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test_tir_ptx_mma.py
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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 sys
import pytest
import tvm
from tvm.script import tir as T
import numpy as np
import tvm.testing
@T.prim_func
def gemm_mma_m8n8k4_row_col_fp64pf64fp64(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [8, 4], dtype="float64")
B = T.match_buffer(b, [8, 4], dtype="float64")
C = T.match_buffer(c, [8, 8], dtype="float64")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([1], "float64", scope="local")
MultiB = T.allocate([1], "float64", scope="local")
Accum = T.allocate([2], "float64", scope="local")
for i in range(2):
Accum[i] = T.float64(0)
MultiA[0] = A[(tx % 32) // 4, (tx % 32) % 4]
MultiB[0] = B[(tx % 32) // 4, (tx % 32) % 4]
T.evaluate(
T.ptx_mma(
"m8n8k4",
"row",
"col",
"fp64",
"fp64",
"fp64",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="float64",
)
)
for mma_accum_c_id in range(2):
C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k4_row_col_fp64pf64fp64():
sch = tvm.tir.Schedule(gemm_mma_m8n8k4_row_col_fp64pf64fp64)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 8:
# Require at least SM80
return
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-1, 1, [8, 4]).astype("float64")
B_np = np.random.uniform(-1, 1, [8, 4]).astype("float64")
C_np = np.zeros([8, 8]).astype("float64")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("float64"), B_np.astype("float64").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m8n8k4_row_row_fp16fp16fp16(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 4], dtype="float16")
B = T.match_buffer(b, [4, 16], dtype="float16")
C = T.match_buffer(c, [16, 16], dtype="float16")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([4], "float16", scope="local")
MultiB = T.allocate([4], "float16", scope="local")
Accum = T.allocate([8], "float16", scope="local")
for i in range(8):
Accum[i] = T.float32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[
((tx % 32) % 4) + (4 * ((((tx % 32) // 16 + (tx % 32) % 16 // 4 * 2)) % 4)),
mma_multi_a_col,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) % 4,
mma_multi_b_col + (4 * ((tx % 32) // 8)),
]
T.evaluate(
T.ptx_mma(
"m8n8k4",
"row",
"row",
"fp16",
"fp16",
"fp16",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="float16",
)
)
for mma_accum_c_id in range(8):
C[
((tx % 32) % 4) + (4 * ((((tx % 32) // 16 + (tx % 32) % 16 // 4 * 2)) % 4)),
mma_accum_c_id % 4 + (4 * ((tx % 32) % 16 // 8)) + mma_accum_c_id // 4 * 8,
] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k4_row_row_fp16fp16fp16():
sch = tvm.tir.Schedule(gemm_mma_m8n8k4_row_row_fp16fp16fp16)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 7:
# Require at least SM70
return
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-1, 1, [16, 4]).astype("float16")
B_np = np.random.uniform(-1, 1, [4, 16]).astype("float16")
C_np = np.zeros([16, 16]).astype("float16")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("float16"), B_np.astype("float16"))
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m8n8k4_row_row_fp16fp16fp32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 4], dtype="float16")
B = T.match_buffer(b, [4, 16], dtype="float16")
C = T.match_buffer(c, [16, 16], dtype="float32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([4], "float16", scope="local")
MultiB = T.allocate([4], "float16", scope="local")
Accum = T.allocate([8], "float32", scope="local")
for i in range(8):
Accum[i] = T.float32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[
((tx % 32) % 4) + (4 * ((((tx % 32) // 16 + (tx % 32) % 16 // 4 * 2)) % 4)),
mma_multi_a_col,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) % 4,
mma_multi_b_col + (4 * ((tx % 32) // 8)),
]
T.evaluate(
T.ptx_mma(
"m8n8k4",
"row",
"row",
"fp16",
"fp16",
"fp32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="float32",
)
)
for mma_accum_c_id in range(8):
C[
((tx % 32) % 2)
+ ((mma_accum_c_id // 2 % 2) * 2)
+ 4 * ((tx % 32) // 16)
+ ((tx % 32) % 16 // 4) % 2 * 8,
(tx % 32) % 4 // 2 * 2
+ (tx % 32) % 16 // 8 * 4
+ mma_accum_c_id % 2
+ mma_accum_c_id // 4 * 8,
] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k4_row_row_fp16fp16fp32():
sch = tvm.tir.Schedule(gemm_mma_m8n8k4_row_row_fp16fp16fp32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 7:
# Require at least SM70
return
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-1, 1, [16, 4]).astype("float16")
B_np = np.random.uniform(-1, 1, [4, 16]).astype("float16")
C_np = np.zeros([16, 16]).astype("float32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("float32"), B_np.astype("float32"))
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m8n8k16_row_col_s8s8s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [8, 16], dtype="int8")
B = T.match_buffer(b, [8, 16], dtype="int8")
C = T.match_buffer(c, [8, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([4], "int8", scope="local")
MultiB = T.allocate([4], "int8", scope="local")
Accum = T.allocate([2], "int32", scope="local")
for i in range(2):
Accum[i] = T.int32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[(tx % 32) // 4, mma_multi_a_col + (tx % 32) % 4 * 4]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[(tx % 32) // 4, mma_multi_b_col + (tx % 32) % 4 * 4]
T.evaluate(
T.ptx_mma(
"m8n8k16",
"row",
"col",
"int8",
"int8",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(2):
C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = Accum[mma_accum_c_id]
# This test uses mma instructions that are not available on NVCC 10.1.
# Failure occurs during the external call to nvcc, when attempting to
# generate the .fatbin file.
@tvm.testing.requires_nvcc_version(11)
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k16_row_col_s8s8s32():
sch = tvm.tir.Schedule(gemm_mma_m8n8k16_row_col_s8s8s32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major * 10 + minor < 75:
# Require at least SM75
return
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-10, 10, [8, 16]).astype("int8")
B_np = np.random.uniform(-10, 10, [8, 16]).astype("int8")
C_np = np.zeros([8, 8]).astype("int32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("int32"), B_np.astype("int32").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m8n8k16_row_col_s8u8s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [8, 16], dtype="int8")
B = T.match_buffer(b, [8, 16], dtype="uint8")
C = T.match_buffer(c, [8, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([4], "int8", scope="local")
MultiB = T.allocate([4], "uint8", scope="local")
Accum = T.allocate([2], "int32", scope="local")
for i in range(2):
Accum[i] = T.int32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[(tx % 32) // 4, mma_multi_a_col + (tx % 32) % 4 * 4]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[(tx % 32) // 4, mma_multi_b_col + (tx % 32) % 4 * 4]
T.evaluate(
T.ptx_mma(
"m8n8k16",
"row",
"col",
"int8",
"uint8",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(2):
C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = Accum[mma_accum_c_id]
# This test uses mma instructions that are not available on NVCC 10.1.
# Failure occurs during the external call to nvcc, when attempting to
# generate the .fatbin file.
@tvm.testing.requires_nvcc_version(11)
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k16_row_col_s8u8s32():
sch = tvm.tir.Schedule(gemm_mma_m8n8k16_row_col_s8u8s32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major * 10 + minor < 75:
# Require at least SM75
return
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-10, 10, [8, 16]).astype("int8")
B_np = np.random.uniform(-10, 10, [8, 16]).astype("uint8")
C_np = np.zeros([8, 8]).astype("int32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("int32"), B_np.astype("int32").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m8n8k32_row_col_s4s4s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [8, 32], dtype="int4")
B = T.match_buffer(b, [8, 32], dtype="int4")
C = T.match_buffer(c, [8, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "int4", scope="local")
MultiB = T.allocate([8], "int4", scope="local")
Accum = T.allocate([2], "int32", scope="local")
for i in range(2):
Accum[i] = T.int32(0)
for mma_multi_a_col in T.vectorized(8):
MultiA[mma_multi_a_col] = A[(tx % 32) // 4, mma_multi_a_col + (tx % 32) % 4 * 8]
for mma_multi_b_col in T.vectorized(8):
MultiB[mma_multi_b_col] = B[(tx % 32) // 4, mma_multi_b_col + (tx % 32) % 4 * 8]
T.evaluate(
T.ptx_mma(
"m8n8k32",
"row",
"col",
"int4",
"int4",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(2):
C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = Accum[mma_accum_c_id]
# This test uses mma instructions that are not available on NVCC 10.1.
# Failure occurs during the external call to nvcc, when attempting to
# generate the .fatbin file.
@tvm.testing.requires_nvcc_version(11)
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k32_row_col_s4s4s32():
sch = tvm.tir.Schedule(gemm_mma_m8n8k32_row_col_s4s4s32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major * 10 + minor < 75:
# Require at least SM75
return
cuda_mod = tvm.build(sch.mod, target="cuda")
ctx = tvm.cuda()
A_tvm = tvm.nd.empty([8, 32], "int4", ctx)
B_tvm = tvm.nd.empty([8, 32], "int4", ctx)
C_tvm = tvm.nd.empty([8, 8], "int32", ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
# Currently the correctness is not checked.
# TODO: add correctness checking here.
@T.prim_func
def gemm_mma_m8n8k32_row_col_s4u4s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [8, 32], dtype="int4")
B = T.match_buffer(b, [8, 32], dtype="uint4")
C = T.match_buffer(c, [8, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "int4", scope="local")
MultiB = T.allocate([8], "uint4", scope="local")
Accum = T.allocate([2], "int32", scope="local")
for i in range(2):
Accum[i] = T.int32(0)
for mma_multi_a_col in T.vectorized(8):
MultiA[mma_multi_a_col] = A[(tx % 32) // 4, mma_multi_a_col + (tx % 32) % 4 * 8]
for mma_multi_b_col in T.vectorized(8):
MultiB[mma_multi_b_col] = B[(tx % 32) // 4, mma_multi_b_col + (tx % 32) % 4 * 8]
T.evaluate(
T.ptx_mma(
"m8n8k32",
"row",
"col",
"int4",
"uint4",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(2):
C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = Accum[mma_accum_c_id]
# This test uses mma instructions that are not available on NVCC 10.1.
# Failure occurs during the external call to nvcc, when attempting to
# generate the .fatbin file.
@tvm.testing.requires_nvcc_version(11)
@tvm.testing.requires_cuda
def test_gemm_mma_m8n8k32_row_col_s4u4s32():
sch = tvm.tir.Schedule(gemm_mma_m8n8k32_row_col_s4u4s32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major * 10 + minor < 75:
# Require at least SM75
return
cuda_mod = tvm.build(sch.mod, target="cuda")
ctx = tvm.cuda()
A_tvm = tvm.nd.empty([8, 32], "int4", ctx)
B_tvm = tvm.nd.empty([8, 32], "uint4", ctx)
C_tvm = tvm.nd.empty([8, 8], "int32", ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
# Currently the correctness is not checked.
# TODO: add correctness checking here.
@T.prim_func
def gemm_mma_m16n8k8_row_col_fp16fp16fp32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 8], dtype="float16")
B = T.match_buffer(b, [8, 8], dtype="float16")
C = T.match_buffer(c, [16, 8], dtype="float32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([4], "float16", scope="local")
MultiB = T.allocate([2], "float16", scope="local")
Accum = T.allocate([4], "float32", scope="local")
for i in range(4):
Accum[i] = T.float32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col // 2 * 8, (tx % 32) % 4 * 2 + mma_multi_a_col % 2
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4 + mma_multi_b_col // 2 * 8, (tx % 32) % 4 * 2 + mma_multi_b_col % 2
]
T.evaluate(
T.ptx_mma(
"m16n8k8",
"row",
"col",
"fp16",
"fp16",
"fp32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="float32",
)
)
for mma_accum_c_id in range(4):
C[(tx % 32) // 4 + mma_accum_c_id // 2 * 8, (tx % 32) % 4 * 2 + mma_accum_c_id % 2] = Accum[
mma_accum_c_id
]
@tvm.testing.requires_cuda
def test_gemm_mma_m16n8k8_row_col_fp16fp16fp32():
sch = tvm.tir.Schedule(gemm_mma_m16n8k8_row_col_fp16fp16fp32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 8:
# Require at least SM80
return
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-1, 1, [16, 8]).astype("float16")
B_np = np.random.uniform(-1, 1, [8, 8]).astype("float16")
C_np = np.zeros([16, 8]).astype("float32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("float32"), B_np.astype("float32").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m16n8k16_row_col_fp16fp16fp16(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 16], dtype="float16")
B = T.match_buffer(b, [8, 16], dtype="float16")
C = T.match_buffer(c, [16, 8], dtype="float16")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "float16", scope="local")
MultiB = T.allocate([4], "float16", scope="local")
Accum = T.allocate([4], "float16", scope="local")
for i in range(4):
Accum[i] = T.float32(0)
for mma_multi_a_col in range(8):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col % 4 // 2 * 8,
(tx % 32) % 4 * 2 + mma_multi_a_col % 2 + mma_multi_a_col // 4 * 8,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 2 + mma_multi_b_col % 2 + mma_multi_b_col // 2 * 8,
]
T.evaluate(
T.ptx_mma(
"m16n8k16",
"row",
"col",
"fp16",
"fp16",
"fp16",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="float16",
)
)
for mma_accum_c_id in range(4):
C[
(tx % 32) // 4 + mma_accum_c_id // 2 * 8,
(tx % 32) % 4 * 2 + mma_accum_c_id % 2,
] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m16n8k16_row_col_fp16fp16fp16():
sch = tvm.tir.Schedule(gemm_mma_m16n8k16_row_col_fp16fp16fp16)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 8:
# Require at least SM80
return
cuda_mod = tvm.build(sch.mod, target="cuda")
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-1, 1, [16, 16]).astype("float16")
B_np = np.random.uniform(-1, 1, [8, 16]).astype("float16")
C_np = np.zeros([16, 8]).astype("float16")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("float16"), B_np.astype("float16").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m16n8k16_row_col_fp16fp16fp32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 16], dtype="float16")
B = T.match_buffer(b, [8, 16], dtype="float16")
C = T.match_buffer(c, [16, 8], dtype="float32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "float16", scope="local")
MultiB = T.allocate([4], "float16", scope="local")
Accum = T.allocate([4], "float32", scope="local")
for i in range(4):
Accum[i] = T.float32(0)
for mma_multi_a_col in range(8):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col % 4 // 2 * 8,
(tx % 32) % 4 * 2 + mma_multi_a_col % 2 + mma_multi_a_col // 4 * 8,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 2 + mma_multi_b_col % 2 + mma_multi_b_col // 2 * 8,
]
T.evaluate(
T.ptx_mma(
"m16n8k16",
"row",
"col",
"fp16",
"fp16",
"fp32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="float32",
)
)
for mma_accum_c_id in range(4):
C[
(tx % 32) // 4 + mma_accum_c_id // 2 * 8,
(tx % 32) % 4 * 2 + mma_accum_c_id % 2,
] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m16n8k16_row_col_fp16fp16fp32():
sch = tvm.tir.Schedule(gemm_mma_m16n8k16_row_col_fp16fp16fp32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 8:
# Require at least SM80
return
cuda_mod = tvm.build(sch.mod, target="cuda")
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-1, 1, [16, 16]).astype("float16")
B_np = np.random.uniform(-1, 1, [8, 16]).astype("float16")
C_np = np.zeros([16, 8]).astype("float32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("float32"), B_np.astype("float32").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m16n8k16_row_col_s8s8s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 16], dtype="int8")
B = T.match_buffer(b, [8, 16], dtype="int8")
C = T.match_buffer(c, [16, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "int8", scope="local")
MultiB = T.allocate([4], "int8", scope="local")
Accum = T.allocate([4], "int32", scope="local")
for i in range(4):
Accum[i] = T.int32(0)
for mma_multi_a_col in range(8):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col // 4 * 8,
(tx % 32) % 4 * 4 + mma_multi_a_col % 4,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 4 + mma_multi_b_col,
]
T.evaluate(
T.ptx_mma(
"m16n8k16",
"row",
"col",
"int8",
"int8",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(4):
C[
(tx % 32) // 4 + mma_accum_c_id // 2 * 8,
(tx % 32) % 4 * 2 + mma_accum_c_id % 2,
] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m16n8k16_row_col_s8s8s32():
sch = tvm.tir.Schedule(gemm_mma_m16n8k16_row_col_s8s8s32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 8:
# Require at least SM80
return
cuda_mod = tvm.build(sch.mod, target="cuda")
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-10, 10, [16, 16]).astype("int8")
B_np = np.random.uniform(-10, 10, [8, 16]).astype("int8")
C_np = np.zeros([16, 8]).astype("int32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("int32"), B_np.astype("int32").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m16n8k16_row_col_s8u8s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 16], dtype="int8")
B = T.match_buffer(b, [8, 16], dtype="uint8")
C = T.match_buffer(c, [16, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "int8", scope="local")
MultiB = T.allocate([4], "uint8", scope="local")
Accum = T.allocate([4], "int32", scope="local")
for i in range(4):
Accum[i] = T.int32(0)
for mma_multi_a_col in range(8):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col // 4 * 8,
(tx % 32) % 4 * 4 + mma_multi_a_col % 4,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 4 + mma_multi_b_col,
]
T.evaluate(
T.ptx_mma(
"m16n8k16",
"row",
"col",
"int8",
"uint8",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(4):
C[
(tx % 32) // 4 + mma_accum_c_id // 2 * 8,
(tx % 32) % 4 * 2 + mma_accum_c_id % 2,
] = Accum[mma_accum_c_id]
@tvm.testing.requires_cuda
def test_gemm_mma_m16n8k16_row_col_s8u8s32():
sch = tvm.tir.Schedule(gemm_mma_m16n8k16_row_col_s8u8s32)
arch = tvm.contrib.nvcc.get_target_compute_version()
major, minor = tvm.contrib.nvcc.parse_compute_version(arch)
if major < 8:
# Require at least SM80
return
cuda_mod = tvm.build(sch.mod, target="cuda")
cuda_mod = tvm.build(sch.mod, target="cuda")
A_np = np.random.uniform(-10, 10, [16, 16]).astype("int8")
B_np = np.random.uniform(-10, 10, [8, 16]).astype("uint8")
C_np = np.zeros([16, 8]).astype("int32")
ctx = tvm.cuda()
A_tvm = tvm.nd.array(A_np, ctx)
B_tvm = tvm.nd.array(B_np, ctx)
C_tvm = tvm.nd.array(C_np, ctx)
cuda_mod(A_tvm, B_tvm, C_tvm)
golden = np.matmul(A_np.astype("int32"), B_np.astype("int32").T)
C_numpy = C_tvm.numpy()
tvm.testing.assert_allclose(golden, C_numpy, atol=1e-3, rtol=1e-3)
@T.prim_func
def gemm_mma_m16n8k32_row_col_s8s8s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 32], dtype="int8")
B = T.match_buffer(b, [8, 32], dtype="int8")
C = T.match_buffer(c, [16, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([16], "int8", scope="local")
MultiB = T.allocate([8], "int8", scope="local")
Accum = T.allocate([4], "int32", scope="local")
for i in range(4):
Accum[i] = T.int32(0)
for mma_multi_a_col in range(16):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col % 8 // 4 * 8,
(tx % 32) % 4 * 4 + mma_multi_a_col % 4 + mma_multi_a_col // 8 * 16,
]
for mma_multi_b_col in range(8):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 4 + mma_multi_b_col % 4 + mma_multi_b_col // 4 * 16,
]
T.evaluate(
T.ptx_mma(
"m16n8k32",
"row",
"col",
"int8",
"int8",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,