# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
from logging import getLogger
from typing import Tuple, Union
import numpy as np
from fusion_base import Fusion
from fusion_utils import NumpyHelper
from onnx import NodeProto, helper, numpy_helper
from onnx_model import OnnxModel
logger = getLogger(__name__)
class FusionMultiHeadAttentionSam2(Fusion):
"""
Fuse MultiHeadAttention subgraph of Segment Anything v2 (SAM2).
"""
def __init__(
self,
model: OnnxModel,
hidden_size: int,
num_heads: int,
):
super().__init__(model, "MultiHeadAttention", ["LayerNormalization"])
self.hidden_size = hidden_size
self.num_heads = num_heads
# Flags to show warning only once
self.num_heads_warning = True
self.hidden_size_warning = True
def get_decoder_num_heads(self, reshape_q: NodeProto) -> int:
"""Detect num_heads from a reshape node.
Args:
reshape_q (NodeProto): reshape node for Q
Returns:
int: num_heads, or 0 if not found
"""
num_heads = 0
# we assume that reshape fusion has done, so the shape is a tensor like [0, 0, num_heads, head_size]
shape_value = self.model.get_constant_value(reshape_q.input[1])
if shape_value is not None:
if isinstance(shape_value, np.ndarray) and list(shape_value.shape) == [4]:
num_heads = int(shape_value[2])
if isinstance(num_heads, int) and num_heads > 0:
return num_heads
return 0
def get_encoder_num_heads(self, reshape_in: NodeProto) -> int:
"""Detect num_heads from a reshape node.
Args:
reshape_q (NodeProto): reshape node for Q
Returns:
int: num_heads, or 0 if not found
"""
num_heads = 0
shape_value = self.model.get_constant_value(reshape_in.input[1])
if shape_value is not None:
if isinstance(shape_value, np.ndarray) and list(shape_value.shape) == [5]:
num_heads = int(shape_value[3])
else:
concat_shape = self.model.match_parent(reshape_in, "Concat", 1)
if concat_shape is not None and len(concat_shape.input) == 5:
# we assume that reshape fusion has done, so the shape is a tensor like [0, 0, num_heads, head_size]
shape_value = self.model.get_constant_value(concat_shape.input[3])
if shape_value is not None:
if isinstance(shape_value, np.ndarray) and list(shape_value.shape) == [1]:
num_heads = int(shape_value[0])
if isinstance(num_heads, int) and num_heads > 0:
return num_heads
return 0
def get_hidden_size(self, layernorm_node):
"""Detect hidden_size from LayerNormalization node.
Args:
layernorm_node (NodeProto): LayerNormalization node before Q, K and V
Returns:
int: hidden_size, or 0 if not found
"""
layernorm_bias = self.model.get_initializer(layernorm_node.input[2])
if layernorm_bias:
return NumpyHelper.to_array(layernorm_bias).shape[0]
return 0
def get_num_heads_and_hidden_size(
self, reshape_q: NodeProto, layernorm_node: NodeProto, is_encoder: bool = False
) -> Tuple[int, int]:
"""Detect num_heads and hidden_size.
Args:
reshape_q (NodeProto): reshape node for Q
layernorm_node (NodeProto): LayerNormalization node before Q, K, V
Returns:
Tuple[int, int]: num_heads and hidden_size
"""
if is_encoder:
num_heads = self.get_encoder_num_heads(reshape_q)
else:
num_heads = self.get_decoder_num_heads(reshape_q)
if num_heads <= 0:
num_heads = self.num_heads # Fall back to user specified value
if self.num_heads > 0 and num_heads != self.num_heads:
if self.num_heads_warning:
logger.warning(f"--num_heads is {self.num_heads}. Detected value is {num_heads}. Using detected value.")
self.num_heads_warning = False # Do not show the warning more than once
hidden_size = self.get_hidden_size(layernorm_node)
if hidden_size <= 0:
hidden_size = self.hidden_size # Fall back to user specified value
if self.hidden_size > 0 and hidden_size != self.hidden_size:
if self.hidden_size_warning:
logger.warning(
f"--hidden_size is {self.hidden_size}. Detected value is {hidden_size}. Using detected value."
)
self.hidden_size_warning = False # Do not show the warning more than once
return num_heads, hidden_size
def create_attention_node(
self,
q_matmul: NodeProto,
q_add: NodeProto,
k_matmul: NodeProto,
k_add: NodeProto,
v_matmul: NodeProto,
v_add: NodeProto,
num_heads: int,
hidden_size: int,
output: str,
) -> Union[NodeProto, None]:
"""Create an Attention node.
Args:
q_matmul (NodeProto): MatMul node in fully connection for Q
q_add (NodeProto): Add bias node in fully connection for Q
k_matmul (NodeProto): MatMul node in fully connection for K
k_add (NodeProto): Add bias node in fully connection for K
v_matmul (NodeProto): MatMul node in fully connection for V
v_add (NodeProto): Add bias node in fully connection for V
num_heads (int): number of attention heads. If a model is pruned, it is the number of heads after pruning.
hidden_size (int): hidden dimension. If a model is pruned, it is the hidden dimension after pruning.
output (str): output name
Returns:
Union[NodeProto, None]: the node created or None if failed.
"""
if hidden_size > 0 and (hidden_size % num_heads) != 0:
logger.debug(f"input hidden size {hidden_size} is not a multiple of num of heads {num_heads}")
return None
q_weight = self.model.get_initializer(q_matmul.input[1])
k_weight = self.model.get_initializer(k_matmul.input[1])
v_weight = self.model.get_initializer(v_matmul.input[1])
if not (q_weight and k_weight and v_weight):
return None
qw = NumpyHelper.to_array(q_weight)
kw = NumpyHelper.to_array(k_weight)
vw = NumpyHelper.to_array(v_weight)
logger.debug(f"qw={qw.shape} kw={kw.shape} vw={vw.shape} hidden_size={hidden_size}")
attention_node_name = self.model.create_node_name("MultiHeadAttention")
attention_inputs = [
q_add.output[0],
k_add.output[0],
v_add.output[0],
]
attention_node = helper.make_node(
"MultiHeadAttention",
inputs=attention_inputs,
outputs=[output],
name=attention_node_name,
)
attention_node.domain = "com.microsoft"
attention_node.attribute.extend([helper.make_attribute("num_heads", num_heads)])
counter_name = "MultiHeadAttention ({})".format("cross attention")
self.increase_counter(counter_name)
return attention_node
def fuse(self, normalize_node, input_name_to_nodes, output_name_to_node):
if self.fuse_sam_encoder_pattern(normalize_node, input_name_to_nodes, output_name_to_node):
return
match_qkv = self.match_attention_subgraph(normalize_node)
if match_qkv is None:
if normalize_node.input[0] not in output_name_to_node:
return
skip_add = output_name_to_node[normalize_node.input[0]]
if skip_add.op_type != "Add":
return
match_qkv = self.match_attention_subgraph(skip_add)
if match_qkv is None:
return
reshape_qkv, transpose_qkv, reshape_q, matmul_q, add_q, matmul_k, add_k, matmul_v, add_v = match_qkv
attention_last_node = reshape_qkv
q_num_heads, q_hidden_size = self.get_num_heads_and_hidden_size(reshape_q, normalize_node, False)
if q_num_heads <= 0:
logger.debug("fuse_attention: failed to detect num_heads")
return
# number of heads are same for all the paths, hence to create attention node, we pass the q_num_heads
new_node = self.create_attention_node(
matmul_q,
add_q,
matmul_k,
add_k,
matmul_v,
add_v,
q_num_heads,
q_hidden_size,
output=attention_last_node.output[0],
)
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
self.nodes_to_remove.extend([attention_last_node, transpose_qkv])
# Use prune graph to remove nodes since they are shared by all attention nodes.
self.prune_graph = True
def match_attention_subgraph(self, node_after_output_projection):
"""Match Q, K and V paths exported by PyTorch 2.*"""
qkv_nodes = self.model.match_parent_path(
node_after_output_projection,
["Add", "MatMul", "Reshape", "Transpose", "MatMul"],
[None, None, None, 0, 0],
)
if qkv_nodes is None:
return None
(_, _, reshape_qkv, transpose_qkv, matmul_qkv) = qkv_nodes
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 None
(_, _, add_v, matmul_v) = v_nodes
qk_nodes = self.model.match_parent_path(matmul_qkv, ["Softmax", "MatMul"], [0, 0])
if qk_nodes is not None:
(_softmax_qk, matmul_qk) = qk_nodes
else:
logger.debug("fuse_attention: failed to match qk path")
return None
q_nodes = self.model.match_parent_path(
matmul_qk, ["Mul", "Transpose", "Reshape", "Add", "MatMul"], [0, None, 0, 0, None]
)
if q_nodes is None:
logger.debug("fuse_attention: failed to match q path")
return None
(mul_q, _transpose_q, reshape_q, add_q, matmul_q) = q_nodes
k_nodes = self.model.match_parent_path(
matmul_qk, ["Mul", "Transpose", "Reshape", "Add", "MatMul"], [1, None, 0, 0, None]
)
if k_nodes is None:
logger.debug("fuse_attention: failed to match k path")
return None
(_mul_k, _, _, add_k, matmul_k) = k_nodes
# The scalar for Q and K is sqrt(1.0/sqrt(head_size)).
mul_q_nodes = self.model.match_parent_path(
mul_q,
["Sqrt", "Div", "Sqrt", "Cast", "Slice", "Shape", "Transpose", "Reshape"],
[None, 0, 1, 0, 0, 0, 0, 0],
)
if mul_q_nodes is None or mul_q_nodes[-1] != reshape_q:
logger.debug("fuse_attention: failed to match mul_q path")
return None
return reshape_qkv, transpose_qkv, reshape_q, matmul_q, add_q, matmul_k, add_k, matmul_v, add_v
# --------------------------------------------------------
# The following are for SAM encoder
# --------------------------------------------------------
def fuse_sam_encoder_pattern(self, normalize_node, input_name_to_nodes, output_name_to_node) -> bool:
# SAM encoder attention layer pattern:
# Add -----------+
# | |
# LayerNorm |
# | |
# Reshape |
# | |
# Transpose |
# | |
# MatMul |
# | |
# Add |
# | |
# Reshape |
# | |
# Split |
# | |
# Self Attention subgraph |
# | |
# Reshape |
# | |
# Transpose |
# | |
# Reshape |
# | |
# Add ----------+
# |
# LayerNorm (starts from here)
nodes = self.model.match_parent_path(
normalize_node,
["Add", "Reshape", "Transpose", "Reshape"],
[0, None, 0, 0],
)
if nodes is None:
nodes = self.model.match_parent_path(
normalize_node,
["Add", "Slice", "Slice", "Reshape", "Transpose", "Reshape"],
[0, None, 0, 0, 0, 0],
)
if nodes is None:
nodes = self.model.match_parent_path(
normalize_node,
["Add"],
[0],
)
if nodes is None:
return False
node_after_output_projection = nodes[-1]
matched_sdpa = self.match_sam_encoder_attention_subgraph(
node_after_output_projection, input_index=1 if len(nodes) == 1 else None
)
if matched_sdpa is None:
return False
reshape_out, transpose_out, split_qkv, transpose_q, transpose_k, transpose_v = matched_sdpa
# B, S, N, H => B, N, S, H
permutation_q = OnnxModel.get_node_attribute(transpose_q, "perm")
if (not isinstance(permutation_q, list)) or permutation_q != [0, 2, 1, 3]:
return False
# B, S, N, H => B, N, H, S
permutation_k = OnnxModel.get_node_attribute(transpose_k, "perm")
if (not isinstance(permutation_k, list)) or permutation_k != [0, 2, 3, 1]:
return False
# B, S, N, H => B, N, S, H
permutation_v = OnnxModel.get_node_attribute(transpose_v, "perm")
if (not isinstance(permutation_v, list)) or permutation_v != [0, 2, 1, 3]:
return False
input_projection_nodes = self.model.match_parent_path(
split_qkv,
["Reshape", "Add", "MatMul"],
[0, 0, None],
)
if input_projection_nodes is None:
return False
reshape_in, add_in, matmul_in = input_projection_nodes
q_num_heads, q_hidden_size = self.get_num_heads_and_hidden_size(reshape_in, normalize_node, True)
if q_num_heads <= 0:
logger.debug("fuse_attention: failed to detect num_heads")
return False
# Add a shape to convert 4D BxSxNxH to 3D BxSxD, which is required by MHA operator.
new_dims_name = "bsnh_to_bsd_reshape_dims"
new_dims = self.model.get_initializer(new_dims_name)
if new_dims is None:
new_dims = numpy_helper.from_array(np.array([0, 0, -1], dtype="int64"), name=new_dims_name)
self.model.add_initializer(new_dims, self.this_graph_name)
reshape_q_name = self.model.create_node_name("Reshape")
reshape_q = helper.make_node(
"Reshape",
inputs=[transpose_q.input[0], new_dims_name],
outputs=[transpose_q.input[0] + "_BSD"],
name=reshape_q_name,
)
self.nodes_to_add.append(reshape_q)
self.node_name_to_graph_name[reshape_q.name] = self.this_graph_name
# Reuse the transpose_q node to transpose K from BSNH to BNSH. Here we update the input and output of the node.
transpose_k_bnsh = transpose_q
transpose_k_bnsh.input[0] = transpose_k.input[0]
transpose_k_bnsh.output[0] = transpose_k.input[0] + "_BNSH"
logger.debug(f"Found MHA: {q_num_heads=} {q_hidden_size=}")
# number of heads are same for all the paths, hence to create attention node, we pass the q_num_heads
new_node = self.create_mha_node(
reshape_q,
transpose_k_bnsh,
transpose_v,
q_num_heads,
)
if new_node is None:
return False
# Update the input of the next node that consumes the output of the MHA.
assert len(self.model.get_children(transpose_out, input_name_to_nodes)) == 1
reshape_out.input[0] = new_node.output[0]
self.nodes_to_add.append(new_node)
self.node_name_to_graph_name[new_node.name] = self.this_graph_name
self.nodes_to_remove.extend([transpose_out])
# Use prune graph to remove nodes since they are shared by all attention nodes.
self.prune_graph = True
return True
def match_sam_encoder_attention_subgraph(self, node_after_output_projection, input_index=None):
"""Match SDPA pattern in SAM2 enconder.*"""
# nodes of output projection and the second MatMul in SDPA.
out_nodes = self.model.match_parent_path(
node_after_output_projection,
["Add", "MatMul", "Reshape", "Transpose", "MatMul"],
[input_index, None, None, 0, 0],
)
if out_nodes is None:
return None
(_, _, reshape_out, transpose_out, matmul_qk_v) = out_nodes
# Split and Reshape is for packed QKV
v_nodes = self.model.match_parent_path(matmul_qk_v, ["Transpose", "Squeeze", "Split", "Reshape"], [1, 0, 0, 0])
if v_nodes is None:
logger.debug("failed to match v path")
return None
(transpose_v, _, split_qkv, reshape_qkv) = v_nodes
qk_nodes = self.model.match_parent_path(matmul_qk_v, ["Softmax", "MatMul"], [0, 0])
if qk_nodes is not None:
(_softmax_qk, matmul_qk) = qk_nodes
else:
logger.debug("failed to match qk path")
return None
q_nodes = self.model.match_parent_path(matmul_qk, ["Mul", "Transpose", "Squeeze", "Split"], [0, None, 0, 0])
if q_nodes is None:
q_nodes = self.model.match_parent_path(
matmul_qk,
["Mul", "Transpose", "Reshape", "Transpose", "MaxPool", "Transpose", "Reshape", "Squeeze", "Split"],
[0, None, 0, 0, 0, 0, 0, 0, 0],
)
if q_nodes is None:
logger.debug("failed to match q path")
return None
if q_nodes[-1] != split_qkv:
return None
transpose_q = q_nodes[1]
k_nodes = self.model.match_parent_path(matmul_qk, ["Mul", "Transpose", "Squeeze", "Split"], [1, None, 0, 0])
if k_nodes is None:
logger.debug("failed to match k path")
return None
if k_nodes[-1] != split_qkv:
return None
(mul_k, transpose_k, _squeeze_k, _) = k_nodes
return reshape_out, transpose_out, split_qkv, transpose_q, transpose_k, transpose_v
def create_mha_node(
self,
reshape_q: NodeProto,
transpose_k: NodeProto,
transpose_v: NodeProto,
num_heads: int,
) -> NodeProto:
"""Create a MultiHeadAttention node for SAM2 encoder.
Args:
reshape_q (NodeProto): Reshape node for Q, output is 3D BxSxNH format
transpose_k (NodeProto): Transpose node for K, output is BNSH format
transpose_v (NodeProto): Transpose node for V, output is BNSH format
num_heads (int): number of attention heads. If a model is pruned, it is the number of heads after pruning.
Returns:
NodeProto: the MultiHeadAttention node created.
"""
attention_node_name = self.model.create_node_name("MultiHeadAttention")
inputs = [
reshape_q.output[0],
transpose_k.output[0],
transpose_v.output[0],
]
# Create a new output name since the shape is 3D, which is different from the original output shape (4D).
output = attention_node_name + "_out"
attention_node = helper.make_node(
"MultiHeadAttention",
inputs=inputs,
outputs=[output],
name=attention_node_name,
)
attention_node.domain = "com.microsoft"
attention_node.attribute.extend([helper.make_attribute("num_heads", num_heads)])
counter_name = "MultiHeadAttention ({})".format("self attention")
self.increase_counter(counter_name)
return attention_node