# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
import logging
from typing import Optional
from fusion_attention import AttentionMask
from fusion_bart_attention import FusionBartAttention
from fusion_options import FusionOptions
from fusion_reshape import FusionReshape
from onnx import numpy_helper
from onnx_model import OnnxModel
from onnx_model_bert import BertOnnxModel
logger = logging.getLogger(__name__)
class FusionBartReshape(FusionReshape):
def __init__(self, model: OnnxModel):
super().__init__(model)
def fuse(self, reshape_node, input_name_to_nodes, output_name_to_node):
if reshape_node.input[1] not in output_name_to_node:
return
concat_node = output_name_to_node[reshape_node.input[1]]
if concat_node.op_type != "Concat" or len(concat_node.input) != 4:
return
path0 = self.model.match_parent_path(
concat_node,
["Unsqueeze", "Gather", "Shape"],
[0, 0, 0],
output_name_to_node,
)
if path0 is None:
return
(_, gather_0, shape_0) = path0
shape = []
gather_value = self.model.get_constant_value(gather_0.input[1])
if gather_value == 0:
shape.append(0)
path1 = self.model.match_parent_path(
concat_node,
["Unsqueeze", "Gather", "Shape"],
[1, 0, 0],
output_name_to_node,
)
if path1 is None:
input_1_proto = self.model.get_initializer(concat_node.input[1])
input_2_proto = self.model.get_initializer(concat_node.input[2])
input_3_proto = self.model.get_initializer(concat_node.input[3])
if input_1_proto is None or input_2_proto is None or input_3_proto is None:
return
input_1 = numpy_helper.to_array(input_1_proto)
input_2 = numpy_helper.to_array(input_2_proto)
input_3 = numpy_helper.to_array(input_3_proto)
if len(input_1) != 1 or len(input_2) != 1 or len(input_3) != 1:
return
if not (input_1[0] == -1 and input_2[0] > 0 and input_3[0] > 0):
return
shape.extend(input_1)
shape.extend(input_2)
shape.extend(input_3)
gemm_path_with_bias = self.model.match_parent_path(
reshape_node, ["Add", "MatMul"], [0, 1], output_name_to_node
)
gemm_path_no_bias = self.model.match_parent_path(reshape_node, ["MatMul"], [0], output_name_to_node)
if gemm_path_with_bias is not None:
gemm_path = gemm_path_with_bias
elif gemm_path_no_bias is not None:
gemm_path = gemm_path_no_bias
else:
return
top_matmul = gemm_path[-1]
root_input = top_matmul.input[0]
self.replace_reshape_node(shape, reshape_node, concat_node)
else:
(_, gather_1, shape_1) = path1
gather_value = self.model.get_constant_value(gather_1.input[1])
if gather_value == 1:
shape.append(0)
input_2_proto = self.model.get_initializer(concat_node.input[2])
input_3_proto = self.model.get_initializer(concat_node.input[3])
if input_2_proto is None or input_3_proto is None:
return
input_2 = numpy_helper.to_array(input_2_proto)
input_3 = numpy_helper.to_array(input_3_proto)
if len(input_2) != 1 or len(input_3) != 1:
return
if not (input_2[0] > 0 and input_3[0] > 0):
return
shape.extend(input_2)
shape.extend(input_3)
gemm_path = self.model.match_parent_path(
reshape_node, ["Mul", "Add", "MatMul"], [0, 0, 1], output_name_to_node
)
if gemm_path is None:
return
top_matmul = gemm_path[-1]
root_input = top_matmul.input[0]
if shape_0.input[0] != root_input or shape_1.input[0] != root_input:
return
self.replace_reshape_node(shape, reshape_node, concat_node)
class BartOnnxModel(BertOnnxModel):
def __init__(self, model, num_heads, hidden_size, model_impl="hf"):
super().__init__(model, num_heads, hidden_size)
self.attention_mask = AttentionMask(self)
self.attention_fusion = FusionBartAttention(self, self.hidden_size, self.num_heads, self.attention_mask)
self.bart_reshape_fusion_preprocess = FusionBartReshape(self)
def optimize(self, options: Optional[FusionOptions] = None, add_dynamic_axes: bool = False):
self.attention_fusion.use_multi_head_attention = False if options is None else options.use_multi_head_attention
self.attention_fusion.disable_multi_head_attention_bias = (
False if options is None else options.disable_multi_head_attention_bias
)
super().optimize(options, add_dynamic_axes)
def fuse_attention(self):
self.attention_fusion.apply()
def preprocess(self):
self.adjust_reshape_and_expand()
self.bart_reshape_fusion_preprocess.apply()