mirror of
https://github.com/hpcaitech/ColossalAI.git
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554aa9592e
* [legacy] move communication to legacy (#4640) * [legacy] refactor logger and clean up legacy codes (#4654) * [legacy] make logger independent to gpc * [legacy] make optim independent to registry * [legacy] move test engine to legacy * [legacy] move nn to legacy (#4656) * [legacy] move nn to legacy * [checkpointio] fix save hf config * [test] remove useledd rpc pp test * [legacy] fix nn init * [example] skip tutorial hybriad parallel example * [devops] test doc check * [devops] test doc check
80 lines
3.4 KiB
Python
80 lines
3.4 KiB
Python
import torch
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from colossalai.legacy.nn.layer.colossalai_layer import Embedding, Linear
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from colossalai.utils import get_current_device
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from .bias_dropout_add import bias_dropout_add_fused_train
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from .bias_gelu import bias_gelu_impl
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JIT_OPTIONS_SET = False
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def set_jit_fusion_options():
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"""Set PyTorch JIT layer fusion options.
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"""
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# LSG: the latest pytorch and CUDA versions may not support
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# the following jit settings
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global JIT_OPTIONS_SET
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if JIT_OPTIONS_SET == False:
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# flags required to enable jit fusion kernels
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TORCH_MAJOR = int(torch.__version__.split('.')[0])
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TORCH_MINOR = int(torch.__version__.split('.')[1])
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if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10):
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# nvfuser
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torch._C._jit_set_profiling_executor(True)
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torch._C._jit_set_profiling_mode(True)
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torch._C._jit_override_can_fuse_on_cpu(False)
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torch._C._jit_override_can_fuse_on_gpu(False)
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torch._C._jit_set_texpr_fuser_enabled(False)
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torch._C._jit_set_nvfuser_enabled(True)
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torch._C._debug_set_autodiff_subgraph_inlining(False)
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else:
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# legacy pytorch fuser
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torch._C._jit_set_profiling_mode(False)
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torch._C._jit_set_profiling_executor(False)
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torch._C._jit_override_can_fuse_on_cpu(True)
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torch._C._jit_override_can_fuse_on_gpu(True)
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JIT_OPTIONS_SET = True
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def warmup_jit_fusion(batch_size: int,
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hidden_size: int,
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seq_length: int = 512,
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vocab_size: int = 32768,
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dtype: torch.dtype = torch.float32):
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""" Compile JIT functions before the main training steps """
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embed = Embedding(vocab_size, hidden_size).to(get_current_device())
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linear_1 = Linear(hidden_size, hidden_size * 4, skip_bias_add=True).to(get_current_device())
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linear_2 = Linear(hidden_size * 4, hidden_size, skip_bias_add=True).to(get_current_device())
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x = torch.randint(vocab_size, (batch_size, seq_length), dtype=torch.long, device=get_current_device())
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x = embed(x)
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y, y_bias = linear_1(x)
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z, z_bias = linear_2(y)
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# Warmup JIT fusions with the input grad_enable state of both forward
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# prop and recomputation
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for bias_grad, input_grad in zip([True, True], [False, True]):
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for _ in range(10):
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bias = torch.rand_like(y_bias, dtype=dtype, device=get_current_device())
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input_ = torch.rand_like(y, dtype=dtype, device=get_current_device())
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bias.requires_grad, input_.requires_grad = bias_grad, input_grad
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bias_gelu_impl(input_, bias)
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# Warmup fused bias+dropout+add
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dropout_rate = 0.1
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# Warmup JIT fusions with the input grad_enable state of both forward
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# prop and recomputation
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for input_grad, bias_grad, residual_grad in zip([False, True], [True, True], [True, True]):
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for _ in range(10):
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input_ = torch.rand_like(z, dtype=dtype, device=get_current_device())
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residual = torch.rand_like(x, dtype=dtype, device=get_current_device())
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bias = torch.rand_like(z_bias, dtype=dtype, device=get_current_device())
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input_.requires_grad = input_grad
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bias.requires_grad = bias_grad
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residual.requires_grad = residual_grad
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bias_dropout_add_fused_train(input_, bias, residual, dropout_rate)
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torch.cuda.empty_cache()
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