Add FP requantize flow for llvm target

Author: Icemist

python/tvm/relay/qnn/op/qnn.py

@@ -92,6 +92,44 @@ def requantize(
     )
 
 
+def upward(data):
+    r"""Upward operator.
+
+    UPWARD is the standard rounding except at midpoints where the value
+    is rounded to positive infinity (for example, -1.5 rounds to -1).
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+
+    return _make.upward(data)
+
+
+def tonearest(data):
+    r"""Tonearest operator.
+
+    TONEAREST is the standard rounding where the value is rounded away
+    from zero at midpoints (for example, -1.5 rounds to -2).
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+
+    return _make.tonearest(data)
+
+
 def quantize(data, output_scale, output_zero_point, axis=-1, out_dtype="int8"):
     r"""Quantize op
     This operator takes float32 as input and produces quantized int8 or unit8 as output.

python/tvm/topi/x86/utils.py

@@ -16,8 +16,26 @@
 # under the License.
 """Common x86 related utilities"""
 import tvm
+import tvm._ffi
 
 
+@tvm._ffi.register_func("tvm.topi.x86.utils.target_has_sse41")
+def target_has_sse41(target):
+    return (
+        target_has_sse42(target)
+        or target_has_avx(target)
+        or target_has_avx2(target)
+        or target_has_avx512(target)
+        or target_has_vnni(target)
+        or target
+        in {
+            "btver2",
+            "penryn",
+        }
+    )
+
+
+@tvm._ffi.register_func("tvm.topi.x86.utils.target_has_sse42")
 def target_has_sse42(target):
     return (
         target_has_avx(target)
@@ -42,6 +60,7 @@ def target_has_sse42(target):
     )
 
 
+@tvm._ffi.register_func("tvm.topi.x86.utils.target_has_avx")
 def target_has_avx(target):
     return (
         target_has_avx2(target)
@@ -51,6 +70,7 @@ def target_has_avx(target):
     )
 
 
+@tvm._ffi.register_func("tvm.topi.x86.utils.target_has_avx2")
 def target_has_avx2(target):
     return (
         target_has_avx512(target)
@@ -70,6 +90,7 @@ def target_has_avx2(target):
     )
 
 
+@tvm._ffi.register_func("tvm.topi.x86.utils.target_has_avx512")
 def target_has_avx512(target):
     return target in {
         "skylake-avx512",
@@ -82,26 +103,28 @@ def target_has_avx512(target):
         "cascadelake",
         "icelake-client",
         "rocketlake",
-        "icelake",
+        "icelake-server",
         "tigerlake",
         "cooperlake",
         "sapphirerapids",
     }
 
 
+@tvm._ffi.register_func("tvm.topi.x86.utils.target_has_vnni")
 def target_has_vnni(target):
     return target in {
         "cascadelake",
         "icelake-client",
         "rocketlake",
-        "icelake",
+        "icelake-server",
         "tigerlake",
         "cooperlake",
         "sapphirerapids",
         "alderlake",
     }
 
 
+@tvm._ffi.register_func("tvm.topi.x86.utils.get_simd_32bit_lanes")
 def get_simd_32bit_lanes():
     mcpu = tvm.target.Target.current().mcpu
     fp32_vec_len = 4

src/relay/qnn/op/requantize.cc

@@ -26,6 +26,7 @@
 #include <tvm/relay/op_attr_types.h>
 #include <tvm/relay/qnn/attrs.h>
 
+#include "../../op/op_common.h"
 #include "../../transforms/infer_layout_utils.h"
 #include "../../transforms/pattern_utils.h"
 #include "../utils.h"
@@ -111,6 +112,107 @@ InferCorrectLayoutOutput RequantizeInferCorrectLayout(const Attrs& attrs,
   return InferCorrectLayoutOutput(input_layouts, output_layouts, Attrs(param));
 }
 
+bool has_current_target_sse41_support() {
+  auto target = Target::Current(true);
+  Optional<String> mcpu =
+      target.defined() ? target->GetAttr<String>("mcpu") : Optional<String>(nullptr);
+  auto target_has_sse41_fn_ptr = tvm::runtime::Registry::Get("tvm.topi.x86.utils.target_has_sse41");
+  ICHECK(target_has_sse41_fn_ptr) << "Function tvm.topi.x86.utils.target_has_sse41 not found";
+  return mcpu && (*target_has_sse41_fn_ptr)(mcpu.value());
+}
+
+/*
+ * \brief TONEAREST is the standard rounding where the value is rounded away
+ *         from zero at midpoints (for example, -1.5 rounds to -2).
+ * \param input_tensor The input tensor to rounding op.
+ * \return The sequence of existing Relay ops.
+ */
+Expr Tonearest(const Expr& input_tensor) {
+  if (has_current_target_sse41_support()) return Round(input_tensor);
+
+  auto half = MakeConstantScalar(DataType::Float(64), 0.5f);
+  auto zero = MakeConstantScalar(DataType::Float(64), 0.f);
+  auto pos_one = MakeConstantScalar(DataType::Float(64), +1.f);
+  auto neg_one = MakeConstantScalar(DataType::Float(64), -1.f);
+  auto multiplier = Where(Less(input_tensor, zero), neg_one, pos_one);
+  auto half_multiplied = Multiply(half, multiplier);
+  auto input_tensor_biased = Add(input_tensor, half_multiplied);
+  auto input_tensor_biased_multiplied = Multiply(input_tensor_biased, multiplier);
+  auto input_tensor_biased_multiplied_int64 =
+      Cast(input_tensor_biased_multiplied, DataType::Int(64));
+  auto input_tensor_biased_multiplied_float64 =
+      Cast(input_tensor_biased_multiplied_int64, DataType::Float(64));
+  auto input_tensor_rounded = Multiply(input_tensor_biased_multiplied_float64, multiplier);
+  return Where(IsFinite(input_tensor), input_tensor_rounded, input_tensor);
+}
+
+/*
+ * \brief UPWARD is the standard rounding except at midpoints where the value
+ *        is rounded to positive infinity (for example, -1.5 rounds to -1).
+ * \param input_tensor The input tensor to rounding op.
+ * \return The sequence of existing Relay ops.
+ */
+Expr Upward(const Expr& input_tensor) {
+  auto half = MakeConstantScalar(DataType::Float(64), 0.5f);
+  auto input_tensor_biased = Add(input_tensor, half);
+  if (has_current_target_sse41_support()) return Floor(input_tensor_biased);
+
+  auto zero = MakeConstantScalar(DataType::Float(64), 0.f);
+  auto one = MakeConstantScalar(DataType::Float(64), +1.f);
+  auto input_tensor_biased_int64 = Cast(input_tensor_biased, DataType::Int(64));
+  auto input_tensor_biased_float64 = Cast(input_tensor_biased_int64, DataType::Float(64));
+  auto is_subtraction_not_necessary =
+      LogicalOr(Equal(input_tensor_biased, input_tensor_biased_float64),
+                GreaterEqual(input_tensor_biased, zero));
+  auto input_tensor_rounded = Where(is_subtraction_not_necessary, input_tensor_biased_float64,
+                                    Subtract(input_tensor_biased_float64, one));
+  return Where(IsFinite(input_tensor), input_tensor_rounded, input_tensor);
+}
+
+// Positional relay function to create tonearest operator
+// used by frontend FFI.
+Expr MakeTonearest(const Attrs& attrs, const Array<Expr>& new_args,
+                   const Array<tvm::relay::Type>& types) {
+  ICHECK_EQ(new_args.size(), 1);
+  auto& data = new_args[0];
+  return Tonearest(data);
+}
+
+RELAY_REGISTER_OP("tonearest")
+    .set_num_inputs(1)
+    .add_argument("data", "Tensor", "The input tensor.")
+    .add_type_rel("Identity", IdentityRel)
+    .set_attr<TOpPattern>("TOpPattern", kElemWise)
+    .set_attr<TOpIsStateful>("TOpIsStateful", false)
+    .set_attr<FTVMLegalize>("FTVMQnnCanonicalize", MakeTonearest);
+
+TVM_REGISTER_GLOBAL("relay.qnn.op._make.tonearest").set_body_typed([](Expr data) {
+  static const Op& op = Op::Get("tonearest");
+  return Call(op, {data}, Attrs(), {});
+});
+
+// Positional relay function to create upward operator
+// used by frontend FFI.
+Expr MakeUpward(const Attrs& attrs, const Array<Expr>& new_args,
+                const Array<tvm::relay::Type>& types) {
+  ICHECK_EQ(new_args.size(), 1);
+  auto& data = new_args[0];
+  return Upward(data);
+}
+
+RELAY_REGISTER_OP("upward")
+    .set_num_inputs(1)
+    .add_argument("data", "Tensor", "The input tensor.")
+    .add_type_rel("Identity", IdentityRel)
+    .set_attr<TOpPattern>("TOpPattern", kElemWise)
+    .set_attr<TOpIsStateful>("TOpIsStateful", false)
+    .set_attr<FTVMLegalize>("FTVMQnnCanonicalize", MakeUpward);
+
+TVM_REGISTER_GLOBAL("relay.qnn.op._make.upward").set_body_typed([](Expr data) {
+  static const Op& op = Op::Get("upward");
+  return Call(op, {data}, Attrs(), {});
+});
+
 // Lowering of qnn.requantize op
 
 /*
@@ -119,7 +221,7 @@ InferCorrectLayoutOutput RequantizeInferCorrectLayout(const Attrs& attrs,
  * \param param The requantize op attrs.
  * \param input_shape The input tensor shape of the requantize op.
  * \return The sequence of existing Relay ops.
- * \note Requantization using only integer computation. Here, the computation is
+ * \note RequantizationInt using only integer computation. Here, the computation is
  *       converted to a fixed point computation by computing output multiplier
  *       and shift. This is useful, if the target device does not support/have
  *       very expensive floating point computations.
@@ -131,10 +233,10 @@ InferCorrectLayoutOutput RequantizeInferCorrectLayout(const Attrs& attrs,
  *       4) Add the output zero point.
  *       5) Cast to the out_dtype.
  */
-Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
-                     const Expr& input_zero_point, const Expr& output_scale,
-                     const Expr& output_zero_point, const RequantizeAttrs* param,
-                     const Array<IndexExpr>& input_shape, const DataType& out_dtype) {
+Expr RequantizeLowerInt(const Expr& input_tensor, const Expr& input_scale,
+                        const Expr& input_zero_point, const Expr& output_scale,
+                        const Expr& output_zero_point, const RequantizeAttrs* param,
+                        const Array<IndexExpr>& input_shape, const DataType& out_dtype) {
   auto tensor = Cast(input_tensor, DataType::Int(32));
   auto zero_scalar = MakeConstantScalar(DataType::Int(32), 0);
   if (!IsEqualScalar(input_zero_point, zero_scalar)) {
@@ -208,6 +310,127 @@ Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
   return Cast(clipped_t, out_dtype);
 }
 
+// Lowering of qnn.requantize op
+
+/*
+ * \brief Lower requantize to a sequence of ops.
+ * \param input_tensor The input tensor to requantize op.
+ * \param param The requantize op attrs.
+ * \param input_shape The input tensor shape of the requantize op.
+ * \return The sequence of existing Relay ops.
+ * \note RequantizationFP using floating computation. All multiplication/sub/sum
+ *       occurs in floating point data type and only at the end is converted to
+ *       int32 data type and clamped for output data type.
+ *
+ *       The whole computation this can be broken down into following steps
+ *       1) Subtract the input zero point.
+ *       2) Perform multiplication.
+ *       3) Add the output zero point.
+ *       4) Cast to the out_dtype.
+ */
+Expr RequantizeLowerFP(const Expr& input_tensor, const Expr& input_scale,
+                       const Expr& input_zero_point, const Expr& output_scale,
+                       const Expr& output_zero_point, const RequantizeAttrs* param,
+                       const Array<IndexExpr>& input_shape, const DataType& out_dtype) {
+  auto tensor = Cast(input_tensor, DataType::Float(64));
+  auto zero_scalar = MakeConstantScalar(DataType::Int(32), 0);
+  if (!IsEqualScalar(input_zero_point, zero_scalar)) {
+    // Broadcast input zero point if needed.
+    int rank = static_cast<int>(input_shape.size());
+    int axis = (param->axis < 0) ? ((rank > 0) ? rank + param->axis : 0) : param->axis;
+    Expr input_zero_broadcast = ExpandBiasToMatchAxis(Reshape(input_zero_point,
+                                                              {
+                                                                  -1,
+                                                              }),
+                                                      rank, {axis});
+    tensor = Subtract(tensor, Cast(input_zero_broadcast, DataType::Float(64)));
+  }
+
+  // 2) If the input and output scales are same, we can skip the multiplication. Check
+  // if the input scale is per-tensor or per-channel. If it is per-tensor, there is single scale for
+  // the whole tensor. For per-channel (aka per-axis), there is a vector of scales for the input
+  // tensor. Depending on the quantization type, the fixed point multiplication routing is called.
+  auto scaled_fp64_t = tensor;
+  double output_scale_float = GetScalarFromConstant<float>(output_scale);
+  if (IsConstScalar(input_scale)) {
+    // This is per-tensor quantization. Single scale.
+    double input_scale_float = GetScalarFromConstant<float>(input_scale);
+    double double_multiplier = input_scale_float / output_scale_float;
+    // Skip if input and output scales are same.
+    if (!IsEqualScalar(input_scale, output_scale)) {
+      double multiplier = double_multiplier;
+      auto m_scalar = MakeConstantScalar(DataType::Float(64), multiplier);
+      scaled_fp64_t = Multiply(m_scalar, scaled_fp64_t);
+    }
+
+  } else {
+    // This is per-channel (per=axis) quantization.
+    std::vector<double> double_multipliers;
+    auto input_axis_scales = GetFloatVectorFromConstant(input_scale);
+    double output_scale_float = GetScalarFromConstant<float>(output_scale);
+    for (auto input_axis_scale : input_axis_scales) {
+      double multiplier = static_cast<double>(input_axis_scale) / output_scale_float;
+      double_multipliers.push_back(multiplier);
+    }
+    int axis = param->axis;
+    axis = (axis == -1) ? input_shape.size() - 1 : axis;
+
+    auto fixed_pt_multiplier_expr = MakeConstantTensor(
+        DataType::Float(64), {(int64_t)double_multipliers.size()}, double_multipliers);
+    size_t n_dim = input_shape.size();
+    auto exp_fixed_pt_multiplier_expr =
+        ExpandBiasToMatchAxis(fixed_pt_multiplier_expr, n_dim, {axis});
+
+    scaled_fp64_t = Multiply(scaled_fp64_t, exp_fixed_pt_multiplier_expr);
+  }
+
+  // 3) Add the output zero point.
+  auto shifted_fp64_t = scaled_fp64_t;
+  if (!IsEqualScalar(output_zero_point, zero_scalar)) {
+    shifted_fp64_t = Add(shifted_fp64_t, Cast(output_zero_point, DataType::Float(64)));
+  }
+
+  if (param->rounding == "UPWARD") {
+    shifted_fp64_t = Upward(shifted_fp64_t);
+  } else /*if (param->rounding == "TONEAREST")*/ {
+    shifted_fp64_t = Tonearest(shifted_fp64_t);
+  }
+
+  shifted_fp64_t = Cast(shifted_fp64_t, DataType::Int(32));
+  // 4) Clip to the out_dtype min/max. Skip clipping if out_dtype is Int32. The fixed point
+  // multiplication keeps the value in int32 range.
+  if (out_dtype == DataType::Int(32)) {
+    return shifted_fp64_t;
+  }
+
+  auto q_min = GetQmin(out_dtype);
+  auto q_max = GetQmax(out_dtype);
+  auto clipped_t = Clip(shifted_fp64_t, q_min, q_max);
+  return Cast(clipped_t, out_dtype);
+}
+
+// Lowering of qnn.requantize op
+/*
+ * \brief Lower requantize to a sequence of ops.
+ * \param input_tensor The input tensor to requantize op.
+ * \param param The requantize op attrs.
+ * \param input_shape The input tensor shape of the requantize op.
+ * \return The sequence of existing Relay ops.
+ */
+Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
+                     const Expr& input_zero_point, const Expr& output_scale,
+                     const Expr& output_zero_point, const RequantizeAttrs* param,
+                     const Array<IndexExpr>& input_shape, const DataType& out_dtype) {
+  auto target = Target::Current(true);
+  if (target.defined() && target->kind->name == "llvm") {
+    return RequantizeLowerFP(input_tensor, input_scale, input_zero_point, output_scale,
+                             output_zero_point, param, input_shape, out_dtype);
+  } else {
+    return RequantizeLowerInt(input_tensor, input_scale, input_zero_point, output_scale,
+                              output_zero_point, param, input_shape, out_dtype);
+  }
+}
+
 /*
  * \brief Forward rewrite the requantize op.
  * \param ref_call The original call that will be lowered.