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Update cal_mfu.py
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@ -11,116 +11,139 @@
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License
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# limitations under the License.
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import json
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import os
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import fire
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import torch
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from transformers import AutoConfig
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import fire
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def model_flops_counter(
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from llamafactory.train.tuner import run_exp
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BASE = 2 # gemm (add + mul)
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def compute_model_flops(
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model_name_or_path: str,
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batch_size: int,
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seqlen: int,
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model_config: dict,
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is_backward: bool = True,
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is_recompute: bool = False,
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is_flashattn: bool = False,
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) -> float:
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seq_length: int,
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include_backward: bool = True,
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include_recompute: bool = False,
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include_flashattn: bool = False,
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) -> int:
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r"""
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Calculates the FLOPs of model per forward/backward pass.
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"""
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calculate the FLOPs of model per iteration
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"""
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hidden_size = model_config.hidden_size
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num_attention_heads = model_config.num_attention_heads
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num_key_value_heads = model_config.num_key_value_heads
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vocab_size = model_config.vocab_size
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intermediate_size = model_config.intermediate_size
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num_hidden_layers = model_config.num_hidden_layers
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"""
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B: batch_size
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S: seqlen
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L: num_hidden_layers
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H: hidden_size
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V: vocab_size
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I: intermediate_size
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"""
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### MLP calculation
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per_mlp_calculation = 2 * hidden_size * intermediate_size
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mlp_calculation_per_layer = per_mlp_calculation * 3
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mlp_calculation = batch_size * seqlen * mlp_calculation_per_layer * num_hidden_layers
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config = AutoConfig.from_pretrained(model_name_or_path)
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hidden_size = getattr(config, "hidden_size", None)
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vocab_size = getattr(config, "vocab_size", None)
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intermediate_size = getattr(config, "intermediate_size", None)
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num_attention_heads = getattr(config, "num_attention_heads", None)
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num_key_value_heads = getattr(config, "num_key_value_heads", None)
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num_hidden_layers = getattr(config, "num_hidden_layers", None)
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tie_word_embeddings = getattr(config, "tie_word_embeddings", False)
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### Attention calculation
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Q_calculation = 2 * hidden_size * hidden_size
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O_calculation = 2 * hidden_size * hidden_size
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K_calculation = 2 * hidden_size * hidden_size * num_key_value_heads / num_attention_heads
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V_calculation = 2 * hidden_size * hidden_size * num_key_value_heads / num_attention_heads
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QKVO_calculation = Q_calculation + O_calculation + K_calculation + V_calculation # 8H^2 / coe
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self_attn_calculation = seqlen * hidden_size * 2 * 2 # (4 * S * H)
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attention_calculation = batch_size * seqlen * num_hidden_layers * (QKVO_calculation + self_attn_calculation) # BSL(8H^2/coe + 4S * H)
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#Embedding and LMhead calculation
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embedding_calculation = hidden_size * vocab_size
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lmhead_calculation = hidden_size * vocab_size
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IO_calculation = 3 * batch_size * seqlen * (embedding_calculation + lmhead_calculation) # 2 *(1+2)BSHV
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E = attention_calculation + mlp_calculation
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coefficient = 3
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fix_term = 0
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if(is_recompute):
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coefficient = 4
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if(is_flashattn):
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fix_term = batch_size *seqlen * self_attn_calculation
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total_calculation = coefficient * E + IO_calculation + fix_term
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return total_calculation
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# mlp module
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mlp_flops_per_token = 3 * BASE * hidden_size * intermediate_size # up, gate, down
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mlp_flops = batch_size * seq_length * num_hidden_layers * mlp_flops_per_token
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# attn projector module
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q_flops_per_token = BASE * hidden_size * hidden_size
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o_flops_per_token = BASE * hidden_size * hidden_size
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k_flops_per_token = BASE * hidden_size * hidden_size * num_key_value_heads // num_attention_heads
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v_flops_per_token = BASE * hidden_size * hidden_size * num_key_value_heads // num_attention_heads
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attn_proj_flops_per_token = q_flops_per_token + o_flops_per_token + k_flops_per_token + v_flops_per_token
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attn_proj_flops = batch_size * seq_length * num_hidden_layers * attn_proj_flops_per_token
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# attn sdpa module
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sdpa_flops_per_layer = 2 * BASE * hidden_size * seq_length * seq_length # (q * k^T) * v
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sdpa_flops = batch_size * num_hidden_layers * sdpa_flops_per_layer
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# embedding module
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embedding_flops_per_token = hidden_size * vocab_size
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embedding_flops = batch_size * seq_length * embedding_flops_per_token
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if tie_word_embeddings is False:
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embedding_flops *= 2
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non_embedding_flops = mlp_flops + attn_proj_flops + sdpa_flops
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non_embedding_coeff, embedding_coeff = 1, 1
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if include_backward:
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non_embedding_coeff += 2
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embedding_coeff += 2
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if include_recompute:
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non_embedding_coeff += 1
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total_flops = non_embedding_coeff * non_embedding_flops + embedding_coeff * embedding_flops
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if include_flashattn:
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total_flops += sdpa_flops
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return total_flops
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def hardware_flops_counter(
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seconds: float, # seconds used in given iterations
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num_gpus: int = 1,
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) -> float:
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if "A100" in torch.cuda.get_device_name():
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return 312 * 1e12 * seconds * num_gpus
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elif "V100" in torch.cuda.get_device_name():
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return 125 * 1e12 * seconds * num_gpus
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def compute_device_flops() -> float:
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device_name = torch.cuda.get_device_name()
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device_count = torch.cuda.device_count()
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if "H100" in device_name or "H800" in device_name:
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return 989 * 1e12 * device_count
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elif "A100" in device_name or "A800" in device_name:
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return 312 * 1e12 * device_count
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elif "V100" in device_name:
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return 125 * 1e12 * device_count
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elif "4090" in device_name:
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return 98 * 1e12 * device_count
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else:
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raise NotImplementedError("Device not supported: {}.".format(device_name))
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def compute_mfu(
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model_name_or_path: str,
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batch_size: int,
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seqlen: int,
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model_config: dict,
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num_iter: int,
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seconds: float,
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num_gpus: int = 1,
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seq_length: int,
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finetuning_type: str = "lora",
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flash_attn: str = "auto",
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deepspeed_stage: int = 0,
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disable_gc: bool = False,
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liger_kernel: bool = False,
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) -> float:
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r"""
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Computes MFU for given model and hyper-params.
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Usage: python cal_mfu.py --model_name_or_path path_to_model --batch_size 1 --seq_length 1024
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"""
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compute MFU given model configuration, training config and training information
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"""
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percentage = (num_iter * model_flops_counter(batch_size,seqlen,model_config)) / hardware_flops_counter(seconds, num_gpus)
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print(f"MFU : {percentage* 100:.2f}%")
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return percentage
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# User input
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args = {
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"model_name_or_path": model_name_or_path,
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"flash_attn": flash_attn,
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"disable_gradient_checkpointing": disable_gc,
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"enable_liger_kernel": liger_kernel,
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"stage": "pt",
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"do_train": True,
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"finetuning_type": finetuning_type,
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"dataset": "c4_demo",
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"cutoff_len": seq_length,
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"output_dir": os.path.join("saves", "test_mfu"),
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"overwrite_output_dir": True,
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"per_device_train_batch_size": batch_size,
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"max_steps": 100,
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"bf16": True,
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}
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if deepspeed_stage in [2, 3]:
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args["deepspeed"] = "examples/deepspeed/ds_z{}_config.json".format(deepspeed_stage)
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### model_name
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model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
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run_exp(args)
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with open(os.path.join("saves", "test_mfu", "all_results.json"), "r", encoding="utf-8") as f:
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result = json.load(f)
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### training config
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batch_size = 8
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seqlen = 1*1024
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num_gpus = 1
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### training information
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num_iter = 225
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seconds = 605 # time used in {num_iter} iterations
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model_config = AutoConfig.from_pretrained(model_name)
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if __name__ == "__main__":
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fire.Fire(
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compute_mfu(
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batch_size=batch_size,
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seqlen=seqlen,
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model_config=model_config,
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num_iter=num_iter,
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seconds=seconds,
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num_gpus=num_gpus
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)
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mfu_value = (
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result["train_samples_per_second"]
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* compute_model_flops(model_name_or_path, batch_size, seq_length)
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/ compute_device_flops()
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)
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print("MFU: {:.2f}%".format(mfu_value * 100))
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if __name__ == "__main__":
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fire.Fire(compute_mfu)
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