def _get_weight_and_bias_for_lstm_cell(self, context):
match = context.cell_match
w_e = match.get_op("cell_kernel")
w = get_weights_from_const_node(self.g, w_e)
if not w:
return None
# check https://www.tensorflow.org/versions/r1.8/api_docs/cc/class/tensorflow/ops/bias-add
# for bias_add data format
bias_add = match.get_op("bias_add")
if bias_add.data_format != "NHWC":
log.debug("BiasAdd data_format is not NHWC, SKIP")
return None
b_e = match.get_op("cell_bias")
b = get_weights_from_const_node(self.g, b_e)
if not b or b.value.shape[0] != w.value.shape[1]:
log.warning(
"cell_kernel and cell_bias's dimensions does not match, skip")
return None
ft_bias = match.get_op("ft_bias")
ft = get_weights_from_const_node(self.g, ft_bias)
if not ft:
return None
if not b.dtype == ft.dtype:
return None
return RnnWeights(w, b, ft)
def get_weight_and_bias(self, match):
# if one of them is not match, just return
w_e = match.get_op("cell_kernel")
w = get_weights_from_const_node(self.g, w_e)
if not w:
return None
# check https://www.tensorflow.org/versions/r1.8/api_docs/cc/class/tensorflow/ops/bias-add
# for bias_add data format
bias_add = match.get_op("bias_add")
if bias_add.data_format != "NHWC":
log.debug("BiasAdd data_format is not NHWC, SKIP")
return None
b_e = match.get_op("cell_bias")
b = get_weights_from_const_node(self.g, b_e)
if not b or b.value.shape[0] != w.value.shape[1]:
log.warning(
"cell_kernel and cell_bias's dimensions does not match, skip")
return None
ft_bias = match.get_op("ft_bias")
ft = get_weights_from_const_node(self.g, ft_bias)
if not ft:
return None
if not (ft.value == 1 and self.g.get_dtype(b_e.output[0])
== self.g.get_dtype(ft_bias.output[0])):
return None
return RnnWeights(w, b, ft)
def _get_weight_and_bias_for_lstmblock_cell(self, context):
cell_match = context.cell_match
w_node = cell_match.get_op("cell_kernel")
w = get_weights_from_const_node(self.g, w_node)
if not w:
log.warning("Cannot find weight, SKIP")
return None
b_node = cell_match.get_op("cell_bias")
b = get_weights_from_const_node(self.g, b_node)
if not b or b.value.shape[0] != w.value.shape[1]:
log.warning(
"cell_kernel and cell_bias's dimension doesn't match, SKIP")
return None
lstm_block_cell = cell_match.get_op("lstm_block_cell")
ft_bias_val = np.array(lstm_block_cell.get_attr("forget_bias").f,
dtype=b.dtype)
ft_bias = RnnWeight(None, ft_bias_val, ft_bias_val.dtype)
return RnnWeights(w, b, ft_bias)