diff options
| -rwxr-xr-x | bin/analyze-trace.py | 196 | ||||
| -rw-r--r-- | lib/behaviour.py | 200 |
2 files changed, 207 insertions, 189 deletions
diff --git a/bin/analyze-trace.py b/bin/analyze-trace.py index 2186951..f721876 100755 --- a/bin/analyze-trace.py +++ b/bin/analyze-trace.py @@ -11,6 +11,7 @@ import dfatool.cli import dfatool.plotter import dfatool.utils import dfatool.functions as df +from dfatool.behaviour import SDKBehaviourModel from dfatool.loader import Logfile from dfatool.model import AnalyticModel from dfatool.validation import CrossValidator @@ -34,179 +35,6 @@ def parse_logfile(filename): return loader.load(f, is_trace=True) -def learn_pta(observations, annotation, delta=dict(), delta_param=dict()): - prev_i = annotation.start.offset - prev = "__init__" - prev_non_kernel = prev - meta_observations = list() - n_seen = dict() - - total_latency_us = 0 - - if annotation.kernels: - # ggf. als dict of tuples, für den Fall dass Schleifen verschieden iterieren können? - for i in range(prev_i, annotation.kernels[0].offset): - this = observations[i]["name"] + " @ " + observations[i]["place"] - - if this in n_seen: - if n_seen[this] == 1: - logging.debug( - f"Loop found in {annotation.start.name} {annotation.end.param}: {this} ⟳" - ) - n_seen[this] += 1 - else: - n_seen[this] = 1 - - if not prev in delta: - delta[prev] = set() - delta[prev].add(this) - - # annotation.start.param may be incomplete, for instance in cases - # where DPUs are allocated before the input file is loadeed (and - # thus before the problem size is known). - # Hence, we must use annotation.end.param whenever we deal - # with possibly problem size-dependent behaviour. - if not (prev, this) in delta_param: - delta_param[(prev, this)] = set() - delta_param[(prev, this)].add( - dfatool.utils.param_dict_to_str(annotation.end.param) - ) - - prev = this - prev_i = i + 1 - - total_latency_us += observations[i]["attribute"].get("latency_us", 0) - - meta_observations.append( - { - "name": f"__trace__ {this}", - "param": annotation.end.param, - "attribute": dict( - filter( - lambda kv: not kv[0].startswith("e_"), - observations[i]["param"].items(), - ) - ), - } - ) - prev_non_kernel = prev - - for kernel in annotation.kernels: - prev = prev_non_kernel - for i in range(prev_i, kernel.offset): - this = observations[i]["name"] + " @ " + observations[i]["place"] - - if not prev in delta: - delta[prev] = set() - delta[prev].add(this) - - if not (prev, this) in delta_param: - delta_param[(prev, this)] = set() - delta_param[(prev, this)].add( - dfatool.utils.param_dict_to_str(annotation.end.param) - ) - - # The last iteration (next block) contains a single kernel, - # so we do not increase total_latency_us here. - # However, this means that we will only ever get one latency - # value for each set of kernels with a common problem size, - # despite potentially having far more data at our fingertips. - # We could provide one total_latency_us for each kernel - # (by combining start latency + kernel latency + teardown latency), - # but for that we first need to distinguish between kernel - # components and teardown components in the following block. - - prev = this - prev_i = i + 1 - - meta_observations.append( - { - "name": f"__trace__ {this}", - "param": annotation.end.param, - "attribute": dict( - filter( - lambda kv: not kv[0].startswith("e_"), - observations[i]["param"].items(), - ) - ), - } - ) - - # There is no kernel end signal in the underlying data, so the last iteration also contains a kernel run. - prev = prev_non_kernel - for i in range(prev_i, annotation.end.offset): - this = observations[i]["name"] + " @ " + observations[i]["place"] - - if this in n_seen: - if n_seen[this] == 1: - logging.debug( - f"Loop found in {annotation.start.name} {annotation.end.param}: {this} ⟳" - ) - n_seen[this] += 1 - else: - n_seen[this] = 1 - - if not prev in delta: - delta[prev] = set() - delta[prev].add(this) - - if not (prev, this) in delta_param: - delta_param[(prev, this)] = set() - delta_param[(prev, this)].add( - dfatool.utils.param_dict_to_str(annotation.end.param) - ) - - total_latency_us += observations[i]["attribute"].get("latency_us", 0) - - prev = this - - meta_observations.append( - { - "name": f"__trace__ {this}", - "param": annotation.end.param, - "attribute": dict( - filter( - lambda kv: not kv[0].startswith("e_"), - observations[i]["param"].items(), - ) - ), - } - ) - - if not prev in delta: - delta[prev] = set() - delta[prev].add("__end__") - if not (prev, "__end__") in delta_param: - delta_param[(prev, "__end__")] = set() - delta_param[(prev, "__end__")].add( - dfatool.utils.param_dict_to_str(annotation.end.param) - ) - - for transition, count in n_seen.items(): - meta_observations.append( - { - "name": f"__loop__ {transition}", - "param": annotation.end.param, - "attribute": {"n_iterations": count}, - } - ) - - if total_latency_us: - meta_observations.append( - { - "name": annotation.start.name, - "param": annotation.end.param, - "attribute": {"latency_us": total_latency_us}, - } - ) - - is_loop = dict( - map(lambda kv: (kv[0], True), filter(lambda kv: kv[1] > 1, n_seen.items())) - ) - - return delta, delta_param, meta_observations, is_loop - - def join_annotations(ref, base, new): offset = len(ref) return base + list(map(lambda x: x.apply_offset(offset), new)) @@ -243,22 +71,12 @@ def main(): map(parse_logfile, args.logfiles), ) - delta_by_name = dict() - delta_param_by_name = dict() - is_loop = dict() - for annotation in annotations: - am_tt_param_names = sorted(annotation.start.param.keys()) - if annotation.name not in delta_by_name: - delta_by_name[annotation.name] = dict() - delta_param_by_name[annotation.name] = dict() - _, _, meta_obs, _is_loop = learn_pta( - observations, - annotation, - delta_by_name[annotation.name], - delta_param_by_name[annotation.name], - ) - observations += meta_obs - is_loop.update(_is_loop) + bm = SDKBehaviourModel(observations, annotations) + observations += bm.meta_observations + is_loop = bm.is_loop + am_tt_param_names = bm.am_tt_param_names + delta_by_name = bm.delta_by_name + delta_param_by_name = bm.delta_param_by_name def format_guard(guard): return "∧".join(map(lambda kv: f"{kv[0]}={kv[1]}", guard)) diff --git a/lib/behaviour.py b/lib/behaviour.py index 1e59d20..156da5f 100644 --- a/lib/behaviour.py +++ b/lib/behaviour.py @@ -6,6 +6,206 @@ from . import utils logger = logging.getLogger(__name__) +class SDKBehaviourModel: + + def __init__(self, observations, annotations): + + meta_observations = list() + delta_by_name = dict() + delta_param_by_name = dict() + is_loop = dict() + + for annotation in annotations: + am_tt_param_names = sorted(annotation.start.param.keys()) + if annotation.name not in delta_by_name: + delta_by_name[annotation.name] = dict() + delta_param_by_name[annotation.name] = dict() + _, _, meta_obs, _is_loop = self.learn_pta( + observations, + annotation, + delta_by_name[annotation.name], + delta_param_by_name[annotation.name], + ) + meta_observations += meta_obs + is_loop.update(_is_loop) + + self.am_tt_param_names = am_tt_param_names + self.delta_by_name = delta_by_name + self.delta_param_by_name = delta_param_by_name + self.meta_observations = meta_observations + self.is_loop = is_loop + + def learn_pta(self, observations, annotation, delta=dict(), delta_param=dict()): + prev_i = annotation.start.offset + prev = "__init__" + prev_non_kernel = prev + meta_observations = list() + n_seen = dict() + + total_latency_us = 0 + + if annotation.kernels: + # ggf. als dict of tuples, für den Fall dass Schleifen verschieden iterieren können? + for i in range(prev_i, annotation.kernels[0].offset): + this = observations[i]["name"] + " @ " + observations[i]["place"] + + if this in n_seen: + if n_seen[this] == 1: + logging.debug( + f"Loop found in {annotation.start.name} {annotation.end.param}: {this} ⟳" + ) + n_seen[this] += 1 + else: + n_seen[this] = 1 + + if not prev in delta: + delta[prev] = set() + delta[prev].add(this) + + # annotation.start.param may be incomplete, for instance in cases + # where DPUs are allocated before the input file is loadeed (and + # thus before the problem size is known). + # Hence, we must use annotation.end.param whenever we deal + # with possibly problem size-dependent behaviour. + if not (prev, this) in delta_param: + delta_param[(prev, this)] = set() + delta_param[(prev, this)].add( + utils.param_dict_to_str(annotation.end.param) + ) + + prev = this + prev_i = i + 1 + + total_latency_us += observations[i]["attribute"].get("latency_us", 0) + + meta_observations.append( + { + "name": f"__trace__ {this}", + "param": annotation.end.param, + "attribute": dict( + filter( + lambda kv: not kv[0].startswith("e_"), + observations[i]["param"].items(), + ) + ), + } + ) + prev_non_kernel = prev + + for kernel in annotation.kernels: + prev = prev_non_kernel + for i in range(prev_i, kernel.offset): + this = observations[i]["name"] + " @ " + observations[i]["place"] + + if not prev in delta: + delta[prev] = set() + delta[prev].add(this) + + if not (prev, this) in delta_param: + delta_param[(prev, this)] = set() + delta_param[(prev, this)].add( + utils.param_dict_to_str(annotation.end.param) + ) + + # The last iteration (next block) contains a single kernel, + # so we do not increase total_latency_us here. + # However, this means that we will only ever get one latency + # value for each set of kernels with a common problem size, + # despite potentially having far more data at our fingertips. + # We could provide one total_latency_us for each kernel + # (by combining start latency + kernel latency + teardown latency), + # but for that we first need to distinguish between kernel + # components and teardown components in the following block. + + prev = this + prev_i = i + 1 + + meta_observations.append( + { + "name": f"__trace__ {this}", + "param": annotation.end.param, + "attribute": dict( + filter( + lambda kv: not kv[0].startswith("e_"), + observations[i]["param"].items(), + ) + ), + } + ) + + # There is no kernel end signal in the underlying data, so the last iteration also contains a kernel run. + prev = prev_non_kernel + for i in range(prev_i, annotation.end.offset): + this = observations[i]["name"] + " @ " + observations[i]["place"] + + if this in n_seen: + if n_seen[this] == 1: + logging.debug( + f"Loop found in {annotation.start.name} {annotation.end.param}: {this} ⟳" + ) + n_seen[this] += 1 + else: + n_seen[this] = 1 + + if not prev in delta: + delta[prev] = set() + delta[prev].add(this) + + if not (prev, this) in delta_param: + delta_param[(prev, this)] = set() + delta_param[(prev, this)].add(utils.param_dict_to_str(annotation.end.param)) + + total_latency_us += observations[i]["attribute"].get("latency_us", 0) + + prev = this + + meta_observations.append( + { + "name": f"__trace__ {this}", + "param": annotation.end.param, + "attribute": dict( + filter( + lambda kv: not kv[0].startswith("e_"), + observations[i]["param"].items(), + ) + ), + } + ) + + if not prev in delta: + delta[prev] = set() + delta[prev].add("__end__") + if not (prev, "__end__") in delta_param: + delta_param[(prev, "__end__")] = set() + delta_param[(prev, "__end__")].add( + utils.param_dict_to_str(annotation.end.param) + ) + + for transition, count in n_seen.items(): + meta_observations.append( + { + "name": f"__loop__ {transition}", + "param": annotation.end.param, + "attribute": {"n_iterations": count}, + } + ) + + if total_latency_us: + meta_observations.append( + { + "name": annotation.start.name, + "param": annotation.end.param, + "attribute": {"latency_us": total_latency_us}, + } + ) + + is_loop = dict( + map(lambda kv: (kv[0], True), filter(lambda kv: kv[1] > 1, n_seen.items())) + ) + + return delta, delta_param, meta_observations, is_loop + + class EventSequenceModel: def __init__(self, models): self.models = models |