{"record":{"id":"d548cbb6235400da","repo":"hpcaitech/Open-Sora","slug":"no-chunks-were-generated-input-shape-x-shape","errorCode":null,"errorMessage":"No chunks were generated. Input shape: {x.shape}","messagePattern":"No chunks were generated\\. Input shape: (.+?)","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"opensora/models/dc_ae/models/nn/vo_ops.py","lineNumber":138,"sourceCode":"    if VERBOSE:\n        print(f\"Input channels: {x.shape[1]}\")\n        print(f\"Chunk size: {chunk_size}\")\n        print(f\"max_channels: {max_channels}\")\n        print(f\"num_chunks: {math.ceil(x.shape[1] / chunk_size)}\")\n\n    chunks = []\n    for i in range(0, x.shape[1], chunk_size):\n        start_idx = i\n        end_idx = min(i + chunk_size, x.shape[1])\n\n        chunk = x[:, start_idx:end_idx, :, :, :]\n\n        interpolated_chunk = F.interpolate(chunk, scale_factor=scale_factor, mode=\"nearest\")\n\n        chunks.append(interpolated_chunk)\n\n    if not chunks:\n        raise ValueError(f\"No chunks were generated. Input shape: {x.shape}\")\n\n    # Concatenate chunks along channel dimension\n    return torch.cat(chunks, dim=1)\n\n\ndef test_chunked_interpolate():\n    # Test case 1: Basic upscaling with scale_factor\n    x1 = torch.randn(2, 16, 16, 32, 32).cuda()\n    scale_factor = (2.0, 2.0, 2.0)\n    assert torch.allclose(\n        chunked_interpolate(x1, scale_factor=scale_factor), F.interpolate(x1, scale_factor=scale_factor, mode=\"nearest\")\n    )\n\n    # Test case 3: Downscaling with scale_factor\n    x3 = torch.randn(2, 16, 32, 64, 64).cuda()\n    scale_factor = (0.5, 0.5, 0.5)\n    assert torch.allclose(\n        chunked_interpolate(x3, scale_factor=scale_factor), F.interpolate(x3, scale_factor=scale_factor, mode=\"nearest\")","sourceCodeStart":120,"sourceCodeEnd":156,"githubUrl":"https://github.com/hpcaitech/Open-Sora/blob/7ad6a96a135feb81f755c84fb391818718f6beb2/opensora/models/dc_ae/models/nn/vo_ops.py#L120-L156","documentation":"Raised by chunked_interpolate in opensora's dc_ae nn ops when the chunking loop produced zero chunks, meaning the input tensor's channel dimension (or chunk size argument) resulted in no slices to interpolate. The function splits x along dim=1 and concatenates interpolated chunks; if chunks is empty the cat would otherwise fail obscurely, so this guard reports the input shape. It almost always indicates a chunk_size <= 0 or an empty/zero-channel input tensor.","triggerScenarios":"Calling chunked_interpolate(x, chunk_size=...) with chunk_size <= 0, or passing a tensor with x.shape[1] == 0 (zero channels). Also reachable via the module's forward() which delegates to this helper.","commonSituations":"Misconfigured chunk size hyperparameter in a dc_ae autoencoder config (e.g. 0 or negative from a YAML typo), or an upstream slicing/concatenation bug that produced an empty channel dimension.","solutions":["Check that chunk_size is a positive integer (>= 1) before calling chunked_interpolate","Verify the input tensor x has a non-zero channel dimension: assert x.shape[1] > 0","Trace where the input tensor was constructed; if channels are computed from a config, validate that value","Add a unit test mirroring test_chunked_interpolate with your exact shapes"],"exampleFix":"// before\nout = chunked_interpolate(x, chunk_size=0)\n// after\nassert x.shape[1] > 0 and chunk_size >= 1\nout = chunked_interpolate(x, chunk_size=chunk_size)","handlingStrategy":"validation","validationCode":"assert x.dim() == 4 and x.shape[1] > 0, f\"bad input {tuple(x.shape)}\"\nassert isinstance(chunk_size, int) and chunk_size >= 1","typeGuard":null,"tryCatchPattern":"try:\n    out = chunked_interpolate(x, chunk_size)\nexcept ValueError as e:\n    if \"No chunks\" in str(e):\n        raise ValueError(f\"chunked_interpolate misconfigured: shape={tuple(x.shape)}, chunk_size={chunk_size}\") from e\n    raise","preventionTips":["Validate chunk_size >= 1 in config loading","Assert non-empty channel dim before VAE forward passes","Add shape smoke tests for new configs"],"tags":["pytorch","tensor-shape","validation","dc-ae"],"backgroundTag":"invalid-tensor-shape","analyzedSha":"7ad6a96a135feb81f755c84fb391818718f6beb2","analyzedAt":"2026-08-28T16:58:37.171Z","schemaVersion":2},"datasetVersion":"2026-08-28T21:17:43.275Z"}