[GitHub] [incubator-tvm] zhiics commented on a change in pull request #6509: [Relay]Allow dynamic batch for arm conv2d

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

zhiics commented on a change in pull request #6509:
URL: https://github.com/apache/incubator-tvm/pull/6509#discussion_r490578631



##########
File path: tests/python/relay/test_any.py
##########
@@ -41,6 +41,7 @@ def check_result(
 ):
     for kind in ["debug", "vm"]:
         targets = targets or tvm.testing.enabled_targets()
+        print(targets)

Review comment:
       remove

##########
File path: tests/python/relay/test_any.py
##########
@@ -423,6 +424,48 @@ def test_any_reshape_like():
     check_result([data_np, shape_like_np], mod, shape_like_np.shape, assert_shape=True)
 
 
+def verify_any_conv2d(
+    data_shape,
+    kernel_shape,
+    strides,
+    padding,
+    dilation,
+    static_data_shape,
+    ref_out_shape,
+):
+    mod = tvm.IRModule()
+    dtype = "float32"
+    data = relay.var("data", shape=data_shape, dtype=dtype)
+    kernel = relay.var("kernel", shape=kernel_shape, dtype=dtype)
+    y = relay.nn.conv2d(data, kernel, strides, padding, dilation, kernel_size=kernel_shape[2:4])
+    mod["main"] = relay.Function([data, kernel], y)
+    data_np = np.random.uniform(size=static_data_shape).astype(dtype)
+    kernel_np = np.random.uniform(size=kernel_shape).astype(dtype)
+    check_result([data_np, kernel_np], mod, ref_out_shape, assert_shape=True)
+
+
+# TODO(@kevinthesun): Support dynamic input height and width.

Review comment:
       decorate gpu?

##########
File path: python/tvm/topi/arm_cpu/conv2d_transpose.py
##########
@@ -84,7 +89,8 @@ def _decl_spatial_pack(
     data_pad = pad(dilated_input, [0, 0, bpad_top, bpad_left], [0, 0, bpad_bottom, bpad_right])
 
     # ==================== define configuration space ====================
-    n, co, oh, ow = cfg.axis(N), cfg.axis(CO), cfg.axis(OH), cfg.axis(OW)
+    n_tuning_axis = N if isinstance(N, int) else 1

Review comment:
       ditto

##########
File path: python/tvm/topi/arm_cpu/conv2d_spatial_pack.py
##########
@@ -54,7 +58,8 @@ def conv2d_spatial_pack_nchw(cfg, data, kernel, strides, padding, dilation, out_
     data_pad = nn.pad(data, [0, 0, pad_top, pad_left], [0, 0, pad_bottom, pad_right])
 
     # ==================== define configuration space ====================
-    n, co, oh, ow = cfg.axis(N), cfg.axis(CO), cfg.axis(OH), cfg.axis(OW)
+    n_tuning_axis = N if isinstance(N, int) else 1

Review comment:
       should we add a TODO for symbolic kernel tuning?




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