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Added BlockLSTMV2 and BlockLSTMGradV2 with IFCO layout

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Note also that BlockLSTMV2 does not allow setting forget_bias (fixed at 0.0)
and defaults cell_clip to 0.0 (disabled).

PiperOrigin-RevId: 263492926
This commit is contained in:
Sergei Lebedev 2019-08-14 21:27:53 -07:00 committed by TensorFlower Gardener
parent 3f1deea3cb
commit 722b96b229
10 changed files with 585 additions and 25 deletions
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171
tensorflow/core/api_def/base_api/api_def_BlockLSTMGradV2.pbtxt Normal file
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@ -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
}

137
tensorflow/core/api_def/base_api/api_def_BlockLSTMV2.pbtxt Normal file
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@ -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
}

70
tensorflow/core/kernels/rnn/lstm_ops.cc
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@ -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);

3
tensorflow/core/kernels/rnn/lstm_ops_gpu.cu.cc
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@ -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);

91
tensorflow/core/ops/rnn_ops.cc
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@ -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

18
tensorflow/python/BUILD
View File

@ -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"],

5
tensorflow/python/ops/rnn_grad.py
View File

@ -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)

99
tensorflow/python/ops/rnn_grad_test.py Normal file
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@ -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()

8
tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt
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@ -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\'], "

8
tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt
View File

@ -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\'], "