GPT-NeoX-20B support in Colab TPU instances

tpu_mtj_backend.py

@@ -46,6 +46,7 @@ import numpy as np
 import optax
 import haiku as hk
 from transformers import AutoTokenizer, GPT2TokenizerFast, AutoModelForCausalLM, GPTNeoForCausalLM
+from tokenizers import Tokenizer
 from mesh_transformer.checkpoint import read_ckpt_lowmem
 from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard, PlaceholderTensor
 from mesh_transformer.util import to_bf16
@@ -800,6 +801,121 @@ def reshard_reverse(x, total_shards, old_shape):
     return out
 
 
+def get_old_shape(t, total_shards, dim=2):
+    if len(t.shape) == 2:
+        shard_shape = t.shape
+        if dim == 1:
+            assert shard_shape[0] % total_shards == 0
+            return (shard_shape[0] // total_shards, shard_shape[1])
+        elif dim == 2:
+            assert shard_shape[1] % total_shards == 0
+            return (shard_shape[0], shard_shape[1] // total_shards)
+        else:
+            raise ValueError(f"Unsupported dim {dim}")
+    if len(t.shape) == 1:
+        assert t.shape[0] % total_shards == 0
+        return (t.shape[0] // total_shards,)
+    else:
+        raise ValueError(f"Unsupported shape {t.shape}")
+
+
+def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
+    assert config["cores_per_replica"] % checkpoint_shards == 0
+    output_shards = config["cores_per_replica"] // checkpoint_shards
+
+    import torch
+    from tqdm.auto import tqdm
+
+    move_xmap = jax.experimental.maps.xmap(
+        fun=lambda x, _: to_bf16(x),
+        in_axes=(["shard", ...], ["batch", ...]),
+        out_axes=["shard", ...],
+        axis_resources={'shard': 'mp', 'batch': 'dp'}
+    )
+
+    path_template = os.path.join(path, "layer_{layer:02d}-model_{shard:02d}-model_states.pt")
+
+    static_mapping = {
+        "word_embeddings.weight": {"module": "embedding_shard/~/linear", "param": "w", "axis": 1},
+        "final_linear.weight": {"module": "projection_shard/~/linear", "param": "w", "axis": 2},
+        "norm.weight": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale", "axis": None},
+        "norm.bias": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset", "axis": None},
+    }
+
+    layer_mapping = {
+        "attention.query_key_value.weight": {"module": "combined_qkv", "param": "w", "axis": 2},
+        "attention.query_key_value.bias": {"module": "combined_qkv", "param": "b", "axis": 1},
+        "attention.dense.weight": {"module": "linear_3", "param": "w", "axis": 1},
+        "attention.dense.bias": {"module": "linear_3", "param": "b", "axis": None},
+        "mlp.dense_h_to_4h.weight": {"module": "linear_4", "param": "w", "axis": 2},
+        "mlp.dense_h_to_4h.bias": {"module": "linear_4", "param": "b", "axis": 1},
+        "mlp.dense_4h_to_h.weight": {"module": "linear_5", "param": "w", "axis": 1},
+        "mlp.dense_4h_to_h.bias": {"module": "linear_5", "param": "b", "axis": None},
+        "input_layernorm.weight": {"module": "replicated_layer_norm", "param": "scale", "axis": None},
+        "input_layernorm.bias": {"module": "replicated_layer_norm", "param": "offset", "axis": None},
+        "post_attention_layernorm.weight": {"module": "replicated_layer_norm_1", "param": "scale", "axis": None},
+        "post_attention_layernorm.bias": {"module": "replicated_layer_norm_1", "param": "offset", "axis": None},
+    }
+
+    tqdm_length = len(static_mapping) + config["layers"]*len(layer_mapping)
+    bar = tqdm(total=tqdm_length, desc="Loading from NeoX checkpoint")
+
+    for checkpoint_layer in range(config["layers"] + 5):
+        if checkpoint_layer in (1, config["layers"] + 2):
+            continue
+        layer = checkpoint_layer - 2
+        shards = []
+        for checkpoint_shard in range(checkpoint_shards):
+            shards.append(torch.load(path_template.format(layer=checkpoint_layer, shard=checkpoint_shard), map_location="cpu"))
+        for key in shards[0]:
+            if key == "attention.rotary_emb.inv_freq":
+                continue
+            elif key in static_mapping:
+                target_module = "causal_transformer_shard/~/" + static_mapping[key]["module"]
+                target_param = static_mapping[key]["param"]
+                target_axis = static_mapping[key]["axis"]
+            elif key in layer_mapping:
+                target_module = f"causal_transformer_shard/~/layer_{layer}/~/" + layer_mapping[key]["module"]
+                target_param = layer_mapping[key]["param"]
+                target_axis = layer_mapping[key]["axis"]
+            else:
+                error = f"{repr(key)} not found in mapping"
+                print("\n\nERROR: ", error, file=sys.stderr)
+                raise RuntimeError(error)
+            original_shape = shards[0][key].shape
+            for checkpoint_shard in range(checkpoint_shards):
+                if key in ("attention.dense.bias", "mlp.dense_4h_to_h.bias"):
+                    shards[checkpoint_shard][key] /= output_shards
+                if key != "word_embeddings.weight":
+                    shards[checkpoint_shard][key] = shards[checkpoint_shard][key].T
+                tensor = shards[checkpoint_shard][key]
+                if target_axis is not None:
+                    target_shape = (output_shards,) + get_old_shape(tensor, total_shards=output_shards, dim=target_axis)
+                else:
+                    target_shape = (output_shards, tensor.shape[0])
+                shards[checkpoint_shard][key] = reshard_reverse(tensor.unsqueeze_(0), output_shards, target_shape)
+            #print(key, ":", original_shape, "->", shards[0][key].shape)
+            tensor = torch.cat([shards[s][key] for s in range(checkpoint_shards)], dim=0)
+            target_shape = state["params"][target_module][target_param].shape
+            if tensor.shape != target_shape:
+                error = f"Weight {repr(key)} has shape {tensor.shape} in checkpoint but shape {target_shape} was requested by MTJ for {target_module} {target_param}"
+                print("\n\nERROR: ", error, file=sys.stderr)
+                raise RuntimeError(error)
+            if tensor.dtype is torch.float16 or tensor.dtype is torch.float32:
+                tensor = tensor.bfloat16()
+            state["params"][target_module][target_param] = move_xmap(
+                jax.dlpack.from_dlpack(torch.utils.dlpack.to_dlpack(tensor)).copy(),
+                np.zeros(config["cores_per_replica"]),
+            )
+            bar.update(1)
+    for mk, mv in state["params"].items():
+        for pk, pv in mv.items():
+            if isinstance(pv, PlaceholderTensor):
+                error = f"{mk} {pk} could not be found in the model checkpoint"
+                print("\n\nERROR:  " + error, file=sys.stderr)
+                raise RuntimeError(error)
+
+
 def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpoint=False, **kwargs) -> None:
     global thread_resources_env, seq, tokenizer, network, params
 
@@ -820,6 +936,23 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
     }
     params = kwargs
 
+    if vars.model == "TPUMeshTransformerGPTNeoX":
+        default_params = {
+            "compat": "neox",
+            "layers": 44,
+            "d_model": 6144,
+            "n_heads": 64,
+            "n_vocab": 50432,
+            "n_vocab_padding": 0,
+            "norm": "doublelayernorm",
+            "pe": "rotary",
+            "pe_rotary_dims": 24,
+            "seq": 2048,
+            "cores_per_replica": 8,
+            "tokenizer_class": "GPT2TokenizerFast",
+            "tokenizer": "gpt2",
+        }
+
     # Try to convert HF config.json to MTJ config
     if hf_checkpoint:
         spec_path = os.path.join("maps", vars.model_type + ".json")
@@ -875,7 +1008,14 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
             params[param] = default_params[param]
 
     # Load tokenizer
-    if not hf_checkpoint:
+    if vars.model == "TPUMeshTransformerGPTNeoX":
+        tokenizer = Tokenizer.from_file(os.path.join(path, "20B_tokenizer.json"))
+        def new_encode(old_encode):
+            def encode(s, *args, **kwargs):
+                return old_encode(s).ids
+            return encode
+        tokenizer.encode = new_encode(tokenizer.encode)
+    elif not hf_checkpoint:
         if not isinstance(params["tokenizer_class"], str) or not any(params["tokenizer_class"].endswith(s) for s in ("Tokenizer", "TokenizerFast")):
             raise ValueError("`tokenizer_class` must be a string ending in 'Tokenizer' or 'TokenizerFast'")
         tokenizer_class = getattr(__import__("transformers"), params["tokenizer_class"])
@@ -917,9 +1057,13 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
 
     network = PenalizingCausalTransformer(params, dematerialized=True)
 
-    if not hf_checkpoint:
+    if not hf_checkpoint and vars.model != "TPUMeshTransformerGPTNeoX":
         network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
-        network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
+        #network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
+        return
+
+    if vars.model == "TPUMeshTransformerGPTNeoX":
+        read_neox_checkpoint(network.state, path, params)
         return
 
     # Convert from HF checkpoint
@@ -945,7 +1089,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
 
     import torch_lazy_loader
     import torch
-    from tqdm import tqdm
+    from tqdm.auto import tqdm
 
     def callback(model_dict, f, **_):
         with zipfile.ZipFile(f, "r") as z:
@@ -1069,4 +1213,4 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
             except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained(vars.model, cache_dir="cache")
 
-    network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
+    #network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))