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new e51ba294d9 [ACL] Prevent offloading of per-channel quantized operators
(#14484)
e51ba294d9 is described below
commit e51ba294d99c93ccbed064ed1fe35b90b1195140
Author: Nicola Lancellotti <[email protected]>
AuthorDate: Wed Apr 5 15:32:46 2023 +0100
[ACL] Prevent offloading of per-channel quantized operators (#14484)
Currently, only per-layer quantization is supported in the
Arm Compute Library runtime. However, there is no check that
prevents the offloading of per-channel quantized operators,
as a consequence, tvm fails during inference if such operators
are found.
---
python/tvm/relay/op/contrib/arm_compute_lib.py | 24 ++++++++++-
.../contrib/test_arm_compute_lib/test_add.py | 31 +++++++++++++-
.../contrib/test_arm_compute_lib/test_conv2d.py | 50 ++++++++++++++++++++++
.../contrib/test_arm_compute_lib/test_dense.py | 43 +++++++++++++++++++
4 files changed, 146 insertions(+), 2 deletions(-)
diff --git a/python/tvm/relay/op/contrib/arm_compute_lib.py
b/python/tvm/relay/op/contrib/arm_compute_lib.py
index 1b9abb0948..6b8d000c66 100644
--- a/python/tvm/relay/op/contrib/arm_compute_lib.py
+++ b/python/tvm/relay/op/contrib/arm_compute_lib.py
@@ -359,6 +359,10 @@ def qnn_conv2d(expr):
kernel_typ = args[1].checked_type
if len(kernel_typ.shape) != 4 or kernel_typ.dtype not in qnn_dtypes:
return False
+ if is_per_channel_quantization(
+ zero_point=args[2], scale=args[4]
+ ) or is_per_channel_quantization(zero_point=args[3], scale=args[5]):
+ return False
is_depthwise = is_depthwise_conv2d(
data_typ.shape,
attrs["data_layout"],
@@ -422,6 +426,10 @@ def qnn_dense(expr):
return False
if attrs.out_dtype != "int32":
return False
+ if is_per_channel_quantization(
+ zero_point=args[2], scale=args[4]
+ ) or is_per_channel_quantization(zero_point=args[3], scale=args[5]):
+ return False
return True
@@ -514,10 +522,24 @@ def qnn_add(expr):
for typ in [args[0].checked_type, args[1].checked_type]:
if typ.dtype not in ["int8", "uint8"]:
return False
-
+ if (
+ is_per_channel_quantization(zero_point=args[3], scale=args[2])
+ or is_per_channel_quantization(zero_point=args[5], scale=args[4])
+ or is_per_channel_quantization(zero_point=args[7], scale=args[6])
+ ):
+ return False
return True
+def is_per_channel_quantization(zero_point, scale):
+ """Check if the quantization is per-channel"""
+ for value in [zero_point, scale]:
+ shape = value.checked_type.shape
+ if len(shape) != 0 and shape[0] != 1:
+ return True
+ return False
+
+
class OpAttrContext(object):
"""Temporarily changes the attr of an op."""
diff --git a/tests/python/contrib/test_arm_compute_lib/test_add.py
b/tests/python/contrib/test_arm_compute_lib/test_add.py
index ee6fcf603c..319105bb5f 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_add.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_add.py
@@ -17,6 +17,7 @@
"""Arm Compute Library integration reshape tests."""
import numpy as np
+import pytest
import tvm
import tvm.testing
@@ -134,6 +135,34 @@ def test_codegen_add():
verify_codegen(func, exp_codegen, 1)
[email protected](
+ "param, param_type",
+ [
+ ("lhs_scale", "float32"),
+ ("lhs_zero_point", "int32"),
+ ("rhs_scale", "float32"),
+ ("rhs_zero_point", "int32"),
+ ],
+)
+def test_codegen_add_per_channel_quantization(param, param_type):
+ if skip_codegen_test():
+ return
+
+ qnn_params = _qnn_params
+ qnn_params[param] = relay.const([1, 2], param_type)
+
+ dtype = "int8"
+ op_name = "qnn.add"
+ op = relay.qnn.op.add
+ inputs = {"a", "b"}
+
+ for shape in [(1, 3, 3, 2)]:
+ func = _get_model(shape, dtype, iter(inputs), op, qnn_params)
+ exp_codegen = _get_expected_codegen(shape, dtype, op_name, qnn_params)
+ verify_codegen(func, exp_codegen, num_acl_modules=0, tvm_ops=1)
+
+
if __name__ == "__main__":
- test_codegen_add()
test_runtime_add()
+ test_codegen_add()
+ test_codegen_add_per_channel_quantization()
diff --git a/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
b/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
index df708020bf..b4fa49ffa2 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
@@ -615,8 +615,58 @@ def test_codegen_qnn_conv2d(trial, dtype):
verify_codegen(func, exp_codegen, 1)
[email protected](
+ "param",
+ ["kernel_sc", "kernel_zp"],
+)
+def test_codegen_qnn_conv2d_per_channel_quantization(param):
+ if skip_codegen_test():
+ return
+
+ dtype = "int8"
+ kernel_h = 2
+ kernel_w = 2
+ pad = (1, 1)
+ stride = (1, 1)
+ dilation = (1, 1)
+ out_channels = 4
+ shape = (1, 10, 10, 14)
+ composite = (False, False, False)
+ groups = 1
+ inputs = {"a"}
+
+ qnn_params = {
+ "input_zp": 1,
+ "input_sc": 1,
+ "kernel_zp": 1,
+ "kernel_sc": 1,
+ "output_zp": 1,
+ "output_sc": 1,
+ }
+ qnn_params[param] = [1, 1, 1, 1]
+
+ args = (shape, kernel_h, kernel_w, pad, stride, dilation, groups, dtype,
out_channels)
+
+ func, params = _get_qnn_model(
+ *args,
+ input_zp=qnn_params["input_zp"],
+ input_sc=qnn_params["input_sc"],
+ kernel_zp=qnn_params["kernel_zp"],
+ kernel_sc=qnn_params["kernel_sc"],
+ output_zp=qnn_params["output_zp"],
+ output_sc=qnn_params["output_sc"],
+ var_names=iter(inputs),
+ has_pad=composite[0],
+ has_bias=composite[1],
+ has_activation=composite[2],
+ )
+ exp_codegen = _get_expected_codegen(*args, has_bias=composite[1],
has_activation=composite[2])
+ verify_codegen(func, exp_codegen, num_acl_modules=0, tvm_ops=2)
+
+
if __name__ == "__main__":
test_conv2d()
test_qnn_conv2d()
test_codegen_conv2d()
test_codegen_qnn_conv2d()
+ test_codegen_qnn_conv2d_per_channel_quantization()
diff --git a/tests/python/contrib/test_arm_compute_lib/test_dense.py
b/tests/python/contrib/test_arm_compute_lib/test_dense.py
index bbcfc4abe6..411f790f34 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_dense.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_dense.py
@@ -380,8 +380,51 @@ def test_codegen_qnn_dense(dtype):
verify_codegen(func, exp_codegen)
[email protected](
+ "param",
+ ["kernel_sc", "kernel_zp"],
+)
+def test_codegen_qnn_dense_per_channel_quantization(param):
+ if skip_codegen_test():
+ return
+
+ np.random.seed(0)
+ dtype = "int8"
+ shape = (1, 2)
+ weight_shape = (2, 2)
+ units = 2
+ composite = True
+ inputs = {"a"}
+ args = (shape, weight_shape, units, dtype)
+
+ qnn_params = {
+ "input_zp": 1,
+ "input_sc": 1,
+ "kernel_zp": 1,
+ "kernel_sc": 1,
+ "output_zp": 1,
+ "output_sc": 1,
+ }
+ qnn_params[param] = [1, 1]
+
+ func, _ = _get_qnn_model(
+ *args,
+ var_names=iter(inputs),
+ input_zp=qnn_params["input_zp"],
+ input_sc=qnn_params["input_sc"],
+ kernel_zp=qnn_params["kernel_zp"],
+ kernel_sc=qnn_params["kernel_sc"],
+ output_zp=qnn_params["output_zp"],
+ output_sc=qnn_params["output_sc"],
+ has_bias=composite,
+ )
+ exp_codegen = _get_expected_codegen(*args, has_bias=composite)
+ verify_codegen(func, exp_codegen, num_acl_modules=0, tvm_ops=3)
+
+
if __name__ == "__main__":
test_dense()
test_qnn_dense()
test_codegen_dense()
test_codegen_qnn_dense()
+ test_codegen_qnn_dense_per_channel_quantization()
