tf.cpp 47.5 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);
Khalique's avatar
Khalique committed
168
        add_mem_op("Cast", &tf_parser::parse_cast, false);
Paul's avatar
Paul committed
169
        add_mem_op("ConcatV2", &tf_parser::parse_concat, false);
Khalique's avatar
Khalique committed
170
        add_mem_op("Const", &tf_parser::parse_constant);
Paul's avatar
Paul committed
171
        add_mem_op("Conv2D", &tf_parser::parse_conv);
Paul's avatar
Paul committed
172
        add_mem_op("DepthwiseConv2dNative", &tf_parser::parse_depthwiseconv);
173
        add_mem_op("ExpandDims", &tf_parser::parse_expanddims, false);
Khalique's avatar
Khalique committed
174
        add_mem_op("FusedBatchNorm", &tf_parser::parse_batchnorm);
Paul's avatar
Paul committed
175
        add_mem_op("MatMul", &tf_parser::parse_matmul, false);
176
        add_mem_op("MaxPool", &tf_parser::parse_pooling);
Khalique's avatar
Khalique committed
177
        add_mem_op("Mean", &tf_parser::parse_mean);
Paul's avatar
Paul committed
178
        add_mem_op("Pack", &tf_parser::parse_pack, false);
Paul's avatar
Paul committed
179
        add_mem_op("Pad", &tf_parser::parse_pad);
Paul's avatar
Paul committed
180
        add_mem_op("Reshape", &tf_parser::parse_reshape, false);
181
        add_mem_op("Softmax", &tf_parser::parse_softmax);
Paul's avatar
Paul committed
182
        add_mem_op("Squeeze", &tf_parser::parse_squeeze, false);
183
        add_mem_op("StridedSlice", &tf_parser::parse_stridedslice);
Khalique's avatar
Khalique committed
184
        add_mem_op("Transpose", &tf_parser::parse_transpose, false);
Khalique's avatar
Khalique committed
185
186
    }

187
    template <class F>
Paul's avatar
Paul committed
188
    void add_op(std::string name, F f, bool transpose = true)
189
    {
Paul's avatar
Paul committed
190
        if(transpose)
Paul's avatar
Paul committed
191
        {
Paul's avatar
Paul committed
192
193
            ops.emplace(name,
                        op_func{[=](const attribute_map& attributes,
Paul's avatar
Paul committed
194
                                    const std::vector<instruction_ref>& args) -> instruction_ref {
Paul's avatar
Paul committed
195
196
                            return to_nhwc(f(attributes, to_nchw(args)));
                        }});
Paul's avatar
Paul committed
197
198
199
200
201
        }
        else
        {
            ops.emplace(name, f);
        }
202
203
    }

Khalique's avatar
Khalique committed
204
    template <class F>
Paul's avatar
Paul committed
205
    void add_mem_op(std::string name, F f, bool transpose = true)
Khalique's avatar
Khalique committed
206
    {
Paul's avatar
Paul committed
207
208
209
210
211
        add_op(name,
               [=](auto&&... xs) {
                   return std::mem_fn(f)(*this, name, std::forward<decltype(xs)>(xs)...);
               },
               transpose);
Khalique's avatar
Khalique committed
212
213
214
215
216
    }

    template <class T>
    void add_binary_op(std::string name, T x)
    {
Paul's avatar
Paul committed
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
        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
232
233
234
235
236
    }

    template <class T>
    instruction_ref add_broadcastable_binary_op(instruction_ref arg0, instruction_ref arg1, T x)
    {
Khalique's avatar
Khalique committed
237
        if(arg0->get_shape().lens() != arg1->get_shape().lens())
Khalique's avatar
Khalique committed
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
        {
            // 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
253
254
            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
255
256
257
258
259

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

260
            std::vector<size_t> output_lens(*s1);
Khalique's avatar
Khalique committed
261
262
263
264
265
266
267
268
269
            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
270
            return to_nhwc(prog.add_instruction(x, to_nchw(l0), to_nchw(l1)));
Khalique's avatar
Khalique committed
271
272
273
        }
        else
        {
Paul's avatar
Paul committed
274
            return to_nhwc(prog.add_instruction(x, {to_nchw(arg0), to_nchw(arg1)}));
Khalique's avatar
Khalique committed
275
276
277
278
        }
    }

    template <class T>
Paul's avatar
Paul committed
279
    void add_generic_op(std::string name, T x, bool transpose = true)
Khalique's avatar
Khalique committed
280
    {
Paul's avatar
Paul committed
281
282
283
284
285
        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
286
287
288
289
290
    }

    instruction_ref
    parse_batchnorm(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
Khalique's avatar
Khalique committed
291
292
293
        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
294
295
296
297
298
299
300
301
        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));
    }

302
    instruction_ref
Khalique's avatar
Khalique committed
303
    parse_biasadd(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
304
    {
305
        uint64_t axis = 1; // assume output of previous layer is in NCHW (broadcast on channel)
Shucai Xiao's avatar
Shucai Xiao committed
306
        auto l0 = prog.add_instruction(op::broadcast{axis, args[0]->get_shape().lens()}, args[1]);
307
        return prog.add_instruction(op::add{}, args[0], l0);
308
309
    }

Khalique's avatar
Khalique committed
310
311
312
313
314
315
316
    instruction_ref
    parse_cast(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        shape::type_t type = parse_type(attributes.at("DstT").type());
        return prog.add_instruction(op::convert{type}, std::move(args));
    }

Khalique's avatar
Khalique committed
317
318
319
320
    instruction_ref
    parse_concat(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        // get index for axis within args
321
        size_t axis_idx = attributes.at("N").i();
Paul's avatar
Paul committed
322
        size_t axis     = args[axis_idx]->eval().at<int64_t>();
Khalique's avatar
Khalique committed
323
        op::concat op{axis};
324
        // return only first N arguments (assuming last index is the axis value)
Paul's avatar
Paul committed
325
326
        return prog.add_instruction(
            op, std::vector<instruction_ref>(args.begin(), args.begin() + args.size() - 1));
Khalique's avatar
Khalique committed
327
328
329
330
331
332
    }

    instruction_ref parse_constant(const std::string&,
                                   attribute_map attributes,
                                   const std::vector<instruction_ref>&)
    {
Paul's avatar
Paul committed
333
        literal v = parse_tensor(attributes.at("value").tensor());
Paul's avatar
Paul committed
334
        return prog.add_literal(v);
Khalique's avatar
Khalique committed
335
336
337
338
339
340
341
342
    }

    instruction_ref
    parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
    {
        op::convolution op;
        if(contains(attributes, "strides"))
        {
343
            std::vector<size_t> stride;
344
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
345
            reorder_data(stride);
346
347
            if(stride.size() != 4)
            {
348
                MIGRAPHX_THROW("strides should have 4 values");
349
            }
350
351
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
Khalique's avatar
Khalique committed
352
353
354
        }
        if(contains(attributes, "dilations"))
        {
355
            std::vector<size_t> dilation;
356
            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
357
            reorder_data(dilation);
358
359
360
361
            if(dilation.size() != 4)
            {
                MIGRAPHX_THROW("dilation should have 4 values");
            }
362
363
            op.dilation[0] = dilation[2];
            op.dilation[1] = dilation[3];
Khalique's avatar
Khalique committed
364
        }
Khalique's avatar
Khalique committed
365

Paul's avatar
Paul committed
366
        auto weights = to_kcxy(args[1]);
Paul's avatar
Paul committed
367
        auto l0      = args[0];
Khalique's avatar
Khalique committed
368
369
370
371
372
        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
373
                op.padding_mode                 = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
374
375
376
                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
377
378

                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
379
380
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
381
382
383
384
385
386
387
388
389
390
391
                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
392
393
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
394
                }
395
396
397
            }
            else if(pad_mode.find("VALID") != std::string::npos)
            {
398
                op.padding_mode = op::padding_mode_t::valid;
Khalique's avatar
Khalique committed
399
            }
Khalique's avatar
Khalique committed
400
            else if(pad_mode.find("EXPLICIT") != std::string::npos)
Khalique's avatar
Khalique committed
401
            {
402
                std::vector<size_t> padding;
403
                copy(attributes.at("explicit_paddings").list().i(), std::back_inserter(padding));
Khalique's avatar
Khalique committed
404
405
406
407
408
409
410
411
412
413
414
415
                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
416
        return prog.add_instruction(op, {l0, to_kcxy(args[1])});
Khalique's avatar
Khalique committed
417
418
    }

Khalique's avatar
Khalique committed
419
420
421
    instruction_ref parse_depthwiseconv(const std::string&,
                                        attribute_map attributes,
                                        std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
422
423
424
    {
        op::convolution op;
        size_t num_channels = args[0]->get_shape().lens()[1];
Khalique's avatar
Khalique committed
425
        op.group            = num_channels;
Khalique's avatar
Khalique committed
426

Khalique's avatar
Khalique committed
427
428
        if(contains(attributes, "strides"))
        {
429
            std::vector<size_t> stride;
430
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
431
            reorder_data(stride);
432
433
            if(stride.size() != 4)
            {
434
                MIGRAPHX_THROW("strides should have 4 values");
435
            }
436
437
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
Khalique's avatar
Khalique committed
438
        }
Paul's avatar
Paul committed
439
440

        auto weights = to_kcxy(args[1]);
Khalique's avatar
Khalique committed
441
442
        if(contains(attributes, "dilations"))
        {
443
            std::vector<size_t> dilation;
444
            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
445
            reorder_data(dilation);
446
447
448
449
            if(dilation.size() != 4)
            {
                MIGRAPHX_THROW("dilation should have 4 values");
            }
450
451
            op.dilation[0] = dilation[2];
            op.dilation[1] = dilation[3];
Khalique's avatar
Khalique committed
452
453
        }

Khalique's avatar
Khalique committed
454
        auto l0 = args[0];
Khalique's avatar
Khalique committed
455
456
457
        if(contains(attributes, "padding"))
        {
            const std::string& pad_mode = attributes.at("padding").s();
Khalique's avatar
Khalique committed
458

Khalique's avatar
Khalique committed
459
460
            if(pad_mode.find("SAME") != std::string::npos)
            {
Khalique's avatar
Khalique committed
461
                op.padding_mode                 = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
462
463
464
465
466
                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
467
468
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
469
470
471
472
473
474
475
476
477
478
479
                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
480
481
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
482
                }
Khalique's avatar
Khalique committed
483
            }
Khalique's avatar
Khalique committed
484
            else if(pad_mode.find("VALID") != std::string::npos)
Khalique's avatar
Khalique committed
485
            {
Khalique's avatar
Khalique committed
486
                op.padding_mode = op::padding_mode_t::valid;
Khalique's avatar
Khalique committed
487
488
            }
        }
Khalique's avatar
Khalique committed
489

Khalique's avatar
Khalique committed
490
491
        std::vector<int64_t> new_weights_shape;
        copy(weights->get_shape().lens(), std::back_inserter(new_weights_shape));
Khalique's avatar
Khalique committed
492
493
494
495

        // 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
496
        int64_t multiplier   = new_weights_shape[0];
Khalique's avatar
Khalique committed
497
498
499
        int64_t out_channels = num_channels * multiplier;
        new_weights_shape[0] = out_channels;
        new_weights_shape[1] = 1;
Paul's avatar
Paul committed
500
        // Make sure weights are contiguous before doing reshape
Paul's avatar
Paul committed
501
502
        auto new_weights =
            prog.add_instruction(op::reshape{new_weights_shape}, make_contiguous(weights));
Khalique's avatar
Khalique committed
503

Khalique's avatar
Khalique committed
504
        return prog.add_instruction(op, {l0, new_weights});
Khalique's avatar
Khalique committed
505
506
    }

Khalique's avatar
Khalique committed
507
508
    instruction_ref
    parse_expanddims(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
509
510
    {
        std::vector<size_t> input_dims = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
511
        std::vector<int64_t> new_dims(input_dims.begin(), input_dims.end());
Khalique's avatar
Khalique committed
512
        size_t num_dims = input_dims.size();
513
        int32_t dim     = args[1]->eval().at<int32_t>();
Khalique's avatar
Khalique committed
514
515

        if(dim < 0)
Khalique's avatar
Khalique committed
516
517
518
519
520
521
522
523
524
525
        {
            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
526
527
    instruction_ref
    parse_matmul(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
528
529
530
    {
        bool transa = false;
        bool transb = false;
Khalique's avatar
Khalique committed
531

532
533
534
535
536
537
538
539
540
541
542
543
        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
544
        std::iter_swap(perm.end() - 1, perm.end() - 2);
545
546
547
548
549
550
551

        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
552
553
    instruction_ref
    parse_mean(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
554
555
    {
        bool keep_dims = attributes.at("keep_dims").b();
Paul's avatar
Paul committed
556
        std::vector<int32_t> hw_axes{2, 3};
Khalique's avatar
Khalique committed
557
        // check if conditions for GlobalAvgPool are met
Khalique's avatar
Khalique committed
558
        auto lens = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
559
560
        auto axes = parse_axes(args[1]->eval().get<int32_t>().to_vector(), lens.size());

Khalique's avatar
Khalique committed
561
        if(axes == hw_axes and lens.size() == 4)
Khalique's avatar
Khalique committed
562
563
        {
            op::pooling op{"average"};
Khalique's avatar
Khalique committed
564
565
            op.lengths[0] = lens[2];
            op.lengths[1] = lens[3];
Khalique's avatar
Khalique committed
566
567
568
569
570
            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
571
572
573
574
        }
        MIGRAPHX_THROW("MIGraphX does not support mean outside of GlobalAvgPool transformation");
    }

Khalique's avatar
Khalique committed
575
576
577
    instruction_ref parse_pack(const std::string&,
                               const attribute_map& attributes,
                               std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
578
579
580
581
582
583
    {
        // reinterpret as unsqueeze with concat
        std::vector<instruction_ref> unsqueezed_args;
        int64_t axis = 0;
        if(contains(attributes, "axis"))
            axis = attributes.at("axis").i();
584
585
586
        size_t input_size = args.front()->get_shape().lens().size();
        if(axis > input_size)
        {
Khalique's avatar
Khalique committed
587
588
            MIGRAPHX_THROW("TF_PARSER: axis value of " + to_string(axis) +
                           " must be smaller than input size " + to_string(input_size));
589
590
        }

Khalique's avatar
Khalique committed
591
592
593
594
595
        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
596
597
        return to_nhwc(
            prog.add_instruction(op::concat{static_cast<size_t>(axis)}, unsqueezed_args));
Khalique's avatar
Khalique committed
598
599
    }

Khalique's avatar
Khalique committed
600
601
602
603
604
    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
605
606
        // 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
607
        std::vector<std::pair<int32_t, int32_t>> pad_per_dim(ndims);
Paul's avatar
Paul committed
608
        auto tf_padding = args[1]->eval().get<int32_t>().to_vector();
Khalique's avatar
Khalique committed
609
        for(size_t i = 0; i < 2 * ndims; i += 2)
Khalique's avatar
Khalique committed
610
        {
Khalique's avatar
Khalique committed
611
612
            pad_per_dim[i / 2].first  = tf_padding[i];
            pad_per_dim[i / 2].second = tf_padding[i + 1];
Khalique's avatar
Khalique committed
613
614
615
616
        }
        reorder_data(pad_per_dim);

        op::pad op;
Khalique's avatar
Khalique committed
617
618
        std::vector<int64_t> pads(ndims * 2);
        for(size_t i = 0; i < ndims; i++)
Khalique's avatar
Khalique committed
619
        {
Khalique's avatar
Khalique committed
620
621
            pads[i]         = pad_per_dim[i].first;
            pads[i + ndims] = pad_per_dim[i].second;
Khalique's avatar
Khalique committed
622
623
        }
        op.pads = pads;
Paul's avatar
Paul committed
624
        return prog.add_instruction(op, args.front());
Khalique's avatar
Khalique committed
625
626
    }

627
628
629
630
631
    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
632

633
634
        if(contains(attributes, "strides"))
        {
635
            std::vector<size_t> stride;
636
            copy(attributes.at("strides").list().i(), std::back_inserter(stride));
637
            reorder_data(stride);
638
639
640
641
            if(stride.size() != 4)
            {
                MIGRAPHX_THROW("strides should have 4 values");
            }
642
643
            op.stride[0] = stride[2];
            op.stride[1] = stride[3];
644
645
646
        }
        if(contains(attributes, "ksize"))
        {
647
            std::vector<size_t> ksize;
648
            copy(attributes.at("ksize").list().i(), std::back_inserter(ksize));
649
            reorder_data(ksize);
650
651
652
            if(ksize.size() != 4)
            {
                MIGRAPHX_THROW("ksize should have 4 values");
Khalique's avatar
Khalique committed
653
            }
654
655
            op.lengths[0] = ksize[2];
            op.lengths[1] = ksize[3];
656
        }
Khalique's avatar
Khalique committed
657
658

        auto l0 = args[0];
Khalique's avatar
Khalique committed
659
660
661
662
663
        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
664
                op.padding_mode = op::padding_mode_t::same;
Khalique's avatar
Khalique committed
665
                auto input_dims = l0->get_shape().lens();
Khalique's avatar
Khalique committed
666
667
                size_t input_h  = input_dims[2];
                size_t input_w  = input_dims[3];
Khalique's avatar
Khalique committed
668
669
670
671
672
673
674
                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
675
676
                    l0                           = prog.add_instruction(
                        migraphx::op::pad{padding, std::numeric_limits<float>::lowest()}, l0);
Khalique's avatar
Khalique committed
677
678
679
                }
                else
                {
Khalique's avatar
Khalique committed
680
681
                    op.padding[0] = pads[0];
                    op.padding[1] = pads[1];
Khalique's avatar
Khalique committed
682
                }
Khalique's avatar
Khalique committed
683
684
685
686
687
688
            }
            else if(pad_mode.find("VALID") != std::string::npos)
            {
                op.padding_mode = op::padding_mode_t::valid;
            }
        }
Khalique's avatar
Khalique committed
689
        return prog.add_instruction(op, l0);
690
    }
Khalique's avatar
Khalique committed
691

692
    instruction_ref
Khalique's avatar
Khalique committed
693
    parse_reshape(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
694
695
696
697
    {
        op::reshape op;
        if(args.size() != 2)
            MIGRAPHX_THROW("reshape needs 2 arguments (input, new_shape)");
Khalique's avatar
Khalique committed
698
        auto s = args[1]->eval();
699
        s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
Paul's avatar
Paul committed
700
        return prog.add_instruction(op, make_contiguous(args[0]));
701
702
    }

Khalique's avatar
Khalique committed
703
704
705
706
707
708
709
710
711
    void parse_from(std::istream& is)
    {
        tensorflow::GraphDef graph;
        if(graph.ParseFromIstream(&is))
        {
            this->parse_graph(graph);
        }
        else
        {
712
            throw std::runtime_error("Failed reading tf file");
Khalique's avatar
Khalique committed
713
714
715
        }
    }

716
717
718
719
720
721
722
723
724
725
    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
726
727
728
    instruction_ref parse_squeeze(const std::string&,
                                  const attribute_map& attributes,
                                  std::vector<instruction_ref> args)
729
730
    {
        op::squeeze op;
Khalique's avatar
Khalique committed
731
        auto input_dims = args[0]->get_shape().lens();
Khalique's avatar
Khalique committed
732
        auto axes       = attributes.at("squeeze_dims").list().i();
733
        copy(axes, std::back_inserter(op.axes));
Khalique's avatar
Khalique committed
734

735
736
        if(op.axes.empty()) // no squeeze_dims provided, remove any dim that equals 1
        {
Khalique's avatar
Khalique committed
737
            for(size_t i = 0; i < input_dims.size(); i++)
738
            {
Khalique's avatar
Khalique committed
739
                if(input_dims.at(i) == 1)
740
741
742
743
                {
                    op.axes.push_back(i);
                }
            }
744
        }
Paul's avatar
Paul committed
745
        return prog.add_instruction(op, make_contiguous(args[0]));
746
747
    }

Khalique's avatar
Khalique committed
748
749
750
    instruction_ref parse_stridedslice(const std::string&,
                                       const attribute_map& attributes,
                                       std::vector<instruction_ref> args)
751
752
    {
        op::slice op;
Khalique's avatar
Khalique committed
753
754
755
        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();
756

Khalique's avatar
Khalique committed
757
758
759
760
        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);
761
        uint32_t shrink_axis_mask = 0;
Khalique's avatar
Khalique committed
762
        uint32_t bitwise_compare  = 1;
763
764
765
        std::vector<int64_t> squeeze_axes;

        if(contains(attributes, "shrink_axis_mask"))
766
            shrink_axis_mask = static_cast<uint32_t>(attributes.at("shrink_axis_mask").i());
767

Khalique's avatar
Khalique committed
768
        for(size_t i = 0; i < num_axes; i++)
769
        {
770
            // the LSB corresponds to axis 0 when determining which axes to squeeze
Khalique's avatar
Khalique committed
771
            if(((shrink_axis_mask >> i) & bitwise_compare) == 1)
772
773
                squeeze_axes.push_back(i);
        }
Khalique's avatar
Khalique committed
774

Paul's avatar
Paul committed
775
776
        auto l0 = prog.add_instruction(op, make_contiguous(args[0]));
        return to_nhwc(prog.add_instruction(op::squeeze{squeeze_axes}, l0));
777
778
    }

Khalique's avatar
Khalique committed
779
780
    instruction_ref
    parse_transpose(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
Khalique's avatar
Khalique committed
781
782
783
784
785
786
787
788
    {
        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
789
790
791
792
793
    void parse_graph(const tensorflow::GraphDef& graph)
    {
        nodes = get_nodes(graph, input_nodes);
        for(auto&& input : input_nodes)
        {
Khalique's avatar
Khalique committed
794
            const std::string& name   = input.name();
Khalique's avatar
Khalique committed
795
            attribute_map input_attrs = get_attributes(input);
Khalique's avatar
Khalique committed
796
797
            shape::type_t shape_type  = parse_type(input_attrs.at("dtype").type());
            std::vector<size_t> dims  = parse_dims(input_attrs.at("shape").shape());
798
            if(is_nhwc and dims.size() >= 4)
799
            {
800
                reorder_data(dims);
801
            }
Khalique's avatar
Khalique committed
802
            shape s            = shape{shape_type, dims};
Paul's avatar
Paul committed
803
            instructions[name] = to_nhwc(prog.add_parameter(name, s));
Khalique's avatar
Khalique committed
804
805
806
        }
        for(auto&& p : nodes)
        {
807
            this->parse_node(p.first);
Khalique's avatar
Khalique committed
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
        }
    }

    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)
            {
834
                instructions[name] = prog.add_instruction(op::unknown{node.op()}, args);
Khalique's avatar
Khalique committed
835
836
837
838
839
840
841
842
843
844
845
            }
            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
846
        for(auto&& attr : node.attr())
Khalique's avatar
Khalique committed
847
848
849
850
851
852
        {
            result[attr.first] = attr.second;
        }
        return result;
    }

Khalique's avatar
Khalique committed
853
    static std::string get_name(const tensorflow::NodeDef& node) { return node.name(); }
Khalique's avatar
Khalique committed
854

Khalique's avatar
Khalique committed
855
856
    static node_map get_nodes(const tensorflow::GraphDef& graph,
                              std::vector<tensorflow::NodeDef>& input_nodes)
Khalique's avatar
Khalique committed
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
911
912
913
914
915
    {
        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
916
917
918
        case tensorflow::DataType::DT_UINT64:
            shape_type = shape::uint64_type;
            break;
Khalique's avatar
Khalique committed
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945

        // 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
946
947
948
949
        }
        return shape_type;
    }

Khalique's avatar
Khalique committed
950
    static literal parse_tensor(const tensorflow::TensorProto& t)
Khalique's avatar
Khalique committed
951
952
    {
        std::vector<size_t> dims = parse_dims(t.tensor_shape());
953
        size_t shape_size = std::accumulate(dims.begin(), dims.end(), 1, std::multiplies<size_t>());
Khalique's avatar
Khalique committed
954
955
        if(!t.tensor_content().empty()) // has raw data
        {
Khalique's avatar
Khalique committed
956
            const std::string& s = t.tensor_content();
Khalique's avatar
Khalique committed
957
958
959
            switch(t.dtype())
            {
            case tensorflow::DataType::DT_INVALID: throw std::runtime_error("");
Khalique's avatar
Khalique committed
960
961
            case tensorflow::DataType::DT_FLOAT:
                return literal{{shape::float_type, dims}, s.data()};
Khalique's avatar
Khalique committed
962
            case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
963
            case tensorflow::DataType::DT_INT8: return literal{{shape::int8_type, dims}, s.data()};
Khalique's avatar
Khalique committed
964
            case tensorflow::DataType::DT_UINT16:
965
                return literal{{shape::uint16_type, dims}, s.data()};
Khalique's avatar
Khalique committed
966
            case tensorflow::DataType::DT_INT16:
967
                return literal{{shape::int16_type, dims}, s.data()};
Khalique's avatar
Khalique committed
968
969
970
971
            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
972
            case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
973
            case tensorflow::DataType::DT_BOOL: return literal{{shape::int8_type, dims}, s.data()};
Khalique's avatar
Khalique committed
974
            case tensorflow::DataType::DT_HALF: return literal{{shape::half_type, dims}, s.data()};
Khalique's avatar
Khalique committed
975
976
            case tensorflow::DataType::DT_DOUBLE:
                return literal{{shape::double_type, dims}, s.data()};
Khalique's avatar
Khalique committed
977
978
979
980
            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
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
1007
1008
1009
1010
1011
            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
1012
1013
1014
1015
            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
1016
1017
1018
1019
1020
1021
1022
            }
            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
1023
1024
            return create_literal(
                shape::float_type, dims, get_data_vals(t.float_val(), shape_size));
Khalique's avatar
Khalique committed
1025
1026
        case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
        case tensorflow::DataType::DT_INT8:
1027
            return create_literal(shape::int8_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1028
        case tensorflow::DataType::DT_UINT16:
1029
            return create_literal(shape::uint16_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1030
        case tensorflow::DataType::DT_INT16:
1031
            return create_literal(shape::int16_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1032
        case tensorflow::DataType::DT_INT32:
1033
            return create_literal(shape::int32_type, dims, get_data_vals(t.int_val(), shape_size));
Khalique's avatar
Khalique committed
1034
        case tensorflow::DataType::DT_INT64:
Khalique's avatar
Khalique committed
1035
1036
            return create_literal(
                shape::int64_type, dims, get_data_vals(t.int64_val(), shape_size));
Khalique's avatar
Khalique committed
1037
1038
        case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
        case tensorflow::DataType::DT_BOOL:
1039
            return create_literal(shape::int32_type, dims, get_data_vals(t.bool_val(), shape_size));
Khalique's avatar
Khalique committed
1040
        case tensorflow::DataType::DT_HALF:
Khalique's avatar
Khalique committed
1041
        {
1042
1043
            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
1044
1045
1046
1047
1048
            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); });
1049
            return create_literal(shape::half_type, dims, data_half);
Khalique's avatar
Khalique committed
1050
        }
Khalique's avatar
Khalique committed
1051
        case tensorflow::DataType::DT_DOUBLE:
Khalique's avatar
Khalique committed
1052
            return literal{{shape::double_type, dims}, get_data_vals(t.double_val(), shape_size)};
Khalique's avatar
Khalique committed
1053
1054
1055
1056
        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
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
1083
1084
1085
1086
1087
        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
1088
1089
1090
1091
        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
1092
1093
1094
1095
        }
        MIGRAPHX_THROW("Invalid tensor type");
    }

1096
    template <class T>
Khalique's avatar
Khalique committed
1097
    static std::vector<T> get_data_vals(const google::protobuf::RepeatedField<T>& data,
1098
                                        const size_t& shape_size)
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
    {
        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
1111
1112
1113
1114
    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
1115
1116
1117
        std::transform(input_dims.begin(),
                       input_dims.end(),
                       std::back_inserter(dims),
Paul's avatar
Paul committed
1118
                       [](const tensorflow::TensorShapeProto_Dim& dim) { return dim.size(); });
Khalique's avatar
Khalique committed
1119
1120
        return dims;
    }
1121
1122

    template <class T>
Khalique's avatar
Khalique committed
1123
    static literal
1124
    create_literal(shape::type_t shape_type, const std::vector<size_t>& dims, std::vector<T> data)
1125
    {
Khalique's avatar
Khalique committed
1126
        // assume if explicit value is mentioned in protobuf and dim size <= 1, treat as scalar
1127
        if(dims.empty() or (dims.size() == 1 and dims.front() == 1))
1128
            return literal{{shape_type}, data};
1129
1130
        return literal{{shape_type, dims}, data};
    }
Khalique's avatar
Khalique committed
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
};

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
1153
    parser.to_nchw(std::prev(parser.prog.end()));
Khalique's avatar
Khalique committed
1154
1155
1156
1157
1158
    return std::move(parser.prog);
}

} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx