def ratio_transform(args):
replicates = args.replicates
tree = read_tree(args.tree, True, True)
taxa_count = len(tree.taxon_namespace)
taxa = []
for node in tree.leaf_node_iter():
taxa.append(Taxon(node.label, {'date': node.date}))
ratios_root_height = Parameter(
"internal_heights", torch.tensor([0.5] * (taxa_count - 2) + [10])
)
tree_model = ReparameterizedTimeTreeModel(
"tree", tree, Taxa('taxa', taxa), ratios_root_height
)
ratios_root_height.tensor = tree_model.transform.inv(
heights_from_branch_lengths(tree)
)
@benchmark
def fn(ratios_root_height):
return tree_model.transform(
ratios_root_height,
)
@benchmark
def fn_grad(ratios_root_height):
heights = tree_model.transform(
ratios_root_height,
)
heights.backward(torch.ones_like(ratios_root_height))
ratios_root_height.grad.data.zero_()
return heights
total_time, heights = fn(args.replicates, ratios_root_height.tensor)
print(f' {replicates} evaluations: {total_time}')
ratios_root_height.requires_grad = True
grad_total_time, heights = fn_grad(args.replicates, ratios_root_height.tensor)
print(f' {replicates} gradient evaluations: {grad_total_time}')
if args.output:
args.output.write(f"ratio_transform,evaluation,off,{total_time},\n")
args.output.write(f"ratio_transform,gradient,off,{grad_total_time},\n")
print(' JIT off')
@benchmark
def fn2(ratios_root_height):
return transform(
tree_model.transform._forward_indices,
tree_model.transform._bounds,
ratios_root_height,
)
@benchmark
def fn2_grad(ratios_root_height):
heights = transform(
tree_model.transform._forward_indices,
tree_model.transform._bounds,
ratios_root_height,
)
heights.backward(torch.ones_like(ratios_root_height))
ratios_root_height.grad.data.zero_()
return heights
ratios_root_height.requires_grad = False
total_time, heights = fn2(args.replicates, ratios_root_height.tensor)
print(f' {replicates} evaluations: {total_time}')
ratios_root_height.requires_grad = True
total_time, heights = fn2_grad(args.replicates, ratios_root_height.tensor)
print(f' {replicates} gradient evaluations: {total_time}')
print(' JIT on')
transform_script = torch.jit.script(transform)
@benchmark
def fn2_jit(ratios_root_height):
return transform_script(
tree_model.transform._forward_indices,
tree_model.transform._bounds,
ratios_root_height,
)
@benchmark
def fn2_grad_jit(ratios_root_height):
heights = transform_script(
tree_model.transform._forward_indices,
tree_model.transform._bounds,
ratios_root_height,
)
heights.backward(torch.ones_like(ratios_root_height))
ratios_root_height.grad.data.zero_()
return heights
ratios_root_height.requires_grad = False
total_time, heights = fn2_jit(args.replicates, ratios_root_height.tensor)
print(f' {replicates} evaluations: {total_time}')
ratios_root_height.requires_grad = True
total_time, heights = fn2_grad_jit(args.replicates, ratios_root_height.tensor)
print(f' {replicates} gradient evaluations: {total_time}')
print('ratio_transform v2 JIT off')
@benchmark
def fn3(ratios_root_height):
return transform2(
tree_model.transform._forward_indices.tolist(),
tree_model.transform._bounds,
ratios_root_height,
)
@benchmark
def fn3_grad(ratios_root_height):
heights = transform2(
tree_model.transform._forward_indices.tolist(),
tree_model.transform._bounds,
ratios_root_height,
)
heights.backward(torch.ones_like(ratios_root_height))
ratios_root_height.grad.data.zero_()
return heights
ratios_root_height.requires_grad = False
total_time, heights = fn3(args.replicates, ratios_root_height.tensor)
print(f' {replicates} evaluations: {total_time}')
ratios_root_height.requires_grad = True
total_time, heights = fn3_grad(args.replicates, ratios_root_height.tensor)
print(f' {replicates} gradient evaluations: {total_time}')
print('ratio_transform v2 JIT on')
transform2_script = torch.jit.script(transform2)
@benchmark
def fn3_jit(ratios_root_height):
return transform2_script(
tree_model.transform._forward_indices.tolist(),
tree_model.transform._bounds,
ratios_root_height,
)
@benchmark
def fn3_grad_jit(ratios_root_height):
heights = transform2_script(
tree_model.transform._forward_indices.tolist(),
tree_model.transform._bounds,
ratios_root_height,
)
heights.backward(torch.ones_like(ratios_root_height))
ratios_root_height.grad.data.zero_()
return heights
ratios_root_height.requires_grad = False
total_time, heights = fn3_jit(args.replicates, ratios_root_height.tensor)
print(f' {replicates} evaluations: {total_time}')
ratios_root_height.requires_grad = True
total_time, heights = fn3_grad_jit(args.replicates, ratios_root_height.tensor)
print(f' {replicates} gradient evaluations: {total_time}')
def constant_coalescent(args):
tree = read_tree(args.tree, True, True)
taxa_count = len(tree.taxon_namespace)
taxa = []
for node in tree.leaf_node_iter():
taxa.append(Taxon(node.label, {'date': node.date}))
ratios_root_height = Parameter(
"internal_heights", torch.tensor([0.5] * (taxa_count - 2) + [20.0])
)
tree_model = TimeTreeModel("tree", tree, Taxa('taxa', taxa), ratios_root_height)
tree_model._internal_heights.tensor = heights_from_branch_lengths(tree)
pop_size = torch.tensor([4.0])
print('JIT off')
@benchmark
def fn(tree_model, pop_size):
return ConstantCoalescent(pop_size).log_prob(tree_model.node_heights)
@benchmark
def fn_grad(tree_model, pop_size):
log_p = ConstantCoalescent(pop_size).log_prob(tree_model.node_heights)
log_p.backward()
ratios_root_height.tensor.grad.data.zero_()
pop_size.grad.data.zero_()
return log_p
total_time, log_p = fn(args.replicates, tree_model, pop_size)
print(f' {args.replicates} evaluations: {total_time} {log_p}')
ratios_root_height.requires_grad = True
pop_size.requires_grad_(True)
grad_total_time, grad_log_p = fn_grad(args.replicates, tree_model, pop_size)
print(f' {args.replicates} gradient evaluations: {grad_total_time}')
if args.output:
args.output.write(
f"coalescent,evaluation,off,{total_time},{log_p.squeeze().item()}\n"
)
args.output.write(
f"coalescent,gradient,off,{grad_total_time},{grad_log_p.squeeze().item()}\n"
)
if args.debug:
tree_model.heights_need_update = True
log_p = ConstantCoalescent(pop_size).log_prob(tree_model.node_heights)
log_p.backward()
print('gradient ratios: ', ratios_root_height.grad)
print('gradient pop size: ', pop_size.grad)
ratios_root_height.tensor.grad.data.zero_()
pop_size.grad.data.zero_()
print('JIT on')
log_prob_script = torch.jit.script(log_prob)
@benchmark
def fn_jit(tree_model, pop_size):
return log_prob_script(tree_model.node_heights, pop_size)
@benchmark
def fn_grad_jit(tree_model, pop_size):
log_p = log_prob_script(tree_model.node_heights, pop_size)
log_p.backward()
ratios_root_height.tensor.grad.data.zero_()
pop_size.grad.data.zero_()
return log_p
ratios_root_height.requires_grad = False
pop_size.requires_grad_(False)
total_time, log_p = fn_jit(args.replicates, tree_model, pop_size)
print(f' {args.replicates} evaluations: {total_time} {log_p}')
ratios_root_height.requires_grad = True
pop_size.requires_grad_(True)
grad_total_time, grad_log_p = fn_grad_jit(args.replicates, tree_model, pop_size)
print(f' {args.replicates} gradient evaluations: {grad_total_time}')
if args.output:
args.output.write(
f"coalescent,evaluation,on,{total_time},{log_p.squeeze().item()}\n"
)
args.output.write(
f"coalescent,gradient,on,{grad_total_time},{grad_log_p.squeeze().item()}\n"
)
if args.all:
print('make sampling times unique and count them:')
@benchmark
def fn3(tree_model, pop_size):
tree_model.heights_need_update = True
node_heights = torch.cat(
(x, tree_model.node_heights[..., tree_model.taxa_count :])
)
return log_prob_squashed(
pop_size, node_heights, counts, tree_model.taxa_count
)
@benchmark
def fn3_grad(tree_model, ratios_root_height, pop_size):
tree_model.heights_need_update = True
node_heights = torch.cat(
(x, tree_model.node_heights[..., tree_model.taxa_count :])
)
log_p = log_prob_squashed(
pop_size, node_heights, counts, tree_model.taxa_count
)
log_p.backward()
ratios_root_height.tensor.grad.data.zero_()
pop_size.grad.data.zero_()
return log_p
x, counts = torch.unique(tree_model.sampling_times, return_counts=True)
counts = torch.cat((counts, torch.tensor([-1] * (taxa_count - 1))))
with torch.no_grad():
total_time, log_p = fn3(args.replicates, tree_model, pop_size)
print(f' {args.replicates} evaluations: {total_time} ({log_p})')
total_time, log_p = fn3_grad(
args.replicates, tree_model, ratios_root_height, pop_size
)
print(f' {args.replicates} gradient evaluations: {total_time}')
def ratio_transform_jacobian(args):
tree = read_tree(args.tree, True, True)
taxa = []
for node in tree.leaf_node_iter():
taxa.append(Taxon(node.label, {'date': node.date}))
taxa_count = len(taxa)
ratios_root_height = Parameter(
"internal_heights", torch.tensor([0.5] * (taxa_count - 1) + [20])
)
tree_model = ReparameterizedTimeTreeModel(
"tree", tree, Taxa('taxa', taxa), ratios_root_height
)
ratios_root_height.tensor = tree_model.transform.inv(
heights_from_branch_lengths(tree)
)
@benchmark
def fn(ratios_root_height):
internal_heights = tree_model.transform(ratios_root_height)
return tree_model.transform.log_abs_det_jacobian(
ratios_root_height, internal_heights
)
@benchmark
def fn_grad(ratios_root_height):
internal_heights = tree_model.transform(ratios_root_height)
log_det_jac = tree_model.transform.log_abs_det_jacobian(
ratios_root_height, internal_heights
)
log_det_jac.backward()
ratios_root_height.grad.data.zero_()
return log_det_jac
print(' JIT off')
total_time, log_det_jac = fn(args.replicates, ratios_root_height.tensor)
print(f' {args.replicates} evaluations: {total_time} ({log_det_jac})')
ratios_root_height.requires_grad = True
grad_total_time, grad_log_det_jac = fn_grad(
args.replicates, ratios_root_height.tensor
)
print(
f' {args.replicates} gradient evaluations: {grad_total_time}'
f' ({grad_log_det_jac})'
)
if args.output:
args.output.write(
f"ratio_transform_jacobian,evaluation,off,{total_time},"
f"{log_det_jac.squeeze().item()}\n"
)
args.output.write(
f"ratio_transform_jacobian,gradient,off,{grad_total_time},"
f"{grad_log_det_jac.squeeze().item()}\n"
)
if args.debug:
internal_heights = tree_model.transform(ratios_root_height.tensor)
log_det_jac = tree_model.transform.log_abs_det_jacobian(
ratios_root_height.tensor, internal_heights
)
log_det_jac.backward()
print(ratios_root_height.grad)
