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| import torch | |
| from diffusers import StableDiffusionPipeline | |
| import torch_mlir | |
| from shark.shark_importer import import_with_fx | |
| import os | |
| model_input = { | |
| "clip": (torch.randint(1, 2, (1, 77)),), | |
| "vae": (torch.randn(1, 4, 128, 128),), |
pashu123
/ int8_elided.mlir
Created
March 20, 2023 15:58
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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) -> (0)> | |
| #map3 = affine_map<(d0) -> (d0)> | |
| #map4 = affine_map<(d0) -> ()> | |
| #map5 = affine_map<(d0, d1) -> (d0, 0)> | |
| #map6 = affine_map<(d0, d1) -> (0, d1)> | |
| #map7 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map8 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map9 = affine_map<(d0, d1) -> (d1)> |
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| from iree import runtime as ireert | |
| from iree.compiler import compile_str | |
| import numpy as np | |
| LINALG_IR = ''' | |
| #map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> | |
| module { | |
| func.func @forward(%arg0: tensor<1x6x32x64x2xf32>) -> tensor<1x6x32x64xcomplex<f32>> { | |
| %c1 = arith.constant 1 : index |
pashu123
/ llama_linalg.mlir
Created
March 3, 2023 15:38
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| func.func @forward(%arg0: tensor<512xf32>, %arg1: tensor<512xf32>, %arg2: tensor<512xf32>, %arg3: tensor<512xf32>, %arg4: tensor<512xf32>, %arg5: tensor<512xf32>, %arg6: tensor<512xf32>, %arg7: tensor<512xf32>, %arg8: tensor<512xf32>, %arg9: tensor<512xf32>, %arg10: tensor<512xf32>, %arg11: tensor<512xf32>, %arg12: tensor<512xf32>, %arg13: tensor<512xf32>, %arg14: tensor<512xf32>, %arg15: tensor<512xf32>, %arg16: tensor<512xf32>, %arg17: tensor<1x512xf32>, %arg18: tensor<512x512xf32>, %arg19: tensor<512x512xf32>, %arg20: tensor<512x512xf32>, %arg21: tensor<512x512xf32>, %arg22: tensor<1536x512xf32>, %arg23: tensor<1536x512xf32>, %arg24: tensor<512x1536xf32>, %arg25: tensor<512x512xf32>, %arg26: tensor<512x512xf32>, %arg27: tensor<512x512xf32>, %arg28: tensor<512x512xf32>, %arg29: tensor<1536x512xf32>, %arg30: tensor<1536x512xf32>, %arg31: tensor<512x1536xf32>, %arg32: tensor<512x512xf32>, %arg33: tensor<512x512xf32>, %arg34: tensor<512x512xf32>, %arg35: tensor<512x512xf32>, %arg36: tensor<1536x512xf32>, %arg3 |
pashu123
/ Transformer.mlir
Created
February 28, 2023 17:48
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| #loc = loc(unknown) | |
| module attributes {torch.debug_module_name = "Transformer"} { | |
| func.func private @__torch__.torch.nn.modules.sparse.Embedding.forward(%arg0: !torch.nn.Module<"__torch__.torch.nn.modules.sparse.Embedding"> loc(unknown), %arg1: !torch.tensor loc(unknown)) -> !torch.tensor { | |
| %185 = torch.tensor_static_info_cast %arg1 : !torch.tensor to !torch.tensor<[1,1],si64> loc(#loc) | |
| %186 = torch.prim.GetAttr %arg0["weight"] : !torch.nn.Module<"__torch__.torch.nn.modules.sparse.Embedding"> -> !torch.tensor loc(#loc) | |
| %int-1 = torch.constant.int -1 loc(#loc1) | |
| %false = torch.constant.bool false loc(#loc1) | |
| %false_0 = torch.constant.bool false loc(#loc1) | |
| %187 = torch.aten.embedding %186, %185, %int-1, %false, %false_0 : !torch.tensor, !torch.tensor<[1,1],si64>, !torch.int, !torch.bool, !torch.bool -> !torch.tensor<[1,1,512],f32> loc(#loc1) | |
| %188 = torch.tensor_static_info_cast %187 : !torch.tensor<[1,1,512],f32> to !torch.tensor loc(#loc1) |
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| /home/prashant/llama/llama/model.py:228: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! | |
| start_pos = int(start_pos_tensor) | |
| /home/prashant/llama/llama/model.py:232: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! | |
| if seqlen > 1: | |
| /home/prashant/llama/llama/model.py:58: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! | |
| assert freqs_cis.shape == (x.shape[1], x.shape[-1]) |
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| Args: {'output_path': '.', 'quant_device': 'cuda', 'batch_size': 2, 'bit_width': 8, 'conv_group_size': 16, 'linear_group_size': 16, 'layer_type': 'conv', 'weight_quant': 'group_scale_param', 'input_quant': 'group_dynamic_sym', 'float16': True, 'inline_ts_graph': False, 'onnx_export': False, 'make_fx': True, 'quantize': True} | |
| Moving model to cuda... | |
| Run model quantization... | |
| Quantize conv2d: conv | |
| Tracing to FX functional representation... | |
| FX graph model... | |
| graph(): | |
| %arg0_1 : [#users=2] = placeholder[target=arg0_1] | |
| %_tensor_constant0 : [#users=1] = get_attr[target=_tensor_constant0] | |
| %lift_fresh_copy : [#users=0] = call_function[target=torch.ops.aten.lift_fresh_copy](args = (%_tensor_constant0,), kwargs = {}) |
pashu123
/ torch_el.mlir
Created
February 14, 2023 17:19
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| module attributes {torch.debug_module_name = "_lambda"} { | |
| func.func @forward(%arg0: !torch.vtensor<[2,4,96,96],f16>, %arg1: !torch.vtensor<[1],f32>, %arg2: !torch.vtensor<[4,77,1024],f16>, %arg3: !torch.vtensor<[],f32>) -> !torch.vtensor<[2,96,96],f16> { | |
| %int1 = torch.constant.int 1 | |
| %int0 = torch.constant.int 0 | |
| %false = torch.constant.bool false | |
| %int4 = torch.constant.int 4 | |
| %float0.000000e00 = torch.constant.float 0.000000e+00 | |
| %int5 = torch.constant.int 5 | |
| %none = torch.constant.none | |
| %int320 = torch.constant.int 320 |
pashu123
/ linalg_el.mlir
Created
February 14, 2023 17:18
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| #map = affine_map<(d0) -> (0)> | |
| #map1 = affine_map<(d0) -> (d0)> | |
| #map2 = affine_map<(d0) -> ()> | |
| #map3 = affine_map<(d0, d1) -> (d0, 0)> | |
| #map4 = affine_map<(d0, d1) -> (0, d1)> | |
| #map5 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map6 = affine_map<(d0, d1) -> (d1, d0)> | |
| #map7 = affine_map<(d0, d1) -> (d1)> | |
| #map8 = affine_map<(d0, d1, d2, d3) -> (d1)> | |
| #map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
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| Package Version Editable project location | |
| ---------------------------- ------------------------ ------------------------------ | |
| absl-py 1.2.0 | |
| aiofiles 22.1.0 | |
| aiohttp 3.8.3 | |
| aiosignal 1.2.0 | |
| albumentations 0.4.3 | |
| altair 4.2.0 | |
| antlr4-python3-runtime 4.8 | |
| anyio 3.6.2 |