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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.py
Created
October 19, 2022 14:06
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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 |
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| #map0 = affine_map<() -> ()> | |
| #map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> | |
| #map2 = affine_map<(d0, d1, d2, d3) -> ()> | |
| #map3 = affine_map<(d0) -> (0)> | |
| #map4 = affine_map<(d0) -> (d0)> | |
| #map5 = affine_map<(d0) -> ()> | |
| #map6 = affine_map<(d0, d1) -> ()> | |
| #map7 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map8 = affine_map<(d0, d1) -> (d0, 0)> | |
| #map9 = affine_map<(d0, d1) -> (0, d1)> |
pashu123
/ xyz_elided.mlir
Created
October 20, 2022 15:35
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| module attributes {torch.debug_module_name = "_lambda"} { | |
| func.func @forward(%arg0: !torch.vtensor<[1,4,64,64],f16>, %arg1: !torch.vtensor<[1],f16>, %arg2: !torch.vtensor<[2,77,768],f16>, %arg3: !torch.vtensor<[],f32>) -> !torch.vtensor<[1,4,64,64],f16> { | |
| %int64 = torch.constant.int 64 | |
| %int320 = torch.constant.int 320 | |
| %int2 = torch.constant.int 2 | |
| %int40960 = torch.constant.int 40960 | |
| %int4096 = torch.constant.int 4096 | |
| %int10 = torch.constant.int 10 | |
| %int32 = torch.constant.int 32 | |
| %int640 = torch.constant.int 640 |
pashu123
/ quant_elided.mlir
Created
October 28, 2022 12:59
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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
/ stable2.0.mlir
Created
November 24, 2022 15:20
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| func.func @forward(%arg0: !torch.vtensor<[2,4,96,96],f32>, %arg1: !torch.vtensor<[2],si64>, %arg2: !torch.vtensor<[2,77,1024],f32>) -> !torch.vtensor<[2,4,96,96],f16> { | |
| %int160 = torch.constant.int 160 | |
| %0 = torch.vtensor.literal(dense<1.250000e-01> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %1 = torch.vtensor.literal(dense<9.9999999999999995E-7> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %2 = torch.vtensor.literal(dense<1.000000e-05> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %3 = torch.vtensor.literal(dense<160> : tensor<si64>) : !torch.vtensor<[],si64> | |
| %4 = torch.vtensor.literal(dense<-9.2103403719761836> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<1280x320xf32>) : !torch.vtensor<[1280,320],f32> | |
| %6 = torch.vtensor.literal(dense_resource<__elided__> : tensor<320x4x3x3xf32>) : !torch.vtensor<[320,4,3,3],f32> | |
| %7 = torch.vtensor.literal(dense_resource<__elided__> : tensor<640x320x3x3xf32>) : !torch.vtensor<[640,320,3,3],f32> |
pashu123
/ stable_linalg.mlir
Created
November 25, 2022 08:51
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| #map = affine_map<(d0) -> (d0)> | |
| #map1 = affine_map<(d0) -> ()> | |
| #map2 = affine_map<() -> ()> | |
| #map3 = affine_map<(d0, d1) -> ()> | |
| #map4 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map5 = affine_map<(d0, d1) -> (d0, 0)> | |
| #map6 = affine_map<(d0, d1) -> (0, d1)> | |
| #map7 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map8 = affine_map<(d0, d1) -> (d1)> | |
| #map9 = affine_map<(d0, d1, d2, d3) -> ()> |
pashu123
/ refine.mlir
Created
November 28, 2022 16:29
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| func.func @forward(%arg0: !torch.vtensor<[1,77],si64>) -> !torch.vtensor<[1,77,1024],f16> { | |
| %0 = torch.vtensor.literal(dense<1.250000e-01> : tensor<f64>) : !torch.vtensor<[],f64> | |
| %1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<1x77xsi64>) : !torch.vtensor<[1,77],si64> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<49408x1024xf16>) : !torch.vtensor<[49408,1024],f16> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<77x1024xf16>) : !torch.vtensor<[77,1024],f16> | |
| %4 = torch.vtensor.literal(dense<-6.550400e+04> : tensor<f32>) : !torch.vtensor<[],f32> | |
| %5 = torch.vtensor.literal(dense_resource<__elided__> : tensor<1024x1024xf16>) : !torch.vtensor<[1024,1024],f16> | |
| %6 = torch.vtensor.literal(dense_resource<__elided__> : tensor<4096x1024xf16>) : !torch.vtensor<[4096,1024],f16> | |
| %7 = torch.vtensor.literal(dense_resource<__elided__> : tensor<4096xf16>) : !torch.vtensor<[4096],f16> | |
| %8 = torch.vtensor.literal(dense_resource<__elided__> : tensor<1024x4096xf16>) : |
pashu123
/ dump1.mlir
Created
November 30, 2022 15:53
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| module attributes {torch.debug_module_name = "_lambda"} { | |
| func.func private @__torch__.torch.fx.graph_module._lambda.forward(%arg0: !torch.nn.Module<"__torch__.torch.fx.graph_module._lambda">, %arg1: !torch.tensor {torch.type_bound = !torch.vtensor<[1,77],si64>}) -> !torch.tensor { | |
| %199 = torch.tensor_static_info_cast %arg1 : !torch.tensor to !torch.tensor<[1,77],si64> | |
| %200 = torch.prim.GetAttr %arg0["_param_constant195"] : !torch.nn.Module<"__torch__.torch.fx.graph_module._lambda"> -> !torch.tensor | |
| %201 = torch.prim.GetAttr %arg0["_param_constant194"] : !torch.nn.Module<"__torch__.torch.fx.graph_module._lambda"> -> !torch.tensor | |
| %202 = torch.prim.GetAttr %arg0["_param_constant193"] : !torch.nn.Module<"__torch__.torch.fx.graph_module._lambda"> -> !torch.tensor | |
| %203 = torch.prim.GetAttr %arg0["_param_constant192"] : !torch.nn.Module<"__torch__.torch.fx.graph_module._lambda"> -> !torch.tensor | |
| %204 = torch.prim.GetAttr %arg0["_param_constant191"] : !torch.nn.Module<"__torch__.torch.fx.g |
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| #map = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, 0)> | |
| module attributes {torch.debug_module_name = "_lambda"} { | |
| func.func @forward(%arg0: tensor<10x9216x9216xf16>, %arg1: tensor<1xf16>, %arg2: tensor<2x77x1024xf16>, %arg3: tensor<f32>) -> tensor<10x9216x9216xf16> { | |
| %cst = arith.constant 0.000000e+00 : f16 | |
| %0 = tensor.empty() : tensor<10x9216x9216xf16> | |
| %1 = tensor.empty() : tensor<10x9216x1xf16> | |
| %2 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel", "parallel"]} ins(%arg0 : tensor<10x9216x9216xf16>) outs(%0 : tensor<10x9216x9216xf16>) { | |
| ^bb0(%in: f16, %out: f16): | |
| %6 = math.exp %in : f16 |