.. DO NOT EDIT. THIS FILE WAS AUTOMATICALLY GENERATED BY .. TVM'S MONKEY-PATCHED VERSION OF SPHINX-GALLERY. TO MAKE .. CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorial/intro_topi.py" .. only:: html .. note:: :class: sphx-glr-download-link-note This tutorial can be used interactively with Google Colab! You can also click :ref:`here ` to run the Jupyter notebook locally. .. image:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg :align: center :target: https://colab.research.google.com/github/apache/tvm-site/blob/asf-site/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb :width: 300px .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorial_intro_topi.py: .. _tutorial-topi: Introduction to TOPI ==================== **Author**: `Ehsan M. Kermani `_ This is an introductory tutorial to TVM Operator Inventory (TOPI). TOPI provides numpy-style generic operations and schedules with higher abstractions than TVM. In this tutorial, we will see how TOPI can save us from writing boilerplate code in TVM. .. GENERATED FROM PYTHON SOURCE LINES 28-35 .. code-block:: default import tvm import tvm.testing from tvm import te from tvm import topi import numpy as np .. GENERATED FROM PYTHON SOURCE LINES 39-45 Basic example ------------- Let's revisit the sum of rows operation (equivalent to :code:`B = numpy.sum(A, axis=1)`') \ To compute the sum of rows of a two dimensional TVM tensor A, we should specify the symbolic operation as well as schedule as follows .. GENERATED FROM PYTHON SOURCE LINES 45-52 .. code-block:: default n = te.var("n") m = te.var("m") A = te.placeholder((n, m), name="A") k = te.reduce_axis((0, m), "k") B = te.compute((n,), lambda i: te.sum(A[i, k], axis=k), name="B") s = te.create_schedule(B.op) .. GENERATED FROM PYTHON SOURCE LINES 53-55 and to examine the IR code in human readable format, we can do .. GENERATED FROM PYTHON SOURCE LINES 55-57 .. code-block:: default print(tvm.lower(s, [A], simple_mode=True)) .. rst-class:: sphx-glr-script-out .. code-block:: none # from tvm.script import ir as I # from tvm.script import tir as T @I.ir_module class Module: @T.prim_func def main(A: T.handle): T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)}) n, m = T.int32(), T.int32() A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto") B = T.allocate([n], "float32", "global") for i in range(n): B_1 = T.Buffer((n,), data=B) B_1[i] = T.float32(0) for k in range(m): A_2 = T.Buffer((A_1.strides[0] * n,), data=A_1.data, buffer_type="auto") B_1[i] = B_1[i] + A_2[i * A_1.strides[0] + k * A_1.strides[1]] .. GENERATED FROM PYTHON SOURCE LINES 58-62 However, for such a common operation we had to define the reduce axis ourselves as well as explicit computation with :code:`te.compute`. Imagine for more complicated operations how much details we need to provide. Fortunately, we can replace those two lines with simple :code:`topi.sum` much like :code:`numpy.sum` .. GENERATED FROM PYTHON SOURCE LINES 62-66 .. code-block:: default C = topi.sum(A, axis=1) ts = te.create_schedule(C.op) print(tvm.lower(ts, [A], simple_mode=True)) .. rst-class:: sphx-glr-script-out .. code-block:: none # from tvm.script import ir as I # from tvm.script import tir as T @I.ir_module class Module: @T.prim_func def main(A: T.handle): T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)}) n, m = T.int32(), T.int32() A_1 = T.match_buffer(A, (n, m), strides=("stride", "stride"), buffer_type="auto") A_red = T.allocate([n], "float32", "global") for ax0 in range(n): A_red_1 = T.Buffer((n,), data=A_red) A_red_1[ax0] = T.float32(0) for k1 in range(m): A_2 = T.Buffer((A_1.strides[0] * n,), data=A_1.data, buffer_type="auto") A_red_1[ax0] = A_red_1[ax0] + A_2[ax0 * A_1.strides[0] + k1 * A_1.strides[1]] .. GENERATED FROM PYTHON SOURCE LINES 67-72 Numpy-style operator overloading -------------------------------- We can add two tensors using :code:`topi.broadcast_add` that have correct (broadcastable with specific) shapes. Even shorter, TOPI provides operator overloading for such common operations. For example, .. GENERATED FROM PYTHON SOURCE LINES 72-78 .. code-block:: default x, y = 100, 10 a = te.placeholder((x, y, y), name="a") b = te.placeholder((y, y), name="b") c = a + b # same as topi.broadcast_add d = a * b # same as topi.broadcast_mul .. GENERATED FROM PYTHON SOURCE LINES 79-80 Overloaded with the same syntax, TOPI handles broadcasting a primitive (`int`, `float`) to a tensor :code:`d - 3.14`. .. GENERATED FROM PYTHON SOURCE LINES 82-90 Generic schedules and fusing operations --------------------------------------- Up to now, we have seen an example of how TOPI can save us from writing explicit computations in lower level API. But it doesn't stop here. Still we did the scheduling as before. TOPI also provides higher level scheduling recipes depending on a given context. For example, for CUDA, we can schedule the following series of operations ending with :code:`topi.sum` using only :code:`topi.generic.schedule_reduce` .. GENERATED FROM PYTHON SOURCE LINES 90-97 .. code-block:: default e = topi.elemwise_sum([c, d]) f = e / 2.0 g = topi.sum(f) with tvm.target.cuda(): sg = topi.cuda.schedule_reduce(g) print(tvm.lower(sg, [a, b], simple_mode=True)) .. rst-class:: sphx-glr-script-out .. code-block:: none /workspace/python/tvm/target/target.py:446: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target. warnings.warn("Try specifying cuda arch by adding 'arch=sm_xx' to your target.") # from tvm.script import ir as I # from tvm.script import tir as T @I.ir_module class Module: @T.prim_func def main(a: T.Buffer((100, 10, 10), "float32"), b: T.Buffer((10, 10), "float32")): T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)}) T_divide_red = T.allocate([1], "float32", "global") threadIdx_x = T.launch_thread("threadIdx.x", 1024) T_divide_red_rf = T.allocate([1], "float32", "local") reduce_temp0 = T.allocate([1], "float32", "local") T_divide_red_rf_1 = T.Buffer((1,), data=T_divide_red_rf, scope="local", align=4) T_divide_red_rf_1[0] = T.float32(0) for k0_k1_fused_k2_fused_outer in range(10): if T.likely(k0_k1_fused_k2_fused_outer * 64 + threadIdx_x // 16 < 625 and k0_k1_fused_k2_fused_outer * 64 + threadIdx_x // 16 < 625 and k0_k1_fused_k2_fused_outer * 64 + threadIdx_x // 16 < 625): a_1 = T.Buffer((10000,), data=a.data) b_1 = T.Buffer((100,), data=b.data) T_divide_red_rf_1[0] = T_divide_red_rf_1[0] + (a_1[k0_k1_fused_k2_fused_outer * 1024 + threadIdx_x] + b_1[(k0_k1_fused_k2_fused_outer * 24 + threadIdx_x) % 100] + a_1[k0_k1_fused_k2_fused_outer * 1024 + threadIdx_x] * b_1[(k0_k1_fused_k2_fused_outer * 24 + threadIdx_x) % 100]) * T.float32(0.5) reduce_temp0_1 = T.Buffer((1,), data=reduce_temp0, scope="local") with T.attr(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))): T.tvm_thread_allreduce(T.uint32(1), T_divide_red_rf_1[0], T.bool(True), reduce_temp0_1[0], threadIdx_x) if threadIdx_x == 0: T_divide_red_1 = T.Buffer((1,), data=T_divide_red, align=4) T_divide_red_1[0] = reduce_temp0_1[0] .. GENERATED FROM PYTHON SOURCE LINES 98-100 As you can see, scheduled stages of computation have been accumulated and we can examine them by .. GENERATED FROM PYTHON SOURCE LINES 100-102 .. code-block:: default print(sg.stages) .. rst-class:: sphx-glr-script-out .. code-block:: none [stage(a, placeholder(a, 0xc96eef0)), stage(b, placeholder(b, 0x24dd1450)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_elemwise_sum, compute(T_elemwise_sum, body=[T_add[ax0, ax1, ax2] + T_multiply[ax0, ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide, compute(T_divide, body=[T_elemwise_sum[ax0, ax1, ax2] / T.float32(2)], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide_red.rf, compute(T_divide_red.rf, body=[T.reduce(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), source=[T_divide[(k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 // 10, (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 % 10, (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) % 10]], init=[], axis=[T.iter_var(k0_k1_fused_k2_fused_outer, T.Range(0, 10), "CommReduce", "")], condition=T.likely((k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 // 10 < 100 and (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 < 1000 and k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024 < 10000), value_index=0)], axis=[T.iter_var(k0_k1_fused_k2_fused_inner, T.Range(0, 1024), "DataPar", "")], reduce_axis=[T.iter_var(k0_k1_fused_k2_fused_outer, T.Range(0, 10), "CommReduce", "")], tag=, attrs={})), stage(T_divide_red, compute(T_divide_red.repl, body=[T.reduce(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), source=[T_divide_red.rf[k0_k1_fused_k2_fused_inner_v]], init=[], axis=[T.iter_var(k0_k1_fused_k2_fused_inner_v, T.Range(0, 1024), "CommReduce", "")], condition=T.bool(True), value_index=0)], axis=[], reduce_axis=[T.iter_var(k0_k1_fused_k2_fused_inner_v, T.Range(0, 1024), "CommReduce", "")], tag=, attrs={}))] .. GENERATED FROM PYTHON SOURCE LINES 103-105 We can test the correctness by comparing with :code:`numpy` result as follows .. GENERATED FROM PYTHON SOURCE LINES 105-116 .. code-block:: default func = tvm.build(sg, [a, b, g], "cuda") dev = tvm.cuda(0) a_np = np.random.uniform(size=(x, y, y)).astype(a.dtype) b_np = np.random.uniform(size=(y, y)).astype(b.dtype) g_np = np.sum(np.add(a_np + b_np, a_np * b_np) / 2.0) a_nd = tvm.nd.array(a_np, dev) b_nd = tvm.nd.array(b_np, dev) g_nd = tvm.nd.array(np.zeros(g_np.shape, dtype=g_np.dtype), dev) func(a_nd, b_nd, g_nd) tvm.testing.assert_allclose(g_nd.numpy(), g_np, rtol=1e-5) .. GENERATED FROM PYTHON SOURCE LINES 117-119 TOPI also provides common neural nets operations such as _softmax_ with optimized schedule .. GENERATED FROM PYTHON SOURCE LINES 119-125 .. code-block:: default tarray = te.placeholder((512, 512), name="tarray") softmax_topi = topi.nn.softmax(tarray) with tvm.target.Target("cuda"): sst = topi.cuda.schedule_softmax(softmax_topi) print(tvm.lower(sst, [tarray], simple_mode=True)) .. rst-class:: sphx-glr-script-out .. code-block:: none # from tvm.script import ir as I # from tvm.script import tir as T @I.ir_module class Module: @T.prim_func def main(tarray: T.Buffer((512, 512), "float32")): T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)}) T_softmax_norm = T.allocate([65536], "float32x4", "global") blockIdx_x = T.launch_thread("blockIdx.x", 512) normal_reduce_temp0 = T.allocate([1], "float32", "local") reduce_temp0 = T.allocate([1], "float32", "local") T_softmax_exp = T.allocate([512], "float32", "warp") normal_reduce_temp0_1 = T.allocate([1], "float32", "local") reduce_temp0_1 = T.allocate([1], "float32", "local") threadIdx_x = T.env_thread("threadIdx.x") T_softmax_exp_1 = T.Buffer((512,), data=T_softmax_exp, scope="warp") with T.launch_thread(threadIdx_x, 32): normal_reduce_temp0_2 = T.Buffer((1,), data=normal_reduce_temp0, scope="local") normal_reduce_temp0_2[0] = T.float32(-3.4028234663852886e+38) tarray_1 = T.Buffer((262144,), data=tarray.data) for k_inner in range(16): normal_reduce_temp0_2[0] = T.max(normal_reduce_temp0_2[0], tarray_1[blockIdx_x * 512 + threadIdx_x * 16 + k_inner]) with T.attr(T.comm_reducer(lambda x, y: T.max(x, y), [T.float32(-3.4028234663852886e+38)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))): reduce_temp0_2 = T.Buffer((1,), data=reduce_temp0, scope="local") T.tvm_thread_allreduce(T.uint32(1), normal_reduce_temp0_2[0], T.bool(True), reduce_temp0_2[0], threadIdx_x) for i1_inner_outer in range(4): cse_var_1: T.int32 = i1_inner_outer * 4 reduce_temp0_2 = T.Buffer((1,), data=reduce_temp0, scope="local", align=4) T_softmax_exp_1[threadIdx_x * 16 + cse_var_1:threadIdx_x * 16 + cse_var_1 + 4] = T.exp(tarray_1[blockIdx_x * 512 + threadIdx_x * 16 + cse_var_1:blockIdx_x * 512 + threadIdx_x * 16 + cse_var_1 + 4] - T.Broadcast(reduce_temp0_2[0], 4)) T.launch_thread(threadIdx_x, 32) normal_reduce_temp0_2 = T.Buffer((1,), data=normal_reduce_temp0_1, scope="local") normal_reduce_temp0_2[0] = T.float32(0) for k_inner in range(16): normal_reduce_temp0_2[0] = normal_reduce_temp0_2[0] + T_softmax_exp_1[threadIdx_x * 16 + k_inner] with T.attr(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))): reduce_temp0_2 = T.Buffer((1,), data=reduce_temp0_1, scope="local") T.tvm_thread_allreduce(T.uint32(1), normal_reduce_temp0_2[0], T.bool(True), reduce_temp0_2[0], threadIdx_x) for i1_inner_outer in range(4): T_softmax_norm_1 = T.Buffer((65536,), "float32x4", data=T_softmax_norm) reduce_temp0_2 = T.Buffer((1,), data=reduce_temp0_1, scope="local", align=4) T_softmax_norm_1[blockIdx_x * 128 + threadIdx_x * 4 + i1_inner_outer] = T_softmax_exp_1[threadIdx_x * 16 + i1_inner_outer * 4:threadIdx_x * 16 + i1_inner_outer * 4 + 4] / T.Broadcast(reduce_temp0_2[0], 4) .. GENERATED FROM PYTHON SOURCE LINES 126-137 Fusing convolutions ------------------- We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together. .. note:: TOPI functions are all generic functions. They have different implementations for different backends to optimize for performance. For each backend, it is necessary to call them under a target scope for both compute declaration and schedule. TVM will choose the right function to call with the target information. .. GENERATED FROM PYTHON SOURCE LINES 137-147 .. code-block:: default data = te.placeholder((1, 3, 224, 224)) kernel = te.placeholder((10, 3, 5, 5)) with tvm.target.Target("cuda"): conv = topi.cuda.conv2d_nchw(data, kernel, 1, 2, 1) out = topi.nn.relu(conv) sconv = topi.cuda.schedule_conv2d_nchw([out]) print(tvm.lower(sconv, [data, kernel], simple_mode=True)) .. rst-class:: sphx-glr-script-out .. code-block:: none # from tvm.script import ir as I # from tvm.script import tir as T @I.ir_module class Module: @T.prim_func def main(placeholder: T.Buffer((1, 3, 224, 224), "float32"), placeholder_1: T.Buffer((10, 3, 5, 5), "float32")): T.func_attr({"from_legacy_te_schedule": T.bool(True), "tir.noalias": T.bool(True)}) compute = T.allocate([501760], "float32", "global") blockIdx_z = T.launch_thread("blockIdx.z", 5) conv2d_nchw = T.allocate([14], "float32", "local") pad_temp_shared = T.allocate([112], "float32", "shared") placeholder_shared = T.allocate([2], "float32", "shared") blockIdx_y = T.launch_thread("blockIdx.y", 224) blockIdx_x = T.launch_thread("blockIdx.x", 2) threadIdx_z = T.launch_thread("threadIdx.z", 1) threadIdx_y = T.launch_thread("threadIdx.y", 1) threadIdx_x = T.launch_thread("threadIdx.x", 16) conv2d_nchw_1 = T.Buffer((4,), data=conv2d_nchw, scope="local", align=8) conv2d_nchw_1[0] = T.float32(0) conv2d_nchw_1[2] = T.float32(0) conv2d_nchw_1[4] = T.float32(0) conv2d_nchw_1[6] = T.float32(0) conv2d_nchw_1[8] = T.float32(0) conv2d_nchw_1[10] = T.float32(0) conv2d_nchw_1[12] = T.float32(0) conv2d_nchw_1[1] = T.float32(0) conv2d_nchw_1[3] = T.float32(0) conv2d_nchw_1[5] = T.float32(0) conv2d_nchw_1[7] = T.float32(0) conv2d_nchw_1[9] = T.float32(0) conv2d_nchw_1[11] = T.float32(0) conv2d_nchw_1[13] = T.float32(0) for rc_outer, ry_outer in T.grid(3, 5): threadIdx_x_1 = T.env_thread("threadIdx.x") pad_temp_shared_1 = T.Buffer((112,), data=pad_temp_shared, scope="shared") placeholder_2 = T.Buffer((150528,), data=placeholder.data) with T.launch_thread("threadIdx.z", 1) as threadIdx_z_1: threadIdx_y_1 = T.launch_thread("threadIdx.y", 1) T.launch_thread(threadIdx_x_1, 16) pad_temp_shared_1[threadIdx_x_1 * 7] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226 and 1 <= blockIdx_x * 56 + threadIdx_x_1 * 7 // 2, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 450], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 1] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226 and 1 <= blockIdx_x * 56 + (threadIdx_x_1 * 7 + 1) // 2, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 449], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 2] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 448], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 3] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 447], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 4] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 446], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 5] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 445], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 6] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 444], T.float32(0)) threadIdx_x_2 = T.env_thread("threadIdx.x") placeholder_shared_1 = T.Buffer((2,), data=placeholder_shared, scope="shared", align=8) placeholder_3 = T.Buffer((750,), data=placeholder_1.data) with T.launch_thread("threadIdx.z", 1) as threadIdx_z_1: threadIdx_y_1 = T.launch_thread("threadIdx.y", 1) T.launch_thread(threadIdx_x_2, 16) if T.likely(threadIdx_x_2 < 2): placeholder_shared_1[threadIdx_x_2] = placeholder_3[blockIdx_z * 150 + threadIdx_x_2 * 75 + rc_outer * 25 + ry_outer * 5] conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[0] conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[0] conv2d_nchw_1[4] = conv2d_nchw_1[4] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[0] conv2d_nchw_1[6] = conv2d_nchw_1[6] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[0] conv2d_nchw_1[8] = conv2d_nchw_1[8] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[0] conv2d_nchw_1[10] = conv2d_nchw_1[10] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[0] conv2d_nchw_1[12] = conv2d_nchw_1[12] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[0] conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[1] conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[1] conv2d_nchw_1[5] = conv2d_nchw_1[5] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[1] conv2d_nchw_1[7] = conv2d_nchw_1[7] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[1] conv2d_nchw_1[9] = conv2d_nchw_1[9] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[1] conv2d_nchw_1[11] = conv2d_nchw_1[11] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[1] conv2d_nchw_1[13] = conv2d_nchw_1[13] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[1] threadIdx_z_1 = T.env_thread("threadIdx.z") threadIdx_y_1 = T.env_thread("threadIdx.y") with T.launch_thread(threadIdx_z_1, 1): T.launch_thread(threadIdx_y_1, 1) T.launch_thread(threadIdx_x_1, 16) pad_temp_shared_1[threadIdx_x_1 * 7] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226 and 1 <= blockIdx_x * 56 + (threadIdx_x_1 * 7 + 1) // 2, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 449], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 1] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 448], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 2] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 447], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 3] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 446], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 4] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 445], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 5] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 444], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 6] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 443], T.float32(0)) threadIdx_z_2 = T.env_thread("threadIdx.z") threadIdx_y_2 = T.env_thread("threadIdx.y") with T.launch_thread(threadIdx_z_2, 1): T.launch_thread(threadIdx_y_2, 1) T.launch_thread(threadIdx_x_2, 16) if T.likely(threadIdx_x_2 < 2): placeholder_shared_1[threadIdx_x_2] = placeholder_3[blockIdx_z * 150 + threadIdx_x_2 * 75 + rc_outer * 25 + ry_outer * 5 + 1] conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[0] conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[0] conv2d_nchw_1[4] = conv2d_nchw_1[4] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[0] conv2d_nchw_1[6] = conv2d_nchw_1[6] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[0] conv2d_nchw_1[8] = conv2d_nchw_1[8] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[0] conv2d_nchw_1[10] = conv2d_nchw_1[10] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[0] conv2d_nchw_1[12] = conv2d_nchw_1[12] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[0] conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[1] conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[1] conv2d_nchw_1[5] = conv2d_nchw_1[5] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[1] conv2d_nchw_1[7] = conv2d_nchw_1[7] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[1] conv2d_nchw_1[9] = conv2d_nchw_1[9] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[1] conv2d_nchw_1[11] = conv2d_nchw_1[11] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[1] conv2d_nchw_1[13] = conv2d_nchw_1[13] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[1] with T.launch_thread(threadIdx_z_1, 1): T.launch_thread(threadIdx_y_1, 1) T.launch_thread(threadIdx_x_1, 16) pad_temp_shared_1[threadIdx_x_1 * 7] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 448], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 1] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 447], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 2] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 446], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 3] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 445], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 4] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 444], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 5] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 443], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 6] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 442], T.float32(0)) with T.launch_thread(threadIdx_z_2, 1): T.launch_thread(threadIdx_y_2, 1) T.launch_thread(threadIdx_x_2, 16) if T.likely(threadIdx_x_2 < 2): placeholder_shared_1[threadIdx_x_2] = placeholder_3[blockIdx_z * 150 + threadIdx_x_2 * 75 + rc_outer * 25 + ry_outer * 5 + 2] conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[0] conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[0] conv2d_nchw_1[4] = conv2d_nchw_1[4] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[0] conv2d_nchw_1[6] = conv2d_nchw_1[6] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[0] conv2d_nchw_1[8] = conv2d_nchw_1[8] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[0] conv2d_nchw_1[10] = conv2d_nchw_1[10] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[0] conv2d_nchw_1[12] = conv2d_nchw_1[12] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[0] conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[1] conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[1] conv2d_nchw_1[5] = conv2d_nchw_1[5] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[1] conv2d_nchw_1[7] = conv2d_nchw_1[7] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[1] conv2d_nchw_1[9] = conv2d_nchw_1[9] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[1] conv2d_nchw_1[11] = conv2d_nchw_1[11] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[1] conv2d_nchw_1[13] = conv2d_nchw_1[13] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[1] with T.launch_thread(threadIdx_z_1, 1): T.launch_thread(threadIdx_y_1, 1) T.launch_thread(threadIdx_x_1, 16) pad_temp_shared_1[threadIdx_x_1 * 7] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 447], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 1] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 446], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 2] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 445], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 3] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 444], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 4] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 443], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 5] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 442], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 6] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226 and blockIdx_x * 16 + threadIdx_x_1 < 31, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 441], T.float32(0)) with T.launch_thread(threadIdx_z_2, 1): T.launch_thread(threadIdx_y_2, 1) T.launch_thread(threadIdx_x_2, 16) if T.likely(threadIdx_x_2 < 2): placeholder_shared_1[threadIdx_x_2] = placeholder_3[blockIdx_z * 150 + threadIdx_x_2 * 75 + rc_outer * 25 + ry_outer * 5 + 3] conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[0] conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[0] conv2d_nchw_1[4] = conv2d_nchw_1[4] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[0] conv2d_nchw_1[6] = conv2d_nchw_1[6] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[0] conv2d_nchw_1[8] = conv2d_nchw_1[8] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[0] conv2d_nchw_1[10] = conv2d_nchw_1[10] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[0] conv2d_nchw_1[12] = conv2d_nchw_1[12] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[0] conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[1] conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[1] conv2d_nchw_1[5] = conv2d_nchw_1[5] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[1] conv2d_nchw_1[7] = conv2d_nchw_1[7] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[1] conv2d_nchw_1[9] = conv2d_nchw_1[9] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[1] conv2d_nchw_1[11] = conv2d_nchw_1[11] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[1] conv2d_nchw_1[13] = conv2d_nchw_1[13] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[1] with T.launch_thread(threadIdx_z_1, 1): T.launch_thread(threadIdx_y_1, 1) T.launch_thread(threadIdx_x_1, 16) pad_temp_shared_1[threadIdx_x_1 * 7] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 446], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 1] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 445], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 2] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 444], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 3] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 443], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 4] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 442], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 5] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226 and blockIdx_x * 16 + threadIdx_x_1 < 31, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 441], T.float32(0)) pad_temp_shared_1[threadIdx_x_1 * 7 + 6] = T.if_then_else(2 <= blockIdx_y + ry_outer and blockIdx_y + ry_outer < 226 and blockIdx_x * 112 + threadIdx_x_1 * 7 < 216, placeholder_2[rc_outer * 50176 + blockIdx_y * 224 + ry_outer * 224 + blockIdx_x * 112 + threadIdx_x_1 * 7 - 440], T.float32(0)) with T.launch_thread(threadIdx_z_2, 1): T.launch_thread(threadIdx_y_2, 1) T.launch_thread(threadIdx_x_2, 16) if T.likely(threadIdx_x_2 < 2): placeholder_shared_1[threadIdx_x_2] = placeholder_3[blockIdx_z * 150 + threadIdx_x_2 * 75 + rc_outer * 25 + ry_outer * 5 + 4] conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[0] conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[0] conv2d_nchw_1[4] = conv2d_nchw_1[4] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[0] conv2d_nchw_1[6] = conv2d_nchw_1[6] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[0] conv2d_nchw_1[8] = conv2d_nchw_1[8] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[0] conv2d_nchw_1[10] = conv2d_nchw_1[10] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[0] conv2d_nchw_1[12] = conv2d_nchw_1[12] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[0] conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * placeholder_shared_1[1] conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 16] * placeholder_shared_1[1] conv2d_nchw_1[5] = conv2d_nchw_1[5] + pad_temp_shared_1[threadIdx_x + 32] * placeholder_shared_1[1] conv2d_nchw_1[7] = conv2d_nchw_1[7] + pad_temp_shared_1[threadIdx_x + 48] * placeholder_shared_1[1] conv2d_nchw_1[9] = conv2d_nchw_1[9] + pad_temp_shared_1[threadIdx_x + 64] * placeholder_shared_1[1] conv2d_nchw_1[11] = conv2d_nchw_1[11] + pad_temp_shared_1[threadIdx_x + 80] * placeholder_shared_1[1] conv2d_nchw_1[13] = conv2d_nchw_1[13] + pad_temp_shared_1[threadIdx_x + 96] * placeholder_shared_1[1] compute_1 = T.Buffer((501760,), data=compute) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x] = T.max(conv2d_nchw_1[0], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 16] = T.max(conv2d_nchw_1[2], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 32] = T.max(conv2d_nchw_1[4], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 48] = T.max(conv2d_nchw_1[6], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 64] = T.max(conv2d_nchw_1[8], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 80] = T.max(conv2d_nchw_1[10], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 96] = T.max(conv2d_nchw_1[12], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50176] = T.max(conv2d_nchw_1[1], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50192] = T.max(conv2d_nchw_1[3], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50208] = T.max(conv2d_nchw_1[5], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50224] = T.max(conv2d_nchw_1[7], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50240] = T.max(conv2d_nchw_1[9], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50256] = T.max(conv2d_nchw_1[11], T.float32(0)) compute_1[blockIdx_z * 100352 + blockIdx_y * 224 + blockIdx_x * 112 + threadIdx_x + 50272] = T.max(conv2d_nchw_1[13], T.float32(0)) .. GENERATED FROM PYTHON SOURCE LINES 148-154 Summary ------- In this tutorial, we have seen - How to use TOPI API for common operations with numpy-style operators. - How TOPI facilitates generic schedules and operator fusion for a context, to generate optimized kernel codes. .. _sphx_glr_download_tutorial_intro_topi.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: intro_topi.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: intro_topi.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_