796
797
798
799
800
801
            }
            else
            {
                instructions[name] = ops[node.op()](get_attributes(node), args);
            }
        }
    }

    static attribute_map get_attributes(const tensorflow::NodeDef& node)
    {
        attribute_map result;
Khalique's avatar
Khalique committed
802
        for(auto&& attr : node.attr())
Khalique's avatar
Khalique committed
803
804
805
806
807
808
        {
            result[attr.first] = attr.second;
        }
        return result;
    }

Khalique's avatar
Khalique committed
809
    static std::string get_name(const tensorflow::NodeDef& node) { return node.name(); }
Khalique's avatar
Khalique committed
810

Khalique's avatar
Khalique committed
811
812
    static node_map get_nodes(const tensorflow::GraphDef& graph,
                              std::vector<tensorflow::NodeDef>& input_nodes)
Khalique's avatar
Khalique committed
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
    {
        node_map result;
        for(auto&& node : graph.node())
        {
            auto node_name = get_name(node);
            // assume each node in graph has an associated name
            if(node_name.empty())
                MIGRAPHX_THROW("tf node with no name found");
            result[node_name] = node;
            if(node.op() == "Placeholder")
            {
                input_nodes.push_back(node);
            }
        }
        return result;
    }

    static shape::type_t parse_type(const tensorflow::DataType t)
    {
        shape::type_t shape_type{};
        switch(t)
        {
        case tensorflow::DataType::DT_INVALID:
            break; // throw std::runtime_error("Unsupported type UNDEFINED");
        case tensorflow::DataType::DT_FLOAT: shape_type = shape::float_type; break;
        case tensorflow::DataType::DT_DOUBLE: shape_type = shape::double_type; break;
        case tensorflow::DataType::DT_INT32: shape_type = shape::int32_type; break;
        case tensorflow::DataType::DT_UINT8:
            break; // throw std::runtime_error("Unsupported type UINT8");
        case tensorflow::DataType::DT_INT16: shape_type = shape::int16_type; break;
        case tensorflow::DataType::DT_INT8: shape_type = shape::int8_type; break;
        case tensorflow::DataType::DT_STRING:
            break; // throw std::runtime_error("Unsupported type STRING");
        case tensorflow::DataType::DT_COMPLEX64:
            break; // throw std::runtime_error("Unsupported type COMPLEX64");
        case tensorflow::DataType::DT_INT64: shape_type = shape::int64_type; break;
        case tensorflow::DataType::DT_BOOL:
            break; // throw std::runtime_error("Unsupported type BOOL");
        case tensorflow::DataType::DT_QINT8:
            break; // throw std::runtime_error("Unsupported type QINT8");
        case tensorflow::DataType::DT_QUINT8:
            break; // throw std::runtime_error("Unsupported type QUINT8");
        case tensorflow::DataType::DT_QINT32:
            break; // throw std::runtime_error("Unsupported type QINT32");
        case tensorflow::DataType::DT_BFLOAT16:
            break; // throw std::runtime_error("Unsupported type BFLOAT16");
        case tensorflow::DataType::DT_QINT16:
            break; // throw std::runtime_error("Unsupported type QINT16");
        case tensorflow::DataType::DT_QUINT16:
            break; // throw std::runtime_error("Unsupported type QUINT16");
        case tensorflow::DataType::DT_UINT16: shape_type = shape::uint16_type; break;
        case tensorflow::DataType::DT_COMPLEX128:
            break; // throw std::runtime_error("Unsupported type COMPLEX128");
        case tensorflow::DataType::DT_HALF: shape_type = shape::half_type; break;
        case tensorflow::DataType::DT_RESOURCE:
            break; // throw std::runtime_error("Unsupported type RESOURCE");
        case tensorflow::DataType::DT_VARIANT:
            break; // throw std::runtime_error("Unsupported type VARIANT");
        case tensorflow::DataType::DT_UINT32: shape_type = shape::uint32_type; break;
Khalique's avatar
Khalique committed
872
873
874
        case tensorflow::DataType::DT_UINT64:
            shape_type = shape::uint64_type;
            break;
Khalique's avatar
Khalique committed
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901

        // tf pb should not use these types
        case tensorflow::DataType::DT_FLOAT_REF: break;
        case tensorflow::DataType::DT_DOUBLE_REF: break;
        case tensorflow::DataType::DT_INT32_REF: break;
        case tensorflow::DataType::DT_UINT8_REF: break;
        case tensorflow::DataType::DT_INT16_REF: break;
        case tensorflow::DataType::DT_INT8_REF: break;
        case tensorflow::DataType::DT_STRING_REF: break;
        case tensorflow::DataType::DT_COMPLEX64_REF: break;
        case tensorflow::DataType::DT_INT64_REF: break;
        case tensorflow::DataType::DT_BOOL_REF: break;
        case tensorflow::DataType::DT_QINT8_REF: break;
        case tensorflow::DataType::DT_QUINT8_REF: break;
        case tensorflow::DataType::DT_QINT32_REF: break;
        case tensorflow::DataType::DT_BFLOAT16_REF: break;
        case tensorflow::DataType::DT_QINT16_REF: break;
        case tensorflow::DataType::DT_QUINT16_REF: break;
        case tensorflow::DataType::DT_UINT16_REF: break;
        case tensorflow::DataType::DT_COMPLEX128_REF: break;
        case tensorflow::DataType::DT_HALF_REF: break;
        case tensorflow::DataType::DT_RESOURCE_REF: break;
        case tensorflow::DataType::DT_VARIANT_REF: break;
        case tensorflow::DataType::DT_UINT32_REF: break;
        case tensorflow::DataType::DT_UINT64_REF: break;
        case tensorflow::DataType::DataType_INT_MAX_SENTINEL_DO_NOT_USE_: break;
        case tensorflow::DataType::DataType_INT_MIN_SENTINEL_DO_NOT_USE_: break;
Khalique's avatar
Khalique committed
902
903
904
905
        }
        return shape_type;
    }

Khalique's avatar
Khalique committed
906
    static literal parse_tensor(const tensorflow::TensorProto& t)
Khalique's avatar
Khalique committed
907
908
    {
        std::vector<size_t> dims = parse_dims(t.tensor_shape());
909
        size_t shape_size = std::accumulate(dims.begin(), dims.end(), 1, std::multiplies<size_t>());
Khalique's avatar
Khalique committed
910
911
        if(!t.tensor_content().empty()) // has raw data
        {
Khalique's avatar
Khalique committed
912
            const std::string& s = t.tensor_content();
Khalique's avatar
Khalique committed
913
914
915
            switch(t.dtype())
            {
            case tensorflow::DataType::DT_INVALID: throw std::runtime_error("");
Khalique's avatar
Khalique committed
916
917
            case tensorflow::DataType::DT_FLOAT:
                return literal{{shape::float_type, dims}, s.data()};
Khalique's avatar
Khalique committed
918
            case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
919
            case tensorflow::DataType::DT_INT8: return literal{{shape::int8_type, dims}, s.data()};
Khalique's avatar
Khalique committed
920
            case tensorflow::DataType::DT_UINT16:
921
                return literal{{shape::uint16_type, dims}, s.data()};
Khalique's avatar
Khalique committed
922
            case tensorflow::DataType::DT_INT16:
923
                return literal{{shape::int16_type, dims}, s.data()};
Khalique's avatar
Khalique committed
924
925
926
927
            case tensorflow::DataType::DT_INT32:
                return literal{{shape::int32_type, dims}, s.data()};
            case tensorflow::DataType::DT_INT64:
                return literal{{shape::int64_type, dims}, s.data()};
Khalique's avatar
Khalique committed
928
            case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
929
            case tensorflow::DataType::DT_BOOL: return literal{{shape::int8_type, dims}, s.data()};
Khalique's avatar
Khalique committed
930
            case tensorflow::DataType::DT_HALF: return literal{{shape::half_type, dims}, s.data()};
Khalique's avatar
Khalique committed
931
932
            case tensorflow::DataType::DT_DOUBLE:
                return literal{{shape::double_type, dims}, s.data()};
Khalique's avatar
Khalique committed
933
934
935
936
            case tensorflow::DataType::DT_UINT32: throw std::runtime_error("");
            case tensorflow::DataType::DT_UINT64: throw std::runtime_error("");
            case tensorflow::DataType::DT_COMPLEX64: throw std::runtime_error("");
            case tensorflow::DataType::DT_COMPLEX128: throw std::runtime_error("");
Khalique's avatar
Khalique committed
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
            case tensorflow::DataType::DT_QINT8: throw std::runtime_error("");
            case tensorflow::DataType::DT_QUINT8: throw std::runtime_error("");
            case tensorflow::DataType::DT_QINT32: throw std::runtime_error("");
            case tensorflow::DataType::DT_BFLOAT16: throw std::runtime_error("");
            case tensorflow::DataType::DT_QINT16: throw std::runtime_error("");
            case tensorflow::DataType::DT_QUINT16: throw std::runtime_error("");
            case tensorflow::DataType::DT_RESOURCE: throw std::runtime_error("");
            case tensorflow::DataType::DT_VARIANT: throw std::runtime_error("");
            case tensorflow::DataType::DT_FLOAT_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_DOUBLE_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_INT32_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_UINT8_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_INT16_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_INT8_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_STRING_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_COMPLEX64_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_INT64_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_BOOL_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_QINT8_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_QUINT8_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_QINT32_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_BFLOAT16_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_QINT16_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_QUINT16_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_UINT16_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_COMPLEX128_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_HALF_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_RESOURCE_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_VARIANT_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_UINT32_REF: throw std::runtime_error("");
            case tensorflow::DataType::DT_UINT64_REF: throw std::runtime_error("");
Khalique's avatar
Khalique committed
968
969
970
971
            case tensorflow::DataType::DataType_INT_MAX_SENTINEL_DO_NOT_USE_:
                throw std::runtime_error("");
            case tensorflow::DataType::DataType_INT_MIN_SENTINEL_DO_NOT_USE_:
                throw std::runtime_error("");
Khalique's avatar
Khalique committed
972
973
974
975
976
977
978
            }
            MIGRAPHX_THROW("Invalid tensor type");
        }
        switch(t.dtype())
        {
        case tensorflow::DataType::DT_INVALID: throw std::runtime_error("");
        case tensorflow::DataType::DT_FLOAT:
Khalique's avatar
Khalique committed
979
980
            return create_literal(
                shape::float_type, dims, get_data_vals(t.float_val(), shape_size));
Khalique's avatar
Khalique committed
981
982
        case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT8:
983
            return create_literal(shape::int8_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
984
        case tensorflow::DataType::DT_UINT16:
985
            return create_literal(shape::uint16_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
986
        case tensorflow::DataType::DT_INT16:
987
            return create_literal(shape::int16_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
988
        case tensorflow::DataType::DT_INT32:
989
            return create_literal(shape::int32_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
990
        case tensorflow::DataType::DT_INT64:
Khalique's avatar
Khalique committed
991
992
            return create_literal(
                shape::int64_type, dims, get_data_vals(t.int64_val(), shape_size));
Khalique's avatar
Khalique committed
993
994
        case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
        case tensorflow::DataType::DT_BOOL:
995
            return create_literal(shape::int32_type, dims, get_data_vals(t.bool_val(), shape_size));
Khalique's avatar
Khalique committed
996
        case tensorflow::DataType::DT_HALF:
Khalique's avatar
Khalique committed
997
        {
998
999
            std::vector<int> data_int32 = get_data_vals(t.half_val(), shape_size);
            std::vector<uint16_t> data_uint16(data_int32.begin(), data_int32.end());
Khalique's avatar
Khalique committed
1000
1001
1002
1003
1004
            std::vector<half> data_half;
            std::transform(data_uint16.begin(),
                           data_uint16.end(),
                           std::back_inserter(data_half),
                           [](uint16_t raw_val) { return *reinterpret_cast<half*>(&raw_val); });
1005
            return create_literal(shape::half_type, dims, data_half);
Khalique's avatar
Khalique committed
1006
        }
Khalique's avatar
Khalique committed
1007
        case tensorflow::DataType::DT_DOUBLE:
Khalique's avatar
Khalique committed
1008
            return literal{{shape::double_type, dims}, get_data_vals(t.double_val(), shape_size)};
Khalique's avatar
Khalique committed
1009
1010
1011
1012
        case tensorflow::DataType::DT_UINT32: throw std::runtime_error("");
        case tensorflow::DataType::DT_UINT64: throw std::runtime_error("");
        case tensorflow::DataType::DT_COMPLEX64: throw std::runtime_error("");
        case tensorflow::DataType::DT_COMPLEX128: throw std::runtime_error("");
Khalique's avatar
Khalique committed
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
        case tensorflow::DataType::DT_QINT8: throw std::runtime_error("");
        case tensorflow::DataType::DT_QUINT8: throw std::runtime_error("");
        case tensorflow::DataType::DT_QINT32: throw std::runtime_error("");
        case tensorflow::DataType::DT_BFLOAT16: throw std::runtime_error("");
        case tensorflow::DataType::DT_QINT16: throw std::runtime_error("");
        case tensorflow::DataType::DT_QUINT16: throw std::runtime_error("");
        case tensorflow::DataType::DT_RESOURCE: throw std::runtime_error("");
        case tensorflow::DataType::DT_VARIANT: throw std::runtime_error("");
        case tensorflow::DataType::DT_FLOAT_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_DOUBLE_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT32_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_UINT8_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT16_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT8_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_STRING_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_COMPLEX64_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT64_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_BOOL_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_QINT8_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_QUINT8_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_QINT32_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_BFLOAT16_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_QINT16_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_QUINT16_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_UINT16_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_COMPLEX128_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_HALF_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_RESOURCE_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_VARIANT_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_UINT32_REF: throw std::runtime_error("");
        case tensorflow::DataType::DT_UINT64_REF: throw std::runtime_error("");
Khalique's avatar
Khalique committed
1044
1045
1046
1047
        case tensorflow::DataType::DataType_INT_MAX_SENTINEL_DO_NOT_USE_:
            throw std::runtime_error("");
        case tensorflow::DataType::DataType_INT_MIN_SENTINEL_DO_NOT_USE_:
            throw std::runtime_error("");
Khalique's avatar
Khalique committed
1048
1049
1050
1051
        }
        MIGRAPHX_THROW("Invalid tensor type");
    }

1052
    template <class T>
Khalique's avatar
Khalique committed
1053
    static std::vector<T> get_data_vals(const google::protobuf::RepeatedField<T>& data,
1054
                                        const size_t& shape_size)
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
    {
        std::vector<T> data_vals(shape_size);
        // check if shape has enough data values given existing fields
        if(data.size() == 1)
        {
            std::fill(data_vals.begin(), data_vals.end(), data[0]);
        }
        else
            copy(data.begin(), data.end(), std::back_inserter(data_vals));
        return data_vals;
    }

Khalique's avatar
Khalique committed
1067
1068
1069
1070
    static std::vector<size_t> parse_dims(const tensorflow::TensorShapeProto& s)
    {
        std::vector<size_t> dims;
        auto input_dims = s.dim();
Khalique's avatar
Khalique committed
1071
1072
1073
        std::transform(input_dims.begin(),
                       input_dims.end(),
                       std::back_inserter(dims),
Paul's avatar
Paul committed
1074
                       [](const tensorflow::TensorShapeProto_Dim& dim) { return dim.size(); });
Khalique's avatar
Khalique committed
1075
1076
        return dims;
    }
1077
1078

    template <class T>
Khalique's avatar
Khalique committed
1079
    static literal
1080
    create_literal(shape::type_t shape_type, const std::vector<size_t>& dims, std::vector<T> data)
1081
    {
Khalique's avatar
Khalique committed
1082
        // assume if explicit value is mentioned in protobuf and dim size <= 1, treat as scalar
1083
        if(dims.empty() or (dims.size() == 1 and dims.front() == 1))
1084
            return literal{{shape_type}, data};
1085
1086
        return literal{{shape_type, dims}, data};
    }
Khalique's avatar
Khalique committed
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
};

program parse_tf(const std::string& name, bool is_nhwc)
{
    std::fstream input(name.c_str(), std::ios::in | std::ios::binary);
    tf_parser parser;
    parser.is_nhwc = is_nhwc;

#ifndef NDEBUG
    // Log the program when it can't be parsed
    try
    {
        parser.parse_from(input);
    }
    catch(...)
    {
        std::cerr << parser.prog << std::endl;
        throw;
    }
#else
    parser.parse_from(input);
#endif
Paul's avatar
Paul committed
1109
    parser.to_nchw(std::prev(parser.prog.end()));
Khalique's avatar
Khalique committed
1110
1111
1112
1113
1114
    return std::move(parser.prog);
}

} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx