Check that AVX (now required) is present.

src/main/java/org/broadinstitute/hellbender/tools/walkers/vqsr/CNNScoreVariants.java

@@ -12,6 +12,7 @@
 import org.broadinstitute.hellbender.engine.*;
 import org.broadinstitute.hellbender.engine.filters.*;
 import org.broadinstitute.hellbender.exceptions.GATKException;
+import org.broadinstitute.hellbender.exceptions.UserException;
 import org.broadinstitute.hellbender.utils.haplotype.HaplotypeBAMWriter;
 import org.broadinstitute.hellbender.utils.io.IOUtils;
 import org.broadinstitute.hellbender.utils.io.Resource;
@@ -110,7 +111,13 @@ public class CNNScoreVariants extends TwoPassVariantWalker {
             "1D models will look at the reference sequence and variant annotations." +
             "2D models look at aligned reads, reference sequence, and variant annotations." +
             "2D models require a BAM file as input as well as the tensor-type argument to be set.";
+<<<<<<< 82e30b8f5001000e3f58efb4b3d6c99ca42b185c
     static final String AVXREQUIRED_ERROR = "This functionality requires hardware that supports the AVX instruction ste.";
+=======
+    static final String AVXREQUIRED_ERROR = "This tool requires AVX instruction set support by default due to its dependency on recent versions of the TensorFlow library.\n" +
+            " If you have an older (pre-1.6) version of TensorFlow installed that does not require AVX, you may attempt to re-run the tool with the --disable-avx-check argument to bypass this check.\n" +
+            " Note that such configurations are not officially supported.";
+>>>>>>> Check that AVX (now required) is present.
 
     private static final int CONTIG_INDEX = 0;
     private static final int POS_INDEX = 1;
@@ -143,6 +150,11 @@ public class CNNScoreVariants extends TwoPassVariantWalker {
     @Argument(fullName="info-annotation-keys", shortName="info-annotation-keys", doc="The VCF info fields to send to python.", optional=true)
     private List<String> annotationKeys = new ArrayList<>(Arrays.asList("MQ", "DP", "SOR", "FS", "QD", "MQRankSum", "ReadPosRankSum"));
 
+    @Advanced
+    @Argument(fullName = "disable-avx-check", shortName = "disable-avx-check", doc = "If set, no check will be made for AVX support.  " +
+            "Use only if you have installed a pre-1.6 TensorFlow build. ", optional = true)
+    private boolean disableAVXCheck = false;
+
     @Advanced
     @Argument(fullName = "inference-batch-size", shortName = "inference-batch-size", doc = "Size of batches for python to do inference on.", minValue = 1, maxValue = 4096, optional = true)
     private int inferenceBatchSize = 256;
@@ -206,12 +218,15 @@ protected String[] customCommandLineValidation() {
             }
         }
 
+<<<<<<< 82e30b8f5001000e3f58efb4b3d6c99ca42b185c
         IntelGKLUtils utils = new IntelGKLUtils();
         if (utils.isAvxSupported() == false)
         {
             return new String[]{CNNScoreVariants.AVXREQUIRED_ERROR};
         }
 
+=======
+>>>>>>> Check that AVX (now required) is present.
         return null;
     }
 
@@ -242,8 +257,23 @@ public List<ReadFilter> getDefaultReadFilters() {
 
     @Override
     public void onTraversalStart() {
+<<<<<<< 82e30b8f5001000e3f58efb4b3d6c99ca42b185c
         if (getHeaderForVariants().getGenotypeSamples().size() > 1) {
             logger.warn("CNNScoreVariants is a single sample tool, but the input VCF has more than 1 sample.");
+=======
+        // Users can disable the AVX check to allow an older version of TF that doesn't require AVX to be used.
+        if(this.disableAVXCheck == false) {
+            IntelGKLUtils utils = new IntelGKLUtils();
+            if (utils.isAvxSupported() == false) {
+                // Give user the bad news, suggest remedies.
+                throw new UserException.HardwareFeatureException(CNNScoreVariants.AVXREQUIRED_ERROR);
+            }
+
+            scoreKey = getScoreKeyAndCheckModelAndReadsHarmony();
+            if (architecture == null && weights == null) {
+                setArchitectureAndWeightsFromResources();
+            }
+>>>>>>> Check that AVX (now required) is present.
         }
 
         // Start the Python process and initialize a stream writer for streaming data to the Python code