Move TorchDynamo into PyTorch core (pytorch#86461)

Context:
pytorch/torchdynamo#1588

This PR moves [TorchDynamo](https://github.com/pytorch/torchdynamo) and TorchInductor into PyTorch core.
- `torchdynamo` becomes `torch._dynamo`
- `torchinductor` becomes `torch._inductor`

This PR was generated by running `copy_to_core.sh` in pytorch/torchdynamo#1538

Pull Request resolved: pytorch#86461
Approved by: https://github.com/voznesenskym

.jenkins/pytorch/win-test-helpers/setup_pytorch_env.bat

@@ -36,7 +36,7 @@ popd
 =======
 :: Pin unittest-xml-reporting to freeze printing test summary logic, related: https://github.com/pytorch/pytorch/issues/69014
 
-pip install "ninja==1.10.0.post1" future "hypothesis==5.35.1" "expecttest==0.1.3" "librosa>=0.6.2" "scipy==1.6.3" psutil pillow "unittest-xml-reporting<=3.2.0,>=2.0.0" pytest pytest-xdist pytest-shard pytest-rerunfailures "xdoctest==1.0.2" "pygments==2.12.0" "opt-einsum>=3.3"
+pip install "ninja==1.10.0.post1" future "hypothesis==5.35.1" "expecttest==0.1.3" "librosa>=0.6.2" "scipy==1.6.3" psutil pillow "unittest-xml-reporting<=3.2.0,>=2.0.0" pytest pytest-xdist pytest-shard pytest-rerunfailures sympy "xdoctest==1.0.2" "pygments==2.12.0" "opt-einsum>=3.3"
 if errorlevel 1 exit /b
 if not errorlevel 0 exit /b
 

benchmarks/dynamo/README.md

@@ -0,0 +1,50 @@
+# Torchdynamo Benchmarks
+
+## What We Benchmark
+TorchDynamo provides a benchmark harness that takes care of uniformly benchmarking different models.  It interleaves runs of eager and dynamo to avoid machine noise/variability issues, and reports results based on medians along with P-values.
+
+The runner integrates with models from TorchBenchmark, HuggingFace and TIMM suites and covers both training and inference.
+
+The infrastructure allows us to specify a loss function. For torchbench models, we use .sum().backward() call in place of the native loss function. For TIMM models, we use a CrossEntropy loss. And HF models contain a loss function inside the model itself, so we don't need any special loss computation handling.
+
+Training benchmarks approximate training by running the model forward, computing loss and then running backward. We entirely skip the optimizer step today.
+
+Inference benchmarks and Training benchmarks measure correctness by comparing dynamo and eager model outputs given fixed inputs and seeds.
+
+## Setup
+
+### Machine
+We run benchmarks on AWS machines (p4d.24xlarge) using 8xNVidia A100 40GB cards.  We suggest using Cuda 11.6 for consistency.
+
+### Benchmarks
+Make sure to carefully follow the [torchbench installation](https://github.com/pytorch/benchmark#installation) instructions, taking care to build the auxiliary libraries (torchvision, torchtext) from a matching version to your pytorch version.
+
+For HF and TIMM models, the scripts already install the transformers and timm package respectively on the first run.
+
+## Runbook
+
+### Basic Usage
+There are a lot of flags in the benchmark runner, and it can be confusing to know which settings to use or what machine to run it on.  In order to support apples-to-apples comparison, we have provided the following 'standard' settings in `runner.py`. This script is a wrapper over the common benchmarking infrastructure and simplifies the flags. We will continually update `runner.py` with the latest and most relevant compilers for training and inference. It also provides some graph utilities to visualize and compare results. Some of the example commands are
+
+**Inference Commands**
+* Inference compilers on torchbench models - `python benchmarks/runner.py --suites=torchbench --inference --dtypes=float16`
+
+**Training Commands**
+* Training compilers on TIMM models - `python benchmarks/runner.py --suites=timm_models --training --dtypes=float32 --output-dir=timm_logs`
+* AOTAutograd Training compiler on TIMM models - `python benchmarks/runner.py --suites=timm_models --training --dtypes=float32 --compilers=aot_nvfuser --output-dir=timm_logs`
+
+Running runner.py generates a file named `run.sh`. This file contains the actual commands that invoke the common benchmarking infrastructure with the appropriate flags. Which brings us to the advanced usage.
+
+### Advanced Usage
+
+One could directly call `torchbench.py`, `huggingface.py` or `timm_models.py` with the necessary flags. There are a lot of flags in the benchmarks runner. Some of the examples are as follows. These are subject to change.
+
+**Inference Commands**
+* TorchScript NVFuser Inference - `python benchmarks/torchbench.py -dcuda -n100 --speedup-ts`
+* TorchInductor CUDA Graphs Inference - `python benchmarks/torchbench.py -dcuda --inductor-settings --float32 -n50 --inductor`
+
+**Training Commands**
+* Torchscript (with TorchDynamo capture) NVFuser Training - `python benchmarks/torchbench.py --float32 -dcuda --training --nvfuser --speedup-dynamo-ts --use-eval-mode`
+* AOTAutograd Torchscript NVFuser Training - `python benchmarks/torchbench.py --float32 -dcuda --training --nvfuser --accuracy-aot-ts-mincut --use-eval-mode`
+
+Above commands are for torchbench models. You can simply replace `torchbench.py` with `huggingface.py` for HF models, and `timm_model.py` for TIMM models.