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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""Onnx Pattern Rewriting.
This script shows how to define a rewriting rule based on patterns.
The objective is to replace some nodes in an onnx model into another
sequence of nodes but more efficient.
First a dummy model
===================
"""
import numpy as np
import onnx
import onnx.helper as oh
import onnx.numpy_helper as onh
from onnxscript import ir
from onnxscript.rewriter import pattern
def get_rotary_model(bad_model=False):
inputs = [
oh.make_tensor_value_info("x", onnx.TensorProto.INT64, shape=[]),
oh.make_tensor_value_info("pos_ids", onnx.TensorProto.FLOAT, shape=[]),
oh.make_tensor_value_info("axis", onnx.TensorProto.INT64, shape=[]),
]
nodes = [
oh.make_node("Unsqueeze", ["x", "axis"], ["_onx_unsqueeze0"]),
oh.make_node("Cast", ["_onx_unsqueeze0"], ["_onx_cast0"], to=1),
oh.make_node("MatMul", ["pos_ids", "_onx_cast0"], ["_onx_matmul0"]),
oh.make_node("Transpose", ["_onx_matmul0"], ["_onx_transpose0"]),
oh.make_node(
"ConcatTrainingBad" if bad_model else "ConcatTraining",
["_onx_transpose0", "_onx_transpose0"],
["_onx_concattraining0", "_onx_concattraining1"],
domain="com.microsoft",
),
oh.make_node("Sin", ["_onx_concattraining0"], ["_onx_sin0"]),
oh.make_node("Cast", ["_onx_sin0"], ["_onx_cast02"], to=1),
oh.make_node("Cos", ["_onx_concattraining0"], ["_onx_cos0"]),
oh.make_node("Cast", ["_onx_cos0"], ["_onx_cast03"], to=1),
]
outputs = [
oh.make_tensor_value_info("_onx_cast02", onnx.TensorProto.UNDEFINED, []),
oh.make_tensor_value_info("_onx_cast03", onnx.TensorProto.UNDEFINED, []),
]
model = oh.make_model(
oh.make_graph(
nodes,
"experiment",
inputs,
outputs,
),
opset_imports=[
oh.make_opsetid("", 18),
oh.make_opsetid("com.microsoft", 18),
],
)
return model
model = get_rotary_model()
ir_model = ir.serde.deserialize_model(model)
####################################
# The rewriting pattern
# =====================
def rotary_match_pattern(op, x, pos_ids, axis):
"""The pattern to match."""
unsqueeze = op.Unsqueeze(x, axis)
cast = op.Cast(unsqueeze, to=onnx.TensorProto.FLOAT)
matmul = op.MatMul(pos_ids, cast)
transpose = op.Transpose(matmul)
output, _length = op.ConcatTraining(
transpose, transpose, domain="com.microsoft", outputs=2
)
sin = op.Sin(output)
cast1 = op.Cast(sin, to=onnx.TensorProto.FLOAT)
cos = op.Cos(output)
cast2 = op.Cast(cos, to=onnx.TensorProto.FLOAT)
return cast1, cast2
def rotary_apply_pattern(op, x, pos_ids, axis):
"""The replacement pattern."""
cos_cache = op.Constant(value=onh.from_array(np.random.rand(256, 256).astype(np.float16)))
sin_cache = op.Constant(value=onh.from_array(np.random.rand(256, 256).astype(np.float16)))
part1, part2 = op.RotaryEmbedding(
x, pos_ids, cos_cache, sin_cache, domain="com.microsoft", outputs=2
)
return part1, part2
###########################
# The rule
# ========
#
# The rule is easy to create.
rule = pattern.RewriteRule(rotary_match_pattern, rotary_apply_pattern, verbose=10)
##########################
# Let's apply it.
rule.apply_to_model(ir_model)
########################
# And finally, we can generate the model.
rewritten_model = ir.serde.serialize_model(ir_model)
########################
# Let's see what it looks like.
for node in rewritten_model.graph.node:
print(f"{node.op_type}({', '.join(node.input)}) -> {', '.join(node.output)}")
#############################
# What if it fails?
# =================
model = get_rotary_model(True)
ir_model = ir.serde.deserialize_model(model)
rule.apply_to_model(ir_model)
rewritten_model = ir.serde.serialize_model(ir_model)
print([n.op_type for n in rewritten_model.graph.node])
################################
# The match did not happen.
# Let's increase the verbosity.
rule = pattern.RewriteRule(rotary_match_pattern, rotary_apply_pattern, verbose=10)
rule.apply_to_model(ir_model)
# TODO(rama): Update the following, the trace-printed looks different now.
######################################
# The logs shows every time the algorithm rejected a pattern.
# We can see the following:
#
# ::
#
# [OnnxGenericPattern.match] NONE - line: 673:onnxscript.rewriter.generic_pattern, op_type=Cast
# --hint--: BACKWARD: different node types
# --pattern
# ConcatTraining(transpose, transpose) -> (output, length)
# -- model
# ConcatTrainingBad(_onx_transpose0, _onx_transpose0) -> (_onx_concattraining0, _onx_concattraining1)
# iteration=1
# --marked-- #2
# Cast(_onx_cos0) ~ Cast(cos) [140186194226496-140186194222320]
# Cos(_onx_concattraining0) ~ Cos(output) [140186194230816-140186194223472]
# len(stacked)=0:[]
#
# Line 673 in file `generic_pattern.py`, the match was rejected.
# It says while comparing two nodes in the backward direction,
# node types do not match.
# It also says that two nodes were actually matched.
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