feat: DeepSeekMoE #32862
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feat: DeepSeekMoE #32862
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
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Hey @ArthurZucker , should be ready for review now, thanks! |
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friendly ping |
| ### Description | ||
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| DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. It employs an innovative MoE architecture, which involves two principal strategies: fine-grained expert segmentation and shared experts isolation. It is trained from scratch on 2T English and Chinese tokens, and exhibits comparable performance with DeepSeek 7B and LLaMA2 7B, with only about 40% of computations. For research purposes, we release the model checkpoints of DeepSeekMoE 16B Base and DeepSeekMoE 16B Chat to the public, which can be deployed on a single GPU with 40GB of memory without the need for quantization. | ||
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maybe missing one line about "this model was contributed by" with your HF username!
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| logger = logging.get_logger(__name__) | ||
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| DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
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| DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
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| moe_layer_freq (`int`, *optional*, defaults to 1): | ||
| The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers. | ||
| first_k_dense_replace (`int`, *optional*, defaults to 0): | ||
| Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). | ||
| \--k dense layers--/ |
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would be nice to have standardization with the other MoE in the library! we usually call this the sparse_step:
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| pretraining_tp (`int`, *optional*, defaults to 1): | ||
| Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this | ||
| document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is | ||
| necessary to ensure exact reproducibility of the pretraining results. Please refer to [this | ||
| issue](https://github.com/pytorch/pytorch/issues/76232). |
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| def _rope_scaling_validation(self): | ||
| """ | ||
| Validate the `rope_scaling` configuration. | ||
| """ | ||
| if self.rope_scaling is None: | ||
| return | ||
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| if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: | ||
| raise ValueError( | ||
| "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " | ||
| f"got {self.rope_scaling}" | ||
| ) | ||
| rope_scaling_type = self.rope_scaling.get("type", None) | ||
| rope_scaling_factor = self.rope_scaling.get("factor", None) | ||
| if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: | ||
| raise ValueError( | ||
| f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" | ||
| ) | ||
| if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: | ||
| raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") |
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Suggested change
| def _rope_scaling_validation(self): | |
| """ | |
| Validate the `rope_scaling` configuration. | |
| """ | |
| if self.rope_scaling is None: | |
| return | |
| if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: | |
| raise ValueError( | |
| "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " | |
| f"got {self.rope_scaling}" | |
| ) | |
| rope_scaling_type = self.rope_scaling.get("type", None) | |
| rope_scaling_factor = self.rope_scaling.get("factor", None) | |
| if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: | |
| raise ValueError( | |
| f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" | |
| ) | |
| if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: | |
| raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") |
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| @torch.no_grad() | ||
| def moe_infer(self, x, flat_expert_indices, flat_expert_weights): | ||
| expert_cache = torch.zeros_like(x) | ||
| idxs = flat_expert_indices.argsort() | ||
| tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) | ||
| token_idxs = idxs // self.num_experts_per_tok | ||
| for i, end_idx in enumerate(tokens_per_expert): | ||
| start_idx = 0 if i == 0 else tokens_per_expert[i - 1] | ||
| if start_idx == end_idx: | ||
| continue | ||
| expert = self.experts[i] | ||
| exp_token_idx = token_idxs[start_idx:end_idx] | ||
| expert_tokens = x[exp_token_idx] | ||
| expert_out = expert(expert_tokens) | ||
| expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) | ||
| expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce="sum") | ||
| return expert_cache |
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I don't really. mind having 2 forwards, one for inference one for training, tho this is not super common
| y = AddAuxiliaryLoss.apply(y, aux_loss) | ||
| else: | ||
| y = self.moe_infer(hidden_states, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) | ||
| if self.config.n_shared_experts is not None: |
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let's avoid code path if the model uses shared experts, let's add this otherwise let's remove!
| return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) | ||
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| # Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->Deepseek |
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we should rather copy from Gemma, removes the need for pretaining tps!
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| if position_embeddings is None: | ||
| logger.warning_once( | ||
| "The attention layers in this model are transitioning from computing the RoPE embeddings internally " | ||
| "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " | ||
| "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be " | ||
| "removed and `position_embeddings` will be mandatory." | ||
| ) | ||
| cos, sin = self.rotary_emb(value_states, position_ids) | ||
| else: |
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as this is a new model, it will not support out of the box legacy behaviours
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| self.mlp = ( | ||
| DeepseekMoE(config) | ||
| if ( | ||
| config.n_routed_experts is not None | ||
| and layer_idx >= config.first_k_dense_replace | ||
| and layer_idx % config.moe_layer_freq == 0 | ||
| ) | ||
| else DeepseekMLP(config) | ||
| ) |
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let's split in 2 lines
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What does this PR do?
Upstream custom code from https://huggingface.co/deepseek-ai/deepseek-moe-16b-base/blob/main/modeling_deepseek.py to huggingface/transformers. This is not DeepSeek V2. The newly released DeepSeek-Prover-V1.5 runs on this architecture for example (though without MoE layers, so it is actually just Llama).
https://huggingface.co/models?other=deepseek
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