diff --git a/tensorflow/core/api_def/base_api/api_def_BlockLSTMGradV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_BlockLSTMGradV2.pbtxt
new file mode 100644
index 00000000000..638d1549804
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_BlockLSTMGradV2.pbtxt
@@ -0,0 +1,171 @@
+op {
+ graph_op_name: "BlockLSTMGradV2"
+ visibility: HIDDEN
+ in_arg {
+ name: "seq_len_max"
+ description: <<END
+Maximum time length actually used by this input. Outputs are padded
+with zeros beyond this length.
+END
+ }
+ in_arg {
+ name: "x"
+ description: <<END
+The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
+END
+ }
+ in_arg {
+ name: "cs_prev"
+ description: <<END
+Value of the initial cell state.
+END
+ }
+ in_arg {
+ name: "h_prev"
+ description: <<END
+Initial output of cell (to be used for peephole).
+END
+ }
+ in_arg {
+ name: "w"
+ description: <<END
+The weight matrix.
+END
+ }
+ in_arg {
+ name: "wci"
+ description: <<END
+The weight matrix for input gate peephole connection.
+END
+ }
+ in_arg {
+ name: "wcf"
+ description: <<END
+The weight matrix for forget gate peephole connection.
+END
+ }
+ in_arg {
+ name: "wco"
+ description: <<END
+The weight matrix for output gate peephole connection.
+END
+ }
+ in_arg {
+ name: "b"
+ description: <<END
+The bias vector.
+END
+ }
+ in_arg {
+ name: "i"
+ description: <<END
+The input gate over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "cs"
+ description: <<END
+The cell state before the tanh over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "f"
+ description: <<END
+The forget gate over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "o"
+ description: <<END
+The output gate over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "ci"
+ description: <<END
+The cell input over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "co"
+ description: <<END
+The cell after the tanh over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "h"
+ description: <<END
+The output h vector over the whole time sequence.
+END
+ }
+ in_arg {
+ name: "cs_grad"
+ description: <<END
+The current gradient of cs.
+END
+ }
+ in_arg {
+ name: "h_grad"
+ description: <<END
+The gradient of h vector.
+END
+ }
+ out_arg {
+ name: "x_grad"
+ description: <<END
+The gradient of x to be back-propped.
+END
+ }
+ out_arg {
+ name: "cs_prev_grad"
+ description: <<END
+The gradient of cs_prev to be back-propped.
+END
+ }
+ out_arg {
+ name: "h_prev_grad"
+ description: <<END
+The gradient of h_prev to be back-propped.
+END
+ }
+ out_arg {
+ name: "w_grad"
+ description: <<END
+The gradient for w to be back-propped.
+END
+ }
+ out_arg {
+ name: "wci_grad"
+ description: <<END
+The gradient for wci to be back-propped.
+END
+ }
+ out_arg {
+ name: "wcf_grad"
+ description: <<END
+The gradient for wcf to be back-propped.
+END
+ }
+ out_arg {
+ name: "wco_grad"
+ description: <<END
+The gradient for wco to be back-propped.
+END
+ }
+ out_arg {
+ name: "b_grad"
+ description: <<END
+The gradient for w to be back-propped.
+END
+ }
+ attr {
+ name: "use_peephole"
+ description: <<END
+Whether to use peephole weights.
+END
+ }
+ summary: "Computes the LSTM cell backward propagation for the entire time sequence."
+ description: <<END
+This implementation is to be used in conjunction of BlockLSTMV2.
+END
+}
diff --git a/tensorflow/core/api_def/base_api/api_def_BlockLSTMV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_BlockLSTMV2.pbtxt
new file mode 100644
index 00000000000..4da9ebaf863
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_BlockLSTMV2.pbtxt
@@ -0,0 +1,137 @@
+op {
+ graph_op_name: "BlockLSTMV2"
+ visibility: HIDDEN
+ in_arg {
+ name: "seq_len_max"
+ description: <<END
+Maximum time length actually used by this input. Outputs are padded
+with zeros beyond this length.
+END
+ }
+ in_arg {
+ name: "x"
+ description: <<END
+The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
+END
+ }
+ in_arg {
+ name: "cs_prev"
+ description: <<END
+Value of the initial cell state.
+END
+ }
+ in_arg {
+ name: "h_prev"
+ description: <<END
+Initial output of cell (to be used for peephole).
+END
+ }
+ in_arg {
+ name: "w"
+ description: <<END
+The weight matrix.
+END
+ }
+ in_arg {
+ name: "wci"
+ description: <<END
+The weight matrix for input gate peephole connection.
+END
+ }
+ in_arg {
+ name: "wcf"
+ description: <<END
+The weight matrix for forget gate peephole connection.
+END
+ }
+ in_arg {
+ name: "wco"
+ description: <<END
+The weight matrix for output gate peephole connection.
+END
+ }
+ in_arg {
+ name: "b"
+ description: <<END
+The bias vector.
+END
+ }
+ out_arg {
+ name: "i"
+ description: <<END
+The input gate over the whole time sequence.
+END
+ }
+ out_arg {
+ name: "cs"
+ description: <<END
+The cell state before the tanh over the whole time sequence.
+END
+ }
+ out_arg {
+ name: "f"
+ description: <<END
+The forget gate over the whole time sequence.
+END
+ }
+ out_arg {
+ name: "o"
+ description: <<END
+The output gate over the whole time sequence.
+END
+ }
+ out_arg {
+ name: "ci"
+ description: <<END
+The cell input over the whole time sequence.
+END
+ }
+ out_arg {
+ name: "co"
+ description: <<END
+The cell after the tanh over the whole time sequence.
+END
+ }
+ out_arg {
+ name: "h"
+ description: <<END
+The output h vector over the whole time sequence.
+END
+ }
+ attr {
+ name: "cell_clip"
+ description: <<END
+Value to clip the 'cs' value to.
+END
+ }
+ attr {
+ name: "use_peephole"
+ description: <<END
+Whether to use peephole weights.
+END
+ }
+ summary: "Computes the LSTM cell forward propagation for all the time steps."
+ description: <<END
+This is equivalent to applying LSTMBlockCell in a loop, like so:
+
+```python
+for x1 in unpack(x):
+ i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
+ x1, cs_prev, h_prev, w, wci, wcf, wco, b)
+ cs_prev = cs1
+ h_prev = h1
+ i.append(i1)
+ cs.append(cs1)
+ f.append(f1)
+ o.append(o1)
+ ci.append(ci1)
+ co.append(co1)
+ h.append(h1)
+return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)
+
+Note that unlike LSTMBlockCell (and BlockLSTM) which uses ICFO gate layout,
+this op uses IFCO. So in order for the following snippet to be equivalent
+all gate-related outputs should be reordered.
+```
+END
+}
diff --git a/tensorflow/core/kernels/rnn/lstm_ops.cc b/tensorflow/core/kernels/rnn/lstm_ops.cc
index 57d3e9b1323..dfd77f9ca5f 100644
--- a/tensorflow/core/kernels/rnn/lstm_ops.cc
+++ b/tensorflow/core/kernels/rnn/lstm_ops.cc
@@ -235,7 +235,9 @@ void LSTMBlockCellBpropWithEigen(
template struct LSTMBlockCellBprop<CPUDevice, T, false /* USE_CUBLAS */, \
GATE_LAYOUT>;
-#define DECLARE_CPU_SPECS(T) DECLARE_CPU_FBPROP(T, ICFO);
+#define DECLARE_CPU_SPECS(T) \
+ DECLARE_CPU_FBPROP(T, ICFO); \
+ DECLARE_CPU_FBPROP(T, IFCO);
DECLARE_CPU_SPECS(Eigen::half);
DECLARE_CPU_SPECS(float);
@@ -827,7 +829,12 @@ template <typename Device, typename T, bool USE_CUBLAS, GateLayout gate_layout>
class BlockLSTMOp : public OpKernel {
public:
explicit BlockLSTMOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
- OP_REQUIRES_OK(ctx, ctx->GetAttr("forget_bias", &forget_bias_));
+ if (ctx->HasAttr("forget_bias")) {
+ OP_REQUIRES_OK(ctx, ctx->GetAttr("forget_bias", &forget_bias_));
+ } else {
+ // V2 version does not have "forget_bias" attribute.
+ forget_bias_ = 0.0;
+ }
OP_REQUIRES_OK(ctx, ctx->GetAttr("cell_clip", &cell_clip_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("use_peephole", &use_peephole_));
}
@@ -1010,10 +1017,13 @@ class BlockLSTMOp : public OpKernel {
bool use_peephole_;
};
-#define REGISTER_KERNEL(T) \
- REGISTER_KERNEL_BUILDER( \
- Name("BlockLSTM").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
- BlockLSTMOp<CPUDevice, T, false, ICFO>);
+#define REGISTER_KERNEL(T) \
+ REGISTER_KERNEL_BUILDER( \
+ Name("BlockLSTM").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+ BlockLSTMOp<CPUDevice, T, false, ICFO>); \
+ REGISTER_KERNEL_BUILDER( \
+ Name("BlockLSTMV2").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+ BlockLSTMOp<CPUDevice, T, false, IFCO>);
REGISTER_KERNEL(Eigen::half);
REGISTER_KERNEL(float);
@@ -1039,12 +1049,17 @@ DECLARE_GPU_SPECS(float);
#undef DECLARE_GPU_SPECS
} // end namespace functor
-#define REGISTER_GPU_KERNEL(T) \
- REGISTER_KERNEL_BUILDER(Name("BlockLSTM") \
- .Device(DEVICE_GPU) \
- .HostMemory("seq_len_max") \
- .TypeConstraint<T>("T"), \
- BlockLSTMOp<GPUDevice, T, true, ICFO>);
+#define REGISTER_GPU_KERNEL(T) \
+ REGISTER_KERNEL_BUILDER(Name("BlockLSTM") \
+ .Device(DEVICE_GPU) \
+ .HostMemory("seq_len_max") \
+ .TypeConstraint<T>("T"), \
+ BlockLSTMOp<GPUDevice, T, true, ICFO>); \
+ REGISTER_KERNEL_BUILDER(Name("BlockLSTMV2") \
+ .Device(DEVICE_GPU) \
+ .HostMemory("seq_len_max") \
+ .TypeConstraint<T>("T"), \
+ BlockLSTMOp<GPUDevice, T, true, IFCO>);
REGISTER_GPU_KERNEL(Eigen::half);
REGISTER_GPU_KERNEL(float);
@@ -1284,10 +1299,13 @@ class BlockLSTMGradOp : public OpKernel {
bool use_peephole_;
};
-#define REGISTER_KERNEL(T) \
- REGISTER_KERNEL_BUILDER( \
- Name("BlockLSTMGrad").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
- BlockLSTMGradOp<CPUDevice, T, false, ICFO>);
+#define REGISTER_KERNEL(T) \
+ REGISTER_KERNEL_BUILDER( \
+ Name("BlockLSTMGrad").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+ BlockLSTMGradOp<CPUDevice, T, false, ICFO>); \
+ REGISTER_KERNEL_BUILDER( \
+ Name("BlockLSTMGradV2").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+ BlockLSTMGradOp<CPUDevice, T, false, IFCO>);
REGISTER_KERNEL(Eigen::half);
REGISTER_KERNEL(float);
@@ -1345,7 +1363,8 @@ namespace functor {
extern template struct TensorCopy<GPUDevice, T>; \
extern template struct TensorAdd<GPUDevice, T>; \
\
- DECLARE_GPU_BPROP(T, ICFO);
+ DECLARE_GPU_BPROP(T, ICFO); \
+ DECLARE_GPU_BPROP(T, IFCO);
DECLARE_GPU_SPECS(Eigen::half);
DECLARE_GPU_SPECS(float);
@@ -1353,12 +1372,17 @@ DECLARE_GPU_SPECS(float);
#undef DECLARE_GPU_BPROP
} // end namespace functor
-#define REGISTER_GPU_KERNEL(T) \
- REGISTER_KERNEL_BUILDER(Name("BlockLSTMGrad") \
- .Device(DEVICE_GPU) \
- .HostMemory("seq_len_max") \
- .TypeConstraint<T>("T"), \
- BlockLSTMGradOp<GPUDevice, T, true, ICFO>);
+#define REGISTER_GPU_KERNEL(T) \
+ REGISTER_KERNEL_BUILDER(Name("BlockLSTMGrad") \
+ .Device(DEVICE_GPU) \
+ .HostMemory("seq_len_max") \
+ .TypeConstraint<T>("T"), \
+ BlockLSTMGradOp<GPUDevice, T, true, ICFO>); \
+ REGISTER_KERNEL_BUILDER(Name("BlockLSTMGradV2") \
+ .Device(DEVICE_GPU) \
+ .HostMemory("seq_len_max") \
+ .TypeConstraint<T>("T"), \
+ BlockLSTMGradOp<GPUDevice, T, true, IFCO>);
REGISTER_GPU_KERNEL(Eigen::half);
REGISTER_GPU_KERNEL(float);
diff --git a/tensorflow/core/kernels/rnn/lstm_ops_gpu.cu.cc b/tensorflow/core/kernels/rnn/lstm_ops_gpu.cu.cc
index f3f37858986..3c1ea27b1ea 100644
--- a/tensorflow/core/kernels/rnn/lstm_ops_gpu.cu.cc
+++ b/tensorflow/core/kernels/rnn/lstm_ops_gpu.cu.cc
@@ -468,7 +468,8 @@ void LSTMBlockCellBpropWithCUDA(
template struct TensorCopyToUnaligned<GPUDevice, T>; \
template struct TensorAdd<GPUDevice, T>; \
\
- DECLARE_GPU_FBPROP(T, ICFO);
+ DECLARE_GPU_FBPROP(T, ICFO); \
+ DECLARE_GPU_FBPROP(T, IFCO);
DECLARE_GPU_SPECS(Eigen::half);
DECLARE_GPU_SPECS(float);
diff --git a/tensorflow/core/ops/rnn_ops.cc b/tensorflow/core/ops/rnn_ops.cc
index b926feb9d2e..af5dc3d26d1 100644
--- a/tensorflow/core/ops/rnn_ops.cc
+++ b/tensorflow/core/ops/rnn_ops.cc
@@ -199,6 +199,45 @@ REGISTER_OP("BlockLSTM")
return Status::OK();
});
+REGISTER_OP("BlockLSTMV2")
+ .Input("seq_len_max: int64")
+ .Input("x: T")
+ .Input("cs_prev: T")
+ .Input("h_prev: T")
+ .Input("w: T")
+ .Input("wci: T")
+ .Input("wcf: T")
+ .Input("wco: T")
+ .Input("b: T")
+ .Output("i: T")
+ .Output("cs: T")
+ .Output("f: T")
+ .Output("o: T")
+ .Output("ci: T")
+ .Output("co: T")
+ .Output("h: T")
+ .Attr("cell_clip: float = 0.0")
+ .Attr("use_peephole: bool = false")
+ .Attr("T: {half, float}")
+ .SetShapeFn([](InferenceContext* c) {
+ ShapeHandle x, b;
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &x));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 1, &b));
+
+ DimensionHandle timelen = c->Dim(x, 0);
+ DimensionHandle batch_size = c->Dim(x, 1);
+ DimensionHandle cell_size;
+ TF_RETURN_IF_ERROR(
+ c->Divide(c->Dim(b, 0), 4, true /* evenly_divisible */, &cell_size));
+
+ DCHECK_EQ(7, c->num_outputs());
+ ShapeHandle output = c->MakeShape({timelen, batch_size, cell_size});
+ for (int i = 0; i < 7; ++i) {
+ c->set_output(i, output);
+ }
+ return Status::OK();
+ });
+
REGISTER_OP("BlockLSTMGrad")
.Input("seq_len_max: int64")
.Input("x: T")
@@ -251,4 +290,56 @@ REGISTER_OP("BlockLSTMGrad")
return Status::OK();
});
+REGISTER_OP("BlockLSTMGradV2")
+ .Input("seq_len_max: int64")
+ .Input("x: T")
+ .Input("cs_prev: T")
+ .Input("h_prev: T")
+ .Input("w: T")
+ .Input("wci: T")
+ .Input("wcf: T")
+ .Input("wco: T")
+ .Input("b: T")
+ .Input("i: T")
+ .Input("cs: T")
+ .Input("f: T")
+ .Input("o: T")
+ .Input("ci: T")
+ .Input("co: T")
+ .Input("h: T")
+ .Input("cs_grad: T")
+ .Input("h_grad: T")
+ .Output("x_grad: T")
+ .Output("cs_prev_grad: T")
+ .Output("h_prev_grad: T")
+ .Output("w_grad: T")
+ .Output("wci_grad: T")
+ .Output("wcf_grad: T")
+ .Output("wco_grad: T")
+ .Output("b_grad: T")
+ .Attr("use_peephole: bool")
+ .Attr("T: {half, float}")
+ .SetShapeFn([](InferenceContext* c) {
+ ShapeHandle x, cs_prev, h_prev, w, wci, wco, wcf, b;
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &x));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 2, &cs_prev));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 2, &h_prev));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 2, &w));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 1, &wci));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 1, &wco));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(7), 1, &wcf));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(8), 1, &b));
+
+ c->set_output(0, x);
+ c->set_output(1, cs_prev);
+ c->set_output(2, h_prev);
+ c->set_output(3, w);
+ c->set_output(4, wci);
+ c->set_output(5, wco);
+ c->set_output(6, wcf);
+ c->set_output(7, b);
+
+ return Status::OK();
+ });
+
} // end namespace tensorflow
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 996c31b8635..77a21caaf63 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3691,6 +3691,24 @@ py_library(
],
)
+py_test(
+ name = "rnn_grad_test",
+ srcs = ["ops/rnn_grad_test.py"],
+ python_version = "PY3",
+ deps = [
+ ":array_ops",
+ ":client_testlib",
+ ":dtypes",
+ ":framework_ops",
+ ":framework_test_lib",
+ ":gradients",
+ ":math_ops",
+ ":rnn_grad",
+ ":rnn_ops_gen",
+ "//third_party/py/numpy",
+ ],
+)
+
py_library(
name = "standard_ops",
srcs = ["ops/standard_ops.py"],
diff --git a/tensorflow/python/ops/rnn_grad.py b/tensorflow/python/ops/rnn_grad.py
index f2707e178b0..e316b7fb8a1 100644
--- a/tensorflow/python/ops/rnn_grad.py
+++ b/tensorflow/python/ops/rnn_grad.py
@@ -21,7 +21,6 @@ from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_rnn_ops
-@ops.RegisterGradient("BlockLSTM")
def _block_lstm_grad(op, *grads):
"""Gradient for the BlockLSTM op."""
seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b = op.inputs
@@ -50,3 +49,7 @@ def _block_lstm_grad(op, *grads):
use_peephole=op.get_attr("use_peephole"))
return (None, x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad,
wco_grad, b_grad)
+
+
+ops.RegisterGradient("BlockLSTM")(_block_lstm_grad)
+ops.RegisterGradient("BlockLSTMV2")(_block_lstm_grad)
diff --git a/tensorflow/python/ops/rnn_grad_test.py b/tensorflow/python/ops/rnn_grad_test.py
new file mode 100644
index 00000000000..2b320234538
--- /dev/null
+++ b/tensorflow/python/ops/rnn_grad_test.py
@@ -0,0 +1,99 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for gradients of (block) LSTM/GRU operations."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+import numpy as np
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_rnn_ops
+from tensorflow.python.ops import gradients
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import rnn_grad # pylint: disable=unused-import
+from tensorflow.python.platform import test
+
+
+class RNNGradTest(test.TestCase):
+
+ @test_util.deprecated_graph_mode_only
+ def testBlockLSTMV1V2Consistency(self):
+ num_steps = 1
+ batch_size = 1
+ input_size = 1
+ hidden_size = 8
+ w = deterministic_random_uniform(
+ [input_size + hidden_size, 4 * hidden_size])
+ b = deterministic_random_uniform([4 * hidden_size])
+ x = deterministic_random_uniform([num_steps, batch_size, input_size])
+ cs_prev = h_prev = deterministic_random_uniform([batch_size, hidden_size])
+
+ all_cs, all_h = self._lstm_block(
+ functools.partial(
+ gen_rnn_ops.BlockLSTM,
+ forget_bias=0.0, # Disable to match V2 default.
+ cell_clip=0.0), # Disable to match V2 default.
+ w, b, x, cs_prev, h_prev)
+ w_grad, b_grad = gradients.gradients(all_cs + all_h, [w, b])
+
+ w_ifco, b_ifco = icfo_to_ifco(w, b)
+ all_cs_ifco, all_h_ifco = self._lstm_block(
+ gen_rnn_ops.BlockLSTMV2, w_ifco, b_ifco, x, cs_prev, h_prev)
+ w_ifco_grad, b_ifco_grad = gradients.gradients(
+ all_cs_ifco + all_h_ifco, [w_ifco, b_ifco])
+
+ self.assertAllEqual(all_cs, all_cs_ifco)
+ self.assertAllEqual(all_h, all_h_ifco)
+ self.assertAllEqual(w_grad, w_ifco_grad)
+ self.assertAllEqual(b_grad, b_ifco_grad)
+
+ def _lstm_block(self, op, w, b, x, cs_prev, h_prev):
+ w_peephole = array_ops.zeros(cs_prev.shape[1:], dtype=w.dtype)
+ _, all_cs, _, _, _, _, all_h = op(
+ seq_len_max=math_ops.cast(array_ops.shape(x)[0], dtypes.int64),
+ x=x,
+ cs_prev=cs_prev,
+ h_prev=h_prev,
+ w=w,
+ wci=w_peephole,
+ wcf=w_peephole,
+ wco=w_peephole,
+ b=b,
+ use_peephole=False)
+ return all_cs, all_h
+
+
+def deterministic_random_uniform(shape):
+ return ops.convert_to_tensor(np.random.random(shape), dtype=dtypes.float32)
+
+
+def icfo_to_ifco(w, b):
+ """Convert gates' weights and biases from ICFO to IFCO layout."""
+ w_i, w_c, w_f, w_o = array_ops.split(w, num_or_size_splits=4, axis=1)
+ b_i, b_c, b_f, b_o = array_ops.split(b, num_or_size_splits=4)
+ w_ifco = array_ops.concat([w_i, w_f, w_c, w_o], axis=1)
+ b_ifco = array_ops.concat([b_i, b_f, b_c, b_o], axis=0)
+ return w_ifco, b_ifco
+
+
+if __name__ == "__main__":
+ test.main()
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt
index 0f9e320753f..1136881b8a5 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt
@@ -472,6 +472,14 @@ tf_module {
name: "BlockLSTMGrad"
argspec: "args=[\'seq_len_max\', \'x\', \'cs_prev\', \'h_prev\', \'w\', \'wci\', \'wcf\', \'wco\', \'b\', \'i\', \'cs\', \'f\', \'o\', \'ci\', \'co\', \'h\', \'cs_grad\', \'h_grad\', \'use_peephole\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
+ member_method {
+ name: "BlockLSTMGradV2"
+ argspec: "args=[\'seq_len_max\', \'x\', \'cs_prev\', \'h_prev\', \'w\', \'wci\', \'wcf\', \'wco\', \'b\', \'i\', \'cs\', \'f\', \'o\', \'ci\', \'co\', \'h\', \'cs_grad\', \'h_grad\', \'use_peephole\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "BlockLSTMV2"
+ argspec: "args=[\'seq_len_max\', \'x\', \'cs_prev\', \'h_prev\', \'w\', \'wci\', \'wcf\', \'wco\', \'b\', \'cell_clip\', \'use_peephole\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'None\'], "
+ }
member_method {
name: "BoostedTreesAggregateStats"
argspec: "args=[\'node_ids\', \'gradients\', \'hessians\', \'feature\', \'max_splits\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt
index 0f9e320753f..1136881b8a5 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt
@@ -472,6 +472,14 @@ tf_module {
name: "BlockLSTMGrad"
argspec: "args=[\'seq_len_max\', \'x\', \'cs_prev\', \'h_prev\', \'w\', \'wci\', \'wcf\', \'wco\', \'b\', \'i\', \'cs\', \'f\', \'o\', \'ci\', \'co\', \'h\', \'cs_grad\', \'h_grad\', \'use_peephole\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
+ member_method {
+ name: "BlockLSTMGradV2"
+ argspec: "args=[\'seq_len_max\', \'x\', \'cs_prev\', \'h_prev\', \'w\', \'wci\', \'wcf\', \'wco\', \'b\', \'i\', \'cs\', \'f\', \'o\', \'ci\', \'co\', \'h\', \'cs_grad\', \'h_grad\', \'use_peephole\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "BlockLSTMV2"
+ argspec: "args=[\'seq_len_max\', \'x\', \'cs_prev\', \'h_prev\', \'w\', \'wci\', \'wcf\', \'wco\', \'b\', \'cell_clip\', \'use_peephole\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'None\'], "
+ }
member_method {
name: "BoostedTreesAggregateStats"
argspec: "args=[\'node_ids\', \'gradients\', \'hessians\', \'feature\', \'max_splits\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "