wehub-resource-sync
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460 lines
14 KiB
Python
460 lines
14 KiB
Python
# Copyright (c) ONNX Project Contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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from typing import Any
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import numpy as np
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import onnx
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from onnx.backend.test.case.base import Base
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from onnx.backend.test.case.node import expect
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def compute_loop_outputs(x, seq, trip_count):
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for i in range(trip_count):
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if seq is None:
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seq = []
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seq += [x[: int(i + 1)]]
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return seq
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class Loop(Base):
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@staticmethod
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def export_loop_11() -> None:
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# Given a tensor x of values [x1, ..., xN], and initial tensor y
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# sum up its elements using a scan
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# returning the final state (y+x1+x2+...+xN) as well the scan_output
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# [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
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y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1])
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y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1])
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scan_out = onnx.helper.make_tensor_value_info(
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"scan_out", onnx.TensorProto.FLOAT, [1]
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)
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cond_in = onnx.helper.make_tensor_value_info(
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"cond_in", onnx.TensorProto.BOOL, []
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)
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cond_out = onnx.helper.make_tensor_value_info(
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"cond_out", onnx.TensorProto.BOOL, []
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)
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iter_count = onnx.helper.make_tensor_value_info(
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"iter_count", onnx.TensorProto.INT64, []
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)
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x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
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y = np.array([-2]).astype(np.float32)
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x_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["x"],
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value=onnx.helper.make_tensor(
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name="const_tensor_x",
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data_type=onnx.TensorProto.FLOAT,
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dims=x.shape,
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vals=x.flatten().astype(float),
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),
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)
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one_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["one"],
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value=onnx.helper.make_tensor(
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name="const_tensor_one",
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data_type=onnx.TensorProto.INT64,
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dims=(),
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vals=[1],
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),
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)
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i_add_node = onnx.helper.make_node(
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"Add", inputs=["iter_count", "one"], outputs=["end"]
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)
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start_unsqueeze_node = onnx.helper.make_node(
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"Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0]
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)
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end_unsqueeze_node = onnx.helper.make_node(
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"Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0]
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)
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slice_node = onnx.helper.make_node(
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"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
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)
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y_add_node = onnx.helper.make_node(
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"Add", inputs=["y_in", "slice_out"], outputs=["y_out"]
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)
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identity_node = onnx.helper.make_node(
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"Identity", inputs=["cond_in"], outputs=["cond_out"]
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)
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scan_identity_node = onnx.helper.make_node(
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"Identity", inputs=["y_out"], outputs=["scan_out"]
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)
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loop_body = onnx.helper.make_graph(
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[
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identity_node,
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x_const_node,
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one_const_node,
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i_add_node,
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start_unsqueeze_node,
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end_unsqueeze_node,
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slice_node,
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y_add_node,
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scan_identity_node,
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],
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"loop_body",
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[iter_count, cond_in, y_in],
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[cond_out, y_out, scan_out],
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)
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node = onnx.helper.make_node(
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"Loop",
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inputs=["trip_count", "cond", "y"],
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outputs=["res_y", "res_scan"],
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body=loop_body,
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)
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trip_count = np.array(5).astype(np.int64)
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res_y = np.array([13]).astype(np.float32)
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cond = np.array(1).astype(bool)
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res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
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expect(
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node,
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inputs=[trip_count, cond, y],
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outputs=[res_y, res_scan],
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name="test_loop11",
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opset_imports=[onnx.helper.make_opsetid("", 11)],
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)
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@staticmethod
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def export_loop_13() -> None:
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# Given a tensor x of values [x1, ..., xN],
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# Return a sequence of tensors of
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# [[x1], [x1, x2], ..., [x1, ..., xN]]
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seq_in = onnx.helper.make_tensor_sequence_value_info(
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"seq_in", onnx.TensorProto.FLOAT, None
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)
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seq_out = onnx.helper.make_tensor_sequence_value_info(
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"seq_out", onnx.TensorProto.FLOAT, None
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)
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cond_in = onnx.helper.make_tensor_value_info(
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"cond_in", onnx.TensorProto.BOOL, []
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)
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cond_out = onnx.helper.make_tensor_value_info(
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"cond_out", onnx.TensorProto.BOOL, []
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)
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iter_count = onnx.helper.make_tensor_value_info(
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"iter_count", onnx.TensorProto.INT64, []
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)
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x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
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x_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["x"],
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value=onnx.helper.make_tensor(
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name="const_tensor_x",
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data_type=onnx.TensorProto.FLOAT,
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dims=x.shape,
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vals=x.flatten().astype(float),
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),
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)
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one_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["one"],
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value=onnx.helper.make_tensor(
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name="const_tensor_one",
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data_type=onnx.TensorProto.INT64,
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dims=(),
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vals=[1],
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),
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)
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zero_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["slice_start"],
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value=onnx.helper.make_tensor(
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name="const_tensor_zero",
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data_type=onnx.TensorProto.INT64,
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dims=(1,),
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vals=[0],
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),
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)
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axes_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["axes"],
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value=onnx.helper.make_tensor(
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name="const_tensor_axes",
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data_type=onnx.TensorProto.INT64,
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dims=(),
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vals=[0],
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),
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)
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add_node = onnx.helper.make_node(
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"Add", inputs=["iter_count", "one"], outputs=["end"]
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)
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end_unsqueeze_node = onnx.helper.make_node(
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"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
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)
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slice_node = onnx.helper.make_node(
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"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
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)
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insert_node = onnx.helper.make_node(
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"SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"]
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)
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identity_node = onnx.helper.make_node(
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"Identity", inputs=["cond_in"], outputs=["cond_out"]
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)
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loop_body = onnx.helper.make_graph(
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[
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identity_node,
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x_const_node,
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one_const_node,
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zero_const_node,
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add_node,
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axes_node,
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end_unsqueeze_node,
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slice_node,
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insert_node,
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],
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"loop_body",
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[iter_count, cond_in, seq_in],
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[cond_out, seq_out],
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)
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node = onnx.helper.make_node(
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"Loop",
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inputs=["trip_count", "cond", "seq_empty"],
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outputs=["seq_res"],
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body=loop_body,
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)
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trip_count = np.array(5).astype(np.int64)
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seq_empty: list[Any] = []
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seq_res = [x[: int(i)] for i in x]
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cond = np.array(1).astype(bool)
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expect(
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node,
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inputs=[trip_count, cond, seq_empty],
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outputs=[seq_res],
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name="test_loop13_seq",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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input_type_protos=[
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onnx.helper.make_tensor_type_proto(
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onnx.TensorProto.INT64, trip_count.shape
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),
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
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onnx.helper.make_sequence_type_proto(
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onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
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),
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],
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)
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@staticmethod
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def export_loop_16_none() -> None:
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# Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
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# Return a concatenated sequence of tensors of
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# [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
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ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
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seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
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opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
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opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp)
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seq_out = onnx.helper.make_tensor_sequence_value_info(
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"seq_out", onnx.TensorProto.FLOAT, []
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)
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cond_in = onnx.helper.make_tensor_value_info(
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"cond_in", onnx.TensorProto.BOOL, []
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)
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cond_out = onnx.helper.make_tensor_value_info(
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"cond_out", onnx.TensorProto.BOOL, []
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)
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iter_count = onnx.helper.make_tensor_value_info(
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"iter_count", onnx.TensorProto.INT64, []
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)
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x0 = np.array(0).astype(np.float32)
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x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
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optional_has_elem_node = onnx.helper.make_node(
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"OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"]
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)
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optional_is_none = onnx.helper.make_node(
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"Not", inputs=["optional_has_elem"], outputs=["optional_is_none"]
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)
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optional_get_elem = onnx.helper.make_node(
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"OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"]
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)
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constant_in = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["constant_in"],
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value=onnx.helper.make_tensor(
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name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0]
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),
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)
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seq_const_in = onnx.helper.make_node(
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"SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"]
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)
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then_seq_out = onnx.helper.make_tensor_sequence_value_info(
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"init_seq_in", onnx.TensorProto.FLOAT, []
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)
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then_body = onnx.helper.make_graph(
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[constant_in, seq_const_in], "then_body", [], [then_seq_out]
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)
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else_seq_out = onnx.helper.make_tensor_sequence_value_info(
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"seq_in", onnx.TensorProto.FLOAT, []
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)
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else_body = onnx.helper.make_graph(
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[optional_get_elem], "else_body", [], [else_seq_out]
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)
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if_node = onnx.helper.make_node(
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"If",
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inputs=["optional_is_none"],
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outputs=["sequence"],
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then_branch=then_body,
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else_branch=else_body,
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)
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x_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["x"],
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value=onnx.helper.make_tensor(
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name="const_tensor_x",
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data_type=onnx.TensorProto.FLOAT,
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dims=x.shape,
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vals=x.flatten().astype(float),
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),
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)
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one_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["one"],
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value=onnx.helper.make_tensor(
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name="const_tensor_one",
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data_type=onnx.TensorProto.INT64,
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dims=(),
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vals=[1],
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),
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)
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zero_const_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["slice_start"],
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value=onnx.helper.make_tensor(
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name="const_tensor_zero",
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data_type=onnx.TensorProto.INT64,
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dims=(1,),
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vals=[0],
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),
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)
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axes_node = onnx.helper.make_node(
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"Constant",
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inputs=[],
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outputs=["axes"],
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value=onnx.helper.make_tensor(
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name="const_tensor_axes",
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data_type=onnx.TensorProto.INT64,
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dims=(),
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vals=[0],
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),
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)
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add_node = onnx.helper.make_node(
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"Add", inputs=["iter_count", "one"], outputs=["end"]
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)
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end_unsqueeze_node = onnx.helper.make_node(
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"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
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)
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slice_node = onnx.helper.make_node(
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"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
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)
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insert_node = onnx.helper.make_node(
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"SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"]
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)
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identity_node = onnx.helper.make_node(
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"Identity", inputs=["cond_in"], outputs=["cond_out"]
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)
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loop_body = onnx.helper.make_graph(
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[
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identity_node,
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optional_has_elem_node,
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optional_is_none,
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if_node,
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x_const_node,
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one_const_node,
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zero_const_node,
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add_node,
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axes_node,
|
|
end_unsqueeze_node,
|
|
slice_node,
|
|
insert_node,
|
|
],
|
|
"loop_body",
|
|
[iter_count, cond_in, opt_in],
|
|
[cond_out, seq_out],
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"Loop",
|
|
inputs=["trip_count", "cond", "opt_seq"],
|
|
outputs=["seq_res"],
|
|
body=loop_body,
|
|
)
|
|
|
|
trip_count = np.array(5).astype(np.int64)
|
|
cond = np.array(1).astype(bool)
|
|
seq_res = compute_loop_outputs(x, [x0], trip_count)
|
|
opt_seq_in: list[Any] = [x0]
|
|
expect(
|
|
node,
|
|
inputs=[trip_count, cond, opt_seq_in],
|
|
outputs=[seq_res],
|
|
name="test_loop16_seq_none",
|
|
opset_imports=[onnx.helper.make_opsetid("", 16)],
|
|
input_type_protos=[
|
|
onnx.helper.make_tensor_type_proto(
|
|
onnx.TensorProto.INT64, trip_count.shape
|
|
),
|
|
onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
|
|
opt_in_tp,
|
|
],
|
|
)
|