From 3eca7f36d112e4d9932fe60b767eebc7d4d61e05 Mon Sep 17 00:00:00 2001 From: Zhaoyue Cheng Date: Mon, 19 Aug 2024 22:25:33 +0000 Subject: [PATCH] Adding Mixtral-8x22b - adding mixtral-8x22b config (to pyconfig as well) - improving the llama and mistral conversion script - in-place weight writing to reduce total RAM usage - better progress tracking adding working 1_test --- MaxText/checkpointing.py | 6 + MaxText/configs/models/mixtral-8x22b.yml | 31 ++ MaxText/llama_or_mistral_ckpt.py | 423 ++++++++++++------ MaxText/pyconfig.py | 1 + .../golden_mixtral-8x22b_export.ipynb | 161 +++++++ .../golden_data_mixtral-8x22b.jsonl | 3 + MaxText/train.py | 20 +- ...tokenizer.mistral => tokenizer.mistral-v1} | Bin assets/tokenizer.mistral-v3 | Bin 0 -> 587404 bytes .../tpu/mixtral/8x22b/1_test_mixtral.sh | 34 ++ .../tpu/mixtral/8x22b/2_test_mixtral.sh | 109 +++++ end_to_end/tpu/mixtral/8x7b/2_test_mixtral.sh | 10 +- 12 files changed, 644 insertions(+), 154 deletions(-) create mode 100644 MaxText/configs/models/mixtral-8x22b.yml create mode 100644 MaxText/scratch_code/golden_mixtral-8x22b_export.ipynb create mode 100644 MaxText/test_assets/golden_data_mixtral-8x22b.jsonl rename assets/{tokenizer.mistral => tokenizer.mistral-v1} (100%) create mode 100644 assets/tokenizer.mistral-v3 create mode 100644 end_to_end/tpu/mixtral/8x22b/1_test_mixtral.sh create mode 100644 end_to_end/tpu/mixtral/8x22b/2_test_mixtral.sh diff --git a/MaxText/checkpointing.py b/MaxText/checkpointing.py index 9da702689..e42752468 100644 --- a/MaxText/checkpointing.py +++ b/MaxText/checkpointing.py @@ -59,9 +59,15 @@ def create_orbax_checkpoint_manager( else: item_names = ("items",) + # local storage checkpoint needs parent directory created + p.mkdir(exist_ok=True, parents=True) + # we need to use ocdbt and zarr3 to control max file size in the checkpoint + # omitting `iter` uses default handler for `iter` + item_handlers = {"items": PyTreeCheckpointHandler(use_ocdbt=True, use_zarr3=True)} mngr = CheckpointManager( p, item_names=item_names, + item_handlers=item_handlers, options=CheckpointManagerOptions( create=True, save_interval_steps=save_interval_steps, diff --git a/MaxText/configs/models/mixtral-8x22b.yml b/MaxText/configs/models/mixtral-8x22b.yml new file mode 100644 index 000000000..ebac00adf --- /dev/null +++ b/MaxText/configs/models/mixtral-8x22b.yml @@ -0,0 +1,31 @@ +# Copyright 2024 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# model config for mixtral-8x22b + +base_emb_dim: 6144 +base_num_query_heads: 48 +base_num_kv_heads: 8 +base_mlp_dim: 16384 +base_num_decoder_layers: 56 +head_dim: 128 +mlp_activations: ["silu","linear"] +vocab_size: 32768 +enable_dropout: False +logits_via_embedding: False +normalization_layer_epsilon: 1.0e-5 +num_experts: 8 +num_experts_per_tok: 2 +rope_max_timescale: 1_000_000 +decoder_block: "mistral" diff --git a/MaxText/llama_or_mistral_ckpt.py b/MaxText/llama_or_mistral_ckpt.py index 4d6977cb4..61f4c7f23 100644 --- a/MaxText/llama_or_mistral_ckpt.py +++ b/MaxText/llama_or_mistral_ckpt.py @@ -15,13 +15,15 @@ Usage: -Get LLaMA pytorch_vars from Meta +Get LLaMA chkpt_vars from Meta Example cmd: To save a ckpt python3 MaxText/llama_or_mistral_ckpt.py --base-model-path \ --maxtext-model-path --model-size llama2-7b +The base model checkpoints should be in the format `{name}.{chkpt_idx}.pth` +For example: `mistral-7b.00.pth` For large size model (e.g. 70B model), this script requires large memory VM. The script load and save weights in a single pass. To fit less memory, modify convert() to load/save weights in multiple passes. @@ -30,25 +32,25 @@ # pylint: disable=g-line-too-long import argparse import pathlib +import os +import gc +import re +import logging +from dataclasses import dataclass -import numpy as np +os.environ["JAX_PLATFORMS"] = "cpu" -import checkpointing +import numpy as np import jax +from jax import tree from flax.training import train_state -import max_logging -from train import save_checkpoint import torch -import sys -import os - -jax.config.update("jax_platform_name", "cpu") +import psutil +from tqdm import tqdm - -def permute_to_match_maxtext_rope(arr): - evens = arr[..., ::2] - odds = arr[..., 1::2] - return jax.numpy.concatenate((evens, odds), axis=arr.ndim - 1) +import max_logging +from train import save_checkpoint +import checkpointing MODEL_PARAMS_DICT = { @@ -106,20 +108,81 @@ def permute_to_match_maxtext_rope(arr): "base_mlp_dim": 14336, "num_experts": 8, }, + "mixtral-8x22b": { + "num_layers": 56, + "num_heads": 48, + "num_kv_heads": 8, + "dims_per_head": 128, + "vocab": 32000, + "base_emb_dim": 6144, + "base_mlp_dim": 16384, + "num_experts": 8, + }, } SIMULATED_CPU_DEVICES_COUNT = 16 +def _hf_mapping(layer_idx: int = -1, expert_idx: int = -1) -> dict: + return { + "tok_embeddings.weight": "model.embed_tokens.weight", + "norm.weight": "model.norm.weight", + "output.weight": "lm_head.weight", + # MOE model + f"layers.{layer_idx}.attention_norm.weight": f"model.layers.{layer_idx}.input_layernorm.weight", + f"layers.{layer_idx}.ffn_norm.weight": f"model.layers.{layer_idx}.post_attention_layernorm.weight", + f"layers.{layer_idx}.attention.wq.weight": f"model.layers.{layer_idx}.self_attn.q_proj.weight", + f"layers.{layer_idx}.attention.wk.weight": f"model.layers.{layer_idx}.self_attn.k_proj.weight", + f"layers.{layer_idx}.attention.wv.weight": f"model.layers.{layer_idx}.self_attn.v_proj.weight", + f"layers.{layer_idx}.attention.wo.weight": f"model.layers.{layer_idx}.self_attn.o_proj.weight", + f"layers.{layer_idx}.feed_forward.gate.weight": f"model.layers.{layer_idx}.block_sparse_moe.gate.weight", + f"layers.{layer_idx}.feed_forward.experts.{expert_idx}.w1.weight": + f"model.layers.{layer_idx}.block_sparse_moe.experts.{expert_idx}.w1.weight", + f"layers.{layer_idx}.feed_forward.experts.{expert_idx}.w2.weight": + f"model.layers.{layer_idx}.block_sparse_moe.experts.{expert_idx}.w2.weight", + f"layers.{layer_idx}.feed_forward.experts.{expert_idx}.w3.weight": + f"model.layers.{layer_idx}.block_sparse_moe.experts.{expert_idx}.w3.weight", + # dense model + f"layers.{layer_idx}.feed_forward.w1.weight": f"model.layers.{layer_idx}.mlp.gate_proj.weight", + f"layers.{layer_idx}.feed_forward.w2.weight": f"model.layers.{layer_idx}.mlp.down_proj.weight", + f"layers.{layer_idx}.feed_forward.w3.weight": f"model.layers.{layer_idx}.mlp.up_proj.weight", + } -def convert(base_model_path, maxtext_model_path, model_size): +@dataclass +class _HFNamespaceMapper: + """A class to dynamically map Mistral/Llama weight names to Huggingface weights + if the checkpoint is from HF. + """ + collection: dict + delimiter: str = "." + + def __getitem__(self, key): + if key in self.collection: + return self.collection[key] # original key takes precedence + fields = key.split(self.delimiter) + num_fields = [int(field) for field in fields if re.match(r"[0-9]+", field) is not None] + mapping = _hf_mapping(*num_fields) + if key not in mapping: + raise ValueError(f"Key `{key}` is missing from the original collection and from the mapping.") + new_key = mapping[key] + if new_key not in self.collection: + raise ValueError(f"New key `{new_key}` mapped from `{key}` is missing from the collection.") + return self.collection[new_key] + + +def permute_to_match_maxtext_rope(arr): + evens = arr[..., ::2] + odds = arr[..., 1::2] + return np.concatenate((evens, odds), axis=arr.ndim - 1) + + +def convert_to_jax_weights(base_model_path, model_size): """ Function to convert the checkpoint at base_model_path into Orbax checkpoint - for MaxText and save at maxtext_model_path + for MaxText and output jax_weights ready for MaxText Attributes: base_model_path: checkpoint path - maxtext_model_path: Path to save the MaxText checkpoint to - model_size: llama2-7b to 70b, mistral-7b, or mixtral-8x7b + model_size: llama2-7b to 70b, mistral-7b, or mixtral-8x7b, mixtral-8x22b """ """Convert model to maxtext.""" model_params = MODEL_PARAMS_DICT[model_size] @@ -129,29 +192,25 @@ def convert(base_model_path, maxtext_model_path, model_size): base_num_kv_heads = model_params["num_kv_heads"] vocab_size = model_params["vocab"] num_experts = model_params["num_experts"] if "num_experts" in model_params else None + mem_info = psutil.Process() + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) - print(f"Loading the base model from {base_model_path}") + max_logging.log(f"Loading the base model from {base_model_path}") # Skip any hidden files for checkpoints ckpt_paths = sorted(pathlib.Path(base_model_path).glob("[!.]*.pth")) - pytorch_vars = {} + chkpt_vars = {} for i, ckpt_path in enumerate(ckpt_paths): - print(f"Loading checkpoint {i+1} of {len(ckpt_paths)} ...") + max_logging.log(f"Loading checkpoint {i+1} of {len(ckpt_paths)} ...") checkpoint = torch.load(ckpt_path, map_location="cpu") - pytorch_vars[int(ckpt_path.name.split(".", maxsplit=2)[1])] = checkpoint - pytorch_vars = [pytorch_vars[i] for i in sorted(list(pytorch_vars.keys()))] + chkpt_vars[int(ckpt_path.name.split(".", maxsplit=2)[1])] = checkpoint + chkpt_vars = [chkpt_vars[i] for i in sorted(list(chkpt_vars.keys()))] + # map weight names if they use HuggingFace instead of PyTorch convention + chkpt_vars = [_HFNamespaceMapper(var) for var in chkpt_vars] - if num_experts: - layer_key = "MoeBlock_0" - else: - layer_key = "mlp" - if model_size[:6] == 'llama3': - token_embedder = np.concatenate( - [var["tok_embeddings.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 - ) - else: - token_embedder = np.concatenate( - [var["tok_embeddings.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=1 - )[:vocab_size, :] + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + # initialize the data structure for storing jax_weights + layer_key = "MoeBlock_0" if num_experts else "mlp" jax_weights = { "decoder": { "layers": { @@ -160,53 +219,58 @@ def convert(base_model_path, maxtext_model_path, model_size): "post_self_attention_layer_norm": {}, "self_attention": {}, }, - "decoder_norm": {"scale": pytorch_vars[0]["norm.weight"].type(torch.float16).numpy()}, - "logits_dense": { - "kernel": np.concatenate( - [var["output.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 - ).transpose()[:, :vocab_size] - }, - }, - "token_embedder": { - "embedding": token_embedder + "decoder_norm": {"scale": None}, + "logits_dense": {"kernel": None}, }, + "token_embedder": {"embedding": None}, } - layer_weight = {"pre_self_attention_layer_norm": {"scale": []}, "post_self_attention_layer_norm": {"scale": []}} + # decoder norm scale ########################################### + max_logging.log("Processing decoder norm scale") + decoder_norm_scale = chkpt_vars[0]["norm.weight"].type(torch.float16).numpy() + jax_weights["decoder"]["decoder_norm"]["scale"] = decoder_norm_scale - if num_experts is None: - layer_weight["mlp"] = { - "wi_0": {"kernel": []}, - "wi_1": {"kernel": []}, - "wo": {"kernel": []}, - } - else: - layer_weight["gate"] = {"kernel": []} + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) - for k in range(num_experts): - jax_weights["decoder"]["layers"]["MoeBlock_0"]["gate"] = {} - layer_weight[f"mlp_{k}"] = { - "wi_0": {"kernel": []}, - "wi_1": {"kernel": []}, - "wo": {"kernel": []}, - } + # logits dense ################################################# + max_logging.log("Processing logits dense") + logits_dense = np.concatenate([var["output.weight"].type( + torch.float16).numpy() for var in chkpt_vars], + axis=0).transpose()[:, :vocab_size] + jax_weights["decoder"]["logits_dense"]["kernel"] = logits_dense + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + # token embedding ############################################## + max_logging.log("Processing token embeddings") + if model_size[:6] == 'llama3': + token_embedder = np.concatenate( + [var["tok_embeddings.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 + ) + else: + token_embedder = np.concatenate( + [var["tok_embeddings.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=1 + )[:vocab_size, :] + jax_weights["token_embedder"]["embedding"] = token_embedder + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + # self attention ############################################### + max_logging.log("Processing self attention") self_attention = { - "query": {"kernel": []}, - "key": {"kernel": []}, - "value": {"kernel": []}, - "out": {"kernel": []}, + "query": {"kernel": None}, + "key": {"kernel": None}, + "value": {"kernel": None}, + "out": {"kernel": None}, } - - for layer_idx in range(base_num_decoder_layers): + for layer_idx in tqdm(range(base_num_decoder_layers), desc="layers", leave=False): wq = np.concatenate( - [var[f"layers.{layer_idx}.attention.wq.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 + [var[f"layers.{layer_idx}.attention.wq.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 ).transpose() wk = np.concatenate( - [var[f"layers.{layer_idx}.attention.wk.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 + [var[f"layers.{layer_idx}.attention.wk.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 ).transpose() wv = np.concatenate( - [var[f"layers.{layer_idx}.attention.wv.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 + [var[f"layers.{layer_idx}.attention.wv.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 ).transpose() wq = np.reshape(wq, [base_num_query_heads * head_dim, base_num_query_heads, head_dim]) @@ -216,98 +280,148 @@ def convert(base_model_path, maxtext_model_path, model_size): wk = permute_to_match_maxtext_rope(wk) w_post = np.concatenate( - [var[f"layers.{layer_idx}.attention.wo.weight"].type(torch.float16).numpy() for var in pytorch_vars], + [var[f"layers.{layer_idx}.attention.wo.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=1, ) w_post = np.reshape(w_post, [base_num_query_heads * head_dim, base_num_query_heads, head_dim]) - self_attention["query"]["kernel"].append(wq) - self_attention["key"]["kernel"].append(wk) - self_attention["value"]["kernel"].append(wv) - self_attention["out"]["kernel"].append(w_post) - pre_self_attention_layernorm = pytorch_vars[0][f"layers.{layer_idx}.attention_norm.weight"].type(torch.float16).numpy() - post_self_attention_layernorm = pytorch_vars[0][f"layers.{layer_idx}.ffn_norm.weight"].type(torch.float16).numpy() - layer_weight["pre_self_attention_layer_norm"]["scale"].append(pre_self_attention_layernorm) - layer_weight["post_self_attention_layer_norm"]["scale"].append(post_self_attention_layernorm) + if self_attention["query"]["kernel"] is None: + stack_shape = (base_num_decoder_layers,) + self_attention["query"]["kernel"] = np.zeros(stack_shape + wq.shape, dtype=np.float16) + self_attention["key"]["kernel"] = np.zeros(stack_shape + wk.shape, dtype=np.float16) + self_attention["value"]["kernel"] = np.zeros(stack_shape + wv.shape, dtype=np.float16) + self_attention["out"]["kernel"] = np.zeros(stack_shape + w_post.shape, dtype=np.float16) + + self_attention["query"]["kernel"][layer_idx, ...] = wq # pylint: disable=E1137 + self_attention["key"]["kernel"][layer_idx, ...] = wk # pylint: disable=E1137 + self_attention["value"]["kernel"][layer_idx, ...] = wv # pylint: disable=E1137 + self_attention["out"]["kernel"][layer_idx, ...] = w_post # pylint: disable=E1137 + self_attention["query"]["kernel"] = np.transpose(self_attention["query"]["kernel"], axes=(1, 0, 2, 3)) + self_attention["key"]["kernel"] = np.transpose(self_attention["key"]["kernel"], axes=(1, 0, 2, 3)) + self_attention["value"]["kernel"] = np.transpose(self_attention["value"]["kernel"], axes=(1, 0, 2, 3)) + # layers, base_num_query_heads * head_dim, base_num_query_heads, head_dim => + # base_num_query_heads, layers,head_dim, base_num_query_heads * head_dim + self_attention["out"]["kernel"] = np.transpose(self_attention["out"]["kernel"], axes=(2, 0, 3, 1)) + + # scale the query weights + self_attention["query"]["kernel"] = self_attention["query"]["kernel"] / np.sqrt(head_dim) + + jax_weights["decoder"]["layers"]["self_attention"] = self_attention + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + # layer weight pre and post self attention norm ################ + max_logging.log("Processing pre and post self attention norms") + layer_weight = {"pre_self_attention_layer_norm": {"scale": None}, + "post_self_attention_layer_norm": {"scale": None}} + + # self attention layer norm and swap the layer index + for layer_idx in tqdm(range(base_num_decoder_layers), desc="layers", leave=False): + pre_self_attention_layernorm = chkpt_vars[0][f"layers.{layer_idx}.attention_norm.weight"].type(torch.float16).numpy() + post_self_attention_layernorm = chkpt_vars[0][f"layers.{layer_idx}.ffn_norm.weight"].type(torch.float16).numpy() + if layer_weight["pre_self_attention_layer_norm"]["scale"] is None: + stack_shape = (base_num_decoder_layers,) + layer_weight["pre_self_attention_layer_norm"]["scale"] = np.zeros( + stack_shape + pre_self_attention_layernorm.shape, dtype=np.float16) + layer_weight["post_self_attention_layer_norm"]["scale"] = np.zeros( + stack_shape + post_self_attention_layernorm.shape, dtype=np.float16) + layer_weight["pre_self_attention_layer_norm"]["scale"][layer_idx, ...] = pre_self_attention_layernorm # pylint: disable=E1137 + layer_weight["post_self_attention_layer_norm"]["scale"][layer_idx, ...] = post_self_attention_layernorm # pylint: disable=E1137 + + layer_weight["pre_self_attention_layer_norm"]["scale"] = np.transpose( + layer_weight["pre_self_attention_layer_norm"]["scale"], axes=(1, 0) + ) + layer_weight["post_self_attention_layer_norm"]["scale"] = np.transpose( + layer_weight["post_self_attention_layer_norm"]["scale"], axes=(1, 0) + ) + + jax_weights["decoder"]["layers"]["pre_self_attention_layer_norm"] = layer_weight["pre_self_attention_layer_norm"] + jax_weights["decoder"]["layers"]["post_self_attention_layer_norm"] = layer_weight["post_self_attention_layer_norm"] + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + # layer weights ################################################ + max_logging.log("Processing layer weights") + if num_experts is None: + layer_weight["mlp"] = { + "wi_0": {"kernel": None}, + "wi_1": {"kernel": None}, + "wo": {"kernel": None}, + } + else: + layer_weight["gate"] = {"kernel": None} + + for k in range(num_experts): + jax_weights["decoder"]["layers"]["MoeBlock_0"]["gate"] = {} + layer_weight["mlp"] = { + "wi_0": {"kernel": None}, + "wi_1": {"kernel": None}, + "wo": {"kernel": None}, + } + + for layer_idx in tqdm(range(base_num_decoder_layers), desc="layers", leave=False): if num_experts is None: wi_0 = np.concatenate( - [var[f"layers.{layer_idx}.feed_forward.w1.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 + [var[f"layers.{layer_idx}.feed_forward.w1.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 ).transpose() wi_1 = np.concatenate( - [var[f"layers.{layer_idx}.feed_forward.w3.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 + [var[f"layers.{layer_idx}.feed_forward.w3.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 ).transpose() wo = np.concatenate( - [var[f"layers.{layer_idx}.feed_forward.w2.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=1 + [var[f"layers.{layer_idx}.feed_forward.w2.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=1 ).transpose() - layer_weight["mlp"]["wi_0"]["kernel"].append(wi_0) - layer_weight["mlp"]["wi_1"]["kernel"].append(wi_1) - layer_weight["mlp"]["wo"]["kernel"].append(wo) + if layer_weight["mlp"]["wi_0"]["kernel"] is None: + stack_shape = (base_num_decoder_layers,) + layer_weight["mlp"]["wi_0"]["kernel"] = np.zeros(stack_shape + wi_0.shape, dtype=np.float16) + layer_weight["mlp"]["wi_1"]["kernel"] = np.zeros(stack_shape + wi_1.shape, dtype=np.float16) + layer_weight["mlp"]["wo"]["kernel"] = np.zeros(stack_shape + wo.shape, dtype=np.float16) + layer_weight["mlp"]["wi_0"]["kernel"][layer_idx, ...] = wi_0 + layer_weight["mlp"]["wi_1"]["kernel"][layer_idx, ...] = wi_1 + layer_weight["mlp"]["wo"]["kernel"][layer_idx, ...] = wo else: gate = np.concatenate( - [var[f"layers.{layer_idx}.feed_forward.gate.weight"].type(torch.float16).numpy() for var in pytorch_vars], axis=0 + [var[f"layers.{layer_idx}.feed_forward.gate.weight"].type(torch.float16).numpy() for var in chkpt_vars], axis=0 ).transpose() - layer_weight["gate"]["kernel"].append(gate) - for k in range(num_experts): + if layer_weight["gate"]["kernel"] is None: + stack_shape = (base_num_decoder_layers,) + layer_weight["gate"]["kernel"] = np.zeros(stack_shape + gate.shape, dtype=np.float16) + layer_weight["gate"]["kernel"][layer_idx, ...] = gate + for k in tqdm(range(num_experts), desc="experts", leave=False): wi_0 = np.concatenate( [ var[f"layers.{layer_idx}.feed_forward.experts.{k}.w1.weight"].type(torch.float16).numpy() - for var in pytorch_vars + for var in chkpt_vars ], axis=0, ).transpose() wi_1 = np.concatenate( [ var[f"layers.{layer_idx}.feed_forward.experts.{k}.w3.weight"].type(torch.float16).numpy() - for var in pytorch_vars + for var in chkpt_vars ], axis=0, ).transpose() wo = np.concatenate( [ var[f"layers.{layer_idx}.feed_forward.experts.{k}.w2.weight"].type(torch.float16).numpy() - for var in pytorch_vars + for var in chkpt_vars ], axis=1, ).transpose() - layer_weight[f"mlp_{k}"]["wi_0"]["kernel"].append(wi_0) - layer_weight[f"mlp_{k}"]["wi_1"]["kernel"].append(wi_1) - layer_weight[f"mlp_{k}"]["wo"]["kernel"].append(wo) - - self_attention["query"]["kernel"] = np.array(self_attention["query"]["kernel"]) - self_attention["key"]["kernel"] = np.array(self_attention["key"]["kernel"]) - self_attention["value"]["kernel"] = np.array(self_attention["value"]["kernel"]) - self_attention["out"]["kernel"] = np.array(self_attention["out"]["kernel"]) - self_attention["query"]["kernel"] = np.transpose(self_attention["query"]["kernel"], axes=(1, 0, 2, 3)) - self_attention["key"]["kernel"] = np.transpose(self_attention["key"]["kernel"], axes=(1, 0, 2, 3)) - self_attention["value"]["kernel"] = np.transpose(self_attention["value"]["kernel"], axes=(1, 0, 2, 3)) - # layers, base_num_query_heads * head_dim, base_num_query_heads, head_dim => - # base_num_query_heads, layers,head_dim, base_num_query_heads * head_dim - self_attention["out"]["kernel"] = np.transpose(self_attention["out"]["kernel"], axes=(2, 0, 3, 1)) - - # scale the query weights - self_attention["query"]["kernel"] = self_attention["query"]["kernel"] / np.sqrt(head_dim) - - jax_weights["decoder"]["layers"]["self_attention"] = self_attention - - # self attention layer norm and swap the layer index - layer_weight["pre_self_attention_layer_norm"]["scale"] = np.array(layer_weight["pre_self_attention_layer_norm"]["scale"]) - layer_weight["post_self_attention_layer_norm"]["scale"] = np.array(layer_weight["post_self_attention_layer_norm"]["scale"]) - layer_weight["pre_self_attention_layer_norm"]["scale"] = np.transpose( - layer_weight["pre_self_attention_layer_norm"]["scale"], axes=(1, 0) - ) - layer_weight["post_self_attention_layer_norm"]["scale"] = np.transpose( - layer_weight["post_self_attention_layer_norm"]["scale"], axes=(1, 0) - ) + if layer_weight["mlp"]["wi_0"]["kernel"] is None: + stack_shape = (num_experts, base_num_decoder_layers) + layer_weight["mlp"]["wi_0"]["kernel"] = np.zeros(stack_shape + wi_0.shape, dtype=np.float16) + layer_weight["mlp"]["wi_1"]["kernel"] = np.zeros(stack_shape + wi_1.shape, dtype=np.float16) + layer_weight["mlp"]["wo"]["kernel"] = np.zeros(stack_shape + wo.shape, dtype=np.float16) + ei, li = k, layer_idx + layer_weight["mlp"]["wi_0"]["kernel"][ei, li, ...] = wi_0 + layer_weight["mlp"]["wi_1"]["kernel"][ei, li, ...] = wi_1 + layer_weight["mlp"]["wo"]["kernel"][ei, li, ...] = wo + gc.collect() + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) - jax_weights["decoder"]["layers"]["pre_self_attention_layer_norm"] = layer_weight["pre_self_attention_layer_norm"] - jax_weights["decoder"]["layers"]["post_self_attention_layer_norm"] = layer_weight["post_self_attention_layer_norm"] if num_experts is None: - layer_weight["mlp"]["wi_0"]["kernel"] = np.array(layer_weight["mlp"]["wi_0"]["kernel"]) - layer_weight["mlp"]["wi_1"]["kernel"] = np.array(layer_weight["mlp"]["wi_1"]["kernel"]) - layer_weight["mlp"]["wo"]["kernel"] = np.array(layer_weight["mlp"]["wo"]["kernel"]) # swap the layer index layer_weight["mlp"]["wi_0"]["kernel"] = np.transpose(layer_weight["mlp"]["wi_0"]["kernel"], axes=(1, 0, 2)) layer_weight["mlp"]["wi_1"]["kernel"] = np.transpose(layer_weight["mlp"]["wi_1"]["kernel"], axes=(1, 0, 2)) @@ -315,26 +429,34 @@ def convert(base_model_path, maxtext_model_path, model_size): jax_weights["decoder"]["layers"]["mlp"] = layer_weight["mlp"] else: - layer_weight["gate"]["kernel"] = np.array(layer_weight["gate"]["kernel"]) layer_weight["gate"]["kernel"] = np.transpose(layer_weight["gate"]["kernel"], axes=(1, 0, 2)) jax_weights["decoder"]["layers"]["MoeBlock_0"]["gate"]["kernel"] = layer_weight["gate"]["kernel"] - all_wi_0 = [] - all_wi_1 = [] - all_wo = [] - for k in range(num_experts): - layer_weight[f"mlp_{k}"]["wi_0"]["kernel"] = np.array(layer_weight[f"mlp_{k}"]["wi_0"]["kernel"]) - layer_weight[f"mlp_{k}"]["wi_1"]["kernel"] = np.array(layer_weight[f"mlp_{k}"]["wi_1"]["kernel"]) - layer_weight[f"mlp_{k}"]["wo"]["kernel"] = np.array(layer_weight[f"mlp_{k}"]["wo"]["kernel"]) + jax_weights["decoder"]["layers"]["MoeBlock_0"]["wi_0"] = layer_weight["mlp"]["wi_0"]["kernel"] + jax_weights["decoder"]["layers"]["MoeBlock_0"]["wi_1"] = layer_weight["mlp"]["wi_1"]["kernel"] + jax_weights["decoder"]["layers"]["MoeBlock_0"]["wo"] = layer_weight["mlp"]["wo"]["kernel"] + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + del chkpt_vars + gc.collect() + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + return jax_weights - all_wi_0.append(layer_weight[f"mlp_{k}"]["wi_0"]["kernel"]) - all_wi_1.append(layer_weight[f"mlp_{k}"]["wi_1"]["kernel"]) - all_wo.append(layer_weight[f"mlp_{k}"]["wo"]["kernel"]) - jax_weights["decoder"]["layers"]["MoeBlock_0"]["wi_0"] = np.array(all_wi_0) - jax_weights["decoder"]["layers"]["MoeBlock_0"]["wi_1"] = np.array(all_wi_1) - jax_weights["decoder"]["layers"]["MoeBlock_0"]["wo"] = np.array(all_wo) +def save_jax_weights_to_checkpoint(maxtext_model_path, jax_weights): + """ + Function to save jax_weights ready for MaxText to a parameters checkpoint + + Attributes: + maxtext_model_path: Path to save the MaxText checkpoint to + model_size: llama2-7b to 70b, mistral-7b, or mixtral-8x7b, mixtral-8x22b + """ + """Save maxtext parameter checkpoint.""" + + mem_info = psutil.Process() + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + gc.collect() mesh = jax.sharding.Mesh(jax.devices(), "checkpoint_sharding_axis") s1 = jax.sharding.NamedSharding(mesh, jax.sharding.PartitionSpec("checkpoint_sharding_axis")) # shards first axis s2 = jax.sharding.NamedSharding(mesh, jax.sharding.PartitionSpec(None, "checkpoint_sharding_axis")) # shards second axis @@ -342,17 +464,26 @@ def convert(base_model_path, maxtext_model_path, model_size): def checkpoint_device_put(arr): if arr.shape[0] % SIMULATED_CPU_DEVICES_COUNT == 0: - print("sharding first axis") + max_logging.log("sharding first axis") return jax.device_put(arr, device=s1) elif len(arr.shape) > 1 and arr.shape[1] % SIMULATED_CPU_DEVICES_COUNT == 0: - print("sharding second axis") + max_logging.log("sharding second axis") return jax.device_put(arr, device=s2) else: - print("no sharding was possible, replicating") + max_logging.log("no sharding was possible, replicating") return jax.device_put(arr, device=s3) # convert all weights to jax.numpy with sharding if applicable - jax_weights = jax.tree_util.tree_map(checkpoint_device_put, jax_weights) + jax_weights_flat, jax_weights_struct = tree.flatten(jax_weights) + jax_weights_new = [] + while len(jax_weights_flat) > 0: + jax_weight = jax_weights_flat.pop(0) + jax_weights_new.append(checkpoint_device_put(jax_weight)) + del jax_weight + gc.collect() + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) + + jax_weights = tree.unflatten(jax_weights_struct, jax_weights_new) # dummy configs for the checkpoint_manager step_number_to_save_new_ckpt = 0 @@ -368,13 +499,12 @@ def checkpoint_device_put(arr): step=0, apply_fn=None, params={"params": jax_weights}, tx=None, opt_state={} # type: ignore ) + logging.debug("Memory usage: %f GB", mem_info.memory_info().rss / (1024 ** 3)) if checkpoint_manager is not None: if save_checkpoint(checkpoint_manager, step_number_to_save_new_ckpt, state_new): max_logging.log(f"saved a checkpoint at step {step_number_to_save_new_ckpt}") # Upon preemption, exit when and only when all ongoing saves are complete. - if checkpoint_manager.reached_preemption(0): - checkpoint_manager.wait_until_finished() - sys.exit() + checkpoint_manager.wait_until_finished() if __name__ == "__main__": @@ -390,4 +520,5 @@ def checkpoint_device_put(arr): os.environ["XLA_FLAGS"] = f"--xla_force_host_platform_device_count={SIMULATED_CPU_DEVICES_COUNT}" - convert(args.base_model_path, args.maxtext_model_path, args.model_size) + save_jax_weights_to_checkpoint(args.maxtext_model_path, + convert_to_jax_weights(args.base_model_path, args.model_size)) diff --git a/MaxText/pyconfig.py b/MaxText/pyconfig.py index d64f4051a..62b61ca7d 100644 --- a/MaxText/pyconfig.py +++ b/MaxText/pyconfig.py @@ -147,6 +147,7 @@ def validate_model_name(s: str) -> bool: "llama3-70b", "mistral-7b", "mixtral-8x7b", + "mixtral-8x22b", "gemma-7b", "gemma-2b", "gemma2-2b", diff --git a/MaxText/scratch_code/golden_mixtral-8x22b_export.ipynb b/MaxText/scratch_code/golden_mixtral-8x22b_export.ipynb new file mode 100644 index 000000000..1b615d3be --- /dev/null +++ b/MaxText/scratch_code/golden_mixtral-8x22b_export.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0d13ebbb", + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\n", + "!pip3 install tokenizers -U\n", + "!pip3 install transformers -U" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6a8a4bb6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rdyro/devel/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import torch \n", + "from transformers import AutoTokenizer, AutoModelForCausalLM \n", + "import jsonlines" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ff804403", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 59/59 [03:54<00:00, 3.97s/it]\n" + ] + } + ], + "source": [ + "# Load the tokenizer and model from Hugging Face \n", + " \n", + "model_id = \"mistralai/Mixtral-8x22B-Instruct-v0.1\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(model_id)\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_id,\n", + " torch_dtype=torch.float16,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "9f218ba6", + "metadata": {}, + "source": [ + "## looping over multiple prompts and logits" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c567f8d9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data saved to golden_data_mixtral-8x22b.jsonl\n" + ] + } + ], + "source": [ + "# Save to disk \n", + "output_path = \"golden_data_mixtral-8x22b.jsonl\" \n", + " \n", + " \n", + "# Your prompt text \n", + "prompt_texts = [\"[INST] I love to [/INST]\", \"[INST] Today is a [/INST]\", \"[INST] What is the [/INST]\"]\n", + "all_data_to_save = []\n", + "\n", + "\n", + "for prompt_text in prompt_texts:\n", + " # Encode the prompt text \n", + " input_ids = tokenizer.encode(prompt_text, return_tensors='pt') \n", + "\n", + " # Get the logits for the prompt + completion \n", + " with torch.no_grad(): \n", + " outputs = model(input_ids) \n", + " logits = outputs.logits \n", + "\n", + " # Convert logits to fp32 \n", + " logits = logits.cpu().numpy().astype('float32') \n", + "\n", + " # Prepare data to be saved \n", + " data_to_save = { \n", + " \"prompt\": prompt_text, \n", + " \"tokens\": input_ids.tolist()[0], \n", + " \"logits\": logits.tolist()[0] # Convert numpy array to list for JSON serialization \n", + " } \n", + " all_data_to_save.append(data_to_save)\n", + " \n", + "with jsonlines.open(output_path,'w') as f: \n", + " f.write_all(all_data_to_save)\n", + "\n", + "\n", + "\n", + "print(f\"Data saved to {output_path}\") \n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82c6e1f7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + 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emergency_checkpoint_manager.CheckpointManager): return checkpoint_manager.save( - step, args=orbax.checkpoint.args.PyTreeSave(state) + step, args=orbax.checkpoint.args.PyTreeSave( + item=state, save_args=save_args, ocdbt_target_data_file_size=_CHUNK_BYTE_SIZE + ) ) if dataset_type == "grain": return checkpoint_manager.save( step, args=orbax.checkpoint.args.Composite( - items=orbax.checkpoint.args.PyTreeSave(item=state), + items=orbax.checkpoint.args.PyTreeSave( + item=state, save_args=save_args, ocdbt_target_data_file_size=_CHUNK_BYTE_SIZE + ), iter=grain.PyGrainCheckpointSave(data_iterator.local_iterator), ), ) else: return checkpoint_manager.save( - step, args=orbax.checkpoint.args.Composite(items=orbax.checkpoint.args.PyTreeSave(item=state)) + step, args=orbax.checkpoint.args.Composite( + items=orbax.checkpoint.args.PyTreeSave( + item=state, save_args=save_args, ocdbt_target_data_file_size=_CHUNK_BYTE_SIZE + ) + ) ) diff --git a/assets/tokenizer.mistral 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Converts the Mistral PyTorch checkpoint to MaxText(orbax) format using a CPU VM. +# 2. Takes the MaxText(orbax) checkpoint to run inference, fine-tuning, and pre-training on a TPU VM. + +# The flow of this file is to convert the Mistral PyTorch checkpoint to MaxText (orbax) format using a CPU VM. + +# Example Usage: export BASE_OUTPUT_PATH=/path/to/GCS/bucket; bash end_to_end/tpu/mixtral/8x22b/1_test_mixtral.sh +# Use the same BASE_OUTPUT_PATH for both 1_test_mixtral.sh & 2_test_mixtral.sh. + +set -ex +MODEL_VARIATION='8x22b' + +if [ -z "${BASE_OUTPUT_PATH}" ]; then + # Non-Googlers please remember to point BASE_OUTPUT_PATH to GCS buckets that you own, this script uses internal buckets for testing. + export BASE_OUTPUT_PATH=gs://runner-maxtext-logs/$(date +%Y-%m-%d-%H-%M)/ + echo "BASE_OUTPUT_PATH is not set, using BASE_OUTPUT_PATH = ${BASE_OUTPUT_PATH}" +fi + +# Download checkpoint +pip3 install torch +MODEL_NAME=Mixtral-8x22B-Instruct-v0.1 +gcloud storage cp -r "gs://maxtext-external/$MODEL_NAME" /tmp + +# Convert it to MaxText(orbax) format - scanned ckpt +JAX_PLATFORMS=cpu python3 MaxText/llama_or_mistral_ckpt.py --base-model-path="/tmp/$MODEL_NAME" --model-size=mixtral-8x22b --maxtext-model-path=${BASE_OUTPUT_PATH}/${MODEL_VARIATION}/scanned_ckpt/ +echo "Wrote MaxText compatible scanned checkpoint to ${BASE_OUTPUT_PATH}/${MODEL_VARIATION}/scanned_ckpt" + +# Generate unscanned ckpt for efficient decoding test +export SCANNED_CHECKPOINT=${BASE_OUTPUT_PATH}/${MODEL_VARIATION}/scanned_ckpt/0/items +export RUN_NAME=unscanned_ckpt +JAX_PLATFORMS=cpu python MaxText/generate_param_only_checkpoint.py MaxText/configs/base.yml async_checkpointing=false base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=${RUN_NAME} model_name='mixtral-8x22b' force_unroll=true megablox=false dtype=float16 weight_dtype=bfloat16 per_device_batch_size=1 max_target_length=1 +echo "Wrote MaxText compatible unscanned checkpoint to ${BASE_OUTPUT_PATH}/${RUN_NAME}/checkpoints" \ No newline at end of file diff --git a/end_to_end/tpu/mixtral/8x22b/2_test_mixtral.sh b/end_to_end/tpu/mixtral/8x22b/2_test_mixtral.sh new file mode 100644 index 000000000..97f56fbb8 --- /dev/null +++ b/end_to_end/tpu/mixtral/8x22b/2_test_mixtral.sh @@ -0,0 +1,109 @@ +#!/bin/bash + +# This file, combined with step 1 in the same directory, runs on daily basis and demonstrates: +# 1. Converts the Mistral PyTorch checkpoint to MaxText(orbax) format using a CPU VM. +# 2. Takes the MaxText(orbax) checkpoint to run inference, fine-tuning, and pre-training on a TPU VM. + +# The flow of this file is to take the MaxText(orbax) checkpoint to run inference, fine-tuning, and pre-training on a TPU VM. +# Please make sure you have run end_to_end/tpu/mixtral/8x22b/1_test_mixtral.sh before running commands from this file. + +# Example Usage: export BASE_OUTPUT_PATH=/path/to/GCS/bucket; bash end_to_end/tpu/mixtral/8x22b/2_test_mixtral.sh +# Use the same BASE_OUTPUT_PATH for both 1_test_mixtral.sh & 2_test_mixtral.sh. + +set -ex +MODEL_VARIATION='8x22b' +PREDICT_LEN=7 +ATOL=60.0 +RTOL=10.0 + +if [ -z "${BASE_OUTPUT_PATH}" ]; then + # Non-Googlers please remember to point BASE_OUTPUT_PATH to GCS buckets that you own, this script uses internal buckets for testing. + export BASE_OUTPUT_PATH=gs://runner-maxtext-logs/$(date +%Y-%m-%d-%H-%M) + echo "BASE_OUTPUT_PATH is not set, using BASE_OUTPUT_PATH = ${BASE_OUTPUT_PATH}" +fi + +export DATASET_PATH=gs://maxtext-dataset + +# `SCANNED_CHECKPOINT` refers to the checkpoint that used for both `train.py` and `decode.py` +# export SCANNED_CHECKPOINT=${BASE_OUTPUT_PATH}/${MODEL_VARIATION}/scanned_ckpt/0/items +export SCANNED_CHECKPOINT=gs://maxtext-external/Mixtral-8x22B-Instruct-v0.1-maxtext/0/items + +export TOKENIZER_PATH=assets/tokenizer.mistral-v3 + +# Run decoding with converted ckpt - matmul implementation +python3 MaxText/decode.py MaxText/configs/base.yml \ + load_parameters_path=${SCANNED_CHECKPOINT} run_name=scanned_decoding \ + per_device_batch_size=1 model_name=mixtral-8x22b async_checkpointing=false \ + tokenizer_path=${TOKENIZER_PATH} ici_tensor_parallelism=1 \ + ici_fsdp_parallelism=-1 max_prefill_predict_length=64 max_target_length=64 \ + prompt="[INST] I love to [/INST]" megablox=False weight_dtype=float16 + +# TODO(rdyro): add decoding test for megablox implementation +#python3 MaxText/decode.py MaxText/configs/base.yml \ +# load_parameters_path=${SCANNED_CHECKPOINT} run_name=scanned_decoding \ +# per_device_batch_size=1 model_name=mixtral-8x22b async_checkpointing=false \ +# tokenizer_path=${TOKENIZER_PATH} ici_tensor_parallelism=1 \ +# ici_fsdp_parallelism=-1 max_prefill_predict_length=16 max_target_length=24 \ +# prompt="[INST] I love to [/INST]" megablox=True weight_dtype=float16 + +# Test whether the forward pass logits match the golden logits - matmul implementation +python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml \ + base_output_directory=${BASE_OUTPUT_PATH} \ + load_parameters_path=${SCANNED_CHECKPOINT} run_name=matmul_forward_pass_test \ + per_device_batch_size=1 model_name=mixtral-8x22b \ + tokenizer_path=${TOKENIZER_PATH} ici_tensor_parallelism=1 \ + ici_fsdp_parallelism=-1 max_prefill_predict_length=$PREDICT_LEN max_target_length=$PREDICT_LEN \ + dataset_type=synthetic dtype=bfloat16 weight_dtype=float16 megablox=False \ + --atol=$ATOL --rtol=$RTOL --token_size=$PREDICT_LEN + # TODO(rdyro): figure out the reason for numerical mismatch for some tokens + +python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml \ + base_output_directory=${BASE_OUTPUT_PATH} \ + load_parameters_path=${SCANNED_CHECKPOINT} run_name=matmul_forward_pass_test \ + per_device_batch_size=1 model_name=mixtral-8x22b \ + tokenizer_path=${TOKENIZER_PATH} ici_tensor_parallelism=1 \ + ici_fsdp_parallelism=-1 max_prefill_predict_length=$PREDICT_LEN max_target_length=$PREDICT_LEN \ + dataset_type=synthetic dtype=bfloat16 weight_dtype=bfloat16 megablox=False \ + --atol=$ATOL --rtol=$RTOL --token_size=$PREDICT_LEN + # TODO(rdyro): figure out the reason for numerical mismatch for some tokens + +# Test whether the forward pass logits match the golden logits - megablox implementation +python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml \ + base_output_directory=${BASE_OUTPUT_PATH} \ + load_parameters_path=${SCANNED_CHECKPOINT} run_name=megablox_forward_pass_test \ + per_device_batch_size=1 model_name=mixtral-8x22b \ + tokenizer_path=${TOKENIZER_PATH} ici_tensor_parallelism=1 \ + ici_fsdp_parallelism=-1 max_prefill_predict_length=$PREDICT_LEN max_target_length=$PREDICT_LEN \ + dataset_type=synthetic dtype=bfloat16 weight_dtype=bfloat16 megablox=True \ + --atol=$ATOL --rtol=$RTOL --token_size=$PREDICT_LEN + # TODO(rdyro): figure out the reason for numerical mismatch for some tokens + +python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml \ + base_output_directory=${BASE_OUTPUT_PATH} \ + load_parameters_path=${SCANNED_CHECKPOINT} run_name=megablox_forward_pass_test \ + per_device_batch_size=1 model_name=mixtral-8x22b \ + tokenizer_path=${TOKENIZER_PATH} ici_tensor_parallelism=1 \ + ici_fsdp_parallelism=-1 max_prefill_predict_length=$PREDICT_LEN max_target_length=$PREDICT_LEN \ + dataset_type=synthetic dtype=bfloat16 weight_dtype=float16 megablox=True \ + --atol=$ATOL --rtol=$RTOL --token_size=$PREDICT_LEN + # TODO(rdyro): figure out the reason for numerical mismatch for some tokens + +# training + +# Run pre-training without load_parameters_path - megablox implementation +python3 MaxText/train.py MaxText/configs/base.yml \ + base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} \ + run_name=pre_training per_device_batch_size=1 enable_checkpointing=false \ + model_name=mixtral-8x22b ici_tensor_parallelism=1 ici_fsdp_parallelism=-1 \ + steps=5 max_target_length=1024 async_checkpointing=false \ + tokenizer_path=${TOKENIZER_PATH} attention=flash dtype=bfloat16 \ + weight_dtype=bfloat16 megablox=True + +# Run fine-tuning - megablox implementation +python3 MaxText/train.py MaxText/configs/base.yml \ + base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} \ + load_parameters_path=${SCANNED_CHECKPOINT} run_name=fine_tuning \ + per_device_batch_size=1 model_name=mixtral-8x22b ici_tensor_parallelism=1 \ + ici_fsdp_parallelism=-1 steps=10 max_target_length=1024 \ + async_checkpointing=false tokenizer_path=${TOKENIZER_PATH} checkpoint_period=100 \ + attention=flash dtype=bfloat16 weight_dtype=bfloat16 megablox=False diff --git a/end_to_end/tpu/mixtral/8x7b/2_test_mixtral.sh b/end_to_end/tpu/mixtral/8x7b/2_test_mixtral.sh index 505230478..488374ae0 100644 --- a/end_to_end/tpu/mixtral/8x7b/2_test_mixtral.sh +++ b/end_to_end/tpu/mixtral/8x7b/2_test_mixtral.sh @@ -26,18 +26,18 @@ export SCANNED_CHECKPOINT=${BASE_OUTPUT_PATH}${MODEL_VARIATION}/scanned_ckpt/0/i # Run decoding with converted ckpt - matmul implementation # TODO(ranran): add decoding test for megablox implementation -python3 MaxText/decode.py MaxText/configs/base.yml load_parameters_path=${SCANNED_CHECKPOINT} run_name=scanned_decoding per_device_batch_size=1 model_name=mixtral-8x7b async_checkpointing=false tokenizer_path=assets/tokenizer.mistral ici_tensor_parallelism=4 ici_fsdp_parallelism=16 max_prefill_predict_length=11 max_target_length=24 prompt="[INST] I love to [/INST]" megablox=False +python3 MaxText/decode.py MaxText/configs/base.yml load_parameters_path=${SCANNED_CHECKPOINT} run_name=scanned_decoding per_device_batch_size=1 model_name=mixtral-8x7b async_checkpointing=false tokenizer_path=assets/tokenizer.mistral-v1 ici_tensor_parallelism=4 ici_fsdp_parallelism=16 max_prefill_predict_length=11 max_target_length=24 prompt="[INST] I love to [/INST]" megablox=False # Test whether the forward pass logits match the golden logits - matmul implementation -python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=matmul_forward_pass_test per_device_batch_size=8 model_name=mixtral-8x7b tokenizer_path=assets/tokenizer.mistral ici_tensor_parallelism=4 ici_fsdp_parallelism=16 max_prefill_predict_length=11 max_target_length=11 dataset_type=synthetic dtype=float32 megablox=False --atol=3 --rtol=1 --token_size=4 +python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=matmul_forward_pass_test per_device_batch_size=8 model_name=mixtral-8x7b tokenizer_path=assets/tokenizer.mistral-v1 ici_tensor_parallelism=4 ici_fsdp_parallelism=16 max_prefill_predict_length=11 max_target_length=11 dataset_type=synthetic dtype=float32 megablox=False --atol=3 --rtol=1 --token_size=4 # Test whether the forward pass logits match the golden logits - megablox implementation -python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=megablox_forward_pass_test per_device_batch_size=8 model_name=mixtral-8x7b tokenizer_path=assets/tokenizer.mistral ici_fsdp_parallelism=64 max_prefill_predict_length=11 max_target_length=11 dataset_type=synthetic dtype=bfloat16 weight_dtype=bfloat16 --atol=4 --rtol=1 --token_size=4 +python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=megablox_forward_pass_test per_device_batch_size=8 model_name=mixtral-8x7b tokenizer_path=assets/tokenizer.mistral-v1 ici_fsdp_parallelism=64 max_prefill_predict_length=11 max_target_length=11 dataset_type=synthetic dtype=bfloat16 weight_dtype=bfloat16 --atol=4 --rtol=1 --token_size=4 # Run fine-tuning - megablox implementation -python3 MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=fine_tuning per_device_batch_size=8 model_name=mixtral-8x7b ici_fsdp_parallelism=64 steps=10 max_target_length=1024 async_checkpointing=false tokenizer_path=assets/tokenizer.mistral checkpoint_period=5 attention=flash dtype=bfloat16 weight_dtype=bfloat16 +python3 MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} load_parameters_path=${SCANNED_CHECKPOINT} run_name=fine_tuning per_device_batch_size=8 model_name=mixtral-8x7b ici_fsdp_parallelism=64 steps=10 max_target_length=1024 async_checkpointing=false tokenizer_path=assets/tokenizer.mistral-v1 checkpoint_period=5 attention=flash dtype=bfloat16 weight_dtype=bfloat16 # Run pre-training without load_parameters_path - megablox implementation -python3 MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} run_name=pre_training per_device_batch_size=8 enable_checkpointing=false model_name=mixtral-8x7b ici_fsdp_parallelism=64 steps=5 max_target_length=1024 async_checkpointing=false tokenizer_path=assets/tokenizer.mistral attention=flash dtype=bfloat16 weight_dtype=bfloat16 +python3 MaxText/train.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} dataset_path=${DATASET_PATH} run_name=pre_training per_device_batch_size=8 enable_checkpointing=false model_name=mixtral-8x7b ici_fsdp_parallelism=64 steps=5 max_target_length=1024 async_checkpointing=false tokenizer_path=assets/tokenizer.mistral-v1 attention=flash dtype=bfloat16 weight_dtype=bfloat16 # TODO(ranran): Run decoding with unscanned ckpt