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[format] applied code formatting on changed files in pull request 4926 (#5007) 

Co-authored-by: github-actions <github-actions@github.com>
pull/5024/head
github-actions[bot] 2023-11-06 17:08:12 +08:00 committed by GitHub
parent 1a3315e336
commit c36e782d80
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2 changed files with 3 additions and 4 deletions
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4
colossalai/inference/tensor_parallel/engine.py
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@ -218,7 +218,7 @@ class TPInferEngine:
), "Discrepancy between the tp size of TPInferEngine and the tp size of shard config" ), "Discrepancy between the tp size of TPInferEngine and the tp size of shard config"
model_name = model.__class__.__name__ model_name = model.__class__.__name__
assert model_name in self.supported_models, f"Unsupported model cls {model_name} for TP inference." assert model_name in self.supported_models, f"Unsupported model cls {model_name} for TP inference."
model = model.model if self.shard_config.inference_gptq else model model = model.model if self.shard_config.inference_gptq else model
policy = get_autopolicy(model, shard_config=self.shard_config) policy = get_autopolicy(model, shard_config=self.shard_config)
@ -311,7 +311,7 @@ class TPInferEngine:
seq_start_indexes[i] = start_index seq_start_indexes[i] = start_index
start_index += curr_seq_len start_index += curr_seq_len
max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device="cuda") block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device="cuda")
batch_infer_state = BatchInferState(batch_size, max_len_in_batch) batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
batch_infer_state.seq_len = seq_lengths.to("cuda") batch_infer_state.seq_len = seq_lengths.to("cuda")

3
tests/test_infer/test_pipeline_infer.py
View File

@ -24,6 +24,7 @@ for k, v in inputs.items():
new_shape[0] = 16 new_shape[0] = 16
inputs[k] = v.to("cuda").repeat(*new_shape) inputs[k] = v.to("cuda").repeat(*new_shape)
def pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size): def pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
model = transformers.LlamaForCausalLM( model = transformers.LlamaForCausalLM(
transformers.LlamaConfig( transformers.LlamaConfig(
@ -58,7 +59,6 @@ def run_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_si
@parameterize("pp_size", [2]) @parameterize("pp_size", [2])
@parameterize("max_output_len", [4]) @parameterize("max_output_len", [4])
@parameterize("micro_batch_size", [1]) @parameterize("micro_batch_size", [1])
@clear_cache_before_run() @clear_cache_before_run()
def run_tp_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size): def run_tp_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size) pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
@ -76,7 +76,6 @@ def check_tp_pipeline_inference(rank, world_size, port):
@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5") @pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
@pytest.mark.dist @pytest.mark.dist
@rerun_if_address_is_in_use() @rerun_if_address_is_in_use()
@clear_cache_before_run() @clear_cache_before_run()