A couple of optimizer and rewriter extensions

[gramalingam]

A few extensions motivated by ongoing transformer fusion optimizations:

Pattern matching:

• Extend pattern-matching pattern to allow specifying that extra-inputs are allowed.

Optimizations:

• Concat (x) can be replaced by Identity(x)
• Redundant cast optimization was missing in core optimizer (though present as a llama rewrite rule).
• Dropout optimizations moved into core optimizer (from rewrite rule; rewrite rule has an issue, use of attribute instead of input, and it seemed better to move it into core optimizer).

In general, for optimizations involving a single node, the core optimizer is a better place (at least, as long as they are generic, and not backend-specific) than rewrite rules. It is more efficient.

• Fix input/output size limit of constant-folding to be number of bytes. (It is currently inconsistent, as number of bytes for one and number of elements for another).

[codecov]

❌ 12 Tests Failed:

Tests completed	Failed	Passed	Skipped
12381	12	12369	1201

View the full list of 3 ❄️ flaky tests

tests.eager_mode_test.TestEagerModeArguments_0_reference_runtime test_function_attribute_by_positional_args

Flake rate in main: 38.09% (Passed 6031 times, Failed 3711 times)

Stack Traces | 0.002s run time

..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:91: in run
    res = self._run(x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:139: in _run
    res = (convert_from_ml_dtypes(res[0]),)
..../test_torch_nightly/lib/python3.12.../onnx/reference/custom_element_types.py:50: in convert_from_ml_dtypes
    return array.view(dtype=dtype)
E   ValueError: Changing the dtype of a 0d array is only supported if the itemsize is unchanged

The above exception was the direct cause of the following exception:
tests/eager_mode_test.py:112: in test_function_attribute_by_positional_args
    self.assertEqual(add_with_alpha(1.0, 2.0, 3.0), 7.0)
onnxscript/values.py:576: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:307: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
tests/eager_mode_test.py:59: in add_with_alpha
    other = op.Mul(other, alpha)
.../onnx_opset/_impl/opset14.py:696: in Mul
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:194: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:524: in _eval
    result = session.run(None, session_run_input)
..../test_torch_nightly/lib/python3.12.../onnx/reference/reference_evaluator.py:599: in run
    outputs = node.run(*inputs, **linked_attributes)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:114: in run
    res = OpRunBinary.run(self, x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:93: in run
    raise TypeError(
E   TypeError: Issues with types <class 'numpy.ndarray'>, <class 'numpy.ndarray'> (binary operator 'Mul').

tests.eager_mode_test.TestEagerModeArguments_0_reference_runtime test_function_input_and_attribute_by_kwargs_out_of_order

Flake rate in main: 38.09% (Passed 6031 times, Failed 3711 times)

Stack Traces | 0.003s run time

..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:91: in run
    res = self._run(x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:139: in _run
    res = (convert_from_ml_dtypes(res[0]),)
..../test_torch_nightly/lib/python3.12.../onnx/reference/custom_element_types.py:50: in convert_from_ml_dtypes
    return array.view(dtype=dtype)
E   ValueError: Changing the dtype of a 0d array is only supported if the itemsize is unchanged

The above exception was the direct cause of the following exception:
tests/eager_mode_test.py:115: in test_function_input_and_attribute_by_kwargs_out_of_order
    self.assertEqual(add_with_alpha(alpha=3.0, other=2.0, this=1.0), 7.0)
onnxscript/values.py:576: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:307: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
tests/eager_mode_test.py:59: in add_with_alpha
    other = op.Mul(other, alpha)
.../onnx_opset/_impl/opset14.py:696: in Mul
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:194: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:524: in _eval
    result = session.run(None, session_run_input)
..../test_torch_nightly/lib/python3.12.../onnx/reference/reference_evaluator.py:599: in run
    outputs = node.run(*inputs, **linked_attributes)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:114: in run
    res = OpRunBinary.run(self, x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:93: in run
    raise TypeError(
E   TypeError: Issues with types <class 'numpy.ndarray'>, <class 'numpy.ndarray'> (binary operator 'Mul').

tests.eager_mode_test.TestEagerModeArguments_0_reference_runtime test_function_some_input_by_kwargs

Flake rate in main: 38.09% (Passed 6031 times, Failed 3711 times)

Stack Traces | 0.003s run time

..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:91: in run
    res = self._run(x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:139: in _run
    res = (convert_from_ml_dtypes(res[0]),)
..../test_torch_nightly/lib/python3.12.../onnx/reference/custom_element_types.py:50: in convert_from_ml_dtypes
    return array.view(dtype=dtype)
E   ValueError: Changing the dtype of a 0d array is only supported if the itemsize is unchanged

The above exception was the direct cause of the following exception:
tests/eager_mode_test.py:106: in test_function_some_input_by_kwargs
    self.assertEqual(add_with_alpha(1.0, other=2.0), 3.0)
onnxscript/values.py:576: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:307: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
tests/eager_mode_test.py:59: in add_with_alpha
    other = op.Mul(other, alpha)
.../onnx_opset/_impl/opset14.py:696: in Mul
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:194: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:524: in _eval
    result = session.run(None, session_run_input)
..../test_torch_nightly/lib/python3.12.../onnx/reference/reference_evaluator.py:599: in run
    outputs = node.run(*inputs, **linked_attributes)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:114: in run
    res = OpRunBinary.run(self, x, y)
..../test_torch_nightly/lib/python3.12.../reference/ops/_op.py:93: in run
    raise TypeError(
E   TypeError: Issues with types <class 'numpy.ndarray'>, <class 'numpy.ndarray'> (binary operator 'Mul').

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[justinchuby]

Thanks!