[Pytorch Lightning support] added pytorch lightning

deepview_profile/export_converter.py

@@ -0,0 +1,158 @@
+import json
+
+
+def convert(message):
+    new_message = {}
+    with open("message.json", "w") as fp:
+        json.dump(message, fp, indent=4)
+
+    new_message["ddp"] = {}
+    new_message["message_type"] = message["message_type"]
+    new_message["project_root"] = message["project_root"]
+    new_message["project_entry_point"] = message["project_entry_point"]
+
+    new_message["hardware_info"] = {
+        "hostname": message["hardware_info"]["hostname"],
+        "os": message["hardware_info"]["os"],
+        "gpus": message["hardware_info"]["gpus"],
+    }
+
+    new_message["throughput"] = {
+        "samples_per_second": message["throughput"]["samples_per_second"],
+        "predicted_max_samples_per_second": message["throughput"][
+            "predicted_max_samples_per_second"
+        ],
+        "run_time_ms": (
+            [
+                message["throughput"]["run_time_ms"]["slope"],
+                message["throughput"]["run_time_ms"]["bias"],
+            ]
+            if "run_time_ms" in message["throughput"]
+            else [0, 0]
+        ),
+        "peak_usage_bytes": (
+            [
+                message["throughput"]["peak_usage_bytes"]["slope"],
+                message["throughput"]["peak_usage_bytes"]["bias"],
+            ]
+            if "peak_usage_bytes" in message["throughput"]
+            else [0, 0]
+        ),
+        "batch_size_context": None,
+        "can_manipulate_batch_size": False,
+    }
+
+    new_message["utilization"] = message["utilization"]
+
+    def fix(a):
+        for d in ["cpu", "gpu"]:
+            for s in ["Forward", "Backward"]:
+                if f"{d}_{s.lower()}" in a:
+                    a[f"{d}{s}"] = a[f"{d}_{s.lower()}"]
+                    del a[f"{d}_{s.lower()}"]
+                else:
+                    a[f"{d}{s}"] = 0
+
+                if f"{d}_{s.lower()}_span" in a:
+                    a[f"{d}{s}Span"] = a[f"{d}_{s.lower()}_span"]
+                    del a[f"{d}_{s.lower()}_span"]
+                else:
+                    a[f"{d}{s}Span"] = 0
+
+        if "children" not in a:
+            a["children"] = []
+            return
+
+        if a:
+            for c in a["children"]:
+                fix(c)
+
+    (
+        fix(new_message["utilization"]["rootNode"])
+        if new_message["utilization"].get("rootNode", None)
+        else None
+    )
+    try:
+        new_message["utilization"]["tensor_core_usage"] = message["utilization"][
+            "tensor_utilization"
+        ]
+    except:
+        new_message["utilization"]["tensor_core_usage"] = 0
+
+    new_message["habitat"] = {
+        "predictions": [
+            (
+                [prediction["device_name"], prediction["runtime_ms"]]
+                if prediction["device_name"] != "unavailable"
+                else ["default_device", 0]
+            )
+            for prediction in message["habitat"]["predictions"]
+        ]
+    }
+
+    new_message["breakdown"] = {
+        "peak_usage_bytes": int(message["breakdown"]["peak_usage_bytes"]),
+        "memory_capacity_bytes": int(message["breakdown"]["memory_capacity_bytes"]),
+        "iteration_run_time_ms": message["breakdown"]["iteration_run_time_ms"],
+        # TODO change these hardcoded numbers
+        "batch_size": 48,
+        "num_nodes_operation_tree": len(message["breakdown"]["operation_tree"]),
+        "num_nodes_weight_tree": 0,
+        "operation_tree": [
+            {
+                "name": op["name"],
+                "num_children": op["num_children"] if "num_children" in op else 0,
+                "forward_ms": op["operation"]["forward_ms"],
+                "backward_ms": op["operation"]["backward_ms"],
+                "size_bytes": (
+                    int(op["operation"]["size_bytes"])
+                    if "size_bytes" in op["operation"]
+                    else 0
+                ),
+                "file_refs": (
+                    [
+                        {
+                            "path": "/".join(ctx["context"]["file_path"]["components"]),
+                            "line_no": ctx["context"]["line_number"],
+                            "run_time_ms": ctx["run_time_ms"],
+                            "size_bytes": (
+                                int(ctx["size_bytes"]) if "size_bytes" in ctx else 0
+                            ),
+                        }
+                        for ctx in op["operation"]["context_info_map"]
+                    ]
+                    if "context_info_map" in op["operation"]
+                    else list()
+                ),
+            }
+            for op in message["breakdown"]["operation_tree"]
+        ],
+    }
+
+    def fix_components(m):
+        for c in m["components"]:
+            if "consumption_joules" not in c:
+                c["consumption"] = 0
+            else:
+                c["consumption"] = c["consumption_joules"]
+                del c["consumption_joules"]
+            c["type"] = c["component_type"]
+            if c["type"] == "ENERGY_NVIDIA":
+                c["type"] = "ENERGY_GPU"
+            del c["component_type"]
+
+    new_message["energy"] = {
+        "current": {
+            "total_consumption": message["energy"]["total_consumption"],
+            "components": message["energy"]["components"],
+            "batch_size": 48,
+        },
+        "past_measurements": message["energy"].get("past_measurements", None),
+    }
+
+    fix_components(new_message["energy"]["current"])
+    if new_message["energy"].get("past_measurements", None):
+        for m in new_message["energy"]["past_measurements"]:
+            fix_components(m)
+
+    return new_message

deepview_profile/pl/deepview_callback.py

@@ -0,0 +1,92 @@
+from typing import Callable, Tuple
+
+import time
+import os
+import json
+import torch
+import sys
+
+try:
+    import pytorch_lightning as pl
+except ImportError:
+    sys.exit("Please install pytorch-lightning:\nuse: pip install lightning\nExiting...")
+
+from termcolor import colored
+from deepview_profile.pl.deepview_interface import trigger_profiling
+
+
+class DeepViewProfilerCallback(pl.Callback):
+    def __init__(self, profile_name: str):
+        super().__init__()
+        self.profiling_triggered = False
+        self.output_filename = f"{profile_name}_{int(time.time())}.json"
+
+    def on_train_batch_end(
+        self,
+        trainer: pl.Trainer,
+        pl_module: pl.LightningModule,
+        outputs,
+        batch,
+        batch_idx,
+    ):
+
+        # only do this once
+        if self.profiling_triggered:
+            return
+
+        print(colored("DeepViewProfiler: Running profiling.", "green"))
+
+        """
+        need 3 things:
+
+            input_provider: just return batch
+            model_provider: just return pl_module
+            iteration_provider: a lambda function that (a) calls pl_module.forward_step and (b) calls loss.backward
+        """
+        initial_batch_size = batch[0].shape[0]
+
+        def input_provider(batch_size: int = initial_batch_size) -> Tuple:
+            model_inputs = list()
+            for elem in batch:
+                # we assume the first dimension is the batch dimension
+                model_inputs.append(
+                    elem[:1].repeat([batch_size] + [1 for _ in elem.shape[1:]])
+                )
+            return (tuple(model_inputs), 0)
+
+        model_provider = lambda: pl_module
+
+        def iteration_provider(module: torch.nn.Module) -> Callable:
+            def iteration(*args, **kwargs):
+                loss = module.training_step(*args, **kwargs)
+                loss.backward()
+
+            return iteration
+
+        project_root = os.getcwd()
+
+        output = trigger_profiling(
+            project_root,
+            "entry_point.py",
+            initial_batch_size,
+            input_provider,
+            model_provider,
+            iteration_provider,
+        )
+
+        with open(self.output_filename, "w") as fp:
+            json.dump(output, fp, indent=4)
+
+        print(
+            colored(
+                f"DeepViewProfiler: Profiling complete! Report written to ", "green"
+            )
+            + colored(self.output_filename, "green", attrs=["bold"])
+        )
+        print(
+            colored(
+                f"DeepViewProfiler: View your report at https://deepview.centml.ai",
+                "green",
+            )
+        )
+        self.profiling_triggered = True

deepview_profile/pl/deepview_interface.py

@@ -0,0 +1,141 @@
+import sys
+from typing import Callable
+import platform
+
+from deepview_profile.analysis.session import AnalysisSession
+from deepview_profile.exceptions import AnalysisError
+from deepview_profile.nvml import NVML
+
+# from deepview_profile.utils import release_memory, next_message_to_dict, files_encoded_unique
+from deepview_profile.utils import release_memory, files_encoded_unique
+from deepview_profile.error_printing import print_analysis_error
+
+from google.protobuf.json_format import MessageToDict
+
+
+def measure_breakdown(session, nvml):
+    print("analysis: running measure_breakdown()")
+    yield session.measure_breakdown(nvml)
+    release_memory()
+
+
+def measure_throughput(session):
+    print("analysis: running measure_throughput()")
+    yield session.measure_throughput()
+    release_memory()
+
+
+def habitat_predict(session):
+    print("analysis: running deepview_predict()")
+    yield session.habitat_predict()
+    release_memory()
+
+
+def measure_utilization(session):
+    print("analysis: running measure_utilization()")
+    yield session.measure_utilization()
+    release_memory()
+
+
+def energy_compute(session):
+    print("analysis: running energy_compute()")
+    yield session.energy_compute()
+    release_memory()
+
+
+def ddp_analysis(session):
+    print("analysis: running ddp_computation()")
+    yield session.ddp_computation()
+    release_memory()
+
+
+def hardware_information(nvml):
+    hardware_info = {
+        "hostname": platform.node(),
+        "os": " ".join(list(platform.uname())),
+        "gpus": nvml.get_device_names(),
+    }
+    return hardware_info
+
+
+class DummyStaticAnalyzer:
+    def batch_size_location(self):
+        return None
+
+
+def next_message_to_dict(a):
+    message = next(a)
+    return MessageToDict(message, preserving_proto_field_name=True)
+
+
+def trigger_profiling(
+    project_root: str,
+    entry_point: str,
+    initial_batch_size: int,
+    input_provider: Callable,
+    model_provider: Callable,
+    iteration_provider: Callable,
+):
+    try:
+        data = {
+            "analysis": {
+                "message_type": "analysis",
+                "project_root": project_root,
+                "project_entry_point": entry_point,
+                "hardware_info": {},
+                "throughput": {},
+                "breakdown": {},
+                "habitat": {},
+                "additionalProviders": "",
+                "energy": {},
+                "utilization": {},
+                "ddp": {},
+            },
+            "epochs": 50,
+            "iterations": 1000,
+            "encodedFiles": [],
+        }
+
+        session = AnalysisSession(
+            project_root,
+            entry_point,
+            project_root,
+            model_provider,
+            input_provider,
+            iteration_provider,
+            initial_batch_size,
+            DummyStaticAnalyzer(),
+        )
+        release_memory()
+
+        exclude_source = False
+
+        with NVML() as nvml:
+            data["analysis"]["hardware_info"] = hardware_information(nvml)
+            data["analysis"]["breakdown"] = next_message_to_dict(
+                measure_breakdown(session, nvml)
+            )
+
+            operation_tree = data["analysis"]["breakdown"]["operation_tree"]
+            if not exclude_source and operation_tree is not None:
+                data["encodedFiles"] = files_encoded_unique(operation_tree)
+
+        data["analysis"]["throughput"] = next_message_to_dict(
+            measure_throughput(session)
+        )
+        data["analysis"]["habitat"] = next_message_to_dict(habitat_predict(session))
+        data["analysis"]["utilization"] = next_message_to_dict(
+            measure_utilization(session)
+        )
+        data["analysis"]["energy"] = next_message_to_dict(energy_compute(session))
+        # data['analysis']['ddp'] = next_message_to_dict(ddp_analysis(session))
+
+        from deepview_profile.export_converter import convert
+
+        data["analysis"] = convert(data["analysis"])
+
+        return data
+
+    except AnalysisError as ex:
+        print_analysis_error(ex)
+        sys.exit(1)