yzh119 commented on code in PR #15106: URL: https://github.com/apache/tvm/pull/15106#discussion_r1232001155
########## tests/python/unittest/test_tir_schedule_tensorize_mfma.py: ########## @@ -0,0 +1,322 @@ +# 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. +# pylint: disable=missing-docstring +import tvm +from tvm import te +from tvm.tir.tensor_intrin.rocm import ( + shared_16x4_to_local_64x1_layout_A, + shared_4x16_to_local_64x1_layout_B, + shared_16x16_to_local_64x4_layout_A, + shared_16x16_to_local_64x4_layout_B, + shared_16x16_to_local_64x4_layout_C, + ROCM_MFMA_fill_16x16_f32_INTRIN, + ROCM_MFMA_LOAD_16x4_A_SHARED_f32_INTRIN, + ROCM_MFMA_LOAD_16x4_B_SHARED_f32_INTRIN, + ROCM_MFMA_f32f32f32_INTRIN, + ROCM_MFMA_STORE_16x16_f32_INTRIN, + ROCM_MFMA_LOAD_16x16_A_SHARED_f16_INTRIN, + ROCM_MFMA_LOAD_16x16_B_SHARED_f16_INTRIN, + ROCM_MFMA_f16f16f32_INTRIN, + ROCM_MFMA_STORE_16x16_f32_INTRIN, + ROCM_MFMA_fill_16x16_i32_INTRIN, + ROCM_MFMA_LOAD_16x16_A_SHARED_s8_INTRIN, + ROCM_MFMA_LOAD_16x16_B_SHARED_s8_INTRIN, + ROCM_MFMA_s8s8s32_INTRIN, + ROCM_MFMA_STORE_16x16_s32_INTRIN, +) +import tvm.testing +import numpy as np +from tvm.testing.tir import mfma_schedule + + +M = 1024 +N = 1024 +K = 1024 +measure_perf = False +gflops = (N * M * K) * 2 / 1e9 + + +def matmul(m, n, k, in_dtype, out_dtype, b_transposed): + b_shape = (n, k) if b_transposed else (k, n) + a = te.placeholder((m, k), name="A", dtype=in_dtype) + b = te.placeholder(b_shape, name="B", dtype=in_dtype) + k = te.reduce_axis((0, k), name="k") + + def maybe_cast(v): + if in_dtype != out_dtype: + return tvm.tir.Cast(out_dtype, v) + return v + + def maybe_swap(i, j): + if b_transposed: + return j, i + return i, j + + c = te.compute( + (m, n), + lambda i, j: te.sum(maybe_cast( + a[i, k]) * maybe_cast(b[maybe_swap(k, j)]), axis=[k]), + name="C", + ) + return (a, b, c) + + +def run_test( + k_inner, + in_dtype, + out_dtype, + b_transposed, + i_factors, + j_factors, + k_factors, + index_map_A, + index_map_B, + index_map_C, + ldmatrix_a_intrin, + ldmatrix_b_intrin, + mma_intrin, + mma_fill_intrin, + mma_store_intrin, +): + sch = mfma_schedule( + te.create_prim_func( + matmul(M, N, K, in_dtype, out_dtype, b_transposed)), + k_inner, + in_dtype, + b_transposed, + i_factors, + j_factors, + k_factors, + index_map_A, + index_map_B, + index_map_C, + ldmatrix_a_intrin, + ldmatrix_b_intrin, + mma_intrin, + mma_fill_intrin, + mma_store_intrin, + ) + + f = tvm.build(sch.mod["main"], target="rocm -mcpu=gfx90a", name="dense") + + dev = tvm.device("rocm", 0) + if in_dtype == "float32": + a_np = np.random.uniform(size=(M, K)).astype("float32") + + if b_transposed: + b_np = np.random.uniform(size=(N, K)).astype("float32") + c_np = np.dot(a_np.astype("float32"), b_np.astype("float32").transpose()).astype( + out_dtype + ) + else: + b_np = np.random.uniform(size=(K, N)).astype("float32") + c_np = np.dot(a_np.astype("float32"), b_np.astype( + "float32")).astype(out_dtype) + elif in_dtype == "float16": + a_np = np.random.uniform(size=(M, K)).astype("float16") + + if b_transposed: + b_np = np.random.uniform(size=(N, K)).astype("float16") + c_np = np.dot(a_np.astype("float32"), b_np.astype("float32").transpose()).astype( + out_dtype + ) + else: + b_np = np.random.uniform(size=(K, N)).astype("float16") + c_np = np.dot(a_np.astype("float32"), b_np.astype( + "float32")).astype(out_dtype) + else: + a_np = np.random.randint(-128, 128, (M, K)).astype("int8") + + if b_transposed: + b_np = np.random.randint(-128, 128, (N, K)).astype("int8") + c_np = np.dot(a_np.astype("float32"), b_np.astype("float32").transpose()).astype( + "int32" + ) + else: + b_np = np.random.randint(-128, 128, (K, N)).astype("int8") + c_np = np.dot(a_np.astype("float32"), + b_np.astype("float32")).astype("int32") + + a = tvm.nd.array(a_np, dev) + b = tvm.nd.array(b_np, dev) + c = tvm.nd.array(np.zeros((M, N), dtype=out_dtype), dev) + + f(a, b, c) + + if in_dtype != "float16": + # The numpy reference is computed with fp32 precision (otherwise too slow). + # So there is non-trivial accuracy difference if TVM result is computed with fp16 accumulation. + tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-2, atol=1e-2) + + return lambda: f.time_evaluator(f.entry_name, dev, number=500)(a, b, c) + + [email protected]_rocm Review Comment: Does all rocm support matrix core? If not, we can create a Feature like `requires_matrixcore`: https://github.com/apache/tvm/blob/7767de9c39a724df6ca4f2bf028630d99f3ee746/python/tvm/testing/utils.py#L866C1-L871 So that we can skip test if matrixcore is not supported on CI. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
