mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-24 01:29:57 +08:00
gongweibao
30f9f33f34
* add fa deter * add ut * add long sentence * fix basic * fix bugs * fix adn * fix first * fix single * fix single * fix single test * refine * add more test * refine comments * add comments of bmm * fix ci * remove probe * add * remove not need * refine tests * fix comments and refine code * refine code * refine test * refine test * mv 4cards tests * fix tests * add * fix comments * fix cover * fix cover --------- Co-authored-by: gongweibao <gognweibao@baidu.com>
292 lines
11 KiB
Python
292 lines
11 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import hashlib
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import json
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import logging
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import time
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import numpy as np
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import paddle
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det_logger = logging.getLogger("fastdeploy.deterministic")
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# ---------------------------------------------------------------------------
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# File paths for deterministic diagnostics (written under /tmp)
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# ---------------------------------------------------------------------------
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_DET_MD5_PATH = "/tmp/fd_det_logits_md5.jsonl"
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_DET_FINGERPRINT_PATH = "/tmp/fd_det_logits.jsonl"
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# ---------------------------------------------------------------------------
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# Shared low-level helpers
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# ---------------------------------------------------------------------------
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def _compute_md5(tensor: paddle.Tensor) -> str:
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"""Compute MD5 hex-digest of a tensor (after casting to float32 if needed).
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Triggers GPU->CPU sync -- use only in diagnostic paths.
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"""
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try:
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data = tensor.cpu().numpy().tobytes()
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except Exception:
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data = tensor.cpu().numpy().astype(np.float32).tobytes()
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return hashlib.md5(data).hexdigest()
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# ---------------------------------------------------------------------------
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# File-based MD5 hash collection (shared across processes via filesystem)
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# ---------------------------------------------------------------------------
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def _reset_logits_md5_file():
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"""Truncate the MD5 hash file (call before each generate run)."""
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with open(_DET_MD5_PATH, "w"):
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pass
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def _read_logits_md5_file():
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"""Read all MD5 hash entries from the file (call after generate completes)."""
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entries = []
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try:
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with open(_DET_MD5_PATH, "r") as f:
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for line in f:
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line = line.strip()
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if line:
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entries.append(json.loads(line))
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except FileNotFoundError:
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pass
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return entries
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def _record_logits_diagnostic(
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logits: paddle.Tensor,
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tag: str = "logits",
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probs: paddle.Tensor = None,
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):
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"""Record both lightweight fingerprint and bit-exact MD5 in one GPU sync.
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Writes a fingerprint line to ``_DET_FINGERPRINT_PATH`` and an MD5 line to
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``_DET_MD5_PATH``. The two files serve different purposes:
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- fingerprint: human-readable summary (sum / argmax / max)
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- MD5: bit-exact cross-run comparison
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WARNING: triggers GPU sync via .cpu().numpy() -- may change CUDA stream
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timing. Use only for diagnostics.
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"""
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with paddle.no_grad():
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fp = logits.astype("float32")
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fp_np = fp.cpu().numpy()
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# --- fingerprint (lightweight summary) ---
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fingerprint = {
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"sum": float(fp_np.sum()),
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"argmax": int(fp_np[0].argmax()),
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"max": float(fp_np[0].max()),
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"batch": logits.shape[0],
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}
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with open(_DET_FINGERPRINT_PATH, "a") as f:
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f.write(json.dumps(fingerprint) + "\n")
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# --- MD5 (bit-exact) ---
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logits_md5 = _compute_md5(fp)
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entry = {"tag": tag, "logits_md5": logits_md5, "probs_md5": ""}
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if probs is not None:
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entry["probs_md5"] = _compute_md5(probs)
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with open(_DET_MD5_PATH, "a") as f:
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f.write(json.dumps(entry) + "\n")
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# Keep old names as aliases so existing callers (sampler.py, tests) still work.
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_record_logits_fingerprint = _record_logits_diagnostic
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_record_logits_md5 = _record_logits_diagnostic
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# ---------------------------------------------------------------------------
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# DeterministicLogger -- per-batch / per-request tensor MD5 logging
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# ---------------------------------------------------------------------------
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class DeterministicLogger:
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"""Helper for logging tensor MD5 hashes and input details to assist determinism debugging."""
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def __init__(self, share_inputs):
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self.share_inputs = share_inputs
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self._current_run_id = None
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self._batch_counter = 0
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# ---- batch lifecycle ----
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def log_batch_start(self, model_forward_batch):
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"""Log batch start with run_id tracking and batch counting."""
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current_run_id = None
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for req in model_forward_batch or []:
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if req is not None:
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parts = req.request_id.split("_")
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if len(parts) > 1:
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current_run_id = parts[-1]
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break
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if current_run_id is not None and current_run_id != self._current_run_id:
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self._current_run_id = current_run_id
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self._batch_counter = 0
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self._batch_counter += 1
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det_logger.info(f"\n{'='*80}")
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det_logger.info(f"[BATCH-START] Run_{self._current_run_id} Batch_{self._batch_counter}")
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det_logger.info(f"{'='*80}\n")
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# ---- tensor MD5 helpers ----
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@staticmethod
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def _compute_tensor_md5(tensor, name="tensor", prefix=""):
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"""Compute MD5 hash of tensor for comparison."""
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if tensor is None:
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return f"{name}_md5=None"
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md5_hash = _compute_md5(tensor)
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return f"{prefix}{name}_md5={md5_hash[:16]}"
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def log_tensor_md5s(self, tensor_dict, forward_batch_reqs_list=None, stage="forward"):
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"""Log MD5 hash values for multiple tensors, including per-request MD5.
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Args:
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tensor_dict: {name: tensor} dictionary
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forward_batch_reqs_list: request list (may contain None)
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stage: Stage identifier (e.g., "prefill", "decode", "forward")
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"""
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batch_size = self._get_batch_size(tensor_dict)
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if batch_size is None:
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return
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prefill_count, decode_count, seq_lens_encoder = self._get_stage_counts(batch_size)
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stage_info = stage
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if prefill_count > 0 or decode_count > 0:
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stage_info += f" (prefill={prefill_count}, decode={decode_count})"
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batch_md5_info = [
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self._compute_tensor_md5(tensor, name, prefix="batch_")
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for name, tensor in tensor_dict.items()
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if tensor is not None
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]
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req_id_str = self._build_req_id_str(forward_batch_reqs_list)
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det_logger.info(
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f"[DETERMINISM-MD5] stage={stage_info} | batch_size={batch_size} | "
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+ (f"requests: {req_id_str} | " if req_id_str else "")
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+ " | ".join(batch_md5_info)
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)
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self._log_per_request_md5s(
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tensor_dict, forward_batch_reqs_list, batch_size, prefill_count, decode_count, seq_lens_encoder
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)
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# ---- internal helpers ----
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@staticmethod
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def _get_batch_size(tensor_dict):
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"""Get batch size from first tensor with a shape."""
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for name, tensor in tensor_dict.items():
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if tensor is not None and hasattr(tensor, "shape"):
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return tensor.shape[0]
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return None
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def _get_stage_counts(self, batch_size):
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"""Get prefill/decode counts and seq_lens_encoder."""
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prefill_count = 0
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decode_count = 0
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seq_lens_encoder = None
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if self.share_inputs is not None and "seq_lens_encoder" in self.share_inputs:
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seq_lens_encoder = self.share_inputs["seq_lens_encoder"].cpu().numpy()
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prefill_count = int((seq_lens_encoder > 0).sum())
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decode_count = int(batch_size - prefill_count)
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return prefill_count, decode_count, seq_lens_encoder
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@staticmethod
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def _build_req_id_str(forward_batch_reqs_list):
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"""Build request ID string from forward_batch_reqs_list."""
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if forward_batch_reqs_list is None:
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return ""
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req_info = [f"[{i}]{req.request_id}" for i, req in enumerate(forward_batch_reqs_list) if req is not None]
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return ", ".join(req_info)
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def _log_per_request_md5s(
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self, tensor_dict, forward_batch_reqs_list, batch_size, prefill_count, decode_count, seq_lens_encoder
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):
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"""Log per-request MD5 for decode requests."""
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if decode_count == 0 or forward_batch_reqs_list is None:
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return
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for i, req in enumerate(forward_batch_reqs_list):
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if req is None or i >= batch_size:
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continue
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if seq_lens_encoder is not None:
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if i >= len(seq_lens_encoder) or int(seq_lens_encoder[i]) != 0:
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continue
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elif prefill_count > 0:
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continue
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req_id = req.request_id
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req_md5_info = [
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self._compute_tensor_md5(tensor[i : i + 1], name)
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for name, tensor in tensor_dict.items()
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if tensor is not None and hasattr(tensor, "shape") and len(tensor.shape) >= 2
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]
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if req_md5_info:
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det_logger.info(f"[DETERMINISM-MD5-REQ] {req_id} | decode | " + " | ".join(req_md5_info))
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# ---- input logging ----
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def log_prefill_input(self, request_id, idx, prefill_start_index, prefill_end_index, input_ids):
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"""Log prefill input details for determinism verification."""
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det_logger.info(
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f"[DETERMINISM] Prefill input - request_id: {request_id}, "
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f"idx: {idx}, prefill_start_index: {prefill_start_index}, "
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f"prefill_end_index: {prefill_end_index}, "
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f"input_ids: {input_ids}"
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)
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def log_deterministic_input(self, forward_meta):
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"""Log determinism inference input information, supports multiple batch requests."""
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ids = forward_meta.ids_remove_padding
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req_ids = self.share_inputs.get("req_ids", None)
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seq_lens_this_time = self.share_inputs.get("seq_lens_this_time", None)
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seq_lens_encoder = self.share_inputs.get("seq_lens_encoder", None)
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seq_lens_decoder = self.share_inputs.get("seq_lens_decoder", None)
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num_requests = len(seq_lens_this_time) if seq_lens_this_time is not None else 0
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det_logger.info(f"[DETERMINISM-INPUT] time={time.time():.6f} | batch_size={num_requests}")
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if num_requests == 0 or ids is None:
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det_logger.info("[DETERMINISM-INPUT] No input data")
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return
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ids_list = ids.cpu().numpy().tolist()
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offset = 0
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for i in range(num_requests):
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req_id = req_ids[i] if req_ids is not None and i < len(req_ids) else f"idx_{i}"
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seq_len = int(seq_lens_this_time[i])
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seq_len_enc = int(seq_lens_encoder[i]) if seq_lens_encoder is not None and i < len(seq_lens_encoder) else 0
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seq_len_dec = int(seq_lens_decoder[i]) if seq_lens_decoder is not None and i < len(seq_lens_decoder) else 0
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request_tokens = ids_list[offset : offset + seq_len] if seq_len > 0 else []
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offset += seq_len
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det_logger.info(
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f"[DETERMINISM-INPUT] req_id={req_id} | tokens={request_tokens} | "
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f"len={seq_len} | seq_len_enc={seq_len_enc} | seq_len_dec={seq_len_dec}"
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)
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