AndrewZhaoLuo commented on a change in pull request #10718:
URL: https://github.com/apache/tvm/pull/10718#discussion_r836712968
##########
File path: tests/python/relay/test_pass_fake_quantization_to_integer.py
##########
@@ -600,13 +600,97 @@ def test_fake_quantize_binary(operator):
compare_fq_to_int(op, [x_np, y_np])
[email protected](
+ "operator",
+ [relay.op.add, relay.op.multiply, relay.op.subtract, relay.op.minimum,
relay.op.maximum],
+)
+def test_fake_quantize_binary_per_channel(operator):
+ def verify_binary_per_channel(lhs_scale, rhs_scale, lhs_zp, rhs_zp,
out_zp, lhs_axis, rhs_axis):
+ if operator == relay.op.multiply:
+ out_scale = relay.const(2.0)
+ rhs_axis = lhs_axis # TODO: Support different axes for
per-channel quantized multiply
+ else:
+ out_scale = relay.const(0.1)
+
+ x = relay.var("x", shape=[1, 3, 224, 224], dtype="int8")
+ x = relay.qnn.op.dequantize(x, relay.const(lhs_scale),
relay.const(lhs_zp), axis=lhs_axis)
+
+ y = relay.var("y", shape=[1, 3, 224, 224], dtype="int8")
+ y = relay.qnn.op.dequantize(y, relay.const(rhs_scale),
relay.const(rhs_zp), axis=rhs_axis)
+
+ op = operator(x, y)
+
+ op = relay.qnn.op.quantize(op, out_scale, relay.const(out_zp),
out_dtype="int8")
+ x_np = np.random.randint(-25, 25, size=[1, 3, 224, 224], dtype="int8")
+ y_np = np.random.randint(-25, 25, size=[1, 3, 224, 224], dtype="int8")
+
+ compare_fq_to_int(op, [x_np, y_np], allow_rounding_error=True)
+
+ # Same axis
+ verify_binary_per_channel(
+ lhs_scale=np.random.uniform(1.0, 5.0, 3),
+ rhs_scale=np.random.uniform(1.0, 5.0, 3),
+ lhs_zp=0,
+ rhs_zp=0,
+ out_zp=0,
+ lhs_axis=1,
+ rhs_axis=1,
+ )
+ verify_binary_per_channel(
+ lhs_scale=np.random.uniform(1.0, 5.0, 3),
+ rhs_scale=np.random.uniform(1.0, 5.0, 3),
+ lhs_zp=np.random.randint(1, 3),
+ rhs_zp=np.random.randint(1, 3),
+ out_zp=0,
+ lhs_axis=1,
+ rhs_axis=1,
+ )
+ verify_binary_per_channel(
+ lhs_scale=np.random.uniform(1.0, 5.0, 3),
+ rhs_scale=np.random.uniform(1.0, 5.0, 3),
+ lhs_zp=np.random.randint(1, 3),
+ rhs_zp=np.random.randint(1, 3),
+ out_zp=np.random.randint(1, 3),
+ lhs_axis=1,
+ rhs_axis=1,
+ )
+
+ # Different axes
+ verify_binary_per_channel(
+ lhs_scale=np.random.uniform(1.0, 5.0, 224),
+ rhs_scale=np.random.uniform(1.0, 5.0, 224),
+ lhs_zp=0,
+ rhs_zp=0,
+ out_zp=0,
+ lhs_axis=2,
+ rhs_axis=3,
+ )
+ verify_binary_per_channel(
+ lhs_scale=np.random.uniform(1.0, 5.0, 224),
+ rhs_scale=np.random.uniform(1.0, 5.0, 224),
+ lhs_zp=np.random.randint(1, 3),
+ rhs_zp=np.random.randint(1, 3),
+ out_zp=0,
+ lhs_axis=2,
+ rhs_axis=3,
+ )
+ verify_binary_per_channel(
+ lhs_scale=np.random.uniform(1.0, 5.0, 224),
+ rhs_scale=np.random.uniform(1.0, 5.0, 224),
+ lhs_zp=np.random.randint(1, 3),
+ rhs_zp=np.random.randint(1, 3),
+ out_zp=np.random.randint(1, 3),
+ lhs_axis=2,
+ rhs_axis=3,
+ )
+
+
Review comment:
Ah nevermind, I seem to have misread, the example has rank = 4, input
tensor is always [1, 3, 224, 224] which is sufficient
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