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RGG implementation
RGG implementation RGG documentation Reformat RGG code Removed global compiler config DepricatedVerifyConfig Use common forge verification Move random seed logic to test_graphs Rename PyBuda to Forge Single RGG pytest test_commands for rgg No reducing microbatch size for forge and torch constants Store test code in pytest report Operator repo update Operator support for RGG classified
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‎forge/forge/op_repo/forge_operators.py‎

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OperatorParamNumber("max", float, 0, 100),
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],
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),
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OperatorDefinition("atan", "forge.op.Atan", 1),
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OperatorDefinition("sine", "forge.op.Sine", 1),
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OperatorDefinition("cosine", "forge.op.Cosine", 1),
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OperatorDefinition("abs", "forge.op.Abs", 1),
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OperatorDefinition("tanh", "forge.op.Tanh", 1),
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OperatorDefinition("cumsum", "forge.op.CumSum", 1),
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OperatorDefinition(
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"cumsum",
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"forge.op.CumSum",
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1,
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forward_params=[
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OperatorParamNumber("dim", int, -3, 3),
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],
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),
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OperatorDefinition("argmax", "forge.op.Argmax", 1),
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OperatorDefinition("logical_not", "forge.op.LogicalNot", 1),
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OperatorDefinition("dropout", "forge.op.Dropout", 1),

‎forge/forge/op_repo/pytorch_operators.py‎

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OperatorDefinition("softmax", "torch.softmax", 1),
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# Unary operators (not implemented)
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OperatorDefinition("acos", "torch.acos", 1),
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OperatorDefinition("arccos", "torch.acos", 1),
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OperatorDefinition("arccos", "torch.arccos", 1),
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OperatorDefinition("acosh", "torch.acosh", 1),
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OperatorDefinition("arccosh", "torch.acosh", 1),
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OperatorDefinition("arccosh", "torch.arccosh", 1),
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OperatorDefinition("angle", "torch.angle", 1),
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OperatorDefinition("asin", "torch.asin", 1),
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OperatorDefinition("arcsin", "torch.asin", 1),
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OperatorDefinition("arcsin", "torch.arcsin", 1),
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OperatorDefinition("asinh", "torch.asinh", 1),
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OperatorDefinition("arcsinh", "torch.asinh", 1),
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OperatorDefinition("arcsinh", "torch.arcsinh", 1),
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OperatorDefinition("atan", "torch.atan", 1),
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OperatorDefinition("arctan", "torch.atan", 1),
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OperatorDefinition("arctan", "torch.arctan", 1),
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OperatorDefinition("atanh", "torch.atanh", 1),
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OperatorDefinition("arctanh", "torch.atanh", 1),
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OperatorDefinition("arctanh", "torch.arctanh", 1),
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OperatorDefinition("bitwise_not", "torch.bitwise_not", 1),
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OperatorDefinition("ceil", "torch.ceil", 1),
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OperatorDefinition("conj_physical", "torch.conj_physical", 1),

‎forge/test/random/__init__.py‎

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# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
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# SPDX-License-Identifier: Apache-2.0

‎forge/test/random/rgg/README.md‎

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## RGG Random Graph Generator
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Random Graph Generator supports testing of randomly generated graphs. Tests based on RGG can be triggered as regular pytests and executed in a single run of pytest. Tests are performed as a bulk operation for the specified number of tests.
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### Execution
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For each random model RGG passes through steps:
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- Generate random model for specific random_seed
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- Verify model via verify_module
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Source code of each randomly generated model with a pytest function can be automatically stored in a folder, ex `test/random_tests/` if configured.
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## Run
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Entrypoint for RGG pytests is in `test_graphs.py` module
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Example command for running Forge RGG tests generated via random graph algorithm
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```shell
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LOGURU_LEVEL=DEBUG FRAMEWORKS=FORGE RANDOM_TEST_SEED=0 RANDOM_TEST_COUNT=5 VERIFICATION_TIMEOUT=60 MIN_DIM=3 MAX_DIM=4 MIN_OP_SIZE_PER_DIM=16 MAX_OP_SIZE_PER_DIM=64 OP_SIZE_QUANTIZATION=1 MIN_MICROBATCH_SIZE=1 MAX_MICROBATCH_SIZE=8 NUM_OF_NODES_MIN=5 NUM_OF_NODES_MAX=10 NUM_OF_FORK_JOINS_MAX=5 CONSTANT_INPUT_RATE=20 SAME_INPUTS_PERCENT_LIMIT=10 pytest -svv forge/test/random/test_graphs.py
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```
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Example command for running PyTorch RGG tests generated via random graph algorithm
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```shell
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LOGURU_LEVEL=DEBUG FRAMEWORKS=PYTORCH RANDOM_TEST_SEED=0 RANDOM_TEST_COUNT=5 VERIFICATION_TIMEOUT=60 MIN_DIM=4 MAX_DIM=4 MIN_OP_SIZE_PER_DIM=4 MAX_OP_SIZE_PER_DIM=8 OP_SIZE_QUANTIZATION=1 MIN_MICROBATCH_SIZE=1 MAX_MICROBATCH_SIZE=1 NUM_OF_NODES_MIN=3 NUM_OF_NODES_MAX=5 NUM_OF_FORK_JOINS_MAX=5 CONSTANT_INPUT_RATE=20 SAME_INPUTS_PERCENT_LIMIT=10 pytest -svv forge/test/random/test_graphs.py
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```
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## Helper scripts
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To start interacting with RGG tests load helper commands via
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```sh
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source forge/test/random/test_commands.sh
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```
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Available commands
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| Command | Description |
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| --------------------- | --------------------------------------------------------------------- |
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| `print_help` | Print commands and current query parameters. |
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| `print_query_docs` | Print docs for all available query parameters. |
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| `print_params` | Print current query parameters values. |
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| `select_test_graphs` | Select test_graps pytest function. |
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| `pytest` | Run all tests specfied via a query parameters. |
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| `with-params pytest` | Print params before and after test run. |
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Usage examples
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Run 5 tests for PYTHORCH framework
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```sh
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RANDOM_TEST_COUNT=5 FRAMEWORKS=PYTORCH with-params pytest
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```
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## Configuration
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Configuration of RGG is supported via `RandomizerConfig`
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Parameters includes configuration of:
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- framework
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- number of tests
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- number of nodes
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- min and max size of an operand dimension
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- ...
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For more details about configuration please take a look at `forge/test/random/rgg/config.py`.
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Please refer to full list of supported enviroment variables in [README.debug.md](../README.debug.md)
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*Test specific environment variables that can be used to fine tune default behavior of Forge RGG tests.*
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## Parameters
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Full list of supported query parameters
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| Parameter | Description | Supported values | Default |
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| ----------------------------- | ----------------------------------------------------------------- | ----------------------------- | --------------------- |
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| FRAMEWORKS | List of frameworks. | PYTORCH, FORGE | |
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| ALGORITHMS | List of algorithms. | RANDOM | RANDOM |
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| CONFIGS | List of config names. | DEFAULT, UNSTABLE, ... | DEFAULT |
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| RANDOM\_TEST\_SEED | Initial seed for RGG. | | 0 |
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| RANDOM\_TEST\_COUNT | Number of random tests to be generated and executed. The parameter generate test_index in range from 0 to RANDOM\_TEST\_COUNT-1. | | 5 |
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| RANDOM\_TESTS\_SELECTED | Limiting random tests to only selected subset defined as comma separated list of test indexes. E.x. "3,4,6" | | Default is no limitation if not specified or empty. |
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| VERIFICATION\_TIMEOUT | Limit time for inference verification in seconds. | | 60 |
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| MIN\_DIM | Minimal number of dimensions of input tensors. | | 3 |
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| MAX\_DIM | Maximum number of dimensions of input tensors. | | 4 |
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| MIN\_OP\_SIZE\_PER\_DIM | Minimal size of an operand dimension. | | 16 |
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| MAX\_OP\_SIZE\_PER\_DIM | Maximum size of an operand dimension. Smaller operand size results in fewer failed tests. | | 512 |
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| OP\_SIZE\_QUANTIZATION | Quantization factor for operand size. | | 1 |
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| MIN_MICROBATCH_SIZE | Minimal size of microbatch of an input tensor. | | 1 |
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| MAX_MICROBATCH_SIZE | Maximum size of microbatch of an input tensor. | | 8 |
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| NUM\_OF\_NODES\_MIN | Minimal number of nodes to be generated by RGG. | | 5 |
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| NUM\_OF\_NODES\_MAX | Maximum number of nodes to be generated by RGG. | | 10 |
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| NUM\_OF\_FORK\_JOINS\_MAX | Maximum number of fork joins to be generated by random graph algorithm in RGG. | | 50 |
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| CONSTANT\_INPUT\_RATE | Rate of constant inputs in RGG in percents. | | 50 |
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| SAME\_INPUTS\_PERCENT\_LIMIT | Percent limit of nodes which have same value on multiple inputes. | | 10 |
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Test configuration parameters
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| Parameter | Description |
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| ------------------------- | --------------------------------------------------------------------------------------------- |
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| SKIP_FORGE_VERIFICATION | Skip Forge model verification including model compiling and inference |
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## Development
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Entrypoint for RGG impplementation is `process_test` module
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Parameters of process_test pytest:
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- test_index - index of a test
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- random_seed - random seed of a test
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- test_device - target test device
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- randomizer_config - test configation parameters
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- graph_builder_type - algorithm
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- framework - target framework

‎forge/test/random/rgg/__init__.py‎

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# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
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# SPDX-License-Identifier: Apache-2.0
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from .datatypes import TensorShape
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from .datatypes import RandomizerConstantNode
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from .datatypes import (
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RandomizerInputNode,
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RandomizerNode,
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ExecutionContext,
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RandomizerParameters,
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RandomizerGraph,
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RandomizerConfig,
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)
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from .datatypes import NodeShapeCalculationContext
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from .datatypes import RandomizerTestContext
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from .datatypes import ModelBuilder, Framework
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from .datatypes import Algorithm
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from .config import get_randomizer_config_default
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from .utils import StrUtils, GraphUtils
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from .utils import DebugUtils
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from .base import GraphBuilder
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from .base import RandomizerRunner, RandomizerCodeGenerator, process_test
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from .frameworks import Frameworks
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from .frameworks import FrameworkTestUtils
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from .algorithms import Algorithms
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from .algorithms import GraphNodeSetup
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from .algorithms import RandomGraphAlgorithm
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__all__ = [
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"TensorShape",
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"RandomizerConstantNode",
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"RandomizerInputNode",
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"RandomizerNode",
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"ExecutionContext",
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"RandomizerParameters",
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"RandomizerGraph",
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"RandomizerConfig",
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"NodeShapeCalculationContext",
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"RandomizerTestContext",
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"ModelBuilder",
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"Framework",
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"get_randomizer_config_default",
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"StrUtils",
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"GraphUtils",
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"DebugUtils",
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"Framework",
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"Algorithm",
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"GraphBuilder",
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"RandomizerRunner",
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"RandomizerCodeGenerator",
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"process_test",
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"Frameworks",
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"FrameworkTestUtils",
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"Algorithms",
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"GraphNodeSetup",
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"RandomGraphAlgorithm",
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]

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