Add Apple system metrics support (#3377)

* Add Apple system metrics support Co-authored-by: Bipradip Chowdhury <[email protected]> Co-authored-by: Rony Leppänen <[email protected]> Co-authored-by: Anders Smedegaard Pedersen <[email protected]> * Fix ModelServerTest.testMetricManager for other HW vendors * Add GPUUtilization as expect metric --------- Co-authored-by: Bipradip Chowdhury <[email protected]> Co-authored-by: Rony Leppänen <[email protected]> Co-authored-by: Anders Smedegaard Pedersen <[email protected]>
pytorch · Dec 20, 2024 · 1a3b18b · 1a3b18b
1 parent 9bcbd22
commit 1a3b18b
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Showing 5 changed files with 36 additions and 16 deletions.
diff --git a/frontend/server/src/main/java/org/pytorch/serve/device/utils/AppleUtil.java b/frontend/server/src/main/java/org/pytorch/serve/device/utils/AppleUtil.java
@@ -5,10 +5,9 @@
 import com.google.gson.JsonObject;
 import com.google.gson.JsonParser;
 import java.util.ArrayList;
+import java.util.Collections;
 import java.util.LinkedHashSet;
 import java.util.List;
-import java.util.stream.Collectors;
-import java.util.stream.IntStream;
 import org.pytorch.serve.device.Accelerator;
 import org.pytorch.serve.device.AcceleratorVendor;
 import org.pytorch.serve.device.interfaces.IAcceleratorUtility;
@@ -75,15 +74,12 @@ public List<JsonObject> extractAccelerators(JsonElement rootObject) {
                         .getAsJsonObject() // Gets the outer object
                         .get("SPDisplaysDataType") // Gets the "SPDisplaysDataType" element
                         .getAsJsonArray();
+
         JsonObject gpuObject = displaysArray.get(0).getAsJsonObject();
-        int number_of_cores = Integer.parseInt(gpuObject.get("sppci_cores").getAsString());
-
-        // add the object `number_of_cores` times to maintain the exsisitng
-        // functionality
-        accelerators =
-                IntStream.range(0, number_of_cores)
-                        .mapToObj(i -> gpuObject)
-                        .collect(Collectors.toList());
+
+        // Create list with only a single accelerator object as
+        // M1, M2, M3 Macs have only single integrated GPU
+        accelerators = Collections.singletonList(gpuObject);
 
         return accelerators;
     }

diff --git a/frontend/server/src/test/java/org/pytorch/serve/ModelServerTest.java b/frontend/server/src/test/java/org/pytorch/serve/ModelServerTest.java
@@ -1372,8 +1372,17 @@ public void testMetricManager() throws JsonParseException, InterruptedException
             Assert.assertTrue(++count < 5);
         }
 
-        // 7 system-level metrics + 3 gpu-specific metrics
-        Assert.assertEquals(metrics.size(), 7 + 3 * configManager.getNumberOfGpu());
+        // Determine if the device is Apple or not
+        String vendor = System.getProperty("os.name");
+        if (vendor != null) {
+            if (vendor.startsWith("Mac")) {
+                // 7 system-level metrics + 2 gpu-specific metrics (per GPU) for Apple devices
+                Assert.assertEquals(metrics.size(), 7 + 2 * configManager.getNumberOfGpu());
+            } else {
+                // 7 system-level metrics + 3 gpu-specific metrics (per GPU) for non-Apple devices
+                Assert.assertEquals(metrics.size(), 7 + 3 * configManager.getNumberOfGpu());
+            }
+        }
 
         for (Metric metric : metrics) {
             String metricName = metric.getMetricName();

diff --git a/frontend/server/src/test/java/org/pytorch/serve/device/utils/AppleUtilTest.java b/frontend/server/src/test/java/org/pytorch/serve/device/utils/AppleUtilTest.java
@@ -76,7 +76,7 @@ public void testExtractAcceleratorId() {
     public void testExtractAccelerators() {
         List<JsonObject> accelerators = appleUtil.extractAccelerators(sampleOutputJson);
 
-        assertEquals(accelerators.size(), 7);
+        assertEquals(accelerators.size(), 1);
         assertEquals(accelerators.get(0).get("sppci_model").getAsString(), "Apple M1");
     }
 
@@ -88,7 +88,7 @@ public void testSmiOutputToUpdatedAccelerators() {
         ArrayList<Accelerator> updatedAccelerators =
                 appleUtil.smiOutputToUpdatedAccelerators(sampleOutputJson.toString(), parsedGpuIds);
 
-        assertEquals(updatedAccelerators.size(), 7);
+        assertEquals(updatedAccelerators.size(), 1);
         Accelerator accelerator = updatedAccelerators.get(0);
         assertEquals(accelerator.getAcceleratorModel(), "Apple M1");
         assertEquals(accelerator.getVendor(), AcceleratorVendor.APPLE);
@@ -112,7 +112,7 @@ public String[] getUtilizationSmiCommand() {
         ArrayList<Accelerator> availableAccelerators =
                 spyAppleUtil.getAvailableAccelerators(availableAcceleratorIds);
 
-        assertEquals(availableAccelerators.size(), 7);
+        assertEquals(availableAccelerators.size(), 1);
         Accelerator accelerator = availableAccelerators.get(0);
         assertEquals(accelerator.getAcceleratorModel(), "Apple M1");
         assertEquals(accelerator.getVendor(), AcceleratorVendor.APPLE);

diff --git a/frontend/server/src/test/java/org/pytorch/serve/util/ConfigManagerTest.java b/frontend/server/src/test/java/org/pytorch/serve/util/ConfigManagerTest.java
@@ -118,7 +118,8 @@ public void testNumGpuM1() throws ReflectiveOperationException, IOException {
         String mac_arm64_cpu_only = System.getenv().getOrDefault("TS_MAC_ARM64_CPU_ONLY", "False");
         if (arch.equals("aarch64")) {
             if (mac_arm64_cpu_only.equals("True")) {
-                Assert.assertEquals(configManager.getNumberOfGpu(), 0);
+                // Mac M1 returns 1 accelerator device
+                Assert.assertEquals(configManager.getNumberOfGpu(), 1);
             } else {
                 Assert.assertTrue(configManager.getNumberOfGpu() > 0);
             }

diff --git a/ts/metrics/system_metrics.py b/ts/metrics/system_metrics.py
@@ -88,6 +88,20 @@ def collect_gpu_metrics(num_of_gpus):
                     amdsmi.amdsmi_shut_down()
                 except amdsmi.AmdSmiException as e:
                     logging.error("Could not shut down AMD-SMI library.")
+        elif torch.backends.mps.is_available():
+            try:
+                total_memory = torch.mps.driver_allocated_memory()
+                mem_used = torch.mps.current_allocated_memory()
+                gpu_mem_utilization = (
+                    (mem_used / total_memory * 100) if total_memory > 0 else 0
+                )
+                # Currently there is no way to calculate GPU utilization with MPS.
+                gpu_utilization = None
+            except Exception as e:
+                logging.error(f"Could not capture MPS memory metrics")
+                mem_used = 0
+                gpu_mem_utilization = 0
+                gpu_utilization = None
 
         dimension_gpu = [
             Dimension("Level", "Host"),