9. IMPLEMENTING LLAMA MODEL ARCHITECTURE¶

In this chapter, we'll walk through the process of defining and implementing the LLaMa 2 Model architecture.

Inspired by original LLaMa 2 Python repository of Meta | llama.cpp

A Quick Reminder:
We've loaded 292 tensors from the model file into a map (PickleDict) of tensors per tensor name via torchModelReader.Load().

Now, NewLlamaTransformer(...) is called to build operation sequence graph of LLaMa architecture.

^{from src/model/loader.go}

func LoadModelEx(modelDir string, includeTensors bool, includeVocab bool) (*Model, error) {
    model := &Model{}
    if includeTensors {
        ...
        modelTensors, err := torchModelReader.Load()
        ...
        model.Tensors = modelTensors
        ...
    }
    ...
    if includeTensors {
        ...
        if model.Transformer, err = NewLlamaTransformer(model); err != nil {
            return nil, err
        }
    }
    return model, nil
}

9.1. Preparation of modelArgs¶

The model.ModelArgs was loaded from the JSON File, "params.json". But, some of them (N_Rep and HeadDim) are "fields that should be calculated" and some of others may have value -1 meaning "default value".

In our project, we used 7B-Chat model. This model has following parameters that are loaded into model.ModelArgs:

Dim:        4096 //dim
N_Layers:   32   //n_layers
N_Heads:    32   //n_heads
N_KVHeads:  -1   //n_kv_heads
VocabSize:  -1   //vocab_size
MultipleOf: 256  //multiple_of

FFNDimMultiplier:  //to be calculated
NormEpsilon:       1e-6 //norm_eps
MaxSequenceLength: //to be calculated

N_Rep:   //to be calculated
HeadDim: //to be calculated

These preparations are done in this function:

modelArgs.VocabSize is -1 in the file, which indicates it wants us to set the default value. modelArgs wants to obey the "tokenizer.model" file. In our case, the tokenizer file contains 32,000 tokens. So, modelArgs.VocabSize was set to 32,000.
If modelArgs.N_KVHeads is not specified in the file, which indicates it wants us to set the default value. The default value is N_Heads.
This N_KVHeads is equal to N_Heads for 7B LLaMa models. It is different for larger models. If you have a n_kv_heads in your "params.json" file, your N_KVHeads value will be other than -1. This means, your larger model applies Grouped Multi-Query Attention. But this is not a tested case in our project, we set it same value as N_Heads.
modelArgs.N_Rep is set to integer value of N_Heads / N_KVHeads, the repetition count for the following operation in original LLaMa code. We haven't implemented it in our project.
modelArgs.HeadDim is set to integer value of modelArgs.Dim / modelArgs.N_Heads. In our case, it is 4096 / 32 = 128. This means we have 32 different attention heads and the dimension of each of these heads is 128.

Sources for Grouped Multi-Query Attention:

Grouped Multi-Query Attention

What's Grouped-Query attention (GQA)? a paper from Google Research

LLaMA explained... - "Grouped Multi-Query Attention" section - Youtube

^{from src/model/llamatransformer.go}

func NewLlamaTransformer(model *Model) (*LlamaTransformer, error) {
    result := &LlamaTransformer{}
    modelArgs := model.ModelArgs

    var err error
    // Compare (VocabSize, Dim) vs. "tok_embeddings.weight" tensor shape
    dim := modelArgs.Dim             // 4096
    vocabSize := modelArgs.VocabSize // 32000

    if modelArgs.N_KVHeads < 0 {
        modelArgs.N_KVHeads = modelArgs.N_Heads
    }
    modelArgs.N_Rep = int(modelArgs.N_Heads / modelArgs.N_KVHeads)
    // Calculate dimension of each head
    modelArgs.HeadDim = int(modelArgs.Dim / modelArgs.N_Heads) // 128
    ...
}

9.2. The First Layer of our Model: Embeddings Layer (tok_embd)¶

Yes! We are at the stage where we REALLY are starting to build the model by laying the first brick!

The getTensor function gets the weights tensor with the name we specified from Model.Tensors map, checks if it has really the expected shape we specified, then returns the ml.Tensor object, or returns "incorrect shape" error.

The weights tensor with name "tok_embeddings.weight" is taken and set to result.tok_embd as first brick. Our result.tok_embd weights tensor is with shape of {vocabSize, dim} = {32000, 4096}.

^{from src/model/llamatransformer.go}

func NewLlamaTransformer(model *Model) (*LlamaTransformer, error) {
    result := &LlamaTransformer{}
    ...
    if result.tok_embd, err = getTensor(model, "tok_embeddings.weight", []int{vocabSize, dim}); err != nil {
        return nil, err
    }
    ...
}

9.3. Building Transformer Blocks¶

The most important part of the transformer models that provide accurate outputs is the attention mechanism. Each "block" of LLaMa consists of a self-attention and a feed-forward neural network parts. The details will be explained further, but also we call these "block"s as "layer"s.

The value of the modelArgs.N_Layers variable corresponds to the number of blocks we have. It is 32, so we will initiate 32 different transformer block LlamaTransformerBlock objects via NewLlamaTransformerBlock(...) function. To achieve this, we instantiate result.Layers array with 32 items, then set each item with instantiating each block.

^{from src/model/llamatransformer.go}

func NewLlamaTransformer(model *Model) (*LlamaTransformer, error) {
    result := &LlamaTransformer{}
    ...
    result.Layers = make([]*LlamaTransformerBlock, modelArgs.N_Layers)

    for layerIdx := 0; layerIdx < modelArgs.N_Layers; layerIdx++ {
        var layer *LlamaTransformerBlock
        if layer, err = NewLlamaTransformerBlock(model, layerIdx); err != nil {
            return nil, err
        }
        result.Layers[layerIdx] = layer
    }
    ...
}

9.4. Building Each Transformer Block (LlamaTransformerBlock)¶

The LlamaTransformerBlock object consists of attn_norm (RMS normalization), attention (Attention mechanism), ffn_norm (RMS normalization), and feedForward (Feed Forward Neural Network) modules. These modules operate respectively.

Type definition:

^{from src/model/llamatransformer.go}

type LlamaTransformerBlock struct {
    LayerIndex int

    attn_norm *RMSNorm // Weights Original: "layers.0.attention_norm.weight"  |  ggml: "blk.0.attn_norm.weight" | shape: [4096] -> [Dim]
    ffn_norm  *RMSNorm // Weights Original: "layers.0.ffn_norm.weight"  |  ggml: "blk.0.ffn_norm.weight" | shape: [4096] -> [Dim]

    attention   *LlamaAttention
    feedForward *LlamaFeedForward
}

In LLaMa models, these normalization modules are operated before their pair models, for e.g., attn_norm is operated before attention module, ffn_norm is operated before feedForward. This approach is called as prenormalization. Root Mean Square Layer Normalization is used as normalization technique.

We will dive into deeper the details in further chapters, at this stage, we should stay zoomed-out view.

At this stage, our steps are:

Taking the weights tensor of attention norm corresponding to current layer index, "layers.%d.attention_norm.weight". In LLaMa model, these weight tensors are named like "layers.0.attention_norm.weight", "layers.1.attention_norm.weight", "layers.2.attention_norm.weight", ..., "layers.31.attention_norm.weight". This weights tensor is with shape of {dim} = {4096},
Instantiating an RMSNorm object with specifying modelArgs.NormEpsilon (1e-6 as epsilon value) and attn_norm_weights tensor via NewRMSNorm(...). Then it is set to result.attn_norm,
Instantiating a LlamaAttention object via NewLlamaAttention(...). Then it is set to result.attention,
Taking the weights tensor of feed-forward neural network norm corresponding to current layer index, "layers.%d.ffn_norm.weight". In LLaMa model, these weight tensors are named like "layers.0.ffn_norm.weight", "layers.1.ffn_norm.weight", "layers.2.ffn_norm.weight", ..., "layers.31.ffn_norm.weight". This weights tensor is with shape of {dim} = {4096},
Instantiating an RMSNorm object with specifying modelArgs.NormEpsilon (1e-6 as epsilon value) and ffn_norm_weights tensor via NewRMSNorm(...). Then it is set to result.ffn_norm,
Instantiating a LlamaFeedForward object via NewLlamaFeedForward(...). Then it is set to result.feedForward,

^{from src/model/llamatransformer.go}

func NewLlamaTransformerBlock(model *Model, layerIndex int) (*LlamaTransformerBlock, error) {
    result := &LlamaTransformerBlock{
        LayerIndex: layerIndex,
    }
    modelArgs := model.ModelArgs
    dim := modelArgs.Dim // 4096
    var err error

    // attention normalization
    attn_norm_weights, err := getLayerTensor(model, "layers.%d.attention_norm.weight", layerIndex, []int{dim})
    if err != nil {
        return nil, err
    }
    result.attn_norm = NewRMSNorm(modelArgs.NormEpsilon, attn_norm_weights)

    if result.attention, err = NewLlamaAttention(model, layerIndex); err != nil {
        return nil, err
    }

    // feed forward normalization
    ffn_norm_weights, err := getLayerTensor(model, "layers.%d.ffn_norm.weight", layerIndex, []int{dim})
    if err != nil {
        return nil, err
    }
    result.ffn_norm = NewRMSNorm(modelArgs.NormEpsilon, ffn_norm_weights)

    if result.feedForward, err = NewLlamaFeedForward(model, layerIndex); err != nil {
        return nil, err
    }

    return result, nil
}

9.4.1. Building an Attention Module (LlamaAttention)¶

The LlamaAttention object consists of:

attn_wq: Attention query weights tensor with shape of {N_Heads * HeadDim, Dim} = {32 * 128, 4096} = {4096, 4096},
attn_wk: Attention key weights tensor with shape of {N_KVHeads * HeadDim, Dim} = {32 * 128, 4096} = {4096, 4096},
attn_wv: Attention value weights tensor with shape of {N_KVHeads * HeadDim, Dim} = {32 * 128, 4096} = {4096, 4096},
attn_wo: Attention output weights tensor with shape of {N_Heads * HeadDim, Dim} = {32 * 128, 4096} = {4096, 4096}.

Type definition:

^{from src/model/llamatransformer.go}

type LlamaAttention struct {
    LayerIndex int

    N_Heads   int
    N_KVHeads int
    N_Rep     int
    HeadDim   int

    attn_wq *ml.Tensor // Original: "layers.0.attention.wq.weight"  |  ggml: "blk.0.attn_q.weight" | [out_features, in_features] -> shape: [4096 4096] -> [N_Heads * HeadDim, Dim]
    attn_wk *ml.Tensor // Original: "layers.0.attention.wk.weight"  |  ggml: "blk.0.attn_k.weight" | [out_features, in_features] -> shape: [4096 4096] -> [N_KVHeads * HeadDim, Dim]
    attn_wv *ml.Tensor // Original: "layers.0.attention.wv.weight"  |  ggml: "blk.0.attn_v.weight" | [out_features, in_features] -> shape: [4096 4096] -> [N_KVHeads * HeadDim, Dim]
    attn_wo *ml.Tensor // Original: "layers.0.attention.wo.weight"  |  ggml: "blk.0.attn_output.weight" | [out_features, in_features] -> shape: [4096 4096] -> [N_Heads * HeadDim, Dim]
}

NewLlamaAttention(...) is called to instantiate a new LlamaAttention object for current layer.

^{from src/model/llamatransformer.go}

func NewLlamaTransformerBlock(model *Model, layerIndex int) (*LlamaTransformerBlock, error) {
    result := &LlamaTransformerBlock{
        LayerIndex: layerIndex,
    }
    ...
    if result.attention, err = NewLlamaAttention(model, layerIndex); err != nil {
        return nil, err
    }
    ...
}

In NewLlamaAttention(...):

Calculating dimension of normal heads and KV heads (key-value heads). In our case, results are both 4096,
Taking the weights tensor of attention query corresponding to current layer index, "layers.%d.attention.wq.weight". Then it is set to result.attn_wq,
Taking the weights tensor of attention key corresponding to current layer index, "layers.%d.attention.wk.weight". Then it is set to result.attn_wk,
Taking the weights tensor of attention value corresponding to current layer index, "layers.%d.attention.wv.weight". Then it is set to result.attn_wv,
Taking the weights tensor of attention output corresponding to current layer index, "layers.%d.attention.wo.weight". Then it is set to result.attn_wo,

^{from src/model/llamatransformer.go}

func NewLlamaAttention(model *Model, layerIndex int) (*LlamaAttention, error) {
    result := &LlamaAttention{
        LayerIndex: layerIndex,
    }
    modelArgs := model.ModelArgs
    dim := modelArgs.Dim // 4096
    var err error

    result.N_Heads = modelArgs.N_Heads
    result.N_KVHeads = modelArgs.N_KVHeads
    result.N_Rep = modelArgs.N_Rep
    // Calculate dimension of each head
    result.HeadDim = modelArgs.HeadDim                        // 128
    normalHeadsTotalDim := modelArgs.N_Heads * result.HeadDim // 4096
    kvHeadsTotalDim := result.N_KVHeads * result.HeadDim      // 4096

    // attn_wq, attn_wk, attn_wv, attn_wo are Linear units, so weight shapes are ordered reversely as [out_features, in_features]
    if result.attn_wq, err = getLayerTensor(model, "layers.%d.attention.wq.weight", layerIndex, []int{normalHeadsTotalDim, dim}); err != nil {
        return nil, err
    }
    if result.attn_wk, err = getLayerTensor(model, "layers.%d.attention.wk.weight", layerIndex, []int{kvHeadsTotalDim, dim}); err != nil {
        return nil, err
    }
    if result.attn_wv, err = getLayerTensor(model, "layers.%d.attention.wv.weight", layerIndex, []int{kvHeadsTotalDim, dim}); err != nil {
        return nil, err
    }
    if result.attn_wo, err = getLayerTensor(model, "layers.%d.attention.wo.weight", layerIndex, []int{normalHeadsTotalDim, dim}); err != nil {
        return nil, err
    }

    return result, nil
}

9.4.2. Building a FeedForward Module (LlamaFeedForward)¶

The LlamaFeedForward object consists of:

ffn_gate: Feed-forward gate weights tensor with shape of {FFNHiddenDim, Dim} = {11008, 4096},
ffn_down: Feed-forward down weights tensor with shape of {Dim, FFNHiddenDim} = {4096, 11008},
ffn_up: Feed-forward up weights tensor with shape of {FFNHiddenDim, Dim} = {11008, 4096},

Note: FFNHiddenDim value is calculated as 11008, we will see how is it calculated below.

Type definition:

^{from src/model/llamatransformer.go}

type LlamaFeedForward struct {
    FFNHiddenDim int

    ffn_gate *ml.Tensor // Original: "layers.0.feed_forward.w1.weight"  |  ggml: "blk.0.ffn_gate.weight" | [out_features, in_features] -> shape: [11008 4096] -> [FFNHiddenDim, Dim] | w1
    ffn_down *ml.Tensor // Original: "layers.0.feed_forward.w2.weight"  |  ggml: "blk.0.ffn_down.weight" | [out_features, in_features] -> shape: [4096 11008] -> [Dim, FFNHiddenDim] | w2
    ffn_up   *ml.Tensor // Original: "layers.0.feed_forward.w3.weight"  |  ggml: "blk.0.ffn_up.weight" | [out_features, in_features] -> shape: [11008 4096] -> [FFNHiddenDim, Dim] | w3
}

NewLlamaFeedForward(...) is called to instantiate a new LlamaFeedForward object for current layer.

^{from src/model/llamatransformer.go}

func NewLlamaTransformerBlock(model *Model, layerIndex int) (*LlamaTransformerBlock, error) {
    result := &LlamaTransformerBlock{
        LayerIndex: layerIndex,
    }
    ...
    if result.feedForward, err = NewLlamaFeedForward(model, layerIndex); err != nil {
        return nil, err
    }
    ...
}

In NewLlamaFeedForward(...):

Calculating dimension of feed forward neural network's hidden layer result.FFNHiddenDim. Actually, I couldn't reasonate well this part, calculation method was taken directly from here and here,
Taking the weights tensor of Feed-forward gate corresponding to current layer index, "layers.%d.feed_forward.w1.weight". Then it is set to result.ffn_gate,
Taking the weights tensor of Feed-forward down corresponding to current layer index, "layers.%d.feed_forward.w2.weight". Then it is set to result.ffn_down,
Taking the weights tensor of Feed-forward up corresponding to current layer index, "layers.%d.feed_forward.w3.weight". Then it is set to result.ffn_up,

Note: ffn_gate, ffn_down, ffn_up are Linear units, so weight shapes are ordered reversely as [out_features, in_features]. At first sight, it may confuse.

^{from src/model/llamatransformer.go}

func NewLlamaFeedForward(model *Model, layerIndex int) (*LlamaFeedForward, error) {
    result := &LlamaFeedForward{}
    modelArgs := model.ModelArgs
    dim := modelArgs.Dim // 4096
    var err error

    // See: https://github.com/facebookresearch/llama/blob/ef351e9cd9496c579bf9f2bb036ef11bdc5ca3d2/llama/model.py#L378
    // Set it to 4 * dim at first
    result.FFNHiddenDim = 4 * modelArgs.Dim
    // See: https://github.com/facebookresearch/llama/blob/ef351e9cd9496c579bf9f2bb036ef11bdc5ca3d2/llama/model.py#L331C4-L331C4
    // Then, do this calculation below:
    result.FFNHiddenDim = int(2 * result.FFNHiddenDim / 3)
    if modelArgs.FFNDimMultiplier > -1 {
        result.FFNHiddenDim = int(modelArgs.FFNDimMultiplier * float64(result.FFNHiddenDim))
    }
    // Ensure ffnHiddenDim is multiple of modelArgs.MultipleOf value
    result.FFNHiddenDim = int(modelArgs.MultipleOf * ((result.FFNHiddenDim + modelArgs.MultipleOf - 1) / modelArgs.MultipleOf))

    // ffn_gate, ffn_down, ffn_up are Linear units, so weight shapes are ordered reversely as [out_features, in_features]
    if result.ffn_gate, err = getLayerTensor(model, "layers.%d.feed_forward.w1.weight", layerIndex, []int{result.FFNHiddenDim, dim}); err != nil {
        return nil, err
    }
    if result.ffn_down, err = getLayerTensor(model, "layers.%d.feed_forward.w2.weight", layerIndex, []int{dim, result.FFNHiddenDim}); err != nil {
        return nil, err
    }
    if result.ffn_up, err = getLayerTensor(model, "layers.%d.feed_forward.w3.weight", layerIndex, []int{result.FFNHiddenDim, dim}); err != nil {
        return nil, err
    }

    return result, nil
}

After completion of this stage, our LlamaTransformerBlock object of the first layer has been built.

This part will be recurred for 32 times for the LLaMa 2 7B models.

9.5. Building the Output Layers of the LlamaTransformer¶

A Quick Reminder:
We've done following things until now:

Built the embedding layer,

Built 32 LlamaTransformerBlock objects, each containing an attention module and a feed-forward module with RMS prenormalization.

After executing these layers, we have a currentTensor object as output of previous "transformer blocks". Then, we need to prenormalize our tensor, then process it with the "output weights".

We continue with:

Taking the weights tensor of output norm, "norm.weight". This weights tensor is with shape of {dim} = {4096},
Instantiating an RMSNorm object with specifying modelArgs.NormEpsilon (1e-6 as epsilon value) and output_norm_weights tensor via NewRMSNorm(...). Then it is set to result.output_norm,
Taking the weights tensor of output, "output.weight". This weights tensor is with shape of {vocabSize, dim} = {32000, 4096}. Then it is set to result.output,

Note: The output is a Linear unit, so weight shapes are ordered reversely as [out_features, in_features]. At first sight, it may confuse.

^{from src/model/llamatransformer.go}

func NewLlamaTransformer(model *Model) (*LlamaTransformer, error) {
    result := &LlamaTransformer{}
    ...
    output_norm_weights, err := getTensor(model, "norm.weight", []int{dim})
    if err != nil {
        return nil, err
    }
    result.output_norm = NewRMSNorm(modelArgs.NormEpsilon, output_norm_weights)

    // output is a Linear unit, so weight shape is ordered reversely as [out_features, in_features]
    if result.output, err = getTensor(model, "output.weight", []int{vocabSize, dim}); err != nil {
        return nil, err
    }
    ...
}

9.6. Precomputing the Frequency Tensor for Complex Exponentials (cis)¶

The code comment from the original LLaMa Python code that explains this:

"""
Precompute the frequency tensor for complex exponentials (cis) with given dimensions.

This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
and the end index 'end'. The 'theta' parameter scales the frequencies.
The returned tensor contains complex values in complex64 data type.

Args:
    dim (int): Dimension of the frequency tensor.
    end (int): End index for precomputing frequencies.
    theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.

Returns:
    torch.Tensor: Precomputed frequency tensor with complex exponentials.
"""

precomputeFreqsCis(...) is called to calculate the frequency tensor for complex exponentials (cis). This tensor values will be used by applyRotaryEmbeddings while applying Rotary Embeddings further.

^{from src/model/llamatransformer.go}

func NewLlamaTransformer(model *Model) (*LlamaTransformer, error) {
    result := &LlamaTransformer{}
    ...
    if result.PrecomputedFreqsCis, err = precomputeFreqsCis(int(dim/modelArgs.N_Heads), modelArgs.MaxSequenceLength*2); err != nil {
        return nil, err
    }
    return result, nil
}

The details of precomputeFreqsCis(...) function is discussed in a dedicated chapter: 10. RoPE (ROTARY POSITIONAL EMBEDDINGS).

Now, we have a complete Model object that contains model arguments, the tokenizer, the LlamaTransformer object at its model.Transformer field, which has a complete LLaMa 2 7B model architecture.