tf.cpp 47.1 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>
83
    parse_axes(const attribute_map& attributes, const std::string& s, const size_t num_dims) const
84
    {
85
86
87
        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
88
        if(is_nhwc)
89
        {
Khalique's avatar
Khalique committed
90
            std::transform(axes.begin(), axes.end(), axes.begin(), [&](size_t axis) {
Khalique's avatar
Khalique committed
91
                return parse_axis(axis, num_dims);
Khalique's avatar
Khalique committed
92
            });
93
94
95
96
        }
        return axes;
    }

Khalique's avatar
Khalique committed
97
    template <class T>
98
    std::vector<T> parse_axes(std::vector<T> axes, const size_t num_dims) const
Khalique's avatar
Khalique committed
99
100
101
    {
        if(is_nhwc)
        {
102
            std::vector<T> new_axes;
Khalique's avatar
Khalique committed
103
104
105
            std::transform(axes.begin(),
                           axes.end(),
                           std::back_inserter(new_axes),
Khalique's avatar
Khalique committed
106
                           [&](size_t axis) { return parse_axis(axis, num_dims); });
107
            return new_axes;
Khalique's avatar
Khalique committed
108
        }
109
        return axes;
Khalique's avatar
Khalique committed
110
111
    }

Khalique's avatar
Khalique committed
112
113
114
    // 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.
115
    template <class T>
116
    void reorder_data(std::vector<T>& prev_data) const
117
118
    {
        std::vector<T> new_data(prev_data.size());
119
        for(size_t i = 0; i < new_data.size(); i++)
120
        {
Khalique's avatar
Khalique committed
121
            auto new_idx         = parse_axis(i, new_data.size());
122
            new_data.at(new_idx) = prev_data.at(i);
123
        }
124
125
126
127
        prev_data = new_data;
    }

    template <class T>
128
    T parse_axis(const T& dim, const size_t num_dims) const
129
    {
Khalique's avatar
Khalique committed
130
        T new_dim = dim;
Khalique's avatar
Khalique committed
131
        if(is_nhwc and num_dims >= 4)
132
133
134
        {
            switch(dim)
            {
Khalique's avatar
Khalique committed
135
136
137
138
139
            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;
140
141
            }
        }
Khalique's avatar
Khalique committed
142
        return new_dim;
143
144
    }

145
146
147
148
149
150
151
    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
152
153
154
155
    tf_parser()
    {
        add_generic_op("Identity", op::identity{});
        add_generic_op("Relu", op::relu{});
Khalique's avatar
Khalique committed
156
        add_generic_op("Relu6", op::clip{6.0, 0.0});
Khalique's avatar
Khalique committed
157
        add_generic_op("Rsqrt", op::rsqrt{});
Khalique's avatar
Khalique committed
158
        add_generic_op("Tanh", op::tanh{});
Khalique's avatar
Khalique committed
159
        add_generic_op("StopGradient", op::identity{});
Khalique's avatar
Khalique committed
160

161
        add_binary_op("Add", op::add{});
Khalique's avatar
Khalique committed
162
        add_binary_op("Mul", op::mul{});
Khalique's avatar
Khalique committed
163
        add_binary_op("SquaredDifference", op::sqdiff{});
Khalique's avatar
Khalique committed
164
        add_binary_op("Sub", op::sub{});
Khalique's avatar
Khalique committed
165

166
        add_mem_op("AvgPool", &tf_parser::parse_pooling);
167
        add_mem_op("BiasAdd", &tf_parser::parse_biasadd);
Paul's avatar
Paul committed
168
        add_mem_op("ConcatV2", &tf_parser::parse_concat, false);
Khalique's avatar
Khalique committed
169
        add_mem_op("Const", &tf_parser::parse_constant);
Paul's avatar
Paul committed
170
        add_mem_op("Conv2D", &tf_parser::parse_conv);
Paul's avatar
Paul committed
171
        add_mem_op("DepthwiseConv2dNative", &tf_parser::parse_depthwiseconv);
172
        add_mem_op("ExpandDims", &tf_parser::parse_expanddims, false);
Khalique's avatar
Khalique committed
173
        add_mem_op("FusedBatchNorm", &tf_parser::parse_batchnorm);
Paul's avatar
Paul committed
174
        add_mem_op("MatMul", &tf_parser::parse_matmul, false);
175
        add_mem_op("MaxPool", &tf_parser::parse_pooling);
Khalique's avatar
Khalique committed
176
        add_mem_op("Mean", &tf_parser::parse_mean);
Paul's avatar
Paul committed
177
        add_mem_op("Pack", &tf_parser::parse_pack, false);
Paul's avatar
Paul committed
178
        add_mem_op("Pad", &tf_parser::parse_pad);
Paul's avatar
Paul committed
179
        add_mem_op("Reshape", &tf_parser::parse_reshape, false);
180
        add_mem_op("Softmax", &tf_parser::parse_softmax);
Paul's avatar
Paul committed
181
        add_mem_op("Squeeze", &tf_parser::parse_squeeze, false);
182
        add_mem_op("StridedSlice", &tf_parser::parse_stridedslice);
Khalique's avatar
Khalique committed
183
        add_mem_op("Transpose", &tf_parser::parse_transpose, false);
Khalique's avatar
Khalique committed
184
185
    }

186
    template <class F>
Paul's avatar
Paul committed
187
    void add_op(std::string name, F f, bool transpose = true)
188
    {
Paul's avatar
Paul committed
189
        if(transpose)
Paul's avatar
Paul committed
190
        {
Paul's avatar
Paul committed
191
192
            ops.emplace(name,
                        op_func{[=](const attribute_map& attributes,
Paul's avatar
Paul committed
193
                                    const std::vector<instruction_ref>& args) -> instruction_ref {
Paul's avatar
Paul committed
194
195
                            return to_nhwc(f(attributes, to_nchw(args)));
                        }});
Paul's avatar
Paul committed
196
197
198
199
200
        }
        else
        {
            ops.emplace(name, f);
        }
201
202
    }

Khalique's avatar
Khalique committed
203
    template <class F>
Paul's avatar
Paul committed
204
    void add_mem_op(std::string name, F f, bool transpose = true)
Khalique's avatar
Khalique committed
205
    {
Paul's avatar
Paul committed
206
207
208
209
210
        add_op(name,
               [=](auto&&... xs) {
                   return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...);
               },
               transpose);
Khalique's avatar
Khalique committed
211
212
213
214
215
    }

    template <class T>
    void add_binary_op(std::string name, T x)
    {
Paul's avatar
Paul committed
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
        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
231
232
233
234
235
    }

    template <class T>
    instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
    {
Khalique's avatar
Khalique committed
236
        if(arg0->get_shape().lens() != arg1->get_shape().lens())
Khalique's avatar
Khalique committed
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
        {
            // 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
252
253
            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
254
255
256
257
258

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

259
            std::vector<size_t> output_lens(*s1);
Khalique's avatar
Khalique committed
260
261
262
263
264
265
266
267
268
            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
269
            return to_nhwc(prog.add_instruction(x, to_nchw(l0), to_nchw(l1)));
Khalique's avatar
Khalique committed
270
271
272
        }
        else
        {
Paul's avatar
Paul committed
273
            return to_nhwc(prog.add_instruction(x, {to_nchw(arg0), to_nchw(arg1)}));
Khalique's avatar
Khalique committed
274
275
276
277
        }
    }

    template <class T>
Paul's avatar
Paul committed
278
    void add_generic_op(std::string name, T x, bool transpose = true)
Khalique's avatar
Khalique committed
279
    {
Paul's avatar
Paul committed
280
281
282
283
284
        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
285
286
287
288
289
    }

    instruction_ref
    parse_batchnorm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
Khalique's avatar
Khalique committed
290
291
292
        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
293
294
295
296
297
298
299
300
        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));
    }

301
    instruction_ref
Khalique's avatar
Khalique committed
302
    parse_biasadd(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
303
    {
304
        uint64_t axis = 1; // assume output of previous layer is in NCHW (broadcast on channel)
Shucai Xiao's avatar
Shucai Xiao committed
305
        auto l0 = prog.add_instruction(op::broadcast{axis, args[0]->get_shape().lens()}, args[1]);
306
        return prog.add_instruction(op::add{}, args[0], l0);
307
308
    }

Khalique's avatar
Khalique committed
309
310
311
312
    instruction_ref
    parse_concat(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        // get index for axis within args
313
        size_t axis_idx = attributes.at("N").i();
Paul's avatar
Paul committed
314
        size_t axis     = args[axis_idx]->eval().at<int64_t>();
Khalique's avatar
Khalique committed
315
        op::concat op{axis};
316
        // return only first N arguments (assuming last index is the axis value)
Paul's avatar
Paul committed
317
318
        return prog.add_instruction(
            op, std::vector<instruction_ref>(args.begin(), args.begin() + args.size() - 1));
Khalique's avatar
Khalique committed
319
320
321
322
323
324
    }

    instruction_ref parse_constant(const std::string&,
                                   attribute_map attributes,
                                   const std::vector<instruction_ref>&)
    {
Paul's avatar
Paul committed
325
        literal v = parse_tensor(attributes.at("value").tensor());
Paul's avatar
Paul committed
326
        return prog.add_literal(v);
Khalique's avatar
Khalique committed
327
328
329
330
331
332
333
334
    }

    instruction_ref
    parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::convolution op;
        if(contains(attributes, "strides"))
        {
335
            std::vector<size_t> stride;
336
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
337
            reorder_data(stride);
338
339
            if(stride.size() != 4)
            {
340
                MIGRAPHX_THROW("strides should have 4 values");
341
            }
342
343
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
Khalique's avatar
Khalique committed
344
345
346
        }
        if(contains(attributes, "dilations"))
        {
347
            std::vector<size_t> dilation;
348
            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
349
            reorder_data(dilation);
350
351
352
353
            if(dilation.size() != 4)
            {
                MIGRAPHX_THROW("dilation should have 4 values");
            }
354
355
            op.dilation[0] = dilation[2];
            op.dilation[1] = dilation[3];
Khalique's avatar
Khalique committed
356
        }
Khalique's avatar
Khalique committed
357

Paul's avatar
Paul committed
358
        auto weights = to_kcxy(args[1]);
Paul's avatar
Paul committed
359
        auto l0      = args[0];
Khalique's avatar
Khalique committed
360
361
362
363
364
        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
365
                op.padding_mode                 = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
366
367
368
                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
369
370

                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
371
372
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
373
374
375
376
377
378
379
380
381
382
383
                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
384
385
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
386
                }
387
388
389
            }
            else if(pad_mode.find("VALID") != std::string::npos)
            {
390
                op.padding_mode = op::padding_mode_t::valid;
Khalique's avatar
Khalique committed
391
            }
Khalique's avatar
Khalique committed
392
            else if(pad_mode.find("EXPLICIT") != std::string::npos)
Khalique's avatar
Khalique committed
393
            {
394
                std::vector<size_t> padding;
395
                copy(attributes.at("explicit_paddings").list().i(), std::back_inserter(padding));
Khalique's avatar
Khalique committed
396
397
398
399
400
401
402
403
404
405
406
407
                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
408
        return prog.add_instruction(op, {l0, to_kcxy(args[1])});
Khalique's avatar
Khalique committed
409
410
    }

Khalique's avatar
Khalique committed
411
412
413
    instruction_ref parse_depthwiseconv(const std::string&,
                                        attribute_map attributes,
                                        std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
414
415
416
    {
        op::convolution op;
        size_t num_channels = args[0]->get_shape().lens()[1];
Khalique's avatar
Khalique committed
417
        op.group            = num_channels;
Khalique's avatar
Khalique committed
418

Khalique's avatar
Khalique committed
419
420
        if(contains(attributes, "strides"))
        {
421
            std::vector<size_t> stride;
422
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
423
            reorder_data(stride);
424
425
            if(stride.size() != 4)
            {
426
                MIGRAPHX_THROW("strides should have 4 values");
427
            }
428
429
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
Khalique's avatar
Khalique committed
430
        }
Paul's avatar
Paul committed
431
432

        auto weights = to_kcxy(args[1]);
Khalique's avatar
Khalique committed
433
434
        if(contains(attributes, "dilations"))
        {
435
            std::vector<size_t> dilation;
436
            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
437
            reorder_data(dilation);
438
439
440
441
            if(dilation.size() != 4)
            {
                MIGRAPHX_THROW("dilation should have 4 values");
            }
442
443
            op.dilation[0] = dilation[2];
            op.dilation[1] = dilation[3];
Khalique's avatar
Khalique committed
444
445
        }

Khalique's avatar
Khalique committed
446
        auto l0 = args[0];
Khalique's avatar
Khalique committed
447
448
449
        if(contains(attributes, "padding"))
        {
            const std::string& pad_mode = attributes.at("padding").s();
Khalique's avatar
Khalique committed
450

Khalique's avatar
Khalique committed
451
452
            if(pad_mode.find("SAME") != std::string::npos)
            {
Khalique's avatar
Khalique committed
453
                op.padding_mode                 = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
454
455
456
457
458
                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
459
460
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
461
462
463
464
465
466
467
468
469
470
471
                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
472
473
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
474
                }
Khalique's avatar
Khalique committed
475
            }
Khalique's avatar
Khalique committed
476
            else if(pad_mode.find("VALID") != std::string::npos)
Khalique's avatar
Khalique committed
477
            {
Khalique's avatar
Khalique committed
478
                op.padding_mode = op::padding_mode_t::valid;
Khalique's avatar
Khalique committed
479
480
            }
        }
Khalique's avatar
Khalique committed
481

Khalique's avatar
Khalique committed
482
483
        std::vector<int64_t> new_weights_shape;
        copy(weights->get_shape().lens(), std::back_inserter(new_weights_shape));
Khalique's avatar
Khalique committed
484
485
486
487

        // 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
488
        int64_t multiplier   = new_weights_shape[0];
Khalique's avatar
Khalique committed
489
490
491
        int64_t out_channels = num_channels * multiplier;
        new_weights_shape[0] = out_channels;
        new_weights_shape[1] = 1;
Paul's avatar
Paul committed
492
        // Make sure weights are contiguous before doing reshape
Paul's avatar
Paul committed
493
494
        auto new_weights =
            prog.add_instruction(op::reshape{new_weights_shape}, make_contiguous(weights));
Khalique's avatar
Khalique committed
495

Khalique's avatar
Khalique committed
496
        return prog.add_instruction(op, {l0, new_weights});
Khalique's avatar
Khalique committed
497
498
    }

Khalique's avatar
Khalique committed
499
500
    instruction_ref
    parse_expanddims(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
501
502
    {
        std::vector<size_t> input_dims = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
503
        std::vector<int64_t> new_dims(input_dims.begin(), input_dims.end());
Khalique's avatar
Khalique committed
504
        size_t num_dims = input_dims.size();
505
        int32_t dim     = args[1]->eval().at<int32_t>();
Khalique's avatar
Khalique committed
506
507

        if(dim < 0)
Khalique's avatar
Khalique committed
508
509
510
511
512
513
514
515
516
517
        {
            new_dims.insert(new_dims.begin() + (num_dims + dim + 1), 1);
        }
        else
        {
            new_dims.insert(new_dims.begin() + dim, 1);
        }
        return prog.add_instruction(op::reshape{new_dims}, args[0]);
    }

Khalique's avatar
Khalique committed
518
519
    instruction_ref
    parse_matmul(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
520
521
522
    {
        bool transa = false;
        bool transb = false;
Khalique's avatar
Khalique committed
523

524
525
526
527
528
529
530
531
532
533
534
535
        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
536
        std::iter_swap(perm.end() - 1, perm.end() - 2);
537
538
539
540
541
542
543

        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
544
545
    instruction_ref
    parse_mean(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
546
547
    {
        bool keep_dims = attributes.at("keep_dims").b();
Paul's avatar
Paul committed
548
        std::vector<int32_t> hw_axes{2, 3};
Khalique's avatar
Khalique committed
549
        // check if conditions for GlobalAvgPool are met
Khalique's avatar
Khalique committed
550
        auto lens = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
551
552
        auto axes = parse_axes(args[1]->eval().get<int32_t>().to_vector(), lens.size());

Khalique's avatar
Khalique committed
553
        if(axes == hw_axes and lens.size() == 4)
Khalique's avatar
Khalique committed
554
555
        {
            op::pooling op{"average"};
Khalique's avatar
Khalique committed
556
557
            op.lengths[0] = lens[2];
            op.lengths[1] = lens[3];
Khalique's avatar
Khalique committed
558
559
560
561
562
            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
563
564
565
566
        }
        MIGRAPHX_THROW("MIGraphX does not support mean outside of GlobalAvgPool transformation");
    }

Khalique's avatar
Khalique committed
567
568
569
    instruction_ref parse_pack(const std::string&,
                               const attribute_map& attributes,
                               std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
570
571
572
573
574
575
    {
        // reinterpret as unsqueeze with concat
        std::vector<instruction_ref> unsqueezed_args;
        int64_t axis = 0;
        if(contains(attributes, "axis"))
            axis = attributes.at("axis").i();
576
577
578
        size_t input_size = args.front()->get_shape().lens().size();
        if(axis > input_size)
        {
Khalique's avatar
Khalique committed
579
580
            MIGRAPHX_THROW("TF_PARSER: axis value of " + to_string(axis) +
                           " must be smaller than input size " + to_string(input_size));
581
582
        }

Khalique's avatar
Khalique committed
583
584
585
586
587
        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
588
589
        return to_nhwc(
            prog.add_instruction(op::concat{static_cast<size_t>(axis)}, unsqueezed_args));
Khalique's avatar
Khalique committed
590
591
    }

Khalique's avatar
Khalique committed
592
593
594
595
596
    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
597
598
        // 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
599
        std::vector<std::pair<int32_t, int32_t>> pad_per_dim(ndims);
Paul's avatar
Paul committed
600
        auto tf_padding = args[1]->eval().get<int32_t>().to_vector();
Khalique's avatar
Khalique committed
601
        for(size_t i = 0; i < 2 * ndims; i += 2)
Khalique's avatar
Khalique committed
602
        {
Khalique's avatar
Khalique committed
603
604
            pad_per_dim[i / 2].first  = tf_padding[i];
            pad_per_dim[i / 2].second = tf_padding[i + 1];
Khalique's avatar
Khalique committed
605
606
607
608
        }
        reorder_data(pad_per_dim);

        op::pad op;
Khalique's avatar
Khalique committed
609
610
        std::vector<int64_t> pads(ndims * 2);
        for(size_t i = 0; i < ndims; i++)
Khalique's avatar
Khalique committed
611
        {
Khalique's avatar
Khalique committed
612
613
            pads[i]         = pad_per_dim[i].first;
            pads[i + ndims] = pad_per_dim[i].second;
Khalique's avatar
Khalique committed
614
615
        }
        op.pads = pads;
Paul's avatar
Paul committed
616
        return prog.add_instruction(op, args.front());
Khalique's avatar
Khalique committed
617
618
    }

619
620
621
622
623
    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
624

625
626
        if(contains(attributes, "strides"))
        {
627
            std::vector<size_t> stride;
628
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
629
            reorder_data(stride);
630
631
632
633
            if(stride.size() != 4)
            {
                MIGRAPHX_THROW("strides should have 4 values");
            }
634
635
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
636
637
638
        }
        if(contains(attributes, "ksize"))
        {
639
            std::vector<size_t> ksize;
640
            copy(attributes.at("ksize").list().i(), std::back_inserter(ksize));
641
            reorder_data(ksize);
642
643
644
            if(ksize.size() != 4)
            {
                MIGRAPHX_THROW("ksize should have 4 values");
Khalique's avatar
Khalique committed
645
            }
646
647
            op.lengths[0] = ksize[2];
            op.lengths[1] = ksize[3];
648
        }
Khalique's avatar
Khalique committed
649
650

        auto l0 = args[0];
Khalique's avatar
Khalique committed
651
652
653
654
655
        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
656
                op.padding_mode = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
657
                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
658
659
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
660
661
662
663
664
665
666
                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
667
668
                    l0                           = prog.add_instruction(
                        migraphx::op::pad{padding, std::numeric_limits<float>::lowest()}, l0);
Khalique's avatar
Khalique committed
669
670
671
                }
                else
                {
Khalique's avatar
Khalique committed
672
673
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
674
                }
Khalique's avatar
Khalique committed
675
676
677
678
679
680
            }
            else if(pad_mode.find("VALID") != std::string::npos)
            {
                op.padding_mode = op::padding_mode_t::valid;
            }
        }
Khalique's avatar
Khalique committed
681
        return prog.add_instruction(op, l0);
682
    }
Khalique's avatar
Khalique committed
683

684
    instruction_ref
Khalique's avatar
Khalique committed
685
    parse_reshape(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
686
687
688
689
    {
        op::reshape op;
        if(args.size() != 2)
            MIGRAPHX_THROW("reshape needs 2 arguments (input, new_shape)");
Khalique's avatar
Khalique committed
690
        auto s = args[1]->eval();
691
        s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
Paul's avatar
Paul committed
692
        return prog.add_instruction(op, make_contiguous(args[0]));
693
694
    }

Khalique's avatar
Khalique committed
695
696
697
698
699
700
701
702
703
    void parse_from(std::istream& is)
    {
        tensorflow::GraphDef graph;
        if(graph.ParseFromIstream(&is))
        {
            this->parse_graph(graph);
        }
        else
        {
704
            throw std::runtime_error("Failed reading tf file");
Khalique's avatar
Khalique committed
705
706
707
        }
    }

Shucai Xiao's avatar
Shucai Xiao committed
708
709
710
    instruction_ref parse_softmax(const std::string&,
                                  const attribute_map& attributes,
                                  std::vector<instruction_ref> args)
711
    {
712
713
714
715
716
717
718
        int axis = 1;
        if(contains(attributes, "axis"))
        {
            axis = static_cast<int>(attributes.at("axis").i());
        }

        return prog.add_instruction(op::softmax{axis}, std::move(args));
719
720
    }

Khalique's avatar
Khalique committed
721
722
723
    instruction_ref parse_squeeze(const std::string&,
                                  const attribute_map& attributes,
                                  std::vector<instruction_ref> args)
724
725
    {
        op::squeeze op;
Khalique's avatar
Khalique committed
726
        auto input_dims = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
727
        auto axes       = attributes.at("squeeze_dims").list().i();
728
        copy(axes, std::back_inserter(op.axes));
Khalique's avatar
Khalique committed
729

730
731
        if(op.axes.empty()) // no squeeze_dims provided, remove any dim that equals 1
        {
Khalique's avatar
Khalique committed
732
            for(size_t i = 0; i < input_dims.size(); i++)
733
            {
Khalique's avatar
Khalique committed
734
                if(input_dims.at(i) == 1)
735
736
737
738
                {
                    op.axes.push_back(i);
                }
            }
739
        }
Paul's avatar
Paul committed
740
        return prog.add_instruction(op, make_contiguous(args[0]));
741
742
    }

Khalique's avatar
Khalique committed
743
744
745
    instruction_ref parse_stridedslice(const std::string&,
                                       const attribute_map& attributes,
                                       std::vector<instruction_ref> args)
746
747
    {
        op::slice op;
Khalique's avatar
Khalique committed
748
749
750
        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();
751

Khalique's avatar
Khalique committed
752
753
754
755
        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);
756
        uint32_t shrink_axis_mask = 0;
Khalique's avatar
Khalique committed
757
        uint32_t bitwise_compare  = 1;
758
759
760
        std::vector<int64_t> squeeze_axes;

        if(contains(attributes, "shrink_axis_mask"))
761
            shrink_axis_mask = static_cast<uint32_t>(attributes.at("shrink_axis_mask").i());
762

Khalique's avatar
Khalique committed
763
        for(size_t i = 0; i < num_axes; i++)
764
        {
765
            // the LSB corresponds to axis 0 when determining which axes to squeeze
Khalique's avatar
Khalique committed
766
            if(((shrink_axis_mask >> i) & bitwise_compare) == 1)
767
768
                squeeze_axes.push_back(i);
        }
Khalique's avatar
Khalique committed
769

Paul's avatar
Paul committed
770
771
        auto l0 = prog.add_instruction(op, make_contiguous(args[0]));
        return to_nhwc(prog.add_instruction(op::squeeze{squeeze_axes}, l0));
772
773
    }

Khalique's avatar
Khalique committed
774
775
    instruction_ref
    parse_transpose(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
776
777
778
779
780
781
782
783
    {
        auto perm = args[1]->eval().get<int32_t>().to_vector();
        op::transpose op;
        op.dims = std::vector<int64_t>(perm.begin(), perm.end());

        return prog.add_instruction(op, args.front());
    }

Khalique's avatar
Khalique committed
784
785
786
787
788
    void parse_graph(const tensorflow::GraphDef& graph)
    {
        nodes = get_nodes(graph, input_nodes);
        for(auto&& input : input_nodes)
        {
Khalique's avatar
Khalique committed
789
            const std::string& name   = input.name();
Khalique's avatar
Khalique committed
790
            attribute_map input_attrs = get_attributes(input);
Khalique's avatar
Khalique committed
791
792
            shape::type_t shape_type  = parse_type(input_attrs.at("dtype").type());
            std::vector<size_t> dims  = parse_dims(input_attrs.at("shape").shape());
793
            if(is_nhwc and dims.size() >= 4)
794
            {
795
                reorder_data(dims);
796
            }
Khalique's avatar
Khalique committed
797
            shape s            = shape{shape_type, dims};
Paul's avatar
Paul committed
798
            instructions[name] = to_nhwc(prog.add_parameter(name, s));
Khalique's avatar
Khalique committed
799
800
801
        }
        for(auto&& p : nodes)
        {
802
            this->parse_node(p.first);
Khalique's avatar
Khalique committed
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
        }
    }

    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)
            {
829
                instructions[name] = prog.add_instruction(op::unknown{node.op()}, args);
Khalique's avatar
Khalique committed
830
831
832
833
834
835
836
837
838
839
840
            }
            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
841
        for(auto&& attr : node.attr())
Khalique's avatar
Khalique committed
842
843
844
845
846
847
        {
            result[attr.first] = attr.second;
        }
        return result;
    }

Khalique's avatar
Khalique committed
848
    static std::string get_name(const tensorflow::NodeDef& node) { return node.name(); }
Khalique's avatar
Khalique committed
849

Khalique's avatar
Khalique committed
850
851
    static node_map get_nodes(const tensorflow::GraphDef& graph,
                              std::vector<tensorflow::NodeDef>& input_nodes)
Khalique's avatar
Khalique committed
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
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
902
903
904
905
906
907
908
909
910
    {
        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
911
912
913
        case tensorflow::DataType::DT_UINT64:
            shape_type = shape::uint64_type;
            break;
Khalique's avatar
Khalique committed
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940

        // 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
941
942
943
944
        }
        return shape_type;
    }

Khalique's avatar
Khalique committed
945
    static literal parse_tensor(const tensorflow::TensorProto& t)
Khalique's avatar
Khalique committed
946
947
    {
        std::vector<size_t> dims = parse_dims(t.tensor_shape());
948
        size_t shape_size = std::accumulate(dims.begin(), dims.end(), 1, std::multiplies<size_t>());
Khalique's avatar
Khalique committed
949
950
        if(!t.tensor_content().empty()) // has raw data
        {
Khalique's avatar
Khalique committed
951
            const std::string& s = t.tensor_content();
Khalique's avatar
Khalique committed
952
953
954
            switch(t.dtype())
            {
            case tensorflow::DataType::DT_INVALID: throw std::runtime_error("");
Khalique's avatar
Khalique committed
955
956
            case tensorflow::DataType::DT_FLOAT:
                return literal{{shape::float_type, dims}, s.data()};
Khalique's avatar
Khalique committed
957
            case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
958
            case tensorflow::DataType::DT_INT8: return literal{{shape::int8_type, dims}, s.data()};
Khalique's avatar
Khalique committed
959
            case tensorflow::DataType::DT_UINT16:
960
                return literal{{shape::uint16_type, dims}, s.data()};
Khalique's avatar
Khalique committed
961
            case tensorflow::DataType::DT_INT16:
962
                return literal{{shape::int16_type, dims}, s.data()};
Khalique's avatar
Khalique committed
963
964
965
966
            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
967
            case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
968
            case tensorflow::DataType::DT_BOOL: return literal{{shape::int8_type, dims}, s.data()};
Khalique's avatar
Khalique committed
969
            case tensorflow::DataType::DT_HALF: return literal{{shape::half_type, dims}, s.data()};
Khalique's avatar
Khalique committed
970
971
            case tensorflow::DataType::DT_DOUBLE:
                return literal{{shape::double_type, dims}, s.data()};
Khalique's avatar
Khalique committed
972
973
974
975
            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
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
            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
1007
1008
1009
1010
            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
1011
1012
1013
1014
1015
1016
1017
            }
            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
1018
1019
            return create_literal(
                shape::float_type, dims, get_data_vals(t.float_val(), shape_size));
Khalique's avatar
Khalique committed
1020
1021
        case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT8:
1022
            return create_literal(shape::int8_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1023
        case tensorflow::DataType::DT_UINT16:
1024
            return create_literal(shape::uint16_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1025
        case tensorflow::DataType::DT_INT16:
1026
            return create_literal(shape::int16_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1027
        case tensorflow::DataType::DT_INT32:
1028
            return create_literal(shape::int32_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1029
        case tensorflow::DataType::DT_INT64:
Khalique's avatar
Khalique committed
1030
1031
            return create_literal(
                shape::int64_type, dims, get_data_vals(t.int64_val(), shape_size));
Khalique's avatar
Khalique committed
1032
1033
        case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
        case tensorflow::DataType::DT_BOOL:
1034
            return create_literal(shape::int32_type, dims, get_data_vals(t.bool_val(), shape_size));
Khalique's avatar
Khalique committed
1035
        case tensorflow::DataType::DT_HALF:
Khalique's avatar
Khalique committed
1036
        {
1037
1038
            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
1039
1040
1041
1042
1043
            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); });
1044
            return create_literal(shape::half_type, dims, data_half);
Khalique's avatar
Khalique committed
1045
        }
Khalique's avatar
Khalique committed
1046
        case tensorflow::DataType::DT_DOUBLE:
Khalique's avatar
Khalique committed
1047
            return literal{{shape::double_type, dims}, get_data_vals(t.double_val(), shape_size)};
Khalique's avatar
Khalique committed
1048
1049
1050
1051
        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
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
        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
1083
1084
1085
1086
        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
1087
1088
1089
1090
        }
        MIGRAPHX_THROW("Invalid tensor type");
    }

1091
    template <class T>
Khalique's avatar
Khalique committed
1092
    static std::vector<T> get_data_vals(const google::protobuf::RepeatedField<T>& data,
1093
                                        const size_t& shape_size)
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
    {
        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
1106
1107
1108
1109
    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
1110
1111
1112
        std::transform(input_dims.begin(),
                       input_dims.end(),
                       std::back_inserter(dims),
Paul's avatar
Paul committed
1113
                       [](const tensorflow::TensorShapeProto_Dim& dim) { return dim.size(); });
Khalique's avatar
Khalique committed
1114
1115
        return dims;
    }
1116
1117

    template <class T>
Khalique's avatar
Khalique committed
1118
    static literal
1119
    create_literal(shape::type_t shape_type, const std::vector<size_t>& dims, std::vector<T> data)
1120
    {
Khalique's avatar
Khalique committed
1121
        // assume if explicit value is mentioned in protobuf and dim size <= 1, treat as scalar
1122
        if(dims.empty() or (dims.size() == 1 and dims.front() == 1))
1123
            return literal{{shape_type}, data};
1124
1125
        return literal{{shape_type, dims}, data};
    }
Khalique's avatar
Khalique committed
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
};

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
1148
    parser.to_nchw(std::prev(parser.prog.end()));
Khalique's avatar
Khalique committed
1149
1150
1151
1152
1153
    return std::move(parser.prog);
}

} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx