Cleanup optimizer

.lintrunner.toml

@@ -46,12 +46,11 @@ exclude_patterns = [
     'onnxscript/onnx_types.py',
     'onnxscript/**/*_test.py', # Skip linting test files for speed
     'onnxscript/function_libs/torch_lib/ops/**', # Operators typing do not play well with mypy
-    'onnxscript/optimizer/evaluator.py',  # FIXME
-    'onnxscript/optimizer/constant_folding.py',  # FIXME
+    'onnxscript/optimizer/legacy/evaluator.py',  # FIXME
+    'onnxscript/optimizer/legacy/constant_folding.py',  # FIXME
     'onnxscript/rewriter/onnxruntime/transformers/fastgelu.py',  # FIXME
     'onnxscript/rewriter/onnxruntime/instance_to_group_normalization.py',  # FIXME
     'onnxscript/_legacy_ir/irbuilder.py',  # FIXME
-    'onnxscript/optimizer/fold_constants_v0.py',  # FIXME
     'onnxscript/rewriter/onnxruntime/transformers/multihead_attention.py',  # FIXME
     'onnxscript/tools/function_unittest_producer.py',  # FIXME
     'onnxscript/_legacy_ir/visitor.py',  # FIXME

onnxscript/optimizer/__init__.py

@@ -2,160 +2,22 @@
 # Licensed under the MIT License.
 from __future__ import annotations
 
-import logging
-from typing import Any
-
 import onnx
-import onnx.shape_inference
-
-from onnxscript import ir, rewriter
-from onnxscript.optimizer import _constant_folding, _inliner
-from onnxscript.optimizer.constant_folding import fold_constants
-from onnxscript.optimizer.remove_unused import remove_unused_nodes
-from onnxscript.optimizer.remove_unused_function import remove_unused_functions
-from onnxscript.optimizer.simple_function_folding import (
-    inline_functions_with_unused_outputs,
-    inline_simple_functions,
-)
-from onnxscript.rewriter import (
-    broadcast_to_matmul,
-    cast_constant_of_shape,
-    gemm_to_matmul_add,
-    no_op,
-)
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_REWRITE_RULES = [
-    *no_op.rules.rules,  # TODO: merge this rule into constant folding?
-    *broadcast_to_matmul.rules.rules,
-    gemm_to_matmul_add.rule,
-    *cast_constant_of_shape.rules.rules,
-]
-
-
-def optimize(
-    model: onnx.ModelProto,
-    num_iterations: int = 2,
-    *,
-    onnx_shape_inference: bool = True,
-    stop_if_no_change: bool = True,
-    external_data_folder: str = "",
-    **kwargs: Any,
-) -> onnx.ModelProto:
-    """Optimize the model. Perform optimizations and clean-ups such as constant folding, dead code elimination, etc.
-
-    Args:
-        model (onnx.ModelProto): The model to optimize.
-        num_iterations (int, optional): Number of iterations to perform.
-        onnx_shape_inference (bool, optional): Whether to perform onnx shape inference on the model.
-            Set this to False to turn off onnx shape inference, and rely on model carried shapes and types.
-            This is useful for models produced by PyTorch 2.2+ dynamo onnx exporter, where the model carries
-            the symbolic shapes recorded from dynamo tracing.
-        stop_if_no_change (bool, optional): Whether to stop if no change is detected.
-        external_data_folder (str, optional): The folder to store external data.
-        **kwargs: Additional keyword arguments. For BC purposes.
-    """
-    if kwargs.pop("function_aware_folding", None) is not None:
-        logger.warning(
-            "'function_aware_folding' is deprecated. 'optimize' now supports both fully inlined models and models with functions. "
-            "To achieve the same behavior as 'function_aware_folding=True' before, set 'onnx_shape_inference=False'. "
-            "This would turn off incremental onnx shape inference and rely on model carried shapes and types. "
-            "See 'onnx_shape_inference' for more details."
-        )
-    for _ in range(num_iterations):
-        if onnx_shape_inference:
-            if model.ByteSize() < 1024 * 1024 * 1024 * 2:
-                # NOTE: strict mode is disabled because it crashes on the models
-                # that have different shapes inferred from the model carried shapes.
-                # The case can be found in:
-                # https://github.com/microsoft/onnxscript/issues/1443
-                model = onnx.shape_inference.infer_shapes(
-                    model, check_type=True, strict_mode=False, data_prop=True
-                )
-            else:
-                logger.warning(
-                    "The model size is too large for full model shape inference. "
-                    "Skipping this step."
-                )
-
-        inline_simple_functions(model)
-        modified = fold_constants(
-            model, external_data_folder, onnx_shape_inference=onnx_shape_inference
-        )
-
-        remove_unused_nodes(model)
-        inline_simple_functions(model)
-        model = remove_unused_functions(model)
-        inline_functions_with_unused_outputs(model)
-        # NOTE: This is general rewrite rules
-        model = rewriter.rewrite(model, pattern_rewrite_rules=_DEFAULT_REWRITE_RULES)
-        if stop_if_no_change and not modified:
-            logger.debug("Stopping after %d iterations.", _)
-            break
-
-    for node in model.graph.node:
-        logger.debug("Node %s::%s name %s.", node.domain, node.op_type, node.name)
-
-    for function in model.functions:
-        for node in function.node:
-            logger.debug(
-                "Function %s::%s node %s::%s name %s.",
-                function.domain,
-                function.name,
-                node.domain,
-                node.op_type,
-                node.name,
-            )
-
-    return model
-
-
-_DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT = (
-    _constant_folding._DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT
-)
-
-_DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT = (
-    _constant_folding._DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT
-)
-
 
-def optimize_ir(
-    model: ir.Model,
-    num_iterations: int = 2,
-    *,
-    onnx_shape_inference: bool = True,
-    stop_if_no_change: bool = True,
-    input_size_limit: int = _DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT,
-    output_size_limit: int = _DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT,
-) -> None:
-    """Optimizes a model.
+import onnxscript.optimizer._legacy._optimizer as legacy_optimizer
+from onnxscript import ir
+from onnxscript.optimizer._constant_folding import basic_constant_propagation
+from onnxscript.optimizer._legacy.constant_folding import fold_constants
+from onnxscript.optimizer._optimizer import optimize_ir
+from onnxscript.optimizer._remove_unused import remove_unused_nodes
 
-    Args:
-        model: The model to be optimized.
-        num_iterations: Number of times the optimization loop is repeated.
-        onnx_shape_inference: Applies node-level shape-inference as part of optimization
-        input_size_limit: Will not apply constant folding to ops with any input of size
-            greater than this. Does not apply to special ops like Shape() and Size().
-        output_size_limit: Will not rewrite any foldable-op into a Constant op if the size
-            of the output tensor is greater than this.
-        stop_if_no_change: Not supported currently (has no effect). Meant to stop the
-            outer optimization loop if no change is detected in one iteration.
-    """
-    del stop_if_no_change  # Looks like rewriter doesn't support this yet.
-    _inliner.inline(model)
-    for _ in range(num_iterations):
-        _constant_folding.fold_constants(
-            model,
-            onnx_shape_inference=onnx_shape_inference,
-            input_size_limit=input_size_limit,
-            output_size_limit=output_size_limit,
-        )
-        rewriter.rewrite(model, pattern_rewrite_rules=_DEFAULT_REWRITE_RULES)
-    remove_unused_nodes(model)
 
+def optimize(model: ir.Model | onnx.ModelProto, *args, **kwargs):
+    if isinstance(model, ir.Model):
+        return optimize_ir(model, *args, **kwargs)
+    else:
+        return legacy_optimizer.optimize(model, *args, **kwargs)
 
-basic_constant_propagation = _constant_folding.basic_constant_propagation
 
 __all__ = [
     "fold_constants",

onnxscript/optimizer/_constant_folding.py

@@ -17,20 +17,32 @@
 
 import onnxscript.ir as ir
 import onnxscript.ir._convenience as _convenience
-import onnxscript.optimizer.constant_folding as constant_folding
 import onnxscript.rewriter.pattern as orp
 import onnxscript.utils.utils as utils
 
+DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT = 1024
+
+DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT = 1024 * 1024
+
 
 def is_control_flow_op(node: ir.Node) -> bool:
     graph_types = {ir.AttributeType.GRAPH, ir.AttributeType.GRAPHS}
     return any(attr.type in graph_types for attr in node.attributes.values())
 
 
+non_deterministic_ops = frozenset(
+    {
+        "RandomUniform",
+        "RandomNormal",
+        "RandomUniformLike",
+        "RandomNormalLike",
+        "Multinomial",
+    }
+)
+
+
 def is_non_deterministic_op(node: ir.Node) -> bool:
-    return node.op_type in constant_folding.non_deterministic_ops and utils.is_onnx_domain(
-        node.domain
-    )
+    return node.op_type in non_deterministic_ops and utils.is_onnx_domain(node.domain)
 
 
 def is_onnx_op(node: ir.Node, op_type: str) -> bool:
@@ -43,10 +55,6 @@ def is_constant_op(node: ir.Node) -> bool:
     )
 
 
-_DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT = 1024
-
-_DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT = constant_folding._DEFAULT_CONSTANT_FOLD_SIZE_LIMIT
-
 logger = logging.getLogger(__name__)
 
 # "Standard" evaluators are used to perform constant-folding.
@@ -787,8 +795,8 @@ def fold_constants(
     external_data_folder: str = "",
     *,
     onnx_shape_inference: bool = False,
-    input_size_limit: int = _DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT,
-    output_size_limit: int = _DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT,
+    input_size_limit: int = DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT,
+    output_size_limit: int = DEFAULT_CONSTANT_FOLD_OUTPUT_SIZE_LIMIT,
 ) -> bool:
     """
     Applies constant folding optimization to the model.

onnxscript/optimizer/constant_folding_test.py → onnxscript/optimizer/_constant_folding_test.py

@@ -8,7 +8,8 @@
 
 import onnxscript.optimizer as optimizer
 from onnxscript.ir import serde
-from onnxscript.optimizer import _constant_folding, constant_folding
+from onnxscript.optimizer import _constant_folding
+from onnxscript.optimizer._legacy import constant_folding
 
 
 @parameterized.parameterized_class(("using_ir",), [(False,), (True,)])

onnxscript/optimizer/_legacy/_optimizer.py

@@ -0,0 +1,98 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import onnx
+import onnx.shape_inference
+
+from onnxscript import rewriter
+from onnxscript.optimizer._legacy._simple_function_folding import (
+    inline_functions_with_unused_outputs,
+    inline_simple_functions,
+)
+from onnxscript.optimizer._legacy.constant_folding import fold_constants
+from onnxscript.optimizer._optimizer import _DEFAULT_REWRITE_RULES
+from onnxscript.optimizer._remove_unused import remove_unused_nodes
+from onnxscript.optimizer._remove_unused_function import remove_unused_functions
+
+logger = logging.getLogger(__name__)
+
+
+def optimize(
+    model: onnx.ModelProto,
+    num_iterations: int = 2,
+    *,
+    onnx_shape_inference: bool = True,
+    stop_if_no_change: bool = True,
+    external_data_folder: str = "",
+    **kwargs: Any,
+) -> onnx.ModelProto:
+    """Optimize the model. Perform optimizations and clean-ups such as constant folding, dead code elimination, etc.
+
+    Args:
+        model (onnx.ModelProto): The model to optimize.
+        num_iterations (int, optional): Number of iterations to perform.
+        onnx_shape_inference (bool, optional): Whether to perform onnx shape inference on the model.
+            Set this to False to turn off onnx shape inference, and rely on model carried shapes and types.
+            This is useful for models produced by PyTorch 2.2+ dynamo onnx exporter, where the model carries
+            the symbolic shapes recorded from dynamo tracing.
+        stop_if_no_change (bool, optional): Whether to stop if no change is detected.
+        external_data_folder (str, optional): The folder to store external data.
+        **kwargs: Additional keyword arguments. For BC purposes.
+    """
+    if kwargs.pop("function_aware_folding", None) is not None:
+        logger.warning(
+            "'function_aware_folding' is deprecated. 'optimize' now supports both fully inlined models and models with functions. "
+            "To achieve the same behavior as 'function_aware_folding=True' before, set 'onnx_shape_inference=False'. "
+            "This would turn off incremental onnx shape inference and rely on model carried shapes and types. "
+            "See 'onnx_shape_inference' for more details."
+        )
+    for _ in range(num_iterations):
+        if onnx_shape_inference:
+            if model.ByteSize() < 1024 * 1024 * 1024 * 2:
+                # NOTE: strict mode is disabled because it crashes on the models
+                # that have different shapes inferred from the model carried shapes.
+                # The case can be found in:
+                # https://github.com/microsoft/onnxscript/issues/1443
+                model = onnx.shape_inference.infer_shapes(
+                    model, check_type=True, strict_mode=False, data_prop=True
+                )
+            else:
+                logger.warning(
+                    "The model size is too large for full model shape inference. "
+                    "Skipping this step."
+                )
+
+        inline_simple_functions(model)
+        modified = fold_constants(
+            model, external_data_folder, onnx_shape_inference=onnx_shape_inference
+        )
+
+        remove_unused_nodes(model)
+        inline_simple_functions(model)
+        model = remove_unused_functions(model)
+        inline_functions_with_unused_outputs(model)
+        # NOTE: This is general rewrite rules
+        model = rewriter.rewrite(model, pattern_rewrite_rules=_DEFAULT_REWRITE_RULES)
+        if stop_if_no_change and not modified:
+            logger.debug("Stopping after %d iterations.", _)
+            break
+
+    for node in model.graph.node:
+        logger.debug("Node %s::%s name %s.", node.domain, node.op_type, node.name)
+
+    for function in model.functions:
+        for node in function.node:
+            logger.debug(
+                "Function %s::%s node %s::%s name %s.",
+                function.domain,
+                function.name,
+                node.domain,
+                node.op_type,
+                node.name,
+            )
+
+    return model

onnxscript/optimizer/simple_function_folding.py → onnxscript/optimizer/_legacy/_simple_function_folding.py

@@ -11,7 +11,7 @@
 
 import onnxscript._legacy_ir as ir
 from onnxscript._legacy_ir import visitor
-from onnxscript.optimizer import remove_unused_proto
+from onnxscript.optimizer._legacy import _remove_unused_proto
 
 logger = logging.getLogger(__name__)
 
@@ -168,7 +168,7 @@ def _find_nodes_with_any_unused_output(
                 # All unused output means the node is not used at all.
                 # Hence do not update used_values with the node's inputs.
                 continue
-            used_values |= remove_unused_proto.compute_used_in_node(node)
+            used_values |= _remove_unused_proto.compute_used_in_node(node)
         return target_nodes
 
     def visit_model(self, model: onnx.ModelProto) -> None: