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cleanup
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@jahatef
jahatef committed Nov 6, 2024
1 parent 49b263a commit 48de682
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Showing 4 changed files with 112 additions and 39 deletions.
8 changes: 4 additions & 4 deletions configs/rwkv/1.5B.yml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
{
# Parallelism is not yet supported for rwkv
"pipe_parallel_size": 1,
"model_parallel_size": 2,
"model_parallel_size": 1,

"num_layers": 24,
"hidden_size": 2048,
Expand All @@ -12,7 +12,7 @@
"output_layer_parallelism": "column",
"norm": "rmsnorm",
"rms_norm_epsilon": 1.0e-5,
"train_micro_batch_size_per_gpu": 1,
"train_micro_batch_size_per_gpu": 4,

"attention_config": [[["rwkv"], 24]],

Expand Down Expand Up @@ -86,8 +86,8 @@
},

# misc. training settings
"train_iters": 1,
"lr_decay_iters": 1,
"train_iters": 320000,
"lr_decay_iters": 320000,
"distributed_backend": "nccl",
"lr_decay_style": "constant",
"warmup": 0.01,
Expand Down
103 changes: 103 additions & 0 deletions configs/rwkv/430M.yml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,103 @@
{
# Parallelism is not yet supported for rwkv
"pipe_parallel_size": 1,
"model_parallel_size": 1,

"num_layers": 24,
"hidden_size": 1024,
"num_attention_heads": 16, # head_size = dim_att / num_attention_heads.
# head_size is 64 for all rwkv models
"seq_length": 4096,
"max_position_embeddings": 4096,
"output_layer_parallelism": "column",
"norm": "rmsnorm",
"rms_norm_epsilon": 1.0e-5,
"train_micro_batch_size_per_gpu": 4,

"attention_config": [[["rwkv"], 24]],

"activation": "silu",

# model settings

#"pos_emb": "rotary",
"rotary_pct": 0.25,
"no_weight_tying": true,
"gpt_j_residual": true,

# these should provide some speedup but takes a while to build, set to true if desired
"scaled_upper_triang_masked_softmax_fusion": false,
"bias_gelu_fusion": false,
"rope_fusion": false,
"layernorm_fusion": false,


# init methods
"init_method": "small_init",
"output_layer_init_method": "wang_init",

# optimizer settings
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.0008,
"betas": [0.9, 0.95],
"eps": 1.0e-8,
}
},
"min_lr": 0.00008,

# for all zero_optimization options, see https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training
"zero_optimization": {
"stage": 1,
"allgather_partitions": True,
"allgather_bucket_size": 500000000,
"overlap_comm": True,
"reduce_scatter": True,
"reduce_bucket_size": 500000000,
"contiguous_gradients": True,
},

# batch / data settings
"data_impl": "mmap",
"num_workers": 1,

# activation checkpointing
"checkpoint_activations": true,
"checkpoint_num_layers": 1,
"partition_activations": true,
"synchronize_each_layer": true,

# regularization
"gradient_clipping": 1.0,
"weight_decay": 0.1,
"hidden_dropout": 0,
"attention_dropout": 0,

# precision settings
"bf16": {
"bf16": true,
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 12,
"hysteresis": 2,
"min_loss_scale": 1,
},

# misc. training settings
"train_iters": 320000,
"lr_decay_iters": 320000,
"distributed_backend": "nccl",
"lr_decay_style": "constant",
"warmup": 0.01,
"checkpoint_factor": 100,
"eval_interval": 100000,
"eval_iters": 10,
"seed": 1234,

# logging
"log_interval": 10,
"steps_per_print": 10,
"wall_clock_breakdown": true,
}
1 change: 0 additions & 1 deletion megatron/model/gpt2_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,6 @@ def cross_entropy(output, labels, _fp16=False):
else:
losses = mpu.vocab_parallel_cross_entropy(output.float().contiguous(), labels)
loss_mask = loss_mask.view(-1)
print(f"model output shape: {output.size()}, loss shape: {losses.size()}")
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
return loss

Expand Down
39 changes: 5 additions & 34 deletions megatron/model/rwkv/v6/rwkv.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -173,9 +173,6 @@ def __init__(self, neox_args, layer_number, init_method):
)

self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
#self.receptance = nn.Linear(
# neox_args.hidden_size, neox_args.dim_att, bias=False
#)
self.receptance = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
Expand All @@ -184,7 +181,6 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False,
)
#self.key = nn.Linear(neox_args.hidden_size, neox_args.dim_att, bias=False)
self.key = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
Expand All @@ -193,7 +189,6 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False,
)
#self.value = nn.Linear(neox_args.hidden_size, neox_args.dim_att, bias=False)
self.value = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
Expand All @@ -202,7 +197,6 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False,
)
#self.output = nn.Linear(neox_args.dim_att, neox_args.hidden_size, bias=False)
self.output = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.dim_att,
Expand All @@ -211,7 +205,6 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False,
)
#self.gate = nn.Linear(neox_args.hidden_size, neox_args.dim_att, bias=False) # column
self.gate = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
Expand All @@ -220,15 +213,6 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False,
)
#self.gate = mpu.RowParallelLinear(
# neox_args=neox_args,
# input_size=neox_args.hidden_size,
# output_size=neox_args.dim_att,
# input_is_parallel=True,
# init_method=init_method,
# parallel_output=False,
# bias=False
# )
self.ln_x = nn.GroupNorm(
neox_args.num_attention_heads, neox_args.dim_att, eps=(1e-5) * (8**2)
)
Expand All @@ -237,7 +221,6 @@ def jit_func(self, x):
B, T, C = x.size()

xx = self.time_shift(x) - x
print(x[0,:,1],xx[0,:,1])

xxx = x + xx * self.time_maa_x
xxx = torch.tanh(xxx @ self.time_maa_w1).view(B * T, 5, -1).transpose(0, 1)
Expand All @@ -256,7 +239,6 @@ def jit_func(self, x):
gated, _ = self.gate(xg)
g = F.silu(gated)

print(f"size of ww matmuls: {self.time_decay_w1.size()}, {self.time_decay_w2.size()}")
ww = torch.tanh(xw @ self.time_decay_w1) @ self.time_decay_w2
w = self.time_decay + ww
w = scatter_to_model_parallel_region(w)
Expand All @@ -268,9 +250,8 @@ def jit_func_2(self, x, g):
x = x.view(B * T, C)

x = self.ln_x(x).view(B, T, C)
print(f"shape of x: {x.size()}, shape of g: {g.size()}")
x, _ = self.output(x * g)
print(f"new shape of x: {x.size()}")

return x

def forward(self, x):
Expand All @@ -279,15 +260,11 @@ def forward(self, x):
H = self.neox_args.num_attention_heads//mpu.get_model_parallel_world_size()
H_tp = H//mpu.get_model_parallel_world_size()

#self.time_faaaa = self.time_faaaa[:self.neox_args.num_attention_heads//2,:]
#self.time_faaaa = scatter_to_model_parallel_region(self.time_faaaa)
r, k, v, g, w = self.jit_func(x)
print(f"shape of r: {r.size()}, k: {k.size()}, v: {v.size()}, g: {g.size()}, w: {w.size()}, H: {H}, B: {B}, T: {T}, C: {C}, time_faaaa: {self.time_faaaa.size()}, \n")

x = RUN_CUDA_RWKV(B, T, C_tp, H, r, k, v, w, u=scatter_to_model_parallel_region(self.time_faaaa.view(-1)).view(H,C_tp//H) )
print(f"size of x after kernel: {x.size()}")

x = gather_from_model_parallel_region(x)
print(f"size of x after allgather: {x.size()}")

return self.jit_func_2(x, g)

Expand Down Expand Up @@ -315,7 +292,6 @@ def __init__(self, neox_args, layer_number, init_method):
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.ffn_dim, bias=False)
self.key = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
Expand All @@ -324,9 +300,7 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False,
)
#self.receptance = nn.Linear(
# neox_args.hidden_size, neox_args.hidden_size, bias=False
#)

self.receptance = mpu.ColumnParallelLinear(
neox_args=neox_args,
input_size=neox_args.hidden_size,
Expand All @@ -335,7 +309,6 @@ def __init__(self, neox_args, layer_number, init_method):
init_method=init_method,
bias=False
)
#self.value = nn.Linear(neox_args.ffn_dim, neox_args.hidden_size, bias=False)
self.value = mpu.RowParallelLinear(
neox_args=neox_args,
input_size=neox_args.ffn_dim,
Expand All @@ -345,6 +318,7 @@ def __init__(self, neox_args, layer_number, init_method):
parallel_output=False,
bias=False
)

def forward(self, x):
xx = self.time_shift(x) - x
xk = x + xx * self.time_maa_k
Expand All @@ -355,7 +329,7 @@ def forward(self, x):
kv, _ = self.value(k)
receptance, _ = self.receptance(xr)
retVal = torch.sigmoid(receptance) * kv
print(f"channel mix output size: {retVal.size()}")

return retVal


Expand All @@ -374,7 +348,6 @@ def __init__(self, neox_args, init_method, layer_number):
neox_args.intermediate_size == None or neox_args.expansion_factor == None
), "Must pass either the absolute intermediate size or the relative expansion factor for rwkv"
if not neox_args.dim_att:
print("replacing dim_att")
neox_args.dim_att = neox_args.hidden_size
if neox_args.intermediate_size:
neox_args.ffn_dim = neox_args.intermediate_size
Expand Down Expand Up @@ -450,7 +423,6 @@ def forward(self, x):

return x


class RWKVResidualLayerPipe(RWKVResidualLayer):
"""
RWKV Pipeline Layer
Expand All @@ -465,5 +437,4 @@ def forward(self, args):
hidden_states = super().forward(hidden_states)
if self.layer_number == self.neox_args.num_layers-1:
hidden_states = hidden_states.transpose(0,1)
print(f"output of model from residual layer pipe: {hidden_states.size()}")
return hidden_states, mask

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