AndrewZhaoLuo commented on code in PR #12213:
URL: https://github.com/apache/tvm/pull/12213#discussion_r934979601
##########
python/tvm/relay/frontend/onnx.py:
##########
@@ -2756,6 +2757,138 @@ def _activation_needs_beta(cls, activation):
]
return activation.decode("utf-8") in needs_beta
+ @classmethod
+ def bidir_rnn_cell(
+ cls,
+ input_seqs,
+ weight_dicts,
+ acts,
+ ):
+ """
+ Bidirectional RNN cell
+ """
+ seq_len = len(input_seqs)
+ forward_outputs, fw_H_t = rnn_cell(
+ input_seqs,
+ **weight_dicts[0],
+ act=acts[0],
+ )
+
+ reverse_outputs, rev_H_t = rnn_cell(
+ input_seqs,
+ **weight_dicts[1],
+ act=acts[1],
+ backwards=True,
+ )
+
+ final_outputs = []
+ for i in range(seq_len):
+ final_outputs.append(
+ _op.stack([forward_outputs[i], reverse_outputs[seq_len - 1 -
i]], axis=0)
+ )
+
+ return (
+ _op.stack(final_outputs, axis=0),
+ _op.stack([fw_H_t, rev_H_t], axis=0),
+ )
+
+ @classmethod
+ def _impl_v7(cls, inputs, attr, params):
Review Comment:
why target v7 when we have an operator v14?
v7 vs v14 don't seem very different, v14 just cuts out the `hidden_size`
attribute which we dont use anyway
Can we change this to _impl_v14?
##########
python/tvm/relay/frontend/onnx.py:
##########
@@ -2756,6 +2757,138 @@ def _activation_needs_beta(cls, activation):
]
return activation.decode("utf-8") in needs_beta
+ @classmethod
+ def bidir_rnn_cell(
+ cls,
+ input_seqs,
+ weight_dicts,
+ acts,
+ ):
+ """
+ Bidirectional RNN cell
+ """
+ seq_len = len(input_seqs)
+ forward_outputs, fw_H_t = rnn_cell(
+ input_seqs,
+ **weight_dicts[0],
+ act=acts[0],
+ )
+
+ reverse_outputs, rev_H_t = rnn_cell(
+ input_seqs,
+ **weight_dicts[1],
+ act=acts[1],
+ backwards=True,
+ )
+
+ final_outputs = []
+ for i in range(seq_len):
+ final_outputs.append(
+ _op.stack([forward_outputs[i], reverse_outputs[seq_len - 1 -
i]], axis=0)
+ )
+
+ return (
+ _op.stack(final_outputs, axis=0),
+ _op.stack([fw_H_t, rev_H_t], axis=0),
+ )
+
+ @classmethod
+ def _impl_v7(cls, inputs, attr, params):
+ # Unpack inputs, note that if optional and not provided then value
will be None.
+ X = inputs[0]
+ Wp = inputs[1]
+ Rp = inputs[2]
+ Bp = inputs[3]
+ # Sequence length currently unused as it can be inferred from shapes.
+ # sequence_lens = inputs['sequence_lens']
+ Hp_0 = inputs[5]
+
+ num_directions = infer_shape(Wp)[0]
+ W_dtype = infer_type(Wp).checked_type.dtype
+
+ if num_directions not in [1, 2]:
+ raise ValueError("num_directions must be either 1 or 2!")
+
+ X_shape = infer_shape(X)
+ hidden_size = infer_shape(Rp)[-1]
+ batch_size = X_shape[1]
+
+ if Hp_0 is None:
+ Hp_0 = _op.zeros((num_directions, batch_size, hidden_size),
W_dtype)
+
+ if "activations" in attr:
Review Comment:
Can you refactor this as it seems identical to what's in LSTM?
##########
tests/python/frontend/onnx/test_forward.py:
##########
@@ -5212,7 +5341,7 @@ def verify_eyelike(indata, dynamic=False):
"test_reduce_sum_keepdims_random",
"test_reduce_sum_negative_axes_keepdims_example",
"test_reduce_sum_negative_axes_keepdims_random",
- "test_rnn_seq_length",
+ "test_rnn_batchwise",
Review Comment:
Why does this fail?
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