ViperEkura
dcc96de12a
- Convert protocol and MoE test classes to plain functions - Add real server/engine integration and generate_async tests - Isolate test model per test and use pytest tmp_path - Reset FastAPI engine state after inference tests - Fix generate_async StopIteration handling on Python 3.12 - Fix HF adapter MoE dense/shared and Gemma qk_norm mapping - Correct dev dependency httpx2 to httpx
365 lines
12 KiB
Python
365 lines
12 KiB
Python
"""Smoke tests for MoE aux loss and diagnostic metrics integration.
|
||
|
||
Does NOT load real data or weights. Uses a tiny randomly-initialized
|
||
MoE model and verifies that aux loss computation and MoE routing
|
||
diagnostics flow end‑to‑end through the strategy layer.
|
||
"""
|
||
|
||
import pytest
|
||
import torch
|
||
|
||
from astrai.config.model_config import AutoRegressiveLMConfig
|
||
from astrai.model.transformer import AutoRegressiveLM
|
||
from astrai.trainer.strategy import (
|
||
SEQStrategy,
|
||
SFTStrategy,
|
||
StrategyFactory,
|
||
_collect_moe_diagnostics,
|
||
)
|
||
from tests.helpers import TINY_CONFIG
|
||
|
||
|
||
def _make_tiny_moe_config(**overrides) -> AutoRegressiveLMConfig:
|
||
return AutoRegressiveLMConfig(
|
||
**{
|
||
**TINY_CONFIG,
|
||
"ffn_type": "moe",
|
||
"n_routed_experts": 4,
|
||
"n_shared_experts": 1,
|
||
"n_activated_experts": 2,
|
||
"topk_method": "greedy",
|
||
**overrides,
|
||
}
|
||
)
|
||
|
||
|
||
def _make_model(config=None) -> AutoRegressiveLM:
|
||
if config is None:
|
||
config = _make_tiny_moe_config()
|
||
return AutoRegressiveLM(config)
|
||
|
||
|
||
def _make_batch(config, batch_size=2, seq_len=8, with_extra=False):
|
||
"""Build a random token batch, optionally with position ids and loss mask."""
|
||
vocab = config.vocab_size
|
||
batch = {
|
||
"input_ids": torch.randint(0, vocab, (batch_size, seq_len)),
|
||
"target_ids": torch.randint(0, vocab, (batch_size, seq_len)),
|
||
}
|
||
if with_extra:
|
||
batch["position_ids"] = (
|
||
torch.arange(seq_len).unsqueeze(0).expand(batch_size, -1)
|
||
)
|
||
batch["loss_mask"] = torch.ones(batch_size, seq_len, dtype=torch.bool)
|
||
return batch
|
||
|
||
|
||
def _make_seq_moe_fixture(device):
|
||
config = _make_tiny_moe_config()
|
||
model = _make_model(config).to(device)
|
||
model.train()
|
||
return config, model
|
||
|
||
|
||
def _make_sft_moe_fixture(device):
|
||
config = _make_tiny_moe_config()
|
||
model = _make_model(config).to(device)
|
||
model.train()
|
||
return config, model
|
||
|
||
|
||
def test_model_forward_contract_uses_dense_training_and_packed_inference():
|
||
from astrai.inference.cache import PagePool, TaskCacheManager
|
||
from astrai.inference.workspace import InferenceWorkspace
|
||
|
||
config = AutoRegressiveLMConfig(**TINY_CONFIG)
|
||
model = AutoRegressiveLM(config).eval()
|
||
dense = model(torch.tensor([[1, 2, 3]]))
|
||
assert dense["logits"].shape == (1, 3, config.vocab_size)
|
||
|
||
pool = PagePool(
|
||
n_layers=config.num_hidden_layers,
|
||
n_kv_heads=config.num_key_value_heads,
|
||
head_dim=config.hidden_size // config.num_attention_heads,
|
||
max_batch_size=1,
|
||
max_seq_len=config.max_position_embeddings,
|
||
device="cpu",
|
||
dtype=torch.float32,
|
||
)
|
||
cache = TaskCacheManager(pool)
|
||
workspace = InferenceWorkspace(
|
||
1,
|
||
config.max_position_embeddings,
|
||
config.num_attention_heads,
|
||
config.hidden_size // config.num_attention_heads,
|
||
torch.device("cpu"),
|
||
torch.float32,
|
||
)
|
||
assert cache.task_alloc("t", [1, 2, 3])
|
||
packed = model(
|
||
torch.tensor([1, 2, 3]),
|
||
position_ids=torch.arange(3),
|
||
kv_cache=cache.bind(["t"], workspace, start_pos=0),
|
||
fwd="prefill",
|
||
)
|
||
assert packed["logits"].shape == (3, config.vocab_size)
|
||
|
||
with pytest.raises(ValueError, match="training input_ids"):
|
||
model(torch.tensor([1, 2, 3]))
|
||
with pytest.raises(ValueError, match="inference input_ids"):
|
||
model(
|
||
torch.tensor([[1, 2, 3]]),
|
||
kv_cache=cache.bind(["t"], workspace, start_pos=0),
|
||
fwd="prefill",
|
||
)
|
||
|
||
|
||
def _router_stats(probs, topk_indices):
|
||
return {"probs": probs, "topk_indices": topk_indices}
|
||
|
||
|
||
def test_collect_moe_diagnostics_returns_all_keys():
|
||
"""_collect_moe_diagnostics should return the four expected keys."""
|
||
# Simulate two MoE layers with uniform routing probabilities
|
||
probs = torch.ones(128, 4) / 4.0
|
||
topk = torch.zeros(128, 2, dtype=torch.long)
|
||
diag = _collect_moe_diagnostics([_router_stats(probs, topk)] * 2)
|
||
|
||
assert set(diag.keys()) == {
|
||
"router_entropy",
|
||
"dead_expert_fraction",
|
||
"load_imbalance_mean",
|
||
"load_imbalance_max",
|
||
}
|
||
for v in diag.values():
|
||
assert isinstance(v, float)
|
||
|
||
|
||
def test_collect_moe_diagnostics_empty_list():
|
||
"""Empty list returns empty dict."""
|
||
assert _collect_moe_diagnostics([]) == {}
|
||
|
||
|
||
def test_collect_moe_diagnostics_uniform_routing():
|
||
"""Uniform routing with top_k=2 → tie-breaking by index.
|
||
|
||
torch.topk breaks ties by index, so with equal probabilities
|
||
experts 0 and 1 always win over experts 2 and 3:
|
||
- dead_expert_fraction = 2/4 = 0.5
|
||
- load_ratios = [2, 2, 0, 0] → |ratio-1| = [1, 1, 1, 1] → mean = 1.0
|
||
- load_imbalance_max = 2.0
|
||
"""
|
||
probs = torch.ones(128, 4) / 4.0
|
||
topk = torch.tensor([[0, 1]] * 128)
|
||
diag = _collect_moe_diagnostics([_router_stats(probs, topk)])
|
||
|
||
assert diag["dead_expert_fraction"] == pytest.approx(0.5, abs=1e-6)
|
||
assert diag["load_imbalance_mean"] == pytest.approx(1.0, abs=1e-6)
|
||
assert diag["load_imbalance_max"] == pytest.approx(2.0, abs=1e-6)
|
||
|
||
|
||
def test_collect_moe_diagnostics_max_entropy():
|
||
"""Uniform probabilities should give log(num_experts) entropy."""
|
||
num_experts = 4
|
||
probs = torch.ones(128, num_experts) / num_experts
|
||
topk = torch.zeros(128, 2, dtype=torch.long)
|
||
diag = _collect_moe_diagnostics([_router_stats(probs, topk)])
|
||
expected_entropy = float(torch.log(torch.tensor(num_experts, dtype=torch.float32)))
|
||
assert diag["router_entropy"] == pytest.approx(expected_entropy, abs=1e-5)
|
||
|
||
|
||
def test_moe_metrics_flow_through_wrapped_model(device):
|
||
"""DDP-like wrappers (no .config / get_moe_router_probs) still collect MoE metrics."""
|
||
import torch.nn as nn
|
||
|
||
from astrai.trainer.strategy import SEQStrategy
|
||
|
||
class ForwardOnlyWrapper(nn.Module):
|
||
def __init__(self, model):
|
||
super().__init__()
|
||
self.module = model
|
||
|
||
def forward(self, *args, **kwargs):
|
||
return self.module(*args, **kwargs)
|
||
|
||
config = _make_tiny_moe_config()
|
||
model = AutoRegressiveLM(config).to(device)
|
||
wrapped = ForwardOnlyWrapper(model)
|
||
wrapped.train()
|
||
|
||
strategy = SEQStrategy(wrapped, device, moe_aux_loss_coef=0.01)
|
||
output = strategy.compute_loss_output(
|
||
{
|
||
"input_ids": torch.randint(0, config.vocab_size, (2, 8)),
|
||
"target_ids": torch.randint(0, config.vocab_size, (2, 8)),
|
||
}
|
||
)
|
||
|
||
assert "moe_aux_loss" in output["metrics"]
|
||
assert "router_entropy" in strategy._moe_metrics
|
||
|
||
|
||
def test_seq_compute_loss_returns_scalar(device):
|
||
"""compute_loss should return a scalar tensor."""
|
||
config, model = _make_seq_moe_fixture(device)
|
||
strategy = SEQStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.01,
|
||
)
|
||
loss = strategy.compute_loss(_make_batch(config))
|
||
assert loss.ndim == 0
|
||
assert loss.requires_grad
|
||
|
||
|
||
def test_seq_compute_loss_output_has_metrics(device):
|
||
"""compute_loss_output dict with moe_aux_loss_coef > 0 includes MoE metrics."""
|
||
config, model = _make_seq_moe_fixture(device)
|
||
strategy = SEQStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.01,
|
||
)
|
||
output = strategy.compute_loss_output(_make_batch(config))
|
||
|
||
assert "loss" in output
|
||
assert "metrics" in output
|
||
assert output["loss"].ndim == 0
|
||
assert output["loss"].requires_grad
|
||
|
||
metrics = output["metrics"]
|
||
# MoE metrics should appear when coef > 0 and model has MoE layers
|
||
for key in ("moe_aux_loss", "moe_aux_loss_weighted", "task_loss", "loss"):
|
||
assert key in metrics, f"Missing metric: {key}"
|
||
assert isinstance(metrics[key], float)
|
||
|
||
|
||
def test_seq_moe_metrics_populated_after_forward(device):
|
||
"""strategy._moe_metrics populated after compute_loss_output."""
|
||
config, model = _make_seq_moe_fixture(device)
|
||
strategy = SEQStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.01,
|
||
)
|
||
strategy.compute_loss_output(_make_batch(config))
|
||
|
||
moe_metrics = strategy._moe_metrics
|
||
assert moe_metrics, "_moe_metrics should not be empty for MoE model"
|
||
for key in (
|
||
"aux_loss",
|
||
"router_entropy",
|
||
"dead_expert_fraction",
|
||
"load_imbalance_mean",
|
||
"load_imbalance_max",
|
||
):
|
||
assert key in moe_metrics, f"Missing _moe_metrics key: {key}"
|
||
assert isinstance(moe_metrics[key], float)
|
||
|
||
|
||
def test_seq_zero_coef_zeroes_weighted_aux(device):
|
||
"""moe_aux_loss_coef=0 → weighted_aux_loss is zero, task_loss == loss."""
|
||
config, model = _make_seq_moe_fixture(device)
|
||
strategy = SEQStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.0,
|
||
)
|
||
output = strategy.compute_loss_output(_make_batch(config))
|
||
metrics = output["metrics"]
|
||
|
||
# task_loss and loss should be equal (aux weighted by zero)
|
||
assert "task_loss" in metrics
|
||
assert "loss" in metrics
|
||
assert metrics["loss"] == pytest.approx(metrics["task_loss"], abs=1e-6)
|
||
|
||
# weighted aux loss is zero
|
||
assert metrics.get("moe_aux_loss_weighted") == pytest.approx(0.0, abs=1e-6)
|
||
|
||
# MoE diagnostics are still collected (monitoring purposes)
|
||
assert strategy._moe_metrics
|
||
assert "router_entropy" in strategy._moe_metrics
|
||
|
||
|
||
def test_seq_aux_loss_added_to_total_loss(device):
|
||
"""Total loss > task_loss when moe_aux_loss_coef > 0."""
|
||
config, model = _make_seq_moe_fixture(device)
|
||
strategy = SEQStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.01,
|
||
)
|
||
output = strategy.compute_loss_output(_make_batch(config))
|
||
assert output["metrics"]["loss"] > output["metrics"]["task_loss"] + 1e-12
|
||
|
||
|
||
def test_seq_factory_creates_strategy_with_coef(device):
|
||
"""StrategyFactory.create passes moe_aux_loss_coef to strategy."""
|
||
_, model = _make_seq_moe_fixture(device)
|
||
strategy = StrategyFactory.create(
|
||
"seq",
|
||
model=model,
|
||
device=device,
|
||
moe_aux_loss_coef=0.02,
|
||
)
|
||
assert strategy.moe_aux_loss_coef == 0.02
|
||
|
||
|
||
def test_seq_no_aux_loss_for_mlp_model(device):
|
||
"""Pure MLP model: model outputs no aux_loss → no MoE metrics."""
|
||
config, _ = _make_seq_moe_fixture(device)
|
||
mlp_config = AutoRegressiveLMConfig(**{**TINY_CONFIG, "ffn_type": "mlp"})
|
||
mlp_model = AutoRegressiveLM(mlp_config).to(device)
|
||
mlp_model.train()
|
||
|
||
strategy = SEQStrategy(
|
||
mlp_model,
|
||
device,
|
||
moe_aux_loss_coef=0.01,
|
||
)
|
||
output = strategy.compute_loss_output(_make_batch(config))
|
||
metrics = output["metrics"]
|
||
|
||
assert "moe_aux_loss" not in metrics
|
||
assert "moe_aux_loss_weighted" not in metrics
|
||
assert metrics["loss"] == pytest.approx(metrics["task_loss"], abs=1e-6)
|
||
assert strategy._moe_metrics == {}
|
||
|
||
|
||
def test_sft_compute_loss_output_with_aux_loss(device):
|
||
"""SFTStrategy produces MoE metrics when coef > 0."""
|
||
config, model = _make_sft_moe_fixture(device)
|
||
strategy = SFTStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.01,
|
||
)
|
||
output = strategy.compute_loss_output(_make_batch(config, with_extra=True))
|
||
|
||
metrics = output["metrics"]
|
||
assert "moe_aux_loss" in metrics
|
||
assert "moe_aux_loss_weighted" in metrics
|
||
assert metrics["loss"] > metrics["task_loss"] + 1e-12
|
||
|
||
moe_metrics = strategy._moe_metrics
|
||
assert "router_entropy" in moe_metrics
|
||
assert "dead_expert_fraction" in moe_metrics
|
||
|
||
|
||
def test_sft_zero_coef_zeroes_weighted_aux(device):
|
||
"""SFTStrategy with zero coef: weighted aux is zero, loss == task_loss."""
|
||
config, model = _make_sft_moe_fixture(device)
|
||
strategy = SFTStrategy(
|
||
model,
|
||
device,
|
||
moe_aux_loss_coef=0.0,
|
||
)
|
||
output = strategy.compute_loss_output(_make_batch(config, with_extra=True))
|
||
metrics = output["metrics"]
|
||
|
||
assert metrics["loss"] == pytest.approx(metrics["task_loss"], abs=1e-6)
|
||
assert metrics.get("moe_aux_loss_weighted") == pytest.approx(0.0, abs=1e-6)
|
||
# Diagnostics still collected
|
||
assert strategy._moe_metrics
|
||
assert "router_entropy" in strategy._moe_metrics
|