Cortex_M backend: Remove int8 requirement for zero_points

The zero point would sometimes get out of range in the hardswish
decomposition (depending on the range of the quantized output)
as it adjusts the zero point. Since the zero_point is applied in int32
accumulation there is no actual reason for the limitation, so
we can just remove it.

Additionally change the input to linear_hardswish to reliably trigger
the behaviour causing the crash before the fix was introduced to
catch future regressions.

Signed-off-by: Adrian Lundell <[email protected]>
Change-Id: Ia300d57acd2f3e63f49bdf1bc8664da8a3f49bf9

Author: AdrianLundell

backends/cortex_m/ops/cortex_m_ops_common.h

@@ -82,13 +82,6 @@ inline void validate_single_quant_params(
   int64_t mult_val = multiplier.to<int64_t>();
   int64_t shift_val = shift.to<int64_t>();
 
-  ET_CHECK_MSG(
-      zp_val >= std::numeric_limits<int8_t>::min() &&
-          zp_val <= std::numeric_limits<int8_t>::max(),
-      "%s zero point must be in int8 range [Value: %d]",
-      param_name,
-      zp_val);
-
   ET_CHECK_MSG(
       mult_val >= std::numeric_limits<int32_t>::min() &&
           mult_val <= std::numeric_limits<int32_t>::max(),

backends/cortex_m/test/ops/test_activation.py

@@ -485,7 +485,7 @@ def forward(self, x):
     ),
     "linear_hardswish": McuTestCase(
         model=CortexMLinearHardswish(in_features=12, out_features=6),
-        example_inputs=(ramp_tensor(-12, 12, (1, 12)),),
+        example_inputs=(ramp_tensor(-2, 0, (1, 12)),),
     ),
     "conv2d_hardsigmoid_inplace": McuTestCase(
         model=CortexMConv2DHardsigmoid(),