[docs] tp training

docs/source/en/_toctree.yml

@@ -202,6 +202,8 @@
       title: FullyShardedDataParallel
     - local: deepspeed
       title: DeepSpeed
+    - local: tensor_parallelism
+      title: Tensor parallelism
     - local: debugging
       title: Multi-GPU debugging
     - local: perf_train_cpu_many

docs/source/en/perf_infer_gpu_multi.md

@@ -309,3 +309,5 @@ The `placement` attribute tells PyTorch how to place a tensor on devices in `Dev
 - Check the [expert parallelism](./expert_parallelism) guide if you're using a mixture-of-experts (MoE) model. These models support tensor parallelism and expert parallelism.
 
 - Read the [Tensor Parallelism (TP) in Transformers: 5 Minutes to Understand](https://huggingface.co/blog/qgallouedec/tp) blog post for a quick overview of tensor parallelism and learn how column and row parallel setups differ.
+
+- See the [Tensor parallelism](./tensor_parallelism) training guide to learn how to use it in a training setting.

docs/source/en/tensor_parallelism.md

@@ -0,0 +1,101 @@
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+
+# Tensor parallelism
+
+Tensor parallelism (TP) splits weight matrices column-wise or row-wise across GPUs. Each GPU holds a shard, computes a partial result, and synchronizes with an all-reduce to produce the full output.
+
+TP relies on frequent cross-GPU communication. It works best on hardware with fast intra-node links such as NVLink.
+
+```text
+    ┌─────────────────────────────┐
+    │       X  (replicated)       │
+    └────┬──────────┬─────────┬───┘
+         │          │         │
+    ┌────▼───┐ ┌────▼───┐ ┌───▼────┐
+    │ ▓▓▓ W₀ │ │ ░░░ W₁ │ │ ███ W₂ │
+    │  X@W₀  │ │  X@W₁  │ │  X@W₂  │
+    └────┬───┘ └────┬───┘ └───┬────┘
+         └──────────┼─────────┘
+               Y₀+Y₁+Y₂
+    ┌────────────────────────────┐
+    │          Y (full)          │
+    └────────────────────────────┘
+```
+
+Transformers supports TP for architectures whose config defines `base_model_tp_plan`. Check that field first to see whether a model supports native TP.
+
+```py
+from transformers import AutoConfig
+
+config = AutoConfig.from_pretrained("Qwen/Qwen3-0.6B")
+print(config.base_model_tp_plan is not None)
+print(config.base_model_tp_plan)
+```
+
+If a model supports TP, set `tp_plan="auto"` in [`~PreTrainedModel.from_pretrained`]. Transformers initializes the device mesh and shards the supported layers for you.
+
+> [!WARNING]
+> Don't use `device_map` with `tp_plan`. The two conflict at the weight-loading level. `device_map` places whole modules on specific GPUs, while `tp_plan` shards those same parameters across all GPUs.
+
+```py
+import torch
+
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B",
+    dtype=torch.bfloat16,
+    tp_plan="auto",
+)
+```
+
+[`Trainer`] detects `tp_plan`, reads `tp_size` from the model, and creates a [`~accelerate.parallelism_config.ParallelismConfig`] automatically.
+
+Launch training on one node with 4 GPUs.
+
+```shell
+torchrun --nproc-per-node 4 train_tp.py
+```
+
+## ParallelismConfig
+
+Pass [`~accelerate.parallelism_config.ParallelismConfig`] explicitly when combining TP with other parallelism techniques like [FSDP](./fsdp).
+
+```py
+import torch
+
+from accelerate import ParallelismConfig
+from transformers import AutoModelForCausalLM, TrainingArguments
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B",
+    dtype=torch.bfloat16,
+    tp_plan="auto",
+)
+
+parallelism_config = ParallelismConfig(tp_size=4)
+
+args = TrainingArguments(
+    ...,
+    parallelism_config=parallelism_config,
+)
+```
+
+## Next steps
+
+- Read the [Tensor Parallelism](https://huggingface.co/spaces/nanotron/ultrascale-playbook?section=tensor_parallelism) chapter from The Ultra-Scale Playbook for more details about how it works.
+- Read the [tensor parallelism inference guide](./perf_infer_gpu_multi) to learn more about partitioning strategies, manual TP plans, and implementation details.