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> |
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| #matmul_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 0, 16, 16, 0], [0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0]]> | |
| #executable_target_embedded_elf_x86_64_ = #hal.executable.target<"llvm-cpu", "embedded-elf-x86_64", {cpu = "znver4", cpu_features = "+avx512f", native_vector_size = 64 : index, target_triple = "x86_64-unknown-unknown-eabi-elf"}> | |
| func.func @mmt4d_bias_relu_fusion_dispatch_0_generic_DxDx16x16_f32() attributes {hal.executable.target = #executable_target_embedded_elf_x86_64_} { | |
| %c0 = arith.constant 0 : index | |
| %c32_i64 = arith.constant 32 : i64 | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %0 = hal.interface.constant.load[0] : i32 | |
| %1 = hal.interface.constant.load[1] : i32 | |
| %2 = hal.interface.constant.load[2] : i32 | |
| %3 = hal.interface.constant.load[3] : i32 |
pashu123
/ new_ir.mlir
Created
June 27, 2024 16:25
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| %46 = linalg.batch_mmt4d {lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 16, 16, 0], [0, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0]]>} ins(%41, %42 : tensor<?x?x3200x16x1xf32>, tensor<?x540x3200x16x1xf16>) outs(%45 : tensor<?x?x540x16x16xf32>) -> tensor<?x?x540x16x16xf32> | |
| 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 : f16 | |
| %c1 = arith.constant 1 : index | |
| %c0 = arith.constant 0 : index | |
| %cst_0 = 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 | |
| %2 = hal.tensor.import %arg0 "input0" : !hal.buffer_view -> tensor<?x?x |
pashu123
/ new_ir.mlir
Created
June 27, 2024 16:17
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| 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 : f16 | |
| %c1 = arith.constant 1 : index | |
| %c0 = arith.constant 0 : index | |
| %cst_0 = 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 | |
| %2 = hal.tensor.import %arg0 "input0" : !hal.buffer_view -> tensor<?x?x3200xf32>{%0, %1} | |
| %3 = hal.tensor.import %arg1 "input1" : !hal.buffer_view -> tensor<8640x3200xf16> | |
| %4 = tensor.empty(%0) : tensor<?x8640x3200xf16> |
pashu123
/ new_ir.mlir
Created
June 27, 2024 15:47
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| //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 | |
| // %2 = hal.tensor.import %arg0 "input0" : !hal.buffer_view -> tensor<?x?x3200xf32>{%0, %1} | |
| // %3 = hal.tensor.import %arg1 "input1" : !hal.buffer_view -> tensor<8640x3200xf16> | |
| // %4 = tensor.empty() : tensor<540x3200x16x1xf16> | |
| // %pack = tensor.pack %3 outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [16, 1] into %4 : tensor<8640x3200xf16> -> tensor<540x3200x16x1xf16> | |
| // %collapsed = tensor.collapse_shape %pack [[0], [1], [2, 3]] : tensor<540x3200x16x1xf16> into tensor<540x3200x16xf16> | |
| // %5 = tensor.empty(%0) : tensor< |
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| module attributes {torch.debug_module_name = "SumModule"} { | |
| ml_program.global private mutable @global_seed(dense<0> : tensor<i64>) : tensor<i64> | |
| func.func @forward(%arg0: tensor<1048576xf32>) -> tensor<f32> { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %0 = tensor.empty() : tensor<f32> | |
| %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<f32>) -> tensor<f32> | |
| %2 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> ()>], iterator_types = ["reduction"]} ins(%arg0 : tensor<1048576xf32>) outs(%1 : tensor<f32>) { | |
| ^bb0(%in: f32, %out: f32): | |
| %3 = arith.addf %in, %out : f32 | |
| linalg.yield %3 : f32 |
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| // -----// IR Dump After AssignTargetDevicesPass (iree-hal-assign-target-devices) //----- // | |
| #executable_target_embedded_elf_x86_64_ = #hal.executable.target<"llvm-cpu", "embedded-elf-x86_64", {cpu = "generic", cpu_features = "", data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-i128:128-f80:128-n8:16:32:64-S128", native_vector_size = 16 : i64, target_triple = "x86_64-unknown-unknown-eabi-elf"}> | |
| #map = affine_map<(d0, d1, d2) -> (d1, d2)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #device_target_local = #hal.device.target<"local", [#executable_target_embedded_elf_x86_64_]> | |
| module attributes {hal.device.targets = [#device_target_local]} { | |
| 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 |