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AmosLewis / module_torch_jit_dispatch_23.mlir
Created August 10, 2024 00:16
hal.executable public @torch_jit_dispatch_23 {
hal.executable.variant public @embedded_elf_x86_64 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"}>) {
hal.executable.export public @torch_jit_dispatch_23_quantized_batch_matmul_56x56x512x128_i8xi8xi32xi32xi32 ordinal(0) layout(#hal.pipeline.layout<push_constants = 0, sets = [<0, bindings = [<0, storage_buffer, ReadOnly>, <1, storage_buffer, ReadOnly>, <2, storage_buffer>], flags = Indirect>]>) attributes {hal.interface.bindings = [#hal.interface.binding<0, 0>, #hal.interface.binding<0, 1>, #hal.interface.binding<0, 2>]} {
^bb0(%arg0: !hal.device):
%x, %y, %z = flow.dispatch.workgroup_count_from_slice
hal.return %x, %y, %z : index, index, index
}
builtin.module {
func.func @torch_jit_dispatch_23_quantized_batch_matmul_5
@AmosLewis
AmosLewis / module_torch_jit_dispatch_15.mlir
Created August 10, 2024 00:15
hal.executable public @torch_jit_dispatch_15 {
hal.executable.variant public @embedded_elf_x86_64 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"}>) {
hal.executable.export public @torch_jit_dispatch_15_quantized_batch_matmul_56x56x128x512_i8xi8xi32xi32xi32 ordinal(0) layout(#hal.pipeline.layout<push_constants = 0, sets = [<0, bindings = [<0, storage_buffer, ReadOnly>, <1, storage_buffer, ReadOnly>, <2, storage_buffer>], flags = Indirect>]>) attributes {hal.interface.bindings = [#hal.interface.binding<0, 0>, #hal.interface.binding<0, 1>, #hal.interface.binding<0, 2>]} {
^bb0(%arg0: !hal.device):
%x, %y, %z = flow.dispatch.workgroup_count_from_slice
hal.return %x, %y, %z : index, index, index
}
builtin.module {
func.func @torch_jit_dispatch_15_quantized_batch_matmul_5
@AmosLewis
AmosLewis / dpn68_vaiq_iree_failed.log
Created August 8, 2024 22:49
failed to translate executables
failed to translate executables
dpn68_vaiq.default.onnx.linalg.mlir:1243:12: error: One or more operations with large vector sizes (8192 bytes) were found:
%180 = linalg.generic {indexing_maps = [#map1, #map2], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%179 : tensor<1x64x56x56xf32>) outs(%107 : tensor<1x64x56x56xi8>) {
^
dpn68_vaiq.default.onnx.linalg.mlir:9:3: note: called from
func.func @main_graph(%arg0: tensor<1x3x224x224xf32>) -> tensor<1x1000xf32> {
^
<unknown>:0: note: %cst_3 = arith.constant dense<1.562500e-02> : vector<200704xf32>
@AmosLewis
AmosLewis / conv_failed_model_list.txt
Created August 6, 2024 04:42
onnx/models/eca_nfnet_l0.ra2_in1k_vaiq
onnx/models/eca_nfnet_l0.ra2_in1k_train_vaiq
onnx/models/eca_nfnet_l1.ra2_in1k_vaiq
onnx/models/eca_nfnet_l1.ra2_in1k_train_vaiq
onnx/models/eca_nfnet_l2.ra3_in1k_vaiq
onnx/models/eca_nfnet_l2.ra3_in1k_train_vaiq
onnx/models/ecaresnet101d_vaiq
onnx/models/ecaresnet101d_pruned_vaiq
onnx/models/ecaresnet101d_pruned_test_vaiq
onnx/models/ecaresnet101d_test_vaiq
@AmosLewis
AmosLewis / Vision_&_P0P1_samemodel.txt
Created July 23, 2024 20:24
modified: onnx/models/dla169/model.py
modified: onnx/models/dla169_vaiq/model.py
modified: onnx/models/efficientnet_b0.ra_in1k/model.py
modified: onnx/models/efficientnet_b0.ra_in1k_vaiq/model.py
modified: onnx/models/efficientnet_b1.ft_in1k/model.py
modified: onnx/models/efficientnet_b1.ft_in1k_vaiq/model.py
modified: onnx/models/efficientnet_b2.ra_in1k/model.py
modified: onnx/models/efficientnet_b2.ra_in1k_vaiq/model.py
modified: onnx/models/efficientnet_b3.ra2_in1k/model.py
modified: onnx/models/efficientnet_b3.ra2_in1k_vaiq/model.py
@AmosLewis
AmosLewis / statusreport_vision_0717.md
Created July 18, 2024 00:26

Status report for run: test-run using mode:onnx todtype:default backend:llvm-cpu

tests model-run onnx-import torch-mlir iree-compile inference
onnx/models/DarkNet53_vaiq passed passed notrun passed mismatch
onnx/models/dla169_vaiq passed passed notrun passed mismatch
onnx/models/efficientnet_b0.ra_in1k_vaiq passed passed notrun passed mismatch
onnx/models/EfficientNet_b0_vaiq passed passed notrun passed mismatch
onnx/models/efficientnet_b1.ft_in1k_vaiq passed passed notrun passed mismatch
onnx/models/EfficientNet_b1_vaiq
@AmosLewis
AmosLewis / opt-125M-awq.default.onnx.linalg.elide.mlir
Created July 3, 2024 16:35
This file has been truncated, but you can view the full file.
#map = affine_map<(d0, d1) -> (d0, d1)>
#map1 = affine_map<() -> ()>
#map2 = affine_map<(d0, d1) -> ()>
#map3 = affine_map<()[s0, s1] -> (s0 * s1)>
#map4 = affine_map<(d0, d1) -> (d0)>
#map5 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map6 = affine_map<(d0) -> (d0)>
#map7 = affine_map<(d0, d1, d2) -> (d0, d1, 0)>
#map8 = affine_map<(d0) -> ()>
#map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
@AmosLewis
AmosLewis / mobilebert-uncased.default.pytorch.linalg.elide.mlir
Created June 24, 2024 23:35
#map = affine_map<(d0, d1) -> (0, d1)>
#map1 = affine_map<(d0, d1) -> (d0, d1)>
#map2 = affine_map<(d0, d1) -> (d0)>
#map3 = affine_map<(d0, d1) -> (d1, d0)>
#map4 = affine_map<()[s0, s1] -> (s0 + s1 + 128)>
#map5 = affine_map<(d0, d1, d2) -> (0, d1, d2)>
#map6 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map7 = affine_map<(d0, d1, d2) -> (d1, d2)>
#map8 = affine_map<(d0, d1, d2) -> (d2)>
#map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
@AmosLewis
AmosLewis / mit-b0.default.pytorch.linalg.elide.mlir
Created June 24, 2024 22:15
#map = affine_map<(d0, d1, d2, d3) -> (d1)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map3 = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
#map4 = affine_map<(d0, d1, d2) -> (d0, d1, 0)>
#map5 = affine_map<(d0, d1, d2) -> (0, d1, 0)>
#map6 = affine_map<(d0, d1, d2) -> (0, d1, d2)>
#map7 = affine_map<(d0, d1, d2) -> (d2)>
#map8 = affine_map<(d0, d1, d2) -> (d1, d2)>
#map9 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d1, d3)>
@AmosLewis
AmosLewis / ConvNeXt_vaiq_int8.default.onnx.linalg.elide.mlir
Created June 12, 2024 03:50
This file has been truncated, but you can view the full file.
#map = affine_map<(d0, d1, d2, d3) -> (0, d1, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map2 = affine_map<(d0) -> (d0)>
#map3 = affine_map<(d0, d1, d2, d3) -> (d1)>
#map4 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d1, d3)>
#map5 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3, d2)>
#map6 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>
#map7 = affine_map<(d0, d1, d2, d3) -> (0, d1, d2, 0)>
#map8 = affine_map<(d0, d1, d2, d3) -> (d3)>
#map9 = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2, d1)>