[CodeGen]Support matmul codegen and runtime in mlir jit

[howin98]

本pr支持在mlir jit中运行matmul op，该pr补全了在gpu dialect之后的codegen中健壮性的一部分问题

[github-actions]

CI failed when running job: Build cpu. PR label automerge has been removed

[github-actions]

Code got formatted by CI. Please request CI again if you still want to have this PR merged. If the PR is from a forked repo, please download the patch files from the GitHub Actions web page and apply them locally.

[github-actions]

Code got formatted by CI. Please request CI again if you still want to have this PR merged. If the PR is from a forked repo, please download the patch files from the GitHub Actions web page and apply them locally.

[github-actions]

CI failed when running job: Build cpu. PR label automerge has been removed

[github-actions]

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/10283/

[github-actions]

Speed stats:

GPU Name: NVIDIA GeForce RTX 3090 

❌ OneFlow resnet50 time: 43.7ms (= 4374.3ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.3ms (= 5729.6ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.31 (= 57.3ms / 43.7ms)

OneFlow resnet50 time: 26.2ms (= 2624.1ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 37.3ms (= 3727.7ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.42 (= 37.3ms / 26.2ms)

OneFlow resnet50 time: 19.5ms (= 3894.0ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 34.8ms (= 6960.3ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.79 (= 34.8ms / 19.5ms)

OneFlow resnet50 time: 19.2ms (= 3840.8ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 31.6ms (= 6316.2ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.64 (= 31.6ms / 19.2ms)

OneFlow resnet50 time: 17.7ms (= 3530.4ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 29.3ms (= 5869.7ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.66 (= 29.3ms / 17.7ms)

OneFlow swin dataloader time: 0.200s (= 39.942s / 200, num_workers=1)
PyTorch swin dataloader time: 0.130s (= 25.953s / 200, num_workers=1)
Relative speed: 0.650 (= 0.130s / 0.200s)

OneFlow swin dataloader time: 0.057s (= 11.316s / 200, num_workers=4)
PyTorch swin dataloader time: 0.032s (= 6.470s / 200, num_workers=4)
Relative speed: 0.572 (= 0.032s / 0.057s)

OneFlow swin dataloader time: 0.031s (= 6.117s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.338s / 200, num_workers=8)
Relative speed: 0.546 (= 0.017s / 0.031s)

❌ OneFlow resnet50 time: 48.5ms (= 4853.4ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 64.9ms (= 6489.8ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.34 (= 64.9ms / 48.5ms)

OneFlow resnet50 time: 35.6ms (= 3559.2ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 50.7ms (= 5073.7ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.43 (= 50.7ms / 35.6ms)

OneFlow resnet50 time: 28.4ms (= 5670.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 40.3ms (= 8060.7ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.42 (= 40.3ms / 28.4ms)

OneFlow resnet50 time: 25.2ms (= 5035.8ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.7ms (= 7738.1ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.54 (= 38.7ms / 25.2ms)

OneFlow resnet50 time: 24.0ms (= 4800.7ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 36.3ms (= 7253.7ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.51 (= 36.3ms / 24.0ms)