mirror of
https://github.com/ollama/ollama.git
synced 2026-09-21 05:28:00 -05:00
Jesse Gross
2e036e7cdf
The MLX runner is the only Go inference runner left and is no longer experimental, so its packages leave x/. The bindings become a top-level mlx package beside the carried patches in mlx/compat, mirroring how llama/ holds the llama.cpp integration, and the runner becomes mlxrunner with the architectures nested under the package they implement. Subpackages move with their parent unless listed. x/mlxrunner/mlx mlx x/internal/mlxthread mlx/mlxthread x/internal/mlxthreadtest mlx/mlxthread/mlxthreadtest x/internal/mlxtest mlx/mlxtest x/quant mlx/quant mlx/compat/*.patch mlx/compat/mlx-c (MLX patches go in mlx/compat/mlx) x/mlxrunner mlxrunner x/models/nn mlxrunner/nn x/models/<arch> mlxrunner/model/<arch> x/mlxrunner/imports.go mlxrunner/model/architectures (new package) x/create create x/safetensors fs/safetensors x/tokenizer mlxrunner/tokenizer Every package keeps its name, so the Go changes are the import path rewrites the moves force, and the CMake, Dockerfile, CI cache keys, drift check and Darwin payload script follow the new paths. Four edits are not paths: the runner's blank architecture imports become the package mlxrunner/model/architectures, so the list to extend for a new model sits beside the architecture directories; a depguard rule keeps the two test harnesses out of non-test code, as the x/internal placement used to; the CI change filter's two entries for the long-deleted x/imagegen/mlx now name the bindings' CMake project and the carried patches, so a change to either builds the payload; and the tokenizer parity test reads its fixtures from its own testdata instead of walking out of x/. x/server and x/imagegen/manifest stay for the next two commits.
119 lines
6.3 KiB
Go
119 lines
6.3 KiB
Go
package create
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import (
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"encoding/json"
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"slices"
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"testing"
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)
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func TestNemotronHImportTransformRegistration(t *testing.T) {
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inv := Inventory{
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Config: sourceModelConfig{Architectures: []string{"NemotronH_Nano_Omni_Reasoning_V3"}},
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RawConfig: json.RawMessage(`{"architectures":["NemotronH_Nano_Omni_Reasoning_V3"],"llm_config":{"num_hidden_layers":52}}`),
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}
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policy, err := newTensorImportTransform(inv)
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if err != nil {
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t.Fatalf("newTensorImportTransform() error = %v", err)
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}
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transform, ok := policy.(nemotronHImportTransform)
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if !ok {
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t.Fatalf("newTensorImportTransform() = %T, want nemotronHImportTransform", policy)
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}
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if transform.numLayers != 52 {
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t.Fatalf("numLayers = %d, want 52", transform.numLayers)
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}
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}
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func TestNemotronHPlanKeepsUnsupportedModalitiesAtSourcePrecision(t *testing.T) {
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transform := nemotronHImportTransform{numLayers: 52}
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inv := Inventory{Dir: "test", Tensors: map[string]SourceTensor{
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"language_model.backbone.embeddings.weight": {Name: "language_model.backbone.embeddings.weight", Dtype: "BF16", Shape: []int32{128, 128}},
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"language_model.backbone.layers.0.mixer.in_proj.weight": {Name: "language_model.backbone.layers.0.mixer.in_proj.weight", Dtype: "BF16", Shape: []int32{128, 128}},
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"vision_model.radio_model.model.patch_generator.proj.weight": {Name: "vision_model.radio_model.model.patch_generator.proj.weight", Dtype: "BF16", Shape: []int32{128, 128}},
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"mlp1.0.weight": {Name: "mlp1.0.weight", Dtype: "BF16", Shape: []int32{128, 128}},
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"sound_encoder.encoder.layers.0.self_attn.q_proj.weight": {Name: "sound_encoder.encoder.layers.0.self_attn.q_proj.weight", Dtype: "BF16", Shape: []int32{128, 128}},
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"sound_projection.adapter.weight": {Name: "sound_projection.adapter.weight", Dtype: "BF16", Shape: []int32{128, 128}},
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}}
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specs, err := Plan(inv, Classification{Kind: SourceFloat, Quantize: "nvfp4"}, transform)
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if err != nil {
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t.Fatalf("Plan() error = %v", err)
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}
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got := specNames(specs)
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want := []string{
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"language_model.backbone.embeddings.weight",
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"language_model.backbone.layers.0.mixer.in_proj.weight",
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"mlp1.0.weight",
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"sound_encoder.encoder.layers.0.self_attn.q_proj.weight",
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"sound_projection.adapter.weight",
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"vision_model.radio_model.model.patch_generator.proj.weight",
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}
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if !slices.Equal(got, want) {
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t.Fatalf("spec names = %v, want %v", got, want)
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}
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for _, name := range []string{
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"mlp1.0.weight",
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"sound_encoder.encoder.layers.0.self_attn.q_proj.weight",
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"sound_projection.adapter.weight",
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"vision_model.radio_model.model.patch_generator.proj.weight",
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} {
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spec, ok := specByName(specs, name)
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if !ok {
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t.Fatalf("missing spec %s", name)
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}
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if got := spec.Tensors[0].Quantize; got != "" {
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t.Fatalf("%s quantize = %q, want source precision", name, got)
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}
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}
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}
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func TestNemotronHImportTransformQuantizationPolicy(t *testing.T) {
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transform := nemotronHImportTransform{numLayers: 52}
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tests := []struct {
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name string
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tensor string
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shape []int32
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quantize string
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want string
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}{
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{"mamba in_proj nvfp4", "language_model.backbone.layers.0.mixer.in_proj.weight", []int32{96, 32}, "nvfp4", "nvfp4"},
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{"mamba out_proj promoted", "language_model.backbone.layers.0.mixer.out_proj.weight", []int32{32, 32}, "nvfp4", "mxfp8"},
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{"attention v_proj promoted", "language_model.backbone.layers.0.mixer.v_proj.weight", []int32{32, 32}, "nvfp4", "mxfp8"},
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{"attention o_proj promoted", "language_model.backbone.layers.0.mixer.o_proj.weight", []int32{32, 32}, "nvfp4", "mxfp8"},
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{"attention o_proj mxfp8", "language_model.backbone.layers.1.mixer.o_proj.weight", []int32{32, 32}, "mxfp8", "mxfp8"},
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{"expert up_proj nvfp4", "language_model.backbone.layers.2.mixer.experts.1.up_proj.weight", []int32{64, 32}, "nvfp4", "nvfp4"},
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{"expert down_proj promoted", "language_model.backbone.layers.2.mixer.experts.1.down_proj.weight", []int32{32, 32}, "nvfp4", "mxfp8"},
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{"shared expert down_proj promoted", "language_model.backbone.layers.2.mixer.shared_experts.down_proj.weight", []int32{32, 32}, "nvfp4", "mxfp8"},
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{"shared expert down_proj mxfp8", "language_model.backbone.layers.2.mixer.shared_experts.down_proj.weight", []int32{32, 64}, "mxfp8", "mxfp8"},
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{"late dense down_proj promoted", "language_model.backbone.layers.51.mixer.down_proj.weight", []int32{32, 32}, "nvfp4", "mxfp8"},
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{"middle dense down_proj stays nvfp4", "language_model.backbone.layers.16.mixer.down_proj.weight", []int32{32, 32}, "nvfp4", "nvfp4"},
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{"router gate kept bf16", "language_model.backbone.layers.2.mixer.gate.weight", []int32{8, 32}, "nvfp4", ""},
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{"conv kept bf16", "language_model.backbone.layers.0.mixer.conv1d.weight", []int32{96, 32}, "nvfp4", ""},
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{"embedding promoted to mxfp8", "language_model.backbone.embeddings.weight", []int32{4096, 32}, "mxfp8", "mxfp8"},
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{"embedding promoted to mxfp8 from nvfp4", "language_model.backbone.embeddings.weight", []int32{4096, 32}, "nvfp4", "mxfp8"},
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{"embedding promoted to int8 from int4", "language_model.backbone.embeddings.weight", []int32{4096, 64}, "int4", "int8"},
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{"embedding promoted to int8", "language_model.backbone.embeddings.weight", []int32{4096, 64}, "int8", "int8"},
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{"embedding unaligned kept bf16", "language_model.backbone.embeddings.weight", []int32{4096, 31}, "mxfp8", ""},
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{"lm head promoted to mxfp8", "language_model.lm_head.weight", []int32{4096, 32}, "nvfp4", "mxfp8"},
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{"lm head promoted to int8 from int4", "language_model.lm_head.weight", []int32{4096, 64}, "int4", "int8"},
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{"lm head promoted to int8", "language_model.lm_head.weight", []int32{4096, 64}, "int8", "int8"},
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{"lm head unaligned kept bf16", "language_model.lm_head.weight", []int32{4096, 31}, "nvfp4", ""},
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{"vision kept bf16", "vision_model.radio_model.model.patch_generator.proj.weight", []int32{32, 32}, "nvfp4", ""},
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{"vision projector kept bf16", "mlp1.0.weight", []int32{32, 32}, "nvfp4", ""},
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{"sound encoder kept bf16", "sound_encoder.encoder.layers.0.self_attn.q_proj.weight", []int32{32, 32}, "nvfp4", ""},
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{"sound projector kept bf16", "sound_projection.adapter.weight", []int32{32, 32}, "nvfp4", ""},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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if got := transform.quantizationType(tt.tensor, tt.shape, tt.quantize); got != tt.want {
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t.Fatalf("quantizationType(%q, %v, %q) = %q, want %q", tt.tensor, tt.shape, tt.quantize, got, tt.want)
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}
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})
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}
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}
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