[Relay][Quantization] Per-Channel FQ2I (apache#8883)

* WIP support per-channel quantization

* more WIP

* More WIP

* fix issue with per-channel bias_add

* Fix fake quantize tests (#4)

* Fixed fake quantize issues.

* Formatting.

* Cleanup unused imports

* Fix real int8 tests.

* Add Relu

* One more little one (#5)

* Fixed fake quantize issues.

* Formatting.

* Cleanup unused imports

* Fix real int8 tests.

* Fix requantize shape bug.

* Non-working Per-channel Dense

* Fix legalization for non spatial operators. (#6)

* Fix legalization for non spatial operators.

* Fix axis checks for end2end functionality.

* fix axis normalization

fix lint

fix lint again

* Per channel fq2i (#8)

* WIP support per-channel quantization

* more WIP

* More WIP

* fix issue with per-channel bias_add

* Fix fake quantize tests (#4)

* Fixed fake quantize issues.

* Formatting.

* Cleanup unused imports

* Fix real int8 tests.

* Add Relu

* One more little one (#5)

* Fixed fake quantize issues.

* Formatting.

* Cleanup unused imports

* Fix real int8 tests.

* Fix requantize shape bug.

* Non-working Per-channel Dense

* Fix legalization for non spatial operators. (#6)

* Fix legalization for non spatial operators.

* Fix axis checks for end2end functionality.

* fix axis normalization

fix lint

fix lint again

* Fix bug in requantize dimension expansion.

* Format.

Co-authored-by: Josh Fromm <jwfromm@octoml.ai>

* respond to review comments

respond to review comments

Co-authored-by: Josh Fromm <jwfromm@octoml.ai>

include/tvm/ir/affine_type.h

@@ -71,24 +71,28 @@ class TensorAffineTypeNode : public AffineTypeNode {
   RelayExpr zero_point;
   /*! \brief The data type of this type */
   DataType dtype;
+  /*! \brief The axis for per-channel quantization */
+  int axis;
 
   void VisitAttrs(tvm::AttrVisitor* v) {
     v->Visit("scale", &scale);
     v->Visit("zero_point", &zero_point);
     v->Visit("dtype", &dtype);
+    v->Visit("axis", &axis);
   }
 
   bool SEqualReduce(const TensorAffineTypeNode* other, SEqualReducer equal) const {
     equal->MarkGraphNode();
     return equal(scale, other->scale) && equal(zero_point, other->zero_point) &&
-           equal(dtype, other->dtype);
+           equal(dtype, other->dtype) && equal(axis, other->axis);
   }
 
   void SHashReduce(SHashReducer hash_reduce) const {
     hash_reduce->MarkGraphNode();
     hash_reduce(scale);
     hash_reduce(zero_point);
     hash_reduce(dtype);
+    hash_reduce(axis);
   }
 
   static constexpr const char* _type_key = "TensorAffineType";
@@ -101,7 +105,7 @@ class TensorAffineTypeNode : public AffineTypeNode {
  */
 class TensorAffineType : public AffineType {
  public:
-  TVM_DLL TensorAffineType(RelayExpr scale, RelayExpr zero_point, DataType dtype);
+  TVM_DLL TensorAffineType(RelayExpr scale, RelayExpr zero_point, DataType dtype, int axis);
 
   TVM_DEFINE_OBJECT_REF_METHODS(TensorAffineType, AffineType, TensorAffineTypeNode);
 };

python/tvm/ir/affine_type.py

@@ -48,10 +48,15 @@ class TensorAffineType(AffineType):
 
     dtype : str
         The content data type.
+
+    axis : int
+        The axis for per-channel quantization.
     """
 
-    def __init__(self, scale, zero_point, dtype):
-        self.__init_handle_by_constructor__(_ffi_api.TensorAffineType, scale, zero_point, dtype)
+    def __init__(self, scale, zero_point, dtype, axis=-1):
+        self.__init_handle_by_constructor__(
+            _ffi_api.TensorAffineType, scale, zero_point, dtype, axis
+        )
 
 
 @tvm._ffi.register_object("TupleAffineType")

python/tvm/relay/frontend/onnx.py

@@ -490,7 +490,7 @@ def _impl_v1(cls, inputs, attr, params):
                 attr["dilations"] = [1] + list(attr["dilations"])
             if "pads" in attr:
                 attr["pads"] = [0, attr["pads"][0], 0, attr["pads"][1]]
-
+        attr["channels"] = kernel_shapes[0][0]
         out = AttrCvt(
             op_name=dimension_picker("conv"),
             transforms={

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

@@ -20,6 +20,7 @@
 
 import tvm
 from tvm import relay
+from tvm._ffi.base import TVMError
 from .. import op as reg
 
 #################################################
@@ -139,11 +140,35 @@ def helper_no_fast_int8_hw_legalization(attrs, inputs, types, relay_op):
     data, kernel, input_zero_point, kernel_zero_point, _, _ = inputs
 
     shift_data = relay.subtract(
-        relay.cast(data, dtype="int16"), relay.cast(input_zero_point, "int16")
-    )
-    shift_kernel = relay.subtract(
-        relay.cast(kernel, dtype="int16"), relay.cast(kernel_zero_point, "int16")
+        relay.cast(data, dtype="int16"), relay.cast(input_zero_point, dtype="int16")
     )
+    # If kernel zero point is a scalar we can directly subtract it.
+    if len(types[3].shape) == 0:
+        shift_kernel = relay.subtract(
+            relay.cast(kernel, dtype="int16"), relay.cast(kernel_zero_point, dtype="int16")
+        )
+    # Otherwise it needs to be broadcast.
+    else:
+        # Determine output axis of kernel for spatial operations.
+        if hasattr(attrs, "kernel_layout"):
+            output_axis = tvm.tir.layout(attrs["kernel_layout"]).index_of("O")
+        # For dense operations, broadcast to [N, K] layout.
+        elif isinstance(attrs, relay.op.op_attrs.DenseAttrs):
+            output_axis = 0
+        # For matrix multiplication instead expand to [K, N] layout.
+        elif isinstance(attrs, relay.op.op_attrs.MatmulAttrs):
+            output_axis = 1
+        else:
+            raise TVMError(
+                "Legalization of %s is not yet supported with per channel parameters"
+                % str(type(attrs))
+            )
+
+        shift_kernel = relay.nn.bias_add(
+            relay.cast(kernel, dtype="int16"),
+            relay.cast(kernel_zero_point, dtype="int16"),
+            output_axis,
+        )
     new_attrs = {k: attrs[k] for k in attrs.keys()}
     return relay_op(shift_data, shift_kernel, **new_attrs)
 

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

@@ -276,8 +276,10 @@ def conv2d(
 ):
     r"""Quantized 2D convolution.
 
-    This operator convolves quantized data with quantized kernel. The scale of
-    the output quantized tensor is the product of the kernel_scale and
+    This operator convolves quantized data with quantized kernel.
+    If doing Per-channel quantization, qnn expects the kernel_zero_scale
+    and optionally the kernel_zero_point will be 1-D vectors instead of scalars.
+    The scale of the output quantized tensor is the product of the kernel_scale and
     input_scale of the input quantized tensors. The zero point of the output
     quantized tensor is 0. By default, the dtype of output is int32. Please also
     refer to Requantize operator to understand how to scale back the int32
@@ -544,6 +546,9 @@ def dense(
 
      `Y = X * W`
 
+    If doing Per-channel quantization, qnn expects the kernel_zero_scale
+    and optionally the kernel_zero_point will be 1-D vectors instead of scalars.
+
     Parameters
     ----------
     data : tvm.relay.Expr

python/tvm/relay/transform/fake_quantization_to_integer.py

@@ -18,13 +18,22 @@
 import tvm
 from tvm import relay
 from tvm.ir import TensorAffineType, TupleAffineType
+from tvm.tir import bijective_layout
 from ..op import register_fake_quantization_to_integer
 
 
 def fold_constant(expr):
     return relay.transform.FoldConstantExpr(expr, tvm.IRModule())
 
 
+def get_zeros(scale):
+    return fold_constant(relay.op.cast(relay.op.zeros_like(scale), "int32"))
+
+
+def infer_shape(expr):
+    return relay.transform.InferType()(tvm.IRModule.from_expr(expr))["main"].body.checked_type.shape
+
+
 @register_fake_quantization_to_integer("qnn.dequantize")
 def dequantize(expr, type_map):
     """Remove dequantize op"""
@@ -52,8 +61,13 @@ def quantize(expr, type_map):
             expr.args[1],
             expr.args[2],
             out_dtype=expr.attrs.out_dtype,
+            axis=t.axis,
         )
-    return [out, TensorAffineType(expr.args[1], expr.args[2], expr.attrs.out_dtype)]
+
+    return [
+        out,
+        TensorAffineType(expr.args[1], expr.args[2], expr.attrs.out_dtype, expr.attrs.axis),
+    ]
 
 
 def register_unary_identity(op_name):
@@ -94,14 +108,19 @@ def bias_add(expr, type_map):
     b_t = type_map[b]
     in_scale = fold_constant(x_t.scale)
     in_zero_point = fold_constant(x_t.zero_point)
-    if not tvm.ir.structural_equal(x_t, b_t):
+    if not (
+        tvm.ir.structural_equal(x_t.scale, b_t.scale)
+        and tvm.ir.structural_equal(x_t.zero_point, b_t.zero_point)
+        and tvm.ir.structural_equal(x_t.dtype, b_t.dtype)
+    ):
         b = relay.qnn.op.requantize(
             b,
             b_t.scale,
             b_t.zero_point,
             in_scale,
             in_zero_point,
             out_dtype=x_t.dtype,
+            axis=0,
         )
     out = relay.op.nn.bias_add(x, b, **expr.attrs)
     return [out, x_t]
@@ -116,11 +135,13 @@ def conv2d(expr, type_map):
     x_t = type_map[x]
     w_t = type_map[weight]
     conv_scale = fold_constant(x_t.scale * w_t.scale)
-    conv_zp = relay.const(0)
+    conv_zp = get_zeros(conv_scale)
     out = relay.qnn.op.conv2d(
         x, weight, x_t.zero_point, w_t.zero_point, x_t.scale, w_t.scale, **attrs
     )
-    return [out, TensorAffineType(conv_scale, conv_zp, out.attrs.out_dtype)]
+    out_layout = attrs["out_layout"] if attrs["out_layout"] != "" else attrs["data_layout"]
+    out_axis = bijective_layout(out_layout, "NCHW").backward_index(list(range(4)))[1]
+    return [out, TensorAffineType(conv_scale, conv_zp, out.attrs.out_dtype, out_axis.value)]
 
 
 @register_fake_quantization_to_integer("nn.dense")
@@ -132,11 +153,11 @@ def dense(expr, type_map):
     x_t = type_map[x]
     w_t = type_map[weight]
     dense_scale = fold_constant(x_t.scale * w_t.scale)
-    dense_zp = relay.const(0)
+    dense_zp = get_zeros(dense_scale)
     out = relay.qnn.op.dense(
         x, weight, x_t.zero_point, w_t.zero_point, x_t.scale, w_t.scale, **attrs
     )
-    return [out, TensorAffineType(dense_scale, dense_zp, out.attrs.out_dtype)]
+    return [out, TensorAffineType(dense_scale, dense_zp, out.attrs.out_dtype, 1)]
 
 
 @register_fake_quantization_to_integer("nn.batch_matmul")
@@ -148,7 +169,7 @@ def batch_matmul(expr, type_map):
     matmul_scale = fold_constant(x_t.scale * y_t.scale)
     matmul_zp = relay.const(0)
     out = relay.qnn.op.batch_matmul(x, y, x_t.zero_point, y_t.zero_point, x_t.scale, y_t.scale)
-    return [out, TensorAffineType(matmul_scale, matmul_zp, out.attrs.out_dtype)]
+    return [out, TensorAffineType(matmul_scale, matmul_zp, out.attrs.out_dtype, x_t.axis)]
 
 
 @register_fake_quantization_to_integer("concatenate")
@@ -198,19 +219,52 @@ def clip(expr, type_map):
     amax = expr.attrs.a_max
     scale = fold_constant(t.scale)
     z_p = fold_constant(t.zero_point)
-    if isinstance(scale, relay.expr.Constant) and isinstance(z_p, relay.expr.Constant):
+    if (
+        isinstance(scale, relay.expr.Constant)
+        and scale.data.numpy().size == 1
+        and isinstance(z_p, relay.expr.Constant)
+        and z_p.data.numpy().size == 1
+    ):
         scale = scale.data.numpy().item()
         z_p = z_p.data.numpy().item()
         new_min = int(amin / scale + z_p)
         new_max = int(amax / scale + z_p)
         out = relay.op.clip(arg, new_min, new_max)
     else:
-        amin = relay.op.round(relay.op.const(amin) / scale + z_p)
-        amax = relay.op.round(relay.op.const(amax) / scale + z_p)
-        out = relay.op.minimum(relay.op.maximum(arg, amin), amax)
+        if not isinstance(amin, relay.expr.Constant):
+            amin = relay.op.const(amin)
+        if not isinstance(amax, relay.expr.Constant):
+            amax = relay.op.const(amax)
+
+        scale_shape = infer_shape(scale)
+        if len(scale_shape) > 0 and scale_shape[0] > 1:
+            b_shape = [1] * len(infer_shape(arg))
+            b_shape[t.axis] = -1
+            amin = relay.op.reshape(relay.op.broadcast_to(amin, scale_shape), b_shape)
+            amax = relay.op.reshape(relay.op.broadcast_to(amax, scale_shape), b_shape)
+        amin = relay.qnn.op.quantize(amin, scale, z_p, t.axis, t.dtype)
+        amax = relay.qnn.op.quantize(amax, scale, z_p, t.axis, t.dtype)
+        out = relay.op.minimum(relay.op.maximum(arg, fold_constant(amin)), fold_constant(amax))
+
     return [out, t]
 
 
+@register_fake_quantization_to_integer("nn.relu")
+def relu(expr, type_map):
+    """Rewrite a relu op"""
+    arg = expr.args[0]
+    t = type_map[arg]
+    scale_shape = infer_shape(t.scale)
+    z_p = t.zero_point
+    assert len(scale_shape) <= 1
+    if len(scale_shape) == 1 and scale_shape[0] > 1:
+        b_shape = [1] * len(infer_shape(arg))
+        b_shape[t.axis] = -1
+        z_p = relay.op.reshape(relay.op.broadcast_to(z_p, scale_shape), b_shape)
+    zero = relay.op.cast(z_p, t.dtype)
+    return [relay.op.maximum(arg, fold_constant(zero)), t]
+
+
 @register_fake_quantization_to_integer("nn.pad")
 def pad(expr, type_map):
     """Rewite an nn.pad op"""
@@ -231,6 +285,7 @@ def pad(expr, type_map):
                 t.scale,
                 t.zero_point,
                 out_dtype=t.dtype,
+                axis=pad_t.axis,
             )
     else:
         ## If the pad-value is a constant, we need to quantize it
@@ -319,6 +374,7 @@ def binary(expr, type_map):
                 out_t.scale,
                 out_t.zero_point,
                 out_dtype=out_t.dtype,
+                axis=left_t.axis,
             )
 
         if right_t != out_t:
@@ -329,6 +385,7 @@ def binary(expr, type_map):
                 out_t.scale,
                 out_t.zero_point,
                 out_dtype=out_t.dtype,
+                axis=right_t.axis,
             )
         out = op(left, right)
         return [out, out_t]

src/ir/affine_type.cc

@@ -30,26 +30,28 @@ namespace tvm {
 using tvm::ReprPrinter;
 using namespace tvm::runtime;
 
-TensorAffineType::TensorAffineType(RelayExpr scale, RelayExpr zero_point, DataType dtype) {
+TensorAffineType::TensorAffineType(RelayExpr scale, RelayExpr zero_point, DataType dtype,
+                                   int axis) {
   ObjectPtr<TensorAffineTypeNode> n = make_object<TensorAffineTypeNode>();
   n->scale = std::move(scale);
   n->zero_point = std::move(zero_point);
   n->dtype = std::move(dtype);
+  n->axis = std::move(axis);
   data_ = std::move(n);
 }
 
 TVM_REGISTER_NODE_TYPE(TensorAffineTypeNode);
 
 TVM_REGISTER_GLOBAL("ir.TensorAffineType")
-    .set_body_typed([](RelayExpr scale, RelayExpr zero_point, DataType dtype) {
-      return TensorAffineType(scale, zero_point, dtype);
+    .set_body_typed([](RelayExpr scale, RelayExpr zero_point, DataType dtype, int axis) {
+      return TensorAffineType(scale, zero_point, dtype, axis);
     });
 
 TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
     .set_dispatch<TensorAffineTypeNode>([](const ObjectRef& ref, ReprPrinter* p) {
       auto* node = static_cast<const TensorAffineTypeNode*>(ref.get());
       p->stream << "TensorAffineType(" << node->scale << ", " << node->zero_point << ", "
-                << node->dtype << ")";
+                << node->dtype << ", " << node->axis << ")";
     });
 
 TupleAffineType::TupleAffineType(Array<TensorAffineType> types) {

src/relay/qnn/op/convolution.cc

@@ -495,7 +495,7 @@ Expr Conv2DSecondTerm(const Expr& padded_data, const Expr& kernel_zero_point,
  * \param input_zero_point The input zero point expr.
  * \param param The qnn conv2d attributes.
  * \param out_channels The number of output channels.
- * \return The sequence of Relay operatos for term3.
+ * \return The sequence of Relay operators for term3.
  * \note The term3 looks like this
  *
  *       Sigma(c,r,s) zp_a * QW(k, c, r, s)
@@ -625,7 +625,7 @@ Expr Conv2DCombineTerms(const Expr& term1, const Expr& term2, const Expr& term3,
  * \node Lowering of the qnn.conv2d operator
  *       A quantized tensor is represented in following manner
  *          A = scale_a x (QA - zp_A)
- *       where QA is quantized tensor, scale_a and zp_A are quantizations
+ *       where QA is quantized tensor, scale_a and zp_A are quantization
  *       params.
  *
  *       Quantized convolution will convolve two quantized tensors and returns a
@@ -662,8 +662,8 @@ Expr Conv2DCombineTerms(const Expr& term1, const Expr& term2, const Expr& term3,
  *         a workaround, we fall back to simpler lowering using int32 conv if
  *         the conv is dilated. We fallback also in case of grouped conv.
  *
- *       For depthwise, we can similarly unroll the computation. The intial compute is as follows
- *       wehere cm = channel_multiplier
+ *       For depthwise, we can similarly unroll the computation. The initial compute is as follows
+ *       where cm = channel_multiplier
  *
  *       Qc(n, oc, oh, ow) = Sigma(r, s) (Qw(oc/m, oc%/m, r, s) - zp_w)
  *                                     * (Qa(n, oc/cm, oh + r, ow + s) - zp_a)
@@ -693,12 +693,13 @@ Expr QnnConv2DCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
   Expr kernel_zero_point = new_args[3];
   const auto* param = attrs.as<Conv2DAttrs>();
   ICHECK(param != nullptr);
-  // Assertion checks for exisiing support.
+  // Assertion checks for existing support.
   ICHECK(param->data_layout == "NCHW" || param->data_layout == "NHWC")
       << "qnn.conv2d supports only NCHW/NHWC input data layout.";
   ICHECK(param->kernel_layout == "OIHW" || param->kernel_layout == "HWIO" ||
          param->kernel_layout == "HWOI")
       << "qnn.conv2d supports only OIHW/HWIO/HWOI kernel data layout.";
+  ICHECK(param->kernel_size.defined()) << "qnn.conv2d requires kernel size to be specified.";
 
   int batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier;
   std::tie(batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier) =