mirror of https://github.com/hpcaitech/ColossalAI
[format] applied code formatting on changed files in pull request 4926 (#5007)
Co-authored-by: github-actions <github-actions@github.com>pull/5024/head
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@ -218,7 +218,7 @@ class TPInferEngine:
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), "Discrepancy between the tp size of TPInferEngine and the tp size of shard config"
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model_name = model.__class__.__name__
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assert model_name in self.supported_models, f"Unsupported model cls {model_name} for TP inference."
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model = model.model if self.shard_config.inference_gptq else model
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policy = get_autopolicy(model, shard_config=self.shard_config)
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@ -311,7 +311,7 @@ class TPInferEngine:
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seq_start_indexes[i] = start_index
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start_index += curr_seq_len
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max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
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block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device="cuda")
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batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
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batch_infer_state.seq_len = seq_lengths.to("cuda")
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@ -24,6 +24,7 @@ for k, v in inputs.items():
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new_shape[0] = 16
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inputs[k] = v.to("cuda").repeat(*new_shape)
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def pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
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model = transformers.LlamaForCausalLM(
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transformers.LlamaConfig(
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@ -58,7 +59,6 @@ def run_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_si
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@parameterize("pp_size", [2])
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@parameterize("max_output_len", [4])
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@parameterize("micro_batch_size", [1])
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@clear_cache_before_run()
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def run_tp_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
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pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
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@ -76,7 +76,6 @@ def check_tp_pipeline_inference(rank, world_size, port):
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@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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