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Add intermediate_size to GPT-NeoX models #1212

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Add intermediate_size to GPT-NeoX models #1212

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6c6a46b
Update transformer.py -> Add `intermediate_size`
dtamayo-nlp May 10, 2024
a80e302
add support for rwkv and mamba and add todos about swiglu
jahatef Jun 19, 2024
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5 changes: 4 additions & 1 deletion megatron/model/mamba/mamba.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,10 @@ def __init__(
self.d_state = 16 # state dimensions per channel
self.d_conv = 4 # convolution width
self.expand = 2 # linear projection expansion factors
self.d_inner = int(self.expand * self.d_model)
if neox_args.intermediate_size == None:
neox_args.d_inner = self.expand * self.d_model
else:
neox_args.d_inner = neox_args.intermediate_size
self.dt_rank = math.ceil(self.d_model / 16) # rank of dt / Delta parameter
self.dt_scale = 1.0

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12 changes: 7 additions & 5 deletions megatron/model/rwkv/v6/rwkv.py
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Expand Up @@ -247,11 +247,11 @@ def __init__(self, neox_args, layer_number):
self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))

self.key = nn.Linear(neox_args.hidden_size, neox_args.dim_ffn, bias=False)
self.key = nn.Linear(neox_args.hidden_size, neox_args.ff_dim, bias=False)
self.receptance = nn.Linear(
neox_args.hidden_size, neox_args.hidden_size, bias=False
)
self.value = nn.Linear(neox_args.dim_ffn, neox_args.hidden_size, bias=False)
self.value = nn.Linear(neox_args.ff_dim, neox_args.hidden_size, bias=False)

def forward(self, x):
xx = self.time_shift(x) - x
Expand All @@ -277,12 +277,14 @@ def __init__(self, neox_args, layer_number):
self.bf16 = neox_args.precision == "bfloat16"
if not hasattr(neox_args, "dim_att"):
neox_args.dim_att = neox_args.hidden_size
if not hasattr(neox_args, "dim_ffn"):
if neox_args.intermediate_size == None:
# Make hidden size 3.5x. Round to nearest multiple of 32 until we add hdim rounding logic
neox_args.dim_ffn = int((neox_args.hidden_size * 3.5) // 32 * 32)
neox_args.ff_dim = int((neox_args.hidden_size * 3.5) // 32 * 32)
else:
neox_args.ff_dim = neox_args.intermediate_size
assert neox_args.hidden_size % 32 == 0
assert neox_args.dim_att % 32 == 0
assert neox_args.dim_ffn % 32 == 0
assert neox_args.ff_dim % 32 == 0
self.neox_args.head_size = neox_args.dim_att // neox_args.num_attention_heads
self.head_size = self.neox_args.head_size
self.num_attention_heads = neox_args.num_attention_heads
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20 changes: 13 additions & 7 deletions megatron/model/transformer.py
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Expand Up @@ -102,13 +102,19 @@ def __init__(
self.activation_type = neox_args.activation
self.bias_gelu_fusion = neox_args.bias_gelu_fusion

# auto scale so geglu has equal parameters
ff_mult = int(4 * 2 / 3) if self.activation_type == "geglu" else 4
ff_dim = (
int(ff_mult * neox_args.hidden_size) * 2
if self.activation_type == "geglu"
else ff_mult * neox_args.hidden_size
)
#TODO: revisit this when we add swiglu to make sure ff_dim is initialized correctly.
if neox_args.intermediate_size:
ff_dim = neox_args.intermediate_size

else:
# auto scale so geglu has equal parameters
ff_mult = int(4 * 2 / 3) if self.activation_type == "geglu" else 4
ff_dim = (
int(ff_mult * neox_args.hidden_size) * 2
if self.activation_type == "geglu"
else int(ff_mult * neox_args.hidden_size)
)

self.dense_h_to_4h = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
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