def test_with_placeholder(): link = chainer.links.DilatedConvolution2D(None, 16, ksize=3, stride=1, pad=1, dilate=2) vx = chainer.Variable(np.random.rand(1, 3, 16, 16).astype(np.float32)) vy = link(vx) N = Placeholder(label="N") H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([N, 3, H, W]) py = link(px) graph = ChainerConverter().convert([px], [py]) x = graph.inputs[0] y = graph.outputs[0] N.value = 1 H.value = 16 W.value = 16 generate_kernel_test_case( description=f"[chainer] L.DilatedConvolution2D with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={x: vx.data}, expected={y: vy.data}, EPS=1e-2 )
def test_with_placeholder(): vx1 = chainer.Variable(np.random.rand(2, 10, 4, 5).astype(np.float32)) vx2 = chainer.Variable(np.random.rand(2, 15, 4, 5).astype(np.float32)) vy = chainer.functions.concat([vx1, vx2], axis=1) N = Placeholder(label="N") C1 = Placeholder(label="C1") C2 = Placeholder(label="C2") H = Placeholder(label="H") W = Placeholder(label="W") px1 = PlaceholderVariable([N, C1, H, W]) px2 = PlaceholderVariable([N, C2, H, W]) py = chainer.functions.concat([px1, px2], axis=1) graph = ChainerConverter().convert([px1, px2], [py]) N.value = 2 C1.value = 10 C2.value = 15 H.value = 4 W.value = 5 generate_kernel_test_case( description=f"[chainer] F.concat with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={ graph.inputs[0]: vx1.data, graph.inputs[1]: vx2.data }, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): vx1 = chainer.Variable(np.random.rand(10, 12).astype(np.float32) * 2 - 1) vx2 = chainer.Variable(np.random.rand(12, 14).astype(np.float32) * 2 - 1) vy = chainer.functions.matmul(vx1, vx2, False, False) M = Placeholder(label="M") K = Placeholder(label="K") N = Placeholder(label="N") px1 = PlaceholderVariable([M, K]) px2 = PlaceholderVariable([K, N]) py = chainer.functions.matmul(px1, px2, False, False) graph = ChainerConverter().convert([px1, px2], [py]) M.value = 10 K.value = 12 N.value = 14 generate_kernel_test_case( description=f"[chainer] F.matmul with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={ graph.inputs[0]: vx1.data, graph.inputs[1]: vx2.data }, expected={graph.outputs[0]: vy.data})
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(1, 3, 16, 16).astype(np.float32)) vy = chainer.functions.local_response_normalization(vx) N = Placeholder(label="N") C = Placeholder(label="C") H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([N, C, H, W]) py = chainer.functions.local_response_normalization(px) graph = ChainerConverter().convert([px], [py]) x = graph.inputs[0] y = graph.outputs[0] N.value = 1 C.value = 3 H.value = 16 W.value = 16 generate_kernel_test_case( description= f"[chainer] F.local_response_normalization with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={x: vx.data}, expected={y: vy.data}, )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(2, 20, 4, 5).astype(np.float32)) vy1, vy2, vy3 = chainer.functions.split_axis(vx, [5, 15], 1) N = Placeholder(label="N") H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([N, 20, H, W]) py1, py2, py3 = chainer.functions.split_axis(px, [5, 15], 1) graph = ChainerConverter().convert([px], [py1, py2, py3]) N.value = 2 H.value = 4 W.value = 5 generate_kernel_test_case( description=f"[chainer] F.split_axis with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={ graph.outputs[0]: vy1.data, graph.outputs[1]: vy2.data, graph.outputs[2]: vy3.data }, )
def test_with_placeholder(): link = chainer.links.BatchNormalization(size=3) vx = chainer.Variable(np.random.rand(1, 3, 16, 16).astype(np.float32)) with chainer.using_config('train', False): vy = link(vx) N = Placeholder(label="N") H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([N, 3, H, W]) with chainer.using_config('train', False): py = link(px) graph = ChainerConverter().convert([px], [py]) x = graph.inputs[0] y = graph.outputs[0] N.value = 1 H.value = 16 W.value = 16 generate_kernel_test_case( description=f"[chainer] L.FixedBatchNormalization with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={x: vx.data}, expected={y: vy.data}, )
def test_with_placeholder(): vx0 = chainer.Variable(np.random.rand(10, 11, 12).astype(np.float32)) vx1 = chainer.Variable(np.random.rand(10, 11, 12).astype(np.float32)) vy = chainer.functions.minimum(vx0, vx1) A = Placeholder(label="A") B = Placeholder(label="B") C = Placeholder(label="C") px0 = PlaceholderVariable([A, B, C]) px1 = PlaceholderVariable([A, B, C]) py = chainer.functions.minimum(px0, px1) graph = ChainerConverter().convert([px0, px1], [py]) A.value = 10 B.value = 11 C.value = 12 generate_kernel_test_case( description=f"[chainer] F.minimum with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={ graph.inputs[0]: vx0.data, graph.inputs[1]: vx1.data }, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(2, 3, 4, 5).astype(np.float32)) vy = chainer.functions.expand_dims(vx, axis=1) N = Placeholder(label="N") C = Placeholder(label="C") H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([N, C, H, W]) py = chainer.functions.expand_dims(px, axis=1) graph = ChainerConverter().convert([px], [py]) x = graph.inputs[0] y = graph.outputs[0] N.value = 2 C.value = 3 H.value = 4 W.value = 5 generate_kernel_test_case( description=f"[chainer] F.expand_dims with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={x: vx.data}, expected={y: vy.data}, )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(2, 5, 4, 8).astype(np.float32)) vy = chainer.functions.space2depth(vx, r=2) N = Placeholder(label="N") C = Placeholder(label="C") px = PlaceholderVariable([N, C, 4, 8]) py = chainer.functions.space2depth(px, r=2) graph = ChainerConverter().convert([px], [py]) N.value = 2 C.value = 5 generate_kernel_test_case( description=f"[chainer] F.space2depth with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(10, 11, 12).astype(np.float32)) vy = chainer.functions.logsumexp(vx, axis=1) A = Placeholder(label="A") C = Placeholder(label="C") px = PlaceholderVariable([A, 11, C]) py = chainer.functions.logsumexp(px, axis=1) graph = ChainerConverter().convert([px], [py]) A.value = 10 C.value = 12 generate_kernel_test_case( description=f"[chainer] F.logsumexp with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data}, EPS=1e-2)
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(1, 3, 16, 16).astype(np.float32)) vy = vx ** 2 H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([1, 3, H, W]) py = px ** 2 graph = ChainerConverter().convert([px], [py]) H.value = 16 W.value = 16 generate_kernel_test_case( description=f"[chainer] F.PowVarConst with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data} )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(2, 16, 7, 7).astype(np.float32)) vy = chainer.functions.unpooling_2d(vx, ksize=3, stride=2, pad=0) N = Placeholder(label="N") C = Placeholder(label="C") px = PlaceholderVariable([N, C, 7, 7]) py = chainer.functions.unpooling_2d(px, ksize=3, stride=2, pad=0) graph = ChainerConverter().convert([px], [py]) N.value = 2 C.value = 16 generate_kernel_test_case( description=f"[chainer] F.unpooling_2d with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): vx1 = chainer.Variable(np.random.rand(2, 8).astype(np.float32)) vx2 = chainer.Variable(np.random.rand(8, 6).astype(np.float32)) vy = vx1 @ vx2 M = Placeholder(label="M") N = Placeholder(label="N") px1 = PlaceholderVariable([M, 8]) px2 = PlaceholderVariable([8, N]) py = px1 @ px2 graph = ChainerConverter().convert([px1, px2], [py]) M.value = 2 N.value = 6 generate_kernel_test_case( description=f"[chainer] F.MatMulVarVar with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx1.data, graph.inputs[1]: vx2.data}, expected={graph.outputs[0]: vy.data} )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(10, 11, 12).astype(np.float32)) vy = chainer.functions.softplus(vx, beta=1.0) A = Placeholder(label="A") B = Placeholder(label="B") C = Placeholder(label="C") px = PlaceholderVariable([A, B, C]) py = chainer.functions.softplus(px, beta=1.0) graph = ChainerConverter().convert([px], [py]) A.value = 10 B.value = 11 C.value = 12 generate_kernel_test_case( description=f"[chainer] F.softplus with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): vx = chainer.Variable(np.random.rand(2, 1, 4, 1).astype(np.float32)) vy = chainer.functions.squeeze(vx, axis=None) N = Placeholder(label="N") C = Placeholder(label="C") H = Placeholder(label="H") W = Placeholder(label="W") px = PlaceholderVariable([N, C, H, W]) py = chainer.functions.squeeze(px, axis=None) graph = ChainerConverter().convert([px], [py]) N.value = 2 C.value = 1 H.value = 4 W.value = 1 generate_kernel_test_case( description=f"[chainer] F.squeeze with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): link = chainer.links.Linear(16, 32) vx = chainer.Variable(np.random.rand(2, 16).astype(np.float32)) vy = link(vx) N = Placeholder(label="N") px = PlaceholderVariable([N, 16]) py = link(px) graph = ChainerConverter().convert([px], [py]) N.value = 2 generate_kernel_test_case( description=f"[chainer] L.Linear with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={graph.inputs[0]: vx.data}, expected={graph.outputs[0]: vy.data}, )
def test_with_placeholder(): rhs = np.random.rand(8, 4) vx = chainer.Variable(np.random.rand(2, 8).astype(np.float32)) vy = vx @ rhs M = Placeholder(label="M") px = PlaceholderVariable([M, 8]) py = px @ rhs graph = ChainerConverter().convert([px], [py]) x = graph.inputs[0] y = graph.outputs[0] M.value = 2 generate_kernel_test_case( description=f"[chainer] F.MatMulVarConstant with placeholder", graph=graph, backend=["webgpu", "webassembly"], inputs={x: vx.data}, expected={y: vy.data}, )