mirror of
https://github.com/ollama/ollama.git
synced 2025-04-16 07:31:35 +02:00
split text model to its own file
This commit is contained in:
Bruce MacDonald
jmorganca
parent
9b57238834
commit
ecc0ef468f
@ -1,156 +1,67 @@
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package mistral3
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import (
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"fmt"
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"math"
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"strings"
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"github.com/ollama/ollama/kvcache"
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"github.com/ollama/ollama/ml"
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"github.com/ollama/ollama/ml/nn"
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"github.com/ollama/ollama/model"
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"github.com/ollama/ollama/model/input"
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)
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type TextOptions struct {
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hiddenSize, numHeads, numKVHeads, headDim int
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eps, ropeBase, ropeScale float32
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ropeDim uint32
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}
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type Model struct {
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model.Base
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model.BytePairEncoding
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*TextModel
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TokenEmbedding *nn.Embedding `gguf:"token_embd"`
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Layers []Layer `gguf:"blk"`
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OutputNorm *nn.RMSNorm `gguf:"output_norm"`
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Output *nn.Linear `gguf:"output,alt:token_embd"`
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// TODO: Add VisionModel field
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// *VisionModel `gguf:"v,vision"`
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*TextOptions
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// TODO: Add MultiModalProjector field for combining vision and text features
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// *MultiModalProjector `gguf:"mm"`
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// TODO: Add ImageProcessor field
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// ImageProcessor
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}
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// TODO: Implement MultimodalProcessor interface
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// var _ model.MultimodalProcessor = (*Model)(nil)
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func New(c ml.Config) (model.Model, error) {
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if !strings.EqualFold(c.String("tokenizer.ggml.model"), "gpt2") {
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return nil, fmt.Errorf("tokenizer %s not yet supported", c.String("tokenizer.ggml.model"))
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textModel, err := NewTextModel(c)
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if err != nil {
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return nil, err
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}
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m := Model{
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BytePairEncoding: model.NewBytePairEncoding(
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c.String("tokenizer.ggml.pretokenizer", `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
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&model.Vocabulary{
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Values: c.Strings("tokenizer.ggml.tokens"),
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Types: c.Uints("tokenizer.ggml.token_type"),
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Merges: c.Strings("tokenizer.ggml.merges"),
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BOS: int32(c.Uint("tokenizer.ggml.bos_token_id", 1)),
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AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
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EOS: int32(c.Uint("tokenizer.ggml.eos_token_id", 2)),
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AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
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},
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),
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Layers: make([]Layer, c.Uint("block_count")),
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TextOptions: &TextOptions{
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hiddenSize: int(c.Uint("embedding_length")),
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numHeads: int(c.Uint("attention.head_count")),
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numKVHeads: int(c.Uint("attention.head_count_kv")),
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headDim: int(c.Uint("attention.key_length")),
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eps: c.Float("attention.layer_norm_rms_epsilon"),
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ropeBase: c.Float("rope.freq_base"),
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ropeScale: c.Float("rope.freq_scale", 1),
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ropeDim: c.Uint("rope.dimension_count"),
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},
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m := &Model{
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TextModel: textModel,
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// TODO: Initialize VisionModel if present
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// VisionModel: newVisionModel(c),
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// TODO: Initialize ImageProcessor
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// ImageProcessor: newImageProcessor(c),
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// TODO: Initialize MultiModalProjector
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// MultiModalProjector: &MultiModalProjector{...},
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}
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m.Cache = kvcache.NewCausalCache(m.Shift)
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m.Cache = kvcache.NewCausalCache(m.TextModel.Shift)
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return &m, nil
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return m, nil
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}
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type SelfAttention struct {
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Query *nn.Linear `gguf:"attn_q"`
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Key *nn.Linear `gguf:"attn_k"`
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Value *nn.Linear `gguf:"attn_v"`
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Output *nn.Linear `gguf:"attn_output"`
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RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
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}
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// TODO: Implement EncodeMultimodal method for processing images
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// func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, error) {
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// // Check if vision model is available
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// // Decode image
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// // Process the image
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// // Pass through vision model
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// // Project vision outputs to text embedding space
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// // Return vision embeddings
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// }
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func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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batchSize := hiddenState.Dim(1)
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ropeType := uint32(0)
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// Get head dimension - use explicit value if available, otherwise calculate
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headDim := opts.headDim
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if headDim == 0 {
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headDim = opts.hiddenSize / opts.numHeads
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}
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// Query projection and reshape
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q := sa.Query.Forward(ctx, hiddenState)
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q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
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q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
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// Key projection and reshape
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k := sa.Key.Forward(ctx, hiddenState)
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k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
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k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
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// Value projection and reshape
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v := sa.Value.Forward(ctx, hiddenState)
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v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
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// Attention computation
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scaleFactor := 1.0 / math.Sqrt(float64(headDim))
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kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
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// Reshape attention output for final projection
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outputDim := headDim * opts.numHeads
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kqv = kqv.Reshape(ctx, outputDim, batchSize)
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// Apply output projection
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return sa.Output.Forward(ctx, kqv)
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}
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func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
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return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
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}
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type MLP struct {
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Up *nn.Linear `gguf:"ffn_up"`
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Down *nn.Linear `gguf:"ffn_down"`
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Gate *nn.Linear `gguf:"ffn_gate"`
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}
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func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
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hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
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return mlp.Down.Forward(ctx, hiddenState)
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}
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type Layer struct {
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AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
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SelfAttention *SelfAttention
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MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
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MLP *MLP
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}
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func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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residual := hiddenState
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hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
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hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
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// In the final layer (outputs != nil), optimize by pruning to just the token positions
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// we need logits for.
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if outputs != nil {
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hiddenState = hiddenState.Rows(ctx, outputs)
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residual = residual.Rows(ctx, outputs)
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}
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hiddenState = hiddenState.Add(ctx, residual)
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residual = hiddenState
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hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
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hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
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return hiddenState.Add(ctx, residual)
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}
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// TODO: Implement PostTokenize method to handle vision tokens
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// func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
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// // Add special tokens around image data
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// // Insert placeholders for image tokens
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// }
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func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
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inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs))
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@ -168,23 +79,10 @@ func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
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return nil, err
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}
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// Process text inputs
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hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
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// TODO: Add handling of multimodal inputs
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// Set image embeddings into hidden state if present in opts.Multimodal
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// Process through text transformer layers
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for i, layer := range m.Layers {
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m.Cache.SetLayer(i)
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var lastLayerOutputs ml.Tensor
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if i == len(m.Layers)-1 {
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lastLayerOutputs = outputs
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}
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hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.TextOptions)
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}
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hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
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return m.Output.Forward(ctx, hiddenState), nil
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return m.TextModel.Forward(ctx, inputs, positions, outputs, opts, m.Cache), nil
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}
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func init() {
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171
model/models/mistral3/model_text.go
Normal file
171
model/models/mistral3/model_text.go
Normal file
@ -0,0 +1,171 @@
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package mistral3
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import (
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"fmt"
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"math"
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"strings"
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"github.com/ollama/ollama/kvcache"
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"github.com/ollama/ollama/ml"
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"github.com/ollama/ollama/ml/nn"
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"github.com/ollama/ollama/model"
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"github.com/ollama/ollama/model/input"
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)
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type TextOptions struct {
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hiddenSize, numHeads, numKVHeads, headDim int
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eps, ropeBase, ropeScale float32
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ropeDim uint32
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}
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type TextModel struct {
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model.Base
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model.BytePairEncoding
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TokenEmbedding *nn.Embedding `gguf:"token_embd"`
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Layers []Layer `gguf:"blk"`
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OutputNorm *nn.RMSNorm `gguf:"output_norm"`
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Output *nn.Linear `gguf:"output,alt:token_embd"`
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*TextOptions
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}
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type SelfAttention struct {
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Query *nn.Linear `gguf:"attn_q"`
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Key *nn.Linear `gguf:"attn_k"`
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Value *nn.Linear `gguf:"attn_v"`
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Output *nn.Linear `gguf:"attn_output"`
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RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
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}
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func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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batchSize := hiddenState.Dim(1)
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ropeType := uint32(0)
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// Get head dimension - use explicit value if available, otherwise calculate
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headDim := opts.headDim
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if headDim == 0 {
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headDim = opts.hiddenSize / opts.numHeads
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}
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// Query projection and reshape
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q := sa.Query.Forward(ctx, hiddenState)
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q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
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q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
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// Key projection and reshape
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k := sa.Key.Forward(ctx, hiddenState)
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k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
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k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
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// Value projection and reshape
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v := sa.Value.Forward(ctx, hiddenState)
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v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
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// Attention computation
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scaleFactor := 1.0 / math.Sqrt(float64(headDim))
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kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
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// Reshape attention output for final projection
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outputDim := headDim * opts.numHeads
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kqv = kqv.Reshape(ctx, outputDim, batchSize)
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// Apply output projection
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return sa.Output.Forward(ctx, kqv)
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}
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func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
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return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
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}
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type MLP struct {
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Up *nn.Linear `gguf:"ffn_up"`
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Down *nn.Linear `gguf:"ffn_down"`
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Gate *nn.Linear `gguf:"ffn_gate"`
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}
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func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
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hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
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return mlp.Down.Forward(ctx, hiddenState)
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}
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type Layer struct {
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AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
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SelfAttention *SelfAttention
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MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
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MLP *MLP
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}
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func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
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residual := hiddenState
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hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
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hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
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// In the final layer (outputs != nil), optimize by pruning to just the token positions
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// we need logits for.
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if outputs != nil {
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hiddenState = hiddenState.Rows(ctx, outputs)
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residual = residual.Rows(ctx, outputs)
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}
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hiddenState = hiddenState.Add(ctx, residual)
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residual = hiddenState
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hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
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hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
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return hiddenState.Add(ctx, residual)
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}
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func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, opts input.Options, cache kvcache.Cache) ml.Tensor {
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// Process text inputs
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hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
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// Process through text transformer layers
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for i, layer := range m.Layers {
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cache.SetLayer(i)
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var lastLayerOutputs ml.Tensor
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if i == len(m.Layers)-1 {
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lastLayerOutputs = outputs
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}
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hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, cache, m.TextOptions)
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}
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hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
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return m.Output.Forward(ctx, hiddenState)
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}
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func NewTextModel(c ml.Config) (*TextModel, error) {
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if !strings.EqualFold(c.String("tokenizer.ggml.model"), "gpt2") {
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return nil, fmt.Errorf("tokenizer %s not yet supported", c.String("tokenizer.ggml.model"))
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}
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textModel := &TextModel{
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BytePairEncoding: model.NewBytePairEncoding(
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c.String("tokenizer.ggml.pretokenizer", `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
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&model.Vocabulary{
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Values: c.Strings("tokenizer.ggml.tokens"),
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Types: c.Uints("tokenizer.ggml.token_type"),
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Merges: c.Strings("tokenizer.ggml.merges"),
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BOS: int32(c.Uint("tokenizer.ggml.bos_token_id", 1)),
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AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
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EOS: int32(c.Uint("tokenizer.ggml.eos_token_id", 2)),
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AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
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},
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),
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Layers: make([]Layer, c.Uint("block_count")),
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TextOptions: &TextOptions{
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hiddenSize: int(c.Uint("embedding_length")),
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numHeads: int(c.Uint("attention.head_count")),
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numKVHeads: int(c.Uint("attention.head_count_kv")),
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headDim: int(c.Uint("attention.key_length")),
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eps: c.Float("attention.layer_norm_rms_epsilon"),
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ropeBase: c.Float("rope.freq_base"),
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ropeScale: c.Float("rope.freq_scale", 1),
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ropeDim: c.Uint("rope.dimension_count"),
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},
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}
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return textModel, nil
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}
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