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# -*- coding: utf-8 -*-
"""
把 DepthART ONNX 中的 com.depthart::SelectiveScan 改写为 pulsar2 可编译的纯 ONNX 子图。
背景：
官方 relative_* ONNX 含自定义算子 SelectiveScan，pulsar2 前端直接报
"dont support SelectiveScan opr"。官方只提供 TensorRT 插件，没有 NPU 实现。
推理脚本里的 Loop 改写可以跑通 onnxruntime，但 AX 工具链不支持 Loop 控制流。
做法：
选择性扫描是线性递推 h_t = exp(dt_t*A) * h_{t-1} + dt_t*B_t*u_t，可写成
h_t = exp(L_t) * h_{-1} + sum_{s<=t} exp(L_t - L_s) * dt_s*B_s*u_s
其中 L_t = cumsum_{r<=t}(dt_r*A)。把时间轴切成固定长度 T（默认 14），每个
分块内用 CumSum + 下三角 mask + MatMul 一次算出，块间只传一个状态。所有
指数项的指数都 <= 0，不会溢出。所有算子（CumSum/Exp/Where/MatMul 等）都在
AX 前端支持列表内，不含 Loop/Scan/自定义算子。
校验：
- 本工具用 Identity 替换 scan 跑一遍，拿到每个 scan 的 D/G/N/L，再按实际
尺寸生成静态图；时间轴不是 T 整数倍时自动补零并在输出处裁掉。
- 改写后的整模型输出与已验证的 Loop 版本最大绝对误差约 6e-6（FP32）。
- 同时把 4D `Einsum` 换成按 k 的批 MatMul（`b k d l, k c d -> b k c l`
和 `b k r l, k d r -> b k d l`）：pulsar2 的量化执行器不支持 4D Einsum，
会报 Op Execution Error。
用法（在 hs/DepthART 目录下执行）：
# 默认：448 模型，块大小 14，输出 onnx/relative_s_448_default_axonnx.onnx
../../venv/bin/python src/tool/depthart_export_axonnx.py
# 指定模型/输出/块大小
../../venv/bin/python src/tool/depthart_export_axonnx.py \\
--model onnx/relative_b_448_default.onnx \\
--output onnx/relative_b_448_default_axonnx.onnx --chunk-size 14
# 改写后用推理脚本验证数值
../../venv/bin/python src/infer/depthart_infer_onnx.py \\
--model onnx/relative_s_448_default_axonnx.onnx --image /path/a.jpg
注意：
- 块内两两指数矩阵形状为 [D*N, T, T]，T 越大显存/内存占用越大；
T=14 时 672 通道层约 1M 元素（4 MB），推荐保持默认。
- 输出是标准 ONNX，不再含自定义算子；原模型文件不会被修改。
"""
import argparse
import logging
from pathlib import Path
import numpy as np
import onnx
import onnxruntime as ort
from onnx import helper, numpy_helper, TensorProto
PROJECT_ROOT = Path(__file__).resolve().parents[2]
DEFAULT_MODEL = PROJECT_ROOT / 'onnx' / 'relative_s_448_default.onnx'
DOMAIN = 'com.depthart'
OP = 'SelectiveScan'
EINSUM_EQUATIONS = {
'b k d l, k c d -> b k c l',
'b k r l, k d r -> b k d l',
}
def collect_runtime_shapes(model):
"""用 ORT 实际跑一遍，读取 scan 与 Einsum 的静态形状。
scan 会被替换成 Identity（输出形状等于 u，不影响下游形状）；Einsum 正常执行。
返回 (scan_shapes, einsum_shapes)：
scan_shapes : 按图顺序的 [(D, G, N, L), ...]
einsum_shapes: {节点名: (x_shape, y_shape)}
"""
nodes = []
scan_outputs, b_aliases = [], {}
einsum_x, einsum_y = {}, {}
for index, node in enumerate(model.graph.node):
if node.domain == DOMAIN and node.op_type == OP:
nodes.append(helper.make_node('Identity', [node.input[0]], [node.output[0]],
name=f'depthart_stub_scan_{index}'))
alias = f'depthart_stub_b_{index}'
nodes.append(helper.make_node('Identity', [node.input[3]], [alias],
name=f'depthart_stub_bnode_{index}'))
scan_outputs.append(node.output[0])
b_aliases[node.output[0]] = alias
else:
if node.op_type == 'Einsum':
x_alias = f'depthart_stub_einsum_x_{index}'
y_alias = f'depthart_stub_einsum_y_{index}'
nodes.append(helper.make_node('Identity', [node.input[0]], [x_alias],
name=f'{x_alias}_node'))
nodes.append(helper.make_node('Identity', [node.output[0]], [y_alias],
name=f'{y_alias}_node'))
einsum_x[node.name] = x_alias
einsum_y[node.name] = y_alias
nodes.append(node)
stub = onnx.ModelProto()
stub.CopyFrom(model)
del stub.graph.node[:]
stub.graph.node.extend(nodes)
for name in scan_outputs:
stub.graph.output.append(helper.make_tensor_value_info(name, TensorProto.FLOAT, None))
stub.graph.output.append(helper.make_tensor_value_info(b_aliases[name],
TensorProto.FLOAT, None))
for x_alias, y_alias in zip(einsum_x.values(), einsum_y.values()):
stub.graph.output.append(helper.make_tensor_value_info(x_alias, TensorProto.FLOAT, None))
stub.graph.output.append(helper.make_tensor_value_info(y_alias, TensorProto.FLOAT, None))
meta = model.graph.input[0]
shape = [dim.dim_value for dim in meta.type.tensor_type.shape.dim]
if not all(shape):
raise ValueError(f'输入必须是静态形状: {meta.name} {shape}')
session = ort.InferenceSession(stub.SerializeToString())
values = dict(zip([out.name for out in session.get_outputs()],
session.run(None, {meta.name: np.zeros(shape, dtype=np.float32)})))
scan_map = {}
for name in scan_outputs:
u_shape = values[name].shape
b_shape = values[b_aliases[name]].shape
if len(u_shape) != 3 or len(b_shape) != 4:
raise ValueError(f'scan 形状不符合预期: u={u_shape} B={b_shape}')
scan_map[name] = (int(u_shape[1]), int(b_shape[1]), int(b_shape[2]), int(u_shape[2]))
scan_shapes = [scan_map[node.output[0]] for node in model.graph.node
if node.domain == DOMAIN and node.op_type == OP]
einsum_shapes = {name: (values[einsum_x[name]].shape, values[einsum_y[name]].shape)
for name in einsum_x}
return scan_shapes, einsum_shapes
class Rewriter:
def __init__(self, model, chunk_size, stub_scan=False, scan_method='pairwise'):
self.model = model
self.chunk_size = chunk_size
self.stub_scan = stub_scan
self.scan_method = scan_method
self.initializers = list(model.graph.initializer)
self.names = {tensor.name for tensor in self.initializers}
self.initializer_shapes = {tensor.name: tuple(tensor.dims)
for tensor in model.graph.initializer}
self.shared = {}
def build_einsum(self, prefix, node, x_shape, y_shape):
"""b k d l, k c d -> b k c l （以及 b k r l, k d r -> b k d l）。
两条式子都是按 k 的批 MatMul：W[k,out,in] @ x[b=1,k,in,l]。
替换掉 4D Einsum，避免量化执行器不支持。
"""
out_name = node.output[0]
w_shape = self.initializer_shapes.get(node.input[1])
if w_shape is None:
raise ValueError(f'{node.name}: 第二个输入不是常量权重，暂不支持')
if len(w_shape) != 3 or len(x_shape) != 4:
raise ValueError(f'{node.name}: Einsum 形状不符合预期: {w_shape} {x_shape}')
if int(x_shape[0]) != 1:
raise ValueError(f'{node.name}: 仅支持 batch=1，实际 {x_shape}')
K, out_dim, in_dim = (int(v) for v in w_shape)
length = int(x_shape[3])
if int(x_shape[1]) != K:
raise ValueError(f'{node.name}: K 不一致: {x_shape} vs {w_shape}')
expected = (1, K, out_dim, length)
if tuple(int(v) for v in y_shape) != expected:
raise ValueError(f'{node.name}: 输出形状 {y_shape} != {expected}')
nodes = []
# 用 Squeeze/Unsqueeze 而不是 Reshape 还原 batch 维：onnxslim 会把
# MatMul 前后的 Reshape 当成冗余节点合并/改名，导致下游张量断链。
nodes.append(helper.make_node(
'Squeeze', [node.input[0], self.shared_const('axes0', np.array([0], np.int64))],
[f'{prefix}_x2']))
nodes.append(helper.make_node('MatMul', [node.input[1], f'{prefix}_x2'],
[f'{prefix}_y3']))
nodes.append(helper.make_node(
'Unsqueeze', [f'{prefix}_y3', self.shared_const('axes0', np.array([0], np.int64))],
[out_name]))
return nodes
def const(self, name, value):
if name not in self.names:
self.initializers.append(numpy_helper.from_array(np.asarray(value), name))
self.names.add(name)
return name
def shared_const(self, key, value):
if key not in self.shared:
self.shared[key] = self.const(f'depthart_{key}', value)
return self.shared[key]
def build_logspace(self, prefix, shapes, inputs):
"""对数空间分块递推：每块 O(T) 而非 O(T^2)，不构造 [D*N,T,T] 两两矩阵。
h_t = exp(L_t) * ( h_prev + cumsum_s( exp(-L_s) * dt_s * B_s * u_s ) )
其中 L 为块内相对累计衰减（L_0=0，L_t<=0）。要求块内 exp(-L) 不超 U16 量程，
因此 T 要小（T=4 时最大约 e^9≈8e3）。
"""
D, G, N, L = shapes
u, delta, a, b, c = inputs[:5]
d = inputs[5] if len(inputs) > 5 and inputs[5] else None
bias = inputs[6] if len(inputs) > 6 and inputs[6] else None
attrs = {item.name: item.i for item in self.node_attrs}
T = self.chunk_size
nodes = []
dt = delta
if bias is not None:
bias3 = self.const(f'{prefix}_bias3_shape', np.array([1, D, 1], np.int64))
nodes.append(helper.make_node('Reshape', [bias, bias3], [f'{prefix}_bias3']))
nodes.append(helper.make_node('Add', [delta, f'{prefix}_bias3'], [f'{prefix}_dtb']))
dt = f'{prefix}_dtb'
if int(attrs.get('delta_softplus', 0)):
nodes.append(helper.make_node('Softplus', [dt], [f'{prefix}_dt']))
dt = f'{prefix}_dt'
# 补零到 T 的整数倍，避免最后一块长度不足
pad = (-L) % T
Lp = L + pad
if pad:
nodes.append(helper.make_node(
'Concat', [dt, self.const(f'{prefix}_pad_dt', np.zeros((1, D, pad), np.float32))],
[f'{prefix}_dt_pad'], axis=2))
nodes.append(helper.make_node(
'Concat', [u, self.const(f'{prefix}_pad_u', np.zeros((1, D, pad), np.float32))],
[f'{prefix}_u_pad'], axis=2))
pad_bc = self.const(f'{prefix}_pad_bc', np.zeros((1, G, N, pad), np.float32))
nodes.append(helper.make_node('Concat', [b, pad_bc], [f'{prefix}_b_pad'], axis=3))
nodes.append(helper.make_node('Concat', [c, pad_bc], [f'{prefix}_c_pad'], axis=3))
dt, u, b, c = (f'{prefix}_dt_pad', f'{prefix}_u_pad',
f'{prefix}_b_pad', f'{prefix}_c_pad')
# dtA = dt * A -> [1,D,N,Lp]
a4 = self.const(f'{prefix}_a4_shape', np.array([1, D, N, 1], np.int64))
nodes.append(helper.make_node('Reshape', [a, a4], [f'{prefix}_a4']))
dt3 = self.const(f'{prefix}_dt3_shape', np.array([1, D, 1, Lp], np.int64))
nodes.append(helper.make_node('Reshape', [dt, dt3], [f'{prefix}_dt4']))
nodes.append(helper.make_node('Mul', [f'{prefix}_dt4', f'{prefix}_a4'],
[f'{prefix}_dtA']))
b_full = self.expand_groups(b, D, G, N, Lp, f'{prefix}_b', nodes)
c_full = self.expand_groups(c, D, G, N, Lp, f'{prefix}_c', nodes)
if d is not None:
nodes.append(helper.make_node('Reshape', [d,
self.const(f'{prefix}_d_shape',
np.array([1, D, 1], np.int64))],
[f'{prefix}_d3']))
d = f'{prefix}_d3'
axes_u2 = self.shared_const('axes_u2', np.array([2], np.int64))
axes_rs = self.shared_const('axes_rs2', np.array([2], np.int64))
axis_t = self.shared_const('axis2', np.array([2], np.int64))
axis_bc = self.shared_const('axis3', np.array([3], np.int64))
h_prev = self.const(f'{prefix}_h0', np.zeros((1, D, N, 1), np.float32))
outputs = []
for k in range(Lp // T):
start, end = k * T, (k + 1) * T
tag = f'{prefix}_c{k}'
starts = self.const(f'{tag}_starts', np.array([start], np.int64))
ends = self.const(f'{tag}_ends', np.array([end], np.int64))
for source, suffix, axis in ((f'{prefix}_dtA', 'dta', axis_bc),
(dt, 'dt', axis_t), (u, 'u', axis_t),
(b_full, 'b', axis_bc), (c_full, 'c', axis_bc)):
nodes.append(helper.make_node('Slice', [source, starts, ends, axis],
[f'{tag}_{suffix}']))
# L = cumsum(dtA)（块内，含首步）；h = exp(L) * (h_prev + cumsum(exp(-L)*b))
nodes.append(helper.make_node('CumSum', [f'{tag}_dta',
self.shared_const('axis3s', np.array(3, np.int64))],
[f'{tag}_L']))
nodes.append(helper.make_node('Exp', [f'{tag}_L'], [f'{tag}_eL']))
nodes.append(helper.make_node('Neg', [f'{tag}_L'], [f'{tag}_negL']))
nodes.append(helper.make_node('Exp', [f'{tag}_negL'], [f'{tag}_eneg']))
# b = dt * u * B；G = cumsum(exp(-Lp) * b)
nodes.append(helper.make_node('Unsqueeze', [f'{tag}_dt', axes_u2], [f'{tag}_dt3']))
nodes.append(helper.make_node('Unsqueeze', [f'{tag}_u', axes_u2], [f'{tag}_u3']))
nodes.append(helper.make_node('Mul', [f'{tag}_dt3', f'{tag}_u3'], [f'{tag}_dtu']))
nodes.append(helper.make_node('Mul', [f'{tag}_dtu', f'{tag}_b'], [f'{tag}_bt']))
nodes.append(helper.make_node('Mul', [f'{tag}_eneg', f'{tag}_bt'], [f'{tag}_g']))
nodes.append(helper.make_node('CumSum', [f'{tag}_g',
self.shared_const('axis3s', np.array(3, np.int64))],
[f'{tag}_G']))
nodes.append(helper.make_node('Add', [f'{tag}_G', h_prev], [f'{tag}_hp']))
nodes.append(helper.make_node('Mul', [f'{tag}_eL', f'{tag}_hp'], [f'{tag}_h']))
nodes.append(helper.make_node('Mul', [f'{tag}_h', f'{tag}_c'], [f'{tag}_hc']))
nodes.append(helper.make_node('ReduceSum', [f'{tag}_hc', axes_rs],
[f'{tag}_ysum'], keepdims=0))
if d is not None:
nodes.append(helper.make_node('Mul', [d, f'{tag}_u'], [f'{tag}_du']))
nodes.append(helper.make_node('Add', [f'{tag}_ysum', f'{tag}_du'],
[f'{tag}_y']))
else:
nodes.append(helper.make_node('Identity', [f'{tag}_ysum'], [f'{tag}_y']))
outputs.append(f'{tag}_y')
nodes.append(helper.make_node('Slice', [f'{tag}_h',
self.const(f'{tag}_hl', np.array([T - 1], np.int64)),
self.const(f'{tag}_he', np.array([T], np.int64)),
axis_bc], [f'{tag}_hnext']))
h_prev = f'{tag}_hnext'
nodes.append(helper.make_node('Concat', outputs, [f'{prefix}_ycat'], axis=2))
if pad:
nodes.append(helper.make_node('Slice', [
f'{prefix}_ycat',
self.const(f'{prefix}_out_start', np.array([0], np.int64)),
self.const(f'{prefix}_out_end', np.array([L], np.int64)),
self.shared_const('axis2', np.array([2], np.int64))], [self.out_name]))
else:
nodes.append(helper.make_node('Identity', [f'{prefix}_ycat'], [self.out_name]))
return nodes
def build(self, prefix, shapes, inputs):
D, G, N, L = shapes
u, delta, a, b, c = inputs[:5]
d = inputs[5] if len(inputs) > 5 and inputs[5] else None
bias = inputs[6] if len(inputs) > 6 and inputs[6] else None
attrs = {item.name: item.i for item in self.node_attrs}
T = self.chunk_size
nodes = []
# dt = softplus(delta + bias)
dt = delta
if bias is not None:
bias3 = self.const(f'{prefix}_bias3_shape', np.array([1, D, 1], np.int64))
nodes.append(helper.make_node('Reshape', [bias, bias3], [f'{prefix}_bias3']))
nodes.append(helper.make_node('Add', [delta, f'{prefix}_bias3'], [f'{prefix}_dtb']))
dt = f'{prefix}_dtb'
if int(attrs.get('delta_softplus', 0)):
nodes.append(helper.make_node('Softplus', [dt], [f'{prefix}_dt']))
dt = f'{prefix}_dt'
# 时间轴补零到 T 的整数倍
pad = (-L) % T
Lp = L + pad
if pad:
nodes.append(helper.make_node(
'Concat', [dt, self.const(f'{prefix}_pad_dt', np.zeros((1, D, pad), np.float32))],
[f'{prefix}_dt_pad'], axis=2))
nodes.append(helper.make_node(
'Concat', [u, self.const(f'{prefix}_pad_u', np.zeros((1, D, pad), np.float32))],
[f'{prefix}_u_pad'], axis=2))
pad_bc = self.const(f'{prefix}_pad_bc', np.zeros((1, G, N, pad), np.float32))
nodes.append(helper.make_node('Concat', [b, pad_bc], [f'{prefix}_b_pad'], axis=3))
nodes.append(helper.make_node('Concat', [c, pad_bc], [f'{prefix}_c_pad'], axis=3))
dt, u, b, c = (f'{prefix}_dt_pad', f'{prefix}_u_pad',
f'{prefix}_b_pad', f'{prefix}_c_pad')
# B/C 按组连续复制：[1,G,N,Lp] -> [1,D,N,Lp]
b_full = self.expand_groups(b, D, G, N, Lp, f'{prefix}_b', nodes)
c_full = self.expand_groups(c, D, G, N, Lp, f'{prefix}_c', nodes)
# A -> [1,D,N,1]，dt -> [1,D,N,T]，再摊平成 [D*N,T] 做 CumSum
a4 = self.const(f'{prefix}_a_shape', np.array([1, D, N, 1], np.int64))
nodes.append(helper.make_node('Reshape', [a, a4], [f'{prefix}_a4']))
if d is not None:
d3 = self.const(f'{prefix}_d_shape', np.array([1, D, 1], np.int64))
nodes.append(helper.make_node('Reshape', [d, d3], [f'{prefix}_d3']))
d = f'{prefix}_d3'
mask = np.tril(np.ones((T, T), dtype=bool)).reshape(1, T, T)
mask_name = self.shared_const(f'mask_{T}', mask)
neg_name = self.shared_const('mask_fill', np.array(-1e4, np.float32))
flat_axis = self.shared_const('axis1', np.array(1, np.int64))
axes_u2 = self.shared_const('axes_u2', np.array([2], np.int64))
axes_u3 = self.shared_const('axes_u3', np.array([3], np.int64))
axes_rs = self.shared_const('axes_rs2', np.array([2], np.int64))
h_prev = self.const(f'{prefix}_h0', np.zeros((1, D, N, 1), np.float32))
outputs = []
for k in range(Lp // T):
start, end = k * T, (k + 1) * T
tag = f'{prefix}_c{k}'
starts = self.const(f'{tag}_starts', np.array([start], np.int64))
ends = self.const(f'{tag}_ends', np.array([end], np.int64))
axis_t = self.shared_const('axis2', np.array([2], np.int64))
axis_bc = self.shared_const('axis3', np.array([3], np.int64))
for source, suffix, axis in ((dt, 'dt', axis_t), (u, 'u', axis_t),
(b_full, 'b', axis_bc), (c_full, 'c', axis_bc)):
nodes.append(helper.make_node('Slice', [source, starts, ends, axis],
[f'{tag}_{suffix}']))
# dtA = dt * A -> [1,D,N,T] -> [D*N,T]，L = cumsum_t(dtA)
nodes.append(helper.make_node('Unsqueeze', [f'{tag}_dt', axes_u2], [f'{tag}_dt3']))
nodes.append(helper.make_node('Mul', [f'{tag}_dt3', f'{prefix}_a4'], [f'{tag}_dtA']))
nodes.append(helper.make_node('Reshape', [f'{tag}_dtA',
self.const(f'{tag}_dtA2_shape',
np.array([D * N, T], np.int64))],
[f'{tag}_dtA2']))
nodes.append(helper.make_node('CumSum', [f'{tag}_dtA2', flat_axis], [f'{tag}_L2']))
nodes.append(helper.make_node('Reshape', [f'{tag}_L2',
self.const(f'{tag}_L_shape',
np.array([1, D, N, T], np.int64))],
[f'{tag}_L']))
# 两两指数矩阵 E[t,s] = exp(L_t - L_s)（s>t 置为 -1e4 后 exp 为 0）
nodes.append(helper.make_node('Reshape', [f'{tag}_L2',
self.const(f'{tag}_Lt_shape',
np.array([D * N, T, 1], np.int64))],
[f'{tag}_Lt']))
nodes.append(helper.make_node('Reshape', [f'{tag}_L2',
self.const(f'{tag}_Ls_shape',
np.array([D * N, 1, T], np.int64))],
[f'{tag}_Ls']))
nodes.append(helper.make_node('Sub', [f'{tag}_Lt', f'{tag}_Ls'], [f'{tag}_diff']))
nodes.append(helper.make_node('Where', [mask_name, f'{tag}_diff', neg_name],
[f'{tag}_diffm']))
nodes.append(helper.make_node('Exp', [f'{tag}_diffm'], [f'{tag}_E']))
# b = dt * u * B，形状 [D*N,1,T]；contrib[t] = sum_{s<=t} E[t,s]*b[s]
nodes.append(helper.make_node('Unsqueeze', [f'{tag}_u', axes_u2], [f'{tag}_u3']))
nodes.append(helper.make_node('Mul', [f'{tag}_dt3', f'{tag}_u3'], [f'{tag}_dtu']))
nodes.append(helper.make_node('Mul', [f'{tag}_dtu', f'{tag}_b'], [f'{tag}_bt']))
nodes.append(helper.make_node('Reshape', [f'{tag}_bt',
self.const(f'{tag}_bt2_shape',
np.array([D * N, 1, T], np.int64))],
[f'{tag}_bt2']))
nodes.append(helper.make_node('Mul', [f'{tag}_E', f'{tag}_bt2'], [f'{tag}_eb']))
nodes.append(helper.make_node('ReduceSum', [f'{tag}_eb',
self.shared_const('axis2s', np.array([2], np.int64))],
[f'{tag}_contrib2'], keepdims=0))
nodes.append(helper.make_node('Reshape', [f'{tag}_contrib2',
self.const(f'{tag}_contrib_shape',
np.array([1, D, N, T], np.int64))],
[f'{tag}_contrib']))
# 块间状态：h_t = exp(L_t) * h_prev + contrib
nodes.append(helper.make_node('Exp', [f'{tag}_L'], [f'{tag}_expL']))
nodes.append(helper.make_node('Mul', [f'{tag}_expL', h_prev], [f'{tag}_carry']))
nodes.append(helper.make_node('Add', [f'{tag}_carry', f'{tag}_contrib'],
[f'{tag}_h']))
# y = sum_n(h*C) + D*u
nodes.append(helper.make_node('Mul', [f'{tag}_h', f'{tag}_c'], [f'{tag}_hc']))
nodes.append(helper.make_node('ReduceSum', [f'{tag}_hc', axes_rs],
[f'{tag}_ysum'], keepdims=0))
if d is not None:
nodes.append(helper.make_node('Mul', [d, f'{tag}_u'], [f'{tag}_du']))
nodes.append(helper.make_node('Add', [f'{tag}_ysum', f'{tag}_du'],
[f'{tag}_y']))
else:
nodes.append(helper.make_node('Identity', [f'{tag}_ysum'], [f'{tag}_y']))
outputs.append(f'{tag}_y')
nodes.append(helper.make_node('Gather', [f'{tag}_h',
self.const(f'{tag}_last', np.array(T - 1, np.int64))],
[f'{tag}_hlast'], axis=3))
nodes.append(helper.make_node('Unsqueeze', [f'{tag}_hlast', axes_u3],
[f'{tag}_hnext']))
h_prev = f'{tag}_hnext'
nodes.append(helper.make_node('Concat', outputs, [f'{prefix}_ycat'], axis=2))
if pad:
nodes.append(helper.make_node('Slice', [
f'{prefix}_ycat',
self.const(f'{prefix}_out_start', np.array([0], np.int64)),
self.const(f'{prefix}_out_end', np.array([L], np.int64)),
self.shared_const('axis2', np.array([2], np.int64))], [self.out_name]))
else:
nodes.append(helper.make_node('Identity', [f'{prefix}_ycat'], [self.out_name]))
return nodes
def expand_groups(self, x, D, G, N, L, prefix, nodes):
gs = D // G
r1_shape = self.const(f'{prefix}_r1_shape', np.array([1, G, 1, N, L], np.int64))
nodes.append(helper.make_node('Reshape', [x, r1_shape], [f'{prefix}_r1']))
target = self.const(f'{prefix}_tgt', np.array([1, G, gs, N, L], np.int64))
nodes.append(helper.make_node('Expand', [f'{prefix}_r1', target], [f'{prefix}_e']))
r2_shape = self.const(f'{prefix}_r2_shape', np.array([1, D, N, L], np.int64))
nodes.append(helper.make_node('Reshape', [f'{prefix}_e', r2_shape], [f'{prefix}_bc4']))
return f'{prefix}_bc4'
def rewrite(self):
shapes, einsum_shapes = collect_runtime_shapes(self.model)
if not shapes:
raise ValueError('模型中没有 SelectiveScan 节点，无需改写')
new_nodes, index, einsum_index = [], 0, 0
for node in self.model.graph.node:
if node.domain == DOMAIN and node.op_type == OP:
if self.stub_scan:
# 仅用于测 NPU 耗时的对照模型：扫描直接透传 u，精度无意义。
stub_node = helper.make_node('Identity', [node.input[0]], [node.output[0]],
name=f'depthart_stub_scan_{index}')
new_nodes.append(stub_node)
index += 1
continue
self.node_attrs = node.attribute
self.out_name = node.output[0]
builder = self.build_logspace if self.scan_method == 'log' else self.build
nodes = builder(f'depthart_pscan_{index}', shapes[index], node.input)
new_nodes.extend(nodes)
index += 1
elif node.op_type == 'Einsum':
equation = None
for attr in node.attribute:
if attr.name == 'equation':
equation = attr.s.decode()
if equation not in EINSUM_EQUATIONS:
raise ValueError(f'{node.name}: 不支持的 Einsum 方程 {equation!r}')
x_shape, y_shape = einsum_shapes[node.name]
nodes = self.build_einsum(f'depthart_einsum_{einsum_index}',
node, x_shape, y_shape)
new_nodes.extend(nodes)
einsum_index += 1
else:
new_nodes.append(node)
out = onnx.ModelProto()
out.CopyFrom(self.model)
del out.graph.node[:]
for position, node in enumerate(new_nodes):
if not node.name:
node.name = f'depthart_gen_{position}'
out.graph.node.extend(new_nodes)
out.graph.initializer.extend(self.initializers[len(self.model.graph.initializer):])
return out
def main():
parser = argparse.ArgumentParser(
description='把 DepthART SelectiveScan 改写为 pulsar2 可编译的纯 ONNX 子图')
parser.add_argument('--model', type=Path, default=DEFAULT_MODEL,
help=f'输入 ONNX，默认 {DEFAULT_MODEL.relative_to(PROJECT_ROOT)}')
parser.add_argument('--output', type=Path,
default=PROJECT_ROOT / 'onnx' / 'relative_s_448_default_axonnx.onnx',
help='输出 ONNX（不会覆盖输入模型）')
parser.add_argument('--chunk-size', type=int, default=14,
help='分块长度 T，默认 14；越大显存占用越高')
parser.add_argument('--stub-scan', action='store_true',
help='把 SelectiveScan 替换为 Identity，生成仅用于测 NPU 耗时的对照模型（精度无意义）')
parser.add_argument('--scan-method', choices=('pairwise', 'log'), default='pairwise',
help='pairwise=两两指数矩阵(默认)；log=对数空间递推，无 T^2 矩阵，T 需小')
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format='%(message)s')
if args.chunk_size < 1:
parser.error('--chunk-size 必须为正数')
if not args.model.is_file():
parser.error(f'--model 不存在: {args.model}')
if args.output.resolve() == args.model.resolve():
parser.error('--output 不能与 --model 相同')
model = onnx.load(str(args.model))
if not any(node.domain == DOMAIN and node.op_type == OP for node in model.graph.node):
raise ValueError(f'{args.model} 中没有 {DOMAIN}::{OP} 节点')
rewriter = Rewriter(model, args.chunk_size, args.stub_scan, args.scan_method)
rewritten = rewriter.rewrite()
args.output.parent.mkdir(parents=True, exist_ok=True)
onnx.save(rewritten, str(args.output))
logging.info('已写出标准 ONNX: %s（节点数 %d）', args.output, len(rewritten.graph.node))
logging.info('下一步：把 pulsar2 配置的 input 指向该文件；'
'可先用 src/infer/depthart_infer_onnx.py 验证数值。')
if __name__ == '__main__':
main()
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