update: support 4 models

examples/awqutils/README.md

@@ -1,5 +1,15 @@
 # AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp
-[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)]
+[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)]
+
+**Supported models:**
+
+- [X] LLaMA 🦙
+- [x] LLaMA 2 🦙🦙
+- [X] MPT
+- [X] Mistral AI v0.1
+- [] Bloom
+- [] Mixtral MoE
+
 
 ## Contents
 
@@ -22,37 +32,26 @@ git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache
 ## Convert
 Example for llama 7b model
 ```bash
-python convert-awq-hf-to-gguf.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --tmp-model-path models/llama-7b-scales --outfile models/llama_7b_fp16.gguf
+# For llama7b and llama27b models
+python examples/awqutils/convert-awq.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --tmp-model-path models/llama-7b-scales --outfile models/llama_7b_fp16.gguf
 ```
 
 ## Quantize
 ```bash
-./build/bin/quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0
+./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0
 ```
 
 ## Benchmark
 The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
 ```bash
-./build/bin/perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw
+./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw
 ```
 
 ## Results
+Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison
+We use three types of llamacpp quantization methods to work with our version, including q4, q4_1, and q2_k
 
-### Llama 7B
+### Llama 7B (Build with OpenBLAS)
-Build with OpenBLAS
-
-#### Memory/Disk Requirements
-
-| Model |     Original  |     AWQ-4bit  | 
-|------:|--------------:|--------------:|
-|  fp16 |     12.853 GB |     12.853 GB |
-|  q4_0 |     3.647  GB |     3.647  GB |
-|  q4_1 |     4.041  GB |     4.041  GB |
-|  q2_k |     2.649  GB |     2.649  GB |
-
-#### Quantization
-
-Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
 
 | Model      | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
 |-----------:|--------------|-------:|-------:|-------:|-------:|
@@ -68,21 +67,7 @@ Several quantization methods are supported. They differ in the resulting model d
 |AWQ-LLama 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
 
 
-### Llama2 7B
+### Llama2 7B (Build with CuBLAS)
-Build with CuBLAS
-
-#### Memory/Disk Requirements
-
-| Model |     Original  |     AWQ-4bit  | 
-|------:|--------------:|--------------:|
-|  fp16 |     12.853 GB |     12.853 GB |
-|  q4_0 |     3.647  GB |     3.647  GB |
-|  q4_1 |     4.041  GB |     4.041  GB |
-|  q2_k |     2.649  GB |     2.649  GB |
-
-#### Quantization
-
-Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
 
 | Model       | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
 |------------:|--------------|-------:|-------:|-------:|-------:|
@@ -98,21 +83,7 @@ Several quantization methods are supported. They differ in the resulting model d
 |AWQ-LLama2 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
 
 
-### Mistral 7B v0.1
+### Mistral 7B v0.1 (Build with CuBLAS)
-Build with CuBLAS
-
-#### Memory/Disk Requirements
-
-| Model |     Original  |     AWQ-4bit  | 
-|------:|--------------:|--------------:|
-|  fp16 |     12.853 GB |     12.853 GB |
-|  q4_0 |     3.647  GB |     3.647  GB |
-|  q4_1 |     4.041  GB |     4.041  GB |
-|  q2_k |     2.649  GB |     2.649  GB |
-
-#### Quantization
-
-Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
 
 | Model        | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
 |-------------:|--------------|-------:|-------:|-------:|-------:|
@@ -126,3 +97,18 @@ Several quantization methods are supported. They differ in the resulting model d
 |AWQ-Mistral 7B| ms/tok @ 4th |     xxx|    xxx |    xxx |    xxx |
 |AWQ-Mistral 7B| ms/tok @ 8th |     xxx|     xx |     xx |     xx |
 |AWQ-Mistral 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
+
+### MPT 7B (Build with OpenBLAS)
+
+| Model        | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
+|-------------:|--------------|-------:|-------:|-------:|-------:|
+|Mistral 7B    | perplexity   | xxxxxx | xxxxxx | xxxxxx | xxxxxx |
+|Mistral 7B    | file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
+|Mistral 7B    | ms/tok @ 4th |    xxx |     xx |     xx |     xx |
+|Mistral 7B    | ms/tok @ 8th |    xxx |     xx |     xx |     xx |
+|Mistral 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
+|AWQ-Mistral 7B| perplexity   | xxxxxx | xxxxxx |  xxxxx | xxxxxx |
+|AWQ-Mistral 7B| file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
+|AWQ-Mistral 7B| ms/tok @ 4th |     xxx|    xxx |    xxx |    xxx |
+|AWQ-Mistral 7B| ms/tok @ 8th |     xxx|     xx |     xx |     xx |
+|AWQ-Mistral 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |

examples/awqutils/apply_awq.py

@@ -1,14 +1,32 @@
+"""
+Original code from:
+1. https://github.com/casper-hansen/AutoAWQ
+2. https://github.com/mit-han-lab/llm-awq
+"""
 import os
 import torch
 import torch.nn as nn
 from transformers import AutoModelForCausalLM, AutoConfig
 
-from transformers.models.bloom.modeling_bloom import BloomBlock, BloomGelu
+from transformers.models.bloom.modeling_bloom import BloomGelu
-from transformers.models.opt.modeling_opt import OPTDecoderLayer
+from transformers.models.llama.modeling_llama import LlamaRMSNorm
-from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRMSNorm
 from transformers.activations import GELUActivation
 
+
 class ScaledActivation(nn.Module):
+    """
+    ScaledActivation module wraps an existing activation function and applies a
+    scale factor to its output.
+
+    Args:
+        module (nn.Module): The activation function to be scaled.
+        scales (torch.Tensor): A tensor of size (num_features,) containing the initial
+            scale factors for each feature.
+
+    Returns:
+        torch.Tensor: The scaled output of the activation function.
+    """
+
     def __init__(self, module, scales):
         super().__init__()
         self.act = module
@@ -17,11 +35,21 @@ class ScaledActivation(nn.Module):
     def forward(self, x):
         return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
 
+
 def set_op_by_name(layer, name, new_module):
-    levels = name.split('.')
+    """
+    Set the new module for given module's name.
+
+    Args:
+        layer (nn.Module): The layer in which to replace the submodule.
+        name (str): The path to the submodule to be replaced, using dot notation
+            to access nested modules.
+        new_module (nn.Module): The new module to replace the existing one.
+    """
+    levels = name.split(".")
     if len(levels) > 1:
         mod_ = layer
-        for l_idx in range(len(levels)-1):
+        for l_idx in range(len(levels) - 1):
             if levels[l_idx].isdigit():
                 mod_ = mod_[int(levels[l_idx])]
             else:
@@ -30,22 +58,45 @@ def set_op_by_name(layer, name, new_module):
     else:
         setattr(layer, name, new_module)
 
+
 def get_op_by_name(module, op_name):
-    # get the op by its name relative to the module
+    """
+    Retrieves a submodule within a given layer based on its name.
+
+    Args:
+        module (nn.Module): The layer containing the submodule to find.
+        op_name (str): The name of the submodule to search for, using dot notation for nested modules.
+
+    Returns:
+        nn.Module: The requested submodule found within the given layer.
+
+    Raises:
+        ValueError: If the specified submodule cannot be found within the layer.
+    """
     for name, m in module.named_modules():
         if name == op_name:
             return m
     raise ValueError(f"Cannot find op {op_name} in module {module}")
 
+
 @torch.no_grad()
 def scale_ln_fcs(ln, fcs, scales):
+    """
+    Scales the weights of a LayerNorm and a list of fully-connected layers proportionally.
+
+    Args:
+        ln (nn.LayerNorm): The LayerNorm module to be scaled.
+        fcs (List[nn.Linear]): A list of fully-connected layers to be scaled.
+        scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
+    """
+
     if not isinstance(fcs, list):
         fcs = [fcs]
 
     scales = scales.to(ln.weight.device)
 
     ln.weight.div_(scales)
-    if hasattr(ln, 'bias') and ln.bias is not None:
+    if hasattr(ln, "bias") and ln.bias is not None:
         ln.bias.div_(scales)
 
     for fc in fcs:
@@ -60,14 +111,20 @@ def scale_ln_fcs(ln, fcs, scales):
 
 @torch.no_grad()
 def scale_fc_fc(fc1, fc2, scales):
+    """
+    Scales the weights of two fully-connected layers in a specific pattern.
+
+    Args:
+        fc1 (nn.Linear): The first fully-connected layer to be scaled.
+        fc2 (nn.Linear): The second fully-connected layer to be scaled.
+        scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
+    """
     assert isinstance(fc1, nn.Linear)
     assert isinstance(fc2, nn.Linear)
-    # assert fc1.out_features == fc2.in_features
 
     scales = scales.to(fc1.weight.device)
 
-    # fc1.weight.div_(scales.view(-1, 1))
+    fc1.weight[-scales.size(0) :].div_(scales.view(-1, 1))
-    fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))
     if fc1.bias is not None:
         fc1.bias.div_(scales.view(-1))
 
@@ -81,6 +138,18 @@ def scale_fc_fc(fc1, fc2, scales):
 
 @torch.no_grad()
 def scale_gelu_fc(gelu, fc, scales):
+    """
+    Scales the weight of a GELU activation and a fully-connected layer proportionally.
+
+    Args:
+        gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled.
+        fc (nn.Linear): The fully-connected layer to be scaled.
+        scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
+
+    Raises:
+        TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`.
+        TypeError: If the `fc` module is not of type `nn.Linear`.
+    """
     assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation))
     assert isinstance(fc, nn.Linear)
 
@@ -91,6 +160,20 @@ def scale_gelu_fc(gelu, fc, scales):
 
 
 def apply_scale(module, scales_list, input_feat_dict=None):
+    """
+    Applies different scaling strategies to layers based on their type and hierarchy within a given module.
+
+    Args:
+        module (nn.Module): The module containing the layers to be scaled.
+        scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing:
+            * prev_op_name (str): The name of the preceding operation or module, relative to which the layers to be
+                                  scaled are located.
+            * layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation.
+            * scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
+        input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding
+            input features (optional). If provided, the input features are also
+            scaled proportionally after scaling the layer weights.
+    """
     for prev_op_name, layer_names, scales in scales_list:
         prev_op = get_op_by_name(module, prev_op_name)
         layers = [get_op_by_name(module, name) for name in layer_names]
@@ -103,15 +186,17 @@ def apply_scale(module, scales_list, input_feat_dict=None):
         if isinstance(prev_op, nn.Linear):
             assert len(layers) == 1
             scale_fc_fc(prev_op, layers[0], scales)
-        elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)):
+        elif (
+            isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm))
+            or "rmsnorm" in str(prev_op.__class__).lower()
+        ):
             scale_ln_fcs(prev_op, layers, scales)
         elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)):
             new_module = ScaledActivation(prev_op, scales)
             set_op_by_name(module, prev_op_name, new_module)
             scale_gelu_fc(prev_op, layers[0], scales)
         else:
-            raise NotImplementedError(
+            raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!")
-                f"prev_op {type(prev_op)} not supported yet!")
 
         # apply the scaling to input feat if given; prepare it for clipping
         if input_feat_dict is not None:
@@ -124,8 +209,18 @@ def apply_scale(module, scales_list, input_feat_dict=None):
             layer.cpu()
         scales.cpu()
 
+
 @torch.no_grad()
 def apply_clip(module, clip_list):
+    """
+    Applies element-wise clipping to the weight of a specific layer within a given module.
+
+    Args:
+        module (nn.Module): The module containing the layer to be clipped.
+        clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing:
+            * name (str): The name of the layer to be clipped, relative to the root of the module.
+            * max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight.
+    """
     for name, max_val in clip_list:
         layer = get_op_by_name(module, name)
         layer.cuda()
@@ -136,25 +231,23 @@ def apply_clip(module, clip_list):
         layer.weight.data = layer.weight.data.reshape(org_shape)
         layer.cpu()
 
-def apply_awq(model, awq_results):
-    apply_scale(model, awq_results["scale"])
-    apply_clip(model, awq_results["clip"])
 
-def add_scale_weights(model, model_path, scale_path, tmp_path):
+def add_scale_weights(model_path, scale_path, tmp_path):
-    print("Loading pre-computed AWQ results from", str(scale_path))
+    """
-    awq_results = torch.load(str(scale_path), map_location="cpu")
+    Adds pre-computed Activation Weight Quantization (AWQ) results to a model, including scaling factors and clipping bounds.
-    apply_awq(model, awq_results)
-    model.save_pretrained(str(tmp_path))
-    os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}")
-    return True
 
-
+    Args:
-if __name__ == "__main__":
+        model_path (str): Path to the pre-trained model to be equipped with AWQ.
-    model_path = "/data/namtd12/llm_models/Llama-2-7b-hf"
+        scale_path (str): Path to the AWQ scale factors (.pt file).
-    scale_path = "awq_cache_pretrained/llama-2-7b-chat-w4-g128.pt"
+        tmp_path (str): Path to the temporary directory where the equipped model will be saved.
-    tmp_path = "debug"
+    """
     config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(
-            model_path, config=config, trust_remote_code=True)
+        model_path, config=config, trust_remote_code=True
+    )
     model.eval()
-    add_scale_weights(model, scale_path, tmp_path)
+    awq_results = torch.load(str(scale_path), map_location="cpu")
+    apply_scale(model, awq_results["scale"])
+    apply_clip(model, awq_results["clip"])
+    model.save_pretrained(str(tmp_path))
+    os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}")

examples/awqutils/convert-awq.py

@@ -25,7 +25,7 @@ from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
 
 import numpy as np
 from sentencepiece import SentencePieceProcessor
-from awqutils.apply_awq import add_scale_weights
+from apply_awq import add_scale_weights
 from transformers import AutoModelForCausalLM, AutoConfig
 
 
@@ -1158,11 +1158,7 @@ def main(args_in: list[str] | None = None) -> None:
 
     args = parser.parse_args(args_in)
     if args.awq_path and args.tmp_model_path:
-        config = AutoConfig.from_pretrained(args.model, trust_remote_code=True)
+        add_scale_weights(str(args.model), str(args.awq_path), str(args.tmp_model_path))
-        model = AutoModelForCausalLM.from_pretrained(
-                args.model, config=config, trust_remote_code=True)
-        model.eval()
-        add_scale_weights(model, args.model, args.awq_path, args.tmp_model_path)
         args.model = args.tmp_model_path
     
     if args.dump_single:

gguf-py/gguf/constants.py

@@ -114,6 +114,7 @@ class MODEL_TENSOR(IntEnum):
     FFN_GATE        = auto()
     FFN_DOWN        = auto()
     FFN_UP          = auto()
+    FFN_ACT         = auto()
     FFN_NORM        = auto()
     ATTN_Q_NORM     = auto()
     ATTN_K_NORM     = auto()
@@ -158,6 +159,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
     MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
     MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
+    MODEL_TENSOR.FFN_ACT:         "blk.{bid}.ffn",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -251,6 +253,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_ACT,
     ],
     MODEL_ARCH.GPTJ: [
         MODEL_TENSOR.TOKEN_EMBD,

gguf-py/gguf/tensor_mapping.py

@@ -164,6 +164,11 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.w1",                             # qwen
         ),
         
+        # Awq-activation gate
+        MODEL_TENSOR.FFN_ACT: (
+            "transformer.blocks.{bid}.ffn.act",  # mpt
+        ),
+
         # Feed-forward gate
         MODEL_TENSOR.FFN_GATE: (
             "model.layers.{bid}.mlp.gate_proj",  # llama-hf refact

llama.cpp

@@ -341,6 +341,7 @@ enum llm_tensor {
     LLM_TENSOR_FFN_GATE,
     LLM_TENSOR_FFN_DOWN,
     LLM_TENSOR_FFN_UP,
+    LLM_TENSOR_FFN_ACT,
     LLM_TENSOR_FFN_NORM,
     LLM_TENSOR_ATTN_Q_NORM,
     LLM_TENSOR_ATTN_K_NORM,
@@ -453,6 +454,7 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
             { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            {LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act"},
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
@@ -1271,6 +1273,7 @@ struct llama_layer {
     // ff bias
     struct ggml_tensor * ffn_down_b; // b2
     struct ggml_tensor * ffn_up_b;   // b3
+    struct ggml_tensor *ffn_act;
 };
 
 struct llama_kv_cell {
@@ -3420,6 +3423,7 @@ static void llm_load_tensors(
                         layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
 
                         layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend);
                         layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
 
                         if (backend == GGML_BACKEND_GPU) {
@@ -3429,6 +3433,7 @@ static void llm_load_tensors(
                                 ggml_nbytes(layer.wo)        +
                                 ggml_nbytes(layer.ffn_norm)  +
                                 ggml_nbytes(layer.ffn_down)  +
+                                ggml_nbytes(layer.ffn_act) +
                                 ggml_nbytes(layer.ffn_up);
                         }
                     }
@@ -3672,6 +3677,7 @@ enum llm_rope_type {
 enum llm_ffn_op_type {
     LLM_FFN_SILU,
     LLM_FFN_GELU,
+    LLM_FFN_GELU_ACT,
     LLM_FFN_RELU,
     LLM_FFN_RELU_SQR,
 };
@@ -3839,6 +3845,7 @@ static struct ggml_tensor * llm_build_ffn(
          struct ggml_tensor * gate_b,
          struct ggml_tensor * down,
          struct ggml_tensor * down_b,
+         struct ggml_tensor *act_scales,
             llm_ffn_op_type   type_op,
           llm_ffn_gate_type   type_gate,
          const llm_build_cb & cb,
@@ -3889,6 +3896,16 @@ static struct ggml_tensor * llm_build_ffn(
                 cur = ggml_relu(ctx, cur);
                 cb(cur, "ffn_relu", il);
             } break;
+        case LLM_FFN_GELU_ACT:
+            {
+                cur = ggml_gelu(ctx, cur);
+                cb(cur, "ffn_relu", il);
+                struct ggml_tensor *repeat = ggml_repeat(ctx, act_scales, cur);
+                cb(repeat, "ffn_repeat(scales)", il);
+                cur = ggml_div(ctx, cur, repeat);
+                cb(cur, "ffn_div(gelu)", il);
+            }
+            break;
         case LLM_FFN_RELU_SQR:
             {
                 cur = ggml_relu(ctx, cur);
@@ -4194,6 +4211,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         model.layers[il].ffn_gate, NULL,
                         model.layers[il].ffn_down, NULL,
+                        NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -4314,6 +4332,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         model.layers[il].ffn_gate, NULL,
                         model.layers[il].ffn_down, NULL,
+                        NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -4432,6 +4451,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         NULL,                      NULL,
                         model.layers[il].ffn_down, NULL,
+                        NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -4540,6 +4560,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                         NULL,                      NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -4748,6 +4769,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                         NULL,                      NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
                         LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -4838,6 +4860,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         model.layers[il].ffn_gate, NULL,
                         model.layers[il].ffn_down, NULL,
+                        NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -4937,6 +4960,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                         NULL,                      NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -5031,7 +5055,8 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         NULL,                      NULL,
                         model.layers[il].ffn_down, NULL,
-                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                        model.layers[il].ffn_act,
+                        LLM_FFN_GELU_ACT, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
@@ -5143,6 +5168,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         model.layers[il].ffn_gate, NULL,
                         model.layers[il].ffn_down, NULL,
+                        NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -5259,6 +5285,7 @@ struct llm_build_context {
                         model.layers[il].ffn_up,   NULL,
                         model.layers[il].ffn_gate, NULL,
                         model.layers[il].ffn_down, NULL,
+                        NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
@@ -5441,6 +5468,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
     { "ffn_gate",                   OFFLOAD_FUNC     },
     { "ffn_gate_b",                 OFFLOAD_FUNC     },
     { "ffn_gate_par",               OFFLOAD_FUNC     },
+    {"ffn_act",                     OFFLOAD_FUNC     },
     { "ffn_down",                   OFFLOAD_FUNC     },
     { "ffn_down_b",                 OFFLOAD_FUNC     },
     { "ffn_out",                    OFFLOAD_FUNC     },