Batch-transpose fusion into `FusedMatMul` sets `transBatchA` on inputs of different ranks, which ONNX Runtime can't compute

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@Syedmudassir1604 已经在做这个了。

开始于 2026年11月5日。

  • #3159 来自 @Syedmudassir1604 —— 未关闭

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Issue 类型
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技术栈
python
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backend, machine-learning

调研方向

Start by finding the ORT fusion rules that fold Transpose into FusedMatMul as transBatchA or transBatchB, then trace how they inspect input shapes and ranks. Reproduce the reported case with rank 3 x and rank 2 w, and check that the fusion is skipped when the ranks differ. Done means the optimized graph runs in ONNX Runtime and matches the unfused result.

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描述

module: optimizer

The rules that fold a Transpose into FusedMatMul as transBatchA / transBatchB don't check the other input's rank. ONNX Runtime's FusedMatMul only batch-transposes when both inputs have the same rank, of at least 3, so the fused graph fails at runtime. A projection of a transposed activation, Transpose(x, [1, 0, 2]) @ w with x rank 3 and w rank 2, is enough.

Repro (onnxscript 0.7.2, onnxruntime 1.31.1):

"""onnxscript folds a batch `Transpose` into `FusedMatMul` (`transBatchA`) when the other input has a lower rank, which
ONNX Runtime can't compute.

`Div(MatMul(Transpose(x, perm=[1, 0, 2]), w), 2)` with `x` rank 3 and `w` rank 2 (a projection of a transposed
activation). onnxscript's ORT fusions turn it into `FusedMatMul(x, w, transBatchA=1, alpha=0.5)`.
"""

import numpy as np
import onnx_ir as ir
import onnxruntime as ort
from onnx import TensorProto, helper
from onnxscript.rewriter.ort_fusions import optimize_for_ort

x = helper.make_tensor_value_info("x", TensorProto.FLOAT, [4, 2, 8])
w = helper.make_tensor("w", TensorProto.FLOAT, [8, 16], np.random.default_rng(0).standard_normal((8, 16)).astype(np.float32).ravel())
two = helper.make_tensor("two", TensorProto.FLOAT, [], [2.0])
nodes = [
    helper.make_node("Transpose", ["x"], ["xt"], perm=[1, 0, 2]),
    helper.make_node("MatMul", ["xt", "w"], ["product"]),
    helper.make_node("Div", ["product", "two"], ["y"]),
]
y = helper.make_tensor_value_info("y", TensorProto.FLOAT, [2, 4, 16])
proto = helper.make_model(helper.make_graph(nodes, "g", [x], [y], [w, two]), opset_imports=[helper.make_opsetid("", 20)], ir_version=10)

feeds = {"x": np.random.default_rng(1).standard_normal((4, 2, 8)).astype(np.float32)}
expected = ort.InferenceSession(proto.SerializeToString(), providers=["CPUExecutionProvider"]).run(None, feeds)[0]

model, _ = optimize_for_ort(ir.serde.deserialize_model(proto))
for node in model.graph.all_nodes():
    print(node.op_type, {k: a.value for k, a in node.attributes.items()}, [str(v.shape) for v in node.inputs if v is not None])
try:
    got = ort.InferenceSession(ir.to_proto(model).SerializeToString(), providers=["CPUExecutionProvider"]).run(None, feeds)[0]
    print("fused graph max diff:", np.abs(got - expected).max())
except Exception as error:
    print("ONNX Runtime:", str(error).split("Status Message: ")[-1].strip())

FusedMatMul {'alpha': 0.5, 'transBatchA': 1} ['[4,2,8]', '[8,16]']
ONNX Runtime: .../matmul_helper.h:73 ... left_num_dims > 2 && left_num_dims == right_num_dims was false. Two inputs should have same rank and rank >= 3 if transBatchA or transBatchB is true

The unfused graph runs and gives [2, 4, 16].

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