File size: 5,539 Bytes
9c4ca75 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
# Copyright 2024 Databricks
# SPDX-License-Identifier: Apache-2.0
from functools import partial
import pytest
import torch
from megablocks.layers.arguments import Arguments
from megablocks.layers.moe import MoE, batched_load_balancing_loss, clear_load_balancing_loss
from megablocks.layers.router import batched_router_zloss, clear_router_zloss
from tests.layers.architectures import FFN
_FORWARD_TESTS = (
(16, 1024, 512, 1, 1),
(16, 1024, 512, 2, 1),
(16, 1024, 512, 4, 1),
(16, 1024, 512, 8, 1),
(8, 2048, 512, 1, 1),
(8, 2048, 512, 2, 1),
(8, 2048, 512, 4, 1),
(16, 1024, 512, 2, 2),
(16, 1024, 512, 4, 2),
(16, 1024, 512, 4, 4),
(16, 1024, 512, 8, 2),
(16, 1024, 512, 8, 4),
(16, 1024, 512, 8, 8),
)
_DENSE_TESTS = (
(16, 1024, 512),
(8, 2048, 512),
)
def construct_moe(
hidden_size: int,
ffn_hidden_size: int,
moe_num_experts: int = 1,
moe_capacity_factor: int = 1,
moe_top_k: int = 1,
moe_zloss_weight: float = 0,
):
# All tests are skipped if triton >=3.2.0 is installed since sparse is not supported
# TODO: Remove this once sparse is supported with triton >=3.2.0
try:
import triton
if triton.__version__ >= '3.2.0':
pytest.skip('Sparse MLP is not supported with triton >=3.2.0')
except ImportError:
pass
init_method = partial(torch.nn.init.normal_, mean=0.0, std=0.1)
args = Arguments(
hidden_size=hidden_size,
ffn_hidden_size=ffn_hidden_size,
moe_num_experts=moe_num_experts,
moe_capacity_factor=moe_capacity_factor,
moe_top_k=moe_top_k,
init_method=init_method,
moe_zloss_weight=moe_zloss_weight,
)
mlp = FFN(args)
moe_mlp = MoE(args)
mlp.cuda(torch.cuda.current_device()).half()
moe_mlp.cuda(torch.cuda.current_device()).half()
# Set the baseline parameters to match exactly.
if moe_num_experts == 1:
with torch.no_grad():
mlp.w1.copy_(moe_mlp.experts.mlp.w1.squeeze())
mlp.w2.copy_(moe_mlp.experts.mlp.w2.squeeze())
return args, mlp, moe_mlp
@pytest.mark.gpu
@pytest.mark.parametrize(('bs', 'sl', 'hs', 'num_experts', 'top_k'), _FORWARD_TESTS)
def test_moe_forward(bs: int, sl: int, hs: int, num_experts: int, top_k: int):
x = torch.randn(sl, bs, hs).half().cuda()
_, _, layer = construct_moe(
hidden_size=hs,
ffn_hidden_size=hs * 2,
moe_num_experts=num_experts,
moe_top_k=top_k,
)
out, _ = layer(x)
assert out.shape == x.shape
clear_load_balancing_loss()
@pytest.mark.gpu
@pytest.mark.parametrize(('bs', 'sl', 'hs', 'num_experts', 'top_k'), _FORWARD_TESTS)
def test_moe_forward_backward(
bs: int,
sl: int,
hs: int,
num_experts: int,
top_k: int,
):
x = torch.randn(sl, bs, hs).half().cuda()
x.requires_grad_(True)
args, _, layer = construct_moe(
hidden_size=hs,
ffn_hidden_size=hs * 2,
moe_num_experts=num_experts,
moe_top_k=top_k,
)
out, _ = layer(x)
assert out.shape == x.shape
loss = out.sum() + batched_load_balancing_loss(args)
loss.backward()
layer.zero_grad(set_to_none=True)
x.grad = None
clear_load_balancing_loss()
@pytest.mark.gpu
@pytest.mark.parametrize(('bs', 'sl', 'hs', 'num_experts', 'top_k'), _FORWARD_TESTS)
def test_moe_forward_backward_with_zloss(
bs: int,
sl: int,
hs: int,
num_experts: int,
top_k: int,
):
x = torch.randn(sl, bs, hs).half().cuda()
x.requires_grad_(True)
args, _, layer = construct_moe(
hidden_size=hs,
ffn_hidden_size=hs * 2,
moe_num_experts=num_experts,
moe_top_k=top_k,
moe_zloss_weight=1e-3,
)
out, _ = layer(x)
assert out.shape == x.shape
loss = out.sum() + batched_load_balancing_loss(args)
loss.backward()
layer.zero_grad(set_to_none=True)
x.grad = None
clear_load_balancing_loss()
clear_router_zloss()
@pytest.mark.gpu
@pytest.mark.parametrize(('bs', 'sl', 'hs'), _DENSE_TESTS)
def test_moe_forward_vs_dense(bs: int, sl: int, hs: int):
x = torch.randn(sl, bs, hs).half().cuda()
_, mlp, moe_mlp = construct_moe(hidden_size=hs, ffn_hidden_size=hs * 2)
expected_out = mlp(x)
out, _ = moe_mlp(x)
assert out.shape == x.shape == expected_out.shape
assert torch.allclose(out, expected_out)
clear_load_balancing_loss()
@pytest.mark.gpu
@pytest.mark.parametrize(('bs', 'sl', 'hs'), _DENSE_TESTS)
def test_moe_forward_backward_vs_dense(bs: int, sl: int, hs: int):
x = torch.randn(sl, bs, hs).half().cuda()
x.requires_grad_(True)
_, mlp, moe_mlp = construct_moe(hidden_size=hs, ffn_hidden_size=hs * 2)
out, _ = moe_mlp(x)
loss = out.sum()
loss.backward()
w1_grad = moe_mlp.experts.mlp.w1.grad.detach().squeeze()
w2_grad = moe_mlp.experts.mlp.w2.grad.detach().squeeze()
moe_mlp.zero_grad(set_to_none=True)
x.grad = None
clear_load_balancing_loss()
expected_out = mlp(x)
expected_loss = expected_out.sum()
expected_loss.backward()
expected_w1_grad = mlp.w1.grad.detach()
expected_w2_grad = mlp.w2.grad.detach()
mlp.zero_grad(set_to_none=True)
x.grad = None
# Verify the gradients match.
assert w1_grad.shape == expected_w1_grad.shape
assert w2_grad.shape == expected_w2_grad.shape
assert torch.allclose(w1_grad, expected_w1_grad)
assert torch.allclose(w2_grad, expected_w2_grad)
clear_load_balancing_loss()
|