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Merged commit includes the following changes:

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237836124  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal changes

--
237831779  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix rnn.reset_states() to throw proper error when before .build() is invoked.

    See https://github.com/tensorflow/tensorflow/issues/25852 for more details.

--
237814078  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix GatherV2 converter. It assumed that the IGatherLayer has same output dimensions as tf.gather, which is not the case.

--
237814023  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Split py_binary into py_binary and py_library to avoid having py_binary in deps.

--

PiperOrigin-RevId: 237836124
This commit is contained in:
A. Unique TensorFlower 2019-03-11 10:54:44 -07:00 committed by TensorFlower Gardener
parent 2b9900638a
commit 6108cbd1db
7 changed files with 314 additions and 246 deletions
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56
tensorflow/compiler/tf2tensorrt/convert/convert_nodes.cc
View File

@ -2243,8 +2243,8 @@ Status ConvertSqueeze(OpConverterParams* params) {
// Make sure target dimension is size 1.
if (input_dims[axis] != 1) {
return errors::InvalidArgument(
"Cannot squeeze a dimension which isn't size 1, at ",
node_def.name());
"Cannot squeeze ", axis, "th dimension ", input_dims[axis],
" which isn't size 1, at ", node_def.name());
}
// Mark dim for removal by setting to 0.
input_dims[axis] = 0;
@ -3700,13 +3700,59 @@ Status ConvertGather(OpConverterParams* params) {
int trt_axis = 0;
TF_RETURN_IF_ERROR(ConvertAxis(axis[0], inputs.at(0).GetTrtDims().nbDims,
node_def.name(), &trt_axis));
TRT_TensorOrWeights params_tensor = inputs.at(0);
TRT_TensorOrWeights indices_tensor = inputs.at(1);
if (indices_tensor.batch_size() != 1) {
return errors::InvalidArgument("Only indices with batch 1 are supported.");
}
// Both input are tensors, and the TF gather result will have rank:
// (params.nbDims + 1) + (indices.nbDims + 1) - 1,
// where "+ 1" adds the batch dim.
const int tf_gather_output_rank = params_tensor.GetTrtDims().nbDims +
indices_tensor.GetTrtDims().nbDims + 1;
if (tf_gather_output_rank > nvinfer1::Dims::MAX_DIMS + 1) {
return errors::InvalidArgument(
"Result of gather has dimension greater than ",
nvinfer1::Dims::MAX_DIMS + 1);
}
if (params->validation_only) return Status::OK();
// Note on how IGatherLayer works: if both the data and indices tensors have
// a batch size dimension of size N, it performs:
// for batchid in xrange(N):
// output[batchid, a0, ..., an, i, ..., j, b0, ..., bn] = (
// data[batchid, a0, ..., an, indices[batchid, i, ..., j] b0, ..., bn])
nvinfer1::IGatherLayer* layer = params->converter->network()->addGather(
*const_cast<nvinfer1::ITensor*>(inputs.at(0).tensor()),
*const_cast<nvinfer1::ITensor*>(inputs.at(1).tensor()), trt_axis);
*const_cast<nvinfer1::ITensor*>(params_tensor.tensor()),
*const_cast<nvinfer1::ITensor*>(indices_tensor.tensor()), trt_axis);
TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name());
params->outputs->push_back(TRT_TensorOrWeights(layer->getOutput(0)));
nvinfer1::ITensor* gather_output = layer->getOutput(0);
nvinfer1::Dims trt_gather_output_dims = gather_output->getDimensions();
// Note for the "- 2": one is for the output batch dim encapsulated by TF-TRT,
// and the other is for the output dimension that is squeezed by IGatherLayer
// because of the implicit batch dim in the indices (see the above note).
if (trt_gather_output_dims.nbDims != tf_gather_output_rank - 2) {
return errors::Internal(
"Get unexpected output dimensions of IGatherLayer. Expect nbDims: ",
tf_gather_output_rank - 2,
", actual nbDims: ", trt_gather_output_dims.nbDims);
}
// Reshape the output so after adding the implicit batch dim it'll match the
// output shape of TF GatherV2.
for (int i = trt_gather_output_dims.nbDims; i > trt_axis; --i) {
trt_gather_output_dims.d[i] = trt_gather_output_dims.d[i - 1];
}
trt_gather_output_dims.d[trt_axis] = 1;
++trt_gather_output_dims.nbDims;
const nvinfer1::ITensor* output_tensor = nullptr;
TF_RETURN_IF_ERROR(params->converter->PrepareTensorForShape(
TRT_TensorOrWeights(gather_output), trt_gather_output_dims,
/*validation_only=*/false, &output_tensor));
params->outputs->push_back(
TRT_TensorOrWeights(const_cast<nvinfer1::ITensor*>(output_tensor)));
return Status::OK();
}

27
tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc
View File

@ -3400,14 +3400,23 @@ void TestConvertGather(OpConverterTest* test) {
// Input is the same {1, 2, 3, 4, 5, 6} for all cases.
const int kGatherOKCases = 5;
const std::vector<CType> params_input = {CType(1), CType(2), CType(3),
CType(4), CType(5), CType(6)};
TestParams ok_params[kGatherOKCases] = {
// Vector indices (output is rank(params)).
TestParams{{1, 2, 3}, {1}, {0}, 3, {1, 2, 1}, {1, 4}},
TestParams{{1, 2, 3}, {1}, {1}, 3, {1, 2, 1}, {2, 5}},
TestParams{{1, 2, 3}, {1}, {2}, -1, {1, 2, 1}, {3, 6}},
TestParams{{1, 2, 3}, {3}, {2, 0, 1}, 3, {1, 2, 3}, {3, 1, 2, 6, 4, 5}},
// Higher rank indices (output is rank(params) + rank(indices) - 1).
TestParams{{1, 2, 3}, {1, 1}, {0}, 2, {1, 1, 1, 3}, {1, 2, 3}},
// Indices are always of rank>1, and output rank is
// rank(params) + rank(indices) - 1.
// TODO(laigd): do we support 0-rank ITensor as indices?
TestParams{{1, 2, 3}, {1}, {0}, 3, {1, 2, 1, 1}, {1, 4}},
TestParams{{1, 2, 3}, {1}, {1}, 3, {1, 2, 1, 1}, {2, 5}},
TestParams{{1, 2, 3}, {1}, {2}, -1, {1, 2, 1, 1}, {3, 6}},
TestParams{
{1, 2, 3}, {3}, {2, 0, 1}, 3, {1, 2, 1, 3}, {3, 1, 2, 6, 4, 5}},
TestParams{{3, 2},
{2, 2},
{0, 0, 1, 0},
2,
{3, 1, 2, 2},
{1, 1, 2, 1, 3, 3, 4, 3, 5, 5, 6, 5}},
};
// Ok.
@ -3426,14 +3435,12 @@ void TestConvertGather(OpConverterTest* test) {
output.tensor()->getDimensions());
// Create input in CType and convert expected output to CType.
std::vector<CType> inputs = {CType(1), CType(2), CType(3),
CType(4), CType(5), CType(6)};
std::vector<CType> converted_expected_output(
ok_params[i].expected_output.begin(),
ok_params[i].expected_output.end());
const DataVec input_data{
{"params", test::AsTensor<CType>(inputs)},
{"params", test::AsTensor<CType>(params_input)},
{"indices", test::AsTensor<int32>(ok_params[i].indices)}};
DataVec output_data{
{"my_gather",

6
tensorflow/lite/experimental/examples/lstm/BUILD
View File

@ -72,7 +72,7 @@ py_test(
"//tensorflow/lite/python:lite",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:platform",
"//tensorflow/python/tools:optimize_for_inference",
"//tensorflow/python/tools:optimize_for_inference_main_lib",
"//third_party/py/numpy",
"@six_archive//:six",
],
@ -95,7 +95,7 @@ py_test(
"//tensorflow/lite/python:lite",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:platform",
"//tensorflow/python/tools:optimize_for_inference",
"//tensorflow/python/tools:optimize_for_inference_main_lib",
"//third_party/py/numpy",
"@six_archive//:six",
],
@ -118,7 +118,7 @@ py_test(
"//tensorflow/lite/python:lite",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:platform",
"//tensorflow/python/tools:optimize_for_inference",
"//tensorflow/python/tools:optimize_for_inference_main_lib",
"//third_party/py/numpy",
"@six_archive//:six",
],

446
tensorflow/opensource_only.files
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@ -1,245 +1,245 @@
tensorflow/contrib/tpu/profiler/pip_package/BUILD
tensorflow/contrib/tpu/profiler/pip_package/setup.py
tensorflow/contrib/tpu/profiler/pip_package/README
tensorflow/contrib/tpu/profiler/pip_package/build_pip_package.sh
tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py
tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/__init__.py
tensorflow/contrib/mpi/BUILD
tensorflow/tools/ci_build/remote/BUILD
tensorflow/tools/pip_package/README
tensorflow/tools/pip_package/MANIFEST.in
tensorflow/tools/pip_package/simple_console.py
tensorflow/tools/pip_package/build_pip_package.sh
tensorflow/tools/pip_package/check_load_py_test.py
tensorflow/tools/pip_package/pip_smoke_test.py
tensorflow/tools/pip_package/simple_console_for_windows.py
tensorflow/tools/pip_package/setup.py
tensorflow/tools/pip_package/BUILD
tensorflow/tools/lib_package/concat_licenses.sh
tensorflow/tools/lib_package/libtensorflow_test.c
tensorflow/tools/lib_package/LibTensorFlowTest.java
tensorflow/tools/lib_package/BUILD
tensorflow/tools/lib_package/libtensorflow_test.sh
tensorflow/tools/lib_package/README.md
tensorflow/tools/lib_package/libtensorflow_java_test.sh
tensorflow/tools/def_file_filter/def_file_filter_configure.bzl
tensorflow/tools/def_file_filter/BUILD
tensorflow/tools/def_file_filter/BUILD.tpl
tensorflow/tools/def_file_filter/def_file_filter.py.tpl
tensorflow/third_party/mkl/MKL_LICENSE
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tensorflow/third_party/eigen3/unsupported/Eigen/MatrixFunctions
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4
tensorflow/python/debug/BUILD
View File

@ -47,7 +47,7 @@ py_library(
":cli_test_utils",
":debug_py",
":grpc_debug_test_server",
":grpc_tensorflow_server",
":grpc_tensorflow_server_lib",
":offline_analyzer_lib",
":session_debug_testlib",
":source_remote",
@ -1124,7 +1124,7 @@ cuda_py_test(
"//tensorflow/python:platform_test",
"//tensorflow/python:variables",
],
data = [":grpc_tensorflow_server"],
data = [":grpc_tensorflow_server_lib"],
grpc_enabled = True,
tags = [
"no_oss", # Incompatible with bazel_pip.

10
tensorflow/python/keras/layers/recurrent.py
View File

@ -833,10 +833,14 @@ class RNN(Layer):
def reset_states(self, states=None):
if not self.stateful:
raise AttributeError('Layer must be stateful.')
if self.time_major:
batch_size = self.input_spec[0].shape[1]
spec_shape = None if self.input_spec is None else self.input_spec[0].shape
if spec_shape is None:
# It is possible to have spec shape to be None, eg when construct a RNN
# with a custom cell, or standard RNN layers (LSTM/GRU) which we only know
# it has 3 dim input, but not its full shape spec before build().
batch_size = None
else:
batch_size = self.input_spec[0].shape[0]
batch_size = spec_shape[1] if self.time_major else spec_shape[0]
if not batch_size:
raise ValueError('If a RNN is stateful, it needs to know '
'its batch size. Specify the batch size '

11
tensorflow/python/keras/layers/recurrent_test.py
View File

@ -1315,6 +1315,17 @@ class RNNTest(keras_parameterized.TestCase):
model = keras.Model([inputs, state_h, state_c], decoder_out)
model.reset_states()
def test_reset_states(self):
# See https://github.com/tensorflow/tensorflow/issues/25852
with self.assertRaisesRegexp(ValueError, 'it needs to know its batch size'):
simple_rnn = keras.layers.SimpleRNN(1, stateful=True)
simple_rnn.reset_states()
with self.assertRaisesRegexp(ValueError, 'it needs to know its batch size'):
cell = Minimal2DRNNCell(1, 2)
custom_rnn = keras.layers.RNN(cell, stateful=True)
custom_rnn.reset_states()
class Minimal2DRNNCell(keras.layers.Layer):
"""The minimal 2D RNN cell is a simple combination of 2 1-D RNN cell.