Add support for onnx fusions

onnxscript/rewriter/onnx_fusions/_core.py

@@ -0,0 +1,37 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import onnx_ir as ir
+from onnxscript.rewriter.onnx_fusions import rms_normalization
+
+
+def _get_onnx_opset_version(model: ir.Model) -> int | None:
+    """Get the ONNX opset version imported by the model."""
+    model_version1 = model.opset_imports.get("")
+    model_version2 = model.opset_imports.get("ai.onnx")
+    if model_version1 is not None and model_version2 is not None:
+        if model_version1 != model_version2:
+            raise ValueError(
+                f"Model imports multiple onnx opsets: {model_version1} and {model_version2}."
+            )
+    return model_version1 or model_version2
+
+
+def _opset_23_fuse(model: ir.Model, *, debug: bool = False) -> dict[str, int]:
+    """
+    Apply fusions targetting ONNX opset 23.
+    """
+    counts: dict[str, int] = {}
+    counts["RMSNormalization"] = rms_normalization.fuse(
+        model, debug=debug
+    )
+
+
+def fuse(model: ir.Model, *, debug: bool = False) -> dict[str, int]:
+    """
+    Apply fusions targetting ONNX ops.
+    """
+    model_opset_version = _get_onnx_opset_version(model)
+    if model_opset_version == 23:
+        return _opset_23_fuse(model, debug=debug)

onnxscript/rewriter/onnx_fusions/rms_normalization.py

@@ -0,0 +1,80 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import onnxscript.ir as ir
+from onnxscript.rewriter import _fusion_utils, _ir_utils, pattern
+
+"""
+RMS Normalization: ONNX Opset 23 op
+See: https://onnx.ai/onnx/operators/onnx__RMSNormalization.html#l-onnx-doc-rmsnormalization
+
+
+Key points for the fusion optimization:
+* Input and scale are allowed to be of different types.
+* The normalization of the input can be done in a different precision than the input type,
+indicated by stash_type.
+* Input (x) must be: float or double or float16 or bfloat16
+* Scale must be: float or double or float16 or bfloat16
+"""
+
+float_types = [
+    ir.DataType.FLOAT,
+    ir.DataType.FLOAT16,
+    ir.DataType.BFLOAT16,
+    ir.DataType.DOUBLE,
+]
+fp_float_types = [ir.DataType.FLOAT, ir.DataType.DOUBLE]
+
+
+class RmsNormFusion(pattern.RewriteRuleClassBase):
+    def pattern(self, op, x, scale, epsilon, compute_dtype, target_dtype):
+        x = pattern.OrValue([op.Cast(x, to=compute_dtype), x])
+        x_square = op.Pow(x, 2.0)
+        mean_square = op.ReduceMean(x_square, [-1], keepdims=1, noop_with_empty_axes=0)
+        mean_square_plus_epsilon = op.Add(mean_square, epsilon)
+        rms = op.Sqrt(mean_square_plus_epsilon)
+        reciprocal_rms = op.Reciprocal(rms)
+        normalized = op.Mul(x, reciprocal_rms)
+        normalized = pattern.OrValue([op.Cast(normalized, to=target_dtype), normalized])
+        return op.Mul(scale, normalized)
+
+    def check(self, op, x, scale, epsilon, compute_dtype, target_dtype, **_) -> pattern.MatchResult:  # type: ignore[name-defined]
+        """Check if the pattern matches conditions for use of SimplifiedLayerNormalization op."""
+        check_result = pattern.MatchResult()
+        # epsilon must be a scalar
+        epsilon_value = _ir_utils.get_singleton_value(epsilon)
+        if not isinstance(epsilon_value, float):  # TODO: support other types
+            return check_result.fail("Epsilon is not a float value.", epsilon)
+        if x.dtype not in float_types:
+            return check_result.fail("Input is not a supported float type.", x)
+        if scale.dtype not in float_types:
+            return check_result.fail("Scale is not a supported float type.", scale)
+        self._stash_dtype = compute_dtype.as_int() if compute_dtype is not None else x.dtype
+        if self._stash_dtype not in fp_float_types:
+            # TODO: ONNX documentation does not specify restrictions on stash_type, though
+            # ORT's SimplifiedLayerNormalization requires it to be float or double.
+            return check_result.fail("Normalization precision is not a float or double type.")
+        # target_dtype is guaranteed to be the same as scale type in a well-typed input
+        # for Mul(scale, normalized) to work. There is no need to check it here for a well-typed input.
+        # TODO (rama): Consider adding checks to protect against incorrectly typed models:
+        return check_result
+
+    def rewrite(self, op, x, scale, epsilon, **_):
+        # Note: ORT's SimplifiedLayerNormalization was placed in onnx domain by mistake.
+        # No need to use com.microsoft domain here; but this is a custom op in ORT.
+        return op.RMSNormalization(
+            x,
+            scale,
+            axis=-1,
+            epsilon=_ir_utils.get_singleton_value(epsilon),
+            stash_type=self._stash_dtype,
+        )
+
+
+_rule = RmsNormFusion.rule()
+rms_normalization_rules = [_rule]
+rms_normalization_ruleset = pattern.RewriteRuleSet(rms_normalization_rules)
+
+
+fuse_rms_normalization = _fusion_utils.apply_fusion_rules(rms_normalization_ruleset)

onnxscript/rewriter/ort_fusions/rms_normalization.py

@@ -28,66 +28,52 @@
 
 
 class RmsNormFusion(pattern.RewriteRuleClassBase):
-    def __init__(self, name: str, *, cast_input: bool, cast_normalized: bool):
-        """
-        Args:
-            name: Name of the rule.
-            cast_input: Whether to cast input to do the normalization in a different precision.
-            cast_normalized: Whether to cast the normalized output to the target dtype (same as scale).
-        """
-        super().__init__(name=name)
-        self._cast_input = cast_input
-        self._cast_normalized = cast_normalized
-
     def pattern(self, op, x, scale, epsilon, compute_dtype, target_dtype):
-        if self._cast_input:
-            x = op.Cast(x, to=compute_dtype)
+        x = pattern.OrValue([op.Cast(x, to=compute_dtype), x])
         x_square = op.Pow(x, 2.0)
         mean_square = op.ReduceMean(x_square, [-1], keepdims=1, noop_with_empty_axes=0)
         mean_square_plus_epsilon = op.Add(mean_square, epsilon)
         rms = op.Sqrt(mean_square_plus_epsilon)
         reciprocal_rms = op.Reciprocal(rms)
         normalized = op.Mul(x, reciprocal_rms)
-        if self._cast_normalized:
-            normalized = op.Cast(normalized, to=target_dtype)
+        normalized = pattern.OrValue([op.Cast(normalized, to=target_dtype), normalized])
         return op.Mul(scale, normalized)
 
-    def check(self, op, x, scale, epsilon, compute_dtype, target_dtype) -> pattern.MatchResult:  # type: ignore[name-defined]
+    def check(
+        self, op, x, scale, epsilon, compute_dtype, target_dtype, **_
+    ) -> pattern.MatchResult:  # type: ignore[name-defined]
         """Check if the pattern matches conditions for use of SimplifiedLayerNormalization op."""
         check_result = pattern.MatchResult()
         # epsilon must be a scalar
         epsilon_value = _ir_utils.get_singleton_value(epsilon)
         if not isinstance(epsilon_value, float):  # TODO: support other types
             return check_result.fail("Epsilon is not a float value.", epsilon)
-        # input and output must be same dtype
         if x.dtype not in float_types:
             return check_result.fail("Input is not a float type.", x)
         if scale.dtype not in float_types:
             return check_result.fail("Scale is not a float type.", scale)
-        stash_dtype = compute_dtype.value if self._cast_input else x.dtype
-        if stash_dtype not in fp_float_types:
+        self._stash_dtype = compute_dtype.as_int() if compute_dtype is not None else x.dtype
+        if self._stash_dtype not in fp_float_types:
             return check_result.fail("Normalization precision is not a float or double type.")
+        # target_dtype is guaranteed to be the same as scale type in a well-typed input
+        # for Mul(scale, normalized) to work. There is no need to check it here for a well-typed input.
+        # TODO (rama): Consider adding checks to protect against incorrectly typed models:
         return check_result
 
-    def rewrite(self, op, x, scale, epsilon, compute_dtype, target_dtype):
-        stash_dtype = compute_dtype.value if self._cast_input else x.dtype
+    def rewrite(self, op, x, scale, epsilon, **_):
         # Note: ORT's SimplifiedLayerNormalization was placed in onnx domain by mistake.
         # No need to use com.microsoft domain here; but this is a custom op in ORT.
         return op.SimplifiedLayerNormalization(
             x,
             scale,
             axis=-1,
             epsilon=_ir_utils.get_singleton_value(epsilon),
-            stash_type=stash_dtype,
+            stash_type=self._stash_dtype,
         )
 
 
-_rule_0 = RmsNormFusion.rule("RmsNorm-0", cast_input=True, cast_normalized=True)
-_rule_1 = RmsNormFusion.rule("RmsNorm-1", cast_input=False, cast_normalized=True)
-_rule_2 = RmsNormFusion.rule("RmsNorm-2", cast_input=True, cast_normalized=False)
-_rule_3 = RmsNormFusion.rule("RmsNorm-3", cast_input=False, cast_normalized=False)
-
-rms_normalization_rules = [_rule_0, _rule_1, _rule_2, _rule_3]
+_rule = RmsNormFusion.rule()
+rms_normalization_rules = [_rule]
 rms_normalization_ruleset = pattern.RewriteRuleSet(rms_normalization_rules)