Add configurable profiler for v1 training

docs/en/hyperparameters/training-argument.md

@@ -1,3 +1,40 @@
 # Training Argument
 
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+## Profiler
+
+LLaMA Factory can collect PyTorch profiler traces during training on CPU, CUDA GPU, and Ascend NPU devices. Enable it in the training YAML:
+
+```yaml
+enable_torch_profiler: true
+profiler_output_dir: ./saves/profile
+profiler_skip_first: 8
+profiler_wait_steps: 0
+profiler_warmup_steps: 1
+profiler_active_steps: 3
+profiler_repeat: 1
+profiler_rank_mode: rank0
+```
+
+The schedule follows the official `torch.profiler.schedule` / `torch_npu.profiler.schedule` semantics: first skip `profiler_skip_first` steps, then each cycle runs `wait -> warmup -> active`, repeated by `profiler_repeat`. The callback calls `prof.step()` once after each optimizer step.
+
+For Ascend NPU, use `profiler_level` and `profiler_aic_metrics` to control collection depth:
+
+```yaml
+profiler_level: level1
+profiler_aic_metrics: pipe_utilization
+profiler_backend_options:
+  npu:
+    data_simplification: true
+    host_sys: [cpu, mem]
+    sys_io: false
+    sys_interconnection: false
+```
+
+String enum values are fixed short values. `profiler_activities` supports `auto`, `all`, `cpu`, `device`; `profiler_rank_mode` supports `all`, `rank0`; `profiler_level` supports `none`, `level0`, `level1`, `level2`; `profiler_aic_metrics` supports `auto`, `none`, `pipe_utilization`, `arithmetic_utilization`, `memory`, `memory_l0`, `memory_ub`, `l2_cache`, `memory_access`, `resource_conflict_ratio`; `profiler_backend_options.npu.host_sys` supports `cpu`, `mem`, `disk`, `network`, `osrt`. Values outside these lists fail validation. `profiler_backend_options` must be a YAML mapping, not a JSON string.
+
+Do not enable Ascend `dynamic_profile` through `PROF_CONFIG_PATH` at the same time as `enable_torch_profiler`.
+
+Official references:
+
+- [PyTorch Profiler](https://docs.pytorch.org/docs/stable/profiler.html)
+- [Ascend CANN 9.0 PyTorch Profiler](https://www.hiascend.com/document/detail/zh/canncommercial/900/devaids/Profiling/atlasprofiling_16_0033.html)

docs/zh/hyperparameters/training-argument.md

@@ -0,0 +1,40 @@
+# 训练参数
+
+## Profiler
+
+LLaMA Factory 支持在训练过程中采集 PyTorch profiler trace，覆盖 CPU、CUDA GPU 和昇腾 NPU。可以在训练 YAML 中开启：
+
+```yaml
+enable_torch_profiler: true
+profiler_output_dir: ./saves/profile
+profiler_skip_first: 8
+profiler_wait_steps: 0
+profiler_warmup_steps: 1
+profiler_active_steps: 3
+profiler_repeat: 1
+profiler_rank_mode: rank0
+```
+
+调度语义对齐官方 `torch.profiler.schedule` / `torch_npu.profiler.schedule`：先跳过 `profiler_skip_first` 个 step，之后每个周期按 `wait -> warmup -> active` 执行，并由 `profiler_repeat` 控制重复次数。callback 在每个 optimizer step 结束后调用一次 `prof.step()`。
+
+昇腾 NPU 上可以通过 `profiler_level` 和 `profiler_aic_metrics` 控制采集深度：
+
+```yaml
+profiler_level: level1
+profiler_aic_metrics: pipe_utilization
+profiler_backend_options:
+  npu:
+    data_simplification: true
+    host_sys: [cpu, mem]
+    sys_io: false
+    sys_interconnection: false
+```
+
+字符串枚举参数固定为短值：`profiler_activities` 支持 `auto`、`all`、`cpu`、`device`；`profiler_rank_mode` 支持 `all`、`rank0`；`profiler_level` 支持 `none`、`level0`、`level1`、`level2`；`profiler_aic_metrics` 支持 `auto`、`none`、`pipe_utilization`、`arithmetic_utilization`、`memory`、`memory_l0`、`memory_ub`、`l2_cache`、`memory_access`、`resource_conflict_ratio`；`profiler_backend_options.npu.host_sys` 支持 `cpu`、`mem`、`disk`、`network`、`osrt`。传入不在列表内的值会直接报错。`profiler_backend_options` 必须是 YAML mapping，不支持 JSON 字符串。
+
+不要同时通过 `PROF_CONFIG_PATH` 开启昇腾 `dynamic_profile` 和 LLaMA Factory 的 `enable_torch_profiler`。
+
+官方参考：
+
+- [PyTorch Profiler](https://docs.pytorch.org/docs/stable/profiler.html)
+- [昇腾 CANN 9.0 PyTorch Profiler](https://www.hiascend.com/document/detail/zh/canncommercial/900/devaids/Profiling/atlasprofiling_16_0033.html)