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Add fused linear for the LLAMA MLP block and multi-head attention block #6425

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merged 4 commits into from
Jul 26, 2023
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Add fused linear for the LLAMA MLP block and multi-head attention block #6425

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0c1a534
Add fused linear for the LLAMA MLP block and multi-head attention block
littsk Jul 17, 2023
178e63b
Refactor the config name 'fuse_attn_qkv' to 'fuse_attention_qkv' for …
littsk Jul 19, 2023
6412892
Added a switch for fuse_mlp_linear and improved the organization of t…
littsk Jul 24, 2023
394608f
add tensor parallel mappings for fused linears
littsk Jul 26, 2023
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9 changes: 7 additions & 2 deletions llm/llama/run_pretrain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,8 +133,12 @@ class ModelArguments:
metadata={"help": "llama, use_fused_rms_norm"},
)
fuse_attention_qkv: bool = field(
default=True,
metadata={"help": "gpt, fuse_attention_qkv"},
default=False,
metadata={"help": "whether to fuse attention qkv"},
)
fuse_mlp_linear: bool = field(
default=False,
metadata={"help": "whether to fuse first up and gate proj in mlp block"},
)
recompute_granularity: str = field(
default="full",
Expand Down Expand Up @@ -404,6 +408,7 @@ def main():
config.use_flash_attention = model_args.use_flash_attention
config.use_fused_rms_norm = model_args.use_fused_rms_norm
config.fuse_attention_qkv = model_args.fuse_attention_qkv
config.fuse_mlp_linear = model_args.fuse_mlp_linear
config.recompute_granularity = model_args.recompute_granularity
config.virtual_pp_degree = model_args.virtual_pp_degree
config.use_recompute = training_args.recompute
Expand Down
4 changes: 4 additions & 0 deletions paddlenlp/transformers/llama/configuration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,7 +208,9 @@ def __init__(
use_cache=True,
use_recompute=False,
recompute_granularity="full",
fuse_attention_qkv=False,
use_flash_attention=False,
fuse_mlp_linear=False,
use_fused_rms_norm=False,
tensor_parallel_output=True,
lm_shift_labels=True,
Expand All @@ -230,7 +232,9 @@ def __init__(
self.use_cache = use_cache
self.use_recompute = use_recompute
self.recompute_granularity = recompute_granularity
self.fuse_attention_qkv = fuse_attention_qkv
self.use_flash_attention = use_flash_attention
self.fuse_mlp_linear = fuse_mlp_linear
self.use_fused_rms_norm = use_fused_rms_norm
self.tensor_parallel_output = tensor_parallel_output
self.lm_shift_labels = lm_shift_labels
Expand Down
143 changes: 94 additions & 49 deletions paddlenlp/transformers/llama/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -316,33 +316,57 @@ def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.tensor_parallel_degree = config.tensor_parallel_degree
self.fuse_mlp_linear = config.fuse_mlp_linear

if config.tensor_parallel_degree > 1:
self.gate_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.intermediate_size,
gather_output=False,
has_bias=False,
)
# 为了减少张量并行的通信量，将两个linear合并成一个
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中文注释 删掉

if config.fuse_mlp_linear:
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这个 fuse_mlp_linear 是不是换个名字更好？
fuse_gate_up_proj ? 或者其他？

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好的好的

self.gate_up_fused_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.intermediate_size * 2,
gather_output=False,
has_bias=False,
)
else:
self.gate_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.intermediate_size,
gather_output=False,
has_bias=False,
)
self.up_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.intermediate_size,
gather_output=False,
has_bias=False,
)

self.down_proj = mpu.RowParallelLinear(
self.intermediate_size,
self.hidden_size,
input_is_parallel=True,
has_bias=False,
)
self.up_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.intermediate_size,
gather_output=False,
has_bias=False,
)
else:
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias_attr=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias_attr=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias_attr=False)

def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
if self.tensor_parallel_degree > 1 and self.fuse_mlp_linear:
# [s, b, 4hp]
intermediate_parallel = self.gate_up_fused_proj(x)
# Special Slicing to accomodate Tensor Parallel
# Even channels is ffc_fc, odd channels is gate
gate_out = intermediate_parallel[..., 0::2]
up_out = intermediate_parallel[..., 1::2]
intermediate_parallel = F.silu(gate_out) * up_out
# [s, b, h]
out = self.down_proj(intermediate_parallel)
else:
out = self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
return out


class LlamaAttention(nn.Layer):
Expand All @@ -354,47 +378,63 @@ def __init__(self, config):
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.max_position_embeddings
self.fuse_attention_qkv = config.fuse_attention_qkv
if config.tensor_parallel_degree > 1:
assert (
self.num_heads % config.tensor_parallel_degree == 0
), f"num_heads: {self.num_heads}, tensor_parallel_degree: {config.tensor_parallel_degree}"
self.num_heads = self.num_heads // config.tensor_parallel_degree

if config.tensor_parallel_degree > 1:
self.q_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
has_bias=False,
gather_output=False,
)
self.k_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
has_bias=False,
gather_output=False,
)
self.v_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
has_bias=False,
gather_output=False,
)
if self.fuse_attention_qkv:
self.qkv_proj = mpu.ColumnParallelLinear(
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预训练参数加载转换，看看能不能根据fuse 搞成自动的。

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请问大佬，具体是啥意思呀

self.hidden_size,
3 * self.hidden_size,
has_bias=False,
gather_output=False,
)
else:
self.q_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
has_bias=False,
gather_output=False,
)
self.k_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
has_bias=False,
gather_output=False,
)
self.v_proj = mpu.ColumnParallelLinear(
self.hidden_size,
self.hidden_size,
has_bias=False,
gather_output=False,
)
else:
self.q_proj = nn.Linear(
self.hidden_size,
self.hidden_size,
bias_attr=False,
)
self.k_proj = nn.Linear(
self.hidden_size,
self.hidden_size,
bias_attr=False,
)
self.v_proj = nn.Linear(
self.hidden_size,
self.hidden_size,
bias_attr=False,
)
if self.fuse_attention_qkv:
self.qkv_proj = nn.Linear(
self.hidden_size,
3 * self.hidden_size,
bias_attr=False,
)
else:
self.q_proj = nn.Linear(
self.hidden_size,
self.hidden_size,
bias_attr=False,
)
self.k_proj = nn.Linear(
self.hidden_size,
self.hidden_size,
bias_attr=False,
)
self.v_proj = nn.Linear(
self.hidden_size,
self.hidden_size,
bias_attr=False,
)
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if config.tensor_parallel_degree > 1:
self.o_proj = mpu.RowParallelLinear(
Expand Down Expand Up @@ -422,9 +462,14 @@ def forward(
) -> Tuple[paddle.Tensor, Optional[paddle.Tensor], Optional[Tuple[paddle.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, q_len, _ = hidden_states.shape
query_states = self.q_proj(hidden_states).reshape(shape=[bsz, q_len, self.num_heads, self.head_dim])
key_states = self.k_proj(hidden_states).reshape(shape=[bsz, q_len, self.num_heads, self.head_dim])
value_states = self.v_proj(hidden_states).reshape(shape=[bsz, q_len, self.num_heads, self.head_dim])
if self.fuse_attention_qkv:
mix_layer = self.qkv_proj(hidden_states)
mix_layer = paddle.reshape_(mix_layer, [0, 0, self.num_heads, 3 * self.head_dim])
query_states, key_states, value_states = paddle.split(mix_layer, num_or_sections=3, axis=-1)
else:
query_states = self.q_proj(hidden_states).reshape(shape=[bsz, q_len, self.num_heads, self.head_dim])
key_states = self.k_proj(hidden_states).reshape(shape=[bsz, q_len, self.num_heads, self.head_dim])
value_states = self.v_proj(hidden_states).reshape(shape=[bsz, q_len, self.num_heads, self.head_dim])

kv_seq_len = key_states.shape[-3]
offset = 0
Expand Down