File size: 4,360 Bytes
d7b3a74 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | def calculate_embedding_flops(seqlen, hidden_size):
return 2 * seqlen * hidden_size
def calculate_lm_head_flops(seqlen, hidden_size, vocab_size):
return 2 * seqlen * hidden_size * vocab_size
def calculate_qkv_projection_flops(args, seqlen, hidden_size, num_attention_heads, num_query_groups):
if args.q_lora_rank is None:
q_flops = 2 * seqlen * hidden_size * num_attention_heads * args.kv_channels
else:
q_flops = (
2
* seqlen
* args.q_lora_rank
* (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.qk_pos_emb_head_dim))
)
if args.kv_lora_rank is None:
kv_flops = 2 * 2 * seqlen * hidden_size * num_query_groups * args.kv_channels
else:
kv_flops = (
2
* seqlen
* (
args.kv_lora_rank
* (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.v_head_dim))
+ args.hidden_size * args.qk_pos_emb_head_dim
)
)
return q_flops + kv_flops
def calculate_attention_flops(args, seqlen, num_attention_heads):
# QK^T with causal
if args.qk_pos_emb_head_dim:
flops = 2 * num_attention_heads * seqlen * seqlen * (args.qk_head_dim + args.qk_pos_emb_head_dim) / 2
else:
flops = 2 * num_attention_heads * seqlen * seqlen * args.kv_channels / 2
# A*V
if args.v_head_dim:
flops += num_attention_heads * seqlen * seqlen * args.v_head_dim
else:
flops += num_attention_heads * seqlen * seqlen * args.kv_channels
return flops
def calculate_output_flops(seqlen, hidden_size):
return 2 * seqlen * hidden_size * hidden_size
def calculate_mlp_flops(seqlen, hidden_size, ffn_hidden_size):
return 2 * seqlen * hidden_size * ffn_hidden_size * 3
def calculate_layer_flops(args, seqlen, hidden_size, num_attention_heads, num_query_groups, ffn_hidden_size):
return (
calculate_qkv_projection_flops(args, seqlen, hidden_size, num_attention_heads, num_query_groups)
+ calculate_attention_flops(args, seqlen, num_attention_heads)
+ calculate_output_flops(seqlen, hidden_size)
+ calculate_mlp_flops(seqlen, hidden_size, ffn_hidden_size)
)
def calculate_fwd_flops(
seqlens,
args,
):
hidden_size = args.hidden_size
num_attention_heads = args.num_attention_heads
num_query_groups = args.num_query_groups
vocab_size = args.vocab_size
total_flops = 0
dense_ffn = args.ffn_hidden_size
if args.num_experts is None:
num_dense_layers = args.num_layers
num_moe_layers = 0
else:
shared_expert_ffn = getattr(args, "moe_shared_expert_intermediate_size", None)
if shared_expert_ffn is None:
shared_expert_ffn = 0
moe_ffn = args.moe_ffn_hidden_size * args.moe_router_topk + shared_expert_ffn
if hasattr(args, "moe_layer_freq"):
if isinstance(args.moe_layer_freq, list):
num_dense_layers = sum(1 for freq in args.moe_layer_freq if freq == 0)
num_moe_layers = sum(1 for freq in args.moe_layer_freq if freq > 0)
else:
num_dense_layers = sum(1 for i in range(args.num_layers) if i % args.moe_layer_freq != 0)
num_moe_layers = sum(1 for i in range(args.num_layers) if i % args.moe_layer_freq == 0)
else:
num_dense_layers = 0
num_moe_layers = args.num_layers
for seqlen in seqlens:
if num_dense_layers > 0:
total_flops += (
calculate_layer_flops(
args,
seqlen,
hidden_size,
num_attention_heads,
num_query_groups,
dense_ffn,
)
* num_dense_layers
)
if num_moe_layers > 0:
total_flops += (
calculate_layer_flops(
args,
seqlen,
hidden_size,
num_attention_heads,
num_query_groups,
moe_ffn,
)
* num_moe_layers
)
total_flops += calculate_lm_head_flops(seqlen, hidden_size, vocab_size)
return total_flops
|