utils.py 2.49 KB
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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
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"""Utilities for models."""

import math

import torch

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from megatron.training import get_args
from megatron.legacy.model import LayerNorm, RMSNorm
from megatron.core.jit import jit_fuser
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import torch._dynamo
torch._dynamo.config.suppress_errors = True
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def init_method_normal(sigma):
    """Init method based on N(0, sigma)."""
    def init_(tensor):
        return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)

    return init_


def scaled_init_method_normal(sigma, num_layers):
    """Init method based on N(0, sigma/sqrt(2*num_layers)."""
    std = sigma / math.sqrt(2.0 * num_layers)
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    def init_(tensor):
        return torch.nn.init.normal_(tensor, mean=0.0, std=std)

    return init_


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def attention_mask_func(attention_scores, attention_mask):
    attention_scores.masked_fill_(attention_mask, -10000.0)
    return attention_scores


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def get_linear_layer(rows, columns, init_method):
    """Simple linear layer with weight initialization."""
    layer = torch.nn.Linear(rows, columns)
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    if get_args().perform_initialization:
        init_method(layer.weight)
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    with torch.no_grad():
        layer.bias.zero_()
    return layer

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@jit_fuser
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def gelu_impl(x):
    """OpenAI's gelu implementation."""
    return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
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                                       (1.0 + 0.044715 * x * x)))
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def openai_gelu(x):
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    return gelu_impl(x)

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#This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter
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@jit_fuser
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def erf_gelu(x):
    return x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype)+torch.ones_like(x).to(dtype=x.dtype))
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@torch.compile(mode="max-autotune-no-cudagraphs")
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def get_norm(config):
    args = get_args()
    if args.normalization == "LayerNorm":
        return LayerNorm(
            config.hidden_size,
            eps=config.layernorm_epsilon,
            no_persist_layer_norm=not config.persist_layer_norm,
            sequence_parallel=config.sequence_parallel,
            apply_layernorm_1p=args.apply_layernorm_1p)
    elif args.normalization == "RMSNorm":
        if args.apply_layernorm_1p:
            raise NotImplementedError('RMSNorm does not currently support the layernorm_1p formulation.')

        return RMSNorm(dim=config.hidden_size,
                       eps=config.layernorm_epsilon,
                       sequence_parallel=config.sequence_parallel)
    else:
        raise Exception(f"unsupported norm type '{args.normalization}'.")