WebNN: Allow 0-D (scalar) reductions

crrev.com/c/5496321 added a restriction on the reduction ops (including argMin/argMax) that the rank of the input tensor must be greater than zero. In webmachinelearning/webnn#681, @fdwr points out that 0-D (scalar) reductions are fine, it's just a no-op. Per that discussion, this removes a restriction. There are already WPT cases for argMin/argMax (although they fail for other reasons); cases are added for the plethora of reduction ops (which also fail for other reasons). Change-Id: Ib10a9b0bb3062f9d2b6001d9a4963b14e751d14f Cq-Include-Trybots: luci.chromium.try:mac14-blink-rel,mac14.arm64-blink-rel,win11-blink-rel
web-platform-tests · May 13, 2024 · ed5beae · ed5beae
1 parent 38a6c3e
commit ed5beae
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Showing 11 changed files with 183 additions and 3 deletions.
diff --git a/webnn/resources/test_data/reduce_l1.json b/webnn/resources/test_data/reduce_l1.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceL1 float32 0D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            5.50882625579834
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 5.50882625579834,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceL1 float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_l2.json b/webnn/resources/test_data/reduce_l2.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceL2 float32 0D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            4.860228061676025
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 4.860228061676025,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceL2 float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_log_sum.json b/webnn/resources/test_data/reduce_log_sum.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceLogSum float32 0D constant tensor all non-negative default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            64.54827117919922
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 4.167413234710693,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceLogSum float32 1D constant tensor all non-negative default options", // default options: {axes: null, keepDimensions: false}
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_log_sum_exp.json b/webnn/resources/test_data/reduce_log_sum_exp.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceLogSumExp float32 0D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            0.7974132895469666
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 0.7974132895469666,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceLogSumExp float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_max.json b/webnn/resources/test_data/reduce_max.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceMax float32 0D constant tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            32.16658401489258
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 32.16658401489258,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceMax float32 1D constant tensor default options",
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_mean.json b/webnn/resources/test_data/reduce_mean.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceMean float32 0D constant tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            95.84498596191406
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 95.84498596191406,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceMean float32 1D constant tensor all positive default options",
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_min.json b/webnn/resources/test_data/reduce_min.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceMin float32 0D constant tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            -58.76195526123047
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -58.76195526123047,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceMin float32 1D constant tensor default options",
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_product.json b/webnn/resources/test_data/reduce_product.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceProduct float32 0D constant tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            -68.75911712646484
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -68.75911712646484,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceProduct float32 1D constant tensor default options",
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_sum.json b/webnn/resources/test_data/reduce_sum.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceSum float32 0D constant tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            69.6038589477539
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 69.6038589477539,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceSum float32 1D constant tensor all positive default options",
       "inputs": {

diff --git a/webnn/resources/test_data/reduce_sum_square.json b/webnn/resources/test_data/reduce_sum_square.json
@@ -1,5 +1,23 @@
 {
   "tests": [
+    {
+      "name": "reduceSumSquare float32 0D constant tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            52.5615348815918
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 2762.71484375,
+        "type": "float32"
+      }
+    },
     {
       "name": "reduceSumSquare float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
       "inputs": {

diff --git a/webnn/resources/utils.js b/webnn/resources/utils.js
@@ -274,9 +274,9 @@ const getReductionPrecisionTolerance = (resources, operationName) => {
   } else {
     sizes = inputShape;
   }
-  const reducedElementCount = sizes.reduce(
-                                  (accumulator, currentValue) => accumulator * currentValue
-  );
+  const reducedElementCount = sizes.length ?
+      sizes.reduce((accumulator, currentValue) => accumulator * currentValue) :
+      1;
   let tolerance;
   switch (operationName) {
     case 'reduceL1':