Initial commit

megatron/core/ssm/mamba_block.py

+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from dataclasses import dataclass
+from functools import partial
+from typing import Union
+
+from torch import Tensor, nn
+
+from megatron.core import parallel_state
+from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols as LayerSymbols
+from megatron.core.ssm.mamba_hybrid_layer_allocation import allocate_layers
+from megatron.core.tensor_parallel import get_cuda_rng_tracker
+from megatron.core.transformer.custom_layers.transformer_engine import TENorm
+from megatron.core.transformer.identity_op import IdentityOp
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import make_viewless_tensor
+
+
+def create_mamba_block(
+    config, mamba_layer_spec, residual_in_fp32=False, layer_idx=None,
+):
+    block = build_module(
+        mamba_layer_spec, config, residual_in_fp32=residual_in_fp32, layer_idx=layer_idx,
+    )
+    block.layer_idx = layer_idx
+    return block
+
+
+# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
+def _init_weights(
+    module,
+    n_layer,
+    initializer_range=0.02,  # Now only used for embedding layer.
+    rescale_prenorm_residual=True,
+    n_residuals_per_layer=1,  # Change to 2 if we have MLP
+):
+    with get_cuda_rng_tracker().fork():
+        if isinstance(module, nn.Linear):
+            if not getattr(module.weight, "_no_reinit", False):
+                nn.init.normal_(module.weight, std=initializer_range)
+            if module.bias is not None:
+                if not getattr(module.bias, "_no_reinit", False):
+                    nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, std=initializer_range)
+
+        for name, p in module.named_parameters():
+            if name in ["in_proj.weight", "x_proj.weight", "conv1d.weight", "out_proj.weight"]:
+                nn.init.kaiming_uniform(p, a=math.sqrt(5))
+
+        if rescale_prenorm_residual:
+            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+            #
+            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+            for name, p in module.named_parameters():
+                if name in ["out_proj.weight", "fc2.weight"]:
+                    # Special Scaled Initialization
+                    nn.init.normal_(
+                        p,
+                        mean=0.0,
+                        std=initializer_range / math.sqrt(n_residuals_per_layer * n_layer),
+                    )
+
+
+@dataclass
+class MambaStackSubmodules:
+    mamba_layer: Union[ModuleSpec, type] = IdentityOp
+    attention_layer: Union[ModuleSpec, type] = IdentityOp
+    mlp_layer: Union[ModuleSpec, type] = IdentityOp
+
+
+class MambaStack(MegatronModule):
+    def __init__(
+        self,
+        config: TransformerConfig,
+        submodules: MambaStackSubmodules,
+        residual_in_fp32=False,
+        pre_process: bool = True,
+        hybrid_attention_ratio: float = 0.0,
+        hybrid_mlp_ratio: float = 0.0,
+        hybrid_override_pattern: str = None,
+        post_layer_norm: bool = True,
+        post_process: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__(config=config)
+        self.residual_in_fp32 = residual_in_fp32
+        self.pre_process = pre_process
+        self.post_layer_norm = post_layer_norm
+        self.post_process = post_process
+
+        # Required for pipeline parallel schedules
+        self.input_tensor = None
+
+        self.hybrid_attention_ratio = hybrid_attention_ratio
+        self.hybrid_mlp_ratio = hybrid_mlp_ratio
+        self.hybrid_override_pattern = hybrid_override_pattern
+
+        layer_type_list = allocate_layers(
+            self.config.num_layers,
+            self.hybrid_attention_ratio,
+            self.hybrid_mlp_ratio,
+            self.hybrid_override_pattern,
+        )
+
+        pp_layer_offset = 0
+        if parallel_state.get_pipeline_model_parallel_world_size() > 1:
+            pp_layer_offset, layer_type_list = self._select_layers_for_pipeline_parallel(
+                layer_type_list
+            )
+
+        self.layers = nn.ModuleList()
+        for i, layer_type in enumerate(layer_type_list):
+            if layer_type == LayerSymbols.MAMBA:
+                layer_idx = i + pp_layer_offset
+                block = create_mamba_block(
+                    self.config,
+                    submodules.mamba_layer,
+                    residual_in_fp32=residual_in_fp32,
+                    layer_idx=layer_idx,
+                )
+            elif layer_type == LayerSymbols.ATTENTION:
+                # Wondering if layer_number should be i+1. See TransformerBlock
+                # and TransformerLayer::sharded_state_dict
+                # Also, transformer layers apply their own pp_layer_offset
+                block = build_module(submodules.attention_layer, config=self.config, layer_number=i)
+            elif layer_type == LayerSymbols.MLP:
+                # Wondering if layer_number should be i+1. See TransformerBlock
+                # and TransformerLayer::sharded_state_dict
+                # Also, transformer layers apply their own pp_layer_offset
+                block = build_module(submodules.mlp_layer, config=self.config, layer_number=i)
+            else:
+                assert True, "unexpected layer_type"
+            self.layers.append(block)
+
+        # Required for activation recomputation
+        self.num_layers_per_pipeline_rank = len(self.layers)
+
+        if self.post_process and self.post_layer_norm:
+            # Final layer norm before output.
+            self.final_norm = TENorm(
+                config=self.config,
+                hidden_size=self.config.hidden_size,
+                eps=self.config.layernorm_epsilon,
+            )
+
+        self.apply(partial(_init_weights, n_layer=self.config.num_layers,))
+
+    def _select_layers_for_pipeline_parallel(self, layer_type_list):
+        pipeline_rank = parallel_state.get_pipeline_model_parallel_rank()
+        num_layers_per_pipeline_rank = (
+            self.config.num_layers // parallel_state.get_pipeline_model_parallel_world_size()
+        )
+
+        assert parallel_state.get_virtual_pipeline_model_parallel_world_size() is None, (
+            "The Mamba hybrid model does not currently support "
+            "virtual/interleaved pipeline parallelism"
+        )
+
+        offset = pipeline_rank * num_layers_per_pipeline_rank
+        selected_list = layer_type_list[offset : offset + num_layers_per_pipeline_rank]
+
+        return offset, selected_list
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        return {
+            i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype)
+            for i, layer in enumerate(self.layers)
+        }
+
+    def set_input_tensor(self, input_tensor: Tensor):
+        """Set input tensor to be used instead of forward()'s input.
+
+        When doing pipeline parallelism the input from the previous
+        stage comes from communication, not from the input, so the
+        model's forward_step_func won't have it. This function is thus
+        used by internal code to bypass the input provided by the
+        forward_step_func"""
+        self.input_tensor = input_tensor
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Tensor,
+        inference_params=None,
+        rotary_pos_emb: Tensor = None,
+    ):
+        if not self.pre_process:
+            # See set_input_tensor()
+            hidden_states = self.input_tensor
+
+        if inference_params:
+            # NOTE(bnorick): match InferenceParams attributes for mamba_ssm.utils.generation.InferenceParams,
+            # this hack supports eval
+            inference_params.max_seqlen = inference_params.max_sequence_length
+            inference_params.seqlen_offset = inference_params.sequence_len_offset
+
+        for layer in self.layers:
+            hidden_states = layer(
+                hidden_states,
+                attention_mask,
+                inference_params=inference_params,
+                rotary_pos_emb=rotary_pos_emb,
+            )
+
+            # The attention layer (currently a simplified transformer layer)
+            # outputs a tuple of (hidden_states, context). Context is intended
+            # for cross-attention, and is not needed in our model.
+            if isinstance(hidden_states, tuple):
+                hidden_states = hidden_states[0]
+
+        # Final layer norm.
+        if self.post_process and self.post_layer_norm:
+            hidden_states = self.final_norm(hidden_states)
+
+        # Ensure that the tensor passed between pipeline parallel stages is
+        # viewless. See related notes in TransformerBlock and TransformerLayer
+        output = make_viewless_tensor(
+            inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True
+        )
+
+        return hidden_states

megatron/core/ssm/mamba_hybrid_layer_allocation.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+import logging
+
+if __name__ != "__main__":
+    from megatron.core.utils import log_single_rank
+else:
+    from typing import Any
+
+    def log_single_rank(logger: logging.Logger, *args: Any, rank: int = 0, **kwargs: Any):
+        print(*args[1:], **kwargs)
+
+
+logger = logging.getLogger(__name__)
+
+
+class Symbols:
+    MAMBA = 'M'
+    ATTENTION = '*'
+    MLP = '-'
+    VALID = {MAMBA, ATTENTION, MLP}
+
+
+def _allocate_auto(
+    total_layers_count: int, target_attention_ratio: float, target_mlp_ratio: float
+) -> list:
+    # First, allocate attention (evenly spaced, starting and ending with mamba)
+    attention_layers_count: int = round(total_layers_count * target_attention_ratio)
+    mamba_layers_count: int = total_layers_count - attention_layers_count
+    mamba_sections_count: int = attention_layers_count + 1
+    mamba_section_length: float = mamba_layers_count / mamba_sections_count
+
+    layer_type_list = [Symbols.MAMBA] * total_layers_count
+    x: float = mamba_section_length
+    for l in range(total_layers_count):
+        if x < 0.5:
+            layer_type_list[l] = Symbols.ATTENTION
+            x += mamba_section_length
+        else:
+            x -= 1
+
+    # Next, allocate mlp
+    # (evenly distributed, but right-justified, not replacing attention)
+    mlp_layers_count: int = round(total_layers_count * target_mlp_ratio)
+    if mlp_layers_count > 0:
+        mamba_layers_count -= mlp_layers_count
+        mamba_to_mlp_ratio: float = mamba_layers_count / mlp_layers_count
+
+        x: float = mamba_to_mlp_ratio
+        for l in range(total_layers_count):
+            if layer_type_list[l] == Symbols.MAMBA:
+                if x < 0.5:
+                    layer_type_list[l] = Symbols.MLP
+                    x += mamba_to_mlp_ratio
+                else:
+                    x -= 1
+
+    return layer_type_list
+
+
+def _allocate_override(total_layers_count: int, override_pattern: str) -> list:
+    layer_type_list = list(override_pattern)
+    override_pattern_length = len(layer_type_list)
+    if override_pattern_length != total_layers_count:
+        raise ValueError(
+            "The hybrid override pattern is the wrong "
+            f"length: got {override_pattern_length}, expected "
+            f"{total_layers_count}"
+        )
+    for l in layer_type_list:
+        if l not in Symbols.VALID:
+            raise ValueError(f"In hybrid override pattern, '{l}' is not " f"one of {Symbols.VALID}")
+
+    return layer_type_list
+
+
+def _layer_counts_match(a: list, b: list) -> bool:
+    for s in Symbols.VALID:
+        if a.count(s) != b.count(s):
+            return False
+    return True
+
+
+def allocate_layers(
+    total_layers_count: int,
+    target_attention_ratio: float,
+    target_mlp_ratio: float,
+    override_pattern: str = None,
+) -> list:
+    assert total_layers_count > 0
+    assert target_attention_ratio >= 0.0 and target_attention_ratio <= 1.0
+    assert target_mlp_ratio >= 0.0 and target_mlp_ratio <= 1.0
+    assert target_attention_ratio + target_mlp_ratio <= 1.0
+    # Note: target_mamba_ratio = 1.0 - target_attention_ratio - target_mlp_ratio
+
+    layer_type_list = _allocate_auto(total_layers_count, target_attention_ratio, target_mlp_ratio)
+
+    if override_pattern is not None:
+        layer_type_list_override = _allocate_override(total_layers_count, override_pattern)
+        log_single_rank(logger, logging.INFO, "Using hybrid override pattern")
+        if (target_attention_ratio > 0.0 or target_mlp_ratio > 0.0) and not _layer_counts_match(
+            layer_type_list_override, layer_type_list
+        ):
+            raise ValueError(
+                "The number of each type of layer in the override "
+                "pattern must match the number in the overridden "
+                "pattern."
+            )
+        if layer_type_list_override == layer_type_list:
+            log_single_rank(
+                logger, logging.INFO, "The override pattern matches the overridden pattern"
+            )
+        else:
+            log_single_rank(logger, logging.INFO, "Warning: overriding pattern A with pattern B")
+            log_single_rank(logger, logging.INFO, f"A: {''.join(layer_type_list)}")
+            log_single_rank(logger, logging.INFO, f"B: {''.join(layer_type_list_override)}")
+        layer_type_list = layer_type_list_override
+
+    if target_attention_ratio > 0.0 or target_mlp_ratio > 0.0 or override_pattern is not None:
+        actual_attention_layers_count = layer_type_list.count(Symbols.ATTENTION)
+        actual_attention_ratio = actual_attention_layers_count / total_layers_count
+        actual_mlp_layers_count = layer_type_list.count(Symbols.MLP)
+        actual_mlp_ratio = actual_mlp_layers_count / total_layers_count
+        allocation_string = ''.join(layer_type_list)
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"Hybrid allocation ({Symbols.MAMBA} is mamba, "
+            f"{Symbols.ATTENTION} is attention, "
+            f"{Symbols.MLP} is mlp):",
+        )
+        log_single_rank(logger, logging.INFO, allocation_string)
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"{actual_attention_layers_count} attention layers in "
+            f"{total_layers_count} total layers.",
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"Target attention ratio: {target_attention_ratio:.2f}. "
+            f"Actual attention ratio: {actual_attention_ratio:.2f}.",
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"{actual_mlp_layers_count} mlp layers in " f"{total_layers_count} total layers.",
+        )
+        log_single_rank(
+            logger,
+            logging.INFO,
+            f"Target mlp ratio: {target_mlp_ratio:.2f}. "
+            f"Actual mlp ratio: {actual_mlp_ratio:.2f}.",
+        )
+    return layer_type_list
+
+
+if __name__ == "__main__":
+    test_cases = [
+        # (10, 0.2, 0.0),
+        # (48, 0.0, 0.0), # will not print anything
+        # (48, 0.1, 0.0),
+        # 48, 0.3, 0.0),
+        # (48, 0.5, 0.0),
+        # (48, 0.6, 0.0),
+        # (48, 0.7, 0.0),
+        # (10, 0.0, 0.1),
+        # (10, 0.0, 0.3),
+        # (10, 0.0, 0.5),
+        # (10, 0.1, 0.1),
+        # (10, 0.2, 0.2),
+        # (10, 0.3, 0.3),
+        # (10, 0.5, 0.5),
+        # (48, 0.2, 0.3),
+        # (48, 0.5, 0.2),
+        # (48, 0.5, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.25, 0.25, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.25, 0.25, "MM-*MM-*MM*-MM*-MM*-MM*-M*M-M*M-M*M-M*M-*MM-*MM-"),
+        # (48, 0.0, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.2, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.0, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
+        # (48, 0.5, 0.5),
+        # (10, 0.3, 0.2, "MMM*-*M*M-"),
+        # (10, 0.3, 0.2, "MM*M-*M*M-"),
+        (9, 0.0, 0.0, "M*-M*-M*-"),
+        (9, 0.0, 0.0, "MMMMMMMMM"),
+    ]
+    for t in test_cases:
+        print("")
+        allocate_layers(*t)

megatron/core/ssm/mamba_layer.py

+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+from typing import Union
+
+import torch
+from torch import Tensor
+
+from megatron.core.transformer.identity_op import IdentityOp
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+@dataclass
+class MambaLayerSubmodules:
+    norm: Union[ModuleSpec, type] = IdentityOp
+    mixer: Union[ModuleSpec, type] = IdentityOp
+
+
+class MambaLayer(MegatronModule):
+    def __init__(
+        self,
+        config: TransformerConfig,
+        submodules: MambaLayerSubmodules,
+        layer_idx=None,
+        residual_in_fp32=False,
+    ):
+        """
+        Top level Mamba Layer
+        """
+        super().__init__(config)
+        self.config = config
+        self.residual_in_fp32 = residual_in_fp32
+        self.mixer = build_module(
+            submodules.mixer, self.config, self.config.hidden_size, layer_idx=layer_idx,
+        )
+        self.norm = build_module(submodules.norm, self.config, self.config.hidden_size)
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Tensor,  # Not used in MambaLayer
+        inference_params=None,
+        rotary_pos_emb: Tensor = None,  # Not used in MambaLayer
+    ):
+
+        residual = hidden_states
+        hidden_states = self.norm(residual.to(dtype=self.norm.weight.dtype))
+        if self.residual_in_fp32:
+            residual = residual.to(torch.float32)
+
+        hidden_states = self.mixer(hidden_states, inference_params=inference_params)
+        return hidden_states + residual
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        return self.mixer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype)

megatron/core/ssm/mamba_mixer.py

+# Copyright (c) 2024, Tri Dao, Albert Gu.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Some of this code was adopted from https://github.com/state-spaces/mamba/
+# This source code is licensed under the Apache license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from megatron.core.parallel_state import get_tensor_model_parallel_world_size
+from megatron.core.tensor_parallel import (
+    ColumnParallelLinear,
+    RowParallelLinear,
+    copy_to_tensor_model_parallel_region,
+    gather_from_sequence_parallel_region,
+    get_cuda_rng_tracker,
+    reduce_from_tensor_model_parallel_region,
+    reduce_scatter_to_sequence_parallel_region,
+)
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+try:
+    from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+except ImportError:
+    selective_state_update = None
+
+try:
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+except ImportError:
+    causal_conv1d_fn = None
+    causal_conv1d_update = None
+
+try:
+    from mamba_ssm.ops.triton.layernorm_gated import RMSNorm as RMSNormGated
+    from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined
+except ImportError:
+    raise ImportError("mamba-ssm is required by the Mamba model but cannot be imported")
+
+try:
+    from einops import rearrange, repeat
+except ImportError:
+    raise ImportError("einops is required by the Mamba model but cannot be imported")
+
+
+class Mamba(MegatronModule):
+    def __init__(
+        self,
+        config: TransformerConfig,
+        d_model,
+        d_state=128,
+        d_conv=4,
+        conv_init=None,
+        expand=2,
+        headdim=64,
+        ngroups=8,
+        A_init_range=(1, 16),
+        D_has_hdim=False,
+        rmsnorm=True,
+        norm_before_gate=False,
+        dt_min=0.001,
+        dt_max=0.1,
+        dt_init="random",
+        dt_scale=1.0,
+        dt_init_floor=1e-4,
+        bias=False,
+        conv_bias=True,
+        # Fused kernel and sharding options
+        chunk_size=128,
+        use_fast_path=True,
+        layer_idx=None,
+    ):
+        super().__init__(config)
+        self.config = config
+        self.d_model = d_model
+        self.d_state = d_state
+        self.d_conv = d_conv
+        self.conv_init = conv_init
+        self.expand = expand
+        self.d_inner = int(self.expand * self.d_model)
+        self.headdim = headdim
+        self.ngroups = ngroups
+        assert self.d_inner % self.headdim == 0
+        self.nheads = self.d_inner // self.headdim
+        self.D_has_hdim = D_has_hdim
+        self.rmsnorm = rmsnorm
+        self.norm_before_gate = norm_before_gate
+        self.chunk_size = chunk_size
+        self.use_fast_path = use_fast_path
+        self.layer_idx = layer_idx
+
+        self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
+        assert self.d_inner % self.tensor_model_parallel_size == 0
+        assert self.ngroups % self.tensor_model_parallel_size == 0
+        assert self.nheads % self.tensor_model_parallel_size == 0
+        assert not bias
+
+        self.d_inner_local = self.d_inner // self.tensor_model_parallel_size
+        self.ngroups_local = self.ngroups // self.tensor_model_parallel_size
+        self.nheads_local = self.nheads // self.tensor_model_parallel_size
+
+        assert self.d_inner_local % self.ngroups_local == 0
+
+        # Assume sequence parallelism: input is already partitioned along the
+        # sequence dimension
+        self.in_proj = ColumnParallelLinear(
+            self.d_model,
+            self.d_inner * 2 + 2 * self.ngroups * self.d_state + self.nheads,
+            config=self.config,
+            init_method=self.config.init_method,
+            gather_output=False,
+            bias=bias,
+        )
+
+        conv_dim = self.d_inner_local + 2 * self.ngroups_local * self.d_state
+        with get_cuda_rng_tracker().fork():
+            self.conv1d = nn.Conv1d(
+                in_channels=conv_dim,
+                out_channels=conv_dim,
+                bias=conv_bias,
+                kernel_size=d_conv,
+                groups=conv_dim,
+                padding=d_conv - 1,
+                device=torch.cuda.current_device(),
+                dtype=config.params_dtype,
+            )
+            setattr(self.conv1d.weight, 'tensor_model_parallel', True)
+            setattr(self.conv1d.bias, 'tensor_model_parallel', True)
+
+            if self.conv_init is not None:
+                nn.init.uniform_(self.conv1d.weight, -self.conv_init, self.conv_init)
+
+        self.activation = "silu"
+        self.act = nn.SiLU()
+
+        with get_cuda_rng_tracker().fork():
+            # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
+            dt = torch.exp(
+                torch.rand(
+                    self.nheads_local, device=torch.cuda.current_device(), dtype=config.params_dtype
+                )
+                * (math.log(dt_max) - math.log(dt_min))
+                + math.log(dt_min)
+            ).clamp(min=dt_init_floor)
+            # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+            inv_dt = dt + torch.log(-torch.expm1(-dt))
+            with torch.no_grad():
+                self.dt_bias = nn.Parameter(inv_dt)
+            # Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit
+            self.dt_bias._no_reinit = True
+            # Just to be explicit. Without this we already don't put wd on dt_bias because of the check
+            # name.endswith("bias") in param_grouping.py
+            self.dt_bias._no_weight_decay = True
+
+            assert A_init_range[0] > 0 and A_init_range[1] >= A_init_range[0]
+            A = torch.empty(
+                self.nheads_local, dtype=torch.float32, device=torch.cuda.current_device()
+            ).uniform_(*A_init_range)
+            A_log = torch.log(A)  # Keep A_log in fp32
+            self.A_log = nn.Parameter(A_log)
+            self.A_log._no_weight_decay = True
+            setattr(self.A_log, 'tensor_model_parallel', True)
+
+        # D "skip" parameter
+        self.D = nn.Parameter(
+            torch.ones(
+                self.d_inner_local if self.D_has_hdim else self.nheads_local,
+                device=torch.cuda.current_device(),
+            )
+        )  # Keep in fp32
+        self.D._no_weight_decay = True
+        setattr(self.D, 'tensor_model_parallel', True)
+
+        if self.rmsnorm:
+            assert RMSNormGated is not None
+            self.norm = RMSNormGated(
+                self.d_inner_local,
+                eps=1e-5,
+                group_size=self.d_inner_local // self.ngroups_local,
+                norm_before_gate=False,
+                device=torch.cuda.current_device(),
+                dtype=config.params_dtype,
+            )
+
+        # Assume sequence parallelism: input is partitioned along d_inner and
+        # output is partitioned along the sequence dimension
+        self.out_proj = RowParallelLinear(
+            self.d_inner,
+            self.d_model,
+            config=self.config,
+            init_method=self.config.output_layer_init_method,
+            bias=bias,
+            input_is_parallel=True,
+            skip_bias_add=False,
+        )
+
+    def forward(self, hidden_states, inference_params=None):
+        """
+        hidden_states: (nL, B, D) / (L B D)
+        Returns: same shape as hidden_states
+        """
+        _, batch, dim = hidden_states.shape
+
+        conv_state, ssm_state = None, None
+        if inference_params is not None:
+            assert not self.config.sequence_parallel
+            conv_state, ssm_state = self._get_states_from_cache(inference_params, batch)
+            if inference_params.seqlen_offset > 0:
+                # The states are updated inplace
+                out, _, _ = self.step(hidden_states, conv_state, ssm_state)
+                return out
+
+        # (nheads_local)
+        A = -torch.exp(self.A_log.float())
+
+        # pl b d ->  l b p(2d)
+        # TODO move transpose to GEMM
+        if self.config.sequence_parallel:
+            # gather data along sequenece dimension
+            hidden_states = gather_from_sequence_parallel_region(hidden_states)
+        else:
+            hidden_states = copy_to_tensor_model_parallel_region(hidden_states)
+        xz = hidden_states @ self.in_proj.weight.t()
+
+        z, xBC, dt = torch.split(
+            xz,
+            [
+                self.d_inner_local,
+                self.d_inner_local + 2 * self.ngroups_local * self.d_state,
+                self.nheads_local,
+            ],
+            dim=-1,
+        )
+
+        # transpose: l b pd --> b pd l
+        xBC = rearrange(xBC, "l b d -> b d l")
+        xBC = xBC.contiguous()
+
+        # Compute short convolution
+        if conv_state is not None:
+            # If we just take x[:, :, -self.d_conv :], it will error if seqlen < self.d_conv
+            # Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.
+            conv_state.copy_(F.pad(xBC, (self.d_conv - xBC.shape[-1], 0)))  # Update state (B D W)
+
+        seqlen = xBC.size(2)
+        if causal_conv1d_fn is None:
+            xBC = self.act(self.conv1d(xBC)[..., :seqlen])
+        else:
+            assert self.activation in ["silu", "swish"]
+            xBC = causal_conv1d_fn(
+                x=xBC,
+                weight=rearrange(self.conv1d.weight, "d 1 w -> d w"),
+                bias=self.conv1d.bias,
+                activation=self.activation,
+            )
+
+        # transpose b pd l --> l b pd
+        xBC = rearrange(xBC, "b d l ->  l b d")
+        xBC = xBC.contiguous()
+
+        x, B, C = torch.split(
+            xBC,
+            [
+                self.d_inner_local,
+                self.ngroups_local * self.d_state,
+                self.ngroups_local * self.d_state,
+            ],
+            dim=-1,
+        )
+
+        # TODO Vijay: fuse most of the transposes with the GEMMS
+        x = rearrange(x, "l b (h p) -> b l h p", p=self.headdim).contiguous()
+        dt = rearrange(dt, "l b d -> b l d").contiguous()
+        B = rearrange(B, "l b (g n) -> b l g n", n=self.d_state).contiguous()
+        C = rearrange(C, "l b (g n) -> b l g n", n=self.d_state).contiguous()
+        z = rearrange(z, "l b (h p) -> b l h p", p=self.headdim).contiguous()
+        y = mamba_chunk_scan_combined(
+            x,
+            dt,
+            A,
+            B,
+            C,
+            self.chunk_size,
+            D=rearrange(self.D.float(), "(h p) -> h p", p=self.headdim)
+            if self.D_has_hdim
+            else self.D,
+            z=z if not self.rmsnorm else None,
+            dt_bias=self.dt_bias.float(),
+            dt_softplus=True,
+            return_final_states=ssm_state is not None,
+        )
+
+        if ssm_state is not None:
+            y, last_state = y
+            ssm_state.copy_(last_state)
+
+        if self.rmsnorm:
+            y = rearrange(y, "b l h p -> b l (h p)").contiguous()
+            z = rearrange(z, "b l h p -> b l (h p)").contiguous()
+            y = self.norm(y, z)
+            y = rearrange(y, "b l d -> l b d").contiguous()
+        else:
+            y = rearrange(y, "b l h p -> l b (h p)").contiguous()
+
+        #  l b pd --> pl b d
+        out_full = y @ self.out_proj.weight.t()
+        if self.config.sequence_parallel:
+            out = reduce_scatter_to_sequence_parallel_region(out_full)
+        else:
+            out = reduce_from_tensor_model_parallel_region(out_full)
+        return out
+
+    def step(self, hidden_states, conv_state, ssm_state):
+        # assert self.ngroups_local == 1, "Only support ngroups=1 for inference for now"
+        dtype = hidden_states.dtype
+        assert hidden_states.shape[0] == 1, "Only support decoding with 1 token at a time for now"
+
+        # l b d --> b d
+        hidden_states = hidden_states.squeeze(0)
+
+        #  b d_model --> b p(2d)
+        xz = hidden_states @ self.in_proj.weight.t()
+
+        z, xBC, dt = torch.split(
+            xz,
+            [
+                self.d_inner_local,
+                self.d_inner_local + 2 * self.ngroups_local * self.d_state,
+                self.nheads_local,
+            ],
+            dim=-1,
+        )
+
+        # Conv step
+        if causal_conv1d_update is None:
+            conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1))  # Update state (B D W)
+            conv_state[:, :, -1] = xBC
+            xBC = torch.sum(
+                conv_state * rearrange(self.conv1d.weight, "d 1 w -> d w"), dim=-1
+            )  # (B D)
+            if self.conv1d.bias is not None:
+                xBC = xBC + self.conv1d.bias
+            xBC = self.act(xBC).to(dtype=dtype)
+        else:
+            xBC = causal_conv1d_update(
+                xBC,
+                conv_state,
+                rearrange(self.conv1d.weight, "d 1 w -> d w"),
+                self.conv1d.bias,
+                self.activation,
+            )
+
+        x, B, C = torch.split(
+            xBC,
+            [
+                self.d_inner_local,
+                self.ngroups_local * self.d_state,
+                self.ngroups_local * self.d_state,
+            ],
+            dim=-1,
+        )
+        A = -torch.exp(self.A_log.float())
+
+        # SSM step
+        if selective_state_update is None:
+            if self.ngroups_local > 1:
+                B = rearrange(B, "b (g n) -> b g n", n=self.d_state)
+                C = rearrange(C, "b (g n) -> b g n", n=self.d_state)
+                B = repeat(B, "b g n -> b (g h) n", h=self.d_inner_local // self.ngroups_local)
+                C = repeat(C, "b g n -> b (g h) n", h=self.d_inner_local // self.ngroups_local)
+
+                dt = repeat(dt, "b h -> b (h p)", p=self.headdim)
+                dt_bias = repeat(self.dt_bias, "h -> (h p)", p=self.headdim)
+                A = repeat(A, "h -> (h p) n", p=self.headdim, n=self.d_state)
+                D = repeat(self.D, "h -> (h p)", p=self.headdim)
+
+                dt = F.softplus(dt + dt_bias.to(dtype=dt.dtype))
+                dA = torch.exp(torch.einsum("bd,dn->bdn", dt, A))
+
+                dB_x = torch.einsum('bd,bdn,bd->bdn', dt, B, x)
+                ssm_state.copy_(
+                    ssm_state * rearrange(dA, "b (h p) n -> b h p n", p=self.headdim)
+                    + rearrange(dB_x, "b (h p) n -> b h p n", p=self.headdim)
+                )
+
+                y = torch.einsum(
+                    "bdn,bdn->bd",
+                    rearrange(ssm_state.to(dtype), "b h p n -> b (h p) n", p=self.headdim),
+                    C,
+                )
+                y = y + D.to(dtype) * x
+                if not self.rmsnorm:
+                    y = y * self.act(z)  # (B D)
+            else:
+                # Discretize A and B (b (g n))
+                dt = F.softplus(dt + self.dt_bias.to(dtype=dt.dtype))  # (batch, nheads)
+                dA = torch.exp(dt * A)
+                x = rearrange(x, "b (h p) -> b h p", p=self.headdim)
+                dBx = torch.einsum("bh,bn,bhp->bhpn", dt, B, x)
+                ssm_state.copy_(ssm_state * rearrange(dA, "b h -> b h 1 1") + dBx)
+                y = torch.einsum("bhpn,bn->bhp", ssm_state.to(dtype), C)
+                y = y + rearrange(self.D.to(dtype), "h -> h 1") * x
+                y = rearrange(y, "b h p -> b (h p)")
+                if not self.rmsnorm:
+                    y = y * self.act(z)  # (B D)
+        else:
+            A = repeat(A, "h -> h p n", p=self.headdim, n=self.d_state).to(dtype=torch.float32)
+            dt = repeat(dt, "b h -> b h p", p=self.headdim)
+            dt_bias = repeat(self.dt_bias, "h -> h p", p=self.headdim)
+            D = repeat(self.D, "h -> h p", p=self.headdim)
+            B = rearrange(B, "b (g n) -> b g n", g=self.ngroups_local)
+            C = rearrange(C, "b (g n) -> b g n", g=self.ngroups_local)
+            x_reshaped = rearrange(x, "b (h p) -> b h p", p=self.headdim)
+            if not self.rmsnorm:
+                z = rearrange(z, "b (h p) -> b h p", p=self.headdim)
+            y = selective_state_update(
+                ssm_state,
+                x_reshaped,
+                dt,
+                A,
+                B,
+                C,
+                D,
+                z=z if not self.rmsnorm else None,
+                dt_bias=dt_bias,
+                dt_softplus=True,
+            )
+            y = rearrange(y, "b h p -> b (h p)")
+
+        if self.rmsnorm:
+            y = self.norm(y, z)
+
+        # b pd --> b d
+        out = y @ self.out_proj.weight.t()
+        out = reduce_from_tensor_model_parallel_region(out)
+        return out.unsqueeze(0), conv_state, ssm_state
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
+        device = self.out_proj.weight.device
+        conv_dtype = self.conv1d.weight.dtype if dtype is None else dtype
+        conv_state = torch.zeros(
+            batch_size, self.conv1d.weight.shape[0], self.d_conv, device=device, dtype=conv_dtype
+        )
+        ssm_dtype = self.in_proj.weight.dtype if dtype is None else dtype
+        # ssm_dtype = torch.float32
+        ssm_state = torch.zeros(
+            batch_size,
+            self.nheads_local,
+            self.headdim,
+            self.d_state,
+            device=device,
+            dtype=ssm_dtype,
+        )
+        return conv_state, ssm_state
+
+    def _get_states_from_cache(self, inference_params, batch_size, initialize_states=False):
+        assert self.layer_idx is not None
+        if self.layer_idx not in inference_params.key_value_memory_dict:
+            conv_state = torch.zeros(
+                batch_size,
+                self.conv1d.weight.shape[0],
+                self.d_conv,
+                device=self.conv1d.weight.device,
+                dtype=self.conv1d.weight.dtype,
+            )
+            ssm_state = torch.zeros(
+                batch_size,
+                self.nheads_local,
+                self.headdim,
+                self.d_state,
+                device=self.in_proj.weight.device,
+                dtype=self.in_proj.weight.dtype,
+            )
+            inference_params.key_value_memory_dict[self.layer_idx] = (conv_state, ssm_state)
+        else:
+            conv_state, ssm_state = inference_params.key_value_memory_dict[self.layer_idx]
+            # TODO: What if batch size changes between generation, and we reuse the same states?
+            if initialize_states:
+                conv_state.zero_()
+                ssm_state.zero_()
+        return conv_state, ssm_state

megatron/core/ssm/triton_cache_manager.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+import os
+import socket
+from pathlib import Path
+
+import torch
+
+try:
+    from triton.runtime.cache import FileCacheManager
+except ImportError:
+    raise ImportError("triton is required by the Mamba model but cannot be imported")
+
+
+def get_rank():
+    return torch.distributed.get_rank()
+
+
+def default_cache_dir():
+    return os.path.join(Path.home(), ".triton", "cache")
+
+
+class ParallelFileCacheManager(FileCacheManager):
+
+    # See https://github.com/triton-lang/triton/blob/main/python/triton/runtime/cache.py
+
+    # When running Triton with multiple ranks, they each create their own cache manager. Their input
+    # keys to that class are mostly (but not entirely) the same across ranks, which leads many ranks
+    # to write to the same 'key' directories in the cache dir at the same time during compilation,
+    # leading to conflicts.  This works around that by making each cache dir be rank specific by
+    # adding "rank_<host>_<pid>" to the cache directory.
+
+    def __init__(self, key):
+        self.key = key
+        self.lock_path = None
+        # create cache directory if it doesn't exist
+        self.cache_dir = os.environ.get('TRITON_CACHE_DIR', default_cache_dir())
+        self.cache_dir = os.path.join(
+            self.cache_dir, "rank_{}_{}".format(socket.gethostname(), os.getpid())
+        )
+        if self.cache_dir:
+            self.cache_dir = os.path.join(self.cache_dir, self.key)
+            self.lock_path = os.path.join(self.cache_dir, "lock")
+            os.makedirs(self.cache_dir, exist_ok=True)

megatron/core/tensor_parallel/__init__.py

+from .cross_entropy import vocab_parallel_cross_entropy
+from .data import broadcast_data
+from .layers import (
+    ColumnParallelLinear,
+    RowParallelLinear,
+    VocabParallelEmbedding,
+    copy_tensor_model_parallel_attributes,
+    linear_with_grad_accumulation_and_async_allreduce,
+    param_is_not_tensor_parallel_duplicate,
+    set_defaults_if_not_set_tensor_model_parallel_attributes,
+    set_tensor_model_parallel_attributes,
+)
+from .mappings import (
+    all_gather_last_dim_from_tensor_parallel_region,
+    all_to_all,
+    all_to_all_hp2sp,
+    all_to_all_sp2hp,
+    copy_to_tensor_model_parallel_region,
+    gather_from_sequence_parallel_region,
+    gather_from_sequence_parallel_region_to_moe,
+    gather_from_tensor_model_parallel_region,
+    reduce_from_tensor_model_parallel_region,
+    reduce_scatter_last_dim_to_tensor_parallel_region,
+    reduce_scatter_to_sequence_parallel_region,
+    reduce_scatter_to_sequence_parallel_region_from_moe,
+    scatter_to_sequence_parallel_region,
+    scatter_to_tensor_model_parallel_region,
+)
+from .random import (
+    checkpoint,
+    get_cuda_rng_tracker,
+    get_data_parallel_rng_tracker_name,
+    model_parallel_cuda_manual_seed,
+)
+from .utils import (
+    gather_split_1d_tensor,
+    split_tensor_along_last_dim,
+    split_tensor_into_1d_equal_chunks,
+)
+
+__all__ = [
+    # cross_entropy.py
+    "vocab_parallel_cross_entropy",
+    # data.py
+    "broadcast_data",
+    # layers.py
+    "ColumnParallelLinear",
+    "RowParallelLinear",
+    "VocabParallelEmbedding",
+    "set_tensor_model_parallel_attributes",
+    "set_defaults_if_not_set_tensor_model_parallel_attributes",
+    "copy_tensor_model_parallel_attributes",
+    "param_is_not_tensor_parallel_duplicate",
+    "linear_with_grad_accumulation_and_async_allreduce",
+    # mappings.py
+    "copy_to_tensor_model_parallel_region",
+    "gather_from_tensor_model_parallel_region",
+    "gather_from_sequence_parallel_region",
+    "reduce_from_tensor_model_parallel_region",
+    "reduce_scatter_to_sequence_parallel_region",
+    "scatter_to_tensor_model_parallel_region",
+    "scatter_to_sequence_parallel_region",
+    # random.py
+    "checkpoint",
+    "get_cuda_rng_tracker",
+    "model_parallel_cuda_manual_seed",
+    # utils.py
+    "split_tensor_along_last_dim",
+    "split_tensor_into_1d_equal_chunks",
+    "gather_split_1d_tensor",
+    "gather_from_sequence_parallel_region_to_moe",
+    "reduce_scatter_to_sequence_parallel_region_from_moe",
+]

megatron/core/tensor_parallel/cross_entropy.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Tuple
+
+import torch
+
+from megatron.core.parallel_state import (
+    get_tensor_model_parallel_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+
+from .utils import VocabUtility
+
+
+class VocabParallelCrossEntropy:
+    """Computes the Cross Entropy Loss splitting the Vocab size across tensor parallel
+       ranks. This implementation is used in both fused and unfused cross entropy implementations
+    """
+
+    @staticmethod
+    def calculate_logits_max(
+        vocab_parallel_logits: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        vocab_parallel_logits = vocab_parallel_logits.float()
+        # Maximum value along vocab dimension across all GPUs.
+        logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
+
+        return vocab_parallel_logits, logits_max
+
+    @staticmethod
+    def calculate_predicted_logits(
+        vocab_parallel_logits: torch.Tensor, target: torch.Tensor, logits_max: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+
+        # In-place subtraction reduces memory pressure.
+        vocab_parallel_logits -= logits_max.unsqueeze(dim=-1)
+
+        # Get the partition's vocab indices
+        get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size
+        partition_vocab_size = vocab_parallel_logits.size()[-1]
+        rank = get_tensor_model_parallel_rank()
+        world_size = get_tensor_model_parallel_world_size()
+        vocab_start_index, vocab_end_index = get_vocab_range(partition_vocab_size, rank, world_size)
+
+        # Create a mask of valid vocab ids (1 means it needs to be masked).
+        target_mask = (target < vocab_start_index) | (target >= vocab_end_index)
+        masked_target = target.clone() - vocab_start_index
+        masked_target[target_mask] = 0
+
+        # Get predicted-logits = logits[target].
+        # For Simplicity, we convert logits to a 2-D tensor with size
+        # [*, partition-vocab-size] and target to a 1-D tensor of size [*].
+        logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
+        masked_target_1d = masked_target.view(-1)
+        arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
+        predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
+        predicted_logits_1d = predicted_logits_1d.clone().contiguous()
+        predicted_logits = predicted_logits_1d.view_as(target)
+        predicted_logits[target_mask] = 0.0
+
+        exp_logits = vocab_parallel_logits
+        torch.exp(vocab_parallel_logits, out=exp_logits)
+        sum_exp_logits = exp_logits.sum(dim=-1)
+
+        return target_mask, masked_target_1d, predicted_logits, sum_exp_logits, exp_logits
+
+    @staticmethod
+    def calculate_cross_entropy_loss(
+        exp_logits: torch.Tensor, predicted_logits: torch.Tensor, sum_exp_logits: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        # Loss = log(sum(exp(logits))) - predicted-logit.
+        loss = torch.log(sum_exp_logits) - predicted_logits
+
+        # Normalize and optionally smooth logits
+        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+
+        return exp_logits, loss
+
+    @staticmethod
+    def prepare_gradient_calculation_operands(
+        softmax: torch.Tensor, target_mask: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+
+        # All the inputs have softmax as thier gradient.
+        grad_input = softmax
+        # For simplicity, work with the 2D gradient.
+        partition_vocab_size = softmax.size()[-1]
+        grad_2d = grad_input.view(-1, partition_vocab_size)
+
+        # Add the gradient from matching classes.
+        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
+
+        softmax_update = 1.0 - target_mask.view(-1).float()
+
+        return grad_2d, arange_1d, softmax_update, grad_input
+
+    @staticmethod
+    def calculate_gradients(
+        grad_2d: torch.Tensor,
+        arange_1d: torch.Tensor,
+        masked_target_1d: torch.Tensor,
+        softmax_update: torch.Tensor,
+        grad_input: torch.Tensor,
+        grad_output: torch.Tensor,
+    ) -> torch.Tensor:
+
+        grad_2d[arange_1d, masked_target_1d] -= softmax_update
+
+        # Finally elementwise multiplication with the output gradients.
+        grad_input.mul_(grad_output.unsqueeze(dim=-1))
+
+        return grad_input
+
+
+class _VocabParallelCrossEntropy(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, vocab_parallel_logits, target, label_smoothing=0.0):
+
+        vocab_parallel_logits, logits_max = VocabParallelCrossEntropy.calculate_logits_max(
+            vocab_parallel_logits
+        )
+        torch.distributed.all_reduce(
+            logits_max, op=torch.distributed.ReduceOp.MAX, group=get_tensor_model_parallel_group()
+        )
+
+        (
+            target_mask,
+            masked_target_1d,
+            predicted_logits,
+            sum_exp_logits,
+            exp_logits,
+        ) = VocabParallelCrossEntropy.calculate_predicted_logits(
+            vocab_parallel_logits, target, logits_max
+        )
+
+        # All reduce is needed to get the chunks from other GPUs.
+        torch.distributed.all_reduce(
+            predicted_logits,
+            op=torch.distributed.ReduceOp.SUM,
+            group=get_tensor_model_parallel_group(),
+        )
+
+        torch.distributed.all_reduce(
+            sum_exp_logits,
+            op=torch.distributed.ReduceOp.SUM,
+            group=get_tensor_model_parallel_group(),
+        )
+
+        exp_logits, loss = VocabParallelCrossEntropy.calculate_cross_entropy_loss(
+            exp_logits, predicted_logits, sum_exp_logits
+        )
+
+        vocab_size = exp_logits.size(-1)
+        if label_smoothing > 0:
+            """
+            We'd like to assign 1 / (K - 1) probability mass to every index that is not the ground truth.
+            = (1 - alpha) * y_gt + alpha * mean(y_{i for i != gt})
+            = (1 - alpha) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i
+            = ((K - 1) * (1 - alpha) / (K - 1)) * y_gt + (alpha / (K - 1)) * \sum_{i != gt} y_i
+            = (K * (1 - alpha) - 1) / (K - 1)) * y_gt  + (alpha / (K - 1)) * \sum_{i} y_i
+            = (1 - (alpha * K) / (K - 1)) * y_gt + ( (alpha * K) / (K - 1) ) * \sum_{i} y_i / K
+            From: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/losses/smoothed_cross_entropy.py
+            """
+            assert 1.0 > label_smoothing > 0.0
+            smoothing = label_smoothing * vocab_size / (vocab_size - 1)
+
+            # Exp logits at this point are normalized probabilities. So we can just take the log to get log-probs.
+            log_probs = torch.log(exp_logits)
+            mean_log_probs = log_probs.mean(dim=-1)
+            loss = (1.0 - smoothing) * loss - smoothing * mean_log_probs
+
+        ctx.label_smoothing, ctx.vocab_size = label_smoothing, vocab_size
+
+        # Store softmax, target-mask and masked-target for backward pass.
+        ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
+
+        return loss
+
+    @staticmethod
+    def backward(ctx, grad_output):
+
+        # Retreive tensors from the forward path.
+        softmax, target_mask, masked_target_1d = ctx.saved_tensors
+        label_smoothing, vocab_size = ctx.label_smoothing, ctx.vocab_size
+
+        (
+            grad_2d,
+            arange_1d,
+            softmax_update,
+            grad_input,
+        ) = VocabParallelCrossEntropy.prepare_gradient_calculation_operands(softmax, target_mask)
+
+        if label_smoothing > 0:
+            smoothing = label_smoothing * vocab_size / (vocab_size - 1)
+            grad_2d[arange_1d, masked_target_1d] -= (1.0 - smoothing) * softmax_update
+            average_grad = 1 / vocab_size
+            grad_2d[arange_1d, :] -= smoothing * average_grad
+
+            # Finally elementwise multiplication with the output gradients.
+            grad_input.mul_(grad_output.unsqueeze(dim=-1))
+        else:
+            grad_input = VocabParallelCrossEntropy.calculate_gradients(
+                grad_2d, arange_1d, masked_target_1d, softmax_update, grad_input, grad_output
+            )
+
+        return grad_input, None, None
+
+
+def vocab_parallel_cross_entropy(vocab_parallel_logits, target, label_smoothing=0.0):
+    """
+    Performs cross entropy loss when logits are split across tensor parallel ranks
+
+    Args:
+        vocab_parallel_logits: logits split across tensor parallel ranks
+                               dimension is [sequence_length, batch_size, vocab_size/num_parallel_ranks]
+
+        target: correct vocab ids of dimseion [sequence_length, micro_batch_size]
+
+        lobal_smoothing: smoothing factor, must be in range [0.0, 1.0)
+                         default is no smoothing (=0.0)
+    """
+    return _VocabParallelCrossEntropy.apply(vocab_parallel_logits, target, label_smoothing)

megatron/core/tensor_parallel/data.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+import torch
+
+from megatron.core.parallel_state import (
+    get_tensor_model_parallel_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_src_rank,
+)
+
+_MAX_DATA_DIM = 5
+
+
+def _check_data_types(keys, data, target_dtype):
+    """Check that all the keys have the same target data type."""
+    for key in keys:
+        assert data[key].dtype == target_dtype, (
+            '{} has data type {} which '
+            'is different than {}'.format(key, data[key].dtype, target_dtype)
+        )
+
+
+def _build_key_size_numel_dictionaries(keys, data):
+    """Build the size on rank 0 and broadcast."""
+    max_dim = _MAX_DATA_DIM
+    sizes = [0 for _ in range(max_dim) for _ in keys]
+
+    # Pack the sizes on rank zero.
+    if get_tensor_model_parallel_rank() == 0:
+        offset = 0
+        for key in keys:
+            assert data[key].dim() < max_dim, 'you should increase MAX_DATA_DIM'
+            size = data[key].size()
+            for i, s in enumerate(size):
+                sizes[i + offset] = s
+            offset += max_dim
+
+    # Move to GPU and broadcast.
+    sizes_cuda = torch.tensor(sizes, dtype=torch.long, device='cuda')
+    torch.distributed.broadcast(
+        sizes_cuda, get_tensor_model_parallel_src_rank(), group=get_tensor_model_parallel_group()
+    )
+
+    # Move back to cpu and unpack.
+    sizes_cpu = sizes_cuda.cpu()
+    key_size = {}
+    key_numel = {}
+    total_numel = 0
+    offset = 0
+    for key in keys:
+        i = 0
+        size = []
+        numel = 1
+        while sizes_cpu[offset + i] > 0:
+            this_size = sizes_cpu[offset + i]
+            size.append(this_size)
+            numel *= this_size
+            i += 1
+        key_size[key] = size
+        key_numel[key] = numel
+        total_numel += numel
+        offset += max_dim
+
+    return key_size, key_numel, total_numel
+
+
+def broadcast_data(keys, data, datatype):
+    """Broadcast data from rank zero of each model parallel group to the
+    members of the same model parallel group.
+
+    Args:
+        keys: list of keys in the data disctionary to be broadcasted
+        data: data dictionary of string keys and cpu tensor values.
+        datatype: torch data type of all tensors in data associated
+                  with keys.
+    """
+    # Build (key, size) and (key, number of elements) dictionaries along
+    # with the total number of elements on all ranks.
+    key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys, data)
+
+    # Pack on rank zero.
+    if get_tensor_model_parallel_rank() == 0:
+        # Check that all keys have the same data type.
+        _check_data_types(keys, data, datatype)
+        # Flatten the data associated with the keys
+        flatten_data = torch.cat([data[key].contiguous().view(-1) for key in keys], dim=0).cuda()
+    else:
+        flatten_data = torch.empty(total_numel, device=torch.cuda.current_device(), dtype=datatype)
+
+    # Broadcast
+    torch.distributed.broadcast(
+        flatten_data, get_tensor_model_parallel_src_rank(), group=get_tensor_model_parallel_group()
+    )
+
+    # Unpack
+    output = {}
+    offset = 0
+    for key in keys:
+        size = key_size[key]
+        numel = key_numel[key]
+        output[key] = flatten_data.narrow(0, offset, numel).view(size)
+        offset += numel
+
+    return output

megatron/core/tensor_parallel/layers.py

+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+# Parts of the code here are adapted from PyTorch
+# repo: https://github.com/pytorch/pytorch
+
+import io
+import math
+import os
+import warnings
+from typing import Any, Callable, List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+import torch.nn.init as init
+from torch.cuda.amp import custom_bwd, custom_fwd
+from torch.nn.parameter import Parameter
+
+from megatron.core.model_parallel_config import ModelParallelConfig
+from megatron.core.parallel_state import (
+    get_global_memory_buffer,
+    get_tensor_and_expert_parallel_rank,
+    get_tensor_and_expert_parallel_world_size,
+    get_tensor_model_parallel_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+
+from ..dist_checkpointing.mapping import ShardedStateDict
+from ..transformer.utils import make_sharded_tensors_for_checkpoint
+from ..utils import make_tp_sharded_tensor_for_checkpoint, prepare_input_tensors_for_wgrad_compute
+from .mappings import (
+    copy_to_tensor_model_parallel_region,
+    gather_from_sequence_parallel_region,
+    gather_from_tensor_model_parallel_region,
+    reduce_from_tensor_model_parallel_region,
+    reduce_scatter_to_sequence_parallel_region,
+    scatter_to_tensor_model_parallel_region,
+)
+from .random import get_cuda_rng_tracker, get_expert_parallel_rng_tracker_name
+from .utils import VocabUtility, divide, split_tensor_along_last_dim
+
+_grad_accum_fusion_available = True
+try:
+    import fused_weight_gradient_mlp_cuda
+except ImportError:
+    _grad_accum_fusion_available = False
+
+_MODEL_PARALLEL_ATTRIBUTE_DEFAULTS = {
+    'tensor_model_parallel': False,
+    'partition_dim': -1,
+    'partition_stride': 1,
+}
+import pdb
+
+
+def param_is_not_tensor_parallel_duplicate(param):
+    return (hasattr(param, 'tensor_model_parallel') and param.tensor_model_parallel) or (
+        get_tensor_model_parallel_rank() == 0
+    )
+
+
+def set_tensor_model_parallel_attributes(tensor, is_parallel, dim, stride):
+    # Make sure the attributes are not set.
+    for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
+        assert not hasattr(tensor, attribute)
+    # Set the attributes.
+    setattr(tensor, 'tensor_model_parallel', is_parallel)
+    setattr(tensor, 'partition_dim', dim)
+    setattr(tensor, 'partition_stride', stride)
+
+
+def set_defaults_if_not_set_tensor_model_parallel_attributes(tensor):
+    def maybe_set(attribute, value):
+        if not hasattr(tensor, attribute):
+            setattr(tensor, attribute, value)
+
+    for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
+        maybe_set(attribute, _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS[attribute])
+
+
+def copy_tensor_model_parallel_attributes(destination_tensor, source_tensor):
+    def maybe_copy(attribute):
+        if hasattr(source_tensor, attribute):
+            setattr(destination_tensor, attribute, getattr(source_tensor, attribute))
+
+    for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
+        maybe_copy(attribute)
+
+
+def _initialize_affine_weight_gpu(
+    weight, init_method, partition_dim, stride=1, expert_parallel=False
+):
+    """Initialize affine weight for model parallel on GPU."""
+
+    set_tensor_model_parallel_attributes(
+        tensor=weight, is_parallel=True, dim=partition_dim, stride=stride
+    )
+
+    if not expert_parallel:
+        with get_cuda_rng_tracker().fork():
+            init_method(weight)
+    else:
+        with get_cuda_rng_tracker().fork(get_expert_parallel_rng_tracker_name()):
+            init_method(weight)
+
+
+def _initialize_affine_weight_cpu(
+    weight,
+    output_size,
+    input_size,
+    per_partition_size,
+    partition_dim,
+    init_method,
+    stride=1,
+    return_master_weight=False,
+    *,
+    params_dtype=torch.float32,
+    rank=None,
+    world_size=None,
+):
+    """Initialize affine weight for model parallel.
+
+    Build the master weight on all processes and scatter
+    the relevant chunk."""
+
+    set_tensor_model_parallel_attributes(
+        tensor=weight, is_parallel=True, dim=partition_dim, stride=stride
+    )
+
+    # Initialize master weight
+    master_weight = torch.empty(output_size, input_size, dtype=torch.float, requires_grad=False)
+    init_method(master_weight)
+    master_weight = master_weight.to(dtype=params_dtype)
+
+    # Split and copy
+    per_partition_per_stride_size = divide(per_partition_size, stride)
+    weight_list = torch.split(master_weight, per_partition_per_stride_size, dim=partition_dim)
+    if rank is None:
+        rank = get_tensor_model_parallel_rank()
+        world_size = get_tensor_model_parallel_world_size()
+    my_weight_list = weight_list[rank::world_size]
+
+    with torch.no_grad():
+        # all tensors must live on the same device
+        cpu_weight = torch.cat(my_weight_list, dim=partition_dim).to_dense()
+        weight.data.copy_(cpu_weight)
+    if return_master_weight:
+        return master_weight
+    return None
+
+
+class VocabParallelEmbedding(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    This is mainly adapted from torch.nn.Embedding and all the default
+    values are kept.
+
+    Args:
+        num_embeddings: vocabulary size.
+        embedding_dim: size of hidden state.
+        reduce_scatter_embeddings: Decides whether to perform ReduceScatter after embedding lookup
+
+    Keyword Args:
+        config: A megatron.core.ModelParallelConfig object
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        *,
+        init_method: Callable,
+        reduce_scatter_embeddings: bool = False,
+        config: ModelParallelConfig,
+    ):
+        super(VocabParallelEmbedding, self).__init__()
+        # Keep the input dimensions.
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.reduce_scatter_embeddings = reduce_scatter_embeddings
+        self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
+        # Divide the weight matrix along the vocaburaly dimension.
+        (
+            self.vocab_start_index,
+            self.vocab_end_index,
+        ) = VocabUtility.vocab_range_from_global_vocab_size(
+            self.num_embeddings, get_tensor_model_parallel_rank(), self.tensor_model_parallel_size
+        )
+        self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index
+        self.deterministic_mode = config.deterministic_mode
+
+        # Allocate weights and initialize.
+        if config.use_cpu_initialization:
+            self.weight = Parameter(
+                torch.empty(
+                    self.num_embeddings_per_partition, self.embedding_dim, dtype=config.params_dtype
+                )
+            )
+            if config.perform_initialization:
+                _initialize_affine_weight_cpu(
+                    self.weight,
+                    self.num_embeddings,
+                    self.embedding_dim,
+                    self.num_embeddings_per_partition,
+                    0,
+                    init_method,
+                    params_dtype=config.params_dtype,
+                )
+        else:
+            self.weight = Parameter(
+                torch.empty(
+                    self.num_embeddings_per_partition,
+                    self.embedding_dim,
+                    device=torch.cuda.current_device(),
+                    dtype=config.params_dtype,
+                )
+            )
+            if config.perform_initialization:
+                _initialize_affine_weight_gpu(self.weight, init_method, partition_dim=0, stride=1)
+
+    def forward(self, input_):
+        if self.tensor_model_parallel_size > 1:
+            # Build the mask.
+            input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
+            # Mask the input.
+            masked_input = input_.clone() - self.vocab_start_index
+            masked_input[input_mask] = 0
+        else:
+            masked_input = input_
+        # Get the embeddings.
+        if self.deterministic_mode:
+            output_parallel = self.weight[masked_input]
+        else:
+            # F.embedding currently has a non-deterministic backward function
+            output_parallel = F.embedding(masked_input, self.weight)
+        # Mask the output embedding.
+        if self.tensor_model_parallel_size > 1:
+            output_parallel[input_mask, :] = 0.0
+
+        if self.reduce_scatter_embeddings:
+            # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+            output_parallel = output_parallel.transpose(0, 1).contiguous()
+            output = reduce_scatter_to_sequence_parallel_region(output_parallel)
+        else:
+            # Reduce across all the model parallel GPUs.
+            output = reduce_from_tensor_model_parallel_region(output_parallel)
+        return output
+
+    def sharded_state_dict(
+        self,
+        prefix: str = '',
+        sharded_offsets: Tuple[Tuple[int, int, int]] = (),
+        metadata: Optional[dict] = None,
+    ) -> ShardedStateDict:
+        """ Non-default implementation for embeddings due to `allow_shape_mismatch` param """
+        state_dict = self.state_dict(prefix='', keep_vars=True)
+
+        weight_prefix = f'{prefix}weight'
+        return {
+            weight_prefix: make_tp_sharded_tensor_for_checkpoint(
+                tensor=state_dict['weight'],
+                key=weight_prefix,
+                allow_shape_mismatch=True,
+                prepend_offsets=sharded_offsets,
+            )
+        }
+
+
+class LinearWithFrozenWeight(torch.autograd.Function):
+    """Linear operator that does not calculate gradient for weight.
+    This op and LinearWithGradAccumulationAndAsyncCommunication performs
+    mathematically-identical forward and DGRAD.
+
+    Conceptually this op is the same as torch.nn.functional.linear with
+    weight.requires_grad==False, but in experiments they are not identical
+    mathematically. """
+
+    @staticmethod
+    @custom_fwd
+    def forward(
+        ctx, input, weight, bias, allreduce_dgrad,
+    ):
+        ctx.save_for_backward(weight)
+        ctx.allreduce_dgrad = allreduce_dgrad
+        output = torch.matmul(input, weight.t())
+        if bias is not None:
+            output = output + bias
+        return output
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, grad_output):
+        (weight,) = ctx.saved_tensors
+        grad_input = grad_output.matmul(weight)
+
+        if ctx.allreduce_dgrad:
+            # All-reduce. Note: here async and sync are effectively the same.
+            torch.distributed.all_reduce(grad_input, group=get_tensor_model_parallel_group())
+
+        return grad_input, None, None, None
+
+
+def linear_with_frozen_weight(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    bias: Optional[torch.Tensor],
+    gradient_accumulation_fusion: bool,
+    async_grad_allreduce: bool,
+    sequence_parallel: bool,
+    grad_output_buffer: Optional[List[torch.Tensor]] = None,
+    allreduce_dgrad: bool = None,
+) -> torch.Tensor:
+    """Linear layer execution with weight.requires_grad == False.
+
+    This function handles linear layers with weight frozen (untrainable).
+    In the forward, it only saves weight and does not save input activations.
+    In the backward, it does not perform weight gradient calculation, or
+    weight gradient allreduce.
+
+    Args:
+
+    input (torch.Tensor required): input like torch.nn.functional.linear
+
+    weight (torch.Tensor required): weight like torch.nn.functional.linear
+
+    bias (torch.Tensor optional): bias like torch.nn.functional.linear
+
+    gradient_accumulation_fusion (bool required): dummy argument, used to
+    keep the API unified between all forward implementation functions.
+
+    async_grad_allreduce (bool required): dummy argument, used to
+    keep the API unified between all forward implementation functions.
+
+    sequence_parallel (bool required): Indicates that sequence
+        parallelism is used and thus in the forward pass the input is
+        all gathered, and the backward pass the input gradients are
+        reduce scattered.
+
+    grad_output_buffer (List[torch.Tensor] optional): dummy argument, used to
+    keep the API unified between all forward implementation functions.
+
+    allreduce_dgrad (bool): Do the allreduce of input gradients.
+        Here, async and sync allreduce are the same. If sequence_parallel is
+        True, this must be False, as no all reduce is performed.
+
+    """
+
+    assert grad_output_buffer is None, (
+        "grad_output_buffer kwarg is only supported with "
+        "linear_with_grad_accumulation_and_async_allreduce"
+    )
+
+    if sequence_parallel:
+        input = gather_from_sequence_parallel_region(input, tensor_parallel_output_grad=True)
+    else:
+        input = input
+
+    if allreduce_dgrad is None:
+        warnings.warn(
+            "async_grad_allreduce is deprecated and will be removed in a future release. use allreduce_dgrad instead."
+        )
+        allreduce_dgrad = async_grad_allreduce
+
+    args = [
+        input,
+        weight,
+        bias,
+        allreduce_dgrad,
+    ]
+
+    return LinearWithFrozenWeight.apply(*args)
+
+
+class LinearWithGradAccumulationAndAsyncCommunication(torch.autograd.Function):
+    """See linear_with_grad_accumulation_and_async_allreduce"""
+
+    @torch.compile(mode="max-autotune-no-cudagraphs")
+    @staticmethod
+    @custom_fwd
+    def forward(
+        ctx,
+        input,
+        weight,
+        bias,
+        gradient_accumulation_fusion,
+        allreduce_dgrad,
+        sequence_parallel,
+        grad_output_buffer,
+    ):
+        ctx.save_for_backward(input, weight)
+        ctx.use_bias = bias is not None
+        ctx.gradient_accumulation_fusion = gradient_accumulation_fusion
+        ctx.allreduce_dgrad = allreduce_dgrad
+        ctx.sequence_parallel = sequence_parallel
+        ctx.grad_output_buffer = grad_output_buffer
+
+        if sequence_parallel:
+            world_size = get_tensor_model_parallel_world_size()
+            dim_size = list(input.size())
+            dim_size[0] = dim_size[0] * world_size
+
+            all_gather_buffer = get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu")
+            torch.distributed._all_gather_base(
+                all_gather_buffer, input, group=get_tensor_model_parallel_group()
+            )
+            total_input = all_gather_buffer
+        else:
+            total_input = input
+
+        output = torch.matmul(total_input, weight.t())
+        if bias is not None:
+            output = output + bias
+        return output
+
+    #@torch.compile(mode="max-autotune-no-cudagraphs")
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, grad_output):
+        input, weight = ctx.saved_tensors
+        use_bias = ctx.use_bias
+        grad_output_buffer = ctx.grad_output_buffer
+
+        wgrad_compute = True
+        if grad_output_buffer is not None:
+            grad_output_buffer.append(grad_output)
+            wgrad_compute = False
+
+        if wgrad_compute:
+            if ctx.sequence_parallel:
+                world_size = get_tensor_model_parallel_world_size()
+                dim_size = list(input.size())
+                dim_size[0] = dim_size[0] * world_size
+
+                all_gather_buffer = get_global_memory_buffer().get_tensor(
+                    dim_size, input.dtype, "mpu"
+                )
+                handle = torch.distributed._all_gather_base(
+                    all_gather_buffer, input, group=get_tensor_model_parallel_group(), async_op=True
+                )
+
+                # Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
+                # gather is scheduled before the input gradient computation
+                total_input = all_gather_buffer
+            else:
+                total_input = input
+        grad_input = grad_output.matmul(weight)
+
+        if ctx.sequence_parallel and wgrad_compute:
+            handle.wait()
+
+        if wgrad_compute:
+            grad_output, total_input = prepare_input_tensors_for_wgrad_compute(
+                grad_output, total_input
+            )
+
+        if ctx.allreduce_dgrad:
+            # Asynchronous all-reduce
+            handle = torch.distributed.all_reduce(
+                grad_input, group=get_tensor_model_parallel_group(), async_op=True
+            )
+            # Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
+            # all-reduce is scheduled before the weight gradient computation
+
+        if ctx.sequence_parallel:
+            assert not ctx.allreduce_dgrad
+            dim_size = list(input.size())
+            sub_grad_input = torch.empty(
+                dim_size, dtype=input.dtype, device=torch.cuda.current_device(), requires_grad=False
+            )
+            # reduce_scatter
+            handle = torch.distributed._reduce_scatter_base(
+                sub_grad_input, grad_input, group=get_tensor_model_parallel_group(), async_op=True
+            )
+            # Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
+            # reduce scatter is scheduled before the weight gradient computation
+
+        if ctx.gradient_accumulation_fusion:
+            if wgrad_compute:
+                if weight.main_grad.dtype == torch.float32:
+                    fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp32(
+                        total_input, grad_output, weight.main_grad
+                    )
+                elif weight.main_grad.dtype in (torch.float16, torch.bfloat16):
+                    fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp16(
+                        total_input, grad_output, weight.main_grad
+                    )
+                else:
+                    raise RuntimeError("Unsupported gradient type for gradient accumulation fusion")
+
+            if hasattr(weight, 'grad_added_to_main_grad'):
+                # When overlap_grad_reduce is True, need to ensure that backward hooks
+                # are all run on the main backprop thread to prevent deadlocks. Setup
+                # dummy grad_weight tensor to prevent backward hooks from being run
+                # in a background thread.
+                if getattr(weight, 'zero_out_wgrad', False):
+                    grad_weight = torch.zeros(
+                        weight.main_grad.shape,
+                        dtype=input.dtype,
+                        device=torch.cuda.current_device(),
+                        requires_grad=False,
+                    )
+                else:
+                    grad_weight = torch.empty(
+                        weight.main_grad.shape,
+                        dtype=input.dtype,
+                        device=torch.cuda.current_device(),
+                        requires_grad=False,
+                    )
+                weight.grad_added_to_main_grad = True
+            else:
+                grad_weight = None
+        else:
+            grad_weight = grad_output.t().matmul(total_input)
+        grad_bias = grad_output.sum(dim=0) if use_bias else None
+
+        if ctx.sequence_parallel:
+            handle.wait()
+            # Need to return None's as gradient has to flow for all the input arguments
+            # provided during forward
+            return sub_grad_input, grad_weight, grad_bias, None, None, None, None
+
+        if ctx.allreduce_dgrad:
+            handle.wait()
+
+        return grad_input, grad_weight, grad_bias, None, None, None, None
+
+
+def linear_with_grad_accumulation_and_async_allreduce(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    bias: Optional[torch.Tensor],
+    gradient_accumulation_fusion: bool,
+    async_grad_allreduce: bool,
+    sequence_parallel: bool,
+    grad_output_buffer: Optional[List[torch.Tensor]] = None,
+    allreduce_dgrad: bool = None,
+) -> torch.Tensor:
+    """Linear layer execution with asynchronous communication and
+    gradient accumulation fusion in backprop.
+
+    This has the option to accumulate the result of backprop
+    calculation into an existing gradient buffer, preventing the need
+    to do an additional addition kernel after the gradient
+    calculation.
+
+    Additionally, the tensor parallel all reduce of the input
+    gradients can be done asynchronously with the calculation of
+    the weight gradients.
+
+    In the case of sequence parallelism, the reduce scatter of the
+    input gradients is done asynchronously with the calcluation of the
+    weight gradients.
+
+    Use of this module requires that the environment variable
+    CUDA_DEVICE_MAX_CONNECTIONS=1. There are a few collective
+    operations, noted in the code, that should be scheduled before
+    compute kernels to overlap the communication with the computation,
+    which is necessary for a speedup but not for correctness so that
+    ordering isn't imposed by the scheduler. Setting
+    CUDA_DEVICE_MAX_CONNECTIONS=1 forces the kernels to be scheduled
+    in the order they are called.
+
+    Args:
+        input (torch.Tensor required): input like torch.nn.functional.linear
+
+        weight (torch.Tensor required): weight like torch.nn.functional.linear
+
+        bias (torch.Tensor optional): bias like torch.nn.functional.linear
+
+        gradient_accumulation_fusion (bool required): Perform the gradient
+            accumulation fusion, requires the custom CUDA extension
+            fused_weight_gradient_mlp_cuda module. To use
+            gradient_accumulation_fusion you must install APEX with
+            --cpp_ext and --cuda_ext. For example: "pip install
+            --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\"
+            " Note that the extension requires CUDA>=11. Otherwise, you
+            must turn off gradient accumulation fusion."
+
+
+        async_grad_allreduce (bool required): Do the allreduce of input
+            gradients asyncronously with the computation of weight
+            gradients. If sequence_parallel is True, this must be
+            False, as no all reduce is performed.
+
+
+        sequence_parallel (bool required): Indicates that sequence
+            parallelism is used and thus in the forward pass the input is
+            all gathered, and the backward pass the input gradients are
+            reduce scattered.
+
+        grad_output_buffer (List[torch.Tensor] optional): Buffer used to save
+            output gradients when embedding table wgrad compute is deferred.
+            Defaults to None.
+
+        allreduce_dgrad (bool): Do the allreduce of input gradients.
+            The allreduce is done asynchronously with the computation of weight
+            gradients. If sequence_parallel is True, this must be
+            False, as no all reduce is performed.
+    """
+    if allreduce_dgrad is None:
+        warnings.warn(
+            "async_grad_allreduce is deprecated and will be removed in a future release. use allreduce_dgrad instead."
+        )
+        allreduce_dgrad = async_grad_allreduce
+
+    args = [
+        input,
+        weight,
+        bias,
+        gradient_accumulation_fusion,
+        allreduce_dgrad,
+        sequence_parallel,
+        grad_output_buffer,
+    ]
+
+    if not linear_with_grad_accumulation_and_async_allreduce.warned:
+        if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
+            if sequence_parallel:
+                warnings.warn(
+                    "When using sequence parallelism it is recommended to set the "
+                    "environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
+                    "maximum speedup"
+                )
+                linear_with_grad_accumulation_and_async_allreduce.warned = True
+
+            if allreduce_dgrad:
+                warnings.warn(
+                    "When using async grad allreduce it is recommended to set the "
+                    "environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
+                    "maximum speedup"
+                )
+                linear_with_grad_accumulation_and_async_allreduce.warned = True
+
+    return LinearWithGradAccumulationAndAsyncCommunication.apply(*args)
+
+
+linear_with_grad_accumulation_and_async_allreduce.warned = False
+
+
+class ColumnParallelLinear(torch.nn.Module):
+    """Linear layer with column parallelism.
+
+    The linear layer is defined as Y = XA + b. A is parallelized along
+    its second dimension as A = [A_1, ..., A_p].
+
+    Args:
+        input_size: first dimension of matrix A.
+        output_size: second dimension of matrix A.
+        bias: If true, add bias
+        gather_output: If true, call all-gather on output and make Y available to all GPUs, otherwise, every GPU will have its output which is Y_i = XA_i
+        init_method: method to initialize weights. Note that bias is always set to zero.
+        stride: For the strided linear layers.
+        keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization.
+        skip_bias_add: If True, do not add the bias term, instead return it to be added by the caller. This enables performance optimations where bias can be fused with other elementwise operations.
+        skip_weight_param_allocation: If True, weight parameter is not allocated and must be passed as a keyword argument `weight` during the forward pass. Note that this does not affect bias, which will be allocated if bias is True. Defaults to False.
+        embedding_activation_buffer: This buffer holds the input activations of the final embedding linear layer on the last pipeline stage when defer_embedding_wgrad_compute is enabled.
+        grad_output_buffer: This buffer holds the gradient outputs of the final embedding linear layer on the last pipeline stage when defer_embedding_wgrad_compute is enabled.
+        is_expert: If True, the layer is treated as an MoE expert layer.
+        config: ModelParallelConfig object
+        tp_comm_buffer_name: Communication buffer name is not used in non-Transformer-Engine modules.
+        disable_grad_reduce: If True, reduction of output gradients across tensor-parallel ranks will be disabled. Defaults to False. This feature is used by Lora Adapter in Nemo to delay and fuse reduction along with other gradients for performance optimization.
+    """
+
+    def __init__(
+        self,
+        input_size,
+        output_size,
+        *,
+        config: ModelParallelConfig,
+        init_method: Callable,
+        bias=True,
+        gather_output=False,
+        stride=1,
+        keep_master_weight_for_test=False,
+        skip_bias_add=False,
+        skip_weight_param_allocation: bool = False,
+        embedding_activation_buffer: Optional[List[torch.Tensor]] = None,
+        grad_output_buffer: Optional[List[torch.Tensor]] = None,
+        is_expert: bool = False,
+        is_mlp: bool = False,
+        tp_comm_buffer_name: str = None,  # Not used
+        disable_grad_reduce: bool = False,
+    ):
+        super(ColumnParallelLinear, self).__init__()
+
+        # Keep input parameters
+        self.input_size = input_size
+        self.output_size = output_size
+        self.gather_output = gather_output
+        # Divide the weight matrix along the last dimension.
+        self.skip_bias_add = skip_bias_add
+        self.is_expert = is_expert
+        self.expert_parallel = config.expert_model_parallel_size > 1
+        self.embedding_activation_buffer = embedding_activation_buffer
+        self.grad_output_buffer = grad_output_buffer
+        self.config = config
+        self.disable_grad_reduce = disable_grad_reduce
+
+        self.explicit_expert_comm = self.is_expert and (
+            config.tensor_model_parallel_size > 1 or self.expert_parallel
+        )
+        if self.explicit_expert_comm and config.moe_extended_tp:
+            world_size = get_tensor_and_expert_parallel_world_size()
+            rank = get_tensor_and_expert_parallel_rank()
+        else:
+            world_size = get_tensor_model_parallel_world_size()
+            rank = get_tensor_model_parallel_rank()
+
+        self.output_size_per_partition = divide(output_size, world_size)
+
+        self.is_mlp = True #is_mlp
+        # Parameters.
+        # Note: torch.nn.functional.linear performs XA^T + b and as a result
+        # we allocate the transpose.
+        # Initialize weight.
+        #pdb.set_trace()
+
+        if not skip_weight_param_allocation:
+            if config.use_cpu_initialization:
+                self.weight = Parameter(
+                    torch.empty(
+                        self.output_size_per_partition, self.input_size, dtype=config.params_dtype
+                    )
+                )
+                if config.perform_initialization:
+                    self.master_weight = _initialize_affine_weight_cpu(
+                        self.weight,
+                        self.output_size,
+                        self.input_size,
+                        self.output_size_per_partition,
+                        0,
+                        init_method,
+                        stride=stride,
+                        return_master_weight=keep_master_weight_for_test,
+                        rank=rank,
+                        world_size=world_size,
+                    )
+            else:
+                if self.is_mlp and self.input_size % 2048 == 0:
+                   tmp_weight = Parameter(torch.empty(
+                           self.output_size_per_partition,
+                           self.input_size+32,
+                           device=torch.cuda.current_device(),
+                           dtype=config.params_dtype,
+                       ))
+                   self.weight = tmp_weight[:,0:self.input_size]
+                else:             
+                   self.weight = Parameter(
+                       torch.empty(
+                           self.output_size_per_partition,
+                           self.input_size,
+                           device=torch.cuda.current_device(),
+                           dtype=config.params_dtype,
+                       )
+                   )
+                if config.perform_initialization:
+                    _initialize_affine_weight_gpu(
+                        self.weight,
+                        init_method,
+                        partition_dim=0,
+                        stride=stride,
+                        expert_parallel=(self.is_expert and self.expert_parallel),
+                    )
+
+            setattr(self.weight, 'allreduce', not (self.is_expert and self.expert_parallel))
+        else:
+            self.weight = None
+        #pdb.set_trace()
+        if bias:
+            if config.use_cpu_initialization:
+                self.bias = Parameter(
+                    torch.empty(self.output_size_per_partition, dtype=config.params_dtype)
+                )
+            else:
+                self.bias = Parameter(
+                    torch.empty(
+                        self.output_size_per_partition,
+                        device=torch.cuda.current_device(),
+                        dtype=config.params_dtype,
+                    )
+                )
+            set_tensor_model_parallel_attributes(self.bias, True, 0, stride)
+            if config.perform_initialization:
+                # Always initialize bias to zero.
+                with torch.no_grad():
+                    self.bias.zero_()
+            setattr(self.bias, 'allreduce', not (self.is_expert and self.expert_parallel))
+        else:
+            self.register_parameter('bias', None)
+
+        self.sequence_parallel = config.sequence_parallel
+        if self.sequence_parallel and world_size <= 1:
+            warnings.warn(
+                f"`sequence_parallel` is set to `True`, but tensor model parallel size is {world_size}. "
+                f"Disabling sequence parallel."
+            )
+            self.sequence_parallel = False
+
+        self.allreduce_dgrad = world_size > 1 and not self.sequence_parallel
+
+        if config.gradient_accumulation_fusion and not _grad_accum_fusion_available:
+            raise RuntimeError(
+                "ColumnParallelLinear was called with gradient_accumulation_fusion set "
+                "to True but the custom CUDA extension fused_weight_gradient_mlp_cuda "
+                "module is not found. To use gradient_accumulation_fusion you must "
+                "install APEX with --cpp_ext and --cuda_ext. For example: "
+                "pip install --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\" "
+                "Note that the extension requires CUDA>=11. Otherwise, you must turn off "
+                "gradient accumulation fusion."
+            )
+        self.gradient_accumulation_fusion = config.gradient_accumulation_fusion
+
+        if self.allreduce_dgrad and self.sequence_parallel:
+            raise RuntimeError(
+                "`allreduce_dgrad` and `sequence_parallel` cannot be enabled at the same time."
+            )
+
+        self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
+
+        # Hook adding a default empty _extra_state for state dict
+        self._register_load_state_dict_pre_hook(
+            lambda state_dict, prefix, *args, **kwargs: state_dict.setdefault(
+                f'{prefix}_extra_state'
+            )
+        )
+    #@torch.compile(mode="max-autotune-no-cudagraphs")
+    def forward(self, input_: torch.Tensor, weight: Optional[torch.Tensor] = None):
+        """Forward of ColumnParallelLinear
+
+        Args:
+            input_: 3D tensor whose order of dimension is [sequence, batch, hidden]
+
+            weight (optional): weight tensor to use, compulsory when
+                skip_weight_param_allocation is True.
+
+        Returns:
+            - output
+            - bias
+
+        """
+        if weight is None:
+            if self.weight is None:
+                raise RuntimeError(
+                    "weight was not supplied to ColumnParallelLinear forward pass "
+                    "and skip_weight_param_allocation is True."
+                )
+            weight = self.weight
+        else:
+            # Check the weight passed in is the correct shape
+            expected_shape = (self.output_size_per_partition, self.input_size)
+            if weight.shape != expected_shape:
+                raise RuntimeError(
+                    f"supplied weight's shape is {tuple(weight.shape)}, "
+                    f"not {expected_shape} as expected"
+                )
+
+        if self.config._cpu_offloading_context is not None:
+            if self.config._cpu_offloading_context.inside_context == True:
+                assert (
+                    self.config.cpu_offloading == False
+                ), "CPU Offloading cannot be enabled while using non-TE modules"
+
+        bias = self.bias if not self.skip_bias_add else None
+
+        if (
+            self.allreduce_dgrad
+            or self.sequence_parallel
+            or self.explicit_expert_comm
+            or self.disable_grad_reduce
+        ):
+            input_parallel = input_
+        else:
+            input_parallel = copy_to_tensor_model_parallel_region(input_)
+
+        if self.config.defer_embedding_wgrad_compute:
+            self.embedding_activation_buffer.append(input_parallel)
+
+        # Matrix multiply.
+        if not weight.requires_grad:
+            self._forward_impl = linear_with_frozen_weight
+        else:
+            self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
+
+        allreduce_dgrad = False if self.explicit_expert_comm else self.allreduce_dgrad
+
+        #pdb.set_trace()
+        output_parallel = self._forward_impl(
+            input=input_parallel,
+            weight=weight,
+            bias=bias,
+            gradient_accumulation_fusion=self.gradient_accumulation_fusion,
+            async_grad_allreduce=allreduce_dgrad,
+            sequence_parallel=False if self.explicit_expert_comm else self.sequence_parallel,
+            grad_output_buffer=self.grad_output_buffer
+            if self.config.defer_embedding_wgrad_compute
+            else None,
+            allreduce_dgrad=allreduce_dgrad,
+        )
+        if self.gather_output:
+            # All-gather across the partitions.
+            assert not self.sequence_parallel
+            output = gather_from_tensor_model_parallel_region(output_parallel)
+        else:
+            output = output_parallel
+        output_bias = self.bias if self.skip_bias_add else None
+        return output, output_bias
+
+    def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None):
+        """ Sharding along axis 0, bias sharded """
+        state_dict = self.state_dict(prefix='', keep_vars=True)
+        return make_sharded_tensors_for_checkpoint(
+            state_dict, prefix, {'weight': 0, 'bias': 0}, sharded_offsets
+        )
+
+    def set_extra_state(self, state: Any):
+        """ Extra state is ignored """
+
+    def get_extra_state(self) -> None:
+        """ Keep compatibility with TE state dict. """
+        return None
+
+
+class RowParallelLinear(torch.nn.Module):
+    """Linear layer with row parallelism.
+
+    The linear layer is defined as Y = XA + b. A is parallelized along its first dimension and X along its second dimension. A = transpose([A_1 .. A_p]) X = [X_1, ..., X_p]
+
+    Args:
+        input_size: first dimension of matrix A.
+        output_size: second dimension of matrix A.
+        bias: If true, add bias. Note that bias is not parallelized.
+        input_is_parallel: If true, we assume that the input is already split across the GPUs and we do not split again.
+        init_method: method to initialize weights. Note that bias is always set to zero.
+        stride: For the strided linear layers.
+        keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization.
+        skip_bias_add: If True, do not add the bias term, instead return it to be added by the caller. This enables performance optimations where bias can be fused with other elementwise operations.
+        is_expert: If True, the layer is treated as an MoE expert layer
+        tp_comm_buffer_name: Communication buffer name. Not used in
+                             non-Transformer-Engine modules.
+        config: ModelParallelConfig object
+
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        *,
+        config: ModelParallelConfig,
+        init_method: Callable,
+        bias: bool,
+        input_is_parallel: bool,
+        skip_bias_add: bool,
+        stride: int = 1,
+        keep_master_weight_for_test: bool = False,
+        is_expert: bool = False,
+        is_mlp: bool = False,
+        tp_comm_buffer_name: str = None,  # Not used
+    ):
+        super(RowParallelLinear, self).__init__()
+
+        # Keep input parameters
+        self.input_size = input_size
+        self.output_size = output_size
+        self.input_is_parallel = input_is_parallel
+        self.skip_bias_add = skip_bias_add
+        self.config = config
+        self.is_expert = is_expert
+        self.expert_parallel = config.expert_model_parallel_size > 1
+        self.gradient_accumulation_fusion = config.gradient_accumulation_fusion
+        self.sequence_parallel = config.sequence_parallel
+        if self.sequence_parallel and not self.input_is_parallel:
+            raise RuntimeError("To enable `sequence_parallel`, `input_is_parallel` must be `True`")
+
+        self.explicit_expert_comm = self.is_expert and (
+            config.tensor_model_parallel_size > 1 or self.expert_parallel
+        )
+
+        # Divide the weight matrix along the last dimension.
+        if self.explicit_expert_comm and config.moe_extended_tp:
+            world_size = get_tensor_and_expert_parallel_world_size()
+            rank = get_tensor_and_expert_parallel_rank()
+        else:
+            world_size = get_tensor_model_parallel_world_size()
+            rank = get_tensor_model_parallel_rank()
+
+        self.input_size_per_partition = divide(input_size, world_size)
+        self.is_mlp = True #is_mlp
+        # Parameters.
+        # Note: torch.nn.functional.linear performs XA^T + b and as a result
+        # we allocate the transpose.
+        # Initialize weight.
+        if config.use_cpu_initialization:
+            self.weight = Parameter(
+                torch.empty(
+                    self.output_size, self.input_size_per_partition, dtype=config.params_dtype
+                )
+            )
+            if config.perform_initialization:
+                self.master_weight = _initialize_affine_weight_cpu(
+                    self.weight,
+                    self.output_size,
+                    self.input_size,
+                    self.input_size_per_partition,
+                    1,
+                    init_method,
+                    stride=stride,
+                    return_master_weight=keep_master_weight_for_test,
+                    params_dtype=config.params_dtype,
+                    rank=rank,
+                    world_size=world_size,
+                )
+        else:
+            if self.is_mlp and self.input_size_per_partition % 2048 == 0:
+               tmp_weight = Parameter(torch.empty(
+                       self.output_size,
+                       self.input_size_per_partition+32,
+                       device=torch.cuda.current_device(),
+                       dtype=config.params_dtype,
+                   ))
+               self.weight = tmp_weight[:,0:self.input_size_per_partition]
+               print("++++++++ weight.size is in RowParallelLinear:", self.weight.size())
+            else:
+               self.weight = Parameter(
+                   torch.empty(
+                       self.output_size,
+                       self.input_size_per_partition,
+                       device=torch.cuda.current_device(),
+                       dtype=config.params_dtype,
+                   )
+               )
+            if config.perform_initialization:
+                _initialize_affine_weight_gpu(
+                    self.weight,
+                    init_method,
+                    partition_dim=1,
+                    stride=stride,
+                    expert_parallel=(self.is_expert and self.expert_parallel),
+                )
+        setattr(self.weight, 'allreduce', not (self.is_expert and self.expert_parallel))
+
+        if bias:
+            if config.use_cpu_initialization:
+                self.bias = Parameter(torch.empty(self.output_size, dtype=config.params_dtype))
+            else:
+                self.bias = Parameter(
+                    torch.empty(
+                        self.output_size,
+                        device=torch.cuda.current_device(),
+                        dtype=config.params_dtype,
+                    )
+                )
+
+            if config.perform_initialization:
+                # Always initialize bias to zero.
+                with torch.no_grad():
+                    self.bias.zero_()
+            setattr(self.bias, 'allreduce', not (self.is_expert and self.expert_parallel))
+            setattr(self.bias, 'sequence_parallel', self.sequence_parallel)
+        else:
+            self.register_parameter('bias', None)
+
+        self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
+
+        # Hook adding a default empty _extra_state for state dict
+        self._register_load_state_dict_pre_hook(
+            lambda state_dict, prefix, *args, **kwargs: state_dict.setdefault(
+                f'{prefix}_extra_state'
+            )
+        )
+    #@torch.compile(mode="max-autotune-no-cudagraphs")
+    def forward(self, input_):
+        """Forward of RowParallelLinear
+
+        Args:
+            input_: 3D tensor whose order of dimension is [sequence, batch, hidden]
+
+        Returns:
+            - output
+            - bias
+        """
+
+        if self.config._cpu_offloading_context is not None:
+            if self.config._cpu_offloading_context.inside_context == True:
+                assert (
+                    self.config.cpu_offloading == False
+                ), "CPU Offloading cannot be enabled while using non-TE modules"
+
+        # Set up backprop all-reduce.
+        if self.input_is_parallel:
+            input_parallel = input_
+        else:
+            assert not self.sequence_parallel
+            input_parallel = scatter_to_tensor_model_parallel_region(input_)
+        # Matrix multiply.
+        if not self.weight.requires_grad:
+            self._forward_impl = linear_with_frozen_weight
+        else:
+            self._forward_impl = linear_with_grad_accumulation_and_async_allreduce
+
+        allreduce_dgrad = False
+
+        output_parallel = self._forward_impl(
+            input=input_parallel,
+            weight=self.weight,
+            bias=None,
+            gradient_accumulation_fusion=self.gradient_accumulation_fusion,
+            async_grad_allreduce=allreduce_dgrad,
+            sequence_parallel=False,
+            grad_output_buffer=None,
+            allreduce_dgrad=allreduce_dgrad,
+        )
+
+        # All-reduce across all the partitions.
+        if self.explicit_expert_comm:
+            assert self.skip_bias_add
+            output_ = output_parallel
+        elif self.sequence_parallel:
+            output_ = reduce_scatter_to_sequence_parallel_region(output_parallel)
+        else:
+            output_ = reduce_from_tensor_model_parallel_region(output_parallel)
+        if not self.skip_bias_add:
+            output = (output_ + self.bias) if self.bias is not None else output_
+            output_bias = None
+        else:
+            output = output_
+            output_bias = self.bias
+        return output, output_bias
+
+    def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None):
+        """ Sharding along axis 1, bias not sharded """
+        state_dict = self.state_dict(prefix='', keep_vars=True)
+        return make_sharded_tensors_for_checkpoint(
+            state_dict, prefix, {'weight': 1}, sharded_offsets
+        )
+
+    def set_extra_state(self, state: Any):
+        """ Extra state is ignored """
+
+    def get_extra_state(self) -> None:
+        """ Keep compatibility with TE state dict. """
+        return None

megatron/core/tensor_parallel/mappings.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+import torch
+
+from megatron.core.parallel_state import (
+    get_expert_model_parallel_group,
+    get_global_memory_buffer,
+    get_tensor_and_expert_parallel_group,
+    get_tensor_model_parallel_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+
+from .utils import split_tensor_along_last_dim
+
+
+def _reduce(input_):
+    """All-reduce the input tensor across model parallel group."""
+
+    # Bypass the function if we are using only 1 GPU.
+    if get_tensor_model_parallel_world_size() == 1:
+        return input_
+
+    # All-reduce.
+    torch.distributed.all_reduce(input_.contiguous(), group=get_tensor_model_parallel_group())
+
+    return input_
+
+
+def _split_along_last_dim(input_):
+    """Split the tensor along its last dimension and keep the
+    corresponding slice."""
+
+    world_size = get_tensor_model_parallel_world_size()
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    # Split along last dimension.
+    input_list = split_tensor_along_last_dim(input_, world_size)
+
+    # Note: torch.split does not create contiguous tensors by default.
+    rank = get_tensor_model_parallel_rank()
+    output = input_list[rank].contiguous()
+
+    return output
+
+
+def _split_along_first_dim(input_):
+    """Split the tensor along its first dimension and keep the
+    corresponding slice."""
+
+    world_size = get_tensor_model_parallel_world_size()
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    # Split along first dimension.
+    dim_size = input_.size()[0]
+    assert (
+        dim_size % world_size == 0
+    ), "First dimension of the tensor should be divisible by tensor parallel size"
+    local_dim_size = dim_size // world_size
+    rank = get_tensor_model_parallel_rank()
+    dim_offset = rank * local_dim_size
+
+    output = input_[dim_offset : dim_offset + local_dim_size].contiguous()
+
+    return output
+
+
+def _gather_along_last_dim(input_):
+    """Gather tensors and concatinate along the last dimension."""
+
+    world_size = get_tensor_model_parallel_world_size()
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    dim_size = list(input_.size())
+    dim_size[0] = dim_size[0] * world_size
+
+    output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
+    torch.distributed.all_gather_into_tensor(
+        output, input_.contiguous(), group=get_tensor_model_parallel_group()
+    )
+    tensor_list = output.chunk(world_size, dim=0)
+    output = torch.cat(tensor_list, dim=-1).contiguous()
+
+    return output
+
+
+def _reduce_scatter_along_last_dim(input_):
+    """Reduce-scatter tensors on the last dimension."""
+    world_size = get_tensor_model_parallel_world_size()
+    target_shape = list(input_.size())
+    target_shape[-1] = target_shape[-1] // world_size
+    input_ = input_.reshape(-1, input_.shape[-1])
+    split_tensors = torch.split(
+        input_, split_size_or_sections=input_.shape[-1] // world_size, dim=1
+    )
+    concat_tensor = torch.cat(split_tensors, dim=0)
+    output = _reduce_scatter_along_first_dim(concat_tensor).reshape(target_shape)
+    return output
+
+
+def _gather_along_first_dim(input_):
+    """Gather tensors and concatinate along the first dimension."""
+
+    world_size = get_tensor_model_parallel_world_size()
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    dim_size = list(input_.size())
+    dim_size[0] = dim_size[0] * world_size
+
+    output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
+    torch.distributed._all_gather_base(
+        output, input_.contiguous(), group=get_tensor_model_parallel_group()
+    )
+
+    return output
+
+
+def _reduce_scatter_along_first_dim(input_):
+    """Reduce-scatter the input tensor across model parallel group."""
+    world_size = get_tensor_model_parallel_world_size()
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    dim_size = list(input_.size())
+    assert (
+        dim_size[0] % world_size == 0
+    ), "First dimension of the tensor should be divisible by tensor parallel size"
+
+    dim_size[0] = dim_size[0] // world_size
+
+    output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
+    torch.distributed._reduce_scatter_base(
+        output, input_.contiguous(), group=get_tensor_model_parallel_group()
+    )
+    return output
+
+
+def _gather_along_first_dim_moe(input_, use_global_buffer=False):
+    """Gather tensors and concatenate along the first dimension."""
+    group = get_tensor_and_expert_parallel_group()
+    world_size = torch.distributed.get_world_size(group=group)
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    dim_size = list(input_.size())
+    dim_size[0] = dim_size[0] * world_size
+
+    if use_global_buffer:
+        output = get_global_memory_buffer().get_tensor(dim_size, input_.dtype, "mpu")
+    else:
+        output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
+    torch.distributed._all_gather_base(output, input_.contiguous(), group=group)
+
+    return output
+
+
+def _reduce_scatter_along_first_dim_moe(input_, use_global_buffer=False):
+    """Reduce-scatter the input tensor across model parallel group."""
+    group = get_tensor_and_expert_parallel_group()
+    world_size = torch.distributed.get_world_size(group=group)
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    dim_size = list(input_.size())
+    assert dim_size[0] % world_size == 0
+    dim_size[0] = dim_size[0] // world_size
+
+    if use_global_buffer:
+        output = get_global_memory_buffer().get_tensor(dim_size, input_.dtype, "mpu")
+    else:
+        output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
+    torch.distributed._reduce_scatter_base(output, input_.contiguous(), group=group)
+    return output
+
+
+def _gather_along_first_dim_expert_parallel(input_):
+    """Gather tensors and concatenate along the first dimension."""
+    group = get_expert_model_parallel_group()
+    world_size = torch.distributed.get_world_size(group=group)
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+
+    dim_size = list(input_.size())
+    dim_size[0] = dim_size[0] * world_size
+
+    output = torch.empty(dim_size, dtype=input_.dtype, device=torch.cuda.current_device())
+    torch.distributed._all_gather_base(output, input_.contiguous(), group=group)
+
+    return output
+
+
+class _CopyToModelParallelRegion(torch.autograd.Function):
+    """Pass the input to the model parallel region."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return input_
+
+    @staticmethod
+    def forward(ctx, input_):
+        return input_
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _reduce(grad_output)
+
+
+class _ReduceFromModelParallelRegion(torch.autograd.Function):
+    """All-reduce the input from the model parallel region."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _reduce(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _reduce(input_)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output
+
+
+class _ScatterToModelParallelRegion(torch.autograd.Function):
+    """Split the input and keep only the corresponding chuck to the rank."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _split_along_last_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _split_along_last_dim(input_)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _gather_along_last_dim(grad_output)
+
+
+class _GatherFromModelParallelRegion(torch.autograd.Function):
+    """Gather the input from model parallel region and concatinate."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _gather_along_last_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _gather_along_last_dim(input_)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _split_along_last_dim(grad_output)
+
+
+class _ScatterToSequenceParallelRegion(torch.autograd.Function):
+    """Split the input and keep only the corresponding chuck to the rank."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _split_along_first_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _split_along_first_dim(input_)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _gather_along_first_dim(grad_output)
+
+
+class _GatherFromSequenceParallelRegion(torch.autograd.Function):
+    """Gather the input from sequence parallel region and concatinate."""
+
+    @staticmethod
+    def symbolic(graph, input_, tensor_parallel_output_grad=True):
+        return _gather_along_first_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_, tensor_parallel_output_grad=True):
+        ctx.tensor_parallel_output_grad = tensor_parallel_output_grad
+        return _gather_along_first_dim(input_)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        tensor_parallel_output_grad = ctx.tensor_parallel_output_grad
+
+        # If the computation graph after the gather operation is
+        # in the tensor parallel mode, output gradients need to reduce
+        # scattered and whereas if the computation is duplicated,
+        # output gradients need to be scattered.
+        if tensor_parallel_output_grad:
+            return _reduce_scatter_along_first_dim(grad_output), None
+        else:
+            return _split_along_first_dim(grad_output), None
+
+
+class _ReduceScatterToSequenceParallelRegion(torch.autograd.Function):
+    """Reduce scatter the input from the model parallel region."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _reduce_scatter_along_first_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _reduce_scatter_along_first_dim(input_)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _gather_along_first_dim(grad_output)
+
+
+class _GatherFromSequenceParallelRegionToMOE(torch.autograd.Function):
+    """Gather the input from model parallel region and concatenate."""  # TODO
+
+    @staticmethod
+    def symbolic(graph, input_, use_global_buffer=False):
+        return _gather_along_first_dim_moe(input_, use_global_buffer)
+
+    @staticmethod
+    def forward(ctx, input_, use_global_buffer=False):
+        ctx.use_global_buffer = use_global_buffer
+        return _gather_along_first_dim_moe(input_, use_global_buffer)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        use_global_buffer = ctx.use_global_buffer
+        return _reduce_scatter_along_first_dim_moe(grad_output, use_global_buffer), None
+
+
+class _ReduceScatterToSequenceParallelRegionFromMOE(torch.autograd.Function):
+    """Reduce scatter the input from the model parallel region."""
+
+    @staticmethod
+    def symbolic(graph, input_, use_global_buffer=False):
+        return _reduce_scatter_along_first_dim_moe(input_, use_global_buffer)
+
+    @staticmethod
+    def forward(ctx, input_, use_global_buffer=False):
+        ctx.use_global_buffer = use_global_buffer
+        return _reduce_scatter_along_first_dim_moe(input_, use_global_buffer)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        use_global_buffer = ctx.use_global_buffer
+        return _gather_along_first_dim_moe(grad_output, use_global_buffer), None
+
+
+class _AllGatherFromTensorParallelRegion(torch.autograd.Function):
+    """Gather the input from model parallel region and concatenate."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _gather_along_last_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _gather_along_last_dim(input_,)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _reduce_scatter_along_last_dim(grad_output)
+
+
+class _ReduceScatterToTensorParallelRegion(torch.autograd.Function):
+    """Reduce scatter the input from the model parallel region."""
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _reduce_scatter_along_last_dim(input_)
+
+    @staticmethod
+    def forward(ctx, input_):
+        return _reduce_scatter_along_last_dim(input_,)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _gather_along_last_dim(grad_output)
+
+
+class _AllToAll(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, group, input, output_split_sizes, input_split_sizes):
+        ctx.group = group
+        ctx.output_split_sizes = output_split_sizes
+        ctx.input_split_sizes = input_split_sizes
+
+        world_size = torch.distributed.get_world_size(group=group)
+        # Bypass the function if we are using only 1 GPU.
+        if world_size == 1:
+            return input
+
+        input = input.contiguous()
+        if output_split_sizes is None:
+            # Equal split (all2all)
+            output = torch.empty_like(input)
+        else:
+            # Unequal split (all2all-v)
+            output = input.new_empty(
+                size=[sum(output_split_sizes)] + list(input.size()[1:]),
+                dtype=input.dtype,
+                device=torch.cuda.current_device(),
+            )
+        torch.distributed.all_to_all_single(
+            output,
+            input,
+            output_split_sizes=output_split_sizes,
+            input_split_sizes=input_split_sizes,
+            group=group,
+        )
+        return output
+
+    @staticmethod
+    def backward(ctx, *grad_output):
+        return (
+            None,
+            _AllToAll.apply(ctx.group, *grad_output, ctx.input_split_sizes, ctx.output_split_sizes),
+            None,
+            None,
+        )
+
+
+# -----------------
+# Helper functions.
+# -----------------
+
+
+def copy_to_tensor_model_parallel_region(input_):
+    return _CopyToModelParallelRegion.apply(input_)
+
+
+def reduce_from_tensor_model_parallel_region(input_):
+    return _ReduceFromModelParallelRegion.apply(input_)
+
+
+def scatter_to_tensor_model_parallel_region(input_):
+    return _ScatterToModelParallelRegion.apply(input_)
+
+
+def gather_from_tensor_model_parallel_region(input_):
+    return _GatherFromModelParallelRegion.apply(input_)
+
+
+def scatter_to_sequence_parallel_region(input_):
+    return _ScatterToSequenceParallelRegion.apply(input_)
+
+
+def gather_from_sequence_parallel_region(input_, tensor_parallel_output_grad=True):
+    return _GatherFromSequenceParallelRegion.apply(input_, tensor_parallel_output_grad)
+
+
+def reduce_scatter_to_sequence_parallel_region(input_):
+    return _ReduceScatterToSequenceParallelRegion.apply(input_)
+
+
+def gather_from_sequence_parallel_region_to_moe(input_, use_global_buffer=False):
+    return _GatherFromSequenceParallelRegionToMOE.apply(input_, use_global_buffer)
+
+
+def reduce_scatter_to_sequence_parallel_region_from_moe(input_, use_global_buffer=False):
+    return _ReduceScatterToSequenceParallelRegionFromMOE.apply(input_, use_global_buffer)
+
+
+def all_gather_last_dim_from_tensor_parallel_region(input_):
+    return _AllGatherFromTensorParallelRegion.apply(input_)
+
+
+def reduce_scatter_last_dim_to_tensor_parallel_region(input_):
+    return _ReduceScatterToTensorParallelRegion.apply(input_)
+
+
+def all_to_all(group, input_, output_split_sizes_=None, input_split_sizes_=None):
+    return _AllToAll.apply(group, input_, output_split_sizes_, input_split_sizes_)
+
+
+def all_to_all_sp2hp(input_):
+    """
+    Perform AlltoAll communication on tensor parallel group, transform the input tensor from shape [num_tokens/TP, H] to [num_tokens, H/TP].
+
+    Args:
+        input_ (torch.Tensor): The input tensor which has been distributed along the sequence dimension.
+
+    Returns:
+        torch.Tensor: The output tensor with shape [num_tokens, H/TP].
+
+    """
+    world_size = get_tensor_model_parallel_world_size()
+    tp_group = get_tensor_model_parallel_group()
+    input_ = input_.reshape(-1, input_.shape[-1])
+    split_tensors = torch.split(
+        input_, split_size_or_sections=input_.shape[-1] // world_size, dim=1
+    )
+    concat_tensor = torch.cat(split_tensors, dim=0)
+    output = all_to_all(tp_group, concat_tensor)
+    return output
+
+
+def all_to_all_hp2sp(input_):
+    """
+    Perform AlltoAll communication on tensor parallel group, transform the input tensor from shape [num_tokens, H/TP] to [num_tokens/TP, H].
+
+    Args:
+        input_ (torch.Tensor): The input tensor which has been distributed along the hidden dimension.
+        
+    Returns:
+        torch.Tensor: The output tensor with shape [num_tokens/TP, H].
+    """
+    world_size = get_tensor_model_parallel_world_size()
+    input_ = input_.reshape(-1, input_.shape[-1])
+    tp_group = get_tensor_model_parallel_group()
+    input_exchanged = all_to_all(tp_group, input_)
+    input_reshaped = input_exchanged.reshape(-1, input_exchanged.shape[-1])
+    split_tensors = torch.split(
+        input_reshaped, split_size_or_sections=input_reshaped.shape[0] // world_size, dim=0
+    )
+    output = torch.cat(split_tensors, dim=-1)
+    return output

megatron/core/tensor_parallel/random.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+# Parts of the code here are adapted from PyTorch
+# repo: https://github.com/pytorch/pytorch
+
+import contextlib
+from importlib.metadata import version
+
+import torch
+from pkg_resources import packaging
+from torch import _C
+from torch.cuda import _lazy_call
+from torch.cuda import device as device_ctx_manager
+from torch.utils.checkpoint import detach_variable
+
+from megatron.core.parallel_state import (
+    get_data_parallel_rank,
+    get_expert_model_parallel_rank,
+    get_tensor_model_parallel_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from megatron.core.utils import safely_set_viewless_tensor_data
+
+from .utils import gather_split_1d_tensor, split_tensor_into_1d_equal_chunks
+
+# Default name for the model parallel rng tracker.
+_MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng'
+_EXPERT_PARALLEL_RNG_TRACKER_NAME = 'expert-parallel-rng'
+_DATA_PARALLEL_RNG_TRACKER_NAME = 'data-parallel-rng'
+
+
+def _set_cuda_rng_state(new_state, device=-1):
+    """Sets the random number generator state of the current GPU.
+
+    Argumentss:
+        new_state (torch.ByteTensor): The desired state
+    This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
+    with a single change: the input state is not cloned. Cloning caused
+    major performance issues for +4 GPU cases.
+    """
+    if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState):
+        # older PyTorch
+        def cb():
+            with device_ctx_manager(device):
+                _C._cuda_setRNGState(new_state)
+
+    else:
+        # newer PyTorch
+        if device == -1:
+            device = torch.device('cuda')
+        elif isinstance(device, str):
+            device = torch.device(device)
+        elif isinstance(device, int):
+            device = torch.device('cuda', device)
+
+        def cb():
+            idx = device.index
+            if idx is None:
+                idx = torch.cuda.current_device()
+            default_generator = torch.cuda.default_generators[idx]
+            default_generator.set_state(new_state)
+
+    _lazy_call(cb)
+
+
+def get_expert_parallel_rng_tracker_name():
+    global _EXPERT_PARALLEL_RNG_TRACKER_NAME
+    return _EXPERT_PARALLEL_RNG_TRACKER_NAME
+
+
+def get_data_parallel_rng_tracker_name():
+    global _DATA_PARALLEL_RNG_TRACKER_NAME
+    return _DATA_PARALLEL_RNG_TRACKER_NAME
+
+
+class CudaRNGStatesTracker:
+    """Tracker for the cuda RNG states.
+
+    Using the `add` method, a cuda rng state is initialized based on
+    the input `seed` and is assigned to `name`. Later, by forking the
+    rng state, we can perform operations and return to our starting
+    cuda state.
+    """
+
+    def __init__(self):
+        self.reset()
+
+    def is_initialized(self):
+        return self._is_initialized
+
+    def reset(self):
+        """Set to the initial state (no tracker)."""
+
+        # Track if initialized.
+        self._is_initialized = False
+
+        # Map from a string name to the cuda rng state.
+        self.states_ = {}
+
+        # Seeds are just for book keeping and ensure no seed is set twice.
+        self.seeds_ = set()
+
+    def get_states(self):
+        """Get rng states. Copy the dictionary so we have direct
+        pointers to the states, not just a pointer to the dictionary."""
+        states = {}
+        for name in self.states_:
+            states[name] = self.states_[name]
+        return states
+
+    def set_states(self, states):
+        """Set the rng states. For efficiency purposes, we do not check
+        the size of seed for compatibility."""
+        self._is_initialized = True
+        self.states_ = states
+
+    def add(self, name, seed):
+        """Track the rng state."""
+        self._is_initialized = True
+        # Check seed is not already used.
+        if seed in self.seeds_:
+            raise Exception('seed {} already exists'.format(seed))
+        self.seeds_.add(seed)
+        # Check that state is not already defined.
+        if name in self.states_:
+            raise Exception('cuda rng state {} already exists'.format(name))
+        # Get the current rng state.
+        orig_rng_state = torch.cuda.get_rng_state()
+        # Set the new state and store it.
+        torch.cuda.manual_seed(seed)
+        self.states_[name] = torch.cuda.get_rng_state()
+        # Reset rng state to what it was.
+        _set_cuda_rng_state(orig_rng_state)
+
+    @contextlib.contextmanager
+    def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME):
+        """Fork the cuda rng state, perform operations, and exit with
+        the original state."""
+        # Check if we have added the state
+        if name not in self.states_:
+            raise Exception('cuda rng state {} is not added'.format(name))
+        # Store current rng state.
+        orig_cuda_rng_state = torch.cuda.get_rng_state()
+        # Set rng state to the desired one
+        _set_cuda_rng_state(self.states_[name])
+        # Do the stuff we wanted to do.
+        try:
+            yield
+        finally:
+            # Update the current rng state for later use.
+            self.states_[name] = torch.cuda.get_rng_state()
+            # And set the state to the original state we started with.
+            _set_cuda_rng_state(orig_cuda_rng_state)
+
+
+# RNG tracker object.
+_CUDA_RNG_STATE_TRACKER = None
+_CUDA_RNG_STATE_TRACKER_INITIALIZED = False
+
+
+def initialize_rng_tracker(use_te_rng_tracker: bool = False):
+    global _CUDA_RNG_STATE_TRACKER
+    global _CUDA_RNG_STATE_TRACKER_INITIALIZED
+    if _CUDA_RNG_STATE_TRACKER_INITIALIZED:
+        return
+    if use_te_rng_tracker:
+        try:
+            import transformer_engine.pytorch as te
+
+            _te_version = packaging.version.Version(version("transformer-engine"))
+            if _te_version < packaging.version.Version("1.5.0"):
+                raise RuntimeError("use_te_rng_tracker requires TransformerEngine version >= 1.5")
+        except:
+            raise RuntimeError("use_te_rng_tracker requires TransformerEngine, but not installed")
+    if use_te_rng_tracker:
+        _CUDA_RNG_STATE_TRACKER = te.distributed.CudaRNGStatesTracker()
+    else:
+        _CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
+    _CUDA_RNG_STATE_TRACKER_INITIALIZED = True
+
+
+def get_cuda_rng_tracker():
+    """Get cuda rng tracker."""
+    initialize_rng_tracker()
+    return _CUDA_RNG_STATE_TRACKER
+
+
+def model_parallel_cuda_manual_seed(seed):
+    """Initialize model parallel cuda seed.
+
+    This function should be called after the model parallel is
+    initialized. Also, no torch.cuda.manual_seed should be called
+    after this function. Basically, this is replacement for that
+    function.
+    Two set of RNG states are tracked:
+    default state: This is for data parallelism and is the same among a set of model parallel GPUs but different across different model paralle groups. This is used for example for dropout in the non-tensor-model-parallel regions.
+    tensor-model-parallel state: This state is different among a set of model parallel GPUs, but the same across data parallel groups. This is used for example for dropout in model parallel regions.
+    """
+    # 2718 is just for fun and any POSITIVE value will work.
+    offset = seed + 2718
+    tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank()
+    # Data parallel gets the original seed.
+    data_parallel_seed = seed
+
+    initialize_rng_tracker()
+    _CUDA_RNG_STATE_TRACKER.reset()
+    # Set the default state.
+    torch.cuda.manual_seed(data_parallel_seed)
+    _CUDA_RNG_STATE_TRACKER.add(_DATA_PARALLEL_RNG_TRACKER_NAME, data_parallel_seed)
+
+    # and model parallel state.
+    _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, tensor_model_parallel_seed)
+
+    expert_parallel_seed = (
+        seed + 1024 + 100 * get_expert_model_parallel_rank() + get_tensor_model_parallel_rank()
+    )
+    _CUDA_RNG_STATE_TRACKER.add(_EXPERT_PARALLEL_RNG_TRACKER_NAME, expert_parallel_seed)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    """Checkpoint Function 
+
+    This function is adapted from torch.utils.checkpoint with two main changes:
+    1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
+    2) the states in the model parallel tracker are also properly tracked/set/reset.
+    """
+
+    @staticmethod
+    def forward(ctx, run_function, distribute_saved_activations, *args):
+        ctx.run_function = run_function
+        ctx.distribute_saved_activations = distribute_saved_activations
+
+        # Copy the rng states.
+        ctx.fwd_cpu_rng_state = torch.get_rng_state()
+        ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
+        ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
+
+        with torch.no_grad():
+            outputs = run_function(*args)
+
+        # Divide hidden states across model parallel group and only keep
+        # the chunk corresponding to the current rank.
+        if distribute_saved_activations:
+            ctx.input_0_shape = args[0].data.shape
+            safely_set_viewless_tensor_data(
+                args[0], split_tensor_into_1d_equal_chunks(args[0].data, new_buffer=True)
+            )
+
+        # Store everything.
+        ctx.save_for_backward(*args)
+
+        return outputs
+
+    @staticmethod
+    def backward(ctx, *args):
+        if not torch.autograd._is_checkpoint_valid():
+            raise RuntimeError(
+                "Checkpointing is not compatible with .grad(), "
+                "please use .backward() if possible"
+            )
+        inputs = ctx.saved_tensors
+        if ctx.distribute_saved_activations:
+            safely_set_viewless_tensor_data(
+                inputs[0], gather_split_1d_tensor(inputs[0].data).view(ctx.input_0_shape)
+            )
+
+        # Store the current states.
+        bwd_cpu_rng_state = torch.get_rng_state()
+        bwd_cuda_rng_state = torch.cuda.get_rng_state()
+        bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
+
+        # Set the states to what it used to be before the forward pass.
+        torch.set_rng_state(ctx.fwd_cpu_rng_state)
+        _set_cuda_rng_state(ctx.fwd_cuda_rng_state)
+        get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)
+
+        # Compute the forward pass.
+        detached_inputs = detach_variable(inputs)
+        with torch.enable_grad():
+            outputs = ctx.run_function(*detached_inputs)
+
+        # Set the states back to what it was at the start of this function.
+        torch.set_rng_state(bwd_cpu_rng_state)
+        _set_cuda_rng_state(bwd_cuda_rng_state)
+        get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)
+
+        if isinstance(outputs, torch.Tensor):
+            outputs = (outputs,)
+
+        # filter out non tensor outputs for backward pass
+        outputs, args = zip(*filter(lambda x: torch.is_tensor(x[0]), zip(outputs, args)))
+        torch.autograd.backward(outputs, args)
+        grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp for inp in detached_inputs)
+        return (None, None) + grads
+
+
+def checkpoint(function, distribute_saved_activations, *args):
+    """Checkpoint a model or part of the model.
+    This has been directly copied from torch.utils.checkpoint."""
+    return CheckpointFunction.apply(function, distribute_saved_activations, *args)

megatron/core/tensor_parallel/utils.py

+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+from typing import List, Sequence
+
+import torch
+
+from megatron.core import parallel_state
+from megatron.core.parallel_state import (
+    get_tensor_model_parallel_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from megatron.core.utils import divide
+
+
+def split_tensor_along_last_dim(
+    tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False,
+) -> List[torch.Tensor]:
+    """ Split a tensor along its last dimension.
+
+        Args:
+            tensor: input tensor.
+            num_partitions: number of partitions to split the tensor
+            contiguous_split_chunks: If True, make each chunk contiguous
+                                     in memory.
+
+        Returns:
+            A list of Tensors
+    """
+    # Get the size and dimension.
+    last_dim = tensor.dim() - 1
+    last_dim_size = divide(tensor.size()[last_dim], num_partitions)
+    # Split.
+    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
+    # Note: torch.split does not create contiguous tensors by default.
+    if contiguous_split_chunks:
+        return tuple(chunk.contiguous() for chunk in tensor_list)
+
+    return tensor_list
+
+
+def split_tensor_into_1d_equal_chunks(tensor, new_buffer=False):
+    """ Break a tensor into equal 1D chunks across tensor parallel ranks.
+
+        Returns a Tensor or View with this rank's portion of the data.
+
+        Args:
+            tensor: The tensor to split
+
+        Keyword Args:
+            new_buffer (bool): If True, returns a new Tensor.
+                               If False, returns a view into the existing Tensor.
+                               Default is False
+
+    """
+    partition_size = torch.numel(tensor) // parallel_state.get_tensor_model_parallel_world_size()
+    start_index = partition_size * parallel_state.get_tensor_model_parallel_rank()
+    end_index = start_index + partition_size
+    if new_buffer:
+        data = torch.empty(
+            partition_size,
+            dtype=tensor.dtype,
+            device=torch.cuda.current_device(),
+            requires_grad=False,
+        )
+        data.copy_(tensor.view(-1)[start_index:end_index])
+    else:
+        data = tensor.view(-1)[start_index:end_index]
+    return data
+
+
+def gather_split_1d_tensor(tensor):
+    """ Opposite of split_tensor_into_1d_equal_chunks. Gather values from tensor
+        model parallel ranks.
+
+        Returns a new Tensor with the gathered data.
+
+        Args:
+            tensor: A Tensor or view of this rank's portion of the data.
+    """
+    numel_gathered = torch.numel(tensor) * parallel_state.get_tensor_model_parallel_world_size()
+    gathered = torch.empty(
+        numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False
+    )
+    # TODO: This API is experimental in pytorch (as of Feb 2022) and
+    # this might break in future pytorch releases. We chose this API
+    # as opposed to torch.distributed.all_gather for efficiency reasons.
+    # This API calls directly NCCL all-gather versus the former does
+    # internal copies and can potentially cause slow down.
+    torch.distributed._all_gather_base(
+        gathered, tensor, group=parallel_state.get_tensor_model_parallel_group()
+    )
+    return gathered
+
+
+class VocabUtility:
+    """ Split the vocabulary into `world_size` chunks and return the first
+        and last index of the vocabulary belonging to the `rank`
+        partition: Note that indices in [fist, last)
+
+    """
+
+    @staticmethod
+    def vocab_range_from_per_partition_vocab_size(
+        per_partition_vocab_size: int, rank, world_size: int
+    ) -> Sequence[int]:
+        index_f = rank * per_partition_vocab_size
+        index_l = index_f + per_partition_vocab_size
+        return index_f, index_l
+
+    @staticmethod
+    def vocab_range_from_global_vocab_size(
+        global_vocab_size: int, rank: int, world_size: int
+    ) -> Sequence[int]:
+        per_partition_vocab_size = divide(global_vocab_size, world_size)
+        return VocabUtility.vocab_range_from_per_partition_vocab_size(
+            per_partition_vocab_size, rank, world_size
+        )

megatron/core/timers.py

+# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+
+"""Megatron timers."""
+
+import time
+from abc import ABC, abstractmethod
+from typing import List
+
+import torch
+
+
+class TimerBase(ABC):
+    def __init__(self, name):
+        self.name = name
+
+    @abstractmethod
+    def start(self, barrier=False):
+        pass
+
+    @abstractmethod
+    def stop(self, barrier=False):
+        pass
+
+    @abstractmethod
+    def reset(self):
+        pass
+
+    @abstractmethod
+    def elapsed(self, reset=True, barrier=False):
+        pass
+
+
+class DummyTimer(TimerBase):
+    def __init__(self):
+        super().__init__('dummy timer')
+
+    def start(self, barrier=False):
+        return
+
+    def stop(self, barrier=False):
+        return
+
+    def reset(self):
+        return
+
+    def elapsed(self, reset=True, barrier=False):
+        raise Exception('dummy timer should not be used to calculate elapsed time')
+
+
+class Timer(TimerBase):
+    """
+    Timer class with ability to start/stop.
+
+    Comment on using `barrier`: If this flag is passed, then all
+    the caller processes will wait till all reach the timing routine.
+    It is up to the user to make sure all the ranks in `barrier_group`
+    call it otherwise, it will result in a hang.
+    Comment on `barrier_group`: By default it is set to None which
+    in torch distributed land, it will result in the global communicator.
+    """
+
+    def __init__(self, name):
+        """Initialize Timer.
+
+        Args:
+            name (str): Name of the timer.
+        """
+        super().__init__(name)
+        self._elapsed = 0.0
+        self._active_time = 0.0
+        self._started = False
+        # Note that None will default to the global process group
+        self._barrier_group = None
+        self._start_time = time.time()
+
+    def set_barrier_group(self, barrier_group):
+        """Sets barrier group.
+
+        Args:
+            barrier_group (ProcessGroup): Torch ProcessGroup for barrier.
+        """
+        self._barrier_group = barrier_group
+
+    def start(self, barrier=False):
+        """Start the timer.
+
+        Args:
+            barrier (bool, optional): Synchronizes ranks before starting. Defaults to False.
+        """
+        assert not self._started, 'timer has already been started'
+        if barrier:
+            torch.distributed.barrier(group=self._barrier_group)
+        torch.cuda.synchronize()
+        self._start_time = time.time()
+        self._started = True
+
+    def stop(self, barrier=False):
+        """Stop the timer.
+
+        Args:
+            barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
+        """
+        assert self._started, 'timer is not started'
+        if barrier:
+            torch.distributed.barrier(group=self._barrier_group)
+        torch.cuda.synchronize()
+        elapsed = time.time() - self._start_time
+        self._elapsed += elapsed
+        self._active_time += elapsed
+        self._started = False
+
+    def reset(self):
+        """Reset timer.
+        """
+        # Don't reset _active_time
+        self._elapsed = 0.0
+        self._started = False
+
+    def elapsed(self, reset=True, barrier=False):
+        """Calculates the elapsed time and restarts timer.
+
+        Args:
+            reset (bool, optional): Resets timer before restarting. Defaults to True.
+            barrier (bool, optional): Synchronizes ranks before stopping. Defaults to False.
+
+        Returns:
+            float: Elapsed time.
+        """
+        _started = self._started
+        # If the timing in progress, end it first.
+        if self._started:
+            self.stop(barrier=barrier)
+        # Get the elapsed time.
+        _elapsed = self._elapsed
+        # Reset the elapsed time
+        if reset:
+            self.reset()
+        # If timing was in progress, set it back.
+        if _started:
+            self.start(barrier=barrier)
+        return _elapsed
+
+    def active_time(self):
+        return self._active_time
+
+
+class Timers:
+    """Class for a group of Timers.
+    """
+
+    def __init__(self, log_level, log_option):
+        """Initialize group of timers.
+
+        Args:
+            log_level (int): Log level to control what timers are enabled.            
+            log_option (str): Setting for logging statistics over ranks for all the timers. Allowed: ['max', 'minmax', 'all'].
+        """
+        self._log_level = log_level
+        allowed_log_options = set(['max', 'minmax', 'all'])
+        assert (
+            log_option in allowed_log_options
+        ), 'input log option {} is invalid. It must be one of {}'.format(
+            log_option, allowed_log_options
+        )
+        self._log_option = log_option
+        self._timers = {}
+        self._log_levels = {}
+        self._dummy_timer = DummyTimer()
+        self._max_log_level = 2
+
+    def __call__(self, name, log_level=None):
+        """Call timer with name and log level."""
+        # If the timer has already been set, then check if the log-level
+        # is provided, it matches the one that the timer was created with.
+        if name in self._timers:
+            if log_level is not None:
+                assert log_level == self._log_levels[name], (
+                    'input log level {} does not match already existing '
+                    'log level {} for {} timer'.format(log_level, self._log_levels[name], name)
+                )
+            return self._timers[name]
+        # If timer does not exist and no log level is provided,
+        # set it to the max log level which is 2.
+        if log_level is None:
+            log_level = self._max_log_level
+        assert (
+            log_level <= self._max_log_level
+        ), 'log level {} is larger than max supported log level {}'.format(
+            log_level, self._max_log_level
+        )
+        # Now if the input log level is larger than the one set for
+        # the timers class, just ignore it and return a dummy timer.
+        if log_level > self._log_level:
+            return self._dummy_timer
+        # Otherwise, initalize the timer and set the level.
+        self._timers[name] = Timer(name)
+        self._log_levels[name] = log_level
+        return self._timers[name]
+
+    def _get_elapsed_time_all_ranks(self, names, reset, barrier):
+        """Returns elapsed times of timers in names.
+        Assumptions:
+            - All the ranks call this function.
+            - `names` are identical on all ranks.
+        If the above assumptions are not met, calling this function will
+        result in hang.
+
+        Args:
+            names (List[str]): list of timer names
+            reset (bool): reset the timer after recording the elapsed time
+            barrier (bool): if set, do a global barrier before time measurments
+
+        Returns:
+            torch.tensor: Tensor of size [world_size, len(names)] with times in float.
+        """
+
+        # First make sure all the callers are in sync.
+        if barrier:
+            torch.distributed.barrier()
+
+        world_size = torch.distributed.get_world_size()
+        rank = torch.distributed.get_rank()
+
+        # Here we can use gather on the rank we want to print the
+        # timing, however, there is no gather_base support in
+        # pytorch yet. It is simpler to deal with a single tensor
+        # and since we are only gathering a small amount of data,
+        # it should be ok to use all-gather instead of gather.
+        rank_name_to_time = torch.zeros(
+            (world_size, len(names)), dtype=torch.float, device=torch.cuda.current_device()
+        )
+        for i, name in enumerate(names):
+            if name in self._timers:
+                # Here we don't need to pass the barrier flag as all
+                # the processes are already in sync. This avoids the
+                # issue of different timers having different barrier
+                # groups inside their class.
+                rank_name_to_time[rank, i] = self._timers[name].elapsed(reset=reset)
+
+        # See the note above for why we are not using gather.
+        torch.distributed._all_gather_base(
+            rank_name_to_time.view(-1), rank_name_to_time[rank, :].view(-1)
+        )
+
+        return rank_name_to_time
+
+    def _get_global_min_max_time(self, names, reset, barrier, normalizer):
+        """Report only min and max times across all ranks."""
+
+        rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset, barrier)
+        name_to_min_max_time = {}
+        for i, name in enumerate(names):
+            rank_to_time = rank_name_to_time[:, i]
+            # filter out the ones we did not have any timings for
+            rank_to_time = rank_to_time[rank_to_time > 0.0]
+            # If the timer exists:
+            if rank_to_time.numel() > 0:
+                name_to_min_max_time[name] = (
+                    rank_to_time.min().item() / normalizer,
+                    rank_to_time.max().item() / normalizer,
+                )
+        return name_to_min_max_time
+
+    def _get_global_min_max_time_string(self, names, reset, barrier, normalizer, max_only):
+        """Report strings for max/minmax times across all ranks."""
+        name_to_min_max_time = self._get_global_min_max_time(names, reset, barrier, normalizer)
+        if not name_to_min_max_time:
+            return None
+        if max_only:
+            output_string = 'max time across ranks (ms):'
+        else:
+            output_string = '(min, max) time across ranks (ms):'
+        for name in name_to_min_max_time:
+            min_time, max_time = name_to_min_max_time[name]
+            if max_only:
+                output_string += '\n    {}: {:.2f}'.format((name + ' ').ljust(48, '.'), max_time)
+            else:
+                output_string += '\n    {}: ({:.2f}, {:.2f})'.format(
+                    (name + ' ').ljust(48, '.'), min_time, max_time
+                )
+        return output_string
+
+    def _get_all_ranks_time_string(self, names, reset, barrier, normalizer):
+        """Report times across all ranks."""
+        rank_name_to_time = self._get_elapsed_time_all_ranks(names, reset, barrier)
+
+        output_string = 'times across ranks (ms):'
+        no_reported_timing = True
+        for i, name in enumerate(names):
+            not_yet_found = True
+            for rank in range(torch.distributed.get_world_size()):
+                if rank_name_to_time[rank, i] > 0:
+                    no_reported_timing = False
+                    if not_yet_found:
+                        not_yet_found = False
+                        output_string += '\n  {}:'.format(name)
+                    output_string += '\n     rank {:2d}: {:.2f}'.format(
+                        rank, rank_name_to_time[rank, i] / normalizer
+                    )
+        if no_reported_timing:
+            return None
+        return output_string
+
+    def get_all_timers_string(
+        self,
+        names: List[str] = None,
+        normalizer: float = 1.0,
+        reset: bool = True,
+        barrier: bool = False,
+    ):
+        """Returns the output string with logged timer values according to configured options.
+
+        Args:
+            names (List[str]): Names of the timers to log. If None, all registered timers are fetched. Defaults to None.
+            normalizer (float, optional): Normalizes the timer values by the factor. Defaults to 1.0.
+            reset (bool, optional): Whether to reset timer values after logging. Defaults to True.
+            barrier (bool, optional): Whether to do a global barrier before time measurments. Defaults to False.
+
+        Raises:
+            Exception: Raises if log option is invalid.
+
+        Returns:
+            str: Formatted string with the timer values.
+        """
+
+        if names == None:  # get all registered timers
+            names = self._timers.keys()
+
+        assert normalizer > 0.0
+        if self._log_option in ['max', 'minmax']:
+            max_only = False
+            if self._log_option == 'max':
+                max_only = True
+            output_string = self._get_global_min_max_time_string(
+                names, reset, barrier, normalizer / 1000.0, max_only
+            )
+        elif self._log_option == 'all':
+            output_string = self._get_all_ranks_time_string(
+                names, reset, barrier, normalizer / 1000.0
+            )
+        else:
+            raise Exception('unknown timing log option {}'.format(self._log_option))
+        return output_string
+
+    def log(
+        self,
+        names: List[str],
+        rank: int = None,
+        normalizer: float = 1.0,
+        reset: bool = True,
+        barrier: bool = False,
+    ):
+        """logs the timers passed in names to stdout. Example usage is to log average per step value for timer 'foo',
+          this function can be called with normalizer factor set to logging interval. 
+
+        Args:
+            names (List[str]): Names of the timers to log.
+            rank (int, optional): logs the timers to a specific rank. If set to None, logs to the last rank. Defaults to None.
+            normalizer (float, optional): Normalizes the timer values by the factor. Defaults to 1.0.
+            reset (bool, optional): Whether to reset timer values after logging. Defaults to True.
+            barrier (bool, optional): Whether to do a global barrier before time measurments. Defaults to False.
+        """
+
+        output_string = self.get_all_timers_string(names, normalizer, reset, barrier)
+        # If no input rank is provided, log on last rank.
+        if rank is None:
+            rank = torch.distributed.get_world_size() - 1
+        if rank == torch.distributed.get_rank() and output_string is not None:
+            print(output_string, flush=True)
+
+    def write(
+        self,
+        names: List[str],
+        writer,
+        iteration: int,
+        normalizer: float = 1.0,
+        reset: bool = True,
+        barrier: bool = False,
+    ):
+        """Write timers to a tensorboard writer. Note that we only report maximum time across ranks to tensorboard.
+
+        Args:
+            names (List[str]): Names of the timers to log.
+            writer (SummaryWriter): Tensorboard SummaryWriter object
+            iteration (int): Current iteration.
+            normalizer (float, optional): Normalizes the timer values by the factor. Defaults to 1.0.
+            reset (bool, optional): Whether to reset timer values after logging. Defaults to True.
+            barrier (bool, optional): Whether to do a global barrier before time measurments. Defaults to False.
+        """
+        # currently when using add_scalars,
+        # torch.utils.add_scalars makes each timer its own run, which
+        # polutes the runs list, so we just add each as a scalar
+        assert normalizer > 0.0
+        name_to_min_max_time = self._get_global_min_max_time(names, reset, barrier, normalizer)
+        if writer is not None:
+            for name in name_to_min_max_time:
+                _, max_time = name_to_min_max_time[name]
+                writer.add_scalar(name + '-time', max_time, iteration)