diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..52373fe24473b1aa44333d318f578ae6bf04b49b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ccf0d87c0d037b254c82797440966d77d737b94e --- /dev/null +++ b/README.md @@ -0,0 +1,224 @@ +--- +license: apache-2.0 +language: +- en +- es +- fr +- de +- it +- pt +- ru +- ar +- hi +- ko +- zh +library_name: transformers +base_model: +- arcee-ai/Trinity-Large-Preview +base_model_relation: quantized +--- + + + + +
+ + Arcee Trinity Large + +
+
+ +# Trinity-Large-Preview-FP8 + +## Introduction + +Trinity-Large-Preview is a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. It is the largest model in Arcee AI's Trinity family, trained on more than 17 trillion tokens and delivering frontier-level performance with strong long-context comprehension. +Trinity-Large-Preview is a lightly post-trained model based on Trinity-Large-Base. + +**This repository contains the FP8 quantized weights of Trinity-Large-Preview.** + +Try it at [chat.arcee.ai](http://chat.arcee.ai/) + +More details on the training of Trinity Large are available in the [technical report](https://github.com/arcee-ai/trinity-large-tech-report/). + + +## Model Variants + +The Trinity Large family consists of three checkpoints from the same training run: + +- **[Trinity-Large-Preview](https://huggingface.co/arcee-ai/Trinity-Large-Preview)**: Lightly post-trained, chat-ready model undergoing active RL +- **[Trinity-Large-TrueBase](https://huggingface.co/arcee-ai/Trinity-Large-TrueBase)**: 10T-token pre-anneal pretraining checkpoint +- **[Trinity-Large-Base](https://huggingface.co/arcee-ai/Trinity-Large-Base)**: Full 17T-token pretrained foundation model with mid-training anneals + +## Architecture + +Trinity-Large-Preview uses a sparse MoE configuration designed to maximize efficiency while maintaining large-scale capacity. + +| Hyperparameter | Value | +|:---|:---:| +| Total parameters | ~398B | +| Active parameters per token | ~13B | +| Experts | 256 (1 shared) | +| Active experts | 4 | +| Routing strategy | 4-of-256 (1.56% sparsity) | +| Dense layers | 6 | +| Pretraining context length | 8,192 | +| Context length after extension | 512k | +| Architecture | Sparse MoE (AfmoeForCausalLM) | + +## Benchmarks + +| Benchmark | Llama 4 Maverick | Trinity-Large Preview | +|-----------|------------------|----------------------| +| MMLU | 85.5 | 87.2 | +| MMLU-Pro | 80.5 | 75.2 | +| GPQA-Diamond | 69.8 | 63.3 | +| AIME 2025 | 19.3 | 24.0 | + +## Training Configuration + +### Pretraining + +- Training tokens: 17 trillion +- Data partner: [Datology](https://www.datologyai.com/) + +
+ + Powered by Datology + +
+ +## Posttraining +- This checkpoint was instruction tuned on 20B tokens. + +### Infrastructure + +- Hardware: 2,048 NVIDIA B300 GPUs +- Parallelism: HSDP + Expert Parallelism +- Compute partner: [Prime Intellect](https://www.primeintellect.ai/) + + +
+ + Powered by Prime Intellect + +
+ +## Usage + +### Running our model + +- [Transformers](https://huggingface.co/arcee-ai/Trinity-Large-Preview#transformers) +- [VLLM](https://huggingface.co/arcee-ai/Trinity-Large-Preview#vllm) +- [llama.cpp](https://huggingface.co/arcee-ai/Trinity-Large-Preview#llamacpp) +- [LM Studio](https://huggingface.co/arcee-ai/Trinity-Large-Preview#lm-studio) +- [API](https://huggingface.co/arcee-ai/Trinity-Large-Preview#api) + + +### Recommended settings + * temperature: + * top_k: + * top_p: + * min_p: + +### Transformers + +Use the `main` transformers branch or pass `trust_remote_code=True` with a released version. + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_id = "arcee-ai/Trinity-Large-Preview" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.bfloat16, + device_map="auto", + trust_remote_code=True +) + +messages = [ + {"role": "user", "content": "Who are you?"}, +] + +input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" +).to(model.device) + +outputs = model.generate( + input_ids, + max_new_tokens=256, + do_sample=True, + temperature=0.5, + top_k=50, + top_p=0.95 +) + +response = tokenizer.decode(outputs[0], skip_special_tokens=True) +print(response) +``` + +### VLLM + +Supported in VLLM release 0.11.1+ + +```bash +vllm serve arcee-ai/Trinity-Large-Preview \ + --dtype bfloat16 \ + --enable-auto-tool-choice \ + --tool-call-parser hermes +``` + +### llama.cpp + +Supported in llama.cpp release b7061+ + +```bash +llama-server -hf arcee-ai/Trinity-Large-Preview-GGUF:q4_k_m +``` + +### LM Studio + +Supported in the latest LM Studio runtime. Search for `arcee-ai/Trinity-Large-Preview-GGUF` in Model Search. + +### API + +Available on OpenRouter: + +```bash +curl -X POST "https://openrouter.ai/v1/chat/completions" \ + -H "Authorization: Bearer $OPENROUTER_API_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "arcee-ai/trinity-large-preview", + "messages": [ + { + "role": "user", + "content": "What are some fun things to do in New York?" + } + ] + }' +``` + + +## License + +Trinity-Large-Preview is released under the Apache License, Version 2.0. + +## Citation + +```bibtex +@misc{arcee_trinity_large_preview, + title = {Trinity-Large-Preview}, + author = {{Arcee AI}}, + year = {2026}, + note = {398B sparse MoE model trained on 17T tokens} +} +``` \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..05f0cd0bf1db4af2bf06100e0c636120d1b7b480 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,65 @@ +{{ bos_token }}{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role + '\n' }} + {% generation %} + {{- content}} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>' }} + {% endgeneration%} + {{- '\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} +{%- endif %} diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..35c5b7304bd66bff92326c63b97ff796a2a68ca7 --- /dev/null +++ b/config.json @@ -0,0 +1,511 @@ +{ + "architectures": [ + "AfmoeForCausalLM" + ], + "attention_dropout": 0.0, + "auto_map": { + "AutoConfig": "configuration_afmoe.AfmoeConfig", + "AutoModel": "modeling_afmoe.AfmoeModel", + "AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM" + }, + "bos_token_id": 0, + "dtype": "bfloat16", + "eos_token_id": 3, + "global_attn_every_n_layers": 4, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 3072, + "initializer_range": 0.02, + "intermediate_size": 12288, + "layer_types": [ + "sliding_attention", + "sliding_attention", + "sliding_attention", + "full_attention", + "sliding_attention", + "sliding_attention", + "sliding_attention", + "full_attention", + 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"compressed-tensors", + "quantization_status": "compressed", + "sparsity_config": {}, + "transform_config": {}, + "version": "0.13.1.a20260116" + }, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 10000, + "route_norm": true, + "route_scale": 2.448, + "score_func": "sigmoid", + "sliding_window": 4096, + "tie_word_embeddings": false, + "topk_group": 1, + "transformers_version": "4.57.3", + "use_cache": true, + "use_grouped_mm": true, + "vocab_size": 200192 +} diff --git a/configuration_afmoe.py b/configuration_afmoe.py new file mode 100644 index 0000000000000000000000000000000000000000..9efecdd517e8e6168f46ebecb3d282bdea34c5dc --- /dev/null +++ b/configuration_afmoe.py @@ -0,0 +1,133 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from transformers.configuration_utils import PretrainedConfig +from transformers.modeling_rope_utils import rope_config_validation +from transformers.configuration_utils import layer_type_validation +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +class AfmoeConfig(PretrainedConfig): + """ + n_group (`int`, *optional*, defaults to 1): + Number of groups for routed experts. + topk_group (`int`, *optional*, defaults to 1): + Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups). + """ + model_type = "afmoe" + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + def __init__( + self, + num_hidden_layers: int = 32, + vocab_size: int = 200192, + hidden_size: int = 2048, + intermediate_size: int = 6144, + moe_intermediate_size=1408, + num_dense_layers=1, + num_attention_heads=16, + num_key_value_heads=None, + head_dim=128, + hidden_act="silu", + max_position_embeddings=16384, + initializer_range=0.02, + rms_norm_eps=1e-5, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + num_experts=64, + num_experts_per_tok=6, + num_shared_experts=2, + num_expert_groups=1, + num_limited_groups=1, + score_func="sigmoid", + route_norm=True, + route_scale=1.0, + global_attn_every_n_layers=4, + sliding_window=1024, + mup_enabled=False, + layer_types=None, + attention_dropout: float = 0.0, + n_group: int = 1, + topk_group: int = 1, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_dense_layers = num_dense_layers + self.num_attention_heads = num_attention_heads + self.head_dim = head_dim + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + + + # MoE specific + self.moe_intermediate_size = moe_intermediate_size + self.num_experts_per_tok = num_experts_per_tok + self.n_group = n_group + self.topk_group = topk_group + self.num_experts = num_experts + self.num_shared_experts = num_shared_experts + self.num_expert_groups = num_expert_groups + self.num_limited_groups = num_limited_groups + self.score_func = score_func + self.route_norm = route_norm + self.route_scale = route_scale + + + # Attention specific + self.attention_dropout = attention_dropout + self.global_attn_every_n_layers = global_attn_every_n_layers + self.sliding_window = sliding_window + self.layer_types = layer_types + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" if bool((i + 1) % global_attn_every_n_layers) else "full_attention" for i in range(self.num_hidden_layers) + ] + layer_type_validation(self.layer_types) + + # muP specific + self.mup_enabled = mup_enabled + + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + + + # Validate rope configs + if self.rope_scaling is not None and "type" in self.rope_scaling: + self.rope_scaling["rope_type"] = self.rope_scaling["type"] + rope_config_validation(self) + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + +__all__ = ["AfmoeConfig"] diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..66125e8274c699a88af4b0ef5c64c1194de047ac --- /dev/null +++ b/generation_config.json @@ -0,0 +1,7 @@ +{ + "_from_model_config": true, + "bos_token_id": 0, + "eos_token_id": 3, + "pad_token_id": 12, + "transformers_version": "4.57.3" +} diff --git a/model-00001-of-00081.safetensors b/model-00001-of-00081.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..081ca497df57ccc9289c008d9d0075931ccc8d90 --- /dev/null +++ b/model-00001-of-00081.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8dbbf27913667dd5daa8f3ab2d6caf94cc06a85f68b62aaed6e4b594ba5d9e87 +size 4992710176 diff --git a/model-00002-of-00081.safetensors b/model-00002-of-00081.safetensors new file mode 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+from transformers.utils import TransformersKwargs +from transformers.cache_utils import Cache, DynamicCache +from transformers.integrations import use_kernel_forward_from_hub + + +try: + from .configuration_afmoe import AfmoeConfig +except: + from configuration_afmoe import AfmoeConfig + +class AfmoeRotaryEmbedding(nn.Module): + + def __init__(self, config: AfmoeConfig, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + def _dynamic_frequency_update(self, position_ids, device): + """ + dynamic RoPE layers should recompute `inv_freq` in the following situations: + 1 - growing beyond the cached sequence length (allow scaling) + 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) + """ + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_seq_len_cached: # growth + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len) + self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation + self.max_seq_len_cached = seq_len + + if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset + # This .to() is needed if the model has been moved to a device after being initialized (because + # the buffer is automatically moved, but not the original copy) + self.original_inv_freq = self.original_inv_freq.to(device) + self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) + self.max_seq_len_cached = self.original_max_seq_len + + @torch.no_grad() + def forward(self, x, position_ids): + if "dynamic" in self.rope_type: + self._dynamic_frequency_update(position_ids, device=x.device) + + # Core RoPE block + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 (see https://github.com/huggingface/transformers/pull/29285) + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float().to(x.device) @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + + # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention + cos = cos * self.attention_scaling + sin = sin * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand( + batch, num_key_value_heads, n_rep, slen, head_dim + ) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + +@use_kernel_forward_from_hub("RMSNorm") +class AfmoeRMSNorm(nn.Module): + def __init__(self, hidden_size: int, eps: float): + """ + AfmoeRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: Optional[torch.Tensor], + scaling: float, + dropout: float = 0.0, + **kwargs, +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to( + query.dtype + ) + attn_weights = nn.functional.dropout( + attn_weights, p=dropout, training=module.training + ) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class AfmoeMLP(nn.Module): + def __init__(self, config, intermediate_size=None): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = intermediate_size or config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +class AfmoeTokenChoiceRouter(nn.Module): + """Token-choice top-K router for MoE routing.""" + + def __init__(self, config): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.num_experts = config.num_experts + self.score_func = config.score_func + self.route_norm = config.route_norm + self.route_scale = config.route_scale + self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False) + + def forward(self, hidden_states, expert_bias: torch.Tensor | None): + _, _, hidden_dim = hidden_states.shape + hidden_states = hidden_states.view(-1, hidden_dim) + + scores = self.gate(hidden_states) + + # Apply scoring function in float32 for stability + if self.score_func == "sigmoid": + scores = torch.sigmoid(scores.to(torch.float32)) + else: + scores = F.softmax(scores.to(torch.float32), dim=-1) + + if expert_bias is not None: + _, selected_experts = torch.topk(scores + expert_bias, k=self.top_k, dim=1) + top_scores = scores.gather(dim=1, index=selected_experts) + else: + top_scores, selected_experts = torch.topk(scores, k=self.top_k, dim=1) + + # Normalize weights if using sigmoid + if self.score_func == "sigmoid" and self.route_norm: + denominator = top_scores.sum(dim=-1, keepdim=True) + 1e-20 + top_scores = top_scores / denominator + + top_scores = top_scores * self.route_scale + return top_scores, selected_experts + +class AfmoeMoE(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.router = AfmoeTokenChoiceRouter(config) + + self.shared_experts = None + if config.num_shared_experts > 0: + self.shared_experts = AfmoeMLP( + config, config.moe_intermediate_size * config.num_shared_experts + ) + self.experts = nn.ModuleList( + [AfmoeMLP( + config, intermediate_size=config.moe_intermediate_size + ) for _ in range(config.num_experts)] + ) + self.expert_bias = nn.Parameter(torch.zeros(config.num_experts, dtype=torch.float32), requires_grad=False) + + + def forward(self, hidden_states): + batch_size, seq_len, hidden_dim = hidden_states.shape + hidden_states_flat = hidden_states.view(-1, hidden_dim) + + # Get routing decisions + top_scores, selected_experts = self.router(hidden_states, self.expert_bias) + + # Process through shared experts + if self.shared_experts is not None: + shared_output = self.shared_experts(hidden_states_flat) + else: + shared_output = torch.zeros_like(hidden_states_flat) + + # Reorder tokens by expert for efficient processing + token_indices_sorted = torch.argsort(selected_experts.view(-1), stable=True) + top_scores_sorted = top_scores.view(-1)[token_indices_sorted] + token_to_expert = selected_experts.view(-1)[token_indices_sorted] + token_indices_sorted = token_indices_sorted // self.config.num_experts_per_tok + + # Gather input tokens + token_indices_expanded = token_indices_sorted.unsqueeze(-1).expand( + -1, hidden_dim + ) + routed_input = torch.gather( + hidden_states_flat, dim=0, index=token_indices_expanded + ) + + routed_output = torch.zeros_like(routed_input) + for expert_id in range(self.config.num_experts): + mask = token_to_expert == expert_id + if mask.any(): + expert_input = routed_input[mask] + expert_out = self.experts[expert_id](expert_input) + routed_output[mask] = expert_out + + routed_output = ( + routed_output.to(torch.float32) * top_scores_sorted.unsqueeze(-1) + ).to(hidden_states.dtype) + + # Scatter back to original positions + output = shared_output.scatter_add( + dim=0, index=token_indices_expanded, src=routed_output + ) + + return output.view(batch_size, seq_len, hidden_dim) + + +class AfmoeAttention(nn.Module): + """Multi-headed attention with local/global pattern and gating.""" + + def __init__(self, config: AfmoeConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_heads = config.num_attention_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention" + self.sliding_window = config.sliding_window if self.is_local_attention else None + + self.q_proj = nn.Linear( + config.hidden_size, self.num_heads * self.head_dim, bias=False + ) + self.k_proj = nn.Linear( + config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False + ) + self.v_proj = nn.Linear( + config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False + ) + self.o_proj = nn.Linear( + self.num_heads * self.head_dim, config.hidden_size, bias=False + ) + + self.q_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps) + self.k_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps) + + self.gate_proj = nn.Linear( + config.hidden_size, self.num_heads * self.head_dim, bias=False + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: Optional[torch.Tensor], + past_key_value: Optional[Cache] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape) + key_states = self.k_proj(hidden_states).view(hidden_shape) + value_states = self.v_proj(hidden_states).view(hidden_shape) + gate_states = self.gate_proj(hidden_states) + + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + if self.is_local_attention: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + cache_kwargs = {"cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + attention_interface: Callable = eager_attention_forward + if self.config._attn_implementation != "eager": + attention_interface = ALL_ATTENTION_FUNCTIONS[ + self.config._attn_implementation + ] + + output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + **kwargs, + ) + + output = output.view(*input_shape, -1).contiguous() + output = output * F.sigmoid(gate_states) + return self.o_proj(output) + + +class AfmoeDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: AfmoeConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.layer_idx = layer_idx + + self.self_attn = AfmoeAttention(config=config, layer_idx=layer_idx) + self.attention_type = config.layer_types[layer_idx] + + # Dual normalization for attention + self.input_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + # Dual normalization for FFN + self.pre_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + # MoE or dense FFN + self.moe_enabled = layer_idx >= config.num_dense_layers + if self.moe_enabled: + self.mlp = AfmoeMoE(config) + else: + self.mlp = AfmoeMLP(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + use_cache: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + # Self Attention with dual normalization + hidden_states = self.input_layernorm(hidden_states) + hidden_states = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + + # FFN with dual normalization + residual = hidden_states + hidden_states = self.pre_mlp_layernorm(hidden_states) + + if self.moe_enabled: + hidden_states = self.mlp(hidden_states) + else: + hidden_states = self.mlp(hidden_states) + + hidden_states = self.post_mlp_layernorm(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +class AfmoePreTrainedModel(PreTrainedModel): + config_class = AfmoeConfig + base_model_prefix = "model" + _no_split_modules = ["AfmoeDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _keep_in_fp32_modules = [ + "input_layernorm", + "post_attention_layernorm", + "pre_mlp_layernorm", + "post_mlp_layernorm", + "q_norm", + "k_norm", + "norm", + ] + _supports_sdpa = True + _supports_attention_backend = True + supports_gradient_checkpointing = True + + +class AfmoeModel(AfmoePreTrainedModel): + _no_split_modules = ["AfmoeDecoderLayer"] + + def __init__(self, config: AfmoeConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding( + config.vocab_size, config.hidden_size, self.padding_idx + ) + self.layers = nn.ModuleList( + [ + AfmoeDecoderLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = AfmoeRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + + def forward( + self, + input_ids: torch.LongTensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[list[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You must specify exactly one of input_ids or inputs_embeds" + ) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if cache_position is None: + past_seen_tokens = ( + past_key_values.get_seq_length() if past_key_values is not None else 0 + ) + cache_position = torch.arange( + past_seen_tokens, + past_seen_tokens + inputs_embeds.shape[1], + device=inputs_embeds.device, + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + mask_kwargs = { + "config": self.config, + "input_embeds": inputs_embeds, + "attention_mask": attention_mask, + "cache_position": cache_position, + "past_key_values": past_key_values, + } + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs), + } + + hidden_states = inputs_embeds + + # Apply muP input scaling if enabled + if self.config.mup_enabled: + hidden_states = hidden_states * (self.config.hidden_size**0.5) + + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for decoder_layer in self.layers: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[decoder_layer.attention_type], + position_ids=position_ids, + past_key_value=past_key_values, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return MoeModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class AfmoeForCausalLM(AfmoePreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + _tp_plan = {"lm_head": "colwise_rep"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = AfmoeModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def forward( + self, + input_ids: torch.LongTensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + token_type_ids: Optional[torch.Tensor] = None, # will be ignored + **kwargs: Unpack[TransformersKwargs], + ) -> Union[Tuple, MoeCausalLMOutputWithPast]: + outputs: MoeModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + cache_position=cache_position, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits + slice_indices = ( + slice(-logits_to_keep, None) + if isinstance(logits_to_keep, int) + else logits_to_keep + ) + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + + return MoeCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) + + +__all__ = [ + "AfmoeForCausalLM", + "AfmoeModel", + "AfmoePreTrainedModel", +] diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..5fd26c542275c864fff98316bef46b902bc68cce --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,23 @@ +{ + 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"single_word": false, + "special": true + }, + "27": { + "content": "<|reserved_special_14|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "28": { + "content": "<|reserved_special_15|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "29": { + "content": "<|reserved_special_16|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "30": { + "content": "<|reserved_special_17|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "31": { + "content": "<|reserved_special_18|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "<|begin_of_text|>", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "extra_special_tokens": {}, + "model_max_length": 65536, + "pad_token": "<|pad|>", + "tokenizer_class": "PreTrainedTokenizerFast", + "use_default_system_prompt": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {%- if '' not in content %}\n {{- '\\n\\n' }}\n {%- endif %}\n {{- content }}\n {%- if '' not in content %}\n {{- '\\n' }}\n {%- endif %}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n{%- endif %}" +} \ No newline at end of file