Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,5 @@ 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
+model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text

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) ~ '</' ~ json_key ~ '>' }}
+            {%- else %}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
+            {%- 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 %}
+    {{- '<tool_call>\n<function=' + (tool_call.name | default('') | string) + '>\n' }}
+    {%- if tool_call.arguments is defined and tool_call.arguments is mapping %}
+        {%- for args_name, args_value in tool_call.arguments.items() %}
+            {{- '<parameter=' + (args_name | string) + '>\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</parameter>\n' }}
+        {%- endfor %}
+    {%- endif %}
+    {{- '</function>\n</tool_call>' }}
+{%- 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<tools>" }}
+        {%- for tool in tools %}
+            {%- if tool.function is defined and tool.function is mapping %}
+                {%- set tool = tool.function %}
+            {%- endif %}
+            {{- '\n<function>\n<name>' ~ (tool.name | default('') | string) ~ '</name>' }}
+            {%- if tool.description is defined and tool.description is not none %}
+                {{- '\n<description>' ~ (tool.description | string | trim) ~ '</description>' }}
+            {%- endif %}
+            {{- '\n<parameters>' }}
+            {%- 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<parameter>\n<name>' ~ (param_name | string) ~ '</name>' }}
+                    {%- if param_fields is mapping and param_fields.type is defined and param_fields.type is not none %}
+                        {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                    {%- endif %}
+                    {%- if param_fields is mapping and param_fields.description is defined and param_fields.description is not none %}
+                        {{- '\n<description>' ~ (param_fields.description | string | trim) ~ '</description>' }}
+                    {%- endif %}
+                    {%- if param_fields is mapping %}
+                        {%- set handled_keys = ['name', 'type', 'description'] %}
+                        {{- render_extra_keys(param_fields, handled_keys) }}
+                    {%- endif %}
+                    {{- '\n</parameter>' }}
+                {%- endfor %}
+            {%- endif %}
+            {%- if tool.parameters is defined %}
+                {%- set handled_keys = ['type', 'properties'] %}
+                {{- render_extra_keys(tool.parameters, handled_keys) }}
+            {%- endif %}
+            {{- '\n</parameters>' }}
+            {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+            {{- render_extra_keys(tool, handled_keys) }}
+            {{- '\n</function>' }}
+        {%- endfor %}
+        {{- "\n</tools>" }}
+        {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> 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</IMPORTANT>' }}
+    {%- 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 %}
+                {{- '<think>' + (reasoning_value | string | trim) + '</think>' }}
+            {%- else %}
+                {{- '<think></think>' }}
+            {%- 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 %}
+        {{- '<tool_response>\n' }}
+        {{- '' if message.content is none else (message.content | string) }}
+        {{- '\n</tool_response>\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<think>' }}
+{%- endif %}

config.json

@@ -0,0 +1,409 @@
+{
+  "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": 256,
+  "num_experts_per_tok": 4,
+  "num_hidden_layers": 60,
+  "num_key_value_heads": 8,
+  "num_limited_groups": 1,
+  "num_shared_experts": 1,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 32,
+          "num_bits": 4,
+          "observer": "mse",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "model.layers.0.self_attn.q_proj",
+      "model.layers.0.self_attn.k_proj",
+      "model.layers.0.self_attn.v_proj",
+      "model.layers.0.self_attn.o_proj",
+      "model.layers.0.self_attn.gate_proj",
+      "model.layers.0.mlp.gate_proj",
+      "model.layers.0.mlp.up_proj",
+      "model.layers.0.mlp.down_proj",
+      "model.layers.1.self_attn.q_proj",
+      "model.layers.1.self_attn.k_proj",
+      "model.layers.1.self_attn.v_proj",
+      "model.layers.1.self_attn.o_proj",
+      "model.layers.1.self_attn.gate_proj",
+      "model.layers.1.mlp.gate_proj",
+      "model.layers.1.mlp.up_proj",
+      "model.layers.1.mlp.down_proj",
+      "model.layers.2.self_attn.q_proj",
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+      "model.layers.2.self_attn.v_proj",
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+      "model.layers.2.mlp.gate_proj",
+      "model.layers.2.mlp.up_proj",
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+      "lm_head"
+    ],
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+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.14.1.a20260326"
+  },
+  "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": "4.57.6",
+  "use_cache": true,
+  "use_grouped_mm": true,
+  "vocab_size": 200192
+}

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"]

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": 3,
+  "pad_token_id": 12,
+  "temperature": 0.8,
+  "top_p": 0.8,
+  "transformers_version": "4.57.6"
+}

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modeling_afmoe.py

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+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",
+]

recipe.yaml

@@ -0,0 +1,45 @@
+default_stage:
+  default_modifiers:
+    AWQModifier:
+      config_groups:
+        group_0:
+          targets: [Linear]
+          weights:
+            num_bits: 4
+            type: int
+            symmetric: true
+            group_size: 32
+            strategy: group
+            block_structure: null
+            dynamic: false
+            actorder: null
+            scale_dtype: null
+            zp_dtype: null
+            observer: mse
+            observer_kwargs: {}
+          input_activations: null
+          output_activations: null
+          format: null
+      targets: [Linear]
+      ignore: ['re:model[.]layers[.]0[.].*', 're:model[.]layers[.]1[.].*', 're:model[.]layers[.]2[.].*',
+        're:model[.]layers[.]3[.].*', 're:model[.]layers[.]4[.].*', 're:model[.]layers[.]5[.].*',
+        're:.*shared_experts.*', 're:.*router[.]gate$', lm_head]
+      bypass_divisibility_checks: false
+      mappings:
+      - smooth_layer: re:.*[.]([6-9]|[1-5][0-9])[.].*input_layernorm
+        balance_layers: ['re:.*[.]([6-9]|[1-5][0-9])[.].*self_attn[.]q_proj', 're:.*[.]([6-9]|[1-5][0-9])[.].*self_attn[.]k_proj',
+          're:.*[.]([6-9]|[1-5][0-9])[.].*self_attn[.]v_proj', 're:.*[.]([6-9]|[1-5][0-9])[.].*self_attn[.]gate_proj']
+        activation_hook_target: null
+        balance_exponent: 1
+      - smooth_layer: re:.*[.]([6-9]|[1-5][0-9])[.].*pre_mlp_layernorm
+        balance_layers: ['re:.*[.]([6-9]|[1-5][0-9])[.].*mlp[.]router[.]gate$', 're:.*[.]([6-9]|[1-5][0-9])[.].*mlp[.].*gate_proj',
+          're:.*[.]([6-9]|[1-5][0-9])[.].*mlp[.].*up_proj']
+        activation_hook_target: router.gate
+        balance_exponent: 1
+      - smooth_layer: re:.*[.]([6-9]|[1-5][0-9])[.].*mlp[.]experts.*up_proj
+        balance_layers: ['re:.*[.]([6-9]|[1-5][0-9])[.].*mlp[.]experts.*down_proj']
+        activation_hook_target: null
+        balance_exponent: 1
+      offload_device: !!python/object/apply:torch.device [cpu]
+      duo_scaling: true
+      n_grid: 20

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
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a93d847b4d3a1da95e9527c30ec10144f63a823e9feec98570274980754897
+size 14614487