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tinygrad
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the big memory gradient didn't even need to be computed

This commit is contained in:
George Hotz 2022-06-16 11:41:29 -07:00
parent 2e58948f6a
commit ce15bf2bdb
2 changed files with 28 additions and 26 deletions
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1
test/test_train.py
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@ -32,7 +32,6 @@ class TestTrain(unittest.TestCase):
Y = np.zeros((BS), dtype=np.int32)
train_one_step(model,X,Y)
@unittest.skip("OOM in GPU test")
def test_vit(self):
model = ViT()
X = np.zeros((BS,3,224,224), dtype=np.float32)

53
tinygrad/mlops.py
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@ -174,30 +174,33 @@ class Conv2D(Function):
def backward(ctx, grad_output):
x, w, C = ctx.saved_tensors
#dx = ctx.processing_op(ProcessingOps.CONVT, grad_output, w, x.shape, C) if ctx.needs_input_grad[0] else None
xt = grad_output
if C.xs > 1 or C.ys > 1: # unstride
xt = ctx.movement_op(MovementOps.RESHAPE, xt, (grad_output.shape[0], grad_output.shape[1], grad_output.shape[2], 1, grad_output.shape[3], 1))
xt = ctx.movement_op(MovementOps.SLICE, xt, ((0,xt.shape[0]), (0,xt.shape[1]), (0,xt.shape[2]), (0,C.ys), (0,xt.shape[4]), (0,C.xs)))
xt = ctx.movement_op(MovementOps.RESHAPE, xt, (xt.shape[0], xt.shape[1], xt.shape[2]*C.ys, xt.shape[4]*C.xs))
wt = ctx.movement_op(MovementOps.RESHAPE, w, (C.groups, C.rcout, C.cin, C.H, C.W))
wt = ctx.movement_op(MovementOps.FLIP, wt, (3, 4))
wt = ctx.movement_op(MovementOps.PERMUTE, wt, (0, 2, 1, 3, 4))
wt = ctx.movement_op(MovementOps.RESHAPE, wt, (C.groups*C.cin, C.rcout, C.H, C.W))
Cdx = get_conv_args(xt.shape, wt.shape, dilation=(C.dy, C.dx), padding=((C.H-1)*C.dy-C.py,(C.W-1)*C.dx-C.px), groups=C.groups)
# TODO: this shape can be wrong. support asymmetric padding to remove the slice
dx = ctx.processing_op(ProcessingOps.CONV, xt, wt, (Cdx.bs, Cdx.cout, Cdx.oy, Cdx.ox), Cdx)
dx = ctx.movement_op(MovementOps.SLICE, dx, [(0,s) for s in x.shape])
dx, dw = None, None
if ctx.needs_input_grad[0]:
#dx = ctx.processing_op(ProcessingOps.CONVT, grad_output, w, x.shape, C) if ctx.needs_input_grad[0] else None
xt = grad_output
if C.xs > 1 or C.ys > 1: # unstride. note, this is really memory intensive for big strides.
xt = ctx.movement_op(MovementOps.RESHAPE, xt, (grad_output.shape[0], grad_output.shape[1], grad_output.shape[2], 1, grad_output.shape[3], 1))
xt = ctx.movement_op(MovementOps.SLICE, xt, ((0,xt.shape[0]), (0,xt.shape[1]), (0,xt.shape[2]), (0,C.ys), (0,xt.shape[4]), (0,C.xs)))
xt = ctx.movement_op(MovementOps.RESHAPE, xt, (xt.shape[0], xt.shape[1], xt.shape[2]*C.ys, xt.shape[4]*C.xs))
wt = ctx.movement_op(MovementOps.RESHAPE, w, (C.groups, C.rcout, C.cin, C.H, C.W))
wt = ctx.movement_op(MovementOps.FLIP, wt, (3, 4))
wt = ctx.movement_op(MovementOps.PERMUTE, wt, (0, 2, 1, 3, 4))
wt = ctx.movement_op(MovementOps.RESHAPE, wt, (C.groups*C.cin, C.rcout, C.H, C.W))
Cdx = get_conv_args(xt.shape, wt.shape, dilation=(C.dy, C.dx), padding=((C.H-1)*C.dy-C.py,(C.W-1)*C.dx-C.px), groups=C.groups)
# TODO: this shape can be wrong. support asymmetric padding to remove the slice
dx = ctx.processing_op(ProcessingOps.CONV, xt, wt, (Cdx.bs, Cdx.cout, Cdx.oy, Cdx.ox), Cdx)
dx = ctx.movement_op(MovementOps.SLICE, dx, [(0,s) for s in x.shape])
# compute derivative of weights using ProcessingOps.CONV
xdw = ctx.movement_op(MovementOps.RESHAPE, x, (C.bs, C.groups, C.cin, C.iy, C.ix))
xdw = ctx.movement_op(MovementOps.PERMUTE, xdw, (2,1,0,3,4))
xdw = ctx.movement_op(MovementOps.RESHAPE, xdw, (C.cin, C.groups*C.bs, C.iy, C.ix))
grad_output_dw = ctx.movement_op(MovementOps.PERMUTE, grad_output, (1,0,2,3))
grad_output_dw = ctx.movement_op(MovementOps.RESHAPE, grad_output_dw, (C.cout, C.bs, C.oy, C.ox))
Cdw = get_conv_args(xdw.shape, grad_output_dw.shape, padding=(C.py, C.px), stride=(C.dy, C.dx), dilation=(C.ys, C.xs), groups=C.groups)
grad_weight = ctx.processing_op(ProcessingOps.CONV, xdw, grad_output_dw, (C.cin, C.cout, Cdw.oy, Cdw.ox), Cdw)
grad_weight = ctx.movement_op(MovementOps.PERMUTE, grad_weight, (1,0,2,3))
# TODO: remove this slice using asymmetric padding
dw = ctx.movement_op(MovementOps.SLICE, grad_weight, ((0, grad_weight.shape[0]), (0, grad_weight.shape[1]), (0, w.shape[2]), (0, w.shape[3])))
if ctx.needs_input_grad[1]:
# compute derivative of weights using ProcessingOps.CONV
xdw = ctx.movement_op(MovementOps.RESHAPE, x, (C.bs, C.groups, C.cin, C.iy, C.ix))
xdw = ctx.movement_op(MovementOps.PERMUTE, xdw, (2,1,0,3,4))
xdw = ctx.movement_op(MovementOps.RESHAPE, xdw, (C.cin, C.groups*C.bs, C.iy, C.ix))
grad_output_dw = ctx.movement_op(MovementOps.PERMUTE, grad_output, (1,0,2,3))
grad_output_dw = ctx.movement_op(MovementOps.RESHAPE, grad_output_dw, (C.cout, C.bs, C.oy, C.ox))
Cdw = get_conv_args(xdw.shape, grad_output_dw.shape, padding=(C.py, C.px), stride=(C.dy, C.dx), dilation=(C.ys, C.xs), groups=C.groups)
grad_weight = ctx.processing_op(ProcessingOps.CONV, xdw, grad_output_dw, (C.cin, C.cout, Cdw.oy, Cdw.ox), Cdw)
grad_weight = ctx.movement_op(MovementOps.PERMUTE, grad_weight, (1,0,2,3))
# TODO: remove this slice using asymmetric padding
dw = ctx.movement_op(MovementOps.SLICE, grad_weight, ((0, grad_weight.shape[0]), (0, grad_weight.shape[1]), (0, w.shape[2]), (0, w.shape[3])))
return dx, dw