Adding mlir quant_dot

src/targets/gpu/fuse_mlir.cpp

@@ -139,7 +139,7 @@ struct find_mlir_op
     auto matcher() const
     {
         auto dot_or_conv = match::skip(match::name("contiguous"))(
-            match::any_of(match::name("dot"), is_mlir_conv()).bind("gemm_based_op"));
+            match::any_of(match::name("dot"), match::name("quant_dot"), is_mlir_conv()).bind("gemm_based_op"));
         return match::name("pointwise")(match::any_of[match::inputs()](dot_or_conv.bind("x")));
     }
 
@@ -205,6 +205,7 @@ struct find_mlir_op
                                     "convolution",
                                     "quant_convolution",
                                     "dot",
+                                    "quant_dot",
                                     "add",
                                     "relu",
                                     "dequantizelinear",

src/targets/gpu/mlir.cpp

@@ -244,8 +244,6 @@ struct mlir_program
 
     MlirAttribute attribute(std::int64_t i) const
     {
-        if(i < 0)
-            MIGRAPHX_THROW("MLIR cant handle negative values since they are ambiguous");
         return mlirIntegerAttrGet(mlirIntegerTypeGet(ctx.get(), 64), i);
     }
     MlirAttribute attribute(std::uint64_t i) const

test/gpu/mlir.cpp

@@ -187,12 +187,39 @@ module {
     EXPECT(verify_mlir(m));
 }
 
+TEST_CASE(quant_dot_add)
+{
+    const std::string mlir_output = R"__migraphx__(
+module {
+  func.func @main(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr"} {
+    %0 = migraphx.quant_dot(%arg0, %arg1) : (tensor<1x5x4xi8>, tensor<1x4x3xi8>) -> tensor<1x5x3xi32>
+    %1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xi32>, tensor<1x5x3xi32>) -> tensor<1x5x3xi32>
+    return %1 : tensor<1x5x3xi32>
+  }
+}
+)__migraphx__";
+    migraphx::module m;
+    auto arg0 = m.add_parameter("arg0", {migraphx::shape::int8_type, {1, 5, 4}});
+    auto arg1 = m.add_parameter("arg1", {migraphx::shape::int8_type, {1, 4, 3}});
+    auto arg2 = m.add_parameter("arg2", {migraphx::shape::int32_type, {1, 5, 3}});
+    auto conv = m.add_instruction(migraphx::make_op("quant_dot"), arg0, arg1);
+    auto add  = m.add_instruction(migraphx::make_op("add"), conv, arg2);
+    m.add_return({add});
+
+    auto s = migraphx::gpu::dump_mlir(m);
+    // Skip test if MLIR is not enabled
+    if(s.empty())
+        return;
+    CHECK(encode(s) == encode(mlir_output));
+    EXPECT(verify_mlir(m));
+}
+
 TEST_CASE(dot_add)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
   func.func @mlir_dot(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr"} {
-    %0 = migraphx.dot(%arg0, %arg1) : tensor<1x5x4xf32>, tensor<1x4x3xf32> -> tensor<1x5x3xf32>
+    %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
     %1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
     return %1 : tensor<1x5x3xf32>
   }