pashu123

import numpy as np


def compare_arrays(expected, computed):
    # Check if the shapes of the arrays match
    if expected.shape != computed.shape:
        print("Arrays have different shapes.")
        return

    # Find where mismatches occur (including handling NaNs)

pashu123

False
False
False
Mismatch at index (np.int64(0), np.int64(2), np.int64(1)): golden=-1.6139899492263794, iree=-0.0
Mismatch at index (np.int64(0), np.int64(2), np.int64(9)): golden=-1.1718499660491943, iree=-0.0
Mismatch at index (np.int64(0), np.int64(2), np.int64(10)): golden=-1.594499945640564, iree=-0.0
Mismatch at index (np.int64(0), np.int64(2), np.int64(11)): golden=-1.9860199689865112, iree=-0.0
Mismatch at index (np.int64(0), np.int64(2), np.int64(18)): golden=-1.1132500171661377, iree=-0.0
Mismatch at index (np.int64(0), np.int64(2), np.int64(19)): golden=-2.1459200382232666, iree=-0.0
Mismatch at index (np.int64(0), np.int64(2), np.int64(20)): golden=-1.3908900022506714, iree=-0.0

pashu123

//func.func @softmax(%arg0: tensor<2x24x1178x1178xf32>) -> tensor<2x24x1178x1178xf32> {
//  %c0 = arith.constant 0 : index
//  %0 = tensor.empty() : tensor<2x24x1178x1178xf32>
//  %1 = linalg.softmax dimension(3) ins(%arg0 : tensor<2x24x1178x1178xf32>) outs(%0 : tensor<2x24x1178x1178xf32>) -> tensor<2x24x1178x1178xf32>
//  return %1 : tensor<2x24x1178x1178xf32>
//}

func.func @softmax(%arg0: tensor<2x24x1178x1178xf32>) -> tensor<2x24x1178xf32> {
  %4 = tensor.empty() : tensor<2x24x1178xf32>
  %cst = arith.constant -3.40282347E+38 : f32

pashu123

func.func @matmul_broad_dispatch_2_batch_mmt4d_DxDx540x3200x16x16x1_f32xf16xf32() attributes {translation_info = #iree_codegen.translation_info<Mmt4dTilingExpert>} {
  %c1 = arith.constant 1 : index
  %c3200 = arith.constant 3200 : index
  %c540 = arith.constant 540 : index
  %c55296000 = arith.constant 55296000 : index
  %c0 = arith.constant 0 : index
  %c32_i64 = arith.constant 32 : i64
  %cst = arith.constant 0.000000e+00 : f32
  %0 = hal.interface.constant.load[0] : i32
  %1 = hal.interface.constant.load[1] : i32

pashu123

func.func @matmul_broad_dispatch_2_batch_mmt4d_DxDx540x3200x16x16x1_f32xf16xf32() attributes {translation_info = #iree_codegen.translation_info<Mmt4dTilingExpert>} {
  %c1 = arith.constant 1 : index
  %c3200 = arith.constant 3200 : index
  %c540 = arith.constant 540 : index
  %c55296000 = arith.constant 55296000 : index
  %c0 = arith.constant 0 : index
  %c32_i64 = arith.constant 32 : i64
  %cst = arith.constant 0.000000e+00 : f32
  %0 = hal.interface.constant.load[0] : i32
  %1 = hal.interface.constant.load[1] : i32

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func.func @pad_and_pack_static(%input: tensor<13x15xf32>, %output: tensor<2x8x8x2xf32>, %pad: f32) -> tensor<2x8x8x2xf32> {
  %0 = tensor.pack %input padding_value(%pad : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<13x15xf32> -> tensor<2x8x8x2xf32>
  return %0 : tensor<2x8x8x2xf32>
}

module attributes {transform.with_named_sequence} {
  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
      %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
      %1, %loops:2 = transform.structured.tile_using_for %0 tile_sizes [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
      transform.yield

pashu123

-- Read the docs: https://www.lunarvim.org/docs/configuration
-- Video Tutorials: https://www.youtube.com/watch?v=sFA9kX-Ud_c&list=PLhoH5vyxr6QqGu0i7tt_XoVK9v-KvZ3m6
-- Forum: https://www.reddit.com/r/lunarvim/
-- Discord: https://discord.com/invite/Xb9B4Ny
--
--
lvim.colorscheme = "lunar"

lvim.format_on_save.enabled = false

pashu123

func.func @img2col(%arg0: tensor<128x1026x1026xf32>) -> tensor<128x3x3x1024x1024xbf16> {
  %0 = tensor.empty() : tensor<128x3x3x1024x1024xbf16>
  %c1 = arith.constant 1 : index
  %c0 = arith.constant 0 : index
  %cst = arith.constant 0.000000e+00 : f32
  %c128 = arith.constant 128 : index
  %c1024 = arith.constant 1024 : index
  %1 = scf.for %arg1 = %c0 to %c128 step %c1 iter_args(%arg2 = %0) -> (tensor<128x3x3x1024x1024xbf16>) {
    %2 = scf.for %arg3 = %c0 to %c1024 step %c1 iter_args(%arg4 = %arg2) -> (tensor<128x3x3x1024x1024xbf16>) {
      %3 = scf.for %arg5 = %c0 to %c1024 step %c1 iter_args(%arg6 = %arg4) -> (tensor<128x3x3x1024x1024xbf16>) {

pashu123

import argparse
import re

parser = argparse.ArgumentParser(description='Convert parameter data type')

parser.add_argument('mlir', type=str, help='MLIR file where all parameters are mentioned')
parser.add_argument('dtype', type=str, help='Required data type of parameters')
parser.add_argument('irpa', type=str, help='destination irpa file')

args = parser.parse_args()

pashu123

 func.func @torch_add(%arg0: !torch.vtensor<[1,1,?,?],i1>, %arg1: !torch.vtensor<[4,1,1,?],i1>) -> !torch.vtensor<[4, 1, ?, ?],i1> {
    %int1 = torch.constant.int 1
    %2 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[1,1,?,?],i1>, !torch.vtensor<[4,1,1,?],i1>, !torch.int -> !torch.vtensor<[4,1,?,?],i1>
    return %2 : !torch.vtensor<[4,1,?,?],i1>
  }