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HyosungSinkHyosungSink
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Merge branch 'master' into roll-migration
2 parents a63d228 + 27531e7 commit 070eda3

18 files changed

Lines changed: 464 additions & 286 deletions

‎.github/labeler.yml‎

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@@ -146,5 +146,6 @@ tests:
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- 'pytest.ini'
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# NOTE: This directory also contains DSA, FLA operator tests
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- 'tests/**'
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- 'benchmark/**'
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# TODO: Move test scripts to tests/scripts
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- 'tools/**'

‎.github/workflows/backend-test.yaml‎

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@@ -61,6 +61,8 @@ jobs:
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ssh-key: ${{ secrets.RUNNER_SSH_KEY }}
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- name: Setup FlagGems
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shell: bash
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env:
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RUNNER_LABEL: ${{ inputs.runner_label }}
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run: |
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if [[ "${RUNNER_LABEL}" != "h20" ]]; then
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export HTTP_PROXY="http://10.1.12.38:38002"
@@ -84,6 +86,7 @@ jobs:
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shell: bash
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env:
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CHANGED_FILES: ${{ inputs.changed_files }}
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RUNNER_LABEL: ${{ inputs.runner_label }}
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run: |
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source .venv/bin/activate
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source tools/set-env.sh ${{ inputs.vendor }}
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tools/test-op.sh ${{ inputs.pr_id }}

‎benchmark/test_batch_norm.py‎

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import pytest
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import torch
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from . import attri_util as attr_utils
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from . import performance_utils as utils
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class NormBenchmark(utils.GenericBenchmark):
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# TODO: add new metric
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def set_more_shapes(self):
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return [
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# 3D shapes represented as [batch_size, channels, hidden_size]
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(16, 16, 64),
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(16, 16, 1024),
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(16, 16, 4098),
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# 4D shapes represented as [batch_size, channels, H, W]
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(1, 8, 4, 4),
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(16, 8, 128, 128),
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]
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def input_fn(shape, dtype, device):
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C = shape[1]
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inp = torch.randn(shape, dtype=dtype, device=device)
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weight = torch.randn((C,), dtype=dtype, device=device)
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bias = torch.randn((C,), dtype=dtype, device=device)
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running_mean = None
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running_var = None
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training = True
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momentum = 0.1
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eps = 1e-5
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cudnn_enabled = True
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yield inp, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled
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if utils.Config.bench_level == utils.BenchLevel.COMPREHENSIVE:
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running_mean = torch.randn((C,), dtype=dtype, device=device)
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running_var = torch.randn((C,), dtype=dtype, device=device)
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yield inp, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled
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@pytest.mark.batch_norm
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def test_batch_norm():
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bench = NormBenchmark(
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op_name="batch_norm",
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input_fn=input_fn,
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torch_op=torch.batch_norm,
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dtypes=attr_utils.FLOAT_DTYPES,
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)
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bench.run()

‎benchmark/test_batch_norm_backward.py‎

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@@ -0,0 +1,96 @@
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import pytest
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import torch
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import flag_gems
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from . import attr_util as attr_utils
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from . import performance_utils as utils
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# TODO(Qiming): Consolidate this to a base package
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class NormBenchmark(utils.GenericBenchmark):
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# TODO: add new metric
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def set_more_shapes(self):
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return [
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# 3D shapes represented as [batch_size, channels, hidden_size]
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(16, 16, 64),
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(16, 16, 1024),
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(16, 16, 4098),
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# 4D shapes represented as [batch_size, channels, H, W]
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(1, 8, 4, 4),
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(16, 8, 128, 128),
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]
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def batchnorm_input_fn(shape, dtype, device):
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C = shape[1]
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inp = torch.randn(shape, dtype=dtype, device=device)
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weight = torch.randn((C,), dtype=dtype, device=device)
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bias = torch.randn((C,), dtype=dtype, device=device)
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running_mean = None
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running_var = None
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training = True
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momentum = 0.1
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eps = 1e-5
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cudnn_enabled = True
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yield inp, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled
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if utils.Config.bench_level == utils.BenchLevel.COMPREHENSIVE:
40+
running_mean = torch.randn((C,), dtype=dtype, device=device)
41+
running_var = torch.randn((C,), dtype=dtype, device=device)
42+
yield inp, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled
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45+
@pytest.mark.batch_norm_backward
46+
def test_batch_norm_backward():
47+
def batch_norm_backward_input_fn(shape, dtype, device):
48+
for forward_args in batchnorm_input_fn(shape, dtype, device):
49+
(
50+
inp,
51+
weight,
52+
bias,
53+
running_mean,
54+
running_var,
55+
training,
56+
_,
57+
eps,
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_,
59+
) = forward_args
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grad_output = torch.randn_like(inp)
62+
channels = weight.shape[0] if weight is not None else inp.shape[1]
63+
64+
if running_mean is None:
65+
running_mean = torch.zeros(channels, dtype=dtype, device=device)
66+
if running_var is None:
67+
running_var = torch.ones(channels, dtype=dtype, device=device)
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69+
save_mean = torch.randn(channels, dtype=torch.float32, device=device)
70+
save_invstd = torch.randn(channels, dtype=torch.float32, device=device)
71+
output_mask = [True, weight is not None, bias is not None]
72+
73+
yield (
74+
grad_output,
75+
inp,
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weight,
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running_mean,
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running_var,
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save_mean,
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save_invstd,
81+
training,
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eps,
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output_mask,
84+
)
85+
86+
bench = NormBenchmark(
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input_fn=batch_norm_backward_input_fn,
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op_name="native_batch_norm_backward",
89+
torch_op=torch.ops.aten.native_batch_norm_backward,
90+
dtypes=[torch.float32]
91+
if flag_gems.vendor_name == "mthreads"
92+
else attr_utils.FLOAT_DTYPES,
93+
)
94+
bench.set_gems(flag_gems.batch_norm_backward)
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bench.run()

‎benchmark/test_group_norm.py‎

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import pytest
2+
import torch
3+
4+
from . import attri_util as attr_utils
5+
from . import performance_utils as utils
6+
7+
8+
# TODO(Qiming): Extract this to a base class
9+
class NormBenchmark(utils.GenericBenchmark):
10+
# TODO: add new metric
11+
12+
def set_more_shapes(self):
13+
return [
14+
# 3D shapes represented as [batch_size, channels, hidden_size]
15+
(16, 16, 64),
16+
(16, 16, 1024),
17+
(16, 16, 4098),
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# 4D shapes represented as [batch_size, channels, H, W]
19+
(1, 8, 4, 4),
20+
(16, 8, 128, 128),
21+
]
22+
23+
24+
def group_norm_input_fn(shape, dtype, device):
25+
inp = torch.randn(shape, dtype=dtype, device=device)
26+
channel = shape[1]
27+
weight = torch.randn(
28+
[
29+
channel,
30+
],
31+
dtype=dtype,
32+
device=device,
33+
)
34+
bias = torch.randn(
35+
[
36+
channel,
37+
],
38+
dtype=dtype,
39+
device=device,
40+
)
41+
yield inp, channel // 2, weight, bias
42+
43+
if utils.Config.bench_level == utils.BenchLevel.COMPREHENSIVE:
44+
yield inp, channel, weight, bias
45+
46+
47+
@pytest.mark.group_norm
48+
def test_group_norm():
49+
bench = NormBenchmark(
50+
input_fn=group_norm_input_fn,
51+
op_name="group_norm",
52+
torch_op=torch.nn.functional.group_norm,
53+
dtypes=attr_utils.FLOAT_DTYPES,
54+
)
55+
bench.run()

‎benchmark/test_instance_norm.py‎

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1+
import pytest
2+
import torch
3+
4+
import flag_gems
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6+
from . import attri_util as attr_utils
7+
from . import performance_utils as utils
8+
9+
10+
class NormBenchmark(utils.GenericBenchmark):
11+
# TODO: add new metric
12+
13+
def set_more_shapes(self):
14+
return [
15+
# 3D shapes represented as [batch_size, channels, hidden_size]
16+
(16, 16, 64),
17+
(16, 16, 1024),
18+
(16, 16, 4098),
19+
# 4D shapes represented as [batch_size, channels, H, W]
20+
(1, 8, 4, 4),
21+
(16, 8, 128, 128),
22+
]
23+
24+
25+
def input_fn(shape, dtype, device):
26+
C = shape[1]
27+
inp = torch.randn(shape, dtype=dtype, device=device)
28+
weight = torch.randn((C,), dtype=dtype, device=device)
29+
bias = torch.randn((C,), dtype=dtype, device=device)
30+
running_mean = None
31+
running_var = None
32+
use_input_stats = True
33+
momentum = 0.1
34+
eps = 1e-5
35+
cudnn_enabled = True
36+
yield inp, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled
37+
if utils.Config.bench_level == utils.BenchLevel.COMPREHENSIVE:
38+
running_mean = torch.randn((C,), dtype=dtype, device=device)
39+
running_var = torch.randn((C,), dtype=dtype, device=device)
40+
yield inp, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled
41+
42+
43+
@pytest.mark.instance_norm
44+
def test_instance_norm(monkeypatch):
45+
if flag_gems.vendor_name == "kunlunxin" and utils.SkipVersion("torch", "<2.5"):
46+
pytest.skip(
47+
"BF16 is not supported in XPytorch 2.0. Please upgrade your PyTorch version >= 2.5"
48+
)
49+
50+
if flag_gems.vendor_name == "mthreads":
51+
# Compatible with older versions of LLVM
52+
monkeypatch.env("DISABLE_LLVM_OPT", "1")
53+
54+
bench = NormBenchmark(
55+
op_name="instance_norm",
56+
input_fn=input_fn,
57+
torch_op=torch.instance_norm,
58+
dtypes=attr_utils.FLOAT_DTYPES,
59+
)
60+
bench.set_gems(flag_gems.instance_norm)
61+
bench.run()

‎benchmark/test_layer_norm.py‎

Lines changed: 40 additions & 0 deletions
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@@ -0,0 +1,40 @@
1+
import pytest
2+
import torch
3+
4+
from . import attri_util as attr_utils
5+
from . import performance_utils as utils
6+
7+
8+
# TODO(Qiming): Extract this to a base class
9+
class NormBenchmark(utils.GenericBenchmark):
10+
# TODO: add new metric
11+
12+
def set_more_shapes(self):
13+
return [
14+
# 3D shapes represented as [batch_size, channels, hidden_size]
15+
(16, 16, 64),
16+
(16, 16, 1024),
17+
(16, 16, 4098),
18+
# 4D shapes represented as [batch_size, channels, H, W]
19+
(1, 8, 4, 4),
20+
(16, 8, 128, 128),
21+
]
22+
23+
24+
def input_fn(shape, dtype, device):
25+
inp = torch.randn(shape, dtype=dtype, device=device)
26+
layer_shape = shape[1:]
27+
weight = torch.randn(layer_shape, dtype=dtype, device=device)
28+
bias = torch.randn(layer_shape, dtype=dtype, device=device)
29+
yield inp, layer_shape, weight, bias
30+
31+
32+
@pytest.mark.layer_norm
33+
def test_layer_norm():
34+
bench = NormBenchmark(
35+
op_name="layer_norm",
36+
input_fn=input_fn,
37+
torch_op=torch.layer_norm,
38+
dtypes=attr_utils.FLOAT_DTYPES,
39+
)
40+
bench.run()

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