Note
Click here to download the full example code
Introduction to torch.compile¶
Author: William Wen
torch.compile is the latest method to speed up your PyTorch code!
torch.compile makes PyTorch code run faster by
JIT-compiling PyTorch code into optimized kernels,
all while requiring minimal code changes.
In this tutorial, we cover basic torch.compile usage,
and demonstrate the advantages of torch.compile over
previous PyTorch compiler solutions, such as
TorchScript and
FX Tracing.
Contents
Required pip Dependencies
torch >= 2.0torchvisionnumpyscipytabulate
NOTE: a modern NVIDIA GPU (H100, A100, or V100) is recommended for this tutorial in order to reproduce the speedup numbers shown below and documented elsewhere.
import torch
import warnings
gpu_ok = False
if torch.cuda.is_available():
device_cap = torch.cuda.get_device_capability()
if device_cap in ((7, 0), (8, 0), (9, 0)):
gpu_ok = True
if not gpu_ok:
warnings.warn(
"GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower "
"than expected."
)
/var/lib/workspace/intermediate_source/torch_compile_tutorial.py:48: UserWarning:
GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower than expected.
Basic Usage¶
torch.compile is included in the latest PyTorch.
Running TorchInductor on GPU requires Triton, which is included with the PyTorch 2.0 nightly
binary. If Triton is still missing, try installing torchtriton via pip
(pip install torchtriton --extra-index-url "https://download.pytorch.org/whl/nightly/cu117"
for CUDA 11.7).
Arbitrary Python functions can be optimized by passing the callable to
torch.compile. We can then call the returned optimized
function in place of the original function.
def foo(x, y):
a = torch.sin(x)
b = torch.cos(y)
return a + b
opt_foo1 = torch.compile(foo)
print(opt_foo1(torch.randn(10, 10), torch.randn(10, 10)))
tensor([[ 1.6850, 1.9924, 1.7090, 0.0034, 1.1414, -0.1822, 0.4861, -0.0536,
-0.2252, 1.9398],
[ 0.3693, -0.0695, 0.1748, 0.3436, 0.1939, 1.5721, 1.9882, -0.2235,
0.3161, 1.2642],
[ 0.2480, 1.8793, 1.7152, 1.6772, 1.8881, 1.4748, 1.3466, 1.7763,
0.7469, 1.0407],
[-0.1121, 1.6015, -0.0188, 0.2128, 0.5218, 1.9838, 0.8185, 0.5093,
-0.3603, 0.1793],
[-1.7890, 1.7532, -0.4040, 0.1222, -0.0029, 1.7975, -0.3877, 0.5123,
0.1673, 0.1330],
[ 1.0627, 0.9609, 0.1019, 1.8814, 0.1142, -0.2338, -0.9621, 0.7631,
0.6506, 0.1853],
[ 0.4584, 1.7648, -0.0444, 1.9610, 1.5884, 0.7353, 1.2190, 1.3662,
1.0938, -0.1587],
[-0.7502, 1.6640, 0.3495, 1.3496, 0.8187, 1.1719, 0.5820, 0.1498,
0.0885, 0.1036],
[ 0.3961, 0.6043, -0.0861, -0.3371, 0.8622, 1.4341, 1.2988, 0.5023,
0.3074, 0.1277],
[ 0.9748, 0.4117, 1.2616, 1.6314, 0.4693, 0.4092, 0.0401, 1.1196,
1.2458, 1.3280]])
Alternatively, we can decorate the function.
@torch.compile
def opt_foo2(x, y):
a = torch.sin(x)
b = torch.cos(y)
return a + b
print(opt_foo2(torch.randn(10, 10), torch.randn(10, 10)))
tensor([[ 0.5360, 0.1697, -0.0561, 0.1890, -0.1310, 1.2276, 1.1739, 0.1944,
-0.1561, 1.6990],
[ 1.0421, 1.9472, 0.2682, 0.2701, 1.3346, 0.7651, 1.0897, 1.1730,
0.6161, 0.9223],
[ 1.5756, 1.5294, 0.0112, -0.1522, -0.7674, 1.8515, -0.2443, 0.3696,
0.2693, 0.8735],
[-0.3701, 1.1190, 1.4164, 1.8648, 1.2080, 0.0732, 1.5274, 0.6868,
1.2440, 1.0715],
[-1.2454, -0.0159, 0.4315, 0.1317, 1.0530, -1.0603, -0.0532, 0.6661,
1.7101, -0.2076],
[-0.7091, 0.7824, 1.7161, 1.2750, 0.6368, 1.2488, 0.4897, 1.2429,
1.3409, 1.3735],
[ 0.8345, 0.0653, 0.3462, 1.2383, -0.4092, 1.6438, -0.0962, 0.4011,
0.2463, -0.5802],
[ 1.6349, 0.7297, 1.2547, -0.3113, 0.9310, 0.1162, 1.7618, 0.4882,
0.7640, 0.2930],
[ 1.1669, -0.7775, 1.2000, 0.6008, -0.2814, 0.5541, 0.5753, 1.4731,
1.6835, 0.7370],
[ 1.5087, 0.6195, 0.1153, 1.2966, 1.8815, 1.1678, 1.5686, 1.6018,
0.2193, 1.3500]])
We can also optimize torch.nn.Module instances.
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.lin = torch.nn.Linear(100, 10)
def forward(self, x):
return torch.nn.functional.relu(self.lin(x))
mod = MyModule()
opt_mod = torch.compile(mod)
print(opt_mod(torch.randn(10, 100)))
tensor([[0.0000, 0.0000, 0.2419, 0.0446, 0.9011, 0.2674, 0.3633, 0.4984, 0.0000,
0.0988],
[0.6906, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.8490, 0.0000, 0.0000,
0.5475],
[0.0852, 0.2762, 0.7441, 0.0000, 0.0000, 0.1820, 0.0000, 0.0000, 0.0000,
0.0334],
[0.3024, 0.0077, 1.2572, 0.0000, 0.0000, 0.6520, 0.0000, 0.0000, 0.0000,
0.8976],
[0.1998, 0.3333, 0.0000, 0.7803, 0.4202, 0.0915, 0.0000, 1.2543, 0.0000,
0.4615],
[0.2487, 0.4187, 0.0000, 0.0000, 0.5124, 0.0000, 0.2512, 0.0000, 0.5850,
0.0000],
[0.0000, 0.0048, 0.0000, 0.0000, 0.0000, 0.2287, 0.0000, 0.4841, 0.3915,
0.0000],
[0.2017, 0.0000, 0.0896, 1.4135, 0.0593, 0.3788, 0.0000, 0.0000, 0.0000,
0.4972],
[0.0000, 0.0000, 1.6580, 0.6414, 0.0000, 0.0000, 0.0000, 0.0000, 0.6491,
0.7755],
[0.0000, 0.0000, 0.6442, 0.0260, 0.7456, 0.1000, 0.0000, 0.0000, 0.5366,
0.1193]], grad_fn=<CompiledFunctionBackward>)
Demonstrating Speedups¶
Let’s now demonstrate that using torch.compile can speed
up real models. We will compare standard eager mode and
torch.compile by evaluating and training a torchvision model on random data.
Before we start, we need to define some utility functions.
# Returns the result of running `fn()` and the time it took for `fn()` to run,
# in seconds. We use CUDA events and synchronization for the most accurate
# measurements.
def timed(fn):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
result = fn()
end.record()
torch.cuda.synchronize()
return result, start.elapsed_time(end) / 1000
# Generates random input and targets data for the model, where `b` is
# batch size.
def generate_data(b):
return (
torch.randn(b, 3, 128, 128).to(torch.float32).cuda(),
torch.randint(1000, (b,)).cuda(),
)
N_ITERS = 10
from torchvision.models import densenet121
def init_model():
return densenet121().to(torch.float32).cuda()
First, let’s compare inference.
Note that in the call to torch.compile, we have the additional
mode argument, which we will discuss below.
model = init_model()
# Reset since we are using a different mode.
import torch._dynamo
torch._dynamo.reset()
model_opt = torch.compile(model, mode="reduce-overhead")
inp = generate_data(16)[0]
with torch.no_grad():
print("eager:", timed(lambda: model(inp))[1])
print("compile:", timed(lambda: model_opt(inp))[1])
eager: 0.31575347900390627
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:124: UserWarning:
TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
compile: 84.2865234375
Notice that torch.compile takes a lot longer to complete
compared to eager. This is because torch.compile compiles
the model into optimized kernels as it executes. In our example, the
structure of the model doesn’t change, and so recompilation is not
needed. So if we run our optimized model several more times, we should
see a significant improvement compared to eager.
eager_times = []
for i in range(N_ITERS):
inp = generate_data(16)[0]
with torch.no_grad():
_, eager_time = timed(lambda: model(inp))
eager_times.append(eager_time)
print(f"eager eval time {i}: {eager_time}")
print("~" * 10)
compile_times = []
for i in range(N_ITERS):
inp = generate_data(16)[0]
with torch.no_grad():
_, compile_time = timed(lambda: model_opt(inp))
compile_times.append(compile_time)
print(f"compile eval time {i}: {compile_time}")
print("~" * 10)
import numpy as np
eager_med = np.median(eager_times)
compile_med = np.median(compile_times)
speedup = eager_med / compile_med
assert(speedup > 1)
print(f"(eval) eager median: {eager_med}, compile median: {compile_med}, speedup: {speedup}x")
print("~" * 10)
eager eval time 0: 0.017733631134033204
eager eval time 1: 0.016097280502319337
eager eval time 2: 0.016043008804321288
eager eval time 3: 0.01603993606567383
eager eval time 4: 0.01599897575378418
eager eval time 5: 0.01594265556335449
eager eval time 6: 0.015932415962219237
eager eval time 7: 0.015960063934326172
eager eval time 8: 0.015898624420166017
eager eval time 9: 0.015954943656921388
~~~~~~~~~~
compile eval time 0: 0.6094346313476563
compile eval time 1: 0.008541184425354004
compile eval time 2: 0.008338432312011718
compile eval time 3: 0.008339455604553223
compile eval time 4: 0.008328191757202149
compile eval time 5: 0.008346624374389648
compile eval time 6: 0.0083374080657959
compile eval time 7: 0.008326144218444824
compile eval time 8: 0.008348671913146973
compile eval time 9: 0.008347647666931152
~~~~~~~~~~
(eval) eager median: 0.015979519844055178, compile median: 0.008343039989471435, speedup: 1.9153114289540332x
~~~~~~~~~~
And indeed, we can see that running our model with torch.compile
results in a significant speedup. Speedup mainly comes from reducing Python overhead and
GPU read/writes, and so the observed speedup may vary on factors such as model
architecture and batch size. For example, if a model’s architecture is simple
and the amount of data is large, then the bottleneck would be
GPU compute and the observed speedup may be less significant.
You may also see different speedup results depending on the chosen mode
argument. The "reduce-overhead" mode uses CUDA graphs to further reduce
the overhead of Python. For your own models,
you may need to experiment with different modes to maximize speedup. You can
read more about modes here.
You may might also notice that the second time we run our model with torch.compile is significantly
slower than the other runs, although it is much faster than the first run. This is because the "reduce-overhead"
mode runs a few warm-up iterations for CUDA graphs.
For general PyTorch benchmarking, you can try using torch.utils.benchmark instead of the timed
function we defined above. We wrote our own timing function in this tutorial to show
torch.compile’s compilation latency.
Now, let’s consider comparing training.
model = init_model()
opt = torch.optim.Adam(model.parameters())
def train(mod, data):
opt.zero_grad(True)
pred = mod(data[0])
loss = torch.nn.CrossEntropyLoss()(pred, data[1])
loss.backward()
opt.step()
eager_times = []
for i in range(N_ITERS):
inp = generate_data(16)
_, eager_time = timed(lambda: train(model, inp))
eager_times.append(eager_time)
print(f"eager train time {i}: {eager_time}")
print("~" * 10)
model = init_model()
opt = torch.optim.Adam(model.parameters())
train_opt = torch.compile(train, mode="reduce-overhead")
compile_times = []
for i in range(N_ITERS):
inp = generate_data(16)
_, compile_time = timed(lambda: train_opt(model, inp))
compile_times.append(compile_time)
print(f"compile train time {i}: {compile_time}")
print("~" * 10)
eager_med = np.median(eager_times)
compile_med = np.median(compile_times)
speedup = eager_med / compile_med
assert(speedup > 1)
print(f"(train) eager median: {eager_med}, compile median: {compile_med}, speedup: {speedup}x")
print("~" * 10)
eager train time 0: 0.3381759948730469
eager train time 1: 0.04996505737304688
eager train time 2: 0.04853657531738281
eager train time 3: 0.04814438247680664
eager train time 4: 0.04834915161132813
eager train time 5: 0.04864921569824219
eager train time 6: 0.04833587265014649
eager train time 7: 0.048277503967285154
eager train time 8: 0.048293888092041014
eager train time 9: 0.048399360656738284
~~~~~~~~~~
compile train time 0: 209.49559375
compile train time 1: 4.77366162109375
compile train time 2: 0.03038617515563965
compile train time 3: 0.023971839904785155
compile train time 4: 0.02370047950744629
compile train time 5: 0.023814144134521483
compile train time 6: 0.02366464042663574
compile train time 7: 0.024535039901733398
compile train time 8: 0.02367692756652832
compile train time 9: 0.023723007202148438
~~~~~~~~~~
(train) eager median: 0.048374256134033206, compile median: 0.02389299201965332, speedup: 2.0246211146030886x
~~~~~~~~~~
Again, we can see that torch.compile takes longer in the first
iteration, as it must compile the model, but in subsequent iterations, we see
significant speedups compared to eager.
We remark that the speedup numbers presented in this tutorial are for demonstration purposes only. Official speedup values can be seen at the TorchInductor performance dashboard.
Comparison to TorchScript and FX Tracing¶
We have seen that torch.compile can speed up PyTorch code.
Why else should we use torch.compile over existing PyTorch
compiler solutions, such as TorchScript or FX Tracing? Primarily, the
advantage of torch.compile lies in its ability to handle
arbitrary Python code with minimal changes to existing code.
One case that torch.compile can handle that other compiler
solutions struggle with is data-dependent control flow (the
if x.sum() < 0: line below).
def f1(x, y):
if x.sum() < 0:
return -y
return y
# Test that `fn1` and `fn2` return the same result, given
# the same arguments `args`. Typically, `fn1` will be an eager function
# while `fn2` will be a compiled function (torch.compile, TorchScript, or FX graph).
def test_fns(fn1, fn2, args):
out1 = fn1(*args)
out2 = fn2(*args)
return torch.allclose(out1, out2)
inp1 = torch.randn(5, 5)
inp2 = torch.randn(5, 5)
TorchScript tracing f1 results in
silently incorrect results, since only the actual control flow path
is traced.
/var/lib/workspace/intermediate_source/torch_compile_tutorial.py:274: TracerWarning:
Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
traced 1, 1: True
traced 1, 2: False
FX tracing f1 results in an error due to the presence of
data-dependent control flow.
import traceback as tb
try:
torch.fx.symbolic_trace(f1)
except:
tb.print_exc()
Traceback (most recent call last):
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 304, in <module>
torch.fx.symbolic_trace(f1)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 1193, in symbolic_trace
graph = tracer.trace(root, concrete_args)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 793, in trace
(self.create_arg(fn(*args)),),
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 274, in f1
if x.sum() < 0:
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 443, in __bool__
return self.tracer.to_bool(self)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 303, in to_bool
raise TraceError('symbolically traced variables cannot be used as inputs to control flow')
torch.fx.proxy.TraceError: symbolically traced variables cannot be used as inputs to control flow
If we provide a value for x as we try to FX trace f1, then
we run into the same problem as TorchScript tracing, as the data-dependent
control flow is removed in the traced function.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:862: UserWarning:
Was not able to add assertion to guarantee correct input x to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with.
fx 1, 1: True
fx 1, 2: False
Now we can see that torch.compile correctly handles
data-dependent control flow.
# Reset since we are using a different mode.
torch._dynamo.reset()
compile_f1 = torch.compile(f1)
print("compile 1, 1:", test_fns(f1, compile_f1, (inp1, inp2)))
print("compile 1, 2:", test_fns(f1, compile_f1, (-inp1, inp2)))
print("~" * 10)
compile 1, 1: True
compile 1, 2: True
~~~~~~~~~~
TorchScript scripting can handle data-dependent control flow, but this solution comes with its own set of problems. Namely, TorchScript scripting can require major code changes and will raise errors when unsupported Python is used.
In the example below, we forget TorchScript type annotations and we receive
a TorchScript error because the input type for argument y, an int,
does not match with the default argument type, torch.Tensor.
def f2(x, y):
return x + y
inp1 = torch.randn(5, 5)
inp2 = 3
script_f2 = torch.jit.script(f2)
try:
script_f2(inp1, inp2)
except:
tb.print_exc()
Traceback (most recent call last):
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 347, in <module>
script_f2(inp1, inp2)
RuntimeError: f2() Expected a value of type 'Tensor (inferred)' for argument 'y' but instead found type 'int'.
Inferred 'y' to be of type 'Tensor' because it was not annotated with an explicit type.
Position: 1
Value: 3
Declaration: f2(Tensor x, Tensor y) -> Tensor
Cast error details: Unable to cast 3 to Tensor
However, torch.compile is easily able to handle f2.
compile_f2 = torch.compile(f2)
print("compile 2:", test_fns(f2, compile_f2, (inp1, inp2)))
print("~" * 10)
compile 2: True
~~~~~~~~~~
Another case that torch.compile handles well compared to
previous compilers solutions is the usage of non-PyTorch functions.
import scipy
def f3(x):
x = x * 2
x = scipy.fft.dct(x.numpy())
x = torch.from_numpy(x)
x = x * 2
return x
TorchScript tracing treats results from non-PyTorch function calls as constants, and so our results can be silently wrong.
inp1 = torch.randn(5, 5)
inp2 = torch.randn(5, 5)
traced_f3 = torch.jit.trace(f3, (inp1,))
print("traced 3:", test_fns(f3, traced_f3, (inp2,)))
/var/lib/workspace/intermediate_source/torch_compile_tutorial.py:365: TracerWarning:
Converting a tensor to a NumPy array might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
/var/lib/workspace/intermediate_source/torch_compile_tutorial.py:366: TracerWarning:
torch.from_numpy results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
traced 3: False
TorchScript scripting and FX tracing disallow non-PyTorch function calls.
try:
torch.jit.script(f3)
except:
tb.print_exc()
try:
torch.fx.symbolic_trace(f3)
except:
tb.print_exc()
Traceback (most recent call last):
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 383, in <module>
torch.jit.script(f3)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/jit/_script.py", line 1395, in script
fn = torch._C._jit_script_compile(
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_jit_internal.py", line 1216, in _try_get_dispatched_fn
return boolean_dispatched.get(fn)
File "/opt/conda/envs/py_3.10/lib/python3.10/weakref.py", line 453, in get
return self.data.get(ref(key),default)
TypeError: cannot create weak reference to 'uarray._Function' object
Traceback (most recent call last):
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 388, in <module>
torch.fx.symbolic_trace(f3)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 1193, in symbolic_trace
graph = tracer.trace(root, concrete_args)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 793, in trace
(self.create_arg(fn(*args)),),
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 365, in f3
x = scipy.fft.dct(x.numpy())
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/scipy/fft/_backend.py", line 25, in __ua_function__
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/scipy/fft/_pocketfft/realtransforms.py", line 19, in _r2r
tmp = _asfarray(x)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/scipy/fft/_pocketfft/helper.py", line 89, in _asfarray
if x.dtype == np.float16:
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 548, in impl
return tracer.create_proxy('call_function', target, args, kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 187, in create_proxy
args_ = self.create_arg(args)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 401, in create_arg
return super().create_arg(a)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 258, in create_arg
return type(a)(self.create_arg(elem) for elem in a)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 258, in <genexpr>
return type(a)(self.create_arg(elem) for elem in a)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 401, in create_arg
return super().create_arg(a)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/proxy.py", line 294, in create_arg
raise NotImplementedError(f"argument of type: {type(a)}")
NotImplementedError: argument of type: <class 'type'>
In comparison, torch.compile is easily able to handle
the non-PyTorch function call.
compile_f3 = torch.compile(f3)
print("compile 3:", test_fns(f3, compile_f3, (inp2,)))
compile 3: True
TorchDynamo and FX Graphs¶
One important component of torch.compile is TorchDynamo.
TorchDynamo is responsible for JIT compiling arbitrary Python code into
FX graphs, which can
then be further optimized. TorchDynamo extracts FX graphs by analyzing Python bytecode
during runtime and detecting calls to PyTorch operations.
Normally, TorchInductor, another component of torch.compile,
further compiles the FX graphs into optimized kernels,
but TorchDynamo allows for different backends to be used. In order to inspect
the FX graphs that TorchDynamo outputs, let us create a custom backend that
outputs the FX graph and simply returns the graph’s unoptimized forward method.
from typing import List
def custom_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
print("custom backend called with FX graph:")
gm.graph.print_tabular()
return gm.forward
# Reset since we are using a different backend.
torch._dynamo.reset()
opt_model = torch.compile(init_model(), backend=custom_backend)
opt_model(generate_data(16)[0])
custom backend called with FX graph:
opcode name target args kwargs
------------- ------------------------------------------------- ---------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ -----------------
placeholder l_x_ L_x_ () {}
call_module l__self___features_conv0 L__self___features_conv0 (l_x_,) {}
call_module l__self___features_norm0 L__self___features_norm0 (l__self___features_conv0,) {}
call_module l__self___features_relu0 L__self___features_relu0 (l__self___features_norm0,) {}
call_module l__self___features_pool0 L__self___features_pool0 (l__self___features_relu0,) {}
call_function concated_features <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0], 1) {}
call_module l__self___features_denseblock1_denselayer1_norm1 L__self___features_denseblock1_denselayer1_norm1 (concated_features,) {}
call_module l__self___features_denseblock1_denselayer1_relu1 L__self___features_denseblock1_denselayer1_relu1 (l__self___features_denseblock1_denselayer1_norm1,) {}
call_module bottleneck_output L__self___features_denseblock1_denselayer1_conv1 (l__self___features_denseblock1_denselayer1_relu1,) {}
call_module l__self___features_denseblock1_denselayer1_norm2 L__self___features_denseblock1_denselayer1_norm2 (bottleneck_output,) {}
call_module l__self___features_denseblock1_denselayer1_relu2 L__self___features_denseblock1_denselayer1_relu2 (l__self___features_denseblock1_denselayer1_norm2,) {}
call_module new_features L__self___features_denseblock1_denselayer1_conv2 (l__self___features_denseblock1_denselayer1_relu2,) {}
call_function concated_features_1 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0, new_features], 1) {}
call_module l__self___features_denseblock1_denselayer2_norm1 L__self___features_denseblock1_denselayer2_norm1 (concated_features_1,) {}
call_module l__self___features_denseblock1_denselayer2_relu1 L__self___features_denseblock1_denselayer2_relu1 (l__self___features_denseblock1_denselayer2_norm1,) {}
call_module bottleneck_output_2 L__self___features_denseblock1_denselayer2_conv1 (l__self___features_denseblock1_denselayer2_relu1,) {}
call_module l__self___features_denseblock1_denselayer2_norm2 L__self___features_denseblock1_denselayer2_norm2 (bottleneck_output_2,) {}
call_module l__self___features_denseblock1_denselayer2_relu2 L__self___features_denseblock1_denselayer2_relu2 (l__self___features_denseblock1_denselayer2_norm2,) {}
call_module new_features_2 L__self___features_denseblock1_denselayer2_conv2 (l__self___features_denseblock1_denselayer2_relu2,) {}
call_function concated_features_2 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0, new_features, new_features_2], 1) {}
call_module l__self___features_denseblock1_denselayer3_norm1 L__self___features_denseblock1_denselayer3_norm1 (concated_features_2,) {}
call_module l__self___features_denseblock1_denselayer3_relu1 L__self___features_denseblock1_denselayer3_relu1 (l__self___features_denseblock1_denselayer3_norm1,) {}
call_module bottleneck_output_4 L__self___features_denseblock1_denselayer3_conv1 (l__self___features_denseblock1_denselayer3_relu1,) {}
call_module l__self___features_denseblock1_denselayer3_norm2 L__self___features_denseblock1_denselayer3_norm2 (bottleneck_output_4,) {}
call_module l__self___features_denseblock1_denselayer3_relu2 L__self___features_denseblock1_denselayer3_relu2 (l__self___features_denseblock1_denselayer3_norm2,) {}
call_module new_features_4 L__self___features_denseblock1_denselayer3_conv2 (l__self___features_denseblock1_denselayer3_relu2,) {}
call_function concated_features_3 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0, new_features, new_features_2, new_features_4], 1) {}
call_module l__self___features_denseblock1_denselayer4_norm1 L__self___features_denseblock1_denselayer4_norm1 (concated_features_3,) {}
call_module l__self___features_denseblock1_denselayer4_relu1 L__self___features_denseblock1_denselayer4_relu1 (l__self___features_denseblock1_denselayer4_norm1,) {}
call_module bottleneck_output_6 L__self___features_denseblock1_denselayer4_conv1 (l__self___features_denseblock1_denselayer4_relu1,) {}
call_module l__self___features_denseblock1_denselayer4_norm2 L__self___features_denseblock1_denselayer4_norm2 (bottleneck_output_6,) {}
call_module l__self___features_denseblock1_denselayer4_relu2 L__self___features_denseblock1_denselayer4_relu2 (l__self___features_denseblock1_denselayer4_norm2,) {}
call_module new_features_6 L__self___features_denseblock1_denselayer4_conv2 (l__self___features_denseblock1_denselayer4_relu2,) {}
call_function concated_features_4 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6], 1) {}
call_module l__self___features_denseblock1_denselayer5_norm1 L__self___features_denseblock1_denselayer5_norm1 (concated_features_4,) {}
call_module l__self___features_denseblock1_denselayer5_relu1 L__self___features_denseblock1_denselayer5_relu1 (l__self___features_denseblock1_denselayer5_norm1,) {}
call_module bottleneck_output_8 L__self___features_denseblock1_denselayer5_conv1 (l__self___features_denseblock1_denselayer5_relu1,) {}
call_module l__self___features_denseblock1_denselayer5_norm2 L__self___features_denseblock1_denselayer5_norm2 (bottleneck_output_8,) {}
call_module l__self___features_denseblock1_denselayer5_relu2 L__self___features_denseblock1_denselayer5_relu2 (l__self___features_denseblock1_denselayer5_norm2,) {}
call_module new_features_8 L__self___features_denseblock1_denselayer5_conv2 (l__self___features_denseblock1_denselayer5_relu2,) {}
call_function concated_features_5 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8], 1) {}
call_module l__self___features_denseblock1_denselayer6_norm1 L__self___features_denseblock1_denselayer6_norm1 (concated_features_5,) {}
call_module l__self___features_denseblock1_denselayer6_relu1 L__self___features_denseblock1_denselayer6_relu1 (l__self___features_denseblock1_denselayer6_norm1,) {}
call_module bottleneck_output_10 L__self___features_denseblock1_denselayer6_conv1 (l__self___features_denseblock1_denselayer6_relu1,) {}
call_module l__self___features_denseblock1_denselayer6_norm2 L__self___features_denseblock1_denselayer6_norm2 (bottleneck_output_10,) {}
call_module l__self___features_denseblock1_denselayer6_relu2 L__self___features_denseblock1_denselayer6_relu2 (l__self___features_denseblock1_denselayer6_norm2,) {}
call_module new_features_10 L__self___features_denseblock1_denselayer6_conv2 (l__self___features_denseblock1_denselayer6_relu2,) {}
call_function cat_6 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8, new_features_10], 1) {}
call_module l__self___features_transition1_norm L__self___features_transition1_norm (cat_6,) {}
call_module l__self___features_transition1_relu L__self___features_transition1_relu (l__self___features_transition1_norm,) {}
call_module l__self___features_transition1_conv L__self___features_transition1_conv (l__self___features_transition1_relu,) {}
call_module l__self___features_transition1_pool L__self___features_transition1_pool (l__self___features_transition1_conv,) {}
call_function concated_features_6 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool], 1) {}
call_module l__self___features_denseblock2_denselayer1_norm1 L__self___features_denseblock2_denselayer1_norm1 (concated_features_6,) {}
call_module l__self___features_denseblock2_denselayer1_relu1 L__self___features_denseblock2_denselayer1_relu1 (l__self___features_denseblock2_denselayer1_norm1,) {}
call_module bottleneck_output_12 L__self___features_denseblock2_denselayer1_conv1 (l__self___features_denseblock2_denselayer1_relu1,) {}
call_module l__self___features_denseblock2_denselayer1_norm2 L__self___features_denseblock2_denselayer1_norm2 (bottleneck_output_12,) {}
call_module l__self___features_denseblock2_denselayer1_relu2 L__self___features_denseblock2_denselayer1_relu2 (l__self___features_denseblock2_denselayer1_norm2,) {}
call_module new_features_12 L__self___features_denseblock2_denselayer1_conv2 (l__self___features_denseblock2_denselayer1_relu2,) {}
call_function concated_features_7 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12], 1) {}
call_module l__self___features_denseblock2_denselayer2_norm1 L__self___features_denseblock2_denselayer2_norm1 (concated_features_7,) {}
call_module l__self___features_denseblock2_denselayer2_relu1 L__self___features_denseblock2_denselayer2_relu1 (l__self___features_denseblock2_denselayer2_norm1,) {}
call_module bottleneck_output_14 L__self___features_denseblock2_denselayer2_conv1 (l__self___features_denseblock2_denselayer2_relu1,) {}
call_module l__self___features_denseblock2_denselayer2_norm2 L__self___features_denseblock2_denselayer2_norm2 (bottleneck_output_14,) {}
call_module l__self___features_denseblock2_denselayer2_relu2 L__self___features_denseblock2_denselayer2_relu2 (l__self___features_denseblock2_denselayer2_norm2,) {}
call_module new_features_14 L__self___features_denseblock2_denselayer2_conv2 (l__self___features_denseblock2_denselayer2_relu2,) {}
call_function concated_features_8 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14], 1) {}
call_module l__self___features_denseblock2_denselayer3_norm1 L__self___features_denseblock2_denselayer3_norm1 (concated_features_8,) {}
call_module l__self___features_denseblock2_denselayer3_relu1 L__self___features_denseblock2_denselayer3_relu1 (l__self___features_denseblock2_denselayer3_norm1,) {}
call_module bottleneck_output_16 L__self___features_denseblock2_denselayer3_conv1 (l__self___features_denseblock2_denselayer3_relu1,) {}
call_module l__self___features_denseblock2_denselayer3_norm2 L__self___features_denseblock2_denselayer3_norm2 (bottleneck_output_16,) {}
call_module l__self___features_denseblock2_denselayer3_relu2 L__self___features_denseblock2_denselayer3_relu2 (l__self___features_denseblock2_denselayer3_norm2,) {}
call_module new_features_16 L__self___features_denseblock2_denselayer3_conv2 (l__self___features_denseblock2_denselayer3_relu2,) {}
call_function concated_features_9 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16], 1) {}
call_module l__self___features_denseblock2_denselayer4_norm1 L__self___features_denseblock2_denselayer4_norm1 (concated_features_9,) {}
call_module l__self___features_denseblock2_denselayer4_relu1 L__self___features_denseblock2_denselayer4_relu1 (l__self___features_denseblock2_denselayer4_norm1,) {}
call_module bottleneck_output_18 L__self___features_denseblock2_denselayer4_conv1 (l__self___features_denseblock2_denselayer4_relu1,) {}
call_module l__self___features_denseblock2_denselayer4_norm2 L__self___features_denseblock2_denselayer4_norm2 (bottleneck_output_18,) {}
call_module l__self___features_denseblock2_denselayer4_relu2 L__self___features_denseblock2_denselayer4_relu2 (l__self___features_denseblock2_denselayer4_norm2,) {}
call_module new_features_18 L__self___features_denseblock2_denselayer4_conv2 (l__self___features_denseblock2_denselayer4_relu2,) {}
call_function concated_features_10 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18], 1) {}
call_module l__self___features_denseblock2_denselayer5_norm1 L__self___features_denseblock2_denselayer5_norm1 (concated_features_10,) {}
call_module l__self___features_denseblock2_denselayer5_relu1 L__self___features_denseblock2_denselayer5_relu1 (l__self___features_denseblock2_denselayer5_norm1,) {}
call_module bottleneck_output_20 L__self___features_denseblock2_denselayer5_conv1 (l__self___features_denseblock2_denselayer5_relu1,) {}
call_module l__self___features_denseblock2_denselayer5_norm2 L__self___features_denseblock2_denselayer5_norm2 (bottleneck_output_20,) {}
call_module l__self___features_denseblock2_denselayer5_relu2 L__self___features_denseblock2_denselayer5_relu2 (l__self___features_denseblock2_denselayer5_norm2,) {}
call_module new_features_20 L__self___features_denseblock2_denselayer5_conv2 (l__self___features_denseblock2_denselayer5_relu2,) {}
call_function concated_features_11 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20], 1) {}
call_module l__self___features_denseblock2_denselayer6_norm1 L__self___features_denseblock2_denselayer6_norm1 (concated_features_11,) {}
call_module l__self___features_denseblock2_denselayer6_relu1 L__self___features_denseblock2_denselayer6_relu1 (l__self___features_denseblock2_denselayer6_norm1,) {}
call_module bottleneck_output_22 L__self___features_denseblock2_denselayer6_conv1 (l__self___features_denseblock2_denselayer6_relu1,) {}
call_module l__self___features_denseblock2_denselayer6_norm2 L__self___features_denseblock2_denselayer6_norm2 (bottleneck_output_22,) {}
call_module l__self___features_denseblock2_denselayer6_relu2 L__self___features_denseblock2_denselayer6_relu2 (l__self___features_denseblock2_denselayer6_norm2,) {}
call_module new_features_22 L__self___features_denseblock2_denselayer6_conv2 (l__self___features_denseblock2_denselayer6_relu2,) {}
call_function concated_features_12 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22], 1) {}
call_module l__self___features_denseblock2_denselayer7_norm1 L__self___features_denseblock2_denselayer7_norm1 (concated_features_12,) {}
call_module l__self___features_denseblock2_denselayer7_relu1 L__self___features_denseblock2_denselayer7_relu1 (l__self___features_denseblock2_denselayer7_norm1,) {}
call_module bottleneck_output_24 L__self___features_denseblock2_denselayer7_conv1 (l__self___features_denseblock2_denselayer7_relu1,) {}
call_module l__self___features_denseblock2_denselayer7_norm2 L__self___features_denseblock2_denselayer7_norm2 (bottleneck_output_24,) {}
call_module l__self___features_denseblock2_denselayer7_relu2 L__self___features_denseblock2_denselayer7_relu2 (l__self___features_denseblock2_denselayer7_norm2,) {}
call_module new_features_24 L__self___features_denseblock2_denselayer7_conv2 (l__self___features_denseblock2_denselayer7_relu2,) {}
call_function concated_features_13 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24], 1) {}
call_module l__self___features_denseblock2_denselayer8_norm1 L__self___features_denseblock2_denselayer8_norm1 (concated_features_13,) {}
call_module l__self___features_denseblock2_denselayer8_relu1 L__self___features_denseblock2_denselayer8_relu1 (l__self___features_denseblock2_denselayer8_norm1,) {}
call_module bottleneck_output_26 L__self___features_denseblock2_denselayer8_conv1 (l__self___features_denseblock2_denselayer8_relu1,) {}
call_module l__self___features_denseblock2_denselayer8_norm2 L__self___features_denseblock2_denselayer8_norm2 (bottleneck_output_26,) {}
call_module l__self___features_denseblock2_denselayer8_relu2 L__self___features_denseblock2_denselayer8_relu2 (l__self___features_denseblock2_denselayer8_norm2,) {}
call_module new_features_26 L__self___features_denseblock2_denselayer8_conv2 (l__self___features_denseblock2_denselayer8_relu2,) {}
call_function concated_features_14 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26], 1) {}
call_module l__self___features_denseblock2_denselayer9_norm1 L__self___features_denseblock2_denselayer9_norm1 (concated_features_14,) {}
call_module l__self___features_denseblock2_denselayer9_relu1 L__self___features_denseblock2_denselayer9_relu1 (l__self___features_denseblock2_denselayer9_norm1,) {}
call_module bottleneck_output_28 L__self___features_denseblock2_denselayer9_conv1 (l__self___features_denseblock2_denselayer9_relu1,) {}
call_module l__self___features_denseblock2_denselayer9_norm2 L__self___features_denseblock2_denselayer9_norm2 (bottleneck_output_28,) {}
call_module l__self___features_denseblock2_denselayer9_relu2 L__self___features_denseblock2_denselayer9_relu2 (l__self___features_denseblock2_denselayer9_norm2,) {}
call_module new_features_28 L__self___features_denseblock2_denselayer9_conv2 (l__self___features_denseblock2_denselayer9_relu2,) {}
call_function concated_features_15 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28], 1) {}
call_module l__self___features_denseblock2_denselayer10_norm1 L__self___features_denseblock2_denselayer10_norm1 (concated_features_15,) {}
call_module l__self___features_denseblock2_denselayer10_relu1 L__self___features_denseblock2_denselayer10_relu1 (l__self___features_denseblock2_denselayer10_norm1,) {}
call_module bottleneck_output_30 L__self___features_denseblock2_denselayer10_conv1 (l__self___features_denseblock2_denselayer10_relu1,) {}
call_module l__self___features_denseblock2_denselayer10_norm2 L__self___features_denseblock2_denselayer10_norm2 (bottleneck_output_30,) {}
call_module l__self___features_denseblock2_denselayer10_relu2 L__self___features_denseblock2_denselayer10_relu2 (l__self___features_denseblock2_denselayer10_norm2,) {}
call_module new_features_30 L__self___features_denseblock2_denselayer10_conv2 (l__self___features_denseblock2_denselayer10_relu2,) {}
call_function concated_features_16 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30], 1) {}
call_module l__self___features_denseblock2_denselayer11_norm1 L__self___features_denseblock2_denselayer11_norm1 (concated_features_16,) {}
call_module l__self___features_denseblock2_denselayer11_relu1 L__self___features_denseblock2_denselayer11_relu1 (l__self___features_denseblock2_denselayer11_norm1,) {}
call_module bottleneck_output_32 L__self___features_denseblock2_denselayer11_conv1 (l__self___features_denseblock2_denselayer11_relu1,) {}
call_module l__self___features_denseblock2_denselayer11_norm2 L__self___features_denseblock2_denselayer11_norm2 (bottleneck_output_32,) {}
call_module l__self___features_denseblock2_denselayer11_relu2 L__self___features_denseblock2_denselayer11_relu2 (l__self___features_denseblock2_denselayer11_norm2,) {}
call_module new_features_32 L__self___features_denseblock2_denselayer11_conv2 (l__self___features_denseblock2_denselayer11_relu2,) {}
call_function concated_features_17 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32], 1) {}
call_module l__self___features_denseblock2_denselayer12_norm1 L__self___features_denseblock2_denselayer12_norm1 (concated_features_17,) {}
call_module l__self___features_denseblock2_denselayer12_relu1 L__self___features_denseblock2_denselayer12_relu1 (l__self___features_denseblock2_denselayer12_norm1,) {}
call_module bottleneck_output_34 L__self___features_denseblock2_denselayer12_conv1 (l__self___features_denseblock2_denselayer12_relu1,) {}
call_module l__self___features_denseblock2_denselayer12_norm2 L__self___features_denseblock2_denselayer12_norm2 (bottleneck_output_34,) {}
call_module l__self___features_denseblock2_denselayer12_relu2 L__self___features_denseblock2_denselayer12_relu2 (l__self___features_denseblock2_denselayer12_norm2,) {}
call_module new_features_34 L__self___features_denseblock2_denselayer12_conv2 (l__self___features_denseblock2_denselayer12_relu2,) {}
call_function cat_19 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32, new_features_34], 1) {}
call_module l__self___features_transition2_norm L__self___features_transition2_norm (cat_19,) {}
call_module l__self___features_transition2_relu L__self___features_transition2_relu (l__self___features_transition2_norm,) {}
call_module l__self___features_transition2_conv L__self___features_transition2_conv (l__self___features_transition2_relu,) {}
call_module l__self___features_transition2_pool L__self___features_transition2_pool (l__self___features_transition2_conv,) {}
call_function concated_features_18 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool], 1) {}
call_module l__self___features_denseblock3_denselayer1_norm1 L__self___features_denseblock3_denselayer1_norm1 (concated_features_18,) {}
call_module l__self___features_denseblock3_denselayer1_relu1 L__self___features_denseblock3_denselayer1_relu1 (l__self___features_denseblock3_denselayer1_norm1,) {}
call_module bottleneck_output_36 L__self___features_denseblock3_denselayer1_conv1 (l__self___features_denseblock3_denselayer1_relu1,) {}
call_module l__self___features_denseblock3_denselayer1_norm2 L__self___features_denseblock3_denselayer1_norm2 (bottleneck_output_36,) {}
call_module l__self___features_denseblock3_denselayer1_relu2 L__self___features_denseblock3_denselayer1_relu2 (l__self___features_denseblock3_denselayer1_norm2,) {}
call_module new_features_36 L__self___features_denseblock3_denselayer1_conv2 (l__self___features_denseblock3_denselayer1_relu2,) {}
call_function concated_features_19 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36], 1) {}
call_module l__self___features_denseblock3_denselayer2_norm1 L__self___features_denseblock3_denselayer2_norm1 (concated_features_19,) {}
call_module l__self___features_denseblock3_denselayer2_relu1 L__self___features_denseblock3_denselayer2_relu1 (l__self___features_denseblock3_denselayer2_norm1,) {}
call_module bottleneck_output_38 L__self___features_denseblock3_denselayer2_conv1 (l__self___features_denseblock3_denselayer2_relu1,) {}
call_module l__self___features_denseblock3_denselayer2_norm2 L__self___features_denseblock3_denselayer2_norm2 (bottleneck_output_38,) {}
call_module l__self___features_denseblock3_denselayer2_relu2 L__self___features_denseblock3_denselayer2_relu2 (l__self___features_denseblock3_denselayer2_norm2,) {}
call_module new_features_38 L__self___features_denseblock3_denselayer2_conv2 (l__self___features_denseblock3_denselayer2_relu2,) {}
call_function concated_features_20 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38], 1) {}
call_module l__self___features_denseblock3_denselayer3_norm1 L__self___features_denseblock3_denselayer3_norm1 (concated_features_20,) {}
call_module l__self___features_denseblock3_denselayer3_relu1 L__self___features_denseblock3_denselayer3_relu1 (l__self___features_denseblock3_denselayer3_norm1,) {}
call_module bottleneck_output_40 L__self___features_denseblock3_denselayer3_conv1 (l__self___features_denseblock3_denselayer3_relu1,) {}
call_module l__self___features_denseblock3_denselayer3_norm2 L__self___features_denseblock3_denselayer3_norm2 (bottleneck_output_40,) {}
call_module l__self___features_denseblock3_denselayer3_relu2 L__self___features_denseblock3_denselayer3_relu2 (l__self___features_denseblock3_denselayer3_norm2,) {}
call_module new_features_40 L__self___features_denseblock3_denselayer3_conv2 (l__self___features_denseblock3_denselayer3_relu2,) {}
call_function concated_features_21 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40], 1) {}
call_module l__self___features_denseblock3_denselayer4_norm1 L__self___features_denseblock3_denselayer4_norm1 (concated_features_21,) {}
call_module l__self___features_denseblock3_denselayer4_relu1 L__self___features_denseblock3_denselayer4_relu1 (l__self___features_denseblock3_denselayer4_norm1,) {}
call_module bottleneck_output_42 L__self___features_denseblock3_denselayer4_conv1 (l__self___features_denseblock3_denselayer4_relu1,) {}
call_module l__self___features_denseblock3_denselayer4_norm2 L__self___features_denseblock3_denselayer4_norm2 (bottleneck_output_42,) {}
call_module l__self___features_denseblock3_denselayer4_relu2 L__self___features_denseblock3_denselayer4_relu2 (l__self___features_denseblock3_denselayer4_norm2,) {}
call_module new_features_42 L__self___features_denseblock3_denselayer4_conv2 (l__self___features_denseblock3_denselayer4_relu2,) {}
call_function concated_features_22 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42], 1) {}
call_module l__self___features_denseblock3_denselayer5_norm1 L__self___features_denseblock3_denselayer5_norm1 (concated_features_22,) {}
call_module l__self___features_denseblock3_denselayer5_relu1 L__self___features_denseblock3_denselayer5_relu1 (l__self___features_denseblock3_denselayer5_norm1,) {}
call_module bottleneck_output_44 L__self___features_denseblock3_denselayer5_conv1 (l__self___features_denseblock3_denselayer5_relu1,) {}
call_module l__self___features_denseblock3_denselayer5_norm2 L__self___features_denseblock3_denselayer5_norm2 (bottleneck_output_44,) {}
call_module l__self___features_denseblock3_denselayer5_relu2 L__self___features_denseblock3_denselayer5_relu2 (l__self___features_denseblock3_denselayer5_norm2,) {}
call_module new_features_44 L__self___features_denseblock3_denselayer5_conv2 (l__self___features_denseblock3_denselayer5_relu2,) {}
call_function concated_features_23 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44], 1) {}
call_module l__self___features_denseblock3_denselayer6_norm1 L__self___features_denseblock3_denselayer6_norm1 (concated_features_23,) {}
call_module l__self___features_denseblock3_denselayer6_relu1 L__self___features_denseblock3_denselayer6_relu1 (l__self___features_denseblock3_denselayer6_norm1,) {}
call_module bottleneck_output_46 L__self___features_denseblock3_denselayer6_conv1 (l__self___features_denseblock3_denselayer6_relu1,) {}
call_module l__self___features_denseblock3_denselayer6_norm2 L__self___features_denseblock3_denselayer6_norm2 (bottleneck_output_46,) {}
call_module l__self___features_denseblock3_denselayer6_relu2 L__self___features_denseblock3_denselayer6_relu2 (l__self___features_denseblock3_denselayer6_norm2,) {}
call_module new_features_46 L__self___features_denseblock3_denselayer6_conv2 (l__self___features_denseblock3_denselayer6_relu2,) {}
call_function concated_features_24 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46], 1) {}
call_module l__self___features_denseblock3_denselayer7_norm1 L__self___features_denseblock3_denselayer7_norm1 (concated_features_24,) {}
call_module l__self___features_denseblock3_denselayer7_relu1 L__self___features_denseblock3_denselayer7_relu1 (l__self___features_denseblock3_denselayer7_norm1,) {}
call_module bottleneck_output_48 L__self___features_denseblock3_denselayer7_conv1 (l__self___features_denseblock3_denselayer7_relu1,) {}
call_module l__self___features_denseblock3_denselayer7_norm2 L__self___features_denseblock3_denselayer7_norm2 (bottleneck_output_48,) {}
call_module l__self___features_denseblock3_denselayer7_relu2 L__self___features_denseblock3_denselayer7_relu2 (l__self___features_denseblock3_denselayer7_norm2,) {}
call_module new_features_48 L__self___features_denseblock3_denselayer7_conv2 (l__self___features_denseblock3_denselayer7_relu2,) {}
call_function concated_features_25 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48], 1) {}
call_module l__self___features_denseblock3_denselayer8_norm1 L__self___features_denseblock3_denselayer8_norm1 (concated_features_25,) {}
call_module l__self___features_denseblock3_denselayer8_relu1 L__self___features_denseblock3_denselayer8_relu1 (l__self___features_denseblock3_denselayer8_norm1,) {}
call_module bottleneck_output_50 L__self___features_denseblock3_denselayer8_conv1 (l__self___features_denseblock3_denselayer8_relu1,) {}
call_module l__self___features_denseblock3_denselayer8_norm2 L__self___features_denseblock3_denselayer8_norm2 (bottleneck_output_50,) {}
call_module l__self___features_denseblock3_denselayer8_relu2 L__self___features_denseblock3_denselayer8_relu2 (l__self___features_denseblock3_denselayer8_norm2,) {}
call_module new_features_50 L__self___features_denseblock3_denselayer8_conv2 (l__self___features_denseblock3_denselayer8_relu2,) {}
call_function concated_features_26 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50], 1) {}
call_module l__self___features_denseblock3_denselayer9_norm1 L__self___features_denseblock3_denselayer9_norm1 (concated_features_26,) {}
call_module l__self___features_denseblock3_denselayer9_relu1 L__self___features_denseblock3_denselayer9_relu1 (l__self___features_denseblock3_denselayer9_norm1,) {}
call_module bottleneck_output_52 L__self___features_denseblock3_denselayer9_conv1 (l__self___features_denseblock3_denselayer9_relu1,) {}
call_module l__self___features_denseblock3_denselayer9_norm2 L__self___features_denseblock3_denselayer9_norm2 (bottleneck_output_52,) {}
call_module l__self___features_denseblock3_denselayer9_relu2 L__self___features_denseblock3_denselayer9_relu2 (l__self___features_denseblock3_denselayer9_norm2,) {}
call_module new_features_52 L__self___features_denseblock3_denselayer9_conv2 (l__self___features_denseblock3_denselayer9_relu2,) {}
call_function concated_features_27 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52], 1) {}
call_module l__self___features_denseblock3_denselayer10_norm1 L__self___features_denseblock3_denselayer10_norm1 (concated_features_27,) {}
call_module l__self___features_denseblock3_denselayer10_relu1 L__self___features_denseblock3_denselayer10_relu1 (l__self___features_denseblock3_denselayer10_norm1,) {}
call_module bottleneck_output_54 L__self___features_denseblock3_denselayer10_conv1 (l__self___features_denseblock3_denselayer10_relu1,) {}
call_module l__self___features_denseblock3_denselayer10_norm2 L__self___features_denseblock3_denselayer10_norm2 (bottleneck_output_54,) {}
call_module l__self___features_denseblock3_denselayer10_relu2 L__self___features_denseblock3_denselayer10_relu2 (l__self___features_denseblock3_denselayer10_norm2,) {}
call_module new_features_54 L__self___features_denseblock3_denselayer10_conv2 (l__self___features_denseblock3_denselayer10_relu2,) {}
call_function concated_features_28 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54], 1) {}
call_module l__self___features_denseblock3_denselayer11_norm1 L__self___features_denseblock3_denselayer11_norm1 (concated_features_28,) {}
call_module l__self___features_denseblock3_denselayer11_relu1 L__self___features_denseblock3_denselayer11_relu1 (l__self___features_denseblock3_denselayer11_norm1,) {}
call_module bottleneck_output_56 L__self___features_denseblock3_denselayer11_conv1 (l__self___features_denseblock3_denselayer11_relu1,) {}
call_module l__self___features_denseblock3_denselayer11_norm2 L__self___features_denseblock3_denselayer11_norm2 (bottleneck_output_56,) {}
call_module l__self___features_denseblock3_denselayer11_relu2 L__self___features_denseblock3_denselayer11_relu2 (l__self___features_denseblock3_denselayer11_norm2,) {}
call_module new_features_56 L__self___features_denseblock3_denselayer11_conv2 (l__self___features_denseblock3_denselayer11_relu2,) {}
call_function concated_features_29 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56], 1) {}
call_module l__self___features_denseblock3_denselayer12_norm1 L__self___features_denseblock3_denselayer12_norm1 (concated_features_29,) {}
call_module l__self___features_denseblock3_denselayer12_relu1 L__self___features_denseblock3_denselayer12_relu1 (l__self___features_denseblock3_denselayer12_norm1,) {}
call_module bottleneck_output_58 L__self___features_denseblock3_denselayer12_conv1 (l__self___features_denseblock3_denselayer12_relu1,) {}
call_module l__self___features_denseblock3_denselayer12_norm2 L__self___features_denseblock3_denselayer12_norm2 (bottleneck_output_58,) {}
call_module l__self___features_denseblock3_denselayer12_relu2 L__self___features_denseblock3_denselayer12_relu2 (l__self___features_denseblock3_denselayer12_norm2,) {}
call_module new_features_58 L__self___features_denseblock3_denselayer12_conv2 (l__self___features_denseblock3_denselayer12_relu2,) {}
call_function concated_features_30 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58], 1) {}
call_module l__self___features_denseblock3_denselayer13_norm1 L__self___features_denseblock3_denselayer13_norm1 (concated_features_30,) {}
call_module l__self___features_denseblock3_denselayer13_relu1 L__self___features_denseblock3_denselayer13_relu1 (l__self___features_denseblock3_denselayer13_norm1,) {}
call_module bottleneck_output_60 L__self___features_denseblock3_denselayer13_conv1 (l__self___features_denseblock3_denselayer13_relu1,) {}
call_module l__self___features_denseblock3_denselayer13_norm2 L__self___features_denseblock3_denselayer13_norm2 (bottleneck_output_60,) {}
call_module l__self___features_denseblock3_denselayer13_relu2 L__self___features_denseblock3_denselayer13_relu2 (l__self___features_denseblock3_denselayer13_norm2,) {}
call_module new_features_60 L__self___features_denseblock3_denselayer13_conv2 (l__self___features_denseblock3_denselayer13_relu2,) {}
call_function concated_features_31 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60], 1) {}
call_module l__self___features_denseblock3_denselayer14_norm1 L__self___features_denseblock3_denselayer14_norm1 (concated_features_31,) {}
call_module l__self___features_denseblock3_denselayer14_relu1 L__self___features_denseblock3_denselayer14_relu1 (l__self___features_denseblock3_denselayer14_norm1,) {}
call_module bottleneck_output_62 L__self___features_denseblock3_denselayer14_conv1 (l__self___features_denseblock3_denselayer14_relu1,) {}
call_module l__self___features_denseblock3_denselayer14_norm2 L__self___features_denseblock3_denselayer14_norm2 (bottleneck_output_62,) {}
call_module l__self___features_denseblock3_denselayer14_relu2 L__self___features_denseblock3_denselayer14_relu2 (l__self___features_denseblock3_denselayer14_norm2,) {}
call_module new_features_62 L__self___features_denseblock3_denselayer14_conv2 (l__self___features_denseblock3_denselayer14_relu2,) {}
call_function concated_features_32 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62], 1) {}
call_module l__self___features_denseblock3_denselayer15_norm1 L__self___features_denseblock3_denselayer15_norm1 (concated_features_32,) {}
call_module l__self___features_denseblock3_denselayer15_relu1 L__self___features_denseblock3_denselayer15_relu1 (l__self___features_denseblock3_denselayer15_norm1,) {}
call_module bottleneck_output_64 L__self___features_denseblock3_denselayer15_conv1 (l__self___features_denseblock3_denselayer15_relu1,) {}
call_module l__self___features_denseblock3_denselayer15_norm2 L__self___features_denseblock3_denselayer15_norm2 (bottleneck_output_64,) {}
call_module l__self___features_denseblock3_denselayer15_relu2 L__self___features_denseblock3_denselayer15_relu2 (l__self___features_denseblock3_denselayer15_norm2,) {}
call_module new_features_64 L__self___features_denseblock3_denselayer15_conv2 (l__self___features_denseblock3_denselayer15_relu2,) {}
call_function concated_features_33 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64], 1) {}
call_module l__self___features_denseblock3_denselayer16_norm1 L__self___features_denseblock3_denselayer16_norm1 (concated_features_33,) {}
call_module l__self___features_denseblock3_denselayer16_relu1 L__self___features_denseblock3_denselayer16_relu1 (l__self___features_denseblock3_denselayer16_norm1,) {}
call_module bottleneck_output_66 L__self___features_denseblock3_denselayer16_conv1 (l__self___features_denseblock3_denselayer16_relu1,) {}
call_module l__self___features_denseblock3_denselayer16_norm2 L__self___features_denseblock3_denselayer16_norm2 (bottleneck_output_66,) {}
call_module l__self___features_denseblock3_denselayer16_relu2 L__self___features_denseblock3_denselayer16_relu2 (l__self___features_denseblock3_denselayer16_norm2,) {}
call_module new_features_66 L__self___features_denseblock3_denselayer16_conv2 (l__self___features_denseblock3_denselayer16_relu2,) {}
call_function concated_features_34 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66], 1) {}
call_module l__self___features_denseblock3_denselayer17_norm1 L__self___features_denseblock3_denselayer17_norm1 (concated_features_34,) {}
call_module l__self___features_denseblock3_denselayer17_relu1 L__self___features_denseblock3_denselayer17_relu1 (l__self___features_denseblock3_denselayer17_norm1,) {}
call_module bottleneck_output_68 L__self___features_denseblock3_denselayer17_conv1 (l__self___features_denseblock3_denselayer17_relu1,) {}
call_module l__self___features_denseblock3_denselayer17_norm2 L__self___features_denseblock3_denselayer17_norm2 (bottleneck_output_68,) {}
call_module l__self___features_denseblock3_denselayer17_relu2 L__self___features_denseblock3_denselayer17_relu2 (l__self___features_denseblock3_denselayer17_norm2,) {}
call_module new_features_68 L__self___features_denseblock3_denselayer17_conv2 (l__self___features_denseblock3_denselayer17_relu2,) {}
call_function concated_features_35 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68], 1) {}
call_module l__self___features_denseblock3_denselayer18_norm1 L__self___features_denseblock3_denselayer18_norm1 (concated_features_35,) {}
call_module l__self___features_denseblock3_denselayer18_relu1 L__self___features_denseblock3_denselayer18_relu1 (l__self___features_denseblock3_denselayer18_norm1,) {}
call_module bottleneck_output_70 L__self___features_denseblock3_denselayer18_conv1 (l__self___features_denseblock3_denselayer18_relu1,) {}
call_module l__self___features_denseblock3_denselayer18_norm2 L__self___features_denseblock3_denselayer18_norm2 (bottleneck_output_70,) {}
call_module l__self___features_denseblock3_denselayer18_relu2 L__self___features_denseblock3_denselayer18_relu2 (l__self___features_denseblock3_denselayer18_norm2,) {}
call_module new_features_70 L__self___features_denseblock3_denselayer18_conv2 (l__self___features_denseblock3_denselayer18_relu2,) {}
call_function concated_features_36 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70], 1) {}
call_module l__self___features_denseblock3_denselayer19_norm1 L__self___features_denseblock3_denselayer19_norm1 (concated_features_36,) {}
call_module l__self___features_denseblock3_denselayer19_relu1 L__self___features_denseblock3_denselayer19_relu1 (l__self___features_denseblock3_denselayer19_norm1,) {}
call_module bottleneck_output_72 L__self___features_denseblock3_denselayer19_conv1 (l__self___features_denseblock3_denselayer19_relu1,) {}
call_module l__self___features_denseblock3_denselayer19_norm2 L__self___features_denseblock3_denselayer19_norm2 (bottleneck_output_72,) {}
call_module l__self___features_denseblock3_denselayer19_relu2 L__self___features_denseblock3_denselayer19_relu2 (l__self___features_denseblock3_denselayer19_norm2,) {}
call_module new_features_72 L__self___features_denseblock3_denselayer19_conv2 (l__self___features_denseblock3_denselayer19_relu2,) {}
call_function concated_features_37 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72], 1) {}
call_module l__self___features_denseblock3_denselayer20_norm1 L__self___features_denseblock3_denselayer20_norm1 (concated_features_37,) {}
call_module l__self___features_denseblock3_denselayer20_relu1 L__self___features_denseblock3_denselayer20_relu1 (l__self___features_denseblock3_denselayer20_norm1,) {}
call_module bottleneck_output_74 L__self___features_denseblock3_denselayer20_conv1 (l__self___features_denseblock3_denselayer20_relu1,) {}
call_module l__self___features_denseblock3_denselayer20_norm2 L__self___features_denseblock3_denselayer20_norm2 (bottleneck_output_74,) {}
call_module l__self___features_denseblock3_denselayer20_relu2 L__self___features_denseblock3_denselayer20_relu2 (l__self___features_denseblock3_denselayer20_norm2,) {}
call_module new_features_74 L__self___features_denseblock3_denselayer20_conv2 (l__self___features_denseblock3_denselayer20_relu2,) {}
call_function concated_features_38 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74], 1) {}
call_module l__self___features_denseblock3_denselayer21_norm1 L__self___features_denseblock3_denselayer21_norm1 (concated_features_38,) {}
call_module l__self___features_denseblock3_denselayer21_relu1 L__self___features_denseblock3_denselayer21_relu1 (l__self___features_denseblock3_denselayer21_norm1,) {}
call_module bottleneck_output_76 L__self___features_denseblock3_denselayer21_conv1 (l__self___features_denseblock3_denselayer21_relu1,) {}
call_module l__self___features_denseblock3_denselayer21_norm2 L__self___features_denseblock3_denselayer21_norm2 (bottleneck_output_76,) {}
call_module l__self___features_denseblock3_denselayer21_relu2 L__self___features_denseblock3_denselayer21_relu2 (l__self___features_denseblock3_denselayer21_norm2,) {}
call_module new_features_76 L__self___features_denseblock3_denselayer21_conv2 (l__self___features_denseblock3_denselayer21_relu2,) {}
call_function concated_features_39 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76], 1) {}
call_module l__self___features_denseblock3_denselayer22_norm1 L__self___features_denseblock3_denselayer22_norm1 (concated_features_39,) {}
call_module l__self___features_denseblock3_denselayer22_relu1 L__self___features_denseblock3_denselayer22_relu1 (l__self___features_denseblock3_denselayer22_norm1,) {}
call_module bottleneck_output_78 L__self___features_denseblock3_denselayer22_conv1 (l__self___features_denseblock3_denselayer22_relu1,) {}
call_module l__self___features_denseblock3_denselayer22_norm2 L__self___features_denseblock3_denselayer22_norm2 (bottleneck_output_78,) {}
call_module l__self___features_denseblock3_denselayer22_relu2 L__self___features_denseblock3_denselayer22_relu2 (l__self___features_denseblock3_denselayer22_norm2,) {}
call_module new_features_78 L__self___features_denseblock3_denselayer22_conv2 (l__self___features_denseblock3_denselayer22_relu2,) {}
call_function concated_features_40 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78], 1) {}
call_module l__self___features_denseblock3_denselayer23_norm1 L__self___features_denseblock3_denselayer23_norm1 (concated_features_40,) {}
call_module l__self___features_denseblock3_denselayer23_relu1 L__self___features_denseblock3_denselayer23_relu1 (l__self___features_denseblock3_denselayer23_norm1,) {}
call_module bottleneck_output_80 L__self___features_denseblock3_denselayer23_conv1 (l__self___features_denseblock3_denselayer23_relu1,) {}
call_module l__self___features_denseblock3_denselayer23_norm2 L__self___features_denseblock3_denselayer23_norm2 (bottleneck_output_80,) {}
call_module l__self___features_denseblock3_denselayer23_relu2 L__self___features_denseblock3_denselayer23_relu2 (l__self___features_denseblock3_denselayer23_norm2,) {}
call_module new_features_80 L__self___features_denseblock3_denselayer23_conv2 (l__self___features_denseblock3_denselayer23_relu2,) {}
call_function concated_features_41 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80], 1) {}
call_module l__self___features_denseblock3_denselayer24_norm1 L__self___features_denseblock3_denselayer24_norm1 (concated_features_41,) {}
call_module l__self___features_denseblock3_denselayer24_relu1 L__self___features_denseblock3_denselayer24_relu1 (l__self___features_denseblock3_denselayer24_norm1,) {}
call_module bottleneck_output_82 L__self___features_denseblock3_denselayer24_conv1 (l__self___features_denseblock3_denselayer24_relu1,) {}
call_module l__self___features_denseblock3_denselayer24_norm2 L__self___features_denseblock3_denselayer24_norm2 (bottleneck_output_82,) {}
call_module l__self___features_denseblock3_denselayer24_relu2 L__self___features_denseblock3_denselayer24_relu2 (l__self___features_denseblock3_denselayer24_norm2,) {}
call_module new_features_82 L__self___features_denseblock3_denselayer24_conv2 (l__self___features_denseblock3_denselayer24_relu2,) {}
call_function cat_44 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80, new_features_82], 1) {}
call_module l__self___features_transition3_norm L__self___features_transition3_norm (cat_44,) {}
call_module l__self___features_transition3_relu L__self___features_transition3_relu (l__self___features_transition3_norm,) {}
call_module l__self___features_transition3_conv L__self___features_transition3_conv (l__self___features_transition3_relu,) {}
call_module l__self___features_transition3_pool L__self___features_transition3_pool (l__self___features_transition3_conv,) {}
call_function concated_features_42 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool], 1) {}
call_module l__self___features_denseblock4_denselayer1_norm1 L__self___features_denseblock4_denselayer1_norm1 (concated_features_42,) {}
call_module l__self___features_denseblock4_denselayer1_relu1 L__self___features_denseblock4_denselayer1_relu1 (l__self___features_denseblock4_denselayer1_norm1,) {}
call_module bottleneck_output_84 L__self___features_denseblock4_denselayer1_conv1 (l__self___features_denseblock4_denselayer1_relu1,) {}
call_module l__self___features_denseblock4_denselayer1_norm2 L__self___features_denseblock4_denselayer1_norm2 (bottleneck_output_84,) {}
call_module l__self___features_denseblock4_denselayer1_relu2 L__self___features_denseblock4_denselayer1_relu2 (l__self___features_denseblock4_denselayer1_norm2,) {}
call_module new_features_84 L__self___features_denseblock4_denselayer1_conv2 (l__self___features_denseblock4_denselayer1_relu2,) {}
call_function concated_features_43 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84], 1) {}
call_module l__self___features_denseblock4_denselayer2_norm1 L__self___features_denseblock4_denselayer2_norm1 (concated_features_43,) {}
call_module l__self___features_denseblock4_denselayer2_relu1 L__self___features_denseblock4_denselayer2_relu1 (l__self___features_denseblock4_denselayer2_norm1,) {}
call_module bottleneck_output_86 L__self___features_denseblock4_denselayer2_conv1 (l__self___features_denseblock4_denselayer2_relu1,) {}
call_module l__self___features_denseblock4_denselayer2_norm2 L__self___features_denseblock4_denselayer2_norm2 (bottleneck_output_86,) {}
call_module l__self___features_denseblock4_denselayer2_relu2 L__self___features_denseblock4_denselayer2_relu2 (l__self___features_denseblock4_denselayer2_norm2,) {}
call_module new_features_86 L__self___features_denseblock4_denselayer2_conv2 (l__self___features_denseblock4_denselayer2_relu2,) {}
call_function concated_features_44 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86], 1) {}
call_module l__self___features_denseblock4_denselayer3_norm1 L__self___features_denseblock4_denselayer3_norm1 (concated_features_44,) {}
call_module l__self___features_denseblock4_denselayer3_relu1 L__self___features_denseblock4_denselayer3_relu1 (l__self___features_denseblock4_denselayer3_norm1,) {}
call_module bottleneck_output_88 L__self___features_denseblock4_denselayer3_conv1 (l__self___features_denseblock4_denselayer3_relu1,) {}
call_module l__self___features_denseblock4_denselayer3_norm2 L__self___features_denseblock4_denselayer3_norm2 (bottleneck_output_88,) {}
call_module l__self___features_denseblock4_denselayer3_relu2 L__self___features_denseblock4_denselayer3_relu2 (l__self___features_denseblock4_denselayer3_norm2,) {}
call_module new_features_88 L__self___features_denseblock4_denselayer3_conv2 (l__self___features_denseblock4_denselayer3_relu2,) {}
call_function concated_features_45 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88], 1) {}
call_module l__self___features_denseblock4_denselayer4_norm1 L__self___features_denseblock4_denselayer4_norm1 (concated_features_45,) {}
call_module l__self___features_denseblock4_denselayer4_relu1 L__self___features_denseblock4_denselayer4_relu1 (l__self___features_denseblock4_denselayer4_norm1,) {}
call_module bottleneck_output_90 L__self___features_denseblock4_denselayer4_conv1 (l__self___features_denseblock4_denselayer4_relu1,) {}
call_module l__self___features_denseblock4_denselayer4_norm2 L__self___features_denseblock4_denselayer4_norm2 (bottleneck_output_90,) {}
call_module l__self___features_denseblock4_denselayer4_relu2 L__self___features_denseblock4_denselayer4_relu2 (l__self___features_denseblock4_denselayer4_norm2,) {}
call_module new_features_90 L__self___features_denseblock4_denselayer4_conv2 (l__self___features_denseblock4_denselayer4_relu2,) {}
call_function concated_features_46 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90], 1) {}
call_module l__self___features_denseblock4_denselayer5_norm1 L__self___features_denseblock4_denselayer5_norm1 (concated_features_46,) {}
call_module l__self___features_denseblock4_denselayer5_relu1 L__self___features_denseblock4_denselayer5_relu1 (l__self___features_denseblock4_denselayer5_norm1,) {}
call_module bottleneck_output_92 L__self___features_denseblock4_denselayer5_conv1 (l__self___features_denseblock4_denselayer5_relu1,) {}
call_module l__self___features_denseblock4_denselayer5_norm2 L__self___features_denseblock4_denselayer5_norm2 (bottleneck_output_92,) {}
call_module l__self___features_denseblock4_denselayer5_relu2 L__self___features_denseblock4_denselayer5_relu2 (l__self___features_denseblock4_denselayer5_norm2,) {}
call_module new_features_92 L__self___features_denseblock4_denselayer5_conv2 (l__self___features_denseblock4_denselayer5_relu2,) {}
call_function concated_features_47 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92], 1) {}
call_module l__self___features_denseblock4_denselayer6_norm1 L__self___features_denseblock4_denselayer6_norm1 (concated_features_47,) {}
call_module l__self___features_denseblock4_denselayer6_relu1 L__self___features_denseblock4_denselayer6_relu1 (l__self___features_denseblock4_denselayer6_norm1,) {}
call_module bottleneck_output_94 L__self___features_denseblock4_denselayer6_conv1 (l__self___features_denseblock4_denselayer6_relu1,) {}
call_module l__self___features_denseblock4_denselayer6_norm2 L__self___features_denseblock4_denselayer6_norm2 (bottleneck_output_94,) {}
call_module l__self___features_denseblock4_denselayer6_relu2 L__self___features_denseblock4_denselayer6_relu2 (l__self___features_denseblock4_denselayer6_norm2,) {}
call_module new_features_94 L__self___features_denseblock4_denselayer6_conv2 (l__self___features_denseblock4_denselayer6_relu2,) {}
call_function concated_features_48 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94], 1) {}
call_module l__self___features_denseblock4_denselayer7_norm1 L__self___features_denseblock4_denselayer7_norm1 (concated_features_48,) {}
call_module l__self___features_denseblock4_denselayer7_relu1 L__self___features_denseblock4_denselayer7_relu1 (l__self___features_denseblock4_denselayer7_norm1,) {}
call_module bottleneck_output_96 L__self___features_denseblock4_denselayer7_conv1 (l__self___features_denseblock4_denselayer7_relu1,) {}
call_module l__self___features_denseblock4_denselayer7_norm2 L__self___features_denseblock4_denselayer7_norm2 (bottleneck_output_96,) {}
call_module l__self___features_denseblock4_denselayer7_relu2 L__self___features_denseblock4_denselayer7_relu2 (l__self___features_denseblock4_denselayer7_norm2,) {}
call_module new_features_96 L__self___features_denseblock4_denselayer7_conv2 (l__self___features_denseblock4_denselayer7_relu2,) {}
call_function concated_features_49 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96], 1) {}
call_module l__self___features_denseblock4_denselayer8_norm1 L__self___features_denseblock4_denselayer8_norm1 (concated_features_49,) {}
call_module l__self___features_denseblock4_denselayer8_relu1 L__self___features_denseblock4_denselayer8_relu1 (l__self___features_denseblock4_denselayer8_norm1,) {}
call_module bottleneck_output_98 L__self___features_denseblock4_denselayer8_conv1 (l__self___features_denseblock4_denselayer8_relu1,) {}
call_module l__self___features_denseblock4_denselayer8_norm2 L__self___features_denseblock4_denselayer8_norm2 (bottleneck_output_98,) {}
call_module l__self___features_denseblock4_denselayer8_relu2 L__self___features_denseblock4_denselayer8_relu2 (l__self___features_denseblock4_denselayer8_norm2,) {}
call_module new_features_98 L__self___features_denseblock4_denselayer8_conv2 (l__self___features_denseblock4_denselayer8_relu2,) {}
call_function concated_features_50 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98], 1) {}
call_module l__self___features_denseblock4_denselayer9_norm1 L__self___features_denseblock4_denselayer9_norm1 (concated_features_50,) {}
call_module l__self___features_denseblock4_denselayer9_relu1 L__self___features_denseblock4_denselayer9_relu1 (l__self___features_denseblock4_denselayer9_norm1,) {}
call_module bottleneck_output_100 L__self___features_denseblock4_denselayer9_conv1 (l__self___features_denseblock4_denselayer9_relu1,) {}
call_module l__self___features_denseblock4_denselayer9_norm2 L__self___features_denseblock4_denselayer9_norm2 (bottleneck_output_100,) {}
call_module l__self___features_denseblock4_denselayer9_relu2 L__self___features_denseblock4_denselayer9_relu2 (l__self___features_denseblock4_denselayer9_norm2,) {}
call_module new_features_100 L__self___features_denseblock4_denselayer9_conv2 (l__self___features_denseblock4_denselayer9_relu2,) {}
call_function concated_features_51 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100], 1) {}
call_module l__self___features_denseblock4_denselayer10_norm1 L__self___features_denseblock4_denselayer10_norm1 (concated_features_51,) {}
call_module l__self___features_denseblock4_denselayer10_relu1 L__self___features_denseblock4_denselayer10_relu1 (l__self___features_denseblock4_denselayer10_norm1,) {}
call_module bottleneck_output_102 L__self___features_denseblock4_denselayer10_conv1 (l__self___features_denseblock4_denselayer10_relu1,) {}
call_module l__self___features_denseblock4_denselayer10_norm2 L__self___features_denseblock4_denselayer10_norm2 (bottleneck_output_102,) {}
call_module l__self___features_denseblock4_denselayer10_relu2 L__self___features_denseblock4_denselayer10_relu2 (l__self___features_denseblock4_denselayer10_norm2,) {}
call_module new_features_102 L__self___features_denseblock4_denselayer10_conv2 (l__self___features_denseblock4_denselayer10_relu2,) {}
call_function concated_features_52 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102], 1) {}
call_module l__self___features_denseblock4_denselayer11_norm1 L__self___features_denseblock4_denselayer11_norm1 (concated_features_52,) {}
call_module l__self___features_denseblock4_denselayer11_relu1 L__self___features_denseblock4_denselayer11_relu1 (l__self___features_denseblock4_denselayer11_norm1,) {}
call_module bottleneck_output_104 L__self___features_denseblock4_denselayer11_conv1 (l__self___features_denseblock4_denselayer11_relu1,) {}
call_module l__self___features_denseblock4_denselayer11_norm2 L__self___features_denseblock4_denselayer11_norm2 (bottleneck_output_104,) {}
call_module l__self___features_denseblock4_denselayer11_relu2 L__self___features_denseblock4_denselayer11_relu2 (l__self___features_denseblock4_denselayer11_norm2,) {}
call_module new_features_104 L__self___features_denseblock4_denselayer11_conv2 (l__self___features_denseblock4_denselayer11_relu2,) {}
call_function concated_features_53 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104], 1) {}
call_module l__self___features_denseblock4_denselayer12_norm1 L__self___features_denseblock4_denselayer12_norm1 (concated_features_53,) {}
call_module l__self___features_denseblock4_denselayer12_relu1 L__self___features_denseblock4_denselayer12_relu1 (l__self___features_denseblock4_denselayer12_norm1,) {}
call_module bottleneck_output_106 L__self___features_denseblock4_denselayer12_conv1 (l__self___features_denseblock4_denselayer12_relu1,) {}
call_module l__self___features_denseblock4_denselayer12_norm2 L__self___features_denseblock4_denselayer12_norm2 (bottleneck_output_106,) {}
call_module l__self___features_denseblock4_denselayer12_relu2 L__self___features_denseblock4_denselayer12_relu2 (l__self___features_denseblock4_denselayer12_norm2,) {}
call_module new_features_106 L__self___features_denseblock4_denselayer12_conv2 (l__self___features_denseblock4_denselayer12_relu2,) {}
call_function concated_features_54 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106], 1) {}
call_module l__self___features_denseblock4_denselayer13_norm1 L__self___features_denseblock4_denselayer13_norm1 (concated_features_54,) {}
call_module l__self___features_denseblock4_denselayer13_relu1 L__self___features_denseblock4_denselayer13_relu1 (l__self___features_denseblock4_denselayer13_norm1,) {}
call_module bottleneck_output_108 L__self___features_denseblock4_denselayer13_conv1 (l__self___features_denseblock4_denselayer13_relu1,) {}
call_module l__self___features_denseblock4_denselayer13_norm2 L__self___features_denseblock4_denselayer13_norm2 (bottleneck_output_108,) {}
call_module l__self___features_denseblock4_denselayer13_relu2 L__self___features_denseblock4_denselayer13_relu2 (l__self___features_denseblock4_denselayer13_norm2,) {}
call_module new_features_108 L__self___features_denseblock4_denselayer13_conv2 (l__self___features_denseblock4_denselayer13_relu2,) {}
call_function concated_features_55 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108], 1) {}
call_module l__self___features_denseblock4_denselayer14_norm1 L__self___features_denseblock4_denselayer14_norm1 (concated_features_55,) {}
call_module l__self___features_denseblock4_denselayer14_relu1 L__self___features_denseblock4_denselayer14_relu1 (l__self___features_denseblock4_denselayer14_norm1,) {}
call_module bottleneck_output_110 L__self___features_denseblock4_denselayer14_conv1 (l__self___features_denseblock4_denselayer14_relu1,) {}
call_module l__self___features_denseblock4_denselayer14_norm2 L__self___features_denseblock4_denselayer14_norm2 (bottleneck_output_110,) {}
call_module l__self___features_denseblock4_denselayer14_relu2 L__self___features_denseblock4_denselayer14_relu2 (l__self___features_denseblock4_denselayer14_norm2,) {}
call_module new_features_110 L__self___features_denseblock4_denselayer14_conv2 (l__self___features_denseblock4_denselayer14_relu2,) {}
call_function concated_features_56 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110], 1) {}
call_module l__self___features_denseblock4_denselayer15_norm1 L__self___features_denseblock4_denselayer15_norm1 (concated_features_56,) {}
call_module l__self___features_denseblock4_denselayer15_relu1 L__self___features_denseblock4_denselayer15_relu1 (l__self___features_denseblock4_denselayer15_norm1,) {}
call_module bottleneck_output_112 L__self___features_denseblock4_denselayer15_conv1 (l__self___features_denseblock4_denselayer15_relu1,) {}
call_module l__self___features_denseblock4_denselayer15_norm2 L__self___features_denseblock4_denselayer15_norm2 (bottleneck_output_112,) {}
call_module l__self___features_denseblock4_denselayer15_relu2 L__self___features_denseblock4_denselayer15_relu2 (l__self___features_denseblock4_denselayer15_norm2,) {}
call_module new_features_112 L__self___features_denseblock4_denselayer15_conv2 (l__self___features_denseblock4_denselayer15_relu2,) {}
call_function concated_features_57 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112], 1) {}
call_module l__self___features_denseblock4_denselayer16_norm1 L__self___features_denseblock4_denselayer16_norm1 (concated_features_57,) {}
call_module l__self___features_denseblock4_denselayer16_relu1 L__self___features_denseblock4_denselayer16_relu1 (l__self___features_denseblock4_denselayer16_norm1,) {}
call_module bottleneck_output_114 L__self___features_denseblock4_denselayer16_conv1 (l__self___features_denseblock4_denselayer16_relu1,) {}
call_module l__self___features_denseblock4_denselayer16_norm2 L__self___features_denseblock4_denselayer16_norm2 (bottleneck_output_114,) {}
call_module l__self___features_denseblock4_denselayer16_relu2 L__self___features_denseblock4_denselayer16_relu2 (l__self___features_denseblock4_denselayer16_norm2,) {}
call_module new_features_114 L__self___features_denseblock4_denselayer16_conv2 (l__self___features_denseblock4_denselayer16_relu2,) {}
call_function cat_61 <built-in method cat of type object at 0x7f84532e6760> ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112, new_features_114], 1) {}
call_module features L__self___features_norm5 (cat_61,) {}
call_function out <function relu at 0x7f837b431510> (features,) {'inplace': True}
call_function out_1 <function adaptive_avg_pool2d at 0x7f837b431000> (out, (1, 1)) {}
call_function out_2 <built-in method flatten of type object at 0x7f84532e6760> (out_1, 1) {}
call_module out_3 L__self___classifier (out_2,) {}
output output output ((out_3,),) {}
tensor([[ 0.0614, -0.4023, -0.2792, ..., -0.5549, 0.0976, -0.0634],
[-0.2032, -0.2706, -0.0935, ..., -0.4815, 0.0758, -0.1038],
[ 0.0637, -0.3492, -0.1492, ..., -0.4841, 0.1776, -0.0723],
...,
[-0.1050, -0.3393, 0.0092, ..., -0.4862, 0.0555, -0.1058],
[ 0.0018, -0.2431, -0.1656, ..., -0.5072, 0.0977, -0.1387],
[ 0.1192, -0.3563, -0.1147, ..., -0.4839, 0.1770, -0.0659]],
device='cuda:0', grad_fn=<AddmmBackward0>)
Using our custom backend, we can now see how TorchDynamo is able to handle
data-dependent control flow. Consider the function below, where the line
if b.sum() < 0 is the source of data-dependent control flow.
def bar(a, b):
x = a / (torch.abs(a) + 1)
if b.sum() < 0:
b = b * -1
return x * b
opt_bar = torch.compile(bar, backend=custom_backend)
inp1 = torch.randn(10)
inp2 = torch.randn(10)
opt_bar(inp1, inp2)
opt_bar(inp1, -inp2)
custom backend called with FX graph:
opcode name target args kwargs
------------- ------ ------------------------------------------------------ ----------- --------
placeholder l_a_ L_a_ () {}
placeholder l_b_ L_b_ () {}
call_function abs_1 <built-in method abs of type object at 0x7f84532e6760> (l_a_,) {}
call_function add <built-in function add> (abs_1, 1) {}
call_function x <built-in function truediv> (l_a_, add) {}
call_method sum_1 sum (l_b_,) {}
call_function lt <built-in function lt> (sum_1, 0) {}
output output output ((x, lt),) {}
custom backend called with FX graph:
opcode name target args kwargs
------------- ------ ----------------------- ------------ --------
placeholder l_x_ L_x_ () {}
placeholder l_b_ L_b_ () {}
call_function mul <built-in function mul> (l_x_, l_b_) {}
output output output ((mul,),) {}
custom backend called with FX graph:
opcode name target args kwargs
------------- ------ ----------------------- ----------- --------
placeholder l_b_ L_b_ () {}
placeholder l_x_ L_x_ () {}
call_function b <built-in function mul> (l_b_, -1) {}
call_function mul_1 <built-in function mul> (l_x_, b) {}
output output output ((mul_1,),) {}
tensor([-0.0176, 1.0753, 0.0282, 0.0756, -0.0176, 0.0633, -0.9161, 0.1333,
-0.1971, -0.3406])
The output reveals that TorchDynamo extracted 3 different FX graphs corresponding the following code (order may differ from the output above):
x = a / (torch.abs(a) + 1)b = b * -1; return x * breturn x * b
When TorchDynamo encounters unsupported Python features, such as data-dependent control flow, it breaks the computation graph, lets the default Python interpreter handle the unsupported code, then resumes capturing the graph.
Let’s investigate by example how TorchDynamo would step through bar.
If b.sum() < 0, then TorchDynamo would run graph 1, let
Python determine the result of the conditional, then run
graph 2. On the other hand, if not b.sum() < 0, then TorchDynamo
would run graph 1, let Python determine the result of the conditional, then
run graph 3.
This highlights a major difference between TorchDynamo and previous PyTorch compiler solutions. When encountering unsupported Python features, previous solutions either raise an error or silently fail. TorchDynamo, on the other hand, will break the computation graph.
We can see where TorchDynamo breaks the graph by using torch._dynamo.explain:
# Reset since we are using a different backend.
torch._dynamo.reset()
explain_output = torch._dynamo.explain(bar)(torch.randn(10), torch.randn(10))
print(explain_output)
Graph Count: 2
Graph Break Count: 1
Op Count: 6
Break Reasons:
Break Reason 1:
Reason: generic_jump TensorVariable()
User Stack:
<FrameSummary file /var/lib/workspace/intermediate_source/torch_compile_tutorial.py, line 434 in bar>
Ops per Graph:
Ops 1:
<built-in method abs of type object at 0x7f84532e6760>
<built-in function add>
<built-in function truediv>
<built-in function lt>
Ops 2:
<built-in function mul>
<built-in function mul>
Out Guards:
Guard 1:
Name: ''
Source: global
Create Function: TORCH_FUNCTION_STATE
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 2:
Name: "G['torch']"
Source: global
Create Function: FUNCTION_MATCH
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 3:
Name: ''
Source: global
Create Function: DEFAULT_DEVICE
Guard Types: ['DEFAULT_DEVICE']
Code List: ['utils_device.CURRENT_DEVICE == None']
Object Weakref: None
Guarded Class Weakref: None
Guard 4:
Name: "G['torch'].abs"
Source: global
Create Function: FUNCTION_MATCH
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 5:
Name: ''
Source: shape_env
Create Function: SHAPE_ENV
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 6:
Name: "L['b']"
Source: local
Create Function: TENSOR_MATCH
Guard Types: ['TENSOR_MATCH']
Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
Object Weakref: <weakref at 0x7f82b2cd12b0; dead>
Guarded Class Weakref: <weakref at 0x7f837bac7c90; to 'torch._C._TensorMeta' at 0x58585a0 (Tensor)>
Guard 7:
Name: ''
Source: global
Create Function: BACKEND_MATCH
Guard Types: ['BACKEND_MATCH']
Code List: ['___check_current_backend(140197382899168)']
Object Weakref: None
Guarded Class Weakref: None
Guard 8:
Name: ''
Source: global
Create Function: GRAD_MODE
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 9:
Name: ''
Source: global
Create Function: DETERMINISTIC_ALGORITHMS
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 10:
Name: "L['a']"
Source: local
Create Function: TENSOR_MATCH
Guard Types: ['TENSOR_MATCH']
Code List: ["hasattr(L['a'], '_dynamo_dynamic_indices') == False"]
Object Weakref: <weakref at 0x7f82eb48d760; dead>
Guarded Class Weakref: <weakref at 0x7f837bac7c90; to 'torch._C._TensorMeta' at 0x58585a0 (Tensor)>
Guard 11:
Name: ''
Source: global
Create Function: TORCH_FUNCTION_STATE
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 12:
Name: ''
Source: global
Create Function: DEFAULT_DEVICE
Guard Types: ['DEFAULT_DEVICE']
Code List: ['utils_device.CURRENT_DEVICE == None']
Object Weakref: None
Guarded Class Weakref: None
Guard 13:
Name: ''
Source: shape_env
Create Function: SHAPE_ENV
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 14:
Name: ''
Source: global
Create Function: BACKEND_MATCH
Guard Types: ['BACKEND_MATCH']
Code List: ['___check_current_backend(140197382899168)']
Object Weakref: None
Guarded Class Weakref: None
Guard 15:
Name: "L['b']"
Source: local
Create Function: TENSOR_MATCH
Guard Types: ['TENSOR_MATCH']
Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
Object Weakref: <weakref at 0x7f82b2cd12b0; dead>
Guarded Class Weakref: <weakref at 0x7f837bac7c90; to 'torch._C._TensorMeta' at 0x58585a0 (Tensor)>
Guard 16:
Name: ''
Source: global
Create Function: GRAD_MODE
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Guard 17:
Name: "L['x']"
Source: local
Create Function: TENSOR_MATCH
Guard Types: ['TENSOR_MATCH']
Code List: ["hasattr(L['x'], '_dynamo_dynamic_indices') == False"]
Object Weakref: <weakref at 0x7f82b2f727a0; dead>
Guarded Class Weakref: <weakref at 0x7f837bac7c90; to 'torch._C._TensorMeta' at 0x58585a0 (Tensor)>
Guard 18:
Name: ''
Source: global
Create Function: DETERMINISTIC_ALGORITHMS
Guard Types: None
Code List: None
Object Weakref: None
Guarded Class Weakref: None
Compile Times: TorchDynamo compilation metrics:
Function Runtimes (s)
------------------------------- --------------
_compile.<locals>.compile_inner 0.0110, 0.0066
OutputGraph.call_user_compiler 0.0001, 0.0000
In order to maximize speedup, graph breaks should be limited.
We can force TorchDynamo to raise an error upon the first graph
break encountered by using fullgraph=True:
opt_bar = torch.compile(bar, fullgraph=True)
try:
opt_bar(torch.randn(10), torch.randn(10))
except:
tb.print_exc()
Traceback (most recent call last):
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 482, in <module>
opt_bar(torch.randn(10), torch.randn(10))
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
return callback(frame, cache_entry, hooks, frame_state, skip=1)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
return _compile(
File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
return func(*args, **kwds)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
r = func(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
out_code = transform_code_object(code, transform)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
transformations(instructions, code_options)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
return fn(*args, **kwargs)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
tracer.run()
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
super().run()
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
and self.step()
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
getattr(self, inst.opname)(inst)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 464, in inner
raise exc.UserError(
torch._dynamo.exc.UserError: Dynamic control flow is not supported at the moment. Please use functorch.experimental.control_flow.cond to explicitly capture the control flow. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#cond-operands
from user code:
File "/var/lib/workspace/intermediate_source/torch_compile_tutorial.py", line 434, in bar
if b.sum() < 0:
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
And below, we demonstrate that TorchDynamo does not break the graph on the model we used above for demonstrating speedups.
opt_model = torch.compile(init_model(), fullgraph=True)
print(opt_model(generate_data(16)[0]))
tensor([[ 0.1344, 0.1976, -0.2056, ..., 0.1861, -0.1632, -0.1642],
[ 0.2680, 0.2349, -0.1903, ..., 0.2170, -0.0055, 0.0309],
[ 0.0164, 0.2180, -0.1115, ..., 0.1709, -0.1681, -0.0635],
...,
[ 0.0807, 0.2680, -0.1889, ..., 0.0381, -0.2073, -0.1444],
[-0.0209, 0.0858, -0.2457, ..., 0.1862, -0.1280, -0.0282],
[-0.0386, 0.0730, -0.1961, ..., 0.0864, -0.2199, -0.1485]],
device='cuda:0', grad_fn=<CompiledFunctionBackward>)
We can use torch.export (from PyTorch 2.1+) to extract a single, exportable
FX graph from the input PyTorch program. The exported graph is intended to be
run on different (i.e. Python-less) environments. One important restriction
is that the torch.export does not support graph breaks. Please check
this tutorial
for more details on torch.export.
Conclusion¶
In this tutorial, we introduced torch.compile by covering
basic usage, demonstrating speedups over eager mode, comparing to previous
PyTorch compiler solutions, and briefly investigating TorchDynamo and its interactions
with FX graphs. We hope that you will give torch.compile a try!
Total running time of the script: ( 6 minutes 10.264 seconds)
