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| import numpy as np | |
| def multiplication_in_int64(array): | |
| return np.cumprod(np.array(array, dtype=np.int64))[-1] | |
| def matrix_operation(shapeA, shapeB): | |
| assert(shapeA[-1] == shapeB[0]) | |
| op = np.cumprod(np.array(shapeA[:-1], np.float64)) | |
| return multiplication_in_int64([2, op[-1], shapeA[-1], shapeB[-1]]) | |
| def word_embedding_operation(model_config, inference_config): | |
| #Given: | |
| #\begin{itemize} | |
| # \item Matrix \( X \) of size \( B \times s \) (representing the batch size and sequence length respectively). | |
| # \item Embedding matrix \( W_e \) of size \( n_{vocab} \times d_{model} \). | |
| #\end{itemize} | |
| #The resultant matrix after the multiplication will be of size \( B \times s \times d_{model} \). | |
| #For each element in this resultant matrix, the number of FLOPs required is \( 2 \times n_{vocab} \). This is because for a single element in the output matrix, we have \( 2N \) FLOPs (with \( N \) being the common dimension), leading to the matrix multiplication FLOP count as: | |
| #\begin{equation} | |
| #2 \times B \times s \times n_{vocab} \times d_{model} | |
| #\end{equation} | |
| A = [inference_config['batchsize'], inference_config['input_seq_length'], model_config['vocab_size']] | |
| B = [model_config['vocab_size'], model_config['hidden_size']] | |
| return matrix_operation(A, B) | |
| def positional_embedding_operation(model_config, inference_config): | |
| return multiplication_in_int64([inference_config['batchsize'], inference_config['input_seq_length'], model_config['hidden_size']]) | |
| ### Below three are the same | |
| def attention_K_operation(model_config, inference_config, seq_length): | |
| A = [inference_config['batchsize'], seq_length, model_config['hidden_size']] | |
| B = [model_config['hidden_size'], model_config['hidden_size']/model_config['num_attention_heads']] | |
| return model_config['num_hidden_layers'] * model_config['num_attention_heads'] * matrix_operation(A, B) | |
| def attention_Q_operation(model_config, inference_config, seq_length): | |
| A = [inference_config['batchsize'], seq_length, model_config['hidden_size']] | |
| B = [model_config['hidden_size'], model_config['hidden_size']/model_config['num_attention_heads']] | |
| return model_config['num_hidden_layers'] * model_config['num_attention_heads'] * matrix_operation(A, B) | |
| def attention_V_operation(model_config, inference_config, seq_length): | |
| A = [inference_config['batchsize'], seq_length, model_config['hidden_size']] | |
| B = [model_config['hidden_size'], model_config['hidden_size']/model_config['num_attention_heads']] | |
| return model_config['num_hidden_layers'] * model_config['num_attention_heads'] * matrix_operation(A, B) | |
| ## | |
| def attention_QK_operation(model_config, inference_config, seq_length_Q, seq_length_K): | |
| A = [inference_config['batchsize'], seq_length_Q, model_config['hidden_size']/model_config['num_attention_heads']] | |
| B = [model_config['hidden_size']/model_config['num_attention_heads'], seq_length_K] | |
| return model_config['num_hidden_layers'] * model_config['num_attention_heads']* matrix_operation(A, B) | |
| def attention_softmax_operation(model_config, inference_config,seq_length): | |
| # Ref: Ouyang, A. (2023). Understanding the Performance of Transformer Inference (Doctoral dissertation, Massachusetts Institute of Technology). | |
| # 3 is a modeled value | |
| softmax_operation = (3*inference_config['batchsize']*seq_length*seq_length) | |
| return model_config['num_hidden_layers'] * model_config['num_attention_heads'] * softmax_operation | |
| def attention_multV_operation(model_config, inference_config, seq_length_Q, seq_length_V): | |
| A = [inference_config['batchsize'], seq_length_Q, seq_length_V] | |
| B = [seq_length_V, model_config['hidden_size']/model_config['num_attention_heads']] | |
| return model_config['num_hidden_layers'] * model_config['num_attention_heads']* matrix_operation(A, B) | |
| def attention_out_operation(model_config, inference_config, seq_length): | |
| A = [inference_config['batchsize'], seq_length, model_config['hidden_size']] | |
| B = [model_config['hidden_size'], model_config['hidden_size']] | |
| return model_config['num_hidden_layers'] * matrix_operation(A, B) | |
| def layernorm_operation(model_config, inference_config, seq_length): | |
| # Ref: Ouyang, A. (2023). Understanding the Performance of Transformer Inference (Doctoral dissertation, Massachusetts Institute of Technology). | |
| # 5 is a modeled value | |
| layernorm_operation = (5*inference_config['batchsize']*seq_length*model_config['hidden_size']) | |
| return model_config['num_hidden_layers'] * model_config['layernorm_operation'] * layernorm_operation | |
| def mlp1_operation(model_config, inference_config, seq_length): | |
| A = [inference_config['batchsize'], seq_length, model_config['hidden_size']] | |
| B = [model_config['hidden_size'], model_config['intermediate_size']] | |
| return model_config['num_hidden_layers'] * matrix_operation(A, B) | |
| def mlp2_operation(model_config, inference_config, seq_length): | |
| A = [inference_config['batchsize'], seq_length, model_config['intermediate_size']] | |
| B = [model_config['intermediate_size'], model_config['hidden_size']] | |
| return model_config['num_hidden_layers'] * matrix_operation(A, B) | |
| def prefilling_operation(model_config, inference_config): | |
| prefilling_operation_count = {} | |
| prefilling_operation_count['word_embedding'] = word_embedding_operation(model_config, inference_config) | |
| prefilling_operation_count['positional_embedding'] = positional_embedding_operation(model_config, inference_config) | |
| prefilling_operation_count['attention_Q'] = attention_Q_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['attention_K'] = attention_K_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['attention_V'] = attention_V_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['attention_QK'] = attention_QK_operation(model_config, inference_config, inference_config['input_seq_length'], inference_config['input_seq_length']) | |
| prefilling_operation_count['attention_softmax'] = attention_softmax_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['attention_multV'] = attention_multV_operation(model_config, inference_config, inference_config['input_seq_length'], inference_config['input_seq_length']) | |
| prefilling_operation_count['attention_out'] = attention_out_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['layernorm'] =layernorm_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['mlp1'] = mlp1_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['mlp2'] = mlp2_operation(model_config, inference_config, inference_config['input_seq_length']) | |
| prefilling_operation_count['embeddings'] = prefilling_operation_count['word_embedding'] + prefilling_operation_count['positional_embedding'] | |
| prefilling_operation_count['attention'] = sum([v for k,v in prefilling_operation_count.items() if 'attention' in k]) | |
| prefilling_operation_count['mlp'] = prefilling_operation_count['mlp1'] + prefilling_operation_count['mlp2'] | |
| prefilling_operation_count['total'] = (prefilling_operation_count['embeddings'] + prefilling_operation_count['attention'] + prefilling_operation_count['mlp'] + prefilling_operation_count['layernorm']) | |
| return prefilling_operation_count | |
| def generation_operation(model_config, inference_config): | |
| generation_operation_count = {} | |
| generation_operation_count['word_embedding'] = 0 | |
| generation_operation_count['positional_embedding'] = 0 | |
| generation_operation_count['attention_K'] = 0 | |
| generation_operation_count['attention_V'] = 0 | |
| generation_operation_count['attention_Q'] = 0 | |
| generation_operation_count['attention_QK'] = 0 | |
| generation_operation_count['attention_softmax'] = 0 | |
| generation_operation_count['attention_multV'] = 0 | |
| generation_operation_count['attention_out'] = 0 | |
| generation_operation_count['mlp1'] = 0 | |
| generation_operation_count['mlp2'] = 0 | |
| generation_operation_count['layernorm'] = 0 | |
| for t in range(inference_config['output_seq_length']): | |
| if inference_config['KV_cache']: | |
| generation_operation_count['attention_K'] += attention_K_operation(model_config, inference_config, 1) | |
| generation_operation_count['attention_V'] += attention_V_operation(model_config, inference_config, 1) | |
| generation_operation_count['attention_Q'] += attention_Q_operation(model_config, inference_config, 1) | |
| generation_operation_count['attention_QK'] += attention_QK_operation(model_config, inference_config, seq_length_Q=1, seq_length_K=(t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_softmax'] += attention_softmax_operation(model_config, inference_config, 1) | |
| generation_operation_count['attention_multV'] += attention_multV_operation(model_config, inference_config, seq_length_Q=1, seq_length_V=(t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_out'] += attention_out_operation(model_config, inference_config, 1) | |
| generation_operation_count['mlp1'] += mlp1_operation(model_config, inference_config, 1) | |
| generation_operation_count['mlp2'] += mlp2_operation(model_config, inference_config, 1) | |
| else: | |
| generation_operation_count['attention_K'] += attention_K_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_V'] += attention_V_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_Q'] += attention_Q_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_QK'] += attention_QK_operation(model_config, inference_config, seq_length_Q=(t+1)+inference_config['input_seq_length'], seq_length_K=(t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_softmax'] += attention_softmax_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_multV'] += attention_multV_operation(model_config, inference_config, seq_length_Q=(t+1)+inference_config['input_seq_length'], seq_length_V=(t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['attention_out'] += attention_out_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['mlp1'] += mlp1_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['mlp2'] += mlp2_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['layernorm'] += layernorm_operation(model_config, inference_config, (t+1)+inference_config['input_seq_length']) | |
| generation_operation_count['embeddings'] = generation_operation_count['word_embedding'] + generation_operation_count['positional_embedding'] | |
| generation_operation_count['attention'] = sum([v for k,v in generation_operation_count.items() if 'attention' in k]) | |
| generation_operation_count['mlp'] = generation_operation_count['mlp1'] + generation_operation_count['mlp2'] | |
| generation_operation_count['total'] = (generation_operation_count['attention'] + generation_operation_count['mlp'] + generation_operation_count['layernorm']) | |
| return generation_operation_count |