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Cherry backprop for conv2d (#281)

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* quick math: 0 + x = x.

* gradient w.r.t. x using cherry for conv

* gradient w.r.t. w for conv on cherry but doing vector dot products

* small optimization

* [cherry] optimize conv backpass for large channel count

* get rid of numpy einsum
This commit is contained in:
Evan Mays
2021-10-30 19:12:19 -04:00
committed by GitHub
parent 3d646272d6
commit 285621aeda
2 changed files with 35 additions and 8 deletions
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12
test/test_ops.py
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@@ -164,6 +164,16 @@ class TestOps(unittest.TestCase):
helper_test_op([(bs,cin,11,28), (6,cin//groups,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_large_input_conv2d(self):
bs = 4
cin = 16
groups = 1
H = 5
W = 2
helper_test_op([(bs,cin,64,64), (6,cin//groups,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_grouped_conv2d(self):
groups = 2
@@ -179,7 +189,7 @@ class TestOps(unittest.TestCase):
H,W = 3,3
helper_test_op([(bs,cin,11,28), (groups*cout,cin//groups,H,W)],
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups).relu(),
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5, forward_only=True)
lambda x,w: Tensor.conv2d(x,w,groups=groups).relu(), atol=1e-4, grad_rtol=1e-5)
def test_strided_conv2d(self):
bs = 4