From 0a40ff8870fb5a3e4ef0c1b27c7b0b5bcb6df9d4 Mon Sep 17 00:00:00 2001
From: Yifan Zhao <yifanz16@illinois.edu>
Date: Thu, 28 Jan 2021 17:56:58 -0600
Subject: [PATCH] Added support for approx config missing operators (default to
baseline)
---
predtuner/approxapp.py | 7 +++++++
predtuner/modeledapp.py | 9 ++++++++-
predtuner/torchapp.py | 4 ++--
3 files changed, 17 insertions(+), 3 deletions(-)
diff --git a/predtuner/approxapp.py b/predtuner/approxapp.py
index 61d203b..5cd82a1 100644
--- a/predtuner/approxapp.py
+++ b/predtuner/approxapp.py
@@ -89,6 +89,13 @@ class ApproxApp(abc.ABC):
knobs.append(baseline_knob)
return baseline_knob
+ def add_baseline_to_knobs(self, approxes: KnobsT):
+ approxes = approxes.copy()
+ for op_name in self.ops:
+ if op_name not in approxes:
+ approxes[op_name] = self.baseline_knob.name
+ return approxes
+
class BaselineKnob(ApproxKnob):
def __init__(self, name: str = "__baseline__"):
diff --git a/predtuner/modeledapp.py b/predtuner/modeledapp.py
index 6c5fe70..06efc8c 100644
--- a/predtuner/modeledapp.py
+++ b/predtuner/modeledapp.py
@@ -144,12 +144,16 @@ class LinearPerfModel(IPerfModel):
"""Weighted linear performance predictor based on cost of each operator."""
def __init__(
- self, op_costs: Dict[str, float], knob_speedups: Dict[str, float]
+ self,
+ app: ModeledApp,
+ op_costs: Dict[str, float],
+ knob_speedups: Dict[str, float],
) -> None:
import numpy as np
import pandas as pd
super().__init__()
+ self.app = app
knob_cost_factor_v = 1 / np.array(list(knob_speedups.values()))
layer_cost_v = np.array(list(op_costs.values()))
costs = np.outer(layer_cost_v, knob_cost_factor_v)
@@ -163,6 +167,7 @@ class LinearPerfModel(IPerfModel):
def measure_perf(self, with_approxes: KnobsT) -> float:
"""We implement this using a weighted linear performance model."""
+ with_approxes = self.app.add_baseline_to_knobs(with_approxes)
return float(
sum(self.cost_df.loc[layer, knob] for layer, knob in with_approxes.items())
)
@@ -204,6 +209,7 @@ class QoSModelP1(IQoSModel):
def measure_qos(self, with_approxes: KnobsT) -> float:
"""Implementation of model."""
assert self.baseline_tensor is not None
+ with_approxes = self.app.add_baseline_to_knobs(with_approxes)
delta_tensors = np.array(
[self.delta_tensors[op][knob] for op, knob in with_approxes.items()]
)
@@ -274,6 +280,7 @@ class QoSModelP2(IQoSModel):
def measure_qos(self, with_approxes: KnobsT) -> float:
assert self.baseline_qos is not None and self.qos_df is not None
+ with_approxes = self.app.add_baseline_to_knobs(with_approxes)
delta_qoses = (
np.array([self.qos_df.loc[kv] for kv in with_approxes.items()])
- self.baseline_qos
diff --git a/predtuner/torchapp.py b/predtuner/torchapp.py
index c93bd8a..0e6fc28 100644
--- a/predtuner/torchapp.py
+++ b/predtuner/torchapp.py
@@ -1,6 +1,6 @@
import abc
from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+from typing import Any, Callable, List, Optional, Set, Tuple, Union
import numpy as np
import torch
@@ -152,7 +152,7 @@ class TorchApp(ModeledApp, abc.ABC):
p1_storage = self.model_storage / "p1.pkl" if self.model_storage else None
p2_storage = self.model_storage / "p2.json" if self.model_storage else None
return [
- LinearPerfModel(self._op_costs, self._knob_speedups),
+ LinearPerfModel(self, self._op_costs, self._knob_speedups),
QoSModelP1(
self, self._get_raw_output_valset, batched_valset_qos, p1_storage
),
--
GitLab