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640 lines
25 KiB
640 lines
25 KiB
# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------------------
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from enum import Enum
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from logging import getLogger
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from os import name
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from sys import path
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from typing import Tuple, Union
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import numpy as np
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from fusion_base import Fusion
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from fusion_options import AttentionMaskFormat
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from fusion_utils import FusionUtils, NumpyHelper
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from onnx import NodeProto, TensorProto, helper, numpy_helper
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from onnx_model import OnnxModel
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from shape_infer_helper import SymbolicShapeInferenceHelper, get_shape_from_type_proto
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logger = getLogger(__name__)
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class AttentionMask:
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"""
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Fuse Attention subgraph into one Attention node.
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"""
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def __init__(self, model: OnnxModel):
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self.model = model
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# A lookup table with mask input as key, and mask index output as value
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self.mask_indice = {}
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# A lookup table with mask input as key, and cast (to int32) output as value
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self.mask_casted = {}
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self.utils = FusionUtils(model)
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self.mask_format = AttentionMaskFormat.MaskIndexEnd
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def set_mask_format(self, mask_format: AttentionMaskFormat):
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self.mask_format = mask_format
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def set_mask_indice(self, mask, mask_index):
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if mask in self.mask_indice:
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assert mask_index == self.mask_indice[mask]
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self.mask_indice[mask] = mask_index
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def get_first_mask(self):
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assert len(self.mask_indice) > 0
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return next(iter(self.mask_indice))
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def process_mask(self, input: str) -> str:
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if self.mask_format == AttentionMaskFormat.NoMask:
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return None
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if input in self.mask_indice:
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return self.mask_indice[input]
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# Add cast to convert int64 to int32
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if self.model.find_graph_input(input):
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casted, input_name = self.utils.cast_graph_input_to_int32(input)
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else:
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input_name, cast_node = self.utils.cast_input_to_int32(input)
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casted = True
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if casted:
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self.mask_casted[input] = input_name
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# Attention supports int32 attention mask (2D) since 1.4.0
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if self.mask_format == AttentionMaskFormat.AttentionMask:
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self.mask_indice[input] = input_name
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return input_name
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# Add a mask processing node to convert attention mask to mask index (1D)
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output_name = self.model.create_node_name("mask_index")
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mask_index_node = helper.make_node(
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"ReduceSum",
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inputs=[input_name],
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outputs=[output_name],
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name=self.model.create_node_name("ReduceSum", "MaskReduceSum"),
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)
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mask_index_node.attribute.extend([helper.make_attribute("axes", [1]), helper.make_attribute("keepdims", 0)])
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self.model.add_node(mask_index_node)
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self.mask_indice[input] = output_name
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return output_name
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class FusionAttention(Fusion):
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"""
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Fuse Attention subgraph into one Attention node.
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"""
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def __init__(
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self,
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model: OnnxModel,
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hidden_size: int,
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num_heads: int,
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attention_mask: AttentionMask,
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use_multi_head_attention: bool = False,
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):
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attention_op_name = "MultiHeadAttention" if use_multi_head_attention else "Attention"
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super().__init__(model, attention_op_name, ["SkipLayerNormalization", "LayerNormalization"])
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self.hidden_size = hidden_size
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self.num_heads = num_heads
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self.attention_mask = attention_mask
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self.use_multi_head_attention = use_multi_head_attention
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self.mask_filter_value = None
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# Flags to show warning only once
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self.num_heads_warning = True
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self.hidden_size_warning = True
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def get_num_heads_and_hidden_size_from_concat(self, concat: NodeProto) -> Tuple[int, int]:
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"""
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Detect num_heads and hidden_size from Concat node in the following subgraph:
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SkipLayerNormalization or EmbedLayerNormalization
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/ |
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MatMul Shape
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Add Gather(indices=0)
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| Unsqueeze
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| Concat (*, -1, 12, 64)
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| /
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Reshape
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Transpose
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"""
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if len(concat.input) == 4:
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num_heads = self.model.get_constant_value(concat.input[2])
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head_size = self.model.get_constant_value(concat.input[3])
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if (
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isinstance(num_heads, np.ndarray)
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and num_heads.size == 1
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and isinstance(head_size, np.ndarray)
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and head_size.size == 1
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):
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return num_heads[0], num_heads[0] * head_size[0]
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return self.num_heads, self.hidden_size
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def get_num_heads_and_hidden_size(self, reshape_q: NodeProto) -> Tuple[int, int]:
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"""Detect num_heads and hidden_size from a reshape node.
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Args:
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reshape_q (NodeProto): reshape node for Q
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Returns:
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Tuple[int, int]: num_heads and hidden_size
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"""
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# we assume that reshape fusion has done, so the shape is a tensor like [0, 0, num_heads, head_size]
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q_shape = self.model.get_initializer(reshape_q.input[1])
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if q_shape is None:
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concat = self.model.get_parent(reshape_q, 1)
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if concat is not None and concat.op_type == "Concat":
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return self.get_num_heads_and_hidden_size_from_concat(concat)
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logger.debug(f"{reshape_q.input[1]} is not initializer.")
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return self.num_heads, self.hidden_size # Fall back to user specified value
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q_shape_value = NumpyHelper.to_array(q_shape)
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if len(q_shape_value) != 4 or (q_shape_value[2] <= 0 or q_shape_value[3] <= 0):
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logger.debug(f"q_shape_value={q_shape_value}. Expected value are like [0, 0, num_heads, head_size].")
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return self.num_heads, self.hidden_size # Fall back to user specified value
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num_heads = q_shape_value[2]
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head_size = q_shape_value[3]
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hidden_size = num_heads * head_size
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if self.num_heads > 0 and num_heads != self.num_heads:
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if self.num_heads_warning:
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logger.warning(f"--num_heads is {self.num_heads}. Detected value is {num_heads}. Using detected value.")
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self.num_heads_warning = False # Do not show the warning more than once
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if self.hidden_size > 0 and hidden_size != self.hidden_size:
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if self.hidden_size_warning:
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logger.warning(
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f"--hidden_size is {self.hidden_size}. Detected value is {hidden_size}. Using detected value."
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)
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self.hidden_size_warning = False # Do not show the warning more than once
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return num_heads, hidden_size
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def get_add_qk_str(self, add_qk: NodeProto):
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shape_infer = self.model.infer_runtime_shape(update=True)
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if shape_infer is None:
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return
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input_0_shape = shape_infer.get_edge_shape(add_qk.input[0])
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input_1_shape = shape_infer.get_edge_shape(add_qk.input[1])
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if input_0_shape is None or input_1_shape is None:
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logger.debug(f"one of the inputs of {add_qk} is None")
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return None
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if input_0_shape != input_1_shape:
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logger.debug(f"the shape of two inputs of {add_qk} is not same")
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return None
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return add_qk.input[1]
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def create_attention_node(
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self,
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mask_index: str,
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q_matmul: NodeProto,
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k_matmul: NodeProto,
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v_matmul: NodeProto,
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q_add: NodeProto,
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k_add: NodeProto,
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v_add: NodeProto,
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num_heads: int,
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hidden_size: int,
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input: str,
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output: str,
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add_qk_str: str,
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) -> Union[NodeProto, None]:
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"""Create an Attention node.
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Args:
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mask_index (str): mask input
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q_matmul (NodeProto): MatMul node in fully connection for Q
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k_matmul (NodeProto): MatMul node in fully connection for K
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v_matmul (NodeProto): MatMul node in fully connection for V
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q_add (NodeProto): Add bias node in fully connection for Q
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k_add (NodeProto): Add bias node in fully connection for K
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v_add (NodeProto): Add bias node in fully connection for V
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num_heads (int): number of attention heads. If a model is pruned, it is the number of heads after pruning.
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hidden_size (int): hidden dimension. If a model is pruned, it is the hidden dimension after pruning.
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input (str): input name
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output (str): output name
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Returns:
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Union[NodeProto, None]: the node created or None if failed.
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"""
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assert num_heads > 0
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if hidden_size > 0 and (hidden_size % num_heads) != 0:
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logger.debug(f"input hidden size {hidden_size} is not a multiple of num of heads {num_heads}")
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return None
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q_weight = self.model.get_initializer(q_matmul.input[1])
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k_weight = self.model.get_initializer(k_matmul.input[1])
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v_weight = self.model.get_initializer(v_matmul.input[1])
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q_bias = self.model.get_initializer(q_add.input[1]) or self.model.get_initializer(q_add.input[0])
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k_bias = self.model.get_initializer(k_add.input[1]) or self.model.get_initializer(k_add.input[0])
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v_bias = self.model.get_initializer(v_add.input[1]) or self.model.get_initializer(v_add.input[0])
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if q_weight is None:
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print(
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f"{q_matmul.input[1]} is not an initializer. "
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"Please set do_constant_folding=True in torch.onnx.export to unblock attention fusion"
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)
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return None
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if not (k_weight and v_weight and q_bias and k_bias):
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return None
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qw = NumpyHelper.to_array(q_weight)
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kw = NumpyHelper.to_array(k_weight)
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vw = NumpyHelper.to_array(v_weight)
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# assert q and k have same shape as expected
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assert qw.shape == kw.shape
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qw_in_size = qw.shape[0]
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kw_in_size = kw.shape[0]
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vw_in_size = vw.shape[0]
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assert qw_in_size == kw_in_size == vw_in_size
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if hidden_size > 0 and hidden_size != qw_in_size:
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logger.warning(
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f"Input hidden size ({hidden_size}) is not same as weight matrix dimension of q,k,v ({qw_in_size}). "
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"Please provide a correct input hidden size or pass in 0"
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)
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is_qkv_diff_dims = False
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if qw.shape != vw.shape:
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is_qkv_diff_dims = True
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# All the matrices can have the same shape or q, k matrics can have the same shape with v being different
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# For 2d weights, the shapes would be [in_size, out_size].
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# For 3d weights, shape would be [in_size, a, b] where a*b = out_size
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qw_out_size = np.prod(qw.shape[1:])
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kw_out_size = np.prod(kw.shape[1:])
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vw_out_size = np.prod(vw.shape[1:])
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qkv_weight_dim = 0
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if is_qkv_diff_dims:
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qkv_weight = np.concatenate((qw, kw, vw), axis=1)
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qkv_weight_dim = qw_out_size + kw_out_size + vw_out_size
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else:
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qkv_weight = np.stack((qw, kw, vw), axis=1)
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qkv_weight_dim = 3 * qw_out_size
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qb = NumpyHelper.to_array(q_bias)
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kb = NumpyHelper.to_array(k_bias)
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vb = NumpyHelper.to_array(v_bias)
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q_bias_shape = np.prod(qb.shape)
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k_bias_shape = np.prod(kb.shape)
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v_bias_shape = np.prod(vb.shape)
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assert q_bias_shape == k_bias_shape == qw_out_size
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assert v_bias_shape == vw_out_size
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qkv_bias_dim = 0
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if is_qkv_diff_dims:
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qkv_bias = np.concatenate((qb, kb, vb), axis=0)
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qkv_bias_dim = q_bias_shape + k_bias_shape + v_bias_shape
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else:
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qkv_bias = np.stack((qb, kb, vb), axis=0)
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qkv_bias_dim = 3 * q_bias_shape
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attention_node_name = self.model.create_node_name("Attention")
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if not self.use_multi_head_attention:
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weight = helper.make_tensor(
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name=attention_node_name + "_qkv_weight",
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data_type=TensorProto.FLOAT,
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dims=[qw_in_size, qkv_weight_dim],
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vals=qkv_weight.flatten().tolist(),
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)
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# Sometimes weights and bias are stored in fp16
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if q_weight.data_type == 10:
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weight.CopyFrom(numpy_helper.from_array(NumpyHelper.to_array(weight).astype(np.float16), weight.name))
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self.model.add_initializer(weight, self.this_graph_name)
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bias = helper.make_tensor(
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name=attention_node_name + "_qkv_bias",
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data_type=TensorProto.FLOAT,
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dims=[qkv_bias_dim],
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vals=qkv_bias.flatten().tolist(),
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)
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if q_bias.data_type == 10:
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bias.CopyFrom(numpy_helper.from_array(NumpyHelper.to_array(bias).astype(np.float16), bias.name))
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self.model.add_initializer(bias, self.this_graph_name)
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# For MultiHeadAttention operator, use separated inputs for query, key and value, and no weights.
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if self.use_multi_head_attention:
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if add_qk_str is not None:
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logger.debug("MultiHeadAttention does not support relative_position_bias: cannot fuse the attention.")
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return None
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attention_inputs = [
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q_matmul.output[0],
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k_matmul.output[0],
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v_matmul.output[0],
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attention_node_name + "_qkv_bias",
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]
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if mask_index is not None:
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attention_inputs.append(mask_index)
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attention_node = helper.make_node(
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"MultiHeadAttention",
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inputs=attention_inputs,
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outputs=[output],
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name=attention_node_name,
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)
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else:
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attention_inputs = [
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input,
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attention_node_name + "_qkv_weight",
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attention_node_name + "_qkv_bias",
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]
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if mask_index is not None:
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attention_inputs.append(mask_index)
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else:
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attention_inputs.append("")
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if add_qk_str is not None:
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attention_inputs.append("") # no past
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attention_inputs.append(add_qk_str)
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attention_node = helper.make_node(
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"Attention",
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inputs=attention_inputs,
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outputs=[output],
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name=attention_node_name,
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)
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attention_node.domain = "com.microsoft"
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attention_node.attribute.extend([helper.make_attribute("num_heads", num_heads)])
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if is_qkv_diff_dims:
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attention_node.attribute.extend(
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[helper.make_attribute("qkv_hidden_sizes", [qw_out_size, kw_out_size, vw_out_size])]
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)
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if self.mask_filter_value is not None:
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attention_node.attribute.extend([helper.make_attribute("mask_filter_value", float(self.mask_filter_value))])
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return attention_node
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def fuse(self, normalize_node, input_name_to_nodes, output_name_to_node):
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# Sometimes we can not fuse skiplayernormalization since the add before layernorm has an output that used by nodes outside skiplayernorm
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# Conceptually we treat add before layernorm as skiplayernorm node since they share the same pattern
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start_node = normalize_node
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if normalize_node.op_type == "LayerNormalization":
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add_before_layernorm = self.model.match_parent(normalize_node, "Add", 0)
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if add_before_layernorm is not None:
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start_node = add_before_layernorm
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else:
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return
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# SkipLayerNormalization has two inputs, and one of them is the root input for attention.
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qkv_nodes = self.model.match_parent_path(
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start_node,
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["Add", "MatMul", "Reshape", "Transpose", "MatMul"],
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[None, None, 0, 0, 0],
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)
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einsum_node = None
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if qkv_nodes is not None:
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(_, _, reshape_qkv, transpose_qkv, matmul_qkv) = qkv_nodes
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else:
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# Match Albert
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qkv_nodes = self.model.match_parent_path(
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start_node, ["Add", "Einsum", "Transpose", "MatMul"], [1, None, 0, 0]
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)
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if qkv_nodes is not None:
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(_, einsum_node, transpose_qkv, matmul_qkv) = qkv_nodes
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else:
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return
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other_inputs = []
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for i, input in enumerate(start_node.input):
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if input not in output_name_to_node:
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continue
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if input == qkv_nodes[0].output[0]:
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continue
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other_inputs.append(input)
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if len(other_inputs) != 1:
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return
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root_input = other_inputs[0]
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"""
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Match flaubert Mask
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Mul --> LayerNormalization --> Attention --> MatMul --> Add
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+---------------------------------------------------------
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"""
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mul_before_layernorm = self.model.match_parent(start_node, "Mul", 0)
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if mul_before_layernorm is not None:
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mul_children = input_name_to_nodes[mul_before_layernorm.output[0]]
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if mul_children is not None and len(mul_children) == 2:
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layernorm_node = mul_children[1]
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if layernorm_node.op_type == "LayerNormalization":
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root_input = layernorm_node.output[0]
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else:
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return
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elif mul_children is not None and len(mul_children) == 5:
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root_input = mul_before_layernorm.output[0]
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else:
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return
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elif normalize_node.op_type == "LayerNormalization":
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children = input_name_to_nodes[root_input]
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for child in children:
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if child.op_type == "LayerNormalization":
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root_input = child.output[0]
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children = input_name_to_nodes[root_input]
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children_types = [child.op_type for child in children]
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if children_types.count("MatMul") != 3:
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|
return
|
|
|
|
v_nodes = self.model.match_parent_path(matmul_qkv, ["Transpose", "Reshape", "Add", "MatMul"], [1, 0, 0, None])
|
|
if v_nodes is None:
|
|
logger.debug("fuse_attention: failed to match v path")
|
|
return
|
|
(_, _, add_v, matmul_v) = v_nodes
|
|
|
|
is_distill = False
|
|
is_distill_add = False
|
|
qk_paths = {
|
|
"path1": (["Softmax", "Add", "Div", "MatMul"], [0, 0, None, 0]),
|
|
"path2": (["Softmax", "Add", "Mul", "MatMul"], [0, 0, None, 0]),
|
|
"path3": (["Softmax", "Where", "MatMul", "Div"], [0, 0, 2, 0]),
|
|
"path4": (["Softmax", "Add", "Where", "MatMul"], [0, 0, 0, 2]),
|
|
}
|
|
|
|
qk_nodes = None
|
|
for k, v in qk_paths.items():
|
|
qk_nodes = self.model.match_parent_path(matmul_qkv, v[0], v[1])
|
|
if qk_nodes is None:
|
|
continue
|
|
if k == "path3":
|
|
is_distill = True
|
|
if k == "path4":
|
|
is_distill_add = True
|
|
break
|
|
|
|
if qk_nodes is None:
|
|
logger.debug("fuse_attention: failed to match qk path")
|
|
return
|
|
|
|
add_qk = None
|
|
matmul_qk = None
|
|
where_qk = None
|
|
if is_distill:
|
|
(_, where_qk, matmul_qk, _) = qk_nodes
|
|
elif is_distill_add:
|
|
(_, add_qk, where_qk, matmul_qk) = qk_nodes
|
|
else:
|
|
(_, add_qk, _, matmul_qk) = qk_nodes
|
|
|
|
q_nodes = self.model.match_parent_path(matmul_qk, ["Transpose", "Reshape", "Add", "MatMul"], [0, 0, 0, None])
|
|
if q_nodes is None:
|
|
q_nodes = self.model.match_parent_path(
|
|
matmul_qk,
|
|
["Div", "Transpose", "Reshape", "Add", "MatMul"],
|
|
[0, 0, 0, 0, None],
|
|
)
|
|
if q_nodes is None:
|
|
logger.debug("fuse_attention: failed to match q path")
|
|
return
|
|
reshape_q = q_nodes[-3]
|
|
add_q = q_nodes[-2]
|
|
matmul_q = q_nodes[-1]
|
|
|
|
k_nodes = self.model.match_parent_path(matmul_qk, ["Transpose", "Reshape", "Add", "MatMul"], [1, 0, 0, None])
|
|
if k_nodes is None:
|
|
k_nodes = self.model.match_parent_path(
|
|
matmul_qk,
|
|
["Transpose", "Transpose", "Reshape", "Add", "MatMul"],
|
|
[1, 0, 0, 0, None],
|
|
)
|
|
if k_nodes is None:
|
|
logger.debug("fuse_attention: failed to match k path")
|
|
return
|
|
add_k = k_nodes[-2]
|
|
matmul_k = k_nodes[-1]
|
|
|
|
# Note that Cast might be removed by OnnxRuntime so we match two patterns here.
|
|
mask_nodes = None
|
|
add_qk_str = None
|
|
if is_distill:
|
|
_, mask_nodes, _ = self.model.match_parent_paths(
|
|
where_qk,
|
|
[
|
|
(["Expand", "Reshape", "Equal"], [0, 0, 0]),
|
|
(["Equal", "Unsqueeze", "Unsqueeze"], [0, 0, 0]),
|
|
(["Cast", "Expand", "Reshape", "Equal"], [0, 0, 0, 0]),
|
|
],
|
|
output_name_to_node,
|
|
)
|
|
elif is_distill_add:
|
|
_, mask_nodes, _ = self.model.match_parent_paths(
|
|
where_qk,
|
|
[
|
|
(["Cast", "Equal", "Unsqueeze", "Unsqueeze"], [0, 0, 0, 0]),
|
|
(["Equal", "Unsqueeze", "Unsqueeze"], [0, 0, 0]),
|
|
],
|
|
output_name_to_node,
|
|
)
|
|
if add_qk is not None:
|
|
add_qk_str = self.get_add_qk_str(add_qk)
|
|
if add_qk_str is None:
|
|
logger.debug(f"fuse_attention: failed to verify shape inference of {add_qk}")
|
|
return
|
|
else:
|
|
_, mask_nodes, _ = self.model.match_parent_paths(
|
|
add_qk,
|
|
[
|
|
(
|
|
["Mul", "Sub", "Cast", "Unsqueeze", "Unsqueeze"],
|
|
[None, 0, 1, 0, 0],
|
|
),
|
|
(["Mul", "Sub", "Unsqueeze", "Unsqueeze"], [None, 0, 1, 0]),
|
|
],
|
|
output_name_to_node,
|
|
)
|
|
if mask_nodes is None:
|
|
logger.debug("fuse_attention: failed to match mask path")
|
|
return
|
|
|
|
if len(mask_nodes) > 1 and mask_nodes[0].op_type == "Mul":
|
|
_, mul_val = self.model.get_constant_input(mask_nodes[0])
|
|
if mul_val != -10000:
|
|
self.mask_filter_value = mul_val
|
|
|
|
if matmul_v.input[0] == root_input and matmul_q.input[0] == root_input and matmul_k.input[0] == root_input:
|
|
mask_index = self.attention_mask.process_mask(mask_nodes[-1].input[0])
|
|
|
|
attention_last_node = reshape_qkv if einsum_node is None else transpose_qkv
|
|
|
|
q_num_heads, q_hidden_size = self.get_num_heads_and_hidden_size(reshape_q)
|
|
# number of heads are same for all the paths, hence to create attention node, we pass the q_num_heads
|
|
# the input_hidden_size represents the input hidden size, this is used as needed but hidden sizes for Q, K are extracted appropriately
|
|
new_node = self.create_attention_node(
|
|
mask_index,
|
|
matmul_q,
|
|
matmul_k,
|
|
matmul_v,
|
|
add_q,
|
|
add_k,
|
|
add_v,
|
|
q_num_heads,
|
|
q_hidden_size,
|
|
root_input,
|
|
attention_last_node.output[0],
|
|
add_qk_str,
|
|
)
|
|
if new_node is None:
|
|
return
|
|
|
|
self.nodes_to_add.append(new_node)
|
|
self.node_name_to_graph_name[new_node.name] = self.this_graph_name
|
|
|
|
if einsum_node is not None:
|
|
unique_index = einsum_node.input[0]
|
|
new_edge = "edge_modified_" + unique_index
|
|
shape_tensor = helper.make_tensor(
|
|
name="shape_modified_tensor" + unique_index,
|
|
data_type=TensorProto.INT64,
|
|
dims=[4],
|
|
vals=np.int64([0, 0, q_num_heads, int(q_hidden_size / q_num_heads)]).tobytes(),
|
|
raw=True,
|
|
)
|
|
self.model.add_initializer(shape_tensor, self.this_graph_name)
|
|
self.model.add_node(
|
|
helper.make_node(
|
|
"Reshape",
|
|
[attention_last_node.output[0], shape_tensor.name],
|
|
[new_edge],
|
|
"reshape_modified_" + unique_index,
|
|
),
|
|
self.this_graph_name,
|
|
)
|
|
einsum_node.input[0] = new_edge
|
|
|
|
self.nodes_to_remove.extend([attention_last_node, transpose_qkv, matmul_qkv])
|
|
self.nodes_to_remove.extend(qk_nodes)
|
|
|
|
# For MultiHeadAttention operator, MatMul nodes for Q/K/V projection shall not be fused.
|
|
self.nodes_to_remove.extend(q_nodes if not self.use_multi_head_attention else q_nodes[:-1])
|
|
self.nodes_to_remove.extend(k_nodes if not self.use_multi_head_attention else k_nodes[:-1])
|
|
self.nodes_to_remove.extend(v_nodes if not self.use_multi_head_attention else v_nodes[:-1])
|
|
|
|
# Use prune graph to remove mask nodes since they are shared by all attention nodes.
|
|
self.prune_graph = True
|