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[ETHOSN] Per-channel int8 quantization for conv2d #10131

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Merged
manupak merged 1 commit into from
Feb 9, 2022
Merged

[ETHOSN] Per-channel int8 quantization for conv2d #10131

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11 changes: 9 additions & 2 deletions tests/python/contrib/test_ethosn/infrastructure.py
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Original file line number Diff line number Diff line change
Expand Up @@ -316,12 +316,18 @@ def get_conv2d(var, shape, dtype):
def get_conv2d_qnn_params(
dtype, input_zp, input_sc, kernel_zp, kernel_sc, kernel_h, kernel_w, channels
):
kernel_sc = (
kernel_sc.numpy() if isinstance(kernel_sc, tvm.runtime.ndarray.NDArray) else [kernel_sc]
)
dtype_min = np.iinfo(dtype).min
dtype_max = np.iinfo(dtype).max

input_max = input_sc * (dtype_max - input_zp)
input_min = input_sc * (dtype_min - input_zp)
kernel_max = kernel_sc * (dtype_max - kernel_zp)
kernel_min = kernel_sc * (dtype_min - kernel_zp)

kernel_max = max(kernel_sc) * (dtype_max - kernel_zp)
kernel_min = min(kernel_sc) * (dtype_min - kernel_zp)

output_limits = [
kernel_max * kernel_h * kernel_w * channels * input_max,
kernel_min * kernel_h * kernel_w * channels * input_max,
Expand All @@ -330,6 +336,7 @@ def get_conv2d_qnn_params(
]
output_max = max(output_limits)
output_min = min(output_limits)

output_sc = (output_max - output_min) / (dtype_max - dtype_min)
output_zp = int(dtype_min - (output_min / output_sc))
return output_zp, output_sc
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42 changes: 27 additions & 15 deletions tests/python/contrib/test_ethosn/test_conv2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,9 +106,13 @@ def _get_model(
)
biasc = relay.const(b, "int32")
bias = relay.nn.bias_add(conv, biasc, axis=3)
if isinstance(kernel_sc, tvm.runtime.ndarray.NDArray):
req_input_sc = [sc * input_sc for sc in kernel_sc.numpy()]
else:
req_input_sc = input_sc * kernel_sc
req = relay.qnn.op.requantize(
bias,
relay.const(input_sc * kernel_sc, "float32"), # input zero scale
relay.const(req_input_sc, "float32"), # input zero scale
relay.const(0, "int32"), # input zero point
relay.const(output_sc, "float32"), # output zero scale
relay.const(output_zp, "int32"), # output zero point
Expand All @@ -123,22 +127,25 @@ def _get_model(
@pytest.mark.parametrize("dtype", ["uint8", "int8"])
def test_conv2d(dtype, depthwise):
trials = [
[(1, 17, 20, 26), 4, 3, 1, "attr", (2, 2), (1, 1)],
[(1, 30, 27, 30), 5, 5, 3, "none", (1, 1), (1, 1)],
[(1, 14, 28, 11), 6, 2, 2, "op", (2, 2), (1, 1)],
[(1, 9, 20, 30), 7, 1, 5, "none", (1, 1), (1, 1)],
[(1, 21, 21, 22), 8, 5, 1, "attr", (2, 2), (1, 1)],
[(1, 21, 25, 29), 9, 2, 5, "op", (1, 1), (1, 1)],
[(1, 31, 28, 15), 10, 1, 2, "attr", (2, 2), (1, 1)],
[(1, 21, 21, 8), 11, 3, 3, "none", (1, 1), (1, 1)],
[(1, 5, 11, 6), 12, 5, 2, "op", (2, 2), (1, 1)],
[(1, 12, 7, 18), 13, 1, 3, "op", (1, 1), (1, 1)],
[(1, 24, 6, 26), 14, 3, 5, "none", (2, 2), (1, 1)],
[(1, 19, 24, 16), 15, 2, 1, "attr", (1, 1), (1, 1)],
[(1, 17, 20, 26), 4, 3, 1, "attr", (2, 2), (1, 1), False],
[(1, 30, 27, 30), 5, 5, 3, "none", (1, 1), (1, 1), False],
[(1, 30, 27, 30), 5, 5, 3, "none", (1, 1), (1, 1), dtype == "int8"],
[(1, 14, 28, 11), 6, 2, 2, "op", (2, 2), (1, 1), False],
[(1, 9, 20, 30), 7, 1, 5, "none", (1, 1), (1, 1), False],
[(1, 21, 21, 22), 8, 5, 1, "attr", (2, 2), (1, 1), False],
[(1, 21, 21, 22), 8, 5, 1, "attr", (2, 2), (1, 1), dtype == "int8"],
[(1, 21, 25, 29), 9, 2, 5, "op", (1, 1), (1, 1), False],
[(1, 21, 25, 29), 9, 2, 5, "op", (1, 1), (1, 1), dtype == "int8"],
[(1, 31, 28, 15), 10, 1, 2, "attr", (2, 2), (1, 1), False],
[(1, 21, 21, 8), 11, 3, 3, "none", (1, 1), (1, 1), False],
[(1, 5, 11, 6), 12, 5, 2, "op", (2, 2), (1, 1), False],
[(1, 12, 7, 18), 13, 1, 3, "op", (1, 1), (1, 1), False],
[(1, 24, 6, 26), 14, 3, 5, "none", (2, 2), (1, 1), False],
[(1, 19, 24, 16), 15, 2, 1, "attr", (1, 1), (1, 1), False],
]

np.random.seed(0)
for shape, out_channels, kernel_h, kernel_w, pad, stride, dilation in trials:
for shape, out_channels, kernel_h, kernel_w, pad, stride, dilation, qnn_per_channel in trials:
if depthwise:
out_channels = shape[3]
groups = out_channels
Expand All @@ -162,7 +169,12 @@ def test_conv2d(dtype, depthwise):
}
input_zp = np.random.randint(np.iinfo(dtype).min, np.iinfo(dtype).max)
input_sc = np.random.random() * 2
kernel_sc = np.random.random() * 2
if qnn_per_channel:
kernel_sc = tvm.nd.array(
np.random.uniform(low=0, high=2, size=(out_channels,)).astype(np.float32)
)
else:
kernel_sc = np.random.random() * 2
kernel_zp = (
0 if dtype == "int8" else np.random.randint(np.iinfo(dtype).min, np.iinfo(dtype).max)
)
Expand Down