masahi commented on a change in pull request #8883:
URL: https://github.com/apache/tvm/pull/8883#discussion_r699878003
##########
File path: 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 data type of this type */
Review comment:
Needs doc update.
##########
File path: python/tvm/relay/transform/fake_quantization_to_integer.py
##########
@@ -198,19 +219,51 @@ 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)
+ zero = relay.const(0, dtype="float32")
+ if len(scale_shape) > 0 and scale_shape[0] > 1:
+ b_shape = [1] * len(infer_shape(arg))
+ b_shape[t.axis] = -1
+ zero = relay.op.reshape(relay.op.broadcast_to(zero, scale_shape),
b_shape)
+ zero = relay.qnn.op.quantize(zero, t.scale, t.zero_point, t.axis, t.dtype)
Review comment:
Why not simply `t.zero_point`?
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