[Hexagon] F2qi avgpool bug fix (#15599)

F2qi avgpool bug fix

python/tvm/relay/transform/fake_quantization_to_integer.py

@@ -119,6 +119,37 @@ def avgpool2d(expr, type_map):
     t = type_map[arg]
     out_t = type_map[expr]
 
+    # dq > nn.avg_pool2d > q
+    # Use the same input quantization parameters for output if the pattern is not the above.
+    # Type_map is a map of graphs and their Tensoraffinetypes
+    # Find the current "nn.avg_pool2d" op after checking for the "qnn.quantize" op in the graph.
+    # Structure for .. dq > op > q will be q [op [dq ..
+    def check(y, expr):
+        if isinstance(y, type(expr)):
+            if y.op.name != "nn.avg_pool2d":
+                return True
+            # check if this is the expr avg_pool
+            if y.attrs != expr.attrs:
+                return True
+        return False
+
+    for x in type_map.items():
+        if isinstance(x[0], type(expr)):
+            if x[0].op.name == "qnn.quantize":
+                prev = x[0]
+                y = prev.args[0]
+                while check(y, expr):
+                    prev = y
+                    y = prev.args[0]
+                if (
+                    isinstance(y, type(expr))
+                    and y.op.name == "nn.avg_pool2d"
+                    and y.attrs == expr.attrs
+                ):
+                    if prev.op.name != "qnn.quantize":
+                        out_t = t
+                    break
+
     out = relay.qnn.op.avg_pool2d(
         arg,
         t.scale,

python/tvm/topi/hexagon/qnn/avg_pool2d.py

@@ -39,6 +39,14 @@ def saturate(x: te.Tensor, dtype: str):
     return te.max(te.min_value(dtype), te.min(x, te.max_value(dtype)))
 
 
+def get_temp_dtype(h, w, dtype):
+    temp_dtype = "int16" if h * w < 256 else "int32"
+    if dtype in ("uint8", "int8"):
+        return temp_dtype
+    else:
+        raise RuntimeError(f"Unsupported output dtype, {odtype}'")
+
+
 def qnn_avg_pool2d_NCHW(
     data: te.Tensor,
     kernel: list,
@@ -59,12 +67,7 @@ def qnn_avg_pool2d_NCHW(
     rh = te.reduce_axis((0, kh), name="rh")
     rw = te.reduce_axis((0, kw), name="rw")
 
-    if odtype == "uint8":
-        temp_dtype = "uint16"
-    elif odtype == "int8":
-        temp_dtype = "int16"
-    else:
-        raise RuntimeError(f"Unsupported output dtype, {odtype}'")
+    temp_dtype = get_temp_dtype(kh, kw, odtype)
 
     sh, sw = stride
     dh, dw = dilation
@@ -155,12 +158,7 @@ def qnn_avg_pool2d_NHWC(
     rh = te.reduce_axis((0, kh), name="rh")
     rw = te.reduce_axis((0, kw), name="rw")
 
-    if odtype == "uint8":
-        temp_dtype = "uint16"
-    elif odtype == "int8":
-        temp_dtype = "int16"
-    else:
-        raise RuntimeError(f"Unsupported output dtype, {odtype}'")
+    temp_dtype = get_temp_dtype(kh, kw, odtype)
 
     sh, sw = stride
     dh, dw = dilation

tests/python/contrib/test_hexagon/infrastructure.py

@@ -21,6 +21,7 @@
 import numpy
 import tvm
 from tvm import te
+from tvm.relay.backend import Executor
 
 
 def ceildiv(o, d):
@@ -112,6 +113,27 @@ def build_and_run(inputs, func, target: str, target_host: str, *args, **kwargs):
     return tensors[-1].asnumpy()
 
 
+def build_module(relay_mod, target):
+    """builds a relay module for a specified target"""
+    params = {}
+    executor = Executor("aot", {"link-params": True})
+    lowered = tvm.relay.build(
+        relay_mod,
+        tvm.target.Target(target, host=target),
+        executor=executor,
+        params=params,
+    )
+    return lowered
+
+
+def run_module(mod, inputs):
+    """invokes run function of specified module with inputs provided"""
+    mod.set_input(**inputs)
+    mod.run()
+    output = mod.get_output(0).numpy()
+    return output
+
+
 def get_conv2d_nhwc_shape(shape_nhwc, kernel_size, strides, padding, dilation, out_channels):
     assert len(shape_nhwc) == 4
     kernel = []