Repository: huggingface/transformers
Pull request: [tests] Add Context-parallel CI tests

# What does this PR do?

Adds two context parallel tests for the CI

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Changed files:
filename: tests/fsdp/test_context_parallel.py
patch: @@ -0,0 +1,224 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import sys
+from pathlib import Path
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    TestCasePlus,
+    execute_subprocess_async,
+    require_accelerate,
+    require_torch_multi_accelerator,
+    run_first,
+    slow,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoModelForCausalLM,
+        AutoTokenizer,
+        DataCollatorForLanguageModeling,
+        HfArgumentParser,
+        Trainer,
+        TrainingArguments,
+    )
+
+
+class TestContextParallel(TestCasePlus):
+    """Test Trainer with Torch context parallelism enabled via accelerate's ParallelismConfig."""
+
+    @require_torch_multi_accelerator
+    @require_accelerate
+    @slow
+    @run_first
+    def test_cp_equivalence(self):
+        """Test that CP produces the same losses as without CP."""
+
+        # Shared setup
+        world_size = 2
+        script_path = __file__
+
+        # Step 1: Run with CP enabled (cp_size=world_size)
+        cp_yes_output_dir = Path(self.get_auto_remove_tmp_dir()).resolve()
+        cp_yes_config_path = cp_yes_output_dir / "context_parallel_config.yaml"
+        cp_yes_losses_path = cp_yes_output_dir / "cp_yes_losses.json"
+
+        # Write config file inline (self-contained test)
+        with open(cp_yes_config_path, "w") as f:
+            f.write(
+                f"""distributed_type: FSDP
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_version: 2
+mixed_precision: bf16
+num_processes: {world_size}
+parallelism_config:
+  parallelism_config_dp_replicate_size: 1
+  parallelism_config_dp_shard_size: 1
+  parallelism_config_tp_size: 1
+  parallelism_config_cp_size: {world_size}
+  parallelism_config_cp_comm_strategy: alltoall
+"""
+            )
+
+        cmd_cp_yes = f"""
+            accelerate launch
+            --config_file {cp_yes_config_path}
+            {script_path}
+            --output_dir {cp_yes_output_dir}
+            --report_to none
+            --max_steps 10
+            --per_device_train_batch_size 1
+            --gradient_accumulation_steps 1
+            --logging_steps 1
+            --remove_unused_columns False
+            --seed 42
+            --loss_output_file {cp_yes_losses_path}
+        """.split()
+
+        execute_subprocess_async(cmd_cp_yes, env=self.get_env())
+
+        # Step 2: Run without CP (FSDP with num_processes=1, no parallelism_config)
+        cp_no_output_dir = Path(self.get_auto_remove_tmp_dir()).resolve()
+        cp_no_config_path = cp_no_output_dir / "context_parallel_config.yaml"
+        cp_no_losses_path = cp_no_output_dir / "cp_no_losses.json"
+
+        # Write config file inline (self-contained test)
+        with open(cp_no_config_path, "w") as f:
+            f.write(
+                """distributed_type: FSDP
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_version: 2
+mixed_precision: bf16
+num_processes: 1
+"""
+            )
+
+        cmd_cp_no = f"""
+            accelerate launch
+            --config_file {cp_no_config_path}
+            {script_path}
+            --output_dir {cp_no_output_dir}
+            --report_to none
+            --max_steps 10
+            --per_device_train_batch_size 1
+            --gradient_accumulation_steps 1
+            --logging_steps 1
+            --remove_unused_columns False
+            --seed 42
+            --loss_output_file {cp_no_losses_path}
+        """.split()
+
+        execute_subprocess_async(cmd_cp_no, env=self.get_env())
+
+        # Compare losses - should be very close since CP just splits sequence computation
+        with open(cp_yes_losses_path) as f:
+            cp_yes_losses = json.load(f)
+        with open(cp_no_losses_path) as f:
+            cp_no_losses = json.load(f)
+
+        assert len(cp_yes_losses) == len(cp_no_losses), (
+            f"Different number of losses: CP has {len(cp_yes_losses)}, no-CP has {len(cp_no_losses)}"
+        )
+
+        # CP should produce very similar results (small numerical differences expected)
+        # The differences come from:
+        # - Different gradient reduction patterns in distributed training
+        # - BF16 mixed precision accumulated differences
+        # - Sequence splitting and gathering in CP mode
+        cp_yes_losses_tensor = torch.tensor(cp_yes_losses)
+        cp_no_losses_tensor = torch.tensor(cp_no_losses)
+
+        # Use torch.testing.assert_close with rtol=2% and atol=0.02
+        # Testing shows actual differences are typically <1.5%
+        torch.testing.assert_close(
+            cp_yes_losses_tensor,
+            cp_no_losses_tensor,
+            rtol=2e-2,  # 2% relative tolerance
+            atol=2e-2,  # 0.02 absolute tolerance
+            msg=f"CP losses {cp_yes_losses} do not match non-CP losses {cp_no_losses}",
+        )
+
+
+if __name__ == "__main__":
+    # Parse custom arguments (not TrainingArguments parameters)
+    loss_output_file = None
+
+    if "--loss_output_file" in sys.argv:
+        idx = sys.argv.index("--loss_output_file")
+        loss_output_file = sys.argv[idx + 1]
+        sys.argv.pop(idx)
+        sys.argv.pop(idx)
+
+    parser = HfArgumentParser((TrainingArguments,))
+    training_args = parser.parse_args_into_dataclasses()[0]
+
+    # Use SmolLM (small Llama-based model that works with CP)
+    model_name = "HuggingFaceTB/SmolLM-135M"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        attn_implementation="sdpa",  # CP requires SDPA
+    )
+
+    # Create simple dataset: just tokenize some text
+    texts = [
+        "The quick brown fox jumps over the lazy dog. " * 10,
+        "Hello world, this is a test sentence for training. " * 10,
+    ] * 4  # 8 samples total
+
+    def tokenize_function(examples):
+        return tokenizer(examples, max_length=128, truncation=True, padding="max_length")
+
+    train_dataset = [tokenize_function(text) for text in texts]
+
+    # Use standard DataCollatorForLanguageModeling for causal LM
+    # pad_to_multiple_of=4 ensures sequences are divisible by cp_size * 2 (for cp_size=2)
+    # Trainer will automatically generate position_ids and shift_labels as needed
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm=False,  # Causal language modeling
+        pad_to_multiple_of=4,
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        data_collator=data_collator,
+    )
+
+    # Train for a few steps
+    trainer.train()
+
+    # Verify training completed
+    assert trainer.state.global_step > 0, "Training should have completed at least one step"
+
+    # Save losses to file if requested (for equivalence testing)
+    if loss_output_file and training_args.process_index == 0:
+        losses = [log["loss"] for log in trainer.state.log_history if "loss" in log]
+        with open(loss_output_file, "w") as f:
+            json.dump(losses, f)