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0847403
1
Parent(s):
a50b299
Fixing naming
Browse files- calc_params.py +43 -0
calc_params.py
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import math
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# Helper function to pretty-print message sizes
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def convert_params(params):
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if params == 0:
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return "0"
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size_name = ("", "K", "M", "B", "T", "P", "E", "Z", "Y")
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i = int(math.floor(math.log(params, 1000)))
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p = math.pow(1000, i)
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s = round(params / p, 2)
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return "%s %s" % (s, size_name[i])
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# Parameter Calculation function
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def calc_params(vocab_size, tied_embeddings, hidden_size, sequence_length, num_layers, moe, num_experts, expert_interval, topk, ffn_expansion_factor, num_mlp_linears, kv_size_ratio):
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if tied_embeddings:
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embedding_params = hidden_size * vocab_size
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else:
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embedding_params = 2 * hidden_size * vocab_size
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position_embedding_params = hidden_size * sequence_length
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attention_params = int(2 * (1 + kv_size_ratio) * num_layers * hidden_size * hidden_size)
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layernorm_params = 13 * num_layers * hidden_size
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if moe:
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num_expert_layers = num_layers / expert_interval
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ffn_expert_params = num_mlp_linears * ffn_expansion_factor * num_expert_layers * num_experts * hidden_size * hidden_size
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ffn_dense_params = num_mlp_linears * ffn_expansion_factor * (num_layers - num_expert_layers) * hidden_size * hidden_size
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ffn_params = ffn_expert_params + ffn_dense_params
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gating_params = num_expert_layers * hidden_size * num_experts
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else:
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ffn_params = num_mlp_linears * ffn_expansion_factor * num_layers * hidden_size * hidden_size
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total_params = embedding_params + attention_params + ffn_params + position_embedding_params + layernorm_params
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if moe:
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total_params += gating_params
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return f"""
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Embedding parameters: {convert_params(embedding_params)}
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Attention parameters: {convert_params(attention_params)}
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FFN parameters: {convert_params(ffn_params)}
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{'Gating parameters: ' + convert_params(gating_params) if moe else ''}
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Total Params in the Model: {convert_params(total_params)}
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"""
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