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e6066e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | # Copyright (c) 2026 SandAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from unittest.mock import patch
import pytest
import torch
import torch.fx as fx
from magi_compiler.config import get_compile_config
from magi_compiler.tokenflow.graph_profile import GraphProfileWrapper
from magi_compiler.tokenflow.utils import CompiledTransformerModel, ModelConfig
from magi_compiler.utils import envs
@pytest.fixture(scope="function")
def simple_graph_profile_wrapper() -> GraphProfileWrapper:
class SimpleModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(128, 128)
def forward(self, x):
y = self.linear(x)
z = torch.relu(y)
return z
model = SimpleModel()
graph_module = fx.symbolic_trace(model)
wrapper = GraphProfileWrapper(graph_module)
return wrapper
def test_resolve_symint_expression(simple_graph_profile_wrapper):
wrapper = simple_graph_profile_wrapper
seq_len = 64
assert wrapper._resolve_symint_expression(128, seq_len) == 128
class FakeSymInt:
def __init__(self, expr_str):
self.expr_str = expr_str
def __str__(self):
return self.expr_str
with patch("torch.SymInt", FakeSymInt):
sym_simple = torch.SymInt("s0")
res_simple = wrapper._resolve_symint_expression(sym_simple, seq_len)
assert res_simple == 64
sym_complex = torch.SymInt("s0 * 2 + 10")
res_complex = wrapper._resolve_symint_expression(sym_complex, seq_len)
assert res_complex == 138
sym_multi = torch.SymInt("s0 + s1")
res_multi = wrapper._resolve_symint_expression(sym_multi, seq_len)
assert res_multi == 128
def test_generate_real_tensor(simple_graph_profile_wrapper):
seq_len = 1
class FakeSymInt:
def __init__(self, *args, **kwargs):
pass
def __str__(self):
return "s0 * 64"
def __int__(self):
return seq_len * 64
with patch("torch.SymInt", FakeSymInt):
wrapper = simple_graph_profile_wrapper
sym_dim = torch.SymInt()
shape = (sym_dim, 128)
stride = (128, 1)
dtype = torch.float32
device = torch.device("cpu")
tensor = wrapper._generate_real_tensor(shape, stride, dtype, device, seq_len)
assert tensor.shape == (seq_len * 64, 128)
assert tensor.stride() == (128, 1)
assert tensor.dtype == dtype
assert not torch.allclose(tensor, torch.zeros_like(tensor))
def test_e2e_correctness():
envs.MAGI_ENABLE_PROFILE = True
# envs.MAGI_ENABLE_FX_GRAPH_VIZ = True
get_compile_config().splitting_ops.extend(["athena::my_attention"])
performer_config = ModelConfig(
hidden_size=4096,
num_layers=1,
num_heads_q=32,
num_heads_kv=8,
head_dim=128,
intermediate_size=16384,
activation_type="gelu",
)
device = "cuda" if torch.cuda.is_available() else "cpu"
class_constructor = CompiledTransformerModel
model = class_constructor(performer_config).to(device).to(performer_config.params_dtype)
uncompiled_model = model.mod
test_seq_lens = [4096, 1014, 512, 101, 64, 7, 1]
for seq_len in test_seq_lens:
x = torch.randn(seq_len, performer_config.hidden_size, device=device, dtype=performer_config.params_dtype)
with torch.no_grad():
output = model(x)
uncompiled_output = uncompiled_model(x)
assert torch.allclose(output, uncompiled_output, atol=1e-3)
if __name__ == "__main__":
pytest.main(["-v", __file__])
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