# 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.

```import tvm
import tvm.testing
from tvm import te
from tvm import topi
import numpy as np
```

## Basic example¶

Let’s revisit the sum of rows operation (equivalent to `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

```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)
```

and to examine the IR code in human readable format, we can do

```print(tvm.lower(s, [A], simple_mode=True))
```
```@main = primfn(A_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [(stride: int32*n: int32)], [], type="auto")}
buffer_map = {A_1: A}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [n, m: int32], [stride, stride_1: int32], type="auto")} {
allocate(B: Pointer(global float32), float32, [n]), storage_scope = global;
for (i: int32, 0, n) {
B_1: Buffer(B, float32, [n], [])[i] = 0f32
for (k: int32, 0, m) {
B_1[i] = (B_1[i] + A[((i*stride) + (k*stride_1))])
}
}
}
```

However, for such a common operation we had to define the reduce axis ourselves as well as explicit computation with `te.compute`. Imagine for more complicated operations how much details we need to provide. Fortunately, we can replace those two lines with simple `topi.sum` much like `numpy.sum`

```C = topi.sum(A, axis=1)
ts = te.create_schedule(C.op)
print(tvm.lower(ts, [A], simple_mode=True))
```
```@main = primfn(A_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {A: Buffer(A_2: Pointer(float32), float32, [(stride: int32*n: int32)], [], type="auto")}
buffer_map = {A_1: A}
preflattened_buffer_map = {A_1: A_3: Buffer(A_2, float32, [n, m: int32], [stride, stride_1: int32], type="auto")} {
allocate(A_red: Pointer(global float32), float32, [n]), storage_scope = global;
for (ax0: int32, 0, n) {
A_red_1: Buffer(A_red, float32, [n], [])[ax0] = 0f32
for (k1: int32, 0, m) {
A_red_1[ax0] = (A_red_1[ax0] + A[((ax0*stride) + (k1*stride_1))])
}
}
}
```

We can add two tensors using `topi.broadcast_add` that have correct (broadcastable with specific) shapes. Even shorter, TOPI provides operator overloading for such common operations. For example,

```x, y = 100, 10
a = te.placeholder((x, y, y), name="a")
b = te.placeholder((y, y), name="b")
d = a * b  # same as topi.broadcast_mul
```

Overloaded with the same syntax, TOPI handles broadcasting a primitive (int, float) to a tensor `d - 3.14`.

## 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 `topi.sum` using only `topi.generic.schedule_reduce`

```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))
```
```/workspace/python/tvm/target/target.py:389: UserWarning: Try specifying cuda arch by adding 'arch=sm_xx' to your target.
@main = primfn(a_1: handle, b_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {a: Buffer(a_2: Pointer(float32), float32, [10000], []),
b: Buffer(b_2: Pointer(float32), float32, [100], [])}
buffer_map = {a_1: a, b_1: b}
preflattened_buffer_map = {a_1: a_3: Buffer(a_2, float32, [100, 10, 10], []), b_1: b_3: Buffer(b_2, float32, [10, 10], [])} {
allocate(T_divide_red: Pointer(global float32), float32, [1]), storage_scope = global;
allocate(T_divide_red.rf: Pointer(local float32), float32, [1]), storage_scope = local;
allocate(reduce_temp0: Pointer(local float32), float32, [1]), storage_scope = local {
T_divide_red.rf_1: Buffer(T_divide_red.rf, float32, [1], [], scope="local", align=4)[0] = 0f32
for (k0.k1.fused.k2.fused.outer: int32, 0, 10) {
if @tir.likely((((((k0.k1.fused.k2.fused.outer*64) + floordiv(threadIdx.x, 16)) < 625) && (((k0.k1.fused.k2.fused.outer*64) + floordiv(threadIdx.x, 16)) < 625)) && (((k0.k1.fused.k2.fused.outer*64) + floordiv(threadIdx.x, 16)) < 625)), dtype=bool) {
}
}
attr [meta[tir.CommReducer][0]] "reduce_scope" = @tir.reinterpret(0u64, dtype=handle);
T_divide_red_1: Buffer(T_divide_red, float32, [1], [], align=4)[0] = reduce_temp0_1[0]
}
}
}
```

As you can see, scheduled stages of computation have been accumulated and we can examine them by

```print(sg.stages)
```
```[stage(a, placeholder(a, 0x230b05e0)), stage(b, placeholder(b, 0x2196e6e0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], 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=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide, compute(T_divide, body=[(T_elemwise_sum[ax0, ax1, ax2]/2f)], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide_red.rf, compute(T_divide_red.rf, body=[reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[0f]), source=[T_divide[floordiv(floordiv((k0.k1.fused.k2.fused.inner + (k0.k1.fused.k2.fused.outer*1024)), 10), 10), floormod(floordiv((k0.k1.fused.k2.fused.inner + (k0.k1.fused.k2.fused.outer*1024)), 10), 10), floormod((k0.k1.fused.k2.fused.inner + (k0.k1.fused.k2.fused.outer*1024)), 10)]], init=[], axis=[iter_var(k0.k1.fused.k2.fused.outer, range(min=0, ext=10))], where=tir.likely((((floordiv(floordiv((k0.k1.fused.k2.fused.inner + (k0.k1.fused.k2.fused.outer*1024)), 10), 10) < 100) && (floordiv((k0.k1.fused.k2.fused.inner + (k0.k1.fused.k2.fused.outer*1024)), 10) < 1000)) && ((k0.k1.fused.k2.fused.inner + (k0.k1.fused.k2.fused.outer*1024)) < 10000))), value_index=0)], axis=[iter_var(k0.k1.fused.k2.fused.inner, range(min=0, ext=1024))], reduce_axis=[iter_var(k0.k1.fused.k2.fused.outer, range(min=0, ext=10))], tag=, attrs={})), stage(T_divide_red, compute(T_divide_red.repl, body=[reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[0f]), source=[T_divide_red.rf[k0.k1.fused.k2.fused.inner.v]], init=[], axis=[iter_var(k0.k1.fused.k2.fused.inner.v, range(min=0, ext=1024))], where=(bool)1, value_index=0)], axis=[], reduce_axis=[iter_var(k0.k1.fused.k2.fused.inner.v, range(min=0, ext=1024))], tag=, attrs={}))]
```

We can test the correctness by comparing with `numpy` result as follows

```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)
```

TOPI also provides common neural nets operations such as _softmax_ with optimized schedule

```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))
```
```@main = primfn(tarray_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {tarray: Buffer(tarray_2: Pointer(float32), float32, [262144], [])}
buffer_map = {tarray_1: tarray}
preflattened_buffer_map = {tarray_1: tarray_3: Buffer(tarray_2, float32, [512, 512], [])} {
allocate(T_softmax_norm: Pointer(global float32x4), float32x4, [65536]), storage_scope = global;
allocate(normal_reduce_temp0: Pointer(local float32), float32, [1]), storage_scope = local;
allocate(reduce_temp0: Pointer(local float32), float32, [1]), storage_scope = local;
allocate(T_softmax_exp: Pointer(warp float32), float32, [512]), storage_scope = warp;
allocate(normal_reduce_temp0_1: Pointer(local float32), float32, [1]), storage_scope = local;
allocate(reduce_temp0_1: Pointer(local float32), float32, [1]), storage_scope = local {
normal_reduce_temp0_2: Buffer(normal_reduce_temp0, float32, [1], [], scope="local")[0] = -3.40282e+38f32
for (k.inner: int32, 0, 16) {
normal_reduce_temp0_2[0] = max(normal_reduce_temp0_2[0], tarray[(((blockIdx.x*512) + (threadIdx.x*16)) + k.inner)])
}
attr [meta[tir.CommReducer][0]] "reduce_scope" = @tir.reinterpret(0u64, dtype=handle);
for (i1.inner.outer: int32, 0, 4) {
let cse_var_1: int32 = (i1.inner.outer*4)
T_softmax_exp_1: Buffer(T_softmax_exp, float32, [512], [], scope="warp")[ramp(((threadIdx.x*16) + cse_var_1), 1, 4)] = @tir.exp((tarray[ramp((((blockIdx.x*512) + (threadIdx.x*16)) + cse_var_1), 1, 4)] - broadcast(reduce_temp0_3: Buffer(reduce_temp0, float32, [1], [], scope="local", align=4)[0], 4)), dtype=float32x4)
}
}
normal_reduce_temp0_3: Buffer(normal_reduce_temp0_1, float32, [1], [], scope="local")[0] = 0f32
for (k.inner_1: int32, 0, 16) {
normal_reduce_temp0_3[0] = (normal_reduce_temp0_3[0] + T_softmax_exp_1[((threadIdx.x*16) + k.inner_1)])
}
attr [meta[tir.CommReducer][1]] "reduce_scope" = @tir.reinterpret(0u64, dtype=handle);
for (i1.inner.outer_1: int32, 0, 4) {
T_softmax_norm_1: Buffer(T_softmax_norm, float32x4, [65536], [])[(((blockIdx.x*128) + (threadIdx.x*4)) + i1.inner.outer_1)] = (T_softmax_exp_1[ramp(((threadIdx.x*16) + (i1.inner.outer_1*4)), 1, 4)] / broadcast(reduce_temp0_5: Buffer(reduce_temp0_1, float32, [1], [], scope="local", align=4)[0], 4))
}
}
}
}
```

## Fusing convolutions¶

We can fuse `topi.nn.conv2d` and `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.

```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))
```
```@main = primfn(placeholder_2: handle, placeholder_3: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
buffers = {placeholder: Buffer(placeholder_4: Pointer(float32), float32, [150528], []),
placeholder_1: Buffer(placeholder_5: Pointer(float32), float32, [750], [])}
buffer_map = {placeholder_2: placeholder, placeholder_3: placeholder_1}
preflattened_buffer_map = {placeholder_2: placeholder_6: Buffer(placeholder_4, float32, [1, 3, 224, 224], []), placeholder_3: placeholder_7: Buffer(placeholder_5, float32, [10, 3, 5, 5], [])} {
allocate(compute: Pointer(global float32), float32, [501760]), storage_scope = global;
allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
allocate(pad_temp.shared: Pointer(shared float32), float32, [112]), storage_scope = shared;
allocate(placeholder.shared: Pointer(shared float32), float32, [2]), storage_scope = shared;
conv2d_nchw_1: Buffer(conv2d_nchw, float32, [4], [], scope="local", align=8)[0] = 0f32
conv2d_nchw_1[2] = 0f32
conv2d_nchw_1[4] = 0f32
conv2d_nchw_1[6] = 0f32
conv2d_nchw_1[8] = 0f32
conv2d_nchw_1[10] = 0f32
conv2d_nchw_1[12] = 0f32
conv2d_nchw_1[1] = 0f32
conv2d_nchw_1[3] = 0f32
conv2d_nchw_1[5] = 0f32
conv2d_nchw_1[7] = 0f32
conv2d_nchw_1[9] = 0f32
conv2d_nchw_1[11] = 0f32
conv2d_nchw_1[13] = 0f32
for (rc.outer: int32, 0, 3) {
for (ry.outer: int32, 0, 5) {
pad_temp.shared_1: Buffer(pad_temp.shared, float32, [112], [], scope="shared")[(threadIdx.x_1*7)] = @tir.if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*56) + floordiv((threadIdx.x_1*7), 2)))), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 450)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 1)] = @tir.if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*56) + floordiv(((threadIdx.x_1*7) + 1), 2)))), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 449)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 2)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 3)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 4)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 5)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 6)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)], 0f32, dtype=float32)
}
if @tir.likely((threadIdx.x_2 < 2), dtype=bool) {
placeholder.shared_1: Buffer(placeholder.shared, float32, [2], [], scope="shared", align=8)[threadIdx.x_2] = placeholder_1[((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5))]
}
pad_temp.shared_1[(threadIdx.x_1*7)] = @tir.if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*56) + floordiv(((threadIdx.x_1*7) + 1), 2)))), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 449)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 1)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 2)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 3)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 4)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 5)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 6)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)], 0f32, dtype=float32)
}
if @tir.likely((threadIdx.x_2 < 2), dtype=bool) {
}
pad_temp.shared_1[(threadIdx.x_1*7)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 1)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 2)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 3)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 4)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 5)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 6)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)], 0f32, dtype=float32)
}
if @tir.likely((threadIdx.x_2 < 2), dtype=bool) {
}
pad_temp.shared_1[(threadIdx.x_1*7)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 1)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 2)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 3)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 4)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 5)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 6)] = @tir.if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*56) + floordiv(((threadIdx.x_1*7) + 9), 2)) < 113)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 441)], 0f32, dtype=float32)
}
if @tir.likely((threadIdx.x_2 < 2), dtype=bool) {
}
pad_temp.shared_1[(threadIdx.x_1*7)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 1)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 2)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 3)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 4)] = @tir.if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 5)] = @tir.if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*56) + floordiv(((threadIdx.x_1*7) + 9), 2)) < 113)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 441)], 0f32, dtype=float32)
pad_temp.shared_1[((threadIdx.x_1*7) + 6)] = @tir.if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*56) + floordiv((threadIdx.x_1*7), 2)) < 108)), placeholder[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 440)], 0f32, dtype=float32)
}
if @tir.likely((threadIdx.x_2 < 2), dtype=bool) {
}
}
}
compute_1: Buffer(compute, float32, [501760], [])[((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x)] = max(conv2d_nchw_1[0], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 16)] = max(conv2d_nchw_1[2], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 32)] = max(conv2d_nchw_1[4], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 48)] = max(conv2d_nchw_1[6], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 64)] = max(conv2d_nchw_1[8], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 80)] = max(conv2d_nchw_1[10], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 96)] = max(conv2d_nchw_1[12], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50176)] = max(conv2d_nchw_1[1], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50192)] = max(conv2d_nchw_1[3], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50208)] = max(conv2d_nchw_1[5], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50224)] = max(conv2d_nchw_1[7], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50240)] = max(conv2d_nchw_1[9], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50256)] = max(conv2d_nchw_1[11], 0f32)
compute_1[(((((blockIdx.z*100352) + (blockIdx.y*224)) + (blockIdx.x*112)) + threadIdx.x) + 50272)] = max(conv2d_nchw_1[13], 0f32)
}
}
```

## 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.

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