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Add profiler annotation for the decoderonly example #8034

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Add profiler annotation for the decoderonly example #8034

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6 changes: 6 additions & 0 deletions examples/decoder_only_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import torch.nn.functional as F
from torch import nn

import torch_xla.debug.profiler as xp

# the default config is intentionally kept low to make it runable on a sigle tpu v2-8 core.
@dataclass
Expand Down Expand Up @@ -44,6 +45,7 @@ def __init__(self, hidden_size, eps=1e-6):
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps

@xp.trace_me("RMSNorm")
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
Expand Down Expand Up @@ -79,6 +81,7 @@ def __init__(self, config: DecoderOnlyConfig):
self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.flash_attention_impl = None

@xp.trace_me("attention")
def forward(
self,
hidden_states: torch.Tensor,
Expand Down Expand Up @@ -153,6 +156,7 @@ def __init__(self, config: DecoderOnlyConfig):
self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = F.silu

@xp.trace_me("MLP")
def forward(self, x):
# [B, S, H] -> [B, S, I]
up_proj = self.up_proj(x)
Expand All @@ -173,6 +177,7 @@ def __init__(self, config: DecoderOnlyConfig):
self.input_layernorm = RMSNorm(config.hidden_size)
self.post_attention_layernorm = RMSNorm(config.hidden_size)

@xp.trace_me("DecoderLayer")
def forward(
self,
hidden_states: torch.Tensor,
Expand Down Expand Up @@ -209,6 +214,7 @@ def __init__(self, config: DecoderOnlyConfig):
self.norm = RMSNorm(config.hidden_size)
self.output = nn.Linear(config.hidden_size, self.vocab_size, bias=False)

@xp.trace_me("DecoderOnlyModel")
def forward(
self,
input_ids: torch.LongTensor = None,
Expand Down
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