[mpact][benchmark] Add new benchmarks (#36)
* add benchmarks
* modify comments
diff --git a/benchmark/python/benchmarks/kernels_benchmark.py b/benchmark/python/benchmarks/kernels_benchmark.py
index 3ad9cce..4dee945 100644
--- a/benchmark/python/benchmarks/kernels_benchmark.py
+++ b/benchmark/python/benchmarks/kernels_benchmark.py
@@ -1,6 +1,7 @@
import torch
import argparse
import numpy as np
+from mpact.models.kernels import *
from mpact_benchmark.utils.benchmark_utils import benchmark, Backends
@@ -23,15 +24,7 @@
)
def matmul() -> torch.nn.Module:
"""Matrix multiplication."""
-
- class Net(torch.nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x, y):
- return torch.matmul(x, y)
-
- return Net()
+ return MMNet()
@benchmark(
@@ -53,15 +46,7 @@
)
def matvec() -> torch.nn.Module:
"""Matrix-vector multiplication."""
-
- class Net(torch.nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x, y):
- return torch.mv(x, y)
-
- return Net()
+ return MVNet()
@benchmark(
@@ -85,15 +70,7 @@
)
def add() -> torch.nn.Module:
"""Element-wise addition."""
-
- class Net(torch.nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x, y):
- return torch.add(x, y)
-
- return Net()
+ return AddNet()
@benchmark(
@@ -115,15 +92,7 @@
)
def elt_mul() -> torch.nn.Module:
"""Element-wise addition."""
-
- class Net(torch.nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x, y):
- return torch.mul(x, y)
-
- return Net()
+ return MulNet()
@benchmark(
@@ -144,15 +113,7 @@
)
def nop() -> torch.nn.Module:
"""Returns matrix unmodified (speed of light)."""
-
- class Net(torch.nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- return x
-
- return Net()
+ return SelfNet()
@benchmark(
@@ -175,15 +136,67 @@
)
def sddmm() -> torch.nn.Module:
"""SDDMM: C = S ○ (A X B) Sampled dense-dense matrix-matrix multiplication."""
+ return SDDMMNet()
- class Net(torch.nn.Module):
- def __init__(self):
- super().__init__()
- def forward(self, x, y, z):
- return torch.mul(x, torch.matmul(y, z))
+@benchmark(
+ [
+ {
+ "name": f"{fmt}_{shape}_{dtype.__name__}",
+ "shape": shape,
+ "formats": (fmt,),
+ "dtype": dtype,
+ # TODO: add mpact and torch inductor once they work.
+ "backends": [
+ b
+ for b in Backends
+ if b.value
+ in (
+ Backends.TORCH_SPARSE_EAGER.value,
+ Backends.TORCH_DENSE_EAGER.value,
+ )
+ ],
+ "drange": (1, 100),
+ "sparsity": [0, 0.5, 0.9, 0.99],
+ }
+ for shape in [([2**i, 2**i],) for i in range(5, 8)]
+ for fmt in ["dense"]
+ for dtype in [np.float64]
+ ]
+)
+def feature_scale() -> torch.nn.Module:
+ """Scales feature matrix in GNN."""
+ return FeatureScale()
- return Net()
+
+@benchmark(
+ [
+ {
+ "name": f"{fmt}_{shape}_{dtype.__name__}",
+ "shape": shape,
+ "formats": (fmt,),
+ "dtype": dtype,
+ # TODO: add mpact and torch inductor once they work.
+ "backends": [
+ b
+ for b in Backends
+ if b.value
+ in (
+ Backends.TORCH_SPARSE_EAGER.value,
+ Backends.TORCH_DENSE_EAGER.value,
+ )
+ ],
+ "drange": (1, 100),
+ "sparsity": [0, 0.5, 0.9, 0.99],
+ }
+ for shape in [([2**i, 2**i],) for i in range(5, 8)]
+ for fmt in ["dense"]
+ for dtype in [np.float64]
+ ]
+)
+def normalization() -> torch.nn.Module:
+ """Normalizes adjacency matrix in GNN."""
+ return Normalization()
if __name__ == "__main__":
@@ -201,6 +214,8 @@
"matvec",
"elt_mul",
"sddmm",
+ "feature_scale",
+ "normalization",
]
if arguments.benchmark_filter:
benchmark_list = arguments.benchmark_filter.split(",")
