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| #!/bin/bash | |
| ################################################################################ | |
| # Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. | |
| # See https://llvm.org/LICENSE.txt for license information. | |
| # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception | |
| ################################################################################ | |
| # | |
| # This script will install the llvm toolchain on the different | |
| # Debian and Ubuntu versions |
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| // -----// IR Dump After AutoInputConversionPipelinePass (iree-auto-input-conversion) //----- // | |
| #map = affine_map<(d0, d1, d2) -> (d1, d2)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| module { | |
| util.func public @matmul_broad(%arg0: tensor<?x?x3200xf32>, %arg1: tensor<8640x3200xf16>) -> tensor<?x?x8640xf32> { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %c0 = arith.constant 0 : index | |
| %c1 = arith.constant 1 : index | |
| %dim = tensor.dim %arg0, %c0 : tensor<?x?x3200xf32> | |
| %dim_0 = tensor.dim %arg0, %c1 : tensor<?x?x3200xf32> |
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| func.func @fuse_multiple_users(%arg0 : tensor<?x?xf32>, | |
| %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> { | |
| %c0 = arith.constant 0 : index | |
| %c1 = arith.constant 1 : index | |
| %c2 = arith.constant 2 : index | |
| %cst0 = arith.constant 0.0 : f32 | |
| %M = tensor.dim %arg0, %c0 : tensor<?x?xf32> | |
| %N = tensor.dim %arg1, %c1 : tensor<?x?xf32> | |
| %K = tensor.dim %arg0, %c1 : tensor<?x?xf32> | |
| %empty = tensor.empty(%M, %N) : tensor<?x?xf32> |
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| func.func @matmul_fusion_test(%arg0 : tensor<?x?xf32>, | |
| %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> { | |
| %c0 = arith.constant 0 : index | |
| %c1 = arith.constant 1 : index | |
| %c2 = arith.constant 2 : index | |
| %cst0 = arith.constant 0.0 : f32 | |
| %M = tensor.dim %arg0, %c0 : tensor<?x?xf32> | |
| %N = tensor.dim %arg1, %c1 : tensor<?x?xf32> | |
| %K = tensor.dim %arg0, %c1 : tensor<?x?xf32> | |
| %empty = tensor.empty(%M, %N) : tensor<?x?xf32> |
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| // -----// IR Dump After AutoInputConversionPipeline (iree-auto-input-conversion) //----- // | |
| #map = affine_map<(d0, d1, d2) -> (d1, d2)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| module { | |
| util.func public @matmul_broad(%arg0: tensor<?x?x3200xf32>, %arg1: tensor<8640x3200xf16>) -> tensor<?x?x8640xf32> { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %c0 = arith.constant 0 : index | |
| %c1 = arith.constant 1 : index | |
| %dim = tensor.dim %arg0, %c0 : tensor<?x?x3200xf32> | |
| %dim_0 = tensor.dim %arg0, %c1 : tensor<?x?x3200xf32> |
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| // -----// IR Dump After AutoInputConversionPipeline (iree-auto-input-conversion) //----- // | |
| #map = affine_map<(d0, d1, d2) -> (d1, d2)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| module { | |
| util.func public @matmul_broad(%arg0: tensor<?x?x3200xf32>, %arg1: tensor<8640x3200xf16>) -> tensor<?x?x8640xf32> { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %c0 = arith.constant 0 : index | |
| %c1 = arith.constant 1 : index | |
| %dim = tensor.dim %arg0, %c0 : tensor<?x?x3200xf32> | |
| %dim_0 = tensor.dim %arg0, %c1 : tensor<?x?x3200xf32> |
pashu123
/ 2ndbug.mlir
Created
August 19, 2024 16:46
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| func.func @matmul_broad_dispatch_1_set_encoding_LHS_DxDx3200() { | |
| %c0 = arith.constant 0 : index | |
| %c32_i64 = arith.constant 32 : i64 | |
| %0 = hal.interface.constant.load layout(<push_constants = 4, sets = [<0, bindings = [<0, storage_buffer, Indirect>], flags = Indirect>]>) ordinal(0) : i32 | |
| %1 = hal.interface.constant.load layout(<push_constants = 4, sets = [<0, bindings = [<0, storage_buffer, Indirect>], flags = Indirect>]>) ordinal(1) : i32 | |
| %2 = hal.interface.constant.load layout(<push_constants = 4, sets = [<0, bindings = [<0, storage_buffer, Indirect>], flags = Indirect>]>) ordinal(2) : i32 | |
| %3 = hal.interface.constant.load layout(<push_constants = 4, sets = [<0, bindings = [<0, storage_buffer, Indirect>], flags = Indirect>]>) ordinal(3) : i32 | |
| %4 = arith.extui %0 : i32 to i64 | |
| %5 = arith.extui %1 : i32 to i64 | |
| %6 = arith.shli %5, %c32_i64 : i64 |
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| #map = affine_map<(d0, d1, d2) -> (d1, d2)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)> | |
| #map3 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)> | |
| #map4 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> | |
| module { | |
| util.func public @matmul_broad(%arg0: !hal.buffer_view, %arg1: !hal.buffer_view) -> !hal.buffer_view attributes {iree.abi.stub, iree.reflection = {iree.abi.declaration = "sync func @matmul_broad(%input0: tensor<?x?x3200xf32>, %input1: tensor<8640x3200xf16>) -> (%output0: tensor<?x?x8640xf32>)"}} { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %0 = hal.buffer_view.dim<%arg0 : !hal.buffer_view>[0] : index | |
| %1 = hal.buffer_view.dim<%arg0 : !hal.buffer_view>[1] : index |
pashu123
/ double_generic.mlir
Created
August 12, 2024 12:34
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| #map = affine_map<(d0) -> (d0)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| module { | |
| func.func @main_graph_dispatch_47_elementwise_64x56x56_f32(%arg0: tensor<200704xi8>, %arg1: tensor<64x56x56xf32>) -> tensor<64x56x56xf32> { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %cst_0 = arith.constant -1.280000e+02 : f32 | |
| %cst_1 = arith.constant 1.270000e+02 : f32 | |
| %cst_2 = arith.constant 1.562500e-02 : f32 | |
| %0 = tensor.empty() : tensor<64x56x56xf32> | |
| %1 = tensor.empty() : tensor<200704xf32> |
pashu123
/ e2e_matmul_cpu_experimental_dt_uk_f16_f16_small_llvm-cpu_local-task_avx512_matmul.mlir
Created
August 9, 2024 18:42
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| func.func @matmul_accumulate_DYNxDYNxf16_times_DYNxDYNxf16_into_DYNxDYNxf16(%lhs: tensor<?x?xf16>, %rhs: tensor<?x?xf16>, %acc: tensor<?x?xf16>) -> tensor<?x?xf16> { | |
| %result = linalg.matmul ins(%lhs, %rhs: tensor<?x?xf16>, tensor<?x?xf16>) outs(%acc: tensor<?x?xf16>) -> tensor<?x?xf16> | |
| return %result: tensor<?x?xf16> | |
| } | |
| func.func @matmul_accumulate_1x1xf16_times_1x1xf16_into_1x1xf16(%lhs: tensor<1x1xf16>, %rhs: tensor<1x1xf16>, %acc: tensor<1x1xf16>) -> tensor<1x1xf16> { | |
| %result = linalg.matmul ins(%lhs, %rhs: tensor<1x1xf16>, tensor<1x1xf16>) outs(%acc: tensor<1x1xf16>) -> tensor<1x1xf16> | |
| return %result: tensor<1x1xf16> |