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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/01/15 05:12:02 UTC

[GitHub] [tvm] junrushao1994 commented on a change in pull request #9909: [PTX-MMA] Add full PTX MMA code generation support

junrushao1994 commented on a change in pull request #9909:
URL: https://github.com/apache/tvm/pull/9909#discussion_r785272559



##########
File path: tests/python/unittest/test_tir_ptx_mma.py
##########
@@ -0,0 +1,1356 @@
+# 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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            0,
+            False,
+            dtype="float64",
+        )
+    )
+    for mma_accum_c_id in range(2):
+        C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = T.load(
+            "float64", 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.random.uniform(-1, 1, [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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            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,
+        ] = T.load("float16", 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.random.uniform(-1, 1, [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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            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,
+        ] = T.load("float32", 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.random.uniform(-1, 1, [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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            0,
+            False,
+            dtype="int32",
+        )
+    )
+    for mma_accum_c_id in range(2):
+        C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = T.load(
+            "int32", Accum, mma_accum_c_id
+        )
+
+
+@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.random.uniform(-1, 1, [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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            0,
+            False,
+            dtype="int32",
+        )
+    )
+    for mma_accum_c_id in range(2):
+        C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = T.load(
+            "int32", Accum, mma_accum_c_id
+        )
+
+
+@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.random.uniform(-1, 1, [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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            0,
+            False,
+            dtype="int32",
+        )
+    )
+    for mma_accum_c_id in range(2):
+        C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = T.load(
+            "int32", Accum, mma_accum_c_id
+        )
+
+
+@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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            0,
+            False,
+            dtype="int32",
+        )
+    )
+    for mma_accum_c_id in range(2):
+        C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = T.load(
+            "int32", Accum, mma_accum_c_id
+        )
+
+
+@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,
+            0,
+            MultiB,
+            0,
+            Accum,
+            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
+        ] = T.load("float32", 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.random.uniform(-1, 1, [16, 8]).astype("float32")

Review comment:
       note that random initialization does follow the convention we have in the tvm repo, so i dont think it confuses anybody. changing to zeros should good too, so i dont have strong opinion




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