Open followtheart opened 11 months ago
followtheart
commented
11 months ago related code: https://github.com/llvm/torch-mlir/blob/ae72eec224e57989f610c6f1d45308125a310f7f/lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp#L175-L195 https://github.com/llvm/torch-mlir/blob/ae72eec224e57989f610c6f1d45308125a310f7f/test/Dialect/Torch/lower-to-backend-contract-error.mlir#L1-L10
maxbartel
commented
11 months ago Same behavior with torchvision.models.mobilenet_v2()
stellaraccident
commented
11 months ago I'm not sure that we have project members who know this part very well anymore: it is part of the original TorchScript backend that we are trying to move off of.
@ramiro050 do you have any experience with this?
ramiro050
commented
11 months ago This is one of those cases where there is so much happening that could trip up torch-mlir that it is hard to know if the InlineGlobalSlots error is the issue or just a consequence of another simplification pass no working correctly.
From looking at the IR, there are a few ops that need support in torch-mlir:
torch.operator "aten.__contains__.str_list"
torch.operator "aten._native_multi_head_attention"
torch.operator "aten.is_autocast_enabled"
torch.operator "aten.is_grad_enabled"
torch.operator "aten.split.sizes"
torch.operator "aten.unflatten.int"
torch.operator "prim.is_nested"
torch.operator "prim.requires_grad"
torch.operator "prim.type"Not having patterns for these ops could be getting in the way of simplifications that would allow inlining global slots.
Additionally, there is quite a bit of control flow in the IR. Torch-MLIR failing to simplify the control flow could be another reason for the error. One way to get around the control flow issue is to set use_tracing=True.
followtheart
commented
11 months ago This script may help.origin from https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit.py ,and fixed one line
import numpy as np
import torch
import torchvision.models as models
#import torch_mlir
import torch
from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
#from https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit.py
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
# classes
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.norm = nn.LayerNorm(dim)
self.attend = nn.Softmax(dim = -1)
self.dropout = nn.Dropout(dropout)
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x):
x = self.norm(x)
qkv = self.to_qkv(x).chunk(3, dim = -1)
# q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
q, k, v = [rearrange(t, 'b n (h d) -> b h n d', h=self.heads) for t in qkv]
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
attn = self.dropout(attn)
out = torch.matmul(attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
FeedForward(dim, mlp_dim, dropout = dropout)
]))
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return self.norm(x)
class ViT(nn.Module):
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
super().__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
nn.LayerNorm(patch_dim),
nn.Linear(patch_dim, dim),
nn.LayerNorm(dim),
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(emb_dropout)
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
self.pool = pool
self.to_latent = nn.Identity()
self.mlp_head = nn.Linear(dim, num_classes)
def forward(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
x = self.to_latent(x)
return self.mlp_head(x)
and
vit = ViT(
image_size = 224,
patch_size = 16,
num_classes = 1000,
dim = 768,
depth = 12,
heads = 12,
mlp_dim = 3072,
dropout = 0.1,
emb_dropout = 0.1
)
img = torch.randn(1, 3, 224, 224)
vit.eval()
#lowering to linalg
vit_mod = torch_mlir.compile(vit, img,
output_type=torch_mlir.OutputType.LINALG_ON_TENSORS,
use_tracing=True)
When I compile vit model into
LINALG_ON_TENSORS,got this error;any help?Error info: