1 | /* Copyright 2017 The TensorFlow Authors. All Rights Reserved. |
2 | |
3 | Licensed under the Apache License, Version 2.0 (the "License"); |
4 | you may not use this file except in compliance with the License. |
5 | You may obtain a copy of the License at |
6 | |
7 | http://www.apache.org/licenses/LICENSE-2.0 |
8 | |
9 | Unless required by applicable law or agreed to in writing, software |
10 | distributed under the License is distributed on an "AS IS" BASIS, |
11 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
12 | See the License for the specific language governing permissions and |
13 | limitations under the License. |
14 | ==============================================================================*/ |
15 | #include "tensorflow/lite/toco/import_tensorflow.h" |
16 | |
17 | #include <memory> |
18 | #include <string> |
19 | #include <utility> |
20 | #include <vector> |
21 | |
22 | #include "google/protobuf/map.h" |
23 | #include "google/protobuf/text_format.h" |
24 | #include "absl/memory/memory.h" |
25 | #include "absl/strings/match.h" |
26 | #include "absl/strings/numbers.h" |
27 | #include "absl/strings/str_cat.h" |
28 | #include "absl/strings/str_split.h" |
29 | #include "absl/strings/strip.h" |
30 | #include "tensorflow/core/common_runtime/device_factory.h" |
31 | #include "tensorflow/core/common_runtime/function.h" |
32 | #include "tensorflow/core/common_runtime/graph_constructor.h" |
33 | #include "tensorflow/core/common_runtime/process_function_library_runtime.h" |
34 | #include "tensorflow/core/framework/attr_value.pb.h" |
35 | #include "tensorflow/core/framework/function.pb.h" |
36 | #include "tensorflow/core/framework/graph.pb.h" |
37 | #include "tensorflow/core/framework/node_def.pb.h" |
38 | #include "tensorflow/core/framework/tensor.pb.h" |
39 | #include "tensorflow/core/framework/tensor_shape.pb.h" |
40 | #include "tensorflow/core/framework/types.pb.h" |
41 | #include "tensorflow/core/lib/core/errors.h" |
42 | #include "tensorflow/core/lib/core/status.h" |
43 | #include "tensorflow/core/platform/logging.h" |
44 | #include "tensorflow/core/public/session_options.h" |
45 | #include "tensorflow/core/public/version.h" |
46 | #include "tensorflow/lite/toco/model.h" |
47 | #include "tensorflow/lite/toco/model_flags.pb.h" |
48 | #include "tensorflow/lite/toco/tensorflow_graph_matching/resolve_cluster.h" |
49 | #include "tensorflow/lite/toco/tensorflow_util.h" |
50 | #include "tensorflow/lite/toco/tooling_util.h" |
51 | |
52 | using tensorflow::AttrValue; |
53 | using tensorflow::DT_BOOL; |
54 | using tensorflow::DT_COMPLEX64; |
55 | using tensorflow::DT_FLOAT; |
56 | using tensorflow::DT_INT16; |
57 | using tensorflow::DT_INT32; |
58 | using tensorflow::DT_INT64; |
59 | using tensorflow::DT_QUINT8; |
60 | using tensorflow::DT_STRING; |
61 | using tensorflow::DT_UINT16; |
62 | using tensorflow::DT_UINT32; |
63 | using tensorflow::DT_UINT8; |
64 | using tensorflow::GraphDef; |
65 | using tensorflow::NodeDef; |
66 | using tensorflow::TensorProto; |
67 | using tensorflow::TensorShapeProto; |
68 | |
69 | namespace toco { |
70 | |
71 | namespace { |
72 | bool HasAttr(const NodeDef& node, const std::string& attr_name) { |
73 | return node.attr().count(attr_name) > 0; |
74 | } |
75 | |
76 | bool HasWildcardDimension(const TensorShapeProto& shape) { |
77 | for (const auto& dim : shape.dim()) { |
78 | if (dim.size() == -1) return true; |
79 | } |
80 | return false; |
81 | } |
82 | |
83 | const std::string& GetStringAttr(const NodeDef& node, |
84 | const std::string& attr_name) { |
85 | CHECK(HasAttr(node, attr_name)); |
86 | const auto& attr = node.attr().at(attr_name); |
87 | CHECK_EQ(attr.value_case(), AttrValue::kS); |
88 | return attr.s(); |
89 | } |
90 | |
91 | int64_t GetIntAttr(const NodeDef& node, const std::string& attr_name) { |
92 | CHECK(HasAttr(node, attr_name)) << attr_name << " not found in:\n" |
93 | << node.DebugString(); |
94 | const auto& attr = node.attr().at(attr_name); |
95 | CHECK_EQ(attr.value_case(), AttrValue::kI); |
96 | return attr.i(); |
97 | } |
98 | |
99 | float GetFloatAttr(const NodeDef& node, const std::string& attr_name) { |
100 | CHECK(HasAttr(node, attr_name)); |
101 | const auto& attr = node.attr().at(attr_name); |
102 | CHECK_EQ(attr.value_case(), AttrValue::kF); |
103 | return attr.f(); |
104 | } |
105 | |
106 | bool GetBoolAttr(const NodeDef& node, const std::string& attr_name) { |
107 | CHECK(HasAttr(node, attr_name)); |
108 | const auto& attr = node.attr().at(attr_name); |
109 | CHECK_EQ(attr.value_case(), AttrValue::kB); |
110 | return attr.b(); |
111 | } |
112 | |
113 | tensorflow::DataType GetDataTypeAttr(const NodeDef& node, |
114 | const std::string& attr_name) { |
115 | CHECK(HasAttr(node, attr_name)); |
116 | const auto& attr = node.attr().at(attr_name); |
117 | CHECK_EQ(attr.value_case(), AttrValue::kType); |
118 | return attr.type(); |
119 | } |
120 | |
121 | const TensorShapeProto& GetShapeAttr(const NodeDef& node, |
122 | const std::string& attr_name) { |
123 | CHECK(HasAttr(node, attr_name)); |
124 | const auto& attr = node.attr().at(attr_name); |
125 | CHECK_EQ(attr.value_case(), AttrValue::kShape); |
126 | return attr.shape(); |
127 | } |
128 | |
129 | const TensorProto& GetTensorAttr(const NodeDef& node, |
130 | const std::string& attr_name) { |
131 | CHECK(HasAttr(node, attr_name)) << "No attr named '" << attr_name << "'" ; |
132 | const auto& attr = node.attr().at(attr_name); |
133 | CHECK_EQ(attr.value_case(), AttrValue::kTensor); |
134 | return attr.tensor(); |
135 | } |
136 | |
137 | const AttrValue::ListValue& GetListAttr(const NodeDef& node, |
138 | const std::string& attr_name) { |
139 | CHECK(HasAttr(node, attr_name)); |
140 | const auto& attr = node.attr().at(attr_name); |
141 | CHECK_EQ(attr.value_case(), AttrValue::kList); |
142 | return attr.list(); |
143 | } |
144 | |
145 | tensorflow::Status CheckOptionalAttr(const NodeDef& node, |
146 | const std::string& attr_name, |
147 | const std::string& expected_value) { |
148 | if (HasAttr(node, attr_name)) { |
149 | const std::string& value = GetStringAttr(node, attr_name); |
150 | if (value != expected_value) { |
151 | return tensorflow::errors::InvalidArgument( |
152 | "Unexpected value for attribute '" + attr_name + "'. Expected '" + |
153 | expected_value + "'" ); |
154 | } |
155 | } |
156 | return ::tensorflow::OkStatus(); |
157 | } |
158 | |
159 | tensorflow::Status CheckOptionalAttr( |
160 | const NodeDef& node, const std::string& attr_name, |
161 | const tensorflow::DataType& expected_value) { |
162 | if (HasAttr(node, attr_name)) { |
163 | const tensorflow::DataType& value = GetDataTypeAttr(node, attr_name); |
164 | if (value != expected_value) { |
165 | return tensorflow::errors::InvalidArgument( |
166 | "Unexpected value for attribute '" + attr_name + "'. Expected '" + |
167 | tensorflow::DataType_Name(expected_value) + "'" ); |
168 | } |
169 | } |
170 | return ::tensorflow::OkStatus(); |
171 | } |
172 | |
173 | template <typename T1, typename T2> |
174 | tensorflow::Status ExpectValue(const T1& v1, const T2& v2, |
175 | const std::string& description) { |
176 | if (v1 == v2) return ::tensorflow::OkStatus(); |
177 | return tensorflow::errors::InvalidArgument(absl::StrCat( |
178 | "Unexpected " , description, ": got " , v1, ", expected " , v2)); |
179 | } |
180 | |
181 | ArrayDataType ConvertDataType(tensorflow::DataType dtype) { |
182 | if (dtype == DT_UINT8) |
183 | return ArrayDataType::kUint8; |
184 | else if (dtype == DT_FLOAT) |
185 | return ArrayDataType::kFloat; |
186 | else if (dtype == DT_BOOL) |
187 | return ArrayDataType::kBool; |
188 | else if (dtype == DT_INT16) |
189 | return ArrayDataType::kInt16; |
190 | else if (dtype == DT_UINT16) |
191 | return ArrayDataType::kUint16; |
192 | else if (dtype == DT_INT32) |
193 | return ArrayDataType::kInt32; |
194 | else if (dtype == DT_UINT32) |
195 | return ArrayDataType::kUint32; |
196 | else if (dtype == DT_INT64) |
197 | return ArrayDataType::kInt64; |
198 | else if (dtype == DT_STRING) |
199 | return ArrayDataType::kString; |
200 | else if (dtype == DT_COMPLEX64) |
201 | return ArrayDataType::kComplex64; |
202 | else |
203 | LOG(INFO) << "Unsupported data type in placeholder op: " << dtype; |
204 | return ArrayDataType::kNone; |
205 | } |
206 | |
207 | tensorflow::Status ImportShape( |
208 | const TFLITE_PROTO_NS::RepeatedPtrField<tensorflow::TensorShapeProto_Dim>& |
209 | input_dims, |
210 | int* input_flat_size, Shape* shape) { |
211 | std::vector<int> input_dims_only_sizes; |
212 | bool zero_sized_shape = false; |
213 | for (auto& d : input_dims) { |
214 | // TensorFlow's shapes use int64s, while TOCO uses ints. |
215 | if (d.size() > std::numeric_limits<int>::max()) { |
216 | return tensorflow::errors::InvalidArgument("Shape element overflows" ); |
217 | } |
218 | if (d.size() == 0) { |
219 | zero_sized_shape = true; |
220 | } |
221 | input_dims_only_sizes.push_back(d.size()); |
222 | } |
223 | |
224 | // Note that up to this point we were OK with the input shape containing |
225 | // elements valued -1 or 0, which are perfectly legal in tensorflow. However |
226 | // our CheckValidShapeDimensions() insists on them being >= 1, with the |
227 | // exception of the "scalar" shape [0]. The main issue with zero-values shape |
228 | // elements is that the corresponding arrays don't contain any data and the |
229 | // allocation code gets a bit confused. It seems that the code expects an |
230 | // empty shape for zero-sized shapes, so we will do just that, except for the |
231 | // [0] case. |
232 | // TODO(b/119325030): In order to correctly import the "scalar" shapes the |
233 | // following test must include "&& input_dims_only_sizes.size() > 1", but |
234 | // that seems to slow everything down a lot. |
235 | if (zero_sized_shape) { |
236 | shape->mutable_dims()->clear(); |
237 | if (input_flat_size != nullptr) *input_flat_size = 0; |
238 | return ::tensorflow::OkStatus(); |
239 | } |
240 | |
241 | *shape->mutable_dims() = input_dims_only_sizes; |
242 | |
243 | if (input_flat_size == nullptr) return ::tensorflow::OkStatus(); |
244 | |
245 | return NumElements(input_dims_only_sizes, input_flat_size); |
246 | } |
247 | |
248 | // Define ways to retrieve data from tensors of different types. |
249 | // TODO(b/80208043): simply use tensorflow::Tensor::FromProto() instead. |
250 | template <typename T> |
251 | struct TensorTraits; |
252 | |
253 | template <> |
254 | struct TensorTraits<float> { |
255 | static int size(const TensorProto& p) { return p.float_val_size(); } |
256 | static float get(const TensorProto& p, int i) { return p.float_val(i); } |
257 | static std::string accessor_name() { return "float_val" ; } |
258 | static std::string type_name() { return "float" ; } |
259 | static void CopyFromContent(const TensorProto& p, std::vector<float>* data) { |
260 | toco::port::CopyToBuffer(p.tensor_content(), |
261 | reinterpret_cast<char*>(data->data())); |
262 | } |
263 | }; |
264 | |
265 | template <> |
266 | struct TensorTraits<uint8_t> { |
267 | static int size(const TensorProto& p) { return p.int_val_size(); } |
268 | static uint8_t get(const TensorProto& p, int i) { return p.int_val(i); } |
269 | static std::string accessor_name() { return "int_val" ; } |
270 | static std::string type_name() { return "uint8" ; } |
271 | static void CopyFromContent(const TensorProto& p, |
272 | std::vector<uint8_t>* data) { |
273 | toco::port::CopyToBuffer(p.tensor_content(), |
274 | reinterpret_cast<char*>(data->data())); |
275 | } |
276 | }; |
277 | |
278 | template <> |
279 | struct TensorTraits<std::complex<float>> { |
280 | static int size(const TensorProto& p) { return p.scomplex_val_size() / 2; } |
281 | static std::complex<float> get(const TensorProto& p, int i) { |
282 | return std::complex<float>(p.scomplex_val(2 * i), |
283 | p.scomplex_val(2 * i + 1)); |
284 | } |
285 | static std::string accessor_name() { return "scomplex_val" ; } |
286 | static std::string type_name() { return "complex64" ; } |
287 | static void CopyFromContent(const TensorProto& p, |
288 | std::vector<std::complex<float>>* data) { |
289 | toco::port::CopyToBuffer(p.tensor_content(), |
290 | reinterpret_cast<char*>(data->data())); |
291 | } |
292 | }; |
293 | |
294 | template <> |
295 | struct TensorTraits<int32> { |
296 | static int size(const TensorProto& p) { return p.int_val_size(); } |
297 | static int32 get(const TensorProto& p, int i) { return p.int_val(i); } |
298 | static std::string accessor_name() { return "int_val" ; } |
299 | static std::string type_name() { return "int32" ; } |
300 | static void CopyFromContent(const TensorProto& p, std::vector<int32>* data) { |
301 | toco::port::CopyToBuffer(p.tensor_content(), |
302 | reinterpret_cast<char*>(data->data())); |
303 | } |
304 | }; |
305 | |
306 | template <> |
307 | struct TensorTraits<uint32> { |
308 | static int size(const TensorProto& p) { return p.uint32_val_size(); } |
309 | static int32 get(const TensorProto& p, int i) { return p.uint32_val(i); } |
310 | static std::string accessor_name() { return "uint32_val" ; } |
311 | static std::string type_name() { return "uint32" ; } |
312 | static void CopyFromContent(const TensorProto& p, std::vector<uint32>* data) { |
313 | toco::port::CopyToBuffer(p.tensor_content(), |
314 | reinterpret_cast<char*>(data->data())); |
315 | } |
316 | }; |
317 | |
318 | template <> |
319 | struct TensorTraits<int64_t> { |
320 | static int size(const TensorProto& p) { return p.int64_val_size(); } |
321 | static int64_t get(const TensorProto& p, int i) { return p.int64_val(i); } |
322 | static std::string accessor_name() { return "int64_val" ; } |
323 | static std::string type_name() { return "int64" ; } |
324 | static void CopyFromContent(const TensorProto& p, |
325 | std::vector<int64_t>* data) { |
326 | toco::port::CopyToBuffer(p.tensor_content(), |
327 | reinterpret_cast<char*>(data->data())); |
328 | } |
329 | }; |
330 | |
331 | template <> |
332 | struct TensorTraits<bool> { |
333 | static int size(const TensorProto& p) { return p.bool_val_size(); } |
334 | static bool get(const TensorProto& p, int i) { return p.bool_val(i); } |
335 | static std::string accessor_name() { return "bool_val" ; } |
336 | static std::string type_name() { return "bool" ; } |
337 | static void CopyFromContent(const TensorProto& p, std::vector<bool>* data) { |
338 | std::vector<char> buf(p.tensor_content().size()); |
339 | toco::port::CopyToBuffer(p.tensor_content(), buf.data()); |
340 | for (int i = 0; i < p.tensor_content().size(); i++) { |
341 | (*data)[i] = static_cast<bool>(buf[i]); |
342 | } |
343 | } |
344 | }; |
345 | |
346 | template <typename T> |
347 | tensorflow::Status ImportTensorData(const TensorProto& input_tensor, |
348 | int input_flat_size, |
349 | std::vector<T>* output_data) { |
350 | CHECK_GE(output_data->size(), input_flat_size); |
351 | int num_elements_in_tensor = TensorTraits<T>::size(input_tensor); |
352 | if (num_elements_in_tensor == input_flat_size) { |
353 | for (int i = 0; i < num_elements_in_tensor; i++) { |
354 | (*output_data)[i] = TensorTraits<T>::get(input_tensor, i); |
355 | } |
356 | } else if (input_tensor.tensor_content().size() == |
357 | input_flat_size * sizeof(T)) { |
358 | TensorTraits<T>::CopyFromContent(input_tensor, output_data); |
359 | } else if (num_elements_in_tensor >= 0 && |
360 | num_elements_in_tensor < input_flat_size) { |
361 | // TODO(b/80208043): use tensorflow::Tensor::FromProto() which is the |
362 | // official way to import tensor data. This particular else-if handles a |
363 | // grappler optimization where the last few elements in a tensor are |
364 | // omitted if they are repeated, and where all elements are omitted if they |
365 | // are zero. |
366 | int i = 0; |
367 | for (; i < num_elements_in_tensor; ++i) { |
368 | (*output_data)[i] = TensorTraits<T>::get(input_tensor, i); |
369 | } |
370 | auto last = i == 0 ? T(0) : (*output_data)[i - 1]; |
371 | for (; i < input_flat_size; ++i) { |
372 | (*output_data)[i] = last; |
373 | } |
374 | } else { |
375 | std::string accessor_name = TensorTraits<T>::accessor_name(); |
376 | std::string type_name = TensorTraits<T>::type_name(); |
377 | return tensorflow::errors::InvalidArgument( |
378 | absl::StrCat("Neither input_content (" , |
379 | input_tensor.tensor_content().size() / sizeof(T), ") nor " , |
380 | accessor_name, " (" , num_elements_in_tensor, |
381 | ") have the right dimensions (" , input_flat_size, |
382 | ") for this " , type_name, " tensor" )); |
383 | } |
384 | return ::tensorflow::OkStatus(); |
385 | } |
386 | |
387 | tensorflow::Status ImportFloatArray(const TensorProto& input_tensor, |
388 | Array* output_array) { |
389 | CHECK_EQ(input_tensor.dtype(), DT_FLOAT); |
390 | const auto& input_shape = input_tensor.tensor_shape(); |
391 | CHECK_LE(input_shape.dim_size(), 6); |
392 | int input_flat_size; |
393 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
394 | output_array->mutable_shape()); |
395 | if (!status.ok()) return status; |
396 | |
397 | auto& output_float_data = |
398 | output_array->GetMutableBuffer<ArrayDataType::kFloat>().data; |
399 | output_float_data.resize(RequiredBufferSizeForShape(output_array->shape()), |
400 | 0.f); |
401 | return ImportTensorData<float>(input_tensor, input_flat_size, |
402 | &output_float_data); |
403 | } |
404 | |
405 | tensorflow::Status ImportComplex64Array(const TensorProto& input_tensor, |
406 | Array* output_array) { |
407 | CHECK_EQ(input_tensor.dtype(), DT_COMPLEX64); |
408 | const auto& input_shape = input_tensor.tensor_shape(); |
409 | CHECK_LE(input_shape.dim_size(), 4); |
410 | int input_flat_size; |
411 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
412 | output_array->mutable_shape()); |
413 | if (!status.ok()) return status; |
414 | |
415 | auto& output_complex_data = |
416 | output_array->GetMutableBuffer<ArrayDataType::kComplex64>().data; |
417 | output_complex_data.resize(RequiredBufferSizeForShape(output_array->shape()), |
418 | std::complex<float>(0.f, 0.f)); |
419 | return ImportTensorData<std::complex<float>>(input_tensor, input_flat_size, |
420 | &output_complex_data); |
421 | } |
422 | |
423 | tensorflow::Status ImportQuint8Array(const TensorProto& input_tensor, |
424 | Array* output_array) { |
425 | CHECK_EQ(input_tensor.dtype(), DT_QUINT8); |
426 | const auto& input_shape = input_tensor.tensor_shape(); |
427 | CHECK_LE(input_shape.dim_size(), 6); |
428 | int input_flat_size; |
429 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
430 | output_array->mutable_shape()); |
431 | if (!status.ok()) return status; |
432 | |
433 | auto& output_int_data = |
434 | output_array->GetMutableBuffer<ArrayDataType::kUint8>().data; |
435 | output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); |
436 | return ImportTensorData<uint8_t>(input_tensor, input_flat_size, |
437 | &output_int_data); |
438 | } |
439 | |
440 | tensorflow::Status ImportInt32Array(const TensorProto& input_tensor, |
441 | Array* output_array) { |
442 | CHECK_EQ(input_tensor.dtype(), DT_INT32); |
443 | const auto& input_shape = input_tensor.tensor_shape(); |
444 | CHECK_LE(input_shape.dim_size(), 6); |
445 | int input_flat_size; |
446 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
447 | output_array->mutable_shape()); |
448 | if (!status.ok()) return status; |
449 | |
450 | auto& output_int_data = |
451 | output_array->GetMutableBuffer<ArrayDataType::kInt32>().data; |
452 | output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); |
453 | return ImportTensorData<int32>(input_tensor, input_flat_size, |
454 | &output_int_data); |
455 | } |
456 | |
457 | tensorflow::Status ImportUint32Array(const TensorProto& input_tensor, |
458 | Array* output_array) { |
459 | CHECK_EQ(input_tensor.dtype(), DT_UINT32); |
460 | const auto& input_shape = input_tensor.tensor_shape(); |
461 | CHECK_LE(input_shape.dim_size(), 6); |
462 | int input_flat_size; |
463 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
464 | output_array->mutable_shape()); |
465 | if (!status.ok()) return status; |
466 | |
467 | auto& output_int_data = |
468 | output_array->GetMutableBuffer<ArrayDataType::kUint32>().data; |
469 | output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); |
470 | return ImportTensorData<uint32>(input_tensor, input_flat_size, |
471 | &output_int_data); |
472 | } |
473 | |
474 | tensorflow::Status ImportInt64Array(const TensorProto& input_tensor, |
475 | Array* output_array) { |
476 | CHECK_EQ(input_tensor.dtype(), DT_INT64); |
477 | const auto& input_shape = input_tensor.tensor_shape(); |
478 | CHECK_LE(input_shape.dim_size(), 6); |
479 | int input_flat_size; |
480 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
481 | output_array->mutable_shape()); |
482 | if (!status.ok()) return status; |
483 | |
484 | auto& output_int_data = |
485 | output_array->GetMutableBuffer<ArrayDataType::kInt64>().data; |
486 | output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); |
487 | return ImportTensorData<int64_t>(input_tensor, input_flat_size, |
488 | &output_int_data); |
489 | } |
490 | |
491 | tensorflow::Status ImportBoolArray(const TensorProto& input_tensor, |
492 | Array* output_array) { |
493 | CHECK_EQ(input_tensor.dtype(), DT_BOOL); |
494 | const auto& input_shape = input_tensor.tensor_shape(); |
495 | CHECK_LE(input_shape.dim_size(), 6); |
496 | int input_flat_size; |
497 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
498 | output_array->mutable_shape()); |
499 | if (!status.ok()) return status; |
500 | |
501 | auto& output_bool_data = |
502 | output_array->GetMutableBuffer<ArrayDataType::kBool>().data; |
503 | output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()), |
504 | false); |
505 | status = |
506 | ImportTensorData<bool>(input_tensor, input_flat_size, &output_bool_data); |
507 | if (!status.ok() && output_bool_data.size() == 1) { |
508 | // Some graphs have bool const nodes without actual value... |
509 | // assuming that 'false' is implied. |
510 | // So far only encountered that in an array with 1 entry, let's |
511 | // require that until we encounter a graph where that's not the case. |
512 | output_bool_data[0] = false; |
513 | return ::tensorflow::OkStatus(); |
514 | } |
515 | return status; |
516 | } |
517 | |
518 | tensorflow::Status ImportStringArray(const TensorProto& input_tensor, |
519 | Array* output_array) { |
520 | CHECK_EQ(input_tensor.dtype(), DT_STRING); |
521 | const auto& input_shape = input_tensor.tensor_shape(); |
522 | CHECK_LE(input_shape.dim_size(), 6); |
523 | int input_flat_size; |
524 | auto status = ImportShape(input_shape.dim(), &input_flat_size, |
525 | output_array->mutable_shape()); |
526 | if (!status.ok()) return status; |
527 | |
528 | if (input_flat_size != input_tensor.string_val_size()) { |
529 | return tensorflow::errors::InvalidArgument( |
530 | "Input_content string_val doesn't have the right dimensions " |
531 | "for this string tensor" ); |
532 | } |
533 | |
534 | auto& output_string_data = |
535 | output_array->GetMutableBuffer<ArrayDataType::kString>().data; |
536 | output_string_data.resize(RequiredBufferSizeForShape(output_array->shape())); |
537 | CHECK_GE(output_string_data.size(), input_flat_size); |
538 | for (int i = 0; i < input_flat_size; ++i) { |
539 | output_string_data[i] = input_tensor.string_val(i); |
540 | } |
541 | return ::tensorflow::OkStatus(); |
542 | } |
543 | |
544 | // Count the number of inputs of a given node. If |
545 | // `tf_import_flags.drop_control_dependency` is true, count the number of |
546 | // non-control-dependency inputs. |
547 | int GetInputsCount(const NodeDef& node, |
548 | const TensorFlowImportFlags& tf_import_flags) { |
549 | if (tf_import_flags.drop_control_dependency) { |
550 | for (size_t i = 0; i < node.input_size(); ++i) { |
551 | if (node.input(i)[0] == '^') { |
552 | return i; |
553 | } |
554 | } |
555 | } |
556 | return node.input_size(); |
557 | } |
558 | |
559 | tensorflow::Status CheckInputsCount( |
560 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
561 | int expected_input_count) { |
562 | if (GetInputsCount(node, tf_import_flags) != expected_input_count) { |
563 | return tensorflow::errors::FailedPrecondition( |
564 | node.op(), " node expects " , expected_input_count, |
565 | " input(s) other than control dependencies: " , node.DebugString()); |
566 | } |
567 | return ::tensorflow::OkStatus(); |
568 | } |
569 | |
570 | template <ArrayDataType T> |
571 | std::string CreateConstArray( |
572 | Model* model, std::string const& name, |
573 | std::vector<typename toco::DataType<T>> const& data) { |
574 | // Utility function to create a const 1D array, useful for input parameters. |
575 | std::string array_name = toco::AvailableArrayName(*model, name); |
576 | auto& array = model->GetOrCreateArray(array_name); |
577 | array.data_type = T; |
578 | array.mutable_shape()->mutable_dims()->emplace_back( |
579 | static_cast<int>(data.size())); |
580 | array.GetMutableBuffer<T>().data = data; |
581 | return array_name; |
582 | } |
583 | |
584 | // Retain TensorFlow NodeDef in Toco Operator. |
585 | // |
586 | // If an op is supported by Toco but not supported by TFLite, TFLite exporter |
587 | // will use the retained NodeDef to populate a Flex op when Flex mode is |
588 | // enabled. |
589 | // |
590 | // This can't be easily applied to all operations, because a TensorFlow node |
591 | // may become multiple Toco operators. Thus we need to call this function in |
592 | // operator conversion functions one by one whenever feasible. |
593 | // |
594 | // This may cause problems if a graph transformation rule changes parameters |
595 | // of the node. When calling this function, please check if any existing |
596 | // graph transformation rule will change an existing operator with the same |
597 | // type. |
598 | // |
599 | // This provides a route to handle Toco-supported & TFLite-unsupported ops |
600 | // in Flex mode. However it's not a solid solution. Eventually we should |
601 | // get rid of this. |
602 | // TODO(b/117327937): Implement all Toco-supported ops in TFLite, and remove |
603 | // this function. |
604 | void RetainTensorFlowNodeDef(const NodeDef& node, Operator* op) { |
605 | node.SerializeToString(&op->tensorflow_node_def); |
606 | } |
607 | |
608 | void GetOutputNamesFromNodeDef(const NodeDef& node, |
609 | const tensorflow::OpDef& op_def, |
610 | TensorFlowUnsupportedOperator* op) { |
611 | int next_output = 0; |
612 | auto add_output = [&node, &next_output, op]() { |
613 | if (next_output == 0) { |
614 | op->outputs.push_back(node.name()); // Implicit :0. |
615 | } else { |
616 | op->outputs.push_back(absl::StrCat(node.name(), ":" , next_output)); |
617 | } |
618 | ++next_output; |
619 | }; |
620 | for (int i = 0; i < op_def.output_arg_size(); ++i) { |
621 | std::string multiples = op_def.output_arg(i).number_attr(); |
622 | if (!multiples.empty()) { |
623 | CHECK(HasAttr(node, multiples)) << "No attr named " << multiples; |
624 | int num_outputs = GetIntAttr(node, multiples); |
625 | for (int j = 0; j < num_outputs; ++j) { |
626 | add_output(); |
627 | } |
628 | } else { |
629 | std::string list = op_def.output_arg(i).type_list_attr(); |
630 | if (!list.empty()) { |
631 | CHECK(HasAttr(node, list)) << "No attr named " << list; |
632 | const AttrValue::ListValue& list_value = GetListAttr(node, list); |
633 | for (int j = 0; j < list_value.type_size(); ++j) { |
634 | add_output(); |
635 | } |
636 | } else { |
637 | add_output(); |
638 | } |
639 | } |
640 | } |
641 | } |
642 | |
643 | void GetOutputTypesFromNodeDef(const NodeDef& node, |
644 | const tensorflow::OpDef& op_def, |
645 | TensorFlowUnsupportedOperator* op) { |
646 | // The given type to the op, or clear the types if invalid. |
647 | auto add_type = [&node, op](tensorflow::DataType type) { |
648 | if (type == tensorflow::DT_INVALID) { |
649 | LOG(WARNING) << "Op node missing output type attribute: " << node.name(); |
650 | op->output_data_types.clear(); |
651 | } else { |
652 | op->output_data_types.push_back(ConvertDataType(type)); |
653 | } |
654 | }; |
655 | |
656 | // Retrieve the data type according to the OpDef definition: either the |
657 | // "type" or "type_attr" field will be set. |
658 | auto get_type = [&node](const tensorflow::OpDef::ArgDef& a) { |
659 | if (a.type() != tensorflow::DT_INVALID) { |
660 | return a.type(); |
661 | } else if (HasAttr(node, a.type_attr())) { |
662 | return GetDataTypeAttr(node, a.type_attr()); |
663 | } else { |
664 | return tensorflow::DT_INVALID; |
665 | } |
666 | }; |
667 | |
668 | for (int i = 0; i < op_def.output_arg_size(); ++i) { |
669 | std::string multiples = op_def.output_arg(i).number_attr(); |
670 | if (!multiples.empty()) { |
671 | CHECK(HasAttr(node, multiples)) << "No attr named " << multiples; |
672 | int num_outputs = GetIntAttr(node, multiples); |
673 | auto type = get_type(op_def.output_arg(i)); |
674 | for (int j = 0; j < num_outputs; ++j) { |
675 | add_type(type); |
676 | } |
677 | } else { |
678 | std::string list = op_def.output_arg(i).type_list_attr(); |
679 | if (!list.empty()) { |
680 | CHECK(HasAttr(node, list)) << "No attr named " << list; |
681 | const AttrValue::ListValue& list_value = GetListAttr(node, list); |
682 | for (int j = 0; j < list_value.type_size(); ++j) { |
683 | add_type(list_value.type(j)); |
684 | } |
685 | } else { |
686 | add_type(get_type(op_def.output_arg(i))); |
687 | } |
688 | } |
689 | } |
690 | } |
691 | |
692 | tensorflow::Status ConvertUnsupportedOperator( |
693 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
694 | const ModelFlags& model_flags, Model* model) { |
695 | // Names of special attributes in TF graph that are used by Toco. |
696 | static constexpr char kAttrOutputQuantized[] = "_output_quantized" ; |
697 | static constexpr char kAttrOutputTypes[] = "_output_types" ; |
698 | static constexpr char kAttrOutputShapes[] = "_output_shapes" ; |
699 | static constexpr char kAttrSupportOutputTypeFloatInQuantizedOp[] = |
700 | "_support_output_type_float_in_quantized_op" ; |
701 | |
702 | LOG(INFO) << "Converting unsupported operation: " << node.op(); |
703 | |
704 | auto* op = new TensorFlowUnsupportedOperator; |
705 | op->tensorflow_op = node.op(); |
706 | |
707 | // For Flex mode. Please read the comments of the function. |
708 | RetainTensorFlowNodeDef(node, op); |
709 | |
710 | model->operators.emplace_back(op); |
711 | |
712 | // Parse inputs. |
713 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
714 | for (int i = 0; i < num_inputs; ++i) { |
715 | op->inputs.push_back(node.input(i)); |
716 | } |
717 | |
718 | // Parse outputs. Name them after the node's name, plus an ordinal suffix. |
719 | // Note that some outputs are to be multiplied by a named attribute. |
720 | const tensorflow::OpDef* op_def = nullptr; |
721 | if (tensorflow::OpRegistry::Global()->LookUpOpDef(node.op(), &op_def).ok()) { |
722 | GetOutputNamesFromNodeDef(node, *op_def, op); |
723 | } else { |
724 | op->outputs.push_back(node.name()); // Implicit :0. |
725 | } |
726 | |
727 | // Parse if the op supports quantization |
728 | if (HasAttr(node, kAttrOutputQuantized)) { |
729 | op->quantized = GetBoolAttr(node, kAttrOutputQuantized); |
730 | } |
731 | // Parse if the quantized op allows output arrays of type float |
732 | if (HasAttr(node, kAttrSupportOutputTypeFloatInQuantizedOp)) { |
733 | op->support_output_type_float_in_quantized_op = |
734 | GetBoolAttr(node, kAttrSupportOutputTypeFloatInQuantizedOp); |
735 | } |
736 | |
737 | // Parse output type(s). |
738 | if (HasAttr(node, kAttrOutputTypes)) { |
739 | const auto& output_types = GetListAttr(node, kAttrOutputTypes); |
740 | for (int i = 0; i < output_types.type_size(); ++i) { |
741 | op->output_data_types.push_back(ConvertDataType(output_types.type(i))); |
742 | } |
743 | } else if (HasAttr(node, "Tout" )) { |
744 | const auto& output_type = GetDataTypeAttr(node, "Tout" ); |
745 | op->output_data_types.push_back(ConvertDataType(output_type)); |
746 | } else if (op_def != nullptr) { |
747 | GetOutputTypesFromNodeDef(node, *op_def, op); |
748 | } else { |
749 | // TODO(b/113613439): Figure out how to propagate types for custom ops |
750 | // that have no OpDef. |
751 | LOG(INFO) << "Unable to determine output type for op: " << node.op(); |
752 | } |
753 | |
754 | // Parse output shape(s). |
755 | if (HasAttr(node, kAttrOutputShapes)) { |
756 | const auto& output_shapes = GetListAttr(node, kAttrOutputShapes); |
757 | Shape output_shape; |
758 | for (int i = 0; i < output_shapes.shape_size(); ++i) { |
759 | const auto& shape = output_shapes.shape(i); |
760 | // TOCO doesn't yet properly handle shapes with wildcard dimensions. |
761 | // TODO(b/113613439): Handle shape inference for unsupported ops that have |
762 | // shapes with wildcard dimensions. |
763 | if (HasWildcardDimension(shape)) { |
764 | LOG(INFO) << "Skipping wildcard output shape(s) for node: " |
765 | << node.name(); |
766 | op->output_shapes.clear(); |
767 | break; |
768 | } |
769 | const auto status = |
770 | ImportShape(shape.dim(), /*input_flat_size=*/nullptr, &output_shape); |
771 | if (!status.ok()) { |
772 | return status; |
773 | } |
774 | op->output_shapes.push_back(output_shape); |
775 | } |
776 | } |
777 | return ::tensorflow::OkStatus(); |
778 | } |
779 | |
780 | tensorflow::Status ConvertConstOperator( |
781 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
782 | const ModelFlags& model_flags, Model* model) { |
783 | CHECK_EQ(node.op(), "Const" ); |
784 | const auto& tensor = GetTensorAttr(node, "value" ); |
785 | const auto dtype = GetDataTypeAttr(node, "dtype" ); |
786 | |
787 | tensorflow::Status status = ::tensorflow::OkStatus(); |
788 | |
789 | auto& array = model->GetOrCreateArray(node.name()); |
790 | switch (dtype) { |
791 | case DT_FLOAT: |
792 | array.data_type = ArrayDataType::kFloat; |
793 | status = ImportFloatArray(tensor, &array); |
794 | break; |
795 | case DT_INT32: |
796 | array.data_type = ArrayDataType::kInt32; |
797 | status = ImportInt32Array(tensor, &array); |
798 | break; |
799 | case DT_UINT32: |
800 | array.data_type = ArrayDataType::kUint32; |
801 | status = ImportUint32Array(tensor, &array); |
802 | break; |
803 | case DT_QUINT8: |
804 | array.data_type = ArrayDataType::kUint8; |
805 | status = ImportQuint8Array(tensor, &array); |
806 | break; |
807 | case DT_INT64: |
808 | array.data_type = ArrayDataType::kInt64; |
809 | status = ImportInt64Array(tensor, &array); |
810 | break; |
811 | case DT_STRING: |
812 | array.data_type = ArrayDataType::kString; |
813 | status = ImportStringArray(tensor, &array); |
814 | break; |
815 | case DT_BOOL: |
816 | array.data_type = ArrayDataType::kBool; |
817 | status = ImportBoolArray(tensor, &array); |
818 | break; |
819 | case DT_COMPLEX64: |
820 | array.data_type = ArrayDataType::kComplex64; |
821 | status = ImportComplex64Array(tensor, &array); |
822 | break; |
823 | default: |
824 | array.data_type = ArrayDataType::kNone; |
825 | // do nothing, silently ignore the Const data. |
826 | // We just make a dummy buffer to indicate that |
827 | // this array does not rely on external input. |
828 | array.GetMutableBuffer<ArrayDataType::kNone>(); |
829 | break; |
830 | } |
831 | TF_RETURN_WITH_CONTEXT_IF_ERROR( |
832 | status, " (while processing node '" + node.name() + "')" ); |
833 | return ::tensorflow::OkStatus(); |
834 | } |
835 | |
836 | tensorflow::Status ConvertConvOperator( |
837 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
838 | const ModelFlags& model_flags, Model* model) { |
839 | CHECK_EQ(node.op(), "Conv2D" ); |
840 | TF_RETURN_IF_ERROR(CheckInputsCount(node, tf_import_flags, 2)); |
841 | |
842 | // We only support NHWC, which is the default data_format. |
843 | // So if data_format is not defined, we're all good. |
844 | TF_RETURN_IF_ERROR(CheckOptionalAttr(node, "data_format" , "NHWC" )); |
845 | TF_RETURN_IF_ERROR(CheckOptionalAttr(node, "T" , DT_FLOAT)); |
846 | |
847 | const auto& input_name = node.input(0); |
848 | const auto& weights_name = node.input(1); |
849 | const auto& reordered_weights_name = |
850 | AvailableArrayName(*model, weights_name + "_reordered" ); |
851 | // Check if a ReorderAxesOperator was already created for these weights |
852 | // (that happens when multiple layers share the same weights). |
853 | const Operator* existing_reorder = |
854 | GetOpWithOutput(*model, reordered_weights_name); |
855 | if (existing_reorder) { |
856 | // Check that it is safe to rely on the _reordered naming of the output |
857 | // array! |
858 | CHECK(existing_reorder->type == OperatorType::kReorderAxes); |
859 | } else { |
860 | // Create a new ReorderAxesOperator |
861 | auto* reorder = new ReorderAxesOperator; |
862 | reorder->inputs = {weights_name}; |
863 | reorder->outputs = {reordered_weights_name}; |
864 | reorder->input_axes_order = AxesOrder::kHWIO; |
865 | reorder->output_axes_order = AxesOrder::kOHWI; |
866 | model->operators.emplace_back(reorder); |
867 | } |
868 | if (!HasAttr(node, "strides" )) { |
869 | return tensorflow::errors::InvalidArgument("Missing attribute 'strides'" ); |
870 | } |
871 | const auto& strides = GetListAttr(node, "strides" ); |
872 | TF_RETURN_IF_ERROR(ExpectValue(strides.i_size(), 4, "number of strides" )); |
873 | TF_RETURN_IF_ERROR(ExpectValue(strides.i(0), 1, "strides(0)" )); |
874 | TF_RETURN_IF_ERROR(ExpectValue(strides.i(3), 1, "strides(3)" )); |
875 | int dilation_height_factor; |
876 | int dilation_width_factor; |
877 | if (HasAttr(node, "dilations" )) { |
878 | const auto& dilations = GetListAttr(node, "dilations" ); |
879 | TF_RETURN_IF_ERROR( |
880 | ExpectValue(dilations.i_size(), 4, "number of dilations" )); |
881 | if (dilations.i(0) != 1 || dilations.i(3) != 1) { |
882 | return tensorflow::errors::InvalidArgument(absl::StrCat( |
883 | "Can only import Conv ops with dilation along the height " |
884 | "(1st) or width (2nd) axis. TensorFlow op \"" , |
885 | node.name(), "\" had dilations:[ " , dilations.i(0), ", " , |
886 | dilations.i(1), ", " , dilations.i(2), ", " , dilations.i(3), "]." )); |
887 | } |
888 | dilation_height_factor = dilations.i(1); |
889 | dilation_width_factor = dilations.i(2); |
890 | } else { |
891 | dilation_height_factor = 1; |
892 | dilation_width_factor = 1; |
893 | } |
894 | const auto& padding = GetStringAttr(node, "padding" ); |
895 | PaddingType padding_type; |
896 | if (padding == "SAME" ) { |
897 | padding_type = PaddingType::kSame; |
898 | } else if (padding == "VALID" ) { |
899 | padding_type = PaddingType::kValid; |
900 | } else { |
901 | return tensorflow::errors::InvalidArgument( |
902 | "Bad padding (only SAME and VALID are supported)" ); |
903 | } |
904 | auto* conv = new ConvOperator; |
905 | conv->inputs = {input_name, reordered_weights_name}; |
906 | conv->outputs = {node.name()}; |
907 | conv->stride_height = strides.i(1); |
908 | conv->stride_width = strides.i(2); |
909 | conv->dilation_height_factor = dilation_height_factor; |
910 | conv->dilation_width_factor = dilation_width_factor; |
911 | conv->padding.type = padding_type; |
912 | model->operators.emplace_back(conv); |
913 | |
914 | return ::tensorflow::OkStatus(); |
915 | } |
916 | |
917 | tensorflow::Status ConvertDepthwiseConvOperator( |
918 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
919 | const ModelFlags& model_flags, Model* model) { |
920 | CHECK_EQ(node.op(), "DepthwiseConv2dNative" ); |
921 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
922 | |
923 | // We only support NHWC, which is the default data_format. |
924 | // So if data_format is not defined, we're all good. |
925 | if (HasAttr(node, "data_format" )) { |
926 | CHECK_EQ(GetStringAttr(node, "data_format" ), "NHWC" ); |
927 | } |
928 | CHECK_EQ(GetDataTypeAttr(node, "T" ), DT_FLOAT); |
929 | |
930 | const auto& input_name = node.input(0); |
931 | const auto& weights_name = node.input(1); |
932 | const auto& reordered_weights_name = weights_name + "_reordered" ; |
933 | // Check if a ReorderAxesOperator was already created for these weights |
934 | // (that happens when multiple layers share the same weights). |
935 | const Operator* existing_reorder = |
936 | GetOpWithOutput(*model, reordered_weights_name); |
937 | if (existing_reorder) { |
938 | // Check that it is safe to rely on the _reordered naming of the output |
939 | // array! |
940 | CHECK(existing_reorder->type == OperatorType::kReorderAxes); |
941 | } else { |
942 | // Create a new ReorderAxesOperator |
943 | auto* reorder = new ReorderAxesOperator; |
944 | reorder->inputs = {weights_name}; |
945 | reorder->outputs = {reordered_weights_name}; |
946 | reorder->input_axes_order = AxesOrder::kHWIM; |
947 | reorder->output_axes_order = AxesOrder::k1HWO; |
948 | model->operators.emplace_back(reorder); |
949 | } |
950 | const auto& strides = GetListAttr(node, "strides" ); |
951 | TF_RETURN_IF_ERROR(ExpectValue(strides.i_size(), 4, "number of strides" )); |
952 | TF_RETURN_IF_ERROR(ExpectValue(strides.i(0), 1, "strides(0)" )); |
953 | TF_RETURN_IF_ERROR(ExpectValue(strides.i(3), 1, "strides(3)" )); |
954 | int dilation_height_factor; |
955 | int dilation_width_factor; |
956 | if (HasAttr(node, "dilations" )) { |
957 | const auto& dilations = GetListAttr(node, "dilations" ); |
958 | TF_RETURN_IF_ERROR( |
959 | ExpectValue(dilations.i_size(), 4, "number of dilations" )); |
960 | if (dilations.i(0) != 1 || dilations.i(3) != 1) { |
961 | return tensorflow::errors::InvalidArgument(absl::StrCat( |
962 | "Can only import Conv ops with dilation along the height " |
963 | "(1st) or width (2nd) axis. TensorFlow op \"" , |
964 | node.name(), "\" had dilations:[ " , dilations.i(0), ", " , |
965 | dilations.i(1), ", " , dilations.i(2), ", " , dilations.i(3), "]." )); |
966 | } |
967 | dilation_height_factor = dilations.i(1); |
968 | dilation_width_factor = dilations.i(2); |
969 | } else { |
970 | dilation_height_factor = 1; |
971 | dilation_width_factor = 1; |
972 | } |
973 | const auto& padding = GetStringAttr(node, "padding" ); |
974 | PaddingType padding_type; |
975 | if (padding == "SAME" ) { |
976 | padding_type = PaddingType::kSame; |
977 | } else if (padding == "VALID" ) { |
978 | padding_type = PaddingType::kValid; |
979 | } else { |
980 | return tensorflow::errors::InvalidArgument( |
981 | "Bad padding (only SAME and VALID are supported)" ); |
982 | } |
983 | auto* conv = new DepthwiseConvOperator; |
984 | conv->inputs = {input_name, reordered_weights_name}; |
985 | conv->outputs = {node.name()}; |
986 | conv->stride_height = strides.i(1); |
987 | conv->stride_width = strides.i(2); |
988 | conv->dilation_height_factor = dilation_height_factor; |
989 | conv->dilation_width_factor = dilation_width_factor; |
990 | conv->padding.type = padding_type; |
991 | model->operators.emplace_back(conv); |
992 | return ::tensorflow::OkStatus(); |
993 | } |
994 | |
995 | tensorflow::Status ConvertDepthToSpaceOperator( |
996 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
997 | const ModelFlags& model_flags, Model* model) { |
998 | CHECK_EQ(node.op(), "DepthToSpace" ); |
999 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1000 | |
1001 | tensorflow::DataType dtype = GetDataTypeAttr(node, "T" ); |
1002 | if (dtype != DT_FLOAT && dtype != DT_UINT8 && dtype != DT_INT32 && |
1003 | dtype != DT_INT64) { |
1004 | const auto* enum_descriptor = tensorflow::DataType_descriptor(); |
1005 | LOG(FATAL) << "TFLite does not support DepthToSpace with type T:" |
1006 | << enum_descriptor->FindValueByNumber(dtype)->name() << ". " |
1007 | << "T must be one of {DT_FLOAT, DT_UINT8, DT_INT32, DT_INT64}." ; |
1008 | } |
1009 | auto* op = new DepthToSpaceOperator; |
1010 | op->inputs.push_back(node.input(0)); |
1011 | op->outputs.push_back(node.name()); |
1012 | op->block_size = GetIntAttr(node, "block_size" ); |
1013 | QCHECK_GE(op->block_size, 2); |
1014 | model->operators.emplace_back(op); |
1015 | return ::tensorflow::OkStatus(); |
1016 | } |
1017 | |
1018 | tensorflow::Status ConvertSpaceToDepthOperator( |
1019 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1020 | const ModelFlags& model_flags, Model* model) { |
1021 | CHECK_EQ(node.op(), "SpaceToDepth" ); |
1022 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1023 | |
1024 | tensorflow::DataType dtype = GetDataTypeAttr(node, "T" ); |
1025 | if (dtype != DT_FLOAT && dtype != DT_UINT8 && dtype != DT_INT32 && |
1026 | dtype != DT_INT64) { |
1027 | const auto* enum_descriptor = tensorflow::DataType_descriptor(); |
1028 | LOG(FATAL) << "TFLite does not support SpaceToDepth with type T:" |
1029 | << enum_descriptor->FindValueByNumber(dtype)->name() << ". " |
1030 | << "T must be one of {DT_FLOAT, DT_UINT8, DT_INT32, DT_INT64}." ; |
1031 | } |
1032 | auto* op = new SpaceToDepthOperator; |
1033 | op->inputs.push_back(node.input(0)); |
1034 | op->outputs.push_back(node.name()); |
1035 | op->block_size = GetIntAttr(node, "block_size" ); |
1036 | QCHECK_GE(op->block_size, 2); |
1037 | model->operators.emplace_back(op); |
1038 | return ::tensorflow::OkStatus(); |
1039 | } |
1040 | |
1041 | tensorflow::Status ConvertBiasAddOperator( |
1042 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1043 | const ModelFlags& model_flags, Model* model) { |
1044 | CHECK_EQ(node.op(), "BiasAdd" ); |
1045 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1046 | |
1047 | const auto& input_name = node.input(0); |
1048 | const auto& bias_name = node.input(1); |
1049 | CHECK_EQ(GetDataTypeAttr(node, "T" ), DT_FLOAT); |
1050 | auto* biasadd = new AddOperator; |
1051 | biasadd->inputs.push_back(input_name); |
1052 | biasadd->inputs.push_back(bias_name); |
1053 | biasadd->outputs.push_back(node.name()); |
1054 | model->operators.emplace_back(biasadd); |
1055 | return ::tensorflow::OkStatus(); |
1056 | } |
1057 | |
1058 | tensorflow::Status ConvertRandomUniform( |
1059 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1060 | const ModelFlags& model_flags, Model* model) { |
1061 | CHECK_EQ(node.op(), "RandomUniform" ); |
1062 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1063 | |
1064 | CHECK_EQ(GetDataTypeAttr(node, "T" ), DT_INT32); |
1065 | auto op = std::make_unique<RandomUniformOperator>(); |
1066 | op->inputs.push_back(node.input(0)); |
1067 | op->outputs.push_back(node.name()); |
1068 | op->dtype = ConvertDataType(GetDataTypeAttr(node, "dtype" )); |
1069 | op->seed = GetIntAttr(node, "seed" ); |
1070 | op->seed2 = GetIntAttr(node, "seed2" ); |
1071 | CHECK(model != nullptr); |
1072 | model->operators.emplace_back(std::move(op)); |
1073 | return ::tensorflow::OkStatus(); |
1074 | } |
1075 | |
1076 | tensorflow::Status ConvertIdentityOperator( |
1077 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1078 | const ModelFlags& model_flags, Model* model) { |
1079 | CHECK(node.op() == "Identity" || node.op() == "CheckNumerics" || |
1080 | node.op() == "PlaceholderWithDefault" || node.op() == "StopGradient" || |
1081 | node.op() == "Snapshot" || node.op() == "EnsureShape" ); |
1082 | auto* op = new TensorFlowIdentityOperator; |
1083 | // Amazingly, some TensorFlow graphs (at least rajeev_lstm.pb) have |
1084 | // identity nodes with multiple inputs, but the other inputs seem |
1085 | // to be gratuitous (in the case of rajeev_lstm.pb, these are |
1086 | // enumerating the LSTM state arrays). We will just ignore extra |
1087 | // inputs beyond the first input. |
1088 | QCHECK_GE(node.input_size(), 1) |
1089 | << node.op() |
1090 | << " node expects at least 1 input other than control dependencies: " |
1091 | << node.DebugString(); |
1092 | const auto& input_name = node.input(0); |
1093 | op->inputs.push_back(input_name); |
1094 | op->outputs.push_back(node.name()); |
1095 | model->operators.emplace_back(op); |
1096 | return ::tensorflow::OkStatus(); |
1097 | } |
1098 | |
1099 | tensorflow::Status ConvertIdentityNOperator( |
1100 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1101 | const ModelFlags& model_flags, Model* model) { |
1102 | CHECK_EQ(node.op(), "IdentityN" ); |
1103 | for (int i = 0; i < node.input_size(); ++i) { |
1104 | auto* op = new TensorFlowIdentityOperator; |
1105 | const auto& input_name = node.input(i); |
1106 | std::string output_name = node.name(); |
1107 | if (i > 0) { |
1108 | output_name = output_name + ":" + std::to_string(i); |
1109 | } |
1110 | op->inputs.push_back(input_name); |
1111 | op->outputs.push_back(output_name); |
1112 | model->operators.emplace_back(op); |
1113 | } |
1114 | return ::tensorflow::OkStatus(); |
1115 | } |
1116 | |
1117 | tensorflow::Status ConvertFakeQuantWithMinMaxArgs( |
1118 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1119 | const ModelFlags& model_flags, Model* model) { |
1120 | CHECK_EQ(node.op(), "FakeQuantWithMinMaxArgs" ); |
1121 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1122 | auto* op = new FakeQuantOperator; |
1123 | op->inputs.push_back(node.input(0)); |
1124 | op->minmax = std::make_unique<MinMax>(); |
1125 | auto& minmax = *op->minmax; |
1126 | minmax.min = GetFloatAttr(node, "min" ); |
1127 | minmax.max = GetFloatAttr(node, "max" ); |
1128 | op->outputs.push_back(node.name()); |
1129 | // tf.fake_quant_with_min_max_args num_bits defaults to 8. |
1130 | op->num_bits = HasAttr(node, "num_bits" ) ? GetIntAttr(node, "num_bits" ) : 8; |
1131 | if (HasAttr(node, "narrow_range" )) { |
1132 | op->narrow_range = GetBoolAttr(node, "narrow_range" ); |
1133 | } |
1134 | model->operators.emplace_back(op); |
1135 | return ::tensorflow::OkStatus(); |
1136 | } |
1137 | |
1138 | tensorflow::Status ConvertFakeQuantWithMinMaxVars( |
1139 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1140 | const ModelFlags& model_flags, Model* model) { |
1141 | CHECK_EQ(node.op(), "FakeQuantWithMinMaxVars" ); |
1142 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
1143 | QCHECK(num_inputs == 3 || num_inputs == 4) |
1144 | << "FakeQuantWithMinMaxVars node expects 3 or 4 inputs other than " |
1145 | "control dependencies: " |
1146 | << node.DebugString(); |
1147 | auto* op = new FakeQuantOperator; |
1148 | for (int i = 0; i < 3; i++) { |
1149 | op->inputs.push_back(node.input(i)); |
1150 | } |
1151 | op->outputs.push_back(node.name()); |
1152 | op->num_bits = HasAttr(node, "num_bits" ) ? GetIntAttr(node, "num_bits" ) : 8; |
1153 | if (HasAttr(node, "narrow_range" )) { |
1154 | op->narrow_range = GetBoolAttr(node, "narrow_range" ); |
1155 | } |
1156 | model->operators.emplace_back(op); |
1157 | return ::tensorflow::OkStatus(); |
1158 | } |
1159 | |
1160 | tensorflow::Status ConvertSqueezeOperator( |
1161 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1162 | const ModelFlags& model_flags, Model* model) { |
1163 | CHECK_EQ(node.op(), "Squeeze" ); |
1164 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1165 | auto* op = new SqueezeOperator; |
1166 | op->inputs.push_back(node.input(0)); |
1167 | op->outputs.push_back(node.name()); |
1168 | |
1169 | // When omitted we are to squeeze all dimensions == 1. |
1170 | if (HasAttr(node, "squeeze_dims" )) { |
1171 | const auto& squeeze_dims = GetListAttr(node, "squeeze_dims" ); |
1172 | for (int i = 0; i < squeeze_dims.i_size(); ++i) { |
1173 | op->squeeze_dims.push_back(squeeze_dims.i(i)); |
1174 | } |
1175 | } |
1176 | |
1177 | model->operators.emplace_back(op); |
1178 | return ::tensorflow::OkStatus(); |
1179 | } |
1180 | |
1181 | tensorflow::Status ConvertSplitOperator( |
1182 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1183 | const ModelFlags& model_flags, Model* model) { |
1184 | CHECK_EQ(node.op(), "Split" ); |
1185 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1186 | auto* op = new TensorFlowSplitOperator; |
1187 | op->inputs.push_back(node.input(0)); |
1188 | op->inputs.push_back(node.input(1)); |
1189 | const int num_split = GetIntAttr(node, "num_split" ); |
1190 | op->outputs.push_back(node.name()); |
1191 | for (int i = 1; i < num_split; i++) { |
1192 | op->outputs.push_back(absl::StrCat(node.name(), ":" , i)); |
1193 | } |
1194 | op->num_split = num_split; |
1195 | model->operators.emplace_back(op); |
1196 | return ::tensorflow::OkStatus(); |
1197 | } |
1198 | |
1199 | tensorflow::Status ConvertSplitVOperator( |
1200 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1201 | const ModelFlags& model_flags, Model* model) { |
1202 | CHECK_EQ(node.op(), "SplitV" ); |
1203 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); |
1204 | auto* op = new TensorFlowSplitVOperator; |
1205 | op->inputs.push_back(node.input(0)); |
1206 | op->inputs.push_back(node.input(1)); |
1207 | op->inputs.push_back(node.input(2)); |
1208 | const int num_split = GetIntAttr(node, "num_split" ); |
1209 | op->outputs.push_back(node.name()); |
1210 | for (int i = 1; i < num_split; i++) { |
1211 | op->outputs.push_back(absl::StrCat(node.name(), ":" , i)); |
1212 | } |
1213 | op->num_split = num_split; |
1214 | model->operators.emplace_back(op); |
1215 | return ::tensorflow::OkStatus(); |
1216 | } |
1217 | |
1218 | tensorflow::Status ConvertSwitchOperator( |
1219 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1220 | const ModelFlags& model_flags, Model* model) { |
1221 | CHECK_EQ(node.op(), "Switch" ); |
1222 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1223 | auto* op = new TensorFlowSwitchOperator; |
1224 | op->inputs.push_back(node.input(0)); |
1225 | op->inputs.push_back(node.input(1)); |
1226 | op->outputs.push_back(node.name()); |
1227 | // Switch operators have two outputs: "name" and "name:1". |
1228 | op->outputs.push_back(node.name() + ":1" ); |
1229 | model->operators.emplace_back(op); |
1230 | return ::tensorflow::OkStatus(); |
1231 | } |
1232 | |
1233 | tensorflow::Status ConvertSoftmaxOperator( |
1234 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1235 | const ModelFlags& model_flags, Model* model) { |
1236 | CHECK_EQ(node.op(), "Softmax" ); |
1237 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1238 | const auto& input_name = node.input(0); |
1239 | auto* softmax = new SoftmaxOperator; |
1240 | softmax->inputs.push_back(input_name); |
1241 | softmax->outputs.push_back(node.name()); |
1242 | // TensorFlow's Softmax doesn't seem to admit a 'beta' parameter. |
1243 | CHECK(!node.attr().count("beta" )); // Stab in the dark, just in case. |
1244 | if (node.attr().count("_softmax_beta" )) { |
1245 | softmax->beta = GetFloatAttr(node, "_softmax_beta" ); |
1246 | } else { |
1247 | softmax->beta = 1.f; |
1248 | } |
1249 | model->operators.emplace_back(softmax); |
1250 | return ::tensorflow::OkStatus(); |
1251 | } |
1252 | |
1253 | tensorflow::Status ConvertLRNOperator( |
1254 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1255 | const ModelFlags& model_flags, Model* model) { |
1256 | CHECK_EQ(node.op(), "LRN" ); |
1257 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1258 | const auto& input_name = node.input(0); |
1259 | auto* lrn = new LocalResponseNormalizationOperator; |
1260 | lrn->inputs.push_back(input_name); |
1261 | lrn->outputs.push_back(node.name()); |
1262 | lrn->range = GetIntAttr(node, "depth_radius" ); |
1263 | lrn->bias = GetFloatAttr(node, "bias" ); |
1264 | lrn->alpha = GetFloatAttr(node, "alpha" ); |
1265 | lrn->beta = GetFloatAttr(node, "beta" ); |
1266 | model->operators.emplace_back(lrn); |
1267 | return ::tensorflow::OkStatus(); |
1268 | } |
1269 | |
1270 | tensorflow::Status ConvertMaxPoolOperator( |
1271 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1272 | const ModelFlags& model_flags, Model* model) { |
1273 | CHECK_EQ(node.op(), "MaxPool" ); |
1274 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1275 | const auto& input_name = node.input(0); |
1276 | // We only support NHWC, which is the default data_format. |
1277 | // So if data_format is not defined, we're all good. |
1278 | if (node.attr().count("data_format" )) { |
1279 | CHECK_EQ(GetStringAttr(node, "data_format" ), "NHWC" ); |
1280 | } |
1281 | if (HasAttr(node, "T" )) { |
1282 | CHECK_EQ(GetDataTypeAttr(node, "T" ), DT_FLOAT); |
1283 | } else { |
1284 | LOG(WARNING) << "Found MaxPool operator missing 'T' attribute" ; |
1285 | } |
1286 | auto* maxpool = new MaxPoolOperator; |
1287 | maxpool->inputs.push_back(input_name); |
1288 | maxpool->outputs.push_back(node.name()); |
1289 | const auto& strides = GetListAttr(node, "strides" ); |
1290 | CHECK_EQ(strides.i_size(), 4); |
1291 | CHECK_EQ(strides.i(0), 1); |
1292 | CHECK_EQ(strides.i(3), 1); |
1293 | maxpool->stride_height = strides.i(1); |
1294 | maxpool->stride_width = strides.i(2); |
1295 | const auto& ksize = GetListAttr(node, "ksize" ); |
1296 | CHECK_EQ(ksize.i_size(), 4); |
1297 | CHECK_EQ(ksize.i(0), 1); |
1298 | CHECK_EQ(ksize.i(3), 1); |
1299 | maxpool->kheight = ksize.i(1); |
1300 | maxpool->kwidth = ksize.i(2); |
1301 | const auto& padding = GetStringAttr(node, "padding" ); |
1302 | if (padding == "SAME" ) { |
1303 | maxpool->padding.type = PaddingType::kSame; |
1304 | } else if (padding == "VALID" ) { |
1305 | maxpool->padding.type = PaddingType::kValid; |
1306 | } else { |
1307 | LOG(FATAL) << "Bad padding (only SAME and VALID are supported)" ; |
1308 | } |
1309 | model->operators.emplace_back(maxpool); |
1310 | return ::tensorflow::OkStatus(); |
1311 | } |
1312 | |
1313 | tensorflow::Status ConvertAvgPoolOperator( |
1314 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1315 | const ModelFlags& model_flags, Model* model) { |
1316 | CHECK_EQ(node.op(), "AvgPool" ); |
1317 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1318 | const auto& input_name = node.input(0); |
1319 | // We only support NHWC, which is the default data_format. |
1320 | // So if data_format is not defined, we're all good. |
1321 | if (node.attr().count("data_format" )) { |
1322 | CHECK_EQ(GetStringAttr(node, "data_format" ), "NHWC" ); |
1323 | } |
1324 | CHECK_EQ(GetDataTypeAttr(node, "T" ), DT_FLOAT); |
1325 | auto* avgpool = new AveragePoolOperator; |
1326 | avgpool->inputs.push_back(input_name); |
1327 | avgpool->outputs.push_back(node.name()); |
1328 | const auto& strides = GetListAttr(node, "strides" ); |
1329 | CHECK_EQ(strides.i_size(), 4); |
1330 | CHECK_EQ(strides.i(0), 1); |
1331 | CHECK_EQ(strides.i(3), 1); |
1332 | avgpool->stride_height = strides.i(1); |
1333 | avgpool->stride_width = strides.i(2); |
1334 | const auto& ksize = GetListAttr(node, "ksize" ); |
1335 | CHECK_EQ(ksize.i_size(), 4); |
1336 | CHECK_EQ(ksize.i(0), 1); |
1337 | CHECK_EQ(ksize.i(3), 1); |
1338 | avgpool->kheight = ksize.i(1); |
1339 | avgpool->kwidth = ksize.i(2); |
1340 | const auto& padding = GetStringAttr(node, "padding" ); |
1341 | if (padding == "SAME" ) { |
1342 | avgpool->padding.type = PaddingType::kSame; |
1343 | } else if (padding == "VALID" ) { |
1344 | avgpool->padding.type = PaddingType::kValid; |
1345 | } else { |
1346 | LOG(FATAL) << "Bad padding (only SAME and VALID are supported)" ; |
1347 | } |
1348 | model->operators.emplace_back(avgpool); |
1349 | return ::tensorflow::OkStatus(); |
1350 | } |
1351 | |
1352 | tensorflow::Status ConvertBatchMatMulOperator( |
1353 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1354 | const ModelFlags& model_flags, Model* model) { |
1355 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1356 | |
1357 | auto* batch_matmul = new BatchMatMulOperator; |
1358 | // https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/matrix_math_functions |
1359 | if (HasAttr(node, "adj_x" )) { |
1360 | batch_matmul->adj_x = GetBoolAttr(node, "adj_x" ); |
1361 | } |
1362 | if (HasAttr(node, "adj_y" )) { |
1363 | batch_matmul->adj_y = GetBoolAttr(node, "adj_y" ); |
1364 | } |
1365 | batch_matmul->inputs = {node.input(0), node.input(1)}; |
1366 | batch_matmul->outputs = {node.name()}; |
1367 | |
1368 | // For Flex mode. Please read the comments of the function. |
1369 | RetainTensorFlowNodeDef(node, batch_matmul); |
1370 | |
1371 | model->operators.emplace_back(batch_matmul); |
1372 | return ::tensorflow::OkStatus(); |
1373 | } |
1374 | |
1375 | tensorflow::Status ConvertMatMulOperator( |
1376 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1377 | const ModelFlags& model_flags, Model* model) { |
1378 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1379 | |
1380 | CHECK(!HasAttr(node, "adjoint_a" ) || |
1381 | (GetBoolAttr(node, "adjoint_a" ) == false)); |
1382 | CHECK(!HasAttr(node, "adjoint_b" ) || |
1383 | (GetBoolAttr(node, "adjoint_b" ) == false)); |
1384 | |
1385 | auto* matmul = new TensorFlowMatMulOperator; |
1386 | if (HasAttr(node, "transpose_a" )) { |
1387 | matmul->transpose_a = GetBoolAttr(node, "transpose_a" ); |
1388 | } |
1389 | if (HasAttr(node, "transpose_b" )) { |
1390 | matmul->transpose_b = GetBoolAttr(node, "transpose_b" ); |
1391 | } |
1392 | |
1393 | matmul->inputs = {node.input(0), node.input(1)}; |
1394 | matmul->outputs = {node.name()}; |
1395 | model->operators.emplace_back(matmul); |
1396 | return ::tensorflow::OkStatus(); |
1397 | } |
1398 | |
1399 | tensorflow::Status ConvertConcatOperator( |
1400 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1401 | const ModelFlags& model_flags, Model* model) { |
1402 | Operator* op = nullptr; |
1403 | if (node.op() == "Concat" ) { |
1404 | op = new TensorFlowConcatOperator; |
1405 | } else if (node.op() == "ConcatV2" ) { |
1406 | op = new TensorFlowConcatV2Operator; |
1407 | } else { |
1408 | LOG(FATAL) << "Expected Concat or ConcatV2" ; |
1409 | } |
1410 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
1411 | QCHECK_GE(num_inputs, 2) |
1412 | << node.op() |
1413 | << " node expects at least 2 inputs other than control dependencies: " |
1414 | << node.DebugString(); |
1415 | CHECK_EQ(num_inputs, 1 + GetIntAttr(node, "N" )); |
1416 | for (int i = 0; i < num_inputs; ++i) { |
1417 | op->inputs.push_back(node.input(i)); |
1418 | } |
1419 | op->outputs.push_back(node.name()); |
1420 | model->operators.emplace_back(op); |
1421 | return ::tensorflow::OkStatus(); |
1422 | } |
1423 | |
1424 | tensorflow::Status ConvertMirrorPadOperator( |
1425 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1426 | const ModelFlags& model_flags, Model* model) { |
1427 | if (node.op() != "MirrorPad" ) { |
1428 | LOG(FATAL) << "Expected MirrorPad." ; |
1429 | } |
1430 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
1431 | CHECK_EQ(num_inputs, 2); |
1432 | auto* op = new MirrorPadOperator; |
1433 | for (int i = 0; i < num_inputs; ++i) { |
1434 | op->inputs.push_back(node.input(i)); |
1435 | } |
1436 | op->outputs.push_back(node.name()); |
1437 | const auto mode = GetStringAttr(node, "mode" ); |
1438 | if (mode == "REFLECT" ) { |
1439 | op->mode = toco::MirrorPadMode::kReflect; |
1440 | } else if (mode == "SYMMETRIC" ) { |
1441 | op->mode = toco::MirrorPadMode::kSymmetric; |
1442 | } |
1443 | |
1444 | model->operators.emplace_back(op); |
1445 | |
1446 | return ::tensorflow::OkStatus(); |
1447 | } |
1448 | |
1449 | static constexpr int kAnyNumInputs = -1; |
1450 | |
1451 | enum FlexSupport { kFlexOk, kFlexNotOk }; |
1452 | |
1453 | // This method supports simple operators without additional attributes. |
1454 | // Converts a simple operator that takes no attributes. The list of inputs is |
1455 | // taken from the given NodeDef, and its number must match NumInputs, unless |
1456 | // kAnyNumInputs is passed in. If kFlexOk is passed in the resulting operator |
1457 | // will be eligible for being exported as a flex op. |
1458 | template <typename Op, int NumInputs, int NumOutputs, FlexSupport flex> |
1459 | tensorflow::Status ConvertSimpleOperatorGeneric( |
1460 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1461 | const ModelFlags& model_flags, Model* model) { |
1462 | if (NumInputs != kAnyNumInputs) { |
1463 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, NumInputs)); |
1464 | } |
1465 | auto* op = new Op; |
1466 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
1467 | for (int i = 0; i < num_inputs; ++i) { |
1468 | op->inputs.push_back(node.input(i)); |
1469 | } |
1470 | op->outputs.push_back(node.name()); |
1471 | if (NumOutputs > 1) { |
1472 | for (int i = 1; i < NumOutputs; ++i) { |
1473 | op->outputs.push_back(node.name() + ":" + std::to_string(i)); |
1474 | } |
1475 | } |
1476 | |
1477 | if (flex == kFlexOk) { |
1478 | RetainTensorFlowNodeDef(node, op); |
1479 | } |
1480 | |
1481 | model->operators.emplace_back(op); |
1482 | return ::tensorflow::OkStatus(); |
1483 | } |
1484 | |
1485 | // Convert a simple operator which is not valid as a flex op. |
1486 | template <typename Op, int NumInputs, int NumOutputs> |
1487 | tensorflow::Status ConvertSimpleOperator( |
1488 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1489 | const ModelFlags& model_flags, Model* model) { |
1490 | return ConvertSimpleOperatorGeneric<Op, NumInputs, NumOutputs, kFlexNotOk>( |
1491 | node, tf_import_flags, model_flags, model); |
1492 | } |
1493 | |
1494 | // Convert a simple operator which is valid as a flex op. |
1495 | template <typename Op, int NumInputs, int NumOutputs> |
1496 | tensorflow::Status ConvertSimpleOperatorFlexOk( |
1497 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1498 | const ModelFlags& model_flags, Model* model) { |
1499 | return ConvertSimpleOperatorGeneric<Op, NumInputs, NumOutputs, kFlexOk>( |
1500 | node, tf_import_flags, model_flags, model); |
1501 | } |
1502 | |
1503 | // Same as ConvertConstOperator, but revert to ConvertUnsupportedOperator if |
1504 | // the types are not supported. Converting Const operators here avoids |
1505 | // expensive copies of the protocol buffers downstream in the flex delegate. |
1506 | tensorflow::Status ConditionallyConvertConstOperator( |
1507 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1508 | const ModelFlags& model_flags, Model* model) { |
1509 | // We avoid incomplete and zero shapes because the resulting arrays |
1510 | // are not completely compatible with Eager/TensorFlow. |
1511 | const auto& tensor = GetTensorAttr(node, "value" ); |
1512 | const auto& shape = tensor.tensor_shape(); |
1513 | for (const auto& dim : shape.dim()) { |
1514 | if (dim.size() <= 0) { |
1515 | return ConvertUnsupportedOperator(node, tf_import_flags, model_flags, |
1516 | model); |
1517 | } |
1518 | } |
1519 | switch (GetDataTypeAttr(node, "dtype" )) { |
1520 | case DT_FLOAT: |
1521 | case DT_INT32: |
1522 | case DT_QUINT8: |
1523 | case DT_INT64: |
1524 | case DT_STRING: |
1525 | case DT_BOOL: |
1526 | case DT_COMPLEX64: |
1527 | return ConvertConstOperator(node, tf_import_flags, model_flags, model); |
1528 | default: |
1529 | return ConvertUnsupportedOperator(node, tf_import_flags, model_flags, |
1530 | model); |
1531 | } |
1532 | } |
1533 | |
1534 | tensorflow::Status ConvertStridedSliceOperator( |
1535 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1536 | const ModelFlags& model_flags, Model* model) { |
1537 | CHECK_EQ(node.op(), "StridedSlice" ); |
1538 | // TODO(soroosh): The 4th input (strides) should be e optional, to be |
1539 | // consistent with TF. |
1540 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); |
1541 | |
1542 | auto* op = new StridedSliceOperator; |
1543 | for (const auto& input : node.input()) { |
1544 | op->inputs.push_back(input); |
1545 | } |
1546 | op->outputs.push_back(node.name()); |
1547 | |
1548 | op->begin_mask = |
1549 | HasAttr(node, "begin_mask" ) ? GetIntAttr(node, "begin_mask" ) : 0; |
1550 | op->ellipsis_mask = |
1551 | HasAttr(node, "ellipsis_mask" ) ? GetIntAttr(node, "ellipsis_mask" ) : 0; |
1552 | op->end_mask = HasAttr(node, "end_mask" ) ? GetIntAttr(node, "end_mask" ) : 0; |
1553 | op->new_axis_mask = |
1554 | HasAttr(node, "new_axis_mask" ) ? GetIntAttr(node, "new_axis_mask" ) : 0; |
1555 | op->shrink_axis_mask = HasAttr(node, "shrink_axis_mask" ) |
1556 | ? GetIntAttr(node, "shrink_axis_mask" ) |
1557 | : 0; |
1558 | |
1559 | model->operators.emplace_back(op); |
1560 | return ::tensorflow::OkStatus(); |
1561 | } |
1562 | |
1563 | tensorflow::Status ConvertPlaceholderOperator( |
1564 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1565 | const ModelFlags& model_flags, Model* model) { |
1566 | CHECK(node.op() == "Placeholder" || node.op() == "LegacyFedInput" ); |
1567 | if (node.op() == "Placeholder" ) { |
1568 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 0)); |
1569 | } |
1570 | |
1571 | bool inside_input_arrays = false; |
1572 | for (const auto& input_array : model_flags.input_arrays()) { |
1573 | if (node.name() == input_array.name()) { |
1574 | inside_input_arrays = true; |
1575 | break; |
1576 | } |
1577 | } |
1578 | |
1579 | if (!inside_input_arrays) { |
1580 | model->AddInvalidInputArray(node.name()); |
1581 | } |
1582 | |
1583 | auto& array = model->GetOrCreateArray(node.name()); |
1584 | if (node.attr().count("dtype" )) { |
1585 | array.data_type = ConvertDataType(GetDataTypeAttr(node, "dtype" )); |
1586 | } |
1587 | if (node.attr().count("shape" )) { |
1588 | const auto& shape = GetShapeAttr(node, "shape" ); |
1589 | auto num_dims = shape.dim_size(); |
1590 | // TODO(b/62716978): This logic needs to be revisited. During dims |
1591 | // refactoring it is an interim fix. |
1592 | if (num_dims > 0 && !HasWildcardDimension(shape)) { |
1593 | auto& dst_array_dims = *array.mutable_shape()->mutable_dims(); |
1594 | dst_array_dims.resize(num_dims); |
1595 | for (std::size_t i = 0; i < num_dims; i++) { |
1596 | dst_array_dims[i] = shape.dim(i).size(); |
1597 | } |
1598 | } |
1599 | } |
1600 | return ::tensorflow::OkStatus(); |
1601 | } |
1602 | |
1603 | tensorflow::Status ConvertNoOpOperator( |
1604 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1605 | const ModelFlags& model_flags, Model* model) { |
1606 | return ::tensorflow::OkStatus(); |
1607 | } |
1608 | |
1609 | tensorflow::Status ConvertCastOperator( |
1610 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1611 | const ModelFlags& model_flags, Model* model) { |
1612 | CHECK_EQ(node.op(), "Cast" ); |
1613 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1614 | const auto tf_src_dtype = GetDataTypeAttr(node, "SrcT" ); |
1615 | const auto tf_dst_dtype = GetDataTypeAttr(node, "DstT" ); |
1616 | auto* op = new CastOperator; |
1617 | op->src_data_type = ConvertDataType(tf_src_dtype); |
1618 | op->dst_data_type = ConvertDataType(tf_dst_dtype); |
1619 | op->inputs.push_back(node.input(0)); |
1620 | op->outputs.push_back(node.name()); |
1621 | model->operators.emplace_back(op); |
1622 | return ::tensorflow::OkStatus(); |
1623 | } |
1624 | |
1625 | tensorflow::Status ConvertFloorOperator( |
1626 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1627 | const ModelFlags& model_flags, Model* model) { |
1628 | CHECK_EQ(node.op(), "Floor" ); |
1629 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1630 | const auto data_type = GetDataTypeAttr(node, "T" ); |
1631 | CHECK(data_type == DT_FLOAT); |
1632 | auto* op = new FloorOperator; |
1633 | op->inputs.push_back(node.input(0)); |
1634 | op->outputs.push_back(node.name()); |
1635 | model->operators.emplace_back(op); |
1636 | return ::tensorflow::OkStatus(); |
1637 | } |
1638 | |
1639 | tensorflow::Status ConvertCeilOperator( |
1640 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1641 | const ModelFlags& model_flags, Model* model) { |
1642 | CHECK_EQ(node.op(), "Ceil" ); |
1643 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1644 | const auto data_type = GetDataTypeAttr(node, "T" ); |
1645 | CHECK(data_type == DT_FLOAT); |
1646 | auto* op = new CeilOperator; |
1647 | op->inputs.push_back(node.input(0)); |
1648 | op->outputs.push_back(node.name()); |
1649 | model->operators.emplace_back(op); |
1650 | return ::tensorflow::OkStatus(); |
1651 | } |
1652 | |
1653 | tensorflow::Status ConvertRoundOperator( |
1654 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1655 | const ModelFlags& model_flags, Model* model) { |
1656 | CHECK_EQ(node.op(), "Round" ); |
1657 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
1658 | const auto data_type = GetDataTypeAttr(node, "T" ); |
1659 | CHECK(data_type == DT_FLOAT); |
1660 | auto* op = new RoundOperator; |
1661 | op->inputs.push_back(node.input(0)); |
1662 | op->outputs.push_back(node.name()); |
1663 | model->operators.emplace_back(op); |
1664 | return ::tensorflow::OkStatus(); |
1665 | } |
1666 | |
1667 | tensorflow::Status ConvertGatherOperator( |
1668 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1669 | const ModelFlags& model_flags, Model* model) { |
1670 | CHECK(node.op() == "Gather" || node.op() == "GatherV2" ); |
1671 | if (node.op() == "Gather" ) |
1672 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1673 | if (node.op() == "GatherV2" ) |
1674 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); |
1675 | const auto indices_data_type = GetDataTypeAttr(node, "Tindices" ); |
1676 | CHECK(indices_data_type == DT_INT32 || indices_data_type == DT_INT64); |
1677 | auto* op = new GatherOperator; |
1678 | op->inputs.push_back(node.input(0)); |
1679 | op->inputs.push_back(node.input(1)); |
1680 | if (node.input_size() >= 3) { |
1681 | // GatherV2 form where we are provided an axis. It may be either a constant |
1682 | // or runtime defined value, so we just wire up the array and let |
1683 | // ResolveGatherAttributes take care of it later on. |
1684 | const auto axis_data_type = GetDataTypeAttr(node, "Taxis" ); |
1685 | CHECK(axis_data_type == DT_INT32 || axis_data_type == DT_INT64); |
1686 | op->inputs.push_back(node.input(2)); |
1687 | } else { |
1688 | // Gather form that assumes axis=0. |
1689 | op->axis = {0}; |
1690 | } |
1691 | op->outputs.push_back(node.name()); |
1692 | model->operators.emplace_back(op); |
1693 | return ::tensorflow::OkStatus(); |
1694 | } |
1695 | |
1696 | tensorflow::Status ConvertGatherNdOperator( |
1697 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1698 | const ModelFlags& model_flags, Model* model) { |
1699 | CHECK_EQ(node.op(), "GatherNd" ); |
1700 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1701 | const auto indices_data_type = GetDataTypeAttr(node, "Tindices" ); |
1702 | CHECK(indices_data_type == DT_INT32 || indices_data_type == DT_INT64); |
1703 | auto* op = new GatherNdOperator; |
1704 | op->inputs.push_back(node.input(0)); |
1705 | op->inputs.push_back(node.input(1)); |
1706 | op->outputs.push_back(node.name()); |
1707 | model->operators.emplace_back(op); |
1708 | return ::tensorflow::OkStatus(); |
1709 | } |
1710 | |
1711 | template <typename Op> |
1712 | tensorflow::Status ConvertArgMinMaxOperator( |
1713 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1714 | const ModelFlags& model_flags, Model* model) { |
1715 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1716 | const auto axis_data_type = |
1717 | HasAttr(node, "Tidx" ) ? GetDataTypeAttr(node, "Tidx" ) : DT_INT32; |
1718 | const auto output_type = HasAttr(node, "output_type" ) |
1719 | ? GetDataTypeAttr(node, "output_type" ) |
1720 | : DT_INT64; |
1721 | CHECK(axis_data_type == DT_INT64 || axis_data_type == DT_INT32); |
1722 | CHECK(output_type == DT_INT64 || output_type == DT_INT32); |
1723 | auto* op = new Op; |
1724 | op->output_data_type = ConvertDataType(output_type); |
1725 | op->inputs.push_back(node.input(0)); |
1726 | op->inputs.push_back(node.input(1)); |
1727 | op->outputs.push_back(node.name()); |
1728 | model->operators.emplace_back(op); |
1729 | return ::tensorflow::OkStatus(); |
1730 | } |
1731 | |
1732 | tensorflow::Status ConvertArgMaxOperator( |
1733 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1734 | const ModelFlags& model_flags, Model* model) { |
1735 | CHECK_EQ(node.op(), "ArgMax" ); |
1736 | return ConvertArgMinMaxOperator<ArgMaxOperator>(node, tf_import_flags, |
1737 | model_flags, model); |
1738 | } |
1739 | |
1740 | tensorflow::Status ConvertArgMinOperator( |
1741 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1742 | const ModelFlags& model_flags, Model* model) { |
1743 | CHECK_EQ(node.op(), "ArgMin" ); |
1744 | return ConvertArgMinMaxOperator<ArgMinOperator>(node, tf_import_flags, |
1745 | model_flags, model); |
1746 | } |
1747 | |
1748 | tensorflow::Status ConvertResizeBilinearOperator( |
1749 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1750 | const ModelFlags& model_flags, Model* model) { |
1751 | CHECK_EQ(node.op(), "ResizeBilinear" ); |
1752 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1753 | auto* op = new ResizeBilinearOperator; |
1754 | |
1755 | op->align_corners = false; |
1756 | op->half_pixel_centers = false; |
1757 | if (HasAttr(node, "align_corners" )) { |
1758 | op->align_corners = GetBoolAttr(node, "align_corners" ); |
1759 | } |
1760 | if (HasAttr(node, "half_pixel_centers" )) { |
1761 | op->half_pixel_centers = GetBoolAttr(node, "half_pixel_centers" ); |
1762 | } |
1763 | |
1764 | op->inputs.push_back(node.input(0)); |
1765 | op->inputs.push_back(node.input(1)); |
1766 | op->outputs.push_back(node.name()); |
1767 | model->operators.emplace_back(op); |
1768 | return ::tensorflow::OkStatus(); |
1769 | } |
1770 | |
1771 | tensorflow::Status ConvertResizeNearestNeighborOperator( |
1772 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1773 | const ModelFlags& model_flags, Model* model) { |
1774 | CHECK_EQ(node.op(), "ResizeNearestNeighbor" ); |
1775 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1776 | auto* op = new ResizeNearestNeighborOperator; |
1777 | |
1778 | op->align_corners = false; |
1779 | op->half_pixel_centers = false; |
1780 | if (HasAttr(node, "align_corners" )) { |
1781 | op->align_corners = GetBoolAttr(node, "align_corners" ); |
1782 | } |
1783 | if (HasAttr(node, "half_pixel_centers" )) { |
1784 | op->half_pixel_centers = GetBoolAttr(node, "half_pixel_centers" ); |
1785 | } |
1786 | |
1787 | op->inputs.push_back(node.input(0)); |
1788 | op->inputs.push_back(node.input(1)); |
1789 | op->outputs.push_back(node.name()); |
1790 | model->operators.emplace_back(op); |
1791 | return ::tensorflow::OkStatus(); |
1792 | } |
1793 | |
1794 | tensorflow::Status ConvertBatchNormWithGlobalNormalizationOperator( |
1795 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1796 | const ModelFlags& model_flags, Model* model) { |
1797 | CHECK_EQ(node.op(), "BatchNormWithGlobalNormalization" ); |
1798 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 5)); |
1799 | |
1800 | // TODO(ahentz): to really match tensorflow we need to add variance_epsilon |
1801 | // to the input, before feeding it into TensorFlowRsqrtOperator. |
1802 | // CHECK_EQ(GetFloatAttr(node, "variance_epsilon"), 0.001f); |
1803 | |
1804 | std::string multiplier = node.name() + "_mul" ; |
1805 | if (GetBoolAttr(node, "scale_after_normalization" )) { |
1806 | // Create graph: |
1807 | // v -> RSQRT -> |
1808 | // MUL -> multiplier |
1809 | // gamma -----> |
1810 | std::string rsqrt = node.name() + "_rsqrt" ; |
1811 | |
1812 | auto* rsqrt_op = new TensorFlowRsqrtOperator; |
1813 | rsqrt_op->inputs.push_back(node.input(2)); |
1814 | rsqrt_op->outputs.push_back(rsqrt); |
1815 | model->operators.emplace_back(rsqrt_op); |
1816 | |
1817 | auto* mul_op = new MulOperator; |
1818 | mul_op->inputs.push_back(rsqrt); |
1819 | mul_op->inputs.push_back(node.input(4)); |
1820 | mul_op->outputs.push_back(multiplier); |
1821 | model->operators.emplace_back(mul_op); |
1822 | } else { |
1823 | // Create graph: |
1824 | // v -> RSQRT -> multiplier |
1825 | auto* rsqrt_op = new TensorFlowRsqrtOperator; |
1826 | rsqrt_op->inputs.push_back(node.input(2)); |
1827 | rsqrt_op->outputs.push_back(multiplier); |
1828 | model->operators.emplace_back(rsqrt_op); |
1829 | } |
1830 | |
1831 | auto* op = new BatchNormalizationOperator; |
1832 | op->global_normalization = true; |
1833 | |
1834 | op->inputs.push_back(node.input(0)); |
1835 | op->inputs.push_back(node.input(1)); |
1836 | op->inputs.push_back(multiplier); |
1837 | op->inputs.push_back(node.input(3)); |
1838 | op->outputs.push_back(node.name()); |
1839 | |
1840 | model->operators.emplace_back(op); |
1841 | return ::tensorflow::OkStatus(); |
1842 | } |
1843 | |
1844 | tensorflow::Status ConvertFusedBatchNormOperator( |
1845 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1846 | const ModelFlags& model_flags, Model* model) { |
1847 | CHECK((node.op() == "FusedBatchNorm" ) || (node.op() == "FusedBatchNormV3" )); |
1848 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 5)); |
1849 | |
1850 | // Declare shortcuts for the inputs. |
1851 | const std::string& gamma_input = node.input(1); |
1852 | const std::string& beta_input = node.input(2); |
1853 | const std::string& moving_mean_input = node.input(3); |
1854 | const std::string& moving_variance_input = node.input(4); |
1855 | |
1856 | // Create an array holding the epsilon value (typically, 0.001). |
1857 | const std::string epsilon_array_name = |
1858 | CreateConstArray<ArrayDataType::kFloat>(model, |
1859 | node.name() + "_epsilon_array" , |
1860 | {GetFloatAttr(node, "epsilon" )}); |
1861 | |
1862 | // Add epsilon to the moving variance. |
1863 | const std::string epsilon_add_op_name = node.name() + "_epsilon" ; |
1864 | auto* epsilon_add_op = new AddOperator; |
1865 | epsilon_add_op->inputs.push_back(moving_variance_input); |
1866 | epsilon_add_op->inputs.push_back(epsilon_array_name); |
1867 | epsilon_add_op->outputs.push_back(epsilon_add_op_name); |
1868 | model->operators.emplace_back(epsilon_add_op); |
1869 | |
1870 | // Take the inverse square root of the (variance + epsilon). |
1871 | const std::string rsqrt_op_name = node.name() + "_rsqrt" ; |
1872 | auto* rsqrt_op = new TensorFlowRsqrtOperator; |
1873 | rsqrt_op->inputs.push_back(epsilon_add_op_name); |
1874 | rsqrt_op->outputs.push_back(rsqrt_op_name); |
1875 | model->operators.emplace_back(rsqrt_op); |
1876 | |
1877 | // Multiply the result by gamma. |
1878 | const std::string multiplier = node.name() + "_mul" ; |
1879 | auto* mul_op = new MulOperator; |
1880 | mul_op->inputs.push_back(rsqrt_op_name); |
1881 | mul_op->inputs.push_back(gamma_input); |
1882 | mul_op->outputs.push_back(multiplier); |
1883 | model->operators.emplace_back(mul_op); |
1884 | |
1885 | // Now we have all required inputs for the BatchNormalizationOperator. |
1886 | auto* op = new BatchNormalizationOperator; |
1887 | op->global_normalization = true; |
1888 | |
1889 | op->inputs.push_back(node.input(0)); |
1890 | op->inputs.push_back(moving_mean_input); |
1891 | op->inputs.push_back(multiplier); |
1892 | op->inputs.push_back(beta_input); |
1893 | op->outputs.push_back(node.name()); |
1894 | |
1895 | model->operators.emplace_back(op); |
1896 | return ::tensorflow::OkStatus(); |
1897 | } |
1898 | |
1899 | tensorflow::Status ConvertSpaceToBatchNDOperator( |
1900 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1901 | const ModelFlags& model_flags, Model* model) { |
1902 | CHECK_EQ(node.op(), "SpaceToBatchND" ); |
1903 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); |
1904 | CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape" ), DT_INT32); |
1905 | CHECK_EQ(GetDataTypeAttr(node, "Tpaddings" ), DT_INT32); |
1906 | auto* op = new SpaceToBatchNDOperator; |
1907 | op->inputs.push_back(node.input(0)); |
1908 | op->inputs.push_back(node.input(1)); |
1909 | op->inputs.push_back(node.input(2)); |
1910 | op->outputs.push_back(node.name()); |
1911 | model->operators.emplace_back(op); |
1912 | return ::tensorflow::OkStatus(); |
1913 | } |
1914 | |
1915 | tensorflow::Status ConvertBatchToSpaceNDOperator( |
1916 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1917 | const ModelFlags& model_flags, Model* model) { |
1918 | CHECK_EQ(node.op(), "BatchToSpaceND" ); |
1919 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); |
1920 | CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape" ), DT_INT32); |
1921 | CHECK_EQ(GetDataTypeAttr(node, "Tcrops" ), DT_INT32); |
1922 | auto* op = new BatchToSpaceNDOperator; |
1923 | op->inputs.push_back(node.input(0)); |
1924 | op->inputs.push_back(node.input(1)); |
1925 | op->inputs.push_back(node.input(2)); |
1926 | op->outputs.push_back(node.name()); |
1927 | model->operators.emplace_back(op); |
1928 | return ::tensorflow::OkStatus(); |
1929 | } |
1930 | |
1931 | template <typename T> |
1932 | tensorflow::Status ConvertReduceOperator( |
1933 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1934 | const ModelFlags& model_flags, Model* model) { |
1935 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
1936 | auto* op = new T; |
1937 | op->inputs.push_back(node.input(0)); |
1938 | op->inputs.push_back(node.input(1)); |
1939 | op->outputs.push_back(node.name()); |
1940 | model->operators.emplace_back(op); |
1941 | if (HasAttr(node, "keepdims" )) { |
1942 | op->keep_dims = GetBoolAttr(node, "keepdims" ); |
1943 | } else if (HasAttr(node, "keep_dims" )) { |
1944 | op->keep_dims = GetBoolAttr(node, "keep_dims" ); |
1945 | } |
1946 | return ::tensorflow::OkStatus(); |
1947 | } |
1948 | |
1949 | // TODO(b/139320642): Add test when fused op is supported. |
1950 | tensorflow::Status ConvertSvdfOperator( |
1951 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1952 | const ModelFlags& model_flags, Model* model) { |
1953 | CHECK_EQ(node.op(), "Svdf" ); |
1954 | const int input_size = GetInputsCount(node, tf_import_flags); |
1955 | QCHECK(input_size == 4 || input_size == 5) |
1956 | << "Svdf node expects 3 or 4 inputs other than control dependencies: " |
1957 | << node.DebugString(); |
1958 | bool has_bias = (input_size == 5); |
1959 | auto* op = new SvdfOperator; |
1960 | int index = 0; |
1961 | op->inputs.push_back(node.input(index++)); |
1962 | op->inputs.push_back(node.input(index++)); |
1963 | op->inputs.push_back(node.input(index++)); |
1964 | if (has_bias) { |
1965 | op->inputs.push_back(node.input(index++)); |
1966 | } |
1967 | op->inputs.push_back(node.input(index)); |
1968 | op->outputs.push_back(node.name()); |
1969 | if (node.attr().at("ActivationFunction" ).s() == "Relu" ) { |
1970 | op->fused_activation_function = FusedActivationFunctionType::kRelu; |
1971 | } else { |
1972 | op->fused_activation_function = FusedActivationFunctionType::kNone; |
1973 | } |
1974 | op->rank = node.attr().at("Rank" ).i(); |
1975 | model->operators.emplace_back(op); |
1976 | return ::tensorflow::OkStatus(); |
1977 | } |
1978 | |
1979 | // This is just bare bones support to get the shapes to propagate. |
1980 | tensorflow::Status ConvertTransposeConvOperator( |
1981 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
1982 | const ModelFlags& model_flags, Model* model) { |
1983 | CHECK_EQ(node.op(), "Conv2DBackpropInput" ); |
1984 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); |
1985 | auto* op = new TransposeConvOperator; |
1986 | op->inputs.push_back(node.input(0)); |
1987 | op->inputs.push_back(node.input(1)); |
1988 | op->inputs.push_back(node.input(2)); |
1989 | op->outputs.push_back(node.name()); |
1990 | const auto& strides = GetListAttr(node, "strides" ); |
1991 | op->stride_height = strides.i(1); |
1992 | op->stride_width = strides.i(2); |
1993 | CHECK_EQ(strides.i_size(), 4) |
1994 | << "Can only import TransposeConv ops with 4D strides. TensorFlow op \"" |
1995 | << node.name() << "\" has " << strides.i_size() << "D strides." ; |
1996 | CHECK((strides.i(0) == 1) && (strides.i(3) == 1)) |
1997 | << "Can only import TransposeConv ops with striding along the height " |
1998 | "(1st) or width (2nd) axis. TensorFlow op \"" |
1999 | << node.name() << "\" had strides:[ " << strides.i(0) << ", " |
2000 | << strides.i(1) << ", " << strides.i(2) << ", " << strides.i(3) << "]." ; |
2001 | op->stride_height = strides.i(1); |
2002 | op->stride_width = strides.i(2); |
2003 | if (HasAttr(node, "dilations" )) { |
2004 | const auto& dilations = GetListAttr(node, "dilations" ); |
2005 | CHECK_EQ(dilations.i_size(), 4) |
2006 | << "Dilation unsupported in TransposeConv. TensorFlow op \"" |
2007 | << node.name() << "\" had dilations" ; |
2008 | CHECK((dilations.i(0) == 1) && (dilations.i(1) == 1) && |
2009 | (dilations.i(2) == 1) && (dilations.i(3) == 1)) |
2010 | << "Dilation unsupported in TransposeConv. TensorFlow op \"" |
2011 | << node.name() << "\" had dilations:[ " << dilations.i(0) << ", " |
2012 | << dilations.i(1) << ", " << dilations.i(2) << ", " << dilations.i(3) |
2013 | << "]." ; |
2014 | } |
2015 | |
2016 | const std::string& weights_name = node.input(TransposeConvOperator::WEIGHTS); |
2017 | const std::string& transposed_weights_name = weights_name + "_transposed" ; |
2018 | // Check if a TransposeOperator was already created for these weights |
2019 | // (can happen when multiple layers share the same weights). |
2020 | const Operator* existing_transpose = |
2021 | GetOpWithOutput(*model, transposed_weights_name); |
2022 | if (existing_transpose) { |
2023 | CHECK(existing_transpose->type == OperatorType::kTranspose); |
2024 | } else { |
2025 | // Transpose weights from HWOI order to OHWI order, which is more efficient |
2026 | // for computation. (Note that TensorFlow considers the order as HWIO |
2027 | // because they consider this a backward conv, inverting the sense of |
2028 | // input/output.) |
2029 | TransposeOperator* transpose = new TransposeOperator; |
2030 | std::string perm_array = CreateConstArray<ArrayDataType::kInt32>( |
2031 | model, node.name() + "_transpose_perm" , {2, 0, 1, 3}); |
2032 | transpose->inputs = {weights_name, perm_array}; |
2033 | transpose->outputs = {transposed_weights_name}; |
2034 | model->operators.emplace_back(transpose); |
2035 | } |
2036 | op->inputs[1] = transposed_weights_name; |
2037 | |
2038 | auto const& padding = GetStringAttr(node, "padding" ); |
2039 | if (padding == "SAME" ) { |
2040 | op->padding.type = PaddingType::kSame; |
2041 | } else if (padding == "VALID" ) { |
2042 | op->padding.type = PaddingType::kValid; |
2043 | } else { |
2044 | LOG(FATAL) << "Only SAME and VALID padding supported on " |
2045 | "Conv2DBackpropInput nodes." ; |
2046 | } |
2047 | model->operators.emplace_back(op); |
2048 | return ::tensorflow::OkStatus(); |
2049 | } |
2050 | |
2051 | tensorflow::Status ConvertRangeOperator( |
2052 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2053 | const ModelFlags& model_flags, Model* model) { |
2054 | CHECK_EQ(node.op(), "Range" ); |
2055 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); |
2056 | auto* op = new RangeOperator; |
2057 | if (HasAttr(node, "Tidx" )) { |
2058 | const auto dtype = toco::GetDataTypeAttr(node, "Tidx" ); |
2059 | CHECK(dtype == DT_UINT8 || dtype == DT_INT32 || dtype == DT_INT64 || |
2060 | dtype == DT_FLOAT); |
2061 | op->dtype = ConvertDataType(dtype); |
2062 | } |
2063 | op->inputs.push_back(node.input(0)); |
2064 | op->inputs.push_back(node.input(1)); |
2065 | op->inputs.push_back(node.input(2)); |
2066 | op->outputs.push_back(node.name()); |
2067 | |
2068 | model->operators.emplace_back(op); |
2069 | return ::tensorflow::OkStatus(); |
2070 | } |
2071 | |
2072 | // Note that it's easy to confuse/conflate "Stack" and "Pack" operators, but |
2073 | // they aren't the same thing. tf.stack results in a "Pack" operator. "Stack" |
2074 | // operators also exist, but involve manipulating the TF runtime stack, and are |
2075 | // not directly related to tf.stack() usage. |
2076 | tensorflow::Status ConvertPackOperator( |
2077 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2078 | const ModelFlags& model_flags, Model* model) { |
2079 | CHECK_EQ(node.op(), "Pack" ); |
2080 | auto op = std::make_unique<PackOperator>(); |
2081 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
2082 | QCHECK_GE(num_inputs, 1) |
2083 | << node.op() |
2084 | << " node expects at least 1 input other than control dependencies: " |
2085 | << node.DebugString(); |
2086 | CHECK_EQ(num_inputs, GetIntAttr(node, "N" )); |
2087 | for (int i = 0; i < num_inputs; ++i) { |
2088 | op->inputs.push_back(node.input(i)); |
2089 | } |
2090 | op->values_count = HasAttr(node, "N" ) ? GetIntAttr(node, "N" ) : num_inputs; |
2091 | op->axis = HasAttr(node, "axis" ) ? GetIntAttr(node, "axis" ) : 0; |
2092 | op->dtype = ConvertDataType(toco::GetDataTypeAttr(node, "T" )); |
2093 | op->outputs.push_back(node.name()); |
2094 | model->operators.emplace_back(std::move(op)); |
2095 | return ::tensorflow::OkStatus(); |
2096 | } |
2097 | |
2098 | tensorflow::Status ConvertUnpackOperator( |
2099 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2100 | const ModelFlags& model_flags, Model* model) { |
2101 | CHECK_EQ(node.op(), "Unpack" ); |
2102 | auto op = std::make_unique<UnpackOperator>(); |
2103 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
2104 | QCHECK_EQ(num_inputs, 1); |
2105 | op->inputs.push_back(node.input(0)); |
2106 | op->num = GetIntAttr(node, "num" ); |
2107 | op->axis = HasAttr(node, "axis" ) ? GetIntAttr(node, "axis" ) : 0; |
2108 | op->dtype = ConvertDataType(toco::GetDataTypeAttr(node, "T" )); |
2109 | |
2110 | op->outputs.push_back(node.name()); // Implicit :0. |
2111 | for (int i = 1; i < op->num; ++i) { |
2112 | op->outputs.push_back(node.name() + ":" + std::to_string(i)); |
2113 | } |
2114 | model->operators.emplace_back(std::move(op)); |
2115 | return ::tensorflow::OkStatus(); |
2116 | } |
2117 | |
2118 | // Some TensorFlow ops only occur in graph cycles, representing |
2119 | // control flow. We do not currently support control flow, so we wouldn't |
2120 | // be able to fully support such graphs, including performing inference, |
2121 | // anyway. However, rather than erroring out early on graphs being cyclic, |
2122 | // it helps to at least support these just enough to allow getting a |
2123 | // graph visualization. This is not trivial, as we require graphs to be |
2124 | // acyclic aside from RNN back-edges. The solution is to special-case |
2125 | // such ops as RNN back-edges, which is technically incorrect (does not |
2126 | // allow representing the op's semantics) but good enough to get a |
2127 | // graph visualization. |
2128 | tensorflow::Status ConvertOperatorSpecialCasedAsRNNBackEdge( |
2129 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2130 | const ModelFlags& model_flags, Model* model) { |
2131 | // At the moment, the only type of operator special-cased in this way is |
2132 | // NextIteration, occurring only in control-flow cycles. |
2133 | CHECK_EQ(node.op(), "NextIteration" ); |
2134 | CHECK_EQ(node.input_size(), 1); |
2135 | auto* rnn_state = model->flags.add_rnn_states(); |
2136 | // This RNN state is not explicitly created by the user, so it's |
2137 | // OK for some later graph transformation to discard it. |
2138 | rnn_state->set_discardable(true); |
2139 | rnn_state->set_state_array(node.name()); |
2140 | rnn_state->set_back_edge_source_array(node.input(0)); |
2141 | // TODO(tianjuny): Temporary set the size to 1 to avoid transient array |
2142 | // allocation crash. The real value should depend on the hidden_size of RNN. |
2143 | rnn_state->set_size(1); |
2144 | return ::tensorflow::OkStatus(); |
2145 | } |
2146 | |
2147 | tensorflow::Status ConvertShapeOperator( |
2148 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2149 | const ModelFlags& model_flags, Model* model) { |
2150 | CHECK_EQ(node.op(), "Shape" ); |
2151 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
2152 | const auto out_type = |
2153 | HasAttr(node, "out_type" ) ? GetDataTypeAttr(node, "out_type" ) : DT_INT32; |
2154 | CHECK(out_type == DT_INT64 || out_type == DT_INT32); |
2155 | auto op = std::make_unique<TensorFlowShapeOperator>(); |
2156 | op->output_data_type = ConvertDataType(out_type); |
2157 | op->inputs.push_back(node.input(0)); |
2158 | op->outputs.push_back(node.name()); |
2159 | model->operators.push_back(std::move(op)); |
2160 | return ::tensorflow::OkStatus(); |
2161 | } |
2162 | |
2163 | tensorflow::Status ConvertReverseSequenceOperator( |
2164 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2165 | const ModelFlags& model_flags, Model* model) { |
2166 | CHECK_EQ(node.op(), "ReverseSequence" ); |
2167 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
2168 | auto op = std::make_unique<ReverseSequenceOperator>(); |
2169 | if (HasAttr(node, "seq_dim" )) { |
2170 | op->seq_dim = GetIntAttr(node, "seq_dim" ); |
2171 | } |
2172 | // In tf.reverse_sequence, batch_dim defaults to 0. |
2173 | op->batch_dim = |
2174 | HasAttr(node, "batch_dim" ) ? GetIntAttr(node, "batch_dim" ) : 0; |
2175 | const int num_inputs = GetInputsCount(node, tf_import_flags); |
2176 | for (int i = 0; i < num_inputs; ++i) { |
2177 | op->inputs.push_back(node.input(i)); |
2178 | } |
2179 | op->outputs.push_back(node.name()); |
2180 | model->operators.push_back(std::move(op)); |
2181 | return ::tensorflow::OkStatus(); |
2182 | } |
2183 | |
2184 | void StripCaretFromArrayNames(Model* model) { |
2185 | for (auto& op : model->operators) { |
2186 | for (auto& input : op->inputs) { |
2187 | input = std::string(absl::StripPrefix(input, "^" )); |
2188 | } |
2189 | for (auto& output : op->outputs) { |
2190 | output = std::string(absl::StripPrefix(output, "^" )); |
2191 | } |
2192 | } |
2193 | for (auto& array : model->GetArrayMap()) { |
2194 | if (absl::StartsWith(array.first, "^" )) { |
2195 | LOG(FATAL) << "What?" ; |
2196 | } |
2197 | } |
2198 | } |
2199 | |
2200 | void StripZeroOutputIndexFromInputs(NodeDef* node) { |
2201 | for (auto& input : *node->mutable_input()) { |
2202 | input = std::string(absl::StripSuffix(input, ":0" )); |
2203 | } |
2204 | } |
2205 | |
2206 | // In TensorFlow GraphDef, when a node has multiple outputs, they are named |
2207 | // name:0, name:1, ... |
2208 | // where 'name' is the node's name(). Just 'name' is an equivalent shorthand |
2209 | // form for name:0. |
2210 | // A TensorFlow GraphDef does not explicitly list all the outputs of each node |
2211 | // (unlike inputs), it being implied by the node's name and operator type |
2212 | // (the latter implies the number of outputs). |
2213 | // This makes it non-trivial for us to reconstruct the list of all arrays |
2214 | // present in the graph and, for each operator, the list of its outputs. |
2215 | // We do that by taking advantage of the fact that |
2216 | // at least each node lists explicitly its inputs, so after we've loaded |
2217 | // all nodes, we can use that information. |
2218 | void (Model* model) { |
2219 | // Construct the list of all arrays consumed by anything in the graph. |
2220 | std::vector<std::string> consumed_arrays; |
2221 | // Add arrays consumed by an op. |
2222 | for (const auto& consumer_op : model->operators) { |
2223 | for (const std::string& input : consumer_op->inputs) { |
2224 | consumed_arrays.push_back(input); |
2225 | } |
2226 | } |
2227 | // Add global outputs of the model. |
2228 | for (const std::string& output_array : model->flags.output_arrays()) { |
2229 | consumed_arrays.push_back(output_array); |
2230 | } |
2231 | // Add arrays consumed by a RNN back-edge. |
2232 | for (const auto& rnn_state : model->flags.rnn_states()) { |
2233 | consumed_arrays.push_back(rnn_state.back_edge_source_array()); |
2234 | } |
2235 | // Now add operator outputs so that all arrays that are consumed, |
2236 | // are produced. |
2237 | for (const std::string& consumed_array : consumed_arrays) { |
2238 | // Test if consumed_array is already the output of some op. |
2239 | // This has occurred in a model where separate nodes had names of the form |
2240 | // foo:$i with the same base name foo. |
2241 | if (GetOpWithOutput(*model, consumed_array)) { |
2242 | continue; |
2243 | } |
2244 | // Split the consumed array name into the form name:output_index. |
2245 | const std::vector<std::string>& split = absl::StrSplit(consumed_array, ':'); |
2246 | // If not of the form name:output_index, then this is not an additional |
2247 | // output of a node with multiple outputs, so nothing to do here. |
2248 | if (split.size() != 2) { |
2249 | continue; |
2250 | } |
2251 | int output_index = 0; |
2252 | if (!absl::SimpleAtoi(split[1], &output_index)) { |
2253 | continue; |
2254 | } |
2255 | // Each op is initially recorded as producing at least the array that |
2256 | // has its name. We use that to identify the producer node. |
2257 | auto* producer_op = GetOpWithOutput(*model, split[0]); |
2258 | if (!producer_op) { |
2259 | continue; |
2260 | } |
2261 | // Add extra outputs to that producer node, all the way to the |
2262 | // output_index. |
2263 | while (producer_op->outputs.size() <= output_index) { |
2264 | using toco::port::StringF; |
2265 | producer_op->outputs.push_back( |
2266 | StringF("%s:%d" , split[0], producer_op->outputs.size())); |
2267 | } |
2268 | } |
2269 | } |
2270 | |
2271 | bool InlineAllFunctions(GraphDef* graphdef) { |
2272 | if (graphdef->library().function().empty()) { |
2273 | VLOG(kLogLevelModelUnchanged) << "No functions to inline." ; |
2274 | return false; |
2275 | } |
2276 | |
2277 | // Override "_noinline" attribute on all functions |
2278 | GraphDef graphdef_copy(*graphdef); |
2279 | for (auto& function : |
2280 | (*graphdef_copy.mutable_library()->mutable_function())) { |
2281 | auto* attributes = function.mutable_attr(); |
2282 | if (attributes->count(tensorflow::kNoInlineAttr) != 0) { |
2283 | (*attributes)[tensorflow::kNoInlineAttr].set_b(false); |
2284 | } |
2285 | } |
2286 | |
2287 | // Construct minimum resources needed to use ExpandInlineFunctions(). |
2288 | tensorflow::SessionOptions options; |
2289 | auto* device_count = options.config.mutable_device_count(); |
2290 | device_count->insert({"CPU" , 1}); |
2291 | std::vector<std::unique_ptr<tensorflow::Device>> devices; |
2292 | TF_CHECK_OK(tensorflow::DeviceFactory::AddDevices( |
2293 | options, "/job:localhost/replica:0/task:0" , &devices)); |
2294 | |
2295 | tensorflow::FunctionLibraryDefinition fld(tensorflow::OpRegistry::Global(), |
2296 | graphdef_copy.library()); |
2297 | tensorflow::StaticDeviceMgr device_mgr(std::move(devices)); |
2298 | tensorflow::ProcessFunctionLibraryRuntime pflr( |
2299 | &device_mgr, tensorflow::Env::Default(), &options.config, |
2300 | TF_GRAPH_DEF_VERSION, &fld, |
2301 | options.config.graph_options().optimizer_options(), nullptr); |
2302 | tensorflow::FunctionLibraryRuntime* flr; |
2303 | flr = pflr.GetFLR("/job:localhost/replica:0/task:0/cpu:0" ); |
2304 | |
2305 | tensorflow::Graph graph(fld); |
2306 | tensorflow::ImportGraphDefOptions gc_opts; |
2307 | gc_opts.validate_shape = false; |
2308 | const auto& tf_convert_status = tensorflow::ImportGraphDef( |
2309 | gc_opts, graphdef_copy, &graph, nullptr, nullptr); |
2310 | if (!tf_convert_status.ok()) { |
2311 | LOG(ERROR) << "tensorflow::ImportGraphDef failed with status: " |
2312 | << tf_convert_status.ToString(); |
2313 | return false; |
2314 | } |
2315 | |
2316 | // Iterate over the graph until there are no more nodes to be inlined. |
2317 | bool graph_modified = false; |
2318 | while (tensorflow::ExpandInlineFunctions(flr, &graph)) { |
2319 | graph_modified = true; |
2320 | } |
2321 | |
2322 | // Output inlined graph |
2323 | if (graph_modified) { |
2324 | LOG(INFO) << "Found and inlined TensorFlow functions." ; |
2325 | graph.ToGraphDef(graphdef); |
2326 | } |
2327 | return graph_modified; |
2328 | } |
2329 | |
2330 | tensorflow::Status ConvertTopKV2Operator( |
2331 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2332 | const ModelFlags& model_flags, Model* model) { |
2333 | CHECK((node.op() == "TopK" ) || (node.op() == "TopKV2" )); |
2334 | auto op = std::make_unique<TopKV2Operator>(); |
2335 | op->inputs.push_back(node.input(0)); |
2336 | // K can be encoded as attr (TopK) convert it to a const. |
2337 | if (HasAttr(node, "k" )) { |
2338 | std::string k_array = CreateConstArray<ArrayDataType::kInt32>( |
2339 | model, node.name() + "k" , {static_cast<int32>(GetIntAttr(node, "k" ))}); |
2340 | op->inputs.push_back(k_array); |
2341 | } else { |
2342 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
2343 | op->inputs.push_back(node.input(1)); |
2344 | } |
2345 | // The op has two outputs. |
2346 | op->outputs.push_back(node.name()); |
2347 | op->outputs.push_back(node.name() + ":1" ); |
2348 | model->operators.emplace_back(op.release()); |
2349 | return ::tensorflow::OkStatus(); |
2350 | } |
2351 | |
2352 | tensorflow::Status ConvertDynamicPartitionOperator( |
2353 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2354 | const ModelFlags& model_flags, Model* model) { |
2355 | auto op = std::make_unique<DynamicPartitionOperator>(); |
2356 | CHECK(HasAttr(node, "num_partitions" )); |
2357 | op->num_partitions = GetIntAttr(node, "num_partitions" ); |
2358 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
2359 | op->inputs.push_back(node.input(0)); |
2360 | op->inputs.push_back(node.input(1)); |
2361 | CHECK_GT(op->num_partitions, 1); |
2362 | op->outputs.push_back(node.name()); // Implicit :0. |
2363 | for (int i = 1; i < op->num_partitions; ++i) { |
2364 | op->outputs.push_back(node.name() + ":" + std::to_string(i)); |
2365 | } |
2366 | model->operators.emplace_back(op.release()); |
2367 | return ::tensorflow::OkStatus(); |
2368 | } |
2369 | |
2370 | tensorflow::Status ConvertDynamicStitchOperator( |
2371 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2372 | const ModelFlags& model_flags, Model* model) { |
2373 | // The parallel and non-parallel variants are the same besides whether they |
2374 | // have a parallel loop; there are no behavioral differences. |
2375 | CHECK(node.op() == "DynamicStitch" || node.op() == "ParallelDynamicStitch" ); |
2376 | auto op = std::make_unique<DynamicStitchOperator>(); |
2377 | CHECK(HasAttr(node, "N" )); |
2378 | op->num_partitions = GetIntAttr(node, "N" ); |
2379 | // Expect all ID partitions + all value partitions. |
2380 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, op->num_partitions * 2)); |
2381 | for (int i = 0; i < op->num_partitions * 2; ++i) { |
2382 | op->inputs.push_back(node.input(i)); |
2383 | } |
2384 | op->outputs.push_back(node.name()); |
2385 | model->operators.emplace_back(op.release()); |
2386 | return ::tensorflow::OkStatus(); |
2387 | } |
2388 | |
2389 | tensorflow::Status ConvertSparseToDenseOperator( |
2390 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2391 | const ModelFlags& model_flags, Model* model) { |
2392 | CHECK_EQ(node.op(), "SparseToDense" ); |
2393 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); |
2394 | |
2395 | auto* op = new SparseToDenseOperator; |
2396 | for (const std::string& input : node.input()) { |
2397 | op->inputs.push_back(input); |
2398 | } |
2399 | op->outputs.push_back(node.name()); |
2400 | |
2401 | op->validate_indices = HasAttr(node, "validate_indices" ) |
2402 | ? GetBoolAttr(node, "validate_indices" ) |
2403 | : true; |
2404 | model->operators.emplace_back(op); |
2405 | return ::tensorflow::OkStatus(); |
2406 | } |
2407 | |
2408 | tensorflow::Status ConvertOneHotOperator( |
2409 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2410 | const ModelFlags& model_flags, Model* model) { |
2411 | CHECK_EQ(node.op(), "OneHot" ); |
2412 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); |
2413 | |
2414 | const auto dtype = GetDataTypeAttr(node, "T" ); |
2415 | // TODO(b/111744875): Support DT_UINT8 and quantization. |
2416 | CHECK(dtype == DT_INT32 || dtype == DT_INT64 || dtype == DT_FLOAT || |
2417 | dtype == DT_BOOL); |
2418 | |
2419 | auto op = std::make_unique<OneHotOperator>(); |
2420 | op->axis = HasAttr(node, "axis" ) ? GetIntAttr(node, "axis" ) : -1; |
2421 | for (const std::string& input : node.input()) { |
2422 | op->inputs.push_back(input); |
2423 | } |
2424 | op->outputs.push_back(node.name()); |
2425 | model->operators.emplace_back(op.release()); |
2426 | return ::tensorflow::OkStatus(); |
2427 | } |
2428 | |
2429 | tensorflow::Status ConvertCTCBeamSearchDecoderOperator( |
2430 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2431 | const ModelFlags& model_flags, Model* model) { |
2432 | CHECK_EQ(node.op(), "CTCBeamSearchDecoder" ); |
2433 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); |
2434 | |
2435 | auto* op = new CTCBeamSearchDecoderOperator; |
2436 | for (const std::string& input : node.input()) { |
2437 | op->inputs.push_back(input); |
2438 | } |
2439 | |
2440 | op->beam_width = |
2441 | HasAttr(node, "beam_width" ) ? GetIntAttr(node, "beam_width" ) : 1; |
2442 | op->top_paths = |
2443 | HasAttr(node, "top_paths" ) ? GetIntAttr(node, "top_paths" ) : 1; |
2444 | op->merge_repeated = HasAttr(node, "merge_repeated" ) |
2445 | ? GetBoolAttr(node, "merge_repeated" ) |
2446 | : true; |
2447 | |
2448 | // There are top_paths + 1 outputs. |
2449 | op->outputs.push_back(node.name()); // Implicit :0. |
2450 | for (int i = 0; i < op->top_paths; ++i) { |
2451 | op->outputs.push_back(node.name() + ":" + std::to_string(i + 1)); |
2452 | } |
2453 | model->operators.emplace_back(op); |
2454 | return ::tensorflow::OkStatus(); |
2455 | } |
2456 | |
2457 | // This isn't a TensorFlow builtin op. Currently this node can only be generated |
2458 | // with TfLite OpHint API. |
2459 | tensorflow::Status ConvertUnidirectionalSequenceLstm( |
2460 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2461 | const ModelFlags& model_flags, Model* model) { |
2462 | DCHECK_EQ(node.op(), "UnidirectionalSequenceLstm" ); |
2463 | |
2464 | const auto& indices = GetListAttr(node, "_tflite_input_indices" ); |
2465 | |
2466 | auto* op = new UnidirectionalSequenceLstmOperator(); |
2467 | |
2468 | // The input size needs to be the same as the TfLite UniDirectionalSequence |
2469 | // Lstm implementation. |
2470 | const int kInputsSize = 20; |
2471 | |
2472 | op->inputs.resize(kInputsSize); |
2473 | |
2474 | if (indices.i_size() != node.input().size()) { |
2475 | // New version, the optional inputs are filled with constant nodes. |
2476 | int count = 0; |
2477 | for (int idx = 0; idx < kInputsSize; ++idx) { |
2478 | if (count < indices.i_size() && indices.i(count) == idx) { |
2479 | // Specified input. |
2480 | op->inputs[idx] = node.input(idx); |
2481 | count++; |
2482 | } else { |
2483 | // Optional input. |
2484 | std::string optional_name = node.name() + "_" + std::to_string(idx); |
2485 | model->CreateOptionalArray(optional_name); |
2486 | op->inputs[idx] = optional_name; |
2487 | } |
2488 | } |
2489 | } else { // Legacy version. |
2490 | std::vector<bool> done(kInputsSize); |
2491 | int idx = 0; |
2492 | for (const std::string& input : node.input()) { |
2493 | int real_index = indices.i(idx); |
2494 | op->inputs[real_index] = (input); |
2495 | done[real_index] = true; |
2496 | idx++; |
2497 | } |
2498 | |
2499 | for (int idx = 0; idx < done.size(); idx++) { |
2500 | if (!done[idx]) { |
2501 | std::string optional_name = node.name() + "_" + std::to_string(idx); |
2502 | model->CreateOptionalArray(optional_name); |
2503 | op->inputs[idx] = optional_name; |
2504 | } |
2505 | } |
2506 | } |
2507 | |
2508 | // There're three outputs, only the last one is required. |
2509 | op->outputs.push_back(node.name() + ":2" ); |
2510 | model->operators.emplace_back(op); |
2511 | |
2512 | return ::tensorflow::OkStatus(); |
2513 | } |
2514 | |
2515 | tensorflow::Status ConvertLeakyReluOperator( |
2516 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2517 | const ModelFlags& model_flags, Model* model) { |
2518 | CHECK_EQ(node.op(), "LeakyRelu" ); |
2519 | TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); |
2520 | CHECK_EQ(GetDataTypeAttr(node, "T" ), DT_FLOAT); |
2521 | const auto& input_name = node.input(0); |
2522 | auto* op = new LeakyReluOperator; |
2523 | op->inputs.push_back(input_name); |
2524 | op->outputs.push_back(node.name()); |
2525 | op->alpha = GetFloatAttr(node, "alpha" ); |
2526 | model->operators.emplace_back(op); |
2527 | return ::tensorflow::OkStatus(); |
2528 | } |
2529 | |
2530 | tensorflow::Status ConvertUnidirectionalSequenceRnn( |
2531 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2532 | const ModelFlags& model_flags, Model* model) { |
2533 | DCHECK_EQ(node.op(), "UnidirectionalSequenceRnn" ); |
2534 | |
2535 | const auto& indices = GetListAttr(node, "_tflite_input_indices" ); |
2536 | if (indices.i_size() != node.input().size()) { |
2537 | return tensorflow::errors::InvalidArgument("Input size does not match." ); |
2538 | } |
2539 | |
2540 | auto* op = new UnidirectionalSequenceRnnOperator(); |
2541 | for (const std::string& input : node.input()) { |
2542 | op->inputs.push_back(input); |
2543 | } |
2544 | // Only use the last one as input. |
2545 | op->outputs.push_back(node.name() + ":1" ); |
2546 | model->operators.emplace_back(op); |
2547 | |
2548 | return ::tensorflow::OkStatus(); |
2549 | } |
2550 | |
2551 | } // namespace |
2552 | |
2553 | namespace internal { |
2554 | |
2555 | using ConverterType = tensorflow::Status (*)( |
2556 | const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, |
2557 | const ModelFlags& model_flags, Model* model); |
2558 | using ConverterMapType = std::unordered_map<std::string, ConverterType>; |
2559 | |
2560 | ConverterMapType GetTensorFlowNodeConverterMapForFlex() { |
2561 | return std::unordered_map<std::string, ConverterType>({ |
2562 | // We need to let TOCO convert Placeholder information into |
2563 | // array data, so that the data types are correct. |
2564 | {"LegacyFedInput" , ConvertPlaceholderOperator}, |
2565 | {"Placeholder" , ConvertPlaceholderOperator}, |
2566 | {"Const" , ConditionallyConvertConstOperator}, |
2567 | }); |
2568 | } |
2569 | |
2570 | ConverterMapType GetTensorFlowNodeConverterMap() { |
2571 | return std::unordered_map<std::string, ConverterType>({ |
2572 | {"Abs" , ConvertSimpleOperator<AbsOperator, kAnyNumInputs, 1>}, |
2573 | {"Add" , ConvertSimpleOperator<AddOperator, 2, 1>}, |
2574 | {"AddV2" , ConvertSimpleOperator<AddOperator, 2, 1>}, |
2575 | {"AddN" , ConvertSimpleOperator<AddNOperator, kAnyNumInputs, 1>}, |
2576 | {"All" , ConvertSimpleOperator<TensorFlowAllOperator, kAnyNumInputs, 1>}, |
2577 | {"Any" , ConvertReduceOperator<TensorFlowAnyOperator>}, |
2578 | {"ArgMax" , ConvertArgMaxOperator}, |
2579 | {"ArgMin" , ConvertArgMinOperator}, |
2580 | {"Assert" , |
2581 | ConvertSimpleOperator<TensorFlowAssertOperator, kAnyNumInputs, 1>}, |
2582 | {"AvgPool" , ConvertAvgPoolOperator}, |
2583 | {"BatchMatMul" , ConvertBatchMatMulOperator}, |
2584 | {"BatchMatMulV2" , ConvertBatchMatMulOperator}, |
2585 | {"BatchNormWithGlobalNormalization" , |
2586 | ConvertBatchNormWithGlobalNormalizationOperator}, |
2587 | {"BatchToSpaceND" , ConvertBatchToSpaceNDOperator}, |
2588 | {"BiasAdd" , ConvertBiasAddOperator}, |
2589 | {"Cast" , ConvertCastOperator}, |
2590 | {"Ceil" , ConvertCeilOperator}, |
2591 | {"CheckNumerics" , ConvertIdentityOperator}, |
2592 | {"Concat" , ConvertConcatOperator}, |
2593 | {"ConcatV2" , ConvertConcatOperator}, |
2594 | {"Const" , ConvertConstOperator}, |
2595 | {"Conv2D" , ConvertConvOperator}, |
2596 | {"Conv2DBackpropInput" , ConvertTransposeConvOperator}, |
2597 | {"Cos" , ConvertSimpleOperator<CosOperator, 1, 1>}, |
2598 | {"CTCBeamSearchDecoder" , ConvertCTCBeamSearchDecoderOperator}, |
2599 | {"DepthToSpace" , ConvertDepthToSpaceOperator}, |
2600 | {"DepthwiseConv2dNative" , ConvertDepthwiseConvOperator}, |
2601 | {"Div" , ConvertSimpleOperator<DivOperator, 2, 1>}, |
2602 | {"DynamicPartition" , ConvertDynamicPartitionOperator}, |
2603 | {"DynamicStitch" , ConvertDynamicStitchOperator}, |
2604 | {"Elu" , ConvertSimpleOperator<EluOperator, 1, 1>}, |
2605 | {"EnsureShape" , ConvertIdentityOperator}, |
2606 | {"Equal" , ConvertSimpleOperator<TensorFlowEqualOperator, 2, 1>}, |
2607 | {"Exp" , ConvertSimpleOperator<ExpOperator, 1, 1>}, |
2608 | {"ExpandDims" , ConvertSimpleOperator<ExpandDimsOperator, 2, 1>}, |
2609 | {"FakeQuantWithMinMaxArgs" , ConvertFakeQuantWithMinMaxArgs}, |
2610 | {"FakeQuantWithMinMaxVars" , ConvertFakeQuantWithMinMaxVars}, |
2611 | {"Fill" , ConvertSimpleOperator<FillOperator, 2, 1>}, |
2612 | {"Floor" , ConvertFloorOperator}, |
2613 | {"FloorDiv" , ConvertSimpleOperator<FloorDivOperator, 2, 1>}, |
2614 | {"FloorMod" , ConvertSimpleOperator<FloorModOperator, 2, 1>}, |
2615 | {"FusedBatchNorm" , ConvertFusedBatchNormOperator}, |
2616 | {"FusedBatchNormV3" , ConvertFusedBatchNormOperator}, |
2617 | {"Gather" , ConvertGatherOperator}, |
2618 | {"GatherV2" , ConvertGatherOperator}, |
2619 | {"GatherNd" , ConvertGatherNdOperator}, |
2620 | {"Greater" , ConvertSimpleOperator<TensorFlowGreaterOperator, 2, 1>}, |
2621 | {"GreaterEqual" , |
2622 | ConvertSimpleOperator<TensorFlowGreaterEqualOperator, 2, 1>}, |
2623 | {"Identity" , ConvertIdentityOperator}, |
2624 | {"IdentityN" , ConvertIdentityNOperator}, |
2625 | {"LRN" , ConvertLRNOperator}, |
2626 | {"LeakyRelu" , ConvertLeakyReluOperator}, |
2627 | {"LegacyFedInput" , ConvertPlaceholderOperator}, |
2628 | {"Less" , ConvertSimpleOperator<TensorFlowLessOperator, 2, 1>}, |
2629 | {"LessEqual" , ConvertSimpleOperator<TensorFlowLessEqualOperator, 2, 1>}, |
2630 | {"Log" , ConvertSimpleOperator<LogOperator, 1, 1>}, |
2631 | {"LogicalAnd" , ConvertSimpleOperator<LogicalAndOperator, 2, 1>}, |
2632 | {"LogicalOr" , ConvertSimpleOperator<LogicalOrOperator, 2, 1>}, |
2633 | {"LogicalNot" , ConvertSimpleOperator<LogicalNotOperator, 1, 1>}, |
2634 | {"LogSoftmax" , ConvertSimpleOperator<LogSoftmaxOperator, 1, 1>}, |
2635 | {"MatMul" , ConvertMatMulOperator}, |
2636 | {"MatrixDiag" , ConvertSimpleOperator<MatrixDiagOperator, 1, 1>}, |
2637 | {"MatrixDiagV2" , ConvertSimpleOperator<MatrixDiagV2Operator, 5, 1>}, |
2638 | // `MatrixDiagV3` has an `align` attribute. However, Toco only converts |
2639 | // `MatrixDiagV3` to `MatrixDiag` with default `k, num_rows, num_cols, |
2640 | // padding_value` inputs. In this case, `align` can be ignored. |
2641 | {"MatrixDiagV3" , ConvertSimpleOperator<MatrixDiagV3Operator, 5, 1>}, |
2642 | {"MatrixSetDiag" , ConvertSimpleOperator<MatrixSetDiagOperator, 2, 1>}, |
2643 | {"MatrixSetDiagV2" , ConvertSimpleOperator<MatrixSetDiagV2Operator, 3, 1>}, |
2644 | // `MatrixSetDiagV3` has an `align` attribute. However, Toco only converts |
2645 | // `MatrixSetDiagV3` to `MatrixSetDiag` with default `k` inputs. In this |
2646 | // case, `align` can be ignored. |
2647 | {"MatrixSetDiagV3" , ConvertSimpleOperator<MatrixSetDiagV3Operator, 3, 1>}, |
2648 | {"Max" , ConvertReduceOperator<TensorFlowMaxOperator>}, |
2649 | {"MaxPool" , ConvertMaxPoolOperator}, |
2650 | {"Maximum" , ConvertSimpleOperator<TensorFlowMaximumOperator, 2, 1>}, |
2651 | {"Mean" , ConvertReduceOperator<MeanOperator>}, |
2652 | {"Merge" , |
2653 | ConvertSimpleOperator<TensorFlowMergeOperator, kAnyNumInputs, 1>}, |
2654 | {"Min" , ConvertReduceOperator<TensorFlowMinOperator>}, |
2655 | {"Minimum" , ConvertSimpleOperator<TensorFlowMinimumOperator, 2, 1>}, |
2656 | {"Mul" , ConvertSimpleOperator<MulOperator, 2, 1>}, |
2657 | {"Neg" , ConvertSimpleOperator<NegOperator, 1, 1>}, |
2658 | {"NextIteration" , ConvertOperatorSpecialCasedAsRNNBackEdge}, |
2659 | {"NoOp" , ConvertNoOpOperator}, |
2660 | {"NotEqual" , ConvertSimpleOperator<TensorFlowNotEqualOperator, 2, 1>}, |
2661 | {"OneHot" , ConvertOneHotOperator}, |
2662 | {"Pack" , ConvertPackOperator}, |
2663 | {"Pad" , ConvertSimpleOperator<PadOperator, 2, 1>}, |
2664 | {"PadV2" , ConvertSimpleOperator<PadV2Operator, 3, 1>}, |
2665 | {"ParallelDynamicStitch" , ConvertDynamicStitchOperator}, |
2666 | {"Placeholder" , ConvertPlaceholderOperator}, |
2667 | {"PlaceholderWithDefault" , ConvertIdentityOperator}, |
2668 | {"Pow" , ConvertSimpleOperator<PowOperator, 2, 1>}, |
2669 | {"Prod" , ConvertReduceOperator<TensorFlowProdOperator>}, |
2670 | {"RandomUniform" , ConvertRandomUniform}, |
2671 | {"Range" , ConvertRangeOperator}, |
2672 | {"Rank" , ConvertSimpleOperator<TensorFlowRankOperator, 1, 1>}, |
2673 | {"RealDiv" , ConvertSimpleOperator<DivOperator, 2, 1>}, |
2674 | {"Relu" , ConvertSimpleOperator<ReluOperator, 1, 1>}, |
2675 | {"Relu6" , ConvertSimpleOperator<Relu6Operator, 1, 1>}, |
2676 | {"Reshape" , ConvertSimpleOperator<TensorFlowReshapeOperator, 2, 1>}, |
2677 | {"ResizeBilinear" , ConvertResizeBilinearOperator}, |
2678 | {"ResizeNearestNeighbor" , ConvertResizeNearestNeighborOperator}, |
2679 | {"ReverseSequence" , ConvertReverseSequenceOperator}, |
2680 | {"ReverseV2" , ConvertSimpleOperator<ReverseV2Operator, 2, 1>}, |
2681 | {"Round" , ConvertRoundOperator}, |
2682 | {"Rsqrt" , ConvertSimpleOperator<TensorFlowRsqrtOperator, 1, 1>}, |
2683 | {"ScatterNd" , ConvertSimpleOperator<ScatterNdOperator, 3, 1>}, |
2684 | {"SegmentSum" , ConvertSimpleOperator<SegmentSumOperator, 2, 1>}, |
2685 | {"Select" , ConvertSimpleOperator<SelectOperator, 3, 1>}, |
2686 | {"SelectV2" , ConvertSimpleOperator<SelectOperator, 3, 1>}, |
2687 | {"Shape" , ConvertShapeOperator}, |
2688 | {"Sigmoid" , ConvertSimpleOperator<LogisticOperator, 1, 1>}, |
2689 | {"Sin" , ConvertSimpleOperator<SinOperator, 1, 1>}, |
2690 | {"Slice" , ConvertSimpleOperator<SliceOperator, 3, 1>}, |
2691 | {"Softmax" , ConvertSoftmaxOperator}, |
2692 | {"SpaceToBatchND" , ConvertSpaceToBatchNDOperator}, |
2693 | {"SpaceToDepth" , ConvertSpaceToDepthOperator}, |
2694 | {"SparseToDense" , ConvertSparseToDenseOperator}, |
2695 | {"Split" , ConvertSplitOperator}, |
2696 | {"SplitV" , ConvertSplitVOperator}, |
2697 | {"Sqrt" , ConvertSimpleOperator<TensorFlowSqrtOperator, 1, 1>}, |
2698 | {"Square" , ConvertSimpleOperator<TensorFlowSquareOperator, 1, 1>}, |
2699 | {"SquaredDifference" , |
2700 | ConvertSimpleOperator<SquaredDifferenceOperator, 2, 1>}, |
2701 | {"Snapshot" , ConvertIdentityOperator}, |
2702 | {"Squeeze" , ConvertSqueezeOperator}, |
2703 | {"StopGradient" , ConvertIdentityOperator}, |
2704 | {"StridedSlice" , ConvertStridedSliceOperator}, |
2705 | {"Sub" , ConvertSimpleOperator<SubOperator, 2, 1>}, |
2706 | {"Sum" , ConvertReduceOperator<TensorFlowSumOperator>}, |
2707 | {"Svdf" , ConvertSvdfOperator}, |
2708 | {"Switch" , ConvertSwitchOperator}, |
2709 | {"Tanh" , ConvertSimpleOperator<TanhOperator, 1, 1>}, |
2710 | {"Tile" , ConvertSimpleOperator<TensorFlowTileOperator, 2, 1>}, |
2711 | {"TopK" , ConvertTopKV2Operator}, |
2712 | {"TopKV2" , ConvertTopKV2Operator}, |
2713 | {"Transpose" , ConvertSimpleOperator<TransposeOperator, 2, 1>}, |
2714 | {"Unpack" , ConvertUnpackOperator}, |
2715 | {"ZerosLike" , ConvertSimpleOperator<TensorFlowZerosLikeOperator, 1, 1>}, |
2716 | {"UnidirectionalSequenceLstm" , ConvertUnidirectionalSequenceLstm}, |
2717 | {"UnidirectionalSequenceRnn" , ConvertUnidirectionalSequenceRnn}, |
2718 | {"MirrorPad" , ConvertMirrorPadOperator}, |
2719 | {"Unique" , ConvertSimpleOperator<UniqueOperator, 1, 2>}, |
2720 | {"Where" , ConvertSimpleOperator<WhereOperator, 1, 1>}, |
2721 | }); |
2722 | } |
2723 | |
2724 | tensorflow::Status ImportTensorFlowNode( |
2725 | const tensorflow::NodeDef& node, |
2726 | const TensorFlowImportFlags& tf_import_flags, const ModelFlags& model_flags, |
2727 | Model* model, const ConverterMapType& converter_map) { |
2728 | auto converter = converter_map.find(node.op()); |
2729 | if (converter == converter_map.end()) { |
2730 | return ConvertUnsupportedOperator(node, tf_import_flags, model_flags, |
2731 | model); |
2732 | } else { |
2733 | return converter->second(node, tf_import_flags, model_flags, model); |
2734 | } |
2735 | } |
2736 | } // namespace internal |
2737 | |
2738 | std::unique_ptr<Model> ImportTensorFlowGraphDef( |
2739 | const ModelFlags& model_flags, const TensorFlowImportFlags& tf_import_flags, |
2740 | const GraphDef& tf_graph) { |
2741 | LogDumpGraphDef(kLogLevelModelChanged, "AT IMPORT" , tf_graph); |
2742 | |
2743 | GraphDef inlined_graph(tf_graph); |
2744 | if (InlineAllFunctions(&inlined_graph)) { |
2745 | LogDumpGraphDef(kLogLevelModelChanged, "AFTER INLINING" , inlined_graph); |
2746 | } |
2747 | |
2748 | // Check input and output specification. |
2749 | for (const auto& specified_input_array : model_flags.input_arrays()) { |
2750 | CHECK(!absl::EndsWith(specified_input_array.name(), ":0" )) |
2751 | << "Unsupported explicit zero output index: " |
2752 | << specified_input_array.name(); |
2753 | } |
2754 | for (const std::string& specified_output_array : |
2755 | model_flags.output_arrays()) { |
2756 | CHECK(!absl::EndsWith(specified_output_array, ":0" )) |
2757 | << "Unsupported explicit zero output index: " << specified_output_array; |
2758 | } |
2759 | |
2760 | Model* model = new Model; |
2761 | internal::ConverterMapType converter_map; |
2762 | |
2763 | // This is used for the TFLite "Full Flex Mode" conversion. All the ops are |
2764 | // imported as `TensorFlowUnsupportedOperator`, and later all these ops are |
2765 | // converted to TFLite Flex ops. |
2766 | if (!tf_import_flags.import_all_ops_as_unsupported) { |
2767 | converter_map = internal::GetTensorFlowNodeConverterMap(); |
2768 | } else { |
2769 | converter_map = internal::GetTensorFlowNodeConverterMapForFlex(); |
2770 | } |
2771 | |
2772 | for (auto node : inlined_graph.node()) { |
2773 | StripZeroOutputIndexFromInputs(&node); |
2774 | auto status = internal::ImportTensorFlowNode( |
2775 | node, tf_import_flags, model_flags, model, converter_map); |
2776 | CHECK(status.ok()) << status.error_message(); |
2777 | } |
2778 | |
2779 | ResolveModelFlags(model_flags, model); |
2780 | |
2781 | StripCaretFromArrayNames(model); |
2782 | AddExtraOutputs(model); |
2783 | FixNoMissingArray(model); |
2784 | FixNoOrphanedArray(model); |
2785 | FixOperatorOrdering(model); |
2786 | CheckInvariants(*model); |
2787 | |
2788 | // if rnn state arrays are constant, make them transient |
2789 | for (const auto& rnn_state : model->flags.rnn_states()) { |
2790 | model->GetArray(rnn_state.state_array()).buffer = nullptr; |
2791 | } |
2792 | |
2793 | return std::unique_ptr<Model>(model); |
2794 | } |
2795 | |
2796 | std::unique_ptr<Model> ImportTensorFlowGraphDef( |
2797 | const ModelFlags& model_flags, const TensorFlowImportFlags& tf_import_flags, |
2798 | const std::string& input_file_contents) { |
2799 | std::unique_ptr<GraphDef> tf_graph(new GraphDef); |
2800 | CHECK(ParseFromStringEitherTextOrBinary(input_file_contents, tf_graph.get())); |
2801 | |
2802 | std::unique_ptr<GraphDef> pruned_graph = |
2803 | MaybeReplaceCompositeSubgraph(*tf_graph); |
2804 | if (pruned_graph) { |
2805 | tf_graph = std::move(pruned_graph); |
2806 | } |
2807 | return ImportTensorFlowGraphDef(model_flags, tf_import_flags, *tf_graph); |
2808 | } |
2809 | |
2810 | } // namespace toco |
2811 | |