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new 6805d54 [PYTORCH]Reduce_ops support added (#5308)
6805d54 is described below
commit 6805d54370ea657a304c58d610e5371c4add4bdf
Author: Samuel <[email protected]>
AuthorDate: Mon Apr 13 15:20:10 2020 +0530
[PYTORCH]Reduce_ops support added (#5308)
* [PYTORCH]Reduce_ops support added
* Review comments updated
* typo bug in qnn test
---
python/tvm/relay/frontend/pytorch.py | 49 +++++++-
tests/python/frontend/pytorch/qnn_test.py | 2 +-
tests/python/frontend/pytorch/test_forward.py | 168 ++++++++++++++++++++++++++
3 files changed, 217 insertions(+), 2 deletions(-)
diff --git a/python/tvm/relay/frontend/pytorch.py
b/python/tvm/relay/frontend/pytorch.py
index 506f6ba..18868cf 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -934,7 +934,50 @@ def _dropout():
def _reduce(name):
def _impl(inputs, input_types):
data = inputs[0]
- return get_relay_op(name)(data)
+ axis = None
+ keepdims = False
+
+ if len(inputs) > 2: # default, torch have only data, axis=None,
keepdims=False
+ if isinstance(inputs[1], int):
+ axis = int(inputs[1])
+ else:
+ axis = list(_infer_shape(inputs[1]))
+ keepdims = bool(inputs[2])
+
+ return get_relay_op(name)(data, axis=axis, keepdims=keepdims)
+
+ return _impl
+
+def _std():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = list(_infer_shape(inputs[1]))
+ keepdims = bool(inputs[3])
+ unbiased = bool(inputs[2])
+
+ if unbiased:
+ msg = "Currently only supports standard-deviation calculated via
the biased "\
+ "estimator. Pytorch's Bessel's correction is not supported."
+ raise NotImplementedError(msg)
+
+ return _op.reduce.std(data, axis=axis, keepdims=keepdims)
+
+ return _impl
+
+def _variance():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = list(_infer_shape(inputs[1]))
+ keepdims = bool(inputs[3])
+ unbiased = bool(inputs[2])
+
+ if unbiased:
+ msg = "Currently only supports standard-deviation calculated via
the biased "\
+ "estimator. Pytorch's Bessel's correction is not supported."
+ raise NotImplementedError(msg)
+
+ return _op.reduce.variance(data, axis=axis, keepdims=keepdims)
+
return _impl
def _mean():
@@ -1381,6 +1424,10 @@ def _get_convert_map(prelude):
"aten::permute" : _transpose(prelude),
"aten::sum" : _reduce("sum"),
"aten::prod" : _reduce("prod"),
+ "aten::argmin" : _reduce("argmin"),
+ "aten::argmax" : _reduce("argmax"),
+ "aten::std" : _std(),
+ "aten::var" : _variance(),
"aten::sqrt" : _sqrt(),
'aten::floor' : _floor(),
"aten::detach" : _identity(),
diff --git a/tests/python/frontend/pytorch/qnn_test.py
b/tests/python/frontend/pytorch/qnn_test.py
index 82e3393..bf5fa98 100644
--- a/tests/python/frontend/pytorch/qnn_test.py
+++ b/tests/python/frontend/pytorch/qnn_test.py
@@ -396,7 +396,7 @@ def test_quantized_imagenet():
mean_abs_diff = np.mean(np.abs(tvm_result - pt_result))
num_identical = np.sum(tvm_result == pt_result)
pt_top3_labels = np.argsort(pt_result)[::-1][:3]
- tvm_top3_labels = np.argsort(pt_result)[::-1][:3]
+ tvm_top3_labels = np.argsort(tvm_result)[::-1][:3]
print("\nModel name: %s" % model_name)
print("PyTorch top3 label:", pt_top3_labels)
diff --git a/tests/python/frontend/pytorch/test_forward.py
b/tests/python/frontend/pytorch/test_forward.py
index 8e99285..91e14c6 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -1279,6 +1279,168 @@ def test_simple_rnn():
verify_script_model(RNNLoop().eval(), [(10, 10, 4)])
+def test_forward_reduce_sum():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class ReduceSum1(Module):
+ def forward(self, *args):
+ return args[0].sum(1)
+
+ class ReduceSum2(Module):
+ def forward(self, *args):
+ return args[0].sum(dim=1, keepdim=False)
+
+ class ReduceSum3(Module):
+ def forward(self, *args):
+ return args[0].sum(dim=2, keepdim=True)
+
+ class ReduceSum4(Module):
+ def forward(self, *args):
+ return args[0].sum(dim=(2,3), keepdim=True)
+
+ class ReduceSum5(Module):
+ def forward(self, *args):
+ return args[0].sum(dim=(2,3), keepdim=False)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(ReduceSum1().float().eval(), input_data=input_data)
+ verify_model(ReduceSum2().float().eval(), input_data=input_data)
+ verify_model(ReduceSum3().float().eval(), input_data=input_data)
+ verify_model(ReduceSum4().float().eval(), input_data=input_data)
+ verify_model(ReduceSum5().float().eval(), input_data=input_data)
+
+
+def test_forward_reduce_prod():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class ReduceProd1(Module):
+ def forward(self, *args):
+ return args[0].prod(1)
+
+ class ReduceProd2(Module):
+ def forward(self, *args):
+ return args[0].prod(dim=1, keepdim=False)
+
+ class ReduceProd3(Module):
+ def forward(self, *args):
+ return args[0].prod(dim=2, keepdim=True)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(ReduceProd1().float().eval(), input_data=input_data)
+ verify_model(ReduceProd2().float().eval(), input_data=input_data)
+ verify_model(ReduceProd3().float().eval(), input_data=input_data)
+
+
+def test_forward_argmin():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class ArgMin1(Module):
+ def forward(self, *args):
+ return args[0].argmin(1)
+
+ class ArgMin2(Module):
+ def forward(self, *args):
+ return args[0].argmin(dim=1, keepdim=False)
+
+ class ArgMin3(Module):
+ def forward(self, *args):
+ return args[0].argmin(dim=2, keepdim=True)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(ArgMin1().float().eval(), input_data=input_data)
+ verify_model(ArgMin2().float().eval(), input_data=input_data)
+ verify_model(ArgMin3().float().eval(), input_data=input_data)
+
+
+def test_forward_argmax():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class ArgMax1(Module):
+ def forward(self, *args):
+ return args[0].argmax(1)
+
+ class ArgMax2(Module):
+ def forward(self, *args):
+ return args[0].argmax(dim=1, keepdim=False)
+
+ class ArgMax3(Module):
+ def forward(self, *args):
+ return args[0].argmax(dim=2, keepdim=True)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(ArgMax1().float().eval(), input_data=input_data)
+ verify_model(ArgMax2().float().eval(), input_data=input_data)
+ verify_model(ArgMax3().float().eval(), input_data=input_data)
+
+
+def test_forward_std():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Std1(Module):
+ def forward(self, *args):
+ return args[0].std(1, unbiased=False)
+
+ class Std2(Module):
+ def forward(self, *args):
+ return args[0].std(dim=1, keepdim=False, unbiased=False)
+
+ class Std3(Module):
+ def forward(self, *args):
+ return args[0].std(dim=2, keepdim=True, unbiased=False)
+
+ class Std4(Module):
+ def forward(self, *args):
+ return args[0].std(dim=(2,3), keepdim=True, unbiased=False)
+
+ class Std5(Module):
+ def forward(self, *args):
+ return args[0].std(dim=(2,3), keepdim=False, unbiased=False)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Std1().float().eval(), input_data=input_data)
+ verify_model(Std2().float().eval(), input_data=input_data)
+ verify_model(Std3().float().eval(), input_data=input_data)
+ verify_model(Std4().float().eval(), input_data=input_data)
+ verify_model(Std5().float().eval(), input_data=input_data)
+
+
+def test_forward_variance():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Variance1(Module):
+ def forward(self, *args):
+ return args[0].var(1, unbiased=False)
+
+ class Variance2(Module):
+ def forward(self, *args):
+ return args[0].var(dim=1, keepdim=False, unbiased=False)
+
+ class Variance3(Module):
+ def forward(self, *args):
+ return args[0].var(dim=2, keepdim=True, unbiased=False)
+
+ class Variance4(Module):
+ def forward(self, *args):
+ return args[0].var(dim=(2,3), keepdim=True, unbiased=False)
+
+ class Variance5(Module):
+ def forward(self, *args):
+ return args[0].var(dim=(2,3), keepdim=False, unbiased=False)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Variance1().float().eval(), input_data=input_data)
+ verify_model(Variance2().float().eval(), input_data=input_data)
+ verify_model(Variance3().float().eval(), input_data=input_data)
+ verify_model(Variance4().float().eval(), input_data=input_data)
+ verify_model(Variance5().float().eval(), input_data=input_data)
+
+
if __name__ == "__main__":
# Single operator tests
test_forward_add()
@@ -1291,6 +1453,12 @@ if __name__ == "__main__":
test_forward_squeeze()
test_forward_unsqueeze()
test_forward_concatenate()
+ test_forward_reduce_sum()
+ test_forward_reduce_prod()
+ test_forward_argmin()
+ test_forward_argmax()
+ test_forward_std()
+ test_forward_variance()
test_forward_relu()
test_forward_prelu()
test_forward_leakyrelu()
