[GitHub] [tvm] ekalda commented on a change in pull request #9910: [microNPU] Add support for requantize

[GitBox <gi...@apache.org>]

ekalda commented on a change in pull request #9910:
URL: https://github.com/apache/tvm/pull/9910#discussion_r788866335



##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -987,5 +990,36 @@ def split_func(x):
     _compare_tvm_with_tflite(split_func, [ifm_shape], accel_type)
 
 
+@pytest.mark.parametrize("accel_type", ACCEL_TYPES)
+@pytest.mark.parametrize(
+    "ifm_shape,ifm_scale,ifm_zp,ofm_scale,ofm_zp",
+    [
+        [(1, 8, 8, 3), 1.0, 0, 1.0, 0],
+        [(1, 20, 30, 3), 1.345, 34, 0.32, -23],
+    ],
+)
+def test_ethosu_requantize(accel_type, ifm_shape, ifm_scale, ifm_zp, ofm_scale, ofm_zp):
+    dtype = "int8"
+    ifm_shape = [1, 8, 8, 3]

Review comment:
       Looks like that `ifm_shape` always overwrites the `ifm_shape` that is specified by the parameter...

##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -949,7 +949,10 @@ def concat_func(*inputs):
         op = tf.concat(list(inputs), axis)
         return op
 
-    _compare_tvm_with_tflite(concat_func, shapes, accel_type)
+    # TODO(lhutton1) For now output is not bit exact with TFLite.
+    # This is because TFLite reference kernels are not being used.
+    # For this, TFLite will need upgrading to 2.6.
+    _compare_tvm_with_tflite(concat_func, shapes, accel_type, output_tolerance=1)

Review comment:
       Thinking about it, I'm quite surprised that previously the concat on U55 + requantize on CPU yielded a bit exact result...

##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -1502,5 +1503,105 @@ def verify(ext_func):
     verify(mod["tvmgen_default_ethos_u_main_0"])
 
 
+@pytest.mark.parametrize(
+    "ifm_shape,ifm_scale,ifm_zp,ofm_scale,ofm_zp",
+    [[(1, 8, 8, 3), 1.0, 0, 1.0, 0], [(1, 20, 30, 3), 1.345, 34, 0.32, -23]],
+)
+def test_ethosu_requantize(ifm_shape, ifm_scale, ifm_zp, ofm_scale, ofm_zp):
+    dtype = "int8"
+    ifm_shape = [1, 8, 8, 3]
+
+    def create_model():
+        ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
+        requantize = relay.qnn.op.requantize(
+            ifm,
+            relay.const(ifm_scale, dtype="float32"),
+            relay.const(ifm_zp, dtype="int32"),
+            relay.const(ofm_scale, dtype="float32"),
+            relay.const(ofm_zp, dtype="int32"),
+        )
+        return tvm.IRModule.from_expr(relay.Function([ifm], requantize))
+
+    def verify(ext_func):
+        op = ext_func.body
+
+        # Check IFM
+        ifm = op.args[0].checked_type
+        assert list(ifm.shape) == list(ifm_shape)
+        assert str(ifm.dtype) == dtype
+
+        # Check OFM
+        ofm = op.checked_type
+        assert list(ofm.shape) == list(ifm_shape)
+        assert str(ofm.dtype) == dtype
+
+        # Check quantization params
+        assert math.isclose(op.attrs.ifm_scale, ifm_scale, abs_tol=1e-7)
+        assert op.attrs.ifm_zero_point == ifm_zp
+        assert math.isclose(op.attrs.ofm_scale, ofm_scale, abs_tol=1e-7)
+        assert op.attrs.ofm_zero_point == ofm_zp
+
+    rewriter = legalize.RequantizeRewriter()
+    pattern_table = [
+        (
+            ethosu.RequantizeParams.composite_name,
+            ethosu.requantize_pattern(),
+            lambda pat: ethosu.RequantizeParams(pat).is_valid(),
+        ),
+    ]
+
+    mod = create_model()
+    mod = partition_ethosu_by_table(mod, pattern_table)
+
+    mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
+        rewriter, mod["tvmgen_default_ethos_u_main_0"]
+    )
+    verify(mod["tvmgen_default_ethos_u_main_0"])
+
+
+def test_multiple_requantize_offload():
+    """
+    Testing requantize offload in the case one requauntize operation is part of

Review comment:
       ```suggestion
       Testing requantize offload in the case one requantize operation is part of
   ```




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