Initial Layerwise Profiler (#124)

### SUMMARY:


Initial layerwise profiler leveraging the
[kineto](https://github.com/pytorch/kineto) base [PyTorch
profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html).

NOTE: we run in eager mode by default so that the stack-trace/event-tree
contains `nn.Module`, otherwise if cuda-graphs is used all the kernels
will be under the `CUDAGraphRunner` module (this is converted to an
nn.Module in this PR so that the `_build_module_tree` code puts all
those kernels under a `CUDAGraphRunner`)

NOTE: vllm kernels like `vllm::reshape_and_cache_kernel` have no trace
or shape informat because they are not registered as a TorchOp (i.e.
using `TORCH_LIBRARY`, they instead just a raw `PYBIND11_MODULE` module)

Example on how to use visualization:

```
pip install -r requirements-dev.txt

python examples/offline_profile.py --model nm-testing/OpenHermes-2.5-Mistral-7B-pruned50 --batch-size 4 --prompt-len 512 --json openhermes7b-dense

### For Breakdown Graphs
# For module level breakdown
python neuralmagic/tools/profiler/visualize_trace.py --json-trace openhermes7b-dense.json --output profile_dense.pdf
# For kernel level breakdown
python neuralmagic/tools/profiler/visualize_trace.py --json-trace openhermes7b-dense.json --output profile_dense_kernels.pdf --level kernel

### For table printing
# Decode Summary Table
python neuralmagic/tools/profiler/print_table.py --json-trace openhermes7b-dense.json --phase decode
# Prefill Summary Table
python neuralmagic/tools/profiler/print_table.py --json-trace openhermes7b-dense.json --phase prefill
# Decode Model Layerwise Table
python neuralmagic/tools/profiler/print_table.py --json-trace openhermes7b-dense.json --phase decode --table model
# Prefill Model Layerwise Table
python neuralmagic/tools/profiler/print_table.py --json-trace openhermes7b-dense.json --phase prefill --table model
```


Example Output:
[profile-example-output.txt](https://github.com/neuralmagic/nm-vllm/files/14596545/profile-example-output.txt)

### TEST PLAN:
- GHA

---------

Co-authored-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>

examples/offline_profile.py

@@ -0,0 +1,241 @@
+import argparse
+import torch
+import sys
+import json
+import inspect
+
+from dataclasses import dataclass, asdict
+from typing import Optional
+from vllm import LLM, SamplingParams
+from vllm.profiler import nm_profile
+
+BATCH_SIZE_DEFAULT = 1
+PROMPT_LEN_DEFAULT = 256
+MAX_SEQ_LEN_DEFAULT = 1024
+
+
+@dataclass
+class ProfileContext:
+    model: str
+    model_revision: str
+    sparsity: str
+    quantization: str
+    max_seq_len: int
+    max_num_batched_tokens: int
+    prompt_len: int
+    batch_size: int
+    tensor_parallel_size: int
+    allow_cuda_graphs: bool
+
+
+def run_profile(context: ProfileContext, csv_output: Optional[str],
+                json_output: Optional[str]):
+    print("Run profile with:")
+    for key, value in asdict(context).items():
+        print(f"  {key} = {value}")
+
+    # Create sampling params
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=8)
+
+    # Create LLM
+    llm = LLM(
+        model=context.model,
+        revision=context.model_revision,
+        sparsity=context.sparsity,
+        enforce_eager=not context.allow_cuda_graphs,
+        tensor_parallel_size=context.tensor_parallel_size,
+        gpu_memory_utilization=0.9,
+        max_model_len=context.max_seq_len,
+        quantization=context.quantization,
+        max_num_batched_tokens=context.max_num_batched_tokens,
+    )
+
+    batch_size = context.batch_size
+    prompt_len = context.prompt_len
+
+    scheduler_config = llm.llm_engine.scheduler_config
+    max_num_batched_tokens = scheduler_config.max_num_batched_tokens
+    max_num_seqs = scheduler_config.max_num_seqs
+
+    if batch_size * prompt_len > max_num_batched_tokens:
+        print(f"ERROR: chosen batch_size * prompt_len "
+              f"({batch_size} * {prompt_len} = {batch_size * prompt_len}) is  "
+              f"larger than max_num_batched_tokens ({max_num_batched_tokens}) "
+              f"and therefore cannot be run in a single profile step, please "
+              f"choose a smaller batch size or prompt length, or increase "
+              f"--max_num_batched_tokens")
+        sys.exit(-1)
+    if batch_size >= max_num_seqs:
+        print(
+            f"ERROR: chosen batch_size ({batch_size}) is larger than "
+            f"max_num_seqs ({max_num_seqs}) and therefore cannot be run in a "
+            f"single profile step, please choose a smaller batch size")
+        sys.exit(-1)
+
+    for i in range(batch_size):
+        llm.llm_engine.add_request(
+            request_id=f"seq{i}",
+            prompt=None,
+            prompt_token_ids=torch.randint(
+                128,  # 128 to skip over special tokens
+                llm.llm_engine.model_config.get_vocab_size() // 2,
+                size=(prompt_len, )).tolist(),
+            sampling_params=sampling_params)
+
+    with nm_profile() as prefill_prof:
+        llm.llm_engine.step()  # First step is prefill
+
+    with nm_profile() as decode_prof:
+        llm.llm_engine.step()
+
+    prefill_results = prefill_prof.results
+    decode_results = decode_prof.results
+
+    print("=" * 80)
+    print(f"= Prefill Model Table "
+          f"(prompt_len={prompt_len}, batch_size={batch_size})")
+    print("=" * 80)
+    print()
+    prefill_results.print_model_table()
+    print()
+    print("=" * 80)
+    print(f"= Decode Model Table "
+          f"(prompt_len={prompt_len}, batch_size={batch_size})")
+    print("=" * 80)
+    print()
+    decode_results.print_model_table()
+    print()
+    print("=" * 80)
+    print(f"= Prefill Summary Table "
+          f"(prompt_len={prompt_len}, batch_size={batch_size})")
+    print("=" * 80)
+    print()
+    prefill_results.print_summary_table()
+    print()
+    print("=" * 80)
+    print(f"= Decode Summary Table "
+          f"(prompt_len={prompt_len}, batch_size={batch_size})")
+    print("=" * 80)
+    print()
+    decode_results.print_summary_table()
+
+    if csv_output:
+        csv_filename_base = csv_output.rstrip(".csv")
+        prefill_results.export_model_stats_table_csv(
+            csv_filename_base + "_prefill_model_table.csv")
+        prefill_results.export_summary_stats_table_csv(
+            csv_filename_base + "_prefill_summary_table.csv")
+        decode_results.export_model_stats_table_csv(\
+            csv_filename_base + "_decode_model_table.csv")
+        decode_results.export_summary_stats_table_csv(
+            csv_filename_base + "_decode_summary_table.csv")
+
+    if json_output:
+        cuda_devices = [
+            torch.cuda.get_device_properties(dev_idx)
+            for dev_idx in range(torch.cuda.device_count())
+        ]
+
+        json_dict = {
+            "context": {
+                "python_version": f"{sys.version}",
+                "torch_version": f"{torch.__version__}",
+                "torch_cuda_version": f"{torch.version.cuda}",
+                "cuda_devices": f"{cuda_devices}",
+                **asdict(context)
+            },
+            "prefill": prefill_results.convert_stats_to_dict(),
+            "decode": decode_results.convert_stats_to_dict()
+        }
+
+        with open(json_output.rstrip(".json") + ".json", "w+") as f:
+            json.dump(json_dict, f, indent=2)
+        pass
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help='The name or path of a HuggingFace Transformers model.')
+    parser.add_argument("--model-revision", type=str, default=None)
+    parser.add_argument(
+        "--csv",
+        type=str,
+        default=None,
+        help="Export the results as multiple csv file. This should be the root "
+        "filename, will create <filename>_prefill_model_table.csv, "
+        "<filename>_prefill_summary_table.csv, "
+        "<filename>_decode_model_table.csv, and "
+        "<filename>_decode_summary_table.csv")
+    parser.add_argument(
+        "--json",
+        type=str,
+        default=None,
+        help="Export the results as a json file. This should be the filename")
+    parser.add_argument(
+        "--sparsity",
+        "-s",
+        type=str,
+        choices=[None, 'sparse_w16a16', 'semi_structured_sparse_w16a16'],
+        help="Method used to compress sparse weights. If "
+        "None, we first check the `sparsity_config` attribute"
+        "in the model config file. If that is None we assume"
+        "the model weights are dense")
+    parser.add_argument(
+        "--quantization",
+        "-q",
+        type=str,
+        choices=['awq', 'gptq', 'squeezellm', 'marlin', None],
+        default=None,
+        help="The method used to quantize the model weights, "
+        "options are \"marlin\", \"awq\", \"gptq\" and \"squeezellm\"")
+    parser.add_argument(
+        "--max-seq-len",
+        type=int,
+        default=MAX_SEQ_LEN_DEFAULT,
+        help=f"Maximum length of a sequence (including prompt and output), "
+        f"default={MAX_SEQ_LEN_DEFAULT}")
+    parser.add_argument(
+        "--max-num-batched-tokens",
+        type=int,
+        default=None,
+        help="Maximum number of tokens to be processed in a single iteration. "
+        " Should be greater than batch-size * prompt-len so the prefill can "
+        " run in a single iteration.")
+    parser.add_argument(
+        "--prompt-len",
+        type=int,
+        default=PROMPT_LEN_DEFAULT,
+        help=f"Length of the random prompt to use when profiling, all batched "
+        f"requests use the same prompt_len, default={PROMPT_LEN_DEFAULT}")
+    parser.add_argument("--batch-size",
+                        type=int,
+                        default=BATCH_SIZE_DEFAULT,
+                        help=f"Number of requests to run as a single batch, "
+                        f"default={BATCH_SIZE_DEFAULT}")
+    parser.add_argument("--tensor-parallel-size",
+                        "-tp",
+                        type=int,
+                        default=1,
+                        help="Number of GPUs to use i.e. tensor parallelism, "
+                        "default=1")
+    parser.add_argument(
+        "--allow-cuda-graphs",
+        action='store_true',
+        help="Enables cuda graphs to be used, well remove a lot of the module "
+        "level info in the profiler results since almost everything runs in "
+        "the graph where we do not have access to an informative stack trace")
+
+    args = parser.parse_args()
+
+    context = ProfileContext(
+        **{
+            k: v
+            for k, v in vars(args).items()
+            if k in inspect.signature(ProfileContext).parameters
+        })
+    run_profile(context, csv_output=args.csv, json_output=args.json)

neuralmagic/tools/profiler/print_table.py

@@ -0,0 +1,77 @@
+import argparse
+import json
+
+from vllm.profiler.nm_profile import SummaryStatsEntry, ModelStatsEntry
+from vllm.profiler.utils import indent_string, TablePrinter
+from typing import Dict
+
+
+def flatten_entries(entry_cls, profile_dict: Dict):
+    entries_and_depth = []
+
+    def get_entries(node, curr_depth=0):
+        entries_and_depth.append((entry_cls(**node["entry"]), curr_depth))
+
+        for child in node["children"]:
+            get_entries(
+                child,
+                curr_depth=curr_depth + 1,
+            )
+
+    for root in profile_dict:
+        get_entries(root)
+
+    return entries_and_depth
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--json-trace",
+                        type=str,
+                        required=True,
+                        help="json trace file output by "
+                        "examples/offline_profile.py")
+    parser.add_argument("--phase",
+                        type=str,
+                        choices=["prefill", "decode"],
+                        required=True,
+                        help="The phase to print the table for.")
+    parser.add_argument("--table",
+                        type=str,
+                        choices=["summary", "model"],
+                        default="summary",
+                        help="Which table to print, the summary table or the "
+                        "layerwise model table")
+
+    args = parser.parse_args()
+
+    with open(args.json_trace, "r") as f:
+        profile_data = json.load(f)
+
+    if args.table == "summary":
+        entries_and_depths = flatten_entries(
+            SummaryStatsEntry, profile_data[args.phase]["summary_stats"])
+        column_widths = dict(name=80,
+                             cuda_time_us=12,
+                             pct_cuda_time=12,
+                             invocations=15)
+    elif args.table == "model":
+        entries_and_depths = flatten_entries(
+            ModelStatsEntry, profile_data[args.phase]["model_stats"])
+        column_widths = dict(name=60,
+                             cpu_time_us=12,
+                             cuda_time_us=12,
+                             pct_cuda_time=12,
+                             trace=60)
+
+    # ident entry names based on the depth
+    entries = []
+    for entry, depth in entries_and_depths:
+        entry.name = indent_string(
+            entry.name,
+            indent=depth,
+            indent_style=lambda indent: "|" + "-" * indent + " ")
+        entries.append(entry)
+
+    TablePrinter(type(entries[0]), column_widths).print_table(entries)