Chi_Liu AmosLewis
AmosLewis
/ ConvNext_vaiq_int8.iree-compile.log
Created
June 12, 2024 03:35
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| failed to translate executables | |
| failed to translate executables | |
| ConvNeXt_vaiq_int8.default.onnx.linalg.mlir:979:12: error: 'func.func' op exceeded stack allocation limit of 32768 bytes for function. Got 401408 bytes | |
| %106 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%105 : tensor<1x56x56x512xf32>) outs(%98 : tensor<1x56x56x512xi8>) { | |
| ^ | |
| ConvNeXt_vaiq_int8.default.onnx.linalg.mlir:24:3: note: called from | |
| func.func @torch_jit(%arg0: tensor<1x3x224x224xf32>) -> tensor<1x1000xf32> { | |
| ^ | |
| ConvNeXt_vaiq_int8.default.onnx.linalg.mlir:979:12: note: see current operation: | |
| "func.func"() <{function_type = () -> (), sym_name = "torch_jit_dispatch_13_quantized_batch_matmul_56x56x512x128_i8xi8xi32xi32xi32"}> ({ |
AmosLewis
/ gpt2-xl.default.pytorch.torch.elide.mlir
Created
May 1, 2024 16:30
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| module { | |
| func.func @main_graph(%arg0: !torch.vtensor<[1,7],si64>) -> (!torch.vtensor<[1,7,50257],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f32>, !torch.vtensor<[1,25,7,64],f |
AmosLewis
/ gpt2-xl.default.pytorch.linalg.elide.mlir
Created
May 1, 2024 16:25
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| #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, d2) -> (0, d1, d2)> | |
| #map4 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map5 = affine_map<(d0, d1, d2) -> (d0, d1, 0)> | |
| #map6 = affine_map<(d0, d1, d2) -> (0, d1, 0)> | |
| #map7 = affine_map<(d0, d1, d2) -> (d2)> | |
| #map8 = affine_map<(d0, d1) -> (d1)> | |
| #map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
AmosLewis
/ resnet50_vaiq_int8.default.onnx.torch.elide.mlir
Created
May 1, 2024 00:39
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| module { | |
| func.func @torch_jit(%arg0: !torch.vtensor<[1,3,224,224],f32>) -> !torch.vtensor<[1,1000],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} { | |
| %0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x3x7x7xf32>) : !torch.vtensor<[64,3,7,7],f32> | |
| %1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x64x1x1xf32>) : !torch.vtensor<[64,64,1,1],f32> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32> | |
| %4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x64x3x3xf32>) : !torch.vtensor<[64,64,3,3],f32> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32> | |
| %6 = torch.vtensor.literal(dense_resource<__elided__> : tensor<256x64x1x |
AmosLewis
/ resnet50_vaiq_int8.default.onnx.linalg.mlir
Created
May 1, 2024 00:38
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| #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, d1, 0, 0)> | |
| #map5 = affine_map<(d0, d1, d2, d3) -> (0, d1, 0, 0)> | |
| #map6 = affine_map<(d0, d1, d2, d3) -> ()> | |
| #map7 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map8 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map9 = affine_map<(d0, d1) -> (0, d1)> |
AmosLewis
/ avgpool2d.mlir
Last active
April 22, 2024 23:38
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| module attributes {torch.debug_module_name = "avgpool2d"} { | |
| func.func @forward(%arg0: !torch.vtensor<[32,384,25,25],f32>) -> !torch.vtensor<[32,384,25,25],f32> { | |
| %int1_0 = torch.constant.int 1 | |
| %int1_1 = torch.constant.int 1 | |
| %int1_2 = torch.constant.int 1 | |
| %int1_3 = torch.constant.int 1 | |
| %int1_4 = torch.constant.int 1 | |
| %int1_5 = torch.constant.int 1 | |
| %int3_0 = torch.constant.int 3 | |
| %int3_1 = torch.constant.int 3 |
AmosLewis
/ yolov8n_vaiq_int8.default.onnx.torch.elide.mlir
Last active
April 22, 2024 18:52
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| module { | |
| func.func @torch_jit(%arg0: !torch.vtensor<[1,3,640,640],f32>) -> (!torch.vtensor<[1,84,8400],f32>, !torch.vtensor<[1,144,80,80],f32>, !torch.vtensor<[1,144,40,40],f32>, !torch.vtensor<[1,144,20,20],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} { | |
| %0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16x3x3x3xf32>) : !torch.vtensor<[16,3,3,3],f32> | |
| %1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16xf32>) : !torch.vtensor<[16],f32> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x16x3x3xf32>) : !torch.vtensor<[32,16,3,3],f32> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32xf32>) : !torch.vtensor<[32],f32> | |
| %4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x32x1x1xf32>) : !torch.vtensor<[32,32,1,1],f32> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32 |
AmosLewis
/ RRDB_ESRGAN_vaiq_int8.default.onnx.torch.elide.mlir
Created
April 22, 2024 18:32
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| module { | |
| func.func @torch_jit(%arg0: !torch.vtensor<[1,3,224,224],f32>) -> !torch.vtensor<[1,3,896,896],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} { | |
| %0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x3x3x3xf32>) : !torch.vtensor<[64,3,3,3],f32> | |
| %1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64xf32>) : !torch.vtensor<[64],f32> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x64x3x3xf32>) : !torch.vtensor<[32,64,3,3],f32> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32xf32>) : !torch.vtensor<[32],f32> | |
| %4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x96x3x3xf32>) : !torch.vtensor<[32,96,3,3],f32> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32xf32>) : !torch.vtensor<[32],f32> | |
| %6 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x1 |
AmosLewis
/ LRASPP_vaiq_int8.default.onnx.torch.elide.mlir
Created
April 22, 2024 18:26
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| module { | |
| func.func @torch_jit(%arg0: !torch.vtensor<[1,3,224,224],f32>) -> !torch.vtensor<[1,21,224,224],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} { | |
| %0 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16x3x3x3xf32>) : !torch.vtensor<[16,3,3,3],f32> | |
| %1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16xf32>) : !torch.vtensor<[16],f32> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16x1x3x3xf32>) : !torch.vtensor<[16,1,3,3],f32> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16xf32>) : !torch.vtensor<[16],f32> | |
| %4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16x16x1x1xf32>) : !torch.vtensor<[16,16,1,1],f32> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16xf32>) : !torch.vtensor<[16],f32> | |
| %6 = torch.vtensor.literal(dense_resource<__elided__> : tensor<64x16 |
AmosLewis
/ pytorch-3dunet_vaiq_int8.default.onnx.torch.elide.mlir
Created
April 22, 2024 16:51
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| module { | |
| func.func @torch_jit(%arg0: !torch.vtensor<[1,1,64,128,128],f32>) -> !torch.vtensor<[1,1,64,128,128],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} { | |
| %0 = torch.vtensor.literal(dense<8.906250e-01> : tensor<1xf32>) : !torch.vtensor<[1],f32> | |
| %1 = torch.vtensor.literal(dense<-0.0849609375> : tensor<1xf32>) : !torch.vtensor<[1],f32> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16x1x3x3x3xf32>) : !torch.vtensor<[16,1,3,3,3],f32> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16xf32>) : !torch.vtensor<[16],f32> | |
| %4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<16xf32>) : !torch.vtensor<[16],f32> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x16x3x3x3xf32>) : !torch.vtensor<[32,16,3,3,3],f32> | |
| %6 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32xf32>) : !torch.vte |