mirror of https://github.com/hpcaitech/ColossalAI
[test] mixtra pp shard test
parent
5a9490a46b
commit
4fc6f9aa98
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@ -411,7 +411,7 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
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reinitialize_optimizer(optimizer, model)
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if self.zero_stage == 0:
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assert self.ep_size > 1
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# assert self.ep_size > 1
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if self.precision in ["fp16", "bf16"]:
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optimizer = HybridParallelAMPOptimizer(
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@ -43,14 +43,17 @@ def data_gen_for_sequence_classification():
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output_transform_fn = lambda x: x
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# define loss function
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loss_fn_for_mixtral_model = lambda x: torch.nn.functional.mse_loss(
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x.last_hidden_state, torch.ones_like(x.last_hidden_state)
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)
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loss_fn_for_mixtral_model = lambda x: torch.nn.functional.mse_loss(x[0], torch.ones_like(x[0]))
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loss_fn = lambda x: x.loss
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loss_fn_for_seq_classification = lambda output: output.logits.mean()
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config = MixtralConfig(
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hidden_size=256, intermediate_size=256, num_attention_heads=64, num_hidden_layers=2, vocab_size=50258
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hidden_size=256,
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intermediate_size=256,
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num_attention_heads=64,
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num_hidden_layers=2,
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vocab_size=50258,
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output_router_logits=True,
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)
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if hasattr(config, "pad_token_id"):
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@ -64,19 +67,19 @@ model_zoo.register(
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loss_fn=loss_fn_for_mixtral_model,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_mixtral_for_casual_lm",
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model_fn=lambda: transformers.MixtralForCausalLM(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_mixtral_for_sequence_classification",
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model_fn=lambda: transformers.MixtralForSequenceClassification(config),
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data_gen_fn=data_gen_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_seq_classification,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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# model_zoo.register(
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# name="transformers_mixtral_for_casual_lm",
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# model_fn=lambda: transformers.MixtralForCausalLM(config),
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# data_gen_fn=data_gen_for_lm,
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# output_transform_fn=output_transform_fn,
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# loss_fn=loss_fn,
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# model_attribute=ModelAttribute(has_control_flow=True),
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# )
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# model_zoo.register(
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# name="transformers_mixtral_for_sequence_classification",
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# model_fn=lambda: transformers.MixtralForSequenceClassification(config),
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# data_gen_fn=data_gen_for_sequence_classification,
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# output_transform_fn=output_transform_fn,
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# loss_fn=loss_fn_for_seq_classification,
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# model_attribute=ModelAttribute(has_control_flow=True),
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# )
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@ -114,37 +114,37 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
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[
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{
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"tp_size": 1,
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"pp_size": 1,
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"ep_size": 4,
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"num_microbatches": 2,
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"pp_size": 4,
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"ep_size": 1,
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"num_microbatches": 4,
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"zero_stage": 0,
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"enable_all_optimization": True,
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"use_lazy_init": False,
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"precision": "fp16",
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"initial_scale": 1,
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},
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{
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"tp_size": 1,
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"pp_size": 1,
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"ep_size": 4,
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"num_microbatches": 2,
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"zero_stage": 1,
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"enable_all_optimization": True,
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"use_lazy_init": False,
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"precision": "fp16",
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"initial_scale": 1,
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},
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{
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"tp_size": 1,
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"pp_size": 1,
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"ep_size": 4,
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"num_microbatches": 2,
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"zero_stage": 2,
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"enable_all_optimization": True,
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"use_lazy_init": False,
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"precision": "fp16",
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"initial_scale": 1,
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},
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# {
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# "tp_size": 1,
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# "pp_size": 1,
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# "ep_size": 4,
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# "num_microbatches": 2,
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# "zero_stage": 1,
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# "enable_all_optimization": True,
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# "use_lazy_init": False,
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# "precision": "fp16",
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# "initial_scale": 1,
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# },
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# {
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# "tp_size": 1,
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# "pp_size": 1,
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# "ep_size": 4,
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# "num_microbatches": 2,
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# "zero_stage": 2,
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# "enable_all_optimization": True,
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# "use_lazy_init": False,
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# "precision": "fp16",
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# "initial_scale": 1,
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# },
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],
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)
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def run_mixtral_test(test_config):
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