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from typing import Optional
import pytest
import torch
import torch.nn as nn
import act_mem
import layers
BATCH_SIZES = (1, 2)
D_MODELS = (128, 256)
SEQ_LENS = (64, 128)
N_HEADS = (2, 4)
DEVICES = ["cpu"]
if torch.cuda.is_available():
DEVICES.append("cuda")
ZERO_MEM_ACT_FNS = [
nn.ReLU(),
nn.Sigmoid(),
nn.Tanh(),
nn.LeakyReLU(inplace=True),
nn.Sigmoid(),
]
ALL_ACT_FNS = ZERO_MEM_ACT_FNS + [
nn.ELU(),
nn.GELU(),
nn.Hardshrink(),
nn.Hardsigmoid(),
nn.Hardswish(),
nn.Hardtanh(),
nn.LeakyReLU(),
nn.SELU(),
nn.SiLU(),
]
class TestSavedTensorContext:
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("d_model", D_MODELS)
@pytest.mark.parametrize("batch_size", BATCH_SIZES)
def test_linear(self, device: str, d_model: int, batch_size: int) -> None:
"""
Test a simple linear layer. The inputs should be saved for backwards
"""
inputs = torch.randn(batch_size, d_model, requires_grad=True, device=device)
lin = nn.Linear(d_model, d_model, device=device)
with act_mem.SavedTensorContext(ignored_tensors=lin.parameters()) as saved:
_ = lin(inputs)
assert saved.saved_tensor_mem == inputs.numel() * inputs.element_size()
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("d_model", D_MODELS)
@pytest.mark.parametrize("batch_size", BATCH_SIZES)
def test_linear_amp(self, device: str, d_model: int, batch_size: int) -> None:
"""
Test a linear layer with AMP. The saved tensors should now be a low-precision version of the
inputs and the low-precision version of the weights version of the weights
"""
inputs = torch.randn(batch_size, d_model, requires_grad=True, device=device)
lin = nn.Linear(d_model, d_model, device=device)
dtype = torch.bfloat16
with torch.autocast(device_type=device, dtype=dtype):
with act_mem.SavedTensorContext(ignored_tensors=lin.parameters()) as saved:
out = lin(inputs)
assert (
saved.saved_tensor_mem
== out.numel() * out.element_size() + lin.weight.numel() * dtype.itemsize
)
@pytest.mark.parametrize("act_fn", ALL_ACT_FNS)
@pytest.mark.parametrize("dropout_prob", (None, 0.5))
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("d_model", D_MODELS)
@pytest.mark.parametrize("batch_size", BATCH_SIZES)
@pytest.mark.parametrize("seq_len", SEQ_LENS)
def test_mlp(
self,
act_fn: nn.Module,
dropout_prob: Optional[float],
device: str,
d_model: int,
batch_size: int,
seq_len: int,
) -> None:
"""
For the transformer MLP layer with a ReLU non-linearity, the initial inputs and the inputs
to the final linear layer (which are four times as large) must always be saved. If the
derivative of the activation function cannot be expressed in terms of the activation
function's *outputs*, then the activation inputs must also be saved (which are again four
times as large as the MLP's inputs). The MLP activation memory can be nearly halved by a
choice of activation function.
"""
inputs = torch.randn(
batch_size, seq_len, d_model, requires_grad=True, device=device
)
expansion_factor = 4
mlp = layers.MLP(
d_model=d_model, act_fn=act_fn, dropout_prob=dropout_prob, device=device
)
with act_mem.SavedTensorContext(ignored_tensors=mlp.parameters()) as saved:
_ = mlp(inputs)
# Compare measured memory against expected
first_lin_input_mem = act_mem.get_tensor_bytes(inputs)
second_lin_input_mem = expansion_factor * first_lin_input_mem
# Only some activations require additional activation memory
activation_input_mem = 0 if act_fn in ZERO_MEM_ACT_FNS else second_lin_input_mem
dropout_act_mem = (
0 if not dropout_prob else inputs.numel() * (4 if device == "cpu" else 1)
)
expected_mem = (
first_lin_input_mem
+ second_lin_input_mem
+ activation_input_mem
+ dropout_act_mem
)
assert saved.saved_tensor_mem == expected_mem
@pytest.mark.parametrize("act_fn", ALL_ACT_FNS)
@pytest.mark.parametrize("dropout_prob", (None, 0.5))
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("d_model", D_MODELS)
@pytest.mark.parametrize("batch_size", BATCH_SIZES)
@pytest.mark.parametrize("seq_len", SEQ_LENS)
def test_mlp_amp(
self,
act_fn: nn.Module,
dropout_prob: Optional[float],
device: str,
d_model: int,
batch_size: int,
seq_len: int,
) -> None:
"""
Similar story with AMP. The only changes come from the modified dtypes and needing to also
save references to the low-precision weights in the Linear layers.
"""
inputs = torch.randn(
batch_size, seq_len, d_model, requires_grad=True, device=device
)
expansion_factor = 4
mlp = layers.MLP(
d_model=d_model, act_fn=act_fn, dropout_prob=dropout_prob, device=device
)
dtype = torch.bfloat16
with torch.autocast(device_type=device, dtype=dtype):
with act_mem.SavedTensorContext(ignored_tensors=mlp.parameters()) as saved:
_ = mlp(inputs)
# Compare measured memory against expected
amp_weight_mem = 2 * expansion_factor * d_model**2 * dtype.itemsize
first_lin_input_mem = inputs.numel() * dtype.itemsize
second_lin_input_mem = expansion_factor * inputs.numel() * dtype.itemsize
# Only some activations require additional activation memory
activation_input_mem = 0 if act_fn in ZERO_MEM_ACT_FNS else second_lin_input_mem
dropout_act_mem = (
0
if not dropout_prob
else inputs.numel() * (dtype.itemsize if device == "cpu" else 1)
)
expected_mem = (
amp_weight_mem
+ first_lin_input_mem
+ second_lin_input_mem
+ activation_input_mem
+ dropout_act_mem
)
assert (
saved.saved_tensor_mem == expected_mem
), f"Failed on {act_fn=}, {dropout_prob=}"
@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda not available")
class TestCUDAMemReadings:
@pytest.mark.parametrize("d_model", D_MODELS)
@pytest.mark.parametrize("batch_size", BATCH_SIZES)
@pytest.mark.parametrize("seq_len", SEQ_LENS)
@pytest.mark.parametrize("act_fn", ALL_ACT_FNS)
def test_mlp(
self, d_model: int, batch_size: int, seq_len: int, act_fn: nn.Module
) -> None:
"""
Track saved tensors and allocated memory and verify they agree.
"""
inputs = torch.randn(batch_size, seq_len, d_model, device="cuda")
mlp = layers.MLP(d_model=d_model, act_fn=act_fn, device="cuda")
with act_mem.AllocatedMemContext() as mem, act_mem.SavedTensorContext(
ignored_tensors=mlp.parameters()
) as saved:
outputs = mlp(inputs)
# AllocatedMemContext captures the outputs, but not inputs, while SavedTensorContext
# captures inputs and not outputs. Nevertheless, the readings agree because inputs and
# outputs are tensors of the same size and `dtype`.
assert mem.delta["current"] == saved.saved_tensor_mem
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