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feat: add new Ollama engine using ggml through cgo This change introduces a new way to run pretrained models. It introduces 3 high level interfaces and a bunch of smaller helper interfaces to facilitate this. - `model.Model` defines the interface for a model architecture. Models such as `llama` and `mllama`, which are provided as examples, can implement the model's forward propagation in the `Forward` method. This method will be called to generate completions. This interface can be found in `model/model.go` - `ml.Backend` defines the interface for a backend tensor library, in this case `ggml`. Among other things, a Backend is responsible for loading a pretrained model into hardware (GPU, CPU, etc) and providing an interface for Models to access loaded tensors. This interface can be found in `ml/backend.go` - `ml.Tensor` defines the interface for a tensor and tensor operations This is the first implementation of the new engine. Follow up PRs will implement more features: - non-greedy sampling (#8410) - integration with Ollama and KV caching (#8301) - more model support (#9080) with more coming soon Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
137 lines
3.1 KiB
Go
137 lines
3.1 KiB
Go
package model
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import (
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"reflect"
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"slices"
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"testing"
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"github.com/google/go-cmp/cmp"
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"github.com/ollama/ollama/ml"
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"github.com/ollama/ollama/ml/backend/ggml"
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"github.com/ollama/ollama/ml/nn"
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)
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func TestParseTags(t *testing.T) {
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cases := []struct {
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value string
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want Tag
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}{
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{
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value: "output",
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want: Tag{
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Name: "output",
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},
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},
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{
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value: "output,alt:token_embd",
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want: Tag{
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Name: "output",
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Alternate: []string{
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"token_embd",
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},
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},
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},
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}
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for _, tt := range cases {
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t.Run(tt.value, func(t *testing.T) {
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got := ParseTags(tt.value)
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if diff := cmp.Diff(tt.want, got); diff != "" {
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t.Errorf("ParseTags() returned unexpected values (-want +got):\n%s", diff)
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}
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})
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}
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}
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type fakeBackend struct {
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*ggml.Backend
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names []string
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}
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type fakeTensor struct {
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*ggml.Tensor
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Name string
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}
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func (m *fakeBackend) Get(name string) ml.Tensor {
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if slices.Contains(m.names, name) {
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return &fakeTensor{Name: name}
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}
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return nil
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}
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func TestPopulateFields(t *testing.T) {
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type fakeLayer 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_o"`
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}
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type fakeModel struct {
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Input *nn.Embedding `gguf:"input"`
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OutputNorm *nn.RMSNorm `gguf:"output_norm"`
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Output *nn.Linear `gguf:"output"`
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Layers [2]fakeLayer `gguf:"blk"`
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}
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var m fakeModel
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v := reflect.ValueOf(&m)
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v.Elem().Set(populateFields(&fakeBackend{
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names: []string{
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"input.weight",
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"blk.0.attn_q.weight",
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"blk.0.attn_k.weight",
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"blk.0.attn_v.weight",
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"blk.1.attn_q.weight",
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"blk.1.attn_k.weight",
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"blk.1.attn_v.weight",
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"output_norm.weight",
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"output.weight",
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},
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}, v))
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if diff := cmp.Diff(fakeModel{
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Input: &nn.Embedding{Weight: &fakeTensor{Name: "input.weight"}},
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OutputNorm: &nn.RMSNorm{Weight: &fakeTensor{Name: "output_norm.weight"}},
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Output: &nn.Linear{Weight: &fakeTensor{Name: "output.weight"}},
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Layers: [2]fakeLayer{
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{
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Query: &nn.Linear{Weight: &fakeTensor{Name: "blk.0.attn_q.weight"}},
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Key: &nn.Linear{Weight: &fakeTensor{Name: "blk.0.attn_k.weight"}},
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Value: &nn.Linear{Weight: &fakeTensor{Name: "blk.0.attn_v.weight"}},
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},
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{
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Query: &nn.Linear{Weight: &fakeTensor{Name: "blk.1.attn_q.weight"}},
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Key: &nn.Linear{Weight: &fakeTensor{Name: "blk.1.attn_k.weight"}},
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Value: &nn.Linear{Weight: &fakeTensor{Name: "blk.1.attn_v.weight"}},
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},
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},
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}, m); diff != "" {
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t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
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}
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}
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func TestPopulateFieldsAlternateName(t *testing.T) {
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type fakeModel struct {
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Input *nn.Embedding `gguf:"input"`
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Output *nn.Linear `gguf:"output,alt:input"`
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}
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m := fakeModel{}
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v := reflect.ValueOf(&m)
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v.Elem().Set(populateFields(&fakeBackend{
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names: []string{
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"input.weight",
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},
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}, v))
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if diff := cmp.Diff(fakeModel{
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Input: &nn.Embedding{Weight: &fakeTensor{Name: "input.weight"}},
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Output: &nn.Linear{Weight: &fakeTensor{Name: "input.weight"}},
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}, m); diff != "" {
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t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
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}
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}
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