Fix gpu test mcore inference utility for Mcore 0.14+

Signed-off-by: Keval Morabia <[email protected]>

Author: kevalmorabia97

tests/_test_utils/torch_dist/plugins/megatron_common.py

@@ -24,6 +24,7 @@
 
 from megatron.core import dist_checkpointing
 from megatron.core.inference.communication_utils import broadcast_from_last_pipeline_stage
+from megatron.core.inference.contexts import StaticInferenceContext
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
@@ -348,15 +349,21 @@ def run_mcore_inference(
             active_hidden_size = model.decoder.layers[0].mlp.linear_fc1.input_size
         else:
             raise ValueError(f"Cannot infer hidden size from {type(model.decoder.layers[0])=}")
+
     inference_wrapper_config = InferenceWrapperConfig(
         hidden_size=active_hidden_size,
         inference_batch_times_seqlen_threshold=batch_size * model.max_sequence_length,
         fp32_residual_connection=False,
         params_dtype=torch.bfloat16 if model.config.bf16 else torch.float32,
         padded_vocab_size=model.vocab_size,
     )
-    wrapped_model = GPTInferenceWrapper(model, inference_wrapper_config)
-    wrapped_model.prep_model_for_inference(prompt_tokens)
+    # Get full sequence output instead of only last token logits
+    inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+    inference_context.materialize_only_last_token_logits = False
+
+    wrapped_model = GPTInferenceWrapper(model, inference_wrapper_config, inference_context)
+    wrapped_model.prep_model_for_inference()
+
     inference_input = wrapped_model.prep_inference_input(prompt_tokens)
     inference_input = wrapped_model.get_batch_for_context_window(
         inference_input, 0, model.max_sequence_length
@@ -375,7 +382,7 @@ def run_mcore_inference(
 def run_mcore_inference_with_dummy_input(
     model: GPTModel | MambaModel, batch_size: int = 2, hidden_size: int | None = None
 ) -> torch.Tensor:
-    """Run inference on a wrapped Megatron GPT or Mamba model."""
+    """Run inference on a Megatron GPT or Mamba model with random dummy input."""
     prompt_tokens = torch.randint(
         0, model.vocab_size, (batch_size, model.max_sequence_length)
     ).cuda()

tests/gpu/torch/quantization/plugins/test_megatron.py

@@ -33,11 +33,9 @@
     auto_quantize_helper,
     tensor_parallel_test_helper,
 )
-from packaging.version import Version
 
 skip_if_no_megatron()
 
-import megatron.core
 from megatron.core.parallel_state import (
     destroy_model_parallel,
     get_data_parallel_group,
@@ -256,10 +254,6 @@ def test_homogeneous_sharded_state_dict(tmp_path, config, compress, meta_device)
         mixed_block_size_config,
     ],
 )
-@pytest.mark.skipif(
-    Version(megatron.core.__version__) <= Version("0.13.0rc1"),
-    reason="This unittest need megatron.core>=0.13 with default heterogenous ckpt support.",
-)
 def test_heterogenous_sharded_state_dict(need_2_gpus, tmp_path, config):
     spawn_multiprocess_job(
         size=2,