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Summary of ChangesHello @wangshankun, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the VAE's decoding capabilities by introducing a distributed 2D decoding mechanism. This allows the model to process large latent representations in parallel across multiple computational ranks, thereby improving performance and scalability, especially in distributed environments. The changes involve adding a new function to manage the distributed logic and updating an existing method to integrate this new functionality based on a parallel execution flag. Highlights
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Code Review
This pull request introduces a new distributed cached decoding function, cached_decode_dist_2d_withflag, and integrates it for parallel execution. The changes look good overall. My feedback focuses on improving code maintainability and robustness by addressing a hardcoded value and duplicated logic within the new function.
| # Calculate H dimension slice | ||
| if cur_rank_h == 0: | ||
| h_start = 0 | ||
| h_end = chunk_h + 2 * padding_size | ||
| elif cur_rank_h == world_size_h - 1: | ||
| h_start = total_h - (chunk_h + 2 * padding_size) | ||
| h_end = total_h | ||
| else: | ||
| h_start = cur_rank_h * chunk_h - padding_size | ||
| h_end = (cur_rank_h + 1) * chunk_h + padding_size | ||
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| # Calculate W dimension slice | ||
| if cur_rank_w == 0: | ||
| w_start = 0 | ||
| w_end = chunk_w + 2 * padding_size | ||
| elif cur_rank_w == world_size_w - 1: | ||
| w_start = total_w - (chunk_w + 2 * padding_size) | ||
| w_end = total_w | ||
| else: | ||
| w_start = cur_rank_w * chunk_w - padding_size | ||
| w_end = (cur_rank_w + 1) * chunk_w + padding_size | ||
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| # Extract the latent chunk for this process | ||
| zs_chunk = zs[:, :, h_start:h_end, w_start:w_end].contiguous() | ||
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| # Decode the chunk | ||
| images_chunk = self.model.cached_decode_withflag(zs_chunk.unsqueeze(0), self.scale, is_first, is_last) | ||
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| # Remove padding from decoded chunk | ||
| spatial_ratio = 8 | ||
| if cur_rank_h == 0: | ||
| decoded_h_start = 0 | ||
| decoded_h_end = chunk_h * spatial_ratio | ||
| elif cur_rank_h == world_size_h - 1: | ||
| decoded_h_start = images_chunk.shape[3] - chunk_h * spatial_ratio | ||
| decoded_h_end = images_chunk.shape[3] | ||
| else: | ||
| decoded_h_start = padding_size * spatial_ratio | ||
| decoded_h_end = images_chunk.shape[3] - padding_size * spatial_ratio | ||
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| if cur_rank_w == 0: | ||
| decoded_w_start = 0 | ||
| decoded_w_end = chunk_w * spatial_ratio | ||
| elif cur_rank_w == world_size_w - 1: | ||
| decoded_w_start = images_chunk.shape[4] - chunk_w * spatial_ratio | ||
| decoded_w_end = images_chunk.shape[4] | ||
| else: | ||
| decoded_w_start = padding_size * spatial_ratio | ||
| decoded_w_end = images_chunk.shape[4] - padding_size * spatial_ratio |
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The logic for calculating slices is duplicated for both the height (h) and width (w) dimensions. This occurs for both the initial latent tensor slicing (lines 1453-1473) and for removing padding from the decoded chunk (lines 1483-1501).
This duplication makes the code harder to read and maintain. Consider refactoring this logic into a private helper method to reduce redundancy and improve clarity. For example, a function like _calculate_slice(total_dim, chunk_dim, padding, rank, world_size) could handle the latent slicing, and a similar one for the decoded chunk slicing.
| images_chunk = self.model.cached_decode_withflag(zs_chunk.unsqueeze(0), self.scale, is_first, is_last) | ||
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| # Remove padding from decoded chunk | ||
| spatial_ratio = 8 |
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The spatial_ratio is hardcoded to 8. This value is related to the model's architecture (specifically, the downsampling factor). Using a hardcoded value can lead to bugs if the model architecture changes. It would be more robust to use the value from the model configuration, like self.model.spatial_compression_ratio.
| spatial_ratio = 8 | |
| spatial_ratio = self.model.spatial_compression_ratio |
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