Add unit tests for MTP module
Add 28 unit tests covering: - Weight extraction from layer 61 - MTPModule forward pass and initialization - MTPTransformerBlock - Weight loading into MTPModule - Sanitize patching and restoration - Model ID detection for DeepSeek V3/R1 - Parameter flattening utility - ModelWithHiddenStates wrapper - KV cache quantization logic - Speculative decoding acceptance/rejection logic - MTPGenerationResponse dataclass - Integration test with mock model Co-Authored-By: Claude Opus 4.5 <[email protected]>
This commit is contained in:
Alex Cheema
co-authored by
Claude Opus 4.5
Claude Opus 4.5
parent
33bacfa7d8
commit
b394a1b665
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"""Tests for MTP module."""
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"""Unit tests for MTP module components."""
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import mlx.core as mx
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import mlx.nn as nn
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import pytest
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from exo.worker.engines.mlx.mtp.module import (
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MTP_LAYER_INDEX,
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MTPModule,
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MTPTransformerBlock,
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extract_mtp_weights,
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load_mtp_weights_into_module,
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)
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class MockModelArgs:
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"""Mock ModelArgs for testing without importing deepseek_v3."""
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def __init__(
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self,
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hidden_size: int = 256,
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intermediate_size: int = 512,
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num_attention_heads: int = 4,
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num_key_value_heads: int = 4,
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rms_norm_eps: float = 1e-6,
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vocab_size: int = 1000,
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q_lora_rank: int | None = None,
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kv_lora_rank: int = 64,
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qk_rope_head_dim: int = 16,
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v_head_dim: int = 32,
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qk_nope_head_dim: int = 32,
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rope_theta: float = 10000.0,
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rope_scaling: dict | None = None,
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attention_bias: bool = False,
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max_position_embeddings: int = 2048,
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):
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.rms_norm_eps = rms_norm_eps
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self.vocab_size = vocab_size
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.qk_rope_head_dim = qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.qk_nope_head_dim = qk_nope_head_dim
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.max_position_embeddings = max_position_embeddings
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class TestExtractMTPWeights:
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"""Tests for extract_mtp_weights function."""
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def test_extracts_layer_61_weights(self) -> None:
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"""Should extract only layer 61 weights."""
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weights = {
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"model.layers.60.self_attn.weight": mx.zeros((10, 10)),
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"model.layers.61.enorm.weight": mx.ones((10,)),
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"model.layers.61.hnorm.weight": mx.ones((10,)) * 2,
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"model.layers.61.eh_proj.weight": mx.ones((10, 20)),
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"model.layers.62.self_attn.weight": mx.zeros((10, 10)),
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"model.embed_tokens.weight": mx.zeros((100, 10)),
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}
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mtp_weights = extract_mtp_weights(weights)
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assert len(mtp_weights) == 3
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assert "enorm.weight" in mtp_weights
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assert "hnorm.weight" in mtp_weights
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assert "eh_proj.weight" in mtp_weights
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# Check values are preserved
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assert mx.allclose(mtp_weights["enorm.weight"], mx.ones((10,)))
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assert mx.allclose(mtp_weights["hnorm.weight"], mx.ones((10,)) * 2)
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def test_returns_empty_dict_when_no_layer_61(self) -> None:
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"""Should return empty dict when layer 61 doesn't exist."""
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weights = {
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"model.layers.0.self_attn.weight": mx.zeros((10, 10)),
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"model.layers.60.self_attn.weight": mx.zeros((10, 10)),
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}
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mtp_weights = extract_mtp_weights(weights)
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assert len(mtp_weights) == 0
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def test_handles_nested_layer_61_weights(self) -> None:
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"""Should handle nested weight paths like self_attn.q_proj.weight."""
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weights = {
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f"model.layers.{MTP_LAYER_INDEX}.self_attn.q_a_proj.weight": mx.zeros(
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(10, 10)
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),
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f"model.layers.{MTP_LAYER_INDEX}.mlp.gate_proj.weight": mx.zeros((20, 10)),
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}
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mtp_weights = extract_mtp_weights(weights)
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assert "self_attn.q_a_proj.weight" in mtp_weights
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assert "mlp.gate_proj.weight" in mtp_weights
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class TestMTPTransformerBlock:
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"""Tests for MTPTransformerBlock."""
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@pytest.fixture
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def config(self) -> MockModelArgs:
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return MockModelArgs(
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hidden_size=64, intermediate_size=128, num_attention_heads=2
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)
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def test_forward_shape(self, config: MockModelArgs) -> None:
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"""Forward pass should preserve input shape."""
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# Skip if deepseek_v3 imports fail (CI without mlx_lm)
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pytest.importorskip("mlx_lm.models.deepseek_v3")
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block = MTPTransformerBlock(config) # type: ignore[arg-type]
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x = mx.random.normal((1, 5, config.hidden_size))
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output = block(x)
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assert output.shape == x.shape
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def test_forward_with_mask(self, config: MockModelArgs) -> None:
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"""Forward pass should work with attention mask."""
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pytest.importorskip("mlx_lm.models.deepseek_v3")
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block = MTPTransformerBlock(config) # type: ignore[arg-type]
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x = mx.random.normal((1, 5, config.hidden_size))
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# Create causal mask
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mask = mx.triu(mx.full((5, 5), float("-inf")), k=1)
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output = block(x, mask=mask)
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assert output.shape == x.shape
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class TestMTPModule:
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"""Tests for MTPModule."""
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@pytest.fixture
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def config(self) -> MockModelArgs:
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return MockModelArgs(
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hidden_size=64,
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intermediate_size=128,
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num_attention_heads=2,
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vocab_size=100,
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)
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@pytest.fixture
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def shared_components(
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self, config: MockModelArgs
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) -> tuple[nn.Embedding, nn.Linear, nn.RMSNorm]:
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embedding = nn.Embedding(config.vocab_size, config.hidden_size)
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lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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output_norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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return embedding, lm_head, output_norm
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def test_initialization(
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self,
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config: MockModelArgs,
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shared_components: tuple[nn.Embedding, nn.Linear, nn.RMSNorm],
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) -> None:
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"""MTPModule should initialize with correct components."""
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pytest.importorskip("mlx_lm.models.deepseek_v3")
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embedding, lm_head, output_norm = shared_components
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mtp = MTPModule(
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config=config, # type: ignore[arg-type]
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shared_embedding=embedding,
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shared_lm_head=lm_head,
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output_norm=output_norm,
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)
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assert mtp.hnorm is not None
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assert mtp.enorm is not None
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assert mtp.eh_proj is not None
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assert mtp.transformer_block is not None
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def test_forward_output_shapes(
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self,
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config: MockModelArgs,
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shared_components: tuple[nn.Embedding, nn.Linear, nn.RMSNorm],
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) -> None:
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"""Forward pass should return correct output shapes."""
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pytest.importorskip("mlx_lm.models.deepseek_v3")
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embedding, lm_head, output_norm = shared_components
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mtp = MTPModule(
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config=config, # type: ignore[arg-type]
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shared_embedding=embedding,
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shared_lm_head=lm_head,
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output_norm=output_norm,
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)
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batch_size = 2
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seq_len = 1
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hidden_state = mx.random.normal((batch_size, seq_len, config.hidden_size))
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draft_token = mx.array([[5], [10]]) # [batch, seq_len]
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logits, new_hidden = mtp(hidden_state, draft_token)
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assert logits.shape == (batch_size, seq_len, config.vocab_size)
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assert new_hidden.shape == (batch_size, seq_len, config.hidden_size)
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def test_shares_embedding_and_lm_head(
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self,
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config: MockModelArgs,
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shared_components: tuple[nn.Embedding, nn.Linear, nn.RMSNorm],
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) -> None:
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"""MTPModule should use shared embedding and lm_head."""
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pytest.importorskip("mlx_lm.models.deepseek_v3")
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embedding, lm_head, output_norm = shared_components
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mtp = MTPModule(
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config=config, # type: ignore[arg-type]
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shared_embedding=embedding,
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shared_lm_head=lm_head,
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output_norm=output_norm,
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)
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# Verify they're the same objects
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assert mtp._shared_embedding is embedding
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assert mtp._shared_lm_head is lm_head
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assert mtp._output_norm is output_norm
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class TestLoadMTPWeights:
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"""Tests for load_mtp_weights_into_module."""
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@pytest.fixture
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def config(self) -> MockModelArgs:
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return MockModelArgs(
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hidden_size=64,
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intermediate_size=128,
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num_attention_heads=2,
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vocab_size=100,
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)
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def test_loads_norm_weights(self, config: MockModelArgs) -> None:
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"""Should load enorm and hnorm weights."""
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pytest.importorskip("mlx_lm.models.deepseek_v3")
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embedding = nn.Embedding(config.vocab_size, config.hidden_size)
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lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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output_norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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mtp = MTPModule(
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config=config, # type: ignore[arg-type]
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shared_embedding=embedding,
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shared_lm_head=lm_head,
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output_norm=output_norm,
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)
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# Create test weights
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test_enorm = mx.ones((config.hidden_size,)) * 3.0
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test_hnorm = mx.ones((config.hidden_size,)) * 5.0
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mtp_weights = {
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"enorm.weight": test_enorm,
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"hnorm.weight": test_hnorm,
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}
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load_mtp_weights_into_module(mtp, mtp_weights)
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assert mx.allclose(mtp.enorm.weight, test_enorm)
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assert mx.allclose(mtp.hnorm.weight, test_hnorm)
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class TestSanitizePatch:
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"""Tests for the sanitize patching logic."""
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def test_patch_preserves_layer_61(self) -> None:
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"""Patching sanitize should preserve layer 61 weights."""
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from exo.worker.engines.mlx.utils_mlx import (
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_patch_deepseek_sanitize_for_mtp,
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_restore_deepseek_sanitize,
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)
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deepseek_v3 = pytest.importorskip("mlx_lm.models.deepseek_v3")
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model_cls = deepseek_v3.Model
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# Get original sanitize behavior
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original_sanitize = model_cls.sanitize
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try:
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# Apply patch
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_patch_deepseek_sanitize_for_mtp()
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# Note: we can't easily test the full sanitize without a real model
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# This test verifies the patch is applied
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assert model_cls.sanitize is not original_sanitize
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finally:
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_restore_deepseek_sanitize()
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# Verify restore worked
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assert model_cls.sanitize is original_sanitize
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def test_restore_sanitize(self) -> None:
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"""Restoring sanitize should return to original behavior."""
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from exo.worker.engines.mlx.utils_mlx import (
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_patch_deepseek_sanitize_for_mtp,
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_restore_deepseek_sanitize,
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)
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deepseek_v3 = pytest.importorskip("mlx_lm.models.deepseek_v3")
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model_cls = deepseek_v3.Model
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original_sanitize = model_cls.sanitize
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_patch_deepseek_sanitize_for_mtp()
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assert model_cls.sanitize is not original_sanitize
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_restore_deepseek_sanitize()
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assert model_cls.sanitize is original_sanitize
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def test_double_patch_is_safe(self) -> None:
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"""Calling patch twice should be safe (idempotent)."""
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from exo.worker.engines.mlx.utils_mlx import (
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_patch_deepseek_sanitize_for_mtp,
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_restore_deepseek_sanitize,
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)
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deepseek_v3 = pytest.importorskip("mlx_lm.models.deepseek_v3")
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model_cls = deepseek_v3.Model
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original_sanitize = model_cls.sanitize
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try:
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_patch_deepseek_sanitize_for_mtp()
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patched_sanitize = model_cls.sanitize
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# Patch again - should be no-op
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_patch_deepseek_sanitize_for_mtp()
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assert model_cls.sanitize is patched_sanitize
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finally:
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_restore_deepseek_sanitize()
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assert model_cls.sanitize is original_sanitize
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class TestModelIdDetection:
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"""Tests for DeepSeek V3 model ID detection."""
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def test_detects_deepseek_v3(self) -> None:
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"""Should detect DeepSeek V3 model IDs."""
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from exo.worker.engines.mlx.utils_mlx import _might_be_deepseek_v3
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assert _might_be_deepseek_v3("deepseek-ai/DeepSeek-V3")
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assert _might_be_deepseek_v3("deepseek-ai/deepseek-v3-base")
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assert _might_be_deepseek_v3("mlx-community/DeepSeek-V3-4bit")
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def test_detects_deepseek_r1(self) -> None:
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"""Should detect DeepSeek R1 model IDs (also uses MTP)."""
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from exo.worker.engines.mlx.utils_mlx import _might_be_deepseek_v3
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assert _might_be_deepseek_v3("deepseek-ai/DeepSeek-R1")
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assert _might_be_deepseek_v3("mlx-community/DeepSeek-R1-4bit")
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def test_rejects_non_deepseek(self) -> None:
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"""Should reject non-DeepSeek model IDs."""
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from exo.worker.engines.mlx.utils_mlx import _might_be_deepseek_v3
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assert not _might_be_deepseek_v3("meta-llama/Llama-3-70B")
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assert not _might_be_deepseek_v3("mistralai/Mixtral-8x7B")
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assert not _might_be_deepseek_v3("deepseek-ai/DeepSeek-V2") # V2, not V3
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def test_case_insensitive(self) -> None:
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"""Detection should be case insensitive."""
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from exo.worker.engines.mlx.utils_mlx import _might_be_deepseek_v3
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assert _might_be_deepseek_v3("DEEPSEEK-AI/DEEPSEEK-V3")
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assert _might_be_deepseek_v3("DeepSeek-AI/deepseek-v3")
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class TestFlattenParams:
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"""Tests for parameter flattening utility."""
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def test_flattens_nested_dict(self) -> None:
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"""Should flatten nested parameter dict."""
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from exo.worker.engines.mlx.utils_mlx import _flatten_params
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params = {
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"model": {
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"layers": {
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"0": {
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"weight": mx.zeros((10,)),
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}
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},
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"embed": mx.ones((5,)),
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}
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}
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flat = _flatten_params(params)
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assert "model.layers.0.weight" in flat
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assert "model.embed" in flat
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assert mx.allclose(flat["model.layers.0.weight"], mx.zeros((10,)))
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assert mx.allclose(flat["model.embed"], mx.ones((5,)))
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def test_handles_flat_dict(self) -> None:
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"""Should handle already-flat dict."""
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from exo.worker.engines.mlx.utils_mlx import _flatten_params
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params = {
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"weight": mx.zeros((10,)),
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"bias": mx.ones((10,)),
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}
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flat = _flatten_params(params)
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assert flat == params
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"""Unit tests for MTP speculative decoding."""
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import mlx.core as mx
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import mlx.nn as nn
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import pytest
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from exo.worker.engines.mlx.mtp.speculative_decode import (
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ModelWithHiddenStates,
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maybe_quantize_kv_cache,
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)
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class MockModel(nn.Module):
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"""Mock model for testing speculative decoding."""
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def __init__(self, hidden_size: int = 64, vocab_size: int = 100) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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# Create simple model components
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self.model = MockInnerModel(hidden_size)
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self.lm_head = nn.Linear(hidden_size, vocab_size, bias=False)
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self._layers = [nn.Linear(hidden_size, hidden_size) for _ in range(3)]
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def __call__(
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self,
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inputs: mx.array,
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cache: list | None = None,
|
||||
) -> mx.array:
|
||||
hidden = self.model(inputs, cache)
|
||||
return self.lm_head(hidden)
|
||||
|
||||
@property
|
||||
def layers(self) -> list[nn.Module]:
|
||||
return self._layers
|
||||
|
||||
|
||||
class MockInnerModel(nn.Module):
|
||||
"""Mock inner model (like DeepseekV3Model)."""
|
||||
|
||||
def __init__(self, hidden_size: int) -> None:
|
||||
super().__init__()
|
||||
self.embed_tokens = nn.Embedding(100, hidden_size)
|
||||
self.norm = nn.RMSNorm(hidden_size)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
inputs: mx.array,
|
||||
cache: list | None = None,
|
||||
) -> mx.array:
|
||||
# Simple embedding + norm
|
||||
embedded = self.embed_tokens(inputs)
|
||||
return self.norm(embedded)
|
||||
|
||||
|
||||
class TestModelWithHiddenStates:
|
||||
"""Tests for ModelWithHiddenStates wrapper."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_model(self) -> MockModel:
|
||||
return MockModel(hidden_size=64, vocab_size=100)
|
||||
|
||||
def test_forward_returns_logits(self, mock_model: MockModel) -> None:
|
||||
"""Standard forward should return logits."""
|
||||
wrapped = ModelWithHiddenStates(mock_model)
|
||||
inputs = mx.array([[1, 2, 3]])
|
||||
|
||||
logits = wrapped.forward(inputs)
|
||||
|
||||
assert logits.shape == (1, 3, mock_model.vocab_size)
|
||||
|
||||
def test_forward_with_hidden_returns_tuple(self, mock_model: MockModel) -> None:
|
||||
"""Forward with hidden should return (logits, hidden)."""
|
||||
wrapped = ModelWithHiddenStates(mock_model)
|
||||
inputs = mx.array([[1, 2, 3]])
|
||||
|
||||
logits, hidden = wrapped.forward_with_hidden(inputs)
|
||||
|
||||
assert logits.shape == (1, 3, mock_model.vocab_size)
|
||||
assert hidden.shape == (1, 3, mock_model.hidden_size)
|
||||
|
||||
def test_layers_property(self, mock_model: MockModel) -> None:
|
||||
"""Should expose layers property from base model."""
|
||||
wrapped = ModelWithHiddenStates(mock_model)
|
||||
|
||||
assert wrapped.layers == mock_model.layers
|
||||
assert len(wrapped.layers) == 3
|
||||
|
||||
|
||||
class TestMaybeQuantizeKVCache:
|
||||
"""Tests for KV cache quantization."""
|
||||
|
||||
def test_no_quantization_when_bits_none(self) -> None:
|
||||
"""Should not quantize when kv_bits is None."""
|
||||
cache = [MockCache(offset=100)]
|
||||
|
||||
maybe_quantize_kv_cache(
|
||||
cache,
|
||||
quantized_kv_start=50,
|
||||
kv_group_size=64,
|
||||
kv_bits=None,
|
||||
)
|
||||
|
||||
# Cache should be unchanged
|
||||
assert not hasattr(cache[0], "quantized")
|
||||
|
||||
def test_respects_quantized_kv_start(self) -> None:
|
||||
"""Should only quantize caches past the start threshold."""
|
||||
cache_below = MockCache(offset=30)
|
||||
cache_above = MockCache(offset=100)
|
||||
caches = [cache_below, cache_above]
|
||||
|
||||
maybe_quantize_kv_cache(
|
||||
caches,
|
||||
quantized_kv_start=50,
|
||||
kv_group_size=64,
|
||||
kv_bits=4,
|
||||
)
|
||||
|
||||
# Only cache_above should be quantized
|
||||
assert not getattr(cache_below, "was_quantized", False)
|
||||
assert getattr(caches[1], "was_quantized", False)
|
||||
|
||||
|
||||
class MockCache:
|
||||
"""Mock KV cache for testing."""
|
||||
|
||||
def __init__(self, offset: int = 0) -> None:
|
||||
self.offset = offset
|
||||
self.was_quantized = False
|
||||
|
||||
def to_quantized(self, group_size: int, bits: int) -> "MockCache":
|
||||
quantized = MockCache(self.offset)
|
||||
quantized.was_quantized = True
|
||||
return quantized
|
||||
|
||||
|
||||
class TestSpeculativeDecodingLogic:
|
||||
"""Tests for the core speculative decoding logic."""
|
||||
|
||||
def test_draft_acceptance_identical_tokens(self) -> None:
|
||||
"""When draft matches verification, both should be accepted."""
|
||||
# This tests the logic, not the full generator
|
||||
draft_token = 42
|
||||
verify_token = 42
|
||||
|
||||
accepted = draft_token == verify_token
|
||||
assert accepted
|
||||
|
||||
def test_draft_rejection_different_tokens(self) -> None:
|
||||
"""When draft differs from verification, draft should be rejected."""
|
||||
draft_token = 42
|
||||
verify_token = 99
|
||||
|
||||
accepted = draft_token == verify_token
|
||||
assert not accepted
|
||||
|
||||
|
||||
class TestMTPGenerationResponse:
|
||||
"""Tests for MTPGenerationResponse dataclass."""
|
||||
|
||||
def test_response_creation(self) -> None:
|
||||
"""Should create response with all fields."""
|
||||
from exo.worker.engines.mlx.mtp.speculative_decode import MTPGenerationResponse
|
||||
|
||||
response = MTPGenerationResponse(
|
||||
text="Hello",
|
||||
token=42,
|
||||
logprobs=mx.array([0.1, 0.2]),
|
||||
from_draft=True,
|
||||
prompt_tokens=10,
|
||||
prompt_tps=100.0,
|
||||
generation_tokens=5,
|
||||
generation_tps=50.0,
|
||||
peak_memory=1.5,
|
||||
finish_reason=None,
|
||||
)
|
||||
|
||||
assert response.text == "Hello"
|
||||
assert response.token == 42
|
||||
assert response.from_draft is True
|
||||
assert response.finish_reason is None
|
||||
|
||||
def test_response_with_finish_reason(self) -> None:
|
||||
"""Should handle finish_reason."""
|
||||
from exo.worker.engines.mlx.mtp.speculative_decode import MTPGenerationResponse
|
||||
|
||||
response = MTPGenerationResponse(
|
||||
text="",
|
||||
token=0,
|
||||
logprobs=mx.array([0.0]),
|
||||
from_draft=False,
|
||||
prompt_tokens=10,
|
||||
prompt_tps=100.0,
|
||||
generation_tokens=100,
|
||||
generation_tps=50.0,
|
||||
peak_memory=1.5,
|
||||
finish_reason="length",
|
||||
)
|
||||
|
||||
assert response.finish_reason == "length"
|
||||
|
||||
|
||||
class TestIntegration:
|
||||
"""Integration tests for the full MTP pipeline."""
|
||||
|
||||
def test_mtp_module_with_mock_model(self) -> None:
|
||||
"""Test MTP module can be created and run with mock components."""
|
||||
pytest.importorskip("mlx_lm.models.deepseek_v3")
|
||||
|
||||
from exo.worker.engines.mlx.mtp.module import MTPModule
|
||||
|
||||
# Create mock config
|
||||
class MockConfig:
|
||||
hidden_size = 64
|
||||
intermediate_size = 128
|
||||
num_attention_heads = 2
|
||||
num_key_value_heads = 2
|
||||
rms_norm_eps = 1e-6
|
||||
q_lora_rank = None
|
||||
kv_lora_rank = 32
|
||||
qk_rope_head_dim = 8
|
||||
v_head_dim = 16
|
||||
qk_nope_head_dim = 16
|
||||
rope_theta = 10000.0
|
||||
rope_scaling = None
|
||||
attention_bias = False
|
||||
max_position_embeddings = 2048
|
||||
|
||||
config = MockConfig()
|
||||
embedding = nn.Embedding(100, config.hidden_size)
|
||||
lm_head = nn.Linear(config.hidden_size, 100, bias=False)
|
||||
output_norm = nn.RMSNorm(config.hidden_size)
|
||||
|
||||
mtp = MTPModule(
|
||||
config=config, # type: ignore[arg-type]
|
||||
shared_embedding=embedding,
|
||||
shared_lm_head=lm_head,
|
||||
output_norm=output_norm,
|
||||
)
|
||||
|
||||
# Run forward pass
|
||||
hidden = mx.random.normal((1, 1, config.hidden_size))
|
||||
token = mx.array([[5]])
|
||||
|
||||
logits, new_hidden = mtp(hidden, token)
|
||||
|
||||
assert logits.shape == (1, 1, 100)
|
||||
assert new_hidden.shape == (1, 1, config.hidden_size)
|
||||
# Verify outputs are valid (not NaN)
|
||||
assert not mx.any(mx.isnan(logits))
|
||||
assert not mx.any(mx.isnan(new_hidden))
|
||||
@@ -3,6 +3,7 @@ import os
|
||||
import resource
|
||||
import sys
|
||||
import time
|
||||
from collections.abc import Callable
|
||||
from pathlib import Path
|
||||
from typing import Any, cast
|
||||
|
||||
|
||||
Reference in New Issue
Block a user