yongwww commented on a change in pull request #5963:
URL: https://github.com/apache/incubator-tvm/pull/5963#discussion_r451332088
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File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -1990,6 +1990,66 @@ def _impl(inputs, attr, params, mod):
return _res
return _impl
+def _LSTMBlockCell():
+ def _impl(inputs, attr, params, mod):
+ """LSTM Block cell.
+ Calculations and return values are described in:
+ https://github.com/tensorflow/tensorflow/blob/
+ r1.8/tensorflow/contrib/rnn/python/ops/lstm_ops.py#L41-L114
+
+ Parameters
+ ----------
+ inputs : relay.Expr
+ Input data
+ in_state_c: list of relay.Expr
+ Cell state input values for all the layers
+ in_state_h: list of relay.Expr
+ Hidden state input values for all the layers
+ attrs : dict
+ Dict of operator attributes
+ params : dict
+ List of pretrained weights and bias
+
+ Returns
+ -------
+ relay.Expr.TupleWapper
+ [dummy, cs, dummy, dummy, dummy, dummy, h]
+ Only cs and h which are useful are returned
+ """
+ in_data = inputs[0]
+ in_state_c = inputs[1]
+ in_state_h = inputs[2]
+ in_weight = inputs[3]
+ in_bias = inputs[7]
+ forget_bias = attr.pop('forget_bias')
+ input_shape = _infer_shape(inputs[0], mod)
+ weight_shape = _infer_shape(inputs[3], mod)
+ batch_size, input_size = input_shape[0], input_shape[1]
+ num_hidden_layers = weight_shape[1]
+
+ in_data = _op.reshape(in_data,
+ newshape=(batch_size, input_size))
+ ixh = _op.concatenate([in_data, in_state_h], axis=1)
+ in_weight = _op.transpose(in_weight, axes=None)
+ gates = _op.nn.dense(ixh, in_weight,
+ units=num_hidden_layers)
+ gates_bias = _op.add(gates, in_bias)
+ gate_list = _op.split(gates_bias, indices_or_sections=4, axis=1)
+ in_gate = _op.sigmoid(gate_list[0])
+ in_transform = _op.tanh(gate_list[1])
+ forget_gate = _op.add(gate_list[2], tvm.relay.const(forget_bias,
attr['T'].name))
+ forget_gate = _op.sigmoid(forget_gate)
+ out_gate = _op.sigmoid(gate_list[3])
+ next_c = _op.add(_op.multiply(forget_gate, in_state_c),
+ _op.multiply(in_gate, in_transform))
+ next_h = out_gate * _op.tanh(next_c)
+ # Return dummy for those unused values
+ dummy = tvm.relay.const(0)
+ return tvm.relay.TupleWrapper(
+ tvm.relay.Tuple([dummy, next_c, dummy, dummy, dummy, dummy,
next_h]), 7)
Review comment:
```
def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1,
cell_clip=3, use_peephole=False, name=None):
r"""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:
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
...
Returns:
A tuple of `Tensor` objects (i, cs, f, o, ci, co, h).
...
```
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