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325 lines
11 KiB
Python
325 lines
11 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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import itertools
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import numpy as np
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import onnx
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from onnx import TensorProto
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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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from onnx.helper import make_tensor, tensor_dtype_to_np_dtype
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F8_TYPES = frozenset({"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"})
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FOUR_BIT_TYPES = frozenset({"UINT4", "INT4", "FLOAT4E2M1"})
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TWO_BIT_TYPES = frozenset({"UINT2", "INT2"})
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class CastLike(Base):
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@staticmethod
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def export() -> None:
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test_cases = [
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("FLOAT", "FLOAT16"),
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("FLOAT", "DOUBLE"),
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("FLOAT16", "FLOAT"),
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("FLOAT16", "DOUBLE"),
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("DOUBLE", "FLOAT"),
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("DOUBLE", "FLOAT16"),
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("FLOAT", "BFLOAT16"),
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("BFLOAT16", "FLOAT"),
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("FLOAT", "FLOAT8E4M3FN"),
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("FLOAT16", "FLOAT8E4M3FN"),
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("FLOAT", "FLOAT8E4M3FNUZ"),
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("FLOAT16", "FLOAT8E4M3FNUZ"),
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("FLOAT8E4M3FN", "FLOAT"),
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("FLOAT8E4M3FN", "FLOAT16"),
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("FLOAT8E4M3FNUZ", "FLOAT"),
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("FLOAT8E4M3FNUZ", "FLOAT16"),
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("FLOAT", "FLOAT8E5M2"),
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("FLOAT16", "FLOAT8E5M2"),
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("FLOAT", "FLOAT8E5M2FNUZ"),
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("FLOAT16", "FLOAT8E5M2FNUZ"),
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("FLOAT8E5M2", "FLOAT"),
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("FLOAT8E5M2", "FLOAT16"),
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("FLOAT8E5M2FNUZ", "FLOAT"),
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("FLOAT8E5M2FNUZ", "FLOAT16"),
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("FLOAT", "UINT4"),
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("FLOAT16", "UINT4"),
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("FLOAT", "INT4"),
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("FLOAT16", "INT4"),
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("UINT4", "FLOAT"),
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("UINT4", "FLOAT16"),
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("UINT4", "UINT8"),
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("INT4", "FLOAT"),
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("INT4", "FLOAT16"),
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("INT4", "INT8"),
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("FLOAT4E2M1", "FLOAT"),
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("FLOAT4E2M1", "FLOAT16"),
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("FLOAT", "FLOAT4E2M1"),
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("FLOAT16", "FLOAT4E2M1"),
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("FLOAT", "UINT2"),
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("FLOAT16", "UINT2"),
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("FLOAT", "INT2"),
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("FLOAT16", "INT2"),
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("UINT2", "FLOAT"),
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("UINT2", "FLOAT16"),
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("UINT2", "UINT8"),
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("INT2", "FLOAT"),
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("INT2", "FLOAT16"),
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("INT2", "INT8"),
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]
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f8_types = {"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"}
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for from_type, to_type in test_cases:
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if from_type == to_type:
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# Skip cases where from_type and to_type are the same
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continue
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from_dtype = getattr(TensorProto, from_type)
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to_dtype = getattr(TensorProto, to_type)
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from_np_dtype = tensor_dtype_to_np_dtype(from_dtype)
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to_np_dtype = tensor_dtype_to_np_dtype(to_dtype)
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if from_type == "BFLOAT16" or to_type == "BFLOAT16":
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np_fp32 = np.array(
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[
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"0.47892547",
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"0.48033667",
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"0.49968487",
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"0.81910545",
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"0.47031248",
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"0.816468",
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"0.21087195",
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"0.7229038",
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"NaN",
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"INF",
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"+INF",
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"-INF",
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],
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dtype=np.float32,
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)
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input_shape = (3, 4)
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elif from_type in f8_types or to_type in f8_types:
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np_fp32 = np.array(
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[
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"0.47892547",
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"0.48033667",
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"0.49968487",
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"0.81910545",
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"0.47031248",
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"0.7229038",
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"1000000",
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"1e-7",
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"NaN",
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"INF",
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"+INF",
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"-INF",
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"-0.0000001",
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"0.0000001",
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"-1000000",
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],
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dtype=np.float32,
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)
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input_shape = (3, 5)
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elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"):
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np_fp32 = np.arange(-9, 16).astype(np.float32)
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input_shape = (5, 5)
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elif from_type in ("UINT2", "INT2") or to_type in ("UINT2", "INT2"):
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np_fp32 = np.arange(-3, 4).astype(np.float32)
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input_shape = (7, 1)
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elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1":
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np_fp32 = np.array(
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[
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"0.48",
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"0.25",
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"1.05",
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"-3.5",
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"-8",
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"9",
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"1000000",
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"1e-7",
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"NaN",
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"INF",
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"+INF",
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"-INF",
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"-4",
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"0.01",
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"-0.0",
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],
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dtype=np.float32,
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)
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input_shape = (3, 5)
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else:
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np_fp32 = np.array(
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[
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"0.47892547",
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"0.48033667",
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"0.49968487",
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"0.81910545",
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"0.47031248",
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"0.816468",
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"0.21087195",
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"0.7229038",
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"NaN",
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"INF",
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"+INF",
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"-INF",
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],
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dtype=np.float32,
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).reshape([3, 4])
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input_shape = (3, 4)
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if from_type in F8_TYPES:
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np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype)
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input = make_tensor(
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"input",
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from_dtype,
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input_shape,
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vals=np_from,
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raw=True,
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)
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elif from_type in FOUR_BIT_TYPES:
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np_from = np_fp32.astype(from_np_dtype)
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packed = onnx.numpy_helper._pack_4bitx2(np_from)
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# No byteswap needed on big-endian machines as _pack_4bitx2()
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# returns a numpy array with uint8 datatype.
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input = make_tensor(
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"input", from_dtype, input_shape, vals=packed.tobytes(), raw=True
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)
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elif from_type in TWO_BIT_TYPES:
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np_from = np_fp32.astype(from_np_dtype)
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packed = onnx.numpy_helper._pack_2bitx4(np_from)
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# No byteswap needed on big-endian machines as _pack_2bitx4()
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# returns a numpy array with uint8 datatype.
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input = make_tensor(
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"input", from_dtype, input_shape, vals=packed.tobytes(), raw=True
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)
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else:
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np_from = np_fp32.astype(from_np_dtype)
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input = make_tensor(
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"input", from_dtype, input_shape, vals=np_from, raw=True
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)
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if to_type in F8_TYPES:
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output = make_tensor(
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"output",
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to_dtype,
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input_shape,
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vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype),
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raw=True,
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)
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elif to_type in FOUR_BIT_TYPES:
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packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype))
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# No byteswap needed on big-endian machines as _pack_4bitx2()
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# returns a numpy array with uint8 datatype.
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output = make_tensor(
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"output", to_dtype, input_shape, vals=packed.tobytes(), raw=True
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)
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elif to_type in TWO_BIT_TYPES:
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packed = onnx.numpy_helper._pack_2bitx4(np_from.astype(to_np_dtype))
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# No byteswap needed on big-endian machines as _pack_2bitx4()
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# returns a numpy array with uint8 datatype.
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output = make_tensor(
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"output", to_dtype, input_shape, vals=packed.tobytes(), raw=True
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)
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else:
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output = make_tensor(
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"output",
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to_dtype,
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input_shape,
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vals=np_from.astype(to_np_dtype),
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raw=True,
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)
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like = make_tensor("like", to_dtype, (0,), vals=[])
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node = onnx.helper.make_node(
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"CastLike",
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inputs=["input", "like"],
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outputs=["output"],
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)
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expect(
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node,
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inputs=[input, like],
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outputs=[output],
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name="test_castlike_" + from_type + "_to_" + to_type,
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)
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@staticmethod
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def export_saturate_false() -> None:
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test_cases = itertools.product(
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[
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"FLOAT",
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"FLOAT16",
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],
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[
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"FLOAT8E4M3FN",
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"FLOAT8E4M3FNUZ",
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"FLOAT8E5M2",
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"FLOAT8E5M2FNUZ",
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],
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)
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input_shape = (3, 5)
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for from_type, to_type in test_cases:
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from_dtype = getattr(TensorProto, from_type)
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to_dtype = getattr(TensorProto, to_type)
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from_np_dtype = tensor_dtype_to_np_dtype(from_dtype)
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to_np_dtype = tensor_dtype_to_np_dtype(to_dtype)
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np_fp32 = np.array(
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[
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"0.47892547",
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"0.48033667",
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"0.49968487",
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"0.81910545",
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"0.47031248",
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"0.7229038",
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"1000000",
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"1e-7",
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"NaN",
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"INF",
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"+INF",
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"-INF",
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"-0.0000001",
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"0.0000001",
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"-1000000",
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],
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dtype=np.float32,
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)
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input = make_tensor(
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"input",
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from_dtype,
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input_shape,
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vals=np_fp32.astype(from_np_dtype),
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raw=True,
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)
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output = make_tensor(
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"output",
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to_dtype,
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input_shape,
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vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype),
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raw=True,
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)
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like = make_tensor("like", to_dtype, (0,), vals=[])
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node = onnx.helper.make_node(
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"CastLike",
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inputs=["input", "like"],
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outputs=["output"],
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saturate=0,
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)
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expect(
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node,
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inputs=[input, like],
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outputs=[output],
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name="test_castlike_no_saturate_" + from_type + "_to_" + to_type,
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)
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