DRAFT CFe fuse Mul Add to Fullyconnected

compiler/luci-pass-value-py-test/test.lst

@@ -2,13 +2,14 @@
 # Format:
 #   eval(MODEL PASS)
 # MODEL: tflite model file name in build/compiler/common-artifacts folder.
-# PASS: Optimization Pass to test. Supports only one Pass for now.
+# PASS: Optimization Pass to test. Supports one or Passes.
 #
 
 # eval(Net_Preactivation_BN_000 fuse_preactivation_batchnorm) : value diff exist
 # --> https://github.com/Samsung/ONE/issues/5782
 eval(FullyConnected_007 replace_non_const_fc_with_batch_matmul)
 eval(HardSwish_001 decompose_hardswish)
+eval(Mul_002 fold_mul)
 eval(Net_Add_FloorMod_Gather_000 remove_gather_guard)
 eval(Net_Add_FullyConnected_000 fuse_add_to_fullyconnected_bias)
 eval(Net_Add_FullyConnected_001 fuse_add_to_fullyconnected_bias)

compiler/luci-pass-value-py-test/test_luci_eval.py

@@ -95,8 +95,13 @@ def luci_eval_verify(test_name,
             assert np.allclose(
                 luci_output_data, intp_output_data, rtol=rtolint, atol=atolint), err_msg
         elif output_details["dtype"] == np.float32:
-            assert np.allclose(
-                luci_output_data, intp_output_data, rtol=rtolf32, atol=atolf32), err_msg
+            diff_comp = np.allclose(
+                luci_output_data, intp_output_data, rtol=rtolf32, atol=atolf32)
+            if not diff_comp:
+                print("\r\ntflite:\r\n", intp_output_data, flush=True)
+                print("\r\ncircle:\r\n", luci_output_data, flush=True)
+                print("\r\nDiff:\r\n", intp_output_data - luci_output_data, flush=True)
+                assert diff_comp, err_msg
         elif output_details["dtype"] == np.int64:
             assert np.allclose(
                 luci_output_data, intp_output_data, rtol=rtolint, atol=atolint), err_msg

res/TensorFlowLiteRecipes/Mul_002/test.recipe

@@ -0,0 +1,35 @@
+operand {
+  name: "ifm1"
+  type: FLOAT32
+  shape { dim: 3 dim: 1 dim: 5 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ifm2"
+  type: FLOAT32
+  shape { dim: 5 }
+  filler {
+    tag: "gaussian"
+    arg: "0.0"
+    arg: "1.0"
+  }
+}
+operand {
+  name: "ofm"
+  type: FLOAT32
+  shape { dim: 3 dim: 1 dim: 5 }
+}
+operation {
+  type: "Mul"
+  input: "ifm1"
+  input: "ifm2"
+  output: "ofm"
+  mul_options {
+    activation: NONE
+  }
+}
+output: "ofm"