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import torch
import torch._inductor.config
from torch._export import aot_compile
from torch.export import Dim
torch.manual_seed(1337)
class Net(torch.nn.Module):
def __init__(self, device, size=4):
super().__init__()
self.w_pre = torch.randn(size, size, device=device)
self.w_add = torch.randn(size, size, device=device)
def forward(self, x):
w_transpose = torch.transpose(self.w_pre, 0, 1)
w_relu = torch.nn.functional.relu(w_transpose)
w = w_relu + self.w_add
return torch.matmul(x, w)
class NetWithTensorConstants(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.w = torch.randn(30, 1, device="cuda")
def forward(self, x, y):
z = self.w * x * y
return z[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17]]
data = {}
large_data = {}
cuda_alloc_data = {}
data_with_tensor_constants = {}
# Basice AOTI model test generation.
def generate_basic_tests():
for device in ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]:
for use_runtime_constant_folding in [True, False]:
if device == "cpu" and use_runtime_constant_folding:
# We do not test runtime const folding for cpu mode.
continue
model = Net(device).to(device=device)
x = torch.randn((4, 4), device=device)
with torch.no_grad():
ref_output = model(x)
torch._dynamo.reset()
with torch.no_grad():
dim0_x = Dim("dim0_x", min=1, max=1024)
dynamic_shapes = {"x": {0: dim0_x}}
model_so_path = aot_compile(
model,
(x,),
dynamic_shapes=dynamic_shapes,
options={
"aot_inductor.use_runtime_constant_folding": use_runtime_constant_folding
},
)
# Also store a .pt2 file using the aoti_compile_and_package API
pt2_package_path = torch._inductor.aoti_compile_and_package(
torch.export.export(
model,
(x,),
dynamic_shapes=dynamic_shapes,
),
inductor_configs={
"aot_inductor.use_runtime_constant_folding": use_runtime_constant_folding
},
)
suffix = f"{device}"
if use_runtime_constant_folding:
suffix += "_use_runtime_constant_folding"
data.update(
{
f"model_so_path_{suffix}": model_so_path,
f"pt2_package_path_{suffix}": pt2_package_path,
f"inputs_{suffix}": [x],
f"outputs_{suffix}": [ref_output],
f"w_pre_{suffix}": model.w_pre,
f"w_add_{suffix}": model.w_add,
}
)
def generate_basic_tests_consts_cpp():
backup_consts_asm_cfg: bool = (
torch._inductor.config.aot_inductor.use_consts_asm_build
)
torch._inductor.config.aot_inductor.use_consts_asm_build = False
# Test consts cpp build again.
generate_basic_tests()
torch._inductor.config.aot_inductor.use_consts_asm_build = backup_consts_asm_cfg
def generate_large_tests():
device = "cuda"
model = Net(device, size=4096).to(device=device)
x = torch.randn((4096, 4096), device=device)
with torch.no_grad():
ref_output = model(x)
torch._dynamo.reset()
for use_runtime_constant_folding in [True, False]:
with torch.no_grad():
model_so_path = aot_compile(
model,
(x,),
options={
"aot_inductor.use_runtime_constant_folding": use_runtime_constant_folding
},
)
# Also store a .pt2 file using the aoti_compile_and_package API
pt2_package_path = torch._inductor.aoti_compile_and_package(
torch.export.export(
model,
(x,),
),
inductor_configs={
"aot_inductor.use_runtime_constant_folding": use_runtime_constant_folding
},
)
suffix = "_use_runtime_constant_folding" if use_runtime_constant_folding else ""
large_data.update(
{ # noqa: F541
f"model_so_path{suffix}": model_so_path,
f"pt2_package_path{suffix}": pt2_package_path,
"inputs": [x],
"outputs": [ref_output],
"w_pre": model.w_pre,
"w_add": model.w_add,
}
)
def generate_cuda_alloc_test():
device = "cuda"
model = Net(device, size=4096).to(device=device)
x = torch.randn((4096, 4096), device=device)
with torch.no_grad():
ref_output = model(x)
torch._dynamo.reset()
with torch.no_grad():
model_so_path = aot_compile(
model,
(x,),
options={"aot_inductor.weight_use_caching_allocator": True},
)
cuda_alloc_data.update(
{ # noqa: F541
"model_so_path": model_so_path,
"inputs": [x],
"outputs": [ref_output],
"w_pre": model.w_pre,
"w_add": model.w_add,
}
)
# AOTI model which will create additional tensors during autograd.
def generate_test_with_additional_tensors():
if not torch.cuda.is_available():
return
model = NetWithTensorConstants()
x = torch.randn((30, 1), device="cuda")
y = torch.randn((30, 1), device="cuda")
with torch.no_grad():
ref_output = model(x, y)
torch._dynamo.reset()
with torch.no_grad():
model_so_path = aot_compile(model, (x, y))
# Also store a .pt2 file using the aoti_compile_and_package API
pt2_package_path = torch._inductor.aoti_compile_and_package(
torch.export.export(model, (x, y))
)
data_with_tensor_constants.update(
{
"model_so_path": model_so_path,
"pt2_package_path": pt2_package_path,
"inputs": [x, y],
"outputs": [ref_output],
"w": model.w,
}
)
generate_basic_tests()
generate_basic_tests_consts_cpp()
generate_large_tests()
generate_test_with_additional_tensors()
generate_cuda_alloc_test()
# Use this to communicate tensors to the cpp code
class Serializer(torch.nn.Module):
def __init__(self, data):
super().__init__()
for key in data:
setattr(self, key, data[key])
torch.jit.script(Serializer(data)).save("data.pt")
torch.jit.script(Serializer(large_data)).save("large_data.pt")
torch.jit.script(Serializer(data_with_tensor_constants)).save(
"data_with_tensor_constants.pt"
)
torch.jit.script(Serializer(cuda_alloc_data)).save("cuda_alloc_data.pt")
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