pashu123

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

==========
VULKANINFO
==========

Vulkan Instance Version: 1.3.231


Instance Extensions: count = 19
===============================
	VK_EXT_acquire_xlib_display            : extension revision 1

pashu123

#composite_of_1731821440b = #util.composite<1731821440xi8, [
    dense_resource<__elided__> : tensor<320x1280xf16>,
    dense_resource<__elided__> : tensor<1280x1280xf16>,
    dense_resource<__elided__> : tensor<1280x320xf16>,
    dense_resource<__elided__> : tensor<320x320xf16>,
    dense_resource<__elided__> : tensor<320x320xf16>,
    dense_resource<__elided__> : tensor<320x320xf16>,
    dense_resource<__elided__> : tensor<320x320xf16>,
    dense_resource<__elided__> : tensor<320x320xf16>,
    dense_resource<__elided__> : tensor<320x320xf16>,

pashu123

import torch
from diffusers import StableDiffusionPipeline
import torch_mlir
from shark.shark_importer import import_with_fx
import os
import torch.fx as fx
import sys

model_input = {

pashu123

graph():
    %arg0_1 : [#users=1] = placeholder[target=arg0_1]
    %arg1_1 : [#users=1] = placeholder[target=arg1_1]
    %arg2_1 : [#users=32] = placeholder[target=arg2_1]
    %expand : [#users=1] = call_function[target=torch.ops.aten.expand](args = (%arg1_1, [2]), kwargs = {})
    %arange : [#users=1] = call_function[target=torch.ops.aten.arange](args = (0, 160), kwargs = {dtype: torch.float32, device: cuda:0, pin_memory: False})
    %mul : [#users=1] = call_function[target=torch.ops.aten.mul](args = (%arange, -9.210340371976184), kwargs = {})
    %div : [#users=1] = call_function[target=torch.ops.aten.div](args = (%mul, 160), kwargs = {})
    %exp : [#users=1] = call_function[target=torch.ops.aten.exp](args = (%div,), kwargs = {})
    %slice_1 : [#users=1] = call_function[target=torch.ops.aten.slice](args = (%expand, 0, 0, 9223372036854775807), kwargs = {})

pashu123

import torch
from diffusers import StableDiffusionPipeline
import torch_mlir
from shark.shark_importer import import_with_fx
import os
import torch.fx as fx
import sys

model_input = {

pashu123

#loc = loc(unknown)
#map = affine_map<(d0) -> (d0)>
module attributes {torch.debug_module_name = "AtenComplexRealModule"} {
  ml_program.global private mutable @global_seed(dense<0> : tensor<i64>) : tensor<i64> loc(#loc)
  func.func @forward(%arg0: tensor<?xcomplex<f32>> loc(unknown)) -> tensor<?xf32> {
    %c0 = arith.constant 0 : index loc(#loc)
    %dim = tensor.dim %arg0, %c0 : tensor<?xcomplex<f32>> loc(#loc)
    %0 = tensor.empty(%dim) : tensor<?xf32> loc(#loc)
    %1 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%arg0 : tensor<?xcomplex<f32>>) outs(%0 : tensor<?xf32>) {
    ^bb0(%in: complex<f32> loc(unknown), %out: f32 loc(unknown)):

pashu123

import torch
import shark
from shark.shark_importer import import_with_fx
from shark.shark_inference import SharkInference
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
)

pashu123

import torch
import shark
from shark.shark_importer import import_with_fx
from shark.shark_inference import SharkInference
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    StoppingCriteria,
    StoppingCriteriaList,
)

pashu123

import torch
from diffusers import StableDiffusionPipeline
import torch_mlir
from shark.shark_importer import import_with_fx
import os
import sys
from diffusers.models.attention_processor import AttnProcessor2_0
from diffusers.models.attention_processor import AttnProcessor