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test_zero_tiled.py
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172 lines (134 loc) · 6.14 KB
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import copy
import torch
import deepspeed
from deepspeed.runtime.zero.tiling import TiledLinear, TiledLinearReturnBias
import pytest
@pytest.mark.parametrize('in_splits,out_splits', [(1, 1), (2, 2), (5, 5), (32, 32)])
def test_tiled_init(in_splits, out_splits):
in_f = 32
out_f = 40
base = torch.nn.Linear(in_f, out_f, bias=True)
l = TiledLinear(in_f,
out_f,
bias=True,
init_linear=copy.deepcopy(base),
out_splits=out_splits,
in_splits=in_splits)
for out_id in range(out_splits):
for in_id in range(in_splits):
local_l = l.linears[out_id][in_id]
assert isinstance(local_l, torch.nn.Linear)
rstart = l.out_parts[out_id]
rstop = l.out_parts[out_id + 1]
cstart = l.in_parts[in_id]
cstop = l.in_parts[in_id + 1]
local_out = rstop - rstart
local_in = cstop - cstart
assert local_l.weight.size()[1] == local_in, f'local[{out_id}][{in_id}].size {local_l.weight.size()}'
assert local_l.weight.size()[0] == local_out
test = base.weight[rstart:rstop, cstart:cstop]
assert local_l.weight.size() == test.size()
assert torch.equal(local_l.weight.data, test.data)
if in_id == in_splits - 1:
assert local_l.bias is not None
assert local_l.bias.size()[0] == local_out
else:
assert local_l.bias is None
@pytest.mark.parametrize('in_splits,out_splits', [(0, 0), (33, 33)])
def test_tiled_baddim(in_splits, out_splits):
dim = 32
with pytest.raises(RuntimeError):
l = TiledLinear(dim, dim, out_splits=out_splits, in_splits=in_splits)
@pytest.mark.skip(reason="seeing nondeterministic failures, skipping for now")
@pytest.mark.parametrize('bias', [False, True])
@pytest.mark.parametrize('in_splits,out_splits', [(1, 1), (2, 2)])
@pytest.mark.parametrize('in_f,out_f', [(32, 32), (23, 29), (29, 23)])
def test_tiled_forward(in_splits, out_splits, bias, in_f, out_f):
base = torch.nn.Linear(in_f, out_f, bias=bias)
test = TiledLinear(in_f,
out_f,
bias=bias,
init_linear=copy.deepcopy(base),
out_splits=out_splits,
in_splits=in_splits)
inp = torch.rand(in_f)
base_out = base(copy.deepcopy(inp))
test_out = test(copy.deepcopy(inp))
assert torch.allclose(base_out, test_out, rtol=1e-4)
@pytest.mark.skip(reason="seeing nondeterministic failures, skipping for now")
@pytest.mark.parametrize('bias', [False, True])
@pytest.mark.parametrize('in_splits,out_splits', [(1, 1), (2, 2)])
@pytest.mark.parametrize('in_f,out_f', [(32, 32), (23, 29), (29, 23)])
def test_tiled_backward(in_splits, out_splits, bias, in_f, out_f):
base = torch.nn.Linear(in_f, out_f, bias=bias)
test = TiledLinear(in_f,
out_f,
bias=bias,
init_linear=copy.deepcopy(base),
out_splits=out_splits,
in_splits=in_splits)
inp = torch.rand(in_f)
base_out = base(copy.deepcopy(inp))
test_out = test(copy.deepcopy(inp))
assert torch.allclose(base_out, test_out, rtol=1e-4)
base_out.sum().backward()
test_out.sum().backward()
# compare grads
for row in range(out_splits):
rstart = test.out_parts[row]
rstop = test.out_parts[row + 1]
for col in range(in_splits):
cstart = test.in_parts[col]
cstop = test.in_parts[col + 1]
local = test.linears[row][col]
base_grad = base.weight.grad[rstart:rstop, cstart:cstop]
assert torch.allclose(base_grad, local.weight.grad, rtol=1e-4)
if local.bias is not None:
base_grad = base.bias.grad[rstart:rstop]
assert torch.allclose(base_grad, local.bias.grad, rtol=1e-4)
class LinearWrapper(torch.nn.Linear):
"""Returns its own bias to simulate Megatron-LM's behavior.
Megatron-LM optionally delays the bias addition to fuse with a proceeding kernel.
"""
def forward(self, input):
out = super().forward(input)
return out, self.bias
@pytest.mark.skip(reason="seeing nondeterministic failures, skipping for now")
@pytest.mark.parametrize('bias', [False, True])
@pytest.mark.parametrize('in_splits,out_splits', [(1, 1), (2, 2)])
@pytest.mark.parametrize('in_f,out_f', [(32, 32), (23, 29), (29, 23)])
def test_tiled_returnbias_backward(in_splits, out_splits, bias, in_f, out_f):
base = LinearWrapper(in_f, out_f, bias=bias)
test = TiledLinearReturnBias(in_f,
out_f,
bias=bias,
linear_cls=LinearWrapper,
init_linear=copy.deepcopy(base),
out_splits=out_splits,
in_splits=in_splits)
inp = torch.rand(in_f)
base_out_t, base_out_b = base(copy.deepcopy(inp))
test_out_t, test_out_b = test(copy.deepcopy(inp))
assert torch.allclose(base_out_t, test_out_t, rtol=1e-4)
if base_out_b is None:
assert test_out_b is None
base_out_b = torch.zeros_like(base_out_t)
test_out_b = torch.zeros_like(test_out_t)
else:
assert test_out_b is not None
assert torch.allclose(base_out_b, test_out_b, rtol=1e-4)
(base_out_t + base_out_b).sum().backward()
(test_out_t + test_out_b).sum().backward()
# compare grads
for row in range(out_splits):
rstart = test.out_parts[row]
rstop = test.out_parts[row + 1]
for col in range(in_splits):
cstart = test.in_parts[col]
cstop = test.in_parts[col + 1]
local = test.linears[row][col]
base_grad = base.weight.grad[rstart:rstop, cstart:cstop]
assert torch.allclose(base_grad, local.weight.grad, rtol=1e-4)
if local.bias is not None:
base_grad = base.bias.grad[rstart:rstop]
assert torch.allclose(base_grad, local.bias.grad, rtol=1e-4)