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Torchscript to CoreML conversion skips for loops
🐞Description
When torchscript code contains for loops and is converted to coreML, the for loops are skipped and the result before the for loop is returned.
To Reproduce
import numpy as np
import torch
import torch.nn as nn
import coremltools as ct
@torch.jit.script
def experiment(val: torch.Tensor):
val = int(val.item())
result = torch.zeros(val)
for i in range(val):
result[i] = i
return result
class Experiment(nn.Module):
def __init__(self):
super(Experiment, self).__init__()
def forward(self, x):
return experiment(x)
exp = Experiment()
# Use a tensor as input, not an integer
input_tensor = torch.tensor(100)
output = exp(input_tensor)
print(output)
traced_exp = torch.jit.script(exp, input_tensor)
traced_exp.eval()
output_traced = traced_exp(input_tensor)
print(output)
# Specify the input type as ct.TensorType(name="x", shape=(1,))
coreml_exp = ct.convert(
traced_exp,
source="pytorch",
inputs=[ct.TensorType(name="x", shape=(1,))],
convert_to="mlprogram"
)
# Create an input dictionary with the necessary input data
input_data = {
'x': np.array([100.0]),
}
# Make a prediction using the model
coreml_output = coreml_exp.predict(input_data)
print(coreml_output)
- Output
tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,
14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.,
56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83.,
84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97.,
98., 99.])
tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,
14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.,
56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83.,
84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97.,
98., 99.])
Converting PyTorch Frontend ==> MIL Ops: 0%| | 0/9 [00:00<?, ? ops/s]
Converting PyTorch Frontend ==> MIL Ops: 78%|███████▊ | 7/9 [00:00<00:00, 3895.47 ops/s]
Running MIL frontend_pytorch pipeline: 100%|██████████| 5/5 [00:00<00:00, 23275.83 passes/s]
Running MIL default pipeline: 0%| | 0/66 [00:00<?, ? passes/s]/Users/abhiroop/Developer/aikynetix/full_model_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/defs/preprocess.py:267: UserWarning: Output, 'result.1', of the source model, has been renamed to 'result_1' in the Core ML model.
warnings.warn(msg.format(var.name, new_name))
Running MIL default pipeline: 100%|██████████| 66/66 [00:00<00:00, 2826.18 passes/s]
Running MIL backend_mlprogram pipeline: 100%|██████████| 12/12 [00:00<00:00, 9124.66 passes/s]
{'result_1': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
dtype=float32)}
System environment (please complete the following information):
- coremltools version: 7.0
- OS (e.g. MacOS version or Linux type): MacOS 14.0
- Any other relevant version information (e.g. PyTorch or TensorFlow version): PyTorch 2.0.1 (same issue for other versions)
Additional Context
The for loop can be easily vectorized, but for simplicity, I wanted to show that the for loops get skipped in coreML conversion.
We only have "expiremental" support for PyTorch models which have not been created by torch.jit.trace.
The same issue persists if the PyTorch model Experiment is traced, but the function experiment is scripted. Does this still fall under experimental support?
@the-neural-networker - the following works for me:
import numpy as np
import torch
import torch.nn as nn
import coremltools as ct
def experiment(val: torch.Tensor):
val = int(val.item())
result = torch.zeros(val)
for i in range(val):
result[i] = i
return result
class Experiment(nn.Module):
def forward(self, x):
return experiment(x)
exp = Experiment().eval()
# Use a tensor as input, not an integer
input_tensor = torch.tensor(100)
traced_model = torch.jit.trace(exp, input_tensor)
y_t = traced_model(input_tensor)
# Specify the input type as ct.TensorType(name="x", shape=(1,))
coreml_exp = ct.convert(
traced_model,
source="pytorch",
inputs=[ct.TensorType(name="x", shape=(1,))],
convert_to="mlprogram"
)
# Create an input dictionary with the necessary input data
input_data = {
'x': np.array([100.0]),
}
# Make a prediction using the model
coreml_output = coreml_exp.predict(input_data)
coreml_output = list(coreml_output.values())[0]
assert all(coreml_output == y_t.numpy())
But won't this fail for other inputs (not 100)? Because technically the experiment function is traced which has a for loop and the function's for loop depends on val, it is not static.
This example will give the wrong prediction when the input is not 100. However that is a result of the tracing not the conversion to Core ML. The PyTorch traced model gives the wrong the results. The model is converted to Core ML correctly.
If you want to do something like this in Core ML, I think you'll need to write your own mlprogram. Take a look at our set of MIL ops:
https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html
Specifically, I think you'll want to look at the ones under:
coremltools.converters.mil.mil.ops.defs.iOS15.control_flow.*