aszc-dev
31774e3324
Carry only the code, tests, and user-facing docs that matter to end users; drop the modernization scaffolding accumulated while building it. - Remove the bench harness, results, and environment captures (bench/). - Remove internal docs and research spikes (docs/). - Remove the Makefile; tests run via uv / pytest directly. - Strip the bench harness and quantization-matrix steps from the Tier 2 workflow. The golden-image test drives conversion through the Core ML Converter node at runtime, so no separate convert step is needed. - Replace phase/handoff annotations across code, tests, and config with neutral docstrings and comments.
123 lines
3.4 KiB
Python
123 lines
3.4 KiB
Python
"""Smoke tests for the pure batch-chunking helpers in coreml_suite.core.
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Uses torch.device('cpu') instead of comfy.model_management.get_torch_device
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so Tier 0 runs without ComfyUI.
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"""
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import pytest
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import torch
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from coreml_suite.core.controlnet import chunk_control
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from coreml_suite.core.inputs import CoreMLInputs
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from coreml_suite.core.latents import chunk_batch, merge_chunks
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CPU = torch.device("cpu")
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@pytest.fixture
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def expected_inputs():
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return {
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"sample": {"shape": (2, 4, 64, 64)},
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"timestep": {"shape": (2,)},
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"timestep_cond": {"shape": (2, 256)},
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"encoder_hidden_states": {"shape": (2, 768, 1, 77)},
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"additional_residual_0": {"shape": (2, 320, 64, 64)},
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"additional_residual_1": {"shape": (2, 640, 32, 32)},
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}
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@pytest.mark.parametrize("batch_size", [1, 2, 4, 5, 9])
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def test_batch_chunking(batch_size):
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latent_image = torch.randn(batch_size, 4, 64, 64).to(CPU)
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target_shape = (4, 4, 64, 64)
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chunked = chunk_batch(latent_image, target_shape)
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for chunk in chunked:
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assert chunk.shape == target_shape
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if batch_size % target_shape[0] != 0:
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assert chunked[-1][batch_size % target_shape[0] :].sum() == 0
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@pytest.mark.parametrize("batch_size", [1, 2, 4, 5, 9])
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def test_merge_chunks(batch_size):
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input_tensor = torch.randn(batch_size, 4, 64, 64).to(CPU)
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target_shape = (4, 4, 64, 64)
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chunked = chunk_batch(input_tensor, target_shape)
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merged = merge_chunks(chunked, input_tensor.shape)
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assert merged.shape == input_tensor.shape
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assert torch.equal(input_tensor, merged)
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@pytest.fixture
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def inputs():
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x = torch.randn(1, 4, 64, 64).to(CPU)
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t = torch.randn([1]).to(CPU)
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c_crossattn = torch.randn(1, 77, 768).to(CPU)
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control = {
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"output": [
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torch.randn(1, 320, 64, 64).to(CPU),
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torch.randn(1, 640, 32, 32).to(CPU),
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],
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}
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timestep_cond = torch.randn(1, 256).to(CPU)
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return CoreMLInputs(x, t, c_crossattn, control, timestep_cond=timestep_cond)
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@pytest.mark.parametrize(
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"b, target_size, num_chunks",
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[
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(1, 2, 1),
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(1, 1, 1),
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(2, 2, 1),
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(3, 2, 2),
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(4, 2, 2),
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(5, 3, 2),
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(9, 4, 3),
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],
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)
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def test_chunking_controlnet(b, target_size, num_chunks):
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cn = {
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"output": [
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torch.randn(b, 320, 64, 64).to(CPU),
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torch.randn(b, 640, 32, 32).to(CPU),
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],
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"middle": [
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torch.randn(b, 1280, 8, 8).to(CPU),
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],
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}
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chunked = chunk_control(cn, target_size)
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assert len(chunked) == num_chunks
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for chunk in chunked:
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assert chunk["output"][0].shape == (target_size, 320, 64, 64)
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assert chunk["output"][1].shape == (target_size, 640, 32, 32)
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assert chunk["middle"][0].shape == (target_size, 1280, 8, 8)
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def test_chunking_no_control():
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cn = None
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target_size = 2
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chunked = chunk_control(cn, target_size)
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assert chunked == [None, None]
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def test_chunking_inputs(expected_inputs, inputs):
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chunked = inputs.chunks(expected_inputs)
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assert len(chunked) == 1
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assert chunked[0].x.shape == (2, 4, 64, 64)
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assert chunked[0].t.shape == (2,)
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assert chunked[0].context.shape == (2, 77, 768)
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assert chunked[0].control["output"][0].shape == (2, 320, 64, 64)
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assert chunked[0].control["output"][1].shape == (2, 640, 32, 32)
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assert chunked[0].ts_cond.shape == (2, 256)
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