[onnx] Add IDF and TFIDF modes to TFIDF Vectorizer

include/torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h

@@ -338,6 +338,31 @@ struct OpBinder {
     return failure();
   }
 
+  ParseResult f32FloatArrayAttr(llvm::SmallVector<float> &values,
+                                StringRef nameSuffix,
+                                ArrayRef<float> defaults) {
+    SmallString<64> name("torch.onnx.");
+    name.append(nameSuffix);
+    auto attr = op->getAttr(name);
+    if (!attr) {
+      values.append(defaults.begin(), defaults.end());
+      return success();
+    }
+    if (auto arrayAttr = dyn_cast<ArrayAttr>(attr)) {
+      for (auto element : arrayAttr) {
+        auto floatAttr = dyn_cast<FloatAttr>(element);
+        if (!floatAttr)
+          return failure();
+        FloatType t = cast<FloatType>(floatAttr.getType());
+        if (t.getWidth() != 32)
+          return failure();
+        values.push_back(floatAttr.getValue().convertToFloat());
+      }
+      return success();
+    }
+    return failure();
+  }
+
   ParseResult stringArrayAttr(llvm::SmallVector<std::string> &values,
                               StringRef nameSuffix) {
     SmallString<64> name("torch.onnx.");

lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp

@@ -4339,6 +4339,7 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         llvm::SmallVector<int64_t> ngram_counts;
         llvm::SmallVector<int64_t> ngram_indexes;
         llvm::SmallVector<int64_t> pool_int64s;
+        llvm::SmallVector<float> weights;
         std::string mode;
         int64_t min_gram_length;
         int64_t max_gram_length;
@@ -4356,9 +4357,10 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
             binder.tensorOperand(input) || binder.tensorResultType(resultType))
           return failure();
 
-        if (mode != "TF")
-          return rewriter.notifyMatchFailure(binder.op,
-                                             "TF mode supported only");
+        llvm::SmallVector<float> defaultWeights(ngram_indexes.size(), 1.0f);
+        if (binder.f32FloatArrayAttr(weights, "weights", defaultWeights))
+          return failure();
+
         if (pool_int64s.size() == 0)
           return rewriter.notifyMatchFailure(
               binder.op, "pool_int64s empty, only integers supported");
@@ -4584,9 +4586,36 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                     binder.getLoc(), loopConditionTrue, ValueRange({count}));
               }
               count = skipLoop.getResult(0);
-              // insert count "tf" into output
               Value countFloat = rewriter.create<Torch::AtenFloatScalarOp>(
                   binder.getLoc(), count);
+              if (mode == "IDF" || mode == "TFIDF") {
+                // both IDF and TFIDF modes use weights
+                float weight = weights[ngram_i];
+                Value constWeight = rewriter.create<Torch::ConstantFloatOp>(
+                    binder.getLoc(), rewriter.getF64FloatAttr(weight));
+
+                // TFIDF
+                Value multiplier = countFloat;
+                if (mode == "IDF") {
+                  // All the counts larger than 1 would be truncated to 1
+                  // and the i-th element in weights would be used to scale
+                  // (by multiplication) the count of the i-th n-gram in pool.
+
+                  Value intCount = rewriter.create<Torch::AtenIntScalarOp>(
+                      binder.getLoc(), count);
+                  // compare intCount > 0
+                  Value gtZeroCount = rewriter.create<Torch::AtenGtIntOp>(
+                      binder.getLoc(), intCount, zero);
+                  gtZeroCount = rewriter.create<Torch::AtenIntBoolOp>(
+                      binder.getLoc(), gtZeroCount);
+                  Value gtZeroCountFloat =
+                      rewriter.create<Torch::AtenFloatScalarOp>(binder.getLoc(),
+                                                                gtZeroCount);
+                  multiplier = gtZeroCountFloat;
+                }
+                countFloat = rewriter.create<Torch::AtenMulFloatOp>(
+                    binder.getLoc(), multiplier, constWeight);
+              }
               Value dataList = rewriter.create<Torch::PrimListConstructOp>(
                   binder.getLoc(),
                   rewriter.getType<Torch::ListType>(