Fix use of integer constants with float/float16 types

This generates warnings on MSVC (and probably other compilers too).

PiperOrigin-RevId: 728489631

Author: dsharletg

bench/rsum-benchmark.h

@@ -38,7 +38,7 @@ void f16_rsum(
   xnnpack::Buffer<xnn_float16, XNN_ALLOCATION_ALIGNMENT> input(
       rows * channels + XNN_EXTRA_BYTES / sizeof(xnn_float16));
   xnnpack::Buffer<xnn_float16> output(rows);
-  std::iota(input.begin(), input.end(), 1);
+  std::iota(input.begin(), input.end(), 1.0f);
 
   // Prepare parameters.
   xnn_f16_scale_params params;
@@ -71,7 +71,7 @@ void f16_f32acc_rsum(
   xnnpack::Buffer<xnn_float16, XNN_ALLOCATION_ALIGNMENT> input(
       rows * channels + XNN_EXTRA_BYTES / sizeof(xnn_float16));
   xnnpack::Buffer<float> output(rows);
-  std::iota(input.begin(), input.end(), 1);
+  std::iota(input.begin(), input.end(), 1.0f);
 
   // Prepare parameters.
   xnn_f16_f32acc_scale_params params;
@@ -104,7 +104,7 @@ void f32_rsum(
   xnnpack::Buffer<float, XNN_ALLOCATION_ALIGNMENT> input(
       rows * channels + XNN_EXTRA_BYTES / sizeof(float));
   xnnpack::Buffer<float> output(rows);
-  std::iota(input.begin(), input.end(), 1);
+  std::iota(input.begin(), input.end(), 1.0f);
 
   // Prepare parameters.
   xnn_f32_scale_params params;

test/convolution-operator-tester.h

@@ -1021,7 +1021,7 @@ class ConvolutionOperatorTester {
         std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)>
             auto_convolution_op(convolution_op2, xnn_delete_operator);
 
-        xnnpack::Buffer<xnn_float16> output2(output.size(), UINT16_C(0xDEAD));
+        xnnpack::Buffer<xnn_float16> output2(output.size());
         size_t workspace_size = SIZE_MAX;
         size_t workspace_alignment = SIZE_MAX;
         ASSERT_EQ(xnn_status_success,

test/fully-connected-operator-tester.h

@@ -1786,7 +1786,7 @@ class FullyConnectedOperatorTester {
             batch_size(),
             /*threadpool=*/nullptr));
 
-        xnnpack::Buffer<xnn_float16> output2(output.size(), UINT16_C(0xDEAD));
+        xnnpack::Buffer<xnn_float16> output2(output.size());
         ASSERT_EQ(xnn_status_success,
           xnn_setup_fully_connected_nc_qd8_f16_qc8w(
             fully_connected_op2,

test/indirection.cc

@@ -168,7 +168,7 @@ class IndirectionTester {
         kernel_size + (output_width - 1) * step_width * kernel_height_;
 
     input_ = xnnpack::Buffer<float>(channels_ * input_height_ * input_width_);
-    std::iota(input_.begin(), input_.end(), 0);
+    std::iota(input_.begin(), input_.end(), 0.0f);
     zero_buffer_ = xnnpack::Buffer<float>(channels_, 0.0f);
 
     const size_t indirect_top_height = divide_round_up(padding_height_ / 2, subsampling_);

test/packq-microkernel-tester.cc

@@ -31,7 +31,7 @@ void PackQMicrokernelTester::Test(xnn_x8_packq_f32qp8_ukernel_fn packq) const {
   xnnpack::Buffer<int8_t, XNN_ALLOCATION_ALIGNMENT> packed_w_ref(packed_size);
 
   // Populate the input and output data.
-  std::iota(input.begin(), input.end(), 0);
+  std::iota(input.begin(), input.end(), 0.0f);
   // TODO(b/372820266): Remove these fill calls that hide uninitialized memory bugs.
   std::fill(packed_w.begin(), packed_w.end(), INT8_C(0x12));
   std::fill(packed_w_ref.begin(), packed_w_ref.end(), INT8_C(0x7B));

test/packw-microkernel-tester.h

@@ -343,10 +343,9 @@ class PackWMicrokernelTester {
         g() * (packed_n() * packed_k() + packed_n()));
     xnnpack::Buffer<xnn_float16> packed_w_ref(g() * (packed_n() * packed_k() + packed_n()));
 
-    const xnn_float16 pad_value = std::max(sr(), kr()) == 1 ? UINT16_C(0xDEAD) : 0;
-    std::iota(weights.begin(), weights.end(), UINT16_C(0x0001));
-    std::iota(bias.begin(), bias.end(), UINT16_C(0x8000));
-    std::fill(packed_w.begin(), packed_w.end(), UINT16_C(0xBEEF));
+    const xnn_float16 pad_value = xnn_float16_from_bits(std::max(sr(), kr()) == 1 ? UINT16_C(0xDEAD) : 0);
+    std::iota(weights.begin(), weights.end(), 1.0f);
+    std::iota(bias.begin(), bias.end(), 0.5f);
     std::fill(packed_w_ref.begin(), packed_w_ref.end(), pad_value);
 
     // Mandate zero-padding of weights to packed_k() in K dimension.
@@ -371,8 +370,8 @@ class PackWMicrokernelTester {
 
     // Call optimized micro-kernel.
     packw(g(), n(), k(), nr(), kr(), sr(),
-          reinterpret_cast<const uint16_t*>(weights.data()), 
-          reinterpret_cast<const uint16_t*>(bias_data), /*scale=*/nullptr, 
+          reinterpret_cast<const uint16_t*>(weights.data()),
+          reinterpret_cast<const uint16_t*>(bias_data), /*scale=*/nullptr,
           reinterpret_cast<uint16_t*>(packed_w.data()),
       /*extra_bytes=*/0, /*params=*/nullptr);
 

test/static-constant-pad.cc

@@ -397,7 +397,7 @@ TEST_F(StaticConstantPadTestF16, matches_operator_api)
   operator_output = xnnpack::Buffer<xnn_float16>(NumElements(output_dims));
   subgraph_output = xnnpack::Buffer<xnn_float16>(operator_output.size());
 
-  std::iota(input.begin(), input.end(), 0);
+  std::iota(input.begin(), input.end(), 0.0f);
 
   ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
 
@@ -470,7 +470,7 @@ TEST_F(StaticConstantPadTestF32, matches_operator_api)
   operator_output = xnnpack::Buffer<float>(NumElements(output_dims));
   subgraph_output = xnnpack::Buffer<float>(operator_output.size());
 
-  std::iota(input.begin(), input.end(), 0);
+  std::iota(input.begin(), input.end(), 0.0f);
 
   ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
 
@@ -540,7 +540,7 @@ TEST_F(StaticConstantPadTestF32, reshape_output)
   }
   subgraph_output = xnnpack::Buffer<float>(NumElements(output_dims));
 
-  std::iota(input.begin(), input.end(), 0);
+  std::iota(input.begin(), input.end(), 0.0f);
 
   ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
 

test/transpose-operator-tester.h

@@ -144,7 +144,7 @@ class TransposeOperatorTester {
     }
     ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
     xnn_operator_t transpose_op = nullptr;
-    std::iota(input.begin(), input.end(), 0);
+    std::iota(input.begin(), input.end(), 0.0f);
 
     ASSERT_EQ(xnn_status_success,
               xnn_create_transpose_nd_x16(0, &transpose_op));
@@ -185,7 +185,7 @@ class TransposeOperatorTester {
       input_stride[i - 1] = input_stride[i] * shape_[i];
       output_stride[i - 1] = output_stride[i] * shape_[perm()[i]];
     }
-    std::iota(input.begin(), input.end(), 0);
+    std::iota(input.begin(), input.end(), 0.0f);
 
     // Call transpose eager API
     ASSERT_EQ(xnn_status_success,