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clock_gettime() failedRUNNING_ON_VALGRINDVALGRIND_SLOWDOWNN10tensorflow4core10RefCountedE???????? ??????????????? ??????????????????? ?????????????????????????????????????????????????????????????????????????????`???????????????????????????????????????????????????????????????0??? ???0???@???????????????????0???@???????P???????`???p???0???@???h???p???x??????????????????????????????????????????????????????????????????????????????? ??????????????????????????? ???????????????????????????????????????????????????????????????????P???0???@???????P???????@???????????0???@???????P???????????P???????`???p???????????????tensorflow/contrib/rnn/kernels/lstm_ops.cccs_prev.dims(0) != batch_size: cs_prev.dims(1) != cell_size: h_prev.dims(0) != batch_size: w.dim_size(0) != input_size + cell_size: w.dim_size(1) != cell_size * 4: b.dim_size(0) != cell_size * 4: LSTMBlockCellOpLSTMBlockCellGradOpBlockLSTMOpBlockLSTMGradOpCheck failed: res.CopyFrom(t.Slice(pos, pos + 1), {t.dim_size(1), t.dim_size(2)}) ./tensorflow/core/framework/tensor.hnew_num_elements == NumElements()cs.dim_size(0) != batch_size: ci.dim_size(0) != batch_size: co.dim_size(0) != batch_size: cs_grad_tensor.dims(0) != batch_size: cs_grad_tensor.dims(1) != cell_size: h_grad_tensor.dims(0) != batch_size: h_grad_tensor.dims(1) != cell_size: Check failed: copy_out_.empty() Check failed: !entry.second.second . This is unnecessary copying. Consider using shapes with even Check failed: !found->second.second Check failed: aligned.shape().IsSameSize(t.shape()) ctx_->allocate_temp(t.dtype(), t.shape(), &aligned)w and b cell_size don't match: BlockLSTMOp is inefficient when both batch_size and input_size are odd. You are using: batch_size=cell_size are odd. You are using: batch_size=wci.dim_size(0) != cell_size: wcf.dim_size(0) != cell_size: wco.dim_size(0) != cell_size: ./tensorflow/core/framework/shape_inference.h
Computes the LSTM cell forward propagation for 1 time step.

This implementation uses 1 weight matrix and 1 bias vector, and there's an
optional peephole connection.

This kernel op implements the following mathematical equations:

```python
xh = [x, h_prev]
[i, f, ci, o] = xh * w + b
f = f + forget_bias

if not use_peephole:
  wci = wcf = wco = 0

i = sigmoid(cs_prev * wci + i)
f = sigmoid(cs_prev * wcf + f)
ci = tanh(ci)

cs = ci .* i + cs_prev .* f
cs = clip(cs, cell_clip)

o = sigmoid(cs * wco + o)
co = tanh(cs)
h = co .* o
```

cell_clip: Value to clip the 'cs' value to.
use_peephole: Whether to use peephole weights.
forget_bias: The forget gate bias.

x: The input to the LSTM cell, shape (batch_size, num_inputs).
cs_prev: Value of the cell state at previous time step.
h_prev: Output of the previous cell at previous time step.
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.

i: The input gate.
cs: The cell state before the tanh.
f: The forget gate.
o: The output gate.
ci: The cell input.
co: The cell after the tanh.
h: The output h vector.

Computes the LSTM cell backward propagation for 1 timestep.

This implementation is to be used in conjunction of LSTMBlockCell.

use_peephole: Whether the cell uses peephole connections.
x: The input to the LSTM cell, shape (batch_size, num_inputs).
cs_prev: The previous cell state.
h_prev: The previous h state.
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.
i: The input gate.
cs: The cell state before the tanh.
f: The forget gate.
o: The output gate.
ci: The cell input.
co: The cell after the tanh.
cs_grad: The current gradient of cs.
h_grad: The gradient of h vector.
cs_prev_grad: The gradient of cs to be back-propped.
dicfo: The derivative wrt to [i, cs, f, o].
wci_grad: The gradient for wci to be back-propped.
wcf_grad: The gradient for wcf to be back-propped.
wco_grad: The gradient for wco to be back-propped.

Computes the LSTM cell forward propagation for all the time steps.

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)
```

cell_clip: Value to clip the 'cs' value to.
use_peephole: Whether to use peephole weights.
forget_bias: The forget gate bias.

seq_len_max: Maximum time length actually used by this input. Outputs are padded
  with zeros beyond this length.
x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
cs_prev: Value of the initial cell state.
h_prev: Initial output of cell (to be used for peephole).
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.

i: The input gate over the whole time sequence.
cs: The cell state before the tanh over the whole time sequence.
f: The forget gate over the whole time sequence.
o: The output gate over the whole time sequence.
ci: The cell input over the whole time sequence.
co: The cell after the tanh over the whole time sequence.
h: The output h vector over the whole time sequence.

Computes the LSTM cell backward propagation for the entire time sequence.

This implementation is to be used in conjunction of LSTMBlock.

use_peephole: Whether to use peephole weights.

seq_len_max: Maximum time length actually used by this input. Outputs are padded
  with zeros beyond this length.
x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
cs_prev: Value of the initial cell state.
h_prev: Initial output of cell (to be used for peephole).
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.
i: The input gate over the whole time sequence.
cs: The cell state before the tanh over the whole time sequence.
f: The forget gate over the whole time sequence.
o: The output gate over the whole time sequence.
ci: The cell input over the whole time sequence.
co: The cell after the tanh over the whole time sequence.
h: The output h vector over the whole time sequence.
cs_grad: The current gradient of cs.
h_grad: The gradient of h vector.

x_grad: The gradient of x to be back-propped.
cs_prev_grad: The gradient of cs_prev to be back-propped.
h_prev_grad: The gradient of h_prev to be back-propped.
w_grad: The gradient for w to be back-propped.
wci_grad: The gradient for wci to be back-propped.
wcf_grad: The gradient for wcf to be back-propped.
wco_grad: The gradient for wco to be back-propped.
b_grad: The gradient for w to be back-propped.
external/com_google_absl/absl/strings/escaping.ccThis can't happen; base64 decoder state = %d\x cannot be followed by a non-hex digit\u must be followed by 4 hex digits: \invalid surrogate character (0xD800-DFFF): \\U must be followed by 8 hex digits: \ exceeds Unicode limit (0x10FFFF)Output pointer must not be nullptr.external/com_google_absl/absl/strings/numbers.ccexternal/com_google_absl/absl/strings/str_split.ccbasic_string::_S_construct null not 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G