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@ -558,7 +558,7 @@ def run_fwd_bwd_vschedule_with_optim(test_config):
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batch_size = test_config["batch_size"]
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num_layers = 8
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assert num_layers % num_model_chunk == 0, f"Model with {num_layers} layer can not dist on {num_model_chunk} chunk"
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in_dim = out_dim = 4096
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in_dim = out_dim = 8192
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before_init_memory = torch.cuda.memory_allocated() / 1024**3
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print(f"Before init Model: {before_init_memory :.3f} GB on device {stage_manager.get_rank()};")
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model = MlpModel(in_dim=in_dim, out_dim=out_dim, num_layers=num_layers).to(rank)
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@ -617,15 +617,15 @@ def run_fwd_bwd_vschedule_with_optim(test_config):
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if rank != 0:
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# w.grad hid_dim * hid_dim * 4(fp32) * 2 (2 layer in each stage) / 1024**3
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# output hid_dim * hid_dim * 4(fp32) / 1024**3
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print(f"rank {rank}: {(after_pp_step_memory - after_init_memory)} == {(in_dim * in_dim * 4 * 3 / 1024**3)}")
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assert (after_pp_step_memory - after_init_memory) == (in_dim * in_dim * 4 * 3 / 1024**3)
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print(f"rank {rank}: {(after_pp_step_memory - after_init_memory)} <= {(in_dim * in_dim * 4 * 3 / 1024**3)}")
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assert (after_pp_step_memory - after_init_memory) <= (in_dim * in_dim * 4 * 3 / 1024**3)
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# pass
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else:
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# rank0 will also hold output;
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print(
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f"rank {rank}: {(after_pp_step_memory - after_init_memory)} == {(in_dim * in_dim * 4 * 3 / 1024**3 + batch_size * in_dim * in_dim * 4 / 1024**3)}"
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f"rank {rank}: {round((after_pp_step_memory - after_init_memory), 5)} <= {round((in_dim * in_dim * 4 * 3 / 1024**3 + batch_size * in_dim * in_dim * 4 / 1024**3), 5)}"
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)
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assert round((after_pp_step_memory - after_init_memory), 5) == round(
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assert round((after_pp_step_memory - after_init_memory), 5) <= round(
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(in_dim * in_dim * 4 * 3 / 1024**3 + batch_size * in_dim * in_dim * 4 / 1024**3), 5
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)
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# pass
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