pashu123’s gists

pashu123 / minilm_canonicalize.mlir

Created June 22, 2022 13:24

After canonicalization minilm.

	module attributes {torch.debug_module_name = "MiniLMSequenceClassification"} {
	func.func @forward(%arg0: !torch.vtensor<[1,128],si32>, %arg1: !torch.vtensor<[1,128],si32>, %arg2: !torch.vtensor<[1,128],si32>) -> !torch.vtensor<[1,2],f32> {
	%int1 = torch.constant.int 1
	%none = torch.constant.none
	%true = torch.constant.bool true
	%float1.000000e00 = torch.constant.float 1.000000e+00
	%int128 = torch.constant.int 128
	%int0 = torch.constant.int 0
	%int9223372036854775807 = torch.constant.int 9223372036854775807
	%float9.999990e-13 = torch.constant.float 9.9999999999999998E-13

pashu123 / v_diff.mlir

Created July 20, 2022 11:16

This file has been truncated, but you can view the full file.

	#map0 = affine_map<(d0, d1) -> (d0, d1)>
	#map1 = affine_map<(d0, d1) -> (d0, 0)>
	#map2 = affine_map<(d0, d1) -> ()>
	#map3 = affine_map<(d0, d1) -> (d1, d0)>
	#map4 = affine_map<(d0, d1) -> (d1)>
	#map5 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (0, d1, 0, d3, 0, 0, 0, 0)>
	#map6 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4, d5, d6, d7)>
	#map7 = affine_map<(d0, d1, d2, d3) -> (d1)>
	#map8 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
	#map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, 0, 0)>

pashu123 / v_diffusion.py

Created July 25, 2022 15:22

	from shark.shark_inference import SharkInference
	from shark.shark_downloader import download_torch_model
	import numpy as np


	mlir_model, func_name, inputs, golden_out = download_torch_model("v_diffusion")

	shark_module = SharkInference(
	mlir_model, func_name, device="cpu", mlir_dialect="linalg"
	)

pashu123 / build_iree.sh

Created July 26, 2022 17:34

	#!/bin/bash

	# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
	# See https://llvm.org/LICENSE.txt for license information.
	# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
	# Also available under a BSD-style license. See LICENSE.

	# Simple script that does a CMake configure of this project as an external
	# LLVM project so it can be tested in isolation to larger assemblies.
	# This is meant for CI's and project maintainers.

pashu123 / v_diff.mlir

Created July 27, 2022 16:46

This file has been truncated, but you can view the full file.

	#map0 = affine_map<(d0, d1) -> (d0, d1)>
	#map1 = affine_map<(d0, d1) -> (d0, 0)>
	#map2 = affine_map<(d0, d1) -> ()>
	#map3 = affine_map<(d0, d1) -> (d1, d0)>
	#map4 = affine_map<(d0, d1) -> (d1)>
	#map5 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (0, d1, 0, d3, 0, 0, 0, 0)>
	#map6 = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4, d5, d6, d7)>
	#map7 = affine_map<(d0, d1, d2, d3) -> (d1)>
	#map8 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
	#map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, 0, 0)>

pashu123 / hello.mlir

Created August 8, 2022 14:17

	module attributes {torch.debug_module_name = "MaskedFillScalarDefaultModule"} {
	func.func @forward(%arg0: !torch.vtensor<[2,3],f32>, %arg1: !torch.vtensor<[2,3],i1>) -> !torch.vtensor<[2,3],f32> {
	%float5.000000e-01 = torch.constant.float 5.000000e-01
	%none = torch.constant.none
	%false = torch.constant.bool false
	%0 = torch.aten.tensor.float %float5.000000e-01, %none, %none, %false : !torch.float, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],f32>
	%1 = torch.aten.Float.Tensor %0 : !torch.vtensor<[],f32> -> !torch.float
	%2 = torch.aten.masked_fill.Scalar %arg0, %arg1, %1 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],i1>, !torch.float -> !torch.vtensor<[2,3],f32>
	return %2 : !torch.vtensor<[2,3],f32>
	}

pashu123 / xyz.py

Created August 10, 2022 08:08

	from PIL import Image
	import requests

	from transformers import AutoModelForMaskedLM, AutoTokenizer
	import torch
	from shark.shark_inference import SharkInference
	from shark.shark_importer import SharkImporter
	from iree.compiler import tf as tfc
	from iree.compiler import compile_str
	from iree import runtime as ireert

pashu123 / hello1.mlir

Created August 11, 2022 05:24

	#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>

pashu123 / dlrm.mlir

Created September 1, 2022 17:32

pashu123 / batch_dlrm.mlir

Created September 2, 2022 02:18

Prashant Kumar pashu123