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# Owner(s): ["module: inductor"]
import glob
import math
import os
import shutil
import tempfile
import torch
import torch._dynamo
import torch._inductor.config as inductor_config
from torch._inductor.test_case import run_tests, TestCase
from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FUSED_ATTENTION
from torch.testing._internal.common_utils import IS_LINUX
from torch.testing._internal.inductor_utils import HAS_CUDA
try:
import pydot # noqa: F401
HAS_PYDOT = True
except ImportError:
HAS_PYDOT = False
HAS_DOT = True if shutil.which("dot") is not None else False
class TestGraphTransformObserver(TestCase):
def test_sdpa_rewriter(self):
if not (
HAS_CUDA and PLATFORM_SUPPORTS_FUSED_ATTENTION and HAS_PYDOT and HAS_DOT
):
return
def dot_prod_attention(
query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
) -> torch.Tensor:
"""Input tensors assumed to have shape (batch_size, n_head, seq_len, embed_dim)"""
return (
torch.matmul(query, key.transpose(-2, -1))
.div(math.sqrt(key.shape[-1]))
.softmax(dim=-1)
.matmul(value)
)
log_url = tempfile.mkdtemp()
inductor_config.trace.log_url_for_graph_xform = log_url
inductor_config.force_disable_caches = True
compiled_fn = torch.compile(dot_prod_attention, fullgraph=True)
tensor_shape = (4, 2, 16, 32)
q = torch.randn(tensor_shape, device="cuda")
k = torch.randn(tensor_shape, device="cuda")
v = torch.randn(tensor_shape, device="cuda")
compiled_fn(q, k, v)
found_input_svg = False
found_output_svg = False
for filepath_object in glob.glob(log_url + "/*"):
if os.path.isfile(filepath_object):
if filepath_object.endswith("input_graph.dot"):
found_input_svg = True
elif filepath_object.endswith("output_graph.dot"):
found_output_svg = True
self.assertTrue(found_input_svg)
self.assertTrue(found_output_svg)
if __name__ == "__main__":
if IS_LINUX:
run_tests()
|
