Add rotary embedding fusion rule (part 1)

onnxscript/optimizer/_constant_folding.py

@@ -300,6 +300,29 @@ def _get_int_attribute(node: ir.Node, name: str, default: int | None = None) ->
     return default
 
 
+@register("Reshape")
+def reshape(node: ir.Node, op, state: OptimizerState) -> ReturnValue:
+    """Replace a Reshape node by Identity when applicable."""
+    input = _get_input(node, 0)
+    shape = _get_input(node, 1)
+    if input is None or shape is None:
+        return None
+    input_shape = input.shape
+    if input_shape is None:
+        return None
+    input_shape_dims = list(input_shape.dims)
+    if any(not isinstance(dim, int) for dim in input_shape_dims):
+        return None
+    shape_value = _get_numpy_value(shape)
+    if shape_value is None:
+        return None
+    target_shape_dims = shape_value.tolist()
+    if input_shape_dims == target_shape_dims:
+        # No need to check for special values like -1, 0, etc. here
+        return op.Identity(input)
+    return None
+
+
 @register("Cast")
 def cast(node: ir.Node, op, state: OptimizerState) -> ReturnValue:
     input = _get_input(node, 0)

onnxscript/rewriter/_ir_utils.py

@@ -2,6 +2,9 @@
 # Licensed under the MIT License.
 from __future__ import annotations
 
+import math
+from typing import Callable
+
 import numpy as np
 
 import onnxscript.ir as ir
@@ -77,3 +80,27 @@ def get_singleton_value(val: ir.Value | None):
     if np_val is not None and np_val.size == 1:
         return np_val.item()
     return None
+
+
+def is_singleton_value(
+    val: ir.Value | None, expected: float | int | Callable, *, rtol: float | None = None
+) -> bool:
+    """Returns True if the value is a single element tensor with given value, and False otherwise."""
+    scalar = get_singleton_value(val)
+    if scalar is None:
+        return False
+    if callable(expected):
+        return expected(scalar)
+    if isinstance(expected, int):
+        return expected == scalar
+    # rtol must be specified for float comparison
+    assert rtol is not None
+    return math.isclose(scalar, expected, rel_tol=rtol)
+
+
+def has_rank(value: ir.Value | None, rank: int) -> bool:
+    """Returns True if the value is statically known to have the given rank, and False otherwise."""
+    if value is None:
+        return False
+    shape = value.shape
+    return (shape is not None) and (shape.rank() == rank)

onnxscript/rewriter/generic_pattern.py

@@ -551,7 +551,12 @@ def match(
         graph_or_function: ir.Graph | ir.Function,
         node: ir.Node,
         verbose: int = 0,
+        remove_nodes: bool = True,
     ) -> orp.MatchResult | None:
+        if not remove_nodes:
+            raise NotImplementedError(
+                "remove_nodes=False is not implemented in GenericPatternMatcher"
+            )
         del model
         del graph_or_function
         self.verbose = verbose

onnxscript/rewriter/onnxruntime/xformers/__init__.py

@@ -1,3 +1,15 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 from __future__ import annotations
+
+from onnxscript.rewriter.onnxruntime.xformers.cos_sin_cache import fuse_cos_sin_cache
+from onnxscript.rewriter.onnxruntime.xformers.rms_normalization import fuse_rms_normalization
+from onnxscript.rewriter.onnxruntime.xformers.rotary_embedding import fuse_rotary_embedding
+from onnxscript.rewriter.onnxruntime.xformers.skip_normalization import fuse_normalization
+
+__all__ = [
+    "fuse_rms_normalization",
+    "fuse_normalization",
+    "fuse_rotary_embedding",
+    "fuse_cos_sin_cache",
+]

onnxscript/rewriter/onnxruntime/xformers/_test_utils.py

@@ -25,7 +25,7 @@ def ort_run(model_name: str, model, inputs):
     providers = ["CPUExecutionProvider"]
     with tempfile.TemporaryDirectory() as temp_dir:
         model_path = os.path.join(temp_dir, f"{model_name}.onnx")
-        io.save(model, model_path)
+        _save(model, model_path)
         # Run model
         session = onnxruntime.InferenceSession(model_path, providers=providers)
         ort_outputs = session.run(None, inputs)

onnxscript/rewriter/onnxruntime/xformers/cos_sin_cache.py

@@ -0,0 +1,102 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import numpy as np
+
+import onnxscript.ir as ir
+from onnxscript.optimizer import remove_unused_nodes
+from onnxscript.rewriter import _ir_utils, pattern
+
+# Rewrite the computation of cos/sin cache into the form expected by ORT's custom ops.
+
+# We match against the following code pattern:
+# Original code (from transformers) for computing cos/sin cache for RoPE:
+# https://github.com/huggingface/transformers/blob/0ade1caa356dce6b70ef8293addeb0898f177206/src/transformers/models/llama/modeling_llama.py#L135
+# position_ids_expanded = position_ids[:, None, :].float()
+# freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+# emb = torch.cat((freqs, freqs), dim=-1)
+# cos = emb.cos()
+# sin = emb.sin()
+#
+# We rewrite this pattern into the following form:
+# inv_freq_values = inv_freq_expanded.reshape(1, -1)
+# pos_id_range = np.arange(max_pos_id, dtype=np.float32).reshape(-1, 1)
+# angles = np.matmul(pos_id_range, inv_freq_values)
+# cos_value = np.cos(angles)
+# sin_value = np.sin(angles)
+# cos_2d = op.Constant(value=ir.tensor(cos_value))
+# sin_2d = op.Constant(value=ir.tensor(sin_value))
+#
+# This produces cos/sin values in a form that can be used by ORT's custom ops.
+
+# TODO: To apply the pattern-rewrite, we need to know the maximum position id.
+# Need to find a way to get this information from the model or its config.
+
+
+class CosSinCacheFusion(pattern.RewriteRuleClassBase):
+    def __init__(self, name: str, max_pos_id: int):
+        # This pattern makes use of shared Cos/Sin values. So, we can't remove the
+        # matched nodes as part of the rewrite-step. We apply a separate final
+        # pass to remove unused nodes.
+        super().__init__(name, remove_nodes=False)
+        self._max_pos_id = max_pos_id
+
+    def pattern(self, op, x, inv_freq, position_ids, interleaved, num_heads):
+        position_ids_expanded = op.Unsqueeze(position_ids, 1)
+        position_ids_expanded = op.Cast(position_ids_expanded, to=ir.DataType.FLOAT)
+        freqs = op.MatMul(inv_freq, position_ids_expanded)
+        freqs = op.Transpose(freqs, perm=[0, 2, 1])
+        emb = op.Concat(freqs, freqs, axis=-1)
+        cos = op.Cos(emb)
+        sin = op.Sin(emb)
+        cos_4d = op.Unsqueeze(cos, 1)  # convert
+        sin_4d = op.Unsqueeze(sin, 1)
+        return op.RotaryEmbedding(
+            x,
+            cos_4d,
+            sin_4d,
+            interleaved=interleaved,
+            num_heads=num_heads,
+            _domain="ai.onnxruntime.fusion",
+        )
+
+    def check(self, context, inv_freq, position_ids, **_):
+        if not _ir_utils.has_rank(position_ids, 2):
+            return False
+        if not _ir_utils.has_rank(inv_freq, 3):
+            return False
+        inv_freq_shape = inv_freq.shape
+        if inv_freq.const_value is None:
+            return False
+        return inv_freq_shape[0] == 1 and inv_freq_shape[2] == 1
+
+    def rewrite(self, op, x, inv_freq, position_ids, interleaved, num_heads, **_):
+        inv_freq_values = inv_freq.const_value.numpy().reshape(1, -1)
+        pos_id_range = np.arange(self._max_pos_id, dtype=np.float32).reshape(-1, 1)
+        angles = np.matmul(pos_id_range, inv_freq_values)
+        cos_value = np.cos(angles)
+        sin_value = np.sin(angles)
+        cos_2d = op.Constant(value=ir.tensor(cos_value))
+        sin_2d = op.Constant(value=ir.tensor(sin_value))
+        return op.RotaryEmbedding(
+            x,
+            position_ids,
+            cos_2d,
+            sin_2d,
+            interleaved=interleaved,
+            num_heads=num_heads,
+            _domain="com.microsoft",
+        )
+
+
+_rule = CosSinCacheFusion.rule("CosSinCache", 2048)
+
+cos_sin_cache_rules = pattern.RewriteRuleSet([_rule])
+
+
+def fuse_cos_sin_cache(model: ir.Model) -> int:
+    count = cos_sin_cache_rules.apply_to_model(model)
+    print(f"CosSinCache count: {count}")
+    remove_unused_nodes(model)
+    return count

onnxscript/rewriter/onnxruntime/xformers/cos_sin_cache_test.py

@@ -0,0 +1,29 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import unittest
+
+import onnxscript.optimizer
+from onnxscript.rewriter.onnxruntime.xformers import fuse_cos_sin_cache, fuse_rotary_embedding
+from onnxscript.rewriter.onnxruntime.xformers._smollm_1layer import _SmollmTestData
+from onnxscript.rewriter.onnxruntime.xformers._test_utils import assert_allclose, ort_run
+
+
+class TestCosSinCacheTransform(unittest.TestCase):
+    def test_smollm(self):
+        smollm_test = _SmollmTestData()
+        model = smollm_test.get_onnx_model()
+        onnxscript.optimizer.optimize(model)
+        inputs = smollm_test.get_ort_inputs()
+        original_outputs = ort_run("original", model, inputs)
+        count = fuse_rotary_embedding(model)
+        self.assertGreater(count, 0)
+        count = fuse_cos_sin_cache(model)
+        self.assertGreater(count, 0)
+        new_outputs = ort_run("optimized", model, inputs)
+        assert_allclose(new_outputs, original_outputs)
+
+
+if __name__ == "__main__":
+    unittest.main()

onnxscript/rewriter/onnxruntime/xformers/rms_normalization.py

@@ -35,14 +35,10 @@ def __init__(self, name: str, *, cast_input: bool, cast_normalized: bool):
             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).
         """
-        self._name = name
+        super().__init__(name=name)
         self._cast_input = cast_input
         self._cast_normalized = cast_normalized
 
-    @property
-    def name(self):
-        return self._name
-
     def pattern(self, op, x, scale, epsilon, compute_dtype, target_dtype):
         if self._cast_input:
             x = op.Cast(x, to=compute_dtype)
@@ -95,5 +91,5 @@ def rewrite(self, op, x, scale, epsilon, compute_dtype, target_dtype):
 
 
 def fuse_rms_normalization(model: ir.Model) -> None:
-    count = rms_normalization_ruleset.apply_to_model(model, verbose=5)
+    count = rms_normalization_ruleset.apply_to_model(model)
     print(f"RMS Normalization count: {count}")

onnxscript/rewriter/onnxruntime/xformers/rms_normalization_test.py

@@ -4,21 +4,12 @@
 
 import unittest
 
-import onnx
-
 import onnxscript.optimizer
 from onnxscript.rewriter.onnxruntime.xformers._smollm_1layer import _SmollmTestData
 from onnxscript.rewriter.onnxruntime.xformers._test_utils import assert_allclose, ort_run
 from onnxscript.rewriter.onnxruntime.xformers.rms_normalization import fuse_rms_normalization
 
 
-def model_repr(self):
-    return f"Model({self.graph.name})"
-
-
-onnx.ModelProto.__repr__ = model_repr
-
-
 class TestRmsNormalization(unittest.TestCase):
     def test_smollm(self):
         smollm_test = _SmollmTestData()

onnxscript/rewriter/onnxruntime/xformers/rotary_embedding.py

@@ -0,0 +1,64 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import onnxscript.ir as ir
+from onnxscript.rewriter import _ir_utils, pattern
+
+# Add first version of the RotaryEmbeddingFusion rule. This considers only one simple pattern
+# for full rotation without interleaving.
+# TODO(rama): Add pattern variations to handle other cases (interleaved, as well as partial rotation).
+
+# Note: This targets the new op being proposed to ONNX. This version does not exist in ORT yet.
+# so it can't be tested by running against ORT. See cos_sin_cache.py for a transformation that
+# rewrites the pattern into one that can be run against ORT.
+
+
+def _rotate_half_pattern(op, x, start1, end1, start2, end2):
+    # Slice(input, starts, ends, axes, steps)
+    x1 = op.Slice(x, start1, end1, [3], [1])
+    x2 = op.Slice(x, start2, end2, [3], [1])
+    minus_x2 = op.Neg(x2)
+    rotated_x = op.Concat(minus_x2, x1, axis=-1)
+    return rotated_x
+
+
+class RotaryEmbeddingFusion(pattern.RewriteRuleClassBase):
+    def pattern(self, op, x, cos, sin, start1, end1, start2, end2):
+        return x * cos + _rotate_half_pattern(op, x, start1, end1, start2, end2) * sin
+
+    def check(self, op, x, start1, end1, start2, end2, **_):
+        # x needs to be a 4D tensor with known last dimension size (== head_size) and known second dimension (num_heads)
+        if x is None or x.shape is None or len(x.shape) != 4:
+            return False
+        if not isinstance(x.shape[1], int):
+            return False
+        head_size = x.shape[3]
+        if not isinstance(head_size, int):
+            return False
+        half_head_size = head_size // 2
+
+        # Check that x is being split into two equal halves of size half_head_size
+        return (
+            _ir_utils.is_singleton_value(start1, 0)
+            and _ir_utils.is_singleton_value(end1, half_head_size)
+            and _ir_utils.is_singleton_value(start2, half_head_size)
+            and _ir_utils.is_singleton_value(end2, lambda x: x >= head_size)
+        )
+
+    def rewrite(self, op, x, cos, sin, **_):
+        num_heads = x.shape[1]
+        return op.RotaryEmbedding(
+            x, cos, sin, interleaved=0, num_heads=num_heads, _domain="ai.onnxruntime.fusion"
+        )
+
+
+_rule = RotaryEmbeddingFusion.rule()
+
+rotary_embedding_rules = pattern.RewriteRuleSet([_rule])
+
+
+def fuse_rotary_embedding(model: ir.Model) -> int:
+    count = rotary_embedding_rules.apply_to_model(model)
+    print(f"Rotary Embedding count: {count}")
+    return count

onnxscript/rewriter/onnxruntime/xformers/rotary_embedding_test.py

@@ -0,0 +1,23 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import unittest
+
+import onnxscript.optimizer
+from onnxscript.rewriter.onnxruntime.xformers._smollm_1layer import _SmollmTestData
+from onnxscript.rewriter.onnxruntime.xformers.rotary_embedding import fuse_rotary_embedding
+
+
+class TestRotaryEmbedding(unittest.TestCase):
+    def test_smollm(self):
+        smollm_test = _SmollmTestData()
+        model = smollm_test.get_onnx_model()
+        onnxscript.optimizer.optimize(model)
+        fuse_rotary_embedding(model)
+        op_types = [n.op_type for n in model.graph]
+        self.assertIn("RotaryEmbedding", op_types)
+
+
+if __name__ == "__main__":
+    unittest.main()