anijain2305 commented on a change in pull request #6782:
URL: https://github.com/apache/incubator-tvm/pull/6782#discussion_r513777765
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File path: python/tvm/relay/frontend/qnn_torch.py
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@@ -826,6 +826,74 @@ def _impl(inputs, _):
return _impl
+def _linear_dynamic():
+ def _calculate_qparam(inp):
+ # reference ATen/native/quantized/cpu/qlinear_dynamic.cpp
+ # ChooseQuantizationParams function
+ mn = _op.min(inp)
+ mx = _op.max(inp)
+
+ # Ensure that the interval contains 0
+ mn = _op.minimum(mn, _op.const(0.0, dtype="float32"))
+ mx = _op.maximum(mx, _op.const(0.0, dtype="float32"))
+
+ qmax = 255
+
+ # reduce_range became True in v1.6
+ if is_version_greater_than("1.5.1"):
+ qmax = 127
+
+ scale = (mx - mn) / _expr.const(qmax, dtype="float32")
+
+ zero_point_from_min = -(mn / scale)
+ zero_point = _op.cast(_op.round(_op.clip(zero_point_from_min, 0.0,
qmax)), "int32")
+
+ return scale, zero_point
+
+ def _impl(inputs, _):
+ weight = inputs[1][0]
+ weight_scale = inputs[1][1]
+ weight_zero_point = inputs[1][2]
+
+ inp = inputs[0]
+
+ input_scale, input_zero_point = _calculate_qparam(inp)
+ qinp = relay.qnn.op.quantize(inp, input_scale, input_zero_point,
out_dtype="uint8")
+
+ data_shape = infer_shape(inp)
+
+ if len(data_shape) > 2:
+ qinp = _op.reverse_reshape(qinp, [-1, 0])
+
+ weight_shape = infer_shape(weight)
+ units = weight_shape[0]
+ dense = relay.qnn.op.dense(
+ qinp,
+ weight,
+ input_zero_point,
+ weight_zero_point,
+ input_scale,
+ weight_scale,
+ units=units,
+ )
+ bias_var = inputs[1][3]
+
+ dequant_scale = input_scale * weight_scale
+ dense_out = _op.cast(dense, "float32") * dequant_scale
Review comment:
First readability, since we are working with qnn ops, one can easily
understand that we are going from int to float domain just by seeing dequantize
op.
Second, dequantize becomes one place where we handle per-tensor and
per-channel quantization scales and non-zero zero points. Internally,
dequantize will lower to the same exact operations that you have currently in
the parser. In future, when we support per-channel scales for dense, we can
just rely on dequantize op.
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