chore: import upstream snapshot with attribution
Docker Image CI / build-ubuntu2004 (push) Has been cancelled
Docker Image CI / build-ubuntu2004 (push) Has been cancelled
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,465 @@
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import numpy as np
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import tensorrt as trt
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from polygraphy import mod, util
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from polygraphy.backend.common import BytesFromPath
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from polygraphy.backend.onnx import OnnxFromPath
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from polygraphy.backend.tf import GraphFromFrozen
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from polygraphy.common import TensorMetadata
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from polygraphy.datatype import DataType
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def model_path(name=None):
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path = os.path.abspath(os.path.dirname(__file__))
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if name is not None:
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path = os.path.join(path, name)
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return path
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class Model:
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def __init__(
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self, path, LoaderType, check_runner, input_metadata=None, ext_data=None
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):
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self.path = path
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self.loader = LoaderType(self.path)
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self.check_runner = check_runner
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self.input_metadata = input_metadata
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self.ext_data = ext_data
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def check_tf_identity(runner):
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feed_dict = {
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"Input:0": np.random.random_sample(size=(1, 15, 25, 30)).astype(np.float32)
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}
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outputs = runner.infer(feed_dict)
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assert np.all(outputs["Identity_2:0"] == feed_dict["Input:0"])
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MODELS_DIR = os.path.join(os.path.dirname(__file__))
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TF_MODELS = {
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"identity": Model(
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path=model_path("tf_identity.pb"),
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LoaderType=GraphFromFrozen,
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check_runner=check_tf_identity,
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),
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}
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def check_identity(runner):
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feed_dict = {"x": np.random.random_sample(size=(1, 1, 2, 2)).astype(np.float32)}
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outputs = runner.infer(feed_dict)
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assert np.all(outputs["y"] == feed_dict["x"])
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def check_identity_identity(runner):
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feed_dict = {"X": np.random.random_sample(size=(64, 64)).astype(np.float32)}
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outputs = runner.infer(feed_dict)
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assert np.all(outputs["identity_out_2"] == feed_dict["X"])
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def check_dynamic_identity(runner, shapes):
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feed_dict = {"X": np.random.random_sample(size=shapes["X"]).astype(np.float32)}
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outputs = runner.infer(feed_dict)
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assert np.array_equal(outputs["Y"], feed_dict["X"])
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def check_empty_tensor_expand(runner, shapes):
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shape = shapes["new_shape"]
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feed_dict = {
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"data": np.zeros(shape=(2, 0, 3, 0), dtype=np.float32),
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"new_shape": np.array(
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shape,
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dtype=(
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np.int32
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if mod.version(trt.__version__) < mod.version("9.0")
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else np.int64
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),
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),
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}
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outputs = runner.infer(feed_dict)
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# Empty tensor will still be empty after broadcast
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assert outputs["expanded"].shape == shape
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assert util.volume(outputs["expanded"].shape) == 0
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def check_reshape(runner):
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feed_dict = {"data": np.random.random_sample(size=(1, 3, 5, 5)).astype(np.float32)}
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outputs = runner.infer(feed_dict)
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assert np.all(outputs["output"] == feed_dict["data"].ravel())
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def check_residual_block(runner, shapes):
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feed_dict = {
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"gpu_0/data_0": np.random.random_sample(size=shapes["gpu_0/data_0"]).astype(
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np.float32
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)
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}
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# Confirm inference can go through without error
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outputs = runner.infer(feed_dict)
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def check_matmul_2layer(runner, shape=(2, 8)):
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feed_dict = {
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"onnx::MatMul_0": np.random.random_sample(size=shape).astype(np.float32)
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}
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# Confirm inference can go through without error
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outputs = runner.infer(feed_dict)
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def no_check_implemented(runner):
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raise NotImplementedError("No check_runner implemented for this model")
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ONNX_MODELS = {
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"identity": Model(
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path=model_path("identity.onnx"),
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LoaderType=BytesFromPath,
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check_runner=check_identity,
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input_metadata=TensorMetadata().add(
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"x", dtype=DataType.FLOAT32, shape=(1, 1, 2, 2)
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),
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),
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"identity_identity": Model(
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path=model_path("identity_identity.onnx"),
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LoaderType=BytesFromPath,
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check_runner=check_identity_identity,
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),
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"dynamic_identity": Model(
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path=model_path("dynamic_identity.onnx"),
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LoaderType=BytesFromPath,
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check_runner=check_dynamic_identity,
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input_metadata=TensorMetadata().add(
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"X", dtype=DataType.FLOAT32, shape=(1, 1, -1, -1)
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),
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),
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"identity_multi_ch": Model(
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path=model_path("identity_multi_ch.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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input_metadata=TensorMetadata().add(
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"x", dtype=DataType.FLOAT32, shape=(2, 4, 3, 3)
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),
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),
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"empty_tensor_expand": Model(
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path=model_path("empty_tensor_expand.onnx"),
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LoaderType=BytesFromPath,
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check_runner=check_empty_tensor_expand,
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),
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"and": Model(
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path=model_path("and.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"scan": Model(
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path=model_path("scan.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"pow_scalar": Model(
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path=model_path("pow_scalar.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"dim_param": Model(
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path=model_path("dim_param.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"tensor_attr": Model(
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path=model_path("tensor_attr.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"identity_with_initializer": Model(
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path=model_path("identity_with_initializer.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"const_foldable": Model(
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path=model_path("const_foldable.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"reshape": Model(
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path=model_path("reshape.onnx"),
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LoaderType=BytesFromPath,
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check_runner=check_reshape,
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),
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"reducable": Model(
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path=model_path("reducable.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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input_metadata=TensorMetadata()
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.add("X0", shape=(1,), dtype=DataType.FLOAT32)
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.add("Y0", shape=(1,), dtype=DataType.FLOAT32),
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),
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"reducable_with_const": Model(
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path=model_path("reducable_with_const.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"ext_weights": Model(
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path=model_path("ext_weights.onnx"),
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LoaderType=OnnxFromPath,
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check_runner=no_check_implemented,
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ext_data=model_path("data"),
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),
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"ext_weights_same_dir": Model(
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path=model_path(os.path.join("ext_weights_same_dir", "ext_weights.onnx")),
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LoaderType=OnnxFromPath,
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check_runner=no_check_implemented,
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ext_data=model_path("ext_weights_same_dir"),
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),
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"capability": Model(
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path=model_path("capability.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"instancenorm": Model(
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path=model_path("instancenorm.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"add_with_dup_inputs": Model(
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path=model_path("add_with_dup_inputs.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"needs_constraints": Model(
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path=model_path("needs_constraints.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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input_metadata=TensorMetadata().add(
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"x", dtype=DataType.FLOAT32, shape=(1, 1, 256, 256)
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),
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),
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"constant_fold_bloater": Model(
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path=model_path("constant_fold_bloater.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"renamable": Model(
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path=model_path("renamable.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"cleanable": Model(
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path=model_path("cleanable.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"nonzero": Model(
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path=model_path("nonzero.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"inp_dim_val_not_set": Model(
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path=model_path("inp_dim_val_not_set.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"multi_output": Model(
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path=model_path("multi_output.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"unbounded_dds": Model(
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path=model_path("unbounded_dds.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"loop": Model(
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path=model_path("loop.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"matmul.fp16": Model(
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path=model_path("matmul.fp16.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"matmul": Model(
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path=model_path("matmul.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"sparse.matmul": Model(
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path=model_path("sparse.matmul.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"matmul.bf16": Model(
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path=model_path("matmul.bf16.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"matmul.bf16.i32data": Model(
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path=model_path("matmul.bf16.i32data.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"matmul_2layer": Model(
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path=model_path("matmul_2layer.onnx"),
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LoaderType=BytesFromPath,
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check_runner=check_matmul_2layer,
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),
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"unsorted": Model(
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path=model_path("unsorted.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"conv": Model(
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path=model_path("conv.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"sparse.conv": Model(
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path=model_path("sparse.conv.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"no_op_reshape": Model(
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path=model_path("no_op_reshape.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"bad_graph_with_dup_value_info": Model(
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path=model_path("bad_graph_with_dup_value_info.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"bad_graph_with_no_name": Model(
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path=model_path("bad_graph_with_no_name.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"bad_graph_with_no_import_domains": Model(
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path=model_path("bad_graph_with_no_import_domains.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
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),
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"bad_graph_with_parallel_invalid_nodes": Model(
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path=model_path("bad_graph_with_parallel_invalid_nodes.onnx"),
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LoaderType=BytesFromPath,
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check_runner=no_check_implemented,
|
||||
),
|
||||
"bad_graph_conditionally_invalid": Model(
|
||||
path=model_path("bad_graph_conditionally_invalid.onnx"),
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||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"custom_op_node": Model(
|
||||
path=model_path("custom_op_node.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"bad_graph_with_duplicate_node_names": Model(
|
||||
path=model_path("bad_graph_with_duplicate_node_names.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"bad_graph_with_multi_level_errors": Model(
|
||||
path=model_path("bad_graph_with_multi_level_errors.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"empty": Model(
|
||||
path=model_path("empty.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"residual_block": Model(
|
||||
path=model_path("residual_block.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=check_residual_block,
|
||||
),
|
||||
"graph_with_subgraph_matching_toy_plugin": Model(
|
||||
path=model_path("toy_subgraph.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"transpose_matmul": Model(
|
||||
path=model_path("transpose_matmul.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"qdq_conv": Model(
|
||||
path=model_path("qdq_conv.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.matmul.fp16": Model(
|
||||
path=model_path("weightless.matmul.fp16.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.matmul.bf16": Model(
|
||||
path=model_path("weightless.matmul.bf16.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.conv": Model(
|
||||
path=model_path("weightless.conv.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.sparse.matmul": Model(
|
||||
path=model_path("weightless.sparse.matmul.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.sparse.conv": Model(
|
||||
path=model_path("weightless.sparse.conv.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.transpose_matmul": Model(
|
||||
path=model_path("weightless.transpose_matmul.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"weightless.qdq_conv": Model(
|
||||
path=model_path("weightless.qdq_conv.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"roialign": Model(
|
||||
path=model_path("roialign.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"attention": Model(
|
||||
path=model_path("attention.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"multi_attention": Model(
|
||||
path=model_path("multi_attention.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
"attention_same_qkv": Model(
|
||||
path=model_path("attention_same_qkv.onnx"),
|
||||
LoaderType=BytesFromPath,
|
||||
check_runner=no_check_implemented,
|
||||
),
|
||||
}
|
||||
Reference in New Issue
Block a user