masahi commented on a change in pull request #4418: [RUNTIME] Add cudnn conv3d
URL: https://github.com/apache/incubator-tvm/pull/4418#discussion_r351924652
##########
File path: tests/python/contrib/test_cudnn.py
##########
@@ -37,52 +38,127 @@ def verify_conv2d(data_dtype, conv_dtype,
tensor_format=0):
if not tvm.module.enabled("cuda"):
print("skip because cuda is not enabled...")
return
- if not tvm.get_global_func("tvm.contrib.cudnn.conv2d.output_shape", True):
+ if not tvm.get_global_func("tvm.contrib.cudnn.conv.output_shape", True):
print("skip because cudnn is not enabled...")
return
-
- xshape = [batch, in_channel, height, weight]
- wshape = cudnn.conv2d_w_shape(in_channel,
- out_channel,
- filter_h,
- filter_w)
+ if tensor_format == 0:
+ xshape = [batch, in_channel, height, weight]
+ wshape = [out_channel, in_channel, filter_h, filter_w]
+ else:
+ xshape = [batch, height, weight, in_channel]
+ wshape = [out_channel, filter_h, filter_w, in_channel]
X = tvm.placeholder(xshape, name='X', dtype=data_dtype)
W = tvm.placeholder(wshape, name='W', dtype=data_dtype)
- Y = cudnn.conv2d_forward(X,
- W,
- stride_h,
- stride_w,
- pad_h,
- pad_w,
- dilation_h,
- dilation_w,
- conv_mode=1,
- tensor_format=tensor_format,
- conv_dtype=conv_dtype,
- algo=-1)
+ Y = cudnn.conv_forward(X,
+ W,
+ 2,
+ [pad_h, pad_w],
+ [stride_h, stride_w],
+ [dilation_h, dilation_w],
+ conv_mode=1,
+ tensor_format=tensor_format,
+ conv_dtype=conv_dtype,
+ algo=-1)
yshape = [x.value for x in Y.shape]
s = tvm.create_schedule(Y.op)
def verify():
ctx = tvm.gpu(0)
f = tvm.build(s, [X, W, Y], "cuda", target_host="llvm", name="conv2d")
- x = tvm.nd.array(np.random.uniform(-1, 1, xshape).astype(data_dtype),
- ctx)
- w = tvm.nd.array(np.random.uniform(-1, 1, wshape).astype(data_dtype),
- ctx)
- y = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(data_dtype),
- ctx)
+ x_np = np.random.uniform(-1, 1, xshape).astype(data_dtype)
+ w_np = np.random.uniform(-1, 1, wshape).astype(data_dtype)
+ y_np = np.zeros(yshape).astype(data_dtype)
+ x = tvm.nd.array(x_np, ctx)
+ w = tvm.nd.array(w_np, ctx)
+ y = tvm.nd.array(y_np, ctx)
+ if tensor_format == 0:
+ c_np = topi.testing.conv2d_nchw_python(x_np, w_np, 1, 1)
+ elif tensor_format == 1:
+ wt = w_np.transpose((1, 2, 3, 0)) #OHWI => HWIO
+ c_np = topi.testing.conv2d_nhwc_python(x_np, wt, 1, 1)
+
f(x, w, y)
+ tvm.testing.assert_allclose(y.asnumpy(), c_np, atol=1e-5, rtol=1e-3)
verify()
def test_conv2d():
verify_conv2d("float32", "float32", tensor_format=0)
verify_conv2d("float16", "float32", tensor_format=1)
- verify_conv2d("float16", "float16", tensor_format=0)
+ #Not pass accuracy test, need check
+ #verify_conv2d("float16", "float16", tensor_format=0)
verify_conv2d("int8", "int32", tensor_format=1)
+def verify_conv3d(data_dtype, conv_dtype, tensor_format=0):
+ in_channel = 4
+ out_channel = 16
+ filter_d = 3
+ filter_h = 3
+ filter_w = 3
+ pad_d = 1
+ pad_h = 1
+ pad_w = 1
+ stride_d = 1
+ stride_h = 1
+ stride_w = 1
+ dilation_d = 1
+ dilation_h = 1
+ dilation_w = 1
+ batch = 3
+ depth = 32
+ height = 32
+ weight = 32
Review comment:
Clean these up using list
Also do the same for conv2d test.
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