282 lines
8.3 KiB
Python
282 lines
8.3 KiB
Python
import pytest
|
|
import torch
|
|
|
|
from astrai.model.components.mlp import MLP, DeepSeekMoE
|
|
from astrai.model.transformer import AutoRegressiveLM
|
|
from tests.helpers import TINY_CONFIG
|
|
|
|
CONFIGS = [
|
|
pytest.param(
|
|
{**TINY_CONFIG, "attn_type": "gqa", "ffn_type": "mlp"},
|
|
id="gqa_mlp",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "mla",
|
|
"ffn_type": "mlp",
|
|
"kv_lora_rank": 4,
|
|
"qk_nope_head_dim": 2,
|
|
"qk_rope_head_dim": 2,
|
|
},
|
|
id="mla_mlp",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"topk_method": "greedy",
|
|
},
|
|
id="gqa_moe",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"topk_method": "greedy",
|
|
"mlp_only_layers": [0],
|
|
},
|
|
id="gqa_moe_dense_first",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"topk_method": "greedy",
|
|
"decoder_sparse_step": 2,
|
|
},
|
|
id="gqa_moe_sparse_step",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"topk_method": "greedy",
|
|
"norm_topk_prob": True,
|
|
},
|
|
id="gqa_moe_norm_topk",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"topk_method": "greedy",
|
|
"moe_intermediate_size": 24,
|
|
"shared_expert_intermediate_size": 20,
|
|
},
|
|
id="gqa_moe_custom_intermediate",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "mlp",
|
|
"rope_theta": 100000.0,
|
|
},
|
|
id="gqa_rope_theta",
|
|
),
|
|
pytest.param(
|
|
{**TINY_CONFIG, "attn_type": "gqa", "ffn_type": "mlp", "use_qk_norm": True},
|
|
id="gqa_qk_norm",
|
|
),
|
|
pytest.param(
|
|
{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "mlp",
|
|
"tie_word_embeddings": True,
|
|
},
|
|
id="gqa_tie_word_embeddings",
|
|
),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("config_kwargs", CONFIGS)
|
|
def test_model_forward(config_kwargs, device):
|
|
from astrai.config.model_config import AutoRegressiveLMConfig
|
|
|
|
config = AutoRegressiveLMConfig(**config_kwargs)
|
|
model = AutoRegressiveLM(config).to(device=device)
|
|
model.eval()
|
|
|
|
batch_size, seq_len = 2, 8
|
|
input_ids = torch.randint(
|
|
0, config.vocab_size, (batch_size, seq_len), device=device
|
|
)
|
|
|
|
with torch.no_grad():
|
|
output = model(input_ids)
|
|
|
|
assert "logits" in output
|
|
assert "hidden_states" in output
|
|
assert output["logits"].shape == (batch_size, seq_len, config.vocab_size)
|
|
assert output["hidden_states"].shape == (
|
|
batch_size,
|
|
seq_len,
|
|
config.hidden_size,
|
|
)
|
|
assert not torch.isnan(output["logits"]).any()
|
|
assert not torch.isnan(output["hidden_states"]).any()
|
|
|
|
|
|
@pytest.mark.parametrize("config_kwargs", CONFIGS)
|
|
def test_model_forward_with_padding(config_kwargs, device):
|
|
from astrai.config.model_config import AutoRegressiveLMConfig
|
|
|
|
config = AutoRegressiveLMConfig(**config_kwargs)
|
|
model = AutoRegressiveLM(config).to(device=device)
|
|
model.eval()
|
|
|
|
batch_size, seq_len = 2, 8
|
|
input_ids = torch.randint(
|
|
0, config.vocab_size, (batch_size, seq_len), device=device
|
|
)
|
|
input_mask = torch.ones(batch_size, seq_len, dtype=torch.bool, device=device)
|
|
input_mask[:, 4:] = False
|
|
|
|
with torch.no_grad():
|
|
output = model(input_ids, input_mask=input_mask)
|
|
|
|
assert output["logits"].shape == (batch_size, seq_len, config.vocab_size)
|
|
assert not torch.isnan(output["logits"]).any()
|
|
|
|
|
|
def test_moe_per_layer_ffn_resolution():
|
|
"""Verify that mlp_only_layers and decoder_sparse_step resolve FFN types correctly."""
|
|
from astrai.config.model_config import AutoRegressiveLMConfig
|
|
|
|
# mlp_only_layers: first layer dense, rest MoE
|
|
config = AutoRegressiveLMConfig(
|
|
**{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"mlp_only_layers": [0],
|
|
}
|
|
)
|
|
model = AutoRegressiveLM(config)
|
|
assert isinstance(model.layers[0].mlp, MLP)
|
|
assert not isinstance(model.layers[0].mlp, DeepSeekMoE)
|
|
assert isinstance(model.layers[1].mlp, DeepSeekMoE)
|
|
|
|
# decoder_sparse_step=2: every other layer is MoE
|
|
config2 = AutoRegressiveLMConfig(
|
|
**{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"decoder_sparse_step": 2,
|
|
}
|
|
)
|
|
model2 = AutoRegressiveLM(config2)
|
|
# layer 0 (id=0): (0+1)%2=1 != 0 -> MLP
|
|
assert isinstance(model2.layers[0].mlp, MLP)
|
|
assert not isinstance(model2.layers[0].mlp, DeepSeekMoE)
|
|
# layer 1 (id=1): (1+1)%2=0 -> MoE
|
|
assert isinstance(model2.layers[1].mlp, DeepSeekMoE)
|
|
|
|
# decoder_sparse_step=1 (default): all layers MoE
|
|
config3 = AutoRegressiveLMConfig(
|
|
**{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
}
|
|
)
|
|
model3 = AutoRegressiveLM(config3)
|
|
for layer in model3.layers:
|
|
assert isinstance(layer.mlp, DeepSeekMoE)
|
|
|
|
|
|
def test_moe_custom_intermediate_shape():
|
|
"""Verify MoE uses custom intermediate sizes when specified."""
|
|
from astrai.config.model_config import AutoRegressiveLMConfig
|
|
|
|
config = AutoRegressiveLMConfig(
|
|
**{
|
|
**TINY_CONFIG,
|
|
"attn_type": "gqa",
|
|
"ffn_type": "moe",
|
|
"n_routed_experts": 4,
|
|
"n_shared_experts": 1,
|
|
"n_activated_experts": 2,
|
|
"moe_intermediate_size": 24,
|
|
"shared_expert_intermediate_size": 20,
|
|
}
|
|
)
|
|
model = AutoRegressiveLM(config)
|
|
moe_layer = model.layers[0].mlp
|
|
assert isinstance(moe_layer, DeepSeekMoE)
|
|
# routed experts use moe_intermediate_size
|
|
for expert in moe_layer.routed_experts:
|
|
assert expert.up.weight.shape[0] == 24
|
|
assert expert.gate.weight.shape[0] == 24
|
|
assert expert.down.weight.shape[1] == 24
|
|
# shared experts use shared_expert_intermediate_size
|
|
for expert in moe_layer.shared_experts:
|
|
assert expert.up.weight.shape[0] == 20
|
|
assert expert.gate.weight.shape[0] == 20
|
|
assert expert.down.weight.shape[1] == 20
|
|
|
|
|
|
def test_moe_defaults_preserve_normalized_routing():
|
|
from astrai.config.model_config import AutoRegressiveLMConfig
|
|
|
|
config = AutoRegressiveLMConfig(
|
|
**TINY_CONFIG,
|
|
ffn_type="moe",
|
|
n_routed_experts=4,
|
|
n_shared_experts=1,
|
|
n_activated_experts=2,
|
|
topk_method="greedy",
|
|
)
|
|
model = AutoRegressiveLM(config)
|
|
|
|
assert config.norm_topk_prob is True
|
|
assert model.layers[0].mlp.norm_topk_prob is True
|
|
|
|
|
|
@pytest.mark.parametrize("decoder_sparse_step", [0, -1])
|
|
def test_moe_rejects_invalid_decoder_sparse_step(decoder_sparse_step):
|
|
from pydantic import ValidationError
|
|
|
|
from astrai.config.model_config import AutoRegressiveLMConfig
|
|
|
|
with pytest.raises(ValidationError, match="decoder_sparse_step must be at least 1"):
|
|
AutoRegressiveLMConfig(
|
|
**TINY_CONFIG,
|
|
ffn_type="moe",
|
|
n_routed_experts=4,
|
|
n_activated_experts=2,
|
|
decoder_sparse_step=decoder_sparse_step,
|
|
)
|