[Metrics] Add input/output token and request size metrics

pkg/ext-proc/handlers/response.go

@@ -73,6 +73,7 @@ func (s *Server) HandleResponseBody(reqCtx *RequestContext, req *extProcPb.Proce
 		return nil, fmt.Errorf("unmarshaling response body: %v", err)
 	}
 	reqCtx.Response = res
+	reqCtx.ResponseSize = len(body.ResponseBody.Body)
 	// ResponseComplete is to indicate the response is complete. In non-streaming
 	// case, it will be set to be true once the response is processed; in
 	// streaming case, it will be set to be true once the last chunk is processed.

pkg/ext-proc/handlers/server.go

@@ -95,6 +95,9 @@ func (s *Server) Process(srv extProcPb.ExternalProcessor_ProcessServer) error {
 			if err == nil && reqCtx.ResponseComplete {
 				reqCtx.ResponseCompleteTimestamp = time.Now()
 				metrics.RecordRequestLatencies(reqCtx.Model, reqCtx.ResolvedTargetModel, reqCtx.RequestReceivedTimestamp, reqCtx.ResponseCompleteTimestamp)
+				metrics.RecordResponseSizes(reqCtx.Model, reqCtx.ResolvedTargetModel, reqCtx.ResponseSize)
+				metrics.RecordInputTokens(reqCtx.Model, reqCtx.ResolvedTargetModel, reqCtx.Response.Usage.PromptTokens)
+				metrics.RecordOutputTokens(reqCtx.Model, reqCtx.ResolvedTargetModel, reqCtx.Response.Usage.CompletionTokens)
 			}
 			klog.V(3).Infof("Request context after HandleResponseBody: %+v", reqCtx)
 		default:
@@ -138,5 +141,6 @@ type RequestContext struct {
 	ResponseCompleteTimestamp time.Time
 	RequestSize               int
 	Response                  Response
+	ResponseSize              int
 	ResponseComplete          bool
 }

pkg/ext-proc/metrics/README.md

@@ -6,13 +6,46 @@ This documentation is the current state of exposed metrics.
 * [Exposed Metrics](#exposed-metrics)
 * [Scrape Metrics](#scrape-metrics)
 
+## Requirements
+
+Response metrics are only supported in non-streaming mode, with the follow up [issue](https://github.com/kubernetes-sigs/gateway-api-inference-extension/issues/178) to address streaming mode.
+
+Currently there are two options:
+- If requests don't use response streaming, then you can enable `Buffered` mode for response in `EnvoyExtensionPolicy`, this will buffer the response body at the proxy and forward it to the endpoint picker, which allows the endpoint picker to report response metrics.
+
+- If requests use response streaming, then it is not recommended to enable `Buffered` mode, the response body processing mode should be left empty in the `EnvoyExtensionPolicy` (default). In this case response bodies will not be forwarded to the endpoint picker, and therefore response metrics will not be reported.
+
+
+```
+apiVersion: gateway.envoyproxy.io/v1alpha1
+kind: EnvoyExtensionPolicy
+metadata:
+  name: ext-proc-policy
+  namespace: default
+spec:
+  extProc:
+    - backendRefs:
+      - group: ""
+        kind: Service
+        name: inference-gateway-ext-proc
+        port: 9002
+      processingMode:
+        request:
+          body: Buffered
+        response:
+          body: Buffered
+```
+
 ## Exposed metrics
 
 | Metric name | Metric Type  | Description | Labels | Status | 
 | ------------|--------------| ----------- | ------ | ------ |
-| inference_model_request_total | Counter      | The counter of requests broken out for each model. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt; ` | ALPHA |
-| inference_model_request_duration_seconds | Distribution | Distribution of response latency. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt; ` | ALPHA |
-| inference_model_request_duration_seconds | Distribution      | Distribution of response latency. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt; ` | ALPHA |
+| inference_model_request_total | Counter      | The counter of requests broken out for each model. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt;  | ALPHA |
+| inference_model_request_duration_seconds | Distribution | Distribution of response latency. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt;  | ALPHA |
+| inference_model_request_sizes | Distribution      | Distribution of request size in bytes. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt;  | ALPHA |
+| inference_model_response_sizes | Distribution      | Distribution of response size in bytes. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt;  | ALPHA |
+| inference_model_input_tokens | Distribution      | Distribution of input token count. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt;  | ALPHA |
+| inference_model_output_tokens | Distribution      | Distribution of output token count. | `model_name`=&lt;model-name&gt; <br> `target_model_name`=&lt;target-model-name&gt;  | ALPHA |
 
 ## Scrape Metrics
 

pkg/ext-proc/metrics/metrics.go

@@ -51,6 +51,43 @@ var (
 		},
 		[]string{"model_name", "target_model_name"},
 	)
+
+	responseSizes = compbasemetrics.NewHistogramVec(
+		&compbasemetrics.HistogramOpts{
+			Subsystem: InferenceModelComponent,
+			Name:      "response_sizes",
+			Help:      "Inference model responses size distribution in bytes for each model and target model.",
+			// Most models have a response token < 8192 tokens. Each token, in average, has 4 characters.
+			// 8192 * 4 = 32768.
+			Buckets:        []float64{1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32778, 65536},
+			StabilityLevel: compbasemetrics.ALPHA,
+		},
+		[]string{"model_name", "target_model_name"},
+	)
+
+	inputTokens = compbasemetrics.NewHistogramVec(
+		&compbasemetrics.HistogramOpts{
+			Subsystem: InferenceModelComponent,
+			Name:      "input_tokens",
+			Help:      "Inference model input token count distribution for requests in each model.",
+			// Most models have a input context window less than 1 million tokens.
+			Buckets:        []float64{1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32778, 65536, 131072, 262144, 524288, 1048576},
+			StabilityLevel: compbasemetrics.ALPHA,
+		},
+		[]string{"model_name", "target_model_name"},
+	)
+
+	outputTokens = compbasemetrics.NewHistogramVec(
+		&compbasemetrics.HistogramOpts{
+			Subsystem: InferenceModelComponent,
+			Name:      "output_tokens",
+			Help:      "Inference model output token count distribution for requests in each model.",
+			// Most models generates output less than 8192 tokens.
+			Buckets:        []float64{1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192},
+			StabilityLevel: compbasemetrics.ALPHA,
+		},
+		[]string{"model_name", "target_model_name"},
+	)
 )
 
 var registerMetrics sync.Once
@@ -61,6 +98,9 @@ func Register() {
 		legacyregistry.MustRegister(requestCounter)
 		legacyregistry.MustRegister(requestLatencies)
 		legacyregistry.MustRegister(requestSizes)
+		legacyregistry.MustRegister(responseSizes)
+		legacyregistry.MustRegister(inputTokens)
+		legacyregistry.MustRegister(outputTokens)
 	})
 }
 
@@ -84,3 +124,22 @@ func RecordRequestLatencies(modelName, targetModelName string, received time.Tim
 	requestLatencies.WithLabelValues(modelName, targetModelName).Observe(elapsedSeconds)
 	return true
 }
+
+// RecordResponseSizes records the response sizes.
+func RecordResponseSizes(modelName, targetModelName string, size int) {
+	responseSizes.WithLabelValues(modelName, targetModelName).Observe(float64(size))
+}
+
+// RecordInputTokens records input tokens count.
+func RecordInputTokens(modelName, targetModelName string, size int) {
+	if size > 0 {
+		inputTokens.WithLabelValues(modelName, targetModelName).Observe(float64(size))
+	}
+}
+
+// RecordOutputTokens records output tokens count.
+func RecordOutputTokens(modelName, targetModelName string, size int) {
+	if size > 0 {
+		outputTokens.WithLabelValues(modelName, targetModelName).Observe(float64(size))
+	}
+}

pkg/ext-proc/metrics/metrics_test.go

@@ -12,6 +12,9 @@ import (
 const RequestTotalMetric = InferenceModelComponent + "_request_total"
 const RequestLatenciesMetric = InferenceModelComponent + "_request_duration_seconds"
 const RequestSizesMetric = InferenceModelComponent + "_request_sizes"
+const ResponseSizesMetric = InferenceModelComponent + "_response_sizes"
+const InputTokensMetric = InferenceModelComponent + "_input_tokens"
+const OutputTokensMetric = InferenceModelComponent + "_output_tokens"
 
 func TestRecordRequestCounterandSizes(t *testing.T) {
 	type requests struct {
@@ -160,3 +163,97 @@ func TestRecordRequestLatencies(t *testing.T) {
 		})
 	}
 }
+
+func TestRecordResponseMetrics(t *testing.T) {
+	type responses struct {
+		modelName       string
+		targetModelName string
+		inputToken      int
+		outputToken     int
+		respSize        int
+	}
+	scenarios := []struct {
+		name string
+		resp []responses
+	}{{
+		name: "multiple requests",
+		resp: []responses{
+			{
+				modelName:       "m10",
+				targetModelName: "t10",
+				respSize:        1200,
+				inputToken:      10,
+				outputToken:     100,
+			},
+			{
+				modelName:       "m10",
+				targetModelName: "t10",
+				respSize:        500,
+				inputToken:      20,
+				outputToken:     200,
+			},
+			{
+				modelName:       "m10",
+				targetModelName: "t11",
+				respSize:        2480,
+				inputToken:      30,
+				outputToken:     300,
+			},
+			{
+				modelName:       "m20",
+				targetModelName: "t20",
+				respSize:        80,
+				inputToken:      40,
+				outputToken:     400,
+			},
+		},
+	}}
+	Register()
+	for _, scenario := range scenarios {
+		t.Run(scenario.name, func(t *testing.T) {
+			for _, resp := range scenario.resp {
+				RecordInputTokens(resp.modelName, resp.targetModelName, resp.inputToken)
+				RecordOutputTokens(resp.modelName, resp.targetModelName, resp.outputToken)
+				RecordResponseSizes(resp.modelName, resp.targetModelName, resp.respSize)
+			}
+			wantResponseSize, err := os.Open("testdata/response_sizes_metric")
+			defer func() {
+				if err := wantResponseSize.Close(); err != nil {
+					t.Error(err)
+				}
+			}()
+			if err != nil {
+				t.Fatal(err)
+			}
+			if err := testutil.GatherAndCompare(legacyregistry.DefaultGatherer, wantResponseSize, ResponseSizesMetric); err != nil {
+				t.Error(err)
+			}
+
+			wantInputToken, err := os.Open("testdata/input_tokens_metric")
+			defer func() {
+				if err := wantInputToken.Close(); err != nil {
+					t.Error(err)
+				}
+			}()
+			if err != nil {
+				t.Fatal(err)
+			}
+			if err := testutil.GatherAndCompare(legacyregistry.DefaultGatherer, wantInputToken, InputTokensMetric); err != nil {
+				t.Error(err)
+			}
+
+			wantOutputToken, err := os.Open("testdata/output_tokens_metric")
+			defer func() {
+				if err := wantOutputToken.Close(); err != nil {
+					t.Error(err)
+				}
+			}()
+			if err != nil {
+				t.Fatal(err)
+			}
+			if err := testutil.GatherAndCompare(legacyregistry.DefaultGatherer, wantOutputToken, OutputTokensMetric); err != nil {
+				t.Error(err)
+			}
+		})
+	}
+}

pkg/ext-proc/metrics/testdata/input_tokens_metric

@@ -0,0 +1,68 @@
+# HELP inference_model_input_tokens [ALPHA] Inference model input token count distribution for requests in each model.
+# TYPE inference_model_input_tokens histogram
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="1"} 0
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="8"} 0
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="16"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="32"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="64"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="128"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="256"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="512"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="1024"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="2048"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="4096"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="8192"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="16384"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="32778"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="65536"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="131072"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="262144"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="524288"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="1.048576e+06"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t10",le="+Inf"} 2
+inference_model_input_tokens_sum{model_name="m10",target_model_name="t10"} 30
+inference_model_input_tokens_count{model_name="m10",target_model_name="t10"} 2
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="1"} 0
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="8"} 0
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="16"} 0
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="32"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="64"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="128"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="256"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="512"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="1024"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="2048"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="4096"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="8192"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="16384"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="32778"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="65536"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="131072"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="262144"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="524288"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="1.048576e+06"} 1
+inference_model_input_tokens_bucket{model_name="m10",target_model_name="t11",le="+Inf"} 1
+inference_model_input_tokens_sum{model_name="m10",target_model_name="t11"} 30
+inference_model_input_tokens_count{model_name="m10",target_model_name="t11"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="1"} 0
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="8"} 0
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="16"} 0
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="32"} 0
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="64"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="128"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="256"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="512"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="1024"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="2048"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="4096"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="8192"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="16384"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="32778"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="65536"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="131072"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="262144"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="524288"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="1.048576e+06"} 1
+inference_model_input_tokens_bucket{model_name="m20",target_model_name="t20",le="+Inf"} 1
+inference_model_input_tokens_sum{model_name="m20",target_model_name="t20"} 40
+inference_model_input_tokens_count{model_name="m20",target_model_name="t20"} 1