mirror of https://github.com/ollama/ollama
354 lines
12 KiB
Go
354 lines
12 KiB
Go
package deepseek2
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// uses deepseek 2 architecture but written based on deepseek 3 model
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import (
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"cmp"
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"math"
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"github.com/ollama/ollama/fs"
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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/ml/nn/rope"
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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 Options struct {
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isMLA bool
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numExpertsUsed int
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numExperts int
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normTopKProb bool
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routedScalingFactor float32
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kvLoraRank,
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qkNopeHeadDim,
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qkRopeHeadDim,
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kqNopeHeadDim,
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qkHeadDim int
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qLoraRank int
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vHeadDim int
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hiddenSize,
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numHeads,
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numKVHeads,
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originalContextLength int
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eps,
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ropeBase,
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ropeScale float32
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kqScale float64
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}
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func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, t, p ml.Tensor) ml.Tensor {
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return nn.RoPE(ctx, t, p, o.qkRopeHeadDim, o.ropeBase, 1./o.ropeScale,
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rope.WithOriginalContextLength(o.originalContextLength),
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rope.WithExtrapolationFactor(1.),
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rope.WithAttentionFactor(float32(1.0/(1.0+0.1*math.Log(float64(o.ropeScale))))),
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)
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}
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type Attention struct {
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Q *nn.Linear `gguf:"attn_q"`
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QA *nn.Linear `gguf:"attn_q_a"`
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QANorm *nn.RMSNorm `gguf:"attn_q_a_norm"`
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QB *nn.Linear `gguf:"attn_q_b"`
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KVA *nn.Linear `gguf:"attn_kv_a_mqa"`
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KVANorm *nn.RMSNorm `gguf:"attn_kv_a_norm"`
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KVB *nn.Linear `gguf:"attn_kv_b"`
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KB *nn.Linear `gguf:"attn_k_b"`
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VB *nn.Linear `gguf:"attn_v_b"`
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Output *nn.Linear `gguf:"attn_out,alt:attn_output"`
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}
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func (attn *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
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seqLength := hiddenStates.Dim(1)
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var query ml.Tensor
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if opts.qLoraRank == 0 {
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query = attn.Q.Forward(ctx, hiddenStates)
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} else {
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query = attn.QA.Forward(ctx, hiddenStates)
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query = attn.QANorm.Forward(ctx, query, opts.eps)
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query = attn.QB.Forward(ctx, query)
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}
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query = query.Reshape(ctx, query.Dim(0)/opts.numHeads, opts.numHeads, seqLength)
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queryChunks := query.ChunkSections(ctx, 0, opts.qkNopeHeadDim, opts.qkRopeHeadDim)
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compressedKV := attn.KVA.Forward(ctx, hiddenStates)
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kPass := compressedKV.Slice(ctx, 0, 0, opts.kvLoraRank, 1)
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kRot := compressedKV.View(ctx,
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opts.kvLoraRank*compressedKV.Stride(0), opts.qkRopeHeadDim,
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compressedKV.Stride(1), 1,
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compressedKV.Stride(1), compressedKV.Dim(1),
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)
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qRot := opts.applyRotaryPositionEmbeddings(ctx, queryChunks[1], positions)
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kRot = opts.applyRotaryPositionEmbeddings(ctx, kRot, positions)
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kPass = attn.KVANorm.Forward(ctx, kPass, opts.eps)
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var attention ml.Tensor
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if !opts.isMLA { // v3
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kPass = attn.KVB.Forward(ctx, kPass)
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kv := kPass.Reshape(ctx, kPass.Dim(0)/opts.numKVHeads, opts.numKVHeads, seqLength)
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kvChunks := kv.ChunkSections(ctx, 0, opts.kqNopeHeadDim, opts.vHeadDim)
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kRot = kRot.Repeat(ctx, 1, queryChunks[0].Dim(1))
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query = qRot.Concat(ctx, queryChunks[0], 0)
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key := kRot.Concat(ctx, kvChunks[0], 0)
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attention = nn.Attention(ctx, query, key, kvChunks[1], opts.kqScale, cache)
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} else { // v3.1
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qPass := queryChunks[0].Permute(ctx, 0, 2, 1, 3)
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qPassAbsorb := attn.KB.Forward(ctx, qPass)
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qPassAbsorb = qPassAbsorb.Permute(ctx, 0, 2, 1, 3)
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query = qRot.Concat(ctx, qPassAbsorb, 0)
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kPass = kPass.Reshape(ctx, opts.kvLoraRank, 1, seqLength)
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key := kRot.Concat(ctx, kPass, 0)
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value := kPass
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attention = nn.AttentionWithVMLA(ctx, query, key, value, nil, attn.VB.Weight, opts.kqScale, cache)
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}
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attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), seqLength)
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return attn.Output.Forward(ctx, attention)
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}
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type MLP interface {
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Forward(ml.Context, ml.Tensor, *Options) ml.Tensor
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}
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type sparse struct {
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Router *nn.Linear `gguf:"ffn_gate_inp"`
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Gate *nn.Linear `gguf:"ffn_gate_exps"`
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Up *nn.Linear `gguf:"ffn_up_exps"`
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Down *nn.Linear `gguf:"ffn_down_exps"`
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SharedExpert *dense `gguf:",suf:_shexp"`
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ExpProbsBias ml.Tensor `gguf:"exp_probs_b.bias,alt:exp_probs_b"`
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}
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func (moe *sparse) Moe(ctx ml.Context, hiddenStates, topKIndices, topKWeights ml.Tensor, opts *Options) ml.Tensor {
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hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
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upStates := moe.Up.Weight.MulmatID(ctx, hiddenStates, topKIndices)
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hiddenStates = moe.Gate.Weight.MulmatID(ctx, hiddenStates, topKIndices)
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hiddenStates = hiddenStates.SILU(ctx, upStates)
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experts := moe.Down.Weight.MulmatID(ctx, hiddenStates, topKIndices)
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experts = experts.Mul(ctx, topKWeights)
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nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
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for i := 1; i < opts.numExpertsUsed; i++ {
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nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
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}
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return nextStates
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}
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func (moe *sparse) topKIndices(ctx ml.Context, scores ml.Tensor, opts *Options) ml.Tensor {
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if moe.ExpProbsBias != nil {
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scores = scores.Add(ctx, moe.ExpProbsBias)
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}
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topKIndices := scores.TopK(ctx, opts.numExpertsUsed)
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return topKIndices
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}
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func (moe *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
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residuals := hiddenStates
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routerLogits := moe.Router.Forward(ctx, hiddenStates)
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scores := routerLogits.Sigmoid(ctx)
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topKIndices := moe.topKIndices(ctx, scores, opts)
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topKWeights := scores.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, topKIndices)
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if opts.normTopKProb {
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topKWeights = topKWeights.Reshape(ctx, opts.numExpertsUsed, hiddenStates.Dim(1))
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topKWeights = topKWeights.Div(ctx, topKWeights.SumRows(ctx))
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topKWeights = topKWeights.Reshape(ctx, 1, opts.numExpertsUsed, hiddenStates.Dim(1))
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}
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topKWeights = topKWeights.Scale(ctx, float64(opts.routedScalingFactor))
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hiddenStates = moe.Moe(ctx, hiddenStates, topKIndices, topKWeights, opts)
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sharedExpertResult := moe.SharedExpert.Forward(ctx, residuals, opts)
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hiddenStates = hiddenStates.Add(ctx, sharedExpertResult)
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return hiddenStates
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}
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type dense struct {
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Gate *nn.Linear `gguf:"ffn_gate"`
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Up *nn.Linear `gguf:"ffn_up"`
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Down *nn.Linear `gguf:"ffn_down"`
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}
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func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
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hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
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return mlp.Down.Forward(ctx, hiddenStates)
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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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Attention *Attention
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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 (t *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
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residual := hiddenStates
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hiddenStates = t.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
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hiddenStates = t.Attention.Forward(ctx, hiddenStates, positions, cache, opts)
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if outputs != nil {
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hiddenStates = hiddenStates.Rows(ctx, outputs)
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residual = residual.Rows(ctx, outputs)
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}
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hiddenStates = hiddenStates.Add(ctx, residual)
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residual = hiddenStates
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hiddenStates = t.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
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hiddenStates = t.MLP.Forward(ctx, hiddenStates, opts)
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hiddenStates = hiddenStates.Add(ctx, residual)
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return hiddenStates
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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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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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*Options
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}
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func New(c fs.Config) (model.Model, error) {
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layers := make([]Layer, c.Uint("block_count"))
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firstDenseLayerIndex := int(c.Uint("leading_dense_block_count"))
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for i := range layers {
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if i < firstDenseLayerIndex {
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layers[i].MLP = &dense{}
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} else {
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layers[i].MLP = &sparse{}
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}
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}
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mScale := float32(1.0 + float64(c.Float("rope.scaling.yarn_log_multiplier"))*math.Log(float64(c.Float("rope.scaling.factor"))))
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kqScale := float64(mScale) * float64(mScale) / math.Sqrt(float64(c.Uint("attention.key_length")))
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isMLA := c.Uint("attention.key_length_mla") != 0 && c.Uint("attention.value_length_mla") != 0
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keyLength := int(cmp.Or(c.Uint("attention.key_length_mla"), c.Uint("attention.key_length")))
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valueLength := int(cmp.Or(c.Uint("attention.value_length_mla"), c.Uint("attention.value_length")))
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var pre []string
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switch c.String("tokenizer.ggml.pre") {
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case "deepseek-v3":
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pre = []string{
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// Split regex into multiple parts (according to DeepSeek3's regex)
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"\\p{N}{1,3}",
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`[一-龥-ゟ゠-ヿ]+`,
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"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
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}
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case "deepseek-llm":
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// TODO: these models haven't been vetted so skip for now
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// pre = []string{
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// "[\r\n]",
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// "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
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// "\\s?[!-/:-~！-／：-～‘-‟ -。]+",
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// "\\s+$",
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// "[一-龥ࠀ-一가-]+",
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// "[0-9]",
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// }
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fallthrough
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default:
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return nil, model.ErrUnsupportedTokenizer
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}
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m := Model{
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BytePairEncoding: model.NewBytePairEncoding(
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&model.Vocabulary{
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Values: c.Strings("tokenizer.ggml.tokens"),
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Types: c.Ints("tokenizer.ggml.token_type"),
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Merges: c.Strings("tokenizer.ggml.merges"),
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AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
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BOS: []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
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AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
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EOS: append(
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[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
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c.Ints("tokenizer.ggml.eos_token_ids")...,
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),
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},
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pre...,
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),
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Layers: layers,
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Options: &Options{
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isMLA: isMLA,
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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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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.scaling.factor", 1),
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numExperts: int(c.Uint("expert_count")),
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numExpertsUsed: int(c.Uint("expert_used_count")),
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normTopKProb: c.Bool("expert_weights_norm", true),
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qLoraRank: int(c.Uint("attention.q_lora_rank")),
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kvLoraRank: int(c.Uint("attention.kv_lora_rank")),
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qkHeadDim: keyLength,
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vHeadDim: valueLength,
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qkRopeHeadDim: int(c.Uint("rope.dimension_count")),
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qkNopeHeadDim: keyLength - int(c.Uint("rope.dimension_count")),
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kqNopeHeadDim: keyLength - int(c.Uint("rope.dimension_count")),
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routedScalingFactor: c.Float("expert_weights_scale"),
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originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
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kqScale: kqScale,
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},
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}
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m.Cache = kvcache.NewCausalCache(m.Shift)
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return &m, nil
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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 m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
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}
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func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
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positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
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hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
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for i, layer := range m.Layers {
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m.Cache.SetLayer(i)
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var outputs ml.Tensor
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if i == len(m.Layers)-1 {
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outputs = batch.Outputs
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}
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hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Options)
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}
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hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
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return m.Output.Forward(ctx, hiddenStates), nil
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}
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func init() {
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model.Register("deepseek2", New)
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}
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