256 lines
11 KiB
Python
256 lines
11 KiB
Python
import pytest
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import torch
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from pytest_mock import MockerFixture
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from tests.helpers.mark import hardware_test
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from vllm_omni.diffusion.models.flux2.flux2_transformer import (
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Flux2PosEmbed,
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Flux2Transformer2DModel,
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)
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# Initialize TP group before tests
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@pytest.fixture(scope="function", autouse=True)
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def setup_tp_group(mocker: MockerFixture):
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"""Set up TP group for each test function"""
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mocker.patch(
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"vllm.model_executor.layers.linear.get_tensor_model_parallel_world_size",
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return_value=2,
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)
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mock_get_tp_group = mocker.patch("vllm.distributed.parallel_state.get_tp_group")
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mock_tp_group = mocker.MagicMock()
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mock_tp_group.world_size = 2
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mock_get_tp_group.return_value = mock_tp_group
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yield
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class TestFlux2TransformerWeightLoading:
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"""Test Flux2Transformer weight loading functionality"""
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@pytest.mark.core_model
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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def test_weight_loading_tp2(self, setup_tp_group):
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"""Verify weights load correctly with TP=2"""
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# Prepare test data
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model = Flux2Transformer2DModel(
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num_layers=2,
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num_single_layers=1,
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num_attention_heads=48,
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attention_head_dim=128,
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joint_attention_dim=15360,
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)
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# Mock TP=2 weight loading
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mock_weights = []
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# 1. Test regular weight loading
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mock_weights.append(("x_embedder.weight", torch.randn(6144, 128)))
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mock_weights.append(("context_embedder.weight", torch.randn(6144, 15360)))
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mock_weights.append(("proj_out.weight", torch.randn(128, 6144)))
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# 2. Test stacked_params_mapping weight loading
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# Full weights - load_weights handles sharding internally
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to_qkv_weight = torch.randn(18432, 6144) # Full weights
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mock_weights.append(("transformer_blocks.0.attn.to_qkv.weight", to_qkv_weight))
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# Add_kv_proj weights
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add_kv_proj_weight = torch.randn(18432, 6144) # Full weights
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mock_weights.append(("transformer_blocks.0.attn.add_kv_proj.weight", add_kv_proj_weight))
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# 3. Test single block weight loading
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single_block_qkv_mlp_weight = torch.randn(18432 + 18432 * 3, 6144) # Full weights
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mock_weights.append(("single_transformer_blocks.0.attn.to_qkv_mlp_proj.weight", single_block_qkv_mlp_weight))
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# Execute weight loading
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loaded_params = model.load_weights(mock_weights)
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# Verify
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assert len(loaded_params) > 0, "Parameters should be loaded"
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# Verify stacked_params_mapping is correctly set
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assert model.stacked_params_mapping is not None
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# Should have 6 mappings: 3 for to_qkv + 3 for add_kv_proj
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assert len(model.stacked_params_mapping) == 6, "Should have 6 mappings"
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# Verify weights are correctly loaded to corresponding layers
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assert hasattr(model.transformer_blocks[0].attn.to_qkv, "weight")
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# With TP=2, weight dimension on each GPU should be 18432/2 = 9216
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assert model.transformer_blocks[0].attn.to_qkv.weight.shape == (9216, 6144), (
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f"With TP=2, to_qkv weight dimension should be (9216, 6144), got {model.transformer_blocks[0].attn.to_qkv.weight.shape}"
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)
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class TestFlux2RopePositionEmbedding:
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"""Test Flux2 RoPE position embedding functionality"""
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@pytest.mark.core_model
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@pytest.mark.cpu
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def test_rope_position_embedding(self):
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"""Verify RoPE produces correct embeddings for 4D coordinates"""
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# Prepare test data - use model default configuration
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# axes_dims_rope default is (32, 32, 32, 32)
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# get_1d_rotary_pos_embed outputs half the dimension for real/imag parts
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# So actual output dimension should be (16+16+16+16) = 64
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axes_dims = (32, 32, 32, 32) # Use model default
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rope_theta = 2000 # Model default is 2000
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pos_embed = Flux2PosEmbed(theta=rope_theta, axes_dim=axes_dims)
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# Create test IDs
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seq_len = 10
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ids = torch.randint(0, 100, (seq_len, 4)) # [S, 4]
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# Forward pass
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freqs_cos, freqs_sin = pos_embed(ids)
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# Verify output shape - based on model config, expected dimension is 64
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# Each axes_dim=32 outputs 16-dim real part, sum of 4 dimensions = 64
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expected_dim = sum(axes_dims) // 2 # 128/2 = 64
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assert freqs_cos.shape == (seq_len, expected_dim), (
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f"Expected shape {(seq_len, expected_dim)}, got {freqs_cos.shape}"
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)
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assert freqs_sin.shape == (seq_len, expected_dim), (
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f"Expected shape {(seq_len, expected_dim)}, got {freqs_sin.shape}"
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)
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# Verify output type - NPU may use float32, other devices use float64
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assert freqs_cos.dtype in [torch.float32, torch.float64], "Should be float type"
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assert freqs_sin.dtype in [torch.float32, torch.float64], "Should be float type"
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# Verify value range
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assert torch.all(freqs_cos >= -1) and torch.all(freqs_cos <= 1), "cos values should be in [-1, 1]"
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assert torch.all(freqs_sin >= -1) and torch.all(freqs_sin <= 1), "sin values should be in [-1, 1]"
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# Verify trigonometric relationship: cos^2 + sin^2 ≈ 1
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cos_sq_sin_sq = freqs_cos**2 + freqs_sin**2
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assert torch.allclose(cos_sq_sin_sq, torch.ones_like(cos_sq_sin_sq), atol=1e-6), "cos^2 + sin^2 should ≈ 1"
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# Verify different positions produce different embeddings
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ids_diff = torch.randint(0, 100, (seq_len, 4))
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freqs_cos_diff, freqs_sin_diff = pos_embed(ids_diff)
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assert not torch.allclose(freqs_cos, freqs_cos_diff), "Different positions should produce different embeddings"
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# Verify same positions produce same embeddings
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ids_same = ids.clone()
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freqs_cos_same, freqs_sin_same = pos_embed(ids_same)
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assert torch.allclose(freqs_cos, freqs_cos_same), "Same positions should produce same embeddings"
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assert torch.allclose(freqs_sin, freqs_sin_same), "Same positions should produce same embeddings"
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class TestFlux2PackedModuleMapping:
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"""Test Flux2 packed module mapping functionality"""
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@pytest.mark.core_model
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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def test_packed_module_mapping(self, setup_tp_group):
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"""Verify to_qkv packing matches HF checkpoint"""
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model = Flux2Transformer2DModel(
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num_layers=1,
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num_single_layers=0,
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num_attention_heads=48,
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attention_head_dim=128,
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joint_attention_dim=15360,
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)
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# Verify stacked_params_mapping is correctly initialized
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model.load_weights([]) # Trigger stacked_params_mapping initialization
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assert model.stacked_params_mapping is not None
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# Verify mapping configuration
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expected_mappings = [
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(".to_qkv.", ".to_q.", "q"),
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(".to_qkv.", ".to_k.", "k"),
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(".to_qkv.", ".to_v.", "v"),
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(".add_kv_proj", ".add_q_proj", "q"),
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(".add_kv_proj", ".add_k_proj", "k"),
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(".add_kv_proj", ".add_v_proj", "v"),
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]
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assert model.stacked_params_mapping == expected_mappings
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# Create mock HF checkpoint weights
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hf_weights = []
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# Mock HF separated Q/K/V weights
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attn_block = model.transformer_blocks[0].attn
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head_dim = 128
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num_heads = 48
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hidden_size = 6144
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# Full weight dimension
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full_shard_size = num_heads * head_dim # 6144
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# Q projection weights (full weights)
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q_weight = torch.randn(full_shard_size, hidden_size)
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hf_weights.append(("transformer_blocks.0.attn.to_q.weight", q_weight))
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# K projection weights (full weights)
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k_weight = torch.randn(full_shard_size, hidden_size)
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hf_weights.append(("transformer_blocks.0.attn.to_k.weight", k_weight))
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# V projection weights (full weights)
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v_weight = torch.randn(full_shard_size, hidden_size)
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hf_weights.append(("transformer_blocks.0.attn.to_v.weight", v_weight))
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# Mock HF separated add_q/k/v projection weights
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add_q_weight = torch.randn(full_shard_size, hidden_size)
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hf_weights.append(("transformer_blocks.0.attn.add_q_proj.weight", add_q_weight))
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add_k_weight = torch.randn(full_shard_size, hidden_size)
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hf_weights.append(("transformer_blocks.0.attn.add_k_proj.weight", add_k_weight))
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add_v_weight = torch.randn(full_shard_size, hidden_size)
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hf_weights.append(("transformer_blocks.0.attn.add_v_proj.weight", add_v_weight))
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# Execute weight loading
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loaded_params = model.load_weights(hf_weights)
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# Verify weights are loaded
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assert len(loaded_params) > 0
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# Verify final QKV weights are correctly combined (considering TP sharding)
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# With TP=2, dimension on each GPU should be half of full dimension
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expected_qkv_shape = (full_shard_size * 3 // 2, hidden_size) # 18432/2 = 9216
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assert attn_block.to_qkv.weight.shape == expected_qkv_shape, (
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f"to_qkv weight dimension should be {expected_qkv_shape}, got {attn_block.to_qkv.weight.shape}"
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)
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expected_add_kv_shape = (full_shard_size * 3 // 2, hidden_size)
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assert attn_block.add_kv_proj.weight.shape == expected_add_kv_shape, (
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f"add_kv_proj weight dimension should be {expected_add_kv_shape}, got {attn_block.add_kv_proj.weight.shape}"
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)
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@pytest.mark.core_model
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@hardware_test(res={"cuda": "L4"}, num_cards=1)
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def test_packed_mapping_edge_cases(self, setup_tp_group):
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"""Test edge cases for packed mapping"""
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model = Flux2Transformer2DModel(
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num_layers=1,
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num_single_layers=1,
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num_attention_heads=48,
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attention_head_dim=128,
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joint_attention_dim=15360,
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)
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model.load_weights([])
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# Test invalid weight names
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invalid_weights = [("invalid.weight", torch.randn(10, 10))]
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loaded_params = model.load_weights(invalid_weights)
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assert len(loaded_params) == 0, "Should not load invalid weights"
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# Test to_out weight renaming
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to_out_weight = torch.randn(6144, 6144)
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weights = [("transformer_blocks.0.attn.to_out.0.weight", to_out_weight)]
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loaded_params = model.load_weights(weights)
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# Check if to_out related weights are loaded
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to_out_loaded = any("to_out" in p for p in loaded_params)
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assert to_out_loaded, "to_out weights should be correctly renamed and loaded"
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# Test partial weight loading
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partial_weights = [
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("x_embedder.weight", torch.randn(6144, 128)),
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("transformer_blocks.0.attn.to_q.weight", torch.randn(6144, 6144)), # Full weights
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]
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loaded_params = model.load_weights(partial_weights)
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assert len(loaded_params) == 2, "Should load two weights"
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assert "x_embedder.weight" in loaded_params
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assert any("to_qkv" in p for p in loaded_params), "to_q should be mapped to to_qkv"
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