# -------------------------------------------------------------------------
# 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, TensorProto, helper
from onnx_model import OnnxModel
logger = getLogger(__name__)
class FusionAttentionUnet(Fusion):
"""
Fuse Attention subgraph of UNet into one Attention node.
"""
def __init__(
self,
model: OnnxModel,
hidden_size: int,
num_heads: int,
is_cross_attention: bool,
enable_packed_qkv: bool,
enable_packed_kv: bool,
):
super().__init__(
model,
"Attention" if is_cross_attention and enable_packed_qkv else "MultiHeadAttention",
["LayerNormalization"],
)
self.hidden_size = hidden_size
self.num_heads = num_heads
self.is_cross_attention = is_cross_attention
# Note: pack Q/K/V or K/V weights into one tensor make it harder for updating initializers for LoRA.
# To support LoRA, it is better to use separated Q, K and V inputs in offline optimization,
# and CUDA operator pre-packs those tensors to preferred format based on available kernels.
# In this way, we can support LoRA and get optimal performance at same time.
self.enable_packed_qkv = enable_packed_qkv
self.enable_packed_kv = enable_packed_kv
# Flags to show warning only once
self.num_heads_warning = True
self.hidden_size_warning = True
def get_num_heads(self, reshape_q: NodeProto, is_torch2: bool = False) -> int:
"""Detect num_heads from a reshape node.
Args:
reshape_q (NodeProto): reshape node for Q
is_torch2 (bool): graph pattern is from PyTorch 2.*
Returns:
int: num_heads, or 0 if not found
"""
num_heads = 0
if is_torch2:
# we assume that reshape fusion has done, so the shape is a tensor like [0, 0, num_heads, head_size]
reshape_parent = self.model.get_parent(reshape_q, 1)
if reshape_parent and reshape_parent.op_type == "Concat" and len(reshape_parent.input) == 4:
num_heads = self.model.get_constant_value(reshape_parent.input[2])
if isinstance(num_heads, np.ndarray) and list(num_heads.shape) == [1]:
num_heads = int(num_heads)
else:
# we assume that reshape fusion has done, so the shape is a tensor like [0, 0, num_heads, head_size]
q_shape_value = self.model.get_constant_value(reshape_q.input[1])
if isinstance(q_shape_value, np.ndarray) and list(q_shape_value.shape) == [4]:
num_heads = int(q_shape_value[2])
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_torch2: bool = False
) -> Tuple[int, int]:
"""Detect num_heads and hidden_size.
Args:
reshape_q (NodeProto): reshape node for Q
is_torch2 (bool): graph pattern is from PyTorch 2.*
layernorm_node (NodeProto): LayerNormalization node before Q, K, V
Returns:
Tuple[int, int]: num_heads and hidden_size
"""
num_heads = self.get_num_heads(reshape_q, is_torch2)
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,
k_matmul: NodeProto,
v_matmul: NodeProto,
num_heads: int,
hidden_size: int,
input: str,
output: str,
) -> Union[NodeProto, None]:
"""Create an Attention node.
Args:
q_matmul (NodeProto): MatMul node in fully connection for Q
k_matmul (NodeProto): MatMul node in fully connection for K
v_matmul (NodeProto): MatMul 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.
input (str): input name
output (str): output name
Returns:
Union[NodeProto, None]: the node created or None if failed.
"""
is_self_attention = not self.is_cross_attention
if is_self_attention:
if q_matmul.input[0] != input or k_matmul.input[0] != input or v_matmul.input[0] != input:
logger.debug(
"For self attention, input hidden state for q and k/v shall be same. Got %s, %s, %s",
q_matmul.input[0],
k_matmul.input[0],
v_matmul.input[0],
)
return None
else:
if q_matmul.input[0] != input or (k_matmul.input[0] != v_matmul.input[0]) or (k_matmul.input[0] == input):
logger.debug(
"For cross attention, input hidden state for q and k/v shall be different. Got %s, %s, %s",
q_matmul.input[0],
k_matmul.input[0],
v_matmul.input[0],
)
return None
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
# Sometimes weights are stored in fp16
float_type = q_weight.data_type
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}")
# assert q and k have same shape as expected
if is_self_attention:
if qw.shape != kw.shape or qw.shape != vw.shape:
return None
qw_in_size = qw.shape[0]
if hidden_size > 0 and hidden_size != qw_in_size:
raise ValueError(
f"Input hidden size ({hidden_size}) is not same as weight dimension of q,k,v ({qw_in_size}). "
"Please provide a correct input hidden size or pass in 0"
)
# All the matrices can have the same shape or q, k matrics can have the same shape with v being different
# For 2d weights, the shapes would be [in_size, out_size].
# For 3d weights, shape would be [in_size, a, b] where a*b = out_size
qw_out_size = int(np.prod(qw.shape[1:]))
if self.enable_packed_qkv:
attention_node_name = self.model.create_node_name("MultiHeadAttention")
c = qw_in_size
n = num_heads
h = qw_out_size // num_heads
# Concat and interleave weights so that the output of fused KV GEMM has [B, S_kv, N, 3, H] shape
qkv_weight = np.dstack([qw.reshape(c, n, h), kw.reshape(c, n, h), vw.reshape(c, n, h)]).reshape(
c, n * 3 * h
)
matmul_node_name = self.model.create_node_name("MatMul", name_prefix="MatMul_QKV")
self.add_initializer(
name=matmul_node_name + "_weight",
data_type=float_type,
dims=[qkv_weight.shape[0], qkv_weight.shape[1]],
vals=qkv_weight,
)
matmul_node = helper.make_node(
"MatMul",
inputs=[k_matmul.input[0], matmul_node_name + "_weight"],
outputs=[matmul_node_name + "_out"],
name=matmul_node_name,
)
self.node_name_to_graph_name[matmul_node.name] = self.this_graph_name
self.add_initializer(
name=matmul_node_name + "_reshape_shape",
data_type=TensorProto.INT64,
dims=[5],
vals=[0, 0, n, 3, h],
raw=False,
)
reshape_node = helper.make_node(
"Reshape",
inputs=[
matmul_node_name + "_out",
matmul_node_name + "_reshape_shape",
],
outputs=[attention_node_name + "_qkv_input"],
name=matmul_node_name + "_reshape",
)
self.node_name_to_graph_name[reshape_node.name] = self.this_graph_name
self.nodes_to_add.extend([matmul_node, reshape_node])
self.nodes_to_remove.extend([q_matmul, k_matmul, v_matmul])
else:
qkv_weight = np.stack((qw, kw, vw), axis=1)
qkv_weight_dim = 3 * qw_out_size
attention_node_name = self.model.create_node_name("Attention")
self.add_initializer(
name=attention_node_name + "_qkv_weight",
data_type=float_type,
dims=[qw_in_size, qkv_weight_dim],
vals=qkv_weight,
)
else: # cross attention
attention_node_name = self.model.create_node_name("MultiHeadAttention")
if self.enable_packed_kv:
if kw.shape != vw.shape:
return None
kw_in_size = kw.shape[0]
vw_in_size = vw.shape[0]
assert kw_in_size == vw_in_size
qw_out_size = qw.shape[1]
kw_out_size = kw.shape[1]
vw_out_size = vw.shape[1]
assert qw_out_size == vw_out_size and kw_out_size == vw_out_size
c = kw_in_size
n = num_heads
h = kw_out_size // num_heads
# Concat and interleave weights so that the output of fused KV GEMM has [B, S_kv, N, 2, H] shape
kv_weight = np.dstack([kw.reshape(c, n, h), vw.reshape(c, n, h)]).reshape(c, n * 2 * h)
matmul_node_name = self.model.create_node_name("MatMul", name_prefix="MatMul_KV")
self.add_initializer(
name=matmul_node_name + "_weight",
data_type=float_type,
dims=[kv_weight.shape[0], kv_weight.shape[1]],
vals=kv_weight,
)
matmul_node = helper.make_node(
"MatMul",
inputs=[k_matmul.input[0], matmul_node_name + "_weight"],
outputs=[matmul_node_name + "_out"],
name=matmul_node_name,
)
self.node_name_to_graph_name[matmul_node.name] = self.this_graph_name
self.add_initializer(
name=matmul_node_name + "_reshape_shape",
data_type=TensorProto.INT64,
dims=[5],
vals=[0, 0, n, 2, h],
raw=False,
)
reshape_node = helper.make_node(
"Reshape",
inputs=[
matmul_node_name + "_out",
matmul_node_name + "_reshape_shape",
],
outputs=[attention_node_name + "_kv_input"],
name=matmul_node_name + "_reshape",
)
self.node_name_to_graph_name[reshape_node.name] = self.this_graph_name
self.nodes_to_add.extend([matmul_node, reshape_node])
self.nodes_to_remove.extend([k_matmul, v_matmul])
# No bias, use zeros
qkv_bias = np.zeros([3, hidden_size], dtype=np.float32)
qkv_bias_dim = 3 * hidden_size
self.add_initializer(
name=attention_node_name + "_qkv_bias",
data_type=float_type,
dims=[qkv_bias_dim],
vals=qkv_bias,
)
if is_self_attention:
if not self.enable_packed_qkv:
attention_inputs = [
input,
attention_node_name + "_qkv_weight",
attention_node_name + "_qkv_bias",
]
else:
attention_inputs = [attention_node_name + "_qkv_input"]
else:
if not self.enable_packed_kv:
attention_inputs = [
q_matmul.output[0],
k_matmul.output[0],
v_matmul.output[0],
attention_node_name + "_qkv_bias",
]
else:
attention_inputs = [
q_matmul.output[0],
attention_node_name + "_kv_input",
]
attention_node = helper.make_node(
"Attention" if (is_self_attention and not self.enable_packed_qkv) else "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 = (
"Attention (self attention)"
if is_self_attention and not self.enable_packed_qkv
else "MultiHeadAttention ({})".format(
"self attention with packed qkv"
if self.enable_packed_qkv
else "cross attention with packed kv" if self.enable_packed_kv else "cross attention"
)
)
self.increase_counter(counter_name)
return attention_node
def create_attention_node_lora(
self,
q_matmul_add: NodeProto,
k_matmul_add: NodeProto,
v_matmul_add: NodeProto,
num_heads: int,
hidden_size: int,
input: str,
output: str,
) -> Union[NodeProto, None]:
"""Create an Attention node.
Args:
q_matmul (NodeProto): MatMul node in fully connection for Q
k_matmul (NodeProto): MatMul node in fully connection for K
v_matmul (NodeProto): MatMul 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.
input (str): input name
output (str): output name
Returns:
Union[NodeProto, None]: the node created or None if failed.
"""
is_self_attention = not self.is_cross_attention
q_matmul = self.model.match_parent(q_matmul_add, "MatMul", 0)
k_matmul = self.model.match_parent(k_matmul_add, "MatMul", 0)
v_matmul = self.model.match_parent(v_matmul_add, "MatMul", 0)
q_lora_nodes = self.match_lora_path(q_matmul_add)
if q_lora_nodes is None:
return None
(q_lora_last_node, q_lora_matmul_1) = q_lora_nodes
k_lora_nodes = self.match_lora_path(k_matmul_add)
if k_lora_nodes is None:
return None
(k_lora_last_node, k_lora_matmul_1) = k_lora_nodes
v_lora_nodes = self.match_lora_path(v_matmul_add)
if v_lora_nodes is None:
return None
(v_lora_last_node, v_lora_matmul_1) = v_lora_nodes
if is_self_attention:
if q_matmul.input[0] != input or k_matmul.input[0] != input or v_matmul.input[0] != input:
logger.debug(
"For self attention, input hidden state for q and k/v shall be same. Got %s, %s, %s",
q_matmul.input[0],
k_matmul.input[0],
v_matmul.input[0],
)
return None
if (
q_lora_matmul_1.input[0] != input
or k_lora_matmul_1.input[0] != input
or v_lora_matmul_1.input[0] != input
):
logger.debug(
"For self attention, input hidden state for LoRA q and k/v weights shall be same. Got %s, %s, %s",
q_lora_matmul_1.input[0],
k_lora_matmul_1.input[0],
v_lora_matmul_1.input[0],
)
return None
else:
if q_matmul.input[0] != input or (k_matmul.input[0] != v_matmul.input[0]) or (k_matmul.input[0] == input):
logger.debug(
"For cross attention, input hidden state for q and k/v shall be different. Got %s, %s, %s",
q_matmul.input[0],
k_matmul.input[0],
v_matmul.input[0],
)
return None
if (
q_lora_matmul_1.input[0] != input
or (k_lora_matmul_1.input[0] != v_lora_matmul_1.input[0])
or (k_matmul.input[0] == input)
):
logger.debug(
(
"For cross attention, input hidden state for LoRA q and k/v weights shall be different. "
"Got %s, %s, %s"
),
q_lora_matmul_1.input[0],
k_lora_matmul_1.input[0],
v_lora_matmul_1.input[0],
)
return None
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
# Sometimes weights are stored in fp16
if q_weight.data_type == 10:
logger.debug("weights are in fp16. Please run fp16 conversion after optimization")
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}")
# assert q and k have same shape as expected
if is_self_attention:
if qw.shape != kw.shape or qw.shape != vw.shape:
return None
qw_in_size = qw.shape[0]
if hidden_size > 0 and hidden_size != qw_in_size:
raise ValueError(
f"Input hidden size ({hidden_size}) is not same as weight dimension of q,k,v ({qw_in_size}). "
"Please provide a correct input hidden size or pass in 0"
)
# All the matrices can have the same shape or q, k matrics can have the same shape with v being different
# For 2d weights, the shapes would be [in_size, out_size].
# For 3d weights, shape would be [in_size, a, b] where a*b = out_size
qw_out_size = int(np.prod(qw.shape[1:]))
if self.enable_packed_qkv:
attention_node_name = self.model.create_node_name("MultiHeadAttention")
c = qw_in_size
n = num_heads
h = qw_out_size // num_heads
# Concat and interleave weights so that the output of fused KV GEMM has [B, S_kv, N, 3, H] shape
qkv_weight = np.dstack([qw.reshape(c, n, h), kw.reshape(c, n, h), vw.reshape(c, n, h)]).reshape(
c, n * 3 * h
)
matmul_node_name = self.model.create_node_name("MatMul", name_prefix="MatMul_QKV")
self.add_initializer(
name=matmul_node_name + "_weight",
data_type=TensorProto.FLOAT,
dims=[qkv_weight.shape[0], qkv_weight.shape[1]],
vals=qkv_weight,
)
matmul_node = helper.make_node(
"MatMul",
inputs=[k_matmul.input[0], matmul_node_name + "_weight"],
outputs=[matmul_node_name + "_out"],
name=matmul_node_name,
)
self.node_name_to_graph_name[matmul_node.name] = self.this_graph_name
# Do the same thing with the LoRA weights, but don't constant fold the result. The goal is to allow
# the Q/K/V weights to be changed without having to re-run the optimizer.
lora_weight_shape_tensor_name = q_lora_last_node.name + "_reshape_shape"
self.add_initializer(
name=lora_weight_shape_tensor_name,
data_type=TensorProto.INT64,
dims=[4],
vals=[0, 0, n, h],
raw=False,
)
# Reshape the LoRA Q weights
q_lora_reshape_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_Q")
q_lora_reshape_node = helper.make_node(
"Reshape",
inputs=[q_lora_last_node.output[0], lora_weight_shape_tensor_name],
outputs=[q_lora_reshape_node_name + "_out"],
name=q_lora_reshape_node_name,
)
self.node_name_to_graph_name[q_lora_reshape_node.name] = self.this_graph_name
# Reshape the LoRA K weights
k_lora_reshape_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_K")
k_lora_reshape_node = helper.make_node(
"Reshape",
inputs=[k_lora_last_node.output[0], lora_weight_shape_tensor_name],
outputs=[k_lora_reshape_node_name + "_out"],
name=k_lora_reshape_node_name,
)
self.node_name_to_graph_name[k_lora_reshape_node.name] = self.this_graph_name
# Reshape the LoRA V weights
v_lora_reshape_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_V")
v_lora_reshape_node = helper.make_node(
"Reshape",
inputs=[v_lora_last_node.output[0], lora_weight_shape_tensor_name],
outputs=[v_lora_reshape_node_name + "_out"],
name=v_lora_reshape_node_name,
)
self.node_name_to_graph_name[v_lora_reshape_node.name] = self.this_graph_name
# Concat the reshaped LoRA Q/K/V weights together on the third axis
qkv_lora_concat_node_name = self.model.create_node_name("Concat", name_prefix="Concat_LoRA_QKV")
qkv_lora_concat_node = helper.make_node(
"Concat",
inputs=[
q_lora_reshape_node.output[0],
k_lora_reshape_node.output[0],
v_lora_reshape_node.output[0],
],
outputs=[qkv_lora_concat_node_name + "_out"],
name=qkv_lora_concat_node_name,
)
qkv_lora_concat_node.attribute.extend([helper.make_attribute("axis", 3)])
self.node_name_to_graph_name[qkv_lora_concat_node.name] = self.this_graph_name
# Reshape the LoRA concatenated weights to [..., n * 3 * h]
reshaped_lora_weights_shape_tensor_name = qkv_lora_concat_node.name + "_reshape_shape"
self.add_initializer(
name=reshaped_lora_weights_shape_tensor_name,
data_type=TensorProto.INT64,
dims=[3],
vals=[0, 0, n * 3 * h],
raw=False,
)
qkv_lora_reshaped_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_QKV")
qkv_lora_reshaped_node = helper.make_node(
"Reshape",
inputs=[qkv_lora_concat_node.output[0], reshaped_lora_weights_shape_tensor_name],
outputs=[qkv_lora_reshaped_node_name + "_out"],
name=qkv_lora_reshaped_node_name,
)
self.node_name_to_graph_name[qkv_lora_reshaped_node.name] = self.this_graph_name
# Add the LoRA Q/K/V weights to the base Q/K/V weights
add_weights_node_name = self.model.create_node_name("Add", name_prefix="Add_Weights_QKV")
add_weights_node = helper.make_node(
"Add",
inputs=[qkv_lora_reshaped_node.output[0], matmul_node.output[0]],
outputs=[add_weights_node_name + "_out"],
name=add_weights_node_name,
)
self.node_name_to_graph_name[add_weights_node.name] = self.this_graph_name
# Finally, reshape the concatenated Q/K/V result to 5D
shape_tensor_name = add_weights_node_name + "_reshape_shape"
self.add_initializer(
name=shape_tensor_name,
data_type=TensorProto.INT64,
dims=[5],
vals=[0, 0, n, 3, h],
raw=False,
)
reshape_node = helper.make_node(
"Reshape",
inputs=[add_weights_node.output[0], shape_tensor_name],
outputs=[attention_node_name + "_qkv_input"],
name=add_weights_node_name + "_reshape",
)
self.node_name_to_graph_name[reshape_node.name] = self.this_graph_name
self.nodes_to_add.extend(
[
matmul_node,
q_lora_reshape_node,
k_lora_reshape_node,
v_lora_reshape_node,
qkv_lora_concat_node,
qkv_lora_reshaped_node,
add_weights_node,
reshape_node,
]
)
self.nodes_to_remove.extend([q_matmul, k_matmul, v_matmul, q_matmul_add, k_matmul_add, v_matmul_add])
else:
# TODO: Support non-packed QKV
return None
else: # cross attention
attention_node_name = self.model.create_node_name("MultiHeadAttention")
if self.enable_packed_kv:
if kw.shape != vw.shape:
return None
kw_in_size = kw.shape[0]
vw_in_size = vw.shape[0]
assert kw_in_size == vw_in_size
qw_out_size = qw.shape[1]
kw_out_size = kw.shape[1]
vw_out_size = vw.shape[1]
assert qw_out_size == vw_out_size and kw_out_size == vw_out_size
c = kw_in_size
n = num_heads
h = kw_out_size // num_heads
# Concat and interleave weights so that the output of fused KV GEMM has [B, S_kv, N, 2, H] shape
kv_weight = np.dstack([kw.reshape(c, n, h), vw.reshape(c, n, h)]).reshape(c, n * 2 * h)
matmul_node_name = self.model.create_node_name("MatMul", name_prefix="MatMul_KV")
self.add_initializer(
name=matmul_node_name + "_weight",
data_type=TensorProto.FLOAT,
dims=[kv_weight.shape[0], kv_weight.shape[1]],
vals=kv_weight,
)
matmul_node = helper.make_node(
"MatMul",
inputs=[k_matmul.input[0], matmul_node_name + "_weight"],
outputs=[matmul_node_name + "_out"],
name=matmul_node_name,
)
self.node_name_to_graph_name[matmul_node.name] = self.this_graph_name
# Do the same thing with the LoRA weights, but don't constant fold the result. The goal is to allow
# the Q/K/V weights to be changed without having to re-run the optimizer.
kv_lora_weight_shape_tensor_name = q_lora_last_node.name + "_reshape_shape"
self.add_initializer(
name=kv_lora_weight_shape_tensor_name,
data_type=TensorProto.INT64,
dims=[4],
vals=[0, 0, n, h],
raw=False,
)
# Reshape the LoRA K weights
k_lora_reshape_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_K")
k_lora_reshape_node = helper.make_node(
"Reshape",
inputs=[k_lora_last_node.output[0], kv_lora_weight_shape_tensor_name],
outputs=[k_lora_reshape_node_name + "_out"],
name=k_lora_reshape_node_name,
)
self.node_name_to_graph_name[k_lora_reshape_node.name] = self.this_graph_name
# Reshape the LoRA V weights
v_lora_reshape_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_V")
v_lora_reshape_node = helper.make_node(
"Reshape",
inputs=[v_lora_last_node.output[0], kv_lora_weight_shape_tensor_name],
outputs=[v_lora_reshape_node_name + "_out"],
name=v_lora_reshape_node_name,
)
self.node_name_to_graph_name[v_lora_reshape_node.name] = self.this_graph_name
# Concat the reshaped LoRA K/V weights together on the third axis
kv_lora_concat_node_name = self.model.create_node_name("Concat", name_prefix="Concat_LoRA_KV")
kv_lora_concat_node = helper.make_node(
"Concat",
inputs=[k_lora_reshape_node.output[0], v_lora_reshape_node.output[0]],
outputs=[kv_lora_concat_node_name + "_out"],
name=kv_lora_concat_node_name,
)
kv_lora_concat_node.attribute.extend([helper.make_attribute("axis", 3)])
self.node_name_to_graph_name[kv_lora_concat_node.name] = self.this_graph_name
# Reshape the LoRA concatenated weights to [..., n * 2 * h]
reshaped_kv_lora_weights_shape_tensor_name = kv_lora_concat_node.name + "_reshape_shape"
self.add_initializer(
name=reshaped_kv_lora_weights_shape_tensor_name,
data_type=TensorProto.INT64,
dims=[3],
vals=[0, 0, n * 2 * h],
raw=False,
)
kv_lora_reshaped_node_name = self.model.create_node_name("Reshape", name_prefix="Reshape_LoRA_KV")
kv_lora_reshaped_node = helper.make_node(
"Reshape",
inputs=[kv_lora_concat_node.output[0], reshaped_kv_lora_weights_shape_tensor_name],
outputs=[kv_lora_reshaped_node_name + "_out"],
name=kv_lora_reshaped_node_name,
)
self.node_name_to_graph_name[kv_lora_reshaped_node.name] = self.this_graph_name
# Add the LoRA K/V weights to the base K/V weights
add_kv_weights_node_name = self.model.create_node_name("Add", name_prefix="Add_Weights_KV")
add_kv_weights_node = helper.make_node(
"Add",
inputs=[kv_lora_reshaped_node.output[0], matmul_node.output[0]],
outputs=[add_kv_weights_node_name + "_out"],
name=add_kv_weights_node_name,
)
self.node_name_to_graph_name[add_kv_weights_node.name] = self.this_graph_name
# Finally, reshape the concatenated K/V result to 5D
shape_tensor_name = add_kv_weights_node_name + "_reshape_shape"
self.add_initializer(
name=shape_tensor_name,
data_type=TensorProto.INT64,
dims=[5],
vals=[0, 0, n, 2, h],
raw=False,
)
reshape_node = helper.make_node(
"Reshape",
inputs=[add_kv_weights_node.output[0], shape_tensor_name],
outputs=[attention_node_name + "_kv_input"],
name=add_kv_weights_node_name + "_reshape",
)
self.node_name_to_graph_name[reshape_node.name] = self.this_graph_name
self.nodes_to_add.extend(
[
matmul_node,
k_lora_reshape_node,
v_lora_reshape_node,
kv_lora_concat_node,
kv_lora_reshaped_node,
add_kv_weights_node,
reshape_node,
]
)
self.nodes_to_remove.extend([k_matmul, v_matmul, k_matmul_add, v_matmul_add])
else:
# TODO: Support non-packed KV
return None
# No bias, use zeros
qkv_bias = np.zeros([3, hidden_size], dtype=np.float32)
qkv_bias_dim = 3 * hidden_size
self.add_initializer(
name=attention_node_name + "_qkv_bias",
data_type=TensorProto.FLOAT,
dims=[qkv_bias_dim],
vals=qkv_bias,
)
if is_self_attention:
if not self.enable_packed_qkv:
# TODO: Support non-packed QKV
return None
else:
attention_inputs = [attention_node_name + "_qkv_input"]
else:
if not self.enable_packed_kv:
# TODO: Support non-packed QKV
return None
else:
attention_inputs = [
q_matmul_add.output[0],
attention_node_name + "_kv_input",
]
attention_node = helper.make_node(
"Attention" if (is_self_attention and not self.enable_packed_qkv) else "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 = (
"Attention (self attention)"
if is_self_attention and not self.enable_packed_qkv
else "MultiHeadAttention ({})".format(
"self attention with packed qkv"
if self.enable_packed_qkv
else "cross attention with packed kv" if self.enable_packed_kv else "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_a1111_fp16(normalize_node, input_name_to_nodes, output_name_to_node):
return
node_before_layernorm = self.model.match_parent(normalize_node, "Add", 0)
# In SD 1.5, for self attention, LayerNorm has parent Reshape
if node_before_layernorm is None and not self.is_cross_attention:
node_before_layernorm = self.model.match_parent(normalize_node, "Reshape", 0)
if node_before_layernorm is None:
return
root_input = node_before_layernorm.output[0]
children_nodes = input_name_to_nodes[root_input]
skip_add = None
for node in children_nodes:
if node.op_type == "Add": # SkipLayerNormalization fusion is not applied yet
skip_add = node
break
if skip_add is None:
return
match_qkv = self.match_qkv_torch1(root_input, skip_add) or self.match_qkv_torch2(root_input, skip_add)
if match_qkv is not None:
is_torch2, reshape_qkv, transpose_qkv, reshape_q, matmul_q, matmul_k, matmul_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, is_torch2)
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,
matmul_k,
matmul_v,
q_num_heads,
q_hidden_size,
input=normalize_node.output[0],
output=attention_last_node.output[0],
)
if new_node is None:
return
else:
# Check if we have a LoRA pattern
match_qkv = self.match_qkv_torch1_lora(root_input, skip_add) or self.match_qkv_torch2_lora(
root_input, skip_add
)
if match_qkv is None:
return
is_torch2, reshape_qkv, transpose_qkv, reshape_q, matmul_add_q, matmul_add_k, matmul_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, is_torch2)
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_lora(
matmul_add_q,
matmul_add_k,
matmul_add_v,
q_num_heads,
q_hidden_size,
input=normalize_node.output[0],
output=attention_last_node.output[0],
)
if new_node is None:
return
q_num_heads, q_hidden_size = self.get_num_heads_and_hidden_size(reshape_q, normalize_node, is_torch2)
if q_num_heads <= 0:
logger.debug("fuse_attention: failed to detect num_heads")
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_qkv_torch1(self, root_input, skip_add):
"""Match Q, K and V paths exported by PyTorch 1.*"""
another_input = 1 if skip_add.input[0] == root_input else 0
qkv_nodes = self.model.match_parent_path(
skip_add,
["Add", "MatMul", "Reshape", "Transpose", "Reshape", "MatMul"],
[another_input, None, None, 0, 0, 0],
)
if qkv_nodes is None:
return None
(_, _, reshape_qkv, transpose_qkv, _, matmul_qkv) = qkv_nodes
# No bias. For cross-attention, the input of the MatMul is encoder_hidden_states graph input.
v_nodes = self.model.match_parent_path(matmul_qkv, ["Reshape", "Transpose", "Reshape", "MatMul"], [1, 0, 0, 0])
if v_nodes is None:
logger.debug("fuse_attention: failed to match v path")
return None
(_, _, _, matmul_v) = v_nodes
qk_nodes = self.model.match_parent_path(matmul_qkv, ["Softmax", "Mul", "MatMul"], [0, 0, 0])
if qk_nodes is not None:
(_softmax_qk, _mul_qk, matmul_qk) = qk_nodes
else:
qk_nodes = self.model.match_parent_path(matmul_qkv, ["Softmax", "Add", "Mul", "MatMul"], [0, 0, 0, 0])
if qk_nodes is not None:
(_softmax_qk, _add_zero, _mul_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, ["Reshape", "Transpose", "Reshape", "MatMul"], [0, 0, 0, 0])
if q_nodes is None:
logger.debug("fuse_attention: failed to match q path")
return None
(_, _transpose_q, reshape_q, matmul_q) = q_nodes
k_nodes = self.model.match_parent_path(
matmul_qk, ["Transpose", "Reshape", "Transpose", "Reshape", "MatMul"], [1, 0, 0, 0, 0]
)
if k_nodes is None:
logger.debug("fuse_attention: failed to match k path")
return None
(_, _, _, _, matmul_k) = k_nodes
return False, reshape_qkv, transpose_qkv, reshape_q, matmul_q, matmul_k, matmul_v
def match_qkv_torch2(self, root_input, skip_add):
"""Match Q, K and V paths exported by PyTorch 2.*"""
another_input = 1 if skip_add.input[0] == root_input else 0
qkv_nodes = self.model.match_parent_path(
skip_add,
["Add", "MatMul", "Reshape", "Transpose", "MatMul"],
[another_input, 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", "MatMul"], [1, 0, 0])
if v_nodes is None:
logger.debug("fuse_attention: failed to match v path")
return None
(_, _, 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", "MatMul"], [0, None, 0, 0])
if q_nodes is None:
logger.debug("fuse_attention: failed to match q path")
return None
(mul_q, _transpose_q, reshape_q, matmul_q) = q_nodes
k_nodes = self.model.match_parent_path(matmul_qk, ["Mul", "Transpose", "Reshape", "MatMul"], [1, None, 0, 0])
if k_nodes is None:
logger.debug("fuse_attention: failed to match k path")
return None
(_mul_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 True, reshape_qkv, transpose_qkv, reshape_q, matmul_q, matmul_k, matmul_v
def match_qkv_torch1_lora(self, root_input, skip_add):
"""Match Q, K and V paths exported by PyTorch 1 that contains LoRA patterns.*"""
another_input = 1 if skip_add.input[0] == root_input else 0
qkv_nodes = self.model.match_parent_path(
skip_add,
["Add", "Add", "MatMul", "Reshape", "Transpose", "Reshape", "MatMul"],
[another_input, 0, None, None, 0, 0, 0],
)
if qkv_nodes is None:
return None
(_, _, _, reshape_qkv, transpose_qkv, _, matmul_qkv) = qkv_nodes
# No bias. For cross-attention, the input of the MatMul is encoder_hidden_states graph input.
v_nodes = self.model.match_parent_path(matmul_qkv, ["Reshape", "Transpose", "Reshape", "Add"], [1, 0, 0, 0])
if v_nodes is None:
logger.debug("fuse_attention: failed to match LoRA v path")
return None
(_, _, _, matmul_add_v) = v_nodes
qk_nodes = self.model.match_parent_path(matmul_qkv, ["Softmax", "Mul", "MatMul"], [0, 0, 0])
if qk_nodes is not None:
(_softmax_qk, _mul_qk, matmul_qk) = qk_nodes
else:
qk_nodes = self.model.match_parent_path(matmul_qkv, ["Softmax", "Add", "Mul", "MatMul"], [0, 0, 0, 0])
if qk_nodes is not None:
(_softmax_qk, _add_zero, _mul_qk, matmul_qk) = qk_nodes
else:
logger.debug("fuse_attention: failed to match LoRA qk path")
return None
q_nodes = self.model.match_parent_path(matmul_qk, ["Reshape", "Transpose", "Reshape", "Add"], [0, 0, 0, 0])
if q_nodes is None:
logger.debug("fuse_attention: failed to match LoRA q path")
return None
(_, _transpose_q, reshape_q, matmul_add_q) = q_nodes
k_nodes = self.model.match_parent_path(
matmul_qk, ["Transpose", "Reshape", "Transpose", "Reshape", "Add"], [1, 0, 0, 0, 0]
)
if k_nodes is None:
logger.debug("fuse_attention: failed to match LoRA k path")
return None
(_, _, _, _, matmul_add_k) = k_nodes
return False, reshape_qkv, transpose_qkv, reshape_q, matmul_add_q, matmul_add_k, matmul_add_v
def match_qkv_torch2_lora(self, root_input, skip_add):
"""Match Q, K and V paths exported by PyTorch 2 that contains LoRA patterns.*"""
another_input = 1 if skip_add.input[0] == root_input else 0
qkv_nodes = self.model.match_parent_path(
skip_add,
["Add", "Add", "MatMul", "Reshape", "Transpose", "MatMul"],
[another_input, 0, 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"], [1, 0, 0])
if v_nodes is None:
logger.debug("fuse_attention: failed to match LoRA v path")
return None
(_, _, matmul_add_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 LoRA qk path")
return None
q_nodes = self.model.match_parent_path(matmul_qk, ["Mul", "Transpose", "Reshape", "Add"], [0, None, 0, 0])
if q_nodes is None:
logger.debug("fuse_attention: failed to match LoRA q path")
return None
(mul_q, _transpose_q, reshape_q, matmul_add_q) = q_nodes
k_nodes = self.model.match_parent_path(matmul_qk, ["Mul", "Transpose", "Reshape", "Add"], [1, None, 0, 0])
if k_nodes is None:
logger.debug("fuse_attention: failed to match LoRA k path")
return None
(_mul_k, _, _, matmul_add_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 LoRA mul_q path")
return None
return True, reshape_qkv, transpose_qkv, reshape_q, matmul_add_q, matmul_add_k, matmul_add_v
def match_lora_path(
self,
add_node: NodeProto,
):
# Lora paths can look like one of the following options:
# MatMul -> MatMul -> Add
# MatMul -> MatMul -> Mul -> Add
# MatMul -> MatMul -> Mul -> Mul -> Add
# Try matching MatMul -> MatMul -> Add
lora_nodes = self.model.match_parent_path(
add_node,
["MatMul", "MatMul"],
[1, 0],
)
if lora_nodes is not None:
(lora_matmul_2_node, lora_matmul_1_node) = lora_nodes
return (lora_matmul_2_node, lora_matmul_1_node)
# Try matching MatMul -> MatMul -> Mul -> Add
lora_nodes = self.model.match_parent_path(
add_node,
["Mul", "MatMul", "MatMul"],
[1, 0, 0],
)
if lora_nodes is not None:
(lora_mul_node, _, lora_matmul_1_node) = lora_nodes
return (lora_mul_node, lora_matmul_1_node)
# Try matching MatMul -> MatMul -> Mul -> Mul -> Add
lora_nodes = self.model.match_parent_path(
add_node,
["Mul", "Mul", "MatMul", "MatMul"],
[1, 0, 0, 0],
)
if lora_nodes is not None:
(lora_mul_node, _, _, lora_matmul_1_node) = lora_nodes
return (lora_mul_node, lora_matmul_1_node)
return None
def fuse_a1111_fp16(self, normalize_node, input_name_to_nodes, output_name_to_node):
"""Fuse attention of fp16 UNet exported in A1111 (stable diffusion webui) extension"""
entry_path = self.model.match_parent_path(normalize_node, ["Cast", "Add"], [0, 0])
if entry_path is None:
entry_path = self.model.match_parent_path(normalize_node, ["Cast", "Reshape"], [0, 0])
if entry_path is None:
return False
_cast, node_before_layernorm = entry_path
root_input = node_before_layernorm.output[0]
children_nodes = input_name_to_nodes[root_input]
skip_add = None
for node in children_nodes:
if node.op_type == "Add": # SkipLayerNormalization fusion is not applied yet
skip_add = node
break
if skip_add is None:
return False
match_qkv = self.match_qkv_a1111(root_input, skip_add)
if match_qkv is None:
return False
(
reshape_qkv,
transpose_qkv,
reshape_q,
matmul_q,
matmul_k,
matmul_v,
) = match_qkv
cast_q = self.model.match_parent(matmul_q, "Cast", 0)
cast_k = self.model.match_parent(matmul_k, "Cast", 0)
cast_v = self.model.match_parent(matmul_v, "Cast", 0)
if not (
cast_q is not None
and cast_k is not None
and (cast_q == cast_k if not self.is_cross_attention else cast_q != cast_k)
and cast_k == cast_v
):
return False
if cast_q.input[0] != normalize_node.output[0]:
return False
attention_last_node = reshape_qkv
q_num_heads = self.get_num_heads(reshape_q, True) or self.get_num_heads(reshape_q, False)
if q_num_heads <= 0:
logger.debug("fuse_attention: failed to detect num_heads")
return False
q_hidden_size = self.get_hidden_size(normalize_node)
# 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,
matmul_k,
matmul_v,
q_num_heads,
q_hidden_size,
input=matmul_q.input[0],
output=attention_last_node.output[0],
)
if new_node is None:
return False
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
return True
def match_qkv_a1111(self, root_input, skip_add):
"""Match Q, K and V paths exported by A1111 (stable diffusion webui) extension"""
another_input = 1 if skip_add.input[0] == root_input else 0
qkv_nodes = self.model.match_parent_path(
skip_add,
["Add", "MatMul", "Reshape", "Transpose", "Reshape", "Einsum"],
[another_input, None, None, 0, 0, 0],
)
if qkv_nodes is None:
return None
(_, _, reshape_qkv, transpose_qkv, reshape_einsum, einsum_qkv) = qkv_nodes
v_nodes = self.model.match_parent_path(einsum_qkv, ["Reshape", "Transpose", "Reshape", "MatMul"], [1, 0, 0, 0])
if v_nodes is None:
logger.debug("fuse_attention: failed to match v path")
return None
(_, _, _, matmul_v) = v_nodes
qk_nodes = self.model.match_parent_path(
einsum_qkv, ["Cast", "Cast", "Softmax", "Mul", "Einsum"], [0, 0, 0, 0, None]
)
if qk_nodes is not None:
(_, _, _softmax_qk, _, einsum_qk) = qk_nodes
else:
logger.debug("fuse_attention: failed to match qk path")
return None
q_nodes = self.model.match_parent_path(einsum_qk, ["Reshape", "Transpose", "Reshape", "MatMul"], [0, 0, 0, 0])
if q_nodes is None:
logger.debug("fuse_attention: failed to match q path")
return None
(_, _transpose_q, reshape_q, matmul_q) = q_nodes
k_nodes = self.model.match_parent_path(einsum_qk, ["Reshape", "Transpose", "Reshape", "MatMul"], [1, 0, 0, 0])
if k_nodes is None:
logger.debug("fuse_attention: failed to match k path")
return None
(_, _, _, matmul_k) = k_nodes
return reshape_qkv, transpose_qkv, reshape_q, matmul_q, matmul_k, matmul_v