First series of P0 patterns to optimize llama

onnxscript/rewriter/llama_rule_sets.py

@@ -0,0 +1,93 @@
+from __future__ import annotations
+
+import onnxscript.ir as ir
+import onnxscript.rewriter.no_op as no_op
+import onnxscript.rewriter.pattern as orp
+from onnxscript.rewriter import pattern
+
+_op = pattern.onnxop
+
+
+def transpose_identity(x, perm):
+    return _op.Transpose(x, perm=perm)
+
+
+def transpose_identity_check(x: ir.Value, perm: ir.Attr | ir.RefAttr) -> bool:
+    if isinstance(perm, ir.RefAttr):
+        return False
+    if perm.type == ir.AttributeType.INTS:
+        if perm.value == list(range(len(perm.value))):
+            return True
+    return False
+
+
+def transpose_identity_rewrite(op, x: ir.Value, perm: ir.Attr | None = None):
+    return op.Identity(x)
+
+
+def transpose_transpose(x, perm1, perm2):
+    return _op.Transpose(_op.Transpose(x, perm=perm1), perm=perm2)
+
+
+def transpose_transpose_check(
+    x: ir.Value, perm1: ir.Attr | ir.RefAttr, perm2: ir.Attr | ir.RefAttr
+) -> bool:
+    if isinstance(perm1, ir.RefAttr) or isinstance(perm2, ir.RefAttr):
+        return False
+    return True
+
+
+def _apply_transpose(perm: tuple[int, ...], on: list[int | str]) -> list[int | str]:
+    assert len(perm) == len(on), "length mismatch"
+    res = [None for i in on]
+    for i, p in enumerate(perm):
+        res[i] = on[p]
+    return res
+
+
+def _apply_transposes(
+    perms: list[tuple[int, ...]], on: list[int | str] | None = None
+) -> list[int | str]:
+    if on is None:
+        on = list(range(len(perms[0])))
+    for p in perms:
+        on = _apply_transpose(p, on)
+    return on
+
+
+def transpose_transpose_rewrite(op, x: ir.Value, perm1: ir.Attr, perm2: ir.Attr):
+    first = list(range(len(perm1.value)))
+    last = _apply_transposes([perm1.value, perm2.value])
+    if first == last:
+        return op.Identity(x)
+    return op.Transpose(x, perm=last)
+
+
+transpose_identity_rule = pattern.RewriteRule(
+    transpose_identity, transpose_identity_rewrite, transpose_identity_check
+)
+transpose_transpose_rule = pattern.RewriteRule(
+    transpose_transpose, transpose_transpose_rewrite, transpose_transpose_check
+)
+
+
+def llama_p0_rule_set(verbose: int = 0) -> orp.RewriteRuleSet:
+    """Returns a set of rules which should be applied
+    before any other one as they usually remove unnecessary computation
+    such as the multiplication by 1 or two consecutive transpose.
+
+    Args:
+        verbose: verbosity
+    Returns:
+        RewriteRuleSet
+    """
+    return orp.RewriteRuleSet(
+        [
+            no_op.mul_by_1_rule,
+            no_op.add_0_rule,
+            no_op.add_0_rule,
+            no_op.div_by_1_rule,
+            transpose_identity_rule,
+            transpose_transpose_rule,
+        ]
+    )

onnxscript/rewriter/llama_rule_sets_tests.py

@@ -0,0 +1,131 @@
+from __future__ import annotations
+
+import unittest
+from typing import Any
+
+import numpy as np
+import onnx
+import onnx.reference
+
+import onnxscript.rewriter.llama_rule_sets as llama_rule_sets
+from onnxscript import ir
+
+FLOAT = onnx.TensorProto.FLOAT
+
+
+class LlamaRuleSetsTest(unittest.TestCase):
+
+    def _get_random_inputs(self, model: onnx.ModelProto) -> dict[str, Any]:
+        feeds: dict[str, Any] = {}
+        for i in model.graph.input:
+            shape = tuple(d + 2 for d in range(len(i.type.tensor_type.shape.dim)))
+            if i.type.tensor_type.elem_type == onnx.TensorProto.FLOAT:
+                feeds[i.name] = np.random.randn(*shape).astype(np.float32)
+            else:
+                raise AssertionError(f"Not implemented for input {i}")
+        return feeds
+
+    def _check_model(
+        self,
+        model: onnx.ModelProto,
+        optimized_model: onnx.ModelProto,
+        feeds: dict[str, Any] | None = None,
+        atol: float = 0.0,
+        rtol: float = 1e-7,
+    ):
+        if not feeds:
+            feeds = self._get_random_inputs(model)
+        ref = onnx.reference.ReferenceEvaluator(model)
+        opt = onnx.reference.ReferenceEvaluator(optimized_model)
+        expected = ref.run(None, feeds)
+        got = opt.run(None, feeds)
+        self.assertEqual(len(expected), len(got))
+        for a, b in zip(expected, got):
+            np.testing.assert_allclose(a, b, atol=atol, rtol=rtol)
+
+    def _identity_models(self):
+        models = [
+            onnx.helper.make_model(
+                onnx.helper.make_graph(
+                    [
+                        onnx.helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 1, 2]),
+                    ],
+                    "name",
+                    [onnx.helper.make_tensor_value_info("X", FLOAT, [None, None, None])],
+                    [onnx.helper.make_tensor_value_info("Y", FLOAT, [None, None, None])],
+                ),
+                opset_imports=[onnx.helper.make_opsetid("", 18)],
+            ),
+            onnx.helper.make_model(
+                onnx.helper.make_graph(
+                    [
+                        onnx.helper.make_node("Mul", ["X", "one"], ["Y"]),
+                    ],
+                    "name",
+                    [onnx.helper.make_tensor_value_info("X", FLOAT, [None])],
+                    [onnx.helper.make_tensor_value_info("Y", FLOAT, [None])],
+                    [
+                        onnx.numpy_helper.from_array(
+                            np.array([1], dtype=np.float32), name="one"
+                        )
+                    ],
+                ),
+                opset_imports=[onnx.helper.make_opsetid("", 18)],
+            ),
+            onnx.helper.make_model(
+                onnx.helper.make_graph(
+                    [
+                        onnx.helper.make_node("Transpose", ["X"], ["xt"], perm=[1, 0]),
+                        onnx.helper.make_node("Transpose", ["xt"], ["Y"], perm=[1, 0]),
+                    ],
+                    "name",
+                    [onnx.helper.make_tensor_value_info("X", FLOAT, [None, None])],
+                    [onnx.helper.make_tensor_value_info("Y", FLOAT, [None, None])],
+                ),
+                opset_imports=[onnx.helper.make_opsetid("", 18)],
+            ),
+        ]
+        return models
+
+    def test_llama_p0_rule_set_identity(self):
+        for model in self._identity_models():
+
+            ir_model = ir.serde.deserialize_model(model)
+            rule_set = llama_rule_sets.llama_p0_rule_set()
+            rule_set.apply_to_model(ir_model)
+            rewritten_model = ir.serde.serialize_model(ir_model)
+
+            self.assertEqual(["Identity"], [n.op_type for n in rewritten_model.graph.node])
+            self._check_model(model, rewritten_model)
+
+    def _transpose_transpose_models(self):
+        models = [
+            onnx.helper.make_model(
+                onnx.helper.make_graph(
+                    [
+                        onnx.helper.make_node("Transpose", ["X"], ["xt"], perm=[1, 2, 0]),
+                        onnx.helper.make_node("Transpose", ["xt"], ["Y"], perm=[1, 2, 0]),
+                    ],
+                    "name",
+                    [onnx.helper.make_tensor_value_info("X", FLOAT, [None, None, None])],
+                    [onnx.helper.make_tensor_value_info("Y", FLOAT, [None, None, None])],
+                ),
+                opset_imports=[onnx.helper.make_opsetid("", 18)],
+            ),
+        ]
+        return models
+
+    def test_llama_p0_rule_set_transpose_transpose(self):
+        for model in self._transpose_transpose_models():
+
+            ir_model = ir.serde.deserialize_model(model)
+            rule_set = llama_rule_sets.llama_p0_rule_set()
+            rule_set.apply_to_model(ir_model)
+            rewritten_model = ir.serde.serialize_model(ir_model)
+
+            self.assertEqual(["Transpose"], [n.op_type for n in rewritten_model.graph.node])
+            self._check_model(model, rewritten_model)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)