pashu123
/ stable_diff_f16_elided.mlir
Created
October 5, 2022 11:50
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| #map0 = affine_map<(d0) -> (0)> | |
| #map1 = affine_map<(d0) -> (d0)> | |
| #map2 = affine_map<(d0) -> ()> | |
| #map3 = affine_map<() -> ()> | |
| #map4 = affine_map<(d0, d1) -> ()> | |
| #map5 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map6 = affine_map<(d0, d1) -> (d0, 0)> | |
| #map7 = affine_map<(d0, d1) -> (0, d1)> | |
| #map8 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map9 = affine_map<(d0, d1) -> (d1)> |
pashu123
/ stable_diff_f16.mlir
Created
October 4, 2022 14:34
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| // -----// IR Dump After DropShapeCalculations (torch-drop-shape-calculations) //----- // | |
| func.func @forward(%arg0: !torch.vtensor<[2,4,64,64],f16>, %arg1: !torch.vtensor<[1],f16>, %arg2: !torch.vtensor<[2,77,768],f16>) -> !torch.vtensor<[2,4,64,64],f16> { | |
| %int160 = torch.constant.int 160 | |
| %float1.600000e02 = torch.constant.float 1.600000e+02 | |
| %str = torch.constant.str "AssertionError: " | |
| %int4 = torch.constant.int 4 | |
| %float0.000000e00 = torch.constant.float 0.000000e+00 | |
| %0 = torch.vtensor.literal(dense<0.079056941504209485> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %1 = torch.vtensor.literal(dense<0.11180339887498948> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %2 = torch.vtensor.literal(dense<0.15811388300841897> : tensor<f64>) : !torch.vtensor<[],f64> |
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| torch-mlir-opt: /home/prashant/torch-mlir/externals/llvm-project/mlir/lib/IR/BuiltinAttributes.cpp:1031: static mlir::DenseElementsAttr mlir::DenseElementsAttr::get(mlir::ShapedType, ArrayRef<mlir::Attribute>): Assertion `intAttr.getType() == eltType && "expected integer attribute type to equal element type"' failed. | |
| PLEASE submit a bug report to https://github.com/llvm/llvm-project/issues/ and include the crash backtrace. | |
| Stack dump: | |
| 0. Program arguments: torch-mlir-opt -pass-pipeline=torch-backend-to-tosa-backend-pipeline xyz.mlir | |
| #0 0x0000557fff3cedd3 llvm::sys::PrintStackTrace(llvm::raw_ostream&, int) (/home/prashant/torch-mlir/build/bin/torch-mlir-opt+0x187cdd3) | |
| #1 0x0000557fff3ccd4e llvm::sys::RunSignalHandlers() (/home/prashant/torch-mlir/build/bin/torch-mlir-opt+0x187ad4e) | |
| #2 0x0000557fff3cf16a SignalHandler(int) Signals.cpp:0:0 | |
| #3 0x00007f745f34c520 (/lib/x86_64-linux-gnu/libc.so.6+0x42520) | |
| #4 0x00007f745f3a0a7c __pthread_kill_implementation ./nptl/./nptl/pthread_kill.c:44:76 | |
| #5 0x00007f74 |
pashu123
/ stable_diff.py
Created
September 24, 2022 09:53
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| from transformers import CLIPTextModel, CLIPTokenizer | |
| from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler | |
| import torch | |
| from PIL import Image | |
| from diffusers import LMSDiscreteScheduler | |
| from tqdm.auto import tqdm | |
| from shark.shark_inference import SharkInference | |
| from torch.fx.experimental.proxy_tensor import make_fx | |
| from torch._decomp import get_decompositions | |
| import torch_mlir |
pashu123
/ vae_torch.mlir
Created
September 23, 2022 17:22
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| #loc0 = loc(unknown) | |
| module attributes {torch.debug_module_name = "_lambda"} { | |
| func.func @forward(%arg0: !torch.vtensor<[1,4,64,64],f32> loc(unknown)) -> !torch.vtensor<[1,3,512,512],f32> { | |
| %int1048576 = torch.constant.int 1048576 loc(#loc1) | |
| %int262144 = torch.constant.int 262144 loc(#loc2) | |
| %int4 = torch.constant.int 4 loc(#loc3) | |
| %int2097152 = torch.constant.int 2097152 loc(#loc4) | |
| %int8 = torch.constant.int 8 loc(#loc5) | |
| %int524288 = torch.constant.int 524288 loc(#loc6) | |
| %int65536 = torch.constant.int 65536 loc(#loc7) |
pashu123
/ _lambda.mlir
Created
September 20, 2022 17:21
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| #loc0 = loc(unknown) | |
| module attributes {torch.debug_module_name = "_lambda"} { | |
| func.func @forward(%arg0: !torch.vtensor<[1,4,64,64],f32> loc(unknown)) -> !torch.vtensor<[1,3,512,512],f32> { | |
| %int1048576 = torch.constant.int 1048576 loc(#loc1) | |
| %int262144 = torch.constant.int 262144 loc(#loc2) | |
| %int4 = torch.constant.int 4 loc(#loc3) | |
| %int2097152 = torch.constant.int 2097152 loc(#loc4) | |
| %int8 = torch.constant.int 8 loc(#loc5) | |
| %int524288 = torch.constant.int 524288 loc(#loc6) | |
| %int65536 = torch.constant.int 65536 loc(#loc7) |
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| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| from torch.fx.experimental.proxy_tensor import make_fx | |
| from torch._decomp import get_decompositions | |
| import tempfile | |
| import torch_mlir | |
| def prepare_sentence_tokens(hf_model: str, sentence: str): | |
| tokenizer = AutoTokenizer.from_pretrained(hf_model) |
pashu123
/ batch_dlrm.mlir
Created
September 2, 2022 02:18
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| #map0 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map1 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map2 = affine_map<(d0, d1) -> (d1)> | |
| #map3 = affine_map<(d0, d1, d2) -> (d1)> | |
| #map4 = affine_map<(d0, d1, d2) -> (d0)> | |
| #map5 = affine_map<(d0, d1, d2) -> (d0, d2)> | |
| #map6 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map7 = affine_map<(d0, d1, d2) -> (d0, d2, d1)> | |
| #map8 = affine_map<(d0, d1) -> (d0, 0)> | |
| #map9 = affine_map<(d0, d1) -> (0)> |
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| #map0 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map1 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map2 = affine_map<(d0, d1) -> (0, d1)> | |
| #map3 = affine_map<(d0, d1) -> (d1)> | |
| #map4 = affine_map<(d0, d1, d2) -> (d1)> | |
| #map5 = affine_map<(d0, d1, d2) -> (d0)> | |
| #map6 = affine_map<(d0, d1, d2) -> (d0, d2)> | |
| #map7 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map8 = affine_map<(d0, d1, d2) -> (d0, d2, d1)> | |
| #map9 = affine_map<(d0, d1) -> (0, 0)> |
pashu123
/ hello1.mlir
Created
August 11, 2022 05:24
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| #map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> | |
| #map1 = affine_map<(d0, d1, d2, d3) -> (d1)> | |
| #map2 = affine_map<(d0, d1, d2, d3) -> (0, d1, d2, d3)> | |
| #map3 = affine_map<(d0, d1) -> (d1)> | |
| #map4 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map5 = affine_map<(d0, d1) -> (d1, d0)> | |
| module attributes {torch.debug_module_name = "VisionModule"} { | |
| func.func @forward(%arg0: tensor<1x3x224x224xf32>) -> tensor<1x1000xf32> { | |
| %false = arith.constant false | |
| %cst = arith.constant dense_resource<__elided__> : tensor<1000xf32> |