Merge weight-loading changes into setup-improvements

openfold/utils/import_weights.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import re
+import logging
 from enum import Enum
 from dataclasses import dataclass
 from functools import partial
@@ -681,15 +682,18 @@ def convert_deprecated_v1_keys(state_dict):
     }
 
     convert_key_re = re.compile("(%s)" % "|".join(map(re.escape, replacements.keys())))
+    template_emb_re = re.compile(r"^((module\.)?(model\.)?)(template(?!_embedder).*)") 
 
     converted_state_dict = {}
     for key, value in state_dict.items():
         # For each match, look-up replacement value in the dictionary
-        new_key = convert_key_re.sub(lambda m: replacements[m.group()], key)
+        new_key = convert_key_re.sub(lambda m: replacements[m.group(1)], key)
 
-        # Add prefix for template modules
-        if new_key.startswith('template'):
-            new_key = f'template_embedder.{new_key}'
+        # Add prefix for template layers 
+        template_match = re.match(template_emb_re, new_key)
+        if template_match:
+            prefix = template_match.group(1)
+            new_key = f'{prefix if prefix else ""}template_embedder.{template_match.group(4)}'
 
         converted_state_dict[new_key] = value
 

scripts/convert_v1_to_v2_weights.py

+# Copyright 2022 AlQuraishi Laboratory
+#
+# 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
+#
+#      http://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.
+#
+# Converts OpenFold .pt checkpoints into AlphaFold .npz ones, which can then be
+# used to run inference using DeepMind's JAX code.
+
+import logging
+import argparse
+import os
+import shutil
+import torch
+
+from openfold.utils.import_weights import convert_deprecated_v1_keys
+from zero_to_fp32 import get_optim_files, parse_optim_states, get_model_state_file
+
+
+def convert_v1_to_v2_weights(args):
+    checkpoint_path = args.input_ckpt_path
+    is_dir = os.path.isdir(checkpoint_path)
+    if is_dir:
+        # A DeepSpeed checkpoint
+        logging.info(
+            'Converting deepspeed checkpoint found at {args.input_checkpoint_path}')
+        state_dict_key = 'module'
+        latest_path = os.path.join(checkpoint_path, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+        ds_checkpoint_dir = os.path.join(checkpoint_path, tag)
+        model_output_path = os.path.join(args.output_ckpt_path, tag)
+        optim_files = get_optim_files(ds_checkpoint_dir)
+        zero_stage, _, _ = parse_optim_states(optim_files, ds_checkpoint_dir)
+        model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
+    else:
+        # A Pytorch Lightning checkpoint
+        logging.info(
+            'Converting pytorch lightning checkpoint found at {args.input_checkpoint_path}')
+        state_dict_key = 'state_dict'
+        model_output_path = args.output_ckpt_path
+        model_file = checkpoint_path
+
+    model_dict = torch.load(model_file, map_location=torch.device('cpu'))
+    model_dict[state_dict_key] = convert_deprecated_v1_keys(
+        model_dict[state_dict_key])
+
+    if 'ema' in model_dict:
+        ema_state_dict = model_dict['ema']['params']
+        model_dict['ema']['params'] = convert_deprecated_v1_keys(
+            ema_state_dict)
+
+    if is_dir:
+        param_shapes = convert_deprecated_v1_keys(
+            model_dict['param_shapes'][0])
+        model_dict['param_shapes'] = [param_shapes]
+
+        shutil.copytree(checkpoint_path, args.output_ckpt_path)
+        out_fname = os.path.join(
+            model_output_path, os.path.basename(model_file))
+
+        for optim_file in optim_files:
+            optim_dict = torch.load(optim_file)
+            new_optim_dict = optim_dict.copy()
+            new_optim_dict['optimizer_state_dict']['param_slice_mappings'][0] = convert_deprecated_v1_keys(
+                optim_dict['optimizer_state_dict']['param_slice_mappings'][0])
+            out_optim_fname = os.path.join(
+                model_output_path, os.path.basename(optim_file))
+            torch.save(new_optim_dict, out_optim_fname)
+    else:
+        out_fname = model_output_path
+
+    torch.save(model_dict, out_fname)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input_ckpt_path", type=str)
+    parser.add_argument("output_ckpt_path", type=str)
+
+    args = parser.parse_args()
+
+    convert_v1_to_v2_weights(args)

scripts/zero_to_fp32.py

 #!/usr/bin/env python
 
-# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
 # copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
 # the future. Once extracted, the weights don't require DeepSpeed and can be used in any
 # application.
@@ -12,13 +17,27 @@ import torch
 import glob
 import math
 import os
-from collections import OrderedDict
 import re
+from collections import OrderedDict
+from dataclasses import dataclass
 
 # while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
 # DeepSpeed data structures it has to be available in the current python environment.
-import deepspeed
 from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
 
 debug = 0
 
@@ -26,12 +45,25 @@ debug = 0
 device = torch.device('cpu')
 
 
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
 def get_model_state_file(checkpoint_dir, zero_stage):
     if not os.path.isdir(checkpoint_dir):
         raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
 
     # there should be only one file
-    if zero_stage == 2:
+    if zero_stage <= 2:
         file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
     elif zero_stage == 3:
         file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
@@ -42,33 +74,68 @@ def get_model_state_file(checkpoint_dir, zero_stage):
     return file
 
 
-def get_optim_files(checkpoint_dir):
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
     # XXX: need to test that this simple glob rule works for multi-node setup too
-    optim_files = sorted(glob.glob(os.path.join(checkpoint_dir, "*_optim_states.pt")))
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
 
-    if len(optim_files) == 0:
-        raise FileNotFoundError(
-            f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
 
-    return optim_files
+    return ckpt_files
 
 
-def parse_model_state(file):
-    state_dict = torch.load(file, map_location=device)
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
 
-    if "buffer_names" not in state_dict:
-        raise ValueError(f"{file} is not a model state checkpoint")
-    buffer_names = state_dict["buffer_names"]
-    if debug:
-        print("Found buffers:", buffer_names)
 
-    # recover just the buffers while restoring them to fp32 if they were saved in fp16
-    buffers = {
-        k: v.float()
-        for k,
-        v in state_dict["module"].items() if k in buffer_names
-    }
-    return buffers
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
 
 
 def parse_optim_states(files, ds_checkpoint_dir):
@@ -76,13 +143,17 @@ def parse_optim_states(files, ds_checkpoint_dir):
     total_files = len(files)
     state_dicts = []
     for f in files:
-        state_dicts.append(torch.load(f, map_location=device))
+        state_dict = torch.load(f, map_location=device)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
 
-    if not "zero_stage" in state_dicts[0]['optimizer_state_dict']:
+    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
         raise ValueError(f"{files[0]} is not a zero checkpoint")
-    zero_stage = state_dicts[0]['optimizer_state_dict']["zero_stage"]
-    world_size = state_dicts[0]['optimizer_state_dict']["partition_count"]
-    param_shapes = state_dicts[0]["param_shapes"]
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
     # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
     # parameters can be different from data parallelism for non-expert parameters. So we can just
     # use the max of the partition_count to get the dp world_size.
@@ -97,18 +168,15 @@ def parse_optim_states(files, ds_checkpoint_dir):
         )
 
     # the groups are named differently in each stage
-    if zero_stage == 2:
-        fp32_groups_key = "single_partition_of_fp32_groups"
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
     elif zero_stage == 3:
-        fp32_groups_key = "fp32_flat_groups"
+        fp32_groups_key = FP32_FLAT_GROUPS
     else:
         raise ValueError(f"unknown zero stage {zero_stage}")
 
-    if zero_stage == 2:
-        fp32_flat_groups = [
-            state_dicts[i]['optimizer_state_dict'][fp32_groups_key]
-            for i in range(len(state_dicts))
-        ]
+    if zero_stage <= 2:
+        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
     elif zero_stage == 3:
         # if there is more than one param group, there will be multiple flattened tensors - one
         # flattened tensor per group - for simplicity merge them into a single tensor
@@ -117,11 +185,10 @@ def parse_optim_states(files, ds_checkpoint_dir):
         # will require matching the sub-lists of param_shapes for each param group flattened tensor
 
         fp32_flat_groups = [
-            torch.cat(state_dicts[i]['optimizer_state_dict'][fp32_groups_key],
-                      0) for i in range(len(state_dicts))
+            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
         ]
 
-    return zero_stage, world_size, param_shapes, fp32_flat_groups
+    return zero_stage, world_size, fp32_flat_groups
 
 
 def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
@@ -135,29 +202,54 @@ def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
     print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
 
     optim_files = get_optim_files(ds_checkpoint_dir)
-    zero_stage, world_size, param_shapes, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
-    print(
-        f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
-    model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
-    buffers = parse_model_state(model_file)
-
-    if zero_stage == 2:
-        return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
-                                                          param_shapes,
-                                                          fp32_flat_groups,
-                                                          buffers)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
     elif zero_stage == 3:
-        return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
-                                                          param_shapes,
-                                                          fp32_flat_groups,
-                                                          buffers)
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
 
 
-def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
-                                               param_shapes,
-                                               fp32_flat_groups,
-                                               buffers):
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
 
     # Reconstruction protocol:
     #
@@ -166,7 +258,7 @@ def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
     if debug:
         for i in range(world_size):
             for j in range(len(fp32_flat_groups[0])):
-                print(f"fp32_flat_groups[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
 
     # XXX: memory usage doubles here (zero2)
     num_param_groups = len(fp32_flat_groups[0])
@@ -175,26 +267,16 @@ def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
         merged_partitions = [sd[i] for sd in fp32_flat_groups]
         full_single_fp32_vector = torch.cat(merged_partitions, 0)
         merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
-    avail_numel = sum([
-        full_single_fp32_vector.numel()
-        for full_single_fp32_vector in merged_single_partition_of_fp32_groups
-    ])
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
 
     if debug:
         wanted_params = sum([len(shapes) for shapes in param_shapes])
-        wanted_numel = sum(
-            [sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
         # not asserting if there is a mismatch due to possible padding
         print(f"Have {avail_numel} numels to process.")
         print(f"Need {wanted_numel} numels in {wanted_params} params.")
 
-    state_dict = OrderedDict()
-
-    # buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
     # params
     # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
     # out-of-core computing solution
@@ -210,13 +292,8 @@ def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
             total_params += 1
 
             if debug:
-                print(
-                    f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} "
-                )
-            state_dict[name] = full_single_fp32_vector.narrow(
-                0,
-                offset,
-                unpartitioned_numel).view(shape)
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
             offset += unpartitioned_numel
 
         # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
@@ -239,12 +316,28 @@ def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
 
         # Sanity check
         if offset != avail_numel:
-            raise ValueError(
-                f"consumed {offset} numels out of {avail_numel} - something is wrong")
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
 
-    print(
-        f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
-    )
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
 
     return state_dict
 
@@ -256,34 +349,61 @@ def zero3_partitioned_param_info(unpartitioned_numel, world_size):
     return partitioned_numel, padding_numel
 
 
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size,
-                                               param_shapes,
-                                               fp32_flat_groups,
-                                               buffers):
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
 
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = fp32_flat_groups[0].numel() * world_size
     # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
     # param, re-consolidating each param, while dealing with padding if any
 
-    avail_numel = fp32_flat_groups[0].numel() * world_size
     # merge list of dicts, preserving order
     param_shapes = {k: v for d in param_shapes for k, v in d.items()}
 
     if debug:
         for i in range(world_size):
-            print(f"fp32_flat_groups[{i}].shape={fp32_flat_groups[i].shape}")
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
 
         wanted_params = len(param_shapes)
         wanted_numel = sum(shape.numel() for shape in param_shapes.values())
         # not asserting if there is a mismatch due to possible padding
-        print(f"Have {avail_numel} numels to process.")
-        print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
-    state_dict = OrderedDict()
-
-    # buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
 
     # params
     # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
@@ -301,30 +421,41 @@ def _get_fp32_state_dict_from_zero3_checkpoint(world_size,
 
         if debug:
             print(
-                f"{total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
             )
 
         # XXX: memory usage doubles here
         state_dict[name] = torch.cat(
-            tuple(fp32_flat_groups[i].narrow(0,
-                                             offset,
-                                             partitioned_numel)
-                  for i in range(world_size)),
-            0).narrow(0,
-                      0,
-                      unpartitioned_numel).view(shape)
+            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
+            0).narrow(0, 0, unpartitioned_numel).view(shape)
         offset += partitioned_numel
 
     offset *= world_size
 
     # Sanity check
     if offset != avail_numel:
-        raise ValueError(
-            f"consumed {offset} numels out of {avail_numel} - something is wrong")
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
 
-    print(
-        f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
-    )
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
 
     return state_dict
 
@@ -447,19 +578,21 @@ def get_global_step_from_zero_checkpoint(checkpoint_dir):
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "checkpoint_dir",
-        type=str,
-        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
     parser.add_argument(
         "output_file",
         type=str,
-        help=
-        "path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)"
-    )
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
     parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
     args = parser.parse_args()
 
     debug = args.debug
 
-    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)

train_openfold.py

@@ -39,6 +39,7 @@ from scripts.zero_to_fp32 import (
     get_fp32_state_dict_from_zero_checkpoint,
     get_global_step_from_zero_checkpoint
 )
+from scripts.zero_to_fp32 import get_optim_files, parse_optim_states, get_model_state_file
 
 from openfold.utils.logger import PerformanceLoggingCallback
 
@@ -294,8 +295,13 @@ def main(args):
             sd = get_fp32_state_dict_from_zero_checkpoint(args.resume_from_ckpt)
         else:
             sd = torch.load(args.resume_from_ckpt)
-        sd = {k[len("module."):]:v for k,v in sd.items()}
-        import_openfold_weights_(model=model_module, state_dict=sd)
+        if 'module' in sd:
+            module_sd = {k[len("module."):]:v for k,v in sd['module'].items()}
+            import_openfold_weights_(model=model_module, state_dict=module_sd)
+        elif 'state_dict' in sd:
+            import_openfold_weights_(model=model_module, state_dict=sd['state_dict'])
+        else:
+            import_openfold_weights_(model=model_module, state_dict=sd)
         logging.info("Successfully loaded model weights...")
     if(args.resume_from_jax_params):
         model_module.load_from_jax(args.resume_from_jax_params)