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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
import tvm | ||
from tvm import te, tir | ||
from tvm.script import tir as T | ||
import tvm.testing | ||
import numpy as np | ||
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@T.prim_func | ||
def ldmatrix_a_desc(a: T.handle, c: T.handle) -> None: | ||
A_shared = T.match_buffer( | ||
a, (16, 8), "float16", align=128, offset_factor=16, scope="shared" | ||
) | ||
A_warp = T.match_buffer( | ||
c, (32, 4), "float16", align=128, offset_factor=16, scope="warp" | ||
) | ||
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with T.block("root"): | ||
T.reads(A_shared[0:16, 0:8]) | ||
T.writes(A_warp[0:32, 0:4]) | ||
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for ax0, ax1 in T.grid(16, 8): | ||
with T.block("A_shared_warp"): | ||
v0, v1 = T.axis.remap("SS", [ax0, ax1]) | ||
T.reads(A_shared[v0, v1]) | ||
T.writes(A_warp[v0 % 8 * 4 + v1 // 2, v0 // 8 * 2 + v1 % 2]) | ||
A_warp[v0 % 8 * 4 + v1 // 2, v0 // 8 * 2 + v1 % 2] = A_shared[v0, v1] | ||
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@T.prim_func | ||
def ldmatrix_a_impl(a: T.handle, c: T.handle) -> None: | ||
s1 = T.var("int32") | ||
s0 = T.var("int32") | ||
A_shared = T.match_buffer( | ||
a, | ||
(16, 8), | ||
"float16", | ||
align=128, | ||
offset_factor=16, | ||
scope="shared", | ||
strides=[s1, s0], | ||
) | ||
A_warp = T.match_buffer( | ||
c, (32, 4), "float16", align=128, offset_factor=16, scope="warp" | ||
) | ||
with T.block("root"): | ||
T.reads(A_shared[0:16, 0:8]) | ||
T.writes(A_warp[0:32, 0:4]) | ||
tx = T.env_thread("threadIdx.x") | ||
T.launch_thread(tx, 32) | ||
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T.evaluate( | ||
T.ptx_ldmatrix( | ||
0, | ||
2, | ||
".b16", | ||
A_warp.data, | ||
4 * tx, | ||
A_shared.data, | ||
8 * (tx % 16), | ||
dtype="float16", | ||
) | ||
) | ||
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@T.prim_func | ||
def ldmatrix_b_desc(a: T.handle, c: T.handle) -> None: | ||
B_shared = T.match_buffer( | ||
a, (8, 8), "float16", align=128, offset_factor=16, scope="shared" | ||
) | ||
B_shared_warp = T.match_buffer( | ||
c, (32, 2), "float16", align=128, offset_factor=16, scope="warp" | ||
) | ||
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with T.block("root"): | ||
T.reads(B_shared[0:8, 0:8]) | ||
T.writes(B_shared_warp[0:32, 0:2]) | ||
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for ax0, ax1 in T.grid(8, 8): | ||
with T.block("A_shared_warp"): | ||
v0, v1 = T.axis.remap("SS", [ax0, ax1]) | ||
T.reads(B_shared[v0, v1]) | ||
T.writes(B_shared_warp[v1 * 4 + v0 // 2, v0 % 2]) | ||
B_shared_warp[v1 * 4 + v0 // 2, v0 % 2] = B_shared[v0, v1] | ||
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@T.prim_func | ||
def ldmatrix_b_impl(a: T.handle, c: T.handle) -> None: | ||
s1 = T.var("int32") | ||
s0 = T.var("int32") | ||
B_shared = T.match_buffer( | ||
a, | ||
(8, 8), | ||
"float16", | ||
align=128, | ||
offset_factor=16, | ||
scope="shared", | ||
strides=[s1, s0], | ||
) | ||
B_warp = T.match_buffer( | ||
c, (32, 2), "float16", align=128, offset_factor=16, scope="warp" | ||
) | ||
with T.block("root"): | ||
T.reads(B_shared[0:8, 0:8]) | ||
T.writes(B_warp[0:32, 0:2]) | ||
tx = T.env_thread("threadIdx.x") | ||
T.launch_thread(tx, 32) | ||
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T.evaluate( | ||
T.ptx_ldmatrix( | ||
0, | ||
1, | ||
".b16", | ||
B_warp.data, | ||
2 * tx, | ||
B_shared.data, | ||
8 * (tx % 8), | ||
dtype="float16", | ||
) | ||
) | ||
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@T.prim_func | ||
def mma_sync_desc(a: T.handle, b: T.handle, c: T.handle) -> None: | ||
A = T.match_buffer(a, [32, 4], dtype="float16", scope="warp") | ||
B = T.match_buffer(b, [32, 2], dtype="float16", scope="warp") | ||
C = T.match_buffer(c, [32, 4], dtype="float32", scope="warp") | ||
with T.block("root"): | ||
T.reads(C[0:32, 0:4], A[0:32, 0:4], B[0:32, 0:2]) | ||
T.writes(C[0:32, 0:4]) | ||
for i0, i1, i2 in T.grid(16, 8, 8): | ||
with T.block("C"): | ||
i, j, k = T.axis.remap("SSR", [i0, i1, i2]) | ||
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T.reads( | ||
C[i % 8 * 4 + j % 8 // 2, i % 16 // 8 * 2 + j % 2], | ||
A[i % 8 * 4 + k % 8 // 2, i % 16 // 8 * 2 + k % 2], | ||
B[k % 8 * 4 + j % 8 // 2, j % 2], | ||
) | ||
T.writes(C[i % 8 * 4 + j % 8 // 2, i % 16 // 8 * 2 + j % 2]) | ||
C[i % 8 * 4 + j % 8 // 2, i % 16 // 8 * 2 + j % 2] = C[ | ||
i % 8 * 4 + j % 8 // 2, i % 16 // 8 * 2 + j % 2 | ||
] + T.cast( | ||
A[i % 8 * 4 + k % 8 // 2, i % 16 // 8 * 2 + k % 2], "float32" | ||
) * T.cast( | ||
B[k % 8 * 4 + j % 8 // 2, j % 2], "float32" | ||
) | ||
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@T.prim_func | ||
def mma_sync_impl(a: T.handle, b: T.handle, c: T.handle) -> None: | ||
A = T.match_buffer(a, (32, 4), "float16", align=128, offset_factor=1, scope="warp") | ||
B = T.match_buffer(b, (32, 2), "float16", align=128, offset_factor=1, scope="warp") | ||
C = T.match_buffer(c, (32, 4), "float32", align=128, offset_factor=1, scope="warp") | ||
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with T.block("root"): | ||
T.reads(C[0:32, 0:4], A[0:32, 0:4], B[0:32, 0:2]) | ||
T.writes(C[0:32, 0:4]) | ||
tx = T.env_thread("threadIdx.x") | ||
T.launch_thread(tx, 32) | ||
T.evaluate( | ||
T.ptx_mma( | ||
"m16n8k8", | ||
"row", | ||
"col", | ||
"fp16", | ||
"fp16", | ||
"fp32", | ||
A.data, | ||
A.elem_offset + tx * 4, | ||
B.data, | ||
B.elem_offset + tx * 2, | ||
C.data, | ||
C.elem_offset + tx * 4, | ||
False, | ||
dtype="float32", | ||
) | ||
) | ||
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tir.TensorIntrin.register("mma.ldmatrix_a", ldmatrix_a_desc, ldmatrix_a_impl) | ||
tir.TensorIntrin.register("mma.ldmatrix_b", ldmatrix_b_desc, ldmatrix_b_impl) | ||
tir.TensorIntrin.register("mma_sync", mma_sync_desc, mma_sync_impl) | ||
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def dense(n: int, m: int, k: int): | ||
a = te.placeholder((n, k), name="A", dtype="float16") | ||
b = te.placeholder((m, k), name="B", dtype="float16") | ||
k = te.reduce_axis((0, k), name="k") | ||
c = te.compute( | ||
(n, m), | ||
lambda i, j: te.sum( | ||
tvm.tir.Cast("float32", a[i, k]) * tvm.tir.Cast("float32", b[j, k]), | ||
axis=[k], | ||
), | ||
name="C", | ||
) | ||
return (a, b, c) | ||
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def test_integration_matmul(): | ||
N = 4096 | ||
M = 4096 | ||
K = 4096 | ||
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workload = te.create_prim_func(dense(n=N, m=M, k=K)) | ||
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def schedule(sch: tir.Schedule): | ||
block = sch.get_block("C") | ||
i, j, k = sch.get_loops(block) | ||
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i, i_tc = sch.split(i, factors=[None, 16]) | ||
j, j_tc = sch.split(j, factors=[None, 8]) | ||
k_outer, k_tc = sch.split(k, factors=[None, 8]) | ||
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sch.reorder( | ||
# fmt: off | ||
i, j, k_outer, | ||
# tensor core | ||
i_tc, j_tc, k_tc | ||
) | ||
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block_outer = sch.blockize(i_tc) | ||
block, _ = block_outer, block | ||
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sch.bind(sch.fuse(i, j), "blockIdx.x") | ||
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def fetch_to_shared(block, idx, ndim): | ||
block_read = sch.cache_read(block, idx, "shared") | ||
sch.compute_at(block_read, k_outer) | ||
warp_size = 32 | ||
fused = sch.fuse(*sch.get_loops(block_read)[-ndim:]) | ||
f_0, f_1 = sch.split(fused, factors=[None, warp_size]) | ||
sch.bind(f_1, "threadIdx.x") | ||
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fetch_to_shared(block, 0, 2) | ||
fetch_to_shared(block, 1, 2) | ||
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# fetch to A_warp 16 * 8 -> 32 * 4 | ||
A_warp = sch.cache_read(block, 0, "warp") | ||
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def lambda_a(i, j): | ||
i_0 = i // 16 | ||
j_0 = j // 8 | ||
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i = i % 16 | ||
j = j % 8 | ||
return ( | ||
i_0, | ||
j_0, | ||
(i % 8) * 4 + (j % 8) // 2, | ||
4 * (j // 8) + (i // 8) * 2 + (j % 8) % 2, | ||
) | ||
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sch.transform_layout(A_warp, 0, "write", index_map=lambda_a) | ||
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sch.tensorize(sch.get_loops(A_warp)[2], "mma.ldmatrix_a") | ||
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def lambda_b(i, j): | ||
i_0 = i // 8 | ||
j_0 = j // 8 | ||
i = i % 8 | ||
j = j % 8 | ||
return i_0, j_0, i // 2 + j * 4, i % 2 | ||
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B_warp = sch.cache_read(block, 1, "warp") | ||
sch.transform_layout( | ||
B_warp, | ||
0, | ||
"write", | ||
index_map=lambda_b, | ||
) | ||
sch.tensorize(sch.get_loops(B_warp)[2], "mma.ldmatrix_b") | ||
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# fetch to C_warp 16 * 8 -> 32 * 4 | ||
C_warp = sch.cache_write(block, 0, "warp") | ||
# sch.reverse_compute_at(C_warp, sch.get_loops(block)[0]) | ||
# need to do a reverse_compute_at to place it under blockidx.x | ||
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sch.transform_layout( | ||
C_warp, | ||
0, | ||
"read", | ||
index_map=lambda_a, | ||
) | ||
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warp_loop1, warp_loop2 = sch.get_loops(C_warp)[-2:] | ||
f_0, f_1 = sch.split(warp_loop1, factors=[None, 8]) | ||
f_2, f_3 = sch.split(warp_loop2, factors=[None, 2]) | ||
sch.reorder(f_1, f_2, f_0, f_3) | ||
fused_1 = sch.fuse(f_1, f_2) | ||
fused_2 = sch.fuse(f_0, f_3) | ||
sch.bind(fused_1, "threadIdx.x") | ||
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block_init_c = sch.decompose_reduction(block, sch.get_loops(block)[1]) | ||
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block_init_c = sch.get_block("C_init") | ||
init_loop1, init_loop2 = sch.get_loops(block_init_c)[-2:] | ||
f_0, f_1 = sch.split(init_loop1, factors=[None, 8]) | ||
f_2, f_3 = sch.split(init_loop2, factors=[None, 2]) | ||
sch.reorder(f_1, f_2, f_0, f_3) | ||
fused_1 = sch.fuse(f_1, f_2) | ||
fused_2 = sch.fuse(f_0, f_3) | ||
sch.bind(fused_1, "threadIdx.x") | ||
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block = sch.get_block("C") | ||
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i1, _, _ = sch.get_loops(block)[-3:] | ||
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sch.tensorize(i1, "mma_sync") | ||
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sch = tir.Schedule(workload) | ||
schedule(sch) | ||
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print(sch.mod["main"].script()) | ||
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target = "cuda" | ||
f = tvm.build(sch.mod["main"], target=target, name="dense") | ||
dev = tvm.device("cuda", 0) | ||
a_np = np.random.uniform(size=(N, K)).astype("float16") | ||
b_np = np.random.uniform(size=(M, K)).astype("float16") | ||
c_np = np.dot(a_np.astype("float32"), b_np.transpose().astype("float32")) | ||
a = tvm.nd.array(a_np, dev) | ||
b = tvm.nd.array(b_np, dev) | ||
c = tvm.nd.array(np.zeros((N, M), dtype="float32"), dev) | ||
# sys.exit(0) | ||
f = tvm.build(sch.mod["main"], target="cuda", name="dense") | ||
f(a, b, c) | ||
print(f.imported_modules[0].get_source()) | ||
tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-3) | ||
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if __name__ == "__main__": | ||
test_integration_matmul() |