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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Integration tests for MoE implementation.
These tests verify cross-module contracts between:
- Modules (moe_modules.py)
- Losses (moe_loss.py)
- Transformer (transformer_2501.py)
"""
import pytest
import torch
from nemo.collections.tts.losses.moe_loss import MoEAuxiliaryLoss
from nemo.collections.tts.modules.moe_modules import PositionwiseConvFFMoE
from nemo.collections.tts.modules.transformer_2501 import Transformer, TransformerLayer
@pytest.mark.unit
class TestMoEIntegration:
"""Integration tests for MoE pipeline: modules, losses, and config handling."""
def test_complete_moe_pipeline(self):
"""Test complete flow: Transformer -> routing_info -> Loss computation."""
transformer = Transformer(
n_layers=2,
d_model=64,
d_ffn=256,
sa_n_heads=4,
kernel_size=1,
use_moe=True,
num_experts=4,
top_k_experts=2,
router_jitter_noise=0.0,
routing_strategy="top_k",
)
loss_module = MoEAuxiliaryLoss(
num_experts=4,
load_balancing_loss_scale=0.01,
router_z_loss_scale=0.001,
)
x = torch.randn(2, 10, 64)
x_mask = torch.ones(2, 10).bool()
transformer.train()
output_dict = transformer(x, x_mask)
# Extract routing info
moe_routing_info = output_dict['moe_routing_info']
assert moe_routing_info is not None
assert len(moe_routing_info) == 2 # n_layers
all_logits = torch.stack([info['router_logits'] for info in moe_routing_info], dim=0)
all_probs = torch.stack([info['router_probs'] for info in moe_routing_info], dim=0)
merged_logits = all_logits.view(-1, all_logits.size(2), all_logits.size(3))
merged_probs = all_probs.view(-1, all_probs.size(2), all_probs.size(3))
# Repeat mask for each layer (for mask-aware loss computation)
n_layers = len(moe_routing_info)
merged_mask = x_mask.unsqueeze(0).repeat(n_layers, 1, 1).view(-1, x_mask.size(1))
load_balancing_loss, router_z_loss, total_loss = loss_module(
router_logits=merged_logits, router_probs=merged_probs, x_mask=merged_mask
)
assert load_balancing_loss.item() >= 0
assert router_z_loss.item() >= 0
assert total_loss.item() >= 0
def test_transformer_from_yaml_config(self):
"""Test creating Transformer from YAML-style config dict."""
config_dict = {
'n_layers': 2,
'd_model': 64,
'd_ffn': 256,
'sa_n_heads': 4,
'kernel_size': 1,
'p_dropout': 0.0,
'has_xattn': False,
'is_causal': True,
'use_moe': True,
'num_experts': 4,
'top_k_experts': 2,
'router_jitter_noise': 0.0,
'routing_strategy': 'top_k',
}
transformer = Transformer(**config_dict)
assert transformer.use_moe is True
@pytest.mark.parametrize(
"cls,kwargs",
[
(
TransformerLayer,
{
'd_model': 64,
'd_ffn': 256,
'sa_n_heads': 4,
'kernel_size': 1,
'p_dropout': 0.0,
'has_xattn': False,
'use_moe': True,
'num_experts': 4,
'top_k_experts': 2,
'router_load_balancing_loss_coeff': 0.01,
},
),
(
Transformer,
{
'n_layers': 2,
'd_model': 64,
'd_ffn': 256,
'sa_n_heads': 4,
'kernel_size': 1,
'use_moe': True,
'num_experts': 4,
'top_k_experts': 2,
'router_z_loss_coeff': 0.001,
},
),
(
PositionwiseConvFFMoE,
{
'd_model': 64,
'd_ffn': 256,
'p_dropout': 0.0,
'num_experts': 4,
'top_k_experts': 2,
'router_load_balancing_loss_coeff': 0.01,
},
),
],
ids=["TransformerLayer", "Transformer", "PositionwiseConvFFMoE"],
)
def test_loss_coefficients_rejected_by_modules(self, cls, kwargs):
"""Test that MoE modules reject loss coefficient parameters (they belong at model level)."""
with pytest.raises(TypeError, match="unexpected keyword argument"):
cls(**kwargs)
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