Merge branch 'main' into justinchu/op-signature

.github/workflows/main.yaml

@@ -71,7 +71,7 @@ jobs:
       - name: Pull Test Data
         run: git lfs pull
       - name: Run tests
-        run: nox -t ${{ matrix.nox-tag }} --forcecolor -- --cov=onnxscript --cov-report=xml --cov-append --cov-branch -n=auto --junit-xml pytest.xml
+        run: nox -t ${{ matrix.nox-tag }} --forcecolor -- --cov=onnxscript --cov-report=xml --cov-append --cov-branch -n=auto --junitxml junit.xml
         env:
           CATCH_ORT_SEGFAULT: "${{ matrix.os == 'ubuntu-latest' && '1' || '0' }}"
           CREATE_REPRODUCTION_REPORT: "${{ matrix.os == 'ubuntu-latest' && '1' || '0' }}"
@@ -80,12 +80,11 @@ jobs:
         uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-      - name: Upload Test Results
-        if: always()
-        uses: actions/upload-artifact@v3
+      - name: Upload test results to Codecov
+        if: ${{ !cancelled() }}
+        uses: codecov/test-results-action@v1
         with:
-          name: Test Results (${{ matrix.name }}-${{ matrix.os }})
-          path: pytest.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
       - name: Upload torchlib error reports
         if: always()
         uses: actions/upload-artifact@v3
@@ -161,23 +160,3 @@ jobs:
             echo "Update readme by running `python docs/update_readme.py`"
             exit 1
           fi
-
-  publish-test-results:
-    name: "Publish Tests Results to Github"
-    needs: test
-    runs-on: ubuntu-latest
-    permissions:
-      checks: write
-      # only needed unless run with comment_mode: off
-      pull-requests: write
-    if: always()
-    steps:
-      - name: Download Artifacts
-        uses: actions/download-artifact@v3
-        with:
-          path: artifacts
-
-      - name: Publish Test Results
-        uses: EnricoMi/publish-unit-test-result-action@v2
-        with:
-          files: "artifacts/**/*.xml"

.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

docs/tutorial/rewriter/examples/allow_other_attributes.py

@@ -0,0 +1,67 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""Onnx Pattern Rewriting with attributes
+
+This script shows how to define a rewriting rule based on patterns that
+are dependent on the attributes of the nodes.
+"""
+
+import onnx
+
+import onnxscript
+from onnxscript import FLOAT, opset18, script
+from onnxscript.rewriter import pattern
+
+
+@script()
+def original_model(A: FLOAT[2, 2], B: FLOAT[2, 2]) -> FLOAT[2, 2]:
+    add = opset18.Add(A, B)
+    result = opset18.Dropout(add, training_mode=False)
+    return result
+
+
+_model = original_model.to_model_proto()
+onnx.checker.check_model(_model)
+
+
+####################################
+# The target pattern
+# =====================
+
+
+def add_pattern(op, input):
+    return op.Dropout(input, training_mode=False, _allow_other_attributes=True)
+
+
+####################################
+# The replacement pattern
+# =====================
+
+
+def add_replacement(op, input, **_):
+    return op.Identity(input)
+
+
+####################################
+# Create Rewrite Rule and Apply to Model
+# =====================
+
+
+def apply_rewrite(model):
+    # Create rewrite rules
+    add_rule = pattern.RewriteRule(
+        add_pattern,  # target pattern
+        add_replacement,  # replacement pattern
+    )
+    # Create a Rewrite Rule Set
+    rewrite_rule_set = pattern.RewriteRuleSet([add_rule])
+    # Apply rewrite while passing match_condition
+    model_with_rewrite = onnxscript.rewriter.rewrite(
+        model,
+        pattern_rewrite_rules=rewrite_rule_set,
+    )
+    return model_with_rewrite
+
+
+_model_with_rewrite = apply_rewrite(_model)
+onnx.checker.check_model(_model_with_rewrite)

docs/tutorial/rewriter/examples/broadcast_matmul.py

@@ -15,7 +15,7 @@
 
 import onnxscript
 from onnxscript import FLOAT, ir, opset18, script
-from onnxscript.rewriter import _ir_utils, pattern
+from onnxscript.rewriter import pattern
 
 logger = logging.getLogger(__name__)
 
@@ -83,8 +83,6 @@ def check_if_not_need_reshape(
 
     input_a_shape = input_a.shape
     input_b_shape = input_b.shape
-    # TODO: Get a helper func to get const_value
-    _ir_utils.propagate_const_value(shape_c)
     shape_c_tensor = shape_c.const_value
     if shape_c_tensor is None:
         logger.info("The value 'shape_c' is not statically known.")

docs/tutorial/rewriter/rewrite_patterns.md

@@ -84,6 +84,29 @@ The graph (on the left) consists of the target pattern before the rewrite rule i
 
 ![target_pattern](examples/img/erfgelu_01.png) ![replacement_pattern](examples/img/erfgelu_02.png)
 
+## Specifying attributes in the pattern
+
+This section demonstrates the use of attribute values in pattern-based rewriting.
+First, write a target pattern and replacement pattern in a similar way to the previous examples.
+The example pattern below will match successfully only against Dropout nodes with the
+attribute value `training_mode` set to `False`.
+The `_allow_other_attributes` option allows the pattern to match nodes that have additional attributes
+not specified in the pattern. If it is set to `False`, then the node must have only the specified
+attribute values, and no other attributes, for a successful match. The default value for this
+option is `True`.
+
+```{literalinclude} examples/allow_other_attributes.py
+:pyobject: add_pattern
+```
+
+```{literalinclude} examples/allow_other_attributes.py
+:pyobject: add_replacement
+```
+
+```{literalinclude} examples/allow_other_attributes.py
+:pyobject: apply_rewrite
+```
+
 
 (heading-target-commute)=
 ## Utilizing `commute` parameter for pattern-matching
@@ -196,3 +219,4 @@ With all the necessary components in place, the pattern rewrite rule with the `m
 The final graph with the applied rewrite looks as follows:
 
 ![broadcast_rewrite](examples/img/broadcast_02.png){align=center}
+

onnxscript/_framework_apis/torch_2_5.py

@@ -17,17 +17,10 @@
 import pathlib
 from typing import Callable
 
-import onnx
-
-from onnxscript import ir
+from onnxscript import ir, optimizer
 from onnxscript.function_libs.torch_lib import registration
 from onnxscript.ir import _external_data
 
-# Internal flag. Will go away.
-_TORCH_ONNX_SAVE_EXTERNAL_DATA_WITH_IR = (
-    os.getenv("TORCH_ONNX_OFFLOAD_EXTERNAL_DATA_WITH_IR") == "1"
-)
-
 
 @dataclasses.dataclass(frozen=True)
 class _OnnxFunctionMeta:
@@ -49,8 +42,10 @@ class _OnnxFunctionMeta:
 
 def optimize(model: ir.Model) -> ir.Model:
     """Optimize the model."""
-
-    # TODO(justinchuby): Use the optimizer
+    # Internal flag. Will go away.
+    enabled = os.getenv("TORCH_ONNX_ENABLE_OPTIMIZATION") == "1"
+    if enabled:
+        optimizer.optimize_ir(model)
     return model
 
 
@@ -81,45 +76,32 @@ def save_model_with_external_data(model: ir.Model, model_path: str | os.PathLike
     """Save the model with external data. The model is unchanged after saving."""
 
     # TODO(#1835): Decide if we want to externalize large attributes as well
-    if _TORCH_ONNX_SAVE_EXTERNAL_DATA_WITH_IR:
-        initializer_values = tuple(model.graph.initializers.values())
-        tensors = [v.const_value for v in initializer_values]
-        for tensor in tensors:
-            if tensor is None:
-                raise ValueError(
-                    "The model contains uninitialized initializer values. "
-                    "Please make sure all initializer values are initialized."
-                )
-        destination_path = pathlib.Path(model_path)
-        base_dir = destination_path.parent
-        data_path = f"{destination_path.name}.data"
-
-        external_tensors = _external_data.convert_tensors_to_external(
-            tensors,  # type: ignore[arg-type]
-            base_dir,
-            data_path,
-        )
-
-        # Replace the initializer values with external tensors and save the model
-        for initializer, external_tensor in zip(initializer_values, external_tensors):
-            initializer.const_value = external_tensor
-        ir.save(model, model_path)
-
-        # Restore the original initializer values so the model is unchanged
-        for initializer, tensor in zip(initializer_values, tensors):
-            initializer.const_value = tensor
-
-    else:
-        destination_path = pathlib.Path(model_path)
-        # Create the directory if it does not exist
-        data_path = f"{destination_path.name}.data"
-        proto = ir.serde.serialize_model(model)
-        onnx.save_model(
-            proto,
-            model_path,
-            save_as_external_data=True,
-            location=data_path,
-        )
+    initializer_values = tuple(model.graph.initializers.values())
+    tensors = [v.const_value for v in initializer_values]
+    for tensor in tensors:
+        if tensor is None:
+            raise ValueError(
+                "The model contains uninitialized initializer values. "
+                "Please make sure all initializer values are initialized."
+            )
+    destination_path = pathlib.Path(model_path)
+    base_dir = destination_path.parent
+    data_path = f"{destination_path.name}.data"
+
+    external_tensors = _external_data.convert_tensors_to_external(
+        tensors,  # type: ignore[arg-type]
+        base_dir,
+        data_path,
+    )
+
+    # Replace the initializer values with external tensors and save the model
+    for initializer, external_tensor in zip(initializer_values, external_tensors):
+        initializer.const_value = external_tensor
+    ir.save(model, model_path)
+
+    # Restore the original initializer values so the model is unchanged
+    for initializer, tensor in zip(initializer_values, tensors):
+        initializer.const_value = tensor
 
 
 def get_torchlib_ops() -> list[_OnnxFunctionMeta]:

onnxscript/_framework_apis/torch_2_6.py

@@ -0,0 +1,26 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""Stable APIs for PyTorch 2.6."""
+
+from __future__ import annotations
+
+__all__ = [
+    "check_model",
+    "convert_version",
+    "get_torchlib_ops",
+    "optimize",
+    "save_model_with_external_data",
+]
+from onnxscript import ir, optimizer
+from onnxscript._framework_apis.torch_2_5 import (
+    check_model,
+    convert_version,
+    get_torchlib_ops,
+    save_model_with_external_data,
+)
+
+
+def optimize(model: ir.Model) -> ir.Model:
+    """Optimize the model."""
+    optimizer.optimize_ir(model)
+    return model