feat(operator): Included LLM spec to CRD

apis/mlops/scheduler/scheduler.proto

@@ -42,9 +42,14 @@ message ModelSpec {
   repeated string requirements = 4; // list of capabilities the server must satisfy to run this model
   optional uint64 memoryBytes = 5; // Requested memory
   optional string server = 6; // the particular model server to load the model. If unspecified will be chosen.
-  optional ExplainerSpec explainer = 7; // optional black box explainer details
   repeated ParameterSpec parameters = 8; // parameters to load with model
   optional ModelRuntimeInfo modelRuntimeInfo = 9; // model specific settings that are sent by the agent
+
+  // ensure only one of explainer or llm is specified at a time
+  oneof model_spec {
+    ExplainerSpec explainer = 7; // optional black box explainer details
+    LlmSpec llm = 10; // optional LLM specific settings
+  }
 }
 
 message ParameterSpec {
@@ -59,6 +64,11 @@ message ExplainerSpec {
   optional string pipelineRef = 3;
 }
 
+message LlmSpec {
+  optional string modelRef = 1;
+  optional string pipelineRef = 2;
+}
+
 message ModelRuntimeInfo {
   oneof modelRuntimeInfo {
     MLServerModelSettings mlserver = 1; 

k8s/helm-charts/seldon-core-v2-crds/templates/seldon-v2-crds.yaml

@@ -235,6 +235,18 @@ spec:
                     description: type of explainer
                     type: string
                 type: object
+              llm:
+                description: Llm spec
+                properties:
+                  modelRef:
+                    description: |-
+                      one of the following need to be set for the llm
+                      Reference to Model
+                    type: string
+                  pipelineRef:
+                    description: Reference to Pipeline
+                    type: string
+                type: object
               logger:
                 description: Payload logging
                 properties:

k8s/yaml/crds.yaml

@@ -238,6 +238,18 @@ spec:
                     description: type of explainer
                     type: string
                 type: object
+              llm:
+                description: Llm spec
+                properties:
+                  modelRef:
+                    description: |-
+                      one of the following need to be set for the llm
+                      Reference to Model
+                    type: string
+                  pipelineRef:
+                    description: Reference to Pipeline
+                    type: string
+                type: object
               logger:
                 description: Payload logging
                 properties:

operator/apis/mlops/v1alpha1/model_types.go

@@ -44,6 +44,15 @@ type ModelSpec struct {
 	Explainer *ExplainerSpec `json:"explainer,omitempty"`
 	// Parameters to load with model
 	Parameters []ParameterSpec `json:"parameters,omitempty"`
+	// Llm spec
+	Llm *LlmSpec `json:"llm,omitempty"`
+}
+
+func (m *ModelSpec) Validate() error {
+	if m.Explainer != nil && m.Llm != nil {
+		return fmt.Errorf("Can't have both explainer and llm in model spec.")
+	}
+	return nil
 }
 
 type ParameterSpec struct {
@@ -64,6 +73,17 @@ type ExplainerSpec struct {
 	PipelineRef *string `json:"pipelineRef,omitempty"`
 }
 
+// Either ModelRef or PipelineRef is required
+type LlmSpec struct {
+	// one of the following need to be set for the llm
+	// Reference to Model
+	// +optional
+	ModelRef *string `json:"modelRef,omitempty"`
+	// Reference to Pipeline
+	// +optional
+	PipelineRef *string `json:"pipelineRef,omitempty"`
+}
+
 type ScalingSpec struct {
 	// Number of replicas - default 1
 	Replicas *int32 `json:"replicas,omitempty"`
@@ -143,6 +163,10 @@ func init() {
 
 // Method to convert Model resource to scheduler proto for communication with Scheduler
 func (m Model) AsSchedulerModel() (*scheduler.Model, error) {
+	// guarantees that Explainer and Llm are mutually exclusive
+	if err := m.Spec.Validate(); err != nil {
+		return nil, err
+	}
 	md := &scheduler.Model{
 		Meta: &scheduler.MetaData{
 			Name: m.Name,
@@ -162,10 +186,20 @@ func (m Model) AsSchedulerModel() (*scheduler.Model, error) {
 		},
 	}
 	if m.Spec.Explainer != nil {
-		md.ModelSpec.Explainer = &scheduler.ExplainerSpec{
-			Type:        m.Spec.Explainer.Type,
-			ModelRef:    m.Spec.Explainer.ModelRef,
-			PipelineRef: m.Spec.Explainer.PipelineRef,
+		md.ModelSpec.ModelSpec = &scheduler.ModelSpec_Explainer{
+			Explainer: &scheduler.ExplainerSpec{
+				Type:        m.Spec.Explainer.Type,
+				ModelRef:    m.Spec.Explainer.ModelRef,
+				PipelineRef: m.Spec.Explainer.PipelineRef,
+			},
+		}
+	}
+	if m.Spec.Llm != nil {
+		md.ModelSpec.ModelSpec = &scheduler.ModelSpec_Llm{
+			Llm: &scheduler.LlmSpec{
+				ModelRef:    m.Spec.Llm.ModelRef,
+				PipelineRef: m.Spec.Llm.PipelineRef,
+			},
 		}
 	}
 	if len(m.Spec.Parameters) > 0 {

operator/apis/mlops/v1alpha1/model_types_test.go

@@ -24,6 +24,55 @@ import (
 	scheduler "github.com/seldonio/seldon-core/apis/go/v2/mlops/scheduler"
 )
 
+func TestModelSpec_Validate(t *testing.T) {
+	g := NewGomegaWithT(t)
+	tests := []struct {
+		name    string
+		spec    *ModelSpec
+		wantErr bool
+	}{
+		{
+			name: "Both explainer and llm set",
+			spec: &ModelSpec{
+				Explainer: &ExplainerSpec{},
+				Llm:       &LlmSpec{},
+			},
+			wantErr: true,
+		},
+		{
+			name: "Only explainer set",
+			spec: &ModelSpec{
+				Explainer: &ExplainerSpec{},
+			},
+			wantErr: false,
+		},
+		{
+			name: "Only llm set",
+			spec: &ModelSpec{
+				Llm: &LlmSpec{},
+			},
+			wantErr: false,
+		},
+		{
+			name:    "Neither explainer nor llm set",
+			spec:    &ModelSpec{},
+			wantErr: false,
+		},
+	}
+
+	for _, tt := range tests {
+		err := tt.spec.Validate()
+
+		if tt.wantErr {
+			g.Expect(err).ToNot(BeNil())
+			g.Expect(err.Error()).To(ContainSubstring("Can't have both explainer and llm in model spec."))
+		} else {
+			g.Expect(err).To(BeNil())
+		}
+	}
+
+}
+
 func TestAsModelDetails(t *testing.T) {
 	g := NewGomegaWithT(t)
 	type test struct {
@@ -39,7 +88,7 @@ func TestAsModelDetails(t *testing.T) {
 	server := "server"
 	m1 := resource.MustParse("1M")
 	m1bytes := uint64(1_000_000)
-	incomeModel := "income"
+	incomeModel, llmModel := "income", "chat-gpt"
 	tests := []test{
 		{
 			name: "simple",
@@ -75,7 +124,7 @@ func TestAsModelDetails(t *testing.T) {
 			},
 		},
 		{
-			name: "complex",
+			name: "complex - explainer",
 			model: &Model{
 				ObjectMeta: metav1.ObjectMeta{
 					Name:       "foo",
@@ -121,9 +170,81 @@ func TestAsModelDetails(t *testing.T) {
 					Requirements:  []string{"a", "b", modelType},
 					StorageConfig: &scheduler.StorageConfig{Config: &scheduler.StorageConfig_StorageSecretName{StorageSecretName: secret}},
 					Server:        &server,
-					Explainer: &scheduler.ExplainerSpec{
-						Type:     "anchor_tabular",
-						ModelRef: &incomeModel,
+					ModelSpec: &scheduler.ModelSpec_Explainer{
+						Explainer: &scheduler.ExplainerSpec{
+							Type:     "anchor_tabular",
+							ModelRef: &incomeModel,
+						},
+					},
+					Parameters: []*scheduler.ParameterSpec{
+						{
+							Name:  "foo",
+							Value: "bar",
+						},
+						{
+							Name:  "foo2",
+							Value: "bar2",
+						},
+					},
+				},
+				DeploymentSpec: &scheduler.DeploymentSpec{
+					Replicas:    4,
+					LogPayloads: true,
+					MinReplicas: 0,
+					MaxReplicas: 0,
+				},
+			},
+		},
+		{
+			name: "complex - llm",
+			model: &Model{
+				ObjectMeta: metav1.ObjectMeta{
+					Name:       "foo",
+					Namespace:  "default",
+					Generation: 1,
+				},
+				Spec: ModelSpec{
+					InferenceArtifactSpec: InferenceArtifactSpec{
+						ModelType:  &modelType,
+						StorageURI: "gs://test",
+						SecretName: &secret,
+					},
+					Logger:       &LoggingSpec{},
+					Requirements: []string{"a", "b"},
+					ScalingSpec:  ScalingSpec{Replicas: &replicas},
+					Server:       &server,
+					Llm: &LlmSpec{
+						ModelRef: &llmModel,
+					},
+					Parameters: []ParameterSpec{
+						{
+							Name:  "foo",
+							Value: "bar",
+						},
+						{
+							Name:  "foo2",
+							Value: "bar2",
+						},
+					},
+				},
+			},
+			modelpb: &scheduler.Model{
+				Meta: &scheduler.MetaData{
+					Name: "foo",
+					KubernetesMeta: &scheduler.KubernetesMeta{
+						Namespace:  "default",
+						Generation: 1,
+					},
+				},
+				ModelSpec: &scheduler.ModelSpec{
+					Uri:           "gs://test",
+					Requirements:  []string{"a", "b", modelType},
+					StorageConfig: &scheduler.StorageConfig{Config: &scheduler.StorageConfig_StorageSecretName{StorageSecretName: secret}},
+					Server:        &server,
+					ModelSpec: &scheduler.ModelSpec_Llm{
+						Llm: &scheduler.LlmSpec{
+							ModelRef: &llmModel,
+						},
 					},
 					Parameters: []*scheduler.ParameterSpec{
 						{

operator/config/crd/bases/mlops.seldon.io_models.yaml

@@ -79,6 +79,18 @@ spec:
                     description: type of explainer
                     type: string
                 type: object
+              llm:
+                description: Llm spec
+                properties:
+                  modelRef:
+                    description: |-
+                      one of the following need to be set for the llm
+                      Reference to Model
+                    type: string
+                  pipelineRef:
+                    description: Reference to Pipeline
+                    type: string
+                type: object
               logger:
                 description: Payload logging
                 properties: