masahi commented on a change in pull request #8781:
URL: https://github.com/apache/tvm/pull/8781#discussion_r693628005
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File path: python/tvm/relay/frontend/common.py
##########
@@ -658,6 +658,92 @@ def unbind(data, axis=0):
return _expr.TupleWrapper(_expr.Tuple(ret), selections)
+def gru_cell(
+ input_seqs,
+ hidden_state,
+ w_inp,
+ w_hid,
+ b_inp=None,
+ b_hid=None,
+ rz_act=_op.sigmoid,
+ n_act=_op.tanh,
+ backwards=False,
+ linear_before_reset=True,
+):
+ """
+ Common implementation of GRU cell for all frontends of TVM
+ TODO(vvchernov): currently it is used by pytorch. Extend for other
frontends
+
+ Parameters
+ ----------
+ input_seqs : List[relay.Expr]
+ The sequence of input tensors
+ Input tensor should be 2d while issue #8412 is not resolved
+ Shape = (batch, feature_size)
+ hidden_state : relay.Expr
+ Hidden state. shape = (batch_size, hidden_size)
+ w_inp, w_hid : relay.Expr
+ weight matrices. wi shape = (3 * hidden_size, feature_size)
+ wh shape = (3 * hidden_size, hidden_size)
+ NOTE: wi = (w_ir|w_iz|w_in) for reset, update and new gates.
+ The order is important for correct GRU calculation!
+ b_inp, b_hid : relay.Expr
+ bias matrices. The same order of internal parts as for weights. shape
= (3 * hidden_size)
+ r_act : relay.op
+ activation funtion for reset gate. it is sigmoid by default
+ z_act : relay.op
+ activation funtion for update gate. it is sigmoid by default
+ n_act : relay.op
+ activation funtion for new gate. it is tanh by default
+ backwards : bool
+ Flag for reverse pass of GRU
+
+ Returns
+ -------
+ result : List[relay.Expr], relay.Expr, relay.Expr
+ The sequence of computed result, final hidden and cell state
+ """
+
+ outputs_list = []
+ for x_t in input_seqs if not backwards else reversed(input_seqs):
+ xwt = _op.nn.dense(x_t, w_inp)
+ if linear_before_reset:
+ hwt = _op.nn.dense(hidden_state, w_hid)
+ # TODO(vvchernov): It is assumed that both bias are or not
Review comment:
What do you mean by `both bias are or not`?
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