| # 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 |
| import tvm.testing |
| from tvm import dlight as dl |
| from tvm.ir import IRModule, assert_structural_equal |
| from tvm.script import ir as I |
| from tvm.script import tir as T |
| from tvm.target import Target |
| |
| |
| def _check(mod_before: IRModule, mod_after: IRModule): |
| target = Target("nvidia/geforce-rtx-3090-ti") |
| with target: |
| mod = dl.ApplyDefaultSchedule( # pylint: disable=not-callable |
| dl.gpu.GeneralReduction(), |
| )(mod_before) |
| assert_structural_equal(mod, mod_after) |
| |
| |
| def test_softmax_1(): |
| # fmt: off |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def main(p_lv44: T.handle, p_output0: T.handle): |
| T.func_attr({"tir.noalias": True}) |
| n, m = T.int64(), T.int64() |
| lv44 = T.match_buffer(p_lv44, (T.int64(1), T.int64(32), n, m)) |
| var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(32), n, m), "float16") |
| # with T.block("root"): |
| T_softmax_maxelem = T.alloc_buffer((T.int64(1), T.int64(32), n)) |
| T_softmax_exp = T.alloc_buffer((T.int64(1), T.int64(32), n, m)) |
| T_softmax_expsum = T.alloc_buffer((T.int64(1), T.int64(32), n)) |
| var_T_softmax_norm_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), n, m)) |
| for i0, i1, i2, k in T.grid(T.int64(1), T.int64(32), n, m): |
| with T.block("T_softmax_maxelem"): |
| v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) |
| T.reads(lv44[v_i0, v_i1, v_i2, v_k]) |
| T.writes(T_softmax_maxelem[v_i0, v_i1, v_i2]) |
| with T.init(): |
| T_softmax_maxelem[v_i0, v_i1, v_i2] = T.float32(-3.4028234663852886e+38) |
| T_softmax_maxelem[v_i0, v_i1, v_i2] = T.max(T_softmax_maxelem[v_i0, v_i1, v_i2], lv44[v_i0, v_i1, v_i2, v_k]) |
| for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m): |
| with T.block("T_softmax_exp"): |
| v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) |
| T.reads(lv44[v_i0, v_i1, v_i2, v_i3], T_softmax_maxelem[v_i0, v_i1, v_i2]) |
| T.writes(T_softmax_exp[v_i0, v_i1, v_i2, v_i3]) |
| T_softmax_exp[v_i0, v_i1, v_i2, v_i3] = T.exp(lv44[v_i0, v_i1, v_i2, v_i3] - T_softmax_maxelem[v_i0, v_i1, v_i2]) |
| for i0, i1, i2, k in T.grid(T.int64(1), T.int64(32), n, m): |
| with T.block("T_softmax_expsum"): |
| v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) |
| T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_k]) |
| T.writes(T_softmax_expsum[v_i0, v_i1, v_i2]) |
| with T.init(): |
| T_softmax_expsum[v_i0, v_i1, v_i2] = T.float32(0) |
| T_softmax_expsum[v_i0, v_i1, v_i2] = T_softmax_expsum[v_i0, v_i1, v_i2] + T_softmax_exp[v_i0, v_i1, v_i2, v_k] |
| for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m): |
| with T.block("T_softmax_norm"): |
| v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) |
| T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_i3], T_softmax_expsum[v_i0, v_i1, v_i2]) |
| T.writes(var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3]) |
| T.block_attr({"axis": 3}) |
| var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3] = T_softmax_exp[v_i0, v_i1, v_i2, v_i3] / T_softmax_expsum[v_i0, v_i1, v_i2] |
| for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m): |
| with T.block("compute"): |
| v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) |
| T.reads(var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3]) |
| T.writes(var_compute_intermediate[v_i0, v_i1, v_i2, v_i3]) |
| var_compute_intermediate[v_i0, v_i1, v_i2, v_i3] = T.Cast("float16", var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3]) |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def main(p_lv44: T.handle, p_output0: T.handle): |
| T.func_attr({"tir.is_scheduled": True, "tir.noalias": True}) |
| n, m = T.int64(), T.int64() |
| lv44 = T.match_buffer(p_lv44, (T.int64(1), T.int64(32), n, m)) |
| var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(32), n, m), "float16") |
| # with T.block("root"): |
| T_softmax_maxelem_shared = T.alloc_buffer((T.int64(1), T.int64(32), n), scope="shared") |
| T_softmax_expsum_shared = T.alloc_buffer((T.int64(1), T.int64(32), n), scope="shared") |
| for ax0_ax1_fused in T.thread_binding(n * T.int64(32), thread="blockIdx.x"): |
| for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): |
| for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_fused_0 in T.serial((m + T.int64(255)) // T.int64(256), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_maxelem"): |
| v0 = T.axis.spatial(T.int64(32), ax0_ax1_fused // n + ax0) |
| v1 = T.axis.spatial(n, ax0_ax1_fused % n + ax1) |
| v2 = T.axis.reduce(m, ax2_fused_0 * T.int64(256) + ax2_fused_1) |
| T.where(ax2_fused_0 * T.int64(256) + ax2_fused_1 < m) |
| T.reads(lv44[T.int64(0), v0, v1, v2]) |
| T.writes(T_softmax_maxelem_shared[T.int64(0), v0, v1]) |
| with T.init(): |
| T_softmax_maxelem_shared[T.int64(0), v0, v1] = T.float32(-3.4028234663852886e+38) |
| T_softmax_maxelem_shared[T.int64(0), v0, v1] = T.max(T_softmax_maxelem_shared[T.int64(0), v0, v1], lv44[T.int64(0), v0, v1, v2]) |
| for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): |
| for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_fused_0 in T.serial((m + T.int64(255)) // T.int64(256), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_expsum"): |
| v0 = T.axis.spatial(T.int64(32), ax0_ax1_fused // n + ax0) |
| v1 = T.axis.spatial(n, ax0_ax1_fused % n + ax1) |
| v2 = T.axis.reduce(m, ax2_fused_0 * T.int64(256) + ax2_fused_1) |
| T.where(ax2_fused_0 * T.int64(256) + ax2_fused_1 < m) |
| T.reads(lv44[T.int64(0), v0, v1, v2], T_softmax_maxelem_shared[T.int64(0), v0, v1]) |
| T.writes(T_softmax_expsum_shared[T.int64(0), v0, v1]) |
| with T.init(): |
| T_softmax_expsum_shared[T.int64(0), v0, v1] = T.float32(0) |
| T_softmax_expsum_shared[T.int64(0), v0, v1] = T_softmax_expsum_shared[T.int64(0), v0, v1] + T.exp(lv44[T.int64(0), v0, v1, v2] - T_softmax_maxelem_shared[T.int64(0), v0, v1]) |
| for ax2_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_0 in T.serial((m + T.int64(255)) // T.int64(256), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("compute"): |
| v0 = T.axis.spatial(T.int64(32), ax0_ax1_fused // n) |
| v1 = T.axis.spatial(n, ax0_ax1_fused % n) |
| v2 = T.axis.spatial(m, ax2_0 * T.int64(256) + ax2_1) |
| T.where(ax2_0 * T.int64(256) + ax2_1 < m) |
| T.reads(lv44[T.int64(0), v0, v1, v2], T_softmax_maxelem_shared[T.int64(0), v0, v1], T_softmax_expsum_shared[T.int64(0), v0, v1]) |
| T.writes(var_compute_intermediate[T.int64(0), v0, v1, v2]) |
| var_compute_intermediate[T.int64(0), v0, v1, v2] = T.Cast("float16", T.exp(lv44[T.int64(0), v0, v1, v2] - T_softmax_maxelem_shared[T.int64(0), v0, v1]) / T_softmax_expsum_shared[T.int64(0), v0, v1]) |
| # fmt: on |
| _check(Before, After) |
| |
| |
| def test_softmax_2(): |
| # fmt: off |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def main(A: T.Buffer((T.int64(1), T.int64(1), T.int64(32000)), "float32"), T_softmax_norm: T.Buffer((T.int64(1), T.int64(1), T.int64(32000)), "float32")): |
| # with T.block("root"): |
| T_softmax_maxelem = T.alloc_buffer((T.int64(1), T.int64(1))) |
| T_softmax_exp = T.alloc_buffer((T.int64(1), T.int64(1), T.int64(32000))) |
| T_softmax_expsum = T.alloc_buffer((T.int64(1), T.int64(1))) |
| for i0, i1, k in T.grid(T.int64(1), T.int64(1), T.int64(32000)): |
| with T.block("T_softmax_maxelem"): |
| v_i0, v_i1, v_k = T.axis.remap("SSR", [i0, i1, k]) |
| T.reads(A[v_i0, v_i1, v_k]) |
| T.writes(T_softmax_maxelem[v_i0, v_i1]) |
| with T.init(): |
| T_softmax_maxelem[v_i0, v_i1] = T.float32(-3.4028234663852886e+38) |
| T_softmax_maxelem[v_i0, v_i1] = T.max(T_softmax_maxelem[v_i0, v_i1], A[v_i0, v_i1, v_k]) |
| for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(32000)): |
| with T.block("T_softmax_exp"): |
| v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) |
| T.reads(A[v_i0, v_i1, v_i2], T_softmax_maxelem[v_i0, v_i1]) |
| T.writes(T_softmax_exp[v_i0, v_i1, v_i2]) |
| T_softmax_exp[v_i0, v_i1, v_i2] = T.exp(A[v_i0, v_i1, v_i2] - T_softmax_maxelem[v_i0, v_i1]) |
| for i0, i1, k in T.grid(T.int64(1), T.int64(1), T.int64(32000)): |
| with T.block("T_softmax_expsum"): |
| v_i0, v_i1, v_k = T.axis.remap("SSR", [i0, i1, k]) |
| T.reads(T_softmax_exp[v_i0, v_i1, v_k]) |
| T.writes(T_softmax_expsum[v_i0, v_i1]) |
| with T.init(): |
| T_softmax_expsum[v_i0, v_i1] = T.float32(0) |
| T_softmax_expsum[v_i0, v_i1] = T_softmax_expsum[v_i0, v_i1] + T_softmax_exp[v_i0, v_i1, v_k] |
| for i0, i1, i2 in T.grid(T.int64(1), T.int64(1), T.int64(32000)): |
| with T.block("T_softmax_norm"): |
| v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) |
| T.reads(T_softmax_exp[v_i0, v_i1, v_i2], T_softmax_expsum[v_i0, v_i1]) |
| T.writes(T_softmax_norm[v_i0, v_i1, v_i2]) |
| T.block_attr({"axis": 2}) |
| T_softmax_norm[v_i0, v_i1, v_i2] = T_softmax_exp[v_i0, v_i1, v_i2] / T_softmax_expsum[v_i0, v_i1] |
| |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def main(A: T.Buffer((T.int64(1), T.int64(1), T.int64(32000)), "float32"), T_softmax_norm: T.Buffer((T.int64(1), T.int64(1), T.int64(32000)), "float32")): |
| T.func_attr({"tir.is_scheduled": True}) |
| # with T.block("root"): |
| T_softmax_maxelem_shared = T.alloc_buffer((T.int64(1), T.int64(1)), scope="shared") |
| T_softmax_expsum_shared = T.alloc_buffer((T.int64(1), T.int64(1)), scope="shared") |
| for ax0_fused in T.thread_binding(T.int64(1), thread="blockIdx.x"): |
| for ax0 in range(T.int64(1)): |
| for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_fused_0 in T.serial(T.int64(125), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_maxelem"): |
| v0 = T.axis.spatial(T.int64(1), ax0) |
| v1 = T.axis.reduce(T.int64(32000), ax1_fused_0 * T.int64(256) + ax1_fused_1) |
| T.reads(A[T.int64(0), T.int64(0), v1]) |
| T.writes(T_softmax_maxelem_shared[T.int64(0), T.int64(0)]) |
| with T.init(): |
| T_softmax_maxelem_shared[T.int64(0), T.int64(0)] = T.float32(-3.4028234663852886e+38) |
| T_softmax_maxelem_shared[T.int64(0), T.int64(0)] = T.max(T_softmax_maxelem_shared[T.int64(0), T.int64(0)], A[T.int64(0), T.int64(0), v1]) |
| for ax0 in range(T.int64(1)): |
| for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_fused_0 in T.serial(T.int64(125), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_expsum"): |
| v0 = T.axis.spatial(T.int64(1), ax0) |
| v1 = T.axis.reduce(T.int64(32000), ax1_fused_0 * T.int64(256) + ax1_fused_1) |
| T.reads(A[T.int64(0), T.int64(0), v1], T_softmax_maxelem_shared[T.int64(0), T.int64(0)]) |
| T.writes(T_softmax_expsum_shared[T.int64(0), T.int64(0)]) |
| with T.init(): |
| T_softmax_expsum_shared[T.int64(0), T.int64(0)] = T.float32(0) |
| T_softmax_expsum_shared[T.int64(0), T.int64(0)] = T_softmax_expsum_shared[T.int64(0), T.int64(0)] + T.exp(A[T.int64(0), T.int64(0), v1] - T_softmax_maxelem_shared[T.int64(0), T.int64(0)]) |
| for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_0 in T.serial(T.int64(125), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_norm"): |
| v0 = T.axis.spatial(T.int64(1), T.int64(0)) |
| v1 = T.axis.spatial(T.int64(32000), ax1_0 * T.int64(256) + ax1_1) |
| T.reads(A[T.int64(0), T.int64(0), v1], T_softmax_maxelem_shared[T.int64(0), T.int64(0)], T_softmax_expsum_shared[T.int64(0), T.int64(0)]) |
| T.writes(T_softmax_norm[T.int64(0), T.int64(0), v1]) |
| T.block_attr({"axis": 2}) |
| T_softmax_norm[T.int64(0), T.int64(0), v1] = T.exp(A[T.int64(0), T.int64(0), v1] - T_softmax_maxelem_shared[T.int64(0), T.int64(0)]) / T_softmax_expsum_shared[T.int64(0), T.int64(0)] |
| |
| # fmt: on |
| _check(Before, After) |
| |
| |
| def test_softmax_3(): |
| # fmt: off |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def main(input: T.Buffer((T.int64(1), T.int64(4), T.int64(32), T.int64(8192)), "float32"), T_softmax_norm: T.Buffer((T.int64(1), T.int64(4), T.int64(32), T.int64(8192)), "float32")): |
| # with T.block("root"): |
| T_softmax_maxelem = T.alloc_buffer((T.int64(1), T.int64(4), T.int64(8192))) |
| T_softmax_exp = T.alloc_buffer((T.int64(1), T.int64(4), T.int64(32), T.int64(8192))) |
| T_softmax_expsum = T.alloc_buffer((T.int64(1), T.int64(4), T.int64(8192))) |
| for i0, i1, i2, k in T.grid(T.int64(1), T.int64(4), T.int64(8192), T.int64(32)): |
| with T.block("T_softmax_maxelem"): |
| v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) |
| T.reads(input[v_i0, v_i1, v_k, v_i2]) |
| T.writes(T_softmax_maxelem[v_i0, v_i1, v_i2]) |
| with T.init(): |
| T_softmax_maxelem[v_i0, v_i1, v_i2] = T.float32(-340282346638528859811704183484516925440.0) |
| T_softmax_maxelem[v_i0, v_i1, v_i2] = T.max(T_softmax_maxelem[v_i0, v_i1, v_i2], input[v_i0, v_i1, v_k, v_i2]) |
| for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(4), T.int64(32), T.int64(8192)): |
| with T.block("T_softmax_exp"): |
| v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) |
| T.reads(input[v_i0, v_i1, v_i2, v_i3], T_softmax_maxelem[v_i0, v_i1, v_i3]) |
| T.writes(T_softmax_exp[v_i0, v_i1, v_i2, v_i3]) |
| T_softmax_exp[v_i0, v_i1, v_i2, v_i3] = T.exp(input[v_i0, v_i1, v_i2, v_i3] - T_softmax_maxelem[v_i0, v_i1, v_i3]) |
| for i0, i1, i2, k in T.grid(T.int64(1), T.int64(4), T.int64(8192), T.int64(32)): |
| with T.block("T_softmax_expsum"): |
| v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) |
| T.reads(T_softmax_exp[v_i0, v_i1, v_k, v_i2]) |
| T.writes(T_softmax_expsum[v_i0, v_i1, v_i2]) |
| with T.init(): |
| T_softmax_expsum[v_i0, v_i1, v_i2] = T.float32(0.0) |
| T_softmax_expsum[v_i0, v_i1, v_i2] = T_softmax_expsum[v_i0, v_i1, v_i2] + T_softmax_exp[v_i0, v_i1, v_k, v_i2] |
| for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(4), T.int64(32), T.int64(8192)): |
| with T.block("T_softmax_norm"): |
| v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) |
| T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_i3], T_softmax_expsum[v_i0, v_i1, v_i3]) |
| T.writes(T_softmax_norm[v_i0, v_i1, v_i2, v_i3]) |
| T.block_attr({"axis": 2}) |
| T_softmax_norm[v_i0, v_i1, v_i2, v_i3] = T_softmax_exp[v_i0, v_i1, v_i2, v_i3] / T_softmax_expsum[v_i0, v_i1, v_i3] |
| |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def main(input: T.Buffer((T.int64(1), T.int64(4), T.int64(32), T.int64(8192)), "float32"), T_softmax_norm: T.Buffer((T.int64(1), T.int64(4), T.int64(32), T.int64(8192)), "float32")): |
| T.func_attr({"tir.is_scheduled": True}) |
| # with T.block("root"): |
| T_softmax_maxelem_shared = T.alloc_buffer((T.int64(1), T.int64(4), T.int64(8192)), scope="shared") |
| T_softmax_expsum_shared = T.alloc_buffer((T.int64(1), T.int64(4), T.int64(8192)), scope="shared") |
| for ax0_ax2_fused in T.thread_binding(T.int64(32768), thread="blockIdx.x"): |
| for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): |
| for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_fused_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_maxelem"): |
| v0 = T.axis.spatial(T.int64(4), ax0_ax2_fused // T.int64(8192) + ax0) |
| v1 = T.axis.spatial(T.int64(8192), ax0_ax2_fused % T.int64(8192) + ax1) |
| v2 = T.axis.reduce(T.int64(32), ax2_fused_0 * T.int64(256) + ax2_fused_1) |
| T.where(ax2_fused_0 * T.int64(256) + ax2_fused_1 < T.int64(32)) |
| T.reads(input[T.int64(0), v0, v2, v1]) |
| T.writes(T_softmax_maxelem_shared[T.int64(0), v0, v1]) |
| with T.init(): |
| T_softmax_maxelem_shared[T.int64(0), v0, v1] = T.float32(-340282346638528859811704183484516925440.0) |
| T_softmax_maxelem_shared[T.int64(0), v0, v1] = T.max(T_softmax_maxelem_shared[T.int64(0), v0, v1], input[T.int64(0), v0, v2, v1]) |
| for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): |
| for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_fused_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_expsum"): |
| v0 = T.axis.spatial(T.int64(4), ax0_ax2_fused // T.int64(8192) + ax0) |
| v1 = T.axis.spatial(T.int64(8192), ax0_ax2_fused % T.int64(8192) + ax1) |
| v2 = T.axis.reduce(T.int64(32), ax2_fused_0 * T.int64(256) + ax2_fused_1) |
| T.where(ax2_fused_0 * T.int64(256) + ax2_fused_1 < T.int64(32)) |
| T.reads(input[T.int64(0), v0, v2, v1], T_softmax_maxelem_shared[T.int64(0), v0, v1]) |
| T.writes(T_softmax_expsum_shared[T.int64(0), v0, v1]) |
| with T.init(): |
| T_softmax_expsum_shared[T.int64(0), v0, v1] = T.float32(0.0) |
| T_softmax_expsum_shared[T.int64(0), v0, v1] = T_softmax_expsum_shared[T.int64(0), v0, v1] + T.exp(input[T.int64(0), v0, v2, v1] - T_softmax_maxelem_shared[T.int64(0), v0, v1]) |
| for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_0 in T.serial(T.int64(1), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_softmax_norm"): |
| v0 = T.axis.spatial(T.int64(4), ax0_ax2_fused // T.int64(8192)) |
| v1 = T.axis.spatial(T.int64(32), ax1_0 * T.int64(256) + ax1_1) |
| v2 = T.axis.spatial(T.int64(8192), ax0_ax2_fused % T.int64(8192)) |
| T.where(ax1_0 * T.int64(256) + ax1_1 < T.int64(32)) |
| T.reads(input[T.int64(0), v0, v1, v2], T_softmax_maxelem_shared[T.int64(0), v0, v2], T_softmax_expsum_shared[T.int64(0), v0, v2]) |
| T.writes(T_softmax_norm[T.int64(0), v0, v1, v2]) |
| T.block_attr({"axis": 2}) |
| T_softmax_norm[T.int64(0), v0, v1, v2] = T.exp(input[T.int64(0), v0, v1, v2] - T_softmax_maxelem_shared[T.int64(0), v0, v2]) / T_softmax_expsum_shared[T.int64(0), v0, v2] |
| # fmt: on |
| _check(Before, After) |
| |
| |
| def test_layer_norm(): |
| # fmt: off |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def main(p_lv6: T.handle, weight1: T.Buffer((T.int64(2560),), "float32"), bias: T.Buffer((T.int64(2560),), "float32"), p_output0: T.handle): |
| T.func_attr({"tir.noalias": True}) |
| n = T.int64() |
| lv6 = T.match_buffer(p_lv6, (T.int64(1), n, T.int64(2560))) |
| var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), n, T.int64(2560)), "float16") |
| # with T.block("root"): |
| A_red_temp_v0 = T.alloc_buffer((T.int64(1), n)) |
| A_red_temp_v1 = T.alloc_buffer((T.int64(1), n)) |
| var_T_layer_norm_intermediate = T.alloc_buffer((T.int64(1), n, T.int64(2560))) |
| for ax0, ax1, k2 in T.grid(T.int64(1), n, T.int64(2560)): |
| with T.block("A_red_temp"): |
| v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) |
| T.reads(lv6[v_ax0, v_ax1, v_k2]) |
| T.writes(A_red_temp_v0[v_ax0, v_ax1], A_red_temp_v1[v_ax0, v_ax1]) |
| with T.init(): |
| A_red_temp_v0[v_ax0, v_ax1] = T.float32(0) |
| A_red_temp_v1[v_ax0, v_ax1] = T.float32(0) |
| v_A_red_temp_v0: T.float32 = A_red_temp_v0[v_ax0, v_ax1] + lv6[v_ax0, v_ax1, v_k2] |
| v_A_red_temp_v1: T.float32 = A_red_temp_v1[v_ax0, v_ax1] + lv6[v_ax0, v_ax1, v_k2] * lv6[v_ax0, v_ax1, v_k2] |
| A_red_temp_v0[v_ax0, v_ax1] = v_A_red_temp_v0 |
| A_red_temp_v1[v_ax0, v_ax1] = v_A_red_temp_v1 |
| for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(2560)): |
| with T.block("T_layer_norm"): |
| v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) |
| T.reads(lv6[v_ax0, v_ax1, v_ax2], A_red_temp_v0[v_ax0, v_ax1], A_red_temp_v1[v_ax0, v_ax1], weight1[v_ax2], bias[v_ax2]) |
| T.writes(var_T_layer_norm_intermediate[v_ax0, v_ax1, v_ax2]) |
| var_T_layer_norm_intermediate[v_ax0, v_ax1, v_ax2] = (lv6[v_ax0, v_ax1, v_ax2] - A_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00039062500000000002)) * T.rsqrt(A_red_temp_v1[v_ax0, v_ax1] * T.float32(0.00039062500000000002) - A_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00039062500000000002) * (A_red_temp_v0[v_ax0, v_ax1] * T.float32(0.00039062500000000002)) + T.float32(1.0000000000000001e-05)) * weight1[v_ax2] + bias[v_ax2] |
| for i0, i1, i2 in T.grid(T.int64(1), n, T.int64(2560)): |
| with T.block("compute"): |
| v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2]) |
| T.reads(var_T_layer_norm_intermediate[v_i0, v_i1, v_i2]) |
| T.writes(var_compute_intermediate[v_i0, v_i1, v_i2]) |
| var_compute_intermediate[v_i0, v_i1, v_i2] = T.Cast("float16", var_T_layer_norm_intermediate[v_i0, v_i1, v_i2]) |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def main(p_lv6: T.handle, weight1: T.Buffer((T.int64(2560),), "float32"), bias: T.Buffer((T.int64(2560),), "float32"), p_output0: T.handle): |
| T.func_attr({"tir.is_scheduled": True, "tir.noalias": True}) |
| n = T.int64() |
| lv6 = T.match_buffer(p_lv6, (T.int64(1), n, T.int64(2560))) |
| var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), n, T.int64(2560)), "float16") |
| # with T.block("root"): |
| A_red_temp_v0_shared = T.alloc_buffer((T.int64(1), n), scope="shared") |
| A_red_temp_v1_shared = T.alloc_buffer((T.int64(1), n), scope="shared") |
| for ax0_fused in T.thread_binding(n, thread="blockIdx.x"): |
| for ax0 in range(T.int64(1)): |
| for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_fused_0 in T.serial(T.int64(10), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("A_red_temp"): |
| v0 = T.axis.spatial(n, ax0_fused + ax0) |
| v1 = T.axis.reduce(T.int64(2560), ax1_fused_0 * T.int64(256) + ax1_fused_1) |
| T.reads(lv6[T.int64(0), v0, v1]) |
| T.writes(A_red_temp_v0_shared[T.int64(0), v0], A_red_temp_v1_shared[T.int64(0), v0]) |
| with T.init(): |
| A_red_temp_v0_shared[T.int64(0), v0] = T.float32(0) |
| A_red_temp_v1_shared[T.int64(0), v0] = T.float32(0) |
| v_A_red_temp_v0: T.float32 = A_red_temp_v0_shared[T.int64(0), v0] + lv6[T.int64(0), v0, v1] |
| v_A_red_temp_v1: T.float32 = A_red_temp_v1_shared[T.int64(0), v0] + lv6[T.int64(0), v0, v1] * lv6[T.int64(0), v0, v1] |
| A_red_temp_v0_shared[T.int64(0), v0] = v_A_red_temp_v0 |
| A_red_temp_v1_shared[T.int64(0), v0] = v_A_red_temp_v1 |
| for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_0 in T.serial(T.int64(10), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("compute"): |
| v0 = T.axis.spatial(n, ax0_fused) |
| v1 = T.axis.spatial(T.int64(2560), ax1_0 * T.int64(256) + ax1_1) |
| T.reads(lv6[T.int64(0), v0, v1], A_red_temp_v0_shared[T.int64(0), v0], A_red_temp_v1_shared[T.int64(0), v0], weight1[v1], bias[v1]) |
| T.writes(var_compute_intermediate[T.int64(0), v0, v1]) |
| var_compute_intermediate[T.int64(0), v0, v1] = T.Cast("float16", (lv6[T.int64(0), v0, v1] - A_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00039062500000000002)) * T.rsqrt(A_red_temp_v1_shared[T.int64(0), v0] * T.float32(0.00039062500000000002) - A_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00039062500000000002) * (A_red_temp_v0_shared[T.int64(0), v0] * T.float32(0.00039062500000000002)) + T.float32(1.0000000000000001e-05)) * weight1[v1] + bias[v1]) |
| # fmt: on |
| _check(Before, After) |
| |
| |
| def test_rms_norm(): |
| # fmt: off |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def main(var_A: T.handle, B: T.Buffer((T.int64(4096),), "float16"), var_rms_norm: T.handle): |
| T.func_attr({"op_pattern": 4, "tir.noalias": True}) |
| n = T.int64() |
| A = T.match_buffer(var_A, (T.int64(1), n, T.int64(4096)), "float16") |
| rms_norm_1 = T.match_buffer(var_rms_norm, (T.int64(1), n, T.int64(4096)), "float16") |
| # with T.block("root"): |
| Ared_temp = T.alloc_buffer((T.int64(1), n)) |
| for bsz, i, k in T.grid(T.int64(1), n, T.int64(4096)): |
| with T.block("Ared_temp"): |
| v_bsz, v_i, v_k = T.axis.remap("SSR", [bsz, i, k]) |
| T.reads(A[v_bsz, v_i, v_k]) |
| T.writes(Ared_temp[v_bsz, v_i]) |
| with T.init(): |
| Ared_temp[v_bsz, v_i] = T.float32(0) |
| Ared_temp[v_bsz, v_i] = Ared_temp[v_bsz, v_i] + T.Cast("float32", A[v_bsz, v_i, v_k]) * T.Cast("float32", A[v_bsz, v_i, v_k]) |
| for bsz, i, k in T.grid(T.int64(1), n, T.int64(4096)): |
| with T.block("rms_norm"): |
| v_bsz, v_i, v_k = T.axis.remap("SSS", [bsz, i, k]) |
| T.reads(B[v_k], A[v_bsz, v_i, v_k], Ared_temp[v_bsz, v_i]) |
| T.writes(rms_norm_1[v_bsz, v_i, v_k]) |
| rms_norm_1[v_bsz, v_i, v_k] = T.Cast("float16", T.Cast("float32", B[v_k]) * (T.Cast("float32", A[v_bsz, v_i, v_k]) / T.sqrt(Ared_temp[v_bsz, v_i] * T.float32(0.000244140625) + T.float32(9.9999999999999995e-07)))) |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def main(var_A: T.handle, B: T.Buffer((T.int64(4096),), "float16"), var_rms_norm: T.handle): |
| T.func_attr({"op_pattern": 4, "tir.is_scheduled": True, "tir.noalias": True}) |
| n = T.int64() |
| A = T.match_buffer(var_A, (T.int64(1), n, T.int64(4096)), "float16") |
| rms_norm_1 = T.match_buffer(var_rms_norm, (T.int64(1), n, T.int64(4096)), "float16") |
| # with T.block("root"): |
| Ared_temp_shared = T.alloc_buffer((T.int64(1), n), scope="shared") |
| for ax0_fused in T.thread_binding(n, thread="blockIdx.x"): |
| for ax0 in range(T.int64(1)): |
| for ax1_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_fused_0 in T.serial(T.int64(16), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("Ared_temp"): |
| v0 = T.axis.spatial(n, ax0_fused + ax0) |
| v1 = T.axis.reduce(T.int64(4096), ax1_fused_0 * T.int64(256) + ax1_fused_1) |
| T.reads(A[T.int64(0), v0, v1]) |
| T.writes(Ared_temp_shared[T.int64(0), v0]) |
| with T.init(): |
| Ared_temp_shared[T.int64(0), v0] = T.float32(0) |
| Ared_temp_shared[T.int64(0), v0] = Ared_temp_shared[T.int64(0), v0] + T.Cast("float32", A[T.int64(0), v0, v1]) * T.Cast("float32", A[T.int64(0), v0, v1]) |
| for ax1_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax1_0 in T.serial(T.int64(16), annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("rms_norm"): |
| v0 = T.axis.spatial(n, ax0_fused) |
| v1 = T.axis.spatial(T.int64(4096), ax1_0 * T.int64(256) + ax1_1) |
| T.reads(B[v1], A[T.int64(0), v0, v1], Ared_temp_shared[T.int64(0), v0]) |
| T.writes(rms_norm_1[T.int64(0), v0, v1]) |
| rms_norm_1[T.int64(0), v0, v1] = T.Cast("float16", T.Cast("float32", B[v1]) * (T.Cast("float32", A[T.int64(0), v0, v1]) / T.sqrt(Ared_temp_shared[T.int64(0), v0] * T.float32(0.000244140625) + T.float32(9.9999999999999995e-07)))) |
| # fmt: on |
| _check(Before, After) |
| |
| |
| def test_group_norm(): |
| # fmt: off |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def main(A: T.Buffer((1, 2048), "float32"), B: T.Buffer((2048,), "float32"), C: T.Buffer((2048,), "float32"), T_reshape: T.Buffer((1, 2048), "float32")): |
| T.func_attr({"tir.noalias": True}) |
| T_reshape_1 = T.alloc_buffer((1, 32, 64)) |
| A_red_temp_v0 = T.alloc_buffer((1, 32)) |
| A_red_temp_v1 = T.alloc_buffer((1, 32)) |
| T_reshape_2 = T.alloc_buffer((32, 64)) |
| T_reshape_3 = T.alloc_buffer((32, 64)) |
| T_group_norm = T.alloc_buffer((1, 32, 64)) |
| for ax0, ax1, ax2 in T.grid(1, 32, 64): |
| with T.block("T_reshape"): |
| v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) |
| T.reads(A[0, (v_ax1 * 64 + v_ax2) % 2048]) |
| T.writes(T_reshape_1[v_ax0, v_ax1, v_ax2]) |
| T_reshape_1[v_ax0, v_ax1, v_ax2] = A[0, (v_ax1 * 64 + v_ax2) % 2048] |
| for ax0, ax1, k2 in T.grid(1, 32, 64): |
| with T.block("A_red_temp"): |
| v_ax0, v_ax1, v_k2 = T.axis.remap("SSR", [ax0, ax1, k2]) |
| T.reads(T_reshape_1[v_ax0, v_ax1, v_k2]) |
| T.writes(A_red_temp_v0[v_ax0, v_ax1], A_red_temp_v1[v_ax0, v_ax1]) |
| with T.init(): |
| A_red_temp_v0[v_ax0, v_ax1] = T.float32(0) |
| A_red_temp_v1[v_ax0, v_ax1] = T.float32(0) |
| v_A_red_temp_v0: T.float32 = A_red_temp_v0[v_ax0, v_ax1] + T_reshape_1[v_ax0, v_ax1, v_k2] |
| v_A_red_temp_v1: T.float32 = A_red_temp_v1[v_ax0, v_ax1] + T_reshape_1[v_ax0, v_ax1, v_k2] * T_reshape_1[v_ax0, v_ax1, v_k2] |
| A_red_temp_v0[v_ax0, v_ax1] = v_A_red_temp_v0 |
| A_red_temp_v1[v_ax0, v_ax1] = v_A_red_temp_v1 |
| for ax0, ax1 in T.grid(32, 64): |
| with T.block("T_reshape_1"): |
| v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) |
| T.reads(B[(v_ax0 * 64 + v_ax1) % 2048]) |
| T.writes(T_reshape_2[v_ax0, v_ax1]) |
| T_reshape_2[v_ax0, v_ax1] = B[(v_ax0 * 64 + v_ax1) % 2048] |
| for ax0, ax1 in T.grid(32, 64): |
| with T.block("T_reshape_2"): |
| v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) |
| T.reads(C[(v_ax0 * 64 + v_ax1) % 2048]) |
| T.writes(T_reshape_3[v_ax0, v_ax1]) |
| T_reshape_3[v_ax0, v_ax1] = C[(v_ax0 * 64 + v_ax1) % 2048] |
| for ax0, ax1, ax2 in T.grid(1, 32, 64): |
| with T.block("T_group_norm"): |
| v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) |
| T.reads(T_reshape_1[v_ax0, v_ax1, v_ax2], A_red_temp_v0[v_ax0, v_ax1], A_red_temp_v1[v_ax0, v_ax1], T_reshape_2[v_ax1, v_ax2], T_reshape_3[v_ax1, v_ax2]) |
| T.writes(T_group_norm[v_ax0, v_ax1, v_ax2]) |
| T_group_norm[v_ax0, v_ax1, v_ax2] = (T_reshape_1[v_ax0, v_ax1, v_ax2] - A_red_temp_v0[v_ax0, v_ax1] * T.float32(0.015625)) * T.rsqrt(A_red_temp_v1[v_ax0, v_ax1] * T.float32(0.015625) - A_red_temp_v0[v_ax0, v_ax1] * T.float32(0.015625) * (A_red_temp_v0[v_ax0, v_ax1] * T.float32(0.015625)) + T.float32(1.0000000000000001e-05)) * T_reshape_2[v_ax1, v_ax2] + T_reshape_3[v_ax1, v_ax2] |
| for ax0, ax1 in T.grid(1, 2048): |
| with T.block("T_reshape_3"): |
| v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) |
| T.reads(T_group_norm[0, v_ax1 % 2048 // 64, v_ax1 % 64]) |
| T.writes(T_reshape[v_ax0, v_ax1]) |
| T_reshape[v_ax0, v_ax1] = T_group_norm[0, v_ax1 % 2048 // 64, v_ax1 % 64] |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def main(A: T.Buffer((1, 2048), "float32"), B: T.Buffer((2048,), "float32"), C: T.Buffer((2048,), "float32"), T_reshape: T.Buffer((1, 2048), "float32")): |
| T.func_attr({"tir.is_scheduled": True, "tir.noalias": True}) |
| # with T.block("root"): |
| A_red_temp_v0_shared = T.alloc_buffer((1, 32), scope="shared") |
| A_red_temp_v1_shared = T.alloc_buffer((1, 32), scope="shared") |
| for ax0_fused in T.thread_binding(T.int64(1), thread="blockIdx.x"): |
| for ax0 in range(32): |
| for ax1_fused_1 in T.thread_binding(256, thread="threadIdx.x"): |
| for ax1_fused_0 in T.serial(1, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("A_red_temp"): |
| v0 = T.axis.spatial(32, ax0) |
| v1 = T.axis.reduce(64, ax1_fused_0 * 256 + ax1_fused_1) |
| T.where(ax1_fused_0 * 256 + ax1_fused_1 < 64) |
| T.reads(A[0, v0 * 64 + v1]) |
| T.writes(A_red_temp_v0_shared[0, v0], A_red_temp_v1_shared[0, v0]) |
| with T.init(): |
| A_red_temp_v0_shared[0, v0] = T.float32(0) |
| A_red_temp_v1_shared[0, v0] = T.float32(0) |
| v_A_red_temp_v0: T.float32 = A_red_temp_v0_shared[0, v0] + A[0, v0 * 64 + v1] |
| v_A_red_temp_v1: T.float32 = A_red_temp_v1_shared[0, v0] + A[0, v0 * 64 + v1] * A[0, v0 * 64 + v1] |
| A_red_temp_v0_shared[0, v0] = v_A_red_temp_v0 |
| A_red_temp_v1_shared[0, v0] = v_A_red_temp_v1 |
| for ax1_1 in T.thread_binding(256, thread="threadIdx.x"): |
| for ax1_0 in T.serial(8, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): |
| with T.block("T_reshape_3"): |
| v0 = T.axis.spatial(T.int64(1), T.int64(0)) |
| v1 = T.axis.spatial(2048, ax1_0 * 256 + ax1_1) |
| T.reads(A[0, v1], A_red_temp_v0_shared[0, v1 // 64], A_red_temp_v1_shared[0, v1 // 64], B[v1], C[v1]) |
| T.writes(T_reshape[0, v1]) |
| T_reshape[0, v1] = (A[0, v1] - A_red_temp_v0_shared[0, v1 // 64] * T.float32(0.015625)) * T.rsqrt(A_red_temp_v1_shared[0, v1 // 64] * T.float32(0.015625) - A_red_temp_v0_shared[0, v1 // 64] * T.float32(0.015625) * (A_red_temp_v0_shared[0, v1 // 64] * T.float32(0.015625)) + T.float32(1.0000000000000001e-05)) * B[v1] + C[v1] # fmt: on |
| _check(Before, After) |
| |
| |
| def test_logsumexp(): |
| @I.ir_module |
| class Before: |
| @T.prim_func |
| def compute_lse(var_A: T.handle, var_blocked_lse: T.handle): |
| T.func_attr({"tir.noalias": True}) |
| batch_size = T.int64(is_size_var=True) |
| vocab_size = T.int64(is_size_var=True) |
| num_chunks = T.int64(is_size_var=True) |
| A = T.match_buffer(var_A, (batch_size, vocab_size), dtype="float32") |
| blocked_lse = T.match_buffer(var_blocked_lse, (batch_size, num_chunks), dtype="float32") |
| A_pad = T.alloc_buffer((batch_size, num_chunks, T.int64(4096)), dtype="float32") |
| temp_max = T.alloc_buffer((batch_size, num_chunks), dtype="float32") |
| temp_sum = T.alloc_buffer((batch_size, num_chunks), dtype="float32") |
| |
| for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): |
| with T.block("pad"): |
| v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) |
| A_pad[v0, v1, v2] = T.if_then_else( |
| v1 * T.int64(4096) + v2 < vocab_size, |
| A[v0, v1 * T.int64(4096) + v2], |
| T.min_value("float32"), |
| ) |
| |
| for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): |
| with T.block("max"): |
| v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) |
| with T.init(): |
| temp_max[v0, v1] = T.min_value("float32") |
| temp_max[v0, v1] = T.max(temp_max[v0, v1], A_pad[v0, v1, v2]) |
| |
| for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): |
| with T.block("sum_exp"): |
| v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) |
| with T.init(): |
| temp_sum[v0, v1] = T.float32(0) |
| temp_sum[v0, v1] += T.if_then_else( |
| v1 * T.int64(4096) + v2 < vocab_size, |
| T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]), |
| T.float32(0), |
| ) |
| |
| for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)): |
| with T.block("log"): |
| v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) |
| blocked_lse[v0, v1] = T.log(temp_sum[v0, v1]) + temp_max[v0, v1] |
| |
| @I.ir_module |
| class After: |
| @T.prim_func |
| def compute_lse(var_A: T.handle, var_blocked_lse: T.handle): |
| T.func_attr({"tir.is_scheduled": True, "tir.noalias": True}) |
| batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) |
| A = T.match_buffer(var_A, (batch_size, vocab_size)) |
| num_chunks = T.int64(is_size_var=True) |
| blocked_lse = T.match_buffer(var_blocked_lse, (batch_size, num_chunks)) |
| temp_max_shared = T.alloc_buffer((batch_size, num_chunks), scope="shared") |
| temp_sum_shared = T.alloc_buffer((batch_size, num_chunks), scope="shared") |
| for ax0_ax1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): |
| for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): |
| for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_fused_0 in T.serial( |
| T.int64(16), |
| annotations={ |
| "pragma_auto_unroll_max_step": 256, |
| "pragma_unroll_explicit": 1, |
| }, |
| ): |
| with T.block("max"): |
| v0 = T.axis.spatial( |
| batch_size, |
| ax0_ax1_fused % (num_chunks * batch_size) // num_chunks + ax0, |
| ) |
| v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks + ax1) |
| v2 = T.axis.reduce( |
| T.int64(4096), ax2_fused_0 * T.int64(256) + ax2_fused_1 |
| ) |
| T.reads(A[v0, v1 * T.int64(4096) + v2]) |
| T.writes(temp_max_shared[v0, v1]) |
| with T.init(): |
| temp_max_shared[v0, v1] = T.min_value("float32") |
| temp_max_shared[v0, v1] = T.max( |
| temp_max_shared[v0, v1], |
| T.if_then_else( |
| v1 * T.int64(4096) + v2 < vocab_size, |
| A[v0, v1 * T.int64(4096) + v2], |
| T.min_value("float32"), |
| ), |
| ) |
| for ax0, ax1 in T.grid(T.int64(1), T.int64(1)): |
| for ax2_fused_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_fused_0 in T.serial( |
| T.int64(16), |
| annotations={ |
| "pragma_auto_unroll_max_step": 256, |
| "pragma_unroll_explicit": 1, |
| }, |
| ): |
| with T.block("sum_exp"): |
| v0 = T.axis.spatial( |
| batch_size, |
| ax0_ax1_fused % (num_chunks * batch_size) // num_chunks + ax0, |
| ) |
| v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks + ax1) |
| v2 = T.axis.reduce( |
| T.int64(4096), ax2_fused_0 * T.int64(256) + ax2_fused_1 |
| ) |
| T.reads(A[v0, v1 * T.int64(4096) + v2], temp_max_shared[v0, v1]) |
| T.writes(temp_sum_shared[v0, v1]) |
| with T.init(): |
| temp_sum_shared[v0, v1] = T.float32(0) |
| temp_sum_shared[v0, v1] = temp_sum_shared[v0, v1] + T.if_then_else( |
| v1 * T.int64(4096) + v2 < vocab_size, |
| T.exp( |
| ( |
| T.if_then_else( |
| v1 * T.int64(4096) + v2 < vocab_size, |
| A[v0, v1 * T.int64(4096) + v2], |
| T.min_value("float32"), |
| ) |
| - temp_max_shared[v0, v1] |
| ) |
| ), |
| T.float32(0), |
| ) |
| for ax2_1 in T.thread_binding(T.int64(256), thread="threadIdx.x"): |
| for ax2_0 in T.serial( |
| T.int64(1), |
| annotations={ |
| "pragma_auto_unroll_max_step": 256, |
| "pragma_unroll_explicit": 1, |
| }, |
| ): |
| with T.block("log"): |
| v0 = T.axis.spatial( |
| batch_size, ax0_ax1_fused % (num_chunks * batch_size) // num_chunks |
| ) |
| v1 = T.axis.spatial(num_chunks, ax0_ax1_fused % num_chunks) |
| v2 = T.axis.spatial(T.int64(1), ax2_0 * T.int64(256) + ax2_1) |
| T.where(ax2_0 * T.int64(256) + ax2_1 < T.int64(1)) |
| T.reads(temp_sum_shared[v0, v1], temp_max_shared[v0, v1]) |
| T.writes(blocked_lse[v0, v1]) |
| blocked_lse[v0, v1] = ( |
| T.log(temp_sum_shared[v0, v1]) + temp_max_shared[v0, v1] |
| ) |
| |
| _check(Before, After) |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |
