{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":30731,"offset":30721,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"```json\n[\n {\"name\": \"\u041f\u0440\u044f\u0436\u0430 \u0434\u043b\u044f \u0440\u0443\u043a\u043e\u0434\u0435\u043b\u0438\u044f\"},\n {\"name\": \"\u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b \u0434\u043b\u044f \u0432\u044b\u0448\u0438\u0432\u043a\u0438\"},\n {\"name\": \"\u041d\u0438\u0442\u0438 \u0434\u043b\u044f \u043a\u0440\u0435\u0430\u0442\u0438\u0432\u043d\u044b\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432\"},\n {\"name\": \"\u041b\u043e\u0441\u043a\u0443\u0442\u043d\u043e\u0435 \u0448\u0438\u0442\u044c\u0435 \u0438 \u043d\u0438\u0442\u043a\u0438\"},\n {\"name\": \"\u041d\u0438\u0442\u043a\u0438 \u0434\u043b\u044f \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432 \u0438 \u0440\u0435\u043c\u0435\u0441\u0435\u043b\"},\n {\"name\": \"\u0422\u0432\u043e\u0440\u0447\u0435\u0441\u043a\u0438\u0435 \u043d\u0438\u0442\u043a\u0438\"},\n {\"name\": \"\u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b \u0434\u043b\u044f \u0432\u044b\u0448\u0438\u0432\u0430\u043d\u0438\u044f\"},\n {\"name\": \"\u041d\u0438\u0442\u0438 \u0434\u043b\u044f \u0431\u0438\u0441\u0435\u0440\u043e\u043f\u043b\u0435\u0442\u0435\u043d\u0438\u044f\"},\n {\"name\": \"\u0421\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u043d\u0438\u0442\u043a\u0438 \u0434\u043b\u044f \u0440\u0443\u043a\u043e\u0434\u0435\u043b\u0438\u044f\"},\n {\"name\": \"\u0425\u0443\u0434\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0435 \u043d\u0438\u0442\u0438\"}\n]\n```","behavior":"degenerate_repetition","conversation_hash":"8e558a2e19e98ccb641173f26a4a8f35","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":649,"n_turns_in_conversation":1,"n_words":76,"source_row_offset":30721,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:28 GMT","turn_id":"a64450f77b5b83128e56ee3e","turn_index":0,"user_text":"\u041d\u0430\u043f\u0438\u0448\u0438 10 \u0430\u043b\u044c\u0442\u0435\u0440\u043d\u0430\u0442\u0438\u0432\u043d\u044b\u0445 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0439 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0438 \"\u0422\u043e\u0432\u0430\u0440\u044b \u0434\u043b\u044f \u0445\u043e\u0431\u0431\u0438/\u0422\u043e\u0432\u0430\u0440\u044b \u0434\u043b\u044f \u0442\u0432\u043e\u0440\u0447\u0435\u0441\u0442\u0432\u0430 \u0438 \u0440\u0443\u043a\u043e\u0434\u0435\u043b\u0438\u044f/\u0422\u043e\u0432\u0430\u0440\u044b \u0434\u043b\u044f \u0432\u044b\u0448\u0438\u0432\u0430\u043d\u0438\u044f \u0438 \u0431\u0438\u0441\u0435\u0440\u043e\u043f\u043b\u0435\u0442\u0435\u043d\u0438\u044f/\u041d\u0438\u0442\u043a\u0438 \u0434\u043b\u044f \u0432\u044b\u0448\u0438\u0432\u0430\u043d\u0438\u044f\". \u041e\u0442\u0432\u0435\u0442 \u0432\u044b\u0434\u0430\u0439 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 JSON, \u043a\u043b\u044e\u0447 - name, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 - \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435"},{"assistant_initiates":false,"asst_text":"\u0412\u043e\u0442 10 \u0430\u043b\u044c\u0442\u0435\u0440\u043d\u0430\u0442\u0438\u0432\u043d\u044b\u0445 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0439 \u0434\u043b\u044f \u0443\u043a\u0430\u0437\u0430\u043d\u043d\u043e\u0439 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0438 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 JSON:\n\n```json\n[\n {\"name\": \"\u0421\u0442\u0440\u043e\u0447\u043d\u044b\u0435 \u043d\u0438\u0442\u0438\"},\n {\"name\": \"\u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b \u0434\u043b\u044f \u043c\u0435\u0448\u043a\u043e\u0437\u0430\u0448\u0438\u0432\u043a\u0438\"},\n {\"name\": \"\u0423\u043f\u0430\u043a\u043e\u0432\u043e\u0447\u043d\u044b\u0435 \u043d\u0438\u0442\u043a\u0438\"},\n {\"name\": \"\u0428\u0432\u0435\u0439\u043d\u044b\u0435 \u043d\u0438\u0442\u0438 \u0434\u043b\u044f \u043c\u0435\u0448\u043a\u043e\u0432\"},\n {\"name\": \"\u0412\u0435\u0440\u0435\u0432\u043e\u0447\u043d\u044b\u0435 \u043d\u0438\u0442\u043a\u0438 \u0434\u043b\u044f \u0443\u043f\u0430\u043a\u043e\u0432\u043a\u0438\"},\n {\"name\": \"\u041c\u0435\u0448\u043a\u043e\u0437\u0430\u0448\u0438\u0432\u043e\u0447\u043d\u044b\u0435 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b\"},\n {\"name\": \"\u041f\u0440\u043e\u0447\u043d\u044b\u0435 \u043d\u0438\u0442\u0438 \u0434\u043b\u044f \u0437\u0430\u0448\u0438\u0432\u043a\u0438\"},\n {\"name\": \"\u041d\u0438\u0442\u0438 \u0434\u043b\u044f \u0448\u0438\u0442\u044c\u044f \u0443\u043f\u0430\u043a\u043e\u0432\u043a\u0438\"},\n {\"name\": \"\u0421\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0435 \u043d\u0438\u0442\u043a\u0438 \u0434\u043b\u044f \u0448\u0432\u043e\u0432\"},\n {\"name\": \"\u0423\u043f\u0430\u043a\u043e\u0432\u043e\u0447\u043d\u044b\u0435 \u043d\u0438\u0442\u043a\u0438 \u0434\u043b\u044f \u043c\u0435\u0448\u043a\u043e\u0432\"}\n]\n```","behavior":"degenerate_repetition","conversation_hash":"2defee6434d6e3c1e9cd7ae1916d48d9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":710,"n_turns_in_conversation":1,"n_words":82,"source_row_offset":30722,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:34 GMT","turn_id":"5d8da7b54ef32e04841f5dd8","turn_index":0,"user_text":"\u041d\u0430\u043f\u0438\u0448\u0438 10 \u0430\u043b\u044c\u0442\u0435\u0440\u043d\u0430\u0442\u0438\u0432\u043d\u044b\u0445 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0439 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0438 \"\u0422\u043e\u0432\u0430\u0440\u044b \u0434\u043b\u044f \u0441\u0444\u0435\u0440\u044b \u0443\u0441\u043b\u0443\u0433/\u0422\u0430\u0440\u0430 \u0438 \u0443\u043f\u0430\u043a\u043e\u0432\u043a\u0430/\u0423\u043f\u0430\u043a\u043e\u0432\u043e\u0447\u043d\u044b\u0435 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b/\u0412\u0435\u0440\u0435\u0432\u043e\u0447\u043d\u044b\u0435 \u0438\u0437\u0434\u0435\u043b\u0438\u044f/\u041d\u0438\u0442\u043a\u0438 \u043c\u0435\u0448\u043a\u043e\u0437\u0430\u0448\u0438\u0432\u043e\u0447\u043d\u044b\u0435\". \u041e\u0442\u0432\u0435\u0442 \u0432\u044b\u0434\u0430\u0439 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 JSON, \u043a\u043b\u044e\u0447 - name, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 - \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435"},{"assistant_initiates":false,"asst_text":"The `CrossMamba` class is clearly a neural network module that is designed to perform some form of cross-modality feature fusion or interaction, specifically tailored for integrating features from two different sources or representations. Let's break down the functionality and role of `CrossMamba` based on the provided code snippet.\n\n### Components:\n\n1. **CrossMamba Layer**:\n - The `cross_mamba` is an instance of the `Mamba` class that takes a dimensionality parameter and a `bimamba_type` (\"v3\" in this case).\n - While the exact implementation of the `Mamba` class isn't given, from the naming conventions and usage, it seems to facilitate cross-modal interaction, combining or mixing information between two input feature sets. The `extra_emb` argument in its invocation suggests extra embeddings from another source (from `m2`).\n\n2. **Layer Normalizations**:\n - `norm1` and `norm2` are layer normalization layers. Their `with_bias` implies that they include a bias term, which can help in stabilizing training and improving performance.\n\n3. **Depth-wise Convolution**:\n - `dwconv` is a depth-wise convolutional layer (3x3 kernel). This is commonly used in efficient network designs (like MobileNet) to reduce computational cost by applying a single convolutional filter per input channel.\n\n### Forward Function:\n\n- **Inputs**:\n - `m1`, `m1_resi`, `m2`: Feature maps from potentially different sources/modalities. `m1` and `m2` may derive from RGB and event-based representations respectively, given the context.\n \n- **Operations**:\n - `m1_resi = m1 + m1_resi`: This implies an initial residual connection or mixture of features, which helps in maintaining information from both feature sources `m1` and `m1_resi`.\n - `global_f = self.cross_mamba(m1, extra_emb=m2)`: Combines or interacts the modified `m1` and `m2` using the `cross_mamba` layer, creating a fused representation.\n - The fused `global_f` is then returned.\n\n### Usage Context:\n\nIn the calling context, `CrossMamba` is used for fusing features derived from different sensory inputs (infrared, event, visual, etc.):\n\n- **Respective Features**:\n - `rgb_feature` and `event_feature` are presumably features extracted by a backbone model from template and search representations.\n \n- **Residual Connections**:\n - `residual_event_f`, `residual_rgb_f` are initially zero, suggesting that later in training, they might hold additional features or corrections for more effective feature integration during backpropagation and optimization.\n \n- **Fusing Features**:\n - `event_f = self.cross_mamba(event_feature, residual_event_f, rgb_feature) + event_feature`\n - `rgb_f = self.cross_mamba(rgb_feature, residual_rgb_f, event_feature) + rgb_feature`\n \n Here, the `CrossMamba` is utilized twice to perform bidirectional feature interaction, enhancing `event_feature` with `rgb_feature` and vice versa. Adding the original feature back (residual connection) ensures that the primary characteristics of each modality are preserved while enriching them with complementary information.\n\n### Conclusion:\n\n`CrossMamba` is strategically designed for cross-modal or cross-representational fusion in neural networks, enabling a richer feature representation that integrates key characteristics from both modalities. This type of module is particularly beneficial in architectures where gaining insight from multiple sensory data types is crucial, such as in computer vision tasks involving multi-spectral imagery, event-based sensing, and other similar scenarios.","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":5216,"n_turns_in_conversation":28,"n_words":549,"source_row_offset":30723,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"9eef9e5c8e6f54463e431171","turn_index":0,"user_text":"\u6a21\u5757\uff1aclass CrossMamba(nn.Module):\n def __init__(self, dim):\n super(CrossMamba, self).__init__()\n self.cross_mamba = Mamba(dim,bimamba_type=\"v3\")\n self.norm1 = LayerNorm(dim,'with_bias')\n self.norm2 = LayerNorm(dim,'with_bias')\n self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n def forward(self,m1,m1_resi,m2): \n m1_resi = m1+m1_resi \n global_f = self.cross_mamba(m1, extra_emb=m2)\n return global_f\uff0c\u88ab\u8c03\u7528\u65f6\uff1a def forward(self, template: torch.Tensor,\n search: torch.Tensor,\n event_template: torch.Tensor, \n event_search: torch.Tensor, \n ce_template_mask=None,\n ce_keep_rate=None,\n return_last_attn=False,\n ):\n \n rgb_feature = self.backbone.forward_features( z=template, x=search, #[B, 320, 384]\n inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False)\n event_feature = self.backbone.forward_features(z=event_template, x=event_search, #[B, 320, 384]\n inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False)\n\n residual_event_f = 0\n residual_rgb_f = 0\n event_f = self.cross_mamba(event_feature,residual_event_f,rgb_feature) + event_feature\n rgb_f = self.cross_mamba(rgb_feature,residual_rgb_f,event_feature) + rgb_feature\uff0c\u90a3\u4e48\u5206\u6790crossMamba\u7684\u4f5c\u7528\u3002"},{"assistant_initiates":false,"asst_text":"`CrossMamba` \u7c7b\u662f\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u6a21\u5757\uff0c\u76ee\u7684\u662f\u8fdb\u884c\u67d0\u79cd\u8de8\u6a21\u6001\u7279\u5f81\u878d\u5408\u6216\u4ea4\u4e92\uff0c\u7279\u522b\u662f\u7528\u4e8e\u6574\u5408\u6765\u81ea\u4e24\u4e2a\u4e0d\u540c\u6765\u6e90\u6216\u8868\u793a\u7684\u7279\u5f81\u3002\u8ba9\u6211\u4eec\u6839\u636e\u63d0\u4f9b\u7684\u4ee3\u7801\u7247\u6bb5\u6765\u5206\u6790 `CrossMamba` \u7684\u529f\u80fd\u548c\u4f5c\u7528\u3002\n\n### \u7ec4\u4ef6\uff1a\n\n1. **CrossMamba \u5c42**\uff1a\n - `cross_mamba` \u662f `Mamba` \u7c7b\u7684\u4e00\u4e2a\u5b9e\u4f8b\uff0c\u63a5\u53d7\u4e00\u4e2a\u7ef4\u5ea6\u53c2\u6570\u548c\u4e00\u4e2a `bimamba_type`\uff08\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u4e3a \"v3\"\uff09\u3002\n - \u867d\u7136\u6ca1\u6709\u7ed9\u51fa `Mamba` \u7c7b\u7684\u5177\u4f53\u5b9e\u73b0\uff0c\u4f46\u4ece\u547d\u540d\u7ea6\u5b9a\u548c\u4f7f\u7528\u60c5\u51b5\u6765\u770b\uff0c\u5b83\u4f3c\u4e4e\u662f\u7528\u4e8e\u8de8\u6a21\u6001\u4ea4\u4e92\uff0c\u7ec4\u5408\u6216\u6df7\u5408\u4e24\u7ec4\u8f93\u5165\u7279\u5f81\u96c6\u4e4b\u95f4\u7684\u4fe1\u606f\u3002\u53ef\u4ee5\u770b\u5230\u5b83\u5728\u8c03\u7528\u65f6\u4f7f\u7528\u4e86 `extra_emb` \u53c2\u6570\uff0c\u8868\u793a\u4ece\u53e6\u4e00\u4e2a\u6765\u6e90\uff08\u201cm2\u201d\uff09\u5f15\u5165\u989d\u5916\u7684\u5d4c\u5165\u3002\n\n2. **\u5c42\u5f52\u4e00\u5316**\uff1a\n - `norm1` \u548c `norm2` \u662f\u5c42\u5f52\u4e00\u5316\u5c42\uff0c`with_bias` \u8868\u793a\u5b83\u4eec\u5305\u542b\u4e00\u4e2a\u504f\u7f6e\u9879\uff0c\u8fd9\u6709\u52a9\u4e8e\u5728\u8bad\u7ec3\u4e2d\u7a33\u5b9a\u548c\u63d0\u9ad8\u6027\u80fd\u3002\n\n3. **\u6df1\u5ea6\u5377\u79ef**\uff1a\n - `dwconv` \u662f\u4e00\u4e2a\u6df1\u5ea6\u5377\u79ef\u5c42\uff083x3 \u7684\u5377\u79ef\u6838\uff09\u3002\u8fd9\u79cd\u5377\u79ef\u901a\u5e38\u7528\u4e8e\u9ad8\u6548\u7684\u7f51\u7edc\u8bbe\u8ba1\uff08\u5982 MobileNet \u4e2d\uff09\uff0c\u901a\u8fc7\u5bf9\u6bcf\u4e2a\u8f93\u5165\u901a\u9053\u5e94\u7528\u5355\u4e2a\u5377\u79ef\u6ee4\u6ce2\u5668\u6765\u964d\u4f4e\u8ba1\u7b97\u6210\u672c\u3002\n\n### \u524d\u5411\u4f20\u64ad\u51fd\u6570\uff1a\n\n- **\u8f93\u5165**\uff1a\n - `m1`, `m1_resi`, `m2`\uff1a\u53ef\u80fd\u6765\u81ea\u4e0d\u540c\u6765\u6e90/\u6a21\u6001\u7684\u7279\u5f81\u56fe\u3002\u5728\u4e0a\u4e0b\u6587\u4e2d\uff0c`m1` \u548c `m2` \u6216\u8bb8\u5206\u522b\u6765\u81ea\u4e8e RGB \u548c\u4e8b\u4ef6\u9a71\u52a8\u7684\u8868\u793a\u3002\n\n- **\u64cd\u4f5c**\uff1a\n - `m1_resi = m1 + m1_resi`\uff1a\u8fd9\u8868\u793a\u521d\u59cb\u7684\u6b8b\u5dee\u8fde\u63a5\u6216\u7279\u5f81\u6df7\u5408\uff0c\u6709\u52a9\u4e8e\u4fdd\u6301\u6765\u81ea `m1` \u548c `m1_resi` \u7684\u4fe1\u606f\u3002\n - `global_f = self.cross_mamba(m1, extra_emb=m2)`\uff1a\u4f7f\u7528 `cross_mamba` \u5c42\u7ec4\u5408\u6216\u4ea4\u4e92\u4fee\u6539\u8fc7\u7684 `m1` \u548c `m2`\uff0c\u521b\u5efa\u878d\u5408\u7684\u8868\u793a\u3002\n - \u8fd4\u56de\u878d\u5408\u540e\u7684 `global_f`\u3002\n\n### \u4f7f\u7528\u4e0a\u4e0b\u6587\uff1a\n\n\u5728\u8c03\u7528\u4e0a\u4e0b\u6587\u4e2d\uff0c`CrossMamba` \u88ab\u7528\u4e8e\u878d\u5408\u4ece\u4e0d\u540c\u611f\u5b98\u8f93\u5165\uff08\u7ea2\u5916\u3001\u4e8b\u4ef6\u3001\u89c6\u89c9\u7b49\uff09\u4e2d\u63d0\u53d6\u7684\u7279\u5f81\uff1a\n\n- **\u76f8\u5e94\u7279\u5f81**\uff1a\n - `rgb_feature` \u548c `event_feature` \u5206\u522b\u662f\u4ece\u6a21\u677f\u548c\u641c\u7d22\u8868\u793a\u4e2d\u901a\u8fc7\u4e00\u4e2a\u9aa8\u5e72\u6a21\u578b\u63d0\u53d6\u7684\u7279\u5f81\u3002\n\n- **\u6b8b\u5dee\u8fde\u63a5**\uff1a\n - `residual_event_f` \u548c `residual_rgb_f` \u521d\u59cb\u5316\u4e3a\u96f6\uff0c\u5efa\u8bae\u5728\u8bad\u7ec3\u540e\u671f\uff0c\u53ef\u80fd\u4f1a\u6301\u6709\u989d\u5916\u7684\u7279\u5f81\u6216\u6821\u6b63\uff0c\u4ee5\u4fbf\u5728\u53cd\u5411\u4f20\u64ad\u548c\u4f18\u5316\u8fc7\u7a0b\u4e2d\u66f4\u6709\u6548\u5730\u8fdb\u884c\u7279\u5f81\u6574\u5408\u3002\n\n- **\u7279\u5f81\u878d\u5408**\uff1a\n - `event_f = self.cross_mamba(event_feature, residual_event_f, rgb_feature) + event_feature`\n - `rgb_f = self.cross_mamba(rgb_feature, residual_rgb_f, event_feature) + rgb_feature`\n\n \u5728\u8fd9\u91cc\uff0c`CrossMamba` \u88ab\u53cc\u5411\u4f7f\u7528\uff0c\u4ee5\u4fc3\u8fdb\u7279\u5f81\u7684\u4ea4\u4e92\uff0c\u7528 `rgb_feature` \u589e\u5f3a `event_feature`\uff0c\u53cd\u4e4b\u4ea6\u7136\u3002\u901a\u8fc7\u52a0\u56de\u539f\u59cb\u7279\u5f81\uff08\u6b8b\u5dee\u8fde\u63a5\uff09\uff0c\u786e\u4fdd\u4e86\u6bcf\u4e2a\u6a21\u6001\u7684\u4e3b\u8981\u7279\u5f81\u5728\u88ab\u8865\u5145\u4e30\u5bcc\u7684\u4fe1\u606f\u540c\u65f6\u5f97\u5230\u4e86\u4fdd\u7559\u3002\n\n### \u7ed3\u8bba\uff1a\n\n`CrossMamba` \u662f\u4e00\u4e2a\u4e13\u4e3a\u5728\u795e\u7ecf\u7f51\u7edc\u4e2d\u5b9e\u73b0\u8de8\u6a21\u6001\u6216\u8de8\u8868\u793a\u878d\u5408\u800c\u8bbe\u8ba1\u7684\u6a21\u5757\uff0c\u4f7f\u5f97\u7279\u5f81\u8868\u793a\u66f4\u4e3a\u4e30\u5bcc\uff0c\u6574\u5408\u4e86\u4e24\u79cd\u6a21\u6001\u7684\u5173\u952e\u7279\u6027\u3002\u8fd9\u79cd\u7c7b\u578b\u7684\u6a21\u5757\u5728\u83b7\u5f97\u591a\u79cd\u611f\u5b98\u6570\u636e\u7c7b\u578b\u7684\u89c1\u89e3\u65f6\u5c24\u5176\u6709\u7528\uff0c\u6bd4\u5982\u5728\u6d89\u53ca\u591a\u5149\u8c31\u56fe\u50cf\u6216\u4e8b\u4ef6\u611f\u77e5\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1537,"n_turns_in_conversation":28,"n_words":122,"source_row_offset":30724,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"672490d86de329efe8e84f3d","turn_index":1,"user_text":"\u4e2d\u6587\u56de\u7b54"},{"assistant_initiates":false,"asst_text":"`Mamba` \u7c7b\u662f\u4e00\u4e2a\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u5757\uff0c\u8bbe\u8ba1\u7528\u4e8e\u5728\u795e\u7ecf\u7f51\u7edc\u4e2d\u8fdb\u884c\u878d\u5408\u548c\u7279\u5f81\u4ea4\u4e92\u3002\u5b83\u5305\u542b\u4e86\u4e00\u7cfb\u5217\u7684\u7ebf\u6027\u6295\u5f71\u3001\u5377\u79ef\u64cd\u4f5c\u548c\u5176\u4ed6\u6570\u5b66\u64cd\u4f5c\uff0c\u7528\u4e8e\u5904\u7406\u8f93\u5165\u5f20\u91cf\u7684\u7279\u5f81\u3002\u901a\u8fc7\u8fd9\u4e9b\u64cd\u4f5c\uff0c`Mamba` \u5b9e\u73b0\u4e86\u591a\u6a21\u6001\u6570\u636e\u7684\u878d\u5408\u3002\u4ee5\u4e0b\u662f\u8fd9\u6bb5\u4ee3\u7801\u7684\u8be6\u7ec6\u89e3\u6790\uff1a\n\n### \u521d\u59cb\u5316\u53c2\u6570\n\n- **d_model**: \u8f93\u5165\u7279\u5f81\u7684\u7ef4\u5ea6\u3002\n- **d_state**: \u7528\u4e8e\u72b6\u6001\u66f4\u65b0\u7684\u5185\u90e8\u7ef4\u5ea6\u3002\n- **d_conv**: \u4e00\u7ef4\u5377\u79ef\u7684\u6838\u5927\u5c0f\u3002\n- **expand**: \u7528\u4e8e\u786e\u5b9a\u5185\u90e8\u7279\u5f81\u7ef4\u5ea6 `d_inner` \u7684\u6269\u5c55\u500d\u7387\u3002\n- **dt_rank, dt_min, dt_max, dt_init, dt_scale, dt_init_floor**: \u53c2\u6570\u7528\u4e8e\u786e\u5b9a\u65f6\u95f4\u66f4\u65b0\u6216\u72b6\u6001\u66f4\u65b0\u7684\u5177\u4f53\u7ec6\u8282\uff0c\u4f7f\u7528\u8fd9\u4e9b\u53c2\u6570\u521d\u59cb\u5316 `dt_proj` \u7684\u6743\u91cd\u548c\u504f\u7f6e\u3002\n- **conv_bias, bias**: \u63a7\u5236\u662f\u5426\u5728\u5377\u79ef\u548c\u7ebf\u6027\u5c42\u4e2d\u4f7f\u7528\u504f\u7f6e\u3002\n- **bimamba_type**: \u51b3\u5b9a\u5907\u7528\u8def\u5f84\u7684\u7c7b\u578b\uff08\u4f8b\u5982 v1, v2, v3\uff09\u3002\n- **if_devide_out**: \u51b3\u5b9a\u5728\u8f93\u51fa\u7ed3\u679c\u65f6\u662f\u5426\u8fdb\u884c\u5e73\u5747\u3002\n\n### \u7c7b\u7ec4\u4ef6\n\n1. **\u7ebf\u6027\u6295\u5f71 (in_proj, x_proj, dt_proj)**:\n - `in_proj`: \u5c06\u8f93\u5165\u7279\u5f81\u6620\u5c04\u5230\u66f4\u9ad8\u7ef4\u5ea6\u7a7a\u95f4\u3002\n - `x_proj`, `dt_proj`: \u5904\u7406\u5377\u79ef\u8f93\u51fa\uff0c\u7528\u4e8e\u7279\u5f81\u72b6\u6001\u66f4\u65b0\u548c\u65f6\u95f4\u6b65\u957f\u8ba1\u7b97\u3002\n\n2. **\u5377\u79ef\u5c42 (conv1d, conv1d_b)**:\n - `conv1d`: \u5bf9\u5f62\u72b6\u4e3a (batch, feature, length) \u7684\u8f93\u5165\u8fdb\u884c\u4e00\u7ef4\u5377\u79ef\u3002\n - `conv1d_b`: \u5907\u7528\u7684\u5377\u79ef\u6a21\u5757\uff0c\u53ef\u5728\u7279\u5b9a\u60c5\u51b5\u4e0b\u4f7f\u7528\u3002\n\n3. **\u6fc0\u6d3b\u51fd\u6570**:\n - \u4f7f\u7528 SiLU\uff08\u53c8\u79f0 Swish\uff09\u4f5c\u4e3a\u6fc0\u6d3b\u51fd\u6570\uff0c\u63d0\u4f9b\u975e\u7ebf\u6027\u53d8\u6362\u3002\n\n4. **\u8f93\u51fa\u6295\u5f71 (out_proj)**:\n - \u4e00\u4e2a\u7ebf\u6027\u5c42\uff0c\u5c06\u5904\u7406\u540e\u7684\u5185\u90e8\u7279\u5f81\u6620\u5c04\u56de\u8f93\u5165\u7ef4\u5ea6\u3002\n\n5. **\u590d\u6742\u8fd0\u7b97(A_log, A_b_log, D, D_b)**:\n - \u7528\u4e8e\u5904\u7406\u7279\u5f81\u72b6\u6001\u66f4\u65b0\u7684\u76f8\u5173\u53c2\u6570\u3002\n - \u4e3a\u53cc\u5411\u8def\u5f84\u51c6\u5907\uff0c\u652f\u6301\u4e0d\u540c\u7c7b\u578b\u7684 `bimamba`\u3002\n\n### \u524d\u5411\u4f20\u64ad\n\n- **\u8f93\u5165\u53c2\u6570**:\n - `hidden_states`: \u8f93\u5165\u7684\u7279\u5f81\u5f20\u91cf\uff0c\u5f62\u72b6\u4e3a (batch, length, dim)\u3002\n - `inference_params`: \u63a8\u7406\u65f6\u7684\u53c2\u6570\uff0c\u7528\u4e8e\u72b6\u6001\u7ba1\u7406\u3002\n - `extra_emb`: \u989d\u5916\u7684\u5d4c\u5165\uff0c\u7528\u4e8e\u7279\u5b9a bimamba \u7c7b\u578b\u3002\n\n- **\u4e3b\u8981\u8ba1\u7b97\u8fc7\u7a0b**:\n - \u5bf9\u8f93\u5165\u7279\u5f81\u8fdb\u884c\u7ebf\u6027\u6295\u5f71\u4ee5\u83b7\u53d6 `xz`\u3002\n - \u4f7f\u7528\u5377\u79ef\u5c42\u5904\u7406\u6295\u5f71\u7ed3\u679c\uff0c\u751f\u6210\u66f4\u65b0\u540e\u7684\u7279\u5f81\u72b6\u6001\u3002\n - \u901a\u8fc7\u4f7f\u7528\u53cc\u5411\u8def\u5f84\uff08\u5982\u679c\u542f\u7528\u7684\u8bdd\uff09\uff0c\u7efc\u5408\u4e24\u6761\u8def\u5f84\u7684\u8f93\u51fa\u3002\n - \u5c06\u7ed3\u679c\u901a\u8fc7\u7ebf\u6027\u6295\u5f71\u6620\u5c04\u4e3a\u6700\u7ec8\u8f93\u51fa\u3002\n\n### \u7279\u70b9\u529f\u80fd\n\n- **\u53cc\u5411\u8def\u5f84 (bimamba_type)**:\n - `v1`, `v2`, `v3` \u7c7b\u578b\u63d0\u4f9b\u4e0d\u540c\u7684\u7279\u5f81\u8def\u5f84\u3002\n - `v3` \u7c7b\u578b\u5141\u8bb8\u901a\u8fc7 `extra_emb` \u5f15\u5165\u989d\u5916\u7684\u7279\u5f81\u5d4c\u5165\u3002\n \n- **\u72b6\u6001\u7ba1\u7406**:\n - \u652f\u6301\u63a8\u7406\u65f6\u7684\u72b6\u6001\u7f13\u5b58\u548c\u66f4\u65b0\u3002\n\n- **\u9ad8\u6548\u8def\u5f84\u9009\u62e9 (use_fast_path)**:\n - \u63d0\u4f9b\u5feb\u901f\u8ba1\u7b97\u8def\u5f84\u6765\u4f18\u5316\u6027\u80fd\u3002\n\n\u901a\u8fc7\u8fd9\u79cd\u8bbe\u8ba1\uff0c`Mamba` \u7c7b\u53ef\u4ee5\u6709\u6548\u5730\u5904\u7406\u548c\u878d\u5408\u591a\u6a21\u6001\u4fe1\u606f\uff0c\u5bf9\u4e8e\u9700\u8981\u7279\u5f81\u4ea4\u4e92\u548c\u96c6\u6210\u7684\u9ad8\u7ea7\u795e\u7ecf\u7f51\u7edc\u5e94\u7528\u573a\u666f\u5c24\u5176\u9002\u7528\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":40222,"n_turns_in_conversation":28,"n_words":3322,"source_row_offset":30725,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"88d9ce476aed13d17f88396c","turn_index":2,"user_text":"mamba\u7684\u5b9a\u4e49\u5982\u4e0b\uff1a# Copyright (c) 2023, Tri Dao, Albert Gu.\n\nimport math\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import Tensor\n\nfrom einops import rearrange, repeat\n\ntry:\n from causal_conv1d import causal_conv1d_fn, causal_conv1d_update\nexcept ImportError:\n causal_conv1d_fn, causal_conv1d_update = None\n\ntry:\n from mamba_ssm.ops.selective_scan_interface import selective_scan_fn, mamba_inner_fn, bimamba_inner_fn, mamba_inner_fn_no_out_proj\nexcept ImportError:\n selective_scan_fn, mamba_inner_fn, bimamba_inner_fn, mamba_inner_fn_no_out_proj = None, None, None, None, None\n\ntry:\n from mamba_ssm.ops.triton.selective_state_update import selective_state_update\nexcept ImportError:\n selective_state_update = None\n\ntry:\n from mamba_ssm.ops.triton.layernorm import RMSNorm, layer_norm_fn, rms_norm_fn\nexcept ImportError:\n RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None\n\n\nclass Mamba(nn.Module):\n def __init__(\n self,\n d_model,\n d_state=16,\n d_conv=4,\n expand=2,\n dt_rank=\"auto\",\n dt_min=0.001,\n dt_max=0.1,\n dt_init=\"random\",\n dt_scale=1.0,\n dt_init_floor=1e-4,\n conv_bias=True,\n bias=False,\n use_fast_path=True, # Fused kernel options\n layer_idx=None,\n device=None,\n dtype=None,\n bimamba_type=\"v2\",\n if_devide_out=False,\n init_layer_scale=None,\n ):\n factory_kwargs = {\"device\": device, \"dtype\": dtype}\n super().__init__()\n self.d_model = d_model\n self.d_state = d_state\n self.d_conv = d_conv\n self.expand = expand\n self.d_inner = int(self.expand * self.d_model)\n self.dt_rank = math.ceil(self.d_model / 16) if dt_rank == \"auto\" else dt_rank\n self.use_fast_path = use_fast_path\n self.layer_idx = layer_idx\n self.bimamba_type = bimamba_type\n self.if_devide_out = if_devide_out\n\n self.init_layer_scale = init_layer_scale\n if init_layer_scale is not None:\n self.gamma = nn.Parameter(init_layer_scale * torch.ones((d_model)), requires_grad=True)\n\n self.in_proj = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)\n\n self.conv1d = nn.Conv1d(\n in_channels=self.d_inner,\n out_channels=self.d_inner,\n bias=conv_bias,\n kernel_size=d_conv,\n groups=self.d_inner,\n padding=d_conv - 1,\n **factory_kwargs,\n )\n\n self.activation = \"silu\"\n self.act = nn.SiLU()\n\n self.x_proj = nn.Linear(\n self.d_inner, self.dt_rank + self.d_state * 2, bias=False, **factory_kwargs\n )\n self.dt_proj = nn.Linear(self.dt_rank, self.d_inner, bias=True, **factory_kwargs)\n\n # Initialize special dt projection to preserve variance at initialization\n dt_init_std = self.dt_rank**-0.5 * dt_scale\n if dt_init == \"constant\":\n nn.init.constant_(self.dt_proj.weight, dt_init_std)\n elif dt_init == \"random\":\n nn.init.uniform_(self.dt_proj.weight, -dt_init_std, dt_init_std)\n else:\n raise NotImplementedError\n\n # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max\n dt = torch.exp(\n torch.rand(self.d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))\n + math.log(dt_min)\n ).clamp(min=dt_init_floor)\n # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759\n inv_dt = dt + torch.log(-torch.expm1(-dt))\n with torch.no_grad():\n self.dt_proj.bias.copy_(inv_dt)\n # Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit\n self.dt_proj.bias._no_reinit = True\n\n # S4D real initialization\n A = repeat(\n torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),\n \"n -> d n\",\n d=self.d_inner,\n ).contiguous()\n A_log = torch.log(A) # Keep A_log in fp32\n self.A_log = nn.Parameter(A_log)\n self.A_log._no_weight_decay = True\n\n # D \"skip\" parameter\n self.D = nn.Parameter(torch.ones(self.d_inner, device=device)) # Keep in fp32\n self.D._no_weight_decay = True\n\n # bidirectional\n if bimamba_type == \"v1\":\n A_b = repeat(\n torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),\n \"n -> d n\",\n d=self.d_inner,\n ).contiguous()\n A_b_log = torch.log(A_b) # Keep A_b_log in fp32\n self.A_b_log = nn.Parameter(A_b_log)\n self.A_b_log._no_weight_decay = True\n elif bimamba_type == \"v2\" or bimamba_type=='v3':\n A_b = repeat(\n torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),\n \"n -> d n\",\n d=self.d_inner,\n ).contiguous()\n A_b_log = torch.log(A_b) # Keep A_b_log in fp32\n self.A_b_log = nn.Parameter(A_b_log)\n self.A_b_log._no_weight_decay = True \n\n self.conv1d_b = nn.Conv1d(\n in_channels=self.d_inner,\n out_channels=self.d_inner,\n bias=conv_bias,\n kernel_size=d_conv,\n groups=self.d_inner,\n padding=d_conv - 1,\n **factory_kwargs,\n )\n\n self.x_proj_b = nn.Linear(\n self.d_inner, self.dt_rank + self.d_state * 2, bias=False, **factory_kwargs\n )\n self.dt_proj_b = nn.Linear(self.dt_rank, self.d_inner, bias=True, **factory_kwargs)\n\n self.D_b = nn.Parameter(torch.ones(self.d_inner, device=device)) # Keep in fp32\n self.D_b._no_weight_decay = True\n self.in_proj_extra = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)\n\n self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)\n\n def forward(self, hidden_states, inference_params=None,extra_emb=None):\n \"\"\"\n hidden_states: (B, L, D)\n Returns: same shape as hidden_states\n \"\"\"\n batch, seqlen, dim = hidden_states.shape\n\n conv_state, ssm_state = None, None\n if inference_params is not None:\n conv_state, ssm_state = self._get_states_from_cache(inference_params, batch)\n if inference_params.seqlen_offset > 0:\n # The states are updated inplace\n out, _, _ = self.step(hidden_states, conv_state, ssm_state)\n return out\n if extra_emb is None:\n # We do matmul and transpose BLH -> HBL at the same time\n xz = rearrange(\n self.in_proj.weight @ rearrange(hidden_states, \"b l d -> d (b l)\"),\n \"d (b l) -> b d l\",\n l=seqlen,\n )\n if self.in_proj.bias is not None:\n xz = xz + rearrange(self.in_proj.bias.to(dtype=xz.dtype), \"d -> d 1\")\n else:\n xz = rearrange(\n self.in_proj.weight @ rearrange(hidden_states, \"b l d -> d (b l)\"),\n \"d (b l) -> b d l\",\n l=seqlen,\n )\n if self.in_proj.bias is not None:\n xz = xz + rearrange(self.in_proj.bias.to(dtype=xz.dtype), \"d -> d 1\")\n extra_emb = rearrange(\n self.in_proj_extra.weight @ rearrange(extra_emb, \"b l d -> d (b l)\"),\n \"d (b l) -> b d l\",\n l=seqlen,\n )\n if self.in_proj_extra.bias is not None:\n extra_emb = extra_emb + rearrange(self.in_proj_extra.bias.to(dtype=xz.dtype), \"d -> d 1\")\n A = -torch.exp(self.A_log.float()) # (d_inner, d_state)\n # In the backward pass we write dx and dz next to each other to avoid torch.cat\n if self.use_fast_path and inference_params is None: # Doesn't support outputting the states\n if self.bimamba_type == \"v1\":\n A_b = -torch.exp(self.A_b_log.float())\n out = bimamba_inner_fn(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n self.out_proj.weight,\n self.out_proj.bias,\n A,\n A_b,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n ) \n elif self.bimamba_type == \"v2\":\n A_b = -torch.exp(self.A_b_log.float())\n out = mamba_inner_fn_no_out_proj(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n A,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n )\n out_b = mamba_inner_fn_no_out_proj(\n xz.flip([-1]),\n self.conv1d_b.weight,\n self.conv1d_b.bias,\n self.x_proj_b.weight,\n self.dt_proj_b.weight,\n A_b,\n None,\n None,\n self.D_b.float(),\n delta_bias=self.dt_proj_b.bias.float(),\n delta_softplus=True,\n )\n # F.linear(rearrange(out_z, \"b d l -> b l d\"), out_proj_weight, out_proj_bias)\n if not self.if_devide_out:\n out = F.linear(rearrange(out + out_b.flip([-1]), \"b d l -> b l d\"), self.out_proj.weight, self.out_proj.bias)\n else:\n out = F.linear(rearrange(out + out_b.flip([-1]), \"b d l -> b l d\") / 2, self.out_proj.weight, self.out_proj.bias)\n elif self.bimamba_type == \"v3\":\n A_b = -torch.exp(self.A_b_log.float())\n out = mamba_inner_fn_no_out_proj(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n A,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n )\n out_b = mamba_inner_fn_no_out_proj(\n extra_emb,\n self.conv1d_b.weight,\n self.conv1d_b.bias,\n self.x_proj_b.weight,\n self.dt_proj_b.weight,\n A_b,\n None,\n None,\n self.D_b.float(),\n delta_bias=self.dt_proj_b.bias.float(),\n delta_softplus=True,\n )\n # F.linear(rearrange(out_z, \"b d l -> b l d\"), out_proj_weight, out_proj_bias)\n if not self.if_devide_out: #True\n out = F.linear(rearrange(out + out_b, \"b d l -> b l d\"), self.out_proj.weight, self.out_proj.bias)\n else: #False\n out = F.linear(rearrange(out + out_b, \"b d l -> b l d\") / 2, self.out_proj.weight, self.out_proj.bias)\n \n else: #False\n out = mamba_inner_fn(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n self.out_proj.weight,\n self.out_proj.bias,\n A,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n )\n else: #False\n x, z = xz.chunk(2, dim=1)\n # Compute short convolution\n if conv_state is not None:\n # If we just take x[:, :, -self.d_conv :], it will error if seqlen < self.d_conv\n # Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.\n conv_state.copy_(F.pad(x, (self.d_conv - x.shape[-1], 0))) # Update state (B D W)\n if causal_conv1d_fn is None:\n x = self.act(self.conv1d(x)[..., :seqlen])\n else:\n assert self.activation in [\"silu\", \"swish\"]\n x = causal_conv1d_fn(\n x=x,\n weight=rearrange(self.conv1d.weight, \"d 1 w -> d w\"),\n bias=self.conv1d.bias,\n activation=self.activation,\n )\n\n # We're careful here about the layout, to avoid extra transposes.\n # We want dt to have d as the slowest moving dimension\n # and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.\n x_dbl = self.x_proj(rearrange(x, \"b d l -> (b l) d\")) # (bl d)\n dt, B, C = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=-1)\n dt = self.dt_proj.weight @ dt.t()\n dt = rearrange(dt, \"d (b l) -> b d l\", l=seqlen)\n B = rearrange(B, \"(b l) dstate -> b dstate l\", l=seqlen).contiguous()\n C = rearrange(C, \"(b l) dstate -> b dstate l\", l=seqlen).contiguous()\n assert self.activation in [\"silu\", \"swish\"]\n y = selective_scan_fn(\n x,\n dt,\n A,\n B,\n C,\n self.D.float(),\n z=z,\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n return_last_state=ssm_state is not None,\n )\n if ssm_state is not None:\n y, last_state = y\n ssm_state.copy_(last_state)\n y = rearrange(y, \"b d l -> b l d\")\n out = self.out_proj(y)\n if self.init_layer_scale is not None: #False\n out = out * self.gamma \n return out\n\n def step(self, hidden_states, conv_state, ssm_state):\n dtype = hidden_states.dtype\n assert hidden_states.shape[1] == 1, \"Only support decoding with 1 token at a time for now\"\n xz = self.in_proj(hidden_states.squeeze(1)) # (B 2D)\n x, z = xz.chunk(2, dim=-1) # (B D)\n\n # Conv step\n if causal_conv1d_update is None:\n conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1)) # Update state (B D W)\n conv_state[:, :, -1] = x\n x = torch.sum(conv_state * rearrange(self.conv1d.weight, \"d 1 w -> d w\"), dim=-1) # (B D)\n if self.conv1d.bias is not None:\n x = x + self.conv1d.bias\n x = self.act(x).to(dtype=dtype)\n else:\n x = causal_conv1d_update(\n x,\n conv_state,\n rearrange(self.conv1d.weight, \"d 1 w -> d w\"),\n self.conv1d.bias,\n self.activation,\n )\n\n x_db = self.x_proj(x) # (B dt_rank+2*d_state)\n dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)\n # Don't add dt_bias here\n dt = F.linear(dt, self.dt_proj.weight) # (B d_inner)\n A = -torch.exp(self.A_log.float()) # (d_inner, d_state)\n\n # SSM step\n if selective_state_update is None:\n # Discretize A and B\n dt = F.softplus(dt + self.dt_proj.bias.to(dtype=dt.dtype))\n dA = torch.exp(torch.einsum(\"bd,dn->bdn\", dt, A))\n dB = torch.einsum(\"bd,bn->bdn\", dt, B)\n ssm_state.copy_(ssm_state * dA + rearrange(x, \"b d -> b d 1\") * dB)\n y = torch.einsum(\"bdn,bn->bd\", ssm_state.to(dtype), C)\n y = y + self.D.to(dtype) * x\n y = y * self.act(z) # (B D)\n else:\n y = selective_state_update(\n ssm_state, x, dt, A, B, C, self.D, z=z, dt_bias=self.dt_proj.bias, dt_softplus=True\n )\n\n out = self.out_proj(y)\n return out.unsqueeze(1), conv_state, ssm_state\n\n def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):\n device = self.out_proj.weight.device\n conv_dtype = self.conv1d.weight.dtype if dtype is None else dtype\n conv_state = torch.zeros(\n batch_size, self.d_model * self.expand, self.d_conv, device=device, dtype=conv_dtype\n )\n ssm_dtype = self.dt_proj.weight.dtype if dtype is None else dtype\n # ssm_dtype = torch.float32\n ssm_state = torch.zeros(\n batch_size, self.d_model * self.expand, self.d_state, device=device, dtype=ssm_dtype\n )\n return conv_state, ssm_state\n\n def _get_states_from_cache(self, inference_params, batch_size, initialize_states=False):\n assert self.layer_idx is not None\n if self.layer_idx not in inference_params.key_value_memory_dict:\n batch_shape = (batch_size,)\n conv_state = torch.zeros(\n batch_size,\n self.d_model * self.expand,\n self.d_conv,\n device=self.conv1d.weight.device,\n dtype=self.conv1d.weight.dtype,\n )\n ssm_state = torch.zeros(\n batch_size,\n self.d_model * self.expand,\n self.d_state,\n device=self.dt_proj.weight.device,\n dtype=self.dt_proj.weight.dtype,\n # dtype=torch.float32,\n )\n inference_params.key_value_memory_dict[self.layer_idx] = (conv_state, ssm_state)\n else:\n conv_state, ssm_state = inference_params.key_value_memory_dict[self.layer_idx]\n # TODO: What if batch size changes between generation, and we reuse the same states?\n if initialize_states:\n conv_state.zero_()\n ssm_state.zero_()\n return conv_state, ssm_state\n\n\nclass Block(nn.Module):\n def __init__(\n self, dim, mixer_cls, norm_cls=nn.LayerNorm, fused_add_norm=False, residual_in_fp32=False\n ):\n \"\"\"\n Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection\"\n\n This Block has a slightly different structure compared to a regular\n prenorm Transformer block.\n The standard block is: LN -> MHA/MLP -> Add.\n [Ref: https://arxiv.org/abs/2002.04745]\n Here we have: Add -> LN -> Mixer, returning both\n the hidden_states (output of the mixer) and the residual.\n This is purely for performance reasons, as we can fuse add and LayerNorm.\n The residual needs to be provided (except for the very first block).\n \"\"\"\n super().__init__()\n self.residual_in_fp32 = residual_in_fp32\n self.fused_add_norm = fused_add_norm\n self.mixer = mixer_cls(dim)\n self.norm = norm_cls(dim)\n if self.fused_add_norm:\n assert RMSNorm is not None, \"RMSNorm import fails\"\n assert isinstance(\n self.norm, (nn.LayerNorm, RMSNorm)\n ), \"Only LayerNorm and RMSNorm are supported for fused_add_norm\"\n\n def forward(\n self, hidden_states: Tensor, residual: Optional[Tensor] = None, inference_params=None\n ):\n r\"\"\"Pass the input through the encoder layer.\n\n Args:\n hidden_states: the sequence to the encoder layer (required).\n residual: hidden_states = Mixer(LN(residual))\n \"\"\"\n if not self.fused_add_norm:\n residual = (hidden_states + residual) if residual is not None else hidden_states\n hidden_states = self.norm(residual.to(dtype=self.norm.weight.dtype))\n if self.residual_in_fp32:\n residual = residual.to(torch.float32)\n else:\n fused_add_norm_fn = rms_norm_fn if isinstance(self.norm, RMSNorm) else layer_norm_fn\n hidden_states, residual = fused_add_norm_fn(\n hidden_states,\n self.norm.weight,\n self.norm.bias,\n residual=residual,\n prenorm=True,\n residual_in_fp32=self.residual_in_fp32,\n eps=self.norm.eps,\n )\n hidden_states = self.mixer(hidden_states, inference_params=inference_params)\n return hidden_states, residual\n\n def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):\n return self.mixer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)\n\n\n\nclass DWMamba(nn.Module):\n def __init__(\n self,\n d_model,\n d_state=16,\n d_conv=4,\n expand=2,\n dt_rank=\"auto\",\n dt_min=0.001,\n dt_max=0.1,\n dt_init=\"random\",\n dt_scale=1.0,\n dt_init_floor=1e-4,\n conv_bias=True,\n bias=False,\n use_fast_path=True, # Fused kernel options\n layer_idx=None,\n device=None,\n dtype=None,\n bimamba_type=\"v2\",\n if_devide_out=False,\n init_layer_scale=None,\n ):\n factory_kwargs = {\"device\": device, \"dtype\": dtype}\n super().__init__()\n self.d_model = d_model\n self.d_state = d_state\n self.d_conv = d_conv\n self.expand = expand\n self.d_inner = int(self.expand * self.d_model)\n self.dt_rank = math.ceil(self.d_model / 16) if dt_rank == \"auto\" else dt_rank\n self.use_fast_path = use_fast_path\n self.layer_idx = layer_idx\n self.bimamba_type = bimamba_type\n self.if_devide_out = if_devide_out\n\n self.init_layer_scale = init_layer_scale\n if init_layer_scale is not None:\n self.gamma = nn.Parameter(init_layer_scale * torch.ones((d_model)), requires_grad=True)\n\n self.in_proj = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)\n\n self.conv1d = nn.Conv1d(\n in_channels=self.d_inner,\n out_channels=self.d_inner,\n bias=conv_bias,\n kernel_size=d_conv,\n groups=self.d_inner,\n padding=d_conv - 1,\n **factory_kwargs,\n )\n\n self.activation = \"silu\"\n self.act = nn.SiLU()\n\n self.x_proj = nn.Linear(\n self.d_inner, self.dt_rank + self.d_state * 2, bias=False, **factory_kwargs\n )\n self.dt_proj = nn.Linear(self.dt_rank, self.d_inner, bias=True, **factory_kwargs)\n\n # Initialize special dt projection to preserve variance at initialization\n dt_init_std = self.dt_rank**-0.5 * dt_scale\n if dt_init == \"constant\":\n nn.init.constant_(self.dt_proj.weight, dt_init_std)\n elif dt_init == \"random\":\n nn.init.uniform_(self.dt_proj.weight, -dt_init_std, dt_init_std)\n else:\n raise NotImplementedError\n\n # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max\n dt = torch.exp(\n torch.rand(self.d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))\n + math.log(dt_min)\n ).clamp(min=dt_init_floor)\n # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759\n inv_dt = dt + torch.log(-torch.expm1(-dt))\n with torch.no_grad():\n self.dt_proj.bias.copy_(inv_dt)\n # Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit\n self.dt_proj.bias._no_reinit = True\n\n # S4D real initialization\n A = repeat(\n torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),\n \"n -> d n\",\n d=self.d_inner,\n ).contiguous()\n A_log = torch.log(A) # Keep A_log in fp32\n self.A_log = nn.Parameter(A_log)\n self.A_log._no_weight_decay = True\n\n # D \"skip\" parameter\n self.D = nn.Parameter(torch.ones(self.d_inner, device=device)) # Keep in fp32\n self.D._no_weight_decay = True\n\n # bidirectional\n if bimamba_type == \"v1\":\n A_b = repeat(\n torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),\n \"n -> d n\",\n d=self.d_inner,\n ).contiguous()\n A_b_log = torch.log(A_b) # Keep A_b_log in fp32\n self.A_b_log = nn.Parameter(A_b_log)\n self.A_b_log._no_weight_decay = True\n elif bimamba_type == \"v2\" or bimamba_type=='v3':\n A_b = repeat(\n torch.arange(1, self.d_state + 1, dtype=torch.float32, device=device),\n \"n -> d n\",\n d=self.d_inner,\n ).contiguous()\n A_b_log = torch.log(A_b) # Keep A_b_log in fp32\n self.A_b_log = nn.Parameter(A_b_log)\n self.A_b_log._no_weight_decay = True \n\n self.conv1d_b = nn.Conv1d(\n in_channels=self.d_inner,\n out_channels=self.d_inner,\n bias=conv_bias,\n kernel_size=d_conv,\n groups=self.d_inner,\n padding=d_conv - 1,\n **factory_kwargs,\n )\n\n self.x_proj_b = nn.Linear(\n self.d_inner, self.dt_rank + self.d_state * 2, bias=False, **factory_kwargs\n )\n self.dt_proj_b = nn.Linear(self.dt_rank, self.d_inner, bias=True, **factory_kwargs)\n\n self.D_b = nn.Parameter(torch.ones(self.d_inner, device=device)) # Keep in fp32\n self.D_b._no_weight_decay = True\n self.in_proj_extra = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)\n\n self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)\n\n def forward(self, hidden_states, inference_params=None,extra_emb=None):\n \"\"\"\n hidden_states: (B, L, D)\n Returns: same shape as hidden_states\n \"\"\"\n batch, seqlen, dim = hidden_states.shape\n\n conv_state, ssm_state = None, None\n if inference_params is not None:\n conv_state, ssm_state = self._get_states_from_cache(inference_params, batch)\n if inference_params.seqlen_offset > 0:\n # The states are updated inplace\n out, _, _ = self.step(hidden_states, conv_state, ssm_state)\n return out\n if extra_emb is None:\n # We do matmul and transpose BLH -> HBL at the same time\n xz = rearrange(\n self.in_proj.weight @ rearrange(hidden_states, \"b l d -> d (b l)\"),\n \"d (b l) -> b d l\",\n l=seqlen,\n )\n if self.in_proj.bias is not None:\n xz = xz + rearrange(self.in_proj.bias.to(dtype=xz.dtype), \"d -> d 1\")\n else:\n xz = rearrange(\n self.in_proj.weight @ rearrange(hidden_states, \"b l d -> d (b l)\"),\n \"d (b l) -> b d l\",\n l=seqlen,\n )\n if self.in_proj.bias is not None:\n xz = xz + rearrange(self.in_proj.bias.to(dtype=xz.dtype), \"d -> d 1\")\n extra_emb = rearrange(\n self.in_proj_extra.weight @ rearrange(extra_emb, \"b l d -> d (b l)\"),\n \"d (b l) -> b d l\",\n l=seqlen,\n )\n if self.in_proj_extra.bias is not None:\n extra_emb = extra_emb + rearrange(self.in_proj_extra.bias.to(dtype=xz.dtype), \"d -> d 1\")\n A = -torch.exp(self.A_log.float()) # (d_inner, d_state)\n # In the backward pass we write dx and dz next to each other to avoid torch.cat\n if self.use_fast_path and inference_params is None: # Doesn't support outputting the states\n if self.bimamba_type == \"v1\":\n A_b = -torch.exp(self.A_b_log.float())\n out = bimamba_inner_fn(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n self.out_proj.weight,\n self.out_proj.bias,\n A,\n A_b,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n ) \n elif self.bimamba_type == \"v2\":\n A_b = -torch.exp(self.A_b_log.float())\n out = mamba_inner_fn_no_out_proj(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n A,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n )\n out_b = mamba_inner_fn_no_out_proj(\n xz.flip([-1]),\n self.conv1d_b.weight,\n self.conv1d_b.bias,\n self.x_proj_b.weight,\n self.dt_proj_b.weight,\n A_b,\n None,\n None,\n self.D_b.float(),\n delta_bias=self.dt_proj_b.bias.float(),\n delta_softplus=True,\n )\n # F.linear(rearrange(out_z, \"b d l -> b l d\"), out_proj_weight, out_proj_bias)\n if not self.if_devide_out:\n out = F.linear(rearrange(out + out_b.flip([-1]), \"b d l -> b l d\"), self.out_proj.weight, self.out_proj.bias)\n else:\n out = F.linear(rearrange(out + out_b.flip([-1]), \"b d l -> b l d\") / 2, self.out_proj.weight, self.out_proj.bias)\n elif self.bimamba_type == \"v3\":\n A_b = -torch.exp(self.A_b_log.float())\n out = mamba_inner_fn_no_out_proj(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n A,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n )\n out_b = mamba_inner_fn_no_out_proj(\n extra_emb,\n self.conv1d_b.weight,\n self.conv1d_b.bias,\n self.x_proj_b.weight,\n self.dt_proj_b.weight,\n A_b,\n None,\n None,\n self.D_b.float(),\n delta_bias=self.dt_proj_b.bias.float(),\n delta_softplus=True,\n )\n # F.linear(rearrange(out_z, \"b d l -> b l d\"), out_proj_weight, out_proj_bias)\n if not self.if_devide_out:\n out = F.linear(rearrange(out + out_b, \"b d l -> b l d\"), self.out_proj.weight, self.out_proj.bias)\n else:\n out = F.linear(rearrange(out + out_b, \"b d l -> b l d\") / 2, self.out_proj.weight, self.out_proj.bias)\n \n else:\n out = mamba_inner_fn(\n xz,\n self.conv1d.weight,\n self.conv1d.bias,\n self.x_proj.weight,\n self.dt_proj.weight,\n self.out_proj.weight,\n self.out_proj.bias,\n A,\n None, # input-dependent B\n None, # input-dependent C\n self.D.float(),\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n )\n else:\n x, z = xz.chunk(2, dim=1)\n # Compute short convolution\n if conv_state is not None:\n # If we just take x[:, :, -self.d_conv :], it will error if seqlen < self.d_conv\n # Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.\n conv_state.copy_(F.pad(x, (self.d_conv - x.shape[-1], 0))) # Update state (B D W)\n if causal_conv1d_fn is None:\n x = self.act(self.conv1d(x)[..., :seqlen])\n else:\n assert self.activation in [\"silu\", \"swish\"]\n x = causal_conv1d_fn(\n x=x,\n weight=rearrange(self.conv1d.weight, \"d 1 w -> d w\"),\n bias=self.conv1d.bias,\n activation=self.activation,\n )\n\n # We're careful here about the layout, to avoid extra transposes.\n # We want dt to have d as the slowest moving dimension\n # and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.\n x_dbl = self.x_proj(rearrange(x, \"b d l -> (b l) d\")) # (bl d)\n dt, B, C = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=-1)\n dt = self.dt_proj.weight @ dt.t()\n dt = rearrange(dt, \"d (b l) -> b d l\", l=seqlen)\n B = rearrange(B, \"(b l) dstate -> b dstate l\", l=seqlen).contiguous()\n C = rearrange(C, \"(b l) dstate -> b dstate l\", l=seqlen).contiguous()\n assert self.activation in [\"silu\", \"swish\"]\n y = selective_scan_fn(\n x,\n dt,\n A,\n B,\n C,\n self.D.float(),\n z=z,\n delta_bias=self.dt_proj.bias.float(),\n delta_softplus=True,\n return_last_state=ssm_state is not None,\n )\n if ssm_state is not None:\n y, last_state = y\n ssm_state.copy_(last_state)\n y = rearrange(y, \"b d l -> b l d\")\n out = self.out_proj(y)\n if self.init_layer_scale is not None:\n out = out * self.gamma \n return out\n\n def step(self, hidden_states, conv_state, ssm_state):\n dtype = hidden_states.dtype\n assert hidden_states.shape[1] == 1, \"Only support decoding with 1 token at a time for now\"\n xz = self.in_proj(hidden_states.squeeze(1)) # (B 2D)\n x, z = xz.chunk(2, dim=-1) # (B D)\n\n # Conv step\n if causal_conv1d_update is None:\n conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1)) # Update state (B D W)\n conv_state[:, :, -1] = x\n x = torch.sum(conv_state * rearrange(self.conv1d.weight, \"d 1 w -> d w\"), dim=-1) # (B D)\n if self.conv1d.bias is not None:\n x = x + self.conv1d.bias\n x = self.act(x).to(dtype=dtype)\n else:\n x = causal_conv1d_update(\n x,\n conv_state,\n rearrange(self.conv1d.weight, \"d 1 w -> d w\"),\n self.conv1d.bias,\n self.activation,\n )\n\n x_db = self.x_proj(x) # (B dt_rank+2*d_state)\n dt, B, C = torch.split(x_db, [self.dt_rank, self.d_state, self.d_state], dim=-1)\n # Don't add dt_bias here\n dt = F.linear(dt, self.dt_proj.weight) # (B d_inner)\n A = -torch.exp(self.A_log.float()) # (d_inner, d_state)\n\n # SSM step\n if selective_state_update is None:\n # Discretize A and B\n dt = F.softplus(dt + self.dt_proj.bias.to(dtype=dt.dtype))\n dA = torch.exp(torch.einsum(\"bd,dn->bdn\", dt, A))\n dB = torch.einsum(\"bd,bn->bdn\", dt, B)\n ssm_state.copy_(ssm_state * dA + rearrange(x, \"b d -> b d 1\") * dB)\n y = torch.einsum(\"bdn,bn->bd\", ssm_state.to(dtype), C)\n y = y + self.D.to(dtype) * x\n y = y * self.act(z) # (B D)\n else:\n y = selective_state_update(\n ssm_state, x, dt, A, B, C, self.D, z=z, dt_bias=self.dt_proj.bias, dt_softplus=True\n )\n\n out = self.out_proj(y)\n return out.unsqueeze(1), conv_state, ssm_state\n\n def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):\n device = self.out_proj.weight.device\n conv_dtype = self.conv1d.weight.dtype if dtype is None else dtype\n conv_state = torch.zeros(\n batch_size, self.d_model * self.expand, self.d_conv, device=device, dtype=conv_dtype\n )\n ssm_dtype = self.dt_proj.weight.dtype if dtype is None else dtype\n # ssm_dtype = torch.float32\n ssm_state = torch.zeros(\n batch_size, self.d_model * self.expand, self.d_state, device=device, dtype=ssm_dtype\n )\n return conv_state, ssm_state\n\n def _get_states_from_cache(self, inference_params, batch_size, initialize_states=False):\n assert self.layer_idx is not None\n if self.layer_idx not in inference_params.key_value_memory_dict:\n batch_shape = (batch_size,)\n conv_state = torch.zeros(\n batch_size,\n self.d_model * self.expand,\n self.d_conv,\n device=self.conv1d.weight.device,\n dtype=self.conv1d.weight.dtype,\n )\n ssm_state = torch.zeros(\n batch_size,\n self.d_model * self.expand,\n self.d_state,\n device=self.dt_proj.weight.device,\n dtype=self.dt_proj.weight.dtype,\n # dtype=torch.float32,\n )\n inference_params.key_value_memory_dict[self.layer_idx] = (conv_state, ssm_state)\n else:\n conv_state, ssm_state = inference_params.key_value_memory_dict[self.layer_idx]\n # TODO: What if batch size changes between generation, and we reuse the same states?\n if initialize_states:\n conv_state.zero_()\n ssm_state.zero_()\n return conv_state, ssm_state"},{"assistant_initiates":false,"asst_text":"\u7ed3\u5408\u4e0a\u6587\u5bf9 `CrossMamba` \u548c `Mamba` \u7c7b\u7684\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u6df1\u523b\u5730\u7406\u89e3 `CrossMamba` \u7684\u4f5c\u7528\u3002\n\n`CrossMamba` \u662f\u4e00\u4e2a\u57fa\u4e8e `Mamba` \u7684\u9ad8\u7ea7\u795e\u7ecf\u7f51\u7edc\u6a21\u5757\uff0c\u4e13\u95e8\u7528\u4e8e\u8de8\u6a21\u6001\u7279\u5f81\u878d\u5408\u3002\u4ee5\u4e0b\u662f\u5176\u5173\u952e\u4f5c\u7528\u5206\u6790\uff1a\n\n### 1. \u8de8\u6a21\u6001\u7279\u5f81\u4ea4\u4e92\n\n`CrossMamba` \u5229\u7528 `Mamba` \u7c7b\u7684\u5185\u5728\u7279\u6027\uff0c\u5904\u7406\u5e76\u878d\u5408\u6765\u81ea\u4e24\u4e2a\u4e0d\u540c\u6a21\u6001\u6216\u7279\u5f81\u6e90\u7684\u4fe1\u606f\u3002\u5728\u7ed9\u5b9a\u7684\u4ee3\u7801\u7247\u6bb5\u4e2d\uff0c`CrossMamba` \u63a5\u53d7\u4e24\u4e2a\u8f93\u5165\uff08`m1` \u548c `m2`\uff09\uff0c\u4ee5\u53ca\u4e00\u4e2a\u6f5c\u5728\u7684\u6b8b\u5dee\u8f93\u5165\uff08`m1_resi`\uff09\uff0c\u8fdb\u884c\u7279\u5f81\u6574\u5408\u3002\n\n- `m1` \u548c `m2` \u53ef\u80fd\u4ee3\u8868\u4e24\u79cd\u4e0d\u540c\u7684\u7279\u5f81\u6a21\u6001\uff0c\u6bd4\u5982 RGB \u56fe\u50cf\u7279\u5f81\u548c\u4e8b\u4ef6\u7279\u5f81\u3002\n- `Mamba` \u7684 `bimamba_type` \u53c2\u6570\u4e3a \"v3\"\uff0c\u610f\u5473\u7740\u5b83\u652f\u6301\u989d\u5916\u7684\u7279\u5f81\u5d4c\u5165 (extra_emb)\uff0c\u5b9e\u73b0\u590d\u6742\u7684\u7279\u5f81\u4ea4\u4e92\u3002\n\n### 2. \u53cc\u5411\u4fe1\u606f\u6d41\n\n\u5728 `Mamba` \u7684 `bimamba_type` \u4e3a \"v3\" \u7684\u60c5\u51b5\u4e0b\uff0c`CrossMamba` \u652f\u6301\u53cc\u5411\u7279\u5f81\u878d\u5408\u7b56\u7565\u3002\u8fd9\u610f\u5473\u7740\u901a\u8fc7 `Mamba` \u4e2d\u7684\u53cc\u5411\u5904\u7406\u8def\u5f84\uff0c\u80fd\u591f\u6709\u6548\u5730\u4ece `m1` \u548c `m2` \u4e2d\u63d0\u53d6\u548c\u878d\u5408\u4fe1\u606f\u3002\n\n- \u8fd9\u79cd\u53cc\u5411\u7279\u5f81\u6d41\u52a8\u5728\u795e\u7ecf\u7f51\u7edc\u4e2d\u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u5168\u9762\u7684\u65b9\u5f0f\u6765\u589e\u5f3a\u7279\u5f81\u8868\u793a\u3002\n- `CrossMamba` \u4e0d\u4ec5\u878d\u5408\u6b63\u5411\u7684\u4fe1\u606f\u6d41\uff0c\u8fd8\u8003\u8651\u4e86\u5728 `Mamba` \u4e2d\u7684\u9006\u5411\u4fe1\u606f\u6d41\uff08\u901a\u8fc7\u5904\u7406 `extra_emb` \u6765\u5b9e\u73b0\uff09\u3002\n\n### 3. \u5c42\u5f52\u4e00\u5316\u4e0e\u5377\u79ef\u589e\u5f3a\n\n- \u5728 `CrossMamba` \u4e2d\uff0c\u5728\u7279\u5f81\u878d\u5408\u4e4b\u524d\uff0c\u91c7\u7528\u4e86 `LayerNorm` \u6765\u8fdb\u884c\u7279\u5f81\u7684\u6807\u51c6\u5316\uff0c\u7a33\u5b9a\u6a21\u578b\u8bad\u7ec3\uff0c\u5e76\u63d0\u5347\u7279\u5f81\u6d41\u52a8\u7684\u8868\u73b0\u3002\n- \u4f7f\u7528\u6df1\u5ea6\u5377\u79ef (`dwconv`) \u589e\u5f3a\u5c40\u90e8\u7279\u5f81\u5904\u7406\uff0c\u5e76\u52a0\u5f3a\u7279\u5f81\u8868\u8fbe\u3002\n\n### 4. \u5f3a\u5316\u7279\u5f81\u5b66\n\n\u901a\u8fc7\u5c06 `Mamba` \u6a21\u578b\u7684\u5b8c\u6574\u5904\u7406\u80fd\u529b\u5e94\u7528\u4e8e\u6a21\u6001\u7279\u5f81\uff0c`CrossMamba` \u5b9e\u73b0\u4e86\u4e00\u79cd\u590d\u5408\u7279\u5f81\u878d\u5408\u7b56\u7565\uff1a\n- \u5b83\u4e0d\u4ec5\u63d0\u53d6\u548c\u5904\u7406\u57fa\u4e8e \u65f6\u95f4 \u548c \u5377\u79ef \u7ed3\u6784\u7684\u7279\u5f81\uff0c\u4e14\u8003\u8651\u4e86\u591a\u6a21\u6001\u7684\u7279\u5f81\u4ea4\u4e92\u3002\n- \u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u5b83\u5c24\u4e3a\u9002\u5408\u4e8e\u9700\u8981\u96c6\u6210\u591a\u6e90\u4fe1\u606f\u7684\u590d\u6742\u4efb\u52a1\uff0c\u5982\u89c6\u9891\u5206\u6790\u3001\u4f20\u611f\u5668\u878d\u5408\u7b49\u3002\n\n### \u603b\u7ed3\n\n\u603b\u4f53\u6765\u8bf4\uff0c`CrossMamba` \u5229\u7528\u4e86 `Mamba` \u7684\u7279\u6027\u63d0\u4f9b\u4e86\u4e00\u79cd\u7075\u6d3b\u800c\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u4e13\u6ce8\u4e8e\u8de8\u6a21\u6001\u7279\u5f81\u878d\u5408\u3002\u5b83\u901a\u8fc7\u53cc\u5411\u7279\u5f81\u6d41\u52a8\u7b56\u7565\u3001\u5f52\u4e00\u5316\u548c\u5377\u79ef\u5904\u7406\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u9c81\u68d2\u548c\u8868\u73b0\u5f3a\u52b2\u7684\u7279\u5f81\u8868\u793a\u7b56\u7565\uff0c\u9002\u5408\u4e8e\u591a\u79cd\u9ad8\u7ea7\u7f51\u7edc\u5e94\u7528\u573a\u666f\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1058,"n_turns_in_conversation":28,"n_words":96,"source_row_offset":30726,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"5e2c1c01882408858199537f","turn_index":3,"user_text":"\u90a3\u4e48\u7ed3\u5408\u4e0a\u6587\u5206\u6790\uff0ccrossMamba\u7684\u4f5c\u7528\u3002"},{"assistant_initiates":false,"asst_text":"### \u65b0\u7684\u8bbe\u8ba1\u76ee\u7684\n\n\u65b0\u7684 `CrossMamba` \u8bbe\u8ba1\u5f15\u5165\u4e86\u4e00\u4e9b\u65b0\u7684\u7ec4\u4ef6\u548c\u673a\u5236\uff0c\u8fd9\u4e9b\u8bbe\u8ba1\u7684\u76ee\u7684\u662f\uff1a\n\n1. **\u95e8\u63a7\u673a\u5236**\uff1a\n - \u4f7f\u7528\u7ebf\u6027\u5c42 `gate` \u548c `sigmoid` \u6fc0\u6d3b\u51fd\u6570\u751f\u6210 `gate_weights`\uff0c\u8fd9\u662f\u4e3a\u4e86\u4ea7\u751f\u4e00\u79cd\u52a8\u6001\u9009\u62e9\u673a\u5236\uff0c\u6839\u636e\u8f93\u5165\u7279\u5f81 `m1` \u786e\u5b9a\u5728\u878d\u5408\u8fc7\u7a0b\u4e2d\u9700\u8981\u7684\u7279\u5f81\u91cd\u8981\u6027\u3002\n\n2. **\u5dee\u5f02\u6807\u51c6\u5316\u4e0e\u975e\u7ebf\u6027\u53d8\u6362**\uff1a\n - \u8ba1\u7b97 `m1` \u548c `m2` \u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u5e76\u901a\u8fc7\u6807\u51c6\u5316\u8be5\u5dee\u5f02\uff08\u4f7f\u7528\u6807\u51c6\u5dee\uff09\uff0c\u5bf9\u5176\u8fdb\u884c `LeakyReLU` \u975e\u7ebf\u6027\u53d8\u6362\u3002\u8fd9\u90e8\u5206\u64cd\u4f5c\u53ef\u80fd\u610f\u5728\u901a\u8fc7\u5f3a\u8c03\u6216\u6291\u5236\u7279\u5b9a\u7684\u7279\u5f81\u53d8\u5316\u6765\u8c03\u6574\u8f93\u5165\u7279\u5f81\u7684\u6743\u91cd\u5206\u914d\u3002\n\n3. **\u591a\u7ebf\u6027\u5c42\u5904\u7406**\uff1a\n - \u4f7f\u7528\u4e24\u4e2a\u7ebf\u6027\u5c42 `linear1` \u548c `linear2` \u5206\u522b\u5904\u7406 `m1` \u548c `m2`\uff0c\u589e\u5f3a\u7279\u5f81\u5904\u7406\u7684\u7075\u6d3b\u6027\u3002\n\n4. **\u7279\u5f81\u52a0\u6743**\uff1a\n - \u5c06\u7ebf\u6027\u53d8\u6362\u540e\u7684 `m1` \u4e0e\u8ba1\u7b97\u5f97\u5230\u7684 `gate_weights` \u548c `probability_weights` \u76f8\u4e58\uff0c\u76ee\u7684\u5728\u4e8e\u6839\u636e\u7279\u5b9a\u6761\u4ef6\u5bf9\u7279\u5f81\u7684\u8d21\u732e\u8fdb\u884c\u653e\u7f29\u3002\n\n### \u53ef\u80fd\u5bfc\u81f4\u6027\u80fd\u53d8\u5dee\u7684\u539f\u56e0\u5206\u6790\n\n1. **\u590d\u6742\u5ea6\u589e\u52a0**\uff1a\n - \u65b0\u7684\u8bbe\u8ba1\u5f15\u5165\u4e86\u8bb8\u591a\u989d\u5916\u7684\u64cd\u4f5c\u548c\u53c2\u6570\uff0c\u4f8b\u5982\u989d\u5916\u7684\u7ebf\u6027\u5c42\u3001\u95e8\u63a7\u673a\u5236\u3001\u6807\u51c6\u5316\u548c\u6fc0\u6d3b\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u66f4\u96be\u4ee5\u8bad\u7ec3\u6216\u5bfc\u81f4\u8fc7\u62df\u5408\u3002\n - \u5728\u6ca1\u6709\u9002\u5f53\u7684\u6b63\u5219\u5316\u6216\u8c03\u4f18\u8fd9\u4e9b\u989d\u5916\u53c2\u6570\u7684\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u5f15\u5165\u566a\u58f0\u6216\u4e0d\u7a33\u5b9a\u6027\uff0c\u4ece\u800c\u6291\u5236\u4e86\u6a21\u578b\u7684\u6574\u4f53\u8868\u73b0\u3002\n\n2. **\u95e8\u63a7\u673a\u5236\u7684\u8fc7\u5ea6\u9650\u5236**\uff1a\n - \u867d\u7136\u95e8\u63a7\u673a\u5236\u4e0e\u6743\u91cd\u8c03\u6574\u65e8\u5728\u7ed9\u4e88\u6a21\u578b\u66f4\u5927\u7684\u5904\u7406\u7075\u6d3b\u6027\uff0c\u4f46\u5982\u679c gate \u548c probability \u6743\u91cd\u6ca1\u6709\u9002\u5f53\u5b66\u4e60\u5230\u6b63\u786e\u7279\u5f81\uff0c\u53ef\u80fd\u5bf9\u5173\u952e\u7684\u7279\u5f81\u8fdb\u884c\u4e86\u9519\u8bef\u7684\u6291\u5236\u6216\u589e\u5f3a\u3002\n\n3. **\u5dee\u5f02\u7684\u8bef\u7528**\uff1a\n - \u6807\u51c6\u5316\u7279\u5f81\u5dee\u5f02\u5e76\u5c06\u5176\u5e94\u7528\u4e8e\u975e\u7ebf\u6027\u6fc0\u6d3b\u5728\u7406\u8bba\u4e0a\u662f\u4e3a\u4e86\u7a81\u663e\u6216\u51cf\u5c11\u7279\u5f81\u53d8\u5316\u7684\u91cd\u8981\u6027\u3002\u7136\u800c\uff0c\u82e5\u7279\u5f81\u5dee\u5f02\u672c\u8eab\u672a\u80fd\u5728\u4e0d\u540c\u7684\u6837\u672c\u4e2d\u4e00\u81f4\u5730\u63d0\u4f9b\u6709\u610f\u4e49\u7684\u529f\u80fd\u6216\u4fe1\u53f7\uff0c\u53ef\u80fd\u5bfc\u81f4\u65e0\u6548\u7684\u6743\u91cd\u5206\u914d\u3002\n\n4. **\u6570\u636e\u4e0d\u534f\u8c03\u95ee\u9898**\uff1a\n - \u5728\u5e94\u7528\u8fd9\u6837\u7684\u7279\u5f81\u6807\u51c6\u5316\u548c\u5dee\u5f02\u8ba1\u7b97\u65f6\uff0c\u6570\u636e\u7684\u89c4\u6a21\u6216\u5c5e\u6027\u4e0a\u7684\u4e0d\u4e00\u81f4\u6027\u53ef\u80fd\u5bfc\u81f4\u7279\u5f81\u504f\u501a\uff0c\u4f8b\u5982\u4e24\u4e2a\u4e0d\u540c\u6a21\u6001\uff08RGB \u548c\u4e8b\u4ef6\u7279\u5f81\uff09\u7684\u7edf\u8ba1\u5dee\u5f02\u5bfc\u81f4\u4e0d\u5408\u7406\u7684 std_diff\u3002\n\n5. **\u53c2\u6570\u521d\u59cb\u5316\u548c\u5b66\u4e60\u7387**\uff1a\n - \u65b0\u589e\u7684\u53c2\u6570\uff08\u5982\u7ebf\u6027\u5c42\u6743\u91cd\u3001\u95e8\u63a7\u53c2\u6570\uff09\u53ef\u80fd\u672a\u9002\u5f53\u5730\u521d\u59cb\u5316\uff0c\u6216\u5b66\u4e60\u7387\u672a\u6839\u636e\u65b0\u8bbe\u8ba1\u8fdb\u884c\u8c03\u6574\uff0c\u5bfc\u81f4\u4f18\u5316\u5668\u65e0\u6cd5\u6709\u6548\u5730\u6536\u655b\u5230\u5408\u7406\u7684\u89e3\u3002\n\n\u7efc\u4e0a\u6240\u8ff0\uff0c\u4e3a\u63d0\u5347\u65b0\u8bbe\u8ba1\u7684\u6027\u80fd\u8868\u73b0\uff0c\u9700\u8981\u4ed4\u7ec6\u5206\u6790\u8fd9\u4e9b\u989d\u5916\u673a\u5236\u7684\u5177\u4f53\u5b9e\u73b0\u5bf9\u6574\u4f53\u6a21\u578b\u7684\u5f71\u54cd\uff0c\u5e76\u9488\u5bf9\u6027\u5730\u4f18\u5316\u53c2\u6570\u8bbe\u7f6e\u548c\u8bad\u7ec3\u7b56\u7565\u3002\u6b64\u8fc7\u7a0b\u5e94\u5305\u62ec\u4e25\u8c28\u7684\u5b9e\u9a8c\u9a8c\u8bc1\u548c\u53c2\u6570\u8c03\u4f18\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":3464,"n_turns_in_conversation":28,"n_words":246,"source_row_offset":30727,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"35b37ca3728ade954c05daf1","turn_index":4,"user_text":"\u90a3\u4e48\u73b0\u5728\u5bf9crossMamba\u8fdb\u884c\u4e86\u65b0\u7684\u8bbe\u8ba1\uff1aclass CrossMamba(nn.Module):\n def __init__(self, dim):\n super(CrossMamba, self).__init__()\n self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n self.norm1 = LayerNorm(dim, 'with_bias')\n self.linear1 = nn.Linear(dim, dim) # \u5904\u7406 m1\n self.linear2 = nn.Linear(dim, dim) # \u5904\u7406 m2\n self.leaky_relu = nn.LeakyReLU(negative_slope=0.01) # \u4f7f\u7528 LeakyReLU\n self.sigmoid = nn.Sigmoid() # \u786e\u4fdd\u6743\u91cd\u5728 [0, 1] \u8303\u56f4\u5185\n self.gate = nn.Linear(dim, 1) # \u95e8\u63a7\u673a\u5236\n def forward(self, m1, m1_resi, m2):\n m1_resi = m1 + m1_resi # (B, N, D)\n gate_values = self.gate(m1)\n gate_weights = self.sigmoid(gate_values)\n diff = m1 - m2\n std_diff = torch.std(diff, dim=-1, keepdim=True)\n std_diff = torch.clamp(std_diff, min=1e-6) # \u907f\u514d\u9664\u4ee5\u96f6\n diff = diff / std_diff\n probability_weights = self.leaky_relu(diff)\n probability_weights = self.sigmoid(probability_weights) # \u4fdd\u8bc1\u6743\u91cd\u5728 [0, 1] \u8303\u56f4\u5185\n weighted_m1 = self.linear1(m1) * gate_weights * probability_weights # (B, N, D)\n m1 = weighted_m1 + m1\n global_f = self.cross_mamba(m1, extra_emb=m2)\n return global_f\uff0c\u4f46\u662f\u5728 def forward(self, template: torch.Tensor,\n search: torch.Tensor,\n event_template: torch.Tensor, \n event_search: torch.Tensor, \n ce_template_mask=None,\n ce_keep_rate=None,\n return_last_attn=False,\n ):\n rgb_feature = self.backbone.forward_features( z=template, x=search, \n inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False)\n event_feature = self.backbone.forward_features(z=event_template, x=event_search, \n inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False)\n residual_event_f = 0\n residual_rgb_f = 0\n event_f = self.cross_mamba(event_feature,residual_event_f,rgb_feature) + event_feature \n rgb_f = self.cross_mamba(rgb_feature,residual_rgb_f,event_feature) + rgb_feature\u8c03\u7528\u65f6\u6027\u80fd\u53cd\u800c\u53d8\u5dee\u4e86\uff0c\u9996\u5148\u5206\u6790\u65b0\u7684\u8bbe\u8ba1\u76ee\u7684\u662f\u4ec0\u4e48\uff1f\u7136\u540e\u5206\u6790\u65b0\u7684\u8bbe\u8ba1\u7248\u672c\u5b58\u5728\u4ec0\u4e48\u95ee\u9898\u5bfc\u81f4\u4e86\u6a21\u578b\u6027\u80fd\u53d8\u5dee\uff1f"},{"assistant_initiates":false,"asst_text":"\u5728\u65b0\u7684 `CrossMamba` \u8bbe\u8ba1\u4e2d\uff0c\u4ee5\u4e0b\u51e0\u4e2a\u64cd\u4f5c\u53ef\u80fd\u5bf9\u6027\u80fd\u7684\u4e0b\u964d\u4ea7\u751f\u4e86\u663e\u8457\u5f71\u54cd\uff1a\n\n### 1. **\u95e8\u63a7\u673a\u5236 (`gate`) \u548c\u975e\u7ebf\u6027\u6fc0\u6d3b**\n\n- **\u95ee\u9898**\uff1a\n - `gate` \u5c42\u751f\u6210\u7684 `gate_weights` \u662f\u5728 `m1` \u4e0a\u901a\u8fc7\u7ebf\u6027\u5c42\u548c `sigmoid` \u6fc0\u6d3b\u751f\u6210\u7684\u3002\u8fd9\u4e00\u64cd\u4f5c\u5bf9\u7279\u5f81\u8fdb\u884c\u4e86\u67d0\u79cd\u52a8\u6001\u52a0\u6743\uff0c\u4f46\u7f3a\u4e4f\u660e\u786e\u6307\u5bfc\u5bfc\u81f4\u8d85\u53c2\u6570\u4f18\u5316\u7684\u96be\u5ea6\u589e\u5927\uff0c\u5bb9\u6613\u51fa\u73b0\u6743\u91cd\u5206\u914d\u4e0d\u5f53\u7684\u95ee\u9898\u3002\n\n- **\u5f71\u54cd**\uff1a\n - \u5982\u679c `gate_weights` \u6ca1\u6709\u5b66\u5230\u9002\u5e94\u4e8e\u8f93\u5165\u6570\u636e\u5206\u5e03\u7684\u6b63\u786e\u6a21\u5f0f\uff0c\u53ef\u80fd\u5bfc\u81f4\u91cd\u8981\u7279\u5f81\u88ab\u4e0d\u5f53\u6291\u5236\uff0c\u4ece\u800c\u5728\u878d\u5408\u8fc7\u7a0b\u4e2d\u4e27\u5931\u4e86\u5173\u952e\u4fe1\u606f\u3002\n\n### 2. **\u5dee\u5f02\u6807\u51c6\u5316\u548c\u975e\u7ebf\u6027\u5904\u7406**\n\n- **\u95ee\u9898**\uff1a\n - \u8ba1\u7b97 `m1` \u548c `m2` \u4e4b\u95f4\u7684\u5dee\u5f02\u5e76\u5bf9\u5176\u8fdb\u884c\u6807\u51c6\u5316\u4ee5\u53ca\u540e\u7eed\u7684 `LeakyReLU` \u548c `sigmoid` \u6fc0\u6d3b\u53ef\u80fd\u4f1a\u5f15\u5165\u8fc7\u591a\u7684\u4e0d\u786e\u5b9a\u56e0\u7d20\u3002\u5728\u8fd9\u79cd\u8bbe\u7f6e\u4e2d\uff0c`diff` \u7684\u6807\u51c6\u5dee\u6807\u51c6\u5316\u53ef\u80fd\u4f1a\u4e22\u5931\u91cd\u8981\u7684\u7279\u5f81\u5bf9\u6bd4\u4fe1\u606f\uff0c\u5c24\u5176\u5f53 `std_diff` \u8fc7\u5c0f\u6216\u504f\u5dee\u5f15\u5165\u8bef\u5bfc\u3002\n\n- **\u5f71\u54cd**\uff1a\n - \u8fd9\u6837\u590d\u6742\u7684\u7279\u5f81\u8ba1\u7b97\u548c\u52a0\u6743\u8c03\u6574\u53ef\u80fd\u5bfc\u81f4\u91cd\u8981\u7279\u5f81\u5728\u4e0d\u540c\u6837\u672c\u4e2d\u8868\u73b0\u4e0d\u4e00\u81f4\uff0c\u5c24\u5176\u5b9e\u7528\u4e8e\u4e0d\u540c\u6a21\u6001\u65f6\uff0c\u53ef\u80fd\u5bfc\u81f4\u8de8\u6a21\u6001\u4e0d\u534f\u8c03\u7684\u95ee\u9898\u3002\n\n### 3. **\u591a\u7ebf\u6027\u5c42\u7684\u8fc7\u5ea6\u4f7f\u7528**\n\n- **\u95ee\u9898**\uff1a\n - \u5f15\u5165\u4e86\u989d\u5916\u7684\u7ebf\u6027\u53d8\u6362\u5c42 (`linear1` \u548c `linear2`)\uff0c\u8fd9\u79cd\u5904\u7406\u5728\u63d0\u4f9b\u66f4\u591a\u53c2\u6570\u7684\u540c\u65f6\uff0c\u4e5f\u589e\u52a0\u4e86\u590d\u5408\u7279\u5f81\u5904\u7406\u7684\u590d\u6742\u6027\uff0c\u800c\u5982\u679c\u6ca1\u6709\u9002\u5408\u7684\u6570\u636e\u652f\u6301\u6216\u8bad\u7ec3\u65f6\u95f4\uff0c\u53cd\u800c\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u96be\u4ee5\u6709\u6548\u5b66\u4e60\u3002\n\n- **\u5f71\u54cd**\uff1a\n - \u8fd9\u4e9b\u989d\u5916\u7684\u7ebf\u6027\u5c42\u53ef\u80fd\u5728\u5904\u7406\u7279\u5f81\u65f6\u5f15\u5165\u591a\u4f59\u7684\u566a\u97f3\uff0c\u7279\u522b\u662f\u5728\u7279\u5f81\u7a7a\u95f4\u7684\u8fc7\u5904\u7406\u4e0b\uff0c\u9501\u5b9a\u5230\u5c40\u90e8\u6b21\u4f18\u89e3\uff0c\u5f71\u54cd\u6574\u4f53\u7279\u5f81\u8868\u793a\u5b66\u4e60\u3002\n\n### \u89e3\u51b3\u5efa\u8bae\n\n- **\u9010\u6b65\u8c03\u8bd5**\uff1a\u53bb\u9664\u6216\u51cf\u5c11\u4ee5\u4e0a\u590d\u6742\u64cd\u4f5c\uff0c\u9010\u6b65\u52a0\u5165\u7279\u5f81\uff0c\u67e5\u770b\u5404\u4e2a\u7ec4\u4ef6\u5bf9\u7ed3\u679c\u7684\u5f71\u54cd\u3002\n- **\u7b80\u5316\u5904\u7406**\uff1a\u521d\u6b65\u53ef\u4ee5\u7b80\u5316\u5230 gate \u63a7\u5236\u548c\u5dee\u5f02\u6807\u51c6\u5316\uff0c\u5e76\u5728\u89c2\u5bdf\u5176\u6548\u679c\u540e\u518d\u8003\u8651\u5f15\u5165\u5176\u4ed6\u7ec4\u4ef6\u3002\n- **\u8d85\u53c2\u6570\u8c03\u4f18**\uff1a\u589e\u52a0\u5b9e\u9a8c\u7684\u5e7f\u5ea6\uff0c\u7279\u522b\u662f\u5728\u95e8\u63a7\u5c42\u53c2\u6570\u548c\u6807\u51c6\u5316\u65b9\u5f0f\u4e0a\u505a\u8fdb\u4e00\u6b65\u7ec6\u5316\u5b9e\u9a8c\uff0c\u627e\u5230\u9002\u5408\u6570\u636e\u96c6\u4e0e\u4efb\u52a1\u7684\u5408\u7406\u53c2\u6570\u8bbe\u7f6e\u3002\n\n\u901a\u8fc7\u9010\u6b65\u51cf\u5c11\u590d\u6742\u64cd\u4f5c\uff0c\u5e76\u8fdb\u884c\u7cfb\u7edf\u5316\u7684\u4f18\u5316\u4e0e\u9a8c\u8bc1\uff0c\u8fd9\u4e9b\u8c03\u6574\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u5e76\u89e3\u51b3\u9020\u6210\u6027\u80fd\u4e0b\u964d\u7684\u5173\u952e\u56e0\u7d20\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":991,"n_turns_in_conversation":28,"n_words":73,"source_row_offset":30728,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"26e0c0114c611ca36c10e581","turn_index":5,"user_text":"\u90a3\u4e48\u7ed9\u51fa\u6700\u5f71\u54cd\u65b0\u8bbe\u8ba1\u6a21\u5757\uff0c\u5bfc\u81f4\u6027\u80fd\u4e0b\u964d\u7684\u64cd\u4f5c"},{"assistant_initiates":false,"asst_text":"\u4e3a\u4e86\u8c03\u8bd5\u548c\u4f18\u5316\u65b0\u7684 `CrossMamba` \u8bbe\u8ba1\uff0c\u6211\u4eec\u53ef\u4ee5\u91c7\u53d6\u9010\u6b65\u7b80\u5316\u548c\u6d4b\u8bd5\u7684\u65b9\u5f0f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u9010\u6b65\u8c03\u8bd5\u7684\u5efa\u8bae\u548c\u76f8\u5e94\u7684\u4ee3\u7801\u5b9e\u73b0\uff1a\n\n### \u9010\u6b65\u8c03\u8bd5\u65b9\u6cd5\uff1a\n\n1. **\u57fa\u7840\u7248\u672c**\uff1a\u4ec5\u4f7f\u7528\u57fa\u672c\u7684\u52a0\u6743\u6216\u7ebf\u6027\u53d8\u6362\u8fdb\u884c\u521d\u6b65\u6d4b\u8bd5\u3002\n2. **\u9010\u6b65\u5f15\u5165**\uff1a\u4e00\u6b65\u4e00\u6b65\u5f15\u5165\u95e8\u63a7\u673a\u5236\u548c\u5dee\u5f02\u6807\u51c6\u5316\uff0c\u89c2\u5bdf\u548c\u8bb0\u5f55\u6bcf\u4e00\u6b65\u7684\u6027\u80fd\u53d8\u5316\u3002\n3. **\u53c2\u6570\u8c03\u4f18**\uff1a\u5728\u6bcf\u4e2a\u6b65\u9aa4\u4e2d\u8c03\u4f18\u8d85\u53c2\u6570\uff0c\u786e\u4fdd\u5728\u5c40\u90e8\u6700\u4f18\u8c03\u6574\u53ef\u884c\u7684\u60c5\u51b5\u4e0b\u6027\u80fd\u63d0\u5347\u3002\n\n### \u8c03\u8bd5\u4ee3\u7801\u5b9e\u73b0\uff1a\n\n```python\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange\n\nclass CrossMamba(nn.Module):\n def __init__(self, dim):\n super(CrossMamba, self).__init__()\n self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n self.linear1 = nn.Linear(dim, dim) # \u7ebf\u6027\u53d8\u6362\n self.sigmoid = nn.Sigmoid() # \u786e\u4fdd\u6743\u91cd\u5728 [0, 1] \u8303\u56f4\u5185\n\n # \u53ef\u9009\u62e9\u9010\u6b65\u6ce8\u91ca\u6389\u8fd9\u51e0\u884c\n self.linear2 = nn.Linear(dim, dim) # \u521d\u59cb\u5316\uff0c\u4f46\u53ef\u4ee5\u6682\u4e0d\u4f7f\u7528\n\n # \u9010\u6b65\u5f15\u5165\u7684\u7ec4\u4ef6\n self.gate = nn.Linear(dim, 1) # \u521d\u59cb\u5316\uff0c\u4f46\u53ef\u4ee5\u6682\u4e0d\u4f7f\u7528\n self.leaky_relu = nn.LeakyReLU(negative_slope=0.01) # \u521d\u59cb\u5316\uff0c\u4f46\u53ef\u4ee5\u6682\u4e0d\u4f7f\u7528\n\n def forward(self, m1, m1_resi, m2):\n m1_resi = m1 + m1_resi # (B, N, D)\n\n # Step 1: \u4ec5\u7ebf\u6027\u53d8\u6362\u548c\u52a0 m1\n weighted_m1 = self.linear1(m1) # (B, N, D)\n m1 = weighted_m1 + m1 # \u521d\u6b65\u878d\u5408\n\n # \u5728\u5b9e\u9645\u8c03\u8bd5\u4e2d\u53ef\u4ee5\u4f9d\u6b21\u53d6\u6d88\u6ce8\u91ca\u4ee5\u9010\u6b65\u5f15\u5165\u590d\u6742\u673a\u5236\n\n # Step 2: \u5f15\u5165\u95e8\u63a7\u673a\u5236\n # gate_values = self.gate(m1)\n # gate_weights = self.sigmoid(gate_values)\n # m1 = weighted_m1 * gate_weights + m1\n\n # Step 3: \u5f15\u5165\u5dee\u5f02\u6807\u51c6\u5316\n # diff = m1 - m2\n # std_diff = torch.std(diff, dim=-1, keepdim=True)\n # std_diff = torch.clamp(std_diff, min=1e-6) # \u907f\u514d\u9664\u4ee5\u96f6\n # diff = diff / std_diff\n # probability_weights = self.leaky_relu(diff)\n # probability_weights = self.sigmoid(probability_weights)\n # m1 = weighted_m1 * gate_weights * probability_weights + m1\n\n global_f = self.cross_mamba(m1, extra_emb=m2)\n return global_f\n\n# Mamba\u7c7b\u7684\u7b80\u5355\u6a21\u62df\u5b9e\u73b0\uff08\u7528\u4e8e\u8c03\u8bd5\uff09\nclass Mamba(nn.Module):\n def __init__(self, dim, bimamba_type=\"v3\"):\n super(Mamba, self).__init__()\n self.out_proj = nn.Linear(dim, dim)\n\n def forward(self, hidden_states, extra_emb=None):\n # \u7b80\u5316\u7684\u8ba1\u7b97\u8fc7\u7a0b\n return self.out_proj(hidden_states)\n\n# \u793a\u4f8b\u8c03\u7528\ndim = 64\ncross_mamba = CrossMamba(dim)\nm1 = torch.rand(32, 10, dim)\nm1_resi = torch.zeros_like(m1)\nm2 = torch.rand(32, 10, dim)\n\noutput = cross_mamba(m1, m1_resi, m2)\nprint(output.shape) # (32, 10, dim)\n```\n\n### \u89e3\u91ca\u4e0e\u6b65\u9aa4\uff1a\n- **\u57fa\u7840\u7ebf\u6027\u53d8\u6362**\uff1a\u9996\u5148\u53ea\u5e94\u7528 `linear1` \u53d8\u6362\uff0c\u7136\u540e\u8fdb\u884c\u7b80\u5355\u7684\u52a0\u6cd5\u64cd\u4f5c\uff0c\u4ee5\u786e\u5b9a\u8fd9\u90e8\u5206\u529f\u80fd\u7684\u57fa\u672c\u5de5\u4f5c\u3002\n- **\u9010\u6b65\u5f15\u5165\u590d\u6742\u7ec4\u4ef6**\uff1a\u901a\u8fc7\u6ce8\u91ca\u548c\u53d6\u6d88\u6ce8\u91ca\u7684\u65b9\u5f0f\u9010\u6b65\u5f15\u5165 `gate` \u548c\u5dee\u5f02\u6807\u51c6\u5316\u7684\u7ec4\u4ef6\uff0c\u5728\u6bcf\u4e00\u6b65\u8fd0\u884c\u5b8c\u540e\u6d4b\u8bd5\u6a21\u578b\u7684\u6027\u80fd\u53d8\u5316\u3002\n- **\u6027\u80fd\u76d1\u6d4b**\uff1a\u5728\u4f7f\u7528\u5b9e\u9645\u6570\u636e\u65f6\uff0c\u6bcf\u6b65\u6dfb\u52a0\u65b0\u7ec4\u4ef6\u540e\uff0c\u76d1\u6d4b\u6a21\u578b\u6027\u80fd\uff0c\u82e5\u53d8\u5316\u6709\u8d1f\u9762\u5f71\u54cd\uff0c\u53ef\u4ee5\u8fdb\u884c\u8fdb\u4e00\u6b65\u8c03\u6574\u6216\u9009\u62e9\u6027\u5f03\u7528\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":2381,"n_turns_in_conversation":28,"n_words":245,"source_row_offset":30729,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"236cc1ef08f4cf1b9a4fad92","turn_index":6,"user_text":"\u90a3\u4e48\u7ed9\u51fa\u9010\u6b65\u8c03\u8bd5\u7684\u4ee3\u7801"},{"assistant_initiates":false,"asst_text":"\u4e3a\u4e86\u589e\u5f3a\u53cc\u6a21\u6001\u4e4b\u95f4\u7684\u4ea4\u4e92\u4e0e\u878d\u5408\uff0c\u9700\u8981\u5728\u7279\u5f81\u5904\u7406\u4e0e\u878d\u5408\u7b56\u7565\u4e0a\u8fdb\u884c\u4f18\u5316\uff0c\u786e\u4fdd\u6a21\u578b\u80fd\u591f\u6709\u6548\u5229\u7528\u4e24\u4e2a\u4e0d\u540c\u6a21\u6001\u7684\u4fe1\u606f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4f18\u5316\u65b9\u6848\uff1a\n\n### 1. **\u52a8\u6001\u7279\u5f81\u52a0\u6743**\n\n- **\u4f18\u5316\u5efa\u8bae**\uff1a\n - \u901a\u8fc7\u66f4\u590d\u6742\u6216\u6570\u636e\u9a71\u52a8\u7684\u65b9\u6cd5\u6765\u786e\u5b9a\u7279\u5f81\u7684\u91cd\u8981\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\u4ee3\u66ff\u7b80\u5355\u7684\u7ebf\u6027\u548c Sigmoid \u95e8\u63a7\uff0c\u5e2e\u52a9\u6a21\u578b\u81ea\u9002\u5e94\u5730\u8c03\u6574\u4e0d\u540c\u6a21\u6001\u95f4\u7684\u4fe1\u606f\u6d41\u3002\n\n- **\u5b9e\u73b0\u63d0\u793a**\uff1a\n ```python\n import torch.nn.functional as F\n \n class CrossMamba(nn.Module):\n # ... \u5176\u4ed6\u90e8\u5206\u4e0d\u53d8 ...\n def __init__(self, dim):\n super(CrossMamba, self).__init__()\n self.attention = nn.MultiheadAttention(embed_dim=dim, num_heads=4)\n # \u5176\u4ed6\u521d\u59cb\u5316\u4e0d\u53d8\n \n def forward(self, m1, m1_resi, m2):\n # \u4f7f\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\u8ba1\u7b97\u52a0\u6743\n m1_resi = m1 + m1_resi\n attn_output, _ = self.attention(m1_resi, m2, m2)\n m1 = m1 + attn_output\n \n global_f = self.cross_mamba(m1, extra_emb=m2)\n return global_f\n ```\n\n### 2. **\u7279\u5f81\u5dee\u5f02\u5b66\u4e60**\n\n- **\u4f18\u5316\u5efa\u8bae**\uff1a\n - \u901a\u8fc7\u5dee\u5f02\u5b66\u4e60\uff08\u5982\u57fa\u4e8e\u635f\u5931\u51fd\u6570\u5f3a\u5236\u76f8\u4f3c/\u5dee\u5f02\u7279\u5f81\u5b66\u4e60\uff09\u6765\u8fdb\u4e00\u6b65\u7cbe\u7ec6\u5316\u53cc\u6a21\u6001\u7684\u878d\u5408\u8fc7\u7a0b\uff0c\u786e\u4fdd\u4e24\u4e2a\u6a21\u6001\u4e4b\u95f4\u7684\u6709\u7528\u4fe1\u606f\u5f97\u4ee5\u63d0\u53d6\u5e76\u5f3a\u5316\u3002\n\n- **\u5b9e\u73b0\u63d0\u793a**\uff1a\n - \u5229\u7528\u5bf9\u6bd4\u5b66\u4e60\u7684\u601d\u60f3\uff0c\u901a\u8fc7\u8c03\u6574 `m1` \u548c `m2` \u7684\u7279\u5f81\u8868\u793a\uff0c\u786e\u4fdd\u90a3\u4e9b\u6709\u610f\u4e49\u7684\u4ea4\u4e92\u7279\u5f81\u53ef\u4ee5\u88ab\u66f4\u5f3a\u70c8\u5730\u5f3a\u8c03\u51fa\u6765\u3002\n\n### 3. **\u878d\u5408\u5c42\u6b63\u5219\u5316**\n\n- **\u4f18\u5316\u5efa\u8bae**\uff1a\n - \u7ed3\u5408 L1/L2 \u6b63\u5219\u5316\u7ea6\u675f\u6216\u8005 Dropout \u63d0\u9ad8\u6cdb\u5316\u80fd\u529b\uff0c\u907f\u514d\u5355\u6a21\u6001\u7279\u5f81\u6216\u8005\u90e8\u5206\u7279\u5f81\u5728\u878d\u5408\u4e2d\u5360\u4e3b\u5bfc\u3002\n\n- **\u5b9e\u73b0\u63d0\u793a**\uff1a\n ```python\n self.dropout = nn.Dropout(p=0.1)\n weighted_m1 = self.dropout(weighted_m1) # \u6b63\u5219\u5316\u5904\u7406\u589e\u52a0\u6cdb\u5316\u6027\n ```\n\n### 4. **\u591a\u5c42\u6b21\u7279\u5f81\u878d\u5408**\n\n- **\u4f18\u5316\u5efa\u8bae**\uff1a\n - \u5229\u7528\u5806\u53e0\u7684\u65b9\u5f0f\u5728\u4e0d\u540c\u5c42\u7ea7\uff08\u5982\u795e\u7ecf\u7f51\u7edc\u7684\u622a\u65ad\u5c42\uff09\u8fdb\u884c\u591a\u6b21\u6a21\u6001\u878d\u5408\uff0c\u4ece\u7c97\u7c92\u5ea6\u5230\u7ec6\u7c92\u5ea6\u9010\u6e10\u589e\u52a0\u4ea4\u4e92\u6df1\u5ea6\u3002\n\n- **\u5b9e\u73b0\u63d0\u793a**\uff1a\n - \u53ef\u4ee5\u901a\u8fc7\u91cd\u590d\u4f7f\u7528 `CrossMamba` \u6a21\u5757\u6216\u8005\u8bbe\u8ba1\u7ea7\u8054\u7ed3\u6784\uff0c\u5728\u524d\u5411\u7f51\u7edc\u7684\u4e0d\u540c\u9636\u6bb5\u9010\u6b65\u589e\u52a0\u878d\u5408\u64cd\u4f5c\u3002\n\n### 5. **\u5e94\u7528\u66f4\u590d\u6742\u7684\u878d\u5408\u673a\u5236**\n\n- **\u4f18\u5316\u5efa\u8bae**\uff1a\n - \u8003\u8651\u91c7\u7528\u66f4\u590d\u6742\u7684\u878d\u5408\u673a\u5236\uff0c\u5982\u589e\u5e7f\u7684 Transformer \u7ed3\u6784\uff0c\u9488\u5bf9\u7279\u5b9a\u6a21\u6001\u7279\u5f81\uff0c\u9644\u52a0\u7279\u5b9a\u7684 Transformer encoder \u4ee5\u4fbf\u5904\u7406\u6a21\u6001\u95f4\u7684\u4fe1\u606f\u6d41\u52a8\u4e0e\u76f8\u4e92\u4f5c\u7528\u3002\n\n- **\u5b9e\u73b0\u63d0\u793a**\uff1a\u53c2\u8003\u6700\u65b0\u7684\u591a\u6a21\u6001\u878d\u5408\u6587\u732e\uff0c\u5b9e\u65bd transformer-based \u53d8\u79cd\u3002\n\n\u901a\u8fc7\u4e0a\u8ff0\u4f18\u5316\uff0c\u6a21\u578b\u5c06\u6709\u53ef\u80fd\u66f4\u597d\u5730\u6355\u6349\u548c\u5229\u7528\u53cc\u6a21\u6001\u6570\u636e\u4e4b\u95f4\u7684\u6f5c\u5728\u5173\u7cfb\u548c\u4fe1\u606f\u4ea4\u4e92\uff0c\u63d0\u5347\u53cc\u6a21\u6001\u878d\u5408\u7684\u6709\u6548\u6027\u548c\u6574\u4f53\u6027\u80fd\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1569,"n_turns_in_conversation":28,"n_words":133,"source_row_offset":30730,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"502ebd5d7cdbddd8da373ce8","turn_index":7,"user_text":"\u6211\u4eec\u7684\u8bbe\u8ba1\u76ee\u7684\u662f\u589e\u52a0\u53cc\u6a21\u6001\u4e4b\u95f4\u7684\u4ea4\u4e92\u878d\u5408\uff0c\u90a3\u4e48\u7ed9\u51fa\u4f18\u5316\u9879"}],"shard":{"index":41,"name":"turns-00041.parquet","rows":42515}}