diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..52373fe24473b1aa44333d318f578ae6bf04b49b 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* 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/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000000000000000000000000000000000000..5b93512b2ca66f53ececeff7a4661df3a4084118
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,159 @@
+<|begin_of_text|>{%- macro render_extra_keys(json_dict, handled_keys) -%}
+ {%- if json_dict is mapping %}
+ {%- for json_key in json_dict if json_key not in handled_keys %}
+ {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}
+ {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '' }}
+ {%- else %}
+ {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }}
+ {%- endif %}
+ {%- endfor %}
+ {%- endif %}
+{%- endmacro -%}
+
+{%- macro render_tool_call(raw_tool_call) -%}
+ {%- if raw_tool_call.function is defined and raw_tool_call.function is mapping %}
+ {%- set tool_call = raw_tool_call.function %}
+ {%- else %}
+ {%- set tool_call = raw_tool_call %}
+ {%- endif %}
+ {{- '\n\n' }}
+ {%- if tool_call.arguments is defined and tool_call.arguments is mapping %}
+ {%- for args_name, args_value in tool_call.arguments.items() %}
+ {{- '\n' }}
+ {%- if args_value is mapping or (args_value is sequence and args_value is not string) %}
+ {{- args_value | tojson | safe }}
+ {%- else %}
+ {{- args_value | string }}
+ {%- endif %}
+ {{- '\n\n' }}
+ {%- endfor %}
+ {%- endif %}
+ {{- '\n' }}
+{%- endmacro -%}
+
+{%- set system_message = none %}
+{%- if messages and messages[0]["role"] == "system" %}
+ {%- set system_message = messages[0]["content"] %}
+ {%- set loop_messages = messages[1:] %}
+{%- else %}
+ {%- set loop_messages = messages %}
+{%- endif %}
+
+{%- if not tools is defined %}
+ {%- set tools = [] %}
+{%- endif %}
+{%- set has_tools = tools is iterable and tools is not string and tools | length > 0 %}
+
+{%- if system_message is not none or has_tools %}
+ {{- '<|im_start|>system\n' }}
+ {%- if system_message is not none %}
+ {{- system_message }}
+ {%- else %}
+ {{- "You are Trinity Large, a helpful assistant developed by Arcee AI, that can interact with a computer to solve tasks." }}
+ {%- endif %}
+ {%- if has_tools %}
+ {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }}
+ {%- for tool in tools %}
+ {%- if tool.function is defined and tool.function is mapping %}
+ {%- set tool = tool.function %}
+ {%- endif %}
+ {{- '\n\n' ~ (tool.name | default('') | string) ~ '' }}
+ {%- if tool.description is defined and tool.description is not none %}
+ {{- '\n' ~ (tool.description | string | trim) ~ '' }}
+ {%- endif %}
+ {{- '\n' }}
+ {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+ {%- for param_name, param_fields in tool.parameters.properties.items() %}
+ {{- '\n\n' ~ (param_name | string) ~ '' }}
+ {%- if param_fields is mapping and param_fields.type is defined and param_fields.type is not none %}
+ {{- '\n' ~ (param_fields.type | string) ~ '' }}
+ {%- endif %}
+ {%- if param_fields is mapping and param_fields.description is defined and param_fields.description is not none %}
+ {{- '\n' ~ (param_fields.description | string | trim) ~ '' }}
+ {%- endif %}
+ {%- if param_fields is mapping %}
+ {%- set handled_keys = ['name', 'type', 'description'] %}
+ {{- render_extra_keys(param_fields, handled_keys) }}
+ {%- endif %}
+ {{- '\n' }}
+ {%- endfor %}
+ {%- endif %}
+ {%- if tool.parameters is defined %}
+ {%- set handled_keys = ['type', 'properties'] %}
+ {{- render_extra_keys(tool.parameters, handled_keys) }}
+ {%- endif %}
+ {{- '\n' }}
+ {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+ {{- render_extra_keys(tool, handled_keys) }}
+ {{- '\n' }}
+ {%- endfor %}
+ {{- "\n" }}
+ {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner block must be nested within XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n' }}
+ {%- endif %}
+ {{- '<|im_end|>\n' }}
+{%- endif %}
+
+{%- for message in loop_messages %}
+ {%- set role = message.role | default('') %}
+ {%- if role == "assistant" %}
+ {%- set content_str = '' if message.content is none else (message.content | string) %}
+ {%- set trimmed_content = content_str | trim %}
+
+ {%- set has_reasoning_content = message.reasoning_content is defined %}
+ {%- set has_reasoning = has_reasoning_content or (message.reasoning is defined) %}
+
+ {%- if has_reasoning_content %}
+ {%- set reasoning_value = message.reasoning_content %}
+ {%- elif message.reasoning is defined %}
+ {%- set reasoning_value = message.reasoning %}
+ {%- else %}
+ {%- set reasoning_value = none %}
+ {%- endif %}
+
+ {%- set has_tool_calls = message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls is not string and message.tool_calls | length > 0 %}
+
+ {{- '<|im_start|>assistant\n' }}
+ {%- if has_reasoning %}
+ {%- if reasoning_value %}
+ {{- '' + (reasoning_value | string | trim) + '' }}
+ {%- else %}
+ {{- '' }}
+ {%- endif %}
+ {%- if trimmed_content %}
+ {{- '\n' + trimmed_content }}
+ {%- endif %}
+ {%- elif has_tool_calls %}
+ {%- if trimmed_content %}
+ {{- trimmed_content }}
+ {%- endif %}
+ {%- else %}
+ {{- content_str }}
+ {%- endif %}
+
+ {%- if has_tool_calls %}
+ {%- for tool_call in message.tool_calls %}
+ {%- set separator = '\n' if ((loop.first and (has_reasoning or trimmed_content)) or (not loop.first)) else '' -%}
+ {{- separator + render_tool_call(tool_call) }}
+ {%- endfor %}
+ {%- endif %}
+ {{- '<|im_end|>\n' }}
+ {%- elif role == "tool" or role == "observation" or role == "function" %}
+ {%- if loop.first or loop.previtem.role not in ["tool", "observation", "function"] %}
+ {{- '<|im_start|>user\n' }}
+ {%- endif %}
+ {{- '\n' }}
+ {{- '' if message.content is none else (message.content | string) }}
+ {{- '\n\n' }}
+ {%- if loop.last or loop.nextitem.role not in ["tool", "observation", "function"] %}
+ {{- '<|im_end|>\n' }}
+ {%- endif %}
+ {%- else %}
+ {{- '<|im_start|>' + (role | string) }}
+ {{- '\n' + ('' if message.content is none else (message.content | string)) }}
+ {{- '<|im_end|>\n' }}
+ {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+ {{- '<|im_start|>assistant\n' }}
+{%- endif %}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d41c1cab62c8162ecdf77621df417690c56c111e
--- /dev/null
+++ b/config.json
@@ -0,0 +1,108 @@
+{
+ "architectures": [
+ "AfmoeForCausalLM"
+ ],
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_afmoe.AfmoeConfig",
+ "AutoModel": "modeling_afmoe.AfmoeModel",
+ "AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM"
+ },
+ "dtype": "bfloat16",
+ "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",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention"
+ ],
+ "load_balance_coeff": 5e-05,
+ "max_position_embeddings": 262144,
+ "model_type": "afmoe",
+ "moe_intermediate_size": 3072,
+ "mup_enabled": true,
+ "n_group": 1,
+ "num_attention_heads": 48,
+ "num_dense_layers": 6,
+ "num_expert_groups": 1,
+ "num_experts": 216,
+ "num_experts_per_tok": 4,
+ "num_hidden_layers": 60,
+ "num_key_value_heads": 8,
+ "num_limited_groups": 1,
+ "num_shared_experts": 1,
+ "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": "4.57.1",
+ "use_cache": true,
+ "use_grouped_mm": true,
+ "vocab_size": 200192
+}
\ No newline at end of file
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..71ac125ab114655c051bfbb2ed3d3fddd27887ba
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,9 @@
+{
+ "_from_model_config": true,
+ "bos_token_id": 0,
+ "eos_token_id": 3,
+ "pad_token_id": 12,
+ "transformers_version": "4.57.3",
+ "temperature": 0.8,
+ "top_p": 0.8
+}
\ No newline at end of file
diff --git a/model-00001.safetensors b/model-00001.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..ca09eb615f45832624e4faf99aa7aa7eb435afc0
--- /dev/null
+++ b/model-00001.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6bd8a3cc805dd3ea7aa74f75868cd85822f0afea34c4f94c492b72c65b8af15d
+size 5360493784
diff --git a/model-00002.safetensors b/model-00002.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..dc83bd862e2d42fcf450ce208af071a9fc6ca45a
--- /dev/null
+++ b/model-00002.safetensors
@@ -0,0 +1,3 @@
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diff --git a/model-00003.safetensors b/model-00003.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..b690218fc4150e24ac89895bdc2ec47d872a3f91
--- /dev/null
+++ b/model-00003.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/model-00004.safetensors b/model-00004.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..b4012cded5ad97dca0ae7831ba3443a8e6baa00a
--- /dev/null
+++ b/model-00004.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/model-00005.safetensors b/model-00005.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..b6f39c8f6d546938047a764c45b9565b8265e1a7
--- /dev/null
+++ b/model-00005.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35b6186979edd8c9ae1709d4849a93a00037b799a90728f64e0c760303cd8ba1
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diff --git a/model-00006.safetensors b/model-00006.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..11120284d146bdb699d010ed7b20a3cfa7552533
--- /dev/null
+++ b/model-00006.safetensors
@@ -0,0 +1,3 @@
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diff --git a/model-00007.safetensors b/model-00007.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..25afb7217153b07894b1bfe7647b59fcc4ab3323
--- /dev/null
+++ b/model-00007.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 5360356072
diff --git a/model-00008.safetensors b/model-00008.safetensors
new file mode 100644
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--- /dev/null
+++ b/model-00008.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:199041c7f6017b29b8a19a690a5013356a2e35b216026d21780cd550990e65bb
+size 5355417768
diff --git a/model-00009.safetensors b/model-00009.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..4a787c22e926dd290d323e6f41903a290ae095a3
--- /dev/null
+++ b/model-00009.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31d0480511c4b6a84d6753c5ada5c2239c71c510ff18de54790896836e3ff108
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diff --git a/model-00010.safetensors b/model-00010.safetensors
new file mode 100644
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--- /dev/null
+++ b/model-00010.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/model-00011.safetensors b/model-00011.safetensors
new file mode 100644
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--- /dev/null
+++ b/model-00011.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/model-00012.safetensors b/model-00012.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..4aa73f3d1061619a945584da13aa95e39fe990b3
--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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\ No newline at end of file
diff --git a/modeling_afmoe.py b/modeling_afmoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c22cca909b47329db43092311406bb22dd07c2c
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+++ b/modeling_afmoe.py
@@ -0,0 +1,680 @@
+from typing import Callable, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import (
+ MoeCausalLMOutputWithPast,
+ MoeModelOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel, ALL_ATTENTION_FUNCTIONS
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.masking_utils import (
+ create_causal_mask,
+ create_sliding_window_causal_mask,
+)
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.processing_utils import Unpack
+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/prune_meta.json b/prune_meta.json
new file mode 100644
index 0000000000000000000000000000000000000000..36039ffeb030dcf7b4292338c6e1e6070b1ea240
--- /dev/null
+++ b/prune_meta.json
@@ -0,0 +1,63 @@
+{
+ "kept_experts": 216,
+ "original_experts": 256,
+ "prune_ratio": 0.1562,
+ "metric": "reap",
+ "moe_layers": [
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59
+ ],
+ "elapsed_s": 1206.3
+}
\ No newline at end of file
diff --git a/smoke_loss_results.json b/smoke_loss_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..b423b6c6722bfbda4b08e7d67e6fb64f8b131492
--- /dev/null
+++ b/smoke_loss_results.json
@@ -0,0 +1,98 @@
+{
+ "math": {
+ "loss": 1.2225,
+ "logits_shape": [
+ 1,
+ 32,
+ 200192
+ ],
+ "top5_next_tokens": [
+ ".",
+ ".\n\n",
+ "\n\n",
+ ".\n",
+ " \n"
+ ],
+ "elapsed_s": 120.6
+ },
+ "coding": {
+ "loss": 2.2573,
+ "logits_shape": [
+ 1,
+ 18,
+ 200192
+ ],
+ "top5_next_tokens": [
+ " ...",
+ " #",
+ " ...\n",
+ " <",
+ " ...\n\n"
+ ],
+ "elapsed_s": 93.4
+ },
+ "agentic": {
+ "loss": 3.5912,
+ "logits_shape": [
+ 1,
+ 23,
+ 200192
+ ],
+ "top5_next_tokens": [
+ " You",
+ " I",
+ " Please",
+ " The",
+ " "
+ ],
+ "elapsed_s": 91.8
+ },
+ "structured_output": {
+ "loss": 3.2387,
+ "logits_shape": [
+ 1,
+ 16,
+ 200192
+ ],
+ "top5_next_tokens": [
+ "<|im_end|>",
+ " Alice",
+ " \n",
+ " output",
+ " \n"
+ ],
+ "elapsed_s": 78.0
+ },
+ "philosophy": {
+ "loss": 2.1287,
+ "logits_shape": [
+ 1,
+ 18,
+ 200192
+ ],
+ "top5_next_tokens": [
+ " The",
+ " This",
+ " \n",
+ " If",
+ " What"
+ ],
+ "elapsed_s": 88.1
+ },
+ "cybersecurity": {
+ "loss": 1.8,
+ "logits_shape": [
+ 1,
+ 18,
+ 200192
+ ],
+ "top5_next_tokens": [
+ "\n\n",
+ ",",
+ "\n",
+ "\n\n\n\n",
+ "\n\n\n"
+ ],
+ "elapsed_s": 73.6
+ }
+}
\ No newline at end of file
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 @@
+{
+ "bos_token": {
+ "content": "<|begin_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "<|im_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<|pad|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..20286e2b0432a2f82d206ea4a2788e51775e376e
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a93d847b4d3a1da95e9527c30ec10144f63a823e9feec98570274980754897
+size 14614487
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..88320d663cb9cbb25a9f5348166bee5af8209901
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,271 @@
+{
+ "add_bos_token": false,
+ "add_eos_token": false,
+ "add_prefix_space": null,
+ "added_tokens_decoder": {
+ "0": {
+ "content": "<|begin_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "1": {
+ "content": "<|end_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "<|im_start|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "3": {
+ "content": "<|im_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "4": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "5": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "6": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "7": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "8": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "9": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
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+ "special": true
+ },
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+ },
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+ },
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+ },
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+ },
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+ },
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+ },
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+ },
+ "25": {
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+ "special": false
+ },
+ "26": {
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+ },
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+ },
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+ },
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+ "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
+}